From ab686a951a041c8f7ee28644db817c8e66a31004 Mon Sep 17 00:00:00 2001
From: Vladislav Golubev <vladislav.golubev@intel.com>
Date: Wed, 24 Nov 2021 23:13:26 +0300
Subject: [PATCH 01/72] [CPU] ExtractImagePatches: dynamic shapes support
 (#8677)

---
 .../mkldnn_extract_image_patches_node.cpp     | 377 +++++++++++-------
 .../nodes/mkldnn_extract_image_patches_node.h |  69 +++-
 .../extract_image_patches.cpp                 | 151 ++++---
 3 files changed, 385 insertions(+), 212 deletions(-)

diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_extract_image_patches_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_extract_image_patches_node.cpp
index 9372c2177fa..29faa1dac52 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_extract_image_patches_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_extract_image_patches_node.cpp
@@ -270,11 +270,7 @@ private:
 
 bool MKLDNNExtractImagePatchesNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
-        const auto extImgPatcher = std::dynamic_pointer_cast<const ngraph::opset3::ExtractImagePatches>(op);
+        auto extImgPatcher = ngraph::as_type_ptr<const ngraph::opset3::ExtractImagePatches>(op);
         if (!extImgPatcher) {
             errorMessage = "Only opset3 ExtractImagePatches operation is supported";
             return false;
@@ -302,21 +298,18 @@ MKLDNNExtractImagePatchesNode::MKLDNNExtractImagePatchesNode(const std::shared_p
     }
 
     errorPrefix = "ExtractImagePatches layer with name '" + op->get_friendly_name() + "' ";
-    const auto extImgPatcher = std::dynamic_pointer_cast<const ngraph::opset3::ExtractImagePatches>(op);
+    auto extImgPatcher = ngraph::as_type_ptr<const ngraph::opset3::ExtractImagePatches>(op);
 
-    if (getOriginalInputsNumber() != 1 || getOriginalOutputsNumber() != 1)
+    if (inputShapes.size() != 1 || outputShapes.size() != 1)
         IE_THROW() << errorPrefix << "has incorrect number of input or output edges!"
-                   << " Input: " << getOriginalInputsNumber() << "; Output: " << getOriginalOutputsNumber();
+                   << " Input: " << inputShapes.size() << "; Output: " << outputShapes.size();
 
-    if (op->get_input_shape(0).size() != 4)
-        IE_THROW() << errorPrefix << "must have 4D input tensor. Actual: " << op->get_input_shape(0).size();
+    if (getInputShapeAtPort(0).getRank() != 4)
+        IE_THROW() << errorPrefix << "must have 4D input tensor. Actual: " << getInputShapeAtPort(0).getRank();
 
-    if (op->get_output_shape(0).size() != 4)
-        IE_THROW() << errorPrefix << "must have 4D output tensor. Actual: " << op->get_output_shape(0).size();
+    if (getOutputShapeAtPort(0).getRank() != 4)
+        IE_THROW() << errorPrefix << "must have 4D output tensor. Actual: " << getOutputShapeAtPort(0).getRank();
 
-    auto ksizes = extImgPatcher->get_sizes();
-    auto strides = extImgPatcher->get_strides();
-    auto rates = extImgPatcher->get_rates();
     if (extImgPatcher->get_auto_pad() == ngraph::op::PadType::VALID) {
         _auto_pad = ExtImgPatcherPadType::VALID;
     } else if (extImgPatcher->get_auto_pad() == ngraph::op::PadType::SAME_LOWER) {
@@ -327,80 +320,46 @@ MKLDNNExtractImagePatchesNode::MKLDNNExtractImagePatchesNode(const std::shared_p
         IE_THROW() << errorPrefix << "has unsupported pad type: " << extImgPatcher->get_auto_pad();
     }
 
-    if (ksizes.size() != 2 || strides.size() != 2 || rates.size() != 2)
+    _ksizes = extImgPatcher->get_sizes();;
+    _strides = extImgPatcher->get_strides();
+    _rates = extImgPatcher->get_rates();
+    if (_ksizes.size() != 2 || _strides.size() != 2 || _rates.size() != 2)
         IE_THROW() << errorPrefix << "must have the following attributes with shape {2}: sizes, strides, rates.";
-    _ksizes.clear();
-    _strides.clear();
-    _rates.clear();
-    for (const auto& x : ksizes)
-        _ksizes.push_back(x);
-    for (const auto& x : strides)
-        _strides.push_back(x);
-    for (const auto& x : rates)
-        _rates.push_back(x);
+}
 
-    SizeVector in_dims = op->get_input_shape(0);
-    _pad_left = 0;
-    _pad_top = 0;
-    jit_extract_image_patches_params jpp;
-    jpp.need_padding = false;
-    if (_auto_pad != ExtImgPatcherPadType::VALID) {
-        const size_t iheight = in_dims[2];
-        const size_t iwidth = in_dims[3];
-        const int64_t ihStep = _ksizes[0] + (_rates[0] - 1) * (_ksizes[0] - 1);
-        const int64_t iwStep = _ksizes[1] + (_rates[1] - 1) * (_ksizes[1] - 1);
-
-        int64_t PW = (std::ceil(1.f * iwidth/_strides[1]) - 1) * _strides[1] + iwStep - iwidth;
-        int64_t PH = (std::ceil(1.f * iheight/_strides[0]) - 1) * _strides[0] + ihStep - iheight;
-
-        int64_t increment_sign = 0;
-        if (_auto_pad == ExtImgPatcherPadType::SAME_LOWER) {
-            increment_sign = 1;
-        } else if (_auto_pad == ExtImgPatcherPadType::SAME_UPPER) {
-            increment_sign = -1;
-        }
-
-        if ((PW > 0) && (PW < iwStep)) {
-            _pad_left = static_cast<size_t>((PW + increment_sign * (PW % 2)) / 2);
-            jpp.need_padding = true;
-        }
-        if ((PH > 0) && (PH < ihStep)) {
-            _pad_top = static_cast<size_t>((PH + increment_sign * (PH % 2)) / 2);
-            jpp.need_padding = true;
-        }
+void MKLDNNExtractImagePatchesNode::createPrimitive() {
+    if (inputShapesDefined()) {
+        if (needPrepareParams())
+            prepareParams();
+        updateLastInputDims();
     }
+}
 
-    jpp.IW = in_dims[3];
-    SizeVector out_dims = op->get_output_shape(0);
-    jpp.OH = out_dims[2];
-    jpp.OW = out_dims[3];
-    jpp.KH = _ksizes[0];
-    jpp.KW = _ksizes[1];
-    jpp.SH = _strides[0];
-    jpp.SW = _strides[1];
-    jpp.dtype_size = getOriginalInputPrecisionAtPort(0).size();
-    jpp.block_size = 1;
+void MKLDNNExtractImagePatchesNode::prepareParams() {
+    const auto& srcMemPtr0 = getParentEdgeAt(0)->getMemoryPtr();
+    const auto& dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
+    if (!srcMemPtr0 || !srcMemPtr0->GetPrimitivePtr())
+        IE_THROW() << "Input memory didn't allocate.";
+    if (!dstMemPtr || !dstMemPtr->GetPrimitivePtr())
+        IE_THROW() << "Destination memory didn't allocate.";
+    if (getSelectedPrimitiveDescriptor() == nullptr)
+        IE_THROW() << "Preferable primitive descriptor is not set.";
 
-    if (mayiuse(x64::avx512_common)) {
-        jpp.block_size = cpu_isa_traits<x64::avx512_common>::vlen / jpp.dtype_size;
-        extract_image_patches_kernel.reset(new jit_extract_image_patches_kernel<x64::avx512_common>(jpp));
-    } else if (mayiuse(x64::avx2)) {
-        jpp.block_size = cpu_isa_traits<x64::avx2>::vlen / jpp.dtype_size;
-        extract_image_patches_kernel.reset(new jit_extract_image_patches_kernel<x64::avx2>(jpp));
-    } else if (mayiuse(x64::sse41)) {
-        jpp.block_size = cpu_isa_traits<x64::sse41>::vlen / jpp.dtype_size;
-        extract_image_patches_kernel.reset(new jit_extract_image_patches_kernel<x64::sse41>(jpp));
+    const auto& in_dims = getParentEdgeAt(0)->getMemory().getStaticDims();
+    const auto& out_dims = getChildEdgesAtPort(0)[0]->getMemory().getStaticDims();
+    const auto prcSize = getOriginalInputPrecisionAtPort(0).size();
+    if (mayiuse(x64::sse41)) {
+        execPtr = std::make_shared<ExtractImagePatchesJitExecutor>(in_dims, out_dims, _ksizes, _strides, _rates, _auto_pad, prcSize);
+    } else {
+        execPtr = std::make_shared<ExtractImagePatchesRefExecutor>(in_dims, out_dims, _ksizes, _strides, _rates, _auto_pad, prcSize);
     }
-
-    if (extract_image_patches_kernel)
-        extract_image_patches_kernel->create_ker();
 }
 
 void MKLDNNExtractImagePatchesNode::initSupportedPrimitiveDescriptors() {
     if (!supportedPrimitiveDescriptors.empty())
         return;
 
-    precision = getOriginalInputPrecisionAtPort(0);
+    const auto precision = getOriginalInputPrecisionAtPort(0);
     if (_supported_precisions_sizes.find(precision.size()) == _supported_precisions_sizes.end())
         IE_THROW() << errorPrefix << "has unsupported precision: " << precision.name();
 
@@ -410,90 +369,208 @@ void MKLDNNExtractImagePatchesNode::initSupportedPrimitiveDescriptors() {
 }
 
 void MKLDNNExtractImagePatchesNode::execute(mkldnn::stream strm) {
-    const char *src_data = reinterpret_cast<const char *>(getParentEdgeAt(0)->getMemoryPtr()->GetPtr());
-    char *dst_data = reinterpret_cast<char *>(getChildEdgesAtPort(0)[0]->getMemoryPtr()->GetPtr());
-    const size_t dtype_size = getOriginalInputPrecisionAtPort(0).size();
-
-    const auto& inDims = getParentEdgeAt(0)->getMemory().getStaticDims();
-    const size_t IC = inDims[1];
-    const size_t IH = inDims[2];
-    const size_t IW = inDims[3];
-
-    const auto& outDims = getChildEdgesAtPort(0)[0]->getMemory().getStaticDims();
-    const size_t OB = outDims[0];
-    const size_t OH = outDims[2];
-    const size_t OW = outDims[3];
-
-    const size_t KH = _ksizes[0], KW = _ksizes[1];
-    const size_t SH = _strides[0], SW = _strides[1];
-    const size_t RH = _rates[0], RW = _rates[1];
-    const size_t PT = _pad_top, PL = _pad_left;
-
-    const std::vector<size_t> istrides = getParentEdgeAt(0)->getMemory().GetDescWithType<BlockedMemoryDesc>()->getStrides();
-    const std::vector<size_t> ostrides = getChildEdgesAtPort(0)[0]->getMemory().GetDescWithType<BlockedMemoryDesc>()->getStrides();
-    const std::vector<size_t> ostrides_partial = {ostrides[0], KW * IC * ostrides[1], IC * ostrides[1], ostrides[1]};
-
-    if (extract_image_patches_kernel) {
-        parallel_for4d(OB, KH, KW, IC, [&](const size_t ob, const size_t kh, const size_t kw, const size_t ic) {
-            const int64_t ih_start = kh * RH - PT;
-            const int64_t iw_start = kw * RW - PL;
-            const size_t ih_lpad = ih_start >= 0 ? 0 : std::ceil(-1.f * ih_start / SH);
-            const size_t iw_lpad = iw_start >= 0 ? 0 : std::ceil(-1.f * iw_start / SW);
-            const size_t ih_hpad = std::ceil((IH - 1.f * ih_start) / SH) > OH ? OH : std::ceil((IH - 1.f * ih_start) / SH);
-            const size_t iw_hpad = std::ceil((IW - 1.f * iw_start) / SW) > OW ? OW : std::ceil((IW - 1.f * iw_start) / SW);
-
-            size_t dst_offset = ob * ostrides_partial[0] + kh * ostrides_partial[1] + kw * ostrides_partial[2] + ic * ostrides_partial[3];
-            size_t src_offset = ob * istrides[0] + ic * istrides[1] + ih_start * istrides[2] + iw_start + ih_lpad * SH * IW;
-
-            auto args = jit_extract_image_patches_args();
-            args.src = src_data + src_offset * dtype_size;
-            args.dst = dst_data + dst_offset * dtype_size;
-            args.h_lo_pad = ih_lpad;
-            args.h_hi_pad = ih_hpad;
-            args.w_lo_pad = iw_lpad;
-            args.w_hi_pad = iw_hpad;
-            (*extract_image_patches_kernel)(&args);
-        });
+    if (execPtr) {
+        auto src = getParentEdgeAt(0)->getMemoryPtr()->GetPtr();
+        auto dst = getChildEdgesAtPort(0)[0]->getMemoryPtr()->GetPtr();
+        const auto inStrides = getParentEdgeAt(0)->getMemory().GetDescWithType<BlockedMemoryDesc>()->getStrides();
+        const auto outStrides = getChildEdgesAtPort(0)[0]->getMemory().GetDescWithType<BlockedMemoryDesc>()->getStrides();
+        execPtr->exec(src, dst, inStrides, outStrides);
     } else {
-        parallel_for4d(OB, KH, KW, IC, [&](const size_t ob, const size_t kh, const size_t kw, const size_t ic) {
-            const int64_t iw_start = kw * RW - PL;
-            const int64_t ih_start = kh * RH - PT;
-            const size_t ih_lpad = ih_start >= 0 ? 0 : std::ceil(- 1.f * ih_start / SH);
-            const size_t iw_lpad = iw_start >= 0 ? 0 : std::ceil(- 1.f * iw_start / SW);
+        IE_THROW() << "Can't execute extract image patches node. Primitive wasn't created";
+    }
+}
 
-            const size_t ih_hpad = std::ceil((IH - 1.f * ih_start) / SH) > OH ? OH : std::ceil((IH + -1.f * ih_start) / SH);
-            const size_t iw_hpad = std::ceil((IW - 1.f * iw_start) / SW) > OW ? OW : std::ceil((IW - 1.f * iw_start) / SW);
+void MKLDNNExtractImagePatchesNode::executeDynamicImpl(mkldnn::stream strm) {
+    return execute(strm);
+}
 
-            char *my_dst_ptr = dst_data +
-                               (ob * ostrides_partial[0] + kh * ostrides_partial[1] + kw * ostrides_partial[2] + ic * ostrides_partial[3]) * dtype_size;
-            const char *my_src_ptr = src_data + (ob * istrides[0] + ic * istrides[1] + ih_start * istrides[2] + iw_start) * dtype_size;
+void MKLDNNExtractImagePatchesNode::ExtractImagePatchesRefExecutor::executeReference(
+    void* src, void* dst, const VectorDims& istrides, const VectorDims& ostrides) const {
+    const char* src_data = reinterpret_cast<const char*>(src);
+    char* dst_data = reinterpret_cast<char*>(dst);
 
-            size_t num_bytes_to_set = ih_lpad * OW * dtype_size;
+    const std::vector<size_t> ostrides_partial = { ostrides[0], jpp.KW * IC * ostrides[1], IC * ostrides[1], ostrides[1] };
+
+    parallel_for4d(OB, jpp.KH, jpp.KW, IC, [&](const size_t ob, const size_t kh, const size_t kw, const size_t ic) {
+        const int64_t iw_start = kw * RW - PL;
+        const int64_t ih_start = kh * RH - PT;
+        const size_t ih_lpad = ih_start >= 0 ? 0 : std::ceil(-1.f * ih_start / jpp.SH);
+        const size_t iw_lpad = iw_start >= 0 ? 0 : std::ceil(-1.f * iw_start / jpp.SW);
+
+        const size_t ih_hpad = std::ceil((IH - 1.f * ih_start) / jpp.SH) > jpp.OH ? jpp.OH : std::ceil((IH + -1.f * ih_start) / jpp.SH);
+        const size_t iw_hpad = std::ceil((jpp.IW - 1.f * iw_start) / jpp.SW) > jpp.OW ? jpp.OW : std::ceil((jpp.IW - 1.f * iw_start) / jpp.SW);
+
+        char* my_dst_ptr = dst_data +
+            (ob * ostrides_partial[0] + kh * ostrides_partial[1] + kw * ostrides_partial[2] + ic * ostrides_partial[3]) * jpp.dtype_size;
+        const char* my_src_ptr = src_data + (ob * istrides[0] + ic * istrides[1] + ih_start * istrides[2] + iw_start) * jpp.dtype_size;
+
+        size_t num_bytes_to_set = ih_lpad * jpp.OW * jpp.dtype_size;
+        memset(my_dst_ptr, 0, num_bytes_to_set);
+        my_dst_ptr += num_bytes_to_set;
+
+        const char* src_ptr_h_stop = my_src_ptr + ih_hpad * jpp.SH * jpp.IW * jpp.dtype_size;
+        for (const char* src_h_ptr = my_src_ptr + ih_lpad * jpp.SH * jpp.IW * jpp.dtype_size;
+            src_h_ptr < src_ptr_h_stop; src_h_ptr += jpp.SH * jpp.IW * jpp.dtype_size) {
+            num_bytes_to_set = iw_lpad * jpp.dtype_size;
             memset(my_dst_ptr, 0, num_bytes_to_set);
             my_dst_ptr += num_bytes_to_set;
 
-            const char* src_ptr_h_stop = my_src_ptr + ih_hpad * SH * IW * dtype_size;
-            for (const char *src_h_ptr = my_src_ptr + ih_lpad * SH * IW * dtype_size;
-                 src_h_ptr < src_ptr_h_stop; src_h_ptr += SH * IW * dtype_size) {
-                num_bytes_to_set = iw_lpad * dtype_size;
-                memset(my_dst_ptr, 0, num_bytes_to_set);
-                my_dst_ptr += num_bytes_to_set;
-
-                const char* src_ptr_w_stop = src_h_ptr + iw_hpad * SW * dtype_size;
-                for (const char* src_w_ptr = src_h_ptr + iw_lpad * SW * dtype_size;
-                     src_w_ptr < src_ptr_w_stop; src_w_ptr += SW * dtype_size) {
-                    num_bytes_to_set = dtype_size;
-                    memcpy(my_dst_ptr, src_w_ptr, num_bytes_to_set);
-                    my_dst_ptr += num_bytes_to_set;
-                }
-                num_bytes_to_set = (OW - iw_hpad) * dtype_size;
-                memset(my_dst_ptr, 0, num_bytes_to_set);
+            const char* src_ptr_w_stop = src_h_ptr + iw_hpad * jpp.SW * jpp.dtype_size;
+            for (const char* src_w_ptr = src_h_ptr + iw_lpad * jpp.SW * jpp.dtype_size;
+                src_w_ptr < src_ptr_w_stop; src_w_ptr += jpp.SW * jpp.dtype_size) {
+                num_bytes_to_set = jpp.dtype_size;
+                memcpy(my_dst_ptr, src_w_ptr, num_bytes_to_set);
                 my_dst_ptr += num_bytes_to_set;
             }
-            num_bytes_to_set = (OH - ih_hpad) * OW * dtype_size;
+            num_bytes_to_set = (jpp.OW - iw_hpad) * jpp.dtype_size;
             memset(my_dst_ptr, 0, num_bytes_to_set);
-        });
+            my_dst_ptr += num_bytes_to_set;
+        }
+        num_bytes_to_set = (jpp.OH - ih_hpad) * jpp.OW * jpp.dtype_size;
+        memset(my_dst_ptr, 0, num_bytes_to_set);
+    });
+}
+
+void MKLDNNExtractImagePatchesNode::ExtractImagePatchesJitExecutor::executeOptimizedGeneric(
+    void* src, void* dst, const VectorDims& istrides, const VectorDims& ostrides) const {
+    const char* src_data = reinterpret_cast<const char*>(src);
+    char* dst_data = reinterpret_cast<char*>(dst);
+    const auto& jpp = pKernel->jpp;
+
+    const std::vector<size_t> ostrides_partial = { ostrides[0], jpp.KW * IC * ostrides[1], IC * ostrides[1], ostrides[1] };
+
+    parallel_for4d(OB, jpp.KH, jpp.KW, IC, [&](const size_t ob, const size_t kh, const size_t kw, const size_t ic) {
+        const int64_t ih_start = kh * RH - PT;
+        const int64_t iw_start = kw * RW - PL;
+        const size_t ih_lpad = ih_start >= 0 ? 0 : std::ceil(-1.f * ih_start / jpp.SH);
+        const size_t iw_lpad = iw_start >= 0 ? 0 : std::ceil(-1.f * iw_start / jpp.SW);
+        const size_t ih_hpad = std::ceil((IH - 1.f * ih_start) / jpp.SH) > jpp.OH ? jpp.OH : std::ceil((IH - 1.f * ih_start) / jpp.SH);
+        const size_t iw_hpad = std::ceil((jpp.IW - 1.f * iw_start) / jpp.SW) > jpp.OW ? jpp.OW : std::ceil((jpp.IW - 1.f * iw_start) / jpp.SW);
+
+        size_t dst_offset = ob * ostrides_partial[0] + kh * ostrides_partial[1] + kw * ostrides_partial[2] + ic * ostrides_partial[3];
+        size_t src_offset = ob * istrides[0] + ic * istrides[1] + ih_start * istrides[2] + iw_start + ih_lpad * jpp.SH * jpp.IW;
+
+        auto args = jit_extract_image_patches_args();
+        args.src = src_data + src_offset * jpp.dtype_size;
+        args.dst = dst_data + dst_offset * jpp.dtype_size;
+        args.h_lo_pad = ih_lpad;
+        args.h_hi_pad = ih_hpad;
+        args.w_lo_pad = iw_lpad;
+        args.w_hi_pad = iw_hpad;
+        (*pKernel)(&args);
+    });
+}
+
+jit_extract_image_patches_params MKLDNNExtractImagePatchesNode::ExtractImagePatchesExecutor::fillJpp(
+    const VectorDims& inDims,
+    const VectorDims& outDims,
+    const VectorDims& kSizes,
+    const VectorDims& strides,
+    const VectorDims& rates,
+    const ExtImgPatcherPadType& padType,
+    const size_t prcSize) {
+    jit_extract_image_patches_params jpp{};
+
+    IC = inDims[1];
+    IH = inDims[2];
+    jpp.IW = inDims[3];
+
+    OB = outDims[0];
+    jpp.OH = outDims[2];
+    jpp.OW = outDims[3];
+
+    jpp.KH = kSizes[0];
+    jpp.KW = kSizes[1];
+
+    jpp.SH = strides[0];
+    jpp.SW = strides[1];
+
+    RH = rates[0];
+    RW = rates[1];
+
+    PL = 0;
+    PT = 0;
+    jpp.need_padding = false;
+    if (padType != ExtImgPatcherPadType::VALID) {
+        const int64_t ihStep = kSizes[0] + (rates[0] - 1) * (kSizes[0] - 1);
+        const int64_t iwStep = kSizes[1] + (rates[1] - 1) * (kSizes[1] - 1);
+
+        int64_t PW = (std::ceil(1.f * jpp.IW / strides[1]) - 1) * strides[1] + iwStep - jpp.IW;
+        int64_t PH = (std::ceil(1.f * IH / strides[0]) - 1) * strides[0] + ihStep - IH;
+
+        int64_t increment_sign = 0;
+        if (padType == ExtImgPatcherPadType::SAME_LOWER) {
+            increment_sign = 1;
+        } else if (padType == ExtImgPatcherPadType::SAME_UPPER) {
+            increment_sign = -1;
+        }
+
+        if ((PW > 0) && (PW < iwStep)) {
+            PL = static_cast<size_t>((PW + increment_sign * (PW % 2)) / 2);
+            jpp.need_padding = true;
+        }
+        if ((PH > 0) && (PH < ihStep)) {
+            PT = static_cast<size_t>((PH + increment_sign * (PH % 2)) / 2);
+            jpp.need_padding = true;
+        }
     }
+
+    jpp.dtype_size = prcSize;
+    if (mayiuse(x64::avx512_common)) {
+        jpp.block_size = cpu_isa_traits<x64::avx512_common>::vlen / prcSize;
+    } else if (mayiuse(x64::avx2)) {
+        jpp.block_size = cpu_isa_traits<x64::avx2>::vlen / prcSize;
+    } else if (mayiuse(x64::sse41)) {
+        jpp.block_size = cpu_isa_traits<x64::sse41>::vlen / prcSize;
+    } else {
+        jpp.block_size = 1;
+    }
+
+    return jpp;
+}
+
+MKLDNNExtractImagePatchesNode::ExtractImagePatchesJitExecutor::ExtractImagePatchesJitExecutor(
+    const VectorDims& inDims,
+    const VectorDims& outDims,
+    const VectorDims& kSizes,
+    const VectorDims& strides,
+    const VectorDims& rates,
+    const ExtImgPatcherPadType& padType,
+    const size_t prcSize) {
+    auto jpp = fillJpp(inDims, outDims, kSizes, strides, rates, padType, prcSize);
+    if (mayiuse(x64::avx512_common)) {
+        pKernel.reset(new jit_extract_image_patches_kernel<x64::avx512_common>(jpp));
+    } else if (mayiuse(x64::avx2)) {
+        pKernel.reset(new jit_extract_image_patches_kernel<x64::avx2>(jpp));
+    } else if (mayiuse(x64::sse41)) {
+        pKernel.reset(new jit_extract_image_patches_kernel<x64::sse41>(jpp));
+    } else {
+        IE_THROW() << "Can't create jit extract image patches kernel";
+    }
+
+    if (pKernel)
+        pKernel->create_ker();
+}
+
+void MKLDNNExtractImagePatchesNode::ExtractImagePatchesJitExecutor::exec(
+    void* src, void* dst, const VectorDims& istrides, const VectorDims& ostrides) {
+    if (!pKernel)
+        IE_THROW() << "Can't execute, kernel for extract image patches node is not compiled";
+    executeOptimizedGeneric(src, dst, istrides, ostrides);
+}
+
+MKLDNNExtractImagePatchesNode::ExtractImagePatchesRefExecutor::ExtractImagePatchesRefExecutor(
+    const VectorDims& inDims,
+    const VectorDims& outDims,
+    const VectorDims& kSizes,
+    const VectorDims& strides,
+    const VectorDims& rates,
+    const ExtImgPatcherPadType& padType,
+    const size_t prcSize) : jpp(fillJpp(inDims, outDims, kSizes, strides, rates, padType, prcSize)) {}
+
+void MKLDNNExtractImagePatchesNode::ExtractImagePatchesRefExecutor::exec(
+    void* src, void* dst, const VectorDims& istrides, const VectorDims& ostrides) {
+    executeReference(src, dst, istrides, ostrides);
 }
 
 const std::set<size_t> MKLDNNExtractImagePatchesNode::_supported_precisions_sizes = {1, 2, 4};
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_extract_image_patches_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_extract_image_patches_node.h
index f78730185aa..62df89c9822 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_extract_image_patches_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_extract_image_patches_node.h
@@ -11,7 +11,6 @@
 #include <vector>
 
 namespace MKLDNNPlugin {
-
 struct jit_extract_image_patches_params {
     size_t IW;
     size_t OH, OW;
@@ -46,10 +45,13 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
+    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
+    void executeDynamicImpl(mkldnn::stream strm) override;
+    void prepareParams() override;
+
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 private:
@@ -62,15 +64,68 @@ private:
     std::vector<size_t> _ksizes;
     std::vector<size_t> _strides;
     std::vector<size_t> _rates;
-    size_t _pad_left;
-    size_t _pad_top;
-    std::shared_ptr<jit_uni_extract_image_patches_kernel> extract_image_patches_kernel;
     static const std::set<size_t> _supported_precisions_sizes;
-
     ExtImgPatcherPadType _auto_pad;
-    InferenceEngine::Precision precision;
 
     std::string errorPrefix;
+
+    struct ExtractImagePatchesExecutor {
+        ExtractImagePatchesExecutor() = default;
+        virtual void exec(void* src, void* dst, const VectorDims& istrides, const VectorDims& ostrides) = 0;
+        jit_extract_image_patches_params fillJpp(
+            const VectorDims& inDims,
+            const VectorDims& outDims,
+            const VectorDims& kSizes,
+            const VectorDims& strides,
+            const VectorDims& rates,
+            const ExtImgPatcherPadType& padType,
+            const size_t prcSize);
+        virtual ~ExtractImagePatchesExecutor() = default;
+
+    protected:
+        size_t IC = 0;
+        size_t IH = 0;
+        size_t OB = 0;
+        size_t RH = 0;
+        size_t RW = 0;
+        size_t PT = 0;
+        size_t PL = 0;
+    };
+
+    using executorPtr = std::shared_ptr<ExtractImagePatchesExecutor>;
+    executorPtr execPtr = nullptr;
+
+    struct ExtractImagePatchesJitExecutor : public ExtractImagePatchesExecutor {
+        ExtractImagePatchesJitExecutor(
+            const VectorDims& inDims,
+            const VectorDims& outDims,
+            const VectorDims& kSizes,
+            const VectorDims& strides,
+            const VectorDims& rates,
+            const ExtImgPatcherPadType& padType,
+            const size_t prcSize);
+        void exec(void* src, void* dst, const VectorDims& istrides, const VectorDims& ostrides) override;
+        void executeOptimizedGeneric(void* src, void* dst, const VectorDims& istrides, const VectorDims& ostrides) const;
+
+    private:
+        std::unique_ptr<jit_uni_extract_image_patches_kernel> pKernel;
+    };
+
+    struct ExtractImagePatchesRefExecutor : public ExtractImagePatchesExecutor {
+        ExtractImagePatchesRefExecutor(
+            const VectorDims& inDims,
+            const VectorDims& outDims,
+            const VectorDims& kSizes,
+            const VectorDims& strides,
+            const VectorDims& rates,
+            const ExtImgPatcherPadType& padType,
+            const size_t prcSize);
+        void exec(void* src, void* dst, const VectorDims& istrides, const VectorDims& ostrides) override;
+        void executeReference(void* src, void* dst, const VectorDims& istrides, const VectorDims& ostrides) const;
+
+    private:
+        jit_extract_image_patches_params jpp;
+    };
 };
 
 }  // namespace MKLDNNPlugin
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/extract_image_patches.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/extract_image_patches.cpp
index 58e532a7f1c..2c58b36392f 100755
--- a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/extract_image_patches.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/extract_image_patches.cpp
@@ -2,91 +2,132 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "shared_test_classes/base/ov_subgraph.hpp"
 #include "test_utils/cpu_test_utils.hpp"
 #include "shared_test_classes/base/layer_test_utils.hpp"
-#include "ngraph_functions/utils/ngraph_helpers.hpp"
-#include <shared_test_classes/single_layer/extract_image_patches.hpp>
+#include "ngraph_functions/builders.hpp"
 
-using namespace InferenceEngine;
 using namespace CPUTestUtils;
+using namespace ov::test;
 
 namespace CPULayerTestsDefinitions {
-using LayerTestsDefinitions::extractImagePatchesTuple;
+using extractImagePatchesParams = typename std::tuple<
+    InputShape,         // input shape
+    ElementType,        // Network precision
+    ov::Shape,          // kernel size
+    ov::Strides,        // strides
+    ov::Shape,          // rates
+    ov::op::PadType>;   // pad type
 
-typedef std::tuple<
-    extractImagePatchesTuple,
-    CPUSpecificParams> extractImagePatchesCPUTestParamsSet;
-
-class ExtractImagePatchesLayerCPUTest : public testing::WithParamInterface<extractImagePatchesCPUTestParamsSet>,
-    virtual public LayerTestsUtils::LayerTestsCommon, public CPUTestsBase {
+class ExtractImagePatchesLayerCPUTest : public testing::WithParamInterface<extractImagePatchesParams>,
+                                        virtual public SubgraphBaseTest, public CPUTestsBase {
 public:
-    static std::string getTestCaseName(testing::TestParamInfo<extractImagePatchesCPUTestParamsSet> obj) {
-        extractImagePatchesTuple basicParamsSet;
-        CPUSpecificParams cpuParams;
-        std::tie(basicParamsSet, cpuParams) = obj.param;
+    static std::string getTestCaseName(testing::TestParamInfo<extractImagePatchesParams> obj) {
+        InputShape inputShapes;
+        ElementType inputPrecision;
+        ov::Shape kernelSize;
+        ov::Strides strides;
+        ov::Shape rates;
+        ov::op::PadType padType;
+        std::tie(inputShapes, inputPrecision, kernelSize, strides, rates, padType) = obj.param;
 
         std::ostringstream result;
-        result << LayerTestsDefinitions::ExtractImagePatchesTest::getTestCaseName(testing::TestParamInfo<extractImagePatchesTuple>(
-            basicParamsSet, 0));
+        result << "netPRC=" << inputPrecision << "_" << "IS=" << CommonTestUtils::partialShape2str({ inputShapes.first }) << "_";
+        result << "TS=";
+        result << "(";
+        for (const auto& targetShape : inputShapes.second) {
+            result << CommonTestUtils::vec2str(targetShape) << "_";
+        }
 
-        result << CPUTestsBase::getTestCaseName(cpuParams);
+        result << ")_" << "kernelSize=" << kernelSize << "_strides=" << strides << "_rates=" << rates << "_padType=" << padType;
         return result.str();
     }
 protected:
     void SetUp() override {
-        extractImagePatchesTuple basicParamsSet;
-        CPUSpecificParams cpuParams;
-        std::tie(basicParamsSet, cpuParams) = this->GetParam();
-        std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+        InputShape inputShapes;
+        ElementType inputPrecision;
+        ov::Shape kernelSize;
+        ov::Strides strides;
+        ov::Shape rates;
+        ov::op::PadType padType;
+        std::tie(inputShapes, inputPrecision, kernelSize, strides, rates, padType) = this->GetParam();
 
-        std::vector<size_t> inputShape, kernel, strides, rates;
-        ngraph::op::PadType pad_type;
-        InferenceEngine::Precision netPrecision;
-        std::tie(inputShape, kernel, strides, rates, pad_type, netPrecision, inPrc, outPrc, inLayout, targetDevice) = basicParamsSet;
-        selectedType = std::string("ref_any_") + netPrecision.name();
+        selectedType = makeSelectedTypeStr("ref_any", inputPrecision);
+        if (inputPrecision == ElementType::bf16) {
+            rel_threshold = 1e-2;
+        }
 
-        auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
-        auto inputNode = std::make_shared<ngraph::opset6::Parameter>(ngPrc, ngraph::Shape(inputShape));
-        ngraph::ParameterVector params = {inputNode};
+        init_input_shapes({ inputShapes });
 
-        auto extImgPatches = std::make_shared<ngraph::opset6::ExtractImagePatches>(
-                inputNode, ngraph::Shape(kernel), ngraph::Strides(strides), ngraph::Shape(rates), pad_type);
-        ngraph::ResultVector results{std::make_shared<ngraph::opset6::Result>(extImgPatches)};
-        function = std::make_shared<ngraph::Function>(results, params, "ExtractImagePatches");
+        auto params = ngraph::builder::makeDynamicParams(inputPrecision, inputDynamicShapes);
+        auto extImgPatches = std::make_shared<ngraph::opset3::ExtractImagePatches>(params[0], kernelSize, strides, rates, padType);
+        function = makeNgraphFunction(inputPrecision, params, extImgPatches, "ExtractImagePatches");
     }
 };
 
 TEST_P(ExtractImagePatchesLayerCPUTest, CompareWithRefs) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
-    Run();
+    run();
     CheckPluginRelatedResults(executableNetwork, "ExtractImagePatches");
 }
 
 namespace {
-    const std::vector<std::vector<size_t>> inShapes = {{2, 3, 13, 37}};
-    const std::vector<std::vector<size_t>> kSizes = {{1, 5}, {3, 4}, {3, 1}};
-    const std::vector<std::vector<size_t>> strides = {{1, 2}, {2, 2}, {2, 1}};
-    const std::vector<std::vector<size_t>> rates = {{1, 3}, {3, 3}, {3, 1}};
+const std::vector<InputShape> inputShapes = {
+    InputShape{{}, {{2, 3, 13, 37}}},
+    InputShape{
+        // dynamic
+        {-1, -1, -1, -1},
+        // static
+        {{2, 3, 13, 37}, {6, 4, 14, 14}, {8, 12, 15, 16}}
+    },
+    InputShape{
+        // dynamic
+        {{5, 15}, {6, 17}, {10, 15}, {13, 16}},
+        // static
+        {{5, 17, 10, 15}, {15, 10, 12, 13}, {10, 10, 15, 16}}
+    },
+};
 
-    const std::vector<ngraph::op::PadType> autoPads = {ngraph::op::PadType::VALID, ngraph::op::PadType::SAME_UPPER, ngraph::op::PadType::SAME_LOWER};
-    const std::vector<Precision> netPrecision = {Precision::I8, Precision::BF16, Precision::FP32};
-    const CPUSpecificParams CPUParams = emptyCPUSpec;
+const std::vector<ElementType> inputPrecisions = {
+    ElementType::i8,
+    ElementType::bf16,
+    ElementType::f32
+};
 
-const auto Layer_params = ::testing::Combine(
-        ::testing::ValuesIn(inShapes),
-        ::testing::ValuesIn(kSizes),
-        ::testing::ValuesIn(strides),
-        ::testing::ValuesIn(rates),
-        ::testing::ValuesIn(autoPads),
-        ::testing::ValuesIn(netPrecision),
-        ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-        ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-        ::testing::Values(InferenceEngine::Layout::ANY),
-        ::testing::Values(CommonTestUtils::DEVICE_CPU));
+const std::vector<ov::Shape> kSizes = {
+    {1, 5},
+    {3, 4},
+    {3, 1}
+};
 
-INSTANTIATE_TEST_SUITE_P(smoke_ExtractImagePatches_CPU, ExtractImagePatchesLayerCPUTest,
-                        ::testing::Combine(Layer_params, ::testing::Values(CPUParams)),
-                        ExtractImagePatchesLayerCPUTest::getTestCaseName);
+const std::vector<ov::Strides> strides = {
+    {1, 2},
+    {2, 2},
+    {2, 1}
+};
+
+const std::vector<ov::Shape> rates = {
+    {1, 3},
+    {3, 3},
+    {3, 1}
+};
+
+const std::vector<ov::op::PadType> autoPads = {
+    ov::op::PadType::VALID,
+    ov::op::PadType::SAME_UPPER,
+    ov::op::PadType::SAME_LOWER
+};
+
+const auto params = ::testing::Combine(
+    ::testing::ValuesIn(inputShapes),
+    ::testing::ValuesIn(inputPrecisions),
+    ::testing::ValuesIn(kSizes),
+    ::testing::ValuesIn(strides),
+    ::testing::ValuesIn(rates),
+    ::testing::ValuesIn(autoPads));
+
+INSTANTIATE_TEST_SUITE_P(smoke_ExtractImagePatches_CPU, ExtractImagePatchesLayerCPUTest, params, ExtractImagePatchesLayerCPUTest::getTestCaseName);
 
 } // namespace
 } // namespace CPULayerTestsDefinitions

From 295c65e1fb3031bac9b4d73ee3a492e2086cd70a Mon Sep 17 00:00:00 2001
From: huzhaoyX <zhaoyangx.hu@intel.com>
Date: Thu, 25 Nov 2021 11:47:10 +0800
Subject: [PATCH 02/72] Add paddle softplus op support. (#8390)

Signed-off-by: huzhaoyX <zhaoyangx.hu@intel.com>
---
 .../frontend/paddlepaddle/src/op/softplus.cpp | 28 ++++++++++++
 ngraph/frontend/paddlepaddle/src/op_table.cpp |  2 +
 .../test/frontend/paddlepaddle/op_fuzzy.cpp   |  1 +
 .../gen_scripts/generate_softplus.py          | 43 +++++++++++++++++++
 4 files changed, 74 insertions(+)
 create mode 100644 ngraph/frontend/paddlepaddle/src/op/softplus.cpp
 create mode 100644 ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_softplus.py

diff --git a/ngraph/frontend/paddlepaddle/src/op/softplus.cpp b/ngraph/frontend/paddlepaddle/src/op/softplus.cpp
new file mode 100644
index 00000000000..35e2ec3f132
--- /dev/null
+++ b/ngraph/frontend/paddlepaddle/src/op/softplus.cpp
@@ -0,0 +1,28 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#include <node_context.hpp>
+
+#include "default_opset.hpp"
+
+namespace ov {
+namespace frontend {
+namespace pdpd {
+namespace op {
+NamedOutputs softplus(const NodeContext& node) {
+    auto data = node.get_ng_input("X");
+    auto beta = node.get_attribute<float>("beta");
+    auto threshold = node.get_attribute<float>("threshold");
+    float supported_beta = 1.0;
+    float supported_threshold = 20.0;
+    const float EPSINON = 1e-6;
+
+    if (!(abs(beta - supported_beta) <= EPSINON) || !(abs(threshold - supported_threshold) <= EPSINON)) {
+        PDPD_OP_VALIDATION_CHECK(node, false, "only support beta==1.0 && threshold==20.0");
+    }
+    return node.default_single_output_mapping({std::make_shared<default_opset::SoftPlus>(data)}, {"Out"});
+}
+}  // namespace op
+}  // namespace pdpd
+}  // namespace frontend
+}  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/op_table.cpp b/ngraph/frontend/paddlepaddle/src/op_table.cpp
index 36b978258f6..e5c508c3237 100644
--- a/ngraph/frontend/paddlepaddle/src/op_table.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op_table.cpp
@@ -63,6 +63,7 @@ OP_CONVERTER(scale);
 OP_CONVERTER(shape);
 OP_CONVERTER(slice);
 OP_CONVERTER(softmax);
+OP_CONVERTER(softplus);
 OP_CONVERTER(sigmoid);
 OP_CONVERTER(split);
 OP_CONVERTER(squeeze);
@@ -136,6 +137,7 @@ std::map<std::string, CreatorFunction> get_supported_ops() {
             {"shape", op::shape},
             {"slice", op::slice},
             {"softmax", op::softmax},
+            {"softplus", op::softplus},
             {"sigmoid", op::sigmoid},
             {"split", op::split},
             {"squeeze2", op::squeeze},
diff --git a/ngraph/test/frontend/paddlepaddle/op_fuzzy.cpp b/ngraph/test/frontend/paddlepaddle/op_fuzzy.cpp
index 0a01edccbc1..7b126c33864 100644
--- a/ngraph/test/frontend/paddlepaddle/op_fuzzy.cpp
+++ b/ngraph/test/frontend/paddlepaddle/op_fuzzy.cpp
@@ -207,6 +207,7 @@ static const std::vector<std::string> models{std::string("argmax"),
                                              std::string("slice_reshape/slice_reshape.pdmodel"),
                                              std::string("softmax"),
                                              std::string("softmax_minus"),
+                                             std::string("softplus_default_params"),
                                              std::string("split_test1"),
                                              std::string("split_test2"),
                                              std::string("split_test3"),
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_softplus.py b/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_softplus.py
new file mode 100644
index 00000000000..f5d31f27fe3
--- /dev/null
+++ b/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_softplus.py
@@ -0,0 +1,43 @@
+#
+# softplus paddle model generator
+#
+import numpy as np
+import sys
+from save_model import saveModel
+
+
+def softplus(name: str, x, beta, threshold):
+    import paddle as pdpd
+    pdpd.enable_static()
+
+    node_x = pdpd.static.data(name='x', shape=x.shape, dtype='float32')
+    out = pdpd.nn.functional.softplus(x=node_x, beta=beta, threshold=threshold)
+
+    cpu = pdpd.static.cpu_places(1)
+    exe = pdpd.static.Executor(cpu[0])
+    # startup program will call initializer to initialize the parameters.
+    exe.run(pdpd.static.default_startup_program())
+
+    outs = exe.run(
+        feed={'x': x},
+        fetch_list=[out])
+
+    saveModel(name, exe, feedkeys=['x'], fetchlist=[out], inputs=[x], outputs=[outs[0]], target_dir=sys.argv[1])
+
+    return outs[0]
+
+
+def main():
+    data = np.array(
+        [[[2.0, 3.0, 4.0, 5.0],
+          [3.0, 4.0, 5.0, 6.0],
+          [7.0, 8.0, 8.0, 9.0]],
+         [[1.0, 2.0, 3.0, 4.0],
+          [5.0, 6.0, 7.0, 8.0],
+          [6.0, 7.0, 8.0, 9.0]]]
+    ).astype(np.float32)
+
+    softplus("softplus_default_params", data, beta=1, threshold=20)
+
+if __name__ == "__main__":
+    main()

From 7fcdff592c47d8d0bc4ca6885abc4eefa10645fa Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Thu, 25 Nov 2021 07:34:31 +0300
Subject: [PATCH 03/72] Fixed postprocessor for top K (#8780)

* Fixed postprocessor for top K

* Changed fix
---
 .../ngraph_reader/topK_tests.cpp              | 74 +++++++++++++++++++
 .../src/preprocess/preprocess_steps_impl.cpp  |  2 +-
 2 files changed, 75 insertions(+), 1 deletion(-)

diff --git a/inference-engine/tests/functional/inference_engine/ngraph_reader/topK_tests.cpp b/inference-engine/tests/functional/inference_engine/ngraph_reader/topK_tests.cpp
index 388a59d9c11..29b3d4ad358 100644
--- a/inference-engine/tests/functional/inference_engine/ngraph_reader/topK_tests.cpp
+++ b/inference-engine/tests/functional/inference_engine/ngraph_reader/topK_tests.cpp
@@ -4,6 +4,8 @@
 
 #include <string>
 #include "ngraph_reader_tests.hpp"
+#include "openvino/runtime/core.hpp"
+
 TEST_F(NGraphReaderTests, DISABLED_ReadTopKNetwork) {
     std::string model = R"V0G0N(
 <net name="Network" version="10">
@@ -248,3 +250,75 @@ TEST_F(NGraphReaderTests, DISABLED_ReadTopKNetwork) {
         data[0] = 5;
     });
 }
+
+TEST_F(NGraphReaderTests, ReadTopKV10Network) {
+    std::string model = R"V0G0N(
+<net name="Network" version="10">
+    <layers>
+        <layer name="in1" type="Parameter" id="0" version="opset1">
+            <data element_type="f32" shape="1,3,22,22"/>
+            <output>
+                <port id="0" precision="FP32">
+                    <dim>1</dim>
+                    <dim>3</dim>
+                    <dim>22</dim>
+                    <dim>22</dim>
+                </port>
+            </output>
+        </layer>
+        <layer id="4" name="1345813459_const" type="Const" version="opset1">
+            <data element_type="i64" offset="0" shape="" size="8"/>
+            <output>
+                <port id="1" precision="I64" />
+            </output>
+        </layer>
+        <layer name="topk" id="1" type="TopK" version="opset3">
+            <data axis="2" index_element_type="i64" mode="max" sort="value"/>
+            <input>
+                <port id="1">
+                    <dim>1</dim>
+                    <dim>3</dim>
+                    <dim>22</dim>
+                    <dim>22</dim>
+                </port>
+                <port id="2"/>
+            </input>
+            <output>
+                <port id="3" precision="FP32">
+                    <dim>1</dim>
+                    <dim>3</dim>
+                    <dim>5</dim>
+                    <dim>22</dim>
+                </port>
+                <port id="4" precision="I64">
+                    <dim>1</dim>
+                    <dim>3</dim>
+                    <dim>5</dim>
+                    <dim>22</dim>
+                </port>
+            </output>
+        </layer>
+        <layer id="2" name="res" type="Result" version="opset1">
+            <input>
+                <port id="0" precision="I64">
+                    <dim>1</dim>
+                    <dim>3</dim>
+                    <dim>5</dim>
+                    <dim>22</dim>
+                </port>
+            </input>
+        </layer>
+    </layers>
+    <edges>
+        <edge from-layer="0" from-port="0" to-layer="1" to-port="1"/>
+        <edge from-layer="4" from-port="1" to-layer="1" to-port="2"/>
+        <edge from-layer="1" from-port="4" to-layer="2" to-port="0"/>
+    </edges>
+</net>
+)V0G0N";
+
+    ov::runtime::Core core;
+    ov::runtime::Tensor t(ov::element::i64, {1});
+    t.data<int64_t>()[0] = 5;
+    core.read_model(model, t);
+}
diff --git a/ngraph/core/src/preprocess/preprocess_steps_impl.cpp b/ngraph/core/src/preprocess/preprocess_steps_impl.cpp
index 4763053a268..f970c61f2a8 100644
--- a/ngraph/core/src/preprocess/preprocess_steps_impl.cpp
+++ b/ngraph/core/src/preprocess/preprocess_steps_impl.cpp
@@ -385,7 +385,7 @@ void PostStepsList::add_convert_impl(const element::Type& type) {
         if (t == element::Type{}) {
             t = ctxt.target_element_type();
         }
-        if (t == node.get_node()->get_element_type()) {
+        if (t == node.get_element_type()) {
             return std::make_tuple(node, false);
         }
         OPENVINO_ASSERT(

From be04b856e259cf8a1e5f61b3ecc3a3b3f68dbe33 Mon Sep 17 00:00:00 2001
From: Aleksandr Korolev <aleksandr.korolev@intel.com>
Date: Thu, 25 Nov 2021 10:32:20 +0300
Subject: [PATCH 04/72] [VPU] coverity issues fix (#8730)

---
 .../vpu/model/data_contents/conv_weights_contents.hpp       | 6 +++---
 .../vpu/model/data_contents/deconvolution_contents.hpp      | 2 +-
 .../src/model/data_contents/conv_weights_contents.cpp       | 6 +++---
 .../src/model/data_contents/deconvolution_contents.cpp      | 2 +-
 4 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/inference-engine/src/vpu/graph_transformer/include/vpu/model/data_contents/conv_weights_contents.hpp b/inference-engine/src/vpu/graph_transformer/include/vpu/model/data_contents/conv_weights_contents.hpp
index b6360c45541..06da36d3bdc 100644
--- a/inference-engine/src/vpu/graph_transformer/include/vpu/model/data_contents/conv_weights_contents.hpp
+++ b/inference-engine/src/vpu/graph_transformer/include/vpu/model/data_contents/conv_weights_contents.hpp
@@ -14,7 +14,7 @@ namespace vpu {
 
 class ConvIm2ColWeightsContent final : public CalculatedDataContent {
 public:
-    explicit ConvIm2ColWeightsContent(const DataContent::Ptr& origContent, DataDesc desc);
+    explicit ConvIm2ColWeightsContent(const DataContent::Ptr& origContent, const DataDesc& desc);
 
     size_t byteSize() const override;
 
@@ -32,7 +32,7 @@ private:
 
 class Conv3x3WeightsContent final : public CalculatedDataContent {
 public:
-    explicit Conv3x3WeightsContent(const DataContent::Ptr& origContent, DataDesc desc);
+    explicit Conv3x3WeightsContent(const DataContent::Ptr& origContent, const DataDesc& desc);
 
     size_t byteSize() const override;
 
@@ -50,7 +50,7 @@ private:
 
 class ConvCHWWeightsContent final : public CalculatedDataContent {
 public:
-    explicit ConvCHWWeightsContent(const DataContent::Ptr& origContent, DataDesc desc);
+    explicit ConvCHWWeightsContent(const DataContent::Ptr& origContent, const DataDesc& desc);
 
     size_t byteSize() const override;
 
diff --git a/inference-engine/src/vpu/graph_transformer/include/vpu/model/data_contents/deconvolution_contents.hpp b/inference-engine/src/vpu/graph_transformer/include/vpu/model/data_contents/deconvolution_contents.hpp
index e0f6e81f912..7797ef2c43d 100644
--- a/inference-engine/src/vpu/graph_transformer/include/vpu/model/data_contents/deconvolution_contents.hpp
+++ b/inference-engine/src/vpu/graph_transformer/include/vpu/model/data_contents/deconvolution_contents.hpp
@@ -78,7 +78,7 @@ class DeconvolutionWeightsContent final : public CalculatedDataContent {
 public:
     DeconvolutionWeightsContent(
             const DataContent::Ptr& origContent,
-            DataDesc desc,
+            const DataDesc& desc,
             int KX, int KY,
             int IC, int OC);
 
diff --git a/inference-engine/src/vpu/graph_transformer/src/model/data_contents/conv_weights_contents.cpp b/inference-engine/src/vpu/graph_transformer/src/model/data_contents/conv_weights_contents.cpp
index 0f0bd88f57b..2291419fe06 100644
--- a/inference-engine/src/vpu/graph_transformer/src/model/data_contents/conv_weights_contents.cpp
+++ b/inference-engine/src/vpu/graph_transformer/src/model/data_contents/conv_weights_contents.cpp
@@ -13,7 +13,7 @@ namespace vpu {
 // ConvIm2ColWeightsContent
 //
 
-ConvIm2ColWeightsContent::ConvIm2ColWeightsContent(const DataContent::Ptr& origContent, DataDesc desc) :
+ConvIm2ColWeightsContent::ConvIm2ColWeightsContent(const DataContent::Ptr& origContent, const DataDesc& desc) :
         _origContent(origContent), _desc(desc) {}
 
 size_t ConvIm2ColWeightsContent::byteSize() const {
@@ -30,7 +30,7 @@ void ConvIm2ColWeightsContent::fillTempBuf(void* tempBuf) const {
 // Conv3x3WeightsContent
 //
 
-Conv3x3WeightsContent::Conv3x3WeightsContent(const DataContent::Ptr& origContent, DataDesc desc) :
+Conv3x3WeightsContent::Conv3x3WeightsContent(const DataContent::Ptr& origContent, const DataDesc& desc) :
         _origContent(origContent), _desc(desc) {
 }
 
@@ -48,7 +48,7 @@ void Conv3x3WeightsContent::fillTempBuf(void* tempBuf) const {
 // ConvCHWWeightsContent
 //
 
-ConvCHWWeightsContent::ConvCHWWeightsContent(const DataContent::Ptr& origContent, DataDesc desc) :
+ConvCHWWeightsContent::ConvCHWWeightsContent(const DataContent::Ptr& origContent, const DataDesc& desc) :
         _origContent(origContent), _desc(desc) {
 }
 
diff --git a/inference-engine/src/vpu/graph_transformer/src/model/data_contents/deconvolution_contents.cpp b/inference-engine/src/vpu/graph_transformer/src/model/data_contents/deconvolution_contents.cpp
index 437b868d5f3..5b30185f87f 100644
--- a/inference-engine/src/vpu/graph_transformer/src/model/data_contents/deconvolution_contents.cpp
+++ b/inference-engine/src/vpu/graph_transformer/src/model/data_contents/deconvolution_contents.cpp
@@ -156,7 +156,7 @@ void deconvolutionRelayout(
 
 DeconvolutionWeightsContent::DeconvolutionWeightsContent(
         const DataContent::Ptr& origContent,
-        DataDesc desc,
+        const DataDesc& desc,
         int KX, int KY,
         int IC, int OC) :
         _origContent(origContent), _desc(desc),

From c10a981f1b34e7db3b759820010f3634f244668d Mon Sep 17 00:00:00 2001
From: Sergey Shlyapnikov <sergey.shlyapnikov@intel.com>
Date: Thu, 25 Nov 2021 10:58:37 +0300
Subject: [PATCH 05/72] [GPU] Do not use OneDNN for FP32 pipeline (#8737)

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

diff --git a/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp b/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp
index e5dbc7daf73..7961d8f4de3 100644
--- a/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp
+++ b/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp
@@ -856,7 +856,7 @@ layout layout_optimizer::get_expected_layout(layout const& current_layout,
     bool non_grouped = prim->groups == 1;
     bool is_2d = input_layout.format.spatial_num() == 2;
     bool onednn_valid_post_ops = get_post_ops_count(node) <= 32;
-    bool use_onednn_impls = _optimization_attributes.use_onednn_impls;
+    bool use_onednn_impls = _optimization_attributes.use_onednn_impls && input_layout.data_type != data_types::f32;
     bool i8_u8_input = input_layout.data_type == data_types::u8 || input_layout.data_type == data_types::i8;
 
     if (use_onednn_impls && onednn_valid_post_ops) {

From b75c91d67c45e05ad5b8876d19f81ff956f9215b Mon Sep 17 00:00:00 2001
From: Vladimir Paramuzov <vladimir.paramuzov@intel.com>
Date: Thu, 25 Nov 2021 10:59:14 +0300
Subject: [PATCH 06/72] [GPU] Fixed setInputBlob behavior test (#8787)

---
 .../plugin/gpu/shared_tests_instances/skip_tests_config.cpp   | 2 ++
 .../include/behavior/infer_request/set_blob_by_type.hpp       | 4 ++--
 inference-engine/thirdparty/clDNN/runtime/kernels_cache.cpp   | 4 +++-
 inference-engine/thirdparty/clDNN/runtime/ocl/ocl_stream.cpp  | 2 +-
 .../clDNN/src/include/deformable_convolution_inst.h           | 1 +
 5 files changed, 9 insertions(+), 4 deletions(-)

diff --git a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp
index e72e81b460d..2354cf299c9 100644
--- a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp
+++ b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp
@@ -96,5 +96,7 @@ std::vector<std::string> disabledTestPatterns() {
             R"(smoke_PrePostProcess.*cvt_color_nv12.*)",
             // TODO: Issue 71215
             R"(smoke_PrePostProcess.*cvt_color_i420.*)",
+            // Unsupported
+            R"(smoke_Behavior/InferRequestSetBlobByType.setInputBlobsByType/BlobType=Batched_Device=GPU_Config=().*)",
     };
 }
diff --git a/inference-engine/tests/functional/plugin/shared/include/behavior/infer_request/set_blob_by_type.hpp b/inference-engine/tests/functional/plugin/shared/include/behavior/infer_request/set_blob_by_type.hpp
index e6162428730..0c6cb85352b 100644
--- a/inference-engine/tests/functional/plugin/shared/include/behavior/infer_request/set_blob_by_type.hpp
+++ b/inference-engine/tests/functional/plugin/shared/include/behavior/infer_request/set_blob_by_type.hpp
@@ -56,13 +56,13 @@ protected:
             case FuncTestUtils::BlobType::NV12:
                 return false;
             case FuncTestUtils::BlobType::Batched: {
-                const std::vector<std::string>& supported_metrics = ie->GetMetric(targetDevice, METRIC_KEY(SUPPORTED_METRICS));
+                std::vector<std::string> supported_metrics = ie->GetMetric(targetDevice, METRIC_KEY(SUPPORTED_METRICS));
                 if (std::find(supported_metrics.begin(), supported_metrics.end(),
                               METRIC_KEY(OPTIMIZATION_CAPABILITIES)) == supported_metrics.end()) {
                     return false;
                 }
 
-                const std::vector<std::string>& optimization_caps =
+                std::vector<std::string> optimization_caps =
                         ie->GetMetric(targetDevice, METRIC_KEY(OPTIMIZATION_CAPABILITIES));
                 return std::find(optimization_caps.begin(), optimization_caps.end(),
                                  METRIC_VALUE(BATCHED_BLOB)) != optimization_caps.end();
diff --git a/inference-engine/thirdparty/clDNN/runtime/kernels_cache.cpp b/inference-engine/thirdparty/clDNN/runtime/kernels_cache.cpp
index 300995163f7..b9943418e4c 100644
--- a/inference-engine/thirdparty/clDNN/runtime/kernels_cache.cpp
+++ b/inference-engine/thirdparty/clDNN/runtime/kernels_cache.cpp
@@ -74,8 +74,10 @@ static std::vector<unsigned char> loadBinaryFromFile(std::string path) {
     if (fp) {
         fseek(fp, 0, SEEK_END);
         auto sz = ftell(fp);
-        if (sz < 0)
+        if (sz < 0) {
+            fclose(fp);
             return {};
+        }
         auto nsize = static_cast<size_t>(sz);
 
         fseek(fp, 0, SEEK_SET);
diff --git a/inference-engine/thirdparty/clDNN/runtime/ocl/ocl_stream.cpp b/inference-engine/thirdparty/clDNN/runtime/ocl/ocl_stream.cpp
index fe11564911c..9083c116a4e 100644
--- a/inference-engine/thirdparty/clDNN/runtime/ocl/ocl_stream.cpp
+++ b/inference-engine/thirdparty/clDNN/runtime/ocl/ocl_stream.cpp
@@ -455,7 +455,7 @@ void ocl_stream::wait_for_events(const std::vector<event::ptr>& events) {
 void ocl_stream::sync_events(std::vector<event::ptr> const& deps, bool is_output) {
     bool needs_barrier = false;
     for (auto& dep : deps) {
-        auto* ocl_base_ev = dynamic_cast<ocl_base_event*>(dep.get());
+        auto* ocl_base_ev = downcast<ocl_base_event>(dep.get());
         if (ocl_base_ev->get_queue_stamp() > _last_barrier) {
             needs_barrier = true;
         }
diff --git a/inference-engine/thirdparty/clDNN/src/include/deformable_convolution_inst.h b/inference-engine/thirdparty/clDNN/src/include/deformable_convolution_inst.h
index bb3e6cecd5b..76f2f136e27 100644
--- a/inference-engine/thirdparty/clDNN/src/include/deformable_convolution_inst.h
+++ b/inference-engine/thirdparty/clDNN/src/include/deformable_convolution_inst.h
@@ -21,6 +21,7 @@ public:
             : parent(prim, prog),
               split(this->get_primitive()->split()),
               depthwise_sep_opt(false),
+              transposed(false),
               groups(this->get_primitive()->groups) {
         support_padding_all(true);
     }

From e25c10075cebcc508f663dd8702fca60cf9fea08 Mon Sep 17 00:00:00 2001
From: Maxim Andronov <maxim.andronov@intel.com>
Date: Thu, 25 Nov 2021 12:52:48 +0300
Subject: [PATCH 07/72] [CPU] Fix makeDummyShape (#8795)

---
 .../memory_desc/cpu_memory_desc_utils.cpp            |  3 ++-
 .../tests/unit/cpu/mkldnn_memory_desc_test.cpp       | 12 ++++++++++++
 2 files changed, 14 insertions(+), 1 deletion(-)

diff --git a/inference-engine/src/mkldnn_plugin/memory_desc/cpu_memory_desc_utils.cpp b/inference-engine/src/mkldnn_plugin/memory_desc/cpu_memory_desc_utils.cpp
index ffb09da736b..38dd1220e29 100644
--- a/inference-engine/src/mkldnn_plugin/memory_desc/cpu_memory_desc_utils.cpp
+++ b/inference-engine/src/mkldnn_plugin/memory_desc/cpu_memory_desc_utils.cpp
@@ -113,11 +113,12 @@ std::shared_ptr<MemoryDesc> MemoryDescUtils::makeDummyDesc(const MemoryDesc &des
 }
 
 Shape MemoryDescUtils::makeDummyShape(const Shape &shape, Dim dummyVal) {
+    const auto& minDims = shape.getMinDims();
     const auto& maxDims = shape.getMaxDims();
     const auto& dims = shape.getDims();
     VectorDims dummyDims(dims.size());
     for (size_t i = 0; i < dims.size(); ++i) {
-        dummyDims[i] = dims[i] == Shape::UNDEFINED_DIM ? std::min(maxDims[i], dummyVal) : dims[i];
+        dummyDims[i] = dims[i] == Shape::UNDEFINED_DIM ? std::min(maxDims[i], std::max(minDims[i], dummyVal)) : dims[i];
     }
     return Shape(dummyDims);
 }
diff --git a/inference-engine/tests/unit/cpu/mkldnn_memory_desc_test.cpp b/inference-engine/tests/unit/cpu/mkldnn_memory_desc_test.cpp
index 90efd5b9f77..c4ff9feb0cb 100644
--- a/inference-engine/tests/unit/cpu/mkldnn_memory_desc_test.cpp
+++ b/inference-engine/tests/unit/cpu/mkldnn_memory_desc_test.cpp
@@ -540,3 +540,15 @@ TEST(cloneWithParamsChange, UndefinedAndDefaultParams) {
         cloneWithParamsChangeCpu(tc);
     }
 }
+
+TEST(makeDummyDesc, LowerBoundMoreThenDummyValie) {
+    Shape shape(ngraph::PartialShape{1, 3, 85, {144, 1444}});
+    auto desc = std::make_shared<DnnlBlockedMemoryDesc>(shape, mkldnn::memory::data_type::f32, mkldnn::memory::format_tag::nchw);
+    ASSERT_FALSE(desc->isDefined());
+
+    MemoryDescPtr definedDesc;
+    ASSERT_NO_THROW(definedDesc = MemoryDescUtils::makeDummyDesc(*desc));
+
+    ASSERT_TRUE(definedDesc->isDefined());
+    ASSERT_EQ((VectorDims{1, 3, 85, 144}), definedDesc->getShape().getStaticDims());
+}

From 163bc458db630bf51271cd7fa668c2a7d49a7338 Mon Sep 17 00:00:00 2001
From: Anton Chetverikov <Anton.Chetverikov@intel.com>
Date: Thu, 25 Nov 2021 12:59:39 +0300
Subject: [PATCH 08/72] [POT] Transformations to remove and add Convert
 operations in graph  (#8672)

* Added special passes to remove and add Convert operations in POT

* Update passes

* Implement changes to support new FP16 models

* Apply codestyle patch

* Revert Cast inserting and add data parameter

* Update FastBC rule

Co-authored-by: Malinin, Nikita <nikita.malinin@intel.com>
---
 tools/pot/openvino/tools/pot/graph/builder.py | 13 ++++++-
 .../openvino/tools/pot/graph/graph_utils.py   |  8 ++--
 tools/pot/openvino/tools/pot/graph/passes.py  | 39 +++++++++++++++++++
 3 files changed, 55 insertions(+), 5 deletions(-)

diff --git a/tools/pot/openvino/tools/pot/graph/builder.py b/tools/pot/openvino/tools/pot/graph/builder.py
index 368d90f8847..1a2adf03910 100644
--- a/tools/pot/openvino/tools/pot/graph/builder.py
+++ b/tools/pot/openvino/tools/pot/graph/builder.py
@@ -2,11 +2,12 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from copy import deepcopy
+import numpy as np
 
 from mo.graph.graph import Graph
 
-from openvino.tools.pot.graph.node_utils import get_node_inputs
-from .editor import create_node, connect_nodes_by_name
+from openvino.tools.pot.graph.node_utils import get_node_input, get_node_inputs
+from .editor import create_node, connect_nodes_by_name, get_node_by_name
 
 
 def build_graph(graph_attrs, meta_data, nodes, edges):
@@ -139,4 +140,12 @@ def build_graph_for_node(model, input_name, input_shape, node, remove_bias=False
     edges.append((node.name, result_name, {'out': 0, 'in': 0}))
     graph = build_graph(*make_copy_graph_attrs(model, input_name, input_shape), nodes, edges)
     graph.ir_v10 = True
+
+    # Add the neccessary attribute to the new graph
+    src_node = get_node_by_name(graph, node.name)
+    weights_node = get_node_input(src_node, 1)
+    weights_node = get_node_input(weights_node, 0) \
+        if weights_node.type == 'FakeQuantize' else weights_node
+    if weights_node.out_port(0).get_data_type() == np.float16:
+        weights_node.out_node(0)['Insert_Convert_operation_after'] = True
     return graph
diff --git a/tools/pot/openvino/tools/pot/graph/graph_utils.py b/tools/pot/openvino/tools/pot/graph/graph_utils.py
index 86d9473b943..e6a1daf3bf5 100644
--- a/tools/pot/openvino/tools/pot/graph/graph_utils.py
+++ b/tools/pot/openvino/tools/pot/graph/graph_utils.py
@@ -10,7 +10,7 @@ from mo.utils.logger import init_logger
 from openvino.inference_engine import IECore  # pylint: disable=E0611
 from openvino.offline_transformations import ApplyPOTTransformations  # pylint: disable=import-error,no-name-in-module
 
-from ..graph.passes import ModelPreprocessor
+from ..graph.passes import ModelPreprocessor, remove_converts, add_removed_converts
 from ..utils.logger import stdout_redirect
 
 init_logger('ERROR', False)
@@ -47,6 +47,7 @@ def load_graph(model_config, target_device='ANY'):
     graph_from_ir.meta_data = meta_data
     graph_from_ir.ir_v10 = True
     graph_from_ir.graph['cmd_params'] = orig_graph_from_ir.graph['cmd_params']
+    remove_converts(graph_from_ir)
     model_preprocessing(graph_from_ir)
     if os.path.exists(serialized_xml_path):
         os.remove(serialized_xml_path)
@@ -71,8 +72,9 @@ def save_graph(graph: Graph, save_path, model_name=None):
         if not os.access(save_path, os.W_OK):
             raise PermissionError(
                 'Output directory {} is not writable for the current user. '.format(save_path))
-
-    save_restored_graph(graph=deepcopy(graph), path=save_path, meta_data=graph.meta_data,
+    graph_copy = deepcopy(graph)
+    add_removed_converts(graph_copy)
+    save_restored_graph(graph=graph_copy, path=save_path, meta_data=graph.meta_data,
                         name=model_name)
 
 
diff --git a/tools/pot/openvino/tools/pot/graph/passes.py b/tools/pot/openvino/tools/pot/graph/passes.py
index 100eac8ab4e..f58cca79754 100644
--- a/tools/pot/openvino/tools/pot/graph/passes.py
+++ b/tools/pot/openvino/tools/pot/graph/passes.py
@@ -13,6 +13,7 @@ import numpy as np
 from extensions.back.ForceStrictPrecision import ForceStrictPrecision
 from extensions.back.compress_quantized_weights import CompressQuantizeWeights
 from extensions.ops.elementwise import Add
+from extensions.ops.Cast import Cast
 from extensions.ops.fakequantize import FakeQuantize
 from mo.back.replacement import BackReplacementPattern
 from mo.front.common.replacement import FrontReplacementSubgraph
@@ -20,6 +21,7 @@ from mo.graph.graph import Graph, Node
 from mo.graph.port import Port
 from mo.middle.pattern_match import apply_pattern
 from mo.ops.const import Const
+from mo.middle.passes.convert_data_type import convert_blob
 from mo.middle.passes.infer import type_infer
 
 from . import editor as ge
@@ -704,6 +706,7 @@ def create_bias_node(graph: Graph, src_node):
 
     for destination_port in destination_ports:
         add_op.out_port(0).connect(destination_port)
+    add_bias.out_node(0)['Insert_Convert_operation_after'] = True
 
 
 def create_fake_quantize_node(graph: Graph, name):
@@ -878,3 +881,39 @@ def find_shape_subgraph_endpoints(out_ports: List[Port], visited: set = None) ->
             visited_nodes.add(in_port.node)
         visited.add(in_port)
     return visited_nodes
+
+
+def remove_converts(graph: Graph):
+    for op in graph.get_op_nodes(type='Convert'):
+        source_op = op.in_port(0).get_source().node
+        if source_op.type == 'Const' and source_op.data_type == np.float16:
+            # Get access to data node after Convert operation and set Insert_Convert_operation_after
+            # to restore Convert operation later
+            op.out_node(0)['Insert_Convert_operation_after'] = True
+            # Mark Const and Convert operation to fold them
+            source_op['need_shape_inference'] = True
+            op['stop_value_propagation'] = False
+            op['need_shape_inference'] = True
+    graph.clean_up()
+
+
+def add_removed_converts(graph: Graph):
+    for data_node_name in graph.get_nodes_with_attributes(Insert_Convert_operation_after=True):
+        data_node = Node(graph, data_node_name)
+        # Get access to Const node connected to data node
+        const_op = data_node.in_node(0)
+        assert const_op.data_type == np.float32, "Error when try to insert Convert operation after Const: {}".\
+            format(const_op.soft_get('name'))
+
+        convert_op = Cast(graph, {'dst_type': np.float32,
+                                  'name': const_op.name + '/restored_convert',
+                                  'stop_value_propagation': True}).create_node()
+
+        # Insert Convert operation after Const operation
+        consumer_port = const_op.out_port(0).get_connection().get_destination()
+        const_op.out_port(0).get_connection().set_destination(convert_op.in_port(0))
+        convert_op.out_port(0).connect(consumer_port)
+
+        # Convert Const value to FP32 to make types in graph consistent
+        const_op.value, _, _ = convert_blob(const_op.value, np.float16)
+        const_op.infer(const_op)

From 629cf83efef236cd9bea3973229341d9a6fe8a9c Mon Sep 17 00:00:00 2001
From: Edward Shogulin <edward.shogulin@intel.com>
Date: Thu, 25 Nov 2021 13:08:53 +0300
Subject: [PATCH 09/72] [LPT] GroupConvolution: 3D tensor support (#8678)

---
 .../src/weightable_layer_transformation.cpp   |   2 +-
 .../convolution_transformation.cpp            |  27 +++++
 .../group_convolution_transformation.cpp      |  49 +++++----
 .../group_convolution_transformation.cpp      |  39 ++++---
 .../group_convolution_transformation.hpp      |   3 +-
 .../convolution_transformation.cpp            |   1 +
 .../group_convolution_transformation.cpp      |  22 ++--
 ...fake_quantize_and_convolution_function.cpp |  33 +++---
 .../src/group_convolution_function.cpp        | 102 ++++++++++++++----
 9 files changed, 198 insertions(+), 80 deletions(-)

diff --git a/inference-engine/src/low_precision_transformations/src/weightable_layer_transformation.cpp b/inference-engine/src/low_precision_transformations/src/weightable_layer_transformation.cpp
index bc5ee3706ba..0a9a2a86690 100644
--- a/inference-engine/src/low_precision_transformations/src/weightable_layer_transformation.cpp
+++ b/inference-engine/src/low_precision_transformations/src/weightable_layer_transformation.cpp
@@ -96,7 +96,7 @@ bool WeightableLayerTransformation::canBeTransformed(const TransformationContext
             }
 
             const auto rankVal = rank.get_length();
-            if ((rankVal != 4) && (rankVal != 5)) {
+            if ((rankVal < 3) || (rankVal > 5)) {
                 return false;
             }
         }
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/convolution_transformation.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/convolution_transformation.cpp
index b2d223c8f31..ffbdd0dbe85 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/convolution_transformation.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/convolution_transformation.cpp
@@ -155,4 +155,31 @@ INSTANTIATE_TEST_SUITE_P(smoke_LPT, ConvolutionWIthIncorrectWeightsTransformatio
         ::testing::ValuesIn(trasformationParamValues),
         ::testing::ValuesIn(incorrectWeightsParams)),
     ConvolutionWIthIncorrectWeightsTransformation::getTestCaseName);
+
+namespace convolution3D {
+const std::vector<LayerTestsDefinitions::ConvolutionTransformationParam> params = {
+    {
+        { 256ul, ngraph::Shape { 1, 1, 1}, { 0.f }, { 255.f }, { 0.f }, { 25.5f } },
+        false,
+        { 255ul, ngraph::Shape { 1, 1, 1}, { 0.f }, { 254.f }, { -12.7f }, { 12.7f } },
+        false,
+        "Convolution",
+        "U8"
+    },
+};
+
+const std::vector<ngraph::Shape> shapes = {
+    { 1, 3, 16 },
+    { 4, 3, 16 }
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_LPT, ConvolutionTransformation,
+     ::testing::Combine(
+             ::testing::ValuesIn(netPrecisions),
+             ::testing::ValuesIn(shapes),
+             ::testing::Values(CommonTestUtils::DEVICE_CPU),
+             ::testing::ValuesIn(trasformationParamValues),
+             ::testing::ValuesIn(params)),
+             ConvolutionTransformation::getTestCaseName);
+}  // namespace convolution3D
 }  // namespace
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/group_convolution_transformation.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/group_convolution_transformation.cpp
index 040396db8bc..a6183d5c013 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/group_convolution_transformation.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/group_convolution_transformation.cpp
@@ -11,22 +11,23 @@ using namespace LayerTestsDefinitions;
 
 namespace {
 const std::vector<ngraph::element::Type> netPrecisions = {
-    ngraph::element::f32,
-    // ngraph::element::f16
+    ngraph::element::f32
 };
 
 const std::vector<ngraph::pass::low_precision::LayerTransformation::Params> trasformationParamValues = {
-    LayerTestsUtils::LayerTransformationParamsNGraphFactory::createParams(),
-    // LayerTestsUtils::LayerTransformationParamsNGraphFactory::createParams().setUpdatePrecisions(false),
+    LayerTestsUtils::LayerTransformationParamsNGraphFactory::createParams()
 };
 
 const std::vector<bool> addPrecisionPreserved = { true, false };
 
+const std::vector<std::pair<ngraph::PartialShape, ngraph::Shape>> inputShapes = {
+    {{ 1, 6, 24, 24 }, { 1, 24, 18, 18 }},
+    {{ 1, 6, 24 }, { 1, 24, 18 }}
+};
+
 const std::vector<LayerTestsDefinitions::GroupConvolutionTransformationParam> params = {
     // group convolution, tensor quantization
     {
-        { 1, 6, 24, 24 },
-        { 1, 24, 18, 18 },
         3ul,
         -1,
         { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 25.5f }, { 0.f }, { 25.5f } },
@@ -36,8 +37,6 @@ const std::vector<LayerTestsDefinitions::GroupConvolutionTransformationParam> pa
     },
     // group convolution, tensor quantization
     {
-        { 1, 6, 24, 24 },
-        { 1, 24, 18, 18 },
         3ul,
         0,
         { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 25.5f }, { 0.f }, { 25.5f } },
@@ -47,8 +46,6 @@ const std::vector<LayerTestsDefinitions::GroupConvolutionTransformationParam> pa
     },
     // group convolution, tensor quantization
     {
-        { 1, 6, 24, 24 },
-        { 1, 24, 18, 18 },
         3ul,
         1,
         { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 25.5f }, { 0.f }, { 25.5f } },
@@ -58,8 +55,6 @@ const std::vector<LayerTestsDefinitions::GroupConvolutionTransformationParam> pa
     },
     // group convolution, per-channel quantization
     {
-        { 1, 6, 24, 24 },
-        { 1, 24, 18, 18 },
         3ul,
         -1,
         {
@@ -73,11 +68,28 @@ const std::vector<LayerTestsDefinitions::GroupConvolutionTransformationParam> pa
         { 255ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 254.f }, { -127.f }, { 127.f } },
         "",
         ""
-    },
+    }
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_LPT, GroupConvolutionTransformation,
+     ::testing::Combine(
+         ::testing::ValuesIn(netPrecisions),
+         ::testing::Values(CommonTestUtils::DEVICE_CPU),
+         ::testing::ValuesIn(trasformationParamValues),
+         ::testing::ValuesIn(inputShapes),
+         ::testing::ValuesIn(params),
+         ::testing::ValuesIn(addPrecisionPreserved)),
+         GroupConvolutionTransformation::getTestCaseName);
+
+namespace depthwise {
+const std::vector<std::pair<ngraph::PartialShape, ngraph::Shape>> inputShapes = {
+    {{ 1, 6, 24, 24 }, { 1, 6, 18, 18 }},
+    {{ 1, 6, 24 }, { 1, 6, 18 }},
+};
+
+const std::vector<LayerTestsDefinitions::GroupConvolutionTransformationParam> params = {
     // depth-wise convolution, tensor quantization
     {
-        { 1, 6, 24, 24 },
-        { 1, 6, 18, 18 },
         6ul,
         -1,
         { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 25.5f }, { 0.f }, { 25.5f } },
@@ -87,8 +99,6 @@ const std::vector<LayerTestsDefinitions::GroupConvolutionTransformationParam> pa
     },
     // depth-wise convolution, per-channel quantization
     {
-        { 1, 6, 24, 24 },
-        { 1, 6, 18, 18 },
         6ul,
         -1,
         {
@@ -105,8 +115,6 @@ const std::vector<LayerTestsDefinitions::GroupConvolutionTransformationParam> pa
     },
     // depth-wise convolution, per-channel quantization
     {
-        ngraph::Shape{ 1, 6, 24, 24 },
-        ngraph::Shape{ 1, 6, 18, 18 },
         6ul,
         -1,
         {
@@ -128,7 +136,10 @@ INSTANTIATE_TEST_SUITE_P(smoke_LPT, GroupConvolutionTransformation,
         ::testing::ValuesIn(netPrecisions),
         ::testing::Values(CommonTestUtils::DEVICE_CPU),
         ::testing::ValuesIn(trasformationParamValues),
+        ::testing::ValuesIn(inputShapes),
         ::testing::ValuesIn(params),
         ::testing::ValuesIn(addPrecisionPreserved)),
     GroupConvolutionTransformation::getTestCaseName);
+} // namespace depthwise
+
 }  // namespace
diff --git a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/low_precision_transformations/group_convolution_transformation.cpp b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/low_precision_transformations/group_convolution_transformation.cpp
index 3543e311e89..dae65e7730d 100644
--- a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/low_precision_transformations/group_convolution_transformation.cpp
+++ b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/low_precision_transformations/group_convolution_transformation.cpp
@@ -17,16 +17,17 @@ const std::vector<ngraph::element::Type> netPrecisions = {
 
 const std::vector<ngraph::pass::low_precision::LayerTransformation::Params> trasformationParamValues = {
     LayerTestsUtils::LayerTransformationParamsNGraphFactory::createParams(),
-    // LayerTestsUtils::LayerTransformationParamsNGraphFactory::createParams().setUpdatePrecisions(false),
 };
 
 const std::vector<bool> addPrecisionPreserved = { true, false };
 
+const std::vector<std::pair<ngraph::PartialShape, ngraph::Shape>> inputShapes = {
+    {{ 1, 6, 24, 24 }, { 1, 24, 18, 18 }}
+};
+
 const std::vector<LayerTestsDefinitions::GroupConvolutionTransformationParam> params = {
     // group convolution, tensor quantization
     {
-        { 1, 6, 24, 24 },
-        ngraph::Shape{ 1, 24, 18, 18 },
         3ul,
         -1,
         { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 25.5f }, { 0.f }, { 25.5f } },
@@ -36,8 +37,6 @@ const std::vector<LayerTestsDefinitions::GroupConvolutionTransformationParam> pa
     },
     // group convolution, tensor quantization
     {
-        { 1, 6, 24, 24 },
-        ngraph::Shape{ 1, 24, 18, 18 },
         3ul,
         0,
         { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 25.5f }, { 0.f }, { 25.5f } },
@@ -47,8 +46,6 @@ const std::vector<LayerTestsDefinitions::GroupConvolutionTransformationParam> pa
     },
     // group convolution, tensor quantization
     {
-        { 1, 6, 24, 24 },
-        ngraph::Shape{ 1, 24, 18, 18 },
         3ul,
         1,
         { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 25.5f }, { 0.f }, { 25.5f } },
@@ -58,8 +55,6 @@ const std::vector<LayerTestsDefinitions::GroupConvolutionTransformationParam> pa
     },
     // group convolution, per-channel quantization
     {
-        { 1, 6, 24, 24 },
-        ngraph::Shape{ 1, 24, 18, 18 },
         3ul,
         -1,
         {
@@ -71,11 +66,27 @@ const std::vector<LayerTestsDefinitions::GroupConvolutionTransformationParam> pa
             { 25.5f, 25.5f, 25.5f / 2.f, 25.5f / 2.f, 25.5f / 4.f, 25.5f / 4.f }
         },
         { 255ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 254.f }, { -127.f }, { 127.f } },
-    },
+    }
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_LPT, GroupConvolutionTransformation,
+    ::testing::Combine(
+        ::testing::ValuesIn(netPrecisions),
+        ::testing::Values(CommonTestUtils::DEVICE_GPU),
+        ::testing::ValuesIn(trasformationParamValues),
+        ::testing::ValuesIn(inputShapes),
+        ::testing::ValuesIn(params),
+        ::testing::ValuesIn(addPrecisionPreserved)),
+    GroupConvolutionTransformation::getTestCaseName);
+
+namespace depthwise {
+const std::vector<std::pair<ngraph::PartialShape, ngraph::Shape>> inputShapes = {
+    {{ 1, 6, 24, 24 }, { 1, 6, 18, 18 }}
+};
+
+const std::vector<LayerTestsDefinitions::GroupConvolutionTransformationParam> params = {
     // depthwise convolution, tensor quantization
     {
-        { 1, 6, 24, 24 },
-        ngraph::Shape{ 1, 6, 18, 18 },
         6ul,
         -1,
         { 256ul, ngraph::Shape { 1, 1, 1, 1 }, { 0.f }, { 25.5f }, { 0.f }, { 25.5f } },
@@ -83,8 +94,6 @@ const std::vector<LayerTestsDefinitions::GroupConvolutionTransformationParam> pa
     },
     // depthwise convolution, per-channel quantization
     {
-        { 1, 6, 24, 24 },
-        ngraph::Shape{ 1, 6, 18, 18 },
         6ul,
         -1,
         {
@@ -104,7 +113,9 @@ INSTANTIATE_TEST_SUITE_P(smoke_LPT, GroupConvolutionTransformation,
         ::testing::ValuesIn(netPrecisions),
         ::testing::Values(CommonTestUtils::DEVICE_GPU),
         ::testing::ValuesIn(trasformationParamValues),
+        ::testing::ValuesIn(inputShapes),
         ::testing::ValuesIn(params),
         ::testing::ValuesIn(addPrecisionPreserved)),
     GroupConvolutionTransformation::getTestCaseName);
+}  // namespace depthwise
 }  // namespace
diff --git a/inference-engine/tests/functional/plugin/shared/include/low_precision_transformations/group_convolution_transformation.hpp b/inference-engine/tests/functional/plugin/shared/include/low_precision_transformations/group_convolution_transformation.hpp
index 237a6b12448..6b0401f3458 100644
--- a/inference-engine/tests/functional/plugin/shared/include/low_precision_transformations/group_convolution_transformation.hpp
+++ b/inference-engine/tests/functional/plugin/shared/include/low_precision_transformations/group_convolution_transformation.hpp
@@ -15,8 +15,6 @@ namespace LayerTestsDefinitions {
 
 class GroupConvolutionTransformationParam {
 public:
-    ngraph::PartialShape inputShape;
-    ngraph::Shape outputShape;
     size_t group;
     int groupCalculationDimention;
     ngraph::builder::subgraph::FakeQuantizeOnData fakeQuantizeOnData;
@@ -29,6 +27,7 @@ typedef std::tuple<
     ngraph::element::Type,
     std::string,
     ngraph::pass::low_precision::LayerTransformation::Params,
+    std::pair<ngraph::PartialShape, ngraph::Shape>,
     GroupConvolutionTransformationParam,
     bool // add precision preserved operation
 > GroupConvolutionTransformationParams;
diff --git a/inference-engine/tests/functional/plugin/shared/src/low_precision_transformations/convolution_transformation.cpp b/inference-engine/tests/functional/plugin/shared/src/low_precision_transformations/convolution_transformation.cpp
index 89b900011a5..7519814f23a 100755
--- a/inference-engine/tests/functional/plugin/shared/src/low_precision_transformations/convolution_transformation.cpp
+++ b/inference-engine/tests/functional/plugin/shared/src/low_precision_transformations/convolution_transformation.cpp
@@ -30,6 +30,7 @@ std::string ConvolutionTransformation::getTestCaseName(const testing::TestParamI
 
     std::ostringstream result;
     result << getTestCaseNameByParams(netPrecision, inputShape, targetDevice, params) << "_" <<
+        inputShape.rank().get_length() << "D_" <<
         param.fakeQuantizeOnData << "_" <<
         param.fakeQuantizeOnWeights;
     return result.str();
diff --git a/inference-engine/tests/functional/plugin/shared/src/low_precision_transformations/group_convolution_transformation.cpp b/inference-engine/tests/functional/plugin/shared/src/low_precision_transformations/group_convolution_transformation.cpp
index 28aee25c464..0f48e736a12 100644
--- a/inference-engine/tests/functional/plugin/shared/src/low_precision_transformations/group_convolution_transformation.cpp
+++ b/inference-engine/tests/functional/plugin/shared/src/low_precision_transformations/group_convolution_transformation.cpp
@@ -24,15 +24,17 @@ std::string GroupConvolutionTransformation::getTestCaseName(const testing::TestP
     ngraph::element::Type netPrecision;
     std::string targetDevice;
     ngraph::pass::low_precision::LayerTransformation::Params params;
+    std::pair<ngraph::PartialShape, ngraph::Shape> inputShapes;
     GroupConvolutionTransformationParam param;
     bool addPrecisionPreserved;
-    std::tie(netPrecision, targetDevice, params, param, addPrecisionPreserved) = obj.param;
+    std::tie(netPrecision, targetDevice, params, inputShapes, param, addPrecisionPreserved) = obj.param;
 
     std::ostringstream result;
     result <<
-        getTestCaseNameByParams(netPrecision, param.inputShape, targetDevice, params) << "_" <<
-        param.inputShape << "_" <<
-        param.outputShape << "_" <<
+        getTestCaseNameByParams(netPrecision, inputShapes.first, targetDevice, params) << "_" <<
+        inputShapes.first.rank().get_length() << "D_" <<
+        inputShapes.first << "_" <<
+        inputShapes.second << "_" <<
         param.group << "_" <<
         param.groupCalculationDimention << "_" <<
         param.fakeQuantizeOnData << "_" <<
@@ -46,14 +48,18 @@ void GroupConvolutionTransformation::SetUp() {
 
     ngraph::element::Type netPrecision;
     ngraph::pass::low_precision::LayerTransformation::Params params;
+    std::pair<ngraph::PartialShape, ngraph::Shape> inputShapes;
     GroupConvolutionTransformationParam param;
     bool addPrecisionPreserved;
-    std::tie(netPrecision, targetDevice, params, param, addPrecisionPreserved) = this->GetParam();
+    std::tie(netPrecision, targetDevice, params, inputShapes, param, addPrecisionPreserved) = this->GetParam();
 
+    while (param.fakeQuantizeOnData.constantShape.size() > inputShapes.first.size()) {
+        param.fakeQuantizeOnData.constantShape.pop_back();
+    }
     function = ngraph::builder::subgraph::GroupConvolutionFunction::getOriginal(
         netPrecision,
-        param.inputShape,
-        param.outputShape,
+        inputShapes.first,
+        inputShapes.second,
         param.group,
         param.groupCalculationDimention,
         param.fakeQuantizeOnData,
@@ -64,7 +70,7 @@ void GroupConvolutionTransformation::SetUp() {
 void GroupConvolutionTransformation::Run() {
     LayerTestsCommon::Run();
 
-    const auto param = std::get<3>(GetParam());
+    const auto param = std::get<4>(GetParam());
     if (!param.layerName.empty()) {
         const auto actualPrecision = getRuntimePrecisionByType(param.layerName);
         auto expectedPrecision = param.expectedKernelType;
diff --git a/inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/src/fake_quantize_and_convolution_function.cpp b/inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/src/fake_quantize_and_convolution_function.cpp
index 1ae071fd508..74ee87c469e 100644
--- a/inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/src/fake_quantize_and_convolution_function.cpp
+++ b/inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/src/fake_quantize_and_convolution_function.cpp
@@ -19,6 +19,10 @@ std::shared_ptr<ngraph::Function> FakeQuantizeAndConvolutionFunction::get(
     const ngraph::PartialShape& inputShape,
     const FakeQuantizeOnData& fqOnData,
     const FakeQuantizeOnWeights& fqOnWeights) {
+    const auto rankLength = inputShape.rank().is_dynamic() ? 4 : inputShape.rank().get_length();
+    if ((rankLength != 3ul) && (rankLength != 4ul)) {
+        throw ov::Exception("not supported input shape rank: " + std::to_string(rankLength));
+    }
 
     const auto input = std::make_shared<ngraph::opset1::Parameter>(precision, inputShape);
     const auto fakeQuantizeOnActivations = fqOnData.empty() ?
@@ -34,28 +38,31 @@ std::shared_ptr<ngraph::Function> FakeQuantizeAndConvolutionFunction::get(
     const size_t outputChannelsCount = 2 * inputShape[1].get_length();
     const auto weights = ngraph::opset1::Constant::create(
         precision,
-        ngraph::Shape{ outputChannelsCount, inputChannelsCount, 1, 1 },
+        rankLength == 3ul ?
+            (ngraph::Shape{ outputChannelsCount, inputChannelsCount, 1}) :
+            (ngraph::Shape{ outputChannelsCount, inputChannelsCount, 1, 1 }),
         std::vector<float>(outputChannelsCount * inputChannelsCount, 1));
 
     auto maxPool = std::make_shared<opset1::MaxPool>(
         fqOnData.empty() ? input : fakeQuantizeOnActivations,
-        Strides{ 1, 1 },
-        Shape{ 1, 1 },
-        Shape{ 0, 0 },
-        Shape{ 2, 2 },
+        Strides(rankLength - 2, 1ul),
+        Shape(rankLength - 2, 1ul),
+        Shape(rankLength - 2, 0ul),
+        Shape(rankLength - 2, 2ul),
         op::RoundingType::FLOOR);
     maxPool->set_friendly_name("maxPool");
 
     const auto convolution = std::make_shared<ngraph::opset1::Convolution>(
         maxPool, //fqOnData.empty() ? input : fakeQuantizeOnActivations,
-        fqOnWeights.empty() ? weights->output(0) :
-        ngraph::builder::makeFakeQuantize(
-            weights, precision, fqOnWeights.quantizationLevel, fqOnWeights.constantShape,
-            fqOnWeights.inputLowValues, fqOnWeights.inputHighValues, fqOnWeights.outputLowValues, fqOnWeights.outputHighValues),
-        ngraph::Strides{ 1, 1 },
-        ngraph::CoordinateDiff{ 0, 0 },
-        ngraph::CoordinateDiff{ 0, 0 },
-        ngraph::Strides{ 1, 1 });
+        fqOnWeights.empty() ?
+            weights->output(0) :
+            ngraph::builder::makeFakeQuantize(
+                weights, precision, fqOnWeights.quantizationLevel, fqOnWeights.constantShape,
+                fqOnWeights.inputLowValues, fqOnWeights.inputHighValues, fqOnWeights.outputLowValues, fqOnWeights.outputHighValues),
+        ngraph::Strides(rankLength - 2, 1ul),
+        ngraph::CoordinateDiff(rankLength - 2, 0ul),
+        ngraph::CoordinateDiff(rankLength - 2, 0ul),
+        ngraph::Strides(rankLength - 2, 1ul));
     convolution->set_friendly_name("convolution");
 
     ngraph::ResultVector results{ std::make_shared<ngraph::opset1::Result>(convolution) };
diff --git a/inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/src/group_convolution_function.cpp b/inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/src/group_convolution_function.cpp
index fb6f82696cb..335f6f44df4 100644
--- a/inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/src/group_convolution_function.cpp
+++ b/inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/src/group_convolution_function.cpp
@@ -22,6 +22,7 @@ namespace builder {
 namespace subgraph {
 
 std::shared_ptr<Node> createWeightsOriginal(
+    const long long int rankLength,
     const ngraph::element::Type precision,
     const size_t inputChannelsCount,
     const size_t outputChannelsCount,
@@ -35,7 +36,9 @@ std::shared_ptr<Node> createWeightsOriginal(
     if (fakeQuantizeOnWeights.empty() && dequantizationOnWeights.empty()) {
         weights = ngraph::opset1::Constant::create(
             precision,
-            ngraph::Shape{ outputChannelsCount, inputChannelsCount, 1, 1 },
+            rankLength == 3 ?
+                ngraph::Shape{ outputChannelsCount, inputChannelsCount, 1 } :
+                ngraph::Shape{ outputChannelsCount, inputChannelsCount, 1, 1 },
             weightsValues.size() == 1ul ?
                 std::vector<float>(outputChannelsCount * inputChannelsCount, weightsValues[0]) :
                 weightsValues);
@@ -43,9 +46,15 @@ std::shared_ptr<Node> createWeightsOriginal(
         const size_t inputChannelsPerGroup = inputChannelsCount / groupCount;
         weights = ngraph::opset1::Constant::create(
             precision,
-            ngraph::Shape{ outputChannelsCount, inputChannelsPerGroup, kernelSize, kernelSize },
+            rankLength == 3 ?
+                ngraph::Shape{ outputChannelsCount, inputChannelsPerGroup, kernelSize } :
+                ngraph::Shape{ outputChannelsCount, inputChannelsPerGroup, kernelSize, kernelSize },
             weightsValues.size() == 1ul ?
-                std::vector<float>(outputChannelsCount * kernelSize * kernelSize * inputChannelsPerGroup, weightsValues[0]) :
+                std::vector<float>(
+                    rankLength == 3 ?
+                        outputChannelsCount * kernelSize * inputChannelsPerGroup :
+                        outputChannelsCount * kernelSize * kernelSize * inputChannelsPerGroup,
+                    weightsValues[0]) :
                 weightsValues);
 
         if (!fakeQuantizeOnWeights.empty()) {
@@ -53,7 +62,9 @@ std::shared_ptr<Node> createWeightsOriginal(
                 weights,
                 precision,
                 fakeQuantizeOnWeights.quantizationLevel,
-                { outputChannelsCount, 1, 1, 1 },
+                rankLength == 3 ?
+                    std::vector<size_t>{ outputChannelsCount, 1, 1 } :
+                    std::vector<size_t>{ outputChannelsCount, 1, 1, 1 },
                 fakeQuantizeOnWeights.inputLowValues,
                 fakeQuantizeOnWeights.inputHighValues,
                 fakeQuantizeOnWeights.outputLowValues,
@@ -68,13 +79,19 @@ std::shared_ptr<Node> createWeightsOriginal(
             weights,
             ngraph::opset1::Constant::create(
                 element::i64,
-                Shape{ 5 },
-                std::vector<int64_t> {
-                    calculatedDimention == 0 ? -1 : static_cast<int64_t>(groupCount),
-                    calculatedDimention == 1 ? -1 : static_cast<int64_t>(outputChannelsCount / groupCount),
-                    static_cast<int64_t>(inputChannelsPerGroup),
-                    static_cast<int64_t>(kernelSize),
-                    static_cast<int64_t>(kernelSize) }),
+                Shape{ static_cast<size_t>(rankLength) + 1ul },
+                rankLength == 3 ?
+                    std::vector<int64_t> {
+                        calculatedDimention == 0 ? -1 : static_cast<int64_t>(groupCount),
+                        calculatedDimention == 1 ? -1 : static_cast<int64_t>(outputChannelsCount / groupCount),
+                        static_cast<int64_t>(inputChannelsPerGroup),
+                        static_cast<int64_t>(kernelSize) } :
+                    std::vector<int64_t> {
+                        calculatedDimention == 0 ? -1 : static_cast<int64_t>(groupCount),
+                        calculatedDimention == 1 ? -1 : static_cast<int64_t>(outputChannelsCount / groupCount),
+                        static_cast<int64_t>(inputChannelsPerGroup),
+                        static_cast<int64_t>(kernelSize),
+                        static_cast<int64_t>(kernelSize) }),
             true);
     }
 
@@ -90,6 +107,11 @@ std::shared_ptr<ngraph::Function> GroupConvolutionFunction::getOriginal(
     const ngraph::builder::subgraph::DequantizationOperations& dequantizationBefore,
     std::shared_ptr<ngraph::opset1::Constant> weightsConst,
     const ngraph::builder::subgraph::FakeQuantizeOnWeights fakeQuantizeOnWeights) {
+    const auto rankLength = inputShape.size();
+    if ((rankLength != 3) && (rankLength != 4)) {
+        throw ov::Exception("not supported input shape rank: " + std::to_string(rankLength));
+    }
+
     const auto input = std::make_shared<ngraph::opset1::Parameter>(precision, inputShape);
     const auto dequantization = makeDequantization(input, dequantizationBefore);
 
@@ -103,6 +125,7 @@ std::shared_ptr<ngraph::Function> GroupConvolutionFunction::getOriginal(
     }
 
     std::shared_ptr<ngraph::Node> weights = createWeightsOriginal(
+        rankLength,
         weightsConst->get_element_type(),
         inputChannelsCount,
         outputChannelsCount,
@@ -135,23 +158,49 @@ std::shared_ptr<ngraph::Function> GroupConvolutionFunction::getOriginal(
     const FakeQuantizeOnData& fakeQuantizeOnData,
     const FakeQuantizeOnWeights& fakeQuantizeOnWeights,
     const bool addPrecisionPreserved) {
+    const auto rankLength = inputShape.rank().is_dynamic() ? 4 : inputShape.rank().get_length();
+    if ((rankLength != 3) && (rankLength != 4)) {
+        throw ov::Exception("not supported input shape rank: " + std::to_string(rankLength));
+    }
+
     const auto input = std::make_shared<ngraph::opset1::Parameter>(precision, inputShape);
 
     std::shared_ptr<ngraph::Node> parent = input;
     if (!fakeQuantizeOnData.empty()) {
         parent = std::make_shared<ngraph::opset1::FakeQuantize>(
             input,
-            std::make_shared<Constant>(precision, Shape{ 1, fakeQuantizeOnData.inputLowValues.size(), 1, 1 }, fakeQuantizeOnData.inputLowValues),
-            std::make_shared<Constant>(precision, Shape{ 1, fakeQuantizeOnData.inputHighValues.size(), 1, 1 }, fakeQuantizeOnData.inputHighValues),
-            std::make_shared<Constant>(precision, Shape{ 1, fakeQuantizeOnData.outputLowValues.size(), 1, 1 }, fakeQuantizeOnData.outputLowValues),
-            std::make_shared<Constant>(precision, Shape{ 1, fakeQuantizeOnData.outputHighValues.size(), 1, 1 }, fakeQuantizeOnData.outputHighValues),
+            std::make_shared<Constant>(
+                precision,
+                rankLength == 3 ?
+                    Shape{ 1, fakeQuantizeOnData.inputLowValues.size(), 1 } :
+                    Shape{ 1, fakeQuantizeOnData.inputLowValues.size(), 1, 1 },
+                fakeQuantizeOnData.inputLowValues),
+            std::make_shared<Constant>(
+                precision,
+                rankLength == 3 ?
+                    Shape{ 1, fakeQuantizeOnData.inputHighValues.size(), 1 } :
+                    Shape{ 1, fakeQuantizeOnData.inputHighValues.size(), 1, 1 },
+                fakeQuantizeOnData.inputHighValues),
+            std::make_shared<Constant>(
+                precision,
+                rankLength == 3 ?
+                    Shape{ 1, fakeQuantizeOnData.outputLowValues.size(), 1 } :
+                    Shape{ 1, fakeQuantizeOnData.outputLowValues.size(), 1, 1 },
+                fakeQuantizeOnData.outputLowValues),
+            std::make_shared<Constant>(
+                precision,
+                rankLength == 3 ?
+                    Shape{ 1, fakeQuantizeOnData.outputHighValues.size(), 1 } :
+                    Shape{ 1, fakeQuantizeOnData.outputHighValues.size(), 1, 1 },
+                fakeQuantizeOnData.outputHighValues),
             fakeQuantizeOnData.quantizationLevel);
     }
 
     if (addPrecisionPreserved) {
-        const std::vector<size_t> stride = { 1, 1 };
-        const std::vector<size_t> padBegin = { 0, 0 };
-        const std::vector<size_t> padEnd = { 0, 0 };
+        const std::vector<size_t> stride(rankLength - 2, 1ul);
+        const std::vector<size_t> padBegin(rankLength - 2, 0ul);
+        const std::vector<size_t> padEnd(rankLength - 2, 0ul);
+        const std::vector<size_t> padKernel(rankLength - 2, 3ul);
         const ngraph::op::PadType padType = ngraph::op::PadType::NOTSET;
         const ngraph::op::RoundingType roundingType = ngraph::op::RoundingType::FLOOR;
         const auto pooling = std::make_shared<ngraph::opset1::MaxPool>(
@@ -159,7 +208,7 @@ std::shared_ptr<ngraph::Function> GroupConvolutionFunction::getOriginal(
             stride,
             padBegin,
             padEnd,
-            ngraph::Shape{ 3, 3 },
+            padKernel,
             roundingType,
             padType);
         parent = pooling;
@@ -173,6 +222,7 @@ std::shared_ptr<ngraph::Function> GroupConvolutionFunction::getOriginal(
 
     std::vector<float> weightsValues = { 1.f };
     std::shared_ptr<ngraph::Node> weights = createWeightsOriginal(
+        rankLength,
         precision,
         inputChannelsCount,
         outputChannelsCount,
@@ -186,10 +236,10 @@ std::shared_ptr<ngraph::Function> GroupConvolutionFunction::getOriginal(
     const auto convolution = std::make_shared<ngraph::opset1::GroupConvolution>(
         parent,
         weights,
-        ngraph::Strides{ 1, 1 },
-        ngraph::CoordinateDiff{ 0, 0 },
-        ngraph::CoordinateDiff{ 0, 0 },
-        ngraph::Strides{ 1, 1 });
+        ngraph::Strides(rankLength - 2, 1ul),
+        ngraph::CoordinateDiff(rankLength - 2, 0),
+        ngraph::CoordinateDiff(rankLength - 2, 0),
+        ngraph::Strides(rankLength - 2, 1));
 
     ngraph::ResultVector results{ std::make_shared<ngraph::opset1::Result>(convolution) };
     return std::make_shared<ngraph::Function>(results, ngraph::ParameterVector{ input }, "GroupConvolutionTransformation");
@@ -208,6 +258,11 @@ std::shared_ptr<ngraph::Function> GroupConvolutionFunction::get(
     const ngraph::element::Type precisionAfterOperation,
     const ngraph::builder::subgraph::DequantizationOperations& dequantizationAfter,
     const ngraph::element::Type precisionAfterDequantization) {
+    const auto rankLength = inputShape.rank().is_dynamic() ? 4 : inputShape.rank().get_length();
+    if ((rankLength != 3) && (rankLength != 4)) {
+        throw ov::Exception("not supported input shape rank: " + std::to_string(rankLength));
+    }
+
     const auto input = std::make_shared<ngraph::opset1::Parameter>(precision, inputShape);
     const auto deqBefore = makeDequantization(input, dequantizationBefore);
 
@@ -235,6 +290,7 @@ std::shared_ptr<ngraph::Function> GroupConvolutionFunction::get(
                 weightsConst->cast_vector<float>()[0]) : weightsConst->cast_vector<float>());
     } else {
         weights = createWeightsOriginal(
+            rankLength,
             weightsConst->get_element_type(),
             inputChannelsCount,
             outputChannelsCount,

From 66b75e60b0938f1557af06d9653e19fd42b95b35 Mon Sep 17 00:00:00 2001
From: Vladislav Golubev <vladislav.golubev@intel.com>
Date: Thu, 25 Nov 2021 14:05:38 +0300
Subject: [PATCH 10/72] [CPU] ScatterUpdate: dynamic shapes support (#8581)

---
 .../nodes/mkldnn_scatter_update_node.cpp      | 103 ++++++-----
 .../nodes/mkldnn_scatter_update_node.h        |  10 +-
 .../single_layer_tests/scatter_ND_update.cpp  | 174 ++++++++++++++++++
 .../scatter_elements_update.cpp               | 174 ++++++++++++++++++
 .../cpu/single_layer_tests/scatter_update.cpp | 147 +++++++++++++++
 5 files changed, 556 insertions(+), 52 deletions(-)
 create mode 100644 inference-engine/tests/functional/plugin/cpu/single_layer_tests/scatter_ND_update.cpp
 create mode 100644 inference-engine/tests/functional/plugin/cpu/single_layer_tests/scatter_elements_update.cpp
 create mode 100644 inference-engine/tests/functional/plugin/cpu/single_layer_tests/scatter_update.cpp

diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_scatter_update_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_scatter_update_node.cpp
index bb68ecfdfc0..566fe7fb00b 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_scatter_update_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_scatter_update_node.cpp
@@ -21,14 +21,10 @@ using namespace InferenceEngine;
 
 bool MKLDNNScatterUpdateNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
-        const auto scatterElemUpd = std::dynamic_pointer_cast<const ngraph::opset3::ScatterElementsUpdate>(op);
-        const auto scatterUpd = std::dynamic_pointer_cast<const ngraph::opset3::ScatterUpdate>(op);
-        const auto scatterNdUpd = std::dynamic_pointer_cast<const ngraph::opset4::ScatterNDUpdate>(op);
-        if (scatterElemUpd == nullptr && scatterUpd == nullptr && scatterNdUpd == nullptr) {
+        auto scatterElemUpd = ngraph::as_type_ptr<const ngraph::opset3::ScatterElementsUpdate>(op);
+        auto scatterUpd = ngraph::as_type_ptr<const ngraph::opset3::ScatterUpdate>(op);
+        auto scatterNdUpd = ngraph::as_type_ptr<const ngraph::opset4::ScatterNDUpdate>(op);
+        if (!scatterElemUpd && !scatterUpd && !scatterNdUpd) {
             const std::string opType = op->get_type_name();
             errorMessage = "Only opset" + opType == "ScatterNDUpdate" ? "4 " : "3 " + opType + " operation is supported";
             return false;
@@ -81,10 +77,10 @@ void MKLDNNScatterUpdateNode::initSupportedPrimitiveDescriptors() {
     if (!supportedPrimitiveDescriptors.empty())
         return;
 
-    auto srcDataDim = getInputShapeAtPort(DATA_ID).getStaticDims();
-    auto indicesDim = getInputShapeAtPort(INDICES_ID).getStaticDims();
-    auto updateDim =  getInputShapeAtPort(UPDATE_ID).getStaticDims();
-    auto dstDataDim = getOutputShapeAtPort(0).getStaticDims();
+    const auto& srcDataDim = getInputShapeAtPort(DATA_ID).getDims();
+    const auto& indicesDim = getInputShapeAtPort(INDICES_ID).getDims();
+    const auto& updateDim =  getInputShapeAtPort(UPDATE_ID).getDims();
+    const auto& dstDataDim = getOutputShapeAtPort(0).getDims();
 
     size_t srcRank = srcDataDim.size();
     size_t indicesRank = indicesDim.size();
@@ -96,9 +92,9 @@ void MKLDNNScatterUpdateNode::initSupportedPrimitiveDescriptors() {
         IE_THROW() << errorPrefix << " should have same rank for input and output tensor";
     } else {
         for (size_t r = 0; r < srcRank; r++) {
-            if (srcDataDim[r] != dstDataDim[r]) {
+            if (!dimsEqualWeak(srcDataDim[r], dstDataDim[r])) {
                 IE_THROW() << errorPrefix << " should have same shape for input and output tensor. The input shape is "
-                                   << srcDataDim[r] << ", while output shape is " << dstDataDim[r] << " for " << r << "th dimension";
+                           << srcDataDim[r] << ", while output shape is " << dstDataDim[r] << " for " << r << "th dimension";
             }
         }
     }
@@ -111,26 +107,28 @@ void MKLDNNScatterUpdateNode::initSupportedPrimitiveDescriptors() {
             break;
         }
         case ScatterUpdateMode::ScatterNDUpdate: {
-            size_t k = indicesDim[indicesRank - 1];
-            if (k > srcRank) {
-                IE_THROW() << errorPrefix << "' do not have an correct indices' last dimension value, "
-                                   << "which should be smaller than or equal to input tensor rank";
-            }
+            if (indicesDim[indicesRank - 1] != Shape::UNDEFINED_DIM) {
+                size_t k = indicesDim[indicesRank - 1];
+                if (k > srcRank) {
+                    IE_THROW() << errorPrefix << "' do not have an correct indices' last dimension value, "
+                        << "which should be smaller than or equal to input tensor rank";
+                }
 
-            SizeVector expectUpdateShape = {};
-            size_t tupleRank = indicesRank - 1;
-            for (size_t ri = 0; ri < tupleRank; ri++) {
-                expectUpdateShape.push_back(indicesDim[ri]);
-            }
-            for (size_t rd = k; rd < srcRank; rd++) {
-                expectUpdateShape.push_back(srcDataDim[rd]);
-            }
-            if (expectUpdateShape.size() != updateRank) {
-                IE_THROW() << errorPrefix << " do not have matched tensor rank relationship for input, indices and update";
-            }
-            for (size_t ru = 0; ru < updateRank; ru++) {
-                if (updateDim[ru] != expectUpdateShape[ru]) {
-                    IE_THROW() << errorPrefix << " do not have matched tensor shape relationship for input, indices and update";
+                SizeVector expectUpdateShape = {};
+                size_t tupleRank = indicesRank - 1;
+                for (size_t ri = 0; ri < tupleRank; ri++) {
+                    expectUpdateShape.push_back(indicesDim[ri]);
+                }
+                for (size_t rd = k; rd < srcRank; rd++) {
+                    expectUpdateShape.push_back(srcDataDim[rd]);
+                }
+                if (expectUpdateShape.size() != updateRank) {
+                    IE_THROW() << errorPrefix << " do not have matched tensor rank relationship for input, indices and update";
+                }
+                for (size_t ru = 0; ru < updateRank; ru++) {
+                    if (!dimsEqualWeak(updateDim[ru], expectUpdateShape[ru])) {
+                        IE_THROW() << errorPrefix << " do not have matched tensor shape relationship for input, indices and update";
+                    }
                 }
             }
             break;
@@ -140,7 +138,7 @@ void MKLDNNScatterUpdateNode::initSupportedPrimitiveDescriptors() {
                 IE_THROW() << errorPrefix << " do not have the same tensor rank for input, indices and update";
             }
             for (size_t ri = 0; ri < indicesRank; ri++) {
-                if (indicesDim[ri] != updateDim[ri]) {
+                if (!dimsEqualWeak(indicesDim[ri], updateDim[ri])) {
                     IE_THROW() << errorPrefix << " do not have the same tensor shape for indices and update";
                 }
             }
@@ -178,8 +176,8 @@ void MKLDNNScatterUpdateNode::initSupportedPrimitiveDescriptors() {
     dataPrec = getOriginalInputPrecisionAtPort(DATA_ID);
     dataSize = dataPrec.size();
 
-    bool canBeInplace = getParentEdgeAt(DATA_ID)->getParent()->getChildEdges().size() == 1 &&
-            !getParentEdgeAt(DATA_ID)->getParent()->isConstant();
+    bool canBeInplace = !isDynamicNode() && getParentEdgeAt(DATA_ID)->getParent()->getChildEdges().size() == 1 &&
+                        !getParentEdgeAt(DATA_ID)->getParent()->isConstant();
 
     NodeConfig config;
     config.dynBatchSupport = false;
@@ -226,6 +224,18 @@ void MKLDNNScatterUpdateNode::createPrimitive() {
         IE_THROW() << errorPrefix << " did not allocate update memory";
     if (getSelectedPrimitiveDescriptor() == nullptr)
         IE_THROW() << errorPrefix << " did not set preferable primitive descriptor";
+
+    if (inputShapesDefined()) {
+        updateLastInputDims();
+    }
+}
+
+bool MKLDNNScatterUpdateNode::needPrepareParams() const {
+    return false;
+}
+
+void MKLDNNScatterUpdateNode::executeDynamicImpl(mkldnn::stream strm) {
+    return execute(strm);
 }
 
 int64_t MKLDNNScatterUpdateNode::getIndicesValue(uint8_t *indices, size_t offset) {
@@ -245,7 +255,7 @@ int64_t MKLDNNScatterUpdateNode::getIndicesValue(uint8_t *indices, size_t offset
 // shapeND: n     c     d     h    w
 // blockND: ncdhw cdhw  dhw   hw   w    1
 // index  : 0      1    2     3    4    5
-static std::vector<size_t> getBlockND(const SizeVector& shape) {
+static std::vector<size_t> getBlockND(const VectorDims& shape) {
     size_t shapeRank = shape.size();
     std::vector<size_t> blockND(shapeRank + 1, 1);
     for (int i = shapeRank - 1; i >= 0; i--) {
@@ -265,8 +275,8 @@ void MKLDNNScatterUpdateNode::execute(mkldnn::stream strm) {
     uint8_t *indicesPtr = reinterpret_cast<uint8_t*>(indicesMemPtr->GetPtr());
     uint8_t *updatePtr = reinterpret_cast<uint8_t*>(updateMemPtr->GetPtr());
 
-    SizeVector srcDataDim = getParentEdgeAt(DATA_ID)->getMemory().getStaticDims();
-    SizeVector indicesDim = getParentEdgeAt(INDICES_ID)->getMemory().getStaticDims();
+    const auto& srcDataDim = getParentEdgeAt(DATA_ID)->getMemory().getStaticDims();
+    const auto& indicesDim = getParentEdgeAt(INDICES_ID)->getMemory().getStaticDims();
     size_t srcRank = srcDataDim.size();
     int axis = 0;
     if (axisRelaxed) {
@@ -362,9 +372,9 @@ void MKLDNNScatterUpdateNode::execute(mkldnn::stream strm) {
 // and indices tensor of shape [i_0, i_1, ..., i_k].
 // Updates tensor shape should be [d_0, d_1, ... d_(axis - 1), i_0, i_1, ..., i_k, d_(axis + 1), ..., d_n].
 void MKLDNNScatterUpdateNode::scatterUpdate(uint8_t *indices, uint8_t *update, int axis, uint8_t *dstData) {
-    SizeVector srcDataDim = getParentEdgeAt(DATA_ID)->getMemory().getStaticDims();
-    SizeVector indicesDim = getParentEdgeAt(INDICES_ID)->getMemory().getStaticDims();
-    SizeVector updateDim = getParentEdgeAt(UPDATE_ID)->getMemory().getStaticDims();
+    const auto& srcDataDim = getParentEdgeAt(DATA_ID)->getMemory().getStaticDims();
+    const auto& indicesDim = getParentEdgeAt(INDICES_ID)->getMemory().getStaticDims();
+    const auto& updateDim = getParentEdgeAt(UPDATE_ID)->getMemory().getStaticDims();
     size_t indicesRank = indicesDim.size();
 
     std::vector<size_t> srcBlockND = getBlockND(srcDataDim);
@@ -395,8 +405,8 @@ void MKLDNNScatterUpdateNode::scatterUpdate(uint8_t *indices, uint8_t *update, i
 // k is indices.shape[-1] and should not be greater than rank of input, q is rank of indicies.
 // updates is a (q-1)-dimension tensor of replacement-slice-values
 void MKLDNNScatterUpdateNode::scatterNDUpdate(uint8_t *indices, uint8_t *update, uint8_t *dstData) {
-    SizeVector srcDataDim = getParentEdgeAt(DATA_ID)->getMemory().getStaticDims();
-    SizeVector indicesDim = getParentEdgeAt(INDICES_ID)->getMemory().getStaticDims();
+    const auto& srcDataDim = getParentEdgeAt(DATA_ID)->getMemory().getStaticDims();
+    const auto& indicesDim = getParentEdgeAt(INDICES_ID)->getMemory().getStaticDims();
     size_t indicesRank = indicesDim.size();
 
     std::vector<size_t> srcBlockND = getBlockND(srcDataDim);
@@ -425,9 +435,8 @@ void MKLDNNScatterUpdateNode::scatterNDUpdate(uint8_t *indices, uint8_t *update,
 // output[i][indices[i][j][k]][k] = updates[i][j][k] if axis = 1,
 // output[i][j][indices[i][j][k]] = updates[i][j][k] if axis = 2.
 void MKLDNNScatterUpdateNode::scatterElementsUpdate(uint8_t *indices, uint8_t *update, int axis, uint8_t *dstData) {
-    SizeVector srcDataDim = getParentEdgeAt(DATA_ID)->getMemory().getStaticDims();
-    SizeVector updateDim = getParentEdgeAt(UPDATE_ID)->getMemory().getStaticDims();
-    SizeVector indicesDim = getParentEdgeAt(INDICES_ID)->getMemory().getStaticDims();
+    const auto& srcDataDim = getParentEdgeAt(DATA_ID)->getMemory().getStaticDims();
+    const auto& updateDim = getParentEdgeAt(UPDATE_ID)->getMemory().getStaticDims();
     size_t updateRank = updateDim.size();
 
     std::vector<size_t> srcBlockND = getBlockND(srcDataDim);
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_scatter_update_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_scatter_update_node.h
index 7fc9d96818f..30b0ca5735d 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_scatter_update_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_scatter_update_node.h
@@ -31,6 +31,9 @@ public:
         return false;
     }
 
+    bool needPrepareParams() const override;
+    void executeDynamicImpl(mkldnn::stream strm) override;
+
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 private:
@@ -40,10 +43,7 @@ private:
     inline int64_t getIndicesValue(uint8_t *indices, size_t offset);
 
     ScatterUpdateMode scatterUpdateMode = ScatterUpdateMode::ScatterUpdate;
-    const size_t DATA_ID = 0;
-    const size_t INDICES_ID = 1;
-    const size_t UPDATE_ID = 2;
-    const size_t AXIS_ID = 3;
+    enum { DATA_ID, INDICES_ID, UPDATE_ID, AXIS_ID };
 
     // if axis can be set other than default 0.
     bool axisRelaxed = false;
@@ -53,4 +53,4 @@ private:
     std::string errorPrefix;
 };
 
-}  // namespace MKLDNNPlugin
\ No newline at end of file
+}  // namespace MKLDNNPlugin
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/scatter_ND_update.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/scatter_ND_update.cpp
new file mode 100644
index 00000000000..e92e40dc299
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/scatter_ND_update.cpp
@@ -0,0 +1,174 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "functional_test_utils/ov_tensor_utils.hpp"
+#include "test_utils/cpu_test_utils.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "ngraph_functions/builders.hpp"
+
+using namespace ngraph;
+using namespace InferenceEngine;
+using namespace CPUTestUtils;
+using namespace ov::test;
+
+namespace CPULayerTestsDefinitions {
+using ScatterNDUpdateShapes = std::vector<InputShape>;
+using IndicesValues = std::vector<std::int64_t>;
+
+struct ScatterNDUpdateLayerParams {
+    ScatterNDUpdateShapes inputShapes;
+    IndicesValues indicesValues;
+};
+
+using scatterUpdateParams = std::tuple<
+    ScatterNDUpdateLayerParams,
+    ElementType,        // input precision
+    ElementType>;       // indices precision
+
+class ScatterNDUpdateLayerCPUTest : public testing::WithParamInterface<scatterUpdateParams>, public SubgraphBaseTest, public CPUTestsBase {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<scatterUpdateParams> obj) {
+        ScatterNDUpdateLayerParams scatterParams;
+        ElementType inputPrecision;
+        ElementType idxPrecision;
+        std::tie(scatterParams, inputPrecision, idxPrecision) = obj.param;
+        const auto inputShapes = scatterParams.inputShapes;
+        const auto indicesValues = scatterParams.indicesValues;
+
+        std::ostringstream result;
+        result << inputPrecision << "_IS=";
+        for (const auto& shape : inputShapes) {
+            result << CommonTestUtils::partialShape2str({ shape.first }) << "_";
+        }
+        result << "TS=";
+        for (const auto& shape : inputShapes) {
+            result << "(";
+            for (const auto& targetShape : shape.second) {
+                result << CommonTestUtils::vec2str(targetShape) << "_";
+            }
+            result << ")_";
+        }
+        result << "indices_values=" << CommonTestUtils::vec2str(indicesValues) << "_idx_precision=" << idxPrecision;
+        return result.str();
+    }
+
+protected:
+    void generate_inputs(const std::vector<ov::Shape>& targetInputStaticShapes) override {
+        inputs.clear();
+        const auto& funcInputs = function->inputs();
+        for (int i = 0; i < funcInputs.size(); ++i) {
+            const auto& funcInput = funcInputs[i];
+            const auto& inputPrecision = funcInput.get_element_type();
+            const auto& targetShape = targetInputStaticShapes[i];
+            ov::runtime::Tensor tensor;
+            if (i == 1) {
+                tensor = ov::runtime::Tensor{ inputPrecision, targetShape };
+                const auto indicesVals = std::get<0>(this->GetParam()).indicesValues;
+                if (inputPrecision == ElementType::i32) {
+                    auto data = tensor.data<std::int32_t>();
+                    for (size_t i = 0; i < tensor.get_size(); ++i) {
+                        data[i] = static_cast<std::int32_t>(indicesVals[i]);
+                    }
+                } else if (inputPrecision == ElementType::i64) {
+                    auto data = tensor.data<std::int64_t>();
+                    for (size_t i = 0; i < tensor.get_size(); ++i) {
+                        data[i] = indicesVals[i];
+                    }
+                } else {
+                    IE_THROW() << "GatherNDUpdate. Unsupported indices precision: " << inputPrecision;
+                }
+            } else {
+                if (inputPrecision.is_real()) {
+                    tensor = ov::test::utils::create_and_fill_tensor(inputPrecision, targetShape, 10, 0, 1000);
+                } else {
+                    tensor = ov::test::utils::create_and_fill_tensor(inputPrecision, targetShape);
+                }
+            }
+            inputs.insert({ funcInput.get_node_shared_ptr(), tensor });
+        }
+    }
+
+    void SetUp() override {
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+        ScatterNDUpdateLayerParams scatterParams;
+        ElementType inputPrecision;
+        ElementType idxPrecision;
+        std::tie(scatterParams, inputPrecision, idxPrecision) = this->GetParam();
+        const auto inputShapes = scatterParams.inputShapes;
+        const auto indicesValues = scatterParams.indicesValues;
+
+        init_input_shapes(inputShapes);
+        selectedType = makeSelectedTypeStr("unknown", inputPrecision);
+
+        auto dataParams = ngraph::builder::makeDynamicParams(inputPrecision, { inputDynamicShapes[0], inputDynamicShapes[2] });
+        auto indicesParam = ngraph::builder::makeDynamicParams(idxPrecision, { inputDynamicShapes[1] });
+        dataParams[0]->set_friendly_name("Param_1");
+        indicesParam[0]->set_friendly_name("Param_2");
+        dataParams[1]->set_friendly_name("Param_3");
+
+        auto scatter = std::make_shared<ngraph::opset4::ScatterNDUpdate>(dataParams[0], indicesParam[0], dataParams[1]);
+
+        ngraph::ParameterVector allParams{ dataParams[0], indicesParam[0], dataParams[1] };
+        function = makeNgraphFunction(inputPrecision, allParams, scatter, "ScatterNDUpdateLayerCPUTest");
+    }
+};
+
+TEST_P(ScatterNDUpdateLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+    run();
+    CheckPluginRelatedResults(executableNetwork, "ScatterUpdate");
+}
+
+const std::vector<ScatterNDUpdateLayerParams> scatterParams = {
+    ScatterNDUpdateLayerParams{
+        ScatterNDUpdateShapes{
+            {{-1, -1, -1, -1, -1}, {{10, 9, 10, 9, 10}, {10, 1, 11, 2, 5}, {10, 15, 8, 1, 7}}},
+            {{2, 2, 1}, {{2, 2, 1}, {2, 2, 1}, {2, 2, 1}}},
+            {{-1, -1, -1, -1, -1, -1}, {{2, 2, 9, 10, 9, 10}, {2, 2, 1, 11, 2, 5}, {2, 2, 15, 8, 1, 7}}},
+        },
+        IndicesValues{ 5, 6, 2, 8 }
+    },
+    ScatterNDUpdateLayerParams{
+        ScatterNDUpdateShapes{
+            {{-1, -1, -1, -1}, {{ 10, 9, 9, 11 }, { 7, 5, 3, 12 }, { 3, 4, 9, 8 }}},
+            {{2, 3}, {{2, 3}, {2, 3}, {2, 3}}},
+            {{-1, -1}, {{2, 11}, {2, 12}, {2, 8}}}
+        },
+        IndicesValues{ 0, 1, 1, 2, 2, 2 }
+    },
+    ScatterNDUpdateLayerParams{
+        ScatterNDUpdateShapes{
+            {{{3, 10}, -1, {3, 9}, -1}, {{ 10, 9, 9, 11 }, { 7, 5, 3, 12 }, { 3, 4, 9, 8 }}},
+            {{2, 3}, {{2, 3}, {2, 3}, {2, 3}}},
+            {{{2, 4}, -1}, {{2, 11}, {2, 12}, {2, 8}}}
+        },
+        IndicesValues{ 0, 1, 1, 2, 2, 2 }
+    },
+    ScatterNDUpdateLayerParams{
+        ScatterNDUpdateShapes{
+            {{{3, 10}, {4, 11}, {3, 9}, {8, 15}}, {{ 10, 9, 9, 11 }, { 7, 5, 3, 12 }, { 3, 4, 9, 8 }}},
+            {{2, 3}, {{2, 3}, {2, 3}, {2, 3}}},
+            {{{2, 4}, -1}, {{2, 11}, {2, 12}, {2, 8}}}
+        },
+        IndicesValues{ 0, 1, 1, 2, 2, 2 }
+    },
+};
+
+const std::vector<ElementType> inputPrecisions = {
+    ElementType::f32,
+    ElementType::i32,
+};
+
+const std::vector<ElementType> constantPrecisions = {
+    ElementType::i32,
+    ElementType::i64,
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs, ScatterNDUpdateLayerCPUTest,
+    ::testing::Combine(
+        ::testing::ValuesIn(scatterParams),
+        ::testing::ValuesIn(inputPrecisions),
+        ::testing::ValuesIn(constantPrecisions)),
+    ScatterNDUpdateLayerCPUTest::getTestCaseName);
+} // namespace CPULayerTestsDefinitions
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/scatter_elements_update.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/scatter_elements_update.cpp
new file mode 100644
index 00000000000..27ef185b74e
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/scatter_elements_update.cpp
@@ -0,0 +1,174 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "test_utils/cpu_test_utils.hpp"
+#include "functional_test_utils/ov_tensor_utils.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "ngraph_functions/builders.hpp"
+
+using namespace ngraph;
+using namespace InferenceEngine;
+using namespace CPUTestUtils;
+using namespace ov::test;
+
+namespace CPULayerTestsDefinitions {
+using ScatterElementsUpdateShapes = std::vector<InputShape>;
+using IndicesValues = std::vector<std::int64_t>;
+
+struct ScatterElementsUpdateLayerParams {
+    ScatterElementsUpdateShapes inputShapes;
+    IndicesValues indicesValues;
+};
+
+using scatterUpdateParams = std::tuple<
+    ScatterElementsUpdateLayerParams,
+    std::int64_t,       // axis
+    ElementType,        // input precision
+    ElementType>;       // indices precision
+
+class ScatterElementsUpdateLayerCPUTest : public testing::WithParamInterface<scatterUpdateParams>, public SubgraphBaseTest, public CPUTestsBase {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<scatterUpdateParams> obj) {
+        ScatterElementsUpdateLayerParams scatterParams;
+        std::int64_t axis;
+        ElementType inputPrecision;
+        ElementType idxPrecision;
+        std::tie(scatterParams, axis, inputPrecision, idxPrecision) = obj.param;
+        const auto inputShapes = scatterParams.inputShapes;
+        const auto indicesVals = scatterParams.indicesValues;
+
+        std::ostringstream result;
+        result << inputPrecision << "_IS=";
+        for (const auto& shape : inputShapes) {
+            result << CommonTestUtils::partialShape2str({ shape.first }) << "_";
+        }
+        result << "TS=";
+        for (const auto& shape : inputShapes) {
+            result << "(";
+            for (const auto& targetShape : shape.second) {
+                result << CommonTestUtils::vec2str(targetShape) << "_";
+            }
+            result << ")_";
+        }
+        result << "_indices_values=" << CommonTestUtils::vec2str(indicesVals)
+               << "axis=" << axis << "_idx_precision=" << idxPrecision;
+        return result.str();
+    }
+
+protected:
+    void generate_inputs(const std::vector<ov::Shape>& targetInputStaticShapes) override {
+        inputs.clear();
+        const auto& funcInputs = function->inputs();
+        for (int i = 0; i < funcInputs.size(); ++i) {
+            const auto& funcInput = funcInputs[i];
+            const auto& inputPrecision = funcInput.get_element_type();
+            const auto& targetShape = targetInputStaticShapes[i];
+            ov::runtime::Tensor tensor;
+            if (i == 1) {
+                tensor = ov::runtime::Tensor{ inputPrecision, targetShape };
+                const auto indicesVals = std::get<0>(this->GetParam()).indicesValues;
+                if (inputPrecision == ElementType::i32) {
+                    auto data = tensor.data<std::int32_t>();
+                    for (size_t i = 0; i < tensor.get_size(); ++i) {
+                        data[i] = static_cast<std::int32_t>(indicesVals[i]);
+                    }
+                } else if (inputPrecision == ElementType::i64) {
+                    auto data = tensor.data<std::int64_t>();
+                    for (size_t i = 0; i < tensor.get_size(); ++i) {
+                        data[i] = indicesVals[i];
+                    }
+                } else {
+                    IE_THROW() << "GatherElementsUpdate. Unsupported indices precision: " << inputPrecision;
+                }
+            } else {
+                if (inputPrecision.is_real()) {
+                    tensor = ov::test::utils::create_and_fill_tensor(inputPrecision, targetShape, 10, 0, 1000);
+                } else {
+                    tensor = ov::test::utils::create_and_fill_tensor(inputPrecision, targetShape);
+                }
+            }
+            inputs.insert({ funcInput.get_node_shared_ptr(), tensor });
+        }
+    }
+
+    void SetUp() override {
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+        ScatterElementsUpdateLayerParams scatterParams;
+        std::int64_t axis;
+        ElementType inputPrecision;
+        ElementType idxPrecision;
+        std::tie(scatterParams, axis, inputPrecision, idxPrecision) = this->GetParam();
+        const auto inputShapes = scatterParams.inputShapes;
+        const auto indicesDescr = scatterParams.indicesValues;
+
+        init_input_shapes(inputShapes);
+        selectedType = makeSelectedTypeStr("unknown", inputPrecision);
+
+        auto dataParams = ngraph::builder::makeDynamicParams(inputPrecision, { inputDynamicShapes[0], inputDynamicShapes[2] });
+        auto indicesParam = ngraph::builder::makeDynamicParams(idxPrecision, { inputDynamicShapes[1] });
+        dataParams[0]->set_friendly_name("Param_1");
+        indicesParam[0]->set_friendly_name("Param_2");
+        dataParams[1]->set_friendly_name("Param_3");
+
+        auto axisNode = ngraph::opset3::Constant::create(idxPrecision, {}, { axis });
+        auto scatter = std::make_shared<ngraph::opset3::ScatterElementsUpdate>(dataParams[0], indicesParam[0], dataParams[1], axisNode);
+
+        ngraph::ParameterVector allParams{ dataParams[0], indicesParam[0], dataParams[1] };
+        function = makeNgraphFunction(inputPrecision, allParams, scatter, "ScatterElementsUpdateLayerCPUTest");
+    }
+};
+
+TEST_P(ScatterElementsUpdateLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+    run();
+    CheckPluginRelatedResults(executableNetwork, "ScatterUpdate");
+}
+
+const std::vector<std::int64_t> axes = { -3, -2, -1, 0, 1, 2 };
+
+const std::vector<ScatterElementsUpdateLayerParams> scatterParams = {
+    ScatterElementsUpdateLayerParams{
+        ScatterElementsUpdateShapes{
+            {{-1, -1, -1}, {{10, 12, 15}, {8, 9, 10}, {11, 8, 12}}},
+            {{-1, -1, -1}, {{1, 2, 4}, {2, 1, 4}, {4, 1, 2}}},
+            {{-1, -1, -1}, {{1, 2, 4}, {2, 1, 4}, {4, 1, 2}}}
+        },
+        IndicesValues{1, 0, 4, 6, 2, 3, 7, 5},
+    },
+    ScatterElementsUpdateLayerParams{
+        ScatterElementsUpdateShapes{
+            {{-1, -1, -1, -1}, {{10, 9, 8, 12}, {8, 12, 10, 9}, {11, 10, 12, 9}}},
+            {{-1, -1, -1, -1}, {{1, 2, 2, 2}, {1, 2, 1, 4}, {1, 2, 2, 2}}},
+            {{-1, -1, -1, -1}, {{1, 2, 2, 2}, {1, 2, 1, 4}, {1, 2, 2, 2}}}
+        },
+        IndicesValues{1, 0, 4, 6, 2, 3, 7, 5},
+    },
+    ScatterElementsUpdateLayerParams{
+        ScatterElementsUpdateShapes{
+            {{{7, 15}, {9, 12}, {1, 12}, {8, 12}}, {{10, 9, 8, 12}, {8, 12, 10, 9}, {11, 10, 12, 9}}},
+            {{-1, -1, -1, -1}, {{1, 2, 2, 2}, {1, 2, 1, 4}, {1, 2, 2, 2}}},
+            {{-1, -1, -1, -1}, {{1, 2, 2, 2}, {1, 2, 1, 4}, {1, 2, 2, 2}}}
+        },
+        IndicesValues{1, 0, 4, 6, 2, 3, 7, 5},
+    },
+};
+
+const std::vector<ElementType> inputPrecisions = {
+    ElementType::f32,
+    ElementType::i32,
+};
+
+const std::vector<ElementType> constantPrecisions = {
+    ElementType::i32,
+    ElementType::i64,
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs, ScatterElementsUpdateLayerCPUTest,
+    ::testing::Combine(
+        ::testing::ValuesIn(scatterParams),
+        ::testing::ValuesIn(axes),
+        ::testing::ValuesIn(inputPrecisions),
+        ::testing::ValuesIn(constantPrecisions)),
+    ScatterElementsUpdateLayerCPUTest::getTestCaseName);
+} // namespace CPULayerTestsDefinitions
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/scatter_update.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/scatter_update.cpp
new file mode 100644
index 00000000000..ded9a8883ed
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/scatter_update.cpp
@@ -0,0 +1,147 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "test_utils/cpu_test_utils.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "ngraph_functions/builders.hpp"
+
+using namespace ngraph;
+using namespace InferenceEngine;
+using namespace CPUTestUtils;
+using namespace ov::test;
+
+namespace CPULayerTestsDefinitions {
+using ScatterUpdateShapes = std::vector<InputShape>;
+using IndicesDescription = std::pair<ov::Shape, std::vector<std::int64_t>>;
+using Axis = std::int64_t;
+
+struct ScatterUpdateLayerParams {
+    ScatterUpdateShapes inputShapes; // shapes for "data" and "updates" inputs
+    IndicesDescription indicesDescriprion; // indices shapes and values
+    Axis axis;
+};
+
+using scatterUpdateParams = std::tuple<
+    ScatterUpdateLayerParams,
+    ElementType,        // input precision
+    ElementType>;       // indices precision
+
+class ScatterUpdateLayerCPUTest : public testing::WithParamInterface<scatterUpdateParams>, public SubgraphBaseTest, public CPUTestsBase {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<scatterUpdateParams> obj) {
+        ScatterUpdateLayerParams scatterParams;
+        ElementType inputPrecision;
+        ElementType idxPrecision;
+        std::tie(scatterParams, inputPrecision, idxPrecision) = obj.param;
+        const auto inputShapes = scatterParams.inputShapes;
+        const auto indicesDescr = scatterParams.indicesDescriprion;
+        const auto axis = scatterParams.axis;
+
+        std::ostringstream result;
+        result << inputPrecision << "_IS=";
+        for (const auto& shape : inputShapes) {
+            result << CommonTestUtils::partialShape2str({ shape.first }) << "_";
+        }
+        result << "TS=";
+        for (const auto& shape : inputShapes) {
+            result << "(";
+            for (const auto& targetShape : shape.second) {
+                result << CommonTestUtils::vec2str(targetShape) << "_";
+            }
+            result << ")_";
+        }
+        result << "indices_shape=" << indicesDescr.first << "_indices_values=" << CommonTestUtils::vec2str(indicesDescr.second)
+               << "axis=" << axis << "_idx_precision=" << idxPrecision;
+        return result.str();
+    }
+
+protected:
+    void SetUp() override {
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+        ScatterUpdateLayerParams scatterParams;
+        ElementType inputPrecision;
+        ElementType idxPrecision;
+        std::tie(scatterParams, inputPrecision, idxPrecision) = this->GetParam();
+        const auto inputShapes = scatterParams.inputShapes;
+        const auto indicesDescr = scatterParams.indicesDescriprion;
+        const auto axis = scatterParams.axis;
+
+        init_input_shapes(inputShapes);
+        selectedType = makeSelectedTypeStr("unknown", inputPrecision);
+
+        auto params = ngraph::builder::makeDynamicParams(inputPrecision, inputDynamicShapes);
+        auto indicesNode = ngraph::opset1::Constant::create(idxPrecision, indicesDescr.first, indicesDescr.second);
+        auto axis_node = ngraph::opset1::Constant::create(idxPrecision, {}, { axis });
+        auto scatter = std::make_shared<ngraph::opset3::ScatterUpdate>(params[0], indicesNode, params[1], axis_node);
+
+        function = makeNgraphFunction(inputPrecision, params, scatter, "ScatterUpdateLayerCPUTest");
+    }
+};
+
+TEST_P(ScatterUpdateLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+    run();
+    CheckPluginRelatedResults(executableNetwork, "ScatterUpdate");
+}
+
+const std::vector<ScatterUpdateLayerParams> scatterParams = {
+    ScatterUpdateLayerParams{
+        ScatterUpdateShapes{
+            {{-1, -1, -1, -1}, {{4, 12, 3, 11}, {7, 11, 2, 3}, {3, 9, 4, 10}}},
+            {{-1, -1, -1, -1}, {{4, 8, 3, 11}, {7, 8, 2, 3}, {3, 8, 4, 10}}}
+        },
+        IndicesDescription{{8}, {0, 2, 4, 6, 1, 3, 5, 7}},
+        Axis{1}
+    },
+    ScatterUpdateLayerParams{
+        ScatterUpdateShapes{
+            {{-1, -1, -1, -1}, {{4, 12, 3, 11}, {7, 9, 1, 12}, {3, 2, 1, 9}}},
+            {{-1, -1, -1, -1}, {{4, 12, 3, 8}, {7, 9, 1, 8}, {3, 2, 1, 8}}}
+        },
+        IndicesDescription{{8}, {0, 2, 4, 6, 1, 3, 5, 7}},
+        Axis{3}
+    },
+    ScatterUpdateLayerParams{
+        ScatterUpdateShapes{
+            {{-1, -1, -1, -1, -1}, {{5, 9, 10, 3, 4}, {7, 8, 11, 2, 2}, {11, 3, 12, 2, 2}}},
+            {{-1, -1, -1, -1, -1, -1}, {{5, 9, 4, 2, 3, 4}, {7, 8, 4, 2, 2, 2}, {11, 3, 4, 2, 2, 2}}}
+        },
+        IndicesDescription{{ 4, 2 }, { 0, 2, 4, 6, 1, 3, 5, 7 }},
+        Axis{2}
+    },
+    ScatterUpdateLayerParams{
+        ScatterUpdateShapes{
+            {{-1, -1, -1, -1, -1}, {{8, 9, 10, 3, 4}, {11, 3, 4, 3, 4}, {12, 9, 11, 2, 2}}},
+            {{-1, -1, -1, -1, -1, -1}, {{4, 2, 9, 10, 3, 4}, {4, 2, 3, 4, 3, 4}, {4, 2, 9, 11, 2, 2}}}
+        },
+        IndicesDescription{{ 4, 2 }, { 0, 2, 4, 6, 1, 3, 5, 7 }},
+        Axis{0}
+    },
+    ScatterUpdateLayerParams{
+        ScatterUpdateShapes{
+            {{{8, 12}, {3, 9}, {4, 11}, {2, 3}, {2, 4}}, {{8, 9, 10, 3, 4}, {11, 3, 4, 3, 4}, {12, 9, 11, 2, 2}}},
+            {{4, 2, {3, 9}, {4, 11}, {2, 3}, {2, 4}}, {{4, 2, 9, 10, 3, 4}, {4, 2, 3, 4, 3, 4}, {4, 2, 9, 11, 2, 2}}}
+        },
+        IndicesDescription{{ 4, 2 }, { 0, 2, 4, 6, 1, 3, 5, 7 }},
+        Axis{0}
+    },
+};
+
+const std::vector<ElementType> inputPrecisions = {
+    ElementType::f32,
+    ElementType::i32,
+};
+
+const std::vector<ElementType> constantPrecisions = {
+    ElementType::i32,
+    ElementType::i64,
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs, ScatterUpdateLayerCPUTest,
+    ::testing::Combine(
+        ::testing::ValuesIn(scatterParams),
+        ::testing::ValuesIn(inputPrecisions),
+        ::testing::ValuesIn(constantPrecisions)),
+    ScatterUpdateLayerCPUTest::getTestCaseName);
+} // namespace CPULayerTestsDefinitions

From cf813f6e83ada6ebccf13aeffe630dc3f66d07cb Mon Sep 17 00:00:00 2001
From: Bartek Szmelczynski <bartosz.szmelczynski@intel.com>
Date: Thu, 25 Nov 2021 12:23:54 +0100
Subject: [PATCH 11/72] add serialize to offline_transformations (#8765)

* add serialize to offline_transformations

* fix namespaces, add tests

* fix python style
---
 .../__init__.py                               |  2 +
 .../core/offline_transformations.cpp          | 25 ++++++++
 .../test_transformations/test_offline_api.py  | 61 ++++++++++++++++++-
 3 files changed, 86 insertions(+), 2 deletions(-)

diff --git a/runtime/bindings/python/src/openvino/offline_transformations_pybind/__init__.py b/runtime/bindings/python/src/openvino/offline_transformations_pybind/__init__.py
index 567d667c072..3b77f599de0 100644
--- a/runtime/bindings/python/src/openvino/offline_transformations_pybind/__init__.py
+++ b/runtime/bindings/python/src/openvino/offline_transformations_pybind/__init__.py
@@ -8,3 +8,5 @@ from openvino.pyopenvino.offline_transformations_pybind import apply_low_latency
 from openvino.pyopenvino.offline_transformations_pybind import apply_pruning_transformation
 from openvino.pyopenvino.offline_transformations_pybind import generate_mapping_file
 from openvino.pyopenvino.offline_transformations_pybind import apply_make_stateful_transformation
+from openvino.pyopenvino.offline_transformations_pybind import serialize
+from openvino.pyopenvino.offline_transformations_pybind import compress_model_transformation
diff --git a/runtime/bindings/python/src/pyopenvino/core/offline_transformations.cpp b/runtime/bindings/python/src/pyopenvino/core/offline_transformations.cpp
index f58bcc6eedf..411087c73ac 100644
--- a/runtime/bindings/python/src/pyopenvino/core/offline_transformations.cpp
+++ b/runtime/bindings/python/src/pyopenvino/core/offline_transformations.cpp
@@ -10,7 +10,10 @@
 #include <openvino/pass/make_stateful.hpp>
 #include <pot_transformations.hpp>
 #include <pruning.hpp>
+#include <transformations/common_optimizations/compress_float_constants.hpp>
+#include <transformations/common_optimizations/mark_precision_sensitive_subgraphs.hpp>
 #include <transformations/common_optimizations/moc_transformations.hpp>
+#include <transformations/serialize.hpp>
 
 #include "openvino/pass/low_latency.hpp"
 #include "openvino/pass/manager.hpp"
@@ -81,4 +84,26 @@ void regmodule_offline_transformations(py::module m) {
         },
         py::arg("function"),
         py::arg("param_res_names"));
+
+    m_offline_transformations.def(
+        "compress_model_transformation",
+        [](std::shared_ptr<ov::Function> function) {
+            ov::pass::Manager manager;
+            manager.register_pass<ov::pass::MarkPrecisionSensitiveSubgraphs>();
+            manager.register_pass<ov::pass::CompressFloatConstants>();
+            manager.run_passes(function);
+        },
+        py::arg("function"));
+
+    // todo: remove as serialize as part of passManager api will be merged
+    m_offline_transformations.def(
+        "serialize",
+        [](std::shared_ptr<ov::Function> function, const std::string& path_to_xml, const std::string& path_to_bin) {
+            ov::pass::Manager manager;
+            manager.register_pass<ov::pass::Serialize>(path_to_xml, path_to_bin);
+            manager.run_passes(function);
+        },
+        py::arg("function"),
+        py::arg("model_path"),
+        py::arg("weights_path"));
 }
diff --git a/runtime/bindings/python/tests/test_transformations/test_offline_api.py b/runtime/bindings/python/tests/test_transformations/test_offline_api.py
index 72b72c811ec..d593085f92c 100644
--- a/runtime/bindings/python/tests/test_transformations/test_offline_api.py
+++ b/runtime/bindings/python/tests/test_transformations/test_offline_api.py
@@ -1,11 +1,14 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
+import os
+import numpy as np
 # TODO: change the module name according to the description in 69196
 from openvino.offline_transformations_pybind import apply_moc_transformations, apply_pot_transformations, \
-    apply_low_latency_transformation, apply_pruning_transformation, apply_make_stateful_transformation
+    apply_low_latency_transformation, apply_pruning_transformation, apply_make_stateful_transformation, \
+    compress_model_transformation, serialize
 
-from openvino import Function, PartialShape
+from openvino import Function, PartialShape, Core
 import openvino as ov
 
 
@@ -60,3 +63,57 @@ def test_make_stateful_transformations():
     assert function is not None
     assert len(function.get_parameters()) == 0
     assert len(function.get_results()) == 0
+
+
+def test_serialize_pass():
+    core = Core()
+    xml_path = "serialized_function.xml"
+    bin_path = "serialized_function.bin"
+
+    func = get_test_function()
+
+    serialize(func, xml_path, bin_path)
+
+    assert func is not None
+
+    res_func = core.read_model(model=xml_path, weights=bin_path)
+
+    assert func.get_parameters() == res_func.get_parameters()
+    assert func.get_ordered_ops() == res_func.get_ordered_ops()
+
+    os.remove(xml_path)
+    os.remove(bin_path)
+
+
+def test_serialize_pass_v2():
+    core = Core()
+    xml_path = "./serialized_function.xml"
+    bin_path = "./serialized_function.bin"
+    shape = [100, 100, 2]
+    parameter_a = ov.opset8.parameter(shape, dtype=np.float32, name="A")
+    parameter_b = ov.opset8.parameter(shape, dtype=np.float32, name="B")
+    model = ov.opset8.floor(ov.opset8.minimum(ov.opset8.abs(parameter_a), parameter_b))
+    func = Function(model, [parameter_a, parameter_b], "Function")
+
+    serialize(func, xml_path, bin_path)
+
+    assert func is not None
+
+    res_func = core.read_model(model=xml_path, weights=bin_path)
+
+    assert func.get_parameters() == res_func.get_parameters()
+    assert func.get_ordered_ops() == res_func.get_ordered_ops()
+
+    os.remove(xml_path)
+    os.remove(bin_path)
+
+
+def test_compress_model_transformation():
+    node_constant = ov.opset8.constant(np.array([[0.0, 0.1, -0.1], [-2.5, 2.5, 3.0]], dtype=np.float32))
+    node_ceil = ov.opset8.ceiling(node_constant)
+    func = Function(node_ceil, [], "TestFunction")
+    assert func.get_ordered_ops()[0].get_element_type().get_type_name() == "f32"
+    compress_model_transformation(func)
+
+    assert func is not None
+    assert func.get_ordered_ops()[0].get_element_type().get_type_name() == "f16"

From f92ae6511bd417adc9d664e329a78dfc416f6761 Mon Sep 17 00:00:00 2001
From: Edward Shogulin <edward.shogulin@intel.com>
Date: Thu, 25 Nov 2021 14:28:00 +0300
Subject: [PATCH 12/72] [LPT] isAsymmetricOnWeights fix: small zero points
 ignoring (#8427)

---
 .../is_asymmetric_on_weights_dequantization.cpp              | 5 ++++-
 .../lp_transformations/is_asymmetric_on_weights_fq.cpp       | 2 ++
 2 files changed, 6 insertions(+), 1 deletion(-)

diff --git a/inference-engine/tests/functional/inference_engine/lp_transformations/is_asymmetric_on_weights_dequantization.cpp b/inference-engine/tests/functional/inference_engine/lp_transformations/is_asymmetric_on_weights_dequantization.cpp
index e5caba16f00..8dc8a6c9ec3 100644
--- a/inference-engine/tests/functional/inference_engine/lp_transformations/is_asymmetric_on_weights_dequantization.cpp
+++ b/inference-engine/tests/functional/inference_engine/lp_transformations/is_asymmetric_on_weights_dequantization.cpp
@@ -21,6 +21,7 @@ using namespace ngraph::pass;
 
 class IsAsymmetricOnWeightsDequantizationTestValues {
 public:
+    TestTransformationParams params;
     ngraph::element::Type precisionBeforeDequantization;
     ngraph::builder::subgraph::DequantizationOperations dequantizationOnActivations;
     std::shared_ptr<ngraph::opset1::Constant> weights;
@@ -58,7 +59,7 @@ public:
         IsAsymmetricOnWeightsDequantizationTestValues testValues = std::get<2>(obj.param);
 
         std::ostringstream result;
-        result <<
+        result << toString(testValues.params) << "_" <<
             netPrecision << "_" <<
             inputShape << "_" <<
             testValues.precisionBeforeDequantization << "_" <<
@@ -95,6 +96,7 @@ const std::vector<ngraph::PartialShape> suitablePartialShapes = {
 
 const std::vector<IsAsymmetricOnWeightsDequantizationTestValues> testValues = {
     {
+        LayerTransformation::createParamsU8I8().setSupportAsymmetricQuantization(true),
         ngraph::element::u8,
         {{ngraph::element::f32}, { 128.f }, { 0.02f }},
         op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f }),
@@ -106,6 +108,7 @@ const std::vector<IsAsymmetricOnWeightsDequantizationTestValues> testValues = {
         true
     },
     {
+        LayerTransformation::createParamsU8I8().setSupportAsymmetricQuantization(true),
         ngraph::element::u8,
         {{ngraph::element::f32}, { 128.f }, { 0.02f }},
         op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f }),
diff --git a/inference-engine/tests/functional/inference_engine/lp_transformations/is_asymmetric_on_weights_fq.cpp b/inference-engine/tests/functional/inference_engine/lp_transformations/is_asymmetric_on_weights_fq.cpp
index 78bd7aa5483..79ac9d62b6d 100644
--- a/inference-engine/tests/functional/inference_engine/lp_transformations/is_asymmetric_on_weights_fq.cpp
+++ b/inference-engine/tests/functional/inference_engine/lp_transformations/is_asymmetric_on_weights_fq.cpp
@@ -21,6 +21,7 @@ using namespace ngraph::pass;
 
 class IsAsymmetricOnWeightsFakeQuantizeTestValues {
 public:
+    TestTransformationParams params;
     ngraph::element::Type precisionBeforeDequantization;
     ngraph::builder::subgraph::DequantizationOperations dequantizationOnActivations;
     std::shared_ptr<ngraph::opset1::Constant> weights;
@@ -106,6 +107,7 @@ const std::vector<ngraph::PartialShape> suitablePartialShapes = {
 
 const std::vector<IsAsymmetricOnWeightsFakeQuantizeTestValues> testValues = {
     {
+        LayerTransformation::createParamsU8I8().setSupportAsymmetricQuantization(true),
         ngraph::element::u8,
         {{ngraph::element::f32}, { 128.f }, { 0.02f }},
         op::Constant::create(ngraph::element::f32, ngraph::Shape{}, std::vector<float>{ 2.f }),

From 1f4aa79a01161bfc4ba39f97f1a2773177203dad Mon Sep 17 00:00:00 2001
From: Andrey Noskov <andrey.noskov@intel.com>
Date: Thu, 25 Nov 2021 15:22:42 +0300
Subject: [PATCH 13/72] [GNA] added split_to_concat_multi_inputs test (#7775)

* added split_to_concat_multi_inputs test

* [GNA] fixes according to comments
  *Deleted commented str
  *Updated opset for MatMul
  *Added gna_sw_exact mode

* [GNA] Added input with n * 512 size

* [GNA] Changed input gen params

* rebase commit
---
 .../split_concat_multi_inputs.cpp             |  46 ++++
 .../split_concat_multi_inputs.hpp             |  14 ++
 .../subgraph/split_concat_multi_inputs.hpp    |  42 ++++
 .../subgraph/split_concat_multi_inputs.cpp    |  62 ++++++
 .../engines/gna/fp32_non_quantized_tests.cpp  | 201 ------------------
 5 files changed, 164 insertions(+), 201 deletions(-)
 create mode 100644 inference-engine/tests/functional/plugin/gna/shared_tests_instances/subgraph_tests/split_concat_multi_inputs.cpp
 create mode 100644 inference-engine/tests/functional/plugin/shared/include/subgraph_tests/split_concat_multi_inputs.hpp
 create mode 100644 inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/split_concat_multi_inputs.hpp
 create mode 100644 inference-engine/tests/functional/shared_test_classes/src/subgraph/split_concat_multi_inputs.cpp

diff --git a/inference-engine/tests/functional/plugin/gna/shared_tests_instances/subgraph_tests/split_concat_multi_inputs.cpp b/inference-engine/tests/functional/plugin/gna/shared_tests_instances/subgraph_tests/split_concat_multi_inputs.cpp
new file mode 100644
index 00000000000..f1b9e3f6272
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/gna/shared_tests_instances/subgraph_tests/split_concat_multi_inputs.cpp
@@ -0,0 +1,46 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <vector>
+
+#include "subgraph_tests/split_concat_multi_inputs.hpp"
+#include "common_test_utils/test_constants.hpp"
+#include "gna/gna_config.hpp"
+
+using namespace SubgraphTestsDefinitions;
+
+namespace {
+std::vector<InferenceEngine::Precision> precisions = {InferenceEngine::Precision::FP32,
+                                                         InferenceEngine::Precision::FP16};
+
+std::vector<std::map<std::string, std::string>> additionalConfig = {
+    {
+        {"GNA_DEVICE_MODE", "GNA_SW_FP32"}
+    },
+    {
+        {"GNA_DEVICE_MODE", "GNA_SW_EXACT"}
+    }
+};
+
+std::vector<std::vector<size_t>> inputShapes = {
+        {1, 10},
+        {1, 50},
+        {1, 32},
+        {1, 512}
+};
+
+std::vector<size_t> splitsNum = {2, 3, 4, 10};
+
+std::vector<bool> isFc = {false, true};
+
+INSTANTIATE_TEST_SUITE_P(smoke_splitConcatMultiInputs, SplitConcatMultiInputsTest,
+                        ::testing::Combine(
+                                ::testing::ValuesIn(precisions),
+                                ::testing::Values(CommonTestUtils::DEVICE_GNA),
+                                ::testing::ValuesIn(additionalConfig),
+                                ::testing::ValuesIn(inputShapes),
+                                ::testing::ValuesIn(splitsNum),
+                                ::testing::ValuesIn(isFc)),
+                        SplitConcatMultiInputsTest::getTestCaseName);
+} // namespace
\ No newline at end of file
diff --git a/inference-engine/tests/functional/plugin/shared/include/subgraph_tests/split_concat_multi_inputs.hpp b/inference-engine/tests/functional/plugin/shared/include/subgraph_tests/split_concat_multi_inputs.hpp
new file mode 100644
index 00000000000..ec91fe02d6c
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/shared/include/subgraph_tests/split_concat_multi_inputs.hpp
@@ -0,0 +1,14 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "shared_test_classes/subgraph/split_concat_multi_inputs.hpp"
+
+namespace SubgraphTestsDefinitions {
+
+TEST_P(SplitConcatMultiInputsTest, CompareWithRefs) {
+    Run();
+};
+}  // namespace SubgraphTestsDefinitions
\ No newline at end of file
diff --git a/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/split_concat_multi_inputs.hpp b/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/split_concat_multi_inputs.hpp
new file mode 100644
index 00000000000..59f61bbd366
--- /dev/null
+++ b/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/subgraph/split_concat_multi_inputs.hpp
@@ -0,0 +1,42 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <tuple>
+#include <vector>
+#include <string>
+#include <memory>
+
+#include "shared_test_classes/base/layer_test_utils.hpp"
+#include "ngraph_functions/utils/ngraph_helpers.hpp"
+#include "ngraph_functions/builders.hpp"
+
+namespace SubgraphTestsDefinitions {
+
+typedef std::tuple<
+        InferenceEngine::Precision,          // Network Precision
+        std::string,                         // Target Device
+        std::map<std::string, std::string>,  // Configuration
+        std::vector<size_t>,                 // Input Shapes
+        size_t,                              // Num of Split outputs (concat inputs)
+        bool                                 // with FC or not
+> SplitConcatMultiInputsParams;
+
+
+class SplitConcatMultiInputsTest : public testing::WithParamInterface<SplitConcatMultiInputsParams>,
+                                   public LayerTestsUtils::LayerTestsCommon {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<SplitConcatMultiInputsParams> obj);
+
+protected:
+    void SetUp() override;
+    InferenceEngine::Blob::Ptr GenerateInput(const InferenceEngine::InputInfo &info) const override;
+
+    float inputDataMin        = 0.0;
+    float inputDataMax        = 0.2;
+    float inputDataResolution = 1;
+    int32_t  seed = 1;
+};
+}  // namespace SubgraphTestsDefinitions
diff --git a/inference-engine/tests/functional/shared_test_classes/src/subgraph/split_concat_multi_inputs.cpp b/inference-engine/tests/functional/shared_test_classes/src/subgraph/split_concat_multi_inputs.cpp
new file mode 100644
index 00000000000..d2dede21c22
--- /dev/null
+++ b/inference-engine/tests/functional/shared_test_classes/src/subgraph/split_concat_multi_inputs.cpp
@@ -0,0 +1,62 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "shared_test_classes/subgraph/split_concat_multi_inputs.hpp"
+#include "ngraph_functions/builders.hpp"
+
+namespace SubgraphTestsDefinitions {
+
+std::string SplitConcatMultiInputsTest::getTestCaseName(testing::TestParamInfo<SplitConcatMultiInputsParams> obj) {
+    std::vector<size_t> inputShape;
+    size_t splitsNum;
+    std::map<std::string, std::string> config;
+    InferenceEngine::Precision netPrecision;
+    std::string targetName;
+    bool withFC;
+    std::tie(netPrecision, targetName, config, inputShape, splitsNum, withFC) = obj.param;
+    std::ostringstream result;
+    result << "IS=" << CommonTestUtils::vec2str(inputShape) << "_";
+    result << "SplitsN=" << splitsNum << "_";
+    result << "netPRC=" << netPrecision.name() << "_";
+    result << "trgDev=" << targetName << "_";
+    result << "FC=" << withFC;
+    for (auto const& configItem : config) {
+        result << "_configItem=" << configItem.first << "_" << configItem.second;
+    }
+    return result.str();
+}
+
+void SplitConcatMultiInputsTest::SetUp() {
+    std::vector<size_t> inputShape;
+    size_t splitsNum;
+    std::map<std::string, std::string> tempConfig;
+    InferenceEngine::Precision netPrecision;
+    bool withFC;
+    std::tie(netPrecision, targetDevice, tempConfig, inputShape, splitsNum, withFC) = this->GetParam();
+    configuration.insert(tempConfig.begin(), tempConfig.end());
+
+    inputShape[1] *= splitsNum;
+    auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
+    auto params = ngraph::builder::makeParams(ngPrc, {inputShape});
+
+    auto split = ngraph::builder::makeSplit(params[0], ngPrc, splitsNum, 1);
+    ngraph::OutputVector concatInputs = split->outputs();
+
+    auto concat = std::make_shared<ngraph::opset7::Concat>(concatInputs, 1);
+
+    if (withFC) {
+        auto mul_const = ngraph::builder::makeConstant<float>(ngPrc, { 10, inputShape[1] },
+            CommonTestUtils::generate_float_numbers(10 * inputShape[1], -0.2f, 0.2f), false);
+        auto matmul = std::make_shared<ngraph::op::MatMul>(concat, mul_const, false, true);
+        function = std::make_shared<ngraph::Function>(matmul, params, "SplitConcatMultiInputs");
+    } else {
+        function = std::make_shared<ngraph::Function>(concat, params, "SplitConcatMultiInputs");
+    }
+}
+
+InferenceEngine::Blob::Ptr SplitConcatMultiInputsTest::GenerateInput(const InferenceEngine::InputInfo &info) const {
+    return FuncTestUtils::createAndFillBlob(info.getTensorDesc(), inputDataMax - inputDataMin, inputDataMin, 1 / inputDataResolution,
+                                            seed);
+}
+}  // namespace SubgraphTestsDefinitions
diff --git a/inference-engine/tests_deprecated/unit/engines/gna/fp32_non_quantized_tests.cpp b/inference-engine/tests_deprecated/unit/engines/gna/fp32_non_quantized_tests.cpp
index 8166d0be1e5..2f7a0e7d1b2 100644
--- a/inference-engine/tests_deprecated/unit/engines/gna/fp32_non_quantized_tests.cpp
+++ b/inference-engine/tests_deprecated/unit/engines/gna/fp32_non_quantized_tests.cpp
@@ -378,113 +378,6 @@ TEST_F(FP32NonQuantizedTest, TwoOutputsDiffPrecisionPropagateForward) {
         .called_with().input("input_1", input_data).result().equals_to(result1).And().equals_to(result2);
 }
 
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith2InputsNotAlignedNoFC) {
-    std::vector<float> input_data(20);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    assert_that().onInferModel(SplitToConcatWith2InputsNotAlignedNoFC())
-            .inNotCompactMode().gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(input_data);
-}
-
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith2By50InputsNotAlignedNoFC) {
-    std::vector<float> input_data(100);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    assert_that().onInferModel(SplitToConcatWith2By50InputsNotAlignedNoFC())
-            .inNotCompactMode().gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(input_data);
-}
-
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith2By50InputsNotAlignedNoFCWithInCopyWithOutCopy) {
-    std::vector<float> input_data(100);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    assert_that().onInferModel(SplitToConcatWith2By50InputsNotAlignedNoFCWithInCopyWithOutCopy())
-            .inNotCompactMode().gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(input_data);
-}
-
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith3InputsNotAlignedNoFC) {
-    std::vector<float> input_data(30);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    assert_that().onInferModel(SplitToConcatWith3InputsNotAlignedNoFC())
-            .inNotCompactMode().gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(input_data);
-}
-
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith4InputsNotAlignedNoFC) {
-    std::vector<float> input_data(40);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    assert_that().onInferModel(SplitToConcatWith4InputsNotAlignedNoFC())
-            .inNotCompactMode().gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(input_data);
-}
-
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith4InputsNotAlignedNoFCWithOutCopy) {
-    std::vector<float> input_data(40);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    assert_that().onInferModel(SplitToConcatWith4InputsNotAlignedNoFCWithOutCopy())
-            .inNotCompactMode().gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(input_data);
-}
-
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith10InputsNotAlignedNoFC) {
-    std::vector<float> input_data(100);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    assert_that().onInferModel(SplitToConcatWith10InputsNotAlignedNoFC())
-            .inNotCompactMode().gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(input_data);
-}
-
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith10InputsNotAlignedNoFCWithOutCopy) {
-    std::vector<float> input_data(100);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    assert_that().onInferModel(SplitToConcatWith10InputsNotAlignedNoFCWithOutCopy())
-            .inNotCompactMode().gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(input_data);
-}
-
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith10By1InputsNotAlignedNoFCWithOutCopy) {
-    std::vector<float> input_data(10);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    assert_that().onInferModel(SplitToConcatWith10By1InputsNotAlignedNoFCWithOutCopy())
-            .inNotCompactMode().gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(input_data);
-}
-
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith2InputsNotAlignedWithFC) {
-    std::vector<float> input_data(20);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    std::vector<float> expected_result(10, 211.0f);
-    assert_that().onInferModel(SplitToConcatWith2InputsNotAlignedWithFC())
-            .inNotCompactMode().withWeigthsPattern({1}).gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(expected_result);
-}
-
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith3InputsNotAlignedWithFC) {
-    std::vector<float> input_data(30);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    std::vector<float> expected_result(10, 466.0f);
-    assert_that().onInferModel(SplitToConcatWith3InputsNotAlignedWithFC())
-            .inNotCompactMode().withWeigthsPattern({1}).gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(expected_result);
-}
-
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith3By512InputsWithOutCopy) {
-    std::vector<float> input_data(1536);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    assert_that().onInferModel(SplitToConcatWith3By512InputsWithOutCopy())
-            .inNotCompactMode().gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(input_data);
-}
-
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith10InputsNotAlignedWithFC) {
-    std::vector<float> input_data(100);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    std::vector<float> expected_result(10, 5051.0f);
-    assert_that().onInferModel(SplitToConcatWith10InputsNotAlignedWithFC())
-            .inNotCompactMode().withWeigthsPattern({1}).gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(expected_result);
-}
-
 TEST_F(FP32NonQuantizedTest, DISABLED_SplitToConcatWith2InputsAlignedNoFC) {
     std::vector<float> input_data(64);
     std::iota(input_data.begin(), input_data.end(), 1.0f);
@@ -501,30 +394,6 @@ TEST_F(FP32NonQuantizedTest, DISABLED_SplitToConcatWith2By64InputsAlignedNoFC) {
             .called_with_input(input_data).equals_to(input_data);
 }
 
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith2Inputs1360NotAlignedNoFC) {
-    std::vector<float> input_data(1360);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    assert_that().onInferModel(SplitToConcatWith2Inputs1360NotAlignedNoFC())
-            .inNotCompactMode().gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(input_data);
-}
-
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith2By64InputsAlignedNoFCWithOutCopy) {
-    std::vector<float> input_data(128);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    assert_that().onInferModel(SplitToConcatWith2By64InputsAlignedNoFCWithOutCopy())
-            .inNotCompactMode().gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(input_data);
-}
-
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith2InputsAlignedNoFCWithInCopyWithOutCopy) {
-    std::vector<float> input_data(64);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    assert_that().onInferModel(SplitToConcatWith2InputsAlignedNoFCWithInCopyWithOutCopy())
-            .inNotCompactMode().gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(input_data);
-}
-
 TEST_F(FP32NonQuantizedTest, DISABLED_SplitToConcatWith3InputsAlignedNoFC) {
     std::vector<float> input_data(96);
     std::iota(input_data.begin(), input_data.end(), 1.0f);
@@ -533,14 +402,6 @@ TEST_F(FP32NonQuantizedTest, DISABLED_SplitToConcatWith3InputsAlignedNoFC) {
             .called_with_input(input_data).equals_to(input_data);
 }
 
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith3InputsAlignedNoFCWithInCopyWithOutCopy) {
-    std::vector<float> input_data(96);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    assert_that().onInferModel(SplitToConcatWith3InputsAlignedNoFCWithInCopyWithOutCopy())
-            .inNotCompactMode().gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(input_data);
-}
-
 TEST_F(FP32NonQuantizedTest, DISABLED_SplitToConcatWith10InputsAlignedNoFC) {
     std::vector<float> input_data(320);
     std::iota(input_data.begin(), input_data.end(), 1.0f);
@@ -549,68 +410,6 @@ TEST_F(FP32NonQuantizedTest, DISABLED_SplitToConcatWith10InputsAlignedNoFC) {
             .called_with_input(input_data).equals_to(input_data);
 }
 
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith10InputsAlignedNoFCWithInCopyWithOutCopy) {
-    std::vector<float> input_data(320);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    assert_that().onInferModel(SplitToConcatWith10InputsAlignedNoFCWithInCopyWithOutCopy())
-            .inNotCompactMode().gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(input_data);
-}
-
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith2InputsAlignedWithFC) {
-    std::vector<float> input_data(64);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    std::vector<float> expected_result(32, 2081.0f);
-    assert_that().onInferModel(SplitToConcatWith2InputsAlignedWithFC())
-            .inNotCompactMode().withWeigthsPattern({1}).gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(expected_result);
-}
-
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith2InputsAlignedWithFCWithInCopy) {
-    std::vector<float> input_data(64);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    std::vector<float> expected_result(32, 2081.0f);
-    assert_that().onInferModel(SplitToConcatWith2InputsAlignedWithFCWithInCopy())
-            .inNotCompactMode().withWeigthsPattern({1}).gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(expected_result);
-}
-
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith3InputsAlignedWithFC) {
-    std::vector<float> input_data(96);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    std::vector<float> expected_result(32, 4657.0f);
-    assert_that().onInferModel(SplitToConcatWith3InputsAlignedWithFC())
-            .inNotCompactMode().withWeigthsPattern({1}).gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(expected_result);
-}
-
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith3InputsAlignedWithFCWithInCopy) {
-    std::vector<float> input_data(96);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    std::vector<float> expected_result(32, 4657.0f);
-    assert_that().onInferModel(SplitToConcatWith3InputsAlignedWithFCWithInCopy())
-            .inNotCompactMode().withWeigthsPattern({1}).gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(expected_result);
-}
-
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith10InputsAlignedWithFC) {
-    std::vector<float> input_data(320);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    std::vector<float> expected_result(32, 51361.0f);
-    assert_that().onInferModel(SplitToConcatWith10InputsAlignedWithFC())
-            .inNotCompactMode().withWeigthsPattern({1}).gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(expected_result);
-}
-
-TEST_F(FP32NonQuantizedTest, SplitToConcatWith10InputsAlignedWithFCWithInCopy) {
-    std::vector<float> input_data(320);
-    std::iota(input_data.begin(), input_data.end(), 1.0f);
-    std::vector<float> expected_result(32, 51361.0f);
-    assert_that().onInferModel(SplitToConcatWith10InputsAlignedWithFCWithInCopy())
-            .inNotCompactMode().withWeigthsPattern({1}).gna().propagate_forward().onCPU()
-            .called_with_input(input_data).equals_to(expected_result);
-}
-
 TEST_F(FP32NonQuantizedTest, ReshapeConvolutionLessThan48Filters) {
     std::vector<float> input_data(800, 1.f);
     std::vector<float> expected_result(1600, 8.f);

From 854df26cadb9a7905b797e0ec098fef9c6f1bb9c Mon Sep 17 00:00:00 2001
From: Nikolay Shchegolev <nikolay.shchegolev@intel.com>
Date: Thu, 25 Nov 2021 15:27:16 +0300
Subject: [PATCH 14/72] [CPU] Dynamic shapes. Broadcast and Tile. (#8041)

---
 .../src/mkldnn_plugin/mkldnn_plugin.cpp       |   2 +
 .../convert_to_cpu_specific_opset.hpp         |   1 -
 .../nodes/common/tile_broadcast_utils.cpp     | 243 ++++++++++++
 .../nodes/common/tile_broadcast_utils.h       |  43 ++
 .../nodes/mkldnn_broadcast_node.cpp           | 272 ++++++++++---
 .../nodes/mkldnn_broadcast_node.h             |  42 +-
 .../mkldnn_plugin/nodes/mkldnn_tile_node.cpp  | 208 ++++++----
 .../mkldnn_plugin/nodes/mkldnn_tile_node.h    |  26 +-
 .../convert_ngraph_to_cnn_network_tests.cpp   |   2 -
 .../single_layer_tests/broadcast.cpp          | 189 +++++----
 .../single_layer_tests/tile.cpp               |  31 +-
 .../cpu/single_layer_tests/broadcast.cpp      | 374 ++++++++++++++++++
 .../plugin/cpu/single_layer_tests/tile.cpp    | 322 +++++++++++++++
 .../src/single_layer/broadcast.cpp            |  14 +-
 .../ngraph_functions/src/broadcast.cpp        |   2 +-
 ngraph/core/src/op/multiply.cpp               |   3 +
 ngraph/core/src/op/tile.cpp                   |  10 +-
 .../python/tests/test_onnx/test_backend.py    |   1 -
 .../test_onnx/test_backend.py                 |   1 -
 19 files changed, 1541 insertions(+), 245 deletions(-)
 create mode 100644 inference-engine/src/mkldnn_plugin/nodes/common/tile_broadcast_utils.cpp
 create mode 100644 inference-engine/src/mkldnn_plugin/nodes/common/tile_broadcast_utils.h
 create mode 100644 inference-engine/tests/functional/plugin/cpu/single_layer_tests/broadcast.cpp
 create mode 100644 inference-engine/tests/functional/plugin/cpu/single_layer_tests/tile.cpp

diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
index d3229df67f4..9de3f2a6af9 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
@@ -35,6 +35,7 @@
 #include <transformations/common_optimizations/nop_elimination.hpp>
 #include <transformations/common_optimizations/wrap_interpolate_into_transposes.hpp>
 #include <transformations/common_optimizations/transpose_sinking.hpp>
+#include <transformations/op_conversions/convert_broadcast_to_tiles.hpp>
 #include <transformations/op_conversions/convert_depth_to_space.hpp>
 #include <transformations/op_conversions/convert_shuffle_channels3.hpp>
 #include <transformations/op_conversions/convert_space_to_depth.hpp>
@@ -337,6 +338,7 @@ static void TransformationUpToCPUSpecificOpSet(std::shared_ptr<ngraph::Function>
     pass_config->disable<ngraph::pass::SimplifyCTCGreedyDecoderSeqLen>();
     pass_config->disable<ngraph::pass::ConvertGather7ToGather1>();
     pass_config->disable<ngraph::pass::ConvertMinimum>();
+    pass_config->disable<ngraph::pass::ConvertBroadcastToTiles>();
 
     pass_config->enable<ngraph::pass::NormalizeL2Decomposition>();
     pass_config->enable<ngraph::pass::ConvertInterpolate1ToInterpolate4>();
diff --git a/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_cpu_specific_opset.hpp b/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_cpu_specific_opset.hpp
index d8c997d2746..3118d3457e7 100644
--- a/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_cpu_specific_opset.hpp
+++ b/inference-engine/src/mkldnn_plugin/ngraph_transformations/convert_to_cpu_specific_opset.hpp
@@ -32,7 +32,6 @@ inline void ConvertToCPUSpecificOpset(std::shared_ptr<ngraph::Function> &nGraphF
     manager.register_pass<Reshape1DMaxPool>();
     manager.register_pass<ConvertMatMulToFC>();
     manager.register_pass<AlignMatMulInputRanks>();
-    manager.register_pass<ConvertBroadcastToTiles>();
     manager.register_pass<ConvertTileToSeqTiles>();
     manager.register_pass<FullyConnectedBiasFusion>();
     manager.register_pass<ReshapeFullyConnected>();
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/tile_broadcast_utils.cpp b/inference-engine/src/mkldnn_plugin/nodes/common/tile_broadcast_utils.cpp
new file mode 100644
index 00000000000..d93a25196c6
--- /dev/null
+++ b/inference-engine/src/mkldnn_plugin/nodes/common/tile_broadcast_utils.cpp
@@ -0,0 +1,243 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "tile_broadcast_utils.h"
+
+#include "cpu_memcpy.h"
+#include "ie_parallel.hpp"
+#include <memory_desc/cpu_memory_desc_utils.h>
+#include "memory_desc/dnnl_blocked_memory_desc.h"
+
+using namespace InferenceEngine;
+using namespace MKLDNNPlugin;
+
+VectorDims TileBroadcastCommon::calculateDenseStrides(const VectorDims &dims) {
+    VectorDims strides(dims.size(), 1);
+
+    for (int i = strides.size() - 2; i >= 0; i--) {
+        strides[i] = strides[i + 1] * dims[i + 1];
+    }
+
+    return strides;
+}
+
+void TileBroadcastCommon::fillOptimizedDimsAndSrcStrides(const VectorDims& srcBlockedDims, const VectorDims& blockedRepeats,
+        VectorDims& optimizedDims, VectorDims& optimizedSrcStrides) {
+    optimizedDims.clear();
+    optimizedSrcStrides.clear();
+    VectorDims srcBlockedStrides = calculateDenseStrides(srcBlockedDims);
+
+    for (int i = 0; i < srcBlockedDims.size(); i++) {
+        optimizedDims.push_back(blockedRepeats[i]);
+        optimizedDims.push_back(srcBlockedDims[i]);
+        optimizedSrcStrides.push_back(0);
+        optimizedSrcStrides.push_back(srcBlockedStrides[i]);
+    }
+
+    int i = 1;
+    while (i < optimizedDims.size() - 1) {
+        if (optimizedDims[i] == 1) {
+            optimizedDims[i + 1] *= optimizedDims[i - 1];
+            optimizedDims.erase(optimizedDims.begin() + i - 1, optimizedDims.begin() + i + 1);
+            optimizedSrcStrides.erase(optimizedSrcStrides.begin() + i - 1, optimizedSrcStrides.begin() + i + 1);
+        } else {
+            i++;
+        }
+    }
+
+    if (optimizedDims[0] == 1 && optimizedDims.size() > 1) {
+        optimizedDims.erase(optimizedDims.begin());
+        optimizedSrcStrides.erase(optimizedSrcStrides.begin());
+    }
+
+    if (optimizedDims[optimizedDims.size() - 1] == 1 && optimizedDims.size() > 1) {
+        optimizedDims.erase(optimizedDims.end() - 1);
+        optimizedSrcStrides.erase(optimizedSrcStrides.end() - 1);
+    }
+}
+
+bool TileBroadcastCommon::canBeExecutedInBlockedLayout(VectorDims srcBlockedDims, VectorDims blockedRepeats,
+        const size_t elemsInBlock) {
+    if (srcBlockedDims.empty() || blockedRepeats.empty() || elemsInBlock == 0lu || srcBlockedDims[1] == Shape::UNDEFINED_DIM ||
+            (blockedRepeats[1] != 1 && srcBlockedDims[1] % elemsInBlock != 0))
+        return false;
+
+    srcBlockedDims[1] = div_up(srcBlockedDims[1], elemsInBlock);
+    srcBlockedDims.push_back(elemsInBlock);
+    blockedRepeats.push_back(1);
+
+    VectorDims optimizedDims, optimizedSrcStrides;
+    fillOptimizedDimsAndSrcStrides(srcBlockedDims, blockedRepeats, optimizedDims, optimizedSrcStrides);
+
+    constexpr size_t maxNDims = 6lu;
+    return optimizedDims.size() <= maxNDims;
+}
+
+bool TileBroadcastCommon::canBeExecutedInNSPCLayout(VectorDims srcBlockedDims, VectorDims blockedRepeats) {
+    srcBlockedDims.push_back(srcBlockedDims[1]);
+    srcBlockedDims.erase(srcBlockedDims.begin() + 1);
+    blockedRepeats.push_back(blockedRepeats[1]);
+    blockedRepeats.erase(blockedRepeats.begin() + 1);
+
+    VectorDims optimizedDims, optimizedSrcStrides;
+    fillOptimizedDimsAndSrcStrides(srcBlockedDims, blockedRepeats, optimizedDims, optimizedSrcStrides);
+
+    constexpr size_t maxNDims = 6lu;
+    return optimizedDims.size() <= maxNDims;
+}
+
+std::vector<NodeDesc> TileBroadcastCommon::getSupportedConfigs(const MKLDNNNode *node) {
+    std::vector<NodeDesc> supportedPrimitiveDescriptors;
+    auto precision = node->getOriginalInputPrecisionAtPort(0);
+    auto dataType = MKLDNNExtensionUtils::IEPrecisionToDataType(precision);
+
+    const auto& srcDims = node->getInputShapeAtPort(0).getDims();
+    const auto& inDataShape = node->getInputShapeAtPort(0);
+    size_t outDataShapeRank = node->getOutputShapeAtPort(0).getRank();
+
+    NodeConfig config;
+    if (repeats.size() != outDataShapeRank && !repeats.empty())
+        IE_THROW() << node->getTypeStr() << " node with name " << node->getName() << " has incorrect Repeats vector."
+                "Repeats rank must be equal to output shape rank. Repeats rank: " << repeats.size() << ", output shape rank: " << outDataShapeRank;
+
+    config.dynBatchSupport = false;
+    config.inConfs.resize(node->getParentEdges().size());
+    config.inConfs[0].inPlace = -1;
+    config.inConfs[0].constant = constMap[0];
+    config.inConfs[1].inPlace = -1;
+    config.inConfs[1].constant = constMap[1];
+    config.inConfs[1].desc = std::make_shared<CpuBlockedMemoryDesc>(Precision::I32, node->getInputShapeAtPort(1));
+    if (config.inConfs.size() == 3) {
+        config.inConfs[2].inPlace = -1;
+        config.inConfs[2].constant = constMap[2];
+        config.inConfs[2].desc = std::make_shared<CpuBlockedMemoryDesc>(Precision::I32, node->getInputShapeAtPort(2));
+    }
+
+    config.outConfs.resize(node->getChildEdges().size());
+
+    auto pushDesc = [&](mkldnn::memory::format_tag inFormat, mkldnn::memory::format_tag outFormat) {
+        config.inConfs[0].desc = std::make_shared<DnnlBlockedMemoryDesc>(node->getInputShapeAtPort(0), dataType, inFormat);
+        for (int i = 0; i < config.outConfs.size(); i++) {
+            config.outConfs[i].inPlace = -1;
+            config.outConfs[i].constant = false;
+            config.outConfs[i].desc = std::make_shared<DnnlBlockedMemoryDesc>(node->getOutputShapeAtPort(0), dataType, outFormat);
+        }
+        supportedPrimitiveDescriptors.push_back({config, impl_desc_type::ref});
+    };
+
+    if (!repeats.empty() && inDataShape.getRank() == outDataShapeRank && (outDataShapeRank == 4 || outDataShapeRank == 5)) {
+        if (canBeExecutedInBlockedLayout(srcDims, repeats, 16)) {
+            if (outDataShapeRank == 4) {
+                pushDesc(mkldnn::memory::format_tag::nChw16c, mkldnn::memory::format_tag::nChw16c);
+            } else {
+                pushDesc(mkldnn::memory::format_tag::nCdhw16c, mkldnn::memory::format_tag::nCdhw16c);
+            }
+        }
+        if (canBeExecutedInBlockedLayout(srcDims, repeats, 8)) {
+            if (outDataShapeRank == 4) {
+                pushDesc(mkldnn::memory::format_tag::nChw8c, mkldnn::memory::format_tag::nChw8c);
+            } else {
+                pushDesc(mkldnn::memory::format_tag::nCdhw8c, mkldnn::memory::format_tag::nCdhw8c);
+            }
+        }
+        if (canBeExecutedInNSPCLayout(srcDims, repeats)) {
+            if (outDataShapeRank == 4) {
+                pushDesc(mkldnn::memory::format_tag::nhwc, mkldnn::memory::format_tag::nhwc);
+            } else {
+                pushDesc(mkldnn::memory::format_tag::ndhwc, mkldnn::memory::format_tag::ndhwc);
+            }
+        }
+    }
+
+    auto inFmt = MKLDNNExtensionUtils::GetPlainFormatByRank(inDataShape.getRank());
+    auto outFmt = MKLDNNExtensionUtils::GetPlainFormatByRank(outDataShapeRank);
+    if (inFmt == mkldnn::memory::format_tag::undef || outFmt == mkldnn::memory::format_tag::undef) {
+        config.inConfs[0].desc = std::make_shared<CpuBlockedMemoryDesc>(precision, node->getInputShapeAtPort(0));
+        for (int i = 0; i < config.outConfs.size(); i++) {
+            config.outConfs[i].inPlace = -1;
+            config.outConfs[i].constant = false;
+            config.outConfs[i].desc = std::make_shared<CpuBlockedMemoryDesc>(precision, node->getOutputShapeAtPort(i));
+        }
+        supportedPrimitiveDescriptors.push_back({config, impl_desc_type::ref});
+    } else {
+        pushDesc(inFmt, outFmt);
+    }
+
+    return supportedPrimitiveDescriptors;
+}
+
+bool TileBroadcastCommon::prepareOptimizedParams(const MKLDNNNode *node, VectorDims& srcBlockedDims, VectorDims& dstBlockedDims) {
+    while (srcBlockedDims.size() < dstBlockedDims.size()) {
+        srcBlockedDims.insert(srcBlockedDims.begin(), 1);
+    }
+
+    VectorDims blockedRepeats = repeats;
+    // for nC(d)hw16c and nC(d)hw8c layouts
+    while (blockedRepeats.size() < dstBlockedDims.size()) {
+        blockedRepeats.push_back(1);
+    }
+    // for NSPC layouts
+    if (node->getBaseMemDescAtInputPort(0)->hasLayoutType(LayoutType::nspc) && one_of(node->getBaseMemDescAtInputPort(0)->getShape().getRank(), 4, 5)) {
+        blockedRepeats.push_back(blockedRepeats[1]);
+        blockedRepeats.erase(blockedRepeats.begin() + 1);
+    }
+
+    VectorDims optimizedDims, optimizedSrcStrides;
+    fillOptimizedDimsAndSrcStrides(srcBlockedDims, blockedRepeats, optimizedDims, optimizedSrcStrides);
+
+    constexpr size_t maxNDims = 6lu;
+    if (optimizedDims.size() > maxNDims)
+        return false;
+
+    while (optimizedDims.size() < maxNDims) {
+        optimizedDims.insert(optimizedDims.begin(), 1);
+        optimizedSrcStrides.insert(optimizedSrcStrides.begin(), 1);
+    }
+
+    VectorDims optimizedDstStrides = calculateDenseStrides(optimizedDims);
+
+    size_t dataSize = node->getSelectedPrimitiveDescriptor()->getConfig().inConfs[0].desc->getPrecision().size();
+    for (int i = 0; i < optimizedDims.size(); i++) {
+        optimizedSrcStrides[i] *= dataSize;
+        optimizedDstStrides[i] *= dataSize;
+    }
+
+    optimizedParams.dims = optimizedDims;
+    optimizedParams.srcStrides = optimizedSrcStrides;
+    optimizedParams.dstStrides = optimizedDstStrides;
+    optimizedParams.copySize = optimizedDims[5] * dataSize;
+
+    return true;
+}
+
+void TileBroadcastCommon::optimizedExecute(const MKLDNNMemoryPtr& srcMemory, const MKLDNNMemoryPtr& dstMemory) {
+    auto srcData = reinterpret_cast<const char *>(srcMemory->GetPtr());
+    auto dstData = reinterpret_cast<char *>(dstMemory->GetPtr());
+
+    if (optimizedParams.srcStrides[5] == 0) {
+        parallel_for5d(optimizedParams.dims[0], optimizedParams.dims[1], optimizedParams.dims[2], optimizedParams.dims[3], optimizedParams.dims[4],
+                [&](int i0, int i1, int i2, int i3, int i4) {
+            auto srcData2 = srcData + (i0 * optimizedParams.srcStrides[0] + i1 * optimizedParams.srcStrides[1] +
+                                                 i2 * optimizedParams.srcStrides[2] + i3 * optimizedParams.srcStrides[3] +
+                                                 i4 * optimizedParams.srcStrides[4]);
+            auto dstData2 = dstData + (i0 * optimizedParams.dstStrides[0] + i1 * optimizedParams.dstStrides[1] +
+                                           i2 * optimizedParams.dstStrides[2] + i3 * optimizedParams.dstStrides[3] +
+                                           i4 * optimizedParams.dstStrides[4]);
+            for (int i = 0; i < optimizedParams.dims[5]; i++) {
+                cpu_memcpy(dstData2 + i * optimizedParams.dstStrides[5], srcData2, optimizedParams.dstStrides[5]);
+            }
+        });
+    } else {
+        parallel_for5d(optimizedParams.dims[0], optimizedParams.dims[1], optimizedParams.dims[2], optimizedParams.dims[3], optimizedParams.dims[4],
+                [&](int i0, int i1, int i2, int i3, int i4) {
+            auto srcData2 = srcData + (i0 * optimizedParams.srcStrides[0] + i1 * optimizedParams.srcStrides[1] +
+                                                 i2 * optimizedParams.srcStrides[2] + i3 * optimizedParams.srcStrides[3] +
+                                                 i4 * optimizedParams.srcStrides[4]);
+            auto dstData2 = dstData + (i0 * optimizedParams.dstStrides[0] + i1 * optimizedParams.dstStrides[1] +
+                                           i2 * optimizedParams.dstStrides[2] + i3 * optimizedParams.dstStrides[3] +
+                                           i4 * optimizedParams.dstStrides[4]);
+            cpu_memcpy(dstData2, srcData2, optimizedParams.copySize);
+        });
+    }
+}
diff --git a/inference-engine/src/mkldnn_plugin/nodes/common/tile_broadcast_utils.h b/inference-engine/src/mkldnn_plugin/nodes/common/tile_broadcast_utils.h
new file mode 100644
index 00000000000..e5a60c81a08
--- /dev/null
+++ b/inference-engine/src/mkldnn_plugin/nodes/common/tile_broadcast_utils.h
@@ -0,0 +1,43 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "mkldnn_node.h"
+
+#include <memory>
+#include <vector>
+
+
+namespace MKLDNNPlugin {
+
+class TileBroadcastCommon {
+protected:
+    static VectorDims calculateDenseStrides(const VectorDims &dims);
+    std::vector<NodeDesc> getSupportedConfigs(const MKLDNNNode *node);
+    bool prepareOptimizedParams(const MKLDNNNode *node, VectorDims& srcBlockedDims, VectorDims& dstBlockedDims);
+
+    void optimizedExecute(const MKLDNNMemoryPtr& srcMemory, const MKLDNNMemoryPtr& dstMemory);
+
+    VectorDims repeats;
+    bool optimizedCase = false;
+    bool constMap[3] = { false };
+    mutable bool needPrepareParamsVar = false;
+
+private:
+    static void fillOptimizedDimsAndSrcStrides(const VectorDims &srcBlockedDims, const VectorDims &blockedRepeats,
+            VectorDims &optimizedDims, VectorDims &optimizedSrcStrides);
+
+    static bool canBeExecutedInBlockedLayout(VectorDims srcDims, VectorDims repeats, const size_t elemsInBlock);
+    static bool canBeExecutedInNSPCLayout(VectorDims srcDims, VectorDims repeats);
+
+    struct {
+        VectorDims dims;
+        VectorDims srcStrides;
+        VectorDims dstStrides;
+        size_t copySize;
+    } optimizedParams;
+};
+
+}  // namespace MKLDNNPlugin
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_broadcast_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_broadcast_node.cpp
index 3b5b561cff3..2930fb71c25 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_broadcast_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_broadcast_node.cpp
@@ -17,23 +17,27 @@
 using namespace MKLDNNPlugin;
 using namespace InferenceEngine;
 
-bool MKLDNNBroadcastNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
+bool MKLDNNBroadcastNode::isSupportedOperation(const std::shared_ptr<const ov::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
+        if (!ov::is_type<ov::op::v1::Broadcast>(op)) {
+            errorMessage = "Only Broadcast operations from opset1 are supported.";
             return false;
         }
-        const auto broadcast = std::dynamic_pointer_cast<const ngraph::opset1::Broadcast>(op);
-        if (!broadcast) {
-            errorMessage = "Only opset1 Broadcast operation is supported";
+        if (!one_of(ov::as_type_ptr<const ov::op::v1::Broadcast>(op)->get_broadcast_spec().m_type,
+                ov::op::AutoBroadcastType::NUMPY, ov::op::AutoBroadcastType::EXPLICIT)) {
+            errorMessage = "Only NUMPY and EXPLICIT broadcast types are supported.";
             return false;
         }
-        if (broadcast->get_broadcast_spec() != ngraph::op::AutoBroadcastType::NUMPY) {
-            errorMessage = "Only NUMPY broadcast type is supported";
+        if (op->get_input_partial_shape(TARGET_SHAPE_IDX).is_dynamic() ||
+                (op->get_input_size() > AXES_MAPPING_IDX && op->get_input_partial_shape(AXES_MAPPING_IDX).is_dynamic())) {
+            errorMessage = "Only static shapes are supported for target shape and axes mapping inputs.";
             return false;
         }
-        if (std::dynamic_pointer_cast<const ngraph::opset1::Constant>(broadcast->get_input_node_shared_ptr(BROADCAST_SHAPE)) == nullptr) {
-            errorMessage = "Only const 'shape' input is supported";
+        if (!isDynamicNgraphNode(op) &&
+                (!ov::is_type<ov::op::v0::Constant>(op->get_input_node_ptr(TARGET_SHAPE_IDX)) ||
+                 (op->get_input_size() > AXES_MAPPING_IDX &&
+                 !ov::is_type<ov::op::v0::Constant>(op->get_input_node_ptr(AXES_MAPPING_IDX))))) {
+            errorMessage = "Only constant target shapes and axis mapping inputs are supported for static shapes.";
             return false;
         }
     } catch (...) {
@@ -42,91 +46,231 @@ bool MKLDNNBroadcastNode::isSupportedOperation(const std::shared_ptr<const ngrap
     return true;
 }
 
-MKLDNNBroadcastNode::MKLDNNBroadcastNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng,
+MKLDNNBroadcastNode::MKLDNNBroadcastNode(const std::shared_ptr<ov::Node>& op, const mkldnn::engine& eng,
         MKLDNNWeightsSharing::Ptr &cache) : MKLDNNNode(op, eng, cache) {
     std::string errorMessage;
     if (!isSupportedOperation(op, errorMessage)) {
         IE_THROW(NotImplemented) << errorMessage;
     }
 
-    errorPrefix = "Broadcast node with name '" + op->get_friendly_name() + "'";
-    if (op->get_input_size() != 2 || op->get_output_size() != 1)
-        IE_THROW() << errorPrefix << " has incorrect number of input/output edges! " << op->get_input_size() << "->" << op->get_output_size();
+    errorPrefix = "Broadcast node with name '" + op->get_friendly_name() + "' ";
+    if (op->get_input_size() != 2 && op->get_input_size() != 3)
+        IE_THROW() << errorPrefix << "has incorrect number of input edges: " << getParentEdges().size();
+    if (op->get_output_size() == 0)
+        IE_THROW() << errorPrefix << "has no output edges.";
 
-    SizeVector shape_dims = op->get_input_shape(BROADCAST_SHAPE);
-    if (shape_dims.size() > 1)
-        IE_THROW() << errorPrefix << " has incorrect 'shape' input rank: " << shape_dims.size();
+    auto broadcastOp = ov::as_type_ptr<const ov::op::v1::Broadcast>(op);
+    if (broadcastOp->get_broadcast_spec().m_type == ov::op::AutoBroadcastType::NUMPY) {
+        broadcastType = NUMPY;
+    } else if (broadcastOp->get_broadcast_spec().m_type == ov::op::AutoBroadcastType::EXPLICIT) {
+        if (op->get_input_size() <= AXES_MAPPING_IDX)
+            IE_THROW() << errorPrefix << " and EXPLICIT mode must have tree input edges: " << getParentEdges().size();
+        broadcastType = EXPLICIT;
+    }
+
+    if (ov::is_type<ov::op::v0::Constant>(op->get_input_node_ptr(TARGET_SHAPE_IDX))) {
+        constMap[TARGET_SHAPE_IDX] = true;
+        targetShape = (ov::as_type<ov::op::v0::Constant>(op->get_input_node_ptr(TARGET_SHAPE_IDX)))->get_vector<int32_t>();
+    }
+    if (broadcastType == EXPLICIT &&
+                ov::is_type<ov::op::v0::Constant>(op->get_input_node_ptr(AXES_MAPPING_IDX))) {
+        constMap[AXES_MAPPING_IDX] = true;
+        axesMapping = ov::as_type<ov::op::v0::Constant>(op->get_input_node_ptr(AXES_MAPPING_IDX))->get_vector<int32_t>();
+    }
+}
+
+void MKLDNNBroadcastNode::getSupportedDescriptors() {
+    if (!isDynamicNode()) {
+        const auto& srcDims = getInputShapeAtPort(INPUT_DATA_IDX).getDims();
+        repeats.assign(targetShape.begin(), targetShape.end());
+        const auto ndims = repeats.size();
+
+        if (broadcastType == NUMPY) {
+            for (size_t i = 0lu; i < srcDims.size(); i++) {
+                repeats[ndims - 1lu - i] /= srcDims[srcDims.size() - 1lu - i];
+            }
+        } else if (broadcastType == EXPLICIT) {
+            for (size_t i = 0lu; i < axesMapping.size(); i++) {
+                repeats[axesMapping[i]] /= srcDims[i];
+            }
+        }
+        needPrepareParamsVar = true;
+    }
 }
 
 void MKLDNNBroadcastNode::initSupportedPrimitiveDescriptors() {
     if (!supportedPrimitiveDescriptors.empty())
         return;
 
-    Precision prec = getOriginalInputPrecisionAtPort(BROADCAST_INPUT);
+    supportedPrimitiveDescriptors = getSupportedConfigs(this);
+}
 
-    addSupportedPrimDesc({{LayoutType::ncsp, prec},
-                          {LayoutType::ncsp, Precision::I32}},
-                         {{LayoutType::ncsp, prec}},
-                         impl_desc_type::ref_any);
+void MKLDNNBroadcastNode::createPrimitive() {
+    if (inputShapesDefined()) {
+        if (needPrepareParams())
+            prepareParams();
+        updateLastInputDims();
+    }
+}
+
+bool MKLDNNBroadcastNode::needPrepareParams() const {
+    return needPrepareParamsVar;
+}
+
+void MKLDNNBroadcastNode::prepareParams() {
+    if (!constMap[TARGET_SHAPE_IDX]) {
+        const auto& targetShapeMem = getParentEdgesAtPort(TARGET_SHAPE_IDX)[0]->getMemory();
+        const int32_t* targetShapeData = reinterpret_cast<const int32_t *>(targetShapeMem.GetPtr());
+        targetShape.assign(targetShapeData, targetShapeData + targetShapeMem.getStaticDims()[0]);
+    }
+    if (broadcastType == EXPLICIT && !constMap[AXES_MAPPING_IDX]) {
+        const auto& axesMapMem = getParentEdgesAtPort(AXES_MAPPING_IDX)[0]->getMemory();
+        const int32_t* axesMapData = reinterpret_cast<const int32_t *>(axesMapMem.GetPtr());
+        axesMapping.assign(axesMapData, axesMapData + axesMapMem.getStaticDims()[0]);
+    }
+
+    const auto& srcDims = getParentEdgesAtPort(INPUT_DATA_IDX)[0]->getMemory().GetShape().getStaticDims();
+    repeats.assign(targetShape.begin(), targetShape.end());
+    const auto ndims = repeats.size();
+
+    auto srcBlockedDims = getParentEdgeAt(INPUT_DATA_IDX)->getMemory().GetDescWithType<BlockedMemoryDesc>()->getBlockDims();
+    auto dstBlockedDims = getChildEdgeAt(0)->getMemory().GetDescWithType<BlockedMemoryDesc>()->getBlockDims();
+
+    if (broadcastType == NUMPY) {
+        for (size_t i = 0lu; i < srcDims.size(); i++) {
+            repeats[ndims - 1lu - i] /= srcDims[srcDims.size() - 1lu - i];
+        }
+    } else if (broadcastType == EXPLICIT) {
+        for (size_t i = 0; i < getInputShapeAtPort(AXES_MAPPING_IDX).getDims()[0]; i++) {
+            repeats[axesMapping[i]] /= srcDims[i];
+        }
+
+        SizeVector newSrcBlockedDims = SizeVector(dstBlockedDims.size(), 1);
+        for (size_t i = 0; i < getInputShapeAtPort(AXES_MAPPING_IDX).getDims()[0]; i++) {
+            newSrcBlockedDims[axesMapping[i]] = srcBlockedDims[i];
+        }
+        srcBlockedDims = newSrcBlockedDims;
+    }
+
+    optimizedCase = prepareOptimizedParams(this, srcBlockedDims, dstBlockedDims);
+}
+
+bool MKLDNNBroadcastNode::needShapeInfer() const {
+    needPrepareParamsVar = true;
+    if (inputShapesModified()) {
+        return true;
+    }
+
+    if (!constMap[TARGET_SHAPE_IDX]) {
+        if (targetShape.empty()) {
+            return true;
+        }
+        const int32_t* targetShapeData = reinterpret_cast<const int32_t *>(getParentEdgesAtPort(TARGET_SHAPE_IDX)[0]->getMemory().GetPtr());
+        for (size_t i = 0lu; i < targetShape.size(); i++) {
+            if (targetShape[i] != targetShapeData[i]) {
+                return true;
+            }
+        }
+    }
+    if (broadcastType == EXPLICIT && !constMap[AXES_MAPPING_IDX]) {
+        if (axesMapping.empty()) {
+            return true;
+        }
+        const int32_t* axesMappingData = reinterpret_cast<const int32_t *>(getParentEdgesAtPort(AXES_MAPPING_IDX)[0]->getMemory().GetPtr());
+        for (size_t i = 0lu; i < axesMapping.size(); i++) {
+            if (axesMapping[i] != axesMappingData[i]) {
+                return true;
+            }
+        }
+    }
+    needPrepareParamsVar = false;
+    return false;
+}
+
+std::vector<VectorDims> MKLDNNBroadcastNode::shapeInfer() const {
+    ngraph::OutputVector inputsForShapeInfer {
+            std::make_shared<ov::op::v0::Parameter>(opToShapeInfer->get_input_element_type(INPUT_DATA_IDX),
+                            getParentEdgesAtPort(INPUT_DATA_IDX)[0]->getMemory().GetShape().toPartialShape()),
+            std::make_shared<ov::op::v0::Constant>(ov::element::Type_t::i32,
+                            getParentEdgesAtPort(TARGET_SHAPE_IDX)[0]->getMemory().GetShape().getStaticDims(),
+                            getParentEdgesAtPort(TARGET_SHAPE_IDX)[0]->getMemory().GetPtr())
+        };
+    if (opToShapeInfer->get_input_size() > AXES_MAPPING_IDX) {
+        inputsForShapeInfer.push_back(std::make_shared<ov::op::v0::Constant>(ov::element::Type_t::i32,
+                        getParentEdgesAtPort(AXES_MAPPING_IDX)[0]->getMemory().GetShape().getStaticDims(),
+                        getParentEdgesAtPort(AXES_MAPPING_IDX)[0]->getMemory().GetPtr()));
+    }
+    const auto localShapeInferOp = opToShapeInfer->clone_with_new_inputs(inputsForShapeInfer);
+
+    localShapeInferOp->validate_and_infer_types();
+
+    std::vector<VectorDims> newOutputShapes(outputShapes.size());
+    for (size_t i = 0lu; i < newOutputShapes.size(); i++) {
+        const auto &partShape = localShapeInferOp->get_output_partial_shape(i);
+        newOutputShapes[i] = partShape.get_shape();
+    }
+
+    return newOutputShapes;
 }
 
 void MKLDNNBroadcastNode::execute(mkldnn::stream strm) {
-    size_t shape_size = (getParentEdgeAt(BROADCAST_SHAPE)->getMemory().getStaticDims())[0];
-    SizeVector dst_dims = getChildEdgeAt(0)->getMemory().getStaticDims();
-    SizeVector src_dims = getParentEdgeAt(BROADCAST_INPUT)->getMemory().getStaticDims();
+    if (optimizedCase) {
+        optimizedExecute(getParentEdgeAt(INPUT_DATA_IDX)->getMemoryPtr(), getChildEdgeAt(0)->getMemoryPtr());
+    } else {
+        plainExecute(strm);
+    }
+}
 
-    auto srcDesc = getParentEdgeAt(BROADCAST_INPUT)->getMemory().GetDescWithType<BlockedMemoryDesc>();
-    SizeVector srcStrides = srcDesc->getStrides();
-    size_t data_size = srcDesc->getPrecision().size();
+void MKLDNNBroadcastNode::plainExecute(mkldnn::stream strm) {
+    VectorDims srcDims = getParentEdgeAt(INPUT_DATA_IDX)->getMemory().getStaticDims();
+    const auto& dstDims = getChildEdgeAt(0)->getMemory().getStaticDims();
+    const auto& dataSrcRank = getParentEdgeAt(INPUT_DATA_IDX)->getMemory().GetShape().getRank();
+    const auto& dataDstRank = getChildEdgeAt(0)->getMemory().GetShape().getRank();
 
-    if (!src_dims.size())
-        src_dims = SizeVector(1, 1);
+    auto srcDesc = getParentEdgeAt(INPUT_DATA_IDX)->getMemory().GetDescWithType<BlockedMemoryDesc>();
+    VectorDims srcStrides = srcDesc->getStrides();
+    const size_t dataSize = srcDesc->getPrecision().size();
+
+    if (!dataSrcRank)
+        srcDims = VectorDims(1, 1);
     if (!srcStrides.size())
-        srcStrides = SizeVector(1, 1);
-
-    if (dst_dims.size() != shape_size) {
-        IE_THROW() << "Output tensor dimension mismatch";
-    }
-
-    if (src_dims.size() > dst_dims.size()) {
-        IE_THROW() << "Output tensor dimension is smaller then input tensor dimension";
-    }
+        srcStrides = VectorDims(1, 1);
 
     auto dstDesc = getChildEdgeAt(0)->getMemory().GetDescWithType<BlockedMemoryDesc>();
-    InferenceEngine::SizeVector dstStrides = dstDesc->getStrides();
-    InferenceEngine::SizeVector src_aligned(dst_dims.size());
-    InferenceEngine::SizeVector srcStrides_aligned(dst_dims.size());
-    size_t prefix_size = dst_dims.size() - src_dims.size();
-    for (size_t i = 0; i < dst_dims.size(); i++) {
-        if (i < prefix_size) {
-            src_aligned[i] = 1;
-            srcStrides_aligned[i] = srcStrides[0];
+    VectorDims dstStrides = dstDesc->getStrides();
+    VectorDims srcAligned(dataDstRank);
+    VectorDims srcStridesAligned(dataDstRank);
+    const size_t prefixSize = dataDstRank - dataSrcRank;
+    for (size_t i = 0lu; i < dataDstRank; i++) {
+        if (i < prefixSize) {
+            srcAligned[i] = 1;
+            srcStridesAligned[i] = srcStrides[0];
         } else {
-            src_aligned[i] = src_dims[i - prefix_size];
-            srcStrides_aligned[i] = srcStrides[i - prefix_size];
+            srcAligned[i] = srcDims[i - prefixSize];
+            srcStridesAligned[i] = srcStrides[i - prefixSize];
         }
     }
 
-    size_t work_amount_dst = dstStrides[0] * dst_dims[0];
-    const auto *src_data = reinterpret_cast<const uint8_t *>(getParentEdgeAt(BROADCAST_INPUT)->getMemoryPtr()->GetPtr());
-    auto *dst_data = reinterpret_cast<uint8_t *>(getChildEdgeAt(0)->getMemoryPtr()->GetPtr());
+    const size_t workAmountDst = dstStrides[0] * dstDims[0];
+    const auto *srcData = reinterpret_cast<const uint8_t *>(getParentEdgeAt(INPUT_DATA_IDX)->getMemoryPtr()->GetPtr());
+    auto *dstData = reinterpret_cast<uint8_t *>(getChildEdgeAt(0)->getMemoryPtr()->GetPtr());
 
     parallel_nt(0, [&](const int ithr, const int nthr) {
-        size_t i, src_idx, start = 0, end = 0;
-        SizeVector counters(dst_dims.size(), 0);
-        splitter(work_amount_dst, nthr, ithr, start, end);
-        for (int j = dst_dims.size() - 1, i = start; j >= 0; j--) {
-            counters[j] = i % dst_dims[j];
-            i /= dst_dims[j];
+        size_t i = 0lu, srcIdx = 0lu, start = 0lu, end = 0lu;
+        VectorDims counters(dataDstRank, 0);
+        splitter(workAmountDst, nthr, ithr, start, end);
+        for (int j = dataDstRank - 1, i = start; j >= 0; j--) {
+            counters[j] = i % dstDims[j];
+            i /= dstDims[j];
         }
-        for (size_t iwork = start * data_size; iwork < end * data_size; iwork += data_size) {
-            for (i = 0, src_idx = 0; i < dst_dims.size(); ++i)
-                src_idx += counters[i] ? ((counters[i] % src_aligned[i]) * srcStrides_aligned[i]) : 0;
+        for (size_t iwork = start * dataSize; iwork < end * dataSize; iwork += dataSize) {
+            for (i = 0lu, srcIdx = 0lu; i < dataDstRank; ++i)
+                srcIdx += counters[i] ? ((counters[i] % srcAligned[i]) * srcStridesAligned[i]) : 0;
 
-            cpu_memcpy(&dst_data[iwork], &src_data[src_idx * data_size], data_size);
+            cpu_memcpy(&dstData[iwork], &srcData[srcIdx * dataSize], dataSize);
 
-            for (int j = dst_dims.size() - 1; j >= 0; j--) {
-                counters[j] = (counters[j] + 1) % dst_dims[j];
+            for (int j = dataDstRank - 1; j >= 0; j--) {
+                counters[j] = (counters[j] + 1) % dstDims[j];
                 if (counters[j] != 0) break;
             }
         }
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_broadcast_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_broadcast_node.h
index 932bded6c7c..4851cf19c9a 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_broadcast_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_broadcast_node.h
@@ -4,29 +4,51 @@
 
 #pragma once
 
-#include <ie_common.h>
-#include <mkldnn_node.h>
-#include <string>
+#include "common/tile_broadcast_utils.h"
+
 #include <memory>
+#include <string>
 #include <vector>
 
+
 namespace MKLDNNPlugin {
 
-class MKLDNNBroadcastNode : public MKLDNNNode {
+class MKLDNNBroadcastNode : public MKLDNNNode, public TileBroadcastCommon {
 public:
-    MKLDNNBroadcastNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache);
+    MKLDNNBroadcastNode(const std::shared_ptr<ov::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache);
 
-    void getSupportedDescriptors() override {};
+    void getSupportedDescriptors() override;
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
+    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
+    void executeDynamicImpl(mkldnn::stream strm) override {
+        execute(strm);
+    }
     bool created() const override;
 
-    static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
+    static bool isSupportedOperation(const std::shared_ptr<const ov::Node>& op, std::string& errorMessage) noexcept;
+
+protected:
+    bool needPrepareParams() const override;
+    void prepareParams() override;
+    bool needShapeInfer() const override;
+    std::vector<VectorDims> shapeInfer() const override;
 
 private:
-    static const size_t BROADCAST_INPUT = 0;
-    static const size_t BROADCAST_SHAPE = 1;
+    void plainExecute(mkldnn::stream strm);
+
+    enum AutoBroadcastType {
+        NUMPY,
+        EXPLICIT
+    };
+    AutoBroadcastType broadcastType;
+
+    static constexpr size_t INPUT_DATA_IDX = 0;
+    static constexpr size_t TARGET_SHAPE_IDX = 1;
+    static constexpr size_t AXES_MAPPING_IDX = 2;
+
+    std::vector<int32_t> targetShape;
+    std::vector<int32_t> axesMapping;
 
     std::string errorPrefix;
 };
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_tile_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_tile_node.cpp
index 00fc9ba7873..74a1d2911c2 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_tile_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_tile_node.cpp
@@ -3,39 +3,24 @@
 //
 
 #include "mkldnn_tile_node.h"
-#include <string>
-#include <mkldnn_types.h>
-#include <mkldnn_extension_utils.h>
 #include "common/cpu_memcpy.h"
-#include <ngraph/opsets/opset1.hpp>
 
-using namespace mkldnn;
-using namespace MKLDNNPlugin;
 using namespace InferenceEngine;
+using namespace MKLDNNPlugin;
 
-bool MKLDNNTileNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
+bool MKLDNNTileNode::isSupportedOperation(const std::shared_ptr<const ov::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
+        if (!ov::is_type<ov::op::v0::Tile>(op)) {
+            errorMessage = "Only opset1 Tile operation is supported.";
             return false;
         }
-        const auto tile = std::dynamic_pointer_cast<const ngraph::opset1::Tile>(op);
-        if (!tile) {
-            errorMessage = "Only opset1 Tile operation is supported";
+        if (op->get_input_partial_shape(TILE_REPEATS).is_dynamic()) {
+            errorMessage = "Only static shape is supported for tile repeats input.";
             return false;
         }
-        if (tile->get_input_shape(TILE_INPUT).size() != tile->get_input_shape(TILE_REPEATS)[0]) {
-            errorMessage = "Doesn't support inputs with different ranks";
-            return false;
-        }
-        const auto repeatsNode = std::dynamic_pointer_cast<const ngraph::opset1::Constant>(tile->get_input_node_shared_ptr(TILE_REPEATS));
-        if (repeatsNode == nullptr) {
-            errorMessage = "Only const 'repeats' input is supported";
-            return false;
-        }
-        const auto repeats = repeatsNode->cast_vector<int64_t>();
-        if (std::count_if(repeats.begin(), repeats.end(), [](int64_t x) { return x > 1; }) > 1) {
-            errorMessage = "Doesn't support 'repeats' with more than one specified axis";
+        if (!isDynamicNgraphNode(op) &&
+                !ov::is_type<ov::op::v0::Constant>(op->get_input_node_ptr(TILE_REPEATS))) {
+            errorMessage = "Only constant 'Repeats' input is supported with static shapes.";
             return false;
         }
     } catch (...) {
@@ -44,77 +29,141 @@ bool MKLDNNTileNode::isSupportedOperation(const std::shared_ptr<const ngraph::No
     return true;
 }
 
-MKLDNNTileNode::MKLDNNTileNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache) :
+MKLDNNTileNode::MKLDNNTileNode(const std::shared_ptr<ov::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache) :
         MKLDNNNode(op, eng, cache) {
     std::string errorMessage;
-    if (isSupportedOperation(op, errorMessage)) {
-        errorPrefix = "Tile node with name '" + getName() + "'";
-
-        const auto tile = std::dynamic_pointer_cast<const ngraph::opset1::Tile>(op);
-        const auto repeatsNode = std::dynamic_pointer_cast<const ngraph::opset1::Constant>(tile->get_input_node_shared_ptr(TILE_REPEATS));
-        const auto repeats = repeatsNode->cast_vector<int64_t>();
-        // At this moment CPU plug-in supports tiling only per single axis
-        // This behavoiur is guaranteed by ConvertTileToSeqTiles
-        for (size_t i = 0; i < repeats.size(); i++) {
-            if (repeats[i] > 1) {
-                axis = i;
-                tiles = repeats[i];
-                break;
-            }
-        }
-        noTiling = axis == -1;
-        if (axis >= static_cast<int>(tile->get_input_shape(TILE_INPUT).size()))
-            IE_THROW() << errorPrefix << " has incorrect tiling axis: " << axis;
-        if (tiles < 1 && !noTiling)
-            IE_THROW() << errorPrefix << " has incorrect 'repeats' value: " << tiles;
-    } else {
+    if (!isSupportedOperation(op, errorMessage)) {
         IE_THROW(NotImplemented) << errorMessage;
     }
+
+    errorPrefix = "Tile node with name '" + getName() + "'";
+
+    if (ov::is_type<ov::op::v0::Constant>(op->get_input_node_ptr(TILE_REPEATS))) {
+        constMap[TILE_REPEATS] = true;
+        repeats = originRepeats = ov::as_type<const ov::op::v0::Constant>(op->get_input_node_ptr(TILE_REPEATS))->cast_vector<size_t>();
+        while (repeats.size() < getInputShapeAtPort(TILE_INPUT).getRank()) {
+            repeats.insert(repeats.begin(), 1lu);
+        }
+    }
 }
 
 void MKLDNNTileNode::getSupportedDescriptors() {
     if (getParentEdges().size() != 2)
-        IE_THROW() << errorPrefix << " has incorrect number of input edges";
-    if (!getChildEdges().size())
-        IE_THROW() << errorPrefix << " has incorrect number of output edges";
+        IE_THROW() << errorPrefix << " has incorrect number of input edges. "
+                "Expected: 2, Actual: " << getParentEdges().size();
+    if (getChildEdges().empty())
+        IE_THROW() << errorPrefix << " has no output edges.";
+    const auto& dstDims0 = getOutputShapeAtPort(0).getDims();
+    for (size_t i = 1lu; i < outputShapes.size(); i++) {
+        const auto& dstDims = getOutputShapeAtPort(i).getDims();
+        if (dstDims.size() != dstDims0.size())
+            IE_THROW() << errorPrefix << " has output edges 0 and " << i << " with different ranks: " << dstDims0.size() << " and " << dstDims.size();
+        for (size_t j = 0; j < dstDims0.size(); j++) {
+            if (dstDims0[j] != dstDims[j]) {
+                IE_THROW() << errorPrefix << " has output edges 0 and " << i << " with different dims: "
+                        << std::string(dstDims0.begin(), dstDims0.end()) << " and " << std::string(dstDims.begin(), dstDims.end());
+            }
+        }
+    }
+    if (constMap[TILE_REPEATS] && getInputShapeAtPort(TILE_INPUT).getRank() > getOutputShapeAtPort(0).getRank())
+        IE_THROW() << errorPrefix << " has incorrect input/output data shape rank. Input shape rank cannot be more than output shape rank. "
+                "Actual input shape size: " << getInputShapeAtPort(TILE_INPUT).getRank() << ", output shape size: " << getOutputShapeAtPort(0).getRank();
+
+    if (!isDynamicNode())
+        needPrepareParamsVar = true;
 }
 
 void MKLDNNTileNode::initSupportedPrimitiveDescriptors() {
     if (!supportedPrimitiveDescriptors.empty())
         return;
 
-    InferenceEngine::Precision precision = getOriginalInputPrecisionAtPort(TILE_INPUT);
-    if (precision.size() != sizeof(PrecisionTrait<Precision::I32>::value_type) &&
-        precision.size() != sizeof(PrecisionTrait<Precision::I16>::value_type) &&
-        precision.size() != sizeof(PrecisionTrait<Precision::I8>::value_type)) {
-        IE_THROW() << errorPrefix << " has unsupported input precision: " << precision;
-    }
-
-    int inPlace = noTiling ? 0 : -1;
-    addSupportedPrimDesc({{LayoutType::ncsp, precision},
-                           {LayoutType::ncsp, Precision::I32}},
-                          {{LayoutType::ncsp, precision, false, inPlace}},
-                           impl_desc_type::unknown,
-                           true);
+    supportedPrimitiveDescriptors = getSupportedConfigs(this);
 }
 
 void MKLDNNTileNode::createPrimitive() {
-    auto& dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
-    auto& srcMemPtr = getParentEdgeAt(0)->getMemoryPtr();
-    if (!dstMemPtr || !dstMemPtr->GetPrimitivePtr())
-        IE_THROW() << errorPrefix << " can't get destination memory";
-    if (!srcMemPtr || !srcMemPtr->GetPrimitivePtr())
-        IE_THROW() << errorPrefix << " can't get input memory";
-    if (getSelectedPrimitiveDescriptor() == nullptr)
-        IE_THROW() << errorPrefix << " has nullable preferable primitive descriptor";
+    if (inputShapesDefined()) {
+        if (needPrepareParams())
+            prepareParams();
+        updateLastInputDims();
+    }
+}
+
+bool MKLDNNTileNode::needPrepareParams() const {
+    return needPrepareParamsVar;
+}
+
+void MKLDNNTileNode::prepareParams() {
+    if (!constMap[TILE_REPEATS]) {
+        const auto& repeatsMem = getParentEdgesAtPort(TILE_REPEATS)[0]->getMemory();
+
+        const int32_t* repeatsData = reinterpret_cast<const int32_t *>(repeatsMem.GetPtr());
+        originRepeats.assign(repeatsData, repeatsData + repeatsMem.getStaticDims()[0]);
+
+        repeats.assign(std::max(originRepeats.size(), getInputShapeAtPort(TILE_INPUT).getRank()), 1lu);
+        const size_t offset = repeats.size() - originRepeats.size();
+        for (size_t i = 0lu; i < originRepeats.size(); i++) {
+            repeats[i + offset] = originRepeats[i];
+        }
+    }
+
+    auto srcBlockedDims = getParentEdgeAt(TILE_INPUT)->getMemory().GetDescWithType<BlockedMemoryDesc>()->getBlockDims();
+    auto dstBlockedDims = getChildEdgeAt(0)->getMemory().GetDescWithType<BlockedMemoryDesc>()->getBlockDims();
+
+    optimizedCase = prepareOptimizedParams(this, srcBlockedDims, dstBlockedDims);
+}
+
+bool MKLDNNTileNode::needShapeInfer() const {
+    needPrepareParamsVar = true;
+    if (inputShapesModified()) {
+        return true;
+    }
+    if (!constMap[TILE_REPEATS]) {
+        if (originRepeats.empty())
+            return true;
+        const int32_t* repeatsData = reinterpret_cast<const int32_t *>(getParentEdgesAtPort(TILE_REPEATS)[0]->getMemory().GetPtr());
+        for (size_t i = 0lu; i < originRepeats.size(); i++) {
+            if (originRepeats[i] != repeatsData[i])
+                return true;
+        }
+    }
+    needPrepareParamsVar = false;
+    return false;
+}
+
+std::vector<VectorDims> MKLDNNTileNode::shapeInfer() const {
+    ngraph::OutputVector inputsForShapeInfer {
+        std::make_shared<ov::op::v0::Parameter>(opToShapeInfer->get_input_element_type(TILE_INPUT),
+                                                getParentEdgesAtPort(TILE_INPUT)[0]->getMemory().GetShape().toPartialShape()),
+        std::make_shared<ov::op::v0::Constant>(ov::element::Type_t::i32,
+                                               getParentEdgesAtPort(TILE_REPEATS)[0]->getMemory().GetShape().getStaticDims(),
+                                               getParentEdgesAtPort(TILE_REPEATS)[0]->getMemory().GetPtr())
+    };
+    const auto localShapeInferOp = opToShapeInfer->clone_with_new_inputs(inputsForShapeInfer);
+
+    localShapeInferOp->validate_and_infer_types();
+
+    std::vector<VectorDims> newOutputShapes(outputShapes.size());
+    for (size_t i = 0lu; i < newOutputShapes.size(); i++) {
+        const auto &partShape = localShapeInferOp->get_output_partial_shape(i);
+        newOutputShapes[i] = partShape.get_shape();
+    }
+    return newOutputShapes;
 }
 
 void MKLDNNTileNode::execute(mkldnn::stream strm) {
+    if (optimizedCase) {
+        optimizedExecute(getParentEdgeAt(TILE_INPUT)->getMemoryPtr(), getChildEdgeAt(0)->getMemoryPtr());
+    } else {
+        plainExecute(strm);
+    }
+}
+
+void MKLDNNTileNode::plainExecute(mkldnn::stream strm) {
     if (noTiling) {
         return;
     }
 
-    auto& srcMemory = getParentEdgeAt(0)->getMemory();
+    auto& srcMemory = getParentEdgeAt(TILE_INPUT)->getMemory();
 
     const uint8_t* src_ptr = reinterpret_cast<const uint8_t*>(srcMemory.GetPtr());
     uint8_t* dst_ptr = reinterpret_cast<uint8_t*>(getChildEdgeAt(0)->getMemory().GetPtr());
@@ -122,14 +171,23 @@ void MKLDNNTileNode::execute(mkldnn::stream strm) {
     int m_inner_dim = 1;
     int m_outer_dim = 1;
     auto inDims = srcMemory.getStaticDims();
-    for (int i=0; i < axis; i++ ) m_outer_dim *= inDims[i];
-    for (int i=axis; i < inDims.size(); i++ ) m_inner_dim *= inDims[i];
+    for (int i = 0; i < axis; i++ )
+        m_outer_dim *= inDims[i];
+    for (int i = axis; i < inDims.size(); i++ )
+        m_inner_dim *= inDims[i];
+
+    int MB = 0;
+    if (isDynamicNode()) {
+        MB = srcMemory.getStaticDims()[0];
+    } else {
+        MB = batchToProcess();
+    }
     if (axis > 0) {
         m_outer_dim /= inDims[0];
-        m_outer_dim *= batchToProcess();
+        m_outer_dim *= MB;
     } else {
         m_inner_dim /= inDims[0];
-        m_inner_dim *= batchToProcess();
+        m_inner_dim *= MB;
     }
 
     if (m_inner_dim == 1 && m_outer_dim % 8 == 0 && srcMemory.getDesc().hasLayoutType(LayoutType::nCsp8c)) {
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_tile_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_tile_node.h
index 2eeb96ae3d8..b973a3a27c9 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_tile_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_tile_node.h
@@ -4,31 +4,43 @@
 
 #pragma once
 
-#include <ie_common.h>
-#include <mkldnn_node.h>
+#include "common/tile_broadcast_utils.h"
+
 #include <string>
 
 namespace MKLDNNPlugin {
 
-class MKLDNNTileNode : public MKLDNNNode {
+class MKLDNNTileNode : public MKLDNNNode, public TileBroadcastCommon {
 public:
-    MKLDNNTileNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache);
+    MKLDNNTileNode(const std::shared_ptr<ov::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache);
 
     void getSupportedDescriptors() override;
     void initSupportedPrimitiveDescriptors() override;
     void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
+    void executeDynamicImpl(mkldnn::stream strm) override {
+        execute(strm);
+    }
     bool created() const override;
 
-    static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
+    static bool isSupportedOperation(const std::shared_ptr<const ov::Node>& op, std::string& errorMessage) noexcept;
+
+protected:
+    bool needPrepareParams() const override;
+    void prepareParams() override;
+    bool needShapeInfer() const override;
+    std::vector<VectorDims> shapeInfer() const override;
 
 private:
-    static const size_t TILE_INPUT = 0;
-    static const size_t TILE_REPEATS = 1;
+    void plainExecute(mkldnn::stream strm);
+
+    static constexpr size_t TILE_INPUT = 0lu;
+    static constexpr size_t TILE_REPEATS = 1lu;
 
     int axis = -1;
     int tiles = 0;
     bool noTiling = false;
+    VectorDims originRepeats;
 
     std::string errorPrefix;
 };
diff --git a/inference-engine/tests/functional/inference_engine/transformations/convert_ngraph_to_cnn_network_tests.cpp b/inference-engine/tests/functional/inference_engine/transformations/convert_ngraph_to_cnn_network_tests.cpp
index c62de865091..d961c561701 100644
--- a/inference-engine/tests/functional/inference_engine/transformations/convert_ngraph_to_cnn_network_tests.cpp
+++ b/inference-engine/tests/functional/inference_engine/transformations/convert_ngraph_to_cnn_network_tests.cpp
@@ -85,7 +85,6 @@ TEST(ConvertFunctionToCNNNetworkTests, ConvertConvolutionNetwork) {
 
 TEST(ConvertFunctionToCNNNetworkTests, OpsShouldBeConvertedToIERepresentation) {
     ngraph::NodeVector should_converted_to_ie = {
-            std::make_shared<ngraph::opset4::Broadcast>(),
             std::make_shared<ngraph::opset4::Convolution>(),
             std::make_shared<ngraph::opset4::GatherTree>(),
             std::make_shared<ngraph::opset4::GroupConvolution>(),
@@ -107,7 +106,6 @@ TEST(ConvertFunctionToCNNNetworkTests, OpsShouldBeConvertedToIERepresentation) {
             std::make_shared<ngraph::opset4::Proposal>(),
             std::make_shared<ngraph::opset4::Selu>(),
             std::make_shared<ngraph::opset4::Swish>(),
-            std::make_shared<ngraph::opset4::Tile>(),
     };
 
     // create simple ngraph function Parameter -> Result
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/broadcast.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/broadcast.cpp
index d30ba6cd215..c9a5ff31bd7 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/broadcast.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/broadcast.cpp
@@ -13,56 +13,103 @@ namespace {
 
 const std::vector<InferenceEngine::Precision> inputPrecisions = {
         InferenceEngine::Precision::FP32,
+        InferenceEngine::Precision::BF16,
         InferenceEngine::Precision::I32,
+        InferenceEngine::Precision::I8,
+        InferenceEngine::Precision::U8
+};
+
+const std::vector<InferenceEngine::Precision> inputTPrecisions = {
+        InferenceEngine::Precision::FP16,
+        InferenceEngine::Precision::I16,
         InferenceEngine::Precision::BOOL
 };
 
-// NUMPY MODE
-
-std::vector<std::vector<size_t>> inShapesNumpy = {
-        {3, 1},
-        {1, 4, 1}
-};
-
-std::vector<std::vector<size_t>> targetShapesNumpy = {
+// NUMPY MODE //////////////////////////////////////////
+// 1D
+std::vector<std::vector<size_t>> targetShapesNumpy1D = {
+        {1},
+        {16},
+        {1, 16},
+        {1, 1, 16},
+        {2, 16},
         {2, 3, 6},
-        {1, 4, 4}
+        {1, 4, 4},
 };
 
-const auto numpyBroadcastParams1 = ::testing::Combine(
-        ::testing::Values(targetShapesNumpy[0]),
+const auto numpyBroadcast1DInputParams = ::testing::Combine(
+        ::testing::ValuesIn(targetShapesNumpy1D),
         ::testing::Values(ngraph::AxisSet{}), //not used in numpy mode
         ::testing::Values(ngraph::op::BroadcastType::NUMPY),
-        ::testing::Values(inShapesNumpy[0]),
+        ::testing::Values(std::vector<size_t>{1}),
         ::testing::ValuesIn(inputPrecisions),
         ::testing::Values(CommonTestUtils::DEVICE_CPU)
 );
 
-INSTANTIATE_TEST_SUITE_P(
-        smoke_TestNumpyBroadcast1,
-        BroadcastLayerTest,
-        numpyBroadcastParams1,
-        BroadcastLayerTest::getTestCaseName
-);
+INSTANTIATE_TEST_CASE_P(smoke_TestNumpyBroadcast1D, BroadcastLayerTest, numpyBroadcast1DInputParams, BroadcastLayerTest::getTestCaseName);
 
-const auto numpyBroadcastParams2 = ::testing::Combine(
-        ::testing::Values(targetShapesNumpy[1]),
+INSTANTIATE_TEST_CASE_P(smoke_PrecTransformation, BroadcastLayerTest,
+        ::testing::Combine(
+            ::testing::Values(targetShapesNumpy1D[0]),
+            ::testing::Values(ngraph::AxisSet{}), //not used in numpy mode
+            ::testing::Values(ngraph::op::BroadcastType::NUMPY),
+            ::testing::Values(std::vector<size_t>{1}),
+            ::testing::ValuesIn(inputTPrecisions),
+            ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+        BroadcastLayerTest::getTestCaseName);
+
+// 2D
+std::vector<std::vector<size_t>> targetShapesNumpy2D = {
+        {3, 1},
+        {3, 6},
+        {2, 3, 6},
+        {2, 2, 3, 6},
+};
+
+const auto numpyBroadcast2DInputParams = ::testing::Combine(
+        ::testing::ValuesIn(targetShapesNumpy2D),
         ::testing::Values(ngraph::AxisSet{}), //not used in numpy mode
         ::testing::Values(ngraph::op::BroadcastType::NUMPY),
-        ::testing::Values(inShapesNumpy[1]),
+        ::testing::Values(std::vector<size_t>{3, 1}),
         ::testing::ValuesIn(inputPrecisions),
         ::testing::Values(CommonTestUtils::DEVICE_CPU)
 );
 
-INSTANTIATE_TEST_SUITE_P(
-        smoke_TestNumpyBroadcast2,
-        BroadcastLayerTest,
-        numpyBroadcastParams2,
-        BroadcastLayerTest::getTestCaseName
+INSTANTIATE_TEST_CASE_P(smoke_TestNumpyBroadcast2D, BroadcastLayerTest, numpyBroadcast2DInputParams, BroadcastLayerTest::getTestCaseName);
+
+// 3D
+std::vector<std::vector<size_t>> targetShapesNumpy3D = {
+        {1, 4, 1},
+        {1, 4, 4},
+        {1, 1, 4, 4},
+        {2, 1, 1, 4, 4},
+};
+
+const auto numpyBroadcast3DInputParams = ::testing::Combine(
+        ::testing::ValuesIn(targetShapesNumpy3D),
+        ::testing::Values(ngraph::AxisSet{}), //not used in numpy mode
+        ::testing::Values(ngraph::op::BroadcastType::NUMPY),
+        ::testing::Values(std::vector<size_t>{1, 4, 1}),
+        ::testing::ValuesIn(inputPrecisions),
+        ::testing::Values(CommonTestUtils::DEVICE_CPU)
 );
 
-// BIDIRECTIONAL MODE
+INSTANTIATE_TEST_CASE_P(smoke_TestNumpyBroadcast3D, BroadcastLayerTest, numpyBroadcast3DInputParams, BroadcastLayerTest::getTestCaseName);
 
+// NGRAPH EVALUATE
+const auto numpyBroadcastNgraphEvaluateParams = ::testing::Combine(
+        ::testing::Values(std::vector<size_t>{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}),
+        ::testing::Values(ngraph::AxisSet{}), //not used in numpy mode
+        ::testing::Values(ngraph::op::BroadcastType::NUMPY),
+        ::testing::Values(std::vector<size_t>{1, 2, 1, 4, 1, 6, 1, 8, 1, 10}),
+        ::testing::ValuesIn(inputPrecisions),
+        ::testing::Values(CommonTestUtils::DEVICE_CPU)
+);
+
+INSTANTIATE_TEST_CASE_P(smoke_TestNumpyBroadcastNgraphEvaluate, BroadcastLayerTest, numpyBroadcastNgraphEvaluateParams, BroadcastLayerTest::getTestCaseName);
+// END NUMPY MODE //////////////////////////////////////
+
+// BIDIRECTIONAL MODE //////////////////////////////////
 std::vector<std::vector<size_t>> inShapesBidi = {
         {4, 1},
         {1, 4, 1},
@@ -70,42 +117,38 @@ std::vector<std::vector<size_t>> inShapesBidi = {
 };
 
 std::vector<std::vector<size_t>> targetShapesBidi = {
-        {2, 1, 4},
+        {4, 1, 4},
         {1, 4, 4},
-        {1, 1, 2, 2}
+        {1, 1, 4, 4}
 };
 
-const auto bidirectionalBroadcastParams1 = ::testing::Combine(
-        ::testing::Values(targetShapesBidi[0]),
+const auto bidirectionalBroadcastParams = ::testing::Combine(
+        ::testing::ValuesIn(targetShapesBidi),
         ::testing::Values(ngraph::AxisSet{}), //not used in bidirectional mode
         ::testing::Values(ngraph::op::BroadcastType::BIDIRECTIONAL),
-        ::testing::Values(inShapesBidi[0]),
+        ::testing::ValuesIn(inShapesBidi),
         ::testing::ValuesIn(inputPrecisions),
         ::testing::Values(CommonTestUtils::DEVICE_CPU)
 );
 
-INSTANTIATE_TEST_SUITE_P(
-        smoke_TestBidirectionalBroadcast1,
-        BroadcastLayerTest,
-        bidirectionalBroadcastParams1,
-        BroadcastLayerTest::getTestCaseName
-);
+INSTANTIATE_TEST_CASE_P(smoke_TestBidirectionalBroadcast, BroadcastLayerTest, bidirectionalBroadcastParams, BroadcastLayerTest::getTestCaseName);
 
-const auto bidirectionalBroadcastParams2 = ::testing::Combine(
-        ::testing::Values(targetShapesBidi[1]),
-        ::testing::Values(ngraph::AxisSet{}), //not used in bidirectional mode
-        ::testing::Values(ngraph::op::BroadcastType::BIDIRECTIONAL),
-        ::testing::Values(inShapesBidi[1]),
+// EXPLICIT MODE ///////////////////////////////////////
+// 1D
+std::vector<std::vector<size_t>> inShapesExplicit1D = { {4} };
+std::vector<std::vector<size_t>> targetShapesExplicit1D = { {4, 2, 4}, {4, 2, 4, 1} };
+std::vector<ngraph::AxisSet> axes1D = { {0}, {2} };
+
+const auto explicitBroadcast1DInputParams = ::testing::Combine(
+        ::testing::ValuesIn(targetShapesExplicit1D),
+        ::testing::ValuesIn(axes1D),
+        ::testing::Values(ngraph::op::BroadcastType::EXPLICIT),
+        ::testing::ValuesIn(inShapesExplicit1D),
         ::testing::ValuesIn(inputPrecisions),
         ::testing::Values(CommonTestUtils::DEVICE_CPU)
 );
 
-INSTANTIATE_TEST_SUITE_P(
-        smoke_TestBidirectionalBroadcast2,
-        BroadcastLayerTest,
-        bidirectionalBroadcastParams2,
-        BroadcastLayerTest::getTestCaseName
-);
+INSTANTIATE_TEST_CASE_P(smoke_TestExplicitBroadcast1D, BroadcastLayerTest, explicitBroadcast1DInputParams, BroadcastLayerTest::getTestCaseName);
 
 const auto bidirectionalBroadcastParams3 = ::testing::Combine(
         ::testing::Values(targetShapesBidi[2]),
@@ -135,40 +178,38 @@ std::vector<std::vector<size_t>> targetShapesExplicit = {
         {2, 3, 4}
 };
 
-std::vector<ngraph::AxisSet> axes = {
-        {1, 2},
-        {0, 2}
-};
+// 2D
+std::vector<std::vector<size_t>> inShapesExplicit2D = { {2, 4} };
+std::vector<std::vector<size_t>> targetShapesExplicit2D = { {2, 2, 4}, {2, 2, 4, 1}};
+std::vector<ngraph::AxisSet> axes2D = { {1, 2}, {0, 2} };
 
-const auto explicitBroadcastParams1 = ::testing::Combine(
-        ::testing::Values(targetShapesExplicit[0]),
-        ::testing::Values(axes[0]),
+const auto explicitBroadcast2DInputParams = ::testing::Combine(
+        ::testing::ValuesIn(targetShapesExplicit2D),
+        ::testing::ValuesIn(axes2D),
         ::testing::Values(ngraph::op::BroadcastType::EXPLICIT),
-        ::testing::Values(inShapesExplicit[0]),
+        ::testing::ValuesIn(inShapesExplicit2D),
         ::testing::ValuesIn(inputPrecisions),
         ::testing::Values(CommonTestUtils::DEVICE_CPU)
 );
 
-INSTANTIATE_TEST_SUITE_P(
-        smoke_TestExplicitBroadcast1,
-        BroadcastLayerTest,
-        explicitBroadcastParams1,
-        BroadcastLayerTest::getTestCaseName
-);
+INSTANTIATE_TEST_CASE_P(smoke_TestExplicitBroadcast2D, BroadcastLayerTest, explicitBroadcast2DInputParams, BroadcastLayerTest::getTestCaseName);
 
-const auto explicitBroadcastParams2 = ::testing::Combine(
-        ::testing::Values(targetShapesExplicit[1]),
-        ::testing::Values(axes[1]),
+// 3D
+std::vector<std::vector<size_t>> inShapesExplicit3D = { {2, 2, 2} };
+std::vector<std::vector<size_t>> targetShapesExplicit3D = { {2, 2, 2, 2} };
+std::vector<ngraph::AxisSet> axes3D = { {0, 1, 2}, {0, 1, 3}, {0, 2, 3}, {1, 2, 3} };
+
+const auto explicitBroadcast3DInputParams = ::testing::Combine(
+        ::testing::ValuesIn(targetShapesExplicit3D),
+        ::testing::ValuesIn(axes3D),
         ::testing::Values(ngraph::op::BroadcastType::EXPLICIT),
-        ::testing::Values(inShapesExplicit[1]),
+        ::testing::ValuesIn(inShapesExplicit3D),
         ::testing::ValuesIn(inputPrecisions),
         ::testing::Values(CommonTestUtils::DEVICE_CPU)
 );
 
-INSTANTIATE_TEST_SUITE_P(
-        smoke_TestExplicitBroadcast2,
-        BroadcastLayerTest,
-        explicitBroadcastParams2,
-        BroadcastLayerTest::getTestCaseName
-);
-}  // namespace
\ No newline at end of file
+INSTANTIATE_TEST_CASE_P(smoke_TestExplicitBroadcast3D, BroadcastLayerTest, explicitBroadcast3DInputParams, BroadcastLayerTest::getTestCaseName);
+// END EXPLICIT MODE ///////////////////////////////////
+
+}  // namespace
+
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/tile.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/tile.cpp
index bff906dd101..8fb376fe0b9 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/tile.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/tile.cpp
@@ -11,11 +11,28 @@ using namespace LayerTestsDefinitions;
 namespace {
 
 const std::vector<InferenceEngine::Precision> netPrecisions = {
+        InferenceEngine::Precision::I8,
+        InferenceEngine::Precision::U8,
+        InferenceEngine::Precision::I32,
+        InferenceEngine::Precision::BF16,
         InferenceEngine::Precision::FP32
 };
 
+const std::vector<InferenceEngine::Precision> netTPrecisions = {
+        InferenceEngine::Precision::I64,
+        InferenceEngine::Precision::I16,
+        InferenceEngine::Precision::FP16,
+};
+
+const std::vector<std::vector<size_t>> inputShapes = {
+        {2, 3, 4},
+        {1, 1, 1},
+};
+
 const std::vector<std::vector<int64_t>> repeats3D = {
         {1, 2, 3},
+        {1, 1, 2, 3},
+        {1, 2, 1, 3},
         {2, 1, 1},
         {2, 3, 1},
         {2, 2, 2},
@@ -30,7 +47,19 @@ INSTANTIATE_TEST_SUITE_P(smoke_Tile, TileLayerTest,
                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
                 ::testing::Values(InferenceEngine::Layout::ANY),
                 ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(std::vector<size_t>({2, 3, 4})),
+                ::testing::ValuesIn(inputShapes),
+                ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+        TileLayerTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_PrecTransformation, TileLayerTest,
+        ::testing::Combine(
+                ::testing::Values(repeats3D[0]),
+                ::testing::ValuesIn(netTPrecisions),
+                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                ::testing::Values(InferenceEngine::Layout::ANY),
+                ::testing::Values(InferenceEngine::Layout::ANY),
+                ::testing::Values(inputShapes[0]),
                 ::testing::Values(CommonTestUtils::DEVICE_CPU)),
         TileLayerTest::getTestCaseName);
 
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/broadcast.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/broadcast.cpp
new file mode 100644
index 00000000000..5833d20f95f
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/broadcast.cpp
@@ -0,0 +1,374 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "test_utils/cpu_test_utils.hpp"
+#include "ngraph_functions/builders.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "functional_test_utils/ov_tensor_utils.hpp"
+
+using namespace CPUTestUtils;
+
+namespace CPULayerTestsDefinitions {
+
+using BroadcastLayerTestParamsSet = typename std::tuple<
+        std::vector<ov::test::InputShape>,     // Shapes
+        std::vector<int64_t>,                  // Target shapes
+        std::vector<int64_t>,                  // Axes mapping
+        ov::op::BroadcastType,                 // Broadcast mode
+        ov::element::Type_t,                   // Network precision
+        std::vector<bool>,                     // Const inputs
+        std::string>;                          // Device name
+
+using BroadcastLayerCPUTestParamsSet = typename std::tuple<
+        BroadcastLayerTestParamsSet,
+        CPUSpecificParams>;
+
+class BroadcastLayerCPUTest : public testing::WithParamInterface<BroadcastLayerCPUTestParamsSet>,
+                              virtual public ov::test::SubgraphBaseTest, public CPUTestsBase {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<BroadcastLayerCPUTestParamsSet> obj) {
+        BroadcastLayerTestParamsSet basicParamsSet;
+        CPUSpecificParams cpuParams;
+        std::tie(basicParamsSet, cpuParams) = obj.param;
+
+        std::vector<ov::test::InputShape> inputShapes;
+        std::vector<int64_t> targetShapes, axesMapping;
+        ov::op::BroadcastType mode;
+        ov::element::Type_t netPrecision;
+        std::vector<bool> isConstInputs;
+        std::string deviceName;
+        std::tie(inputShapes, targetShapes, axesMapping, mode, netPrecision, isConstInputs, deviceName) = basicParamsSet;
+
+        std::ostringstream result;
+        result << "IS=(";
+        for (const auto& shape : inputShapes) {
+            result << CommonTestUtils::partialShape2str({shape.first}) << "_";
+        }
+        result << ")_TS=(";
+        for (const auto& shape : inputShapes) {
+            for (const auto& item : shape.second) {
+                result << CommonTestUtils::vec2str(item) << "_";
+            }
+        }
+        result << "targetShape=" << CommonTestUtils::vec2str(targetShapes)  << "_";
+        result << "axesMapping=" << CommonTestUtils::vec2str(axesMapping)  << "_";
+        result << "mode=" << mode << "_";
+        result << "netPrec=" << netPrecision << "_";
+        result << "constIn=(" << (isConstInputs[0] ? "True" : "False") << "." << (isConstInputs[1] ? "True" : "False") << ")_";
+        result << "trgDev=" << deviceName;
+
+        result << CPUTestsBase::getTestCaseName(cpuParams);
+
+        return result.str();
+    }
+
+protected:
+    void SetUp() override {
+        BroadcastLayerTestParamsSet basicParamsSet;
+        CPUSpecificParams cpuParams;
+        std::tie(basicParamsSet, cpuParams) = this->GetParam();
+
+        std::vector<ov::test::InputShape> inputShapes;
+        ov::op::BroadcastType mode;
+        ov::element::Type_t netPrecision;
+        std::vector<bool> isConstInput;
+        std::tie(inputShapes, targetShape, axesMapping, mode, netPrecision, isConstInput, targetDevice) = basicParamsSet;
+        bool isTargetShapeConst = isConstInput[0], isAxesMapConst = isConstInput[1];
+        const auto targetShapeRank = targetShape.size();
+        const auto axesMappingRank = axesMapping.size();
+
+        std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
+
+        selectedType += std::string("_") + InferenceEngine::details::convertPrecision(netPrecision).name();
+
+        if (inputShapes.front().first.rank() != 0) {
+            inputDynamicShapes.push_back(inputShapes.front().first);
+            if (!isTargetShapeConst) {
+                inputDynamicShapes.push_back({ static_cast<int64_t>(targetShape.size()) });
+            }
+            if (!isAxesMapConst) {
+                inputDynamicShapes.push_back({ static_cast<int64_t>(axesMapping.size()) });
+            }
+        }
+        const size_t targetStaticShapeSize = inputShapes.front().second.size();
+        targetStaticShapes.resize(targetStaticShapeSize);
+        for (size_t i = 0lu; i < targetStaticShapeSize; ++i) {
+            targetStaticShapes[i].push_back(inputShapes.front().second[i]);
+            if (!isTargetShapeConst)
+                targetStaticShapes[i].push_back({ targetShape.size() });
+            if (!isAxesMapConst)
+                targetStaticShapes[i].push_back({ axesMapping.size() });
+        }
+
+        ov::ParameterVector functionParams;
+        if (inputDynamicShapes.empty()) {
+            functionParams.push_back(std::make_shared<ov::op::v0::Parameter>(netPrecision, targetStaticShapes.front().front()));
+        } else {
+            functionParams.push_back(std::make_shared<ov::op::v0::Parameter>(netPrecision, inputDynamicShapes.front()));
+            if (!isTargetShapeConst) {
+                functionParams.push_back(std::make_shared<ov::op::v0::Parameter>(ov::element::i64, inputDynamicShapes[1]));
+                functionParams.back()->set_friendly_name("targetShape");
+            }
+            if (!isAxesMapConst) {
+                functionParams.push_back(std::make_shared<ov::op::v0::Parameter>(ov::element::i64, inputDynamicShapes.back()));
+                functionParams.back()->set_friendly_name("axesMapping");
+            }
+        }
+        functionParams.front()->set_friendly_name("data");
+
+        auto paramOuts = ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ov::op::v0::Parameter>(functionParams));
+
+        std::shared_ptr<ov::op::v3::Broadcast> broadcastOp;
+        if (mode == ov::op::BroadcastType::EXPLICIT) {
+            std::shared_ptr<ov::Node> targetShapeOp;
+            std::shared_ptr<ov::Node> axesMappingOp;
+            if (isTargetShapeConst) {
+                targetShapeOp = ov::op::v0::Constant::create(ov::element::i64, {targetShapeRank}, targetShape);
+            } else {
+                targetShapeOp = functionParams[0];
+            }
+            if (isAxesMapConst) {
+                axesMappingOp = ov::op::v0::Constant::create(ov::element::i64, {axesMappingRank}, axesMapping);
+            } else {
+                axesMappingOp = functionParams.size() > 2 ? functionParams[2] : functionParams[1];
+            }
+            broadcastOp = std::make_shared<ov::op::v3::Broadcast>(paramOuts[0],
+                                                               targetShapeOp,
+                                                               axesMappingOp,
+                                                               mode);
+        } else if (mode == ov::op::BroadcastType::NUMPY) {
+            if (isTargetShapeConst) {
+                auto targetShapeConst = ov::op::v0::Constant::create(ov::element::i64, {targetShapeRank}, targetShape);
+                broadcastOp = std::make_shared<ov::op::v3::Broadcast>(paramOuts[0],
+                                                                      targetShapeConst,
+                                                                      mode);
+            } else {
+                broadcastOp = std::make_shared<ov::op::v3::Broadcast>(paramOuts[0],
+                                                                      paramOuts[1],
+                                                                      mode);
+            }
+        }
+
+        function = makeNgraphFunction(netPrecision, functionParams, broadcastOp, "Broadcast");
+    }
+
+    void generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) override {
+        inputs.clear();
+        const auto& funcInputs = function->inputs();
+        for (size_t i = 0lu; i < funcInputs.size(); i++) {
+            const auto& funcInput = funcInputs[i];
+            ov::runtime::Tensor tensor;
+            if (funcInput.get_node()->get_friendly_name() == "targetShape") {
+                tensor = ov::runtime::Tensor{ov::element::i64, targetInputStaticShapes[i]};
+                auto data = tensor.data<ov::element_type_traits<ov::element::i64>::value_type>();
+                for (size_t i = 0lu; i < targetShape.size(); i++) {
+                    data[i] = targetShape[i];
+                }
+            } else if (funcInput.get_node()->get_friendly_name() == "axesMapping") {
+                tensor = ov::runtime::Tensor{ov::element::i64, targetInputStaticShapes[i]};
+                auto data = tensor.data<ov::element_type_traits<ov::element::i64>::value_type>();
+                for (size_t i = 0lu; i < axesMapping.size(); i++) {
+                    data[i] = axesMapping[i];
+                }
+            } else {
+                if (funcInput.get_element_type().is_real()) {
+                    tensor = ov::test::utils::create_and_fill_tensor(
+                        funcInput.get_element_type(), targetInputStaticShapes[i], 10, 0, 1000);
+                } else {
+                    tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i]);
+                }
+            }
+            inputs.insert({funcInput.get_node_shared_ptr(), tensor});
+        }
+    }
+
+    std::vector<int64_t> targetShape, axesMapping;
+};
+
+TEST_P(BroadcastLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+
+    run();
+    CheckPluginRelatedResults(executableNetwork, "Broadcast");
+}
+
+namespace {
+
+/* CPU PARAMS */
+const auto cpuParams_nChw16c = CPUSpecificParams {{nChw16c}, {nChw16c}, {}, "ref"};
+const auto cpuParams_nCdhw16c = CPUSpecificParams {{nCdhw16c}, {nCdhw16c}, {}, "ref"};
+
+const auto cpuParams_nChw8c = CPUSpecificParams {{nChw8c}, {nChw8c}, {}, "ref"};
+const auto cpuParams_nCdhw8c = CPUSpecificParams {{nCdhw8c}, {nCdhw8c}, {}, "ref"};
+
+const auto cpuParams_nhwc = CPUSpecificParams {{nhwc}, {nhwc}, {}, "ref"};
+const auto cpuParams_ndhwc = CPUSpecificParams {{ndhwc}, {ndhwc}, {}, "ref"};
+/* ========== */
+
+/* COMMON PARAMS */
+const std::vector<ov::element::Type_t> inputPrecisions = {
+    ov::element::f32,
+    ov::element::bf16,
+    ov::element::i32,
+    ov::element::i8
+};
+/* ============= */
+
+/* INSTANCES */
+// 4D
+const std::vector<CPUSpecificParams> CPUParams4D = {
+        cpuParams_nChw16c,
+        cpuParams_nChw8c,
+        cpuParams_nhwc
+};
+
+const std::vector<std::vector<ov::test::InputShape>> staticInputShapes4D = {
+    {
+        {{},
+            { // Static shapes
+                {1, 16, 1, 1}
+            }
+        }
+    },
+    {
+        {{},
+            { // Static shapes
+                {50, 50}
+            }
+        }
+    }
+};
+
+INSTANTIATE_TEST_CASE_P(smoke_StaticShape4D, BroadcastLayerCPUTest,
+                    ::testing::Combine(
+                            ::testing::Combine(
+                            ::testing::Values(staticInputShapes4D[0]),
+                            ::testing::ValuesIn(std::vector<std::vector<int64_t>>{{1, 16, 3, 3}, {1, 16, 1, 3}}),
+                            ::testing::Values(std::vector<int64_t>{}),
+                            ::testing::Values(ov::op::BroadcastType::NUMPY),
+                            ::testing::ValuesIn(inputPrecisions),
+                            ::testing::Values(std::vector<bool>{true, true}),
+                            ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                        ::testing::ValuesIn(CPUParams4D)),
+                    BroadcastLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_CASE_P(smoke_StaticShape4DE, BroadcastLayerCPUTest,
+                    ::testing::Combine(
+                        ::testing::Combine(
+                            ::testing::Values(staticInputShapes4D[1]),
+                            ::testing::Values(std::vector<int64_t>{1, 50, 50, 16}),
+                            ::testing::Values(std::vector<int64_t>{1, 2}),
+                            ::testing::Values(ov::op::BroadcastType::EXPLICIT),
+                            ::testing::ValuesIn(inputPrecisions),
+                            ::testing::Values(std::vector<bool>{true, true}),
+                            ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                        ::testing::Values(CPUSpecificParams{{}, {}, {}, "ref"})),
+                    BroadcastLayerCPUTest::getTestCaseName);
+
+const std::vector<std::vector<ov::test::InputShape>> dynamicInputShapes4D = {
+    {
+        { // Origin dynamic shapes
+            {ov::Dimension(1, 20), ov::Dimension(1, 20), ov::Dimension(1, 20), ov::Dimension(1, 20)},
+            { // Dynamic shapes instances
+                {1, 16, 1, 1},
+                {8, 1, 1, 7},
+                {1, 1, 1, 7}
+            }
+        },
+    },
+    {
+        { // Origin dynamic shapes
+            {-1, -1, -1, -1},
+            { // Dynamic shapes instances
+                {{1, 16, 1, 1}},
+                {{8, 1, 1, 1}}
+            }
+        }
+    }
+};
+
+INSTANTIATE_TEST_CASE_P(smoke_DynamicShape4D, BroadcastLayerCPUTest,
+                    ::testing::Combine(::testing::Combine(
+                            ::testing::ValuesIn(dynamicInputShapes4D),
+                            ::testing::ValuesIn(std::vector<std::vector<int64_t>>{{8, 16,  1, 7}, {8, 16, 10, 7}}),
+                            ::testing::Values(std::vector<int64_t>{}),
+                            ::testing::Values(ov::op::BroadcastType::NUMPY),
+                            ::testing::ValuesIn(inputPrecisions),
+                            ::testing::ValuesIn(std::vector<std::vector<bool>>{{true, true}, {false, true}}),
+                            ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                        ::testing::Values(CPUSpecificParams{{}, {}, {}, "ref"})),
+                    BroadcastLayerCPUTest::getTestCaseName);
+
+// 5D
+const std::vector<std::vector<ov::test::InputShape>> staticInputShapes5D = {
+    {
+        {{},
+            { // Static shapes
+                {1, 16, 1, 1, 1}
+            }
+        }
+    }
+};
+const std::vector<std::vector<ov::test::InputShape>> dynamicInputShapes5D = {
+    {
+        { // Origin dynamic shapes
+            {ov::Dimension(1, 20), ov::Dimension(1, 20), ov::Dimension(1, 20), ov::Dimension(1, 20), ov::Dimension(1, 20)},
+            { // Dynamic shapes instances
+                {1, 16, 1, 1, 1},
+                {8, 1, 1, 7, 1},
+                {8, 1, 1, 1, 1}
+            }
+        }
+    },
+    {
+        { // Origin dynamic shapes
+            {-1, -1, -1, -1, -1},
+            { // Dynamic shapes instances
+                {1, 16, 1, 1, 1},
+                {8, 16, 1, 7, 1}
+            }
+        }
+    }
+};
+std::vector<std::vector<int64_t>> targetShapes5D {
+    {8, 16,  1, 7, 1},
+    {8, 16, 10, 7, 4}
+};
+
+const std::vector<CPUSpecificParams> CPUParams5D = {
+        cpuParams_nCdhw16c,
+        cpuParams_nCdhw8c,
+        cpuParams_ndhwc,
+};
+
+INSTANTIATE_TEST_CASE_P(smoke_StaticShape5D, BroadcastLayerCPUTest,
+                    ::testing::Combine(
+                        ::testing::Combine(
+                            ::testing::ValuesIn(staticInputShapes5D),
+                            ::testing::ValuesIn(std::vector<std::vector<int64_t>>{{1, 16, 1, 1, 3}, {1, 16, 3, 1, 3}}),
+                            ::testing::Values(std::vector<int64_t>{}),
+                            ::testing::Values(ov::op::BroadcastType::NUMPY),
+                            ::testing::ValuesIn(inputPrecisions),
+                            ::testing::Values(std::vector<bool>{true, true}),
+                            ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                        ::testing::ValuesIn(CPUParams5D)),
+                    BroadcastLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_CASE_P(smoke_DynamicShape5D, BroadcastLayerCPUTest,
+                    ::testing::Combine(
+                        ::testing::Combine(
+                            ::testing::ValuesIn(dynamicInputShapes5D),
+                            ::testing::ValuesIn(targetShapes5D),
+                            ::testing::Values(std::vector<int64_t>{}),
+                            ::testing::Values(ov::op::BroadcastType::NUMPY),
+                            ::testing::ValuesIn(inputPrecisions),
+                            ::testing::ValuesIn(std::vector<std::vector<bool>>{{true, true}, {false, true}}),
+                            ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                        ::testing::Values(CPUSpecificParams{{}, {}, {}, "ref"})),
+                    BroadcastLayerCPUTest::getTestCaseName);
+/* ========= */
+
+} // namespace
+
+} // namespace CPULayerTestsDefinitions
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/tile.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/tile.cpp
new file mode 100644
index 00000000000..aef8df59eee
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/tile.cpp
@@ -0,0 +1,322 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "test_utils/cpu_test_utils.hpp"
+#include "ngraph_functions/builders.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "functional_test_utils/ov_tensor_utils.hpp"
+
+using namespace CPUTestUtils;
+
+namespace CPULayerTestsDefinitions {
+
+using TileLayerTestParamsSet = typename std::tuple<
+        std::vector<ov::test::InputShape>,     // Input shapes
+        std::vector<int64_t>,                  // Repeats
+        ov::element::Type_t,                   // Network precision
+        bool,                                  // Is Repeats input constant
+        std::string>;                          // Device name
+
+typedef std::tuple<
+        TileLayerTestParamsSet,
+        CPUSpecificParams> TileLayerCPUTestParamsSet;
+
+class TileLayerCPUTest : public testing::WithParamInterface<TileLayerCPUTestParamsSet>,
+                         virtual public ov::test::SubgraphBaseTest, public CPUTestsBase {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<TileLayerCPUTestParamsSet> obj) {
+        TileLayerTestParamsSet basicParamsSet;
+        CPUSpecificParams cpuParams;
+        std::tie(basicParamsSet, cpuParams) = obj.param;
+
+        std::vector<ov::test::InputShape> inputShapes;
+        std::vector<int64_t> repeats;
+        ov::element::Type_t netPrecision;
+        bool isRepeatsConst;
+        std::string deviceName;
+        std::tie(inputShapes, repeats, netPrecision, isRepeatsConst, deviceName) = basicParamsSet;
+
+        std::ostringstream result;
+        result << "IS=(";
+        for (const auto& shape : inputShapes) {
+            result << CommonTestUtils::partialShape2str({shape.first}) << "_";
+        }
+        result << ")_TS=(";
+        for (const auto& shape : inputShapes) {
+            for (const auto& item : shape.second) {
+                result << CommonTestUtils::vec2str(item) << "_";
+            }
+        }
+        result << "Repeats=" << CommonTestUtils::vec2str(repeats)  << "_";
+        result << "netPrec=" << netPrecision << "_";
+        result << "constRepeats=" << (isRepeatsConst ? "True" : "False") << "_";
+        result << "trgDev=" << deviceName;
+
+        result << CPUTestsBase::getTestCaseName(cpuParams);
+
+        return result.str();
+    }
+
+protected:
+    void SetUp() override {
+        TileLayerTestParamsSet basicParamsSet;
+        CPUSpecificParams cpuParams;
+        std::tie(basicParamsSet, cpuParams) = this->GetParam();
+
+        std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
+
+        std::vector<ov::test::InputShape> inputShapes;
+        ov::element::Type_t netPrecision;
+        bool isRepeatsConst;
+        std::tie(inputShapes, repeatsData, netPrecision, isRepeatsConst, targetDevice) = basicParamsSet;
+
+        selectedType += std::string("_") + InferenceEngine::details::convertPrecision(netPrecision).name();
+
+        if (inputShapes.front().first.rank() != 0) {
+            inputDynamicShapes.push_back(inputShapes.front().first);
+            if (!isRepeatsConst) {
+                inputDynamicShapes.push_back({ static_cast<int64_t>(repeatsData.size()) });
+            }
+        }
+        const size_t targetStaticShapeSize = inputShapes.front().second.size();
+        targetStaticShapes.resize(targetStaticShapeSize);
+        for (size_t i = 0lu; i < targetStaticShapeSize; ++i) {
+            targetStaticShapes[i].push_back(inputShapes.front().second[i]);
+            if (!isRepeatsConst)
+                targetStaticShapes[i].push_back({ repeatsData.size() });
+        }
+
+        ov::ParameterVector functionParams;
+        if (inputDynamicShapes.empty()) {
+            functionParams.push_back(std::make_shared<ov::op::v0::Parameter>(netPrecision, targetStaticShapes.front().front()));
+        } else {
+            functionParams.push_back(std::make_shared<ov::op::v0::Parameter>(netPrecision, inputDynamicShapes.front()));
+            if (!isRepeatsConst) {
+                functionParams.push_back(std::make_shared<ov::op::v0::Parameter>(ov::element::i64, inputDynamicShapes[1]));
+                functionParams.back()->set_friendly_name("repeats");
+            }
+        }
+        functionParams.front()->set_friendly_name("data");
+
+        auto paramOuts = ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ov::op::v0::Parameter>(functionParams));
+        std::shared_ptr<ov::Node> tileNode;
+        if (isRepeatsConst) {
+            tileNode = std::make_shared<ov::op::v0::Tile>(paramOuts[0],
+                    ov::op::v0::Constant::create(ov::element::i64, { repeatsData.size() }, repeatsData));
+        } else {
+            tileNode = std::make_shared<ov::op::v0::Tile>(paramOuts[0], paramOuts[1]);
+        }
+        function = makeNgraphFunction(netPrecision, functionParams, tileNode, "CPUTile");
+    }
+
+    void generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) override {
+        inputs.clear();
+        const auto& funcInputs = function->inputs();
+        for (size_t i = 0lu; i < funcInputs.size(); i++) {
+            const auto& funcInput = funcInputs[i];
+            ov::runtime::Tensor tensor;
+            if (funcInput.get_node()->get_friendly_name() == "repeats") {
+                tensor = ov::runtime::Tensor{ov::element::i64, targetInputStaticShapes[i]};
+                auto data = tensor.data<ov::element_type_traits<ov::element::i64>::value_type>();
+                for (size_t i = 0lu; i < repeatsData.size(); i++) {
+                    data[i] = repeatsData[i];
+                }
+            } else {
+                if (funcInput.get_element_type().is_real()) {
+                    tensor = ov::test::utils::create_and_fill_tensor(
+                        funcInput.get_element_type(), targetInputStaticShapes[i], 10, 0, 1000);
+                } else {
+                    tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i]);
+                }
+            }
+            inputs.insert({funcInput.get_node_shared_ptr(), tensor});
+        }
+    }
+
+    std::vector<int64_t> repeatsData;
+};
+
+TEST_P(TileLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+
+    run();
+    CheckPluginRelatedResults(executableNetwork, "Tile");
+}
+
+namespace {
+
+/* CPU PARAMS */
+const auto cpuParams_nchw = CPUSpecificParams{{nchw}, {nchw}, {}, "ref"};
+const auto cpuParams_ncdhw = CPUSpecificParams{{ncdhw}, {ncdhw}, {}, "ref"};
+
+const auto cpuParams_nChw16c = CPUSpecificParams{{nChw16c}, {nChw16c}, {}, "ref"};
+const auto cpuParams_nCdhw16c = CPUSpecificParams{{nCdhw16c}, {nCdhw16c}, {}, "ref"};
+
+const auto cpuParams_nChw8c = CPUSpecificParams{{nChw8c}, {nChw8c}, {}, "ref"};
+const auto cpuParams_nCdhw8c = CPUSpecificParams{{nCdhw8c}, {nCdhw8c}, {}, "ref"};
+
+const auto cpuParams_nhwc = CPUSpecificParams{{nhwc}, {nhwc}, {}, "ref"};
+const auto cpuParams_ndhwc = CPUSpecificParams{{ndhwc}, {ndhwc}, {}, "ref"};
+/* ========== */
+
+/* PARAMS */
+const std::vector<ov::element::Type_t> netPrecisions = {
+    ov::element::f32,
+    ov::element::bf16,
+    ov::element::i32,
+    ov::element::i8
+};
+
+const std::vector<std::vector<ov::test::InputShape>> staticInputShapes4D = {
+    {
+        {{},
+            { // Static shapes
+                {2, 16, 3, 4}
+            }
+        }
+    },
+    {
+        {{},
+            { // Static shapes
+                {1, 16, 1, 1}
+            }
+        }
+    }
+};
+const std::vector<std::vector<ov::test::InputShape>> dynamicInputShapes4D = {
+    {
+        { // Origin dynamic shapes
+            {ov::Dimension(1, 20), ov::Dimension(10, 20), ov::Dimension(1, 20), ov::Dimension(1, 20)},
+            { // Dynamic shapes instances
+                {2, 16, 3, 4},
+                {1, 16, 1, 1},
+                {1, 16, 2, 3}
+            }
+         }
+    },
+    {
+        { // Origin dynamic shapes
+            {-1, -1, -1, -1},
+            { // Dynamic shapes instances
+                {3, 15, 5, 7},
+                {4, 55, 8, 24}
+            }
+        }
+    }
+};
+
+const std::vector<std::vector<ov::test::InputShape>> staticInputShapes5D = {
+    {
+        {{},
+            { // Static shapes
+                {2, 16, 2, 3, 4}
+            }
+        }
+    }
+};
+const std::vector<std::vector<ov::test::InputShape>> dynamicInputShapes5D = {
+    {
+        { // Origin dynamic shapes
+            {ov::Dimension(1, 20), ov::Dimension(1, 20), ov::Dimension(1, 20), ov::Dimension(1, 20), ov::Dimension(1, 70)},
+            { // Dynamic shapes instances
+                {2, 16, 2, 3, 4},
+                {1, 16, 8, 5, 4},
+                {8, 1, 2, 3, 64}
+            }
+        }
+    },
+    {
+        { // Origin dynamic shapes
+            {-1, -1, -1, -1, -1},
+            { // Dynamic shapes instances
+                {2, 16, 2, 3, 4},
+                {1, 16, 8, 5, 4},
+                {8, 1, 2, 3, 64}
+            }
+        }
+    }
+};
+
+const std::vector<std::vector<int64_t>> repeats4D = {
+        {2, 3},
+        {1, 2, 3},
+        {1, 1, 1, 1},
+        {1, 1, 2, 3},
+        {1, 2, 1, 3},
+        {2, 1, 1, 1},
+        {2, 3, 1, 1}
+};
+const std::vector<std::vector<int64_t>> repeats5D = {
+        {1, 2, 3},
+        {1, 1, 2, 3},
+        {1, 1, 1, 2, 3},
+        {1, 2, 1, 1, 3},
+        {2, 1, 1, 1, 1},
+        {2, 3, 1, 1, 1}
+};
+
+const std::vector<CPUSpecificParams> CPUParams4D = {
+        cpuParams_nchw,
+        cpuParams_nChw16c,
+        cpuParams_nChw8c,
+        cpuParams_nhwc,
+};
+
+const std::vector<CPUSpecificParams> CPUParams5D = {
+        cpuParams_ncdhw,
+        cpuParams_nCdhw16c,
+        cpuParams_nCdhw8c,
+        cpuParams_ndhwc,
+};
+/* ============= */
+
+/* INSTANCES */
+INSTANTIATE_TEST_CASE_P(smoke_StaticShape4D, TileLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Combine(
+                                        ::testing::ValuesIn(staticInputShapes4D),
+                                        ::testing::ValuesIn(repeats4D),
+                                        ::testing::ValuesIn(netPrecisions),
+                                        ::testing::Values(true),
+                                        ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                                ::testing::ValuesIn(CPUParams4D)),
+                        TileLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_CASE_P(smoke_DynamicShape4D, TileLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Combine(
+                                        ::testing::ValuesIn(dynamicInputShapes4D),
+                                        ::testing::ValuesIn(repeats4D),
+                                        ::testing::ValuesIn(netPrecisions),
+                                        ::testing::Values(true, false),
+                                        ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                                ::testing::Values(CPUSpecificParams{{}, {}, {}, "ref"})),
+                        TileLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_CASE_P(smoke_StaticShape5D, TileLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Combine(
+                                        ::testing::ValuesIn(staticInputShapes5D),
+                                        ::testing::ValuesIn(repeats5D),
+                                        ::testing::ValuesIn(netPrecisions),
+                                        ::testing::Values(true),
+                                        ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                                ::testing::ValuesIn(CPUParams5D)),
+                        TileLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_CASE_P(smoke_DynamicShape5D, TileLayerCPUTest,
+                        ::testing::Combine(
+                                ::testing::Combine(
+                                        ::testing::ValuesIn(dynamicInputShapes5D),
+                                        ::testing::ValuesIn(repeats5D),
+                                        ::testing::ValuesIn(netPrecisions),
+                                        ::testing::Values(true, false),
+                                        ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                                ::testing::Values(CPUSpecificParams{{}, {}, {}, "ref"})),
+                        TileLayerCPUTest::getTestCaseName);
+/* ========= */
+
+} // namespace
+
+} // namespace CPULayerTestsDefinitions
diff --git a/inference-engine/tests/functional/shared_test_classes/src/single_layer/broadcast.cpp b/inference-engine/tests/functional/shared_test_classes/src/single_layer/broadcast.cpp
index 2961551648a..1cb86e1bf88 100644
--- a/inference-engine/tests/functional/shared_test_classes/src/single_layer/broadcast.cpp
+++ b/inference-engine/tests/functional/shared_test_classes/src/single_layer/broadcast.cpp
@@ -7,8 +7,8 @@
 namespace LayerTestsDefinitions {
 std::string BroadcastLayerTest::getTestCaseName(const testing::TestParamInfo<BroadcastParamsTuple>& obj) {
     InferenceEngine::SizeVector targetShape;
-    ngraph::AxisSet axesMapping;
-    ngraph::op::BroadcastType mode;
+    ov::AxisSet axesMapping;
+    ov::op::BroadcastType mode;
     InferenceEngine::SizeVector inputShape;
     InferenceEngine::Precision networkPrecision;
     std::string deviceName;
@@ -26,19 +26,19 @@ std::string BroadcastLayerTest::getTestCaseName(const testing::TestParamInfo<Bro
 
 void BroadcastLayerTest::SetUp() {
     InferenceEngine::SizeVector targetShape;
-    ngraph::AxisSet axesMapping;
-    ngraph::op::BroadcastType mode;
+    ov::AxisSet axesMapping;
+    ov::op::BroadcastType mode;
     InferenceEngine::SizeVector inputShape;
     InferenceEngine::Precision networkPrecision;
     std::tie(targetShape, axesMapping, mode, inputShape, networkPrecision, targetDevice) = this->GetParam();
     auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(networkPrecision);
 
-    auto target_shape_const = ngraph::opset3::Constant::create(ngraph::element::i64, {targetShape.size()}, targetShape);
+    auto target_shape_const = ov::op::v0::Constant::create(ov::element::i64, {targetShape.size()}, targetShape);
     auto params = ngraph::builder::makeParams(ngPrc, {inputShape});
 
     auto broadcast = ngraph::builder::makeBroadcast(params[0], target_shape_const, mode, axesMapping);
-    ngraph::ResultVector results{std::make_shared<ngraph::opset4::Result>(broadcast)};
-    function = std::make_shared<ngraph::Function>(results, params, "BroadcastInference");
+    ov::ResultVector results{std::make_shared<ov::op::v0::Result>(broadcast)};
+    function = std::make_shared<ov::Function>(results, params, "BroadcastInference");
 }
 
 }  // namespace LayerTestsDefinitions
diff --git a/inference-engine/tests/ngraph_helpers/ngraph_functions/src/broadcast.cpp b/inference-engine/tests/ngraph_helpers/ngraph_functions/src/broadcast.cpp
index 5e825ec369a..a30044725bb 100644
--- a/inference-engine/tests/ngraph_helpers/ngraph_functions/src/broadcast.cpp
+++ b/inference-engine/tests/ngraph_helpers/ngraph_functions/src/broadcast.cpp
@@ -19,7 +19,7 @@ std::shared_ptr<ngraph::Node> makeBroadcast(const ngraph::Output<Node> &in,
                                                            target_shape,
                                                            axisSetConst,
                                                            mode);
-    } else { // numpy/bidiractional modes
+    } else { // numpy/bidirectional modes
         return std::make_shared<ngraph::opset5::Broadcast>(in,
                                                            target_shape,
                                                            mode);
diff --git a/ngraph/core/src/op/multiply.cpp b/ngraph/core/src/op/multiply.cpp
index a72a561e22e..0441ab3c9a9 100644
--- a/ngraph/core/src/op/multiply.cpp
+++ b/ngraph/core/src/op/multiply.cpp
@@ -41,6 +41,9 @@ bool evaluate_multiply(const HostTensorPtr& arg0,
         NGRAPH_TYPE_CASE(evaluate_multiply, f16, arg0, arg1, out, broadcast_spec);
         NGRAPH_TYPE_CASE(evaluate_multiply, f32, arg0, arg1, out, broadcast_spec);
         NGRAPH_TYPE_CASE(evaluate_multiply, bf16, arg0, arg1, out, broadcast_spec);
+        NGRAPH_TYPE_CASE(evaluate_multiply, u8, arg0, arg1, out, broadcast_spec);
+        NGRAPH_TYPE_CASE(evaluate_multiply, i16, arg0, arg1, out, broadcast_spec);
+        NGRAPH_TYPE_CASE(evaluate_multiply, u16, arg0, arg1, out, broadcast_spec);
     default:
         rc = false;
         break;
diff --git a/ngraph/core/src/op/tile.cpp b/ngraph/core/src/op/tile.cpp
index 1f6d95fd650..138f4be1f9b 100644
--- a/ngraph/core/src/op/tile.cpp
+++ b/ngraph/core/src/op/tile.cpp
@@ -58,7 +58,15 @@ void op::v0::Tile::validate_and_infer_types() {
             output_shape[i] = data_shape[i] * repeats_value[i];
         set_output_type(0, arg_et, output_shape);
     } else {
-        set_output_type(0, arg_et, ov::PartialShape::dynamic());
+        Rank outRank = Rank::dynamic();
+        if (arg_shape.rank().is_static() && repeats_shape.is_static()) {
+            std::vector<Dimension> data_shape(arg_shape);
+            auto data_rank = data_shape.size();
+            auto repeats_rank = repeats_value.size();
+            auto output_rank = std::max(data_rank, repeats_rank);
+            outRank = Rank(output_rank);
+        }
+        set_output_type(0, arg_et, ov::PartialShape::dynamic(outRank));
     }
 
     set_input_is_relevant_to_shape(0);
diff --git a/runtime/bindings/python/tests/test_onnx/test_backend.py b/runtime/bindings/python/tests/test_onnx/test_backend.py
index 51a95bb416f..daeace44ce5 100644
--- a/runtime/bindings/python/tests/test_onnx/test_backend.py
+++ b/runtime/bindings/python/tests/test_onnx/test_backend.py
@@ -128,7 +128,6 @@ tests_expected_to_fail = [
     (
         xfail_issue_39658,
         "OnnxBackendNodeModelTest.test_tile_cpu",
-        "OnnxBackendNodeModelTest.test_tile_precomputed_cpu",
     ),
     (
         xfail_issue_39662,
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/test_backend.py b/runtime/bindings/python/tests_compatibility/test_onnx/test_backend.py
index bd4d8284b2c..c6bbe3d6a57 100644
--- a/runtime/bindings/python/tests_compatibility/test_onnx/test_backend.py
+++ b/runtime/bindings/python/tests_compatibility/test_onnx/test_backend.py
@@ -113,7 +113,6 @@ tests_expected_to_fail = [
     (
         xfail_issue_39658,
         "OnnxBackendNodeModelTest.test_tile_cpu",
-        "OnnxBackendNodeModelTest.test_tile_precomputed_cpu",
     ),
     (
         xfail_issue_39662,

From afd811890a5692c80aa1432ccfbe98c5bb1c7dc1 Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Thu, 25 Nov 2021 16:44:46 +0300
Subject: [PATCH 15/72] Added note about Graphviz (#8827)

* Added pp header to util library

* Added note about Graphviz dependency for VisualizeTree pass

* Revert "Added pp header to util library"

This reverts commit a5d16d62364acaf815b84defcf4850f2934dc77e.
---
 docs/nGraph_DG/nGraph_debug_capabilities.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/nGraph_DG/nGraph_debug_capabilities.md b/docs/nGraph_DG/nGraph_debug_capabilities.md
index bda4dff150c..971db2eeaed 100644
--- a/docs/nGraph_DG/nGraph_debug_capabilities.md
+++ b/docs/nGraph_DG/nGraph_debug_capabilities.md
@@ -14,3 +14,5 @@ std::shared_ptr<ngraph::Function> nGraph;
 ...
 ngraph::pass::VisualizeTree("after.png").run_on_function(nGraph);     // Visualize the nGraph function to an image
 ```
+
+> **NOTE**: Graphviz is required for visualization to image.

From 78eb1aa85f6ef7cd708a75d849c7dc32ba5a7906 Mon Sep 17 00:00:00 2001
From: Artur Kulikowski <artur.kulikowski@intel.com>
Date: Thu, 25 Nov 2021 14:54:40 +0100
Subject: [PATCH 16/72] [Python API] Expose missing methods of class Function
 (#8733)

---
 .../core/include/openvino/core/function.hpp   |   9 +-
 ngraph/core/src/function.cpp                  |   8 +-
 .../pyngraph/ops/util/index_reduction.cpp     |   2 +-
 .../src/openvino/impl/op/util/__init__.py     |   3 +-
 .../python/src/pyopenvino/graph/function.cpp  | 317 +++++++++++++++++-
 .../graph/ops/util/index_reduction.cpp        |   2 +-
 .../ops/util/regmodule_graph_op_util.cpp      |   1 +
 .../ops/util/regmodule_graph_op_util.hpp      |   2 +
 .../pyopenvino/graph/ops/util/variable.cpp    |  28 ++
 .../pyopenvino/graph/ops/util/variable.hpp    |  11 +
 .../test_inference_engine/test_function.py    | 111 +++++-
 .../test_ngraph/test_ops_util_variable.py     |  46 +++
 12 files changed, 521 insertions(+), 19 deletions(-)
 create mode 100644 runtime/bindings/python/src/pyopenvino/graph/ops/util/variable.cpp
 create mode 100644 runtime/bindings/python/src/pyopenvino/graph/ops/util/variable.hpp
 create mode 100644 runtime/bindings/python/tests/test_ngraph/test_ops_util_variable.py

diff --git a/ngraph/core/include/openvino/core/function.hpp b/ngraph/core/include/openvino/core/function.hpp
index e61621079f6..c14904c0ed4 100644
--- a/ngraph/core/include/openvino/core/function.hpp
+++ b/ngraph/core/include/openvino/core/function.hpp
@@ -192,9 +192,16 @@ public:
     /// Index for parameter, or -1
     int64_t get_parameter_index(const std::shared_ptr<ov::op::v0::Parameter>& parameter) const;
 
-    /// Index for value or result referencing it, or -1
+    /// \brief Return the index of this function's Result represented by the "value" Output object.
+    /// This method returns -1 if an the passed output is not related to the Results of a function.
+    /// \param value Output containing Node
     int64_t get_result_index(const ov::Output<ov::Node>& value) const;
 
+    /// \brief Return the index of this function's Result represented by the "value" Output object.
+    /// This method returns -1 if an the passed output is not related to the Results of a function.
+    /// \param value Output containing Node
+    int64_t get_result_index(const ov::Output<const ov::Node>& value) const;
+
     /// \deprecated Use evaluate with ov::runtime::Tensor instead
     /// \brief Evaluate the function on inputs, putting results in outputs.
     /// \param output_tensors Tensors for the outputs to compute. One for each result
diff --git a/ngraph/core/src/function.cpp b/ngraph/core/src/function.cpp
index 7fbeb39f07c..2e8c35683cd 100644
--- a/ngraph/core/src/function.cpp
+++ b/ngraph/core/src/function.cpp
@@ -436,6 +436,10 @@ int64_t ov::Function::get_parameter_index(const std::shared_ptr<ngraph::op::Para
 }
 
 int64_t ov::Function::get_result_index(const Output<Node>& value) const {
+    return get_result_index(Output<const Node>(value.get_node(), value.get_index()));
+}
+
+int64_t ov::Function::get_result_index(const Output<const Node>& value) const {
     int64_t pos = 0;
     if (is_type<ngraph::op::Result>(value.get_node_shared_ptr())) {
         auto result = value.get_node_shared_ptr();
@@ -447,7 +451,9 @@ int64_t ov::Function::get_result_index(const Output<Node>& value) const {
         }
     } else {
         for (auto r : get_results()) {
-            if (r->input_value(0) == value) {
+            const auto& input_value = r->input_value(0);
+            const auto result_input = Output<const Node>(input_value.get_node(), input_value.get_index());
+            if (result_input == value) {
                 return pos;
             }
             pos++;
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/ops/util/index_reduction.cpp b/runtime/bindings/python/src/compatibility/pyngraph/ops/util/index_reduction.cpp
index 26734aae7d4..51e7352b0b6 100644
--- a/runtime/bindings/python/src/compatibility/pyngraph/ops/util/index_reduction.cpp
+++ b/runtime/bindings/python/src/compatibility/pyngraph/ops/util/index_reduction.cpp
@@ -15,7 +15,7 @@ namespace py = pybind11;
 void regclass_pyngraph_op_util_IndexReduction(py::module m) {
     py::class_<ngraph::op::util::IndexReduction, std::shared_ptr<ngraph::op::util::IndexReduction>> indexReduction(
         m,
-        "IndexRedection",
+        "IndexReduction",
         py::module_local());
 
     indexReduction.def("get_reduction_axis", &ngraph::op::util::IndexReduction::get_reduction_axis);
diff --git a/runtime/bindings/python/src/openvino/impl/op/util/__init__.py b/runtime/bindings/python/src/openvino/impl/op/util/__init__.py
index 26c7dce579c..f29de64f8d7 100644
--- a/runtime/bindings/python/src/openvino/impl/op/util/__init__.py
+++ b/runtime/bindings/python/src/openvino/impl/op/util/__init__.py
@@ -11,6 +11,7 @@ from openvino.pyopenvino.op.util import UnaryElementwiseArithmetic
 from openvino.pyopenvino.op.util import BinaryElementwiseComparison
 from openvino.pyopenvino.op.util import BinaryElementwiseArithmetic
 from openvino.pyopenvino.op.util import BinaryElementwiseLogical
-from openvino.pyopenvino.op.util import OpAnnotations
 from openvino.pyopenvino.op.util import ArithmeticReduction
 from openvino.pyopenvino.op.util import IndexReduction
+from openvino.pyopenvino.op.util import VariableInfo
+from openvino.pyopenvino.op.util import Variable
diff --git a/runtime/bindings/python/src/pyopenvino/graph/function.cpp b/runtime/bindings/python/src/pyopenvino/graph/function.cpp
index c064ddbdd3c..685eabba812 100644
--- a/runtime/bindings/python/src/pyopenvino/graph/function.cpp
+++ b/runtime/bindings/python/src/pyopenvino/graph/function.cpp
@@ -10,12 +10,20 @@
 #include "openvino/core/partial_shape.hpp"
 #include "openvino/op/parameter.hpp"  // ov::op::v0::Parameter
 #include "openvino/op/sink.hpp"
+#include "pyopenvino/core/tensor.hpp"
 #include "pyopenvino/graph/function.hpp"
+#include "pyopenvino/graph/ops/result.hpp"
+#include "pyopenvino/graph/ops/util/variable.hpp"
+#include "pyopenvino/graph/rt_map.hpp"
 
 namespace py = pybind11;
 
 static const char* CAPSULE_NAME = "openvino_function";
 
+using PyRTMap = std::map<std::string, std::shared_ptr<ov::Variant>>;
+
+PYBIND11_MAKE_OPAQUE(PyRTMap);
+
 void set_tensor_names(const ov::ParameterVector& parameters) {
     for (const auto& param : parameters) {
         ov::Output<ov::Node> p = param;
@@ -26,6 +34,16 @@ void set_tensor_names(const ov::ParameterVector& parameters) {
     }
 }
 
+ov::SinkVector cast_to_sink_vector(const std::vector<std::shared_ptr<ov::Node>>& nodes) {
+    ov::SinkVector sinks;
+    for (const auto& node : nodes) {
+        auto sink = std::dynamic_pointer_cast<ov::op::Sink>(node);
+        NGRAPH_CHECK(sink != nullptr, "Node {} is not instance of Sink");
+        sinks.push_back(sink);
+    }
+    return sinks;
+}
+
 void regclass_graph_Function(py::module m) {
     py::class_<ov::Function, std::shared_ptr<ov::Function>> function(m, "Function", py::module_local());
     function.doc() = "openvino.impl.Function wraps ov::Function";
@@ -34,12 +52,8 @@ void regclass_graph_Function(py::module m) {
                              const std::vector<std::shared_ptr<ov::Node>>& nodes,
                              const ov::ParameterVector& params,
                              const std::string& name) {
-                     ov::SinkVector sinks;
-                     for (const auto& node : nodes) {
-                         auto sink = std::dynamic_pointer_cast<ov::op::Sink>(node);
-                         NGRAPH_CHECK(sink != nullptr, "Node {} is not instance of Sink");
-                         sinks.push_back(sink);
-                     }
+                     set_tensor_names(params);
+                     const auto sinks = cast_to_sink_vector(nodes);
                      return std::make_shared<ov::Function>(res, sinks, params, name);
                  }),
                  py::arg("results"),
@@ -88,11 +102,11 @@ void regclass_graph_Function(py::module m) {
                         String to set as function's friendly name.
                  )");
 
-    function.def(py::init([](const std::shared_ptr<ov::Node>& results,
+    function.def(py::init([](const std::shared_ptr<ov::Node>& result,
                              const ov::ParameterVector& parameters,
                              const std::string& name) {
                      set_tensor_names(parameters);
-                     return std::make_shared<ov::Function>(results, parameters, name);
+                     return std::make_shared<ov::Function>(result, parameters, name);
                  }),
                  py::arg("result"),
                  py::arg("parameters"),
@@ -102,7 +116,7 @@ void regclass_graph_Function(py::module m) {
 
                     Parameters
                     ----------
-                    results : Node
+                    result : Node
                         Node to be used as result.
 
                     parameters : List[op.Parameter]
@@ -112,6 +126,188 @@ void regclass_graph_Function(py::module m) {
                         String to set as function's friendly name.
                  )");
 
+    function.def(
+        py::init([](const ov::OutputVector& results, const ov::ParameterVector& parameters, const std::string& name) {
+            set_tensor_names(parameters);
+            return std::make_shared<ov::Function>(results, parameters, name);
+        }),
+        py::arg("results"),
+        py::arg("parameters"),
+        py::arg("name") = ""),
+        R"(
+            Create user-defined Function which is a representation of a model
+
+            Parameters
+            ----------
+            results : List[Output]
+                List of outputs.
+
+            parameters : List[op.Parameter]
+                List of parameters.
+
+            name : str
+                String to set as function's friendly name.
+        )";
+
+    function.def(py::init([](const ov::OutputVector& results,
+                             const std::vector<std::shared_ptr<ov::Node>>& nodes,
+                             const ov::ParameterVector& parameters,
+                             const std::string& name) {
+                     set_tensor_names(parameters);
+                     const auto sinks = cast_to_sink_vector(nodes);
+                     return std::make_shared<ov::Function>(results, sinks, parameters, name);
+                 }),
+                 py::arg("results"),
+                 py::arg("sinks"),
+                 py::arg("parameters"),
+                 py::arg("name") = ""),
+        R"(
+            Create user-defined Function which is a representation of a model
+
+            Parameters
+            ----------
+            results : List[Output]
+                List of outputs.
+
+            sinks : List[Node]
+                List of Nodes to be used as Sinks (e.g. Assign ops).
+
+            parameters : List[op.Parameter]
+                List of parameters.
+
+            name : str
+                String to set as function's friendly name.
+            )";
+    function.def(py::init([](const ov::ResultVector& results,
+                             const std::vector<std::shared_ptr<ov::Node>>& nodes,
+                             const ov::ParameterVector& parameters,
+                             const ov::op::util::VariableVector& variables,
+                             const std::string& name) {
+                     set_tensor_names(parameters);
+                     const auto sinks = cast_to_sink_vector(nodes);
+                     return std::make_shared<ov::Function>(results, sinks, parameters, variables, name);
+                 }),
+                 py::arg("results"),
+                 py::arg("sinks"),
+                 py::arg("parameters"),
+                 py::arg("variables"),
+                 py::arg("name") = ""),
+        R"(
+            Create user-defined Function which is a representation of a model
+
+            Parameters
+            ----------
+            results : List[op.Result]
+                List of results.
+
+            sinks : List[Node]
+                List of Nodes to be used as Sinks (e.g. Assign ops).
+
+            parameters : List[op.Parameter]
+                List of parameters.
+
+            variables : List[op.util.Variable]
+                List of variables.
+
+            name : str
+                String to set as function's friendly name.
+            )";
+
+    function.def(py::init([](const ov::OutputVector& results,
+                             const std::vector<std::shared_ptr<ov::Node>>& nodes,
+                             const ov::ParameterVector& parameters,
+                             const ov::op::util::VariableVector& variables,
+                             const std::string& name) {
+                     set_tensor_names(parameters);
+                     const auto sinks = cast_to_sink_vector(nodes);
+                     return std::make_shared<ov::Function>(results, sinks, parameters, variables, name);
+                 }),
+                 py::arg("results"),
+                 py::arg("sinks"),
+                 py::arg("parameters"),
+                 py::arg("variables"),
+                 py::arg("name") = ""),
+        R"(
+            Create user-defined Function which is a representation of a model
+
+            Parameters
+            ----------
+            results : List[Output]
+                List of results.
+
+            sinks : List[Node]
+                List of Nodes to be used as Sinks (e.g. Assign ops).
+
+            parameters : List[op.Parameter]
+                List of parameters.
+
+            variables : List[op.util.Variable]
+                List of variables.
+
+            name : str
+                String to set as function's friendly name.
+        )";
+
+    function.def(py::init([](const ov::ResultVector& results,
+                             const ov::ParameterVector& parameters,
+                             const ov::op::util::VariableVector& variables,
+                             const std::string& name) {
+                     set_tensor_names(parameters);
+                     return std::make_shared<ov::Function>(results, parameters, variables, name);
+                 }),
+                 py::arg("results"),
+                 py::arg("parameters"),
+                 py::arg("variables"),
+                 py::arg("name") = ""),
+        R"(
+            Create user-defined Function which is a representation of a model
+
+            Parameters
+            ----------
+            results : List[op.Result]
+                List of results.
+
+            parameters : List[op.Parameter]
+                List of parameters.
+
+            variables : List[op.util.Variable]
+                List of variables.
+
+            name : str
+                String to set as function's friendly name.
+        )";
+
+    function.def(py::init([](const ov::OutputVector& results,
+                             const ov::ParameterVector& parameters,
+                             const ov::op::util::VariableVector& variables,
+                             const std::string& name) {
+                     set_tensor_names(parameters);
+                     return std::make_shared<ov::Function>(results, parameters, variables, name);
+                 }),
+                 py::arg("results"),
+                 py::arg("parameters"),
+                 py::arg("variables"),
+                 py::arg("name") = ""),
+        R"(
+            Create user-defined Function which is a representation of a model
+
+            Parameters
+            ----------
+            results : List[Output]
+                List of results.
+
+            parameters : List[op.Parameter]
+                List of parameters.
+
+            variables : List[op.util.Variable]
+                List of variables.
+
+            name : str
+                String to set as function's friendly name.
+        )";
+
+    function.def("validate_nodes_and_infer_types", &ov::Function::validate_nodes_and_infer_types);
+
     function.def(
         "reshape",
         [](ov::Function& self, const std::map<std::string, ov::PartialShape>& partial_shapes) {
@@ -270,6 +466,43 @@ void regclass_graph_Function(py::module m) {
                     get_result : Node
                         Node object representing result.
                  )");
+    function.def("get_result_index",
+                 (int64_t(ov::Function::*)(const ov::Output<ov::Node>&) const) & ov::Function::get_result_index,
+                 py::arg("value"),
+                 R"(
+                    Return index of result.
+
+                    Return -1 if `value` not matched.
+
+                    Parameters
+                    ----------
+                    value : Output
+                        Output containing Node
+                    
+                    Returns
+                    ----------
+                    get_result_index : int
+                        Index for value referencing it. 
+                 )");
+    function.def("get_result_index",
+                 (int64_t(ov::Function::*)(const ov::Output<const ov::Node>&) const) & ov::Function::get_result_index,
+                 py::arg("value"),
+                 R"(
+                    Return index of result.
+
+                    Return -1 if `value` not matched.
+
+                    Parameters
+                    ----------
+                    value : Output
+                        Output containing Node
+                    
+                    Returns
+                    ----------
+                    get_result_index : int
+                        Index for value referencing it.
+                 )");
+
     function.def("get_name",
                  &ov::Function::get_name,
                  R"(
@@ -388,6 +621,72 @@ void regclass_graph_Function(py::module m) {
         },
         py::arg("outputs"));
 
+    function.def("replace_parameter",
+                 &ov::Function::replace_parameter,
+                 py::arg("parameter_index"),
+                 py::arg("parameter"),
+                 R"(
+                    Replace the `parameter_index`th parameter of the function with `parameter`.
+    
+                    All users of the `parameter_index`th parameter are redirected to `parameter`, and the
+                    `parameter_index`th entry in the function parameter list is replaced with `parameter`.
+
+                    Parameters
+                    ----------
+                    parameter_index : int
+                        The index of the parameter to replace.
+                    parameter: op.Parameter
+                        The parameter to substitute for the `parameter_index`th parameter.
+        )");
+
+    function.def("get_parameter_index",
+                 (int64_t(ov::Function::*)(const std::shared_ptr<ov::op::v0::Parameter>&) const) &
+                     ov::Function::get_parameter_index,
+                 py::arg("parameter"),
+                 R"(
+                    Return the index position of `parameter`.
+
+                    Return -1 if parameter not matched.
+
+                    Parameters
+                    ----------
+                    parameter : op.Parameter
+
+                    Returns
+                    ----------
+                    get_parameter_index : int
+                        Index for parameter
+                 )");
+
+    function.def(
+        "evaluate",
+        [](ov::Function& self,
+           ov::runtime::TensorVector& output_tensors,
+           const ov::runtime::TensorVector& input_tensors,
+           PyRTMap evaluation_context) -> bool {
+            return self.evaluate(output_tensors, input_tensors, evaluation_context);
+        },
+        py::arg("output_tensors"),
+        py::arg("input_tensors"),
+        py::arg("evaluation_context") = PyRTMap(),
+        R"(
+            Evaluate the function on inputs, putting results in outputs
+
+            Parameters
+            ----------
+            output_tensors : List[op.Tensor]
+                Tensors for the outputs to compute. One for each result
+            input_tensors : List[op.Tensor]
+                Tensors for the inputs. One for each inputs.
+            evaluation_context: PyRTMap
+                Storage of additional settings and attributes that can be used
+                when evaluating the function. This additional information can be shared across nodes.
+
+            Returns
+            ----------
+            evaluate : bool
+
+        )");
     function.def("__repr__", [](const ov::Function& self) {
         std::string class_name = py::cast(self).get_type().attr("__name__").cast<std::string>();
         std::stringstream shapes_ss;
diff --git a/runtime/bindings/python/src/pyopenvino/graph/ops/util/index_reduction.cpp b/runtime/bindings/python/src/pyopenvino/graph/ops/util/index_reduction.cpp
index bcbd8c1e668..a2285b6b759 100644
--- a/runtime/bindings/python/src/pyopenvino/graph/ops/util/index_reduction.cpp
+++ b/runtime/bindings/python/src/pyopenvino/graph/ops/util/index_reduction.cpp
@@ -15,7 +15,7 @@ namespace py = pybind11;
 void regclass_graph_op_util_IndexReduction(py::module m) {
     py::class_<ov::op::util::IndexReduction, std::shared_ptr<ov::op::util::IndexReduction>> indexReduction(
         m,
-        "IndexRedection");
+        "IndexReduction");
 
     indexReduction.def("get_reduction_axis", &ov::op::util::IndexReduction::get_reduction_axis);
     indexReduction.def("set_reduction_axis", &ov::op::util::IndexReduction::set_reduction_axis);
diff --git a/runtime/bindings/python/src/pyopenvino/graph/ops/util/regmodule_graph_op_util.cpp b/runtime/bindings/python/src/pyopenvino/graph/ops/util/regmodule_graph_op_util.cpp
index 1ae197b06f0..0e4f1e5d852 100644
--- a/runtime/bindings/python/src/pyopenvino/graph/ops/util/regmodule_graph_op_util.cpp
+++ b/runtime/bindings/python/src/pyopenvino/graph/ops/util/regmodule_graph_op_util.cpp
@@ -19,4 +19,5 @@ void regmodule_graph_op_util(py::module m) {
     //    regclass_graph_op_util_UnaryElementwise(m_util);
     regclass_graph_op_util_UnaryElementwiseArithmetic(m_util);
     regclass_graph_op_util_IndexReduction(m_util);
+    regclass_graph_op_util_Variable(m_util);
 }
diff --git a/runtime/bindings/python/src/pyopenvino/graph/ops/util/regmodule_graph_op_util.hpp b/runtime/bindings/python/src/pyopenvino/graph/ops/util/regmodule_graph_op_util.hpp
index fcd016ac64b..746e424a81a 100644
--- a/runtime/bindings/python/src/pyopenvino/graph/ops/util/regmodule_graph_op_util.hpp
+++ b/runtime/bindings/python/src/pyopenvino/graph/ops/util/regmodule_graph_op_util.hpp
@@ -5,12 +5,14 @@
 #pragma once
 
 #include <pybind11/pybind11.h>
+
 #include "pyopenvino/graph/ops/util/arithmetic_reduction.hpp"
 #include "pyopenvino/graph/ops/util/binary_elementwise_arithmetic.hpp"
 #include "pyopenvino/graph/ops/util/binary_elementwise_comparison.hpp"
 #include "pyopenvino/graph/ops/util/binary_elementwise_logical.hpp"
 #include "pyopenvino/graph/ops/util/index_reduction.hpp"
 #include "pyopenvino/graph/ops/util/unary_elementwise_arithmetic.hpp"
+#include "pyopenvino/graph/ops/util/variable.hpp"
 
 namespace py = pybind11;
 
diff --git a/runtime/bindings/python/src/pyopenvino/graph/ops/util/variable.cpp b/runtime/bindings/python/src/pyopenvino/graph/ops/util/variable.cpp
new file mode 100644
index 00000000000..04354883444
--- /dev/null
+++ b/runtime/bindings/python/src/pyopenvino/graph/ops/util/variable.cpp
@@ -0,0 +1,28 @@
+#include "pyopenvino/graph/ops/util/variable.hpp"
+
+#include <pybind11/numpy.h>
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+
+#include "openvino/op/util/variable.hpp"
+
+namespace py = pybind11;
+
+void regclass_graph_op_util_Variable(py::module m) {
+    py::class_<ov::op::util::VariableInfo> variable_info(m, "VariableInfo");
+    variable_info.doc() = "openvino.impl.op.util.VariableInfo wraps ov::op::util::VariableInfo";
+    variable_info.def(py::init<>());
+    variable_info.def_readwrite("data_shape", &ov::op::util::VariableInfo::data_shape);
+    variable_info.def_readwrite("data_type", &ov::op::util::VariableInfo::data_type);
+    variable_info.def_readwrite("variable_id", &ov::op::util::VariableInfo::variable_id);
+
+    py::class_<ov::op::util::Variable, std::shared_ptr<ov::op::util::Variable>> variable(m, "Variable");
+    variable.doc() = "openvino.impl.op.util.Variable wraps ov::op::util::Variable";
+    variable.def(py::init([](const ov::op::util::VariableInfo& info) {
+                     return ov::op::util::Variable{info};
+                 }),
+                 py::arg("info"));
+    variable.def_property_readonly("info", &ov::op::util::Variable::get_info);
+    variable.def("get_info", &ov::op::util::Variable::get_info);
+    variable.def("update", &ov::op::util::Variable::update, py::arg("variable_info"));
+}
diff --git a/runtime/bindings/python/src/pyopenvino/graph/ops/util/variable.hpp b/runtime/bindings/python/src/pyopenvino/graph/ops/util/variable.hpp
new file mode 100644
index 00000000000..fe984017654
--- /dev/null
+++ b/runtime/bindings/python/src/pyopenvino/graph/ops/util/variable.hpp
@@ -0,0 +1,11 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <pybind11/pybind11.h>
+
+namespace py = pybind11;
+
+void regclass_graph_op_util_Variable(py::module m);
diff --git a/runtime/bindings/python/tests/test_inference_engine/test_function.py b/runtime/bindings/python/tests/test_inference_engine/test_function.py
index 7db18b1fdb0..3d2bca9180e 100644
--- a/runtime/bindings/python/tests/test_inference_engine/test_function.py
+++ b/runtime/bindings/python/tests/test_inference_engine/test_function.py
@@ -6,9 +6,10 @@ import pytest
 
 import openvino.opset8 as ops
 
-from openvino import Function
-from openvino.descriptor import Tensor
-from openvino.impl import PartialShape
+from openvino import Function, Tensor
+from openvino.descriptor import Tensor as DescriptorTensor
+
+from openvino.impl import PartialShape, Shape
 
 
 def test_function_add_outputs_tensor_name():
@@ -22,7 +23,7 @@ def test_function_add_outputs_tensor_name():
     assert len(function.get_results()) == 1
     function.add_outputs("relu_t1")
     assert len(function.get_results()) == 2
-    assert isinstance(function.outputs[1].get_tensor(), Tensor)
+    assert isinstance(function.outputs[1].get_tensor(), DescriptorTensor)
     assert "relu_t1" in function.outputs[1].get_tensor().names
 
 
@@ -73,7 +74,9 @@ def test_function_add_output_incorrect_idx():
     assert len(function.get_results()) == 1
     with pytest.raises(RuntimeError) as e:
         function.add_outputs(("relu1", 10))
-    assert "Cannot add output to port 10 operation relu1 has only 1 outputs." in str(e.value)
+    assert "Cannot add output to port 10 operation relu1 has only 1 outputs." in str(
+        e.value
+    )
 
 
 def test_function_add_output_incorrect_name():
@@ -140,3 +143,101 @@ def test_add_outputs_incorrect_outputs_list():
     with pytest.raises(TypeError) as e:
         function.add_outputs([0, 0])
     assert "Incorrect type of a value to add as output at index 0" in str(e.value)
+
+
+def test_validate_nodes_and_infer_types():
+    param1 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data1")
+    param2 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data2")
+    add = ops.add(param1, param2)
+    func = Function(add, [param1, param2], "TestFunction")
+
+    invalid_shape = Shape([3, 7])
+    param3 = ops.parameter(invalid_shape, dtype=np.float32, name="data3")
+    func.replace_parameter(0, param3)
+
+    with pytest.raises(RuntimeError) as e:
+        func.validate_nodes_and_infer_types()
+    assert "Argument shapes are inconsistent" in str(e.value)
+
+
+def test_get_result_index():
+    input_shape = PartialShape([1])
+    param = ops.parameter(input_shape, dtype=np.float32, name="data")
+    relu = ops.relu(param, name="relu")
+    function = Function(relu, [param], "TestFunction")
+    assert len(function.outputs) == 1
+    assert function.get_result_index(function.outputs[0]) == 0
+
+
+def test_get_result_index_invalid():
+    shape1 = PartialShape([1])
+    param1 = ops.parameter(shape1, dtype=np.float32, name="data1")
+    relu1 = ops.relu(param1, name="relu1")
+    function = Function(relu1, [param1], "TestFunction")
+
+    shape2 = PartialShape([2])
+    param2 = ops.parameter(shape2, dtype=np.float32, name="data2")
+    relu2 = ops.relu(param2, name="relu2")
+    invalid_output = relu2.outputs()[0]
+    assert len(function.outputs) == 1
+    assert function.get_result_index(invalid_output) == -1
+
+
+def test_parameter_index():
+    input_shape = PartialShape([1])
+    param = ops.parameter(input_shape, dtype=np.float32, name="data")
+    relu = ops.relu(param, name="relu")
+    function = Function(relu, [param], "TestFunction")
+    assert function.get_parameter_index(param) == 0
+
+
+def test_parameter_index_invalid():
+    shape1 = PartialShape([1])
+    param1 = ops.parameter(shape1, dtype=np.float32, name="data1")
+    relu = ops.relu(param1, name="relu")
+    function = Function(relu, [param1], "TestFunction")
+    shape2 = PartialShape([2])
+    param2 = ops.parameter(shape2, dtype=np.float32, name="data2")
+    assert function.get_parameter_index(param2) == -1
+
+
+def test_replace_parameter():
+    shape1 = PartialShape([1])
+    param1 = ops.parameter(shape1, dtype=np.float32, name="data")
+    shape2 = PartialShape([2])
+    param2 = ops.parameter(shape2, dtype=np.float32, name="data")
+    relu = ops.relu(param1, name="relu")
+
+    function = Function(relu, [param1], "TestFunction")
+    param_index = function.get_parameter_index(param1)
+    function.replace_parameter(param_index, param2)
+    assert function.get_parameter_index(param2) == param_index
+    assert function.get_parameter_index(param1) == -1
+
+
+def test_evaluate():
+    param1 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data1")
+    param2 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data2")
+    add = ops.add(param1, param2)
+    func = Function(add, [param1, param2], "TestFunction")
+
+    input1 = np.array([2, 1], dtype=np.float32).reshape(2, 1)
+    input2 = np.array([3, 7], dtype=np.float32).reshape(2, 1)
+    out_tensor = Tensor("float32", Shape([2, 1]))
+
+    assert func.evaluate([out_tensor], [Tensor(input1), Tensor(input2)])
+    assert np.allclose(out_tensor.data, np.array([5, 8]).reshape(2, 1))
+
+
+def test_evaluate_invalid_input_shape():
+    param1 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data1")
+    param2 = ops.parameter(Shape([2, 1]), dtype=np.float32, name="data2")
+    add = ops.add(param1, param2)
+    func = Function(add, [param1, param2], "TestFunction")
+
+    with pytest.raises(RuntimeError) as e:
+        assert func.evaluate(
+            [Tensor("float32", Shape([2, 1]))],
+            [Tensor("float32", Shape([3, 1])), Tensor("float32", Shape([3, 1]))],
+        )
+    assert "must be compatible with the partial shape: {2,1}" in str(e.value)
diff --git a/runtime/bindings/python/tests/test_ngraph/test_ops_util_variable.py b/runtime/bindings/python/tests/test_ngraph/test_ops_util_variable.py
new file mode 100644
index 00000000000..dd41a4af32e
--- /dev/null
+++ b/runtime/bindings/python/tests/test_ngraph/test_ops_util_variable.py
@@ -0,0 +1,46 @@
+# Copyright (C) 2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+from openvino.impl import PartialShape, Type
+from openvino.impl.op.util import VariableInfo, Variable
+
+
+def test_info_as_property():
+    info = VariableInfo()
+    info.data_shape = PartialShape([1])
+    info.data_type = Type.f32
+    info.variable_id = "test_id"
+    variable = Variable(info)
+    assert variable.info.data_shape == info.data_shape
+    assert variable.info.data_type == info.data_type
+    assert variable.info.variable_id == info.variable_id
+
+
+def test_get_info():
+    info = VariableInfo()
+    info.data_shape = PartialShape([1])
+    info.data_type = Type.f32
+    info.variable_id = "test_id"
+    variable = Variable(info)
+    assert variable.get_info().data_shape == info.data_shape
+    assert variable.get_info().data_type == info.data_type
+    assert variable.get_info().variable_id == info.variable_id
+
+
+def test_info_update():
+    info1 = VariableInfo()
+    info1.data_shape = PartialShape([1])
+    info1.data_type = Type.f32
+    info1.variable_id = "test_id"
+
+    variable = Variable(info1)
+
+    info2 = VariableInfo()
+    info2.data_shape = PartialShape([2, 1])
+    info2.data_type = Type.i64
+    info2.variable_id = "test_id2"
+
+    variable.update(info2)
+    assert variable.info.data_shape == info2.data_shape
+    assert variable.info.data_type == info2.data_type
+    assert variable.info.variable_id == info2.variable_id

From c66c37807306fe2f82d9864328fff23aa0e1b9bb Mon Sep 17 00:00:00 2001
From: Vladislav Golubev <vladislav.golubev@intel.com>
Date: Thu, 25 Nov 2021 17:26:00 +0300
Subject: [PATCH 17/72] Failed test disabled (#8823)

---
 .../plugin/cpu/shared_tests_instances/skip_tests_config.cpp     | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
index 3e681558a71..c97ab5f6ca2 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
@@ -153,6 +153,8 @@ std::vector<std::string> disabledTestPatterns() {
         // TODO : CVS-69533
         R"(.*ConvolutionLayerCPUTest.*IS=\{.+\}.*_Fused=.*Add\(Parameters\).*)",
         R"(.*GroupConvolutionLayerCPUTest.*IS=\{.+\}.*_Fused=.*Add\(Parameters\).*)",
+        // Issue: 71968
+        R"(.*LSTMSequenceCommonZeroClip.*PURE.*CONST.*hidden_size=10.*sigmoid.sigmoid.sigmoid.*reverse.*FP32_targetDevice=CPU.*)",
     };
 
 #define FIX_62820 0

From 3743552ea7361be80679c5afcd297ea93d1f072e Mon Sep 17 00:00:00 2001
From: Gorokhov Dmitriy <dmitry.gorokhov@intel.com>
Date: Thu, 25 Nov 2021 19:39:31 +0300
Subject: [PATCH 18/72] [CPU] Enable brgemm primitives (#8169)

---
 .../mkldnn_plugin/mkldnn/iml_type_mapper.cpp  | 28 ++++++-
 .../mkldnn_plugin/mkldnn/iml_type_mapper.h    | 58 ++++++++++----
 .../src/mkldnn_plugin/mkldnn_node.cpp         | 11 +++
 .../nodes/mkldnn_fullyconnected_node.cpp      |  9 +++
 .../nodes/mkldnn_matmul_node.cpp              | 37 +++++++++
 .../mkldnn_plugin/nodes/mkldnn_matmul_node.h  |  1 +
 .../plugin/cpu/single_layer_tests/mat_mul.cpp | 76 +++++++++++++++++--
 inference-engine/thirdparty/CMakeLists.txt    |  2 +-
 inference-engine/thirdparty/mkl-dnn           |  2 +-
 9 files changed, 199 insertions(+), 25 deletions(-)

diff --git a/inference-engine/src/mkldnn_plugin/mkldnn/iml_type_mapper.cpp b/inference-engine/src/mkldnn_plugin/mkldnn/iml_type_mapper.cpp
index ebbebaa5dc3..adfe783f25c 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn/iml_type_mapper.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn/iml_type_mapper.cpp
@@ -22,11 +22,15 @@ impl_desc_type MKLDNNPlugin::parse_impl_name(std::string impl_desc_name) {
 
     SEARCH_WORD(ref);
     SEARCH_WORD(jit);
-    SEARCH_WORD(gemm);
+    SEARCH_WORD(brgconv);
+    SEARCH_WORD(brgemm);
+    if ((res & impl_desc_type::brgemm) != impl_desc_type::brgemm)
+        SEARCH_WORD(gemm);
     SEARCH_WORD(blas);
     SEARCH_WORD(sse42);
     SEARCH_WORD_2(sse41, sse42);
     SEARCH_WORD(avx2);
+    SEARCH_WORD(amx);
     SEARCH_WORD(avx512);
     SEARCH_WORD(any);
     SEARCH_WORD(_1x1);
@@ -82,6 +86,28 @@ const char* MKLDNNPlugin::impl_type_to_string(impl_desc_type type) {
     CASE(jit_avx_dw);
     CASE(jit_sse42_dw);
     CASE(jit_uni_dw);
+    CASE(jit_avx512_amx);
+    CASE(jit_avx512_amx_1x1);
+    CASE(jit_avx512_amx_dw);
+    CASE(brgconv_avx512);
+    CASE(brgconv_avx2);
+    CASE(brgconv_avx);
+    CASE(brgconv_sse42);
+    CASE(brgconv_uni);
+    CASE(brgconv_avx512_amx);
+    CASE(brgconv_avx512_1x1);
+    CASE(brgconv_avx2_1x1);
+    CASE(brgconv_avx_1x1);
+    CASE(brgconv_sse42_1x1);
+    CASE(brgconv_uni_1x1);
+    CASE(brgconv_avx512_amx_1x1);
+    CASE(brgemm_avx512);
+    CASE(brgemm_avx2);
+    CASE(brgemm_avx);
+    CASE(brgemm_sse42);
+    CASE(brgemm_uni);
+    CASE(brgemm_avx512_amx);
+
 #undef CASE
     return "unknown";
 }
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn/iml_type_mapper.h b/inference-engine/src/mkldnn_plugin/mkldnn/iml_type_mapper.h
index 207c51c76e8..9cd5529eca4 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn/iml_type_mapper.h
+++ b/inference-engine/src/mkldnn_plugin/mkldnn/iml_type_mapper.h
@@ -12,25 +12,29 @@ enum impl_desc_type {
     unknown = 0x00000000,
     undef,
     // Optimization approach
-    simple = 1<<6,
-    ref    = 1<<7,
-    jit    = 1<<8,
-    gemm   = 1<<9,
+    simple  = 1<<6,
+    ref     = 1<<7,
+    jit     = 1<<8,
+    gemm    = 1<<9,
+    brgconv = 1<<10,
+    brgemm  = 1<<11,
     // CPU version
-    sse42  = 1<<10,
-    avx    = 1<<11,
-    avx2   = 1<<12,
-    avx512 = 1<<13,
-    blas   = 1<<14,
-    any    = 1<<15,
-    uni    = 1<<16,
+    sse42  = 1<<12,
+    avx    = 1<<13,
+    avx2   = 1<<14,
+    avx512 = 1<<15,
+    amx    = 1<<16,
+    blas   = 1<<17,
+    any    = 1<<18,
+    uni    = 1<<19,
     // Other specificator
-    _1x1    = 1<<17,
-    _dw     = 1<<18,
+    _1x1    = 1<<20,
+    _dw     = 1<<21,
     // Other info
-    reorder = 1<<19,
+    reorder = 1<<22,
     // winograd
-    winograd = 1<<20,
+    winograd = 1<<23,
+
     // real types
     ref_any             = ref  | any,
 
@@ -49,18 +53,42 @@ enum impl_desc_type {
     jit_avx             = jit  | avx,
     jit_sse42           = jit  | sse42,
     jit_uni             = jit  | uni,
+    jit_avx512_amx      = jit  | avx512 | amx,
 
     jit_avx512_1x1      = jit  | avx512 | _1x1,
     jit_avx2_1x1        = jit  | avx2   | _1x1,
     jit_avx_1x1         = jit  | avx    | _1x1,
     jit_sse42_1x1       = jit  | sse42  | _1x1,
     jit_uni_1x1         = jit  | uni    | _1x1,
+    jit_avx512_amx_1x1  = jit  | avx512 | amx | _1x1,
 
     jit_avx512_dw       = jit  | avx512 | _dw,
     jit_avx2_dw         = jit  | avx2   | _dw,
     jit_avx_dw          = jit  | avx    | _dw,
     jit_sse42_dw        = jit  | sse42  | _dw,
     jit_uni_dw          = jit  | uni    | _dw,
+    jit_avx512_amx_dw   = jit  | avx512 | amx | _dw,
+
+    brgconv_avx512      = brgconv  | avx512,
+    brgconv_avx2        = brgconv  | avx2,
+    brgconv_avx         = brgconv  | avx,
+    brgconv_sse42       = brgconv  | sse42,
+    brgconv_uni         = brgconv  | uni,
+    brgconv_avx512_amx  = brgconv  | avx512 | amx,
+
+    brgconv_avx512_1x1      = brgconv  | avx512 | _1x1,
+    brgconv_avx2_1x1        = brgconv  | avx2 | _1x1,
+    brgconv_avx_1x1         = brgconv  | avx | _1x1,
+    brgconv_sse42_1x1       = brgconv  | sse42 | _1x1,
+    brgconv_uni_1x1         = brgconv  | uni | _1x1,
+    brgconv_avx512_amx_1x1  = brgconv  | avx512 | amx | _1x1,
+
+    brgemm_avx512      = brgemm  | avx512,
+    brgemm_avx2        = brgemm  | avx2,
+    brgemm_avx         = brgemm  | avx,
+    brgemm_sse42       = brgemm  | sse42,
+    brgemm_uni         = brgemm  | uni,
+    brgemm_avx512_amx  = brgemm  | avx512 | amx,
 };
 
 const char * impl_type_to_string(impl_desc_type type);
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp
index 97169290492..4db90997d19 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp
@@ -391,9 +391,12 @@ std::string MKLDNNNode::getPrimitiveDescriptorType() {
     SEARCH_TYPE(reorder);
     SEARCH_TYPE(jit);
     SEARCH_TYPE(gemm);
+    SEARCH_TYPE(brgconv);
+    SEARCH_TYPE(brgemm);
     SEARCH_TYPE(ref);
 
     SEARCH_TYPE(avx512);
+    SEARCH_TYPE(amx);
     SEARCH_TYPE(avx2);
     SEARCH_TYPE(avx);
     SEARCH_TYPE(sse42);
@@ -838,6 +841,14 @@ void MKLDNNNode::cleanup() {
 const std::vector<impl_desc_type>& MKLDNNNode::getPrimitivesPriority() {
     std::vector<impl_desc_type> priorities = {
             impl_desc_type::unknown,
+            impl_desc_type::brgconv_avx512_amx_1x1,
+            impl_desc_type::brgconv_avx512_amx,
+            impl_desc_type::jit_avx512_amx_dw,
+            impl_desc_type::jit_avx512_amx_1x1,
+            impl_desc_type::jit_avx512_amx,
+            // Brgconv kernels disabled in order to prevent perf degradations on non AMX HW
+//            impl_desc_type::brgconv_avx512_1x1,
+//            impl_desc_type::brgconv_avx512,
             impl_desc_type::jit_uni_dw,
             impl_desc_type::jit_uni_1x1,
             impl_desc_type::jit_uni,
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fullyconnected_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fullyconnected_node.cpp
index 6226ef03bfb..68bad2f2f92 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fullyconnected_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_fullyconnected_node.cpp
@@ -261,6 +261,15 @@ const std::vector<impl_desc_type>& MKLDNNFullyConnectedNode::getPrimitivesPriori
             impl_desc_type::jit_sse42,
             impl_desc_type::ref,
     };
+
+    // WA: brgemm kernel contains bug that may lead to segfault in case of added post-ops and unaligned number of channels
+    size_t simdWidth = 16;
+    auto inputDims = inputShapes[0].getDims();
+    if (inputDims.back() != Shape::UNDEFINED_DIM && inputDims.back() % simdWidth == 0) {
+        priorities.insert(priorities.begin() + 1, impl_desc_type::brgemm_avx512_amx);
+        priorities.insert(priorities.begin() + 2, impl_desc_type::brgemm_avx512);
+    }
+
     for (const auto& impl : priorities) {
         if (std::find(implPriorities.begin(), implPriorities.end(), impl) == implPriorities.end())
             implPriorities.push_back(impl);
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matmul_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matmul_node.cpp
index fa277a11209..e9ebde02cff 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matmul_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matmul_node.cpp
@@ -353,4 +353,41 @@ void MKLDNNMatMulNode::executeDynamicImpl(dnnl::stream strm) {
     MKLDNNNode::execute(strm);
 }
 
+const std::vector<impl_desc_type>& MKLDNNMatMulNode::getPrimitivesPriority() {
+    std::vector<impl_desc_type> priorities = {
+            impl_desc_type::unknown,
+            impl_desc_type::brgemm_avx512_amx,
+            impl_desc_type::brgemm_avx512,
+            impl_desc_type::gemm_blas,
+            impl_desc_type::gemm_avx512,
+            impl_desc_type::gemm_avx2,
+            impl_desc_type::gemm_avx,
+            impl_desc_type::gemm_sse42,
+            impl_desc_type::gemm_any,
+            impl_desc_type::gemm,
+            impl_desc_type::jit_gemm,
+            impl_desc_type::jit_uni_dw,
+            impl_desc_type::jit_uni_1x1,
+            impl_desc_type::jit_uni,
+            impl_desc_type::jit_avx512_dw,
+            impl_desc_type::jit_avx512_1x1,
+            impl_desc_type::jit_avx512,
+            impl_desc_type::jit_avx2_dw,
+            impl_desc_type::jit_avx2_1x1,
+            impl_desc_type::jit_avx2,
+            impl_desc_type::jit_avx_dw,
+            impl_desc_type::jit_avx_1x1,
+            impl_desc_type::jit_avx,
+            impl_desc_type::jit_sse42_dw,
+            impl_desc_type::jit_sse42_1x1,
+            impl_desc_type::jit_sse42,
+            impl_desc_type::ref,
+    };
+    for (const auto& impl : priorities) {
+        if (std::find(implPriorities.begin(), implPriorities.end(), impl) == implPriorities.end())
+            implPriorities.push_back(impl);
+    }
+    return implPriorities;
+}
+
 REG_MKLDNN_PRIM_FOR(MKLDNNMatMulNode, MatMul);
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matmul_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matmul_node.h
index e0046594f53..9c9489df237 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matmul_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matmul_node.h
@@ -36,6 +36,7 @@ public:
     void executeDynamicImpl(mkldnn::stream strm) override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
+    const std::vector<impl_desc_type>& getPrimitivesPriority() override;
 
 protected:
     AttrPtr initPrimitiveAttr() const override;
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/mat_mul.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/mat_mul.cpp
index 417da7b48b2..367886f31e2 100644
--- a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/mat_mul.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/mat_mul.cpp
@@ -35,7 +35,8 @@ typedef std::tuple<
 
 using MatMulLayerCPUTestParamSet = std::tuple<MatMulLayerTestParamsSet,
                                               MatMulNodeType,
-                                              fusingSpecificParams>;
+                                              fusingSpecificParams,
+                                              CPUSpecificParams>;
 
 class MatMulLayerCPUTest : public testing::WithParamInterface<MatMulLayerCPUTestParamSet>,
                            virtual public SubgraphBaseTest, public CpuTestWithFusing {
@@ -44,8 +45,9 @@ public:
         MatMulLayerTestParamsSet basicParamsSet;
         MatMulNodeType nodeType;
         fusingSpecificParams fusingParams;
+        CPUSpecificParams cpuParams;
 
-        std::tie(basicParamsSet, nodeType, fusingParams) = obj.param;
+        std::tie(basicParamsSet, nodeType, fusingParams, cpuParams) = obj.param;
 
         ElementType netType;
         ElementType inType, outType;
@@ -86,6 +88,7 @@ public:
         }
         result << ")";
         result << CpuTestWithFusing::getTestCaseName(fusingParams);
+        result << CPUTestsBase::getTestCaseName(cpuParams);
 
         return result.str();
     }
@@ -103,8 +106,10 @@ protected:
         MatMulLayerTestParamsSet basicParamsSet;
         MatMulNodeType nodeType;
         fusingSpecificParams fusingParams;
+        CPUSpecificParams cpuParams;
 
-        std::tie(basicParamsSet, nodeType, fusingParams) = this->GetParam();
+        std::tie(basicParamsSet, nodeType, fusingParams, cpuParams) = this->GetParam();
+        std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
 
         ShapeRelatedParams shapeRelatedParams;
         ElementType netType;
@@ -149,7 +154,7 @@ protected:
             inType = outType = netType;
 
         cpuNodeType = nodeType == MatMulNodeType::MatMul ? "MatMul" : "FullyConnected";
-        selectedType = makeSelectedTypeStr("jit_gemm", outType);
+        selectedType = makeSelectedTypeStr(selectedType, outType);
 
         auto params = builder::makeDynamicParams(netType, {inShapeA});
 
@@ -184,6 +189,13 @@ const std::vector<ElementType> netPRCs {
     ElementType::bf16
 };
 
+std::vector<CPUSpecificParams> filterSpecificParams() {
+    std::vector<CPUSpecificParams> specificParams;
+    specificParams.push_back(CPUSpecificParams{{}, {}, {"jit_gemm"}, "jit_gemm"});
+
+    return specificParams;
+}
+
 /* ============= FullyConnected ============= */
 namespace fullyConnected {
 
@@ -233,7 +245,8 @@ const auto fullyConnectedParams2D = ::testing::Combine(::testing::ValuesIn(IS2D)
 
 const auto testParams2D = ::testing::Combine(fullyConnectedParams2D,
                                              ::testing::Values(MatMulNodeType::FullyConnected),
-                                             ::testing::ValuesIn(fusingParamsSet2D));
+                                             ::testing::ValuesIn(fusingParamsSet2D),
+                                             ::testing::ValuesIn(filterSpecificParams()));
 
 INSTANTIATE_TEST_SUITE_P(smoke_FC_2D, MatMulLayerCPUTest, testParams2D, MatMulLayerCPUTest::getTestCaseName);
 
@@ -266,10 +279,58 @@ const auto fullyConnectedParams3D = ::testing::Combine(::testing::ValuesIn(IS3D)
 
 const auto testParams3D = ::testing::Combine(fullyConnectedParams3D,
                                              ::testing::Values(MatMulNodeType::FullyConnected),
-                                             ::testing::ValuesIn(fusingParamsSet3D));
+                                             ::testing::ValuesIn(fusingParamsSet3D),
+                                             ::testing::ValuesIn(filterSpecificParams()));
 
 INSTANTIATE_TEST_SUITE_P(smoke_FC_3D, MatMulLayerCPUTest, testParams3D, MatMulLayerCPUTest::getTestCaseName);
 
+std::vector<std::map<std::string, std::string>> filterAdditionalConfig_Brgemm() {
+    std::vector<std::map<std::string, std::string>> additionalConfig = {
+        std::map<std::string, std::string>{/* empty config */}
+    };
+    if (with_cpu_x86_bfloat16()) {
+        additionalConfig.push_back({{PluginConfigParams::KEY_ENFORCE_BF16, PluginConfigParams::YES}});
+    }
+
+    return additionalConfig;
+}
+
+const std::vector<ShapeRelatedParams> IS2D_Brgemm = {
+    {static_shapes_to_test_representation({{59, 16}, {16, 120}}), {false, false}},
+    {static_shapes_to_test_representation({{59, 16}, {16, 120}}), {true, false}},
+    {static_shapes_to_test_representation({{59, 16}, {16, 120}}), {false, true}},
+    {static_shapes_to_test_representation({{59, 16}, {16, 120}}), {true, true}},
+
+    {static_shapes_to_test_representation({{71, 128}, {128, 20}}), {false, false}},
+    {static_shapes_to_test_representation({{71, 128}, {128, 20}}), {true, false}},
+    {static_shapes_to_test_representation({{71, 128}, {128, 20}}), {false, true}},
+    {static_shapes_to_test_representation({{71, 128}, {128, 20}}), {true, true}},
+};
+
+std::vector<CPUSpecificParams> filterSpecificParams_Brgemm() {
+    std::vector<CPUSpecificParams> specificParams;
+    if (with_cpu_x86_avx512_core()) {
+        specificParams.push_back(CPUSpecificParams{{}, {}, {"brgemm_avx512"}, "brgemm_avx512"});
+    }
+
+    return specificParams;
+}
+
+const auto fullyConnectedParams2D_Brgemm = ::testing::Combine(::testing::ValuesIn(IS2D_Brgemm),
+                                                       ::testing::Values(ElementType::f32),
+                                                       ::testing::Values(ElementType::undefined),
+                                                       ::testing::Values(ElementType::undefined),
+                                                       ::testing::Values(helpers::InputLayerType::CONSTANT),
+                                                       ::testing::Values(CommonTestUtils::DEVICE_CPU),
+                                                       ::testing::ValuesIn(filterAdditionalConfig_Brgemm()));
+
+const auto testParams2D_Brgemm = ::testing::Combine(fullyConnectedParams2D_Brgemm,
+                                             ::testing::Values(MatMulNodeType::FullyConnected),
+                                             ::testing::ValuesIn(fusingParamsSet2D),
+                                             ::testing::ValuesIn(filterSpecificParams_Brgemm()));
+
+INSTANTIATE_TEST_SUITE_P(smoke_FC_2D_Brgemm, MatMulLayerCPUTest, testParams2D_Brgemm, MatMulLayerCPUTest::getTestCaseName);
+
 } // namespace fullyConnected
 
 
@@ -459,7 +520,8 @@ const auto matMulParams = ::testing::Combine(::testing::ValuesIn(IS),
 
 const auto testParams = ::testing::Combine(matMulParams,
                                            ::testing::Values(MatMulNodeType::MatMul),
-                                           ::testing::ValuesIn(matmulFusingParams));
+                                           ::testing::ValuesIn(matmulFusingParams),
+                                           ::testing::ValuesIn(filterSpecificParams()));
 
 INSTANTIATE_TEST_SUITE_P(smoke_MM, MatMulLayerCPUTest, testParams, MatMulLayerCPUTest::getTestCaseName);
 
diff --git a/inference-engine/thirdparty/CMakeLists.txt b/inference-engine/thirdparty/CMakeLists.txt
index 10d9e495254..ef56e007e62 100644
--- a/inference-engine/thirdparty/CMakeLists.txt
+++ b/inference-engine/thirdparty/CMakeLists.txt
@@ -32,7 +32,7 @@ function(ie_add_mkldnn)
     set(DNNL_ENABLE_ITT_TASKS ${BUILD_SHARED_LIBS} CACHE BOOL "" FORCE)
     set(DNNL_ENABLE_CONCURRENT_EXEC ON CACHE BOOL "" FORCE)
     set(DNNL_ENABLE_PRIMITIVE_CACHE OFF CACHE BOOL "" FORCE) ## TODO: try it later
-    set(DNNL_ENABLE_MAX_CPU_ISA OFF CACHE BOOL "" FORCE)     ## TODO: try it later
+    set(DNNL_ENABLE_MAX_CPU_ISA ON CACHE BOOL "" FORCE)
     set(DNNL_LIBRARY_TYPE STATIC CACHE BOOL "" FORCE)
     set(DNNL_BUILD_EXAMPLES OFF CACHE BOOL "" FORCE)
     set(DNNL_BUILD_TESTS OFF CACHE BOOL "" FORCE)
diff --git a/inference-engine/thirdparty/mkl-dnn b/inference-engine/thirdparty/mkl-dnn
index 93ad2b1bf5a..cdc3fd7c168 160000
--- a/inference-engine/thirdparty/mkl-dnn
+++ b/inference-engine/thirdparty/mkl-dnn
@@ -1 +1 @@
-Subproject commit 93ad2b1bf5aa9c6677b09fbd80967e341b50168c
+Subproject commit cdc3fd7c1687a31b5db8eba6d46a44e0513b1e2d

From 342d83555234fc6c5c6a04b3312333cbb043e227 Mon Sep 17 00:00:00 2001
From: Zhang Yi <yi3.zhang@intel.com>
Date: Fri, 26 Nov 2021 01:50:21 +0800
Subject: [PATCH 19/72] =?UTF-8?q?[CPU]=20Dynamic=20shape=20for=20ctc=5Fgre?=
 =?UTF-8?q?edy=5Fdecoder=20&=20ctc=5Fgreedy=5Fdecoder=5Fseq=E2=80=A6=20(#8?=
 =?UTF-8?q?671)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .../nodes/mkldnn_ctc_greedy_decoder_node.cpp  |  24 +-
 .../nodes/mkldnn_ctc_greedy_decoder_node.h    |   5 +-
 ...mkldnn_ctc_greedy_decoder_seq_len_node.cpp |  22 +-
 .../mkldnn_ctc_greedy_decoder_seq_len_node.h  |   4 +-
 .../single_layer_tests/ctc_greedy_decoder.cpp | 174 ++++++++++++++
 .../ctc_greedy_decoder_seq_len.cpp            | 216 ++++++++++++++++++
 6 files changed, 431 insertions(+), 14 deletions(-)
 create mode 100644 inference-engine/tests/functional/plugin/cpu/single_layer_tests/ctc_greedy_decoder.cpp
 create mode 100644 inference-engine/tests/functional/plugin/cpu/single_layer_tests/ctc_greedy_decoder_seq_len.cpp

diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_node.cpp
index bc4e88b3f95..b6881da496f 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_node.cpp
@@ -14,10 +14,6 @@ using namespace InferenceEngine;
 
 bool MKLDNNCTCGreedyDecoderNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
         const auto greedyDecOp = ngraph::as_type_ptr<const ngraph::op::v0::CTCGreedyDecoder>(op);
         if (!greedyDecOp) {
             errorMessage = "Node is not an instance of the CTCGreedyDecoder operation from operation set v0.";
@@ -42,8 +38,10 @@ MKLDNNCTCGreedyDecoderNode::MKLDNNCTCGreedyDecoderNode(const std::shared_ptr<ngr
     if (getOriginalOutputsNumber() != 1)
         IE_THROW() << errorPrefix << "has invalid number of outputs edges: " << getOriginalOutputsNumber();
 
-    if (op->get_input_shape(DATA_INDEX)[0] != op->get_input_shape(SEQUENCE_LENGTH_INDEX)[0] &&
-        op->get_input_shape(DATA_INDEX)[1] != op->get_input_shape(SEQUENCE_LENGTH_INDEX)[1])
+    const auto& dataDims = getInputShapeAtPort(DATA_INDEX).getDims();
+    const auto& seqDims = getInputShapeAtPort(SEQUENCE_LENGTH_INDEX).getDims();
+
+    if (!dimsEqualWeak(dataDims[0], seqDims[0]) || !dimsEqualWeak(dataDims[1], seqDims[1]))
         IE_THROW() << errorPrefix << "has invalid input shapes.";
 
     auto greedyDecOp = ngraph::as_type_ptr<const ngraph::op::v0::CTCGreedyDecoder>(op);
@@ -167,4 +165,18 @@ bool MKLDNNCTCGreedyDecoderNode::created() const {
     return getType() == CTCGreedyDecoder;
 }
 
+void MKLDNNCTCGreedyDecoderNode::executeDynamicImpl(dnnl::stream strm) {
+    MKLDNNCTCGreedyDecoderNode::execute(strm);
+}
+
+void MKLDNNCTCGreedyDecoderNode::createPrimitive() {
+    if (inputShapesDefined()) {
+        updateLastInputDims();
+    }
+}
+
+bool MKLDNNCTCGreedyDecoderNode::needPrepareParams() const {
+    return false;
+}
+
 REG_MKLDNN_PRIM_FOR(MKLDNNCTCGreedyDecoderNode, CTCGreedyDecoder)
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_node.h
index aa42dda6a97..a936d47d75d 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_node.h
@@ -15,12 +15,13 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
+    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
+    void executeDynamicImpl(dnnl::stream strm) override;
+    bool needPrepareParams() const override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
-
 private:
     const size_t DATA_INDEX = 0lu;
     const size_t SEQUENCE_LENGTH_INDEX = 1lu;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.cpp
index ab6a5c79e0a..81e94cee450 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.cpp
@@ -14,10 +14,6 @@ using namespace InferenceEngine;
 
 bool MKLDNNCTCGreedyDecoderSeqLenNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
         const auto greedyDecOp = ngraph::as_type_ptr<const ngraph::op::v6::CTCGreedyDecoderSeqLen>(op);
         if (!greedyDecOp) {
             errorMessage = "Node is not an instance of the CTCGreedyDecoderSeqLen operation from operation set v6.";
@@ -42,7 +38,9 @@ MKLDNNCTCGreedyDecoderSeqLenNode::MKLDNNCTCGreedyDecoderSeqLenNode(const std::sh
     if (getOriginalOutputsNumber() != 2)
         IE_THROW() << errorPrefix << "has invalid number of outputs edges: " << getOriginalOutputsNumber();
 
-    if (op->get_input_shape(DATA_INDEX)[0] != op->get_input_shape(SEQUENCE_LENGTH_INDEX)[0])
+    const auto& dataDims = getInputShapeAtPort(DATA_INDEX).getDims();
+    const auto& seqDims = getInputShapeAtPort(SEQUENCE_LENGTH_INDEX).getDims();
+    if (!dimsEqualWeak(dataDims[0], seqDims[0]))
         IE_THROW() << errorPrefix << "has invalid input shapes.";
 
     auto greedyDecOp = ngraph::as_type_ptr<const ngraph::op::v6::CTCGreedyDecoderSeqLen>(op);
@@ -170,4 +168,18 @@ bool MKLDNNCTCGreedyDecoderSeqLenNode::created() const {
     return getType() == CTCGreedyDecoderSeqLen;
 }
 
+void MKLDNNCTCGreedyDecoderSeqLenNode::createPrimitive() {
+    if (inputShapesDefined()) {
+        updateLastInputDims();
+    }
+}
+
+void MKLDNNCTCGreedyDecoderSeqLenNode::executeDynamicImpl(dnnl::stream strm) {
+    MKLDNNCTCGreedyDecoderSeqLenNode::execute(strm);
+}
+
+bool MKLDNNCTCGreedyDecoderSeqLenNode::needPrepareParams() const {
+    return false;
+}
+
 REG_MKLDNN_PRIM_FOR(MKLDNNCTCGreedyDecoderSeqLenNode, CTCGreedyDecoderSeqLen)
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.h
index 95ef66222c1..023a9e8f409 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_greedy_decoder_seq_len_node.h
@@ -15,9 +15,11 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
+    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
+    void executeDynamicImpl(dnnl::stream strm) override;
+    bool needPrepareParams() const override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/ctc_greedy_decoder.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/ctc_greedy_decoder.cpp
new file mode 100644
index 00000000000..9d9689350d4
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/ctc_greedy_decoder.cpp
@@ -0,0 +1,174 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <functional_test_utils/ov_tensor_utils.hpp>
+#include <ngraph_functions/builders.hpp>
+#include <shared_test_classes/single_layer/ctc_greedy_decoder.hpp>
+#include <string>
+#include <tuple>
+#include <vector>
+
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "test_utils/cpu_test_utils.hpp"
+
+using namespace InferenceEngine;
+using namespace CPUTestUtils;
+using namespace ov::test;
+
+namespace CPULayerTestsDefinitions {
+
+using CtcGreedyDecoderParams = std::tuple<size_t,   // Sequence length T
+                                          size_t,   // Batch size N
+                                          size_t>;  // Number of classes
+
+using InputShapeParams = std::pair<std::vector<ov::Dimension>,            // bounds for T, N, C
+                                   std::vector<CtcGreedyDecoderParams>>;  // target input dimensions
+
+using CTCGreedyDecoderLayerCPUTestParams = std::tuple<InputShapeParams,  // Input Shape
+                                                      ElementType,       // Input precision
+                                                      bool               // mergeRepeated
+                                                      >;
+
+class CTCGreedyDecoderLayerCPUTest : public testing::WithParamInterface<CTCGreedyDecoderLayerCPUTestParams>,
+                                     virtual public SubgraphBaseTest,
+                                     public CPUTestsBase {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<CTCGreedyDecoderLayerCPUTestParams>& obj) {
+        ElementType inType;
+        bool mergeRepeated;
+        InputShapeParams shapes;
+        std::tie(shapes, inType, mergeRepeated) = obj.param;
+        std::ostringstream results;
+        results << "IS=" << CommonTestUtils::partialShape2str({shapes.first}) << "_";
+        results << "TS=";
+        for (const auto& shape : shapes.second) {
+            size_t T;
+            size_t N;
+            size_t C;
+            std::tie(T, N, C) = shape;
+            results << "{" << T << "," << N << "," << C << "}"
+                    << "_";
+        }
+
+        results << "Prc=" << inType << "_";
+        results << "MergeRepeated=" << mergeRepeated;
+
+        return results.str();
+    }
+
+protected:
+    void SetUp() override {
+        ElementType inType;
+        bool mergeRepeated;
+        InputShapeParams shapes;
+        std::tie(shapes, inType, mergeRepeated) = GetParam();
+        selectedType = "ref_any_FP32";
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+        // construct input shapes
+        ASSERT_EQ(shapes.first.size(), 3);
+        const auto& in_dyn_T = shapes.first[0];
+        const auto& in_dyn_N = shapes.first[1];
+        const auto& in_dyc_C = shapes.first[2];
+        inputDynamicShapes = {ov::PartialShape{in_dyn_T, in_dyn_N, in_dyc_C}, ov::PartialShape{in_dyn_T, in_dyn_N}};
+
+        for (const auto& shape : shapes.second) {
+            size_t T;
+            size_t N;
+            size_t C;
+            std::tie(T, N, C) = shape;
+            targetStaticShapes.push_back({{T, N, C}, {T, N}});
+        }
+
+        auto params = ngraph::builder::makeDynamicParams(inType, inputDynamicShapes);
+        auto ctcGreedyDecoder = std::make_shared<ov::op::v0::CTCGreedyDecoder>(params[0], params[1], mergeRepeated);
+
+        ngraph::ResultVector results{std::make_shared<ngraph::opset1::Result>(ctcGreedyDecoder)};
+        function = std::make_shared<ngraph::Function>(results, params, "CTCGreedyDecoderCPU");
+    };
+
+    void generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) override {
+        inputs.clear();
+        const auto& funcInputs = function->inputs();
+        for (int i = 0; i < funcInputs.size(); ++i) {
+            const auto& funcInput = funcInputs[i];
+            ov::runtime::Tensor tensor;
+            if (i == 0) {
+                if (funcInput.get_element_type().is_real()) {
+                    tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(),
+                                                                     targetInputStaticShapes[i],
+                                                                     10,
+                                                                     0,
+                                                                     1000);
+                } else {
+                    tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(),
+                                                                     targetInputStaticShapes[i]);
+                }
+            } else {
+                auto T = targetInputStaticShapes[i][0];
+                auto B = targetInputStaticShapes[i][1];
+                std::mt19937 gen(1);
+                std::uniform_int_distribution<unsigned long> dist(1, T);
+
+                std::vector<float> sequenceMaskData(B * T, 0);
+                for (int b = 0; b < B; b++) {
+                    int len = dist(gen);
+                    for (int t = 0; t < len; t++) {
+                        sequenceMaskData[t * B + b] = 1;
+                    }
+                }
+                tensor = ov::runtime::Tensor{funcInput.get_element_type(), targetInputStaticShapes[i]};
+                float* begin = tensor.data<float>();
+                std::copy(sequenceMaskData.begin(), sequenceMaskData.end(), begin);
+            }
+
+            inputs.insert({funcInput.get_node_shared_ptr(), tensor});
+        }
+    }
+};
+
+TEST_P(CTCGreedyDecoderLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED();
+    run();
+    CheckPluginRelatedResults(executableNetwork, "CTCGreedyDecoder");
+}
+
+namespace {
+// Common params
+// ElementType::f16 is not support by CPU Plugin yet
+const std::vector<ElementType> netPrecisions = {ElementType::f32};
+const std::vector<bool> mergeRepeated{true, false};
+const std::vector<InputShapeParams> inputShapesCTCDecoder = {
+    {{ov::Dimension{1, 50}, ov::Dimension{1, 3}, ov::Dimension{2, 150}},
+     {CtcGreedyDecoderParams{1, 1, 16},
+      CtcGreedyDecoderParams{50, 3, 3},
+      CtcGreedyDecoderParams{50, 3, 7},
+      CtcGreedyDecoderParams{50, 3, 8},
+      CtcGreedyDecoderParams{50, 3, 16},
+      CtcGreedyDecoderParams{50, 3, 128},
+      CtcGreedyDecoderParams{50, 3, 49},
+      CtcGreedyDecoderParams{50, 3, 55}}},
+    {{ov::Dimension{-1}, ov::Dimension{-1}, ov::Dimension{-1}},
+     {CtcGreedyDecoderParams{50, 3, 3},
+      CtcGreedyDecoderParams{50, 3, 7},
+      CtcGreedyDecoderParams{50, 3, 8},
+      CtcGreedyDecoderParams{50, 3, 16},
+      CtcGreedyDecoderParams{50, 3, 128},
+      CtcGreedyDecoderParams{50, 3, 49},
+      CtcGreedyDecoderParams{50, 3, 55},
+      CtcGreedyDecoderParams{1, 1, 16}}},
+};
+
+const auto basicCases = ::testing::Combine(::testing::ValuesIn(inputShapesCTCDecoder),
+                                           ::testing::ValuesIn(netPrecisions),
+                                           ::testing::ValuesIn(mergeRepeated));
+
+INSTANTIATE_TEST_SUITE_P(smoke_CtcGreedyDecoderCPU,
+                         CTCGreedyDecoderLayerCPUTest,
+                         basicCases,
+                         CTCGreedyDecoderLayerCPUTest::getTestCaseName);
+}  // namespace
+
+}  // namespace CPULayerTestsDefinitions
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/ctc_greedy_decoder_seq_len.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/ctc_greedy_decoder_seq_len.cpp
new file mode 100644
index 00000000000..7094be74e89
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/ctc_greedy_decoder_seq_len.cpp
@@ -0,0 +1,216 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <functional_test_utils/ov_tensor_utils.hpp>
+#include <ngraph_functions/builders.hpp>
+#include <string>
+#include <tuple>
+#include <vector>
+
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "test_utils/cpu_test_utils.hpp"
+
+using namespace InferenceEngine;
+using namespace CPUTestUtils;
+using namespace ov::test;
+
+namespace CPULayerTestsDefinitions {
+
+using CtcGreedyDecoderSeqLenParams = std::tuple<size_t,   // Batch size N
+                                                size_t,   // Sequence length T
+                                                size_t>;  // Number of classes
+
+using InputShapeParams = std::pair<std::vector<ov::Dimension>,                  // bounds N, T, C, blank
+                                   std::vector<CtcGreedyDecoderSeqLenParams>>;  // target input dimensions
+
+using InputElementParams = std::vector<ElementType>;
+
+using CTCGreedyDecoderSeqLenLayerCPUTestParams = std::tuple<InputShapeParams,    // Input Shape
+                                                            InputElementParams,  // Input precision
+                                                            ElementType,         // Index Type
+                                                            bool                 // mergeRepeated
+                                                            >;
+ngraph::ParameterVector makeDynamicParams(const std::vector<ElementType>& types,
+                                          const std::vector<ov::PartialShape>& shapes) {
+    ngraph::ParameterVector outs;
+    NGRAPH_CHECK(types.size() == shapes.size());
+    for (size_t i = 0; i < types.size(); i++) {
+        auto paramNode = std::make_shared<ov::op::v0::Parameter>(types[i], shapes[i]);
+        outs.push_back(paramNode);
+    }
+    return outs;
+}
+
+class CTCGreedyDecoderSeqLenLayerCPUTest : public testing::WithParamInterface<CTCGreedyDecoderSeqLenLayerCPUTestParams>,
+                                           virtual public SubgraphBaseTest,
+                                           public CPUTestsBase {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<CTCGreedyDecoderSeqLenLayerCPUTestParams>& obj) {
+        InputElementParams inType;
+        bool mergeRepeated;
+        InputShapeParams shapes;
+        ElementType indexType;
+        std::tie(shapes, inType, indexType, mergeRepeated) = obj.param;
+        std::ostringstream results;
+        results << "IS=" << CommonTestUtils::partialShape2str({shapes.first}) << "_";
+        results << "TS=";
+        for (const auto& shape : shapes.second) {
+            size_t N;
+            size_t T;
+            size_t C;
+            std::tie(N, T, C) = shape;
+            results << "{" << N << "," << T << "," << C << "}"
+                    << "_";
+        }
+        for (const auto& type : inType) {
+            results << "Prc=" << type << "_";
+        }
+
+        results << "IndexType=" << indexType << "_";
+
+        results << "MergeRepeated=" << mergeRepeated;
+
+        return results.str();
+    }
+
+protected:
+    void SetUp() override {
+        InputElementParams inType;
+        bool mergeRepeated;
+        InputShapeParams shapes;
+        ElementType indexType;
+        std::tie(shapes, inType, indexType, mergeRepeated) = GetParam();
+        selectedType = "ref_any_FP32";
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+        ASSERT_EQ(shapes.first.size(), 4);
+        const auto& in_dyn_N = shapes.first[0];
+        const auto& in_dyn_T = shapes.first[1];
+        const auto& in_dyc_C = shapes.first[2];
+        const auto& in_dyc_blank = shapes.first[3];
+        const size_t blank_rank = in_dyc_blank.get_length();
+        ASSERT_TRUE(blank_rank == 0 || blank_rank == 1);
+        inputDynamicShapes = {ov::PartialShape{in_dyn_N, in_dyn_T, in_dyc_C},
+                              ov::PartialShape{in_dyn_N},
+                              blank_rank == 0 ? ov::PartialShape{} : ov::PartialShape{1}};
+
+        for (auto& shape : shapes.second) {
+            size_t N;
+            size_t T;
+            size_t C;
+            std::tie(N, T, C) = shape;
+            if (blank_rank == 0)
+                targetStaticShapes.push_back({{N, T, C}, {N}, {}});
+            else
+                targetStaticShapes.push_back({{N, T, C}, {N}, {1}});
+        }
+
+        auto params = makeDynamicParams(inType, inputDynamicShapes);
+        auto ctcGreedyDecoderSeqLen = std::make_shared<ov::op::v6::CTCGreedyDecoderSeqLen>(params[0],
+                                                                                           params[1],
+                                                                                           params[2],
+                                                                                           mergeRepeated,
+                                                                                           indexType,
+                                                                                           indexType);
+
+        ngraph::ResultVector results{std::make_shared<ngraph::opset1::Result>(ctcGreedyDecoderSeqLen)};
+        function = std::make_shared<ngraph::Function>(results, params, "CTCGreedyDecoderSeqLenCPU");
+    };
+
+    void generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) override {
+        inputs.clear();
+        const auto& funcInputs = function->inputs();
+        const auto& dataShape = targetInputStaticShapes[0];
+        for (int i = 0; i < funcInputs.size(); ++i) {
+            const auto& funcInput = funcInputs[i];
+            ov::runtime::Tensor tensor;
+            if (i == 0) {
+                if (funcInput.get_element_type().is_real()) {
+                    tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(),
+                                                                     targetInputStaticShapes[i],
+                                                                     10,
+                                                                     0,
+                                                                     1000);
+                } else {
+                    tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(),
+                                                                     targetInputStaticShapes[i]);
+                }
+            } else if (i == 1) {
+                const auto seqLen = dataShape[1];
+                const auto B = dataShape[0];
+                std::mt19937 gen(42);
+                std::uniform_int_distribution<unsigned long> dist(1, seqLen);
+
+                std::vector<int32_t> sequenceLenData(B, 0);
+                for (int b = 0; b < B; b++) {
+                    const int len = dist(gen);
+                    sequenceLenData[b] = len;
+                }
+                tensor = ov::runtime::Tensor{funcInput.get_element_type(), targetInputStaticShapes[i]};
+                if (funcInput.get_element_type() == ElementType::i32) {
+                    auto begin = tensor.data<int32_t>();
+                    std::copy(sequenceLenData.begin(), sequenceLenData.end(), begin);
+                } else if (funcInput.get_element_type() == ElementType::i64) {
+                    auto begin = tensor.data<int64_t>();
+                    std::copy(sequenceLenData.begin(), sequenceLenData.end(), begin);
+                }
+
+            } else if (i == 2) {
+                // blank should be valid class type
+                tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(),
+                                                                 targetInputStaticShapes[i],
+                                                                 dataShape[2],
+                                                                 0);
+            }
+            inputs.insert({funcInput.get_node_shared_ptr(), tensor});
+        }
+    }
+};
+
+TEST_P(CTCGreedyDecoderSeqLenLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED();
+    run();
+    CheckPluginRelatedResults(executableNetwork, "CTCGreedyDecoderSeqLen");
+}
+
+namespace {
+// Common params
+// ElementType::f16 is not support by CPU Plugin yet
+const std::vector<ElementType> inputType = {ElementType::f32, ElementType::i32, ElementType::i32};
+const std::vector<bool> mergeRepeated{true, false};
+const std::vector<ElementType> indexType = {ElementType::i64, ElementType::i32};
+const std::vector<InputShapeParams> inputShapesCTCDecoder = {
+    {{ov::Dimension{-1}, ov::Dimension{-1}, ov::Dimension{-1}, ov::Dimension{0}},
+     {CtcGreedyDecoderSeqLenParams{1, 1, 1},
+      CtcGreedyDecoderSeqLenParams{1, 6, 10},
+      CtcGreedyDecoderSeqLenParams{3, 3, 16},
+      CtcGreedyDecoderSeqLenParams{5, 3, 55}}},
+    {{ov::Dimension{-1}, ov::Dimension{-1}, ov::Dimension{-1}, ov::Dimension{1}},
+     {CtcGreedyDecoderSeqLenParams{1, 1, 1},
+      CtcGreedyDecoderSeqLenParams{1, 6, 10},
+      CtcGreedyDecoderSeqLenParams{3, 3, 16},
+      CtcGreedyDecoderSeqLenParams{5, 3, 55}}},
+    {{ov::Dimension{1, 5}, ov::Dimension{1, 6}, ov::Dimension{1, 60}, ov::Dimension{0}},
+     {CtcGreedyDecoderSeqLenParams{1, 6, 10},
+      CtcGreedyDecoderSeqLenParams{3, 3, 16},
+      CtcGreedyDecoderSeqLenParams{5, 3, 55}}},
+    {{ov::Dimension{1, 5}, ov::Dimension{1, 6}, ov::Dimension{1, 60}, ov::Dimension{1}},
+     {CtcGreedyDecoderSeqLenParams{1, 6, 10},
+      CtcGreedyDecoderSeqLenParams{3, 3, 16},
+      CtcGreedyDecoderSeqLenParams{5, 3, 55}}},
+};
+
+const auto basicCases = ::testing::Combine(::testing::ValuesIn(inputShapesCTCDecoder),
+                                           ::testing::Values(inputType),
+                                           ::testing::ValuesIn(indexType),
+                                           ::testing::ValuesIn(mergeRepeated));
+
+INSTANTIATE_TEST_SUITE_P(smoke_CtcGreedyDecoderSeqLenCPU,
+                         CTCGreedyDecoderSeqLenLayerCPUTest,
+                         basicCases,
+                         CTCGreedyDecoderSeqLenLayerCPUTest::getTestCaseName);
+}  // namespace
+
+}  // namespace CPULayerTestsDefinitions

From 387c35cc6e7f1076296218f4d34f6c7c866cfd56 Mon Sep 17 00:00:00 2001
From: Piotr Szmelczynski <piotr.szmelczynski@intel.com>
Date: Thu, 25 Nov 2021 21:40:19 +0100
Subject: [PATCH 20/72] Pszmel/bind preprocessing (#8791)

* Use reference_wrapper for preprocess bindings

* Update tests

* add bindings to I420_SINGLE_PLANE and I420_THREE_PLANES

* remove init from all classes except PrePostProcessor and add RGBX and BGRX to ColorFormat enum

* update test name

* add test for taking input and output by name

* move ref_wrapper to common.hpp

* add common.hpp include to PrePostProcessor

* update comment on ref_wrapper
---
 .../python/src/pyopenvino/core/common.hpp     |  13 +
 .../graph/preprocess/pre_post_process.cpp     | 340 ++++++------------
 .../tests/test_ngraph/test_preprocess.py      | 246 ++++++-------
 3 files changed, 235 insertions(+), 364 deletions(-)

diff --git a/runtime/bindings/python/src/pyopenvino/core/common.hpp b/runtime/bindings/python/src/pyopenvino/core/common.hpp
index 7e2e0f0dfc9..68d28ec6396 100644
--- a/runtime/bindings/python/src/pyopenvino/core/common.hpp
+++ b/runtime/bindings/python/src/pyopenvino/core/common.hpp
@@ -43,4 +43,17 @@ namespace Common
     PyObject* parse_parameter(const InferenceEngine::Parameter& param);
 
     uint32_t get_optimal_number_of_requests(const ov::runtime::ExecutableNetwork& actual);
+
+    // Use only with classes that are not creatable by users on Python's side, because
+    // Objects created in Python that are wrapped with such wrapper will cause memory leaks.
+    template <typename T>
+    class ref_wrapper {
+        std::reference_wrapper<T> impl;
+
+    public:
+        explicit ref_wrapper(T* p) : impl(*p) {}
+        T* get() const {
+            return &impl.get();
+    }
+};
 }; // namespace Common
diff --git a/runtime/bindings/python/src/pyopenvino/graph/preprocess/pre_post_process.cpp b/runtime/bindings/python/src/pyopenvino/graph/preprocess/pre_post_process.cpp
index 2bc775c1dfc..1393ff7494c 100644
--- a/runtime/bindings/python/src/pyopenvino/graph/preprocess/pre_post_process.cpp
+++ b/runtime/bindings/python/src/pyopenvino/graph/preprocess/pre_post_process.cpp
@@ -10,22 +10,23 @@
 #include "openvino/core/function.hpp"
 #include "openvino/core/node.hpp"
 #include "openvino/core/preprocess/pre_post_process.hpp"
+#include "pyopenvino/core/common.hpp"
 
 namespace py = pybind11;
 
+// Custom holder wrapping returned references to preprocessing objects
+PYBIND11_DECLARE_HOLDER_TYPE(T, Common::ref_wrapper<T>)
+
 static void regclass_graph_PreProcessSteps(py::module m) {
-    py::class_<ov::preprocess::PreProcessSteps, std::shared_ptr<ov::preprocess::PreProcessSteps>> steps(
+    py::class_<ov::preprocess::PreProcessSteps, Common::ref_wrapper<ov::preprocess::PreProcessSteps>> steps(
         m,
         "PreProcessSteps");
     steps.doc() = "openvino.impl.preprocess.PreProcessSteps wraps ov::preprocess::PreProcessSteps";
 
-    steps.def(py::init<>());
-
     steps.def(
         "mean",
-        [](const std::shared_ptr<ov::preprocess::PreProcessSteps>& me, float value) {
-            me->mean(value);
-            return me;
+        [](ov::preprocess::PreProcessSteps& me, float value) {
+            return &me.mean(value);
         },
         py::arg("value"),
         R"(
@@ -42,9 +43,8 @@ static void regclass_graph_PreProcessSteps(py::module m) {
               )");
     steps.def(
         "mean",
-        [](const std::shared_ptr<ov::preprocess::PreProcessSteps>& me, const std::vector<float>& values) {
-            me->mean(values);
-            return me;
+        [](ov::preprocess::PreProcessSteps& me, const std::vector<float>& values) {
+            return &me.mean(values);
         },
         py::arg("values"),
         R"(
@@ -61,9 +61,8 @@ static void regclass_graph_PreProcessSteps(py::module m) {
               )");
     steps.def(
         "scale",
-        [](const std::shared_ptr<ov::preprocess::PreProcessSteps>& me, float value) {
-            me->scale(value);
-            return me;
+        [](ov::preprocess::PreProcessSteps& me, float value) {
+            return &me.scale(value);
         },
         py::arg("value"),
         R"(
@@ -80,9 +79,8 @@ static void regclass_graph_PreProcessSteps(py::module m) {
               )");
     steps.def(
         "scale",
-        [](const std::shared_ptr<ov::preprocess::PreProcessSteps>& me, const std::vector<float>& values) {
-            me->scale(values);
-            return me;
+        [](ov::preprocess::PreProcessSteps& me, const std::vector<float>& values) {
+            return &me.scale(values);
         },
         py::arg("values"),
         R"(
@@ -99,9 +97,8 @@ static void regclass_graph_PreProcessSteps(py::module m) {
               )");
     steps.def(
         "convert_element_type",
-        [](const std::shared_ptr<ov::preprocess::PreProcessSteps>& me, ov::element::Type type) {
-            me->convert_element_type(type);
-            return me;
+        [](ov::preprocess::PreProcessSteps& me, ov::element::Type type) {
+            return &me.convert_element_type(type);
         },
         py::arg("type"),
         R"(
@@ -118,9 +115,8 @@ static void regclass_graph_PreProcessSteps(py::module m) {
               )");
     steps.def(
         "custom",
-        [](const std::shared_ptr<ov::preprocess::PreProcessSteps>& me, py::function op) {
-            me->custom(op.cast<const ov::preprocess::PreProcessSteps::CustomPreprocessOp>());
-            return me;
+        [](ov::preprocess::PreProcessSteps& me, py::function op) {
+            return &me.custom(op.cast<const ov::preprocess::PreProcessSteps::CustomPreprocessOp>());
         },
         py::arg("operation"),
         R"(
@@ -135,63 +131,54 @@ static void regclass_graph_PreProcessSteps(py::module m) {
               )");
     steps.def(
         "convert_color",
-        [](const std::shared_ptr<ov::preprocess::PreProcessSteps>& me, const ov::preprocess::ColorFormat& dst_format) {
-            me->convert_color(dst_format);
-            return me;
+        [](ov::preprocess::PreProcessSteps& me, const ov::preprocess::ColorFormat& dst_format) {
+            return &me.convert_color(dst_format);
         },
         py::arg("dst_format"));
     steps.def(
         "resize",
-        [](const std::shared_ptr<ov::preprocess::PreProcessSteps>& me,
+        [](ov::preprocess::PreProcessSteps& me,
            ov::preprocess::ResizeAlgorithm alg,
            size_t dst_height,
            size_t dst_width) {
-            me->resize(alg, dst_height, dst_width);
-            return me;
+            return &me.resize(alg, dst_height, dst_width);
         },
         py::arg("alg"),
         py::arg("dst_height"),
         py::arg("dst_width"));
     steps.def(
         "resize",
-        [](const std::shared_ptr<ov::preprocess::PreProcessSteps>& me, ov::preprocess::ResizeAlgorithm alg) {
-            me->resize(alg);
-            return me;
+        [](ov::preprocess::PreProcessSteps& me, ov::preprocess::ResizeAlgorithm alg) {
+            return &me.resize(alg);
         },
         py::arg("alg"));
     steps.def(
         "convert_layout",
-        [](const std::shared_ptr<ov::preprocess::PreProcessSteps>& me, const ov::Layout& layout = {}) {
-            me->convert_layout(layout);
-            return me;
+        [](ov::preprocess::PreProcessSteps& me, const ov::Layout& layout = {}) {
+            return &me.convert_layout(layout);
         },
         py::arg("dst_layout"));
     steps.def(
         "convert_layout",
-        [](const std::shared_ptr<ov::preprocess::PreProcessSteps>& me, const std::vector<uint64_t>& dims) {
-            me->convert_layout(dims);
-            return me;
+        [](ov::preprocess::PreProcessSteps& me, const std::vector<uint64_t>& dims) {
+            return &me.convert_layout(dims);
         },
         py::arg("dims"));
-    steps.def("reverse_channels", [](const std::shared_ptr<ov::preprocess::PreProcessSteps>& me) {
-        me->reverse_channels();
-        return me;
+    steps.def("reverse_channels", [](ov::preprocess::PreProcessSteps& me) {
+        return &me.reverse_channels();
     });
 }
 
 static void regclass_graph_PostProcessSteps(py::module m) {
-    py::class_<ov::preprocess::PostProcessSteps, std::shared_ptr<ov::preprocess::PostProcessSteps>> steps(
+    py::class_<ov::preprocess::PostProcessSteps, Common::ref_wrapper<ov::preprocess::PostProcessSteps>> steps(
         m,
         "PostProcessSteps");
     steps.doc() = "openvino.impl.preprocess.PostprocessSteps wraps ov::preprocess::PostProcessSteps";
 
-    steps.def(py::init<>());
-
     steps.def(
         "convert_element_type",
-        [](const std::shared_ptr<ov::preprocess::PostProcessSteps>& me, ov::element::Type type) {
-            me->convert_element_type(type);
-            return me;
+        [](ov::preprocess::PostProcessSteps& me, ov::element::Type type) {
+            return &me.convert_element_type(type);
         },
         py::arg("type"),
         R"(
@@ -208,23 +195,20 @@ static void regclass_graph_PostProcessSteps(py::module m) {
               )");
     steps.def(
         "convert_layout",
-        [](const std::shared_ptr<ov::preprocess::PostProcessSteps>& me, const ov::Layout& layout = {}) {
-            me->convert_layout(layout);
-            return me;
+        [](ov::preprocess::PostProcessSteps& me, const ov::Layout& layout = {}) {
+            return &me.convert_layout(layout);
         },
         py::arg("dst_layout"));
     steps.def(
         "convert_layout",
-        [](const std::shared_ptr<ov::preprocess::PostProcessSteps>& me, const std::vector<uint64_t>& dims) {
-            me->convert_layout(dims);
-            return me;
+        [](ov::preprocess::PostProcessSteps& me, const std::vector<uint64_t>& dims) {
+            return &me.convert_layout(dims);
         },
         py::arg("dims"));
     steps.def(
         "custom",
-        [](const std::shared_ptr<ov::preprocess::PostProcessSteps>& me, py::function op) {
-            me->custom(op.cast<const ov::preprocess::PostProcessSteps::CustomPostprocessOp>());
-            return me;
+        [](ov::preprocess::PostProcessSteps& me, py::function op) {
+            return &me.custom(op.cast<const ov::preprocess::PostProcessSteps::CustomPostprocessOp>());
         },
         py::arg("operation"),
         R"(
@@ -240,18 +224,15 @@ static void regclass_graph_PostProcessSteps(py::module m) {
 }
 
 static void regclass_graph_InputTensorInfo(py::module m) {
-    py::class_<ov::preprocess::InputTensorInfo, std::shared_ptr<ov::preprocess::InputTensorInfo>> info(
+    py::class_<ov::preprocess::InputTensorInfo, Common::ref_wrapper<ov::preprocess::InputTensorInfo>> info(
         m,
         "InputTensorInfo");
     info.doc() = "openvino.impl.preprocess.InputTensorInfo wraps ov::preprocess::InputTensorInfo";
 
-    info.def(py::init<>());
-
     info.def(
         "set_element_type",
-        [](const std::shared_ptr<ov::preprocess::InputTensorInfo>& me, const ov::element::Type& type) {
-            me->set_element_type(type);
-            return me;
+        [](ov::preprocess::InputTensorInfo& me, const ov::element::Type& type) {
+            return &me.set_element_type(type);
         },
         py::arg("type"),
         R"(
@@ -266,41 +247,34 @@ static void regclass_graph_InputTensorInfo(py::module m) {
                 tensor : InputTensorInfo
                     Reference to itself to allow chaining of calls in client's code in a builder-like manner.
               )");
-    info.def("set_layout", [](const std::shared_ptr<ov::preprocess::InputTensorInfo>& me, const ov::Layout& layout) {
-        me->set_layout(layout);
-        return me;
+    info.def("set_layout", [](ov::preprocess::InputTensorInfo& me, const ov::Layout& layout) {
+        return &me.set_layout(layout);
     });
-    info.def("set_spatial_dynamic_shape", [](const std::shared_ptr<ov::preprocess::InputTensorInfo>& me) {
-        me->set_spatial_dynamic_shape();
-        return me;
+    info.def("set_spatial_dynamic_shape", [](ov::preprocess::InputTensorInfo& me) {
+        return &me.set_spatial_dynamic_shape();
+    });
+    info.def("set_spatial_static_shape", [](ov::preprocess::InputTensorInfo& me, size_t height, size_t width) {
+        return &me.set_spatial_static_shape(height, width);
+        ;
     });
-    info.def("set_spatial_static_shape",
-             [](const std::shared_ptr<ov::preprocess::InputTensorInfo>& me, size_t height, size_t width) {
-                 me->set_spatial_static_shape(height, width);
-                 return me;
-             });
     info.def("set_color_format",
-             [](const std::shared_ptr<ov::preprocess::InputTensorInfo>& me,
+             [](ov::preprocess::InputTensorInfo& me,
                 const ov::preprocess::ColorFormat& format,
                 const std::vector<std::string>& sub_names = {}) {
-                 me->set_color_format(format, sub_names);
-                 return me;
+                 return &me.set_color_format(format, sub_names);
              });
 }
 
 static void regclass_graph_OutputTensorInfo(py::module m) {
-    py::class_<ov::preprocess::OutputTensorInfo, std::shared_ptr<ov::preprocess::OutputTensorInfo>> info(
+    py::class_<ov::preprocess::OutputTensorInfo, Common::ref_wrapper<ov::preprocess::OutputTensorInfo>> info(
         m,
         "OutputTensorInfo");
     info.doc() = "openvino.impl.preprocess.OutputTensorInfo wraps ov::preprocess::OutputTensorInfo";
 
-    info.def(py::init<>());
-
     info.def(
         "set_element_type",
-        [](const std::shared_ptr<ov::preprocess::OutputTensorInfo>& me, const ov::element::Type& type) {
-            me->set_element_type(type);
-            return me;
+        [](ov::preprocess::OutputTensorInfo& me, const ov::element::Type& type) {
+            return &me.set_element_type(type);
         },
         py::arg("type"),
         R"(
@@ -315,150 +289,60 @@ static void regclass_graph_OutputTensorInfo(py::module m) {
                 tensor : OutputTensorInfo
                     Reference to itself to allow chaining of calls in client's code in a builder-like manner.
               )");
-    info.def("set_layout", [](const std::shared_ptr<ov::preprocess::OutputTensorInfo>& me, const ov::Layout& layout) {
-        me->set_layout(layout);
-        return me;
+    info.def("set_layout", [](ov::preprocess::OutputTensorInfo& me, const ov::Layout& layout) {
+        return &me.set_layout(layout);
     });
 }
 
 static void regclass_graph_InputInfo(py::module m) {
-    py::class_<ov::preprocess::InputInfo, std::shared_ptr<ov::preprocess::InputInfo>> inp(m, "InputInfo");
+    py::class_<ov::preprocess::InputInfo, Common::ref_wrapper<ov::preprocess::InputInfo>> inp(m, "InputInfo");
     inp.doc() = "openvino.impl.preprocess.InputInfo wraps ov::preprocess::InputInfo";
 
-    inp.def(py::init<>(), R"(Default constructor, can be used only for networks with exactly one input)");
-    inp.def(py::init<size_t>(), R"(Constructor with parameter index as argument)");
-    inp.def(py::init<const std::string&>(), R"(Constructor with input tensor name as argument)");
-
-    inp.def(
-        "tensor",
-        [](const std::shared_ptr<ov::preprocess::InputInfo>& me,
-           const std::shared_ptr<ov::preprocess::InputTensorInfo>& inputTensorInfo) {
-            me->tensor(std::move(*inputTensorInfo));
-            return me;
-        },
-        py::arg("tensor"),
-        R"(
-                Adds builder for actual tensor information of client's input.
-                Parameters
-                ----------
-                tensor : InputTensorInfo
-                    Client's input tensor information. It's internal data will be moved to parent InputInfo object.
-                Returns
-                ----------
-                tensor : InputInfo
-                    Reference to itself to allow chaining of calls in client's code in a builder-like manner.
-              )");
-    inp.def(
-        "preprocess",
-        [](const std::shared_ptr<ov::preprocess::InputInfo>& me,
-           const std::shared_ptr<ov::preprocess::PreProcessSteps>& preProcessSteps) {
-            me->preprocess(std::move(*preProcessSteps));
-            return me;
-        },
-        py::arg("pre_process_steps"),
-        R"(
-                Adds builder for actual preprocessing steps for input parameter.
-                Steps can specify various actions, like 'mean', 'scale' and others.
-                Parameters
-                ----------
-                pre_process_steps : PreProcessSteps
-                    Preprocessing steps. It's internal data will be moved to parent InputInfo object.
-                Returns
-                ----------
-                preprocess : InputInfo
-                    Reference to itself to allow chaining of calls in client's code in a builder-like manner.
-              )");
-    inp.def(
-        "network",
-        [](const std::shared_ptr<ov::preprocess::InputInfo>& me,
-           const std::shared_ptr<ov::preprocess::InputNetworkInfo>& inputNetworkInfo) {
-            me->network(std::move(*inputNetworkInfo));
-            return me;
-        },
-        py::arg("input_network_info"));
+    inp.def("tensor", [](ov::preprocess::InputInfo& me) {
+        return &me.tensor();
+    });
+    inp.def("preprocess", [](ov::preprocess::InputInfo& me) {
+        return &me.preprocess();
+    });
+    inp.def("network", [](ov::preprocess::InputInfo& me) {
+        return &me.network();
+    });
 }
 
 static void regclass_graph_OutputInfo(py::module m) {
-    py::class_<ov::preprocess::OutputInfo, std::shared_ptr<ov::preprocess::OutputInfo>> out(m, "OutputInfo");
+    py::class_<ov::preprocess::OutputInfo, Common::ref_wrapper<ov::preprocess::OutputInfo>> out(m, "OutputInfo");
     out.doc() = "openvino.impl.preprocess.OutputInfo wraps ov::preprocess::OutputInfo";
 
-    out.def(py::init<>(), R"(Default constructor, can be used only for networks with exactly one output)");
-    out.def(py::init<size_t>(), R"(Constructor with parameter index as argument)");
-    out.def(py::init<const std::string&>(), R"(Constructor with tensor name as argument)");
-
-    out.def(
-        "tensor",
-        [](const std::shared_ptr<ov::preprocess::OutputInfo>& me,
-           const std::shared_ptr<ov::preprocess::OutputTensorInfo>& outputTensorInfo) {
-            me->tensor(std::move(*outputTensorInfo));
-            return me;
-        },
-        py::arg("tensor"),
-        R"(
-                Adds builder for actual tensor information of client's output.
-                Parameters
-                ----------
-                tensor : OutputTensorInfo
-                    Client's output tensor information. It's internal data will be moved to parent OutputInfo object.
-                Returns
-                ----------
-                tensor : OutputInfo
-                    Reference to itself to allow chaining of calls in client's code in a builder-like manner.
-              )");
-    out.def(
-        "postprocess",
-        [](const std::shared_ptr<ov::preprocess::OutputInfo>& me,
-           const std::shared_ptr<ov::preprocess::PostProcessSteps>& postProcessSteps) {
-            me->postprocess(std::move(*postProcessSteps));
-            return me;
-        },
-        py::arg("post_process_steps"),
-        R"(
-                Adds builder for actual postprocessing steps for output parameter.
-                Parameters
-                ----------
-                post_process_steps : PostProcessSteps
-                    Postprocessing steps. It's internal data will be moved to parent OutputInfo object.
-                Returns
-                ----------
-                preprocess : OutputInfo
-                    Reference to itself to allow chaining of calls in client's code in a builder-like manner.
-              )");
-    out.def(
-        "network",
-        [](const std::shared_ptr<ov::preprocess::OutputInfo>& me,
-           const std::shared_ptr<ov::preprocess::OutputNetworkInfo>& outputNetworkInfo) {
-            me->network(std::move(*outputNetworkInfo));
-            return me;
-        },
-        py::arg("output_network_info"));
+    out.def("tensor", [](ov::preprocess::OutputInfo& me) {
+        return &me.tensor();
+    });
+    out.def("postprocess", [](ov::preprocess::OutputInfo& me) {
+        return &me.postprocess();
+    });
+    out.def("network", [](ov::preprocess::OutputInfo& me) {
+        return &me.network();
+    });
 }
 
 static void regclass_graph_OutputNetworkInfo(py::module m) {
-    py::class_<ov::preprocess::OutputNetworkInfo, std::shared_ptr<ov::preprocess::OutputNetworkInfo>> info(
+    py::class_<ov::preprocess::OutputNetworkInfo, Common::ref_wrapper<ov::preprocess::OutputNetworkInfo>> info(
         m,
         "OutputNetworkInfo");
     info.doc() = "openvino.impl.preprocess.OutputNetworkInfo wraps ov::preprocess::OutputNetworkInfo";
 
-    info.def(py::init<>());
-
-    info.def("set_layout", [](const std::shared_ptr<ov::preprocess::OutputNetworkInfo>& me, const ov::Layout& layout) {
-        me->set_layout(layout);
-        return me;
+    info.def("set_layout", [](ov::preprocess::OutputNetworkInfo& me, const ov::Layout& layout) {
+        return &me.set_layout(layout);
     });
 }
 
 static void regclass_graph_InputNetworkInfo(py::module m) {
-    py::class_<ov::preprocess::InputNetworkInfo, std::shared_ptr<ov::preprocess::InputNetworkInfo>> info(
+    py::class_<ov::preprocess::InputNetworkInfo, Common::ref_wrapper<ov::preprocess::InputNetworkInfo>> info(
         m,
         "InputNetworkInfo");
     info.doc() = "openvino.impl.preprocess.InputNetworkInfo wraps ov::preprocess::InputNetworkInfo";
 
-    info.def(py::init<>());
-
-    info.def("set_layout", [](const std::shared_ptr<ov::preprocess::InputNetworkInfo>& me, const ov::Layout& layout) {
-        me->set_layout(layout);
-        return me;
+    info.def("set_layout", [](ov::preprocess::InputNetworkInfo& me, const ov::Layout& layout) {
+        return &me.set_layout(layout);
     });
 }
 
@@ -467,8 +351,12 @@ static void regenum_graph_ColorFormat(py::module m) {
         .value("UNDEFINED", ov::preprocess::ColorFormat::UNDEFINED)
         .value("NV12_SINGLE_PLANE", ov::preprocess::ColorFormat::NV12_SINGLE_PLANE)
         .value("NV12_TWO_PLANES", ov::preprocess::ColorFormat::NV12_TWO_PLANES)
+        .value("I420_SINGLE_PLANE", ov::preprocess::ColorFormat::I420_SINGLE_PLANE)
+        .value("I420_THREE_PLANES", ov::preprocess::ColorFormat::I420_THREE_PLANES)
         .value("RGB", ov::preprocess::ColorFormat::RGB)
         .value("BGR", ov::preprocess::ColorFormat::BGR)
+        .value("RGBX", ov::preprocess::ColorFormat::RGBX)
+        .value("BGRX", ov::preprocess::ColorFormat::BGRX)
         .export_values();
 }
 
@@ -498,43 +386,35 @@ void regclass_graph_PrePostProcessor(py::module m) {
 
     proc.def(py::init<const std::shared_ptr<ov::Function>&>());
 
+    proc.def("input", [](ov::preprocess::PrePostProcessor& me) {
+        return &me.input();
+    });
     proc.def(
         "input",
-        [](const std::shared_ptr<ov::preprocess::PrePostProcessor>& me,
-           const std::shared_ptr<ov::preprocess::InputInfo>& info) {
-            me->input(std::move(*info));
-            return me;
+        [](ov::preprocess::PrePostProcessor& me, const std::string& tensor_name) {
+            return &me.input(tensor_name);
         },
-        py::arg("input_info"),
-        R"(
-                Adds builder for preprocessing info for input parameter.
-                Parameters
-                ----------
-                input_info : InputInfo
-                    Preprocessing info for input parameter. It's internal data will be moved to PreProcessing object.
-                Returns
-                ----------
-                in : PrePostProcessor
-                    Reference to itself to allow chaining of calls in client's code.
-              )");
+        py::arg("tensor_name"));
+    proc.def(
+        "input",
+        [](ov::preprocess::PrePostProcessor& me, size_t input_index) {
+            return &me.input(input_index);
+        },
+        py::arg("input_index"));
+    proc.def("output", [](ov::preprocess::PrePostProcessor& me) {
+        return &me.output();
+    });
     proc.def(
         "output",
-        [](const std::shared_ptr<ov::preprocess::PrePostProcessor>& me,
-           const std::shared_ptr<ov::preprocess::OutputInfo>& info) {
-            me->output(std::move(*info));
-            return me;
+        [](ov::preprocess::PrePostProcessor& me, const std::string& tensor_name) {
+            return &me.output(tensor_name);
         },
-        py::arg("output_info"),
-        R"(
-                Adds builder for preprocessing info for output parameter.
-                Parameters
-                ----------
-                output_info : OutputInfo
-                    Preprocessing info for output parameter. It's internal data will be moved to PreProcessing object.
-                Returns
-                ----------
-                in : PrePostProcessor
-                    Reference to itself to allow chaining of calls in client's code.
-              )");
+        py::arg("tensor_name"));
+    proc.def(
+        "output",
+        [](ov::preprocess::PrePostProcessor& me, size_t output_index) {
+            return &me.output(output_index);
+        },
+        py::arg("output_index"));
     proc.def("build", &ov::preprocess::PrePostProcessor::build);
 }
diff --git a/runtime/bindings/python/tests/test_ngraph/test_preprocess.py b/runtime/bindings/python/tests/test_ngraph/test_preprocess.py
index a5afaa5c3ee..df811d21da5 100644
--- a/runtime/bindings/python/tests/test_ngraph/test_preprocess.py
+++ b/runtime/bindings/python/tests/test_ngraph/test_preprocess.py
@@ -10,9 +10,7 @@ from openvino.impl import Function, Output, Type
 from openvino.utils.decorators import custom_preprocess_function
 from openvino import Core
 from tests.runtime import get_runtime
-from openvino.preprocess import PrePostProcessor, InputInfo, PreProcessSteps, InputTensorInfo, \
-    OutputTensorInfo, OutputNetworkInfo, InputNetworkInfo, ColorFormat, OutputInfo, \
-    PostProcessSteps, ResizeAlgorithm
+from openvino.preprocess import PrePostProcessor, ColorFormat, ResizeAlgorithm
 
 
 def test_ngraph_preprocess_mean():
@@ -21,13 +19,11 @@ def test_ngraph_preprocess_mean():
     model = parameter_a
     function = Function(model, [parameter_a], "TestFunction")
 
-    function = PrePostProcessor(function)\
-        .input(InputInfo()
-               .preprocess(PreProcessSteps()
-                           .mean(1.)
-                           )
-               )\
-        .build()
+    p = PrePostProcessor(function)
+    inp = p.input()
+    prep = inp.preprocess()
+    prep.mean(1.0)
+    function = p.build()
 
     input_data = np.array([[1, 2], [3, 4]]).astype(np.float32)
     expected_output = np.array([[0, 1], [2, 3]]).astype(np.float32)
@@ -45,14 +41,10 @@ def test_ngraph_preprocess_mean_vector():
     function = Function(model, [parameter_a], "TestFunction")
     layout = ov.Layout("NCHW")
 
-    function = PrePostProcessor(function)\
-        .input(InputInfo()
-               .tensor(InputTensorInfo().set_layout(layout))
-               .preprocess(PreProcessSteps()
-                           .mean([1., 2.])
-                           )
-               )\
-        .build()
+    p = PrePostProcessor(function)
+    p.input().tensor().set_layout(layout)
+    p.input().preprocess().mean([1., 2.])
+    function = p.build()
 
     input_data = np.array([[1, 2], [3, 4]]).astype(np.float32)
     expected_output = np.array([[0, 0], [2, 2]]).astype(np.float32)
@@ -70,14 +62,11 @@ def test_ngraph_preprocess_scale_vector():
     function = Function(model, [parameter_a], "TestFunction")
     layout = ov.Layout("NCHW")
 
-    function = PrePostProcessor(function)\
-        .input(InputInfo()
-               .tensor(InputTensorInfo().set_layout(layout))
-               .preprocess(PreProcessSteps()
-                           .scale([0.5, 2.])
-                           )
-               )\
-        .build()
+    p = PrePostProcessor(function)
+    inp = p.input()
+    inp.tensor().set_layout(layout)
+    inp.preprocess().scale([0.5, 2.0])
+    function = p.build()
 
     input_data = np.array([[1, 2], [3, 4]]).astype(np.float32)
     expected_output = np.array([[2, 1], [6, 2]]).astype(np.float32)
@@ -98,24 +87,13 @@ def test_ngraph_preprocess_mean_scale_convert():
     def custom_preprocess(output: Output):
         return ops.abs(output)
 
-    function = PrePostProcessor(function) \
-        .input(InputInfo(1)
-               .tensor(InputTensorInfo()
-                       .set_element_type(Type.i32))
-               .preprocess(PreProcessSteps()
-                           .convert_element_type(Type.f32)
-                           .mean(1.)
-                           .scale(2.)
-                           )
-               ) \
-        .input(InputInfo(0)
-               .preprocess(PreProcessSteps()
-                           .convert_element_type(Type.f32)
-                           .mean(1.)
-                           .custom(custom_preprocess)
-                           )
-               ) \
-        .build()
+    p = PrePostProcessor(function)
+    inp2 = p.input(1)
+    inp2.tensor().set_element_type(Type.i32)
+    inp2.preprocess().convert_element_type(Type.f32).mean(1.).scale(2.)
+    inp1 = p.input(0)
+    inp1.preprocess().convert_element_type(Type.f32).mean(1.).custom(custom_preprocess)
+    function = p.build()
 
     input_data1 = np.array([[0, 1], [2, -2]]).astype(np.int32)
     input_data2 = np.array([[1, 3], [5, 7]]).astype(np.int32)
@@ -129,6 +107,40 @@ def test_ngraph_preprocess_mean_scale_convert():
     assert np.equal(output2, expected_output2).all()
 
 
+def test_ngraph_preprocess_input_output_by_name():
+    shape = [2, 2]
+    param1 = ops.parameter(shape, dtype=np.int32, name="A")
+    param2 = ops.parameter(shape, dtype=np.int32, name="B")
+    function = Function([param1, param2], [param1, param2], "TestFunction")
+
+    @custom_preprocess_function
+    def custom_preprocess(output: Output):
+        return ops.abs(output)
+
+    p = PrePostProcessor(function)
+    inp2 = p.input("B")
+    inp2.tensor().set_element_type(Type.i32)
+    inp2.preprocess().convert_element_type(Type.f32).mean(1.).scale(2.)
+    inp1 = p.input("A")
+    inp1.preprocess().convert_element_type(Type.f32).mean(1.)
+    out1 = p.output("A")
+    out1.postprocess().custom(custom_preprocess)
+    out2 = p.output("B")
+    out2.postprocess().custom(custom_preprocess)
+    function = p.build()
+
+    input_data1 = np.array([[0, 1], [2, -2]]).astype(np.int32)
+    input_data2 = np.array([[-1, 3], [5, 7]]).astype(np.int32)
+    expected_output1 = np.array([[1, 0], [1, 3]]).astype(np.float32)
+    expected_output2 = np.array([[1, 1], [2, 3]]).astype(np.float32)
+
+    runtime = get_runtime()
+    computation = runtime.computation(function)
+    [output1, output2] = computation(input_data1, input_data2)
+    assert np.equal(output1, expected_output1).all()
+    assert np.equal(output2, expected_output2).all()
+
+
 def test_ngraph_preprocess_output_postprocess():
     shape = [2, 2]
     parameter_a = ops.parameter(shape, dtype=np.int32, name="A")
@@ -142,20 +154,15 @@ def test_ngraph_preprocess_output_postprocess():
     def custom_postprocess(output: Output):
         return ops.abs(output)
 
-    function = PrePostProcessor(function)\
-        .input(InputInfo()
-               .tensor(InputTensorInfo().set_layout(layout1))
-               .preprocess(PreProcessSteps()
-                           .convert_element_type(Type.f32)
-                           .mean([1., 2.])
-                           )
-               ) \
-        .output(OutputInfo().postprocess(PostProcessSteps()
-                                         .convert_element_type(Type.f32)
-                                         .convert_layout(layout2)
-                                         .convert_layout(layout3)
-                                         .custom(custom_postprocess))) \
-        .build()
+    p = PrePostProcessor(function)
+    inp = p.input()
+    inp.tensor().set_layout(layout1)
+    inp.preprocess().convert_element_type(Type.f32).mean([1., 2.])
+    out = p.output()
+    out.postprocess().convert_element_type(Type.f32) \
+                     .convert_layout(layout2) \
+                     .convert_layout(layout3).custom(custom_postprocess)
+    function = p.build()
 
     input_data = np.array([[-1, -2], [-3, -4]]).astype(np.int32)
     expected_output = np.array([[2, 4], [4, 6]]).astype(np.float32)
@@ -174,24 +181,16 @@ def test_ngraph_preprocess_spatial_static_shape():
     layout = ov.Layout("CHW")
 
     color_format = ColorFormat.RGB
-    function = PrePostProcessor(function)\
-        .input(InputInfo()
-               .tensor(InputTensorInfo()
-                       .set_layout(layout)
-                       .set_spatial_static_shape(2, 2)
-                       .set_color_format(color_format, []))
-               .preprocess(PreProcessSteps()
-                           .convert_element_type(Type.f32)
-                           .mean([1., 2])
-                           )
-               .network(InputNetworkInfo().set_layout(layout))
-               ) \
-        .output(OutputInfo()
-                .tensor(OutputTensorInfo()
-                        .set_layout(layout)
-                        .set_element_type(Type.f32))
-                .network(OutputNetworkInfo().set_layout(layout))) \
-        .build()
+
+    p = PrePostProcessor(function)
+    inp = p.input()
+    inp.tensor().set_layout(layout).set_spatial_static_shape(2, 2).set_color_format(color_format, [])
+    inp.preprocess().convert_element_type(Type.f32).mean([1., 2.])
+    inp.network().set_layout(layout)
+    out = p.output()
+    out.tensor().set_layout(layout).set_element_type(Type.f32)
+    out.network().set_layout(layout)
+    function = p.build()
 
     input_data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]).astype(np.int32)
     expected_output = np.array([[[0, 1], [2, 3]], [[3, 4], [5, 6]]]).astype(np.float32)
@@ -205,11 +204,20 @@ def test_ngraph_preprocess_spatial_static_shape():
 @pytest.mark.parametrize(
     "algorithm, color_format1, color_format2, is_failing",
     [(ResizeAlgorithm.RESIZE_LINEAR, ColorFormat.UNDEFINED, ColorFormat.BGR, True),
+     (ResizeAlgorithm.RESIZE_NEAREST, ColorFormat.RGB, ColorFormat.I420_SINGLE_PLANE, True),
+     (ResizeAlgorithm.RESIZE_NEAREST, ColorFormat.RGB, ColorFormat.I420_THREE_PLANES, True),
      (ResizeAlgorithm.RESIZE_LINEAR, ColorFormat.RGB, ColorFormat.NV12_SINGLE_PLANE, True),
+     (ResizeAlgorithm.RESIZE_LINEAR, ColorFormat.RGB, ColorFormat.RGBX, True),
+     (ResizeAlgorithm.RESIZE_LINEAR, ColorFormat.RGB, ColorFormat.BGRX, True),
      (ResizeAlgorithm.RESIZE_LINEAR, ColorFormat.RGB, ColorFormat.NV12_TWO_PLANES, True),
+     (ResizeAlgorithm.RESIZE_LINEAR, ColorFormat.UNDEFINED, ColorFormat.I420_SINGLE_PLANE, True),
      (ResizeAlgorithm.RESIZE_CUBIC, ColorFormat.RGB, ColorFormat.UNDEFINED, True),
      (ResizeAlgorithm.RESIZE_CUBIC, ColorFormat.RGB, ColorFormat.BGR, False),
      (ResizeAlgorithm.RESIZE_CUBIC, ColorFormat.BGR, ColorFormat.RGB, False),
+     (ResizeAlgorithm.RESIZE_CUBIC, ColorFormat.BGR, ColorFormat.RGBX, True),
+     (ResizeAlgorithm.RESIZE_CUBIC, ColorFormat.BGR, ColorFormat.BGRX, True),
+     (ResizeAlgorithm.RESIZE_NEAREST, ColorFormat.BGR, ColorFormat.I420_SINGLE_PLANE, True),
+     (ResizeAlgorithm.RESIZE_NEAREST, ColorFormat.BGR, ColorFormat.I420_THREE_PLANES, True),
      (ResizeAlgorithm.RESIZE_NEAREST, ColorFormat.BGR, ColorFormat.NV12_SINGLE_PLANE, True),
      (ResizeAlgorithm.RESIZE_NEAREST, ColorFormat.BGR, ColorFormat.NV12_TWO_PLANES, True),
      (ResizeAlgorithm.RESIZE_NEAREST, ColorFormat.BGR, ColorFormat.UNDEFINED, True)])
@@ -220,16 +228,12 @@ def test_ngraph_preprocess_steps(algorithm, color_format1, color_format2, is_fai
     function = Function(model, [parameter_a], "TestFunction")
     layout1 = ov.Layout("NCWH")
     layout2 = ov.Layout("NCHW")
-    custom_processor = PrePostProcessor(function)\
-        .input(InputInfo()
-               .tensor(InputTensorInfo()
-                       .set_layout(layout1)
-                       .set_color_format(color_format1, []))
-               .preprocess(PreProcessSteps()
-                           .mean(1.)
-                           .resize(algorithm, 3, 3)
-                           .convert_layout(layout2)
-                           .convert_color(color_format2)))
+
+    custom_processor = PrePostProcessor(function)
+    inp = custom_processor.input()
+    inp.tensor().set_layout(layout1).set_color_format(color_format1, [])
+    inp.preprocess().mean(1.).resize(algorithm, 3, 3).convert_layout(layout2).convert_color(color_format2)
+
     if is_failing:
         with pytest.raises(RuntimeError) as e:
             function = custom_processor.build()
@@ -253,20 +257,13 @@ def test_ngraph_preprocess_postprocess_layout():
     layout1 = ov.Layout("NCWH")
     layout2 = ov.Layout("NCHW")
 
-    function = PrePostProcessor(function)\
-        .input(InputInfo()
-               .tensor(InputTensorInfo()
-                       .set_layout(layout1))
-               .preprocess(PreProcessSteps()
-                           .mean(1.)
-                           .convert_layout(layout2)
-                           .reverse_channels()
-                           )
-               ) \
-        .output(OutputInfo()
-                .postprocess(PostProcessSteps()
-                             .convert_layout([0, 1, 2, 3]))) \
-        .build()
+    p = PrePostProcessor(function)
+    inp = p.input()
+    inp.tensor().set_layout(layout1)
+    inp.preprocess().mean(1.).convert_layout(layout2).reverse_channels()
+    out = p.output()
+    out.postprocess().convert_layout([0, 1, 2, 3])
+    function = p.build()
 
     input_data = np.array([[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]]).astype(np.float32)
     expected_output = np.array([[[[0, 3, 6], [1, 4, 7], [2, 5, 8]]]]).astype(np.float32)
@@ -284,16 +281,11 @@ def test_ngraph_preprocess_reverse_channels():
     function = Function(model, [parameter_a], "TestFunction")
     layout1 = ov.Layout("NCWH")
 
-    function = PrePostProcessor(function)\
-        .input(InputInfo()
-               .tensor(InputTensorInfo()
-                       .set_layout(layout1))
-               .preprocess(PreProcessSteps()
-                           .mean(1.)
-                           .reverse_channels()
-                           )
-               ) \
-        .build()
+    p = PrePostProcessor(function)
+    inp = p.input()
+    inp.tensor().set_layout(layout1)
+    inp.preprocess().mean(1.).reverse_channels()
+    function = p.build()
 
     input_data = np.array([[[[1, 2], [3, 4]], [[5, 6], [7, 8]]]]).astype(np.float32)
     expected_output = np.array([[[[4, 5], [6, 7]], [[0, 1], [2, 3]]]]).astype(np.float32)
@@ -312,15 +304,11 @@ def test_ngraph_preprocess_resize_algorithm():
     resize_alg = ResizeAlgorithm.RESIZE_CUBIC
     layout1 = ov.Layout("NCWH")
 
-    function = PrePostProcessor(function)\
-        .input(InputInfo()
-               .tensor(InputTensorInfo()
-                       .set_layout(layout1))
-               .preprocess(PreProcessSteps()
-                           .mean(1.)
-                           .resize(resize_alg, 3, 3))
-               )\
-        .build()
+    p = PrePostProcessor(function)
+    inp = p.input()
+    inp.tensor().set_layout(layout1)
+    inp.preprocess().mean(1.).resize(resize_alg, 3, 3)
+    function = p.build()
 
     input_data = np.array([[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]]).astype(np.float32)
     expected_output = np.array([[[[0, 1, 2], [3, 4, 5], [6, 7, 8]]]]).astype(np.float32)
@@ -398,21 +386,11 @@ def test_ngraph_preprocess_model():
     def custom_preprocess(output: Output):
         return ops.abs(output)
 
-    function = PrePostProcessor(function) \
-        .input(InputInfo(1)
-               .preprocess(PreProcessSteps()
-                           .convert_element_type(Type.f32)
-                           .scale(0.5)
-                           )
-               ) \
-        .input(InputInfo(0)
-               .preprocess(PreProcessSteps()
-                           .convert_element_type(Type.f32)
-                           .mean(5.))) \
-        .output(OutputInfo(0)
-                .postprocess(PostProcessSteps()
-                             .custom(custom_preprocess))) \
-        .build()
+    p = PrePostProcessor(function)
+    p.input(1).preprocess().convert_element_type(Type.f32).scale(0.5)
+    p.input(0).preprocess().convert_element_type(Type.f32).mean(5.)
+    p.output(0).postprocess().custom(custom_preprocess)
+    function = p.build()
 
     input_data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]).astype(np.float32)
     expected_output = np.array([[[2, 1], [4, 7]], [[10, 13], [16, 19]]]).astype(np.float32)

From d01e6368ec817a989fd2532794c16df560ea9f8f Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Fri, 26 Nov 2021 06:27:23 +0300
Subject: [PATCH 21/72] Added pp header to util library (#8814)

* Added pp header to util library

* Fixed code style and typo

* Fixed arm
---
 .../inference_engine/include/ie/ie_common.h   |  2 +-
 .../interface/ie_iplugin_internal.hpp         |  2 +-
 .../include/openvino/cc/selective_build.h     |  2 +-
 openvino/itt/CMakeLists.txt                   |  2 +-
 openvino/itt/include/openvino/itt.hpp         |  2 +-
 openvino/util/include/openvino/util/pp.hpp    | 50 +++++++++++++++++++
 6 files changed, 55 insertions(+), 5 deletions(-)
 create mode 100644 openvino/util/include/openvino/util/pp.hpp

diff --git a/inference-engine/src/inference_engine/include/ie/ie_common.h b/inference-engine/src/inference_engine/include/ie/ie_common.h
index 936b7f52b30..28025b09b33 100644
--- a/inference-engine/src/inference_engine/include/ie/ie_common.h
+++ b/inference-engine/src/inference_engine/include/ie/ie_common.h
@@ -403,7 +403,7 @@ struct ThrowNow final {
 #    define IE_LOCATION ""
 #endif  // NDEBUG
 
-// WARNING: DO NOT USE THIS MACRO! Use openvino/pp.hpp macro library
+// WARNING: DO NOT USE THIS MACRO! Use openvino/util/pp.hpp macro library
 #define IE_PP_EXPAND(X)             X
 #define IE_PP_NARG(...)             IE_PP_EXPAND(IE_PP_NARG_(__VA_ARGS__, IE_PP_RSEQ_N()))
 #define IE_PP_NARG_(...)            IE_PP_EXPAND(IE_PP_ARG_N(__VA_ARGS__))
diff --git a/inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iplugin_internal.hpp b/inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iplugin_internal.hpp
index e9799495680..d8a08e3d0d3 100644
--- a/inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iplugin_internal.hpp
+++ b/inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iplugin_internal.hpp
@@ -19,7 +19,7 @@
 #include "ie_iextension.h"
 #include "ie_input_info.hpp"
 #include "ie_parameter.hpp"
-#include "openvino/pp.hpp"
+#include "openvino/util/pp.hpp"
 #include "so_ptr.hpp"
 
 namespace ov {
diff --git a/openvino/conditional_compilation/include/openvino/cc/selective_build.h b/openvino/conditional_compilation/include/openvino/cc/selective_build.h
index 756179695a3..fe36e50ef03 100644
--- a/openvino/conditional_compilation/include/openvino/cc/selective_build.h
+++ b/openvino/conditional_compilation/include/openvino/cc/selective_build.h
@@ -55,7 +55,7 @@
     *
 */
 
-#include <openvino/pp.hpp>
+#include <openvino/util/pp.hpp>
 #include <openvino/itt.hpp>
 
 #define OV_CC_EXPAND OV_PP_EXPAND
diff --git a/openvino/itt/CMakeLists.txt b/openvino/itt/CMakeLists.txt
index fbeab9f3e0d..e5abb9199ec 100644
--- a/openvino/itt/CMakeLists.txt
+++ b/openvino/itt/CMakeLists.txt
@@ -10,7 +10,7 @@ add_library(${TARGET_NAME} STATIC ${SOURCES})
 
 add_library(openvino::itt ALIAS ${TARGET_NAME})
 
-target_link_libraries(${TARGET_NAME} PUBLIC openvino::pp)
+target_link_libraries(${TARGET_NAME} PUBLIC openvino::pp openvino::util)
 
 if(TARGET ittnotify)
     target_link_libraries(${TARGET_NAME} PUBLIC ittnotify)
diff --git a/openvino/itt/include/openvino/itt.hpp b/openvino/itt/include/openvino/itt.hpp
index 8a22efa54fe..ee276fa1feb 100644
--- a/openvino/itt/include/openvino/itt.hpp
+++ b/openvino/itt/include/openvino/itt.hpp
@@ -9,7 +9,7 @@
 
 #pragma once
 #include <openvino/function_name.hpp>
-#include <openvino/pp.hpp>
+#include <openvino/util/pp.hpp>
 #include <string>
 #include <utility>
 
diff --git a/openvino/util/include/openvino/util/pp.hpp b/openvino/util/include/openvino/util/pp.hpp
new file mode 100644
index 00000000000..935485a8e2d
--- /dev/null
+++ b/openvino/util/include/openvino/util/pp.hpp
@@ -0,0 +1,50 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+/**
+ * @brief Set of macro used by openvino
+ * @file openvino/util/pp.hpp
+ */
+
+#pragma once
+
+// Macros for string conversion
+#define OV_PP_TOSTRING(...)  OV_PP_TOSTRING_(__VA_ARGS__)
+#define OV_PP_TOSTRING_(...) #__VA_ARGS__
+
+#define OV_PP_EXPAND(X) X
+
+#define OV_PP_NARG(...)                         OV_PP_EXPAND(OV_PP_NARG_(__VA_ARGS__, OV_PP_RSEQ_N()))
+#define OV_PP_NARG_(...)                        OV_PP_EXPAND(OV_PP_ARG_N(__VA_ARGS__))
+#define OV_PP_ARG_N(_0, _1, _2, _3, _4, N, ...) N
+#define OV_PP_RSEQ_N()                          0, 4, 3, 2, 1, 0
+#define OV_PP_NO_ARGS(NAME)                     , , , ,
+
+// Macros for names concatenation
+#define OV_PP_CAT_(x, y)        x##y
+#define OV_PP_CAT(x, y)         OV_PP_CAT_(x, y)
+#define OV_PP_CAT3_(x, y, z)    x##y##z
+#define OV_PP_CAT3(x, y, z)     OV_PP_CAT3_(x, y, z)
+#define OV_PP_CAT4_(x, y, z, w) x##y##z##w
+#define OV_PP_CAT4(x, y, z, w)  OV_PP_CAT4_(x, y, z, w)
+
+#define OV_PP_OVERLOAD(NAME, ...) \
+    OV_PP_EXPAND(OV_PP_CAT3(NAME, _, OV_PP_EXPAND(OV_PP_NARG(OV_PP_NO_ARGS __VA_ARGS__(NAME))))(__VA_ARGS__))
+
+// Placeholder for first macro argument
+#define OV_PP_ARG_PLACEHOLDER_1 0,
+
+// This macro returns second argument, first argument is ignored
+#define OV_PP_SECOND_ARG(...)                   OV_PP_EXPAND(OV_PP_SECOND_ARG_(__VA_ARGS__, 0))
+#define OV_PP_SECOND_ARG_(...)                  OV_PP_EXPAND(OV_PP_SECOND_ARG_GET(__VA_ARGS__))
+#define OV_PP_SECOND_ARG_GET(ignored, val, ...) val
+
+// Return macro argument value
+#define OV_PP_IS_ENABLED(x) OV_PP_IS_ENABLED1(x)
+
+// Generate junk macro or {0, } sequence if val is 1
+#define OV_PP_IS_ENABLED1(val) OV_PP_IS_ENABLED2(OV_PP_CAT(OV_PP_ARG_PLACEHOLDER_, val))
+
+// Return second argument from possible sequences {1, 0}, {0, 1, 0}
+#define OV_PP_IS_ENABLED2(arg1_or_junk) OV_PP_SECOND_ARG(arg1_or_junk 1, 0)

From 1ac1720df09ad97a424ce0338dbeb381c277858e Mon Sep 17 00:00:00 2001
From: Ivan Tikhonov <ivan.tikhonov@intel.com>
Date: Fri, 26 Nov 2021 07:18:56 +0300
Subject: [PATCH 22/72] Use new headers and namespaces in Paddle FrontEnd
 (#8834)

---
 ngraph/frontend/paddlepaddle/CMakeLists.txt   |  2 +-
 .../paddlepaddle_frontend/frontend.hpp        | 12 ++--
 .../include/paddlepaddle_frontend/model.hpp   |  3 +-
 .../include/paddlepaddle_frontend/place.hpp   |  3 +-
 ngraph/frontend/paddlepaddle/src/decoder.cpp  | 34 ++++-----
 ngraph/frontend/paddlepaddle/src/decoder.hpp  | 10 +--
 .../paddlepaddle/src/default_opset.hpp        |  4 +-
 ngraph/frontend/paddlepaddle/src/frontend.cpp | 26 +++----
 ngraph/frontend/paddlepaddle/src/model.cpp    | 31 ++++----
 .../paddlepaddle/src/node_context.hpp         | 30 ++++----
 .../frontend/paddlepaddle/src/op/argmax.cpp   | 22 +++---
 .../paddlepaddle/src/op/assign_value.cpp      |  5 +-
 .../paddlepaddle/src/op/batch_norm.cpp        | 36 +++++-----
 ngraph/frontend/paddlepaddle/src/op/cast.cpp  |  7 +-
 ngraph/frontend/paddlepaddle/src/op/clip.cpp  |  5 +-
 .../frontend/paddlepaddle/src/op/concat.cpp   |  3 +-
 .../frontend/paddlepaddle/src/op/conv2d.cpp   |  3 +-
 .../paddlepaddle/src/op/conv2d_transpose.cpp  |  2 +-
 .../paddlepaddle/src/op/conv2d_utils.cpp      | 17 +++--
 .../paddlepaddle/src/op/conv2d_utils.hpp      | 36 +++++-----
 .../frontend/paddlepaddle/src/op/cumsum.cpp   |  2 +-
 .../paddlepaddle/src/op/deformable_conv.cpp   | 50 ++++++-------
 .../frontend/paddlepaddle/src/op/dropout.cpp  | 10 +--
 .../paddlepaddle/src/op/elementwise_ops.cpp   | 34 ++++-----
 .../paddlepaddle/src/op/embedding.cpp         |  3 +-
 .../paddlepaddle/src/op/expand_v2.cpp         | 28 ++++----
 .../paddlepaddle/src/op/fill_any_like.cpp     |  2 +-
 .../paddlepaddle/src/op/fill_constant.cpp     |  5 +-
 .../src/op/fill_constant_batch_size_like.cpp  | 70 +++++++++----------
 .../src/op/flatten_contiguous_range.cpp       |  4 +-
 ngraph/frontend/paddlepaddle/src/op/gelu.cpp  |  2 +-
 .../paddlepaddle/src/op/hard_sigmoid.cpp      | 12 ++--
 .../paddlepaddle/src/op/hard_swish.cpp        |  7 +-
 .../frontend/paddlepaddle/src/op/interp.cpp   | 24 +++----
 .../paddlepaddle/src/op/layer_norm.cpp        |  4 +-
 .../paddlepaddle/src/op/leakyrelu.cpp         |  7 +-
 ngraph/frontend/paddlepaddle/src/op/log.cpp   |  7 +-
 .../paddlepaddle/src/op/logical_not.cpp       |  5 +-
 ngraph/frontend/paddlepaddle/src/op/lstm.cpp  | 46 ++++++------
 .../frontend/paddlepaddle/src/op/matmul.cpp   | 10 +--
 .../paddlepaddle/src/op/matrix_nms.cpp        |  6 +-
 .../paddlepaddle/src/op/multiclass_nms.cpp    |  6 +-
 ngraph/frontend/paddlepaddle/src/op/pad3d.cpp | 33 ++++-----
 .../frontend/paddlepaddle/src/op/pool2d.cpp   | 70 +++++++++----------
 ngraph/frontend/paddlepaddle/src/op/pow.cpp   | 11 +--
 .../paddlepaddle/src/op/prior_box.cpp         | 10 +--
 ngraph/frontend/paddlepaddle/src/op/range.cpp | 13 ++--
 ngraph/frontend/paddlepaddle/src/op/relu.cpp  |  6 +-
 ngraph/frontend/paddlepaddle/src/op/relu6.cpp |  7 +-
 .../frontend/paddlepaddle/src/op/reshape2.cpp | 17 ++---
 ngraph/frontend/paddlepaddle/src/op/rnn.cpp   |  2 +-
 ngraph/frontend/paddlepaddle/src/op/scale.cpp |  4 +-
 ngraph/frontend/paddlepaddle/src/op/shape.cpp |  5 +-
 .../frontend/paddlepaddle/src/op/sigmoid.cpp  |  5 +-
 .../frontend/paddlepaddle/src/op/softmax.cpp  |  5 +-
 ngraph/frontend/paddlepaddle/src/op/split.cpp | 10 +--
 .../frontend/paddlepaddle/src/op/squeeze.cpp  |  9 +--
 .../paddlepaddle/src/op/transpose2.cpp        |  8 +--
 .../paddlepaddle/src/op/unsqueeze.cpp         |  9 +--
 .../frontend/paddlepaddle/src/op/yolo_box.cpp |  9 +--
 ngraph/frontend/paddlepaddle/src/op_table.hpp |  1 -
 .../paddlepaddle/src/pdpd_fw_node.cpp         |  1 -
 .../paddlepaddle/src/pdpd_fw_node.hpp         |  2 +-
 .../frontend/paddlepaddle/src/pdpd_utils.cpp  | 14 ++++
 .../frontend/paddlepaddle/src/pdpd_utils.hpp  |  2 +
 ngraph/frontend/paddlepaddle/src/place.cpp    |  2 +-
 66 files changed, 460 insertions(+), 430 deletions(-)
 create mode 100644 ngraph/frontend/paddlepaddle/src/pdpd_utils.cpp

diff --git a/ngraph/frontend/paddlepaddle/CMakeLists.txt b/ngraph/frontend/paddlepaddle/CMakeLists.txt
index 89f6fb9f97a..8f9930c5b65 100644
--- a/ngraph/frontend/paddlepaddle/CMakeLists.txt
+++ b/ngraph/frontend/paddlepaddle/CMakeLists.txt
@@ -77,7 +77,7 @@ ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
 link_system_libraries(${TARGET_NAME} PRIVATE ${Protobuf_LITE_LIBRARIES})
 
 target_link_libraries(${TARGET_NAME} PRIVATE frontend_common::static
-                                     PRIVATE ngraph::builder inference_engine_transformations)
+                                     PRIVATE inference_engine_transformations)
 
 add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME}
                         EXCLUDE_PATTERNS ${PROTO_SRCS} ${PROTO_HDRS})
diff --git a/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp b/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp
index dcda3d0bca4..55275c4b6a3 100644
--- a/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp
+++ b/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/frontend.hpp
@@ -18,12 +18,12 @@ public:
     FrontEndPDPD() = default;
 
     /// \brief Completely convert the remaining, not converted part of a function.
-    /// \param partiallyConverted partially converted nGraph function
-    /// \return fully converted nGraph function
+    /// \param partiallyConverted partially converted OV function
+    /// \return fully converted OV function
     std::shared_ptr<Function> convert(InputModel::Ptr model) const override;
 
     /// \brief Completely convert the remaining, not converted part of a function.
-    /// \param partiallyConverted partially converted nGraph function
+    /// \param partiallyConverted partially converted OV function
     void convert(std::shared_ptr<Function> partiallyConverted) const override;
 
     /// \brief Convert only those parts of the model that can be converted leaving others
@@ -31,14 +31,14 @@ public:
     /// function or another form of convert function should be called to finalize the
     /// conversion process.
     /// \param model Input model
-    /// \return partially converted nGraph function
+    /// \return partially converted OV function
     std::shared_ptr<Function> convert_partially(InputModel::Ptr model) const override;
 
     /// \brief Convert operations with one-to-one mapping with decoding nodes.
-    /// Each decoding node is an nGraph node representing a single FW operation node with
+    /// Each decoding node is an OV node representing a single FW operation node with
     /// all attributes represented in FW-independent way.
     /// \param model Input model
-    /// \return nGraph function after decoding
+    /// \return OV function after decoding
     std::shared_ptr<Function> decode(InputModel::Ptr model) const override;
 
     /// \brief Gets name of this FrontEnd. Can be used by clients
diff --git a/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/model.hpp b/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/model.hpp
index 54ba4d49834..abd51f0956c 100644
--- a/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/model.hpp
+++ b/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/model.hpp
@@ -5,7 +5,8 @@
 #pragma once
 
 #include <manager.hpp>
-#include <paddlepaddle_frontend/utility.hpp>
+
+#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
diff --git a/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/place.hpp b/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/place.hpp
index f8905bf4437..e41e39564db 100644
--- a/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/place.hpp
+++ b/ngraph/frontend/paddlepaddle/include/paddlepaddle_frontend/place.hpp
@@ -5,7 +5,8 @@
 #pragma once
 
 #include <manager.hpp>
-#include <paddlepaddle_frontend/exceptions.hpp>
+
+#include "paddlepaddle_frontend/exceptions.hpp"
 
 namespace paddle {
 namespace framework {
diff --git a/ngraph/frontend/paddlepaddle/src/decoder.cpp b/ngraph/frontend/paddlepaddle/src/decoder.cpp
index d157a349a8f..56f21f0d15b 100644
--- a/ngraph/frontend/paddlepaddle/src/decoder.cpp
+++ b/ngraph/frontend/paddlepaddle/src/decoder.cpp
@@ -19,17 +19,17 @@ namespace ov {
 namespace frontend {
 using namespace paddle::framework;
 
-std::map<paddle::framework::proto::VarType_Type, ngraph::element::Type> TYPE_MAP{
-    {proto::VarType_Type::VarType_Type_BOOL, ngraph::element::boolean},
-    {proto::VarType_Type::VarType_Type_INT16, ngraph::element::i16},
-    {proto::VarType_Type::VarType_Type_INT32, ngraph::element::i32},
-    {proto::VarType_Type::VarType_Type_INT64, ngraph::element::i64},
-    {proto::VarType_Type::VarType_Type_FP16, ngraph::element::f16},
-    {proto::VarType_Type::VarType_Type_FP32, ngraph::element::f32},
-    {proto::VarType_Type::VarType_Type_FP64, ngraph::element::f64},
-    {proto::VarType_Type::VarType_Type_UINT8, ngraph::element::u8},
-    {proto::VarType_Type::VarType_Type_INT8, ngraph::element::i8},
-    {proto::VarType_Type::VarType_Type_BF16, ngraph::element::bf16}};
+std::map<paddle::framework::proto::VarType_Type, ov::element::Type> TYPE_MAP{
+    {proto::VarType_Type::VarType_Type_BOOL, ov::element::boolean},
+    {proto::VarType_Type::VarType_Type_INT16, ov::element::i16},
+    {proto::VarType_Type::VarType_Type_INT32, ov::element::i32},
+    {proto::VarType_Type::VarType_Type_INT64, ov::element::i64},
+    {proto::VarType_Type::VarType_Type_FP16, ov::element::f16},
+    {proto::VarType_Type::VarType_Type_FP32, ov::element::f32},
+    {proto::VarType_Type::VarType_Type_FP64, ov::element::f64},
+    {proto::VarType_Type::VarType_Type_UINT8, ov::element::u8},
+    {proto::VarType_Type::VarType_Type_INT8, ov::element::i8},
+    {proto::VarType_Type::VarType_Type_BF16, ov::element::bf16}};
 
 std::shared_ptr<Variant> DecoderPDPDProto::get_attribute(const std::string& name,
                                                          const VariantTypeInfo& type_info) const {
@@ -55,9 +55,9 @@ std::shared_ptr<Variant> DecoderPDPDProto::get_attribute(const std::string& name
     } else if (type_info == VariantWrapper<std::vector<float>>::get_type_info_static()) {
         auto floats = std::vector<float>(attrs[0].floats().begin(), attrs[0].floats().end());
         return std::make_shared<VariantWrapper<std::vector<float>>>(floats);
-    } else if (type_info == VariantWrapper<ngraph::element::Type>::get_type_info_static()) {
+    } else if (type_info == VariantWrapper<ov::element::Type>::get_type_info_static()) {
         auto data_type = (paddle::framework::proto::VarType_Type)attrs[0].i();
-        return std::make_shared<VariantWrapper<ngraph::element::Type>>(TYPE_MAP[data_type]);
+        return std::make_shared<VariantWrapper<ov::element::Type>>(TYPE_MAP[data_type]);
     } else if (type_info == VariantWrapper<bool>::get_type_info_static()) {
         return std::make_shared<VariantWrapper<bool>>(attrs[0].b());
     }
@@ -82,8 +82,8 @@ size_t DecoderPDPDProto::get_output_size() const {
     return res;
 }
 
-std::map<std::string, std::vector<ngraph::element::Type>> DecoderPDPDProto::get_output_type_map() const {
-    std::map<std::string, std::vector<ngraph::element::Type>> output_types;
+std::map<std::string, std::vector<ov::element::Type>> DecoderPDPDProto::get_output_type_map() const {
+    std::map<std::string, std::vector<ov::element::Type>> output_types;
     for (const auto& out_port_pair : op_place->get_output_ports()) {
         for (const auto& p_place : out_port_pair.second) {
             output_types[out_port_pair.first].push_back(p_place->get_target_tensor_pdpd()->get_element_type());
@@ -92,8 +92,8 @@ std::map<std::string, std::vector<ngraph::element::Type>> DecoderPDPDProto::get_
     return output_types;
 }
 
-ngraph::element::Type DecoderPDPDProto::get_out_port_type(const std::string& port_name) const {
-    std::vector<ngraph::element::Type> output_types;
+ov::element::Type DecoderPDPDProto::get_out_port_type(const std::string& port_name) const {
+    std::vector<ov::element::Type> output_types;
     for (const auto& out_port : op_place->get_output_ports().at(port_name)) {
         output_types.push_back(out_port->get_target_tensor_pdpd()->get_element_type());
     }
diff --git a/ngraph/frontend/paddlepaddle/src/decoder.hpp b/ngraph/frontend/paddlepaddle/src/decoder.hpp
index a87bf04f591..29ef5ee9eda 100644
--- a/ngraph/frontend/paddlepaddle/src/decoder.hpp
+++ b/ngraph/frontend/paddlepaddle/src/decoder.hpp
@@ -9,18 +9,18 @@
 #include <fstream>
 #include <map>
 #include <memory>
-#include <paddlepaddle_frontend/frontend.hpp>
-#include <paddlepaddle_frontend/place.hpp>
 #include <string>
 #include <utility>
 #include <vector>
 
 #include "framework.pb.h"
 #include "node_context.hpp"
+#include "paddlepaddle_frontend/frontend.hpp"
+#include "paddlepaddle_frontend/place.hpp"
 
 namespace ov {
 namespace frontend {
-extern std::map<paddle::framework::proto::VarType_Type, ngraph::element::Type> TYPE_MAP;
+extern std::map<paddle::framework::proto::VarType_Type, ov::element::Type> TYPE_MAP;
 
 class DecoderPDPDProto : public pdpd::DecoderBase {
 public:
@@ -32,11 +32,11 @@ public:
 
     size_t get_output_size() const override;
 
-    ngraph::element::Type get_out_port_type(const std::string& port_name) const override;
+    ov::element::Type get_out_port_type(const std::string& port_name) const override;
 
     std::string get_op_type() const override;
 
-    std::map<std::string, std::vector<ngraph::element::Type>> get_output_type_map() const;
+    std::map<std::string, std::vector<ov::element::Type>> get_output_type_map() const;
 
     std::map<std::string, OutputVector> map_for_each_input(
         const std::function<Output<Node>(const std::string&, size_t)>& func) const;
diff --git a/ngraph/frontend/paddlepaddle/src/default_opset.hpp b/ngraph/frontend/paddlepaddle/src/default_opset.hpp
index 677e48045cf..852018f88a1 100644
--- a/ngraph/frontend/paddlepaddle/src/default_opset.hpp
+++ b/ngraph/frontend/paddlepaddle/src/default_opset.hpp
@@ -2,13 +2,13 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "ngraph/opsets/opset8.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
-namespace default_opset = ngraph::opset8;
+namespace default_opset = ov::opset8;
 
 }  // namespace op
 }  // namespace pdpd
diff --git a/ngraph/frontend/paddlepaddle/src/frontend.cpp b/ngraph/frontend/paddlepaddle/src/frontend.cpp
index 009626b6d15..584dcff6dff 100644
--- a/ngraph/frontend/paddlepaddle/src/frontend.cpp
+++ b/ngraph/frontend/paddlepaddle/src/frontend.cpp
@@ -2,15 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "paddlepaddle_frontend/frontend.hpp"
+
 #include <fstream>
 #include <map>
-#include <ngraph/ngraph.hpp>
-#include <ngraph/variant.hpp>
-#include <openvino/opsets/opset7.hpp>
-#include <paddlepaddle_frontend/exceptions.hpp>
-#include <paddlepaddle_frontend/frontend.hpp>
-#include <paddlepaddle_frontend/model.hpp>
-#include <paddlepaddle_frontend/place.hpp>
 #include <string>
 #include <vector>
 
@@ -18,6 +13,11 @@
 #include "framework.pb.h"
 #include "node_context.hpp"
 #include "op_table.hpp"
+#include "openvino/core/variant.hpp"
+#include "openvino/opsets/opset7.hpp"
+#include "paddlepaddle_frontend/exceptions.hpp"
+#include "paddlepaddle_frontend/model.hpp"
+#include "paddlepaddle_frontend/place.hpp"
 #include "pdpd_fw_node.hpp"
 #include "pdpd_utils.hpp"
 
@@ -178,7 +178,7 @@ std::shared_ptr<Function> FrontEndPDPD::convert_each_node(
                         const auto& ng_outputs = named_outputs.at(port.parameter());
                         FRONT_END_OP_CONVERSION_CHECK(ng_outputs.size() == port.arguments_size(),
                                                       "The number of output tensors must be equal to "
-                                                      "the number of outputs of the ngraph node.");
+                                                      "the number of outputs of the OV node.");
                         for (size_t idx = 0; idx < ng_outputs.size(); ++idx) {
                             const auto& var_name = port.arguments()[idx];
                             ng_outputs[idx].get_tensor().set_names({var_name});
@@ -202,7 +202,7 @@ std::shared_ptr<Function> FrontEndPDPD::convert_each_node(
         result_nodes.push_back(result);
     }
 
-    return std::make_shared<ngraph::Function>(result_nodes, parameter_nodes);
+    return std::make_shared<ov::Function>(result_nodes, parameter_nodes);
 }
 
 bool FrontEndPDPD::supported_impl(const std::vector<std::shared_ptr<Variant>>& variants) const {
@@ -276,7 +276,7 @@ InputModel::Ptr FrontEndPDPD::load_impl(const std::vector<std::shared_ptr<Varian
     PDPD_THROW("Model can be loaded either from 1 or 2 files/streams");
 }
 
-std::shared_ptr<ngraph::Function> FrontEndPDPD::convert(InputModel::Ptr model) const {
+std::shared_ptr<ov::Function> FrontEndPDPD::convert(InputModel::Ptr model) const {
     auto pdpd_model = std::dynamic_pointer_cast<InputModelPDPD>(model);
     std::map<std::string, pdpd::CreatorFunction> CREATORS_MAP = pdpd::get_supported_ops();
     auto f = convert_each_node(
@@ -287,7 +287,7 @@ std::shared_ptr<ngraph::Function> FrontEndPDPD::convert(InputModel::Ptr model) c
     return f;
 }
 
-void FrontEndPDPD::convert(std::shared_ptr<ngraph::Function> partiallyConverted) const {
+void FrontEndPDPD::convert(std::shared_ptr<ov::Function> partiallyConverted) const {
     for (const auto& node : partiallyConverted->get_ordered_ops()) {
         if (ov::is_type<PDPDFrameworkNode>(node)) {
             pdpd::normalize_framework_node(std::dynamic_pointer_cast<PDPDFrameworkNode>(node),
@@ -299,7 +299,7 @@ void FrontEndPDPD::convert(std::shared_ptr<ngraph::Function> partiallyConverted)
     }
 }
 
-std::shared_ptr<ngraph::Function> FrontEndPDPD::convert_partially(InputModel::Ptr model) const {
+std::shared_ptr<ov::Function> FrontEndPDPD::convert_partially(InputModel::Ptr model) const {
     auto pdpd_model = std::dynamic_pointer_cast<InputModelPDPD>(model);
     std::map<std::string, pdpd::CreatorFunction> CREATORS_MAP = pdpd::get_supported_ops();
     auto f = convert_each_node(
@@ -316,7 +316,7 @@ std::shared_ptr<ngraph::Function> FrontEndPDPD::convert_partially(InputModel::Pt
     return f;
 }
 
-std::shared_ptr<ngraph::Function> FrontEndPDPD::decode(InputModel::Ptr model) const {
+std::shared_ptr<ov::Function> FrontEndPDPD::decode(InputModel::Ptr model) const {
     auto pdpd_model = std::dynamic_pointer_cast<InputModelPDPD>(model);
     std::map<std::string, pdpd::CreatorFunction> CREATORS_MAP = pdpd::get_supported_ops();
     auto f = convert_each_node(pdpd_model, pdpd::make_framework_node);
diff --git a/ngraph/frontend/paddlepaddle/src/model.cpp b/ngraph/frontend/paddlepaddle/src/model.cpp
index 0cbb9038e87..0cd4c7376a8 100644
--- a/ngraph/frontend/paddlepaddle/src/model.cpp
+++ b/ngraph/frontend/paddlepaddle/src/model.cpp
@@ -2,16 +2,17 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "paddlepaddle_frontend/model.hpp"
+
 #include <fstream>
-#include <openvino/opsets/opset7.hpp>
-#include <paddlepaddle_frontend/exceptions.hpp>
-#include <paddlepaddle_frontend/model.hpp>
-#include <paddlepaddle_frontend/place.hpp>
 #include <queue>
 
 #include "decoder.hpp"
 #include "framework.pb.h"
 #include "node_context.hpp"
+#include "openvino/opsets/opset7.hpp"
+#include "paddlepaddle_frontend/exceptions.hpp"
+#include "paddlepaddle_frontend/place.hpp"
 #include "pdpd_utils.hpp"
 
 #if defined(OPENVINO_ENABLE_UNICODE_PATH_SUPPORT) && defined(_WIN32)
@@ -34,10 +35,10 @@ public:
     void overrideAllOutputs(const std::vector<Place::Ptr>& outputs);
     void overrideAllInputs(const std::vector<Place::Ptr>& inputs);
     void extractSubgraph(const std::vector<Place::Ptr>& inputs, const std::vector<Place::Ptr>& outputs);
-    void setDefaultShape(Place::Ptr place, const ngraph::Shape&);
-    void setPartialShape(Place::Ptr place, const ngraph::PartialShape&);
-    ngraph::PartialShape getPartialShape(Place::Ptr place) const;
-    void setElementType(Place::Ptr place, const ngraph::element::Type&);
+    void setDefaultShape(Place::Ptr place, const ov::Shape&);
+    void setPartialShape(Place::Ptr place, const ov::PartialShape&);
+    ov::PartialShape getPartialShape(Place::Ptr place) const;
+    void setElementType(Place::Ptr place, const ov::element::Type&);
     void setTensorValue(Place::Ptr place, const void* value);
 
     std::vector<std::shared_ptr<OpPlacePDPD>> get_op_places() const;
@@ -374,19 +375,19 @@ void InputModelPDPD::InputModelPDPDImpl::extractSubgraph(const std::vector<Place
     overrideAllOutputs(outputs);
 }
 
-void InputModelPDPD::InputModelPDPDImpl::setDefaultShape(Place::Ptr place, const ngraph::Shape& shape) {
+void InputModelPDPD::InputModelPDPDImpl::setDefaultShape(Place::Ptr place, const ov::Shape& shape) {
     FRONT_END_NOT_IMPLEMENTED("setDefaultShape");
 }
 
-void InputModelPDPD::InputModelPDPDImpl::setPartialShape(Place::Ptr place, const ngraph::PartialShape& p_shape) {
+void InputModelPDPD::InputModelPDPDImpl::setPartialShape(Place::Ptr place, const ov::PartialShape& p_shape) {
     castToTensorPlace(place)->set_partial_shape(p_shape);
 }
 
-ngraph::PartialShape InputModelPDPD::InputModelPDPDImpl::getPartialShape(Place::Ptr place) const {
+ov::PartialShape InputModelPDPD::InputModelPDPDImpl::getPartialShape(Place::Ptr place) const {
     return castToTensorPlace(place)->get_partial_shape();
 }
 
-void InputModelPDPD::InputModelPDPDImpl::setElementType(Place::Ptr place, const ngraph::element::Type& type) {
+void InputModelPDPD::InputModelPDPDImpl::setElementType(Place::Ptr place, const ov::element::Type& type) {
     castToTensorPlace(place)->set_element_type(type);
 }
 
@@ -446,15 +447,15 @@ void InputModelPDPD::extract_subgraph(const std::vector<Place::Ptr>& inputs, con
     _impl->extractSubgraph(inputs, outputs);
 }
 
-void InputModelPDPD::set_partial_shape(Place::Ptr place, const ngraph::PartialShape& p_shape) {
+void InputModelPDPD::set_partial_shape(Place::Ptr place, const ov::PartialShape& p_shape) {
     _impl->setPartialShape(place, p_shape);
 }
 
-ngraph::PartialShape InputModelPDPD::get_partial_shape(Place::Ptr place) const {
+ov::PartialShape InputModelPDPD::get_partial_shape(Place::Ptr place) const {
     return _impl->getPartialShape(place);
 }
 
-void InputModelPDPD::set_element_type(Place::Ptr place, const ngraph::element::Type& type) {
+void InputModelPDPD::set_element_type(Place::Ptr place, const ov::element::Type& type) {
     _impl->setElementType(place, type);
 }
 
diff --git a/ngraph/frontend/paddlepaddle/src/node_context.hpp b/ngraph/frontend/paddlepaddle/src/node_context.hpp
index 2e02119c485..28461725362 100644
--- a/ngraph/frontend/paddlepaddle/src/node_context.hpp
+++ b/ngraph/frontend/paddlepaddle/src/node_context.hpp
@@ -8,7 +8,7 @@
 #include "paddlepaddle_frontend/exceptions.hpp"
 #include "paddlepaddle_frontend/utility.hpp"
 
-#define NGRAPH_VARIANT_DECLARATION(TYPE, info)                                            \
+#define OPENVINO_VARIANT_DECLARATION(TYPE, info)                                          \
     template <>                                                                           \
     class VariantWrapper<TYPE> : public VariantImpl<TYPE> {                               \
     public:                                                                               \
@@ -17,13 +17,13 @@
     }
 
 namespace ov {
-NGRAPH_VARIANT_DECLARATION(int32_t, "Variant::int32");
-NGRAPH_VARIANT_DECLARATION(std::vector<int32_t>, "Variant::int32_vector");
-NGRAPH_VARIANT_DECLARATION(float, "Variant::float");
-NGRAPH_VARIANT_DECLARATION(std::vector<float>, "Variant::float_vector");
-NGRAPH_VARIANT_DECLARATION(bool, "Variant::bool");
-NGRAPH_VARIANT_DECLARATION(ngraph::element::Type, "Variant::element_type");
-NGRAPH_VARIANT_DECLARATION(std::vector<int64_t>, "Variant::int64_vector");
+OPENVINO_VARIANT_DECLARATION(int32_t, "Variant::int32");
+OPENVINO_VARIANT_DECLARATION(std::vector<int32_t>, "Variant::int32_vector");
+OPENVINO_VARIANT_DECLARATION(float, "Variant::float");
+OPENVINO_VARIANT_DECLARATION(std::vector<float>, "Variant::float_vector");
+OPENVINO_VARIANT_DECLARATION(bool, "Variant::bool");
+OPENVINO_VARIANT_DECLARATION(ov::element::Type, "Variant::element_type");
+OPENVINO_VARIANT_DECLARATION(std::vector<int64_t>, "Variant::int64_vector");
 namespace frontend {
 namespace pdpd {
 using InPortName = std::string;
@@ -54,7 +54,7 @@ public:
     /// \param port_name Port name for the node
     ///
     /// \return Type of specified output port
-    virtual ngraph::element::Type get_out_port_type(const std::string& port_name) const = 0;
+    virtual ov::element::Type get_out_port_type(const std::string& port_name) const = 0;
 
     virtual std::string get_op_type() const = 0;
 };
@@ -170,7 +170,7 @@ public:
         return decoder.get_output_names();
     }
 
-    ngraph::element::Type get_out_port_type(const std::string& port_name) const {
+    ov::element::Type get_out_port_type(const std::string& port_name) const {
         return decoder.get_out_port_type(port_name);
     }
 
@@ -178,21 +178,21 @@ public:
         return decoder.get_op_type();
     }
 
-    NamedOutputs default_single_output_mapping(const std::shared_ptr<Node>& ngraph_node,
+    NamedOutputs default_single_output_mapping(const std::shared_ptr<Node>& node,
                                                const std::vector<OutPortName>& required_pdpd_out_names) const;
 };
 
 inline NamedOutputs NodeContext::default_single_output_mapping(
-    const std::shared_ptr<Node>& ngraph_node,
+    const std::shared_ptr<Node>& node,
     const std::vector<OutPortName>& required_pdpd_out_names) const {
     NamedOutputs named_outputs;
-    const auto& ngraph_outputs = ngraph_node->outputs();
+    const auto& outputs = node->outputs();
     const auto& pdpd_op_output_names = this->get_output_names();
-    FRONT_END_GENERAL_CHECK(ngraph_outputs.size() == 1, "nGraph node must have exactly one output");
+    FRONT_END_GENERAL_CHECK(outputs.size() == 1, "OV node must have exactly one output");
     for (const auto& pdpd_name : pdpd_op_output_names) {
         if (std::find(required_pdpd_out_names.begin(), required_pdpd_out_names.end(), pdpd_name) !=
             required_pdpd_out_names.end())
-            named_outputs[pdpd_name] = {ngraph_outputs[0]};
+            named_outputs[pdpd_name] = {outputs[0]};
     }
     return named_outputs;
 }
diff --git a/ngraph/frontend/paddlepaddle/src/op/argmax.cpp b/ngraph/frontend/paddlepaddle/src/op/argmax.cpp
index e2ad9a8fe6d..265c12ad879 100644
--- a/ngraph/frontend/paddlepaddle/src/op/argmax.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/argmax.cpp
@@ -2,9 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
+#include "openvino/opsets/opset6.hpp"
+
 namespace ov {
 namespace frontend {
 namespace pdpd {
@@ -13,24 +14,23 @@ NamedOutputs argmax(const NodeContext& node) {
     auto data = node.get_ng_input("X");
     bool flatten = node.get_attribute<bool>("flatten");
     const element::Type& index_element_type = element::i64;
-    const Output<ngraph::Node> k = ngraph::opset6::Constant::create(ngraph::element::i64, {}, {1});
+    const Output<ov::Node> k = ov::opset6::Constant::create(ov::element::i64, {}, {1});
 
     if (!flatten) {
         auto axis = node.get_attribute<int64_t>("axis");
-        const auto axis_to_remove = ngraph::opset6::Constant::create(element::u64, Shape{}, {axis});
-        auto node_topk = std::make_shared<ngraph::opset6::TopK>(data, k, axis, "max", "index", index_element_type);
-        const auto reshaped_indices = std::make_shared<ngraph::opset6::Squeeze>(node_topk->output(1), axis_to_remove);
+        const auto axis_to_remove = ov::opset6::Constant::create(element::u64, Shape{}, {axis});
+        auto node_topk = std::make_shared<ov::opset6::TopK>(data, k, axis, "max", "index", index_element_type);
+        const auto reshaped_indices = std::make_shared<ov::opset6::Squeeze>(node_topk->output(1), axis_to_remove);
         return node.default_single_output_mapping(
-            {std::make_shared<ngraph::opset6::Convert>(reshaped_indices, element::i64)},
+            {std::make_shared<ov::opset6::Convert>(reshaped_indices, element::i64)},
             {"Out"});
     } else {
         int64_t axis = 0;
-        const Output<ngraph::Node> reshape_flatten = ngraph::opset6::Constant::create(ngraph::element::i64, {1}, {-1});
-        auto node_reshape = std::make_shared<ngraph::opset6::Reshape>(data, reshape_flatten, true);
-        auto node_topk =
-            std::make_shared<ngraph::opset6::TopK>(node_reshape, k, axis, "max", "index", index_element_type);
+        const Output<ov::Node> reshape_flatten = ov::opset6::Constant::create(ov::element::i64, {1}, {-1});
+        auto node_reshape = std::make_shared<ov::opset6::Reshape>(data, reshape_flatten, true);
+        auto node_topk = std::make_shared<ov::opset6::TopK>(node_reshape, k, axis, "max", "index", index_element_type);
         return node.default_single_output_mapping(
-            {std::make_shared<ngraph::opset6::Convert>(node_topk->output(1), element::i64)},
+            {std::make_shared<ov::opset6::Convert>(node_topk->output(1), element::i64)},
             {"Out"});
     }
 }
diff --git a/ngraph/frontend/paddlepaddle/src/op/assign_value.cpp b/ngraph/frontend/paddlepaddle/src/op/assign_value.cpp
index ad5482f8d67..93b2fdfb08e 100644
--- a/ngraph/frontend/paddlepaddle/src/op/assign_value.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/assign_value.cpp
@@ -3,14 +3,15 @@
 //
 
 #include <node_context.hpp>
-#include <openvino/opsets/opset6.hpp>
+
+#include "openvino/opsets/opset6.hpp"
 namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
 NamedOutputs assign_value(const NodeContext& node) {
     std::vector<int32_t> shape = node.get_attribute<std::vector<int32_t>>("shape");
-    auto dtype = node.get_attribute<ngraph::element::Type>("dtype");
+    auto dtype = node.get_attribute<ov::element::Type>("dtype");
     std::shared_ptr<Node> const_node;
 
     switch (dtype) {
diff --git a/ngraph/frontend/paddlepaddle/src/op/batch_norm.cpp b/ngraph/frontend/paddlepaddle/src/op/batch_norm.cpp
index 1d52fecd0d3..86f4cea41fc 100644
--- a/ngraph/frontend/paddlepaddle/src/op/batch_norm.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/batch_norm.cpp
@@ -2,9 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
+#include "openvino/opsets/opset6.hpp"
+
 namespace ov {
 namespace frontend {
 namespace pdpd {
@@ -20,26 +21,25 @@ NamedOutputs batch_norm(const NodeContext& node) {
     PDPD_ASSERT((data_layout == "NCHW" || data_layout == "NHWC"), "Not supported input data layout!");
     if (data_layout == "NCHW") {
         return node.default_single_output_mapping(
-            {std::make_shared<ngraph::opset6::BatchNormInference>(data,
-                                                                  gamma,
-                                                                  beta,
-                                                                  mean,
-                                                                  variance,
-                                                                  node.get_attribute<float>("epsilon"))},
+            {std::make_shared<ov::opset6::BatchNormInference>(data,
+                                                              gamma,
+                                                              beta,
+                                                              mean,
+                                                              variance,
+                                                              node.get_attribute<float>("epsilon"))},
             {"Y"});
     } else {
-        auto input_order = ngraph::opset6::Constant::create(ngraph::element::i64, {4}, {0, 3, 1, 2});
-        auto data_nchw = std::make_shared<ngraph::opset6::Transpose>(data, input_order);
-        auto node_batch_norm =
-            std::make_shared<ngraph::opset6::BatchNormInference>(data_nchw,
-                                                                 gamma,
-                                                                 beta,
-                                                                 mean,
-                                                                 variance,
-                                                                 node.get_attribute<float>("epsilon"));
-        auto output_order = ngraph::opset6::Constant::create(ngraph::element::i64, {4}, {0, 2, 3, 1});
+        auto input_order = ov::opset6::Constant::create(ov::element::i64, {4}, {0, 3, 1, 2});
+        auto data_nchw = std::make_shared<ov::opset6::Transpose>(data, input_order);
+        auto node_batch_norm = std::make_shared<ov::opset6::BatchNormInference>(data_nchw,
+                                                                                gamma,
+                                                                                beta,
+                                                                                mean,
+                                                                                variance,
+                                                                                node.get_attribute<float>("epsilon"));
+        auto output_order = ov::opset6::Constant::create(ov::element::i64, {4}, {0, 2, 3, 1});
         return node.default_single_output_mapping(
-            {std::make_shared<ngraph::opset6::Transpose>(node_batch_norm, output_order)},
+            {std::make_shared<ov::opset6::Transpose>(node_batch_norm, output_order)},
             {"Y"});
     }
 }
diff --git a/ngraph/frontend/paddlepaddle/src/op/cast.cpp b/ngraph/frontend/paddlepaddle/src/op/cast.cpp
index 75cf919a12c..4530705483a 100644
--- a/ngraph/frontend/paddlepaddle/src/op/cast.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/cast.cpp
@@ -2,18 +2,19 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
+#include "openvino/opsets/opset6.hpp"
+
 namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
 NamedOutputs cast(const NodeContext& node) {
     auto data = node.get_ng_input("X");
-    auto out_dtype = node.get_attribute<ngraph::element::Type>("out_dtype");
+    auto out_dtype = node.get_attribute<ov::element::Type>("out_dtype");
 
-    return node.default_single_output_mapping({std::make_shared<ngraph::opset6::Convert>(data, out_dtype)}, {"Out"});
+    return node.default_single_output_mapping({std::make_shared<ov::opset6::Convert>(data, out_dtype)}, {"Out"});
 }
 
 }  // namespace op
diff --git a/ngraph/frontend/paddlepaddle/src/op/clip.cpp b/ngraph/frontend/paddlepaddle/src/op/clip.cpp
index 551548f89b8..c34806a1d53 100644
--- a/ngraph/frontend/paddlepaddle/src/op/clip.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/clip.cpp
@@ -2,9 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
+#include "openvino/opsets/opset6.hpp"
+
 namespace ov {
 namespace frontend {
 namespace pdpd {
@@ -15,7 +16,7 @@ NamedOutputs clip(const NodeContext& node) {
     auto max = node.get_attribute<float>("max");
     PDPD_OP_VALIDATION_CHECK(node, max >= min, "clip: max value must greater than min value!");
 
-    return node.default_single_output_mapping({std::make_shared<ngraph::opset6::Clamp>(data, min, max)}, {"Out"});
+    return node.default_single_output_mapping({std::make_shared<ov::opset6::Clamp>(data, min, max)}, {"Out"});
 }
 
 }  // namespace op
diff --git a/ngraph/frontend/paddlepaddle/src/op/concat.cpp b/ngraph/frontend/paddlepaddle/src/op/concat.cpp
index 13fb8b45318..512df6cc806 100644
--- a/ngraph/frontend/paddlepaddle/src/op/concat.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/concat.cpp
@@ -3,7 +3,8 @@
 //
 
 #include <node_context.hpp>
-#include <openvino/opsets/opset6.hpp>
+
+#include "openvino/opsets/opset6.hpp"
 
 namespace ov {
 namespace frontend {
diff --git a/ngraph/frontend/paddlepaddle/src/op/conv2d.cpp b/ngraph/frontend/paddlepaddle/src/op/conv2d.cpp
index 87b0b9f53f9..cd71b250fcd 100644
--- a/ngraph/frontend/paddlepaddle/src/op/conv2d.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/conv2d.cpp
@@ -2,9 +2,8 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <openvino/opsets/opset6.hpp>
-
 #include "conv2d_utils.hpp"
+#include "openvino/opsets/opset6.hpp"
 
 namespace ov {
 namespace frontend {
diff --git a/ngraph/frontend/paddlepaddle/src/op/conv2d_transpose.cpp b/ngraph/frontend/paddlepaddle/src/op/conv2d_transpose.cpp
index 57133dd626c..e0f8db7655f 100644
--- a/ngraph/frontend/paddlepaddle/src/op/conv2d_transpose.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/conv2d_transpose.cpp
@@ -3,9 +3,9 @@
 //
 
 #include <node_context.hpp>
-#include <openvino/opsets/opset6.hpp>
 
 #include "conv2d_utils.hpp"
+#include "openvino/opsets/opset6.hpp"
 
 namespace ov {
 namespace frontend {
diff --git a/ngraph/frontend/paddlepaddle/src/op/conv2d_utils.cpp b/ngraph/frontend/paddlepaddle/src/op/conv2d_utils.cpp
index 31dd422f53f..d1b7689d561 100644
--- a/ngraph/frontend/paddlepaddle/src/op/conv2d_utils.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/conv2d_utils.cpp
@@ -4,28 +4,27 @@
 
 #include "conv2d_utils.hpp"
 
-#include <openvino/opsets/opset6.hpp>
-
 #include "node_context.hpp"
+#include "openvino/opsets/opset6.hpp"
 
 namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
-ngraph::op::PadType get_auto_pad(const NodeContext& node) {
+ov::op::PadType get_auto_pad(const NodeContext& node) {
     // Default value means use explicitly provided padding values.
-    ngraph::op::PadType pad_type{ngraph::op::PadType::NOTSET};
+    ov::op::PadType pad_type{ov::op::PadType::NOTSET};
     auto padding_algorithm = node.get_attribute<std::string>("padding_algorithm");
-    static std::unordered_map<std::string, ngraph::op::PadType> auto_pad_values{
-        {"VALID", ngraph::op::PadType::VALID},
-        {"SAME", ngraph::op::PadType::SAME_UPPER},
-        {"NOTSET", ngraph::op::PadType::NOTSET},
+    static std::unordered_map<std::string, ov::op::PadType> auto_pad_values{
+        {"VALID", ov::op::PadType::VALID},
+        {"SAME", ov::op::PadType::SAME_UPPER},
+        {"NOTSET", ov::op::PadType::NOTSET},
     };
 
     const auto pad_val_it = auto_pad_values.find(padding_algorithm);
 
     if (pad_val_it == auto_pad_values.end()) {
-        pad_type = ngraph::op::PadType::NOTSET;
+        pad_type = ov::op::PadType::NOTSET;
     } else {
         pad_type = pad_val_it->second;
     }
diff --git a/ngraph/frontend/paddlepaddle/src/op/conv2d_utils.hpp b/ngraph/frontend/paddlepaddle/src/op/conv2d_utils.hpp
index 0dc62aec2d7..f5632612191 100644
--- a/ngraph/frontend/paddlepaddle/src/op/conv2d_utils.hpp
+++ b/ngraph/frontend/paddlepaddle/src/op/conv2d_utils.hpp
@@ -9,7 +9,7 @@ namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
-ngraph::op::PadType get_auto_pad(const NodeContext& node);
+ov::op::PadType get_auto_pad(const NodeContext& node);
 std::pair<CoordinateDiff, CoordinateDiff> get_pads(const NodeContext& node);
 std::shared_ptr<Node> get_reshaped_filter(const Output<Node>& filters, int32_t groups);
 
@@ -32,25 +32,23 @@ NamedOutputs conv2d_base(const NodeContext& node) {
     if (groups > 1) {
         const auto reshaped_filters = get_reshaped_filter(filters, groups);
 
-        return node.default_single_output_mapping(
-            {std::make_shared<T1>(data,
-                                  reshaped_filters,
-                                  ngraph::Strides(strides.begin(), strides.end()),
-                                  pads_begin,
-                                  pads_end,
-                                  ngraph::Strides(dilations.begin(), dilations.end()),
-                                  auto_pad_type)},
-            {"Output"});
+        return node.default_single_output_mapping({std::make_shared<T1>(data,
+                                                                        reshaped_filters,
+                                                                        ov::Strides(strides.begin(), strides.end()),
+                                                                        pads_begin,
+                                                                        pads_end,
+                                                                        ov::Strides(dilations.begin(), dilations.end()),
+                                                                        auto_pad_type)},
+                                                  {"Output"});
     } else {
-        return node.default_single_output_mapping(
-            {std::make_shared<T2>(data,
-                                  filters,
-                                  ngraph::Strides(strides.begin(), strides.end()),
-                                  pads_begin,
-                                  pads_end,
-                                  ngraph::Strides(dilations.begin(), dilations.end()),
-                                  auto_pad_type)},
-            {"Output"});
+        return node.default_single_output_mapping({std::make_shared<T2>(data,
+                                                                        filters,
+                                                                        ov::Strides(strides.begin(), strides.end()),
+                                                                        pads_begin,
+                                                                        pads_end,
+                                                                        ov::Strides(dilations.begin(), dilations.end()),
+                                                                        auto_pad_type)},
+                                                  {"Output"});
     }
 }
 
diff --git a/ngraph/frontend/paddlepaddle/src/op/cumsum.cpp b/ngraph/frontend/paddlepaddle/src/op/cumsum.cpp
index 13916a8efd2..a56724f0bf5 100644
--- a/ngraph/frontend/paddlepaddle/src/op/cumsum.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/cumsum.cpp
@@ -17,7 +17,7 @@ NamedOutputs cumsum(const NodeContext& node) {
     const auto reverse = node.get_attribute<bool>("reverse", false);
     const auto exclusive = node.get_attribute<bool>("exclusive", false);
 
-    std::shared_ptr<ngraph::Node> input = x.get_node_shared_ptr();
+    std::shared_ptr<ov::Node> input = x.get_node_shared_ptr();
     if (flatten) {
         // convert to 1-d tensor
         input = std::make_shared<default_opset::Reshape>(x,
diff --git a/ngraph/frontend/paddlepaddle/src/op/deformable_conv.cpp b/ngraph/frontend/paddlepaddle/src/op/deformable_conv.cpp
index 5c5b5a7689d..93b03e9d9ec 100644
--- a/ngraph/frontend/paddlepaddle/src/op/deformable_conv.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/deformable_conv.cpp
@@ -1,10 +1,10 @@
 // Copyright (C) 2018-2021 Intel Corporation
 // SPDX-License-Identifier: Apache-2.0
 //
-#include <ngraph/opsets/opset8.hpp>
 #include <node_context.hpp>
 
 #include "conv2d_utils.hpp"
+#include "openvino/opsets/opset8.hpp"
 
 namespace ov {
 namespace frontend {
@@ -25,37 +25,37 @@ NamedOutputs deformable_conv(const NodeContext& node) {
     const auto pads_begin = paddings.first;
     const auto pads_end = paddings.second;
 
-    const ngraph::op::PadType auto_pad{ngraph::op::PadType::EXPLICIT};
+    const ov::op::PadType auto_pad{ov::op::PadType::EXPLICIT};
 
     std::shared_ptr<Node> output_node;
     if (node.has_ng_input("Mask")) {
         auto mask = node.get_ng_input("Mask");
         output_node =
-            std::make_shared<ngraph::opset8::DeformableConvolution>(input,
-                                                                    offset,
-                                                                    filter,
-                                                                    mask,
-                                                                    ngraph::Strides(strides.begin(), strides.end()),
-                                                                    pads_begin,
-                                                                    pads_end,
-                                                                    ngraph::Strides(dilations.begin(), dilations.end()),
-                                                                    auto_pad,
-                                                                    groups,
-                                                                    deformable_groups,
-                                                                    true);
+            std::make_shared<ov::opset8::DeformableConvolution>(input,
+                                                                offset,
+                                                                filter,
+                                                                mask,
+                                                                ov::Strides(strides.begin(), strides.end()),
+                                                                pads_begin,
+                                                                pads_end,
+                                                                ov::Strides(dilations.begin(), dilations.end()),
+                                                                auto_pad,
+                                                                groups,
+                                                                deformable_groups,
+                                                                true);
     } else {
         output_node =
-            std::make_shared<ngraph::opset8::DeformableConvolution>(input,
-                                                                    offset,
-                                                                    filter,
-                                                                    ngraph::Strides(strides.begin(), strides.end()),
-                                                                    pads_begin,
-                                                                    pads_end,
-                                                                    ngraph::Strides(dilations.begin(), dilations.end()),
-                                                                    auto_pad,
-                                                                    groups,
-                                                                    deformable_groups,
-                                                                    true);
+            std::make_shared<ov::opset8::DeformableConvolution>(input,
+                                                                offset,
+                                                                filter,
+                                                                ov::Strides(strides.begin(), strides.end()),
+                                                                pads_begin,
+                                                                pads_end,
+                                                                ov::Strides(dilations.begin(), dilations.end()),
+                                                                auto_pad,
+                                                                groups,
+                                                                deformable_groups,
+                                                                true);
     }
 
     return node.default_single_output_mapping({output_node}, {"Output"});
diff --git a/ngraph/frontend/paddlepaddle/src/op/dropout.cpp b/ngraph/frontend/paddlepaddle/src/op/dropout.cpp
index 3548e63b226..fd4d5546184 100644
--- a/ngraph/frontend/paddlepaddle/src/op/dropout.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/dropout.cpp
@@ -2,9 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
+#include "openvino/opsets/opset6.hpp"
+
 namespace ov {
 namespace frontend {
 namespace pdpd {
@@ -17,10 +18,9 @@ NamedOutputs dropout(const NodeContext& node) {
         (dropout_implementation == "downgrade_in_infer" || dropout_implementation == "upscale_in_train"),
         "Unsupported dropout mode!");
     if (dropout_implementation == "downgrade_in_infer") {
-        auto dropout_prob = ngraph::opset6::Constant::create(ngraph::element::f32,
-                                                             {1},
-                                                             {1 - node.get_attribute<float>("dropout_prob")});
-        return node.default_single_output_mapping({std::make_shared<ngraph::opset6::Multiply>(data, dropout_prob)},
+        auto dropout_prob =
+            ov::opset6::Constant::create(ov::element::f32, {1}, {1 - node.get_attribute<float>("dropout_prob")});
+        return node.default_single_output_mapping({std::make_shared<ov::opset6::Multiply>(data, dropout_prob)},
                                                   {"Out"});
     } else {
         return node.default_single_output_mapping(data.get_node_shared_ptr(), {"Out"});
diff --git a/ngraph/frontend/paddlepaddle/src/op/elementwise_ops.cpp b/ngraph/frontend/paddlepaddle/src/op/elementwise_ops.cpp
index dd08bdc7b15..7dcf4a62a11 100644
--- a/ngraph/frontend/paddlepaddle/src/op/elementwise_ops.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/elementwise_ops.cpp
@@ -3,9 +3,10 @@
 //
 
 #include <map>
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
+#include "openvino/opsets/opset6.hpp"
+
 namespace ov {
 namespace frontend {
 namespace pdpd {
@@ -25,9 +26,9 @@ NamedOutputs elementwise_ops(const NodeContext& node) {
     if ((axis == -1) || (axis == x_rank - 1) || (x_rank == y_rank)) {
         return node.default_single_output_mapping({std::make_shared<T>(x, y)}, {"Out"});
     } else {
-        // This broadcast can be implemented by either ngraph::Reshape or
-        // ngraph::Broadcast. Since PDPD implicates y_shape is a subsequence of
-        // x_shape starting from axis, to use ngraph::Reshape like Paddle2ONNX,
+        // This broadcast can be implemented by either ov::Reshape or
+        // ov::Broadcast. Since PDPD implicates y_shape is a subsequence of
+        // x_shape starting from axis, to use ov::Reshape like Paddle2ONNX,
         // which is more friendly to PnP.
         auto broadcast_shape = std::vector<int64_t>(x_rank, 1);
         PartialShape y_shape = y.get_partial_shape();
@@ -35,49 +36,48 @@ NamedOutputs elementwise_ops(const NodeContext& node) {
         for (auto it = y_shape.begin(); it != y_shape.end(); ++i, ++it)
             broadcast_shape[axis + i] = (*it).get_length();
 
-        auto reshape_node = ngraph::opset6::Constant::create(ngraph::element::i64,
-                                                             ngraph::Shape{broadcast_shape.size()},
-                                                             broadcast_shape);
-        auto y_node = std::make_shared<ngraph::opset6::Reshape>(y, reshape_node, false);
+        auto reshape_node =
+            ov::opset6::Constant::create(ov::element::i64, ov::Shape{broadcast_shape.size()}, broadcast_shape);
+        auto y_node = std::make_shared<ov::opset6::Reshape>(y, reshape_node, false);
         return node.default_single_output_mapping({std::make_shared<T>(x, y_node)}, {"Out"});
     }
 }
 
 //
 NamedOutputs elementwise_add(const NodeContext& node_context) {
-    return elementwise_ops<ngraph::opset6::Add>(node_context);
+    return elementwise_ops<ov::opset6::Add>(node_context);
 }
 
 NamedOutputs elementwise_sub(const NodeContext& node_context) {
-    return elementwise_ops<ngraph::opset6::Subtract>(node_context);
+    return elementwise_ops<ov::opset6::Subtract>(node_context);
 }
 
 NamedOutputs elementwise_mul(const NodeContext& node_context) {
-    return elementwise_ops<ngraph::opset6::Multiply>(node_context);
+    return elementwise_ops<ov::opset6::Multiply>(node_context);
 }
 
 NamedOutputs elementwise_div(const NodeContext& node_context) {
-    return elementwise_ops<ngraph::opset6::Divide>(node_context);
+    return elementwise_ops<ov::opset6::Divide>(node_context);
 }
 
 NamedOutputs elementwise_min(const NodeContext& node_context) {
-    return elementwise_ops<ngraph::opset6::Minimum>(node_context);
+    return elementwise_ops<ov::opset6::Minimum>(node_context);
 }
 
 NamedOutputs elementwise_max(const NodeContext& node_context) {
-    return elementwise_ops<ngraph::opset6::Maximum>(node_context);
+    return elementwise_ops<ov::opset6::Maximum>(node_context);
 }
 
 NamedOutputs elementwise_pow(const NodeContext& node_context) {
-    return elementwise_ops<ngraph::opset6::Power>(node_context);
+    return elementwise_ops<ov::opset6::Power>(node_context);
 }
 
 NamedOutputs elementwise_equal(const NodeContext& node_context) {
-    return elementwise_ops<ngraph::opset6::Equal>(node_context);
+    return elementwise_ops<ov::opset6::Equal>(node_context);
 }
 
 NamedOutputs elementwise_greater_equal(const NodeContext& node_context) {
-    return elementwise_ops<ngraph::opset6::GreaterEqual>(node_context);
+    return elementwise_ops<ov::opset6::GreaterEqual>(node_context);
 }
 
 }  // namespace op
diff --git a/ngraph/frontend/paddlepaddle/src/op/embedding.cpp b/ngraph/frontend/paddlepaddle/src/op/embedding.cpp
index 7a2c337b753..e8cfe77f4e8 100644
--- a/ngraph/frontend/paddlepaddle/src/op/embedding.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/embedding.cpp
@@ -3,7 +3,8 @@
 //
 
 #include <node_context.hpp>
-#include <openvino/opsets/opset8.hpp>
+
+#include "openvino/opsets/opset8.hpp"
 
 namespace ov {
 namespace frontend {
diff --git a/ngraph/frontend/paddlepaddle/src/op/expand_v2.cpp b/ngraph/frontend/paddlepaddle/src/op/expand_v2.cpp
index c9ab3347d1e..3cc59b41778 100644
--- a/ngraph/frontend/paddlepaddle/src/op/expand_v2.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/expand_v2.cpp
@@ -2,9 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
-#include <paddlepaddle_frontend/utility.hpp>
+
+#include "openvino/opsets/opset6.hpp"
+#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
@@ -17,12 +18,12 @@ NamedOutputs expand_v2(const NodeContext& node) {
         shape_expected_node = node.get_ng_input("Shape");
     } else if (node.has_ng_input("expand_shapes_tensor")) {
         auto inputs = node.get_ng_inputs("expand_shapes_tensor");
-        ngraph::NodeVector node_vec;
+        ov::NodeVector node_vec;
         for (auto& input : inputs) {
-            auto cast = std::make_shared<ngraph::opset6::Convert>(input, element::i32);
+            auto cast = std::make_shared<ov::opset6::Convert>(input, element::i32);
             node_vec.push_back(cast);
         }
-        shape_expected_node = std::make_shared<ngraph::opset6::Concat>(node_vec, 0);
+        shape_expected_node = std::make_shared<ov::opset6::Concat>(node_vec, 0);
     } else {
         std::vector<int32_t> shape_expected;
         if (node.has_attribute<std::vector<int32_t>>("shape")) {
@@ -30,20 +31,17 @@ NamedOutputs expand_v2(const NodeContext& node) {
         } else {
             throw std::runtime_error("expand: has no shape attribute");
         }
-        shape_expected_node =
-            ngraph::opset6::Constant::create(ngraph::element::i32, {shape_expected.size()}, shape_expected);
+        shape_expected_node = ov::opset6::Constant::create(ov::element::i32, {shape_expected.size()}, shape_expected);
     }
     // if -1 in shape we will copy the orginal value from input
-    auto zero_node = ngraph::opset6::Constant::create(ngraph::element::i32, {1}, {0});
-    auto mask_node = std::make_shared<ngraph::opset6::Greater>(shape_expected_node, zero_node);
-    auto input_shape_node = std::make_shared<ngraph::opset6::ShapeOf>(x, element::i32);
-    auto fixed_shape_node = std::make_shared<ngraph::opset6::Select>(mask_node, shape_expected_node, input_shape_node);
-    auto repeated_node = std::make_shared<ngraph::opset6::Divide>(fixed_shape_node, input_shape_node, false);
+    auto zero_node = ov::opset6::Constant::create(ov::element::i32, {1}, {0});
+    auto mask_node = std::make_shared<ov::opset6::Greater>(shape_expected_node, zero_node);
+    auto input_shape_node = std::make_shared<ov::opset6::ShapeOf>(x, element::i32);
+    auto fixed_shape_node = std::make_shared<ov::opset6::Select>(mask_node, shape_expected_node, input_shape_node);
+    auto repeated_node = std::make_shared<ov::opset6::Divide>(fixed_shape_node, input_shape_node, false);
 
     return node.default_single_output_mapping(
-        {std::make_shared<ngraph::opset6::Tile>(
-            x,
-            std::make_shared<ngraph::opset6::Convert>(repeated_node, element::i64))},
+        {std::make_shared<ov::opset6::Tile>(x, std::make_shared<ov::opset6::Convert>(repeated_node, element::i64))},
         {"Out"});
 }
 
diff --git a/ngraph/frontend/paddlepaddle/src/op/fill_any_like.cpp b/ngraph/frontend/paddlepaddle/src/op/fill_any_like.cpp
index 11a2371c0d4..af52a2581b4 100644
--- a/ngraph/frontend/paddlepaddle/src/op/fill_any_like.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/fill_any_like.cpp
@@ -12,7 +12,7 @@ namespace pdpd {
 namespace op {
 NamedOutputs fill_any_like(const NodeContext& node) {
     const auto x = node.get_ng_input("X");
-    auto dtype = node.get_attribute<ngraph::element::Type>("dtype", element::undefined);
+    auto dtype = node.get_attribute<ov::element::Type>("dtype", element::undefined);
     const auto value = node.get_attribute<float>("value");
     if (dtype == element::undefined) {
         // when type does not define, use the input type
diff --git a/ngraph/frontend/paddlepaddle/src/op/fill_constant.cpp b/ngraph/frontend/paddlepaddle/src/op/fill_constant.cpp
index ffc92c3397b..0983bbabe41 100644
--- a/ngraph/frontend/paddlepaddle/src/op/fill_constant.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/fill_constant.cpp
@@ -3,7 +3,8 @@
 //
 
 #include <node_context.hpp>
-#include <openvino/opsets/opset6.hpp>
+
+#include "openvino/opsets/opset6.hpp"
 
 namespace ov {
 namespace frontend {
@@ -11,7 +12,7 @@ namespace pdpd {
 namespace op {
 NamedOutputs fill_constant(const NodeContext& node) {
     auto shape = node.get_attribute<std::vector<int64_t>>("shape");
-    auto dtype = node.get_attribute<ngraph::element::Type>("dtype");
+    auto dtype = node.get_attribute<ov::element::Type>("dtype");
     Output<Node> value_node;
     Output<Node> shape_node;
     if (node.has_ng_input("ValueTensor")) {
diff --git a/ngraph/frontend/paddlepaddle/src/op/fill_constant_batch_size_like.cpp b/ngraph/frontend/paddlepaddle/src/op/fill_constant_batch_size_like.cpp
index 04676913e74..e0f4c5728b0 100644
--- a/ngraph/frontend/paddlepaddle/src/op/fill_constant_batch_size_like.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/fill_constant_batch_size_like.cpp
@@ -4,55 +4,56 @@
 
 #include <limits.h>
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
-#include <paddlepaddle_frontend/utility.hpp>
+
+#include "openvino/opsets/opset6.hpp"
+#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
 static std::shared_ptr<Node> get_val(int32_t idx, const Output<Node>& data) {
-    auto startsNode = ngraph::opset6::Constant::create(element::i32, {1}, {idx});
-    auto endsNode = ngraph::opset6::Constant::create(element::i32, {1}, {idx + 1});
-    auto stridesNode = ngraph::opset6::Constant::create(element::i32, {1}, {1});
-    return std::make_shared<ngraph::opset6::StridedSlice>(data,
-                                                          startsNode,
-                                                          endsNode,
-                                                          stridesNode,
-                                                          std::vector<int64_t>(1, 0),
-                                                          std::vector<int64_t>(1, 0));
+    auto startsNode = ov::opset6::Constant::create(element::i32, {1}, {idx});
+    auto endsNode = ov::opset6::Constant::create(element::i32, {1}, {idx + 1});
+    auto stridesNode = ov::opset6::Constant::create(element::i32, {1}, {1});
+    return std::make_shared<ov::opset6::StridedSlice>(data,
+                                                      startsNode,
+                                                      endsNode,
+                                                      stridesNode,
+                                                      std::vector<int64_t>(1, 0),
+                                                      std::vector<int64_t>(1, 0));
 }
 
 static std::shared_ptr<Node> set_val(int32_t idx, std::shared_ptr<Node> val_node, std::shared_ptr<Node> array_node) {
     NodeVector nodes;
     if (idx > 0) {
         // [0, idx)
-        auto startsNode = ngraph::opset6::Constant::create(element::i32, {1}, {0});
-        auto endsNode = ngraph::opset6::Constant::create(element::i32, {1}, {idx});
-        auto stridesNode = ngraph::opset6::Constant::create(element::i32, {1}, {1});
-        auto head = std::make_shared<ngraph::opset6::StridedSlice>(array_node,
-                                                                   startsNode,
-                                                                   endsNode,
-                                                                   stridesNode,
-                                                                   std::vector<int64_t>(1, 0),
-                                                                   std::vector<int64_t>(1, 0));
-        nodes.push_back(head);
-    }
-    nodes.push_back(val_node);
-    // [idx + 1, max)
-    auto startsNode = ngraph::opset6::Constant::create(element::i32, {1}, {idx + 1});
-    auto endsNode = ngraph::opset6::Constant::create(element::i32, {1}, {INT_MAX});
-    auto stridesNode = ngraph::opset6::Constant::create(element::i32, {1}, {1});
-    auto tail = std::make_shared<ngraph::opset6::StridedSlice>(array_node,
+        auto startsNode = ov::opset6::Constant::create(element::i32, {1}, {0});
+        auto endsNode = ov::opset6::Constant::create(element::i32, {1}, {idx});
+        auto stridesNode = ov::opset6::Constant::create(element::i32, {1}, {1});
+        auto head = std::make_shared<ov::opset6::StridedSlice>(array_node,
                                                                startsNode,
                                                                endsNode,
                                                                stridesNode,
                                                                std::vector<int64_t>(1, 0),
                                                                std::vector<int64_t>(1, 0));
+        nodes.push_back(head);
+    }
+    nodes.push_back(val_node);
+    // [idx + 1, max)
+    auto startsNode = ov::opset6::Constant::create(element::i32, {1}, {idx + 1});
+    auto endsNode = ov::opset6::Constant::create(element::i32, {1}, {INT_MAX});
+    auto stridesNode = ov::opset6::Constant::create(element::i32, {1}, {1});
+    auto tail = std::make_shared<ov::opset6::StridedSlice>(array_node,
+                                                           startsNode,
+                                                           endsNode,
+                                                           stridesNode,
+                                                           std::vector<int64_t>(1, 0),
+                                                           std::vector<int64_t>(1, 0));
     nodes.push_back(tail);
 
-    return std::make_shared<ngraph::opset6::Concat>(nodes, 0);
+    return std::make_shared<ov::opset6::Concat>(nodes, 0);
 }
 
 template <element::Type_t Type,
@@ -66,12 +67,12 @@ static Output<Node> get_seed_node(const NodeContext& node) {
     auto str_value = node.get_attribute<std::string>("str_value");
     if (str_value.empty()) {
         auto float_value = node.get_attribute<float>("value");
-        val_node = ngraph::opset6::Constant::create(dtype, {1}, {static_cast<StorageDataType>(float_value)});
+        val_node = ov::opset6::Constant::create(dtype, {1}, {static_cast<StorageDataType>(float_value)});
     } else {
         std::stringstream ss(str_value);
         StorageDataType tmp_value;
         ss >> tmp_value;
-        val_node = ngraph::opset6::Constant::create(dtype, {1}, {static_cast<StorageDataType>(tmp_value)});
+        val_node = ov::opset6::Constant::create(dtype, {1}, {static_cast<StorageDataType>(tmp_value)});
     }
     return val_node;
 }
@@ -105,18 +106,17 @@ NamedOutputs fill_constant_batch_size_like(const NodeContext& node) {
     auto output_dim_idx = node.get_attribute<int32_t>("output_dim_idx");
     auto shapes = node.get_attribute<std::vector<int32_t>>("shape");
     auto input = node.get_ng_input("Input");
-    auto input_shape = std::make_shared<ngraph::opset6::ShapeOf>(input, element::i32);
+    auto input_shape = std::make_shared<ov::opset6::ShapeOf>(input, element::i32);
     // 1, cat the array:
     //   shape[0, shape[output_dim_idx]) + input_shape[input_dim_idx] +
     //   shape[shape[output_dim_idx + 1], -1]
     auto input_val_node = get_val(input_dim_idx, input_shape);
-    auto shapes_node = ngraph::opset6::Constant::create(ngraph::element::i32, {shapes.size()}, shapes);
+    auto shapes_node = ov::opset6::Constant::create(ov::element::i32, {shapes.size()}, shapes);
     auto shape_node = set_val(output_dim_idx, input_val_node, shapes_node);
 
     // 2, use the shape broadcast the node
     auto val_node = get_seed_node(node);
-    return node.default_single_output_mapping({std::make_shared<ngraph::opset6::Broadcast>(val_node, shape_node)},
-                                              {"Out"});
+    return node.default_single_output_mapping({std::make_shared<ov::opset6::Broadcast>(val_node, shape_node)}, {"Out"});
 }
 
 }  // namespace op
diff --git a/ngraph/frontend/paddlepaddle/src/op/flatten_contiguous_range.cpp b/ngraph/frontend/paddlepaddle/src/op/flatten_contiguous_range.cpp
index 0a32a251ac5..7a395248b1d 100644
--- a/ngraph/frontend/paddlepaddle/src/op/flatten_contiguous_range.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/flatten_contiguous_range.cpp
@@ -2,9 +2,9 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/builder/reshape.hpp>
 #include <node_context.hpp>
-#include <openvino/opsets/opset6.hpp>
+
+#include "openvino/opsets/opset6.hpp"
 
 namespace ov {
 namespace frontend {
diff --git a/ngraph/frontend/paddlepaddle/src/op/gelu.cpp b/ngraph/frontend/paddlepaddle/src/op/gelu.cpp
index 6f0593e9215..a5ed25ec636 100644
--- a/ngraph/frontend/paddlepaddle/src/op/gelu.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/gelu.cpp
@@ -13,7 +13,7 @@ namespace op {
 NamedOutputs gelu(const NodeContext& node) {
     const auto data = node.get_ng_input("X");
     const auto approximate = node.get_attribute<bool>("approximate", false);
-    const auto mode = approximate ? ngraph::op::GeluApproximationMode::TANH : ngraph::op::GeluApproximationMode::ERF;
+    const auto mode = approximate ? ov::op::GeluApproximationMode::TANH : ov::op::GeluApproximationMode::ERF;
 
     return node.default_single_output_mapping({std::make_shared<default_opset::Gelu>(data, mode)}, {"Out"});
 }
diff --git a/ngraph/frontend/paddlepaddle/src/op/hard_sigmoid.cpp b/ngraph/frontend/paddlepaddle/src/op/hard_sigmoid.cpp
index b3a840c4618..fa993e5e444 100644
--- a/ngraph/frontend/paddlepaddle/src/op/hard_sigmoid.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/hard_sigmoid.cpp
@@ -2,9 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
-#include <paddlepaddle_frontend/utility.hpp>
+
+#include "openvino/opsets/opset6.hpp"
+#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
@@ -15,10 +16,9 @@ NamedOutputs hard_sigmoid(const NodeContext& node) {
     auto dtype = data.get_element_type();
     float slope = node.get_attribute<float>("slope", 0.2f);
     float offset = node.get_attribute<float>("offset", 0.5f);
-    auto alpha = ngraph::opset6::Constant::create(dtype, Shape{}, {slope});
-    auto beta = ngraph::opset6::Constant::create(dtype, Shape{}, {offset});
-    return node.default_single_output_mapping({std::make_shared<ngraph::opset6::HardSigmoid>(data, alpha, beta)},
-                                              {"Out"});
+    auto alpha = ov::opset6::Constant::create(dtype, Shape{}, {slope});
+    auto beta = ov::opset6::Constant::create(dtype, Shape{}, {offset});
+    return node.default_single_output_mapping({std::make_shared<ov::opset6::HardSigmoid>(data, alpha, beta)}, {"Out"});
 }
 
 }  // namespace op
diff --git a/ngraph/frontend/paddlepaddle/src/op/hard_swish.cpp b/ngraph/frontend/paddlepaddle/src/op/hard_swish.cpp
index 618d29ae3bc..196705e0496 100644
--- a/ngraph/frontend/paddlepaddle/src/op/hard_swish.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/hard_swish.cpp
@@ -2,9 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
-#include <paddlepaddle_frontend/utility.hpp>
+
+#include "openvino/opsets/opset6.hpp"
+#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
@@ -24,7 +25,7 @@ NamedOutputs hard_swish(const NodeContext& node) {
         auto offset = node.get_attribute<float>("offset");
         PDPD_ASSERT(std::abs(offset - 3.0) < 0.001, "hard_swish: Only offset = 3.0 is currently supported");
     }
-    return node.default_single_output_mapping({std::make_shared<ngraph::opset6::HSwish>(data)}, {"Out"});
+    return node.default_single_output_mapping({std::make_shared<ov::opset6::HSwish>(data)}, {"Out"});
 }
 
 }  // namespace op
diff --git a/ngraph/frontend/paddlepaddle/src/op/interp.cpp b/ngraph/frontend/paddlepaddle/src/op/interp.cpp
index e1a6fdca36e..7831ce06389 100644
--- a/ngraph/frontend/paddlepaddle/src/op/interp.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/interp.cpp
@@ -12,10 +12,10 @@ namespace pdpd {
 namespace op {
 using namespace default_opset;
 
-static std::shared_ptr<ngraph::Node> calculate_output_shape_based_on_scales(const Output<ngraph::Node>& data,
-                                                                            const std::vector<float>& scale,
-                                                                            Output<ngraph::Node>& scales,
-                                                                            const int space_dim) {
+static std::shared_ptr<ov::Node> calculate_output_shape_based_on_scales(const Output<ov::Node>& data,
+                                                                        const std::vector<float>& scale,
+                                                                        Output<ov::Node>& scales,
+                                                                        const int space_dim) {
     const size_t scale_size = static_cast<size_t>(space_dim + 2);
     FRONT_END_GENERAL_CHECK(scale.size() > 0 && scale.size() <= scale_size);
 
@@ -25,25 +25,25 @@ static std::shared_ptr<ngraph::Node> calculate_output_shape_based_on_scales(cons
 
     const auto shape_of_data = std::make_shared<Convert>(std::make_shared<ShapeOf>(data), scales.get_element_type());
     const auto multiply = std::make_shared<Multiply>(shape_of_data, scales);
-    const auto output_shape = std::make_shared<Convert>(multiply, ngraph::element::i64);
+    const auto output_shape = std::make_shared<Convert>(multiply, ov::element::i64);
 
     return output_shape;
 }
 
-static std::shared_ptr<ngraph::Node> calculate_scales_based_on_sizes(const Output<ngraph::Node>& data,
-                                                                     const Output<ngraph::Node>& sizes) {
+static std::shared_ptr<ov::Node> calculate_scales_based_on_sizes(const Output<ov::Node>& data,
+                                                                 const Output<ov::Node>& sizes) {
     const float epsilon = 1.0e-5;
-    const auto shape_of_data = std::make_shared<Convert>(std::make_shared<ShapeOf>(data), ngraph::element::f32);
-    const auto converted_sizes = std::make_shared<Convert>(sizes, ngraph::element::f32);
+    const auto shape_of_data = std::make_shared<Convert>(std::make_shared<ShapeOf>(data), ov::element::f32);
+    const auto converted_sizes = std::make_shared<Convert>(sizes, ov::element::f32);
     const auto divide = std::make_shared<Divide>(converted_sizes, shape_of_data);
-    const auto eps_node = std::make_shared<Constant>(ngraph::element::f32, Shape{}, epsilon);
+    const auto eps_node = std::make_shared<Constant>(ov::element::f32, Shape{}, epsilon);
     const auto scales = std::make_shared<Add>(divide, eps_node);
 
     return scales;
 }
 
-static std::shared_ptr<ngraph::Node> extract_out_sizes(const Output<ngraph::Node>& data,
-                                                       const std::vector<int64_t>& out_sizes) {
+static std::shared_ptr<ov::Node> extract_out_sizes(const Output<ov::Node>& data,
+                                                   const std::vector<int64_t>& out_sizes) {
     const auto shape_of_x = std::make_shared<ShapeOf>(data);
     const auto shape_begin = Constant::create(element::i64, {1}, {0});
     const int end_idx = static_cast<int>(out_sizes.size());
diff --git a/ngraph/frontend/paddlepaddle/src/op/layer_norm.cpp b/ngraph/frontend/paddlepaddle/src/op/layer_norm.cpp
index f28468a1523..12599ad99bc 100644
--- a/ngraph/frontend/paddlepaddle/src/op/layer_norm.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/layer_norm.cpp
@@ -37,8 +37,8 @@ NamedOutputs layer_norm(const NodeContext& node) {
                                                                  std::vector<int64_t>{0},
                                                                  std::vector<int64_t>{1});
 
-    const auto mvn = std::make_shared<MVN>(data, axis, true, epsilon, ngraph::op::MVNEpsMode::INSIDE_SQRT);
-    std::shared_ptr<ngraph::Node> result = mvn;
+    const auto mvn = std::make_shared<MVN>(data, axis, true, epsilon, ov::op::MVNEpsMode::INSIDE_SQRT);
+    std::shared_ptr<ov::Node> result = mvn;
     if (node.has_ng_input("Scale")) {
         const auto s = node.get_ng_input("Scale");
         const auto reshaped_s = std::make_shared<Reshape>(s, scale_bias_shape, false);
diff --git a/ngraph/frontend/paddlepaddle/src/op/leakyrelu.cpp b/ngraph/frontend/paddlepaddle/src/op/leakyrelu.cpp
index c5ab5b74f21..4a54ccb5173 100644
--- a/ngraph/frontend/paddlepaddle/src/op/leakyrelu.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/leakyrelu.cpp
@@ -2,17 +2,18 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
+#include "openvino/opsets/opset6.hpp"
+
 namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
 NamedOutputs leaky_relu(const NodeContext& node) {
     auto data = node.get_ng_input("X");
-    auto alpha = ngraph::opset6::Constant::create(ngraph::element::f32, {1}, {node.get_attribute<float>("alpha")});
-    return node.default_single_output_mapping({std::make_shared<ngraph::opset6::PRelu>(data, alpha)}, {"Out"});
+    auto alpha = ov::opset6::Constant::create(ov::element::f32, {1}, {node.get_attribute<float>("alpha")});
+    return node.default_single_output_mapping({std::make_shared<ov::opset6::PRelu>(data, alpha)}, {"Out"});
 }
 }  // namespace op
 }  // namespace pdpd
diff --git a/ngraph/frontend/paddlepaddle/src/op/log.cpp b/ngraph/frontend/paddlepaddle/src/op/log.cpp
index 8b0e18c5826..a38e802cb5a 100644
--- a/ngraph/frontend/paddlepaddle/src/op/log.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/log.cpp
@@ -2,9 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
-#include <paddlepaddle_frontend/utility.hpp>
+
+#include "openvino/opsets/opset6.hpp"
+#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
@@ -12,7 +13,7 @@ namespace pdpd {
 namespace op {
 NamedOutputs log(const NodeContext& node) {
     auto x = node.get_ng_input("X");
-    return node.default_single_output_mapping({std::make_shared<ngraph::opset6::Log>(x)}, {"Out"});
+    return node.default_single_output_mapping({std::make_shared<ov::opset6::Log>(x)}, {"Out"});
 }
 }  // namespace op
 }  // namespace pdpd
diff --git a/ngraph/frontend/paddlepaddle/src/op/logical_not.cpp b/ngraph/frontend/paddlepaddle/src/op/logical_not.cpp
index 71d571d56c8..f182be450ec 100644
--- a/ngraph/frontend/paddlepaddle/src/op/logical_not.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/logical_not.cpp
@@ -2,16 +2,17 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
+#include "openvino/opsets/opset6.hpp"
+
 namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
 NamedOutputs logical_not(const NodeContext& node) {
     auto data = node.get_ng_input("X");
-    return node.default_single_output_mapping({std::make_shared<ngraph::opset6::LogicalNot>(data)}, {"Out"});
+    return node.default_single_output_mapping({std::make_shared<ov::opset6::LogicalNot>(data)}, {"Out"});
 }
 }  // namespace op
 }  // namespace pdpd
diff --git a/ngraph/frontend/paddlepaddle/src/op/lstm.cpp b/ngraph/frontend/paddlepaddle/src/op/lstm.cpp
index 3f34629b394..da8c93a2cff 100644
--- a/ngraph/frontend/paddlepaddle/src/op/lstm.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/lstm.cpp
@@ -3,10 +3,10 @@
 //
 
 #include <node_context.hpp>
-#include <openvino/opsets/opset6.hpp>
 
-#include "ngraph/builder/reshape.hpp"
+#include "openvino/opsets/opset6.hpp"
 #include "paddlepaddle_frontend/utility.hpp"
+#include "pdpd_utils.hpp"
 
 namespace ov {
 namespace frontend {
@@ -28,7 +28,7 @@ enum class LSTMInput {
 
 struct LSTMNgInputMap {
     explicit LSTMNgInputMap(const NodeContext& node, Output<Node>& prev_output, int layer) {
-        auto input_x = ngraph::builder::opset1::reorder_axes(prev_output, {1, 0, 2});
+        auto input_x = reorder_axes(prev_output, {1, 0, 2});
         //[begin. end)
         auto weight_list = node.get_ng_inputs("WeightList");
         auto weight_begin = weight_list.begin();
@@ -115,39 +115,39 @@ struct LSTMNgInputMap {
         auto c_end = opset6::Constant::create(element::i64, {1}, {layer * bidirect_len + bidirect_len});
 
         m_input_map[LSTMInput::LSTM_INPUT_INIT_H] =
-            ngraph::builder::opset1::reorder_axes(std::make_shared<opset6::StridedSlice>(init_states[0],
-                                                                                         h_begin,
-                                                                                         h_end,
-                                                                                         std::vector<int64_t>{0},
-                                                                                         std::vector<int64_t>{0}),
-                                                  {1, 0, 2});
+            reorder_axes(std::make_shared<opset6::StridedSlice>(init_states[0],
+                                                                h_begin,
+                                                                h_end,
+                                                                std::vector<int64_t>{0},
+                                                                std::vector<int64_t>{0}),
+                         {1, 0, 2});
         m_input_map[LSTMInput::LSTM_INPUT_INIT_C] =
-            ngraph::builder::opset1::reorder_axes(std::make_shared<opset6::StridedSlice>(init_states[1],
-                                                                                         c_begin,
-                                                                                         c_end,
-                                                                                         std::vector<int64_t>{0},
-                                                                                         std::vector<int64_t>{0}),
-                                                  {1, 0, 2});
+            reorder_axes(std::make_shared<opset6::StridedSlice>(init_states[1],
+                                                                c_begin,
+                                                                c_end,
+                                                                std::vector<int64_t>{0},
+                                                                std::vector<int64_t>{0}),
+                         {1, 0, 2});
     }
 
-    Output<ngraph::Node>& at(const LSTMInput& key) {
+    Output<ov::Node>& at(const LSTMInput& key) {
         return m_input_map.at(key);
     }
 
-    std::map<LSTMInput, Output<ngraph::Node>> m_input_map;
+    std::map<LSTMInput, Output<ov::Node>> m_input_map;
 };
 
 // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ATTRIBUTES PARSING ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 struct LSTMAttributes {
     explicit LSTMAttributes(const NodeContext& node)
-        : m_direction(node.get_attribute<bool>("is_bidirec") ? ngraph::op::RecurrentSequenceDirection::BIDIRECTIONAL
-                                                             : ngraph::op::RecurrentSequenceDirection::FORWARD),
+        : m_direction(node.get_attribute<bool>("is_bidirec") ? ov::op::RecurrentSequenceDirection::BIDIRECTIONAL
+                                                             : ov::op::RecurrentSequenceDirection::FORWARD),
           m_hidden_size(node.get_attribute<int32_t>("hidden_size")),
           m_layers(node.get_attribute<int32_t>("num_layers"))
 
               {};
 
-    ngraph::op::RecurrentSequenceDirection m_direction;
+    ov::op::RecurrentSequenceDirection m_direction;
     int32_t m_hidden_size;
     int32_t m_layers;
 };
@@ -172,12 +172,12 @@ NamedOutputs lstm(const NodeContext& node) {
                                                                     input_map.at(LSTMInput::LSTM_INPUT_B),
                                                                     attrs.m_hidden_size,
                                                                     attrs.m_direction);
-        prev_output = ngraph::builder::opset1::reorder_axes(lstm_sequence->output(0), {2, 0, 1, 3});
+        prev_output = reorder_axes(lstm_sequence->output(0), {2, 0, 1, 3});
         auto out_shape = opset6::Constant::create(element::i64, Shape{3}, {0, 0, -1});
         prev_output = std::make_shared<opset6::Reshape>(prev_output, out_shape, true);
 
-        final_h.push_back(ngraph::builder::opset1::reorder_axes(lstm_sequence->output(1), {1, 0, 2}));
-        final_c.push_back(ngraph::builder::opset1::reorder_axes(lstm_sequence->output(2), {1, 0, 2}));
+        final_h.push_back(reorder_axes(lstm_sequence->output(1), {1, 0, 2}));
+        final_c.push_back(reorder_axes(lstm_sequence->output(2), {1, 0, 2}));
     }
 
     NamedOutputs named_outputs;
diff --git a/ngraph/frontend/paddlepaddle/src/op/matmul.cpp b/ngraph/frontend/paddlepaddle/src/op/matmul.cpp
index 4e7f76d0e57..37a4a69eeb5 100644
--- a/ngraph/frontend/paddlepaddle/src/op/matmul.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/matmul.cpp
@@ -1,9 +1,10 @@
 // Copyright (C) 2018-2021 Intel Corporation
 // SPDX-License-Identifier: Apache-2.0
 //
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
+#include "openvino/opsets/opset6.hpp"
+
 namespace ov {
 namespace frontend {
 namespace pdpd {
@@ -14,13 +15,12 @@ NamedOutputs matmul(const NodeContext& node) {
     auto alpha = node.get_attribute<float>("alpha", 1);
     auto transpose_a = node.get_attribute<bool>("transpose_X", false);
     auto transpose_b = node.get_attribute<bool>("transpose_Y", false);
-    auto mm = std::make_shared<ngraph::opset6::MatMul>(x, y, transpose_a, transpose_b);
+    auto mm = std::make_shared<ov::opset6::MatMul>(x, y, transpose_a, transpose_b);
     if (alpha == 1) {
         return node.default_single_output_mapping({mm}, {"Out"});
     } else {
-        auto alpha_node = ngraph::opset6::Constant::create(ngraph::element::f32, {1}, {alpha});
-        return node.default_single_output_mapping({std::make_shared<ngraph::opset6::Multiply>(mm, alpha_node)},
-                                                  {"Out"});
+        auto alpha_node = ov::opset6::Constant::create(ov::element::f32, {1}, {alpha});
+        return node.default_single_output_mapping({std::make_shared<ov::opset6::Multiply>(mm, alpha_node)}, {"Out"});
     }
 }
 
diff --git a/ngraph/frontend/paddlepaddle/src/op/matrix_nms.cpp b/ngraph/frontend/paddlepaddle/src/op/matrix_nms.cpp
index 85ee304832c..12a7df7cd1d 100644
--- a/ngraph/frontend/paddlepaddle/src/op/matrix_nms.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/matrix_nms.cpp
@@ -1,16 +1,16 @@
 // Copyright (C) 2018-2021 Intel Corporation
 // SPDX-License-Identifier: Apache-2.0
 //
-#include <ngraph/opsets/opset8.hpp>
 #include <node_context.hpp>
-#include <paddlepaddle_frontend/utility.hpp>
+
+#include "openvino/opsets/opset8.hpp"
+#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
 NamedOutputs matrix_nms(const NodeContext& node) {
-    using namespace ngraph;
     using namespace opset8;
     using namespace element;
 
diff --git a/ngraph/frontend/paddlepaddle/src/op/multiclass_nms.cpp b/ngraph/frontend/paddlepaddle/src/op/multiclass_nms.cpp
index ced2a327107..88871e680ea 100644
--- a/ngraph/frontend/paddlepaddle/src/op/multiclass_nms.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/multiclass_nms.cpp
@@ -1,16 +1,16 @@
 // Copyright (C) 2018-2021 Intel Corporation
 // SPDX-License-Identifier: Apache-2.0
 //
-#include <ngraph/opsets/opset8.hpp>
 #include <node_context.hpp>
-#include <paddlepaddle_frontend/utility.hpp>
+
+#include "openvino/opsets/opset8.hpp"
+#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
 NamedOutputs multiclass_nms(const NodeContext& node) {
-    using namespace ngraph;
     using namespace opset8;
     using namespace element;
 
diff --git a/ngraph/frontend/paddlepaddle/src/op/pad3d.cpp b/ngraph/frontend/paddlepaddle/src/op/pad3d.cpp
index 5c92ad7d1e9..8a2a8f1b555 100644
--- a/ngraph/frontend/paddlepaddle/src/op/pad3d.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/pad3d.cpp
@@ -2,9 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
+#include "openvino/opsets/opset6.hpp"
+
 namespace ov {
 namespace frontend {
 namespace pdpd {
@@ -28,27 +29,27 @@ NamedOutputs pad3d(const NodeContext& node) {
         for (int i = 0; i < 6; i++)
             paddings[i] = padding_int;
     } else {
-        throw ngraph::ngraph_error("Unsupported paddings attribute!");
+        PDPD_OP_VALIDATION_CHECK(node, false, "Unsupported paddings attribute!");
     }
 
     auto pads_begin = std::vector<int32_t>(5, 0);
     auto pads_end = std::vector<int32_t>(5, 0);
 
-    Output<ngraph::Node> values;
-    Output<ngraph::Node> padding_begin;
-    Output<ngraph::Node> padding_end;
+    Output<ov::Node> values;
+    Output<ov::Node> padding_begin;
+    Output<ov::Node> padding_end;
 
-    ngraph::op::PadMode pad_mode;
+    ov::op::PadMode pad_mode;
     // TODO Support Circular mode in #55704
     if (mode == "constant") {
-        pad_mode = ngraph::op::PadMode::CONSTANT;
-        values = ngraph::opset6::Constant::create(element::f32, ngraph::Shape{}, {value});
+        pad_mode = ov::op::PadMode::CONSTANT;
+        values = ov::opset6::Constant::create(element::f32, ov::Shape{}, {value});
     } else if (mode == "reflect") {
-        pad_mode = ngraph::op::PadMode::REFLECT;
+        pad_mode = ov::op::PadMode::REFLECT;
     } else if (mode == "replicate") {
-        pad_mode = ngraph::op::PadMode::EDGE;
+        pad_mode = ov::op::PadMode::EDGE;
     } else {
-        throw ngraph::ngraph_error("Unsupported 3d paddings mode: [" + mode + "]");
+        PDPD_OP_VALIDATION_CHECK(node, false, "Unsupported 3d paddings mode: [" + mode + "]");
     }
 
     if (data_format == "NCDHW") {
@@ -66,19 +67,19 @@ NamedOutputs pad3d(const NodeContext& node) {
         pads_begin[1] = paddings[4];  // front
         pads_end[1] = paddings[5];    // back
     } else {
-        throw ngraph::ngraph_error("Unsupported 3d paddings data_format: [" + data_format + "]");
+        PDPD_OP_VALIDATION_CHECK(node, false, "Unsupported 3d paddings data_format: [" + data_format + "]");
     }
 
-    padding_begin = ngraph::opset6::Constant::create(element::i32, ngraph::Shape{pads_begin.size()}, pads_begin);
-    padding_end = ngraph::opset6::Constant::create(element::i32, ngraph::Shape{pads_end.size()}, pads_end);
+    padding_begin = ov::opset6::Constant::create(element::i32, ov::Shape{pads_begin.size()}, pads_begin);
+    padding_end = ov::opset6::Constant::create(element::i32, ov::Shape{pads_end.size()}, pads_end);
 
     if (mode == "constant")
         return node.default_single_output_mapping(
-            {std::make_shared<ngraph::opset6::Pad>(data, padding_begin, padding_end, values, pad_mode)},
+            {std::make_shared<ov::opset6::Pad>(data, padding_begin, padding_end, values, pad_mode)},
             {"Out"});
     else
         return node.default_single_output_mapping(
-            {std::make_shared<ngraph::opset6::Pad>(data, padding_begin, padding_end, pad_mode)},
+            {std::make_shared<ov::opset6::Pad>(data, padding_begin, padding_end, pad_mode)},
             {"Out"});
 }
 }  // namespace op
diff --git a/ngraph/frontend/paddlepaddle/src/op/pool2d.cpp b/ngraph/frontend/paddlepaddle/src/op/pool2d.cpp
index 8869a0c387b..d7606aa85bb 100644
--- a/ngraph/frontend/paddlepaddle/src/op/pool2d.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/pool2d.cpp
@@ -4,33 +4,31 @@
 //
 //*****************************************************************************
 
-#include <ngraph/opsets/opset6.hpp>
-#include <ngraph/opsets/opset8.hpp>
 #include <node_context.hpp>
 
+#include "openvino/opsets/opset6.hpp"
+#include "openvino/opsets/opset8.hpp"
+
 namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
 // helper func - get pad_begin and pad_end
-static void get_paddings(const NodeContext& node,
-                         ngraph::Shape& pad_begin,
-                         ngraph::Shape& pad_end,
-                         ngraph::op::PadType& auto_pad) {
+static void get_paddings(const NodeContext& node, ov::Shape& pad_begin, ov::Shape& pad_end, ov::op::PadType& auto_pad) {
     if (node.has_attribute<std::string>("padding_algorithm")) {
         auto pad_algo = node.get_attribute<std::string>("padding_algorithm");
         if (pad_algo == "SAME") {
-            auto_pad = ngraph::op::PadType::SAME_UPPER;
+            auto_pad = ov::op::PadType::SAME_UPPER;
         } else if (pad_algo == "VALID") {
-            auto_pad = ngraph::op::PadType::VALID;
+            auto_pad = ov::op::PadType::VALID;
         } else if (pad_algo == "EXPLICIT") {
-            auto_pad = ngraph::op::PadType::EXPLICIT;
+            auto_pad = ov::op::PadType::EXPLICIT;
         } else {
             throw std::runtime_error("Unsupported pooling padding_algorithm " + pad_algo);
         }
     } else {
         // adaptive_maxpool with no such attr.
-        auto_pad = ngraph::op::PadType::EXPLICIT;
+        auto_pad = ov::op::PadType::EXPLICIT;
     }
 
     /*If pool padding size is a tuple or list, it could be in three forms:
@@ -69,7 +67,7 @@ NamedOutputs pool2d(const NodeContext& node) {
     auto kernel_shape = node.get_attribute<std::vector<int32_t>>("ksize");
 
     auto rounding_type =
-        node.get_attribute<bool>("ceil_mode", false) ? ngraph::op::RoundingType::CEIL : ngraph::op::RoundingType::FLOOR;
+        node.get_attribute<bool>("ceil_mode", false) ? ov::op::RoundingType::CEIL : ov::op::RoundingType::FLOOR;
 
     if (pooling_type.empty()) {
         pooling_type = "max";
@@ -83,20 +81,20 @@ NamedOutputs pool2d(const NodeContext& node) {
     int32_t input_rank = input_shape.rank().get_length();
     PDPD_ASSERT(input_rank >= 2, "input tensor rank must be greater than 2");
 
-    auto auto_pad = ngraph::op::PadType::EXPLICIT;
-    ngraph::Shape pad_begin, pad_end;
+    auto auto_pad = ov::op::PadType::EXPLICIT;
+    ov::Shape pad_begin, pad_end;
     get_paddings(node, pad_begin, pad_end, auto_pad);
 
     if (global_pooling || (adaptive && std::any_of(kernel_shape.begin(), kernel_shape.end(), [](int32_t i) {
                                return i == 1;
                            }))) {
         if (pooling_type == "max") {
-            auto axes = ngraph::opset6::Constant::create(ngraph::element::i64, {2}, {input_rank - 2, input_rank - 1});
-            return node.default_single_output_mapping({std::make_shared<ngraph::opset6::ReduceMax>(data, axes, true)},
+            auto axes = ov::opset6::Constant::create(ov::element::i64, {2}, {input_rank - 2, input_rank - 1});
+            return node.default_single_output_mapping({std::make_shared<ov::opset6::ReduceMax>(data, axes, true)},
                                                       {"Out"});
         } else {
-            auto axes = ngraph::opset6::Constant::create(ngraph::element::i64, {2}, {input_rank - 2, input_rank - 1});
-            return node.default_single_output_mapping({std::make_shared<ngraph::opset6::ReduceMean>(data, axes, true)},
+            auto axes = ov::opset6::Constant::create(ov::element::i64, {2}, {input_rank - 2, input_rank - 1});
+            return node.default_single_output_mapping({std::make_shared<ov::opset6::ReduceMean>(data, axes, true)},
                                                       {"Out"});
         }
     } else if (adaptive) {
@@ -111,20 +109,20 @@ NamedOutputs pool2d(const NodeContext& node) {
             pool_size[1] = kernel_shape[1];
         }
 
-        const Output<ngraph::Node> output_shape =
-            ngraph::opset6::Constant::create(ngraph::element::i64, {pool_size.size()}, pool_size);
+        const Output<ov::Node> output_shape =
+            ov::opset6::Constant::create(ov::element::i64, {pool_size.size()}, pool_size);
 
         if (pooling_type == "max") {
             std::vector<Output<Node>> pool_outputs;
             pool_outputs =
-                std::make_shared<ngraph::opset8::AdaptiveMaxPool>(data, output_shape, ngraph::element::i32)->outputs();
+                std::make_shared<ov::opset8::AdaptiveMaxPool>(data, output_shape, ov::element::i32)->outputs();
             NamedOutputs outputs;
             outputs["Out"] = {pool_outputs[0]};
             outputs["Mask"] = {pool_outputs[1]};
             return outputs;
         } else {
             return node.default_single_output_mapping(
-                {std::make_shared<ngraph::opset8::AdaptiveAvgPool>(data, output_shape)},
+                {std::make_shared<ov::opset8::AdaptiveAvgPool>(data, output_shape)},
                 {"Out"});
         }
     } else {
@@ -158,25 +156,25 @@ NamedOutputs pool2d(const NodeContext& node) {
 
         if (pooling_type == "max") {
             return node.default_single_output_mapping(
-                {std::make_shared<ngraph::opset6::MaxPool>(data,
-                                                           ngraph::Strides(strides.begin(), strides.end()),
-                                                           pad_begin,
-                                                           pad_end,
-                                                           ngraph::Shape{kernel_h, kernel_w},
-                                                           rounding_type,
-                                                           auto_pad)},
+                {std::make_shared<ov::opset6::MaxPool>(data,
+                                                       ov::Strides(strides.begin(), strides.end()),
+                                                       pad_begin,
+                                                       pad_end,
+                                                       ov::Shape{kernel_h, kernel_w},
+                                                       rounding_type,
+                                                       auto_pad)},
                 {"Out"});
         } else {
             bool exclude_pad = node.get_attribute<bool>("exclusive", false);
             return node.default_single_output_mapping(
-                {std::make_shared<ngraph::opset6::AvgPool>(data,
-                                                           ngraph::Strides(strides.begin(), strides.end()),
-                                                           pad_begin,
-                                                           pad_end,
-                                                           ngraph::Shape{kernel_h, kernel_w},
-                                                           exclude_pad,
-                                                           rounding_type,
-                                                           auto_pad)},
+                {std::make_shared<ov::opset6::AvgPool>(data,
+                                                       ov::Strides(strides.begin(), strides.end()),
+                                                       pad_begin,
+                                                       pad_end,
+                                                       ov::Shape{kernel_h, kernel_w},
+                                                       exclude_pad,
+                                                       rounding_type,
+                                                       auto_pad)},
                 {"Out"});
         }
     }
diff --git a/ngraph/frontend/paddlepaddle/src/op/pow.cpp b/ngraph/frontend/paddlepaddle/src/op/pow.cpp
index 52182946a6e..22cbc91461e 100644
--- a/ngraph/frontend/paddlepaddle/src/op/pow.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/pow.cpp
@@ -1,9 +1,10 @@
 // Copyright (C) 2018-2021 Intel Corporation
 // SPDX-License-Identifier: Apache-2.0
 //
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
-#include <paddlepaddle_frontend/utility.hpp>
+
+#include "openvino/opsets/opset6.hpp"
+#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
@@ -16,12 +17,12 @@ NamedOutputs pow(const NodeContext& node) {
     if (node.has_ng_input("FactorTensor")) {
         factor_node = node.get_ng_input("FactorTensor");
         if (factor_node.get_element_type() != dtype)
-            factor_node = std::make_shared<ngraph::opset6::Convert>(factor_node, dtype);
+            factor_node = std::make_shared<ov::opset6::Convert>(factor_node, dtype);
     } else {
-        factor_node = ngraph::opset6::Constant::create(dtype, Shape{1}, {node.get_attribute<float>("factor")});
+        factor_node = ov::opset6::Constant::create(dtype, Shape{1}, {node.get_attribute<float>("factor")});
     }
 
-    return node.default_single_output_mapping({std::make_shared<ngraph::opset6::Power>(x, factor_node)}, {"Out"});
+    return node.default_single_output_mapping({std::make_shared<ov::opset6::Power>(x, factor_node)}, {"Out"});
 }
 
 }  // namespace op
diff --git a/ngraph/frontend/paddlepaddle/src/op/prior_box.cpp b/ngraph/frontend/paddlepaddle/src/op/prior_box.cpp
index a6ffb1a90bc..4b18ad839f9 100644
--- a/ngraph/frontend/paddlepaddle/src/op/prior_box.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/prior_box.cpp
@@ -2,7 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "ngraph/op/prior_box.hpp"
+#include "openvino/op/prior_box.hpp"
 
 #include <node_context.hpp>
 
@@ -16,7 +16,7 @@ using namespace default_opset;
 using namespace element;
 namespace detail {
 namespace {
-std::shared_ptr<StridedSlice> make_slice(const std::shared_ptr<ngraph::Node>& node, int64_t start, int64_t end) {
+std::shared_ptr<StridedSlice> make_slice(const std::shared_ptr<ov::Node>& node, int64_t start, int64_t end) {
     return std::make_shared<StridedSlice>(node,
                                           Constant::create(i64, Shape{1}, std::vector<int64_t>{start}),
                                           Constant::create(i64, Shape{1}, std::vector<int64_t>{end}),
@@ -33,7 +33,7 @@ NamedOutputs prior_box(const NodeContext& node) {
     const auto output_shape_slice = detail::make_slice(input_shape, 2, 4);
     const auto image_shape_slice = detail::make_slice(Image_shape, 2, 4);
 
-    ngraph::op::PriorBoxAttrs attrs;
+    PriorBox::Attributes attrs;
     attrs.min_size = node.get_attribute<std::vector<float>>("min_sizes", {});
     attrs.max_size = node.get_attribute<std::vector<float>>("max_sizes", {});
     attrs.aspect_ratio = node.get_attribute<std::vector<float>>("aspect_ratios", {1.0});
@@ -48,7 +48,7 @@ NamedOutputs prior_box(const NodeContext& node) {
 
     const auto ov_prior_box_node = std::make_shared<PriorBox>(output_shape_slice, image_shape_slice, attrs);
 
-    const auto split_axis_node = Constant::create(i64, ngraph::Shape{}, {0});
+    const auto split_axis_node = Constant::create(i64, ov::Shape{}, {0});
     const auto node_prior_box_split = std::make_shared<Split>(ov_prior_box_node, split_axis_node, 2);
 
     const auto node_boxes_origin = node_prior_box_split->output(0);
@@ -60,7 +60,7 @@ NamedOutputs prior_box(const NodeContext& node) {
     auto node_boxes_reshape = std::make_shared<Reshape>(node_boxes_origin, out_shape, true);
     const auto node_variances_reshape = std::make_shared<Reshape>(node_variances_origin, out_shape, true);
 
-    int64_t total_aspect_ratios = ngraph::op::PriorBox::normalized_aspect_ratio(attrs.aspect_ratio, attrs.flip).size();
+    int64_t total_aspect_ratios = PriorBox::normalized_aspect_ratio(attrs.aspect_ratio, attrs.flip).size();
     if ((total_aspect_ratios > 1) && !attrs.min_size.empty() && !attrs.max_size.empty() &&
         !min_max_aspect_ratios_order) {
         std::vector<int64_t> mask{1, 1, 1, 0, 1};
diff --git a/ngraph/frontend/paddlepaddle/src/op/range.cpp b/ngraph/frontend/paddlepaddle/src/op/range.cpp
index a90f60d0cb5..9ebeff92d06 100644
--- a/ngraph/frontend/paddlepaddle/src/op/range.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/range.cpp
@@ -2,9 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
+#include "openvino/opsets/opset6.hpp"
+
 namespace ov {
 namespace frontend {
 namespace pdpd {
@@ -15,13 +16,13 @@ NamedOutputs range(const NodeContext& node) {
     auto step = node.get_ng_input("Step");
     auto type = node.get_out_port_type("Out");
 
-    const auto axis = ngraph::opset6::Constant::create(element::i64, Shape{}, {0});
-    auto start_scalar = std::make_shared<ngraph::opset6::Squeeze>(start, axis);
-    auto stop_scalar = std::make_shared<ngraph::opset6::Squeeze>(stop, axis);
-    auto step_scalar = std::make_shared<ngraph::opset6::Squeeze>(step, axis);
+    const auto axis = ov::opset6::Constant::create(element::i64, Shape{}, {0});
+    auto start_scalar = std::make_shared<ov::opset6::Squeeze>(start, axis);
+    auto stop_scalar = std::make_shared<ov::opset6::Squeeze>(stop, axis);
+    auto step_scalar = std::make_shared<ov::opset6::Squeeze>(step, axis);
 
     return node.default_single_output_mapping(
-        {std::make_shared<ngraph::opset6::Range>(start_scalar, stop_scalar, step_scalar, type)},
+        {std::make_shared<ov::opset6::Range>(start_scalar, stop_scalar, step_scalar, type)},
         {"Out"});
 }
 
diff --git a/ngraph/frontend/paddlepaddle/src/op/relu.cpp b/ngraph/frontend/paddlepaddle/src/op/relu.cpp
index 4aa63bda1dc..9277594cc9a 100644
--- a/ngraph/frontend/paddlepaddle/src/op/relu.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/relu.cpp
@@ -2,16 +2,16 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
+#include "openvino/opsets/opset6.hpp"
+
 namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
 NamedOutputs relu(const NodeContext& node) {
-    return node.default_single_output_mapping({std::make_shared<ngraph::opset6::Relu>(node.get_ng_input("X"))},
-                                              {"Out"});
+    return node.default_single_output_mapping({std::make_shared<ov::opset6::Relu>(node.get_ng_input("X"))}, {"Out"});
 }
 
 }  // namespace op
diff --git a/ngraph/frontend/paddlepaddle/src/op/relu6.cpp b/ngraph/frontend/paddlepaddle/src/op/relu6.cpp
index 65aa1354cdd..2c37f0ac81f 100644
--- a/ngraph/frontend/paddlepaddle/src/op/relu6.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/relu6.cpp
@@ -2,9 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
-#include <paddlepaddle_frontend/utility.hpp>
+
+#include "openvino/opsets/opset6.hpp"
+#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
@@ -13,7 +14,7 @@ namespace op {
 NamedOutputs relu6(const NodeContext& node) {
     auto data = node.get_ng_input("X");
     auto threshold = node.get_attribute<float>("threshold", 6.0f);
-    return node.default_single_output_mapping({std::make_shared<ngraph::opset6::Clamp>(data, 0.0, threshold)}, {"Out"});
+    return node.default_single_output_mapping({std::make_shared<ov::opset6::Clamp>(data, 0.0, threshold)}, {"Out"});
 }
 
 }  // namespace op
diff --git a/ngraph/frontend/paddlepaddle/src/op/reshape2.cpp b/ngraph/frontend/paddlepaddle/src/op/reshape2.cpp
index 6cffb7557ec..20e2bccea10 100644
--- a/ngraph/frontend/paddlepaddle/src/op/reshape2.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/reshape2.cpp
@@ -2,9 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
-#include <paddlepaddle_frontend/utility.hpp>
+
+#include "openvino/opsets/opset6.hpp"
+#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
@@ -14,8 +15,8 @@ NamedOutputs reshape2(const NodeContext& node) {
     auto data = node.get_ng_input("X");
     if (!node.has_ng_input("Shape") && !node.has_ng_input("ShapeTensor")) {
         auto shape_attr = node.get_attribute<std::vector<int32_t>>("shape");
-        auto shape_node = ngraph::opset6::Constant::create(ngraph::element::i32, {shape_attr.size()}, shape_attr);
-        return node.default_single_output_mapping({std::make_shared<ngraph::opset6::Reshape>(data, shape_node, true)},
+        auto shape_node = ov::opset6::Constant::create(ov::element::i32, {shape_attr.size()}, shape_attr);
+        return node.default_single_output_mapping({std::make_shared<ov::opset6::Reshape>(data, shape_node, true)},
                                                   {"Out"});
     } else {
         std::string name = "Shape";
@@ -24,14 +25,14 @@ NamedOutputs reshape2(const NodeContext& node) {
         }
 
         auto nodes = node.get_ng_inputs(name);
-        ngraph::NodeVector node_vec;
+        ov::NodeVector node_vec;
         for (auto& input_node : nodes) {
-            auto cast = std::make_shared<ngraph::opset6::Convert>(input_node, element::i64);
+            auto cast = std::make_shared<ov::opset6::Convert>(input_node, element::i64);
             node_vec.push_back(cast);
         }
 
-        auto shape_node = std::make_shared<ngraph::opset6::Concat>(node_vec, 0);
-        return node.default_single_output_mapping({std::make_shared<ngraph::opset6::Reshape>(data, shape_node, true)},
+        auto shape_node = std::make_shared<ov::opset6::Concat>(node_vec, 0);
+        return node.default_single_output_mapping({std::make_shared<ov::opset6::Reshape>(data, shape_node, true)},
                                                   {"Out"});
     }
 }
diff --git a/ngraph/frontend/paddlepaddle/src/op/rnn.cpp b/ngraph/frontend/paddlepaddle/src/op/rnn.cpp
index f0e594079b9..0e779c168de 100644
--- a/ngraph/frontend/paddlepaddle/src/op/rnn.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/rnn.cpp
@@ -2,9 +2,9 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
+#include "openvino/opsets/opset6.hpp"
 #include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
diff --git a/ngraph/frontend/paddlepaddle/src/op/scale.cpp b/ngraph/frontend/paddlepaddle/src/op/scale.cpp
index 9b67eedc70a..da8dd865178 100644
--- a/ngraph/frontend/paddlepaddle/src/op/scale.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/scale.cpp
@@ -2,9 +2,9 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/builder/make_constant.hpp>
 #include <node_context.hpp>
-#include <openvino/opsets/opset6.hpp>
+
+#include "openvino/opsets/opset6.hpp"
 
 namespace ov {
 namespace frontend {
diff --git a/ngraph/frontend/paddlepaddle/src/op/shape.cpp b/ngraph/frontend/paddlepaddle/src/op/shape.cpp
index 3c777cb87a9..b2594edcba3 100644
--- a/ngraph/frontend/paddlepaddle/src/op/shape.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/shape.cpp
@@ -2,16 +2,17 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
+#include "openvino/opsets/opset6.hpp"
+
 namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
 NamedOutputs shape(const NodeContext& node) {
     auto data = node.get_ng_input("Input");
-    auto shape_node = std::make_shared<ngraph::opset6::ShapeOf>(data, element::i32);
+    auto shape_node = std::make_shared<ov::opset6::ShapeOf>(data, element::i32);
     return node.default_single_output_mapping({shape_node}, {"Out"});
 }
 
diff --git a/ngraph/frontend/paddlepaddle/src/op/sigmoid.cpp b/ngraph/frontend/paddlepaddle/src/op/sigmoid.cpp
index f9122d2562f..b4145c68695 100644
--- a/ngraph/frontend/paddlepaddle/src/op/sigmoid.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/sigmoid.cpp
@@ -2,16 +2,17 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
+#include "openvino/opsets/opset6.hpp"
+
 namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
 NamedOutputs sigmoid(const NodeContext& node) {
     auto data = node.get_ng_input("X");
-    return node.default_single_output_mapping({std::make_shared<ngraph::opset6::Sigmoid>(data)}, {"Out"});
+    return node.default_single_output_mapping({std::make_shared<ov::opset6::Sigmoid>(data)}, {"Out"});
 }
 
 }  // namespace op
diff --git a/ngraph/frontend/paddlepaddle/src/op/softmax.cpp b/ngraph/frontend/paddlepaddle/src/op/softmax.cpp
index 0646df0b704..b70dd6971d9 100644
--- a/ngraph/frontend/paddlepaddle/src/op/softmax.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/softmax.cpp
@@ -2,9 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
+#include "openvino/opsets/opset6.hpp"
+
 namespace ov {
 namespace frontend {
 namespace pdpd {
@@ -17,7 +18,7 @@ NamedOutputs softmax(const NodeContext& node) {
         auto data_rank = data.get_partial_shape().rank().get_length();
         axis = data_rank + axis;
     }
-    return node.default_single_output_mapping({std::make_shared<ngraph::opset6::Softmax>(data, axis)}, {"Out"});
+    return node.default_single_output_mapping({std::make_shared<ov::opset6::Softmax>(data, axis)}, {"Out"});
 }
 }  // namespace op
 }  // namespace pdpd
diff --git a/ngraph/frontend/paddlepaddle/src/op/split.cpp b/ngraph/frontend/paddlepaddle/src/op/split.cpp
index e4fff9fd0cc..843f2bb6df5 100644
--- a/ngraph/frontend/paddlepaddle/src/op/split.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/split.cpp
@@ -2,16 +2,16 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset7.hpp>
 #include <node_context.hpp>
-#include <paddlepaddle_frontend/utility.hpp>
+
+#include "openvino/opsets/opset7.hpp"
+#include "paddlepaddle_frontend/utility.hpp"
 
 namespace ov {
 namespace frontend {
 namespace pdpd {
 namespace op {
 NamedOutputs split(const NodeContext& node) {
-    using namespace ngraph;
     using namespace opset7;
     const auto& data = node.get_ng_input("X");
     Output<Node> axis;
@@ -24,7 +24,7 @@ NamedOutputs split(const NodeContext& node) {
         if (node.has_attribute<int32_t>("axis")) {
             dim = node.get_attribute<int32_t>("axis");
         }
-        axis = std::make_shared<Constant>(ngraph::element::i32, Shape{}, dim);
+        axis = std::make_shared<Constant>(ov::element::i32, Shape{}, dim);
     }
     auto num_or_sections = node.get_attribute<int32_t>("num");
     NamedOutputs named_outputs;
@@ -33,7 +33,7 @@ NamedOutputs split(const NodeContext& node) {
         Output<Node> sections_node;
         if (node.has_ng_input("SectionsTensorList")) {
             auto inputs = node.get_ng_inputs("SectionsTensorList");
-            sections_node = std::make_shared<ngraph::opset7::Concat>(inputs, 0);
+            sections_node = std::make_shared<ov::opset7::Concat>(inputs, 0);
         } else {
             PDPD_ASSERT(node.has_attribute<std::vector<int32_t>>("sections"),
                         "split: num==0 && no sections is invalid.");
diff --git a/ngraph/frontend/paddlepaddle/src/op/squeeze.cpp b/ngraph/frontend/paddlepaddle/src/op/squeeze.cpp
index ec52228e43d..2c3472df019 100644
--- a/ngraph/frontend/paddlepaddle/src/op/squeeze.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/squeeze.cpp
@@ -2,9 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
+#include "openvino/opsets/opset6.hpp"
+
 namespace ov {
 namespace frontend {
 namespace pdpd {
@@ -18,10 +19,10 @@ NamedOutputs squeeze(const NodeContext& node) {
 
     std::shared_ptr<Node> out;
     if (!axes.empty()) {
-        auto axesNode = ngraph::opset6::Constant::create(ngraph::element::i32, {axes.size()}, axes);
-        out = std::make_shared<ngraph::opset6::Squeeze>(data, axesNode);
+        auto axesNode = ov::opset6::Constant::create(ov::element::i32, {axes.size()}, axes);
+        out = std::make_shared<ov::opset6::Squeeze>(data, axesNode);
     } else {
-        out = std::make_shared<ngraph::opset6::Squeeze>(data);
+        out = std::make_shared<ov::opset6::Squeeze>(data);
     }
     return node.default_single_output_mapping(out, {"Out"});
 }
diff --git a/ngraph/frontend/paddlepaddle/src/op/transpose2.cpp b/ngraph/frontend/paddlepaddle/src/op/transpose2.cpp
index ece4118d602..2c2464fd01b 100644
--- a/ngraph/frontend/paddlepaddle/src/op/transpose2.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/transpose2.cpp
@@ -2,9 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
+#include "openvino/opsets/opset6.hpp"
+
 namespace ov {
 namespace frontend {
 namespace pdpd {
@@ -12,9 +13,8 @@ namespace op {
 NamedOutputs transpose2(const NodeContext& node) {
     auto data = node.get_ng_input("X");
     auto perm = node.get_attribute<std::vector<int>>("axis");
-    auto input_order = ngraph::opset6::Constant::create(ngraph::element::i64, {perm.size()}, perm);
-    return node.default_single_output_mapping({std::make_shared<ngraph::opset6::Transpose>(data, input_order)},
-                                              {"Out"});
+    auto input_order = ov::opset6::Constant::create(ov::element::i64, {perm.size()}, perm);
+    return node.default_single_output_mapping({std::make_shared<ov::opset6::Transpose>(data, input_order)}, {"Out"});
 }
 
 }  // namespace op
diff --git a/ngraph/frontend/paddlepaddle/src/op/unsqueeze.cpp b/ngraph/frontend/paddlepaddle/src/op/unsqueeze.cpp
index 34970c70639..0d4a583b5a8 100644
--- a/ngraph/frontend/paddlepaddle/src/op/unsqueeze.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/unsqueeze.cpp
@@ -2,9 +2,10 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <ngraph/opsets/opset6.hpp>
 #include <node_context.hpp>
 
+#include "openvino/opsets/opset6.hpp"
+
 namespace ov {
 namespace frontend {
 namespace pdpd {
@@ -16,12 +17,12 @@ NamedOutputs unsqueeze(const NodeContext& node) {
         axesNode = node.get_ng_input("AxesTensor");
     } else if (node.has_ng_input("AxesTensorList")) {
         auto inputs = node.get_ng_inputs("AxesTensorList");
-        axesNode = std::make_shared<ngraph::opset6::Concat>(inputs, 0);
+        axesNode = std::make_shared<ov::opset6::Concat>(inputs, 0);
     } else {
         auto axes = node.get_attribute<std::vector<int32_t>>("axes");
-        axesNode = ngraph::opset6::Constant::create(ngraph::element::i32, {axes.size()}, axes);
+        axesNode = ov::opset6::Constant::create(ov::element::i32, {axes.size()}, axes);
     }
-    return node.default_single_output_mapping({std::make_shared<ngraph::opset6::Unsqueeze>(data, axesNode)}, {"Out"});
+    return node.default_single_output_mapping({std::make_shared<ov::opset6::Unsqueeze>(data, axesNode)}, {"Out"});
 }
 }  // namespace op
 }  // namespace pdpd
diff --git a/ngraph/frontend/paddlepaddle/src/op/yolo_box.cpp b/ngraph/frontend/paddlepaddle/src/op/yolo_box.cpp
index c2ad5f91b1a..4393304ef99 100644
--- a/ngraph/frontend/paddlepaddle/src/op/yolo_box.cpp
+++ b/ngraph/frontend/paddlepaddle/src/op/yolo_box.cpp
@@ -4,7 +4,8 @@
 #include <limits>  // std::numeric_limits
 #include <node_context.hpp>
 #include <numeric>
-#include <openvino/opsets/opset6.hpp>
+
+#include "openvino/opsets/opset6.hpp"
 
 namespace ov {
 namespace frontend {
@@ -109,8 +110,8 @@ NamedOutputs yolo_box(const NodeContext& node_context) {
     auto node_prob = node_split_input->output(5);
 
     // x/y
-    std::shared_ptr<ngraph::Node> node_box_x_sigmoid = std::make_shared<Sigmoid>(node_box_x);
-    std::shared_ptr<ngraph::Node> node_box_y_sigmoid = std::make_shared<Sigmoid>(node_box_y);
+    std::shared_ptr<ov::Node> node_box_x_sigmoid = std::make_shared<Sigmoid>(node_box_x);
+    std::shared_ptr<ov::Node> node_box_y_sigmoid = std::make_shared<Sigmoid>(node_box_y);
 
     if (std::fabs(scale_x_y - default_scale) > 1e-6) {  // float not-equal
         float bias_x_y = -0.5 * (scale_x_y - 1.0);
@@ -270,7 +271,7 @@ NamedOutputs yolo_box(const NodeContext& node_context) {
     auto node_pred_box_x2_squeeze = std::make_shared<Multiply>(node_pred_box_x2_reshape, node_img_width_cast);
     auto node_pred_box_y2_squeeze = std::make_shared<Multiply>(node_pred_box_y2_reshape, node_img_height_cast);
 
-    std::shared_ptr<ngraph::Node> node_pred_box_result;
+    std::shared_ptr<ov::Node> node_pred_box_result;
     if (clip_bbox) {
         auto node_number_one = Constant::create<float>(f32, {1}, {1.0});
         auto node_new_img_height = std::make_shared<Subtract>(node_img_height_cast, node_number_one);
diff --git a/ngraph/frontend/paddlepaddle/src/op_table.hpp b/ngraph/frontend/paddlepaddle/src/op_table.hpp
index 76ee7d5e3c3..b24f2f94180 100644
--- a/ngraph/frontend/paddlepaddle/src/op_table.hpp
+++ b/ngraph/frontend/paddlepaddle/src/op_table.hpp
@@ -6,7 +6,6 @@
 
 #include <functional>
 #include <map>
-#include <ngraph/output_vector.hpp>
 #include <string>
 
 #include "node_context.hpp"
diff --git a/ngraph/frontend/paddlepaddle/src/pdpd_fw_node.cpp b/ngraph/frontend/paddlepaddle/src/pdpd_fw_node.cpp
index e91df788b85..b4e43718790 100644
--- a/ngraph/frontend/paddlepaddle/src/pdpd_fw_node.cpp
+++ b/ngraph/frontend/paddlepaddle/src/pdpd_fw_node.cpp
@@ -6,7 +6,6 @@
 
 namespace ov {
 namespace frontend {
-NGRAPH_RTTI_DEFINITION(PDPDFrameworkNode, "PDPDFrameworkNode", 1);
 
 void PDPDFrameworkNode::validate_and_infer_types() {
     FrameworkNode::validate_and_infer_types();
diff --git a/ngraph/frontend/paddlepaddle/src/pdpd_fw_node.hpp b/ngraph/frontend/paddlepaddle/src/pdpd_fw_node.hpp
index 558b29b0e75..81ede7fa33e 100644
--- a/ngraph/frontend/paddlepaddle/src/pdpd_fw_node.hpp
+++ b/ngraph/frontend/paddlepaddle/src/pdpd_fw_node.hpp
@@ -12,7 +12,7 @@ namespace ov {
 namespace frontend {
 class PDPDFrameworkNode : public ov::op::util::FrameworkNode {
 public:
-    NGRAPH_RTTI_DECLARATION;
+    OPENVINO_OP("PDPDFrameworkNode", "util", ov::op::util::FrameworkNode);
 
     PDPDFrameworkNode(const DecoderPDPDProto& decoder,
                       const OutputVector& inputs,
diff --git a/ngraph/frontend/paddlepaddle/src/pdpd_utils.cpp b/ngraph/frontend/paddlepaddle/src/pdpd_utils.cpp
new file mode 100644
index 00000000000..5545de213bb
--- /dev/null
+++ b/ngraph/frontend/paddlepaddle/src/pdpd_utils.cpp
@@ -0,0 +1,14 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "pdpd_utils.hpp"
+
+std::shared_ptr<ov::Node> ov::frontend::pdpd::reorder_axes(const ov::Output<ov::Node>& value,
+                                                           std::vector<size_t> axes_order) {
+    const auto axes_order_const =
+        std::make_shared<opset6::Constant>(element::i64,
+                                           Shape{axes_order.size()},
+                                           std::vector<int64_t>(axes_order.begin(), axes_order.end()));
+    return std::make_shared<opset6::Transpose>(value, axes_order_const);
+}
diff --git a/ngraph/frontend/paddlepaddle/src/pdpd_utils.hpp b/ngraph/frontend/paddlepaddle/src/pdpd_utils.hpp
index 286f6e11b9b..6f98dcd16db 100644
--- a/ngraph/frontend/paddlepaddle/src/pdpd_utils.hpp
+++ b/ngraph/frontend/paddlepaddle/src/pdpd_utils.hpp
@@ -5,6 +5,7 @@
 #pragma once
 
 #include "common/frontend_exceptions.hpp"
+#include "openvino/opsets/opset6.hpp"
 
 namespace ov {
 namespace frontend {
@@ -38,6 +39,7 @@ bool endsWith(const std::basic_string<T>& str, const std::basic_string<T>& suffi
     return false;
 }
 
+std::shared_ptr<Node> reorder_axes(const Output<Node>& value, std::vector<size_t> axes_order);
 }  // namespace pdpd
 }  // namespace frontend
 }  // namespace ov
diff --git a/ngraph/frontend/paddlepaddle/src/place.cpp b/ngraph/frontend/paddlepaddle/src/place.cpp
index 873eca5a24e..cfc4ecf2585 100644
--- a/ngraph/frontend/paddlepaddle/src/place.cpp
+++ b/ngraph/frontend/paddlepaddle/src/place.cpp
@@ -2,7 +2,7 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <paddlepaddle_frontend/place.hpp>
+#include "paddlepaddle_frontend/place.hpp"
 
 #include "decoder.hpp"
 #include "framework.pb.h"

From 60fb05bb6a58b9de708124004e8906c34fc7b249 Mon Sep 17 00:00:00 2001
From: Vladimir Paramuzov <vladimir.paramuzov@intel.com>
Date: Fri, 26 Nov 2021 10:17:07 +0300
Subject: [PATCH 23/72] [GPU] Fix strided slice -> crop replacement. Int8
 support in strided slice (#8764)

---
 .../src/cldnn_engine/ops/convolution.cpp      |  6 +--
 .../src/cldnn_engine/ops/strided_slice.cpp    |  4 +-
 .../single_layer_tests/strided_slice.cpp      |  2 +
 .../skip_tests_config.cpp                     |  2 -
 .../strided_slice_kernel_ref.cpp              |  4 ++
 .../graph_optimizer/pre_replace_deconv.cpp    |  3 +-
 .../clDNN/src/impls/ocl/strided_slice.cpp     |  5 ++
 .../test_cases/strided_slice_gpu_test.cpp     | 53 +++++++++++++++++++
 8 files changed, 72 insertions(+), 7 deletions(-)

diff --git a/inference-engine/src/cldnn_engine/ops/convolution.cpp b/inference-engine/src/cldnn_engine/ops/convolution.cpp
index ac654e9c2e5..dd053634a7d 100644
--- a/inference-engine/src/cldnn_engine/ops/convolution.cpp
+++ b/inference-engine/src/cldnn_engine/ops/convolution.cpp
@@ -39,19 +39,19 @@ static ConvoltuionParameters GetConvolutionParameters(const ngraph::CoordinateDi
     switch (strides.size()) {
         case 3: {
             stride = cldnn::tensor(cldnn::batch(1), cldnn::feature(1), cldnn::spatial(strides[2], strides[1], strides[0]));
-            padding = cldnn::tensor(cldnn::batch(0), cldnn::feature(0), cldnn::spatial(pads_begin[2], pads_begin[1], pads_begin[0]));
+            padding = cldnn::tensor({0, 0, TensorValue(pads_begin[2]), TensorValue(pads_begin[1]), TensorValue(pads_begin[0])}, 0);
             dilation = cldnn::tensor(cldnn::batch(1), cldnn::feature(1), cldnn::spatial(dilations[2], dilations[1], dilations[0]));
             break;
         }
         case 2: {
             stride = cldnn::tensor(cldnn::batch(1), cldnn::feature(1), cldnn::spatial(strides[1], strides[0], 1));
-            padding = cldnn::tensor(cldnn::batch(0), cldnn::feature(0), cldnn::spatial(pads_begin[1], pads_begin[0], 0));
+            padding = cldnn::tensor({0, 0, TensorValue(pads_begin[1]), TensorValue(pads_begin[0])}, 0);
             dilation = cldnn::tensor(cldnn::batch(1), cldnn::feature(1), cldnn::spatial(dilations[1], dilations[0], 1));
             break;
         }
         case 1: {
             stride = cldnn::tensor(cldnn::batch(1), cldnn::feature(1), cldnn::spatial(strides[0], 1, 1));
-            padding = cldnn::tensor(cldnn::batch(0), cldnn::feature(0), cldnn::spatial(pads_begin[0], 0, 0));
+            padding = cldnn::tensor({0, 0, TensorValue(pads_begin[0]), 0}, 0);
             dilation = cldnn::tensor(cldnn::batch(1), cldnn::feature(1), cldnn::spatial(dilations[0], 1, 1));
             break;
         }
diff --git a/inference-engine/src/cldnn_engine/ops/strided_slice.cpp b/inference-engine/src/cldnn_engine/ops/strided_slice.cpp
index 98b7169bf6e..696a0cab959 100644
--- a/inference-engine/src/cldnn_engine/ops/strided_slice.cpp
+++ b/inference-engine/src/cldnn_engine/ops/strided_slice.cpp
@@ -217,7 +217,7 @@ static void CreateStridedSliceOp(Program& p, const std::shared_ptr<ngraph::op::v
 
         auto cropPrim = cldnn::crop(layerName, inPrimitive, refSize, offSize, op->get_friendly_name());
         p.AddPrimitive(cropPrim);
-        p.AddPrimitiveToProfiler(layerName, op);
+        auto last_layer_primitive = layerName;
 
         // Reshape in case of deleting of axis
         if (!shrink_axis_mask.empty()) {
@@ -226,7 +226,9 @@ static void CreateStridedSliceOp(Program& p, const std::shared_ptr<ngraph::op::v
             auto reshapePrim = cldnn::reshape(reshapeOutName, layerName, targetShape, op->get_friendly_name());
             p.AddPrimitive(reshapePrim);
             p.AddInnerPrimitiveToProfiler(reshapeOutName, layerName, op);
+            last_layer_primitive = reshapeOutName;
         }
+        p.AddPrimitiveToProfiler(op, last_layer_primitive);
         return;
     } while (false);
 
diff --git a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/strided_slice.cpp b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/strided_slice.cpp
index 1b4447a75da..a784cc44ced 100644
--- a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/strided_slice.cpp
+++ b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/strided_slice.cpp
@@ -44,6 +44,8 @@ std::vector<StridedSliceSpecificParams> ss_only_test_cases_fp32 = {
                             {1, 1, 1, 1}, {1, 1, 1, 1},  {},  {},  {} },
         StridedSliceSpecificParams{ { 2, 2, 2, 2 }, { 1, 1, 1, 1 }, { 2, 2, 2, 2 }, { 1, 1, 1, 1 },
                             {0, 0, 0, 0}, {1, 1, 1, 1},  {},  {},  {} },
+        StridedSliceSpecificParams{ { 1, 2, 128, 2 }, { 0, 0, 0, 1 }, { 0, 1, 0, 2 }, { 1, 1, 1, 1 },
+                            {1, 0, 1, 0}, {1, 0, 1, 0},  {0, 0, 0, 0},  {0, 1, 0, 1},  {0, 0, 0, 0} },
         StridedSliceSpecificParams{ { 2, 2, 2, 2 }, { 1, 1, 1, 1 }, { 2, 2, 2, 2 }, { 1, 1, 1, 1 },
                             {0, 0, 0, 0}, {0, 0, 0, 0},  {},  {},  {} },
         StridedSliceSpecificParams{ { 2, 2, 4, 3 }, { 0, 0, 0, 0 }, { 2, 2, 4, 3 }, { 1, 1, 2, 1 },
diff --git a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp
index 2354cf299c9..a2f53ac7fcf 100644
--- a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp
+++ b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp
@@ -46,8 +46,6 @@ std::vector<std::string> disabledTestPatterns() {
             R"(.*(smoke_DetectionOutput5In).*)",
             // TODO: Issue: 47773
             R"(.*(ProposalLayerTest).*)",
-            // INT8 StridedSlice not supported
-            R"(.*(LPT/StridedSliceTransformation).*)",
             // TODO: Issue: 48106
             R"(.*ConstantResultSubgraphTest.*inPrc=I16.*)",
             // TODO: Issue: 54194
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/strided_slice/strided_slice_kernel_ref.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/strided_slice/strided_slice_kernel_ref.cpp
index 6326c208519..63fa006c0ac 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/strided_slice/strided_slice_kernel_ref.cpp
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/strided_slice/strided_slice_kernel_ref.cpp
@@ -41,10 +41,14 @@ ParamsKey StridedSliceKernelRef::GetSupportedKey() const {
     ParamsKey k;
     k.EnableInputDataType(Datatype::F16);
     k.EnableInputDataType(Datatype::F32);
+    k.EnableInputDataType(Datatype::UINT8);
+    k.EnableInputDataType(Datatype::INT8);
     k.EnableInputDataType(Datatype::INT32);
     k.EnableInputDataType(Datatype::INT64);
     k.EnableOutputDataType(Datatype::F16);
     k.EnableOutputDataType(Datatype::F32);
+    k.EnableOutputDataType(Datatype::UINT8);
+    k.EnableOutputDataType(Datatype::INT8);
     k.EnableOutputDataType(Datatype::INT32);
     k.EnableOutputDataType(Datatype::INT64);
     k.EnableAllInputLayout();
diff --git a/inference-engine/thirdparty/clDNN/src/graph_optimizer/pre_replace_deconv.cpp b/inference-engine/thirdparty/clDNN/src/graph_optimizer/pre_replace_deconv.cpp
index 0be55da5cd8..1d95823d426 100644
--- a/inference-engine/thirdparty/clDNN/src/graph_optimizer/pre_replace_deconv.cpp
+++ b/inference-engine/thirdparty/clDNN/src/graph_optimizer/pre_replace_deconv.cpp
@@ -84,9 +84,10 @@ void pre_replace_deconv::run(program& p) {
                     p.remove_connection(*weights_node_ptr, deconv_node);
                 }
 
+                auto filter_z = deconv_prim->grouped_weights_shape ? 1 : (filter_size.spatial[2] - 1);
                 pad.spatial[0] = (filter_size.spatial[0] - 1) - std::abs(pad.spatial[0]);
                 pad.spatial[1] = (filter_size.spatial[1] - 1) - std::abs(pad.spatial[1]);
-                pad.spatial[2] = (filter_size.spatial[2] - 1) - std::abs(pad.spatial[2]);
+                pad.spatial[2] = filter_z - std::abs(pad.spatial[2]);
 
                 std::vector<std::shared_ptr<program_node>> bias_connections;
                 for (auto& bias_id : biases_nodes_id) {
diff --git a/inference-engine/thirdparty/clDNN/src/impls/ocl/strided_slice.cpp b/inference-engine/thirdparty/clDNN/src/impls/ocl/strided_slice.cpp
index bc788c5a5e4..81c3d1c4737 100644
--- a/inference-engine/thirdparty/clDNN/src/impls/ocl/strided_slice.cpp
+++ b/inference-engine/thirdparty/clDNN/src/impls/ocl/strided_slice.cpp
@@ -118,10 +118,15 @@ attach_strided_slice_impl::attach_strided_slice_impl() {
         std::make_tuple(data_types::f16, format::bfyx),
         std::make_tuple(data_types::i32, format::bfyx),
         std::make_tuple(data_types::i64, format::bfyx),
+        std::make_tuple(data_types::i8, format::bfyx),
+        std::make_tuple(data_types::u8, format::bfyx),
+
         std::make_tuple(data_types::f32, format::bfzyx),
         std::make_tuple(data_types::f16, format::bfzyx),
         std::make_tuple(data_types::i32, format::bfzyx),
         std::make_tuple(data_types::i64, format::bfzyx),
+        std::make_tuple(data_types::i8, format::bfzyx),
+        std::make_tuple(data_types::u8, format::bfzyx),
     });
 }
 
diff --git a/inference-engine/thirdparty/clDNN/tests/test_cases/strided_slice_gpu_test.cpp b/inference-engine/thirdparty/clDNN/tests/test_cases/strided_slice_gpu_test.cpp
index aa4ec3aa0d3..470623e28d8 100644
--- a/inference-engine/thirdparty/clDNN/tests/test_cases/strided_slice_gpu_test.cpp
+++ b/inference-engine/thirdparty/clDNN/tests/test_cases/strided_slice_gpu_test.cpp
@@ -1081,6 +1081,59 @@ TEST(strided_slice_gpu_f32_i64, test_2x2x2x1x1) {
     }
 }
 
+
+TEST(strided_slice_gpu_i8_i64, test_2x2x2x1x1) {
+    // Input (BFZYX): 2x2x2x1x1
+    // Output (BFZYX): 1x2x2x1x1
+
+    auto& engine = get_test_engine();
+    auto input = engine.allocate_memory({ data_types::i8, format::bfzyx, { 2, 2, 1, 1, 2 } });
+    auto begin = engine.allocate_memory({ data_types::i64, format::bfyx, { 3, 1, 1, 1 } });
+    auto end = engine.allocate_memory({ data_types::i64, format::bfyx, { 3, 1, 1, 1 } });
+    auto strides = engine.allocate_memory({ data_types::i64, format::bfyx, { 3, 1, 1, 1 } });
+
+    set_values<int8_t>(input, {
+            0, 1, 2, 3, 4, 5, 6, 7
+    });
+    set_values<int64_t>(begin, {
+            0, 0, 0
+    });
+    set_values<int64_t>(end, {
+            1, 2, 2
+    });
+    set_values<int64_t>(strides, {
+            1, 1, 1
+    });
+
+    topology topology;
+    topology.add(input_layout("input", input->get_layout()));
+    topology.add(data("input2", begin));
+    topology.add(data("input3", end));
+    topology.add(data("input4", strides));
+    topology.add(strided_slice("strided_slice", "input", "input2", "input3", "input4", {}, {}, {}, {}, tensor{1, 2, 1, 2}));
+
+    network network(engine, topology);
+
+    network.set_input_data("input", input);
+
+    auto outputs = network.execute();
+
+    EXPECT_EQ(outputs.size(), size_t(1));
+    EXPECT_EQ(outputs.begin()->first, "strided_slice");
+
+    auto output = outputs.at("strided_slice").get_memory();
+
+    std::vector<int8_t> answers = {
+            0, 1, 2, 3
+    };
+
+    cldnn::mem_lock<int8_t> output_ptr(output, get_test_stream());
+
+    for (size_t i = 0; i < answers.size(); ++i) {
+        EXPECT_TRUE(are_equal(answers[i], output_ptr[i]));
+    }
+}
+
 TEST(strided_slice_gpu_f32_i32, test_2x2x2x1x1_2) {
     // Input (BFZYX): 2x2x2x1x1
     // Output (BFZYX): 2x1x1x1x1

From bf504c00d3cb4e7073a35cb2cd5bfb83df444368 Mon Sep 17 00:00:00 2001
From: Katarzyna Mitrus <katarzyna.mitrus@intel.com>
Date: Fri, 26 Nov 2021 08:30:45 +0100
Subject: [PATCH 24/72] [ONNX FE] Support for ONNX ATen (embedding bag) (#8802)

---
 ngraph/frontend/onnx/frontend/src/op/aten.cpp |  93 +++++++++++++
 ngraph/frontend/onnx/frontend/src/op/aten.hpp |  21 +++
 .../frontend/onnx/frontend/src/ops_bridge.cpp |   2 +
 .../aten_embedding_sum_many_outputs.prototxt  |  96 ++++++++++++++
 .../aten_embedding_sum_offset_3in.prototxt    |  93 +++++++++++++
 .../aten_embedding_sum_offset_4in.prototxt    | 107 +++++++++++++++
 .../aten_embedding_sum_packed_2in.prototxt    |  82 ++++++++++++
 ...edding_sum_packed_3in_offset_none.prototxt |  83 ++++++++++++
 ...sum_packed_4in_per_sample_weights.prototxt | 100 ++++++++++++++
 ...embedding_sum_packed_4in_two_none.prototxt |  84 ++++++++++++
 .../aten_unsupported_embedding_mode.prototxt  |  93 +++++++++++++
 .../onnx/aten_unsupported_operator.prototxt   |  93 +++++++++++++
 ngraph/test/onnx/onnx_import.in.cpp           | 123 ++++++++++++++++++
 13 files changed, 1070 insertions(+)
 create mode 100644 ngraph/frontend/onnx/frontend/src/op/aten.cpp
 create mode 100644 ngraph/frontend/onnx/frontend/src/op/aten.hpp
 create mode 100644 ngraph/test/models/onnx/aten_embedding_sum_many_outputs.prototxt
 create mode 100644 ngraph/test/models/onnx/aten_embedding_sum_offset_3in.prototxt
 create mode 100644 ngraph/test/models/onnx/aten_embedding_sum_offset_4in.prototxt
 create mode 100644 ngraph/test/models/onnx/aten_embedding_sum_packed_2in.prototxt
 create mode 100644 ngraph/test/models/onnx/aten_embedding_sum_packed_3in_offset_none.prototxt
 create mode 100644 ngraph/test/models/onnx/aten_embedding_sum_packed_4in_per_sample_weights.prototxt
 create mode 100644 ngraph/test/models/onnx/aten_embedding_sum_packed_4in_two_none.prototxt
 create mode 100644 ngraph/test/models/onnx/aten_unsupported_embedding_mode.prototxt
 create mode 100644 ngraph/test/models/onnx/aten_unsupported_operator.prototxt

diff --git a/ngraph/frontend/onnx/frontend/src/op/aten.cpp b/ngraph/frontend/onnx/frontend/src/op/aten.cpp
new file mode 100644
index 00000000000..3c507559f22
--- /dev/null
+++ b/ngraph/frontend/onnx/frontend/src/op/aten.cpp
@@ -0,0 +1,93 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "op/aten.hpp"
+
+#include "default_opset.hpp"
+#include "exceptions.hpp"
+#include "onnx_import/core/node.hpp"
+#include "onnx_import/core/null_node.hpp"
+#include "openvino/opsets/opset8.hpp"
+
+namespace ngraph {
+namespace onnx_import {
+namespace op {
+namespace set_1 {
+
+OutputVector aten(const Node& node) {
+    OutputVector inputs{node.get_ng_inputs()};
+
+    const auto operator_name = node.get_attribute_value<std::string>("operator", "");
+    CHECK_VALID_NODE(node,
+                     operator_name == "embedding_bag",
+                     "Only `embedding_bag` is supported as ATen `operator` attribute. Got: ",
+                     operator_name);
+
+    const auto mode = node.get_attribute_value<int64_t>("mode");
+    CHECK_VALID_NODE(node,
+                     mode == 0,
+                     "Unsupported mode, only `0` (sum) is supported as ATen embedding_bag `mode` attribute. Got: ",
+                     mode);
+    CHECK_VALID_NODE(node, inputs.size() >= 2, "Minimum 2 inputs are required. Got: ", inputs.size());
+
+    const bool is_packed_two_inputs =
+        inputs.size() == 2 || (inputs.size() == 3 && ngraph::op::is_null(inputs[2])) ||
+        (inputs.size() == 4 && ngraph::op::is_null(inputs[2]) && ngraph::op::is_null(inputs[3]));
+    const bool is_packed_three_inputs =
+        inputs.size() == 4 && ngraph::op::is_null(inputs[2]) && !ngraph::op::is_null(inputs[3]);
+    const bool is_offsets_three_inputs = inputs.size() == 3 && !ngraph::op::is_null(inputs[2]);
+
+    Output<ov::Node> embedding_bag;
+    if (is_packed_two_inputs) {
+        embedding_bag = std::make_shared<default_opset::EmbeddingBagPackedSum>(inputs[0], inputs[1]);
+    } else if (is_packed_three_inputs) {
+        embedding_bag = std::make_shared<default_opset::EmbeddingBagPackedSum>(inputs[0], inputs[1], inputs[3]);
+    } else if (is_offsets_three_inputs) {
+        embedding_bag = std::make_shared<default_opset::EmbeddingBagOffsetsSum>(inputs[0], inputs[1], inputs[2]);
+    } else if (inputs.size() >= 4) {
+        // Need to expand embedding table with zeros (default values for empty bags)
+        const auto& emb_tbl_in = inputs[0];
+        const auto& indices_in = inputs[1];
+        const auto& offsets_in = inputs[2];
+        const auto& per_sample_weights_in = inputs[3];
+
+        const auto data_type = emb_tbl_in.get_element_type();
+        const auto ind_type = indices_in.get_element_type();
+
+        const auto zero_const = std::make_shared<default_opset::Constant>(ind_type, Shape{}, 0);
+
+        // Shape aligned node, filled with zeros
+        const auto zero_of_data_type_const = std::make_shared<default_opset::Constant>(data_type, Shape{1}, 0);
+        const auto weights_shape_node = std::make_shared<default_opset::ShapeOf>(emb_tbl_in, ind_type);
+        const auto weights_last_dim_idx = std::make_shared<default_opset::Constant>(element::i32, Shape{1}, -1);
+        const auto weights_last_dim =
+            std::make_shared<ov::opset8::Gather>(weights_shape_node, weights_last_dim_idx, zero_const);
+        const auto zero_col_node =
+            std::make_shared<default_opset::Broadcast>(zero_of_data_type_const, weights_last_dim);
+        const auto default_embeddings_node = std::make_shared<default_opset::Unsqueeze>(zero_col_node, zero_const);
+
+        // Expanded embedding table weights
+        const auto weights_concat =
+            std::make_shared<default_opset::Concat>(OutputVector{emb_tbl_in, default_embeddings_node}, 0);
+        // Index in embedding table to fill empty bags
+        const auto weights_first_dim = std::make_shared<default_opset::Squeeze>(
+            std::make_shared<default_opset::Gather>(weights_shape_node, zero_const, zero_const));
+
+        embedding_bag = std::make_shared<default_opset::EmbeddingBagOffsetsSum>(weights_concat,
+                                                                                indices_in,
+                                                                                offsets_in,
+                                                                                weights_first_dim,  // default index
+                                                                                per_sample_weights_in);
+
+    } else {
+        OPENVINO_UNREACHABLE("Unsupported inputs configuration for ATen `embedding_bag` operation.");
+    }
+    // Enable import onnx Node with duplicated outputs
+    return OutputVector(node.get_outputs_size(), embedding_bag);
+}
+
+}  // namespace set_1
+}  // namespace op
+}  // namespace onnx_import
+}  // namespace ngraph
diff --git a/ngraph/frontend/onnx/frontend/src/op/aten.hpp b/ngraph/frontend/onnx/frontend/src/op/aten.hpp
new file mode 100644
index 00000000000..89b0ea3e7c2
--- /dev/null
+++ b/ngraph/frontend/onnx/frontend/src/op/aten.hpp
@@ -0,0 +1,21 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "default_opset.hpp"
+#include "ngraph/node.hpp"
+#include "onnx_import/core/node.hpp"
+
+namespace ngraph {
+namespace onnx_import {
+namespace op {
+namespace set_1 {
+
+OutputVector aten(const Node& node);
+
+}  // namespace set_1
+}  // namespace op
+}  // namespace onnx_import
+}  // namespace ngraph
diff --git a/ngraph/frontend/onnx/frontend/src/ops_bridge.cpp b/ngraph/frontend/onnx/frontend/src/ops_bridge.cpp
index 7d12281d446..4bd94665bbb 100644
--- a/ngraph/frontend/onnx/frontend/src/ops_bridge.cpp
+++ b/ngraph/frontend/onnx/frontend/src/ops_bridge.cpp
@@ -24,6 +24,7 @@
 #include "op/asinh.hpp"
 #include "op/atan.hpp"
 #include "op/atanh.hpp"
+#include "op/aten.hpp"
 #include "op/average_pool.hpp"
 #include "op/batch_norm.hpp"
 #include "op/bitshift.hpp"
@@ -293,6 +294,7 @@ OperatorsBridge::OperatorsBridge() {
     REGISTER_OPERATOR("Asin", 1, asin);
     REGISTER_OPERATOR("Asinh", 1, asinh);
     REGISTER_OPERATOR("Atan", 1, atan);
+    REGISTER_OPERATOR("ATen", 1, aten);
     REGISTER_OPERATOR("Atanh", 1, atanh);
     REGISTER_OPERATOR("AveragePool", 1, average_pool);
     REGISTER_OPERATOR("BatchNormalization", 1, batch_norm);
diff --git a/ngraph/test/models/onnx/aten_embedding_sum_many_outputs.prototxt b/ngraph/test/models/onnx/aten_embedding_sum_many_outputs.prototxt
new file mode 100644
index 00000000000..c89bd70373e
--- /dev/null
+++ b/ngraph/test/models/onnx/aten_embedding_sum_many_outputs.prototxt
@@ -0,0 +1,96 @@
+ir_version: 3
+producer_name: "onnx_import_test"
+graph {
+ node {
+    input: "emb_tbl"
+    input: "indices"
+    input: "offsets"
+    output: "result_0"
+    output: "result_1"
+    output: "result_2"
+    output: "result_3"
+    op_type: "ATen"
+    attribute {
+      name: "mode"
+      i: 0
+      type: INT
+    }
+    attribute {
+      name: "operator"
+      s: "embedding_bag"
+      type: STRING
+    }
+    attribute {
+      name: "scale_grad_by_freq"
+      i: 0
+      type: INT
+    }
+    attribute {
+      name: "sparse"
+      i: 1
+      type: INT
+    }
+  }
+  name: "test_aten_model"
+  input {
+    name: "emb_tbl"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 5
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "indices"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 4
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "offsets"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "result_0"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 1
+}
diff --git a/ngraph/test/models/onnx/aten_embedding_sum_offset_3in.prototxt b/ngraph/test/models/onnx/aten_embedding_sum_offset_3in.prototxt
new file mode 100644
index 00000000000..be788ecde53
--- /dev/null
+++ b/ngraph/test/models/onnx/aten_embedding_sum_offset_3in.prototxt
@@ -0,0 +1,93 @@
+ir_version: 3
+producer_name: "onnx_import_test"
+graph {
+ node {
+    input: "emb_tbl"
+    input: "indices"
+    input: "offsets"
+    output: "result_0"
+    op_type: "ATen"
+    attribute {
+      name: "mode"
+      i: 0
+      type: INT
+    }
+    attribute {
+      name: "operator"
+      s: "embedding_bag"
+      type: STRING
+    }
+    attribute {
+      name: "scale_grad_by_freq"
+      i: 0
+      type: INT
+    }
+    attribute {
+      name: "sparse"
+      i: 1
+      type: INT
+    }
+  }
+  name: "test_aten_model"
+  input {
+    name: "emb_tbl"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 5
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "indices"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 4
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "offsets"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "result_0"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 1
+}
diff --git a/ngraph/test/models/onnx/aten_embedding_sum_offset_4in.prototxt b/ngraph/test/models/onnx/aten_embedding_sum_offset_4in.prototxt
new file mode 100644
index 00000000000..5e2fe46532c
--- /dev/null
+++ b/ngraph/test/models/onnx/aten_embedding_sum_offset_4in.prototxt
@@ -0,0 +1,107 @@
+ir_version: 3
+producer_name: "onnx_import_test"
+graph {
+ node {
+    input: "emb_tbl"
+    input: "indices"
+    input: "offsets"
+    input: "per_sample_weights"
+    output: "result_0"
+    op_type: "ATen"
+    attribute {
+      name: "mode"
+      i: 0
+      type: INT
+    }
+    attribute {
+      name: "operator"
+      s: "embedding_bag"
+      type: STRING
+    }
+    attribute {
+      name: "scale_grad_by_freq"
+      i: 0
+      type: INT
+    }
+    attribute {
+      name: "sparse"
+      i: 1
+      type: INT
+    }
+  }
+  name: "test_aten_model"
+  input {
+    name: "emb_tbl"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 5
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "indices"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 4
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "offsets"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "per_sample_weights"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 4
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "result_0"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 1
+}
diff --git a/ngraph/test/models/onnx/aten_embedding_sum_packed_2in.prototxt b/ngraph/test/models/onnx/aten_embedding_sum_packed_2in.prototxt
new file mode 100644
index 00000000000..72576cdcbe4
--- /dev/null
+++ b/ngraph/test/models/onnx/aten_embedding_sum_packed_2in.prototxt
@@ -0,0 +1,82 @@
+ir_version: 3
+producer_name: "onnx_import_test"
+graph {
+ node {
+    input: "emb_tbl"
+    input: "indices"
+    output: "result_0"
+    op_type: "ATen"
+    attribute {
+      name: "mode"
+      i: 0
+      type: INT
+    }
+    attribute {
+      name: "operator"
+      s: "embedding_bag"
+      type: STRING
+    }
+    attribute {
+      name: "scale_grad_by_freq"
+      i: 0
+      type: INT
+    }
+    attribute {
+      name: "sparse"
+      i: 1
+      type: INT
+    }
+  }
+  name: "test_aten_model"
+  input {
+    name: "emb_tbl"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 5
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "indices"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "result_0"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 1
+}
diff --git a/ngraph/test/models/onnx/aten_embedding_sum_packed_3in_offset_none.prototxt b/ngraph/test/models/onnx/aten_embedding_sum_packed_3in_offset_none.prototxt
new file mode 100644
index 00000000000..6f25b45f6f2
--- /dev/null
+++ b/ngraph/test/models/onnx/aten_embedding_sum_packed_3in_offset_none.prototxt
@@ -0,0 +1,83 @@
+ir_version: 3
+producer_name: "onnx_import_test"
+graph {
+ node {
+    input: "emb_tbl"
+    input: "indices"
+    input: ""
+    output: "result_0"
+    op_type: "ATen"
+    attribute {
+      name: "mode"
+      i: 0
+      type: INT
+    }
+    attribute {
+      name: "operator"
+      s: "embedding_bag"
+      type: STRING
+    }
+    attribute {
+      name: "scale_grad_by_freq"
+      i: 0
+      type: INT
+    }
+    attribute {
+      name: "sparse"
+      i: 1
+      type: INT
+    }
+  }
+  name: "test_aten_model"
+  input {
+    name: "emb_tbl"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 5
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "indices"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "result_0"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 1
+}
diff --git a/ngraph/test/models/onnx/aten_embedding_sum_packed_4in_per_sample_weights.prototxt b/ngraph/test/models/onnx/aten_embedding_sum_packed_4in_per_sample_weights.prototxt
new file mode 100644
index 00000000000..bc15c5b616d
--- /dev/null
+++ b/ngraph/test/models/onnx/aten_embedding_sum_packed_4in_per_sample_weights.prototxt
@@ -0,0 +1,100 @@
+ir_version: 3
+producer_name: "onnx_import_test"
+graph {
+ node {
+    input: "emb_tbl"
+    input: "indices"
+    input: ""
+    input: "per_sample_weights"
+    output: "result_0"
+    op_type: "ATen"
+    attribute {
+      name: "mode"
+      i: 0
+      type: INT
+    }
+    attribute {
+      name: "operator"
+      s: "embedding_bag"
+      type: STRING
+    }
+    attribute {
+      name: "scale_grad_by_freq"
+      i: 0
+      type: INT
+    }
+    attribute {
+      name: "sparse"
+      i: 1
+      type: INT
+    }
+  }
+  name: "test_aten_model"
+  input {
+    name: "emb_tbl"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 5
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "indices"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "per_sample_weights"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "result_0"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 1
+}
diff --git a/ngraph/test/models/onnx/aten_embedding_sum_packed_4in_two_none.prototxt b/ngraph/test/models/onnx/aten_embedding_sum_packed_4in_two_none.prototxt
new file mode 100644
index 00000000000..74f0576e90e
--- /dev/null
+++ b/ngraph/test/models/onnx/aten_embedding_sum_packed_4in_two_none.prototxt
@@ -0,0 +1,84 @@
+ir_version: 3
+producer_name: "onnx_import_test"
+graph {
+ node {
+    input: "emb_tbl"
+    input: "indices"
+    input: ""
+    input: ""
+    output: "result_0"
+    op_type: "ATen"
+    attribute {
+      name: "mode"
+      i: 0
+      type: INT
+    }
+    attribute {
+      name: "operator"
+      s: "embedding_bag"
+      type: STRING
+    }
+    attribute {
+      name: "scale_grad_by_freq"
+      i: 0
+      type: INT
+    }
+    attribute {
+      name: "sparse"
+      i: 1
+      type: INT
+    }
+  }
+  name: "test_aten_model"
+  input {
+    name: "emb_tbl"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 5
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "indices"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "result_0"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 1
+}
diff --git a/ngraph/test/models/onnx/aten_unsupported_embedding_mode.prototxt b/ngraph/test/models/onnx/aten_unsupported_embedding_mode.prototxt
new file mode 100644
index 00000000000..e83c561c2c6
--- /dev/null
+++ b/ngraph/test/models/onnx/aten_unsupported_embedding_mode.prototxt
@@ -0,0 +1,93 @@
+ir_version: 3
+producer_name: "onnx_import_test"
+graph {
+ node {
+    input: "emb_tbl"
+    input: "indices"
+    input: "offsets"
+    output: "result_0"
+    op_type: "ATen"
+    attribute {
+      name: "mode"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "operator"
+      s: "embedding_bag"
+      type: STRING
+    }
+    attribute {
+      name: "scale_grad_by_freq"
+      i: 0
+      type: INT
+    }
+    attribute {
+      name: "sparse"
+      i: 1
+      type: INT
+    }
+  }
+  name: "test_aten_model"
+  input {
+    name: "emb_tbl"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 5
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "indices"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 4
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "offsets"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "result_0"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 1
+}
diff --git a/ngraph/test/models/onnx/aten_unsupported_operator.prototxt b/ngraph/test/models/onnx/aten_unsupported_operator.prototxt
new file mode 100644
index 00000000000..95139fef850
--- /dev/null
+++ b/ngraph/test/models/onnx/aten_unsupported_operator.prototxt
@@ -0,0 +1,93 @@
+ir_version: 3
+producer_name: "onnx_import_test"
+graph {
+ node {
+    input: "emb_tbl"
+    input: "indices"
+    input: "offsets"
+    output: "result_0"
+    op_type: "ATen"
+    attribute {
+      name: "mode"
+      i: 1
+      type: INT
+    }
+    attribute {
+      name: "operator"
+      s: "test_unsupported_operator"
+      type: STRING
+    }
+    attribute {
+      name: "scale_grad_by_freq"
+      i: 0
+      type: INT
+    }
+    attribute {
+      name: "sparse"
+      i: 1
+      type: INT
+    }
+  }
+  name: "test_aten_model"
+  input {
+    name: "emb_tbl"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 5
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "indices"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 4
+          }
+        }
+      }
+    }
+  }
+  input {
+    name: "offsets"
+    type {
+      tensor_type {
+        elem_type: 6
+        shape {
+          dim {
+            dim_value: 3
+          }
+        }
+      }
+    }
+  }
+  output {
+    name: "result_0"
+    type {
+      tensor_type {
+        elem_type: 1
+        shape {
+          dim {
+            dim_value: 3
+          }
+          dim {
+            dim_value: 2
+          }
+        }
+      }
+    }
+  }
+}
+opset_import {
+  version: 1
+}
diff --git a/ngraph/test/onnx/onnx_import.in.cpp b/ngraph/test/onnx/onnx_import.in.cpp
index 8775d91e54d..09c502f2708 100644
--- a/ngraph/test/onnx/onnx_import.in.cpp
+++ b/ngraph/test/onnx/onnx_import.in.cpp
@@ -4419,3 +4419,126 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_model_random_normal_like) {
     test_case.add_expected_output<float>(Shape{2, 2}, {13.459274, 41.75028, -19.311913, 131.79282});
     test_case.run();
 }
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_aten_embedding_bag_packed_sum_2in) {
+    const auto function =
+        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/aten_embedding_sum_packed_2in.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    test_case.add_input<float>(Shape{5, 2}, {-0.2, -0.6, -0.1, -0.4, -1.9, -1.8, -1., 1.5, 0.8, -0.7});
+    test_case.add_input<int32_t>(Shape{3, 2}, {0, 2, 1, 2, 3, 4});  // indices
+
+    test_case.add_expected_output<float>(Shape{3, 2}, {-2.1, -2.4, -2., -2.2, -0.19999999, 0.8});
+    test_case.run();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_aten_embedding_bag_packed_sum_3in_offsets_none) {
+    const auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/aten_embedding_sum_packed_3in_offset_none.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    test_case.add_input<float>(Shape{5, 2}, {-0.2, -0.6, -0.1, -0.4, -1.9, -1.8, -1., 1.5, 0.8, -0.7});
+    test_case.add_input<int32_t>(Shape{3, 2}, {0, 2, 1, 2, 3, 4});  // indices
+
+    test_case.add_expected_output<float>(Shape{3, 2}, {-2.1, -2.4, -2., -2.2, -0.19999999, 0.8});
+    test_case.run();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_aten_embedding_bag_packed_sum_4in_per_sample_weights) {
+    const auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/aten_embedding_sum_packed_4in_per_sample_weights.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    test_case.add_input<float>(Shape{5, 2}, {-0.2, -0.6, -0.1, -0.4, -1.9, -1.8, -1., 1.5, 0.8, -0.7});
+    test_case.add_input<int32_t>(Shape{3, 2}, {0, 2, 1, 2, 3, 4});            // indices
+    test_case.add_input<float>(Shape{3, 2}, {0.5, 0.5, 0.5, 0.5, 0.5, 0.5});  // per_sample_weights
+
+    test_case.add_expected_output<float>(Shape{3, 2}, {-1.05, -1.2, -1., -1.1, -0.09999999, 0.4});
+    test_case.run();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_aten_embedding_bag_packed_sum_4in_two_none) {
+    const auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/aten_embedding_sum_packed_4in_two_none.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    test_case.add_input<float>(Shape{5, 2}, {-0.2, -0.6, -0.1, -0.4, -1.9, -1.8, -1., 1.5, 0.8, -0.7});
+    test_case.add_input<int32_t>(Shape{3, 2}, {0, 2, 1, 2, 3, 4});  // indices
+
+    test_case.add_expected_output<float>(Shape{3, 2}, {-2.1, -2.4, -2., -2.2, -0.19999999, 0.8});
+    test_case.run();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_aten_embedding_bag_offsets_sum_3in) {
+    const auto function =
+        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/aten_embedding_sum_offset_3in.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    test_case.add_input<float>(Shape{5, 2}, {-0.2, -0.6, -0.1, -0.4, -1.9, -1.8, -1., 1.5, 0.8, -0.7});
+    test_case.add_input<int32_t>(Shape{4}, {0, 2, 3, 4});  // indices
+    test_case.add_input<int32_t>(Shape{3}, {0, 2, 2});     // offsets
+
+    test_case.add_expected_output<float>(Shape{3, 2}, {-2.1, -2.4, 0, 0, -0.2, 0.8});
+    test_case.run();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_aten_embedding_bag_offsets_sum_4in) {
+    const auto function =
+        onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/aten_embedding_sum_offset_4in.onnx"));
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    test_case.add_input<float>(Shape{5, 2}, {-0.2, -0.6, -0.1, -0.4, -1.9, -1.8, -1., 1.5, 0.8, -0.7});
+    test_case.add_input<int32_t>(Shape{4}, {0, 2, 3, 4});        // indices
+    test_case.add_input<int32_t>(Shape{3}, {0, 2, 2});           // offsets
+    test_case.add_input<float>(Shape{4}, {0.5, 0.5, 0.5, 0.5});  // per_sample_weights
+
+    test_case.add_expected_output<float>(Shape{3, 2}, {-1.05, -1.2, 0., 0., -0.09999999, 0.4});
+    test_case.run();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_aten_embedding_bag_many_node_outputs) {
+    const auto function = onnx_import::import_onnx_model(
+        file_util::path_join(SERIALIZED_ZOO, "onnx/aten_embedding_sum_many_outputs.onnx"));
+
+    // 4 outputs in onnx Node (1 connected and 3 not connected)
+    EXPECT_EQ(function->outputs().size(), 1);
+    EXPECT_EQ(function->get_results().size(), 1);
+
+    auto test_case = test::TestCase<TestEngine>(function);
+    test_case.add_input<float>(Shape{5, 2}, {-0.2, -0.6, -0.1, -0.4, -1.9, -1.8, -1., 1.5, 0.8, -0.7});
+    test_case.add_input<int32_t>(Shape{4}, {0, 2, 3, 4});  // indices
+    test_case.add_input<int32_t>(Shape{3}, {0, 2, 2});     // offsets
+
+    test_case.add_expected_output<float>(Shape{3, 2}, {-2.1, -2.4, 0, 0, -0.2, 0.8});
+    test_case.run();
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_aten_unsupported_embedding_mode) {
+    try {
+        const auto function = onnx_import::import_onnx_model(
+            file_util::path_join(SERIALIZED_ZOO, "onnx/aten_unsupported_embedding_mode.onnx"));
+        FAIL() << "Expected exception was not thrown.";
+    } catch (const ngraph::ngraph_error& e) {
+        EXPECT_HAS_SUBSTRING(
+            e.what(),
+            std::string(
+                "Unsupported mode, only `0` (sum) is supported as ATen embedding_bag `mode` attribute. Got: 1"));
+    } catch (...) {
+        FAIL() << "Other exception than expected was thrown.";
+    }
+}
+
+NGRAPH_TEST(${BACKEND_NAME}, onnx_aten_unsupported_operator) {
+    try {
+        const auto function =
+            onnx_import::import_onnx_model(file_util::path_join(SERIALIZED_ZOO, "onnx/aten_unsupported_operator.onnx"));
+        FAIL() << "Expected exception was not thrown.";
+    } catch (const ngraph::ngraph_error& e) {
+        EXPECT_HAS_SUBSTRING(
+            e.what(),
+            std::string(
+                "Only `embedding_bag` is supported as ATen `operator` attribute. Got: test_unsupported_operator"));
+    } catch (...) {
+        FAIL() << "Other exception than expected was thrown.";
+    }
+}

From 68cf41cbdf655e21b3765da4af35fa76b50ffdc5 Mon Sep 17 00:00:00 2001
From: Vladislav Golubev <vladislav.golubev@intel.com>
Date: Fri, 26 Nov 2021 11:00:09 +0300
Subject: [PATCH 25/72] [CPU] TopK: dynamic shapes support (#8474)

---
 .../mkldnn_plugin/nodes/mkldnn_topk_node.cpp  |  98 ++++++----
 .../mkldnn_plugin/nodes/mkldnn_topk_node.h    |  16 +-
 .../plugin/cpu/single_layer_tests/topk.cpp    | 172 ++++++++++++++++++
 3 files changed, 240 insertions(+), 46 deletions(-)
 create mode 100644 inference-engine/tests/functional/plugin/cpu/single_layer_tests/topk.cpp

diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_topk_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_topk_node.cpp
index ffef15396cd..c1d200f79c3 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_topk_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_topk_node.cpp
@@ -4,6 +4,8 @@
 
 #include <cmath>
 
+#include <ngraph/opsets/opset1.hpp>
+#include <ie_ngraph_utils.hpp>
 #include <ngraph/op/topk.hpp>
 #include "ie_parallel.hpp"
 #include "mkldnn_topk_node.h"
@@ -18,10 +20,6 @@ using namespace InferenceEngine;
 
 bool MKLDNNTopKNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
         const auto topKOp = ngraph::as_type_ptr<const ngraph::op::v1::TopK>(op);
         if (!topKOp) {
             errorMessage = "Node is not an instance of the TopK from the operations set v1 or v3";
@@ -52,36 +50,9 @@ MKLDNNTopKNode::MKLDNNTopKNode(const std::shared_ptr<ngraph::Node>& op, const mk
     }
     auto topK1Op = ngraph::as_type_ptr<ngraph::op::v1::TopK>(op);
 
-    VectorDims dstDims = topK1Op->get_output_shape(TOPK_VALUE);
-    src_dims = topK1Op->get_input_shape(TOPK_DATA);
-
     axis = topK1Op->get_axis();
-
-    if (topK1Op->get_mode() == ngraph::op::TopKMode::MAX)
-        mode_max = true;
-    else
-        mode_max = false;
-
-    if (topK1Op->get_sort_type() == ngraph::op::TopKSortType::SORT_VALUES)
-        sort_value = true;
-    else
-        sort_value = false;
-
-    int j;
-    for (j = src_dims.size() - 1; j >= 0; j--) {
-        if (src_dims[j] != 1) break;
-    }
-    if (static_cast<size_t>(j) == axis) is_last_dim = true;
-
-    for (size_t i = 0; i < axis; i++) {
-        axis_step *= src_dims[i];
-    }
-    axis_dim = src_dims[axis];
-    for (size_t i = (axis + 1); i < src_dims.size(); i++) {
-        axis_stride *= src_dims[i];
-    }
-    dim = static_cast<int>(src_dims[axis]);
-    before_num = count(src_dims, 0, axis);
+    mode_max = topK1Op->get_mode() == ngraph::op::TopKMode::MAX;
+    sort_value = topK1Op->get_sort_type() == ngraph::op::TopKSortType::SORT_VALUES;
 }
 
 void MKLDNNTopKNode::initSupportedPrimitiveDescriptors() {
@@ -134,11 +105,23 @@ void MKLDNNTopKNode::execute(mkldnn::stream strm) {
         IE_THROW() << errorMsg;
     }
 
-    if (src_dims[axis] < static_cast<size_t>(src_k))
-        src_k = src_dims[axis];
-
     const VectorDims& in_dims = getParentEdgeAt(TOPK_DATA)->getMemory().getStaticDims();
 
+    if (in_dims[axis] < static_cast<size_t>(src_k))
+        src_k = in_dims[axis];
+
+    bool is_last_dim = false;
+    for (int j = in_dims.size() - 1; j >= 0; j--) {
+        if (in_dims[j] != 1) {
+            if (static_cast<size_t>(j) == axis)
+                is_last_dim = true;
+            break;
+        }
+    }
+
+    dim = static_cast<int>(in_dims[axis]);
+    before_num = count(in_dims, 0, axis);
+
     if (src_k == 1) {
         if (is_last_dim) {
             if (mode_max)
@@ -170,6 +153,49 @@ bool MKLDNNTopKNode::created() const {
     return getType() == TopK;
 }
 
+bool MKLDNNTopKNode::needPrepareParams() const {
+    return false;
+}
+
+void MKLDNNTopKNode::executeDynamicImpl(mkldnn::stream strm) {
+    return execute(strm);
+}
+
+void MKLDNNTopKNode::createPrimitive() {
+    if (inputShapesDefined()) {
+        updateLastInputDims();
+    }
+}
+
+bool MKLDNNTopKNode::needShapeInfer() const {
+    const int kValue = reinterpret_cast<int*>(getParentEdgeAt(TOPK_K)->getMemoryPtr()->GetPtr())[0];
+    return inputShapesModified() || kValue != src_k;
+}
+
+std::vector<VectorDims> MKLDNNTopKNode::shapeInfer() const {
+    if (dynamic_cast<ngraph::opset1::Constant*>(opToShapeInfer->get_input_node_ptr(1))) {
+        return MKLDNNNode::shapeInfer();
+    }
+
+    opToShapeInfer->get_input_tensor(0).set_partial_shape(getParentEdgesAtPort(0)[0]->getMemory().getDesc().getShape().toPartialShape());
+
+    const auto& kMemory = getParentEdgesAtPort(1)[0]->getMemory();
+    const auto ngPrecision = InferenceEngine::details::convertPrecision(kMemory.getDesc().getPrecision());
+    const auto kConst = ngraph::opset1::Constant::create(ngPrecision, VectorDims{}, kMemory.GetPtr());
+
+    const auto localShapeInferOp = opToShapeInfer->clone_with_new_inputs({ opToShapeInfer->input_value(0), kConst });
+    localShapeInferOp->validate_and_infer_types();
+
+    std::vector<VectorDims> newOutputShapes(outputShapes.size());
+    for (size_t i = 0; i < newOutputShapes.size(); ++i) {
+        const auto& pShape = localShapeInferOp->get_output_partial_shape(i);
+        if (pShape.is_dynamic())
+            IE_THROW(NotImplemented) << "CPU plug-in doesn't support default shape infer for nodes with internal dynamism";
+        newOutputShapes[i] = pShape.get_shape();
+    }
+    return newOutputShapes;
+}
+
 template <class Compare1, template <typename> class Compare2>
 void MKLDNNTopKNode::top1_axis(const float* src_data, float* dst_data, int* dst_idx, VectorDims in_dims) {
     int after_num = count(in_dims, axis + 1, in_dims.size());
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_topk_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_topk_node.h
index 3cf879f4338..83773eb7eec 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_topk_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_topk_node.h
@@ -16,15 +16,16 @@ public:
                    MKLDNNWeightsSharing::Ptr &cache);
 
     void getSupportedDescriptors() override {};
-
     void initSupportedPrimitiveDescriptors() override;
-
-    void createPrimitive() override {};
-
+    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
-
     bool created() const override;
 
+    bool needPrepareParams() const override;
+    void executeDynamicImpl(mkldnn::stream strm) override;
+    bool needShapeInfer() const override;
+    std::vector<VectorDims> shapeInfer() const override;
+
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node> &op, std::string &errorMessage) noexcept;
 
 #if defined(HAVE_AVX512F)
@@ -85,12 +86,7 @@ private:
     const size_t TOPK_VALUE = 0;
     const size_t TOPK_INDEX = 1;
 
-    InferenceEngine::SizeVector src_dims;
     size_t axis;
-    size_t axis_dim;
-    size_t axis_stride = 1;
-    size_t axis_step = 1;
-    bool is_last_dim = false;
     int src_k = 1;
 
     bool sort_value = false;
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/topk.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/topk.cpp
new file mode 100644
index 00000000000..df461039f90
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/topk.cpp
@@ -0,0 +1,172 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "functional_test_utils/ov_tensor_utils.hpp"
+#include "common_test_utils/data_utils.hpp"
+#include "test_utils/cpu_test_utils.hpp"
+
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "ngraph_functions/builders.hpp"
+
+using namespace ngraph;
+using namespace InferenceEngine;
+using namespace CPUTestUtils;
+using namespace ov::test;
+
+namespace CPULayerTestsDefinitions {
+using topKParams = std::tuple<
+    ngraph::element::Type, // data precision
+    InputShape, // data shape
+    std::int64_t, // axis
+    ngraph::opset4::TopK::Mode,
+    ngraph::opset4::TopK::SortType>;
+
+class TopKLayerCPUTest : public testing::WithParamInterface<topKParams>, public ov::test::SubgraphBaseTest, public CPUTestsBase {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<topKParams> obj) {
+        ngraph::element::Type inputPrecision;
+        InputShape inputShape;
+        std::int64_t axis;
+        ngraph::opset4::TopK::Mode mode;
+        ngraph::opset4::TopK::SortType sortType;
+        std::tie(inputPrecision, inputShape, axis, mode, sortType) = obj.param;
+
+        std::ostringstream result;
+        result << inputPrecision << "_" << "IS=" << CommonTestUtils::partialShape2str({ inputShape.first }) << "_" << "TS=(";
+        for (const auto& shape : inputShape.second) {
+            result << CommonTestUtils::vec2str(shape) << "_";
+        }
+
+        result << ")_axis=" << axis << "_mode=" << mode << "_sortType=" << sortType;
+        return result.str();
+    }
+
+protected:
+    void generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) override {
+        inputs.clear();
+        const auto& funcInputs = function->inputs();
+
+        ov::runtime::Tensor data_tensor;
+        const auto& dataPrecision = funcInputs[0].get_element_type();
+        const auto& dataShape = targetInputStaticShapes.front();
+        if (funcInputs[0].get_element_type().is_real()) {
+            data_tensor = ov::test::utils::create_and_fill_tensor(dataPrecision, dataShape, 10, 0, 1000);
+        } else {
+            data_tensor = ov::test::utils::create_and_fill_tensor(dataPrecision, dataShape);
+        }
+
+        const auto axis = std::get<2>(this->GetParam());
+        const auto& kPrecision = funcInputs[1].get_element_type();
+        const auto& kShape = targetInputStaticShapes[1];
+
+        const size_t startFrom = 1;
+        const size_t range = targetInputStaticShapes[0][axis] - 1;
+        const size_t seed = inferRequestNum++;
+        const auto kTensor = ov::test::utils::create_and_fill_tensor(kPrecision, kShape, range, startFrom, 1, seed);
+
+        inputs.insert({ funcInputs[0].get_node_shared_ptr(), data_tensor });
+        inputs.insert({ funcInputs[1].get_node_shared_ptr(), kTensor });
+    }
+
+    void SetUp() override {
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+        ngraph::element::Type inputPrecision;
+        InputShape inputShape;
+        std::int64_t axis;
+        ngraph::opset4::TopK::Mode mode;
+        ngraph::opset4::TopK::SortType sortType;
+        std::tie(inputPrecision, inputShape, axis, mode, sortType) = this->GetParam();
+
+        inputDynamicShapes = { inputShape.first, {} };
+        for (size_t i = 0; i < inputShape.second.size(); ++i) {
+            targetStaticShapes.push_back({ inputShape.second[i], {} });
+        }
+
+        selectedType = makeSelectedTypeStr("ref_any", inputPrecision);
+
+        auto params = ngraph::builder::makeDynamicParams(inputPrecision, { inputDynamicShapes[0] });
+        auto k_param = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::i32, inputDynamicShapes[1]);
+        params.push_back(k_param);
+        auto topk = std::make_shared<ngraph::opset1::TopK>(params[0], k_param, axis, mode, sortType);
+
+        ngraph::ResultVector results;
+        for (size_t i = 0; i < topk->get_output_size(); ++i) {
+            results.push_back(std::make_shared<ngraph::opset1::Result>(topk->output(i)));
+        }
+
+        function = std::make_shared<ngraph::Function>(results, params, "TopKLayerCPUTest");
+        functionRefs = ngraph::clone_function(*function);
+    }
+
+private:
+    size_t inferRequestNum = 0;
+};
+
+TEST_P(TopKLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+
+    run();
+    CheckPluginRelatedResults(executableNetwork, "TopK");
+}
+
+const ngraph::element::TypeVector inputPrecisions = {
+    ngraph::element::f32,
+};
+
+const std::vector<int64_t> axes = { 0, 1, 2 };
+
+const std::vector<ngraph::opset4::TopK::Mode> modes = {
+    ngraph::opset4::TopK::Mode::MIN,
+    ngraph::opset4::TopK::Mode::MAX
+};
+
+const std::vector<ngraph::opset4::TopK::SortType> sortTypes = {
+    ngraph::opset4::TopK::SortType::SORT_INDICES,
+    ngraph::opset4::TopK::SortType::SORT_VALUES,
+};
+
+const std::vector<InputShape> inShapes = {
+    InputShape{
+        // dynamic
+        {-1, -1, -1},
+        // target
+        {
+            {10, 13, 11},
+            {15, 10, 5},
+            {5, 5, 8}
+        }
+    },
+    InputShape{
+        // dynamic
+        {-1, { 7, 25 }, -1},
+        // target
+        {
+            {10, 7, 10},
+            {15, 20, 5},
+            {5, 25, 7}
+        }
+    },
+    InputShape{
+        // dynamic
+        {{8, 10}, {7, 17}, {12, 27}},
+        // target
+        {
+            {8, 7, 12},
+            {9, 12, 25},
+            {10, 17, 27}
+        }
+    },
+};
+
+const auto testCases = ::testing::Combine(
+    ::testing::ValuesIn(inputPrecisions),
+    ::testing::ValuesIn(inShapes),
+    ::testing::ValuesIn(axes),
+    ::testing::ValuesIn(modes),
+    ::testing::ValuesIn(sortTypes)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs, TopKLayerCPUTest, testCases, TopKLayerCPUTest::getTestCaseName);
+
+} // namespace CPULayerTestsDefinitions

From 8f636ed1047a304be6bcbf10dd633e1bdaa9e0ea Mon Sep 17 00:00:00 2001
From: Polina Brzezinskaya <polina.brzezinskaya@intel.com>
Date: Fri, 26 Nov 2021 11:25:10 +0300
Subject: [PATCH 26/72] [VPU]Fix dynamic shape API Mac build error (#8821)

---
 .../src/vpu/myriad_plugin/myriad_executable_network.cpp    | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/inference-engine/src/vpu/myriad_plugin/myriad_executable_network.cpp b/inference-engine/src/vpu/myriad_plugin/myriad_executable_network.cpp
index c33762dc1d5..25c1b344d29 100644
--- a/inference-engine/src/vpu/myriad_plugin/myriad_executable_network.cpp
+++ b/inference-engine/src/vpu/myriad_plugin/myriad_executable_network.cpp
@@ -99,10 +99,9 @@ ExecutableNetwork::ExecutableNetwork(
         }
         copyNetwork = ie::CNNNetwork(function);
         for (const auto& inputInf : network.getInputsInfo()) {
-            auto& copyInput = copyNetwork.getInputsInfo()[inputInf.first];
-            copyInput->setPrecision(inputInf.second->getPrecision());
-            copyInput->setLayout(inputInf.second->getLayout());
-            copyInput->getPreProcess() = inputInf.second->getPreProcess();
+            copyNetwork.getInputsInfo()[inputInf.first]->setPrecision(inputInf.second->getPrecision());
+            copyNetwork.getInputsInfo()[inputInf.first]->setLayout(inputInf.second->getLayout());
+            copyNetwork.getInputsInfo()[inputInf.first]->getPreProcess() = inputInf.second->getPreProcess();
         }
         for (const auto& outputInf : network.getOutputsInfo()) {
             *copyNetwork.getOutputsInfo()[outputInf.first].get() = *outputInf.second.get();

From 7a16de290aca623f8ed377b30775681c7458b199 Mon Sep 17 00:00:00 2001
From: Vladimir Paramuzov <vladimir.paramuzov@intel.com>
Date: Fri, 26 Nov 2021 11:35:03 +0300
Subject: [PATCH 27/72] [GPU] Fixed NHWC interp. Added exec precision to impl
 name in perf counters (#8803)

---
 .../src/cldnn_engine/cldnn_graph.cpp          | 53 +----------
 .../src/cldnn_engine/cldnn_graph.h            |  2 -
 .../cldnn_transformations_pipeline.cpp        |  5 +
 .../skip_tests_config.cpp                     |  2 -
 .../clDNN/api/cldnn/graph/network.hpp         |  1 +
 .../clDNN/api/cldnn/graph/program.hpp         |  2 +
 .../resample/resample_kernel_base.cpp         |  1 +
 .../src/graph_optimizer/compile_graph.cpp     |  4 +
 .../thirdparty/clDNN/src/network.cpp          |  4 +
 .../thirdparty/clDNN/src/program.cpp          | 92 ++++++++++---------
 .../clDNN/tests/test_cases/cache_test.cpp     | 11 +--
 11 files changed, 77 insertions(+), 100 deletions(-)

diff --git a/inference-engine/src/cldnn_engine/cldnn_graph.cpp b/inference-engine/src/cldnn_engine/cldnn_graph.cpp
index 0727747030f..567f209257c 100644
--- a/inference-engine/src/cldnn_engine/cldnn_graph.cpp
+++ b/inference-engine/src/cldnn_engine/cldnn_graph.cpp
@@ -84,8 +84,6 @@ void CLDNNGraph::Build() {
         m_networks.emplace_back(network);
     }
 
-    UpdateImplementationsMap();
-
     GPU_DEBUG_GET_INSTANCE(debug_config);
     GPU_DEBUG_IF(!debug_config->dry_run_path.empty()) {
         CNNNetwork net(GetExecGraphInfo());
@@ -545,49 +543,6 @@ bool CLDNNGraph::IsLoaded() const {
     return GetNetwork() != nullptr;
 }
 
-void CLDNNGraph::UpdateImplementationsMap() {
-    OV_ITT_SCOPED_TASK(itt::domains::CLDNNPlugin, "CLDNNGraph::UpdateImplementationsMap");
-    if (m_config.useProfiling) {
-        auto extractImplementationFromInfo = [](const std::string& info) -> std::string {
-            std::string def_implementation = "undef";
-            std::string impl_section = "implementation :";
-            std::string::size_type pos = info.find(impl_section);
-            if (pos == std::string::npos) {
-                return def_implementation;
-            }
-
-            std::string::size_type end_pos = info.find(',', pos);
-            if (end_pos == std::string::npos) {
-                return def_implementation;
-            }
-
-            std::string::size_type length = end_pos - pos - impl_section.size();
-
-            auto trim = [](const std::string& str) {
-                size_t first = str.find_first_not_of(' ');
-                if (std::string::npos == first) {
-                    return str;
-                }
-                size_t last = str.find_last_not_of(' ');
-                return str.substr(first, (last - first + 1));
-            };
-            std::string tmp = trim(info.substr(pos + impl_section.size(), length));
-
-            return tmp.length() > 1 ? tmp : def_implementation;
-        };
-
-        // Parse primitive info and extract implementation name.
-        for (auto& id : profilingIDs) {
-            std::string prim_info = "";
-            try {
-                prim_info = GetNetwork()->get_primitive_info(id);
-            } catch (std::exception& /*e*/) { }
-
-            implementationsMap.insert({id, extractImplementationFromInfo(prim_info)});
-        }
-    }
-}
-
 std::map<std::string, InferenceEngine::InferenceEngineProfileInfo> CLDNNGraph::GetPerformanceCounts() const {
     OV_ITT_SCOPED_TASK(itt::domains::CLDNNPlugin, "CLDNNGraph::GetPerformanceCounts");
     std::map<std::string, InferenceEngine::InferenceEngineProfileInfo> result;
@@ -632,7 +587,7 @@ std::map<std::string, InferenceEngine::InferenceEngineProfileInfo> CLDNNGraph::G
             static const std::string cpuExecType("CPU");
             cpuExecType.copy(extPerfEntry.exec_type, cpuExecType.length());  // Override execType as CPU
         } else {
-            std::string impl = implementationsMap.at(primId);
+            std::string impl = GetNetwork()->get_implementation_info(primId);
             impl.copy(extPerfEntry.exec_type, impl.length());
         }
 
@@ -659,8 +614,10 @@ std::map<std::string, InferenceEngine::InferenceEngineProfileInfo> CLDNNGraph::G
                     allIds.erase(std::find(allIds.begin(), allIds.end(), id));
                 }
             }
-            if (!kernelId.empty())
-                implementationsMap.at(kernelId).copy(extPerfEntry.exec_type, implementationsMap.at(kernelId).length());
+            if (!kernelId.empty()) {
+                std::string impl_info = GetNetwork()->get_implementation_info(kernelId);
+                std::memcpy(extPerfEntry.exec_type, &impl_info[0], impl_info.length());
+            }
         }
 
         getUpperCaseName(perfCounter.layerType).copy(extPerfEntry.layer_type, perfCounter.layerType.length());
diff --git a/inference-engine/src/cldnn_engine/cldnn_graph.h b/inference-engine/src/cldnn_engine/cldnn_graph.h
index 27a9a3f029a..d2a4cf95084 100644
--- a/inference-engine/src/cldnn_engine/cldnn_graph.h
+++ b/inference-engine/src/cldnn_engine/cldnn_graph.h
@@ -88,7 +88,6 @@ protected:
     std::map<std::string, std::vector<cldnn::primitive_id>> prevPrimitiveIDs;
 
     std::map<cldnn::primitive_id, std::pair<std::string, PerfCounter>> perfMap;
-    std::map<cldnn::primitive_id, std::string> implementationsMap;
     std::vector<cldnn::primitive_id> profilingIDs;
 
     std::map<std::string, InferenceEngine::SizeVector> outputDims;
@@ -99,7 +98,6 @@ protected:
     std::shared_ptr<cldnn::network> BuildNetwork(std::shared_ptr<cldnn::program> program);
     void Build();
     void UpdateLayersMaps();
-    void UpdateImplementationsMap();
     std::shared_ptr<ngraph::Function> GetExecGraphInfoByPrimitivesInfo(std::vector<cldnn::primitive_info>& pi,
                                                                        bool filter_const_primitives = true);
 };
diff --git a/inference-engine/src/cldnn_engine/cldnn_transformations_pipeline.cpp b/inference-engine/src/cldnn_engine/cldnn_transformations_pipeline.cpp
index 68353d8268b..50a280ed2d6 100644
--- a/inference-engine/src/cldnn_engine/cldnn_transformations_pipeline.cpp
+++ b/inference-engine/src/cldnn_engine/cldnn_transformations_pipeline.cpp
@@ -35,6 +35,9 @@
 #include <transformations/common_optimizations/weights_dequantize_to_fake_quantize.hpp>
 #include "transformations/common_optimizations/convert_quantize_dequantize.hpp"
 #include "transformations/common_optimizations/convert_compression_only_to_legacy.hpp"
+#include <transformations/common_optimizations/wrap_interpolate_into_transposes.hpp>
+#include <transformations/common_optimizations/transpose_sinking.hpp>
+
 #include <transformations/op_conversions/convert_depth_to_space.hpp>
 #include <transformations/op_conversions/convert_space_to_depth.hpp>
 #include <transformations/op_conversions/convert_gelu.hpp>
@@ -120,6 +123,8 @@ void TransformationsPipeline::apply(std::shared_ptr<ov::Function> func) {
 
         manager.register_pass<ngraph::pass::InitNodeInfo>();
         manager.register_pass<ngraph::pass::CommonOptimizations>();
+        manager.register_pass<ngraph::pass::WrapInterpolateIntoTransposes>();
+        manager.register_pass<ngraph::pass::TransposeSinking>();
 
         if (!config.enable_loop_unrolling) {
             manager.register_pass<ngraph::pass::BidirectionalLSTMSequenceDecomposition>();
diff --git a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp
index a2f53ac7fcf..5e678cb5de4 100644
--- a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp
+++ b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/skip_tests_config.cpp
@@ -88,8 +88,6 @@ std::vector<std::string> disabledTestPatterns() {
             R"(.*(SoftMaxLayerTest).*)",
             // TODO: Issue: 68712
             R"(.*.MatMul.*CompareWithRefs.*IS0=\(1.5\)_IS1=\(1.5\).*transpose_a=0.*transpose_b=1.*CONSTANT.*FP16.*UNSPECIFIED.*UNSPECIFIED.*ANY.*)",
-            // TODO: Issue 66685
-            R"(smoke_PrePostProcess.*resize_linear_nhwc.*)",
             // TODO: Issue 69187
             R"(smoke_PrePostProcess.*cvt_color_nv12.*)",
             // TODO: Issue 71215
diff --git a/inference-engine/thirdparty/clDNN/api/cldnn/graph/network.hpp b/inference-engine/thirdparty/clDNN/api/cldnn/graph/network.hpp
index f2fecc79412..ea3af7000ad 100644
--- a/inference-engine/thirdparty/clDNN/api/cldnn/graph/network.hpp
+++ b/inference-engine/thirdparty/clDNN/api/cldnn/graph/network.hpp
@@ -160,6 +160,7 @@ public:
     // Implementation specific calls
     std::shared_ptr<primitive_inst> get_primitive(const primitive_id& id);
     std::string get_primitive_info(const primitive_id& id) const;
+    std::string get_implementation_info(const primitive_id& id) const;
     const event::ptr& get_primitive_event(const primitive_id& id) const { return _events.at(id); }
     bool has_event(const primitive_id& id) const { return _events.count(id); }
     std::vector<std::shared_ptr<primitive_inst>> get_primitives(const std::vector<primitive_id>& ids);
diff --git a/inference-engine/thirdparty/clDNN/api/cldnn/graph/program.hpp b/inference-engine/thirdparty/clDNN/api/cldnn/graph/program.hpp
index 98184293bd3..0d65175db0e 100644
--- a/inference-engine/thirdparty/clDNN/api/cldnn/graph/program.hpp
+++ b/inference-engine/thirdparty/clDNN/api/cldnn/graph/program.hpp
@@ -206,6 +206,8 @@ public:
                       std::function<bool(program_node const&)> const& filter = nullptr) const;
 
     const primitives_info& get_primitives_info() const;
+    data_types get_inference_precision(const program_node& node) const;
+    std::string get_implementation_info(const primitive_id& id) const;
     const graph_optimizer_info& get_optimizer_passes_info() const;
     void save_pass_info(std::string pass_name);
 
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/resample/resample_kernel_base.cpp b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/resample/resample_kernel_base.cpp
index dbb4376ebb3..2b1c714dd01 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/resample/resample_kernel_base.cpp
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/actual_kernels/resample/resample_kernel_base.cpp
@@ -107,6 +107,7 @@ bool ResampleKernelBase::Validate(const Params& p, const optional_params& o) con
     const auto& input = params.inputs[0];
     if ((input.GetDType() == Datatype::UINT8 || input.GetDType() == Datatype::INT8) &&
         params.resampleType != ResampleType::NEAREST_NEIGHBOR &&
+        params.resampleType != ResampleType::CAFFE_BILINEAR_INTERP &&
         params.resampleType != ResampleType::BILINEAR_INTERP)
         return false;
 
diff --git a/inference-engine/thirdparty/clDNN/src/graph_optimizer/compile_graph.cpp b/inference-engine/thirdparty/clDNN/src/graph_optimizer/compile_graph.cpp
index 98cb74dfd17..4dac88c97d9 100644
--- a/inference-engine/thirdparty/clDNN/src/graph_optimizer/compile_graph.cpp
+++ b/inference-engine/thirdparty/clDNN/src/graph_optimizer/compile_graph.cpp
@@ -54,5 +54,9 @@ void compile_graph::run(program& p) {
 
         task_executor->runAndWait(tasks);
         tasks.clear();
+
+        if (exception) {
+            std::rethrow_exception(exception);
+        }
     }
 }
diff --git a/inference-engine/thirdparty/clDNN/src/network.cpp b/inference-engine/thirdparty/clDNN/src/network.cpp
index 6d126d9b481..59703aed498 100644
--- a/inference-engine/thirdparty/clDNN/src/network.cpp
+++ b/inference-engine/thirdparty/clDNN/src/network.cpp
@@ -466,6 +466,10 @@ std::string network::get_primitive_info(const primitive_id& id) const {
     return node.type()->to_string(node);
 }
 
+std::string network::get_implementation_info(const primitive_id& id) const {
+    return _program->get_implementation_info(id);
+}
+
 memory::ptr network::get_output_memory(const primitive_id& output_id) {
     return get_primitive(output_id)->output_memory_ptr();
 }
diff --git a/inference-engine/thirdparty/clDNN/src/program.cpp b/inference-engine/thirdparty/clDNN/src/program.cpp
index 9764b03b04b..dbf1a264b5d 100644
--- a/inference-engine/thirdparty/clDNN/src/program.cpp
+++ b/inference-engine/thirdparty/clDNN/src/program.cpp
@@ -192,7 +192,7 @@ program_node& program::get_node(primitive_id const& id) {
     try {
         return *nodes_map.at(id);
     } catch (...) {
-        throw std::runtime_error("Program doesn't contain primtive node: " + id);
+        throw std::runtime_error("Program doesn't contain primitive node: " + id);
     }
 }
 
@@ -200,7 +200,7 @@ program_node const& program::get_node(primitive_id const& id) const {
     try {
         return *nodes_map.at(id);
     } catch (...) {
-        throw std::runtime_error("Program doesn't contain primtive node: " + id);
+        throw std::runtime_error("Program doesn't contain primitive node: " + id);
     }
 }
 
@@ -1122,47 +1122,57 @@ void program::dump_program(const char* stage,
     dump_graph_optimized(graph, *this);
 }
 
+data_types program::get_inference_precision(const program_node& node) const {
+    if (node.is_input()) {
+        return node.get_output_layout().data_type;
+    }
+    std::vector<data_types> input_dts;
+    for (auto& dep : node.get_dependencies()) {
+        input_dts.push_back(dep->get_output_layout().data_type);
+    }
+    data_types output_dt = node.get_output_layout().data_type;
+
+    assert(!input_dts.empty());
+    if (node.is_type<reorder>()) {
+        // If reorder has different input/output types - pick the max one as runtime precision
+        return data_type_traits::max_type(input_dts[0], output_dt);
+    } else if (node.is_type<quantize>()) {
+        if (data_type_traits::is_quantized(output_dt))
+            return output_dt;
+        return data_type_traits::max_type(input_dts[0], output_dt);
+    } else if (node.is_type<eltwise>()) {
+        auto max_dt = input_dts[0];
+        for (size_t i = 1; i < input_dts.size(); i++) {
+            max_dt = data_type_traits::max_type(max_dt, input_dts[i]);
+        }
+        return max_dt;
+    } else if (node.is_type<convolution>() || node.is_type<deconvolution>() || node.is_type<fully_connected>() || node.is_type<gemm>()) {
+        if (input_dts.size() < 2) {
+            throw std::runtime_error("[clDNN] Invalid inputs count in node " + node.id() + " during stage info collection. Expected >= 2 inputs");
+        }
+        if (data_type_traits::is_quantized(input_dts[0]) && data_type_traits::is_quantized(input_dts[1])) {
+            return input_dts[0];
+        } else {
+            return data_type_traits::max_type(input_dts[0], input_dts[1]);
+        }
+    }
+
+    return input_dts[0];
+}
+
+std::string program::get_implementation_info(const primitive_id& id) const {
+    try {
+        const auto& node = get_node(id);
+        auto impl = node.get_selected_impl();
+        return impl ? (impl->get_kernel_name() + "__" + dt_to_str(get_inference_precision(node))) : "undef";
+    } catch (...) { }
+
+    return "undef";
+}
+
 program::primitives_info program::get_current_stage_info() const {
     primitives_info info;
 
-    auto get_inference_precision = [](program_node& node) -> data_types {
-        if (node.is_input()) {
-            return node.get_output_layout().data_type;
-        }
-        std::vector<data_types> input_dts;
-        for (auto& dep : node.get_dependencies()) {
-            input_dts.push_back(dep->get_output_layout().data_type);
-        }
-        data_types output_dt = node.get_output_layout().data_type;
-
-        assert(!input_dts.empty());
-        if (node.is_type<reorder>()) {
-            // If reorder has different input/output types - pick the max one as runtime precision
-            return data_type_traits::max_type(input_dts[0], output_dt);
-        } else if (node.is_type<quantize>()) {
-            if (data_type_traits::is_quantized(output_dt))
-                return output_dt;
-            return data_type_traits::max_type(input_dts[0], output_dt);
-        } else if (node.is_type<eltwise>()) {
-            auto max_dt = input_dts[0];
-            for (size_t i = 1; i < input_dts.size(); i++) {
-                max_dt = data_type_traits::max_type(max_dt, input_dts[i]);
-            }
-            return max_dt;
-        } else if (node.is_type<convolution>() || node.is_type<deconvolution>() || node.is_type<fully_connected>() || node.is_type<gemm>()) {
-            if (input_dts.size() < 2) {
-                throw std::runtime_error("[clDNN] Invalid inputs count in node " + node.id() + " during stage info collection. Expected >= 2 inputs");
-            }
-            if (data_type_traits::is_quantized(input_dts[0]) && data_type_traits::is_quantized(input_dts[1])) {
-                return input_dts[0];
-            } else {
-                return data_type_traits::max_type(input_dts[0], input_dts[1]);
-            }
-        }
-
-        return input_dts[0];
-    };
-
     // Get info for actually executed graph nodes
     int exec_id = 0;
     for (auto& p : get_processing_order()) {
@@ -1191,7 +1201,7 @@ program::primitives_info program::get_current_stage_info() const {
                           fused,
                           p->get_output_layout(),
                           fmt_to_str(p->get_output_layout().format),
-                          p->selected_impl ? p->selected_impl->get_kernel_name() : "",
+                          get_implementation_info(p->id()),
                           get_inference_precision(*p),
                           p->selected_impl ? p->selected_impl->is_cpu() : false,
                           exec_id++);
diff --git a/inference-engine/thirdparty/clDNN/tests/test_cases/cache_test.cpp b/inference-engine/thirdparty/clDNN/tests/test_cases/cache_test.cpp
index 24cdc27cc49..0c49194320c 100644
--- a/inference-engine/thirdparty/clDNN/tests/test_cases/cache_test.cpp
+++ b/inference-engine/thirdparty/clDNN/tests/test_cases/cache_test.cpp
@@ -223,13 +223,10 @@ public:
         network.execute();
 
         if (compare_implementation.compare) {
-            std::string exec_impl;
-            for (auto& info : network.get_primitives_info()) {
-                if (info.original_id == "conv") {
-                    exec_impl = info.kernel_id;
-                    break;
-                }
-            }
+            std::string exec_impl = network.get_implementation_info("conv");
+            auto precision_pos = exec_impl.find("__");
+            exec_impl = exec_impl.substr(0, precision_pos);
+
             if (compare_implementation.not_equal) {
                 EXPECT_NE(exec_impl, compare_implementation.value);
             } else {

From 5b3c3bc1490ff1c05fba6687a2c95eced5662f19 Mon Sep 17 00:00:00 2001
From: Svetlana Dolinina <svetlana.a.dolinina@intel.com>
Date: Fri, 26 Nov 2021 11:45:05 +0300
Subject: [PATCH 28/72] Dragan: random uniform fix (#8716)

* fix random uniform input constant layout

* added test for 4D
---
 model-optimizer/extensions/ops/random_uniform.py            | 3 +++
 tests/layer_tests/tensorflow_tests/test_tf_RandomUniform.py | 3 ++-
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/model-optimizer/extensions/ops/random_uniform.py b/model-optimizer/extensions/ops/random_uniform.py
index 86dfcec2656..fde3719de1d 100644
--- a/model-optimizer/extensions/ops/random_uniform.py
+++ b/model-optimizer/extensions/ops/random_uniform.py
@@ -4,6 +4,7 @@
 import numpy as np
 
 from mo.graph.graph import Graph, Node
+from mo.graph.perm_inputs import PermuteInputs
 from mo.middle.passes.convert_data_type import np_data_type_to_destination_type
 from mo.ops.op import Op
 
@@ -51,6 +52,8 @@ class RandomUniform(Op):
         node.in_node(1)['correct_data_type'] = True
         node.in_node(2)['correct_data_type'] = True
 
+        PermuteInputs().set_input_permutation(node.in_node(0), node, 'output:0', 'shape')
+
 
 class AttributedRandomUniform(Op):
     """ RandomUniform operation that generates a sequence of random values from uniform distribution.
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_RandomUniform.py b/tests/layer_tests/tensorflow_tests/test_tf_RandomUniform.py
index 347c092cddf..09e5185d3ba 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_RandomUniform.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_RandomUniform.py
@@ -26,7 +26,7 @@ class TestTFRandomUniform(CommonTFLayerTest):
             x = tf.compat.v1.placeholder(input_type, tf_x_shape, 'Input')
             if global_seed is not None:
                 tf.compat.v1.random.set_random_seed(global_seed)
-            random_uniform = tf.random.uniform(x_shape, seed=op_seed, dtype=input_type, minval=min_val,
+            random_uniform = tf.random.uniform(tf_x_shape, seed=op_seed, dtype=input_type, minval=min_val,
                                                maxval=max_val) + x
 
             tf.compat.v1.global_variables_initializer()
@@ -80,6 +80,7 @@ class TestTFRandomUniform(CommonTFLayerTest):
         dict(global_seed=32465, op_seed=48971, min_val=0.0, max_val=1.0, x_shape=[3, 7], input_type=tf.float32),
         marks=pytest.mark.precommit),
         dict(global_seed=None, op_seed=56197, min_val=-100, max_val=100, x_shape=[6], input_type=tf.float32),
+        dict(global_seed=None, op_seed=56197, min_val=-100, max_val=100, x_shape=[1, 2, 1, 1], input_type=tf.float32),
         dict(global_seed=78132, op_seed=None, min_val=-200, max_val=-50, x_shape=[5, 8], input_type=tf.int32),
         dict(global_seed=4571, op_seed=48971, min_val=1.5, max_val=2.3, x_shape=[7], input_type=tf.float32),
         dict(global_seed=32465, op_seed=12335, min_val=-150, max_val=-100, x_shape=[18], input_type=tf.int32)]

From d8d9954ad6e83e9b6dc1f69914809e818fc10d43 Mon Sep 17 00:00:00 2001
From: Nikita Malinin <nikita.malinin@intel.com>
Date: Fri, 26 Nov 2021 12:08:01 +0300
Subject: [PATCH 29/72] Fix bug with the outputs naming (#8556)

(cherry picked from commit 13c99e88828a9df273e612dcc7d9ae698488fcbf)
---
 tools/pot/openvino/tools/pot/graph/passes.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/pot/openvino/tools/pot/graph/passes.py b/tools/pot/openvino/tools/pot/graph/passes.py
index f58cca79754..81ccbe66adc 100644
--- a/tools/pot/openvino/tools/pot/graph/passes.py
+++ b/tools/pot/openvino/tools/pot/graph/passes.py
@@ -656,7 +656,7 @@ class FakeQuantizeNameSwapper(BackReplacementPattern):
                 new_fq_name = copy(input_node['orig_node_name'])
 
             input_node_outputs = get_all_node_outputs(input_node)
-            if all([op.type == 'FakeQuantize' for op in input_node_outputs]):
+            if len(input_node_outputs) > 1 and all([op.type == 'FakeQuantize' for op in input_node_outputs]):
                 new_fq_name += '.{}'.format(fq_node.in_port(0).get_source().idx)
 
             fq_node['orig_fq_name'] = copy(fq_node.name)

From 8db9168d4162bb4812b6ccf30b857c2328f518ce Mon Sep 17 00:00:00 2001
From: Indira Salyahova <indira.salyahova@intel.com>
Date: Fri, 26 Nov 2021 12:10:33 +0300
Subject: [PATCH 30/72] [POT] Check node is not None (#8732)

* fix: check the node is not None

* input port is not None
---
 tools/pot/openvino/tools/pot/graph/node_utils.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tools/pot/openvino/tools/pot/graph/node_utils.py b/tools/pot/openvino/tools/pot/graph/node_utils.py
index b5bdbc335ca..1aabeafa6a9 100644
--- a/tools/pot/openvino/tools/pot/graph/node_utils.py
+++ b/tools/pot/openvino/tools/pot/graph/node_utils.py
@@ -23,7 +23,8 @@ def get_node_input_ports(node: Node):
     :return: list of node inputs
     """
     sources_ports = [parent.get_source() for parent in node.in_ports().values()]
-    return [port if port is not None else None for port in sources_ports]
+    return [port for port in sources_ports if port is not None]
+
 
 def get_node_input(node: Node, in_port: int):
     """

From e32a569fb4583244ac3364f54f898c53d82b0711 Mon Sep 17 00:00:00 2001
From: Indira Salyahova <indira.salyahova@intel.com>
Date: Fri, 26 Nov 2021 12:17:43 +0300
Subject: [PATCH 31/72] fix: quantize only one input mvn operation (#8822)

---
 tools/pot/openvino/tools/pot/graph/passes.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/pot/openvino/tools/pot/graph/passes.py b/tools/pot/openvino/tools/pot/graph/passes.py
index 81ccbe66adc..a0162f9f51d 100644
--- a/tools/pot/openvino/tools/pot/graph/passes.py
+++ b/tools/pot/openvino/tools/pot/graph/passes.py
@@ -113,7 +113,7 @@ class InsertFakeQuantize(BackReplacementPattern):
     @staticmethod
     def quantize_only_input(node: Node):
         if node.type in ['Interpolate', 'Power', 'ReduceMean', 'NormalizeL2',
-                         'Assign', 'PReLU', 'ReLU', 'Sigmoid', 'Tanh', 'Clamp']:
+                         'Assign', 'PReLU', 'ReLU', 'Sigmoid', 'Tanh', 'Clamp', 'MVN']:
             return True
         # ScaleSift case, FQ only for input
         if node.type == 'Multiply' and nu.check_input_data_is_const(node, 1):

From bd3e4fbe7800ed33cd57b0fb93947fcfb826bae3 Mon Sep 17 00:00:00 2001
From: Anton Pankratov <anton.pankratov@intel.com>
Date: Fri, 26 Nov 2021 12:23:25 +0300
Subject: [PATCH 32/72] Added openvino any (#8288)

---
 .../ncc_naming_style/openvino.style           |   8 +-
 docs/snippets/InferenceEngine_Caching3.cpp    |   2 +-
 .../behavior/plugin/core_integration.cpp      |   4 +-
 .../ie/gpu/details/gpu_context_helpers.hpp    |   6 +-
 .../include/ie/ie_parameter.hpp               |  15 +-
 .../include/openvino/runtime/core.hpp         |   4 +-
 .../openvino/runtime/executable_network.hpp   |   4 +-
 .../include/openvino/runtime/parameter.hpp    | 342 +---------
 .../openvino/runtime/remote_tensor.hpp        |   1 +
 .../src/cpp/ie_executable_network.cpp         |   8 +-
 .../inference_engine/src/cpp/ie_plugin.hpp    |   8 +-
 .../src/cpp/ie_remote_context.cpp             |  12 +-
 .../src/inference_engine/src/ie_common.cpp    |  34 -
 .../src/inference_engine/src/ie_core.cpp      |  59 +-
 .../inference_engine/src/remote_tensor.cpp    |   6 +-
 .../include/low_precision/network_helper.hpp  |   2 +-
 .../rt_info/precisions_attribute.hpp          |   2 +-
 .../quantization_alignment_attribute.hpp      |   2 +-
 .../src/rt_info/precisions_attribute.cpp      |   4 +-
 .../quantization_alignment_attribute.cpp      |   2 +-
 .../src/mkldnn_plugin/utils/ngraph_utils.hpp  |   2 +-
 .../rt_info/memory_formats_attribute.cpp      |  21 +-
 .../rt_info/memory_formats_attribute.hpp      |  87 +--
 .../src/snippets/src/generator.cpp            |   6 +-
 .../transformations/rt_info/attributes.hpp    |   9 +-
 .../rt_info/fused_names_attribute.hpp         |   4 +-
 .../rt_info/primitives_priority_attribute.hpp |   6 +-
 .../transformations/rt_info/attributes.cpp    |   9 +-
 .../inference_engine/caching_test.cpp         |   4 +-
 .../layer_transformation.hpp                  |   4 +-
 .../inference_engine/parameter_tests.cpp      | 418 ------------
 .../inference_engine/snippets/registers.cpp   |  11 +-
 .../plugin/cpu/test_utils/cpu_test_utils.cpp  |   4 +-
 .../plugin/cpu/test_utils/cpu_test_utils.hpp  |   2 +-
 .../behavior/ov_plugin/core_integration.cpp   |   6 +-
 .../behavior/ov_plugin/core_integration.cpp   |   2 +-
 .../ov_executable_network/get_metric.hpp      |  42 +-
 .../behavior/ov_plugin/core_integration.hpp   |  46 +-
 .../shared/include/behavior/ov_remote.hpp     |   1 +
 .../src/behavior/caching/caching_tests.cpp    |   2 +-
 .../common_test_utils/ov_common_utils.hpp     |  27 +
 .../ie_executable_network_test.cpp            |   2 +-
 ngraph/core/include/ngraph/node.hpp           |   3 +-
 ngraph/core/include/ngraph/variant.hpp        |   1 +
 ngraph/core/include/openvino/core/any.hpp     | 638 ++++++++++++++++++
 .../openvino/core/descriptor/tensor.hpp       |   3 +-
 ngraph/core/include/openvino/core/node.hpp    |   4 +-
 ngraph/core/include/openvino/core/type.hpp    |  25 +-
 ngraph/core/include/openvino/core/variant.hpp |  17 +-
 ngraph/core/src/any.cpp                       |  88 +++
 ngraph/core/src/pass/init_node_info.cpp       |   3 +-
 .../pass/rt_info/fused_names_attribute.cpp    |   6 +-
 .../rt_info/primitives_priority_attribute.cpp |   4 +-
 ngraph/core/src/pass/serialize.cpp            |   2 +
 ngraph/core/src/pass/visualize_tree.cpp       |   4 +-
 ngraph/core/src/rt_info.cpp                   |  13 +-
 ngraph/core/src/variant.cpp                   |   9 +-
 ngraph/frontend/ir/src/ir_deserializer.cpp    |   2 +-
 .../include/tensorflow_frontend/decoder.hpp   |   1 +
 .../include/tensorflow_frontend/frontend.hpp  |   1 +
 .../tensorflow_frontend/graph_iterator.hpp    |   1 +
 .../frontend/tensorflow/src/node_context.hpp  |   1 +
 ngraph/frontend/tensorflow/src/utils.hpp      |   1 +
 ngraph/test/CMakeLists.txt                    |   1 +
 ngraph/test/any.cpp                           | 446 ++++++++++++
 .../src/compatibility/pyngraph/node.cpp       |   2 +-
 .../src/compatibility/pyngraph/rt_map.cpp     |  16 +-
 .../src/compatibility/pyngraph/variant.cpp    |  27 +-
 .../src/compatibility/pyngraph/variant.hpp    |  52 +-
 .../pyopenvino/graph/descriptors/tensor.cpp   |   2 +-
 .../python/src/pyopenvino/graph/function.cpp  |  12 +-
 .../python/src/pyopenvino/graph/node.cpp      |   8 +-
 .../python/src/pyopenvino/graph/rt_map.cpp    |  20 +-
 .../python/src/pyopenvino/graph/variant.cpp   |  33 +-
 .../python/src/pyopenvino/graph/variant.hpp   |  50 +-
 samples/cpp/hello_query_device/main.cpp       |  15 +-
 76 files changed, 1609 insertions(+), 1142 deletions(-)
 delete mode 100644 inference-engine/tests/functional/inference_engine/parameter_tests.cpp
 create mode 100644 inference-engine/tests/ie_test_utils/common_test_utils/ov_common_utils.hpp
 create mode 100644 ngraph/core/include/openvino/core/any.hpp
 create mode 100644 ngraph/core/src/any.cpp
 create mode 100644 ngraph/test/any.cpp

diff --git a/cmake/developer_package/ncc_naming_style/openvino.style b/cmake/developer_package/ncc_naming_style/openvino.style
index 4f9df1dfebd..f983b8b84ad 100644
--- a/cmake/developer_package/ncc_naming_style/openvino.style
+++ b/cmake/developer_package/ncc_naming_style/openvino.style
@@ -12,7 +12,7 @@ TemplateNonTypeParameter: '^\w*$'
 ClassTemplate: '^([A-Z][\w]+|element_type_traits)$'
 TemplateTypeParameter: '^\w*$'
 ParameterName: '^\w*$'
-FunctionTemplate: '^(operator.+|\w+)$'
+FunctionTemplate: '^(operator.+|[\w]+|Impl<.*>)$'
 TypeAliasName: '^\w+$'
 VariableReference: '^\w+$'
 
@@ -25,7 +25,7 @@ TypedefName: '^.*$'
 CxxDynamicCastExpression: '^.*$'
 
 # not needed values
-ClassTemplatePartialSpecialization: 'XXXX'
+ClassTemplatePartialSpecialization: '^.*$'
 ConversionFunction: '^.*$'
 UsingDirective: 'XXXX'
 ClassAccessSpecifier: '^.*$' # looks like can be fixed
@@ -64,9 +64,9 @@ GenericSelectionExpression: 'XXXX'
 GnuNullExpression: 'XXXX'
 CxxStaticCastExpression: '^.*$'
 CxxReinterpretCastExpression: '^.*$'
-CxxConstCastExpression: 'XXXX'
+CxxConstCastExpression: '^.*$'
 CxxFunctionalCastExpression: '^.*$'
-CxxTypeidExpression: 'XXXX'
+CxxTypeidExpression: '^.*$'
 CxxBoolLiteralExpression: '^.*$'
 CxxNullPointerLiteralExpression: '^.*$'
 CxxThisExpression: '^.*$'
diff --git a/docs/snippets/InferenceEngine_Caching3.cpp b/docs/snippets/InferenceEngine_Caching3.cpp
index ce91a798552..282d07b1dc9 100644
--- a/docs/snippets/InferenceEngine_Caching3.cpp
+++ b/docs/snippets/InferenceEngine_Caching3.cpp
@@ -14,7 +14,7 @@ using namespace InferenceEngine;
     auto it = std::find(keys.begin(), keys.end(), METRIC_KEY(IMPORT_EXPORT_SUPPORT));
 
     // If metric 'IMPORT_EXPORT_SUPPORT' exists, check it's value
-    bool cachingSupported = (it != keys.end()) && ie.GetMetric(deviceName, METRIC_KEY(IMPORT_EXPORT_SUPPORT));
+    bool cachingSupported = (it != keys.end()) && ie.GetMetric(deviceName, METRIC_KEY(IMPORT_EXPORT_SUPPORT)).as<bool>();
 //! [part3]
     return 0;
 }
diff --git a/docs/template_plugin/tests/functional/shared_tests_instances/behavior/plugin/core_integration.cpp b/docs/template_plugin/tests/functional/shared_tests_instances/behavior/plugin/core_integration.cpp
index 765b28d3bab..78cf8bfaecf 100644
--- a/docs/template_plugin/tests/functional/shared_tests_instances/behavior/plugin/core_integration.cpp
+++ b/docs/template_plugin/tests/functional/shared_tests_instances/behavior/plugin/core_integration.cpp
@@ -130,10 +130,10 @@ TEST_F(IEClassGetConfigTestTEMPLATE, smoke_GetConfigNoThrow) {
             std::string defaultDeviceID = ie.GetConfig(deviceName, CONFIG_KEY(DEVICE_ID));
             std::cout << CONFIG_KEY(DEVICE_ID) << " : " << defaultDeviceID << std::endl;
         } else if (CONFIG_KEY(PERF_COUNT) == confKey) {
-            bool defaultPerfCount = ie.GetConfig(deviceName, CONFIG_KEY(PERF_COUNT));
+            bool defaultPerfCount = ie.GetConfig(deviceName, CONFIG_KEY(PERF_COUNT)).as<bool>();
             std::cout << CONFIG_KEY(PERF_COUNT) << " : " << defaultPerfCount << std::endl;
         } else if (CONFIG_KEY(EXCLUSIVE_ASYNC_REQUESTS) == confKey) {
-            bool defaultExclusive = ie.GetConfig(deviceName, CONFIG_KEY(EXCLUSIVE_ASYNC_REQUESTS));
+            bool defaultExclusive = ie.GetConfig(deviceName, CONFIG_KEY(EXCLUSIVE_ASYNC_REQUESTS)).as<bool>();
             std::cout << CONFIG_KEY(EXCLUSIVE_ASYNC_REQUESTS) << " : " << defaultExclusive << std::endl;
         }
     }
diff --git a/inference-engine/src/inference_engine/include/ie/gpu/details/gpu_context_helpers.hpp b/inference-engine/src/inference_engine/include/ie/gpu/details/gpu_context_helpers.hpp
index 3f2e7b77212..0c90a5e2156 100644
--- a/inference-engine/src/inference_engine/include/ie/gpu/details/gpu_context_helpers.hpp
+++ b/inference-engine/src/inference_engine/include/ie/gpu/details/gpu_context_helpers.hpp
@@ -47,8 +47,7 @@ protected:
             IE_THROW() << "No parameter " << handle_Key << " found";
         }
 
-        Tmp handle = itrHandle->second;
-        return static_cast<Result>(handle);
+        return static_cast<Result>(itrHandle->second.as<Tmp>());
     }
 
     /**
@@ -62,8 +61,7 @@ protected:
             IE_THROW() << "No parameter " << handle_Key << " found";
         }
 
-        Result handle = itrHandle->second;
-        return handle;
+        return itrHandle->second.as<Result>();
     }
 
     /**
diff --git a/inference-engine/src/inference_engine/include/ie/ie_parameter.hpp b/inference-engine/src/inference_engine/include/ie/ie_parameter.hpp
index 0be3fd9d786..7f1bf0ef02a 100644
--- a/inference-engine/src/inference_engine/include/ie/ie_parameter.hpp
+++ b/inference-engine/src/inference_engine/include/ie/ie_parameter.hpp
@@ -24,17 +24,10 @@
 
 namespace InferenceEngine {
 
-using ov::runtime::Parameter;
+/**
+ * @brief Alias for type that can store any value
+ */
+using Parameter = ov::Any;
 using ov::runtime::ParamMap;
 
 }  // namespace InferenceEngine
-
-namespace ov {
-namespace runtime {
-
-#ifdef __ANDROID__
-extern template struct INFERENCE_ENGINE_API_CLASS(InferenceEngine::Parameter::RealData<InferenceEngine::Blob::Ptr>);
-#endif
-
-}  // namespace runtime
-}  // namespace ov
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/core.hpp b/inference-engine/src/inference_engine/include/openvino/runtime/core.hpp
index ca4e85de4e3..1044a2d91bf 100644
--- a/inference-engine/src/inference_engine/include/openvino/runtime/core.hpp
+++ b/inference-engine/src/inference_engine/include/openvino/runtime/core.hpp
@@ -281,7 +281,7 @@ public:
      * @param name  - config key.
      * @return Value of config corresponding to config key.
      */
-    Parameter get_config(const std::string& deviceName, const std::string& name) const;
+    Any get_config(const std::string& deviceName, const std::string& name) const;
 
     /**
      * @brief Gets general runtime metric for dedicated hardware.
@@ -293,7 +293,7 @@ public:
      * @param name - metric name to request.
      * @return Metric value corresponding to metric key.
      */
-    Parameter get_metric(const std::string& deviceName, const std::string& name) const;
+    Any get_metric(const std::string& deviceName, const std::string& name) const;
 
     /**
      * @brief Returns devices available for neural networks inference
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/executable_network.hpp b/inference-engine/src/inference_engine/include/openvino/runtime/executable_network.hpp
index 0587e9afcc7..7c966434445 100644
--- a/inference-engine/src/inference_engine/include/openvino/runtime/executable_network.hpp
+++ b/inference-engine/src/inference_engine/include/openvino/runtime/executable_network.hpp
@@ -148,7 +148,7 @@ public:
      * @param name config key, can be found in ie_plugin_config.hpp
      * @return Configuration parameter value
      */
-    Parameter get_config(const std::string& name) const;
+    Any get_config(const std::string& name) const;
 
     /**
      * @brief Gets general runtime metric for an executable network.
@@ -159,7 +159,7 @@ public:
      * @param name metric name to request
      * @return Metric parameter value
      */
-    Parameter get_metric(const std::string& name) const;
+    Any get_metric(const std::string& name) const;
 
     /**
      * @brief Returns pointer to plugin-specific shared context
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/parameter.hpp b/inference-engine/src/inference_engine/include/openvino/runtime/parameter.hpp
index 026b0fd4dff..0fe1c9ded2f 100644
--- a/inference-engine/src/inference_engine/include/openvino/runtime/parameter.hpp
+++ b/inference-engine/src/inference_engine/include/openvino/runtime/parameter.hpp
@@ -8,353 +8,15 @@
  */
 #pragma once
 
-#include <algorithm>
-#include <cctype>
-#include <iterator>
 #include <map>
-#include <memory>
-#include <string>
-#include <tuple>
-#include <typeinfo>
-#include <utility>
-#include <vector>
 
-#include "openvino/core/except.hpp"
-#include "openvino/runtime/common.hpp"
+#include "openvino/core/any.hpp"
 
 namespace ov {
 namespace runtime {
-
-/**
- * @brief This class represents an object to work with different parameters
- *
- */
-class OPENVINO_RUNTIME_API Parameter {
-public:
-    /**
-     * @brief Default constructor
-     */
-    Parameter() = default;
-
-    /**
-     * @brief Move constructor
-     *
-     * @param parameter Parameter object
-     */
-    Parameter(Parameter&& parameter) noexcept {
-        std::swap(ptr, parameter.ptr);
-    }
-
-    /**
-     * @brief Copy constructor
-     *
-     * @param parameter Parameter object
-     */
-    Parameter(const Parameter& parameter) {
-        *this = parameter;
-    }
-
-    /**
-     * @brief Constructor creates parameter with object
-     *
-     * @tparam T Parameter type
-     * @tparam U Identity type-transformation
-     * @param parameter object
-     */
-    template <class T,
-              typename = typename std::enable_if<!std::is_same<typename std::decay<T>::type, Parameter>::value &&
-                                                 !std::is_abstract<typename std::decay<T>::type>::value>::type>
-    Parameter(T&& parameter) {
-        static_assert(!std::is_same<typename std::decay<T>::type, Parameter>::value, "To prevent recursion");
-        ptr = new RealData<typename std::decay<T>::type>(std::forward<T>(parameter));
-    }
-
-    /**
-     * @brief Constructor creates string parameter from char *
-     *
-     * @param str char array
-     */
-    Parameter(const char* str) : Parameter(std::string(str)) {}
-
-    /**
-     * @brief Destructor
-     */
-    virtual ~Parameter();
-
-    /**
-     * Copy operator for Parameter
-     * @param parameter Parameter object
-     * @return Parameter
-     */
-    Parameter& operator=(const Parameter& parameter) {
-        if (this == &parameter) {
-            return *this;
-        }
-        clear();
-        if (!parameter.empty())
-            ptr = parameter.ptr->copy();
-        return *this;
-    }
-
-    /**
-     * Remove a value from parameter
-     */
-    void clear() {
-        delete ptr;
-        ptr = nullptr;
-    }
-
-    /**
-     * Checks that parameter contains a value
-     * @return false if parameter contains a value else false
-     */
-    bool empty() const noexcept {
-        return nullptr == ptr;
-    }
-
-    /**
-     * Checks the type of value
-     * @tparam T Type of value
-     * @return true if type of value is correct
-     */
-    template <class T>
-    bool is() const {
-        return empty() ? false : ptr->is(typeid(T));
-    }
-
-    /**
-     * Dynamic cast to specified type
-     * @tparam T type
-     * @return casted object
-     */
-    template <typename T>
-    T&& as() && {
-        return std::move(dyn_cast<T>(ptr));
-    }
-
-    /**
-     * Dynamic cast to specified type
-     * @tparam T type
-     * @return casted object
-     */
-    template <class T>
-    T& as() & {
-        return dyn_cast<T>(ptr);
-    }
-    /**
-     * Dynamic cast to specified type
-     * @tparam T type
-     * @return casted object
-     */
-    template <class T>
-    const T& as() const& {
-        return dyn_cast<T>(ptr);
-    }
-
-    /**
-     * Dynamic cast to specified type
-     * @tparam T type
-     * @return casted object
-     */
-    template <class T>
-    operator T &&() && {
-        return std::move(dyn_cast<typename std::remove_cv<T>::type>(ptr));
-    }
-
-    /**
-     * Dynamic cast to specified type
-     * @tparam T type
-     * @return casted object
-     */
-    template <class T>
-    operator T&() & {
-        return dyn_cast<typename std::remove_cv<T>::type>(ptr);
-    }
-
-    /**
-     * Dynamic cast to specified type
-     * @tparam T type
-     * @return casted object
-     */
-    template <class T>
-    operator const T&() const& {
-        return dyn_cast<typename std::remove_cv<T>::type>(ptr);
-    }
-
-    /**
-     * Dynamic cast to specified type
-     * @tparam T type
-     * @return casted object
-     */
-    template <class T>
-    operator T&() const& {
-        return dyn_cast<typename std::remove_cv<T>::type>(ptr);
-    }
-
-    /**
-     * @brief The comparison operator for the Parameter
-     *
-     * @param rhs object to compare
-     * @return true if objects are equal
-     */
-    bool operator==(const Parameter& rhs) const {
-        return *ptr == *(rhs.ptr);
-    }
-    /**
-     * @brief The comparison operator for the Parameter
-     *
-     * @param rhs object to compare
-     * @return true if objects aren't equal
-     */
-    bool operator!=(const Parameter& rhs) const {
-        return !(*this == rhs);
-    }
-
-    /**
-     * @brief Prints underlying object to the given output stream.
-     * Uses operator<< if it is defined, leaves stream unchanged otherwise.
-     * In case of empty parameter or nullptr stream immediately returns.
-     *
-     * @param object Object to be printed to the given output stream.
-     * @param stream Output stream object will be printed to.
-     */
-    friend void PrintTo(const Parameter& object, std::ostream* stream) {
-        if (object.empty() || !stream) {
-            return;
-        }
-        object.ptr->print(*stream);
-    }
-
-private:
-    template <class T, class EqualTo>
-    struct CheckOperatorEqual {
-        template <class U, class V>
-        static auto test(U*) -> decltype(std::declval<U>() == std::declval<V>()) {
-            return false;
-        }
-
-        template <typename, typename>
-        static auto test(...) -> std::false_type {
-            return {};
-        }
-
-        using type = typename std::is_same<bool, decltype(test<T, EqualTo>(nullptr))>::type;
-    };
-
-    template <class T, class EqualTo = T>
-    struct HasOperatorEqual : CheckOperatorEqual<T, EqualTo>::type {};
-
-    template <class T, class U>
-    struct CheckOutputStreamOperator {
-        template <class V, class W>
-        static auto test(W*) -> decltype(std::declval<V&>() << std::declval<W>(), std::true_type()) {
-            return {};
-        }
-
-        template <typename, typename>
-        static auto test(...) -> std::false_type {
-            return {};
-        }
-
-        using type = typename std::is_same<std::true_type, decltype(test<T, U>(nullptr))>::type;
-    };
-
-    template <class T>
-    struct HasOutputStreamOperator : CheckOutputStreamOperator<std::ostream, T>::type {};
-
-    struct Any {
-#ifdef __ANDROID__
-        virtual ~Any();
-#else
-        virtual ~Any() = default;
-#endif
-        virtual bool is(const std::type_info&) const = 0;
-        virtual Any* copy() const = 0;
-        virtual bool operator==(const Any& rhs) const = 0;
-        virtual void print(std::ostream&) const = 0;
-    };
-
-    template <class T>
-    struct RealData : Any, std::tuple<T> {
-        using std::tuple<T>::tuple;
-
-        bool is(const std::type_info& id) const override {
-            return id == typeid(T);
-        }
-        Any* copy() const override {
-            return new RealData{get()};
-        }
-
-        T& get() & {
-            return std::get<0>(*static_cast<std::tuple<T>*>(this));
-        }
-
-        const T& get() const& {
-            return std::get<0>(*static_cast<const std::tuple<T>*>(this));
-        }
-
-        template <class U>
-        typename std::enable_if<!HasOperatorEqual<U>::value, bool>::type equal(const Any& left, const Any& rhs) const {
-            throw ov::Exception("Parameter doesn't contain equal operator");
-        }
-
-        template <class U>
-        typename std::enable_if<HasOperatorEqual<U>::value, bool>::type equal(const Any& left, const Any& rhs) const {
-            return dyn_cast<U>(&left) == dyn_cast<U>(&rhs);
-        }
-
-        bool operator==(const Any& rhs) const override {
-            return rhs.is(typeid(T)) && equal<T>(*this, rhs);
-        }
-
-        template <class U, typename std::enable_if<!HasOutputStreamOperator<U>::value, bool>::type = true>
-        void print(std::ostream& stream, const U& object) const {}
-
-        template <class U, typename std::enable_if<HasOutputStreamOperator<U>::value, bool>::type = true>
-        void print(std::ostream& stream, const U& object) const {
-            stream << object;
-        }
-
-        void print(std::ostream& stream) const override {
-            print<T>(stream, get());
-        }
-    };
-
-    template <typename T>
-    static T& dyn_cast(Any* obj) {
-        OPENVINO_ASSERT(obj != nullptr, "Parameter is empty!");
-        return dynamic_cast<RealData<T>&>(*obj).get();
-    }
-
-    template <typename T>
-    static const T& dyn_cast(const Any* obj) {
-        OPENVINO_ASSERT(obj != nullptr, "Parameter is empty!");
-        return dynamic_cast<const RealData<T>&>(*obj).get();
-    }
-
-    Any* ptr = nullptr;
-};
-
 /**
  * @brief An std::map object containing parameters
  */
-using ParamMap = std::map<std::string, Parameter>;
-
-#ifdef __ANDROID__
-extern template struct OPENVINO_RUNTIME_API ov::runtime::Parameter::RealData<int>;
-extern template struct OPENVINO_RUNTIME_API ov::runtime::Parameter::RealData<bool>;
-extern template struct OPENVINO_RUNTIME_API ov::runtime::Parameter::RealData<float>;
-extern template struct OPENVINO_RUNTIME_API ov::runtime::Parameter::RealData<uint32_t>;
-extern template struct OPENVINO_RUNTIME_API ov::runtime::Parameter::RealData<std::string>;
-extern template struct OPENVINO_RUNTIME_API ov::runtime::Parameter::RealData<unsigned long>;
-extern template struct OPENVINO_RUNTIME_API ov::runtime::Parameter::RealData<std::vector<int>>;
-extern template struct OPENVINO_RUNTIME_API ov::runtime::Parameter::RealData<std::vector<std::string>>;
-extern template struct OPENVINO_RUNTIME_API ov::runtime::Parameter::RealData<std::vector<unsigned long>>;
-extern template struct OPENVINO_RUNTIME_API ov::runtime::Parameter::RealData<std::tuple<unsigned int, unsigned int>>;
-extern template struct OPENVINO_RUNTIME_API
-    ov::runtime::Parameter::RealData<std::tuple<unsigned int, unsigned int, unsigned int>>;
-#endif
-
+using ParamMap = std::map<std::string, Any>;
 }  // namespace runtime
-
 }  // namespace ov
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/remote_tensor.hpp b/inference-engine/src/inference_engine/include/openvino/runtime/remote_tensor.hpp
index 0f6545b2cac..b0f60afe2e5 100644
--- a/inference-engine/src/inference_engine/include/openvino/runtime/remote_tensor.hpp
+++ b/inference-engine/src/inference_engine/include/openvino/runtime/remote_tensor.hpp
@@ -9,6 +9,7 @@
  */
 #pragma once
 
+#include "openvino/runtime/common.hpp"
 #include "openvino/runtime/parameter.hpp"
 #include "openvino/runtime/tensor.hpp"
 
diff --git a/inference-engine/src/inference_engine/src/cpp/ie_executable_network.cpp b/inference-engine/src/inference_engine/src/cpp/ie_executable_network.cpp
index 9c0d444acd1..53a026de1d1 100644
--- a/inference-engine/src/inference_engine/src/cpp/ie_executable_network.cpp
+++ b/inference-engine/src/inference_engine/src/cpp/ie_executable_network.cpp
@@ -90,11 +90,11 @@ void ExecutableNetwork::SetConfig(const std::map<std::string, Parameter>& config
 }
 
 Parameter ExecutableNetwork::GetConfig(const std::string& name) const {
-    EXEC_NET_CALL_STATEMENT(return _impl->GetConfig(name));
+    EXEC_NET_CALL_STATEMENT(return {_so, _impl->GetConfig(name)});
 }
 
 Parameter ExecutableNetwork::GetMetric(const std::string& name) const {
-    EXEC_NET_CALL_STATEMENT(return _impl->GetMetric(name));
+    EXEC_NET_CALL_STATEMENT(return {_so, _impl->GetMetric(name)});
 }
 
 RemoteContext::Ptr ExecutableNetwork::GetContext() const {
@@ -203,11 +203,11 @@ void ExecutableNetwork::set_config(const ie::ParamMap& config) {
 }
 
 ie::Parameter ExecutableNetwork::get_config(const std::string& name) const {
-    OV_EXEC_NET_CALL_STATEMENT(return _impl->GetConfig(name));
+    OV_EXEC_NET_CALL_STATEMENT(return {_so, _impl->GetConfig(name)});
 }
 
 ie::Parameter ExecutableNetwork::get_metric(const std::string& name) const {
-    OV_EXEC_NET_CALL_STATEMENT(return _impl->GetMetric(name));
+    OV_EXEC_NET_CALL_STATEMENT(return {_so, _impl->GetMetric(name)});
 }
 
 RemoteContext ExecutableNetwork::get_context() const {
diff --git a/inference-engine/src/inference_engine/src/cpp/ie_plugin.hpp b/inference-engine/src/inference_engine/src/cpp/ie_plugin.hpp
index e525103c06c..26a02391f74 100644
--- a/inference-engine/src/inference_engine/src/cpp/ie_plugin.hpp
+++ b/inference-engine/src/inference_engine/src/cpp/ie_plugin.hpp
@@ -100,7 +100,7 @@ struct InferencePlugin {
     }
 
     Parameter GetMetric(const std::string& name, const std::map<std::string, Parameter>& options) const {
-        PLUGIN_CALL_STATEMENT(return _ptr->GetMetric(name, options));
+        PLUGIN_CALL_STATEMENT(return {_so, _ptr->GetMetric(name, options)});
     }
 
     ov::runtime::SoPtr<RemoteContext> CreateContext(const ParamMap& params) {
@@ -112,7 +112,7 @@ struct InferencePlugin {
     }
 
     Parameter GetConfig(const std::string& name, const std::map<std::string, Parameter>& options) const {
-        PLUGIN_CALL_STATEMENT(return _ptr->GetConfig(name, options));
+        PLUGIN_CALL_STATEMENT(return {_so, _ptr->GetConfig(name, options)});
     }
 };
 }  // namespace InferenceEngine
@@ -210,7 +210,7 @@ public:
     }
 
     ie::Parameter get_metric(const std::string& name, const ie::ParamMap& options) const {
-        OV_PLUGIN_CALL_STATEMENT(return _ptr->GetMetric(name, options));
+        OV_PLUGIN_CALL_STATEMENT(return {_so, _ptr->GetMetric(name, options)});
     }
 
     SoPtr<ie::RemoteContext> create_context(const ie::ParamMap& params) {
@@ -222,7 +222,7 @@ public:
     }
 
     ie::Parameter get_config(const std::string& name, const ie::ParamMap& options) const {
-        OV_PLUGIN_CALL_STATEMENT(return _ptr->GetConfig(name, options));
+        OV_PLUGIN_CALL_STATEMENT(return {_so, _ptr->GetConfig(name, options)});
     }
 };
 
diff --git a/inference-engine/src/inference_engine/src/cpp/ie_remote_context.cpp b/inference-engine/src/inference_engine/src/cpp/ie_remote_context.cpp
index 1c3942bfc10..0c873efa297 100644
--- a/inference-engine/src/inference_engine/src/cpp/ie_remote_context.cpp
+++ b/inference-engine/src/inference_engine/src/cpp/ie_remote_context.cpp
@@ -59,7 +59,7 @@ std::string RemoteContext::get_device_name() const {
 
 RemoteTensor RemoteContext::create_tensor(const element::Type& element_type,
                                           const Shape& shape,
-                                          const ie::ParamMap& params) {
+                                          const ParamMap& params) {
     OV_REMOTE_CONTEXT_STATEMENT({
         auto blob = _impl->CreateBlob(
             {ie::details::convertPrecision(element_type), shape, ie::TensorDesc::getLayoutByRank(shape.size())},
@@ -78,8 +78,14 @@ Tensor RemoteContext::create_host_tensor(const element::Type element_type, const
     });
 }
 
-ie::ParamMap RemoteContext::get_params() const {
-    OV_REMOTE_CONTEXT_STATEMENT(return _impl->getParams());
+ParamMap RemoteContext::get_params() const {
+    ParamMap paramMap;
+    OV_REMOTE_CONTEXT_STATEMENT({
+        for (auto&& param : _impl->getParams()) {
+            paramMap.emplace(param.first, Any{_so, param.second});
+        }
+    });
+    return paramMap;
 }
 
 }  // namespace runtime
diff --git a/inference-engine/src/inference_engine/src/ie_common.cpp b/inference-engine/src/inference_engine/src/ie_common.cpp
index 1a269837baf..da54d47b01f 100644
--- a/inference-engine/src/inference_engine/src/ie_common.cpp
+++ b/inference-engine/src/inference_engine/src/ie_common.cpp
@@ -116,37 +116,3 @@ StatusCode InferenceEngineException::getStatus() const {
 IE_SUPPRESS_DEPRECATED_END
 
 }  // namespace InferenceEngine
-
-namespace ov {
-
-namespace runtime {
-
-//
-// openvino/runtime/parameter.hpp
-//
-
-Parameter::~Parameter() {
-    clear();
-}
-
-#ifdef __ANDROID__
-Parameter::Any::~Any() {}
-
-template struct Parameter::RealData<InferenceEngine::Blob::Ptr>;
-template struct Parameter::RealData<int>;
-template struct Parameter::RealData<bool>;
-template struct Parameter::RealData<float>;
-template struct Parameter::RealData<double>;
-template struct Parameter::RealData<uint32_t>;
-template struct Parameter::RealData<std::string>;
-template struct Parameter::RealData<unsigned long>;
-template struct Parameter::RealData<std::vector<int>>;
-template struct Parameter::RealData<std::vector<std::string>>;
-template struct Parameter::RealData<std::vector<unsigned long>>;
-template struct Parameter::RealData<std::tuple<unsigned int, unsigned int>>;
-template struct Parameter::RealData<std::tuple<unsigned int, unsigned int, unsigned int>>;
-#endif
-
-}  // namespace runtime
-
-}  // namespace ov
diff --git a/inference-engine/src/inference_engine/src/ie_core.cpp b/inference-engine/src/inference_engine/src/ie_core.cpp
index e3465cf0428..8cd03ba10ed 100644
--- a/inference-engine/src/inference_engine/src/ie_core.cpp
+++ b/inference-engine/src/inference_engine/src/ie_core.cpp
@@ -105,34 +105,6 @@ Parsed<T> parseDeviceNameIntoConfig(const std::string& deviceName, const std::ma
     return {deviceName_, config_};
 }
 
-ie::Parameter copyParameterValue(const ie::Parameter& value) {
-    if (value.is<bool>()) {
-        return {value.as<bool>()};
-    } else if (value.is<int>()) {
-        return {value.as<int>()};
-    } else if (value.is<unsigned int>()) {
-        return {value.as<unsigned int>()};
-    } else if (value.is<float>()) {
-        return {value.as<float>()};
-    } else if (value.is<std::string>()) {
-        return {value.as<std::string>()};
-    } else if (value.is<std::vector<std::string>>()) {
-        return {value.as<std::vector<std::string>>()};
-    } else if (value.is<std::vector<int>>()) {
-        return {value.as<std::vector<int>>()};
-    } else if (value.is<std::vector<float>>()) {
-        return {value.as<std::vector<float>>()};
-    } else if (value.is<std::vector<unsigned int>>()) {
-        return {value.as<std::vector<unsigned int>>()};
-    } else if (value.is<std::tuple<unsigned int, unsigned int, unsigned int>>()) {
-        return {value.as<std::tuple<unsigned int, unsigned int, unsigned int>>()};
-    } else if (value.is<std::tuple<unsigned int, unsigned int>>()) {
-        return {value.as<std::tuple<unsigned int, unsigned int>>()};
-    }
-
-    return std::move(value);
-}
-
 template <typename F>
 void allowNotImplemented(F&& f) {
     try {
@@ -230,7 +202,8 @@ class CoreImpl : public ie::ICore, public std::enable_shared_from_this<ie::ICore
     bool DeviceSupportsImportExport(const ov::runtime::InferencePlugin& plugin) const {
         std::vector<std::string> supportedMetricKeys = plugin.get_metric(METRIC_KEY(SUPPORTED_METRICS), {});
         auto it = std::find(supportedMetricKeys.begin(), supportedMetricKeys.end(), METRIC_KEY(IMPORT_EXPORT_SUPPORT));
-        bool supported = (it != supportedMetricKeys.end()) && plugin.get_metric(METRIC_KEY(IMPORT_EXPORT_SUPPORT), {});
+        bool supported =
+            (it != supportedMetricKeys.end()) && plugin.get_metric(METRIC_KEY(IMPORT_EXPORT_SUPPORT), {}).as<bool>();
         return supported;
     }
 
@@ -703,19 +676,12 @@ public:
             parsed._config.insert(o);
         }
 
-        // we need to return a copy of Parameter object which is created on Core side,
-        // not in InferenceEngine plugin side, which can be unloaded from Core in a parallel thread
-        // TODO: remove this WA after *-31417 is resolved
-        return copyParameterValue(GetCPPPluginByName(parsed._deviceName).get_metric(name, parsed._config));
+        return GetCPPPluginByName(parsed._deviceName).get_metric(name, parsed._config);
     }
 
     ie::Parameter GetConfig(const std::string& deviceName, const std::string& name) const override {
         auto parsed = parseDeviceNameIntoConfig(deviceName);
-
-        // we need to return a copy of Parameter object which is created on Core side,
-        // not in InferenceEngine plugin side, which can be unloaded from Core in a parallel thread
-        // TODO: remove this WA after *-31417 is resolved
-        return copyParameterValue(GetCPPPluginByName(parsed._deviceName).get_config(name, parsed._config));
+        return GetCPPPluginByName(parsed._deviceName).get_config(name, parsed._config);
     }
 
     /**
@@ -1382,12 +1348,7 @@ Parameter Core::GetConfig(const std::string& deviceName, const std::string& name
     }
 
     auto parsed = ov::runtime::parseDeviceNameIntoConfig(deviceName);
-
-    // we need to return a copy of Parameter object which is created on Core side,
-    // not in InferenceEngine plugin side, which can be unloaded from Core in a parallel thread
-    // TODO: remove this WA after *-31417 is resolved
-    return ov::runtime::copyParameterValue(
-        _impl->GetCPPPluginByName(parsed._deviceName).get_config(name, parsed._config));
+    return _impl->GetCPPPluginByName(parsed._deviceName).get_config(name, parsed._config);
 }
 
 Parameter Core::GetMetric(const std::string& deviceName, const std::string& name, const ParamMap& options) const {
@@ -1594,7 +1555,7 @@ void Core::set_config(const ConfigMap& config, const std::string& deviceName) {
     });
 }
 
-Parameter Core::get_config(const std::string& deviceName, const std::string& name) const {
+Any Core::get_config(const std::string& deviceName, const std::string& name) const {
     OPENVINO_ASSERT(deviceName.find("HETERO:") != 0,
                     "You can only get_config of the HETERO itself (without devices). "
                     "get_config is also possible for the individual devices before creating the HETERO on top.");
@@ -1607,15 +1568,11 @@ Parameter Core::get_config(const std::string& deviceName, const std::string& nam
 
     OV_CORE_CALL_STATEMENT({
         auto parsed = parseDeviceNameIntoConfig(deviceName);
-
-        // we need to return a copy of Parameter object which is created on Core side,
-        // not in ie plugin side, which can be unloaded from Core in a parallel thread
-        // TODO: remove this WA after *-31417 is resolved
-        return copyParameterValue(_impl->GetCPPPluginByName(parsed._deviceName).get_config(name, parsed._config));
+        return _impl->GetCPPPluginByName(parsed._deviceName).get_config(name, parsed._config);
     });
 }
 
-Parameter Core::get_metric(const std::string& deviceName, const std::string& name) const {
+Any Core::get_metric(const std::string& deviceName, const std::string& name) const {
     OV_CORE_CALL_STATEMENT(return _impl->GetMetric(deviceName, name););
 }
 
diff --git a/inference-engine/src/inference_engine/src/remote_tensor.cpp b/inference-engine/src/inference_engine/src/remote_tensor.cpp
index 88af4062adf..d1bf97474cd 100644
--- a/inference-engine/src/inference_engine/src/remote_tensor.cpp
+++ b/inference-engine/src/inference_engine/src/remote_tensor.cpp
@@ -38,7 +38,11 @@ ie::ParamMap RemoteTensor::get_params() const {
     type_check(*this);
     auto remote_impl = static_cast<ie::RemoteBlob*>(_impl.get());
     try {
-        return remote_impl->getParams();
+        ParamMap paramMap;
+        for (auto&& param : remote_impl->getParams()) {
+            paramMap.emplace(param.first, Any{_so, param.second});
+        }
+        return paramMap;
     } catch (const std::exception& ex) {
         throw ov::Exception(ex.what());
     } catch (...) {
diff --git a/inference-engine/src/low_precision_transformations/include/low_precision/network_helper.hpp b/inference-engine/src/low_precision_transformations/include/low_precision/network_helper.hpp
index cb0f56e51fc..9da92e72631 100644
--- a/inference-engine/src/low_precision_transformations/include/low_precision/network_helper.hpp
+++ b/inference-engine/src/low_precision_transformations/include/low_precision/network_helper.hpp
@@ -246,7 +246,7 @@ public:
                     auto it = rt.find(name);
                     if (it != rt.end()) {
                         const auto currentAttribute = it->second;
-                        if (oldAttribute.get() == currentAttribute.get()) {
+                        if (oldAttribute == as_type_ptr<ngraph::Variant>(currentAttribute)) {
                             rt[name] = newAttribute;
                         }
                         handleConnectedNodes = true;
diff --git a/inference-engine/src/low_precision_transformations/include/low_precision/rt_info/precisions_attribute.hpp b/inference-engine/src/low_precision_transformations/include/low_precision/rt_info/precisions_attribute.hpp
index 65d0a50ea25..b7057464c4f 100644
--- a/inference-engine/src/low_precision_transformations/include/low_precision/rt_info/precisions_attribute.hpp
+++ b/inference-engine/src/low_precision_transformations/include/low_precision/rt_info/precisions_attribute.hpp
@@ -51,7 +51,7 @@ public:
 
     VariantWrapper(const value_type& value) : VariantImpl<value_type>(value) {}
 
-    std::shared_ptr<Variant> init(const std::shared_ptr<ngraph::Node>& node) override;
+    ov::Any init(const std::shared_ptr<ngraph::Node>& node) override;
 
     std::shared_ptr<ngraph::PrecisionsAttribute> get() { return this->m_value; }
 
diff --git a/inference-engine/src/low_precision_transformations/include/low_precision/rt_info/quantization_alignment_attribute.hpp b/inference-engine/src/low_precision_transformations/include/low_precision/rt_info/quantization_alignment_attribute.hpp
index 28503344f23..57f43d06cfe 100644
--- a/inference-engine/src/low_precision_transformations/include/low_precision/rt_info/quantization_alignment_attribute.hpp
+++ b/inference-engine/src/low_precision_transformations/include/low_precision/rt_info/quantization_alignment_attribute.hpp
@@ -50,7 +50,7 @@ public:
 
     VariantWrapper(const value_type& value) : VariantImpl<value_type>(value) {}
 
-    std::shared_ptr<Variant> init(const std::shared_ptr<ngraph::Node>& node) override;
+    ov::Any init(const std::shared_ptr<ngraph::Node>& node) override;
 
     std::shared_ptr<ngraph::QuantizationAlignmentAttribute> get() { return this->m_value; }
 
diff --git a/inference-engine/src/low_precision_transformations/src/rt_info/precisions_attribute.cpp b/inference-engine/src/low_precision_transformations/src/rt_info/precisions_attribute.cpp
index 2cc92e68e21..f7158632319 100644
--- a/inference-engine/src/low_precision_transformations/src/rt_info/precisions_attribute.cpp
+++ b/inference-engine/src/low_precision_transformations/src/rt_info/precisions_attribute.cpp
@@ -57,8 +57,8 @@ void VariantWrapper<std::shared_ptr<PrecisionsAttribute>>::merge(
     }
 }
 
-std::shared_ptr<ngraph::Variant> VariantWrapper<std::shared_ptr<PrecisionsAttribute>>::init(const std::shared_ptr<ngraph::Node>& node) {
-    return nullptr;
+ov::Any VariantWrapper<std::shared_ptr<PrecisionsAttribute>>::init(const std::shared_ptr<ngraph::Node>& node) {
+    return {};
 }
 
 std::string VariantWrapper<std::shared_ptr<PrecisionsAttribute>>::to_string() {
diff --git a/inference-engine/src/low_precision_transformations/src/rt_info/quantization_alignment_attribute.cpp b/inference-engine/src/low_precision_transformations/src/rt_info/quantization_alignment_attribute.cpp
index 26fd6711c34..f275f836f6d 100644
--- a/inference-engine/src/low_precision_transformations/src/rt_info/quantization_alignment_attribute.cpp
+++ b/inference-engine/src/low_precision_transformations/src/rt_info/quantization_alignment_attribute.cpp
@@ -24,7 +24,7 @@ template class ngraph::VariantImpl<QuantizationAlignmentAttributePtr>;
 
 constexpr VariantTypeInfo VariantWrapper<QuantizationAlignmentAttributePtr>::type_info;
 
-std::shared_ptr<ngraph::Variant> VariantWrapper<QuantizationAlignmentAttributePtr>::init(const std::shared_ptr<ngraph::Node>& node) {
+ov::Any VariantWrapper<QuantizationAlignmentAttributePtr>::init(const std::shared_ptr<ngraph::Node>& node) {
     return nullptr;
 }
 
diff --git a/inference-engine/src/mkldnn_plugin/utils/ngraph_utils.hpp b/inference-engine/src/mkldnn_plugin/utils/ngraph_utils.hpp
index 964b2e17015..08ae121a7db 100644
--- a/inference-engine/src/mkldnn_plugin/utils/ngraph_utils.hpp
+++ b/inference-engine/src/mkldnn_plugin/utils/ngraph_utils.hpp
@@ -10,7 +10,7 @@
 
 namespace MKLDNNPlugin {
 
-inline std::string getRTInfoValue(const std::map<std::string, std::shared_ptr<ngraph::Variant>>& rtInfo, std::string paramName) {
+inline std::string getRTInfoValue(const std::map<std::string, ov::Any>& rtInfo, std::string paramName) {
     auto it = rtInfo.find(paramName);
     if (it != rtInfo.end()) {
         auto value = std::dynamic_pointer_cast<ngraph::VariantImpl<std::string>>(it->second);
diff --git a/inference-engine/src/mkldnn_plugin/utils/rt_info/memory_formats_attribute.cpp b/inference-engine/src/mkldnn_plugin/utils/rt_info/memory_formats_attribute.cpp
index 91ae496d918..5dca9699645 100644
--- a/inference-engine/src/mkldnn_plugin/utils/rt_info/memory_formats_attribute.cpp
+++ b/inference-engine/src/mkldnn_plugin/utils/rt_info/memory_formats_attribute.cpp
@@ -11,15 +11,28 @@
 using namespace ngraph;
 using namespace ov;
 
-template class ov::MLKDNNMemoryFormatsHelper<MLKDNNInputMemoryFormats>;
+MLKDNNInputMemoryFormats::~MLKDNNInputMemoryFormats() = default;
 
 std::string ngraph::getMLKDNNInputMemoryFormats(const std::shared_ptr<ngraph::Node>& node) {
-    return MLKDNNMemoryFormatsHelper<MLKDNNInputMemoryFormats>::getMemoryFormats(node);
+    auto it_info = node->get_rt_info().find(MLKDNNInputMemoryFormatsAttr);
+    if (it_info != node->get_rt_info().end()) {
+        if (auto ptr = it_info->second.as<std::shared_ptr<MLKDNNInputMemoryFormats>>()) {
+            return ptr->getMemoryFormats();
+        }
+    }
+    return {};
 }
 
-template class ov::MLKDNNMemoryFormatsHelper<MLKDNNOutputMemoryFormats>;
+MLKDNNOutputMemoryFormats::~MLKDNNOutputMemoryFormats() = default;
 
 std::string ngraph::getMLKDNNOutputMemoryFormats(const std::shared_ptr<ngraph::Node>& node) {
-    return MLKDNNMemoryFormatsHelper<MLKDNNOutputMemoryFormats>::getMemoryFormats(node);
+    auto it_info = node->get_rt_info().find(MLKDNNOutputMemoryFormatsAttr);
+    if (it_info != node->get_rt_info().end()) {
+        if (auto ptr = it_info->second.as<std::shared_ptr<MLKDNNOutputMemoryFormats>>()) {
+            return ptr->getMemoryFormats();
+        }
+    }
+    return {};
 }
 
+
diff --git a/inference-engine/src/mkldnn_plugin/utils/rt_info/memory_formats_attribute.hpp b/inference-engine/src/mkldnn_plugin/utils/rt_info/memory_formats_attribute.hpp
index cdd47e604e1..c2e7498bb58 100644
--- a/inference-engine/src/mkldnn_plugin/utils/rt_info/memory_formats_attribute.hpp
+++ b/inference-engine/src/mkldnn_plugin/utils/rt_info/memory_formats_attribute.hpp
@@ -15,7 +15,8 @@ namespace ngraph {
 constexpr const char *MLKDNNInputMemoryFormatsAttr = "MLKDNNInputMemoryFormats";
 constexpr const char *MLKDNNOutputMemoryFormatsAttr = "MLKDNNOutputMemoryFormats";
 
-class MLKDNNMemoryFormats {
+template<typename MemoryFormat>
+class MLKDNNMemoryFormats : public Variant {
 protected:
     std::string memory_format;
 
@@ -23,82 +24,54 @@ public:
     MLKDNNMemoryFormats() = default;
     explicit MLKDNNMemoryFormats(const std::string &_memory_format) : memory_format(_memory_format) {}
     std::string getMemoryFormats() const { return memory_format; }
-};
 
-
-class MLKDNNInputMemoryFormats : public MLKDNNMemoryFormats {
-public:
-    MLKDNNInputMemoryFormats() = default;
-    explicit MLKDNNInputMemoryFormats(const std::string &_memory_format) : MLKDNNMemoryFormats(_memory_format) {}
-};
-
-std::string getMLKDNNInputMemoryFormats(const std::shared_ptr<ngraph::Node>& node);
-
-class MLKDNNOutputMemoryFormats : public MLKDNNMemoryFormats {
-public:
-    MLKDNNOutputMemoryFormats() = default;
-    explicit MLKDNNOutputMemoryFormats(const std::string &_memory_format) : MLKDNNMemoryFormats(_memory_format) {}
-};
-std::string getMLKDNNOutputMemoryFormats(const std::shared_ptr<ngraph::Node>& node);
-
-}  // namespace ngraph
-
-namespace ov {
-template <typename MemoryFormatsType>
-class MLKDNNMemoryFormatsHelper : public VariantImpl<MemoryFormatsType> {
-public:
-    MLKDNNMemoryFormatsHelper(const MemoryFormatsType& value) : VariantImpl<MemoryFormatsType>(value) {}
-
-    static std::string getMemoryFormats(const std::shared_ptr<ngraph::Node>& node) {
-        const auto &rtInfo = node->get_rt_info();
-        using MemoryFormatsWrapper = VariantWrapper<MemoryFormatsType>;
-        if (!rtInfo.count(MemoryFormatsWrapper::get_type_info_static().name)) return "";
-        const auto &attr = rtInfo.at(MemoryFormatsWrapper::get_type_info_static().name);
-        MemoryFormatsType mem_format = ngraph::as_type_ptr<MemoryFormatsWrapper>(attr)->get();
-        return mem_format.getMemoryFormats();
-    }
-
-    std::shared_ptr<ngraph::Variant> merge(const ngraph::NodeVector & nodes) override {
+    ov::Any merge(const ngraph::NodeVector & nodes) override {
         std::set<std::string> unique_mem_format;
 
         for (auto &node : nodes) {
-            std::string mem_format = getMemoryFormats(node);
-            if (!mem_format.empty()) unique_mem_format.insert(mem_format);
+            auto it_info = node->get_rt_info().find(MemoryFormat::get_type_info_static().name);
+            if (it_info != node->get_rt_info().end()) {
+                if (auto ptr = it_info->second.template as<std::shared_ptr<MemoryFormat>>()) {
+                    std::string mem_format = ptr->getMemoryFormats();
+                    if (!mem_format.empty()) {
+                        unique_mem_format.insert(mem_format);
+                    }
+                }
+            }
         }
 
         if (unique_mem_format.size() > 1) {
-            throw ngraph::ngraph_error(std::string(VariantWrapper<MemoryFormatsType>::get_type_info_static().name) + " no rule defined for multiple values.");
+            throw ngraph::ngraph_error(
+                std::string(MemoryFormat::get_type_info_static().name) +
+                " no rule defined for multiple values.");
         }
 
         std::string final_mem_format;
         if (unique_mem_format.size() == 1) {
             final_mem_format = *unique_mem_format.begin();
         }
-        return std::make_shared<VariantWrapper<MemoryFormatsType>>(MemoryFormatsType(final_mem_format));
-    }
-
-    std::shared_ptr<ngraph::Variant> init(const std::shared_ptr<ngraph::Node> & node) override {
-        throw ngraph::ngraph_error(std::string(VariantWrapper<MemoryFormatsType>::get_type_info_static().name) + " has no default initialization.");
+        return std::make_shared<MemoryFormat>(final_mem_format);
     }
 };
-extern template class MLKDNNMemoryFormatsHelper<ngraph::MLKDNNInputMemoryFormats>;
 
-template<>
-class VariantWrapper<ngraph::MLKDNNInputMemoryFormats> : public MLKDNNMemoryFormatsHelper<ngraph::MLKDNNInputMemoryFormats> {
+
+class MLKDNNInputMemoryFormats : public MLKDNNMemoryFormats<MLKDNNInputMemoryFormats> {
 public:
-    OPENVINO_RTTI(ngraph::MLKDNNInputMemoryFormatsAttr);
-
-    VariantWrapper(const ngraph::MLKDNNInputMemoryFormats &value) : MLKDNNMemoryFormatsHelper<ngraph::MLKDNNInputMemoryFormats>(value) {}
+    OPENVINO_RTTI(MLKDNNInputMemoryFormatsAttr);
+    MLKDNNInputMemoryFormats() = default;
+    explicit MLKDNNInputMemoryFormats(const std::string &_memory_format) : MLKDNNMemoryFormats(_memory_format) {}
+    ~MLKDNNInputMemoryFormats() override;
 };
 
-extern template class MLKDNNMemoryFormatsHelper<ngraph::MLKDNNOutputMemoryFormats>;
+std::string getMLKDNNInputMemoryFormats(const std::shared_ptr<ngraph::Node>& node);
 
-template<>
-class VariantWrapper<ngraph::MLKDNNOutputMemoryFormats> : public MLKDNNMemoryFormatsHelper<ngraph::MLKDNNOutputMemoryFormats> {
+class MLKDNNOutputMemoryFormats : public MLKDNNMemoryFormats<MLKDNNOutputMemoryFormats> {
 public:
-    OPENVINO_RTTI(ngraph::MLKDNNOutputMemoryFormatsAttr);
-
-    VariantWrapper(const ngraph::MLKDNNOutputMemoryFormats &value) : MLKDNNMemoryFormatsHelper<ngraph::MLKDNNOutputMemoryFormats>(value) {}
+    OPENVINO_RTTI(MLKDNNOutputMemoryFormatsAttr);
+    MLKDNNOutputMemoryFormats() = default;
+    explicit MLKDNNOutputMemoryFormats(const std::string &_memory_format) : MLKDNNMemoryFormats(_memory_format) {}
+    ~MLKDNNOutputMemoryFormats() override;
 };
+std::string getMLKDNNOutputMemoryFormats(const std::shared_ptr<ngraph::Node>& node);
 
-}  // namespace ov
+}  // namespace ngraph
diff --git a/inference-engine/src/snippets/src/generator.cpp b/inference-engine/src/snippets/src/generator.cpp
index 91a50ad576d..33b373f2d29 100644
--- a/inference-engine/src/snippets/src/generator.cpp
+++ b/inference-engine/src/snippets/src/generator.cpp
@@ -17,7 +17,8 @@ auto ngraph::snippets::getRegisters(std::shared_ptr<ngraph::Node>& n) -> ngraph:
 
     // ToDo: change to reg_t
     std::vector<size_t> rout;
-    if (auto rinfo = rt["reginfo"]) {
+    auto rinfo = rt["reginfo"];
+    if (!rinfo.empty()) {
         auto reginfo = ngraph::as_type_ptr<ngraph::VariantWrapper<std::vector<size_t>>>(rinfo)->get();
         for (auto reg : reginfo) {
             rout.push_back(reg);
@@ -27,7 +28,8 @@ auto ngraph::snippets::getRegisters(std::shared_ptr<ngraph::Node>& n) -> ngraph:
     std::vector<size_t> rin;
     for (auto input : n->inputs()) {
         auto rt = input.get_source_output().get_node_shared_ptr()->get_rt_info();
-        if (auto rinfo = rt["reginfo"]) {
+        auto rinfo = rt["reginfo"];
+        if (!rinfo.empty()) {
             auto reginfo = ngraph::as_type_ptr<ngraph::VariantWrapper<std::vector<size_t>>>(rinfo)->get();
             for (auto reg : reginfo) {
                 rin.push_back(reg);
diff --git a/inference-engine/src/transformations/include/transformations/rt_info/attributes.hpp b/inference-engine/src/transformations/include/transformations/rt_info/attributes.hpp
index 0866171601d..11a0d515ccb 100644
--- a/inference-engine/src/transformations/include/transformations/rt_info/attributes.hpp
+++ b/inference-engine/src/transformations/include/transformations/rt_info/attributes.hpp
@@ -11,6 +11,7 @@
 #include <ngraph/node.hpp>
 #include <openvino/core/preprocess/input_tensor_info.hpp>
 #include <openvino/core/variant.hpp>
+#include <openvino/core/any.hpp>
 #include <set>
 #include <transformations/rt_info/disable_constant_folding.hpp>
 #include <transformations/rt_info/disable_fp16_compression.hpp>
@@ -31,15 +32,17 @@ public:
     Attributes();
     ~Attributes();
 
-    Variant* create_by_type_info(const ov::DiscreteTypeInfo& type_info);
+    std::shared_ptr<Variant> create_by_type_info(const ov::DiscreteTypeInfo& type_info);
 
 private:
     template <class T>
     void register_factory() {
-        m_factory_registry.register_factory<T>(ngraph::FactoryRegistry<T>::template get_default_factory<T>());
+        m_factory_registry.emplace(T::get_type_info_static(), [] {
+            return std::make_shared<T>();
+        });
     }
 
-    ngraph::FactoryRegistry<Variant> m_factory_registry;
+    std::unordered_map<DiscreteTypeInfo, std::function<std::shared_ptr<Variant>()>> m_factory_registry;
 };
 }  // namespace pass
 }  // namespace ov
diff --git a/inference-engine/src/transformations/include/transformations/rt_info/fused_names_attribute.hpp b/inference-engine/src/transformations/include/transformations/rt_info/fused_names_attribute.hpp
index c80a4b1536b..98ab5b7ae9f 100644
--- a/inference-engine/src/transformations/include/transformations/rt_info/fused_names_attribute.hpp
+++ b/inference-engine/src/transformations/include/transformations/rt_info/fused_names_attribute.hpp
@@ -96,9 +96,9 @@ public:
 
     VariantWrapper(const value_type &value) : VariantImpl<value_type>(value) {}
 
-    std::shared_ptr<ngraph::Variant> merge(const ngraph::NodeVector & nodes) override;
+    Any merge(const ngraph::NodeVector & nodes) override;
 
-    std::shared_ptr<ngraph::Variant> init(const std::shared_ptr<ngraph::Node> & node) override;
+    Any init(const std::shared_ptr<ngraph::Node> & node) override;
 
     bool visit_attributes(AttributeVisitor & visitor) override;
 };
diff --git a/inference-engine/src/transformations/include/transformations/rt_info/primitives_priority_attribute.hpp b/inference-engine/src/transformations/include/transformations/rt_info/primitives_priority_attribute.hpp
index eb387a57762..49ccfaf674f 100644
--- a/inference-engine/src/transformations/include/transformations/rt_info/primitives_priority_attribute.hpp
+++ b/inference-engine/src/transformations/include/transformations/rt_info/primitives_priority_attribute.hpp
@@ -32,11 +32,13 @@ public:
 
     PrimitivesPriority() = default;
 
+    ~PrimitivesPriority() override = default;
+
     PrimitivesPriority(const value_type &value) : VariantImpl<value_type>(value) {}
 
-    std::shared_ptr<ov::Variant> merge(const ngraph::NodeVector & nodes) override;
+    Any merge(const ngraph::NodeVector & nodes) override;
 
-    std::shared_ptr<ov::Variant> init(const std::shared_ptr<ngraph::Node> & node) override;
+    Any init(const std::shared_ptr<ngraph::Node> & node) override;
 
     bool visit_attributes(AttributeVisitor & visitor) override;
 };
diff --git a/inference-engine/src/transformations/src/transformations/rt_info/attributes.cpp b/inference-engine/src/transformations/src/transformations/rt_info/attributes.cpp
index c9201466bcd..641fc08d2be 100644
--- a/inference-engine/src/transformations/src/transformations/rt_info/attributes.cpp
+++ b/inference-engine/src/transformations/src/transformations/rt_info/attributes.cpp
@@ -18,8 +18,13 @@ ov::pass::Attributes::Attributes() {
     register_factory<ov::preprocess::TensorInfoMemoryType>();
 }
 
-ov::Variant* ov::pass::Attributes::create_by_type_info(const ov::DiscreteTypeInfo& type_info) {
-    return m_factory_registry.create(type_info);
+std::shared_ptr<ov::Variant> ov::pass::Attributes::create_by_type_info(const ov::DiscreteTypeInfo& type_info) {
+    auto it_type = m_factory_registry.find(type_info);
+    if (it_type != m_factory_registry.end()) {
+        return it_type->second();
+    } else {
+        return {};
+    }
 }
 
 ov::pass::Attributes::~Attributes() = default;
diff --git a/inference-engine/tests/functional/inference_engine/caching_test.cpp b/inference-engine/tests/functional/inference_engine/caching_test.cpp
index b6065d928c0..a7214cd6481 100644
--- a/inference-engine/tests/functional/inference_engine/caching_test.cpp
+++ b/inference-engine/tests/functional/inference_engine/caching_test.cpp
@@ -360,7 +360,9 @@ private:
         }));
 
         ON_CALL(plugin, GetMetric(METRIC_KEY(DEVICE_ARCHITECTURE), _)).
-                WillByDefault(Return("mock"));
+                        WillByDefault(Invoke([&](const std::string &, const std::map<std::string, Parameter> &) {
+            return "mock";
+        }));
 
         ON_CALL(plugin, ImportNetwork(_, _, _)).
                 WillByDefault(Invoke([&](std::istream &istr, const RemoteContext::Ptr&,
diff --git a/inference-engine/tests/functional/inference_engine/lp_transformations/layer_transformation.hpp b/inference-engine/tests/functional/inference_engine/lp_transformations/layer_transformation.hpp
index d704936eecf..b90717de7ac 100644
--- a/inference-engine/tests/functional/inference_engine/lp_transformations/layer_transformation.hpp
+++ b/inference-engine/tests/functional/inference_engine/lp_transformations/layer_transformation.hpp
@@ -139,7 +139,7 @@ public:
 
     template <class Attribute>
     static bool checkIfOutputAttributesAreTheSame(const NodeVector& nodes) {
-        Variant* first = nullptr;
+        void* first = nullptr;
         for (auto node : nodes) {
             for (auto output : node->outputs()) {
                 auto& rt = output.get_rt_info();
@@ -150,11 +150,13 @@ public:
                 }
 
                 auto value = it->second;
+                OPENVINO_SUPPRESS_DEPRECATED_START
                 if (first == nullptr) {
                     first = value.get();
                 } else if (value.get() != first) {
                     return false;
                 }
+                OPENVINO_SUPPRESS_DEPRECATED_END
             }
         }
         return true;
diff --git a/inference-engine/tests/functional/inference_engine/parameter_tests.cpp b/inference-engine/tests/functional/inference_engine/parameter_tests.cpp
deleted file mode 100644
index 16ed9c339ff..00000000000
--- a/inference-engine/tests/functional/inference_engine/parameter_tests.cpp
+++ /dev/null
@@ -1,418 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include <gtest/gtest.h>
-
-#include <ie_parameter.hpp>
-#include <ie_layouts.h>
-
-using namespace InferenceEngine;
-
-class DestructorTest {
-public:
-    DestructorTest() {
-        constructorCount++;
-    }
-
-    DestructorTest(const DestructorTest& c) {
-        constructorCount++;
-    }
-
-    DestructorTest(const DestructorTest&& c) {
-        constructorCount++;
-    }
-
-    ~DestructorTest() {
-        destructorCount++;
-    }
-
-    static size_t destructorCount;
-    static size_t constructorCount;
-};
-size_t DestructorTest::destructorCount = 0;
-size_t DestructorTest::constructorCount = 0;
-
-class ParameterTests : public ::testing::Test {
-public:
-    void SetUp() override {
-        DestructorTest::destructorCount = 0;
-        DestructorTest::constructorCount = 0;
-    }
-};
-
-TEST_F(ParameterTests, ParameterAsInt) {
-    Parameter p = 4;
-    ASSERT_TRUE(p.is<int>());
-    int test = p;
-    ASSERT_EQ(4, test);
-}
-
-TEST_F(ParameterTests, ParameterAsUInt) {
-    Parameter p = 4u;
-    ASSERT_TRUE(p.is<unsigned int>());
-#ifdef __i386__
-    ASSERT_TRUE(p.is<size_t>());
-#else
-    ASSERT_FALSE(p.is<size_t>());
-#endif
-    unsigned int test = p;
-    ASSERT_EQ(4, test);
-}
-
-TEST_F(ParameterTests, ParameterAsSize_t) {
-    size_t ref = 4;
-    Parameter p = ref;
-    ASSERT_TRUE(p.is<size_t>());
-    size_t test = p;
-    ASSERT_EQ(ref, test);
-}
-
-TEST_F(ParameterTests, ParameterAsFloat) {
-    Parameter p = 4.f;
-    ASSERT_TRUE(p.is<float>());
-    float test = p;
-    ASSERT_EQ(4.f, test);
-}
-
-TEST_F(ParameterTests, ParameterAsString) {
-    std::string ref = "test";
-    Parameter p = ref;
-    std::string test = p;
-    ASSERT_TRUE(p.is<std::string>());
-    ASSERT_EQ(ref, test);
-}
-
-TEST_F(ParameterTests, ParameterAsStringInLine) {
-    Parameter p = "test";
-    std::string test = p;
-    ASSERT_TRUE(p.is<std::string>());
-    ASSERT_EQ("test", test);
-}
-
-TEST_F(ParameterTests, IntParameterAsString) {
-    Parameter p = 4;
-    ASSERT_TRUE(p.is<int>());
-    ASSERT_FALSE(p.is<std::string>());
-    ASSERT_THROW(std::string test = p, std::bad_cast);
-    ASSERT_THROW(std::string test = p.as<std::string>(), std::bad_cast);
-}
-
-TEST_F(ParameterTests, StringParameterAsInt) {
-    Parameter p = "4";
-    ASSERT_FALSE(p.is<int>());
-    ASSERT_TRUE(p.is<std::string>());
-    ASSERT_THROW((void)static_cast<int>(p), std::bad_cast);
-    ASSERT_THROW((void)p.as<int>(), std::bad_cast);
-}
-
-TEST_F(ParameterTests, ParameterAsTensorDesc) {
-    TensorDesc ref(Precision::FP32, {1, 3, 2, 2}, Layout::NCHW);
-    Parameter p = ref;
-    ASSERT_TRUE(p.is<TensorDesc>());
-    TensorDesc test = p;
-    ASSERT_EQ(ref, test);
-}
-
-TEST_F(ParameterTests, ParameterAsInts) {
-    std::vector<int> ref = {1, 2, 3, 4, 5};
-    Parameter p = ref;
-    ASSERT_TRUE(p.is<std::vector<int>>());
-    std::vector<int> test = p;
-    ASSERT_EQ(ref.size(), test.size());
-    for (size_t i = 0; i < ref.size(); i++) {
-        ASSERT_EQ(ref[i], test[i]);
-    }
-}
-
-TEST_F(ParameterTests, ParameterAsUInts) {
-    std::vector<unsigned int> ref = {1, 2, 3, 4, 5};
-    Parameter p = ref;
-    ASSERT_TRUE(p.is<std::vector<unsigned int>>());
-    std::vector<unsigned int> test = p;
-    ASSERT_EQ(ref.size(), test.size());
-    for (size_t i = 0; i < ref.size(); i++) {
-        ASSERT_EQ(ref[i], test[i]);
-    }
-}
-
-TEST_F(ParameterTests, ParameterAsSize_ts) {
-    std::vector<size_t> ref = {1, 2, 3, 4, 5};
-    Parameter p = ref;
-    ASSERT_TRUE(p.is<std::vector<size_t>>());
-    std::vector<size_t> test = p;
-    ASSERT_EQ(ref.size(), test.size());
-    for (size_t i = 0; i < ref.size(); i++) {
-        ASSERT_EQ(ref[i], test[i]);
-    }
-}
-
-TEST_F(ParameterTests, ParameterAsFloats) {
-    std::vector<float> ref = {1, 2, 3, 4, 5};
-    Parameter p = ref;
-    ASSERT_TRUE(p.is<std::vector<float>>());
-    std::vector<float> test = p;
-    ASSERT_EQ(ref.size(), test.size());
-    for (size_t i = 0; i < ref.size(); i++) {
-        ASSERT_EQ(ref[i], test[i]);
-    }
-}
-
-TEST_F(ParameterTests, ParameterAsStrings) {
-    std::vector<std::string> ref = {"test1", "test2", "test3", "test4", "test1"};
-    Parameter p = ref;
-    ASSERT_TRUE(p.is<std::vector<std::string>>());
-    std::vector<std::string> test = p;
-    ASSERT_EQ(ref.size(), test.size());
-    for (size_t i = 0; i < ref.size(); i++) {
-        ASSERT_EQ(ref[i], test[i]);
-    }
-}
-
-TEST_F(ParameterTests, ParameterAsMapOfParameters) {
-    std::map<std::string, Parameter> refMap;
-    refMap["testParamInt"] = 4;
-    refMap["testParamString"] = "test";
-    Parameter p = refMap;
-    bool isMap = p.is<std::map<std::string, Parameter>>();
-    ASSERT_TRUE(isMap);
-    std::map<std::string, Parameter> testMap = p;
-
-    ASSERT_NE(testMap.find("testParamInt"), testMap.end());
-    ASSERT_NE(testMap.find("testParamString"), testMap.end());
-
-    int testInt = testMap["testParamInt"];
-    std::string testString = testMap["testParamString"];
-
-    ASSERT_EQ(refMap["testParamInt"].as<int>(), testInt);
-    ASSERT_EQ(refMap["testParamString"].as<std::string>(), testString);
-}
-
-TEST_F(ParameterTests, ParameterNotEmpty) {
-    Parameter p = 4;
-    ASSERT_FALSE(p.empty());
-}
-
-TEST_F(ParameterTests, ParameterEmpty) {
-    Parameter p;
-    ASSERT_TRUE(p.empty());
-}
-
-TEST_F(ParameterTests, ParameterClear) {
-    Parameter p = 4;
-    ASSERT_FALSE(p.empty());
-    p.clear();
-    ASSERT_TRUE(p.empty());
-}
-
-TEST_F(ParameterTests, ParametersNotEqualByType) {
-    Parameter p1 = 4;
-    Parameter p2 = "string";
-    ASSERT_TRUE(p1 != p2);
-    ASSERT_FALSE(p1 == p2);
-}
-
-TEST_F(ParameterTests, ParametersNotEqualByValue) {
-    Parameter p1 = 4;
-    Parameter p2 = 5;
-    ASSERT_TRUE(p1 != p2);
-    ASSERT_FALSE(p1 == p2);
-}
-
-TEST_F(ParameterTests, ParametersEqual) {
-    Parameter p1 = 4;
-    Parameter p2 = 4;
-    ASSERT_TRUE(p1 == p2);
-    ASSERT_FALSE(p1 != p2);
-}
-
-TEST_F(ParameterTests, ParametersStringEqual) {
-    std::string s1 = "abc";
-    std::string s2 = std::string("a") + "bc";
-    Parameter p1 = s1;
-    Parameter p2 = s2;
-    ASSERT_TRUE(s1 == s2);
-    ASSERT_TRUE(p1 == p2);
-    ASSERT_FALSE(p1 != p2);
-}
-
-TEST_F(ParameterTests, ParametersCStringEqual) {
-    const char s1[] = "abc";
-    const char s2[] = "abc";
-    Parameter p1 = s1;
-    Parameter p2 = s2;
-    ASSERT_TRUE(s1 != s2);
-    ASSERT_TRUE(p1 == p2);
-    ASSERT_FALSE(p1 != p2);
-}
-
-TEST_F(ParameterTests, MapOfParametersEqual) {
-    std::map<std::string, Parameter> map0;
-    map0["testParamInt"] = 4;
-    map0["testParamString"] = "test";
-    const auto map1 = map0;
-
-    Parameter p0 = map0;
-    Parameter p1 = map1;
-    ASSERT_TRUE(p0 == p1);
-    ASSERT_FALSE(p0 != p1);
-}
-
-TEST_F(ParameterTests, CompareParametersWithoutEqualOperator) {
-    class TestClass {
-    public:
-        TestClass(int test, int* testPtr): test(test), testPtr(testPtr) {}
-
-    private:
-        int test;
-        int* testPtr;
-    };
-
-    TestClass a(2, reinterpret_cast<int*>(0x234));
-    TestClass b(2, reinterpret_cast<int*>(0x234));
-    TestClass c(3, reinterpret_cast<int*>(0x234));
-    Parameter parA = a;
-    Parameter parB = b;
-    Parameter parC = c;
-
-    ASSERT_THROW((void)(parA == parB), ov::Exception);
-    ASSERT_THROW((void)(parA != parB), ov::Exception);
-    ASSERT_THROW((void)(parA == parC), ov::Exception);
-    ASSERT_THROW((void)(parA != parC), ov::Exception);
-}
-
-TEST_F(ParameterTests, ParameterRemovedRealObject) {
-    ASSERT_EQ(0, DestructorTest::constructorCount);
-    ASSERT_EQ(0, DestructorTest::destructorCount);
-    {
-        DestructorTest t;
-        Parameter p1 = t;
-    }
-    ASSERT_EQ(2, DestructorTest::constructorCount);
-    ASSERT_EQ(2, DestructorTest::destructorCount);
-}
-
-TEST_F(ParameterTests, ParameterRemovedRealObjectWithDuplication) {
-    ASSERT_EQ(0, DestructorTest::constructorCount);
-    ASSERT_EQ(0, DestructorTest::destructorCount);
-    {
-        DestructorTest t;
-        Parameter p = t;
-        ASSERT_EQ(0, DestructorTest::destructorCount);
-        p = t;
-        ASSERT_EQ(2, DestructorTest::destructorCount);
-    }
-    ASSERT_EQ(4, DestructorTest::constructorCount);
-    ASSERT_EQ(4, DestructorTest::destructorCount);
-}
-
-TEST_F(ParameterTests, ParameterRemovedRealObjectPointerWithDuplication) {
-    ASSERT_EQ(0, DestructorTest::constructorCount);
-    ASSERT_EQ(0, DestructorTest::destructorCount);
-    {
-        auto * t = new DestructorTest();
-        Parameter p = t;
-        ASSERT_EQ(1, DestructorTest::constructorCount);
-        ASSERT_EQ(0, DestructorTest::destructorCount);
-        p = t;
-        ASSERT_TRUE(p.is<DestructorTest *>());
-        DestructorTest* t2 = p;
-        ASSERT_EQ(0, DestructorTest::destructorCount);
-        delete t;
-        auto * t3 = p.as<DestructorTest *>();
-        ASSERT_EQ(t2, t3);
-    }
-    ASSERT_EQ(1, DestructorTest::constructorCount);
-    ASSERT_EQ(1, DestructorTest::destructorCount);
-}
-
-TEST_F(ParameterTests, PrintToEmptyParameterDoesNothing) {
-    Parameter p;
-    std::stringstream stream;
-    ASSERT_NO_THROW(PrintTo(p, &stream));
-    ASSERT_EQ(stream.str(), std::string{});
-}
-
-TEST_F(ParameterTests, PrintToIntParameter) {
-    int value = -5;
-    Parameter p = value;
-    std::stringstream stream;
-    ASSERT_NO_THROW(PrintTo(p, &stream));
-    ASSERT_EQ(stream.str(), std::to_string(value));
-}
-
-TEST_F(ParameterTests, PrintToUIntParameter) {
-    unsigned int value = 5;
-    Parameter p = value;
-    std::stringstream stream;
-    ASSERT_NO_THROW(PrintTo(p, &stream));
-    ASSERT_EQ(stream.str(), std::to_string(value));
-}
-
-TEST_F(ParameterTests, PrintToSize_tParameter) {
-    std::size_t value = 5;
-    Parameter p = value;
-    std::stringstream stream;
-    ASSERT_NO_THROW(PrintTo(p, &stream));
-    ASSERT_EQ(stream.str(), std::to_string(value));
-}
-
-TEST_F(ParameterTests, PrintToFloatParameter) {
-    Parameter p = 5.5f;
-    std::stringstream stream;
-    ASSERT_NO_THROW(PrintTo(p, &stream));
-    ASSERT_EQ(stream.str(), std::string{"5.5"});
-}
-
-TEST_F(ParameterTests, PrintToStringParameter) {
-    std::string value = "some text";
-    Parameter p = value;
-    std::stringstream stream;
-    ASSERT_NO_THROW(PrintTo(p, &stream));
-    ASSERT_EQ(stream.str(), value);
-}
-
-TEST_F(ParameterTests, PrintToVectorOfIntsParameterDoesNothing) {
-    Parameter p = std::vector<int>{-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5};
-    std::stringstream stream;
-    ASSERT_NO_THROW(PrintTo(p, &stream));
-    ASSERT_EQ(stream.str(), std::string{});
-}
-
-TEST_F(ParameterTests, PrintToVectorOfUIntsParameterDoesNothing) {
-    Parameter p = std::vector<unsigned int>{0, 1, 2, 3, 4, 5};
-    std::stringstream stream;
-    ASSERT_NO_THROW(PrintTo(p, &stream));
-    ASSERT_EQ(stream.str(), std::string{});
-}
-
-TEST_F(ParameterTests, PrintToVectorOfSize_tParameterDoesNothing) {
-    Parameter p = std::vector<std::size_t>{0, 1, 2, 3, 4, 5};
-    std::stringstream stream;
-    ASSERT_NO_THROW(PrintTo(p, &stream));
-    ASSERT_EQ(stream.str(), std::string{});
-}
-
-TEST_F(ParameterTests, PrintToVectorOfFloatsParameterDoesNothing) {
-    Parameter p = std::vector<float>{0.0f, 1.1f, 2.2f, 3.3f, 4.4f, 5.5f};
-    std::stringstream stream;
-    ASSERT_NO_THROW(PrintTo(p, &stream));
-    ASSERT_EQ(stream.str(), std::string{});
-}
-
-TEST_F(ParameterTests, PrintToVectorOfStringsParameterDoesNothing) {
-    Parameter p = std::vector<std::string>{"zero", "one", "two", "three", "four", "five"};
-    std::stringstream stream;
-    ASSERT_NO_THROW(PrintTo(p, &stream));
-    ASSERT_EQ(stream.str(), std::string{});
-}
-
-TEST_F(ParameterTests, PrintToMapOfParametersDoesNothing) {
-    std::map<std::string, Parameter> refMap;
-    refMap["testParamInt"] = 4;
-    refMap["testParamString"] = "test";
-    Parameter p = refMap;
-    std::stringstream stream;
-    ASSERT_NO_THROW(PrintTo(p, &stream));
-    ASSERT_EQ(stream.str(), std::string{});
-}
diff --git a/inference-engine/tests/functional/inference_engine/snippets/registers.cpp b/inference-engine/tests/functional/inference_engine/snippets/registers.cpp
index e76d8318f59..54ce843308a 100644
--- a/inference-engine/tests/functional/inference_engine/snippets/registers.cpp
+++ b/inference-engine/tests/functional/inference_engine/snippets/registers.cpp
@@ -51,8 +51,9 @@ TEST(TransformationTests, AssignRegisters) {
         for (auto& op : f->get_ordered_ops()) {
             auto& rt = op->get_rt_info();
 
-            if (auto rinfo = rt["reginfo"]) {
-                auto reginfo = ov::as_type_ptr<VariantWrapper<std::vector<size_t>>>(rinfo)->get();
+            auto it_rinfo = rt.find("reginfo");
+            if (it_rinfo != rt.end()) {
+                auto reginfo = ov::as_type_ptr<VariantWrapper<std::vector<size_t>>>(it_rinfo->second)->get();
                 auto reg = reginfo[0];
                 ASSERT_TRUE(ref_registers[op->get_friendly_name()] == reg);
                 total_ops++;
@@ -124,9 +125,9 @@ TEST(TransformationTests, AssignRegisters2) {
         auto total_ops = 0;
         for (auto& op : f->get_ordered_ops()) {
             auto& rt = op->get_rt_info();
-
-            if (auto rinfo = rt["reginfo"]) {
-                auto reginfo = ov::as_type_ptr<VariantWrapper<std::vector<size_t>>>(rinfo)->get();
+            auto it_rinfo = rt.find("reginfo");
+            if (it_rinfo != rt.end()) {
+                auto reginfo = ov::as_type_ptr<VariantWrapper<std::vector<size_t>>>(it_rinfo->second)->get();
                 auto reg = reginfo[0];
                 ASSERT_TRUE(ref_registers[op->get_friendly_name()] == reg);
                 total_ops++;
diff --git a/inference-engine/tests/functional/plugin/cpu/test_utils/cpu_test_utils.cpp b/inference-engine/tests/functional/plugin/cpu/test_utils/cpu_test_utils.cpp
index 3af2767bbbc..d92bcdfb1e1 100644
--- a/inference-engine/tests/functional/plugin/cpu/test_utils/cpu_test_utils.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/test_utils/cpu_test_utils.cpp
@@ -266,11 +266,11 @@ CPUTestsBase::makeCPUInfo(std::vector<cpu_memory_format_t> inFmts, std::vector<c
 
     if (!inFmts.empty()) {
         cpuInfo.insert({std::string(ngraph::MLKDNNInputMemoryFormatsAttr),
-                std::make_shared<ngraph::VariantWrapper<ngraph::MLKDNNInputMemoryFormats>>(ngraph::MLKDNNInputMemoryFormats(fmts2str(inFmts, "cpu:")))});
+                std::make_shared<ngraph::MLKDNNInputMemoryFormats>(fmts2str(inFmts, "cpu:"))});
     }
     if (!outFmts.empty()) {
         cpuInfo.insert({std::string(ngraph::MLKDNNOutputMemoryFormatsAttr),
-                std::make_shared<ngraph::VariantWrapper<ngraph::MLKDNNOutputMemoryFormats>>(ngraph::MLKDNNOutputMemoryFormats(fmts2str(outFmts, "cpu:")))});
+                std::make_shared<ngraph::MLKDNNOutputMemoryFormats>(fmts2str(outFmts, "cpu:"))});
     }
     if (!priority.empty()) {
         cpuInfo.insert({"PrimitivesPriority", std::make_shared<ngraph::VariantWrapper<std::string>>(impls2str(priority))});
diff --git a/inference-engine/tests/functional/plugin/cpu/test_utils/cpu_test_utils.hpp b/inference-engine/tests/functional/plugin/cpu/test_utils/cpu_test_utils.hpp
index 08c50459baa..b62e5c6c206 100644
--- a/inference-engine/tests/functional/plugin/cpu/test_utils/cpu_test_utils.hpp
+++ b/inference-engine/tests/functional/plugin/cpu/test_utils/cpu_test_utils.hpp
@@ -116,7 +116,7 @@ namespace CPUTestUtils {
 
 class CPUTestsBase {
 public:
-    typedef std::map<std::string, std::shared_ptr<ngraph::Variant>> CPUInfo;
+    typedef std::map<std::string, ov::Any> CPUInfo;
 
 public:
     static std::string getTestCaseName(CPUSpecificParams params);
diff --git a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_plugin/core_integration.cpp b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_plugin/core_integration.cpp
index 12cc9381901..2ed71181c73 100644
--- a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_plugin/core_integration.cpp
+++ b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/ov_plugin/core_integration.cpp
@@ -76,7 +76,7 @@ INSTANTIATE_TEST_SUITE_P(nightly_OVClassGetAvailableDevices, OVClassGetAvailable
 using OVClassGetMetricTest_GPU_DEVICE_TOTAL_MEM_SIZE = OVClassBaseTestP;
 TEST_P(OVClassGetMetricTest_GPU_DEVICE_TOTAL_MEM_SIZE, GetMetricAndPrintNoThrow) {
     ov::runtime::Core ie;
-    ov::runtime::Parameter p;
+    ov::Any p;
 
     ASSERT_NO_THROW(p = ie.get_metric(deviceName, GPU_METRIC_KEY(DEVICE_TOTAL_MEM_SIZE)));
     uint64_t t = p;
@@ -93,7 +93,7 @@ INSTANTIATE_TEST_SUITE_P(nightly_OVClassGetMetricTest,
 using OVClassGetMetricTest_GPU_UARCH_VERSION = OVClassBaseTestP;
 TEST_P(OVClassGetMetricTest_GPU_UARCH_VERSION, GetMetricAndPrintNoThrow) {
     ov::runtime::Core ie;
-    ov::runtime::Parameter p;
+    ov::Any p;
 
     ASSERT_NO_THROW(p = ie.get_metric(deviceName, GPU_METRIC_KEY(UARCH_VERSION)));
     std::string t = p;
@@ -109,7 +109,7 @@ INSTANTIATE_TEST_SUITE_P(nightly_OVClassGetMetricTest,
 using OVClassGetMetricTest_GPU_EXECUTION_UNITS_COUNT = OVClassBaseTestP;
 TEST_P(OVClassGetMetricTest_GPU_EXECUTION_UNITS_COUNT, GetMetricAndPrintNoThrow) {
     ov::runtime::Core ie;
-    ov::runtime::Parameter p;
+    ov::Any p;
 
     ASSERT_NO_THROW(p = ie.get_metric(deviceName, GPU_METRIC_KEY(EXECUTION_UNITS_COUNT)));
     int t = p;
diff --git a/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/behavior/ov_plugin/core_integration.cpp b/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/behavior/ov_plugin/core_integration.cpp
index ebb6f9819d7..8f6d3bab139 100644
--- a/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/behavior/ov_plugin/core_integration.cpp
+++ b/inference-engine/tests/functional/plugin/myriad/shared_tests_instances/behavior/ov_plugin/core_integration.cpp
@@ -37,7 +37,7 @@ TEST_P(OVClassNetworkTestP_VPU_GetMetric, smoke_OptimizationCapabilitiesReturnsF
     ov::runtime::Core ie;
     ASSERT_METRIC_SUPPORTED(METRIC_KEY(OPTIMIZATION_CAPABILITIES))
 
-    ov::runtime::Parameter optimizationCapabilitiesParameter;
+    ov::Any optimizationCapabilitiesParameter;
     ASSERT_NO_THROW(optimizationCapabilitiesParameter =
                             ie.get_metric(deviceName, METRIC_KEY(OPTIMIZATION_CAPABILITIES)));
 
diff --git a/inference-engine/tests/functional/plugin/shared/include/behavior/ov_executable_network/get_metric.hpp b/inference-engine/tests/functional/plugin/shared/include/behavior/ov_executable_network/get_metric.hpp
index 16cbd649aaf..3cd1e7d58f5 100644
--- a/inference-engine/tests/functional/plugin/shared/include/behavior/ov_executable_network/get_metric.hpp
+++ b/inference-engine/tests/functional/plugin/shared/include/behavior/ov_executable_network/get_metric.hpp
@@ -101,7 +101,7 @@ TEST_P(OVClassImportExportTestP, smoke_ImportNetworkNoThrowWithDeviceName) {
 //
 TEST_P(OVClassExecutableNetworkGetMetricTest_SUPPORTED_CONFIG_KEYS, GetMetricNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     auto exeNetwork = ie.compile_model(simpleNetwork, deviceName);
 
@@ -119,7 +119,7 @@ TEST_P(OVClassExecutableNetworkGetMetricTest_SUPPORTED_CONFIG_KEYS, GetMetricNoT
 
 TEST_P(OVClassExecutableNetworkGetMetricTest_SUPPORTED_METRICS, GetMetricNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     auto exeNetwork = ie.compile_model(simpleNetwork, deviceName);
 
@@ -137,7 +137,7 @@ TEST_P(OVClassExecutableNetworkGetMetricTest_SUPPORTED_METRICS, GetMetricNoThrow
 
 TEST_P(OVClassExecutableNetworkGetMetricTest_NETWORK_NAME, GetMetricNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     auto exeNetwork = ie.compile_model(simpleNetwork, deviceName);
 
@@ -151,7 +151,7 @@ TEST_P(OVClassExecutableNetworkGetMetricTest_NETWORK_NAME, GetMetricNoThrow) {
 
 TEST_P(OVClassExecutableNetworkGetMetricTest_OPTIMAL_NUMBER_OF_INFER_REQUESTS, GetMetricNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     auto exeNetwork = ie.compile_model(simpleNetwork, deviceName);
 
@@ -165,7 +165,7 @@ TEST_P(OVClassExecutableNetworkGetMetricTest_OPTIMAL_NUMBER_OF_INFER_REQUESTS, G
 
 TEST_P(OVClassExecutableNetworkGetMetricTest_ThrowsUnsupported, GetMetricThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     auto exeNetwork = ie.compile_model(simpleNetwork, deviceName);
 
@@ -174,7 +174,7 @@ TEST_P(OVClassExecutableNetworkGetMetricTest_ThrowsUnsupported, GetMetricThrow)
 
 TEST_P(OVClassExecutableNetworkGetConfigTest, GetConfigNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     auto exeNetwork = ie.compile_model(simpleNetwork, deviceName);
 
@@ -182,7 +182,7 @@ TEST_P(OVClassExecutableNetworkGetConfigTest, GetConfigNoThrow) {
     std::vector<std::string> configValues = p;
 
     for (auto&& confKey : configValues) {
-        runtime::Parameter defaultValue;
+        ov::Any defaultValue;
         ASSERT_NO_THROW(defaultValue = ie.get_config(deviceName, confKey));
         ASSERT_FALSE(defaultValue.empty());
     }
@@ -190,7 +190,7 @@ TEST_P(OVClassExecutableNetworkGetConfigTest, GetConfigNoThrow) {
 
 TEST_P(OVClassExecutableNetworkGetConfigTest, GetConfigThrows) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     auto exeNetwork = ie.compile_model(simpleNetwork, deviceName);
 
@@ -199,7 +199,7 @@ TEST_P(OVClassExecutableNetworkGetConfigTest, GetConfigThrows) {
 
 TEST_P(OVClassExecutableNetworkSetConfigTest, SetConfigThrows) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     auto exeNetwork = ie.compile_model(simpleNetwork, deviceName);
 
@@ -208,7 +208,7 @@ TEST_P(OVClassExecutableNetworkSetConfigTest, SetConfigThrows) {
 
 TEST_P(OVClassExecutableNetworkSupportedConfigTest, SupportedConfigWorks) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     auto exeNetwork = ie.compile_model(simpleNetwork, deviceName);
 
@@ -227,7 +227,7 @@ TEST_P(OVClassExecutableNetworkUnsupportedConfigTest, UnsupportedConfigThrows) {
 
 TEST_P(OVClassExecutableNetworkGetConfigTest, GetConfigNoEmptyNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     ASSERT_NO_THROW(p = ie.get_metric(deviceName, METRIC_KEY(SUPPORTED_CONFIG_KEYS)));
     std::vector<std::string> devConfigValues = p;
@@ -241,15 +241,15 @@ TEST_P(OVClassExecutableNetworkGetConfigTest, GetConfigNoEmptyNoThrow) {
     for (auto && configKey : devConfigValues) {
         ASSERT_NE(execConfigValues.end(), std::find(execConfigValues.begin(), execConfigValues.end(), configKey));
 
-        runtime::Parameter configValue;
-        ASSERT_NO_THROW(Parameter configValue = exeNetwork.get_config(configKey));
+        ov::Any configValue;
+        ASSERT_NO_THROW(ov::Any configValue = exeNetwork.get_config(configKey));
     }
     */
 }
 
 TEST_P(OVClassHeteroExecutableNetworkGetMetricTest_SUPPORTED_CONFIG_KEYS, GetMetricNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter pHetero, pDevice;
+    ov::Any pHetero, pDevice;
 
     auto heteroExeNetwork = ie.compile_model(actualNetwork, heteroDeviceName);
     auto deviceExeNetwork = ie.compile_model(actualNetwork, deviceName);
@@ -270,8 +270,8 @@ TEST_P(OVClassHeteroExecutableNetworkGetMetricTest_SUPPORTED_CONFIG_KEYS, GetMet
         auto it = std::find(heteroConfigValues.begin(), heteroConfigValues.end(), deviceConf);
         ASSERT_TRUE(it != heteroConfigValues.end());
 
-        runtime::Parameter heteroConfigValue = heteroExeNetwork.get_config(deviceConf);
-        runtime::Parameter deviceConfigValue = deviceExeNetwork.get_config(deviceConf);
+        ov::Any heteroConfigValue = heteroExeNetwork.get_config(deviceConf);
+        ov::Any deviceConfigValue = deviceExeNetwork.get_config(deviceConf);
 
         // HETERO returns EXCLUSIVE_ASYNC_REQUESTS as a boolean value
         if (CONFIG_KEY(EXCLUSIVE_ASYNC_REQUESTS) != deviceConf) {
@@ -282,7 +282,7 @@ TEST_P(OVClassHeteroExecutableNetworkGetMetricTest_SUPPORTED_CONFIG_KEYS, GetMet
 
 TEST_P(OVClassHeteroExecutableNetworkGetMetricTest_SUPPORTED_METRICS, GetMetricNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter pHetero, pDevice;
+    ov::Any pHetero, pDevice;
 
     auto heteroExeNetwork = ie.compile_model(actualNetwork, heteroDeviceName);
     auto deviceExeNetwork = ie.compile_model(actualNetwork, deviceName);
@@ -306,8 +306,8 @@ TEST_P(OVClassHeteroExecutableNetworkGetMetricTest_SUPPORTED_METRICS, GetMetricN
         auto it = std::find(heteroMetricValues.begin(), heteroMetricValues.end(), deviceMetricName);
         ASSERT_TRUE(it != heteroMetricValues.end());
 
-        runtime::Parameter heteroMetricValue = heteroExeNetwork.get_metric(deviceMetricName);
-        runtime::Parameter deviceMetricValue = deviceExeNetwork.get_metric(deviceMetricName);
+        ov::Any heteroMetricValue = heteroExeNetwork.get_metric(deviceMetricName);
+        ov::Any deviceMetricValue = deviceExeNetwork.get_metric(deviceMetricName);
 
         if (std::find(heteroSpecificMetrics.begin(), heteroSpecificMetrics.end(), deviceMetricName) ==
             heteroSpecificMetrics.end()) {
@@ -318,7 +318,7 @@ TEST_P(OVClassHeteroExecutableNetworkGetMetricTest_SUPPORTED_METRICS, GetMetricN
 
 TEST_P(OVClassHeteroExecutableNetworkGetMetricTest_NETWORK_NAME, GetMetricNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     auto exeNetwork = ie.compile_model(actualNetwork, heteroDeviceName);
 
@@ -330,7 +330,7 @@ TEST_P(OVClassHeteroExecutableNetworkGetMetricTest_NETWORK_NAME, GetMetricNoThro
 
 TEST_P(OVClassHeteroExecutableNetworkGetMetricTest_TARGET_FALLBACK, GetMetricNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     setHeteroNetworkAffinity(deviceName);
 
diff --git a/inference-engine/tests/functional/plugin/shared/include/behavior/ov_plugin/core_integration.hpp b/inference-engine/tests/functional/plugin/shared/include/behavior/ov_plugin/core_integration.hpp
index 1b1704b472c..6b301c27a68 100644
--- a/inference-engine/tests/functional/plugin/shared/include/behavior/ov_plugin/core_integration.hpp
+++ b/inference-engine/tests/functional/plugin/shared/include/behavior/ov_plugin/core_integration.hpp
@@ -470,7 +470,7 @@ TEST_P(OVClassNetworkTestP, QueryNetworkMultiThrows) {
 
 TEST(OVClassBasicTest, smoke_GetMetricSupportedMetricsHeteroNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
     std::string deviceName = CommonTestUtils::DEVICE_HETERO;
 
     ASSERT_NO_THROW(p = ie.get_metric(deviceName, METRIC_KEY(SUPPORTED_METRICS)));
@@ -486,7 +486,7 @@ TEST(OVClassBasicTest, smoke_GetMetricSupportedMetricsHeteroNoThrow) {
 
 TEST(OVClassBasicTest, smoke_GetMetricSupportedConfigKeysHeteroNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
     std::string deviceName = CommonTestUtils::DEVICE_HETERO;
 
     ASSERT_NO_THROW(p = ie.get_metric(deviceName, METRIC_KEY(SUPPORTED_CONFIG_KEYS)));
@@ -509,7 +509,7 @@ TEST(OVClassBasicTest, smoke_GetMetricSupportedConfigKeysHeteroThrows) {
 
 TEST_P(OVClassGetMetricTest_SUPPORTED_METRICS, GetMetricAndPrintNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     ASSERT_NO_THROW(p = ie.get_metric(deviceName, METRIC_KEY(SUPPORTED_METRICS)));
     std::vector<std::string> t = p;
@@ -524,7 +524,7 @@ TEST_P(OVClassGetMetricTest_SUPPORTED_METRICS, GetMetricAndPrintNoThrow) {
 
 TEST_P(OVClassGetMetricTest_SUPPORTED_CONFIG_KEYS, GetMetricAndPrintNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     ASSERT_NO_THROW(p = ie.get_metric(deviceName, METRIC_KEY(SUPPORTED_CONFIG_KEYS)));
     std::vector<std::string> t = p;
@@ -539,7 +539,7 @@ TEST_P(OVClassGetMetricTest_SUPPORTED_CONFIG_KEYS, GetMetricAndPrintNoThrow) {
 
 TEST_P(OVClassGetMetricTest_AVAILABLE_DEVICES, GetMetricAndPrintNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     ASSERT_NO_THROW(p = ie.get_metric(deviceName, METRIC_KEY(AVAILABLE_DEVICES)));
     std::vector<std::string> t = p;
@@ -554,7 +554,7 @@ TEST_P(OVClassGetMetricTest_AVAILABLE_DEVICES, GetMetricAndPrintNoThrow) {
 
 TEST_P(OVClassGetMetricTest_FULL_DEVICE_NAME, GetMetricAndPrintNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     ASSERT_NO_THROW(p = ie.get_metric(deviceName, METRIC_KEY(FULL_DEVICE_NAME)));
     std::string t = p;
@@ -565,7 +565,7 @@ TEST_P(OVClassGetMetricTest_FULL_DEVICE_NAME, GetMetricAndPrintNoThrow) {
 
 TEST_P(OVClassGetMetricTest_OPTIMIZATION_CAPABILITIES, GetMetricAndPrintNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     ASSERT_NO_THROW(p = ie.get_metric(deviceName, METRIC_KEY(OPTIMIZATION_CAPABILITIES)));
     std::vector<std::string> t = p;
@@ -580,7 +580,7 @@ TEST_P(OVClassGetMetricTest_OPTIMIZATION_CAPABILITIES, GetMetricAndPrintNoThrow)
 
 TEST_P(OVClassGetMetricTest_DEVICE_GOPS, GetMetricAndPrintNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     ASSERT_NO_THROW(p = ie.get_metric(deviceName, METRIC_KEY(DEVICE_GOPS)));
     std::map<InferenceEngine::Precision, float> t = p;
@@ -595,7 +595,7 @@ TEST_P(OVClassGetMetricTest_DEVICE_GOPS, GetMetricAndPrintNoThrow) {
 
 TEST_P(OVClassGetMetricTest_DEVICE_TYPE, GetMetricAndPrintNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     ASSERT_NO_THROW(p = ie.get_metric(deviceName, METRIC_KEY(DEVICE_TYPE)));
     InferenceEngine::Metrics::DeviceType t = p;
@@ -607,7 +607,7 @@ TEST_P(OVClassGetMetricTest_DEVICE_TYPE, GetMetricAndPrintNoThrow) {
 
 TEST_P(OVClassGetMetricTest_NUMBER_OF_WAITING_INFER_REQUESTS, GetMetricAndPrintNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     ASSERT_NO_THROW(p = ie.get_metric(deviceName, METRIC_KEY(NUMBER_OF_WAITING_INFER_REQUESTS)));
     unsigned int t = p;
@@ -619,7 +619,7 @@ TEST_P(OVClassGetMetricTest_NUMBER_OF_WAITING_INFER_REQUESTS, GetMetricAndPrintN
 
 TEST_P(OVClassGetMetricTest_NUMBER_OF_EXEC_INFER_REQUESTS, GetMetricAndPrintNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     ASSERT_NO_THROW(p = ie.get_metric(deviceName, METRIC_KEY(NUMBER_OF_EXEC_INFER_REQUESTS)));
     unsigned int t = p;
@@ -631,7 +631,7 @@ TEST_P(OVClassGetMetricTest_NUMBER_OF_EXEC_INFER_REQUESTS, GetMetricAndPrintNoTh
 
 TEST_P(OVClassGetMetricTest_RANGE_FOR_ASYNC_INFER_REQUESTS, GetMetricAndPrintNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     ASSERT_NO_THROW(p = ie.get_metric(deviceName, METRIC_KEY(RANGE_FOR_ASYNC_INFER_REQUESTS)));
     std::tuple<unsigned int, unsigned int, unsigned int> t = p;
@@ -653,7 +653,7 @@ TEST_P(OVClassGetMetricTest_RANGE_FOR_ASYNC_INFER_REQUESTS, GetMetricAndPrintNoT
 
 TEST_P(OVClassGetMetricTest_RANGE_FOR_STREAMS, GetMetricAndPrintNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     ASSERT_NO_THROW(p = ie.get_metric(deviceName, METRIC_KEY(RANGE_FOR_STREAMS)));
     std::tuple<unsigned int, unsigned int> t = p;
@@ -672,20 +672,20 @@ TEST_P(OVClassGetMetricTest_RANGE_FOR_STREAMS, GetMetricAndPrintNoThrow) {
 
 TEST_P(OVClassGetMetricTest_ThrowUnsupported, GetMetricThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     ASSERT_THROW(p = ie.get_metric(deviceName, "unsupported_metric"), ov::Exception);
 }
 
 TEST_P(OVClassGetConfigTest, GetConfigNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     ASSERT_NO_THROW(p = ie.get_metric(deviceName, METRIC_KEY(SUPPORTED_CONFIG_KEYS)));
     std::vector<std::string> configValues = p;
 
     for (auto&& confKey : configValues) {
-        runtime::Parameter defaultValue;
+        ov::Any defaultValue;
         ASSERT_NO_THROW(defaultValue = ie.get_config(deviceName, confKey));
         ASSERT_FALSE(defaultValue.empty());
     }
@@ -693,7 +693,7 @@ TEST_P(OVClassGetConfigTest, GetConfigNoThrow) {
 
 TEST_P(OVClassGetConfigTest, GetConfigHeteroNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     ASSERT_NO_THROW(p = ie.get_metric(deviceName, METRIC_KEY(SUPPORTED_CONFIG_KEYS)));
     std::vector<std::string> configValues = p;
@@ -705,14 +705,14 @@ TEST_P(OVClassGetConfigTest, GetConfigHeteroNoThrow) {
 
 TEST_P(OVClassGetConfigTest_ThrowUnsupported, GetConfigHeteroThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     ASSERT_THROW(p = ie.get_config(CommonTestUtils::DEVICE_HETERO, "unsupported_config"), ov::Exception);
 }
 
 TEST_P(OVClassGetConfigTest_ThrowUnsupported, GetConfigHeteroWithDeviceThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     ASSERT_THROW(p = ie.get_config(CommonTestUtils::DEVICE_HETERO + std::string(":") + deviceName,
                                    HETERO_CONFIG_KEY(DUMP_GRAPH_DOT)),
@@ -721,14 +721,14 @@ TEST_P(OVClassGetConfigTest_ThrowUnsupported, GetConfigHeteroWithDeviceThrow) {
 
 TEST_P(OVClassGetConfigTest_ThrowUnsupported, GetConfigThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     ASSERT_THROW(p = ie.get_config(deviceName, "unsupported_config"), ov::Exception);
 }
 
 TEST_P(OVClassSpecificDeviceTestGetConfig, GetConfigSpecificDeviceNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
-    runtime::Parameter p;
+    ov::Any p;
 
     std::string deviceID, clearDeviceName;
     auto pos = deviceName.find('.');
@@ -748,7 +748,7 @@ TEST_P(OVClassSpecificDeviceTestGetConfig, GetConfigSpecificDeviceNoThrow) {
     std::vector<std::string> configValues = p;
 
     for (auto &&confKey : configValues) {
-        runtime::Parameter defaultValue;
+        ov::Any defaultValue;
         ASSERT_NO_THROW(defaultValue = ie.get_config(deviceName, confKey));
         ASSERT_FALSE(defaultValue.empty());
     }
@@ -1152,7 +1152,7 @@ TEST_P(OVClassSetGlobalConfigTest, SetGlobalConfigNoThrow) {
     ov::runtime::Core ie = createCoreWithTemplate();
 
     std::vector<std::string> deviceIDs = ie.get_metric(deviceName, METRIC_KEY(AVAILABLE_DEVICES));
-    runtime::Parameter ref, src;
+    ov::Any ref, src;
     for (auto& dev_id : deviceIDs) {
         ASSERT_NO_THROW(ie.set_config({{ InferenceEngine::PluginConfigParams::KEY_PERF_COUNT, InferenceEngine::PluginConfigParams::NO }},
                                       deviceName + "." + dev_id));
diff --git a/inference-engine/tests/functional/plugin/shared/include/behavior/ov_remote.hpp b/inference-engine/tests/functional/plugin/shared/include/behavior/ov_remote.hpp
index b6339de49a7..9988cc7c2b4 100644
--- a/inference-engine/tests/functional/plugin/shared/include/behavior/ov_remote.hpp
+++ b/inference-engine/tests/functional/plugin/shared/include/behavior/ov_remote.hpp
@@ -11,6 +11,7 @@
 #include "openvino/runtime/executable_network.hpp"
 #include "openvino/op/parameter.hpp"
 #include "functional_test_utils/ov_plugin_cache.hpp"
+#include "common_test_utils/ov_common_utils.hpp"
 
 namespace ov {
 namespace test {
diff --git a/inference-engine/tests/functional/plugin/shared/src/behavior/caching/caching_tests.cpp b/inference-engine/tests/functional/plugin/shared/src/behavior/caching/caching_tests.cpp
index 8a6918a51d1..734630d61e5 100644
--- a/inference-engine/tests/functional/plugin/shared/src/behavior/caching/caching_tests.cpp
+++ b/inference-engine/tests/functional/plugin/shared/src/behavior/caching/caching_tests.cpp
@@ -121,7 +121,7 @@ bool LoadNetworkCacheTestBase::importExportSupported(InferenceEngine::Core& ie)
     auto it = std::find(supportedMetricKeys.begin(), supportedMetricKeys.end(),
                         METRIC_KEY(IMPORT_EXPORT_SUPPORT));
     bool supported = (it != supportedMetricKeys.end()) &&
-                     ie.GetMetric(targetDevice, METRIC_KEY(IMPORT_EXPORT_SUPPORT));
+                     ie.GetMetric(targetDevice, METRIC_KEY(IMPORT_EXPORT_SUPPORT)).as<bool>();
     return supported;
 }
 
diff --git a/inference-engine/tests/ie_test_utils/common_test_utils/ov_common_utils.hpp b/inference-engine/tests/ie_test_utils/common_test_utils/ov_common_utils.hpp
new file mode 100644
index 00000000000..728d335c2dc
--- /dev/null
+++ b/inference-engine/tests/ie_test_utils/common_test_utils/ov_common_utils.hpp
@@ -0,0 +1,27 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <algorithm>
+#include <string>
+#include <sstream>
+#include <iterator>
+#include <vector>
+#include <set>
+#include <chrono>
+#include <ostream>
+#include <memory>
+
+#include <cpp/ie_cnn_network.h>
+#include <ngraph/function.hpp>
+#include <openvino/core/any.hpp>
+
+namespace ov {
+namespace test {
+    static void PrintTo(const Any& any, std::ostream* os) {
+        any.print(*os);
+    }
+}  // namespace test
+}  // namespace ov
diff --git a/inference-engine/tests/unit/inference_engine/ie_executable_network_test.cpp b/inference-engine/tests/unit/inference_engine/ie_executable_network_test.cpp
index 3595c5194f4..9c1e54a5d60 100644
--- a/inference-engine/tests/unit/inference_engine/ie_executable_network_test.cpp
+++ b/inference-engine/tests/unit/inference_engine/ie_executable_network_test.cpp
@@ -29,7 +29,7 @@ using testing::SetArgReferee;
 //  1. void Export(const std::string& modelFileName)
 //  2. void Export(std::ostream& networkModel)
 //  4. CNNNetwork GetExecGraphInfo()
-//  5. void SetConfig(const std::map<std::string, Parameter>& config)
+//  5. void SetConfig(const std::map<std::string, Any>& config)
 //  6. Parameter GetConfig(const std::string& name) const
 //  7. Parameter GetMetric(const std::string& name) const
 //  8. RemoteContext::Ptr GetContext()
diff --git a/ngraph/core/include/ngraph/node.hpp b/ngraph/core/include/ngraph/node.hpp
index 631bb424a7b..f8ed87f5a55 100644
--- a/ngraph/core/include/ngraph/node.hpp
+++ b/ngraph/core/include/ngraph/node.hpp
@@ -36,6 +36,7 @@
 #include "ngraph/strides.hpp"
 #include "ngraph/type.hpp"
 #include "ngraph/variant.hpp"
+#include "openvino/core/any.hpp"
 #include "openvino/core/node.hpp"
 
 namespace ov {
@@ -63,7 +64,7 @@ using ov::op::v0::Result;
 }
 }  // namespace op
 
-using EvaluationContext = std::map<std::string, std::shared_ptr<Variant>>;
+using EvaluationContext = ov::EvaluationContext;
 using ResultVector = std::vector<std::shared_ptr<ngraph::op::v0::Result>>;
 
 const auto node_validation_failure_loc_string = ov::node_validation_failure_loc_string;
diff --git a/ngraph/core/include/ngraph/variant.hpp b/ngraph/core/include/ngraph/variant.hpp
index cf42b298296..a203a35d2df 100644
--- a/ngraph/core/include/ngraph/variant.hpp
+++ b/ngraph/core/include/ngraph/variant.hpp
@@ -7,6 +7,7 @@
 #include <map>
 #include <string>
 
+#include "openvino/core/any.hpp"  // used for ov::RTMap
 #include "openvino/core/variant.hpp"
 
 namespace ov {
diff --git a/ngraph/core/include/openvino/core/any.hpp b/ngraph/core/include/openvino/core/any.hpp
new file mode 100644
index 00000000000..db9ad76ee0c
--- /dev/null
+++ b/ngraph/core/include/openvino/core/any.hpp
@@ -0,0 +1,638 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+/**
+ * @brief A header file for the Any class
+ * @file openvino/runtime/any.hpp
+ */
+#pragma once
+
+#include <map>
+#include <memory>
+#include <string>
+#include <typeinfo>
+#include <unordered_map>
+#include <utility>
+
+#include "openvino/core/attribute_visitor.hpp"
+#include "openvino/core/except.hpp"
+#include "openvino/core/variant.hpp"
+
+namespace InferenceEngine {
+class InferencePlugin;
+class ExecutableNetwork;
+}  // namespace InferenceEngine
+
+namespace ov {
+
+class Node;
+class Variant;
+class ParamMap;
+
+namespace runtime {
+class ExecutableNetwork;
+class RemoteContext;
+class RemoteTensor;
+class InferencePlugin;
+}  // namespace runtime
+
+/**
+ * @brief This class represents an object to work with different types
+ */
+class OPENVINO_API Any {
+    std::shared_ptr<void> _so;
+
+    template <typename T>
+    using decay_t = typename std::decay<T>::type;
+
+    template <typename T>
+    struct IsNullPointer : std::is_same<std::nullptr_t, typename std::remove_cv<T>::type> {};
+
+    template <class T>
+    struct Ostreamable {
+        template <class U>
+        static auto test(U*) -> decltype(std::declval<std::ostream&>() << std::declval<U>(), std::true_type()) {
+            return {};
+        }
+        template <typename>
+        static auto test(...) -> std::false_type {
+            return {};
+        }
+        constexpr static const auto value = std::is_same<std::true_type, decltype(test<T>(nullptr))>::value;
+    };
+
+    template <typename T>
+    struct EqualityComparable {
+        static void* conv(bool);
+        template <typename U>
+        static char test(decltype(conv(std::declval<U>() == std::declval<U>())));
+        template <typename U>
+        static long test(...);
+        constexpr static const bool value = sizeof(test<T>(nullptr)) == sizeof(char);
+    };
+
+    template <typename... T>
+    struct EqualityComparable<std::map<T...>> {
+        static void* conv(bool);
+        template <typename U>
+        static char test(decltype(conv(std::declval<typename U::key_type>() == std::declval<typename U::key_type>() &&
+                                       std::declval<typename U::mapped_type>() ==
+                                           std::declval<typename U::mapped_type>())));
+        template <typename U>
+        static long test(...);
+        constexpr static const bool value = sizeof(test<std::map<T...>>(nullptr)) == sizeof(char);
+    };
+
+    /**
+     * @brief Base API of erased type
+     */
+    class OPENVINO_API Base : public std::enable_shared_from_this<Base> {
+    public:
+        void type_check(const std::type_info&) const;
+
+        using Ptr = std::shared_ptr<Base>;
+        virtual const std::type_info& type_info() const = 0;
+        virtual const void* addressof() const = 0;
+        void* addressof() {
+            return const_cast<void*>(const_cast<const Base*>(this)->addressof());
+        }
+        virtual Base::Ptr copy() const = 0;
+        virtual bool equal(const Base& rhs) const = 0;
+        virtual void print(std::ostream&) const = 0;
+
+        virtual const DiscreteTypeInfo& get_type_info() const = 0;
+        virtual std::shared_ptr<Variant> as_variant() const;
+        virtual bool is_copyable() const;
+        virtual Any init(const std::shared_ptr<Node>& node);
+        virtual Any merge(const std::vector<std::shared_ptr<Node>>& nodes);
+        virtual std::string to_string();
+        std::string to_string() const;
+        virtual bool visit_attributes(AttributeVisitor&);
+        bool visit_attributes(AttributeVisitor&) const;
+
+        template <class T>
+        bool is() const {
+            return typeid(decay_t<T>) == type_info();
+        }
+
+        template <class T>
+        T& as() & {
+            type_check(typeid(decay_t<T>));
+            return *static_cast<decay_t<T>*>(addressof());
+        }
+
+        template <class T>
+        const T& as() const& {
+            type_check(typeid(decay_t<T>));
+            return *static_cast<const decay_t<T>*>(addressof());
+        }
+
+    protected:
+        ~Base() = default;
+    };
+
+    template <class T, typename = void>
+    struct Impl;
+    template <class T>
+    struct Impl<T, typename std::enable_if<std::is_convertible<T, std::shared_ptr<Variant>>::value>::type>
+        : public Base {
+        const DiscreteTypeInfo& get_type_info() const override {
+            return static_cast<Variant*>(variant.get())->get_type_info();
+        }
+        std::shared_ptr<Variant> as_variant() const override {
+            return std::dynamic_pointer_cast<Variant>(variant);
+        }
+        bool is_copyable() const override {
+            return static_cast<Variant*>(variant.get())->is_copyable();
+        }
+        Any init(const std::shared_ptr<Node>& node) override {
+            return static_cast<Variant*>(variant.get())->init(node);
+        }
+        Any merge(const std::vector<std::shared_ptr<Node>>& nodes) override {
+            return static_cast<Variant*>(variant.get())->merge(nodes);
+        }
+        std::string to_string() override {
+            return static_cast<Variant*>(variant.get())->to_string();
+        }
+        bool visit_attributes(AttributeVisitor& visitor) override {
+            return static_cast<Variant*>(variant.get())->visit_attributes(visitor);
+        }
+
+        Impl(const T& variant_) : variant{variant_} {}
+
+        const std::type_info& type_info() const override {
+            return typeid(T);
+        }
+
+        const void* addressof() const override {
+            return std::addressof(variant);
+        }
+
+        Base::Ptr copy() const override {
+            return std::make_shared<Impl<T>>(this->variant);
+        }
+
+        template <class U>
+        static typename std::enable_if<EqualityComparable<U>::value, bool>::type equal_impl(const U& rhs,
+                                                                                            const U& lhs) {
+            return rhs == lhs;
+        }
+        template <class U>
+        [[noreturn]] static typename std::enable_if<!EqualityComparable<U>::value, bool>::type equal_impl(const U&,
+                                                                                                          const U&) {
+            throw ov::Exception{"Could not compare types without equality operator"};
+        }
+
+        bool equal(const Base& rhs) const override {
+            if (rhs.is<T>()) {
+                return equal_impl(this->variant, rhs.as<T>());
+            }
+            return false;
+        }
+
+        void print(std::ostream& os) const override {
+            os << variant->to_string();
+        }
+
+        T variant;
+    };
+
+    template <class T>
+    struct Impl<T, typename std::enable_if<!std::is_convertible<T, std::shared_ptr<Variant>>::value>::type>
+        : public Base {
+        OPENVINO_RTTI(typeid(T).name());
+
+        template <typename... Args>
+        Impl(Args&&... args) : value(std::forward<Args>(args)...) {}
+
+        const std::type_info& type_info() const override {
+            return typeid(T);
+        }
+
+        const void* addressof() const override {
+            return std::addressof(value);
+        }
+
+        Base::Ptr copy() const override {
+            return std::make_shared<Impl<T>>(this->value);
+        }
+
+        template <class U>
+        static typename std::enable_if<EqualityComparable<U>::value, bool>::type equal_impl(const U& rhs,
+                                                                                            const U& lhs) {
+            return rhs == lhs;
+        }
+        template <class U>
+        [[noreturn]] static typename std::enable_if<!EqualityComparable<U>::value, bool>::type equal_impl(const U&,
+                                                                                                          const U&) {
+            throw ov::Exception{"Could not compare types without equality operator"};
+        }
+
+        bool equal(const Base& rhs) const override {
+            if (rhs.is<T>()) {
+                return equal_impl(this->value, rhs.as<T>());
+            }
+            return false;
+        }
+
+        template <class U>
+        static typename std::enable_if<Ostreamable<U>::value>::type print_impl(std::ostream& os, const U& value) {
+            os << value;
+        }
+
+        template <class U>
+        static typename std::enable_if<!Ostreamable<U>::value>::type print_impl(std::ostream&, const U&) {}
+
+        void print(std::ostream& os) const override {
+            print_impl(os, value);
+        }
+
+        T value;
+    };
+
+    friend class ::ov::Variant;
+    friend class ::ov::ParamMap;
+    friend class ::InferenceEngine::InferencePlugin;
+    friend class ::InferenceEngine::ExecutableNetwork;
+    friend class ::ov::runtime::ExecutableNetwork;
+    friend class ::ov::runtime::RemoteContext;
+    friend class ::ov::runtime::RemoteTensor;
+    friend class ::ov::runtime::InferencePlugin;
+
+    Any(const std::shared_ptr<void>& so, const Any& other);
+
+    void impl_check() const;
+
+    mutable Base::Ptr _variant_impl;
+
+    Base::Ptr _impl;
+
+public:
+    /**
+     * @brief Default constructor
+     */
+    Any() = default;
+
+    /**
+     * @brief Constructor creates any with object
+     *
+     * @tparam T Any type
+     * @param value object
+     */
+    template <typename T,
+              typename std::enable_if<!std::is_same<decay_t<T>, Any>::value && !std::is_abstract<decay_t<T>>::value &&
+                                          !std::is_convertible<decay_t<T>, Base::Ptr>::value,
+                                      bool>::type = true>
+    Any(T&& value) : _impl{std::make_shared<Impl<decay_t<T>>>(std::forward<T>(value))} {}
+
+    /**
+     * @brief Constructor creates string any from char *
+     *
+     * @param str char array
+     */
+    Any(const char* str);
+
+    /**
+     * @brief Empty constructor
+     *
+     */
+    Any(const std::nullptr_t);
+
+    /**
+     * @brief Inplace value construction function
+     *
+     * @tparam T Any type
+     * @tparam Args pack of paramter types passed to T constructor
+     * @param args pack of paramters passed to T constructor
+     */
+    template <typename T, typename... Args>
+    static Any make(Args&&... args) {
+        Any any;
+        any._impl = std::make_shared<Impl<decay_t<T>>>(std::forward<Args>(args)...);
+        return any;
+    }
+
+    /**
+     * Checks that any contains a value
+     * @return false if any contains a value else false
+     */
+    bool empty() const;
+
+    /**
+     * @brief check the type of value in any
+     * @tparam T Type of value
+     * @return true if type of value is correct
+     */
+    template <class T>
+    typename std::enable_if<!std::is_convertible<T, std::shared_ptr<Variant>>::value, bool>::type is() const {
+        if (_impl != nullptr) {
+            return _impl->type_info() == typeid(decay_t<T>);
+        } else {
+            return false;
+        }
+    }
+
+    /**
+     * @brief check the type of value in any
+     * @tparam T Type of value
+     * @return true if type of value is correct
+     */
+    template <class T>
+    typename std::enable_if<std::is_convertible<T, std::shared_ptr<Variant>>::value, bool>::type is() const {
+        if (_impl != nullptr) {
+            if (_impl->type_info() == typeid(decay_t<T>)) {
+                return true;
+            } else {
+                auto variant = _impl->as_variant();
+                if (variant == nullptr) {
+                    return false;
+                } else {
+                    return std::dynamic_pointer_cast<typename T::element_type>(variant) != nullptr;
+                }
+            }
+        } else {
+            return false;
+        }
+    }
+
+    /**
+     * Dynamic cast to specified type
+     * @tparam T type
+     * @return casted object
+     */
+    template <typename T>
+    typename std::enable_if<std::is_convertible<T, std::shared_ptr<Variant>>::value, T>::type&& as() && {
+        if (_impl == nullptr) {
+            _variant_impl = std::make_shared<Impl<decay_t<T>>>(T{});
+            return _variant_impl->as<T>();
+        } else {
+            if (_impl->type_info() == typeid(decay_t<T>)) {
+                return std::move(*static_cast<decay_t<T>*>(_impl->addressof()));
+            } else {
+                auto variant = _impl->as_variant();
+                if (variant == nullptr) {
+                    throw ov::Exception{std::string{"Any does not contains pointer to variant. It contains "} +
+                                        _impl->type_info().name()};
+                }
+                auto vptr = std::dynamic_pointer_cast<typename T::element_type>(variant);
+                if (vptr == nullptr && T::element_type::get_type_info_static() != variant->get_type_info() &&
+                    T::element_type::get_type_info_static() != Variant::get_type_info_static()) {
+                    throw ov::Exception{std::string{"Could not cast Any variant to "} + typeid(T).name() + " from " +
+                                        _impl->type_info().name() + "; from " +
+                                        static_cast<std::string>(variant->get_type_info()) + " to " +
+                                        static_cast<std::string>(T::element_type::get_type_info_static())};
+                }
+                vptr = std::static_pointer_cast<typename T::element_type>(variant);
+                _variant_impl = std::make_shared<Impl<decay_t<T>>>(vptr);
+                return _variant_impl->as<T>();
+            }
+        }
+    }
+
+    /**
+     * Dynamic cast to specified type
+     * @tparam T type
+     * @return casted object
+     */
+    template <class T>
+    typename std::enable_if<std::is_convertible<T, std::shared_ptr<Variant>>::value, T>::type& as() & {
+        if (_impl == nullptr) {
+            _variant_impl = std::make_shared<Impl<decay_t<T>>>(T{});
+            return _variant_impl->as<T>();
+        } else {
+            if (_impl->type_info() == typeid(decay_t<T>)) {
+                return *static_cast<decay_t<T>*>(_impl->addressof());
+            } else {
+                auto variant = _impl->as_variant();
+                if (variant == nullptr) {
+                    throw ov::Exception{std::string{"Any does not contains pointer to variant. It contains "} +
+                                        _impl->type_info().name()};
+                }
+                auto vptr = std::dynamic_pointer_cast<typename T::element_type>(variant);
+                if (vptr == nullptr && T::element_type::get_type_info_static() != variant->get_type_info() &&
+                    T::element_type::get_type_info_static() != Variant::get_type_info_static()) {
+                    throw ov::Exception{std::string{"Could not cast Any variant to "} + typeid(T).name() + " from " +
+                                        _impl->type_info().name() + "; from " +
+                                        static_cast<std::string>(variant->get_type_info()) + " to " +
+                                        static_cast<std::string>(T::element_type::get_type_info_static())};
+                }
+                vptr = std::static_pointer_cast<typename T::element_type>(variant);
+                _variant_impl = std::make_shared<Impl<decay_t<T>>>(vptr);
+                return _variant_impl->as<T>();
+            }
+        }
+    }
+
+    /**
+     * Dynamic cast to specified type
+     * @tparam T type
+     * @return casted object
+     */
+    template <class T>
+    const typename std::enable_if<std::is_convertible<T, std::shared_ptr<Variant>>::value, T>::type& as() const& {
+        if (_impl == nullptr) {
+            _variant_impl = std::make_shared<Impl<decay_t<T>>>(T{});
+            return _variant_impl->as<T>();
+        } else {
+            if (_impl->type_info() == typeid(decay_t<T>)) {
+                return *static_cast<const decay_t<T>*>(_impl->addressof());
+            } else {
+                auto variant = _impl->as_variant();
+                if (variant == nullptr) {
+                    throw ov::Exception{std::string{"Any does not contains pointer to variant. It contains "} +
+                                        _impl->type_info().name()};
+                }
+                auto vptr = std::dynamic_pointer_cast<typename T::element_type>(variant);
+                if (vptr == nullptr && T::element_type::get_type_info_static() != variant->get_type_info() &&
+                    T::element_type::get_type_info_static() != Variant::get_type_info_static()) {
+                    throw ov::Exception{std::string{"Could not cast Any variant to "} + typeid(T).name() + " from " +
+                                        _impl->type_info().name() + "; from " +
+                                        static_cast<std::string>(variant->get_type_info()) + " to " +
+                                        static_cast<std::string>(T::element_type::get_type_info_static())};
+                }
+                vptr = std::static_pointer_cast<typename T::element_type>(variant);
+                _variant_impl = std::make_shared<Impl<decay_t<T>>>(vptr);
+                return _variant_impl->as<T>();
+            }
+        }
+    }
+
+    /**
+     * Dynamic cast to specified type
+     * @tparam T type
+     * @return casted object
+     */
+    template <typename T>
+    typename std::enable_if<!std::is_convertible<T, std::shared_ptr<Variant>>::value, T>::type&& as() && {
+        impl_check();
+        _impl->type_check(typeid(decay_t<T>));
+        return std::move(*static_cast<decay_t<T>*>(_impl->addressof()));
+    }
+
+    /**
+     * Dynamic cast to specified type
+     * @tparam T type
+     * @return casted object
+     */
+    template <class T>
+    typename std::enable_if<!std::is_convertible<T, std::shared_ptr<Variant>>::value, T>::type& as() & {
+        impl_check();
+        _impl->type_check(typeid(decay_t<T>));
+        return *static_cast<decay_t<T>*>(_impl->addressof());
+    }
+
+    /**
+     * Dynamic cast to specified type
+     * @tparam T type
+     * @return casted object
+     */
+    template <class T>
+    const typename std::enable_if<!std::is_convertible<T, std::shared_ptr<Variant>>::value, T>::type& as() const& {
+        impl_check();
+        _impl->type_check(typeid(decay_t<T>));
+        return *static_cast<const decay_t<T>*>(_impl->addressof());
+    }
+
+    /**
+     * @brief Converts to specified type
+     * @tparam T type
+     * @return casted object
+     */
+    template <typename T>
+    operator T&() & {
+        return as<T>();
+    }
+
+    /**
+     * @brief Converts to specified type
+     * @tparam T type
+     * @return casted object
+     */
+    template <typename T>
+    operator const T&() const& {
+        return as<T>();
+    }
+
+    /**
+     * @brief Converts to specified type
+     * @tparam T type
+     * @return casted object
+     */
+    template <typename T>
+    operator T&() const& {
+        return const_cast<Any*>(this)->as<T>();
+    }
+
+    /**
+     * @brief Converts to specified type
+     * @tparam T type
+     * @return casted object
+     */
+    template <typename T>
+    operator T &&() && {
+        return std::move(as<T&&>());
+    }
+
+    /**
+     * @brief The comparison operator for the Any
+     *
+     * @param other object to compare
+     * @return true if objects are equal
+     */
+    bool operator==(const Any& other) const;
+
+    /**
+     * @brief The comparison operator for the Any
+     *
+     * @param other object to compare
+     * @return true if objects are equal
+     */
+    bool operator==(const std::nullptr_t&) const;
+
+    /**
+     * @brief The comparison operator for the Any
+     *
+     * @param other object to compare
+     * @return true if objects aren't equal
+     */
+    bool operator!=(const Any& other) const;
+
+    /**
+     * @brief Standard pointer operator
+     * @return underlined interface
+     */
+    OPENVINO_DEPRECATED("Please use as() method")
+    Base* operator->();
+
+    /**
+     * @brief Standard pointer operator
+     * @return underlined interface
+     */
+    OPENVINO_DEPRECATED("Please use as() method")
+    const Base* operator->() const;
+
+    /**
+     * @brief Prints underlying object to the given output stream.
+     * Uses operator<< if it is defined, leaves stream unchanged otherwise.
+     * In case of empty any or nullptr stream immediately returns.
+     *
+     * @param stream Output stream object will be printed to.
+     */
+    void print(std::ostream& stream) const;
+
+    /**
+     * @brief Return pointer to underlined interface
+     * @return underlined interface
+     */
+    OPENVINO_DEPRECATED("Please use as() method")
+    Base* get() {
+        impl_check();
+        return _impl.get();
+    }
+
+    /**
+     * @brief Return pointer to underlined interface
+     * @return underlined interface
+     */
+    OPENVINO_DEPRECATED("Please use as() method")
+    const Base* get() const {
+        impl_check();
+        return _impl.get();
+    }
+};
+
+namespace util {
+template <>
+struct AsTypePtr<Any> {
+    template <typename T>
+    static std::shared_ptr<T> call(const Any& any) {
+        if (any.is<std::shared_ptr<T>>()) {
+            return any.as<std::shared_ptr<T>>();
+        } else {
+            return {};
+        }
+    }
+};
+}  // namespace util
+
+using RTMap = std::map<std::string, Any>;
+
+}  // namespace ov
+
+namespace std {
+template <typename T>
+std::shared_ptr<T> dynamic_pointer_cast(const ::ov::Any& any) {
+    if (any.is<std::shared_ptr<T>>()) {
+        return any.as<std::shared_ptr<T>>();
+    } else {
+        return {};
+    }
+}
+
+template <typename T>
+std::shared_ptr<T> static_pointer_cast(const ::ov::Any& any) {
+    return any.as<std::shared_ptr<T>>();
+}
+
+}  // namespace std
diff --git a/ngraph/core/include/openvino/core/descriptor/tensor.hpp b/ngraph/core/include/openvino/core/descriptor/tensor.hpp
index 0f9f7f970c8..58aed63e321 100644
--- a/ngraph/core/include/openvino/core/descriptor/tensor.hpp
+++ b/ngraph/core/include/openvino/core/descriptor/tensor.hpp
@@ -11,6 +11,7 @@
 #include <string>
 #include <unordered_set>
 
+#include "openvino/core/any.hpp"
 #include "openvino/core/core_visibility.hpp"
 #include "openvino/core/partial_shape.hpp"
 #include "openvino/core/shape.hpp"
@@ -114,7 +115,7 @@ protected:
     std::string m_name;
 
     std::unordered_set<std::string> m_names;
-    std::map<std::string, std::shared_ptr<Variant>> m_rt_info;
+    std::map<std::string, Any> m_rt_info;
 };
 
 OPENVINO_API
diff --git a/ngraph/core/include/openvino/core/node.hpp b/ngraph/core/include/openvino/core/node.hpp
index aa918ec3475..4abdd23b1ea 100644
--- a/ngraph/core/include/openvino/core/node.hpp
+++ b/ngraph/core/include/openvino/core/node.hpp
@@ -77,7 +77,7 @@ class SharedRTInfo;
 
 /// EvaluationContext stores and manages a context (additional parameters, values and
 /// environment) for evaluating ov::Function.
-using EvaluationContext = std::map<std::string, std::shared_ptr<Variant>>;
+using EvaluationContext = ov::RTMap;
 
 OPENVINO_API
 std::string node_validation_failure_loc_string(const Node* node);
@@ -405,7 +405,7 @@ public:
     /// True if this and node have one output with same element type and shape
     bool has_same_type(std::shared_ptr<const Node> node) const;
 
-    using RTMap = std::map<std::string, std::shared_ptr<Variant>>;
+    using RTMap = std::map<std::string, Any>;
 
     RTMap& get_rt_info() {
         return m_rt_info;
diff --git a/ngraph/core/include/openvino/core/type.hpp b/ngraph/core/include/openvino/core/type.hpp
index 08eb5c24070..6b89f4c5f03 100644
--- a/ngraph/core/include/openvino/core/type.hpp
+++ b/ngraph/core/include/openvino/core/type.hpp
@@ -87,7 +87,6 @@ typename std::enable_if<
 is_type(Value value) {
     return value->get_type_info().is_castable(Type::type_info);
 }
-OPENVINO_SUPPRESS_DEPRECATED_END
 
 template <typename Type, typename Value>
 typename std::enable_if<
@@ -98,6 +97,7 @@ typename std::enable_if<
 is_type(Value value) {
     return value->get_type_info().is_castable(Type::get_type_info_static());
 }
+OPENVINO_SUPPRESS_DEPRECATED_END
 
 /// Casts a Value* to a Type* if it is of type Type, nullptr otherwise
 template <typename Type, typename Value>
@@ -107,14 +107,25 @@ as_type(Value value) {
     return ov::is_type<Type>(value) ? static_cast<Type*>(value) : nullptr;
 }
 
+namespace util {
+template <typename T>
+struct AsTypePtr;
 /// Casts a std::shared_ptr<Value> to a std::shared_ptr<Type> if it is of type
 /// Type, nullptr otherwise
-template <typename Type, typename Value>
-typename std::enable_if<
-    std::is_convertible<decltype(std::static_pointer_cast<Type>(std::declval<Value>())), std::shared_ptr<Type>>::value,
-    std::shared_ptr<Type>>::type
-as_type_ptr(Value value) {
-    return ov::is_type<Type>(value) ? std::static_pointer_cast<Type>(value) : std::shared_ptr<Type>();
+template <typename In>
+struct AsTypePtr<std::shared_ptr<In>> {
+    template <typename Type>
+    static std::shared_ptr<Type> call(const std::shared_ptr<In>& value) {
+        return ov::is_type<Type>(value) ? std::static_pointer_cast<Type>(value) : std::shared_ptr<Type>();
+    }
+};
+}  // namespace util
+
+/// Casts a std::shared_ptr<Value> to a std::shared_ptr<Type> if it is of type
+/// Type, nullptr otherwise
+template <typename T, typename U>
+auto as_type_ptr(const U& value) -> decltype(::ov::util::AsTypePtr<U>::template call<T>(value)) {
+    return ::ov::util::AsTypePtr<U>::template call<T>(value);
 }
 }  // namespace ov
 
diff --git a/ngraph/core/include/openvino/core/variant.hpp b/ngraph/core/include/openvino/core/variant.hpp
index 5802fe14f9f..015fb744bf1 100644
--- a/ngraph/core/include/openvino/core/variant.hpp
+++ b/ngraph/core/include/openvino/core/variant.hpp
@@ -15,16 +15,22 @@
 namespace ov {
 class Node;
 class AttributeVisitor;
+class Any;
 using VariantTypeInfo = DiscreteTypeInfo;
 
 class OPENVINO_API Variant {
 public:
+    static const ::ov::DiscreteTypeInfo& get_type_info_static() {
+        static const ::ov::DiscreteTypeInfo type_info{"Variant", 0};
+        return type_info;
+    }
     virtual ~Variant();
-    virtual const VariantTypeInfo& get_type_info() const = 0;
-
+    virtual const VariantTypeInfo& get_type_info() const {
+        return get_type_info_static();
+    }
     virtual bool is_copyable() const;
-    virtual std::shared_ptr<ov::Variant> init(const std::shared_ptr<Node>& node);
-    virtual std::shared_ptr<ov::Variant> merge(const ov::NodeVector& nodes);
+    virtual Any init(const std::shared_ptr<Node>& node);
+    virtual Any merge(const ov::NodeVector& nodes);
     virtual std::string to_string() {
         return "";
     }
@@ -38,6 +44,7 @@ public:
 template <typename VT>
 class VariantImpl : public Variant {
 public:
+    OPENVINO_RTTI(typeid(VT).name(), "0");
     using value_type = VT;
 
     VariantImpl() = default;
@@ -103,7 +110,5 @@ inline std::shared_ptr<Variant> make_variant(const wchar_t (&s)[N]) {
     return std::dynamic_pointer_cast<VariantImpl<std::wstring>>(std::make_shared<VariantWrapper<std::wstring>>(s));
 }
 #endif
-
-using RTMap = std::map<std::string, std::shared_ptr<Variant>>;
 using VariantVector = std::vector<std::shared_ptr<Variant>>;
 }  // namespace ov
diff --git a/ngraph/core/src/any.cpp b/ngraph/core/src/any.cpp
new file mode 100644
index 00000000000..2c28f292e04
--- /dev/null
+++ b/ngraph/core/src/any.cpp
@@ -0,0 +1,88 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/core/any.hpp"
+
+namespace ov {
+
+void Any::Base::type_check(const std::type_info& type_info_) const {
+    OPENVINO_ASSERT(type_info() == type_info_, "Bad cast from: ", type_info().name(), " To type: ", type_info_.name());
+}
+
+std::shared_ptr<Variant> Any::Base::as_variant() const {
+    return {};
+}
+
+bool Any::Base::is_copyable() const {
+    return true;
+}
+Any Any::Base::init(const std::shared_ptr<Node>&) {
+    return {};
+}
+Any Any::Base::merge(const std::vector<std::shared_ptr<Node>>&) {
+    return {};
+}
+std::string Any::Base::to_string() {
+    std::stringstream strm;
+    print(strm);
+    return strm.str();
+}
+
+std::string Any::Base::to_string() const {
+    return const_cast<Any::Base*>(this)->to_string();
+}
+
+bool Any::Base::visit_attributes(AttributeVisitor&) {
+    return false;
+}
+bool Any::Base::visit_attributes(AttributeVisitor& visitor) const {
+    return const_cast<Any::Base*>(this)->visit_attributes(visitor);
+}
+
+Any::Any(const std::shared_ptr<void>& so, const Any& other) : _so{so}, _impl{other._impl} {}
+
+Any::Any(const char* str) : Any(std::string{str}) {}
+
+Any::Any(const std::nullptr_t) : Any() {}
+
+void Any::impl_check() const {
+    OPENVINO_ASSERT(_impl != nullptr, "Any was not initialized.");
+}
+
+bool Any::empty() const {
+    return _impl == nullptr;
+}
+
+void Any::print(std::ostream& ostream) const {
+    if (_impl != nullptr) {
+        _impl->print(ostream);
+    }
+}
+
+bool Any::operator==(const Any& other) const {
+    if (_impl == nullptr && other._impl == nullptr) {
+        return false;
+    }
+    if (_impl == other._impl) {
+        return true;
+    }
+    return _impl->equal(*other._impl);
+}
+
+bool Any::operator==(const std::nullptr_t&) const {
+    return _impl == nullptr;
+}
+
+bool Any::operator!=(const Any& other) const {
+    return !operator==(other);
+}
+
+Any::Base* Any::operator->() {
+    return _impl.get();
+}
+
+const Any::Base* Any::operator->() const {
+    return _impl.get();
+}
+}  // namespace ov
diff --git a/ngraph/core/src/pass/init_node_info.cpp b/ngraph/core/src/pass/init_node_info.cpp
index 89fcb9cf52e..a40837fbd6d 100644
--- a/ngraph/core/src/pass/init_node_info.cpp
+++ b/ngraph/core/src/pass/init_node_info.cpp
@@ -41,7 +41,8 @@ bool ngraph::pass::InitNodeInfo::run_on_function(std::shared_ptr<ngraph::Functio
             // Skip initialization if attribute has been already set
             if (rtInfo.count(attr->get_type_info()))
                 continue;
-            if (auto init_attr = attr->init(node)) {
+            auto init_attr = attr->init(node);
+            if (!init_attr.empty()) {
                 rtInfo[attr->get_type_info()] = init_attr;
             }
         }
diff --git a/ngraph/core/src/pass/rt_info/fused_names_attribute.cpp b/ngraph/core/src/pass/rt_info/fused_names_attribute.cpp
index e3315e9d817..8a25cec593f 100644
--- a/ngraph/core/src/pass/rt_info/fused_names_attribute.cpp
+++ b/ngraph/core/src/pass/rt_info/fused_names_attribute.cpp
@@ -63,7 +63,7 @@ std::vector<std::string> ngraph::getFusedNamesVector(const std::shared_ptr<ngrap
 
 template class ov::VariantImpl<FusedNames>;
 
-std::shared_ptr<ngraph::Variant> VariantWrapper<FusedNames>::merge(const ngraph::NodeVector& nodes) {
+Any VariantWrapper<FusedNames>::merge(const ngraph::NodeVector& nodes) {
     FusedNames mergedNames;
     for (auto& node : nodes) {
         const auto& rtInfo = node->get_rt_info();
@@ -79,11 +79,11 @@ std::shared_ptr<ngraph::Variant> VariantWrapper<FusedNames>::merge(const ngraph:
     return std::make_shared<VariantWrapper<FusedNames>>(mergedNames);
 }
 
-std::shared_ptr<ngraph::Variant> VariantWrapper<FusedNames>::init(const std::shared_ptr<ngraph::Node>& node) {
+Any VariantWrapper<FusedNames>::init(const std::shared_ptr<ngraph::Node>& node) {
     return std::make_shared<VariantWrapper<FusedNames>>(FusedNames(node->get_friendly_name()));
 }
 
 bool VariantWrapper<FusedNames>::visit_attributes(AttributeVisitor& visitor) {
     visitor.on_attribute("value", m_value.fused_names);
     return true;
-}
\ No newline at end of file
+}
diff --git a/ngraph/core/src/pass/rt_info/primitives_priority_attribute.cpp b/ngraph/core/src/pass/rt_info/primitives_priority_attribute.cpp
index 9936336d98c..036580253cc 100644
--- a/ngraph/core/src/pass/rt_info/primitives_priority_attribute.cpp
+++ b/ngraph/core/src/pass/rt_info/primitives_priority_attribute.cpp
@@ -27,7 +27,7 @@ std::string ov::getPrimitivesPriority(const std::shared_ptr<ngraph::Node>& node)
     return ov::as_type_ptr<PrimitivesPriority>(attr)->get();
 }
 
-std::shared_ptr<ngraph::Variant> PrimitivesPriority::merge(const ngraph::NodeVector& nodes) {
+Any PrimitivesPriority::merge(const ngraph::NodeVector& nodes) {
     auto isConvolutionBased = [](const std::shared_ptr<Node>& node) -> bool {
         if (std::dynamic_pointer_cast<ngraph::opset1::Convolution>(node) ||
             std::dynamic_pointer_cast<ngraph::opset1::GroupConvolution>(node) ||
@@ -59,7 +59,7 @@ std::shared_ptr<ngraph::Variant> PrimitivesPriority::merge(const ngraph::NodeVec
     return std::make_shared<PrimitivesPriority>(final_primitives_priority);
 }
 
-std::shared_ptr<ngraph::Variant> PrimitivesPriority::init(const std::shared_ptr<ngraph::Node>& node) {
+Any PrimitivesPriority::init(const std::shared_ptr<ngraph::Node>& node) {
     throw ngraph_error(std::string(get_type_info()) + " has no default initialization.");
 }
 
diff --git a/ngraph/core/src/pass/serialize.cpp b/ngraph/core/src/pass/serialize.cpp
index 08becb27c50..b1dc8049364 100644
--- a/ngraph/core/src/pass/serialize.cpp
+++ b/ngraph/core/src/pass/serialize.cpp
@@ -885,6 +885,7 @@ void ngfunction_2_ir(pugi::xml_node& netXml,
             bool has_attrs = false;
             for (const auto& item : attributes) {
                 auto attribute_node = rt_node.append_child("attribute");
+                OPENVINO_SUPPRESS_DEPRECATED_START
                 attribute_node.append_attribute("name").set_value(item.second->get_type_info().name);
                 attribute_node.append_attribute("version").set_value(
                     item.second->get_type_info().get_version().c_str());
@@ -894,6 +895,7 @@ void ngfunction_2_ir(pugi::xml_node& netXml,
                 } else {
                     has_attrs = true;
                 }
+                OPENVINO_SUPPRESS_DEPRECATED_END
             }
             if (!has_attrs) {
                 node.remove_child(rt_node);
diff --git a/ngraph/core/src/pass/visualize_tree.cpp b/ngraph/core/src/pass/visualize_tree.cpp
index 8361c064aef..7f69d491055 100644
--- a/ngraph/core/src/pass/visualize_tree.cpp
+++ b/ngraph/core/src/pass/visualize_tree.cpp
@@ -155,13 +155,15 @@ static std::string label_edge(const std::shared_ptr<Node>& /* src */,
     return ss.str();
 }
 
-static std::string get_attribute_values(const std::map<std::string, std::shared_ptr<Variant>>& attributes,
+static std::string get_attribute_values(const std::map<std::string, ov::Any>& attributes,
                                         const std::string& delimiter = ", ") {
     stringstream ss;
     bool first = true;
     for (const auto& item : attributes) {
         ss << (first ? " " : delimiter) << item.first;
+        OPENVINO_SUPPRESS_DEPRECATED_START
         const auto attributeValue = item.second == nullptr ? "[EMPTY]" : item.second->to_string();
+        OPENVINO_SUPPRESS_DEPRECATED_END
         if (!attributeValue.empty())
             ss << "{" << attributeValue << "}";
 
diff --git a/ngraph/core/src/rt_info.cpp b/ngraph/core/src/rt_info.cpp
index b07b2000057..eaed0197af7 100644
--- a/ngraph/core/src/rt_info.cpp
+++ b/ngraph/core/src/rt_info.cpp
@@ -10,7 +10,8 @@
 namespace {
 
 ngraph::Node::RTMap mergeRuntimeInfo(const ngraph::NodeVector& nodes) {
-    std::unordered_map<std::string, std::vector<std::shared_ptr<ngraph::Variant>>> attrs;
+    std::unordered_map<std::string, std::vector<ov::Any>> attrs;
+    OPENVINO_SUPPRESS_DEPRECATED_START
     for (const auto& node : nodes) {
         for (const auto& item : node->get_rt_info()) {
             if (item.second->is_copyable() && item.first != "opset") {
@@ -24,10 +25,14 @@ ngraph::Node::RTMap mergeRuntimeInfo(const ngraph::NodeVector& nodes) {
         auto attr = *item.second.begin();
         if (item.second.size() == 1) {
             merged_attrs[item.first] = attr;
-        } else if (auto merge_attr = attr->merge(nodes)) {
-            merged_attrs[item.first] = merge_attr;
+        } else {
+            auto merge_attr = attr->merge(nodes);
+            if (!merge_attr.empty()) {
+                merged_attrs[item.first] = merge_attr;
+            }
         }
     }
+    OPENVINO_SUPPRESS_DEPRECATED_END
 
     return merged_attrs;
 }
@@ -56,9 +61,11 @@ void ngraph::copy_runtime_info(std::shared_ptr<ngraph::Node> from, std::shared_p
     attrs.clear();
 
     for (const auto& item : from->get_rt_info()) {
+        OPENVINO_SUPPRESS_DEPRECATED_START
         if (item.second->is_copyable() && item.first != "opset") {
             attrs[item.first] = item.second;
         }
+        OPENVINO_SUPPRESS_DEPRECATED_END
     }
 
     if (opset) {
diff --git a/ngraph/core/src/variant.cpp b/ngraph/core/src/variant.cpp
index 9988b24cc7b..4e3c73bfcf4 100644
--- a/ngraph/core/src/variant.cpp
+++ b/ngraph/core/src/variant.cpp
@@ -5,18 +5,19 @@
 #include "ngraph/variant.hpp"
 
 #include "ngraph/node.hpp"
+#include "openvino/core/any.hpp"
 #include "openvino/core/attribute_visitor.hpp"
 
 using namespace ngraph;
 
 Variant::~Variant() = default;
 
-std::shared_ptr<ngraph::Variant> Variant::init(const std::shared_ptr<ngraph::Node>& node) {
-    return nullptr;
+ov::Any Variant::init(const std::shared_ptr<ngraph::Node>& node) {
+    return {};
 }
 
-std::shared_ptr<ngraph::Variant> Variant::merge(const ngraph::NodeVector& nodes) {
-    return nullptr;
+ov::Any Variant::merge(const ngraph::NodeVector& nodes) {
+    return {};
 }
 
 bool Variant::is_copyable() const {
diff --git a/ngraph/frontend/ir/src/ir_deserializer.cpp b/ngraph/frontend/ir/src/ir_deserializer.cpp
index 8908fb6c03a..7271e190eca 100644
--- a/ngraph/frontend/ir/src/ir_deserializer.cpp
+++ b/ngraph/frontend/ir/src/ir_deserializer.cpp
@@ -733,7 +733,7 @@ std::shared_ptr<ngraph::Node> XmlDeserializer::createNode(const std::vector<ngra
             if (auto attr = attrs_factory.create_by_type_info(type_info)) {
                 RTInfoDeserializer attribute_visitor(item);
                 if (attr->visit_attributes(attribute_visitor)) {
-                    rt_info[type_info] = std::shared_ptr<Variant>(attr);
+                    rt_info[type_info] = attr;
                 } else {
                     IE_THROW() << "VisitAttributes is not supported for: " << item.name() << " attribute";
                 }
diff --git a/ngraph/frontend/tensorflow/include/tensorflow_frontend/decoder.hpp b/ngraph/frontend/tensorflow/include/tensorflow_frontend/decoder.hpp
index 7d9f2a32f4a..80ecb99935f 100644
--- a/ngraph/frontend/tensorflow/include/tensorflow_frontend/decoder.hpp
+++ b/ngraph/frontend/tensorflow/include/tensorflow_frontend/decoder.hpp
@@ -4,6 +4,7 @@
 
 #pragma once
 
+#include "openvino/core/any.hpp"
 #include "openvino/core/variant.hpp"
 #include "tensorflow_frontend/utility.hpp"
 
diff --git a/ngraph/frontend/tensorflow/include/tensorflow_frontend/frontend.hpp b/ngraph/frontend/tensorflow/include/tensorflow_frontend/frontend.hpp
index 64adfedee71..ab4a8062904 100644
--- a/ngraph/frontend/tensorflow/include/tensorflow_frontend/frontend.hpp
+++ b/ngraph/frontend/tensorflow/include/tensorflow_frontend/frontend.hpp
@@ -9,6 +9,7 @@
 
 #include "common/frontend.hpp"
 #include "common/input_model.hpp"
+#include "openvino/core/any.hpp"
 #include "openvino/core/node_vector.hpp"
 #include "openvino/core/variant.hpp"
 #include "tensorflow_frontend/utility.hpp"
diff --git a/ngraph/frontend/tensorflow/include/tensorflow_frontend/graph_iterator.hpp b/ngraph/frontend/tensorflow/include/tensorflow_frontend/graph_iterator.hpp
index 16fe1501707..613035894f5 100644
--- a/ngraph/frontend/tensorflow/include/tensorflow_frontend/graph_iterator.hpp
+++ b/ngraph/frontend/tensorflow/include/tensorflow_frontend/graph_iterator.hpp
@@ -4,6 +4,7 @@
 
 #pragma once
 
+#include "openvino/core/any.hpp"
 #include "openvino/core/variant.hpp"
 #include "tensorflow_frontend/decoder.hpp"
 #include "tensorflow_frontend/utility.hpp"
diff --git a/ngraph/frontend/tensorflow/src/node_context.hpp b/ngraph/frontend/tensorflow/src/node_context.hpp
index 31d583def6d..28a8729140f 100644
--- a/ngraph/frontend/tensorflow/src/node_context.hpp
+++ b/ngraph/frontend/tensorflow/src/node_context.hpp
@@ -5,6 +5,7 @@
 #pragma once
 
 #include "exceptions.hpp"
+#include "openvino/core/any.hpp"
 #include "openvino/core/variant.hpp"
 #include "place.hpp"
 #include "tensor.pb.h"
diff --git a/ngraph/frontend/tensorflow/src/utils.hpp b/ngraph/frontend/tensorflow/src/utils.hpp
index 27183ea5ec8..7ab6aa9f093 100644
--- a/ngraph/frontend/tensorflow/src/utils.hpp
+++ b/ngraph/frontend/tensorflow/src/utils.hpp
@@ -99,6 +99,7 @@ void values_from_const_node(const NodeContext& node, ov::Shape* const_tensor_sha
     auto tensor_proto_var =
         decoder->get_attribute("value", ::ov::VariantWrapper<::tensorflow::TensorProto>::get_type_info_static());
     FRONT_END_GENERAL_CHECK(tensor_proto_var != nullptr);
+
     auto tensor_proto =
         std::dynamic_pointer_cast<::ov::VariantWrapper<::tensorflow::TensorProto>>(tensor_proto_var)->get();
 
diff --git a/ngraph/test/CMakeLists.txt b/ngraph/test/CMakeLists.txt
index 7422efa5078..17509d4de49 100644
--- a/ngraph/test/CMakeLists.txt
+++ b/ngraph/test/CMakeLists.txt
@@ -94,6 +94,7 @@ set(SRC
     opset1.cpp
     ov_default_allocator_test.cpp
     ov_tensor_test.cpp
+    any.cpp
     partial_shape.cpp
     pass_config.cpp
     pass_manager.cpp
diff --git a/ngraph/test/any.cpp b/ngraph/test/any.cpp
new file mode 100644
index 00000000000..47428262111
--- /dev/null
+++ b/ngraph/test/any.cpp
@@ -0,0 +1,446 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/core/any.hpp"
+
+#include <string>
+
+#include "gtest/gtest.h"
+#include "openvino/core/variant.hpp"
+
+using namespace ov;
+
+class DestructorTest {
+public:
+    DestructorTest() {
+        constructorCount++;
+    }
+
+    DestructorTest(const DestructorTest& c) {
+        constructorCount++;
+    }
+
+    DestructorTest(const DestructorTest&& c) {
+        constructorCount++;
+    }
+
+    ~DestructorTest() {
+        destructorCount++;
+    }
+
+    static size_t destructorCount;
+    static size_t constructorCount;
+};
+size_t DestructorTest::destructorCount = 0;
+size_t DestructorTest::constructorCount = 0;
+
+class AnyTests : public ::testing::Test {
+public:
+    void SetUp() override {
+        DestructorTest::destructorCount = 0;
+        DestructorTest::constructorCount = 0;
+    }
+};
+
+TEST_F(AnyTests, parameter_std_string) {
+    auto parameter = Any{"My string"};
+    ASSERT_TRUE(parameter.is<std::string>());
+    EXPECT_EQ(parameter.as<std::string>(), "My string");
+}
+
+TEST_F(AnyTests, parameter_int64_t) {
+    auto parameter = Any{int64_t(27)};
+    ASSERT_TRUE(parameter.is<int64_t>());
+    EXPECT_FALSE(parameter.is<std::string>());
+    EXPECT_EQ(parameter.as<int64_t>(), 27);
+}
+
+struct Ship {
+    Ship(const std::string& name_, const int16_t x_, const int16_t y_) : name{name_}, x{x_}, y{y_} {}
+    std::string name;
+    int16_t x;
+    int16_t y;
+};
+
+TEST_F(AnyTests, parameter_ship) {
+    {
+        auto parameter = Any{Ship{"Lollipop", 3, 4}};
+        ASSERT_TRUE(parameter.is<Ship>());
+        Ship& ship = parameter.as<Ship>();
+        EXPECT_EQ(ship.name, "Lollipop");
+        EXPECT_EQ(ship.x, 3);
+        EXPECT_EQ(ship.y, 4);
+    }
+    {
+        auto parameter = Any::make<Ship>("Lollipop", int16_t(3), int16_t(4));
+        ASSERT_TRUE(parameter.is<Ship>());
+        Ship& ship = parameter.as<Ship>();
+        EXPECT_EQ(ship.name, "Lollipop");
+        EXPECT_EQ(ship.x, 3);
+        EXPECT_EQ(ship.y, 4);
+    }
+    {
+        auto parameter = Any::make<Ship>("Lollipop", int16_t(3), int16_t(4));
+        ASSERT_TRUE(parameter.is<Ship>());
+        Ship ship = parameter;
+        EXPECT_EQ(ship.name, "Lollipop");
+        EXPECT_EQ(ship.x, 3);
+        EXPECT_EQ(ship.y, 4);
+    }
+}
+
+TEST_F(AnyTests, AnyAsInt) {
+    Any p = 4;
+    ASSERT_TRUE(p.is<int>());
+    int test = p;
+    ASSERT_EQ(4, test);
+}
+
+TEST_F(AnyTests, AnyAsUInt) {
+    Any p = uint32_t(4);
+    ASSERT_TRUE(p.is<uint32_t>());
+    ASSERT_TRUE(p.is<uint32_t>());
+    uint32_t test = p;
+    ASSERT_EQ(4, test);
+}
+
+TEST_F(AnyTests, AnyAsString) {
+    std::string ref = "test";
+    Any p = ref;
+    std::string test = p;
+    ASSERT_TRUE(p.is<std::string>());
+    ASSERT_EQ(ref, test);
+}
+
+TEST_F(AnyTests, AnyAsStringInLine) {
+    Any p = "test";
+    std::string test = p;
+    ASSERT_TRUE(p.is<std::string>());
+    ASSERT_EQ("test", test);
+}
+
+TEST_F(AnyTests, IntAnyAsString) {
+    Any p = 4;
+    ASSERT_TRUE(p.is<int>());
+    ASSERT_FALSE(p.is<std::string>());
+    ASSERT_THROW(std::string test = p, ov::Exception);
+    ASSERT_THROW(std::string test = p.as<std::string>(), ov::Exception);
+}
+
+TEST_F(AnyTests, StringAnyAsInt) {
+    Any p = "4";
+    ASSERT_FALSE(p.is<int>());
+    ASSERT_TRUE(p.is<std::string>());
+    ASSERT_THROW((void)static_cast<int>(p), ov::Exception);
+    ASSERT_THROW((void)p.as<int>(), ov::Exception);
+}
+
+TEST_F(AnyTests, AnyAsInts) {
+    std::vector<int> ref = {1, 2, 3, 4, 5};
+    Any p = ref;
+    ASSERT_TRUE(p.is<std::vector<int>>());
+    std::vector<int> test = p;
+    ASSERT_EQ(ref.size(), test.size());
+    for (size_t i = 0; i < ref.size(); i++) {
+        ASSERT_EQ(ref[i], test[i]);
+    }
+}
+
+TEST_F(AnyTests, AnyAsMapOfAnys) {
+    std::map<std::string, Any> refMap;
+    refMap["testParamInt"] = 4;
+    refMap["testParamString"] = "test";
+    Any p = refMap;
+    bool isMap = p.is<std::map<std::string, Any>>();
+    ASSERT_TRUE(isMap);
+    std::map<std::string, Any> testMap = p;
+
+    ASSERT_NE(testMap.find("testParamInt"), testMap.end());
+    ASSERT_NE(testMap.find("testParamString"), testMap.end());
+
+    int testInt = testMap["testParamInt"];
+    std::string testString = testMap["testParamString"];
+
+    ASSERT_EQ(refMap["testParamInt"].as<int>(), testInt);
+    ASSERT_EQ(refMap["testParamString"].as<std::string>(), testString);
+}
+
+TEST_F(AnyTests, AnyNotEmpty) {
+    Any p = 4;
+    ASSERT_FALSE(p.empty());
+}
+
+TEST_F(AnyTests, AnyEmpty) {
+    Any p;
+    ASSERT_TRUE(p.empty());
+}
+
+TEST_F(AnyTests, AnyClear) {
+    Any p = 4;
+    ASSERT_FALSE(p.empty());
+    p = {};
+    ASSERT_TRUE(p.empty());
+}
+
+TEST_F(AnyTests, AnysNotEqualByType) {
+    Any p1 = 4;
+    Any p2 = "string";
+    ASSERT_TRUE(p1 != p2);
+    ASSERT_FALSE(p1 == p2);
+}
+
+TEST_F(AnyTests, AnysNotEqualByValue) {
+    Any p1 = 4;
+    Any p2 = 5;
+    ASSERT_TRUE(p1 != p2);
+    ASSERT_FALSE(p1 == p2);
+}
+
+TEST_F(AnyTests, AnysEqual) {
+    Any p1 = 4;
+    Any p2 = 4;
+    ASSERT_TRUE(p1 == p2);
+    ASSERT_FALSE(p1 != p2);
+}
+
+TEST_F(AnyTests, AnysStringEqual) {
+    std::string s1 = "abc";
+    std::string s2 = std::string("a") + "bc";
+    Any p1 = s1;
+    Any p2 = s2;
+    ASSERT_TRUE(s1 == s2);
+    ASSERT_TRUE(p1 == p2);
+    ASSERT_FALSE(p1 != p2);
+}
+
+TEST_F(AnyTests, MapOfAnysEqual) {
+    std::map<std::string, Any> map0;
+    map0["testParamInt"] = 4;
+    map0["testParamString"] = "test";
+    const auto map1 = map0;
+
+    Any p0 = map0;
+    Any p1 = map1;
+    ASSERT_TRUE(p0 == p1);
+    ASSERT_FALSE(p0 != p1);
+}
+
+TEST_F(AnyTests, CompareAnysWithoutEqualOperator) {
+    class TestClass {
+    public:
+        TestClass(int test, int* testPtr) : test(test), testPtr(testPtr) {}
+
+    private:
+        int test;
+        int* testPtr;
+    };
+
+    TestClass a(2, reinterpret_cast<int*>(0x234));
+    TestClass b(2, reinterpret_cast<int*>(0x234));
+    TestClass c(3, reinterpret_cast<int*>(0x234));
+    Any parA = a;
+    Any parB = b;
+    Any parC = c;
+
+    ASSERT_THROW((void)(parA == parB), ov::Exception);
+    ASSERT_THROW((void)(parA != parB), ov::Exception);
+    ASSERT_THROW((void)(parA == parC), ov::Exception);
+    ASSERT_THROW((void)(parA != parC), ov::Exception);
+}
+
+TEST_F(AnyTests, AnyRemovedRealObject) {
+    ASSERT_EQ(0, DestructorTest::constructorCount);
+    ASSERT_EQ(0, DestructorTest::destructorCount);
+    {
+        DestructorTest t;
+        Any p1 = t;
+    }
+    ASSERT_EQ(2, DestructorTest::constructorCount);
+    ASSERT_EQ(2, DestructorTest::destructorCount);
+}
+
+TEST_F(AnyTests, AnyRemovedRealObjectWithDuplication) {
+    ASSERT_EQ(0, DestructorTest::constructorCount);
+    ASSERT_EQ(0, DestructorTest::destructorCount);
+    {
+        DestructorTest t;
+        Any p = t;
+        ASSERT_EQ(0, DestructorTest::destructorCount);
+        p = t;
+        ASSERT_EQ(1, DestructorTest::destructorCount);
+    }
+    ASSERT_EQ(3, DestructorTest::constructorCount);
+    ASSERT_EQ(3, DestructorTest::destructorCount);
+}
+
+TEST_F(AnyTests, AnyRemovedRealObjectPointerWithDuplication) {
+    ASSERT_EQ(0, DestructorTest::constructorCount);
+    ASSERT_EQ(0, DestructorTest::destructorCount);
+    {
+        auto* t = new DestructorTest();
+        Any p = t;
+        ASSERT_EQ(1, DestructorTest::constructorCount);
+        ASSERT_EQ(0, DestructorTest::destructorCount);
+        p = t;
+        ASSERT_TRUE(p.is<DestructorTest*>());
+        DestructorTest* t2 = p;
+        ASSERT_EQ(0, DestructorTest::destructorCount);
+        delete t;
+        auto* t3 = p.as<DestructorTest*>();
+        ASSERT_EQ(t2, t3);
+    }
+    ASSERT_EQ(1, DestructorTest::constructorCount);
+    ASSERT_EQ(1, DestructorTest::destructorCount);
+}
+
+void PrintTo(const Any& object, std::ostream* stream) {
+    if (object.empty() || !stream) {
+        return;
+    }
+    object.print(*stream);
+}
+
+TEST_F(AnyTests, PrintToEmptyAnyDoesNothing) {
+    Any p;
+    std::stringstream stream;
+    ASSERT_NO_THROW(p.print(stream));
+    ASSERT_EQ(stream.str(), std::string{});
+}
+
+TEST_F(AnyTests, PrintToIntAny) {
+    int value = -5;
+    Any p = value;
+    std::stringstream stream;
+    ASSERT_NO_THROW(p.print(stream));
+    ASSERT_EQ(stream.str(), std::to_string(value));
+}
+
+TEST_F(AnyTests, PrintToUIntAny) {
+    unsigned int value = 5;
+    Any p = value;
+    std::stringstream stream;
+    ASSERT_NO_THROW(p.print(stream));
+    ASSERT_EQ(stream.str(), std::to_string(value));
+}
+
+TEST_F(AnyTests, PrintToSize_tAny) {
+    std::size_t value = 5;
+    Any p = value;
+    std::stringstream stream;
+    ASSERT_NO_THROW(p.print(stream));
+    ASSERT_EQ(stream.str(), std::to_string(value));
+}
+
+TEST_F(AnyTests, PrintToFloatAny) {
+    Any p = 5.5f;
+    std::stringstream stream;
+    ASSERT_NO_THROW(p.print(stream));
+    ASSERT_EQ(stream.str(), std::string{"5.5"});
+}
+
+TEST_F(AnyTests, PrintToStringAny) {
+    std::string value = "some text";
+    Any p = value;
+    std::stringstream stream;
+    ASSERT_NO_THROW(p.print(stream));
+    ASSERT_EQ(stream.str(), value);
+}
+
+TEST_F(AnyTests, PrintToVectorOfIntsAnyDoesNothing) {
+    Any p = std::vector<int>{-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5};
+    std::stringstream stream;
+    ASSERT_NO_THROW(p.print(stream));
+    ASSERT_EQ(stream.str(), std::string{});
+}
+
+TEST_F(AnyTests, PrintToVectorOfUIntsAnyDoesNothing) {
+    Any p = std::vector<unsigned int>{0, 1, 2, 3, 4, 5};
+    std::stringstream stream;
+    ASSERT_NO_THROW(p.print(stream));
+    ASSERT_EQ(stream.str(), std::string{});
+}
+
+TEST_F(AnyTests, PrintToVectorOfSize_tAnyDoesNothing) {
+    Any p = std::vector<std::size_t>{0, 1, 2, 3, 4, 5};
+    std::stringstream stream;
+    ASSERT_NO_THROW(p.print(stream));
+    ASSERT_EQ(stream.str(), std::string{});
+}
+
+TEST_F(AnyTests, PrintToVectorOfFloatsAnyDoesNothing) {
+    Any p = std::vector<float>{0.0f, 1.1f, 2.2f, 3.3f, 4.4f, 5.5f};
+    std::stringstream stream;
+    ASSERT_NO_THROW(p.print(stream));
+    ASSERT_EQ(stream.str(), std::string{});
+}
+
+TEST_F(AnyTests, PrintToVectorOfStringsAnyDoesNothing) {
+    Any p = std::vector<std::string>{"zero", "one", "two", "three", "four", "five"};
+    std::stringstream stream;
+    ASSERT_NO_THROW(p.print(stream));
+    ASSERT_EQ(stream.str(), std::string{});
+}
+
+TEST_F(AnyTests, PrintToMapOfAnysDoesNothing) {
+    std::map<std::string, Any> refMap;
+    refMap["testParamInt"] = 4;
+    refMap["testParamString"] = "test";
+    Any p = refMap;
+    std::stringstream stream;
+    ASSERT_NO_THROW(p.print(stream));
+    ASSERT_EQ(stream.str(), std::string{});
+}
+
+TEST_F(AnyTests, constructFromVariantImpl) {
+    auto parameter = Any{4};
+    auto get_impl = [&] {
+        return std::make_shared<VariantImpl<int>>();
+    };
+    auto other_parameter = Any{get_impl()};
+}
+
+TEST_F(AnyTests, dynamicPointerCastToVariant) {
+    Any p = std::make_shared<VariantWrapper<std::string>>("42");
+    auto str_variant = std::dynamic_pointer_cast<VariantWrapper<std::string>>(p);
+    ASSERT_EQ("42", str_variant->get());
+}
+
+TEST_F(AnyTests, asTypePtrToVariant) {
+    Any p = std::make_shared<VariantWrapper<std::string>>("42");
+    auto str_variant = ov::as_type_ptr<VariantWrapper<std::string>>(p);
+    ASSERT_EQ("42", str_variant->get());
+}
+
+TEST_F(AnyTests, castToVariant) {
+    {
+        Any p = std::make_shared<VariantWrapper<std::string>>("42");
+        std::shared_ptr<VariantWrapper<std::string>> str_variant = p;
+        ASSERT_EQ("42", str_variant->get());
+    }
+    {
+        Any p = std::make_shared<VariantWrapper<std::string>>("42");
+        auto f = [](const std::shared_ptr<VariantWrapper<std::string>>& str_variant) {
+            ASSERT_NE(nullptr, str_variant);
+            ASSERT_EQ("42", str_variant->get());
+        };
+        f(p);
+    }
+    {
+        Any p = std::make_shared<VariantWrapper<std::string>>("42");
+        auto f = [](std::shared_ptr<VariantWrapper<std::string>>& str_variant) {
+            ASSERT_NE(nullptr, str_variant);
+            ASSERT_EQ("42", str_variant->get());
+        };
+        f(p);
+    }
+    {
+        std::shared_ptr<Variant> v = std::make_shared<VariantWrapper<std::string>>("42");
+        Any p = v;
+        auto f = [](std::shared_ptr<VariantWrapper<std::string>>& str_variant) {
+            ASSERT_NE(nullptr, str_variant);
+            ASSERT_EQ("42", str_variant->get());
+        };
+        f(p);
+    }
+}
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/node.cpp b/runtime/bindings/python/src/compatibility/pyngraph/node.cpp
index 956297a2357..ab8c8e910be 100644
--- a/runtime/bindings/python/src/compatibility/pyngraph/node.cpp
+++ b/runtime/bindings/python/src/compatibility/pyngraph/node.cpp
@@ -31,7 +31,7 @@ public:
 
 namespace py = pybind11;
 
-using PyRTMap = std::map<std::string, std::shared_ptr<ngraph::Variant>>;
+using PyRTMap = ngraph::Node::RTMap;
 
 PYBIND11_MAKE_OPAQUE(PyRTMap);
 
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/rt_map.cpp b/runtime/bindings/python/src/compatibility/pyngraph/rt_map.cpp
index ebd49a9a86b..535594f0564 100644
--- a/runtime/bindings/python/src/compatibility/pyngraph/rt_map.cpp
+++ b/runtime/bindings/python/src/compatibility/pyngraph/rt_map.cpp
@@ -20,29 +20,19 @@
 
 namespace py = pybind11;
 
-using PyRTMap = std::map<std::string, std::shared_ptr<ngraph::Variant>>;
+using PyRTMap = std::map<std::string, ov::Any>;
 
 PYBIND11_MAKE_OPAQUE(PyRTMap);
 
-template <typename T>
-void _set_with_variant(PyRTMap& m, const std::string& k, const T v) {
-    auto new_v = std::make_shared<ngraph::VariantWrapper<T>>(ngraph::VariantWrapper<T>(v));
-    auto it = m.find(k);
-    if (it != m.end())
-        it->second = new_v;
-    else
-        m.emplace(k, new_v);
-}
-
 void regclass_pyngraph_PyRTMap(py::module m) {
     auto py_map = py::bind_map<PyRTMap>(m, "PyRTMap");
     py_map.doc() = "ngraph.impl.PyRTMap makes bindings for std::map<std::string, "
                    "std::shared_ptr<ngraph::Variant>>, which can later be used as ngraph::Node::RTMap";
 
     py_map.def("__setitem__", [](PyRTMap& m, const std::string& k, const std::string v) {
-        _set_with_variant(m, k, v);
+        m[k] = v;
     });
     py_map.def("__setitem__", [](PyRTMap& m, const std::string& k, const int64_t v) {
-        _set_with_variant(m, k, v);
+        m[k] = v;
     });
 }
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/variant.cpp b/runtime/bindings/python/src/compatibility/pyngraph/variant.cpp
index 602954fee14..62fc1486912 100644
--- a/runtime/bindings/python/src/compatibility/pyngraph/variant.cpp
+++ b/runtime/bindings/python/src/compatibility/pyngraph/variant.cpp
@@ -11,8 +11,33 @@
 namespace py = pybind11;
 
 void regclass_pyngraph_Variant(py::module m) {
-    py::class_<ngraph::Variant, std::shared_ptr<ngraph::Variant>> variant_base(m, "Variant", py::module_local());
+    py::class_<ov::Any> variant_base(m, "Variant", py::module_local());
     variant_base.doc() = "ngraph.impl.Variant wraps ngraph::Variant";
+
+    variant_base.def(
+        "__eq__",
+        [](const ov::Any& a, const ov::Any& b) {
+            return a == b;
+        },
+        py::is_operator());
+    variant_base.def(
+        "__eq__",
+        [](const ov::Any& a, const std::string& b) {
+            return a.as<std::string>() == b;
+        },
+        py::is_operator());
+    variant_base.def(
+        "__eq__",
+        [](const ov::Any& a, const int64_t& b) {
+            return a.as<int64_t>() == b;
+        },
+        py::is_operator());
+
+    variant_base.def("__repr__", [](const ov::Any self) {
+        std::stringstream ret;
+        self.print(ret);
+        return ret.str();
+    });
 }
 
 template void regclass_pyngraph_VariantWrapper<std::string>(py::module m, std::string typestring);
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/variant.hpp b/runtime/bindings/python/src/compatibility/pyngraph/variant.hpp
index 7041f5cd0ce..81e5abea860 100644
--- a/runtime/bindings/python/src/compatibility/pyngraph/variant.hpp
+++ b/runtime/bindings/python/src/compatibility/pyngraph/variant.hpp
@@ -10,67 +10,77 @@
 #include <sstream>
 #include <string>
 
-#include "ngraph/variant.hpp" // ngraph::Variant
+#include "openvino/core/any.hpp"
 
 namespace py = pybind11;
 
 void regclass_pyngraph_Variant(py::module m);
 
+template<typename T>
+struct AnyT : public ov::Any {
+    using ov::Any::Any;
+};
+
+
 template <typename VT>
-extern void regclass_pyngraph_VariantWrapper(py::module m, std::string typestring)
-{
+extern void regclass_pyngraph_VariantWrapper(py::module m, std::string typestring) {
     auto pyclass_name = py::detail::c_str((std::string("Variant") + typestring));
-    py::class_<ngraph::VariantWrapper<VT>,
-               std::shared_ptr<ngraph::VariantWrapper<VT>>,
-               ngraph::Variant>
+    py::class_<AnyT<VT>, ov::Any>
         variant_wrapper(m, pyclass_name, py::module_local());
     variant_wrapper.doc() =
-        "ngraph.impl.Variant[typestring] wraps ngraph::VariantWrapper<typestring>";
+        "openvino.impl.Variant[" + typestring + "] wraps ov::Any with " + typestring;
 
     variant_wrapper.def(py::init<const VT&>());
 
     variant_wrapper.def(
         "__eq__",
-        [](const ngraph::VariantWrapper<VT>& a, const ngraph::VariantWrapper<VT>& b) {
-            return a.get() == b.get();
+        [](const AnyT<VT>& a, const AnyT<VT>& b) {
+            return a.template as<VT>() == b.template as<VT>();
         },
         py::is_operator());
     variant_wrapper.def(
         "__eq__",
-        [](const ngraph::VariantWrapper<std::string>& a, const std::string& b) {
-            return a.get() == b;
+        [](const AnyT<VT>& a, const std::string& b) {
+            return a.template as<std::string>() == b;
         },
         py::is_operator());
     variant_wrapper.def(
         "__eq__",
-        [](const ngraph::VariantWrapper<int64_t>& a, const int64_t& b) { return a.get() == b; },
+        [](const AnyT<VT>& a, const int64_t& b) { return a.template as<int64_t>() == b; },
         py::is_operator());
 
-    variant_wrapper.def("__repr__", [](const ngraph::VariantWrapper<VT> self) {
+    variant_wrapper.def("__repr__", [](const AnyT<VT> self) {
         std::stringstream ret;
-        ret << self.get();
+        self.print(ret);
         return ret.str();
     });
 
     variant_wrapper.def("get",
-                        (VT & (ngraph::VariantWrapper<VT>::*)()) & ngraph::VariantWrapper<VT>::get,
+                        [] (const AnyT<VT>& self) {
+                            return self.template as<VT>();
+                        },
                         R"(
                             Returns
                             ----------
                             get : Variant
-                                Value of Variant.
+                                Value of ov::Any.
                         )");
     variant_wrapper.def("set",
-                        &ngraph::VariantWrapper<VT>::set,
+                        [] (AnyT<VT>& self, const VT value) {
+                            self = value;
+                        },
                         R"(
                             Parameters
                             ----------
                             set : str or int
-                                Value to be set in Variant.
+                                Value to be set in ov::Any.
                         )");
 
     variant_wrapper.def_property("value",
-                                 (VT & (ngraph::VariantWrapper<VT>::*)()) &
-                                     ngraph::VariantWrapper<VT>::get,
-                                 &ngraph::VariantWrapper<VT>::set);
+                                 [] (const AnyT<VT>& self) {
+                                    return self.template as<VT>();
+                                 },
+                                 [] (AnyT<VT>& self, const VT value) {
+                                    self = value;
+                                 });
 }
diff --git a/runtime/bindings/python/src/pyopenvino/graph/descriptors/tensor.cpp b/runtime/bindings/python/src/pyopenvino/graph/descriptors/tensor.cpp
index 547b9d8d3c6..b23a103368c 100644
--- a/runtime/bindings/python/src/pyopenvino/graph/descriptors/tensor.cpp
+++ b/runtime/bindings/python/src/pyopenvino/graph/descriptors/tensor.cpp
@@ -13,7 +13,7 @@
 
 namespace py = pybind11;
 
-using PyRTMap = std::map<std::string, std::shared_ptr<ov::Variant>>;
+using PyRTMap = ov::RTMap;
 
 PYBIND11_MAKE_OPAQUE(PyRTMap);
 
diff --git a/runtime/bindings/python/src/pyopenvino/graph/function.cpp b/runtime/bindings/python/src/pyopenvino/graph/function.cpp
index 685eabba812..1a692f5cdd1 100644
--- a/runtime/bindings/python/src/pyopenvino/graph/function.cpp
+++ b/runtime/bindings/python/src/pyopenvino/graph/function.cpp
@@ -20,7 +20,7 @@ namespace py = pybind11;
 
 static const char* CAPSULE_NAME = "openvino_function";
 
-using PyRTMap = std::map<std::string, std::shared_ptr<ov::Variant>>;
+using PyRTMap = ov::RTMap;
 
 PYBIND11_MAKE_OPAQUE(PyRTMap);
 
@@ -478,11 +478,11 @@ void regclass_graph_Function(py::module m) {
                     ----------
                     value : Output
                         Output containing Node
-                    
+
                     Returns
                     ----------
                     get_result_index : int
-                        Index for value referencing it. 
+                        Index for value referencing it.
                  )");
     function.def("get_result_index",
                  (int64_t(ov::Function::*)(const ov::Output<const ov::Node>&) const) & ov::Function::get_result_index,
@@ -496,7 +496,7 @@ void regclass_graph_Function(py::module m) {
                     ----------
                     value : Output
                         Output containing Node
-                    
+
                     Returns
                     ----------
                     get_result_index : int
@@ -542,7 +542,7 @@ void regclass_graph_Function(py::module m) {
     function.def("is_dynamic",
                  &ov::Function::is_dynamic,
                  R"(
-                    Returns true if any of the op's defined in the function 
+                    Returns true if any of the op's defined in the function
                     contains partial shape.
 
                     Returns
@@ -627,7 +627,7 @@ void regclass_graph_Function(py::module m) {
                  py::arg("parameter"),
                  R"(
                     Replace the `parameter_index`th parameter of the function with `parameter`.
-    
+
                     All users of the `parameter_index`th parameter are redirected to `parameter`, and the
                     `parameter_index`th entry in the function parameter list is replaced with `parameter`.
 
diff --git a/runtime/bindings/python/src/pyopenvino/graph/node.cpp b/runtime/bindings/python/src/pyopenvino/graph/node.cpp
index ce20f8147fc..adaefe67966 100644
--- a/runtime/bindings/python/src/pyopenvino/graph/node.cpp
+++ b/runtime/bindings/python/src/pyopenvino/graph/node.cpp
@@ -31,7 +31,7 @@ public:
 
 namespace py = pybind11;
 
-using PyRTMap = std::map<std::string, std::shared_ptr<ov::Variant>>;
+using PyRTMap = ov::Node::RTMap;
 
 PYBIND11_MAKE_OPAQUE(PyRTMap);
 
@@ -174,7 +174,7 @@ void regclass_graph_Node(py::module m) {
                 Returns
                 ----------
                 get_type_name : str
-                    String repesenting Type's name. 
+                    String repesenting Type's name.
              )");
     node.def("get_name",
              &ov::Node::get_name,
@@ -189,7 +189,7 @@ void regclass_graph_Node(py::module m) {
     node.def("get_friendly_name",
              &ov::Node::get_friendly_name,
              R"(
-                Gets the friendly name for a node. If no friendly name has 
+                Gets the friendly name for a node. If no friendly name has
                 been set via set_friendly_name then the node's unique name
                 is returned.
 
@@ -204,7 +204,7 @@ void regclass_graph_Node(py::module m) {
              py::arg("name"),
              R"(
                 Sets a friendly name for a node. This does not overwrite the unique name
-                of the node and is retrieved via get_friendly_name(). Used mainly for 
+                of the node and is retrieved via get_friendly_name(). Used mainly for
                 debugging. The friendly name may be set exactly once.
 
                 Parameters
diff --git a/runtime/bindings/python/src/pyopenvino/graph/rt_map.cpp b/runtime/bindings/python/src/pyopenvino/graph/rt_map.cpp
index ba52c2ca5c5..95f1d8f0f42 100644
--- a/runtime/bindings/python/src/pyopenvino/graph/rt_map.cpp
+++ b/runtime/bindings/python/src/pyopenvino/graph/rt_map.cpp
@@ -20,29 +20,19 @@
 
 namespace py = pybind11;
 
-using PyRTMap = std::map<std::string, std::shared_ptr<ov::Variant>>;
+using PyRTMap = ov::RTMap;
 
 PYBIND11_MAKE_OPAQUE(PyRTMap);
 
-template <typename T>
-void _set_with_variant(PyRTMap& m, const std::string& k, const T v) {
-    auto new_v = std::make_shared<ov::VariantWrapper<T>>(ov::VariantWrapper<T>(v));
-    auto it = m.find(k);
-    if (it != m.end())
-        it->second = new_v;
-    else
-        m.emplace(k, new_v);
-}
-
 void regclass_graph_PyRTMap(py::module m) {
     auto py_map = py::bind_map<PyRTMap>(m, "PyRTMap");
-    py_map.doc() = "ngraph.impl.PyRTMap makes bindings for std::map<std::string, "
-                   "std::shared_ptr<ov::Variant>>, which can later be used as ov::Node::RTMap";
+    py_map.doc() = "openvino.impl.PyRTMap makes bindings for std::map<std::string, "
+                   "ov::Any, which can later be used as ov::Node::RTMap";
 
     py_map.def("__setitem__", [](PyRTMap& m, const std::string& k, const std::string v) {
-        _set_with_variant(m, k, v);
+        m[k] = v;
     });
     py_map.def("__setitem__", [](PyRTMap& m, const std::string& k, const int64_t v) {
-        _set_with_variant(m, k, v);
+        m[k] = v;
     });
 }
diff --git a/runtime/bindings/python/src/pyopenvino/graph/variant.cpp b/runtime/bindings/python/src/pyopenvino/graph/variant.cpp
index 407b3965797..ebf388d1c3c 100644
--- a/runtime/bindings/python/src/pyopenvino/graph/variant.cpp
+++ b/runtime/bindings/python/src/pyopenvino/graph/variant.cpp
@@ -2,17 +2,42 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "openvino/core/variant.hpp"  // ov::Variant
+#include "pyopenvino/graph/variant.hpp"
 
 #include <pybind11/pybind11.h>
 
-#include "pyopenvino/graph/variant.hpp"
+#include "openvino/core/any.hpp"
 
 namespace py = pybind11;
 
 void regclass_graph_Variant(py::module m) {
-    py::class_<ov::Variant, std::shared_ptr<ov::Variant>> variant_base(m, "Variant");
-    variant_base.doc() = "openvino.impl.Variant wraps ov::Variant";
+    py::class_<ov::Any> variant_base(m, "Variant", py::module_local());
+    variant_base.doc() = "openvino.impl.Variant wraps ov::Any";
+
+    variant_base.def(
+        "__eq__",
+        [](const ov::Any& a, const ov::Any& b) {
+            return a == b;
+        },
+        py::is_operator());
+    variant_base.def(
+        "__eq__",
+        [](const ov::Any& a, const std::string& b) {
+            return a.as<std::string>() == b;
+        },
+        py::is_operator());
+    variant_base.def(
+        "__eq__",
+        [](const ov::Any& a, const int64_t& b) {
+            return a.as<int64_t>() == b;
+        },
+        py::is_operator());
+
+    variant_base.def("__repr__", [](const ov::Any self) {
+        std::stringstream ret;
+        self.print(ret);
+        return ret.str();
+    });
 }
 
 template void regclass_graph_VariantWrapper<std::string>(py::module m, std::string typestring);
diff --git a/runtime/bindings/python/src/pyopenvino/graph/variant.hpp b/runtime/bindings/python/src/pyopenvino/graph/variant.hpp
index 6d4bc06f0f5..3dee3c6d6f0 100644
--- a/runtime/bindings/python/src/pyopenvino/graph/variant.hpp
+++ b/runtime/bindings/python/src/pyopenvino/graph/variant.hpp
@@ -10,67 +10,77 @@
 #include <sstream>
 #include <string>
 
-#include "openvino/core/variant.hpp" // ov::Variant
+#include "openvino/core/any.hpp" // ov::Variant
 
 namespace py = pybind11;
 
 void regclass_graph_Variant(py::module m);
 
+template<typename T>
+struct AnyT : public ov::Any {
+    using ov::Any::Any;
+};
+
 template <typename VT>
 extern void regclass_graph_VariantWrapper(py::module m, std::string typestring)
 {
     auto pyclass_name = py::detail::c_str((std::string("Variant") + typestring));
-    py::class_<ov::VariantWrapper<VT>,
-               std::shared_ptr<ov::VariantWrapper<VT>>,
-               ov::Variant>
-        variant_wrapper(m, pyclass_name );
+    py::class_<AnyT<VT>, ov::Any>
+        variant_wrapper(m, pyclass_name, py::module_local());
     variant_wrapper.doc() =
-        "openvino.impl.Variant[typestring] wraps ov::VariantWrapper<typestring>";
+        "openvino.impl.Variant[" + typestring + "] wraps ov::Any with " + typestring;
 
     variant_wrapper.def(py::init<const VT&>());
 
     variant_wrapper.def(
         "__eq__",
-        [](const ov::VariantWrapper<VT>& a, const ov::VariantWrapper<VT>& b) {
-            return a.get() == b.get();
+        [](const ov::Any& a, const ov::Any& b) {
+            return a.as<VT>() == b.as<VT>();
         },
         py::is_operator());
     variant_wrapper.def(
         "__eq__",
-        [](const ov::VariantWrapper<std::string>& a, const std::string& b) {
-            return a.get() == b;
+        [](const ov::Any& a, const std::string& b) {
+            return a.as<std::string>() == b;
         },
         py::is_operator());
     variant_wrapper.def(
         "__eq__",
-        [](const ov::VariantWrapper<int64_t>& a, const int64_t& b) { return a.get() == b; },
+        [](const ov::Any& a, const int64_t& b) { return a.as<int64_t>() == b; },
         py::is_operator());
 
-    variant_wrapper.def("__repr__", [](const ov::VariantWrapper<VT> self) {
+    variant_wrapper.def("__repr__", [](const ov::Any self) {
         std::stringstream ret;
-        ret << self.get();
+        self.print(ret);
         return ret.str();
     });
 
     variant_wrapper.def("get",
-                        (VT & (ov::VariantWrapper<VT>::*)()) & ov::VariantWrapper<VT>::get,
+                        [] (const ov::Any& self) {
+                            return self.as<VT>();
+                        },
                         R"(
                             Returns
                             ----------
                             get : Variant
-                                Value of Variant.
+                                Value of ov::Any.
                         )");
     variant_wrapper.def("set",
-                        &ov::VariantWrapper<VT>::set,
+                        [] (ov::Any& self, const VT value) {
+                            self = value;
+                        },
                         R"(
                             Parameters
                             ----------
                             set : str or int
-                                Value to be set in Variant.
+                                Value to be set in ov::Any.
                         )");
 
     variant_wrapper.def_property("value",
-                                 (VT & (ov::VariantWrapper<VT>::*)()) &
-                                     ov::VariantWrapper<VT>::get,
-                                 &ov::VariantWrapper<VT>::set);
+                                 [] (const ov::Any& self) {
+                                    return self.as<VT>();
+                                 },
+                                 [] (ov::Any& self, const VT value) {
+                                    self = value;
+                                 });
 }
diff --git a/samples/cpp/hello_query_device/main.cpp b/samples/cpp/hello_query_device/main.cpp
index 9bf079dd85c..2e204b7f7f9 100644
--- a/samples/cpp/hello_query_device/main.cpp
+++ b/samples/cpp/hello_query_device/main.cpp
@@ -32,7 +32,7 @@ std::ostream& operator<<(std::ostream& stream, const std::vector<T>& v) {
  * @param reference on IE Parameter
  * @return void
  */
-void printParameterValue(const ov::runtime::Parameter& value) {
+void printAnyValue(const ov::Any& value) {
     if (value.empty()) {
         std::cout << "EMPTY VALUE" << std::endl;
     } else if (value.is<bool>()) {
@@ -83,7 +83,14 @@ void printParameterValue(const ov::runtime::Parameter& value) {
         std::cout << " }";
         std::cout << std::endl;
     } else {
-        std::cout << "UNSUPPORTED TYPE" << std::endl;
+        std::stringstream strm;
+        value.print(strm);
+        auto str = strm.str();
+        if (str.empty()) {
+            std::cout << "UNSUPPORTED TYPE" << std::endl;
+        } else {
+            std::cout << str << std::endl;
+        }
     }
 }
 
@@ -117,7 +124,7 @@ int main(int argc, char* argv[]) {
             for (auto&& metricName : supportedMetrics) {
                 if (metricName != METRIC_KEY(SUPPORTED_METRICS) && metricName != METRIC_KEY(SUPPORTED_CONFIG_KEYS)) {
                     std::cout << "\t\t" << metricName << " : " << std::flush;
-                    printParameterValue(core.get_metric(device, metricName));
+                    printAnyValue(core.get_metric(device, metricName));
                 }
             }
 
@@ -129,7 +136,7 @@ int main(int argc, char* argv[]) {
                     core.get_metric(device, METRIC_KEY(SUPPORTED_CONFIG_KEYS));
                 for (auto&& configKey : supportedConfigKeys) {
                     std::cout << "\t\t" << configKey << " : " << std::flush;
-                    printParameterValue(core.get_config(device, configKey));
+                    printAnyValue(core.get_config(device, configKey));
                 }
             }
 

From 7f6b004a33fb14a3d31ace475889cb0c370b8d00 Mon Sep 17 00:00:00 2001
From: Mateusz Tabaka <mateusz.tabaka@intel.com>
Date: Fri, 26 Nov 2021 10:31:21 +0100
Subject: [PATCH 33/72] [VPU] filter out unused outputs in RNN layer handler
 (#8778)

[VPU] Filter out unused outputs in RNN layer handler
Ticket: 60540
---
 .../vpu/graph_transformer/src/stages/rnn.cpp  |  5 +--
 .../myriad/subgraph_tests/lstm_transpose.cpp  | 34 +++++++++++++++++++
 2 files changed, 37 insertions(+), 2 deletions(-)
 create mode 100644 inference-engine/tests/functional/plugin/myriad/subgraph_tests/lstm_transpose.cpp

diff --git a/inference-engine/src/vpu/graph_transformer/src/stages/rnn.cpp b/inference-engine/src/vpu/graph_transformer/src/stages/rnn.cpp
index 8cae451119c..0c0d676b18c 100644
--- a/inference-engine/src/vpu/graph_transformer/src/stages/rnn.cpp
+++ b/inference-engine/src/vpu/graph_transformer/src/stages/rnn.cpp
@@ -198,8 +198,9 @@ void FrontEnd::parseRNN(const Model& model, const ie::CNNLayerPtr& _layer, const
     };
 
     auto newWeights = model->addConstData(_layer->name + "@weights", weights->desc(), generator);
-    auto outputData = outputs;
-    if (outputs.size() == 1) {
+    DataVector outputData;
+    std::copy_if(outputs.cbegin(), outputs.cend(), std::back_inserter(outputData), [](const Data& handle) { return !!handle;});
+    if (outputData.size() == 1) {
         outputData.push_back(model->addFakeData());
     }
 
diff --git a/inference-engine/tests/functional/plugin/myriad/subgraph_tests/lstm_transpose.cpp b/inference-engine/tests/functional/plugin/myriad/subgraph_tests/lstm_transpose.cpp
new file mode 100644
index 00000000000..6a7314efb73
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/myriad/subgraph_tests/lstm_transpose.cpp
@@ -0,0 +1,34 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <shared_test_classes/base/layer_test_utils.hpp>
+#include <ngraph/opsets/opset8.hpp>
+
+namespace {
+
+using namespace ngraph;
+
+class LSTMTranspose : virtual public LayerTestsUtils::LayerTestsCommon {
+protected:
+    void SetUp() override {
+        targetDevice = CommonTestUtils::DEVICE_MYRIAD;
+
+        auto X = std::make_shared<opset8::Parameter>(element::f32, Shape{1, 26, 64});
+        auto hidden_state = std::make_shared<opset8::Parameter>(element::f32, Shape{1, 1, 32});
+        auto cell_state = std::make_shared<opset8::Parameter>(element::f32, Shape{1, 1, 32});
+        auto seq_len = opset8::Constant::create(element::i32, Shape{1}, {26});
+        auto W = opset8::Constant::create(element::f32, Shape{1, 128, 64}, {1});
+        auto R = opset8::Constant::create(element::f32, Shape{1, 128, 32}, {1});
+        auto B = opset8::Constant::create(element::f32, Shape{1, 128}, {1});
+        auto lstm = std::make_shared<opset8::LSTMSequence>(X, hidden_state, cell_state, seq_len, W, R, B, 32, op::RecurrentSequenceDirection::FORWARD);
+        auto transpose = std::make_shared<opset8::Transpose>(lstm->output(0), opset8::Constant::create(element::i32, Shape{4}, {2, 0, 1, 3}));
+        function = std::make_shared<ngraph::Function>(transpose, ParameterVector{X, hidden_state, cell_state});
+    }
+};
+
+TEST_F(LSTMTranspose, CompareWithRefs) {
+    Run();
+}
+
+}  // namespace

From 2d0ae6028a7cc3e2f2d7a9ba145c1c5b2a2f4260 Mon Sep 17 00:00:00 2001
From: Liubov Batanina <piccione-mail@yandex.ru>
Date: Fri, 26 Nov 2021 12:42:33 +0300
Subject: [PATCH 34/72] [POT] Added quantization inside subgraphs (#8530)

* [POT] Support subgraphs

* Add support for inplace statistics

* Fixed cascade models

* Fixed bug

* Fixed import

* Fix name

* Add test

* Fix comments

* Replace test model

* Replace test model

* Add recursively flag

* Fix pylint

* Updated AA node names

* Fix adding results to node

* Disable subgraph quantization

* Disable recursion search in graph

* Skip test with subgraphs quantization
---
 .../tools/pot/algorithms/algorithm.py         |   4 +
 .../pot/algorithms/finetuning/algorithm.py    |   8 +-
 .../accuracy_aware_common/algorithm.py        |  12 +-
 .../accuracy_aware_common/mixed_precision.py  |  12 +-
 .../accuracy_aware_common/utils.py            |  11 ++
 .../quantization/bias_correction/algorithm.py |  62 ++++----
 .../channel_alignment/algorithm.py            |  22 +--
 .../quantization/datafree/algorithm.py        |  43 +++---
 .../algorithms/quantization/fake_quantize.py  |  44 +++---
 .../fake_quantize_configuration.py            |  24 ++--
 .../fast_bias_correction/algorithm.py         |  24 ++--
 .../layerwise_finetuning/algorithm.py         |  16 +--
 .../quantization/minmax/algorithm.py          |  32 ++---
 .../quantization/optimization/rangeopt.py     |  14 +-
 .../outlier_channel_splitting/algorithm.py    |   4 +-
 .../overflow_correction/algorithm.py          |  12 +-
 .../qnoise_estimator/algorithm.py             |  31 ++--
 .../quantization/qnoise_estimator/utils.py    |   4 +-
 .../quantization/ranger/algorithm.py          |  14 +-
 .../tunable_quantization/algorithm.py         |   2 +-
 .../weight_bias_correction/algorithm.py       |   4 +-
 .../pot/algorithms/sparsity/default/utils.py  |   2 +-
 .../layerwise_finetuning/algorithm.py         |   8 +-
 .../sparsity/magnitude_sparsity/algorithm.py  |   8 +-
 .../openvino/tools/pot/algorithms/utils.py    |  15 ++
 .../tools/pot/data_loaders/creator.py         |   2 +-
 .../openvino/tools/pot/engines/ac_engine.py   |  16 ++-
 .../openvino/tools/pot/engines/ie_engine.py   |  16 ++-
 tools/pot/openvino/tools/pot/engines/utils.py |  37 ++++-
 tools/pot/openvino/tools/pot/graph/editor.py  |  88 ++++++++++--
 .../openvino/tools/pot/graph/model_utils.py   |  16 ++-
 .../openvino/tools/pot/graph/node_utils.py    |   8 +-
 .../pot/openvino/tools/pot/graph/nx_model.py  |  21 ++-
 tools/pot/openvino/tools/pot/graph/passes.py  |  20 +--
 .../openvino/tools/pot/graph/transformer.py   |   5 +
 tools/pot/openvino/tools/pot/graph/utils.py   |   6 +-
 .../pot/statistics/statistic_graph_builder.py | 132 ++++++++++++------
 .../pot/tests/data/hardware_configs/cpu.json  |   7 +
 .../reference/cpu_flatten.json                |   2 +-
 .../tensor_iterator_example.json              |   3 +
 .../tensor_iterator_example.pb                |   3 +
 .../tensor_iterator_example_tf.xml            |   3 +
 tools/pot/tests/test_graph.py                 |   3 +-
 tools/pot/tests/test_statistic_builder.py     |   2 +-
 44 files changed, 539 insertions(+), 283 deletions(-)
 create mode 100644 tools/pot/openvino/tools/pot/algorithms/utils.py
 create mode 100644 tools/pot/tests/data/models/tensor_iterator_example/tensor_iterator_example.json
 create mode 100644 tools/pot/tests/data/models/tensor_iterator_example/tensor_iterator_example.pb
 create mode 100644 tools/pot/tests/data/reference_models/tensor_iterator_example_tf.xml

diff --git a/tools/pot/openvino/tools/pot/algorithms/algorithm.py b/tools/pot/openvino/tools/pot/algorithms/algorithm.py
index 04305ddc193..4a8352c010e 100644
--- a/tools/pot/openvino/tools/pot/algorithms/algorithm.py
+++ b/tools/pot/openvino/tools/pot/algorithms/algorithm.py
@@ -4,6 +4,7 @@
 from abc import ABC, abstractmethod
 from copy import deepcopy
 
+from .utils import process_ignored_scope
 from ..api.engine import Engine
 
 
@@ -23,6 +24,9 @@ class Algorithm(ABC):
         self.default_steps_size = 0.05
         self.total_exec_steps = 0
 
+        if isinstance(self._config.ignored, dict) and 'scope' in self._config.ignored:
+            self._config.ignored.scope = process_ignored_scope(self._config.ignored.scope)
+
     @property
     def config(self):
         return self._config
diff --git a/tools/pot/openvino/tools/pot/algorithms/finetuning/algorithm.py b/tools/pot/openvino/tools/pot/algorithms/finetuning/algorithm.py
index 0f6ac6bc675..7ef3c6c3b53 100644
--- a/tools/pot/openvino/tools/pot/algorithms/finetuning/algorithm.py
+++ b/tools/pot/openvino/tools/pot/algorithms/finetuning/algorithm.py
@@ -125,7 +125,7 @@ class LayerwiseModelFinetuning(Algorithm):
                     logger.warning('Undefined parameter found: {}'.format(name))
                     continue
         else:
-            logger.warning('Was not able to wrap layer {} with PyTorch'.format(op_node.name))
+            logger.warning('Was not able to wrap layer {} with PyTorch'.format(op_node.fullname))
         return wrapped_op, params
 
     def _fine_tuning_loop(
@@ -285,7 +285,7 @@ class LayerwiseModelFinetuning(Algorithm):
         base_algo.register_statistics(self._original_model, self.algo_collector)
         collect_statistics(self._engine, self._original_model, [base_algo])
         base_model = base_algo.run(deepcopy(self._original_model))
-        output_node_name = nu.get_node_input(self._original_model.get_final_output_nodes()[0], 0).name
+        output_node_name = nu.get_node_input(self._original_model.get_final_output_nodes()[0], 0).fullname
 
         stats_layout = {output_node_name: {'output_logits': TensorStatistic(lambda logits: logits)}}
         metric_subset_size = int(self._dataset_size * self._metric_subset_ratio)
@@ -344,9 +344,9 @@ class LayerwiseModelFinetuning(Algorithm):
                 fp_model_callbacks[input_node_name] = {'output': lambda tensor: tensor}
             else:
                 modified_model_callbacks[input_node_name] = {'output': lambda tensor: tensor}
-            fp_model_callbacks[output_node.name] = {'output': lambda tensor: tensor}
+            fp_model_callbacks[output_node.fullname] = {'output': lambda tensor: tensor}
             self._nodes_to_tune_input[op_name] = input_node_name
-            self._nodes_to_tune_output[op_name] = output_node.name
+            self._nodes_to_tune_output[op_name] = output_node.fullname
 
         return fp_model_callbacks, modified_model_callbacks
 
diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware_common/algorithm.py b/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware_common/algorithm.py
index 17e0d5e504c..23978777c3d 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware_common/algorithm.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware_common/algorithm.py
@@ -9,7 +9,7 @@ from sys import maxsize
 import numpy as np
 
 from .utils import create_metric_config, is_preset_performance, \
-    get_mixed_preset_config, evaluate_model, get_num_of_quantized_ops
+    get_mixed_preset_config, evaluate_model, get_num_of_quantized_ops, prepare_nodes_for_logger
 from ..utils import load_hardware_config
 from ...algorithm import Algorithm
 from ...algorithm_selector import COMPRESSION_ALGORITHMS
@@ -356,7 +356,7 @@ class AccuracyAwareCommon(Algorithm):
             logger.debug('Changed FakeQuantize nodes:\n %s', '\n'.join(all_changed_nodes_names))
             logger.info(' %d out of %d layers have been reverted back to the %s precision: %s',
                         len(all_ops_in_targeted_prec), self._quantized_layers_num, self._precision_change_to,
-                        ', '.join(all_ops_in_targeted_prec))
+                        ', '.join(prepare_nodes_for_logger(all_ops_in_targeted_prec)))
             send_event("result_aa", self._get_result_aa(metrics_accuracy_drop, len(all_ops_in_targeted_prec)))
 
         logger.update_progress(self.total_exec_steps)
@@ -418,10 +418,10 @@ class AccuracyAwareCommon(Algorithm):
 
         fake_quantize_nodes = get_nodes_by_type(model, ['FakeQuantize'])
         for node in fake_quantize_nodes:
-            if excluded_nodes and node.name in excluded_nodes:
+            if excluded_nodes and node.fullname in excluded_nodes:
                 continue
-            if node.name not in change_fqs:
-                modified_model, modified_fq_layers, _ = self._modify_model_in_scope(deepcopy(model), [node.name])
+            if node.fullname not in change_fqs:
+                modified_model, modified_fq_layers, _ = self._modify_model_in_scope(deepcopy(model), [node.fullname])
                 if not modified_fq_layers:
                     continue
                 logger.debug('Changed\\Removed a block of %d FQ layers: %s', len(modified_fq_layers),
@@ -434,7 +434,7 @@ class AccuracyAwareCommon(Algorithm):
                 self._engine.allow_pairwise_subset = False
                 logger.update_progress(self._config.ranking_subset_size)
                 ranking_metric = self._metrics_config[metric_name].ranking
-                node_importance_score[node.name] = ranking_metric.comparator(metrics[ranking_metric.name])
+                node_importance_score[node.fullname] = ranking_metric.comparator(metrics[ranking_metric.name])
 
         eu.reset_dataset_to_default(self._engine)
 
diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware_common/mixed_precision.py b/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware_common/mixed_precision.py
index 3234adbb4ce..0cd61e85e00 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware_common/mixed_precision.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware_common/mixed_precision.py
@@ -43,7 +43,7 @@ class INT4MixedQuantization(AccuracyAwareCommon):
     # pylint: disable=W0221, W0212
     def _quantize_model(self, model, quantize_with_low_bitwidth=True):
         convolution_fq_nodes = [
-            node.name
+            node.fullname
             for node in mu.get_nodes_by_type(model, ['FakeQuantize'])
             if self._can_set_fq_to_low_bitwidth(node)
         ]
@@ -67,9 +67,9 @@ class INT4MixedQuantization(AccuracyAwareCommon):
         cut_fqs = []
         cut_model = deepcopy(model)
         for node in mu.get_nodes_by_type(model, ['FakeQuantize']):
-            if node.name not in cut_fqs:
+            if node.fullname not in cut_fqs:
                 cut_model, cut_fq_layers, _ = self._graph_transformer.remove_fq_nodes(
-                    cut_model, [node.name]
+                    cut_model, [node.fullname]
                 )
                 cut_fqs += cut_fq_layers
         return cut_model
@@ -136,9 +136,9 @@ class INT4MixedQuantization(AccuracyAwareCommon):
         ]
 
         for node in convolution_fq_nodes:
-            if node.name not in cut_fqs:
+            if node.fullname not in cut_fqs:
                 cut_model, cut_fq_layers, _ = self._modify_model_in_scope(
-                    model, [node.name]
+                    model, [node.fullname]
                 )
                 logger.info(
                     'Removed a block of %d FQ layers: %s',
@@ -155,7 +155,7 @@ class INT4MixedQuantization(AccuracyAwareCommon):
                 self._engine.allow_pairwise_subset = False
                 logger.update_progress(self._config.ranking_subset_size)
                 ranking_metric = self._metrics_config[metric_name].ranking
-                node_importance_score[node.name] = ranking_metric.comparator(
+                node_importance_score[node.fullname] = ranking_metric.comparator(
                     metrics[ranking_metric.name]
                 )
 
diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware_common/utils.py b/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware_common/utils.py
index 7c9f0d80c57..554425326e8 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware_common/utils.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/accuracy_aware_common/utils.py
@@ -338,3 +338,14 @@ def process_per_sample_metrics(metrics_per_sample, metrics_config,
             processed_metrics_per_sample[name] = [values[i] for i in indices]
 
     return processed_metrics_per_sample
+
+
+def prepare_nodes_for_logger(nodes_names):
+    postprocessed_nodes_names = []
+    for name in nodes_names:
+        subgraphs = name.split('|')
+        if len(subgraphs) > 1:
+            postprocessed_nodes_names.append(str(subgraphs))
+        else:
+            postprocessed_nodes_names.append(subgraphs[0])
+    return postprocessed_nodes_names
diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/bias_correction/algorithm.py b/tools/pot/openvino/tools/pot/algorithms/quantization/bias_correction/algorithm.py
index ddf74a68a87..d36f8602fa5 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/bias_correction/algorithm.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/bias_correction/algorithm.py
@@ -112,16 +112,16 @@ class BiasCorrection(Algorithm):
 
         def skip_node(node):
             if not nu.node_with_quantized_weights(node) and not self._apply_for_all_nodes:
-                logger.debug('%s skipped because it does not have FQ weights.', node.name)
+                logger.debug('%s skipped because it does not have FQ weights.', node.fullname)
                 return True
 
             if not nu.check_const_input(node):
-                logger.debug('%s skipped because channel axis is not defined', node.name)
+                logger.debug('%s skipped because channel axis is not defined', node.fullname)
                 return True
 
             bias_node = nu.get_bias_for_node(node)
             if bias_node is None:
-                logger.debug('%s skipped because its bias is empty.', node.name)
+                logger.debug('%s skipped because its bias is empty.', node.fullname)
                 return True
 
             return False
@@ -135,9 +135,9 @@ class BiasCorrection(Algorithm):
                 continue
             input_nodes, stats_nodes, output_nodes = self._get_subgraph_data_for_node(node)
             subgraphs_data[node_name] = {
-                'input_nodes': [n.name for n in input_nodes],
-                'stats_nodes': [n.name for n in stats_nodes],
-                'output_nodes': [n.name for n in output_nodes]
+                'input_nodes': [n.fullname for n in input_nodes],
+                'stats_nodes': [n.fullname for n in stats_nodes],
+                'output_nodes': [n.fullname for n in output_nodes]
             }
         del model_copy
         return subgraphs_data
@@ -147,7 +147,7 @@ class BiasCorrection(Algorithm):
         fq_names_to_cut = []
         for fq_node in fq_nodes:
             if nu.get_node_input(fq_node, 0).type != 'Const':
-                fq_names_to_cut.append(fq_node.name)
+                fq_names_to_cut.append(fq_node.fullname)
         self._graph_transformer.remove_fq_nodes(model, fq_names_to_cut, True)
 
     def _get_subgraph_data_for_node(self, main_node):
@@ -170,9 +170,9 @@ class BiasCorrection(Algorithm):
                     walk_to_parents(stat_node_parent)
 
         def fill_output_nodes():
-            assigns = ge.get_nodes_by_type(main_node.graph, ['Assign'])
+            assigns = ge.get_nodes_by_type(main_node.graph, ['Assign'], recursively=False)
             for node_name in checked_input_names:
-                node = ge.get_node_by_name(main_node.graph, node_name)
+                node = ge.get_node_by_name(main_node.graph, node_name, recursively=False)
                 if node.type == 'ReadValue':
                     output_nodes.extend(nu.get_lstm_ends(node, assigns, checked_input_names))
 
@@ -185,11 +185,11 @@ class BiasCorrection(Algorithm):
                     node_input_0 = nu.get_node_input(node_input_0, 0)
             if node.type in self._types_with_bias \
                     and (nu.node_with_quantized_weights(node) and not self._apply_for_all_nodes):
-                if node_input_0.name not in checked_stat_names:
-                    checked_stat_names.append(node_input_0.name)
-                    checked_input_names.append(node_input_0.name)
+                if node_input_0.fullname not in checked_stat_names:
+                    checked_stat_names.append(node_input_0.fullname)
+                    checked_input_names.append(node_input_0.fullname)
                     stats_nodes.append(node_input_0)
-                    self._collected_stat_inputs.append(node_input_0.name)
+                    self._collected_stat_inputs.append(node_input_0.fullname)
             elif is_this_branch_node and len(node_parents) > 1:
                 return
             else:
@@ -200,10 +200,10 @@ class BiasCorrection(Algorithm):
 
         def walk_to_parents(node):
             node_parents = self.get_node_parents(node)
-            if node.name in checked_input_names:
+            if node.fullname in checked_input_names:
                 return
-            checked_input_names.append(node.name)
-            if node.name in self._collected_stat_inputs:
+            checked_input_names.append(node.fullname)
+            if node.fullname in self._collected_stat_inputs:
                 if node not in input_nodes:
                     input_nodes.append(node)
             else:
@@ -225,11 +225,11 @@ class BiasCorrection(Algorithm):
             model.clean_up()
         else:
             input_nodes = [mu.get_node_by_name(model, name) for name in
-                           self._subgraphs_data[node.name]['input_nodes']]
+                           self._subgraphs_data[node.fullname]['input_nodes']]
             stats_nodes = [mu.get_node_by_name(model, name) for name in
-                           self._subgraphs_data[node.name]['stats_nodes']]
+                           self._subgraphs_data[node.fullname]['stats_nodes']]
             output_nodes = [mu.get_node_by_name(model, name) for name in
-                            self._subgraphs_data[node.name]['output_nodes']]
+                            self._subgraphs_data[node.fullname]['output_nodes']]
 
             self._remove_default_results(node.graph)
             if node_bias_add not in output_nodes:
@@ -248,7 +248,7 @@ class BiasCorrection(Algorithm):
 
     @staticmethod
     def _remove_default_results(graph):
-        graph_outputs = ge.get_nodes_by_type(graph, ['Result'])
+        graph_outputs = ge.get_nodes_by_type(graph, ['Result'], recursively=False)
         for graph_output in graph_outputs:
             graph.remove_node(graph_output.id)
 
@@ -294,7 +294,7 @@ class BiasCorrection(Algorithm):
         return outputs_data
 
     def _update_split_subgraphs(self, model_copy):
-        for node_split in mu.get_nodes_by_type(model_copy, self._split_types):
+        for node_split in mu.get_nodes_by_type(model_copy, self._split_types, recursively=False):
             for port_id in node_split.out_ports():
                 split_result_name = '{}/result/{}'.format(node_split.name, port_id)
                 split_result = ge.create_node(node_split.graph, split_result_name, 'Result', {})
@@ -322,7 +322,7 @@ class BiasCorrection(Algorithm):
             self._launcher.set_model(model_copy, md_shapes=feed_shapes)
 
     def _compute_bias_shift(self, model_copy, **params):
-        add_name = params['node_bias_add'].name
+        add_name = params['node_bias_add'].fullname
         fp32_output = agf.mean(self._fp32_statistics[add_name]['mean_per_channel'])
 
         if model_copy.is_cascade:
@@ -382,7 +382,7 @@ class BiasCorrection(Algorithm):
             if trigger:
                 for stat_node_name in self._subgraphs_data[node_name]['input_nodes']:
                     input_node = mu.get_node_by_name(graph, stat_node_name)
-                    stat_nodes_list.append(input_node.name)
+                    stat_nodes_list.append(input_node.fullname)
         return stat_nodes_list
 
     def register_statistics(self, model, stats_collector):
@@ -395,18 +395,18 @@ class BiasCorrection(Algorithm):
         fqut.insert_fake_quantize_nodes(self._config, quantized_model)
         topological_biased_ops = self._get_topological_biased_ops(quantized_model)
 
-        self._nodes_with_bias_names = [node.name for node in topological_biased_ops]
-        parameter_nodes = mu.get_nodes_by_type(model, ['Parameter'])
+        self._nodes_with_bias_names = [node.fullname for node in topological_biased_ops]
+        parameter_nodes = mu.get_nodes_by_type(model, ['Parameter'], recursively=False)
         biased_after_param_nodes = self._get_biased_after_params(parameter_nodes)
         for node in topological_biased_ops:
             add_node = self._get_add_node_for_bias(node)
-            add_node_name = add_node.name
+            add_node_name = add_node.fullname
             if 'orig_node_name' in add_node:
-                add_node_name = add_node['orig_node_name']
+                add_node_name = nu.reset_node_fullname(add_node_name, add_node['orig_node_name'])
             axis = OPERATIONS_CHANNEL_AXIS[node.type]
             self._channel_axis[add_node_name] = axis
-            if node.name in biased_after_param_nodes:
-                input_name = biased_after_param_nodes[node.name]
+            if node.fullname in biased_after_param_nodes:
+                input_name = biased_after_param_nodes[node.fullname]
                 statistics_layout[input_name] = {'batch_mean_param_in': agf.batch_mean}
                 self._collected_stat_inputs.append(input_name)
             statistics_layout[add_node_name] =\
@@ -447,13 +447,13 @@ class BiasCorrection(Algorithm):
             node_children = self.get_node_children(node)
             if node.type in self._types_with_bias:
                 node_input = nu.get_node_input(node, 0)
-                biased_after_param_nodes[node.name] = node_input.name
+                biased_after_param_nodes[node.fullname] = node_input.fullname
                 return
             for node_child in node_children:
                 walk_to_children(node_child, parameter_name)
 
         for param_node in parameter_nodes:
-            walk_to_children(param_node, param_node.name)
+            walk_to_children(param_node, param_node.fullname)
 
         return biased_after_param_nodes
 
diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/channel_alignment/algorithm.py b/tools/pot/openvino/tools/pot/algorithms/quantization/channel_alignment/algorithm.py
index 4d6a9632867..d7454dddf92 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/channel_alignment/algorithm.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/channel_alignment/algorithm.py
@@ -75,7 +75,7 @@ class ActivationChannelAlignment(Algorithm):
             # Step over bias Add node
             if nu.get_bias_for_node(node_in):
                 node_in = nu.get_node_output(node_in, 0)[0]
-            name = node_in.name
+            name = node_in.fullname
             stats_layout[name] = {'channel_range_min': TensorStatistic(asf.quantile_per_channel, q=1e-4),
                                   'channel_range_max': TensorStatistic(asf.quantile_per_channel, q=1-1e-4)}
 
@@ -86,7 +86,7 @@ class ActivationChannelAlignment(Algorithm):
         # Step over bias Add node
         if nu.get_bias_for_node(node_in):
             node_in = nu.get_node_output(node_in, 0)[0]
-        name = node_in.name
+        name = node_in.fullname
 
         amin = stats[name]['channel_range_min']
         amax = stats[name]['channel_range_max']
@@ -151,7 +151,7 @@ class ActivationChannelAlignment(Algorithm):
 
     def find_node_pairs(self, model):
         node_pairs_list = []
-        nodes = sorted([(n.name, n) for n in mu.get_nodes_by_type(model, ['Convolution'])])
+        nodes = sorted([(n.fullname, n) for n in mu.get_nodes_by_type(model, ['Convolution'])])
         for _, node_out in nodes:
             if not self.check_conv_node(node_out):
                 continue
@@ -173,7 +173,7 @@ class ActivationChannelAlignment(Algorithm):
             node_pairs_list.append((node_in, weights_in, bias_in,
                                     node_out, weights_out, bias_out))
             # align activations channels inside this sequence
-            logger.debug('{} -> {}'.format(node_in.name, node_out.name))
+            logger.debug('{} -> {}'.format(node_in.fullname, node_out.fullname))
 
         return node_pairs_list
 
@@ -184,15 +184,15 @@ class ActivationChannelAlignment(Algorithm):
         if not node_out.has_valid('pads_begin') or not node_out.has_valid('pads_end') or \
                 not np.all(np.array(node_out.pads_begin) == 0) or not np.all(np.array(node_out.pads_end) == 0):
             logger.debug('Pad of {} Convolution node != 0 '
-                         'Do not align activations for this node pair.'.format(node_out.name))
+                         'Do not align activations for this node pair.'.format(node_out.fullname))
             return False
         if not node_out.has_valid('strides') or not np.all(np.array(node_out.strides) == 1):
             logger.debug('Strides of {} Convolution node != 1 '
-                         'Do not align activations for this node pair.'.format(node_out.name))
+                         'Do not align activations for this node pair.'.format(node_out.fullname))
             return False
         if not node_out.has_valid('strides') or not np.all(np.array(node_out.dilations) == 1):
             logger.debug('Dilation of {} Convolution node != 1 '
-                         'Do not align activations for this node pair.'.format(node_out.name))
+                         'Do not align activations for this node pair.'.format(node_out.fullname))
             return False
         return True
 
@@ -203,13 +203,13 @@ class ActivationChannelAlignment(Algorithm):
         node_out_producer_port = node_out.in_port(0).get_source()
         if len(node_out_producer_port.get_destinations()) > 1:
             logger.debug('{} has a producer that feeds many nodes. '
-                         'Do not align activations for this node pair.'.format(node_out.name))
+                         'Do not align activations for this node pair.'.format(node_out.fullname))
             return False
         # check that producer is convolution
         if node_in.type not in ['Convolution', 'MatMul']:
-            logger.debug('{} gets data from {} {}'.format(node_out.name, node_in.name, node_in.type))
+            logger.debug('{} gets data from {} {}'.format(node_out.fullname, node_in.fullname, node_in.type))
             logger.debug('{} has no Convolution producer. '
-                         'Do not align activations for this node pair.'.format(node_out.name))
+                         'Do not align activations for this node pair.'.format(node_out.fullname))
             return False
         return True
 
@@ -241,7 +241,7 @@ class ActivationChannelAlignment(Algorithm):
         if w_out.type != 'Const':
             w_out = None
             logger.debug('{} has no const weights. '
-                         'Do not align activations for this node pair.'.format(node_out.name))
+                         'Do not align activations for this node pair.'.format(node_out.fullname))
 
         # get consumer convolution bias
         b_out = nu.get_bias_for_node(node_out)
diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/datafree/algorithm.py b/tools/pot/openvino/tools/pot/algorithms/quantization/datafree/algorithm.py
index 44654c6ce16..4f9874e005b 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/datafree/algorithm.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/datafree/algorithm.py
@@ -11,7 +11,6 @@ from ...algorithm_selector import COMPRESSION_ALGORITHMS
 from ...quantization import fake_quantize as fqut
 from ....graph import editor as ge
 from ....graph import node_utils as nu
-from ....graph.editor import get_nodes_by_type
 from ....graph.special_operations import is_eltwise
 
 
@@ -36,7 +35,7 @@ class DataFreeQuantization(Algorithm):
         # init BatchNorm dict
         for node in ge.get_all_operation_nodes(model):
             if 'bn_weights' in node:
-                self._bn_dict[node.name] = node['bn_weights']
+                self._bn_dict[node.fullname] = node['bn_weights']
 
         fqut.insert_fake_quantize_nodes(self._config, model)
         fake_quantize_config = fqut.compute_stats_layouts(self._config, model)
@@ -48,7 +47,7 @@ class DataFreeQuantization(Algorithm):
         # compute weights statistics
         weights_stats = fqut.compute_weights_stats(model, weights_stats_layout)
 
-        for fq_ in get_nodes_by_type(model, ['FakeQuantize']):
+        for fq_ in ge.get_nodes_by_type(model, ['FakeQuantize']):
             # get zero parent because this is FakeQuantize node input
             _node_input = fqut.get_fake_quantize_input(fq_)
             if _node_input.type != 'Const':
@@ -60,10 +59,10 @@ class DataFreeQuantization(Algorithm):
                                         self._std_multiplier * stats_dict['std'])
                 else:
                     tensor_min, tensor_max = stats_dict['min'], stats_dict['max']
-                fq_input_stats[_node_input.name] = {'min': tensor_min,
-                                                    'max': max(tensor_max, np.abs(tensor_min))}
+                fq_input_stats[_node_input.fullname] = {'min': tensor_min,
+                                                        'max': max(tensor_max, np.abs(tensor_min))}
 
-            if fake_quantize_config[fq_.name]['mode'] == 'symmetric':
+            if fake_quantize_config[fq_.fullname]['mode'] == 'symmetric':
                 min_level_, max_level_ = fqut.symmetric_range(_node_input, fq_, weights_stats,
                                                               fq_input_stats, fake_quantize_config)
             else:
@@ -88,15 +87,15 @@ class DataFreeQuantization(Algorithm):
             range_estimator_config = get_range_estimator_config(config, 'weights', scale_, q_mode_)
             return get_tensor_statistics(range_estimator_config, for_weights=True)
 
-        fq_nodes = get_nodes_by_type(model, ['FakeQuantize'])
+        fq_nodes = ge.get_nodes_by_type(model, ['FakeQuantize'])
         weights_layout = {}
         for fq in fq_nodes:
             fq_input = fqut.get_fake_quantize_input(fq)
-            layer_config = fake_quantize_config[fq.name]
+            layer_config = fake_quantize_config[fq.fullname]
             scale = layer_config['granularity'] if layer_config['granularity'] else 'pertensor'
             q_mode = layer_config['mode']
             if fq_input.type == 'Const':
-                weights_layout[fq.name] = _get_stats(scale, q_mode)
+                weights_layout[fq.fullname] = _get_stats(scale, q_mode)
         return weights_layout
 
     @staticmethod
@@ -120,11 +119,11 @@ class DataFreeQuantization(Algorithm):
                                            node_min, node_max)
         tensor_max = np.max(bn_weight_dict['mean'] + self._std_multiplier * bn_weight_dict['std'])
         tensor_min = np.min(bn_weight_dict['mean'] - self._std_multiplier * bn_weight_dict['std'])
-        self._bn_dict[node.name] = {'mean': clipped_mean,
-                                    'std': clipped_sigma,
-                                    'max': np.array(min(tensor_max, node_max)),
-                                    'min': np.array(max(tensor_min, node_min))}
-        return self._bn_dict[node.name]
+        self._bn_dict[node.fullname] = {'mean': clipped_mean,
+                                        'std': clipped_sigma,
+                                        'max': np.array(min(tensor_max, node_max)),
+                                        'min': np.array(max(tensor_min, node_min))}
+        return self._bn_dict[node.fullname]
 
     def _propagate_stats(self, bn_weights, node):
         # will need to be handled, when perchannel quantization of activations will be supported
@@ -143,9 +142,9 @@ class DataFreeQuantization(Algorithm):
         pass_ops = channel_changing_ops + \
             stat_prop_agnostic_ops
 
-        if node.name in self._bn_dict:
+        if node.fullname in self._bn_dict:
             stats_dict = bn_weights
-            if 'max' not in self._bn_dict[node.name]:
+            if 'max' not in self._bn_dict[node.fullname]:
                 tensor_max = np.max(bn_weights['mean'] +
                                     self._std_multiplier * bn_weights['std'])
                 tensor_min = np.min(bn_weights['mean'] -
@@ -170,8 +169,8 @@ class DataFreeQuantization(Algorithm):
                 node.type == 'ScaleShift' and nu.get_node_input(node, 0).type == 'Parameter':
             stats_dict = bn_weights
         else:
-            raise RuntimeError('{} layer {} does not support stats propagation'.format(node.type, node.name))
-        self._bn_dict[node.name] = stats_dict
+            raise RuntimeError('{} layer {} does not support stats propagation'.format(node.type, node.fullname))
+        self._bn_dict[node.fullname] = stats_dict
         return stats_dict
 
     def _find_stats_in_branch(self, start_node):
@@ -179,13 +178,13 @@ class DataFreeQuantization(Algorithm):
         current_node = start_node
         visited_nodes = [start_node]
         while True:
-            if current_node.name in self._bn_dict:
-                bn_weights = self._bn_dict[current_node.name]
+            if current_node.fullname in self._bn_dict:
+                bn_weights = self._bn_dict[current_node.fullname]
                 for node in visited_nodes[::-1]:
                     bn_weights = self._propagate_stats(bn_weights, node)
                 return bn_weights
             if branching_ops(current_node):
-                self._bn_dict[current_node.name] = self._collect_stats_for_branching_op(current_node)
+                self._bn_dict[current_node.fullname] = self._collect_stats_for_branching_op(current_node)
                 continue
             if nu.get_node_inputs(current_node):
                 current_node = nu.get_node_input(current_node, 0)
@@ -194,7 +193,7 @@ class DataFreeQuantization(Algorithm):
                 if current_node.type != 'Parameter':
                     raise RuntimeError('Node has no parents and stats were not found')
                 bn_weights = {'mean': np.array(0.0), 'std': np.array(1.0)}
-                self._bn_dict[current_node.name] = bn_weights
+                self._bn_dict[current_node.fullname] = bn_weights
                 for node in visited_nodes[::-1]:
                     bn_weights = self._propagate_stats(bn_weights, node)
                 return bn_weights
diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/fake_quantize.py b/tools/pot/openvino/tools/pot/algorithms/quantization/fake_quantize.py
index 306def42cb6..e1e23dd3900 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/fake_quantize.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/fake_quantize.py
@@ -131,18 +131,18 @@ def compute_stats_layouts(config, model, qscheme=None):
         node_input = get_node_input(fq, 0)
         is_weights = node_input.type == 'Const'
         if is_weights:
-            fq_config = copy(fq_configuration[fq.name]['weights'])
+            fq_config = copy(fq_configuration[fq.fullname]['weights'])
         else:
-            fq_config = copy(fq_configuration[fq.name]['activations'])
-        fake_quantize_config[fq.name] = fq_config
-        if fq.name in config.layerwise_configs[0]:
-            fq_config = Dict(merge_nested_dicts(fq_config, config.layerwise_configs[0][fq.name]))
+            fq_config = copy(fq_configuration[fq.fullname]['activations'])
+        fake_quantize_config[fq.fullname] = fq_config
+        if fq.fullname in config.layerwise_configs[0]:
+            fq_config = Dict(merge_nested_dicts(fq_config, config.layerwise_configs[0][fq.fullname]))
 
         fq_config['signed'] = False
         if 'level_low' in fq_config and 'level_high' in fq_config and fq_config['level_low'] < 0:
             fq_config['signed'] = True
 
-        fake_quantize_config[fq.name] = fq_config
+        fake_quantize_config[fq.fullname] = fq_config
         fq.levels = compute_levels(fq_config, is_weights)
 
     return fake_quantize_config
@@ -242,19 +242,19 @@ def symmetric_range(node, fq, weights_stats,
     name = get_quantized_input_key(fq)
     if node.type == 'Const' or get_input_data_value(fq, 0) is not None:
         node_output = get_fake_quantize_first_output(fq)
-        max_level = weights_stats[node_output.name]['max']
+        max_level = weights_stats[node_output.fullname]['max']
         max_level = fix_zero_filters_symmetric(max_level)
         min_level = -max_level
     elif name in batch_inputs_stats:
         max_level = batch_inputs_stats[name]['max']
         min_level = batch_inputs_stats[name]['min']
         max_level = fix_zero_filters_symmetric(max_level)
-        signed = fake_quantize_config[fq.name]['signed']
+        signed = fake_quantize_config[fq.fullname]['signed']
         min_level = np.zeros(max_level.shape) if np.all(min_level >= 0) and not signed else \
             -max_level * fq.levels / (fq.levels - 2)
     else:
         raise Exception(
-            'WARNING: Fake quantize node {} is missed'.format(fq.name))
+            'WARNING: Fake quantize node {} is missed'.format(fq.fullname))
     min_level, max_level = broadcast_fq_values(fq, node, min_level, max_level, fake_quantize_config)
     return min_level, max_level
 
@@ -264,14 +264,14 @@ def asymmetric_range(node, fq, weights_stats,
     name = get_quantized_input_key(fq)
     if node.type == 'Const' or get_input_data_value(fq, 0) is not None:
         node_output = get_fake_quantize_first_output(fq)
-        max_level = weights_stats[node_output.name]['max']
-        min_level = weights_stats[node_output.name]['min']
+        max_level = weights_stats[node_output.fullname]['max']
+        min_level = weights_stats[node_output.fullname]['min']
     elif name in batch_inputs_stats:
         max_level = batch_inputs_stats[name]['max']
         min_level = batch_inputs_stats[name]['min']
     else:
         raise Exception(
-            'WARNING: Fake quantize node {} is missed'.format(fq.name))
+            'WARNING: Fake quantize node {} is missed'.format(fq.fullname))
 
     max_level, min_level = fix_zero_filters_asymmetric(max_level, min_level)
     min_level = np.where(min_level < 0.0, min_level, 0.0)
@@ -279,13 +279,13 @@ def asymmetric_range(node, fq, weights_stats,
     if unify_zp:
         if name in batch_inputs_stats:
             raise Exception(
-                'WARING: unify zero point of fake quantize node {} not supported'.format(fq.name)
+                'WARING: unify zero point of fake quantize node {} not supported'.format(fq.fullname)
             )
         min_level, max_level = tune_range_unify_zp(
-            min_level, max_level, fake_quantize_config[fq.name]['bits'])
+            min_level, max_level, fake_quantize_config[fq.fullname]['bits'])
     else:
         min_level, max_level = tune_range(
-            min_level, max_level, fake_quantize_config[fq.name]['bits'])
+            min_level, max_level, fake_quantize_config[fq.fullname]['bits'])
 
     min_level, max_level = broadcast_fq_values(fq, node, min_level, max_level, fake_quantize_config)
     return min_level, max_level
@@ -341,11 +341,11 @@ def compute_weights_stats(model, stats_layout):
         if weights_node.type != 'Const' and weights_value is None:
             raise Exception('Incorrect stats layout for weights:'
                             ' {} is activation'.format(weights_node.name))
-        if node.name not in weights_stats:
-            weights_stats[node.name] = {}
+        if node.fullname not in weights_stats:
+            weights_stats[node.fullname] = {}
         for stat_name, stat_fn in stats.items():
             weights = weights_value.astype(np.float32)
-            weights_stats[node.name][stat_name] = stat_fn(weights)
+            weights_stats[node.fullname][stat_name] = stat_fn(weights)
     return weights_stats
 
 
@@ -375,7 +375,7 @@ def broadcast_fq_values(fq, node, min_level, max_level, fq_config):
         else:
             bounds_shape[0] = input_shape[0]
     else:
-        if fq_config[fq.name]['granularity'] == 'perchannel':
+        if fq_config[fq.fullname]['granularity'] == 'perchannel':
             bounds_shape[1] = input_shape[1]
 
     min_level = min_level.reshape(bounds_shape)
@@ -395,18 +395,18 @@ def set_rescaling_factors(target_device, model, scaling_factor=2.0):
     """
     fqs_to_rescale = []
 
-    if target_device not in ['CPU', 'ANY'] or not get_nodes_by_type(model, ['Convolution', ]):
+    if target_device not in ['CPU', 'ANY'] or not get_nodes_by_type(model, ['Convolution'], recursively=False):
         return {'scaling_factor': 1.0,
                 'fqs_to_rescale': fqs_to_rescale}
 
-    input_nodes = get_nodes_by_type(model, ['Parameter'])
+    input_nodes = get_nodes_by_type(model, ['Parameter'], recursively=False)
 
     input_convolutions = get_first_convolutions(input_nodes)
 
     for node in input_convolutions:
         fqs_to_rescale.append(get_node_input(node, 1).name)
 
-    conv_nodes_to_rescale = get_nodes_by_type(model, [op['type'] for op in OPERATIONS_WITH_WEIGHTS])
+    conv_nodes_to_rescale = get_nodes_by_type(model, [op['type'] for op in OPERATIONS_WITH_WEIGHTS], recursively=False)
     conv_fqs_to_rescale = [get_node_input(node, 1).name for node in conv_nodes_to_rescale if
                            'need_rescale' in node and node['need_rescale']]
     fqs_to_rescale.extend(conv_fqs_to_rescale)
diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/fake_quantize_configuration.py b/tools/pot/openvino/tools/pot/algorithms/quantization/fake_quantize_configuration.py
index a8ba9f332fb..63a00cc0cbc 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/fake_quantize_configuration.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/fake_quantize_configuration.py
@@ -130,11 +130,11 @@ def read_all_fake_quantize_configurations(config, hardware_config, model):
                     if not _is_quantizable(child):
                         queue.append(child)
                     elif child.type not in descendants:
-                        descendants.append((child.name,
+                        descendants.append((child.fullname,
                                             get_hardware_config_operation_type(child, available_types)))
                     if current.type == 'Split' \
                             and child.type == 'Concat' \
-                            and len({child_.name for child_ in children}) == 1:
+                            and len({child_.fullname for child_ in children}) == 1:
                         break
             return descendants
 
@@ -146,7 +146,7 @@ def read_all_fake_quantize_configurations(config, hardware_config, model):
         available_types = [layer['type'] for layer in hardware_config]
         for fq in get_nodes_by_type(model, ['FakeQuantize']):
             node_input = get_node_input(fq, 0)
-            out[fq.name] = (_get_node_valuable_descendant(fq), node_input.type == 'Const')
+            out[fq.fullname] = (_get_node_valuable_descendant(fq), node_input.type == 'Const')
 
         return out
 
@@ -320,7 +320,7 @@ def find_fqs_to_unify(model, config):
             'Concat': _is_concat_unify_condition
         }
         if node.type in check_map:
-            logger.debug('Checking {} node with {} type'.format(node.name, node.type))
+            logger.debug('Checking {} node with {} type'.format(node.fullname, node.type))
             return check_map[node.type](node)
         return True
 
@@ -331,7 +331,7 @@ def find_fqs_to_unify(model, config):
             elif input_node.type in [n['type'] for n in CONCAT_UNIFY_OUTPUTS]:
                 concat_stack.clear()
                 logger.debug('Found %s %s as Concat %s output',
-                             input_node.type, input_node.name, node.name)
+                             input_node.type, input_node.fullname, node.fullname)
                 return True
             return False
 
@@ -340,7 +340,7 @@ def find_fqs_to_unify(model, config):
         for concat_input in concat_inputs:
             if concat_input.type not in [n['type'] for n in CONCAT_UNIFY_INPUTS]:
                 logger.debug('Concat %s without FQ or Concat as input will not unified',
-                             node.name)
+                             node.fullname)
                 return res
         concat_stack = [node]
         while concat_stack:
@@ -363,24 +363,24 @@ def find_fqs_to_unify(model, config):
         return 'Const' in [parent.type for parent in get_node_inputs(layer) if parent]
 
     def _process_node(node_, stack_, visited_, to_unify_):
-        visited_[node_.name] = True
+        visited_[node_.fullname] = True
         if _is_unified_scales_op(node_) or _is_agnostic_branching_op(node_):
             if not _has_const_input(node_):
-                to_unify_[0].append(node_.name)
+                to_unify_[0].append(node_.fullname)
         elif node_.type == 'FakeQuantize' and get_node_input(node_, 0).type != 'Const':
-            to_unify_[1].append(node_.name)
+            to_unify_[1].append(node_.fullname)
         # traverse down
         if node_.type == 'FakeQuantize' or _is_quantize_agnostic_op(node_):
             for child in get_all_node_outputs(node_):
                 node_data_type = get_node_data_type(child)
-                if not visited_[child.name] and is_data_type_quantizable(node_data_type) and \
+                if not visited_[child.fullname] and is_data_type_quantizable(node_data_type) and \
                         (_is_quantize_agnostic_op(child) or _is_unified_scales_op(child)):
                     stack_.append(child)
         # traverse up
         if node_.type != 'FakeQuantize':
             for parent in get_node_inputs(node_):
                 node_data_type = get_node_data_type(parent)
-                if parent and not visited_[parent.name] and is_data_type_quantizable(node_data_type) and \
+                if parent and not visited_[parent.fullname] and is_data_type_quantizable(node_data_type) and \
                         (parent.type == 'FakeQuantize' or _is_quantize_agnostic_op(parent)):
                     stack_.append(parent)
 
@@ -396,7 +396,7 @@ def find_fqs_to_unify(model, config):
     if model is None:
         return fqs_to_unify
     for fq in get_nodes_by_type(model, ['FakeQuantize']):
-        if not visited[fq.name] and get_node_input(fq, 0).type != 'Const':
+        if not visited[fq.fullname] and get_node_input(fq, 0).type != 'Const':
             stack = [fq]
             to_unify = [[], []]
             while stack:
diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/fast_bias_correction/algorithm.py b/tools/pot/openvino/tools/pot/algorithms/quantization/fast_bias_correction/algorithm.py
index 28361e060dc..e0693af0a32 100755
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/fast_bias_correction/algorithm.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/fast_bias_correction/algorithm.py
@@ -60,11 +60,11 @@ class FastBiasCorrection(Algorithm):
 
             bias_node = nu.get_bias_for_node(op_node)
             if bias_node is None:
-                logger.debug('{} skipped because of bias is empty'.format(op_node.name))
+                logger.debug('{} skipped because of bias is empty'.format(op_node.fullname))
                 continue
 
             if not nu.check_const_input(op_node):
-                logger.debug('{} skipped because channel axis is not defiened'.format(op_node.name))
+                logger.debug('{} skipped because channel axis is not defiened'.format(op_node.fullname))
                 continue
 
             input_node = nu.get_node_input(op_node, 0)
@@ -74,7 +74,7 @@ class FastBiasCorrection(Algorithm):
                 quantized_node = nu.get_node_input(op_node, 0)
 
             input_shape = nu.get_input_shape_for_bias(op_node)
-            op_model = mu.build_model_for_node(model, input_node.name, input_shape, op_node,
+            op_model = mu.build_model_for_node(model, input_node.fullname, input_shape, op_node,
                                                remove_bias=True, target_device=self._config['target_device'])
 
             # We need to get output from the biased operation
@@ -84,10 +84,10 @@ class FastBiasCorrection(Algorithm):
 
             input_node_name = get_quantized_input_key(quantized_node)
             fp32_inputs = agf.mean(activations_statistics[input_node_name]["mean_per_channel"])
-            fp32_outputs = agf.mean(activations_statistics[after_biased_conv.name]["mean_per_channel"])
+            fp32_outputs = agf.mean(activations_statistics[after_biased_conv.fullname]["mean_per_channel"])
 
             bias_shift = self._calculate_bias_shift(
-                launcher, input_node.name, input_shape, op_model, fp32_inputs, fp32_outputs)
+                launcher, input_node.fullname, input_shape, op_model, fp32_inputs, fp32_outputs)
             current_bias_value = nu.get_node_value(bias_node)
             # Reshaped since bias are broadcasted
             add_out_shape = nu.get_input_shape_for_bias(after_biased_conv)
@@ -102,14 +102,14 @@ class FastBiasCorrection(Algorithm):
                 bias_shift_magnitude = np.max(np.abs((bias_shift - current_bias_value) / current_bias_value))
             bias_original_value = nu.get_node_value(bias_node)
             if bias_original_value.shape != bias_shift.shape:
-                logger.debug('{} skipped because shift shape and original shape are inconsistent'.format(op_node.name))
+                logger.debug(f'{op_node.fullname} skipped because shift shape and original shape are inconsistent')
                 continue
 
             if bias_shift_magnitude < self._threshold:
                 op_node['original_bias'] = current_bias_value
                 nu.set_node_value(bias_node, bias_shift)
             else:
-                logger.debug('{} skipped by threshold'.format(op_node.name))
+                logger.debug('{} skipped by threshold'.format(op_node.fullname))
         return model
 
     def register_statistics(self, model, stats_collector):
@@ -132,14 +132,14 @@ class FastBiasCorrection(Algorithm):
                 if nu.get_node_input(quantized_node, 0).type == 'FakeQuantize':
                     quantized_node = nu.get_node_input(op_node, 0)
 
-                op_output_name = op_node.name
-                if op_node.name not in inputs_outputs_layout:
+                op_output_name = op_node.fullname
+                if op_node.fullname not in inputs_outputs_layout:
                     # Conv -> Add, MatMul -> Add cases
                     # We need to get output from the biased operation
                     if nu.get_bias_for_node(op_node):
                         bias = nu.get_bias_for_node(op_node)
                         op_node_output = nu.get_node_output(bias, 0)[0]
-                        op_output_name = op_node_output.name
+                        op_output_name = op_node_output.fullname
                     inputs_outputs_layout[op_output_name] = {
                         "mean_per_channel": TensorStatisticAxis(inplace_statistics=inplace_statistics,
                                                                 granularity='perchannel', type='mean',
@@ -191,8 +191,8 @@ class FastBiasCorrection(Algorithm):
 
             axis = OPERATIONS_CHANNEL_AXIS[op_node.type]
 
-            self._channel_axis[input_node.name] = axis
-            add_name = after_biased_conv.name
+            self._channel_axis[input_node.fullname] = axis
+            add_name = after_biased_conv.fullname
             if 'orig_node_name' in after_biased_conv:
                 add_name = after_biased_conv['orig_node_name']
             self._channel_axis[add_name] = axis
diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/layerwise_finetuning/algorithm.py b/tools/pot/openvino/tools/pot/algorithms/quantization/layerwise_finetuning/algorithm.py
index 210481cea83..029a65fe2b4 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/layerwise_finetuning/algorithm.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/layerwise_finetuning/algorithm.py
@@ -61,9 +61,9 @@ class QuantizeModelFinetuning(LayerwiseModelFinetuning):
         quantized_model = model
 
         # Add first convolutions and their weight FQ's to tuning ignored scope
-        input_nodes = mu.get_nodes_by_type(model, ['Parameter'])
+        input_nodes = mu.get_nodes_by_type(model, ['Parameter'], recursively=False)
         input_convolutions = get_first_convolutions(input_nodes)
-        input_convolutions_names = [node.name for node in input_convolutions]
+        input_convolutions_names = [node.fullname for node in input_convolutions]
         self._tconf['tuning_ignored_scope'].extend(input_convolutions_names)
         logger.debug('Tuning ignored scope updated with: {}'.format(input_convolutions_names))
 
@@ -111,8 +111,8 @@ class QuantizeModelFinetuning(LayerwiseModelFinetuning):
             fq_node = mu.get_node_by_name(modified_model, op_name)
             wrapped_op, params = self._wrap_node(fq_node, FakeQuantize, op_info['params'])
             if wrapped_op:
-                wrapped_ops[fq_node.name] = wrapped_op
-                ops_parameters[fq_node.name] = params
+                wrapped_ops[fq_node.fullname] = wrapped_op
+                ops_parameters[fq_node.fullname] = params
 
         for op_name, op_info in nodes_to_tune.items():
             if op_name in wrapped_ops:
@@ -120,13 +120,13 @@ class QuantizeModelFinetuning(LayerwiseModelFinetuning):
             conv_node = mu.get_node_by_name(modified_model, op_name)
             conv_node_input = nu.get_node_input(conv_node, 0)
             input_fq = None
-            if conv_node_input.type == 'FakeQuantize' and conv_node_input.name in wrapped_ops:
-                input_fq = wrapped_ops[conv_node_input.name]
+            if conv_node_input.type == 'FakeQuantize' and conv_node_input.fullname in wrapped_ops:
+                input_fq = wrapped_ops[conv_node_input.fullname]
             op_info['input_fq'] = input_fq
             wrapped_op, params = self._wrap_node(conv_node, LinearModule, op_info['params'])
             if wrapped_op:
-                wrapped_ops[conv_node.name] = wrapped_op
-                ops_parameters[conv_node.name] = params
+                wrapped_ops[conv_node.fullname] = wrapped_op
+                ops_parameters[conv_node.fullname] = params
 
         return wrapped_ops, ops_parameters
 
diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/minmax/algorithm.py b/tools/pot/openvino/tools/pot/algorithms/quantization/minmax/algorithm.py
index 561ada1a1ad..d344eeb1ae1 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/minmax/algorithm.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/minmax/algorithm.py
@@ -93,15 +93,15 @@ class MinMaxQuantization(Algorithm):
         for fq in fq_nodes:
             fq_input = fqut.get_fake_quantize_input(fq)
             fq_input_value = fqut.get_fake_quantize_input_value(fq)
-            layer_config = fake_quantize_config[fq.name]
+            layer_config = fake_quantize_config[fq.fullname]
             is_weights = fq_input.type == 'Const' or fq_input_value is not None
             ts_args = {}
             if is_weights is True and for_weights is True:
                 node = fqut.get_fake_quantize_first_output(fq)
                 if node.type in [op['type'] for op in TRANSPOSED_OPERATIONS]:
                     ts_args.update({'transpose': True})
-                statistics_layout[fq.name] = get_tensor_statistics(layer_config['range_estimator'],
-                                                                   for_weights=True, **ts_args)
+                statistics_layout[fq.fullname] = get_tensor_statistics(layer_config['range_estimator'],
+                                                                       for_weights=True, **ts_args)
             elif is_weights is False and for_weights is False:
                 fq_input_key = nu.get_quantized_input_key(fq)
                 ts_args['inplace_statistics'] = inplace_statistics
@@ -124,39 +124,39 @@ class MinMaxQuantization(Algorithm):
             fq_input = fqut.get_fake_quantize_input(fq)
             fq_input_value = fqut.get_fake_quantize_input_value(fq)
             stat_config_keys = {}
-            if fq.name not in fake_quantize_config:
+            if fq.fullname not in fake_quantize_config:
                 continue
-            for stat_type in fake_quantize_config[fq.name]['range_estimator']:
-                stat_config_keys[stat_type] = get_stat_name_by_config(fake_quantize_config[fq.name]['range_estimator'],
-                                                                      stat_type)
+            for stat_type in fake_quantize_config[fq.fullname]['range_estimator']:
+                stat_config_keys[stat_type] = get_stat_name_by_config(
+                    fake_quantize_config[fq.fullname]['range_estimator'], stat_type)
             if fq_input.type != 'Const' and fq_input_value is None:
                 fq_input_key = nu.get_quantized_input_key(fq)
                 max_values = inputs_stats[fq_input_key][stat_config_keys['max']]
                 min_values = inputs_stats[fq_input_key][stat_config_keys['min']]
 
                 min_aggregator, max_aggregator = \
-                    get_aggregator(fake_quantize_config[fq.name]['range_estimator'])
+                    get_aggregator(fake_quantize_config[fq.fullname]['range_estimator'])
                 batch_inputs_stats[fq_input_key] = {'max': max_aggregator(max_values),
                                                     'min': min_aggregator(min_values)}
             else:
                 node_output = fqut.get_fake_quantize_first_output(fq)
-                batch_weights_stats[node_output.name] = {}
+                batch_weights_stats[node_output.fullname] = {}
                 for stat_type in ['min', 'max']:
                     if stat_type in stat_config_keys:
-                        batch_weights_stats[node_output.name][stat_type] = weights_stats[node_output.name][
+                        batch_weights_stats[node_output.fullname][stat_type] = weights_stats[node_output.fullname][
                             stat_config_keys[stat_type]]
 
         for fq_ in fake_quantizations:
             # get first input because this is FakeQuantize node input
             _node_input = fqut.get_fake_quantize_input(fq_)
-            if fq_.name not in fake_quantize_config:
+            if fq_.fullname not in fake_quantize_config:
                 continue
-            if fake_quantize_config[fq_.name]['mode'] == 'symmetric':
+            if fake_quantize_config[fq_.fullname]['mode'] == 'symmetric':
                 min_level_, max_level_ = fqut.symmetric_range(_node_input, fq_, batch_weights_stats,
                                                               batch_inputs_stats, fake_quantize_config)
             else:
-                if 'unified_zeropoint' in fake_quantize_config[fq_.name].keys():
-                    unify_zp = fake_quantize_config[fq_.name]['unified_zeropoint']
+                if 'unified_zeropoint' in fake_quantize_config[fq_.fullname].keys():
+                    unify_zp = fake_quantize_config[fq_.fullname]['unified_zeropoint']
                 else:
                     unify_zp = False
                 min_level_, max_level_ = fqut.asymmetric_range(_node_input, fq_, batch_weights_stats,
@@ -170,13 +170,13 @@ class MinMaxQuantization(Algorithm):
                     return stat_value
                 return stat_value
 
-            range_estimator_config = fake_quantize_config[fq_.name]['range_estimator']
+            range_estimator_config = fake_quantize_config[fq_.fullname]['range_estimator']
             min_level_ = clip_stat(min_level_, 'min', range_estimator_config) \
                 if 'min' in range_estimator_config else min_level_
             max_level_ = clip_stat(max_level_, 'max', range_estimator_config) \
                 if 'max' in range_estimator_config else max_level_
 
-            if fq_.name in fake_quantize_config['fqs_to_rescale']:
+            if fq_.fullname in fake_quantize_config['fqs_to_rescale']:
                 weights_node = nu.get_node_input(fq_, 0)
                 weights = nu.get_node_value(weights_node)
                 conv_node = nu.get_node_output(fq_, 0)[0]
diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/optimization/rangeopt.py b/tools/pot/openvino/tools/pot/algorithms/quantization/optimization/rangeopt.py
index e8b613a38aa..003dc2fcc3d 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/optimization/rangeopt.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/optimization/rangeopt.py
@@ -63,20 +63,20 @@ class RangeOptimization(OptimizationAlgorithm):
             parents = nu.get_node_inputs(fq)
             if parents[0].type != 'Const':
                 if parents[0].type in ('Clamp', 'ReLU'):
-                    out[parents[0].name] = [nu.get_node_value(parents[2])]
-                    self._optimization_scope[parents[0].name] = 'ReLU'
-                if parents[0].name not in self._optimization_scope:
-                    out[parents[0].name] = [
+                    out[parents[0].fullname] = [nu.get_node_value(parents[2])]
+                    self._optimization_scope[parents[0].fullname] = 'ReLU'
+                if parents[0].fullname not in self._optimization_scope:
+                    out[parents[0].fullname] = [
                         nu.get_node_value(val) for val in parents[1:3]
                     ]
         return out
 
     def _set_parameter_values(self, model, param_values):
-        for fq in model.get_op_nodes(type='FakeQuantize'):
+        for fq in mu.get_nodes_by_type(model, ['FakeQuantize']):
             # get zero parent because this is FakeQuantize node input
             _node_input = nu.get_node_input(fq, 0)
-            if _node_input.name in self._activation_ranges:
-                min_level_, max_level_ = param_values[_node_input.name]
+            if _node_input.fullname in self._activation_ranges:
+                min_level_, max_level_ = param_values[_node_input.fullname]
                 fqut.fill_fake_quantize_node(fq, min_level_, max_level_)
 
         return model
diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/outlier_channel_splitting/algorithm.py b/tools/pot/openvino/tools/pot/algorithms/quantization/outlier_channel_splitting/algorithm.py
index 1fa44e4a886..60d0d080678 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/outlier_channel_splitting/algorithm.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/outlier_channel_splitting/algorithm.py
@@ -39,7 +39,7 @@ class OutlierChannelSplitting(Algorithm):
             if self.add_input_channels_for_conv_node(conv_node, ocs_channels_idxs):
                 nu.set_node_value(weights_node, ocs_weights)
                 logger.debug('Node {}: Channels {} were splitted'.
-                             format(conv_node.name, ','.join(str(idx) for idx in ocs_channels_idxs)))
+                             format(conv_node.fullname, ','.join(str(idx) for idx in ocs_channels_idxs)))
 
         model.clean_up()
         return model
@@ -85,7 +85,7 @@ class OutlierChannelSplitting(Algorithm):
 
     def get_conv_nodes(self, model):
         conv_nodes_list = []
-        nodes = sorted([(n.name, n) for n in mu.get_nodes_by_type(model, ['Convolution'])])
+        nodes = sorted([(n.fullname, n) for n in mu.get_nodes_by_type(model, ['Convolution'])])
         for _, node in nodes:
             if not self.check_conv_node(node):
                 continue
diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/overflow_correction/algorithm.py b/tools/pot/openvino/tools/pot/algorithms/quantization/overflow_correction/algorithm.py
index 34147d8cfa2..a3e6d981a27 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/overflow_correction/algorithm.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/overflow_correction/algorithm.py
@@ -47,20 +47,20 @@ class OverflowCorrection(Algorithm):
             if bias_node is None:
                 continue
             add_node = nu.get_node_output(bias_node, 0)[0]
-            add_node_name = add_node.name
+            add_node_name = add_node.fullname
             if add_node_name not in activation_statistics \
                     or 'max_per_tensor' not in activation_statistics[add_node_name]:
-                logger.debug('Skipping {}'.format(weighted_node.name))
+                logger.debug('Skipping {}'.format(weighted_node.fullname))
                 continue
-            logger.debug('Processing {}'.format(weighted_node.name))
+            logger.debug('Processing {}'.format(weighted_node.fullname))
             weight_fq = nu.get_node_input(weighted_node, 1)
             if weight_fq.levels <= np.iinfo(np.uint8).max:
-                logger.debug('Skipping {} due to INT8 weights quantization'.format(weighted_node.name))
+                logger.debug('Skipping {} due to INT8 weights quantization'.format(weighted_node.fullname))
                 continue
             rescale_value = correct_node_overflow(weighted_node, activation_statistics[add_node_name]['max_per_tensor'])
             if rescale_value:
                 logger.debug('Weights and scales for node {} '
-                             'updated with scale coefficient: {}'.format(weighted_node.name, rescale_value))
+                             'updated with scale coefficient: {}'.format(weighted_node.fullname, rescale_value))
         return model
 
     def register_statistics(self, model, stats_collector):
@@ -72,7 +72,7 @@ class OverflowCorrection(Algorithm):
             if bias_node is None:
                 continue
             add_node = nu.get_node_output(bias_node, 0)[0]
-            stats_layout[add_node.name] = {'max_per_tensor': acf.abs_max_per_tensor}
+            stats_layout[add_node.fullname] = {'max_per_tensor': acf.abs_max_per_tensor}
         quantized_model = deepcopy(model)
         fqut.insert_fake_quantize_nodes(self._config, quantized_model)
         layers_mapping = fqut.create_renamed_layers_mapping(quantized_model, stats_layout)
diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/qnoise_estimator/algorithm.py b/tools/pot/openvino/tools/pot/algorithms/quantization/qnoise_estimator/algorithm.py
index 90ecaba3b1a..88984f1dc45 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/qnoise_estimator/algorithm.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/qnoise_estimator/algorithm.py
@@ -5,6 +5,7 @@ from copy import deepcopy
 
 import numpy as np
 import pandas as pd
+from mo.graph.graph import rename_node
 
 from .utils import get_composite_model
 from ..utils import load_hardware_config
@@ -57,7 +58,8 @@ class QuantNoiseEstimator(Algorithm):
         fully_quantized_model = deepcopy(model)
         model = self.get_nonquantized_model(model)
         for node in mu.get_all_operation_nodes(fully_quantized_model):
-            node.name = node.name + self.q_suffix
+            rename_node(node, node.name + self.q_suffix)
+            node.fullname += self.q_suffix
 
         composite_model = get_composite_model(
             model, fully_quantized_model, quantized_suffix=self.q_suffix
@@ -75,13 +77,13 @@ class QuantNoiseEstimator(Algorithm):
             add_after_conv = nu.get_node_output(conv, 0)[0]
             if add_after_conv.type == 'Add':
                 # needs special layout for input/output stats
-                stat_calculation_layers.update({add_after_conv.name: conv.name})
-                inputs_outputs_layout[add_after_conv.name] = {
+                stat_calculation_layers.update({add_after_conv.fullname: conv.fullname})
+                inputs_outputs_layout[add_after_conv.fullname] = {
                     'layerwise_stat': SQNRStatistic(
                         self.activation_stats, self.q_suffix
                     )
                 }
-                inputs_outputs_layout[add_after_conv.name + self.q_suffix] = {}
+                inputs_outputs_layout[add_after_conv.fullname + self.q_suffix] = {}
 
         del model, fully_quantized_model
         self._engine.set_model(composite_model)
@@ -108,7 +110,7 @@ class QuantNoiseEstimator(Algorithm):
             fq_cut_node_list = fq_remover.find_fq_nodes_to_cut(fq_node)
             cut_fqs = []
             fq_names = [
-                node.name for node in mu.get_nodes_by_type(model, ['FakeQuantize'])
+                node.fullname for node in mu.get_nodes_by_type(model, ['FakeQuantize'])
             ]
             for node_name in fq_names:
                 if node_name not in cut_fqs and node_name not in fq_cut_node_list:
@@ -132,7 +134,7 @@ class QuantNoiseEstimator(Algorithm):
             if conv_input_node.type == 'FakeQuantize' and add_after_conv.type == 'Add':
                 logger.info(
                     'Calculating stats for quantized convolution {}'.format(
-                        conv_node.name
+                        conv_node.fullname
                     )
                 )
                 single_fq_layer_model = get_single_fq_model(
@@ -140,7 +142,8 @@ class QuantNoiseEstimator(Algorithm):
                 )
 
                 for node in mu.get_all_operation_nodes(single_fq_layer_model):
-                    node.name = node.name + self.q_suffix
+                    rename_node(node, node.name + self.q_suffix)
+                    node.fullname += self.q_suffix
 
                 composite_model = get_composite_model(
                     deepcopy(model), single_fq_layer_model
@@ -149,15 +152,15 @@ class QuantNoiseEstimator(Algorithm):
                 # collect convolution output residuals for original vs. quantized model
                 inputs_outputs_layout = {}
                 add_after_conv = nu.get_node_output(
-                    mu.get_node_by_name(composite_model, conv_node.name), 0
+                    mu.get_node_by_name(composite_model, conv_node.fullname), 0
                 )[0]
                 # needs special layout for input/output stats
-                inputs_outputs_layout[add_after_conv.name] = {
+                inputs_outputs_layout[add_after_conv.fullname] = {
                     'layerwise_stat': SQNRStatistic(
                         self.activation_stats, self.q_suffix
                     )
                 }
-                inputs_outputs_layout[add_after_conv.name + self.q_suffix] = {}
+                inputs_outputs_layout[add_after_conv.fullname + self.q_suffix] = {}
 
                 self._engine.set_model(composite_model)
                 _, accumulated_stats = self._engine.predict(stats_layout=inputs_outputs_layout,
@@ -165,10 +168,10 @@ class QuantNoiseEstimator(Algorithm):
                                                                 range(self._config['stat_subset_size'])))
                 qnoise_values.append(
                     self.mean_estimator(
-                        accumulated_stats[add_after_conv.name]['layerwise_stat']
+                        accumulated_stats[add_after_conv.fullname]['layerwise_stat']
                     )
                 )
-                node_names.append(conv_node.name)
+                node_names.append(conv_node.fullname)
 
         noise_data = {'noise_metric': qnoise_values, 'layer_name': node_names}
         if 'results_dump_filename' in self._config:
@@ -179,9 +182,9 @@ class QuantNoiseEstimator(Algorithm):
         cut_fqs = []
         cut_model = deepcopy(model)
         for node in mu.get_nodes_by_type(model, ['FakeQuantize']):
-            if node.name not in cut_fqs:
+            if node.fullname not in cut_fqs:
                 cut_model, cut_fq_layers, _ = self._graph_transformer.remove_fq_nodes(
-                    cut_model, [node.name]
+                    cut_model, [node.fullname]
                 )
                 cut_fqs += cut_fq_layers
         return cut_model
diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/qnoise_estimator/utils.py b/tools/pot/openvino/tools/pot/algorithms/quantization/qnoise_estimator/utils.py
index 7210b71f789..c52c199427e 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/qnoise_estimator/utils.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/qnoise_estimator/utils.py
@@ -22,12 +22,12 @@ def get_composite_model(model, quantized_model, quantized_suffix='_quantized'):
     composite_model = model_disjoint_union(model, quantized_model)
     fq_inputs = [
         node
-        for node in get_nodes_by_type(composite_model, ['Parameter'])
+        for node in get_nodes_by_type(composite_model, ['Parameter'], recursively=False)
         if quantized_suffix not in node.name
     ]
     q_inputs = [
         node
-        for node in get_nodes_by_type(composite_model, ['Parameter'])
+        for node in get_nodes_by_type(composite_model, ['Parameter'], recursively=False)
         if quantized_suffix in node.name
     ]
     for fp_input_node, q_input_node in zip(fq_inputs, q_inputs):
diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/ranger/algorithm.py b/tools/pot/openvino/tools/pot/algorithms/quantization/ranger/algorithm.py
index 771e0f75a38..191fdc5c61b 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/ranger/algorithm.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/ranger/algorithm.py
@@ -7,6 +7,7 @@ from ...algorithm_selector import COMPRESSION_ALGORITHMS
 from ....algorithms.algorithm import Algorithm
 from ....graph import editor as ge
 from ....graph import model_utils as mu
+from ....graph import node_utils as nu
 from ....samplers.creator import create_sampler
 from ....statistics.functions import activations as acf
 from ....utils.logger import get_logger
@@ -39,14 +40,15 @@ class Ranger(Algorithm):
 
         act_nodes = mu.get_nodes_by_type(model, self._act_types)
         for act_node in act_nodes:
-            if act_node.name not in activation_statistics:
-                logger.debug('Stats After {} not found!'.format(act_node.name))
+            if act_node.fullname not in activation_statistics:
+                logger.debug('Stats After {} not found!'.format(act_node.fullname))
                 continue
-            min_after_act = np.min(activation_statistics[act_node.name]['min_per_tensor'])
-            max_after_act = np.max(activation_statistics[act_node.name]['max_per_tensor'])
+            min_after_act = np.min(activation_statistics[act_node.fullname]['min_per_tensor'])
+            max_after_act = np.max(activation_statistics[act_node.fullname]['max_per_tensor'])
             clamp_attrs = {'min': min_after_act, 'max': max_after_act}
             clamp_name = act_node.name + '/min_max_Clamp'
             clamp_node = ge.create_node(act_node.graph, clamp_name, 'AttributedClamp', clamp_attrs)
+            clamp_node['fullname'] = nu.reset_node_fullname(act_node.fullname, clamp_name)
 
             dest_ports = act_node.out_port(0).get_destinations()
             act_node.out_port(0).disconnect()
@@ -61,8 +63,8 @@ class Ranger(Algorithm):
         act_nodes = mu.get_nodes_by_type(model, self._act_types)
         stats_layout = {}
         for act_node in act_nodes:
-            stats_layout[act_node.name] = {'max_per_tensor': acf.max_per_tensor,
-                                           'min_per_tensor': acf.min_per_tensor}
+            stats_layout[act_node.fullname] = {'max_per_tensor': acf.max_per_tensor,
+                                               'min_per_tensor': acf.min_per_tensor}
         stats_collector.register(self.name, stats_layout, self._sampler)
 
     @property
diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/tunable_quantization/algorithm.py b/tools/pot/openvino/tools/pot/algorithms/quantization/tunable_quantization/algorithm.py
index 58b8fe8926e..d95d709be45 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/tunable_quantization/algorithm.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/tunable_quantization/algorithm.py
@@ -72,7 +72,7 @@ class TunableQuantization(MinMaxQuantization):
         for fq in get_nodes_by_type(model, ['FakeQuantize']):
             node_input = get_node_input(fq, 0)
             op_type = 'weights' if node_input.type == 'Const' else 'activations'
-            fq_node_config = fq_configuration[fq.name][op_type]
+            fq_node_config = fq_configuration[fq.fullname][op_type]
             for child_name, child_config in fq_node_config:
                 if child_name not in nodes_config:
                     nodes_config[child_name] = {'weights': [], 'activations': []}
diff --git a/tools/pot/openvino/tools/pot/algorithms/quantization/weight_bias_correction/algorithm.py b/tools/pot/openvino/tools/pot/algorithms/quantization/weight_bias_correction/algorithm.py
index a60151de049..5d75d9bdc98 100644
--- a/tools/pot/openvino/tools/pot/algorithms/quantization/weight_bias_correction/algorithm.py
+++ b/tools/pot/openvino/tools/pot/algorithms/quantization/weight_bias_correction/algorithm.py
@@ -35,7 +35,7 @@ class WeightBiasCorrection(Algorithm):
         for fq in mu.get_nodes_by_type(model, ['FakeQuantize']):
             node_input = nu.get_node_input(fq, 0)
             if node_input.type == 'Const':
-                quantized_weights_layout[fq.name] = {'tensor': lambda tensor: tensor}
+                quantized_weights_layout[fq.fullname] = {'tensor': lambda tensor: tensor}
 
         self._engine.set_model(model)
         _, quantized_weights = self._engine.predict(quantized_weights_layout, range(1))
@@ -61,7 +61,7 @@ class WeightBiasCorrection(Algorithm):
         fq_weights_node = nu.get_node_input(op_node, 1)
         weights_node = nu.get_node_input(fq_weights_node, 0)
         fp32_weights = weights_node.value * first_conv_scaling_factor
-        int_weights = quantized_weights[fq_weights_node.name]['tensor'][0]
+        int_weights = quantized_weights[fq_weights_node.fullname]['tensor'][0]
         axis = tuple(range(1, len(fp32_weights.shape)))
         variance_per_channel_shift = np.std(fp32_weights, axis=axis) / (
             np.std(int_weights, axis=axis) + safety_eps
diff --git a/tools/pot/openvino/tools/pot/algorithms/sparsity/default/utils.py b/tools/pot/openvino/tools/pot/algorithms/sparsity/default/utils.py
index e2e78eb259c..dc7a2f32c96 100644
--- a/tools/pot/openvino/tools/pot/algorithms/sparsity/default/utils.py
+++ b/tools/pot/openvino/tools/pot/algorithms/sparsity/default/utils.py
@@ -35,7 +35,7 @@ def check_model_sparsity_level(model,
         all_nodes_with_weights = [
             node
             for node in all_nodes_with_weights
-            if (node.name not in sparsity_ignored_scope)
+            if (node.fullname not in sparsity_ignored_scope)
         ]
     for node in all_nodes_with_weights:
         weight_node = nu.get_weights_for_node(node)
diff --git a/tools/pot/openvino/tools/pot/algorithms/sparsity/layerwise_finetuning/algorithm.py b/tools/pot/openvino/tools/pot/algorithms/sparsity/layerwise_finetuning/algorithm.py
index 6abc0187765..6063b2ee0d5 100644
--- a/tools/pot/openvino/tools/pot/algorithms/sparsity/layerwise_finetuning/algorithm.py
+++ b/tools/pot/openvino/tools/pot/algorithms/sparsity/layerwise_finetuning/algorithm.py
@@ -143,8 +143,8 @@ class SparseModelFinetuning(LayerwiseModelFinetuning):
     def _collect_nodes_to_tune(self, modified_model):
         nodes_to_tune = {}
         for op_node in mu.get_nodes_by_type(modified_model, self._weighted_operations):
-            if op_node.name not in self._tconf['tuning_ignored_scope']:
-                nodes_to_tune[op_node.name] = {
+            if op_node.fullname not in self._tconf['tuning_ignored_scope']:
+                nodes_to_tune[op_node.fullname] = {
                     'type': op_node.type,
                     'params': {}
                 }
@@ -158,8 +158,8 @@ class SparseModelFinetuning(LayerwiseModelFinetuning):
             op_node = mu.get_node_by_name(modified_model, op_name)
             wrapped_op, params = self._wrap_node(op_node, LinearModule, op_info['params'])
             if wrapped_op:
-                wrapped_ops[op_node.name] = wrapped_op
-                ops_parameters[op_node.name] = params
+                wrapped_ops[op_node.fullname] = wrapped_op
+                ops_parameters[op_node.fullname] = params
         return wrapped_ops, ops_parameters
 
     def _fallback_to_baseline(self):
diff --git a/tools/pot/openvino/tools/pot/algorithms/sparsity/magnitude_sparsity/algorithm.py b/tools/pot/openvino/tools/pot/algorithms/sparsity/magnitude_sparsity/algorithm.py
index a565d17e5fb..ca8c8db8bcd 100644
--- a/tools/pot/openvino/tools/pot/algorithms/sparsity/magnitude_sparsity/algorithm.py
+++ b/tools/pot/openvino/tools/pot/algorithms/sparsity/magnitude_sparsity/algorithm.py
@@ -58,7 +58,7 @@ class MagnitudeSparsity(Algorithm):
                 self._safety_eps_variance_factor,
             )
             conv_node = get_node_output(node, 0)[0]
-            self.sparsity_levels_per_layer[conv_node.name] = node_sparsity_mask.mean()
+            self.sparsity_levels_per_layer[conv_node.fullname] = node_sparsity_mask.mean()
 
         self.statistics_table = self._sparsity_statistics(model)
         return model
@@ -87,7 +87,7 @@ class MagnitudeSparsity(Algorithm):
 
         if self.ignored_scope is not None:
             all_nodes_with_weights = [node for node in all_nodes_with_weights
-                                      if node.name not in self.ignored_scope]
+                                      if node.fullname not in self.ignored_scope]
 
         for node in all_nodes_with_weights:
             if model.is_node_ignored(self.ignored_params, node, skipped=False):
@@ -166,8 +166,8 @@ class MagnitudeSparsity(Algorithm):
             weight_tensor = self._get_weights_value(node)
             drow['Weight tensor shape'] = list(weight_tensor.shape)
             conv_node = get_node_output(node, 0)[0]
-            drow['Layer name'] = conv_node.name
-            drow['Sparsity rate'] = 100 * self.sparsity_levels_per_layer[conv_node.name]
+            drow['Layer name'] = conv_node.fullname
+            drow['Sparsity rate'] = 100 * self.sparsity_levels_per_layer[conv_node.fullname]
             row = [drow[h] for h in header]
             data.append(row)
 
diff --git a/tools/pot/openvino/tools/pot/algorithms/utils.py b/tools/pot/openvino/tools/pot/algorithms/utils.py
new file mode 100644
index 00000000000..86bf0f0d3d8
--- /dev/null
+++ b/tools/pot/openvino/tools/pot/algorithms/utils.py
@@ -0,0 +1,15 @@
+# Copyright (C) 2020-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+def process_ignored_scope(ignored_scope):
+    """ Returns ignored scope with names containing all nested subgraphs for nodes
+    :param ignored_scope: ignored_scope from algorithm specific config
+    :return ignored scope with fullnames for nodes
+     """
+    ignored_scope_with_fullnames = []
+    for layer in ignored_scope:
+        if isinstance(layer, (tuple, list)):
+            ignored_scope_with_fullnames.append('|'.join(layer))
+        else:
+            ignored_scope_with_fullnames.append(layer)
+    return ignored_scope_with_fullnames
diff --git a/tools/pot/openvino/tools/pot/data_loaders/creator.py b/tools/pot/openvino/tools/pot/data_loaders/creator.py
index fd0aef7315e..14e76e92f00 100644
--- a/tools/pot/openvino/tools/pot/data_loaders/creator.py
+++ b/tools/pot/openvino/tools/pot/data_loaders/creator.py
@@ -13,7 +13,7 @@ def create_data_loader(config, model):
     :return: instance of DataLoader descendant class
     """
 
-    inputs = get_nodes_by_type(model, ['Parameter'])
+    inputs = get_nodes_by_type(model, ['Parameter'], recursively=False)
 
     if len(inputs) > 1 and\
             not any([tuple(i.shape) == (1, 3) for i in inputs]):
diff --git a/tools/pot/openvino/tools/pot/engines/ac_engine.py b/tools/pot/openvino/tools/pot/engines/ac_engine.py
index 8791dae3179..47ce1b57110 100644
--- a/tools/pot/openvino/tools/pot/engines/ac_engine.py
+++ b/tools/pot/openvino/tools/pot/engines/ac_engine.py
@@ -9,7 +9,8 @@ from functools import partial
 
 import numpy as np
 
-from .utils import append_stats, process_accumulated_stats
+from .utils import append_stats, process_accumulated_stats, \
+    restore_original_node_names, align_stat_names_with_results
 from ..api.engine import Engine
 from ..data_loaders.ac_data_loader import ACDataLoader
 from ..graph.model_utils import save_model, add_outputs
@@ -122,12 +123,20 @@ class ACEngine(Engine):
         callback_layout, stat_names_aliases = {}, {}
         # add outputs for activation statistics collection
         if stats_layout is not None:
-            model_with_stat_op, nodes_name = self._statistic_graph_builder.\
+            model_with_stat_op, nodes_name, output_to_node_names = self._statistic_graph_builder.\
                 insert_statistic(copy.deepcopy(self._nx_model),
                                  stats_layout, stat_aliases)
             self.set_model(model_with_stat_op)
             add_outputs(self._model, nodes_name)
             self._model_evaluator.load_network(self._model)
+
+            model_output_names = [out for m_dict in self._model for out in m_dict['model'].outputs.keys()]
+            align_stat_names_with_results(model_output_names,
+                                          nodes_name,
+                                          output_to_node_names,
+                                          stats_layout,
+                                          stat_aliases)
+
             # Creating statistics layout with IE-like names
             stat_names_aliases = {convert_output_key(key): key for key in stats_layout}
             callback_layout = {convert_output_key(key): value
@@ -189,6 +198,9 @@ class ACEngine(Engine):
         self._per_sample_metrics.clear()
         self.dump_prediction_to_annotation = False
 
+        if stats_layout:
+            restore_original_node_names(output_to_node_names, accumulated_stats, stats_layout, stat_aliases)
+
         return metrics, accumulated_stats
 
     def _load_model(self, paths):
diff --git a/tools/pot/openvino/tools/pot/engines/ie_engine.py b/tools/pot/openvino/tools/pot/engines/ie_engine.py
index 1267facb6a6..5fc59655e95 100644
--- a/tools/pot/openvino/tools/pot/engines/ie_engine.py
+++ b/tools/pot/openvino/tools/pot/engines/ie_engine.py
@@ -9,7 +9,8 @@ import copy
 import numpy as np
 from openvino.inference_engine import IECore, StatusCode   # pylint: disable=E0611
 
-from .utils import append_stats, process_accumulated_stats
+from .utils import append_stats, process_accumulated_stats, \
+    restore_original_node_names, align_stat_names_with_results
 from ..api.engine import Engine
 from ..graph.model_utils import save_model
 from ..samplers.batch_sampler import BatchSampler
@@ -90,11 +91,19 @@ class IEEngine(Engine):
 
         stat_names_aliases = None
         if stats_layout:
-            model_with_stat_op, nodes_name = self._statistic_graph_builder.\
+            model_with_stat_op, nodes_name, output_to_node_names = self._statistic_graph_builder.\
                 insert_statistic(copy.deepcopy(self._nx_model),
                                  stats_layout, stat_aliases)
             self.set_model(model_with_stat_op)
             self._add_outputs(nodes_name)
+
+            model_output_names = self._model.outputs.keys()
+            align_stat_names_with_results(model_output_names,
+                                          nodes_name,
+                                          output_to_node_names,
+                                          stats_layout,
+                                          stat_aliases)
+
             # Creating statistics layout with IE-like names
             stats_layout, stat_names_aliases = self._convert_stats_names(stats_layout)
 
@@ -109,6 +118,9 @@ class IEEngine(Engine):
             process_accumulated_stats(accumulated_stats=self._accumulated_layer_stats,
                                       stat_names_aliases=stat_names_aliases)
 
+        if stats_layout:
+            restore_original_node_names(output_to_node_names, accumulated_stats, stats_layout, stat_aliases)
+
         # Calculate metrics of required type. Reset collected statistics
         metrics = None
         if self._metric:
diff --git a/tools/pot/openvino/tools/pot/engines/utils.py b/tools/pot/openvino/tools/pot/engines/utils.py
index a3fa808924b..fd9cf941144 100644
--- a/tools/pot/openvino/tools/pot/engines/utils.py
+++ b/tools/pot/openvino/tools/pot/engines/utils.py
@@ -64,7 +64,7 @@ def get_per_layer_stat_mapping(stats_layout):
         for stat_name, _ in stats.items():
             layer_stat_name = layer
             if hasattr(stat_name, 'kwargs') and stat_name.kwargs.get('inplace_statistics', False):
-                layer_stat_name = stat_name.kwargs['type'] + '_' + layer
+                layer_stat_name = stat_name.kwargs.get('layer_stat_name', stat_name.kwargs['type'] + '_' + layer)
             old_names_mapping[layer_stat_name], stat_names_by_layer[layer_stat_name] = layer, stat_name
 
     return stat_names_by_layer, old_names_mapping
@@ -76,7 +76,7 @@ def get_inplace_stats_mapping(stats_layout):
         for stat_name, _ in stats.items():
             layer_stat_name = layer
             if hasattr(stat_name, 'kwargs') and stat_name.kwargs.get('inplace_statistics', False):
-                layer_stat_name = stat_name.kwargs['type'] + '_' + layer
+                layer_stat_name = stat_name.kwargs.get('layer_stat_name', stat_name.kwargs['type'] + '_' + layer)
             if layer not in old_name_stat_new_name:
                 old_name_stat_new_name[layer] = {stat_name: layer_stat_name}
             else:
@@ -100,3 +100,36 @@ def get_sequential_activations(activations, layer, activation_seq, stats_layout,
         activation_seq[layer].append(activations)
     elif old_names_mapping.get(layer, None) in stats_layout and callable(stat_names_by_layer[layer]):
         activation_seq[layer].append(activations)
+
+
+def update_stats(stats_layout: dict, stat_aliases: dict, old_key: str, new_key: str):
+    stats_layout[new_key] = stats_layout.pop(old_key)
+    for algo_name in stat_aliases:
+        if old_key in stat_aliases[algo_name]:
+            stat_aliases[algo_name][new_key] = stat_aliases[algo_name].pop(old_key)
+
+
+def restore_original_node_names(output2node, accumulated_stats, stats_layout, stat_aliases):
+    if output2node and stats_layout:
+        for out_name, original_node_name in output2node.items():
+            accumulated_stats[original_node_name] = accumulated_stats.pop(out_name)
+            update_stats(stats_layout, stat_aliases, out_name, original_node_name)
+
+
+def align_stat_names_with_results(result_names, nodes_name, output2node, stats_layout, stat_aliases):
+    """ Change node name in stast to result name if in the original model the subgraph had 1 output,
+    but after adding outputs in the subgraph, the number of output ports increased.
+    For such nodes, it is necessary to add a '.0' to the original output name
+    :param: result_names: names of Result nodes
+    :param: nodes_name: node name in graph
+    :param: output2node: a dict storing the matching of the result to the node
+    :param: stats_layout: dict of stats collection functions
+    :param: stat_aliases: dict of algorithms collections stats
+    """
+    if output2node:
+        for original_out_name in nodes_name:
+            if original_out_name not in result_names and (original_out_name, 0) not in stats_layout:
+                out_name_with_port = original_out_name + '.0'
+                assert out_name_with_port in result_names
+                update_stats(stats_layout, stat_aliases, original_out_name, out_name_with_port)
+                output2node[out_name_with_port] = original_out_name
diff --git a/tools/pot/openvino/tools/pot/graph/editor.py b/tools/pot/openvino/tools/pot/graph/editor.py
index ed407b4262f..a0b6ae972ba 100644
--- a/tools/pot/openvino/tools/pot/graph/editor.py
+++ b/tools/pot/openvino/tools/pot/graph/editor.py
@@ -1,11 +1,32 @@
 # Copyright (C) 2020-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
+from functools import partial
+
 from mo.graph.graph import Graph, Node
+from mo.middle.pattern_match import for_graph_and_each_sub_graph_recursively
 
 from .ops import OPERATIONS
 
 
+class FunctionResultsAccumulator:
+    """
+    Run function on graph and store each call result in the list
+    """
+    def __init__(self, func):
+        self._results = []
+        self._func = func
+
+    @property
+    def results(self):
+        return self._results
+
+    def __call__(self, graph):
+        result = self._func(graph)
+        if not result is None:
+            self._results.extend(result)
+
+
 def find_node(graph: Graph, name):
     """ Get node by name
     :param graph: NetworkX model to search in
@@ -19,13 +40,27 @@ def find_node(graph: Graph, name):
     return None
 
 
-def get_node_by_name(graph: Graph, name: str) -> Node:
+# TODO: set recursively = True to enable subgraphs quantization
+def get_node_by_name(graph: Graph, name: str, recursively: bool = False) -> Node:
     """ Returns node by name
     :param graph: NetworkX model to take node
     :param name: name of the node
+    :param recursively: whether return all nodes from the graph
+    and each subgraph or only from the external graph
     :return node from NetworkX model (of type Node or None if there's no such node)
     """
-    node = graph.get_op_nodes(name=name)
+    if recursively:
+        def get_node_by_fullname(graph: Graph, name: str) -> Node:
+            nodes = graph.get_nodes_with_attributes(**dict(kind='op', fullname=name))
+            return [Node(graph, nodes[0])] if nodes else None
+
+        partial_get_node_by_fullname = partial(get_node_by_fullname, name=name)
+        get_node_by_fullname_func = FunctionResultsAccumulator(partial_get_node_by_fullname)
+        for_graph_and_each_sub_graph_recursively(graph, get_node_by_fullname_func)
+        node = get_node_by_fullname_func.results
+    else:
+        node = graph.get_op_nodes(name=name)
+
     return node[0] if node else None
 
 
@@ -59,37 +94,70 @@ def connect_nodes_by_name(graph: Graph, src_node_name, src_port, dst_node_name,
     :param dst_node_name: name of the destination node
     :param dst_port: index of the port for destination node
      """
-    src_node = get_node_by_name(graph, src_node_name)
+    src_node = get_node_by_name(graph, src_node_name, recursively=False)
     if src_node is None:
         raise Exception('There\'s no node with {} name'.format(src_node_name))
-    dst_node = get_node_by_name(graph, dst_node_name)
+    dst_node = get_node_by_name(graph, dst_node_name, recursively=False)
     if dst_node is None:
         raise Exception('There\'s no node with {} name'.format(dst_node_name))
 
     connect_nodes(src_node, src_port, dst_node, dst_port)
 
 
-def get_all_operation_nodes(graph: Graph):
+# TODO: set recursively = True to enable subgraphs quantization
+def get_all_operation_nodes(graph: Graph, recursively: bool = False):
     """ Returns sequence of all nodes in graph
     :param graph: NetworkX model to take nodes
+    :param recursively: whether return all nodes from the graph
+    and each subgraph or only from the external graph
     :return list of all nodes
     """
+    if recursively:
+        get_all_op_nodes_func = FunctionResultsAccumulator(lambda graph: graph.get_op_nodes())
+        for_graph_and_each_sub_graph_recursively(graph, get_all_op_nodes_func)
+        return get_all_op_nodes_func.results
+
     return graph.get_op_nodes()
 
 
-def get_nodes_by_type(graph: Graph, types: list):
+# TODO: set recursively = True to enable subgraphs quantization
+def get_nodes_by_type(graph: Graph, types: list, recursively: bool = False) -> list:
     """ Returns all nodes with type from types collection
      :param graph: NetworkX model to collect nodes
      :param types: list of required types
+     :param recursively: whether return all nodes from the graph
+     and each subgraph or only from the main graph
      :return list of nodes filtered by 'types' collection
       """
-    nodes = []
-    for t in types:
-        for node in graph.get_op_nodes(type=t):
-            nodes.append(node)
+    def get_nodes_by_type_from_main_graph(graph, types):
+        return [node for t in types for node in graph.get_op_nodes(type=t)]
+
+    if recursively:
+        partial_get_nodes_by_type = partial(get_nodes_by_type_from_main_graph, types=types)
+        get_nodes_by_type_recursively = FunctionResultsAccumulator(partial_get_nodes_by_type)
+        for_graph_and_each_sub_graph_recursively(graph, get_nodes_by_type_recursively)
+        nodes = [node for node in get_nodes_by_type_recursively.results if node]
+    else:
+        nodes = get_nodes_by_type_from_main_graph(graph, types)
     return nodes
 
 
+def add_fullname_for_nodes(graph: Graph):
+    def set_fullname(graph, subgraphs=None):
+        if subgraphs is None:
+            subgraphs = []
+        for node in graph:
+            node = Node(graph, node)
+            if node.has_valid('sub_graphs'):
+                for sub_graph_name in node.sub_graphs:
+                    subgraphs.append(node.name)
+                    set_fullname(node[sub_graph_name], subgraphs)
+                    subgraphs = subgraphs[:-1]
+            node['fullname'] = '|'.join(subgraphs + [node.name])
+
+    set_fullname(graph)
+
+
 def create_node(graph: Graph, node_name, node_type, node_attrs):
     """ Create node in graph
     :param graph: NetworkX model
diff --git a/tools/pot/openvino/tools/pot/graph/model_utils.py b/tools/pot/openvino/tools/pot/graph/model_utils.py
index cd0e9eb568f..1f29a66486b 100644
--- a/tools/pot/openvino/tools/pot/graph/model_utils.py
+++ b/tools/pot/openvino/tools/pot/graph/model_utils.py
@@ -52,18 +52,21 @@ def compress_model_weights(model: NXModel):
         compress_weights(model_dict['model'])
 
 
-def get_nodes_by_type(model: NXModel, types: list):
+# TODO: set recursively = True to enable subgraphs quantization
+def get_nodes_by_type(model: NXModel, types: list, recursively: bool = False):
     """ Returns all nodes with type from types collection
     :param model: NXModel model
     :param types: list of required types
+    :param recursively: whether return all nodes from the model
+    and each subgraph or only from the external model
     :return list of nodes filtered by 'types' collection
     """
     return [node for model_dict in model.models
-            for node in ge.get_nodes_by_type(model_dict['model'], types)]
+            for node in ge.get_nodes_by_type(model_dict['model'], types, recursively)]
 
 
 def get_node_by_name(model: NXModel, name: str) -> Node:
-    """ Returns node by name
+    """ Returns node by name found in the graph and each subgraph
     :param model: NXModel model
     :param name: name of the node
     :return node from model (of type Node or None if there's no such node)
@@ -77,13 +80,16 @@ def get_node_by_name(model: NXModel, name: str) -> Node:
     return names[0] if names else None
 
 
-def get_all_operation_nodes(model: NXModel):
+# TODO: set recursively = True to enable subgraphs quantization
+def get_all_operation_nodes(model: NXModel, recursively: bool = False):
     """ Returns sequence of all nodes in all graphs
     :param model: NXModel model
+    :param recursively: whether return all nodes from the model
+    and each subgraph or only from the external model
     :return list of all nodes
     """
     return [node for model_dict in model.models
-            for node in ge.get_all_operation_nodes(model_dict['model'])]
+            for node in ge.get_all_operation_nodes(model_dict['model'], recursively)]
 
 
 def build_model_for_node(nx_model, input_name, input_shape, node, remove_bias=False,
diff --git a/tools/pot/openvino/tools/pot/graph/node_utils.py b/tools/pot/openvino/tools/pot/graph/node_utils.py
index 1aabeafa6a9..51cae922f43 100644
--- a/tools/pot/openvino/tools/pot/graph/node_utils.py
+++ b/tools/pot/openvino/tools/pot/graph/node_utils.py
@@ -192,10 +192,10 @@ def get_quantized_input_key(quantized_node):
     Otherwise, key is tuple (fq_input name, output port number)
     """
     quantized_input = get_node_input(quantized_node, 0)
-    key = quantized_input.name
+    key = quantized_input.fullname
     if len(quantized_input.out_ports()) > 1:
         port_number = quantized_node.in_port(0).get_source().out
-        key = (quantized_input.name, port_number)
+        key = (quantized_input.fullname, port_number)
     return key
 
 
@@ -272,3 +272,7 @@ def get_node_data_type(node):
             and node.in_port(0).get_source().is_data_type_defined():
         return node.in_port(0).get_source().get_data_type()
     return None
+
+
+def reset_node_fullname(old_fullname, node_name):
+    return '|'.join(old_fullname.split('|')[:-1] + [node_name])
diff --git a/tools/pot/openvino/tools/pot/graph/nx_model.py b/tools/pot/openvino/tools/pot/graph/nx_model.py
index 2898e268550..1767a96b1a1 100644
--- a/tools/pot/openvino/tools/pot/graph/nx_model.py
+++ b/tools/pot/openvino/tools/pot/graph/nx_model.py
@@ -5,6 +5,7 @@ import os
 from copy import deepcopy
 from addict import Dict
 import networkx as nx
+from mo.graph.graph import rename_node
 
 from openvino.tools.pot.graph.graph_utils import load_graph, save_graph
 from openvino.tools.pot.graph import editor as ge
@@ -42,6 +43,9 @@ class NXModel:
             raise TypeError('Unable to load models. Invalid keyword argument. '
                             'Either model config (config=) or NetworkX graph (graph=) is expected.')
 
+        for model in self._models:
+            ge.add_fullname_for_nodes(model['model'])
+
     def _from_config(self, model_config, target_device='ANY'):
         if not isinstance(model_config, Dict):
             model_config = Dict(model_config)
@@ -59,8 +63,11 @@ class NXModel:
         self._is_cascade = len(self._models) > 1
         if self._is_cascade:
             self._add_models_prefix()
+        for model in self._models:
+            ge.add_fullname_for_nodes(model['model'])
 
     def _from_graph(self, graph):
+        ge.add_fullname_for_nodes(graph)
         self._models.append({'model': graph})
         self._is_cascade = False
 
@@ -134,7 +141,7 @@ class NXModel:
     def get_final_output_nodes(self):
         """Returns list of Result nodes from the last model of cascade"""
         last_model = self._models[-1]['model']
-        return ge.get_nodes_by_type(last_model, ['Result'])
+        return ge.get_nodes_by_type(last_model, ['Result'], recursively=False)
 
     def clean_up(self):
         for model_dict in self._models:
@@ -178,8 +185,8 @@ class NXModel:
             self._prefix_is_applied = True
             for model_dict in self._models:
                 model_name, model = model_dict['name'], model_dict['model']
-                for node in ge.get_all_operation_nodes(model):
-                    node.name = '{}_{}'.format(model_name, node.name)
+                for node in ge.get_all_operation_nodes(model, recursively=False):
+                    rename_node(node, f'{model_name}_{node.name}')
 
     def _remove_models_prefix(self):
         """Removes model name prefix from node names"""
@@ -188,9 +195,9 @@ class NXModel:
             for model_dict in self._models:
                 model_name, model = model_dict['name'], model_dict['model']
                 self._cache.node_names[model_name] = []
-                for node in ge.get_all_operation_nodes(model):
+                for node in ge.get_all_operation_nodes(model, recursively=False):
                     if node.name.startswith(model_name):
-                        node.name = node.name.replace(model_name + '_', '', 1)
+                        rename_node(node, node.name.replace(model_name + '_', '', 1))
                         self._cache.node_names[model_name].append(node.name)
 
     def _restore_models_prefix(self):
@@ -199,7 +206,7 @@ class NXModel:
             self._prefix_is_applied = True
             for model_dict in self._models:
                 model_name, model = model_dict['name'], model_dict['model']
-                for node in ge.get_all_operation_nodes(model):
+                for node in ge.get_all_operation_nodes(model, recursively=False):
                     if node.name in self._cache.node_names[model_name]:
-                        node.name = '{}_{}'.format(model_name, node.name)
+                        rename_node(node, f'{model_name}_{node.name}')
             self._cache.pop('node_names')
diff --git a/tools/pot/openvino/tools/pot/graph/passes.py b/tools/pot/openvino/tools/pot/graph/passes.py
index a0162f9f51d..1c8f11a55a4 100644
--- a/tools/pot/openvino/tools/pot/graph/passes.py
+++ b/tools/pot/openvino/tools/pot/graph/passes.py
@@ -17,7 +17,7 @@ from extensions.ops.Cast import Cast
 from extensions.ops.fakequantize import FakeQuantize
 from mo.back.replacement import BackReplacementPattern
 from mo.front.common.replacement import FrontReplacementSubgraph
-from mo.graph.graph import Graph, Node
+from mo.graph.graph import Graph, Node, rename_node
 from mo.graph.port import Port
 from mo.middle.pattern_match import apply_pattern
 from mo.ops.const import Const
@@ -131,6 +131,8 @@ class InsertFakeQuantize(BackReplacementPattern):
 
         if m_op.type in ['Convolution', 'ConvolutionBackpropData', 'MatMul']:
             insert_fake_quantize(graph, m_op, [0, 1], ['fq_input', 'fq_weights'])
+        elif m_op.type == 'LSTMCell':
+            insert_fake_quantize(graph, m_op, [0, 1, 2, 3, 4])
         elif self.quantize_only_input(m_op):
             insert_fake_quantize(graph, m_op, [0])
         else:
@@ -345,7 +347,7 @@ class FakeQuantizePropagation(BackReplacementPattern):
                         _skip_multibranch_ascent_ops[name] = skip_ascent_map[name]
                     else:
                         _skip_multibranch_ascent_ops[name] = _is_node_skippable(
-                            ge.get_node_by_name(graph, name), skip_ascent_map)
+                            ge.get_node_by_name(graph, name, recursively=False), skip_ascent_map)
                 skip_ascent_map.update(_skip_multibranch_ascent_ops)
                 return any(_skip_multibranch_ascent_ops.values())
 
@@ -411,7 +413,7 @@ class FakeQuantizeOptimization(BackReplacementPattern):
 
 class RemoveFakeQuantize:
     def find_and_remove_node(self, graph, node_name, force=False):
-        node = ge.get_node_by_name(graph, node_name)
+        node = ge.get_node_by_name(graph, node_name, recursively=False)
         if not node:
             return [], []
 
@@ -508,9 +510,11 @@ class RemoveFakeQuantize:
     @staticmethod
     def undo_renaming(graph, fq_node):
         if 'orig_fq_name' in fq_node:
-            node = ge.get_node_by_name(graph, '{fq_name}/pre_fq_input'.format(fq_name=fq_node.name))
-            node.name = node['orig_node_name']
-            fq_node.name = fq_node['orig_fq_name']
+            node = ge.get_node_by_name(graph,
+                                       '{fq_name}/pre_fq_input'.format(fq_name=fq_node.fullname),
+                                       recursively=False)
+            rename_node(node, node['orig_node_name'])
+            rename_node(fq_node, fq_node['orig_fq_name'])
 
     @property
     def quantize_agnostic_operations(self):
@@ -660,11 +664,11 @@ class FakeQuantizeNameSwapper(BackReplacementPattern):
                 new_fq_name += '.{}'.format(fq_node.in_port(0).get_source().idx)
 
             fq_node['orig_fq_name'] = copy(fq_node.name)
-            fq_node.name = copy(new_fq_name)
+            rename_node(fq_node, new_fq_name)
 
             if 'orig_node_name' not in input_node:
                 input_node['orig_node_name'] = copy(input_node.name)
-                input_node.name = '{original_name}/pre_fq_input'.format(original_name=input_node.name)
+                rename_node(input_node, f'{input_node.name}/pre_fq_input')
 
         pattern = get_fq_result_pattern()
         apply_pattern(
diff --git a/tools/pot/openvino/tools/pot/graph/transformer.py b/tools/pot/openvino/tools/pot/graph/transformer.py
index 44e1fb4e8e8..179b6c3c392 100644
--- a/tools/pot/openvino/tools/pot/graph/transformer.py
+++ b/tools/pot/openvino/tools/pot/graph/transformer.py
@@ -1,6 +1,7 @@
 # Copyright (C) 2020-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
+from .editor import add_fullname_for_nodes
 from .special_operations import QUANTIZE_AGNOSTIC_OPERATIONS
 from .passes import InsertFakeQuantize, FakeQuantizePropagation, FakeQuantizeOptimization, RemoveFakeQuantize, \
     SpecialBlocksMarker, FakeQuantizeNameSwapper
@@ -68,6 +69,10 @@ class GraphTransformer:
             self.fq_insertion.ignored_params = ignored_params_[model_dict['name']] if model.is_cascade \
                 else ignored_params_
             self._insert_fake_quantize(model_dict['model'])
+            # TODO: Uncomment to enable subgraphs quantization
+            # from mo.middle.pattern_match import for_graph_and_each_sub_graph_recursively
+            # for_graph_and_each_sub_graph_recursively(model_dict['model'], self._insert_fake_quantize)
+            add_fullname_for_nodes(model_dict['model'])
         return model
 
     def _remove_fq_nodes(self, graph, node_names, force=False):
diff --git a/tools/pot/openvino/tools/pot/graph/utils.py b/tools/pot/openvino/tools/pot/graph/utils.py
index bce43fd8823..0067b56d693 100644
--- a/tools/pot/openvino/tools/pot/graph/utils.py
+++ b/tools/pot/openvino/tools/pot/graph/utils.py
@@ -142,7 +142,7 @@ def is_ignored(ignored_params, op, skipped=True):
     """
     if ignored_params.get('skip_model') or \
             skipped and 'skipped' in op and op['skipped'] or\
-            op.name in ignored_params['scope']:
+            op.fullname in ignored_params['scope']:
         return True
     for operation in ignored_params['operations']:
         if op.type == operation['type']:
@@ -188,7 +188,7 @@ def check_agnostic_and_ignored_params(model, ignored_params):
         children = [node for node in get_all_node_outputs(node) if node is not None]
         for child in children:
             if child not in quantize_agnostic:
-                ignored_params['scope'].append(child.name)
+                ignored_params['scope'].append(child.fullname)
             else:
                 add_new_ignored_params(model, node, quantize_agnostic,\
                                        ignored_params, model_is_cascade)
@@ -200,7 +200,7 @@ def check_agnostic_and_ignored_params(model, ignored_params):
         ignored_params_operation = [op['type'] for op in dict_ignored_operation_model['operations']]
 
         for node in model_dict['model'].get_op_nodes():
-            if (node.type in ignored_params_operation or node.name in dict_ignored_operation_model['scope']) \
+            if (node.type in ignored_params_operation or node.fullname in dict_ignored_operation_model['scope']) \
                                                                        and node.type in quantize_agnostic:
 
                 new_ignored_params = add_new_ignored_params(model_dict['model'], node,
diff --git a/tools/pot/openvino/tools/pot/statistics/statistic_graph_builder.py b/tools/pot/openvino/tools/pot/statistics/statistic_graph_builder.py
index 6e537a29604..d66c962fbe8 100644
--- a/tools/pot/openvino/tools/pot/statistics/statistic_graph_builder.py
+++ b/tools/pot/openvino/tools/pot/statistics/statistic_graph_builder.py
@@ -1,94 +1,148 @@
 # Copyright (C) 2020-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
+from copy import deepcopy
+from mo.ops.op import Op
 from mo.ops.result import Result
 from mo.front.tf.graph_utils import create_op_node_with_second_input
 from mo.front.common.partial_infer.utils import int64_array
+
+try:
+    from extensions.back.add_outputs_recursive import AddOutputRecursive
+except ImportError:
+    pass  # we try to import AddOutputRecursive for subgraphs quantization
+
 from extensions.ops.ReduceOps import ReduceMin, ReduceMax, ReduceMean
 from extensions.ops.activation_ops import Abs
 
 from ..graph.model_utils import get_node_by_name
-from ..graph.node_utils import get_output_shape
+from ..graph.node_utils import get_output_shape, reset_node_fullname
 from ..statistics.statistics import Statistic, TensorStatistic, TensorStatisticAxis
 from ..statistics.function_selector import ACTIVATIONS, get_stats_function
 
-from ..utils.utils import convert_output_key
-
-
 
 class StatisticGraphBuilder:
     def insert_statistic(self, model, stats_layout, stat_aliases=None):
+        output_to_node_names = {}
         if stat_aliases is None or model is None:
-            return model, list(stats_layout.keys())
+            return model, list(stats_layout.keys()), output_to_node_names
         nodes_names = []
-        for algo_name, node_stats in stat_aliases.items():
+        copy_stat_aliases = deepcopy(stat_aliases)
+        for algo_name, node_stats in copy_stat_aliases.items():
             for node_name, stats in node_stats.items():
                 node_name_in_graph = node_name[0] if isinstance(node_name, tuple) else node_name
+                node_name_in_graph = node_name_in_graph.replace('/pre_fq_input', '')
                 node = get_node_by_name(model, node_name_in_graph)
-                node_name = convert_output_key(node_name)
+                node_in_main_graph = get_node_by_name(model, node_name_in_graph.split('|')[0])
+                model_graph = node_in_main_graph.graph
                 for stat, _ in list(stats.items()):
                     if not isinstance(stat, Statistic) or not stat.kwargs.get('inplace_statistics', False):
-                        nodes_names.append(node_name_in_graph)
+                        if node_name_in_graph not in nodes_names:
+                            nodes_names.append(node_name_in_graph)
                         continue
                     type_stat = stat.kwargs['type']
-                    add_output_node = getattr(self, f'insert_{type_stat}')(node, type_stat, node_name, **stat.kwargs)
+                    add_output_node, op_name = getattr(self, f'insert_{type_stat}')(model_graph,
+                                                                                    node,
+                                                                                    type_stat,
+                                                                                    node.name,
+                                                                                    **stat.kwargs)
                     if add_output_node:
-                        nodes_names.append(add_output_node)
+                        if node_name_in_graph not in nodes_names:
+                            nodes_names.append(op_name)
                         class_statistic = TensorStatistic if isinstance(stat, TensorStatistic) else TensorStatisticAxis
-
-                        del stats_layout[node_name][stat]
                         fn = get_stats_function(ACTIVATIONS, type_stat, stat.kwargs.get('granularity'),
                                                 'compute_statistic')
                         new_stat = class_statistic(fn,
                                                    channel=stat.kwargs.get('channel', {}),
                                                    inplace_statistics=False,
                                                    granularity=stat.kwargs.get('granularity'),
-                                                   type=type_stat)
-                        stats_layout[node_name][new_stat] = new_stat
+                                                   type=type_stat,
+                                                   layer_stat_name=op_name)
+                    else:
+                        new_stat = deepcopy(stat)
+                        new_stat.kwargs['layer_stat_name'] = op_name
 
-                        stat_name = stat_aliases[algo_name][node_name][stat]
-                        del stat_aliases[algo_name][node_name][stat]
-                        stat_aliases[algo_name][node_name][new_stat] = stat_name
+                    del stats_layout[node_name][stat]
+                    stats_layout[node_name][new_stat] = new_stat
 
-        return model, nodes_names
+                    stat_name = stat_aliases[algo_name][node_name][stat]
+                    del stat_aliases[algo_name][node_name][stat]
+                    stat_aliases[algo_name][node_name][new_stat] = stat_name
 
-    def insert_reduce(self, insert_op, node, granularity, type_stat, node_name, axis=1):
+                # add output if node in subgraph
+                if model_graph != node.graph:
+                    if node_name_in_graph in nodes_names:
+                        nodes_names.remove(node_name_in_graph)
+
+                    # Don't need adding extra output to the same node, but for another algo
+                    if node_name_in_graph in output_to_node_names.values():
+                        result_name = next((result for result, node in output_to_node_names.items()
+                                            if node == node_name_in_graph))
+                    else:
+                        model_graph.graph['additional_outputs'] = node_name_in_graph.split('|')
+                        results = AddOutputRecursive().find_and_replace_pattern(model_graph)
+                        assert len(results) == 1
+                        result_name = results[0].name
+                    if node_name in stats_layout:
+                        stats_layout[result_name] = stats_layout.pop(node_name)
+                    stat_aliases[algo_name][result_name] = stat_aliases[algo_name].pop(node_name)
+                    output_to_node_names[result_name] = node_name_in_graph
+
+        return model, nodes_names, output_to_node_names
+
+    def insert_reduce(self, model_graph, insert_op, node, granularity, type_stat, node_name, axis=1):
         axis_const = self.find_axis(node, granularity, axis)
         if isinstance(axis_const, str):
-            return node.name
+            return (True, node.name)
         reduce_op = create_op_node_with_second_input(node.graph, insert_op, int64_array(axis_const),
                                                      dict(name=f'{type_stat}_{node_name}'))
-
+        reduce_op['fullname'] = reset_node_fullname(node.fullname, reduce_op.name)
+        if node.graph != model_graph:
+            Op.create_data_node(reduce_op.graph, reduce_op, {'shape': [1]})
         node.out_port(0).connect(reduce_op.in_port(0))
-        self.insert_result(node, reduce_op, type_stat)
-        return None
+        return self.insert_result(model_graph, node, reduce_op, type_stat)
 
-    def insert_min(self, node, type_stat, node_name, **kwargs):
-        return self.insert_reduce(ReduceMin, node, kwargs.get('granularity'), type_stat, node_name)
+    def insert_min(self, model_graph, node, type_stat, node_name, **kwargs):
+        return self.insert_reduce(model_graph, ReduceMin, node, kwargs.get('granularity'), type_stat, node_name)
 
-    def insert_max(self, node, type_stat, node_name, **kwargs):
-        return self.insert_reduce(ReduceMax, node, kwargs.get('granularity'), type_stat, node_name)
+    def insert_max(self, model_graph, node, type_stat, node_name, **kwargs):
+        return self.insert_reduce(model_graph, ReduceMax, node, kwargs.get('granularity'), type_stat, node_name)
 
-    def insert_mean(self, node, type_stat, node_name, **kwargs):
+    def insert_mean(self, model_graph, node, type_stat, node_name, **kwargs):
         axis_channel = kwargs.get('channel', None).get(node.name, 1)
-        return self.insert_reduce(ReduceMean, node, kwargs.get('granularity'), type_stat, node_name, axis_channel)
+        return self.insert_reduce(model_graph,
+                                  ReduceMean,
+                                  node,
+                                  kwargs.get('granularity'),
+                                  type_stat,
+                                  node_name,
+                                  axis_channel)
 
-    def insert_abs_max(self, node, type_stat, node_name, **kwargs):
+    def insert_abs_max(self, model_graph, node, type_stat, node_name, **kwargs):
         axis_const = self.find_axis(node, kwargs.get('granularity'))
         if isinstance(axis_const, str):
-            return node.name
-        abs_node = Abs(node.graph, {"name": type_stat + node_name}).create_node()
-        abs_node.in_port(0).connect(node.out_port(0))
+            return (True, node.name)
+        abs_node = Abs(node.graph, {"name": f'abs_{node_name}'}).create_node_with_data([node.out_node(0)]).in_node(0)
         max_op = create_op_node_with_second_input(node.graph, ReduceMax, int64_array(axis_const),
-                                                  dict(name='abs_max_' + node_name))
+                                                  dict(name=f'{type_stat}_{node_name}'))
+
+        if node.graph != model_graph:
+            Op.create_data_node(max_op.graph, max_op, {'shape': [1]})
+        max_op['fullname'] = reset_node_fullname(node.fullname, max_op.name)
         abs_node.out_port(0).connect(max_op.in_port(0))
-        self.insert_result(node, max_op, type_stat)
-        return None
+        return self.insert_result(model_graph, node, max_op, type_stat)
 
     @staticmethod
-    def insert_result(node, child_node, name):
-        res_op = Result(node.graph, {'name': f'Result_{name}_{node.name}'}).create_node()
-        child_node.out_port(0).connect(res_op.in_port(0))
+    def insert_result(model_graph, node, child_node, name):
+        if node.graph != model_graph:
+            model_graph.graph['additional_outputs'] = child_node.fullname.split('|')
+            res_op = AddOutputRecursive().find_and_replace_pattern(model_graph)
+            ie_result_name = res_op[0].name
+        else:
+            ie_result_name = f'{name}_{node.name}'
+            res_op = Result(node.graph, {'name': f'Result_{ie_result_name}'}).create_node()
+            child_node.out_port(0).connect(res_op.in_port(0))
+        return (False, ie_result_name)
 
     @staticmethod
     def find_axis(node, granularity, axis=1):
diff --git a/tools/pot/tests/data/hardware_configs/cpu.json b/tools/pot/tests/data/hardware_configs/cpu.json
index 8d79ae1048d..5415fe71fae 100755
--- a/tools/pot/tests/data/hardware_configs/cpu.json
+++ b/tools/pot/tests/data/hardware_configs/cpu.json
@@ -173,6 +173,13 @@
                 "weights": ["q8_w_sym", "q8_w_asym"]
             }
         },
+        {
+            "type": "LSTMCell",
+            "quantization": {
+                "activations": "q8_a",
+                "weights": ["q8_w_sym", "q8_w_asym"]
+            }
+        },
         {
             "type": "Power",
             "quantization": {
diff --git a/tools/pot/tests/data/hardware_configs/reference/cpu_flatten.json b/tools/pot/tests/data/hardware_configs/reference/cpu_flatten.json
index 4fd824a0cbd..718d578ef77 100644
--- a/tools/pot/tests/data/hardware_configs/reference/cpu_flatten.json
+++ b/tools/pot/tests/data/hardware_configs/reference/cpu_flatten.json
@@ -1 +1 @@
-[{"target_device": "CPU"}, {"type": "Convolution", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "DepthWiseConvolution", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "MatMul", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "Add", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "Multiply", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "Maximum", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "Less", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "LessEqual", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "Greater", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "GreaterEqual", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "Divide", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "Minimum", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "Equal", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "Subtract", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "NotEqual", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "FloorMod", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "LogicalOr", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "LogicalXor", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "LogicalAnd", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "LogicalNot", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "Power", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}]}}, {"type": "AvgPool", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}]}}, {"type": "NormalizeL2", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}]}}, {"type": "ReduceMean", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}]}}, {"type": "MaxPool"}, {"type": "ReduceMax"}, {"type": "Interpolate", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}]}}, {"type": "MVN", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}]}}, {"type": "Concat", "attributes": {"scales": "unified"}}, {"type": "Reshape"}, {"type": "Flatten"}, {"type": "Squeeze"}, {"type": "Unsqueeze"}, {"type": "Split"}, {"type": "VariadicSplit"}, {"type": "Crop"}, {"type": "Transpose"}, {"type": "Tile"}, {"type": "StridedSlice"}, {"type": "ShuffleChannels"}, {"type": "Broadcast"}, {"type": "Pad"}, {"type": "ConvertLike"}]
\ No newline at end of file
+[{"target_device": "CPU"}, {"type": "Convolution", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "DepthWiseConvolution", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "MatMul", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "Add", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "Multiply", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "Maximum", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "Less", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "LessEqual", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "Greater", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "GreaterEqual", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "Divide", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "Minimum", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "Equal", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "Subtract", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "NotEqual", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "FloorMod", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "LogicalOr", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "LogicalXor", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "LogicalAnd", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "LogicalNot", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "LSTMCell", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}], "weights": [{"bits": 8, "mode": "symmetric", "level_low": -127, "level_high": 127, "granularity": "perchannel"}, {"bits": 8, "mode": "asymmetric", "granularity": "perchannel"}]}}, {"type": "Power", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}]}}, {"type": "AvgPool", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}]}}, {"type": "NormalizeL2", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}]}}, {"type": "ReduceMean", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}]}}, {"type": "MaxPool"}, {"type": "ReduceMax"}, {"type": "Interpolate", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}]}}, {"type": "MVN", "quantization": {"activations": [{"bits": 8, "mode": "symmetric", "granularity": "pertensor"}, {"bits": 8, "mode": "asymmetric", "granularity": "pertensor"}]}}, {"type": "Concat", "attributes": {"scales": "unified"}}, {"type": "Reshape"}, {"type": "Flatten"}, {"type": "Squeeze"}, {"type": "Unsqueeze"}, {"type": "Split"}, {"type": "VariadicSplit"}, {"type": "Crop"}, {"type": "Transpose"}, {"type": "Tile"}, {"type": "StridedSlice"}, {"type": "ShuffleChannels"}, {"type": "Broadcast"}, {"type": "Pad"}, {"type": "ConvertLike"}]
\ No newline at end of file
diff --git a/tools/pot/tests/data/models/tensor_iterator_example/tensor_iterator_example.json b/tools/pot/tests/data/models/tensor_iterator_example/tensor_iterator_example.json
new file mode 100644
index 00000000000..d075306e4dc
--- /dev/null
+++ b/tools/pot/tests/data/models/tensor_iterator_example/tensor_iterator_example.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:66bb19335e67bff48904f44c1aca979dccde693db87acaeea084f66ad141af36
+size 259
diff --git a/tools/pot/tests/data/models/tensor_iterator_example/tensor_iterator_example.pb b/tools/pot/tests/data/models/tensor_iterator_example/tensor_iterator_example.pb
new file mode 100644
index 00000000000..7cacf91f28f
--- /dev/null
+++ b/tools/pot/tests/data/models/tensor_iterator_example/tensor_iterator_example.pb
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4026069adcbce63e2cb892f20af75610df01fdb5a4a5cfb018e8f1c58b9b721b
+size 1983799
diff --git a/tools/pot/tests/data/reference_models/tensor_iterator_example_tf.xml b/tools/pot/tests/data/reference_models/tensor_iterator_example_tf.xml
new file mode 100644
index 00000000000..865815910ee
--- /dev/null
+++ b/tools/pot/tests/data/reference_models/tensor_iterator_example_tf.xml
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:81a8f061fbe4e248b0150151a1506b90ad1f6b66e9a37399d6e371fae215eb76
+size 23986
diff --git a/tools/pot/tests/test_graph.py b/tools/pot/tests/test_graph.py
index 5ee601eb565..fd4654d1141 100755
--- a/tools/pot/tests/test_graph.py
+++ b/tools/pot/tests/test_graph.py
@@ -25,7 +25,8 @@ TEST_MODELS = [
     ('densenet121_example', 'pytorch', 'ANY'),
     ('multiple_out_ports_net', 'tf', 'ANY'),
     ('lstm_example', 'pytorch', 'GNA'),
-    ('multiple_outputs_net_example', 'dldt', 'GNA')
+    ('multiple_outputs_net_example', 'dldt', 'GNA'),
+    # ('tensor_iterator_example', 'tf', 'ANY'),
 ]
 
 CASCADE_MAP = Dict({
diff --git a/tools/pot/tests/test_statistic_builder.py b/tools/pot/tests/test_statistic_builder.py
index 80f7437ca12..297dfa511ab 100644
--- a/tools/pot/tests/test_statistic_builder.py
+++ b/tools/pot/tests/test_statistic_builder.py
@@ -101,7 +101,7 @@ def test_statistics_collector_subsets(tmp_path, models, model_name, model_framew
     algo.register_statistics(model, collector)
     statistic_graph_builder = StatisticGraphBuilder()
     act_stats_layout, stat_aliases = merge_stats_by_algo_names([algorithm.name], collector._layout_by_algo)
-    model_with_nodes, nodes_names = statistic_graph_builder.insert_statistic(model, act_stats_layout, stat_aliases)
+    model_with_nodes, nodes_names, _ = statistic_graph_builder.insert_statistic(model, act_stats_layout, stat_aliases)
     ir_name = f'{model_name}_stat_{type_max}_{type_min}' if type_min is not None \
         else f'{model_name}_stat_mean'
     check_model(tmp_path, model_with_nodes, ir_name, model_framework)

From b3869cb1274491392e343b5ef3014aba392953f7 Mon Sep 17 00:00:00 2001
From: Anastasia Popova <anastasia.popova@intel.com>
Date: Fri, 26 Nov 2021 13:02:46 +0300
Subject: [PATCH 35/72] Fixed tensors propagation in GlobalPoolingToReduce.
 (#8835)

---
 .../front/global_pooling_to_reduce.py         |  4 +-
 .../front/GlobalPoolingToReduce_test.py       | 55 +++++++++++++++++++
 2 files changed, 57 insertions(+), 2 deletions(-)
 create mode 100644 model-optimizer/unit_tests/extensions/front/GlobalPoolingToReduce_test.py

diff --git a/model-optimizer/extensions/front/global_pooling_to_reduce.py b/model-optimizer/extensions/front/global_pooling_to_reduce.py
index 135dd22548c..9a5fb98d310 100644
--- a/model-optimizer/extensions/front/global_pooling_to_reduce.py
+++ b/model-optimizer/extensions/front/global_pooling_to_reduce.py
@@ -45,8 +45,8 @@ class GlobalPoolingToReduce(FrontReplacementPattern):
 
             pooling.out_port(0).get_connection().set_source(reduce.out_port(0))
             src = pooling.in_port(0).get_connection().get_source()
-            pooling.in_port(0).disconnect()
-            src.connect(reduce.in_port(0))
+
+            reduce.in_port(0).get_connection().set_source(src)
 
             start = Const(graph, {'value': int64_array(2)}).create_node()
             end = Rank(graph, {'name': name + '/input_rank'}).create_node()
diff --git a/model-optimizer/unit_tests/extensions/front/GlobalPoolingToReduce_test.py b/model-optimizer/unit_tests/extensions/front/GlobalPoolingToReduce_test.py
new file mode 100644
index 00000000000..051c175771a
--- /dev/null
+++ b/model-optimizer/unit_tests/extensions/front/GlobalPoolingToReduce_test.py
@@ -0,0 +1,55 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import unittest
+
+from extensions.front.global_pooling_to_reduce import GlobalPoolingToReduce
+from mo.front.common.partial_infer.utils import int64_array
+from mo.graph.graph import Node
+from mo.utils.ir_engine.compare_graphs import compare_graphs
+from unit_tests.utils.graph import build_graph, regular_op, result, build_graph_with_edge_attrs, const
+
+nodes = {**regular_op('input', {'type': 'Parameter'}),
+
+         **regular_op('relu', {'type': 'Relu'}),
+         **regular_op('pooling', {'type': 'Pooling', 'global_pool': True, 'pool_method': 'avg'}),
+
+         **result('result'),
+
+         **regular_op('rank', {'type': 'Rank'}),
+         **regular_op('reduce_mean', {'type': 'ReduceMean'}),
+         **regular_op('range', {'type': 'Range'}),
+         **const('const_1', int64_array(2)),
+         **const('const_2', int64_array(1)),
+
+         }
+edges = [('input', 'relu', {'in': 0, 'out': 0}), ('relu', 'pooling', {'in': 0, 'out': 0}),
+         ('pooling', 'result', {'in': 0, 'out': 0})]
+ref_edges = [('input', 'relu', {'in': 0, 'out': 0}), ('relu', 'rank', {'in': 0, 'out': 0}),
+             ('rank', 'range', {'in': 1, 'out': 0}),
+             ('relu', 'reduce_mean', {'in': 0, 'out': 0}),
+             ('const_1', 'range', {'in': 0, 'out': 0}), ('const_2', 'range', {'in': 2, 'out': 0}),
+             ('range', 'reduce_mean', {'in': 1, 'out': 0}),
+             ('reduce_mean', 'result', {'in': 0, 'out': 0})]
+
+
+class GlobalPoolingToReduceTest(unittest.TestCase):
+    def test_global_pooling_to_reduce(self):
+        graph = build_graph_with_edge_attrs(nodes, edges)
+
+        graph_ref = build_graph(nodes, ref_edges)
+        graph.stage = 'front'
+        graph.graph['layout'] = 'NCHW'
+        node = Node(graph, 'relu')
+        node.out_edge(0)['fw_tensor_debug_info'] = [('Relu_0', 'Relu_tensor')]
+
+        GlobalPoolingToReduce().find_and_replace_pattern(graph)
+        graph.clean_up()
+
+        (flag, resp) = compare_graphs(graph, graph_ref, 'result')
+        self.assertTrue(flag, resp)
+
+        node = Node(graph, 'relu')
+        edge_attrs = node.out_port(0).get_destinations()[0].get_in_edge_attrs()
+        self.assertTrue('fw_tensor_debug_info' in edge_attrs)
+        self.assertTrue(edge_attrs['fw_tensor_debug_info'] == [('Relu_0', 'Relu_tensor')])

From 533bab2b78a689828c6b190ef1b9fc12af12881e Mon Sep 17 00:00:00 2001
From: Ilya Lavrenov <ilya.lavrenov@intel.com>
Date: Fri, 26 Nov 2021 13:40:27 +0300
Subject: [PATCH 36/72] Export more enum names from nrgaph (#8828)

---
 ngraph/core/src/distributed.cpp            | 2 +-
 ngraph/core/src/op/interpolate.cpp         | 2 +-
 ngraph/core/src/op/lstm_cell.cpp           | 2 +-
 ngraph/core/src/op/non_max_suppression.cpp | 6 +++---
 ngraph/core/src/op/reverse.cpp             | 2 +-
 ngraph/core/src/op/round.cpp               | 2 +-
 6 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/ngraph/core/src/distributed.cpp b/ngraph/core/src/distributed.cpp
index 10f16f5c961..5c9c1cc3b36 100644
--- a/ngraph/core/src/distributed.cpp
+++ b/ngraph/core/src/distributed.cpp
@@ -12,7 +12,7 @@ using namespace ngraph;
 
 namespace ov {
 template <>
-EnumNames<ngraph::reduction::Type>& EnumNames<ngraph::reduction::Type>::get() {
+NGRAPH_API EnumNames<ngraph::reduction::Type>& EnumNames<ngraph::reduction::Type>::get() {
     static auto enum_names = ov::EnumNames<ngraph::reduction::Type>("reduction::Type",
                                                                     {{"SUM", ngraph::reduction::Type::SUM},
                                                                      {"PROD", ngraph::reduction::Type::PROD},
diff --git a/ngraph/core/src/op/interpolate.cpp b/ngraph/core/src/op/interpolate.cpp
index 2aed5f92af7..1dfe5f1b72a 100644
--- a/ngraph/core/src/op/interpolate.cpp
+++ b/ngraph/core/src/op/interpolate.cpp
@@ -75,7 +75,7 @@ std::ostream& ov::operator<<(std::ostream& s, const op::v0::Interpolate::Interpo
 
 namespace ov {
 template <>
-EnumNames<ngraph::op::v0::Interpolate::InterpolateMode>&
+NGRAPH_API EnumNames<ngraph::op::v0::Interpolate::InterpolateMode>&
 EnumNames<ngraph::op::v0::Interpolate::InterpolateMode>::get() {
     static auto enum_names = EnumNames<ngraph::op::v0::Interpolate::InterpolateMode>(
         "op::v0::Interpolate::InterpolateMode",
diff --git a/ngraph/core/src/op/lstm_cell.cpp b/ngraph/core/src/op/lstm_cell.cpp
index b62403adf02..22de84fb11e 100644
--- a/ngraph/core/src/op/lstm_cell.cpp
+++ b/ngraph/core/src/op/lstm_cell.cpp
@@ -336,7 +336,7 @@ shared_ptr<Node> op::v0::LSTMCell::clone_with_new_inputs(const OutputVector& new
 
 namespace ov {
 template <>
-EnumNames<ngraph::op::LSTMWeightsFormat>& EnumNames<ngraph::op::LSTMWeightsFormat>::get() {
+NGRAPH_API EnumNames<ngraph::op::LSTMWeightsFormat>& EnumNames<ngraph::op::LSTMWeightsFormat>::get() {
     static auto enum_names = EnumNames<ngraph::op::LSTMWeightsFormat>("op::LSTMWeightsFormat",
                                                                       {{"fico", ngraph::op::LSTMWeightsFormat::FICO},
                                                                        {"icof", ngraph::op::LSTMWeightsFormat::ICOF},
diff --git a/ngraph/core/src/op/non_max_suppression.cpp b/ngraph/core/src/op/non_max_suppression.cpp
index 8180b5a32e2..dec0ba4b136 100644
--- a/ngraph/core/src/op/non_max_suppression.cpp
+++ b/ngraph/core/src/op/non_max_suppression.cpp
@@ -177,7 +177,7 @@ int64_t op::v1::NonMaxSuppression::max_boxes_output_from_input() const {
 
 namespace ov {
 template <>
-EnumNames<ngraph::op::v1::NonMaxSuppression::BoxEncodingType>&
+NGRAPH_API EnumNames<ngraph::op::v1::NonMaxSuppression::BoxEncodingType>&
 EnumNames<ngraph::op::v1::NonMaxSuppression::BoxEncodingType>::get() {
     static auto enum_names = EnumNames<ngraph::op::v1::NonMaxSuppression::BoxEncodingType>(
         "op::v1::NonMaxSuppression::BoxEncodingType",
@@ -365,7 +365,7 @@ int64_t op::v3::NonMaxSuppression::max_boxes_output_from_input() const {
 
 namespace ov {
 template <>
-EnumNames<ngraph::op::v3::NonMaxSuppression::BoxEncodingType>&
+NGRAPH_API EnumNames<ngraph::op::v3::NonMaxSuppression::BoxEncodingType>&
 EnumNames<ngraph::op::v3::NonMaxSuppression::BoxEncodingType>::get() {
     static auto enum_names = EnumNames<ngraph::op::v3::NonMaxSuppression::BoxEncodingType>(
         "op::v3::NonMaxSuppression::BoxEncodingType",
@@ -825,7 +825,7 @@ std::ostream& ov::operator<<(std::ostream& s, const op::v5::NonMaxSuppression::B
 
 namespace ov {
 template <>
-EnumNames<ngraph::op::v5::NonMaxSuppression::BoxEncodingType>&
+NGRAPH_API EnumNames<ngraph::op::v5::NonMaxSuppression::BoxEncodingType>&
 EnumNames<ngraph::op::v5::NonMaxSuppression::BoxEncodingType>::get() {
     static auto enum_names = EnumNames<ngraph::op::v5::NonMaxSuppression::BoxEncodingType>(
         "op::v5::NonMaxSuppression::BoxEncodingType",
diff --git a/ngraph/core/src/op/reverse.cpp b/ngraph/core/src/op/reverse.cpp
index ef4a97a571b..b3778e4c049 100644
--- a/ngraph/core/src/op/reverse.cpp
+++ b/ngraph/core/src/op/reverse.cpp
@@ -203,7 +203,7 @@ std::ostream& ov::operator<<(std::ostream& s, const op::v1::Reverse::Mode& type)
 
 namespace ov {
 template <>
-EnumNames<ngraph::op::v1::Reverse::Mode>& EnumNames<ngraph::op::v1::Reverse::Mode>::get() {
+NGRAPH_API EnumNames<ngraph::op::v1::Reverse::Mode>& EnumNames<ngraph::op::v1::Reverse::Mode>::get() {
     static auto enum_names = EnumNames<ngraph::op::v1::Reverse::Mode>(
         "op::v1::Reverse::Mode",
         {{"index", ngraph::op::v1::Reverse::Mode::INDEX}, {"mask", ngraph::op::v1::Reverse::Mode::MASK}});
diff --git a/ngraph/core/src/op/round.cpp b/ngraph/core/src/op/round.cpp
index bc15db2efef..4dbe2e97d08 100644
--- a/ngraph/core/src/op/round.cpp
+++ b/ngraph/core/src/op/round.cpp
@@ -121,7 +121,7 @@ std::ostream& ov::operator<<(std::ostream& s, const op::v5::Round::RoundMode& ty
 
 namespace ov {
 template <>
-EnumNames<ngraph::op::v5::Round::RoundMode>& EnumNames<ngraph::op::v5::Round::RoundMode>::get() {
+NGRAPH_API EnumNames<ngraph::op::v5::Round::RoundMode>& EnumNames<ngraph::op::v5::Round::RoundMode>::get() {
     static auto enum_names = EnumNames<ngraph::op::v5::Round::RoundMode>(
         "op::v5::Round::RoundMode",
         {{"half_to_even", ngraph::op::v5::Round::RoundMode::HALF_TO_EVEN},

From 31ae69ac2d77a00f58e0a1c1ee854077eace9f4e Mon Sep 17 00:00:00 2001
From: Tatyana Raguzova <Tatyana.Raguzova@intel.com>
Date: Fri, 26 Nov 2021 13:42:13 +0300
Subject: [PATCH 37/72] [setupvars] Removed unnecessary double brackets (#8796)

they cause a problem on raspbianOS
---
 scripts/setupvars/setupvars.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/setupvars/setupvars.sh b/scripts/setupvars/setupvars.sh
index 8a6676db1e6..deaf26adad9 100755
--- a/scripts/setupvars/setupvars.sh
+++ b/scripts/setupvars/setupvars.sh
@@ -8,7 +8,7 @@ INSTALLDIR="${SCRIPT_DIR}"
 export INTEL_OPENVINO_DIR="$INSTALLDIR"
 
 # parse command line options
-while [[ $# -gt 0 ]]
+while [ $# -gt 0 ]
 do
 key="$1"
 case $key in

From bcf0879785f48259aabd133d1545abac41b61b6b Mon Sep 17 00:00:00 2001
From: Jan Iwaszkiewicz <jan.iwaszkiewicz@intel.com>
Date: Fri, 26 Nov 2021 12:44:11 +0100
Subject: [PATCH 38/72] Fix queue fails on callbacks (#8809)

---
 .../src/pyopenvino/core/async_infer_queue.cpp | 16 +++++--
 .../test_infer_request.py                     | 46 +++++++++++++++++++
 2 files changed, 59 insertions(+), 3 deletions(-)

diff --git a/runtime/bindings/python/src/pyopenvino/core/async_infer_queue.cpp b/runtime/bindings/python/src/pyopenvino/core/async_infer_queue.cpp
index 160a3ef2307..e8cbd691649 100644
--- a/runtime/bindings/python/src/pyopenvino/core/async_infer_queue.cpp
+++ b/runtime/bindings/python/src/pyopenvino/core/async_infer_queue.cpp
@@ -40,7 +40,8 @@ public:
         _cv.wait(lock, [this] {
             return !(_idle_handles.empty());
         });
-
+        if (_errors.size() > 0)
+            throw _errors.front();
         return !(_idle_handles.empty());
     }
 
@@ -51,7 +52,8 @@ public:
         _cv.wait(lock, [this] {
             return !(_idle_handles.empty());
         });
-
+        if (_errors.size() > 0)
+            throw _errors.front();
         return _idle_handles.front();
     }
 
@@ -63,6 +65,8 @@ public:
         _cv.wait(lock, [this] {
             return _idle_handles.size() == _requests.size();
         });
+        if (_errors.size() > 0)
+            throw _errors.front();
     }
 
     void set_default_callbacks() {
@@ -90,7 +94,12 @@ public:
                 }
                 // Acquire GIL, execute Python function
                 py::gil_scoped_acquire acquire;
-                f_callback(_requests[handle], _user_ids[handle]);
+                try {
+                    f_callback(_requests[handle], _user_ids[handle]);
+                } catch (py::error_already_set py_error) {
+                    assert(PyErr_Occurred());
+                    _errors.push(py_error);
+                }
                 // Add idle handle to queue
                 _idle_handles.push(handle);
                 // Notify locks in getIdleRequestId() or waitAll() functions
@@ -104,6 +113,7 @@ public:
     std::vector<py::object> _user_ids;  // user ID can be any Python object
     std::mutex _mutex;
     std::condition_variable _cv;
+    std::queue<py::error_already_set> _errors;
 };
 
 void regclass_AsyncInferQueue(py::module m) {
diff --git a/runtime/bindings/python/tests/test_inference_engine/test_infer_request.py b/runtime/bindings/python/tests/test_inference_engine/test_infer_request.py
index 0785e200e31..b97ed6e6a2d 100644
--- a/runtime/bindings/python/tests/test_inference_engine/test_infer_request.py
+++ b/runtime/bindings/python/tests/test_inference_engine/test_infer_request.py
@@ -212,6 +212,52 @@ def test_infer_queue(device):
     assert all(job["latency"] > 0 for job in jobs_done)
 
 
+def test_infer_queue_fail_on_cpp_func(device):
+    jobs = 6
+    num_request = 4
+    core = Core()
+    func = core.read_model(test_net_xml, test_net_bin)
+    exec_net = core.compile_model(func, device)
+    infer_queue = AsyncInferQueue(exec_net, num_request)
+
+    def callback(request, _):
+        request.get_tensor("Unknown")
+
+    img = read_image()
+    infer_queue.set_callback(callback)
+    assert infer_queue.is_ready
+
+    with pytest.raises(RuntimeError) as e:
+        for _ in range(jobs):
+            infer_queue.start_async({"data": img})
+        infer_queue.wait_all()
+
+    assert "Port for tensor name Unknown was not found" in str(e.value)
+
+
+def test_infer_queue_fail_on_py_func(device):
+    jobs = 1
+    num_request = 1
+    core = Core()
+    func = core.read_model(test_net_xml, test_net_bin)
+    exec_net = core.compile_model(func, device)
+    infer_queue = AsyncInferQueue(exec_net, num_request)
+
+    def callback(request, _):
+        request = request + 21
+
+    img = read_image()
+    infer_queue.set_callback(callback)
+    assert infer_queue.is_ready
+
+    with pytest.raises(TypeError) as e:
+        for _ in range(jobs):
+            infer_queue.start_async({"data": img})
+        infer_queue.wait_all()
+
+    assert "unsupported operand type(s) for +" in str(e.value)
+
+
 @pytest.mark.parametrize("data_type",
                          [np.float32,
                           np.int32,

From 734185c04c4ef2fa5b0ccc1f6db6a5d2296aa28c Mon Sep 17 00:00:00 2001
From: Vladislav Golubev <vladislav.golubev@intel.com>
Date: Fri, 26 Nov 2021 17:41:48 +0300
Subject: [PATCH 39/72] [CPU] LRN: dynamic shapes support (#8724)

---
 .../mkldnn_plugin/nodes/mkldnn_lrn_node.cpp   |  83 +++++++++----
 .../src/mkldnn_plugin/nodes/mkldnn_lrn_node.h |   6 +-
 .../plugin/cpu/single_layer_tests/lrn.cpp     | 116 ++++++++++++++++++
 3 files changed, 183 insertions(+), 22 deletions(-)
 create mode 100644 inference-engine/tests/functional/plugin/cpu/single_layer_tests/lrn.cpp

diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_lrn_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_lrn_node.cpp
index b4e1e28891b..e1430776725 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_lrn_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_lrn_node.cpp
@@ -14,23 +14,18 @@ using namespace InferenceEngine;
 
 bool MKLDNNLrnNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
-
-        const auto lrn = std::dynamic_pointer_cast<const ngraph::opset1::LRN>(op);
+        auto lrn = ngraph::as_type_ptr<const ngraph::opset1::LRN>(op);
         if (!lrn) {
             errorMessage = "Only opset1 LRN operation is supported";
             return false;
         }
 
-        const auto dataDims = lrn->get_input_shape(0);
+        const auto& dataDims = lrn->get_input_partial_shape(0);
         if (dataDims.size() < 2 || dataDims.size() > 5) {
             errorMessage = "Doesn't support 'data' input with rank: " + std::to_string(dataDims.size());
             return false;
         }
-        const auto axesNode = std::dynamic_pointer_cast<const ngraph::opset1::Constant>(lrn->get_input_node_shared_ptr(1));
+        auto axesNode = ngraph::as_type_ptr<const ngraph::opset1::Constant>(lrn->get_input_node_shared_ptr(1));
         if (!axesNode) {
             errorMessage = "Only Constant operation on 'axis' input is supported";
             return false;
@@ -69,9 +64,10 @@ MKLDNNLrnNode::MKLDNNLrnNode(const std::shared_ptr<ngraph::Node>& op, const mkld
     if (isSupportedOperation(op, errorMessage)) {
         errorPrefix = "LRN node with name '" + getName() + "'";
 
-        const auto lrn = std::dynamic_pointer_cast<const ngraph::opset1::LRN>(op);
-        const auto axes = std::dynamic_pointer_cast<const ngraph::opset1::Constant>(lrn->get_input_node_shared_ptr(1))->cast_vector<int64_t>();
-        isAcrossMaps = (axes.size() == 1 && axes[0] == 1);
+        auto lrn = ngraph::as_type_ptr<const ngraph::opset1::LRN>(op);
+        auto axes = ngraph::as_type_ptr<const ngraph::opset1::Constant>(lrn->get_input_node_shared_ptr(1))->cast_vector<int64_t>();
+        bool isAcrossMaps = (axes.size() == 1 && axes[0] == 1);
+        alg = isAcrossMaps ? mkldnn::algorithm::lrn_across_channels : mkldnn::algorithm::lrn_within_channel;
         alpha = static_cast<float>(lrn->get_alpha());
         beta = static_cast<float>(lrn->get_beta());
         k = static_cast<float>(lrn->get_bias());
@@ -107,21 +103,56 @@ std::shared_ptr<MemoryDesc> MKLDNNLrnNode::getSrcMemDesc(mkldnn::primitive_desc_
     if (idx > 0) {
         return std::make_shared<CpuBlockedMemoryDesc>(getOriginalInputPrecisionAtPort(idx), getInputShapeAtPort(idx));
     } else {
-        return MKLDNNExtensionUtils::makeDescriptor(primitive_desc_it.dst_desc(idx));
+        if (getInputShapeAtPort(idx).isDynamic()) {
+            return MKLDNNExtensionUtils::makeUndefinedDesc(primitive_desc_it.src_desc(idx), getInputShapeAtPort(idx));
+        }
+        return MKLDNNExtensionUtils::makeDescriptor(primitive_desc_it.src_desc(idx));
     }
 }
 
 void MKLDNNLrnNode::createPrimitive() {
-    if (prim)
-        return;
+    if (inputShapesDefined()) {
+        if (needPrepareParams())
+            prepareParams();
+        updateLastInputDims();
+    }
+}
 
-    auto prim_desc = createPrimitiveDescriptor<mkldnn::lrn_forward::primitive_desc, mkldnn::lrn_forward::desc>();
+void MKLDNNLrnNode::prepareParams() {
+    auto& srcMemPtr = getParentEdgeAt(0)->getMemoryPtr();
+    auto& dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
+    if (!srcMemPtr || !srcMemPtr->GetPrimitivePtr())
+        IE_THROW() << errorPrefix << " input memory did not allocate";
+    if (!dstMemPtr || !dstMemPtr->GetPrimitivePtr())
+        IE_THROW() << errorPrefix << "destination memory did not allocate";
+
+    const NodeDesc* selected_pd = getSelectedPrimitiveDescriptor();
+    if (selected_pd == nullptr)
+        IE_THROW() << errorPrefix << "preferable primitive descriptor did not set";
+
+    auto inpDesc = getParentEdgeAt(0)->getMemory().GetDescWithType<DnnlMemoryDesc>();
+    const auto& in_candidate = inpDesc->getDnnlDesc();
+    MKLDNNDescriptor desc(std::shared_ptr<mkldnn::lrn_forward::desc>(
+        new mkldnn::lrn_forward::desc(mkldnn::prop_kind::forward_scoring, alg, in_candidate, size, alpha, beta, k)));
+
+    mkldnn::lrn_forward::primitive_desc prim_desc;
+    dnnl::primitive_desc_iterator itpd = desc.createPrimitiveDescriptorIterator(getEngine());
+    while (static_cast<bool>(itpd)) {
+        impl_desc_type impl_type = parse_impl_name(itpd.impl_info_str());
+
+        if (impl_type == selected_pd->getImplementationType()) {
+            prim_desc = itpd.get();
+            break;
+        }
+        if (!itpd.next_impl())
+            IE_THROW() << "Primitive descriptor was not found for node " << getName() << ".";
+    }
 
     prim.reset(new mkldnn::lrn_forward(prim_desc));
 
-    auto src = getParentEdgesAtPort(0)[0]->getMemoryPtr()->GetPrimitive();
-    auto dst = getChildEdgesAtPort(0)[0]->getMemoryPtr()->GetPrimitive();
-    primArgs = {{DNNL_ARG_SRC, src}, {DNNL_ARG_DST, dst}};
+    auto src = srcMemPtr->GetPrimitive();
+    auto dst = dstMemPtr->GetPrimitive();
+    primArgs = { {DNNL_ARG_SRC, src}, {DNNL_ARG_DST, dst} };
 }
 
 bool MKLDNNLrnNode::created() const {
@@ -130,11 +161,21 @@ bool MKLDNNLrnNode::created() const {
 
 void MKLDNNLrnNode::createDescriptor(const std::vector<MemoryDescPtr> &inputDesc,
                                      const std::vector<MemoryDescPtr> &outputDesc) {
-    mkldnn::algorithm alg = isAcrossMaps ? mkldnn::algorithm::lrn_across_channels : mkldnn::algorithm::lrn_within_channel;
+    auto inpDesc = inputDesc[0]->isDefined() ? inputDesc[0] : MemoryDescUtils::makeDummyDesc(*inputDesc[0]);
+    DnnlMemoryDescPtr definedInpMemDesc = MemoryDescUtils::convertToDnnlMemoryDesc(inpDesc);
+    const auto& in_candidate = definedInpMemDesc->getDnnlDesc();
+
     MKLDNNDescriptor desc(std::shared_ptr<mkldnn::lrn_forward::desc>(
-            new mkldnn::lrn_forward::desc(mkldnn::prop_kind::forward_scoring, alg, MemoryDescUtils::convertToDnnlMemoryDesc(inputDesc[0])->getDnnlDesc(),
-                                          size, alpha, beta, k)));
+            new mkldnn::lrn_forward::desc(mkldnn::prop_kind::forward_scoring, alg, in_candidate, size, alpha, beta, k)));
     descs.push_back(desc);
 }
 
+std::vector<VectorDims> MKLDNNLrnNode::shapeInfer() const {
+    return { getParentEdgesAtPort(0).front()->getMemory().getStaticDims() };
+}
+
+void MKLDNNLrnNode::executeDynamicImpl(dnnl::stream strm) {
+    MKLDNNNode::execute(strm);
+}
+
 REG_MKLDNN_PRIM_FOR(MKLDNNLrnNode, Lrn);
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_lrn_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_lrn_node.h
index 8ab180829ad..97ef5163f43 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_lrn_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_lrn_node.h
@@ -29,10 +29,14 @@ public:
         return false;
     }
 
+    void prepareParams() override;
+    void executeDynamicImpl(mkldnn::stream strm) override;
+    std::vector<VectorDims> shapeInfer() const override;
+
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 private:
-    bool isAcrossMaps = false;
+    mkldnn::algorithm alg;
     size_t size = 1;
     int k = 1;
     float alpha = 1.0f;
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/lrn.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/lrn.cpp
new file mode 100644
index 00000000000..061d451c6e6
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/lrn.cpp
@@ -0,0 +1,116 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "functional_test_utils/ov_tensor_utils.hpp"
+#include "test_utils/cpu_test_utils.hpp"
+
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "ngraph_functions/builders.hpp"
+
+using namespace ngraph;
+using namespace CPUTestUtils;
+using namespace ov::test;
+
+namespace CPULayerTestsDefinitions {
+using LRNParams = std::tuple<
+    ElementType,           // data precision
+    InputShape,            // data shape
+    double,                // alpha
+    double,                // beta
+    double,                // bias
+    size_t,                // size
+    std::vector<int64_t>>; // axes to reduction
+
+class LRNLayerCPUTest : public testing::WithParamInterface<LRNParams>, public ov::test::SubgraphBaseTest, public CPUTestsBase {
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<LRNParams> obj) {
+        ElementType inputPrecision;
+        InputShape inputShapes;
+        double alpha, beta, bias;
+        size_t size;
+        std::vector<int64_t> axes;
+        std::tie(inputPrecision, inputShapes, alpha, beta, bias, size, axes) = obj.param;
+
+        std::ostringstream result;
+        result << inputPrecision << "_" << "IS=" << CommonTestUtils::partialShape2str({ inputShapes.first }) << "_" << "TS=(";
+        for (const auto& shape : inputShapes.second) {
+            result << CommonTestUtils::vec2str(shape) << "_";
+        }
+
+        result << ")_alpha=" << alpha << "_beta=" << beta << "_bias=" << bias << "_size=" << size << "_axes=" << CommonTestUtils::vec2str(axes);
+        return result.str();
+    }
+
+protected:
+    void SetUp() override {
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+        ElementType inputPrecision;
+        InputShape inputShapes;
+        double alpha, beta, bias;
+        size_t size;
+        std::vector<int64_t> axes;
+        std::tie(inputPrecision, inputShapes, alpha, beta, bias, size, axes) = this->GetParam();
+
+        init_input_shapes({ inputShapes });
+        selectedType = makeSelectedTypeStr("ref_any", inputPrecision);
+
+        auto params = ngraph::builder::makeDynamicParams(inputPrecision, { inputDynamicShapes });
+        auto axesNode = ngraph::opset1::Constant::create(ngraph::element::i32, { axes.size() }, axes);
+        auto lrn = std::make_shared<ngraph::opset3::LRN>(params[0], axesNode, alpha, beta, bias, size);
+        function = makeNgraphFunction(inputPrecision, params, lrn, "LRN");
+    }
+};
+
+TEST_P(LRNLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+
+    run();
+    CheckPluginRelatedResults(executableNetwork, "LRN");
+}
+
+const std::vector<ElementType> inputPrecisions = {
+    ngraph::element::f32,
+};
+
+const std::vector<std::vector<std::int64_t>> axes = {
+    { 1 },
+    { 2, 3 },
+    { 3, 2 },
+    { 1, 2, 3 }
+};
+
+const std::vector<double> alpha = { 9.9e-05 };
+const std::vector<double> beta = { 2. };
+const std::vector<double> bias = { 1. };
+const std::vector<size_t> size = { 5ul };
+
+const std::vector<InputShape> inputShapes = {
+    InputShape{{}, {{10, 10, 3, 8}}},
+    InputShape{
+        // dynamic
+        {-1, -1, -1, -1},
+        // static
+        {{15, 5, 7, 8}, {10, 10, 3, 8}, {1, 3, 5, 5}}
+    },
+    InputShape{
+        // dynamic
+        {{1, 15}, {3, 10}, {3, 7}, {5, 8}},
+        // static
+        {{15, 5, 7, 8}, {10, 10, 3, 8}, {1, 3, 5, 5}}
+    },
+};
+
+const auto testCases = ::testing::Combine(
+    ::testing::ValuesIn(inputPrecisions),
+    ::testing::ValuesIn(inputShapes),
+    ::testing::ValuesIn(alpha),
+    ::testing::ValuesIn(beta),
+    ::testing::ValuesIn(bias),
+    ::testing::ValuesIn(size),
+    ::testing::ValuesIn(axes)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_CompareWithRefs, LRNLayerCPUTest, testCases, LRNLayerCPUTest::getTestCaseName);
+
+} // namespace CPULayerTestsDefinitions

From f59ece3cdeb863574aab539fef4ee87ffbf9174f Mon Sep 17 00:00:00 2001
From: Vladislav Golubev <vladislav.golubev@intel.com>
Date: Fri, 26 Nov 2021 18:45:51 +0300
Subject: [PATCH 40/72] [CPU] ROIAlign: dynamic shapes support (#8571)

---
 .../nodes/mkldnn_roi_align_node.cpp           |  34 +--
 .../nodes/mkldnn_roi_align_node.h             |   3 +
 .../cpu/single_layer_tests/roialign.cpp       | 193 ++++++++++++------
 3 files changed, 157 insertions(+), 73 deletions(-)

diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.cpp
index 045930139ad..bbd4d12ac5e 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.cpp
@@ -25,11 +25,7 @@ using ngPoolingMode = ngraph::op::v3::ROIAlign::PoolingMode;
 
 bool MKLDNNROIAlignNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
-        const auto roiAlign = std::dynamic_pointer_cast<const ngraph::opset3::ROIAlign>(op);
+        auto roiAlign = ngraph::as_type_ptr<const ngraph::opset3::ROIAlign>(op);
         if (!roiAlign) {
             errorMessage = "Only opset3 ROIAlign operation is supported";
             return false;
@@ -52,7 +48,7 @@ MKLDNNROIAlignNode::MKLDNNROIAlignNode(const std::shared_ptr<ngraph::Node>& op,
     if (isSupportedOperation(op, errorMessage)) {
         errorPrefix = "ROIPooling layer with name '" + getName() + "' ";
 
-        const auto roiAlign = std::dynamic_pointer_cast<const ngraph::opset3::ROIAlign>(op);
+        auto roiAlign = ngraph::as_type_ptr<const ngraph::opset3::ROIAlign>(op);
         pooledH = roiAlign->get_pooled_h();
         pooledW = roiAlign->get_pooled_w();
         spatialScale = roiAlign->get_spatial_scale();
@@ -93,15 +89,15 @@ void MKLDNNROIAlignNode::getSupportedDescriptors() {
         IE_THROW() << errorPrefix << "doesn't support output with rank: " << getOutputShapeAtPort(0).getRank();
     }
 
-    if (getInputShapeAtPort(1).getStaticDims()[1] != 4) {
-        IE_THROW() << errorPrefix << "has invalid shape on 1st input: ["
-                           << getInputShapeAtPort(1).getStaticDims()[0] << "," << getInputShapeAtPort(1).getStaticDims()[1] << "]";
+    const auto& proposalsDims = getInputShapeAtPort(1).getDims();
+    if (proposalsDims[1] != 4) {
+        IE_THROW() << errorPrefix << "has invalid shape on 1st input: [" << proposalsDims[0] << "," << proposalsDims[1] << "]";
     }
 
-    if (getInputShapeAtPort(1).getStaticDims()[0] != getInputShapeAtPort(2).getStaticDims()[0]) {
+    const auto& indexesDims = getInputShapeAtPort(2).getDims();
+    if (!dimsEqualWeak(proposalsDims[0], indexesDims[0])) {
         IE_THROW() << errorPrefix << "has different sizes of inputs for proposals ("
-                           << getInputShapeAtPort(1).getStaticDims()[0] << ") and indexes ("
-                           << getInputShapeAtPort(2).getStaticDims()[0] << ")";
+                   << proposalsDims[0] << ") and indexes (" << indexesDims[0] << ")";
     }
 }
 
@@ -368,6 +364,18 @@ bool MKLDNNROIAlignNode::created() const {
     return getType() == ROIAlign;
 }
 
-void MKLDNNROIAlignNode::createPrimitive() {}
+bool MKLDNNROIAlignNode::needPrepareParams() const {
+    return false;
+}
+
+void MKLDNNROIAlignNode::executeDynamicImpl(mkldnn::stream strm) {
+    return execute(strm);
+}
+
+void MKLDNNROIAlignNode::createPrimitive() {
+    if (inputShapesDefined()) {
+        updateLastInputDims();
+    }
+}
 
 REG_MKLDNN_PRIM_FOR(MKLDNNROIAlignNode, ROIAlign)
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.h
index 4e6b835c95f..31d470d4bca 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_roi_align_node.h
@@ -23,6 +23,9 @@ public:
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
+    bool needPrepareParams() const override;
+    void executeDynamicImpl(mkldnn::stream strm) override;
+
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 private:
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/roialign.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/roialign.cpp
index 78a6fdcd0cb..cd7864c7fe8 100644
--- a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/roialign.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/roialign.cpp
@@ -3,70 +3,129 @@
 //
 
 #include "test_utils/cpu_test_utils.hpp"
+#include "functional_test_utils/ov_tensor_utils.hpp"
 
+#include "shared_test_classes/base/ov_subgraph.hpp"
 #include "ngraph_functions/builders.hpp"
 #include "ngraph_functions/utils/ngraph_helpers.hpp"
 
 using namespace InferenceEngine;
 using namespace CPUTestUtils;
+using namespace ov::test;
 
 namespace CPULayerTestsDefinitions {
-namespace {
-    int pooledH;
-    int pooledW;
-    float spatialScale;
-    int samplingRatio;
-    std::pair<std::vector<float>, std::vector<size_t>> proposal;
-    std::string mode;
-    std::vector<size_t> inputShape;
-}  // namespace
+using ROIAlignShapes = std::vector<InputShape>;
 
-typedef std::tuple<
+using ROIAlignSpecificParams =  std::tuple<
         int,                                                 // bin's column count
         int,                                                 // bin's row count
         float,                                               // scale for given region considering actual input size
         int,                                                 // pooling ratio
-        std::pair<std::vector<float>, std::vector<size_t>>,  // united proposal vector of coordinates and indexes
         std::string,                                         // pooling mode
-        std::vector<size_t>                                  // feature map shape
-> ROIAlignSpecificParams;
+        ROIAlignShapes
+>;
 
-typedef std::tuple<
+using ROIAlignLayerTestParams = std::tuple<
         ROIAlignSpecificParams,
-        InferenceEngine::Precision,     // Net precision
+        ElementType,                    // Net precision
         LayerTestsUtils::TargetDevice   // Device name
-> ROIAlignLayerTestParams;
+>;
 
-typedef std::tuple<
+using ROIAlignLayerCPUTestParamsSet = std::tuple<
         CPULayerTestsDefinitions::ROIAlignLayerTestParams,
-        CPUSpecificParams> ROIAlignLayerCPUTestParamsSet;
+        CPUSpecificParams>;
 
 class ROIAlignLayerCPUTest : public testing::WithParamInterface<ROIAlignLayerCPUTestParamsSet>,
-                             virtual public LayerTestsUtils::LayerTestsCommon, public CPUTestsBase {
+                             public SubgraphBaseTest, public CPUTestsBase {
 public:
     static std::string getTestCaseName(testing::TestParamInfo<ROIAlignLayerCPUTestParamsSet> obj) {
         CPULayerTestsDefinitions::ROIAlignLayerTestParams basicParamsSet;
         CPUSpecificParams cpuParams;
         std::tie(basicParamsSet, cpuParams) = obj.param;
         std::string td;
-        Precision netPr;
+        ElementType netPrecision;
         ROIAlignSpecificParams roiPar;
-        std::tie(roiPar, netPr, td) = basicParamsSet;
-        std::tie(pooledH, pooledW, spatialScale, samplingRatio,
-                 proposal, mode, inputShape) = roiPar;
+        std::tie(roiPar, netPrecision, td) = basicParamsSet;
+
+        int pooledH;
+        int pooledW;
+        float spatialScale;
+        int samplingRatio;
+        std::string mode;
+        ROIAlignShapes inputShapes;
+        std::tie(pooledH, pooledW, spatialScale, samplingRatio, mode, inputShapes) = roiPar;
         std::ostringstream result;
-        result << "ROIAlignTest_";
-        result << std::to_string(obj.index);
+
+        result << netPrecision << "_IS=";
+        for (const auto& shape : inputShapes) {
+            result << CommonTestUtils::partialShape2str({ shape.first }) << "_";
+        }
+        result << "TS=";
+        for (const auto& shape : inputShapes) {
+            result << "(";
+            for (const auto& targetShape : shape.second) {
+                result << CommonTestUtils::vec2str(targetShape) << "_";
+            }
+            result << ")_";
+        }
+
         result << "pooledH=" << pooledH << "_";
         result << "pooledW=" << pooledW << "_";
         result << "spatialScale=" << spatialScale << "_";
         result << "samplingRatio=" << samplingRatio << "_";
-        result << (netPr == Precision::FP32 ? "FP32" : "BF16") << "_";
         result << mode << "_";
         result << CPUTestsBase::getTestCaseName(cpuParams);
+
         return result.str();
     }
 protected:
+    void generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) override {
+        inputs.clear();
+        const auto& funcInputs = function->inputs();
+
+        ov::runtime::Tensor data_tensor;
+        const auto& dataPrecision = funcInputs[0].get_element_type();
+        const auto& dataShape = targetInputStaticShapes.front();
+        data_tensor = ov::test::utils::create_and_fill_tensor(dataPrecision, dataShape, 10, 0, 1000);
+
+        const auto& coordsET = funcInputs[1].get_element_type();
+        auto coordsTensor = ov::runtime::Tensor{ coordsET, targetInputStaticShapes[1] };
+        if (coordsET == ElementType::f32) {
+            auto coordsTensorData = static_cast<float*>(coordsTensor.data());
+            for (size_t i = 0; i < coordsTensor.get_size(); i += 4) {
+                coordsTensorData[i] = 1.f;
+                coordsTensorData[i] = 1.f;
+                coordsTensorData[i] = 19.f;
+                coordsTensorData[i] = 19.f;
+            }
+        } else if (coordsET == ElementType::bf16) {
+            auto coordsTensorData = static_cast<std::int16_t*>(coordsTensor.data());
+            for (size_t i = 0; i < coordsTensor.get_size(); i += 4) {
+                coordsTensorData[i] = static_cast<std::int16_t>(ngraph::bfloat16(1.f).to_bits());
+                coordsTensorData[i] = static_cast<std::int16_t>(ngraph::bfloat16(1.f).to_bits());
+                coordsTensorData[i] = static_cast<std::int16_t>(ngraph::bfloat16(19.f).to_bits());
+                coordsTensorData[i] = static_cast<std::int16_t>(ngraph::bfloat16(19.f).to_bits());
+            }
+        } else {
+            IE_THROW() << "roi align. Unsupported precision: " << coordsET;
+        }
+
+        auto roisIdxTensor = ov::runtime::Tensor{ funcInputs[2].get_element_type(), targetInputStaticShapes[2] };
+        auto roisIdxTensorData = static_cast<std::int32_t*>(roisIdxTensor.data());
+        if (roisIdxTensor.get_size() == 1) {
+            roisIdxTensorData[0] = 1;
+        } else if (roisIdxTensor.get_size() == 2) {
+            roisIdxTensorData[0] = 0;
+            roisIdxTensorData[1] = 1;
+        } else {
+            IE_THROW() << "Unexpected roiIdx size: " << roisIdxTensor.get_size();
+        }
+
+        inputs.insert({ funcInputs[0].get_node_shared_ptr(), data_tensor });
+        inputs.insert({ funcInputs[1].get_node_shared_ptr(), coordsTensor });
+        inputs.insert({ funcInputs[2].get_node_shared_ptr(), roisIdxTensor });
+    }
+
     void SetUp() override {
         CPULayerTestsDefinitions::ROIAlignLayerTestParams basicParamsSet;
         CPUSpecificParams cpuParams;
@@ -74,36 +133,38 @@ protected:
         std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
 
         CPULayerTestsDefinitions::ROIAlignSpecificParams roiAlignParams;
-        auto netPrecision = InferenceEngine::Precision::UNSPECIFIED;
-        std::tie(roiAlignParams, netPrecision, targetDevice) = basicParamsSet;
-        inPrc = outPrc = netPrecision;
-        std::tie(pooledH, pooledW, spatialScale, samplingRatio,
-                 proposal, mode, inputShape) = roiAlignParams;
+        ElementType inputPrecision;
+        std::tie(roiAlignParams, inputPrecision, targetDevice) = basicParamsSet;
 
-        std::vector<float> proposalVector = proposal.first;
-        std::vector<size_t> roiIdxVector = proposal.second;
+        int pooledH;
+        int pooledW;
+        float spatialScale;
+        int samplingRatio;
+        std::string mode;
+        ROIAlignShapes inputShapes;
+        std::tie(pooledH, pooledW, spatialScale, samplingRatio, mode, inputShapes) = roiAlignParams;
 
-        ngraph::Shape coordsShape = { proposalVector.size() / 4, 4 };
-        ngraph::Shape idxVectorShape = { roiIdxVector.size() };
+        init_input_shapes(inputShapes);
 
-        auto roisIdx = ngraph::builder::makeConstant<size_t>(ngraph::element::i32, idxVectorShape, roiIdxVector);
-        auto coords = ngraph::builder::makeConstant<float>(ngraph::element::f32, coordsShape, proposalVector);
-        auto params = ngraph::builder::makeParams(ngraph::element::f32, {inputShape});
+        auto float_params = ngraph::builder::makeDynamicParams(inputPrecision, { inputDynamicShapes[0], inputDynamicShapes[1] });
+        auto int_params = ngraph::builder::makeDynamicParams(ngraph::element::i32, { inputDynamicShapes[2] });
 
-        auto roialign = std::make_shared<ngraph::opset3::ROIAlign>(params[0], coords, roisIdx, pooledH, pooledW,
+        auto roialign = std::make_shared<ngraph::opset3::ROIAlign>(float_params[0], float_params[1], int_params[0], pooledH, pooledW,
                                                                    samplingRatio, spatialScale, mode);
-        roialign->get_rt_info() = getCPUInfo();
-        selectedType = std::string("unknown_") + inPrc.name();
 
-        threshold = 1e-2;
-        const ngraph::ResultVector results{std::make_shared<ngraph::opset3::Result>(roialign)};
-        function = std::make_shared<ngraph::Function>(results, params, "ROIAlign");
+        selectedType = makeSelectedTypeStr("unknown", inputPrecision);
+        if (inputPrecision == ElementType::bf16) {
+            rel_threshold = 1e-2;
+        }
+
+        ngraph::ParameterVector params{ float_params[0], float_params[1], int_params[0] };
+        function = makeNgraphFunction(inputPrecision, params, roialign, "ROIAlign");
     }
 };
 
 TEST_P(ROIAlignLayerCPUTest, CompareWithRefs) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
-    Run();
+    run();
     CheckPluginRelatedResults(executableNetwork, "ROIAlign");
 }
 
@@ -122,9 +183,9 @@ std::vector<CPUSpecificParams> filterCPUInfoForDevice() {
     return resCPUParams;
 }
 
-const std::vector<InferenceEngine::Precision> netPrecisions = {
-        InferenceEngine::Precision::FP32,
-        InferenceEngine::Precision::BF16
+const std::vector<ElementType> netPrecisions = {
+        ElementType::f32,
+        ElementType::bf16,
 };
 
 const std::vector<int> spatialBinXVector = { 2 };
@@ -140,17 +201,30 @@ const std::vector<std::string> modeVector = {
         "max"
 };
 
-const std::vector<std::vector<size_t>> inputShapeVector = {
-        SizeVector({ 2, 18, 20, 20 }),
-        SizeVector({ 2, 4, 20, 20 }),
-        SizeVector({ 2, 4, 20, 40 }),
-        SizeVector({ 10, 1, 20, 20 })
-};
-
-
-const std::vector<std::pair<std::vector<float>, std::vector<size_t>>> propVector = {
-        {{ 1, 1, 19, 19, 1, 1, 19, 19, }, { 0, 1 }},
-        {{ 1, 1, 19, 19 }, { 1 }}
+const std::vector<ROIAlignShapes> inputShapeVector = {
+    ROIAlignShapes{{{}, {{ 2, 18, 20, 20 }}}, {{}, {{2, 4}}}, {{}, {{2}}}},
+    ROIAlignShapes{{{}, {{ 2, 4, 20, 20 }}}, {{}, {{2, 4}}}, {{}, {{2}}}},
+    ROIAlignShapes{{{}, {{ 2, 4, 20, 40 }}}, {{}, {{2, 4}}}, {{}, {{2}}}},
+    ROIAlignShapes{{{}, {{ 10, 1, 20, 20 }}}, {{}, {{2, 4}}}, {{}, {{2}}}},
+    ROIAlignShapes{{{}, {{ 2, 18, 20, 20 }}}, {{}, {{1, 4}}}, {{}, {{1}}}},
+    ROIAlignShapes{{{}, {{ 2, 4, 20, 20 }}}, {{}, {{1, 4}}}, {{}, {{1}}}},
+    ROIAlignShapes{{{}, {{ 2, 4, 20, 40 }}}, {{}, {{1, 4}}}, {{}, {{1}}}},
+    ROIAlignShapes{{{}, {{ 10, 1, 20, 20 }}}, {{}, {{1, 4}}}, {{}, {{1}}}},
+    ROIAlignShapes{
+        {{-1, -1, -1, -1}, {{ 10, 1, 20, 20 }, { 2, 4, 20, 20 }, { 2, 18, 20, 20 }}},
+        {{-1, 4}, {{1, 4}, {2, 4}, {1, 4}}},
+        {{-1}, {{1}, {2}, {1}}}
+    },
+    ROIAlignShapes{
+        {{{2, 10}, { 1, 5 }, -1, -1}, {{ 2, 1, 20, 20 }, { 10, 5, 30, 20 }, { 4, 4, 40, 40 }}},
+        {{-1, 4}, {{2, 4}, {2, 4}, {1, 4}}},
+        {{-1}, {{2}, {2}, {1}}}
+    },
+    ROIAlignShapes{
+        {{{2, 10}, {1, 18}, {10, 30}, {15, 25}}, {{ 10, 1, 10, 15 }, { 2, 4, 20, 20 }, { 7, 18, 30, 25 }}},
+        {{{1, 2}, 4}, {{1, 4}, {2, 4}, {1, 4}}},
+        {{{1, 2}}, {{1}, {2}, {1}}}
+    },
 };
 
 const auto roiAlignParams = ::testing::Combine(
@@ -158,7 +232,6 @@ const auto roiAlignParams = ::testing::Combine(
         ::testing::ValuesIn(spatialBinYVector),       // bin's row count
         ::testing::ValuesIn(spatialScaleVector),      // scale for given region considering actual input size
         ::testing::ValuesIn(poolingRatioVector),      // pooling ratio for bin
-        ::testing::ValuesIn(propVector),              // united vector of coordinates and batch id's
         ::testing::ValuesIn(modeVector),              // pooling mode
         ::testing::ValuesIn(inputShapeVector)         // feature map shape
 );

From 38aebd44631555104d35515c701e61fc15559140 Mon Sep 17 00:00:00 2001
From: Luo Cheng <cheng.luo@intel.com>
Date: Sat, 27 Nov 2021 01:23:26 +0800
Subject: [PATCH 41/72] [CPU] MulticlassNms/MatrixNms support dynamic shape
 (#8161)

---
 .../src/cnn_network_ngraph_impl.cpp           |   5 +-
 .../src/mkldnn_plugin/mkldnn_plugin.cpp       |  30 +-
 .../nodes/mkldnn_matrix_nms_node.cpp          | 173 +++++-----
 .../nodes/mkldnn_matrix_nms_node.h            |  11 +-
 .../nodes/mkldnn_multiclass_nms.cpp           | 301 ++++++++++--------
 .../nodes/mkldnn_multiclass_nms.hpp           |  43 +--
 .../ngraph_ops/nms_static_shape_ie.hpp        |   4 +-
 .../convert_matrix_nms_to_matrix_nms_ie.hpp   |   2 +-
 ...rt_multiclass_nms_to_multiclass_nms_ie.hpp |   2 +-
 .../convert_matrix_nms_to_matrix_nms_ie.cpp   |  13 +-
 ...rt_multiclass_nms_to_multiclass_nms_ie.cpp |  13 +-
 .../serialization/single_layer/matrix_nms.cpp |  35 +-
 .../single_layer/multiclass_nms.cpp           |  30 +-
 .../transformations/convert_precision.cpp     |  53 +++
 .../single_layer_tests/matrix_nms.cpp         |  64 ++--
 .../single_layer_tests/multiclass_nms.cpp     |  52 ++-
 .../include/single_layer_tests/matrix_nms.hpp |  10 +-
 .../single_layer_tests/multiclass_nms.hpp     |  10 +-
 .../single_layer/matrix_nms.hpp               |  41 +--
 .../single_layer/multiclass_nms.hpp           |  44 +--
 .../src/single_layer/matrix_nms.cpp           | 274 +++++++++-------
 .../src/single_layer/multiclass_nms.cpp       | 286 ++++++++++-------
 ngraph/core/src/pass/convert_precision.cpp    |  32 ++
 23 files changed, 950 insertions(+), 578 deletions(-)

diff --git a/inference-engine/src/inference_engine/src/cnn_network_ngraph_impl.cpp b/inference-engine/src/inference_engine/src/cnn_network_ngraph_impl.cpp
index 24484c2f8f2..015859e9274 100644
--- a/inference-engine/src/inference_engine/src/cnn_network_ngraph_impl.cpp
+++ b/inference-engine/src/inference_engine/src/cnn_network_ngraph_impl.cpp
@@ -423,8 +423,9 @@ void CNNNetworkNGraphImpl::reshape(const std::map<std::string, ngraph::PartialSh
                 ::ngraph::pass::Manager manager;
                 // resolves dynamism by replacing dynamic operation with static version
                 manager.register_pass<::ngraph::pass::ConvertNMS5ToLegacyMatcher>(false);
-                manager.register_pass<::ngraph::pass::ConvertMulticlassNmsToMulticlassNmsIE>();
-                manager.register_pass<::ngraph::pass::ConvertMatrixNmsToMatrixNmsIE>();
+                // TODO [DS NMS]: remove when nodes from models where nms is not last node in model supports DS
+                manager.register_pass<::ngraph::pass::ConvertMulticlassNmsToMulticlassNmsIE>(false);
+                manager.register_pass<::ngraph::pass::ConvertMatrixNmsToMatrixNmsIE>(false);
                 manager.register_pass<::ngraph::pass::DisableConvertConstantFoldingOnConstPath>();
                 manager.register_pass<::ngraph::pass::ConstantFolding>();
                 // OneHotToLegacy changes output precision
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
index 9de3f2a6af9..c6b398de27c 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
@@ -314,7 +314,35 @@ static void TransformationUpToCPUSpecificOpSet(std::shared_ptr<ngraph::Function>
                 for (size_t i = 0; i < node->get_output_size(); i++) {
                     const auto outputs = node->get_output_target_inputs(i);
                     for (const auto &out : outputs) {
-                        if (out.get_node()->get_type_info() != ngraph::op::v0::Result::get_type_info_static()) {
+                        if (!ngraph::op::is_output(out.get_node())) {
+                            return false;
+                        }
+                    }
+                }
+                return true;
+            });
+
+    // TODO [DS NMS]: remove when nodes from models where nms is not last node in model supports DS
+    pass_config->set_callback<ngraph::pass::ConvertMulticlassNmsToMulticlassNmsIE>(
+            [](const_node_ptr &node) -> bool {
+                for (size_t i = 0; i < node->get_output_size(); i++) {
+                    const auto outputs = node->get_output_target_inputs(i);
+                    for (const auto &out : outputs) {
+                        if (!ngraph::op::is_output(out.get_node())) {
+                            return false;
+                        }
+                    }
+                }
+                return true;
+            });
+
+    // TODO [DS NMS]: remove when nodes from models where nms is not last node in model supports DS
+    pass_config->set_callback<ngraph::pass::ConvertMatrixNmsToMatrixNmsIE>(
+            [](const_node_ptr &node) -> bool {
+                for (size_t i = 0; i < node->get_output_size(); i++) {
+                    const auto outputs = node->get_output_target_inputs(i);
+                    for (const auto &out : outputs) {
+                        if (!ngraph::op::is_output(out.get_node())) {
                             return false;
                         }
                     }
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.cpp
index 4317596fd32..b01aa731e68 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.cpp
@@ -12,25 +12,19 @@
 
 #include "ie_parallel.hpp"
 #include "ngraph/opsets/opset8.hpp"
-#include "ngraph_ops/nms_static_shape_ie.hpp"
 #include "utils/general_utils.h"
 
 using namespace MKLDNNPlugin;
 using namespace InferenceEngine;
-using MatrixNmsIEInternal = ngraph::op::internal::NmsStaticShapeIE<ngraph::op::v8::MatrixNms>;
 
 using ngNmsSortResultType = ngraph::op::util::NmsBase::SortResultType;
 using ngNmseDcayFunction = ngraph::op::v8::MatrixNms::DecayFunction;
 
 bool MKLDNNMatrixNmsNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
-        const auto nms = std::dynamic_pointer_cast<const MatrixNmsIEInternal>(op);
+        const auto nms = std::dynamic_pointer_cast<const ngraph::op::v8::MatrixNms>(op);
         if (!nms) {
-            errorMessage = "Only internal MatrixNms operation is supported";
+            errorMessage = "Only MatrixNms operation is supported";
             return false;
         }
         const auto& attrs = nms->get_attrs();
@@ -57,36 +51,16 @@ MKLDNNMatrixNmsNode::MKLDNNMatrixNmsNode(const std::shared_ptr<ngraph::Node>& op
         IE_THROW(NotImplemented) << errorMessage;
     }
 
-    errorPrefix = "MatrixNMS layer with name '" + getName() + "' ";
-    const auto matrix_nms = std::dynamic_pointer_cast<const MatrixNmsIEInternal>(op);
+    m_errorPrefix = "MatrixNMS layer with name '" + getName() + "' ";
 
     if (getOriginalInputsNumber() != 2)
-        IE_THROW() << errorPrefix << "has incorrect number of input edges: " << getOriginalInputsNumber();
+        IE_THROW() << m_errorPrefix << "has incorrect number of input edges: " << getOriginalInputsNumber();
 
     if (getOriginalOutputsNumber() != 3)
-        IE_THROW() << errorPrefix << "has incorrect number of output edges: " << getOriginalOutputsNumber();
+        IE_THROW() << m_errorPrefix << "has incorrect number of output edges: " << getOriginalOutputsNumber();
 
-    const SizeVector& boxes_dims = inputShapes[NMS_BOXES].getStaticDims();
-    const SizeVector& scores_dims = inputShapes[NMS_SCORES].getStaticDims();
-    if (!(boxes_dims[0] == scores_dims[0] && boxes_dims[1] == scores_dims[2])) {
-        IE_THROW() << errorPrefix << "has incompatible 'boxes' and 'scores' input dmensions";
-    }
+    const auto matrix_nms = std::dynamic_pointer_cast<const ngraph::op::v8::MatrixNms>(op);
 
-    m_numBatches = boxes_dims[0];
-    m_numBoxes = boxes_dims[1];
-    if (boxes_dims.size() != 3)
-        IE_THROW() << errorPrefix << "has unsupported 'boxes' input rank: " << boxes_dims.size();
-    if (boxes_dims[2] != 4)
-        IE_THROW() << errorPrefix << "has unsupported 'boxes' input 3rd dimension size: " << boxes_dims[2];
-
-    m_numClasses = scores_dims[1];
-    if (scores_dims.size() != 3)
-        IE_THROW() << errorPrefix << "has unsupported 'scores' input rank: " << scores_dims.size();
-
-    if (m_numBatches != scores_dims[0])
-        IE_THROW() << errorPrefix << " num_batches is different in 'boxes' and 'scores' inputs";
-    if (m_numBoxes != scores_dims[2])
-        IE_THROW() << errorPrefix << " num_boxes is different in 'boxes' and 'scores' inputs";
     auto& attrs = matrix_nms->get_attrs();
     if (attrs.sort_result_type == ngraph::op::util::NmsBase::SortResultType::CLASSID)
         m_sortResultType = MatrixNmsSortResultType::CLASSID;
@@ -109,35 +83,6 @@ MKLDNNMatrixNmsNode::MKLDNNMatrixNmsNode(const std::shared_ptr<ngraph::Node>& op
     m_gaussianSigma = attrs.gaussian_sigma;
     m_postThreshold = attrs.post_threshold;
     m_normalized = attrs.normalized;
-    int64_t max_output_boxes_per_class = 0;
-    size_t real_num_classes = m_backgroundClass == -1 ? m_numClasses : m_numClasses - 1;
-    if (m_nmsTopk >= 0)
-        max_output_boxes_per_class = std::min(m_numBoxes, static_cast<size_t>(m_nmsTopk));
-    else
-        max_output_boxes_per_class = m_numBoxes;
-
-    m_maxBoxesPerBatch = max_output_boxes_per_class * real_num_classes;
-    if (m_keepTopk >= 0)
-        m_maxBoxesPerBatch = std::min(m_maxBoxesPerBatch, static_cast<size_t>(m_keepTopk));
-}
-
-void MKLDNNMatrixNmsNode::initSupportedPrimitiveDescriptors() {
-    if (!supportedPrimitiveDescriptors.empty())
-        return;
-
-    m_realNumClasses = m_backgroundClass == -1 ? m_numClasses : m_numClasses - 1;
-    m_realNumBoxes = m_nmsTopk == -1 ? m_numBoxes : std::min(m_nmsTopk, static_cast<int>(m_numBoxes));
-    m_numPerBatch.resize(m_numBatches);
-    m_filteredBoxes.resize(m_numBatches * m_realNumClasses * m_realNumBoxes);
-    m_numPerBatchClass.resize(m_numBatches, std::vector<int64_t>(m_numClasses, 0));
-    m_classOffset.resize(m_numClasses, 0);
-
-    for (size_t i = 0, count = 0; i < m_numClasses; i++) {
-        if (i == m_backgroundClass)
-            continue;
-        m_classOffset[i] = (count++) * m_realNumBoxes;
-    }
-
     if (m_decayFunction == MatrixNmsDecayFunction::LINEAR) {
         m_decay_fn = [](float iou, float max_iou, float sigma) -> float {
             return (1. - iou) / (1. - max_iou + 1e-10f);
@@ -148,16 +93,29 @@ void MKLDNNMatrixNmsNode::initSupportedPrimitiveDescriptors() {
         };
     }
 
+    const auto& boxes_dims = getInputShapeAtPort(NMS_BOXES).getDims();
+    if (boxes_dims.size() != 3)
+        IE_THROW() << m_errorPrefix << "has unsupported 'boxes' input rank: " << boxes_dims.size();
+    if (boxes_dims[2] != 4)
+        IE_THROW() << m_errorPrefix << "has unsupported 'boxes' input 3rd dimension size: " << boxes_dims[2];
+    const auto& scores_dims = getInputShapeAtPort(NMS_SCORES).getDims();
+    if (scores_dims.size() != 3)
+        IE_THROW() << m_errorPrefix << "has unsupported 'scores' input rank: " << scores_dims.size();
+}
+
+void MKLDNNMatrixNmsNode::initSupportedPrimitiveDescriptors() {
+    if (!supportedPrimitiveDescriptors.empty())
+        return;
+
     const std::vector<Precision> supportedFloatPrecision = {Precision::FP32};
     const std::vector<Precision> supportedIntOutputPrecision = {Precision::I32, Precision::I64};
 
-    checkPrecision(getOriginalInputPrecisionAtPort(NMS_BOXES), supportedFloatPrecision, "boxes", inType);
+    checkPrecision(getOriginalInputPrecisionAtPort(NMS_BOXES), supportedFloatPrecision, "boxes", m_inType);
+    checkPrecision(getOriginalInputPrecisionAtPort(NMS_SCORES), supportedFloatPrecision, "scores", m_inType);
 
-    checkPrecision(getOriginalInputPrecisionAtPort(NMS_SCORES), supportedFloatPrecision, "scores", inType);
-
-    checkPrecision(getOriginalOutputPrecisionAtPort(NMS_SELECTED_INDICES), supportedIntOutputPrecision, "selected_indices", outType);
-    checkPrecision(getOriginalOutputPrecisionAtPort(NMS_SELECTED_OUTPUTS), supportedFloatPrecision, "selected_outputs", outType);
-    checkPrecision(getOriginalOutputPrecisionAtPort(NMS_VALID_OUTPUTS), supportedIntOutputPrecision, "valid_outputs", outType);
+    checkPrecision(getOriginalOutputPrecisionAtPort(NMS_SELECTED_INDICES), supportedIntOutputPrecision, "selected_indices", m_outType);
+    checkPrecision(getOriginalOutputPrecisionAtPort(NMS_SELECTED_OUTPUTS), supportedFloatPrecision, "selected_outputs", m_outType);
+    checkPrecision(getOriginalOutputPrecisionAtPort(NMS_VALID_OUTPUTS), supportedIntOutputPrecision, "valid_outputs", m_outType);
 
     addSupportedPrimDesc({{LayoutType::ncsp, Precision::FP32},
                           {LayoutType::ncsp, Precision::FP32}},
@@ -282,6 +240,54 @@ size_t MKLDNNMatrixNmsNode::nmsMatrix(const float* boxesData, const float* score
     return numDet;
 }
 
+void MKLDNNMatrixNmsNode::createPrimitive() {
+    if (inputShapesDefined()) {
+        prepareParams();
+        updateLastInputDims();
+    }
+}
+
+void MKLDNNMatrixNmsNode::prepareParams() {
+    const auto& boxes_dims = getParentEdgeAt(NMS_BOXES)->getMemory().getStaticDims();
+    const auto& scores_dims = getParentEdgeAt(NMS_SCORES)->getMemory().getStaticDims();
+    if (!(boxes_dims[0] == scores_dims[0] && boxes_dims[1] == scores_dims[2])) {
+        IE_THROW() << m_errorPrefix << "has incompatible 'boxes' and 'scores' input dmensions";
+    }
+
+    m_numBatches = boxes_dims[0];
+    m_numBoxes = boxes_dims[1];
+
+    m_numClasses = scores_dims[1];
+
+    int64_t max_output_boxes_per_class = 0;
+    size_t real_num_classes = m_backgroundClass == -1 ? m_numClasses :
+        m_backgroundClass < m_numClasses ? m_numClasses - 1 : m_numClasses;
+    if (m_nmsTopk >= 0)
+        max_output_boxes_per_class = std::min(m_numBoxes, static_cast<size_t>(m_nmsTopk));
+    else
+        max_output_boxes_per_class = m_numBoxes;
+
+    m_maxBoxesPerBatch = max_output_boxes_per_class * real_num_classes;
+    if (m_keepTopk >= 0)
+        m_maxBoxesPerBatch = std::min(m_maxBoxesPerBatch, static_cast<size_t>(m_keepTopk));
+
+    m_realNumClasses = real_num_classes;
+    m_realNumBoxes = m_nmsTopk == -1 ? m_numBoxes : std::min(m_nmsTopk, static_cast<int>(m_numBoxes));
+    m_numPerBatch.resize(m_numBatches);
+    m_filteredBoxes.resize(m_numBatches * m_realNumClasses * m_realNumBoxes);
+    m_numPerBatchClass.resize(m_numBatches);
+    for (auto &numPerBatch : m_numPerBatchClass) {
+        numPerBatch.resize(m_numClasses, 0);
+    }
+    m_classOffset.resize(m_numClasses, 0);
+
+    for (size_t i = 0, count = 0; i < m_numClasses; i++) {
+        if (i == m_backgroundClass)
+            continue;
+        m_classOffset[i] = (count++) * m_realNumBoxes;
+    }
+}
+
 void MKLDNNMatrixNmsNode::execute(mkldnn::stream strm) {
     const float* boxes = reinterpret_cast<const float*>(getParentEdgeAt(NMS_BOXES)->getMemoryPtr()->GetPtr());
     const float* scores = reinterpret_cast<const float*>(getParentEdgeAt(NMS_SCORES)->getMemoryPtr()->GetPtr());
@@ -352,9 +358,20 @@ void MKLDNNMatrixNmsNode::execute(mkldnn::stream strm) {
         }
     }
 
-    float* selectedOutputs = reinterpret_cast<float*>(getChildEdgesAtPort(NMS_SELECTED_OUTPUTS)[0]->getMemoryPtr()->GetPtr());
-    int* selectedIndices = reinterpret_cast<int*>(getChildEdgesAtPort(NMS_SELECTED_INDICES)[0]->getMemoryPtr()->GetPtr());
-    int* validOutputs = reinterpret_cast<int*>(getChildEdgesAtPort(NMS_VALID_OUTPUTS)[0]->getMemoryPtr()->GetPtr());
+    auto selectedOutputsMemPtr = getChildEdgesAtPort(NMS_SELECTED_OUTPUTS)[0]->getMemoryPtr();
+    auto selectedIndicesMemPtr = getChildEdgesAtPort(NMS_SELECTED_INDICES)[0]->getMemoryPtr();
+    auto validOutputsMemPtr = getChildEdgesAtPort(NMS_VALID_OUTPUTS)[0]->getMemoryPtr();
+
+    // TODO [DS NMS]: remove when nodes from models where nms is not last node in model supports DS
+    if (isDynamicNode()) {
+        size_t totalBox = std::accumulate(m_numPerBatch.begin(), m_numPerBatch.end(), 0);
+        selectedOutputsMemPtr->redefineDesc(getBaseMemDescAtOutputPort(NMS_SELECTED_OUTPUTS)->cloneWithNewDims({totalBox, 6}));
+        selectedIndicesMemPtr->redefineDesc(getBaseMemDescAtOutputPort(NMS_SELECTED_INDICES)->cloneWithNewDims({totalBox, 1}));
+        validOutputsMemPtr->redefineDesc(getBaseMemDescAtOutputPort(NMS_VALID_OUTPUTS)->cloneWithNewDims({m_numBatches}));
+    }
+    float* selectedOutputs = reinterpret_cast<float*>(selectedOutputsMemPtr->GetPtr());
+    int* selectedIndices = reinterpret_cast<int*>(selectedIndicesMemPtr->GetPtr());
+    int* validOutputs = reinterpret_cast<int*>(validOutputsMemPtr->GetPtr());
     std::copy(m_numPerBatch.begin(), m_numPerBatch.end(), validOutputs);
 
     int64_t outputOffset = 0;
@@ -372,16 +389,22 @@ void MKLDNNMatrixNmsNode::execute(mkldnn::stream strm) {
             selectedBase[4] = m_filteredBoxes[originalIndex].box.x2;
             selectedBase[5] = m_filteredBoxes[originalIndex].box.y2;
         }
-        std::fill_n(selectedOutputs + (outputOffset + real_boxes) * 6, (m_maxBoxesPerBatch - real_boxes) * 6, -1);
-        std::fill_n(selectedIndices + (outputOffset + real_boxes), m_maxBoxesPerBatch - real_boxes, -1);
-        outputOffset += m_maxBoxesPerBatch;
-        originalOffset += real_boxes;
+        // TODO [DS NMS]: remove when nodes from models where nms is not last node in model supports DS
+        if (!isDynamicNode()) {
+            std::fill_n(selectedOutputs + (outputOffset + real_boxes) * 6, (m_maxBoxesPerBatch - real_boxes) * 6, -1);
+            std::fill_n(selectedIndices + (outputOffset + real_boxes), m_maxBoxesPerBatch - real_boxes, -1);
+            outputOffset += m_maxBoxesPerBatch;
+            originalOffset += real_boxes;
+        } else {
+            outputOffset += real_boxes;
+            originalOffset += real_boxes;
+        }
     }
 }
 
 void MKLDNNMatrixNmsNode::checkPrecision(const Precision prec, const std::vector<Precision> precList, const std::string name, const std::string type) {
     if (std::find(precList.begin(), precList.end(), prec) == precList.end())
-        IE_THROW() << errorPrefix << "has unsupported '" << name << "' " << type << " precision: " << prec;
+        IE_THROW() << m_errorPrefix << "has unsupported '" << name << "' " << type << " precision: " << prec;
 }
 
 REG_MKLDNN_PRIM_FOR(MKLDNNMatrixNmsNode, MatrixNms);
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.h
index 879caff6d70..c1f272bd2b2 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.h
@@ -27,12 +27,17 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
+    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
+    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+
+    bool needShapeInfer() const override { return false; }
+    void prepareParams() override;
+
 private:
     // input
     static const size_t NMS_BOXES = 0;
@@ -82,8 +87,8 @@ private:
         int64_t classIndex = -1;
         float score = 0.0f;
     };
-    std::string errorPrefix;
-    const std::string inType = "input", outType = "output";
+    std::string m_errorPrefix;
+    const std::string m_inType = "input", m_outType = "output";
     std::vector<int64_t> m_numPerBatch;
     std::vector<std::vector<int64_t>> m_numPerBatchClass;
     std::vector<BoxInfo> m_filteredBoxes;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.cpp
index d3ad18d0595..85d13de3829 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.cpp
@@ -9,7 +9,6 @@
 #include <chrono>
 #include <cmath>
 #include <ie_ngraph_utils.hpp>
-#include <ngraph_ops/nms_static_shape_ie.hpp>
 #include <queue>
 #include <string>
 #include <utility>
@@ -22,17 +21,12 @@ using namespace MKLDNNPlugin;
 using namespace InferenceEngine;
 
 using ngNmsSortResultType = ngraph::op::util::NmsBase::SortResultType;
-using MulticlassNmsIEInternal = ngraph::op::internal::NmsStaticShapeIE<ngraph::op::v8::MulticlassNms>;
 
 bool MKLDNNMultiClassNmsNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
-        const auto nms = std::dynamic_pointer_cast<const MulticlassNmsIEInternal>(op);
+        const auto nms = std::dynamic_pointer_cast<const ngraph::op::v8::MulticlassNms>(op);
         if (!nms) {
-            errorMessage = "Only internal MulitClassNonMaxSuppression operation is supported";
+            errorMessage = "Only MulticlassNms operation is supported";
             return false;
         }
         const auto& atrri = nms->get_attrs();
@@ -53,79 +47,55 @@ MKLDNNMultiClassNmsNode::MKLDNNMultiClassNmsNode(const std::shared_ptr<ngraph::N
     if (!isSupportedOperation(op, errorMessage)) {
         IE_THROW(NotImplemented) << errorMessage;
     }
-    errorPrefix = "MultiClassNms layer with name '" + getName() + "' ";
-    const auto nms = std::dynamic_pointer_cast<const MulticlassNmsIEInternal>(op);
+    m_errorPrefix = "MultiClassNms layer with name '" + getName() + "' ";
 
     if (getOriginalInputsNumber() != 2)
-        IE_THROW() << errorPrefix << "has incorrect number of input edges: " << getOriginalInputsNumber();
+        IE_THROW() << m_errorPrefix << "has incorrect number of input edges: " << getOriginalInputsNumber();
 
     if (getOriginalOutputsNumber() != 3)
-        IE_THROW() << errorPrefix << "has incorrect number of output edges: " << getOriginalOutputsNumber();
+        IE_THROW() << m_errorPrefix << "has incorrect number of output edges: " << getOriginalOutputsNumber();
+
+    const auto nms = std::dynamic_pointer_cast<const ngraph::op::v8::MulticlassNms>(op);
 
     auto& atrri = nms->get_attrs();
-    sort_result_across_batch = atrri.sort_result_across_batch;
-    max_output_boxes_per_class = atrri.nms_top_k;
-    iou_threshold = atrri.iou_threshold;
-    score_threshold = atrri.score_threshold;
-    background_class = atrri.background_class;
-    keep_top_k = atrri.keep_top_k;
+    m_sortResultAcrossBatch = atrri.sort_result_across_batch;
+    m_nmsTopK = atrri.nms_top_k;
+    m_iouThreshold = atrri.iou_threshold;
+    m_scoreThreshold = atrri.score_threshold;
+    m_backgroundClass = atrri.background_class;
+    m_keepTopK = atrri.keep_top_k;
     if (atrri.sort_result_type == ngNmsSortResultType::CLASSID)
-        sort_result_type = MulticlassNmsSortResultType::CLASSID;
+        m_sortResultType = MulticlassNmsSortResultType::CLASSID;
     else if (atrri.sort_result_type == ngNmsSortResultType::SCORE)
-        sort_result_type = MulticlassNmsSortResultType::SCORE;
+        m_sortResultType = MulticlassNmsSortResultType::SCORE;
     else if (atrri.sort_result_type == ngNmsSortResultType::NONE)
-        sort_result_type = MulticlassNmsSortResultType::NONE;
-    nms_eta = atrri.nms_eta;
-    normalized = atrri.normalized;
+        m_sortResultType = MulticlassNmsSortResultType::NONE;
+    m_nmsEta = atrri.nms_eta;
+    m_normalized = atrri.normalized;
 
-    const SizeVector& boxes_dims = inputShapes[NMS_BOXES].getStaticDims();
+    const auto& boxes_dims = getInputShapeAtPort(NMS_BOXES).getDims();
     if (boxes_dims.size() != 3)
-        IE_THROW() << errorPrefix << "has unsupported 'boxes' input rank: " << boxes_dims.size();
+        IE_THROW() << m_errorPrefix << "has unsupported 'boxes' input rank: " << boxes_dims.size();
     if (boxes_dims[2] != 4)
-        IE_THROW() << errorPrefix << "has unsupported 'boxes' input 3rd dimension size: " << boxes_dims[2];
-
-    const SizeVector& scores_dims = inputShapes[NMS_SCORES].getStaticDims();
+        IE_THROW() << m_errorPrefix << "has unsupported 'boxes' input 3rd dimension size: " << boxes_dims[2];
+    const auto& scores_dims = getInputShapeAtPort(NMS_SCORES).getDims();
     if (scores_dims.size() != 3)
-        IE_THROW() << errorPrefix << "has unsupported 'scores' input rank: " << scores_dims.size();
-
-    if (boxes_dims[0] != scores_dims[0])
-        IE_THROW() << errorPrefix << " num_batches is different in 'boxes' and 'scores' inputs";
-    if (boxes_dims[1] != scores_dims[2])
-        IE_THROW() << errorPrefix << " num_boxes is different in 'boxes' and 'scores' inputs";
-
-    const SizeVector& valid_outputs_dims = outputShapes[NMS_SELECTEDNUM].getStaticDims();
-    if (valid_outputs_dims.size() != 1)
-        IE_THROW() << errorPrefix << "has unsupported 'valid_outputs' output rank: " << valid_outputs_dims.size();
-    if (valid_outputs_dims[0] != boxes_dims[0])  // valid_outputs_dims[0] != num_batches
-        IE_THROW() << errorPrefix << "has unsupported 'valid_outputs' output 1st dimension size: " << valid_outputs_dims[0];
+        IE_THROW() << m_errorPrefix << "has unsupported 'scores' input rank: " << scores_dims.size();
 }
 
 void MKLDNNMultiClassNmsNode::initSupportedPrimitiveDescriptors() {
     if (!supportedPrimitiveDescriptors.empty())
         return;
-    const SizeVector& boxes_dims = inputShapes[NMS_BOXES].getStaticDims();
-    num_batches = boxes_dims[0];
-    num_boxes = boxes_dims[1];
-    const SizeVector& scores_dims = inputShapes[NMS_SCORES].getStaticDims();
-    num_classes = scores_dims[1];
-    numFiltBox.resize(num_batches, std::vector<size_t>(num_classes));  // batches
-    numBoxOffset.resize(num_batches);
-
-    if (max_output_boxes_per_class) {
-        max_output_boxes_per_class = (max_output_boxes_per_class == -1) ? num_boxes : max_output_boxes_per_class;
-        filtBoxes.resize(max_output_boxes_per_class * num_batches * num_classes);
-    }
 
     const std::vector<Precision> supportedFloatPrecision = {Precision::FP32, Precision::BF16};
     const std::vector<Precision> supportedIntOutputPrecision = {Precision::I32, Precision::I64};
 
-    checkPrecision(getOriginalInputPrecisionAtPort(NMS_BOXES), supportedFloatPrecision, "boxes", inType);
+    checkPrecision(getOriginalInputPrecisionAtPort(NMS_BOXES), supportedFloatPrecision, "boxes", m_inType);
+    checkPrecision(getOriginalInputPrecisionAtPort(NMS_SCORES), supportedFloatPrecision, "scores", m_inType);
 
-    checkPrecision(getOriginalInputPrecisionAtPort(NMS_SCORES), supportedFloatPrecision, "scores", inType);
-
-    checkPrecision(getOriginalOutputPrecisionAtPort(NMS_SELECTEDINDICES), supportedIntOutputPrecision, "selected_indices", outType);
-    checkPrecision(getOriginalOutputPrecisionAtPort(NMS_SELECTEDOUTPUTS), supportedFloatPrecision, "selected_outputs", outType);
-    checkPrecision(getOriginalOutputPrecisionAtPort(NMS_SELECTEDNUM), supportedIntOutputPrecision, "selected_num", outType);
+    checkPrecision(getOriginalOutputPrecisionAtPort(NMS_SELECTEDINDICES), supportedIntOutputPrecision, "selected_indices", m_outType);
+    checkPrecision(getOriginalOutputPrecisionAtPort(NMS_SELECTEDOUTPUTS), supportedFloatPrecision, "selected_outputs", m_outType);
+    checkPrecision(getOriginalOutputPrecisionAtPort(NMS_SELECTEDNUM), supportedIntOutputPrecision, "selected_num", m_outType);
 
     addSupportedPrimDesc({{LayoutType::ncsp, Precision::FP32},
                           {LayoutType::ncsp, Precision::FP32}},
@@ -135,100 +105,138 @@ void MKLDNNMultiClassNmsNode::initSupportedPrimitiveDescriptors() {
                          impl_desc_type::ref_any);
 }
 
+void MKLDNNMultiClassNmsNode::createPrimitive() {
+    if (inputShapesDefined()) {
+        prepareParams();
+        updateLastInputDims();
+    }
+}
+
+void MKLDNNMultiClassNmsNode::prepareParams() {
+    const auto& boxes_dims = getParentEdgeAt(NMS_BOXES)->getMemory().getStaticDims();
+    const auto& scores_dims = getParentEdgeAt(NMS_SCORES)->getMemory().getStaticDims();
+    if (!(boxes_dims[0] == scores_dims[0] && boxes_dims[1] == scores_dims[2])) {
+        IE_THROW() << m_errorPrefix << "has incompatible 'boxes' and 'scores' input dmensions";
+    }
+
+    m_numBatches = boxes_dims[0];
+    m_numBoxes = boxes_dims[1];
+
+    m_numClasses = scores_dims[1];
+
+    int max_output_boxes_per_class = 0;
+    size_t real_num_classes = m_backgroundClass == -1 ? m_numClasses :
+        m_backgroundClass < m_numClasses ? m_numClasses - 1 : m_numClasses;
+    if (m_nmsTopK) {
+        max_output_boxes_per_class = (m_nmsTopK == -1) ? m_numBoxes :
+            std::min(m_nmsTopK, static_cast<int>(m_numBoxes));
+        m_filtBoxes.resize(max_output_boxes_per_class * m_numBatches * m_numClasses);
+    }
+    m_nmsRealTopk = max_output_boxes_per_class;
+
+    m_maxBoxesPerBatch = max_output_boxes_per_class * real_num_classes;
+    if (m_keepTopK >= 0)
+        m_maxBoxesPerBatch = std::min(m_maxBoxesPerBatch, static_cast<size_t>(m_keepTopK));
+
+    m_numFiltBox.resize(m_numBatches);
+    for (auto &numPerBatch : m_numFiltBox) {
+        numPerBatch.resize(m_numClasses, 0);
+    }
+    m_numBoxOffset.resize(m_numBatches);
+}
+
 void MKLDNNMultiClassNmsNode::execute(mkldnn::stream strm) {
     const float* boxes = reinterpret_cast<const float*>(getParentEdgeAt(NMS_BOXES)->getMemoryPtr()->GetPtr());
     const float* scores = reinterpret_cast<const float*>(getParentEdgeAt(NMS_SCORES)->getMemoryPtr()->GetPtr());
 
     auto dims_boxes = getParentEdgeAt(NMS_BOXES)->getMemory().getStaticDims();
 
-    if (max_output_boxes_per_class == 0)
+    if (m_nmsRealTopk == 0)
         return;
 
-    int* selected_indices = reinterpret_cast<int*>(getChildEdgesAtPort(NMS_SELECTEDINDICES)[0]->getMemoryPtr()->GetPtr());
-
-    float* selected_outputs = reinterpret_cast<float*>(getChildEdgesAtPort(NMS_SELECTEDOUTPUTS)[0]->getMemoryPtr()->GetPtr());
-
-    int* selected_num = reinterpret_cast<int*>(getChildEdgesAtPort(NMS_SELECTEDNUM)[0]->getMemoryPtr()->GetPtr());
+    auto selectedOutputsMemPtr = getChildEdgesAtPort(NMS_SELECTEDOUTPUTS)[0]->getMemoryPtr();
+    auto selectedIndicesMemPtr = getChildEdgesAtPort(NMS_SELECTEDINDICES)[0]->getMemoryPtr();
+    auto validOutputsMemPtr = getChildEdgesAtPort(NMS_SELECTEDNUM)[0]->getMemoryPtr();
 
     auto boxesStrides = getParentEdgeAt(NMS_BOXES)->getMemory().GetDescWithType<BlockedMemoryDesc>()->getStrides();
     auto scoresStrides = getParentEdgeAt(NMS_SCORES)->getMemory().GetDescWithType<BlockedMemoryDesc>()->getStrides();
 
-    if ((nms_eta >= 0) && (nms_eta < 1)) {
+    if ((m_nmsEta >= 0) && (m_nmsEta < 1)) {
         nmsWithEta(boxes, scores, boxesStrides, scoresStrides);
     } else {
         nmsWithoutEta(boxes, scores, boxesStrides, scoresStrides);
     }
 
-    size_t startOffset = numFiltBox[0][0];
-    numBoxOffset[0] = 0;
-    for (size_t b = 0; b < numFiltBox.size(); b++) {
+    size_t startOffset = m_numFiltBox[0][0];
+    m_numBoxOffset[0] = 0;
+    for (size_t b = 0; b < m_numFiltBox.size(); b++) {
         size_t batchOffsetNew = 0;
-        size_t batchOffset = b * num_classes * max_output_boxes_per_class;
-        for (size_t c = (b == 0 ? 1 : 0); c < numFiltBox[b].size(); c++) {
-            size_t offset = batchOffset + c * max_output_boxes_per_class;
-            for (size_t i = 0; i < numFiltBox[b][c]; i++) {
-                filtBoxes[startOffset + i] = filtBoxes[offset + i];
+        size_t batchOffset = b * m_numClasses * m_nmsRealTopk;
+        for (size_t c = (b == 0 ? 1 : 0); c < m_numFiltBox[b].size(); c++) {
+            size_t offset = batchOffset + c * m_nmsRealTopk;
+            for (size_t i = 0; i < m_numFiltBox[b][c]; i++) {
+                m_filtBoxes[startOffset + i] = m_filtBoxes[offset + i];
             }
-            startOffset += numFiltBox[b][c];
-            batchOffsetNew += numFiltBox[b][c];
+            startOffset += m_numFiltBox[b][c];
+            batchOffsetNew += m_numFiltBox[b][c];
         }
-        numBoxOffset[b] = batchOffsetNew;
+        m_numBoxOffset[b] = batchOffsetNew;
         if (b == 0)
-            numBoxOffset[b] += numFiltBox[0][0];
+            m_numBoxOffset[b] += m_numFiltBox[0][0];
     }
     // sort element before go through keep_top_k
-    parallel_sort(filtBoxes.begin(), filtBoxes.begin() + startOffset, [](const filteredBoxes& l, const filteredBoxes& r) {
+    parallel_sort(m_filtBoxes.begin(), m_filtBoxes.begin() + startOffset, [](const filteredBoxes& l, const filteredBoxes& r) {
         return ((l.batch_index < r.batch_index) ||
                 ((l.batch_index == r.batch_index) && ((l.score > r.score) || ((std::fabs(l.score - r.score) < 1e-6) && l.class_index < r.class_index) ||
                                                       ((std::fabs(l.score - r.score) < 1e-6) && l.class_index == r.class_index && l.box_index < r.box_index))));
     });
 
-    if (keep_top_k > -1) {
+    if (m_keepTopK > -1) {
         startOffset = 0;
         size_t offset = 0;
-        for (size_t b = 0; b < numFiltBox.size(); b++) {
-            if (numBoxOffset[b] > keep_top_k) {
+        for (size_t b = 0; b < m_numFiltBox.size(); b++) {
+            if (m_numBoxOffset[b] > m_keepTopK) {
                 if (startOffset == offset) {
-                    startOffset += keep_top_k;
-                    offset += numBoxOffset[b];
+                    startOffset += m_keepTopK;
+                    offset += m_numBoxOffset[b];
                 } else {
-                    for (size_t i = 0; i < keep_top_k; i++) {
-                        filtBoxes[startOffset + i] = filtBoxes[offset + i];
+                    for (size_t i = 0; i < m_keepTopK; i++) {
+                        m_filtBoxes[startOffset + i] = m_filtBoxes[offset + i];
                     }
-                    startOffset += keep_top_k;
-                    offset += numBoxOffset[b];
+                    startOffset += m_keepTopK;
+                    offset += m_numBoxOffset[b];
                 }
             } else {
                 if (startOffset == offset) {
-                    startOffset += numBoxOffset[b];
-                    offset += numBoxOffset[b];
+                    startOffset += m_numBoxOffset[b];
+                    offset += m_numBoxOffset[b];
                 } else {
-                    for (size_t i = 0; i < numBoxOffset[b]; i++) {
-                        filtBoxes[startOffset + i] = filtBoxes[offset + i];
+                    for (size_t i = 0; i < m_numBoxOffset[b]; i++) {
+                        m_filtBoxes[startOffset + i] = m_filtBoxes[offset + i];
                     }
-                    startOffset += numBoxOffset[b];
-                    offset += numBoxOffset[b];
+                    startOffset += m_numBoxOffset[b];
+                    offset += m_numBoxOffset[b];
                 }
             }
         }
     }
 
-    if (sort_result_across_batch) {
-        if (sort_result_type == MulticlassNmsSortResultType::SCORE) {
-            parallel_sort(filtBoxes.begin(), filtBoxes.begin() + startOffset, [](const filteredBoxes& l, const filteredBoxes& r) {
+    if (m_sortResultAcrossBatch) {
+        if (m_sortResultType == MulticlassNmsSortResultType::SCORE) {
+            parallel_sort(m_filtBoxes.begin(), m_filtBoxes.begin() + startOffset, [](const filteredBoxes& l, const filteredBoxes& r) {
                 return (l.score > r.score) || (l.score == r.score && l.batch_index < r.batch_index) ||
                        (l.score == r.score && l.batch_index == r.batch_index && l.class_index < r.class_index) ||
                        (l.score == r.score && l.batch_index == r.batch_index && l.class_index == r.class_index && l.box_index < r.box_index);
             });
-        } else if (sort_result_type == MulticlassNmsSortResultType::CLASSID) {
-            parallel_sort(filtBoxes.begin(), filtBoxes.begin() + startOffset, [](const filteredBoxes& l, const filteredBoxes& r) {
+        } else if (m_sortResultType == MulticlassNmsSortResultType::CLASSID) {
+            parallel_sort(m_filtBoxes.begin(), m_filtBoxes.begin() + startOffset, [](const filteredBoxes& l, const filteredBoxes& r) {
                 return (l.class_index < r.class_index) || (l.class_index == r.class_index && l.batch_index < r.batch_index) ||
                        (l.class_index == r.class_index && l.batch_index == r.batch_index && l.score > r.score) ||
                        (l.class_index == r.class_index && l.batch_index == r.batch_index && l.score == r.score && l.box_index < r.box_index);
             });
         }
-    } else if (sort_result_type == MulticlassNmsSortResultType::CLASSID) {
-        parallel_sort(filtBoxes.begin(), filtBoxes.begin() + startOffset, [](const filteredBoxes& l, const filteredBoxes& r) {
+    } else if (m_sortResultType == MulticlassNmsSortResultType::CLASSID) {
+        parallel_sort(m_filtBoxes.begin(), m_filtBoxes.begin() + startOffset, [](const filteredBoxes& l, const filteredBoxes& r) {
             return ((l.batch_index < r.batch_index) ||
                     ((l.batch_index == r.batch_index) &&
                      ((l.class_index < r.class_index) || ((l.class_index == r.class_index) && l.score > r.score) ||
@@ -236,18 +244,28 @@ void MKLDNNMultiClassNmsNode::execute(mkldnn::stream strm) {
         });
     }
 
-    const size_t selectedBoxesNum = getChildEdgeAt(NMS_SELECTEDINDICES)->getMemory().getStaticDims()[0];
-    const size_t validOutputs = std::min(startOffset, selectedBoxesNum);
+    const size_t validOutputs = std::min(startOffset, m_maxBoxesPerBatch * dims_boxes[0]);
 
     std::vector<size_t> m_selected_num;
     m_selected_num.resize(dims_boxes[0]);
 
-    const size_t selectedBoxesNum_perBatch = selectedBoxesNum / dims_boxes[0];
+    const size_t selectedBoxesNum_perBatch = m_maxBoxesPerBatch;
 
     for (size_t idx = 0lu; idx < validOutputs; idx++) {
-        m_selected_num[filtBoxes[idx].batch_index]++;
+        m_selected_num[m_filtBoxes[idx].batch_index]++;
     }
 
+    // TODO [DS NMS]: remove when nodes from models where nms is not last node in model supports DS
+    if (isDynamicNode()) {
+        size_t totalBox = std::accumulate(m_selected_num.begin(), m_selected_num.end(), 0);
+        selectedOutputsMemPtr->redefineDesc(getBaseMemDescAtOutputPort(NMS_SELECTEDOUTPUTS)->cloneWithNewDims({totalBox, 6}));
+        selectedIndicesMemPtr->redefineDesc(getBaseMemDescAtOutputPort(NMS_SELECTEDINDICES)->cloneWithNewDims({totalBox, 1}));
+        validOutputsMemPtr->redefineDesc(getBaseMemDescAtOutputPort(NMS_SELECTEDNUM)->cloneWithNewDims({m_numBatches}));
+    }
+    int* selected_indices = reinterpret_cast<int*>(selectedIndicesMemPtr->GetPtr());
+    float* selected_outputs = reinterpret_cast<float*>(selectedOutputsMemPtr->GetPtr());
+    int* selected_num = reinterpret_cast<int*>(validOutputsMemPtr->GetPtr());
+
     int64_t output_offset = 0;
     int64_t original_offset = 0;
     for (size_t i = 0; i < dims_boxes[0]; i++) {
@@ -256,19 +274,25 @@ void MKLDNNMultiClassNmsNode::execute(mkldnn::stream strm) {
 
         for (size_t j = 0; j < real_boxes; j++) {
             auto original_index = original_offset + j;
-            selected_indices[j + output_offset] = filtBoxes[original_index].batch_index * dims_boxes[1] + filtBoxes[original_index].box_index;
+            selected_indices[j + output_offset] = m_filtBoxes[original_index].batch_index * dims_boxes[1] + m_filtBoxes[original_index].box_index;
             auto selected_base = selected_outputs + (output_offset + j) * 6;
-            selected_base[0] = filtBoxes[original_index].class_index;
-            selected_base[1] = filtBoxes[original_index].score;
+            selected_base[0] = m_filtBoxes[original_index].class_index;
+            selected_base[1] = m_filtBoxes[original_index].score;
             selected_base[2] = boxes[selected_indices[j + output_offset] * 4];
             selected_base[3] = boxes[selected_indices[j + output_offset] * 4 + 1];
             selected_base[4] = boxes[selected_indices[j + output_offset] * 4 + 2];
             selected_base[5] = boxes[selected_indices[j + output_offset] * 4 + 3];
         }
-        std::fill_n(selected_outputs + (output_offset + real_boxes) * 6, (selectedBoxesNum_perBatch - real_boxes) * 6, -1);
-        std::fill_n(selected_indices + (output_offset + real_boxes), selectedBoxesNum_perBatch - real_boxes, -1);
-        output_offset += selectedBoxesNum_perBatch;
-        original_offset += real_boxes;
+        // TODO [DS NMS]: remove when nodes from models where nms is not last node in model supports DS
+        if (!isDynamicNode()) {
+            std::fill_n(selected_outputs + (output_offset + real_boxes) * 6, (selectedBoxesNum_perBatch - real_boxes) * 6, -1);
+            std::fill_n(selected_indices + (output_offset + real_boxes), selectedBoxesNum_perBatch - real_boxes, -1);
+            output_offset += selectedBoxesNum_perBatch;
+            original_offset += real_boxes;
+        } else {
+            output_offset += real_boxes;
+            original_offset += real_boxes;
+        }
     }
 }
 
@@ -309,21 +333,21 @@ void MKLDNNMultiClassNmsNode::nmsWithEta(const float* boxes, const float* scores
         return iou <= adaptive_threshold ? 1.0f : 0.0f;
     };
 
-    parallel_for2d(num_batches, num_classes, [&](int batch_idx, int class_idx) {
-        if (class_idx != background_class) {
+    parallel_for2d(m_numBatches, m_numClasses, [&](int batch_idx, int class_idx) {
+        if (class_idx != m_backgroundClass) {
             std::vector<filteredBoxes> fb;
             const float* boxesPtr = boxes + batch_idx * boxesStrides[0];
             const float* scoresPtr = scores + batch_idx * scoresStrides[0] + class_idx * scoresStrides[1];
 
             std::priority_queue<boxInfo, std::vector<boxInfo>, decltype(less)> sorted_boxes(less);
-            for (int box_idx = 0; box_idx < num_boxes; box_idx++) {
-                if (scoresPtr[box_idx] >= score_threshold)  // algin with ref
+            for (int box_idx = 0; box_idx < m_numBoxes; box_idx++) {
+                if (scoresPtr[box_idx] >= m_scoreThreshold)  // algin with ref
                     sorted_boxes.emplace(boxInfo({scoresPtr[box_idx], box_idx, 0}));
             }
             fb.reserve(sorted_boxes.size());
             if (sorted_boxes.size() > 0) {
-                auto adaptive_threshold = iou_threshold;
-                int max_out_box = (max_output_boxes_per_class > sorted_boxes.size()) ? sorted_boxes.size() : max_output_boxes_per_class;
+                auto adaptive_threshold = m_iouThreshold;
+                int max_out_box = (m_nmsRealTopk > sorted_boxes.size()) ? sorted_boxes.size() : m_nmsRealTopk;
                 while (max_out_box && !sorted_boxes.empty()) {
                     boxInfo currBox = sorted_boxes.top();
                     float origScore = currBox.score;
@@ -332,49 +356,49 @@ void MKLDNNMultiClassNmsNode::nmsWithEta(const float* boxes, const float* scores
 
                     bool box_is_selected = true;
                     for (int idx = static_cast<int>(fb.size()) - 1; idx >= currBox.suppress_begin_index; idx--) {
-                        float iou = intersectionOverUnion(&boxesPtr[currBox.idx * 4], &boxesPtr[fb[idx].box_index * 4], normalized);
+                        float iou = intersectionOverUnion(&boxesPtr[currBox.idx * 4], &boxesPtr[fb[idx].box_index * 4], m_normalized);
                         currBox.score *= func(iou, adaptive_threshold);
                         if (iou >= adaptive_threshold) {
                             box_is_selected = false;
                             break;
                         }
-                        if (currBox.score <= score_threshold)
+                        if (currBox.score <= m_scoreThreshold)
                             break;
                     }
 
                     currBox.suppress_begin_index = fb.size();
                     if (box_is_selected) {
-                        if (nms_eta < 1 && adaptive_threshold > 0.5) {
-                            adaptive_threshold *= nms_eta;
+                        if (m_nmsEta < 1 && adaptive_threshold > 0.5) {
+                            adaptive_threshold *= m_nmsEta;
                         }
                         if (currBox.score == origScore) {
                             fb.push_back({currBox.score, batch_idx, class_idx, currBox.idx});
                             continue;
                         }
-                        if (currBox.score > score_threshold) {
+                        if (currBox.score > m_scoreThreshold) {
                             sorted_boxes.push(currBox);
                         }
                     }
                 }
             }
-            numFiltBox[batch_idx][class_idx] = fb.size();
-            size_t offset = batch_idx * num_classes * max_output_boxes_per_class + class_idx * max_output_boxes_per_class;
+            m_numFiltBox[batch_idx][class_idx] = fb.size();
+            size_t offset = batch_idx * m_numClasses * m_nmsRealTopk + class_idx * m_nmsRealTopk;
             for (size_t i = 0; i < fb.size(); i++) {
-                filtBoxes[offset + i] = fb[i];
+                m_filtBoxes[offset + i] = fb[i];
             }
         }
     });
 }
 
 void MKLDNNMultiClassNmsNode::nmsWithoutEta(const float* boxes, const float* scores, const SizeVector& boxesStrides, const SizeVector& scoresStrides) {
-    parallel_for2d(num_batches, num_classes, [&](int batch_idx, int class_idx) {
-        if (class_idx != background_class) {
+    parallel_for2d(m_numBatches, m_numClasses, [&](int batch_idx, int class_idx) {
+        if (class_idx != m_backgroundClass) {
             const float* boxesPtr = boxes + batch_idx * boxesStrides[0];
             const float* scoresPtr = scores + batch_idx * scoresStrides[0] + class_idx * scoresStrides[1];
 
             std::vector<std::pair<float, int>> sorted_boxes;
-            for (int box_idx = 0; box_idx < num_boxes; box_idx++) {
-                if (scoresPtr[box_idx] >= score_threshold)  // algin with ref
+            for (int box_idx = 0; box_idx < m_numBoxes; box_idx++) {
+                if (scoresPtr[box_idx] >= m_scoreThreshold)  // algin with ref
                     sorted_boxes.emplace_back(std::make_pair(scoresPtr[box_idx], box_idx));
             }
 
@@ -383,35 +407,36 @@ void MKLDNNMultiClassNmsNode::nmsWithoutEta(const float* boxes, const float* sco
                 parallel_sort(sorted_boxes.begin(), sorted_boxes.end(), [](const std::pair<float, int>& l, const std::pair<float, int>& r) {
                     return (l.first > r.first || ((l.first == r.first) && (l.second < r.second)));
                 });
-                int offset = batch_idx * num_classes * max_output_boxes_per_class + class_idx * max_output_boxes_per_class;
-                filtBoxes[offset + 0] = filteredBoxes(sorted_boxes[0].first, batch_idx, class_idx, sorted_boxes[0].second);
+                int offset = batch_idx * m_numClasses * m_nmsRealTopk + class_idx * m_nmsRealTopk;
+                m_filtBoxes[offset + 0] = filteredBoxes(sorted_boxes[0].first, batch_idx, class_idx, sorted_boxes[0].second);
                 io_selection_size++;
-                int max_out_box = (max_output_boxes_per_class > sorted_boxes.size()) ? sorted_boxes.size() : max_output_boxes_per_class;
+                int max_out_box = (m_nmsRealTopk > sorted_boxes.size()) ? sorted_boxes.size() : m_nmsRealTopk;
                 for (size_t box_idx = 1; box_idx < max_out_box; box_idx++) {
                     bool box_is_selected = true;
                     for (int idx = io_selection_size - 1; idx >= 0; idx--) {
-                        float iou =
-                            intersectionOverUnion(&boxesPtr[sorted_boxes[box_idx].second * 4], &boxesPtr[filtBoxes[offset + idx].box_index * 4], normalized);
-                        if (iou >= iou_threshold) {
+                        float iou = intersectionOverUnion(&boxesPtr[sorted_boxes[box_idx].second * 4],
+                            &boxesPtr[m_filtBoxes[offset + idx].box_index * 4], m_normalized);
+                        if (iou >= m_iouThreshold) {
                             box_is_selected = false;
                             break;
                         }
                     }
 
                     if (box_is_selected) {
-                        filtBoxes[offset + io_selection_size] = filteredBoxes(sorted_boxes[box_idx].first, batch_idx, class_idx, sorted_boxes[box_idx].second);
+                        m_filtBoxes[offset + io_selection_size] = filteredBoxes(sorted_boxes[box_idx].first, batch_idx, class_idx,
+                            sorted_boxes[box_idx].second);
                         io_selection_size++;
                     }
                 }
             }
-            numFiltBox[batch_idx][class_idx] = io_selection_size;
+            m_numFiltBox[batch_idx][class_idx] = io_selection_size;
         }
     });
 }
 
 void MKLDNNMultiClassNmsNode::checkPrecision(const Precision prec, const std::vector<Precision> precList, const std::string name, const std::string type) {
     if (std::find(precList.begin(), precList.end(), prec) == precList.end())
-        IE_THROW() << errorPrefix << "has unsupported '" << name << "' " << type << " precision: " << prec;
+        IE_THROW() << m_errorPrefix << "has unsupported '" << name << "' " << type << " precision: " << prec;
 }
 
 REG_MKLDNN_PRIM_FOR(MKLDNNMultiClassNmsNode, MulticlassNms)
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.hpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.hpp
index fd6d040b3b0..6acf3755001 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.hpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_multiclass_nms.hpp
@@ -23,12 +23,17 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
+    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
+    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+
+    bool needShapeInfer() const override { return false; }
+    void prepareParams() override;
+
 private:
     // input (port Num)
     const size_t NMS_BOXES = 0;
@@ -39,27 +44,29 @@ private:
     const size_t NMS_SELECTEDINDICES = 1;
     const size_t NMS_SELECTEDNUM = 2;
 
-    bool sort_result_across_batch = false;
-    MulticlassNmsSortResultType sort_result_type = MulticlassNmsSortResultType::NONE;
+    bool m_sortResultAcrossBatch = false;
+    MulticlassNmsSortResultType m_sortResultType = MulticlassNmsSortResultType::NONE;
 
-    size_t num_batches = 0;
-    size_t num_boxes = 0;
-    size_t num_classes = 0;
+    size_t m_numBatches = 0;
+    size_t m_numBoxes = 0;
+    size_t m_numClasses = 0;
+    size_t m_maxBoxesPerBatch = 0;
 
-    int max_output_boxes_per_class = 0;
-    float iou_threshold = 0.0f;
-    float score_threshold = 0.0f;
+    int m_nmsRealTopk = 0;
+    int m_nmsTopK = 0;
+    float m_iouThreshold = 0.0f;
+    float m_scoreThreshold = 0.0f;
 
-    int32_t background_class = 0;
-    int32_t keep_top_k = 0;
-    float nms_eta = 0.0f;
-    bool normalized = true;
+    int32_t m_backgroundClass = 0;
+    int32_t m_keepTopK = 0;
+    float m_nmsEta = 0.0f;
+    bool m_normalized = true;
 
-    std::string errorPrefix;
+    std::string m_errorPrefix;
 
-    std::vector<std::vector<size_t>> numFiltBox;
-    std::vector<size_t> numBoxOffset;
-    const std::string inType = "input", outType = "output";
+    std::vector<std::vector<size_t>> m_numFiltBox;
+    std::vector<size_t> m_numBoxOffset;
+    const std::string m_inType = "input", m_outType = "output";
 
     struct filteredBoxes {
         float score;
@@ -77,7 +84,7 @@ private:
         int suppress_begin_index;
     };
 
-    std::vector<filteredBoxes> filtBoxes;
+    std::vector<filteredBoxes> m_filtBoxes;
 
     void checkPrecision(const InferenceEngine::Precision prec, const std::vector<InferenceEngine::Precision> precList, const std::string name,
                         const std::string type);
diff --git a/inference-engine/src/transformations/include/ngraph_ops/nms_static_shape_ie.hpp b/inference-engine/src/transformations/include/ngraph_ops/nms_static_shape_ie.hpp
index 2d33d7c3386..1328110266c 100644
--- a/inference-engine/src/transformations/include/ngraph_ops/nms_static_shape_ie.hpp
+++ b/inference-engine/src/transformations/include/ngraph_ops/nms_static_shape_ie.hpp
@@ -64,8 +64,8 @@ void NmsStaticShapeIE<BaseNmsOp>::validate_and_infer_types() {
         if (num_boxes_boxes.is_static() && scores_ps[0].is_static() && scores_ps[1].is_static()) {
             const auto num_boxes = num_boxes_boxes.get_length();
             auto num_classes = scores_ps[1].get_length();
-            if (this->m_attrs.background_class >=0 && this->m_attrs.background_class <= num_classes) {
-                num_classes = num_classes - 1;
+            if (this->m_attrs.background_class >= 0 && this->m_attrs.background_class < num_classes) {
+                num_classes = std::max(int64_t{1}, num_classes - 1);
             }
             int64_t max_output_boxes_per_class = 0;
             if (this->m_attrs.nms_top_k >= 0)
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_matrix_nms_to_matrix_nms_ie.hpp b/inference-engine/src/transformations/include/transformations/op_conversions/convert_matrix_nms_to_matrix_nms_ie.hpp
index 080a0868322..42705003123 100644
--- a/inference-engine/src/transformations/include/transformations/op_conversions/convert_matrix_nms_to_matrix_nms_ie.hpp
+++ b/inference-engine/src/transformations/include/transformations/op_conversions/convert_matrix_nms_to_matrix_nms_ie.hpp
@@ -22,5 +22,5 @@ class TRANSFORMATIONS_API ConvertMatrixNmsToMatrixNmsIE;
 class ngraph::pass::ConvertMatrixNmsToMatrixNmsIE: public ngraph::pass::MatcherPass {
 public:
     NGRAPH_RTTI_DECLARATION;
-    ConvertMatrixNmsToMatrixNmsIE();
+    ConvertMatrixNmsToMatrixNmsIE(bool force_i32_output_type = true);
 };
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_multiclass_nms_to_multiclass_nms_ie.hpp b/inference-engine/src/transformations/include/transformations/op_conversions/convert_multiclass_nms_to_multiclass_nms_ie.hpp
index b639364b24e..b818f425077 100644
--- a/inference-engine/src/transformations/include/transformations/op_conversions/convert_multiclass_nms_to_multiclass_nms_ie.hpp
+++ b/inference-engine/src/transformations/include/transformations/op_conversions/convert_multiclass_nms_to_multiclass_nms_ie.hpp
@@ -22,5 +22,5 @@ class TRANSFORMATIONS_API ConvertMulticlassNmsToMulticlassNmsIE;
 class ngraph::pass::ConvertMulticlassNmsToMulticlassNmsIE: public ngraph::pass::MatcherPass {
 public:
     NGRAPH_RTTI_DECLARATION;
-    ConvertMulticlassNmsToMulticlassNmsIE();
+    ConvertMulticlassNmsToMulticlassNmsIE(bool force_i32_output_type = true);
 };
diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_matrix_nms_to_matrix_nms_ie.cpp b/inference-engine/src/transformations/src/transformations/op_conversions/convert_matrix_nms_to_matrix_nms_ie.cpp
index 34163fc4860..d52a290a957 100644
--- a/inference-engine/src/transformations/src/transformations/op_conversions/convert_matrix_nms_to_matrix_nms_ie.cpp
+++ b/inference-engine/src/transformations/src/transformations/op_conversions/convert_matrix_nms_to_matrix_nms_ie.cpp
@@ -18,13 +18,18 @@
 
 NGRAPH_RTTI_DEFINITION(ngraph::pass::ConvertMatrixNmsToMatrixNmsIE, "ConvertMatrixNmsToMatrixNmsIE", 0);
 
-ngraph::pass::ConvertMatrixNmsToMatrixNmsIE::ConvertMatrixNmsToMatrixNmsIE() {
+ngraph::pass::ConvertMatrixNmsToMatrixNmsIE::ConvertMatrixNmsToMatrixNmsIE(bool force_i32_output_type) {
     MATCHER_SCOPE(ConvertMatrixNmsToMatrixNmsIE);
     auto nms = ngraph::pattern::wrap_type<ngraph::opset8::MatrixNms>();
 
-    ngraph::matcher_pass_callback callback = [](pattern::Matcher &m) {
+    ngraph::matcher_pass_callback callback = [=](pattern::Matcher &m) {
         auto nms = std::dynamic_pointer_cast<ngraph::opset8::MatrixNms>(m.get_match_root());
-        if (!nms) {
+        if (!nms || transformation_callback(nms)) {
+            return false;
+        }
+
+        // if input shape is dynamic force the output shape must be dynamic too
+        if (nms->get_input_partial_shape(0).is_dynamic() || nms->get_input_partial_shape(1).is_dynamic()) {
             return false;
         }
 
@@ -32,7 +37,7 @@ ngraph::pass::ConvertMatrixNmsToMatrixNmsIE::ConvertMatrixNmsToMatrixNmsIE() {
         // vector of new nGraph operations
         NodeVector new_ops;
         auto attrs = nms->get_attrs();
-        attrs.output_type = element::i32;
+        attrs.output_type = force_i32_output_type ? element::i32 : nms->get_output_type();
         auto nms_new = std::make_shared<op::internal::NmsStaticShapeIE<ngraph::opset8::MatrixNms>>(
                 new_args.at(0),
                 new_args.at(1),
diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_multiclass_nms_to_multiclass_nms_ie.cpp b/inference-engine/src/transformations/src/transformations/op_conversions/convert_multiclass_nms_to_multiclass_nms_ie.cpp
index 1f236610e53..2c09e289337 100644
--- a/inference-engine/src/transformations/src/transformations/op_conversions/convert_multiclass_nms_to_multiclass_nms_ie.cpp
+++ b/inference-engine/src/transformations/src/transformations/op_conversions/convert_multiclass_nms_to_multiclass_nms_ie.cpp
@@ -18,13 +18,18 @@
 
 NGRAPH_RTTI_DEFINITION(ngraph::pass::ConvertMulticlassNmsToMulticlassNmsIE, "ConvertMulticlassNmsToMulticlassNmsIE", 0);
 
-ngraph::pass::ConvertMulticlassNmsToMulticlassNmsIE::ConvertMulticlassNmsToMulticlassNmsIE() {
+ngraph::pass::ConvertMulticlassNmsToMulticlassNmsIE::ConvertMulticlassNmsToMulticlassNmsIE(bool force_i32_output_type) {
     MATCHER_SCOPE(ConvertMulticlassNmsToMulticlassNmsIE);
     auto nms = ngraph::pattern::wrap_type<ngraph::opset8::MulticlassNms>();
 
-    ngraph::matcher_pass_callback callback = [](pattern::Matcher &m) {
+    ngraph::matcher_pass_callback callback = [=](pattern::Matcher &m) {
         auto nms = std::dynamic_pointer_cast<ngraph::opset8::MulticlassNms>(m.get_match_root());
-        if (!nms) {
+        if (!nms || transformation_callback(nms)) {
+            return false;
+        }
+
+        // if input shape is dynamic force the output shape must be dynamic too
+        if (nms->get_input_partial_shape(0).is_dynamic() || nms->get_input_partial_shape(1).is_dynamic()) {
             return false;
         }
 
@@ -32,7 +37,7 @@ ngraph::pass::ConvertMulticlassNmsToMulticlassNmsIE::ConvertMulticlassNmsToMulti
         // vector of new nGraph operations
         NodeVector new_ops;
         auto attrs = nms->get_attrs();
-        attrs.output_type = element::i32;
+        attrs.output_type = force_i32_output_type ? element::i32 : nms->get_output_type();
 
         auto nms_new = std::make_shared<op::internal::NmsStaticShapeIE<ngraph::opset8::MulticlassNms>>(
                 new_args.at(0),
diff --git a/inference-engine/tests/functional/inference_engine/serialization/single_layer/matrix_nms.cpp b/inference-engine/tests/functional/inference_engine/serialization/single_layer/matrix_nms.cpp
index 9e5910160b1..6cc30ad8860 100644
--- a/inference-engine/tests/functional/inference_engine/serialization/single_layer/matrix_nms.cpp
+++ b/inference-engine/tests/functional/inference_engine/serialization/single_layer/matrix_nms.cpp
@@ -7,22 +7,31 @@
 #include "shared_test_classes/single_layer/matrix_nms.hpp"
 
 using namespace ngraph;
-using namespace LayerTestsDefinitions;
+using namespace ov::test::subgraph;
 
 namespace {
     TEST_P(MatrixNmsLayerTest, Serialize) {
-        Serialize();
+        serialize();
     }
 
-    const std::vector<InferenceEngine::Precision> netPrecisions = {
-            InferenceEngine::Precision::FP32,
-            InferenceEngine::Precision::FP16
+    const std::vector<ov::test::ElementType> netPrecisions = {
+            ov::element::f32,
+            ov::element::f16
     };
 
-    const std::vector<InputShapeParams> inShapeParams = {
-        InputShapeParams{3, 100, 5},
-        InputShapeParams{1, 10, 50},
-        InputShapeParams{2, 50, 50}
+    const std::vector<std::vector<ov::test::InputShape>> shapeParams = {
+        // num_batches, num_boxes, 4
+        {{{ngraph::Dimension::dynamic(), ngraph::Dimension::dynamic(), 4},
+            {{1, 10, 4}, {2, 100, 4}}},
+        // num_batches, num_classes, num_boxes
+        {{ngraph::Dimension::dynamic(), ngraph::Dimension::dynamic(), ngraph::Dimension::dynamic()},
+            {{1, 3, 10}, {2, 5, 100}}}},
+        // num_batches, num_boxes, 4
+        {{{ngraph::Dimension(1, 10), ngraph::Dimension(1, 100), 4},
+            {{1, 10, 4}, {2, 100, 4}}},
+        // num_batches, num_classes, num_boxes
+        {{{ngraph::Dimension(1, 10), ngraph::Dimension(1, 100), ngraph::Dimension(1, 100)}},
+            {{1, 3, 10}, {2, 5, 100}}}}
     };
 
     const std::vector<op::v8::MatrixNms::SortResultType> sortResultType = {op::v8::MatrixNms::SortResultType::CLASSID,
@@ -43,10 +52,10 @@ namespace {
     const std::vector<bool> normalized = {true, false};
     const std::vector<op::v8::MatrixNms::DecayFunction> decayFunction = {op::v8::MatrixNms::DecayFunction::GAUSSIAN,
                                                     op::v8::MatrixNms::DecayFunction::LINEAR};
-    const auto nmsParams = ::testing::Combine(::testing::ValuesIn(inShapeParams),
-                                          ::testing::Combine(::testing::Values(InferenceEngine::Precision::FP32),
-                                                             ::testing::Values(InferenceEngine::Precision::I32),
-                                                             ::testing::Values(InferenceEngine::Precision::FP32)),
+    const auto nmsParams = ::testing::Combine(::testing::ValuesIn(shapeParams),
+                                          ::testing::Combine(::testing::Values(ov::element::f32),
+                                                             ::testing::Values(ov::element::i32),
+                                                             ::testing::Values(ov::element::f32)),
                                           ::testing::ValuesIn(sortResultType),
                                           ::testing::ValuesIn(outType),
                                           ::testing::ValuesIn(topKParams),
diff --git a/inference-engine/tests/functional/inference_engine/serialization/single_layer/multiclass_nms.cpp b/inference-engine/tests/functional/inference_engine/serialization/single_layer/multiclass_nms.cpp
index f53954dda8c..179fcd8a42a 100644
--- a/inference-engine/tests/functional/inference_engine/serialization/single_layer/multiclass_nms.cpp
+++ b/inference-engine/tests/functional/inference_engine/serialization/single_layer/multiclass_nms.cpp
@@ -7,19 +7,25 @@
 #include "shared_test_classes/single_layer/multiclass_nms.hpp"
 
 using namespace ngraph;
-using namespace LayerTestsDefinitions;
+using namespace ov::test::subgraph;
 
 namespace {
 TEST_P(MulticlassNmsLayerTest, Serialize) {
-    Serialize();
+    serialize();
 }
 
-const std::vector<InferenceEngine::Precision> netPrecisions = {
-    InferenceEngine::Precision::FP32, InferenceEngine::Precision::FP16};
-
-const std::vector<InputShapeParams> inShapeParams = {
-    InputShapeParams{3, 100, 5}, InputShapeParams{1, 10, 50},
-    InputShapeParams{2, 50, 50}};
+const std::vector<std::vector<ov::test::InputShape>> shapeParams = {
+    // num_batches, num_boxes, 4
+    {{{ngraph::Dimension::dynamic(), ngraph::Dimension::dynamic(), 4},
+        {{1, 10, 4}, {2, 100, 4}}},
+    // num_batches, num_classes, num_boxes
+     {{ngraph::Dimension::dynamic(), ngraph::Dimension::dynamic(), ngraph::Dimension::dynamic()},
+        {{1, 3, 10}, {2, 5, 100}}}},
+    {{{ngraph::Dimension(1, 10), ngraph::Dimension(1, 100), 4},
+        {{1, 10, 4}, {2, 100, 4}}},
+    {{{ngraph::Dimension(1, 10), ngraph::Dimension(1, 100), ngraph::Dimension(1, 100)}},
+        {{1, 3, 10}, {2, 5, 100}}}}
+};
 
 const std::vector<int32_t> nmsTopK = {-1, 20};
 const std::vector<float> iouThreshold = {0.7f};
@@ -37,10 +43,10 @@ const std::vector<float> nmsEta = {0.6f, 1.0f};
 const std::vector<bool> normalized = {true, false};
 
 const auto nmsParams = ::testing::Combine(
-    ::testing::ValuesIn(inShapeParams),
-    ::testing::Combine(::testing::Values(InferenceEngine::Precision::FP32),
-                       ::testing::Values(InferenceEngine::Precision::I32),
-                       ::testing::Values(InferenceEngine::Precision::FP32)),
+    ::testing::ValuesIn(shapeParams),
+    ::testing::Combine(::testing::Values(ov::element::f32),
+                       ::testing::Values(ov::element::i32),
+                       ::testing::Values(ov::element::f32)),
     ::testing::ValuesIn(nmsTopK),
     ::testing::Combine(::testing::ValuesIn(iouThreshold),
                        ::testing::ValuesIn(scoreThreshold),
diff --git a/inference-engine/tests/functional/inference_engine/transformations/convert_precision.cpp b/inference-engine/tests/functional/inference_engine/transformations/convert_precision.cpp
index 032cd71e5d9..0d384efdef6 100644
--- a/inference-engine/tests/functional/inference_engine/transformations/convert_precision.cpp
+++ b/inference-engine/tests/functional/inference_engine/transformations/convert_precision.cpp
@@ -14,6 +14,7 @@
 #include <ngraph/opsets/opset3.hpp>
 #include <ngraph/opsets/opset4.hpp>
 #include <ngraph/opsets/opset5.hpp>
+#include <ngraph/opsets/opset8.hpp>
 #include <transformations/convert_precision.hpp>
 #include <transformations/utils/utils.hpp>
 #include <ngraph/pass/manager.hpp>
@@ -125,6 +126,58 @@ TEST(TransformationTests, ConvertPrecision_NMS5) {
     ASSERT_FALSE(has_type<ngraph::element::Type_t::f32>(f));
 }
 
+TEST(TransformationTests, ConvertPrecision_MatrixNms) {
+    std::shared_ptr<ngraph::Function> f;
+    {
+        auto boxes = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f16, ngraph::Shape{1, 1000, 4});
+        auto scores = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f16, ngraph::Shape{1, 1, 1000});
+        op::v8::MatrixNms::Attributes attrs;
+        attrs.output_type = ngraph::element::i64;
+        auto nms = std::make_shared<ngraph::opset8::MatrixNms>(boxes, scores, attrs);
+
+        auto result1 = std::make_shared<ngraph::opset8::Result>(nms->output(0));
+        auto result2 = std::make_shared<ngraph::opset8::Result>(nms->output(1));
+        auto result3 = std::make_shared<ngraph::opset8::Result>(nms->output(2));
+        f = std::make_shared<ngraph::Function>(ngraph::ResultVector{result1, result2, result3}, ngraph::ParameterVector{boxes, scores});
+    }
+
+    pass::Manager manager;
+    static const precisions_array precisions = {
+            { ngraph::element::i64, ngraph::element::i32 },
+            { ngraph::element::f16, ngraph::element::f32 }
+    };
+    manager.register_pass<ngraph::pass::ConvertPrecision>(precisions);
+    manager.run_passes(f);
+    ASSERT_FALSE(has_type<ngraph::element::Type_t::i64>(f));
+    ASSERT_FALSE(has_type<ngraph::element::Type_t::f16>(f));
+}
+
+TEST(TransformationTests, ConvertPrecision_MulticlassNms) {
+    std::shared_ptr<ngraph::Function> f;
+    {
+        auto boxes = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f16, ngraph::Shape{1, 1000, 4});
+        auto scores = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f16, ngraph::Shape{1, 1, 1000});
+        op::v8::MulticlassNms::Attributes attrs;
+        attrs.output_type = ngraph::element::i64;
+        auto nms = std::make_shared<ngraph::opset8::MulticlassNms>(boxes, scores, attrs);
+
+        auto result1 = std::make_shared<ngraph::opset8::Result>(nms->output(0));
+        auto result2 = std::make_shared<ngraph::opset8::Result>(nms->output(1));
+        auto result3 = std::make_shared<ngraph::opset8::Result>(nms->output(2));
+        f = std::make_shared<ngraph::Function>(ngraph::ResultVector{result1, result2, result3}, ngraph::ParameterVector{boxes, scores});
+    }
+
+    pass::Manager manager;
+    static const precisions_array precisions = {
+            { ngraph::element::i64, ngraph::element::i32 },
+            { ngraph::element::f16, ngraph::element::f32 }
+    };
+    manager.register_pass<ngraph::pass::ConvertPrecision>(precisions);
+    manager.run_passes(f);
+    ASSERT_FALSE(has_type<ngraph::element::Type_t::i64>(f));
+    ASSERT_FALSE(has_type<ngraph::element::Type_t::f16>(f));
+}
+
 TEST(TransformationTests, ConvertPrecision_ShapeOf) {
     std::shared_ptr<Function> f(nullptr);
     {
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/matrix_nms.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/matrix_nms.cpp
index 816a5b9c1c1..477e4e1fc74 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/matrix_nms.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/matrix_nms.cpp
@@ -8,14 +8,25 @@
 #include "single_layer_tests/matrix_nms.hpp"
 #include "common_test_utils/test_constants.hpp"
 
-using namespace LayerTestsDefinitions;
+using namespace ov::test::subgraph;
 using namespace InferenceEngine;
 using namespace ngraph;
+const std::vector<std::vector<ov::Shape>> inStaticShapeParams = {
+    {{3, 100, 4}, {3,   1, 100}},
+    {{1, 10,  4}, {1, 100, 10 }}
+};
 
-const std::vector<InputShapeParams> inShapeParams = {
-    InputShapeParams{3, 100, 5},
-    InputShapeParams{1, 10, 50},
-    InputShapeParams{2, 50, 50}
+const std::vector<std::vector<ov::test::InputShape>> inDynamicShapeParams = {
+    // num_batches, num_boxes, 4
+    {{{ngraph::Dimension::dynamic(), ngraph::Dimension::dynamic(), 4},
+        {{1, 10, 4}, {2, 100, 4}}},
+    // num_batches, num_classes, num_boxes
+     {{ngraph::Dimension::dynamic(), ngraph::Dimension::dynamic(), ngraph::Dimension::dynamic()},
+        {{1, 3, 10}, {2, 5, 100}}}},
+    {{{ngraph::Dimension(1, 10), ngraph::Dimension(1, 100), 4},
+        {{1, 10, 4}, {2, 100, 4}}},
+    {{{ngraph::Dimension(1, 10), ngraph::Dimension(1, 100), ngraph::Dimension(1, 100)}},
+        {{1, 3, 10}, {2, 5, 100}}}}
 };
 
 const std::vector<op::v8::MatrixNms::SortResultType> sortResultType = {op::v8::MatrixNms::SortResultType::CLASSID,
@@ -32,23 +43,38 @@ const std::vector<ThresholdParams> thresholdParams = {
 };
 const std::vector<int> nmsTopK = {-1, 100};
 const std::vector<int> keepTopK = {-1, 5};
-const std::vector<int> backgroudClass = {-1, 0};
+const std::vector<int> backgroudClass = {-1, 1};
 const std::vector<bool> normalized = {true, false};
 const std::vector<op::v8::MatrixNms::DecayFunction> decayFunction = {op::v8::MatrixNms::DecayFunction::GAUSSIAN,
                                                 op::v8::MatrixNms::DecayFunction::LINEAR};
 
-const auto nmsParams = ::testing::Combine(::testing::ValuesIn(inShapeParams),
-                                          ::testing::Combine(::testing::Values(Precision::FP32),
-                                                             ::testing::Values(Precision::I32),
-                                                             ::testing::Values(Precision::FP32)),
-                                          ::testing::ValuesIn(sortResultType),
-                                          ::testing::ValuesIn(outType),
-                                          ::testing::ValuesIn(topKParams),
-                                          ::testing::ValuesIn(thresholdParams),
-                                          ::testing::ValuesIn(backgroudClass),
-                                          ::testing::ValuesIn(normalized),
-                                          ::testing::ValuesIn(decayFunction),
-                                          ::testing::Values(CommonTestUtils::DEVICE_CPU)
+const auto nmsParamsStatic = ::testing::Combine(::testing::ValuesIn(ov::test::static_shapes_to_test_representation(inStaticShapeParams)),
+                                                ::testing::Combine(::testing::Values(ov::element::f32),
+                                                                   ::testing::Values(ov::element::i32),
+                                                                   ::testing::Values(ov::element::f32)),
+                                                ::testing::ValuesIn(sortResultType),
+                                                ::testing::ValuesIn(outType),
+                                                ::testing::ValuesIn(topKParams),
+                                                ::testing::ValuesIn(thresholdParams),
+                                                ::testing::ValuesIn(backgroudClass),
+                                                ::testing::ValuesIn(normalized),
+                                                ::testing::ValuesIn(decayFunction),
+                                                ::testing::Values(CommonTestUtils::DEVICE_CPU)
 );
 
-INSTANTIATE_TEST_SUITE_P(smoke_MatrixNmsLayerTest, MatrixNmsLayerTest, nmsParams, MatrixNmsLayerTest::getTestCaseName);
+const auto nmsParamsDynamic = ::testing::Combine(::testing::ValuesIn(inDynamicShapeParams),
+                                                 ::testing::Combine(::testing::Values(ov::element::f32),
+                                                                    ::testing::Values(ov::element::i32),
+                                                                    ::testing::Values(ov::element::f32)),
+                                                 ::testing::ValuesIn(sortResultType),
+                                                 ::testing::ValuesIn(outType),
+                                                 ::testing::ValuesIn(topKParams),
+                                                 ::testing::ValuesIn(thresholdParams),
+                                                 ::testing::ValuesIn(backgroudClass),
+                                                 ::testing::ValuesIn(normalized),
+                                                 ::testing::ValuesIn(decayFunction),
+                                                 ::testing::Values(CommonTestUtils::DEVICE_CPU)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MatrixNmsLayerTest_static, MatrixNmsLayerTest, nmsParamsStatic, MatrixNmsLayerTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_MatrixNmsLayerTest_dynamic, MatrixNmsLayerTest, nmsParamsDynamic, MatrixNmsLayerTest::getTestCaseName);
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/multiclass_nms.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/multiclass_nms.cpp
index 33844763173..1988ef53e2a 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/multiclass_nms.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/multiclass_nms.cpp
@@ -8,16 +8,32 @@
 
 #include "common_test_utils/test_constants.hpp"
 
-using namespace LayerTestsDefinitions;
+using namespace ov::test::subgraph;
 using namespace InferenceEngine;
 using namespace ngraph;
 
-const std::vector<InputShapeParams> inShapeParams = {InputShapeParams {3, 100, 5}, InputShapeParams {1, 10, 50}, InputShapeParams {2, 50, 50}};
+const std::vector<std::vector<ov::Shape>> inStaticShapeParams = {
+    {{3, 100, 4}, {3,   1, 100}},
+    {{1, 10,  4}, {1, 100, 10 }}
+};
+
+const std::vector<std::vector<ov::test::InputShape>> inDynamicShapeParams = {
+    // num_batches, num_boxes, 4
+    {{{ngraph::Dimension::dynamic(), ngraph::Dimension::dynamic(), 4},
+        {{1, 10, 4}, {2, 100, 4}}},
+    // num_batches, num_classes, num_boxes
+     {{ngraph::Dimension::dynamic(), ngraph::Dimension::dynamic(), ngraph::Dimension::dynamic()},
+        {{1, 3, 10}, {2, 5, 100}}}},
+    {{{ngraph::Dimension(1, 10), ngraph::Dimension(1, 100), 4},
+        {{1, 10, 4}, {2, 100, 4}}},
+    {{{ngraph::Dimension(1, 10), ngraph::Dimension(1, 100), ngraph::Dimension(1, 100)}},
+        {{1, 3, 10}, {2, 5, 100}}}}
+};
 
 const std::vector<int32_t> nmsTopK = {-1, 20};
 const std::vector<float> iouThreshold = {0.7f};
 const std::vector<float> scoreThreshold = {0.7f};
-const std::vector<int32_t> backgroundClass = {-1, 0};
+const std::vector<int32_t> backgroundClass = {-1, 1};
 const std::vector<int32_t> keepTopK = {-1, 30};
 const std::vector<element::Type> outType = {element::i32, element::i64};
 
@@ -27,11 +43,29 @@ const std::vector<bool> sortResDesc = {true, false};
 const std::vector<float> nmsEta = {0.6f, 1.0f};
 const std::vector<bool> normalized = {true, false};
 
-const auto nmsParams = ::testing::Combine(
-    ::testing::ValuesIn(inShapeParams),
-    ::testing::Combine(::testing::Values(Precision::FP32), ::testing::Values(Precision::I32), ::testing::Values(Precision::FP32)), ::testing::ValuesIn(nmsTopK),
+const auto nmsParamsStatic = ::testing::Combine(
+    ::testing::ValuesIn(ov::test::static_shapes_to_test_representation(inStaticShapeParams)),
+    ::testing::Combine(::testing::Values(ov::element::f32), ::testing::Values(ov::element::i32), ::testing::Values(ov::element::f32)),
+    ::testing::ValuesIn(nmsTopK),
     ::testing::Combine(::testing::ValuesIn(iouThreshold), ::testing::ValuesIn(scoreThreshold), ::testing::ValuesIn(nmsEta)),
-    ::testing::ValuesIn(backgroundClass), ::testing::ValuesIn(keepTopK), ::testing::ValuesIn(outType), ::testing::ValuesIn(sortResultType),
-    ::testing::Combine(::testing::ValuesIn(sortResDesc), ::testing::ValuesIn(normalized)), ::testing::Values(CommonTestUtils::DEVICE_CPU));
+    ::testing::ValuesIn(backgroundClass),
+    ::testing::ValuesIn(keepTopK),
+    ::testing::ValuesIn(outType),
+    ::testing::ValuesIn(sortResultType),
+    ::testing::Combine(::testing::ValuesIn(sortResDesc), ::testing::ValuesIn(normalized)),
+    ::testing::Values(CommonTestUtils::DEVICE_CPU));
 
-INSTANTIATE_TEST_SUITE_P(smoke_MulticlassNmsLayerTest, MulticlassNmsLayerTest, nmsParams, MulticlassNmsLayerTest::getTestCaseName);
+const auto nmsParamsDynamic = ::testing::Combine(
+    ::testing::ValuesIn(inDynamicShapeParams),
+    ::testing::Combine(::testing::Values(ov::element::f32), ::testing::Values(ov::element::i32), ::testing::Values(ov::element::f32)),
+    ::testing::ValuesIn(nmsTopK),
+    ::testing::Combine(::testing::ValuesIn(iouThreshold), ::testing::ValuesIn(scoreThreshold), ::testing::ValuesIn(nmsEta)),
+    ::testing::ValuesIn(backgroundClass),
+    ::testing::ValuesIn(keepTopK),
+    ::testing::ValuesIn(outType),
+    ::testing::ValuesIn(sortResultType),
+    ::testing::Combine(::testing::ValuesIn(sortResDesc), ::testing::ValuesIn(normalized)),
+    ::testing::Values(CommonTestUtils::DEVICE_CPU));
+
+INSTANTIATE_TEST_SUITE_P(smoke_MulticlassNmsLayerTest_static, MulticlassNmsLayerTest, nmsParamsStatic, MulticlassNmsLayerTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_MulticlassNmsLayerTest_dynamic, MulticlassNmsLayerTest, nmsParamsDynamic, MulticlassNmsLayerTest::getTestCaseName);
\ No newline at end of file
diff --git a/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/matrix_nms.hpp b/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/matrix_nms.hpp
index 21e89bf0474..18f75890588 100644
--- a/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/matrix_nms.hpp
+++ b/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/matrix_nms.hpp
@@ -6,10 +6,14 @@
 
 #include "shared_test_classes/single_layer/matrix_nms.hpp"
 
-namespace LayerTestsDefinitions {
+namespace ov {
+namespace test {
+namespace subgraph {
 
 TEST_P(MatrixNmsLayerTest, CompareWithRefs) {
-    Run();
+    run();
 };
 
-}  // namespace LayerTestsDefinitions
+} // namespace subgraph
+} // namespace test
+} // namespace ov
diff --git a/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/multiclass_nms.hpp b/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/multiclass_nms.hpp
index e89ba2d126c..28ae8cea0c4 100644
--- a/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/multiclass_nms.hpp
+++ b/inference-engine/tests/functional/plugin/shared/include/single_layer_tests/multiclass_nms.hpp
@@ -6,10 +6,14 @@
 
 #include "shared_test_classes/single_layer/multiclass_nms.hpp"
 
-namespace LayerTestsDefinitions {
+namespace ov {
+namespace test {
+namespace subgraph {
 
 TEST_P(MulticlassNmsLayerTest, CompareWithRefs) {
-    Run();
+    run();
 };
 
-}  // namespace LayerTestsDefinitions
+} // namespace subgraph
+} // namespace test
+} // namespace ov
diff --git a/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/matrix_nms.hpp b/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/matrix_nms.hpp
index f318c6930af..2ee3eff36b2 100644
--- a/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/matrix_nms.hpp
+++ b/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/matrix_nms.hpp
@@ -7,18 +7,18 @@
 #include <tuple>
 #include <string>
 
-#include "shared_test_classes/base/layer_test_utils.hpp"
-#include "ngraph_functions/builders.hpp"
+#include "ngraph_functions/utils/ngraph_helpers.hpp"
+#include "common_test_utils/common_utils.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
 
-namespace LayerTestsDefinitions {
+namespace ov {
+namespace test {
+namespace subgraph {
 
-using InputShapeParams = std::tuple<size_t,  // Number of batches
-                                    size_t,  // Number of boxes
-                                    size_t>; // Number of classes
-
-using InputPrecisions = std::tuple<InferenceEngine::Precision,  // boxes and scores precisions
-                                   InferenceEngine::Precision,  // max_output_boxes_per_class precision
-                                   InferenceEngine::Precision>; // iou_threshold, score_threshold, soft_nms_sigma precisions
+using InputPrecisions = std::tuple<ElementType,   // boxes and scores precisions
+                                   ElementType,   // max_output_boxes_per_class
+                                                  // precision
+                                   ElementType>;  // iou_threshold, score_threshold,
 
 using TopKParams = std::tuple<int,      // Maximum number of boxes to be selected per class
                               int>;     // Maximum number of boxes to be selected per batch element
@@ -27,7 +27,7 @@ using ThresholdParams = std::tuple<float,   // minimum score to consider box for
                                    float,   // gaussian_sigma parameter for gaussian decay_function
                                    float>;  // filter out boxes with low confidence score after decaying
 
-using NmsParams = std::tuple<InputShapeParams,                                   // Params using to create 1st and 2nd inputs
+using NmsParams = std::tuple<std::vector<InputShape>,                            // Params using to create 1st and 2nd inputs
                              InputPrecisions,                                    // Input precisions
                              ngraph::op::v8::MatrixNms::SortResultType,          // Order of output elements
                              ngraph::element::Type,                              // Output type
@@ -38,21 +38,22 @@ using NmsParams = std::tuple<InputShapeParams,
                              ngraph::op::v8::MatrixNms::DecayFunction,           // Decay function
                              std::string>;                                       // Device name
 
-class MatrixNmsLayerTest : public testing::WithParamInterface<NmsParams>, virtual public LayerTestsUtils::LayerTestsCommon {
+class MatrixNmsLayerTest : public testing::WithParamInterface<NmsParams>,
+                           virtual public SubgraphBaseTest {
 public:
     static std::string getTestCaseName(const testing::TestParamInfo<NmsParams>& obj);
-    void GenerateInputs() override;
-    void Compare(const std::vector<std::pair<ngraph::element::Type, std::vector<std::uint8_t>>> &expectedOutputs,
-                 const std::vector<InferenceEngine::Blob::Ptr> &actualOutputs)
-    override;
+    void generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) override;
+    void compare(const std::vector<ov::runtime::Tensor> &expected, const std::vector<ov::runtime::Tensor> &actual) override;
 
 protected:
     void SetUp() override;
 
 private:
-    size_t numBatches, numBoxes, numClasses;
-    size_t maxOutputBoxesPerClass;
-    size_t maxOutputBoxesPerBatch;
+    void GetOutputParams(size_t& numBatches, size_t& maxOutputBoxesPerBatch);
+    ngraph::op::v8::MatrixNms::Attributes m_attrs;
+    bool m_outStaticShape;
 };
 
-}  // namespace LayerTestsDefinitions
+} // namespace subgraph
+} // namespace test
+} // namespace ov
diff --git a/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/multiclass_nms.hpp b/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/multiclass_nms.hpp
index 4dd7777b4b9..bf6c141d1af 100644
--- a/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/multiclass_nms.hpp
+++ b/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/multiclass_nms.hpp
@@ -7,20 +7,19 @@
 #include <string>
 #include <tuple>
 
-#include "ngraph_functions/builders.hpp"
-#include "shared_test_classes/base/layer_test_utils.hpp"
+#include "ngraph_functions/utils/ngraph_helpers.hpp"
+#include "common_test_utils/common_utils.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
 
-namespace LayerTestsDefinitions {
+namespace ov {
+namespace test {
+namespace subgraph {
 
-using InputShapeParams = std::tuple<size_t,   // Number of batches
-                                    size_t,   // Number of boxes
-                                    size_t>;  // Number of classes
-
-using InputPrecisions = std::tuple<InferenceEngine::Precision,   // boxes and scores precisions
-                                   InferenceEngine::Precision,   // max_output_boxes_per_class
-                                                                 // precision
-                                   InferenceEngine::Precision>;  // iou_threshold, score_threshold,
-                                                                 // soft_nms_sigma precisions
+using InputPrecisions = std::tuple<ElementType,   // boxes and scores precisions
+                                   ElementType,   // max_output_boxes_per_class
+                                                  // precision
+                                   ElementType>;  // iou_threshold, score_threshold,
+                                                  // soft_nms_sigma precisions
 
 using InputfloatVar = std::tuple<float,   // iouThreshold
                                  float,   // scoreThreshold
@@ -29,7 +28,7 @@ using InputfloatVar = std::tuple<float,   // iouThreshold
 using InputboolVar = std::tuple<bool,   // nmsEta
                                 bool>;  // normalized
 
-using MulticlassNmsParams = std::tuple<InputShapeParams,                           // Params using to create 1st and 2nd inputs
+using MulticlassNmsParams = std::tuple<std::vector<InputShape>,                    // Params using to create 1st and 2nd inputs
                                        InputPrecisions,                            // Input precisions
                                        int32_t,                                    // Max output boxes per class
                                        InputfloatVar,                              // iouThreshold, scoreThreshold, nmsEta
@@ -40,20 +39,21 @@ using MulticlassNmsParams = std::tuple<InputShapeParams,
                                        InputboolVar,                               // Sort result across batch, normalized
                                        std::string>;
 
-class MulticlassNmsLayerTest : public testing::WithParamInterface<MulticlassNmsParams>, virtual public LayerTestsUtils::LayerTestsCommon {
+class MulticlassNmsLayerTest : public testing::WithParamInterface<MulticlassNmsParams>,
+                               virtual public SubgraphBaseTest {
 public:
     static std::string getTestCaseName(const testing::TestParamInfo<MulticlassNmsParams>& obj);
-    void GenerateInputs() override;
-    void Compare(const std::vector<std::pair<ngraph::element::Type, std::vector<std::uint8_t>>>& expectedOutputs,
-                 const std::vector<InferenceEngine::Blob::Ptr>& actualOutputs) override;
+    void generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) override;
+    void compare(const std::vector<ov::runtime::Tensor> &expected, const std::vector<ov::runtime::Tensor> &actual) override;
 
 protected:
     void SetUp() override;
 
 private:
-    size_t numBatches, numBoxes, numClasses;
-    size_t maxOutputBoxesPerClass;
-    size_t maxOutputBoxesPerBatch;
+    void GetOutputParams(size_t& numBatches, size_t& maxOutputBoxesPerBatch);
+    ngraph::op::v8::MulticlassNms::Attributes m_attrs;
+    bool m_outStaticShape;
 };
-
-}  // namespace LayerTestsDefinitions
+} // namespace subgraph
+} // namespace test
+} // namespace ov
diff --git a/inference-engine/tests/functional/shared_test_classes/src/single_layer/matrix_nms.cpp b/inference-engine/tests/functional/shared_test_classes/src/single_layer/matrix_nms.cpp
index de4787cdf22..ddc3cc708aa 100644
--- a/inference-engine/tests/functional/shared_test_classes/src/single_layer/matrix_nms.cpp
+++ b/inference-engine/tests/functional/shared_test_classes/src/single_layer/matrix_nms.cpp
@@ -2,16 +2,22 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "ngraph_functions/builders.hpp"
+#include "functional_test_utils/ov_tensor_utils.hpp"
 #include "shared_test_classes/single_layer/matrix_nms.hpp"
+#include "shared_test_classes/base/layer_test_utils.hpp"
 
-namespace LayerTestsDefinitions {
+#include "functional_test_utils/plugin_cache.hpp"
+
+namespace ov {
+namespace test {
+namespace subgraph {
 
 using namespace ngraph;
 using namespace InferenceEngine;
-using namespace FuncTestUtils::PrecisionUtils;
 
 std::string MatrixNmsLayerTest::getTestCaseName(const testing::TestParamInfo<NmsParams>& obj) {
-    InputShapeParams inShapeParams;
+    std::vector<InputShape> shapes;
     InputPrecisions inPrecisions;
     op::v8::MatrixNms::SortResultType sortResultType;
     element::Type outType;
@@ -21,13 +27,10 @@ std::string MatrixNmsLayerTest::getTestCaseName(const testing::TestParamInfo<Nms
     ThresholdParams thresholdParams;
     bool normalized;
     std::string targetDevice;
-    std::tie(inShapeParams, inPrecisions, sortResultType, outType, topKParams, thresholdParams,
+    std::tie(shapes, inPrecisions, sortResultType, outType, topKParams, thresholdParams,
         backgroudClass, normalized, decayFunction, targetDevice) = obj.param;
 
-    size_t numBatches, numBoxes, numClasses;
-    std::tie(numBatches, numBoxes, numClasses) = inShapeParams;
-
-    Precision paramsPrec, maxBoxPrec, thrPrec;
+    ElementType paramsPrec, maxBoxPrec, thrPrec;
     std::tie(paramsPrec, maxBoxPrec, thrPrec) = inPrecisions;
 
     int nmsTopK, keepTopK;
@@ -37,8 +40,18 @@ std::string MatrixNmsLayerTest::getTestCaseName(const testing::TestParamInfo<Nms
     std::tie(score_threshold, gaussian_sigma, post_threshold) = thresholdParams;
 
     std::ostringstream result;
-    result << "numBatches=" << numBatches << "_numBoxes=" << numBoxes << "_numClasses=" << numClasses << "_";
-    result << "paramsPrec=" << paramsPrec << "_maxBoxPrec=" << maxBoxPrec << "_thrPrec=" << thrPrec << "_";
+    result << "IS=(";
+    for (const auto& shape : shapes) {
+        result << CommonTestUtils::partialShape2str({shape.first}) << "_";
+    }
+    result << ")_TS=(";
+    for (const auto& shape : shapes) {
+        for (const auto& item : shape.second) {
+            result << CommonTestUtils::vec2str(item) << "_";
+        }
+    }
+
+    result << ")_paramsPrec=" << paramsPrec << "_maxBoxPrec=" << maxBoxPrec << "_thrPrec=" << thrPrec << "_";
     result << "sortResultType=" << sortResultType << "_normalized=" << normalized << "_";
     result << "outType=" << outType << "_nmsTopK=" << nmsTopK << "_keepTopK=" << keepTopK << "_";
     result << "backgroudClass=" << backgroudClass << "_decayFunction=" << decayFunction << "_";
@@ -47,38 +60,86 @@ std::string MatrixNmsLayerTest::getTestCaseName(const testing::TestParamInfo<Nms
     return result.str();
 }
 
-void MatrixNmsLayerTest::GenerateInputs() {
-    size_t it = 0;
-    for (const auto &input : cnnNetwork.getInputsInfo()) {
-        const auto &info = input.second;
-        Blob::Ptr blob;
+void MatrixNmsLayerTest::generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) {
+    inputs.clear();
 
-        if (it == 1) {
-            blob = make_blob_with_precision(info->getTensorDesc());
-            blob->allocate();
-            CommonTestUtils::fill_data_random_float<Precision::FP32>(blob, 1, 0, 100000);
+    const auto& funcInputs = function->inputs();
+    for (int i = 0; i < funcInputs.size(); ++i) {
+        const auto& funcInput = funcInputs[i];
+        ov::runtime::Tensor tensor;
+
+        if (i == 1) {
+            tensor = ov::runtime::Tensor(funcInput.get_element_type(), targetInputStaticShapes[i]);
+
+            const size_t range = 1;
+            const size_t startFrom = 0;
+            const size_t k = 1000;
+            const int seed = 1;
+            std::default_random_engine random(seed);
+            std::uniform_int_distribution<int32_t> distribution(k * startFrom, k * (startFrom + range));
+
+            auto *dataPtr = tensor.data<float>();
+            for (size_t i = 0; i < tensor.get_size(); i++) {
+                auto value = static_cast<float>(distribution(random));
+                dataPtr[i] = value / static_cast<float>(k);
+            }
         } else {
-            blob = GenerateInput(*info);
+            tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i]);
         }
-        inputs.push_back(blob);
-        it++;
+
+        inputs.insert({funcInput.get_node_shared_ptr(), tensor});
     }
 }
 
-void MatrixNmsLayerTest::Compare(const std::vector<std::pair<ngraph::element::Type, std::vector<std::uint8_t>>> &expectedOutputs,
-                                     const std::vector<Blob::Ptr> &actualOutputs) {
+void MatrixNmsLayerTest::GetOutputParams(size_t& numBatches, size_t& maxOutputBoxesPerBatch) {
+    size_t it = 0;
+    size_t numBoxes = 0, numClasses = 0;
+    const auto& funcInputs = function->inputs();
+    for (int i = 0; i < funcInputs.size(); ++i) {
+        const auto& funcInput = funcInputs[i];
+        const auto& dims = inputs[funcInput.get_node_shared_ptr()].get_shape();
+
+        if (it == 1) {
+            numClasses = dims[1];
+        } else {
+            numBatches = dims[0];
+            numBoxes = dims[1];
+        }
+        it++;
+    }
+
+    ASSERT_TRUE(numBatches > 0 && numBoxes > 0 && numClasses > 0)
+        << "Expected numBatches, numBoxes, numClasses > 0, got:" << numBatches << ", " << numBoxes << ", " << numClasses;
+
+    auto realClasses = numClasses;
+    if (m_attrs.background_class >= 0 && m_attrs.background_class < numClasses) {
+       realClasses = realClasses - 1;
+    }
+
+    size_t maxOutputBoxesPerClass = 0;
+    if (m_attrs.nms_top_k >= 0)
+       maxOutputBoxesPerClass = std::min(numBoxes, static_cast<size_t>(m_attrs.nms_top_k));
+    else
+       maxOutputBoxesPerClass = numBoxes;
+
+    maxOutputBoxesPerBatch  = maxOutputBoxesPerClass * realClasses;
+    if (m_attrs.keep_top_k >= 0)
+       maxOutputBoxesPerBatch =
+               std::min(maxOutputBoxesPerBatch, static_cast<size_t>(m_attrs.keep_top_k));
+}
+
+void MatrixNmsLayerTest::compare(const std::vector<ov::runtime::Tensor> &expectedOutputs,
+                                 const std::vector<ov::runtime::Tensor> &actualOutputs) {
     auto batchIndex = -1;
+    size_t numBatches, maxOutputBoxesPerBatch;
+    GetOutputParams(numBatches, maxOutputBoxesPerBatch);
     std::vector<int32_t> numPerBatch(numBatches);
     for (int outputIndex = static_cast<int>(expectedOutputs.size()) - 1; outputIndex >= 0 ; outputIndex--) {
         const auto& actual = actualOutputs[outputIndex];
-        const auto _dims = actual->getTensorDesc().getDims();
+        const auto _dims = actual.get_shape();
         if (_dims.size() == 1 && _dims[0] == numBatches) {
             batchIndex = outputIndex;
-            auto memory = InferenceEngine::as<InferenceEngine::MemoryBlob>(actual);
-            IE_ASSERT(memory);
-            const auto lockedMemory = memory->wmap();
-            const auto actualBuffer = lockedMemory.as<const uint8_t *>();
-            auto buffer = reinterpret_cast<const int32_t *>(actualBuffer);
+            auto buffer = reinterpret_cast<const int32_t*>(actual.data());
             std::copy_n(buffer, numBatches, numPerBatch.begin());
         }
     }
@@ -86,39 +147,30 @@ void MatrixNmsLayerTest::Compare(const std::vector<std::pair<ngraph::element::Ty
     for (int outputIndex = static_cast<int>(expectedOutputs.size()) - 1; outputIndex >= 0 ; outputIndex--) {
         const auto& expected = expectedOutputs[outputIndex];
         const auto& actual = actualOutputs[outputIndex];
+        const auto actualBuffer = static_cast<uint8_t*>(actual.data());
+        const auto expectedBuffer = static_cast<uint8_t*>(expected.data());
 
         //Compare Selected Outputs & Selected Indices
         if (outputIndex != batchIndex) {
-            const auto &expectedBuffer = expected.second.data();
-            auto memory = InferenceEngine::as<InferenceEngine::MemoryBlob>(actual);
-            IE_ASSERT(memory);
-            const auto lockedMemory = memory->wmap();
-            const auto actualBuffer = lockedMemory.as<const uint8_t *>();
-
-            auto k =  static_cast<float>(expected.first.size()) / actual->getTensorDesc().getPrecision().size();
-            // W/A for int4, uint4
-            if (expected.first == ngraph::element::Type_t::u4 || expected.first == ngraph::element::Type_t::i4) {
-                k /= 2;
-            }
             if (outputIndex == 2) {
-                if (expected.second.size() != k * actual->byteSize())
+                if (expected.get_size() != actual.get_size())
                     throw std::runtime_error("Expected and actual size 3rd output have different size");
             }
 
-            const auto &precision = actual->getTensorDesc().getPrecision();
+            const auto& precision = actual.get_element_type();
             auto expected_offset = 0;
             auto actual_offset = 0;
             for (size_t i = 0; i < numPerBatch.size(); i++) {
                 auto validNums = numPerBatch[i];
                 switch (precision) {
-                    case InferenceEngine::Precision::FP32: {
-                        switch (expected.first) {
-                            case ngraph::element::Type_t::f32:
+                    case ov::element::f32: {
+                        switch (expected.get_element_type()) {
+                            case ov::element::f32:
                                 LayerTestsUtils::LayerTestsCommon::Compare(
                                         reinterpret_cast<const float *>(expectedBuffer) + expected_offset * 6,
                                         reinterpret_cast<const float *>(actualBuffer) + actual_offset * 6, validNums * 6, 1e-5f);
                                 break;
-                            case ngraph::element::Type_t::f64:
+                            case ov::element::f64:
                                 LayerTestsUtils::LayerTestsCommon::Compare(
                                         reinterpret_cast<const double *>(expectedBuffer) + expected_offset * 6,
                                         reinterpret_cast<const float *>(actualBuffer) + actual_offset * 6, validNums *6, 1e-5f);
@@ -126,22 +178,23 @@ void MatrixNmsLayerTest::Compare(const std::vector<std::pair<ngraph::element::Ty
                             default:
                                 break;
                         }
-
-                        const auto fBuffer = lockedMemory.as<const float *>();
-                        for (size_t tailing = validNums * 6; tailing < maxOutputBoxesPerBatch * 6; tailing++) {
-                            ASSERT_TRUE(std::abs(fBuffer[(actual_offset * 6 + tailing)] - -1.f) < 1e-5)
-                                << "Invalid default value: " << fBuffer[i] << " at index: " << i;
+                        if (m_outStaticShape) {
+                            const auto fBuffer = static_cast<float*>(actual.data());
+                            for (size_t tailing = validNums * 6; tailing < maxOutputBoxesPerBatch * 6; tailing++) {
+                                ASSERT_TRUE(std::abs(fBuffer[(actual_offset * 6 + tailing)] - -1.f) < 1e-5)
+                                    << "Invalid default value: " << fBuffer[i] << " at index: " << i;
+                            }
                         }
                         break;
                     }
-                    case InferenceEngine::Precision::I32: {
-                        switch (expected.first) {
-                            case ngraph::element::Type_t::i32:
+                    case ov::element::i32: {
+                        switch (expected.get_element_type()) {
+                            case ov::element::i32:
                                 LayerTestsUtils::LayerTestsCommon::Compare(
                                         reinterpret_cast<const int32_t *>(expectedBuffer) + expected_offset,
                                         reinterpret_cast<const int32_t *>(actualBuffer) + actual_offset, validNums, 0);
                                 break;
-                            case ngraph::element::Type_t::i64:
+                            case ov::element::i64:
                                 LayerTestsUtils::LayerTestsCommon::Compare(
                                         reinterpret_cast<const int64_t *>(expectedBuffer) + expected_offset,
                                         reinterpret_cast<const int32_t *>(actualBuffer) + actual_offset, validNums, 0);
@@ -149,46 +202,42 @@ void MatrixNmsLayerTest::Compare(const std::vector<std::pair<ngraph::element::Ty
                             default:
                                 break;
                         }
-                        const auto iBuffer = lockedMemory.as<const int *>();
-                        for (size_t tailing = validNums; tailing < maxOutputBoxesPerBatch; tailing++) {
-                            ASSERT_TRUE(iBuffer[actual_offset + tailing] == -1) << "Invalid default value: " << iBuffer[i] << " at index: " << i;
+                        if (m_outStaticShape) {
+                            const auto iBuffer = static_cast<int*>(actual.data());
+                            for (size_t tailing = validNums; tailing < maxOutputBoxesPerBatch; tailing++) {
+                                ASSERT_TRUE(iBuffer[actual_offset + tailing] == -1) << "Invalid default value: " << iBuffer[i] << " at index: " << i;
+                            }
                         }
                         break;
                     }
                     default:
                         FAIL() << "Comparator for " << precision << " precision isn't supported";
                 }
-                expected_offset += validNums;
-                actual_offset += maxOutputBoxesPerBatch;
+                if (!m_outStaticShape) {
+                    expected_offset += validNums;
+                    actual_offset += validNums;
+                } else {
+                    expected_offset += validNums;
+                    actual_offset += maxOutputBoxesPerBatch;
+                }
             }
         } else {
-            const auto &expectedBuffer = expected.second.data();
-            auto memory = InferenceEngine::as<InferenceEngine::MemoryBlob>(actual);
-            IE_ASSERT(memory);
-            const auto lockedMemory = memory->wmap();
-            const auto actualBuffer = lockedMemory.as<const uint8_t *>();
-
-            auto k =  static_cast<float>(expected.first.size()) / actual->getTensorDesc().getPrecision().size();
-            // W/A for int4, uint4
-            if (expected.first == ngraph::element::Type_t::u4 || expected.first == ngraph::element::Type_t::i4) {
-                k /= 2;
-            }
             if (outputIndex == 2) {
-                if (expected.second.size() != k * actual->byteSize())
+                if (expected.get_size() != actual.get_size())
                     throw std::runtime_error("Expected and actual size 3rd output have different size");
             }
 
-            const auto &precision = actual->getTensorDesc().getPrecision();
-            size_t size = expected.second.size() / (k * actual->getTensorDesc().getPrecision().size());
+            const auto& precision = actual.get_element_type();
+            size_t size = expected.get_size();
             switch (precision) {
-                case InferenceEngine::Precision::I32: {
-                    switch (expected.first) {
-                        case ngraph::element::Type_t::i32:
+                case ov::element::i32: {
+                    switch (expected.get_element_type()) {
+                        case ov::element::i32:
                             LayerTestsUtils::LayerTestsCommon::Compare(
                                     reinterpret_cast<const int32_t *>(expectedBuffer),
                                     reinterpret_cast<const int32_t *>(actualBuffer), size, 0);
                             break;
-                        case ngraph::element::Type_t::i64:
+                        case ov::element::i64:
                             LayerTestsUtils::LayerTestsCommon::Compare(
                                     reinterpret_cast<const int64_t *>(expectedBuffer),
                                     reinterpret_cast<const int32_t *>(actualBuffer), size, 0);
@@ -206,45 +255,48 @@ void MatrixNmsLayerTest::Compare(const std::vector<std::pair<ngraph::element::Ty
 }
 
 void MatrixNmsLayerTest::SetUp() {
-    InputShapeParams inShapeParams;
+    std::vector<InputShape> shapes;
     InputPrecisions inPrecisions;
-    op::v8::MatrixNms::Attributes attrs;
     TopKParams topKParams;
     ThresholdParams thresholdParams;
 
-    std::tie(inShapeParams, inPrecisions, attrs.sort_result_type, attrs.output_type, topKParams, thresholdParams,
-        attrs.background_class, attrs.normalized, attrs.decay_function, targetDevice) = this->GetParam();
+    std::tie(shapes, inPrecisions, m_attrs.sort_result_type, m_attrs.output_type, topKParams, thresholdParams,
+        m_attrs.background_class, m_attrs.normalized, m_attrs.decay_function, targetDevice) = this->GetParam();
 
-    std::tie(attrs.nms_top_k, attrs.keep_top_k) = topKParams;
-    std::tie(attrs.score_threshold, attrs.gaussian_sigma, attrs.post_threshold) = thresholdParams;
-    std::tie(numBatches, numBoxes, numClasses) = inShapeParams;
-    auto realClasses = numClasses;
-    if (attrs.background_class >=0 && attrs.background_class <= numClasses) {
-        realClasses = realClasses - 1;
-    }
+    std::tie(m_attrs.nms_top_k, m_attrs.keep_top_k) = topKParams;
+    std::tie(m_attrs.score_threshold, m_attrs.gaussian_sigma, m_attrs.post_threshold) = thresholdParams;
 
-    maxOutputBoxesPerClass = 0;
-    if (attrs.nms_top_k >= 0)
-        maxOutputBoxesPerClass = std::min(numBoxes, static_cast<size_t>(attrs.nms_top_k));
-    else
-        maxOutputBoxesPerClass = numBoxes;
+    init_input_shapes(shapes);
 
-    maxOutputBoxesPerBatch  = maxOutputBoxesPerClass * realClasses;
-    if (attrs.keep_top_k >= 0)
-        maxOutputBoxesPerBatch =
-                std::min(maxOutputBoxesPerBatch, static_cast<size_t>(attrs.keep_top_k));
-    Precision paramsPrec, maxBoxPrec, thrPrec;
+    // input is dynamic shape -> output will be dynamic shape
+    // input is static shape -> output will be static shape
+    const auto inputDynamicParam = {shapes[0].first, shapes[1].first};
+    m_outStaticShape = std::any_of(inputDynamicParam.begin(), inputDynamicParam.end(), [](const ov::PartialShape& shape) {
+        return shape.rank() == 0;
+    });
+
+    ElementType paramsPrec, maxBoxPrec, thrPrec;
     std::tie(paramsPrec, maxBoxPrec, thrPrec) = inPrecisions;
-
-    const std::vector<size_t> boxesShape{numBatches, numBoxes, 4}, scoresShape{numBatches, numClasses, numBoxes};
-    auto ngPrc = convertIE2nGraphPrc(paramsPrec);
-    auto params = builder::makeParams(ngPrc, {boxesShape, scoresShape});
-    auto paramOuts = helpers::convert2OutputVector(helpers::castOps2Nodes<op::Parameter>(params));
-    auto nms = std::make_shared<opset8::MatrixNms>(paramOuts[0], paramOuts[1], attrs);
-    auto nms_0_identity = std::make_shared<opset5::Multiply>(nms->output(0), opset5::Constant::create(element::f32, Shape{1}, {1}));
-    auto nms_1_identity = std::make_shared<opset5::Multiply>(nms->output(1), opset5::Constant::create(attrs.output_type, Shape{1}, {1}));
-    auto nms_2_identity = std::make_shared<opset5::Multiply>(nms->output(2), opset5::Constant::create(attrs.output_type, Shape{1}, {1}));
-    function = std::make_shared<Function>(OutputVector{nms_0_identity, nms_1_identity, nms_2_identity}, params, "NMS");
+    const auto params = ngraph::builder::makeDynamicParams(paramsPrec, inputDynamicShapes);
+    const auto paramOuts =
+            ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
+    auto nms = std::make_shared<opset8::MatrixNms>(paramOuts[0], paramOuts[1], m_attrs);
+    if (!m_outStaticShape) {
+        auto result = std::make_shared<opset5::Result>(nms);
+        function = std::make_shared<Function>(result, params, "MatrixNMS");
+    } else {
+        auto nms_0_identity = std::make_shared<opset5::Multiply>(nms->output(0), opset5::Constant::create(element::f32, Shape{1}, {1}));
+        auto nms_1_identity = std::make_shared<opset5::Multiply>(nms->output(1), opset5::Constant::create(m_attrs.output_type, Shape{1}, {1}));
+        auto nms_2_identity = std::make_shared<opset5::Multiply>(nms->output(2), opset5::Constant::create(m_attrs.output_type, Shape{1}, {1}));
+        OutputVector results = {
+            std::make_shared<opset5::Result>(nms_0_identity),
+            std::make_shared<opset5::Result>(nms_1_identity),
+            std::make_shared<opset5::Result>(nms_2_identity)
+        };
+        function = std::make_shared<Function>(results, params, "MatrixNMS");
+    }
 }
 
-}  // namespace LayerTestsDefinitions
+} // namespace subgraph
+} // namespace test
+} // namespace ov
diff --git a/inference-engine/tests/functional/shared_test_classes/src/single_layer/multiclass_nms.cpp b/inference-engine/tests/functional/shared_test_classes/src/single_layer/multiclass_nms.cpp
index dd42206f63f..4cfb200878c 100644
--- a/inference-engine/tests/functional/shared_test_classes/src/single_layer/multiclass_nms.cpp
+++ b/inference-engine/tests/functional/shared_test_classes/src/single_layer/multiclass_nms.cpp
@@ -2,16 +2,22 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "ngraph_functions/builders.hpp"
+#include "functional_test_utils/ov_tensor_utils.hpp"
 #include "shared_test_classes/single_layer/multiclass_nms.hpp"
+#include "shared_test_classes/base/layer_test_utils.hpp"
 
-namespace LayerTestsDefinitions {
+#include "functional_test_utils/plugin_cache.hpp"
+
+namespace ov {
+namespace test {
+namespace subgraph {
 
 using namespace ngraph;
 using namespace InferenceEngine;
-using namespace FuncTestUtils::PrecisionUtils;
 
 std::string MulticlassNmsLayerTest::getTestCaseName(const testing::TestParamInfo<MulticlassNmsParams>& obj) {
-    InputShapeParams inShapeParams;
+    std::vector<InputShape> shapes;
     InputPrecisions inPrecisions;
     int32_t nmsTopK, backgroundClass, keepTopK;
     element::Type outType;
@@ -23,12 +29,9 @@ std::string MulticlassNmsLayerTest::getTestCaseName(const testing::TestParamInfo
 
     std::string targetDevice;
 
-    std::tie(inShapeParams, inPrecisions, nmsTopK, inFloatVar, backgroundClass, keepTopK, outType, sortResultType, inboolVar, targetDevice) = obj.param;
+    std::tie(shapes, inPrecisions, nmsTopK, inFloatVar, backgroundClass, keepTopK, outType, sortResultType, inboolVar, targetDevice) = obj.param;
 
-    size_t numBatches, numBoxes, numClasses;
-    std::tie(numBatches, numBoxes, numClasses) = inShapeParams;
-
-    Precision paramsPrec, maxBoxPrec, thrPrec;
+    ElementType paramsPrec, maxBoxPrec, thrPrec;
     std::tie(paramsPrec, maxBoxPrec, thrPrec) = inPrecisions;
 
     float iouThr, scoreThr, nmsEta;
@@ -38,8 +41,18 @@ std::string MulticlassNmsLayerTest::getTestCaseName(const testing::TestParamInfo
     std::tie(sortResCB, normalized) = inboolVar;
 
     std::ostringstream result;
-    result << "numBatches=" << numBatches << "_numBoxes=" << numBoxes << "_numClasses=" << numClasses << "_";
-    result << "paramsPrec=" << paramsPrec << "_maxBoxPrec=" << maxBoxPrec << "_thrPrec=" << thrPrec << "_";
+    result << "IS=(";
+    for (const auto& shape : shapes) {
+        result << CommonTestUtils::partialShape2str({shape.first}) << "_";
+    }
+    result << ")_TS=(";
+    for (const auto& shape : shapes) {
+        for (const auto& item : shape.second) {
+            result << CommonTestUtils::vec2str(item) << "_";
+        }
+    }
+
+    result << ")_paramsPrec=" << paramsPrec << "_maxBoxPrec=" << maxBoxPrec << "_thrPrec=" << thrPrec << "_";
     result << "nmsTopK=" << nmsTopK << "_";
     result << "iouThr=" << iouThr << "_scoreThr=" << scoreThr << "_backgroundClass=" << backgroundClass << "_";
     result << "keepTopK=" << keepTopK << "_outType=" << outType << "_";
@@ -48,38 +61,86 @@ std::string MulticlassNmsLayerTest::getTestCaseName(const testing::TestParamInfo
     return result.str();
 }
 
-void MulticlassNmsLayerTest::GenerateInputs() {
-    size_t it = 0;
-    for (const auto& input : cnnNetwork.getInputsInfo()) {
-        const auto& info = input.second;
-        Blob::Ptr blob;
+void MulticlassNmsLayerTest::generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) {
+    inputs.clear();
 
-        if (it == 1) {
-            blob = make_blob_with_precision(info->getTensorDesc());
-            blob->allocate();
-            CommonTestUtils::fill_data_random_float<Precision::FP32>(blob, 1, 0, 1000);
+    const auto& funcInputs = function->inputs();
+    for (int i = 0; i < funcInputs.size(); ++i) {
+        const auto& funcInput = funcInputs[i];
+        ov::runtime::Tensor tensor;
+
+        if (i == 1) {
+            tensor = ov::runtime::Tensor(funcInput.get_element_type(), targetInputStaticShapes[i]);
+
+            const size_t range = 1;
+            const size_t startFrom = 0;
+            const size_t k = 1000;
+            const int seed = 1;
+            std::default_random_engine random(seed);
+            std::uniform_int_distribution<int32_t> distribution(k * startFrom, k * (startFrom + range));
+
+            auto *dataPtr = tensor.data<float>();
+            for (size_t i = 0; i < tensor.get_size(); i++) {
+                auto value = static_cast<float>(distribution(random));
+                dataPtr[i] = value / static_cast<float>(k);
+            }
         } else {
-            blob = GenerateInput(*info);
+            tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i]);
         }
-        inputs.push_back(blob);
-        it++;
+
+        inputs.insert({funcInput.get_node_shared_ptr(), tensor});
     }
 }
 
-void MulticlassNmsLayerTest::Compare(const std::vector<std::pair<ngraph::element::Type, std::vector<std::uint8_t>>>& expectedOutputs,
-                                     const std::vector<Blob::Ptr>& actualOutputs) {
+void MulticlassNmsLayerTest::GetOutputParams(size_t& numBatches, size_t& maxOutputBoxesPerBatch) {
+    size_t it = 0;
+    size_t numBoxes = 0, numClasses = 0;
+    const auto& funcInputs = function->inputs();
+    for (int i = 0; i < funcInputs.size(); ++i) {
+        const auto& funcInput = funcInputs[i];
+        const auto& dims = inputs[funcInput.get_node_shared_ptr()].get_shape();
+
+        if (it == 1) {
+            numClasses = dims[1];
+        } else {
+            numBatches = dims[0];
+            numBoxes = dims[1];
+        }
+        it++;
+    }
+
+    ASSERT_TRUE(numBatches > 0 && numBoxes > 0 && numClasses > 0)
+        << "Expected numBatches, numBoxes, numClasses > 0, got:" << numBatches << ", " << numBoxes << ", " << numClasses;
+
+    auto realClasses = numClasses;
+    if (m_attrs.background_class >= 0 && m_attrs.background_class < numClasses) {
+       realClasses = realClasses - 1;
+    }
+
+    size_t maxOutputBoxesPerClass = 0;
+    if (m_attrs.nms_top_k >= 0)
+       maxOutputBoxesPerClass = std::min(numBoxes, static_cast<size_t>(m_attrs.nms_top_k));
+    else
+       maxOutputBoxesPerClass = numBoxes;
+
+    maxOutputBoxesPerBatch  = maxOutputBoxesPerClass * realClasses;
+    if (m_attrs.keep_top_k >= 0)
+       maxOutputBoxesPerBatch =
+               std::min(maxOutputBoxesPerBatch, static_cast<size_t>(m_attrs.keep_top_k));
+}
+
+void MulticlassNmsLayerTest::compare(const std::vector<ov::runtime::Tensor> &expectedOutputs,
+                                     const std::vector<ov::runtime::Tensor> &actualOutputs) {
     auto batchIndex = -1;
+    size_t numBatches, maxOutputBoxesPerBatch;
+    GetOutputParams(numBatches, maxOutputBoxesPerBatch);
     std::vector<int32_t> numPerBatch(numBatches);
     for (int outputIndex = static_cast<int>(expectedOutputs.size()) - 1; outputIndex >= 0; outputIndex--) {
         const auto& actual = actualOutputs[outputIndex];
-        const auto _dims = actual->getTensorDesc().getDims();
+        const auto _dims = actual.get_shape();
         if (_dims.size() == 1 && _dims[0] == numBatches) {
             batchIndex = outputIndex;
-            auto memory = InferenceEngine::as<InferenceEngine::MemoryBlob>(actual);
-            IE_ASSERT(memory);
-            const auto lockedMemory = memory->wmap();
-            const auto actualBuffer = lockedMemory.as<const uint8_t*>();
-            auto buffer = reinterpret_cast<const int32_t*>(actualBuffer);
+            auto buffer = reinterpret_cast<const int32_t*>(actual.data());
             std::copy_n(buffer, numBatches, numPerBatch.begin());
         }
     }
@@ -87,39 +148,30 @@ void MulticlassNmsLayerTest::Compare(const std::vector<std::pair<ngraph::element
     for (int outputIndex = static_cast<int>(expectedOutputs.size()) - 1; outputIndex >= 0; outputIndex--) {
         const auto& expected = expectedOutputs[outputIndex];
         const auto& actual = actualOutputs[outputIndex];
+        const auto actualBuffer = static_cast<uint8_t*>(actual.data());
+        const auto expectedBuffer = static_cast<uint8_t*>(expected.data());
 
         // Compare Selected Outputs & Selected Indices
         if (outputIndex != batchIndex) {
-            const auto& expectedBuffer = expected.second.data();
-            auto memory = InferenceEngine::as<InferenceEngine::MemoryBlob>(actual);
-            IE_ASSERT(memory);
-            const auto lockedMemory = memory->wmap();
-            const auto actualBuffer = lockedMemory.as<const uint8_t*>();
-
-            auto k = static_cast<float>(expected.first.size()) / actual->getTensorDesc().getPrecision().size();
-            // W/A for int4, uint4
-            if (expected.first == ngraph::element::Type_t::u4 || expected.first == ngraph::element::Type_t::i4) {
-                k /= 2;
-            }
             if (outputIndex == 2) {
-                if (expected.second.size() != k * actual->byteSize())
+                if (expected.get_size() != actual.get_size())
                     throw std::runtime_error("Expected and actual size 3rd output have different "
                                              "size");
             }
 
-            const auto& precision = actual->getTensorDesc().getPrecision();
+            const auto& precision = actual.get_element_type();
             auto expected_offset = 0;
             auto actual_offset = 0;
             for (size_t i = 0; i < numPerBatch.size(); i++) {
                 auto validNums = numPerBatch[i];
                 switch (precision) {
-                case InferenceEngine::Precision::FP32: {
-                    switch (expected.first) {
-                    case ngraph::element::Type_t::f32:
+                case ov::element::f32: {
+                    switch (expected.get_element_type()) {
+                    case ov::element::f32:
                         LayerTestsUtils::LayerTestsCommon::Compare(reinterpret_cast<const float*>(expectedBuffer) + expected_offset * 6,
                                                                    reinterpret_cast<const float*>(actualBuffer) + actual_offset * 6, validNums * 6, 1e-5f);
                         break;
-                    case ngraph::element::Type_t::f64:
+                    case ov::element::f64:
                         LayerTestsUtils::LayerTestsCommon::Compare(reinterpret_cast<const double*>(expectedBuffer) + expected_offset * 6,
                                                                    reinterpret_cast<const float*>(actualBuffer) + actual_offset * 6, validNums * 6, 1e-5f);
                         break;
@@ -127,66 +179,64 @@ void MulticlassNmsLayerTest::Compare(const std::vector<std::pair<ngraph::element
                         break;
                     }
 
-                    const auto fBuffer = lockedMemory.as<const float*>();
-                    for (size_t tailing = validNums * 6; tailing < maxOutputBoxesPerBatch * 6; tailing++) {
-                        ASSERT_TRUE(std::abs(fBuffer[(actual_offset * 6 + tailing)] - -1.f) < 1e-5)
-                            << "Invalid default value: " << fBuffer[i] << " at index: " << i;
+                    if (m_outStaticShape) {
+                        const auto fBuffer = static_cast<float*>(actual.data());
+                        for (size_t tailing = validNums * 6; tailing < maxOutputBoxesPerBatch * 6; tailing++) {
+                            ASSERT_TRUE(std::abs(fBuffer[(actual_offset * 6 + tailing)] - -1.f) < 1e-5)
+                                << "Invalid default value: " << fBuffer[i] << " at index: " << i;
+                        }
                     }
                     break;
                 }
-                case InferenceEngine::Precision::I32: {
-                    switch (expected.first) {
-                    case ngraph::element::Type_t::i32:
+                case ov::element::i32: {
+                    switch (expected.get_element_type()) {
+                    case ov::element::i32:
                         LayerTestsUtils::LayerTestsCommon::Compare(reinterpret_cast<const int32_t*>(expectedBuffer) + expected_offset,
                                                                    reinterpret_cast<const int32_t*>(actualBuffer) + actual_offset, validNums, 0);
                         break;
-                    case ngraph::element::Type_t::i64:
+                    case ov::element::i64:
                         LayerTestsUtils::LayerTestsCommon::Compare(reinterpret_cast<const int64_t*>(expectedBuffer) + expected_offset,
                                                                    reinterpret_cast<const int32_t*>(actualBuffer) + actual_offset, validNums, 0);
                         break;
                     default:
                         break;
                     }
-                    const auto iBuffer = lockedMemory.as<const int*>();
-                    for (size_t tailing = validNums; tailing < maxOutputBoxesPerBatch; tailing++) {
-                        ASSERT_TRUE(iBuffer[actual_offset + tailing] == -1) << "Invalid default value: " << iBuffer[i] << " at index: " << i;
+                    if (m_outStaticShape) {
+                        const auto iBuffer = static_cast<int*>(actual.data());
+                        for (size_t tailing = validNums; tailing < maxOutputBoxesPerBatch; tailing++) {
+                            ASSERT_TRUE(iBuffer[actual_offset + tailing] == -1) << "Invalid default value: " << iBuffer[i] << " at index: " << i;
+                        }
                     }
                     break;
                 }
                 default:
                     FAIL() << "Comparator for " << precision << " precision isn't supported";
                 }
-                expected_offset += validNums;
-                actual_offset += maxOutputBoxesPerBatch;
+                if (!m_outStaticShape) {
+                    expected_offset += validNums;
+                    actual_offset += validNums;
+                } else {
+                    expected_offset += validNums;
+                    actual_offset += maxOutputBoxesPerBatch;
+                }
             }
         } else {
-            const auto& expectedBuffer = expected.second.data();
-            auto memory = InferenceEngine::as<InferenceEngine::MemoryBlob>(actual);
-            IE_ASSERT(memory);
-            const auto lockedMemory = memory->wmap();
-            const auto actualBuffer = lockedMemory.as<const uint8_t*>();
-
-            auto k = static_cast<float>(expected.first.size()) / actual->getTensorDesc().getPrecision().size();
-            // W/A for int4, uint4
-            if (expected.first == ngraph::element::Type_t::u4 || expected.first == ngraph::element::Type_t::i4) {
-                k /= 2;
-            }
             if (outputIndex == 2) {
-                if (expected.second.size() != k * actual->byteSize())
+                if (expected.get_size() != actual.get_size())
                     throw std::runtime_error("Expected and actual size 3rd output have different "
                                              "size");
             }
 
-            const auto& precision = actual->getTensorDesc().getPrecision();
-            size_t size = expected.second.size() / (k * actual->getTensorDesc().getPrecision().size());
+            const auto& precision = actual.get_element_type();
+            size_t size = expected.get_size();
             switch (precision) {
-            case InferenceEngine::Precision::I32: {
-                switch (expected.first) {
-                case ngraph::element::Type_t::i32:
+            case ov::element::i32: {
+                switch (expected.get_element_type()) {
+                case ov::element::i32:
                     LayerTestsUtils::LayerTestsCommon::Compare(reinterpret_cast<const int32_t*>(expectedBuffer), reinterpret_cast<const int32_t*>(actualBuffer),
                                                                size, 0);
                     break;
-                case ngraph::element::Type_t::i64:
+                case ov::element::i64:
                     LayerTestsUtils::LayerTestsCommon::Compare(reinterpret_cast<const int64_t*>(expectedBuffer), reinterpret_cast<const int32_t*>(actualBuffer),
                                                                size, 0);
                     break;
@@ -203,9 +253,8 @@ void MulticlassNmsLayerTest::Compare(const std::vector<std::pair<ngraph::element
 }
 
 void MulticlassNmsLayerTest::SetUp() {
-    InputShapeParams inShapeParams;
+    std::vector<InputShape> shapes;
     InputPrecisions inPrecisions;
-    op::v8::MulticlassNms::Attributes attrs;
     size_t maxOutBoxesPerClass, backgroundClass, keepTopK;
     element::Type outType;
 
@@ -214,27 +263,19 @@ void MulticlassNmsLayerTest::SetUp() {
     InputfloatVar inFloatVar;
     InputboolVar inboolVar;
 
-    std::tie(inShapeParams, inPrecisions, maxOutBoxesPerClass, inFloatVar, backgroundClass, keepTopK, outType, sortResultType, inboolVar, targetDevice) =
+    std::tie(shapes, inPrecisions, maxOutBoxesPerClass, inFloatVar, backgroundClass, keepTopK, outType, sortResultType, inboolVar, targetDevice) =
         this->GetParam();
 
-    // size_t numBatches, numBoxes, numClasses;
-    std::tie(numBatches, numBoxes, numClasses) = inShapeParams;
-    auto realClasses = numClasses;
-    if (backgroundClass >= 0 && backgroundClass <= numClasses) {
-        realClasses = realClasses - 1;
-    }
+    init_input_shapes(shapes);
 
-    maxOutputBoxesPerClass = 0;
-    if (maxOutBoxesPerClass >= 0)
-        maxOutputBoxesPerClass = std::min(numBoxes, static_cast<size_t>(maxOutBoxesPerClass));
-    else
-        maxOutputBoxesPerClass = numBoxes;
+    // input is dynamic shape -> output will be dynamic shape
+    // input is static shape -> output will be static shape
+    const auto inputDynamicParam = {shapes[0].first, shapes[1].first};
+    m_outStaticShape = std::any_of(inputDynamicParam.begin(), inputDynamicParam.end(), [](const ov::PartialShape& shape) {
+        return shape.rank() == 0;
+    });
 
-    maxOutputBoxesPerBatch = maxOutputBoxesPerClass * realClasses;
-    if (keepTopK >= 0)
-        maxOutputBoxesPerBatch = std::min(maxOutputBoxesPerBatch, static_cast<size_t>(keepTopK));
-
-    Precision paramsPrec, maxBoxPrec, thrPrec;
+    ElementType paramsPrec, maxBoxPrec, thrPrec;
     std::tie(paramsPrec, maxBoxPrec, thrPrec) = inPrecisions;
 
     float iouThr, scoreThr, nmsEta;
@@ -243,28 +284,39 @@ void MulticlassNmsLayerTest::SetUp() {
     bool sortResCB, normalized;
     std::tie(sortResCB, normalized) = inboolVar;
 
-    const std::vector<size_t> boxesShape {numBatches, numBoxes, 4}, scoresShape {numBatches, numClasses, numBoxes};
-    auto ngPrc = convertIE2nGraphPrc(paramsPrec);
-    auto params = builder::makeParams(ngPrc, {boxesShape, scoresShape});
-    auto paramOuts = helpers::convert2OutputVector(helpers::castOps2Nodes<op::Parameter>(params));
+    const auto params = ngraph::builder::makeDynamicParams(paramsPrec, inputDynamicShapes);
+    const auto paramOuts =
+            ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
 
-    attrs.iou_threshold = iouThr;
-    attrs.score_threshold = scoreThr;
-    attrs.nms_eta = nmsEta;
-    attrs.sort_result_type = sortResultType;
-    attrs.sort_result_across_batch = sortResCB;
-    attrs.output_type = outType;
-    attrs.nms_top_k = maxOutBoxesPerClass;
-    attrs.keep_top_k = keepTopK;
-    attrs.background_class = backgroundClass;
-    attrs.normalized = normalized;
+    m_attrs.iou_threshold = iouThr;
+    m_attrs.score_threshold = scoreThr;
+    m_attrs.nms_eta = nmsEta;
+    m_attrs.sort_result_type = sortResultType;
+    m_attrs.sort_result_across_batch = sortResCB;
+    m_attrs.output_type = outType;
+    m_attrs.nms_top_k = maxOutBoxesPerClass;
+    m_attrs.keep_top_k = keepTopK;
+    m_attrs.background_class = backgroundClass;
+    m_attrs.normalized = normalized;
 
-    auto nms = std::make_shared<opset8::MulticlassNms>(paramOuts[0], paramOuts[1], attrs);
+    auto nms = std::make_shared<opset8::MulticlassNms>(paramOuts[0], paramOuts[1], m_attrs);
 
-    auto nms_0_identity = std::make_shared<opset5::Multiply>(nms->output(0), opset5::Constant::create(ngPrc, Shape {1}, {1}));
-    auto nms_1_identity = std::make_shared<opset5::Multiply>(nms->output(1), opset5::Constant::create(outType, Shape {1}, {1}));
-    auto nms_2_identity = std::make_shared<opset5::Multiply>(nms->output(2), opset5::Constant::create(outType, Shape {1}, {1}));
-    function = std::make_shared<Function>(OutputVector {nms_0_identity, nms_1_identity, nms_2_identity}, params, "MulticlassNMS");
+    if (!m_outStaticShape) {
+        auto result = std::make_shared<opset5::Result>(nms);
+        function = std::make_shared<Function>(result, params, "MulticlassNMS");
+    } else {
+        auto nms_0_identity = std::make_shared<opset5::Multiply>(nms->output(0), opset5::Constant::create(paramsPrec, Shape {1}, {1}));
+        auto nms_1_identity = std::make_shared<opset5::Multiply>(nms->output(1), opset5::Constant::create(outType, Shape {1}, {1}));
+        auto nms_2_identity = std::make_shared<opset5::Multiply>(nms->output(2), opset5::Constant::create(outType, Shape {1}, {1}));
+        OutputVector results = {
+            std::make_shared<opset5::Result>(nms_0_identity),
+            std::make_shared<opset5::Result>(nms_1_identity),
+            std::make_shared<opset5::Result>(nms_2_identity)
+        };
+        function = std::make_shared<Function>(results, params, "MulticlassNMS");
+    }
 }
 
-}  // namespace LayerTestsDefinitions
+} // namespace subgraph
+} // namespace test
+} // namespace ov
diff --git a/ngraph/core/src/pass/convert_precision.cpp b/ngraph/core/src/pass/convert_precision.cpp
index b11ee9fe029..da987a2c9b1 100644
--- a/ngraph/core/src/pass/convert_precision.cpp
+++ b/ngraph/core/src/pass/convert_precision.cpp
@@ -30,6 +30,8 @@ bool fuse_type_to_convert(const std::shared_ptr<ngraph::Node>& node, ngraph::ele
 bool fuse_type_to_nms3(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx);
 bool fuse_type_to_nms4(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx);
 bool fuse_type_to_nms5(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx);
+bool fuse_type_to_matrix_nms(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx);
+bool fuse_type_to_multiclass_nms(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx);
 bool fuse_type_to_topk(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx);
 bool fuse_type_to_maxpool(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx);
 bool fuse_type_to_nonzero(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx);
@@ -253,6 +255,8 @@ bool ngraph::pass::ConvertPrecision::run_on_function(std::shared_ptr<ngraph::Fun
         {opset3::NonMaxSuppression::get_type_info_static(), fuse_type_to_nms3},
         {opset4::NonMaxSuppression::get_type_info_static(), fuse_type_to_nms4},
         {opset5::NonMaxSuppression::get_type_info_static(), fuse_type_to_nms5},
+        {opset8::MatrixNms::get_type_info_static(), fuse_type_to_matrix_nms},
+        {opset8::MulticlassNms::get_type_info_static(), fuse_type_to_multiclass_nms},
         {opset6::CTCGreedyDecoderSeqLen::get_type_info_static(), fuse_type_to_ctc_greedy_decoder_seq_len},
         {opset4::TopK::get_type_info_static(), fuse_type_to_topk},
         {opset8::MaxPool::get_type_info_static(), fuse_type_to_maxpool},
@@ -385,6 +389,34 @@ bool fuse_type_to_nms5(const std::shared_ptr<ngraph::Node>& node, ngraph::elemen
     return true;
 }
 
+bool fuse_type_to_matrix_nms(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx) {
+    auto nms = ov::as_type_ptr<opset8::MatrixNms>(node);
+    if (!nms) {
+        return false;
+    }
+
+    if ((idx == 1 || idx == 2) && (to == element::i32 || to == element::i64)) {
+        nms->set_output_type(to);
+        return true;
+    }
+
+    return false;
+}
+
+bool fuse_type_to_multiclass_nms(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx) {
+    auto nms = ov::as_type_ptr<opset8::MulticlassNms>(node);
+    if (!nms) {
+        return false;
+    }
+
+    if ((idx == 1 || idx == 2) && (to == element::i32 || to == element::i64)) {
+        nms->set_output_type(to);
+        return true;
+    }
+
+    return false;
+}
+
 bool fuse_type_to_topk(const std::shared_ptr<ngraph::Node>& node, ngraph::element::Type to, size_t idx) {
     if (auto topk = ov::as_type_ptr<opset4::TopK>(node)) {
         if (idx == 1 && (to == element::i32 || to == element::i64)) {

From 42f5034892d4a0017361a5f32b49276a2d6d16fa Mon Sep 17 00:00:00 2001
From: Steve Yoo <steve.yoo@intel.com>
Date: Sat, 27 Nov 2021 04:35:43 +0900
Subject: [PATCH 42/72] Migrate Sort (MatrixNms-8, MulticlassNms-8,
 NonMaxSuppression-5) (#8394)

* Migrate Sort (MatrixNms-8, MulticlassNms-8, NonMaxSuppression-5)

* Add right multiclass_nms.cpp

* Add struct Attributes to matrix_nms

* Add constructors to opset8::MatrixNms::Attributes

* Fix code format of opset8::MatrixNms::Attributes::Attributes
---
 .../functional/op_reference/matrix_nms.cpp    | 481 ++++++++++++
 .../op_reference/multiclass_nms.cpp           | 567 ++++++++++++++
 .../op_reference/non_max_suppression.cpp      | 489 ++++++++++++
 .../tests/functional/skip_tests_config.cpp    |   6 +
 .../core/include/openvino/op/matrix_nms.hpp   |  25 +
 ngraph/test/CMakeLists.txt                    |   3 -
 ngraph/test/backend/matrix_nms.in.cpp         | 597 --------------
 ngraph/test/backend/multiclass_nms.in.cpp     | 735 ------------------
 .../test/backend/non_max_suppression.in.cpp   | 638 ---------------
 9 files changed, 1568 insertions(+), 1973 deletions(-)
 create mode 100644 docs/template_plugin/tests/functional/op_reference/matrix_nms.cpp
 create mode 100644 docs/template_plugin/tests/functional/op_reference/multiclass_nms.cpp
 create mode 100644 docs/template_plugin/tests/functional/op_reference/non_max_suppression.cpp
 delete mode 100644 ngraph/test/backend/matrix_nms.in.cpp
 delete mode 100644 ngraph/test/backend/multiclass_nms.in.cpp
 delete mode 100644 ngraph/test/backend/non_max_suppression.in.cpp

diff --git a/docs/template_plugin/tests/functional/op_reference/matrix_nms.cpp b/docs/template_plugin/tests/functional/op_reference/matrix_nms.cpp
new file mode 100644
index 00000000000..0f56d574fa4
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/matrix_nms.cpp
@@ -0,0 +1,481 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "openvino/opsets/opset8.hpp"
+#include "openvino/opsets/opset1.hpp"
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct MatrixNmsParams {
+    MatrixNmsParams(
+        const opset8::MatrixNms::Attributes& attrs,
+        const Tensor& boxes, const Tensor& scores,
+        const Tensor& expectedSelectedScores, const Tensor& expectedSelectedIndices,
+        const Tensor& expectedValidOutputs, const std::string& testcaseName = "") :
+        attrs(attrs),
+        boxes(boxes), scores(scores),
+        expectedSelectedScores(expectedSelectedScores), expectedSelectedIndices(expectedSelectedIndices),
+        expectedValidOutputs(expectedValidOutputs), testcaseName(testcaseName) {}
+
+    opset8::MatrixNms::Attributes attrs;
+    Tensor boxes;
+    Tensor scores;
+    Tensor expectedSelectedScores;
+    Tensor expectedSelectedIndices;
+    Tensor expectedValidOutputs;
+    std::string testcaseName;
+};
+
+class ReferenceMatrixNmsTest : public testing::TestWithParam<MatrixNmsParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.boxes.data, params.scores.data};
+        refOutData = {params.expectedSelectedScores.data,
+                      params.expectedSelectedIndices.data,
+                      params.expectedValidOutputs.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<MatrixNmsParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "bType=" << param.boxes.type;
+        result << "_bShape=" << param.boxes.shape;
+        result << "_sType=" << param.scores.type;
+        result << "_sShape=" << param.scores.shape;
+        result << "_escType=" << param.expectedSelectedScores.type;
+        result << "_escShape=" << param.expectedSelectedScores.shape;
+        result << "_esiType=" << param.expectedSelectedIndices.type;
+        result << "_esiShape=" << param.expectedSelectedIndices.shape;
+        result << "_evoType=" << param.expectedValidOutputs.type;
+        if (param.testcaseName != "") {
+            result << "_evoShape=" << param.expectedValidOutputs.shape;
+            result << "_=" << param.testcaseName;
+        } else {
+            result << "_evoShape=" << param.expectedValidOutputs.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const MatrixNmsParams& params) {
+        const auto boxes = std::make_shared<opset1::Parameter>(params.boxes.type, PartialShape::dynamic());
+        const auto scores = std::make_shared<opset1::Parameter>(params.scores.type, PartialShape::dynamic());
+        const auto nms = std::make_shared<opset8::MatrixNms>(boxes, scores, params.attrs);
+        const auto f = std::make_shared<Function>(nms->outputs(), ParameterVector{boxes, scores});
+        return f;
+    }
+};
+
+TEST_P(ReferenceMatrixNmsTest, CompareWithRefs) {
+    Exec();
+}
+
+template <element::Type_t ET, element::Type_t ET_TH, element::Type_t ET_IND>
+std::vector<MatrixNmsParams> generateParams() {
+    using T = typename element_type_traits<ET>::value_type;
+    using T_TH = typename element_type_traits<ET_TH>::value_type;
+    using T_IND = typename element_type_traits<ET_IND>::value_type;
+    std::vector<MatrixNmsParams> params {
+        MatrixNmsParams(
+            {
+                opset8::MatrixNms::SortResultType::SCORE,   // sort_result_type
+                false,                                      // sort_result_across_batch
+                ET_IND,                                     // output_type
+                0.0f,                                       // score_threshold
+                3,                                          // nms_top_k
+                -1,                                         // keep_top_k
+                0,                                          // background_class
+                opset8::MatrixNms::DecayFunction::LINEAR,   // decay_function
+                2.0f,                                       // gaussian_sigma
+                0.0f,                                       // post_threshold
+                true,                                       // normalized
+            },
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {1, 2, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3}),      // scores
+            Tensor(ET_TH, {3, 6}, std::vector<T_TH>{
+                1.00, 0.95, 0.00, 0.00, 1.00, 1.00, 1.00, 0.8, 0.00,
+                10.00, 1.00, 11.00, 1.00, 0.13636364, 0.0, 0.1, 1.0, 1.1}),             // expected_selected_scores
+            Tensor(ET_IND, {3, 1}, std::vector<T_IND>{0, 3, 1}),                        // expected_selected_indices
+            Tensor(ET_IND, {1}, std::vector<T_IND>{3}),                                 // expected_valid_outputs
+            "matrix_nms_output_type_i64"),
+        MatrixNmsParams(
+            {
+                opset8::MatrixNms::SortResultType::SCORE,   // sort_result_type
+                false,                                      // sort_result_across_batch
+                ET_IND,                                     // output_type
+                0.0f,                                       // score_threshold
+                3,                                          // nms_top_k
+                -1,                                         // keep_top_k
+                0,                                          // background_class
+                opset8::MatrixNms::DecayFunction::LINEAR,   // decay_function
+                2.0f,                                       // gaussian_sigma
+                0.0f,                                       // post_threshold
+                true,                                       // normalized
+            },
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {1, 2, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3}),      // scores
+            Tensor(ET_TH, {3, 6}, std::vector<T_TH>{
+                1.00, 0.95, 0.00, 0.00, 1.00, 1.00, 1.00, 0.8, 0.00,
+                10.00, 1.00, 11.00, 1.00, 0.13636364, 0.0, 0.1, 1.0, 1.1}),             // expected_selected_scores
+            Tensor(ET_IND, {3, 1}, std::vector<T_IND>{0, 3, 1}),                        // expected_selected_indices
+            Tensor(ET_IND, {1}, std::vector<T_IND>{3}),                                 // expected_valid_outputs
+            "matrix_nms_output_type_i32"),
+        MatrixNmsParams(
+            {
+                opset8::MatrixNms::SortResultType::SCORE,   // sort_result_type
+                false,                                      // sort_result_across_batch
+                ET_IND,                                     // output_type
+                0.0f,                                       // score_threshold
+                3,                                          // nms_top_k
+                -1,                                         // keep_top_k
+                0,                                          // background_class
+                opset8::MatrixNms::DecayFunction::GAUSSIAN, // decay_function
+                2.0f,                                       // gaussian_sigma
+                0.0f,                                       // post_threshold
+                true,                                       // normalized
+            },
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {1, 2, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3}),      // scores
+            Tensor(ET_TH, {3, 6}, std::vector<T_TH>{
+                1.00,  0.95, 0.00,  0.00, 1.00,      1.00, 1.00, 0.8, 0.00,
+                10.00, 1.00, 11.00, 1.00, 0.1966116, 0.0,  0.1,  1.0, 1.1}),            // expected_selected_scores
+            Tensor(ET_IND, {3, 1}, std::vector<T_IND>{0, 3, 1}),                        // expected_selected_indices
+            Tensor(ET_IND, {1}, std::vector<T_IND>{3}),                                 // expected_valid_outputs
+            "matrix_nms_gaussian"),
+        MatrixNmsParams(
+            {
+                opset8::MatrixNms::SortResultType::SCORE,   // sort_result_type
+                false,                                      // sort_result_across_batch
+                ET_IND,                                     // output_type
+                0.0f,                                       // score_threshold
+                3,                                          // nms_top_k
+                -1,                                         // keep_top_k
+                0,                                          // background_class
+                opset8::MatrixNms::DecayFunction::LINEAR,   // decay_function
+                2.0f,                                       // gaussian_sigma
+                0.0f,                                       // post_threshold
+                true,                                       // normalized
+            },
+            Tensor(ET, {2, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {2, 2, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3}),      // scores
+            Tensor(ET_TH, {6, 6}, std::vector<T_TH>{
+                1.00, 0.95, 0.00, 0.00, 1.00, 1.00, 1.00, 0.8, 0.00, 10.00, 1.00, 11.00, 1.00, 0.13636364, 0.0, 0.1, 1.0, 1.1,
+                1.00, 0.95, 0.00, 0.00, 1.00, 1.00, 1.00, 0.8, 0.00, 10.00, 1.00, 11.00, 1.00, 0.13636364, 0.0, 0.1, 1.0, 1.1}),
+                                                                                        // expected_selected_scores
+            Tensor(ET_IND, {6, 1}, std::vector<T_IND>{0, 3, 1, 6, 9, 7}),               // expected_selected_indices
+            Tensor(ET_IND, {2}, std::vector<T_IND>{3, 3}),                              // expected_valid_outputs
+            "matrix_nms_two_batches_two_classes"),
+        MatrixNmsParams(
+            {
+                opset8::MatrixNms::SortResultType::SCORE,   // sort_result_type
+                true,                                       // sort_result_across_batch
+                ET_IND,                                     // output_type
+                0.0f,                                       // score_threshold
+                3,                                          // nms_top_k
+                -1,                                         // keep_top_k
+                -1,                                         // background_class
+                opset8::MatrixNms::DecayFunction::LINEAR,   // decay_function
+                2.0f,                                       // gaussian_sigma
+                0.5f,                                       // post_threshold
+                true,                                       // normalized
+            },
+            Tensor(ET, {2, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {2, 2, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3}),      // scores
+            Tensor(ET_TH, {8, 6}, std::vector<T_TH>{
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00,
+                1.00, 0.95, 0.00, 0.00,  1.00, 1.00,
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00,
+                1.00, 0.95, 0.00, 0.00,  1.00, 1.00,
+                0.00, 0.90, 0.00, 0.00,  1.00, 1.00,
+                0.00, 0.90, 0.00, 0.00,  1.00, 1.00,
+                1.00, 0.80, 0.00, 10.00, 1.00, 11.00,
+                1.00, 0.80, 0.00, 10.00, 1.00, 11.00}),                                 // expected_selected_scores
+            Tensor(ET_IND, {8, 1}, std::vector<T_IND>{3, 0, 9, 6, 0, 6, 3, 9}),         // expected_selected_indices
+            Tensor(ET_IND, {2}, std::vector<T_IND>{4, 4}),                              // expected_valid_outputs
+            "matrix_nms_two_batches_two_classes_by_score_cross_batch"),
+        MatrixNmsParams(
+            {
+                opset8::MatrixNms::SortResultType::CLASSID, // sort_result_type
+                true,                                       // sort_result_across_batch
+                ET_IND,                                     // output_type
+                0.0f,                                       // score_threshold
+                3,                                          // nms_top_k
+                -1,                                         // keep_top_k
+                -1,                                         // background_class
+                opset8::MatrixNms::DecayFunction::LINEAR,   // decay_function
+                2.0f,                                       // gaussian_sigma
+                0.5f,                                       // post_threshold
+                true,                                       // normalized
+            },
+            Tensor(ET, {2, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {2, 2, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3}),      // scores
+            Tensor(ET_TH, {8, 6}, std::vector<T_TH>{
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00,
+                0.00, 0.90, 0.00, 0.00,  1.00, 1.00,
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00,
+                0.00, 0.90, 0.00, 0.00,  1.00, 1.00,
+                1.00, 0.95, 0.00, 0.00,  1.00, 1.00,
+                1.00, 0.80, 0.00, 10.00, 1.00, 11.00,
+                1.00, 0.95, 0.00, 0.00,  1.00, 1.00,
+                1.00, 0.80, 0.00, 10.00, 1.00, 11.00}),                                 // expected_selected_scores
+            Tensor(ET_IND, {8, 1}, std::vector<T_IND>{3, 0, 9, 6, 0, 3, 6, 9}),         // expected_selected_indices
+            Tensor(ET_IND, {2}, std::vector<T_IND>{4, 4}),                              // expected_valid_outputs
+            "matrix_nms_two_batches_two_classes_by_classid_cross_batch"),
+        MatrixNmsParams(
+            {
+                opset8::MatrixNms::SortResultType::CLASSID, // sort_result_type
+                false,                                      // sort_result_across_batch
+                ET_IND,                                     // output_type
+                0.0f,                                       // score_threshold
+                3,                                          // nms_top_k
+                3,                                          // keep_top_k
+                0,                                          // background_class
+                opset8::MatrixNms::DecayFunction::LINEAR,   // decay_function
+                2.0f,                                       // gaussian_sigma
+                0.0f,                                       // post_threshold
+                true,                                       // normalized
+            },
+            Tensor(ET, {2, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {2, 2, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3}),      // scores
+            Tensor(ET_TH, {6, 6}, std::vector<T_TH>{
+                1.00, 0.95, 0.00, 0.00, 1.00, 1.00, 1.00, 0.8, 0.00, 10.00, 1.00, 11.00, 1.00, 0.13636364, 0.0, 0.1, 1.0, 1.1,
+                1.00, 0.95, 0.00, 0.00, 1.00, 1.00, 1.00, 0.8, 0.00, 10.00, 1.00, 11.00, 1.00, 0.13636364, 0.0, 0.1, 1.0, 1.1}),
+                                                                                        // expected_selected_scores
+            Tensor(ET_IND, {6, 1}, std::vector<T_IND>{0, 3, 1, 6, 9, 7}),               // expected_selected_indices
+            Tensor(ET_IND, {2}, std::vector<T_IND>{3, 3}),                              // expected_valid_outputs
+            "matrix_nms_by_keep_top_k"),
+        MatrixNmsParams(
+            {
+                opset8::MatrixNms::SortResultType::SCORE,   // sort_result_type
+                false,                                      // sort_result_across_batch
+                ET_IND,                                     // output_type
+                0.0f,                                       // score_threshold
+                3,                                          // nms_top_k
+                -1,                                         // keep_top_k
+                -1,                                         // background_class
+                opset8::MatrixNms::DecayFunction::LINEAR,   // decay_function
+                2.0f,                                       // gaussian_sigma
+                0.0f,                                       // post_threshold
+                true,                                       // normalized
+            },
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {1, 2, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3}),      // scores
+            Tensor(ET_TH, {6, 6}, std::vector<T_TH>{
+                0.00, 0.95, 0.0, 10.0, 1.0, 11.0, 1.00, 0.95,       0.0, 0.0, 1.0, 1.0, 0.00, 0.9,        0.0, 0.0, 1.0, 1.0,
+                1.00, 0.8,  0.0, 10.0, 1.0, 11.0, 0.00, 0.13636364, 0.0, 0.1, 1.0, 1.1, 1.00, 0.13636364, 0.0, 0.1, 1.0, 1.1}),
+                                                                                        // expected_selected_scores
+            Tensor(ET_IND, {6, 1}, std::vector<T_IND>{3, 0, 0, 3, 1, 1}),               // expected_selected_indices
+            Tensor(ET_IND, {1}, std::vector<T_IND>{6}),                                 // expected_valid_outputs
+            "matrix_nms_background"),
+        MatrixNmsParams(
+            {
+                opset8::MatrixNms::SortResultType::SCORE,   // sort_result_type
+                false,                                      // sort_result_across_batch
+                ET_IND,                                     // output_type
+                0.0f,                                       // score_threshold
+                3,                                          // nms_top_k
+                -1,                                         // keep_top_k
+                -1,                                         // background_class
+                opset8::MatrixNms::DecayFunction::LINEAR,   // decay_function
+                2.0f,                                       // gaussian_sigma
+                0.0f,                                       // post_threshold
+                true,                                       // normalized
+            },
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                1.0, 1.0,  0.0, 0.0,  0.0, 0.1,  1.0, 1.1,  0.0, 0.9,   1.0, -0.1,
+                0.0, 10.0, 1.0, 11.0, 1.0, 10.1, 0.0, 11.1, 1.0, 101.0, 0.0, 100.0}),   // boxes
+            Tensor(ET_TH, {1, 1, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3}),                                       // scores
+            Tensor(ET_TH, {3, 6}, std::vector<T_TH>{
+                0.00, 0.95, 0.0, 10.0, 1.0, 11.0, 0.00, 0.9, 1.0, 1.0, 0.0, 0.0, 0.00, 0.75, 0.0, 0.1, 1.0, 1.1}),
+                                                                                        // expected_selected_scores
+            Tensor(ET_IND, {3, 1}, std::vector<T_IND>{3, 0, 1}),                        // expected_selected_indices
+            Tensor(ET_IND, {1}, std::vector<T_IND>{3}),                                 // expected_valid_outputs
+            "matrix_nms_flipped_coordinates"),
+        MatrixNmsParams(
+            {
+                opset8::MatrixNms::SortResultType::SCORE,   // sort_result_type
+                false,                                      // sort_result_across_batch
+                ET_IND,                                     // output_type
+                0.0f,                                       // score_threshold
+                3,                                          // nms_top_k
+                -1,                                         // keep_top_k
+                -1,                                         // background_class
+                opset8::MatrixNms::DecayFunction::LINEAR,   // decay_function
+                2.0f,                                       // gaussian_sigma
+                0.8f,                                       // post_threshold
+                true,                                       // normalized
+            },
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {1, 1, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3}),                                       // scores
+            Tensor(ET_TH, {2, 6}, std::vector<T_TH>{
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.9, 0.00, 0.00, 1.00, 1.00}),
+                                                                                        // expected_selected_scores
+            Tensor(ET_IND, {2, 1}, std::vector<T_IND>{3, 0}),                           // expected_selected_indices
+            Tensor(ET_IND, {1}, std::vector<T_IND>{2}),                                 // expected_valid_outputs
+            "matrix_nms_post_threshold"),
+        MatrixNmsParams(
+            {
+                opset8::MatrixNms::SortResultType::SCORE,   // sort_result_type
+                false,                                      // sort_result_across_batch
+                ET_IND,                                     // output_type
+                0.0f,                                       // score_threshold
+                3,                                          // nms_top_k
+                -1,                                         // keep_top_k
+                -1,                                         // background_class
+                opset8::MatrixNms::DecayFunction::LINEAR,   // decay_function
+                2.0f,                                       // gaussian_sigma
+                0.3f,                                       // post_threshold
+                true,                                       // normalized
+            },
+            Tensor(ET, {1, 10, 4}, std::vector<T>{
+                0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0,
+                1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0,
+                0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0}),           // boxes
+            Tensor(ET_TH, {1, 1, 10}, std::vector<T_TH>{
+                0.4, 0.01, 0.2, 0.09, 0.15, 0.05, 0.02, 0.03, 0.05, 0.0}),              // scores
+            Tensor(ET_TH, {1, 6}, std::vector<T_TH>{
+                0.00, 0.40, 0.00, 0.00, 1.00, 1.00}),                                   // expected_selected_scores
+            Tensor(ET_IND, {1, 1}, std::vector<T_IND>{0}),                              // expected_selected_indices
+            Tensor(ET_IND, {1}, std::vector<T_IND>{1}),                                 // expected_valid_outputs
+            "matrix_nms_identical_boxes"),
+        MatrixNmsParams(
+            {
+                opset8::MatrixNms::SortResultType::SCORE,   // sort_result_type
+                false,                                      // sort_result_across_batch
+                ET_IND,                                     // output_type
+                0.0f,                                       // score_threshold
+                2,                                          // nms_top_k
+                -1,                                         // keep_top_k
+                -1,                                         // background_class
+                opset8::MatrixNms::DecayFunction::LINEAR,   // decay_function
+                2.0f,                                       // gaussian_sigma
+                0.0f,                                       // post_threshold
+                true,                                       // normalized
+            },
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {1, 1, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3}),                                       // scores
+            Tensor(ET_TH, {2, 6}, std::vector<T_TH>{
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.90, 0.00, 0.00, 1.00, 1.00}),
+                                                                                        // expected_selected_scores
+            Tensor(ET_IND, {2, 1}, std::vector<T_IND>{3, 0}),                           // expected_selected_indices
+            Tensor(ET_IND, {1}, std::vector<T_IND>{2}),                                 // expected_valid_outputs
+            "matrix_nms_nms_top_k"),
+        MatrixNmsParams(
+            {
+                opset8::MatrixNms::SortResultType::SCORE,   // sort_result_type
+                false,                                      // sort_result_across_batch
+                ET_IND,                                     // output_type
+                0.0f,                                       // score_threshold
+                3,                                          // nms_top_k
+                -1,                                         // keep_top_k
+                -1,                                         // background_class
+                opset8::MatrixNms::DecayFunction::LINEAR,   // decay_function
+                2.0f,                                       // gaussian_sigma
+                0.0f,                                       // post_threshold
+                true,                                       // normalized
+            },
+            Tensor(ET, {1, 1, 4}, std::vector<T>{0.0, 0.0, 1.0, 1.0}),                  // boxes
+            Tensor(ET_TH, {1, 1, 1}, std::vector<T_TH>{0.9}),                           // scores
+            Tensor(ET_TH, {1, 6}, std::vector<T_TH>{
+                0.00, 0.90, 0.00, 0.00, 1.00, 1.00}),                                   // expected_selected_scores
+            Tensor(ET_IND, {1, 1}, std::vector<T_IND>{0}),                              // expected_selected_indices
+            Tensor(ET_IND, {1}, std::vector<T_IND>{1}),                                 // expected_valid_outputs
+            "matrix_nms_single_box"),
+        MatrixNmsParams(
+            {
+                opset8::MatrixNms::SortResultType::SCORE,   // sort_result_type
+                false,                                      // sort_result_across_batch
+                ET_IND,                                     // output_type
+                2.0f,                                       // score_threshold
+                3,                                          // nms_top_k
+                -1,                                         // keep_top_k
+                -1,                                         // background_class
+                opset8::MatrixNms::DecayFunction::LINEAR,   // decay_function
+                2.0f,                                       // gaussian_sigma
+                0.0f,                                       // post_threshold
+                true,                                       // normalized
+            },
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {1, 1, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3}),                                       // scores
+            Tensor(ET_TH, {0, 6}, std::vector<T_TH>{}),                                 // expected_selected_scores
+            Tensor(ET_IND, {0, 1}, std::vector<T_IND>{}),                               // expected_selected_indices
+            Tensor(ET_IND, {1}, std::vector<T_IND>{0}),                                 // expected_valid_outputs
+            "matrix_nms_no_output"),
+    };
+    return params;
+}
+
+std::vector<MatrixNmsParams> generateCombinedParams() {
+    const std::vector<std::vector<MatrixNmsParams>> generatedParams {
+        generateParams<element::Type_t::bf16, element::Type_t::f32, element::Type_t::i32>(),
+        generateParams<element::Type_t::f16, element::Type_t::f32, element::Type_t::i32>(),
+        generateParams<element::Type_t::f32, element::Type_t::f32, element::Type_t::i32>(),
+        generateParams<element::Type_t::bf16, element::Type_t::f32, element::Type_t::i64>(),
+        generateParams<element::Type_t::f16, element::Type_t::f32, element::Type_t::i64>(),
+        generateParams<element::Type_t::f32, element::Type_t::f32, element::Type_t::i64>(),
+    };
+    std::vector<MatrixNmsParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_MatrixNms_With_Hardcoded_Refs, ReferenceMatrixNmsTest,
+    testing::ValuesIn(generateCombinedParams()), ReferenceMatrixNmsTest::getTestCaseName);
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/op_reference/multiclass_nms.cpp b/docs/template_plugin/tests/functional/op_reference/multiclass_nms.cpp
new file mode 100644
index 00000000000..730675a8a12
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/multiclass_nms.cpp
@@ -0,0 +1,567 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "openvino/op/multiclass_nms.hpp"
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct MulticlassNmsParams {
+    MulticlassNmsParams(
+        const int nms_top_k,
+        const float iou_threshold,
+        const float score_threshold,
+        const op::v8::MulticlassNms::SortResultType sort_result_type,
+        const int keep_top_k,
+        const int background_class,
+        const float nms_eta,
+        const ov::element::Type output_type,
+        const bool sort_result_across_batch,
+        const bool normalized,
+        const Tensor& boxes, const Tensor& scores,
+        const Tensor& expectedSelectedScores, const Tensor& expectedSelectedIndices,
+        const Tensor& expectedValidOutputs, const std::string& testcaseName = "") :
+        nms_top_k(nms_top_k),
+        iou_threshold(iou_threshold),
+        score_threshold(score_threshold),
+        sort_result_type(sort_result_type),
+        keep_top_k(keep_top_k),
+        background_class(background_class),
+        nms_eta(nms_eta),
+        output_type(output_type),
+        sort_result_across_batch(sort_result_across_batch),
+        normalized(normalized),
+        boxes(boxes), scores(scores),
+        expectedSelectedScores(expectedSelectedScores), expectedSelectedIndices(expectedSelectedIndices),
+        expectedValidOutputs(expectedValidOutputs), testcaseName(testcaseName) {}
+
+    int nms_top_k;
+    float iou_threshold;
+    float score_threshold;
+    op::v8::MulticlassNms::SortResultType sort_result_type;
+    int keep_top_k;
+    int background_class;
+    float nms_eta;
+    ov::element::Type output_type;
+
+    bool sort_result_across_batch = false;
+    bool normalized = true;
+
+    Tensor boxes;
+    Tensor scores;
+    Tensor expectedSelectedScores;
+    Tensor expectedSelectedIndices;
+    Tensor expectedValidOutputs;
+    std::string testcaseName;
+};
+
+class ReferenceMulticlassNmsTest : public testing::TestWithParam<MulticlassNmsParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.boxes.data, params.scores.data};
+        refOutData = {params.expectedSelectedScores.data,
+                      params.expectedSelectedIndices.data,
+                      params.expectedValidOutputs.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<MulticlassNmsParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "bType=" << param.boxes.type;
+        result << "_bShape=" << param.boxes.shape;
+        result << "_sType=" << param.scores.type;
+        result << "_sShape=" << param.scores.shape;
+        result << "_escType=" << param.expectedSelectedScores.type;
+        result << "_escShape=" << param.expectedSelectedScores.shape;
+        result << "_esiType=" << param.expectedSelectedIndices.type;
+        result << "_esiShape=" << param.expectedSelectedIndices.shape;
+        result << "_evoType=" << param.expectedValidOutputs.type;
+        if (param.testcaseName != "") {
+            result << "_evoShape=" << param.expectedValidOutputs.shape;
+            result << "_=" << param.testcaseName;
+        } else {
+            result << "_evoShape=" << param.expectedValidOutputs.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const MulticlassNmsParams& params) {
+        op::v8::MulticlassNms::Attributes attrs;
+        attrs.nms_top_k = params.nms_top_k;
+        attrs.iou_threshold = params.iou_threshold;
+        attrs.score_threshold = params.score_threshold;
+        attrs.sort_result_type = params.sort_result_type;
+        attrs.keep_top_k = params.keep_top_k;
+        attrs.background_class = params.background_class;
+        attrs.nms_eta = params.nms_eta;
+        attrs.output_type = params.output_type;
+        attrs.sort_result_across_batch = params.sort_result_across_batch;
+        attrs.normalized = params.normalized;
+        const auto boxes = std::make_shared<op::v0::Parameter>(params.boxes.type, PartialShape::dynamic());
+        const auto scores = std::make_shared<op::v0::Parameter>(params.scores.type, PartialShape::dynamic());
+        const auto nms = std::make_shared<op::v8::MulticlassNms>(boxes, scores, attrs);
+        const auto f = std::make_shared<Function>(nms->outputs(), ParameterVector{boxes, scores});
+        return f;
+    }
+};
+
+TEST_P(ReferenceMulticlassNmsTest, CompareWithRefs) {
+    Exec();
+}
+
+template <element::Type_t ET, element::Type_t ET_TH, element::Type_t ET_IND>
+std::vector<MulticlassNmsParams> generateParams() {
+    using T = typename element_type_traits<ET>::value_type;
+    using T_TH = typename element_type_traits<ET_TH>::value_type;
+    using T_IND = typename element_type_traits<ET_IND>::value_type;
+    std::vector<MulticlassNmsParams> params {
+        MulticlassNmsParams(
+            3,                                              // nms_top_k
+            0.5f,                                           // iou_threshold
+            0.0f,                                           // score_threshold
+            op::v8::MulticlassNms::SortResultType::SCORE,   // sort_result_type
+            -1,                                             // keep_top_k
+            -1,                                             // background_class
+            1.0f,                                           // nms_eta
+            ET_IND,                                         // output_type
+            false,                                          // sort_result_across_batch
+            true,                                           // normalized
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {1, 2, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3}),      // scores
+            Tensor(ET_TH, {4, 6}, std::vector<T_TH>{
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 1.00, 0.95,
+                0.00, 0.00, 1.00, 1.00,  0.00, 0.90,  0.00, 0.00,
+                1.00, 1.00, 1.00, 0.80,  0.00, 10.00, 1.00, 11.00}),                    // expected_selected_scores
+            Tensor(ET_IND, {4, 1}, std::vector<T_IND>{3, 0, 0, 3}),                     // expected_selected_indices
+            Tensor(ET_IND, {1}, std::vector<T_IND>{4}),                                 // expected_valid_outputs
+            "multiclass_nms_by_score"),
+        MulticlassNmsParams(
+            3,                                              // nms_top_k
+            0.5f,                                           // iou_threshold
+            0.0f,                                           // score_threshold
+            op::v8::MulticlassNms::SortResultType::CLASSID, // sort_result_type
+            -1,                                             // keep_top_k
+            -1,                                             // background_class
+            1.0f,                                           // nms_eta
+            ET_IND,                                         // output_type
+            false,                                          // sort_result_across_batch
+            true,                                           // normalized
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {1, 2, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3}),      // scores
+            Tensor(ET_TH, {4, 6}, std::vector<T_TH>{
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.90,
+                0.00, 0.00, 1.00, 1.00,  1.00, 0.95,  0.00, 0.00,
+                1.00, 1.00, 1.00, 0.80,  0.00, 10.00, 1.00, 11.00}),                    // expected_selected_scores
+            Tensor(ET_IND, {4, 1}, std::vector<T_IND>{3, 0, 0, 3}),                     // expected_selected_indices
+            Tensor(ET_IND, {1}, std::vector<T_IND>{4}),                                 // expected_valid_outputs
+            "multiclass_nms_by_class_id"),
+        MulticlassNmsParams(
+            3,                                              // nms_top_k
+            0.5f,                                           // iou_threshold
+            0.0f,                                           // score_threshold
+            op::v8::MulticlassNms::SortResultType::CLASSID, // sort_result_type
+            -1,                                             // keep_top_k
+            -1,                                             // background_class
+            1.0f,                                           // nms_eta
+            ET_IND,                                         // output_type
+            false,                                          // sort_result_across_batch
+            true,                                           // normalized
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {1, 2, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3}),      // scores
+            Tensor(ET_TH, {4, 6}, std::vector<T_TH>{
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.90,
+                0.00, 0.00, 1.00, 1.00,  1.00, 0.95,  0.00, 0.00,
+                1.00, 1.00, 1.00, 0.80,  0.00, 10.00, 1.00, 11.00}),                    // expected_selected_scores
+            Tensor(ET_IND, {4, 1}, std::vector<T_IND>{3, 0, 0, 3}),                     // expected_selected_indices
+            Tensor(ET_IND, {1}, std::vector<T_IND>{4}),                                 // expected_valid_outputs
+            "multiclass_nms_output_type_i32"),
+        MulticlassNmsParams(
+            3,                                              // nms_top_k
+            0.5f,                                           // iou_threshold
+            0.0f,                                           // score_threshold
+            op::v8::MulticlassNms::SortResultType::SCORE,   // sort_result_type
+            -1,                                             // keep_top_k
+            -1,                                             // background_class
+            1.0f,                                           // nms_eta
+            ET_IND,                                         // output_type
+            false,                                          // sort_result_across_batch
+            true,                                           // normalized
+            Tensor(ET, {2, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {2, 2, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3}),      // scores
+            Tensor(ET_TH, {8, 6}, std::vector<T_TH>{
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 1.00, 0.95, 0.00, 0.00,  1.00, 1.00,
+                0.00, 0.90, 0.00, 0.00,  1.00, 1.00,  1.00, 0.80, 0.00, 10.00, 1.00, 11.00,
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 1.00, 0.95, 0.00, 0.00,  1.00, 1.00,
+                0.00, 0.90, 0.00, 0.00,  1.00, 1.00,  1.00, 0.80, 0.00, 10.00, 1.00, 11.00}),
+                                                                                        // expected_selected_scores
+            Tensor(ET_IND, {8, 1}, std::vector<T_IND>{3, 0, 0, 3, 9, 6, 6, 9}),         // expected_selected_indices
+            Tensor(ET_IND, {2}, std::vector<T_IND>{4, 4}),                              // expected_valid_outputs
+            "multiclass_nms_two_batches_two_classes_by_score"),
+        MulticlassNmsParams(
+            3,                                              // nms_top_k
+            0.5f,                                           // iou_threshold
+            0.0f,                                           // score_threshold
+            op::v8::MulticlassNms::SortResultType::CLASSID, // sort_result_type
+            -1,                                             // keep_top_k
+            -1,                                             // background_class
+            1.0f,                                           // nms_eta
+            ET_IND,                                         // output_type
+            false,                                          // sort_result_across_batch
+            true,                                           // normalized
+            Tensor(ET, {2, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {2, 2, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3}),      // scores
+            Tensor(ET_TH, {8, 6}, std::vector<T_TH>{
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.90, 0.00, 0.00,  1.00, 1.00,
+                1.00, 0.95, 0.00, 0.00,  1.00, 1.00,  1.00, 0.80, 0.00, 10.00, 1.00, 11.00,
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.90, 0.00, 0.00,  1.00, 1.00,
+                1.00, 0.95, 0.00, 0.00,  1.00, 1.00,  1.00, 0.80, 0.00, 10.00, 1.00, 11.00}),
+                                                                                        // expected_selected_scores
+            Tensor(ET_IND, {8, 1}, std::vector<T_IND>{3, 0, 0, 3, 9, 6, 6, 9}),         // expected_selected_indices
+            Tensor(ET_IND, {2}, std::vector<T_IND>{4, 4}),                              // expected_valid_outputs
+            "multiclass_nms_two_batches_two_classes_by_class_id"),
+        MulticlassNmsParams(
+            3,                                              // nms_top_k
+            0.5f,                                           // iou_threshold
+            0.0f,                                           // score_threshold
+            op::v8::MulticlassNms::SortResultType::SCORE,   // sort_result_type
+            -1,                                             // keep_top_k
+            -1,                                             // background_class
+            1.0f,                                           // nms_eta
+            ET_IND,                                         // output_type
+            true,                                           // sort_result_across_batch
+            true,                                           // normalized
+            Tensor(ET, {2, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {2, 2, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3}),      // scores
+            Tensor(ET_TH, {8, 6}, std::vector<T_TH>{
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00,
+                1.00, 0.95, 0.00, 0.00,  1.00, 1.00,
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00,
+                1.00, 0.95, 0.00, 0.00,  1.00, 1.00,
+                0.00, 0.90, 0.00, 0.00,  1.00, 1.00,
+                0.00, 0.90, 0.00, 0.00,  1.00, 1.00,
+                1.00, 0.80, 0.00, 10.00, 1.00, 11.00,
+                1.00, 0.80, 0.00, 10.00, 1.00, 11.00}),                                 // expected_selected_scores
+            Tensor(ET_IND, {8, 1}, std::vector<T_IND>{3, 0, 9, 6, 0, 6, 3, 9}),         // expected_selected_indices
+            Tensor(ET_IND, {2}, std::vector<T_IND>{4, 4}),                              // expected_valid_outputs
+            "multiclass_nms_two_batches_two_classes_by_score_cross_batch"),
+        MulticlassNmsParams(
+            3,                                              // nms_top_k
+            0.5f,                                           // iou_threshold
+            0.0f,                                           // score_threshold
+            op::v8::MulticlassNms::SortResultType::CLASSID, // sort_result_type
+            -1,                                             // keep_top_k
+            -1,                                             // background_class
+            1.0f,                                           // nms_eta
+            ET_IND,                                         // output_type
+            true,                                           // sort_result_across_batch
+            true,                                           // normalized
+            Tensor(ET, {2, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {2, 2, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3}),      // scores
+            Tensor(ET_TH, {8, 6}, std::vector<T_TH>{
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00,
+                0.00, 0.90, 0.00, 0.00,  1.00, 1.00,
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00,
+                0.00, 0.90, 0.00, 0.00,  1.00, 1.00,
+                1.00, 0.95, 0.00, 0.00,  1.00, 1.00,
+                1.00, 0.80, 0.00, 10.00, 1.00, 11.00,
+                1.00, 0.95, 0.00, 0.00,  1.00, 1.00,
+                1.00, 0.80, 0.00, 10.00, 1.00, 11.00}),                                 // expected_selected_scores
+            Tensor(ET_IND, {8, 1}, std::vector<T_IND>{3, 0, 9, 6, 0, 3, 6, 9}),         // expected_selected_indices
+            Tensor(ET_IND, {2}, std::vector<T_IND>{4, 4}),                              // expected_valid_outputs
+            "multiclass_nms_two_batches_two_classes_by_class_id_cross_batch"),
+        MulticlassNmsParams(
+            3,                                              // nms_top_k
+            0.5f,                                           // iou_threshold
+            0.0f,                                           // score_threshold
+            op::v8::MulticlassNms::SortResultType::SCORE,   // sort_result_type
+            -1,                                             // keep_top_k
+            -1,                                             // background_class
+            1.0f,                                           // nms_eta
+            ET_IND,                                         // output_type
+            false,                                          // sort_result_across_batch
+            true,                                           // normalized
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                1.0, 1.0,  0.0, 0.0,  0.0, 0.1,  1.0, 1.1,  0.0, 0.9,   1.0, -0.1,
+                0.0, 10.0, 1.0, 11.0, 1.0, 10.1, 0.0, 11.1, 1.0, 101.0, 0.0, 100.0}),   // boxes
+            Tensor(ET_TH, {1, 1, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3}),                                       // scores
+            Tensor(ET_TH, {3, 6}, std::vector<T_TH>{
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.90, 1.00, 1.00, 0.00, 0.00, 0.00, 0.75, 0.00, 0.10, 1.00, 1.10}),
+                                                                                        // expected_selected_scores
+            Tensor(ET_IND, {3, 1}, std::vector<T_IND>{3, 0, 1}),                        // expected_selected_indices
+            Tensor(ET_IND, {1}, std::vector<T_IND>{3}),                                 // expected_valid_outputs
+            "multiclass_nms_flipped_coordinates"),
+        MulticlassNmsParams(
+            3,                                              // nms_top_k
+            0.5f,                                           // iou_threshold
+            0.0f,                                           // score_threshold
+            op::v8::MulticlassNms::SortResultType::SCORE,   // sort_result_type
+            -1,                                             // keep_top_k
+            -1,                                             // background_class
+            1.0f,                                           // nms_eta
+            ET_IND,                                         // output_type
+            false,                                          // sort_result_across_batch
+            true,                                           // normalized
+            Tensor(ET, {1, 10, 4}, std::vector<T>{
+                0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0,
+                1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0,
+                0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0}),           // boxes
+            Tensor(ET_TH, {1, 1, 10}, std::vector<T_TH>{
+                0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9}),                     // scores
+            Tensor(ET_TH, {1, 6}, std::vector<T_TH>{
+                0.00, 0.90, 0.00, 0.00, 1.00, 1.00}),
+                                                                                        // expected_selected_scores
+            Tensor(ET_IND, {1, 1}, std::vector<T_IND>{0}),                              // expected_selected_indices
+            Tensor(ET_IND, {1}, std::vector<T_IND>{1}),                                 // expected_valid_outputs
+            "multiclass_nms_identical_boxes"),
+        MulticlassNmsParams(
+            2,                                              // nms_top_k
+            0.5f,                                           // iou_threshold
+            0.0f,                                           // score_threshold
+            op::v8::MulticlassNms::SortResultType::SCORE,   // sort_result_type
+            -1,                                             // keep_top_k
+            -1,                                             // background_class
+            1.0f,                                           // nms_eta
+            ET_IND,                                         // output_type
+            false,                                          // sort_result_across_batch
+            true,                                           // normalized
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {1, 1, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3}),                                       // scores
+            Tensor(ET_TH, {2, 6}, std::vector<T_TH>{
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.90, 0.00, 0.00, 1.00, 1.00}),
+                                                                                        // expected_selected_scores
+            Tensor(ET_IND, {2, 1}, std::vector<T_IND>{3, 0}),                           // expected_selected_indices
+            Tensor(ET_IND, {1}, std::vector<T_IND>{2}),                                 // expected_valid_outputs
+            "multiclass_nms_limit_output_size"),
+        MulticlassNmsParams(
+            3,                                              // nms_top_k
+            0.5f,                                           // iou_threshold
+            0.0f,                                           // score_threshold
+            op::v8::MulticlassNms::SortResultType::SCORE,   // sort_result_type
+            -1,                                             // keep_top_k
+            -1,                                             // background_class
+            1.0f,                                           // nms_eta
+            ET_IND,                                         // output_type
+            false,                                          // sort_result_across_batch
+            true,                                           // normalized
+            Tensor(ET, {1, 1, 4}, std::vector<T>{0.0, 0.0, 1.0, 1.0}),                  // boxes
+            Tensor(ET_TH, {1, 1, 1}, std::vector<T_TH>{0.9}),                           // scores
+            Tensor(ET_TH, {1, 6}, std::vector<T_TH>{
+                0.00, 0.90, 0.00, 0.00, 1.00, 1.00}),                                   // expected_selected_scores
+            Tensor(ET_IND, {1, 1}, std::vector<T_IND>{0}),                              // expected_selected_indices
+            Tensor(ET_IND, {1}, std::vector<T_IND>{1}),                                 // expected_valid_outputs
+            "multiclass_nms_single_box"),
+        MulticlassNmsParams(
+            3,                                              // nms_top_k
+            0.2f,                                           // iou_threshold
+            0.0f,                                           // score_threshold
+            op::v8::MulticlassNms::SortResultType::SCORE,   // sort_result_type
+            -1,                                             // keep_top_k
+            -1,                                             // background_class
+            1.0f,                                           // nms_eta
+            ET_IND,                                         // output_type
+            false,                                          // sort_result_across_batch
+            true,                                           // normalized
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {1, 1, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3}),                                       // scores
+            Tensor(ET_TH, {2, 6}, std::vector<T_TH>{
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.90, 0.00, 0.00, 1.00, 1.00}),
+                                                                                        // expected_selected_scores
+            Tensor(ET_IND, {2, 1}, std::vector<T_IND>{3, 0}),                           // expected_selected_indices
+            Tensor(ET_IND, {1}, std::vector<T_IND>{2}),                                 // expected_valid_outputs
+            "multiclass_nms_by_IOU"),
+        MulticlassNmsParams(
+            3,                                              // nms_top_k
+            0.5f,                                           // iou_threshold
+            0.95f,                                          // score_threshold
+            op::v8::MulticlassNms::SortResultType::SCORE,   // sort_result_type
+            -1,                                             // keep_top_k
+            -1,                                             // background_class
+            1.0f,                                           // nms_eta
+            ET_IND,                                         // output_type
+            false,                                          // sort_result_across_batch
+            true,                                           // normalized
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {1, 1, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3}),                                       // scores
+            Tensor(ET_TH, {1, 6}, std::vector<T_TH>{
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00}),                                 // expected_selected_scores
+            Tensor(ET_IND, {1, 1}, std::vector<T_IND>{3}),                              // expected_selected_indices
+            Tensor(ET_IND, {1}, std::vector<T_IND>{1}),                                 // expected_valid_outputs
+            "multiclass_nms_by_IOU_and_scores"),
+        MulticlassNmsParams(
+            3,                                              // nms_top_k
+            0.5f,                                           // iou_threshold
+            2.0f,                                           // score_threshold
+            op::v8::MulticlassNms::SortResultType::SCORE, // sort_result_type
+            -1,                                             // keep_top_k
+            -1,                                             // background_class
+            1.0f,                                           // nms_eta
+            ET_IND,                                         // output_type
+            false,                                          // sort_result_across_batch
+            true,                                           // normalized
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {1, 1, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3}),                                       // scores
+            Tensor(ET_TH, {0, 6}, std::vector<T_TH>{}),                                 // expected_selected_scores
+            Tensor(ET_IND, {0, 1}, std::vector<T_IND>{}),                               // expected_selected_indices
+            Tensor(ET_IND, {1}, std::vector<T_IND>{0}),                                 // expected_valid_outputs
+            "multiclass_nms_no_output"),
+        MulticlassNmsParams(
+            3,                                              // nms_top_k
+            0.5f,                                           // iou_threshold
+            0.0f,                                           // score_threshold
+            op::v8::MulticlassNms::SortResultType::CLASSID, // sort_result_type
+            -1,                                             // keep_top_k
+            0,                                              // background_class
+            1.0f,                                           // nms_eta
+            ET_IND,                                         // output_type
+            false,                                          // sort_result_across_batch
+            true,                                           // normalized
+            Tensor(ET, {2, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {2, 2, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3}),      // scores
+            Tensor(ET_TH, {4, 6}, std::vector<T_TH>{
+                1.00, 0.95, 0.00, 0.00, 1.00, 1.00, 1.00, 0.80, 0.00, 10.00, 1.00, 11.00,
+                1.00, 0.95, 0.00, 0.00, 1.00, 1.00, 1.00, 0.80, 0.00, 10.00, 1.00, 11.00}),
+                                                                                        // expected_selected_scores
+            Tensor(ET_IND, {4, 1}, std::vector<T_IND>{0, 3, 6, 9}),                     // expected_selected_indices
+            Tensor(ET_IND, {2}, std::vector<T_IND>{2, 2}),                              // expected_valid_outputs
+            "multiclass_nms_by_background"),
+        MulticlassNmsParams(
+            3,                                              // nms_top_k
+            0.5f,                                           // iou_threshold
+            0.0f,                                           // score_threshold
+            op::v8::MulticlassNms::SortResultType::CLASSID, // sort_result_type
+            3,                                              // keep_top_k
+            -1,                                             // background_class
+            1.0f,                                           // nms_eta
+            ET_IND,                                         // output_type
+            false,                                          // sort_result_across_batch
+            true,                                           // normalized
+            Tensor(ET, {2, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {2, 2, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3}),      // scores
+            Tensor(ET_TH, {6, 6}, std::vector<T_TH>{
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.90, 0.00,
+                0.00, 1.00, 1.00, 1.00,  0.95, 0.00,  0.00, 1.00, 1.00,
+                0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.90, 0.00,
+                0.00, 1.00, 1.00, 1.00,  0.95, 0.00,  0.00, 1.00, 1.00}),               // expected_selected_scores
+            Tensor(ET_IND, {6, 1}, std::vector<T_IND>{3, 0, 0, 9, 6, 6}),               // expected_selected_indices
+            Tensor(ET_IND, {2}, std::vector<T_IND>{3, 3}),                              // expected_valid_outputs
+            "multiclass_nms_by_keep_top_k"),
+        MulticlassNmsParams(
+            -1,                                             // nms_top_k
+            1.0f,                                           // iou_threshold
+            0.0f,                                           // score_threshold
+            op::v8::MulticlassNms::SortResultType::CLASSID, // sort_result_type
+            -1,                                             // keep_top_k
+            -1,                                             // background_class
+            0.1f,                                           // nms_eta
+            ET_IND,                                         // output_type
+            false,                                          // sort_result_across_batch
+            true,                                           // normalized
+            Tensor(ET, {2, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),   // boxes
+            Tensor(ET_TH, {2, 2, 6}, std::vector<T_TH>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3}),      // scores
+            Tensor(ET_TH, {12, 6}, std::vector<T_TH>{
+                0.00,   0.95, 0.00,   10.00,  1.00, 11.00,  0.00,   0.90, 0.00,   0.00,   1.00, 1.00,  0.00,  0.30, 0.00,
+                100.00, 1.00, 101.00, 1.00,   0.95, 0.00,   0.00,   1.00, 1.00,   1.00,   0.80, 0.00,  10.00, 1.00, 11.00,
+                1.00,   0.30, 0.00,   100.00, 1.00, 101.00, 0.00,   0.95, 0.00,   10.00,  1.00, 11.00, 0.00,  0.90, 0.00,
+                0.00,   1.00, 1.00,   0.00,   0.30, 0.00,   100.00, 1.00, 101.00, 1.00,   0.95, 0.00,  0.00,  1.00, 1.00,
+                1.00,   0.80, 0.00,   10.00,  1.00, 11.00,  1.00,   0.30, 0.00,   100.00, 1.00, 101.00}),
+                                                                                        // expected_selected_scores
+            Tensor(ET_IND, {12, 1}, std::vector<T_IND>{
+                3, 0, 5, 0, 3, 5, 9, 6, 11, 6, 9, 11}),                                 // expected_selected_indices
+            Tensor(ET_IND, {2}, std::vector<T_IND>{6, 6}),                              // expected_valid_outputs
+            "multiclass_nms_by_nms_eta"),
+    };
+    return params;
+}
+
+std::vector<MulticlassNmsParams> generateCombinedParams() {
+    const std::vector<std::vector<MulticlassNmsParams>> generatedParams {
+        generateParams<element::Type_t::bf16, element::Type_t::f32, element::Type_t::i32>(),
+        generateParams<element::Type_t::f16, element::Type_t::f32, element::Type_t::i32>(),
+        generateParams<element::Type_t::f32, element::Type_t::f32, element::Type_t::i32>(),
+        generateParams<element::Type_t::bf16, element::Type_t::f32, element::Type_t::i64>(),
+        generateParams<element::Type_t::f16, element::Type_t::f32, element::Type_t::i64>(),
+        generateParams<element::Type_t::f32, element::Type_t::f32, element::Type_t::i64>(),
+    };
+    std::vector<MulticlassNmsParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_MulticlassNms_With_Hardcoded_Refs, ReferenceMulticlassNmsTest,
+    testing::ValuesIn(generateCombinedParams()), ReferenceMulticlassNmsTest::getTestCaseName);
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/op_reference/non_max_suppression.cpp b/docs/template_plugin/tests/functional/op_reference/non_max_suppression.cpp
new file mode 100644
index 00000000000..0e0b7a5e0fa
--- /dev/null
+++ b/docs/template_plugin/tests/functional/op_reference/non_max_suppression.cpp
@@ -0,0 +1,489 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include "openvino/op/non_max_suppression.hpp"
+#include "openvino/op/constant.hpp"
+#include "base_reference_test.hpp"
+
+using namespace reference_tests;
+using namespace ov;
+
+namespace {
+struct NonMaxSuppressionParams {
+    NonMaxSuppressionParams(
+        const Tensor& boxes, const Tensor& scores,
+        const Tensor& maxOutputBoxesPerClass, const Tensor& iouThreshold, const Tensor& scoreThreshold,
+        const Tensor& softNmsSigma, const op::v5::NonMaxSuppression::BoxEncodingType boxEncoding,
+        const Tensor& expectedSelectedIndices, const Tensor& expectedSelectedScores,
+        const Tensor& expectedValidOutputs, const std::string& testcaseName = "") :
+        boxes(boxes), scores(scores),
+        maxOutputBoxesPerClass(maxOutputBoxesPerClass), iouThreshold(iouThreshold), scoreThreshold(scoreThreshold),
+        softNmsSigma(softNmsSigma), boxEncoding(boxEncoding),
+        expectedSelectedIndices(expectedSelectedIndices), expectedSelectedScores(expectedSelectedScores),
+        expectedValidOutputs(expectedValidOutputs), testcaseName(testcaseName) {}
+
+    Tensor boxes;
+    Tensor scores;
+    Tensor maxOutputBoxesPerClass;
+    Tensor iouThreshold;
+    Tensor scoreThreshold;
+    Tensor softNmsSigma;
+    op::v5::NonMaxSuppression::BoxEncodingType boxEncoding;
+    Tensor expectedSelectedIndices;
+    Tensor expectedSelectedScores;
+    Tensor expectedValidOutputs;
+    std::string testcaseName;
+};
+
+class ReferenceNonMaxSuppressionTest : public testing::TestWithParam<NonMaxSuppressionParams>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.boxes.data, params.scores.data};
+        refOutData = {params.expectedSelectedIndices.data,
+                      params.expectedSelectedScores.data,
+                      params.expectedValidOutputs.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<NonMaxSuppressionParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "bType=" << param.boxes.type;
+        result << "_bShape=" << param.boxes.shape;
+        result << "_sType=" << param.scores.type;
+        result << "_sShape=" << param.scores.shape;
+        result << "_escType=" << param.expectedSelectedScores.type;
+        result << "_escShape=" << param.expectedSelectedScores.shape;
+        result << "_esiType=" << param.expectedSelectedIndices.type;
+        result << "_esiShape=" << param.expectedSelectedIndices.shape;
+        result << "_evoType=" << param.expectedValidOutputs.type;
+        if (param.testcaseName != "") {
+            result << "_evoShape=" << param.expectedValidOutputs.shape;
+            result << "_=" << param.testcaseName;
+        } else {
+            result << "_evoShape=" << param.expectedValidOutputs.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const NonMaxSuppressionParams& params) {
+        const auto boxes = std::make_shared<op::v0::Parameter>(params.boxes.type, params.boxes.shape);
+        const auto scores = std::make_shared<op::v0::Parameter>(params.scores.type, params.scores.shape);
+        const auto max_output_boxes_per_class = std::make_shared<op::v0::Constant>(
+            params.maxOutputBoxesPerClass.type, params.maxOutputBoxesPerClass.shape, params.maxOutputBoxesPerClass.data.data());
+        const auto iou_threshold = std::make_shared<op::v0::Constant>(
+            params.iouThreshold.type, params.iouThreshold.shape, params.iouThreshold.data.data());
+        const auto score_threshold = std::make_shared<op::v0::Constant>(
+            params.scoreThreshold.type, params.scoreThreshold.shape, params.scoreThreshold.data.data());
+        const auto soft_nms_sigma = std::make_shared<op::v0::Constant>(
+            params.softNmsSigma.type, params.softNmsSigma.shape, params.softNmsSigma.data.data());
+        const auto nms = std::make_shared<op::v5::NonMaxSuppression>(boxes,
+                                                                     scores,
+                                                                     max_output_boxes_per_class,
+                                                                     iou_threshold,
+                                                                     score_threshold,
+                                                                     soft_nms_sigma,
+                                                                     params.boxEncoding,
+                                                                     false);
+        const auto f = std::make_shared<Function>(nms->outputs(), ParameterVector{boxes, scores});
+        return f;
+    }
+};
+
+class ReferenceNonMaxSuppressionTestWithoutConstants : public ReferenceNonMaxSuppressionTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {params.boxes.data, params.scores.data, params.maxOutputBoxesPerClass.data,
+                     params.iouThreshold.data, params.scoreThreshold.data, params.softNmsSigma.data};
+        refOutData = {params.expectedSelectedIndices.data,
+                      params.expectedSelectedScores.data,
+                      params.expectedValidOutputs.data};
+    }
+
+    static std::string getTestCaseName(const testing::TestParamInfo<NonMaxSuppressionParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "bType=" << param.boxes.type;
+        result << "_bShape=" << param.boxes.shape;
+        result << "_sType=" << param.scores.type;
+        result << "_sShape=" << param.scores.shape;
+        result << "_escType=" << param.expectedSelectedScores.type;
+        result << "_escShape=" << param.expectedSelectedScores.shape;
+        result << "_esiType=" << param.expectedSelectedIndices.type;
+        result << "_esiShape=" << param.expectedSelectedIndices.shape;
+        result << "_evoType=" << param.expectedValidOutputs.type;
+        if (param.testcaseName != "") {
+            result << "_evoShape=" << param.expectedValidOutputs.shape;
+            result << "_=" << param.testcaseName;
+        } else {
+            result << "_evoShape=" << param.expectedValidOutputs.shape;
+        }
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const NonMaxSuppressionParams& params) {
+        const auto boxes = std::make_shared<op::v0::Parameter>(params.boxes.type, params.boxes.shape);
+        const auto scores = std::make_shared<op::v0::Parameter>(params.scores.type, params.scores.shape);
+        const auto max_output_boxes_per_class = std::make_shared<op::v0::Parameter>(
+            params.maxOutputBoxesPerClass.type, params.maxOutputBoxesPerClass.shape);
+        const auto iou_threshold = std::make_shared<op::v0::Parameter>(
+            params.iouThreshold.type, params.iouThreshold.shape);
+        const auto score_threshold = std::make_shared<op::v0::Parameter>(
+            params.scoreThreshold.type, params.scoreThreshold.shape);
+        const auto soft_nms_sigma = std::make_shared<op::v0::Parameter>(
+            params.softNmsSigma.type, params.softNmsSigma.shape);
+        const auto nms = std::make_shared<op::v5::NonMaxSuppression>(boxes,
+                                                                     scores,
+                                                                     max_output_boxes_per_class,
+                                                                     iou_threshold,
+                                                                     score_threshold,
+                                                                     soft_nms_sigma,
+                                                                     params.boxEncoding,
+                                                                     false);
+        const auto f = std::make_shared<Function>(nms->outputs(),
+                                                  ParameterVector{boxes, scores, max_output_boxes_per_class,
+                                                                  iou_threshold, score_threshold, soft_nms_sigma});
+        return f;
+    }
+};
+
+TEST_P(ReferenceNonMaxSuppressionTest, CompareWithRefs) {
+    Exec();
+}
+
+TEST_P(ReferenceNonMaxSuppressionTestWithoutConstants, CompareWithRefs) {
+    Exec();
+}
+
+template <element::Type_t ET, element::Type_t ET_BOX, element::Type_t ET_TH, element::Type_t ET_IND>
+std::vector<NonMaxSuppressionParams> generateParams() {
+    using T = typename element_type_traits<ET>::value_type;
+    using T_BOX = typename element_type_traits<ET_BOX>::value_type;
+    using T_TH = typename element_type_traits<ET_TH>::value_type;
+    using T_IND = typename element_type_traits<ET_IND>::value_type;
+    std::vector<NonMaxSuppressionParams> params {
+        NonMaxSuppressionParams(
+            // boxes
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                0.5, 0.5,  1.0, 1.0, 0.5, 0.6,  1.0, 1.0, 0.5, 0.4,   1.0, 1.0,
+                0.5, 10.5, 1.0, 1.0, 0.5, 10.6, 1.0, 1.0, 0.5, 100.5, 1.0, 1.0}),
+            // scores
+            Tensor(ET, {1, 1, 6}, std::vector<T>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3}),
+            // max_output_boxes_per_class
+            Tensor(ET_BOX, {}, std::vector<T_BOX>{3}),
+            // iou_threshold
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.5f}),
+            // score_threshold
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.0f}),
+            // soft_nms_sigma
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.0f}),
+            // box_encoding
+            op::v5::NonMaxSuppression::BoxEncodingType::CENTER,
+            // selected_indices
+            Tensor(ET_IND, {3, 3}, std::vector<T_IND>{
+                0, 0, 3, 0, 0, 0, 0, 0, 5}),
+            // selected_scores
+            Tensor(ET_TH, {3, 3}, std::vector<T_TH>{
+                0.0, 0.0, 0.95, 0.0, 0.0, 0.9, 0.0, 0.0, 0.3}),
+            // valid_outputs
+            Tensor(ET_IND, {1}, std::vector<T_IND>{3}),
+            "nonmaxsuppression_center_point_box_format"),
+        NonMaxSuppressionParams(
+            // boxes
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                1.0, 1.0,  0.0, 0.0,  0.0, 0.1,  1.0, 1.1,  0.0, 0.9,   1.0, -0.1,
+                0.0, 10.0, 1.0, 11.0, 1.0, 10.1, 0.0, 11.1, 1.0, 101.0, 0.0, 100.0}),
+            // scores
+            Tensor(ET, {1, 1, 6}, std::vector<T>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3}),
+            // max_output_boxes_per_class
+            Tensor(ET_BOX, {}, std::vector<T_BOX>{3}),
+            // iou_threshold
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.5f}),
+            // score_threshold
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.0f}),
+            // soft_nms_sigma
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.0f}),
+            // box_encoding
+            op::v5::NonMaxSuppression::BoxEncodingType::CORNER,
+            // selected_indices
+            Tensor(ET_IND, {3, 3}, std::vector<T_IND>{
+                0, 0, 3, 0, 0, 0, 0, 0, 5}),
+            // selected_scores
+            Tensor(ET_TH, {3, 3}, std::vector<T_TH>{
+                0.0, 0.0, 0.95, 0.0, 0.0, 0.9, 0.0, 0.0, 0.3}),
+            // valid_outputs
+            Tensor(ET_IND, {1}, std::vector<T_IND>{3}),
+            "nonmaxsuppression_flipped_coordinates"),
+        NonMaxSuppressionParams(
+            // boxes
+            Tensor(ET, {1, 10, 4}, std::vector<T>{
+                0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0,
+                1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0,
+                0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0}),
+            // scores
+            Tensor(ET, {1, 1, 10}, std::vector<T>{
+                0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9}),
+            // max_output_boxes_per_class
+            Tensor(ET_BOX, {}, std::vector<T_BOX>{3}),
+            // iou_threshold
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.5f}),
+            // score_threshold
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.0f}),
+            // soft_nms_sigma
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.0f}),
+            // box_encoding
+            op::v5::NonMaxSuppression::BoxEncodingType::CORNER,
+            // selected_indices
+            Tensor(ET_IND, {1, 3}, std::vector<T_IND>{0, 0, 0}),
+            // selected_scores
+            Tensor(ET_TH, {1, 3}, std::vector<T_TH>{0.0, 0.0, 0.9}),
+            // valid_outputs
+            Tensor(ET_IND, {1}, std::vector<T_IND>{1}),
+            "nonmaxsuppression_identical_boxes"),
+        NonMaxSuppressionParams(
+            // boxes
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),
+            // scores
+            Tensor(ET, {1, 1, 6}, std::vector<T>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3}),
+            // max_output_boxes_per_class
+            Tensor(ET_BOX, {}, std::vector<T_BOX>{2}),
+            // iou_threshold
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.5f}),
+            // score_threshold
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.0f}),
+            // soft_nms_sigma
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.0f}),
+            // box_encoding
+            op::v5::NonMaxSuppression::BoxEncodingType::CORNER,
+            // selected_indices
+            Tensor(ET_IND, {2, 3}, std::vector<T_IND>{0, 0, 3, 0, 0, 0}),
+            // selected_scores
+            Tensor(ET_TH, {2, 3}, std::vector<T_TH>{
+                0.0, 0.0, 0.95, 0.0, 0.0, 0.9}),
+            // valid_outputs
+            Tensor(ET_IND, {1}, std::vector<T_IND>{2}),
+            "nonmaxsuppression_limit_output_size"),
+        NonMaxSuppressionParams(
+            // boxes
+            Tensor(ET, {1, 1, 4}, std::vector<T>{0.0, 0.0, 1.0, 1.0}),
+            // scores
+            Tensor(ET, {1, 1, 1}, std::vector<T>{0.9}),
+            // max_output_boxes_per_class
+            Tensor(ET_BOX, {}, std::vector<T_BOX>{3}),
+            // iou_threshold
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.5f}),
+            // score_threshold
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.0f}),
+            // soft_nms_sigma
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.0f}),
+            // box_encoding
+            op::v5::NonMaxSuppression::BoxEncodingType::CORNER,
+            // selected_indices
+            Tensor(ET_IND, {1, 3}, std::vector<T_IND>{0, 0, 0}),
+            // selected_scores
+            Tensor(ET_TH, {1, 3}, std::vector<T_TH>{0.0, 0.0, 0.9}),
+            // valid_outputs
+            Tensor(ET_IND, {1}, std::vector<T_IND>{1}),
+            "nonmaxsuppression_single_box"),
+        NonMaxSuppressionParams(
+            // boxes
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),
+            // scores
+            Tensor(ET, {1, 1, 6}, std::vector<T>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3}),
+            // max_output_boxes_per_class
+            Tensor(ET_BOX, {}, std::vector<T_BOX>{3}),
+            // iou_threshold
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.5f}),
+            // score_threshold
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.0f}),
+            // soft_nms_sigma
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.0f}),
+            // box_encoding
+            op::v5::NonMaxSuppression::BoxEncodingType::CORNER,
+            // selected_indices
+            Tensor(ET_IND, {3, 3}, std::vector<T_IND>{
+                0, 0, 3, 0, 0, 0, 0, 0, 5}),
+            // selected_scores
+            Tensor(ET_TH, {3, 3}, std::vector<T_TH>{
+                0.0, 0.0, 0.95, 0.0, 0.0, 0.9, 0.0, 0.0, 0.3}),
+            // valid_outputs
+            Tensor(ET_IND, {1}, std::vector<T_IND>{3}),
+            "nonmaxsuppression_suppress_by_IOU"),
+        NonMaxSuppressionParams(
+            // boxes
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),
+            // scores
+            Tensor(ET, {1, 1, 6}, std::vector<T>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3}),
+            // max_output_boxes_per_class
+            Tensor(ET_BOX, {}, std::vector<T_BOX>{3}),
+            // iou_threshold
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.5f}),
+            // score_threshold
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.4f}),
+            // soft_nms_sigma
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.0f}),
+            // box_encoding
+            op::v5::NonMaxSuppression::BoxEncodingType::CORNER,
+            // selected_indices
+            Tensor(ET_IND, {2, 3}, std::vector<T_IND>{
+                0, 0, 3, 0, 0, 0}),
+            // selected_scores
+            Tensor(ET_TH, {2, 3}, std::vector<T_TH>{
+                0.0, 0.0, 0.95, 0.0, 0.0, 0.9}),
+            // valid_outputs
+            Tensor(ET_IND, {1}, std::vector<T_IND>{2}),
+            "nonmaxsuppression_suppress_by_IOU_and_scores"),
+        NonMaxSuppressionParams(
+            // boxes
+            Tensor(ET, {2, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),
+            // scores
+            Tensor(ET, {2, 1, 6}, std::vector<T>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.9, 0.75, 0.6, 0.95, 0.5, 0.3}),
+            // max_output_boxes_per_class
+            Tensor(ET_BOX, {}, std::vector<T_BOX>{2}),
+            // iou_threshold
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.5f}),
+            // score_threshold
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.0f}),
+            // soft_nms_sigma
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.0f}),
+            // box_encoding
+            op::v5::NonMaxSuppression::BoxEncodingType::CORNER,
+            // selected_indices
+            Tensor(ET_IND, {4, 3}, std::vector<T_IND>{
+                0, 0, 3, 0, 0, 0, 1, 0, 3, 1, 0, 0}),
+            // selected_scores
+            Tensor(ET_TH, {4, 3}, std::vector<T_TH>{
+                0.0, 0.0, 0.95, 0.0, 0.0, 0.9, 1.0, 0.0, 0.95, 1.0, 0.0, 0.9}),
+            // valid_outputs
+            Tensor(ET_IND, {1}, std::vector<T_IND>{4}),
+            "nonmaxsuppression_two_batches"),
+        NonMaxSuppressionParams(
+            // boxes
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
+                0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0}),
+            // scores
+            Tensor(ET, {1, 2, 6}, std::vector<T>{
+                0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.9, 0.75, 0.6, 0.95, 0.5, 0.3}),
+            // max_output_boxes_per_class
+            Tensor(ET_BOX, {}, std::vector<T_BOX>{2}),
+            // iou_threshold
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.5f}),
+            // score_threshold
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.0f}),
+            // soft_nms_sigma
+            Tensor(ET_TH, {}, std::vector<T_TH>{0.0f}),
+            // box_encoding
+            op::v5::NonMaxSuppression::BoxEncodingType::CORNER,
+            // selected_indices
+            Tensor(ET_IND, {4, 3}, std::vector<T_IND>{
+                0, 0, 3, 0, 0, 0, 0, 1, 3, 0, 1, 0}),
+            // selected_scores
+            Tensor(ET_TH, {4, 3}, std::vector<T_TH>{
+                0.0, 0.0, 0.95, 0.0, 0.0, 0.9, 0.0, 1.0, 0.95, 0.0, 1.0, 0.9}),
+            // valid_outputs
+            Tensor(ET_IND, {1}, std::vector<T_IND>{4}),
+            "nonmaxsuppression_two_classes"),
+    };
+    return params;
+}
+
+std::vector<NonMaxSuppressionParams> generateCombinedParams() {
+    const std::vector<std::vector<NonMaxSuppressionParams>> generatedParams {
+        generateParams<element::Type_t::bf16, element::Type_t::i32, element::Type_t::f32, element::Type_t::i32>(),
+        generateParams<element::Type_t::f16, element::Type_t::i32, element::Type_t::f32, element::Type_t::i32>(),
+        generateParams<element::Type_t::f32, element::Type_t::i32, element::Type_t::f32, element::Type_t::i32>(),
+        generateParams<element::Type_t::bf16, element::Type_t::i32, element::Type_t::f32, element::Type_t::i64>(),
+        generateParams<element::Type_t::f16, element::Type_t::i32, element::Type_t::f32, element::Type_t::i64>(),
+        generateParams<element::Type_t::f32, element::Type_t::i32, element::Type_t::f32, element::Type_t::i64>(),
+    };
+    std::vector<NonMaxSuppressionParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+template <element::Type_t ET, element::Type_t ET_BOX, element::Type_t ET_TH, element::Type_t ET_IND>
+std::vector<NonMaxSuppressionParams> generateParamsWithoutConstants() {
+    using T = typename element_type_traits<ET>::value_type;
+    using T_BOX = typename element_type_traits<ET_BOX>::value_type;
+    using T_TH = typename element_type_traits<ET_TH>::value_type;
+    using T_IND = typename element_type_traits<ET_IND>::value_type;
+    std::vector<NonMaxSuppressionParams> params {
+        NonMaxSuppressionParams(
+            // boxes
+            Tensor(ET, {1, 6, 4}, std::vector<T>{
+                0.0f, 0.0f,  1.0f, 1.0f,  0.0f, 0.1f,  1.0f, 1.1f,  0.0f, -0.1f,  1.0f, 0.9f,
+                0.0f, 10.0f, 1.0f, 11.0f, 0.0f, 10.1f, 1.0f, 11.1f, 0.0f, 100.0f, 1.0f, 101.0f}),
+            // scores
+            Tensor(ET, {1, 1, 6}, std::vector<T>{
+                0.9f, 0.75f, 0.6f, 0.95f, 0.5f, 0.3f}),
+            // max_output_boxes_per_class
+            Tensor(ET_BOX, {1}, std::vector<T_BOX>{1}),
+            // iou_threshold
+            Tensor(ET_TH, {1}, std::vector<T_TH>{0.4f}),
+            // score_threshold
+            Tensor(ET_TH, {1}, std::vector<T_TH>{0.2f}),
+            // soft_nms_sigma
+            Tensor(ET_TH, {1}, std::vector<T_TH>{0.0f}),
+            // box_encoding
+            op::v5::NonMaxSuppression::BoxEncodingType::CORNER,
+            // selected_indices
+            Tensor(ET_IND, {1, 3}, std::vector<T_IND>{0, 0, 3}),
+            // selected_scores
+            Tensor(ET_TH, {1, 3}, std::vector<T_TH>{0.0f, 0.0f, 0.95f}),
+            // valid_outputs
+            Tensor(ET_IND, {1}, std::vector<T_IND>{1}),
+            "nonmaxsuppression_suppress_by_IOU_and_scores_without_constants"),
+    };
+    return params;
+}
+
+std::vector<NonMaxSuppressionParams> generateCombinedParamsWithoutConstants() {
+    const std::vector<std::vector<NonMaxSuppressionParams>> generatedParams {
+        generateParamsWithoutConstants<element::Type_t::bf16, element::Type_t::i32, element::Type_t::f32, element::Type_t::i32>(),
+        generateParamsWithoutConstants<element::Type_t::f16, element::Type_t::i32, element::Type_t::f32, element::Type_t::i32>(),
+        generateParamsWithoutConstants<element::Type_t::f32, element::Type_t::i32, element::Type_t::f32, element::Type_t::i32>(),
+        generateParamsWithoutConstants<element::Type_t::bf16, element::Type_t::i32, element::Type_t::f32, element::Type_t::i64>(),
+        generateParamsWithoutConstants<element::Type_t::f16, element::Type_t::i32, element::Type_t::f32, element::Type_t::i64>(),
+        generateParamsWithoutConstants<element::Type_t::f32, element::Type_t::i32, element::Type_t::f32, element::Type_t::i64>(),
+    };
+    std::vector<NonMaxSuppressionParams> combinedParams;
+
+    for (const auto& params : generatedParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_NonMaxSuppression_With_Hardcoded_Refs, ReferenceNonMaxSuppressionTest,
+    testing::ValuesIn(generateCombinedParams()), ReferenceNonMaxSuppressionTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_NonMaxSuppression_With_Hardcoded_Refs, ReferenceNonMaxSuppressionTestWithoutConstants,
+    testing::ValuesIn(generateCombinedParamsWithoutConstants()), ReferenceNonMaxSuppressionTest::getTestCaseName);
+} // namespace
\ No newline at end of file
diff --git a/docs/template_plugin/tests/functional/skip_tests_config.cpp b/docs/template_plugin/tests/functional/skip_tests_config.cpp
index bc20772d640..e7e16f48f62 100644
--- a/docs/template_plugin/tests/functional/skip_tests_config.cpp
+++ b/docs/template_plugin/tests/functional/skip_tests_config.cpp
@@ -100,6 +100,12 @@ std::vector<std::string> disabledTestPatterns() {
         R"(.*ReferenceTopKTest.*aType=f64.*)",
         // CVS-63947
         R"(.*ReferenceConcatTest.*concat_zero_.*)",
+        // CVS-64119
+        R"(.*ReferenceMatrixNmsTest.*esiType=i64.*evoType=i64.*)",
+        // CVS-64121
+        R"(.*ReferenceMulticlassNmsTest.*esiType=i64.*evoType=i64.*)",
+        // CVS-64096
+        R"(.*ReferenceNonMaxSuppressionTest.*esiType=i32.*evoType=i32.*)",
     };
 
 #ifdef _WIN32
diff --git a/ngraph/core/include/openvino/op/matrix_nms.hpp b/ngraph/core/include/openvino/op/matrix_nms.hpp
index 2567e36e6a2..907ecc6ec79 100644
--- a/ngraph/core/include/openvino/op/matrix_nms.hpp
+++ b/ngraph/core/include/openvino/op/matrix_nms.hpp
@@ -45,6 +45,31 @@ public:
         float post_threshold = 0.0f;
         // specifies whether boxes are normalized or not
         bool normalized = true;
+
+        Attributes() {}
+
+        Attributes(op::v8::MatrixNms::SortResultType sort_result_type,
+                   bool sort_result_across_batch,
+                   ov::element::Type output_type,
+                   float score_threshold,
+                   int nms_top_k,
+                   int keep_top_k,
+                   int background_class,
+                   op::v8::MatrixNms::DecayFunction decay_function,
+                   float gaussian_sigma,
+                   float post_threshold,
+                   bool normalized)
+            : sort_result_type(sort_result_type),
+              sort_result_across_batch(sort_result_across_batch),
+              output_type(output_type),
+              score_threshold(score_threshold),
+              nms_top_k(nms_top_k),
+              keep_top_k(keep_top_k),
+              background_class(background_class),
+              decay_function(decay_function),
+              gaussian_sigma(gaussian_sigma),
+              post_threshold(post_threshold),
+              normalized(normalized) {}
     };
 
     MatrixNms();
diff --git a/ngraph/test/CMakeLists.txt b/ngraph/test/CMakeLists.txt
index 17509d4de49..d5d1655ea84 100644
--- a/ngraph/test/CMakeLists.txt
+++ b/ngraph/test/CMakeLists.txt
@@ -492,12 +492,9 @@ set(MULTI_TEST_SRC
     backend/experimental_detectron_prior_grid.in.cpp
     backend/function_name.in.cpp
     backend/interpolate.in.cpp
-    backend/matrix_nms.in.cpp
-    backend/multiclass_nms.in.cpp
     backend/multiple_backends.in.cpp
     backend/multiple_result.in.cpp
     backend/node_name.in.cpp
-    backend/non_max_suppression.in.cpp
     backend/region_yolo.in.cpp
     backend/sqrt.in.cpp
     backend/unhandled_op.in.cpp
diff --git a/ngraph/test/backend/matrix_nms.in.cpp b/ngraph/test/backend/matrix_nms.in.cpp
deleted file mode 100644
index 09c1d59f46a..00000000000
--- a/ngraph/test/backend/matrix_nms.in.cpp
+++ /dev/null
@@ -1,597 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-// clang-format off
-#ifdef ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
-#define DEFAULT_FLOAT_TOLERANCE_BITS ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
-#endif
-
-#ifdef ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
-#define DEFAULT_DOUBLE_TOLERANCE_BITS ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
-#endif
-// clang-format on
-
-#include "gtest/gtest.h"
-#include "ngraph/ngraph.hpp"
-#include "engines_util/test_engines.hpp"
-#include "engines_util/test_case.hpp"
-#include "util/test_control.hpp"
-
-using namespace std;
-using namespace ngraph;
-
-static string s_manifest = "${MANIFEST}";
-using TestEngine = test::ENGINE_CLASS_NAME(${BACKEND_NAME});
-
-NGRAPH_TEST(${BACKEND_NAME}, matrix_nms_output_type_i64) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3};
-
-    op::v8::MatrixNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MatrixNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = 0;
-
-    const auto boxes_shape = Shape{1, 6, 4};  // N 1, C 2, M 6
-    const auto scores_shape = Shape{1, 2, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    attrs.decay_function = op::v8::MatrixNms::DecayFunction::LINEAR;
-    attrs.gaussian_sigma = 2.0f;
-    attrs.post_threshold = 0.0f;
-
-    auto nms = make_shared<op::v8::MatrixNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {0, 3, 1};
-    std::vector<float> expected_selected_scores =
-        {1.00, 0.95, 0.00, 0.00, 1.00, 1.00, 1.00, 0.8, 0.00, 10.00, 1.00, 11.00, 1.00, 0.13636364, 0.0, 0.1, 1.0, 1.1};
-    std::vector<int64_t> expected_valid_outputs = {3};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({3, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({3, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({1}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, matrix_nms_output_type_i32) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3};
-
-    op::v8::MatrixNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MatrixNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = 0;
-
-    const auto boxes_shape = Shape{1, 6, 4};  // N 1, C 2, M 6
-    const auto scores_shape = Shape{1, 2, 6};
-    attrs.decay_function = op::v8::MatrixNms::DecayFunction::LINEAR;
-    attrs.gaussian_sigma = 2.0f;
-    attrs.post_threshold = 0.0f;
-    attrs.output_type = ngraph::element::i32;
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-
-    auto nms = make_shared<op::v8::MatrixNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int32_t> expected_selected_indices = {0, 3, 1};
-    std::vector<float> expected_selected_scores =
-        {1.00, 0.95, 0.00, 0.00, 1.00, 1.00, 1.00, 0.8, 0.00, 10.00, 1.00, 11.00, 1.00, 0.13636364, 0.0, 0.1, 1.0, 1.1};
-    std::vector<int32_t> expected_valid_outputs = {3};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({3, 6}, expected_selected_scores);
-    test_case.add_expected_output<int32_t>({3, 1}, expected_selected_indices);
-    test_case.add_expected_output<int32_t>({1}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, matrix_nms_gaussian) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3};
-
-    op::v8::MatrixNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MatrixNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = 0;
-
-    const auto boxes_shape = Shape{1, 6, 4};  // N 1, C 2, M 6
-    const auto scores_shape = Shape{1, 2, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    attrs.decay_function = op::v8::MatrixNms::DecayFunction::GAUSSIAN;
-    attrs.gaussian_sigma = 2.0f;
-    attrs.post_threshold = 0.0f;
-
-    auto nms = make_shared<op::v8::MatrixNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {0, 3, 1};
-    std::vector<float> expected_selected_scores =
-        {1.00, 0.95, 0.00, 0.00, 1.00, 1.00, 1.00, 0.8, 0.00, 10.00, 1.00, 11.00, 1.00, 0.1966116, 0.0, 0.1, 1.0, 1.1};
-    std::vector<int64_t> expected_valid_outputs = {3};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({3, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({3, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({1}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, matrix_nms_two_batches_two_classes) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,  // 0
-                                     0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};  // 1
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,   // 0
-                                      0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3};  // 1
-
-    op::v8::MatrixNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MatrixNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = 0;
-
-    const auto boxes_shape = Shape{2, 6, 4};  // N 2, C 2, M 6
-    const auto scores_shape = Shape{2, 2, 6};
-    attrs.decay_function = op::v8::MatrixNms::DecayFunction::LINEAR;
-    attrs.gaussian_sigma = 2.0f;
-    attrs.post_threshold = 0.0f;
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-
-    auto nms = make_shared<op::v8::MatrixNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {0, 3, 1, 6, 9, 7};
-    std::vector<float> expected_selected_scores = {
-        1.00, 0.95, 0.00, 0.00, 1.00, 1.00, 1.00, 0.8, 0.00, 10.00, 1.00, 11.00, 1.00, 0.13636364, 0.0, 0.1, 1.0, 1.1,
-        1.00, 0.95, 0.00, 0.00, 1.00, 1.00, 1.00, 0.8, 0.00, 10.00, 1.00, 11.00, 1.00, 0.13636364, 0.0, 0.1, 1.0, 1.1};
-    std::vector<int64_t> expected_valid_outputs = {3, 3};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({6, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({6, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({2}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, matrix_nms_two_batches_two_classes_by_score_cross_batch) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,  // 0
-                                     0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};  // 1
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,   // 0
-                                      0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3};  // 1
-
-    op::v8::MatrixNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MatrixNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-
-    const auto boxes_shape = Shape{2, 6, 4};  // N 2, C 2, M 6
-    const auto scores_shape = Shape{2, 2, 6};
-    attrs.decay_function = op::v8::MatrixNms::DecayFunction::LINEAR;
-    attrs.gaussian_sigma = 2.0f;
-    attrs.post_threshold = 0.5;
-    attrs.sort_result_across_batch = true;
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-
-    auto nms = make_shared<op::v8::MatrixNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {3, 0, 9, 6, 0, 6, 3, 9};
-    std::vector<float> expected_selected_scores = {0.00, 0.95, 0.00, 10.00, 1.00, 11.00,   // 3
-                                                   1.00, 0.95, 0.00, 0.00,  1.00, 1.00,    // 0
-                                                   0.00, 0.95, 0.00, 10.00, 1.00, 11.00,   // 9
-                                                   1.00, 0.95, 0.00, 0.00,  1.00, 1.00,    // 6
-                                                   0.00, 0.90, 0.00, 0.00,  1.00, 1.00,    // 0
-                                                   0.00, 0.90, 0.00, 0.00,  1.00, 1.00,    // 6
-                                                   1.00, 0.80, 0.00, 10.00, 1.00, 11.00,   // 3
-                                                   1.00, 0.80, 0.00, 10.00, 1.00, 11.00};  // 9
-    std::vector<int64_t> expected_valid_outputs = {4, 4};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({8, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({8, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({2}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, matrix_nms_two_batches_two_classes_by_classid_cross_batch) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,  // 0
-                                     0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};  // 1
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,   // 0
-                                      0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3};  // 1
-
-    op::v8::MatrixNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MatrixNms::SortResultType::CLASSID;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-
-    const auto boxes_shape = Shape{2, 6, 4};  // N 2, C 2, M 6
-    const auto scores_shape = Shape{2, 2, 6};
-    attrs.decay_function = op::v8::MatrixNms::DecayFunction::LINEAR;
-    attrs.gaussian_sigma = 2.0f;
-    attrs.post_threshold = 0.5;
-    attrs.sort_result_across_batch = true;
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-
-    auto nms = make_shared<op::v8::MatrixNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {3, 0, 9, 6, 0, 3, 6, 9};
-    std::vector<float> expected_selected_scores = {0.00, 0.95, 0.00, 10.00, 1.00, 11.00,   // 3
-                                                   0.00, 0.90, 0.00, 0.00,  1.00, 1.00,    // 0
-                                                   0.00, 0.95, 0.00, 10.00, 1.00, 11.00,   // 9
-                                                   0.00, 0.90, 0.00, 0.00,  1.00, 1.00,    // 6
-                                                   1.00, 0.95, 0.00, 0.00,  1.00, 1.00,    // 0
-                                                   1.00, 0.80, 0.00, 10.00, 1.00, 11.00,   // 3
-                                                   1.00, 0.95, 0.00, 0.00,  1.00, 1.00,    // 6
-                                                   1.00, 0.80, 0.00, 10.00, 1.00, 11.00};  // 9
-    std::vector<int64_t> expected_valid_outputs = {4, 4};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({8, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({8, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({2}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, matrix_nms_by_keep_top_k) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,  // 0
-                                     0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};  // 1
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,   // 0
-                                      0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3};  // 1
-
-    op::v8::MatrixNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MatrixNms::SortResultType::CLASSID;
-    attrs.keep_top_k = 3;
-    attrs.background_class = 0;
-
-    const auto boxes_shape = Shape{2, 6, 4};  // N 2, C 2, M 6
-    const auto scores_shape = Shape{2, 2, 6};
-    attrs.decay_function = op::v8::MatrixNms::DecayFunction::LINEAR;
-    attrs.gaussian_sigma = 2.0f;
-    attrs.post_threshold = 0.0f;
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto nms = make_shared<op::v8::MatrixNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {0, 3, 1, 6, 9, 7};
-    std::vector<float> expected_selected_scores = {
-        1.00, 0.95, 0.00, 0.00, 1.00, 1.00, 1.00, 0.8, 0.00, 10.00, 1.00, 11.00, 1.00, 0.13636364, 0.0, 0.1, 1.0, 1.1,
-        1.00, 0.95, 0.00, 0.00, 1.00, 1.00, 1.00, 0.8, 0.00, 10.00, 1.00, 11.00, 1.00, 0.13636364, 0.0, 0.1, 1.0, 1.1};
-    std::vector<int64_t> expected_valid_outputs = {3, 3};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({6, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({6, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({2}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, matrix_nms_background) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3};
-
-    op::v8::MatrixNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MatrixNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-
-    const auto boxes_shape = Shape{1, 6, 4};  // N 1, C 2, M 6
-    const auto scores_shape = Shape{1, 2, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    attrs.decay_function = op::v8::MatrixNms::DecayFunction::LINEAR;
-    attrs.gaussian_sigma = 2.0f;
-    attrs.post_threshold = 0.0f;
-
-    auto nms = make_shared<op::v8::MatrixNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {3, 0, 0, 3, 1, 1};
-    std::vector<float> expected_selected_scores = {
-        0.00, 0.95, 0.0, 10.0, 1.0, 11.0, 1.00, 0.95,       0.0, 0.0, 1.0, 1.0, 0.00, 0.9,        0.0, 0.0, 1.0, 1.0,
-        1.00, 0.8,  0.0, 10.0, 1.0, 11.0, 0.00, 0.13636364, 0.0, 0.1, 1.0, 1.1, 1.00, 0.13636364, 0.0, 0.1, 1.0, 1.1};
-    std::vector<int64_t> expected_valid_outputs = {6};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({6, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({6, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({1}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, matrix_nms_flipped_coordinates) {
-    std::vector<float> boxes_data = {1.0, 1.0,  0.0, 0.0,  0.0, 0.1,  1.0, 1.1,  0.0, 0.9,   1.0, -0.1,
-                                     0.0, 10.0, 1.0, 11.0, 1.0, 10.1, 0.0, 11.1, 1.0, 101.0, 0.0, 100.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3};
-
-    op::v8::MatrixNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MatrixNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-
-    const auto boxes_shape = Shape{1, 6, 4};  // N 1, C 1, M 6
-    const auto scores_shape = Shape{1, 1, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    attrs.decay_function = op::v8::MatrixNms::DecayFunction::LINEAR;
-    attrs.gaussian_sigma = 2.0f;
-    attrs.post_threshold = 0.0f;
-
-    auto nms = make_shared<op::v8::MatrixNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {3, 0, 1};
-    std::vector<float> expected_selected_scores =
-        {0.00, 0.95, 0.0, 10.0, 1.0, 11.0, 0.00, 0.9, 1.0, 1.0, 0.0, 0.0, 0.00, 0.75, 0.0, 0.1, 1.0, 1.1};
-    std::vector<int64_t> expected_valid_outputs = {3};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({3, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({3, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({1}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, matrix_nms_post_threshold) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3};
-
-    op::v8::MatrixNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.score_threshold = 0.00;
-    attrs.sort_result_type = op::v8::MatrixNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-
-    const auto boxes_shape = Shape{1, 6, 4};  // N 1, C 2, M 6
-    const auto scores_shape = Shape{1, 1, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    attrs.decay_function = op::v8::MatrixNms::DecayFunction::LINEAR;
-    attrs.gaussian_sigma = 2.0f;
-    attrs.post_threshold = 0.8;
-
-    auto nms = make_shared<op::v8::MatrixNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {3, 0};
-    std::vector<float> expected_selected_scores =
-        {0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.9, 0.00, 0.00, 1.00, 1.00};
-    std::vector<int64_t> expected_valid_outputs = {2};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({2, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({2, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({1}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, matrix_nms_identical_boxes) {
-    std::vector<float> boxes_data = {0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0,
-                                     1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0,
-                                     0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0};
-
-    std::vector<float> scores_data = {0.4, 0.01, 0.2, 0.09, 0.15, 0.05, 0.02, 0.03, 0.05, 0.0};
-
-    op::v8::MatrixNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MatrixNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-
-    const auto boxes_shape = Shape{1, 10, 4};  // N 1, C 1, M 10
-    const auto scores_shape = Shape{1, 1, 10};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    attrs.decay_function = op::v8::MatrixNms::DecayFunction::LINEAR;
-    attrs.gaussian_sigma = 2.0f;
-    attrs.post_threshold = 0.3;
-
-    auto nms = make_shared<op::v8::MatrixNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {0};
-    std::vector<float> expected_selected_scores = {0.00, 0.40, 0.00, 0.00, 1.00, 1.00};
-    std::vector<int64_t> expected_valid_outputs = {1};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({1, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({1, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({1}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, matrix_nms_nms_top_k) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3};
-
-    op::v8::MatrixNms::Attributes attrs;
-    attrs.nms_top_k = 2;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MatrixNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-
-    const auto boxes_shape = Shape{1, 6, 4};
-    const auto scores_shape = Shape{1, 1, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    attrs.decay_function = op::v8::MatrixNms::DecayFunction::LINEAR;
-    attrs.gaussian_sigma = 2.0f;
-    attrs.post_threshold = 0.0f;
-
-    auto nms = make_shared<op::v8::MatrixNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {3, 0};
-    std::vector<float> expected_selected_scores =
-        {0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.90, 0.00, 0.00, 1.00, 1.00};
-    std::vector<int64_t> expected_valid_outputs = {2};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({2, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({2, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({1}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, matrix_nms_single_box) {
-    std::vector<float> boxes_data = {0.0, 0.0, 1.0, 1.0};
-
-    std::vector<float> scores_data = {0.9};
-
-    op::v8::MatrixNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MatrixNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-
-    const auto boxes_shape = Shape{1, 1, 4};
-    const auto scores_shape = Shape{1, 1, 1};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    attrs.decay_function = op::v8::MatrixNms::DecayFunction::LINEAR;
-    attrs.gaussian_sigma = 2.0f;
-    attrs.post_threshold = 0.0f;
-
-    auto nms = make_shared<op::v8::MatrixNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {0};
-    std::vector<float> expected_selected_scores = {0.00, 0.90, 0.00, 0.00, 1.00, 1.00};
-    std::vector<int64_t> expected_valid_outputs = {1};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({1, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({1, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({1}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, matrix_nms_no_output) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3};
-
-    op::v8::MatrixNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.score_threshold = 2.0f;
-    attrs.sort_result_type = op::v8::MatrixNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-
-    const auto boxes_shape = Shape{1, 6, 4};
-    const auto scores_shape = Shape{1, 1, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    attrs.decay_function = op::v8::MatrixNms::DecayFunction::LINEAR;
-    attrs.gaussian_sigma = 2.0f;
-    attrs.post_threshold = 0.0f;
-
-    auto nms = make_shared<op::v8::MatrixNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {};
-    std::vector<float> expected_selected_scores = {};
-    std::vector<int64_t> expected_valid_outputs = {0};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({0, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({0, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({1}, expected_valid_outputs);
-    test_case.run();
-}
diff --git a/ngraph/test/backend/multiclass_nms.in.cpp b/ngraph/test/backend/multiclass_nms.in.cpp
deleted file mode 100644
index 6143f9d1c54..00000000000
--- a/ngraph/test/backend/multiclass_nms.in.cpp
+++ /dev/null
@@ -1,735 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-// clang-format off
-#ifdef ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
-#define DEFAULT_FLOAT_TOLERANCE_BITS ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
-#endif
-
-#ifdef ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
-#define DEFAULT_DOUBLE_TOLERANCE_BITS ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
-#endif
-// clang-format on
-
-#include "gtest/gtest.h"
-#include "ngraph/ngraph.hpp"
-#include "engines_util/test_engines.hpp"
-#include "engines_util/test_case.hpp"
-#include "util/test_control.hpp"
-
-using namespace std;
-using namespace ngraph;
-
-static string s_manifest = "${MANIFEST}";
-using TestEngine = test::ENGINE_CLASS_NAME(${BACKEND_NAME});
-
-NGRAPH_TEST(${BACKEND_NAME}, multiclass_nms_by_score) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3};
-
-    op::v8::MulticlassNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.iou_threshold = 0.5f;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MulticlassNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-    attrs.nms_eta = 1.0f;
-
-    const auto boxes_shape = Shape{1, 6, 4};  // N 1, C 2, M 6
-    const auto scores_shape = Shape{1, 2, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-
-    auto nms = make_shared<op::v8::MulticlassNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {3, 0, 0, 3};
-    std::vector<float> expected_selected_scores = {0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 1.00, 0.95,
-                                                   0.00, 0.00, 1.00, 1.00,  0.00, 0.90,  0.00, 0.00,
-                                                   1.00, 1.00, 1.00, 0.80,  0.00, 10.00, 1.00, 11.00};
-    std::vector<int64_t> expected_valid_outputs = {4};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({4, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({4, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({1}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, multiclass_nms_by_class_id) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3};
-
-    op::v8::MulticlassNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.iou_threshold = 0.5f;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MulticlassNms::SortResultType::CLASSID;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-    attrs.nms_eta = 1.0f;
-
-    const auto boxes_shape = Shape{1, 6, 4};  // N 1, C 2, M 6
-    const auto scores_shape = Shape{1, 2, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-
-    auto nms = make_shared<op::v8::MulticlassNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {3, 0, 0, 3};
-    std::vector<float> expected_selected_scores = {0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.90,
-                                                   0.00, 0.00, 1.00, 1.00,  1.00, 0.95,  0.00, 0.00,
-                                                   1.00, 1.00, 1.00, 0.80,  0.00, 10.00, 1.00, 11.00};
-    std::vector<int64_t> expected_valid_outputs = {4};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({4, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({4, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({1}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, multiclass_nms_output_type_i32) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3};
-
-    op::v8::MulticlassNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.iou_threshold = 0.5f;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MulticlassNms::SortResultType::CLASSID;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-    attrs.nms_eta = 1.0f;
-    attrs.output_type = element::i32;
-
-    const auto boxes_shape = Shape{1, 6, 4};  // N 1, C 2, M 6
-    const auto scores_shape = Shape{1, 2, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-
-    auto nms = make_shared<op::v8::MulticlassNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int32_t> expected_selected_indices = {3, 0, 0, 3};
-    std::vector<float> expected_selected_scores = {0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.90,
-                                                   0.00, 0.00, 1.00, 1.00,  1.00, 0.95,  0.00, 0.00,
-                                                   1.00, 1.00, 1.00, 0.80,  0.00, 10.00, 1.00, 11.00};
-    std::vector<int32_t> expected_valid_outputs = {4};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({4, 6}, expected_selected_scores);
-    test_case.add_expected_output<int32_t>({4, 1}, expected_selected_indices);
-    test_case.add_expected_output<int32_t>({1}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, multiclass_nms_two_batches_two_classes_by_score) {
-    std::vector<float> boxes_data = {
-        0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-        0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,  // 0
-        0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-        0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0  // 1
-    };
-
-    std::vector<float> scores_data = {
-        0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,  // 0
-        0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3   // 1
-    };
-
-    op::v8::MulticlassNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.iou_threshold = 0.5f;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MulticlassNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-    attrs.nms_eta = 1.0f;
-
-    const auto boxes_shape = Shape{2, 6, 4};  // N 2, C 2, M 6
-    const auto scores_shape = Shape{2, 2, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-
-    auto nms = make_shared<op::v8::MulticlassNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {3, 0, 0, 3, 9, 6, 6, 9};
-    std::vector<float> expected_selected_scores = {
-        0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 1.00, 0.95, 0.00, 0.00,  1.00, 1.00,
-        0.00, 0.90, 0.00, 0.00,  1.00, 1.00,  1.00, 0.80, 0.00, 10.00, 1.00, 11.00,  // 0
-        0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 1.00, 0.95, 0.00, 0.00,  1.00, 1.00,
-        0.00, 0.90, 0.00, 0.00,  1.00, 1.00,  1.00, 0.80, 0.00, 10.00, 1.00, 11.00};  // 1
-    std::vector<int64_t> expected_valid_outputs = {4, 4};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({8, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({8, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({2}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, multiclass_nms_two_batches_two_classes_by_class_id) {
-    std::vector<float> boxes_data = {
-        0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-        0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,  // 0
-        0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-        0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0  // 1
-    };
-
-    std::vector<float> scores_data = {
-        0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,  // 0
-        0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3   // 1
-    };
-
-    op::v8::MulticlassNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.iou_threshold = 0.5f;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MulticlassNms::SortResultType::CLASSID;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-    attrs.nms_eta = 1.0f;
-
-    const auto boxes_shape = Shape{2, 6, 4};  // N 2, C 2, M 6
-    const auto scores_shape = Shape{2, 2, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto nms = make_shared<op::v8::MulticlassNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {3, 0, 0, 3, 9, 6, 6, 9};
-    std::vector<float> expected_selected_scores = {
-        0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.90, 0.00, 0.00,  1.00, 1.00,
-        1.00, 0.95, 0.00, 0.00,  1.00, 1.00,  1.00, 0.80, 0.00, 10.00, 1.00, 11.00,  // 0
-        0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.90, 0.00, 0.00,  1.00, 1.00,
-        1.00, 0.95, 0.00, 0.00,  1.00, 1.00,  1.00, 0.80, 0.00, 10.00, 1.00, 11.00};  // 1
-    std::vector<int64_t> expected_valid_outputs = {4, 4};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({8, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({8, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({2}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, multiclass_nms_two_batches_two_classes_by_score_cross_batch) {
-    std::vector<float> boxes_data = {
-        0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-        0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,  // 0
-        0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-        0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0  // 1
-    };
-
-    std::vector<float> scores_data = {
-        0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,  // 0
-        0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3   // 1
-    };
-
-    op::v8::MulticlassNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.iou_threshold = 0.5f;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MulticlassNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-    attrs.nms_eta = 1.0f;
-    attrs.sort_result_across_batch = true;
-
-    const auto boxes_shape = Shape{2, 6, 4};  // N 2, C 2, M 6
-    const auto scores_shape = Shape{2, 2, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-
-    auto nms = make_shared<op::v8::MulticlassNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {3, 0, 9, 6, 0, 6, 3, 9};
-    std::vector<float> expected_selected_scores = {0.00, 0.95, 0.00, 10.00, 1.00, 11.00,   // 3
-                                                   1.00, 0.95, 0.00, 0.00,  1.00, 1.00,    // 0
-                                                   0.00, 0.95, 0.00, 10.00, 1.00, 11.00,   // 9
-                                                   1.00, 0.95, 0.00, 0.00,  1.00, 1.00,    // 6
-                                                   0.00, 0.90, 0.00, 0.00,  1.00, 1.00,    // 0
-                                                   0.00, 0.90, 0.00, 0.00,  1.00, 1.00,    // 6
-                                                   1.00, 0.80, 0.00, 10.00, 1.00, 11.00,   // 3
-                                                   1.00, 0.80, 0.00, 10.00, 1.00, 11.00};  // 9
-    std::vector<int64_t> expected_valid_outputs = {4, 4};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({8, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({8, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({2}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, multiclass_nms_two_batches_two_classes_by_class_id_cross_batch) {
-    std::vector<float> boxes_data = {
-        0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-        0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,  // 0
-        0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-        0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0  // 1
-    };
-
-    std::vector<float> scores_data = {
-        0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,  // 0
-        0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3   // 1
-    };
-
-    op::v8::MulticlassNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.iou_threshold = 0.5f;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MulticlassNms::SortResultType::CLASSID;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-    attrs.nms_eta = 1.0f;
-    attrs.sort_result_across_batch = true;
-
-    const auto boxes_shape = Shape{2, 6, 4};  // N 2, C 2, M 6
-    const auto scores_shape = Shape{2, 2, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto nms = make_shared<op::v8::MulticlassNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {3, 0, 9, 6, 0, 3, 6, 9};
-    std::vector<float> expected_selected_scores = {0.00, 0.95, 0.00, 10.00, 1.00, 11.00,   // 3
-                                                   0.00, 0.90, 0.00, 0.00,  1.00, 1.00,    // 0
-                                                   0.00, 0.95, 0.00, 10.00, 1.00, 11.00,   // 9
-                                                   0.00, 0.90, 0.00, 0.00,  1.00, 1.00,    // 6
-                                                   1.00, 0.95, 0.00, 0.00,  1.00, 1.00,    // 0
-                                                   1.00, 0.80, 0.00, 10.00, 1.00, 11.00,   // 3
-                                                   1.00, 0.95, 0.00, 0.00,  1.00, 1.00,    // 6
-                                                   1.00, 0.80, 0.00, 10.00, 1.00, 11.00};  // 9
-    std::vector<int64_t> expected_valid_outputs = {4, 4};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({8, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({8, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({2}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, multiclass_nms_flipped_coordinates) {
-    std::vector<float> boxes_data = {1.0, 1.0,  0.0, 0.0,  0.0, 0.1,  1.0, 1.1,  0.0, 0.9,   1.0, -0.1,
-                                     0.0, 10.0, 1.0, 11.0, 1.0, 10.1, 0.0, 11.1, 1.0, 101.0, 0.0, 100.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3};
-
-    op::v8::MulticlassNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.iou_threshold = 0.5f;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MulticlassNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-    attrs.nms_eta = 1.0f;
-
-    const auto boxes_shape = Shape{1, 6, 4};  // N 1, C 1, M 6
-    const auto scores_shape = Shape{1, 1, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto nms = make_shared<op::v8::MulticlassNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {3, 0, 1};
-    std::vector<float> expected_selected_scores =
-        {0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.90, 1.00, 1.00, 0.00, 0.00, 0.00, 0.75, 0.00, 0.10, 1.00, 1.10};
-    std::vector<int64_t> expected_valid_outputs = {3};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({3, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({3, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({1}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, multiclass_nms_identical_boxes) {
-    std::vector<float> boxes_data = {0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0,
-                                     1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0,
-                                     0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0};
-
-    std::vector<float> scores_data = {0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9};
-
-    op::v8::MulticlassNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.iou_threshold = 0.5f;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MulticlassNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-    attrs.nms_eta = 1.0f;
-
-    const auto boxes_shape = Shape{1, 10, 4};  // N 1, C 1, M 10
-    const auto scores_shape = Shape{1, 1, 10};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto nms = make_shared<op::v8::MulticlassNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {0};
-    std::vector<float> expected_selected_scores = {0.00, 0.90, 0.00, 0.00, 1.00, 1.00};
-    std::vector<int64_t> expected_valid_outputs = {1};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({1, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({1, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({1}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, multiclass_nms_limit_output_size) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3};
-
-    op::v8::MulticlassNms::Attributes attrs;
-    attrs.nms_top_k = 2;
-    attrs.iou_threshold = 0.5f;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MulticlassNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-    attrs.nms_eta = 1.0f;
-
-    const auto boxes_shape = Shape{1, 6, 4};
-    const auto scores_shape = Shape{1, 1, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto nms = make_shared<op::v8::MulticlassNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {3, 0};
-    std::vector<float> expected_selected_scores =
-        {0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.90, 0.00, 0.00, 1.00, 1.00};
-    std::vector<int64_t> expected_valid_outputs = {2};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({2, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({2, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({1}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, multiclass_nms_single_box) {
-    std::vector<float> boxes_data = {0.0, 0.0, 1.0, 1.0};
-
-    std::vector<float> scores_data = {0.9};
-
-    op::v8::MulticlassNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.iou_threshold = 0.5f;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MulticlassNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-    attrs.nms_eta = 1.0f;
-
-    const auto boxes_shape = Shape{1, 1, 4};
-    const auto scores_shape = Shape{1, 1, 1};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto nms = make_shared<op::v8::MulticlassNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {0};
-    std::vector<float> expected_selected_scores = {0.00, 0.90, 0.00, 0.00, 1.00, 1.00};
-    std::vector<int64_t> expected_valid_outputs = {1};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({1, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({1, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({1}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, multiclass_nms_by_IOU) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3};
-
-    op::v8::MulticlassNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.iou_threshold = 0.2f;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MulticlassNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-    attrs.nms_eta = 1.0f;
-
-    const auto boxes_shape = Shape{1, 6, 4};
-    const auto scores_shape = Shape{1, 1, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto nms = make_shared<op::v8::MulticlassNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {3, 0};
-    std::vector<float> expected_selected_scores =
-        {0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.90, 0.00, 0.00, 1.00, 1.00};
-    std::vector<int64_t> expected_valid_outputs = {2};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({2, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({2, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({1}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, multiclass_nms_by_IOU_and_scores) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3};
-
-    op::v8::MulticlassNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.iou_threshold = 0.5f;
-    attrs.score_threshold = 0.95f;
-    attrs.sort_result_type = op::v8::MulticlassNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-    attrs.nms_eta = 1.0f;
-
-    const auto boxes_shape = Shape{1, 6, 4};
-    const auto scores_shape = Shape{1, 1, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto nms = make_shared<op::v8::MulticlassNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {3};
-    std::vector<float> expected_selected_scores = {0.00, 0.95, 0.00, 10.00, 1.00, 11.00};
-    std::vector<int64_t> expected_valid_outputs = {1};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({1, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({1, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({1}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, multiclass_nms_no_output) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3};
-
-    op::v8::MulticlassNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.iou_threshold = 0.5f;
-    attrs.score_threshold = 2.0f;
-    attrs.sort_result_type = op::v8::MulticlassNms::SortResultType::SCORE;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-    attrs.nms_eta = 1.0f;
-
-    const auto boxes_shape = Shape{1, 6, 4};
-    const auto scores_shape = Shape{1, 1, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto nms = make_shared<op::v8::MulticlassNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {};
-    std::vector<float> expected_selected_scores = {};
-    std::vector<int64_t> expected_valid_outputs = {0};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({0, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({0, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({1}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, multiclass_nms_by_background) {
-    std::vector<float> boxes_data = {
-        0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-        0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,  // 0
-        0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-        0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0  // 1
-    };
-
-    std::vector<float> scores_data = {
-        0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,  // 0
-        0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3   // 1
-    };
-
-    op::v8::MulticlassNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.iou_threshold = 0.5f;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MulticlassNms::SortResultType::CLASSID;
-    attrs.keep_top_k = -1;
-    attrs.background_class = 0;
-    attrs.nms_eta = 1.0f;
-
-    const auto boxes_shape = Shape{2, 6, 4};  // N 2, C 2, M 6
-    const auto scores_shape = Shape{2, 2, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto nms = make_shared<op::v8::MulticlassNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {0, 3, 6, 9};
-    std::vector<float> expected_selected_scores = {
-        1.00, 0.95, 0.00, 0.00, 1.00, 1.00, 1.00, 0.80, 0.00, 10.00, 1.00, 11.00,   // 0
-        1.00, 0.95, 0.00, 0.00, 1.00, 1.00, 1.00, 0.80, 0.00, 10.00, 1.00, 11.00};  // 1
-    std::vector<int64_t> expected_valid_outputs = {2, 2};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({4, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({4, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({2}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, multiclass_nms_by_keep_top_k) {
-    std::vector<float> boxes_data = {
-        0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-        0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,  // 0
-        0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-        0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0  // 1
-    };
-
-    std::vector<float> scores_data = {
-        0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,  // 0
-        0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3   // 1
-    };
-
-    op::v8::MulticlassNms::Attributes attrs;
-    attrs.nms_top_k = 3;
-    attrs.iou_threshold = 0.5f;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MulticlassNms::SortResultType::CLASSID;
-    attrs.keep_top_k = 3;
-    attrs.background_class = -1;
-    attrs.nms_eta = 1.0f;
-
-    const auto boxes_shape = Shape{2, 6, 4};  // N 2, C 2, M 6
-    const auto scores_shape = Shape{2, 2, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto nms = make_shared<op::v8::MulticlassNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {3, 0, 0, 9, 6, 6};
-    std::vector<float> expected_selected_scores = {0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.90, 0.00,
-                                                   0.00, 1.00, 1.00, 1.00,  0.95, 0.00,  0.00, 1.00, 1.00,  // 0
-                                                   0.00, 0.95, 0.00, 10.00, 1.00, 11.00, 0.00, 0.90, 0.00,
-                                                   0.00, 1.00, 1.00, 1.00,  0.95, 0.00,  0.00, 1.00, 1.00};  // 1
-    std::vector<int64_t> expected_valid_outputs = {3, 3};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({6, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({6, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({2}, expected_valid_outputs);
-    test_case.run();
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, multiclass_nms_by_nms_eta) {
-    std::vector<float> boxes_data = {
-        0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-        0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,  // 0
-        0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-        0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0  // 1
-    };
-
-    std::vector<float> scores_data = {
-        0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3,  // 0
-        0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.95, 0.75, 0.6, 0.80, 0.5, 0.3   // 1
-    };
-
-    op::v8::MulticlassNms::Attributes attrs;
-    attrs.nms_top_k = -1;
-    attrs.iou_threshold = 1.0f;
-    attrs.score_threshold = 0.0f;
-    attrs.sort_result_type = op::v8::MulticlassNms::SortResultType::CLASSID;
-    attrs.keep_top_k = -1;
-    attrs.background_class = -1;
-    attrs.nms_eta = 0.1f;
-
-    const auto boxes_shape = Shape{2, 6, 4};  // N 2, C 2, M 6
-    const auto scores_shape = Shape{2, 2, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto nms = make_shared<op::v8::MulticlassNms>(boxes, scores, attrs);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    std::vector<int64_t> expected_selected_indices = {3, 0, 5, 0, 3, 5, 9, 6, 11, 6, 9, 11};
-    std::vector<float> expected_selected_scores = {
-        0.00,   0.95, 0.00,   10.00,  1.00, 11.00,  0.00,   0.90, 0.00,   0.00,   1.00, 1.00,  0.00,  0.30, 0.00,
-        100.00, 1.00, 101.00, 1.00,   0.95, 0.00,   0.00,   1.00, 1.00,   1.00,   0.80, 0.00,  10.00, 1.00, 11.00,
-        1.00,   0.30, 0.00,   100.00, 1.00, 101.00, 0.00,   0.95, 0.00,   10.00,  1.00, 11.00, 0.00,  0.90, 0.00,
-        0.00,   1.00, 1.00,   0.00,   0.30, 0.00,   100.00, 1.00, 101.00, 1.00,   0.95, 0.00,  0.00,  1.00, 1.00,
-        1.00,   0.80, 0.00,   10.00,  1.00, 11.00,  1.00,   0.30, 0.00,   100.00, 1.00, 101.00};
-    std::vector<int64_t> expected_valid_outputs = {6, 6};
-
-    auto test_case = test::TestCase<TestEngine, test::TestCaseType::DYNAMIC>(f);
-    test_case.add_multiple_inputs<float>({boxes_data, scores_data});
-    test_case.add_expected_output<float>({12, 6}, expected_selected_scores);
-    test_case.add_expected_output<int64_t>({12, 1}, expected_selected_indices);
-    test_case.add_expected_output<int64_t>({2}, expected_valid_outputs);
-    test_case.run();
-}
diff --git a/ngraph/test/backend/non_max_suppression.in.cpp b/ngraph/test/backend/non_max_suppression.in.cpp
deleted file mode 100644
index 652e2bfafa2..00000000000
--- a/ngraph/test/backend/non_max_suppression.in.cpp
+++ /dev/null
@@ -1,638 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-// clang-format off
-#ifdef ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
-#define DEFAULT_FLOAT_TOLERANCE_BITS ${BACKEND_NAME}_FLOAT_TOLERANCE_BITS
-#endif
-
-#ifdef ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
-#define DEFAULT_DOUBLE_TOLERANCE_BITS ${BACKEND_NAME}_DOUBLE_TOLERANCE_BITS
-#endif
-// clang-format on
-
-#include "gtest/gtest.h"
-#include "runtime/backend.hpp"
-#include "ngraph/runtime/tensor.hpp"
-#include "ngraph/ngraph.hpp"
-#include "util/all_close.hpp"
-#include "util/all_close_f.hpp"
-#include "util/ndarray.hpp"
-#include "util/test_control.hpp"
-#include "engines_util/execute_tools.hpp"
-
-NGRAPH_SUPPRESS_DEPRECATED_START
-
-using namespace std;
-using namespace ngraph;
-
-static string s_manifest = "${MANIFEST}";
-
-NGRAPH_TEST(${BACKEND_NAME}, nonmaxsuppression_center_point_box_format) {
-    std::vector<float> boxes_data = {0.5, 0.5,  1.0, 1.0, 0.5, 0.6,  1.0, 1.0, 0.5, 0.4,   1.0, 1.0,
-                                     0.5, 10.5, 1.0, 1.0, 0.5, 10.6, 1.0, 1.0, 0.5, 100.5, 1.0, 1.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3};
-
-    const int64_t max_output_boxes_per_class_data = 3;
-    const float iou_threshold_data = 0.5f;
-    const float score_threshold_data = 0.0f;
-    const auto box_encoding = op::v5::NonMaxSuppression::BoxEncodingType::CENTER;
-    const auto boxes_shape = Shape{1, 6, 4};
-    const auto scores_shape = Shape{1, 1, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto max_output_boxes_per_class =
-        op::Constant::create<int64_t>(element::i64, Shape{}, {max_output_boxes_per_class_data});
-    auto iou_threshold = op::Constant::create<float>(element::f32, Shape{}, {iou_threshold_data});
-    auto score_threshold = op::Constant::create<float>(element::f32, Shape{}, {score_threshold_data});
-    auto soft_nms_sigma = op::Constant::create<float>(element::f32, Shape{}, {0.0f});
-    auto nms = make_shared<op::v5::NonMaxSuppression>(boxes,
-                                                      scores,
-                                                      max_output_boxes_per_class,
-                                                      iou_threshold,
-                                                      score_threshold,
-                                                      soft_nms_sigma,
-                                                      box_encoding,
-                                                      false);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    auto selected_indeces = backend->create_tensor(element::i64, Shape{3, 3});
-    auto selected_scores = backend->create_tensor(element::f32, Shape{3, 3});
-    auto valid_outputs = backend->create_tensor(element::i64, Shape{1});
-
-    auto backend_boxes = backend->create_tensor(element::f32, boxes_shape);
-    auto backend_scores = backend->create_tensor(element::f32, scores_shape);
-    copy_data(backend_boxes, boxes_data);
-    copy_data(backend_scores, scores_data);
-
-    auto handle = backend->compile(f);
-
-    handle->call({selected_indeces, selected_scores, valid_outputs}, {backend_boxes, backend_scores});
-
-    auto selected_indeces_value = read_vector<int64_t>(selected_indeces);
-    auto selected_scores_value = read_vector<float>(selected_scores);
-    auto valid_outputs_value = read_vector<int64_t>(valid_outputs);
-
-    std::vector<int64_t> expected_selected_indices = {0, 0, 3, 0, 0, 0, 0, 0, 5};
-    std::vector<float> expected_selected_scores = {0.0, 0.0, 0.95, 0.0, 0.0, 0.9, 0.0, 0.0, 0.3};
-    std::vector<int64_t> expected_valid_outputs = {3};
-
-    EXPECT_EQ(expected_selected_indices, selected_indeces_value);
-    EXPECT_EQ(expected_selected_scores, selected_scores_value);
-    EXPECT_EQ(expected_valid_outputs, valid_outputs_value);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, nonmaxsuppression_flipped_coordinates) {
-    std::vector<float> boxes_data = {1.0, 1.0,  0.0, 0.0,  0.0, 0.1,  1.0, 1.1,  0.0, 0.9,   1.0, -0.1,
-                                     0.0, 10.0, 1.0, 11.0, 1.0, 10.1, 0.0, 11.1, 1.0, 101.0, 0.0, 100.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3};
-
-    const int64_t max_output_boxes_per_class_data = 3;
-    const float iou_threshold_data = 0.5f;
-    const float score_threshold_data = 0.0f;
-    const auto box_encoding = op::v5::NonMaxSuppression::BoxEncodingType::CORNER;
-    const auto boxes_shape = Shape{1, 6, 4};
-    const auto scores_shape = Shape{1, 1, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto max_output_boxes_per_class =
-        op::Constant::create<int64_t>(element::i64, Shape{}, {max_output_boxes_per_class_data});
-    auto iou_threshold = op::Constant::create<float>(element::f32, Shape{}, {iou_threshold_data});
-    auto score_threshold = op::Constant::create<float>(element::f32, Shape{}, {score_threshold_data});
-    auto soft_nms_sigma = op::Constant::create<float>(element::f32, Shape{}, {0.0f});
-    auto nms = make_shared<op::v5::NonMaxSuppression>(boxes,
-                                                      scores,
-                                                      max_output_boxes_per_class,
-                                                      iou_threshold,
-                                                      score_threshold,
-                                                      soft_nms_sigma,
-                                                      box_encoding,
-                                                      false);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    auto selected_indeces = backend->create_tensor(element::i64, Shape{3, 3});
-    auto selected_scores = backend->create_tensor(element::f32, Shape{3, 3});
-    auto valid_outputs = backend->create_tensor(element::i64, Shape{1});
-
-    auto backend_boxes = backend->create_tensor(element::f32, boxes_shape);
-    auto backend_scores = backend->create_tensor(element::f32, scores_shape);
-    copy_data(backend_boxes, boxes_data);
-    copy_data(backend_scores, scores_data);
-
-    auto handle = backend->compile(f);
-
-    handle->call({selected_indeces, selected_scores, valid_outputs}, {backend_boxes, backend_scores});
-
-    auto selected_indeces_value = read_vector<int64_t>(selected_indeces);
-    auto selected_scores_value = read_vector<float>(selected_scores);
-    auto valid_outputs_value = read_vector<int64_t>(valid_outputs);
-
-    std::vector<int64_t> expected_selected_indices = {0, 0, 3, 0, 0, 0, 0, 0, 5};
-    std::vector<float> expected_selected_scores = {0.0, 0.0, 0.95, 0.0, 0.0, 0.9, 0.0, 0.0, 0.3};
-    std::vector<int64_t> expected_valid_outputs = {3};
-
-    EXPECT_EQ(expected_selected_indices, selected_indeces_value);
-    EXPECT_EQ(expected_selected_scores, selected_scores_value);
-    EXPECT_EQ(expected_valid_outputs, valid_outputs_value);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, nonmaxsuppression_identical_boxes) {
-    std::vector<float> boxes_data = {0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0,
-                                     1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0,
-                                     0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0};
-
-    std::vector<float> scores_data = {0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9};
-
-    const int64_t max_output_boxes_per_class_data = 3;
-    const float iou_threshold_data = 0.5f;
-    const float score_threshold_data = 0.0f;
-    const auto box_encoding = op::v5::NonMaxSuppression::BoxEncodingType::CORNER;
-    const auto boxes_shape = Shape{1, 10, 4};
-    const auto scores_shape = Shape{1, 1, 10};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto max_output_boxes_per_class =
-        op::Constant::create<int64_t>(element::i64, Shape{}, {max_output_boxes_per_class_data});
-    auto iou_threshold = op::Constant::create<float>(element::f32, Shape{}, {iou_threshold_data});
-    auto score_threshold = op::Constant::create<float>(element::f32, Shape{}, {score_threshold_data});
-    auto soft_nms_sigma = op::Constant::create<float>(element::f32, Shape{}, {0.0f});
-    auto nms = make_shared<op::v5::NonMaxSuppression>(boxes,
-                                                      scores,
-                                                      max_output_boxes_per_class,
-                                                      iou_threshold,
-                                                      score_threshold,
-                                                      soft_nms_sigma,
-                                                      box_encoding,
-                                                      false);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    auto selected_indeces = backend->create_tensor(element::i64, Shape{1, 3});
-    auto selected_scores = backend->create_tensor(element::f32, Shape{1, 3});
-    auto valid_outputs = backend->create_tensor(element::i64, Shape{1});
-
-    auto backend_boxes = backend->create_tensor(element::f32, boxes_shape);
-    auto backend_scores = backend->create_tensor(element::f32, scores_shape);
-    copy_data(backend_boxes, boxes_data);
-    copy_data(backend_scores, scores_data);
-
-    auto handle = backend->compile(f);
-
-    handle->call({selected_indeces, selected_scores, valid_outputs}, {backend_boxes, backend_scores});
-
-    auto selected_indeces_value = read_vector<int64_t>(selected_indeces);
-    auto selected_scores_value = read_vector<float>(selected_scores);
-    auto valid_outputs_value = read_vector<int64_t>(valid_outputs);
-
-    std::vector<int64_t> expected_selected_indices = {0, 0, 0};
-    std::vector<float> expected_selected_scores = {0.0, 0.0, 0.9};
-    std::vector<int64_t> expected_valid_outputs = {1};
-
-    EXPECT_EQ(expected_selected_indices, selected_indeces_value);
-    EXPECT_EQ(expected_selected_scores, selected_scores_value);
-    EXPECT_EQ(expected_valid_outputs, valid_outputs_value);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, nonmaxsuppression_limit_output_size) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3};
-
-    const int64_t max_output_boxes_per_class_data = 2;
-    const float iou_threshold_data = 0.5f;
-    const float score_threshold_data = 0.0f;
-    const auto box_encoding = op::v5::NonMaxSuppression::BoxEncodingType::CORNER;
-    const auto boxes_shape = Shape{1, 6, 4};
-    const auto scores_shape = Shape{1, 1, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto max_output_boxes_per_class =
-        op::Constant::create<int64_t>(element::i64, Shape{}, {max_output_boxes_per_class_data});
-    auto iou_threshold = op::Constant::create<float>(element::f32, Shape{}, {iou_threshold_data});
-    auto score_threshold = op::Constant::create<float>(element::f32, Shape{}, {score_threshold_data});
-    auto soft_nms_sigma = op::Constant::create<float>(element::f32, Shape{}, {0.0f});
-    auto nms = make_shared<op::v5::NonMaxSuppression>(boxes,
-                                                      scores,
-                                                      max_output_boxes_per_class,
-                                                      iou_threshold,
-                                                      score_threshold,
-                                                      soft_nms_sigma,
-                                                      box_encoding,
-                                                      false);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    auto selected_indeces = backend->create_tensor(element::i64, Shape{2, 3});
-    auto selected_scores = backend->create_tensor(element::f32, Shape{2, 3});
-    auto valid_outputs = backend->create_tensor(element::i64, Shape{1});
-
-    auto backend_boxes = backend->create_tensor(element::f32, boxes_shape);
-    auto backend_scores = backend->create_tensor(element::f32, scores_shape);
-    copy_data(backend_boxes, boxes_data);
-    copy_data(backend_scores, scores_data);
-
-    auto handle = backend->compile(f);
-
-    handle->call({selected_indeces, selected_scores, valid_outputs}, {backend_boxes, backend_scores});
-
-    auto selected_indeces_value = read_vector<int64_t>(selected_indeces);
-    auto selected_scores_value = read_vector<float>(selected_scores);
-    auto valid_outputs_value = read_vector<int64_t>(valid_outputs);
-
-    std::vector<int64_t> expected_selected_indices = {0, 0, 3, 0, 0, 0};
-    std::vector<float> expected_selected_scores = {0.0, 0.0, 0.95, 0.0, 0.0, 0.9};
-    std::vector<int64_t> expected_valid_outputs = {2};
-
-    EXPECT_EQ(expected_selected_indices, selected_indeces_value);
-    EXPECT_EQ(expected_selected_scores, selected_scores_value);
-    EXPECT_EQ(expected_valid_outputs, valid_outputs_value);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, nonmaxsuppression_single_box) {
-    std::vector<float> boxes_data = {0.0, 0.0, 1.0, 1.0};
-
-    std::vector<float> scores_data = {0.9};
-
-    const int64_t max_output_boxes_per_class_data = 3;
-    const float iou_threshold_data = 0.5f;
-    const float score_threshold_data = 0.0f;
-    const auto box_encoding = op::v5::NonMaxSuppression::BoxEncodingType::CORNER;
-    const auto boxes_shape = Shape{1, 1, 4};
-    const auto scores_shape = Shape{1, 1, 1};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto max_output_boxes_per_class =
-        op::Constant::create<int64_t>(element::i64, Shape{}, {max_output_boxes_per_class_data});
-    auto iou_threshold = op::Constant::create<float>(element::f32, Shape{}, {iou_threshold_data});
-    auto score_threshold = op::Constant::create<float>(element::f32, Shape{}, {score_threshold_data});
-    auto soft_nms_sigma = op::Constant::create<float>(element::f32, Shape{}, {0.0f});
-    auto nms = make_shared<op::v5::NonMaxSuppression>(boxes,
-                                                      scores,
-                                                      max_output_boxes_per_class,
-                                                      iou_threshold,
-                                                      score_threshold,
-                                                      soft_nms_sigma,
-                                                      box_encoding,
-                                                      false);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    auto selected_indeces = backend->create_tensor(element::i64, Shape{1, 3});
-    auto selected_scores = backend->create_tensor(element::f32, Shape{1, 3});
-    auto valid_outputs = backend->create_tensor(element::i64, Shape{1});
-
-    auto backend_boxes = backend->create_tensor(element::f32, boxes_shape);
-    auto backend_scores = backend->create_tensor(element::f32, scores_shape);
-    copy_data(backend_boxes, boxes_data);
-    copy_data(backend_scores, scores_data);
-
-    auto handle = backend->compile(f);
-
-    handle->call({selected_indeces, selected_scores, valid_outputs}, {backend_boxes, backend_scores});
-
-    auto selected_indeces_value = read_vector<int64_t>(selected_indeces);
-    auto selected_scores_value = read_vector<float>(selected_scores);
-    auto valid_outputs_value = read_vector<int64_t>(valid_outputs);
-
-    std::vector<int64_t> expected_selected_indices = {0, 0, 0};
-    std::vector<float> expected_selected_scores = {0.0, 0.0, 0.9};
-    std::vector<int64_t> expected_valid_outputs = {1};
-
-    EXPECT_EQ(expected_selected_indices, selected_indeces_value);
-    EXPECT_EQ(expected_selected_scores, selected_scores_value);
-    EXPECT_EQ(expected_valid_outputs, valid_outputs_value);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, nonmaxsuppression_suppress_by_IOU) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3};
-
-    const int64_t max_output_boxes_per_class_data = 3;
-    const float iou_threshold_data = 0.5f;
-    const float score_threshold_data = 0.0f;
-    const auto box_encoding = op::v5::NonMaxSuppression::BoxEncodingType::CORNER;
-    const auto boxes_shape = Shape{1, 6, 4};
-    const auto scores_shape = Shape{1, 1, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto max_output_boxes_per_class =
-        op::Constant::create<int64_t>(element::i64, Shape{}, {max_output_boxes_per_class_data});
-    auto iou_threshold = op::Constant::create<float>(element::f32, Shape{}, {iou_threshold_data});
-    auto score_threshold = op::Constant::create<float>(element::f32, Shape{}, {score_threshold_data});
-    auto soft_nms_sigma = op::Constant::create<float>(element::f32, Shape{}, {0.0f});
-    auto nms = make_shared<op::v5::NonMaxSuppression>(boxes,
-                                                      scores,
-                                                      max_output_boxes_per_class,
-                                                      iou_threshold,
-                                                      score_threshold,
-                                                      soft_nms_sigma,
-                                                      box_encoding,
-                                                      false);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    auto selected_indeces = backend->create_tensor(element::i64, Shape{3, 3});
-    auto selected_scores = backend->create_tensor(element::f32, Shape{3, 3});
-    auto valid_outputs = backend->create_tensor(element::i64, Shape{1});
-
-    auto backend_boxes = backend->create_tensor(element::f32, boxes_shape);
-    auto backend_scores = backend->create_tensor(element::f32, scores_shape);
-    copy_data(backend_boxes, boxes_data);
-    copy_data(backend_scores, scores_data);
-
-    auto handle = backend->compile(f);
-
-    handle->call({selected_indeces, selected_scores, valid_outputs}, {backend_boxes, backend_scores});
-
-    auto selected_indeces_value = read_vector<int64_t>(selected_indeces);
-    auto selected_scores_value = read_vector<float>(selected_scores);
-    auto valid_outputs_value = read_vector<int64_t>(valid_outputs);
-
-    std::vector<int64_t> expected_selected_indices = {0, 0, 3, 0, 0, 0, 0, 0, 5};
-    std::vector<float> expected_selected_scores = {0.0, 0.0, 0.95, 0.0, 0.0, 0.9, 0.0, 0.0, 0.3};
-    std::vector<int64_t> expected_valid_outputs = {3};
-
-    EXPECT_EQ(expected_selected_indices, selected_indeces_value);
-    EXPECT_EQ(expected_selected_scores, selected_scores_value);
-    EXPECT_EQ(expected_valid_outputs, valid_outputs_value);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, nonmaxsuppression_suppress_by_IOU_and_scores) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3};
-
-    const int64_t max_output_boxes_per_class_data = 3;
-    const float iou_threshold_data = 0.5f;
-    const float score_threshold_data = 0.4f;
-    const auto box_encoding = op::v5::NonMaxSuppression::BoxEncodingType::CORNER;
-    const auto boxes_shape = Shape{1, 6, 4};
-    const auto scores_shape = Shape{1, 1, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto max_output_boxes_per_class =
-        op::Constant::create<int64_t>(element::i64, Shape{}, {max_output_boxes_per_class_data});
-    auto iou_threshold = op::Constant::create<float>(element::f32, Shape{}, {iou_threshold_data});
-    auto score_threshold = op::Constant::create<float>(element::f32, Shape{}, {score_threshold_data});
-    auto soft_nms_sigma = op::Constant::create<float>(element::f32, Shape{}, {0.0f});
-    auto nms = make_shared<op::v5::NonMaxSuppression>(boxes,
-                                                      scores,
-                                                      max_output_boxes_per_class,
-                                                      iou_threshold,
-                                                      score_threshold,
-                                                      soft_nms_sigma,
-                                                      box_encoding,
-                                                      false);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    auto selected_indeces = backend->create_tensor(element::i64, Shape{2, 3});
-    auto selected_scores = backend->create_tensor(element::f32, Shape{2, 3});
-    auto valid_outputs = backend->create_tensor(element::i64, Shape{1});
-
-    auto backend_boxes = backend->create_tensor(element::f32, boxes_shape);
-    auto backend_scores = backend->create_tensor(element::f32, scores_shape);
-    copy_data(backend_boxes, boxes_data);
-    copy_data(backend_scores, scores_data);
-
-    auto handle = backend->compile(f);
-
-    handle->call({selected_indeces, selected_scores, valid_outputs}, {backend_boxes, backend_scores});
-
-    auto selected_indeces_value = read_vector<int64_t>(selected_indeces);
-    auto selected_scores_value = read_vector<float>(selected_scores);
-    auto valid_outputs_value = read_vector<int64_t>(valid_outputs);
-
-    std::vector<int64_t> expected_selected_indices = {0, 0, 3, 0, 0, 0};
-    std::vector<float> expected_selected_scores = {0.0, 0.0, 0.95, 0.0, 0.0, 0.9};
-    std::vector<int64_t> expected_valid_outputs = {2};
-
-    EXPECT_EQ(expected_selected_indices, selected_indeces_value);
-    EXPECT_EQ(expected_selected_scores, selected_scores_value);
-    EXPECT_EQ(expected_valid_outputs, valid_outputs_value);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, nonmaxsuppression_two_batches) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0,
-                                     0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.9, 0.75, 0.6, 0.95, 0.5, 0.3};
-
-    const int64_t max_output_boxes_per_class_data = 2;
-    const float iou_threshold_data = 0.5f;
-    const float score_threshold_data = 0.0f;
-    const auto box_encoding = op::v5::NonMaxSuppression::BoxEncodingType::CORNER;
-    const auto boxes_shape = Shape{2, 6, 4};
-    const auto scores_shape = Shape{2, 1, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto max_output_boxes_per_class =
-        op::Constant::create<int64_t>(element::i64, Shape{}, {max_output_boxes_per_class_data});
-    auto iou_threshold = op::Constant::create<float>(element::f32, Shape{}, {iou_threshold_data});
-    auto score_threshold = op::Constant::create<float>(element::f32, Shape{}, {score_threshold_data});
-    auto soft_nms_sigma = op::Constant::create<float>(element::f32, Shape{}, {0.0f});
-    auto nms = make_shared<op::v5::NonMaxSuppression>(boxes,
-                                                      scores,
-                                                      max_output_boxes_per_class,
-                                                      iou_threshold,
-                                                      score_threshold,
-                                                      soft_nms_sigma,
-                                                      box_encoding,
-                                                      false);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    auto selected_indeces = backend->create_tensor(element::i64, Shape{4, 3});
-    auto selected_scores = backend->create_tensor(element::f32, Shape{4, 3});
-    auto valid_outputs = backend->create_tensor(element::i64, Shape{1});
-
-    auto backend_boxes = backend->create_tensor(element::f32, boxes_shape);
-    auto backend_scores = backend->create_tensor(element::f32, scores_shape);
-    copy_data(backend_boxes, boxes_data);
-    copy_data(backend_scores, scores_data);
-
-    auto handle = backend->compile(f);
-
-    handle->call({selected_indeces, selected_scores, valid_outputs}, {backend_boxes, backend_scores});
-
-    auto selected_indeces_value = read_vector<int64_t>(selected_indeces);
-    auto selected_scores_value = read_vector<float>(selected_scores);
-    auto valid_outputs_value = read_vector<int64_t>(valid_outputs);
-
-    std::vector<int64_t> expected_selected_indices = {0, 0, 3, 0, 0, 0, 1, 0, 3, 1, 0, 0};
-    std::vector<float> expected_selected_scores = {0.0, 0.0, 0.95, 0.0, 0.0, 0.9, 1.0, 0.0, 0.95, 1.0, 0.0, 0.9};
-    std::vector<int64_t> expected_valid_outputs = {4};
-
-    EXPECT_EQ(expected_selected_indices, selected_indeces_value);
-    EXPECT_EQ(expected_selected_scores, selected_scores_value);
-    EXPECT_EQ(expected_valid_outputs, valid_outputs_value);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, nonmaxsuppression_two_classes) {
-    std::vector<float> boxes_data = {0.0, 0.0,  1.0, 1.0,  0.0, 0.1,  1.0, 1.1,  0.0, -0.1,  1.0, 0.9,
-                                     0.0, 10.0, 1.0, 11.0, 0.0, 10.1, 1.0, 11.1, 0.0, 100.0, 1.0, 101.0};
-
-    std::vector<float> scores_data = {0.9, 0.75, 0.6, 0.95, 0.5, 0.3, 0.9, 0.75, 0.6, 0.95, 0.5, 0.3};
-
-    const int64_t max_output_boxes_per_class_data = 2;
-    const float iou_threshold_data = 0.5f;
-    const float score_threshold_data = 0.0f;
-    const auto box_encoding = op::v5::NonMaxSuppression::BoxEncodingType::CORNER;
-    const auto boxes_shape = Shape{1, 6, 4};
-    const auto scores_shape = Shape{1, 2, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    auto max_output_boxes_per_class =
-        op::Constant::create<int64_t>(element::i64, Shape{}, {max_output_boxes_per_class_data});
-    auto iou_threshold = op::Constant::create<float>(element::f32, Shape{}, {iou_threshold_data});
-    auto score_threshold = op::Constant::create<float>(element::f32, Shape{}, {score_threshold_data});
-    auto soft_nms_sigma = op::Constant::create<float>(element::f32, Shape{}, {0.0f});
-    auto nms = make_shared<op::v5::NonMaxSuppression>(boxes,
-                                                      scores,
-                                                      max_output_boxes_per_class,
-                                                      iou_threshold,
-                                                      score_threshold,
-                                                      soft_nms_sigma,
-                                                      box_encoding,
-                                                      false);
-
-    auto f = make_shared<Function>(nms, ParameterVector{boxes, scores});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    auto selected_indeces = backend->create_tensor(element::i64, Shape{4, 3});
-    auto selected_scores = backend->create_tensor(element::f32, Shape{4, 3});
-    auto valid_outputs = backend->create_tensor(element::i64, Shape{1});
-
-    auto backend_boxes = backend->create_tensor(element::f32, boxes_shape);
-    auto backend_scores = backend->create_tensor(element::f32, scores_shape);
-    copy_data(backend_boxes, boxes_data);
-    copy_data(backend_scores, scores_data);
-
-    auto handle = backend->compile(f);
-
-    handle->call({selected_indeces, selected_scores, valid_outputs}, {backend_boxes, backend_scores});
-
-    auto selected_indeces_value = read_vector<int64_t>(selected_indeces);
-    auto selected_scores_value = read_vector<float>(selected_scores);
-    auto valid_outputs_value = read_vector<int64_t>(valid_outputs);
-
-    std::vector<int64_t> expected_selected_indices = {0, 0, 3, 0, 0, 0, 0, 1, 3, 0, 1, 0};
-    std::vector<float> expected_selected_scores = {0.0, 0.0, 0.95, 0.0, 0.0, 0.9, 0.0, 1.0, 0.95, 0.0, 1.0, 0.9};
-    std::vector<int64_t> expected_valid_outputs = {4};
-
-    EXPECT_EQ(expected_selected_indices, selected_indeces_value);
-    EXPECT_EQ(expected_selected_scores, selected_scores_value);
-    EXPECT_EQ(expected_valid_outputs, valid_outputs_value);
-}
-
-NGRAPH_TEST(${BACKEND_NAME}, nonmaxsuppression_suppress_by_IOU_and_scores_without_constants) {
-    std::vector<float> boxes_data = {0.0f, 0.0f,  1.0f, 1.0f,  0.0f, 0.1f,  1.0f, 1.1f,  0.0f, -0.1f,  1.0f, 0.9f,
-                                     0.0f, 10.0f, 1.0f, 11.0f, 0.0f, 10.1f, 1.0f, 11.1f, 0.0f, 100.0f, 1.0f, 101.0f};
-
-    std::vector<float> scores_data = {0.9f, 0.75f, 0.6f, 0.95f, 0.5f, 0.3f};
-
-    std::vector<int64_t> max_output_boxes_per_class_data = {1};
-    std::vector<float> iou_threshold_data = {0.4f};
-    std::vector<float> score_threshold_data = {0.2f};
-    const auto box_encoding = op::v5::NonMaxSuppression::BoxEncodingType::CORNER;
-    const auto boxes_shape = Shape{1, 6, 4};
-    const auto scores_shape = Shape{1, 1, 6};
-
-    const auto boxes = make_shared<op::Parameter>(element::f32, boxes_shape);
-    const auto scores = make_shared<op::Parameter>(element::f32, scores_shape);
-    const auto max_output_boxes_per_class = make_shared<op::Parameter>(element::i64, Shape{1});
-    const auto score_treshold = make_shared<op::Parameter>(element::f32, Shape{1});
-    const auto iou_threshold = make_shared<op::Parameter>(element::f32, Shape{1});
-    const auto soft_nms_sigma = make_shared<op::Parameter>(element::f32, Shape{1});
-
-    auto nms = make_shared<op::v5::NonMaxSuppression>(boxes,
-                                                      scores,
-                                                      max_output_boxes_per_class,
-                                                      iou_threshold,
-                                                      score_treshold,
-                                                      soft_nms_sigma,
-                                                      box_encoding,
-                                                      false);
-
-    auto f = make_shared<Function>(
-        nms,
-        ParameterVector{boxes, scores, max_output_boxes_per_class, iou_threshold, score_treshold, soft_nms_sigma});
-
-    auto backend = runtime::Backend::create("${BACKEND_NAME}");
-
-    auto selected_indeces = backend->create_tensor(element::i64, Shape{1, 3});
-    auto selected_scores = backend->create_tensor(element::f32, Shape{1, 3});
-    auto valid_outputs = backend->create_tensor(element::i64, Shape{1});
-
-    auto backend_boxes = backend->create_tensor(element::f32, boxes_shape);
-    auto backend_scores = backend->create_tensor(element::f32, scores_shape);
-    auto backend_max_output_boxes_per_class = backend->create_tensor(element::i64, {1});
-    auto backend_iou_threshold = backend->create_tensor(element::f32, {1});
-    auto backend_score_threshold = backend->create_tensor(element::f32, {1});
-    auto backend_soft_nms_sigma = backend->create_tensor(element::f32, {1});
-    copy_data(backend_boxes, boxes_data);
-    copy_data(backend_scores, scores_data);
-    copy_data(backend_max_output_boxes_per_class, max_output_boxes_per_class_data);
-    copy_data(backend_iou_threshold, iou_threshold_data);
-    copy_data(backend_score_threshold, score_threshold_data);
-    copy_data(backend_soft_nms_sigma, std::vector<float>(0.0));
-
-    auto handle = backend->compile(f);
-
-    handle->call({selected_indeces, selected_scores, valid_outputs},
-                 {backend_boxes,
-                  backend_scores,
-                  backend_max_output_boxes_per_class,
-                  backend_iou_threshold,
-                  backend_score_threshold,
-                  backend_soft_nms_sigma});
-
-    auto selected_indeces_value = read_vector<int64_t>(selected_indeces);
-    auto selected_scores_value = read_vector<float>(selected_scores);
-    auto valid_outputs_value = read_vector<int64_t>(valid_outputs);
-
-    std::vector<int64_t> expected_selected_indices = {0, 0, 3};
-    std::vector<float> expected_selected_scores = {0.0f, 0.0f, 0.95f};
-    std::vector<int64_t> expected_valid_outputs = {1};
-
-    EXPECT_EQ(expected_selected_indices, selected_indeces_value);
-    EXPECT_EQ(expected_selected_scores, selected_scores_value);
-    EXPECT_EQ(expected_valid_outputs, valid_outputs_value);
-}

From 1cc9ca481f782d5c7368247b1f9539933fd4fca6 Mon Sep 17 00:00:00 2001
From: Gorokhov Dmitriy <dmitry.gorokhov@intel.com>
Date: Fri, 26 Nov 2021 23:42:55 +0300
Subject: [PATCH 43/72] Fixed master build (#8857)

* [Tests] Added missed ostream operators

* Disabled ROIAlign tests with bf16

Co-authored-by: Alexandra Sidorova <alexandra.sidorova@intel.com>
---
 .../skip_tests_config.cpp                     |  2 ++
 .../src/single_layer/matrix_nms.cpp           |  1 +
 .../src/single_layer/multiclass_nms.cpp       |  1 +
 .../ngraph_functions/utils/ngraph_helpers.hpp |  4 +++
 .../src/utils/ngraph_helpers.cpp              | 31 +++++++++++++++++++
 5 files changed, 39 insertions(+)

diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
index c97ab5f6ca2..78d98a023e4 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
@@ -155,6 +155,8 @@ std::vector<std::string> disabledTestPatterns() {
         R"(.*GroupConvolutionLayerCPUTest.*IS=\{.+\}.*_Fused=.*Add\(Parameters\).*)",
         // Issue: 71968
         R"(.*LSTMSequenceCommonZeroClip.*PURE.*CONST.*hidden_size=10.*sigmoid.sigmoid.sigmoid.*reverse.*FP32_targetDevice=CPU.*)",
+        // Issue: 72151
+        R"(.*smoke_ROIAlignLayoutTest.*bf16.*)",
     };
 
 #define FIX_62820 0
diff --git a/inference-engine/tests/functional/shared_test_classes/src/single_layer/matrix_nms.cpp b/inference-engine/tests/functional/shared_test_classes/src/single_layer/matrix_nms.cpp
index ddc3cc708aa..237b52bc0d4 100644
--- a/inference-engine/tests/functional/shared_test_classes/src/single_layer/matrix_nms.cpp
+++ b/inference-engine/tests/functional/shared_test_classes/src/single_layer/matrix_nms.cpp
@@ -15,6 +15,7 @@ namespace subgraph {
 
 using namespace ngraph;
 using namespace InferenceEngine;
+using ngraph::helpers::operator<<;
 
 std::string MatrixNmsLayerTest::getTestCaseName(const testing::TestParamInfo<NmsParams>& obj) {
     std::vector<InputShape> shapes;
diff --git a/inference-engine/tests/functional/shared_test_classes/src/single_layer/multiclass_nms.cpp b/inference-engine/tests/functional/shared_test_classes/src/single_layer/multiclass_nms.cpp
index 4cfb200878c..48363ae778f 100644
--- a/inference-engine/tests/functional/shared_test_classes/src/single_layer/multiclass_nms.cpp
+++ b/inference-engine/tests/functional/shared_test_classes/src/single_layer/multiclass_nms.cpp
@@ -15,6 +15,7 @@ namespace subgraph {
 
 using namespace ngraph;
 using namespace InferenceEngine;
+using ngraph::helpers::operator<<;
 
 std::string MulticlassNmsLayerTest::getTestCaseName(const testing::TestParamInfo<MulticlassNmsParams>& obj) {
     std::vector<InputShape> shapes;
diff --git a/inference-engine/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/utils/ngraph_helpers.hpp b/inference-engine/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/utils/ngraph_helpers.hpp
index 76e4db937db..37533b5d446 100644
--- a/inference-engine/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/utils/ngraph_helpers.hpp
+++ b/inference-engine/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/utils/ngraph_helpers.hpp
@@ -320,6 +320,10 @@ std::ostream& operator<<(std::ostream & os, SequenceTestsMode type);
 
 std::ostream& operator<<(std::ostream & os, MemoryTransformation type);
 
+std::ostream& operator<<(std::ostream & os, op::util::NmsBase::SortResultType type);
+
+std::ostream& operator<<(std::ostream & os, op::v8::MatrixNms::DecayFunction type);
+
 void resize_function(std::shared_ptr<ov::Function> function, const std::vector<ov::Shape>& targetInputStaticShapes);
 
 }  // namespace helpers
diff --git a/inference-engine/tests/ngraph_helpers/ngraph_functions/src/utils/ngraph_helpers.cpp b/inference-engine/tests/ngraph_helpers/ngraph_functions/src/utils/ngraph_helpers.cpp
index 5d1c813a8b2..4f52894c941 100644
--- a/inference-engine/tests/ngraph_helpers/ngraph_functions/src/utils/ngraph_helpers.cpp
+++ b/inference-engine/tests/ngraph_helpers/ngraph_functions/src/utils/ngraph_helpers.cpp
@@ -903,6 +903,37 @@ std::ostream& operator<<(std::ostream & os, MemoryTransformation type) {
     return os;
 }
 
+std::ostream& operator<<(std::ostream & os, ngraph::op::util::NmsBase::SortResultType type) {
+    switch (type) {
+        case op::util::NmsBase::SortResultType::CLASSID:
+            os << "CLASSID";
+            break;
+        case op::util::NmsBase::SortResultType::SCORE:
+            os << "SCORE";
+            break;
+        case op::util::NmsBase::SortResultType::NONE:
+            os << "NONE";
+            break;
+        default:
+            throw std::runtime_error("NOT_SUPPORTED_TYPE");
+    }
+    return os;
+}
+
+std::ostream& operator<<(std::ostream & os, op::v8::MatrixNms::DecayFunction type) {
+    switch (type) {
+        case op::v8::MatrixNms::DecayFunction::GAUSSIAN:
+            os << "GAUSSIAN";
+            break;
+        case op::v8::MatrixNms::DecayFunction::LINEAR:
+            os << "LINEAR";
+            break;
+        default:
+            throw std::runtime_error("NOT_SUPPORTED_TYPE");
+    }
+    return os;
+}
+
 void resize_function(std::shared_ptr<ov::Function> function,
                      const std::vector<ov::Shape>& targetInputStaticShapes) {
     auto inputs = function->inputs();

From 5e91ef46400d09c78ea554d372f672e4f7488ec7 Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Sat, 27 Nov 2021 07:34:38 +0300
Subject: [PATCH 44/72] Fixed error message for unrecognized attribute (#8815)

---
 ngraph/frontend/ir/src/ir_deserializer.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ngraph/frontend/ir/src/ir_deserializer.cpp b/ngraph/frontend/ir/src/ir_deserializer.cpp
index 7271e190eca..c19c1e4d98c 100644
--- a/ngraph/frontend/ir/src/ir_deserializer.cpp
+++ b/ngraph/frontend/ir/src/ir_deserializer.cpp
@@ -735,10 +735,10 @@ std::shared_ptr<ngraph::Node> XmlDeserializer::createNode(const std::vector<ngra
                 if (attr->visit_attributes(attribute_visitor)) {
                     rt_info[type_info] = attr;
                 } else {
-                    IE_THROW() << "VisitAttributes is not supported for: " << item.name() << " attribute";
+                    IE_THROW() << "VisitAttributes is not supported for: " << attribute_name << " attribute";
                 }
             } else {
-                IE_THROW() << "Attribute: " << item.name() << " is not recognized";
+                IE_THROW() << "Attribute: " << attribute_name << " is not recognized";
             }
         }
     };

From 909dea8b5d4c28d52cbbfa643a5e68a60a018178 Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Sat, 27 Nov 2021 10:06:25 +0300
Subject: [PATCH 45/72] Removed preprocessor library (#8845)

---
 openvino/CMakeLists.txt             |  3 +-
 openvino/itt/CMakeLists.txt         |  2 +-
 openvino/pp/CMakeLists.txt          | 14 ---------
 openvino/pp/include/openvino/pp.hpp | 49 -----------------------------
 4 files changed, 2 insertions(+), 66 deletions(-)
 delete mode 100644 openvino/pp/CMakeLists.txt
 delete mode 100644 openvino/pp/include/openvino/pp.hpp

diff --git a/openvino/CMakeLists.txt b/openvino/CMakeLists.txt
index e13df8efbf0..94487b286e4 100644
--- a/openvino/CMakeLists.txt
+++ b/openvino/CMakeLists.txt
@@ -2,9 +2,8 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-add_subdirectory(pp)
 add_subdirectory(itt)
 add_subdirectory(conditional_compilation)
 add_subdirectory(util)
 
-openvino_developer_export_targets(COMPONENT openvino_common TARGETS openvino::pp openvino::itt openvino::conditional_compilation)
+openvino_developer_export_targets(COMPONENT openvino_common TARGETS openvino::itt openvino::conditional_compilation)
diff --git a/openvino/itt/CMakeLists.txt b/openvino/itt/CMakeLists.txt
index e5abb9199ec..c427f242ed7 100644
--- a/openvino/itt/CMakeLists.txt
+++ b/openvino/itt/CMakeLists.txt
@@ -10,7 +10,7 @@ add_library(${TARGET_NAME} STATIC ${SOURCES})
 
 add_library(openvino::itt ALIAS ${TARGET_NAME})
 
-target_link_libraries(${TARGET_NAME} PUBLIC openvino::pp openvino::util)
+target_link_libraries(${TARGET_NAME} PUBLIC openvino::util)
 
 if(TARGET ittnotify)
     target_link_libraries(${TARGET_NAME} PUBLIC ittnotify)
diff --git a/openvino/pp/CMakeLists.txt b/openvino/pp/CMakeLists.txt
deleted file mode 100644
index e2060db6b3f..00000000000
--- a/openvino/pp/CMakeLists.txt
+++ /dev/null
@@ -1,14 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-#
-
-set(TARGET_NAME openvino_preprocessor)
-
-add_library(${TARGET_NAME} INTERFACE)
-
-add_library(openvino::pp ALIAS ${TARGET_NAME})
-
-target_include_directories(${TARGET_NAME} INTERFACE
-    $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>)
-
-ov_install_static_lib(${TARGET_NAME} ngraph)
diff --git a/openvino/pp/include/openvino/pp.hpp b/openvino/pp/include/openvino/pp.hpp
deleted file mode 100644
index 6931c8f1845..00000000000
--- a/openvino/pp/include/openvino/pp.hpp
+++ /dev/null
@@ -1,49 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-/**
- * @brief Set of macro used by openvino
- * @file pp.hpp
- */
-
-#pragma once
-
-// Macros for string conversion
-#define OV_PP_TOSTRING(...) OV_PP_TOSTRING_(__VA_ARGS__)
-#define OV_PP_TOSTRING_(...) #__VA_ARGS__
-
-#define OV_PP_EXPAND(X) X
-
-#define OV_PP_NARG(...) OV_PP_EXPAND( OV_PP_NARG_(__VA_ARGS__, OV_PP_RSEQ_N()) )
-#define OV_PP_NARG_(...) OV_PP_EXPAND( OV_PP_ARG_N(__VA_ARGS__) )
-#define OV_PP_ARG_N(_0, _1, _2, _3, _4, N, ...) N
-#define OV_PP_RSEQ_N() 0, 4, 3, 2, 1, 0
-#define OV_PP_NO_ARGS(NAME) ,,,,
-
-// Macros for names concatenation
-#define OV_PP_CAT_(x, y) x ## y
-#define OV_PP_CAT(x, y) OV_PP_CAT_(x, y)
-#define OV_PP_CAT3_(x, y, z) x ## y ## z
-#define OV_PP_CAT3(x, y, z) OV_PP_CAT3_(x, y, z)
-#define OV_PP_CAT4_(x, y, z, w) x ## y ## z ## w
-#define OV_PP_CAT4(x, y, z, w) OV_PP_CAT4_(x, y, z, w)
-
-#define OV_PP_OVERLOAD(NAME, ...) OV_PP_EXPAND( OV_PP_CAT3(NAME, _, OV_PP_EXPAND( OV_PP_NARG(OV_PP_NO_ARGS __VA_ARGS__ (NAME)) ))(__VA_ARGS__) )
-
-// Placeholder for first macro argument
-#define OV_PP_ARG_PLACEHOLDER_1 0,
-
-// This macro returns second argument, first argument is ignored
-#define OV_PP_SECOND_ARG(...) OV_PP_EXPAND(OV_PP_SECOND_ARG_(__VA_ARGS__, 0))
-#define OV_PP_SECOND_ARG_(...) OV_PP_EXPAND(OV_PP_SECOND_ARG_GET(__VA_ARGS__))
-#define OV_PP_SECOND_ARG_GET(ignored, val, ...) val
-
-// Return macro argument value
-#define OV_PP_IS_ENABLED(x) OV_PP_IS_ENABLED1(x)
-
-// Generate junk macro or {0, } sequence if val is 1
-#define OV_PP_IS_ENABLED1(val) OV_PP_IS_ENABLED2(OV_PP_CAT(OV_PP_ARG_PLACEHOLDER_, val))
-
-// Return second argument from possible sequences {1, 0}, {0, 1, 0}
-#define OV_PP_IS_ENABLED2(arg1_or_junk) OV_PP_SECOND_ARG(arg1_or_junk 1, 0)

From 7cb378c92b81f861aef9973e97fd4e2e30de1f06 Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Sat, 27 Nov 2021 11:08:48 +0300
Subject: [PATCH 46/72] Changed add_output API (#8833)

* Changed add_output API

* Added C++ tests

* Added bython tests

* Try to fix python tests

* Fixed python tests

* Try to fix build

* Cannot compare pointers
---
 .../core/include/openvino/core/function.hpp   |  6 ++--
 ngraph/core/src/function.cpp                  | 17 +++++------
 ngraph/test/function.cpp                      | 15 +++++++---
 .../python/src/pyopenvino/graph/function.cpp  | 10 +++++--
 .../test_inference_engine/test_function.py    | 29 +++++++++++++++----
 5 files changed, 53 insertions(+), 24 deletions(-)

diff --git a/ngraph/core/include/openvino/core/function.hpp b/ngraph/core/include/openvino/core/function.hpp
index c14904c0ed4..9fb61bbc853 100644
--- a/ngraph/core/include/openvino/core/function.hpp
+++ b/ngraph/core/include/openvino/core/function.hpp
@@ -115,9 +115,9 @@ public:
     ov::Output<const ov::Node> input(size_t i) const;
     ov::Output<const ov::Node> input(const std::string& tensor_name) const;
 
-    void add_output(const std::string& tensor_name);
-    void add_output(const std::string& op_name, size_t output_idx);
-    void add_output(const ov::Output<ov::Node>& port);
+    ov::Output<ov::Node> add_output(const std::string& tensor_name);
+    ov::Output<ov::Node> add_output(const std::string& op_name, size_t output_idx);
+    ov::Output<ov::Node> add_output(const ov::Output<ov::Node>& port);
 
     void reshape(const std::map<std::string, ov::PartialShape>& partial_shapes);
     void reshape(const std::map<ov::Output<ov::Node>, ov::PartialShape>& partial_shapes);
diff --git a/ngraph/core/src/function.cpp b/ngraph/core/src/function.cpp
index 2e8c35683cd..88b4355ea6c 100644
--- a/ngraph/core/src/function.cpp
+++ b/ngraph/core/src/function.cpp
@@ -868,22 +868,21 @@ void ov::Function::reshape(const std::map<ov::Output<ov::Node>, ov::PartialShape
     }
 }
 
-void ov::Function::add_output(const std::string& tensor_name) {
+ov::Output<ov::Node> ov::Function::add_output(const std::string& tensor_name) {
     for (const auto& op : get_ops()) {
         if (ov::op::util::is_output(op))
             continue;
         for (const auto& output : op->outputs()) {
             const auto& names = output.get_tensor().get_names();
             if (names.find(tensor_name) != names.end()) {
-                add_output(output);
-                return;
+                return add_output(output);
             }
         }
     }
     throw ov::Exception("Tensor name " + tensor_name + " was not found.");
 }
 
-void ov::Function::add_output(const std::string& op_name, size_t output_idx) {
+ov::Output<ov::Node> ov::Function::add_output(const std::string& op_name, size_t output_idx) {
     for (const auto& op : get_ops()) {
         if (op->get_friendly_name() == op_name) {
             OPENVINO_ASSERT(output_idx < op->get_output_size(),
@@ -894,23 +893,23 @@ void ov::Function::add_output(const std::string& op_name, size_t output_idx) {
                             " has only ",
                             std::to_string(op->get_output_size()),
                             " outputs.");
-            add_output(op->output(output_idx));
-            return;
+            return add_output(op->output(output_idx));
         }
     }
     throw ov::Exception("Port " + std::to_string(output_idx) + " for operation with name " + op_name +
                         " was not found.");
 }
 
-void ov::Function::add_output(const ov::Output<ov::Node>& port) {
+ov::Output<ov::Node> ov::Function::add_output(const ov::Output<ov::Node>& port) {
     if (ov::op::util::is_output(port.get_node()))
-        return;
+        return port;
     for (const auto& input : port.get_target_inputs()) {
         // Do not add result if port is already connected with result
         if (ov::op::util::is_output(input.get_node())) {
-            return;
+            return input.get_node()->output(0);
         }
     }
     auto result = std::make_shared<ov::op::v0::Result>(port);
     add_results({result});
+    return result->output(0);
 }
diff --git a/ngraph/test/function.cpp b/ngraph/test/function.cpp
index 213e027dba0..6d417359600 100644
--- a/ngraph/test/function.cpp
+++ b/ngraph/test/function.cpp
@@ -830,11 +830,14 @@ TEST(function, add_output_tensor_name) {
 
     EXPECT_EQ(f->get_results().size(), 1);
 
-    EXPECT_NO_THROW(f->add_output("relu_t1"));
+    ov::Output<ov::Node> out1, out2;
+    EXPECT_NO_THROW(out1 = f->add_output("relu_t1"));
     EXPECT_EQ(f->get_results().size(), 2);
-    EXPECT_NO_THROW(f->add_output("relu_t1"));
+    EXPECT_NO_THROW(out2 = f->add_output("relu_t1"));
     EXPECT_EQ(f->get_results().size(), 2);
     EXPECT_EQ(f->get_results()[1]->input_value(0).get_node(), relu1.get());
+    EXPECT_EQ(out1, out2);
+    EXPECT_EQ(out1.get_node(), f->get_results()[1].get());
 }
 
 TEST(function, add_output_op_name) {
@@ -880,7 +883,9 @@ TEST(function, add_output_port) {
 
     EXPECT_EQ(f->get_results().size(), 1);
 
-    EXPECT_NO_THROW(f->add_output(relu1->output(0)));
+    ov::Output<ov::Node> out;
+    EXPECT_NO_THROW(out = f->add_output(relu1->output(0)));
+    EXPECT_EQ(out.get_node(), f->get_results()[1].get());
     EXPECT_EQ(f->get_results().size(), 2);
     EXPECT_EQ(f->get_results()[1]->input_value(0).get_node(), relu1.get());
 }
@@ -966,8 +971,10 @@ TEST(function, add_output_port_to_result) {
 
     EXPECT_EQ(f->get_results().size(), 1);
 
-    EXPECT_NO_THROW(f->add_output(result->output(0)));
+    ov::Output<ov::Node> out;
+    EXPECT_NO_THROW(out = f->add_output(result->output(0)));
     EXPECT_EQ(f->get_results().size(), 1);
+    EXPECT_EQ(out, result->output(0));
 }
 
 namespace {
diff --git a/runtime/bindings/python/src/pyopenvino/graph/function.cpp b/runtime/bindings/python/src/pyopenvino/graph/function.cpp
index 1a692f5cdd1..60443aceff5 100644
--- a/runtime/bindings/python/src/pyopenvino/graph/function.cpp
+++ b/runtime/bindings/python/src/pyopenvino/graph/function.cpp
@@ -589,6 +589,7 @@ void regclass_graph_Function(py::module m) {
         "add_outputs",
         [](ov::Function& self, py::handle& outputs) {
             int i = 0;
+            std::vector<ov::Output<ov::Node>> new_outputs;
             py::list _outputs;
             if (!py::isinstance<py::list>(outputs)) {
                 if (py::isinstance<py::str>(outputs)) {
@@ -605,19 +606,22 @@ void regclass_graph_Function(py::module m) {
             }
 
             for (py::handle output : _outputs) {
+                ov::Output<ov::Node> out;
                 if (py::isinstance<py::str>(_outputs[i])) {
-                    self.add_output(output.cast<std::string>());
+                    out = self.add_output(output.cast<std::string>());
                 } else if (py::isinstance<py::tuple>(output)) {
                     py::tuple output_tuple = output.cast<py::tuple>();
-                    self.add_output(output_tuple[0].cast<std::string>(), output_tuple[1].cast<int>());
+                    out = self.add_output(output_tuple[0].cast<std::string>(), output_tuple[1].cast<int>());
                 } else if (py::isinstance<ov::Output<ov::Node>>(_outputs[i])) {
-                    self.add_output(output.cast<ov::Output<ov::Node>>());
+                    out = self.add_output(output.cast<ov::Output<ov::Node>>());
                 } else {
                     throw py::type_error("Incorrect type of a value to add as output at index " + std::to_string(i) +
                                          ".");
                 }
+                new_outputs.emplace_back(out);
                 i++;
             }
+            return new_outputs;
         },
         py::arg("outputs"));
 
diff --git a/runtime/bindings/python/tests/test_inference_engine/test_function.py b/runtime/bindings/python/tests/test_inference_engine/test_function.py
index 3d2bca9180e..6929ca56c47 100644
--- a/runtime/bindings/python/tests/test_inference_engine/test_function.py
+++ b/runtime/bindings/python/tests/test_inference_engine/test_function.py
@@ -21,10 +21,13 @@ def test_function_add_outputs_tensor_name():
     relu2 = ops.relu(relu1, name="relu2")
     function = Function(relu2, [param], "TestFunction")
     assert len(function.get_results()) == 1
-    function.add_outputs("relu_t1")
+    new_outs = function.add_outputs("relu_t1")
     assert len(function.get_results()) == 2
     assert isinstance(function.outputs[1].get_tensor(), DescriptorTensor)
     assert "relu_t1" in function.outputs[1].get_tensor().names
+    assert len(new_outs) == 1
+    assert new_outs[0].get_node() == function.outputs[1].get_node()
+    assert new_outs[0].get_index() == function.outputs[1].get_index()
 
 
 def test_function_add_outputs_op_name():
@@ -35,8 +38,11 @@ def test_function_add_outputs_op_name():
     relu2 = ops.relu(relu1, name="relu2")
     function = Function(relu2, [param], "TestFunction")
     assert len(function.get_results()) == 1
-    function.add_outputs(("relu1", 0))
+    new_outs = function.add_outputs(("relu1", 0))
     assert len(function.get_results()) == 2
+    assert len(new_outs) == 1
+    assert new_outs[0].get_node() == function.outputs[1].get_node()
+    assert new_outs[0].get_index() == function.outputs[1].get_index()
 
 
 def test_function_add_output_port():
@@ -47,8 +53,11 @@ def test_function_add_output_port():
     relu2 = ops.relu(relu1, name="relu2")
     function = Function(relu2, [param], "TestFunction")
     assert len(function.get_results()) == 1
-    function.add_outputs(relu1.output(0))
+    new_outs = function.add_outputs(relu1.output(0))
     assert len(function.get_results()) == 2
+    assert len(new_outs) == 1
+    assert new_outs[0].get_node() == function.outputs[1].get_node()
+    assert new_outs[0].get_index() == function.outputs[1].get_index()
 
 
 def test_function_add_output_incorrect_tensor_name():
@@ -102,8 +111,13 @@ def test_add_outputs_several_tensors():
     relu3 = ops.relu(relu2, name="relu3")
     function = Function(relu3, [param], "TestFunction")
     assert len(function.get_results()) == 1
-    function.add_outputs(["relu_t1", "relu_t2"])
+    new_outs = function.add_outputs(["relu_t1", "relu_t2"])
     assert len(function.get_results()) == 3
+    assert len(new_outs) == 2
+    assert new_outs[0].get_node() == function.outputs[1].get_node()
+    assert new_outs[0].get_index() == function.outputs[1].get_index()
+    assert new_outs[1].get_node() == function.outputs[2].get_node()
+    assert new_outs[1].get_index() == function.outputs[2].get_index()
 
 
 def test_add_outputs_several_ports():
@@ -116,8 +130,13 @@ def test_add_outputs_several_ports():
     relu3 = ops.relu(relu2, name="relu3")
     function = Function(relu3, [param], "TestFunction")
     assert len(function.get_results()) == 1
-    function.add_outputs([("relu1", 0), ("relu2", 0)])
+    new_outs = function.add_outputs([("relu1", 0), ("relu2", 0)])
     assert len(function.get_results()) == 3
+    assert len(new_outs) == 2
+    assert new_outs[0].get_node() == function.outputs[1].get_node()
+    assert new_outs[0].get_index() == function.outputs[1].get_index()
+    assert new_outs[1].get_node() == function.outputs[2].get_node()
+    assert new_outs[1].get_index() == function.outputs[2].get_index()
 
 
 def test_add_outputs_incorrect_value():

From b1fba3a6758e5695a95bfa19190129543617ed08 Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Sat, 27 Nov 2021 11:28:25 +0300
Subject: [PATCH 47/72] Rename runtime to src (#8842)

* Renamed runtime to src

* Removed old paths

* Fixed cmake

* Fixed doc
---
 .ci/azure/linux_ngraph_onnx.yml                     |   2 +-
 .ci/openvino-onnx/Dockerfile                        |   2 +-
 .ci/openvino-onnx/Jenkinsfile                       |   4 ++--
 .github/dependabot.yml                              |   2 +-
 .gitmodules                                         |   4 ++--
 CMakeLists.txt                                      |   2 +-
 docs/CMakeLists.txt                                 |  10 +++++-----
 docs/IE_DG/PythonPackage_Overview.md                |   2 +-
 .../tf_specific/Convert_GNMT_From_Tensorflow.md     |   2 +-
 scripts/CMakeLists.txt                              |   6 +++---
 {runtime => src}/CMakeLists.txt                     |   0
 {runtime => src}/bindings/python/.clang-format      |   0
 {runtime => src}/bindings/python/.gitignore         |   0
 {runtime => src}/bindings/python/BUILDING.md        |   0
 {runtime => src}/bindings/python/CMakeLists.txt     |   0
 .../bindings/python/docs/api_overview.md            |   0
 {runtime => src}/bindings/python/requirements.txt   |   0
 .../bindings/python/requirements_test.txt           |   0
 {runtime => src}/bindings/python/setup.py           |   2 +-
 {runtime => src}/bindings/python/src/CMakeLists.txt |   0
 .../python/src/compatibility/CMakeLists.txt         |   0
 .../python/src/compatibility/ngraph/__init__.py     |   0
 .../python/src/compatibility/ngraph/exceptions.py   |   0
 .../src/compatibility/ngraph/frontend/__init__.py   |   0
 .../python/src/compatibility/ngraph/helpers.py      |   0
 .../src/compatibility/ngraph/impl/__init__.py       |   0
 .../src/compatibility/ngraph/impl/op/__init__.py    |   0
 .../compatibility/ngraph/impl/op/util/__init__.py   |   0
 .../compatibility/ngraph/impl/passes/__init__.py    |   0
 .../src/compatibility/ngraph/opset1/__init__.py     |   0
 .../python/src/compatibility/ngraph/opset1/ops.py   |   0
 .../src/compatibility/ngraph/opset2/__init__.py     |   0
 .../python/src/compatibility/ngraph/opset2/ops.py   |   0
 .../src/compatibility/ngraph/opset3/__init__.py     |   0
 .../python/src/compatibility/ngraph/opset3/ops.py   |   0
 .../src/compatibility/ngraph/opset4/__init__.py     |   0
 .../python/src/compatibility/ngraph/opset4/ops.py   |   0
 .../src/compatibility/ngraph/opset5/__init__.py     |   0
 .../python/src/compatibility/ngraph/opset5/ops.py   |   0
 .../src/compatibility/ngraph/opset6/__init__.py     |   0
 .../python/src/compatibility/ngraph/opset6/ops.py   |   0
 .../src/compatibility/ngraph/opset7/__init__.py     |   0
 .../python/src/compatibility/ngraph/opset7/ops.py   |   0
 .../src/compatibility/ngraph/opset8/__init__.py     |   0
 .../python/src/compatibility/ngraph/opset8/ops.py   |   0
 .../python/src/compatibility/ngraph/opset_utils.py  |   0
 .../src/compatibility/ngraph/utils/__init__.py      |   0
 .../src/compatibility/ngraph/utils/broadcasting.py  |   0
 .../src/compatibility/ngraph/utils/decorators.py    |   0
 .../compatibility/ngraph/utils/input_validation.py  |   0
 .../src/compatibility/ngraph/utils/node_factory.py  |   0
 .../src/compatibility/ngraph/utils/reduction.py     |   0
 .../ngraph/utils/tensor_iterator_types.py           |   0
 .../python/src/compatibility/ngraph/utils/types.py  |   0
 .../src/compatibility/pyngraph/CMakeLists.txt       |   0
 .../python/src/compatibility/pyngraph/axis_set.cpp  |   0
 .../python/src/compatibility/pyngraph/axis_set.hpp  |   0
 .../src/compatibility/pyngraph/axis_vector.cpp      |   0
 .../src/compatibility/pyngraph/axis_vector.hpp      |   0
 .../src/compatibility/pyngraph/coordinate.cpp       |   0
 .../src/compatibility/pyngraph/coordinate.hpp       |   0
 .../src/compatibility/pyngraph/coordinate_diff.cpp  |   0
 .../src/compatibility/pyngraph/coordinate_diff.hpp  |   0
 .../pyngraph/dict_attribute_visitor.cpp             |   0
 .../pyngraph/dict_attribute_visitor.hpp             |   0
 .../python/src/compatibility/pyngraph/dimension.cpp |   0
 .../python/src/compatibility/pyngraph/dimension.hpp |   0
 .../compatibility/pyngraph/discrete_type_info.cpp   |   0
 .../compatibility/pyngraph/discrete_type_info.hpp   |   0
 .../compatibility/pyngraph/frontend/frontend.cpp    |   0
 .../compatibility/pyngraph/frontend/frontend.hpp    |   0
 .../pyngraph/frontend/frontend_manager.cpp          |   0
 .../pyngraph/frontend/frontend_manager.hpp          |   0
 .../compatibility/pyngraph/frontend/inputmodel.cpp  |   0
 .../compatibility/pyngraph/frontend/inputmodel.hpp  |   0
 .../src/compatibility/pyngraph/frontend/place.cpp   |   0
 .../src/compatibility/pyngraph/frontend/place.hpp   |   0
 .../python/src/compatibility/pyngraph/function.cpp  |   0
 .../python/src/compatibility/pyngraph/function.hpp  |   0
 .../python/src/compatibility/pyngraph/node.cpp      |   0
 .../python/src/compatibility/pyngraph/node.hpp      |   0
 .../src/compatibility/pyngraph/node_factory.cpp     |   0
 .../src/compatibility/pyngraph/node_factory.hpp     |   0
 .../src/compatibility/pyngraph/node_input.cpp       |   0
 .../src/compatibility/pyngraph/node_input.hpp       |   0
 .../src/compatibility/pyngraph/node_output.cpp      |   0
 .../src/compatibility/pyngraph/node_output.hpp      |   0
 .../src/compatibility/pyngraph/ops/constant.cpp     |   0
 .../src/compatibility/pyngraph/ops/constant.hpp     |   0
 .../src/compatibility/pyngraph/ops/parameter.cpp    |   0
 .../src/compatibility/pyngraph/ops/parameter.hpp    |   0
 .../src/compatibility/pyngraph/ops/result.cpp       |   0
 .../src/compatibility/pyngraph/ops/result.hpp       |   0
 .../pyngraph/ops/util/arithmetic_reduction.cpp      |   0
 .../pyngraph/ops/util/arithmetic_reduction.hpp      |   0
 .../ops/util/binary_elementwise_arithmetic.cpp      |   0
 .../ops/util/binary_elementwise_arithmetic.hpp      |   0
 .../ops/util/binary_elementwise_comparison.cpp      |   0
 .../ops/util/binary_elementwise_comparison.hpp      |   0
 .../ops/util/binary_elementwise_logical.cpp         |   0
 .../ops/util/binary_elementwise_logical.hpp         |   0
 .../pyngraph/ops/util/index_reduction.cpp           |   0
 .../pyngraph/ops/util/index_reduction.hpp           |   0
 .../pyngraph/ops/util/op_annotations.cpp            |   0
 .../pyngraph/ops/util/op_annotations.hpp            |   0
 .../ops/util/regmodule_pyngraph_op_util.cpp         |   0
 .../ops/util/regmodule_pyngraph_op_util.hpp         |   0
 .../ops/util/unary_elementwise_arithmetic.cpp       |   0
 .../ops/util/unary_elementwise_arithmetic.hpp       |   0
 .../src/compatibility/pyngraph/partial_shape.cpp    |   0
 .../src/compatibility/pyngraph/partial_shape.hpp    |   0
 .../src/compatibility/pyngraph/passes/manager.cpp   |   0
 .../src/compatibility/pyngraph/passes/manager.hpp   |   0
 .../pyngraph/passes/regmodule_pyngraph_passes.cpp   |   0
 .../pyngraph/passes/regmodule_pyngraph_passes.hpp   |   0
 .../python/src/compatibility/pyngraph/pyngraph.cpp  |   0
 .../python/src/compatibility/pyngraph/rt_map.cpp    |   0
 .../python/src/compatibility/pyngraph/rt_map.hpp    |   0
 .../python/src/compatibility/pyngraph/shape.cpp     |   0
 .../python/src/compatibility/pyngraph/shape.hpp     |   0
 .../python/src/compatibility/pyngraph/strides.cpp   |   0
 .../python/src/compatibility/pyngraph/strides.hpp   |   0
 .../compatibility/pyngraph/types/element_type.cpp   |   0
 .../compatibility/pyngraph/types/element_type.hpp   |   0
 .../pyngraph/types/regmodule_pyngraph_types.cpp     |   0
 .../pyngraph/types/regmodule_pyngraph_types.hpp     |   0
 .../python/src/compatibility/pyngraph/util.cpp      |   0
 .../python/src/compatibility/pyngraph/util.hpp      |   0
 .../python/src/compatibility/pyngraph/variant.cpp   |   0
 .../python/src/compatibility/pyngraph/variant.hpp   |   0
 .../bindings/python/src/openvino/__init__.py        |   0
 .../python/src/openvino/descriptor/__init__.py      |   0
 .../bindings/python/src/openvino/exceptions.py      |   0
 .../bindings/python/src/openvino/ie_api.py          |   0
 .../bindings/python/src/openvino/impl/__init__.py   |   0
 .../python/src/openvino/impl/op/__init__.py         |   0
 .../python/src/openvino/impl/op/util/__init__.py    |   0
 .../python/src/openvino/impl/passes/__init__.py     |   0
 .../offline_transformations_pybind/__init__.py      |   0
 .../bindings/python/src/openvino/opset1/__init__.py |   0
 .../bindings/python/src/openvino/opset1/ops.py      |   0
 .../bindings/python/src/openvino/opset2/__init__.py |   0
 .../bindings/python/src/openvino/opset2/ops.py      |   0
 .../bindings/python/src/openvino/opset3/__init__.py |   0
 .../bindings/python/src/openvino/opset3/ops.py      |   0
 .../bindings/python/src/openvino/opset4/__init__.py |   0
 .../bindings/python/src/openvino/opset4/ops.py      |   0
 .../bindings/python/src/openvino/opset5/__init__.py |   0
 .../bindings/python/src/openvino/opset5/ops.py      |   0
 .../bindings/python/src/openvino/opset6/__init__.py |   0
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 .../bindings/python/src/openvino/opset7/__init__.py |   0
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 .../bindings/python/src/openvino/opset8/__init__.py |   0
 .../bindings/python/src/openvino/opset8/ops.py      |   0
 .../bindings/python/src/openvino/opset_utils.py     |   0
 .../python/src/openvino/preprocess/__init__.py      |   0
 .../bindings/python/src/openvino/utils/__init__.py  |   0
 .../python/src/openvino/utils/broadcasting.py       |   0
 .../python/src/openvino/utils/decorators.py         |   0
 .../python/src/openvino/utils/input_validation.py   |   0
 .../python/src/openvino/utils/node_factory.py       |   0
 .../bindings/python/src/openvino/utils/reduction.py |   0
 .../src/openvino/utils/tensor_iterator_types.py     |   0
 .../bindings/python/src/openvino/utils/types.py     |   0
 .../bindings/python/src/pyopenvino/CMakeLists.txt   |   0
 .../src/pyopenvino/core/async_infer_queue.cpp       |   0
 .../src/pyopenvino/core/async_infer_queue.hpp       |   0
 .../bindings/python/src/pyopenvino/core/common.cpp  |   0
 .../bindings/python/src/pyopenvino/core/common.hpp  |   0
 .../python/src/pyopenvino/core/containers.cpp       |   0
 .../python/src/pyopenvino/core/containers.hpp       |   0
 .../bindings/python/src/pyopenvino/core/core.cpp    |   0
 .../bindings/python/src/pyopenvino/core/core.hpp    |   0
 .../src/pyopenvino/core/executable_network.cpp      |   0
 .../src/pyopenvino/core/executable_network.hpp      |   0
 .../python/src/pyopenvino/core/ie_parameter.cpp     |   0
 .../python/src/pyopenvino/core/ie_parameter.hpp     |   0
 .../python/src/pyopenvino/core/infer_request.cpp    |   0
 .../python/src/pyopenvino/core/infer_request.hpp    |   0
 .../src/pyopenvino/core/offline_transformations.cpp |   0
 .../src/pyopenvino/core/offline_transformations.hpp |   0
 .../python/src/pyopenvino/core/profiling_info.cpp   |   0
 .../python/src/pyopenvino/core/profiling_info.hpp   |   0
 .../bindings/python/src/pyopenvino/core/tensor.cpp  |   0
 .../bindings/python/src/pyopenvino/core/tensor.hpp  |   0
 .../python/src/pyopenvino/core/variable_state.cpp   |   0
 .../python/src/pyopenvino/core/variable_state.hpp   |   0
 .../bindings/python/src/pyopenvino/core/version.cpp |   0
 .../bindings/python/src/pyopenvino/core/version.hpp |   0
 .../python/src/pyopenvino/graph/axis_set.cpp        |   0
 .../python/src/pyopenvino/graph/axis_set.hpp        |   0
 .../python/src/pyopenvino/graph/axis_vector.cpp     |   0
 .../python/src/pyopenvino/graph/axis_vector.hpp     |   0
 .../python/src/pyopenvino/graph/coordinate.cpp      |   0
 .../python/src/pyopenvino/graph/coordinate.hpp      |   0
 .../python/src/pyopenvino/graph/coordinate_diff.cpp |   0
 .../python/src/pyopenvino/graph/coordinate_diff.hpp |   0
 .../src/pyopenvino/graph/descriptors/tensor.cpp     |   0
 .../src/pyopenvino/graph/descriptors/tensor.hpp     |   0
 .../src/pyopenvino/graph/dict_attribute_visitor.cpp |   0
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 .../python/src/pyopenvino/graph/dimension.cpp       |   0
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 .../python/src/pyopenvino/graph/function.cpp        |   0
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 .../bindings/python/src/pyopenvino/graph/layout.cpp |   0
 .../bindings/python/src/pyopenvino/graph/layout.hpp |   0
 .../python/src/pyopenvino/graph/layout_helpers.cpp  |   0
 .../python/src/pyopenvino/graph/layout_helpers.hpp  |   0
 .../bindings/python/src/pyopenvino/graph/node.cpp   |   0
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 .../python/src/pyopenvino/graph/node_factory.cpp    |   0
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 .../python/src/pyopenvino/graph/node_input.cpp      |   0
 .../python/src/pyopenvino/graph/node_input.hpp      |   0
 .../python/src/pyopenvino/graph/node_output.cpp     |   0
 .../python/src/pyopenvino/graph/node_output.hpp     |   0
 .../python/src/pyopenvino/graph/ops/constant.cpp    |   0
 .../python/src/pyopenvino/graph/ops/constant.hpp    |   0
 .../python/src/pyopenvino/graph/ops/parameter.cpp   |   0
 .../python/src/pyopenvino/graph/ops/parameter.hpp   |   0
 .../python/src/pyopenvino/graph/ops/result.cpp      |   0
 .../python/src/pyopenvino/graph/ops/result.hpp      |   0
 .../graph/ops/util/arithmetic_reduction.cpp         |   0
 .../graph/ops/util/arithmetic_reduction.hpp         |   0
 .../ops/util/binary_elementwise_arithmetic.cpp      |   0
 .../ops/util/binary_elementwise_arithmetic.hpp      |   0
 .../ops/util/binary_elementwise_comparison.cpp      |   0
 .../ops/util/binary_elementwise_comparison.hpp      |   0
 .../graph/ops/util/binary_elementwise_logical.cpp   |   0
 .../graph/ops/util/binary_elementwise_logical.hpp   |   0
 .../pyopenvino/graph/ops/util/index_reduction.cpp   |   0
 .../pyopenvino/graph/ops/util/index_reduction.hpp   |   0
 .../graph/ops/util/regmodule_graph_op_util.cpp      |   0
 .../graph/ops/util/regmodule_graph_op_util.hpp      |   0
 .../graph/ops/util/unary_elementwise_arithmetic.cpp |   0
 .../graph/ops/util/unary_elementwise_arithmetic.hpp |   0
 .../src/pyopenvino/graph/ops/util/variable.cpp      |   0
 .../src/pyopenvino/graph/ops/util/variable.hpp      |   0
 .../python/src/pyopenvino/graph/partial_shape.cpp   |   0
 .../python/src/pyopenvino/graph/partial_shape.hpp   |   0
 .../python/src/pyopenvino/graph/passes/manager.cpp  |   0
 .../python/src/pyopenvino/graph/passes/manager.hpp  |   0
 .../graph/passes/regmodule_graph_passes.cpp         |   0
 .../graph/passes/regmodule_graph_passes.hpp         |   0
 .../graph/preprocess/pre_post_process.cpp           |   0
 .../graph/preprocess/pre_post_process.hpp           |   0
 .../bindings/python/src/pyopenvino/graph/rt_map.cpp |   0
 .../bindings/python/src/pyopenvino/graph/rt_map.hpp |   0
 .../bindings/python/src/pyopenvino/graph/shape.cpp  |   0
 .../bindings/python/src/pyopenvino/graph/shape.hpp  |   0
 .../python/src/pyopenvino/graph/strides.cpp         |   0
 .../python/src/pyopenvino/graph/strides.hpp         |   0
 .../src/pyopenvino/graph/types/element_type.cpp     |   0
 .../src/pyopenvino/graph/types/element_type.hpp     |   0
 .../graph/types/regmodule_graph_types.cpp           |   0
 .../graph/types/regmodule_graph_types.hpp           |   0
 .../bindings/python/src/pyopenvino/graph/util.cpp   |   0
 .../bindings/python/src/pyopenvino/graph/util.hpp   |   0
 .../bindings/python/src/pyopenvino/graph/util.py    |   0
 .../python/src/pyopenvino/graph/variant.cpp         |   0
 .../python/src/pyopenvino/graph/variant.hpp         |   0
 .../bindings/python/src/pyopenvino/pyopenvino.cpp   |   0
 {runtime => src}/bindings/python/tests/__init__.py  |   0
 {runtime => src}/bindings/python/tests/conftest.py  |   0
 .../tests/mock/mock_py_ov_frontend/CMakeLists.txt   |   0
 .../mock/mock_py_ov_frontend/mock_py_frontend.cpp   |   0
 .../mock/mock_py_ov_frontend/mock_py_frontend.hpp   |   0
 .../tests/mock/pyngraph_fe_mock_api/CMakeLists.txt  |   0
 .../pyngraph_mock_frontend_api.cpp                  |   0
 {runtime => src}/bindings/python/tests/runtime.py   |   0
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 rename {runtime => src}/bindings/python/src/pyopenvino/graph/node_output.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/constant.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/constant.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/parameter.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/parameter.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/result.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/result.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/util/arithmetic_reduction.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/util/arithmetic_reduction.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/util/binary_elementwise_arithmetic.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/util/binary_elementwise_arithmetic.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/util/binary_elementwise_comparison.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/util/binary_elementwise_comparison.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/util/binary_elementwise_logical.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/util/binary_elementwise_logical.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/util/index_reduction.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/util/index_reduction.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/util/regmodule_graph_op_util.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/util/regmodule_graph_op_util.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/util/unary_elementwise_arithmetic.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/util/unary_elementwise_arithmetic.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/util/variable.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/ops/util/variable.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/partial_shape.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/partial_shape.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/passes/manager.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/passes/manager.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/passes/regmodule_graph_passes.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/passes/regmodule_graph_passes.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/preprocess/pre_post_process.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/preprocess/pre_post_process.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/rt_map.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/rt_map.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/shape.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/shape.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/strides.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/strides.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/types/element_type.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/types/element_type.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/types/regmodule_graph_types.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/types/regmodule_graph_types.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/util.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/util.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/util.py (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/variant.cpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/graph/variant.hpp (100%)
 rename {runtime => src}/bindings/python/src/pyopenvino/pyopenvino.cpp (100%)
 rename {runtime => src}/bindings/python/tests/__init__.py (100%)
 rename {runtime => src}/bindings/python/tests/conftest.py (100%)
 rename {runtime => src}/bindings/python/tests/mock/mock_py_ov_frontend/CMakeLists.txt (100%)
 rename {runtime => src}/bindings/python/tests/mock/mock_py_ov_frontend/mock_py_frontend.cpp (100%)
 rename {runtime => src}/bindings/python/tests/mock/mock_py_ov_frontend/mock_py_frontend.hpp (100%)
 rename {runtime => src}/bindings/python/tests/mock/pyngraph_fe_mock_api/CMakeLists.txt (100%)
 rename {runtime => src}/bindings/python/tests/mock/pyngraph_fe_mock_api/pyngraph_mock_frontend_api.cpp (100%)
 rename {runtime => src}/bindings/python/tests/runtime.py (100%)
 rename {runtime => src}/bindings/python/tests/test_inference_engine/__init__.py (100%)
 rename {runtime => src}/bindings/python/tests/test_inference_engine/test_core.py (100%)
 rename {runtime => src}/bindings/python/tests/test_inference_engine/test_executable_network.py (100%)
 rename {runtime => src}/bindings/python/tests/test_inference_engine/test_function.py (100%)
 rename {runtime => src}/bindings/python/tests/test_inference_engine/test_infer_request.py (100%)
 rename {runtime => src}/bindings/python/tests/test_inference_engine/test_output_const_node.py (100%)
 rename {runtime => src}/bindings/python/tests/test_inference_engine/test_tensor.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/__init__.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_adaptive_pool.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_basic.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_convolution.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_core.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_create_op.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_ctc_loss.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_data_movement.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_descriptor.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_dft.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_dyn_attributes.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_einsum.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_gather.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_idft.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_input_validation.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_log_softmax.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_manager.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_node_factory.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_normalization.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_ops.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_ops_binary.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_ops_fused.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_ops_matmul.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_ops_multioutput.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_ops_reshape.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_ops_scatter.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_ops_unary.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_ops_util_variable.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_pooling.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_preprocess.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_proposal.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_random_uniform.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_reduction.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_roll.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_sequence_processing.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_swish.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/test_utils.py (100%)
 rename {runtime => src}/bindings/python/tests/test_ngraph/util.py (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/__init__.py (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/model_zoo_preprocess.sh (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/models/add_abc.onnx (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/models/data/tensor.data (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/models/external_data.onnx (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/test_backend.py (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/test_onnx_external_data.py (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/test_onnx_import.py (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/test_ops_batchnorm.py (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/test_ops_binary.py (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/test_ops_convpool.py (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/test_ops_logical.py (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/test_ops_matmul.py (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/test_ops_nonlinear.py (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/test_ops_random.py (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/test_ops_reduction.py (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/test_ops_reshape.py (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/test_ops_unary.py (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/test_ops_variadic.py (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/test_zoo_models.py (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/utils/__init__.py (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/utils/model_importer.py (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/utils/onnx_backend.py (100%)
 rename {runtime => src}/bindings/python/tests/test_onnx/utils/onnx_helpers.py (100%)
 rename {runtime => src}/bindings/python/tests/test_transformations/test_offline_api.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/__init__.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/conftest.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/mock/mock_py_ngraph_frontend/CMakeLists.txt (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/mock/mock_py_ngraph_frontend/mock_py_frontend.cpp (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/mock/mock_py_ngraph_frontend/mock_py_frontend.hpp (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/mock/pyngraph_fe_mock_api/CMakeLists.txt (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/mock/pyngraph_fe_mock_api/pyngraph_mock_frontend_api.cpp (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/runtime.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_frontend/test_frontend_onnx.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_frontend/test_frontend_onnx_editor.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_frontend/test_frontendmanager.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/__init__.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_adaptive_pool.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_basic.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_convolution.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_core.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_create_op.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_ctc_loss.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_data_movement.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_dft.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_dyn_attributes.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_einsum.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_gather.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_idft.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_input_validation.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_log_softmax.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_manager.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_node_factory.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_normalization.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_ops.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_ops_binary.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_ops_fused.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_ops_matmul.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_ops_multioutput.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_ops_reshape.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_ops_scatter.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_ops_unary.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_pooling.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_proposal.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_random_uniform.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_reduction.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_roll.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_sequence_processing.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_swish.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/test_utils.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_ngraph/util.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/__init__.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/model_zoo_preprocess.sh (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/models/add_abc.onnx (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/models/data/tensor.data (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/models/external_data.onnx (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/test_backend.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/test_onnx_external_data.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/test_onnx_import.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/test_ops_batchnorm.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/test_ops_binary.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/test_ops_convpool.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/test_ops_logical.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/test_ops_matmul.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/test_ops_nonlinear.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/test_ops_reduction.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/test_ops_reshape.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/test_ops_unary.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/test_ops_variadic.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/test_zoo_models.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/utils/__init__.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/utils/model_importer.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/utils/onnx_backend.py (100%)
 rename {runtime => src}/bindings/python/tests_compatibility/test_onnx/utils/onnx_helpers.py (100%)
 rename {runtime => src}/bindings/python/thirdparty/pybind11 (100%)
 rename {runtime => src}/bindings/python/tox.ini (100%)

diff --git a/.ci/azure/linux_ngraph_onnx.yml b/.ci/azure/linux_ngraph_onnx.yml
index 6858024e877..f8166f26b4d 100644
--- a/.ci/azure/linux_ngraph_onnx.yml
+++ b/.ci/azure/linux_ngraph_onnx.yml
@@ -79,7 +79,7 @@ jobs:
     workingDirectory: $(WORK_DIR)
     displayName: 'Install dependencies'
 
-  - script: runtime/bindings/python/tests/test_onnx/model_zoo_preprocess.sh -d $(MODELS_DIR)/models_data -o -s "$(ONNX_MODEL_ZOO_SHA)"
+  - script: src/bindings/python/tests/test_onnx/model_zoo_preprocess.sh -d $(MODELS_DIR)/models_data -o -s "$(ONNX_MODEL_ZOO_SHA)"
     displayName: 'Update models'
     condition: ne(variables['BUILD_TYPE'], 'Debug')
 
diff --git a/.ci/openvino-onnx/Dockerfile b/.ci/openvino-onnx/Dockerfile
index 0992ea21220..c4b56c2c7b4 100644
--- a/.ci/openvino-onnx/Dockerfile
+++ b/.ci/openvino-onnx/Dockerfile
@@ -72,7 +72,7 @@ RUN cmake .. \
 RUN make -j $(nproc) install
 
 # Run tests via tox
-WORKDIR /openvino/runtime/bindings/python
+WORKDIR /openvino/src/bindings/python
 ENV OpenVINO_DIR=/openvino/dist/runtime/cmake
 ENV LD_LIBRARY_PATH=/openvino/dist/runtime/lib:/openvino/dist/runtime/3rdparty/tbb/lib
 ENV PYTHONPATH=/openvino/bin/intel64/${BUILD_TYPE}/lib/python_api/python3.8:${PYTHONPATH}
diff --git a/.ci/openvino-onnx/Jenkinsfile b/.ci/openvino-onnx/Jenkinsfile
index cfe866c23eb..6b8b19193d4 100644
--- a/.ci/openvino-onnx/Jenkinsfile
+++ b/.ci/openvino-onnx/Jenkinsfile
@@ -93,7 +93,7 @@ def prepare_repository(String workdir) {
 
 def updateModels() {
     sh """
-        ./runtime/bindings/python/tests/test_onnx/model_zoo_preprocess.sh -d ${HOME}/ONNX_CI/models_data -o -s ${ONNX_MODEL_ZOO_SHA}
+        ./src/bindings/python/tests/test_onnx/model_zoo_preprocess.sh -d ${HOME}/ONNX_CI/models_data -o -s ${ONNX_MODEL_ZOO_SHA}
     """
 }
 
@@ -210,4 +210,4 @@ pipeline {
 
         }
     }
-}
\ No newline at end of file
+}
diff --git a/.github/dependabot.yml b/.github/dependabot.yml
index 863c4603442..036611ee975 100644
--- a/.github/dependabot.yml
+++ b/.github/dependabot.yml
@@ -4,7 +4,7 @@ version: 2
 updates:
   # Enable version updates for nGraph Python API
   - package-ecosystem: pip
-    directory: "/runtime/bindings/python"
+    directory: "/src/bindings/python"
     schedule:
       interval: weekly
       day: monday
diff --git a/.gitmodules b/.gitmodules
index 128209adef3..e9174b4e3ac 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -44,8 +44,8 @@
 [submodule "thirdparty/protobuf"]
 	path = thirdparty/protobuf/protobuf
 	url = https://github.com/protocolbuffers/protobuf.git
-[submodule "runtime/bindings/python/thirdparty/pybind11"]
-	path = runtime/bindings/python/thirdparty/pybind11
+[submodule "src/bindings/python/thirdparty/pybind11"]
+	path = src/bindings/python/thirdparty/pybind11
 	url = https://github.com/pybind/pybind11.git
 [submodule "thirdparty/ittapi/ittapi"]
 	path = thirdparty/ittapi/ittapi
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 8ada4c8cfdd..b400b9835aa 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -94,7 +94,7 @@ add_subdirectory(thirdparty)
 add_subdirectory(openvino)
 add_subdirectory(ngraph)
 add_subdirectory(inference-engine)
-add_subdirectory(runtime)
+add_subdirectory(src)
 add_subdirectory(samples)
 include(cmake/extra_modules.cmake)
 add_subdirectory(model-optimizer)
diff --git a/docs/CMakeLists.txt b/docs/CMakeLists.txt
index b2ccee1dcdd..43ad34db591 100644
--- a/docs/CMakeLists.txt
+++ b/docs/CMakeLists.txt
@@ -70,7 +70,7 @@ function(build_docs)
     set(C_API "${IE_SOURCE_DIR}/ie_bridges/c/include")
     set(PLUGIN_API_DIR "${DOCS_BUILD_DIR}/IE_PLUGIN_DG")
     set(NGRAPH_DIR "${OpenVINO_SOURCE_DIR}/ngraph")
-    set(NGRAPH_PY_DIR "${OpenVINO_SOURCE_DIR}/runtime/bindings/python/src/compatibility/ngraph/")
+    set(NGRAPH_PY_DIR "${OpenVINO_SOURCE_DIR}/src/bindings/python/src/compatibility/ngraph/")
     set(NGRAPH_CPP_DIR "${NGRAPH_DIR}/core/include/" "${NGRAPH_DIR}/frontend/onnx_import/include")
 
     # Preprocessing scripts
@@ -222,10 +222,10 @@ function(build_docs)
         "${OpenVINO_SOURCE_DIR}/inference-engine/*.png"
         "${OpenVINO_SOURCE_DIR}/inference-engine/*.gif"
         "${OpenVINO_SOURCE_DIR}/inference-engine/*.jpg"
-        "${OpenVINO_SOURCE_DIR}/runtime/*.md"
-        "${OpenVINO_SOURCE_DIR}/runtime/*.png"
-        "${OpenVINO_SOURCE_DIR}/runtime/*.gif"
-        "${OpenVINO_SOURCE_DIR}/runtime/*.jpg"
+        "${OpenVINO_SOURCE_DIR}/src/*.md"
+        "${OpenVINO_SOURCE_DIR}/src/*.png"
+        "${OpenVINO_SOURCE_DIR}/src/*.gif"
+        "${OpenVINO_SOURCE_DIR}/src/*.jpg"
         "${OpenVINO_SOURCE_DIR}/samples/*.md"
         "${OpenVINO_SOURCE_DIR}/samples/*.png"
         "${OpenVINO_SOURCE_DIR}/samples/*.gif"
diff --git a/docs/IE_DG/PythonPackage_Overview.md b/docs/IE_DG/PythonPackage_Overview.md
index a86a146d7fe..19f97d4c235 100644
--- a/docs/IE_DG/PythonPackage_Overview.md
+++ b/docs/IE_DG/PythonPackage_Overview.md
@@ -7,7 +7,7 @@ The OpenVINO™ Python\* package available in the `<INSTALL_DIR>/python/python3.
 
 The OpenVINO™ Python\* package includes the following sub-packages:
 
- - [openvino.inference_engine](../../runtime/bindings/python/docs/api_overview.md) - Python\* wrapper on OpenVINO™ Inference Engine.
+ - [openvino.inference_engine](../../src/bindings/python/docs/api_overview.md) - Python\* wrapper on OpenVINO™ Inference Engine.
  - `openvino.tools.accuracy_checker` - Measure accuracy.
  - `openvino.tools.benchmark` - Measure latency and throughput.
 
diff --git a/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_GNMT_From_Tensorflow.md b/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_GNMT_From_Tensorflow.md
index 989290b8f8a..18a770d32e1 100644
--- a/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_GNMT_From_Tensorflow.md
+++ b/docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_GNMT_From_Tensorflow.md
@@ -274,4 +274,4 @@ exec_net = ie.load_network(network=net, device_name="CPU")
 result_ie = exec_net.infer(input_data)
 ```
 
-For more information about Python API, refer to [Inference Engine Python API Overview](../../../../../runtime/bindings/python/docs/api_overview.md).
+For more information about Python API, refer to [Inference Engine Python API Overview](../../../../../src/bindings/python/docs/api_overview.md).
diff --git a/scripts/CMakeLists.txt b/scripts/CMakeLists.txt
index d4b41058500..1afd8a06c20 100644
--- a/scripts/CMakeLists.txt
+++ b/scripts/CMakeLists.txt
@@ -10,15 +10,15 @@ ie_shellcheck_process(DIRECTORY "${OpenVINO_SOURCE_DIR}"
                       SKIP "${OpenVINO_SOURCE_DIR}/bin"
                            "${OpenVINO_SOURCE_DIR}/build"
                            "${OpenVINO_SOURCE_DIR}/thirdparty"
-                           "${OpenVINO_SOURCE_DIR}/runtime/bindings/python/thirdparty/pybind11"
+                           "${OpenVINO_SOURCE_DIR}/src/bindings/python/thirdparty/pybind11"
                            "${IE_MAIN_SOURCE_DIR}/thirdparty"
                            "${OpenVINO_SOURCE_DIR}/tools/pot/thirdparty"
                            "${TEMP}"
                            # TODO fix and enable back:
                            "${OpenVINO_SOURCE_DIR}/scripts/install_dependencies/install_NEO_OCL_driver.sh"
                            "${OpenVINO_SOURCE_DIR}/scripts/install_dependencies/install_openvino_dependencies.sh"
-                           "${OpenVINO_SOURCE_DIR}/runtime/bindings/python/tests/test_onnx/model_zoo_preprocess.sh"
-                           "${OpenVINO_SOURCE_DIR}/runtime/bindings/python/tests_compatibility/test_onnx/model_zoo_preprocess.sh"
+                           "${OpenVINO_SOURCE_DIR}/src/bindings/python/tests/test_onnx/model_zoo_preprocess.sh"
+                           "${OpenVINO_SOURCE_DIR}/src/bindings/python/tests_compatibility/test_onnx/model_zoo_preprocess.sh"
                            )
 
 #
diff --git a/runtime/CMakeLists.txt b/src/CMakeLists.txt
similarity index 100%
rename from runtime/CMakeLists.txt
rename to src/CMakeLists.txt
diff --git a/runtime/bindings/python/.clang-format b/src/bindings/python/.clang-format
similarity index 100%
rename from runtime/bindings/python/.clang-format
rename to src/bindings/python/.clang-format
diff --git a/runtime/bindings/python/.gitignore b/src/bindings/python/.gitignore
similarity index 100%
rename from runtime/bindings/python/.gitignore
rename to src/bindings/python/.gitignore
diff --git a/runtime/bindings/python/BUILDING.md b/src/bindings/python/BUILDING.md
similarity index 100%
rename from runtime/bindings/python/BUILDING.md
rename to src/bindings/python/BUILDING.md
diff --git a/runtime/bindings/python/CMakeLists.txt b/src/bindings/python/CMakeLists.txt
similarity index 100%
rename from runtime/bindings/python/CMakeLists.txt
rename to src/bindings/python/CMakeLists.txt
diff --git a/runtime/bindings/python/docs/api_overview.md b/src/bindings/python/docs/api_overview.md
similarity index 100%
rename from runtime/bindings/python/docs/api_overview.md
rename to src/bindings/python/docs/api_overview.md
diff --git a/runtime/bindings/python/requirements.txt b/src/bindings/python/requirements.txt
similarity index 100%
rename from runtime/bindings/python/requirements.txt
rename to src/bindings/python/requirements.txt
diff --git a/runtime/bindings/python/requirements_test.txt b/src/bindings/python/requirements_test.txt
similarity index 100%
rename from runtime/bindings/python/requirements_test.txt
rename to src/bindings/python/requirements_test.txt
diff --git a/runtime/bindings/python/setup.py b/src/bindings/python/setup.py
similarity index 99%
rename from runtime/bindings/python/setup.py
rename to src/bindings/python/setup.py
index 0881c0c2110..60d2365500e 100644
--- a/runtime/bindings/python/setup.py
+++ b/src/bindings/python/setup.py
@@ -19,7 +19,7 @@ from distutils.command.build import build as _build
 __version__ = os.environ.get("NGRAPH_VERSION", "0.0.0.dev0")
 PYTHON_API_ROOT_DIR = os.path.abspath(os.path.dirname(__file__))
 OPENVINO_ROOT_DIR = os.path.normpath(os.path.join(PYTHON_API_ROOT_DIR, "../../.."))
-# Change current working directory to runtime/bindings/python
+# Change current working directory to src/bindings/python
 os.chdir(PYTHON_API_ROOT_DIR)
 
 NGRAPH_LIBS = ["ngraph", "openvino"]
diff --git a/runtime/bindings/python/src/CMakeLists.txt b/src/bindings/python/src/CMakeLists.txt
similarity index 100%
rename from runtime/bindings/python/src/CMakeLists.txt
rename to src/bindings/python/src/CMakeLists.txt
diff --git a/runtime/bindings/python/src/compatibility/CMakeLists.txt b/src/bindings/python/src/compatibility/CMakeLists.txt
similarity index 100%
rename from runtime/bindings/python/src/compatibility/CMakeLists.txt
rename to src/bindings/python/src/compatibility/CMakeLists.txt
diff --git a/runtime/bindings/python/src/compatibility/ngraph/__init__.py b/src/bindings/python/src/compatibility/ngraph/__init__.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/__init__.py
rename to src/bindings/python/src/compatibility/ngraph/__init__.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/exceptions.py b/src/bindings/python/src/compatibility/ngraph/exceptions.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/exceptions.py
rename to src/bindings/python/src/compatibility/ngraph/exceptions.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/frontend/__init__.py b/src/bindings/python/src/compatibility/ngraph/frontend/__init__.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/frontend/__init__.py
rename to src/bindings/python/src/compatibility/ngraph/frontend/__init__.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/helpers.py b/src/bindings/python/src/compatibility/ngraph/helpers.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/helpers.py
rename to src/bindings/python/src/compatibility/ngraph/helpers.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/impl/__init__.py b/src/bindings/python/src/compatibility/ngraph/impl/__init__.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/impl/__init__.py
rename to src/bindings/python/src/compatibility/ngraph/impl/__init__.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/impl/op/__init__.py b/src/bindings/python/src/compatibility/ngraph/impl/op/__init__.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/impl/op/__init__.py
rename to src/bindings/python/src/compatibility/ngraph/impl/op/__init__.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/impl/op/util/__init__.py b/src/bindings/python/src/compatibility/ngraph/impl/op/util/__init__.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/impl/op/util/__init__.py
rename to src/bindings/python/src/compatibility/ngraph/impl/op/util/__init__.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/impl/passes/__init__.py b/src/bindings/python/src/compatibility/ngraph/impl/passes/__init__.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/impl/passes/__init__.py
rename to src/bindings/python/src/compatibility/ngraph/impl/passes/__init__.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/opset1/__init__.py b/src/bindings/python/src/compatibility/ngraph/opset1/__init__.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/opset1/__init__.py
rename to src/bindings/python/src/compatibility/ngraph/opset1/__init__.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/opset1/ops.py b/src/bindings/python/src/compatibility/ngraph/opset1/ops.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/opset1/ops.py
rename to src/bindings/python/src/compatibility/ngraph/opset1/ops.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/opset2/__init__.py b/src/bindings/python/src/compatibility/ngraph/opset2/__init__.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/opset2/__init__.py
rename to src/bindings/python/src/compatibility/ngraph/opset2/__init__.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/opset2/ops.py b/src/bindings/python/src/compatibility/ngraph/opset2/ops.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/opset2/ops.py
rename to src/bindings/python/src/compatibility/ngraph/opset2/ops.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/opset3/__init__.py b/src/bindings/python/src/compatibility/ngraph/opset3/__init__.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/opset3/__init__.py
rename to src/bindings/python/src/compatibility/ngraph/opset3/__init__.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/opset3/ops.py b/src/bindings/python/src/compatibility/ngraph/opset3/ops.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/opset3/ops.py
rename to src/bindings/python/src/compatibility/ngraph/opset3/ops.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/opset4/__init__.py b/src/bindings/python/src/compatibility/ngraph/opset4/__init__.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/opset4/__init__.py
rename to src/bindings/python/src/compatibility/ngraph/opset4/__init__.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/opset4/ops.py b/src/bindings/python/src/compatibility/ngraph/opset4/ops.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/opset4/ops.py
rename to src/bindings/python/src/compatibility/ngraph/opset4/ops.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/opset5/__init__.py b/src/bindings/python/src/compatibility/ngraph/opset5/__init__.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/opset5/__init__.py
rename to src/bindings/python/src/compatibility/ngraph/opset5/__init__.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/opset5/ops.py b/src/bindings/python/src/compatibility/ngraph/opset5/ops.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/opset5/ops.py
rename to src/bindings/python/src/compatibility/ngraph/opset5/ops.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/opset6/__init__.py b/src/bindings/python/src/compatibility/ngraph/opset6/__init__.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/opset6/__init__.py
rename to src/bindings/python/src/compatibility/ngraph/opset6/__init__.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/opset6/ops.py b/src/bindings/python/src/compatibility/ngraph/opset6/ops.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/opset6/ops.py
rename to src/bindings/python/src/compatibility/ngraph/opset6/ops.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/opset7/__init__.py b/src/bindings/python/src/compatibility/ngraph/opset7/__init__.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/opset7/__init__.py
rename to src/bindings/python/src/compatibility/ngraph/opset7/__init__.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/opset7/ops.py b/src/bindings/python/src/compatibility/ngraph/opset7/ops.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/opset7/ops.py
rename to src/bindings/python/src/compatibility/ngraph/opset7/ops.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/opset8/__init__.py b/src/bindings/python/src/compatibility/ngraph/opset8/__init__.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/opset8/__init__.py
rename to src/bindings/python/src/compatibility/ngraph/opset8/__init__.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/opset8/ops.py b/src/bindings/python/src/compatibility/ngraph/opset8/ops.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/opset8/ops.py
rename to src/bindings/python/src/compatibility/ngraph/opset8/ops.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/opset_utils.py b/src/bindings/python/src/compatibility/ngraph/opset_utils.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/opset_utils.py
rename to src/bindings/python/src/compatibility/ngraph/opset_utils.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/utils/__init__.py b/src/bindings/python/src/compatibility/ngraph/utils/__init__.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/utils/__init__.py
rename to src/bindings/python/src/compatibility/ngraph/utils/__init__.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/utils/broadcasting.py b/src/bindings/python/src/compatibility/ngraph/utils/broadcasting.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/utils/broadcasting.py
rename to src/bindings/python/src/compatibility/ngraph/utils/broadcasting.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/utils/decorators.py b/src/bindings/python/src/compatibility/ngraph/utils/decorators.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/utils/decorators.py
rename to src/bindings/python/src/compatibility/ngraph/utils/decorators.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/utils/input_validation.py b/src/bindings/python/src/compatibility/ngraph/utils/input_validation.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/utils/input_validation.py
rename to src/bindings/python/src/compatibility/ngraph/utils/input_validation.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/utils/node_factory.py b/src/bindings/python/src/compatibility/ngraph/utils/node_factory.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/utils/node_factory.py
rename to src/bindings/python/src/compatibility/ngraph/utils/node_factory.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/utils/reduction.py b/src/bindings/python/src/compatibility/ngraph/utils/reduction.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/utils/reduction.py
rename to src/bindings/python/src/compatibility/ngraph/utils/reduction.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/utils/tensor_iterator_types.py b/src/bindings/python/src/compatibility/ngraph/utils/tensor_iterator_types.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/utils/tensor_iterator_types.py
rename to src/bindings/python/src/compatibility/ngraph/utils/tensor_iterator_types.py
diff --git a/runtime/bindings/python/src/compatibility/ngraph/utils/types.py b/src/bindings/python/src/compatibility/ngraph/utils/types.py
similarity index 100%
rename from runtime/bindings/python/src/compatibility/ngraph/utils/types.py
rename to src/bindings/python/src/compatibility/ngraph/utils/types.py
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/CMakeLists.txt b/src/bindings/python/src/compatibility/pyngraph/CMakeLists.txt
similarity index 100%
rename from runtime/bindings/python/src/compatibility/pyngraph/CMakeLists.txt
rename to src/bindings/python/src/compatibility/pyngraph/CMakeLists.txt
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/axis_set.cpp b/src/bindings/python/src/compatibility/pyngraph/axis_set.cpp
similarity index 100%
rename from runtime/bindings/python/src/compatibility/pyngraph/axis_set.cpp
rename to src/bindings/python/src/compatibility/pyngraph/axis_set.cpp
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/axis_set.hpp b/src/bindings/python/src/compatibility/pyngraph/axis_set.hpp
similarity index 100%
rename from runtime/bindings/python/src/compatibility/pyngraph/axis_set.hpp
rename to src/bindings/python/src/compatibility/pyngraph/axis_set.hpp
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/axis_vector.cpp b/src/bindings/python/src/compatibility/pyngraph/axis_vector.cpp
similarity index 100%
rename from runtime/bindings/python/src/compatibility/pyngraph/axis_vector.cpp
rename to src/bindings/python/src/compatibility/pyngraph/axis_vector.cpp
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/axis_vector.hpp b/src/bindings/python/src/compatibility/pyngraph/axis_vector.hpp
similarity index 100%
rename from runtime/bindings/python/src/compatibility/pyngraph/axis_vector.hpp
rename to src/bindings/python/src/compatibility/pyngraph/axis_vector.hpp
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/coordinate.cpp b/src/bindings/python/src/compatibility/pyngraph/coordinate.cpp
similarity index 100%
rename from runtime/bindings/python/src/compatibility/pyngraph/coordinate.cpp
rename to src/bindings/python/src/compatibility/pyngraph/coordinate.cpp
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/coordinate.hpp b/src/bindings/python/src/compatibility/pyngraph/coordinate.hpp
similarity index 100%
rename from runtime/bindings/python/src/compatibility/pyngraph/coordinate.hpp
rename to src/bindings/python/src/compatibility/pyngraph/coordinate.hpp
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/coordinate_diff.cpp b/src/bindings/python/src/compatibility/pyngraph/coordinate_diff.cpp
similarity index 100%
rename from runtime/bindings/python/src/compatibility/pyngraph/coordinate_diff.cpp
rename to src/bindings/python/src/compatibility/pyngraph/coordinate_diff.cpp
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/coordinate_diff.hpp b/src/bindings/python/src/compatibility/pyngraph/coordinate_diff.hpp
similarity index 100%
rename from runtime/bindings/python/src/compatibility/pyngraph/coordinate_diff.hpp
rename to src/bindings/python/src/compatibility/pyngraph/coordinate_diff.hpp
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/dict_attribute_visitor.cpp b/src/bindings/python/src/compatibility/pyngraph/dict_attribute_visitor.cpp
similarity index 100%
rename from runtime/bindings/python/src/compatibility/pyngraph/dict_attribute_visitor.cpp
rename to src/bindings/python/src/compatibility/pyngraph/dict_attribute_visitor.cpp
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/dict_attribute_visitor.hpp b/src/bindings/python/src/compatibility/pyngraph/dict_attribute_visitor.hpp
similarity index 100%
rename from runtime/bindings/python/src/compatibility/pyngraph/dict_attribute_visitor.hpp
rename to src/bindings/python/src/compatibility/pyngraph/dict_attribute_visitor.hpp
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/dimension.cpp b/src/bindings/python/src/compatibility/pyngraph/dimension.cpp
similarity index 100%
rename from runtime/bindings/python/src/compatibility/pyngraph/dimension.cpp
rename to src/bindings/python/src/compatibility/pyngraph/dimension.cpp
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/dimension.hpp b/src/bindings/python/src/compatibility/pyngraph/dimension.hpp
similarity index 100%
rename from runtime/bindings/python/src/compatibility/pyngraph/dimension.hpp
rename to src/bindings/python/src/compatibility/pyngraph/dimension.hpp
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/discrete_type_info.cpp b/src/bindings/python/src/compatibility/pyngraph/discrete_type_info.cpp
similarity index 100%
rename from runtime/bindings/python/src/compatibility/pyngraph/discrete_type_info.cpp
rename to src/bindings/python/src/compatibility/pyngraph/discrete_type_info.cpp
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/discrete_type_info.hpp b/src/bindings/python/src/compatibility/pyngraph/discrete_type_info.hpp
similarity index 100%
rename from runtime/bindings/python/src/compatibility/pyngraph/discrete_type_info.hpp
rename to src/bindings/python/src/compatibility/pyngraph/discrete_type_info.hpp
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/frontend/frontend.cpp b/src/bindings/python/src/compatibility/pyngraph/frontend/frontend.cpp
similarity index 100%
rename from runtime/bindings/python/src/compatibility/pyngraph/frontend/frontend.cpp
rename to src/bindings/python/src/compatibility/pyngraph/frontend/frontend.cpp
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/frontend/frontend.hpp b/src/bindings/python/src/compatibility/pyngraph/frontend/frontend.hpp
similarity index 100%
rename from runtime/bindings/python/src/compatibility/pyngraph/frontend/frontend.hpp
rename to src/bindings/python/src/compatibility/pyngraph/frontend/frontend.hpp
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/frontend/frontend_manager.cpp b/src/bindings/python/src/compatibility/pyngraph/frontend/frontend_manager.cpp
similarity index 100%
rename from runtime/bindings/python/src/compatibility/pyngraph/frontend/frontend_manager.cpp
rename to src/bindings/python/src/compatibility/pyngraph/frontend/frontend_manager.cpp
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/frontend/frontend_manager.hpp b/src/bindings/python/src/compatibility/pyngraph/frontend/frontend_manager.hpp
similarity index 100%
rename from runtime/bindings/python/src/compatibility/pyngraph/frontend/frontend_manager.hpp
rename to src/bindings/python/src/compatibility/pyngraph/frontend/frontend_manager.hpp
diff --git a/runtime/bindings/python/src/compatibility/pyngraph/frontend/inputmodel.cpp b/src/bindings/python/src/compatibility/pyngraph/frontend/inputmodel.cpp
similarity index 100%
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diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_create_op.py b/src/bindings/python/tests_compatibility/test_ngraph/test_create_op.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_create_op.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_create_op.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_ctc_loss.py b/src/bindings/python/tests_compatibility/test_ngraph/test_ctc_loss.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_ctc_loss.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_ctc_loss.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_data_movement.py b/src/bindings/python/tests_compatibility/test_ngraph/test_data_movement.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_data_movement.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_data_movement.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_dft.py b/src/bindings/python/tests_compatibility/test_ngraph/test_dft.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_dft.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_dft.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_dyn_attributes.py b/src/bindings/python/tests_compatibility/test_ngraph/test_dyn_attributes.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_dyn_attributes.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_dyn_attributes.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_einsum.py b/src/bindings/python/tests_compatibility/test_ngraph/test_einsum.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_einsum.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_einsum.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_gather.py b/src/bindings/python/tests_compatibility/test_ngraph/test_gather.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_gather.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_gather.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_idft.py b/src/bindings/python/tests_compatibility/test_ngraph/test_idft.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_idft.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_idft.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_input_validation.py b/src/bindings/python/tests_compatibility/test_ngraph/test_input_validation.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_input_validation.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_input_validation.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_log_softmax.py b/src/bindings/python/tests_compatibility/test_ngraph/test_log_softmax.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_log_softmax.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_log_softmax.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_manager.py b/src/bindings/python/tests_compatibility/test_ngraph/test_manager.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_manager.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_manager.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_node_factory.py b/src/bindings/python/tests_compatibility/test_ngraph/test_node_factory.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_node_factory.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_node_factory.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_normalization.py b/src/bindings/python/tests_compatibility/test_ngraph/test_normalization.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_normalization.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_normalization.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_ops.py b/src/bindings/python/tests_compatibility/test_ngraph/test_ops.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_ops.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_ops.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_ops_binary.py b/src/bindings/python/tests_compatibility/test_ngraph/test_ops_binary.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_ops_binary.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_ops_binary.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_ops_fused.py b/src/bindings/python/tests_compatibility/test_ngraph/test_ops_fused.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_ops_fused.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_ops_fused.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_ops_matmul.py b/src/bindings/python/tests_compatibility/test_ngraph/test_ops_matmul.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_ops_matmul.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_ops_matmul.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_ops_multioutput.py b/src/bindings/python/tests_compatibility/test_ngraph/test_ops_multioutput.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_ops_multioutput.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_ops_multioutput.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_ops_reshape.py b/src/bindings/python/tests_compatibility/test_ngraph/test_ops_reshape.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_ops_reshape.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_ops_reshape.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_ops_scatter.py b/src/bindings/python/tests_compatibility/test_ngraph/test_ops_scatter.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_ops_scatter.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_ops_scatter.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_ops_unary.py b/src/bindings/python/tests_compatibility/test_ngraph/test_ops_unary.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_ops_unary.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_ops_unary.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_pooling.py b/src/bindings/python/tests_compatibility/test_ngraph/test_pooling.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_pooling.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_pooling.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_proposal.py b/src/bindings/python/tests_compatibility/test_ngraph/test_proposal.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_proposal.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_proposal.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_random_uniform.py b/src/bindings/python/tests_compatibility/test_ngraph/test_random_uniform.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_random_uniform.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_random_uniform.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_reduction.py b/src/bindings/python/tests_compatibility/test_ngraph/test_reduction.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_reduction.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_reduction.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_roll.py b/src/bindings/python/tests_compatibility/test_ngraph/test_roll.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_roll.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_roll.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_sequence_processing.py b/src/bindings/python/tests_compatibility/test_ngraph/test_sequence_processing.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_sequence_processing.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_sequence_processing.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_swish.py b/src/bindings/python/tests_compatibility/test_ngraph/test_swish.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_swish.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_swish.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/test_utils.py b/src/bindings/python/tests_compatibility/test_ngraph/test_utils.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/test_utils.py
rename to src/bindings/python/tests_compatibility/test_ngraph/test_utils.py
diff --git a/runtime/bindings/python/tests_compatibility/test_ngraph/util.py b/src/bindings/python/tests_compatibility/test_ngraph/util.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_ngraph/util.py
rename to src/bindings/python/tests_compatibility/test_ngraph/util.py
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/__init__.py b/src/bindings/python/tests_compatibility/test_onnx/__init__.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/__init__.py
rename to src/bindings/python/tests_compatibility/test_onnx/__init__.py
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/model_zoo_preprocess.sh b/src/bindings/python/tests_compatibility/test_onnx/model_zoo_preprocess.sh
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/model_zoo_preprocess.sh
rename to src/bindings/python/tests_compatibility/test_onnx/model_zoo_preprocess.sh
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/models/add_abc.onnx b/src/bindings/python/tests_compatibility/test_onnx/models/add_abc.onnx
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/models/add_abc.onnx
rename to src/bindings/python/tests_compatibility/test_onnx/models/add_abc.onnx
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/models/data/tensor.data b/src/bindings/python/tests_compatibility/test_onnx/models/data/tensor.data
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/models/data/tensor.data
rename to src/bindings/python/tests_compatibility/test_onnx/models/data/tensor.data
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/models/external_data.onnx b/src/bindings/python/tests_compatibility/test_onnx/models/external_data.onnx
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/models/external_data.onnx
rename to src/bindings/python/tests_compatibility/test_onnx/models/external_data.onnx
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/test_backend.py b/src/bindings/python/tests_compatibility/test_onnx/test_backend.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/test_backend.py
rename to src/bindings/python/tests_compatibility/test_onnx/test_backend.py
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/test_onnx_external_data.py b/src/bindings/python/tests_compatibility/test_onnx/test_onnx_external_data.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/test_onnx_external_data.py
rename to src/bindings/python/tests_compatibility/test_onnx/test_onnx_external_data.py
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/test_onnx_import.py b/src/bindings/python/tests_compatibility/test_onnx/test_onnx_import.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/test_onnx_import.py
rename to src/bindings/python/tests_compatibility/test_onnx/test_onnx_import.py
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/test_ops_batchnorm.py b/src/bindings/python/tests_compatibility/test_onnx/test_ops_batchnorm.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/test_ops_batchnorm.py
rename to src/bindings/python/tests_compatibility/test_onnx/test_ops_batchnorm.py
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/test_ops_binary.py b/src/bindings/python/tests_compatibility/test_onnx/test_ops_binary.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/test_ops_binary.py
rename to src/bindings/python/tests_compatibility/test_onnx/test_ops_binary.py
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/test_ops_convpool.py b/src/bindings/python/tests_compatibility/test_onnx/test_ops_convpool.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/test_ops_convpool.py
rename to src/bindings/python/tests_compatibility/test_onnx/test_ops_convpool.py
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/test_ops_logical.py b/src/bindings/python/tests_compatibility/test_onnx/test_ops_logical.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/test_ops_logical.py
rename to src/bindings/python/tests_compatibility/test_onnx/test_ops_logical.py
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/test_ops_matmul.py b/src/bindings/python/tests_compatibility/test_onnx/test_ops_matmul.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/test_ops_matmul.py
rename to src/bindings/python/tests_compatibility/test_onnx/test_ops_matmul.py
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/test_ops_nonlinear.py b/src/bindings/python/tests_compatibility/test_onnx/test_ops_nonlinear.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/test_ops_nonlinear.py
rename to src/bindings/python/tests_compatibility/test_onnx/test_ops_nonlinear.py
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/test_ops_reduction.py b/src/bindings/python/tests_compatibility/test_onnx/test_ops_reduction.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/test_ops_reduction.py
rename to src/bindings/python/tests_compatibility/test_onnx/test_ops_reduction.py
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/test_ops_reshape.py b/src/bindings/python/tests_compatibility/test_onnx/test_ops_reshape.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/test_ops_reshape.py
rename to src/bindings/python/tests_compatibility/test_onnx/test_ops_reshape.py
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/test_ops_unary.py b/src/bindings/python/tests_compatibility/test_onnx/test_ops_unary.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/test_ops_unary.py
rename to src/bindings/python/tests_compatibility/test_onnx/test_ops_unary.py
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/test_ops_variadic.py b/src/bindings/python/tests_compatibility/test_onnx/test_ops_variadic.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/test_ops_variadic.py
rename to src/bindings/python/tests_compatibility/test_onnx/test_ops_variadic.py
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/test_zoo_models.py b/src/bindings/python/tests_compatibility/test_onnx/test_zoo_models.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/test_zoo_models.py
rename to src/bindings/python/tests_compatibility/test_onnx/test_zoo_models.py
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/utils/__init__.py b/src/bindings/python/tests_compatibility/test_onnx/utils/__init__.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/utils/__init__.py
rename to src/bindings/python/tests_compatibility/test_onnx/utils/__init__.py
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/utils/model_importer.py b/src/bindings/python/tests_compatibility/test_onnx/utils/model_importer.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/utils/model_importer.py
rename to src/bindings/python/tests_compatibility/test_onnx/utils/model_importer.py
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/utils/onnx_backend.py b/src/bindings/python/tests_compatibility/test_onnx/utils/onnx_backend.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/utils/onnx_backend.py
rename to src/bindings/python/tests_compatibility/test_onnx/utils/onnx_backend.py
diff --git a/runtime/bindings/python/tests_compatibility/test_onnx/utils/onnx_helpers.py b/src/bindings/python/tests_compatibility/test_onnx/utils/onnx_helpers.py
similarity index 100%
rename from runtime/bindings/python/tests_compatibility/test_onnx/utils/onnx_helpers.py
rename to src/bindings/python/tests_compatibility/test_onnx/utils/onnx_helpers.py
diff --git a/runtime/bindings/python/thirdparty/pybind11 b/src/bindings/python/thirdparty/pybind11
similarity index 100%
rename from runtime/bindings/python/thirdparty/pybind11
rename to src/bindings/python/thirdparty/pybind11
diff --git a/runtime/bindings/python/tox.ini b/src/bindings/python/tox.ini
similarity index 100%
rename from runtime/bindings/python/tox.ini
rename to src/bindings/python/tox.ini

From 5719192687454b4e6a768c924eb20abba8fad273 Mon Sep 17 00:00:00 2001
From: Ilya Lavrenov <ilya.lavrenov@intel.com>
Date: Sat, 27 Nov 2021 14:58:24 +0300
Subject: [PATCH 48/72] Use ninja multi-config (#8757)

* Use ninja multu-config

* Reused OV_GENERATOR_MULTI_CONFIG

* Try to use config in api_validator post build task

* Fixes

* Revert "[GPU] Fix multi configuration build of onednn_gpu (#8743)"

This reverts commit a8623317329cc244a76ccb96bcec85559c8397fc.

* Propogate disable deprecated for _RELEASE flags as well
---
 .ci/azure/windows.yml                         |  2 +-
 CMakeLists.txt                                |  1 +
 .../IEDevScriptsConfig.cmake                  | 19 ++---
 .../api_validator/api_validator.cmake         | 10 ++-
 .../compile_flags/os_flags.cmake              |  8 +-
 .../developer_package/compile_flags/sdl.cmake | 84 +++++++++----------
 cmake/developer_package/features.cmake        |  6 +-
 cmake/developer_package/target_flags.cmake    |  2 +
 .../ie_bridges/python/cmake/UseCython.cmake   |  5 +-
 .../src/cldnn_engine/CMakeLists.txt           |  5 +-
 .../subgraphs_dumper/CMakeLists.txt           |  7 +-
 .../thirdparty/clDNN/CMakeLists.txt           |  3 -
 .../clDNN/kernel_selector/CMakeLists.txt      |  5 +-
 .../thirdparty/clDNN/runtime/CMakeLists.txt   | 15 +---
 .../thirdparty/clDNN/src/CMakeLists.txt       | 15 +---
 samples/cpp/CMakeLists.txt                    |  6 +-
 tests/fuzz/CMakeLists.txt                     |  6 +-
 tests/memory_tests/CMakeLists.txt             |  3 +-
 tests/stress_tests/CMakeLists.txt             |  6 +-
 thirdparty/zlib/CMakeLists.txt                |  3 +-
 20 files changed, 93 insertions(+), 118 deletions(-)

diff --git a/.ci/azure/windows.yml b/.ci/azure/windows.yml
index 07d62f33878..f689e8c95ed 100644
--- a/.ci/azure/windows.yml
+++ b/.ci/azure/windows.yml
@@ -132,7 +132,7 @@ jobs:
 
   - script: |
       set PATH=$(WORK_DIR)\ninja-win;%PATH%
-      call "$(MSVS_VARS_PATH)" && $(CMAKE_CMD) -GNinja -DENABLE_ONEDNN_FOR_GPU=OFF -DENABLE_GNA=$(CMAKE_BUILD_SHARED_LIBS) -DENABLE_CLDNN=$(CMAKE_BUILD_SHARED_LIBS) -DENABLE_GAPI_PREPROCESSING=$(CMAKE_BUILD_SHARED_LIBS) -DBUILD_SHARED_LIBS=$(CMAKE_BUILD_SHARED_LIBS) -DENABLE_REQUIREMENTS_INSTALL=OFF -DENABLE_FASTER_BUILD=ON -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_TESTS=ON -DENABLE_STRICT_DEPENDENCIES=OFF -DENABLE_PYTHON=ON -DPYTHON_EXECUTABLE="C:\hostedtoolcache\windows\Python\3.7.6\x64\python.exe" -DPYTHON_INCLUDE_DIR="C:\hostedtoolcache\windows\Python\3.7.6\x64\include" -DPYTHON_LIBRARY="C:\hostedtoolcache\windows\Python\3.7.6\x64\libs\python37.lib" -DIE_EXTRA_MODULES=$(OPENVINO_CONTRIB_REPO_DIR)\modules -DCMAKE_C_COMPILER:PATH="$(MSVC_COMPILER_PATH)" -DCMAKE_CXX_COMPILER:PATH="$(MSVC_COMPILER_PATH)" $(REPO_DIR)
+      call "$(MSVS_VARS_PATH)" && $(CMAKE_CMD) -G "Ninja Multi-Config" -DENABLE_ONEDNN_FOR_GPU=OFF -DENABLE_GNA=$(CMAKE_BUILD_SHARED_LIBS) -DENABLE_CLDNN=$(CMAKE_BUILD_SHARED_LIBS) -DENABLE_GAPI_PREPROCESSING=$(CMAKE_BUILD_SHARED_LIBS) -DBUILD_SHARED_LIBS=$(CMAKE_BUILD_SHARED_LIBS) -DENABLE_REQUIREMENTS_INSTALL=OFF -DENABLE_FASTER_BUILD=ON -DCMAKE_BUILD_TYPE=$(BUILD_TYPE) -DENABLE_TESTS=ON -DENABLE_STRICT_DEPENDENCIES=OFF -DENABLE_PYTHON=ON -DPYTHON_EXECUTABLE="C:\hostedtoolcache\windows\Python\3.7.6\x64\python.exe" -DPYTHON_INCLUDE_DIR="C:\hostedtoolcache\windows\Python\3.7.6\x64\include" -DPYTHON_LIBRARY="C:\hostedtoolcache\windows\Python\3.7.6\x64\libs\python37.lib" -DIE_EXTRA_MODULES=$(OPENVINO_CONTRIB_REPO_DIR)\modules -DCMAKE_C_COMPILER:PATH="$(MSVC_COMPILER_PATH)" -DCMAKE_CXX_COMPILER:PATH="$(MSVC_COMPILER_PATH)" $(REPO_DIR)
     workingDirectory: $(BUILD_DIR)
     displayName: 'CMake'
 
diff --git a/CMakeLists.txt b/CMakeLists.txt
index b400b9835aa..d822d31c460 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -36,6 +36,7 @@ message (STATUS "CMAKE_BINARY_DIR ...................... " ${CMAKE_BINARY_DIR})
 message (STATUS "OpenVINO_SOURCE_DIR ................... " ${OpenVINO_SOURCE_DIR})
 message (STATUS "CMAKE_GENERATOR ....................... " ${CMAKE_GENERATOR})
 message (STATUS "CMAKE_C_COMPILER_ID ................... " ${CMAKE_C_COMPILER_ID})
+message (STATUS "CMAKE_CXX_COMPILER_ID ................. " ${CMAKE_CXX_COMPILER_ID})
 message (STATUS "CMAKE_BUILD_TYPE ...................... " ${CMAKE_BUILD_TYPE})
 message (STATUS "CMAKE_TOOLCHAIN_FILE .................. " ${CMAKE_TOOLCHAIN_FILE})
 
diff --git a/cmake/developer_package/IEDevScriptsConfig.cmake b/cmake/developer_package/IEDevScriptsConfig.cmake
index bb7327ea144..dfbb3833056 100644
--- a/cmake/developer_package/IEDevScriptsConfig.cmake
+++ b/cmake/developer_package/IEDevScriptsConfig.cmake
@@ -106,17 +106,11 @@ else()
     set(BIN_FOLDER "bin/${ARCH_FOLDER}")
 endif()
 
-if(NOT DEFINED CMAKE_BUILD_TYPE OR CMAKE_BUILD_TYPE STREQUAL "")
-    message(STATUS "CMAKE_BUILD_TYPE not defined, 'Release' will be used")
-    set(CMAKE_BUILD_TYPE "Release")
-else()
-    set(RELEASE_TYPES "Debug" "Release" "RelWithDebInfo" "MinSizeRel")
-    list(FIND RELEASE_TYPES ${CMAKE_BUILD_TYPE} INDEX_FOUND)
-    if (INDEX_FOUND EQUAL -1)
-        message(FATAL_ERROR "CMAKE_BUILD_TYPE must be one of Debug, Release, RelWithDebInfo, or MinSizeRel")
-    endif()
+set(CMAKE_BUILD_TYPE "Release" CACHE STRING "CMake build type")
+set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Release;Debug;RelWithDebInfo;MinSizeRel")
+if(CMAKE_GENERATOR MATCHES "^Ninja Multi-Config$")
+    set(CMAKE_DEFAULT_BUILD_TYPE "Release" CACHE STRING "CMake default build type")
 endif()
-message(STATUS "CMAKE_BUILD_TYPE: ${CMAKE_BUILD_TYPE}")
 
 if(USE_BUILD_TYPE_SUBFOLDER)
     set(BIN_FOLDER "${BIN_FOLDER}/${CMAKE_BUILD_TYPE}")
@@ -152,8 +146,8 @@ endif()
 set(CMAKE_DEBUG_POSTFIX ${IE_DEBUG_POSTFIX})
 set(CMAKE_RELEASE_POSTFIX ${IE_RELEASE_POSTFIX})
 
-if (MSVC OR CMAKE_GENERATOR STREQUAL "Xcode")
-    # Support CMake multiconfiguration for Visual Studio or Xcode build
+# Support CMake multi-configuration for Visual Studio / Ninja or Xcode build
+if (OV_GENERATOR_MULTI_CONFIG)
     set(IE_BUILD_POSTFIX $<$<CONFIG:Debug>:${IE_DEBUG_POSTFIX}>$<$<CONFIG:Release>:${IE_RELEASE_POSTFIX}>)
 else ()
     if (CMAKE_BUILD_TYPE STREQUAL "Debug")
@@ -162,7 +156,6 @@ else ()
         set(IE_BUILD_POSTFIX ${IE_RELEASE_POSTFIX})
     endif()
 endif()
-
 add_definitions(-DIE_BUILD_POSTFIX=\"${IE_BUILD_POSTFIX}\")
 
 if(NOT UNIX)
diff --git a/cmake/developer_package/api_validator/api_validator.cmake b/cmake/developer_package/api_validator/api_validator.cmake
index ed168178fe3..d58ba745742 100644
--- a/cmake/developer_package/api_validator/api_validator.cmake
+++ b/cmake/developer_package/api_validator/api_validator.cmake
@@ -96,7 +96,7 @@ function(_ie_add_api_validator_post_build_step)
         get_target_property(IS_IMPORTED ${target} IMPORTED)
         get_target_property(orig_target ${target} ALIASED_TARGET)
         if(IS_IMPORTED)
-            get_target_property(target_location ${target} LOCATION)  
+            get_target_property(target_location ${target} LOCATION)
             get_filename_component(target_name "${target_location}" NAME_WE)
         elseif(TARGET "${orig_target}")
             set(target_name ${orig_target})
@@ -107,10 +107,14 @@ function(_ie_add_api_validator_post_build_step)
 
     foreach(target IN LISTS API_VALIDATOR_TARGETS)
         api_validator_get_target_name()
-        set(output_file "${CMAKE_BINARY_DIR}/api_validator/${target_name}.txt")
+        if(CMAKE_VERSION VERSION_GREATER_EQUAL 3.21 AND OV_GENERATOR_MULTI_CONFIG)
+            set(output_file "${CMAKE_BINARY_DIR}/api_validator/$<CONFIG>/${target_name}.txt")
+        else()
+            set(output_file "${CMAKE_BINARY_DIR}/api_validator/${target_name}.txt")
+        endif()
 
         add_custom_command(TARGET ${API_VALIDATOR_TARGET} POST_BUILD
-            COMMAND ${CMAKE_COMMAND}
+            COMMAND ${CMAKE_COMMAND} --config $<CONFIG>
                 -D UWP_API_VALIDATOR=${UWP_API_VALIDATOR}
                 -D UWP_API_VALIDATOR_TARGET=$<TARGET_FILE:${target}>
                 -D UWP_API_VALIDATOR_APIS=${UWP_API_VALIDATOR_APIS}
diff --git a/cmake/developer_package/compile_flags/os_flags.cmake b/cmake/developer_package/compile_flags/os_flags.cmake
index a6b3ac4ee43..e3945d9ecc4 100644
--- a/cmake/developer_package/compile_flags/os_flags.cmake
+++ b/cmake/developer_package/compile_flags/os_flags.cmake
@@ -28,6 +28,8 @@ macro(disable_deprecated_warnings)
         message(WARNING "Unsupported CXX compiler ${CMAKE_CXX_COMPILER_ID}")
     endif()
 
+    set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} ${ie_c_cxx_deprecated}")
+    set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} ${ie_c_cxx_deprecated}")
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${ie_c_cxx_deprecated}")
     set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${ie_c_cxx_deprecated}")
 endmacro()
@@ -42,7 +44,9 @@ macro(ie_deprecated_no_errors)
             set(ie_c_cxx_deprecated_no_errors "/Qdiag-warning:1478,1786")
         elseif(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
             # show 4996 only for /w4
-            set(ie_c_cxx_deprecated_no_errors "/w44996")
+            set(ie_c_cxx_deprecated_no_errors "/wd4996")
+            # WA for VPUX plugin
+            set(ie_c_cxx_deprecated_no_errors "${ie_c_cxx_deprecated_no_errors} /wd4146 /wd4703")
         endif()
     else()
         if(CMAKE_CXX_COMPILER_ID STREQUAL "Intel")
@@ -56,6 +60,8 @@ macro(ie_deprecated_no_errors)
         endif()
     endif()
 
+    set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} ${ie_c_cxx_deprecated_no_errors}")
+    set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} ${ie_c_cxx_deprecated_no_errors}")
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${ie_c_cxx_deprecated_no_errors}")
     set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${ie_c_cxx_deprecated_no_errors}")
 endmacro()
diff --git a/cmake/developer_package/compile_flags/sdl.cmake b/cmake/developer_package/compile_flags/sdl.cmake
index 7690a9031d8..0b2a8c7d510 100644
--- a/cmake/developer_package/compile_flags/sdl.cmake
+++ b/cmake/developer_package/compile_flags/sdl.cmake
@@ -2,49 +2,47 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-if (CMAKE_BUILD_TYPE STREQUAL "Release")
-    if(UNIX)
-        set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -Wformat -Wformat-security")
-        if (NOT ENABLE_SANITIZER)
-            # ASan does not support fortification https://github.com/google/sanitizers/issues/247
-            set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -D_FORTIFY_SOURCE=2")
-        endif()
-        if(NOT APPLE)
-            set(CMAKE_EXE_LINKER_FLAGS_RELEASE "${CMAKE_EXE_LINKER_FLAGS_RELEASE} -pie")
-        endif()
-
-        if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
-            set(IE_LINKER_FLAGS "${IE_LINKER_FLAGS} -z noexecstack -z relro -z now")
-            if(CMAKE_CXX_COMPILER_VERSION VERSION_LESS 4.9)
-                set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -fstack-protector-all")
-            else()
-                set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -fstack-protector-strong")
-            endif()
-            if (NOT ENABLE_SANITIZER)
-                set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -s")
-            endif()
-        elseif(OV_COMPILER_IS_CLANG)
-            set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -fstack-protector-all")
-        elseif(CMAKE_CXX_COMPILER_ID STREQUAL "Intel")
-            if (NOT ENABLE_SANITIZER)
-                set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -Wl,--strip-all")
-            endif()
-            set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -fstack-protector-strong")
-            set(IE_LINKER_FLAGS "${IE_LINKER_FLAGS} -z noexecstack -z relro -z now")
-        endif()
-    else()
-        if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
-            set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} /sdl")
-        endif()
-        set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} /guard:cf")
-        if(ENABLE_INTEGRITYCHECK)
-            set(CMAKE_SHARED_LINKER_FLAGS_RELEASE "${CMAKE_SHARED_LINKER_FLAGS_RELEASE} /INTEGRITYCHECK")
-        endif()
+if(UNIX)
+    set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -Wformat -Wformat-security")
+    if (NOT ENABLE_SANITIZER)
+        # ASan does not support fortification https://github.com/google/sanitizers/issues/247
+        set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -D_FORTIFY_SOURCE=2")
+    endif()
+    if(NOT APPLE)
+        set(CMAKE_EXE_LINKER_FLAGS_RELEASE "${CMAKE_EXE_LINKER_FLAGS_RELEASE} -pie")
     endif()
 
-    set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${IE_C_CXX_FLAGS}")
-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${IE_C_CXX_FLAGS}")
-    set(CMAKE_SHARED_LINKER_FLAGS_RELEASE "${CMAKE_SHARED_LINKER_FLAGS_RELEASE} ${IE_LINKER_FLAGS}")
-    set(CMAKE_MODULE_LINKER_FLAGS_RELEASE "${CMAKE_MODULE_LINKER_FLAGS_RELEASE} ${IE_LINKER_FLAGS}")
-    set(CMAKE_EXE_LINKER_FLAGS_RELEASE "${CMAKE_EXE_LINKER_FLAGS_RELEASE} ${IE_LINKER_FLAGS}")
+    if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
+        set(IE_LINKER_FLAGS "${IE_LINKER_FLAGS} -z noexecstack -z relro -z now")
+        if(CMAKE_CXX_COMPILER_VERSION VERSION_LESS 4.9)
+            set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -fstack-protector-all")
+        else()
+            set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -fstack-protector-strong")
+        endif()
+        if (NOT ENABLE_SANITIZER)
+            set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -s")
+        endif()
+    elseif(OV_COMPILER_IS_CLANG)
+        set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -fstack-protector-all")
+    elseif(CMAKE_CXX_COMPILER_ID STREQUAL "Intel")
+        if (NOT ENABLE_SANITIZER)
+            set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -Wl,--strip-all")
+        endif()
+        set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} -fstack-protector-strong")
+        set(IE_LINKER_FLAGS "${IE_LINKER_FLAGS} -z noexecstack -z relro -z now")
+    endif()
+else()
+    if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
+        set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} /sdl")
+    endif()
+    set(IE_C_CXX_FLAGS "${IE_C_CXX_FLAGS} /guard:cf")
+    if(ENABLE_INTEGRITYCHECK)
+        set(CMAKE_SHARED_LINKER_FLAGS_RELEASE "${CMAKE_SHARED_LINKER_FLAGS_RELEASE} /INTEGRITYCHECK")
+    endif()
 endif()
+
+set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} ${IE_C_CXX_FLAGS}")
+set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} ${IE_C_CXX_FLAGS}")
+set(CMAKE_SHARED_LINKER_FLAGS_RELEASE "${CMAKE_SHARED_LINKER_FLAGS_RELEASE} ${IE_LINKER_FLAGS}")
+set(CMAKE_MODULE_LINKER_FLAGS_RELEASE "${CMAKE_MODULE_LINKER_FLAGS_RELEASE} ${IE_LINKER_FLAGS}")
+set(CMAKE_EXE_LINKER_FLAGS_RELEASE "${CMAKE_EXE_LINKER_FLAGS_RELEASE} ${IE_LINKER_FLAGS}")
diff --git a/cmake/developer_package/features.cmake b/cmake/developer_package/features.cmake
index a0d4d17db32..a35b53bc630 100644
--- a/cmake/developer_package/features.cmake
+++ b/cmake/developer_package/features.cmake
@@ -11,10 +11,10 @@ ie_dependent_option (ENABLE_LTO "Enable Link Time Optimization" OFF "LINUX;NOT C
 
 ie_option (OS_FOLDER "create OS dedicated folder in output" OFF)
 
-if(UNIX)
-    ie_option(USE_BUILD_TYPE_SUBFOLDER "Create dedicated sub-folder per build type for output binaries" ON)
-else()
+if(OV_GENERATOR_MULTI_CONFIG)
     ie_option(USE_BUILD_TYPE_SUBFOLDER "Create dedicated sub-folder per build type for output binaries" OFF)
+else()
+    ie_option(USE_BUILD_TYPE_SUBFOLDER "Create dedicated sub-folder per build type for output binaries" ON)
 endif()
 
 # FIXME: ARM cross-compiler generates several "false positive" warnings regarding __builtin_memcpy buffer overflow
diff --git a/cmake/developer_package/target_flags.cmake b/cmake/developer_package/target_flags.cmake
index d4fd9837647..a359937a702 100644
--- a/cmake/developer_package/target_flags.cmake
+++ b/cmake/developer_package/target_flags.cmake
@@ -61,3 +61,5 @@ if(CMAKE_CXX_COMPILER_ID MATCHES "^(Apple)?Clang$")
 else()
     set(OV_COMPILER_IS_CLANG OFF)
 endif()
+
+get_property(OV_GENERATOR_MULTI_CONFIG GLOBAL PROPERTY GENERATOR_IS_MULTI_CONFIG)
diff --git a/inference-engine/ie_bridges/python/cmake/UseCython.cmake b/inference-engine/ie_bridges/python/cmake/UseCython.cmake
index 08939b3ad4f..4aa1b482798 100644
--- a/inference-engine/ie_bridges/python/cmake/UseCython.cmake
+++ b/inference-engine/ie_bridges/python/cmake/UseCython.cmake
@@ -230,10 +230,7 @@ function( compile_pyx _name generated_file )
     set( no_docstrings_arg "--no-docstrings" )
   endif()
 
-  if( "${CMAKE_BUILD_TYPE}" STREQUAL "Debug" OR
-        "${CMAKE_BUILD_TYPE}" STREQUAL "RelWithDebInfo" )
-      set( cython_debug_arg "--gdb" )
-  endif()
+  set( cython_debug_arg "$<$<OR:$<CONFIG:Debug>,$<CONFIG:RelWithDebInfo>>:--gdb>" )
 
   if( "${PYTHONLIBS_VERSION_STRING}" MATCHES "^3." )
     set( version_arg "-3" )
diff --git a/inference-engine/src/cldnn_engine/CMakeLists.txt b/inference-engine/src/cldnn_engine/CMakeLists.txt
index 58b7536e63e..cab62ab79ea 100644
--- a/inference-engine/src/cldnn_engine/CMakeLists.txt
+++ b/inference-engine/src/cldnn_engine/CMakeLists.txt
@@ -26,9 +26,8 @@ ie_add_plugin(NAME ${TARGET_NAME}
               SOURCES ${MAIN_SRC} ${LIBRARY_HEADERS}
               VERSION_DEFINES_FOR cldnn_engine.cpp)
 
-if(CMAKE_BUILD_TYPE STREQUAL "Release")
-  target_compile_options(${TARGET_NAME} PRIVATE -Os)
-endif()
+target_compile_options(${TARGET_NAME} PRIVATE
+    $<$<CONFIG:Release>:$<IF:$<CXX_COMPILER_ID:MSVC>,/Os,-Os>>)
 
 target_link_libraries(${TARGET_NAME} PRIVATE clDNN_lib pugixml::static
                                              inference_engine_transformations
diff --git a/inference-engine/tests/functional/plugin/conformance/subgraphs_dumper/CMakeLists.txt b/inference-engine/tests/functional/plugin/conformance/subgraphs_dumper/CMakeLists.txt
index 8a97d7698d2..6d1c07b38fb 100644
--- a/inference-engine/tests/functional/plugin/conformance/subgraphs_dumper/CMakeLists.txt
+++ b/inference-engine/tests/functional/plugin/conformance/subgraphs_dumper/CMakeLists.txt
@@ -9,9 +9,6 @@ addIeTargetTest(
         ROOT ${CMAKE_CURRENT_SOURCE_DIR}
         INCLUDES
             ${CMAKE_CURRENT_SOURCE_DIR}/include
-            $<TARGET_PROPERTY:inference_engine,INTERFACE_INCLUDE_DIRECTORIES>
-        DEPENDENCIES
-            ir_ov_frontend
         LINK_LIBRARIES
             PRIVATE
                 gflags
@@ -21,4 +18,8 @@ addIeTargetTest(
         ADD_CPPLINT
 )
 
+if(TARGET ir_ov_frontend)
+    add_dependencies(${TARGET_NAME} ir_ov_frontend)
+endif()
+
 ie_faster_build(${TARGET_NAME} UNITY)
diff --git a/inference-engine/thirdparty/clDNN/CMakeLists.txt b/inference-engine/thirdparty/clDNN/CMakeLists.txt
index 954fda6f1ca..26e249d0ba2 100644
--- a/inference-engine/thirdparty/clDNN/CMakeLists.txt
+++ b/inference-engine/thirdparty/clDNN/CMakeLists.txt
@@ -12,9 +12,6 @@ project("${CLDNN__PROJ_NAME}")
 # ====================================== HELPER CONSTANT VARIABLES =====================================
 # ======================================================================================================
 # ======================================================================================================
-if (ENABLE_ONEDNN_FOR_GPU)
-    set(ONEDNN_LIBRARY onednn_gpu_tgt)
-endif()
 
 if(ENABLE_GPU_DEBUG_CAPS)
   add_definitions(-DGPU_DEBUG_CONFIG=1)
diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/CMakeLists.txt b/inference-engine/thirdparty/clDNN/kernel_selector/CMakeLists.txt
index 2622eac0004..a8f804318a7 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/CMakeLists.txt
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/CMakeLists.txt
@@ -125,9 +125,8 @@ add_library("${CLDNN_BUILD__PROJ}" STATIC
     ${__CLDNN_AllSources}
   )
 
-if(CMAKE_BUILD_TYPE STREQUAL "Release")
-  target_compile_options(${CLDNN_BUILD__PROJ} PRIVATE -Os)
-endif()
+target_compile_options("${CLDNN_BUILD__PROJ}" PRIVATE
+  $<$<CONFIG:Release>:$<IF:$<CXX_COMPILER_ID:MSVC>,/Os,-Os>>)
 
 set_property(TARGET "${CLDNN_BUILD__PROJ}" PROPERTY PROJECT_LABEL "${CLDNN_BUILD__PROJ_LABEL}")
 
diff --git a/inference-engine/thirdparty/clDNN/runtime/CMakeLists.txt b/inference-engine/thirdparty/clDNN/runtime/CMakeLists.txt
index d5ec92eb561..26d02fba2f7 100644
--- a/inference-engine/thirdparty/clDNN/runtime/CMakeLists.txt
+++ b/inference-engine/thirdparty/clDNN/runtime/CMakeLists.txt
@@ -47,9 +47,8 @@ add_library("${CLDNN_BUILD__PROJ}" STATIC
     ${__CLDNN_AllSources}
   )
 
-if(CMAKE_BUILD_TYPE STREQUAL "Release")
-  target_compile_options(${CLDNN_BUILD__PROJ} PRIVATE -Os)
-endif()
+target_compile_options("${CLDNN_BUILD__PROJ}" PRIVATE
+  $<$<CONFIG:Release>:$<IF:$<CXX_COMPILER_ID:MSVC>,/Os,-Os>>)
 
 set_property(TARGET "${CLDNN_BUILD__PROJ}" PROPERTY PROJECT_LABEL "${CLDNN_BUILD__PROJ_LABEL}")
 
@@ -67,15 +66,7 @@ target_link_libraries("${CLDNN_BUILD__PROJ}" PRIVATE
   )
 
 if(ENABLE_ONEDNN_FOR_GPU)
-  get_property(isMultiConfig GLOBAL PROPERTY GENERATOR_IS_MULTI_CONFIG)
-  if (isMultiConfig)
-    add_dependencies(${CLDNN_BUILD__PROJ} onednn_gpu_build)
-	foreach(dir IN LISTS ONEDNN_INCLUDE_DIRS)
-	  target_include_directories("${CLDNN_BUILD__PROJ}" PUBLIC $<BUILD_INTERFACE:${dir}>)
-	endforeach()
-	target_compile_definitions("${CLDNN_BUILD__PROJ}" PUBLIC ENABLE_ONEDNN_FOR_GPU)
-  endif()
-  target_link_libraries("${CLDNN_BUILD__PROJ}" PUBLIC ${ONEDNN_LIBRARY})
+  target_link_libraries("${CLDNN_BUILD__PROJ}" PUBLIC onednn_gpu_tgt)
 endif()
 
 if(WIN32)
diff --git a/inference-engine/thirdparty/clDNN/src/CMakeLists.txt b/inference-engine/thirdparty/clDNN/src/CMakeLists.txt
index d90f593fdd7..1da07e6055e 100644
--- a/inference-engine/thirdparty/clDNN/src/CMakeLists.txt
+++ b/inference-engine/thirdparty/clDNN/src/CMakeLists.txt
@@ -111,9 +111,8 @@ add_library("${CLDNN_BUILD__PROJ}" STATIC
     ${__CLDNN_AllSources}
   )
 
-if(CMAKE_BUILD_TYPE STREQUAL "Release")
-  target_compile_options(${CLDNN_BUILD__PROJ} PRIVATE -Os)
-endif()
+target_compile_options("${CLDNN_BUILD__PROJ}" PRIVATE
+  $<$<CONFIG:Release>:$<IF:$<CXX_COMPILER_ID:MSVC>,/Os,-Os>>)
 
 set_property(TARGET "${CLDNN_BUILD__PROJ}" PROPERTY PROJECT_LABEL "${CLDNN_BUILD__PROJ_LABEL}")
 
@@ -128,15 +127,7 @@ target_link_libraries("${CLDNN_BUILD__PROJ}" PRIVATE
 )
 
 if(ENABLE_ONEDNN_FOR_GPU)
-  get_property(isMultiConfig GLOBAL PROPERTY GENERATOR_IS_MULTI_CONFIG)
-  if (isMultiConfig)
-    add_dependencies(${CLDNN_BUILD__PROJ} onednn_gpu_build)
-	foreach(dir IN LISTS ONEDNN_INCLUDE_DIRS)
-	  target_include_directories("${CLDNN_BUILD__PROJ}" PUBLIC $<BUILD_INTERFACE:${dir}>)
-	endforeach()
-	target_compile_definitions("${CLDNN_BUILD__PROJ}" PUBLIC ENABLE_ONEDNN_FOR_GPU)
-  endif()
-  target_link_libraries("${CLDNN_BUILD__PROJ}" PUBLIC ${ONEDNN_LIBRARY})
+  target_link_libraries("${CLDNN_BUILD__PROJ}" PUBLIC onednn_gpu_tgt)
 endif()
 
 if(COMMAND add_cpplint_target)
diff --git a/samples/cpp/CMakeLists.txt b/samples/cpp/CMakeLists.txt
index 7e08d4f6697..6c6d14af94e 100644
--- a/samples/cpp/CMakeLists.txt
+++ b/samples/cpp/CMakeLists.txt
@@ -9,10 +9,8 @@ cmake_policy(SET CMP0025 NEW)
 
 project(Samples)
 
-if (CMAKE_BUILD_TYPE STREQUAL "")
-    message(STATUS "CMAKE_BUILD_TYPE not defined, 'Release' will be used")
-    set(CMAKE_BUILD_TYPE "Release")
-endif()
+set(CMAKE_BUILD_TYPE "Release" CACHE STRING "CMake build type")
+set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Release" "Debug" "RelWithDebInfo" "MinSizeRel")
 
 set_property(GLOBAL PROPERTY USE_FOLDERS ON)
 
diff --git a/tests/fuzz/CMakeLists.txt b/tests/fuzz/CMakeLists.txt
index a5f46c5a07e..b0fbdf5cbe0 100644
--- a/tests/fuzz/CMakeLists.txt
+++ b/tests/fuzz/CMakeLists.txt
@@ -6,10 +6,8 @@ project(openvino_fuzzing)
 
 cmake_minimum_required(VERSION 3.13)
 
-if (CMAKE_BUILD_TYPE STREQUAL "")
-    message(STATUS "CMAKE_BUILD_TYPE not defined, 'Release' will be used")
-    set(CMAKE_BUILD_TYPE "Release")
-endif()
+set(CMAKE_BUILD_TYPE "Release" CACHE STRING "CMake build type")
+set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Release" "Debug" "RelWithDebInfo" "MinSizeRel")
 
 set(OpenVINO_SOURCE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../../")
 
diff --git a/tests/memory_tests/CMakeLists.txt b/tests/memory_tests/CMakeLists.txt
index d2ca2f7eccd..27700420b6c 100644
--- a/tests/memory_tests/CMakeLists.txt
+++ b/tests/memory_tests/CMakeLists.txt
@@ -11,7 +11,8 @@ if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
     set (CMAKE_CXX_FLAGS "-std=c++11 ${CMAKE_CXX_FLAGS}")
 endif()
 
-set (CMAKE_BUILD_TYPE "Release" CACHE STRING "Choose the build type")
+set(CMAKE_BUILD_TYPE "Release" CACHE STRING "CMake build type")
+set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Release" "Debug" "RelWithDebInfo" "MinSizeRel")
 
 project(memory_tests)
 
diff --git a/tests/stress_tests/CMakeLists.txt b/tests/stress_tests/CMakeLists.txt
index a2c328b16c3..62b96f2f042 100644
--- a/tests/stress_tests/CMakeLists.txt
+++ b/tests/stress_tests/CMakeLists.txt
@@ -11,10 +11,8 @@ if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
     set (CMAKE_CXX_FLAGS "-std=c++11 ${CMAKE_CXX_FLAGS}")
 endif()
 
-if (CMAKE_BUILD_TYPE STREQUAL "")
-    message(STATUS "CMAKE_BUILD_TYPE not defined, 'Release' will be used")
-    set(CMAKE_BUILD_TYPE "Release")
-endif()
+set(CMAKE_BUILD_TYPE "Release" CACHE STRING "CMake build type")
+set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Release" "Debug" "RelWithDebInfo" "MinSizeRel")
 
 # Define directory where artifacts will be placed
 set(OUTPUT_ROOT ${CMAKE_CURRENT_SOURCE_DIR})
diff --git a/thirdparty/zlib/CMakeLists.txt b/thirdparty/zlib/CMakeLists.txt
index d887e275611..9a24a5daff7 100644
--- a/thirdparty/zlib/CMakeLists.txt
+++ b/thirdparty/zlib/CMakeLists.txt
@@ -5,7 +5,8 @@
 project(zlib)
 
 if(CMAKE_C_COMPILER_ID STREQUAL "MSVC")
-    set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS}  /wd4996  /W3")
+    set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /wd4996 /W3")
+    set (CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} /wd4995 /wd4996")
 else()
     set (CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wno-all")
     if (CMAKE_CXX_COMPILER_ID STREQUAL "Intel")

From ab3a892d48186f1cddbe4521abf6b005a3809ba6 Mon Sep 17 00:00:00 2001
From: Vladimir Paramuzov <vladimir.paramuzov@intel.com>
Date: Sat, 27 Nov 2021 15:06:54 +0300
Subject: [PATCH 49/72] [GPU] Fixed typo in cache.json (#8756)

---
 .../clDNN/kernel_selector/core/cache/cache.json      |  4 ++--
 .../precommit_configs/desktop_references_config.xml  | 12 ++++++------
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache.json b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache.json
index 57cd1548eb5..f19efc6a793 100644
--- a/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache.json
+++ b/inference-engine/thirdparty/clDNN/kernel_selector/core/cache/cache.json
@@ -1436,7 +1436,7 @@
                 "F32_BFYX_v1_p0_0_v1_p0_0_v2048_p0_0_v300_p0_0;F32_FB_v300_p0_0_v21_p0_0": ["fully_connected_gpu_fb_oi_ref", 0],
                 "F32_BFYX_v1_p0_0_v1_p0_0_v2048_p0_0_v300_p0_0;F32_FB_v300_p0_0_v84_p0_0": ["fully_connected_gpu_fb_oi_ref", 0],
                 "F32_BFYX_v1792_p0_0_v1_p0_0_v1_p0_0_v1_p0_0;F32_BF_v512_p0_0_v1_p0_0": ["fully_connected_gpu_bf_io_input_spatial", 2]
-            },
+            }
         },
         "64": {
             "CONVOLUTION": {
@@ -2320,7 +2320,7 @@
                 "F16_YXFB_v300_p0_0_v4096_p0_0_v1_p0_0_v1_p0_0;F16_FB_v300_p0_0_v6565_p0_0": ["fully_connected_gpu_yxfb_ref", 0],
                 "F16_YXFB_v300_p0_0_v4096_p0_0_v1_p0_0_v1_p0_0;F16_FB_v300_p0_0_v101_p0_0": ["fully_connected_gpu_fb_oi_ref", 2],
                 "F16_BFYX_v71_p0_0_v88_p0_0_v1_p0_0_v1_p0_0;F16_BF_v128_p0_0_v1_p0_0": ["fully_connected_gpu_bf_io_gemm", 1]
-            },
+            }
         }
     },
     "version_1": {
diff --git a/tests/stress_tests/.automation/memcheck_tests/precommit_configs/desktop_references_config.xml b/tests/stress_tests/.automation/memcheck_tests/precommit_configs/desktop_references_config.xml
index 05d67549066..4b054cd07bc 100644
--- a/tests/stress_tests/.automation/memcheck_tests/precommit_configs/desktop_references_config.xml
+++ b/tests/stress_tests/.automation/memcheck_tests/precommit_configs/desktop_references_config.xml
@@ -11,11 +11,11 @@
         <model path="public/mobilenet-ssd/FP32/mobilenet-ssd.xml" precision="FP32" test="infer_request_inference" device="GPU" vmsize="1667380" vmpeak="1667380" vmrss="472123" vmhwm="753719" />
 
         <model path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" precision="FP32" test="create_exenetwork" device="CPU" vmsize="908018" vmpeak="908018" vmrss="41142" vmhwm="41142" />
-        <model path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" precision="FP32" test="create_exenetwork" device="GPU" vmsize="2108095" vmpeak="2230602" vmrss="215618" vmhwm="281486" />
+        <model path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" precision="FP32" test="create_exenetwork" device="GPU" vmsize="2108095" vmpeak="2230602" vmrss="235618" vmhwm="301486" />
         <model path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" precision="FP32" test="inference_with_streams" device="CPU" vmsize="1008342" vmpeak="1093008" vmrss="43602" vmhwm="43602" />
-        <model path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" precision="FP32" test="inference_with_streams" device="GPU" vmsize="2203952" vmpeak="2241272" vmrss="214328" vmhwm="280103" />
+        <model path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" precision="FP32" test="inference_with_streams" device="GPU" vmsize="2203952" vmpeak="2241272" vmrss="244328" vmhwm="300103" />
         <model path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" precision="FP32" test="infer_request_inference" device="CPU" vmsize="908549" vmpeak="908549" vmrss="43492" vmhwm="43492" />
-        <model path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" precision="FP32" test="infer_request_inference" device="GPU" vmsize="2108100" vmpeak="2230612" vmrss="193330" vmhwm="280550" />
+        <model path="public/mtcnn/mtcnn-r/FP32/mtcnn-r.xml" precision="FP32" test="infer_request_inference" device="GPU" vmsize="2108100" vmpeak="2230612" vmrss="223996" vmhwm="300550" />
 
         <model path="public/ssd300/FP32/ssd300.xml" precision="FP32" test="create_exenetwork" device="CPU" vmsize="1054892" vmpeak="1054892" vmrss="292962" vmhwm="292962" />
         <model path="public/ssd300/FP32/ssd300.xml" precision="FP32" test="create_exenetwork" device="GPU" vmsize="1887948" vmpeak="1925471" vmrss="792849" vmhwm="1081917" />
@@ -40,11 +40,11 @@
         <model path="public/mobilenet-ssd/FP16/mobilenet-ssd.xml" precision="FP16" test="infer_request_inference" device="GPU" vmsize="1623206" vmpeak="1708402" vmrss="459144" vmhwm="762455" />
 
         <model path="public/mtcnn/mtcnn-r/FP16/mtcnn-r.xml" precision="FP16" test="create_exenetwork" device="CPU" vmsize="908169" vmpeak="908169" vmrss="42021" vmhwm="42021" />
-        <model path="public/mtcnn/mtcnn-r/FP16/mtcnn-r.xml" precision="FP16" test="create_exenetwork" device="GPU" vmsize="2107846" vmpeak="2145161" vmrss="192961" vmhwm="280217" />
+        <model path="public/mtcnn/mtcnn-r/FP16/mtcnn-r.xml" precision="FP16" test="create_exenetwork" device="GPU" vmsize="2107846" vmpeak="2145161" vmrss="236984" vmhwm="303448" />
         <model path="public/mtcnn/mtcnn-r/FP16/mtcnn-r.xml" precision="FP16" test="inference_with_streams" device="CPU" vmsize="1008113" vmpeak="1088786" vmrss="44028" vmhwm="44028" />
-        <model path="public/mtcnn/mtcnn-r/FP16/mtcnn-r.xml" precision="FP16" test="inference_with_streams" device="GPU" vmsize="2203697" vmpeak="2222500" vmrss="193143" vmhwm="279931" />
+        <model path="public/mtcnn/mtcnn-r/FP16/mtcnn-r.xml" precision="FP16" test="inference_with_streams" device="GPU" vmsize="2203697" vmpeak="2222500" vmrss="237996" vmhwm="303904" />
         <model path="public/mtcnn/mtcnn-r/FP16/mtcnn-r.xml" precision="FP16" test="infer_request_inference" device="CPU" vmsize="908700" vmpeak="908700" vmrss="43227" vmhwm="43227" />
-        <model path="public/mtcnn/mtcnn-r/FP16/mtcnn-r.xml" precision="FP16" test="infer_request_inference" device="GPU" vmsize="2107846" vmpeak="2145161" vmrss="194516" vmhwm="281372" />
+        <model path="public/mtcnn/mtcnn-r/FP16/mtcnn-r.xml" precision="FP16" test="infer_request_inference" device="GPU" vmsize="2107846" vmpeak="2145161" vmrss="235248" vmhwm="305996" />
 
         <model path="public/ssd300/FP16/ssd300.xml" precision="FP16" test="create_exenetwork" device="CPU" vmsize="1120184" vmpeak="1120184" vmrss="359200" vmhwm="359200" />
         <model path="public/ssd300/FP16/ssd300.xml" precision="FP16" test="create_exenetwork" device="GPU" vmsize="1630569" vmpeak="1752530" vmrss="546364" vmhwm="874426" />

From e76fc14ae11b6f7e933658b1a7b2172c43f560f6 Mon Sep 17 00:00:00 2001
From: Sergey Shlyapnikov <sergey.shlyapnikov@intel.com>
Date: Sat, 27 Nov 2021 15:08:06 +0300
Subject: [PATCH 50/72] [GPU] Use b_fs_yx_fsv16 format for OneDNN convolutins
 in case of FP32 output (#8808)

---
 .../thirdparty/clDNN/src/layout_optimizer.cpp | 19 ++++++++++++++++++-
 1 file changed, 18 insertions(+), 1 deletion(-)

diff --git a/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp b/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp
index 7961d8f4de3..59c87e3d28d 100644
--- a/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp
+++ b/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp
@@ -876,6 +876,18 @@ layout layout_optimizer::get_expected_layout(layout const& current_layout,
     }
 
     if (use_onednn_impls) {
+        std::function<bool(const program_node&)> has_any_convolutions_below;
+        has_any_convolutions_below = [&](const program_node& node) -> bool {
+            if (node.get_users().empty())
+                return false;
+            for (auto& usr : node.get_users()) {
+                if (usr->is_type<convolution>())
+                    return true;
+                return has_any_convolutions_below(*usr);
+            }
+            return false;
+        };
+
         /* ***************************** OneDNN impls format selection part ****************************** */
         bool valid_grouped = !is_dw && prim->groups > 1 && (ofm_per_group % compute_block == 0 && ifm_per_group % compute_block == 0);
         if (i8_u8_input) {
@@ -883,7 +895,12 @@ layout layout_optimizer::get_expected_layout(layout const& current_layout,
                 if (input_layout.size.batch[0] % 16 == 0) {
                     expected_format = cldnn::format::bs_fs_yx_bsv32_fsv32;
                 } else {
-                    expected_format = cldnn::format::b_fs_yx_fsv32;
+                    if (data_type_traits::is_floating_point(output_layout.data_type) &&
+                        !has_any_convolutions_below(node)) {
+                        expected_format = cldnn::format::b_fs_yx_fsv16;
+                    } else {
+                        expected_format = cldnn::format::b_fs_yx_fsv32;
+                    }
                 }
             } else if ((_optimization_attributes.b_fs_yx_fsv16_network &&
                        convolution_b_fs_yx_fsv16_opt(input_layout, output_layout, weights_layout, prim)) && is_2d) {

From 9b3e12006fae11aee37a047cdb7c4db39a62405f Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Sat, 27 Nov 2021 15:54:35 +0300
Subject: [PATCH 51/72] Fixed error: expression result unused (#8862)

---
 .../python/src/pyopenvino/graph/function.cpp  | 34 +++++++++----------
 1 file changed, 17 insertions(+), 17 deletions(-)

diff --git a/src/bindings/python/src/pyopenvino/graph/function.cpp b/src/bindings/python/src/pyopenvino/graph/function.cpp
index 60443aceff5..24f81fa5789 100644
--- a/src/bindings/python/src/pyopenvino/graph/function.cpp
+++ b/src/bindings/python/src/pyopenvino/graph/function.cpp
@@ -133,7 +133,7 @@ void regclass_graph_Function(py::module m) {
         }),
         py::arg("results"),
         py::arg("parameters"),
-        py::arg("name") = ""),
+        py::arg("name") = "",
         R"(
             Create user-defined Function which is a representation of a model
 
@@ -147,7 +147,7 @@ void regclass_graph_Function(py::module m) {
 
             name : str
                 String to set as function's friendly name.
-        )";
+        )");
 
     function.def(py::init([](const ov::OutputVector& results,
                              const std::vector<std::shared_ptr<ov::Node>>& nodes,
@@ -160,8 +160,8 @@ void regclass_graph_Function(py::module m) {
                  py::arg("results"),
                  py::arg("sinks"),
                  py::arg("parameters"),
-                 py::arg("name") = ""),
-        R"(
+                 py::arg("name") = "",
+                 R"(
             Create user-defined Function which is a representation of a model
 
             Parameters
@@ -177,7 +177,7 @@ void regclass_graph_Function(py::module m) {
 
             name : str
                 String to set as function's friendly name.
-            )";
+            )");
     function.def(py::init([](const ov::ResultVector& results,
                              const std::vector<std::shared_ptr<ov::Node>>& nodes,
                              const ov::ParameterVector& parameters,
@@ -191,8 +191,8 @@ void regclass_graph_Function(py::module m) {
                  py::arg("sinks"),
                  py::arg("parameters"),
                  py::arg("variables"),
-                 py::arg("name") = ""),
-        R"(
+                 py::arg("name") = "",
+                 R"(
             Create user-defined Function which is a representation of a model
 
             Parameters
@@ -211,7 +211,7 @@ void regclass_graph_Function(py::module m) {
 
             name : str
                 String to set as function's friendly name.
-            )";
+            )");
 
     function.def(py::init([](const ov::OutputVector& results,
                              const std::vector<std::shared_ptr<ov::Node>>& nodes,
@@ -226,8 +226,8 @@ void regclass_graph_Function(py::module m) {
                  py::arg("sinks"),
                  py::arg("parameters"),
                  py::arg("variables"),
-                 py::arg("name") = ""),
-        R"(
+                 py::arg("name") = "",
+                 R"(
             Create user-defined Function which is a representation of a model
 
             Parameters
@@ -246,7 +246,7 @@ void regclass_graph_Function(py::module m) {
 
             name : str
                 String to set as function's friendly name.
-        )";
+        )");
 
     function.def(py::init([](const ov::ResultVector& results,
                              const ov::ParameterVector& parameters,
@@ -258,8 +258,8 @@ void regclass_graph_Function(py::module m) {
                  py::arg("results"),
                  py::arg("parameters"),
                  py::arg("variables"),
-                 py::arg("name") = ""),
-        R"(
+                 py::arg("name") = "",
+                 R"(
             Create user-defined Function which is a representation of a model
 
             Parameters
@@ -275,7 +275,7 @@ void regclass_graph_Function(py::module m) {
 
             name : str
                 String to set as function's friendly name.
-        )";
+        )");
 
     function.def(py::init([](const ov::OutputVector& results,
                              const ov::ParameterVector& parameters,
@@ -287,8 +287,8 @@ void regclass_graph_Function(py::module m) {
                  py::arg("results"),
                  py::arg("parameters"),
                  py::arg("variables"),
-                 py::arg("name") = ""),
-        R"(
+                 py::arg("name") = "",
+                 R"(
             Create user-defined Function which is a representation of a model
 
             Parameters
@@ -304,7 +304,7 @@ void regclass_graph_Function(py::module m) {
 
             name : str
                 String to set as function's friendly name.
-        )";
+        )");
 
     function.def("validate_nodes_and_infer_types", &ov::Function::validate_nodes_and_infer_types);
 

From 77f6a0076b4281716f81b92814b03e1ce6ee260d Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Sat, 27 Nov 2021 18:53:12 +0300
Subject: [PATCH 52/72] Move core components (#8869)

* Moved openvino to src

* Moved ngraph and frontends to src

* Fixed cmake generation

* Moved inference_engine libs to src

* Moved C API to src

* Fixed CMake generation

* Moved readers to tests, snippets and preprocessing to common

* Fixed CMake

* Moved transformations and lp_transformations

* Fixed transformations cmake

* Fixed build

* Fixed unit-tests and ci paths

* Fixed docs

* Fixed C API build

* Try to fix static build

* More clear order

* Renamed inference_engine_legacy_api to legacy

* Fixed some cmake scripts

* Fixed path to legacy

* Fixed Myriad plugin

* Fixed v7 reader

* Fixed plugin.hpp

* Fixed developer config

* Fixed ie_parallel
---
 .ci/azure/linux.yml                           |   4 +-
 .ci/azure/linux_lohika.yml                    |   4 +-
 .ci/azure/mac.yml                             |   2 +-
 .ci/azure/windows.yml                         |   4 +-
 CMakeLists.txt                                |   6 +-
 cmake/dependencies.cmake                      |   2 +-
 cmake/developer_package/plugins/plugins.cmake |   2 +-
 cmake/developer_package/version.cmake         |   4 +-
 cmake/extra_modules.cmake                     |   4 +-
 .../cmake => cmake}/ie_parallel.cmake         |   0
 ...renceEngineDeveloperPackageConfig.cmake.in |   2 +-
 cmake/test_model_zoo.cmake                    |   8 +-
 docs/CMakeLists.txt                           |   7 +-
 docs/doxygen/doxygen-ignore.txt               |  29 +---
 docs/doxygen/ie_docs.config                   |   2 +-
 docs/doxygen/ie_plugin_api.config             |   6 +-
 docs/doxygen/ngraph_cpp_api.config            |   6 +-
 inference-engine/CMakeLists.txt               |   4 -
 .../ie_bridges/python/wheel/CMakeLists.txt    |   2 +-
 inference-engine/src/CMakeLists.txt           |  45 ------
 inference-engine/src/readers/CMakeLists.txt   |  22 ---
 .../myriad_executable_network.cpp             |   3 +-
 .../inference_engine/CMakeLists.txt           |   2 +-
 .../tests_deprecated/CMakeLists.txt           |   1 +
 .../tests_deprecated/readers/CMakeLists.txt   |   8 +
 .../readers/ir_reader_v7/CMakeLists.txt       |   2 +-
 .../readers/ir_reader_v7/ie_blob_proxy.hpp    |   0
 .../ir_reader_v7/ie_cnn_net_reader_impl.cpp   |   0
 .../ir_reader_v7/ie_cnn_net_reader_impl.h     |   0
 .../readers/ir_reader_v7/ie_format_parser.cpp |   0
 .../readers/ir_reader_v7/ie_format_parser.h   |   0
 .../readers/ir_reader_v7/ie_ir_itt.hpp        |   0
 .../readers/ir_reader_v7/ie_ir_parser.cpp     |  18 +--
 .../readers/ir_reader_v7/ie_ir_parser.hpp     |   0
 .../readers/ir_reader_v7/ie_ir_reader.cpp     |   2 +-
 .../readers/ir_reader_v7/ie_ir_reader.hpp     |   8 +-
 .../readers/ir_reader_v7/ie_layer_parsers.cpp |   0
 .../readers/ir_reader_v7/ie_layer_parsers.h   |   0
 .../ir_reader_v7/ie_layer_validators.cpp      |   0
 .../ir_reader_v7/ie_layer_validators.hpp      |   0
 .../readers/ir_reader_v7/parsers.h            |   0
 ngraph/.gitignore                             | 123 ---------------
 ngraph/CMakeLists.txt                         |  31 ----
 ngraph/environment_variables.md               |  16 --
 .../test/util/unit-test-execution/README.md   |  24 ---
 .../test/util/unit-test-execution/conftest.py |  96 ------------
 .../util/unit-test-execution/requirements.txt |   1 -
 .../unit_test_executable.py                   | 143 ------------------
 openvino/CMakeLists.txt                       |   9 --
 .../inference_engine => src}/.clang-format    |   0
 src/CMakeLists.txt                            |  42 ++++-
 src/bindings/CMakeLists.txt                   |  10 ++
 .../bindings}/c/CMakeLists.txt                |   0
 .../bindings}/c/docs/api_overview.md          |   0
 .../bindings}/c/include/c_api/ie_c_api.h      |   0
 .../bindings}/c/src/CMakeLists.txt            |   0
 .../bindings}/c/src/ie_c_api.cpp              |   0
 .../bindings}/c/tests/CMakeLists.txt          |   0
 .../bindings}/c/tests/ie_c_api_test.cpp       |   0
 .../bindings}/c/tests/test_model_repo.hpp     |   0
 src/common/CMakeLists.txt                     |  19 +++
 .../conditional_compilation/CMakeLists.txt    |   1 +
 .../include/openvino/cc/factory.h             |   0
 .../include/openvino/cc/ngraph/itt.hpp        |   0
 .../include/openvino/cc/selective_build.h     |   0
 .../scripts/ccheader.py                       |   0
 {openvino => src/common}/itt/CMakeLists.txt   |   1 +
 .../common}/itt/cmake/ITTConfig.cmake         |   0
 .../itt/include/openvino/function_name.hpp    |   0
 .../common}/itt/include/openvino/itt.hpp      |   0
 {openvino => src/common}/itt/src/itt.cpp      |   0
 .../common/legacy}/CMakeLists.txt             |   0
 .../include/legacy/cnn_network_impl.hpp       |   0
 .../convert_function_to_cnn_network.hpp       |   0
 .../details/ie_cnn_network_iterator.hpp       |   0
 .../legacy/details/ie_cnn_network_tools.h     |   0
 .../legacy}/include/legacy/graph_tools.hpp    |   0
 .../include/legacy/graph_transformer.h        |   0
 .../legacy/include/legacy}/ie_ir_version.hpp  |   0
 .../common/legacy}/include/legacy/ie_layers.h |   0
 .../include/legacy/ie_layers_internal.hpp     |   0
 .../include/legacy/ie_layers_property.hpp     |   0
 .../legacy/include/legacy}/ie_reader.hpp      |   0
 .../include/legacy/ie_util_internal.hpp       |   0
 .../include/legacy/layer_transform.hpp        |   0
 .../common/legacy}/include/legacy/net_pass.h  |   0
 .../include/legacy/ngraph_ops/crop_ie.hpp     |   0
 .../include/legacy/ngraph_ops/eltwise.hpp     |   0
 .../legacy/ngraph_ops/fully_connected.hpp     |   0
 .../include/legacy/ngraph_ops/gather_ie.hpp   |   0
 .../legacy/ngraph_ops/gather_tree_ie.hpp      |   0
 .../include/legacy/ngraph_ops/gru_cell_ie.hpp |   0
 .../legacy/ngraph_ops/gru_sequence_ie.hpp     |   0
 .../legacy/ngraph_ops/hard_sigmoid_ie.hpp     |   0
 .../include/legacy/ngraph_ops/interp.hpp      |   0
 .../include/legacy/ngraph_ops/lrn_ie.hpp      |   0
 .../legacy/ngraph_ops/lstm_cell_ie.hpp        |   0
 .../legacy/ngraph_ops/lstm_sequence_ie.hpp    |   0
 .../include/legacy/ngraph_ops/nms_ie.hpp      |   0
 .../legacy/ngraph_ops/normalize_ie.hpp        |   0
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 .../onnx_common/src/onnx_model_validator.cpp  |   0
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 .../paddlepaddle/src/op/matmul_v2.cpp         |   0
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 .../frontends}/paddlepaddle/src/op/pow.cpp    |   0
 .../paddlepaddle/src/op/prior_box.cpp         |   0
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 .../paddlepaddle/src/op/reshape2.cpp          |   0
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 .../frontends}/paddlepaddle/src/op/scale.cpp  |   0
 .../frontends}/paddlepaddle/src/op/shape.cpp  |   0
 .../paddlepaddle/src/op/sigmoid.cpp           |   0
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 .../frontends}/paddlepaddle/src/op/split.cpp  |   0
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 .../paddlepaddle/src/op/yolo_box.cpp          |   0
 .../frontends}/paddlepaddle/src/op_table.cpp  |   0
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 .../paddlepaddle/src/pdpd_fw_node.cpp         |   0
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 .../paddlepaddle/src/pdpd_utils.cpp           |   0
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 .../paddlepaddle/src/proto/framework.proto    |   0
 .../frontends}/tensorflow/CMakeLists.txt      |   0
 .../include/tensorflow_frontend/decoder.hpp   |   0
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 .../tensorflow_frontend/graph_iterator.hpp    |   0
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 .../tensorflow/src/decoder_proto.cpp          |   0
 .../tensorflow/src/decoder_proto.hpp          |   0
 .../frontends}/tensorflow/src/exceptions.cpp  |   0
 .../frontends}/tensorflow/src/exceptions.hpp  |   0
 .../frontends}/tensorflow/src/frontend.cpp    |   0
 .../tensorflow/src/graph_iterator_proto.hpp   |   0
 .../frontends}/tensorflow/src/model.cpp       |   0
 .../frontends}/tensorflow/src/model.hpp       |   0
 .../tensorflow/src/node_context.hpp           |   0
 .../frontends}/tensorflow/src/op/addN.cpp     |   0
 .../tensorflow/src/op/arg_min_max.cpp         |   0
 .../frontends}/tensorflow/src/op/avg_pool.cpp |   0
 .../frontends}/tensorflow/src/op/bias_add.cpp |   0
 .../tensorflow/src/op/binary_op.cpp           |   0
 .../frontends}/tensorflow/src/op/cast.cpp     |   0
 .../frontends}/tensorflow/src/op/concat.cpp   |   0
 .../frontends}/tensorflow/src/op/const.cpp    |   0
 .../frontends}/tensorflow/src/op/conv_2d.cpp  |   0
 .../tensorflow/src/op/conv_2d_backprop.cpp    |   0
 .../frontends}/tensorflow/src/op/conv_3d.cpp  |   0
 .../tensorflow/src/op/crop_and_resize.cpp     |   0
 .../frontends}/tensorflow/src/op/cumsum.cpp   |   0
 .../tensorflow/src/op/depth_to_space.cpp      |   0
 .../tensorflow/src/op/depthwise_conv_2d.cpp   |   0
 .../frontends}/tensorflow/src/op/elu.cpp      |   0
 .../tensorflow/src/op/expand_dims.cpp         |   0
 .../src/op/fake_quant_min_max_vars.cpp        |   0
 .../frontends}/tensorflow/src/op/fill.cpp     |   0
 .../tensorflow/src/op/fused_batch_norm.cpp    |   0
 .../frontends}/tensorflow/src/op/gather.cpp   |   0
 .../frontends}/tensorflow/src/op/identity.cpp |   0
 .../tensorflow/src/op/interpolate.cpp         |   0
 .../tensorflow/src/op/is_finite.cpp           |   0
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 .../tensorflow/src/op/leaky_relu.cpp          |   0
 .../frontends}/tensorflow/src/op/log1p.cpp    |   0
 .../tensorflow/src/op/log_softmax.cpp         |   0
 .../frontends}/tensorflow/src/op/lrn.cpp      |   0
 .../frontends}/tensorflow/src/op/matmul.cpp   |   0
 .../frontends}/tensorflow/src/op/max_pool.cpp |   0
 .../frontends}/tensorflow/src/op/no_op.cpp    |   0
 .../tensorflow/src/op/non_max_suppression.cpp |   0
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 .../frontends}/tensorflow/src/op/pack.cpp     |   0
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 .../tensorflow/src/op/placeholder.cpp         |   0
 .../tensorflow/src/op/random_uniform.cpp      |   0
 .../frontends}/tensorflow/src/op/range.cpp    |   0
 .../frontends}/tensorflow/src/op/rank.cpp     |   0
 .../tensorflow/src/op/reciprocal.cpp          |   0
 .../frontends}/tensorflow/src/op/reduce.cpp   |   0
 .../frontends}/tensorflow/src/op/relu_6.cpp   |   0
 .../frontends}/tensorflow/src/op/reshape.cpp  |   0
 .../frontends}/tensorflow/src/op/reverse.cpp  |   0
 .../frontends}/tensorflow/src/op/roll.cpp     |   0
 .../frontends}/tensorflow/src/op/round.cpp    |   0
 .../frontends}/tensorflow/src/op/rsqrt.cpp    |   0
 .../frontends}/tensorflow/src/op/select.cpp   |   0
 .../frontends}/tensorflow/src/op/shape.cpp    |   0
 .../frontends}/tensorflow/src/op/size.cpp     |   0
 .../frontends}/tensorflow/src/op/slice.cpp    |   0
 .../frontends}/tensorflow/src/op/softmax.cpp  |   0
 .../tensorflow/src/op/space_to_batch_nd.cpp   |   0
 .../tensorflow/src/op/space_to_depth.cpp      |   0
 .../frontends}/tensorflow/src/op/split.cpp    |   0
 .../frontends}/tensorflow/src/op/sqrt.cpp     |   0
 .../frontends}/tensorflow/src/op/square.cpp   |   0
 .../frontends}/tensorflow/src/op/squeeze.cpp  |   0
 .../tensorflow/src/op/strided_slice.cpp       |   0
 .../frontends}/tensorflow/src/op/tile.cpp     |   0
 .../frontends}/tensorflow/src/op/top_k.cpp    |   0
 .../tensorflow/src/op/transpose.cpp           |   0
 .../frontends}/tensorflow/src/op/unary_op.cpp |   0
 .../frontends}/tensorflow/src/op/unpack.cpp   |   0
 .../frontends}/tensorflow/src/op/where.cpp    |   0
 .../frontends}/tensorflow/src/op/xdivy.cpp    |   0
 .../tensorflow/src/op/zeros_like.cpp          |   0
 .../frontends}/tensorflow/src/op_table.cpp    |   0
 .../frontends}/tensorflow/src/op_table.hpp    |   0
 .../tensorflow/src/openvino_conversions.cpp   |   0
 .../tensorflow/src/openvino_conversions.hpp   |   0
 .../tensorflow/src/pass/transpose_sinking.cpp |   0
 .../tensorflow/src/pass/transpose_sinking.hpp |   0
 .../frontends}/tensorflow/src/place.cpp       |   0
 .../frontends}/tensorflow/src/place.hpp       |   0
 .../src/proto/allocation_description.proto    |   0
 .../tensorflow/src/proto/api_def.proto        |   0
 .../tensorflow/src/proto/attr_value.proto     |   0
 .../tensorflow/src/proto/cost_graph.proto     |   0
 .../src/proto/dataset_options.proto           |   0
 .../src/proto/device_attributes.proto         |   0
 .../tensorflow/src/proto/function.proto       |   0
 .../tensorflow/src/proto/graph.proto          |   0
 .../src/proto/graph_transfer_info.proto       |   0
 .../tensorflow/src/proto/kernel_def.proto     |   0
 .../tensorflow/src/proto/log_memory.proto     |   0
 .../tensorflow/src/proto/model.proto          |   0
 .../tensorflow/src/proto/node_def.proto       |   0
 .../tensorflow/src/proto/op_def.proto         |   0
 .../tensorflow/src/proto/reader_base.proto    |   0
 .../remote_fused_graph_execute_info.proto     |   0
 .../src/proto/resource_handle.proto           |   0
 .../tensorflow/src/proto/step_stats.proto     |   0
 .../tensorflow/src/proto/summary.proto        |   0
 .../tensorflow/src/proto/tensor.proto         |   0
 .../src/proto/tensor_description.proto        |   0
 .../tensorflow/src/proto/tensor_shape.proto   |   0
 .../tensorflow/src/proto/tensor_slice.proto   |   0
 .../tensorflow/src/proto/types.proto          |   0
 .../tensorflow/src/proto/variable.proto       |   0
 .../tensorflow/src/proto/versions.proto       |   0
 .../frontends}/tensorflow/src/tensorflow.cpp  |   0
 .../tensorflow/src/tf_framework_node.cpp      |   0
 .../tensorflow/src/tf_framework_node.hpp      |   0
 .../frontends}/tensorflow/src/utils.cpp       |   0
 .../frontends}/tensorflow/src/utils.hpp       |   0
 {ngraph => src/inference}/.clang-format       |   0
 .../inference}/CMakeLists.txt                 |  18 +--
 .../inference/dev_api}/.clang-format          |   0
 .../inference/dev_api}/blob_factory.hpp       |   0
 .../inference/dev_api}/blob_transform.hpp     |   0
 .../inference/dev_api}/caseless.hpp           |   0
 ...executable_network_thread_safe_default.hpp |   0
 ...nfer_async_request_thread_safe_default.hpp |   0
 .../ie_iexecutable_network_internal.hpp       |   0
 .../interface/ie_iinfer_request_internal.hpp  |   0
 .../interface/ie_internal_plugin_config.hpp   |   0
 .../interface/ie_iplugin_internal.hpp         |   0
 .../interface/ie_ivariable_state_internal.hpp |   0
 .../dev_api}/cpp_interfaces/plugin_itt.hpp    |   0
 .../inference/dev_api}/debug.h                |   0
 .../inference/dev_api}/description_buffer.hpp |   0
 .../inference/dev_api}/exec_graph_info.hpp    |   0
 .../inference/dev_api}/file_utils.h           |   0
 .../inference/dev_api}/ie_algorithm.hpp       |   0
 .../inference/dev_api}/ie_icore.hpp           |   0
 .../inference/dev_api}/ie_memcpy.h            |   0
 .../inference/dev_api}/ie_metric_helpers.hpp  |   0
 .../inference/dev_api}/ie_ngraph_utils.hpp    |   0
 .../dev_api}/ie_performance_hints.hpp         |   0
 .../inference/dev_api}/ie_system_conf.h       |   0
 .../inference/dev_api}/memory_solver.hpp      |   0
 .../dev_api}/performance_heuristics.hpp       |   0
 .../inference/dev_api}/precision_utils.h      |   0
 .../inference/dev_api}/so_ptr.hpp             |   0
 .../threading/ie_cpu_streams_executor.hpp     |   0
 .../threading/ie_executor_manager.hpp         |   0
 .../threading/ie_immediate_executor.hpp       |   0
 .../threading/ie_istreams_executor.hpp        |   0
 .../dev_api}/threading/ie_itask_executor.hpp  |   0
 .../threading/ie_tbb_streams_executor.hpp     |   0
 .../dev_api}/threading/ie_thread_local.hpp    |   0
 .../inference/dev_api}/xml_parse_utils.h      |   0
 .../include/ie/cldnn/cldnn_config.hpp         |   0
 .../include/ie/cpp/ie_cnn_network.h           |   0
 .../include/ie/cpp/ie_executable_network.hpp  |   0
 .../include/ie/cpp/ie_infer_request.hpp       |   0
 .../include/ie/cpp/ie_memory_state.hpp        |   0
 .../include/ie/details/ie_blob_iterator.hpp   |   0
 .../include/ie/details/ie_exception.hpp       |   0
 .../include/ie/details/ie_pre_allocator.hpp   |   0
 .../include/ie/details/ie_so_loader.h         |   0
 .../include/ie/details/ie_so_pointer.hpp      |   0
 .../inference}/include/ie/gna/gna_config.hpp  |   0
 .../ie/gpu/details/gpu_context_helpers.hpp    |   0
 .../inference}/include/ie/gpu/gpu_config.hpp  |   0
 .../include/ie/gpu/gpu_context_api_dx.hpp     |   0
 .../include/ie/gpu/gpu_context_api_ocl.hpp    |   0
 .../include/ie/gpu/gpu_context_api_va.hpp     |   0
 .../include/ie/gpu/gpu_ocl_wrapper.hpp        |   0
 .../inference}/include/ie/gpu/gpu_params.hpp  |   0
 .../ie/hetero/hetero_plugin_config.hpp        |   0
 .../inference}/include/ie/ie_allocator.hpp    |   0
 .../inference}/include/ie/ie_api.h            |   0
 .../inference}/include/ie/ie_blob.h           |   0
 .../inference}/include/ie/ie_common.h         |   0
 .../inference}/include/ie/ie_compound_blob.h  |   0
 .../inference}/include/ie/ie_core.hpp         |   0
 .../inference}/include/ie/ie_data.h           |   0
 .../inference}/include/ie/ie_extension.h      |   0
 .../inference}/include/ie/ie_icnn_network.hpp |   0
 .../include/ie/ie_iexecutable_network.hpp     |   0
 .../inference}/include/ie/ie_iextension.h     |   0
 .../include/ie/ie_iinfer_request.hpp          |   0
 .../inference}/include/ie/ie_input_info.hpp   |   0
 .../inference}/include/ie/ie_layouts.h        |   0
 .../include/ie/ie_locked_memory.hpp           |   0
 .../inference}/include/ie/ie_parallel.hpp     |   0
 .../inference}/include/ie/ie_parameter.hpp    |   0
 .../include/ie/ie_plugin_config.hpp           |   0
 .../inference}/include/ie/ie_precision.hpp    |   0
 .../inference}/include/ie/ie_preprocess.hpp   |   0
 .../inference}/include/ie/ie_remote_blob.hpp  |   0
 .../include/ie/ie_remote_context.hpp          |   0
 .../include/ie/ie_transformations.hpp         |   0
 .../inference}/include/ie/ie_version.hpp      |   0
 .../include/ie/inference_engine.hpp           |   0
 .../ie/multi-device/multi_device_config.hpp   |   0
 .../inference}/include/ie/vpu/hddl_config.hpp |   0
 .../include/ie/vpu/hddl_plugin_config.hpp     |   0
 .../include/ie/vpu/myriad_config.hpp          |   0
 .../include/ie/vpu/myriad_plugin_config.hpp   |   0
 .../inference}/include/ie/vpu/vpu_config.hpp  |   0
 .../include/ie/vpu/vpu_plugin_config.hpp      |   0
 .../inference}/include/openvino/openvino.hpp  |   0
 .../include/openvino/runtime/common.hpp       |   0
 .../include/openvino/runtime/core.hpp         |   0
 .../include/openvino/runtime/exception.hpp    |   0
 .../openvino/runtime/executable_network.hpp   |   0
 .../include/openvino/runtime/gpu/ocl/dx.hpp   |   0
 .../include/openvino/runtime/gpu/ocl/ocl.hpp  |   0
 .../openvino/runtime/gpu/ocl/ocl_wrapper.hpp  |   0
 .../include/openvino/runtime/gpu/ocl/va.hpp   |   0
 .../openvino/runtime/infer_request.hpp        |   0
 .../include/openvino/runtime/parameter.hpp    |   0
 .../openvino/runtime/profiling_info.hpp       |   0
 .../openvino/runtime/remote_context.hpp       |   0
 .../openvino/runtime/remote_tensor.hpp        |   0
 .../include/openvino/runtime/runtime.hpp      |   0
 .../openvino/runtime/variable_state.hpp       |   0
 .../inference}/src/blob_transform.cpp         |   0
 .../src/cnn_network_ngraph_impl.cpp           |   0
 .../src/cnn_network_ngraph_impl.hpp           |   0
 .../inference}/src/compilation_context.cpp    |   0
 .../inference}/src/compilation_context.hpp    |   0
 .../inference}/src/cpp/exception2status.hpp   |   0
 .../inference}/src/cpp/ie_cnn_network.cpp     |   0
 .../src/cpp/ie_executable_network.cpp         |   0
 .../src/cpp/ie_executable_network_base.hpp    |   0
 .../inference}/src/cpp/ie_extension.cpp       |   0
 .../src/cpp/ie_infer_async_request_base.hpp   |   0
 .../inference}/src/cpp/ie_infer_request.cpp   |   0
 .../inference}/src/cpp/ie_plugin.hpp          |   0
 .../inference}/src/cpp/ie_remote_context.cpp  |   0
 .../inference}/src/cpp/ie_variable_state.cpp  |   0
 .../ie_iexecutable_network_internal.cpp       |   0
 .../interface/ie_iinfer_request_internal.cpp  |   0
 .../interface/ie_iplugin_internal.cpp         |   0
 .../interface/ie_ivariable_state_internal.cpp |   0
 .../cpu_x86_sse42/blob_transform_sse42.cpp    |   0
 .../cpu_x86_sse42/blob_transform_sse42.hpp    |   0
 .../inference}/src/file_utils.cpp             |   0
 .../inference}/src/ie_cache_guard.cpp         |   0
 .../inference}/src/ie_cache_guard.hpp         |   0
 .../inference}/src/ie_cache_manager.hpp       |   0
 .../inference}/src/ie_common.cpp              |   0
 .../inference}/src/ie_compound_blob.cpp       |   0
 .../inference}/src/ie_core.cpp                |   0
 .../inference}/src/ie_data.cpp                |   0
 .../inference}/src/ie_itt.hpp                 |   0
 .../inference}/src/ie_memcpy.cpp              |   0
 .../inference}/src/ie_network_reader.cpp      |   4 +-
 .../inference}/src/ie_network_reader.hpp      |   0
 .../inference}/src/ie_ngraph_utils.cpp        |   0
 .../inference}/src/ie_remote_context.cpp      |   0
 .../inference}/src/ie_system_conf.cpp         |   0
 .../inference}/src/ie_transformations.cpp     |   0
 .../inference}/src/ie_version.cpp             |   0
 .../inference}/src/os/lin/lin_system_conf.cpp |   0
 .../inference}/src/os/win/win_system_conf.cpp |   0
 .../inference}/src/precision_utils.cpp        |   0
 .../inference}/src/precomp.hpp                |   0
 .../inference}/src/remote_tensor.cpp          |   0
 .../inference}/src/shared_object_loader.cpp   |   0
 .../inference}/src/system_allocator.hpp       |   0
 .../src/threading/ie_cpu_streams_executor.cpp |   0
 .../src/threading/ie_executor_manager.cpp     |   0
 .../src/threading/ie_istreams_executor.cpp    |   0
 .../src/threading/ie_itask_executor.cpp       |   0
 .../threading/ie_parallel_custom_arena.cpp    |   0
 .../threading/ie_parallel_custom_arena.hpp    |   0
 .../src/threading/ie_tbb_streams_executor.cpp |   0
 .../src/threading/ie_thread_affinity.cpp      |   0
 .../src/threading/ie_thread_affinity.hpp      |   0
 .../inference}/src/xml_parse_utils.cpp        |   0
 3909 files changed, 166 insertions(+), 617 deletions(-)
 rename {inference-engine/cmake => cmake}/ie_parallel.cmake (100%)
 delete mode 100644 inference-engine/src/readers/CMakeLists.txt
 create mode 100644 inference-engine/tests_deprecated/readers/CMakeLists.txt
 rename inference-engine/{src => tests_deprecated}/readers/ir_reader_v7/CMakeLists.txt (95%)
 rename inference-engine/{src => tests_deprecated}/readers/ir_reader_v7/ie_blob_proxy.hpp (100%)
 rename inference-engine/{src => tests_deprecated}/readers/ir_reader_v7/ie_cnn_net_reader_impl.cpp (100%)
 rename inference-engine/{src => tests_deprecated}/readers/ir_reader_v7/ie_cnn_net_reader_impl.h (100%)
 rename inference-engine/{src => tests_deprecated}/readers/ir_reader_v7/ie_format_parser.cpp (100%)
 rename inference-engine/{src => tests_deprecated}/readers/ir_reader_v7/ie_format_parser.h (100%)
 rename inference-engine/{src => tests_deprecated}/readers/ir_reader_v7/ie_ir_itt.hpp (100%)
 rename inference-engine/{src => tests_deprecated}/readers/ir_reader_v7/ie_ir_parser.cpp (76%)
 rename inference-engine/{src => tests_deprecated}/readers/ir_reader_v7/ie_ir_parser.hpp (100%)
 rename inference-engine/{src => tests_deprecated}/readers/ir_reader_v7/ie_ir_reader.cpp (97%)
 rename inference-engine/{src => tests_deprecated}/readers/ir_reader_v7/ie_ir_reader.hpp (85%)
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 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/eltwise_base_transformation.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/fake_quantize.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/fake_quantize_decomposition.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/fold_convert.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/fold_fake_quantize.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/fuse_convert.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/fuse_fake_quantize.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/fuse_multiply_to_fake_quantize.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/fuse_subtract_to_fake_quantize.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/group_convolution.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/interpolate.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/layer_transformation.hpp (100%)
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 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/markup_can_be_quantized.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/markup_per_tensor_quantization.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/markup_precisions.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/mat_mul.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/max_pool.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/move_fake_quantize.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/multiply.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/multiply_to_group_convolution.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/mvn.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/network_helper.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/normalize_l2.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/pad.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/prelu.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/propagate_precisions.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/propagate_shared_value.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/propagate_through_precision_preserved.hpp (100%)
 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/propagate_to_input.hpp (100%)
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 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/quantization_details.hpp (100%)
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 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/reshape.hpp (100%)
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 rename {inference-engine/src => src/common}/low_precision_transformations/include/low_precision/rt_info/precision_preserved_attribute.hpp (100%)
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 rename {ngraph/test => src/core/tests}/visitors/op/lrn.cpp (100%)
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 rename {ngraph/test => src/core/tests}/visitors/op/maximum.cpp (100%)
 rename {ngraph/test => src/core/tests}/visitors/op/minimum.cpp (100%)
 rename {ngraph/test => src/core/tests}/visitors/op/mish.cpp (100%)
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 rename {ngraph/test => src/core/tests}/visitors/op/multiclass_nms.cpp (100%)
 rename {ngraph/test => src/core/tests}/visitors/op/multiply.cpp (100%)
 rename {ngraph/test => src/core/tests}/visitors/op/mvn.cpp (100%)
 rename {ngraph/test => src/core/tests}/visitors/op/negative.cpp (100%)
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 rename {ngraph/test => src/core/tests}/visitors/op/pad.cpp (100%)
 rename {ngraph/test => src/core/tests}/visitors/op/parameter.cpp (100%)
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 rename {ngraph/test => src/core/tests}/visitors/op/prelu.cpp (100%)
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 rename {ngraph/test => src/core/tests}/visitors/op/proposal.cpp (100%)
 rename {ngraph/test => src/core/tests}/visitors/op/psroi_pooling.cpp (100%)
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 rename {ngraph/test => src/core/tests}/visitors/op/reduce_logical_or.cpp (100%)
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 rename {ngraph/test => src/core/tests}/visitors/op/reduce_mean.cpp (100%)
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 rename {ngraph/test => src/core/tests}/visitors/op/reverse.cpp (100%)
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 rename {ngraph/test => src/core/tests}/visitors/op/rnn_cell.cpp (100%)
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 rename {ngraph/test => src/core/tests}/visitors/op/roll.cpp (100%)
 rename {ngraph/test => src/core/tests}/visitors/op/round.cpp (100%)
 rename {ngraph/test => src/core/tests}/visitors/op/scatter_elements_update.cpp (100%)
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 rename {ngraph/test => src/core/tests}/visitors/op/select.cpp (100%)
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 rename {ngraph/test => src/core/tests}/visitors/op/sin.cpp (100%)
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 rename {ngraph/test => src/core/tests}/visitors/op/sqrt.cpp (100%)
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 rename {ngraph/frontend => src/frontends}/onnx/frontend/src/op/global_max_pool.cpp (100%)
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 rename {inference-engine/src/inference_engine => src/inference}/include/openvino/runtime/core.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/include/openvino/runtime/exception.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/include/openvino/runtime/executable_network.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/include/openvino/runtime/gpu/ocl/dx.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/include/openvino/runtime/gpu/ocl/ocl.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/include/openvino/runtime/gpu/ocl/ocl_wrapper.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/include/openvino/runtime/gpu/ocl/va.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/include/openvino/runtime/infer_request.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/include/openvino/runtime/parameter.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/include/openvino/runtime/profiling_info.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/include/openvino/runtime/remote_context.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/include/openvino/runtime/remote_tensor.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/include/openvino/runtime/runtime.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/include/openvino/runtime/variable_state.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/blob_transform.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/cnn_network_ngraph_impl.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/cnn_network_ngraph_impl.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/compilation_context.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/compilation_context.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/cpp/exception2status.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/cpp/ie_cnn_network.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/cpp/ie_executable_network.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/cpp/ie_executable_network_base.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/cpp/ie_extension.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/cpp/ie_infer_async_request_base.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/cpp/ie_infer_request.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/cpp/ie_plugin.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/cpp/ie_remote_context.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/cpp/ie_variable_state.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/cpp_interfaces/interface/ie_iexecutable_network_internal.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/cpp_interfaces/interface/ie_iinfer_request_internal.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/cpp_interfaces/interface/ie_iplugin_internal.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/cpp_interfaces/interface/ie_ivariable_state_internal.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/cpu_x86_sse42/blob_transform_sse42.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/cpu_x86_sse42/blob_transform_sse42.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/file_utils.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/ie_cache_guard.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/ie_cache_guard.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/ie_cache_manager.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/ie_common.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/ie_compound_blob.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/ie_core.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/ie_data.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/ie_itt.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/ie_memcpy.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/ie_network_reader.cpp (99%)
 rename {inference-engine/src/inference_engine => src/inference}/src/ie_network_reader.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/ie_ngraph_utils.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/ie_remote_context.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/ie_system_conf.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/ie_transformations.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/ie_version.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/os/lin/lin_system_conf.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/os/win/win_system_conf.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/precision_utils.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/precomp.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/remote_tensor.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/shared_object_loader.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/system_allocator.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/threading/ie_cpu_streams_executor.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/threading/ie_executor_manager.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/threading/ie_istreams_executor.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/threading/ie_itask_executor.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/threading/ie_parallel_custom_arena.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/threading/ie_parallel_custom_arena.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/threading/ie_tbb_streams_executor.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/threading/ie_thread_affinity.cpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/threading/ie_thread_affinity.hpp (100%)
 rename {inference-engine/src/inference_engine => src/inference}/src/xml_parse_utils.cpp (100%)

diff --git a/.ci/azure/linux.yml b/.ci/azure/linux.yml
index 1060f3f7a68..4c8d7340f0f 100644
--- a/.ci/azure/linux.yml
+++ b/.ci/azure/linux.yml
@@ -117,9 +117,9 @@ jobs:
       # For running Python API tests
       python3 -m pip install -r $(REPO_DIR)/inference-engine/ie_bridges/python/src/requirements-dev.txt
       # For running PaddlePaddle frontend unit tests
-      python3 -m pip install -r $(REPO_DIR)/ngraph/test/frontend/paddlepaddle/requirements_dev.txt
+      python3 -m pip install -r $(REPO_DIR)/src/core/tests/frontend/paddlepaddle/requirements_dev.txt
       # For running ONNX frontend unit tests
-      python3 -m pip install -r $(REPO_DIR)/ngraph/test/requirements_test_onnx.txt
+      python3 -m pip install -r $(REPO_DIR)/src/core/tests/requirements_test_onnx.txt
       # For MO unit tests
       python3 -m pip install -r $(REPO_DIR)/model-optimizer/requirements.txt
       python3 -m pip install -r $(REPO_DIR)/model-optimizer/requirements_dev.txt
diff --git a/.ci/azure/linux_lohika.yml b/.ci/azure/linux_lohika.yml
index dca5cbc4c28..8d9e8e2b735 100644
--- a/.ci/azure/linux_lohika.yml
+++ b/.ci/azure/linux_lohika.yml
@@ -86,9 +86,9 @@ jobs:
       # For running Python API tests
       python3 -m pip install -r $(REPO_DIR)/inference-engine/ie_bridges/python/src/requirements-dev.txt
       # For running PaddlePaddle frontend unit tests
-      python3 -m pip install -r $(REPO_DIR)/ngraph/test/frontend/paddlepaddle/requirements_dev.txt
+      python3 -m pip install -r $(REPO_DIR)/src/core/tests/frontend/paddlepaddle/requirements_dev.txt
       # For running ONNX frontend unit tests
-      python3 -m pip install -r $(REPO_DIR)/ngraph/test/requirements_test_onnx.txt
+      python3 -m pip install -r $(REPO_DIR)/src/core/tests/requirements_test_onnx.txt
       # For MO unit tests
       python3 -m pip install -r $(REPO_DIR)/model-optimizer/requirements.txt
       python3 -m pip install -r $(REPO_DIR)/model-optimizer/requirements_dev.txt
diff --git a/.ci/azure/mac.yml b/.ci/azure/mac.yml
index 517f2b93dd1..bf61f09c8b2 100644
--- a/.ci/azure/mac.yml
+++ b/.ci/azure/mac.yml
@@ -84,7 +84,7 @@ jobs:
   - script: |
       brew install cython
       brew install automake
-      python3 -m pip install -r $(REPO_DIR)/ngraph/test/requirements_test_onnx.txt
+      python3 -m pip install -r $(REPO_DIR)/src/core/tests/requirements_test_onnx.txt
       # Speed up build
       brew install ninja
       # Speed up tests
diff --git a/.ci/azure/windows.yml b/.ci/azure/windows.yml
index f689e8c95ed..b5094e51aa6 100644
--- a/.ci/azure/windows.yml
+++ b/.ci/azure/windows.yml
@@ -110,9 +110,9 @@ jobs:
       rem For running Python API tests
       python -m pip install -r $(REPO_DIR)\inference-engine\ie_bridges\python\src\requirements-dev.txt
       rem For running PaddlePaddle frontend unit tests
-      python -m pip install -r $(REPO_DIR)\ngraph\test\frontend\paddlepaddle\requirements_dev.txt
+      python -m pip install -r $(REPO_DIR)\src\core\tests\frontend\paddlepaddle\requirements_dev.txt
       rem For running ONNX frontend unit tests
-      python -m pip install -r $(REPO_DIR)\ngraph\test\requirements_test_onnx.txt
+      python -m pip install -r $(REPO_DIR)\src\core\tests\requirements_test_onnx.txt
       rem For MO unit tests
       python -m pip install -r $(REPO_DIR)\model-optimizer\requirements.txt
       python -m pip install -r $(REPO_DIR)\model-optimizer\requirements_dev.txt
diff --git a/CMakeLists.txt b/CMakeLists.txt
index d822d31c460..d2f18b3ae1d 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -92,11 +92,11 @@ ie_cpack_add_component(ngraph_dev REQUIRED DEPENDS ngraph)
 include(cmake/test_model_zoo.cmake)
 
 add_subdirectory(thirdparty)
-add_subdirectory(openvino)
-add_subdirectory(ngraph)
-add_subdirectory(inference-engine)
+add_subdirectory(inference-engine/thirdparty)
+add_subdirectory(inference-engine/src)
 add_subdirectory(src)
 add_subdirectory(samples)
+add_subdirectory(inference-engine)
 include(cmake/extra_modules.cmake)
 add_subdirectory(model-optimizer)
 add_subdirectory(docs)
diff --git a/cmake/dependencies.cmake b/cmake/dependencies.cmake
index 9104bcd9923..8472af490b7 100644
--- a/cmake/dependencies.cmake
+++ b/cmake/dependencies.cmake
@@ -265,7 +265,7 @@ else()
     reset_deps_cache(OpenCV_DIR)
 endif()
 
-include(${IE_MAIN_SOURCE_DIR}/cmake/ie_parallel.cmake)
+include(${OpenVINO_SOURCE_DIR}/cmake/ie_parallel.cmake)
 
 if(ENABLE_GNA)
     reset_deps_cache(
diff --git a/cmake/developer_package/plugins/plugins.cmake b/cmake/developer_package/plugins/plugins.cmake
index 268b90fced6..6f35ba93385 100644
--- a/cmake/developer_package/plugins/plugins.cmake
+++ b/cmake/developer_package/plugins/plugins.cmake
@@ -311,7 +311,7 @@ function(ie_generate_plugins_hpp)
         ie_target_link_plugins(inference_engine_s)
     endif()
 
-    set(ie_plugins_hpp "${CMAKE_BINARY_DIR}/inference-engine/src/inference_engine/ie_plugins.hpp")
+    set(ie_plugins_hpp "${CMAKE_BINARY_DIR}/src/inference/ie_plugins.hpp")
     set(plugins_hpp_in "${IEDevScripts_DIR}/plugins/plugins.hpp.in")
 
     add_custom_command(OUTPUT "${ie_plugins_hpp}"
diff --git a/cmake/developer_package/version.cmake b/cmake/developer_package/version.cmake
index e8d4fdb3d3d..3f51cc1f336 100644
--- a/cmake/developer_package/version.cmake
+++ b/cmake/developer_package/version.cmake
@@ -44,12 +44,12 @@ macro(ie_parse_ci_build_number)
             return()
         endif()
 
-        set(ie_version_hpp "${OpenVINO_SOURCE_DIR}/inference-engine/src/inference_engine/include/ie/ie_version.hpp")
+        set(ie_version_hpp "${OpenVINO_SOURCE_DIR}/src/inference/include/ie/ie_version.hpp")
         if(NOT EXISTS ${ie_version_hpp})
             message(FATAL_ERROR "File ie_version.hpp with IE_VERSION definitions is not found")
         endif()
 
-        set(ov_version_hpp "${OpenVINO_SOURCE_DIR}/ngraph/core/include/openvino/core/version.hpp")
+        set(ov_version_hpp "${OpenVINO_SOURCE_DIR}/src/core/include/openvino/core/version.hpp")
         if(NOT EXISTS ${ov_version_hpp})
             message(FATAL_ERROR "File openvino/core/version.hpp with OPENVINO_VERSION definitions is not found")
         endif()
diff --git a/cmake/extra_modules.cmake b/cmake/extra_modules.cmake
index d72497749ac..3b22125d009 100644
--- a/cmake/extra_modules.cmake
+++ b/cmake/extra_modules.cmake
@@ -6,7 +6,7 @@ function(ie_generate_dev_package_config)
     # dummy check that OpenCV is here
     find_package(OpenCV QUIET)
 
-    set(all_dev_targets gflags ie_libraries)
+    set(all_dev_targets gflags ov_runtime_libraries)
     foreach(component IN LISTS openvino_export_components)
         # export all targets with prefix and use them during extra modules build
         export(TARGETS ${${component}} NAMESPACE IE::
@@ -102,7 +102,7 @@ openvino_developer_export_targets(COMPONENT ngraph TARGETS ngraph_backend interp
 ie_generate_dev_package_config()
 
 # extra modules must be registered after inference_engine library
-# and all other IE common libraries (ie_libraries) are creared
+# and all other IE common libraries (ov_runtime_libraries) are creared
 # because 'register_extra_modules' creates fake InferenceEngineDeveloperPackageConfig.cmake
 # with all imported developer targets
 register_extra_modules()
diff --git a/inference-engine/cmake/ie_parallel.cmake b/cmake/ie_parallel.cmake
similarity index 100%
rename from inference-engine/cmake/ie_parallel.cmake
rename to cmake/ie_parallel.cmake
diff --git a/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in b/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in
index 0f1f0d5e374..bf7f04d8f93 100644
--- a/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in
+++ b/cmake/templates/InferenceEngineDeveloperPackageConfig.cmake.in
@@ -45,7 +45,7 @@ find_dependency(InferenceEngine
                 NO_DEFAULT_PATH)
 
 find_dependency(ngraph
-                PATHS "${CMAKE_CURRENT_LIST_DIR}/ngraph"
+                PATHS "${CMAKE_CURRENT_LIST_DIR}/src/core"
                 NO_CMAKE_FIND_ROOT_PATH
                 NO_DEFAULT_PATH)
 
diff --git a/cmake/test_model_zoo.cmake b/cmake/test_model_zoo.cmake
index c8ec6691ca9..a9f6356826a 100644
--- a/cmake/test_model_zoo.cmake
+++ b/cmake/test_model_zoo.cmake
@@ -5,7 +5,7 @@
 set_property(GLOBAL PROPERTY JOB_POOLS four_jobs=4)
 
 function(ov_model_convert SRC DST OUT)
-    set(onnx_gen_script ${OpenVINO_SOURCE_DIR}/ngraph/test/models/onnx/onnx_prototxt_converter.py)
+    set(onnx_gen_script ${OpenVINO_SOURCE_DIR}/src/core/tests/models/onnx/onnx_prototxt_converter.py)
 
     file(GLOB_RECURSE prototxt_models RELATIVE "${SRC}" "${SRC}/*.prototxt")
     file(GLOB_RECURSE xml_models RELATIVE "${SRC}" "${SRC}/*.xml")
@@ -62,8 +62,8 @@ function(ov_model_convert SRC DST OUT)
     set(${OUT} ${files} PARENT_SCOPE)
 endfunction()
 
-ov_model_convert("${CMAKE_CURRENT_SOURCE_DIR}/ngraph/test"
-                 "${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/test_model_zoo/ngraph"
+ov_model_convert("${CMAKE_CURRENT_SOURCE_DIR}/src/core/tests"
+                 "${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/test_model_zoo/core"
                   onnx_out_files)
 
 set(rel_path "inference-engine/tests/functional/plugin/shared/models")
@@ -117,7 +117,7 @@ if(ENABLE_TESTS)
             list(APPEND args --use-feature=2020-resolver)
         endif()
 
-        set(reqs "${OpenVINO_SOURCE_DIR}/ngraph/test/requirements_test_onnx.txt")
+        set(reqs "${OpenVINO_SOURCE_DIR}/src/core/tests/requirements_test_onnx.txt")
         add_custom_target(test_pip_prerequsites ALL
                           "${PYTHON_EXECUTABLE}" -m pip install ${args} -r ${reqs}
                           COMMENT "Install requirements_test.txt"
diff --git a/docs/CMakeLists.txt b/docs/CMakeLists.txt
index 43ad34db591..b0cbcdf899d 100644
--- a/docs/CMakeLists.txt
+++ b/docs/CMakeLists.txt
@@ -65,13 +65,14 @@ function(build_docs)
     set(DOCS_BUILD_DIR "${CMAKE_CURRENT_BINARY_DIR}")
     set(DOXYGEN_DIR "${OpenVINO_SOURCE_DIR}/docs/doxygen")
     set(IE_SOURCE_DIR "${OpenVINO_SOURCE_DIR}/inference-engine")
+    set(OV_INFERENCE_DIR "${OpenVINO_SOURCE_DIR}/src/inference")
     set(PYTHON_API_IN "${IE_SOURCE_DIR}/ie_bridges/python/src/openvino/inference_engine/ie_api.pyx")
     set(PYTHON_API_OUT "${DOCS_BUILD_DIR}/python_api/ie_api.pyx")
-    set(C_API "${IE_SOURCE_DIR}/ie_bridges/c/include")
+    set(C_API "${OpenVINO_SOURCE_DIR}/bindings/c/include")
     set(PLUGIN_API_DIR "${DOCS_BUILD_DIR}/IE_PLUGIN_DG")
-    set(NGRAPH_DIR "${OpenVINO_SOURCE_DIR}/ngraph")
+    set(CORE_DIR "${OpenVINO_SOURCE_DIR}/src/core")
+    set(FRONTENDS_DIR "${OpenVINO_SOURCE_DIR}/src/frontends")
     set(NGRAPH_PY_DIR "${OpenVINO_SOURCE_DIR}/src/bindings/python/src/compatibility/ngraph/")
-    set(NGRAPH_CPP_DIR "${NGRAPH_DIR}/core/include/" "${NGRAPH_DIR}/frontend/onnx_import/include")
 
     # Preprocessing scripts
     set(DOXY_MD_FILTER "${DOXYGEN_DIR}/doxy_md_filter.py")
diff --git a/docs/doxygen/doxygen-ignore.txt b/docs/doxygen/doxygen-ignore.txt
index f623f4826e1..77e59419d8f 100644
--- a/docs/doxygen/doxygen-ignore.txt
+++ b/docs/doxygen/doxygen-ignore.txt
@@ -1,6 +1,5 @@
 openvino/samples/cpp/hello_reshape_ssd/README.md
 openvino/docs/index.md
-inference-engine/include/ie_icnn_network.hpp
 openvino/docs/get_started/get_started_dl_workbench.md
 openvino/docs/get_started/get_started_linux.md
 openvino/docs/get_started/get_started_raspbian.md
@@ -10,29 +9,13 @@ openvino/docs/HOWTO/Custom_Layers_Guide.md
 openvino/docs/install_guides/deployment-manager-tool.md
 openvino/docs/MO_DG/prepare_model/customize_model_optimizer/Customize_Model_Optimizer.md
 openvino/docs/ovsa/ovsa_get_started.md
-openvino/inference-engine/ie_bridges/c/docs/api_overview.md
-inference-engine/include/cpp/ie_infer_request.hpp
-inference-engine/include/ie_parallel.hpp
-inference-engine/include/gpu/gpu_context_api_ocl.hpp
-inference-engine/include/gpu/gpu_context_api_va.hpp
-inference-engine/include/ie_plugin_config.hpp
-inference-engine/include/ie_unicode.hpp
-inference-engine/include/vpu/myriad_config.hpp
-inference-engine/include/vpu/vpu_config.hpp
-inference-engine/include/vpu/vpu_plugin_config.hpp
+openvino/src/bindings/c/docs/api_overview.md
 openvino/docs/benchmarks/performance_int8_vs_fp32.md
 openvino/docs/get_started/get_started_macos.md
-inference-engine/include/details/ie_so_pointer.hpp
-inference-engine/include/ie_compound_blob.h
-inference-engine/include/ie_data.h
-inference-engine/include/ie_blob.h
-inference-engine/include/ie_precision.hpp
-inference-engine/include/ie_remote_context.hpp
-inference-engine/include/gpu/gpu_context_api_dx.hpp
 build/docs/openvino_docs.xml
 openvino/docs/install_guides/installing-openvino-linux-ivad-vpu.md
-inference-engine/src/inference_engine/include/ie/ie_parallel.hpp
-inference-engine/src/inference_engine/include/ie/ie_plugin_config.hpp
-inference-engine/src/inference_engine/include/ie/vpu/myriad_config.hpp
-inference-engine/src/inference_engine/include/ie/vpu/vpu_config.hpp
-inference-engine/src/inference_engine/include/ie/vpu/vpu_plugin_config.hpp
+src/inference/include/ie/ie_parallel.hpp
+src/inference/include/ie/ie_plugin_config.hpp
+src/inference/include/ie/vpu/myriad_config.hpp
+src/inference/include/ie/vpu/vpu_config.hpp
+src/inference/include/ie/vpu/vpu_plugin_config.hpp
diff --git a/docs/doxygen/ie_docs.config b/docs/doxygen/ie_docs.config
index d8050d0e4a5..fd5ca445ceb 100644
--- a/docs/doxygen/ie_docs.config
+++ b/docs/doxygen/ie_docs.config
@@ -824,7 +824,7 @@ WARN_LOGFILE           = "@DOCS_BUILD_DIR@/ie_docs.log"
 # Note: If this tag is empty the current directory is searched.
 
 INPUT                  = "@DOCS_BUILD_DIR@" \
-                         "@IE_SOURCE_DIR@/src/inference_engine/include"
+                         "@OV_INFERENCE_DIR@/include"
 
 # This tag can be used to specify the character encoding of the source files
 # that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
diff --git a/docs/doxygen/ie_plugin_api.config b/docs/doxygen/ie_plugin_api.config
index 470d2dd845a..b6c72a139e6 100644
--- a/docs/doxygen/ie_plugin_api.config
+++ b/docs/doxygen/ie_plugin_api.config
@@ -24,9 +24,9 @@ GENERATE_TAGFILE       = "@DOCS_BUILD_DIR@/ie_plugin_api.tag"
 EXTRACT_LOCAL_CLASSES  = NO
 
 INPUT                  = "@DOCS_BUILD_DIR@/docs/IE_PLUGIN_DG" \
-                         "@IE_SOURCE_DIR@/src/plugin_api" \
-                         "@IE_SOURCE_DIR@/src/transformations/include" \
-                         "@OpenVINO_SOURCE_DIR@/openvino/itt/include/openvino"
+                         "@OV_INFERENCE_DIR@/dev_api" \
+                         "@OpenVINO_SOURCE_DIR@/src/common/transformations/include" \
+                         "@OpenVINO_SOURCE_DIR@/src/common/itt/include/openvino"
 
 
 RECURSIVE              = YES
diff --git a/docs/doxygen/ngraph_cpp_api.config b/docs/doxygen/ngraph_cpp_api.config
index 2b0b50b6d70..5ee7a2d6f24 100644
--- a/docs/doxygen/ngraph_cpp_api.config
+++ b/docs/doxygen/ngraph_cpp_api.config
@@ -28,9 +28,9 @@ FILE_PATTERNS          = *.cpp \
 
 LAYOUT_FILE            = "@NGRAPH_CPP_LAYOUT_BUILD@"
 
-INPUT                  = "@NGRAPH_DIR@/core/include/" \
-                         "@NGRAPH_DIR@/frontend/onnx/frontend/include/" \
-                         "@NGRAPH_DIR@/frontend/paddlepaddle/frontend/include/"
+INPUT                  = "@CORE_DIR@/core/include/" \
+                         "@FRONTENDS_DIR@/onnx/frontend/include/" \
+                         "@FRONTENDS_DIR@/paddlepaddle/frontend/include/"
 
 HTML_OUTPUT            = "@NGRAPH_CPP_OUTPUT@"
 
diff --git a/inference-engine/CMakeLists.txt b/inference-engine/CMakeLists.txt
index 71d80f1f974..2e935e206d1 100644
--- a/inference-engine/CMakeLists.txt
+++ b/inference-engine/CMakeLists.txt
@@ -4,10 +4,6 @@
 
 project(InferenceEngine)
 
-add_subdirectory(thirdparty)
-add_subdirectory(src)
-add_subdirectory(ie_bridges/c)
-
 if(ENABLE_PYTHON)
     add_subdirectory(ie_bridges/python)
 endif()
diff --git a/inference-engine/ie_bridges/python/wheel/CMakeLists.txt b/inference-engine/ie_bridges/python/wheel/CMakeLists.txt
index 211e6349237..61d4a5bf2f5 100644
--- a/inference-engine/ie_bridges/python/wheel/CMakeLists.txt
+++ b/inference-engine/ie_bridges/python/wheel/CMakeLists.txt
@@ -47,7 +47,7 @@ endif()
 # create target for openvino.wheel
 
 set(openvino_wheel_deps ie_api offline_transformations_api)
-foreach(_target ie_libraries ie_plugins _pyngraph pyopenvino)
+foreach(_target ov_runtime_libraries ie_plugins _pyngraph pyopenvino)
     if(TARGET ${_target})
         list(APPEND openvino_wheel_deps ${_target})
     endif()
diff --git a/inference-engine/src/CMakeLists.txt b/inference-engine/src/CMakeLists.txt
index 83ef2917b0d..1090339f5c6 100644
--- a/inference-engine/src/CMakeLists.txt
+++ b/inference-engine/src/CMakeLists.txt
@@ -7,14 +7,8 @@ if(CMAKE_COMPILER_IS_GNUCXX)
     ie_add_compiler_flags(-Wmissing-declarations)
 endif()
 
-add_subdirectory(transformations)
-
-add_subdirectory(low_precision_transformations)
-
 add_subdirectory(offline_transformations)
 
-add_subdirectory(snippets)
-
 if(ENABLE_MKL_DNN)
     add_subdirectory(mkldnn_plugin)
 endif()
@@ -38,42 +32,3 @@ endif()
 if(ENABLE_MULTI)
     add_subdirectory(multi_device)
 endif()
-
-add_subdirectory(inference_engine)
-
-add_subdirectory(legacy_api)
-
-add_subdirectory(readers)
-
-add_subdirectory(preprocessing)
-
-# add a custom target to build all Inference Engine Core libraries
-
-add_custom_target(ie_libraries ALL
-                  DEPENDS inference_engine_transformations inference_engine_legacy
-                          inference_engine inference_engine_preproc
-                          inference_engine_lp_transformations inference_engine_snippets)
-
-if(ENABLE_IR_V7_READER)
-    add_dependencies(ie_libraries inference_engine_ir_v7_reader)
-endif()
-
-if(NGRAPH_IR_FRONTEND_ENABLE)
-    if(BUILD_SHARED_LIBS)
-        add_dependencies(ie_libraries ir_ov_frontend)
-    endif()
-    # use this one once CVS-69781 is fixed
-    # add_dependencies(inference_engine ir_ov_frontend)
-endif()
-
-if(NGRAPH_ONNX_FRONTEND_ENABLE)
-    add_dependencies(inference_engine onnx_ov_frontend)
-endif()
-
-if(NGRAPH_PDPD_FRONTEND_ENABLE)
-    add_dependencies(inference_engine paddlepaddle_ov_frontend)
-endif()
-
-if(NGRAPH_TF_FRONTEND_ENABLE)
-    add_dependencies(inference_engine tensorflow_ov_frontend)
-endif()
diff --git a/inference-engine/src/readers/CMakeLists.txt b/inference-engine/src/readers/CMakeLists.txt
deleted file mode 100644
index 29cd6e6c327..00000000000
--- a/inference-engine/src/readers/CMakeLists.txt
+++ /dev/null
@@ -1,22 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-#
-
-set(TARGET_NAME inference_engine_reader_api)
-
-# Reader API interface library
-add_library(${TARGET_NAME} INTERFACE)
-
-target_include_directories(${TARGET_NAME} INTERFACE
-    $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/reader_api>
-    $<TARGET_PROPERTY:inference_engine,INTERFACE_INCLUDE_DIRECTORIES>)
-
-ov_install_static_lib(${TARGET_NAME} core)
-
-file(GLOB_RECURSE reader_api_hpp "${CMAKE_CURRENT_SOURCE_DIR}/reader_api/*.hpp")
-
-add_cpplint_target(${TARGET_NAME}_cpplint FOR_SOURCES ${reader_api_hpp})
-
-if(ENABLE_IR_V7_READER)
-    add_subdirectory(ir_reader_v7)
-endif()
diff --git a/inference-engine/src/vpu/myriad_plugin/myriad_executable_network.cpp b/inference-engine/src/vpu/myriad_plugin/myriad_executable_network.cpp
index 25c1b344d29..90c12142c23 100644
--- a/inference-engine/src/vpu/myriad_plugin/myriad_executable_network.cpp
+++ b/inference-engine/src/vpu/myriad_plugin/myriad_executable_network.cpp
@@ -21,7 +21,8 @@
 #include <vpu/ngraph/operations/dynamic_shape_resolver.hpp>
 #include <vpu/ngraph/transformations/dynamic_to_static_shape.hpp>
 #include <ngraph/opsets/opset3.hpp>
-#include "../../../ngraph/core/include/openvino/core/interval.hpp"
+// FIXME: Please remove relative path
+#include "../../../src/core/include/openvino/core/interval.hpp"
 
 using namespace InferenceEngine;
 
diff --git a/inference-engine/tests/functional/inference_engine/CMakeLists.txt b/inference-engine/tests/functional/inference_engine/CMakeLists.txt
index b0b09aea2cb..bd504546d66 100644
--- a/inference-engine/tests/functional/inference_engine/CMakeLists.txt
+++ b/inference-engine/tests/functional/inference_engine/CMakeLists.txt
@@ -127,7 +127,7 @@ function(ie_headers_compilation_with_custom_flags)
             "${OpenVINO_SOURCE_DIR}/inference-engine/src/plugin_api/")
     else()
         set(include_dirs
-            "${OpenVINO_SOURCE_DIR}/ngraph/core/include"
+            "${OpenVINO_SOURCE_DIR}/src/core/include"
             "${OpenVINO_SOURCE_DIR}/inference-engine/src/inference_engine/include/ie"
             "${OpenVINO_SOURCE_DIR}/inference-engine/src/inference_engine/include/")
     endif()
diff --git a/inference-engine/tests_deprecated/CMakeLists.txt b/inference-engine/tests_deprecated/CMakeLists.txt
index 31f5a9f7f96..996b8796461 100644
--- a/inference-engine/tests_deprecated/CMakeLists.txt
+++ b/inference-engine/tests_deprecated/CMakeLists.txt
@@ -8,6 +8,7 @@ if(NOT MSVC)
     ie_add_compiler_flags(-Wno-unused-variable)
 endif()
 
+add_subdirectory(readers)
 add_subdirectory(helpers)
 
 if (ENABLE_GAPI_TESTS)
diff --git a/inference-engine/tests_deprecated/readers/CMakeLists.txt b/inference-engine/tests_deprecated/readers/CMakeLists.txt
new file mode 100644
index 00000000000..108468dcfa9
--- /dev/null
+++ b/inference-engine/tests_deprecated/readers/CMakeLists.txt
@@ -0,0 +1,8 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+if(ENABLE_IR_V7_READER)
+    add_subdirectory(ir_reader_v7)
+    add_dependencies(ov_runtime_libraries inference_engine_ir_v7_reader)
+endif()
diff --git a/inference-engine/src/readers/ir_reader_v7/CMakeLists.txt b/inference-engine/tests_deprecated/readers/ir_reader_v7/CMakeLists.txt
similarity index 95%
rename from inference-engine/src/readers/ir_reader_v7/CMakeLists.txt
rename to inference-engine/tests_deprecated/readers/ir_reader_v7/CMakeLists.txt
index 663649c4048..e69de57f879 100644
--- a/inference-engine/src/readers/ir_reader_v7/CMakeLists.txt
+++ b/inference-engine/tests_deprecated/readers/ir_reader_v7/CMakeLists.txt
@@ -34,7 +34,7 @@ target_compile_definitions(${TARGET_NAME} PRIVATE IMPLEMENT_INFERENCE_ENGINE_PLU
 
 target_include_directories(${TARGET_NAME} PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/")
 
-target_link_libraries(${TARGET_NAME} PRIVATE inference_engine_reader_api inference_engine_plugin_api
+target_link_libraries(${TARGET_NAME} PRIVATE inference_engine_plugin_api
                                              inference_engine_legacy pugixml::static openvino::itt)
 
 ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
diff --git a/inference-engine/src/readers/ir_reader_v7/ie_blob_proxy.hpp b/inference-engine/tests_deprecated/readers/ir_reader_v7/ie_blob_proxy.hpp
similarity index 100%
rename from inference-engine/src/readers/ir_reader_v7/ie_blob_proxy.hpp
rename to inference-engine/tests_deprecated/readers/ir_reader_v7/ie_blob_proxy.hpp
diff --git a/inference-engine/src/readers/ir_reader_v7/ie_cnn_net_reader_impl.cpp b/inference-engine/tests_deprecated/readers/ir_reader_v7/ie_cnn_net_reader_impl.cpp
similarity index 100%
rename from inference-engine/src/readers/ir_reader_v7/ie_cnn_net_reader_impl.cpp
rename to inference-engine/tests_deprecated/readers/ir_reader_v7/ie_cnn_net_reader_impl.cpp
diff --git a/inference-engine/src/readers/ir_reader_v7/ie_cnn_net_reader_impl.h b/inference-engine/tests_deprecated/readers/ir_reader_v7/ie_cnn_net_reader_impl.h
similarity index 100%
rename from inference-engine/src/readers/ir_reader_v7/ie_cnn_net_reader_impl.h
rename to inference-engine/tests_deprecated/readers/ir_reader_v7/ie_cnn_net_reader_impl.h
diff --git a/inference-engine/src/readers/ir_reader_v7/ie_format_parser.cpp b/inference-engine/tests_deprecated/readers/ir_reader_v7/ie_format_parser.cpp
similarity index 100%
rename from inference-engine/src/readers/ir_reader_v7/ie_format_parser.cpp
rename to inference-engine/tests_deprecated/readers/ir_reader_v7/ie_format_parser.cpp
diff --git a/inference-engine/src/readers/ir_reader_v7/ie_format_parser.h b/inference-engine/tests_deprecated/readers/ir_reader_v7/ie_format_parser.h
similarity index 100%
rename from inference-engine/src/readers/ir_reader_v7/ie_format_parser.h
rename to inference-engine/tests_deprecated/readers/ir_reader_v7/ie_format_parser.h
diff --git a/inference-engine/src/readers/ir_reader_v7/ie_ir_itt.hpp b/inference-engine/tests_deprecated/readers/ir_reader_v7/ie_ir_itt.hpp
similarity index 100%
rename from inference-engine/src/readers/ir_reader_v7/ie_ir_itt.hpp
rename to inference-engine/tests_deprecated/readers/ir_reader_v7/ie_ir_itt.hpp
diff --git a/inference-engine/src/readers/ir_reader_v7/ie_ir_parser.cpp b/inference-engine/tests_deprecated/readers/ir_reader_v7/ie_ir_parser.cpp
similarity index 76%
rename from inference-engine/src/readers/ir_reader_v7/ie_ir_parser.cpp
rename to inference-engine/tests_deprecated/readers/ir_reader_v7/ie_ir_parser.cpp
index 8d15d0f5752..e52431de5cd 100644
--- a/inference-engine/src/readers/ir_reader_v7/ie_ir_parser.cpp
+++ b/inference-engine/tests_deprecated/readers/ir_reader_v7/ie_ir_parser.cpp
@@ -2,13 +2,13 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "ie_reader.hpp"
 #include "ie_ir_parser.hpp"
 #include "ie_cnn_net_reader_impl.h"
+#include "legacy/ie_reader.hpp"
 
 using namespace InferenceEngine;
 
-IRParser::IRParser(size_t version): IRParser(version, {}) {}
+IRParser::IRParser(size_t version) : IRParser(version, {}) {}
 IRParser::IRParser(size_t version, const std::vector<InferenceEngine::IExtensionPtr>& exts) {
     if (version < 10) {
         parser = std::make_shared<CNNParser>();
@@ -29,29 +29,25 @@ class WeightsHolderBlob : public TBlob<uint8_t> {
     Blob::CPtr originBlob;
 
 public:
-    explicit WeightsHolderBlob(const Blob::CPtr& weights) :
-        TBlob<uint8_t>(weights->getTensorDesc(),
-                       weights->cbuffer().as<uint8_t*>()),
-        originBlob(weights) { }
+    explicit WeightsHolderBlob(const Blob::CPtr& weights)
+        : TBlob<uint8_t>(weights->getTensorDesc(), weights->cbuffer().as<uint8_t*>()), originBlob(weights) {}
 };
 
 CNNNetwork CNNParser::parse(const pugi::xml_node& root, const Blob::CPtr& weights) {
     details::CNNNetReaderImpl reader(std::make_shared<details::V2FormatParserCreator>());
     ResponseDesc resp;
     StatusCode ret = reader.ReadNetwork(root, &resp);
-    if (ret != OK)
-        IE_THROW() << resp.msg;
+    if (ret != OK) IE_THROW() << resp.msg;
 
     TBlob<uint8_t>::Ptr weightsPtr;
 
     if (weights != nullptr) {
         weightsPtr = TBlob<uint8_t>::Ptr(new WeightsHolderBlob(weights));
     } else {
-        weightsPtr = std::make_shared<TBlob<uint8_t>>(TensorDesc(Precision::U8, { 0 }, Layout::C));
+        weightsPtr = std::make_shared<TBlob<uint8_t>>(TensorDesc(Precision::U8, {0}, Layout::C));
         weightsPtr->allocate();
     }
     ret = reader.SetWeights(weightsPtr, &resp);
-    if (ret != OK)
-        IE_THROW() << resp.msg;
+    if (ret != OK) IE_THROW() << resp.msg;
     return reader.getNetwork();
 }
diff --git a/inference-engine/src/readers/ir_reader_v7/ie_ir_parser.hpp b/inference-engine/tests_deprecated/readers/ir_reader_v7/ie_ir_parser.hpp
similarity index 100%
rename from inference-engine/src/readers/ir_reader_v7/ie_ir_parser.hpp
rename to inference-engine/tests_deprecated/readers/ir_reader_v7/ie_ir_parser.hpp
diff --git a/inference-engine/src/readers/ir_reader_v7/ie_ir_reader.cpp b/inference-engine/tests_deprecated/readers/ir_reader_v7/ie_ir_reader.cpp
similarity index 97%
rename from inference-engine/src/readers/ir_reader_v7/ie_ir_reader.cpp
rename to inference-engine/tests_deprecated/readers/ir_reader_v7/ie_ir_reader.cpp
index 1e27c8b6fb7..193fd6e0f19 100644
--- a/inference-engine/src/readers/ir_reader_v7/ie_ir_reader.cpp
+++ b/inference-engine/tests_deprecated/readers/ir_reader_v7/ie_ir_reader.cpp
@@ -5,7 +5,7 @@
 #include <xml_parse_utils.h>
 
 #include "openvino/runtime/common.hpp"
-#include <ie_ir_version.hpp>
+#include <legacy/ie_ir_version.hpp>
 #include <ie_ir_reader.hpp>
 #include <memory>
 #include <string>
diff --git a/inference-engine/src/readers/ir_reader_v7/ie_ir_reader.hpp b/inference-engine/tests_deprecated/readers/ir_reader_v7/ie_ir_reader.hpp
similarity index 85%
rename from inference-engine/src/readers/ir_reader_v7/ie_ir_reader.hpp
rename to inference-engine/tests_deprecated/readers/ir_reader_v7/ie_ir_reader.hpp
index ad72c1e52d3..cb0ed861410 100644
--- a/inference-engine/src/readers/ir_reader_v7/ie_ir_reader.hpp
+++ b/inference-engine/tests_deprecated/readers/ir_reader_v7/ie_ir_reader.hpp
@@ -9,7 +9,7 @@
 #include <ie_common.h>
 #include <ie_iextension.h>
 
-#include <ie_reader.hpp>
+#include <legacy/ie_reader.hpp>
 #include <map>
 #include <memory>
 #include <string>
@@ -25,7 +25,7 @@ namespace InferenceEngine {
 /**
  * @brief This class is the main interface to build and parse a network from a given IR
  */
-class IRReader: public IReader {
+class IRReader : public IReader {
 public:
     /**
      * @brief Checks that reader supports format of the model
@@ -49,7 +49,9 @@ public:
      *
      * @return CNNNetwork
      */
-    CNNNetwork read(std::istream& model, const Blob::CPtr& weights, const std::vector<IExtensionPtr>& exts) const override;
+    CNNNetwork read(std::istream& model,
+                    const Blob::CPtr& weights,
+                    const std::vector<IExtensionPtr>& exts) const override;
 
     std::vector<std::string> getDataFileExtensions() const override {
         return {"bin"};
diff --git a/inference-engine/src/readers/ir_reader_v7/ie_layer_parsers.cpp b/inference-engine/tests_deprecated/readers/ir_reader_v7/ie_layer_parsers.cpp
similarity index 100%
rename from inference-engine/src/readers/ir_reader_v7/ie_layer_parsers.cpp
rename to inference-engine/tests_deprecated/readers/ir_reader_v7/ie_layer_parsers.cpp
diff --git a/inference-engine/src/readers/ir_reader_v7/ie_layer_parsers.h b/inference-engine/tests_deprecated/readers/ir_reader_v7/ie_layer_parsers.h
similarity index 100%
rename from inference-engine/src/readers/ir_reader_v7/ie_layer_parsers.h
rename to inference-engine/tests_deprecated/readers/ir_reader_v7/ie_layer_parsers.h
diff --git a/inference-engine/src/readers/ir_reader_v7/ie_layer_validators.cpp b/inference-engine/tests_deprecated/readers/ir_reader_v7/ie_layer_validators.cpp
similarity index 100%
rename from inference-engine/src/readers/ir_reader_v7/ie_layer_validators.cpp
rename to inference-engine/tests_deprecated/readers/ir_reader_v7/ie_layer_validators.cpp
diff --git a/inference-engine/src/readers/ir_reader_v7/ie_layer_validators.hpp b/inference-engine/tests_deprecated/readers/ir_reader_v7/ie_layer_validators.hpp
similarity index 100%
rename from inference-engine/src/readers/ir_reader_v7/ie_layer_validators.hpp
rename to inference-engine/tests_deprecated/readers/ir_reader_v7/ie_layer_validators.hpp
diff --git a/inference-engine/src/readers/ir_reader_v7/parsers.h b/inference-engine/tests_deprecated/readers/ir_reader_v7/parsers.h
similarity index 100%
rename from inference-engine/src/readers/ir_reader_v7/parsers.h
rename to inference-engine/tests_deprecated/readers/ir_reader_v7/parsers.h
diff --git a/ngraph/.gitignore b/ngraph/.gitignore
deleted file mode 100644
index 588daa92a64..00000000000
--- a/ngraph/.gitignore
+++ /dev/null
@@ -1,123 +0,0 @@
-# Compiled Object files
-*.slo
-*.lo
-*.o
-*.obj
-*.pyc
-
-# Precompiled Headers
-*.gch
-*.pch
-
-# Compiled Dynamic libraries
-*.so
-*.dylib
-*.dll
-
-*.db
-
-# Fortran module files
-*.mod
-
-# Compiled Static libraries
-*.lai
-*.la
-*.a
-*.lib
-
-# Executables
-*.exe
-*.out
-*.app
-
-# QT Creator
-*.creator
-*.config
-*.files
-*.includes
-*.creator.user*
-*.autosave
-
-.DS_Store
-@eaDir/
-.d/
-bin/
-*.log
-output/
-*.png
-*.jpg
-*.mp2
-*.mpg
-*.cpio
-*.wav
-*.backup
-doc/source/generated
-.cache/
-nervana_aeon.egg-info/
-*.xsd
-*.xslt
-.vscode
-
-# emacs
-*~
-
-# vim
-*.swp
-*.swo
-tags
-
-build/
-scripts/
-
-# makeenv and test intermediate files
-tmp/
-
-# Apple
-*.AppleDouble
-
-config_args.txt
-.nfs*
-venv/
-.vscode/
-.vs/
-
-# VisualGDB files
-VisualGDB/
-toolchain.cmake
-
-# docbuild artifacts
-doc/sphinx/build/*
-doc/doxygen/*.xml
-doc/doxygen/*.html
-doc/doxygen/man/*
-doc/doxygen/latex/*
-doc/doxygen/xml/*
-doc/doxygen/html/*
-
-
-# python venv things that might get added accidentally
-../
-python-wheels/
-.whl
-*.whl
-.venv
-
-# python wheels
-python/share/*
-
-# git merge
-*.orig
-\#*
-\.#*
-
-# remnants from a failed in-source build
-CMakeCache.txt
-CMakeFiles/
-CMakeSettings.json
-
-# don't add dot-save files!
-
-.save
-
-# YouCompleteMe
-.ycm_extra_conf.py
diff --git a/ngraph/CMakeLists.txt b/ngraph/CMakeLists.txt
deleted file mode 100644
index 9681640b96b..00000000000
--- a/ngraph/CMakeLists.txt
+++ /dev/null
@@ -1,31 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-#
-
-if(ENABLE_LTO)
-    set(CMAKE_INTERPROCEDURAL_OPTIMIZATION_RELEASE ON)
-endif()
-
-set(NGRAPH_INCLUDE_PATH
-    ${CMAKE_CURRENT_SOURCE_DIR}/core/include
-)
-
-project (ngraph)
-
-#-----------------------------------------------------------------------------------------------
-# Installation logic...
-#-----------------------------------------------------------------------------------------------
-
-set(NGRAPH_TARGETS_FILE "${CMAKE_CURRENT_BINARY_DIR}/ngraphTargets.cmake")
-
-add_definitions(-DPROJECT_ROOT_DIR="${CMAKE_CURRENT_SOURCE_DIR}")
-
-#-----------------------------------------------------------------------------------------------
-# External projects install directory
-#-----------------------------------------------------------------------------------------------
-
-add_subdirectory(core)
-
-add_subdirectory(frontend)
-
-add_subdirectory(test)
diff --git a/ngraph/environment_variables.md b/ngraph/environment_variables.md
deleted file mode 100644
index 505ced47448..00000000000
--- a/ngraph/environment_variables.md
+++ /dev/null
@@ -1,16 +0,0 @@
-# Environment Variables
-
-| Name | Default | Description |
-| ------------------------------------|:---:| --- |
-| NGRAPH_ENABLE_REPLACE_CHECK | |
-| NGRAPH_ENABLE_TRACING | |
-| NGRAPH_ENABLE_VISUALIZE_TRACING | |
-| NGRAPH_FAIL_MATCH_AT | |
-| NGRAPH_GRAPH_REWRITE_RERUN_DYNAMIC_CHECK | |
-| NGRAPH_GTEST_INFO | |
-| NGRAPH_PROFILE_PASS_ENABLE | |
-| NGRAPH_VISUALIZE_EDGE_JUMP_DISTANCE | |
-| NGRAPH_VISUALIZE_EDGE_LABELS | |
-| NGRAPH_VISUALIZE_TRACING_FORMAT | |
-| NGRAPH_VISUALIZE_TREE_OUTPUT_SHAPES | |
-| NGRAPH_VISUALIZE_TREE_OUTPUT_TYPES | |
diff --git a/ngraph/test/util/unit-test-execution/README.md b/ngraph/test/util/unit-test-execution/README.md
deleted file mode 100644
index f6bb6b52538..00000000000
--- a/ngraph/test/util/unit-test-execution/README.md
+++ /dev/null
@@ -1,24 +0,0 @@
-These test executes 'unit-test'.
-
-To run please do the following:
-1. To run tests required installing some dependencies:
-    - pip3 install -r requirements.txt
-2. Set environment variable:
-    a. Required:
-        export PATH_TO_EXE=<path where nGraph unit-test locates>
-3. To run all tests:
-    a. cd folder where unit_test_executable.py locates
-    b. pytest --gtest_filter="*"
-4. To run exact test:
-    a. cd folder where unit_test_executable.py locates
-    b. pytest --gtest_filter="<your test name>"
-5. To get html report add "--html=report.html" to pytest cmd line
-    (but before install this module "pip install pytest-html")
-6.This test get result of opset1 operation (passed and failed) and also creates csv file 'nodes_coverage.csv' and
-    'nodes_coverage.html' after execution. Here you may find name of operations and its passrate and coverage
-    for several plugins.
-    Example:
-    Operation | GPU passed / total | CPU passed / total
-    Abs       | 1/2                | 1/2
-
-    Here operation 'Abs': 1 test of 2 passed on GPU and CPU
\ No newline at end of file
diff --git a/ngraph/test/util/unit-test-execution/conftest.py b/ngraph/test/util/unit-test-execution/conftest.py
deleted file mode 100644
index 796e74b3f40..00000000000
--- a/ngraph/test/util/unit-test-execution/conftest.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-import logging as log
-import sys
-import subprocess
-import os
-import pytest
-
-
-def pytest_addoption(parser):
-    parser.addoption(
-        "--gtest_filter",
-        help="Attributes to gtest",
-        type=str,
-        required=True,
-    )
-
-
-@pytest.fixture(scope="session")
-def gtest_filter(request):
-    return request.config.getoption('gtest_filter')
-
-
-def shell(cmd, env=None):
-    """
-    Run command execution in specified environment
-    :param cmd: list containing command and its parameters
-    :param env: set of environment variables to set for this command
-    :return:
-    """
-    if sys.platform.startswith('linux') or sys.platform == 'darwin':
-        cmd = ['/bin/bash', '-c', "unset OMP_NUM_THREADS; " + cmd]
-    else:
-        cmd = " ".join(cmd)
-
-    sys.stdout.write("Running command:\n" + "".join(cmd) + "\n")
-    p = subprocess.Popen(cmd, env=env, stdout=subprocess.PIPE, stderr=subprocess.STDOUT,
-                         universal_newlines=True)
-    stdout = []
-    while True:
-        line = p.stdout.readline()
-        stdout.append(line)
-        print(line.rstrip())
-        if line == '' and p.poll() != None:
-            break
-    return p.returncode, ''.join(stdout)
-
-
-def create_list_test(stdout):
-    # Example of stdout content:
-    # 'CPU'
-    # ' zero_sized_abs'
-    # ' zero_sized_ceiling'
-    # ...
-    # So, list of test will be concatenation of 'CPU' and the second part (starting with ' '):
-    # 'CPU.zero_sized_abs'
-    # 'CPU.zero_sized_ceiling'
-    list_test = []
-    first_name, second_name = [''] * 2
-    for line in stdout:
-        if not line.startswith(' '):
-            first_name = line
-        else:
-            second_name = line
-            # Several test has gtest mark 'DISABLED' inside test - no test will be executed
-            if not 'DISABLED' in line:
-                list_test.append(first_name + second_name.strip())
-    return list_test
-
-
-def pytest_generate_tests(metafunc):
-    gtest_filter = metafunc.config.getoption(name='gtest_filter')
-    if 'gtest_filter' in metafunc.fixturenames and gtest_filter is not None:
-        executable = os.path.join(os.environ.get('PATH_TO_EXE'), "unit-test")
-        cmd_line = executable + ' --gtest_filter=' + gtest_filter + ' --gtest_list_tests'
-        log.info('Executing {} for getting list of test'.format(executable))
-        retcode, stdout = shell(cmd=cmd_line)
-        assert retcode == 0, "unit-test --gtest_list_tests execution failed. Return code: {}".format(retcode)
-        stdout = stdout.split('\n')
-        list_test = create_list_test(stdout)
-
-        # Find all opset1 operations: execute test 'opset.dump'
-        cmd_line_all_op = executable + ' --gtest_filter=opset.dump'
-        log.info('Executing {} for getting list of test'.format(cmd_line_all_op))
-        retcode_op1, stdout_op1 = shell(cmd=cmd_line_all_op)
-        assert retcode_op1 == 0, "unit-test --gtest_filter=opset.dump execution failed. Return code: {}".format(retcode)
-        # Parsing stdout to storing name of opset1 operations
-        stdout_op1 = stdout_op1.split('\n')
-        operation_opset1 = []
-        for line in stdout_op1:
-            if 'All opset1 operations:' in line:
-                operation_opset1 = list(set(line.replace('All opset1 operations:', '').strip().split(' ')))
-        for op in operation_opset1:
-            pytest.operation_dictionary[op] = {}
-        metafunc.parametrize(argnames="gtest_filter", argvalues=list_test)
diff --git a/ngraph/test/util/unit-test-execution/requirements.txt b/ngraph/test/util/unit-test-execution/requirements.txt
deleted file mode 100644
index 55b033e901c..00000000000
--- a/ngraph/test/util/unit-test-execution/requirements.txt
+++ /dev/null
@@ -1 +0,0 @@
-pytest
\ No newline at end of file
diff --git a/ngraph/test/util/unit-test-execution/unit_test_executable.py b/ngraph/test/util/unit-test-execution/unit_test_executable.py
deleted file mode 100644
index 173d973f089..00000000000
--- a/ngraph/test/util/unit-test-execution/unit_test_executable.py
+++ /dev/null
@@ -1,143 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-
-import logging as log
-import sys
-import os
-import csv
-import pytest
-import re
-from conftest import shell
-
-log.basicConfig(format="[ %(levelname)s ]  %(msg)s", stream=sys.stdout, level=log.INFO)
-
-pytest.operation_dictionary = {}
-pytest.avaliable_plugins = []
-
-
-def save_coverage_to_csv(csv_path, header):
-    with open(csv_path, 'w', newline='') as f:
-        csv_writer = csv.writer(f, delimiter='|', quotechar='|', quoting=csv.QUOTE_MINIMAL)
-        csv_writer.writerow(i for i in header)
-        i = 1
-        for key in sorted(pytest.operation_dictionary):
-            line = [i, key]
-            for plugin in pytest.avaliable_plugins:
-                if not plugin in pytest.operation_dictionary[key]:
-                    line.append('0/0')
-                else:
-                    line.append('/'.join(str(x) for x in pytest.operation_dictionary[key][plugin]))
-            csv_writer.writerow(line)
-            i += 1
-
-
-def get_color(value):
-    if '/' in value:
-        passed, total = [int(x.strip()) for x in value.split('/')]
-        if passed == total and total != 0:
-            return "#d1ffd3"
-        elif passed == total and total == 0:
-            return "#dadada"
-        else:
-            return "#ffdbdb"
-    else:
-        return "white"
-
-
-def csv_to_html_table(csv_path, html_path, headers=None, delimiter=","):
-    with open(csv_path) as f:
-        content = f.readlines()
-
-    # reading file content into list
-    rows = [x.strip() for x in content]
-    table = "<!DOCTYPE html><html><head><title>Opset1 operations results</title></head><body><table border=1>"
-
-    # creating HTML header row if header is provided
-    if headers is not None:
-        table += "<tr>"
-        table += "".join(["<th>" + cell + "</th>" for cell in headers])
-        table += "</tr>"
-    else:
-        table += "<tr>"
-        table += "".join(["<th>" + cell + "</th>" for cell in rows[0].split(delimiter)])
-        table += "</tr>"
-        rows = rows[1:]
-
-    # Converting csv to html row by row
-    for row in rows:
-        table += "<tr>" + "".join(["<td style=background-color:%s>" % (get_color(cell)) + cell + "</td>"
-                                   for cell in row.split(delimiter)]) + "</tr>" + "\n"
-    table += "</table></body></html><br>"
-
-    # Saving html file
-    fh = open(html_path, "w")
-    fh.write(table)
-    fh.close()
-
-
-def setup_module():
-    try:
-        os.environ.get('PATH_TO_EXE')
-    except KeyError:
-        raise ImportError('PATH_TO_EXE is upsent in your environment variables. '
-                          'Please, do "export PATH_TO_EXE=<path to unit-test>')
-
-
-def teardown_module():
-    """
-    Creating CSV file at the end of test with nGraph nodes coverage
-    :return:
-    """
-    csv_path = "nodes_coverage.csv"
-    header = ["#", "Operation"] + [p + " passed / total" for p in pytest.avaliable_plugins]
-    save_coverage_to_csv(csv_path=csv_path, header=header)
-
-    # Convert csv file to html for better visualization
-    html_path = "nodes_coverage.html"
-    csv_to_html_table(csv_path=csv_path, html_path=html_path, delimiter="|")
-
-
-def test(gtest_filter):
-    executable = os.path.join(os.environ.get('PATH_TO_EXE'), "unit-test")
-    cmd_line = executable + ' --gtest_filter=' + gtest_filter
-    retcode, stdout = shell(cmd=cmd_line)
-
-    # Parsing output of single test
-    stdout = stdout.split('\n')
-    nodes_list = []
-    for line in stdout:
-        if 'UNSUPPORTED OPS DETECTED!' in line:
-            pytest.skip('Skip from pytest because unit-test send error UNSUPPORTED OPS DETECTED!')
-        elif 'Nodes in test:' in line:
-            nodes_list = list(set(line.replace('Nodes in test:', '').strip().split(' ')))
-
-    if not nodes_list:
-        pytest.skip('Skip from pytest because inside test no one ngraph function created')
-
-    # Added one more loop, because condition below must be executed only if some nodes_list found
-    # (it means that test includes opset1 operations)
-    for line in stdout:
-        if re.match('.*1 test from\s([A-Z]+)', line):
-            matches = re.search(r'.*1 test from\s([A-Z]+)', line)
-            plugin = matches.group(1)
-            if plugin not in pytest.avaliable_plugins:
-                pytest.avaliable_plugins.append(plugin)
-
-    # Filling dictionary with operation coverage
-    # How many time one operation is tested
-    for n in nodes_list:
-        if plugin in pytest.operation_dictionary[n]:
-            numerator, denominator = pytest.operation_dictionary[n][plugin]
-            pytest.operation_dictionary[n][plugin] = (numerator if retcode != 0 else numerator + 1,
-                                                      denominator + 1)
-        else:
-            pytest.operation_dictionary[n][plugin] = (0, 1) if retcode != 0 else (1, 1)
-
-    # This check is at the end, because with 99% it will return 0 or 1 (when function check of test failed)
-    # Because the same cmd line executed by pytest_generate_tests with --gtest_list_tests.
-    # So, most of the issue cached there.
-    assert retcode == 0, "unit-test execution failed. Gtest failed. Return code: {}".format(retcode)
-
-
-if __name__ == '__main__':
-    log.warning("Please run {} by pytest like so:\npytest {} --gtest_filter=<attributes for gtest_filter>")
diff --git a/openvino/CMakeLists.txt b/openvino/CMakeLists.txt
deleted file mode 100644
index 94487b286e4..00000000000
--- a/openvino/CMakeLists.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-#
-
-add_subdirectory(itt)
-add_subdirectory(conditional_compilation)
-add_subdirectory(util)
-
-openvino_developer_export_targets(COMPONENT openvino_common TARGETS openvino::itt openvino::conditional_compilation)
diff --git a/inference-engine/src/inference_engine/.clang-format b/src/.clang-format
similarity index 100%
rename from inference-engine/src/inference_engine/.clang-format
rename to src/.clang-format
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 88bc7064127..0f7b4f529b0 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -2,6 +2,44 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-if(ENABLE_PYTHON)
-    add_subdirectory(bindings/python)
+if(ENABLE_LTO)
+    set(CMAKE_INTERPROCEDURAL_OPTIMIZATION_RELEASE ON)
+endif()
+add_definitions(-DPROJECT_ROOT_DIR="${CMAKE_CURRENT_SOURCE_DIR}")
+
+add_custom_target(ov_runtime_libraries ALL)
+
+# TODO: Fix Wall for core, frontends, tests and  c binding
+add_subdirectory(core)
+add_subdirectory(frontends)
+# TODO: remove ngraph/ngraph.hpp usage
+add_subdirectory(core/tests)
+add_subdirectory(bindings)
+
+if(CMAKE_COMPILER_IS_GNUCXX)
+    ie_add_compiler_flags(-Wall)
+    ie_add_compiler_flags(-Wmissing-declarations)
+endif()
+
+add_subdirectory(inference)
+add_subdirectory(common)
+
+
+if(NGRAPH_IR_FRONTEND_ENABLE)
+    if(BUILD_SHARED_LIBS)
+        add_dependencies(ov_runtime_libraries ir_ov_frontend)
+    endif()
+    # use this one once CVS-69781 is fixed
+    # add_dependencies(inference_engine ir_ov_frontend)
+endif()
+if(NGRAPH_ONNX_FRONTEND_ENABLE)
+    add_dependencies(inference_engine onnx_ov_frontend)
+endif()
+
+if(NGRAPH_PDPD_FRONTEND_ENABLE)
+    add_dependencies(inference_engine paddlepaddle_ov_frontend)
+endif()
+
+if(NGRAPH_TF_FRONTEND_ENABLE)
+    add_dependencies(inference_engine tensorflow_ov_frontend)
 endif()
diff --git a/src/bindings/CMakeLists.txt b/src/bindings/CMakeLists.txt
new file mode 100644
index 00000000000..4811a833112
--- /dev/null
+++ b/src/bindings/CMakeLists.txt
@@ -0,0 +1,10 @@
+# Copyright (C) 2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+add_subdirectory(c)
+
+if(ENABLE_PYTHON)
+    add_subdirectory(python)
+endif()
+
diff --git a/inference-engine/ie_bridges/c/CMakeLists.txt b/src/bindings/c/CMakeLists.txt
similarity index 100%
rename from inference-engine/ie_bridges/c/CMakeLists.txt
rename to src/bindings/c/CMakeLists.txt
diff --git a/inference-engine/ie_bridges/c/docs/api_overview.md b/src/bindings/c/docs/api_overview.md
similarity index 100%
rename from inference-engine/ie_bridges/c/docs/api_overview.md
rename to src/bindings/c/docs/api_overview.md
diff --git a/inference-engine/ie_bridges/c/include/c_api/ie_c_api.h b/src/bindings/c/include/c_api/ie_c_api.h
similarity index 100%
rename from inference-engine/ie_bridges/c/include/c_api/ie_c_api.h
rename to src/bindings/c/include/c_api/ie_c_api.h
diff --git a/inference-engine/ie_bridges/c/src/CMakeLists.txt b/src/bindings/c/src/CMakeLists.txt
similarity index 100%
rename from inference-engine/ie_bridges/c/src/CMakeLists.txt
rename to src/bindings/c/src/CMakeLists.txt
diff --git a/inference-engine/ie_bridges/c/src/ie_c_api.cpp b/src/bindings/c/src/ie_c_api.cpp
similarity index 100%
rename from inference-engine/ie_bridges/c/src/ie_c_api.cpp
rename to src/bindings/c/src/ie_c_api.cpp
diff --git a/inference-engine/ie_bridges/c/tests/CMakeLists.txt b/src/bindings/c/tests/CMakeLists.txt
similarity index 100%
rename from inference-engine/ie_bridges/c/tests/CMakeLists.txt
rename to src/bindings/c/tests/CMakeLists.txt
diff --git a/inference-engine/ie_bridges/c/tests/ie_c_api_test.cpp b/src/bindings/c/tests/ie_c_api_test.cpp
similarity index 100%
rename from inference-engine/ie_bridges/c/tests/ie_c_api_test.cpp
rename to src/bindings/c/tests/ie_c_api_test.cpp
diff --git a/inference-engine/ie_bridges/c/tests/test_model_repo.hpp b/src/bindings/c/tests/test_model_repo.hpp
similarity index 100%
rename from inference-engine/ie_bridges/c/tests/test_model_repo.hpp
rename to src/bindings/c/tests/test_model_repo.hpp
diff --git a/src/common/CMakeLists.txt b/src/common/CMakeLists.txt
new file mode 100644
index 00000000000..35f24cd8c13
--- /dev/null
+++ b/src/common/CMakeLists.txt
@@ -0,0 +1,19 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+add_subdirectory(itt)
+add_subdirectory(conditional_compilation)
+add_subdirectory(util)
+
+add_subdirectory(snippets)
+add_subdirectory(legacy)
+add_subdirectory(preprocessing)
+add_subdirectory(transformations)
+add_subdirectory(low_precision_transformations)
+
+add_dependencies(ov_runtime_libraries inference_engine_legacy
+                                      inference_engine_transformations
+                                      inference_engine_lp_transformations
+                                      inference_engine_preproc
+                                      inference_engine_snippets)
diff --git a/openvino/conditional_compilation/CMakeLists.txt b/src/common/conditional_compilation/CMakeLists.txt
similarity index 95%
rename from openvino/conditional_compilation/CMakeLists.txt
rename to src/common/conditional_compilation/CMakeLists.txt
index 70fb4484e76..541cf953907 100644
--- a/openvino/conditional_compilation/CMakeLists.txt
+++ b/src/common/conditional_compilation/CMakeLists.txt
@@ -50,3 +50,4 @@ ov_install_static_lib(${TARGET_NAME} ngraph)
 
 file(GLOB_RECURSE hdrs ${CMAKE_CURRENT_SOURCE_DIR}/include/*.h ${CMAKE_CURRENT_SOURCE_DIR}/include/*.hpp)
 add_cpplint_target(${TARGET_NAME}_cpplint FOR_SOURCES ${hdrs})
+openvino_developer_export_targets(COMPONENT openvino_common TARGETS openvino::conditional_compilation)
diff --git a/openvino/conditional_compilation/include/openvino/cc/factory.h b/src/common/conditional_compilation/include/openvino/cc/factory.h
similarity index 100%
rename from openvino/conditional_compilation/include/openvino/cc/factory.h
rename to src/common/conditional_compilation/include/openvino/cc/factory.h
diff --git a/openvino/conditional_compilation/include/openvino/cc/ngraph/itt.hpp b/src/common/conditional_compilation/include/openvino/cc/ngraph/itt.hpp
similarity index 100%
rename from openvino/conditional_compilation/include/openvino/cc/ngraph/itt.hpp
rename to src/common/conditional_compilation/include/openvino/cc/ngraph/itt.hpp
diff --git a/openvino/conditional_compilation/include/openvino/cc/selective_build.h b/src/common/conditional_compilation/include/openvino/cc/selective_build.h
similarity index 100%
rename from openvino/conditional_compilation/include/openvino/cc/selective_build.h
rename to src/common/conditional_compilation/include/openvino/cc/selective_build.h
diff --git a/openvino/conditional_compilation/scripts/ccheader.py b/src/common/conditional_compilation/scripts/ccheader.py
similarity index 100%
rename from openvino/conditional_compilation/scripts/ccheader.py
rename to src/common/conditional_compilation/scripts/ccheader.py
diff --git a/openvino/itt/CMakeLists.txt b/src/common/itt/CMakeLists.txt
similarity index 93%
rename from openvino/itt/CMakeLists.txt
rename to src/common/itt/CMakeLists.txt
index c427f242ed7..da5421f6737 100644
--- a/openvino/itt/CMakeLists.txt
+++ b/src/common/itt/CMakeLists.txt
@@ -36,3 +36,4 @@ target_include_directories(${TARGET_NAME} PUBLIC
 ov_install_static_lib(${TARGET_NAME} openvino_common)
 
 add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME})
+openvino_developer_export_targets(COMPONENT openvino_common TARGETS openvino::itt)
diff --git a/openvino/itt/cmake/ITTConfig.cmake b/src/common/itt/cmake/ITTConfig.cmake
similarity index 100%
rename from openvino/itt/cmake/ITTConfig.cmake
rename to src/common/itt/cmake/ITTConfig.cmake
diff --git a/openvino/itt/include/openvino/function_name.hpp b/src/common/itt/include/openvino/function_name.hpp
similarity index 100%
rename from openvino/itt/include/openvino/function_name.hpp
rename to src/common/itt/include/openvino/function_name.hpp
diff --git a/openvino/itt/include/openvino/itt.hpp b/src/common/itt/include/openvino/itt.hpp
similarity index 100%
rename from openvino/itt/include/openvino/itt.hpp
rename to src/common/itt/include/openvino/itt.hpp
diff --git a/openvino/itt/src/itt.cpp b/src/common/itt/src/itt.cpp
similarity index 100%
rename from openvino/itt/src/itt.cpp
rename to src/common/itt/src/itt.cpp
diff --git a/inference-engine/src/legacy_api/CMakeLists.txt b/src/common/legacy/CMakeLists.txt
similarity index 100%
rename from inference-engine/src/legacy_api/CMakeLists.txt
rename to src/common/legacy/CMakeLists.txt
diff --git a/inference-engine/src/legacy_api/include/legacy/cnn_network_impl.hpp b/src/common/legacy/include/legacy/cnn_network_impl.hpp
similarity index 100%
rename from inference-engine/src/legacy_api/include/legacy/cnn_network_impl.hpp
rename to src/common/legacy/include/legacy/cnn_network_impl.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/convert_function_to_cnn_network.hpp b/src/common/legacy/include/legacy/convert_function_to_cnn_network.hpp
similarity index 100%
rename from inference-engine/src/legacy_api/include/legacy/convert_function_to_cnn_network.hpp
rename to src/common/legacy/include/legacy/convert_function_to_cnn_network.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/details/ie_cnn_network_iterator.hpp b/src/common/legacy/include/legacy/details/ie_cnn_network_iterator.hpp
similarity index 100%
rename from inference-engine/src/legacy_api/include/legacy/details/ie_cnn_network_iterator.hpp
rename to src/common/legacy/include/legacy/details/ie_cnn_network_iterator.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/details/ie_cnn_network_tools.h b/src/common/legacy/include/legacy/details/ie_cnn_network_tools.h
similarity index 100%
rename from inference-engine/src/legacy_api/include/legacy/details/ie_cnn_network_tools.h
rename to src/common/legacy/include/legacy/details/ie_cnn_network_tools.h
diff --git a/inference-engine/src/legacy_api/include/legacy/graph_tools.hpp b/src/common/legacy/include/legacy/graph_tools.hpp
similarity index 100%
rename from inference-engine/src/legacy_api/include/legacy/graph_tools.hpp
rename to src/common/legacy/include/legacy/graph_tools.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/graph_transformer.h b/src/common/legacy/include/legacy/graph_transformer.h
similarity index 100%
rename from inference-engine/src/legacy_api/include/legacy/graph_transformer.h
rename to src/common/legacy/include/legacy/graph_transformer.h
diff --git a/inference-engine/src/readers/ir_reader_v7/ie_ir_version.hpp b/src/common/legacy/include/legacy/ie_ir_version.hpp
similarity index 100%
rename from inference-engine/src/readers/ir_reader_v7/ie_ir_version.hpp
rename to src/common/legacy/include/legacy/ie_ir_version.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ie_layers.h b/src/common/legacy/include/legacy/ie_layers.h
similarity index 100%
rename from inference-engine/src/legacy_api/include/legacy/ie_layers.h
rename to src/common/legacy/include/legacy/ie_layers.h
diff --git a/inference-engine/src/legacy_api/include/legacy/ie_layers_internal.hpp b/src/common/legacy/include/legacy/ie_layers_internal.hpp
similarity index 100%
rename from inference-engine/src/legacy_api/include/legacy/ie_layers_internal.hpp
rename to src/common/legacy/include/legacy/ie_layers_internal.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ie_layers_property.hpp b/src/common/legacy/include/legacy/ie_layers_property.hpp
similarity index 100%
rename from inference-engine/src/legacy_api/include/legacy/ie_layers_property.hpp
rename to src/common/legacy/include/legacy/ie_layers_property.hpp
diff --git a/inference-engine/src/readers/reader_api/ie_reader.hpp b/src/common/legacy/include/legacy/ie_reader.hpp
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rename from inference-engine/src/readers/reader_api/ie_reader.hpp
rename to src/common/legacy/include/legacy/ie_reader.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ie_util_internal.hpp b/src/common/legacy/include/legacy/ie_util_internal.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ie_util_internal.hpp
rename to src/common/legacy/include/legacy/ie_util_internal.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/layer_transform.hpp b/src/common/legacy/include/legacy/layer_transform.hpp
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rename from inference-engine/src/legacy_api/include/legacy/layer_transform.hpp
rename to src/common/legacy/include/legacy/layer_transform.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/net_pass.h b/src/common/legacy/include/legacy/net_pass.h
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rename from inference-engine/src/legacy_api/include/legacy/net_pass.h
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diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/crop_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/crop_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/crop_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/crop_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/eltwise.hpp b/src/common/legacy/include/legacy/ngraph_ops/eltwise.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/eltwise.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/eltwise.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/fully_connected.hpp b/src/common/legacy/include/legacy/ngraph_ops/fully_connected.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/fully_connected.hpp
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diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/gather_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/gather_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/gather_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/gather_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/gather_tree_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/gather_tree_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/gather_tree_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/gather_tree_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/gru_cell_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/gru_cell_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/gru_cell_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/gru_cell_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/gru_sequence_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/gru_sequence_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/gru_sequence_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/gru_sequence_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/hard_sigmoid_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/hard_sigmoid_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/hard_sigmoid_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/hard_sigmoid_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/interp.hpp b/src/common/legacy/include/legacy/ngraph_ops/interp.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/interp.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/interp.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/lrn_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/lrn_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/lrn_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/lrn_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/lstm_cell_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/lstm_cell_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/lstm_cell_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/lstm_cell_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/lstm_sequence_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/lstm_sequence_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/lstm_sequence_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/lstm_sequence_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/nms_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/nms_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/nms_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/nms_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/normalize_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/normalize_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/normalize_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/normalize_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/onehot_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/onehot_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/onehot_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/onehot_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/pad_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/pad_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/pad_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/pad_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/power.hpp b/src/common/legacy/include/legacy/ngraph_ops/power.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/power.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/power.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/proposal_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/proposal_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/proposal_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/proposal_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/relu_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/relu_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/relu_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/relu_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/rnn_cell_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/rnn_cell_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/rnn_cell_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/rnn_cell_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/rnn_sequence_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/rnn_sequence_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/rnn_sequence_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/rnn_sequence_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/scaleshift.hpp b/src/common/legacy/include/legacy/ngraph_ops/scaleshift.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/scaleshift.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/scaleshift.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/selu_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/selu_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/selu_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/selu_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/swish_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/swish_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/swish_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/swish_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/tile_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/tile_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/tile_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/tile_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/ngraph_ops/topk_ie.hpp b/src/common/legacy/include/legacy/ngraph_ops/topk_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/ngraph_ops/topk_ie.hpp
rename to src/common/legacy/include/legacy/ngraph_ops/topk_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_cells_to_cells_ie.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_cells_to_cells_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_cells_to_cells_ie.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_cells_to_cells_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_gather_to_gather_ie.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_gather_to_gather_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_gather_to_gather_ie.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_gather_to_gather_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_gathertree_to_gathertree_ie.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_gathertree_to_gathertree_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_gathertree_to_gathertree_ie.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_gathertree_to_gathertree_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_hard_sigmoid_to_hard_sigmoid_ie.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_hard_sigmoid_to_hard_sigmoid_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_hard_sigmoid_to_hard_sigmoid_ie.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_hard_sigmoid_to_hard_sigmoid_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_interpolate_to_interp_or_resample.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_interpolate_to_interp_or_resample.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_interpolate_to_interp_or_resample.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_interpolate_to_interp_or_resample.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_lrn_to_lrn_ie.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_lrn_to_lrn_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_lrn_to_lrn_ie.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_lrn_to_lrn_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_matmul_to_fc_or_gemm.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_matmul_to_fc_or_gemm.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_matmul_to_fc_or_gemm.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_matmul_to_fc_or_gemm.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_mul_add_to_scaleshift_or_power.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_mul_add_to_scaleshift_or_power.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_mul_add_to_scaleshift_or_power.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_mul_add_to_scaleshift_or_power.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_mul_or_add_finally.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_mul_or_add_finally.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_mul_or_add_finally.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_mul_or_add_finally.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_nms_5_to_legacy.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_nms_5_to_legacy.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_nms_5_to_legacy.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_nms_5_to_legacy.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_nms_to_nms_ie.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_nms_to_nms_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_nms_to_nms_ie.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_nms_to_nms_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_normalizel2_to_normalize_ie.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_normalizel2_to_normalize_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_normalizel2_to_normalize_ie.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_normalizel2_to_normalize_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_one_hot_to_one_hot_ie.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_one_hot_to_one_hot_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_one_hot_to_one_hot_ie.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_one_hot_to_one_hot_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_opset1_to_legacy.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_opset1_to_legacy.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_opset1_to_legacy.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_opset1_to_legacy.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_pad_to_pad_ie.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_pad_to_pad_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_pad_to_pad_ie.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_pad_to_pad_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_power_to_power_ie.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_power_to_power_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_power_to_power_ie.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_power_to_power_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_prelu_to_relu_ie.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_prelu_to_relu_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_prelu_to_relu_ie.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_prelu_to_relu_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_proposal_to_proposal_ie.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_proposal_to_proposal_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_proposal_to_proposal_ie.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_proposal_to_proposal_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_selu_to_selu_ie.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_selu_to_selu_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_selu_to_selu_ie.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_selu_to_selu_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_sequences_to_sequences_ie.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_sequences_to_sequences_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_sequences_to_sequences_ie.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_sequences_to_sequences_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_sqrt_to_power_ie.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_sqrt_to_power_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_sqrt_to_power_ie.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_sqrt_to_power_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_strided_slice_to_crop.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_strided_slice_to_crop.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_strided_slice_to_crop.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_strided_slice_to_crop.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_swish_to_swish_ie.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_swish_to_swish_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_swish_to_swish_ie.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_swish_to_swish_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_tile_to_ie_tile.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_tile_to_ie_tile.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_tile_to_ie_tile.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_tile_to_ie_tile.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_topk_to_topk_ie.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_topk_to_topk_ie.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/convert_topk_to_topk_ie.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/convert_topk_to_topk_ie.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/fc_bias_fusion.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/fc_bias_fusion.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/fc_bias_fusion.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/fc_bias_fusion.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/reshape_1d_ops.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/reshape_1d_ops.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/reshape_1d_ops.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/reshape_1d_ops.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/reshape_fc_fusion.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/reshape_fc_fusion.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/reshape_fc_fusion.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/reshape_fc_fusion.hpp
diff --git a/inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/reshape_fully_connected.hpp b/src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/reshape_fully_connected.hpp
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rename from inference-engine/src/legacy_api/include/legacy/transformations/convert_opset1_to_legacy/reshape_fully_connected.hpp
rename to src/common/legacy/include/legacy/transformations/convert_opset1_to_legacy/reshape_fully_connected.hpp
diff --git a/inference-engine/src/legacy_api/src/cnn_network_impl.cpp b/src/common/legacy/src/cnn_network_impl.cpp
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rename from inference-engine/src/legacy_api/src/cnn_network_impl.cpp
rename to src/common/legacy/src/cnn_network_impl.cpp
diff --git a/inference-engine/src/legacy_api/src/convert_function_to_cnn_network.cpp b/src/common/legacy/src/convert_function_to_cnn_network.cpp
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diff --git a/inference-engine/src/legacy_api/src/graph_tools.cpp b/src/common/legacy/src/graph_tools.cpp
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rename from inference-engine/src/legacy_api/src/graph_tools.cpp
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diff --git a/inference-engine/src/legacy_api/src/graph_transformer.cpp b/src/common/legacy/src/graph_transformer.cpp
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rename to src/common/legacy/src/graph_transformer.cpp
diff --git a/inference-engine/src/legacy_api/src/ie_layer_validators.cpp b/src/common/legacy/src/ie_layer_validators.cpp
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diff --git a/inference-engine/src/legacy_api/src/ie_layer_validators.hpp b/src/common/legacy/src/ie_layer_validators.hpp
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rename from inference-engine/src/legacy_api/src/ie_layer_validators.hpp
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diff --git a/inference-engine/src/legacy_api/src/ie_layers.cpp b/src/common/legacy/src/ie_layers.cpp
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diff --git a/inference-engine/src/legacy_api/src/ie_layers_internal.cpp b/src/common/legacy/src/ie_layers_internal.cpp
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diff --git a/inference-engine/src/legacy_api/src/ie_legacy_itt.hpp b/src/common/legacy/src/ie_legacy_itt.hpp
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rename from inference-engine/src/legacy_api/src/ie_legacy_itt.hpp
rename to src/common/legacy/src/ie_legacy_itt.hpp
diff --git a/inference-engine/src/legacy_api/src/ie_util_internal.cpp b/src/common/legacy/src/ie_util_internal.cpp
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rename from inference-engine/src/legacy_api/src/ie_util_internal.cpp
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diff --git a/inference-engine/src/legacy_api/src/layer_transform.cpp b/src/common/legacy/src/layer_transform.cpp
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rename from inference-engine/src/legacy_api/src/layer_transform.cpp
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diff --git a/inference-engine/src/legacy_api/src/net_pass.cpp b/src/common/legacy/src/net_pass.cpp
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rename from inference-engine/src/legacy_api/src/net_pass.cpp
rename to src/common/legacy/src/net_pass.cpp
diff --git a/inference-engine/src/legacy_api/src/network_serializer_v7.cpp b/src/common/legacy/src/network_serializer_v7.cpp
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diff --git a/inference-engine/src/legacy_api/src/network_serializer_v7.hpp b/src/common/legacy/src/network_serializer_v7.hpp
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rename from inference-engine/src/legacy_api/src/network_serializer_v7.hpp
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diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/crop_ie.cpp b/src/common/legacy/src/ngraph_ops/crop_ie.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/crop_ie.cpp
rename to src/common/legacy/src/ngraph_ops/crop_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/eltwise.cpp b/src/common/legacy/src/ngraph_ops/eltwise.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/eltwise.cpp
rename to src/common/legacy/src/ngraph_ops/eltwise.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/fully_connected.cpp b/src/common/legacy/src/ngraph_ops/fully_connected.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/fully_connected.cpp
rename to src/common/legacy/src/ngraph_ops/fully_connected.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/gather_ie.cpp b/src/common/legacy/src/ngraph_ops/gather_ie.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/gather_ie.cpp
rename to src/common/legacy/src/ngraph_ops/gather_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/gather_tree_ie.cpp b/src/common/legacy/src/ngraph_ops/gather_tree_ie.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/gather_tree_ie.cpp
rename to src/common/legacy/src/ngraph_ops/gather_tree_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/gru_cell_ie.cpp b/src/common/legacy/src/ngraph_ops/gru_cell_ie.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/gru_cell_ie.cpp
rename to src/common/legacy/src/ngraph_ops/gru_cell_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/gru_sequence_ie.cpp b/src/common/legacy/src/ngraph_ops/gru_sequence_ie.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/gru_sequence_ie.cpp
rename to src/common/legacy/src/ngraph_ops/gru_sequence_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/hard_sigmoid_ie.cpp b/src/common/legacy/src/ngraph_ops/hard_sigmoid_ie.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/hard_sigmoid_ie.cpp
rename to src/common/legacy/src/ngraph_ops/hard_sigmoid_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/interp.cpp b/src/common/legacy/src/ngraph_ops/interp.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/interp.cpp
rename to src/common/legacy/src/ngraph_ops/interp.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/lrn_ie.cpp b/src/common/legacy/src/ngraph_ops/lrn_ie.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/lrn_ie.cpp
rename to src/common/legacy/src/ngraph_ops/lrn_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/lstm_cell_ie.cpp b/src/common/legacy/src/ngraph_ops/lstm_cell_ie.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/lstm_cell_ie.cpp
rename to src/common/legacy/src/ngraph_ops/lstm_cell_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/lstm_sequence_ie.cpp b/src/common/legacy/src/ngraph_ops/lstm_sequence_ie.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/lstm_sequence_ie.cpp
rename to src/common/legacy/src/ngraph_ops/lstm_sequence_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/nms_ie.cpp b/src/common/legacy/src/ngraph_ops/nms_ie.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/nms_ie.cpp
rename to src/common/legacy/src/ngraph_ops/nms_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/normalize_ie.cpp b/src/common/legacy/src/ngraph_ops/normalize_ie.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/normalize_ie.cpp
rename to src/common/legacy/src/ngraph_ops/normalize_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/onehot_ie.cpp b/src/common/legacy/src/ngraph_ops/onehot_ie.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/onehot_ie.cpp
rename to src/common/legacy/src/ngraph_ops/onehot_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/pad_ie.cpp b/src/common/legacy/src/ngraph_ops/pad_ie.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/pad_ie.cpp
rename to src/common/legacy/src/ngraph_ops/pad_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/power.cpp b/src/common/legacy/src/ngraph_ops/power.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/power.cpp
rename to src/common/legacy/src/ngraph_ops/power.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/proposal_ie.cpp b/src/common/legacy/src/ngraph_ops/proposal_ie.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/proposal_ie.cpp
rename to src/common/legacy/src/ngraph_ops/proposal_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/relu_ie.cpp b/src/common/legacy/src/ngraph_ops/relu_ie.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/relu_ie.cpp
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diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/rnn_cell_ie.cpp b/src/common/legacy/src/ngraph_ops/rnn_cell_ie.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/rnn_cell_ie.cpp
rename to src/common/legacy/src/ngraph_ops/rnn_cell_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/rnn_sequence_ie.cpp b/src/common/legacy/src/ngraph_ops/rnn_sequence_ie.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/rnn_sequence_ie.cpp
rename to src/common/legacy/src/ngraph_ops/rnn_sequence_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/scaleshift.cpp b/src/common/legacy/src/ngraph_ops/scaleshift.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/scaleshift.cpp
rename to src/common/legacy/src/ngraph_ops/scaleshift.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/selu_ie.cpp b/src/common/legacy/src/ngraph_ops/selu_ie.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/selu_ie.cpp
rename to src/common/legacy/src/ngraph_ops/selu_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/swish_ie.cpp b/src/common/legacy/src/ngraph_ops/swish_ie.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/swish_ie.cpp
rename to src/common/legacy/src/ngraph_ops/swish_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/tile_ie.cpp b/src/common/legacy/src/ngraph_ops/tile_ie.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/tile_ie.cpp
rename to src/common/legacy/src/ngraph_ops/tile_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/ngraph_ops/topk_ie.cpp b/src/common/legacy/src/ngraph_ops/topk_ie.cpp
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rename from inference-engine/src/legacy_api/src/ngraph_ops/topk_ie.cpp
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diff --git a/inference-engine/src/legacy_api/src/precomp.hpp b/src/common/legacy/src/precomp.hpp
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rename from inference-engine/src/legacy_api/src/precomp.hpp
rename to src/common/legacy/src/precomp.hpp
diff --git a/inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_cells_to_cells_ie.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_cells_to_cells_ie.cpp
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rename from inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_cells_to_cells_ie.cpp
rename to src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_cells_to_cells_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_gather_to_gather_ie.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_gather_to_gather_ie.cpp
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rename from inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_gather_to_gather_ie.cpp
rename to src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_gather_to_gather_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_gathertree_to_gathertree_ie.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_gathertree_to_gathertree_ie.cpp
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rename from inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_gathertree_to_gathertree_ie.cpp
rename to src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_gathertree_to_gathertree_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_hard_sigmoid_to_hard_sigmoid_ie.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_hard_sigmoid_to_hard_sigmoid_ie.cpp
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rename from inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_hard_sigmoid_to_hard_sigmoid_ie.cpp
rename to src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_hard_sigmoid_to_hard_sigmoid_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_interpolate_to_interp_or_resample.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_interpolate_to_interp_or_resample.cpp
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rename from inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_interpolate_to_interp_or_resample.cpp
rename to src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_interpolate_to_interp_or_resample.cpp
diff --git a/inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_lrn_to_lrn_ie.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_lrn_to_lrn_ie.cpp
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rename from inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_lrn_to_lrn_ie.cpp
rename to src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_lrn_to_lrn_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_matmul_to_fc_or_gemm.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_matmul_to_fc_or_gemm.cpp
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rename from inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_matmul_to_fc_or_gemm.cpp
rename to src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_matmul_to_fc_or_gemm.cpp
diff --git a/inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_mul_add_to_scaleshift_or_power.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_mul_add_to_scaleshift_or_power.cpp
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rename from inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_mul_add_to_scaleshift_or_power.cpp
rename to src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_mul_add_to_scaleshift_or_power.cpp
diff --git a/inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_mul_or_add_finally.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_mul_or_add_finally.cpp
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rename from inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_mul_or_add_finally.cpp
rename to src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_mul_or_add_finally.cpp
diff --git a/inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_nms_5_to_legacy.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_nms_5_to_legacy.cpp
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rename from inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_nms_5_to_legacy.cpp
rename to src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_nms_5_to_legacy.cpp
diff --git a/inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_nms_to_nms_ie.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_nms_to_nms_ie.cpp
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rename from inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_nms_to_nms_ie.cpp
rename to src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_nms_to_nms_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_normalizel2_to_normalize_ie.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_normalizel2_to_normalize_ie.cpp
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rename from inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_normalizel2_to_normalize_ie.cpp
rename to src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_normalizel2_to_normalize_ie.cpp
diff --git a/inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_one_hot_to_one_hot_ie.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_one_hot_to_one_hot_ie.cpp
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diff --git a/inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_pad_to_pad_ie.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_pad_to_pad_ie.cpp
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diff --git a/inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_power_to_power_ie.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_power_to_power_ie.cpp
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diff --git a/inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_prelu_to_relu_ie.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_prelu_to_relu_ie.cpp
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diff --git a/inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_selu_to_selu_ie.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_selu_to_selu_ie.cpp
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diff --git a/inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_strided_slice_to_crop.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_strided_slice_to_crop.cpp
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diff --git a/inference-engine/src/legacy_api/src/transformations/convert_opset1_to_legacy/convert_tile_to_ie_tile.cpp b/src/common/legacy/src/transformations/convert_opset1_to_legacy/convert_tile_to_ie_tile.cpp
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diff --git a/inference-engine/src/snippets/src/pass/load_movebroadcast_to_broadcastload.cpp b/src/common/snippets/src/pass/load_movebroadcast_to_broadcastload.cpp
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diff --git a/inference-engine/src/snippets/src/pass/vector_to_scalar.cpp b/src/common/snippets/src/pass/vector_to_scalar.cpp
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diff --git a/inference-engine/src/transformations/CMakeLists.txt b/src/common/transformations/CMakeLists.txt
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diff --git a/inference-engine/src/transformations/include/ngraph_ops/nms_static_shape_ie.hpp b/src/common/transformations/include/ngraph_ops/nms_static_shape_ie.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/add_fake_quantize_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/add_fake_quantize_fusion.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/algebraic_simplification.hpp b/src/common/transformations/include/transformations/common_optimizations/algebraic_simplification.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/batch_to_space_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/batch_to_space_fusion.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/binarize_weights.hpp b/src/common/transformations/include/transformations/common_optimizations/binarize_weights.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/broadcast_elementwise_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/broadcast_elementwise_fusion.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/clamp_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/clamp_fusion.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/compress_float_constants.hpp b/src/common/transformations/include/transformations/common_optimizations/compress_float_constants.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/conv_bias_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/conv_bias_fusion.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/conv_mul_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/conv_mul_fusion.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/conv_to_binary_conv.hpp b/src/common/transformations/include/transformations/common_optimizations/conv_to_binary_conv.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/convert_compression_only_to_legacy.hpp b/src/common/transformations/include/transformations/common_optimizations/convert_compression_only_to_legacy.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/convert_nms_gather_path_to_unsigned.hpp b/src/common/transformations/include/transformations/common_optimizations/convert_nms_gather_path_to_unsigned.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/convert_quantize_dequantize.hpp b/src/common/transformations/include/transformations/common_optimizations/convert_quantize_dequantize.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/depth_to_space_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/depth_to_space_fusion.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/dilated_convolution_converter.hpp b/src/common/transformations/include/transformations/common_optimizations/dilated_convolution_converter.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/disable_random_uniform_constant_folding.hpp b/src/common/transformations/include/transformations/common_optimizations/disable_random_uniform_constant_folding.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/disable_shapeof_constant_folding.hpp b/src/common/transformations/include/transformations/common_optimizations/disable_shapeof_constant_folding.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/divide_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/divide_fusion.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/dropout_with_random_uniform_replacer.hpp b/src/common/transformations/include/transformations/common_optimizations/dropout_with_random_uniform_replacer.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/eliminate_unsqueeze_gather.hpp b/src/common/transformations/include/transformations/common_optimizations/eliminate_unsqueeze_gather.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/fq_mul_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/fq_mul_fusion.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/gelu_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/gelu_fusion.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/hsigmoid_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/hsigmoid_fusion.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/interpolate_sequence_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/interpolate_sequence_fusion.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/interpolate_sequence_fusion.hpp
rename to src/common/transformations/include/transformations/common_optimizations/interpolate_sequence_fusion.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/leaky_relu_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/leaky_relu_fusion.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/leaky_relu_fusion.hpp
rename to src/common/transformations/include/transformations/common_optimizations/leaky_relu_fusion.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/lin_op_sequence_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/lin_op_sequence_fusion.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/lin_op_sequence_fusion.hpp
rename to src/common/transformations/include/transformations/common_optimizations/lin_op_sequence_fusion.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/low_latency.hpp b/src/common/transformations/include/transformations/common_optimizations/low_latency.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/low_latency.hpp
rename to src/common/transformations/include/transformations/common_optimizations/low_latency.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/mark_precision_sensitive_subgraphs.hpp b/src/common/transformations/include/transformations/common_optimizations/mark_precision_sensitive_subgraphs.hpp
similarity index 100%
rename from inference-engine/src/transformations/include/transformations/common_optimizations/mark_precision_sensitive_subgraphs.hpp
rename to src/common/transformations/include/transformations/common_optimizations/mark_precision_sensitive_subgraphs.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/mish_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/mish_fusion.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/mish_fusion.hpp
rename to src/common/transformations/include/transformations/common_optimizations/mish_fusion.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/moc_transformations.hpp b/src/common/transformations/include/transformations/common_optimizations/moc_transformations.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/moc_transformations.hpp
rename to src/common/transformations/include/transformations/common_optimizations/moc_transformations.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/mul_conv_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/mul_conv_fusion.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/mul_conv_fusion.hpp
rename to src/common/transformations/include/transformations/common_optimizations/mul_conv_fusion.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/mul_fake_quantize_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/mul_fake_quantize_fusion.hpp
similarity index 100%
rename from inference-engine/src/transformations/include/transformations/common_optimizations/mul_fake_quantize_fusion.hpp
rename to src/common/transformations/include/transformations/common_optimizations/mul_fake_quantize_fusion.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/mvn_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/mvn_fusion.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/mvn_fusion.hpp
rename to src/common/transformations/include/transformations/common_optimizations/mvn_fusion.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/nop_elimination.hpp b/src/common/transformations/include/transformations/common_optimizations/nop_elimination.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/nop_elimination.hpp
rename to src/common/transformations/include/transformations/common_optimizations/nop_elimination.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/normalize_l2_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/normalize_l2_fusion.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/normalize_l2_fusion.hpp
rename to src/common/transformations/include/transformations/common_optimizations/normalize_l2_fusion.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/optimize_strided_slice.hpp b/src/common/transformations/include/transformations/common_optimizations/optimize_strided_slice.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/optimize_strided_slice.hpp
rename to src/common/transformations/include/transformations/common_optimizations/optimize_strided_slice.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/pad_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/pad_fusion.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/pad_fusion.hpp
rename to src/common/transformations/include/transformations/common_optimizations/pad_fusion.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/pull_transpose_through_fq.hpp b/src/common/transformations/include/transformations/common_optimizations/pull_transpose_through_fq.hpp
similarity index 100%
rename from inference-engine/src/transformations/include/transformations/common_optimizations/pull_transpose_through_fq.hpp
rename to src/common/transformations/include/transformations/common_optimizations/pull_transpose_through_fq.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/random_uniform_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/random_uniform_fusion.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/random_uniform_fusion.hpp
rename to src/common/transformations/include/transformations/common_optimizations/random_uniform_fusion.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/relu_fake_quantize_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/relu_fake_quantize_fusion.hpp
similarity index 100%
rename from inference-engine/src/transformations/include/transformations/common_optimizations/relu_fake_quantize_fusion.hpp
rename to src/common/transformations/include/transformations/common_optimizations/relu_fake_quantize_fusion.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/remove_filtering_boxes_by_size.hpp b/src/common/transformations/include/transformations/common_optimizations/remove_filtering_boxes_by_size.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/remove_filtering_boxes_by_size.hpp
rename to src/common/transformations/include/transformations/common_optimizations/remove_filtering_boxes_by_size.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/reshape_sequence_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/reshape_sequence_fusion.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/reshape_sequence_fusion.hpp
rename to src/common/transformations/include/transformations/common_optimizations/reshape_sequence_fusion.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/shuffle_channels_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/shuffle_channels_fusion.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/shuffle_channels_fusion.hpp
rename to src/common/transformations/include/transformations/common_optimizations/shuffle_channels_fusion.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/simplify_shape_of_sub_graph.hpp b/src/common/transformations/include/transformations/common_optimizations/simplify_shape_of_sub_graph.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/simplify_shape_of_sub_graph.hpp
rename to src/common/transformations/include/transformations/common_optimizations/simplify_shape_of_sub_graph.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/softmax_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/softmax_fusion.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/softmax_fusion.hpp
rename to src/common/transformations/include/transformations/common_optimizations/softmax_fusion.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/softplus_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/softplus_fusion.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/softplus_fusion.hpp
rename to src/common/transformations/include/transformations/common_optimizations/softplus_fusion.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/softplus_to_mish_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/softplus_to_mish_fusion.hpp
similarity index 100%
rename from inference-engine/src/transformations/include/transformations/common_optimizations/softplus_to_mish_fusion.hpp
rename to src/common/transformations/include/transformations/common_optimizations/softplus_to_mish_fusion.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/space_to_batch_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/space_to_batch_fusion.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/space_to_batch_fusion.hpp
rename to src/common/transformations/include/transformations/common_optimizations/space_to_batch_fusion.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/split_concat_pair_to_interpolate_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/split_concat_pair_to_interpolate_fusion.hpp
similarity index 100%
rename from inference-engine/src/transformations/include/transformations/common_optimizations/split_concat_pair_to_interpolate_fusion.hpp
rename to src/common/transformations/include/transformations/common_optimizations/split_concat_pair_to_interpolate_fusion.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/split_squeeze_concat_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/split_squeeze_concat_fusion.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/split_squeeze_concat_fusion.hpp
rename to src/common/transformations/include/transformations/common_optimizations/split_squeeze_concat_fusion.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/strides_optimization.hpp b/src/common/transformations/include/transformations/common_optimizations/strides_optimization.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/strides_optimization.hpp
rename to src/common/transformations/include/transformations/common_optimizations/strides_optimization.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/subtract_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/subtract_fusion.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/subtract_fusion.hpp
rename to src/common/transformations/include/transformations/common_optimizations/subtract_fusion.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/swish_fusion.hpp b/src/common/transformations/include/transformations/common_optimizations/swish_fusion.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/swish_fusion.hpp
rename to src/common/transformations/include/transformations/common_optimizations/swish_fusion.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/transpose_sinking.hpp b/src/common/transformations/include/transformations/common_optimizations/transpose_sinking.hpp
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rename from inference-engine/src/transformations/include/transformations/common_optimizations/transpose_sinking.hpp
rename to src/common/transformations/include/transformations/common_optimizations/transpose_sinking.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/transpose_to_reshape.hpp b/src/common/transformations/include/transformations/common_optimizations/transpose_to_reshape.hpp
similarity index 100%
rename from inference-engine/src/transformations/include/transformations/common_optimizations/transpose_to_reshape.hpp
rename to src/common/transformations/include/transformations/common_optimizations/transpose_to_reshape.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/weights_dequantize_to_fake_quantize.hpp b/src/common/transformations/include/transformations/common_optimizations/weights_dequantize_to_fake_quantize.hpp
similarity index 100%
rename from inference-engine/src/transformations/include/transformations/common_optimizations/weights_dequantize_to_fake_quantize.hpp
rename to src/common/transformations/include/transformations/common_optimizations/weights_dequantize_to_fake_quantize.hpp
diff --git a/inference-engine/src/transformations/include/transformations/common_optimizations/wrap_interpolate_into_transposes.hpp b/src/common/transformations/include/transformations/common_optimizations/wrap_interpolate_into_transposes.hpp
similarity index 100%
rename from inference-engine/src/transformations/include/transformations/common_optimizations/wrap_interpolate_into_transposes.hpp
rename to src/common/transformations/include/transformations/common_optimizations/wrap_interpolate_into_transposes.hpp
diff --git a/inference-engine/src/transformations/include/transformations/control_flow/unroll_if.hpp b/src/common/transformations/include/transformations/control_flow/unroll_if.hpp
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rename from inference-engine/src/transformations/include/transformations/control_flow/unroll_if.hpp
rename to src/common/transformations/include/transformations/control_flow/unroll_if.hpp
diff --git a/inference-engine/src/transformations/include/transformations/control_flow/unroll_tensor_iterator.hpp b/src/common/transformations/include/transformations/control_flow/unroll_tensor_iterator.hpp
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rename from inference-engine/src/transformations/include/transformations/control_flow/unroll_tensor_iterator.hpp
rename to src/common/transformations/include/transformations/control_flow/unroll_tensor_iterator.hpp
diff --git a/inference-engine/src/transformations/include/transformations/convert_precision.hpp b/src/common/transformations/include/transformations/convert_precision.hpp
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rename from inference-engine/src/transformations/include/transformations/convert_precision.hpp
rename to src/common/transformations/include/transformations/convert_precision.hpp
diff --git a/inference-engine/src/transformations/include/transformations/disable_decompression_convert_constant_folding.hpp b/src/common/transformations/include/transformations/disable_decompression_convert_constant_folding.hpp
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rename from inference-engine/src/transformations/include/transformations/disable_decompression_convert_constant_folding.hpp
rename to src/common/transformations/include/transformations/disable_decompression_convert_constant_folding.hpp
diff --git a/inference-engine/src/transformations/include/transformations/hash.hpp b/src/common/transformations/include/transformations/hash.hpp
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rename from inference-engine/src/transformations/include/transformations/hash.hpp
rename to src/common/transformations/include/transformations/hash.hpp
diff --git a/inference-engine/src/transformations/include/transformations/init_node_info.hpp b/src/common/transformations/include/transformations/init_node_info.hpp
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rename from inference-engine/src/transformations/include/transformations/init_node_info.hpp
rename to src/common/transformations/include/transformations/init_node_info.hpp
diff --git a/inference-engine/src/transformations/include/transformations/low_precision/disable_convert_constant_folding_on_const_path.hpp b/src/common/transformations/include/transformations/low_precision/disable_convert_constant_folding_on_const_path.hpp
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rename from inference-engine/src/transformations/include/transformations/low_precision/disable_convert_constant_folding_on_const_path.hpp
rename to src/common/transformations/include/transformations/low_precision/disable_convert_constant_folding_on_const_path.hpp
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/batch_norm_decomposition.hpp b/src/common/transformations/include/transformations/op_conversions/batch_norm_decomposition.hpp
similarity index 100%
rename from inference-engine/src/transformations/include/transformations/op_conversions/batch_norm_decomposition.hpp
rename to src/common/transformations/include/transformations/op_conversions/batch_norm_decomposition.hpp
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/bidirectional_sequences_decomposition.hpp b/src/common/transformations/include/transformations/op_conversions/bidirectional_sequences_decomposition.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/bidirectional_sequences_decomposition.hpp
rename to src/common/transformations/include/transformations/op_conversions/bidirectional_sequences_decomposition.hpp
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_batch_to_space.hpp b/src/common/transformations/include/transformations/op_conversions/convert_batch_to_space.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_batch_to_space.hpp
rename to src/common/transformations/include/transformations/op_conversions/convert_batch_to_space.hpp
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_broadcast3.hpp b/src/common/transformations/include/transformations/op_conversions/convert_broadcast3.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_broadcast3.hpp
rename to src/common/transformations/include/transformations/op_conversions/convert_broadcast3.hpp
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_broadcast_to_tiles.hpp b/src/common/transformations/include/transformations/op_conversions/convert_broadcast_to_tiles.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_broadcast_to_tiles.hpp
rename to src/common/transformations/include/transformations/op_conversions/convert_broadcast_to_tiles.hpp
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_convolutions.hpp b/src/common/transformations/include/transformations/op_conversions/convert_convolutions.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_convolutions.hpp
rename to src/common/transformations/include/transformations/op_conversions/convert_convolutions.hpp
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_deformable_conv_v8_to_v1.hpp b/src/common/transformations/include/transformations/op_conversions/convert_deformable_conv_v8_to_v1.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_deformable_conv_v8_to_v1.hpp
rename to src/common/transformations/include/transformations/op_conversions/convert_deformable_conv_v8_to_v1.hpp
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_depth_to_space.hpp b/src/common/transformations/include/transformations/op_conversions/convert_depth_to_space.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_depth_to_space.hpp
rename to src/common/transformations/include/transformations/op_conversions/convert_depth_to_space.hpp
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_divide.hpp b/src/common/transformations/include/transformations/op_conversions/convert_divide.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_divide.hpp
rename to src/common/transformations/include/transformations/op_conversions/convert_divide.hpp
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_gather_0d.hpp b/src/common/transformations/include/transformations/op_conversions/convert_gather_0d.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_gather_0d.hpp
rename to src/common/transformations/include/transformations/op_conversions/convert_gather_0d.hpp
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_gather_downgrade.hpp b/src/common/transformations/include/transformations/op_conversions/convert_gather_downgrade.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_gather_downgrade.hpp
rename to src/common/transformations/include/transformations/op_conversions/convert_gather_downgrade.hpp
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_gather_upgrade.hpp b/src/common/transformations/include/transformations/op_conversions/convert_gather_upgrade.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_gather_upgrade.hpp
rename to src/common/transformations/include/transformations/op_conversions/convert_gather_upgrade.hpp
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_gelu.hpp b/src/common/transformations/include/transformations/op_conversions/convert_gelu.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_gelu.hpp
rename to src/common/transformations/include/transformations/op_conversions/convert_gelu.hpp
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_interpolate1_to_interpolate4.hpp b/src/common/transformations/include/transformations/op_conversions/convert_interpolate1_to_interpolate4.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_interpolate1_to_interpolate4.hpp
rename to src/common/transformations/include/transformations/op_conversions/convert_interpolate1_to_interpolate4.hpp
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_matrix_nms_to_matrix_nms_ie.hpp b/src/common/transformations/include/transformations/op_conversions/convert_matrix_nms_to_matrix_nms_ie.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_matrix_nms_to_matrix_nms_ie.hpp
rename to src/common/transformations/include/transformations/op_conversions/convert_matrix_nms_to_matrix_nms_ie.hpp
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_maxpool_downgrade.hpp b/src/common/transformations/include/transformations/op_conversions/convert_maxpool_downgrade.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_maxpool_downgrade.hpp
rename to src/common/transformations/include/transformations/op_conversions/convert_maxpool_downgrade.hpp
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_minimum_to_power_and_max.hpp b/src/common/transformations/include/transformations/op_conversions/convert_minimum_to_power_and_max.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_minimum_to_power_and_max.hpp
rename to src/common/transformations/include/transformations/op_conversions/convert_minimum_to_power_and_max.hpp
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_mod.hpp b/src/common/transformations/include/transformations/op_conversions/convert_mod.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_mod.hpp
rename to src/common/transformations/include/transformations/op_conversions/convert_mod.hpp
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_multiclass_nms_to_multiclass_nms_ie.hpp b/src/common/transformations/include/transformations/op_conversions/convert_multiclass_nms_to_multiclass_nms_ie.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_multiclass_nms_to_multiclass_nms_ie.hpp
rename to src/common/transformations/include/transformations/op_conversions/convert_multiclass_nms_to_multiclass_nms_ie.hpp
diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_mvn1_to_mvn6.hpp b/src/common/transformations/include/transformations/op_conversions/convert_mvn1_to_mvn6.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_mvn1_to_mvn6.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_negative.hpp b/src/common/transformations/include/transformations/op_conversions/convert_negative.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_nms_to_nms_ie_internal.hpp b/src/common/transformations/include/transformations/op_conversions/convert_nms_to_nms_ie_internal.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_nms_to_nms_ie_internal.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_pad_to_group_conv.hpp b/src/common/transformations/include/transformations/op_conversions/convert_pad_to_group_conv.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_pad_to_group_conv.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_previous_nms_to_nms_5.hpp b/src/common/transformations/include/transformations/op_conversions/convert_previous_nms_to_nms_5.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_previous_nms_to_nms_5.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_reduce_to_pooling.hpp b/src/common/transformations/include/transformations/op_conversions/convert_reduce_to_pooling.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_reduce_to_pooling.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_scatter_elements_to_scatter.hpp b/src/common/transformations/include/transformations/op_conversions/convert_scatter_elements_to_scatter.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_scatter_elements_to_scatter.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_sequences_to_tensor_iterator.hpp b/src/common/transformations/include/transformations/op_conversions/convert_sequences_to_tensor_iterator.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_sequences_to_tensor_iterator.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_shapeof3.hpp b/src/common/transformations/include/transformations/op_conversions/convert_shapeof3.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_shuffle_channels3.hpp b/src/common/transformations/include/transformations/op_conversions/convert_shuffle_channels3.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/convert_shuffle_channels3.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_slice_to_strided_slice.hpp b/src/common/transformations/include/transformations/op_conversions/convert_slice_to_strided_slice.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_space_to_batch.hpp b/src/common/transformations/include/transformations/op_conversions/convert_space_to_batch.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_space_to_depth.hpp b/src/common/transformations/include/transformations/op_conversions/convert_space_to_depth.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_subtract.hpp b/src/common/transformations/include/transformations/op_conversions/convert_subtract.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_ti_to_sequences.hpp b/src/common/transformations/include/transformations/op_conversions/convert_ti_to_sequences.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/convert_topk3.hpp b/src/common/transformations/include/transformations/op_conversions/convert_topk3.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/einsum_decomposition.hpp b/src/common/transformations/include/transformations/op_conversions/einsum_decomposition.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/fq_decomposition.hpp b/src/common/transformations/include/transformations/op_conversions/fq_decomposition.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/gather_normalize_negative_indices.hpp b/src/common/transformations/include/transformations/op_conversions/gather_normalize_negative_indices.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/gather_normalize_negative_indices.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/gelu7_downgrade.hpp b/src/common/transformations/include/transformations/op_conversions/gelu7_downgrade.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/gru_cell_decomposition.hpp b/src/common/transformations/include/transformations/op_conversions/gru_cell_decomposition.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/hsigmoid_decomposition.hpp b/src/common/transformations/include/transformations/op_conversions/hsigmoid_decomposition.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/hswish_decomposition.hpp b/src/common/transformations/include/transformations/op_conversions/hswish_decomposition.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/log_softmax_decomposition.hpp b/src/common/transformations/include/transformations/op_conversions/log_softmax_decomposition.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/lstm_cell_decomposition.hpp b/src/common/transformations/include/transformations/op_conversions/lstm_cell_decomposition.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/mvn6_decomposition.hpp b/src/common/transformations/include/transformations/op_conversions/mvn6_decomposition.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/normalize_l2_decomposition.hpp b/src/common/transformations/include/transformations/op_conversions/normalize_l2_decomposition.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/reduce_l1_decomposition.hpp b/src/common/transformations/include/transformations/op_conversions/reduce_l1_decomposition.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/rnn_cell_decomposition.hpp b/src/common/transformations/include/transformations/op_conversions/rnn_cell_decomposition.hpp
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rename from inference-engine/src/transformations/include/transformations/op_conversions/rnn_cell_decomposition.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/simplify_ctc_greedy_decoder_seq_len.hpp b/src/common/transformations/include/transformations/op_conversions/simplify_ctc_greedy_decoder_seq_len.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/softmax_decomposition.hpp b/src/common/transformations/include/transformations/op_conversions/softmax_decomposition.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/op_conversions/softplus_decomposition.hpp b/src/common/transformations/include/transformations/op_conversions/softplus_decomposition.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/opset_conversions/convert_opset2_to_opset1.hpp b/src/common/transformations/include/transformations/opset_conversions/convert_opset2_to_opset1.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/opset_conversions/convert_opset3_to_opset2.hpp b/src/common/transformations/include/transformations/opset_conversions/convert_opset3_to_opset2.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/rt_info/attributes.hpp b/src/common/transformations/include/transformations/rt_info/attributes.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/rt_info/disable_constant_folding.hpp b/src/common/transformations/include/transformations/rt_info/disable_constant_folding.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/rt_info/disable_fp16_compression.hpp b/src/common/transformations/include/transformations/rt_info/disable_fp16_compression.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/rt_info/fused_names_attribute.hpp b/src/common/transformations/include/transformations/rt_info/fused_names_attribute.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/rt_info/nms_selected_indices.hpp b/src/common/transformations/include/transformations/rt_info/nms_selected_indices.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/rt_info/old_api_map_element_type_attribute.hpp b/src/common/transformations/include/transformations/rt_info/old_api_map_element_type_attribute.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/rt_info/old_api_map_order_attribute.hpp b/src/common/transformations/include/transformations/rt_info/old_api_map_order_attribute.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/rt_info/primitives_priority_attribute.hpp b/src/common/transformations/include/transformations/rt_info/primitives_priority_attribute.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/serialize.hpp b/src/common/transformations/include/transformations/serialize.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/smart_reshape/mimic_set_batch_size.hpp b/src/common/transformations/include/transformations/smart_reshape/mimic_set_batch_size.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/smart_reshape/proposal_scales_stridedslice.hpp b/src/common/transformations/include/transformations/smart_reshape/proposal_scales_stridedslice.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/smart_reshape/reshape_to_1D.hpp b/src/common/transformations/include/transformations/smart_reshape/reshape_to_1D.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/smart_reshape/set_batch_size.hpp b/src/common/transformations/include/transformations/smart_reshape/set_batch_size.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/smart_reshape/strided_slice_squeeze.hpp b/src/common/transformations/include/transformations/smart_reshape/strided_slice_squeeze.hpp
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diff --git a/inference-engine/src/transformations/include/transformations/utils/utils.hpp b/src/common/transformations/include/transformations/utils/utils.hpp
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diff --git a/inference-engine/src/transformations/src/ngraph_ops/deconvolution_ie.cpp b/src/common/transformations/src/ngraph_ops/deconvolution_ie.cpp
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diff --git a/inference-engine/src/transformations/src/ngraph_ops/nms_static_shape_ie.cpp b/src/common/transformations/src/ngraph_ops/nms_static_shape_ie.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/add_fake_quantize_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/add_fake_quantize_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/algebraic_simplification.cpp b/src/common/transformations/src/transformations/common_optimizations/algebraic_simplification.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/batch_to_space_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/batch_to_space_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/binarize_weights.cpp b/src/common/transformations/src/transformations/common_optimizations/binarize_weights.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/broadcast_elementwise_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/broadcast_elementwise_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/clamp_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/clamp_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/common_optimizations.cpp b/src/common/transformations/src/transformations/common_optimizations/common_optimizations.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/compress_float_constants.cpp b/src/common/transformations/src/transformations/common_optimizations/compress_float_constants.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/conv_bias_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/conv_bias_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/conv_mul_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/conv_mul_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/conv_to_binary_conv.cpp b/src/common/transformations/src/transformations/common_optimizations/conv_to_binary_conv.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/convert_compression_only_to_legacy.cpp b/src/common/transformations/src/transformations/common_optimizations/convert_compression_only_to_legacy.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/convert_nms_gather_path_to_unsigned.cpp b/src/common/transformations/src/transformations/common_optimizations/convert_nms_gather_path_to_unsigned.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/convert_quantize_dequantize.cpp b/src/common/transformations/src/transformations/common_optimizations/convert_quantize_dequantize.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/depth_to_space_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/depth_to_space_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/dilated_convolution_converter.cpp b/src/common/transformations/src/transformations/common_optimizations/dilated_convolution_converter.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/disable_random_uniform_constant_folding.cpp b/src/common/transformations/src/transformations/common_optimizations/disable_random_uniform_constant_folding.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/disable_shapeof_constant_folding.cpp b/src/common/transformations/src/transformations/common_optimizations/disable_shapeof_constant_folding.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/divide_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/divide_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/dropout_with_random_uniform_replacer.cpp b/src/common/transformations/src/transformations/common_optimizations/dropout_with_random_uniform_replacer.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/eliminate_unsqueeze_gather.cpp b/src/common/transformations/src/transformations/common_optimizations/eliminate_unsqueeze_gather.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/fq_mul_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/fq_mul_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/fq_reshape_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/fq_reshape_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/gelu_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/gelu_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/hsigmoid_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/hsigmoid_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/hswish_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/hswish_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/interpolate_sequence_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/interpolate_sequence_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/leaky_relu_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/leaky_relu_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/lin_op_sequence_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/lin_op_sequence_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/mark_precision_sensitive_subgraphs.cpp b/src/common/transformations/src/transformations/common_optimizations/mark_precision_sensitive_subgraphs.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/mish_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/mish_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/moc_transformations.cpp b/src/common/transformations/src/transformations/common_optimizations/moc_transformations.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/mul_conv_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/mul_conv_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/mul_fake_quantize_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/mul_fake_quantize_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/mvn_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/mvn_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/nop_elimination.cpp b/src/common/transformations/src/transformations/common_optimizations/nop_elimination.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/normalize_l2_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/normalize_l2_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/optimize_strided_slice.cpp b/src/common/transformations/src/transformations/common_optimizations/optimize_strided_slice.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/pad_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/pad_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/pull_transpose_through_fq.cpp b/src/common/transformations/src/transformations/common_optimizations/pull_transpose_through_fq.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/random_uniform_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/random_uniform_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/relu_fake_quantize_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/relu_fake_quantize_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/remove_filtering_boxes_by_size.cpp b/src/common/transformations/src/transformations/common_optimizations/remove_filtering_boxes_by_size.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/reshape_sequence_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/reshape_sequence_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/shuffle_channels_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/shuffle_channels_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/simplify_shape_of_sub_graph.cpp b/src/common/transformations/src/transformations/common_optimizations/simplify_shape_of_sub_graph.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/softmax_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/softmax_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/softplus_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/softplus_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/softplus_to_mish_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/softplus_to_mish_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/space_to_batch_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/space_to_batch_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/split_concat_pair_to_interpolate_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/split_concat_pair_to_interpolate_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/split_squeeze_concat_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/split_squeeze_concat_fusion.cpp
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rename from inference-engine/src/transformations/src/transformations/common_optimizations/split_squeeze_concat_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/strides_optimization.cpp b/src/common/transformations/src/transformations/common_optimizations/strides_optimization.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/subtract_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/subtract_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/swish_fusion.cpp b/src/common/transformations/src/transformations/common_optimizations/swish_fusion.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/transpose_sinking.cpp b/src/common/transformations/src/transformations/common_optimizations/transpose_sinking.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/transpose_to_reshape.cpp b/src/common/transformations/src/transformations/common_optimizations/transpose_to_reshape.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/weights_dequantize_to_fake_quantize.cpp b/src/common/transformations/src/transformations/common_optimizations/weights_dequantize_to_fake_quantize.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/common_optimizations/wrap_interpolate_into_transposes.cpp b/src/common/transformations/src/transformations/common_optimizations/wrap_interpolate_into_transposes.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/control_flow/unroll_if.cpp b/src/common/transformations/src/transformations/control_flow/unroll_if.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/control_flow/unroll_tensor_iterator.cpp b/src/common/transformations/src/transformations/control_flow/unroll_tensor_iterator.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/disable_decompression_convert_constant_folding.cpp b/src/common/transformations/src/transformations/disable_decompression_convert_constant_folding.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/batch_norm_decomposition.cpp b/src/common/transformations/src/transformations/op_conversions/batch_norm_decomposition.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/bidirectional_sequences_decomposition.cpp b/src/common/transformations/src/transformations/op_conversions/bidirectional_sequences_decomposition.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_batch_to_space.cpp b/src/common/transformations/src/transformations/op_conversions/convert_batch_to_space.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_deformable_conv_v8_to_v1.cpp b/src/common/transformations/src/transformations/op_conversions/convert_deformable_conv_v8_to_v1.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_depth_to_space.cpp b/src/common/transformations/src/transformations/op_conversions/convert_depth_to_space.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_divide.cpp b/src/common/transformations/src/transformations/op_conversions/convert_divide.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_gather_0d.cpp b/src/common/transformations/src/transformations/op_conversions/convert_gather_0d.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_gelu.cpp b/src/common/transformations/src/transformations/op_conversions/convert_gelu.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_interpolate1_to_interpolate4.cpp b/src/common/transformations/src/transformations/op_conversions/convert_interpolate1_to_interpolate4.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_matrix_nms_to_matrix_nms_ie.cpp b/src/common/transformations/src/transformations/op_conversions/convert_matrix_nms_to_matrix_nms_ie.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_maxpool_downgrade.cpp b/src/common/transformations/src/transformations/op_conversions/convert_maxpool_downgrade.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_minimum_to_power_and_max.cpp b/src/common/transformations/src/transformations/op_conversions/convert_minimum_to_power_and_max.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_mod.cpp b/src/common/transformations/src/transformations/op_conversions/convert_mod.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_multiclass_nms_to_multiclass_nms_ie.cpp b/src/common/transformations/src/transformations/op_conversions/convert_multiclass_nms_to_multiclass_nms_ie.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_mvn1_to_mvn6.cpp b/src/common/transformations/src/transformations/op_conversions/convert_mvn1_to_mvn6.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_negative.cpp b/src/common/transformations/src/transformations/op_conversions/convert_negative.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_nms_to_nms_ie_internal.cpp b/src/common/transformations/src/transformations/op_conversions/convert_nms_to_nms_ie_internal.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_pad_to_group_conv.cpp b/src/common/transformations/src/transformations/op_conversions/convert_pad_to_group_conv.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_previous_nms_to_nms_5.cpp b/src/common/transformations/src/transformations/op_conversions/convert_previous_nms_to_nms_5.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_reduce_to_pooling.cpp b/src/common/transformations/src/transformations/op_conversions/convert_reduce_to_pooling.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_scatter_elements_to_scatter.cpp b/src/common/transformations/src/transformations/op_conversions/convert_scatter_elements_to_scatter.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_sequences_to_tensor_iterator.cpp b/src/common/transformations/src/transformations/op_conversions/convert_sequences_to_tensor_iterator.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_shapeof3.cpp b/src/common/transformations/src/transformations/op_conversions/convert_shapeof3.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_shuffle_channels3.cpp b/src/common/transformations/src/transformations/op_conversions/convert_shuffle_channels3.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_slice_to_strided_slice.cpp b/src/common/transformations/src/transformations/op_conversions/convert_slice_to_strided_slice.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_space_to_batch.cpp b/src/common/transformations/src/transformations/op_conversions/convert_space_to_batch.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_space_to_depth.cpp b/src/common/transformations/src/transformations/op_conversions/convert_space_to_depth.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_subtract.cpp b/src/common/transformations/src/transformations/op_conversions/convert_subtract.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_ti_to_sequences.cpp b/src/common/transformations/src/transformations/op_conversions/convert_ti_to_sequences.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/convert_topk3.cpp b/src/common/transformations/src/transformations/op_conversions/convert_topk3.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/einsum_decomposition.cpp b/src/common/transformations/src/transformations/op_conversions/einsum_decomposition.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/fq_decomposition.cpp b/src/common/transformations/src/transformations/op_conversions/fq_decomposition.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/gather_normalize_negative_indices.cpp b/src/common/transformations/src/transformations/op_conversions/gather_normalize_negative_indices.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/gelu7_downgrade.cpp b/src/common/transformations/src/transformations/op_conversions/gelu7_downgrade.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/gru_cell_decomposition.cpp b/src/common/transformations/src/transformations/op_conversions/gru_cell_decomposition.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/hsigmoid_decomposition.cpp b/src/common/transformations/src/transformations/op_conversions/hsigmoid_decomposition.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/log_softmax_decomposition.cpp b/src/common/transformations/src/transformations/op_conversions/log_softmax_decomposition.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/lstm_cell_decomposition.cpp b/src/common/transformations/src/transformations/op_conversions/lstm_cell_decomposition.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/mvn6_decomposition.cpp b/src/common/transformations/src/transformations/op_conversions/mvn6_decomposition.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/normalize_l2_decomposition.cpp b/src/common/transformations/src/transformations/op_conversions/normalize_l2_decomposition.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/reduce_l1_decomposition.cpp b/src/common/transformations/src/transformations/op_conversions/reduce_l1_decomposition.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/rnn_cell_decomposition.cpp b/src/common/transformations/src/transformations/op_conversions/rnn_cell_decomposition.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/simplify_ctc_greedy_decoder_seq_len.cpp b/src/common/transformations/src/transformations/op_conversions/simplify_ctc_greedy_decoder_seq_len.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/op_conversions/softmax_decomposition.cpp b/src/common/transformations/src/transformations/op_conversions/softmax_decomposition.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/opset_conversions/convert_opset2_to_opset1.cpp b/src/common/transformations/src/transformations/opset_conversions/convert_opset2_to_opset1.cpp
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diff --git a/inference-engine/src/transformations/src/transformations/opset_conversions/convert_opset3_to_opset2.cpp b/src/common/transformations/src/transformations/opset_conversions/convert_opset3_to_opset2.cpp
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rename from inference-engine/src/transformations/src/transformations/rt_info/nms_selected_indices.cpp
rename to src/common/transformations/src/transformations/rt_info/nms_selected_indices.cpp
diff --git a/inference-engine/src/transformations/src/transformations/rt_info/old_api_map_element_type_attribute.cpp b/src/common/transformations/src/transformations/rt_info/old_api_map_element_type_attribute.cpp
similarity index 100%
rename from inference-engine/src/transformations/src/transformations/rt_info/old_api_map_element_type_attribute.cpp
rename to src/common/transformations/src/transformations/rt_info/old_api_map_element_type_attribute.cpp
diff --git a/inference-engine/src/transformations/src/transformations/rt_info/old_api_map_order_attribute.cpp b/src/common/transformations/src/transformations/rt_info/old_api_map_order_attribute.cpp
similarity index 100%
rename from inference-engine/src/transformations/src/transformations/rt_info/old_api_map_order_attribute.cpp
rename to src/common/transformations/src/transformations/rt_info/old_api_map_order_attribute.cpp
diff --git a/inference-engine/src/transformations/src/transformations/rt_info/strides_property.cpp b/src/common/transformations/src/transformations/rt_info/strides_property.cpp
similarity index 100%
rename from inference-engine/src/transformations/src/transformations/rt_info/strides_property.cpp
rename to src/common/transformations/src/transformations/rt_info/strides_property.cpp
diff --git a/inference-engine/src/transformations/src/transformations/utils/utils.cpp b/src/common/transformations/src/transformations/utils/utils.cpp
similarity index 100%
rename from inference-engine/src/transformations/src/transformations/utils/utils.cpp
rename to src/common/transformations/src/transformations/utils/utils.cpp
diff --git a/inference-engine/src/plugin_api/.clang-format b/src/common/util/.clang-format
similarity index 100%
rename from inference-engine/src/plugin_api/.clang-format
rename to src/common/util/.clang-format
diff --git a/openvino/util/CMakeLists.txt b/src/common/util/CMakeLists.txt
similarity index 100%
rename from openvino/util/CMakeLists.txt
rename to src/common/util/CMakeLists.txt
diff --git a/openvino/util/include/openvino/util/common_util.hpp b/src/common/util/include/openvino/util/common_util.hpp
similarity index 100%
rename from openvino/util/include/openvino/util/common_util.hpp
rename to src/common/util/include/openvino/util/common_util.hpp
diff --git a/openvino/util/include/openvino/util/env_util.hpp b/src/common/util/include/openvino/util/env_util.hpp
similarity index 100%
rename from openvino/util/include/openvino/util/env_util.hpp
rename to src/common/util/include/openvino/util/env_util.hpp
diff --git a/openvino/util/include/openvino/util/file_util.hpp b/src/common/util/include/openvino/util/file_util.hpp
similarity index 100%
rename from openvino/util/include/openvino/util/file_util.hpp
rename to src/common/util/include/openvino/util/file_util.hpp
diff --git a/openvino/util/include/openvino/util/log.hpp b/src/common/util/include/openvino/util/log.hpp
similarity index 100%
rename from openvino/util/include/openvino/util/log.hpp
rename to src/common/util/include/openvino/util/log.hpp
diff --git a/openvino/util/include/openvino/util/pp.hpp b/src/common/util/include/openvino/util/pp.hpp
similarity index 100%
rename from openvino/util/include/openvino/util/pp.hpp
rename to src/common/util/include/openvino/util/pp.hpp
diff --git a/openvino/util/include/openvino/util/shared_object.hpp b/src/common/util/include/openvino/util/shared_object.hpp
similarity index 100%
rename from openvino/util/include/openvino/util/shared_object.hpp
rename to src/common/util/include/openvino/util/shared_object.hpp
diff --git a/openvino/util/include/openvino/util/util.hpp b/src/common/util/include/openvino/util/util.hpp
similarity index 100%
rename from openvino/util/include/openvino/util/util.hpp
rename to src/common/util/include/openvino/util/util.hpp
diff --git a/openvino/util/src/common_util.cpp b/src/common/util/src/common_util.cpp
similarity index 100%
rename from openvino/util/src/common_util.cpp
rename to src/common/util/src/common_util.cpp
diff --git a/openvino/util/src/env_util.cpp b/src/common/util/src/env_util.cpp
similarity index 100%
rename from openvino/util/src/env_util.cpp
rename to src/common/util/src/env_util.cpp
diff --git a/openvino/util/src/file_util.cpp b/src/common/util/src/file_util.cpp
similarity index 100%
rename from openvino/util/src/file_util.cpp
rename to src/common/util/src/file_util.cpp
diff --git a/openvino/util/src/log.cpp b/src/common/util/src/log.cpp
similarity index 100%
rename from openvino/util/src/log.cpp
rename to src/common/util/src/log.cpp
diff --git a/openvino/util/src/os/lin/lin_shared_object_loader.cpp b/src/common/util/src/os/lin/lin_shared_object_loader.cpp
similarity index 100%
rename from openvino/util/src/os/lin/lin_shared_object_loader.cpp
rename to src/common/util/src/os/lin/lin_shared_object_loader.cpp
diff --git a/openvino/util/src/os/win/win_shared_object_loader.cpp b/src/common/util/src/os/win/win_shared_object_loader.cpp
similarity index 100%
rename from openvino/util/src/os/win/win_shared_object_loader.cpp
rename to src/common/util/src/os/win/win_shared_object_loader.cpp
diff --git a/ngraph/core/CMakeLists.txt b/src/core/CMakeLists.txt
similarity index 94%
rename from ngraph/core/CMakeLists.txt
rename to src/core/CMakeLists.txt
index 3d566271bbe..e28b939a82d 100644
--- a/ngraph/core/CMakeLists.txt
+++ b/src/core/CMakeLists.txt
@@ -4,6 +4,8 @@
 
 add_definitions(-DIN_NGRAPH_LIBRARY)
 
+set(NGRAPH_INCLUDE_PATH ${CMAKE_CURRENT_SOURCE_DIR}/include)
+
 if(CMAKE_COMPILER_IS_GNUCXX)
     ie_add_compiler_flags(-Wmissing-declarations)
 endif()
@@ -134,14 +136,14 @@ install(DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/include/
             PATTERN "*.h")
 
 configure_package_config_file(${OpenVINO_SOURCE_DIR}/cmake/templates/ngraphConfig.cmake.in
-    ${ngraph_BINARY_DIR}/ngraphConfig.cmake
+    ${CMAKE_CURRENT_BINARY_DIR}/ngraphConfig.cmake
     INSTALL_DESTINATION cmake)
 
-write_basic_package_version_file(${ngraph_BINARY_DIR}/ngraphConfigVersion.cmake
+write_basic_package_version_file(${CMAKE_CURRENT_BINARY_DIR}/ngraphConfigVersion.cmake
     VERSION ${IE_VERSION_MAJOR}.${IE_VERSION_MINOR}.${IE_VERSION_PATCH}
     COMPATIBILITY SameMajorVersion)
 
-install(FILES ${ngraph_BINARY_DIR}/ngraphConfig.cmake
-              ${ngraph_BINARY_DIR}/ngraphConfigVersion.cmake
+install(FILES ${CMAKE_CURRENT_BINARY_DIR}/ngraphConfig.cmake
+              ${CMAKE_CURRENT_BINARY_DIR}/ngraphConfigVersion.cmake
         DESTINATION "runtime/cmake"
         COMPONENT ngraph_dev)
diff --git a/ngraph/core/builder/CMakeLists.txt b/src/core/builder/CMakeLists.txt
similarity index 100%
rename from ngraph/core/builder/CMakeLists.txt
rename to src/core/builder/CMakeLists.txt
diff --git a/ngraph/core/builder/include/ngraph/builder/autobroadcast.hpp b/src/core/builder/include/ngraph/builder/autobroadcast.hpp
similarity index 100%
rename from ngraph/core/builder/include/ngraph/builder/autobroadcast.hpp
rename to src/core/builder/include/ngraph/builder/autobroadcast.hpp
diff --git a/ngraph/core/builder/include/ngraph/builder/make_constant.hpp b/src/core/builder/include/ngraph/builder/make_constant.hpp
similarity index 100%
rename from ngraph/core/builder/include/ngraph/builder/make_constant.hpp
rename to src/core/builder/include/ngraph/builder/make_constant.hpp
diff --git a/ngraph/core/builder/include/ngraph/builder/norm.hpp b/src/core/builder/include/ngraph/builder/norm.hpp
similarity index 100%
rename from ngraph/core/builder/include/ngraph/builder/norm.hpp
rename to src/core/builder/include/ngraph/builder/norm.hpp
diff --git a/ngraph/core/builder/include/ngraph/builder/reduce_ops.hpp b/src/core/builder/include/ngraph/builder/reduce_ops.hpp
similarity index 100%
rename from ngraph/core/builder/include/ngraph/builder/reduce_ops.hpp
rename to src/core/builder/include/ngraph/builder/reduce_ops.hpp
diff --git a/ngraph/core/builder/include/ngraph/builder/reshape.hpp b/src/core/builder/include/ngraph/builder/reshape.hpp
similarity index 100%
rename from ngraph/core/builder/include/ngraph/builder/reshape.hpp
rename to src/core/builder/include/ngraph/builder/reshape.hpp
diff --git a/ngraph/core/builder/include/ngraph/builder/split.hpp b/src/core/builder/include/ngraph/builder/split.hpp
similarity index 100%
rename from ngraph/core/builder/include/ngraph/builder/split.hpp
rename to src/core/builder/include/ngraph/builder/split.hpp
diff --git a/ngraph/core/builder/src/builder/autobroadcast.cpp b/src/core/builder/src/builder/autobroadcast.cpp
similarity index 100%
rename from ngraph/core/builder/src/builder/autobroadcast.cpp
rename to src/core/builder/src/builder/autobroadcast.cpp
diff --git a/ngraph/core/builder/src/builder/make_constant.cpp b/src/core/builder/src/builder/make_constant.cpp
similarity index 100%
rename from ngraph/core/builder/src/builder/make_constant.cpp
rename to src/core/builder/src/builder/make_constant.cpp
diff --git a/ngraph/core/builder/src/builder/norm.cpp b/src/core/builder/src/builder/norm.cpp
similarity index 100%
rename from ngraph/core/builder/src/builder/norm.cpp
rename to src/core/builder/src/builder/norm.cpp
diff --git a/ngraph/core/builder/src/builder/reduce_ops.cpp b/src/core/builder/src/builder/reduce_ops.cpp
similarity index 100%
rename from ngraph/core/builder/src/builder/reduce_ops.cpp
rename to src/core/builder/src/builder/reduce_ops.cpp
diff --git a/ngraph/core/builder/src/builder/reshape.cpp b/src/core/builder/src/builder/reshape.cpp
similarity index 100%
rename from ngraph/core/builder/src/builder/reshape.cpp
rename to src/core/builder/src/builder/reshape.cpp
diff --git a/ngraph/core/builder/src/builder/split.cpp b/src/core/builder/src/builder/split.cpp
similarity index 100%
rename from ngraph/core/builder/src/builder/split.cpp
rename to src/core/builder/src/builder/split.cpp
diff --git a/ngraph/core/builder/src/precomp.hpp b/src/core/builder/src/precomp.hpp
similarity index 100%
rename from ngraph/core/builder/src/precomp.hpp
rename to src/core/builder/src/precomp.hpp
diff --git a/ngraph/core/include/ngraph/attribute_adapter.hpp b/src/core/include/ngraph/attribute_adapter.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/attribute_adapter.hpp
rename to src/core/include/ngraph/attribute_adapter.hpp
diff --git a/ngraph/core/include/ngraph/attribute_visitor.hpp b/src/core/include/ngraph/attribute_visitor.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/attribute_visitor.hpp
rename to src/core/include/ngraph/attribute_visitor.hpp
diff --git a/ngraph/core/include/ngraph/axis_set.hpp b/src/core/include/ngraph/axis_set.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/axis_set.hpp
rename to src/core/include/ngraph/axis_set.hpp
diff --git a/ngraph/core/include/ngraph/axis_vector.hpp b/src/core/include/ngraph/axis_vector.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/axis_vector.hpp
rename to src/core/include/ngraph/axis_vector.hpp
diff --git a/ngraph/core/include/ngraph/check.hpp b/src/core/include/ngraph/check.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/check.hpp
rename to src/core/include/ngraph/check.hpp
diff --git a/ngraph/core/include/ngraph/compatibility.hpp b/src/core/include/ngraph/compatibility.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/compatibility.hpp
rename to src/core/include/ngraph/compatibility.hpp
diff --git a/ngraph/core/include/ngraph/coordinate.hpp b/src/core/include/ngraph/coordinate.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/coordinate.hpp
rename to src/core/include/ngraph/coordinate.hpp
diff --git a/ngraph/core/include/ngraph/coordinate_diff.hpp b/src/core/include/ngraph/coordinate_diff.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/coordinate_diff.hpp
rename to src/core/include/ngraph/coordinate_diff.hpp
diff --git a/ngraph/core/include/ngraph/deprecated.hpp b/src/core/include/ngraph/deprecated.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/deprecated.hpp
rename to src/core/include/ngraph/deprecated.hpp
diff --git a/ngraph/core/include/ngraph/descriptor/input.hpp b/src/core/include/ngraph/descriptor/input.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/descriptor/input.hpp
rename to src/core/include/ngraph/descriptor/input.hpp
diff --git a/ngraph/core/include/ngraph/descriptor/output.hpp b/src/core/include/ngraph/descriptor/output.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/descriptor/output.hpp
rename to src/core/include/ngraph/descriptor/output.hpp
diff --git a/ngraph/core/include/ngraph/descriptor/tensor.hpp b/src/core/include/ngraph/descriptor/tensor.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/descriptor/tensor.hpp
rename to src/core/include/ngraph/descriptor/tensor.hpp
diff --git a/ngraph/core/include/ngraph/dimension.hpp b/src/core/include/ngraph/dimension.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/dimension.hpp
rename to src/core/include/ngraph/dimension.hpp
diff --git a/ngraph/core/include/ngraph/distributed.hpp b/src/core/include/ngraph/distributed.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/distributed.hpp
rename to src/core/include/ngraph/distributed.hpp
diff --git a/ngraph/core/include/ngraph/enum_names.hpp b/src/core/include/ngraph/enum_names.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/enum_names.hpp
rename to src/core/include/ngraph/enum_names.hpp
diff --git a/ngraph/core/include/ngraph/env_util.hpp b/src/core/include/ngraph/env_util.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/env_util.hpp
rename to src/core/include/ngraph/env_util.hpp
diff --git a/ngraph/core/include/ngraph/evaluator.hpp b/src/core/include/ngraph/evaluator.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/evaluator.hpp
rename to src/core/include/ngraph/evaluator.hpp
diff --git a/ngraph/core/include/ngraph/except.hpp b/src/core/include/ngraph/except.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/except.hpp
rename to src/core/include/ngraph/except.hpp
diff --git a/ngraph/core/include/ngraph/factory.hpp b/src/core/include/ngraph/factory.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/factory.hpp
rename to src/core/include/ngraph/factory.hpp
diff --git a/ngraph/core/include/ngraph/factory_adapter.hpp b/src/core/include/ngraph/factory_adapter.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/factory_adapter.hpp
rename to src/core/include/ngraph/factory_adapter.hpp
diff --git a/ngraph/core/include/ngraph/file_util.hpp b/src/core/include/ngraph/file_util.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/file_util.hpp
rename to src/core/include/ngraph/file_util.hpp
diff --git a/ngraph/core/include/ngraph/function.hpp b/src/core/include/ngraph/function.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/function.hpp
rename to src/core/include/ngraph/function.hpp
diff --git a/ngraph/core/include/ngraph/graph_util.hpp b/src/core/include/ngraph/graph_util.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/graph_util.hpp
rename to src/core/include/ngraph/graph_util.hpp
diff --git a/ngraph/core/include/ngraph/interval.hpp b/src/core/include/ngraph/interval.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/interval.hpp
rename to src/core/include/ngraph/interval.hpp
diff --git a/ngraph/core/include/ngraph/log.hpp b/src/core/include/ngraph/log.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/log.hpp
rename to src/core/include/ngraph/log.hpp
diff --git a/ngraph/core/include/ngraph/ngraph.hpp b/src/core/include/ngraph/ngraph.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/ngraph.hpp
rename to src/core/include/ngraph/ngraph.hpp
diff --git a/ngraph/core/include/ngraph/ngraph_visibility.hpp b/src/core/include/ngraph/ngraph_visibility.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/ngraph_visibility.hpp
rename to src/core/include/ngraph/ngraph_visibility.hpp
diff --git a/ngraph/core/include/ngraph/node.hpp b/src/core/include/ngraph/node.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/node.hpp
rename to src/core/include/ngraph/node.hpp
diff --git a/ngraph/core/include/ngraph/node_input.hpp b/src/core/include/ngraph/node_input.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/node_input.hpp
rename to src/core/include/ngraph/node_input.hpp
diff --git a/ngraph/core/include/ngraph/node_output.hpp b/src/core/include/ngraph/node_output.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/node_output.hpp
rename to src/core/include/ngraph/node_output.hpp
diff --git a/ngraph/core/include/ngraph/op/abs.hpp b/src/core/include/ngraph/op/abs.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/abs.hpp
rename to src/core/include/ngraph/op/abs.hpp
diff --git a/ngraph/core/include/ngraph/op/acos.hpp b/src/core/include/ngraph/op/acos.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/acos.hpp
rename to src/core/include/ngraph/op/acos.hpp
diff --git a/ngraph/core/include/ngraph/op/acosh.hpp b/src/core/include/ngraph/op/acosh.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/acosh.hpp
rename to src/core/include/ngraph/op/acosh.hpp
diff --git a/ngraph/core/include/ngraph/op/adaptive_avg_pool.hpp b/src/core/include/ngraph/op/adaptive_avg_pool.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/adaptive_avg_pool.hpp
rename to src/core/include/ngraph/op/adaptive_avg_pool.hpp
diff --git a/ngraph/core/include/ngraph/op/adaptive_max_pool.hpp b/src/core/include/ngraph/op/adaptive_max_pool.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/adaptive_max_pool.hpp
rename to src/core/include/ngraph/op/adaptive_max_pool.hpp
diff --git a/ngraph/core/include/ngraph/op/add.hpp b/src/core/include/ngraph/op/add.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/add.hpp
rename to src/core/include/ngraph/op/add.hpp
diff --git a/ngraph/core/include/ngraph/op/and.hpp b/src/core/include/ngraph/op/and.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/and.hpp
rename to src/core/include/ngraph/op/and.hpp
diff --git a/ngraph/core/include/ngraph/op/asin.hpp b/src/core/include/ngraph/op/asin.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/asin.hpp
rename to src/core/include/ngraph/op/asin.hpp
diff --git a/ngraph/core/include/ngraph/op/asinh.hpp b/src/core/include/ngraph/op/asinh.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/asinh.hpp
rename to src/core/include/ngraph/op/asinh.hpp
diff --git a/ngraph/core/include/ngraph/op/assign.hpp b/src/core/include/ngraph/op/assign.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/assign.hpp
rename to src/core/include/ngraph/op/assign.hpp
diff --git a/ngraph/core/include/ngraph/op/atan.hpp b/src/core/include/ngraph/op/atan.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/atan.hpp
rename to src/core/include/ngraph/op/atan.hpp
diff --git a/ngraph/core/include/ngraph/op/atanh.hpp b/src/core/include/ngraph/op/atanh.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/atanh.hpp
rename to src/core/include/ngraph/op/atanh.hpp
diff --git a/ngraph/core/include/ngraph/op/avg_pool.hpp b/src/core/include/ngraph/op/avg_pool.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/avg_pool.hpp
rename to src/core/include/ngraph/op/avg_pool.hpp
diff --git a/ngraph/core/include/ngraph/op/batch_norm.hpp b/src/core/include/ngraph/op/batch_norm.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/batch_norm.hpp
rename to src/core/include/ngraph/op/batch_norm.hpp
diff --git a/ngraph/core/include/ngraph/op/batch_to_space.hpp b/src/core/include/ngraph/op/batch_to_space.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/batch_to_space.hpp
rename to src/core/include/ngraph/op/batch_to_space.hpp
diff --git a/ngraph/core/include/ngraph/op/binary_convolution.hpp b/src/core/include/ngraph/op/binary_convolution.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/binary_convolution.hpp
rename to src/core/include/ngraph/op/binary_convolution.hpp
diff --git a/ngraph/core/include/ngraph/op/broadcast.hpp b/src/core/include/ngraph/op/broadcast.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/broadcast.hpp
rename to src/core/include/ngraph/op/broadcast.hpp
diff --git a/ngraph/core/include/ngraph/op/bucketize.hpp b/src/core/include/ngraph/op/bucketize.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/bucketize.hpp
rename to src/core/include/ngraph/op/bucketize.hpp
diff --git a/ngraph/core/include/ngraph/op/ceiling.hpp b/src/core/include/ngraph/op/ceiling.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/ceiling.hpp
rename to src/core/include/ngraph/op/ceiling.hpp
diff --git a/ngraph/core/include/ngraph/op/clamp.hpp b/src/core/include/ngraph/op/clamp.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/clamp.hpp
rename to src/core/include/ngraph/op/clamp.hpp
diff --git a/ngraph/core/include/ngraph/op/concat.hpp b/src/core/include/ngraph/op/concat.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/concat.hpp
rename to src/core/include/ngraph/op/concat.hpp
diff --git a/ngraph/core/include/ngraph/op/constant.hpp b/src/core/include/ngraph/op/constant.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/constant.hpp
rename to src/core/include/ngraph/op/constant.hpp
diff --git a/ngraph/core/include/ngraph/op/convert.hpp b/src/core/include/ngraph/op/convert.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/convert.hpp
rename to src/core/include/ngraph/op/convert.hpp
diff --git a/ngraph/core/include/ngraph/op/convert_like.hpp b/src/core/include/ngraph/op/convert_like.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/convert_like.hpp
rename to src/core/include/ngraph/op/convert_like.hpp
diff --git a/ngraph/core/include/ngraph/op/convolution.hpp b/src/core/include/ngraph/op/convolution.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/convolution.hpp
rename to src/core/include/ngraph/op/convolution.hpp
diff --git a/ngraph/core/include/ngraph/op/cos.hpp b/src/core/include/ngraph/op/cos.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/cos.hpp
rename to src/core/include/ngraph/op/cos.hpp
diff --git a/ngraph/core/include/ngraph/op/cosh.hpp b/src/core/include/ngraph/op/cosh.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/cosh.hpp
rename to src/core/include/ngraph/op/cosh.hpp
diff --git a/ngraph/core/include/ngraph/op/ctc_greedy_decoder.hpp b/src/core/include/ngraph/op/ctc_greedy_decoder.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/ctc_greedy_decoder.hpp
rename to src/core/include/ngraph/op/ctc_greedy_decoder.hpp
diff --git a/ngraph/core/include/ngraph/op/ctc_greedy_decoder_seq_len.hpp b/src/core/include/ngraph/op/ctc_greedy_decoder_seq_len.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/ctc_greedy_decoder_seq_len.hpp
rename to src/core/include/ngraph/op/ctc_greedy_decoder_seq_len.hpp
diff --git a/ngraph/core/include/ngraph/op/ctc_loss.hpp b/src/core/include/ngraph/op/ctc_loss.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/ctc_loss.hpp
rename to src/core/include/ngraph/op/ctc_loss.hpp
diff --git a/ngraph/core/include/ngraph/op/cum_sum.hpp b/src/core/include/ngraph/op/cum_sum.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/cum_sum.hpp
rename to src/core/include/ngraph/op/cum_sum.hpp
diff --git a/ngraph/core/include/ngraph/op/deformable_convolution.hpp b/src/core/include/ngraph/op/deformable_convolution.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/deformable_convolution.hpp
rename to src/core/include/ngraph/op/deformable_convolution.hpp
diff --git a/ngraph/core/include/ngraph/op/deformable_psroi_pooling.hpp b/src/core/include/ngraph/op/deformable_psroi_pooling.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/deformable_psroi_pooling.hpp
rename to src/core/include/ngraph/op/deformable_psroi_pooling.hpp
diff --git a/ngraph/core/include/ngraph/op/depth_to_space.hpp b/src/core/include/ngraph/op/depth_to_space.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/depth_to_space.hpp
rename to src/core/include/ngraph/op/depth_to_space.hpp
diff --git a/ngraph/core/include/ngraph/op/detection_output.hpp b/src/core/include/ngraph/op/detection_output.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/detection_output.hpp
rename to src/core/include/ngraph/op/detection_output.hpp
diff --git a/ngraph/core/include/ngraph/op/dft.hpp b/src/core/include/ngraph/op/dft.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/dft.hpp
rename to src/core/include/ngraph/op/dft.hpp
diff --git a/ngraph/core/include/ngraph/op/divide.hpp b/src/core/include/ngraph/op/divide.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/divide.hpp
rename to src/core/include/ngraph/op/divide.hpp
diff --git a/ngraph/core/include/ngraph/op/einsum.hpp b/src/core/include/ngraph/op/einsum.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/einsum.hpp
rename to src/core/include/ngraph/op/einsum.hpp
diff --git a/ngraph/core/include/ngraph/op/elu.hpp b/src/core/include/ngraph/op/elu.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/elu.hpp
rename to src/core/include/ngraph/op/elu.hpp
diff --git a/ngraph/core/include/ngraph/op/embedding_segments_sum.hpp b/src/core/include/ngraph/op/embedding_segments_sum.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/embedding_segments_sum.hpp
rename to src/core/include/ngraph/op/embedding_segments_sum.hpp
diff --git a/ngraph/core/include/ngraph/op/embeddingbag_offsets_sum.hpp b/src/core/include/ngraph/op/embeddingbag_offsets_sum.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/embeddingbag_offsets_sum.hpp
rename to src/core/include/ngraph/op/embeddingbag_offsets_sum.hpp
diff --git a/ngraph/core/include/ngraph/op/embeddingbag_packedsum.hpp b/src/core/include/ngraph/op/embeddingbag_packedsum.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/embeddingbag_packedsum.hpp
rename to src/core/include/ngraph/op/embeddingbag_packedsum.hpp
diff --git a/ngraph/core/include/ngraph/op/equal.hpp b/src/core/include/ngraph/op/equal.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/equal.hpp
rename to src/core/include/ngraph/op/equal.hpp
diff --git a/ngraph/core/include/ngraph/op/erf.hpp b/src/core/include/ngraph/op/erf.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/erf.hpp
rename to src/core/include/ngraph/op/erf.hpp
diff --git a/ngraph/core/include/ngraph/op/exp.hpp b/src/core/include/ngraph/op/exp.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/exp.hpp
rename to src/core/include/ngraph/op/exp.hpp
diff --git a/ngraph/core/include/ngraph/op/experimental_detectron_detection_output.hpp b/src/core/include/ngraph/op/experimental_detectron_detection_output.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/experimental_detectron_detection_output.hpp
rename to src/core/include/ngraph/op/experimental_detectron_detection_output.hpp
diff --git a/ngraph/core/include/ngraph/op/experimental_detectron_generate_proposals.hpp b/src/core/include/ngraph/op/experimental_detectron_generate_proposals.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/experimental_detectron_generate_proposals.hpp
rename to src/core/include/ngraph/op/experimental_detectron_generate_proposals.hpp
diff --git a/ngraph/core/include/ngraph/op/experimental_detectron_prior_grid_generator.hpp b/src/core/include/ngraph/op/experimental_detectron_prior_grid_generator.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/experimental_detectron_prior_grid_generator.hpp
rename to src/core/include/ngraph/op/experimental_detectron_prior_grid_generator.hpp
diff --git a/ngraph/core/include/ngraph/op/experimental_detectron_roi_feature.hpp b/src/core/include/ngraph/op/experimental_detectron_roi_feature.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/experimental_detectron_roi_feature.hpp
rename to src/core/include/ngraph/op/experimental_detectron_roi_feature.hpp
diff --git a/ngraph/core/include/ngraph/op/experimental_detectron_topkrois.hpp b/src/core/include/ngraph/op/experimental_detectron_topkrois.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/experimental_detectron_topkrois.hpp
rename to src/core/include/ngraph/op/experimental_detectron_topkrois.hpp
diff --git a/ngraph/core/include/ngraph/op/extractimagepatches.hpp b/src/core/include/ngraph/op/extractimagepatches.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/extractimagepatches.hpp
rename to src/core/include/ngraph/op/extractimagepatches.hpp
diff --git a/ngraph/core/include/ngraph/op/fake_quantize.hpp b/src/core/include/ngraph/op/fake_quantize.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/fake_quantize.hpp
rename to src/core/include/ngraph/op/fake_quantize.hpp
diff --git a/ngraph/core/include/ngraph/op/floor.hpp b/src/core/include/ngraph/op/floor.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/floor.hpp
rename to src/core/include/ngraph/op/floor.hpp
diff --git a/ngraph/core/include/ngraph/op/floor_mod.hpp b/src/core/include/ngraph/op/floor_mod.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/floor_mod.hpp
rename to src/core/include/ngraph/op/floor_mod.hpp
diff --git a/ngraph/core/include/ngraph/op/gather.hpp b/src/core/include/ngraph/op/gather.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/gather.hpp
rename to src/core/include/ngraph/op/gather.hpp
diff --git a/ngraph/core/include/ngraph/op/gather_elements.hpp b/src/core/include/ngraph/op/gather_elements.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/gather_elements.hpp
rename to src/core/include/ngraph/op/gather_elements.hpp
diff --git a/ngraph/core/include/ngraph/op/gather_nd.hpp b/src/core/include/ngraph/op/gather_nd.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/gather_nd.hpp
rename to src/core/include/ngraph/op/gather_nd.hpp
diff --git a/ngraph/core/include/ngraph/op/gather_tree.hpp b/src/core/include/ngraph/op/gather_tree.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/gather_tree.hpp
rename to src/core/include/ngraph/op/gather_tree.hpp
diff --git a/ngraph/core/include/ngraph/op/gelu.hpp b/src/core/include/ngraph/op/gelu.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/gelu.hpp
rename to src/core/include/ngraph/op/gelu.hpp
diff --git a/ngraph/core/include/ngraph/op/greater.hpp b/src/core/include/ngraph/op/greater.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/greater.hpp
rename to src/core/include/ngraph/op/greater.hpp
diff --git a/ngraph/core/include/ngraph/op/greater_eq.hpp b/src/core/include/ngraph/op/greater_eq.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/greater_eq.hpp
rename to src/core/include/ngraph/op/greater_eq.hpp
diff --git a/ngraph/core/include/ngraph/op/grn.hpp b/src/core/include/ngraph/op/grn.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/grn.hpp
rename to src/core/include/ngraph/op/grn.hpp
diff --git a/ngraph/core/include/ngraph/op/group_conv.hpp b/src/core/include/ngraph/op/group_conv.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/group_conv.hpp
rename to src/core/include/ngraph/op/group_conv.hpp
diff --git a/ngraph/core/include/ngraph/op/gru_cell.hpp b/src/core/include/ngraph/op/gru_cell.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/gru_cell.hpp
rename to src/core/include/ngraph/op/gru_cell.hpp
diff --git a/ngraph/core/include/ngraph/op/gru_sequence.hpp b/src/core/include/ngraph/op/gru_sequence.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/gru_sequence.hpp
rename to src/core/include/ngraph/op/gru_sequence.hpp
diff --git a/ngraph/core/include/ngraph/op/hard_sigmoid.hpp b/src/core/include/ngraph/op/hard_sigmoid.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/hard_sigmoid.hpp
rename to src/core/include/ngraph/op/hard_sigmoid.hpp
diff --git a/ngraph/core/include/ngraph/op/hsigmoid.hpp b/src/core/include/ngraph/op/hsigmoid.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/hsigmoid.hpp
rename to src/core/include/ngraph/op/hsigmoid.hpp
diff --git a/ngraph/core/include/ngraph/op/hswish.hpp b/src/core/include/ngraph/op/hswish.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/hswish.hpp
rename to src/core/include/ngraph/op/hswish.hpp
diff --git a/ngraph/core/include/ngraph/op/i420_to_bgr.hpp b/src/core/include/ngraph/op/i420_to_bgr.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/i420_to_bgr.hpp
rename to src/core/include/ngraph/op/i420_to_bgr.hpp
diff --git a/ngraph/core/include/ngraph/op/i420_to_rgb.hpp b/src/core/include/ngraph/op/i420_to_rgb.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/i420_to_rgb.hpp
rename to src/core/include/ngraph/op/i420_to_rgb.hpp
diff --git a/ngraph/core/include/ngraph/op/idft.hpp b/src/core/include/ngraph/op/idft.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/idft.hpp
rename to src/core/include/ngraph/op/idft.hpp
diff --git a/ngraph/core/include/ngraph/op/if.hpp b/src/core/include/ngraph/op/if.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/if.hpp
rename to src/core/include/ngraph/op/if.hpp
diff --git a/ngraph/core/include/ngraph/op/interpolate.hpp b/src/core/include/ngraph/op/interpolate.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/interpolate.hpp
rename to src/core/include/ngraph/op/interpolate.hpp
diff --git a/ngraph/core/include/ngraph/op/less.hpp b/src/core/include/ngraph/op/less.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/less.hpp
rename to src/core/include/ngraph/op/less.hpp
diff --git a/ngraph/core/include/ngraph/op/less_eq.hpp b/src/core/include/ngraph/op/less_eq.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/less_eq.hpp
rename to src/core/include/ngraph/op/less_eq.hpp
diff --git a/ngraph/core/include/ngraph/op/log.hpp b/src/core/include/ngraph/op/log.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/log.hpp
rename to src/core/include/ngraph/op/log.hpp
diff --git a/ngraph/core/include/ngraph/op/log_softmax.hpp b/src/core/include/ngraph/op/log_softmax.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/log_softmax.hpp
rename to src/core/include/ngraph/op/log_softmax.hpp
diff --git a/ngraph/core/include/ngraph/op/loop.hpp b/src/core/include/ngraph/op/loop.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/loop.hpp
rename to src/core/include/ngraph/op/loop.hpp
diff --git a/ngraph/core/include/ngraph/op/lrn.hpp b/src/core/include/ngraph/op/lrn.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/lrn.hpp
rename to src/core/include/ngraph/op/lrn.hpp
diff --git a/ngraph/core/include/ngraph/op/lstm_cell.hpp b/src/core/include/ngraph/op/lstm_cell.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/lstm_cell.hpp
rename to src/core/include/ngraph/op/lstm_cell.hpp
diff --git a/ngraph/core/include/ngraph/op/lstm_sequence.hpp b/src/core/include/ngraph/op/lstm_sequence.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/lstm_sequence.hpp
rename to src/core/include/ngraph/op/lstm_sequence.hpp
diff --git a/ngraph/core/include/ngraph/op/matmul.hpp b/src/core/include/ngraph/op/matmul.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/matmul.hpp
rename to src/core/include/ngraph/op/matmul.hpp
diff --git a/ngraph/core/include/ngraph/op/matrix_nms.hpp b/src/core/include/ngraph/op/matrix_nms.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/matrix_nms.hpp
rename to src/core/include/ngraph/op/matrix_nms.hpp
diff --git a/ngraph/core/include/ngraph/op/max.hpp b/src/core/include/ngraph/op/max.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/max.hpp
rename to src/core/include/ngraph/op/max.hpp
diff --git a/ngraph/core/include/ngraph/op/max_pool.hpp b/src/core/include/ngraph/op/max_pool.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/max_pool.hpp
rename to src/core/include/ngraph/op/max_pool.hpp
diff --git a/ngraph/core/include/ngraph/op/maximum.hpp b/src/core/include/ngraph/op/maximum.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/maximum.hpp
rename to src/core/include/ngraph/op/maximum.hpp
diff --git a/ngraph/core/include/ngraph/op/min.hpp b/src/core/include/ngraph/op/min.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/min.hpp
rename to src/core/include/ngraph/op/min.hpp
diff --git a/ngraph/core/include/ngraph/op/minimum.hpp b/src/core/include/ngraph/op/minimum.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/minimum.hpp
rename to src/core/include/ngraph/op/minimum.hpp
diff --git a/ngraph/core/include/ngraph/op/mish.hpp b/src/core/include/ngraph/op/mish.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/mish.hpp
rename to src/core/include/ngraph/op/mish.hpp
diff --git a/ngraph/core/include/ngraph/op/mod.hpp b/src/core/include/ngraph/op/mod.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/mod.hpp
rename to src/core/include/ngraph/op/mod.hpp
diff --git a/ngraph/core/include/ngraph/op/multiclass_nms.hpp b/src/core/include/ngraph/op/multiclass_nms.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/multiclass_nms.hpp
rename to src/core/include/ngraph/op/multiclass_nms.hpp
diff --git a/ngraph/core/include/ngraph/op/multiply.hpp b/src/core/include/ngraph/op/multiply.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/multiply.hpp
rename to src/core/include/ngraph/op/multiply.hpp
diff --git a/ngraph/core/include/ngraph/op/mvn.hpp b/src/core/include/ngraph/op/mvn.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/mvn.hpp
rename to src/core/include/ngraph/op/mvn.hpp
diff --git a/ngraph/core/include/ngraph/op/negative.hpp b/src/core/include/ngraph/op/negative.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/negative.hpp
rename to src/core/include/ngraph/op/negative.hpp
diff --git a/ngraph/core/include/ngraph/op/non_max_suppression.hpp b/src/core/include/ngraph/op/non_max_suppression.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/non_max_suppression.hpp
rename to src/core/include/ngraph/op/non_max_suppression.hpp
diff --git a/ngraph/core/include/ngraph/op/non_zero.hpp b/src/core/include/ngraph/op/non_zero.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/non_zero.hpp
rename to src/core/include/ngraph/op/non_zero.hpp
diff --git a/ngraph/core/include/ngraph/op/normalize_l2.hpp b/src/core/include/ngraph/op/normalize_l2.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/normalize_l2.hpp
rename to src/core/include/ngraph/op/normalize_l2.hpp
diff --git a/ngraph/core/include/ngraph/op/not.hpp b/src/core/include/ngraph/op/not.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/not.hpp
rename to src/core/include/ngraph/op/not.hpp
diff --git a/ngraph/core/include/ngraph/op/not_equal.hpp b/src/core/include/ngraph/op/not_equal.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/not_equal.hpp
rename to src/core/include/ngraph/op/not_equal.hpp
diff --git a/ngraph/core/include/ngraph/op/nv12_to_bgr.hpp b/src/core/include/ngraph/op/nv12_to_bgr.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/nv12_to_bgr.hpp
rename to src/core/include/ngraph/op/nv12_to_bgr.hpp
diff --git a/ngraph/core/include/ngraph/op/nv12_to_rgb.hpp b/src/core/include/ngraph/op/nv12_to_rgb.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/nv12_to_rgb.hpp
rename to src/core/include/ngraph/op/nv12_to_rgb.hpp
diff --git a/ngraph/core/include/ngraph/op/one_hot.hpp b/src/core/include/ngraph/op/one_hot.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/one_hot.hpp
rename to src/core/include/ngraph/op/one_hot.hpp
diff --git a/ngraph/core/include/ngraph/op/op.hpp b/src/core/include/ngraph/op/op.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/op.hpp
rename to src/core/include/ngraph/op/op.hpp
diff --git a/ngraph/core/include/ngraph/op/or.hpp b/src/core/include/ngraph/op/or.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/or.hpp
rename to src/core/include/ngraph/op/or.hpp
diff --git a/ngraph/core/include/ngraph/op/pad.hpp b/src/core/include/ngraph/op/pad.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/pad.hpp
rename to src/core/include/ngraph/op/pad.hpp
diff --git a/ngraph/core/include/ngraph/op/parameter.hpp b/src/core/include/ngraph/op/parameter.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/parameter.hpp
rename to src/core/include/ngraph/op/parameter.hpp
diff --git a/ngraph/core/include/ngraph/op/power.hpp b/src/core/include/ngraph/op/power.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/power.hpp
rename to src/core/include/ngraph/op/power.hpp
diff --git a/ngraph/core/include/ngraph/op/prelu.hpp b/src/core/include/ngraph/op/prelu.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/prelu.hpp
rename to src/core/include/ngraph/op/prelu.hpp
diff --git a/ngraph/core/include/ngraph/op/prior_box.hpp b/src/core/include/ngraph/op/prior_box.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/prior_box.hpp
rename to src/core/include/ngraph/op/prior_box.hpp
diff --git a/ngraph/core/include/ngraph/op/prior_box_clustered.hpp b/src/core/include/ngraph/op/prior_box_clustered.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/prior_box_clustered.hpp
rename to src/core/include/ngraph/op/prior_box_clustered.hpp
diff --git a/ngraph/core/include/ngraph/op/proposal.hpp b/src/core/include/ngraph/op/proposal.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/proposal.hpp
rename to src/core/include/ngraph/op/proposal.hpp
diff --git a/ngraph/core/include/ngraph/op/psroi_pooling.hpp b/src/core/include/ngraph/op/psroi_pooling.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/psroi_pooling.hpp
rename to src/core/include/ngraph/op/psroi_pooling.hpp
diff --git a/ngraph/core/include/ngraph/op/random_uniform.hpp b/src/core/include/ngraph/op/random_uniform.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/random_uniform.hpp
rename to src/core/include/ngraph/op/random_uniform.hpp
diff --git a/ngraph/core/include/ngraph/op/range.hpp b/src/core/include/ngraph/op/range.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/range.hpp
rename to src/core/include/ngraph/op/range.hpp
diff --git a/ngraph/core/include/ngraph/op/read_value.hpp b/src/core/include/ngraph/op/read_value.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/read_value.hpp
rename to src/core/include/ngraph/op/read_value.hpp
diff --git a/ngraph/core/include/ngraph/op/reduce_l1.hpp b/src/core/include/ngraph/op/reduce_l1.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/reduce_l1.hpp
rename to src/core/include/ngraph/op/reduce_l1.hpp
diff --git a/ngraph/core/include/ngraph/op/reduce_l2.hpp b/src/core/include/ngraph/op/reduce_l2.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/reduce_l2.hpp
rename to src/core/include/ngraph/op/reduce_l2.hpp
diff --git a/ngraph/core/include/ngraph/op/reduce_logical_and.hpp b/src/core/include/ngraph/op/reduce_logical_and.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/reduce_logical_and.hpp
rename to src/core/include/ngraph/op/reduce_logical_and.hpp
diff --git a/ngraph/core/include/ngraph/op/reduce_logical_or.hpp b/src/core/include/ngraph/op/reduce_logical_or.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/reduce_logical_or.hpp
rename to src/core/include/ngraph/op/reduce_logical_or.hpp
diff --git a/ngraph/core/include/ngraph/op/reduce_mean.hpp b/src/core/include/ngraph/op/reduce_mean.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/reduce_mean.hpp
rename to src/core/include/ngraph/op/reduce_mean.hpp
diff --git a/ngraph/core/include/ngraph/op/reduce_prod.hpp b/src/core/include/ngraph/op/reduce_prod.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/reduce_prod.hpp
rename to src/core/include/ngraph/op/reduce_prod.hpp
diff --git a/ngraph/core/include/ngraph/op/reduce_sum.hpp b/src/core/include/ngraph/op/reduce_sum.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/reduce_sum.hpp
rename to src/core/include/ngraph/op/reduce_sum.hpp
diff --git a/ngraph/core/include/ngraph/op/region_yolo.hpp b/src/core/include/ngraph/op/region_yolo.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/region_yolo.hpp
rename to src/core/include/ngraph/op/region_yolo.hpp
diff --git a/ngraph/core/include/ngraph/op/relu.hpp b/src/core/include/ngraph/op/relu.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/relu.hpp
rename to src/core/include/ngraph/op/relu.hpp
diff --git a/ngraph/core/include/ngraph/op/reorg_yolo.hpp b/src/core/include/ngraph/op/reorg_yolo.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/reorg_yolo.hpp
rename to src/core/include/ngraph/op/reorg_yolo.hpp
diff --git a/ngraph/core/include/ngraph/op/reshape.hpp b/src/core/include/ngraph/op/reshape.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/reshape.hpp
rename to src/core/include/ngraph/op/reshape.hpp
diff --git a/ngraph/core/include/ngraph/op/result.hpp b/src/core/include/ngraph/op/result.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/result.hpp
rename to src/core/include/ngraph/op/result.hpp
diff --git a/ngraph/core/include/ngraph/op/reverse.hpp b/src/core/include/ngraph/op/reverse.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/reverse.hpp
rename to src/core/include/ngraph/op/reverse.hpp
diff --git a/ngraph/core/include/ngraph/op/reverse_sequence.hpp b/src/core/include/ngraph/op/reverse_sequence.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/reverse_sequence.hpp
rename to src/core/include/ngraph/op/reverse_sequence.hpp
diff --git a/ngraph/core/include/ngraph/op/rnn_cell.hpp b/src/core/include/ngraph/op/rnn_cell.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/rnn_cell.hpp
rename to src/core/include/ngraph/op/rnn_cell.hpp
diff --git a/ngraph/core/include/ngraph/op/rnn_sequence.hpp b/src/core/include/ngraph/op/rnn_sequence.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/rnn_sequence.hpp
rename to src/core/include/ngraph/op/rnn_sequence.hpp
diff --git a/ngraph/core/include/ngraph/op/roi_align.hpp b/src/core/include/ngraph/op/roi_align.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/roi_align.hpp
rename to src/core/include/ngraph/op/roi_align.hpp
diff --git a/ngraph/core/include/ngraph/op/roi_pooling.hpp b/src/core/include/ngraph/op/roi_pooling.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/roi_pooling.hpp
rename to src/core/include/ngraph/op/roi_pooling.hpp
diff --git a/ngraph/core/include/ngraph/op/roll.hpp b/src/core/include/ngraph/op/roll.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/roll.hpp
rename to src/core/include/ngraph/op/roll.hpp
diff --git a/ngraph/core/include/ngraph/op/round.hpp b/src/core/include/ngraph/op/round.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/round.hpp
rename to src/core/include/ngraph/op/round.hpp
diff --git a/ngraph/core/include/ngraph/op/scatter_elements_update.hpp b/src/core/include/ngraph/op/scatter_elements_update.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/scatter_elements_update.hpp
rename to src/core/include/ngraph/op/scatter_elements_update.hpp
diff --git a/ngraph/core/include/ngraph/op/scatter_nd_update.hpp b/src/core/include/ngraph/op/scatter_nd_update.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/scatter_nd_update.hpp
rename to src/core/include/ngraph/op/scatter_nd_update.hpp
diff --git a/ngraph/core/include/ngraph/op/scatter_update.hpp b/src/core/include/ngraph/op/scatter_update.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/scatter_update.hpp
rename to src/core/include/ngraph/op/scatter_update.hpp
diff --git a/ngraph/core/include/ngraph/op/select.hpp b/src/core/include/ngraph/op/select.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/select.hpp
rename to src/core/include/ngraph/op/select.hpp
diff --git a/ngraph/core/include/ngraph/op/selu.hpp b/src/core/include/ngraph/op/selu.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/selu.hpp
rename to src/core/include/ngraph/op/selu.hpp
diff --git a/ngraph/core/include/ngraph/op/shape_of.hpp b/src/core/include/ngraph/op/shape_of.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/shape_of.hpp
rename to src/core/include/ngraph/op/shape_of.hpp
diff --git a/ngraph/core/include/ngraph/op/shuffle_channels.hpp b/src/core/include/ngraph/op/shuffle_channels.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/shuffle_channels.hpp
rename to src/core/include/ngraph/op/shuffle_channels.hpp
diff --git a/ngraph/core/include/ngraph/op/sigmoid.hpp b/src/core/include/ngraph/op/sigmoid.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/sigmoid.hpp
rename to src/core/include/ngraph/op/sigmoid.hpp
diff --git a/ngraph/core/include/ngraph/op/sign.hpp b/src/core/include/ngraph/op/sign.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/sign.hpp
rename to src/core/include/ngraph/op/sign.hpp
diff --git a/ngraph/core/include/ngraph/op/sin.hpp b/src/core/include/ngraph/op/sin.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/sin.hpp
rename to src/core/include/ngraph/op/sin.hpp
diff --git a/ngraph/core/include/ngraph/op/sinh.hpp b/src/core/include/ngraph/op/sinh.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/sinh.hpp
rename to src/core/include/ngraph/op/sinh.hpp
diff --git a/ngraph/core/include/ngraph/op/sink.hpp b/src/core/include/ngraph/op/sink.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/sink.hpp
rename to src/core/include/ngraph/op/sink.hpp
diff --git a/ngraph/core/include/ngraph/op/slice.hpp b/src/core/include/ngraph/op/slice.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/slice.hpp
rename to src/core/include/ngraph/op/slice.hpp
diff --git a/ngraph/core/include/ngraph/op/softmax.hpp b/src/core/include/ngraph/op/softmax.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/softmax.hpp
rename to src/core/include/ngraph/op/softmax.hpp
diff --git a/ngraph/core/include/ngraph/op/softplus.hpp b/src/core/include/ngraph/op/softplus.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/softplus.hpp
rename to src/core/include/ngraph/op/softplus.hpp
diff --git a/ngraph/core/include/ngraph/op/space_to_batch.hpp b/src/core/include/ngraph/op/space_to_batch.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/space_to_batch.hpp
rename to src/core/include/ngraph/op/space_to_batch.hpp
diff --git a/ngraph/core/include/ngraph/op/space_to_depth.hpp b/src/core/include/ngraph/op/space_to_depth.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/space_to_depth.hpp
rename to src/core/include/ngraph/op/space_to_depth.hpp
diff --git a/ngraph/core/include/ngraph/op/split.hpp b/src/core/include/ngraph/op/split.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/split.hpp
rename to src/core/include/ngraph/op/split.hpp
diff --git a/ngraph/core/include/ngraph/op/sqrt.hpp b/src/core/include/ngraph/op/sqrt.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/sqrt.hpp
rename to src/core/include/ngraph/op/sqrt.hpp
diff --git a/ngraph/core/include/ngraph/op/squared_difference.hpp b/src/core/include/ngraph/op/squared_difference.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/squared_difference.hpp
rename to src/core/include/ngraph/op/squared_difference.hpp
diff --git a/ngraph/core/include/ngraph/op/squeeze.hpp b/src/core/include/ngraph/op/squeeze.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/squeeze.hpp
rename to src/core/include/ngraph/op/squeeze.hpp
diff --git a/ngraph/core/include/ngraph/op/strided_slice.hpp b/src/core/include/ngraph/op/strided_slice.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/strided_slice.hpp
rename to src/core/include/ngraph/op/strided_slice.hpp
diff --git a/ngraph/core/include/ngraph/op/subtract.hpp b/src/core/include/ngraph/op/subtract.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/subtract.hpp
rename to src/core/include/ngraph/op/subtract.hpp
diff --git a/ngraph/core/include/ngraph/op/swish.hpp b/src/core/include/ngraph/op/swish.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/swish.hpp
rename to src/core/include/ngraph/op/swish.hpp
diff --git a/ngraph/core/include/ngraph/op/tan.hpp b/src/core/include/ngraph/op/tan.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/tan.hpp
rename to src/core/include/ngraph/op/tan.hpp
diff --git a/ngraph/core/include/ngraph/op/tanh.hpp b/src/core/include/ngraph/op/tanh.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/tanh.hpp
rename to src/core/include/ngraph/op/tanh.hpp
diff --git a/ngraph/core/include/ngraph/op/tensor_iterator.hpp b/src/core/include/ngraph/op/tensor_iterator.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/tensor_iterator.hpp
rename to src/core/include/ngraph/op/tensor_iterator.hpp
diff --git a/ngraph/core/include/ngraph/op/tile.hpp b/src/core/include/ngraph/op/tile.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/tile.hpp
rename to src/core/include/ngraph/op/tile.hpp
diff --git a/ngraph/core/include/ngraph/op/topk.hpp b/src/core/include/ngraph/op/topk.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/topk.hpp
rename to src/core/include/ngraph/op/topk.hpp
diff --git a/ngraph/core/include/ngraph/op/transpose.hpp b/src/core/include/ngraph/op/transpose.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/transpose.hpp
rename to src/core/include/ngraph/op/transpose.hpp
diff --git a/ngraph/core/include/ngraph/op/unsqueeze.hpp b/src/core/include/ngraph/op/unsqueeze.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/unsqueeze.hpp
rename to src/core/include/ngraph/op/unsqueeze.hpp
diff --git a/ngraph/core/include/ngraph/op/util/activation_functions.hpp b/src/core/include/ngraph/op/util/activation_functions.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/activation_functions.hpp
rename to src/core/include/ngraph/op/util/activation_functions.hpp
diff --git a/ngraph/core/include/ngraph/op/util/arithmetic_reduction.hpp b/src/core/include/ngraph/op/util/arithmetic_reduction.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/arithmetic_reduction.hpp
rename to src/core/include/ngraph/op/util/arithmetic_reduction.hpp
diff --git a/ngraph/core/include/ngraph/op/util/arithmetic_reductions_keep_dims.hpp b/src/core/include/ngraph/op/util/arithmetic_reductions_keep_dims.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/arithmetic_reductions_keep_dims.hpp
rename to src/core/include/ngraph/op/util/arithmetic_reductions_keep_dims.hpp
diff --git a/ngraph/core/include/ngraph/op/util/attr_types.hpp b/src/core/include/ngraph/op/util/attr_types.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/attr_types.hpp
rename to src/core/include/ngraph/op/util/attr_types.hpp
diff --git a/ngraph/core/include/ngraph/op/util/binary_elementwise_arithmetic.hpp b/src/core/include/ngraph/op/util/binary_elementwise_arithmetic.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/binary_elementwise_arithmetic.hpp
rename to src/core/include/ngraph/op/util/binary_elementwise_arithmetic.hpp
diff --git a/ngraph/core/include/ngraph/op/util/binary_elementwise_comparison.hpp b/src/core/include/ngraph/op/util/binary_elementwise_comparison.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/binary_elementwise_comparison.hpp
rename to src/core/include/ngraph/op/util/binary_elementwise_comparison.hpp
diff --git a/ngraph/core/include/ngraph/op/util/binary_elementwise_logical.hpp b/src/core/include/ngraph/op/util/binary_elementwise_logical.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/binary_elementwise_logical.hpp
rename to src/core/include/ngraph/op/util/binary_elementwise_logical.hpp
diff --git a/ngraph/core/include/ngraph/op/util/broadcast_base.hpp b/src/core/include/ngraph/op/util/broadcast_base.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/broadcast_base.hpp
rename to src/core/include/ngraph/op/util/broadcast_base.hpp
diff --git a/ngraph/core/include/ngraph/op/util/deformable_convolution_base.hpp b/src/core/include/ngraph/op/util/deformable_convolution_base.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/deformable_convolution_base.hpp
rename to src/core/include/ngraph/op/util/deformable_convolution_base.hpp
diff --git a/ngraph/core/include/ngraph/op/util/elementwise_args.hpp b/src/core/include/ngraph/op/util/elementwise_args.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/elementwise_args.hpp
rename to src/core/include/ngraph/op/util/elementwise_args.hpp
diff --git a/ngraph/core/include/ngraph/op/util/embeddingbag_offsets_base.hpp b/src/core/include/ngraph/op/util/embeddingbag_offsets_base.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/embeddingbag_offsets_base.hpp
rename to src/core/include/ngraph/op/util/embeddingbag_offsets_base.hpp
diff --git a/ngraph/core/include/ngraph/op/util/embeddingbag_packed_base.hpp b/src/core/include/ngraph/op/util/embeddingbag_packed_base.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/embeddingbag_packed_base.hpp
rename to src/core/include/ngraph/op/util/embeddingbag_packed_base.hpp
diff --git a/ngraph/core/include/ngraph/op/util/eval_copy.hpp b/src/core/include/ngraph/op/util/eval_copy.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/eval_copy.hpp
rename to src/core/include/ngraph/op/util/eval_copy.hpp
diff --git a/ngraph/core/include/ngraph/op/util/evaluate_helpers.hpp b/src/core/include/ngraph/op/util/evaluate_helpers.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/evaluate_helpers.hpp
rename to src/core/include/ngraph/op/util/evaluate_helpers.hpp
diff --git a/ngraph/core/include/ngraph/op/util/fft_base.hpp b/src/core/include/ngraph/op/util/fft_base.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/fft_base.hpp
rename to src/core/include/ngraph/op/util/fft_base.hpp
diff --git a/ngraph/core/include/ngraph/op/util/framework_node.hpp b/src/core/include/ngraph/op/util/framework_node.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/framework_node.hpp
rename to src/core/include/ngraph/op/util/framework_node.hpp
diff --git a/ngraph/core/include/ngraph/op/util/gather_base.hpp b/src/core/include/ngraph/op/util/gather_base.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/gather_base.hpp
rename to src/core/include/ngraph/op/util/gather_base.hpp
diff --git a/ngraph/core/include/ngraph/op/util/gather_nd_base.hpp b/src/core/include/ngraph/op/util/gather_nd_base.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/gather_nd_base.hpp
rename to src/core/include/ngraph/op/util/gather_nd_base.hpp
diff --git a/ngraph/core/include/ngraph/op/util/index_reduction.hpp b/src/core/include/ngraph/op/util/index_reduction.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/index_reduction.hpp
rename to src/core/include/ngraph/op/util/index_reduction.hpp
diff --git a/ngraph/core/include/ngraph/op/util/logical_reduction.hpp b/src/core/include/ngraph/op/util/logical_reduction.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/logical_reduction.hpp
rename to src/core/include/ngraph/op/util/logical_reduction.hpp
diff --git a/ngraph/core/include/ngraph/op/util/logical_reduction_keep_dims.hpp b/src/core/include/ngraph/op/util/logical_reduction_keep_dims.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/logical_reduction_keep_dims.hpp
rename to src/core/include/ngraph/op/util/logical_reduction_keep_dims.hpp
diff --git a/ngraph/core/include/ngraph/op/util/max_pool_base.hpp b/src/core/include/ngraph/op/util/max_pool_base.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/max_pool_base.hpp
rename to src/core/include/ngraph/op/util/max_pool_base.hpp
diff --git a/ngraph/core/include/ngraph/op/util/multi_subgraph_base.hpp b/src/core/include/ngraph/op/util/multi_subgraph_base.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/multi_subgraph_base.hpp
rename to src/core/include/ngraph/op/util/multi_subgraph_base.hpp
diff --git a/ngraph/core/include/ngraph/op/util/nms_base.hpp b/src/core/include/ngraph/op/util/nms_base.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/nms_base.hpp
rename to src/core/include/ngraph/op/util/nms_base.hpp
diff --git a/ngraph/core/include/ngraph/op/util/op_annotations.hpp b/src/core/include/ngraph/op/util/op_annotations.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/op_annotations.hpp
rename to src/core/include/ngraph/op/util/op_annotations.hpp
diff --git a/ngraph/core/include/ngraph/op/util/op_types.hpp b/src/core/include/ngraph/op/util/op_types.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/op_types.hpp
rename to src/core/include/ngraph/op/util/op_types.hpp
diff --git a/ngraph/core/include/ngraph/op/util/recurrent_sequence.hpp b/src/core/include/ngraph/op/util/recurrent_sequence.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/recurrent_sequence.hpp
rename to src/core/include/ngraph/op/util/recurrent_sequence.hpp
diff --git a/ngraph/core/include/ngraph/op/util/reduction_base.hpp b/src/core/include/ngraph/op/util/reduction_base.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/reduction_base.hpp
rename to src/core/include/ngraph/op/util/reduction_base.hpp
diff --git a/ngraph/core/include/ngraph/op/util/rnn_cell_base.hpp b/src/core/include/ngraph/op/util/rnn_cell_base.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/rnn_cell_base.hpp
rename to src/core/include/ngraph/op/util/rnn_cell_base.hpp
diff --git a/ngraph/core/include/ngraph/op/util/scatter_base.hpp b/src/core/include/ngraph/op/util/scatter_base.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/scatter_base.hpp
rename to src/core/include/ngraph/op/util/scatter_base.hpp
diff --git a/ngraph/core/include/ngraph/op/util/scatter_nd_base.hpp b/src/core/include/ngraph/op/util/scatter_nd_base.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/scatter_nd_base.hpp
rename to src/core/include/ngraph/op/util/scatter_nd_base.hpp
diff --git a/ngraph/core/include/ngraph/op/util/slice_plan.hpp b/src/core/include/ngraph/op/util/slice_plan.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/slice_plan.hpp
rename to src/core/include/ngraph/op/util/slice_plan.hpp
diff --git a/ngraph/core/include/ngraph/op/util/sub_graph_base.hpp b/src/core/include/ngraph/op/util/sub_graph_base.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/sub_graph_base.hpp
rename to src/core/include/ngraph/op/util/sub_graph_base.hpp
diff --git a/ngraph/core/include/ngraph/op/util/unary_elementwise_arithmetic.hpp b/src/core/include/ngraph/op/util/unary_elementwise_arithmetic.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/unary_elementwise_arithmetic.hpp
rename to src/core/include/ngraph/op/util/unary_elementwise_arithmetic.hpp
diff --git a/ngraph/core/include/ngraph/op/util/variable.hpp b/src/core/include/ngraph/op/util/variable.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/variable.hpp
rename to src/core/include/ngraph/op/util/variable.hpp
diff --git a/ngraph/core/include/ngraph/op/util/variable_context.hpp b/src/core/include/ngraph/op/util/variable_context.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/variable_context.hpp
rename to src/core/include/ngraph/op/util/variable_context.hpp
diff --git a/ngraph/core/include/ngraph/op/util/variable_extension.hpp b/src/core/include/ngraph/op/util/variable_extension.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/variable_extension.hpp
rename to src/core/include/ngraph/op/util/variable_extension.hpp
diff --git a/ngraph/core/include/ngraph/op/util/variable_value.hpp b/src/core/include/ngraph/op/util/variable_value.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/util/variable_value.hpp
rename to src/core/include/ngraph/op/util/variable_value.hpp
diff --git a/ngraph/core/include/ngraph/op/variadic_split.hpp b/src/core/include/ngraph/op/variadic_split.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/variadic_split.hpp
rename to src/core/include/ngraph/op/variadic_split.hpp
diff --git a/ngraph/core/include/ngraph/op/xor.hpp b/src/core/include/ngraph/op/xor.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/op/xor.hpp
rename to src/core/include/ngraph/op/xor.hpp
diff --git a/ngraph/core/include/ngraph/ops.hpp b/src/core/include/ngraph/ops.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/ops.hpp
rename to src/core/include/ngraph/ops.hpp
diff --git a/ngraph/core/include/ngraph/opsets/opset.hpp b/src/core/include/ngraph/opsets/opset.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/opsets/opset.hpp
rename to src/core/include/ngraph/opsets/opset.hpp
diff --git a/ngraph/core/include/ngraph/opsets/opset1.hpp b/src/core/include/ngraph/opsets/opset1.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/opsets/opset1.hpp
rename to src/core/include/ngraph/opsets/opset1.hpp
diff --git a/ngraph/core/include/ngraph/opsets/opset1_tbl.hpp b/src/core/include/ngraph/opsets/opset1_tbl.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/opsets/opset1_tbl.hpp
rename to src/core/include/ngraph/opsets/opset1_tbl.hpp
diff --git a/ngraph/core/include/ngraph/opsets/opset2.hpp b/src/core/include/ngraph/opsets/opset2.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/opsets/opset2.hpp
rename to src/core/include/ngraph/opsets/opset2.hpp
diff --git a/ngraph/core/include/ngraph/opsets/opset2_tbl.hpp b/src/core/include/ngraph/opsets/opset2_tbl.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/opsets/opset2_tbl.hpp
rename to src/core/include/ngraph/opsets/opset2_tbl.hpp
diff --git a/ngraph/core/include/ngraph/opsets/opset3.hpp b/src/core/include/ngraph/opsets/opset3.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/opsets/opset3.hpp
rename to src/core/include/ngraph/opsets/opset3.hpp
diff --git a/ngraph/core/include/ngraph/opsets/opset3_tbl.hpp b/src/core/include/ngraph/opsets/opset3_tbl.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/opsets/opset3_tbl.hpp
rename to src/core/include/ngraph/opsets/opset3_tbl.hpp
diff --git a/ngraph/core/include/ngraph/opsets/opset4.hpp b/src/core/include/ngraph/opsets/opset4.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/opsets/opset4.hpp
rename to src/core/include/ngraph/opsets/opset4.hpp
diff --git a/ngraph/core/include/ngraph/opsets/opset4_tbl.hpp b/src/core/include/ngraph/opsets/opset4_tbl.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/opsets/opset4_tbl.hpp
rename to src/core/include/ngraph/opsets/opset4_tbl.hpp
diff --git a/ngraph/core/include/ngraph/opsets/opset5.hpp b/src/core/include/ngraph/opsets/opset5.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/opsets/opset5.hpp
rename to src/core/include/ngraph/opsets/opset5.hpp
diff --git a/ngraph/core/include/ngraph/opsets/opset5_tbl.hpp b/src/core/include/ngraph/opsets/opset5_tbl.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/opsets/opset5_tbl.hpp
rename to src/core/include/ngraph/opsets/opset5_tbl.hpp
diff --git a/ngraph/core/include/ngraph/opsets/opset6.hpp b/src/core/include/ngraph/opsets/opset6.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/opsets/opset6.hpp
rename to src/core/include/ngraph/opsets/opset6.hpp
diff --git a/ngraph/core/include/ngraph/opsets/opset6_tbl.hpp b/src/core/include/ngraph/opsets/opset6_tbl.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/opsets/opset6_tbl.hpp
rename to src/core/include/ngraph/opsets/opset6_tbl.hpp
diff --git a/ngraph/core/include/ngraph/opsets/opset7.hpp b/src/core/include/ngraph/opsets/opset7.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/opsets/opset7.hpp
rename to src/core/include/ngraph/opsets/opset7.hpp
diff --git a/ngraph/core/include/ngraph/opsets/opset7_tbl.hpp b/src/core/include/ngraph/opsets/opset7_tbl.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/opsets/opset7_tbl.hpp
rename to src/core/include/ngraph/opsets/opset7_tbl.hpp
diff --git a/ngraph/core/include/ngraph/opsets/opset8.hpp b/src/core/include/ngraph/opsets/opset8.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/opsets/opset8.hpp
rename to src/core/include/ngraph/opsets/opset8.hpp
diff --git a/ngraph/core/include/ngraph/opsets/opset8_tbl.hpp b/src/core/include/ngraph/opsets/opset8_tbl.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/opsets/opset8_tbl.hpp
rename to src/core/include/ngraph/opsets/opset8_tbl.hpp
diff --git a/ngraph/core/include/ngraph/output_vector.hpp b/src/core/include/ngraph/output_vector.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/output_vector.hpp
rename to src/core/include/ngraph/output_vector.hpp
diff --git a/ngraph/core/include/ngraph/partial_shape.hpp b/src/core/include/ngraph/partial_shape.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/partial_shape.hpp
rename to src/core/include/ngraph/partial_shape.hpp
diff --git a/ngraph/core/include/ngraph/pass/constant_folding.hpp b/src/core/include/ngraph/pass/constant_folding.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pass/constant_folding.hpp
rename to src/core/include/ngraph/pass/constant_folding.hpp
diff --git a/ngraph/core/include/ngraph/pass/convert_fp32_to_fp16.hpp b/src/core/include/ngraph/pass/convert_fp32_to_fp16.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pass/convert_fp32_to_fp16.hpp
rename to src/core/include/ngraph/pass/convert_fp32_to_fp16.hpp
diff --git a/ngraph/core/include/ngraph/pass/graph_rewrite.hpp b/src/core/include/ngraph/pass/graph_rewrite.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pass/graph_rewrite.hpp
rename to src/core/include/ngraph/pass/graph_rewrite.hpp
diff --git a/ngraph/core/include/ngraph/pass/low_latency.hpp b/src/core/include/ngraph/pass/low_latency.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pass/low_latency.hpp
rename to src/core/include/ngraph/pass/low_latency.hpp
diff --git a/ngraph/core/include/ngraph/pass/manager.hpp b/src/core/include/ngraph/pass/manager.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pass/manager.hpp
rename to src/core/include/ngraph/pass/manager.hpp
diff --git a/ngraph/core/include/ngraph/pass/pass.hpp b/src/core/include/ngraph/pass/pass.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pass/pass.hpp
rename to src/core/include/ngraph/pass/pass.hpp
diff --git a/ngraph/core/include/ngraph/pass/pass_config.hpp b/src/core/include/ngraph/pass/pass_config.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pass/pass_config.hpp
rename to src/core/include/ngraph/pass/pass_config.hpp
diff --git a/ngraph/core/include/ngraph/pass/serialize.hpp b/src/core/include/ngraph/pass/serialize.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pass/serialize.hpp
rename to src/core/include/ngraph/pass/serialize.hpp
diff --git a/ngraph/core/include/ngraph/pass/validate.hpp b/src/core/include/ngraph/pass/validate.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pass/validate.hpp
rename to src/core/include/ngraph/pass/validate.hpp
diff --git a/ngraph/core/include/ngraph/pass/visualize_tree.hpp b/src/core/include/ngraph/pass/visualize_tree.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pass/visualize_tree.hpp
rename to src/core/include/ngraph/pass/visualize_tree.hpp
diff --git a/ngraph/core/include/ngraph/pattern/matcher.hpp b/src/core/include/ngraph/pattern/matcher.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pattern/matcher.hpp
rename to src/core/include/ngraph/pattern/matcher.hpp
diff --git a/ngraph/core/include/ngraph/pattern/op/any.hpp b/src/core/include/ngraph/pattern/op/any.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pattern/op/any.hpp
rename to src/core/include/ngraph/pattern/op/any.hpp
diff --git a/ngraph/core/include/ngraph/pattern/op/any_of.hpp b/src/core/include/ngraph/pattern/op/any_of.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pattern/op/any_of.hpp
rename to src/core/include/ngraph/pattern/op/any_of.hpp
diff --git a/ngraph/core/include/ngraph/pattern/op/any_output.hpp b/src/core/include/ngraph/pattern/op/any_output.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pattern/op/any_output.hpp
rename to src/core/include/ngraph/pattern/op/any_output.hpp
diff --git a/ngraph/core/include/ngraph/pattern/op/branch.hpp b/src/core/include/ngraph/pattern/op/branch.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pattern/op/branch.hpp
rename to src/core/include/ngraph/pattern/op/branch.hpp
diff --git a/ngraph/core/include/ngraph/pattern/op/capture.hpp b/src/core/include/ngraph/pattern/op/capture.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pattern/op/capture.hpp
rename to src/core/include/ngraph/pattern/op/capture.hpp
diff --git a/ngraph/core/include/ngraph/pattern/op/label.hpp b/src/core/include/ngraph/pattern/op/label.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pattern/op/label.hpp
rename to src/core/include/ngraph/pattern/op/label.hpp
diff --git a/ngraph/core/include/ngraph/pattern/op/or.hpp b/src/core/include/ngraph/pattern/op/or.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pattern/op/or.hpp
rename to src/core/include/ngraph/pattern/op/or.hpp
diff --git a/ngraph/core/include/ngraph/pattern/op/pattern.hpp b/src/core/include/ngraph/pattern/op/pattern.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pattern/op/pattern.hpp
rename to src/core/include/ngraph/pattern/op/pattern.hpp
diff --git a/ngraph/core/include/ngraph/pattern/op/skip.hpp b/src/core/include/ngraph/pattern/op/skip.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pattern/op/skip.hpp
rename to src/core/include/ngraph/pattern/op/skip.hpp
diff --git a/ngraph/core/include/ngraph/pattern/op/true.hpp b/src/core/include/ngraph/pattern/op/true.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pattern/op/true.hpp
rename to src/core/include/ngraph/pattern/op/true.hpp
diff --git a/ngraph/core/include/ngraph/pattern/op/wrap_type.hpp b/src/core/include/ngraph/pattern/op/wrap_type.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/pattern/op/wrap_type.hpp
rename to src/core/include/ngraph/pattern/op/wrap_type.hpp
diff --git a/ngraph/core/include/ngraph/rank.hpp b/src/core/include/ngraph/rank.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/rank.hpp
rename to src/core/include/ngraph/rank.hpp
diff --git a/ngraph/core/include/ngraph/rt_info.hpp b/src/core/include/ngraph/rt_info.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/rt_info.hpp
rename to src/core/include/ngraph/rt_info.hpp
diff --git a/ngraph/core/include/ngraph/runtime/aligned_buffer.hpp b/src/core/include/ngraph/runtime/aligned_buffer.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/runtime/aligned_buffer.hpp
rename to src/core/include/ngraph/runtime/aligned_buffer.hpp
diff --git a/ngraph/core/include/ngraph/runtime/host_tensor.hpp b/src/core/include/ngraph/runtime/host_tensor.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/runtime/host_tensor.hpp
rename to src/core/include/ngraph/runtime/host_tensor.hpp
diff --git a/ngraph/core/include/ngraph/runtime/shared_buffer.hpp b/src/core/include/ngraph/runtime/shared_buffer.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/runtime/shared_buffer.hpp
rename to src/core/include/ngraph/runtime/shared_buffer.hpp
diff --git a/ngraph/core/include/ngraph/runtime/tensor.hpp b/src/core/include/ngraph/runtime/tensor.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/runtime/tensor.hpp
rename to src/core/include/ngraph/runtime/tensor.hpp
diff --git a/ngraph/core/include/ngraph/shape.hpp b/src/core/include/ngraph/shape.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/shape.hpp
rename to src/core/include/ngraph/shape.hpp
diff --git a/ngraph/core/include/ngraph/shape_util.hpp b/src/core/include/ngraph/shape_util.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/shape_util.hpp
rename to src/core/include/ngraph/shape_util.hpp
diff --git a/ngraph/core/include/ngraph/slice_plan.hpp b/src/core/include/ngraph/slice_plan.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/slice_plan.hpp
rename to src/core/include/ngraph/slice_plan.hpp
diff --git a/ngraph/core/include/ngraph/specialize_function.hpp b/src/core/include/ngraph/specialize_function.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/specialize_function.hpp
rename to src/core/include/ngraph/specialize_function.hpp
diff --git a/ngraph/core/include/ngraph/strides.hpp b/src/core/include/ngraph/strides.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/strides.hpp
rename to src/core/include/ngraph/strides.hpp
diff --git a/ngraph/core/include/ngraph/type.hpp b/src/core/include/ngraph/type.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/type.hpp
rename to src/core/include/ngraph/type.hpp
diff --git a/ngraph/core/include/ngraph/type/bfloat16.hpp b/src/core/include/ngraph/type/bfloat16.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/type/bfloat16.hpp
rename to src/core/include/ngraph/type/bfloat16.hpp
diff --git a/ngraph/core/include/ngraph/type/element_type.hpp b/src/core/include/ngraph/type/element_type.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/type/element_type.hpp
rename to src/core/include/ngraph/type/element_type.hpp
diff --git a/ngraph/core/include/ngraph/type/element_type_traits.hpp b/src/core/include/ngraph/type/element_type_traits.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/type/element_type_traits.hpp
rename to src/core/include/ngraph/type/element_type_traits.hpp
diff --git a/ngraph/core/include/ngraph/type/float16.hpp b/src/core/include/ngraph/type/float16.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/type/float16.hpp
rename to src/core/include/ngraph/type/float16.hpp
diff --git a/ngraph/core/include/ngraph/util.hpp b/src/core/include/ngraph/util.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/util.hpp
rename to src/core/include/ngraph/util.hpp
diff --git a/ngraph/core/include/ngraph/validation_util.hpp b/src/core/include/ngraph/validation_util.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/validation_util.hpp
rename to src/core/include/ngraph/validation_util.hpp
diff --git a/ngraph/core/include/ngraph/variant.hpp b/src/core/include/ngraph/variant.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/variant.hpp
rename to src/core/include/ngraph/variant.hpp
diff --git a/ngraph/core/include/ngraph/version.hpp b/src/core/include/ngraph/version.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/version.hpp
rename to src/core/include/ngraph/version.hpp
diff --git a/ngraph/core/include/ngraph/visibility.hpp b/src/core/include/ngraph/visibility.hpp
similarity index 100%
rename from ngraph/core/include/ngraph/visibility.hpp
rename to src/core/include/ngraph/visibility.hpp
diff --git a/ngraph/core/include/openvino/core/any.hpp b/src/core/include/openvino/core/any.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/any.hpp
rename to src/core/include/openvino/core/any.hpp
diff --git a/ngraph/core/include/openvino/core/attribute_adapter.hpp b/src/core/include/openvino/core/attribute_adapter.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/attribute_adapter.hpp
rename to src/core/include/openvino/core/attribute_adapter.hpp
diff --git a/ngraph/core/include/openvino/core/attribute_visitor.hpp b/src/core/include/openvino/core/attribute_visitor.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/attribute_visitor.hpp
rename to src/core/include/openvino/core/attribute_visitor.hpp
diff --git a/ngraph/core/include/openvino/core/axis_set.hpp b/src/core/include/openvino/core/axis_set.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/axis_set.hpp
rename to src/core/include/openvino/core/axis_set.hpp
diff --git a/ngraph/core/include/openvino/core/axis_vector.hpp b/src/core/include/openvino/core/axis_vector.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/axis_vector.hpp
rename to src/core/include/openvino/core/axis_vector.hpp
diff --git a/ngraph/core/include/openvino/core/coordinate.hpp b/src/core/include/openvino/core/coordinate.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/coordinate.hpp
rename to src/core/include/openvino/core/coordinate.hpp
diff --git a/ngraph/core/include/openvino/core/coordinate_diff.hpp b/src/core/include/openvino/core/coordinate_diff.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/coordinate_diff.hpp
rename to src/core/include/openvino/core/coordinate_diff.hpp
diff --git a/ngraph/core/include/openvino/core/core.hpp b/src/core/include/openvino/core/core.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/core.hpp
rename to src/core/include/openvino/core/core.hpp
diff --git a/ngraph/core/include/openvino/core/core_visibility.hpp b/src/core/include/openvino/core/core_visibility.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/core_visibility.hpp
rename to src/core/include/openvino/core/core_visibility.hpp
diff --git a/ngraph/core/include/openvino/core/deprecated.hpp b/src/core/include/openvino/core/deprecated.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/deprecated.hpp
rename to src/core/include/openvino/core/deprecated.hpp
diff --git a/ngraph/core/include/openvino/core/descriptor/input.hpp b/src/core/include/openvino/core/descriptor/input.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/descriptor/input.hpp
rename to src/core/include/openvino/core/descriptor/input.hpp
diff --git a/ngraph/core/include/openvino/core/descriptor/output.hpp b/src/core/include/openvino/core/descriptor/output.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/descriptor/output.hpp
rename to src/core/include/openvino/core/descriptor/output.hpp
diff --git a/ngraph/core/include/openvino/core/descriptor/tensor.hpp b/src/core/include/openvino/core/descriptor/tensor.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/descriptor/tensor.hpp
rename to src/core/include/openvino/core/descriptor/tensor.hpp
index 58aed63e321..10d3fa00da1 100644
--- a/ngraph/core/include/openvino/core/descriptor/tensor.hpp
+++ b/src/core/include/openvino/core/descriptor/tensor.hpp
@@ -106,7 +106,6 @@ protected:
     // It was decided to move m_shape and m_partial_shape synchronization point there and
     // to keep methods signature backward compatible.
     mutable std::mutex m_mutex;
-    mutable std::atomic_bool m_shape_changed;
     mutable Shape m_shape;
     // TODO: end
 
@@ -116,6 +115,7 @@ protected:
 
     std::unordered_set<std::string> m_names;
     std::map<std::string, Any> m_rt_info;
+    mutable std::atomic_bool m_shape_changed;
 };
 
 OPENVINO_API
diff --git a/ngraph/core/include/openvino/core/dimension.hpp b/src/core/include/openvino/core/dimension.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/dimension.hpp
rename to src/core/include/openvino/core/dimension.hpp
diff --git a/ngraph/core/include/openvino/core/enum_mask.hpp b/src/core/include/openvino/core/enum_mask.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/enum_mask.hpp
rename to src/core/include/openvino/core/enum_mask.hpp
diff --git a/ngraph/core/include/openvino/core/enum_names.hpp b/src/core/include/openvino/core/enum_names.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/enum_names.hpp
rename to src/core/include/openvino/core/enum_names.hpp
diff --git a/ngraph/core/include/openvino/core/except.hpp b/src/core/include/openvino/core/except.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/except.hpp
rename to src/core/include/openvino/core/except.hpp
diff --git a/ngraph/core/include/openvino/core/extension.hpp b/src/core/include/openvino/core/extension.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/extension.hpp
rename to src/core/include/openvino/core/extension.hpp
diff --git a/ngraph/core/include/openvino/core/function.hpp b/src/core/include/openvino/core/function.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/function.hpp
rename to src/core/include/openvino/core/function.hpp
diff --git a/ngraph/core/include/openvino/core/graph_util.hpp b/src/core/include/openvino/core/graph_util.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/graph_util.hpp
rename to src/core/include/openvino/core/graph_util.hpp
diff --git a/ngraph/core/include/openvino/core/interval.hpp b/src/core/include/openvino/core/interval.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/interval.hpp
rename to src/core/include/openvino/core/interval.hpp
diff --git a/ngraph/core/include/openvino/core/layout.hpp b/src/core/include/openvino/core/layout.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/layout.hpp
rename to src/core/include/openvino/core/layout.hpp
diff --git a/ngraph/core/include/openvino/core/node.hpp b/src/core/include/openvino/core/node.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/node.hpp
rename to src/core/include/openvino/core/node.hpp
diff --git a/ngraph/core/include/openvino/core/node_input.hpp b/src/core/include/openvino/core/node_input.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/node_input.hpp
rename to src/core/include/openvino/core/node_input.hpp
diff --git a/ngraph/core/include/openvino/core/node_output.hpp b/src/core/include/openvino/core/node_output.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/node_output.hpp
rename to src/core/include/openvino/core/node_output.hpp
diff --git a/ngraph/core/include/openvino/core/node_vector.hpp b/src/core/include/openvino/core/node_vector.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/node_vector.hpp
rename to src/core/include/openvino/core/node_vector.hpp
diff --git a/ngraph/core/include/openvino/core/op_extension.hpp b/src/core/include/openvino/core/op_extension.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/op_extension.hpp
rename to src/core/include/openvino/core/op_extension.hpp
diff --git a/ngraph/core/include/openvino/core/partial_shape.hpp b/src/core/include/openvino/core/partial_shape.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/partial_shape.hpp
rename to src/core/include/openvino/core/partial_shape.hpp
diff --git a/ngraph/core/include/openvino/core/preprocess/color_format.hpp b/src/core/include/openvino/core/preprocess/color_format.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/preprocess/color_format.hpp
rename to src/core/include/openvino/core/preprocess/color_format.hpp
diff --git a/ngraph/core/include/openvino/core/preprocess/input_info.hpp b/src/core/include/openvino/core/preprocess/input_info.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/preprocess/input_info.hpp
rename to src/core/include/openvino/core/preprocess/input_info.hpp
diff --git a/ngraph/core/include/openvino/core/preprocess/input_network_info.hpp b/src/core/include/openvino/core/preprocess/input_network_info.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/preprocess/input_network_info.hpp
rename to src/core/include/openvino/core/preprocess/input_network_info.hpp
diff --git a/ngraph/core/include/openvino/core/preprocess/input_tensor_info.hpp b/src/core/include/openvino/core/preprocess/input_tensor_info.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/preprocess/input_tensor_info.hpp
rename to src/core/include/openvino/core/preprocess/input_tensor_info.hpp
diff --git a/ngraph/core/include/openvino/core/preprocess/output_info.hpp b/src/core/include/openvino/core/preprocess/output_info.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/preprocess/output_info.hpp
rename to src/core/include/openvino/core/preprocess/output_info.hpp
diff --git a/ngraph/core/include/openvino/core/preprocess/output_network_info.hpp b/src/core/include/openvino/core/preprocess/output_network_info.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/preprocess/output_network_info.hpp
rename to src/core/include/openvino/core/preprocess/output_network_info.hpp
diff --git a/ngraph/core/include/openvino/core/preprocess/output_tensor_info.hpp b/src/core/include/openvino/core/preprocess/output_tensor_info.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/preprocess/output_tensor_info.hpp
rename to src/core/include/openvino/core/preprocess/output_tensor_info.hpp
diff --git a/ngraph/core/include/openvino/core/preprocess/postprocess_steps.hpp b/src/core/include/openvino/core/preprocess/postprocess_steps.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/preprocess/postprocess_steps.hpp
rename to src/core/include/openvino/core/preprocess/postprocess_steps.hpp
diff --git a/ngraph/core/include/openvino/core/preprocess/pre_post_process.hpp b/src/core/include/openvino/core/preprocess/pre_post_process.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/preprocess/pre_post_process.hpp
rename to src/core/include/openvino/core/preprocess/pre_post_process.hpp
diff --git a/ngraph/core/include/openvino/core/preprocess/preprocess_steps.hpp b/src/core/include/openvino/core/preprocess/preprocess_steps.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/preprocess/preprocess_steps.hpp
rename to src/core/include/openvino/core/preprocess/preprocess_steps.hpp
diff --git a/ngraph/core/include/openvino/core/preprocess/resize_algorithm.hpp b/src/core/include/openvino/core/preprocess/resize_algorithm.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/preprocess/resize_algorithm.hpp
rename to src/core/include/openvino/core/preprocess/resize_algorithm.hpp
diff --git a/ngraph/core/include/openvino/core/rank.hpp b/src/core/include/openvino/core/rank.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/rank.hpp
rename to src/core/include/openvino/core/rank.hpp
diff --git a/ngraph/core/include/openvino/core/rtti.hpp b/src/core/include/openvino/core/rtti.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/rtti.hpp
rename to src/core/include/openvino/core/rtti.hpp
diff --git a/ngraph/core/include/openvino/core/shape.hpp b/src/core/include/openvino/core/shape.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/shape.hpp
rename to src/core/include/openvino/core/shape.hpp
diff --git a/ngraph/core/include/openvino/core/strides.hpp b/src/core/include/openvino/core/strides.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/strides.hpp
rename to src/core/include/openvino/core/strides.hpp
diff --git a/ngraph/core/include/openvino/core/type.hpp b/src/core/include/openvino/core/type.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/type.hpp
rename to src/core/include/openvino/core/type.hpp
diff --git a/ngraph/core/include/openvino/core/type/bfloat16.hpp b/src/core/include/openvino/core/type/bfloat16.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/type/bfloat16.hpp
rename to src/core/include/openvino/core/type/bfloat16.hpp
diff --git a/ngraph/core/include/openvino/core/type/element_type.hpp b/src/core/include/openvino/core/type/element_type.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/type/element_type.hpp
rename to src/core/include/openvino/core/type/element_type.hpp
diff --git a/ngraph/core/include/openvino/core/type/element_type_traits.hpp b/src/core/include/openvino/core/type/element_type_traits.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/type/element_type_traits.hpp
rename to src/core/include/openvino/core/type/element_type_traits.hpp
diff --git a/ngraph/core/include/openvino/core/type/float16.hpp b/src/core/include/openvino/core/type/float16.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/type/float16.hpp
rename to src/core/include/openvino/core/type/float16.hpp
diff --git a/ngraph/core/include/openvino/core/validation_util.hpp b/src/core/include/openvino/core/validation_util.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/validation_util.hpp
rename to src/core/include/openvino/core/validation_util.hpp
diff --git a/ngraph/core/include/openvino/core/variant.hpp b/src/core/include/openvino/core/variant.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/variant.hpp
rename to src/core/include/openvino/core/variant.hpp
diff --git a/ngraph/core/include/openvino/core/version.hpp b/src/core/include/openvino/core/version.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/version.hpp
rename to src/core/include/openvino/core/version.hpp
diff --git a/ngraph/core/include/openvino/core/visibility.hpp b/src/core/include/openvino/core/visibility.hpp
similarity index 100%
rename from ngraph/core/include/openvino/core/visibility.hpp
rename to src/core/include/openvino/core/visibility.hpp
diff --git a/ngraph/core/include/openvino/op/abs.hpp b/src/core/include/openvino/op/abs.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/abs.hpp
rename to src/core/include/openvino/op/abs.hpp
diff --git a/ngraph/core/include/openvino/op/acos.hpp b/src/core/include/openvino/op/acos.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/acos.hpp
rename to src/core/include/openvino/op/acos.hpp
diff --git a/ngraph/core/include/openvino/op/acosh.hpp b/src/core/include/openvino/op/acosh.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/acosh.hpp
rename to src/core/include/openvino/op/acosh.hpp
diff --git a/ngraph/core/include/openvino/op/adaptive_avg_pool.hpp b/src/core/include/openvino/op/adaptive_avg_pool.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/adaptive_avg_pool.hpp
rename to src/core/include/openvino/op/adaptive_avg_pool.hpp
diff --git a/ngraph/core/include/openvino/op/adaptive_max_pool.hpp b/src/core/include/openvino/op/adaptive_max_pool.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/adaptive_max_pool.hpp
rename to src/core/include/openvino/op/adaptive_max_pool.hpp
diff --git a/ngraph/core/include/openvino/op/add.hpp b/src/core/include/openvino/op/add.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/add.hpp
rename to src/core/include/openvino/op/add.hpp
diff --git a/ngraph/core/include/openvino/op/asin.hpp b/src/core/include/openvino/op/asin.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/asin.hpp
rename to src/core/include/openvino/op/asin.hpp
diff --git a/ngraph/core/include/openvino/op/asinh.hpp b/src/core/include/openvino/op/asinh.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/asinh.hpp
rename to src/core/include/openvino/op/asinh.hpp
diff --git a/ngraph/core/include/openvino/op/assign.hpp b/src/core/include/openvino/op/assign.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/assign.hpp
rename to src/core/include/openvino/op/assign.hpp
diff --git a/ngraph/core/include/openvino/op/atan.hpp b/src/core/include/openvino/op/atan.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/atan.hpp
rename to src/core/include/openvino/op/atan.hpp
diff --git a/ngraph/core/include/openvino/op/atanh.hpp b/src/core/include/openvino/op/atanh.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/atanh.hpp
rename to src/core/include/openvino/op/atanh.hpp
diff --git a/ngraph/core/include/openvino/op/avg_pool.hpp b/src/core/include/openvino/op/avg_pool.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/avg_pool.hpp
rename to src/core/include/openvino/op/avg_pool.hpp
diff --git a/ngraph/core/include/openvino/op/batch_norm.hpp b/src/core/include/openvino/op/batch_norm.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/batch_norm.hpp
rename to src/core/include/openvino/op/batch_norm.hpp
diff --git a/ngraph/core/include/openvino/op/batch_to_space.hpp b/src/core/include/openvino/op/batch_to_space.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/batch_to_space.hpp
rename to src/core/include/openvino/op/batch_to_space.hpp
diff --git a/ngraph/core/include/openvino/op/binary_convolution.hpp b/src/core/include/openvino/op/binary_convolution.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/binary_convolution.hpp
rename to src/core/include/openvino/op/binary_convolution.hpp
diff --git a/ngraph/core/include/openvino/op/broadcast.hpp b/src/core/include/openvino/op/broadcast.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/broadcast.hpp
rename to src/core/include/openvino/op/broadcast.hpp
diff --git a/ngraph/core/include/openvino/op/bucketize.hpp b/src/core/include/openvino/op/bucketize.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/bucketize.hpp
rename to src/core/include/openvino/op/bucketize.hpp
diff --git a/ngraph/core/include/openvino/op/ceiling.hpp b/src/core/include/openvino/op/ceiling.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/ceiling.hpp
rename to src/core/include/openvino/op/ceiling.hpp
diff --git a/ngraph/core/include/openvino/op/clamp.hpp b/src/core/include/openvino/op/clamp.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/clamp.hpp
rename to src/core/include/openvino/op/clamp.hpp
diff --git a/ngraph/core/include/openvino/op/concat.hpp b/src/core/include/openvino/op/concat.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/concat.hpp
rename to src/core/include/openvino/op/concat.hpp
diff --git a/ngraph/core/include/openvino/op/constant.hpp b/src/core/include/openvino/op/constant.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/constant.hpp
rename to src/core/include/openvino/op/constant.hpp
diff --git a/ngraph/core/include/openvino/op/convert.hpp b/src/core/include/openvino/op/convert.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/convert.hpp
rename to src/core/include/openvino/op/convert.hpp
diff --git a/ngraph/core/include/openvino/op/convert_like.hpp b/src/core/include/openvino/op/convert_like.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/convert_like.hpp
rename to src/core/include/openvino/op/convert_like.hpp
diff --git a/ngraph/core/include/openvino/op/convolution.hpp b/src/core/include/openvino/op/convolution.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/convolution.hpp
rename to src/core/include/openvino/op/convolution.hpp
diff --git a/ngraph/core/include/openvino/op/cos.hpp b/src/core/include/openvino/op/cos.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/cos.hpp
rename to src/core/include/openvino/op/cos.hpp
diff --git a/ngraph/core/include/openvino/op/cosh.hpp b/src/core/include/openvino/op/cosh.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/cosh.hpp
rename to src/core/include/openvino/op/cosh.hpp
diff --git a/ngraph/core/include/openvino/op/ctc_greedy_decoder.hpp b/src/core/include/openvino/op/ctc_greedy_decoder.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/ctc_greedy_decoder.hpp
rename to src/core/include/openvino/op/ctc_greedy_decoder.hpp
diff --git a/ngraph/core/include/openvino/op/ctc_greedy_decoder_seq_len.hpp b/src/core/include/openvino/op/ctc_greedy_decoder_seq_len.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/ctc_greedy_decoder_seq_len.hpp
rename to src/core/include/openvino/op/ctc_greedy_decoder_seq_len.hpp
diff --git a/ngraph/core/include/openvino/op/ctc_loss.hpp b/src/core/include/openvino/op/ctc_loss.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/ctc_loss.hpp
rename to src/core/include/openvino/op/ctc_loss.hpp
diff --git a/ngraph/core/include/openvino/op/cum_sum.hpp b/src/core/include/openvino/op/cum_sum.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/cum_sum.hpp
rename to src/core/include/openvino/op/cum_sum.hpp
diff --git a/ngraph/core/include/openvino/op/deformable_convolution.hpp b/src/core/include/openvino/op/deformable_convolution.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/deformable_convolution.hpp
rename to src/core/include/openvino/op/deformable_convolution.hpp
diff --git a/ngraph/core/include/openvino/op/deformable_psroi_pooling.hpp b/src/core/include/openvino/op/deformable_psroi_pooling.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/deformable_psroi_pooling.hpp
rename to src/core/include/openvino/op/deformable_psroi_pooling.hpp
diff --git a/ngraph/core/include/openvino/op/depth_to_space.hpp b/src/core/include/openvino/op/depth_to_space.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/depth_to_space.hpp
rename to src/core/include/openvino/op/depth_to_space.hpp
diff --git a/ngraph/core/include/openvino/op/detection_output.hpp b/src/core/include/openvino/op/detection_output.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/detection_output.hpp
rename to src/core/include/openvino/op/detection_output.hpp
diff --git a/ngraph/core/include/openvino/op/dft.hpp b/src/core/include/openvino/op/dft.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/dft.hpp
rename to src/core/include/openvino/op/dft.hpp
diff --git a/ngraph/core/include/openvino/op/divide.hpp b/src/core/include/openvino/op/divide.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/divide.hpp
rename to src/core/include/openvino/op/divide.hpp
diff --git a/ngraph/core/include/openvino/op/einsum.hpp b/src/core/include/openvino/op/einsum.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/einsum.hpp
rename to src/core/include/openvino/op/einsum.hpp
diff --git a/ngraph/core/include/openvino/op/elu.hpp b/src/core/include/openvino/op/elu.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/elu.hpp
rename to src/core/include/openvino/op/elu.hpp
diff --git a/ngraph/core/include/openvino/op/embedding_segments_sum.hpp b/src/core/include/openvino/op/embedding_segments_sum.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/embedding_segments_sum.hpp
rename to src/core/include/openvino/op/embedding_segments_sum.hpp
diff --git a/ngraph/core/include/openvino/op/embeddingbag_offsets_sum.hpp b/src/core/include/openvino/op/embeddingbag_offsets_sum.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/embeddingbag_offsets_sum.hpp
rename to src/core/include/openvino/op/embeddingbag_offsets_sum.hpp
diff --git a/ngraph/core/include/openvino/op/embeddingbag_packedsum.hpp b/src/core/include/openvino/op/embeddingbag_packedsum.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/embeddingbag_packedsum.hpp
rename to src/core/include/openvino/op/embeddingbag_packedsum.hpp
diff --git a/ngraph/core/include/openvino/op/equal.hpp b/src/core/include/openvino/op/equal.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/equal.hpp
rename to src/core/include/openvino/op/equal.hpp
diff --git a/ngraph/core/include/openvino/op/erf.hpp b/src/core/include/openvino/op/erf.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/erf.hpp
rename to src/core/include/openvino/op/erf.hpp
diff --git a/ngraph/core/include/openvino/op/exp.hpp b/src/core/include/openvino/op/exp.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/exp.hpp
rename to src/core/include/openvino/op/exp.hpp
diff --git a/ngraph/core/include/openvino/op/experimental_detectron_detection_output.hpp b/src/core/include/openvino/op/experimental_detectron_detection_output.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/experimental_detectron_detection_output.hpp
rename to src/core/include/openvino/op/experimental_detectron_detection_output.hpp
diff --git a/ngraph/core/include/openvino/op/experimental_detectron_generate_proposals.hpp b/src/core/include/openvino/op/experimental_detectron_generate_proposals.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/experimental_detectron_generate_proposals.hpp
rename to src/core/include/openvino/op/experimental_detectron_generate_proposals.hpp
diff --git a/ngraph/core/include/openvino/op/experimental_detectron_prior_grid_generator.hpp b/src/core/include/openvino/op/experimental_detectron_prior_grid_generator.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/experimental_detectron_prior_grid_generator.hpp
rename to src/core/include/openvino/op/experimental_detectron_prior_grid_generator.hpp
diff --git a/ngraph/core/include/openvino/op/experimental_detectron_roi_feature.hpp b/src/core/include/openvino/op/experimental_detectron_roi_feature.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/experimental_detectron_roi_feature.hpp
rename to src/core/include/openvino/op/experimental_detectron_roi_feature.hpp
diff --git a/ngraph/core/include/openvino/op/experimental_detectron_topkrois.hpp b/src/core/include/openvino/op/experimental_detectron_topkrois.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/experimental_detectron_topkrois.hpp
rename to src/core/include/openvino/op/experimental_detectron_topkrois.hpp
diff --git a/ngraph/core/include/openvino/op/extractimagepatches.hpp b/src/core/include/openvino/op/extractimagepatches.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/extractimagepatches.hpp
rename to src/core/include/openvino/op/extractimagepatches.hpp
diff --git a/ngraph/core/include/openvino/op/fake_quantize.hpp b/src/core/include/openvino/op/fake_quantize.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/fake_quantize.hpp
rename to src/core/include/openvino/op/fake_quantize.hpp
diff --git a/ngraph/core/include/openvino/op/floor.hpp b/src/core/include/openvino/op/floor.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/floor.hpp
rename to src/core/include/openvino/op/floor.hpp
diff --git a/ngraph/core/include/openvino/op/floor_mod.hpp b/src/core/include/openvino/op/floor_mod.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/floor_mod.hpp
rename to src/core/include/openvino/op/floor_mod.hpp
diff --git a/ngraph/core/include/openvino/op/gather.hpp b/src/core/include/openvino/op/gather.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/gather.hpp
rename to src/core/include/openvino/op/gather.hpp
diff --git a/ngraph/core/include/openvino/op/gather_elements.hpp b/src/core/include/openvino/op/gather_elements.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/gather_elements.hpp
rename to src/core/include/openvino/op/gather_elements.hpp
diff --git a/ngraph/core/include/openvino/op/gather_nd.hpp b/src/core/include/openvino/op/gather_nd.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/gather_nd.hpp
rename to src/core/include/openvino/op/gather_nd.hpp
diff --git a/ngraph/core/include/openvino/op/gather_tree.hpp b/src/core/include/openvino/op/gather_tree.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/gather_tree.hpp
rename to src/core/include/openvino/op/gather_tree.hpp
diff --git a/ngraph/core/include/openvino/op/gelu.hpp b/src/core/include/openvino/op/gelu.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/gelu.hpp
rename to src/core/include/openvino/op/gelu.hpp
diff --git a/ngraph/core/include/openvino/op/greater.hpp b/src/core/include/openvino/op/greater.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/greater.hpp
rename to src/core/include/openvino/op/greater.hpp
diff --git a/ngraph/core/include/openvino/op/greater_eq.hpp b/src/core/include/openvino/op/greater_eq.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/greater_eq.hpp
rename to src/core/include/openvino/op/greater_eq.hpp
diff --git a/ngraph/core/include/openvino/op/grn.hpp b/src/core/include/openvino/op/grn.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/grn.hpp
rename to src/core/include/openvino/op/grn.hpp
diff --git a/ngraph/core/include/openvino/op/group_conv.hpp b/src/core/include/openvino/op/group_conv.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/group_conv.hpp
rename to src/core/include/openvino/op/group_conv.hpp
diff --git a/ngraph/core/include/openvino/op/gru_cell.hpp b/src/core/include/openvino/op/gru_cell.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/gru_cell.hpp
rename to src/core/include/openvino/op/gru_cell.hpp
diff --git a/ngraph/core/include/openvino/op/gru_sequence.hpp b/src/core/include/openvino/op/gru_sequence.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/gru_sequence.hpp
rename to src/core/include/openvino/op/gru_sequence.hpp
diff --git a/ngraph/core/include/openvino/op/hard_sigmoid.hpp b/src/core/include/openvino/op/hard_sigmoid.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/hard_sigmoid.hpp
rename to src/core/include/openvino/op/hard_sigmoid.hpp
diff --git a/ngraph/core/include/openvino/op/hsigmoid.hpp b/src/core/include/openvino/op/hsigmoid.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/hsigmoid.hpp
rename to src/core/include/openvino/op/hsigmoid.hpp
diff --git a/ngraph/core/include/openvino/op/hswish.hpp b/src/core/include/openvino/op/hswish.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/hswish.hpp
rename to src/core/include/openvino/op/hswish.hpp
diff --git a/ngraph/core/include/openvino/op/i420_to_bgr.hpp b/src/core/include/openvino/op/i420_to_bgr.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/i420_to_bgr.hpp
rename to src/core/include/openvino/op/i420_to_bgr.hpp
diff --git a/ngraph/core/include/openvino/op/i420_to_rgb.hpp b/src/core/include/openvino/op/i420_to_rgb.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/i420_to_rgb.hpp
rename to src/core/include/openvino/op/i420_to_rgb.hpp
diff --git a/ngraph/core/include/openvino/op/idft.hpp b/src/core/include/openvino/op/idft.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/idft.hpp
rename to src/core/include/openvino/op/idft.hpp
diff --git a/ngraph/core/include/openvino/op/if.hpp b/src/core/include/openvino/op/if.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/if.hpp
rename to src/core/include/openvino/op/if.hpp
diff --git a/ngraph/core/include/openvino/op/interpolate.hpp b/src/core/include/openvino/op/interpolate.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/interpolate.hpp
rename to src/core/include/openvino/op/interpolate.hpp
diff --git a/ngraph/core/include/openvino/op/less.hpp b/src/core/include/openvino/op/less.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/less.hpp
rename to src/core/include/openvino/op/less.hpp
diff --git a/ngraph/core/include/openvino/op/less_eq.hpp b/src/core/include/openvino/op/less_eq.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/less_eq.hpp
rename to src/core/include/openvino/op/less_eq.hpp
diff --git a/ngraph/core/include/openvino/op/log.hpp b/src/core/include/openvino/op/log.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/log.hpp
rename to src/core/include/openvino/op/log.hpp
diff --git a/ngraph/core/include/openvino/op/log_softmax.hpp b/src/core/include/openvino/op/log_softmax.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/log_softmax.hpp
rename to src/core/include/openvino/op/log_softmax.hpp
diff --git a/ngraph/core/include/openvino/op/logical_and.hpp b/src/core/include/openvino/op/logical_and.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/logical_and.hpp
rename to src/core/include/openvino/op/logical_and.hpp
diff --git a/ngraph/core/include/openvino/op/logical_not.hpp b/src/core/include/openvino/op/logical_not.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/logical_not.hpp
rename to src/core/include/openvino/op/logical_not.hpp
diff --git a/ngraph/core/include/openvino/op/logical_or.hpp b/src/core/include/openvino/op/logical_or.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/logical_or.hpp
rename to src/core/include/openvino/op/logical_or.hpp
diff --git a/ngraph/core/include/openvino/op/logical_xor.hpp b/src/core/include/openvino/op/logical_xor.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/logical_xor.hpp
rename to src/core/include/openvino/op/logical_xor.hpp
diff --git a/ngraph/core/include/openvino/op/loop.hpp b/src/core/include/openvino/op/loop.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/loop.hpp
rename to src/core/include/openvino/op/loop.hpp
diff --git a/ngraph/core/include/openvino/op/lrn.hpp b/src/core/include/openvino/op/lrn.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/lrn.hpp
rename to src/core/include/openvino/op/lrn.hpp
diff --git a/ngraph/core/include/openvino/op/lstm_cell.hpp b/src/core/include/openvino/op/lstm_cell.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/lstm_cell.hpp
rename to src/core/include/openvino/op/lstm_cell.hpp
diff --git a/ngraph/core/include/openvino/op/lstm_sequence.hpp b/src/core/include/openvino/op/lstm_sequence.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/lstm_sequence.hpp
rename to src/core/include/openvino/op/lstm_sequence.hpp
diff --git a/ngraph/core/include/openvino/op/matmul.hpp b/src/core/include/openvino/op/matmul.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/matmul.hpp
rename to src/core/include/openvino/op/matmul.hpp
diff --git a/ngraph/core/include/openvino/op/matrix_nms.hpp b/src/core/include/openvino/op/matrix_nms.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/matrix_nms.hpp
rename to src/core/include/openvino/op/matrix_nms.hpp
diff --git a/ngraph/core/include/openvino/op/max_pool.hpp b/src/core/include/openvino/op/max_pool.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/max_pool.hpp
rename to src/core/include/openvino/op/max_pool.hpp
diff --git a/ngraph/core/include/openvino/op/maximum.hpp b/src/core/include/openvino/op/maximum.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/maximum.hpp
rename to src/core/include/openvino/op/maximum.hpp
diff --git a/ngraph/core/include/openvino/op/minimum.hpp b/src/core/include/openvino/op/minimum.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/minimum.hpp
rename to src/core/include/openvino/op/minimum.hpp
diff --git a/ngraph/core/include/openvino/op/mish.hpp b/src/core/include/openvino/op/mish.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/mish.hpp
rename to src/core/include/openvino/op/mish.hpp
diff --git a/ngraph/core/include/openvino/op/mod.hpp b/src/core/include/openvino/op/mod.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/mod.hpp
rename to src/core/include/openvino/op/mod.hpp
diff --git a/ngraph/core/include/openvino/op/multiclass_nms.hpp b/src/core/include/openvino/op/multiclass_nms.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/multiclass_nms.hpp
rename to src/core/include/openvino/op/multiclass_nms.hpp
diff --git a/ngraph/core/include/openvino/op/multiply.hpp b/src/core/include/openvino/op/multiply.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/multiply.hpp
rename to src/core/include/openvino/op/multiply.hpp
diff --git a/ngraph/core/include/openvino/op/mvn.hpp b/src/core/include/openvino/op/mvn.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/mvn.hpp
rename to src/core/include/openvino/op/mvn.hpp
diff --git a/ngraph/core/include/openvino/op/negative.hpp b/src/core/include/openvino/op/negative.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/negative.hpp
rename to src/core/include/openvino/op/negative.hpp
diff --git a/ngraph/core/include/openvino/op/non_max_suppression.hpp b/src/core/include/openvino/op/non_max_suppression.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/non_max_suppression.hpp
rename to src/core/include/openvino/op/non_max_suppression.hpp
diff --git a/ngraph/core/include/openvino/op/non_zero.hpp b/src/core/include/openvino/op/non_zero.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/non_zero.hpp
rename to src/core/include/openvino/op/non_zero.hpp
diff --git a/ngraph/core/include/openvino/op/normalize_l2.hpp b/src/core/include/openvino/op/normalize_l2.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/normalize_l2.hpp
rename to src/core/include/openvino/op/normalize_l2.hpp
diff --git a/ngraph/core/include/openvino/op/not_equal.hpp b/src/core/include/openvino/op/not_equal.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/not_equal.hpp
rename to src/core/include/openvino/op/not_equal.hpp
diff --git a/ngraph/core/include/openvino/op/nv12_to_bgr.hpp b/src/core/include/openvino/op/nv12_to_bgr.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/nv12_to_bgr.hpp
rename to src/core/include/openvino/op/nv12_to_bgr.hpp
diff --git a/ngraph/core/include/openvino/op/nv12_to_rgb.hpp b/src/core/include/openvino/op/nv12_to_rgb.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/nv12_to_rgb.hpp
rename to src/core/include/openvino/op/nv12_to_rgb.hpp
diff --git a/ngraph/core/include/openvino/op/one_hot.hpp b/src/core/include/openvino/op/one_hot.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/one_hot.hpp
rename to src/core/include/openvino/op/one_hot.hpp
diff --git a/ngraph/core/include/openvino/op/op.hpp b/src/core/include/openvino/op/op.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/op.hpp
rename to src/core/include/openvino/op/op.hpp
diff --git a/ngraph/core/include/openvino/op/ops.hpp b/src/core/include/openvino/op/ops.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/ops.hpp
rename to src/core/include/openvino/op/ops.hpp
diff --git a/ngraph/core/include/openvino/op/pad.hpp b/src/core/include/openvino/op/pad.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/pad.hpp
rename to src/core/include/openvino/op/pad.hpp
diff --git a/ngraph/core/include/openvino/op/parameter.hpp b/src/core/include/openvino/op/parameter.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/parameter.hpp
rename to src/core/include/openvino/op/parameter.hpp
diff --git a/ngraph/core/include/openvino/op/power.hpp b/src/core/include/openvino/op/power.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/power.hpp
rename to src/core/include/openvino/op/power.hpp
diff --git a/ngraph/core/include/openvino/op/prelu.hpp b/src/core/include/openvino/op/prelu.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/prelu.hpp
rename to src/core/include/openvino/op/prelu.hpp
diff --git a/ngraph/core/include/openvino/op/prior_box.hpp b/src/core/include/openvino/op/prior_box.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/prior_box.hpp
rename to src/core/include/openvino/op/prior_box.hpp
diff --git a/ngraph/core/include/openvino/op/prior_box_clustered.hpp b/src/core/include/openvino/op/prior_box_clustered.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/prior_box_clustered.hpp
rename to src/core/include/openvino/op/prior_box_clustered.hpp
diff --git a/ngraph/core/include/openvino/op/proposal.hpp b/src/core/include/openvino/op/proposal.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/proposal.hpp
rename to src/core/include/openvino/op/proposal.hpp
diff --git a/ngraph/core/include/openvino/op/psroi_pooling.hpp b/src/core/include/openvino/op/psroi_pooling.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/psroi_pooling.hpp
rename to src/core/include/openvino/op/psroi_pooling.hpp
diff --git a/ngraph/core/include/openvino/op/random_uniform.hpp b/src/core/include/openvino/op/random_uniform.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/random_uniform.hpp
rename to src/core/include/openvino/op/random_uniform.hpp
diff --git a/ngraph/core/include/openvino/op/range.hpp b/src/core/include/openvino/op/range.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/range.hpp
rename to src/core/include/openvino/op/range.hpp
diff --git a/ngraph/core/include/openvino/op/read_value.hpp b/src/core/include/openvino/op/read_value.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/read_value.hpp
rename to src/core/include/openvino/op/read_value.hpp
diff --git a/ngraph/core/include/openvino/op/reduce_l1.hpp b/src/core/include/openvino/op/reduce_l1.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/reduce_l1.hpp
rename to src/core/include/openvino/op/reduce_l1.hpp
diff --git a/ngraph/core/include/openvino/op/reduce_l2.hpp b/src/core/include/openvino/op/reduce_l2.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/reduce_l2.hpp
rename to src/core/include/openvino/op/reduce_l2.hpp
diff --git a/ngraph/core/include/openvino/op/reduce_logical_and.hpp b/src/core/include/openvino/op/reduce_logical_and.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/reduce_logical_and.hpp
rename to src/core/include/openvino/op/reduce_logical_and.hpp
diff --git a/ngraph/core/include/openvino/op/reduce_logical_or.hpp b/src/core/include/openvino/op/reduce_logical_or.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/reduce_logical_or.hpp
rename to src/core/include/openvino/op/reduce_logical_or.hpp
diff --git a/ngraph/core/include/openvino/op/reduce_max.hpp b/src/core/include/openvino/op/reduce_max.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/reduce_max.hpp
rename to src/core/include/openvino/op/reduce_max.hpp
diff --git a/ngraph/core/include/openvino/op/reduce_mean.hpp b/src/core/include/openvino/op/reduce_mean.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/reduce_mean.hpp
rename to src/core/include/openvino/op/reduce_mean.hpp
diff --git a/ngraph/core/include/openvino/op/reduce_min.hpp b/src/core/include/openvino/op/reduce_min.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/reduce_min.hpp
rename to src/core/include/openvino/op/reduce_min.hpp
diff --git a/ngraph/core/include/openvino/op/reduce_prod.hpp b/src/core/include/openvino/op/reduce_prod.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/reduce_prod.hpp
rename to src/core/include/openvino/op/reduce_prod.hpp
diff --git a/ngraph/core/include/openvino/op/reduce_sum.hpp b/src/core/include/openvino/op/reduce_sum.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/reduce_sum.hpp
rename to src/core/include/openvino/op/reduce_sum.hpp
diff --git a/ngraph/core/include/openvino/op/region_yolo.hpp b/src/core/include/openvino/op/region_yolo.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/region_yolo.hpp
rename to src/core/include/openvino/op/region_yolo.hpp
diff --git a/ngraph/core/include/openvino/op/relu.hpp b/src/core/include/openvino/op/relu.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/relu.hpp
rename to src/core/include/openvino/op/relu.hpp
diff --git a/ngraph/core/include/openvino/op/reorg_yolo.hpp b/src/core/include/openvino/op/reorg_yolo.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/reorg_yolo.hpp
rename to src/core/include/openvino/op/reorg_yolo.hpp
diff --git a/ngraph/core/include/openvino/op/reshape.hpp b/src/core/include/openvino/op/reshape.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/reshape.hpp
rename to src/core/include/openvino/op/reshape.hpp
diff --git a/ngraph/core/include/openvino/op/result.hpp b/src/core/include/openvino/op/result.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/result.hpp
rename to src/core/include/openvino/op/result.hpp
diff --git a/ngraph/core/include/openvino/op/reverse.hpp b/src/core/include/openvino/op/reverse.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/reverse.hpp
rename to src/core/include/openvino/op/reverse.hpp
diff --git a/ngraph/core/include/openvino/op/reverse_sequence.hpp b/src/core/include/openvino/op/reverse_sequence.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/reverse_sequence.hpp
rename to src/core/include/openvino/op/reverse_sequence.hpp
diff --git a/ngraph/core/include/openvino/op/rnn_cell.hpp b/src/core/include/openvino/op/rnn_cell.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/rnn_cell.hpp
rename to src/core/include/openvino/op/rnn_cell.hpp
diff --git a/ngraph/core/include/openvino/op/rnn_sequence.hpp b/src/core/include/openvino/op/rnn_sequence.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/rnn_sequence.hpp
rename to src/core/include/openvino/op/rnn_sequence.hpp
diff --git a/ngraph/core/include/openvino/op/roi_align.hpp b/src/core/include/openvino/op/roi_align.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/roi_align.hpp
rename to src/core/include/openvino/op/roi_align.hpp
diff --git a/ngraph/core/include/openvino/op/roi_pooling.hpp b/src/core/include/openvino/op/roi_pooling.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/roi_pooling.hpp
rename to src/core/include/openvino/op/roi_pooling.hpp
diff --git a/ngraph/core/include/openvino/op/roll.hpp b/src/core/include/openvino/op/roll.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/roll.hpp
rename to src/core/include/openvino/op/roll.hpp
diff --git a/ngraph/core/include/openvino/op/round.hpp b/src/core/include/openvino/op/round.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/round.hpp
rename to src/core/include/openvino/op/round.hpp
diff --git a/ngraph/core/include/openvino/op/scatter_elements_update.hpp b/src/core/include/openvino/op/scatter_elements_update.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/scatter_elements_update.hpp
rename to src/core/include/openvino/op/scatter_elements_update.hpp
diff --git a/ngraph/core/include/openvino/op/scatter_nd_update.hpp b/src/core/include/openvino/op/scatter_nd_update.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/scatter_nd_update.hpp
rename to src/core/include/openvino/op/scatter_nd_update.hpp
diff --git a/ngraph/core/include/openvino/op/scatter_update.hpp b/src/core/include/openvino/op/scatter_update.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/scatter_update.hpp
rename to src/core/include/openvino/op/scatter_update.hpp
diff --git a/ngraph/core/include/openvino/op/select.hpp b/src/core/include/openvino/op/select.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/select.hpp
rename to src/core/include/openvino/op/select.hpp
diff --git a/ngraph/core/include/openvino/op/selu.hpp b/src/core/include/openvino/op/selu.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/selu.hpp
rename to src/core/include/openvino/op/selu.hpp
diff --git a/ngraph/core/include/openvino/op/shape_of.hpp b/src/core/include/openvino/op/shape_of.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/shape_of.hpp
rename to src/core/include/openvino/op/shape_of.hpp
diff --git a/ngraph/core/include/openvino/op/shuffle_channels.hpp b/src/core/include/openvino/op/shuffle_channels.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/shuffle_channels.hpp
rename to src/core/include/openvino/op/shuffle_channels.hpp
diff --git a/ngraph/core/include/openvino/op/sigmoid.hpp b/src/core/include/openvino/op/sigmoid.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/sigmoid.hpp
rename to src/core/include/openvino/op/sigmoid.hpp
diff --git a/ngraph/core/include/openvino/op/sign.hpp b/src/core/include/openvino/op/sign.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/sign.hpp
rename to src/core/include/openvino/op/sign.hpp
diff --git a/ngraph/core/include/openvino/op/sin.hpp b/src/core/include/openvino/op/sin.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/sin.hpp
rename to src/core/include/openvino/op/sin.hpp
diff --git a/ngraph/core/include/openvino/op/sinh.hpp b/src/core/include/openvino/op/sinh.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/sinh.hpp
rename to src/core/include/openvino/op/sinh.hpp
diff --git a/ngraph/core/include/openvino/op/sink.hpp b/src/core/include/openvino/op/sink.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/sink.hpp
rename to src/core/include/openvino/op/sink.hpp
diff --git a/ngraph/core/include/openvino/op/slice.hpp b/src/core/include/openvino/op/slice.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/slice.hpp
rename to src/core/include/openvino/op/slice.hpp
diff --git a/ngraph/core/include/openvino/op/softmax.hpp b/src/core/include/openvino/op/softmax.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/softmax.hpp
rename to src/core/include/openvino/op/softmax.hpp
diff --git a/ngraph/core/include/openvino/op/softplus.hpp b/src/core/include/openvino/op/softplus.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/softplus.hpp
rename to src/core/include/openvino/op/softplus.hpp
diff --git a/ngraph/core/include/openvino/op/space_to_batch.hpp b/src/core/include/openvino/op/space_to_batch.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/space_to_batch.hpp
rename to src/core/include/openvino/op/space_to_batch.hpp
diff --git a/ngraph/core/include/openvino/op/space_to_depth.hpp b/src/core/include/openvino/op/space_to_depth.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/space_to_depth.hpp
rename to src/core/include/openvino/op/space_to_depth.hpp
diff --git a/ngraph/core/include/openvino/op/split.hpp b/src/core/include/openvino/op/split.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/split.hpp
rename to src/core/include/openvino/op/split.hpp
diff --git a/ngraph/core/include/openvino/op/sqrt.hpp b/src/core/include/openvino/op/sqrt.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/sqrt.hpp
rename to src/core/include/openvino/op/sqrt.hpp
diff --git a/ngraph/core/include/openvino/op/squared_difference.hpp b/src/core/include/openvino/op/squared_difference.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/squared_difference.hpp
rename to src/core/include/openvino/op/squared_difference.hpp
diff --git a/ngraph/core/include/openvino/op/squeeze.hpp b/src/core/include/openvino/op/squeeze.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/squeeze.hpp
rename to src/core/include/openvino/op/squeeze.hpp
diff --git a/ngraph/core/include/openvino/op/strided_slice.hpp b/src/core/include/openvino/op/strided_slice.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/strided_slice.hpp
rename to src/core/include/openvino/op/strided_slice.hpp
diff --git a/ngraph/core/include/openvino/op/subtract.hpp b/src/core/include/openvino/op/subtract.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/subtract.hpp
rename to src/core/include/openvino/op/subtract.hpp
diff --git a/ngraph/core/include/openvino/op/swish.hpp b/src/core/include/openvino/op/swish.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/swish.hpp
rename to src/core/include/openvino/op/swish.hpp
diff --git a/ngraph/core/include/openvino/op/tan.hpp b/src/core/include/openvino/op/tan.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/tan.hpp
rename to src/core/include/openvino/op/tan.hpp
diff --git a/ngraph/core/include/openvino/op/tanh.hpp b/src/core/include/openvino/op/tanh.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/tanh.hpp
rename to src/core/include/openvino/op/tanh.hpp
diff --git a/ngraph/core/include/openvino/op/tensor_iterator.hpp b/src/core/include/openvino/op/tensor_iterator.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/tensor_iterator.hpp
rename to src/core/include/openvino/op/tensor_iterator.hpp
diff --git a/ngraph/core/include/openvino/op/tile.hpp b/src/core/include/openvino/op/tile.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/tile.hpp
rename to src/core/include/openvino/op/tile.hpp
diff --git a/ngraph/core/include/openvino/op/topk.hpp b/src/core/include/openvino/op/topk.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/topk.hpp
rename to src/core/include/openvino/op/topk.hpp
diff --git a/ngraph/core/include/openvino/op/transpose.hpp b/src/core/include/openvino/op/transpose.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/transpose.hpp
rename to src/core/include/openvino/op/transpose.hpp
diff --git a/ngraph/core/include/openvino/op/unsqueeze.hpp b/src/core/include/openvino/op/unsqueeze.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/unsqueeze.hpp
rename to src/core/include/openvino/op/unsqueeze.hpp
diff --git a/ngraph/core/include/openvino/op/util/activation_functions.hpp b/src/core/include/openvino/op/util/activation_functions.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/activation_functions.hpp
rename to src/core/include/openvino/op/util/activation_functions.hpp
diff --git a/ngraph/core/include/openvino/op/util/arithmetic_reduction.hpp b/src/core/include/openvino/op/util/arithmetic_reduction.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/arithmetic_reduction.hpp
rename to src/core/include/openvino/op/util/arithmetic_reduction.hpp
diff --git a/ngraph/core/include/openvino/op/util/arithmetic_reductions_keep_dims.hpp b/src/core/include/openvino/op/util/arithmetic_reductions_keep_dims.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/arithmetic_reductions_keep_dims.hpp
rename to src/core/include/openvino/op/util/arithmetic_reductions_keep_dims.hpp
diff --git a/ngraph/core/include/openvino/op/util/assign_base.hpp b/src/core/include/openvino/op/util/assign_base.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/assign_base.hpp
rename to src/core/include/openvino/op/util/assign_base.hpp
diff --git a/ngraph/core/include/openvino/op/util/attr_types.hpp b/src/core/include/openvino/op/util/attr_types.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/attr_types.hpp
rename to src/core/include/openvino/op/util/attr_types.hpp
diff --git a/ngraph/core/include/openvino/op/util/binary_elementwise_arithmetic.hpp b/src/core/include/openvino/op/util/binary_elementwise_arithmetic.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/binary_elementwise_arithmetic.hpp
rename to src/core/include/openvino/op/util/binary_elementwise_arithmetic.hpp
diff --git a/ngraph/core/include/openvino/op/util/binary_elementwise_comparison.hpp b/src/core/include/openvino/op/util/binary_elementwise_comparison.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/binary_elementwise_comparison.hpp
rename to src/core/include/openvino/op/util/binary_elementwise_comparison.hpp
diff --git a/ngraph/core/include/openvino/op/util/binary_elementwise_logical.hpp b/src/core/include/openvino/op/util/binary_elementwise_logical.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/binary_elementwise_logical.hpp
rename to src/core/include/openvino/op/util/binary_elementwise_logical.hpp
diff --git a/ngraph/core/include/openvino/op/util/broadcast_base.hpp b/src/core/include/openvino/op/util/broadcast_base.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/broadcast_base.hpp
rename to src/core/include/openvino/op/util/broadcast_base.hpp
diff --git a/ngraph/core/include/openvino/op/util/convert_color_i420_base.hpp b/src/core/include/openvino/op/util/convert_color_i420_base.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/convert_color_i420_base.hpp
rename to src/core/include/openvino/op/util/convert_color_i420_base.hpp
diff --git a/ngraph/core/include/openvino/op/util/convert_color_nv12_base.hpp b/src/core/include/openvino/op/util/convert_color_nv12_base.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/convert_color_nv12_base.hpp
rename to src/core/include/openvino/op/util/convert_color_nv12_base.hpp
diff --git a/ngraph/core/include/openvino/op/util/deformable_convolution_base.hpp b/src/core/include/openvino/op/util/deformable_convolution_base.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/deformable_convolution_base.hpp
rename to src/core/include/openvino/op/util/deformable_convolution_base.hpp
diff --git a/ngraph/core/include/openvino/op/util/elementwise_args.hpp b/src/core/include/openvino/op/util/elementwise_args.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/elementwise_args.hpp
rename to src/core/include/openvino/op/util/elementwise_args.hpp
diff --git a/ngraph/core/include/openvino/op/util/embeddingbag_offsets_base.hpp b/src/core/include/openvino/op/util/embeddingbag_offsets_base.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/embeddingbag_offsets_base.hpp
rename to src/core/include/openvino/op/util/embeddingbag_offsets_base.hpp
diff --git a/ngraph/core/include/openvino/op/util/embeddingbag_packed_base.hpp b/src/core/include/openvino/op/util/embeddingbag_packed_base.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/embeddingbag_packed_base.hpp
rename to src/core/include/openvino/op/util/embeddingbag_packed_base.hpp
diff --git a/ngraph/core/include/openvino/op/util/fft_base.hpp b/src/core/include/openvino/op/util/fft_base.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/fft_base.hpp
rename to src/core/include/openvino/op/util/fft_base.hpp
diff --git a/ngraph/core/include/openvino/op/util/framework_node.hpp b/src/core/include/openvino/op/util/framework_node.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/framework_node.hpp
rename to src/core/include/openvino/op/util/framework_node.hpp
diff --git a/ngraph/core/include/openvino/op/util/gather_base.hpp b/src/core/include/openvino/op/util/gather_base.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/gather_base.hpp
rename to src/core/include/openvino/op/util/gather_base.hpp
diff --git a/ngraph/core/include/openvino/op/util/gather_nd_base.hpp b/src/core/include/openvino/op/util/gather_nd_base.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/gather_nd_base.hpp
rename to src/core/include/openvino/op/util/gather_nd_base.hpp
diff --git a/ngraph/core/include/openvino/op/util/index_reduction.hpp b/src/core/include/openvino/op/util/index_reduction.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/index_reduction.hpp
rename to src/core/include/openvino/op/util/index_reduction.hpp
diff --git a/ngraph/core/include/openvino/op/util/logical_reduction.hpp b/src/core/include/openvino/op/util/logical_reduction.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/logical_reduction.hpp
rename to src/core/include/openvino/op/util/logical_reduction.hpp
diff --git a/ngraph/core/include/openvino/op/util/logical_reduction_keep_dims.hpp b/src/core/include/openvino/op/util/logical_reduction_keep_dims.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/logical_reduction_keep_dims.hpp
rename to src/core/include/openvino/op/util/logical_reduction_keep_dims.hpp
diff --git a/ngraph/core/include/openvino/op/util/max_pool_base.hpp b/src/core/include/openvino/op/util/max_pool_base.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/max_pool_base.hpp
rename to src/core/include/openvino/op/util/max_pool_base.hpp
diff --git a/ngraph/core/include/openvino/op/util/multi_subgraph_base.hpp b/src/core/include/openvino/op/util/multi_subgraph_base.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/multi_subgraph_base.hpp
rename to src/core/include/openvino/op/util/multi_subgraph_base.hpp
diff --git a/ngraph/core/include/openvino/op/util/nms_base.hpp b/src/core/include/openvino/op/util/nms_base.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/nms_base.hpp
rename to src/core/include/openvino/op/util/nms_base.hpp
diff --git a/ngraph/core/include/openvino/op/util/op_types.hpp b/src/core/include/openvino/op/util/op_types.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/op_types.hpp
rename to src/core/include/openvino/op/util/op_types.hpp
diff --git a/ngraph/core/include/openvino/op/util/precision_sensitive_attribute.hpp b/src/core/include/openvino/op/util/precision_sensitive_attribute.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/precision_sensitive_attribute.hpp
rename to src/core/include/openvino/op/util/precision_sensitive_attribute.hpp
diff --git a/ngraph/core/include/openvino/op/util/read_value_base.hpp b/src/core/include/openvino/op/util/read_value_base.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/read_value_base.hpp
rename to src/core/include/openvino/op/util/read_value_base.hpp
diff --git a/ngraph/core/include/openvino/op/util/reduction_base.hpp b/src/core/include/openvino/op/util/reduction_base.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/reduction_base.hpp
rename to src/core/include/openvino/op/util/reduction_base.hpp
diff --git a/ngraph/core/include/openvino/op/util/rnn_cell_base.hpp b/src/core/include/openvino/op/util/rnn_cell_base.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/rnn_cell_base.hpp
rename to src/core/include/openvino/op/util/rnn_cell_base.hpp
diff --git a/ngraph/core/include/openvino/op/util/scatter_base.hpp b/src/core/include/openvino/op/util/scatter_base.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/scatter_base.hpp
rename to src/core/include/openvino/op/util/scatter_base.hpp
diff --git a/ngraph/core/include/openvino/op/util/scatter_nd_base.hpp b/src/core/include/openvino/op/util/scatter_nd_base.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/scatter_nd_base.hpp
rename to src/core/include/openvino/op/util/scatter_nd_base.hpp
diff --git a/ngraph/core/include/openvino/op/util/sub_graph_base.hpp b/src/core/include/openvino/op/util/sub_graph_base.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/sub_graph_base.hpp
rename to src/core/include/openvino/op/util/sub_graph_base.hpp
diff --git a/ngraph/core/include/openvino/op/util/unary_elementwise_arithmetic.hpp b/src/core/include/openvino/op/util/unary_elementwise_arithmetic.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/unary_elementwise_arithmetic.hpp
rename to src/core/include/openvino/op/util/unary_elementwise_arithmetic.hpp
diff --git a/ngraph/core/include/openvino/op/util/variable.hpp b/src/core/include/openvino/op/util/variable.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/variable.hpp
rename to src/core/include/openvino/op/util/variable.hpp
diff --git a/ngraph/core/include/openvino/op/util/variable_context.hpp b/src/core/include/openvino/op/util/variable_context.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/variable_context.hpp
rename to src/core/include/openvino/op/util/variable_context.hpp
diff --git a/ngraph/core/include/openvino/op/util/variable_extension.hpp b/src/core/include/openvino/op/util/variable_extension.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/variable_extension.hpp
rename to src/core/include/openvino/op/util/variable_extension.hpp
diff --git a/ngraph/core/include/openvino/op/util/variable_value.hpp b/src/core/include/openvino/op/util/variable_value.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/util/variable_value.hpp
rename to src/core/include/openvino/op/util/variable_value.hpp
diff --git a/ngraph/core/include/openvino/op/variadic_split.hpp b/src/core/include/openvino/op/variadic_split.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/variadic_split.hpp
rename to src/core/include/openvino/op/variadic_split.hpp
diff --git a/ngraph/core/include/openvino/op/xor.hpp b/src/core/include/openvino/op/xor.hpp
similarity index 100%
rename from ngraph/core/include/openvino/op/xor.hpp
rename to src/core/include/openvino/op/xor.hpp
diff --git a/ngraph/core/include/openvino/opsets/opset.hpp b/src/core/include/openvino/opsets/opset.hpp
similarity index 100%
rename from ngraph/core/include/openvino/opsets/opset.hpp
rename to src/core/include/openvino/opsets/opset.hpp
diff --git a/ngraph/core/include/openvino/opsets/opset1.hpp b/src/core/include/openvino/opsets/opset1.hpp
similarity index 100%
rename from ngraph/core/include/openvino/opsets/opset1.hpp
rename to src/core/include/openvino/opsets/opset1.hpp
diff --git a/ngraph/core/include/openvino/opsets/opset1_tbl.hpp b/src/core/include/openvino/opsets/opset1_tbl.hpp
similarity index 100%
rename from ngraph/core/include/openvino/opsets/opset1_tbl.hpp
rename to src/core/include/openvino/opsets/opset1_tbl.hpp
diff --git a/ngraph/core/include/openvino/opsets/opset2.hpp b/src/core/include/openvino/opsets/opset2.hpp
similarity index 100%
rename from ngraph/core/include/openvino/opsets/opset2.hpp
rename to src/core/include/openvino/opsets/opset2.hpp
diff --git a/ngraph/core/include/openvino/opsets/opset2_tbl.hpp b/src/core/include/openvino/opsets/opset2_tbl.hpp
similarity index 100%
rename from ngraph/core/include/openvino/opsets/opset2_tbl.hpp
rename to src/core/include/openvino/opsets/opset2_tbl.hpp
diff --git a/ngraph/core/include/openvino/opsets/opset3.hpp b/src/core/include/openvino/opsets/opset3.hpp
similarity index 100%
rename from ngraph/core/include/openvino/opsets/opset3.hpp
rename to src/core/include/openvino/opsets/opset3.hpp
diff --git a/ngraph/core/include/openvino/opsets/opset3_tbl.hpp b/src/core/include/openvino/opsets/opset3_tbl.hpp
similarity index 100%
rename from ngraph/core/include/openvino/opsets/opset3_tbl.hpp
rename to src/core/include/openvino/opsets/opset3_tbl.hpp
diff --git a/ngraph/core/include/openvino/opsets/opset4.hpp b/src/core/include/openvino/opsets/opset4.hpp
similarity index 100%
rename from ngraph/core/include/openvino/opsets/opset4.hpp
rename to src/core/include/openvino/opsets/opset4.hpp
diff --git a/ngraph/core/include/openvino/opsets/opset4_tbl.hpp b/src/core/include/openvino/opsets/opset4_tbl.hpp
similarity index 100%
rename from ngraph/core/include/openvino/opsets/opset4_tbl.hpp
rename to src/core/include/openvino/opsets/opset4_tbl.hpp
diff --git a/ngraph/core/include/openvino/opsets/opset5.hpp b/src/core/include/openvino/opsets/opset5.hpp
similarity index 100%
rename from ngraph/core/include/openvino/opsets/opset5.hpp
rename to src/core/include/openvino/opsets/opset5.hpp
diff --git a/ngraph/core/include/openvino/opsets/opset5_tbl.hpp b/src/core/include/openvino/opsets/opset5_tbl.hpp
similarity index 100%
rename from ngraph/core/include/openvino/opsets/opset5_tbl.hpp
rename to src/core/include/openvino/opsets/opset5_tbl.hpp
diff --git a/ngraph/core/include/openvino/opsets/opset6.hpp b/src/core/include/openvino/opsets/opset6.hpp
similarity index 100%
rename from ngraph/core/include/openvino/opsets/opset6.hpp
rename to src/core/include/openvino/opsets/opset6.hpp
diff --git a/ngraph/core/include/openvino/opsets/opset6_tbl.hpp b/src/core/include/openvino/opsets/opset6_tbl.hpp
similarity index 100%
rename from ngraph/core/include/openvino/opsets/opset6_tbl.hpp
rename to src/core/include/openvino/opsets/opset6_tbl.hpp
diff --git a/ngraph/core/include/openvino/opsets/opset7.hpp b/src/core/include/openvino/opsets/opset7.hpp
similarity index 100%
rename from ngraph/core/include/openvino/opsets/opset7.hpp
rename to src/core/include/openvino/opsets/opset7.hpp
diff --git a/ngraph/core/include/openvino/opsets/opset7_tbl.hpp b/src/core/include/openvino/opsets/opset7_tbl.hpp
similarity index 100%
rename from ngraph/core/include/openvino/opsets/opset7_tbl.hpp
rename to src/core/include/openvino/opsets/opset7_tbl.hpp
diff --git a/ngraph/core/include/openvino/opsets/opset8.hpp b/src/core/include/openvino/opsets/opset8.hpp
similarity index 100%
rename from ngraph/core/include/openvino/opsets/opset8.hpp
rename to src/core/include/openvino/opsets/opset8.hpp
diff --git a/ngraph/core/include/openvino/opsets/opset8_tbl.hpp b/src/core/include/openvino/opsets/opset8_tbl.hpp
similarity index 100%
rename from ngraph/core/include/openvino/opsets/opset8_tbl.hpp
rename to src/core/include/openvino/opsets/opset8_tbl.hpp
diff --git a/ngraph/core/include/openvino/pass/constant_folding.hpp b/src/core/include/openvino/pass/constant_folding.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/constant_folding.hpp
rename to src/core/include/openvino/pass/constant_folding.hpp
diff --git a/ngraph/core/include/openvino/pass/convert_fp32_to_fp16.hpp b/src/core/include/openvino/pass/convert_fp32_to_fp16.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/convert_fp32_to_fp16.hpp
rename to src/core/include/openvino/pass/convert_fp32_to_fp16.hpp
diff --git a/ngraph/core/include/openvino/pass/graph_rewrite.hpp b/src/core/include/openvino/pass/graph_rewrite.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/graph_rewrite.hpp
rename to src/core/include/openvino/pass/graph_rewrite.hpp
diff --git a/ngraph/core/include/openvino/pass/low_latency.hpp b/src/core/include/openvino/pass/low_latency.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/low_latency.hpp
rename to src/core/include/openvino/pass/low_latency.hpp
diff --git a/ngraph/core/include/openvino/pass/make_stateful.hpp b/src/core/include/openvino/pass/make_stateful.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/make_stateful.hpp
rename to src/core/include/openvino/pass/make_stateful.hpp
diff --git a/ngraph/core/include/openvino/pass/manager.hpp b/src/core/include/openvino/pass/manager.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/manager.hpp
rename to src/core/include/openvino/pass/manager.hpp
diff --git a/ngraph/core/include/openvino/pass/pass.hpp b/src/core/include/openvino/pass/pass.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/pass.hpp
rename to src/core/include/openvino/pass/pass.hpp
diff --git a/ngraph/core/include/openvino/pass/pass_config.hpp b/src/core/include/openvino/pass/pass_config.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/pass_config.hpp
rename to src/core/include/openvino/pass/pass_config.hpp
diff --git a/ngraph/core/include/openvino/pass/pattern/matcher.hpp b/src/core/include/openvino/pass/pattern/matcher.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/pattern/matcher.hpp
rename to src/core/include/openvino/pass/pattern/matcher.hpp
diff --git a/ngraph/core/include/openvino/pass/pattern/op/any.hpp b/src/core/include/openvino/pass/pattern/op/any.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/pattern/op/any.hpp
rename to src/core/include/openvino/pass/pattern/op/any.hpp
diff --git a/ngraph/core/include/openvino/pass/pattern/op/any_of.hpp b/src/core/include/openvino/pass/pattern/op/any_of.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/pattern/op/any_of.hpp
rename to src/core/include/openvino/pass/pattern/op/any_of.hpp
diff --git a/ngraph/core/include/openvino/pass/pattern/op/any_output.hpp b/src/core/include/openvino/pass/pattern/op/any_output.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/pattern/op/any_output.hpp
rename to src/core/include/openvino/pass/pattern/op/any_output.hpp
diff --git a/ngraph/core/include/openvino/pass/pattern/op/branch.hpp b/src/core/include/openvino/pass/pattern/op/branch.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/pattern/op/branch.hpp
rename to src/core/include/openvino/pass/pattern/op/branch.hpp
diff --git a/ngraph/core/include/openvino/pass/pattern/op/capture.hpp b/src/core/include/openvino/pass/pattern/op/capture.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/pattern/op/capture.hpp
rename to src/core/include/openvino/pass/pattern/op/capture.hpp
diff --git a/ngraph/core/include/openvino/pass/pattern/op/label.hpp b/src/core/include/openvino/pass/pattern/op/label.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/pattern/op/label.hpp
rename to src/core/include/openvino/pass/pattern/op/label.hpp
diff --git a/ngraph/core/include/openvino/pass/pattern/op/or.hpp b/src/core/include/openvino/pass/pattern/op/or.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/pattern/op/or.hpp
rename to src/core/include/openvino/pass/pattern/op/or.hpp
diff --git a/ngraph/core/include/openvino/pass/pattern/op/pattern.hpp b/src/core/include/openvino/pass/pattern/op/pattern.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/pattern/op/pattern.hpp
rename to src/core/include/openvino/pass/pattern/op/pattern.hpp
diff --git a/ngraph/core/include/openvino/pass/pattern/op/skip.hpp b/src/core/include/openvino/pass/pattern/op/skip.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/pattern/op/skip.hpp
rename to src/core/include/openvino/pass/pattern/op/skip.hpp
diff --git a/ngraph/core/include/openvino/pass/pattern/op/true.hpp b/src/core/include/openvino/pass/pattern/op/true.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/pattern/op/true.hpp
rename to src/core/include/openvino/pass/pattern/op/true.hpp
diff --git a/ngraph/core/include/openvino/pass/pattern/op/wrap_type.hpp b/src/core/include/openvino/pass/pattern/op/wrap_type.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/pattern/op/wrap_type.hpp
rename to src/core/include/openvino/pass/pattern/op/wrap_type.hpp
diff --git a/ngraph/core/include/openvino/pass/serialize.hpp b/src/core/include/openvino/pass/serialize.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/serialize.hpp
rename to src/core/include/openvino/pass/serialize.hpp
diff --git a/ngraph/core/include/openvino/pass/validate.hpp b/src/core/include/openvino/pass/validate.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/validate.hpp
rename to src/core/include/openvino/pass/validate.hpp
diff --git a/ngraph/core/include/openvino/pass/visualize_tree.hpp b/src/core/include/openvino/pass/visualize_tree.hpp
similarity index 100%
rename from ngraph/core/include/openvino/pass/visualize_tree.hpp
rename to src/core/include/openvino/pass/visualize_tree.hpp
diff --git a/ngraph/core/include/openvino/runtime/allocator.hpp b/src/core/include/openvino/runtime/allocator.hpp
similarity index 100%
rename from ngraph/core/include/openvino/runtime/allocator.hpp
rename to src/core/include/openvino/runtime/allocator.hpp
diff --git a/ngraph/core/include/openvino/runtime/tensor.hpp b/src/core/include/openvino/runtime/tensor.hpp
similarity index 100%
rename from ngraph/core/include/openvino/runtime/tensor.hpp
rename to src/core/include/openvino/runtime/tensor.hpp
diff --git a/ngraph/core/reference/CMakeLists.txt b/src/core/reference/CMakeLists.txt
similarity index 100%
rename from ngraph/core/reference/CMakeLists.txt
rename to src/core/reference/CMakeLists.txt
diff --git a/ngraph/core/reference/include/ngraph/coordinate_index.hpp b/src/core/reference/include/ngraph/coordinate_index.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/coordinate_index.hpp
rename to src/core/reference/include/ngraph/coordinate_index.hpp
diff --git a/ngraph/core/reference/include/ngraph/coordinate_range.hpp b/src/core/reference/include/ngraph/coordinate_range.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/coordinate_range.hpp
rename to src/core/reference/include/ngraph/coordinate_range.hpp
diff --git a/ngraph/core/reference/include/ngraph/coordinate_transform.hpp b/src/core/reference/include/ngraph/coordinate_transform.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/coordinate_transform.hpp
rename to src/core/reference/include/ngraph/coordinate_transform.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/opt_kernel/reshape.hpp b/src/core/reference/include/ngraph/runtime/opt_kernel/reshape.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/opt_kernel/reshape.hpp
rename to src/core/reference/include/ngraph/runtime/opt_kernel/reshape.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/abs.hpp b/src/core/reference/include/ngraph/runtime/reference/abs.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/abs.hpp
rename to src/core/reference/include/ngraph/runtime/reference/abs.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/acos.hpp b/src/core/reference/include/ngraph/runtime/reference/acos.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/acos.hpp
rename to src/core/reference/include/ngraph/runtime/reference/acos.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/acosh.hpp b/src/core/reference/include/ngraph/runtime/reference/acosh.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/acosh.hpp
rename to src/core/reference/include/ngraph/runtime/reference/acosh.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/adaptive_avg_pool.hpp b/src/core/reference/include/ngraph/runtime/reference/adaptive_avg_pool.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/adaptive_avg_pool.hpp
rename to src/core/reference/include/ngraph/runtime/reference/adaptive_avg_pool.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/adaptive_max_pool.hpp b/src/core/reference/include/ngraph/runtime/reference/adaptive_max_pool.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/adaptive_max_pool.hpp
rename to src/core/reference/include/ngraph/runtime/reference/adaptive_max_pool.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/add.hpp b/src/core/reference/include/ngraph/runtime/reference/add.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/add.hpp
rename to src/core/reference/include/ngraph/runtime/reference/add.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/and.hpp b/src/core/reference/include/ngraph/runtime/reference/and.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/and.hpp
rename to src/core/reference/include/ngraph/runtime/reference/and.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/asin.hpp b/src/core/reference/include/ngraph/runtime/reference/asin.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/asin.hpp
rename to src/core/reference/include/ngraph/runtime/reference/asin.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/asinh.hpp b/src/core/reference/include/ngraph/runtime/reference/asinh.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/asinh.hpp
rename to src/core/reference/include/ngraph/runtime/reference/asinh.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/atan.hpp b/src/core/reference/include/ngraph/runtime/reference/atan.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/atan.hpp
rename to src/core/reference/include/ngraph/runtime/reference/atan.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/atan2.hpp b/src/core/reference/include/ngraph/runtime/reference/atan2.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/atan2.hpp
rename to src/core/reference/include/ngraph/runtime/reference/atan2.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/atanh.hpp b/src/core/reference/include/ngraph/runtime/reference/atanh.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/atanh.hpp
rename to src/core/reference/include/ngraph/runtime/reference/atanh.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/autobroadcast_binop.hpp b/src/core/reference/include/ngraph/runtime/reference/autobroadcast_binop.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/autobroadcast_binop.hpp
rename to src/core/reference/include/ngraph/runtime/reference/autobroadcast_binop.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/avg_pool.hpp b/src/core/reference/include/ngraph/runtime/reference/avg_pool.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/avg_pool.hpp
rename to src/core/reference/include/ngraph/runtime/reference/avg_pool.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/batch_norm.hpp b/src/core/reference/include/ngraph/runtime/reference/batch_norm.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/batch_norm.hpp
rename to src/core/reference/include/ngraph/runtime/reference/batch_norm.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/binary_convolution.hpp b/src/core/reference/include/ngraph/runtime/reference/binary_convolution.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/binary_convolution.hpp
rename to src/core/reference/include/ngraph/runtime/reference/binary_convolution.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/broadcast.hpp b/src/core/reference/include/ngraph/runtime/reference/broadcast.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/broadcast.hpp
rename to src/core/reference/include/ngraph/runtime/reference/broadcast.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/bucketize.hpp b/src/core/reference/include/ngraph/runtime/reference/bucketize.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/bucketize.hpp
rename to src/core/reference/include/ngraph/runtime/reference/bucketize.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/ceiling.hpp b/src/core/reference/include/ngraph/runtime/reference/ceiling.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/ceiling.hpp
rename to src/core/reference/include/ngraph/runtime/reference/ceiling.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/clamp.hpp b/src/core/reference/include/ngraph/runtime/reference/clamp.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/clamp.hpp
rename to src/core/reference/include/ngraph/runtime/reference/clamp.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/concat.hpp b/src/core/reference/include/ngraph/runtime/reference/concat.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/concat.hpp
rename to src/core/reference/include/ngraph/runtime/reference/concat.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/constant.hpp b/src/core/reference/include/ngraph/runtime/reference/constant.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/constant.hpp
rename to src/core/reference/include/ngraph/runtime/reference/constant.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/convert.hpp b/src/core/reference/include/ngraph/runtime/reference/convert.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/convert.hpp
rename to src/core/reference/include/ngraph/runtime/reference/convert.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/convert_color_nv12.hpp b/src/core/reference/include/ngraph/runtime/reference/convert_color_nv12.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/convert_color_nv12.hpp
rename to src/core/reference/include/ngraph/runtime/reference/convert_color_nv12.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/convolution.hpp b/src/core/reference/include/ngraph/runtime/reference/convolution.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/convolution.hpp
rename to src/core/reference/include/ngraph/runtime/reference/convolution.hpp
diff --git a/ngraph/core/reference/include/ngraph/runtime/reference/convolution_backprop_data.hpp b/src/core/reference/include/ngraph/runtime/reference/convolution_backprop_data.hpp
similarity index 100%
rename from ngraph/core/reference/include/ngraph/runtime/reference/convolution_backprop_data.hpp
rename to src/core/reference/include/ngraph/runtime/reference/convolution_backprop_data.hpp
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rename from ngraph/core/reference/include/ngraph/runtime/reference/ctc_greedy_decoder_seq_len.hpp
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diff --git a/ngraph/core/src/op/loop.cpp b/src/core/src/op/loop.cpp
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diff --git a/ngraph/core/src/op/nv12_to_bgr.cpp b/src/core/src/op/nv12_to_bgr.cpp
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diff --git a/ngraph/core/src/op/roi_align.cpp b/src/core/src/op/roi_align.cpp
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diff --git a/ngraph/core/src/op/round.cpp b/src/core/src/op/round.cpp
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diff --git a/ngraph/core/src/op/scatter_elements_update.cpp b/src/core/src/op/scatter_elements_update.cpp
similarity index 100%
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similarity index 100%
rename from ngraph/core/src/op/scatter_nd_update.cpp
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similarity index 100%
rename from ngraph/core/src/op/scatter_update.cpp
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diff --git a/ngraph/core/src/op/select.cpp b/src/core/src/op/select.cpp
similarity index 100%
rename from ngraph/core/src/op/select.cpp
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diff --git a/ngraph/core/src/op/selu.cpp b/src/core/src/op/selu.cpp
similarity index 100%
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diff --git a/ngraph/core/src/op/shape_of.cpp b/src/core/src/op/shape_of.cpp
similarity index 100%
rename from ngraph/core/src/op/shape_of.cpp
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diff --git a/ngraph/core/src/op/shuffle_channels.cpp b/src/core/src/op/shuffle_channels.cpp
similarity index 100%
rename from ngraph/core/src/op/shuffle_channels.cpp
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diff --git a/ngraph/core/src/op/sigmoid.cpp b/src/core/src/op/sigmoid.cpp
similarity index 100%
rename from ngraph/core/src/op/sigmoid.cpp
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diff --git a/ngraph/core/src/op/sign.cpp b/src/core/src/op/sign.cpp
similarity index 100%
rename from ngraph/core/src/op/sign.cpp
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diff --git a/ngraph/core/src/op/sin.cpp b/src/core/src/op/sin.cpp
similarity index 100%
rename from ngraph/core/src/op/sin.cpp
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diff --git a/ngraph/core/src/op/sinh.cpp b/src/core/src/op/sinh.cpp
similarity index 100%
rename from ngraph/core/src/op/sinh.cpp
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diff --git a/ngraph/core/src/op/sink.cpp b/src/core/src/op/sink.cpp
similarity index 100%
rename from ngraph/core/src/op/sink.cpp
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similarity index 100%
rename from ngraph/core/src/op/slice.cpp
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diff --git a/ngraph/core/src/op/softmax.cpp b/src/core/src/op/softmax.cpp
similarity index 100%
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diff --git a/ngraph/core/src/op/softplus.cpp b/src/core/src/op/softplus.cpp
similarity index 100%
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diff --git a/ngraph/core/src/op/space_to_batch.cpp b/src/core/src/op/space_to_batch.cpp
similarity index 100%
rename from ngraph/core/src/op/space_to_batch.cpp
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diff --git a/ngraph/core/src/op/space_to_depth.cpp b/src/core/src/op/space_to_depth.cpp
similarity index 100%
rename from ngraph/core/src/op/space_to_depth.cpp
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diff --git a/ngraph/core/src/op/split.cpp b/src/core/src/op/split.cpp
similarity index 100%
rename from ngraph/core/src/op/split.cpp
rename to src/core/src/op/split.cpp
diff --git a/ngraph/core/src/op/sqrt.cpp b/src/core/src/op/sqrt.cpp
similarity index 100%
rename from ngraph/core/src/op/sqrt.cpp
rename to src/core/src/op/sqrt.cpp
diff --git a/ngraph/core/src/op/squared_difference.cpp b/src/core/src/op/squared_difference.cpp
similarity index 100%
rename from ngraph/core/src/op/squared_difference.cpp
rename to src/core/src/op/squared_difference.cpp
diff --git a/ngraph/core/src/op/squeeze.cpp b/src/core/src/op/squeeze.cpp
similarity index 100%
rename from ngraph/core/src/op/squeeze.cpp
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diff --git a/ngraph/core/src/op/strided_slice.cpp b/src/core/src/op/strided_slice.cpp
similarity index 100%
rename from ngraph/core/src/op/strided_slice.cpp
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diff --git a/ngraph/core/src/op/subtract.cpp b/src/core/src/op/subtract.cpp
similarity index 100%
rename from ngraph/core/src/op/subtract.cpp
rename to src/core/src/op/subtract.cpp
diff --git a/ngraph/core/src/op/swish.cpp b/src/core/src/op/swish.cpp
similarity index 100%
rename from ngraph/core/src/op/swish.cpp
rename to src/core/src/op/swish.cpp
diff --git a/ngraph/core/src/op/tan.cpp b/src/core/src/op/tan.cpp
similarity index 100%
rename from ngraph/core/src/op/tan.cpp
rename to src/core/src/op/tan.cpp
diff --git a/ngraph/core/src/op/tanh.cpp b/src/core/src/op/tanh.cpp
similarity index 100%
rename from ngraph/core/src/op/tanh.cpp
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diff --git a/ngraph/core/src/op/tensor_iterator.cpp b/src/core/src/op/tensor_iterator.cpp
similarity index 100%
rename from ngraph/core/src/op/tensor_iterator.cpp
rename to src/core/src/op/tensor_iterator.cpp
diff --git a/ngraph/core/src/op/tile.cpp b/src/core/src/op/tile.cpp
similarity index 100%
rename from ngraph/core/src/op/tile.cpp
rename to src/core/src/op/tile.cpp
diff --git a/ngraph/core/src/op/topk.cpp b/src/core/src/op/topk.cpp
similarity index 100%
rename from ngraph/core/src/op/topk.cpp
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diff --git a/ngraph/core/src/op/transpose.cpp b/src/core/src/op/transpose.cpp
similarity index 100%
rename from ngraph/core/src/op/transpose.cpp
rename to src/core/src/op/transpose.cpp
diff --git a/ngraph/core/src/op/type_relaxed.cpp b/src/core/src/op/type_relaxed.cpp
similarity index 100%
rename from ngraph/core/src/op/type_relaxed.cpp
rename to src/core/src/op/type_relaxed.cpp
diff --git a/ngraph/core/src/op/unsqueeze.cpp b/src/core/src/op/unsqueeze.cpp
similarity index 100%
rename from ngraph/core/src/op/unsqueeze.cpp
rename to src/core/src/op/unsqueeze.cpp
diff --git a/ngraph/core/src/op/util/activation_functions.cpp b/src/core/src/op/util/activation_functions.cpp
similarity index 100%
rename from ngraph/core/src/op/util/activation_functions.cpp
rename to src/core/src/op/util/activation_functions.cpp
diff --git a/ngraph/core/src/op/util/arithmetic_reduction.cpp b/src/core/src/op/util/arithmetic_reduction.cpp
similarity index 100%
rename from ngraph/core/src/op/util/arithmetic_reduction.cpp
rename to src/core/src/op/util/arithmetic_reduction.cpp
diff --git a/ngraph/core/src/op/util/arithmetic_reductions_keep_dims.cpp b/src/core/src/op/util/arithmetic_reductions_keep_dims.cpp
similarity index 100%
rename from ngraph/core/src/op/util/arithmetic_reductions_keep_dims.cpp
rename to src/core/src/op/util/arithmetic_reductions_keep_dims.cpp
diff --git a/ngraph/core/src/op/util/assign_base.cpp b/src/core/src/op/util/assign_base.cpp
similarity index 100%
rename from ngraph/core/src/op/util/assign_base.cpp
rename to src/core/src/op/util/assign_base.cpp
diff --git a/ngraph/core/src/op/util/attr_types.cpp b/src/core/src/op/util/attr_types.cpp
similarity index 100%
rename from ngraph/core/src/op/util/attr_types.cpp
rename to src/core/src/op/util/attr_types.cpp
diff --git a/ngraph/core/src/op/util/binary_elementwise_arithmetic.cpp b/src/core/src/op/util/binary_elementwise_arithmetic.cpp
similarity index 100%
rename from ngraph/core/src/op/util/binary_elementwise_arithmetic.cpp
rename to src/core/src/op/util/binary_elementwise_arithmetic.cpp
diff --git a/ngraph/core/src/op/util/binary_elementwise_comparison.cpp b/src/core/src/op/util/binary_elementwise_comparison.cpp
similarity index 100%
rename from ngraph/core/src/op/util/binary_elementwise_comparison.cpp
rename to src/core/src/op/util/binary_elementwise_comparison.cpp
diff --git a/ngraph/core/src/op/util/binary_elementwise_logical.cpp b/src/core/src/op/util/binary_elementwise_logical.cpp
similarity index 100%
rename from ngraph/core/src/op/util/binary_elementwise_logical.cpp
rename to src/core/src/op/util/binary_elementwise_logical.cpp
diff --git a/ngraph/core/src/op/util/broadcast_base.cpp b/src/core/src/op/util/broadcast_base.cpp
similarity index 100%
rename from ngraph/core/src/op/util/broadcast_base.cpp
rename to src/core/src/op/util/broadcast_base.cpp
diff --git a/ngraph/core/src/op/util/convert_color_i420_base.cpp b/src/core/src/op/util/convert_color_i420_base.cpp
similarity index 100%
rename from ngraph/core/src/op/util/convert_color_i420_base.cpp
rename to src/core/src/op/util/convert_color_i420_base.cpp
diff --git a/ngraph/core/src/op/util/convert_color_nv12_base.cpp b/src/core/src/op/util/convert_color_nv12_base.cpp
similarity index 100%
rename from ngraph/core/src/op/util/convert_color_nv12_base.cpp
rename to src/core/src/op/util/convert_color_nv12_base.cpp
diff --git a/ngraph/core/src/op/util/deformable_convolution_base.cpp b/src/core/src/op/util/deformable_convolution_base.cpp
similarity index 100%
rename from ngraph/core/src/op/util/deformable_convolution_base.cpp
rename to src/core/src/op/util/deformable_convolution_base.cpp
diff --git a/ngraph/core/src/op/util/elementwise_args.cpp b/src/core/src/op/util/elementwise_args.cpp
similarity index 100%
rename from ngraph/core/src/op/util/elementwise_args.cpp
rename to src/core/src/op/util/elementwise_args.cpp
diff --git a/ngraph/core/src/op/util/embeddingbag_offsets_base.cpp b/src/core/src/op/util/embeddingbag_offsets_base.cpp
similarity index 100%
rename from ngraph/core/src/op/util/embeddingbag_offsets_base.cpp
rename to src/core/src/op/util/embeddingbag_offsets_base.cpp
diff --git a/ngraph/core/src/op/util/embeddingbag_packed_base.cpp b/src/core/src/op/util/embeddingbag_packed_base.cpp
similarity index 100%
rename from ngraph/core/src/op/util/embeddingbag_packed_base.cpp
rename to src/core/src/op/util/embeddingbag_packed_base.cpp
diff --git a/ngraph/core/src/op/util/evaluate_helpers.cpp b/src/core/src/op/util/evaluate_helpers.cpp
similarity index 100%
rename from ngraph/core/src/op/util/evaluate_helpers.cpp
rename to src/core/src/op/util/evaluate_helpers.cpp
diff --git a/ngraph/core/src/op/util/evaluation_context.cpp b/src/core/src/op/util/evaluation_context.cpp
similarity index 100%
rename from ngraph/core/src/op/util/evaluation_context.cpp
rename to src/core/src/op/util/evaluation_context.cpp
diff --git a/ngraph/core/src/op/util/fft_base.cpp b/src/core/src/op/util/fft_base.cpp
similarity index 100%
rename from ngraph/core/src/op/util/fft_base.cpp
rename to src/core/src/op/util/fft_base.cpp
diff --git a/ngraph/core/src/op/util/framework_node.cpp b/src/core/src/op/util/framework_node.cpp
similarity index 100%
rename from ngraph/core/src/op/util/framework_node.cpp
rename to src/core/src/op/util/framework_node.cpp
diff --git a/ngraph/core/src/op/util/gather_base.cpp b/src/core/src/op/util/gather_base.cpp
similarity index 100%
rename from ngraph/core/src/op/util/gather_base.cpp
rename to src/core/src/op/util/gather_base.cpp
diff --git a/ngraph/core/src/op/util/gather_nd_base.cpp b/src/core/src/op/util/gather_nd_base.cpp
similarity index 100%
rename from ngraph/core/src/op/util/gather_nd_base.cpp
rename to src/core/src/op/util/gather_nd_base.cpp
diff --git a/ngraph/core/src/op/util/index_reduction.cpp b/src/core/src/op/util/index_reduction.cpp
similarity index 100%
rename from ngraph/core/src/op/util/index_reduction.cpp
rename to src/core/src/op/util/index_reduction.cpp
diff --git a/ngraph/core/src/op/util/logical_reduction.cpp b/src/core/src/op/util/logical_reduction.cpp
similarity index 100%
rename from ngraph/core/src/op/util/logical_reduction.cpp
rename to src/core/src/op/util/logical_reduction.cpp
diff --git a/ngraph/core/src/op/util/logical_reduction_keep_dims.cpp b/src/core/src/op/util/logical_reduction_keep_dims.cpp
similarity index 100%
rename from ngraph/core/src/op/util/logical_reduction_keep_dims.cpp
rename to src/core/src/op/util/logical_reduction_keep_dims.cpp
diff --git a/ngraph/core/src/op/util/max_pool_base.cpp b/src/core/src/op/util/max_pool_base.cpp
similarity index 100%
rename from ngraph/core/src/op/util/max_pool_base.cpp
rename to src/core/src/op/util/max_pool_base.cpp
diff --git a/ngraph/core/src/op/util/multi_subgraph_base.cpp b/src/core/src/op/util/multi_subgraph_base.cpp
similarity index 100%
rename from ngraph/core/src/op/util/multi_subgraph_base.cpp
rename to src/core/src/op/util/multi_subgraph_base.cpp
diff --git a/ngraph/core/src/op/util/nms_base.cpp b/src/core/src/op/util/nms_base.cpp
similarity index 100%
rename from ngraph/core/src/op/util/nms_base.cpp
rename to src/core/src/op/util/nms_base.cpp
diff --git a/ngraph/core/src/op/util/op_types.cpp b/src/core/src/op/util/op_types.cpp
similarity index 100%
rename from ngraph/core/src/op/util/op_types.cpp
rename to src/core/src/op/util/op_types.cpp
diff --git a/ngraph/core/src/op/util/precision_sensitive_attribute.cpp b/src/core/src/op/util/precision_sensitive_attribute.cpp
similarity index 100%
rename from ngraph/core/src/op/util/precision_sensitive_attribute.cpp
rename to src/core/src/op/util/precision_sensitive_attribute.cpp
diff --git a/ngraph/core/src/op/util/read_value_base.cpp b/src/core/src/op/util/read_value_base.cpp
similarity index 100%
rename from ngraph/core/src/op/util/read_value_base.cpp
rename to src/core/src/op/util/read_value_base.cpp
diff --git a/ngraph/core/src/op/util/recurrent_sequence.cpp b/src/core/src/op/util/recurrent_sequence.cpp
similarity index 100%
rename from ngraph/core/src/op/util/recurrent_sequence.cpp
rename to src/core/src/op/util/recurrent_sequence.cpp
diff --git a/ngraph/core/src/op/util/reduction_base.cpp b/src/core/src/op/util/reduction_base.cpp
similarity index 100%
rename from ngraph/core/src/op/util/reduction_base.cpp
rename to src/core/src/op/util/reduction_base.cpp
diff --git a/ngraph/core/src/op/util/rnn_cell_base.cpp b/src/core/src/op/util/rnn_cell_base.cpp
similarity index 100%
rename from ngraph/core/src/op/util/rnn_cell_base.cpp
rename to src/core/src/op/util/rnn_cell_base.cpp
diff --git a/ngraph/core/src/op/util/scatter_base.cpp b/src/core/src/op/util/scatter_base.cpp
similarity index 100%
rename from ngraph/core/src/op/util/scatter_base.cpp
rename to src/core/src/op/util/scatter_base.cpp
diff --git a/ngraph/core/src/op/util/scatter_nd_base.cpp b/src/core/src/op/util/scatter_nd_base.cpp
similarity index 100%
rename from ngraph/core/src/op/util/scatter_nd_base.cpp
rename to src/core/src/op/util/scatter_nd_base.cpp
diff --git a/ngraph/core/src/op/util/slice_plan.cpp b/src/core/src/op/util/slice_plan.cpp
similarity index 100%
rename from ngraph/core/src/op/util/slice_plan.cpp
rename to src/core/src/op/util/slice_plan.cpp
diff --git a/ngraph/core/src/op/util/sub_graph_base.cpp b/src/core/src/op/util/sub_graph_base.cpp
similarity index 100%
rename from ngraph/core/src/op/util/sub_graph_base.cpp
rename to src/core/src/op/util/sub_graph_base.cpp
diff --git a/ngraph/core/src/op/util/unary_elementwise_arithmetic.cpp b/src/core/src/op/util/unary_elementwise_arithmetic.cpp
similarity index 100%
rename from ngraph/core/src/op/util/unary_elementwise_arithmetic.cpp
rename to src/core/src/op/util/unary_elementwise_arithmetic.cpp
diff --git a/ngraph/core/src/op/util/variable.cpp b/src/core/src/op/util/variable.cpp
similarity index 100%
rename from ngraph/core/src/op/util/variable.cpp
rename to src/core/src/op/util/variable.cpp
diff --git a/ngraph/core/src/op/variadic_split.cpp b/src/core/src/op/variadic_split.cpp
similarity index 100%
rename from ngraph/core/src/op/variadic_split.cpp
rename to src/core/src/op/variadic_split.cpp
diff --git a/ngraph/core/src/op/xor.cpp b/src/core/src/op/xor.cpp
similarity index 100%
rename from ngraph/core/src/op/xor.cpp
rename to src/core/src/op/xor.cpp
diff --git a/ngraph/core/src/opsets/opset.cpp b/src/core/src/opsets/opset.cpp
similarity index 100%
rename from ngraph/core/src/opsets/opset.cpp
rename to src/core/src/opsets/opset.cpp
diff --git a/ngraph/core/src/partial_shape.cpp b/src/core/src/partial_shape.cpp
similarity index 100%
rename from ngraph/core/src/partial_shape.cpp
rename to src/core/src/partial_shape.cpp
diff --git a/ngraph/core/src/pass/constant_folding.cpp b/src/core/src/pass/constant_folding.cpp
similarity index 100%
rename from ngraph/core/src/pass/constant_folding.cpp
rename to src/core/src/pass/constant_folding.cpp
diff --git a/ngraph/core/src/pass/convert_fp32_to_fp16.cpp b/src/core/src/pass/convert_fp32_to_fp16.cpp
similarity index 100%
rename from ngraph/core/src/pass/convert_fp32_to_fp16.cpp
rename to src/core/src/pass/convert_fp32_to_fp16.cpp
diff --git a/ngraph/core/src/pass/convert_precision.cpp b/src/core/src/pass/convert_precision.cpp
similarity index 100%
rename from ngraph/core/src/pass/convert_precision.cpp
rename to src/core/src/pass/convert_precision.cpp
diff --git a/ngraph/core/src/pass/graph_rewrite.cpp b/src/core/src/pass/graph_rewrite.cpp
similarity index 100%
rename from ngraph/core/src/pass/graph_rewrite.cpp
rename to src/core/src/pass/graph_rewrite.cpp
diff --git a/ngraph/core/src/pass/init_node_info.cpp b/src/core/src/pass/init_node_info.cpp
similarity index 100%
rename from ngraph/core/src/pass/init_node_info.cpp
rename to src/core/src/pass/init_node_info.cpp
diff --git a/ngraph/core/src/pass/low_latency.cpp b/src/core/src/pass/low_latency.cpp
similarity index 100%
rename from ngraph/core/src/pass/low_latency.cpp
rename to src/core/src/pass/low_latency.cpp
diff --git a/ngraph/core/src/pass/make_stateful.cpp b/src/core/src/pass/make_stateful.cpp
similarity index 100%
rename from ngraph/core/src/pass/make_stateful.cpp
rename to src/core/src/pass/make_stateful.cpp
diff --git a/ngraph/core/src/pass/manager.cpp b/src/core/src/pass/manager.cpp
similarity index 100%
rename from ngraph/core/src/pass/manager.cpp
rename to src/core/src/pass/manager.cpp
diff --git a/ngraph/core/src/pass/pass.cpp b/src/core/src/pass/pass.cpp
similarity index 100%
rename from ngraph/core/src/pass/pass.cpp
rename to src/core/src/pass/pass.cpp
diff --git a/ngraph/core/src/pass/pass_config.cpp b/src/core/src/pass/pass_config.cpp
similarity index 100%
rename from ngraph/core/src/pass/pass_config.cpp
rename to src/core/src/pass/pass_config.cpp
diff --git a/ngraph/core/src/pass/perf_counters.cpp b/src/core/src/pass/perf_counters.cpp
similarity index 100%
rename from ngraph/core/src/pass/perf_counters.cpp
rename to src/core/src/pass/perf_counters.cpp
diff --git a/ngraph/core/src/pass/perf_counters.hpp b/src/core/src/pass/perf_counters.hpp
similarity index 100%
rename from ngraph/core/src/pass/perf_counters.hpp
rename to src/core/src/pass/perf_counters.hpp
diff --git a/ngraph/core/src/pass/rt_info/fused_names_attribute.cpp b/src/core/src/pass/rt_info/fused_names_attribute.cpp
similarity index 100%
rename from ngraph/core/src/pass/rt_info/fused_names_attribute.cpp
rename to src/core/src/pass/rt_info/fused_names_attribute.cpp
diff --git a/ngraph/core/src/pass/rt_info/primitives_priority_attribute.cpp b/src/core/src/pass/rt_info/primitives_priority_attribute.cpp
similarity index 100%
rename from ngraph/core/src/pass/rt_info/primitives_priority_attribute.cpp
rename to src/core/src/pass/rt_info/primitives_priority_attribute.cpp
diff --git a/ngraph/core/src/pass/serialize.cpp b/src/core/src/pass/serialize.cpp
similarity index 100%
rename from ngraph/core/src/pass/serialize.cpp
rename to src/core/src/pass/serialize.cpp
diff --git a/ngraph/core/src/pass/smart_reshape/matmul_sr.cpp b/src/core/src/pass/smart_reshape/matmul_sr.cpp
similarity index 100%
rename from ngraph/core/src/pass/smart_reshape/matmul_sr.cpp
rename to src/core/src/pass/smart_reshape/matmul_sr.cpp
diff --git a/ngraph/core/src/pass/smart_reshape/mimic_set_batch_size.cpp b/src/core/src/pass/smart_reshape/mimic_set_batch_size.cpp
similarity index 100%
rename from ngraph/core/src/pass/smart_reshape/mimic_set_batch_size.cpp
rename to src/core/src/pass/smart_reshape/mimic_set_batch_size.cpp
diff --git a/ngraph/core/src/pass/smart_reshape/proposal_scales_stridedslice.cpp b/src/core/src/pass/smart_reshape/proposal_scales_stridedslice.cpp
similarity index 100%
rename from ngraph/core/src/pass/smart_reshape/proposal_scales_stridedslice.cpp
rename to src/core/src/pass/smart_reshape/proposal_scales_stridedslice.cpp
diff --git a/ngraph/core/src/pass/smart_reshape/reshape_to_1D.cpp b/src/core/src/pass/smart_reshape/reshape_to_1D.cpp
similarity index 100%
rename from ngraph/core/src/pass/smart_reshape/reshape_to_1D.cpp
rename to src/core/src/pass/smart_reshape/reshape_to_1D.cpp
diff --git a/ngraph/core/src/pass/smart_reshape/set_batch_size.cpp b/src/core/src/pass/smart_reshape/set_batch_size.cpp
similarity index 100%
rename from ngraph/core/src/pass/smart_reshape/set_batch_size.cpp
rename to src/core/src/pass/smart_reshape/set_batch_size.cpp
diff --git a/ngraph/core/src/pass/smart_reshape/smart_reshape.cpp b/src/core/src/pass/smart_reshape/smart_reshape.cpp
similarity index 100%
rename from ngraph/core/src/pass/smart_reshape/smart_reshape.cpp
rename to src/core/src/pass/smart_reshape/smart_reshape.cpp
diff --git a/ngraph/core/src/pass/smart_reshape/strided_slice_squeeze.cpp b/src/core/src/pass/smart_reshape/strided_slice_squeeze.cpp
similarity index 100%
rename from ngraph/core/src/pass/smart_reshape/strided_slice_squeeze.cpp
rename to src/core/src/pass/smart_reshape/strided_slice_squeeze.cpp
diff --git a/ngraph/core/src/pass/validate.cpp b/src/core/src/pass/validate.cpp
similarity index 100%
rename from ngraph/core/src/pass/validate.cpp
rename to src/core/src/pass/validate.cpp
diff --git a/ngraph/core/src/pass/visualize_tree.cpp b/src/core/src/pass/visualize_tree.cpp
similarity index 100%
rename from ngraph/core/src/pass/visualize_tree.cpp
rename to src/core/src/pass/visualize_tree.cpp
diff --git a/ngraph/core/src/pattern/matcher.cpp b/src/core/src/pattern/matcher.cpp
similarity index 100%
rename from ngraph/core/src/pattern/matcher.cpp
rename to src/core/src/pattern/matcher.cpp
diff --git a/ngraph/core/src/pattern/op/any.cpp b/src/core/src/pattern/op/any.cpp
similarity index 100%
rename from ngraph/core/src/pattern/op/any.cpp
rename to src/core/src/pattern/op/any.cpp
diff --git a/ngraph/core/src/pattern/op/any_of.cpp b/src/core/src/pattern/op/any_of.cpp
similarity index 100%
rename from ngraph/core/src/pattern/op/any_of.cpp
rename to src/core/src/pattern/op/any_of.cpp
diff --git a/ngraph/core/src/pattern/op/any_output.cpp b/src/core/src/pattern/op/any_output.cpp
similarity index 100%
rename from ngraph/core/src/pattern/op/any_output.cpp
rename to src/core/src/pattern/op/any_output.cpp
diff --git a/ngraph/core/src/pattern/op/branch.cpp b/src/core/src/pattern/op/branch.cpp
similarity index 100%
rename from ngraph/core/src/pattern/op/branch.cpp
rename to src/core/src/pattern/op/branch.cpp
diff --git a/ngraph/core/src/pattern/op/capture.cpp b/src/core/src/pattern/op/capture.cpp
similarity index 100%
rename from ngraph/core/src/pattern/op/capture.cpp
rename to src/core/src/pattern/op/capture.cpp
diff --git a/ngraph/core/src/pattern/op/label.cpp b/src/core/src/pattern/op/label.cpp
similarity index 100%
rename from ngraph/core/src/pattern/op/label.cpp
rename to src/core/src/pattern/op/label.cpp
diff --git a/ngraph/core/src/pattern/op/or.cpp b/src/core/src/pattern/op/or.cpp
similarity index 100%
rename from ngraph/core/src/pattern/op/or.cpp
rename to src/core/src/pattern/op/or.cpp
diff --git a/ngraph/core/src/pattern/op/pattern.cpp b/src/core/src/pattern/op/pattern.cpp
similarity index 100%
rename from ngraph/core/src/pattern/op/pattern.cpp
rename to src/core/src/pattern/op/pattern.cpp
diff --git a/ngraph/core/src/pattern/op/skip.cpp b/src/core/src/pattern/op/skip.cpp
similarity index 100%
rename from ngraph/core/src/pattern/op/skip.cpp
rename to src/core/src/pattern/op/skip.cpp
diff --git a/ngraph/core/src/pattern/op/true.cpp b/src/core/src/pattern/op/true.cpp
similarity index 100%
rename from ngraph/core/src/pattern/op/true.cpp
rename to src/core/src/pattern/op/true.cpp
diff --git a/ngraph/core/src/pattern/op/wrap_type.cpp b/src/core/src/pattern/op/wrap_type.cpp
similarity index 100%
rename from ngraph/core/src/pattern/op/wrap_type.cpp
rename to src/core/src/pattern/op/wrap_type.cpp
diff --git a/ngraph/core/src/precomp.hpp b/src/core/src/precomp.hpp
similarity index 100%
rename from ngraph/core/src/precomp.hpp
rename to src/core/src/precomp.hpp
diff --git a/ngraph/core/src/preprocess/color_utils.cpp b/src/core/src/preprocess/color_utils.cpp
similarity index 100%
rename from ngraph/core/src/preprocess/color_utils.cpp
rename to src/core/src/preprocess/color_utils.cpp
diff --git a/ngraph/core/src/preprocess/color_utils.hpp b/src/core/src/preprocess/color_utils.hpp
similarity index 100%
rename from ngraph/core/src/preprocess/color_utils.hpp
rename to src/core/src/preprocess/color_utils.hpp
diff --git a/ngraph/core/src/preprocess/function_guard.hpp b/src/core/src/preprocess/function_guard.hpp
similarity index 100%
rename from ngraph/core/src/preprocess/function_guard.hpp
rename to src/core/src/preprocess/function_guard.hpp
diff --git a/ngraph/core/src/preprocess/pre_post_process.cpp b/src/core/src/preprocess/pre_post_process.cpp
similarity index 100%
rename from ngraph/core/src/preprocess/pre_post_process.cpp
rename to src/core/src/preprocess/pre_post_process.cpp
diff --git a/ngraph/core/src/preprocess/preprocess_steps_impl.cpp b/src/core/src/preprocess/preprocess_steps_impl.cpp
similarity index 100%
rename from ngraph/core/src/preprocess/preprocess_steps_impl.cpp
rename to src/core/src/preprocess/preprocess_steps_impl.cpp
diff --git a/ngraph/core/src/preprocess/preprocess_steps_impl.hpp b/src/core/src/preprocess/preprocess_steps_impl.hpp
similarity index 100%
rename from ngraph/core/src/preprocess/preprocess_steps_impl.hpp
rename to src/core/src/preprocess/preprocess_steps_impl.hpp
diff --git a/ngraph/core/src/rt_info.cpp b/src/core/src/rt_info.cpp
similarity index 100%
rename from ngraph/core/src/rt_info.cpp
rename to src/core/src/rt_info.cpp
diff --git a/ngraph/core/src/runtime/aligned_buffer.cpp b/src/core/src/runtime/aligned_buffer.cpp
similarity index 100%
rename from ngraph/core/src/runtime/aligned_buffer.cpp
rename to src/core/src/runtime/aligned_buffer.cpp
diff --git a/ngraph/core/src/runtime/allocator.cpp b/src/core/src/runtime/allocator.cpp
similarity index 100%
rename from ngraph/core/src/runtime/allocator.cpp
rename to src/core/src/runtime/allocator.cpp
diff --git a/ngraph/core/src/runtime/blob_allocator.hpp b/src/core/src/runtime/blob_allocator.hpp
similarity index 100%
rename from ngraph/core/src/runtime/blob_allocator.hpp
rename to src/core/src/runtime/blob_allocator.hpp
diff --git a/ngraph/core/src/runtime/host_tensor.cpp b/src/core/src/runtime/host_tensor.cpp
similarity index 100%
rename from ngraph/core/src/runtime/host_tensor.cpp
rename to src/core/src/runtime/host_tensor.cpp
diff --git a/ngraph/core/src/runtime/ov_tensor.cpp b/src/core/src/runtime/ov_tensor.cpp
similarity index 100%
rename from ngraph/core/src/runtime/ov_tensor.cpp
rename to src/core/src/runtime/ov_tensor.cpp
diff --git a/ngraph/core/src/runtime/tensor.cpp b/src/core/src/runtime/tensor.cpp
similarity index 100%
rename from ngraph/core/src/runtime/tensor.cpp
rename to src/core/src/runtime/tensor.cpp
diff --git a/ngraph/core/src/shape.cpp b/src/core/src/shape.cpp
similarity index 100%
rename from ngraph/core/src/shape.cpp
rename to src/core/src/shape.cpp
diff --git a/ngraph/core/src/shape_util.cpp b/src/core/src/shape_util.cpp
similarity index 100%
rename from ngraph/core/src/shape_util.cpp
rename to src/core/src/shape_util.cpp
diff --git a/ngraph/core/src/shared_node_info.hpp b/src/core/src/shared_node_info.hpp
similarity index 100%
rename from ngraph/core/src/shared_node_info.hpp
rename to src/core/src/shared_node_info.hpp
diff --git a/ngraph/core/src/so_extension.cpp b/src/core/src/so_extension.cpp
similarity index 100%
rename from ngraph/core/src/so_extension.cpp
rename to src/core/src/so_extension.cpp
diff --git a/ngraph/core/src/so_extension.hpp b/src/core/src/so_extension.hpp
similarity index 100%
rename from ngraph/core/src/so_extension.hpp
rename to src/core/src/so_extension.hpp
diff --git a/ngraph/core/src/specialize_function.cpp b/src/core/src/specialize_function.cpp
similarity index 100%
rename from ngraph/core/src/specialize_function.cpp
rename to src/core/src/specialize_function.cpp
diff --git a/ngraph/core/src/strides.cpp b/src/core/src/strides.cpp
similarity index 100%
rename from ngraph/core/src/strides.cpp
rename to src/core/src/strides.cpp
diff --git a/ngraph/core/src/tensor_name_util.hpp b/src/core/src/tensor_name_util.hpp
similarity index 100%
rename from ngraph/core/src/tensor_name_util.hpp
rename to src/core/src/tensor_name_util.hpp
diff --git a/ngraph/core/src/type.cpp b/src/core/src/type.cpp
similarity index 100%
rename from ngraph/core/src/type.cpp
rename to src/core/src/type.cpp
diff --git a/ngraph/core/src/type/bfloat16.cpp b/src/core/src/type/bfloat16.cpp
similarity index 100%
rename from ngraph/core/src/type/bfloat16.cpp
rename to src/core/src/type/bfloat16.cpp
diff --git a/ngraph/core/src/type/element_type.cpp b/src/core/src/type/element_type.cpp
similarity index 100%
rename from ngraph/core/src/type/element_type.cpp
rename to src/core/src/type/element_type.cpp
diff --git a/ngraph/core/src/type/float16.cpp b/src/core/src/type/float16.cpp
similarity index 100%
rename from ngraph/core/src/type/float16.cpp
rename to src/core/src/type/float16.cpp
diff --git a/ngraph/core/src/util.cpp b/src/core/src/util.cpp
similarity index 100%
rename from ngraph/core/src/util.cpp
rename to src/core/src/util.cpp
diff --git a/ngraph/core/src/validation_util.cpp b/src/core/src/validation_util.cpp
similarity index 100%
rename from ngraph/core/src/validation_util.cpp
rename to src/core/src/validation_util.cpp
diff --git a/ngraph/core/src/variant.cpp b/src/core/src/variant.cpp
similarity index 100%
rename from ngraph/core/src/variant.cpp
rename to src/core/src/variant.cpp
diff --git a/ngraph/core/src/version.cpp b/src/core/src/version.cpp
similarity index 100%
rename from ngraph/core/src/version.cpp
rename to src/core/src/version.cpp
diff --git a/ngraph/test/CMakeLists.txt b/src/core/tests/CMakeLists.txt
similarity index 99%
rename from ngraph/test/CMakeLists.txt
rename to src/core/tests/CMakeLists.txt
index d5d1655ea84..8510fd51abc 100644
--- a/ngraph/test/CMakeLists.txt
+++ b/src/core/tests/CMakeLists.txt
@@ -7,13 +7,13 @@ if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fuse-ld=gold")
 endif()
 
-add_definitions(-DSERIALIZED_ZOO=\"${TEST_MODEL_ZOO}/ngraph/models\")
+add_definitions(-DSERIALIZED_ZOO=\"${TEST_MODEL_ZOO}/core/models\")
 set(NGRAPH_ONNX_NAMESPACE ngraph_onnx)
 
 add_subdirectory(runtime)
 
 if(ENABLE_TESTS)
-    add_definitions("-DTEST_FILES=\"${TEST_MODEL_ZOO}/ngraph/files\"")
+    add_definitions("-DTEST_FILES=\"${TEST_MODEL_ZOO}/core/files\"")
     add_subdirectory(util)
     add_subdirectory(engines_util)
     if (NGRAPH_ONNX_FRONTEND_ENABLE)
@@ -436,7 +436,7 @@ set_source_files_properties(${CMAKE_CURRENT_SOURCE_DIR}/threading.cpp
 
 # Add include path to so_extension.hpp
 set_property(SOURCE ${CMAKE_CURRENT_SOURCE_DIR}/extension.cpp
-                    APPEND PROPERTY INCLUDE_DIRECTORIES "${OpenVINO_SOURCE_DIR}/ngraph/core/src/")
+                    APPEND PROPERTY INCLUDE_DIRECTORIES "${OpenVINO_SOURCE_DIR}/src/core/src/")
 
 if(SUGGEST_OVERRIDE_SUPPORTED)
     set_source_files_properties(ov_tensor_test.cpp
diff --git a/ngraph/test/aligned_buffer.cpp b/src/core/tests/aligned_buffer.cpp
similarity index 100%
rename from ngraph/test/aligned_buffer.cpp
rename to src/core/tests/aligned_buffer.cpp
diff --git a/ngraph/test/all_close_f.cpp b/src/core/tests/all_close_f.cpp
similarity index 100%
rename from ngraph/test/all_close_f.cpp
rename to src/core/tests/all_close_f.cpp
diff --git a/ngraph/test/any.cpp b/src/core/tests/any.cpp
similarity index 100%
rename from ngraph/test/any.cpp
rename to src/core/tests/any.cpp
diff --git a/ngraph/test/backend/abc.in.cpp b/src/core/tests/backend/abc.in.cpp
similarity index 100%
rename from ngraph/test/backend/abc.in.cpp
rename to src/core/tests/backend/abc.in.cpp
diff --git a/ngraph/test/backend/aliased_output.in.cpp b/src/core/tests/backend/aliased_output.in.cpp
similarity index 100%
rename from ngraph/test/backend/aliased_output.in.cpp
rename to src/core/tests/backend/aliased_output.in.cpp
diff --git a/ngraph/test/backend/api.in.cpp b/src/core/tests/backend/api.in.cpp
similarity index 100%
rename from ngraph/test/backend/api.in.cpp
rename to src/core/tests/backend/api.in.cpp
diff --git a/ngraph/test/backend/auto_broadcast.in.cpp b/src/core/tests/backend/auto_broadcast.in.cpp
similarity index 100%
rename from ngraph/test/backend/auto_broadcast.in.cpp
rename to src/core/tests/backend/auto_broadcast.in.cpp
diff --git a/ngraph/test/backend/builder_reduce_ops_opset1.in.cpp b/src/core/tests/backend/builder_reduce_ops_opset1.in.cpp
similarity index 100%
rename from ngraph/test/backend/builder_reduce_ops_opset1.in.cpp
rename to src/core/tests/backend/builder_reduce_ops_opset1.in.cpp
diff --git a/ngraph/test/backend/dyn_reshape.in.cpp b/src/core/tests/backend/dyn_reshape.in.cpp
similarity index 100%
rename from ngraph/test/backend/dyn_reshape.in.cpp
rename to src/core/tests/backend/dyn_reshape.in.cpp
diff --git a/ngraph/test/backend/dynamic.in.cpp b/src/core/tests/backend/dynamic.in.cpp
similarity index 100%
rename from ngraph/test/backend/dynamic.in.cpp
rename to src/core/tests/backend/dynamic.in.cpp
diff --git a/ngraph/test/backend/experimental_detectron_prior_grid.in.cpp b/src/core/tests/backend/experimental_detectron_prior_grid.in.cpp
similarity index 100%
rename from ngraph/test/backend/experimental_detectron_prior_grid.in.cpp
rename to src/core/tests/backend/experimental_detectron_prior_grid.in.cpp
diff --git a/ngraph/test/backend/function_name.in.cpp b/src/core/tests/backend/function_name.in.cpp
similarity index 100%
rename from ngraph/test/backend/function_name.in.cpp
rename to src/core/tests/backend/function_name.in.cpp
diff --git a/ngraph/test/backend/interpolate.in.cpp b/src/core/tests/backend/interpolate.in.cpp
similarity index 100%
rename from ngraph/test/backend/interpolate.in.cpp
rename to src/core/tests/backend/interpolate.in.cpp
diff --git a/ngraph/test/backend/layer_norm.in.cpp b/src/core/tests/backend/layer_norm.in.cpp
similarity index 100%
rename from ngraph/test/backend/layer_norm.in.cpp
rename to src/core/tests/backend/layer_norm.in.cpp
diff --git a/ngraph/test/backend/multiple_backends.in.cpp b/src/core/tests/backend/multiple_backends.in.cpp
similarity index 100%
rename from ngraph/test/backend/multiple_backends.in.cpp
rename to src/core/tests/backend/multiple_backends.in.cpp
diff --git a/ngraph/test/backend/multiple_result.in.cpp b/src/core/tests/backend/multiple_result.in.cpp
similarity index 100%
rename from ngraph/test/backend/multiple_result.in.cpp
rename to src/core/tests/backend/multiple_result.in.cpp
diff --git a/ngraph/test/backend/node_name.in.cpp b/src/core/tests/backend/node_name.in.cpp
similarity index 100%
rename from ngraph/test/backend/node_name.in.cpp
rename to src/core/tests/backend/node_name.in.cpp
diff --git a/ngraph/test/backend/partial_slice.in.cpp b/src/core/tests/backend/partial_slice.in.cpp
similarity index 100%
rename from ngraph/test/backend/partial_slice.in.cpp
rename to src/core/tests/backend/partial_slice.in.cpp
diff --git a/ngraph/test/backend/quantize_dequantize.in.cpp b/src/core/tests/backend/quantize_dequantize.in.cpp
similarity index 100%
rename from ngraph/test/backend/quantize_dequantize.in.cpp
rename to src/core/tests/backend/quantize_dequantize.in.cpp
diff --git a/ngraph/test/backend/region_yolo.in.cpp b/src/core/tests/backend/region_yolo.in.cpp
similarity index 100%
rename from ngraph/test/backend/region_yolo.in.cpp
rename to src/core/tests/backend/region_yolo.in.cpp
diff --git a/ngraph/test/backend/slice.in.cpp b/src/core/tests/backend/slice.in.cpp
similarity index 100%
rename from ngraph/test/backend/slice.in.cpp
rename to src/core/tests/backend/slice.in.cpp
diff --git a/ngraph/test/backend/sqrt.in.cpp b/src/core/tests/backend/sqrt.in.cpp
similarity index 100%
rename from ngraph/test/backend/sqrt.in.cpp
rename to src/core/tests/backend/sqrt.in.cpp
diff --git a/ngraph/test/backend/unhandled_op.in.cpp b/src/core/tests/backend/unhandled_op.in.cpp
similarity index 100%
rename from ngraph/test/backend/unhandled_op.in.cpp
rename to src/core/tests/backend/unhandled_op.in.cpp
diff --git a/ngraph/test/backend/validate_call.in.cpp b/src/core/tests/backend/validate_call.in.cpp
similarity index 100%
rename from ngraph/test/backend/validate_call.in.cpp
rename to src/core/tests/backend/validate_call.in.cpp
diff --git a/ngraph/test/backend/zero_sized.in.cpp b/src/core/tests/backend/zero_sized.in.cpp
similarity index 100%
rename from ngraph/test/backend/zero_sized.in.cpp
rename to src/core/tests/backend/zero_sized.in.cpp
diff --git a/ngraph/test/backend_api.cpp b/src/core/tests/backend_api.cpp
similarity index 100%
rename from ngraph/test/backend_api.cpp
rename to src/core/tests/backend_api.cpp
diff --git a/ngraph/test/backend_debug_api.cpp b/src/core/tests/backend_debug_api.cpp
similarity index 100%
rename from ngraph/test/backend_debug_api.cpp
rename to src/core/tests/backend_debug_api.cpp
diff --git a/ngraph/test/bfloat16.cpp b/src/core/tests/bfloat16.cpp
similarity index 100%
rename from ngraph/test/bfloat16.cpp
rename to src/core/tests/bfloat16.cpp
diff --git a/ngraph/test/build_graph.cpp b/src/core/tests/build_graph.cpp
similarity index 100%
rename from ngraph/test/build_graph.cpp
rename to src/core/tests/build_graph.cpp
diff --git a/ngraph/test/builder.cpp b/src/core/tests/builder.cpp
similarity index 100%
rename from ngraph/test/builder.cpp
rename to src/core/tests/builder.cpp
diff --git a/ngraph/test/builder_autobroadcast.cpp b/src/core/tests/builder_autobroadcast.cpp
similarity index 100%
rename from ngraph/test/builder_autobroadcast.cpp
rename to src/core/tests/builder_autobroadcast.cpp
diff --git a/ngraph/test/check.cpp b/src/core/tests/check.cpp
similarity index 100%
rename from ngraph/test/check.cpp
rename to src/core/tests/check.cpp
diff --git a/ngraph/test/conditional_compilation/ngraph_cc_collect.cpp b/src/core/tests/conditional_compilation/ngraph_cc_collect.cpp
similarity index 96%
rename from ngraph/test/conditional_compilation/ngraph_cc_collect.cpp
rename to src/core/tests/conditional_compilation/ngraph_cc_collect.cpp
index 833ce4b51f8..80d721cc374 100644
--- a/ngraph/test/conditional_compilation/ngraph_cc_collect.cpp
+++ b/src/core/tests/conditional_compilation/ngraph_cc_collect.cpp
@@ -16,7 +16,7 @@
 
 #define SELECTIVE_BUILD_ANALYZER
 
-#include "../core/src/itt.hpp"
+#include "../src/itt.hpp"
 
 using namespace std;
 
diff --git a/ngraph/test/conditional_compilation/ngraph_cc_off.cpp b/src/core/tests/conditional_compilation/ngraph_cc_off.cpp
similarity index 96%
rename from ngraph/test/conditional_compilation/ngraph_cc_off.cpp
rename to src/core/tests/conditional_compilation/ngraph_cc_off.cpp
index 51a2849f412..00d7dcaa972 100644
--- a/ngraph/test/conditional_compilation/ngraph_cc_off.cpp
+++ b/src/core/tests/conditional_compilation/ngraph_cc_off.cpp
@@ -14,7 +14,7 @@
 #    undef SELECTIVE_BUILD
 #endif
 
-#include "../core/src/itt.hpp"
+#include "../src/itt.hpp"
 
 using namespace std;
 
diff --git a/ngraph/test/conditional_compilation/ngraph_cc_on.cpp b/src/core/tests/conditional_compilation/ngraph_cc_on.cpp
similarity index 96%
rename from ngraph/test/conditional_compilation/ngraph_cc_on.cpp
rename to src/core/tests/conditional_compilation/ngraph_cc_on.cpp
index 462b9e28c2c..03259b4b4f8 100644
--- a/ngraph/test/conditional_compilation/ngraph_cc_on.cpp
+++ b/src/core/tests/conditional_compilation/ngraph_cc_on.cpp
@@ -16,7 +16,7 @@
 
 #define SELECTIVE_BUILD
 
-#include "../core/src/itt.hpp"
+#include "../src/itt.hpp"
 
 using namespace std;
 
diff --git a/ngraph/test/constant.cpp b/src/core/tests/constant.cpp
similarity index 100%
rename from ngraph/test/constant.cpp
rename to src/core/tests/constant.cpp
diff --git a/ngraph/test/constant_folding.cpp b/src/core/tests/constant_folding.cpp
similarity index 100%
rename from ngraph/test/constant_folding.cpp
rename to src/core/tests/constant_folding.cpp
diff --git a/ngraph/test/control_dependencies.cpp b/src/core/tests/control_dependencies.cpp
similarity index 100%
rename from ngraph/test/control_dependencies.cpp
rename to src/core/tests/control_dependencies.cpp
diff --git a/ngraph/test/convert_u1_to_string.cpp b/src/core/tests/convert_u1_to_string.cpp
similarity index 100%
rename from ngraph/test/convert_u1_to_string.cpp
rename to src/core/tests/convert_u1_to_string.cpp
diff --git a/ngraph/test/coordinate.cpp b/src/core/tests/coordinate.cpp
similarity index 100%
rename from ngraph/test/coordinate.cpp
rename to src/core/tests/coordinate.cpp
diff --git a/ngraph/test/coordinate_range.cpp b/src/core/tests/coordinate_range.cpp
similarity index 100%
rename from ngraph/test/coordinate_range.cpp
rename to src/core/tests/coordinate_range.cpp
diff --git a/ngraph/test/copy.cpp b/src/core/tests/copy.cpp
similarity index 100%
rename from ngraph/test/copy.cpp
rename to src/core/tests/copy.cpp
diff --git a/ngraph/test/element_type.cpp b/src/core/tests/element_type.cpp
similarity index 100%
rename from ngraph/test/element_type.cpp
rename to src/core/tests/element_type.cpp
diff --git a/ngraph/test/engines_util/CMakeLists.txt b/src/core/tests/engines_util/CMakeLists.txt
similarity index 100%
rename from ngraph/test/engines_util/CMakeLists.txt
rename to src/core/tests/engines_util/CMakeLists.txt
diff --git a/ngraph/test/engines_util/engine_factory.hpp b/src/core/tests/engines_util/engine_factory.hpp
similarity index 100%
rename from ngraph/test/engines_util/engine_factory.hpp
rename to src/core/tests/engines_util/engine_factory.hpp
diff --git a/ngraph/test/engines_util/engine_traits.hpp b/src/core/tests/engines_util/engine_traits.hpp
similarity index 100%
rename from ngraph/test/engines_util/engine_traits.hpp
rename to src/core/tests/engines_util/engine_traits.hpp
diff --git a/ngraph/test/engines_util/execute_tools.cpp b/src/core/tests/engines_util/execute_tools.cpp
similarity index 100%
rename from ngraph/test/engines_util/execute_tools.cpp
rename to src/core/tests/engines_util/execute_tools.cpp
diff --git a/ngraph/test/engines_util/execute_tools.hpp b/src/core/tests/engines_util/execute_tools.hpp
similarity index 100%
rename from ngraph/test/engines_util/execute_tools.hpp
rename to src/core/tests/engines_util/execute_tools.hpp
diff --git a/ngraph/test/engines_util/ie_engines.cpp b/src/core/tests/engines_util/ie_engines.cpp
similarity index 100%
rename from ngraph/test/engines_util/ie_engines.cpp
rename to src/core/tests/engines_util/ie_engines.cpp
diff --git a/ngraph/test/engines_util/ie_engines.hpp b/src/core/tests/engines_util/ie_engines.hpp
similarity index 100%
rename from ngraph/test/engines_util/ie_engines.hpp
rename to src/core/tests/engines_util/ie_engines.hpp
diff --git a/ngraph/test/engines_util/interpreter_engine.cpp b/src/core/tests/engines_util/interpreter_engine.cpp
similarity index 100%
rename from ngraph/test/engines_util/interpreter_engine.cpp
rename to src/core/tests/engines_util/interpreter_engine.cpp
diff --git a/ngraph/test/engines_util/interpreter_engine.hpp b/src/core/tests/engines_util/interpreter_engine.hpp
similarity index 100%
rename from ngraph/test/engines_util/interpreter_engine.hpp
rename to src/core/tests/engines_util/interpreter_engine.hpp
diff --git a/ngraph/test/engines_util/random.hpp b/src/core/tests/engines_util/random.hpp
similarity index 100%
rename from ngraph/test/engines_util/random.hpp
rename to src/core/tests/engines_util/random.hpp
diff --git a/ngraph/test/engines_util/shared_utils.cpp b/src/core/tests/engines_util/shared_utils.cpp
similarity index 100%
rename from ngraph/test/engines_util/shared_utils.cpp
rename to src/core/tests/engines_util/shared_utils.cpp
diff --git a/ngraph/test/engines_util/shared_utils.hpp b/src/core/tests/engines_util/shared_utils.hpp
similarity index 100%
rename from ngraph/test/engines_util/shared_utils.hpp
rename to src/core/tests/engines_util/shared_utils.hpp
diff --git a/ngraph/test/engines_util/test_case.cpp b/src/core/tests/engines_util/test_case.cpp
similarity index 100%
rename from ngraph/test/engines_util/test_case.cpp
rename to src/core/tests/engines_util/test_case.cpp
diff --git a/ngraph/test/engines_util/test_case.hpp b/src/core/tests/engines_util/test_case.hpp
similarity index 100%
rename from ngraph/test/engines_util/test_case.hpp
rename to src/core/tests/engines_util/test_case.hpp
diff --git a/ngraph/test/engines_util/test_case_engine.hpp b/src/core/tests/engines_util/test_case_engine.hpp
similarity index 100%
rename from ngraph/test/engines_util/test_case_engine.hpp
rename to src/core/tests/engines_util/test_case_engine.hpp
diff --git a/ngraph/test/engines_util/test_engines.hpp b/src/core/tests/engines_util/test_engines.hpp
similarity index 100%
rename from ngraph/test/engines_util/test_engines.hpp
rename to src/core/tests/engines_util/test_engines.hpp
diff --git a/ngraph/test/eval.cpp b/src/core/tests/eval.cpp
similarity index 100%
rename from ngraph/test/eval.cpp
rename to src/core/tests/eval.cpp
diff --git a/ngraph/test/extension.cpp b/src/core/tests/extension.cpp
similarity index 100%
rename from ngraph/test/extension.cpp
rename to src/core/tests/extension.cpp
diff --git a/ngraph/test/file_util.cpp b/src/core/tests/file_util.cpp
similarity index 100%
rename from ngraph/test/file_util.cpp
rename to src/core/tests/file_util.cpp
diff --git a/ngraph/test/files/onnx/convtranspose_output_shape/w.bin b/src/core/tests/files/onnx/convtranspose_output_shape/w.bin
similarity index 100%
rename from ngraph/test/files/onnx/convtranspose_output_shape/w.bin
rename to src/core/tests/files/onnx/convtranspose_output_shape/w.bin
diff --git a/ngraph/test/files/onnx/convtranspose_output_shape/x.bin b/src/core/tests/files/onnx/convtranspose_output_shape/x.bin
similarity index 100%
rename from ngraph/test/files/onnx/convtranspose_output_shape/x.bin
rename to src/core/tests/files/onnx/convtranspose_output_shape/x.bin
diff --git a/ngraph/test/files/onnx/convtranspose_output_shape/y.bin b/src/core/tests/files/onnx/convtranspose_output_shape/y.bin
similarity index 100%
rename from ngraph/test/files/onnx/convtranspose_output_shape/y.bin
rename to src/core/tests/files/onnx/convtranspose_output_shape/y.bin
diff --git a/ngraph/test/files/onnx/qlinearconv2d/x.bin b/src/core/tests/files/onnx/qlinearconv2d/x.bin
similarity index 100%
rename from ngraph/test/files/onnx/qlinearconv2d/x.bin
rename to src/core/tests/files/onnx/qlinearconv2d/x.bin
diff --git a/ngraph/test/files/onnx/qlinearconv2d/y.bin b/src/core/tests/files/onnx/qlinearconv2d/y.bin
similarity index 100%
rename from ngraph/test/files/onnx/qlinearconv2d/y.bin
rename to src/core/tests/files/onnx/qlinearconv2d/y.bin
diff --git a/ngraph/test/files/region_in_yolov2_caffe.data b/src/core/tests/files/region_in_yolov2_caffe.data
similarity index 100%
rename from ngraph/test/files/region_in_yolov2_caffe.data
rename to src/core/tests/files/region_in_yolov2_caffe.data
diff --git a/ngraph/test/files/region_in_yolov3_mxnet.data b/src/core/tests/files/region_in_yolov3_mxnet.data
similarity index 100%
rename from ngraph/test/files/region_in_yolov3_mxnet.data
rename to src/core/tests/files/region_in_yolov3_mxnet.data
diff --git a/ngraph/test/files/region_out_yolov2_caffe.data b/src/core/tests/files/region_out_yolov2_caffe.data
similarity index 100%
rename from ngraph/test/files/region_out_yolov2_caffe.data
rename to src/core/tests/files/region_out_yolov2_caffe.data
diff --git a/ngraph/test/files/region_out_yolov3_mxnet.data b/src/core/tests/files/region_out_yolov3_mxnet.data
similarity index 100%
rename from ngraph/test/files/region_out_yolov3_mxnet.data
rename to src/core/tests/files/region_out_yolov3_mxnet.data
diff --git a/ngraph/test/float16.cpp b/src/core/tests/float16.cpp
similarity index 100%
rename from ngraph/test/float16.cpp
rename to src/core/tests/float16.cpp
diff --git a/ngraph/test/framework_node.cpp b/src/core/tests/framework_node.cpp
similarity index 100%
rename from ngraph/test/framework_node.cpp
rename to src/core/tests/framework_node.cpp
diff --git a/ngraph/test/frontend/CMakeLists.txt b/src/core/tests/frontend/CMakeLists.txt
similarity index 100%
rename from ngraph/test/frontend/CMakeLists.txt
rename to src/core/tests/frontend/CMakeLists.txt
diff --git a/ngraph/test/frontend/frontend_manager.cpp b/src/core/tests/frontend/frontend_manager.cpp
similarity index 100%
rename from ngraph/test/frontend/frontend_manager.cpp
rename to src/core/tests/frontend/frontend_manager.cpp
diff --git a/ngraph/test/frontend/mock_frontend.cpp b/src/core/tests/frontend/mock_frontend.cpp
similarity index 100%
rename from ngraph/test/frontend/mock_frontend.cpp
rename to src/core/tests/frontend/mock_frontend.cpp
diff --git a/ngraph/test/frontend/onnx/CMakeLists.txt b/src/core/tests/frontend/onnx/CMakeLists.txt
similarity index 92%
rename from ngraph/test/frontend/onnx/CMakeLists.txt
rename to src/core/tests/frontend/onnx/CMakeLists.txt
index 1b28c00d837..6bbf19ea106 100644
--- a/ngraph/test/frontend/onnx/CMakeLists.txt
+++ b/src/core/tests/frontend/onnx/CMakeLists.txt
@@ -17,7 +17,7 @@ install(TARGETS ${TARGET_NAME}
         COMPONENT tests
         EXCLUDE_FROM_ALL)
 
-set(TEST_ONNX_MODELS_DIRNAME ${TEST_MODEL_ZOO}/ngraph/models/onnx)
+set(TEST_ONNX_MODELS_DIRNAME ${TEST_MODEL_ZOO}/src/core/models/onnx)
 target_compile_definitions(${TARGET_NAME} PRIVATE -D TEST_ONNX_MODELS_DIRNAME=\"${TEST_ONNX_MODELS_DIRNAME}/\")
 set(MANIFEST ${CMAKE_CURRENT_SOURCE_DIR}/unit_test.manifest)
 target_compile_definitions(${TARGET_NAME} PRIVATE -D MANIFEST=\"${MANIFEST}\")
diff --git a/ngraph/test/frontend/onnx/load_from.cpp b/src/core/tests/frontend/onnx/load_from.cpp
similarity index 100%
rename from ngraph/test/frontend/onnx/load_from.cpp
rename to src/core/tests/frontend/onnx/load_from.cpp
diff --git a/ngraph/test/frontend/onnx/main.cpp b/src/core/tests/frontend/onnx/main.cpp
similarity index 100%
rename from ngraph/test/frontend/onnx/main.cpp
rename to src/core/tests/frontend/onnx/main.cpp
diff --git a/ngraph/test/frontend/onnx/onnx_utils.hpp b/src/core/tests/frontend/onnx/onnx_utils.hpp
similarity index 100%
rename from ngraph/test/frontend/onnx/onnx_utils.hpp
rename to src/core/tests/frontend/onnx/onnx_utils.hpp
diff --git a/ngraph/test/frontend/onnx/unit_test.manifest b/src/core/tests/frontend/onnx/unit_test.manifest
similarity index 100%
rename from ngraph/test/frontend/onnx/unit_test.manifest
rename to src/core/tests/frontend/onnx/unit_test.manifest
diff --git a/ngraph/test/frontend/paddlepaddle/CMakeLists.txt b/src/core/tests/frontend/paddlepaddle/CMakeLists.txt
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/CMakeLists.txt
rename to src/core/tests/frontend/paddlepaddle/CMakeLists.txt
diff --git a/ngraph/test/frontend/paddlepaddle/basic_api.cpp b/src/core/tests/frontend/paddlepaddle/basic_api.cpp
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/basic_api.cpp
rename to src/core/tests/frontend/paddlepaddle/basic_api.cpp
diff --git a/ngraph/test/frontend/paddlepaddle/convert_model.cpp b/src/core/tests/frontend/paddlepaddle/convert_model.cpp
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/convert_model.cpp
rename to src/core/tests/frontend/paddlepaddle/convert_model.cpp
diff --git a/ngraph/test/frontend/paddlepaddle/convert_unsupported.cpp b/src/core/tests/frontend/paddlepaddle/convert_unsupported.cpp
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/convert_unsupported.cpp
rename to src/core/tests/frontend/paddlepaddle/convert_unsupported.cpp
diff --git a/ngraph/test/frontend/paddlepaddle/cut_specific_model.cpp b/src/core/tests/frontend/paddlepaddle/cut_specific_model.cpp
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/cut_specific_model.cpp
rename to src/core/tests/frontend/paddlepaddle/cut_specific_model.cpp
diff --git a/ngraph/test/frontend/paddlepaddle/incorrect_cut_model.cpp b/src/core/tests/frontend/paddlepaddle/incorrect_cut_model.cpp
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/incorrect_cut_model.cpp
rename to src/core/tests/frontend/paddlepaddle/incorrect_cut_model.cpp
diff --git a/ngraph/test/frontend/paddlepaddle/load_from.cpp b/src/core/tests/frontend/paddlepaddle/load_from.cpp
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/load_from.cpp
rename to src/core/tests/frontend/paddlepaddle/load_from.cpp
diff --git a/ngraph/test/frontend/paddlepaddle/main.cpp b/src/core/tests/frontend/paddlepaddle/main.cpp
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/main.cpp
rename to src/core/tests/frontend/paddlepaddle/main.cpp
diff --git a/ngraph/test/frontend/paddlepaddle/op_fuzzy.cpp b/src/core/tests/frontend/paddlepaddle/op_fuzzy.cpp
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/op_fuzzy.cpp
rename to src/core/tests/frontend/paddlepaddle/op_fuzzy.cpp
diff --git a/ngraph/test/frontend/paddlepaddle/paddle_utils.hpp b/src/core/tests/frontend/paddlepaddle/paddle_utils.hpp
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/paddle_utils.hpp
rename to src/core/tests/frontend/paddlepaddle/paddle_utils.hpp
diff --git a/ngraph/test/frontend/paddlepaddle/partial_shape.cpp b/src/core/tests/frontend/paddlepaddle/partial_shape.cpp
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/partial_shape.cpp
rename to src/core/tests/frontend/paddlepaddle/partial_shape.cpp
diff --git a/ngraph/test/frontend/paddlepaddle/places.cpp b/src/core/tests/frontend/paddlepaddle/places.cpp
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/places.cpp
rename to src/core/tests/frontend/paddlepaddle/places.cpp
diff --git a/ngraph/test/frontend/paddlepaddle/requirements_dev.txt b/src/core/tests/frontend/paddlepaddle/requirements_dev.txt
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/requirements_dev.txt
rename to src/core/tests/frontend/paddlepaddle/requirements_dev.txt
diff --git a/ngraph/test/frontend/paddlepaddle/set_element_type.cpp b/src/core/tests/frontend/paddlepaddle/set_element_type.cpp
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/set_element_type.cpp
rename to src/core/tests/frontend/paddlepaddle/set_element_type.cpp
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_2in_2out.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_2in_2out.py
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_2in_2out.py
rename to src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_2in_2out.py
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_2in_2out_dynbatch.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_2in_2out_dynbatch.py
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_2in_2out_dynbatch.py
rename to src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_2in_2out_dynbatch.py
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_argmax.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_argmax.py
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_argmax.py
rename to src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_argmax.py
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_assign_value.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_assign_value.py
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_assign_value.py
rename to src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_assign_value.py
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_batch_norm.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_batch_norm.py
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_batch_norm.py
rename to src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_batch_norm.py
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_bmm.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_bmm.py
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_bmm.py
rename to src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_bmm.py
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_clip.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_clip.py
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_clip.py
rename to src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_clip.py
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d.py
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d.py
rename to src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d.py
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_combinations.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_combinations.py
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_combinations.py
rename to src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_combinations.py
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_relu.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_relu.py
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_relu.py
rename to src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_relu.py
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_s.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_s.py
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_s.py
rename to src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_s.py
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_transpose.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_transpose.py
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_transpose.py
rename to src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_conv2d_transpose.py
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_cumsum.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_cumsum.py
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_cumsum.py
rename to src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_cumsum.py
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_dropout.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_dropout.py
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_dropout.py
rename to src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_dropout.py
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_dynamic_pool2d.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_dynamic_pool2d.py
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_dynamic_pool2d.py
rename to src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_dynamic_pool2d.py
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_elementwise_ops.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_elementwise_ops.py
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_elementwise_ops.py
rename to src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_elementwise_ops.py
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_embedding.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_embedding.py
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_embedding.py
rename to src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_embedding.py
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_equal.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_equal.py
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_equal.py
rename to src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_equal.py
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_exp.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_exp.py
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_exp.py
rename to src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_exp.py
diff --git a/ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_expand_v2.py b/src/core/tests/frontend/paddlepaddle/test_models/gen_scripts/generate_expand_v2.py
similarity index 100%
rename from ngraph/test/frontend/paddlepaddle/test_models/gen_scripts/generate_expand_v2.py
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rename from ngraph/test/type_prop/softmax.cpp
rename to src/core/tests/type_prop/softmax.cpp
diff --git a/ngraph/test/type_prop/softplus.cpp b/src/core/tests/type_prop/softplus.cpp
similarity index 100%
rename from ngraph/test/type_prop/softplus.cpp
rename to src/core/tests/type_prop/softplus.cpp
diff --git a/ngraph/test/type_prop/space_to_batch.cpp b/src/core/tests/type_prop/space_to_batch.cpp
similarity index 100%
rename from ngraph/test/type_prop/space_to_batch.cpp
rename to src/core/tests/type_prop/space_to_batch.cpp
diff --git a/ngraph/test/type_prop/space_to_depth.cpp b/src/core/tests/type_prop/space_to_depth.cpp
similarity index 100%
rename from ngraph/test/type_prop/space_to_depth.cpp
rename to src/core/tests/type_prop/space_to_depth.cpp
diff --git a/ngraph/test/type_prop/split.cpp b/src/core/tests/type_prop/split.cpp
similarity index 100%
rename from ngraph/test/type_prop/split.cpp
rename to src/core/tests/type_prop/split.cpp
diff --git a/ngraph/test/type_prop/sqrt.cpp b/src/core/tests/type_prop/sqrt.cpp
similarity index 100%
rename from ngraph/test/type_prop/sqrt.cpp
rename to src/core/tests/type_prop/sqrt.cpp
diff --git a/ngraph/test/type_prop/squared_difference.cpp b/src/core/tests/type_prop/squared_difference.cpp
similarity index 100%
rename from ngraph/test/type_prop/squared_difference.cpp
rename to src/core/tests/type_prop/squared_difference.cpp
diff --git a/ngraph/test/type_prop/squeeze.cpp b/src/core/tests/type_prop/squeeze.cpp
similarity index 100%
rename from ngraph/test/type_prop/squeeze.cpp
rename to src/core/tests/type_prop/squeeze.cpp
diff --git a/ngraph/test/type_prop/strided_slice.cpp b/src/core/tests/type_prop/strided_slice.cpp
similarity index 100%
rename from ngraph/test/type_prop/strided_slice.cpp
rename to src/core/tests/type_prop/strided_slice.cpp
diff --git a/ngraph/test/type_prop/subtract.cpp b/src/core/tests/type_prop/subtract.cpp
similarity index 100%
rename from ngraph/test/type_prop/subtract.cpp
rename to src/core/tests/type_prop/subtract.cpp
diff --git a/ngraph/test/type_prop/swish.cpp b/src/core/tests/type_prop/swish.cpp
similarity index 100%
rename from ngraph/test/type_prop/swish.cpp
rename to src/core/tests/type_prop/swish.cpp
diff --git a/ngraph/test/type_prop/tan.cpp b/src/core/tests/type_prop/tan.cpp
similarity index 100%
rename from ngraph/test/type_prop/tan.cpp
rename to src/core/tests/type_prop/tan.cpp
diff --git a/ngraph/test/type_prop/tanh.cpp b/src/core/tests/type_prop/tanh.cpp
similarity index 100%
rename from ngraph/test/type_prop/tanh.cpp
rename to src/core/tests/type_prop/tanh.cpp
diff --git a/ngraph/test/type_prop/ti.cpp b/src/core/tests/type_prop/ti.cpp
similarity index 100%
rename from ngraph/test/type_prop/ti.cpp
rename to src/core/tests/type_prop/ti.cpp
diff --git a/ngraph/test/type_prop/tile.cpp b/src/core/tests/type_prop/tile.cpp
similarity index 100%
rename from ngraph/test/type_prop/tile.cpp
rename to src/core/tests/type_prop/tile.cpp
diff --git a/ngraph/test/type_prop/top_k.cpp b/src/core/tests/type_prop/top_k.cpp
similarity index 100%
rename from ngraph/test/type_prop/top_k.cpp
rename to src/core/tests/type_prop/top_k.cpp
diff --git a/ngraph/test/type_prop/transpose.cpp b/src/core/tests/type_prop/transpose.cpp
similarity index 100%
rename from ngraph/test/type_prop/transpose.cpp
rename to src/core/tests/type_prop/transpose.cpp
diff --git a/ngraph/test/type_prop/unary_elementwise.cpp b/src/core/tests/type_prop/unary_elementwise.cpp
similarity index 100%
rename from ngraph/test/type_prop/unary_elementwise.cpp
rename to src/core/tests/type_prop/unary_elementwise.cpp
diff --git a/ngraph/test/type_prop/unary_ops.hpp b/src/core/tests/type_prop/unary_ops.hpp
similarity index 100%
rename from ngraph/test/type_prop/unary_ops.hpp
rename to src/core/tests/type_prop/unary_ops.hpp
diff --git a/ngraph/test/type_prop/unsqueeze.cpp b/src/core/tests/type_prop/unsqueeze.cpp
similarity index 100%
rename from ngraph/test/type_prop/unsqueeze.cpp
rename to src/core/tests/type_prop/unsqueeze.cpp
diff --git a/ngraph/test/type_prop/variadic_split.cpp b/src/core/tests/type_prop/variadic_split.cpp
similarity index 100%
rename from ngraph/test/type_prop/variadic_split.cpp
rename to src/core/tests/type_prop/variadic_split.cpp
diff --git a/ngraph/test/type_prop_layers.cpp b/src/core/tests/type_prop_layers.cpp
similarity index 100%
rename from ngraph/test/type_prop_layers.cpp
rename to src/core/tests/type_prop_layers.cpp
diff --git a/ngraph/test/uint4.cpp b/src/core/tests/uint4.cpp
similarity index 100%
rename from ngraph/test/uint4.cpp
rename to src/core/tests/uint4.cpp
diff --git a/ngraph/test/util.cpp b/src/core/tests/util.cpp
similarity index 100%
rename from ngraph/test/util.cpp
rename to src/core/tests/util.cpp
diff --git a/ngraph/test/util/CMakeLists.txt b/src/core/tests/util/CMakeLists.txt
similarity index 85%
rename from ngraph/test/util/CMakeLists.txt
rename to src/core/tests/util/CMakeLists.txt
index 70ec394bf5f..a43400183d0 100644
--- a/ngraph/test/util/CMakeLists.txt
+++ b/src/core/tests/util/CMakeLists.txt
@@ -14,7 +14,5 @@ target_include_directories(ngraph_test_util PUBLIC ${CMAKE_CURRENT_SOURCE_DIR})
 file(GLOB_RECURSE all_util_src "${CMAKE_CURRENT_SOURCE_DIR}/*.cpp" "${CMAKE_CURRENT_SOURCE_DIR}/*.hpp")
 add_clang_format_target(ngraph_test_util_clang FOR_SOURCES ${all_util_src})
 
-set_source_files_properties(${all_util_src} PROPERTIES INCLUDE_DIRECTORIES ${CMAKE_CURRENT_SOURCE_DIR}/../../core/src/)
-
 # developer package
 openvino_developer_export_targets(COMPONENT ngraph TARGETS ngraph_test_util)
diff --git a/ngraph/test/util/all_close.cpp b/src/core/tests/util/all_close.cpp
similarity index 100%
rename from ngraph/test/util/all_close.cpp
rename to src/core/tests/util/all_close.cpp
diff --git a/ngraph/test/util/all_close.hpp b/src/core/tests/util/all_close.hpp
similarity index 100%
rename from ngraph/test/util/all_close.hpp
rename to src/core/tests/util/all_close.hpp
diff --git a/ngraph/test/util/all_close_f.cpp b/src/core/tests/util/all_close_f.cpp
similarity index 100%
rename from ngraph/test/util/all_close_f.cpp
rename to src/core/tests/util/all_close_f.cpp
diff --git a/ngraph/test/util/all_close_f.hpp b/src/core/tests/util/all_close_f.hpp
similarity index 100%
rename from ngraph/test/util/all_close_f.hpp
rename to src/core/tests/util/all_close_f.hpp
diff --git a/ngraph/test/util/float_util.cpp b/src/core/tests/util/float_util.cpp
similarity index 100%
rename from ngraph/test/util/float_util.cpp
rename to src/core/tests/util/float_util.cpp
diff --git a/ngraph/test/util/float_util.hpp b/src/core/tests/util/float_util.hpp
similarity index 100%
rename from ngraph/test/util/float_util.hpp
rename to src/core/tests/util/float_util.hpp
diff --git a/ngraph/test/util/graph_comparator.cpp b/src/core/tests/util/graph_comparator.cpp
similarity index 100%
rename from ngraph/test/util/graph_comparator.cpp
rename to src/core/tests/util/graph_comparator.cpp
diff --git a/ngraph/test/util/graph_comparator.hpp b/src/core/tests/util/graph_comparator.hpp
similarity index 100%
rename from ngraph/test/util/graph_comparator.hpp
rename to src/core/tests/util/graph_comparator.hpp
diff --git a/ngraph/test/util/matcher.hpp b/src/core/tests/util/matcher.hpp
similarity index 100%
rename from ngraph/test/util/matcher.hpp
rename to src/core/tests/util/matcher.hpp
diff --git a/ngraph/test/util/ndarray.hpp b/src/core/tests/util/ndarray.hpp
similarity index 100%
rename from ngraph/test/util/ndarray.hpp
rename to src/core/tests/util/ndarray.hpp
diff --git a/ngraph/test/util/test_common.cpp b/src/core/tests/util/test_common.cpp
similarity index 100%
rename from ngraph/test/util/test_common.cpp
rename to src/core/tests/util/test_common.cpp
diff --git a/ngraph/test/util/test_common.hpp b/src/core/tests/util/test_common.hpp
similarity index 96%
rename from ngraph/test/util/test_common.hpp
rename to src/core/tests/util/test_common.hpp
index f34cac6b61f..9e8514e307a 100644
--- a/ngraph/test/util/test_common.hpp
+++ b/src/core/tests/util/test_common.hpp
@@ -8,11 +8,13 @@
 
 #include <memory>
 #include <openvino/core/function.hpp>
-#include <shared_node_info.hpp>
 #include <string>
 #include <utility>
 
 namespace ov {
+
+class SharedRTInfo;
+
 namespace test {
 
 class TestsCommon : virtual public ::testing::Test {
diff --git a/ngraph/test/util/test_control.cpp b/src/core/tests/util/test_control.cpp
similarity index 100%
rename from ngraph/test/util/test_control.cpp
rename to src/core/tests/util/test_control.cpp
diff --git a/ngraph/test/util/test_control.hpp b/src/core/tests/util/test_control.hpp
similarity index 100%
rename from ngraph/test/util/test_control.hpp
rename to src/core/tests/util/test_control.hpp
diff --git a/ngraph/test/util/test_tools.hpp b/src/core/tests/util/test_tools.hpp
similarity index 100%
rename from ngraph/test/util/test_tools.hpp
rename to src/core/tests/util/test_tools.hpp
diff --git a/ngraph/test/util/type_prop.hpp b/src/core/tests/util/type_prop.hpp
similarity index 100%
rename from ngraph/test/util/type_prop.hpp
rename to src/core/tests/util/type_prop.hpp
diff --git a/ngraph/test/util/visitor.hpp b/src/core/tests/util/visitor.hpp
similarity index 100%
rename from ngraph/test/util/visitor.hpp
rename to src/core/tests/util/visitor.hpp
diff --git a/ngraph/test/visitors/op/acos.cpp b/src/core/tests/visitors/op/acos.cpp
similarity index 100%
rename from ngraph/test/visitors/op/acos.cpp
rename to src/core/tests/visitors/op/acos.cpp
diff --git a/ngraph/test/visitors/op/acosh.cpp b/src/core/tests/visitors/op/acosh.cpp
similarity index 100%
rename from ngraph/test/visitors/op/acosh.cpp
rename to src/core/tests/visitors/op/acosh.cpp
diff --git a/ngraph/test/visitors/op/adaptive_avg_pool.cpp b/src/core/tests/visitors/op/adaptive_avg_pool.cpp
similarity index 100%
rename from ngraph/test/visitors/op/adaptive_avg_pool.cpp
rename to src/core/tests/visitors/op/adaptive_avg_pool.cpp
diff --git a/ngraph/test/visitors/op/adaptive_max_pool.cpp b/src/core/tests/visitors/op/adaptive_max_pool.cpp
similarity index 100%
rename from ngraph/test/visitors/op/adaptive_max_pool.cpp
rename to src/core/tests/visitors/op/adaptive_max_pool.cpp
diff --git a/ngraph/test/visitors/op/add.cpp b/src/core/tests/visitors/op/add.cpp
similarity index 100%
rename from ngraph/test/visitors/op/add.cpp
rename to src/core/tests/visitors/op/add.cpp
diff --git a/ngraph/test/visitors/op/asin.cpp b/src/core/tests/visitors/op/asin.cpp
similarity index 100%
rename from ngraph/test/visitors/op/asin.cpp
rename to src/core/tests/visitors/op/asin.cpp
diff --git a/ngraph/test/visitors/op/asinh.cpp b/src/core/tests/visitors/op/asinh.cpp
similarity index 100%
rename from ngraph/test/visitors/op/asinh.cpp
rename to src/core/tests/visitors/op/asinh.cpp
diff --git a/ngraph/test/visitors/op/atan.cpp b/src/core/tests/visitors/op/atan.cpp
similarity index 100%
rename from ngraph/test/visitors/op/atan.cpp
rename to src/core/tests/visitors/op/atan.cpp
diff --git a/ngraph/test/visitors/op/atanh.cpp b/src/core/tests/visitors/op/atanh.cpp
similarity index 100%
rename from ngraph/test/visitors/op/atanh.cpp
rename to src/core/tests/visitors/op/atanh.cpp
diff --git a/ngraph/test/visitors/op/avg_pool.cpp b/src/core/tests/visitors/op/avg_pool.cpp
similarity index 100%
rename from ngraph/test/visitors/op/avg_pool.cpp
rename to src/core/tests/visitors/op/avg_pool.cpp
diff --git a/ngraph/test/visitors/op/batch_norm.cpp b/src/core/tests/visitors/op/batch_norm.cpp
similarity index 100%
rename from ngraph/test/visitors/op/batch_norm.cpp
rename to src/core/tests/visitors/op/batch_norm.cpp
diff --git a/ngraph/test/visitors/op/batch_to_space.cpp b/src/core/tests/visitors/op/batch_to_space.cpp
similarity index 100%
rename from ngraph/test/visitors/op/batch_to_space.cpp
rename to src/core/tests/visitors/op/batch_to_space.cpp
diff --git a/ngraph/test/visitors/op/binary_convolution.cpp b/src/core/tests/visitors/op/binary_convolution.cpp
similarity index 100%
rename from ngraph/test/visitors/op/binary_convolution.cpp
rename to src/core/tests/visitors/op/binary_convolution.cpp
diff --git a/ngraph/test/visitors/op/binary_ops.hpp b/src/core/tests/visitors/op/binary_ops.hpp
similarity index 100%
rename from ngraph/test/visitors/op/binary_ops.hpp
rename to src/core/tests/visitors/op/binary_ops.hpp
diff --git a/ngraph/test/visitors/op/broadcast.cpp b/src/core/tests/visitors/op/broadcast.cpp
similarity index 100%
rename from ngraph/test/visitors/op/broadcast.cpp
rename to src/core/tests/visitors/op/broadcast.cpp
diff --git a/ngraph/test/visitors/op/bucketize.cpp b/src/core/tests/visitors/op/bucketize.cpp
similarity index 100%
rename from ngraph/test/visitors/op/bucketize.cpp
rename to src/core/tests/visitors/op/bucketize.cpp
diff --git a/ngraph/test/visitors/op/ceiling.cpp b/src/core/tests/visitors/op/ceiling.cpp
similarity index 100%
rename from ngraph/test/visitors/op/ceiling.cpp
rename to src/core/tests/visitors/op/ceiling.cpp
diff --git a/ngraph/test/visitors/op/clamp.cpp b/src/core/tests/visitors/op/clamp.cpp
similarity index 100%
rename from ngraph/test/visitors/op/clamp.cpp
rename to src/core/tests/visitors/op/clamp.cpp
diff --git a/ngraph/test/visitors/op/constant.cpp b/src/core/tests/visitors/op/constant.cpp
similarity index 100%
rename from ngraph/test/visitors/op/constant.cpp
rename to src/core/tests/visitors/op/constant.cpp
diff --git a/ngraph/test/visitors/op/convert.cpp b/src/core/tests/visitors/op/convert.cpp
similarity index 100%
rename from ngraph/test/visitors/op/convert.cpp
rename to src/core/tests/visitors/op/convert.cpp
diff --git a/ngraph/test/visitors/op/convert_color_i420.cpp b/src/core/tests/visitors/op/convert_color_i420.cpp
similarity index 100%
rename from ngraph/test/visitors/op/convert_color_i420.cpp
rename to src/core/tests/visitors/op/convert_color_i420.cpp
diff --git a/ngraph/test/visitors/op/convert_color_nv12.cpp b/src/core/tests/visitors/op/convert_color_nv12.cpp
similarity index 100%
rename from ngraph/test/visitors/op/convert_color_nv12.cpp
rename to src/core/tests/visitors/op/convert_color_nv12.cpp
diff --git a/ngraph/test/visitors/op/convolution.cpp b/src/core/tests/visitors/op/convolution.cpp
similarity index 100%
rename from ngraph/test/visitors/op/convolution.cpp
rename to src/core/tests/visitors/op/convolution.cpp
diff --git a/ngraph/test/visitors/op/convolution_backprop.cpp b/src/core/tests/visitors/op/convolution_backprop.cpp
similarity index 100%
rename from ngraph/test/visitors/op/convolution_backprop.cpp
rename to src/core/tests/visitors/op/convolution_backprop.cpp
diff --git a/ngraph/test/visitors/op/cos.cpp b/src/core/tests/visitors/op/cos.cpp
similarity index 100%
rename from ngraph/test/visitors/op/cos.cpp
rename to src/core/tests/visitors/op/cos.cpp
diff --git a/ngraph/test/visitors/op/cosh.cpp b/src/core/tests/visitors/op/cosh.cpp
similarity index 100%
rename from ngraph/test/visitors/op/cosh.cpp
rename to src/core/tests/visitors/op/cosh.cpp
diff --git a/ngraph/test/visitors/op/ctc_greedy_decoder.cpp b/src/core/tests/visitors/op/ctc_greedy_decoder.cpp
similarity index 100%
rename from ngraph/test/visitors/op/ctc_greedy_decoder.cpp
rename to src/core/tests/visitors/op/ctc_greedy_decoder.cpp
diff --git a/ngraph/test/visitors/op/ctc_greedy_decoder_seq_len.cpp b/src/core/tests/visitors/op/ctc_greedy_decoder_seq_len.cpp
similarity index 100%
rename from ngraph/test/visitors/op/ctc_greedy_decoder_seq_len.cpp
rename to src/core/tests/visitors/op/ctc_greedy_decoder_seq_len.cpp
diff --git a/ngraph/test/visitors/op/ctc_loss.cpp b/src/core/tests/visitors/op/ctc_loss.cpp
similarity index 100%
rename from ngraph/test/visitors/op/ctc_loss.cpp
rename to src/core/tests/visitors/op/ctc_loss.cpp
diff --git a/ngraph/test/visitors/op/cum_sum.cpp b/src/core/tests/visitors/op/cum_sum.cpp
similarity index 100%
rename from ngraph/test/visitors/op/cum_sum.cpp
rename to src/core/tests/visitors/op/cum_sum.cpp
diff --git a/ngraph/test/visitors/op/deformable_convolution.cpp b/src/core/tests/visitors/op/deformable_convolution.cpp
similarity index 100%
rename from ngraph/test/visitors/op/deformable_convolution.cpp
rename to src/core/tests/visitors/op/deformable_convolution.cpp
diff --git a/ngraph/test/visitors/op/deformable_psroi_pooling.cpp b/src/core/tests/visitors/op/deformable_psroi_pooling.cpp
similarity index 100%
rename from ngraph/test/visitors/op/deformable_psroi_pooling.cpp
rename to src/core/tests/visitors/op/deformable_psroi_pooling.cpp
diff --git a/ngraph/test/visitors/op/depth_to_space.cpp b/src/core/tests/visitors/op/depth_to_space.cpp
similarity index 100%
rename from ngraph/test/visitors/op/depth_to_space.cpp
rename to src/core/tests/visitors/op/depth_to_space.cpp
diff --git a/ngraph/test/visitors/op/detection_output.cpp b/src/core/tests/visitors/op/detection_output.cpp
similarity index 100%
rename from ngraph/test/visitors/op/detection_output.cpp
rename to src/core/tests/visitors/op/detection_output.cpp
diff --git a/ngraph/test/visitors/op/dft.cpp b/src/core/tests/visitors/op/dft.cpp
similarity index 100%
rename from ngraph/test/visitors/op/dft.cpp
rename to src/core/tests/visitors/op/dft.cpp
diff --git a/ngraph/test/visitors/op/divide.cpp b/src/core/tests/visitors/op/divide.cpp
similarity index 100%
rename from ngraph/test/visitors/op/divide.cpp
rename to src/core/tests/visitors/op/divide.cpp
diff --git a/ngraph/test/visitors/op/einsum.cpp b/src/core/tests/visitors/op/einsum.cpp
similarity index 100%
rename from ngraph/test/visitors/op/einsum.cpp
rename to src/core/tests/visitors/op/einsum.cpp
diff --git a/ngraph/test/visitors/op/elu.cpp b/src/core/tests/visitors/op/elu.cpp
similarity index 100%
rename from ngraph/test/visitors/op/elu.cpp
rename to src/core/tests/visitors/op/elu.cpp
diff --git a/ngraph/test/visitors/op/embedding_segments_sum.cpp b/src/core/tests/visitors/op/embedding_segments_sum.cpp
similarity index 100%
rename from ngraph/test/visitors/op/embedding_segments_sum.cpp
rename to src/core/tests/visitors/op/embedding_segments_sum.cpp
diff --git a/ngraph/test/visitors/op/embeddingbag_offsetssum.cpp b/src/core/tests/visitors/op/embeddingbag_offsetssum.cpp
similarity index 100%
rename from ngraph/test/visitors/op/embeddingbag_offsetssum.cpp
rename to src/core/tests/visitors/op/embeddingbag_offsetssum.cpp
diff --git a/ngraph/test/visitors/op/embeddingbag_packedsum.cpp b/src/core/tests/visitors/op/embeddingbag_packedsum.cpp
similarity index 100%
rename from ngraph/test/visitors/op/embeddingbag_packedsum.cpp
rename to src/core/tests/visitors/op/embeddingbag_packedsum.cpp
diff --git a/ngraph/test/visitors/op/equal.cpp b/src/core/tests/visitors/op/equal.cpp
similarity index 100%
rename from ngraph/test/visitors/op/equal.cpp
rename to src/core/tests/visitors/op/equal.cpp
diff --git a/ngraph/test/visitors/op/erf.cpp b/src/core/tests/visitors/op/erf.cpp
similarity index 100%
rename from ngraph/test/visitors/op/erf.cpp
rename to src/core/tests/visitors/op/erf.cpp
diff --git a/ngraph/test/visitors/op/exp.cpp b/src/core/tests/visitors/op/exp.cpp
similarity index 100%
rename from ngraph/test/visitors/op/exp.cpp
rename to src/core/tests/visitors/op/exp.cpp
diff --git a/ngraph/test/visitors/op/experimental_detectron_detection_output.cpp b/src/core/tests/visitors/op/experimental_detectron_detection_output.cpp
similarity index 100%
rename from ngraph/test/visitors/op/experimental_detectron_detection_output.cpp
rename to src/core/tests/visitors/op/experimental_detectron_detection_output.cpp
diff --git a/ngraph/test/visitors/op/experimental_detectron_generate_proposals.cpp b/src/core/tests/visitors/op/experimental_detectron_generate_proposals.cpp
similarity index 100%
rename from ngraph/test/visitors/op/experimental_detectron_generate_proposals.cpp
rename to src/core/tests/visitors/op/experimental_detectron_generate_proposals.cpp
diff --git a/ngraph/test/visitors/op/experimental_detectron_topkrois.cpp b/src/core/tests/visitors/op/experimental_detectron_topkrois.cpp
similarity index 100%
rename from ngraph/test/visitors/op/experimental_detectron_topkrois.cpp
rename to src/core/tests/visitors/op/experimental_detectron_topkrois.cpp
diff --git a/ngraph/test/visitors/op/extractimagepatches.cpp b/src/core/tests/visitors/op/extractimagepatches.cpp
similarity index 100%
rename from ngraph/test/visitors/op/extractimagepatches.cpp
rename to src/core/tests/visitors/op/extractimagepatches.cpp
diff --git a/ngraph/test/visitors/op/fake_quantize.cpp b/src/core/tests/visitors/op/fake_quantize.cpp
similarity index 100%
rename from ngraph/test/visitors/op/fake_quantize.cpp
rename to src/core/tests/visitors/op/fake_quantize.cpp
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similarity index 100%
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rename from ngraph/test/visitors/op/matrix_nms.cpp
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rename to src/core/tests/visitors/op/minimum.cpp
diff --git a/ngraph/test/visitors/op/mish.cpp b/src/core/tests/visitors/op/mish.cpp
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diff --git a/ngraph/test/visitors/op/mod.cpp b/src/core/tests/visitors/op/mod.cpp
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rename to src/core/tests/visitors/op/mod.cpp
diff --git a/ngraph/test/visitors/op/multiclass_nms.cpp b/src/core/tests/visitors/op/multiclass_nms.cpp
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rename from ngraph/test/visitors/op/multiclass_nms.cpp
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diff --git a/ngraph/test/visitors/op/multiply.cpp b/src/core/tests/visitors/op/multiply.cpp
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diff --git a/ngraph/test/visitors/op/mvn.cpp b/src/core/tests/visitors/op/mvn.cpp
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diff --git a/ngraph/test/visitors/op/negative.cpp b/src/core/tests/visitors/op/negative.cpp
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rename to src/core/tests/visitors/op/negative.cpp
diff --git a/ngraph/test/visitors/op/non_max_suppression.cpp b/src/core/tests/visitors/op/non_max_suppression.cpp
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rename from ngraph/test/visitors/op/non_max_suppression.cpp
rename to src/core/tests/visitors/op/non_max_suppression.cpp
diff --git a/ngraph/test/visitors/op/non_zero.cpp b/src/core/tests/visitors/op/non_zero.cpp
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rename from ngraph/test/visitors/op/non_zero.cpp
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diff --git a/ngraph/test/visitors/op/normalize_l2.cpp b/src/core/tests/visitors/op/normalize_l2.cpp
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rename from ngraph/test/visitors/op/normalize_l2.cpp
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diff --git a/ngraph/test/visitors/op/not_equal.cpp b/src/core/tests/visitors/op/not_equal.cpp
similarity index 100%
rename from ngraph/test/visitors/op/not_equal.cpp
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diff --git a/ngraph/test/visitors/op/one_hot.cpp b/src/core/tests/visitors/op/one_hot.cpp
similarity index 100%
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diff --git a/ngraph/test/visitors/op/pad.cpp b/src/core/tests/visitors/op/pad.cpp
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rename from ngraph/test/visitors/op/pad.cpp
rename to src/core/tests/visitors/op/pad.cpp
diff --git a/ngraph/test/visitors/op/parameter.cpp b/src/core/tests/visitors/op/parameter.cpp
similarity index 100%
rename from ngraph/test/visitors/op/parameter.cpp
rename to src/core/tests/visitors/op/parameter.cpp
diff --git a/ngraph/test/visitors/op/power.cpp b/src/core/tests/visitors/op/power.cpp
similarity index 100%
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rename to src/core/tests/visitors/op/power.cpp
diff --git a/ngraph/test/visitors/op/prelu.cpp b/src/core/tests/visitors/op/prelu.cpp
similarity index 100%
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diff --git a/ngraph/test/visitors/op/prior_box.cpp b/src/core/tests/visitors/op/prior_box.cpp
similarity index 100%
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diff --git a/ngraph/test/visitors/op/prior_box_clustered.cpp b/src/core/tests/visitors/op/prior_box_clustered.cpp
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diff --git a/ngraph/test/visitors/op/proposal.cpp b/src/core/tests/visitors/op/proposal.cpp
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diff --git a/ngraph/test/visitors/op/psroi_pooling.cpp b/src/core/tests/visitors/op/psroi_pooling.cpp
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diff --git a/ngraph/test/visitors/op/random_uniform.cpp b/src/core/tests/visitors/op/random_uniform.cpp
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diff --git a/ngraph/test/visitors/op/reduce_l1.cpp b/src/core/tests/visitors/op/reduce_l1.cpp
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diff --git a/ngraph/test/visitors/op/reduce_l2.cpp b/src/core/tests/visitors/op/reduce_l2.cpp
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diff --git a/ngraph/test/visitors/op/reduce_logical_and.cpp b/src/core/tests/visitors/op/reduce_logical_and.cpp
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diff --git a/ngraph/test/visitors/op/reduce_logical_or.cpp b/src/core/tests/visitors/op/reduce_logical_or.cpp
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rename to src/core/tests/visitors/op/reduce_logical_or.cpp
diff --git a/ngraph/test/visitors/op/reduce_max.cpp b/src/core/tests/visitors/op/reduce_max.cpp
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diff --git a/ngraph/test/visitors/op/reduce_mean.cpp b/src/core/tests/visitors/op/reduce_mean.cpp
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diff --git a/ngraph/test/visitors/op/reduce_min.cpp b/src/core/tests/visitors/op/reduce_min.cpp
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diff --git a/ngraph/test/visitors/op/reduce_ops.hpp b/src/core/tests/visitors/op/reduce_ops.hpp
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rename from ngraph/test/visitors/op/reduce_ops.hpp
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diff --git a/ngraph/test/visitors/op/reduce_prod.cpp b/src/core/tests/visitors/op/reduce_prod.cpp
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diff --git a/ngraph/test/visitors/op/reduce_sum.cpp b/src/core/tests/visitors/op/reduce_sum.cpp
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diff --git a/ngraph/test/visitors/op/region_yolo.cpp b/src/core/tests/visitors/op/region_yolo.cpp
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diff --git a/ngraph/test/visitors/op/relu.cpp b/src/core/tests/visitors/op/relu.cpp
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diff --git a/ngraph/test/visitors/op/reorg_yolo.cpp b/src/core/tests/visitors/op/reorg_yolo.cpp
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diff --git a/ngraph/test/visitors/op/reshape.cpp b/src/core/tests/visitors/op/reshape.cpp
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diff --git a/ngraph/test/visitors/op/result.cpp b/src/core/tests/visitors/op/result.cpp
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diff --git a/ngraph/test/visitors/op/rnn_cell.cpp b/src/core/tests/visitors/op/rnn_cell.cpp
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diff --git a/ngraph/test/visitors/op/rnn_sequence.cpp b/src/core/tests/visitors/op/rnn_sequence.cpp
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diff --git a/ngraph/test/visitors/op/roi_pooling.cpp b/src/core/tests/visitors/op/roi_pooling.cpp
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diff --git a/ngraph/test/visitors/op/roll.cpp b/src/core/tests/visitors/op/roll.cpp
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diff --git a/ngraph/test/visitors/op/round.cpp b/src/core/tests/visitors/op/round.cpp
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diff --git a/ngraph/test/visitors/op/scatter_elements_update.cpp b/src/core/tests/visitors/op/scatter_elements_update.cpp
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diff --git a/ngraph/test/visitors/op/select.cpp b/src/core/tests/visitors/op/select.cpp
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diff --git a/ngraph/test/visitors/op/sigmoid.cpp b/src/core/tests/visitors/op/sigmoid.cpp
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diff --git a/ngraph/test/visitors/op/sin.cpp b/src/core/tests/visitors/op/sin.cpp
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diff --git a/ngraph/test/visitors/op/sinh.cpp b/src/core/tests/visitors/op/sinh.cpp
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diff --git a/ngraph/test/visitors/op/softmax.cpp b/src/core/tests/visitors/op/softmax.cpp
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diff --git a/ngraph/test/visitors/op/softplus.cpp b/src/core/tests/visitors/op/softplus.cpp
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diff --git a/ngraph/test/visitors/op/space_to_batch.cpp b/src/core/tests/visitors/op/space_to_batch.cpp
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diff --git a/ngraph/test/visitors/op/split.cpp b/src/core/tests/visitors/op/split.cpp
similarity index 100%
rename from ngraph/test/visitors/op/split.cpp
rename to src/core/tests/visitors/op/split.cpp
diff --git a/ngraph/test/visitors/op/sqrt.cpp b/src/core/tests/visitors/op/sqrt.cpp
similarity index 100%
rename from ngraph/test/visitors/op/sqrt.cpp
rename to src/core/tests/visitors/op/sqrt.cpp
diff --git a/ngraph/test/visitors/op/squared_difference.cpp b/src/core/tests/visitors/op/squared_difference.cpp
similarity index 100%
rename from ngraph/test/visitors/op/squared_difference.cpp
rename to src/core/tests/visitors/op/squared_difference.cpp
diff --git a/ngraph/test/visitors/op/squeeze.cpp b/src/core/tests/visitors/op/squeeze.cpp
similarity index 100%
rename from ngraph/test/visitors/op/squeeze.cpp
rename to src/core/tests/visitors/op/squeeze.cpp
diff --git a/ngraph/test/visitors/op/strided_slice.cpp b/src/core/tests/visitors/op/strided_slice.cpp
similarity index 100%
rename from ngraph/test/visitors/op/strided_slice.cpp
rename to src/core/tests/visitors/op/strided_slice.cpp
diff --git a/ngraph/test/visitors/op/subtract.cpp b/src/core/tests/visitors/op/subtract.cpp
similarity index 100%
rename from ngraph/test/visitors/op/subtract.cpp
rename to src/core/tests/visitors/op/subtract.cpp
diff --git a/ngraph/test/visitors/op/swish.cpp b/src/core/tests/visitors/op/swish.cpp
similarity index 100%
rename from ngraph/test/visitors/op/swish.cpp
rename to src/core/tests/visitors/op/swish.cpp
diff --git a/ngraph/test/visitors/op/tan.cpp b/src/core/tests/visitors/op/tan.cpp
similarity index 100%
rename from ngraph/test/visitors/op/tan.cpp
rename to src/core/tests/visitors/op/tan.cpp
diff --git a/ngraph/test/visitors/op/tanh.cpp b/src/core/tests/visitors/op/tanh.cpp
similarity index 100%
rename from ngraph/test/visitors/op/tanh.cpp
rename to src/core/tests/visitors/op/tanh.cpp
diff --git a/ngraph/test/visitors/op/topk.cpp b/src/core/tests/visitors/op/topk.cpp
similarity index 100%
rename from ngraph/test/visitors/op/topk.cpp
rename to src/core/tests/visitors/op/topk.cpp
diff --git a/ngraph/test/visitors/op/transpose.cpp b/src/core/tests/visitors/op/transpose.cpp
similarity index 100%
rename from ngraph/test/visitors/op/transpose.cpp
rename to src/core/tests/visitors/op/transpose.cpp
diff --git a/ngraph/test/visitors/op/unary_ops.hpp b/src/core/tests/visitors/op/unary_ops.hpp
similarity index 100%
rename from ngraph/test/visitors/op/unary_ops.hpp
rename to src/core/tests/visitors/op/unary_ops.hpp
diff --git a/ngraph/test/visitors/op/unsqueeze.cpp b/src/core/tests/visitors/op/unsqueeze.cpp
similarity index 100%
rename from ngraph/test/visitors/op/unsqueeze.cpp
rename to src/core/tests/visitors/op/unsqueeze.cpp
diff --git a/ngraph/test/visitors/op/variadic_split.cpp b/src/core/tests/visitors/op/variadic_split.cpp
similarity index 100%
rename from ngraph/test/visitors/op/variadic_split.cpp
rename to src/core/tests/visitors/op/variadic_split.cpp
diff --git a/ngraph/test/visitors/partial_shape.cpp b/src/core/tests/visitors/partial_shape.cpp
similarity index 100%
rename from ngraph/test/visitors/partial_shape.cpp
rename to src/core/tests/visitors/partial_shape.cpp
diff --git a/ngraph/test/visitors/user_op.cpp b/src/core/tests/visitors/user_op.cpp
similarity index 100%
rename from ngraph/test/visitors/user_op.cpp
rename to src/core/tests/visitors/user_op.cpp
diff --git a/ngraph/test/visitors/value_map.cpp b/src/core/tests/visitors/value_map.cpp
similarity index 100%
rename from ngraph/test/visitors/value_map.cpp
rename to src/core/tests/visitors/value_map.cpp
diff --git a/ngraph/frontend/CMakeLists.txt b/src/frontends/CMakeLists.txt
similarity index 100%
rename from ngraph/frontend/CMakeLists.txt
rename to src/frontends/CMakeLists.txt
diff --git a/ngraph/frontend/common/CMakeLists.txt b/src/frontends/common/CMakeLists.txt
similarity index 99%
rename from ngraph/frontend/common/CMakeLists.txt
rename to src/frontends/common/CMakeLists.txt
index 697a6994f38..12f4ed14fc0 100644
--- a/ngraph/frontend/common/CMakeLists.txt
+++ b/src/frontends/common/CMakeLists.txt
@@ -14,7 +14,7 @@ file(GLOB_RECURSE LIBRARY_PUBLIC_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/include/*.h
 
 # Add include path to so_extension.hpp
 set_property(SOURCE ${CMAKE_CURRENT_SOURCE_DIR}/src/frontend_manager.cpp
-                    APPEND PROPERTY INCLUDE_DIRECTORIES "${OpenVINO_SOURCE_DIR}/ngraph/core/src/")
+                    APPEND PROPERTY INCLUDE_DIRECTORIES "${OpenVINO_SOURCE_DIR}/src/core/src/")
 
 set(FRONTEND_INCLUDE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/include)
 
diff --git a/ngraph/frontend/common/include/common/frontend.hpp b/src/frontends/common/include/common/frontend.hpp
similarity index 100%
rename from ngraph/frontend/common/include/common/frontend.hpp
rename to src/frontends/common/include/common/frontend.hpp
diff --git a/ngraph/frontend/common/include/common/frontend_defs.hpp b/src/frontends/common/include/common/frontend_defs.hpp
similarity index 100%
rename from ngraph/frontend/common/include/common/frontend_defs.hpp
rename to src/frontends/common/include/common/frontend_defs.hpp
diff --git a/ngraph/frontend/common/include/common/frontend_exceptions.hpp b/src/frontends/common/include/common/frontend_exceptions.hpp
similarity index 100%
rename from ngraph/frontend/common/include/common/frontend_exceptions.hpp
rename to src/frontends/common/include/common/frontend_exceptions.hpp
diff --git a/ngraph/frontend/common/include/common/input_model.hpp b/src/frontends/common/include/common/input_model.hpp
similarity index 100%
rename from ngraph/frontend/common/include/common/input_model.hpp
rename to src/frontends/common/include/common/input_model.hpp
diff --git a/ngraph/frontend/common/include/common/parameters.hpp b/src/frontends/common/include/common/parameters.hpp
similarity index 100%
rename from ngraph/frontend/common/include/common/parameters.hpp
rename to src/frontends/common/include/common/parameters.hpp
diff --git a/ngraph/frontend/common/include/common/place.hpp b/src/frontends/common/include/common/place.hpp
similarity index 100%
rename from ngraph/frontend/common/include/common/place.hpp
rename to src/frontends/common/include/common/place.hpp
diff --git a/ngraph/frontend/common/include/manager.hpp b/src/frontends/common/include/manager.hpp
similarity index 100%
rename from ngraph/frontend/common/include/manager.hpp
rename to src/frontends/common/include/manager.hpp
diff --git a/ngraph/frontend/common/src/frontend_manager.cpp b/src/frontends/common/src/frontend_manager.cpp
similarity index 100%
rename from ngraph/frontend/common/src/frontend_manager.cpp
rename to src/frontends/common/src/frontend_manager.cpp
diff --git a/ngraph/frontend/common/src/parameters.cpp b/src/frontends/common/src/parameters.cpp
similarity index 100%
rename from ngraph/frontend/common/src/parameters.cpp
rename to src/frontends/common/src/parameters.cpp
diff --git a/ngraph/frontend/common/src/plugin_loader.cpp b/src/frontends/common/src/plugin_loader.cpp
similarity index 100%
rename from ngraph/frontend/common/src/plugin_loader.cpp
rename to src/frontends/common/src/plugin_loader.cpp
diff --git a/ngraph/frontend/common/src/plugin_loader.hpp b/src/frontends/common/src/plugin_loader.hpp
similarity index 100%
rename from ngraph/frontend/common/src/plugin_loader.hpp
rename to src/frontends/common/src/plugin_loader.hpp
diff --git a/ngraph/frontend/common/src/utils.cpp b/src/frontends/common/src/utils.cpp
similarity index 100%
rename from ngraph/frontend/common/src/utils.cpp
rename to src/frontends/common/src/utils.cpp
diff --git a/ngraph/frontend/common/src/utils.hpp b/src/frontends/common/src/utils.hpp
similarity index 100%
rename from ngraph/frontend/common/src/utils.hpp
rename to src/frontends/common/src/utils.hpp
diff --git a/ngraph/frontend/ir/CMakeLists.txt b/src/frontends/ir/CMakeLists.txt
similarity index 98%
rename from ngraph/frontend/ir/CMakeLists.txt
rename to src/frontends/ir/CMakeLists.txt
index b43bfcdc797..c1f25db68ce 100644
--- a/ngraph/frontend/ir/CMakeLists.txt
+++ b/src/frontends/ir/CMakeLists.txt
@@ -12,7 +12,7 @@ set(${TARGET_NAME}_INCLUDE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/include)
 
 # Add include path to so_extension.hpp
 set_property(SOURCE ${CMAKE_CURRENT_SOURCE_DIR}/src/frontend.cpp
-                    APPEND PROPERTY INCLUDE_DIRECTORIES "${OpenVINO_SOURCE_DIR}/ngraph/core/src/")
+                    APPEND PROPERTY INCLUDE_DIRECTORIES "${OpenVINO_SOURCE_DIR}/src/core/src/")
 
 # Create named folders for the sources within the .vcproj
 # Empty name lists them directly under the .vcproj
diff --git a/ngraph/frontend/ir/include/ir_frontend/frontend.hpp b/src/frontends/ir/include/ir_frontend/frontend.hpp
similarity index 100%
rename from ngraph/frontend/ir/include/ir_frontend/frontend.hpp
rename to src/frontends/ir/include/ir_frontend/frontend.hpp
diff --git a/ngraph/frontend/ir/include/ir_frontend/model.hpp b/src/frontends/ir/include/ir_frontend/model.hpp
similarity index 100%
rename from ngraph/frontend/ir/include/ir_frontend/model.hpp
rename to src/frontends/ir/include/ir_frontend/model.hpp
diff --git a/ngraph/frontend/ir/include/ir_frontend/utility.hpp b/src/frontends/ir/include/ir_frontend/utility.hpp
similarity index 100%
rename from ngraph/frontend/ir/include/ir_frontend/utility.hpp
rename to src/frontends/ir/include/ir_frontend/utility.hpp
diff --git a/ngraph/frontend/ir/src/frontend.cpp b/src/frontends/ir/src/frontend.cpp
similarity index 100%
rename from ngraph/frontend/ir/src/frontend.cpp
rename to src/frontends/ir/src/frontend.cpp
diff --git a/ngraph/frontend/ir/src/ir_deserializer.cpp b/src/frontends/ir/src/ir_deserializer.cpp
similarity index 100%
rename from ngraph/frontend/ir/src/ir_deserializer.cpp
rename to src/frontends/ir/src/ir_deserializer.cpp
diff --git a/ngraph/frontend/ir/src/ir_deserializer.hpp b/src/frontends/ir/src/ir_deserializer.hpp
similarity index 100%
rename from ngraph/frontend/ir/src/ir_deserializer.hpp
rename to src/frontends/ir/src/ir_deserializer.hpp
diff --git a/ngraph/frontend/ir/src/model.cpp b/src/frontends/ir/src/model.cpp
similarity index 100%
rename from ngraph/frontend/ir/src/model.cpp
rename to src/frontends/ir/src/model.cpp
diff --git a/ngraph/frontend/ir/src/rt_info_deserializer.cpp b/src/frontends/ir/src/rt_info_deserializer.cpp
similarity index 100%
rename from ngraph/frontend/ir/src/rt_info_deserializer.cpp
rename to src/frontends/ir/src/rt_info_deserializer.cpp
diff --git a/ngraph/frontend/ir/src/rt_info_deserializer.hpp b/src/frontends/ir/src/rt_info_deserializer.hpp
similarity index 100%
rename from ngraph/frontend/ir/src/rt_info_deserializer.hpp
rename to src/frontends/ir/src/rt_info_deserializer.hpp
diff --git a/ngraph/frontend/ir/src/utils.cpp b/src/frontends/ir/src/utils.cpp
similarity index 100%
rename from ngraph/frontend/ir/src/utils.cpp
rename to src/frontends/ir/src/utils.cpp
diff --git a/ngraph/frontend/ir/src/utils.hpp b/src/frontends/ir/src/utils.hpp
similarity index 100%
rename from ngraph/frontend/ir/src/utils.hpp
rename to src/frontends/ir/src/utils.hpp
diff --git a/ngraph/frontend/onnx/CMakeLists.txt b/src/frontends/onnx/CMakeLists.txt
similarity index 100%
rename from ngraph/frontend/onnx/CMakeLists.txt
rename to src/frontends/onnx/CMakeLists.txt
diff --git a/ngraph/frontend/onnx/frontend/CMakeLists.txt b/src/frontends/onnx/frontend/CMakeLists.txt
similarity index 100%
rename from ngraph/frontend/onnx/frontend/CMakeLists.txt
rename to src/frontends/onnx/frontend/CMakeLists.txt
diff --git a/ngraph/frontend/onnx/frontend/include/onnx_frontend/frontend.hpp b/src/frontends/onnx/frontend/include/onnx_frontend/frontend.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/include/onnx_frontend/frontend.hpp
rename to src/frontends/onnx/frontend/include/onnx_frontend/frontend.hpp
diff --git a/ngraph/frontend/onnx/frontend/include/onnx_import/core/node.hpp b/src/frontends/onnx/frontend/include/onnx_import/core/node.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/include/onnx_import/core/node.hpp
rename to src/frontends/onnx/frontend/include/onnx_import/core/node.hpp
diff --git a/ngraph/frontend/onnx/frontend/include/onnx_import/core/null_node.hpp b/src/frontends/onnx/frontend/include/onnx_import/core/null_node.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/include/onnx_import/core/null_node.hpp
rename to src/frontends/onnx/frontend/include/onnx_import/core/null_node.hpp
diff --git a/ngraph/frontend/onnx/frontend/include/onnx_import/core/operator_set.hpp b/src/frontends/onnx/frontend/include/onnx_import/core/operator_set.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/include/onnx_import/core/operator_set.hpp
rename to src/frontends/onnx/frontend/include/onnx_import/core/operator_set.hpp
diff --git a/ngraph/frontend/onnx/frontend/include/onnx_import/onnx.hpp b/src/frontends/onnx/frontend/include/onnx_import/onnx.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/include/onnx_import/onnx.hpp
rename to src/frontends/onnx/frontend/include/onnx_import/onnx.hpp
diff --git a/ngraph/frontend/onnx/frontend/include/onnx_import/onnx_importer_visibility.hpp b/src/frontends/onnx/frontend/include/onnx_import/onnx_importer_visibility.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/include/onnx_import/onnx_importer_visibility.hpp
rename to src/frontends/onnx/frontend/include/onnx_import/onnx_importer_visibility.hpp
diff --git a/ngraph/frontend/onnx/frontend/include/onnx_import/onnx_utils.hpp b/src/frontends/onnx/frontend/include/onnx_import/onnx_utils.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/include/onnx_import/onnx_utils.hpp
rename to src/frontends/onnx/frontend/include/onnx_import/onnx_utils.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/core/attribute.cpp b/src/frontends/onnx/frontend/src/core/attribute.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/core/attribute.cpp
rename to src/frontends/onnx/frontend/src/core/attribute.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/core/attribute.hpp b/src/frontends/onnx/frontend/src/core/attribute.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/core/attribute.hpp
rename to src/frontends/onnx/frontend/src/core/attribute.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/core/graph.cpp b/src/frontends/onnx/frontend/src/core/graph.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/core/graph.cpp
rename to src/frontends/onnx/frontend/src/core/graph.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/core/graph.hpp b/src/frontends/onnx/frontend/src/core/graph.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/core/graph.hpp
rename to src/frontends/onnx/frontend/src/core/graph.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/core/graph_cache.cpp b/src/frontends/onnx/frontend/src/core/graph_cache.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/core/graph_cache.cpp
rename to src/frontends/onnx/frontend/src/core/graph_cache.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/core/graph_cache.hpp b/src/frontends/onnx/frontend/src/core/graph_cache.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/core/graph_cache.hpp
rename to src/frontends/onnx/frontend/src/core/graph_cache.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/core/model.cpp b/src/frontends/onnx/frontend/src/core/model.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/core/model.cpp
rename to src/frontends/onnx/frontend/src/core/model.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/core/model.hpp b/src/frontends/onnx/frontend/src/core/model.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/core/model.hpp
rename to src/frontends/onnx/frontend/src/core/model.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/core/node.cpp b/src/frontends/onnx/frontend/src/core/node.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/core/node.cpp
rename to src/frontends/onnx/frontend/src/core/node.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/core/null_node.cpp b/src/frontends/onnx/frontend/src/core/null_node.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/core/null_node.cpp
rename to src/frontends/onnx/frontend/src/core/null_node.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/core/sparse_tensor.hpp b/src/frontends/onnx/frontend/src/core/sparse_tensor.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/core/sparse_tensor.hpp
rename to src/frontends/onnx/frontend/src/core/sparse_tensor.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/core/tensor.hpp b/src/frontends/onnx/frontend/src/core/tensor.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/core/tensor.hpp
rename to src/frontends/onnx/frontend/src/core/tensor.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/core/transform.cpp b/src/frontends/onnx/frontend/src/core/transform.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/core/transform.cpp
rename to src/frontends/onnx/frontend/src/core/transform.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/core/transform.hpp b/src/frontends/onnx/frontend/src/core/transform.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/core/transform.hpp
rename to src/frontends/onnx/frontend/src/core/transform.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/core/value_info.hpp b/src/frontends/onnx/frontend/src/core/value_info.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/core/value_info.hpp
rename to src/frontends/onnx/frontend/src/core/value_info.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/default_opset.hpp b/src/frontends/onnx/frontend/src/default_opset.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/default_opset.hpp
rename to src/frontends/onnx/frontend/src/default_opset.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/detail/subgraph_extraction.cpp b/src/frontends/onnx/frontend/src/detail/subgraph_extraction.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/detail/subgraph_extraction.cpp
rename to src/frontends/onnx/frontend/src/detail/subgraph_extraction.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/detail/subgraph_extraction.hpp b/src/frontends/onnx/frontend/src/detail/subgraph_extraction.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/detail/subgraph_extraction.hpp
rename to src/frontends/onnx/frontend/src/detail/subgraph_extraction.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/edge_mapper.cpp b/src/frontends/onnx/frontend/src/edge_mapper.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/edge_mapper.cpp
rename to src/frontends/onnx/frontend/src/edge_mapper.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/edge_mapper.hpp b/src/frontends/onnx/frontend/src/edge_mapper.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/edge_mapper.hpp
rename to src/frontends/onnx/frontend/src/edge_mapper.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/editor.cpp b/src/frontends/onnx/frontend/src/editor.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/editor.cpp
rename to src/frontends/onnx/frontend/src/editor.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/editor.hpp b/src/frontends/onnx/frontend/src/editor.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/editor.hpp
rename to src/frontends/onnx/frontend/src/editor.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/editor_types.hpp b/src/frontends/onnx/frontend/src/editor_types.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/editor_types.hpp
rename to src/frontends/onnx/frontend/src/editor_types.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/exceptions.cpp b/src/frontends/onnx/frontend/src/exceptions.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/exceptions.cpp
rename to src/frontends/onnx/frontend/src/exceptions.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/exceptions.hpp b/src/frontends/onnx/frontend/src/exceptions.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/exceptions.hpp
rename to src/frontends/onnx/frontend/src/exceptions.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/frontend.cpp b/src/frontends/onnx/frontend/src/frontend.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/frontend.cpp
rename to src/frontends/onnx/frontend/src/frontend.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/input_model.cpp b/src/frontends/onnx/frontend/src/input_model.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/input_model.cpp
rename to src/frontends/onnx/frontend/src/input_model.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/input_model.hpp b/src/frontends/onnx/frontend/src/input_model.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/input_model.hpp
rename to src/frontends/onnx/frontend/src/input_model.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/onnx.cpp b/src/frontends/onnx/frontend/src/onnx.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/onnx.cpp
rename to src/frontends/onnx/frontend/src/onnx.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/onnx_framework_node.cpp b/src/frontends/onnx/frontend/src/onnx_framework_node.cpp
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rename from ngraph/frontend/onnx/frontend/src/onnx_framework_node.cpp
rename to src/frontends/onnx/frontend/src/onnx_framework_node.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/onnx_framework_node.hpp b/src/frontends/onnx/frontend/src/onnx_framework_node.hpp
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rename to src/frontends/onnx/frontend/src/onnx_framework_node.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/onnx_utils.cpp b/src/frontends/onnx/frontend/src/onnx_utils.cpp
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rename from ngraph/frontend/onnx/frontend/src/onnx_utils.cpp
rename to src/frontends/onnx/frontend/src/onnx_utils.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/abs.hpp b/src/frontends/onnx/frontend/src/op/abs.hpp
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rename to src/frontends/onnx/frontend/src/op/abs.hpp
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rename to src/frontends/onnx/frontend/src/op/acos.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/acosh.hpp b/src/frontends/onnx/frontend/src/op/acosh.hpp
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rename to src/frontends/onnx/frontend/src/op/acosh.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/add.cpp b/src/frontends/onnx/frontend/src/op/add.cpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/affine.cpp b/src/frontends/onnx/frontend/src/op/affine.cpp
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rename to src/frontends/onnx/frontend/src/op/affine.cpp
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rename to src/frontends/onnx/frontend/src/op/affine.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/and.hpp b/src/frontends/onnx/frontend/src/op/and.hpp
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rename to src/frontends/onnx/frontend/src/op/argmax.cpp
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rename to src/frontends/onnx/frontend/src/op/argmin.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/argmin.hpp b/src/frontends/onnx/frontend/src/op/argmin.hpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/asin.hpp b/src/frontends/onnx/frontend/src/op/asin.hpp
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rename to src/frontends/onnx/frontend/src/op/asin.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/asinh.hpp b/src/frontends/onnx/frontend/src/op/asinh.hpp
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rename to src/frontends/onnx/frontend/src/op/asinh.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/atan.hpp b/src/frontends/onnx/frontend/src/op/atan.hpp
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rename to src/frontends/onnx/frontend/src/op/atan.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/atanh.hpp b/src/frontends/onnx/frontend/src/op/atanh.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/atanh.hpp
rename to src/frontends/onnx/frontend/src/op/atanh.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/aten.cpp b/src/frontends/onnx/frontend/src/op/aten.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/aten.cpp
rename to src/frontends/onnx/frontend/src/op/aten.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/aten.hpp b/src/frontends/onnx/frontend/src/op/aten.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/aten.hpp
rename to src/frontends/onnx/frontend/src/op/aten.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/average_pool.cpp b/src/frontends/onnx/frontend/src/op/average_pool.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/average_pool.cpp
rename to src/frontends/onnx/frontend/src/op/average_pool.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/average_pool.hpp b/src/frontends/onnx/frontend/src/op/average_pool.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/average_pool.hpp
rename to src/frontends/onnx/frontend/src/op/average_pool.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/batch_norm.cpp b/src/frontends/onnx/frontend/src/op/batch_norm.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/batch_norm.cpp
rename to src/frontends/onnx/frontend/src/op/batch_norm.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/batch_norm.hpp b/src/frontends/onnx/frontend/src/op/batch_norm.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/batch_norm.hpp
rename to src/frontends/onnx/frontend/src/op/batch_norm.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/bitshift.cpp b/src/frontends/onnx/frontend/src/op/bitshift.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/bitshift.cpp
rename to src/frontends/onnx/frontend/src/op/bitshift.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/bitshift.hpp b/src/frontends/onnx/frontend/src/op/bitshift.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/bitshift.hpp
rename to src/frontends/onnx/frontend/src/op/bitshift.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/cast.cpp b/src/frontends/onnx/frontend/src/op/cast.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/cast.cpp
rename to src/frontends/onnx/frontend/src/op/cast.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/cast.hpp b/src/frontends/onnx/frontend/src/op/cast.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/cast.hpp
rename to src/frontends/onnx/frontend/src/op/cast.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/cast_like.cpp b/src/frontends/onnx/frontend/src/op/cast_like.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/cast_like.cpp
rename to src/frontends/onnx/frontend/src/op/cast_like.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/cast_like.hpp b/src/frontends/onnx/frontend/src/op/cast_like.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/cast_like.hpp
rename to src/frontends/onnx/frontend/src/op/cast_like.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/ceil.hpp b/src/frontends/onnx/frontend/src/op/ceil.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/ceil.hpp
rename to src/frontends/onnx/frontend/src/op/ceil.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/clip.cpp b/src/frontends/onnx/frontend/src/op/clip.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/clip.cpp
rename to src/frontends/onnx/frontend/src/op/clip.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/clip.hpp b/src/frontends/onnx/frontend/src/op/clip.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/clip.hpp
rename to src/frontends/onnx/frontend/src/op/clip.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/com.microsoft/attention.cpp b/src/frontends/onnx/frontend/src/op/com.microsoft/attention.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/com.microsoft/attention.cpp
rename to src/frontends/onnx/frontend/src/op/com.microsoft/attention.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/com.microsoft/attention.hpp b/src/frontends/onnx/frontend/src/op/com.microsoft/attention.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/com.microsoft/attention.hpp
rename to src/frontends/onnx/frontend/src/op/com.microsoft/attention.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/com.microsoft/bias_gelu.cpp b/src/frontends/onnx/frontend/src/op/com.microsoft/bias_gelu.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/com.microsoft/bias_gelu.cpp
rename to src/frontends/onnx/frontend/src/op/com.microsoft/bias_gelu.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/com.microsoft/bias_gelu.hpp b/src/frontends/onnx/frontend/src/op/com.microsoft/bias_gelu.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/com.microsoft/bias_gelu.hpp
rename to src/frontends/onnx/frontend/src/op/com.microsoft/bias_gelu.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/com.microsoft/embed_layer_normalization.cpp b/src/frontends/onnx/frontend/src/op/com.microsoft/embed_layer_normalization.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/com.microsoft/embed_layer_normalization.cpp
rename to src/frontends/onnx/frontend/src/op/com.microsoft/embed_layer_normalization.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/com.microsoft/embed_layer_normalization.hpp b/src/frontends/onnx/frontend/src/op/com.microsoft/embed_layer_normalization.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/com.microsoft/embed_layer_normalization.hpp
rename to src/frontends/onnx/frontend/src/op/com.microsoft/embed_layer_normalization.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/com.microsoft/skip_layer_normalization.cpp b/src/frontends/onnx/frontend/src/op/com.microsoft/skip_layer_normalization.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/com.microsoft/skip_layer_normalization.cpp
rename to src/frontends/onnx/frontend/src/op/com.microsoft/skip_layer_normalization.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/com.microsoft/skip_layer_normalization.hpp b/src/frontends/onnx/frontend/src/op/com.microsoft/skip_layer_normalization.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/com.microsoft/skip_layer_normalization.hpp
rename to src/frontends/onnx/frontend/src/op/com.microsoft/skip_layer_normalization.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/compress.cpp b/src/frontends/onnx/frontend/src/op/compress.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/compress.cpp
rename to src/frontends/onnx/frontend/src/op/compress.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/compress.hpp b/src/frontends/onnx/frontend/src/op/compress.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/compress.hpp
rename to src/frontends/onnx/frontend/src/op/compress.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/concat.cpp b/src/frontends/onnx/frontend/src/op/concat.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/concat.cpp
rename to src/frontends/onnx/frontend/src/op/concat.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/concat.hpp b/src/frontends/onnx/frontend/src/op/concat.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/concat.hpp
rename to src/frontends/onnx/frontend/src/op/concat.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/constant.cpp b/src/frontends/onnx/frontend/src/op/constant.cpp
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rename to src/frontends/onnx/frontend/src/op/constant.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/constant.hpp b/src/frontends/onnx/frontend/src/op/constant.hpp
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rename to src/frontends/onnx/frontend/src/op/constant.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/constant_fill.cpp b/src/frontends/onnx/frontend/src/op/constant_fill.cpp
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rename to src/frontends/onnx/frontend/src/op/constant_fill.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/constant_fill.hpp b/src/frontends/onnx/frontend/src/op/constant_fill.hpp
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rename to src/frontends/onnx/frontend/src/op/constant_fill.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/constant_of_shape.cpp b/src/frontends/onnx/frontend/src/op/constant_of_shape.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/constant_of_shape.cpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/constant_of_shape.hpp b/src/frontends/onnx/frontend/src/op/constant_of_shape.hpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/conv_integer.cpp b/src/frontends/onnx/frontend/src/op/conv_integer.cpp
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rename to src/frontends/onnx/frontend/src/op/conv_integer.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/conv_integer.hpp b/src/frontends/onnx/frontend/src/op/conv_integer.hpp
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rename to src/frontends/onnx/frontend/src/op/conv_integer.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/elu.cpp
rename to src/frontends/onnx/frontend/src/op/elu.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/elu.hpp b/src/frontends/onnx/frontend/src/op/elu.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/elu.hpp
rename to src/frontends/onnx/frontend/src/op/elu.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/equal.hpp b/src/frontends/onnx/frontend/src/op/equal.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/equal.hpp
rename to src/frontends/onnx/frontend/src/op/equal.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/erf.hpp b/src/frontends/onnx/frontend/src/op/erf.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/erf.hpp
rename to src/frontends/onnx/frontend/src/op/erf.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/exp.hpp b/src/frontends/onnx/frontend/src/op/exp.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/exp.hpp
rename to src/frontends/onnx/frontend/src/op/exp.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/expand.cpp b/src/frontends/onnx/frontend/src/op/expand.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/expand.cpp
rename to src/frontends/onnx/frontend/src/op/expand.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/expand.hpp b/src/frontends/onnx/frontend/src/op/expand.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/expand.hpp
rename to src/frontends/onnx/frontend/src/op/expand.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/eye_like.cpp b/src/frontends/onnx/frontend/src/op/eye_like.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/eye_like.cpp
rename to src/frontends/onnx/frontend/src/op/eye_like.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/eye_like.hpp b/src/frontends/onnx/frontend/src/op/eye_like.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/eye_like.hpp
rename to src/frontends/onnx/frontend/src/op/eye_like.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/flatten.cpp b/src/frontends/onnx/frontend/src/op/flatten.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/flatten.cpp
rename to src/frontends/onnx/frontend/src/op/flatten.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/flatten.hpp b/src/frontends/onnx/frontend/src/op/flatten.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/flatten.hpp
rename to src/frontends/onnx/frontend/src/op/flatten.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/floor.hpp b/src/frontends/onnx/frontend/src/op/floor.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/floor.hpp
rename to src/frontends/onnx/frontend/src/op/floor.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/gather.hpp b/src/frontends/onnx/frontend/src/op/gather.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/gather.hpp
rename to src/frontends/onnx/frontend/src/op/gather.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/gather_elements.hpp b/src/frontends/onnx/frontend/src/op/gather_elements.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/gather_elements.hpp
rename to src/frontends/onnx/frontend/src/op/gather_elements.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/gather_nd.cpp b/src/frontends/onnx/frontend/src/op/gather_nd.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/gather_nd.cpp
rename to src/frontends/onnx/frontend/src/op/gather_nd.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/gather_nd.hpp b/src/frontends/onnx/frontend/src/op/gather_nd.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/gather_nd.hpp
rename to src/frontends/onnx/frontend/src/op/gather_nd.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/gemm.cpp b/src/frontends/onnx/frontend/src/op/gemm.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/gemm.cpp
rename to src/frontends/onnx/frontend/src/op/gemm.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/gemm.hpp b/src/frontends/onnx/frontend/src/op/gemm.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/gemm.hpp
rename to src/frontends/onnx/frontend/src/op/gemm.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/global_average_pool.cpp b/src/frontends/onnx/frontend/src/op/global_average_pool.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/global_average_pool.cpp
rename to src/frontends/onnx/frontend/src/op/global_average_pool.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/global_average_pool.hpp b/src/frontends/onnx/frontend/src/op/global_average_pool.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/global_average_pool.hpp
rename to src/frontends/onnx/frontend/src/op/global_average_pool.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/global_max_pool.cpp b/src/frontends/onnx/frontend/src/op/global_max_pool.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/global_max_pool.cpp
rename to src/frontends/onnx/frontend/src/op/global_max_pool.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/global_max_pool.hpp b/src/frontends/onnx/frontend/src/op/global_max_pool.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/global_max_pool.hpp
rename to src/frontends/onnx/frontend/src/op/global_max_pool.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/greater.hpp b/src/frontends/onnx/frontend/src/op/greater.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/greater.hpp
rename to src/frontends/onnx/frontend/src/op/greater.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/gru.cpp b/src/frontends/onnx/frontend/src/op/gru.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/gru.cpp
rename to src/frontends/onnx/frontend/src/op/gru.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/gru.hpp b/src/frontends/onnx/frontend/src/op/gru.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/gru.hpp
rename to src/frontends/onnx/frontend/src/op/gru.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/hard_sigmoid.cpp b/src/frontends/onnx/frontend/src/op/hard_sigmoid.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/hard_sigmoid.cpp
rename to src/frontends/onnx/frontend/src/op/hard_sigmoid.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/hard_sigmoid.hpp b/src/frontends/onnx/frontend/src/op/hard_sigmoid.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/hard_sigmoid.hpp
rename to src/frontends/onnx/frontend/src/op/hard_sigmoid.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/hard_swish.hpp b/src/frontends/onnx/frontend/src/op/hard_swish.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/hard_swish.hpp
rename to src/frontends/onnx/frontend/src/op/hard_swish.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/hardmax.cpp b/src/frontends/onnx/frontend/src/op/hardmax.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/hardmax.cpp
rename to src/frontends/onnx/frontend/src/op/hardmax.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/hardmax.hpp b/src/frontends/onnx/frontend/src/op/hardmax.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/hardmax.hpp
rename to src/frontends/onnx/frontend/src/op/hardmax.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/identity.hpp b/src/frontends/onnx/frontend/src/op/identity.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/identity.hpp
rename to src/frontends/onnx/frontend/src/op/identity.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/if.cpp b/src/frontends/onnx/frontend/src/op/if.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/if.cpp
rename to src/frontends/onnx/frontend/src/op/if.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/if.hpp b/src/frontends/onnx/frontend/src/op/if.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/if.hpp
rename to src/frontends/onnx/frontend/src/op/if.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/image_scaler.cpp b/src/frontends/onnx/frontend/src/op/image_scaler.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/image_scaler.cpp
rename to src/frontends/onnx/frontend/src/op/image_scaler.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/image_scaler.hpp b/src/frontends/onnx/frontend/src/op/image_scaler.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/image_scaler.hpp
rename to src/frontends/onnx/frontend/src/op/image_scaler.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/instance_norm.cpp b/src/frontends/onnx/frontend/src/op/instance_norm.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/instance_norm.cpp
rename to src/frontends/onnx/frontend/src/op/instance_norm.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/instance_norm.hpp b/src/frontends/onnx/frontend/src/op/instance_norm.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/instance_norm.hpp
rename to src/frontends/onnx/frontend/src/op/instance_norm.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/leaky_relu.cpp b/src/frontends/onnx/frontend/src/op/leaky_relu.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/leaky_relu.cpp
rename to src/frontends/onnx/frontend/src/op/leaky_relu.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/leaky_relu.hpp b/src/frontends/onnx/frontend/src/op/leaky_relu.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/leaky_relu.hpp
rename to src/frontends/onnx/frontend/src/op/leaky_relu.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/less.hpp b/src/frontends/onnx/frontend/src/op/less.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/less.hpp
rename to src/frontends/onnx/frontend/src/op/less.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/log.cpp b/src/frontends/onnx/frontend/src/op/log.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/log.cpp
rename to src/frontends/onnx/frontend/src/op/log.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/log.hpp b/src/frontends/onnx/frontend/src/op/log.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/log.hpp
rename to src/frontends/onnx/frontend/src/op/log.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/log_softmax.cpp b/src/frontends/onnx/frontend/src/op/log_softmax.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/log_softmax.cpp
rename to src/frontends/onnx/frontend/src/op/log_softmax.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/log_softmax.hpp b/src/frontends/onnx/frontend/src/op/log_softmax.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/log_softmax.hpp
rename to src/frontends/onnx/frontend/src/op/log_softmax.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/loop.cpp b/src/frontends/onnx/frontend/src/op/loop.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/loop.cpp
rename to src/frontends/onnx/frontend/src/op/loop.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/loop.hpp b/src/frontends/onnx/frontend/src/op/loop.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/loop.hpp
rename to src/frontends/onnx/frontend/src/op/loop.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/lp_norm.cpp b/src/frontends/onnx/frontend/src/op/lp_norm.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/lp_norm.cpp
rename to src/frontends/onnx/frontend/src/op/lp_norm.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/lp_norm.hpp b/src/frontends/onnx/frontend/src/op/lp_norm.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/lp_norm.hpp
rename to src/frontends/onnx/frontend/src/op/lp_norm.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/lp_pool.cpp b/src/frontends/onnx/frontend/src/op/lp_pool.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/lp_pool.cpp
rename to src/frontends/onnx/frontend/src/op/lp_pool.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/lp_pool.hpp b/src/frontends/onnx/frontend/src/op/lp_pool.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/lp_pool.hpp
rename to src/frontends/onnx/frontend/src/op/lp_pool.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/lrn.cpp b/src/frontends/onnx/frontend/src/op/lrn.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/lrn.cpp
rename to src/frontends/onnx/frontend/src/op/lrn.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/lrn.hpp b/src/frontends/onnx/frontend/src/op/lrn.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/lrn.hpp
rename to src/frontends/onnx/frontend/src/op/lrn.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/lstm.cpp b/src/frontends/onnx/frontend/src/op/lstm.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/lstm.cpp
rename to src/frontends/onnx/frontend/src/op/lstm.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/lstm.hpp b/src/frontends/onnx/frontend/src/op/lstm.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/lstm.hpp
rename to src/frontends/onnx/frontend/src/op/lstm.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/matmul.hpp b/src/frontends/onnx/frontend/src/op/matmul.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/matmul.hpp
rename to src/frontends/onnx/frontend/src/op/matmul.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/matmul_integer.cpp b/src/frontends/onnx/frontend/src/op/matmul_integer.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/matmul_integer.cpp
rename to src/frontends/onnx/frontend/src/op/matmul_integer.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/matmul_integer.hpp b/src/frontends/onnx/frontend/src/op/matmul_integer.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/matmul_integer.hpp
rename to src/frontends/onnx/frontend/src/op/matmul_integer.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/max.hpp b/src/frontends/onnx/frontend/src/op/max.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/max.hpp
rename to src/frontends/onnx/frontend/src/op/max.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/max_pool.cpp b/src/frontends/onnx/frontend/src/op/max_pool.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/max_pool.cpp
rename to src/frontends/onnx/frontend/src/op/max_pool.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/max_pool.hpp b/src/frontends/onnx/frontend/src/op/max_pool.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/max_pool.hpp
rename to src/frontends/onnx/frontend/src/op/max_pool.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/mean.cpp b/src/frontends/onnx/frontend/src/op/mean.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/mean.cpp
rename to src/frontends/onnx/frontend/src/op/mean.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/mean.hpp b/src/frontends/onnx/frontend/src/op/mean.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/mean.hpp
rename to src/frontends/onnx/frontend/src/op/mean.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/mean_variance_normalization.cpp b/src/frontends/onnx/frontend/src/op/mean_variance_normalization.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/mean_variance_normalization.cpp
rename to src/frontends/onnx/frontend/src/op/mean_variance_normalization.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/mean_variance_normalization.hpp b/src/frontends/onnx/frontend/src/op/mean_variance_normalization.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/mean_variance_normalization.hpp
rename to src/frontends/onnx/frontend/src/op/mean_variance_normalization.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/min.hpp b/src/frontends/onnx/frontend/src/op/min.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/min.hpp
rename to src/frontends/onnx/frontend/src/op/min.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/mod.cpp b/src/frontends/onnx/frontend/src/op/mod.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/mod.cpp
rename to src/frontends/onnx/frontend/src/op/mod.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/mod.hpp b/src/frontends/onnx/frontend/src/op/mod.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/mod.hpp
rename to src/frontends/onnx/frontend/src/op/mod.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/mul.hpp b/src/frontends/onnx/frontend/src/op/mul.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/mul.hpp
rename to src/frontends/onnx/frontend/src/op/mul.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/neg.hpp b/src/frontends/onnx/frontend/src/op/neg.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/neg.hpp
rename to src/frontends/onnx/frontend/src/op/neg.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/non_max_suppression.cpp b/src/frontends/onnx/frontend/src/op/non_max_suppression.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/non_max_suppression.cpp
rename to src/frontends/onnx/frontend/src/op/non_max_suppression.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/non_max_suppression.hpp b/src/frontends/onnx/frontend/src/op/non_max_suppression.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/non_max_suppression.hpp
rename to src/frontends/onnx/frontend/src/op/non_max_suppression.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/non_zero.cpp b/src/frontends/onnx/frontend/src/op/non_zero.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/non_zero.cpp
rename to src/frontends/onnx/frontend/src/op/non_zero.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/non_zero.hpp b/src/frontends/onnx/frontend/src/op/non_zero.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/non_zero.hpp
rename to src/frontends/onnx/frontend/src/op/non_zero.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/not.hpp b/src/frontends/onnx/frontend/src/op/not.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/not.hpp
rename to src/frontends/onnx/frontend/src/op/not.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/onehot.cpp b/src/frontends/onnx/frontend/src/op/onehot.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/onehot.cpp
rename to src/frontends/onnx/frontend/src/op/onehot.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/onehot.hpp b/src/frontends/onnx/frontend/src/op/onehot.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/onehot.hpp
rename to src/frontends/onnx/frontend/src/op/onehot.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/or.hpp b/src/frontends/onnx/frontend/src/op/or.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/or.hpp
rename to src/frontends/onnx/frontend/src/op/or.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/deformable_conv_2d.cpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/deformable_conv_2d.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/deformable_conv_2d.cpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/deformable_conv_2d.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/deformable_conv_2d.hpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/deformable_conv_2d.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/deformable_conv_2d.hpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/deformable_conv_2d.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/detection_output.cpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/detection_output.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/detection_output.cpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/detection_output.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/detection_output.hpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/detection_output.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/detection_output.hpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/detection_output.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/detection_output.cpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/detection_output.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/detection_output.cpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/detection_output.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/detection_output.hpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/detection_output.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/detection_output.hpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/detection_output.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/generate_proposals_single_image.cpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/generate_proposals_single_image.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/generate_proposals_single_image.cpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/generate_proposals_single_image.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/generate_proposals_single_image.hpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/generate_proposals_single_image.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/generate_proposals_single_image.hpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/generate_proposals_single_image.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/prior_grid_generator.cpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/prior_grid_generator.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/prior_grid_generator.cpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/prior_grid_generator.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/prior_grid_generator.hpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/prior_grid_generator.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/prior_grid_generator.hpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/prior_grid_generator.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/roi_feature_extractor.cpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/roi_feature_extractor.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/roi_feature_extractor.cpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/roi_feature_extractor.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/roi_feature_extractor.hpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/roi_feature_extractor.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/roi_feature_extractor.hpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/roi_feature_extractor.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/topk_rios.cpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/topk_rios.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/topk_rios.cpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/topk_rios.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/topk_rios.hpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/topk_rios.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/topk_rios.hpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/experimental_detectron/topk_rios.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/fake_quantize.cpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/fake_quantize.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/fake_quantize.cpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/fake_quantize.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/fake_quantize.hpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/fake_quantize.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/fake_quantize.hpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/fake_quantize.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/group_norm.cpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/group_norm.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/group_norm.cpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/group_norm.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/group_norm.hpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/group_norm.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/group_norm.hpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/group_norm.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/normalize.cpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/normalize.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/normalize.cpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/normalize.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/normalize.hpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/normalize.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/normalize.hpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/normalize.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/prior_box.cpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/prior_box.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/prior_box.cpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/prior_box.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/prior_box.hpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/prior_box.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/prior_box.hpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/swish.cpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/swish.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/swish.cpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/swish.hpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/swish.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/org.openvinotoolkit/swish.hpp
rename to src/frontends/onnx/frontend/src/op/org.openvinotoolkit/swish.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/pad.cpp b/src/frontends/onnx/frontend/src/op/pad.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/pad.cpp
rename to src/frontends/onnx/frontend/src/op/pad.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/pad.hpp b/src/frontends/onnx/frontend/src/op/pad.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/pad.hpp
rename to src/frontends/onnx/frontend/src/op/pad.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/pow.cpp b/src/frontends/onnx/frontend/src/op/pow.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/pow.cpp
rename to src/frontends/onnx/frontend/src/op/pow.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/pow.hpp b/src/frontends/onnx/frontend/src/op/pow.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/pow.hpp
rename to src/frontends/onnx/frontend/src/op/pow.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/prelu.cpp b/src/frontends/onnx/frontend/src/op/prelu.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/prelu.cpp
rename to src/frontends/onnx/frontend/src/op/prelu.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/prelu.hpp b/src/frontends/onnx/frontend/src/op/prelu.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/prelu.hpp
rename to src/frontends/onnx/frontend/src/op/prelu.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/qlinear_conv.cpp b/src/frontends/onnx/frontend/src/op/qlinear_conv.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/qlinear_conv.cpp
rename to src/frontends/onnx/frontend/src/op/qlinear_conv.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/qlinear_conv.hpp b/src/frontends/onnx/frontend/src/op/qlinear_conv.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/qlinear_conv.hpp
rename to src/frontends/onnx/frontend/src/op/qlinear_conv.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/qlinear_matmul.cpp b/src/frontends/onnx/frontend/src/op/qlinear_matmul.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/qlinear_matmul.cpp
rename to src/frontends/onnx/frontend/src/op/qlinear_matmul.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/qlinear_matmul.hpp b/src/frontends/onnx/frontend/src/op/qlinear_matmul.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/qlinear_matmul.hpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/quant_conv.cpp b/src/frontends/onnx/frontend/src/op/quant_conv.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/quant_conv.cpp
rename to src/frontends/onnx/frontend/src/op/quant_conv.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/quant_conv.hpp b/src/frontends/onnx/frontend/src/op/quant_conv.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/quant_conv.hpp
rename to src/frontends/onnx/frontend/src/op/quant_conv.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/quantize_linear.cpp b/src/frontends/onnx/frontend/src/op/quantize_linear.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/quantize_linear.cpp
rename to src/frontends/onnx/frontend/src/op/quantize_linear.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/quantize_linear.hpp b/src/frontends/onnx/frontend/src/op/quantize_linear.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/quantize_linear.hpp
rename to src/frontends/onnx/frontend/src/op/quantize_linear.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/random_normal.cpp b/src/frontends/onnx/frontend/src/op/random_normal.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/random_normal.cpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/random_normal.hpp b/src/frontends/onnx/frontend/src/op/random_normal.hpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/random_normal_like.cpp b/src/frontends/onnx/frontend/src/op/random_normal_like.cpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/random_normal_like.hpp b/src/frontends/onnx/frontend/src/op/random_normal_like.hpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/random_uniform.cpp b/src/frontends/onnx/frontend/src/op/random_uniform.cpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/random_uniform.hpp b/src/frontends/onnx/frontend/src/op/random_uniform.hpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/random_uniform_like.cpp b/src/frontends/onnx/frontend/src/op/random_uniform_like.cpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/random_uniform_like.hpp b/src/frontends/onnx/frontend/src/op/random_uniform_like.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/random_uniform_like.hpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/range.cpp b/src/frontends/onnx/frontend/src/op/range.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/range.cpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/range.hpp b/src/frontends/onnx/frontend/src/op/range.hpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/reciprocal.cpp b/src/frontends/onnx/frontend/src/op/reciprocal.cpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/reciprocal.hpp b/src/frontends/onnx/frontend/src/op/reciprocal.hpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/reduce.cpp b/src/frontends/onnx/frontend/src/op/reduce.cpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/relu.hpp b/src/frontends/onnx/frontend/src/op/relu.hpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/reshape.cpp b/src/frontends/onnx/frontend/src/op/reshape.cpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/reshape.hpp b/src/frontends/onnx/frontend/src/op/reshape.hpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/resize.cpp b/src/frontends/onnx/frontend/src/op/resize.cpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/resize.hpp b/src/frontends/onnx/frontend/src/op/resize.hpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/reverse_sequence.cpp b/src/frontends/onnx/frontend/src/op/reverse_sequence.cpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/rnn.cpp b/src/frontends/onnx/frontend/src/op/rnn.cpp
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rename to src/frontends/onnx/frontend/src/op/rnn.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/roi_align.cpp b/src/frontends/onnx/frontend/src/op/roi_align.cpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/round.cpp b/src/frontends/onnx/frontend/src/op/round.cpp
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diff --git a/ngraph/frontend/onnx/frontend/src/op/scatter_elements.cpp b/src/frontends/onnx/frontend/src/op/scatter_elements.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/scatter_elements.cpp
rename to src/frontends/onnx/frontend/src/op/scatter_elements.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/scatter_elements.hpp b/src/frontends/onnx/frontend/src/op/scatter_elements.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/scatter_elements.hpp
rename to src/frontends/onnx/frontend/src/op/scatter_elements.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/scatter_nd.cpp b/src/frontends/onnx/frontend/src/op/scatter_nd.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/scatter_nd.cpp
rename to src/frontends/onnx/frontend/src/op/scatter_nd.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/scatter_nd.hpp b/src/frontends/onnx/frontend/src/op/scatter_nd.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/scatter_nd.hpp
rename to src/frontends/onnx/frontend/src/op/scatter_nd.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/selu.cpp b/src/frontends/onnx/frontend/src/op/selu.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/selu.cpp
rename to src/frontends/onnx/frontend/src/op/selu.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/selu.hpp b/src/frontends/onnx/frontend/src/op/selu.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/selu.hpp
rename to src/frontends/onnx/frontend/src/op/selu.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/shape.cpp b/src/frontends/onnx/frontend/src/op/shape.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/shape.cpp
rename to src/frontends/onnx/frontend/src/op/shape.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/shape.hpp b/src/frontends/onnx/frontend/src/op/shape.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/shape.hpp
rename to src/frontends/onnx/frontend/src/op/shape.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/shrink.cpp b/src/frontends/onnx/frontend/src/op/shrink.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/shrink.cpp
rename to src/frontends/onnx/frontend/src/op/shrink.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/shrink.hpp b/src/frontends/onnx/frontend/src/op/shrink.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/shrink.hpp
rename to src/frontends/onnx/frontend/src/op/shrink.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/sigmoid.hpp b/src/frontends/onnx/frontend/src/op/sigmoid.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/sigmoid.hpp
rename to src/frontends/onnx/frontend/src/op/sigmoid.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/sign.hpp b/src/frontends/onnx/frontend/src/op/sign.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/sign.hpp
rename to src/frontends/onnx/frontend/src/op/sign.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/sin.hpp b/src/frontends/onnx/frontend/src/op/sin.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/sin.hpp
rename to src/frontends/onnx/frontend/src/op/sin.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/sinh.hpp b/src/frontends/onnx/frontend/src/op/sinh.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/sinh.hpp
rename to src/frontends/onnx/frontend/src/op/sinh.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/size.cpp b/src/frontends/onnx/frontend/src/op/size.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/size.cpp
rename to src/frontends/onnx/frontend/src/op/size.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/size.hpp b/src/frontends/onnx/frontend/src/op/size.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/size.hpp
rename to src/frontends/onnx/frontend/src/op/size.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/slice.cpp b/src/frontends/onnx/frontend/src/op/slice.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/slice.cpp
rename to src/frontends/onnx/frontend/src/op/slice.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/slice.hpp b/src/frontends/onnx/frontend/src/op/slice.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/slice.hpp
rename to src/frontends/onnx/frontend/src/op/slice.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/softmax.cpp b/src/frontends/onnx/frontend/src/op/softmax.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/softmax.cpp
rename to src/frontends/onnx/frontend/src/op/softmax.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/softmax.hpp b/src/frontends/onnx/frontend/src/op/softmax.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/softmax.hpp
rename to src/frontends/onnx/frontend/src/op/softmax.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/softplus.cpp b/src/frontends/onnx/frontend/src/op/softplus.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/softplus.cpp
rename to src/frontends/onnx/frontend/src/op/softplus.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/softplus.hpp b/src/frontends/onnx/frontend/src/op/softplus.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/softplus.hpp
rename to src/frontends/onnx/frontend/src/op/softplus.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/softsign.cpp b/src/frontends/onnx/frontend/src/op/softsign.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/softsign.cpp
rename to src/frontends/onnx/frontend/src/op/softsign.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/softsign.hpp b/src/frontends/onnx/frontend/src/op/softsign.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/softsign.hpp
rename to src/frontends/onnx/frontend/src/op/softsign.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/space_to_depth.cpp b/src/frontends/onnx/frontend/src/op/space_to_depth.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/space_to_depth.cpp
rename to src/frontends/onnx/frontend/src/op/space_to_depth.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/space_to_depth.hpp b/src/frontends/onnx/frontend/src/op/space_to_depth.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/space_to_depth.hpp
rename to src/frontends/onnx/frontend/src/op/space_to_depth.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/split.cpp b/src/frontends/onnx/frontend/src/op/split.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/split.cpp
rename to src/frontends/onnx/frontend/src/op/split.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/split.hpp b/src/frontends/onnx/frontend/src/op/split.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/split.hpp
rename to src/frontends/onnx/frontend/src/op/split.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/sqrt.hpp b/src/frontends/onnx/frontend/src/op/sqrt.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/sqrt.hpp
rename to src/frontends/onnx/frontend/src/op/sqrt.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/squeeze.cpp b/src/frontends/onnx/frontend/src/op/squeeze.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/squeeze.cpp
rename to src/frontends/onnx/frontend/src/op/squeeze.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/squeeze.hpp b/src/frontends/onnx/frontend/src/op/squeeze.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/squeeze.hpp
rename to src/frontends/onnx/frontend/src/op/squeeze.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/sub.hpp b/src/frontends/onnx/frontend/src/op/sub.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/sub.hpp
rename to src/frontends/onnx/frontend/src/op/sub.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/sum.hpp b/src/frontends/onnx/frontend/src/op/sum.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/sum.hpp
rename to src/frontends/onnx/frontend/src/op/sum.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/tan.hpp b/src/frontends/onnx/frontend/src/op/tan.hpp
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rename from ngraph/frontend/onnx/frontend/src/op/tan.hpp
rename to src/frontends/onnx/frontend/src/op/tan.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/tanh.hpp b/src/frontends/onnx/frontend/src/op/tanh.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/tanh.hpp
rename to src/frontends/onnx/frontend/src/op/tanh.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/thresholded_relu.cpp b/src/frontends/onnx/frontend/src/op/thresholded_relu.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/thresholded_relu.cpp
rename to src/frontends/onnx/frontend/src/op/thresholded_relu.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/thresholded_relu.hpp b/src/frontends/onnx/frontend/src/op/thresholded_relu.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/thresholded_relu.hpp
rename to src/frontends/onnx/frontend/src/op/thresholded_relu.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/tile.cpp b/src/frontends/onnx/frontend/src/op/tile.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/tile.cpp
rename to src/frontends/onnx/frontend/src/op/tile.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/tile.hpp b/src/frontends/onnx/frontend/src/op/tile.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/tile.hpp
rename to src/frontends/onnx/frontend/src/op/tile.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/topk.cpp b/src/frontends/onnx/frontend/src/op/topk.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/topk.cpp
rename to src/frontends/onnx/frontend/src/op/topk.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/topk.hpp b/src/frontends/onnx/frontend/src/op/topk.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/topk.hpp
rename to src/frontends/onnx/frontend/src/op/topk.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/transpose.cpp b/src/frontends/onnx/frontend/src/op/transpose.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/transpose.cpp
rename to src/frontends/onnx/frontend/src/op/transpose.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/transpose.hpp b/src/frontends/onnx/frontend/src/op/transpose.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/transpose.hpp
rename to src/frontends/onnx/frontend/src/op/transpose.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/unsqueeze.cpp b/src/frontends/onnx/frontend/src/op/unsqueeze.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/unsqueeze.cpp
rename to src/frontends/onnx/frontend/src/op/unsqueeze.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/unsqueeze.hpp b/src/frontends/onnx/frontend/src/op/unsqueeze.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/unsqueeze.hpp
rename to src/frontends/onnx/frontend/src/op/unsqueeze.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/upsample.cpp b/src/frontends/onnx/frontend/src/op/upsample.cpp
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rename from ngraph/frontend/onnx/frontend/src/op/upsample.cpp
rename to src/frontends/onnx/frontend/src/op/upsample.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/upsample.hpp b/src/frontends/onnx/frontend/src/op/upsample.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/upsample.hpp
rename to src/frontends/onnx/frontend/src/op/upsample.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/where.hpp b/src/frontends/onnx/frontend/src/op/where.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/where.hpp
rename to src/frontends/onnx/frontend/src/op/where.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/op/xor.hpp b/src/frontends/onnx/frontend/src/op/xor.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/op/xor.hpp
rename to src/frontends/onnx/frontend/src/op/xor.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/ops_bridge.cpp b/src/frontends/onnx/frontend/src/ops_bridge.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/ops_bridge.cpp
rename to src/frontends/onnx/frontend/src/ops_bridge.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/ops_bridge.hpp b/src/frontends/onnx/frontend/src/ops_bridge.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/ops_bridge.hpp
rename to src/frontends/onnx/frontend/src/ops_bridge.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/place.cpp b/src/frontends/onnx/frontend/src/place.cpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/place.cpp
rename to src/frontends/onnx/frontend/src/place.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/place.hpp b/src/frontends/onnx/frontend/src/place.hpp
similarity index 100%
rename from ngraph/frontend/onnx/frontend/src/place.hpp
rename to src/frontends/onnx/frontend/src/place.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/precomp.hpp b/src/frontends/onnx/frontend/src/precomp.hpp
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rename from ngraph/frontend/onnx/frontend/src/precomp.hpp
rename to src/frontends/onnx/frontend/src/precomp.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/utils/arg_min_max_factory.cpp b/src/frontends/onnx/frontend/src/utils/arg_min_max_factory.cpp
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rename from ngraph/frontend/onnx/frontend/src/utils/arg_min_max_factory.cpp
rename to src/frontends/onnx/frontend/src/utils/arg_min_max_factory.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/utils/arg_min_max_factory.hpp b/src/frontends/onnx/frontend/src/utils/arg_min_max_factory.hpp
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rename from ngraph/frontend/onnx/frontend/src/utils/arg_min_max_factory.hpp
rename to src/frontends/onnx/frontend/src/utils/arg_min_max_factory.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/utils/common.cpp b/src/frontends/onnx/frontend/src/utils/common.cpp
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rename from ngraph/frontend/onnx/frontend/src/utils/common.cpp
rename to src/frontends/onnx/frontend/src/utils/common.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/utils/common.hpp b/src/frontends/onnx/frontend/src/utils/common.hpp
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rename from ngraph/frontend/onnx/frontend/src/utils/common.hpp
rename to src/frontends/onnx/frontend/src/utils/common.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/utils/conv_factory.cpp b/src/frontends/onnx/frontend/src/utils/conv_factory.cpp
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rename from ngraph/frontend/onnx/frontend/src/utils/conv_factory.cpp
rename to src/frontends/onnx/frontend/src/utils/conv_factory.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/utils/conv_factory.hpp b/src/frontends/onnx/frontend/src/utils/conv_factory.hpp
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rename from ngraph/frontend/onnx/frontend/src/utils/conv_factory.hpp
rename to src/frontends/onnx/frontend/src/utils/conv_factory.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/utils/convpool.cpp b/src/frontends/onnx/frontend/src/utils/convpool.cpp
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rename from ngraph/frontend/onnx/frontend/src/utils/convpool.cpp
rename to src/frontends/onnx/frontend/src/utils/convpool.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/utils/convpool.hpp b/src/frontends/onnx/frontend/src/utils/convpool.hpp
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rename from ngraph/frontend/onnx/frontend/src/utils/convpool.hpp
rename to src/frontends/onnx/frontend/src/utils/convpool.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/utils/onnx_internal.cpp b/src/frontends/onnx/frontend/src/utils/onnx_internal.cpp
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rename from ngraph/frontend/onnx/frontend/src/utils/onnx_internal.cpp
rename to src/frontends/onnx/frontend/src/utils/onnx_internal.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/utils/onnx_internal.hpp b/src/frontends/onnx/frontend/src/utils/onnx_internal.hpp
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rename from ngraph/frontend/onnx/frontend/src/utils/onnx_internal.hpp
rename to src/frontends/onnx/frontend/src/utils/onnx_internal.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/utils/pooling_factory.cpp b/src/frontends/onnx/frontend/src/utils/pooling_factory.cpp
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rename from ngraph/frontend/onnx/frontend/src/utils/pooling_factory.cpp
rename to src/frontends/onnx/frontend/src/utils/pooling_factory.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/utils/pooling_factory.hpp b/src/frontends/onnx/frontend/src/utils/pooling_factory.hpp
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rename from ngraph/frontend/onnx/frontend/src/utils/pooling_factory.hpp
rename to src/frontends/onnx/frontend/src/utils/pooling_factory.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/utils/random_normal.cpp b/src/frontends/onnx/frontend/src/utils/random_normal.cpp
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rename from ngraph/frontend/onnx/frontend/src/utils/random_normal.cpp
rename to src/frontends/onnx/frontend/src/utils/random_normal.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/utils/random_normal.hpp b/src/frontends/onnx/frontend/src/utils/random_normal.hpp
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rename from ngraph/frontend/onnx/frontend/src/utils/random_normal.hpp
rename to src/frontends/onnx/frontend/src/utils/random_normal.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/utils/recurrent.cpp b/src/frontends/onnx/frontend/src/utils/recurrent.cpp
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rename from ngraph/frontend/onnx/frontend/src/utils/recurrent.cpp
rename to src/frontends/onnx/frontend/src/utils/recurrent.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/utils/recurrent.hpp b/src/frontends/onnx/frontend/src/utils/recurrent.hpp
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rename from ngraph/frontend/onnx/frontend/src/utils/recurrent.hpp
rename to src/frontends/onnx/frontend/src/utils/recurrent.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/utils/reshape.cpp b/src/frontends/onnx/frontend/src/utils/reshape.cpp
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rename from ngraph/frontend/onnx/frontend/src/utils/reshape.cpp
rename to src/frontends/onnx/frontend/src/utils/reshape.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/utils/reshape.hpp b/src/frontends/onnx/frontend/src/utils/reshape.hpp
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rename from ngraph/frontend/onnx/frontend/src/utils/reshape.hpp
rename to src/frontends/onnx/frontend/src/utils/reshape.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/utils/tensor_external_data.cpp b/src/frontends/onnx/frontend/src/utils/tensor_external_data.cpp
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rename from ngraph/frontend/onnx/frontend/src/utils/tensor_external_data.cpp
rename to src/frontends/onnx/frontend/src/utils/tensor_external_data.cpp
diff --git a/ngraph/frontend/onnx/frontend/src/utils/tensor_external_data.hpp b/src/frontends/onnx/frontend/src/utils/tensor_external_data.hpp
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rename from ngraph/frontend/onnx/frontend/src/utils/tensor_external_data.hpp
rename to src/frontends/onnx/frontend/src/utils/tensor_external_data.hpp
diff --git a/ngraph/frontend/onnx/frontend/src/utils/variadic.hpp b/src/frontends/onnx/frontend/src/utils/variadic.hpp
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rename from ngraph/frontend/onnx/frontend/src/utils/variadic.hpp
rename to src/frontends/onnx/frontend/src/utils/variadic.hpp
diff --git a/ngraph/frontend/onnx/onnx_common/CMakeLists.txt b/src/frontends/onnx/onnx_common/CMakeLists.txt
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rename from ngraph/frontend/onnx/onnx_common/CMakeLists.txt
rename to src/frontends/onnx/onnx_common/CMakeLists.txt
diff --git a/ngraph/frontend/onnx/onnx_common/include/onnx_common/onnx_model_validator.hpp b/src/frontends/onnx/onnx_common/include/onnx_common/onnx_model_validator.hpp
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rename from ngraph/frontend/onnx/onnx_common/include/onnx_common/onnx_model_validator.hpp
rename to src/frontends/onnx/onnx_common/include/onnx_common/onnx_model_validator.hpp
diff --git a/ngraph/frontend/onnx/onnx_common/include/onnx_common/parser.hpp b/src/frontends/onnx/onnx_common/include/onnx_common/parser.hpp
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rename from ngraph/frontend/onnx/onnx_common/include/onnx_common/parser.hpp
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rename from ngraph/frontend/onnx/onnx_common/include/onnx_common/utils.hpp
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rename from ngraph/frontend/onnx/onnx_common/src/onnx_model_validator.cpp
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rename from ngraph/frontend/onnx/onnx_common/src/parser.cpp
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rename from ngraph/frontend/onnx/onnx_common/src/utils.cpp
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diff --git a/ngraph/frontend/paddlepaddle/CMakeLists.txt b/src/frontends/paddlepaddle/CMakeLists.txt
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rename from ngraph/frontend/paddlepaddle/src/op/conv2d_utils.cpp
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diff --git a/ngraph/frontend/paddlepaddle/src/op/fill_constant_batch_size_like.cpp b/src/frontends/paddlepaddle/src/op/fill_constant_batch_size_like.cpp
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diff --git a/ngraph/frontend/paddlepaddle/src/op/gelu.cpp b/src/frontends/paddlepaddle/src/op/gelu.cpp
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diff --git a/ngraph/frontend/paddlepaddle/src/op/hard_swish.cpp b/src/frontends/paddlepaddle/src/op/hard_swish.cpp
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rename to src/frontends/paddlepaddle/src/op/layer_norm.cpp
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similarity index 100%
rename from ngraph/frontend/tensorflow/src/op/zeros_like.cpp
rename to src/frontends/tensorflow/src/op/zeros_like.cpp
diff --git a/ngraph/frontend/tensorflow/src/op_table.cpp b/src/frontends/tensorflow/src/op_table.cpp
similarity index 100%
rename from ngraph/frontend/tensorflow/src/op_table.cpp
rename to src/frontends/tensorflow/src/op_table.cpp
diff --git a/ngraph/frontend/tensorflow/src/op_table.hpp b/src/frontends/tensorflow/src/op_table.hpp
similarity index 100%
rename from ngraph/frontend/tensorflow/src/op_table.hpp
rename to src/frontends/tensorflow/src/op_table.hpp
diff --git a/ngraph/frontend/tensorflow/src/openvino_conversions.cpp b/src/frontends/tensorflow/src/openvino_conversions.cpp
similarity index 100%
rename from ngraph/frontend/tensorflow/src/openvino_conversions.cpp
rename to src/frontends/tensorflow/src/openvino_conversions.cpp
diff --git a/ngraph/frontend/tensorflow/src/openvino_conversions.hpp b/src/frontends/tensorflow/src/openvino_conversions.hpp
similarity index 100%
rename from ngraph/frontend/tensorflow/src/openvino_conversions.hpp
rename to src/frontends/tensorflow/src/openvino_conversions.hpp
diff --git a/ngraph/frontend/tensorflow/src/pass/transpose_sinking.cpp b/src/frontends/tensorflow/src/pass/transpose_sinking.cpp
similarity index 100%
rename from ngraph/frontend/tensorflow/src/pass/transpose_sinking.cpp
rename to src/frontends/tensorflow/src/pass/transpose_sinking.cpp
diff --git a/ngraph/frontend/tensorflow/src/pass/transpose_sinking.hpp b/src/frontends/tensorflow/src/pass/transpose_sinking.hpp
similarity index 100%
rename from ngraph/frontend/tensorflow/src/pass/transpose_sinking.hpp
rename to src/frontends/tensorflow/src/pass/transpose_sinking.hpp
diff --git a/ngraph/frontend/tensorflow/src/place.cpp b/src/frontends/tensorflow/src/place.cpp
similarity index 100%
rename from ngraph/frontend/tensorflow/src/place.cpp
rename to src/frontends/tensorflow/src/place.cpp
diff --git a/ngraph/frontend/tensorflow/src/place.hpp b/src/frontends/tensorflow/src/place.hpp
similarity index 100%
rename from ngraph/frontend/tensorflow/src/place.hpp
rename to src/frontends/tensorflow/src/place.hpp
diff --git a/ngraph/frontend/tensorflow/src/proto/allocation_description.proto b/src/frontends/tensorflow/src/proto/allocation_description.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/allocation_description.proto
rename to src/frontends/tensorflow/src/proto/allocation_description.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/api_def.proto b/src/frontends/tensorflow/src/proto/api_def.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/api_def.proto
rename to src/frontends/tensorflow/src/proto/api_def.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/attr_value.proto b/src/frontends/tensorflow/src/proto/attr_value.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/attr_value.proto
rename to src/frontends/tensorflow/src/proto/attr_value.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/cost_graph.proto b/src/frontends/tensorflow/src/proto/cost_graph.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/cost_graph.proto
rename to src/frontends/tensorflow/src/proto/cost_graph.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/dataset_options.proto b/src/frontends/tensorflow/src/proto/dataset_options.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/dataset_options.proto
rename to src/frontends/tensorflow/src/proto/dataset_options.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/device_attributes.proto b/src/frontends/tensorflow/src/proto/device_attributes.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/device_attributes.proto
rename to src/frontends/tensorflow/src/proto/device_attributes.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/function.proto b/src/frontends/tensorflow/src/proto/function.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/function.proto
rename to src/frontends/tensorflow/src/proto/function.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/graph.proto b/src/frontends/tensorflow/src/proto/graph.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/graph.proto
rename to src/frontends/tensorflow/src/proto/graph.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/graph_transfer_info.proto b/src/frontends/tensorflow/src/proto/graph_transfer_info.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/graph_transfer_info.proto
rename to src/frontends/tensorflow/src/proto/graph_transfer_info.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/kernel_def.proto b/src/frontends/tensorflow/src/proto/kernel_def.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/kernel_def.proto
rename to src/frontends/tensorflow/src/proto/kernel_def.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/log_memory.proto b/src/frontends/tensorflow/src/proto/log_memory.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/log_memory.proto
rename to src/frontends/tensorflow/src/proto/log_memory.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/model.proto b/src/frontends/tensorflow/src/proto/model.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/model.proto
rename to src/frontends/tensorflow/src/proto/model.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/node_def.proto b/src/frontends/tensorflow/src/proto/node_def.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/node_def.proto
rename to src/frontends/tensorflow/src/proto/node_def.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/op_def.proto b/src/frontends/tensorflow/src/proto/op_def.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/op_def.proto
rename to src/frontends/tensorflow/src/proto/op_def.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/reader_base.proto b/src/frontends/tensorflow/src/proto/reader_base.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/reader_base.proto
rename to src/frontends/tensorflow/src/proto/reader_base.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/remote_fused_graph_execute_info.proto b/src/frontends/tensorflow/src/proto/remote_fused_graph_execute_info.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/remote_fused_graph_execute_info.proto
rename to src/frontends/tensorflow/src/proto/remote_fused_graph_execute_info.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/resource_handle.proto b/src/frontends/tensorflow/src/proto/resource_handle.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/resource_handle.proto
rename to src/frontends/tensorflow/src/proto/resource_handle.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/step_stats.proto b/src/frontends/tensorflow/src/proto/step_stats.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/step_stats.proto
rename to src/frontends/tensorflow/src/proto/step_stats.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/summary.proto b/src/frontends/tensorflow/src/proto/summary.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/summary.proto
rename to src/frontends/tensorflow/src/proto/summary.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/tensor.proto b/src/frontends/tensorflow/src/proto/tensor.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/tensor.proto
rename to src/frontends/tensorflow/src/proto/tensor.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/tensor_description.proto b/src/frontends/tensorflow/src/proto/tensor_description.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/tensor_description.proto
rename to src/frontends/tensorflow/src/proto/tensor_description.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/tensor_shape.proto b/src/frontends/tensorflow/src/proto/tensor_shape.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/tensor_shape.proto
rename to src/frontends/tensorflow/src/proto/tensor_shape.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/tensor_slice.proto b/src/frontends/tensorflow/src/proto/tensor_slice.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/tensor_slice.proto
rename to src/frontends/tensorflow/src/proto/tensor_slice.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/types.proto b/src/frontends/tensorflow/src/proto/types.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/types.proto
rename to src/frontends/tensorflow/src/proto/types.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/variable.proto b/src/frontends/tensorflow/src/proto/variable.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/variable.proto
rename to src/frontends/tensorflow/src/proto/variable.proto
diff --git a/ngraph/frontend/tensorflow/src/proto/versions.proto b/src/frontends/tensorflow/src/proto/versions.proto
similarity index 100%
rename from ngraph/frontend/tensorflow/src/proto/versions.proto
rename to src/frontends/tensorflow/src/proto/versions.proto
diff --git a/ngraph/frontend/tensorflow/src/tensorflow.cpp b/src/frontends/tensorflow/src/tensorflow.cpp
similarity index 100%
rename from ngraph/frontend/tensorflow/src/tensorflow.cpp
rename to src/frontends/tensorflow/src/tensorflow.cpp
diff --git a/ngraph/frontend/tensorflow/src/tf_framework_node.cpp b/src/frontends/tensorflow/src/tf_framework_node.cpp
similarity index 100%
rename from ngraph/frontend/tensorflow/src/tf_framework_node.cpp
rename to src/frontends/tensorflow/src/tf_framework_node.cpp
diff --git a/ngraph/frontend/tensorflow/src/tf_framework_node.hpp b/src/frontends/tensorflow/src/tf_framework_node.hpp
similarity index 100%
rename from ngraph/frontend/tensorflow/src/tf_framework_node.hpp
rename to src/frontends/tensorflow/src/tf_framework_node.hpp
diff --git a/ngraph/frontend/tensorflow/src/utils.cpp b/src/frontends/tensorflow/src/utils.cpp
similarity index 100%
rename from ngraph/frontend/tensorflow/src/utils.cpp
rename to src/frontends/tensorflow/src/utils.cpp
diff --git a/ngraph/frontend/tensorflow/src/utils.hpp b/src/frontends/tensorflow/src/utils.hpp
similarity index 100%
rename from ngraph/frontend/tensorflow/src/utils.hpp
rename to src/frontends/tensorflow/src/utils.hpp
diff --git a/ngraph/.clang-format b/src/inference/.clang-format
similarity index 100%
rename from ngraph/.clang-format
rename to src/inference/.clang-format
diff --git a/inference-engine/src/inference_engine/CMakeLists.txt b/src/inference/CMakeLists.txt
similarity index 96%
rename from inference-engine/src/inference_engine/CMakeLists.txt
rename to src/inference/CMakeLists.txt
index 0ffabc46306..fe587bd9912 100644
--- a/inference-engine/src/inference_engine/CMakeLists.txt
+++ b/src/inference/CMakeLists.txt
@@ -27,10 +27,10 @@ file (GLOB LIBRARY_SRC
 
 # Add include path to so_extension.hpp
 set_property(SOURCE ${CMAKE_CURRENT_SOURCE_DIR}/src/ie_core.cpp
-                    APPEND PROPERTY INCLUDE_DIRECTORIES "${OpenVINO_SOURCE_DIR}/ngraph/core/src/")
+                    APPEND PROPERTY INCLUDE_DIRECTORIES "${OpenVINO_SOURCE_DIR}/src/core/src/")
 
 # TODO: WA for OneHot pass usage in reshape
-set(LEGACY_SRC_ROOT "${IE_MAIN_SOURCE_DIR}/src/legacy_api/src")
+set(LEGACY_SRC_ROOT "${OpenVINO_SOURCE_DIR}/src/common/legacy/src")
 set(LEGACY_LIBRARY_SHARED_SRCS
     "${LEGACY_SRC_ROOT}/transformations/convert_opset1_to_legacy/convert_one_hot_to_one_hot_ie.cpp"
     "${LEGACY_SRC_ROOT}/transformations/convert_opset1_to_legacy/convert_nms_5_to_legacy.cpp"
@@ -108,7 +108,7 @@ add_library(${TARGET_NAME}_plugin_api INTERFACE)
 
 target_include_directories(${TARGET_NAME}_plugin_api INTERFACE
     $<TARGET_PROPERTY:${TARGET_NAME}_preproc,INTERFACE_INCLUDE_DIRECTORIES>
-    $<BUILD_INTERFACE:${IE_MAIN_SOURCE_DIR}/src/plugin_api>
+    $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/dev_api>
     $<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}>
     $<BUILD_INTERFACE:${PUBLIC_HEADERS_DIR}/ie>)
 
@@ -119,8 +119,8 @@ target_link_libraries(${TARGET_NAME}_plugin_api INTERFACE pugixml::static openvi
 
 set_ie_threading_interface_for(${TARGET_NAME}_plugin_api)
 
-file(GLOB_RECURSE plugin_api_src "${IE_MAIN_SOURCE_DIR}/src/plugin_api/*.hpp"
-                                 "${IE_MAIN_SOURCE_DIR}/src/plugin_api/*.h")
+file(GLOB_RECURSE plugin_api_src "${CMAKE_CURRENT_SOURCE_DIR}/dev_api/*.hpp"
+                                 "${CMAKE_CURRENT_SOURCE_DIR}/dev_api/*.h")
 
 add_clang_format_target(${TARGET_NAME}_plugin_api_clang FOR_SOURCES ${plugin_api_src})
 
@@ -152,7 +152,7 @@ target_include_directories(${TARGET_NAME}_obj PRIVATE "${CMAKE_CURRENT_SOURCE_DI
                                                       $<TARGET_PROPERTY:${TARGET_NAME}_transformations,INTERFACE_INCLUDE_DIRECTORIES>
                                                       $<TARGET_PROPERTY:${TARGET_NAME}_plugin_api,INTERFACE_INCLUDE_DIRECTORIES>)
 
-target_link_libraries(${TARGET_NAME}_obj PRIVATE ${TARGET_NAME}_reader_api openvino::itt openvino::util)
+target_link_libraries(${TARGET_NAME}_obj PRIVATE openvino::itt openvino::util)
 
 set_ie_threading_interface_for(${TARGET_NAME}_obj)
 if (TBBBIND_2_5_FOUND)
@@ -351,9 +351,9 @@ install(EXPORT OpenVINOTargets
         DESTINATION runtime/cmake
         COMPONENT core_dev)
 
-set(IE_NGRAPH_DIR "${CMAKE_BINARY_DIR}/ngraph")
+set(IE_NGRAPH_DIR "${CMAKE_BINARY_DIR}/src/core")
 set(IE_INCLUDE_DIR "${PUBLIC_HEADERS_DIR}/ie")
-set(IE_PARALLEL_CMAKE "${InferenceEngine_SOURCE_DIR}/cmake/ie_parallel.cmake")
+set(IE_PARALLEL_CMAKE "${OpenVINO_SOURCE_DIR}/cmake/ie_parallel.cmake")
 
 configure_package_config_file("${OpenVINO_SOURCE_DIR}/cmake/templates/InferenceEngineConfig.cmake.in"
                               "${CMAKE_BINARY_DIR}/InferenceEngineConfig.cmake"
@@ -388,7 +388,7 @@ configure_file("${OpenVINO_SOURCE_DIR}/cmake/templates/OpenVINOConfig-version.cm
 
 install(FILES "${CMAKE_BINARY_DIR}/share/InferenceEngineConfig.cmake"
               "${CMAKE_BINARY_DIR}/InferenceEngineConfig-version.cmake"
-              "${InferenceEngine_SOURCE_DIR}/cmake/ie_parallel.cmake"
+              "${OpenVINO_SOURCE_DIR}/cmake/ie_parallel.cmake"
         DESTINATION runtime/cmake
         COMPONENT core_dev)
 
diff --git a/openvino/util/.clang-format b/src/inference/dev_api/.clang-format
similarity index 100%
rename from openvino/util/.clang-format
rename to src/inference/dev_api/.clang-format
diff --git a/inference-engine/src/plugin_api/blob_factory.hpp b/src/inference/dev_api/blob_factory.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/blob_factory.hpp
rename to src/inference/dev_api/blob_factory.hpp
diff --git a/inference-engine/src/plugin_api/blob_transform.hpp b/src/inference/dev_api/blob_transform.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/blob_transform.hpp
rename to src/inference/dev_api/blob_transform.hpp
diff --git a/inference-engine/src/plugin_api/caseless.hpp b/src/inference/dev_api/caseless.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/caseless.hpp
rename to src/inference/dev_api/caseless.hpp
diff --git a/inference-engine/src/plugin_api/cpp_interfaces/impl/ie_executable_network_thread_safe_default.hpp b/src/inference/dev_api/cpp_interfaces/impl/ie_executable_network_thread_safe_default.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/cpp_interfaces/impl/ie_executable_network_thread_safe_default.hpp
rename to src/inference/dev_api/cpp_interfaces/impl/ie_executable_network_thread_safe_default.hpp
diff --git a/inference-engine/src/plugin_api/cpp_interfaces/impl/ie_infer_async_request_thread_safe_default.hpp b/src/inference/dev_api/cpp_interfaces/impl/ie_infer_async_request_thread_safe_default.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/cpp_interfaces/impl/ie_infer_async_request_thread_safe_default.hpp
rename to src/inference/dev_api/cpp_interfaces/impl/ie_infer_async_request_thread_safe_default.hpp
diff --git a/inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iexecutable_network_internal.hpp b/src/inference/dev_api/cpp_interfaces/interface/ie_iexecutable_network_internal.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iexecutable_network_internal.hpp
rename to src/inference/dev_api/cpp_interfaces/interface/ie_iexecutable_network_internal.hpp
diff --git a/inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iinfer_request_internal.hpp b/src/inference/dev_api/cpp_interfaces/interface/ie_iinfer_request_internal.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iinfer_request_internal.hpp
rename to src/inference/dev_api/cpp_interfaces/interface/ie_iinfer_request_internal.hpp
diff --git a/inference-engine/src/plugin_api/cpp_interfaces/interface/ie_internal_plugin_config.hpp b/src/inference/dev_api/cpp_interfaces/interface/ie_internal_plugin_config.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/cpp_interfaces/interface/ie_internal_plugin_config.hpp
rename to src/inference/dev_api/cpp_interfaces/interface/ie_internal_plugin_config.hpp
diff --git a/inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iplugin_internal.hpp b/src/inference/dev_api/cpp_interfaces/interface/ie_iplugin_internal.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/cpp_interfaces/interface/ie_iplugin_internal.hpp
rename to src/inference/dev_api/cpp_interfaces/interface/ie_iplugin_internal.hpp
diff --git a/inference-engine/src/plugin_api/cpp_interfaces/interface/ie_ivariable_state_internal.hpp b/src/inference/dev_api/cpp_interfaces/interface/ie_ivariable_state_internal.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/cpp_interfaces/interface/ie_ivariable_state_internal.hpp
rename to src/inference/dev_api/cpp_interfaces/interface/ie_ivariable_state_internal.hpp
diff --git a/inference-engine/src/plugin_api/cpp_interfaces/plugin_itt.hpp b/src/inference/dev_api/cpp_interfaces/plugin_itt.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/cpp_interfaces/plugin_itt.hpp
rename to src/inference/dev_api/cpp_interfaces/plugin_itt.hpp
diff --git a/inference-engine/src/plugin_api/debug.h b/src/inference/dev_api/debug.h
similarity index 100%
rename from inference-engine/src/plugin_api/debug.h
rename to src/inference/dev_api/debug.h
diff --git a/inference-engine/src/plugin_api/description_buffer.hpp b/src/inference/dev_api/description_buffer.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/description_buffer.hpp
rename to src/inference/dev_api/description_buffer.hpp
diff --git a/inference-engine/src/plugin_api/exec_graph_info.hpp b/src/inference/dev_api/exec_graph_info.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/exec_graph_info.hpp
rename to src/inference/dev_api/exec_graph_info.hpp
diff --git a/inference-engine/src/plugin_api/file_utils.h b/src/inference/dev_api/file_utils.h
similarity index 100%
rename from inference-engine/src/plugin_api/file_utils.h
rename to src/inference/dev_api/file_utils.h
diff --git a/inference-engine/src/plugin_api/ie_algorithm.hpp b/src/inference/dev_api/ie_algorithm.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/ie_algorithm.hpp
rename to src/inference/dev_api/ie_algorithm.hpp
diff --git a/inference-engine/src/plugin_api/ie_icore.hpp b/src/inference/dev_api/ie_icore.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/ie_icore.hpp
rename to src/inference/dev_api/ie_icore.hpp
diff --git a/inference-engine/src/plugin_api/ie_memcpy.h b/src/inference/dev_api/ie_memcpy.h
similarity index 100%
rename from inference-engine/src/plugin_api/ie_memcpy.h
rename to src/inference/dev_api/ie_memcpy.h
diff --git a/inference-engine/src/plugin_api/ie_metric_helpers.hpp b/src/inference/dev_api/ie_metric_helpers.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/ie_metric_helpers.hpp
rename to src/inference/dev_api/ie_metric_helpers.hpp
diff --git a/inference-engine/src/plugin_api/ie_ngraph_utils.hpp b/src/inference/dev_api/ie_ngraph_utils.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/ie_ngraph_utils.hpp
rename to src/inference/dev_api/ie_ngraph_utils.hpp
diff --git a/inference-engine/src/plugin_api/ie_performance_hints.hpp b/src/inference/dev_api/ie_performance_hints.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/ie_performance_hints.hpp
rename to src/inference/dev_api/ie_performance_hints.hpp
diff --git a/inference-engine/src/plugin_api/ie_system_conf.h b/src/inference/dev_api/ie_system_conf.h
similarity index 100%
rename from inference-engine/src/plugin_api/ie_system_conf.h
rename to src/inference/dev_api/ie_system_conf.h
diff --git a/inference-engine/src/plugin_api/memory_solver.hpp b/src/inference/dev_api/memory_solver.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/memory_solver.hpp
rename to src/inference/dev_api/memory_solver.hpp
diff --git a/inference-engine/src/plugin_api/performance_heuristics.hpp b/src/inference/dev_api/performance_heuristics.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/performance_heuristics.hpp
rename to src/inference/dev_api/performance_heuristics.hpp
diff --git a/inference-engine/src/plugin_api/precision_utils.h b/src/inference/dev_api/precision_utils.h
similarity index 100%
rename from inference-engine/src/plugin_api/precision_utils.h
rename to src/inference/dev_api/precision_utils.h
diff --git a/inference-engine/src/plugin_api/so_ptr.hpp b/src/inference/dev_api/so_ptr.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/so_ptr.hpp
rename to src/inference/dev_api/so_ptr.hpp
diff --git a/inference-engine/src/plugin_api/threading/ie_cpu_streams_executor.hpp b/src/inference/dev_api/threading/ie_cpu_streams_executor.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/threading/ie_cpu_streams_executor.hpp
rename to src/inference/dev_api/threading/ie_cpu_streams_executor.hpp
diff --git a/inference-engine/src/plugin_api/threading/ie_executor_manager.hpp b/src/inference/dev_api/threading/ie_executor_manager.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/threading/ie_executor_manager.hpp
rename to src/inference/dev_api/threading/ie_executor_manager.hpp
diff --git a/inference-engine/src/plugin_api/threading/ie_immediate_executor.hpp b/src/inference/dev_api/threading/ie_immediate_executor.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/threading/ie_immediate_executor.hpp
rename to src/inference/dev_api/threading/ie_immediate_executor.hpp
diff --git a/inference-engine/src/plugin_api/threading/ie_istreams_executor.hpp b/src/inference/dev_api/threading/ie_istreams_executor.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/threading/ie_istreams_executor.hpp
rename to src/inference/dev_api/threading/ie_istreams_executor.hpp
diff --git a/inference-engine/src/plugin_api/threading/ie_itask_executor.hpp b/src/inference/dev_api/threading/ie_itask_executor.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/threading/ie_itask_executor.hpp
rename to src/inference/dev_api/threading/ie_itask_executor.hpp
diff --git a/inference-engine/src/plugin_api/threading/ie_tbb_streams_executor.hpp b/src/inference/dev_api/threading/ie_tbb_streams_executor.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/threading/ie_tbb_streams_executor.hpp
rename to src/inference/dev_api/threading/ie_tbb_streams_executor.hpp
diff --git a/inference-engine/src/plugin_api/threading/ie_thread_local.hpp b/src/inference/dev_api/threading/ie_thread_local.hpp
similarity index 100%
rename from inference-engine/src/plugin_api/threading/ie_thread_local.hpp
rename to src/inference/dev_api/threading/ie_thread_local.hpp
diff --git a/inference-engine/src/plugin_api/xml_parse_utils.h b/src/inference/dev_api/xml_parse_utils.h
similarity index 100%
rename from inference-engine/src/plugin_api/xml_parse_utils.h
rename to src/inference/dev_api/xml_parse_utils.h
diff --git a/inference-engine/src/inference_engine/include/ie/cldnn/cldnn_config.hpp b/src/inference/include/ie/cldnn/cldnn_config.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/cldnn/cldnn_config.hpp
rename to src/inference/include/ie/cldnn/cldnn_config.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/cpp/ie_cnn_network.h b/src/inference/include/ie/cpp/ie_cnn_network.h
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/cpp/ie_cnn_network.h
rename to src/inference/include/ie/cpp/ie_cnn_network.h
diff --git a/inference-engine/src/inference_engine/include/ie/cpp/ie_executable_network.hpp b/src/inference/include/ie/cpp/ie_executable_network.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/cpp/ie_executable_network.hpp
rename to src/inference/include/ie/cpp/ie_executable_network.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/cpp/ie_infer_request.hpp b/src/inference/include/ie/cpp/ie_infer_request.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/cpp/ie_infer_request.hpp
rename to src/inference/include/ie/cpp/ie_infer_request.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/cpp/ie_memory_state.hpp b/src/inference/include/ie/cpp/ie_memory_state.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/cpp/ie_memory_state.hpp
rename to src/inference/include/ie/cpp/ie_memory_state.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/details/ie_blob_iterator.hpp b/src/inference/include/ie/details/ie_blob_iterator.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/details/ie_blob_iterator.hpp
rename to src/inference/include/ie/details/ie_blob_iterator.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/details/ie_exception.hpp b/src/inference/include/ie/details/ie_exception.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/details/ie_exception.hpp
rename to src/inference/include/ie/details/ie_exception.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/details/ie_pre_allocator.hpp b/src/inference/include/ie/details/ie_pre_allocator.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/details/ie_pre_allocator.hpp
rename to src/inference/include/ie/details/ie_pre_allocator.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/details/ie_so_loader.h b/src/inference/include/ie/details/ie_so_loader.h
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/details/ie_so_loader.h
rename to src/inference/include/ie/details/ie_so_loader.h
diff --git a/inference-engine/src/inference_engine/include/ie/details/ie_so_pointer.hpp b/src/inference/include/ie/details/ie_so_pointer.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/details/ie_so_pointer.hpp
rename to src/inference/include/ie/details/ie_so_pointer.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/gna/gna_config.hpp b/src/inference/include/ie/gna/gna_config.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/gna/gna_config.hpp
rename to src/inference/include/ie/gna/gna_config.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/gpu/details/gpu_context_helpers.hpp b/src/inference/include/ie/gpu/details/gpu_context_helpers.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/gpu/details/gpu_context_helpers.hpp
rename to src/inference/include/ie/gpu/details/gpu_context_helpers.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/gpu/gpu_config.hpp b/src/inference/include/ie/gpu/gpu_config.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/gpu/gpu_config.hpp
rename to src/inference/include/ie/gpu/gpu_config.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/gpu/gpu_context_api_dx.hpp b/src/inference/include/ie/gpu/gpu_context_api_dx.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/gpu/gpu_context_api_dx.hpp
rename to src/inference/include/ie/gpu/gpu_context_api_dx.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/gpu/gpu_context_api_ocl.hpp b/src/inference/include/ie/gpu/gpu_context_api_ocl.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/gpu/gpu_context_api_ocl.hpp
rename to src/inference/include/ie/gpu/gpu_context_api_ocl.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/gpu/gpu_context_api_va.hpp b/src/inference/include/ie/gpu/gpu_context_api_va.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/gpu/gpu_context_api_va.hpp
rename to src/inference/include/ie/gpu/gpu_context_api_va.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/gpu/gpu_ocl_wrapper.hpp b/src/inference/include/ie/gpu/gpu_ocl_wrapper.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/gpu/gpu_ocl_wrapper.hpp
rename to src/inference/include/ie/gpu/gpu_ocl_wrapper.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/gpu/gpu_params.hpp b/src/inference/include/ie/gpu/gpu_params.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/gpu/gpu_params.hpp
rename to src/inference/include/ie/gpu/gpu_params.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/hetero/hetero_plugin_config.hpp b/src/inference/include/ie/hetero/hetero_plugin_config.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/hetero/hetero_plugin_config.hpp
rename to src/inference/include/ie/hetero/hetero_plugin_config.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/ie_allocator.hpp b/src/inference/include/ie/ie_allocator.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_allocator.hpp
rename to src/inference/include/ie/ie_allocator.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/ie_api.h b/src/inference/include/ie/ie_api.h
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_api.h
rename to src/inference/include/ie/ie_api.h
diff --git a/inference-engine/src/inference_engine/include/ie/ie_blob.h b/src/inference/include/ie/ie_blob.h
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_blob.h
rename to src/inference/include/ie/ie_blob.h
diff --git a/inference-engine/src/inference_engine/include/ie/ie_common.h b/src/inference/include/ie/ie_common.h
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_common.h
rename to src/inference/include/ie/ie_common.h
diff --git a/inference-engine/src/inference_engine/include/ie/ie_compound_blob.h b/src/inference/include/ie/ie_compound_blob.h
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_compound_blob.h
rename to src/inference/include/ie/ie_compound_blob.h
diff --git a/inference-engine/src/inference_engine/include/ie/ie_core.hpp b/src/inference/include/ie/ie_core.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_core.hpp
rename to src/inference/include/ie/ie_core.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/ie_data.h b/src/inference/include/ie/ie_data.h
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_data.h
rename to src/inference/include/ie/ie_data.h
diff --git a/inference-engine/src/inference_engine/include/ie/ie_extension.h b/src/inference/include/ie/ie_extension.h
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_extension.h
rename to src/inference/include/ie/ie_extension.h
diff --git a/inference-engine/src/inference_engine/include/ie/ie_icnn_network.hpp b/src/inference/include/ie/ie_icnn_network.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_icnn_network.hpp
rename to src/inference/include/ie/ie_icnn_network.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/ie_iexecutable_network.hpp b/src/inference/include/ie/ie_iexecutable_network.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_iexecutable_network.hpp
rename to src/inference/include/ie/ie_iexecutable_network.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/ie_iextension.h b/src/inference/include/ie/ie_iextension.h
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_iextension.h
rename to src/inference/include/ie/ie_iextension.h
diff --git a/inference-engine/src/inference_engine/include/ie/ie_iinfer_request.hpp b/src/inference/include/ie/ie_iinfer_request.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_iinfer_request.hpp
rename to src/inference/include/ie/ie_iinfer_request.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/ie_input_info.hpp b/src/inference/include/ie/ie_input_info.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_input_info.hpp
rename to src/inference/include/ie/ie_input_info.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/ie_layouts.h b/src/inference/include/ie/ie_layouts.h
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_layouts.h
rename to src/inference/include/ie/ie_layouts.h
diff --git a/inference-engine/src/inference_engine/include/ie/ie_locked_memory.hpp b/src/inference/include/ie/ie_locked_memory.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_locked_memory.hpp
rename to src/inference/include/ie/ie_locked_memory.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/ie_parallel.hpp b/src/inference/include/ie/ie_parallel.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_parallel.hpp
rename to src/inference/include/ie/ie_parallel.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/ie_parameter.hpp b/src/inference/include/ie/ie_parameter.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_parameter.hpp
rename to src/inference/include/ie/ie_parameter.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/ie_plugin_config.hpp b/src/inference/include/ie/ie_plugin_config.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_plugin_config.hpp
rename to src/inference/include/ie/ie_plugin_config.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/ie_precision.hpp b/src/inference/include/ie/ie_precision.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_precision.hpp
rename to src/inference/include/ie/ie_precision.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/ie_preprocess.hpp b/src/inference/include/ie/ie_preprocess.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_preprocess.hpp
rename to src/inference/include/ie/ie_preprocess.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/ie_remote_blob.hpp b/src/inference/include/ie/ie_remote_blob.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_remote_blob.hpp
rename to src/inference/include/ie/ie_remote_blob.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/ie_remote_context.hpp b/src/inference/include/ie/ie_remote_context.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_remote_context.hpp
rename to src/inference/include/ie/ie_remote_context.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/ie_transformations.hpp b/src/inference/include/ie/ie_transformations.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_transformations.hpp
rename to src/inference/include/ie/ie_transformations.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/ie_version.hpp b/src/inference/include/ie/ie_version.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/ie_version.hpp
rename to src/inference/include/ie/ie_version.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/inference_engine.hpp b/src/inference/include/ie/inference_engine.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/inference_engine.hpp
rename to src/inference/include/ie/inference_engine.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/multi-device/multi_device_config.hpp b/src/inference/include/ie/multi-device/multi_device_config.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/multi-device/multi_device_config.hpp
rename to src/inference/include/ie/multi-device/multi_device_config.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/vpu/hddl_config.hpp b/src/inference/include/ie/vpu/hddl_config.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/vpu/hddl_config.hpp
rename to src/inference/include/ie/vpu/hddl_config.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/vpu/hddl_plugin_config.hpp b/src/inference/include/ie/vpu/hddl_plugin_config.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/vpu/hddl_plugin_config.hpp
rename to src/inference/include/ie/vpu/hddl_plugin_config.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/vpu/myriad_config.hpp b/src/inference/include/ie/vpu/myriad_config.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/vpu/myriad_config.hpp
rename to src/inference/include/ie/vpu/myriad_config.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/vpu/myriad_plugin_config.hpp b/src/inference/include/ie/vpu/myriad_plugin_config.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/vpu/myriad_plugin_config.hpp
rename to src/inference/include/ie/vpu/myriad_plugin_config.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/vpu/vpu_config.hpp b/src/inference/include/ie/vpu/vpu_config.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/vpu/vpu_config.hpp
rename to src/inference/include/ie/vpu/vpu_config.hpp
diff --git a/inference-engine/src/inference_engine/include/ie/vpu/vpu_plugin_config.hpp b/src/inference/include/ie/vpu/vpu_plugin_config.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/ie/vpu/vpu_plugin_config.hpp
rename to src/inference/include/ie/vpu/vpu_plugin_config.hpp
diff --git a/inference-engine/src/inference_engine/include/openvino/openvino.hpp b/src/inference/include/openvino/openvino.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/openvino/openvino.hpp
rename to src/inference/include/openvino/openvino.hpp
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/common.hpp b/src/inference/include/openvino/runtime/common.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/openvino/runtime/common.hpp
rename to src/inference/include/openvino/runtime/common.hpp
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/core.hpp b/src/inference/include/openvino/runtime/core.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/openvino/runtime/core.hpp
rename to src/inference/include/openvino/runtime/core.hpp
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/exception.hpp b/src/inference/include/openvino/runtime/exception.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/openvino/runtime/exception.hpp
rename to src/inference/include/openvino/runtime/exception.hpp
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/executable_network.hpp b/src/inference/include/openvino/runtime/executable_network.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/openvino/runtime/executable_network.hpp
rename to src/inference/include/openvino/runtime/executable_network.hpp
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl/dx.hpp b/src/inference/include/openvino/runtime/gpu/ocl/dx.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl/dx.hpp
rename to src/inference/include/openvino/runtime/gpu/ocl/dx.hpp
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl/ocl.hpp b/src/inference/include/openvino/runtime/gpu/ocl/ocl.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl/ocl.hpp
rename to src/inference/include/openvino/runtime/gpu/ocl/ocl.hpp
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl/ocl_wrapper.hpp b/src/inference/include/openvino/runtime/gpu/ocl/ocl_wrapper.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl/ocl_wrapper.hpp
rename to src/inference/include/openvino/runtime/gpu/ocl/ocl_wrapper.hpp
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl/va.hpp b/src/inference/include/openvino/runtime/gpu/ocl/va.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/openvino/runtime/gpu/ocl/va.hpp
rename to src/inference/include/openvino/runtime/gpu/ocl/va.hpp
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/infer_request.hpp b/src/inference/include/openvino/runtime/infer_request.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/openvino/runtime/infer_request.hpp
rename to src/inference/include/openvino/runtime/infer_request.hpp
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/parameter.hpp b/src/inference/include/openvino/runtime/parameter.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/openvino/runtime/parameter.hpp
rename to src/inference/include/openvino/runtime/parameter.hpp
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/profiling_info.hpp b/src/inference/include/openvino/runtime/profiling_info.hpp
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rename from inference-engine/src/inference_engine/include/openvino/runtime/profiling_info.hpp
rename to src/inference/include/openvino/runtime/profiling_info.hpp
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/remote_context.hpp b/src/inference/include/openvino/runtime/remote_context.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/openvino/runtime/remote_context.hpp
rename to src/inference/include/openvino/runtime/remote_context.hpp
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/remote_tensor.hpp b/src/inference/include/openvino/runtime/remote_tensor.hpp
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rename from inference-engine/src/inference_engine/include/openvino/runtime/remote_tensor.hpp
rename to src/inference/include/openvino/runtime/remote_tensor.hpp
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/runtime.hpp b/src/inference/include/openvino/runtime/runtime.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/include/openvino/runtime/runtime.hpp
rename to src/inference/include/openvino/runtime/runtime.hpp
diff --git a/inference-engine/src/inference_engine/include/openvino/runtime/variable_state.hpp b/src/inference/include/openvino/runtime/variable_state.hpp
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rename from inference-engine/src/inference_engine/include/openvino/runtime/variable_state.hpp
rename to src/inference/include/openvino/runtime/variable_state.hpp
diff --git a/inference-engine/src/inference_engine/src/blob_transform.cpp b/src/inference/src/blob_transform.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/blob_transform.cpp
rename to src/inference/src/blob_transform.cpp
diff --git a/inference-engine/src/inference_engine/src/cnn_network_ngraph_impl.cpp b/src/inference/src/cnn_network_ngraph_impl.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/cnn_network_ngraph_impl.cpp
rename to src/inference/src/cnn_network_ngraph_impl.cpp
diff --git a/inference-engine/src/inference_engine/src/cnn_network_ngraph_impl.hpp b/src/inference/src/cnn_network_ngraph_impl.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/cnn_network_ngraph_impl.hpp
rename to src/inference/src/cnn_network_ngraph_impl.hpp
diff --git a/inference-engine/src/inference_engine/src/compilation_context.cpp b/src/inference/src/compilation_context.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/compilation_context.cpp
rename to src/inference/src/compilation_context.cpp
diff --git a/inference-engine/src/inference_engine/src/compilation_context.hpp b/src/inference/src/compilation_context.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/compilation_context.hpp
rename to src/inference/src/compilation_context.hpp
diff --git a/inference-engine/src/inference_engine/src/cpp/exception2status.hpp b/src/inference/src/cpp/exception2status.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/cpp/exception2status.hpp
rename to src/inference/src/cpp/exception2status.hpp
diff --git a/inference-engine/src/inference_engine/src/cpp/ie_cnn_network.cpp b/src/inference/src/cpp/ie_cnn_network.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/cpp/ie_cnn_network.cpp
rename to src/inference/src/cpp/ie_cnn_network.cpp
diff --git a/inference-engine/src/inference_engine/src/cpp/ie_executable_network.cpp b/src/inference/src/cpp/ie_executable_network.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/cpp/ie_executable_network.cpp
rename to src/inference/src/cpp/ie_executable_network.cpp
diff --git a/inference-engine/src/inference_engine/src/cpp/ie_executable_network_base.hpp b/src/inference/src/cpp/ie_executable_network_base.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/cpp/ie_executable_network_base.hpp
rename to src/inference/src/cpp/ie_executable_network_base.hpp
diff --git a/inference-engine/src/inference_engine/src/cpp/ie_extension.cpp b/src/inference/src/cpp/ie_extension.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/cpp/ie_extension.cpp
rename to src/inference/src/cpp/ie_extension.cpp
diff --git a/inference-engine/src/inference_engine/src/cpp/ie_infer_async_request_base.hpp b/src/inference/src/cpp/ie_infer_async_request_base.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/cpp/ie_infer_async_request_base.hpp
rename to src/inference/src/cpp/ie_infer_async_request_base.hpp
diff --git a/inference-engine/src/inference_engine/src/cpp/ie_infer_request.cpp b/src/inference/src/cpp/ie_infer_request.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/cpp/ie_infer_request.cpp
rename to src/inference/src/cpp/ie_infer_request.cpp
diff --git a/inference-engine/src/inference_engine/src/cpp/ie_plugin.hpp b/src/inference/src/cpp/ie_plugin.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/cpp/ie_plugin.hpp
rename to src/inference/src/cpp/ie_plugin.hpp
diff --git a/inference-engine/src/inference_engine/src/cpp/ie_remote_context.cpp b/src/inference/src/cpp/ie_remote_context.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/cpp/ie_remote_context.cpp
rename to src/inference/src/cpp/ie_remote_context.cpp
diff --git a/inference-engine/src/inference_engine/src/cpp/ie_variable_state.cpp b/src/inference/src/cpp/ie_variable_state.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/cpp/ie_variable_state.cpp
rename to src/inference/src/cpp/ie_variable_state.cpp
diff --git a/inference-engine/src/inference_engine/src/cpp_interfaces/interface/ie_iexecutable_network_internal.cpp b/src/inference/src/cpp_interfaces/interface/ie_iexecutable_network_internal.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/cpp_interfaces/interface/ie_iexecutable_network_internal.cpp
rename to src/inference/src/cpp_interfaces/interface/ie_iexecutable_network_internal.cpp
diff --git a/inference-engine/src/inference_engine/src/cpp_interfaces/interface/ie_iinfer_request_internal.cpp b/src/inference/src/cpp_interfaces/interface/ie_iinfer_request_internal.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/cpp_interfaces/interface/ie_iinfer_request_internal.cpp
rename to src/inference/src/cpp_interfaces/interface/ie_iinfer_request_internal.cpp
diff --git a/inference-engine/src/inference_engine/src/cpp_interfaces/interface/ie_iplugin_internal.cpp b/src/inference/src/cpp_interfaces/interface/ie_iplugin_internal.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/cpp_interfaces/interface/ie_iplugin_internal.cpp
rename to src/inference/src/cpp_interfaces/interface/ie_iplugin_internal.cpp
diff --git a/inference-engine/src/inference_engine/src/cpp_interfaces/interface/ie_ivariable_state_internal.cpp b/src/inference/src/cpp_interfaces/interface/ie_ivariable_state_internal.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/cpp_interfaces/interface/ie_ivariable_state_internal.cpp
rename to src/inference/src/cpp_interfaces/interface/ie_ivariable_state_internal.cpp
diff --git a/inference-engine/src/inference_engine/src/cpu_x86_sse42/blob_transform_sse42.cpp b/src/inference/src/cpu_x86_sse42/blob_transform_sse42.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/cpu_x86_sse42/blob_transform_sse42.cpp
rename to src/inference/src/cpu_x86_sse42/blob_transform_sse42.cpp
diff --git a/inference-engine/src/inference_engine/src/cpu_x86_sse42/blob_transform_sse42.hpp b/src/inference/src/cpu_x86_sse42/blob_transform_sse42.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/cpu_x86_sse42/blob_transform_sse42.hpp
rename to src/inference/src/cpu_x86_sse42/blob_transform_sse42.hpp
diff --git a/inference-engine/src/inference_engine/src/file_utils.cpp b/src/inference/src/file_utils.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/file_utils.cpp
rename to src/inference/src/file_utils.cpp
diff --git a/inference-engine/src/inference_engine/src/ie_cache_guard.cpp b/src/inference/src/ie_cache_guard.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/ie_cache_guard.cpp
rename to src/inference/src/ie_cache_guard.cpp
diff --git a/inference-engine/src/inference_engine/src/ie_cache_guard.hpp b/src/inference/src/ie_cache_guard.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/ie_cache_guard.hpp
rename to src/inference/src/ie_cache_guard.hpp
diff --git a/inference-engine/src/inference_engine/src/ie_cache_manager.hpp b/src/inference/src/ie_cache_manager.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/ie_cache_manager.hpp
rename to src/inference/src/ie_cache_manager.hpp
diff --git a/inference-engine/src/inference_engine/src/ie_common.cpp b/src/inference/src/ie_common.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/ie_common.cpp
rename to src/inference/src/ie_common.cpp
diff --git a/inference-engine/src/inference_engine/src/ie_compound_blob.cpp b/src/inference/src/ie_compound_blob.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/ie_compound_blob.cpp
rename to src/inference/src/ie_compound_blob.cpp
diff --git a/inference-engine/src/inference_engine/src/ie_core.cpp b/src/inference/src/ie_core.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/ie_core.cpp
rename to src/inference/src/ie_core.cpp
diff --git a/inference-engine/src/inference_engine/src/ie_data.cpp b/src/inference/src/ie_data.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/ie_data.cpp
rename to src/inference/src/ie_data.cpp
diff --git a/inference-engine/src/inference_engine/src/ie_itt.hpp b/src/inference/src/ie_itt.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/ie_itt.hpp
rename to src/inference/src/ie_itt.hpp
diff --git a/inference-engine/src/inference_engine/src/ie_memcpy.cpp b/src/inference/src/ie_memcpy.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/ie_memcpy.cpp
rename to src/inference/src/ie_memcpy.cpp
diff --git a/inference-engine/src/inference_engine/src/ie_network_reader.cpp b/src/inference/src/ie_network_reader.cpp
similarity index 99%
rename from inference-engine/src/inference_engine/src/ie_network_reader.cpp
rename to src/inference/src/ie_network_reader.cpp
index b4e04f139c6..473394e7ae2 100644
--- a/inference-engine/src/inference_engine/src/ie_network_reader.cpp
+++ b/src/inference/src/ie_network_reader.cpp
@@ -20,10 +20,10 @@
 #include "ie_input_info.hpp"
 #include "manager.hpp"
 #ifdef ENABLE_IR_V7_READER
-#    include "ie_ir_version.hpp"
+#    include "legacy/ie_ir_version.hpp"
 #endif
 #include "ie_itt.hpp"
-#include "ie_reader.hpp"
+#include "legacy/ie_reader.hpp"
 #include "ngraph/function.hpp"
 #include "ngraph/type/element_type.hpp"
 #include "ngraph/variant.hpp"
diff --git a/inference-engine/src/inference_engine/src/ie_network_reader.hpp b/src/inference/src/ie_network_reader.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/ie_network_reader.hpp
rename to src/inference/src/ie_network_reader.hpp
diff --git a/inference-engine/src/inference_engine/src/ie_ngraph_utils.cpp b/src/inference/src/ie_ngraph_utils.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/ie_ngraph_utils.cpp
rename to src/inference/src/ie_ngraph_utils.cpp
diff --git a/inference-engine/src/inference_engine/src/ie_remote_context.cpp b/src/inference/src/ie_remote_context.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/ie_remote_context.cpp
rename to src/inference/src/ie_remote_context.cpp
diff --git a/inference-engine/src/inference_engine/src/ie_system_conf.cpp b/src/inference/src/ie_system_conf.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/ie_system_conf.cpp
rename to src/inference/src/ie_system_conf.cpp
diff --git a/inference-engine/src/inference_engine/src/ie_transformations.cpp b/src/inference/src/ie_transformations.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/ie_transformations.cpp
rename to src/inference/src/ie_transformations.cpp
diff --git a/inference-engine/src/inference_engine/src/ie_version.cpp b/src/inference/src/ie_version.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/ie_version.cpp
rename to src/inference/src/ie_version.cpp
diff --git a/inference-engine/src/inference_engine/src/os/lin/lin_system_conf.cpp b/src/inference/src/os/lin/lin_system_conf.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/os/lin/lin_system_conf.cpp
rename to src/inference/src/os/lin/lin_system_conf.cpp
diff --git a/inference-engine/src/inference_engine/src/os/win/win_system_conf.cpp b/src/inference/src/os/win/win_system_conf.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/os/win/win_system_conf.cpp
rename to src/inference/src/os/win/win_system_conf.cpp
diff --git a/inference-engine/src/inference_engine/src/precision_utils.cpp b/src/inference/src/precision_utils.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/precision_utils.cpp
rename to src/inference/src/precision_utils.cpp
diff --git a/inference-engine/src/inference_engine/src/precomp.hpp b/src/inference/src/precomp.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/precomp.hpp
rename to src/inference/src/precomp.hpp
diff --git a/inference-engine/src/inference_engine/src/remote_tensor.cpp b/src/inference/src/remote_tensor.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/remote_tensor.cpp
rename to src/inference/src/remote_tensor.cpp
diff --git a/inference-engine/src/inference_engine/src/shared_object_loader.cpp b/src/inference/src/shared_object_loader.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/shared_object_loader.cpp
rename to src/inference/src/shared_object_loader.cpp
diff --git a/inference-engine/src/inference_engine/src/system_allocator.hpp b/src/inference/src/system_allocator.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/system_allocator.hpp
rename to src/inference/src/system_allocator.hpp
diff --git a/inference-engine/src/inference_engine/src/threading/ie_cpu_streams_executor.cpp b/src/inference/src/threading/ie_cpu_streams_executor.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/threading/ie_cpu_streams_executor.cpp
rename to src/inference/src/threading/ie_cpu_streams_executor.cpp
diff --git a/inference-engine/src/inference_engine/src/threading/ie_executor_manager.cpp b/src/inference/src/threading/ie_executor_manager.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/threading/ie_executor_manager.cpp
rename to src/inference/src/threading/ie_executor_manager.cpp
diff --git a/inference-engine/src/inference_engine/src/threading/ie_istreams_executor.cpp b/src/inference/src/threading/ie_istreams_executor.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/threading/ie_istreams_executor.cpp
rename to src/inference/src/threading/ie_istreams_executor.cpp
diff --git a/inference-engine/src/inference_engine/src/threading/ie_itask_executor.cpp b/src/inference/src/threading/ie_itask_executor.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/threading/ie_itask_executor.cpp
rename to src/inference/src/threading/ie_itask_executor.cpp
diff --git a/inference-engine/src/inference_engine/src/threading/ie_parallel_custom_arena.cpp b/src/inference/src/threading/ie_parallel_custom_arena.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/threading/ie_parallel_custom_arena.cpp
rename to src/inference/src/threading/ie_parallel_custom_arena.cpp
diff --git a/inference-engine/src/inference_engine/src/threading/ie_parallel_custom_arena.hpp b/src/inference/src/threading/ie_parallel_custom_arena.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/threading/ie_parallel_custom_arena.hpp
rename to src/inference/src/threading/ie_parallel_custom_arena.hpp
diff --git a/inference-engine/src/inference_engine/src/threading/ie_tbb_streams_executor.cpp b/src/inference/src/threading/ie_tbb_streams_executor.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/threading/ie_tbb_streams_executor.cpp
rename to src/inference/src/threading/ie_tbb_streams_executor.cpp
diff --git a/inference-engine/src/inference_engine/src/threading/ie_thread_affinity.cpp b/src/inference/src/threading/ie_thread_affinity.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/threading/ie_thread_affinity.cpp
rename to src/inference/src/threading/ie_thread_affinity.cpp
diff --git a/inference-engine/src/inference_engine/src/threading/ie_thread_affinity.hpp b/src/inference/src/threading/ie_thread_affinity.hpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/threading/ie_thread_affinity.hpp
rename to src/inference/src/threading/ie_thread_affinity.hpp
diff --git a/inference-engine/src/inference_engine/src/xml_parse_utils.cpp b/src/inference/src/xml_parse_utils.cpp
similarity index 100%
rename from inference-engine/src/inference_engine/src/xml_parse_utils.cpp
rename to src/inference/src/xml_parse_utils.cpp

From 7edcf474547a077a2c5821f909dbc565aeea8612 Mon Sep 17 00:00:00 2001
From: Chenhu Wang <chenhu.wang@intel.com>
Date: Sun, 28 Nov 2021 22:44:07 +0800
Subject: [PATCH 53/72] [CPU] CTCLoss dynamism support (#8777)

---
 .../nodes/mkldnn_ctc_loss_node.cpp            |  12 +-
 .../nodes/mkldnn_ctc_loss_node.h              |   5 +-
 .../skip_tests_config.cpp                     |   6 +
 .../cpu/single_layer_tests/ctc_Loss.cpp       | 251 ++++++++++++++++++
 .../include/ngraph_functions/builders.hpp     |   2 +
 .../ngraph_functions/src/params_vector.cpp    |  10 +
 6 files changed, 281 insertions(+), 5 deletions(-)
 create mode 100644 inference-engine/tests/functional/plugin/cpu/single_layer_tests/ctc_Loss.cpp

diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_loss_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_loss_node.cpp
index d3e0f149c7c..3019cf20e86 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_loss_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_loss_node.cpp
@@ -13,10 +13,6 @@ using namespace InferenceEngine;
 
 bool MKLDNNCTCLossNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
         const auto ctcLossOp = ngraph::as_type_ptr<const ngraph::op::v4::CTCLoss>(op);
         if (!ctcLossOp) {
             errorMessage = "Node is not an instance of the CTCLoss operation from operation set v4.";
@@ -61,6 +57,14 @@ void MKLDNNCTCLossNode::initSupportedPrimitiveDescriptors() {
                          impl_desc_type::ref_any);
 }
 
+void MKLDNNCTCLossNode::createPrimitive() {
+    if (inputShapesDefined()) {
+        if (needPrepareParams())
+            prepareParams();
+        updateLastInputDims();
+    }
+}
+
 void MKLDNNCTCLossNode::execute(mkldnn::stream strm) {
     StatusCode returnCode = OK;
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_loss_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_loss_node.h
index 8b5a0253b36..b37f4500f3e 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_loss_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_ctc_loss_node.h
@@ -15,12 +15,15 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
+    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
+    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); };
+    bool needPrepareParams() const override { return false; };
+
 private:
     bool ctcMergeRepeated;
     bool preprocessCollapseRepeated;
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
index 78d98a023e4..3fb94dea712 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
@@ -155,6 +155,12 @@ std::vector<std::string> disabledTestPatterns() {
         R"(.*GroupConvolutionLayerCPUTest.*IS=\{.+\}.*_Fused=.*Add\(Parameters\).*)",
         // Issue: 71968
         R"(.*LSTMSequenceCommonZeroClip.*PURE.*CONST.*hidden_size=10.*sigmoid.sigmoid.sigmoid.*reverse.*FP32_targetDevice=CPU.*)",
+        // Issue: 72005
+        // there are some inconsistency between cpu plugin and ng ref
+        // for ctcMergeRepeated is true when legal randomized inputs value.
+        // Failure happened on win and macos for current seeds.
+        R"(.*CTCLossLayerTest.*CMR=1.*)",
+        R"(.*CTCLossLayerCPUTest.*ctcMergeRepeated=1.*)",
         // Issue: 72151
         R"(.*smoke_ROIAlignLayoutTest.*bf16.*)",
     };
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/ctc_Loss.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/ctc_Loss.cpp
new file mode 100644
index 00000000000..48927a83b67
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/ctc_Loss.cpp
@@ -0,0 +1,251 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <functional_test_utils/ov_tensor_utils.hpp>
+#include <ngraph_functions/builders.hpp>
+#include <string>
+#include <tuple>
+#include <vector>
+
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "test_utils/cpu_test_utils.hpp"
+
+using namespace InferenceEngine;
+using namespace CPUTestUtils;
+using namespace ov::test;
+
+namespace CPULayerTestsDefinitions {
+
+// N,T,C
+using CTCLossShapeParams = std::pair<std::vector<ngraph::PartialShape>, std::vector<std::vector<ngraph::Shape>>>;
+
+using CTCLossLayerCPUTestParams = std::tuple<CTCLossShapeParams,            // [N, T, C]
+                                            int,                            // blank value
+                                            bool,                           // preprocessCollapseRepeated
+                                            bool,                           // ctcMergeRepeated
+                                            bool,                           // unique
+                                            ngraph::element::Type,          // fp precision for logits
+                                            ngraph::element::Type           // int precision for label and length
+                                            >;
+
+class CTCLossLayerCPUTest : public testing::WithParamInterface<CTCLossLayerCPUTestParams>, virtual public SubgraphBaseTest, public CPUTestsBase {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<CTCLossLayerCPUTestParams>& obj) {
+        CTCLossShapeParams shapes;
+        int blank;
+        bool preprocessCollapseRepeated;
+        bool ctcMergeRepeated;
+        bool unique;
+        ngraph::element::Type fPrecision;
+        ngraph::element::Type iPrecision;
+        std::tie(shapes, blank, preprocessCollapseRepeated, ctcMergeRepeated, unique, fPrecision, iPrecision) = obj.param;
+        std::ostringstream results;
+        results << "IS=" << CommonTestUtils::partialShape2str({shapes.first}) << "_";
+        results << "TS=";
+        for (std::vector<ngraph::Shape>& staticShapes : shapes.second) {
+            for (ngraph::Shape& shape : staticShapes) {
+                size_t N = shape[0];
+                size_t T = shape[1];
+                size_t C = shape[2];
+                results << "{" << N << "," << T << "," << C << "}"
+                    << "_";
+            }
+        }
+        results << "blank=" << blank << "_";
+        results << "preprocessCollapseRepeated=" << preprocessCollapseRepeated << "_";
+        results << "ctcMergeRepeated=" << ctcMergeRepeated << "_";
+        results << "unique=" << unique << "_";
+
+        results << "fPrecision=" << fPrecision << "_";
+        results << "iPrecision=" << iPrecision << "_";
+
+        return results.str();
+    }
+
+protected:
+    void SetUp() override {
+        CTCLossShapeParams shapes;
+        bool preprocessCollapseRepeated;
+        bool ctcMergeRepeated;
+        bool unique;
+        ngraph::element::Type fPrecision;
+        ngraph::element::Type iPrecision;
+        std::tie(shapes, blank, preprocessCollapseRepeated, ctcMergeRepeated, unique, fPrecision, iPrecision) = GetParam();
+
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+        selectedType = std::string("ref_any_FP32");
+
+        for (std::vector<ngraph::Shape>& staticShapes : shapes.second) {
+            for (ngraph::Shape& shape : staticShapes) {
+                size_t N = shape[0];
+                size_t T = shape[1];
+                size_t C = shape[2];
+                targetStaticShapes.push_back({{N, T, C}, {N}, {N, T}, {N}});
+            }
+        }
+
+        auto inputDynamicShapesValues = shapes.first.front();
+        ov::PartialShape shapeN{inputDynamicShapesValues[0]};
+        ov::PartialShape shapeNT{inputDynamicShapesValues[0], inputDynamicShapesValues[1]};
+        ov::PartialShape shapeNTC{inputDynamicShapesValues[0], inputDynamicShapesValues[1], inputDynamicShapesValues[2]};
+        inputDynamicShapes = {shapeNTC, shapeN, shapeNT, shapeN};
+
+        std::vector<ngraph::element::Type> types{fPrecision, iPrecision, iPrecision, iPrecision};
+        std::vector<ov::PartialShape> partialShapes{inputDynamicShapesValues, shapeN, shapeNT, shapeN};
+
+        auto params = ngraph::builder::makeDynamicParams(types, partialShapes);
+        auto bankNode = ngraph::op::Constant::create(ngraph::element::i64, ngraph::Shape{ }, {blank});
+
+        auto ctcLoss = std::make_shared<ngraph::opset4::CTCLoss>(params[0], params[1], params[2],
+            params[3], bankNode, preprocessCollapseRepeated, ctcMergeRepeated, unique);
+        ngraph::ResultVector results{std::make_shared<ngraph::opset1::Result>(ctcLoss)};
+        function = std::make_shared<ngraph::Function>(results, params, "CTCLossLayerCPUTest");
+    };
+
+    void generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) override {
+        inputs.clear();
+        const auto& funcInputs = function->inputs();
+        const auto& dataShape = targetInputStaticShapes[0];
+        const auto N = dataShape[0];
+        const auto T = dataShape[1];
+        const auto C = dataShape[2];
+        ngraph::Shape shapeN{N};
+        ngraph::Shape shapeNT{N, T};
+
+        std::mt19937 gen(42);
+        std::uniform_int_distribution<unsigned long> dist(1, T);
+        std::vector<int32_t> logitLength(N, 0);
+        for (int n = 0; n < N; n++) {
+            logitLength[n] = dist(gen);
+        }
+        for (int i = 0; i < funcInputs.size(); ++i) {
+            const auto& funcInput = funcInputs[i];
+            ov::runtime::Tensor tensor;
+            if (i == 0) {
+                tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(),
+                                                                 dataShape,
+                                                                 10,
+                                                                 0,
+                                                                 10);
+            } else if (i == 1) {
+                tensor = ov::runtime::Tensor{funcInput.get_element_type(), {shapeN}};
+                if (funcInput.get_element_type() == ElementType::i32) {
+                    auto begin = tensor.data<int32_t>();
+                    std::copy(logitLength.begin(), logitLength.end(), begin);
+                } else if (funcInput.get_element_type() == ElementType::i64) {
+                    auto begin = tensor.data<int64_t>();
+                    std::copy(logitLength.begin(), logitLength.end(), begin);
+                }
+            } else if (i == 2) {
+                std::mt19937 genLable(42);
+                std::uniform_int_distribution<unsigned long> distLabel(0, C - 1);
+                std::vector<int32_t> labels(N * T, 0);
+                for (int n = 0; n < N * T; n++) {
+                    int value;
+                    // make sure blank not be inclded in labels
+                    while ((value = distLabel(genLable)) == blank) {}
+                    labels[n] = value;
+                }
+                tensor = ov::runtime::Tensor{funcInput.get_element_type(), {shapeNT}};
+                if (funcInput.get_element_type() == ElementType::i32) {
+                    auto begin = tensor.data<int32_t>();
+                    std::copy(labels.begin(), labels.end(), begin);
+                } else if (funcInput.get_element_type() == ElementType::i64) {
+                    auto begin = tensor.data<int64_t>();
+                    std::copy(labels.begin(), labels.end(), begin);
+                }
+            } else if (i == 3) {
+                std::mt19937 gen(24);
+                std::uniform_int_distribution<unsigned long> dist(1, T);
+
+                std::vector<int32_t> labelLength(N, 0);
+                for (int n = 0; n < N; n++) {
+                    const int len = dist(gen);
+                    // make sure lableLen <= logitLen
+                    labelLength[n] = std::min(len, logitLength[n]);
+                }
+
+                tensor = ov::runtime::Tensor{funcInput.get_element_type(), {shapeN}};
+                if (funcInput.get_element_type() == ElementType::i32) {
+                    auto begin = tensor.data<int32_t>();
+                    std::copy(labelLength.begin(), labelLength.end(), begin);
+                } else if (funcInput.get_element_type() == ElementType::i64) {
+                    auto begin = tensor.data<int64_t>();
+                    std::copy(labelLength.begin(), labelLength.end(), begin);
+                }
+            }
+            inputs.insert({funcInput.get_node_shared_ptr(), tensor});
+        }
+    }
+
+private:
+    int blank;
+};
+
+TEST_P(CTCLossLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED();
+
+    run();
+    CheckPluginRelatedResults(executableNetwork, "CTCLoss");
+}
+
+namespace {
+    const ngraph::element::TypeVector fPrecisions = {
+        ngraph::element::f32
+        // ngraph::element::f16
+    };
+
+    const ngraph::element::TypeVector iPrecisions = {
+        ngraph::element::i32,
+        ngraph::element::i64
+    };
+
+    const std::vector<bool> preprocessCollapseRepeated = {true, false};
+    const std::vector<bool> ctcMergeRepeated = {true, false};
+    const std::vector<bool> unique = {true, false};
+
+    const std::vector<CTCLossShapeParams> shapes = {
+        {
+            // dynamic undifined
+            {
+                {-1, -1, -1},
+            },
+            // target
+            {
+                {{3, 6, 8}, {2, 5, 6}, {5, 6, 10}}
+            }
+        },
+        {
+            // dynamic lower/upper bound
+            {
+                {{1, 10}, {5, 10}, {6, 12}},
+            },
+            // target
+            {
+                {{1, 5, 6}, {10, 10, 12}, {5, 7, 8}}
+            }
+        },
+    };
+
+    const std::vector<int> blanks = {
+        0, 2, 5
+    };
+
+const auto basicCases = ::testing::Combine(::testing::ValuesIn(shapes),
+                                           ::testing::ValuesIn(blanks),
+                                           ::testing::ValuesIn(preprocessCollapseRepeated),
+                                           ::testing::ValuesIn(ctcMergeRepeated),
+                                           ::testing::ValuesIn(unique),
+                                           ::testing::ValuesIn(fPrecisions),
+                                           ::testing::ValuesIn(iPrecisions));
+
+INSTANTIATE_TEST_SUITE_P(smoke_CTCLossCPU,
+                         CTCLossLayerCPUTest,
+                         basicCases,
+                         CTCLossLayerCPUTest::getTestCaseName);
+}  // namespace
+
+}  // namespace CPULayerTestsDefinitions
diff --git a/inference-engine/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/builders.hpp b/inference-engine/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/builders.hpp
index be1b755ae0f..473788f19e5 100644
--- a/inference-engine/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/builders.hpp
+++ b/inference-engine/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/builders.hpp
@@ -26,6 +26,8 @@ ngraph::ParameterVector makeParams(const element::Type &type, const std::vector<
 
 ngraph::ParameterVector makeDynamicParams(const element::Type &type, const std::vector<ov::PartialShape> &shapes);
 
+ngraph::ParameterVector makeDynamicParams(const std::vector<element::Type>& types, const std::vector<ov::PartialShape>& shapes);
+
 ngraph::ParameterVector
 makeParams(const element::Type &type, const std::vector<std::pair<std::string, std::vector<size_t>>> &inputs);
 
diff --git a/inference-engine/tests/ngraph_helpers/ngraph_functions/src/params_vector.cpp b/inference-engine/tests/ngraph_helpers/ngraph_functions/src/params_vector.cpp
index cddc5361823..fe396baf6db 100644
--- a/inference-engine/tests/ngraph_helpers/ngraph_functions/src/params_vector.cpp
+++ b/inference-engine/tests/ngraph_helpers/ngraph_functions/src/params_vector.cpp
@@ -43,5 +43,15 @@ ngraph::ParameterVector makeDynamicParams(const element::Type &type, const std::
     return outs;
 }
 
+ngraph::ParameterVector makeDynamicParams(const std::vector<element::Type>& types, const std::vector<ov::PartialShape>& shapes) {
+    ngraph::ParameterVector outs;
+    NGRAPH_CHECK(types.size() == shapes.size());
+    for (size_t i = 0; i < types.size(); i++) {
+        auto paramNode = std::make_shared<ov::op::v0::Parameter>(types[i], shapes[i]);
+        outs.push_back(paramNode);
+    }
+    return outs;
+}
+
 }  // namespace builder
 }  // namespace ngraph
\ No newline at end of file

From cf3c9ae04b5e849215a7a7114a600b7e2e0fe946 Mon Sep 17 00:00:00 2001
From: Mang Guo <mang.guo@intel.com>
Date: Sun, 28 Nov 2021 22:45:29 +0800
Subject: [PATCH 54/72] [CPU]Adaptive pooling dynamic shape support (#8703)

---
 .../nodes/mkldnn_adaptive_pooling.cpp         |  44 ++-
 .../nodes/mkldnn_adaptive_pooling.h           |   7 +
 .../single_layer_tests/adaptive_pooling.cpp   | 325 ++++++++++++++----
 3 files changed, 304 insertions(+), 72 deletions(-)

diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_adaptive_pooling.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_adaptive_pooling.cpp
index 20819bdd448..7cad050c49a 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_adaptive_pooling.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_adaptive_pooling.cpp
@@ -23,10 +23,6 @@ using namespace mkldnn::impl::cpu::x64;
 
 bool MKLDNNAdaptivePoolingNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
         if (one_of(op->get_type_info(), ngraph::op::v8::AdaptiveAvgPool::get_type_info_static())) {
             auto adaPool = std::dynamic_pointer_cast<const ngraph::opset8::AdaptiveAvgPool>(op);
             if (!adaPool) {
@@ -63,6 +59,7 @@ MKLDNNAdaptivePoolingNode::MKLDNNAdaptivePoolingNode(const std::shared_ptr<ngrap
         algorithm = Algorithm::AdaptivePoolingMax;
     }
     spatialDimsCount = getInputShapeAtPort(0).getRank() - 2;
+    spatialDimsValue.resize(spatialDimsCount);
 }
 
 void MKLDNNAdaptivePoolingNode::getSupportedDescriptors() {
@@ -74,12 +71,9 @@ void MKLDNNAdaptivePoolingNode::getSupportedDescriptors() {
     if (getChildEdges().size() != (algorithm == AdaptivePoolingMax ? 2 : 1))
         IE_THROW() << errorPrefix << "has incorrect number of output edges: " << getParentEdges().size();
 
-    auto parentDims = getInputShapeAtPort(0).getStaticDims();
-    auto childDims = getOutputShapeAtPort(0).getStaticDims();
-
-    spatialDimsCount = parentDims.size() - 2;
+    auto srcRank = getInputShapeAtPort(0).getRank();
     if (!one_of(spatialDimsCount, 1, 2, 3)) {
-        IE_THROW() << errorPrefix << "doesn't support 0th input with rank: " << getInputShapeAtPort(0).getRank();
+        IE_THROW() << errorPrefix << "doesn't support 0th input with rank: " << srcRank;
     }
 
     if (getInputShapeAtPort(1).getRank() != 1) {
@@ -91,6 +85,35 @@ void MKLDNNAdaptivePoolingNode::getSupportedDescriptors() {
     }
 }
 
+bool MKLDNNAdaptivePoolingNode::needShapeInfer() const {
+    const auto newSpatialDimsPtr = reinterpret_cast<int32_t *>(getParentEdgesAtPort(1)[0]->getMemoryPtr()->GetPtr());
+    for (size_t i = 0; i < spatialDimsCount; i++) {
+        if (spatialDimsValue[i] != newSpatialDimsPtr[i])
+            return true;
+    }
+    return MKLDNNNode::needShapeInfer();
+}
+
+std::vector<VectorDims> MKLDNNAdaptivePoolingNode::shapeInfer() const {
+    const auto inputDims = getParentEdgesAtPort(0)[0]->getMemory().GetShape().getStaticDims();
+    const auto spatialDims = getParentEdgesAtPort(1)[0]->getMemory().GetShape().getStaticDims();
+    const auto inputRank = inputDims.size();
+    const auto spatialDimsSize = spatialDims[0];
+
+    VectorDims outputDims(inputRank);
+    outputDims[0] = inputDims[0];
+    outputDims[1] = inputDims[1];
+    auto newSpatialDimsPtr = reinterpret_cast<int32_t *>(getParentEdgesAtPort(1)[0]->getMemoryPtr()->GetPtr());
+    for (size_t i = 0; i < spatialDimsSize; i++) {
+        outputDims[i + 2] = newSpatialDimsPtr[i];
+        spatialDimsValue[i] = newSpatialDimsPtr[i];
+    }
+
+    std::vector<VectorDims> result = {};
+    result.resize(outputShapes.size(), outputDims);
+    return result;
+}
+
 void MKLDNNAdaptivePoolingNode::initSupportedPrimitiveDescriptors() {
     if (!supportedPrimitiveDescriptors.empty())
         return;
@@ -104,7 +127,8 @@ void MKLDNNAdaptivePoolingNode::initSupportedPrimitiveDescriptors() {
     config.outConfs.resize((algorithm == Algorithm::AdaptivePoolingAvg ? 1 : 2));
 
     std::vector<LayoutType> dataFormats{ LayoutType::ncsp };
-    if (getInputShapeAtPort(0).getStaticDims()[1] != 1) {
+    const auto &inDims = getInputShapeAtPort(0).getDims();
+    if (inDims[1] != Shape::UNDEFINED_DIM && inDims[1] != 1) {
         dataFormats.push_back(LayoutType::nspc);
         dataFormats.push_back(LayoutType::nCsp16c);
         dataFormats.push_back(LayoutType::nCsp8c);
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_adaptive_pooling.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_adaptive_pooling.h
index 77a7e14d87a..c47740e75ec 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_adaptive_pooling.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_adaptive_pooling.h
@@ -26,10 +26,17 @@ public:
 
 private:
     int spatialDimsCount;
+    mutable std::vector<Dim> spatialDimsValue = {};
     InferenceEngine::Precision precision = InferenceEngine::Precision::FP32;
     inline void setBinBorders(size_t *startPtr, size_t *endPtr, size_t idx, size_t inputLength, size_t outputLength);
 
     std::string errorPrefix;
+
+protected:
+    bool needShapeInfer() const override;
+    std::vector<VectorDims> shapeInfer() const override;
+    bool needPrepareParams() const override { return false; };
+    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); };
 };
 
 }  // namespace MKLDNNPlugin
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/adaptive_pooling.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/adaptive_pooling.cpp
index e8232476ec4..f9b1f59108a 100644
--- a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/adaptive_pooling.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/adaptive_pooling.cpp
@@ -2,53 +2,65 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include <functional_test_utils/ov_tensor_utils.hpp>
+#include "shared_test_classes/base/ov_subgraph.hpp"
 #include "test_utils/cpu_test_utils.hpp"
 #include "ngraph_functions/builders.hpp"
 #include "ngraph_functions/utils/ngraph_helpers.hpp"
 
 using namespace InferenceEngine;
 using namespace CPUTestUtils;
+using namespace ov::test;
 
 namespace CPULayerTestsDefinitions {
 namespace {
     std::vector<int> pooledSpatialShape;
     std::string mode;
-    std::vector<size_t> inputShape;
+    std::vector<InputShape> inputShape;
 }  // namespace
 
-typedef std::tuple<
+using AdaPoolSpecificParams = std::tuple<
         std::vector<int>,        // pooled vector
-        std::vector<size_t>      // feature map shape
-> AdaPoolSpecificParams;
+        std::vector<InputShape>>;      // feature map shape
 
-typedef std::tuple<
+using AdaPoolLayerTestParams = std::tuple<
         AdaPoolSpecificParams,
-        std::string,                        // mode
-        InferenceEngine::Precision,         // Net precision
-        LayerTestsUtils::TargetDevice       // Device name
-> AdaPoolLayerTestParams;
+        std::string,         // mode
+        bool,                // second Input is Constant
+        ElementType,         // Net precision
+        TargetDevice>;       // Device name
 
-typedef std::tuple<
+using AdaPoolLayerCPUTestParamsSet = std::tuple<
         CPULayerTestsDefinitions::AdaPoolLayerTestParams,
-        CPUSpecificParams> AdaPoolLayerCPUTestParamsSet;
+        CPUSpecificParams>;
 
 class AdaPoolLayerCPUTest : public testing::WithParamInterface<AdaPoolLayerCPUTestParamsSet>,
-                            virtual public LayerTestsUtils::LayerTestsCommon, public CPUTestsBase {
+                            virtual public SubgraphBaseTest, public CPUTestsBase {
 public:
     static std::string getTestCaseName(testing::TestParamInfo<AdaPoolLayerCPUTestParamsSet> obj) {
         CPULayerTestsDefinitions::AdaPoolLayerTestParams basicParamsSet;
         CPUSpecificParams cpuParams;
         std::tie(basicParamsSet, cpuParams) = obj.param;
         std::string td;
-        Precision netPr;
+        ElementType netPr;
+        bool isStatic;
         AdaPoolSpecificParams adaPar;
-        std::tie(adaPar, mode, netPr, td) = basicParamsSet;
+        std::tie(adaPar, mode, isStatic, netPr, td) = basicParamsSet;
         std::tie(pooledSpatialShape, inputShape) = adaPar;
         std::ostringstream result;
         result << "AdaPoolTest_";
-        result << "IS=" << CommonTestUtils::vec2str(inputShape) << "_";
+        result << "IS=(";
+        for (const auto& shape : inputShape) {
+            result << CommonTestUtils::partialShape2str({shape.first}) << "_";
+        }
+        result << ")_TS=(";
+        for (const auto& shape : inputShape) {
+            for (const auto& item : shape.second) {
+                result << CommonTestUtils::vec2str(item) << "_";
+            }
+        }
         result << "OS=" << CommonTestUtils::vec2str(pooledSpatialShape) << "(spat.)_";
-        result << netPr.name() << "_";
+        result << netPr << "_";
         result << mode << "_";
         result << CPUTestsBase::getTestCaseName(cpuParams) << "_";
         result << std::to_string(obj.index);
@@ -62,31 +74,103 @@ protected:
         std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
 
         CPULayerTestsDefinitions::AdaPoolSpecificParams adaPoolParams;
-        auto netPrecision = InferenceEngine::Precision::UNSPECIFIED;
-        std::tie(adaPoolParams, mode,  netPrecision, targetDevice) = basicParamsSet;
-        inPrc = outPrc = netPrecision;
-        std::tie(pooledSpatialShape, inputShape) = adaPoolParams;
+        ElementType netPrecision;
+        bool isStatic;
+        std::tie(adaPoolParams, mode, isStatic, netPrecision, targetDevice) = basicParamsSet;
+        std::tie(pooledVector, inputShape) = adaPoolParams;
 
-        ngraph::Shape coordsShape = {pooledSpatialShape.size() };
-        auto pooledParam = ngraph::builder::makeConstant<int32_t>(ngraph::element::i32, coordsShape, pooledSpatialShape);
-        auto params = ngraph::builder::makeParams(ngraph::element::f32, {inputShape});
-
-        // we cannot create abstract Op to use polymorphism
-        auto adapoolMax = std::make_shared<ngraph::opset8::AdaptiveMaxPool>(params[0], pooledParam, ngraph::element::i32);
-        adapoolMax->get_rt_info() = getCPUInfo();
-        auto adapoolAvg = std::make_shared<ngraph::opset8::AdaptiveAvgPool>(params[0], pooledParam);
-        adapoolAvg->get_rt_info() = getCPUInfo();
+        init_input_shapes(inputShape);
+        if (!isStatic) {
+            for (auto &target : targetStaticShapes) {
+                target.push_back({pooledVector.size()});
+            }
+        }
 
         selectedType = std::string("unknown_FP32");
-        threshold = 1e-2;
-        function = (mode == "max" ? std::make_shared<ngraph::Function>(adapoolMax->outputs(), params, "AdaPoolMax") :
-                    std::make_shared<ngraph::Function>(adapoolAvg->outputs(), params, "AdaPoolAvg"));
+        if (netPrecision == ElementType::bf16) {
+            rel_threshold = 1e-2;
+        }
+        function = createFunction(isStatic);
     }
+
+    void generatePooledVector() {
+        std::random_device rd;
+        std::uniform_int_distribution<int32_t> distribution(1, 5);
+        for (int i = 0; i < pooledVector.size(); i++) {
+            pooledVector[i] = distribution(rd);
+        }
+    }
+
+    std::shared_ptr<ngraph::Function> createFunction(bool secondInputConst) {
+        auto params = ngraph::builder::makeDynamicParams(ngraph::element::f32, { inputDynamicShapes[0] });
+        params.front()->set_friendly_name("ParamsInput");
+        std::shared_ptr<ov::Node> secondInput;
+        if (secondInputConst) {
+            secondInput = ngraph::op::Constant::create(ngraph::element::i32, ngraph::Shape{pooledVector.size()}, pooledVector);
+        } else {
+            auto pooledParam = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::i32, ngraph::Shape{pooledVector.size()});
+            pooledParam->set_friendly_name("ParamSecondInput");
+            params.push_back(pooledParam);
+            secondInput = pooledParam;
+        }
+
+        auto adapoolMax = std::make_shared<ngraph::opset8::AdaptiveMaxPool>(params[0], secondInput, ngraph::element::i32);
+        adapoolMax->get_rt_info() = getCPUInfo();
+        auto adapoolAvg = std::make_shared<ngraph::opset8::AdaptiveAvgPool>(params[0], secondInput);
+        adapoolAvg->get_rt_info() = getCPUInfo();
+
+        auto function = (mode == "max" ? std::make_shared<ngraph::Function>(adapoolMax->outputs(), params, "AdaPoolMax") :
+                    std::make_shared<ngraph::Function>(adapoolAvg->outputs(), params, "AdaPoolAvg"));
+        return function;
+    }
+
+    void init_ref_function(std::shared_ptr<ov::Function> &funcRef, const std::vector<ov::Shape>& targetInputStaticShapes) override {
+        if (function->get_parameters().size() == 2) {
+            generatePooledVector();
+            funcRef = createFunction(true);
+        }
+        ngraph::helpers::resize_function(funcRef, targetInputStaticShapes);
+    }
+
+    void validate() override {
+        if (function->get_parameters().size() == 2) {
+            auto pos = std::find_if(inputs.begin(), inputs.end(),
+                                    [](const std::pair<std::shared_ptr<ov::Node>, ov::runtime::Tensor> &params) {
+                                        return params.first->get_friendly_name() == "ParamSecondInput";
+                                    });
+            IE_ASSERT(pos != inputs.end());
+            inputs.erase(pos);
+        }
+        SubgraphBaseTest::validate();
+    }
+
+    void generate_inputs(const std::vector<ov::Shape>& targetInputStaticShapes) override {
+        inputs.clear();
+        const auto& funcInputs = function->inputs();
+        for (int i = 0; i < funcInputs.size(); ++i) {
+            const auto& funcInput = funcInputs[i];
+            ov::runtime::Tensor tensor;
+
+            if (i == 1) {
+                tensor = ov::runtime::Tensor(funcInput.get_element_type(), targetInputStaticShapes[i]);
+                auto *dataPtr = tensor.data<int32_t>();
+                for (size_t i = 0; i < pooledVector.size(); i++) {
+                    dataPtr[i] = pooledVector[i];
+                }
+            } else {
+                    tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i], 2560, 0, 256);
+            }
+            inputs.insert({funcInput.get_node_shared_ptr(), tensor});
+        }
+    }
+
+private:
+    std::vector<int> pooledVector;
 };
 
 TEST_P(AdaPoolLayerCPUTest, CompareWithRefs) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
-    Run();
+    run();
     CheckPluginRelatedResults(executableNetwork, "AdaptivePooling");
 }
 
@@ -151,9 +235,9 @@ std::vector<CPUSpecificParams> filterCPUInfoForDevice(std::string dims = "3D", s
     return resCPUParams;
 }
 
-const std::vector<InferenceEngine::Precision> netPrecisions = {
-        InferenceEngine::Precision::FP32,
-        InferenceEngine::Precision::BF16
+const std::vector<ElementType> netPrecisions = {
+        ElementType::f32,
+        ElementType::bf16
 };
 
 const std::vector<std::vector<int>> pooled3DVector = {
@@ -173,24 +257,36 @@ const std::vector<std::vector<int>> pooled5DVector = {
         { 3, 5, 3 },
 };
 
-const std::vector<std::vector<size_t>> input3DShapeVector = {
-        SizeVector({ 1, 17, 3 }),
-        SizeVector({ 3, 17, 5 }),
+std::vector<std::vector<ov::Shape>> staticInput3DShapeVector = {{{1, 17, 3}, {3, 7, 5}}};
+
+const std::vector<std::vector<InputShape>> input3DShapeVector = {
+        {
+                {{{-1, 17, -1}, {{1, 17, 3}, {3, 17, 5}, {3, 17, 5}}}},
+                {{{{1, 10}, 20, {1, 10}}, {{1, 20, 5}, {2, 20, 4}, {3, 20, 6}}}}
+        }
 };
 
-const std::vector<std::vector<size_t>> input4DShapeVector = {
-        SizeVector({ 1, 3, 1, 1 }),
-        SizeVector({ 3, 17, 5, 2 }),
+std::vector<std::vector<ov::Shape>> staticInput4DShapeVector = {{{1, 3, 1, 1}, {3, 17, 5, 2}}};
+
+const std::vector<std::vector<InputShape>> input4DShapeVector = {
+        {
+                {{{-1, 3, -1, -1}, {{1, 3, 1, 1}, {3, 3, 5, 2}, {3, 3, 5, 2}}}},
+                {{{{1, 10}, 3, {1, 10}, {1, 10}}, {{2, 3, 10, 6}, {3, 3, 6, 5}, {3, 3, 6, 5}}}}
+        }
 };
 
-const std::vector<std::vector<size_t>> input5DShapeVector = {
-        SizeVector({ 1, 17, 2, 5, 2 }),
-        SizeVector({ 3, 17, 4, 5, 4 }),
+std::vector<std::vector<ov::Shape>> staticInput5DShapeVector = {{{ 1, 17, 2, 5, 2}, {3, 17, 4, 5, 4}}};
+
+const std::vector<std::vector<InputShape>> input5DShapeVector = {
+        {
+                {{{-1, 17, -1, -1, -1}, {{1, 17, 2, 5, 2}, {3, 17, 4, 5, 4}, {3, 17, 4, 5, 4}}}},
+                {{{{1, 10}, 3, {1, 10}, {1, 10}, {1, 10}}, {{3, 3, 2, 5, 2}, {1, 3, 4, 5, 4}, {1, 3, 4, 5, 4}}}}
+        }
 };
 
 const auto adaPool3DParams = ::testing::Combine(
         ::testing::ValuesIn(pooled3DVector),         // output spatial shape
-        ::testing::ValuesIn(input3DShapeVector)     // feature map shape
+        ::testing::ValuesIn(input3DShapeVector)      // feature map shape
 );
 
 const auto adaPool4DParams = ::testing::Combine(
@@ -203,11 +299,27 @@ const auto adaPool5DParams = ::testing::Combine(
         ::testing::ValuesIn(input5DShapeVector)     // feature map shape
 );
 
+const auto staticAdaPool3DParams = ::testing::Combine(
+        ::testing::ValuesIn(pooled3DVector),         // output spatial shape
+        ::testing::ValuesIn(static_shapes_to_test_representation(staticInput3DShapeVector))      // feature map shape
+);
+
+const auto staticAdaPool4DParams = ::testing::Combine(
+        ::testing::ValuesIn(pooled4DVector),         // output spatial shape
+        ::testing::ValuesIn(static_shapes_to_test_representation(staticInput4DShapeVector))     // feature map shape
+);
+
+const auto staticAdaPool5DParams = ::testing::Combine(
+        ::testing::ValuesIn(pooled5DVector),         // output spatial shape
+        ::testing::ValuesIn(static_shapes_to_test_representation(staticInput5DShapeVector))     // feature map shape
+);
+
 INSTANTIATE_TEST_SUITE_P(smoke_AdaPoolAvg3DLayoutTest, AdaPoolLayerCPUTest,
                          ::testing::Combine(
                                  ::testing::Combine(
                                          adaPool3DParams,
                                          ::testing::Values("avg"),
+                                         ::testing::Values(false),
                                          ::testing::ValuesIn(netPrecisions),
                                          ::testing::Values(CommonTestUtils::DEVICE_CPU)),
                                  ::testing::ValuesIn(filterCPUInfoForDevice("3D", "avg"))),
@@ -218,6 +330,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_AdaPoolAvg4DLayoutTest, AdaPoolLayerCPUTest,
                                  ::testing::Combine(
                                          adaPool4DParams,
                                          ::testing::Values("avg"),
+                                         ::testing::Values(false),
                                          ::testing::ValuesIn(netPrecisions),
                                          ::testing::Values(CommonTestUtils::DEVICE_CPU)),
                                  ::testing::ValuesIn(filterCPUInfoForDevice("4D", "avg"))),
@@ -228,6 +341,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_AdaPoolAvg5DLayoutTest, AdaPoolLayerCPUTest,
                                  ::testing::Combine(
                                          adaPool5DParams,
                                          ::testing::Values("avg"),
+                                         ::testing::Values(false),
                                          ::testing::ValuesIn(netPrecisions),
                                          ::testing::Values(CommonTestUtils::DEVICE_CPU)),
                                  ::testing::ValuesIn(filterCPUInfoForDevice("5D", "avg"))),
@@ -238,6 +352,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_AdaPoolMax3DLayoutTest, AdaPoolLayerCPUTest,
                                  ::testing::Combine(
                                          adaPool3DParams,
                                          ::testing::Values("max"),
+                                         ::testing::Values(false),
                                          ::testing::ValuesIn(netPrecisions),
                                          ::testing::Values(CommonTestUtils::DEVICE_CPU)),
                                  ::testing::ValuesIn(filterCPUInfoForDevice("3D", "max"))),
@@ -248,6 +363,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_AdaPoolMax4DLayoutTest, AdaPoolLayerCPUTest,
                                  ::testing::Combine(
                                          adaPool4DParams,
                                          ::testing::Values("max"),
+                                         ::testing::Values(false),
                                          ::testing::ValuesIn(netPrecisions),
                                          ::testing::Values(CommonTestUtils::DEVICE_CPU)),
                                  ::testing::ValuesIn(filterCPUInfoForDevice("4D", "max"))),
@@ -258,22 +374,111 @@ INSTANTIATE_TEST_SUITE_P(smoke_AdaPoolMax5DLayoutTest, AdaPoolLayerCPUTest,
                                  ::testing::Combine(
                                          adaPool5DParams,
                                          ::testing::Values("max"),
+                                         ::testing::Values(false),
                                          ::testing::ValuesIn(netPrecisions),
                                          ::testing::Values(CommonTestUtils::DEVICE_CPU)),
                                  ::testing::ValuesIn(filterCPUInfoForDevice("5D", "max"))),
                          AdaPoolLayerCPUTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_StaticAdaPoolAvg3DLayoutTest, AdaPoolLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::Combine(
+                                         staticAdaPool3DParams,
+                                         ::testing::Values("avg"),
+                                         ::testing::Values(true),
+                                         ::testing::ValuesIn(netPrecisions),
+                                         ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                                 ::testing::ValuesIn(filterCPUInfoForDevice("3D", "avg"))),
+                         AdaPoolLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_StaticAdaPoolAvg4DLayoutTest, AdaPoolLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::Combine(
+                                         staticAdaPool4DParams,
+                                         ::testing::Values("avg"),
+                                         ::testing::Values(true),
+                                         ::testing::ValuesIn(netPrecisions),
+                                         ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                                 ::testing::ValuesIn(filterCPUInfoForDevice("4D", "avg"))),
+                         AdaPoolLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_StaticAdaPoolAvg5DLayoutTest, AdaPoolLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::Combine(
+                                         staticAdaPool5DParams,
+                                         ::testing::Values("avg"),
+                                         ::testing::Values(true),
+                                         ::testing::ValuesIn(netPrecisions),
+                                         ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                                 ::testing::ValuesIn(filterCPUInfoForDevice("5D", "avg"))),
+                         AdaPoolLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_StaticAdaPoolMax3DLayoutTest, AdaPoolLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::Combine(
+                                         staticAdaPool3DParams,
+                                         ::testing::Values("max"),
+                                         ::testing::Values(true),
+                                         ::testing::ValuesIn(netPrecisions),
+                                         ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                                 ::testing::ValuesIn(filterCPUInfoForDevice("3D", "max"))),
+                         AdaPoolLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_StaticAdaPoolMax4DLayoutTest, AdaPoolLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::Combine(
+                                         staticAdaPool4DParams,
+                                         ::testing::Values("max"),
+                                         ::testing::Values(true),
+                                         ::testing::ValuesIn(netPrecisions),
+                                         ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                                 ::testing::ValuesIn(filterCPUInfoForDevice("4D", "max"))),
+                         AdaPoolLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_StaticAdaPoolMax5DLayoutTest, AdaPoolLayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::Combine(
+                                         staticAdaPool5DParams,
+                                         ::testing::Values("max"),
+                                         ::testing::Values(true),
+                                         ::testing::ValuesIn(netPrecisions),
+                                         ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                                 ::testing::ValuesIn(filterCPUInfoForDevice("5D", "max"))),
+                         AdaPoolLayerCPUTest::getTestCaseName);
+
+
 // in 1-channel cases  {..., 1, 1, 1} shape cannot be correctly resolved on oneDnn level, so it was removed from instances
 
+const std::vector<std::vector<InputShape>> input3DShape1Channel = {
+        {
+                {{{-1, -1, -1}, {{1, 1, 2}, {1, 1, 2}, {1, 1, 2}}}},
+                {{{{1, 10}, {1, 10}, {1, 10}}, {{1, 1, 2}, {2, 1, 2}, {2, 1, 2}}}}
+        }
+};
+
+const std::vector<std::vector<InputShape>> input4DShape1Channel = {
+        {
+                {{{-1, -1, -1, -1}, {{1, 1, 1, 2}, {2, 1, 2, 1}, {2, 1, 2, 1}}}},
+                {{{{1, 10}, {1, 10}, {1, 10}, {1, 10}}, {{1, 1, 1, 2}, {1, 1, 1, 2}, {2, 1, 2, 1}}}}
+        }
+};
+
+const std::vector<std::vector<InputShape>> input5DShape1Channel = {
+        {
+                {{{-1, -1, -1, -1, -1}, {{1, 1, 1, 1, 2}, {1, 1, 1, 1, 2}, {2, 1, 1, 2, 1}}}},
+                {{{{1, 10}, {1, 10}, {1, 10}, {1, 10}, {1, 10}}, {{1, 1, 1, 1, 2}, {1, 1, 1, 1, 2}, {2, 1, 1, 2, 1}}}}
+        }
+};
+
 INSTANTIATE_TEST_SUITE_P(smoke_AdaPool_1ch_Avg3DLayoutTest, AdaPoolLayerCPUTest,
                          ::testing::Combine(
                                  ::testing::Combine(
                                          ::testing::Combine(
                                                  ::testing::ValuesIn(std::vector<std::vector<int>> {
                                                          {1}, {2}}),
-                                                 ::testing::ValuesIn(std::vector<std::vector<size_t>> {
-                                                         SizeVector{1, 1, 2}, SizeVector{2, 1, 2}})),
+                                                 ::testing::ValuesIn(input3DShape1Channel)),
                                          ::testing::Values("avg"),
+                                         ::testing::Values(true),
                                          ::testing::ValuesIn(netPrecisions),
                                          ::testing::Values(CommonTestUtils::DEVICE_CPU)),
                                  ::testing::Values(CPUSpecificParams{{ncw, x}, {ncw}, {}, {}})),
@@ -287,11 +492,9 @@ INSTANTIATE_TEST_SUITE_P(smoke_AdaPool_1ch_Avg4DLayoutTest, AdaPoolLayerCPUTest,
                                                          {1, 1},
                                                          {2, 2}
                                                  }),
-                                                 ::testing::ValuesIn(std::vector<std::vector<size_t>> {
-                                                         SizeVector{1, 1, 1, 2},
-                                                         SizeVector{2, 1, 2, 1}
-                                                 })),
+                                                 ::testing::ValuesIn(input4DShape1Channel)),
                                          ::testing::Values("avg"),
+                                         ::testing::Values(true),
                                          ::testing::ValuesIn(netPrecisions),
                                          ::testing::Values(CommonTestUtils::DEVICE_CPU)),
                                  ::testing::Values(CPUSpecificParams{{nchw, x}, {nchw}, {}, {}})),
@@ -303,9 +506,9 @@ INSTANTIATE_TEST_SUITE_P(smoke_AdaPool_1ch_Avg5DLayoutTest, AdaPoolLayerCPUTest,
                                          ::testing::Combine(
                                                  ::testing::ValuesIn(std::vector<std::vector<int>> {
                                                          {1, 1, 1}, {2, 2, 2}}),
-                                                 ::testing::ValuesIn(std::vector<std::vector<size_t>> {
-                                                         SizeVector{1, 1, 1, 1, 2}, SizeVector{2, 1, 1, 2, 1}})),
+                                                 ::testing::ValuesIn(input5DShape1Channel)),
                                          ::testing::Values("avg"),
+                                         ::testing::Values(true),
                                          ::testing::ValuesIn(netPrecisions),
                                          ::testing::Values(CommonTestUtils::DEVICE_CPU)),
                                  ::testing::Values(CPUSpecificParams{{ncdhw, x}, {ncdhw}, {}, {}})),
@@ -318,9 +521,9 @@ INSTANTIATE_TEST_SUITE_P(smoke_AdaPool_1ch_Max3DLayoutTest, AdaPoolLayerCPUTest,
                                          ::testing::Combine(
                                                  ::testing::ValuesIn(std::vector<std::vector<int>> {
                                                          {1}, {2}}),
-                                                 ::testing::ValuesIn(std::vector<std::vector<size_t>> {
-                                                         SizeVector{1, 1, 2}, SizeVector{2, 1, 2}})),
+                                                 ::testing::ValuesIn(input3DShape1Channel)),
                                          ::testing::Values("max"),
+                                         ::testing::Values(true),
                                          ::testing::ValuesIn(netPrecisions),
                                          ::testing::Values(CommonTestUtils::DEVICE_CPU)),
                                  ::testing::Values(CPUSpecificParams{{ncw, x}, {ncw}, {}, {}})),
@@ -332,9 +535,9 @@ INSTANTIATE_TEST_SUITE_P(smoke_AdaPool_1ch_Max4DLayoutTest, AdaPoolLayerCPUTest,
                                          ::testing::Combine(
                                                  ::testing::ValuesIn(std::vector<std::vector<int>> {
                                                          {1, 1}, {2, 2}}),
-                                                 ::testing::ValuesIn(std::vector<std::vector<size_t>> {
-                                                         SizeVector{1, 1, 1, 2}, SizeVector{2, 1, 2, 1}})),
+                                                 ::testing::ValuesIn(input4DShape1Channel)),
                                          ::testing::Values("max"),
+                                         ::testing::Values(true),
                                          ::testing::ValuesIn(netPrecisions),
                                          ::testing::Values(CommonTestUtils::DEVICE_CPU)),
                                  ::testing::Values(CPUSpecificParams{{nchw, x}, {nchw}, {}, {}})),
@@ -348,11 +551,9 @@ INSTANTIATE_TEST_SUITE_P(smoke_AdaPool_1ch_Max5DLayoutTest, AdaPoolLayerCPUTest,
                                                          {1, 1, 1},
                                                          {2, 2, 2}
                                                  }),
-                                                 ::testing::ValuesIn(std::vector<std::vector<size_t>> {
-                                                         SizeVector{1, 1, 1, 1, 2},
-                                                         SizeVector{2, 1, 1, 2, 1}
-                                                 })),
+                                                 ::testing::ValuesIn(input5DShape1Channel)),
                                          ::testing::Values("max"),
+                                         ::testing::Values(true),
                                          ::testing::ValuesIn(netPrecisions),
                                          ::testing::Values(CommonTestUtils::DEVICE_CPU)),
                                  ::testing::Values(CPUSpecificParams{{ncdhw, x}, {ncdhw}, {}, {}})),

From c02fe4b813f8ab47b0478f18a29063ce29aa20f0 Mon Sep 17 00:00:00 2001
From: Ilya Lavrenov <ilya.lavrenov@intel.com>
Date: Sun, 28 Nov 2021 21:36:24 +0300
Subject: [PATCH 55/72] Static OpenVINO frontends (#8868)

* Static OpenVINO frontends

* ONNX tests with shared extension CustomOpUser_ONNXImporter

* Resolved issues with ONNX's NCC style check

* Reverted openvino => ngraph for frontend includes install

* Try to fix ONNX / Protobuf

* Try to fix CI

* Try

* Fixed cmake stage with BUILD_SHARED_LIBS

* Fixed export for linkable frontends

* Fixed warnings on Linux

* Fixed after ngraph => src
---
 .ci/azure/windows.yml                         |   2 -
 CMakeLists.txt                                |   1 +
 .../IEDevScriptsConfig.cmake                  |   1 +
 .../frontends/create_frontends_hpp.cmake      |  34 +++
 .../frontends/frontends.cmake                 | 241 ++++++++++++++++++
 .../frontends/ov_frontends.hpp.in             |  27 ++
 cmake/developer_package/plugins/plugins.cmake |  24 +-
 cmake/features.cmake                          |   8 +-
 .../ir_serialization/custom_ops.cpp           |  12 +-
 src/frontends/CMakeLists.txt                  |  14 +-
 src/frontends/common/CMakeLists.txt           |  14 +-
 src/frontends/common/src/plugin_loader.cpp    |  52 +++-
 src/frontends/common/src/plugin_loader.hpp    |   3 +-
 src/frontends/common/src/utils.cpp            |  17 +-
 src/frontends/ir/CMakeLists.txt               |  57 +----
 .../ir/include/ir_frontend/utility.hpp        |  16 +-
 src/frontends/ir/src/frontend.cpp             |   4 +-
 src/frontends/onnx/frontend/CMakeLists.txt    |  69 +----
 .../include/onnx_frontend/frontend.hpp        |  15 +-
 .../onnx_import/onnx_importer_visibility.hpp  |  12 +-
 .../onnx/frontend/src/core/model.cpp          |   2 +-
 src/frontends/onnx/frontend/src/frontend.cpp  |   4 +-
 .../onnx/frontend/src/op/quant_conv.cpp       |  33 ++-
 .../onnx/frontend/src/op/quant_conv.hpp       |  34 ++-
 src/frontends/onnx/onnx_common/CMakeLists.txt |   9 +-
 src/frontends/paddlepaddle/CMakeLists.txt     |  98 +------
 .../include/paddlepaddle_frontend/utility.hpp |  16 +-
 src/frontends/paddlepaddle/src/frontend.cpp   |   4 +-
 src/frontends/tensorflow/CMakeLists.txt       | 101 +-------
 .../include/tensorflow_frontend/utility.hpp   |  15 +-
 src/frontends/tensorflow/src/tensorflow.cpp   |   4 +-
 thirdparty/CMakeLists.txt                     |   8 +-
 thirdparty/onnx/CMakeLists.txt                |  47 +++-
 thirdparty/protobuf/CMakeLists.txt            |  12 +-
 34 files changed, 581 insertions(+), 429 deletions(-)
 create mode 100644 cmake/developer_package/frontends/create_frontends_hpp.cmake
 create mode 100644 cmake/developer_package/frontends/frontends.cmake
 create mode 100644 cmake/developer_package/frontends/ov_frontends.hpp.in

diff --git a/.ci/azure/windows.yml b/.ci/azure/windows.yml
index b5094e51aa6..9f7b1256bad 100644
--- a/.ci/azure/windows.yml
+++ b/.ci/azure/windows.yml
@@ -210,12 +210,10 @@ jobs:
   - script: call $(SETUPVARS) && $(INSTALL_TEST_DIR)\paddlepaddle_tests --gtest_print_time=1 --gtest_output=xml:TEST-PaddlePaddle.xml
     displayName: 'PaddlePaddle Frontend UT'
     continueOnError: false
-    condition: eq(variables['CMAKE_BUILD_SHARED_LIBS'], 'ON')
 
   - script: call $(SETUPVARS) && $(INSTALL_TEST_DIR)\tensorflow_tests --gtest_print_time=1 --gtest_output=xml:TEST-Tensorflow.xml
     displayName: 'Tensorflow Frontend UT'
     continueOnError: false
-    condition: eq(variables['CMAKE_BUILD_SHARED_LIBS'], 'ON')
 
   - script: |
       set PATH=$(IB_DIR);%PATH%
diff --git a/CMakeLists.txt b/CMakeLists.txt
index d2f18b3ae1d..c73e5f52b4f 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -32,6 +32,7 @@ endif()
 
 # resolving dependencies for the project
 message (STATUS "PROJECT ............................... " ${PROJECT_NAME})
+message (STATUS "CMAKE_VERSION ......................... " ${CMAKE_VERSION})
 message (STATUS "CMAKE_BINARY_DIR ...................... " ${CMAKE_BINARY_DIR})
 message (STATUS "OpenVINO_SOURCE_DIR ................... " ${OpenVINO_SOURCE_DIR})
 message (STATUS "CMAKE_GENERATOR ....................... " ${CMAKE_GENERATOR})
diff --git a/cmake/developer_package/IEDevScriptsConfig.cmake b/cmake/developer_package/IEDevScriptsConfig.cmake
index dfbb3833056..fcd8726f923 100644
--- a/cmake/developer_package/IEDevScriptsConfig.cmake
+++ b/cmake/developer_package/IEDevScriptsConfig.cmake
@@ -228,6 +228,7 @@ include(api_validator/api_validator)
 
 include(vs_version/vs_version)
 include(plugins/plugins)
+include(frontends/frontends)
 include(add_ie_target)
 include(CMakePackageConfigHelpers)
 
diff --git a/cmake/developer_package/frontends/create_frontends_hpp.cmake b/cmake/developer_package/frontends/create_frontends_hpp.cmake
new file mode 100644
index 00000000000..a4f972ac00f
--- /dev/null
+++ b/cmake/developer_package/frontends/create_frontends_hpp.cmake
@@ -0,0 +1,34 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+foreach(var OV_FRONTENDS_HPP_HEADER_IN OV_FRONTENDS_HPP_HEADER FRONTEND_NAMES)
+    if(NOT DEFINED ${var})
+        message(FATAL_ERROR "${var} is required, but not defined")
+    endif()
+endforeach()
+
+# configure variables
+
+set(OV_FRONTEND_DECLARATIONS "")
+set(OV_FRONTEND_MAP_DEFINITION "    FrontendsStaticRegistry registry = {")
+
+foreach(frontend IN LISTS FRONTEND_NAMES)
+    # common
+    set(_OV_FRONTEND_DATA_FUNC "GetFrontEndData${frontend}")
+    set(_OV_VERSION_FUNC "GetAPIVersion${frontend}")
+
+    # declarations
+    set(OV_FRONTEND_DECLARATIONS "${OV_FRONTEND_DECLARATIONS}
+ov::frontend::FrontEndVersion ${_OV_VERSION_FUNC}();
+void* ${_OV_FRONTEND_DATA_FUNC}();")
+
+    set(OV_FRONTEND_MAP_DEFINITION "${OV_FRONTEND_MAP_DEFINITION}
+        { Value { ${_OV_FRONTEND_DATA_FUNC}, ${_OV_VERSION_FUNC} } },")
+endforeach()
+
+set(OV_FRONTEND_MAP_DEFINITION "${OV_FRONTEND_MAP_DEFINITION}
+    };
+    return registry;")
+
+configure_file("${OV_FRONTENDS_HPP_HEADER_IN}" "${OV_FRONTENDS_HPP_HEADER}" @ONLY)
diff --git a/cmake/developer_package/frontends/frontends.cmake b/cmake/developer_package/frontends/frontends.cmake
new file mode 100644
index 00000000000..4fa8084d35d
--- /dev/null
+++ b/cmake/developer_package/frontends/frontends.cmake
@@ -0,0 +1,241 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+set(FRONTEND_INSTALL_INCLUDE "runtime/include/ngraph/frontend")
+set(FRONTEND_NAME_SUFFIX "_ov_frontend")
+
+set(FRONTEND_NAMES "" CACHE INTERNAL "")
+
+if(NOT TARGET ov_frontends)
+    add_custom_target(ov_frontends)
+endif()
+
+#
+# ov_target_link_frontends(<TARGET_NAME>)
+#
+function(ov_target_link_frontends TARGET_NAME)
+    if(BUILD_SHARED_LIBS)
+        return()
+    endif()
+
+    foreach(name IN LISTS FRONTEND_NAMES)
+        set(frontend_target_name "${name}${FRONTEND_NAME_SUFFIX}")
+        target_link_libraries(${TARGET_NAME} PRIVATE ${frontend_target_name})
+    endforeach()
+endfunction()
+
+#
+# ov_generate_frontends_hpp()
+#
+function(ov_generate_frontends_hpp)
+    if(BUILD_SHARED_LIBS)
+        return()
+    endif()
+
+    # add frontends to libraries including ov_frontends.hpp
+    ov_target_link_frontends(frontend_common)
+
+    set(ov_frontends_hpp "${CMAKE_BINARY_DIR}/src/frontends/common/src/ov_frontends.hpp")
+    set(frontends_hpp_in "${IEDevScripts_DIR}/frontends/ov_frontends.hpp.in")
+
+    add_custom_command(OUTPUT "${ov_frontends_hpp}"
+                       COMMAND
+                        "${CMAKE_COMMAND}"
+                        -D "OV_FRONTENDS_HPP_HEADER_IN=${frontends_hpp_in}"
+                        -D "OV_FRONTENDS_HPP_HEADER=${ov_frontends_hpp}"
+                        -D "FRONTEND_NAMES=${FRONTEND_NAMES}"
+                        -P "${IEDevScripts_DIR}/frontends/create_frontends_hpp.cmake"
+                       DEPENDS
+                         "${frontends_hpp_in}"
+                         "${IEDevScripts_DIR}/frontends/create_frontends_hpp.cmake"
+                       COMMENT
+                         "Generate ov_frontends.hpp for static build"
+                       VERBATIM)
+
+    # for some reason dependency on source files does not work
+    # so, we have to use explicit target and make it dependency for frontend_common
+    add_custom_target(_ov_frontends_hpp DEPENDS ${ov_frontends_hpp})
+    add_dependencies(frontend_common _ov_frontends_hpp)
+
+    # add dependency for object files
+    get_target_property(sources frontend_common::static SOURCES)
+    foreach(source IN LISTS sources)
+        if("${source}" MATCHES "\\$\\<TARGET_OBJECTS\\:([A-Za-z0-9_]*)\\>")
+            # object library
+            set(obj_library ${CMAKE_MATCH_1})
+            get_target_property(obj_sources ${obj_library} SOURCES)
+            list(APPEND all_sources ${obj_sources})
+        else()
+            # usual source
+            list(APPEND all_sources ${source})
+        endif()
+    endforeach()
+
+    # add dependency on header file generation for all inference_engine source files
+    set_source_files_properties(${all_sources} PROPERTIES OBJECT_DEPENDS ${ov_frontends_hpp})
+endfunction()
+
+unset(protobuf_lite_installed CACHE)
+unset(protobuf_installed CACHE)
+
+#
+# ov_add_frontend(NAME <IR|ONNX|...>
+#                 FILEDESCRIPTION <description>
+#                 [LINKABLE_FRONTEND]
+#                 [SKIP_INSTALL]
+#                 [PROTOBUF_LITE]
+#                 [LINK_LIBRARIES <lib1 lib2 ...>])
+#
+macro(ov_add_frontend)
+    set(options LINKABLE_FRONTEND PROTOBUF_LITE SKIP_NCC_STYLE SKIP_INSTALL)
+    set(oneValueArgs NAME FILEDESCRIPTION)
+    set(multiValueArgs LINK_LIBRARIES PROTO_FILES)
+    cmake_parse_arguments(OV_FRONTEND "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+
+    foreach(prop NAME FILEDESCRIPTION)
+        if(NOT DEFINED OV_FRONTEND_${prop})
+            message(FATAL_ERROR "Frontend ${prop} property is not defined")
+        endif()
+    endforeach()
+
+    set(TARGET_NAME "${OV_FRONTEND_NAME}${FRONTEND_NAME_SUFFIX}")
+
+    list(APPEND FRONTEND_NAMES ${OV_FRONTEND_NAME})
+    set(FRONTEND_NAMES "${FRONTEND_NAMES}" CACHE INTERNAL "" FORCE)
+
+    file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp)
+    file(GLOB_RECURSE LIBRARY_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/src/*.hpp)
+    file(GLOB_RECURSE LIBRARY_PUBLIC_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/include/*.hpp)
+
+    set(${TARGET_NAME}_INCLUDE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/include)
+
+    # Create named folders for the sources within the .vcproj
+    # Empty name lists them directly under the .vcproj
+
+    source_group("src" FILES ${LIBRARY_SRC})
+    source_group("include" FILES ${LIBRARY_HEADERS})
+    source_group("public include" FILES ${LIBRARY_PUBLIC_HEADERS})
+
+    # Generate protobuf file on build time for each '.proto' file in src/proto
+    file(GLOB proto_files ${CMAKE_CURRENT_SOURCE_DIR}/src/proto/*.proto)
+
+    foreach(INFILE IN LISTS proto_files)
+        get_filename_component(FILE_DIR ${INFILE} DIRECTORY)
+        get_filename_component(FILE_WE ${INFILE} NAME_WE)
+        set(OUTPUT_PB_SRC ${CMAKE_CURRENT_BINARY_DIR}/${FILE_WE}.pb.cc)
+        set(OUTPUT_PB_HEADER ${CMAKE_CURRENT_BINARY_DIR}/${FILE_WE}.pb.h)
+        set(GENERATED_PROTO ${INFILE})
+        add_custom_command(
+                OUTPUT "${OUTPUT_PB_SRC}" "${OUTPUT_PB_HEADER}"
+                COMMAND ${PROTOC_EXECUTABLE} ARGS --cpp_out ${CMAKE_CURRENT_BINARY_DIR} -I ${FILE_DIR} ${FILE_WE}.proto
+                DEPENDS ${PROTOC_EXECUTABLE} ${GENERATED_PROTO}
+                COMMENT "Running C++ protocol buffer compiler (${PROTOC_EXECUTABLE}) on ${GENERATED_PROTO}"
+                VERBATIM
+                COMMAND_EXPAND_LISTS)
+        list(APPEND PROTO_SRCS "${OUTPUT_PB_SRC}")
+        list(APPEND PROTO_HDRS "${OUTPUT_PB_HEADER}")
+    endforeach()
+
+    # Disable all warnings for generated code
+    set_source_files_properties(${PROTO_SRCS} ${PROTO_HDRS} PROPERTIES COMPILE_OPTIONS -w GENERATED TRUE)
+
+    # Create library
+    add_library(${TARGET_NAME} ${LIBRARY_SRC} ${LIBRARY_HEADERS} ${LIBRARY_PUBLIC_HEADERS} ${PROTO_SRCS} ${PROTO_HDRS})
+
+    if(OV_FRONTEND_LINKABLE_FRONTEND)
+        # create beautiful alias
+        add_library(openvino::frontend::${OV_FRONTEND_NAME} ALIAS ${TARGET_NAME})
+    endif()
+
+    if(NOT BUILD_SHARED_LIBS)
+        # override default function names
+        target_compile_definitions(${TARGET_NAME} PRIVATE
+            "-DGetFrontEndData=GetFrontEndData${OV_FRONTEND_NAME}"
+            "-DGetAPIVersion=GetAPIVersion${OV_FRONTEND_NAME}")
+    endif()
+
+    if(OV_FRONTEND_SKIP_NCC_STYLE)
+        # frontend's CMakeLists.txt must define its own custom 'ov_ncc_naming_style' step
+    else()
+        ov_ncc_naming_style(FOR_TARGET ${TARGET_NAME}
+                            INCLUDE_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/include"
+                            ADDITIONAL_INCLUDE_DIRECTORIES
+                                $<TARGET_PROPERTY:frontend_common::static,INTERFACE_INCLUDE_DIRECTORIES>)
+    endif()
+
+    target_include_directories(${TARGET_NAME}
+            PUBLIC
+                $<BUILD_INTERFACE:${${TARGET_NAME}_INCLUDE_DIR}>
+            PRIVATE
+                ${CMAKE_CURRENT_SOURCE_DIR}/src
+                ${CMAKE_CURRENT_BINARY_DIR})
+
+    ie_add_vs_version_file(NAME ${TARGET_NAME}
+                           FILEDESCRIPTION ${OV_FRONTEND_FILEDESCRIPTION})
+
+    ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
+
+    target_link_libraries(${TARGET_NAME} PRIVATE frontend_common::static ${OV_FRONTEND_LINK_LIBRARIES})
+
+    # WA for TF frontends which always requires protobuf (not protobuf-lite)
+    # if TF FE is built in static mode, use protobuf for all other FEs
+    if(FORCE_FRONTENDS_USE_PROTOBUF)
+        set(OV_FRONTEND_PROTOBUF_LITE OFF)
+    endif()
+
+    if(proto_files)
+        if(OV_FRONTEND_PROTOBUF_LITE)
+            if(NOT protobuf_lite_installed)
+                ov_install_static_lib(${Protobuf_LITE_LIBRARIES} ngraph)
+                set(protobuf_lite_installed ON CACHE INTERNAL "" FORCE)
+            endif()
+            link_system_libraries(${TARGET_NAME} PRIVATE ${Protobuf_LITE_LIBRARIES})
+        else()
+            if(NOT protobuf_installed)
+                ov_install_static_lib(${Protobuf_LIBRARIES} ngraph)
+                set(protobuf_installed ON CACHE INTERNAL "" FORCE)
+            endif()
+            link_system_libraries(${TARGET_NAME} PRIVATE ${Protobuf_LIBRARIES})
+        endif()
+
+        # prptobuf generated code emits -Wsuggest-override error
+        if(SUGGEST_OVERRIDE_SUPPORTED)
+            target_compile_options(${TARGET_NAME} PRIVATE -Wno-suggest-override)
+        endif()
+    endif()
+
+    add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME}
+                            EXCLUDE_PATTERNS ${PROTO_SRCS} ${PROTO_HDRS})
+
+    add_dependencies(ov_frontends ${TARGET_NAME})
+
+    if(NOT OV_FRONTEND_SKIP_INSTALL)
+        if(BUILD_SHARED_LIBS)
+            if(OV_FRONTEND_LINKABLE_FRONTEND)
+                set(export_set EXPORT OpenVINOTargets)
+            endif()
+            install(TARGETS ${TARGET_NAME} ${export_set}
+                    RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT ngraph
+                    ARCHIVE DESTINATION ${IE_CPACK_ARCHIVE_PATH} COMPONENT ngraph
+                    LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT ngraph)
+        else()
+            ov_install_static_lib(${TARGET_NAME} ngraph)
+        endif()
+
+        if(OV_FRONTEND_LINKABLE_FRONTEND)
+            # install -dev part
+            install(DIRECTORY ${${TARGET_NAME}_INCLUDE_DIR}/${OV_FRONTEND_NAME}_frontend
+                    DESTINATION ${FRONTEND_INSTALL_INCLUDE}
+                    COMPONENT ngraph_dev
+                    FILES_MATCHING PATTERN "*.hpp")
+
+            set_target_properties(${TARGET_NAME} PROPERTIES EXPORT_NAME frontend::${OV_FRONTEND_NAME})
+            export(TARGETS ${TARGET_NAME} NAMESPACE openvino::
+                   APPEND FILE "${CMAKE_BINARY_DIR}/OpenVINOTargets.cmake")
+        endif()
+    else()
+        # skipped frontend has to be installed in static libraries case
+        ov_install_static_lib(${TARGET_NAME} ngraph)
+    endif()
+endmacro()
diff --git a/cmake/developer_package/frontends/ov_frontends.hpp.in b/cmake/developer_package/frontends/ov_frontends.hpp.in
new file mode 100644
index 00000000000..0293054160d
--- /dev/null
+++ b/cmake/developer_package/frontends/ov_frontends.hpp.in
@@ -0,0 +1,27 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "common/frontend.hpp"
+
+@OV_FRONTEND_DECLARATIONS@
+
+namespace {
+
+using GetFrontEndDataFunc = void*();
+using GetAPIVersionFunc = ov::frontend::FrontEndVersion();
+
+struct Value {
+    GetFrontEndDataFunc* m_dataFunc;
+    GetAPIVersionFunc* m_versionFunc;
+};
+
+using FrontendsStaticRegistry = std::vector<Value>;
+
+const FrontendsStaticRegistry getStaticFrontendsRegistry() {
+@OV_FRONTEND_MAP_DEFINITION@
+}
+
+} // namespace
diff --git a/cmake/developer_package/plugins/plugins.cmake b/cmake/developer_package/plugins/plugins.cmake
index 6f35ba93385..caef045711f 100644
--- a/cmake/developer_package/plugins/plugins.cmake
+++ b/cmake/developer_package/plugins/plugins.cmake
@@ -75,8 +75,6 @@ function(ie_add_plugin)
                 target_compile_definitions(${IE_PLUGIN_NAME} PRIVATE
                     IE_CREATE_EXTENSION=CreateExtensionShared${IE_PLUGIN_DEVICE_NAME})
             endif()
-            # install static plugins
-            ov_install_static_lib(${IE_PLUGIN_NAME} core)
         endif()
 
         ie_add_vs_version_file(NAME ${IE_PLUGIN_NAME}
@@ -137,13 +135,17 @@ function(ie_add_plugin)
         endif()
 
         # install rules
-        if(NOT IE_PLUGIN_SKIP_INSTALL)
+        if(NOT IE_PLUGIN_SKIP_INSTALL OR NOT BUILD_SHARED_LIBS)
             string(TOLOWER "${IE_PLUGIN_DEVICE_NAME}" install_component)
             ie_cpack_add_component(${install_component} REQUIRED DEPENDS core)
 
-            install(TARGETS ${IE_PLUGIN_NAME}
-                    LIBRARY DESTINATION ${IE_CPACK_RUNTIME_PATH}
-                    COMPONENT ${install_component})
+            if(BUILD_SHARED_LIBS)
+                install(TARGETS ${IE_PLUGIN_NAME}
+                        LIBRARY DESTINATION ${IE_CPACK_RUNTIME_PATH}
+                        COMPONENT ${install_component})
+            else()
+                ov_install_static_lib(${IE_PLUGIN_NAME} ${install_component})
+            endif()
         endif()
     endif()
 
@@ -244,12 +246,18 @@ macro(ie_register_plugins_dynamic)
                       VERBATIM)
 endmacro()
 
+#
+# ie_register_plugins()
+#
 macro(ie_register_plugins)
     if(BUILD_SHARED_LIBS)
         ie_register_plugins_dynamic(${ARGN})
     endif()
 endmacro()
 
+#
+# ie_target_link_plugins(<TARGET_NAME>)
+#
 function(ie_target_link_plugins TARGET_NAME)
     if(BUILD_SHARED_LIBS)
         return()
@@ -332,8 +340,8 @@ function(ie_generate_plugins_hpp)
 
     # for some reason dependency on source files does not work
     # so, we have to use explicit target and make it dependency for inference_engine
-    add_custom_target(ie_generate_hpp DEPENDS ${ie_plugins_hpp})
-    add_dependencies(inference_engine ie_generate_hpp)
+    add_custom_target(_ie_plugins_hpp DEPENDS ${ie_plugins_hpp})
+    add_dependencies(inference_engine _ie_plugins_hpp)
 
     # add dependency for object files
     get_target_property(sources inference_engine SOURCES)
diff --git a/cmake/features.cmake b/cmake/features.cmake
index b7c152a0561..a40a5f25d0f 100644
--- a/cmake/features.cmake
+++ b/cmake/features.cmake
@@ -161,13 +161,19 @@ ie_dependent_option(NGRAPH_PDPD_FRONTEND_ENABLE "Enable PaddlePaddle FrontEnd" O
 ie_option(NGRAPH_IR_FRONTEND_ENABLE "Enable IR FrontEnd" ON)
 ie_dependent_option(NGRAPH_TF_FRONTEND_ENABLE "Enable TensorFlow FrontEnd" ON "protoc_available" OFF)
 ie_dependent_option(NGRAPH_USE_SYSTEM_PROTOBUF "Use system protobuf" OFF
-    "NGRAPH_ONNX_FRONTEND_ENABLE OR NGRAPH_PDPD_FRONTEND_ENABLE OR NGRAPH_TF_FRONTEND_ENABLE" OFF)
+    "NGRAPH_ONNX_FRONTEND_ENABLE OR NGRAPH_PDPD_FRONTEND_ENABLE OR NGRAPH_TF_FRONTEND_ENABLE;BUILD_SHARED_LIBS" OFF)
 ie_dependent_option(NGRAPH_UNIT_TEST_ENABLE "Enables ngraph unit tests" ON "ENABLE_TESTS;NOT ANDROID" OFF)
 ie_dependent_option(NGRAPH_UNIT_TEST_BACKENDS_ENABLE "Control the building of unit tests using backends" ON
     "NGRAPH_UNIT_TEST_ENABLE" OFF)
 ie_option(OPENVINO_DEBUG_ENABLE "Enable output for OPENVINO_DEBUG statements" OFF)
 ie_option(ENABLE_REQUIREMENTS_INSTALL "Dynamic dependencies install" ON)
 
+if(NOT BUILD_SHARED_LIBS AND NGRAPH_TF_FRONTEND_ENABLE)
+    set(FORCE_FRONTENDS_USE_PROTOBUF ON)
+else()
+    set(FORCE_FRONTENDS_USE_PROTOBUF OFF)
+endif()
+
 # WA for ngraph python build on Windows debug
 list(REMOVE_ITEM IE_OPTIONS NGRAPH_UNIT_TEST_ENABLE NGRAPH_UNIT_TEST_BACKENDS_ENABLE)
 
diff --git a/inference-engine/tests/functional/inference_engine/ir_serialization/custom_ops.cpp b/inference-engine/tests/functional/inference_engine/ir_serialization/custom_ops.cpp
index e5e130dbb03..25a3a6e28e5 100644
--- a/inference-engine/tests/functional/inference_engine/ir_serialization/custom_ops.cpp
+++ b/inference-engine/tests/functional/inference_engine/ir_serialization/custom_ops.cpp
@@ -20,7 +20,7 @@
 #endif
 
 #ifndef IE_BUILD_POSTFIX  // should be already defined by cmake
-#define IE_BUILD_POSTFIX ""
+# error "IE_BUILD_POSTFIX is not defined"
 #endif
 
 static std::string get_extension_path() {
@@ -69,6 +69,12 @@ TEST_F(CustomOpsSerializationTest, CustomOpUser_MO) {
 
 #ifdef NGRAPH_ONNX_FRONTEND_ENABLE
 
+// This test will not work because template_extension for ONNX registers
+// extension via `register_operator` function which registers operator
+// is template_extension's copy of onnx_importer. So, extensions as
+// a shared library for ONNX don't make sence in static OpenVINO build
+#ifndef OPENVINO_STATIC_LIBRARY
+
 TEST_F(CustomOpsSerializationTest, CustomOpUser_ONNXImporter) {
     const std::string model = CommonTestUtils::getModelFromTestModelZoo(
         IR_SERIALIZATION_MODELS_PATH "custom_op.onnx");
@@ -90,7 +96,9 @@ TEST_F(CustomOpsSerializationTest, CustomOpUser_ONNXImporter) {
     ASSERT_TRUE(success) << message;
 }
 
-#endif
+#endif // OPENVINO_STATIC_LIBRARY
+
+#endif // NGRAPH_ONNX_FRONTEND_ENABLE
 
 TEST_F(CustomOpsSerializationTest, CustomOpTransformation) {
     const std::string model = CommonTestUtils::getModelFromTestModelZoo(
diff --git a/src/frontends/CMakeLists.txt b/src/frontends/CMakeLists.txt
index bf81fe58baa..d8db4a68ed6 100644
--- a/src/frontends/CMakeLists.txt
+++ b/src/frontends/CMakeLists.txt
@@ -2,23 +2,23 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-set(FRONTEND_INSTALL_INCLUDE "runtime/include/ngraph/frontend")
-set(FRONTEND_NAME_SUFFIX "_ov_frontend")
-
 add_subdirectory(common)
 
-if (NGRAPH_ONNX_FRONTEND_ENABLE)
+if(NGRAPH_ONNX_FRONTEND_ENABLE)
     add_subdirectory(onnx)
 endif()
 
-if (NGRAPH_PDPD_FRONTEND_ENABLE)
+if(NGRAPH_PDPD_FRONTEND_ENABLE)
     add_subdirectory(paddlepaddle)
 endif()
 
-if (NGRAPH_IR_FRONTEND_ENABLE)
+if(NGRAPH_IR_FRONTEND_ENABLE)
     add_subdirectory(ir)
 endif()
 
-if (NGRAPH_TF_FRONTEND_ENABLE)
+if(NGRAPH_TF_FRONTEND_ENABLE)
     add_subdirectory(tensorflow)
 endif()
+
+# used for static build
+ov_generate_frontends_hpp()
diff --git a/src/frontends/common/CMakeLists.txt b/src/frontends/common/CMakeLists.txt
index 12f4ed14fc0..9d5ab39c6df 100644
--- a/src/frontends/common/CMakeLists.txt
+++ b/src/frontends/common/CMakeLists.txt
@@ -28,15 +28,19 @@ add_library(${TARGET_NAME} ${LIBRARY_SRC} ${LIBRARY_HEADERS} ${LIBRARY_PUBLIC_HE
 add_library(ngraph::${TARGET_NAME} ALIAS ${TARGET_NAME})
 add_library(openvino::frontend::common ALIAS ${TARGET_NAME})
 
-target_include_directories(${TARGET_NAME} PUBLIC
+target_include_directories(${TARGET_NAME}
+    PUBLIC
         $<BUILD_INTERFACE:${FRONTEND_INCLUDE_DIR}>
-        $<INSTALL_INTERFACE:${FRONTEND_INSTALL_INCLUDE}>)
+        $<INSTALL_INTERFACE:${FRONTEND_INSTALL_INCLUDE}>
+    PRIVATE
+        # for ov_frontends.hpp in static build
+        "${CMAKE_CURRENT_BINARY_DIR}/src")
+
 target_include_directories(${TARGET_NAME} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/src)
 target_link_libraries(${TARGET_NAME} PRIVATE ${CMAKE_DL_LIBS} openvino::util PUBLIC ngraph)
 set_property(SOURCE ${CMAKE_CURRENT_SOURCE_DIR}/src/plugin_loader.cpp APPEND PROPERTY COMPILE_DEFINITIONS
         FRONTEND_LIB_PREFIX="${CMAKE_SHARED_LIBRARY_PREFIX}"
-        FRONTEND_LIB_SUFFIX="${FRONTEND_NAME_SUFFIX}${IE_BUILD_POSTFIX}${CMAKE_SHARED_LIBRARY_SUFFIX}"
-        )
+        FRONTEND_LIB_SUFFIX="${FRONTEND_NAME_SUFFIX}${IE_BUILD_POSTFIX}${CMAKE_SHARED_LIBRARY_SUFFIX}")
 
 add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
 
@@ -76,7 +80,7 @@ install(DIRECTORY ${FRONTEND_INCLUDE_DIR}/common
         DESTINATION ${FRONTEND_INSTALL_INCLUDE}
         COMPONENT ngraph_dev
         FILES_MATCHING PATTERN "*.hpp")
-        
+
 install(DIRECTORY ${FRONTEND_INCLUDE_DIR}/
         DESTINATION ${FRONTEND_INSTALL_INCLUDE}
         COMPONENT ngraph_dev
diff --git a/src/frontends/common/src/plugin_loader.cpp b/src/frontends/common/src/plugin_loader.cpp
index 0be0739389d..b09d83bb77c 100644
--- a/src/frontends/common/src/plugin_loader.cpp
+++ b/src/frontends/common/src/plugin_loader.cpp
@@ -26,6 +26,26 @@
 using namespace ov;
 using namespace ov::frontend;
 
+#ifdef OPENVINO_STATIC_LIBRARY
+
+#    include "ov_frontends.hpp"
+
+namespace {
+
+std::vector<PluginData> load_static_plugins() {
+    std::vector<PluginData> res;
+    for (const auto& frontend : getStaticFrontendsRegistry()) {
+        PluginHandle fakeGuard([]() {});
+        std::unique_ptr<FrontEndPluginInfo> fact{reinterpret_cast<FrontEndPluginInfo*>(frontend.m_dataFunc())};
+        res.emplace_back(std::move(fakeGuard), std::move(*fact));
+    }
+    return res;
+}
+
+}  // namespace
+
+#endif  // OPENVINO_STATIC_LIBRARY
+
 #ifdef WIN32
 #    define DLOPEN(file_str) LoadLibrary(TEXT(file_str.c_str()))
 #    define DLSYM(obj, func) GetProcAddress(obj, func)
@@ -43,8 +63,8 @@ static std::vector<std::string> list_files(const std::string& path) {
     NGRAPH_SUPPRESS_DEPRECATED_START
     std::vector<std::string> res;
     try {
-        auto prefix = std::string(FRONTEND_LIB_PREFIX);
-        auto suffix = std::string(FRONTEND_LIB_SUFFIX);
+        const auto prefix = std::string(FRONTEND_LIB_PREFIX);
+        const auto suffix = std::string(FRONTEND_LIB_SUFFIX);
         ov::util::iterate_files(
             path,
             [&res, &prefix, &suffix](const std::string& file_path, bool is_dir) {
@@ -55,12 +75,7 @@ static std::vector<std::string> list_files(const std::string& path) {
                     res.push_back(file_path);
                 }
             },
-            // ilavreno: this is current solution for static runtime
-            // since frontends are still dynamic libraries and they are located in
-            // a different folder with compare to frontend_common one (in ./lib)
-            // we are trying to use recursive search. Can be reverted back in future
-            // once the frontends are static too.
-            true,
+            false,
             true);
     } catch (...) {
         // Ignore exceptions
@@ -69,10 +84,9 @@ static std::vector<std::string> list_files(const std::string& path) {
     NGRAPH_SUPPRESS_DEPRECATED_END
 }
 
-std::vector<PluginData> ov::frontend::load_plugins(const std::string& dir_name) {
-    auto files = list_files(dir_name);
+static std::vector<PluginData> load_dynamic_plugins(const std::string& dir_name) {
     std::vector<PluginData> res;
-    for (const auto& file : files) {
+    for (const auto& file : list_files(dir_name)) {
         auto shared_object = DLOPEN(file);
         if (!shared_object) {
             NGRAPH_DEBUG << "Error loading FrontEnd " << file << " " << DLERROR() << std::endl;
@@ -105,3 +119,19 @@ std::vector<PluginData> ov::frontend::load_plugins(const std::string& dir_name)
     }
     return res;
 }
+
+std::vector<PluginData> ov::frontend::load_plugins(const std::string& dir_name) {
+    std::vector<PluginData> res;
+#ifdef OPENVINO_STATIC_LIBRARY
+    res = load_static_plugins();
+#endif  // OPENVINO_STATIC_LIBRARY
+    for (auto&& fe : load_dynamic_plugins(dir_name)) {
+        // if frontend is registered as static one, skip dynamic version
+        if (std::find_if(res.begin(), res.end(), [&fe](const PluginData& pd) {
+                return pd.m_plugin_info.m_name == fe.m_plugin_info.m_name;
+            }) == res.end()) {
+            res.emplace_back(std::move(fe));
+        }
+    }
+    return res;
+}
diff --git a/src/frontends/common/src/plugin_loader.hpp b/src/frontends/common/src/plugin_loader.hpp
index 86036fbbf34..9e5fbd813df 100644
--- a/src/frontends/common/src/plugin_loader.hpp
+++ b/src/frontends/common/src/plugin_loader.hpp
@@ -7,10 +7,8 @@
 #include <manager.hpp>
 
 #ifdef _WIN32
-static const char FileSeparator[] = "\\";
 static const char PathSeparator[] = ";";
 #else
-static const char FileSeparator[] = "/";
 static const char PathSeparator[] = ":";
 #endif  // _WIN32
 
@@ -42,6 +40,7 @@ private:
 
 struct PluginData {
     PluginData(PluginHandle&& h, FrontEndPluginInfo&& info) : m_lib_handle(std::move(h)), m_plugin_info(info) {}
+    PluginData(PluginData&& pd) : m_lib_handle(std::move(pd.m_lib_handle)), m_plugin_info(pd.m_plugin_info) {}
 
     PluginHandle m_lib_handle;  // Shall be destroyed when plugin is not needed anymore to free memory
     FrontEndPluginInfo m_plugin_info;
diff --git a/src/frontends/common/src/utils.cpp b/src/frontends/common/src/utils.cpp
index 742f7a8b8f9..98e62ec12e2 100644
--- a/src/frontends/common/src/utils.cpp
+++ b/src/frontends/common/src/utils.cpp
@@ -5,6 +5,7 @@
 #include "utils.hpp"
 
 #include "common/frontend_exceptions.hpp"
+#include "openvino/util/file_util.hpp"
 #include "plugin_loader.hpp"
 
 #ifndef _WIN32
@@ -25,18 +26,6 @@
 #    include <Windows.h>
 #endif
 
-namespace {
-std::string get_path_name(const std::string& s) {
-    size_t i = s.rfind(FileSeparator, s.length());
-    if (i != std::string::npos) {
-        return (s.substr(0, i));
-    }
-
-    return {};
-}
-
-}  // namespace
-
 static std::string _get_frontend_library_path() {
 #ifdef _WIN32
     CHAR ie_library_path[MAX_PATH];
@@ -47,11 +36,11 @@ static std::string _get_frontend_library_path() {
         FRONT_END_INITIALIZATION_CHECK(false, "GetModuleHandle returned ", GetLastError());
     }
     GetModuleFileNameA(hm, (LPSTR)ie_library_path, sizeof(ie_library_path));
-    return get_path_name(std::string(ie_library_path));
+    return ov::util::get_directory(std::string(ie_library_path));
 #elif defined(__APPLE__) || defined(__linux__)
     Dl_info info;
     dladdr(reinterpret_cast<void*>(ov::frontend::get_frontend_library_path), &info);
-    return get_path_name(std::string(info.dli_fname)).c_str();
+    return ov::util::get_directory(std::string(info.dli_fname)).c_str();
 #else
 #    error "Unsupported OS"
 #endif  // _WIN32
diff --git a/src/frontends/ir/CMakeLists.txt b/src/frontends/ir/CMakeLists.txt
index c1f25db68ce..120bae4b6d5 100644
--- a/src/frontends/ir/CMakeLists.txt
+++ b/src/frontends/ir/CMakeLists.txt
@@ -2,55 +2,12 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-set(TARGET_NAME "ir_ov_frontend")
-
-file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp)
-file(GLOB_RECURSE LIBRARY_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/src/*.hpp)
-file(GLOB_RECURSE LIBRARY_PUBLIC_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/include/*.hpp)
-
-set(${TARGET_NAME}_INCLUDE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/include)
+ov_add_frontend(NAME ir
+                FILEDESCRIPTION "FrontEnd to load OpenVINO IR file format"
+                LINK_LIBRARIES inference_engine_transformations pugixml::static
+                               # TODO: remove dependencies below in CVS-69781
+                               inference_engine inference_engine_plugin_api)
 
 # Add include path to so_extension.hpp
-set_property(SOURCE ${CMAKE_CURRENT_SOURCE_DIR}/src/frontend.cpp
-                    APPEND PROPERTY INCLUDE_DIRECTORIES "${OpenVINO_SOURCE_DIR}/src/core/src/")
-
-# Create named folders for the sources within the .vcproj
-# Empty name lists them directly under the .vcproj
-
-source_group("src" FILES ${LIBRARY_SRC})
-source_group("include" FILES ${LIBRARY_HEADERS})
-source_group("public include" FILES ${LIBRARY_PUBLIC_HEADERS})
-
-# Create library
-add_library(${TARGET_NAME} SHARED ${LIBRARY_SRC} ${LIBRARY_HEADERS} ${LIBRARY_PUBLIC_HEADERS})
-
-ov_ncc_naming_style(FOR_TARGET ${TARGET_NAME}
-                    INCLUDE_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/include"
-                    ADDITIONAL_INCLUDE_DIRECTORIES
-                        $<TARGET_PROPERTY:frontend_common::static,INTERFACE_INCLUDE_DIRECTORIES>)
-
-target_include_directories(${TARGET_NAME}
-        PUBLIC
-            $<BUILD_INTERFACE:${${TARGET_NAME}_INCLUDE_DIR}>
-        PRIVATE
-            ${CMAKE_CURRENT_SOURCE_DIR}/src
-            ${CMAKE_CURRENT_BINARY_DIR})
-
-target_include_directories(${TARGET_NAME} SYSTEM PRIVATE ${CMAKE_CURRENT_BINARY_DIR})
-
-ie_add_vs_version_file(NAME ${TARGET_NAME}
-                       FILEDESCRIPTION "FrontEnd to load and convert OpenVINO IR file format")
-
-ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
-
-target_link_libraries(${TARGET_NAME} PRIVATE frontend_common::static
-        ngraph::builder inference_engine_transformations
-        inference_engine pugixml::static inference_engine_plugin_api)
-
-add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME}
-                        EXCLUDE_PATTERNS ${PROTO_SRCS} ${PROTO_HDRS})
-
-install(TARGETS ${TARGET_NAME}
-        RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT ngraph
-        ARCHIVE DESTINATION ${IE_CPACK_ARCHIVE_PATH} COMPONENT ngraph
-        LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT ngraph)
+set_source_files_properties(${CMAKE_CURRENT_SOURCE_DIR}/src/frontend.cpp
+    PROPERTIES INCLUDE_DIRECTORIES "${OpenVINO_SOURCE_DIR}/src/core/src/")
diff --git a/src/frontends/ir/include/ir_frontend/utility.hpp b/src/frontends/ir/include/ir_frontend/utility.hpp
index 7164541cadf..5e9555fc50d 100644
--- a/src/frontends/ir/include/ir_frontend/utility.hpp
+++ b/src/frontends/ir/include/ir_frontend/utility.hpp
@@ -6,12 +6,18 @@
 
 #include <openvino/core/visibility.hpp>
 
-// Defined if we are building the plugin DLL (instead of using it)
-#ifdef ir_ov_frontend_EXPORTS
-#    define IR_API OPENVINO_CORE_EXPORTS
+#ifdef OPENVINO_STATIC_LIBRARY
+#    define IR_API
+#    define IR_C_API
 #else
-#    define IR_API OPENVINO_CORE_IMPORTS
-#endif  // ir_ov_frontend_EXPORTS
+#    ifdef ir_ov_frontend_EXPORTS
+#        define IR_API   OPENVINO_CORE_EXPORTS
+#        define IR_C_API OPENVINO_EXTERN_C OPENVINO_CORE_EXPORTS
+#    else
+#        define IR_API   OPENVINO_CORE_IMPORTS
+#        define IR_C_API OPENVINO_EXTERN_C OPENVINO_CORE_IMPORTS
+#    endif  // ir_ov_frontend_EXPORTS
+#endif      // OPENVINO_STATIC_LIBRARY
 
 #define IR_ASSERT(ex, msg)                 \
     {                                      \
diff --git a/src/frontends/ir/src/frontend.cpp b/src/frontends/ir/src/frontend.cpp
index b02cb3a58ed..d7a7cc15ba3 100644
--- a/src/frontends/ir/src/frontend.cpp
+++ b/src/frontends/ir/src/frontend.cpp
@@ -236,11 +236,11 @@ std::string FrontEndIR::get_name() const {
 }  // namespace frontend
 }  // namespace ov
 
-extern "C" IR_API ov::frontend::FrontEndVersion GetAPIVersion() {
+IR_C_API ov::frontend::FrontEndVersion GetAPIVersion() {
     return OV_FRONTEND_API_VERSION;
 }
 
-extern "C" IR_API void* GetFrontEndData() {
+IR_C_API void* GetFrontEndData() {
     frontend::FrontEndPluginInfo* res = new frontend::FrontEndPluginInfo();
     res->m_name = "ir";
     res->m_creator = []() {
diff --git a/src/frontends/onnx/frontend/CMakeLists.txt b/src/frontends/onnx/frontend/CMakeLists.txt
index 2ebb43e038d..d671a9fc7a6 100644
--- a/src/frontends/onnx/frontend/CMakeLists.txt
+++ b/src/frontends/onnx/frontend/CMakeLists.txt
@@ -2,67 +2,24 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-set(TARGET_NAME onnx_ov_frontend)
+ov_add_frontend(NAME onnx
+                LINKABLE_FRONTEND
+                PROTOBUF_LITE
+                SKIP_NCC_STYLE
+                FILEDESCRIPTION "FrontEnd to load and convert ONNX file format"
+                LINK_LIBRARIES ngraph::builder openvino::util onnx_common inference_engine_transformations)
 
 set(ONNX_OPSET_VERSION 13 CACHE INTERNAL "Supported version of ONNX operator set")
-set(ONNX_FRONTEND_INCLUDE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/include)
-
-file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp)
-file(GLOB_RECURSE LIBRARY_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/src/*.hpp)
-file(GLOB_RECURSE LIBRARY_PUBLIC_HEADERS ${ONNX_FRONTEND_INCLUDE_DIR}/*.hpp)
-
-# Remove disabled ops
-list(REMOVE_ITEM LIBRARY_SRC
-    ${CMAKE_CURRENT_SOURCE_DIR}/src/op/quant_conv.cpp
-    )
-list(REMOVE_ITEM LIBRARY_HEADERS
-    ${CMAKE_CURRENT_SOURCE_DIR}/src/op/quant_conv.hpp
-    )
-
-# Create named folders for the sources within the .vcproj
-# Empty name lists them directly under the .vcproj
-
-source_group("src" FILES ${LIBRARY_SRC})
-source_group("include" FILES ${LIBRARY_HEADERS})
-source_group("public include" FILES ${LIBRARY_PUBLIC_HEADERS})
-
-# Create library
-add_library(${TARGET_NAME} SHARED ${LIBRARY_SRC} ${LIBRARY_HEADERS} ${LIBRARY_PUBLIC_HEADERS})
-add_library(openvino::frontend::onnx ALIAS ${TARGET_NAME})
-
-add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
-
-ov_ncc_naming_style(FOR_TARGET ${TARGET_NAME}
-                   INCLUDE_DIRECTORY "${ONNX_FRONTEND_INCLUDE_DIR}"
-                   DEFINITIONS
-                       $<TARGET_PROPERTY:onnx,INTERFACE_COMPILE_DEFINITIONS>
-                   ADDITIONAL_INCLUDE_DIRECTORIES
-                       $<TARGET_PROPERTY:frontend_common::static,INTERFACE_INCLUDE_DIRECTORIES>)
-
-ie_add_vs_version_file(NAME ${TARGET_NAME}
-                       FILEDESCRIPTION "nGraph ONNX frontend library")
-
-ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
-
-target_link_libraries(${TARGET_NAME} PUBLIC ngraph PRIVATE frontend_common ngraph::builder openvino::util onnx_common inference_engine_transformations)
-
-target_include_directories(${TARGET_NAME} PUBLIC $<BUILD_INTERFACE:${ONNX_FRONTEND_INCLUDE_DIR}>
-                                                $<INSTALL_INTERFACE:${FRONTEND_INSTALL_INCLUDE}>)
-target_include_directories(${TARGET_NAME} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/src)
-
 target_compile_definitions(${TARGET_NAME} PRIVATE ONNX_OPSET_VERSION=${ONNX_OPSET_VERSION})
 
-set_target_properties(${TARGET_NAME} PROPERTIES EXPORT_NAME frontend::onnx)
-install(TARGETS ${TARGET_NAME} EXPORT OpenVINOTargets
-        RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT ngraph
-        ARCHIVE DESTINATION ${IE_CPACK_ARCHIVE_PATH} COMPONENT ngraph
-        LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT ngraph)
+ov_ncc_naming_style(FOR_TARGET ${TARGET_NAME}
+                    INCLUDE_DIRECTORY "${${TARGET_NAME}_INCLUDE_DIR}"
+                    DEFINITIONS
+                        $<TARGET_PROPERTY:onnx,INTERFACE_COMPILE_DEFINITIONS>
+                    ADDITIONAL_INCLUDE_DIRECTORIES
+                        $<TARGET_PROPERTY:frontend_common::static,INTERFACE_INCLUDE_DIRECTORIES>)
 
-install(DIRECTORY ${ONNX_FRONTEND_INCLUDE_DIR}/onnx_frontend
-                  ${ONNX_FRONTEND_INCLUDE_DIR}/onnx_import
+install(DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/include/onnx_import
         DESTINATION ${FRONTEND_INSTALL_INCLUDE}
         COMPONENT ngraph_dev
         FILES_MATCHING PATTERN "*.hpp")
-
-export(TARGETS ${TARGET_NAME} NAMESPACE openvino::
-       APPEND FILE "${CMAKE_BINARY_DIR}/OpenVINOTargets.cmake")
diff --git a/src/frontends/onnx/frontend/include/onnx_frontend/frontend.hpp b/src/frontends/onnx/frontend/include/onnx_frontend/frontend.hpp
index e3f900d6f03..ddd1ef66b4e 100644
--- a/src/frontends/onnx/frontend/include/onnx_frontend/frontend.hpp
+++ b/src/frontends/onnx/frontend/include/onnx_frontend/frontend.hpp
@@ -6,11 +6,18 @@
 
 #include <common/frontend.hpp>
 
-#ifdef onnx_ov_frontend_EXPORTS
-#    define ONNX_FRONTEND_API OPENVINO_CORE_EXPORTS
+#ifdef OPENVINO_STATIC_LIBRARY
+#    define ONNX_FRONTEND_API
+#    define ONNX_FRONTEND_C_API
 #else
-#    define ONNX_FRONTEND_API OPENVINO_CORE_IMPORTS
-#endif
+#    ifdef onnx_ov_frontend_EXPORTS
+#        define ONNX_FRONTEND_API   OPENVINO_CORE_EXPORTS
+#        define ONNX_FRONTEND_C_API OPENVINO_EXTERN_C OPENVINO_CORE_EXPORTS
+#    else
+#        define ONNX_FRONTEND_API   OPENVINO_CORE_IMPORTS
+#        define ONNX_FRONTEND_C_API OPENVINO_EXTERN_C OPENVINO_CORE_IMPORTS
+#    endif  // onnx_ov_frontend_EXPORTS
+#endif      // OPENVINO_STATIC_LIBRARY
 
 namespace ov {
 namespace frontend {
diff --git a/src/frontends/onnx/frontend/include/onnx_import/onnx_importer_visibility.hpp b/src/frontends/onnx/frontend/include/onnx_import/onnx_importer_visibility.hpp
index d2bd18de52c..fcce9aaa746 100644
--- a/src/frontends/onnx/frontend/include/onnx_import/onnx_importer_visibility.hpp
+++ b/src/frontends/onnx/frontend/include/onnx_import/onnx_importer_visibility.hpp
@@ -4,8 +4,12 @@
 
 #include "ngraph/visibility.hpp"
 
-#ifdef onnx_ov_frontend_EXPORTS
-#    define ONNX_IMPORTER_API OPENVINO_CORE_EXPORTS
+#ifdef OPENVINO_STATIC_LIBRARY
+#    define ONNX_IMPORTER_API
 #else
-#    define ONNX_IMPORTER_API OPENVINO_CORE_IMPORTS
-#endif
+#    ifdef onnx_ov_frontend_EXPORTS
+#        define ONNX_IMPORTER_API OPENVINO_CORE_EXPORTS
+#    else
+#        define ONNX_IMPORTER_API OPENVINO_CORE_IMPORTS
+#    endif  // onnx_ov_frontend_EXPORTS
+#endif      // OPENVINO_STATIC_LIBRARY
diff --git a/src/frontends/onnx/frontend/src/core/model.cpp b/src/frontends/onnx/frontend/src/core/model.cpp
index fe86093825d..a3141e7e582 100644
--- a/src/frontends/onnx/frontend/src/core/model.cpp
+++ b/src/frontends/onnx/frontend/src/core/model.cpp
@@ -71,7 +71,7 @@ void Model::enable_opset_domain(const std::string& domain) {
     if (m_opset.find(domain) == std::end(m_opset)) {
         OperatorSet opset{OperatorsBridge::get_operator_set(domain)};
         if (opset.empty()) {
-            NGRAPH_WARN << "Couldn't enable domain: " << domain << " since it hasn't any registered operators.";
+            NGRAPH_WARN << "Couldn't enable domain: " << domain << " since it does not have any registered operators.";
 
             return;
         }
diff --git a/src/frontends/onnx/frontend/src/frontend.cpp b/src/frontends/onnx/frontend/src/frontend.cpp
index afa90828f99..8f9b333a074 100644
--- a/src/frontends/onnx/frontend/src/frontend.cpp
+++ b/src/frontends/onnx/frontend/src/frontend.cpp
@@ -20,11 +20,11 @@ using VariantString = VariantWrapper<std::string>;
 using VariantWString = VariantWrapper<std::wstring>;
 using VariantIstreamPtr = VariantWrapper<std::istream*>;
 
-extern "C" ONNX_FRONTEND_API FrontEndVersion GetAPIVersion() {
+ONNX_FRONTEND_C_API FrontEndVersion GetAPIVersion() {
     return OV_FRONTEND_API_VERSION;
 }
 
-extern "C" ONNX_FRONTEND_API void* GetFrontEndData() {
+ONNX_FRONTEND_C_API void* GetFrontEndData() {
     FrontEndPluginInfo* res = new FrontEndPluginInfo();
     res->m_name = "onnx";
     res->m_creator = []() {
diff --git a/src/frontends/onnx/frontend/src/op/quant_conv.cpp b/src/frontends/onnx/frontend/src/op/quant_conv.cpp
index fe359f3cd91..4f2e180012a 100644
--- a/src/frontends/onnx/frontend/src/op/quant_conv.cpp
+++ b/src/frontends/onnx/frontend/src/op/quant_conv.cpp
@@ -2,22 +2,25 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-// Disabled in CMakeList
-// Update to higher opset required
+// Disabled in CMakeLists.txt
+// Update to higher opset is required
 
-#include <cstddef>
-#include <memory>
-#include <vector>
+#if 0
 
-#include "default_opset.hpp"
-#include "exceptions.hpp"
-#include "ngraph/builder/quantization/quantized_linear_convolution.hpp"
-#include "ngraph/coordinate_diff.hpp"
-#include "ngraph/frontend/onnx_import/utils/convpool.hpp"
-#include "ngraph/op/util/attr_types.hpp"
-#include "ngraph/opsets/opset0.hpp"
-#include "ngraph/strides.hpp"
-#include "op/quant_conv.hpp"
+#    include "op/quant_conv.hpp"
+
+#    include <cstddef>
+#    include <memory>
+#    include <vector>
+
+#    include "default_opset.hpp"
+#    include "exceptions.hpp"
+#    include "ngraph/builder/quantization/quantized_linear_convolution.hpp"
+#    include "ngraph/coordinate_diff.hpp"
+#    include "ngraph/frontend/onnx_import/utils/convpool.hpp"
+#    include "ngraph/op/util/attr_types.hpp"
+#    include "ngraph/opsets/opset0.hpp"
+#    include "ngraph/strides.hpp"
 
 namespace ngraph
 {
@@ -269,3 +272,5 @@ namespace ngraph
     } // namespace onnx_import
 
 } // namespace ngraph
+
+#endif
diff --git a/src/frontends/onnx/frontend/src/op/quant_conv.hpp b/src/frontends/onnx/frontend/src/op/quant_conv.hpp
index 01f2034cd32..8df3eba0f2b 100644
--- a/src/frontends/onnx/frontend/src/op/quant_conv.hpp
+++ b/src/frontends/onnx/frontend/src/op/quant_conv.hpp
@@ -10,26 +10,22 @@
 #include "ngraph/node.hpp"
 #include "onnx_import/core/node.hpp"
 
-namespace ngraph
-{
-    namespace onnx_import
-    {
-        namespace op
-        {
-            namespace set_1
-            {
-                /// \brief Performs ONNX Quant Conv operation.
-                ///
-                /// \param node   The ONNX node object representing this operation.
-                ///
-                /// \return The vector containing Ngraph nodes producing output of ONNX quantizied
-                ///         convolution operation.
-                OutputVector quant_conv(const Node& node);
+namespace ngraph {
+namespace onnx_import {
+namespace op {
+namespace set_1 {
+/// \brief Performs ONNX Quant Conv operation.
+///
+/// \param node   The ONNX node object representing this operation.
+///
+/// \return The vector containing Ngraph nodes producing output of ONNX quantizied
+///         convolution operation.
+OutputVector quant_conv(const Node& node);
 
-            } // namespace set_1
+}  // namespace set_1
 
-        } // namespace op
+}  // namespace op
 
-    } // namespace onnx_import
+}  // namespace onnx_import
 
-} // namespace ngraph
+}  // namespace ngraph
diff --git a/src/frontends/onnx/onnx_common/CMakeLists.txt b/src/frontends/onnx/onnx_common/CMakeLists.txt
index 1c4d330290d..4d90061a21f 100644
--- a/src/frontends/onnx/onnx_common/CMakeLists.txt
+++ b/src/frontends/onnx/onnx_common/CMakeLists.txt
@@ -26,7 +26,14 @@ target_include_directories(${TARGET_NAME} PUBLIC $<BUILD_INTERFACE:${ONNX_COMMON
                                                  $<INSTALL_INTERFACE:${FRONTEND_INSTALL_INCLUDE}>)
 
 target_link_libraries(${TARGET_NAME} PRIVATE ngraph)
-link_system_libraries(${TARGET_NAME} PUBLIC onnx_proto onnx ${Protobuf_LITE_LIBRARIES})
+
+if(ONNX_USE_LITE_PROTO)
+    link_system_libraries(${TARGET_NAME} PUBLIC onnx_proto onnx ${Protobuf_LITE_LIBRARIES})
+else()
+    link_system_libraries(${TARGET_NAME} PUBLIC onnx_proto onnx ${Protobuf_LIBRARIES})
+endif()
 
 target_include_directories(${TARGET_NAME} PRIVATE ${ONNX_COMMON_SRC_DIR})
 add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
+
+ov_install_static_lib(${TARGET_NAME} ngraph)
diff --git a/src/frontends/paddlepaddle/CMakeLists.txt b/src/frontends/paddlepaddle/CMakeLists.txt
index 8f9930c5b65..e4d064b2388 100644
--- a/src/frontends/paddlepaddle/CMakeLists.txt
+++ b/src/frontends/paddlepaddle/CMakeLists.txt
@@ -2,96 +2,8 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-set(TARGET_NAME "paddlepaddle_ov_frontend")
-
-file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp)
-file(GLOB_RECURSE LIBRARY_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/src/*.hpp)
-file(GLOB_RECURSE LIBRARY_PUBLIC_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/include/*.hpp)
-
-set(${TARGET_NAME}_INCLUDE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/include)
-
-# Create named folders for the sources within the .vcproj
-# Empty name lists them directly under the .vcproj
-
-source_group("src" FILES ${LIBRARY_SRC})
-source_group("include" FILES ${LIBRARY_HEADERS})
-source_group("public include" FILES ${LIBRARY_PUBLIC_HEADERS})
-
-set(PROTO_SRCS)
-set(PROTO_HDRS)
-
-# Generate protobuf file on build time for each '.proto' file in src/proto
-file(GLOB proto_files ${CMAKE_CURRENT_SOURCE_DIR}/src/proto/*.proto)
-
-foreach(INFILE ${proto_files})
-    get_filename_component(FILE_DIR ${INFILE} DIRECTORY)
-    get_filename_component(FILE_WE ${INFILE} NAME_WE)
-    set(OUTPUT_PB_SRC ${CMAKE_CURRENT_BINARY_DIR}/${FILE_WE}.pb.cc)
-    set(OUTPUT_PB_HEADER ${CMAKE_CURRENT_BINARY_DIR}/${FILE_WE}.pb.h)
-    set(GENERATED_PROTO ${INFILE})
-    add_custom_command(
-            OUTPUT "${OUTPUT_PB_SRC}" "${OUTPUT_PB_HEADER}"
-            COMMAND ${PROTOC_EXECUTABLE} ARGS --cpp_out ${CMAKE_CURRENT_BINARY_DIR} -I ${FILE_DIR} ${FILE_WE}.proto
-            DEPENDS ${PROTOC_EXECUTABLE} ${GENERATED_PROTO}
-            COMMENT "Running C++ protocol buffer compiler (${PROTOC_EXECUTABLE}) on ${GENERATED_PROTO}"
-            VERBATIM
-            COMMAND_EXPAND_LISTS)
-    list(APPEND PROTO_SRCS "${OUTPUT_PB_SRC}")
-    list(APPEND PROTO_HDRS "${OUTPUT_PB_HEADER}")
-endforeach()
-
-add_custom_target(${TARGET_NAME}_proto DEPENDS ${PROTO_SRCS} ${PROTO_HDRS})
-
-set_source_files_properties(${PROTO_SRCS} ${PROTO_HDRS} PROPERTIES GENERATED TRUE)
-
-# Disable all warnings for generated code
-set_source_files_properties(${PROTO_SRCS} ${PROTO_HDRS} PROPERTIES COMPILE_OPTIONS -w)
-
-# Create library
-add_library(${TARGET_NAME} SHARED ${LIBRARY_SRC} ${LIBRARY_HEADERS} ${LIBRARY_PUBLIC_HEADERS} ${PROTO_SRCS} ${PROTO_HDRS})
-add_library(openvino::frontend::paddlepaddle ALIAS ${TARGET_NAME})
-
-add_dependencies(${TARGET_NAME} ${TARGET_NAME}_proto)
-
-ov_ncc_naming_style(FOR_TARGET ${TARGET_NAME}
-                    INCLUDE_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/include"
-                    ADDITIONAL_INCLUDE_DIRECTORIES
-                        $<TARGET_PROPERTY:frontend_common::static,INTERFACE_INCLUDE_DIRECTORIES>)
-
-target_include_directories(${TARGET_NAME}
-        PUBLIC
-            $<BUILD_INTERFACE:${${TARGET_NAME}_INCLUDE_DIR}>
-            $<INSTALL_INTERFACE:${FRONTEND_INSTALL_INCLUDE}>
-        PRIVATE
-            ${CMAKE_CURRENT_SOURCE_DIR}/src
-            ${CMAKE_CURRENT_BINARY_DIR})
-
-target_include_directories(${TARGET_NAME} SYSTEM PRIVATE ${Protobuf_INCLUDE_DIRS}
-                                                         ${CMAKE_CURRENT_BINARY_DIR})
-
-ie_add_vs_version_file(NAME ${TARGET_NAME}
-                       FILEDESCRIPTION "FrontEnd to load and convert PaddlePaddle file format")
-
-ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
-
-link_system_libraries(${TARGET_NAME} PRIVATE ${Protobuf_LITE_LIBRARIES})
-
-target_link_libraries(${TARGET_NAME} PRIVATE frontend_common::static
-                                     PRIVATE inference_engine_transformations)
-
-add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME}
-                        EXCLUDE_PATTERNS ${PROTO_SRCS} ${PROTO_HDRS})
-
-set_target_properties(${TARGET_NAME} PROPERTIES EXPORT_NAME frontend::paddlepaddle)
-install(TARGETS ${TARGET_NAME} EXPORT OpenVINOTargets
-        RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT ngraph
-        ARCHIVE DESTINATION ${IE_CPACK_ARCHIVE_PATH} COMPONENT ngraph
-        LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT ngraph)
-
-install(DIRECTORY ${${TARGET_NAME}_INCLUDE_DIR}/paddlepaddle_frontend
-        DESTINATION ${FRONTEND_INSTALL_INCLUDE}
-        COMPONENT ngraph_dev
-        FILES_MATCHING PATTERN "*.hpp")
-
-export(TARGETS ${TARGET_NAME} NAMESPACE openvino::
-       APPEND FILE "${CMAKE_BINARY_DIR}/OpenVINOTargets.cmake")
+ov_add_frontend(NAME paddlepaddle
+                LINKABLE_FRONTEND
+                PROTOBUF_LITE
+                FILEDESCRIPTION "FrontEnd to load and convert PaddlePaddle file format"
+                LINK_LIBRARIES ngraph::builder)
diff --git a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/utility.hpp b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/utility.hpp
index 55ba86cb1c8..8d61af08ff6 100644
--- a/src/frontends/paddlepaddle/include/paddlepaddle_frontend/utility.hpp
+++ b/src/frontends/paddlepaddle/include/paddlepaddle_frontend/utility.hpp
@@ -6,12 +6,18 @@
 
 #include <manager.hpp>
 
-// Defined if we are building the plugin DLL (instead of using it)
-#ifdef paddlepaddle_ov_frontend_EXPORTS
-#    define PDPD_API OPENVINO_CORE_EXPORTS
+#ifdef OPENVINO_STATIC_LIBRARY
+#    define PDPD_API
+#    define PDPD_C_API
 #else
-#    define PDPD_API OPENVINO_CORE_IMPORTS
-#endif  // paddlepaddle_ov_frontend_EXPORTS
+#    ifdef paddlepaddle_ov_frontend_EXPORTS
+#        define PDPD_API   OPENVINO_CORE_EXPORTS
+#        define PDPD_C_API OPENVINO_EXTERN_C OPENVINO_CORE_EXPORTS
+#    else
+#        define PDPD_API   OPENVINO_CORE_IMPORTS
+#        define PDPD_C_API OPENVINO_EXTERN_C OPENVINO_CORE_IMPORTS
+#    endif  // paddlepaddle_ov_frontend_EXPORTS
+#endif      // OPENVINO_STATIC_LIBRARY
 
 #define PDPD_ASSERT(ex, msg)               \
     {                                      \
diff --git a/src/frontends/paddlepaddle/src/frontend.cpp b/src/frontends/paddlepaddle/src/frontend.cpp
index 584dcff6dff..113e57632c7 100644
--- a/src/frontends/paddlepaddle/src/frontend.cpp
+++ b/src/frontends/paddlepaddle/src/frontend.cpp
@@ -329,11 +329,11 @@ std::string FrontEndPDPD::get_name() const {
 }  // namespace frontend
 }  // namespace ov
 
-extern "C" PDPD_API FrontEndVersion GetAPIVersion() {
+PDPD_C_API FrontEndVersion GetAPIVersion() {
     return OV_FRONTEND_API_VERSION;
 }
 
-extern "C" PDPD_API void* GetFrontEndData() {
+PDPD_C_API void* GetFrontEndData() {
     FrontEndPluginInfo* res = new FrontEndPluginInfo();
     res->m_name = "paddle";
     res->m_creator = []() {
diff --git a/src/frontends/tensorflow/CMakeLists.txt b/src/frontends/tensorflow/CMakeLists.txt
index e419e13a6ad..1e049c6d66d 100644
--- a/src/frontends/tensorflow/CMakeLists.txt
+++ b/src/frontends/tensorflow/CMakeLists.txt
@@ -2,103 +2,14 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-set(TARGET_NAME "tensorflow_ov_frontend")
-
-file(GLOB_RECURSE LIBRARY_SRC ${CMAKE_CURRENT_SOURCE_DIR}/src/*.cpp)
-file(GLOB_RECURSE LIBRARY_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/src/*.hpp)
-file(GLOB_RECURSE LIBRARY_PUBLIC_HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/include/*.hpp)
-
-set(${TARGET_NAME}_INCLUDE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/include)
-
-# Create named folders for the sources within the .vcproj
-# Empty name lists them directly under the .vcproj
-
-source_group("src" FILES ${LIBRARY_SRC})
-source_group("include" FILES ${LIBRARY_HEADERS})
-source_group("public include" FILES ${LIBRARY_PUBLIC_HEADERS})
-
-set(PROTO_SRCS)
-set(PROTO_HDRS)
-
-# Generate protobuf file on build time for each '.proto' file in src/proto
-file(GLOB proto_files ${CMAKE_CURRENT_SOURCE_DIR}/src/proto/*.proto)
-
-foreach(INFILE ${proto_files})
-    get_filename_component(FILE_DIR ${INFILE} DIRECTORY)
-    get_filename_component(FILE_WE ${INFILE} NAME_WE)
-    set(OUTPUT_PB_SRC ${CMAKE_CURRENT_BINARY_DIR}/${FILE_WE}.pb.cc)
-    set(OUTPUT_PB_HEADER ${CMAKE_CURRENT_BINARY_DIR}/${FILE_WE}.pb.h)
-    set(GENERATED_PROTO ${INFILE})
-    add_custom_command(
-            OUTPUT "${OUTPUT_PB_SRC}" "${OUTPUT_PB_HEADER}"
-            COMMAND ${PROTOC_EXECUTABLE} ARGS --cpp_out ${CMAKE_CURRENT_BINARY_DIR} -I ${FILE_DIR} ${FILE_WE}.proto
-            DEPENDS ${PROTOC_EXECUTABLE} ${GENERATED_PROTO}
-            COMMENT "Running C++ protocol buffer compiler (${PROTOC_EXECUTABLE}) on ${GENERATED_PROTO}"
-            VERBATIM
-            COMMAND_EXPAND_LISTS)
-    list(APPEND PROTO_SRCS "${OUTPUT_PB_SRC}")
-    list(APPEND PROTO_HDRS "${OUTPUT_PB_HEADER}")
-endforeach()
-
-add_custom_target(${TARGET_NAME}_proto DEPENDS ${PROTO_SRCS} ${PROTO_HDRS})
-
-set_source_files_properties(${PROTO_SRCS} ${PROTO_HDRS} PROPERTIES GENERATED TRUE)
-
-# Disable all warnings for generated code
-set_source_files_properties(${PROTO_SRCS} ${PROTO_HDRS} PROPERTIES COMPILE_OPTIONS -w)
-
-# Create library
-add_library(${TARGET_NAME} SHARED ${LIBRARY_SRC} ${LIBRARY_HEADERS} ${LIBRARY_PUBLIC_HEADERS} ${PROTO_SRCS} ${PROTO_HDRS})
-add_library(openvino::frontend::tensorflow ALIAS ${TARGET_NAME})
-
-add_dependencies(${TARGET_NAME} ${TARGET_NAME}_proto)
-
-ov_ncc_naming_style(FOR_TARGET ${TARGET_NAME}
-                    INCLUDE_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/include"
-                    ADDITIONAL_INCLUDE_DIRECTORIES
-                        $<TARGET_PROPERTY:frontend_common::static,INTERFACE_INCLUDE_DIRECTORIES>)
-
-target_include_directories(${TARGET_NAME}
-        PUBLIC
-            $<BUILD_INTERFACE:${${TARGET_NAME}_INCLUDE_DIR}>
-            $<INSTALL_INTERFACE:${FRONTEND_INSTALL_INCLUDE}>
-        PRIVATE
-            ${CMAKE_CURRENT_SOURCE_DIR}/src
-            ${CMAKE_CURRENT_BINARY_DIR})
-
-target_include_directories(${TARGET_NAME} SYSTEM PRIVATE ${Protobuf_INCLUDE_DIRS}
-                                                         ${CMAKE_CURRENT_BINARY_DIR})
-
-ie_add_vs_version_file(NAME ${TARGET_NAME}
-                       FILEDESCRIPTION "FrontEnd to load and convert TensorFlow file format")
-
-ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
-
-link_system_libraries(${TARGET_NAME} PRIVATE ${Protobuf_LITE_LIBRARIES})
-
-target_link_libraries(${TARGET_NAME} PRIVATE frontend_common::static
-                                     PRIVATE inference_engine_transformations libprotobuf openvino::util)
-
-add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME}
-                        EXCLUDE_PATTERNS ${PROTO_SRCS} ${PROTO_HDRS})
-
-set_target_properties(${TARGET_NAME} PROPERTIES EXPORT_NAME frontend::tensorflow)
+ov_add_frontend(NAME tensorflow
+                LINKABLE_FRONTEND
+                SKIP_INSTALL
+                FILEDESCRIPTION "FrontEnd to load and convert TensorFlow file format"
+                LINK_LIBRARIES openvino::util)
 
+# TODO: once TensorFlow FE become releasable component, need to remove these lines and SKIP_INSTALL above
 install(TARGETS ${TARGET_NAME}
         RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT tests EXCLUDE_FROM_ALL
         ARCHIVE DESTINATION ${IE_CPACK_ARCHIVE_PATH} COMPONENT tests EXCLUDE_FROM_ALL
         LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT tests EXCLUDE_FROM_ALL)
-
-# TODO: add install commands once TensorFlow frontend is complete, and delete the install above
-#install(TARGETS ${TARGET_NAME} EXPORT OpenVINOTargets
-#        RUNTIME DESTINATION ${IE_CPACK_RUNTIME_PATH} COMPONENT ngraph
-#        ARCHIVE DESTINATION ${IE_CPACK_ARCHIVE_PATH} COMPONENT ngraph
-#        LIBRARY DESTINATION ${IE_CPACK_LIBRARY_PATH} COMPONENT ngraph)
-
-#install(DIRECTORY ${${TARGET_NAME}_INCLUDE_DIR}/tensorflow_frontend
-#        DESTINATION ${FRONTEND_INSTALL_INCLUDE}
-#        COMPONENT ngraph_dev
-#        FILES_MATCHING PATTERN "*.hpp")
-#
-#export(TARGETS ${TARGET_NAME} NAMESPACE openvino::
-#       APPEND FILE "${CMAKE_BINARY_DIR}/OpenVINOTargets.cmake")
diff --git a/src/frontends/tensorflow/include/tensorflow_frontend/utility.hpp b/src/frontends/tensorflow/include/tensorflow_frontend/utility.hpp
index 50c922f0673..2e6c3abf6b2 100644
--- a/src/frontends/tensorflow/include/tensorflow_frontend/utility.hpp
+++ b/src/frontends/tensorflow/include/tensorflow_frontend/utility.hpp
@@ -6,8 +6,15 @@
 
 #include "common/frontend_exceptions.hpp"
 
-#ifdef tensorflow_ov_frontend_EXPORTS
-#    define TF_API OPENVINO_CORE_EXPORTS
+#ifdef OPENVINO_STATIC_LIBRARY
+#    define TF_API
+#    define TF_C_API
 #else
-#    define TF_API OPENVINO_CORE_IMPORTS
-#endif  // tensorflow_ov_frontend_EXPORTS
+#    ifdef tensorflow_ov_frontend_EXPORTS
+#        define TF_API   OPENVINO_CORE_EXPORTS
+#        define TF_C_API OPENVINO_EXTERN_C OPENVINO_CORE_EXPORTS
+#    else
+#        define TF_API   OPENVINO_CORE_IMPORTS
+#        define TF_C_API OPENVINO_EXTERN_C OPENVINO_CORE_IMPORTS
+#    endif  // tensorflow_ov_frontend_EXPORTS
+#endif      // OPENVINO_STATIC_LIBRARY
diff --git a/src/frontends/tensorflow/src/tensorflow.cpp b/src/frontends/tensorflow/src/tensorflow.cpp
index 7dec8591b78..24824017e42 100644
--- a/src/frontends/tensorflow/src/tensorflow.cpp
+++ b/src/frontends/tensorflow/src/tensorflow.cpp
@@ -5,11 +5,11 @@
 #include "manager.hpp"
 #include "tensorflow_frontend/frontend.hpp"
 
-extern "C" OPENVINO_CORE_EXPORTS ov::frontend::FrontEndVersion GetAPIVersion() {
+TF_C_API ov::frontend::FrontEndVersion GetAPIVersion() {
     return OV_FRONTEND_API_VERSION;
 }
 
-extern "C" OPENVINO_CORE_EXPORTS void* GetFrontEndData() {
+TF_C_API void* GetFrontEndData() {
     auto res = new ov::frontend::FrontEndPluginInfo();
     res->m_name = "tf";
     res->m_creator = []() {
diff --git a/thirdparty/CMakeLists.txt b/thirdparty/CMakeLists.txt
index a913e797b27..8a4733aba8a 100644
--- a/thirdparty/CMakeLists.txt
+++ b/thirdparty/CMakeLists.txt
@@ -90,24 +90,26 @@ if(NGRAPH_PDPD_FRONTEND_ENABLE OR NGRAPH_ONNX_FRONTEND_ENABLE OR NGRAPH_TF_FRONT
         endif()
         find_package(Protobuf 3.9.0 REQUIRED)
         set(Protobuf_LITE_LIBRARIES protobuf::libprotobuf-lite)
+        set(Protobuf_LIBRARIES protobuf::libprotobuf)
         set(SYSTEM_PROTOC protobuf::protoc)
         set(PROTOC_EXECUTABLE ${SYSTEM_PROTOC})
 
-        foreach(target ${SYSTEM_PROTOC} ${Protobuf_LITE_LIBRARIES})
+        foreach(target ${SYSTEM_PROTOC} ${Protobuf_LIBRARIES} ${Protobuf_LITE_LIBRARIES})
             set_property(TARGET ${target} PROPERTY IMPORTED_GLOBAL TRUE)
         endforeach()
     else()
-        add_subdirectory(protobuf)
+        add_subdirectory(protobuf EXCLUDE_FROM_ALL)
     endif()
 
     # forward variables used in the other places
     set(SYSTEM_PROTOC ${SYSTEM_PROTOC} PARENT_SCOPE)
     set(PROTOC_EXECUTABLE ${PROTOC_EXECUTABLE} PARENT_SCOPE)
+    set(Protobuf_LIBRARIES ${Protobuf_LIBRARIES} PARENT_SCOPE)
     set(Protobuf_LITE_LIBRARIES ${Protobuf_LITE_LIBRARIES} PARENT_SCOPE)
     set(Protobuf_INCLUDE_DIRS ${Protobuf_INCLUDE_DIRS} PARENT_SCOPE)
 
     # set public / interface compile options
-    foreach(target IN LISTS Protobuf_LITE_LIBRARIES)
+    foreach(target IN LISTS Protobuf_LITE_LIBRARIES Protobuf_LIBRARIES)
         set(link_type PUBLIC)
         if(NGRAPH_USE_SYSTEM_PROTOBUF)
             set(link_type INTERFACE)
diff --git a/thirdparty/onnx/CMakeLists.txt b/thirdparty/onnx/CMakeLists.txt
index 263802dd42f..0bafecc72c5 100644
--- a/thirdparty/onnx/CMakeLists.txt
+++ b/thirdparty/onnx/CMakeLists.txt
@@ -8,26 +8,51 @@
 
 set(CMAKE_INTERPROCEDURAL_OPTIMIZATION_RELEASE OFF)
 set(NGRAPH_ONNX_NAMESPACE ngraph_onnx)
-set(BUILD_SHARED_LIBS OFF)
 
 if(NOT DEFINED ONNX_USE_MSVC_STATIC_RUNTIME)
     set(ONNX_USE_MSVC_STATIC_RUNTIME OFF)
 endif()
 
-macro(onnx_set_target_properties)
-    target_include_directories(onnx SYSTEM PRIVATE "${Protobuf_INCLUDE_DIRS}")
-    target_include_directories(onnx_proto SYSTEM PRIVATE "${Protobuf_INCLUDE_DIRS}")
+if(FORCE_FRONTENDS_USE_PROTOBUF)
+    set(ONNX_USE_LITE_PROTO_DEFAULT OFF)
+else()
+    set(ONNX_USE_LITE_PROTO_DEFAULT ON)
+endif()
 
-    ov_disable_all_warnings(onnx onnx_proto)
-endmacro()
-
-set(ONNX_USE_PROTOBUF_SHARED_LIBS ${BUILD_SHARED_LIBS} CACHE BOOL "Use dynamic protobuf by ONNX library" FORCE)
+set(ONNX_USE_PROTOBUF_SHARED_LIBS CACHE BOOL "Use dynamic protobuf by ONNX library" FORCE)
 set(ONNX_NAMESPACE ${NGRAPH_ONNX_NAMESPACE})
-set(ONNX_USE_LITE_PROTO ON CACHE BOOL "Use protobuf lite for ONNX library" FORCE)
+set(ONNX_USE_LITE_PROTO ${ONNX_USE_LITE_PROTO_DEFAULT} CACHE BOOL "Use protobuf lite for ONNX library" FORCE)
 set(ONNX_ML ON CACHE BOOL "Use ONNX ML" FORCE)
 if(CMAKE_CROSSCOMPILING)
     set(ONNX_CUSTOM_PROTOC_EXECUTABLE ${SYSTEM_PROTOC})
 endif()
 
-add_subdirectory(onnx EXCLUDE_FROM_ALL)
-onnx_set_target_properties()
+# build targets
+
+function(ov_onnx_build_static)
+    set(BUILD_SHARED_LIBS OFF)
+    add_subdirectory(onnx EXCLUDE_FROM_ALL)
+endfunction()
+
+ov_onnx_build_static()
+
+target_include_directories(onnx SYSTEM PRIVATE "${Protobuf_INCLUDE_DIRS}")
+target_include_directories(onnx_proto SYSTEM PRIVATE "${Protobuf_INCLUDE_DIRS}")
+
+ov_disable_all_warnings(onnx onnx_proto)
+
+# install
+
+ov_install_static_lib(onnx ngraph)
+ov_install_static_lib(onnx_proto ngraph)
+
+# WA for ONNX: protobuf must be in the same export set of ONNX targets
+if(NOT BUILD_SHARED_LIBS)
+    if(ONNX_USE_LITE_PROTO)
+        install(TARGETS ${Protobuf_LITE_LIBRARIES} EXPORT ONNXTargets
+                ARCHIVE DESTINATION ${IE_CPACK_ARCHIVE_PATH} COMPONENT ngraph)
+    else()
+        install(TARGETS ${Protobuf_LIBRARIES} EXPORT ONNXTargets
+                ARCHIVE DESTINATION ${IE_CPACK_ARCHIVE_PATH} COMPONENT ngraph)
+    endif()
+endif()
diff --git a/thirdparty/protobuf/CMakeLists.txt b/thirdparty/protobuf/CMakeLists.txt
index 97cb66b5462..c3867073e83 100644
--- a/thirdparty/protobuf/CMakeLists.txt
+++ b/thirdparty/protobuf/CMakeLists.txt
@@ -16,9 +16,11 @@ if(OV_COMPILER_IS_CLANG)
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-inconsistent-missing-override")
 endif()
 
-set(protobuf_BUILD_TESTS OFF CACHE BOOL "Build tests")
-set(protobuf_BUILD_SHARED_LIBS ${BUILD_SHARED_LIBS} CACHE BOOL "Build shared libs" FORCE)
-set(protobuf_WITH_ZLIB OFF CACHE BOOL "Build with zlib support")
+set(protobuf_VERBOSE ON)
+set(protobuf_BUILD_TESTS OFF CACHE BOOL "Build tests" FORCE)
+set(protobuf_BUILD_SHARED_LIBS OFF CACHE BOOL "Build shared libs" FORCE)
+set(protobuf_WITH_ZLIB OFF CACHE BOOL "Build with zlib support" FORCE)
+set(protobuf_BUILD_CONFORMANCE OFF CACHE BOOL "" FORCE)
 
 # When we build dll libraries. These flags make sure onnx and protobuf build with /MD, not /MT.
 # These two options can't be mixed, because they requires link two imcompatiable runtime.
@@ -77,7 +79,8 @@ if(CMAKE_CROSSCOMPILING AND NOT PROTOC_VERSION VERSION_EQUAL protobuf_VERSION)
     message(WARNING "system protobuf version does not match with the compiled one, please update system protobuf or submodule")
 endif()
 
-# forward variables used in the other places
+# set to parent scope
+
 if(SYSTEM_PROTOC)
     set(SYSTEM_PROTOC ${SYSTEM_PROTOC} PARENT_SCOPE)
     set(PROTOC_EXECUTABLE ${SYSTEM_PROTOC} PARENT_SCOPE)
@@ -87,4 +90,5 @@ endif()
 
 set(protobuf_VERSION ${protobuf_VERSION} PARENT_SCOPE)
 set(Protobuf_LITE_LIBRARIES libprotobuf-lite PARENT_SCOPE)
+set(Protobuf_LIBRARIES libprotobuf PARENT_SCOPE)
 set(Protobuf_INCLUDE_DIRS ${Protobuf_INCLUDE_DIRS} PARENT_SCOPE)

From a09f4ded7946714064d9d38cb001cba25829e708 Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Mon, 29 Nov 2021 01:15:56 +0300
Subject: [PATCH 56/72] Fixed codeowners (#8875)

---
 CODEOWNERS | 23 +++++++++++++----------
 1 file changed, 13 insertions(+), 10 deletions(-)

diff --git a/CODEOWNERS b/CODEOWNERS
index 9dd779166a0..2fc110e3bed 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -19,24 +19,26 @@ azure-pipelines.yml  @openvinotoolkit/openvino-admins
 # IE Core:
 /inference-engine/  @openvinotoolkit/openvino-ie-maintainers
 /inference-engine/ie_bridges/python @openvinotoolkit/openvino-ie-python-api-maintainers
-/inference-engine/src/transformations/  @GlebKazantaev @ilyachur
-/inference-engine/src/legacy_api/  @openvinotoolkit/openvino-ngraph-maintainers
-/inference-engine/src/readers/  @openvinotoolkit/openvino-ngraph-maintainers
+/src/common/transformations/  @GlebKazantaev @ilyachur
+/src/common/legacy/  @openvinotoolkit/openvino-ngraph-maintainers
+/src/common/  @openvinotoolkit/openvino-ie-maintainers
+/inference-engine/tests_deprecated/readers/  @openvinotoolkit/openvino-ngraph-maintainers
 
 # IE CPU:
 /inference-engine/src/mkldnn_plugin/  @openvinotoolkit/openvino-ie-cpu-maintainers @openvinotoolkit/openvino-ie-cpu-developers
-/inference-engine/src/low_precision_transformations/  @openvinotoolkit/openvino-ie-cpu-maintainers @openvinotoolkit/openvino-ie-cpu-developers
+/src/common/low_precision_transformations/  @openvinotoolkit/openvino-ie-cpu-maintainers @openvinotoolkit/openvino-ie-cpu-developers
 /inference-engine/thirdparty/mkl-dnn/  @openvinotoolkit/openvino-ie-cpu-maintainers @openvinotoolkit/openvino-ie-cpu-developers
 
 # IE GPU:
 /inference-engine/src/cldnn_engine/  @openvinotoolkit/openvino-ie-gpu-maintainers @openvinotoolkit/openvino-ie-gpu-developers
-/inference-engine/src/inference_engine/include/gpu/  @openvinotoolkit/openvino-ie-gpu-maintainers @openvinotoolkit/openvino-ie-gpu-developers
-/inference-engine/src/inference_engine/include/cldnn/  @openvinotoolkit/openvino-ie-gpu-maintainers @openvinotoolkit/openvino-ie-gpu-developers
+/src/inference/include/ie/gpu/  @openvinotoolkit/openvino-ie-gpu-maintainers @openvinotoolkit/openvino-ie-gpu-developers
+/src/inference/include/ie/cldnn/  @openvinotoolkit/openvino-ie-gpu-maintainers @openvinotoolkit/openvino-ie-gpu-developers
+/src/inference/include/openvino/runtime/gpu/  @openvinotoolkit/openvino-ie-gpu-maintainers @openvinotoolkit/openvino-ie-gpu-developers
 /inference-engine/thirdparty/clDNN/  @openvinotoolkit/openvino-ie-gpu-maintainers @openvinotoolkit/openvino-ie-gpu-developers
 
 # IE VPU:
 /inference-engine/src/vpu/  @openvinotoolkit/openvino-ie-vpu-maintainers
-/inference-engine/src/inference_engine/include/vpu/  @openvinotoolkit/openvino-ie-vpu-maintainers
+/src/inference/include/ie/vpu/  @openvinotoolkit/openvino-ie-vpu-maintainers
 /inference-engine/thirdparty/movidius/  @openvinotoolkit/openvino-ie-vpu-maintainers
 /inference-engine/tests_deprecated/unit/engines/vpu/  @openvinotoolkit/openvino-ie-vpu-maintainers @openvinotoolkit/openvino-ie-tests-maintainers
 /inference-engine/tests_deprecated/functional/vpu/  @openvinotoolkit/openvino-ie-vpu-maintainers @openvinotoolkit/openvino-ie-tests-maintainers
@@ -48,11 +50,11 @@ azure-pipelines.yml  @openvinotoolkit/openvino-admins
 
 # IE GNA:
 /inference-engine/src/gna_plugin/  @openvinotoolkit/openvino-ie-gna-maintainers
-/inference-engine/src/inference_engine/include/gna/  @openvinotoolkit/openvino-ie-gna-maintainers
+/src/inference/include/ie/gna/  @openvinotoolkit/openvino-ie-gna-maintainers
 
 # IE MULTI:
 /inference-engine/src/multi_device/  @openvinotoolkit/openvino-ie-multi-maintainers
-/inference-engine/src/inference_engine/include/multi-device/  @openvinotoolkit/openvino-ie-multi-maintainers
+/src/inference/include/ie/multi-device/  @openvinotoolkit/openvino-ie-multi-maintainers
 
 # IE Tests:
 /inference-engine/tests/  @openvinotoolkit/openvino-ie-tests-maintainers
@@ -64,7 +66,8 @@ azure-pipelines.yml  @openvinotoolkit/openvino-admins
 /model-optimizer/  @openvinotoolkit/openvino-mo-maintainers
 
 # nGraph:
-/ngraph/  @openvinotoolkit/openvino-ngraph-maintainers
+/src/core/  @openvinotoolkit/openvino-ngraph-maintainers
+/src/frontends/  @openvinotoolkit/openvino-ngraph-maintainers
 
 # POT Tools
 /tools/pot/  @openvinotoolkit/openvino-pot-maintainers

From 497aaf9b4671c1a1165d67e9be5b3a1a8527117a Mon Sep 17 00:00:00 2001
From: Sergey Shlyapnikov <sergey.shlyapnikov@intel.com>
Date: Mon, 29 Nov 2021 05:41:39 +0300
Subject: [PATCH 57/72] [GPU] Relax batch size limitaions for DO GPU
 implementation usage for some of Gen12HP dGPUs (#8801)

---
 inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp b/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp
index 59c87e3d28d..9549399c2d6 100644
--- a/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp
+++ b/inference-engine/thirdparty/clDNN/src/layout_optimizer.cpp
@@ -1164,7 +1164,9 @@ impl_types layout_optimizer::get_preferred_impl_type(program_node& node, format
         auto& detection_output_node = node.as<detection_output>();
         auto confidence_layout = detection_output_node.confidence().get_output_layout();
         auto prim = detection_output_node.get_primitive();
-        if (confidence_layout.size.batch[0] <= lws_max && confidence_layout.size.batch[0] >= 4 && prim->confidence_threshold >= 0.1 &&
+        auto batch_size_limitations = (device_info.supports_immad && device_info.execution_units_count >= 256) ? true : confidence_layout.size.batch[0] >= 4;
+        if (confidence_layout.size.batch[0] <= lws_max && batch_size_limitations &&
+            prim->confidence_threshold >= 0.1 &&
             prim->top_k <= 400 && prim->num_classes >= 16 && confidence_layout.size.feature[0] > 10000)
             preferred_impl = impl_types::ocl;
         else

From 07651aa5c0f1fdd270fa189acec44b2afb2ce51b Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Mon, 29 Nov 2021 07:17:36 +0300
Subject: [PATCH 58/72] Fixed some inference-engine paths (#8878)

---
 docs/IE_PLUGIN_DG/Doxyfile                                  | 6 +++---
 docs/nGraph_DG/nGraphTransformation.md                      | 2 +-
 .../tests/functional/inference_engine/CMakeLists.txt        | 6 +++---
 tools/pot/README_dev.md                                     | 2 +-
 4 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/docs/IE_PLUGIN_DG/Doxyfile b/docs/IE_PLUGIN_DG/Doxyfile
index 8c275fe9f0c..a9367a25888 100644
--- a/docs/IE_PLUGIN_DG/Doxyfile
+++ b/docs/IE_PLUGIN_DG/Doxyfile
@@ -781,9 +781,9 @@ WARN_LOGFILE           =
 # Note: If this tag is empty the current directory is searched.
 
 INPUT                  = . \
-						 ../../inference-engine/src/transformations/include/transformations \
-						 ../../inference-engine/src/plugin_api \
-						 ../../openvino/itt/include/openvino
+						 ../../src/common/transformations/include/transformations \
+						 ../../src/inference/dev_api \
+						 ../../src/common/itt/include/openvino
 
 # This tag can be used to specify the character encoding of the source files
 # that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
diff --git a/docs/nGraph_DG/nGraphTransformation.md b/docs/nGraph_DG/nGraphTransformation.md
index e46f0dd8a02..03777180ad8 100644
--- a/docs/nGraph_DG/nGraphTransformation.md
+++ b/docs/nGraph_DG/nGraphTransformation.md
@@ -11,7 +11,7 @@ Before creating a transformation, do the following:
 
 ### Transformation Library Structure
 Transformation library is independent from Inference Engine target library named as `inference_engine_transformations`
-and is located in the `inference-engine/src/transformations` directory.
+and is located in the `src/common/transformations` directory.
 
 Transformations root directory contains two folders:
 * `ngraph_ops` - Contains internal opset operations that are common for plugins.
diff --git a/inference-engine/tests/functional/inference_engine/CMakeLists.txt b/inference-engine/tests/functional/inference_engine/CMakeLists.txt
index bd504546d66..11463cf8bcb 100644
--- a/inference-engine/tests/functional/inference_engine/CMakeLists.txt
+++ b/inference-engine/tests/functional/inference_engine/CMakeLists.txt
@@ -124,12 +124,12 @@ function(ie_headers_compilation_with_custom_flags)
 
     if(IE_TEST_PLUGIN_API)
         set(include_dirs
-            "${OpenVINO_SOURCE_DIR}/inference-engine/src/plugin_api/")
+            "${OpenVINO_SOURCE_DIR}/src/inference/dev_api/")
     else()
         set(include_dirs
             "${OpenVINO_SOURCE_DIR}/src/core/include"
-            "${OpenVINO_SOURCE_DIR}/inference-engine/src/inference_engine/include/ie"
-            "${OpenVINO_SOURCE_DIR}/inference-engine/src/inference_engine/include/")
+            "${OpenVINO_SOURCE_DIR}/src/inference/include/ie"
+            "${OpenVINO_SOURCE_DIR}/src/inference/include/")
     endif()
 
     foreach(include_dir IN LISTS include_dirs)
diff --git a/tools/pot/README_dev.md b/tools/pot/README_dev.md
index b5ec6b69dd1..7c5d30ddb9f 100644
--- a/tools/pot/README_dev.md
+++ b/tools/pot/README_dev.md
@@ -30,7 +30,7 @@ Post-Training Optimization Tool includes standalone command-line tool and Python
    git submodule init
    git submodule update
    ```
-3) Clone DLDT repo: `git clone https://gitlab-icv.inn.intel.com/inference-engine/dldt` (Not into the post-training-compression-tool)
+3) Clone DLDT repo: `git clone https://github.com/openvinotoolkit/openvino` (Not into the post-training-compression-tool)
 4) Switch dldt to required branch: `feature/low_precision/develop_fp_v10`
 5) Build inference engine (Instruction can be found in dldt repo)
 6) Switch dldt to _mkaglins/poc_ branch (Inference engine is built from _feature/low_precision/develop_fp_v10_ branch to support `FakeQuantize` layers. ModelOptimizer is used from _mkaglins/poc_ branch. So stay on _mkaglins/poc_ branch as you've built IE and don't build it from there again)

From 25b2131d21453e91782d6e48c4edbcecda64b920 Mon Sep 17 00:00:00 2001
From: Bo Liu <bo4.liu@intel.com>
Date: Mon, 29 Nov 2021 12:28:49 +0800
Subject: [PATCH 59/72] priorbox_8 ngraph and inference-engine parts (#8114)

* ngraph and inference-engine parts

* add priorbox_8 python api

* remove 'PriorBoxAttrs' and 'PriorBox' from outside of opset namespace

* add common nGraph transformation 'ConvertPriorBox8To0'

* remove redundant alias of PriorBox::Attributes

* use new Tensor api for evaluate method

* change v0operation back to the former api, pass Attribute structure to the reference implement

* use new Tensor api for constant_fold

* add support for dynamic shapes of constant_fold new Tensor api

* fix Node 'create temp tensors' issue when shape==0'

* revert to 'HostTensor' api for PriorBox8

* Apply suggestions from code review and 'template_plugin reference' testcase replaced 'backend INTERPRETER' testcase

* transformation part Apply suggestions from code review

* python init file updated for opset8

* keep backward compatibility to fix CI issue

* rebase to new structure of OpenVINO repo

* revert 'thirdparty/onednn_gpu' mistake changes
---
 .../functional/op_reference/prior_box.cpp     | 175 ++++++++++++--
 .../serialization/single_layer/prior_box.cpp  |   7 +-
 .../const_folding_prior_box.cpp               | 113 ++++++++-
 .../convert_prior_box_v8_to_v0_test.cpp       |  77 +++++++
 .../single_layer_tests/prior_box.cpp          |   6 +-
 .../single_layer/prior_box.hpp                |   4 +-
 .../src/single_layer/prior_box.cpp            |  16 +-
 .../compatibility/ngraph/opset8/__init__.py   |   2 +-
 .../src/compatibility/ngraph/opset8/ops.py    | 105 +++++++++
 .../python/src/openvino/opset8/__init__.py    |   2 +-
 .../python/src/openvino/opset8/ops.py         | 106 +++++++++
 .../convert_prior_box_v8_to_v0.hpp            |  26 +++
 .../common_optimizations.cpp                  |   2 +
 .../convert_prior_box_v8_to_v0.cpp            |  54 +++++
 src/core/include/ngraph/op/prior_box.hpp      |   3 +
 src/core/include/openvino/op/prior_box.hpp    |  61 +++++
 .../include/openvino/opsets/opset8_tbl.hpp    |   2 +-
 .../ngraph/runtime/reference/prior_box.hpp    |  53 +++--
 src/core/src/op/prior_box.cpp                 | 216 +++++++++++++++++-
 .../runtime/interpreter/evaluates_map.cpp     |  16 +-
 .../runtime/interpreter/int_executable.cpp    |   2 +-
 .../runtime/interpreter/opset_int_tbl.hpp     |   1 +
 src/core/tests/type_prop/prior_box.cpp        |  39 +++-
 src/core/tests/visitors/op/prior_box.cpp      |  47 +++-
 .../src/op/org.openvinotoolkit/prior_box.cpp  |   4 +-
 .../paddlepaddle/src/op/prior_box.cpp         |  44 +---
 26 files changed, 1056 insertions(+), 127 deletions(-)
 create mode 100644 inference-engine/tests/functional/inference_engine/transformations/convert_prior_box_v8_to_v0_test.cpp
 create mode 100644 src/common/transformations/include/transformations/op_conversions/convert_prior_box_v8_to_v0.hpp
 create mode 100644 src/common/transformations/src/transformations/op_conversions/convert_prior_box_v8_to_v0.cpp

diff --git a/docs/template_plugin/tests/functional/op_reference/prior_box.cpp b/docs/template_plugin/tests/functional/op_reference/prior_box.cpp
index e24ae7a72b0..b382373a1a9 100644
--- a/docs/template_plugin/tests/functional/op_reference/prior_box.cpp
+++ b/docs/template_plugin/tests/functional/op_reference/prior_box.cpp
@@ -2,11 +2,12 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+#include "openvino/op/prior_box.hpp"
+
 #include <gtest/gtest.h>
 
-#include "openvino/op/prior_box.hpp"
 #include "base_reference_test.hpp"
-#include "openvino/opsets/opset1.hpp"
+#include "openvino/op/constant.hpp"
 
 using namespace reference_tests;
 using namespace ov;
@@ -17,9 +18,11 @@ struct PriorBoxParams {
     PriorBoxParams(const std::vector<float>& min_size,
                    const std::vector<float>& aspect_ratio,
                    const bool scale_all_size,
-                   const ov::Shape& layerShapeShape, const ov::Shape& imageShapeShape,
+                   const ov::Shape& layerShapeShape,
+                   const ov::Shape& imageShapeShape,
                    const ov::element::Type& iType,
-                   const std::vector<IT>& layerShapeValues, const std::vector<IT>& imageShapeValues,
+                   const std::vector<IT>& layerShapeValues,
+                   const std::vector<IT>& imageShapeValues,
                    const std::vector<float>& oValues,
                    const std::string& testcaseName = "")
         : layerShapeShape(layerShapeShape),
@@ -30,10 +33,10 @@ struct PriorBoxParams {
           imageShapeData(CreateTensor(iType, imageShapeValues)),
           refData(CreateTensor(outType, oValues)),
           testcaseName(testcaseName) {
-              attrs.min_size = min_size;
-              attrs.aspect_ratio = aspect_ratio;
-              attrs.scale_all_sizes = scale_all_size;
-          }
+        attrs.min_size = min_size;
+        attrs.aspect_ratio = aspect_ratio;
+        attrs.scale_all_sizes = scale_all_size;
+    }
 
     ov::op::v0::PriorBox::Attributes attrs;
     ov::Shape layerShapeShape;
@@ -46,6 +49,46 @@ struct PriorBoxParams {
     std::string testcaseName;
 };
 
+struct PriorBoxV8Params {
+    template <class IT>
+    PriorBoxV8Params(const std::vector<float>& min_size,
+                     const std::vector<float>& max_size,
+                     const std::vector<float>& aspect_ratio,
+                     const bool scale_all_size,
+                     const bool min_max_aspect_ratios_order,
+                     const ov::Shape& layerShapeShape,
+                     const ov::Shape& imageShapeShape,
+                     const ov::element::Type& iType,
+                     const std::vector<IT>& layerShapeValues,
+                     const std::vector<IT>& imageShapeValues,
+                     const std::vector<float>& oValues,
+                     const std::string& testcaseName = "")
+        : layerShapeShape(layerShapeShape),
+          imageShapeShape(imageShapeShape),
+          inType(iType),
+          outType(ov::element::Type_t::f32),
+          layerShapeData(CreateTensor(iType, layerShapeValues)),
+          imageShapeData(CreateTensor(iType, imageShapeValues)),
+          refData(CreateTensor(outType, oValues)),
+          testcaseName(testcaseName) {
+        attrs.min_size = min_size;
+        attrs.max_size = max_size;
+        attrs.aspect_ratio = aspect_ratio;
+        attrs.scale_all_sizes = scale_all_size;
+        attrs.min_max_aspect_ratios_order = min_max_aspect_ratios_order;
+    }
+
+    ov::op::v8::PriorBox::Attributes attrs;
+    ov::Shape layerShapeShape;
+    ov::Shape imageShapeShape;
+    ov::element::Type inType;
+    ov::element::Type outType;
+    ov::runtime::Tensor layerShapeData;
+    ov::runtime::Tensor imageShapeData;
+    ov::runtime::Tensor refData;
+    std::string testcaseName;
+};
+
 class ReferencePriorBoxLayerTest : public testing::TestWithParam<PriorBoxParams>, public CommonReferenceTest {
 public:
     void SetUp() override {
@@ -68,10 +111,43 @@ public:
 
 private:
     static std::shared_ptr<Function> CreateFunction(const PriorBoxParams& params) {
-        auto LS = std::make_shared<opset1::Constant>(params.inType, params.layerShapeShape, params.layerShapeData.data());
-        auto IS = std::make_shared<opset1::Constant>(params.inType, params.imageShapeShape, params.imageShapeData.data());
+        auto LS =
+            std::make_shared<op::v0::Constant>(params.inType, params.layerShapeShape, params.layerShapeData.data());
+        auto IS =
+            std::make_shared<op::v0::Constant>(params.inType, params.imageShapeShape, params.imageShapeData.data());
         const auto PriorBox = std::make_shared<op::v0::PriorBox>(LS, IS, params.attrs);
-        return std::make_shared<ov::Function>(NodeVector {PriorBox}, ParameterVector {});
+        return std::make_shared<ov::Function>(NodeVector{PriorBox}, ParameterVector{});
+    }
+};
+
+class ReferencePriorBoxV8LayerTest : public testing::TestWithParam<PriorBoxV8Params>, public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        inputData = {};
+        refOutData = {params.refData};
+    }
+    static std::string getTestCaseName(const testing::TestParamInfo<PriorBoxV8Params>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "layerShapeShape=" << param.layerShapeShape << "_";
+        result << "imageShapeShape=" << param.imageShapeShape << "_";
+        result << "iType=" << param.inType << "_";
+        result << "oType=" << param.outType;
+        if (param.testcaseName != "")
+            result << "_" << param.testcaseName;
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const PriorBoxV8Params& params) {
+        auto LS =
+            std::make_shared<op::v0::Constant>(params.inType, params.layerShapeShape, params.layerShapeData.data());
+        auto IS =
+            std::make_shared<op::v0::Constant>(params.inType, params.imageShapeShape, params.imageShapeData.data());
+        const auto PriorBoxV8 = std::make_shared<op::v8::PriorBox>(LS, IS, params.attrs);
+        return std::make_shared<ov::Function>(NodeVector{PriorBoxV8}, ParameterVector{});
     }
 };
 
@@ -79,13 +155,20 @@ TEST_P(ReferencePriorBoxLayerTest, CompareWithRefs) {
     Exec();
 }
 
+TEST_P(ReferencePriorBoxV8LayerTest, CompareWithRefs) {
+    Exec();
+}
+
 template <element::Type_t IN_ET>
 std::vector<PriorBoxParams> generatePriorBoxFloatParams() {
     using T = typename element_type_traits<IN_ET>::value_type;
 
-    std::vector<PriorBoxParams> priorBoxParams {
-        PriorBoxParams({2.0f}, {1.5f}, false,
-                       {2}, {2},
+    std::vector<PriorBoxParams> priorBoxParams{
+        PriorBoxParams({2.0f},
+                       {1.5f},
+                       false,
+                       {2},
+                       {2},
                        IN_ET,
                        std::vector<T>{2, 2},
                        std::vector<T>{10, 10},
@@ -101,8 +184,37 @@ std::vector<PriorBoxParams> generatePriorBoxFloatParams() {
     return priorBoxParams;
 }
 
+template <element::Type_t IN_ET>
+std::vector<PriorBoxV8Params> generatePriorBoxV8FloatParams() {
+    using T = typename element_type_traits<IN_ET>::value_type;
+
+    std::vector<PriorBoxV8Params> priorBoxV8Params{
+        PriorBoxV8Params(
+            {2.0f},
+            {5.0f},
+            {1.5f},
+            true,
+            false,
+            {2},
+            {2},
+            IN_ET,
+            std::vector<T>{2, 2},
+            std::vector<T>{10, 10},
+            std::vector<float>{
+                0.15, 0.15, 0.35, 0.35, 0.127526, 0.16835, 0.372474, 0.33165, 0.0918861, 0.0918861, 0.408114, 0.408114,
+                0.65, 0.15, 0.85, 0.35, 0.627526, 0.16835, 0.872474, 0.33165, 0.591886,  0.0918861, 0.908114, 0.408114,
+                0.15, 0.65, 0.35, 0.85, 0.127526, 0.66835, 0.372474, 0.83165, 0.0918861, 0.591886,  0.408114, 0.908114,
+                0.65, 0.65, 0.85, 0.85, 0.627526, 0.66835, 0.872474, 0.83165, 0.591886,  0.591886,  0.908114, 0.908114,
+                0.1,  0.1,  0.1,  0.1,  0.1,      0.1,     0.1,      0.1,     0.1,       0.1,       0.1,      0.1,
+                0.1,  0.1,  0.1,  0.1,  0.1,      0.1,     0.1,      0.1,     0.1,       0.1,       0.1,      0.1,
+                0.1,  0.1,  0.1,  0.1,  0.1,      0.1,     0.1,      0.1,     0.1,       0.1,       0.1,      0.1,
+                0.1,  0.1,  0.1,  0.1,  0.1,      0.1,     0.1,      0.1,     0.1,       0.1,       0.1,      0.1}),
+    };
+    return priorBoxV8Params;
+}
+
 std::vector<PriorBoxParams> generatePriorBoxCombinedParams() {
-    const std::vector<std::vector<PriorBoxParams>> priorBoxTypeParams {
+    const std::vector<std::vector<PriorBoxParams>> priorBoxTypeParams{
         generatePriorBoxFloatParams<element::Type_t::i64>(),
         generatePriorBoxFloatParams<element::Type_t::i32>(),
         generatePriorBoxFloatParams<element::Type_t::i16>(),
@@ -111,7 +223,7 @@ std::vector<PriorBoxParams> generatePriorBoxCombinedParams() {
         generatePriorBoxFloatParams<element::Type_t::u32>(),
         generatePriorBoxFloatParams<element::Type_t::u16>(),
         generatePriorBoxFloatParams<element::Type_t::u8>(),
-        };
+    };
     std::vector<PriorBoxParams> combinedParams;
 
     for (const auto& params : priorBoxTypeParams) {
@@ -120,7 +232,32 @@ std::vector<PriorBoxParams> generatePriorBoxCombinedParams() {
     return combinedParams;
 }
 
-INSTANTIATE_TEST_SUITE_P(smoke_PriorBox_With_Hardcoded_Refs, ReferencePriorBoxLayerTest,
-    testing::ValuesIn(generatePriorBoxCombinedParams()), ReferencePriorBoxLayerTest::getTestCaseName);
+std::vector<PriorBoxV8Params> generatePriorBoxV8CombinedParams() {
+    const std::vector<std::vector<PriorBoxV8Params>> priorBoxV8TypeParams{
+        generatePriorBoxV8FloatParams<element::Type_t::i64>(),
+        generatePriorBoxV8FloatParams<element::Type_t::i32>(),
+        generatePriorBoxV8FloatParams<element::Type_t::i16>(),
+        generatePriorBoxV8FloatParams<element::Type_t::i8>(),
+        generatePriorBoxV8FloatParams<element::Type_t::u64>(),
+        generatePriorBoxV8FloatParams<element::Type_t::u32>(),
+        generatePriorBoxV8FloatParams<element::Type_t::u16>(),
+        generatePriorBoxV8FloatParams<element::Type_t::u8>(),
+    };
+    std::vector<PriorBoxV8Params> combinedParams;
 
-} // namespace
\ No newline at end of file
+    for (const auto& params : priorBoxV8TypeParams) {
+        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
+    }
+    return combinedParams;
+}
+
+INSTANTIATE_TEST_SUITE_P(smoke_PriorBox_With_Hardcoded_Refs,
+                         ReferencePriorBoxLayerTest,
+                         testing::ValuesIn(generatePriorBoxCombinedParams()),
+                         ReferencePriorBoxLayerTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_PriorBoxV8_With_Hardcoded_Refs,
+                         ReferencePriorBoxV8LayerTest,
+                         testing::ValuesIn(generatePriorBoxV8CombinedParams()),
+                         ReferencePriorBoxV8LayerTest::getTestCaseName);
+}  // namespace
\ No newline at end of file
diff --git a/inference-engine/tests/functional/inference_engine/serialization/single_layer/prior_box.cpp b/inference-engine/tests/functional/inference_engine/serialization/single_layer/prior_box.cpp
index 91def7d74c8..38404e9868b 100644
--- a/inference-engine/tests/functional/inference_engine/serialization/single_layer/prior_box.cpp
+++ b/inference-engine/tests/functional/inference_engine/serialization/single_layer/prior_box.cpp
@@ -66,6 +66,10 @@ namespace {
         true, false
     };
 
+    const std::vector<bool> min_max_aspect_ratios_order = {
+        true, false
+    };
+
     const std::vector<size_t> inputShape = {128, 128};
     const std::vector<size_t> imageShape = {50, 50};
 
@@ -81,7 +85,8 @@ namespace {
             ::testing::ValuesIn(steps),
             ::testing::ValuesIn(offsets),
             ::testing::ValuesIn(variances),
-            ::testing::ValuesIn(scale_all_sizes));
+            ::testing::ValuesIn(scale_all_sizes),
+            ::testing::ValuesIn(min_max_aspect_ratios_order));
 
     INSTANTIATE_TEST_SUITE_P(smoke_PriorBox_Basic, PriorBoxLayerTest,
                             ::testing::Combine(
diff --git a/inference-engine/tests/functional/inference_engine/transformations/const_folding_prior_box.cpp b/inference-engine/tests/functional/inference_engine/transformations/const_folding_prior_box.cpp
index a56ea2b612e..b62629d0728 100644
--- a/inference-engine/tests/functional/inference_engine/transformations/const_folding_prior_box.cpp
+++ b/inference-engine/tests/functional/inference_engine/transformations/const_folding_prior_box.cpp
@@ -10,6 +10,7 @@
 #include <memory>
 
 #include <ngraph/opsets/opset3.hpp>
+#include <ngraph/opsets/opset8.hpp>
 #include <ngraph/function.hpp>
 #include <transformations/init_node_info.hpp>
 #include <ngraph/pass/constant_folding.hpp>
@@ -24,7 +25,7 @@ TEST(TransformationTests, ConstFoldingPriorBox) {
 
     {
         auto in = std::make_shared<ngraph::opset3::Parameter>(ngraph::element::i64, ngraph::Shape{2});
-        ngraph::op::PriorBoxAttrs attrs;
+        ngraph::op::v0::PriorBox::Attributes attrs;
         attrs.min_size = {256.0f};
         attrs.max_size = {315.0f};
         attrs.aspect_ratio = {2.0f};
@@ -119,7 +120,7 @@ TEST(TransformationTests, ConstFoldingPriorBoxSubgraph) {
     {
         auto in = std::make_shared<ngraph::opset3::Parameter>(ngraph::element::i64, ngraph::Shape{2, 3, 1, 1});
         auto in_2 = std::make_shared<ngraph::opset3::Parameter>(ngraph::element::i64, ngraph::Shape{2, 3, 300, 300});
-        ngraph::op::PriorBoxAttrs attrs;
+        ngraph::op::v0::PriorBox::Attributes attrs;
         attrs.min_size = {256.0f};
         attrs.max_size = {315.0f};
         attrs.aspect_ratio = {2.0f};
@@ -225,3 +226,111 @@ TEST(TransformationTests, ConstFoldingPriorBoxClusteredSubgraph) {
     EXPECT_TRUE(ref != nullptr);
     EXPECT_TRUE(fused->get_vector<float>() == ref->get_vector<float>());
 }
+
+TEST(TransformationTests, ConstFoldingPriorBox8) {
+    std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+
+    {
+        auto in = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape {2});
+        ngraph::op::v8::PriorBox::Attributes attrs;
+        attrs.min_size = {2.0f};
+        attrs.max_size = {5.0f};
+        attrs.aspect_ratio = {1.5f};
+        attrs.scale_all_sizes = true;
+        attrs.min_max_aspect_ratios_order = false;
+
+        auto layer_shape = ngraph::opset8::Constant::create<int64_t>(ngraph::element::i64, ngraph::Shape {2}, {2, 2});
+        auto image_shape = ngraph::opset8::Constant::create<int64_t>(ngraph::element::i64, ngraph::Shape {2}, {10, 10});
+        auto pb = std::make_shared<ngraph::opset8::PriorBox>(layer_shape, image_shape, attrs);
+        auto res = std::make_shared<ngraph::opset8::Result>(pb);
+        f = std::make_shared<ngraph::Function>(ngraph::NodeVector {res}, ngraph::ParameterVector {in});
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::InitNodeInfo>();
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+        manager.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+
+    {
+        auto layer_shape = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape {2});
+        auto const_prior_box = ngraph::opset8::Constant::create<float>(
+            ngraph::element::f32, ngraph::Shape {2, 48},
+            {0.15,     0.15,    0.35,     0.35,     0.127526,  0.16835,  0.372474, 0.33165,   0.0918861, 0.0918861, 0.408114, 0.408114, 0.65,     0.15,
+             0.85,     0.35,    0.627526, 0.16835,  0.872474,  0.33165,  0.591886, 0.0918861, 0.908114,  0.408114,  0.15,     0.65,     0.35,     0.85,
+             0.127526, 0.66835, 0.372474, 0.83165,  0.0918861, 0.591886, 0.408114, 0.908114,  0.65,      0.65,      0.85,     0.85,     0.627526, 0.66835,
+             0.872474, 0.83165, 0.591886, 0.591886, 0.908114,  0.908114, 0.1,      0.1,       0.1,       0.1,       0.1,      0.1,      0.1,      0.1,
+             0.1,      0.1,     0.1,      0.1,      0.1,       0.1,      0.1,      0.1,       0.1,       0.1,       0.1,      0.1,      0.1,      0.1,
+             0.1,      0.1,     0.1,      0.1,      0.1,       0.1,      0.1,      0.1,       0.1,       0.1,       0.1,      0.1,      0.1,      0.1,
+             0.1,      0.1,     0.1,      0.1,      0.1,       0.1,      0.1,      0.1,       0.1,       0.1,       0.1,      0.1});
+        auto res = std::make_shared<ngraph::opset8::Result>(const_prior_box);
+        f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector {res}, ngraph::ParameterVector {layer_shape});
+    }
+
+    auto res = compare_functions(f, f_ref);
+    ASSERT_TRUE(res.first) << res.second;
+
+    auto fused = std::dynamic_pointer_cast<ngraph::opset8::Constant>(f->get_result()->input_value(0).get_node_shared_ptr());
+    auto ref = std::dynamic_pointer_cast<ngraph::opset8::Constant>(f->get_result()->input_value(0).get_node_shared_ptr());
+
+    EXPECT_TRUE(fused != nullptr);
+    EXPECT_TRUE(ref != nullptr);
+    EXPECT_TRUE(fused->get_vector<float>() == ref->get_vector<float>());
+}
+
+TEST(TransformationTests, ConstFoldingPriorBox8Subgraph) {
+    std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+
+    {
+        auto in = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape {2, 3, 2, 2});
+        auto in_2 = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape {2, 3, 10, 10});
+        ngraph::op::v8::PriorBox::Attributes attrs;
+        attrs.min_size = {2.0f};
+        attrs.max_size = {5.0f};
+        attrs.aspect_ratio = {1.5f};
+        attrs.scale_all_sizes = true;
+        attrs.min_max_aspect_ratios_order = false;
+
+        auto layer_shape = std::make_shared<ngraph::opset8::ShapeOf>(in);
+        auto image_shape = std::make_shared<ngraph::opset8::ShapeOf>(in_2);
+
+        auto begin = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape {1}, {2});
+        auto end = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape {1}, {4});
+        auto stride = ngraph::opset8::Constant::create(ngraph::element::i64, ngraph::Shape {1}, {1});
+        auto ss_data = std::make_shared<ngraph::opset8::StridedSlice>(layer_shape, begin, end, stride, std::vector<int64_t> {0}, std::vector<int64_t> {0});
+
+        auto ss_image = std::make_shared<ngraph::opset8::StridedSlice>(image_shape, begin, end, stride, std::vector<int64_t> {0}, std::vector<int64_t> {0});
+        auto pb = std::make_shared<ngraph::opset8::PriorBox>(ss_data, ss_image, attrs);
+        auto res = std::make_shared<ngraph::opset8::Result>(pb);
+        f = std::make_shared<ngraph::Function>(ngraph::NodeVector {res}, ngraph::ParameterVector {in, in_2});
+        ngraph::pass::Manager manager;
+        manager.register_pass<ngraph::pass::InitNodeInfo>();
+        manager.register_pass<ngraph::pass::ConstantFolding>();
+        manager.run_passes(f);
+        ASSERT_NO_THROW(check_rt_info(f));
+    }
+
+    {
+        auto layer_shape = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::i64, ngraph::Shape {2});
+        auto const_prior_box = ngraph::opset8::Constant::create<float>(
+            ngraph::element::f32, ngraph::Shape {2, 48},
+            {0.15,     0.15,    0.35,     0.35,     0.127526,  0.16835,  0.372474, 0.33165,   0.0918861, 0.0918861, 0.408114, 0.408114, 0.65,     0.15,
+             0.85,     0.35,    0.627526, 0.16835,  0.872474,  0.33165,  0.591886, 0.0918861, 0.908114,  0.408114,  0.15,     0.65,     0.35,     0.85,
+             0.127526, 0.66835, 0.372474, 0.83165,  0.0918861, 0.591886, 0.408114, 0.908114,  0.65,      0.65,      0.85,     0.85,     0.627526, 0.66835,
+             0.872474, 0.83165, 0.591886, 0.591886, 0.908114,  0.908114, 0.1,      0.1,       0.1,       0.1,       0.1,      0.1,      0.1,      0.1,
+             0.1,      0.1,     0.1,      0.1,      0.1,       0.1,      0.1,      0.1,       0.1,       0.1,       0.1,      0.1,      0.1,      0.1,
+             0.1,      0.1,     0.1,      0.1,      0.1,       0.1,      0.1,      0.1,       0.1,       0.1,       0.1,      0.1,      0.1,      0.1,
+             0.1,      0.1,     0.1,      0.1,      0.1,       0.1,      0.1,      0.1,       0.1,       0.1,       0.1,      0.1});
+        auto res = std::make_shared<ngraph::opset8::Result>(const_prior_box);
+        f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector {res}, ngraph::ParameterVector {layer_shape});
+    }
+
+    auto res = compare_functions(f, f_ref);
+    ASSERT_TRUE(res.first) << res.second;
+
+    auto fused = std::dynamic_pointer_cast<ngraph::opset8::Constant>(f->get_result()->input_value(0).get_node_shared_ptr());
+    auto ref = std::dynamic_pointer_cast<ngraph::opset8::Constant>(f->get_result()->input_value(0).get_node_shared_ptr());
+
+    EXPECT_TRUE(fused != nullptr);
+    EXPECT_TRUE(ref != nullptr);
+    EXPECT_TRUE(fused->get_vector<float>() == ref->get_vector<float>());
+}
diff --git a/inference-engine/tests/functional/inference_engine/transformations/convert_prior_box_v8_to_v0_test.cpp b/inference-engine/tests/functional/inference_engine/transformations/convert_prior_box_v8_to_v0_test.cpp
new file mode 100644
index 00000000000..56bcab84104
--- /dev/null
+++ b/inference-engine/tests/functional/inference_engine/transformations/convert_prior_box_v8_to_v0_test.cpp
@@ -0,0 +1,77 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <memory>
+#include <ngraph/function.hpp>
+#include <ngraph/opsets/opset1.hpp>
+#include <ngraph/opsets/opset8.hpp>
+#include <ngraph/pass/manager.hpp>
+#include <string>
+#include <transformations/init_node_info.hpp>
+#include <transformations/op_conversions/convert_prior_box_v8_to_v0.hpp>
+
+#include "common_test_utils/ngraph_test_utils.hpp"
+
+using namespace testing;
+using namespace ngraph;
+
+TEST_F(TransformationTestsF, ConvertPriorBox8To0) {
+    {
+        const Shape input_shape {2, 2};
+        const Shape image_Shape {10, 10};
+        op::v8::PriorBox::Attributes attrs;
+        attrs.min_size = {2.0f};
+        attrs.max_size = {5.0f};
+        attrs.aspect_ratio = {1.5f};
+        attrs.scale_all_sizes = true;
+
+        auto input = std::make_shared<opset8::Parameter>(element::i64, input_shape);
+        auto image = std::make_shared<opset8::Parameter>(element::i64, image_Shape);
+
+        auto prior_box = std::make_shared<opset8::PriorBox>(input, image, attrs);
+
+        function = std::make_shared<Function>(NodeVector {prior_box}, ParameterVector {input, image});
+        manager.register_pass<pass::ConvertPriorBox8To0>();
+    }
+
+    {
+        const Shape input_shape {2, 2};
+        const Shape image_Shape {10, 10};
+        op::v0::PriorBox::Attributes attrs;
+        attrs.min_size = {2.0f};
+        attrs.max_size = {5.0f};
+        attrs.aspect_ratio = {1.5f};
+        attrs.scale_all_sizes = true;
+
+        auto input = std::make_shared<opset1::Parameter>(element::i64, input_shape);
+        auto image = std::make_shared<opset1::Parameter>(element::i64, image_Shape);
+
+        auto prior_box = std::make_shared<opset1::PriorBox>(input, image, attrs);
+
+        function_ref = std::make_shared<Function>(NodeVector {prior_box}, ParameterVector {input, image});
+    }
+}
+
+TEST_F(TransformationTestsF, ConvertPriorBox8To0_min_max_aspect_ratios_order) {
+    {
+        const Shape input_shape {2, 2};
+        const Shape image_Shape {10, 10};
+        op::v8::PriorBox::Attributes attrs;
+        attrs.min_size = {2.0f};
+        attrs.max_size = {5.0f};
+        attrs.aspect_ratio = {1.5f};
+        attrs.scale_all_sizes = true;
+        attrs.min_max_aspect_ratios_order = false;
+
+        auto input = std::make_shared<opset8::Parameter>(element::i64, input_shape);
+        auto image = std::make_shared<opset8::Parameter>(element::i64, image_Shape);
+
+        auto prior_box = std::make_shared<opset8::PriorBox>(input, image, attrs);
+
+        function = std::make_shared<Function>(NodeVector {prior_box}, ParameterVector {input, image});
+        manager.register_pass<pass::ConvertPriorBox8To0>();
+    }
+}
\ No newline at end of file
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/prior_box.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/prior_box.cpp
index d4c56aa0078..4be78d242b8 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/prior_box.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/prior_box.cpp
@@ -50,6 +50,9 @@ const std::vector<std::vector<float>> variances = {
 const std::vector<bool> scale_all_sizes = {
     false, true};
 
+const std::vector<bool> min_max_aspect_ratios_order = {
+    false, true};
+
 const std::vector<size_t> inputShape = {300, 300};
 const std::vector<size_t> imageShape = {32, 32};
 
@@ -65,7 +68,8 @@ const auto layerSpecificParams = ::testing::Combine(
     ::testing::ValuesIn(steps),
     ::testing::ValuesIn(offsets),
     ::testing::ValuesIn(variances),
-    ::testing::ValuesIn(scale_all_sizes));
+    ::testing::ValuesIn(scale_all_sizes),
+    ::testing::ValuesIn(min_max_aspect_ratios_order));
 
 INSTANTIATE_TEST_SUITE_P(smoke_PriorBox_Basic, PriorBoxLayerTest,
                             ::testing::Combine(
diff --git a/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/prior_box.hpp b/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/prior_box.hpp
index 15fcb6d1127..466112a1d96 100644
--- a/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/prior_box.hpp
+++ b/inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/prior_box.hpp
@@ -39,7 +39,8 @@ using priorBoxSpecificParams =  std::tuple<
         float,              // step
         float,              // offset
         std::vector<float>, // variance
-        bool>;              // scale_all_sizes
+        bool,               // scale_all_sizes
+        bool>;              // min_max_aspect_ratios_order
 
 typedef std::tuple<
         priorBoxSpecificParams,
@@ -73,6 +74,7 @@ protected:
     bool clip;
     bool flip;
     bool scale_all_sizes;
+    bool min_max_aspect_ratios_order;
 
     void SetUp() override;
 };
diff --git a/inference-engine/tests/functional/shared_test_classes/src/single_layer/prior_box.cpp b/inference-engine/tests/functional/shared_test_classes/src/single_layer/prior_box.cpp
index f3b95141ae0..b901dfd7bfd 100644
--- a/inference-engine/tests/functional/shared_test_classes/src/single_layer/prior_box.cpp
+++ b/inference-engine/tests/functional/shared_test_classes/src/single_layer/prior_box.cpp
@@ -21,10 +21,10 @@ std::string PriorBoxLayerTest::getTestCaseName(const testing::TestParamInfo<prio
 
     std::vector<float> min_size, max_size, aspect_ratio, density, fixed_ratio, fixed_size, variance;
     float step, offset;
-    bool clip, flip, scale_all_sizes;
+    bool clip, flip, scale_all_sizes, min_max_aspect_ratios_order;
     std::tie(min_size, max_size, aspect_ratio,
              density, fixed_ratio, fixed_size, clip,
-             flip, step, offset, variance, scale_all_sizes) = specParams;
+             flip, step, offset, variance, scale_all_sizes, min_max_aspect_ratios_order) = specParams;
 
     std::ostringstream result;
     const char separator = '_';
@@ -47,6 +47,7 @@ std::string PriorBoxLayerTest::getTestCaseName(const testing::TestParamInfo<prio
     result << "clip=" << clip << separator;
     result << "flip=" << flip<< separator;
     result << "scale_all=" << scale_all_sizes << separator;
+    result << "min_max_aspect_ratios_order=" << min_max_aspect_ratios_order << separator;
     result << "trgDev=" << targetDevice;
 
     return result.str();
@@ -58,14 +59,14 @@ void PriorBoxLayerTest::SetUp() {
              inPrc, outPrc, inLayout, outLayout,
              inputShapes, imageShapes, targetDevice) = GetParam();
 
-    std::tie(min_size, max_size, aspect_ratio,
-             density, fixed_ratio, fixed_size, clip,
-             flip, step, offset, variance, scale_all_sizes) = specParams;
+    std::tie(min_size, max_size, aspect_ratio, density, fixed_ratio, fixed_size,
+             clip, flip, step, offset, variance, scale_all_sizes,
+             min_max_aspect_ratios_order) = specParams;
 
     auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
     auto params = ngraph::builder::makeParams(ngPrc, {inputShapes, imageShapes});
 
-    ngraph::op::PriorBoxAttrs attributes;
+    ngraph::op::v8::PriorBox::Attributes attributes;
     attributes.min_size = min_size;
     attributes.max_size = max_size;
     attributes.aspect_ratio = aspect_ratio;
@@ -77,10 +78,11 @@ void PriorBoxLayerTest::SetUp() {
     attributes.offset = offset;
     attributes.clip = clip;
     attributes.flip = flip;
+    attributes.min_max_aspect_ratios_order = min_max_aspect_ratios_order;
 
     auto shape_of_1 = std::make_shared<ngraph::opset3::ShapeOf>(params[0]);
     auto shape_of_2 = std::make_shared<ngraph::opset3::ShapeOf>(params[1]);
-    auto priorBox = std::make_shared<ngraph::op::PriorBox>(
+    auto priorBox = std::make_shared<ngraph::op::v8::PriorBox>(
         shape_of_1,
         shape_of_2,
         attributes);
diff --git a/src/bindings/python/src/compatibility/ngraph/opset8/__init__.py b/src/bindings/python/src/compatibility/ngraph/opset8/__init__.py
index 74029d8869e..8c00720f620 100644
--- a/src/bindings/python/src/compatibility/ngraph/opset8/__init__.py
+++ b/src/bindings/python/src/compatibility/ngraph/opset8/__init__.py
@@ -102,7 +102,7 @@ from ngraph.opset1.ops import pad
 from ngraph.opset1.ops import parameter
 from ngraph.opset1.ops import power
 from ngraph.opset1.ops import prelu
-from ngraph.opset1.ops import prior_box
+from ngraph.opset8.ops import prior_box
 from ngraph.opset1.ops import prior_box_clustered
 from ngraph.opset1.ops import psroi_pooling
 from ngraph.opset4.ops import proposal
diff --git a/src/bindings/python/src/compatibility/ngraph/opset8/ops.py b/src/bindings/python/src/compatibility/ngraph/opset8/ops.py
index fdf71ea6f86..19d8f9cf731 100644
--- a/src/bindings/python/src/compatibility/ngraph/opset8/ops.py
+++ b/src/bindings/python/src/compatibility/ngraph/opset8/ops.py
@@ -417,3 +417,108 @@ def gather_nd(
     }
 
     return _get_node_factory_opset8().create("GatherND", inputs, attributes)
+
+
+def prior_box(
+    layer_shape: Node, image_shape: NodeInput, attrs: dict, name: Optional[str] = None
+) -> Node:
+    """Generate prior boxes of specified sizes and aspect ratios across all dimensions.
+
+    @param  layer_shape:  Shape of layer for which prior boxes are computed.
+    @param  image_shape:  Shape of image to which prior boxes are scaled.
+    @param  attrs:        The dictionary containing key, value pairs for attributes.
+    @param  name:         Optional name for the output node.
+    @return Node representing prior box operation.
+    Available attributes are:
+    * min_size                      The minimum box size (in pixels).
+                                    Range of values: positive floating point numbers
+                                    Default value: []
+                                    Required: no
+    * max_size                      The maximum box size (in pixels).
+                                    Range of values: positive floating point numbers
+                                    Default value: []
+                                    Required: no
+    * aspect_ratio                  Aspect ratios of prior boxes.
+                                    Range of values: set of positive floating point numbers
+                                    Default value: []
+                                    Required: no
+    * flip                          The flag that denotes that each aspect_ratio is duplicated and flipped.
+                                    Range of values: {True, False}
+                                    Default value: False
+                                    Required: no
+    * clip                          The flag that denotes if each value in the output tensor should be clipped
+                                    to [0,1] interval.
+                                    Range of values: {True, False}
+                                    Default value: False
+                                    Required: no
+    * step                          The distance between box centers.
+                                    Range of values: floating point non-negative number
+                                    Default value: 0
+                                    Required: no
+    * offset                        This is a shift of box respectively to top left corner.
+                                    Range of values: floating point non-negative number
+                                    Default value: None
+                                    Required: yes
+    * variance                      The variance denotes a variance of adjusting bounding boxes. The attribute
+                                    could contain 0, 1 or 4 elements.
+                                    Range of values: floating point positive numbers
+                                    Default value: []
+                                    Required: no
+    * scale_all_sizes               The flag that denotes type of inference.
+                                    Range of values: False - max_size is ignored
+                                                     True  - max_size is used
+                                    Default value: True
+                                    Required: no
+    * fixed_ratio                   This is an aspect ratio of a box.
+                                    Range of values: a list of positive floating-point numbers
+                                    Default value: None
+                                    Required: no
+    * fixed_size                    This is an initial box size (in pixels).
+                                    Range of values: a list of positive floating-point numbers
+                                    Default value: None
+                                    Required: no
+    * density                       This is the square root of the number of boxes of each type.
+                                    Range of values: a list of positive floating-point numbers
+                                    Default value: None
+                                    Required: no
+    * min_max_aspect_ratios_order   The flag that denotes the order of output prior box.
+                                    Range of values: False - the output prior box is in order of
+                                                             [min, aspect_ratios, max]
+                                                     True  - the output prior box is in order of
+                                                             [min, max, aspect_ratios]
+                                    Default value: True
+                                    Required: no
+    Example of attribute dictionary:
+    @code{.py}
+        # just required ones
+        attrs = {
+            'offset': 85,
+        }
+        attrs = {
+            'offset': 85,
+            'flip': True,
+            'clip': True,
+            'fixed_size': [32, 64, 128]
+        }
+    @endcode
+    Optional attributes which are absent from dictionary will be set with corresponding default.
+    """
+    requirements = [
+        ("offset", True, np.floating, is_non_negative_value),
+        ("min_size", False, np.floating, is_positive_value),
+        ("max_size", False, np.floating, is_positive_value),
+        ("aspect_ratio", False, np.floating, is_positive_value),
+        ("flip", False, np.bool_, None),
+        ("clip", False, np.bool_, None),
+        ("step", False, np.floating, is_non_negative_value),
+        ("variance", False, np.floating, is_positive_value),
+        ("scale_all_sizes", False, np.bool_, None),
+        ("fixed_ratio", False, np.floating, is_positive_value),
+        ("fixed_size", False, np.floating, is_positive_value),
+        ("density", False, np.floating, is_positive_value),
+        ("min_max_aspect_ratios_order", False, np.bool_, None),
+    ]
+
+    check_valid_attributes("PriorBox", attrs, requirements)
+
+    return _get_node_factory_opset8().create("PriorBox", [layer_shape, as_node(image_shape)], attrs)
diff --git a/src/bindings/python/src/openvino/opset8/__init__.py b/src/bindings/python/src/openvino/opset8/__init__.py
index c53da9026b8..7e188be3b6a 100644
--- a/src/bindings/python/src/openvino/opset8/__init__.py
+++ b/src/bindings/python/src/openvino/opset8/__init__.py
@@ -102,7 +102,7 @@ from openvino.opset1.ops import pad
 from openvino.opset1.ops import parameter
 from openvino.opset1.ops import power
 from openvino.opset1.ops import prelu
-from openvino.opset1.ops import prior_box
+from openvino.opset8.ops import prior_box
 from openvino.opset1.ops import prior_box_clustered
 from openvino.opset1.ops import psroi_pooling
 from openvino.opset4.ops import proposal
diff --git a/src/bindings/python/src/openvino/opset8/ops.py b/src/bindings/python/src/openvino/opset8/ops.py
index 67559a8dc49..728ea50dc5d 100644
--- a/src/bindings/python/src/openvino/opset8/ops.py
+++ b/src/bindings/python/src/openvino/opset8/ops.py
@@ -417,3 +417,109 @@ def gather_nd(
     }
 
     return _get_node_factory_opset8().create("GatherND", inputs, attributes)
+
+
+@nameable_op
+def prior_box(
+    layer_shape: Node, image_shape: NodeInput, attrs: dict, name: Optional[str] = None
+) -> Node:
+    """Generate prior boxes of specified sizes and aspect ratios across all dimensions.
+
+    @param  layer_shape:  Shape of layer for which prior boxes are computed.
+    @param  image_shape:  Shape of image to which prior boxes are scaled.
+    @param  attrs:        The dictionary containing key, value pairs for attributes.
+    @param  name:         Optional name for the output node.
+    @return Node representing prior box operation.
+    Available attributes are:
+    * min_size                      The minimum box size (in pixels).
+                                    Range of values: positive floating point numbers
+                                    Default value: []
+                                    Required: no
+    * max_size                      The maximum box size (in pixels).
+                                    Range of values: positive floating point numbers
+                                    Default value: []
+                                    Required: no
+    * aspect_ratio                  Aspect ratios of prior boxes.
+                                    Range of values: set of positive floating point numbers
+                                    Default value: []
+                                    Required: no
+    * flip                          The flag that denotes that each aspect_ratio is duplicated and flipped.
+                                    Range of values: {True, False}
+                                    Default value: False
+                                    Required: no
+    * clip                          The flag that denotes if each value in the output tensor should be clipped
+                                    to [0,1] interval.
+                                    Range of values: {True, False}
+                                    Default value: False
+                                    Required: no
+    * step                          The distance between box centers.
+                                    Range of values: floating point non-negative number
+                                    Default value: 0
+                                    Required: no
+    * offset                        This is a shift of box respectively to top left corner.
+                                    Range of values: floating point non-negative number
+                                    Default value: None
+                                    Required: yes
+    * variance                      The variance denotes a variance of adjusting bounding boxes. The attribute
+                                    could contain 0, 1 or 4 elements.
+                                    Range of values: floating point positive numbers
+                                    Default value: []
+                                    Required: no
+    * scale_all_sizes               The flag that denotes type of inference.
+                                    Range of values: False - max_size is ignored
+                                                     True  - max_size is used
+                                    Default value: True
+                                    Required: no
+    * fixed_ratio                   This is an aspect ratio of a box.
+                                    Range of values: a list of positive floating-point numbers
+                                    Default value: None
+                                    Required: no
+    * fixed_size                    This is an initial box size (in pixels).
+                                    Range of values: a list of positive floating-point numbers
+                                    Default value: None
+                                    Required: no
+    * density                       This is the square root of the number of boxes of each type.
+                                    Range of values: a list of positive floating-point numbers
+                                    Default value: None
+                                    Required: no
+    * min_max_aspect_ratios_order   The flag that denotes the order of output prior box.
+                                    Range of values: False - the output prior box is in order of
+                                                             [min, aspect_ratios, max]
+                                                     True  - the output prior box is in order of
+                                                             [min, max, aspect_ratios]
+                                    Default value: True
+                                    Required: no
+    Example of attribute dictionary:
+    @code{.py}
+        # just required ones
+        attrs = {
+            'offset': 85,
+        }
+        attrs = {
+            'offset': 85,
+            'flip': True,
+            'clip': True,
+            'fixed_size': [32, 64, 128]
+        }
+    @endcode
+    Optional attributes which are absent from dictionary will be set with corresponding default.
+    """
+    requirements = [
+        ("offset", True, np.floating, is_non_negative_value),
+        ("min_size", False, np.floating, is_positive_value),
+        ("max_size", False, np.floating, is_positive_value),
+        ("aspect_ratio", False, np.floating, is_positive_value),
+        ("flip", False, np.bool_, None),
+        ("clip", False, np.bool_, None),
+        ("step", False, np.floating, is_non_negative_value),
+        ("variance", False, np.floating, is_positive_value),
+        ("scale_all_sizes", False, np.bool_, None),
+        ("fixed_ratio", False, np.floating, is_positive_value),
+        ("fixed_size", False, np.floating, is_positive_value),
+        ("density", False, np.floating, is_positive_value),
+        ("min_max_aspect_ratios_order", False, np.bool_, None),
+    ]
+
+    check_valid_attributes("PriorBox", attrs, requirements)
+
+    return _get_node_factory_opset8().create("PriorBox", [layer_shape, as_node(image_shape)], attrs)
diff --git a/src/common/transformations/include/transformations/op_conversions/convert_prior_box_v8_to_v0.hpp b/src/common/transformations/include/transformations/op_conversions/convert_prior_box_v8_to_v0.hpp
new file mode 100644
index 00000000000..593ec33f818
--- /dev/null
+++ b/src/common/transformations/include/transformations/op_conversions/convert_prior_box_v8_to_v0.hpp
@@ -0,0 +1,26 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <ngraph/pass/graph_rewrite.hpp>
+#include <transformations_visibility.hpp>
+
+namespace ngraph {
+namespace pass {
+
+class TRANSFORMATIONS_API ConvertPriorBox8To0;
+
+}  // namespace pass
+}  // namespace ngraph
+
+/**
+ * @ingroup ie_transformation_common_api
+ * @brief ConvertPriorBox8To1 converts v8::PriorBox into v0::PriorBox.
+ */
+class ngraph::pass::ConvertPriorBox8To0 : public ngraph::pass::MatcherPass {
+public:
+    NGRAPH_RTTI_DECLARATION;
+    ConvertPriorBox8To0();
+};
\ No newline at end of file
diff --git a/src/common/transformations/src/transformations/common_optimizations/common_optimizations.cpp b/src/common/transformations/src/transformations/common_optimizations/common_optimizations.cpp
index a0456b926e1..ed32b6a1ae5 100644
--- a/src/common/transformations/src/transformations/common_optimizations/common_optimizations.cpp
+++ b/src/common/transformations/src/transformations/common_optimizations/common_optimizations.cpp
@@ -81,6 +81,7 @@
 #include "transformations/op_conversions/convert_deformable_conv_v8_to_v1.hpp"
 #include "transformations/op_conversions/convert_maxpool_downgrade.hpp"
 #include "transformations/disable_decompression_convert_constant_folding.hpp"
+#include "transformations/op_conversions/convert_prior_box_v8_to_v0.hpp"
 
 #include <ngraph/pass/manager.hpp>
 #include <ngraph/pass/constant_folding.hpp>
@@ -174,6 +175,7 @@ bool ngraph::pass::CommonOptimizations::run_on_function(std::shared_ptr<ngraph::
     manager.register_pass<ngraph::pass::ConvertGather7ToGather8, false>();
     manager.register_pass<ngraph::pass::ConvertDeformableConv8To1>();
     manager.register_pass<ngraph::pass::ConvertMaxPool8ToMaxPool1>();
+    manager.register_pass<ngraph::pass::ConvertPriorBox8To0>();  // not plugins implemented priorbox8
 
     auto fq_fusions = manager.register_pass<ngraph::pass::GraphRewrite>();
     fq_fusions->add_matcher<ngraph::pass::FakeQuantizeMulFusion>();
diff --git a/src/common/transformations/src/transformations/op_conversions/convert_prior_box_v8_to_v0.cpp b/src/common/transformations/src/transformations/op_conversions/convert_prior_box_v8_to_v0.cpp
new file mode 100644
index 00000000000..fb6b94ccd0f
--- /dev/null
+++ b/src/common/transformations/src/transformations/op_conversions/convert_prior_box_v8_to_v0.cpp
@@ -0,0 +1,54 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "transformations/op_conversions/convert_prior_box_v8_to_v0.hpp"
+
+#include <ngraph/opsets/opset1.hpp>
+#include <ngraph/opsets/opset8.hpp>
+#include <ngraph/pattern/op/wrap_type.hpp>
+#include <ngraph/rt_info.hpp>
+
+#include "itt.hpp"
+
+NGRAPH_RTTI_DEFINITION(ngraph::pass::ConvertPriorBox8To0, "ConvertPriorBox8To0", 0);
+
+ngraph::pass::ConvertPriorBox8To0::ConvertPriorBox8To0() {
+    MATCHER_SCOPE(ConvertPriorBox8To0);
+
+    auto prior_box_v8 = pattern::wrap_type<ngraph::opset8::PriorBox>();
+
+    ngraph::matcher_pass_callback callback = [=](pattern::Matcher& m) {
+        auto prior_box_v8_node = std::dynamic_pointer_cast<ngraph::opset8::PriorBox>(m.get_match_root());
+        if (!prior_box_v8_node)
+            return false;
+
+        ngraph::opset8::PriorBox::Attributes attrs_v8 = prior_box_v8_node->get_attrs();
+        if (!attrs_v8.min_max_aspect_ratios_order)
+            return false;
+
+        ngraph::opset1::PriorBox::Attributes attrs_v0;
+        attrs_v0.min_size = attrs_v8.min_size;
+        attrs_v0.max_size = attrs_v8.max_size;
+        attrs_v0.aspect_ratio = attrs_v8.aspect_ratio;
+        attrs_v0.density = attrs_v8.density;
+        attrs_v0.fixed_ratio = attrs_v8.fixed_ratio;
+        attrs_v0.fixed_size = attrs_v8.fixed_size;
+        attrs_v0.clip = attrs_v8.clip;
+        attrs_v0.flip = attrs_v8.flip;
+        attrs_v0.step = attrs_v8.step;
+        attrs_v0.offset = attrs_v8.offset;
+        attrs_v0.variance = attrs_v8.variance;
+        attrs_v0.scale_all_sizes = attrs_v8.scale_all_sizes;
+
+        auto prior_box_v0 = std::make_shared<ngraph::opset1::PriorBox>(prior_box_v8_node->input_value(0), prior_box_v8_node->input_value(1), attrs_v0);
+        prior_box_v0->set_friendly_name(prior_box_v8_node->get_friendly_name());
+        ngraph::copy_runtime_info(prior_box_v8_node, prior_box_v0);
+        ngraph::replace_node(prior_box_v8_node, prior_box_v0);
+
+        return true;
+    };
+
+    auto m = std::make_shared<pattern::Matcher>(prior_box_v8, matcher_name);
+    register_matcher(m, callback);
+}
diff --git a/src/core/include/ngraph/op/prior_box.hpp b/src/core/include/ngraph/op/prior_box.hpp
index 0f0b760ef3c..6f3fd9ced9e 100644
--- a/src/core/include/ngraph/op/prior_box.hpp
+++ b/src/core/include/ngraph/op/prior_box.hpp
@@ -13,6 +13,9 @@ using PriorBoxAttrs = ov::op::v0::PriorBox::Attributes;
 namespace v0 {
 using ov::op::v0::PriorBox;
 }  // namespace v0
+namespace v8 {
+using ov::op::v8::PriorBox;
+}  // namespace v8
 using v0::PriorBox;
 }  // namespace op
 }  // namespace ngraph
diff --git a/src/core/include/openvino/op/prior_box.hpp b/src/core/include/openvino/op/prior_box.hpp
index 53924c19d73..53d8b8c5b70 100644
--- a/src/core/include/openvino/op/prior_box.hpp
+++ b/src/core/include/openvino/op/prior_box.hpp
@@ -66,5 +66,66 @@ private:
     Attributes m_attrs;
 };
 }  // namespace v0
+
+namespace v8 {
+/// \brief Layer which generates prior boxes of specified sizes
+/// normalized to input image size
+class OPENVINO_API PriorBox : public Op {
+public:
+    OPENVINO_OP("PriorBox", "opset8");
+    BWDCMP_RTTI_DECLARATION;
+    struct Attributes {
+        // min_size                    Desired min_size of prior boxes
+        // max_size                    Desired max_size of prior boxes
+        // aspect_ratio                Aspect ratios of prior boxes
+        // clip                        Clip output to [0,1]
+        // flip                        Flip aspect ratios
+        // step                        Distance between prior box centers
+        // offset                      Box offset relative to top center of image
+        // variance                    Values to adjust prior boxes with
+        // scale_all_sizes             Scale all sizes
+        // min_max_aspect_ratios_order Order of output prior box
+        std::vector<float> min_size;
+        std::vector<float> max_size;
+        std::vector<float> aspect_ratio;
+        std::vector<float> density;
+        std::vector<float> fixed_ratio;
+        std::vector<float> fixed_size;
+        bool clip = false;
+        bool flip = false;
+        float step = 0.0f;
+        float offset = 0.0f;
+        std::vector<float> variance;
+        bool scale_all_sizes = true;
+        bool min_max_aspect_ratios_order = true;
+    };
+
+    PriorBox() = default;
+    /// \brief Constructs a PriorBox operation
+    ///
+    /// \param layer_shape    Shape of layer for which prior boxes are computed
+    /// \param image_shape    Shape of image to which prior boxes are scaled
+    /// \param attrs          PriorBox attributes
+    PriorBox(const Output<Node>& layer_shape, const Output<Node>& image_shape, const Attributes& attrs);
+
+    void validate_and_infer_types() override;
+    std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
+
+    static int64_t number_of_priors(const Attributes& attrs);
+
+    static std::vector<float> normalized_aspect_ratio(const std::vector<float>& aspect_ratio, bool flip);
+    const Attributes& get_attrs() const {
+        return m_attrs;
+    }
+    bool visit_attributes(AttributeVisitor& visitor) override;
+    OPENVINO_SUPPRESS_DEPRECATED_START
+    bool evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const override;
+    OPENVINO_SUPPRESS_DEPRECATED_END
+    bool has_evaluate() const override;
+
+private:
+    Attributes m_attrs;
+};
+}  // namespace v8
 }  // namespace op
 }  // namespace ov
diff --git a/src/core/include/openvino/opsets/opset8_tbl.hpp b/src/core/include/openvino/opsets/opset8_tbl.hpp
index 14f9dbc8d46..a11dd507302 100644
--- a/src/core/include/openvino/opsets/opset8_tbl.hpp
+++ b/src/core/include/openvino/opsets/opset8_tbl.hpp
@@ -71,7 +71,6 @@ _OPENVINO_OP_REG(PSROIPooling, ov::op::v0)
 _OPENVINO_OP_REG(Pad, ov::op::v1)
 _OPENVINO_OP_REG(Parameter, ov::op::v0)
 _OPENVINO_OP_REG(Power, ov::op::v1)
-_OPENVINO_OP_REG(PriorBox, ov::op::v0)
 _OPENVINO_OP_REG(PriorBoxClustered, ov::op::v0)
 _OPENVINO_OP_REG(Proposal, ov::op::v4)
 _OPENVINO_OP_REG(Range, ov::op::v4)
@@ -188,3 +187,4 @@ _OPENVINO_OP_REG(NV12toRGB, ov::op::v8)
 _OPENVINO_OP_REG(RandomUniform, ov::op::v8)
 _OPENVINO_OP_REG(Slice, ov::op::v8)
 _OPENVINO_OP_REG(If, ov::op::v8)
+_OPENVINO_OP_REG(PriorBox, ov::op::v8)
diff --git a/src/core/reference/include/ngraph/runtime/reference/prior_box.hpp b/src/core/reference/include/ngraph/runtime/reference/prior_box.hpp
index 3ac49ef2f86..3b2dfc39524 100644
--- a/src/core/reference/include/ngraph/runtime/reference/prior_box.hpp
+++ b/src/core/reference/include/ngraph/runtime/reference/prior_box.hpp
@@ -23,7 +23,11 @@ static inline float clip_less(float x, float threshold) {
 }
 
 template <typename T>
-void prior_box(const T* data, const T* img, float* dst_data, const Shape& out_shape, const op::PriorBoxAttrs& attrs) {
+void prior_box(const T* data,
+               const T* img,
+               float* dst_data,
+               const Shape& out_shape,
+               const op::v8::PriorBox::Attributes& attrs) {
     const int64_t W = data[1];
     const int64_t H = data[0];
     const int64_t IW = img[1];
@@ -51,7 +55,7 @@ void prior_box(const T* data, const T* img, float* dst_data, const Shape& out_sh
     if (variance.empty())
         variance.push_back(0.1f);
 
-    int64_t num_priors = op::PriorBox::number_of_priors(attrs);
+    int64_t num_priors = op::v8::PriorBox::number_of_priors(attrs);
 
     float step = attrs.step;
     auto min_size = attrs.min_size;
@@ -162,21 +166,42 @@ void prior_box(const T* data, const T* img, float* dst_data, const Shape& out_sh
                 box_height = min_size[ms_idx] * 0.5f;
                 calculate_data(center_x, center_y, box_width, box_height, false);
 
-                if (attrs.max_size.size() > ms_idx) {
-                    box_width = box_height = std::sqrt(min_size[ms_idx] * attrs.max_size[ms_idx]) * 0.5f;
-                    calculate_data(center_x, center_y, box_width, box_height, false);
-                }
+                if (attrs.min_max_aspect_ratios_order) {
+                    if (attrs.max_size.size() > ms_idx) {
+                        box_width = box_height = std::sqrt(min_size[ms_idx] * attrs.max_size[ms_idx]) * 0.5f;
+                        calculate_data(center_x, center_y, box_width, box_height, false);
+                    }
 
-                if (attrs.scale_all_sizes || (!attrs.scale_all_sizes && (ms_idx == min_size.size() - 1))) {
-                    size_t s_idx = attrs.scale_all_sizes ? ms_idx : 0;
-                    for (float ar : aspect_ratios) {
-                        if (std::fabs(ar - 1.0f) < 1e-6) {
-                            continue;
+                    if (attrs.scale_all_sizes || (!attrs.scale_all_sizes && (ms_idx == min_size.size() - 1))) {
+                        size_t s_idx = attrs.scale_all_sizes ? ms_idx : 0;
+                        for (float ar : aspect_ratios) {
+                            if (std::fabs(ar - 1.0f) < 1e-6) {
+                                continue;
+                            }
+
+                            ar = std::sqrt(ar);
+                            box_width = min_size[s_idx] * 0.5f * ar;
+                            box_height = min_size[s_idx] * 0.5f / ar;
+                            calculate_data(center_x, center_y, box_width, box_height, false);
                         }
+                    }
+                } else {
+                    if (attrs.scale_all_sizes || (!attrs.scale_all_sizes && (ms_idx == min_size.size() - 1))) {
+                        size_t s_idx = attrs.scale_all_sizes ? ms_idx : 0;
+                        for (float ar : aspect_ratios) {
+                            if (std::fabs(ar - 1.0f) < 1e-6) {
+                                continue;
+                            }
 
-                        ar = std::sqrt(ar);
-                        box_width = min_size[s_idx] * 0.5f * ar;
-                        box_height = min_size[s_idx] * 0.5f / ar;
+                            ar = std::sqrt(ar);
+                            box_width = min_size[s_idx] * 0.5f * ar;
+                            box_height = min_size[s_idx] * 0.5f / ar;
+                            calculate_data(center_x, center_y, box_width, box_height, false);
+                        }
+                    }
+
+                    if (attrs.max_size.size() > ms_idx) {
+                        box_width = box_height = std::sqrt(min_size[ms_idx] * attrs.max_size[ms_idx]) * 0.5f;
                         calculate_data(center_x, center_y, box_width, box_height, false);
                     }
                 }
diff --git a/src/core/src/op/prior_box.cpp b/src/core/src/op/prior_box.cpp
index b19b136cd09..41efcd9739d 100644
--- a/src/core/src/op/prior_box.cpp
+++ b/src/core/src/op/prior_box.cpp
@@ -10,21 +10,22 @@
 #include "ngraph/op/constant.hpp"
 #include "ngraph/runtime/host_tensor.hpp"
 #include "ngraph/runtime/reference/prior_box.hpp"
+#include "openvino/runtime/tensor.hpp"
 
 using namespace std;
 using namespace ngraph;
 
 BWDCMP_RTTI_DEFINITION(op::v0::PriorBox);
 
-op::PriorBox::PriorBox(const Output<Node>& layer_shape,
-                       const Output<Node>& image_shape,
-                       const PriorBox::Attributes& attrs)
+op::v0::PriorBox::PriorBox(const Output<Node>& layer_shape,
+                           const Output<Node>& image_shape,
+                           const PriorBox::Attributes& attrs)
     : Op({layer_shape, image_shape}),
       m_attrs(attrs) {
     constructor_validate_and_infer_types();
 }
 
-void op::PriorBox::validate_and_infer_types() {
+void op::v0::PriorBox::validate_and_infer_types() {
     NGRAPH_OP_SCOPE(v0_PriorBox_validate_and_infer_types);
     // shape node should have integer data type. For now we only allow i64
     auto layer_shape_et = get_input_element_type(0);
@@ -67,13 +68,13 @@ void op::PriorBox::validate_and_infer_types() {
     }
 }
 
-shared_ptr<Node> op::PriorBox::clone_with_new_inputs(const OutputVector& new_args) const {
+shared_ptr<Node> op::v0::PriorBox::clone_with_new_inputs(const OutputVector& new_args) const {
     NGRAPH_OP_SCOPE(v0_PriorBox_clone_with_new_inputs);
     check_new_args_count(this, new_args);
     return make_shared<PriorBox>(new_args.at(0), new_args.at(1), m_attrs);
 }
 
-int64_t op::PriorBox::number_of_priors(const PriorBox::Attributes& attrs) {
+int64_t op::v0::PriorBox::number_of_priors(const PriorBox::Attributes& attrs) {
     // Starting with 0 number of prior and then various conditions on attributes will contribute
     // real number of prior boxes as PriorBox is a fat thing with several modes of
     // operation that will be checked in order in the next statements.
@@ -102,7 +103,7 @@ int64_t op::PriorBox::number_of_priors(const PriorBox::Attributes& attrs) {
     return num_priors;
 }
 
-std::vector<float> op::PriorBox::normalized_aspect_ratio(const std::vector<float>& aspect_ratio, bool flip) {
+std::vector<float> op::v0::PriorBox::normalized_aspect_ratio(const std::vector<float>& aspect_ratio, bool flip) {
     std::set<float> unique_ratios;
     for (auto ratio : aspect_ratio) {
         unique_ratios.insert(std::round(ratio * 1e6) / 1e6);
@@ -113,7 +114,7 @@ std::vector<float> op::PriorBox::normalized_aspect_ratio(const std::vector<float
     return std::vector<float>(unique_ratios.begin(), unique_ratios.end());
 }
 
-bool op::PriorBox::visit_attributes(AttributeVisitor& visitor) {
+bool op::v0::PriorBox::visit_attributes(AttributeVisitor& visitor) {
     NGRAPH_OP_SCOPE(v0_PriorBox_visit_attributes);
     visitor.on_attribute("min_size", m_attrs.min_size);
     visitor.on_attribute("max_size", m_attrs.max_size);
@@ -136,19 +137,32 @@ template <element::Type_t ET>
 bool evaluate(const HostTensorPtr& arg0,
               const HostTensorPtr& arg1,
               const HostTensorPtr& out,
-              op::PriorBox::Attributes attrs) {
+              op::v0::PriorBox::Attributes attrs) {
+    op::v8::PriorBox::Attributes attrs_v8;
+    attrs_v8.min_size = attrs.min_size;
+    attrs_v8.max_size = attrs.max_size;
+    attrs_v8.aspect_ratio = attrs.aspect_ratio;
+    attrs_v8.density = attrs.density;
+    attrs_v8.fixed_ratio = attrs.fixed_ratio;
+    attrs_v8.fixed_size = attrs.fixed_size;
+    attrs_v8.clip = attrs.clip;
+    attrs_v8.flip = attrs.flip;
+    attrs_v8.step = attrs.step;
+    attrs_v8.offset = attrs.offset;
+    attrs_v8.variance = attrs.variance;
+    attrs_v8.scale_all_sizes = attrs.scale_all_sizes;
     runtime::reference::prior_box(arg0->get_data_ptr<ET>(),
                                   arg1->get_data_ptr<ET>(),
                                   out->get_data_ptr<float>(),
                                   out->get_shape(),
-                                  attrs);
+                                  attrs_v8);
     return true;
 }
 
 bool evaluate_prior_box(const HostTensorPtr& arg0,
                         const HostTensorPtr& arg1,
                         const HostTensorPtr& out,
-                        const op::PriorBox::Attributes& attrs) {
+                        const op::v0::PriorBox::Attributes& attrs) {
     bool rc = true;
     switch (arg0->get_element_type()) {
         NGRAPH_TYPE_CASE(evaluate_prior_box, i8, arg0, arg1, out, attrs);
@@ -190,3 +204,183 @@ bool op::v0::PriorBox::has_evaluate() const {
     }
     return false;
 }
+
+// ------------------------------ V8 ------------------------------
+
+BWDCMP_RTTI_DEFINITION(op::v8::PriorBox);
+
+op::v8::PriorBox::PriorBox(const Output<Node>& layer_shape,
+                           const Output<Node>& image_shape,
+                           const PriorBox::Attributes& attrs)
+    : Op({layer_shape, image_shape}),
+      m_attrs(attrs) {
+    constructor_validate_and_infer_types();
+}
+
+void op::v8::PriorBox::validate_and_infer_types() {
+    NGRAPH_OP_SCOPE(v8_PriorBox_validate_and_infer_types);
+    // shape node should have integer data type. For now we only allow i64
+    auto layer_shape_et = get_input_element_type(0);
+    NODE_VALIDATION_CHECK(this,
+                          layer_shape_et.is_integral_number(),
+                          "layer shape input must be an integral number, but is: ",
+                          layer_shape_et);
+
+    auto image_shape_et = get_input_element_type(1);
+    NODE_VALIDATION_CHECK(this,
+                          image_shape_et.is_integral_number(),
+                          "image shape input must be an integral number, but is: ",
+                          image_shape_et);
+
+    auto layer_shape_rank = get_input_partial_shape(0).rank();
+    auto image_shape_rank = get_input_partial_shape(1).rank();
+    NODE_VALIDATION_CHECK(this,
+                          layer_shape_rank.compatible(image_shape_rank),
+                          "layer shape input rank ",
+                          layer_shape_rank,
+                          " must match image shape input rank ",
+                          image_shape_rank);
+
+    set_input_is_relevant_to_shape(0);
+
+    if (auto const_shape = get_constant_from_source(input_value(0))) {
+        NODE_VALIDATION_CHECK(this,
+                              shape_size(const_shape->get_shape()) == 2,
+                              "Layer shape must have rank 2",
+                              const_shape->get_shape());
+
+        auto layer_shape = const_shape->get_shape_val();
+
+        set_output_type(
+            0,
+            element::f32,
+            ov::Shape{2, 4 * layer_shape[0] * layer_shape[1] * static_cast<size_t>(number_of_priors(m_attrs))});
+    } else {
+        set_output_type(0, element::f32, ov::PartialShape{2, Dimension::dynamic()});
+    }
+}
+
+shared_ptr<Node> op::v8::PriorBox::clone_with_new_inputs(const OutputVector& new_args) const {
+    NGRAPH_OP_SCOPE(v8_PriorBox_clone_with_new_inputs);
+    check_new_args_count(this, new_args);
+    return make_shared<PriorBox>(new_args.at(0), new_args.at(1), m_attrs);
+}
+
+int64_t op::v8::PriorBox::number_of_priors(const PriorBox::Attributes& attrs) {
+    // Starting with 0 number of prior and then various conditions on attributes will contribute
+    // real number of prior boxes as PriorBox is a fat thing with several modes of
+    // operation that will be checked in order in the next statements.
+    int64_t num_priors = 0;
+
+    // Total number of boxes around each point; depends on whether flipped boxes are included
+    // plus one box 1x1.
+    int64_t total_aspect_ratios = normalized_aspect_ratio(attrs.aspect_ratio, attrs.flip).size();
+
+    if (attrs.scale_all_sizes)
+        num_priors = total_aspect_ratios * attrs.min_size.size() + attrs.max_size.size();
+    else
+        num_priors = total_aspect_ratios + attrs.min_size.size() - 1;
+
+    if (!attrs.fixed_size.empty())
+        num_priors = total_aspect_ratios * attrs.fixed_size.size();
+
+    for (auto density : attrs.density) {
+        auto rounded_density = static_cast<int64_t>(density);
+        auto density_2d = (rounded_density * rounded_density - 1);
+        if (!attrs.fixed_ratio.empty())
+            num_priors += attrs.fixed_ratio.size() * density_2d;
+        else
+            num_priors += total_aspect_ratios * density_2d;
+    }
+    return num_priors;
+}
+
+std::vector<float> op::v8::PriorBox::normalized_aspect_ratio(const std::vector<float>& aspect_ratio, bool flip) {
+    std::set<float> unique_ratios;
+    for (auto ratio : aspect_ratio) {
+        unique_ratios.insert(std::round(ratio * 1e6) / 1e6);
+        if (flip)
+            unique_ratios.insert(std::round(1 / ratio * 1e6) / 1e6);
+    }
+    unique_ratios.insert(1);
+    return std::vector<float>(unique_ratios.begin(), unique_ratios.end());
+}
+
+bool op::v8::PriorBox::visit_attributes(AttributeVisitor& visitor) {
+    NGRAPH_OP_SCOPE(v8_PriorBox_visit_attributes);
+    visitor.on_attribute("min_size", m_attrs.min_size);
+    visitor.on_attribute("max_size", m_attrs.max_size);
+    visitor.on_attribute("aspect_ratio", m_attrs.aspect_ratio);
+    visitor.on_attribute("density", m_attrs.density);
+    visitor.on_attribute("fixed_ratio", m_attrs.fixed_ratio);
+    visitor.on_attribute("fixed_size", m_attrs.fixed_size);
+    visitor.on_attribute("clip", m_attrs.clip);
+    visitor.on_attribute("flip", m_attrs.flip);
+    visitor.on_attribute("step", m_attrs.step);
+    visitor.on_attribute("offset", m_attrs.offset);
+    visitor.on_attribute("variance", m_attrs.variance);
+    visitor.on_attribute("scale_all_sizes", m_attrs.scale_all_sizes);
+    visitor.on_attribute("min_max_aspect_ratios_order", m_attrs.min_max_aspect_ratios_order);
+    return true;
+}
+
+namespace prior_box_v8 {
+namespace {
+template <element::Type_t ET>
+bool evaluate(const HostTensorPtr& arg0,
+              const HostTensorPtr& arg1,
+              const HostTensorPtr& out,
+              op::v8::PriorBox::Attributes attrs) {
+    runtime::reference::prior_box(arg0->get_data_ptr<ET>(),
+                                  arg1->get_data_ptr<ET>(),
+                                  out->get_data_ptr<float>(),
+                                  out->get_shape(),
+                                  attrs);
+    return true;
+}
+
+bool evaluate_prior_box(const HostTensorPtr& arg0,
+                        const HostTensorPtr& arg1,
+                        const HostTensorPtr& out,
+                        const op::v8::PriorBox::Attributes& attrs) {
+    bool rc = true;
+    switch (arg0->get_element_type()) {
+        NGRAPH_TYPE_CASE(evaluate_prior_box, i8, arg0, arg1, out, attrs);
+        NGRAPH_TYPE_CASE(evaluate_prior_box, i16, arg0, arg1, out, attrs);
+        NGRAPH_TYPE_CASE(evaluate_prior_box, i32, arg0, arg1, out, attrs);
+        NGRAPH_TYPE_CASE(evaluate_prior_box, i64, arg0, arg1, out, attrs);
+        NGRAPH_TYPE_CASE(evaluate_prior_box, u8, arg0, arg1, out, attrs);
+        NGRAPH_TYPE_CASE(evaluate_prior_box, u16, arg0, arg1, out, attrs);
+        NGRAPH_TYPE_CASE(evaluate_prior_box, u32, arg0, arg1, out, attrs);
+        NGRAPH_TYPE_CASE(evaluate_prior_box, u64, arg0, arg1, out, attrs);
+    default:
+        rc = false;
+        break;
+    }
+    return rc;
+}
+}  // namespace
+}  // namespace prior_box_v8
+
+bool op::v8::PriorBox::evaluate(const HostTensorVector& outputs, const HostTensorVector& inputs) const {
+    NGRAPH_OP_SCOPE(v8_PriorBox_evaluate);
+    return prior_box_v8::evaluate_prior_box(inputs[0], inputs[1], outputs[0], get_attrs());
+}
+
+bool op::v8::PriorBox::has_evaluate() const {
+    NGRAPH_OP_SCOPE(v8_PriorBox_has_evaluate);
+    switch (get_input_element_type(0)) {
+    case ngraph::element::i8:
+    case ngraph::element::i16:
+    case ngraph::element::i32:
+    case ngraph::element::i64:
+    case ngraph::element::u8:
+    case ngraph::element::u16:
+    case ngraph::element::u32:
+    case ngraph::element::u64:
+        return true;
+    default:
+        break;
+    }
+    return false;
+}
diff --git a/src/core/tests/runtime/interpreter/evaluates_map.cpp b/src/core/tests/runtime/interpreter/evaluates_map.cpp
index ccc4cf9071c..b83769c5757 100644
--- a/src/core/tests/runtime/interpreter/evaluates_map.cpp
+++ b/src/core/tests/runtime/interpreter/evaluates_map.cpp
@@ -1526,17 +1526,6 @@ bool evaluate(const shared_ptr<op::v0::Elu>& op, const HostTensorVector& outputs
     return true;
 }
 
-template <element::Type_t ET>
-bool evaluate(const shared_ptr<op::v0::PriorBox>& op, const HostTensorVector& outputs, const HostTensorVector& inputs) {
-    using T = typename element_type_traits<ET>::value_type;
-    runtime::reference::prior_box<T>(inputs[0]->get_data_ptr<T>(),
-                                     inputs[1]->get_data_ptr<T>(),
-                                     outputs[0]->get_data_ptr<float>(),
-                                     outputs[0]->get_shape(),
-                                     op->get_attrs());
-    return true;
-}
-
 template <element::Type_t ET>
 bool evaluate(const shared_ptr<op::v0::Proposal>& op, const HostTensorVector& outputs, const HostTensorVector& inputs) {
     using T = typename element_type_traits<ET>::value_type;
@@ -2704,11 +2693,8 @@ bool evaluate(const shared_ptr<op::v8::Gather>& op, const HostTensorVector& outp
 template <typename T>
 bool evaluate_node(std::shared_ptr<Node> node, const HostTensorVector& outputs, const HostTensorVector& inputs) {
     auto element_type = node->get_output_element_type(0);
-    if (ov::is_type<op::v1::Select>(node)) {
+    if (ov::is_type<op::v1::Select>(node))
         element_type = node->get_input_element_type(1);
-    } else if (ov::is_type<op::v0::PriorBox>(node)) {
-        element_type = node->get_input_element_type(0);
-    }
 
     switch (element_type) {
     case element::Type_t::boolean:
diff --git a/src/core/tests/runtime/interpreter/int_executable.cpp b/src/core/tests/runtime/interpreter/int_executable.cpp
index 45b5302541e..3aa12c2740e 100644
--- a/src/core/tests/runtime/interpreter/int_executable.cpp
+++ b/src/core/tests/runtime/interpreter/int_executable.cpp
@@ -128,7 +128,7 @@ bool runtime::interpreter::INTExecutable::call(const vector<shared_ptr<runtime::
 
         // get op type
         element::Type type;
-        if (ov::is_type<op::Convert>(op) || ov::is_type<op::PriorBox>(op)) {
+        if (ov::is_type<op::Convert>(op) || ov::is_type<op::v0::PriorBox>(op) || ov::is_type<op::v8::PriorBox>(op)) {
             type = op->get_input_element_type(0);
         } else if (ov::is_type<op::v1::Equal>(op) || ov::is_type<op::v1::Greater>(op) ||
                    ov::is_type<op::v1::GreaterEqual>(op) || ov::is_type<op::v1::Less>(op) ||
diff --git a/src/core/tests/runtime/interpreter/opset_int_tbl.hpp b/src/core/tests/runtime/interpreter/opset_int_tbl.hpp
index 8c58e32a3c2..e100660e9a9 100644
--- a/src/core/tests/runtime/interpreter/opset_int_tbl.hpp
+++ b/src/core/tests/runtime/interpreter/opset_int_tbl.hpp
@@ -111,3 +111,4 @@ NGRAPH_OP(Sigmoid, op::v0)
 NGRAPH_OP(Tanh, op::v0)
 NGRAPH_OP(Exp, op::v0)
 NGRAPH_OP(Log, op::v0)
+NGRAPH_OP(PriorBox, ngraph::op::v8)
diff --git a/src/core/tests/type_prop/prior_box.cpp b/src/core/tests/type_prop/prior_box.cpp
index 251ad801794..a980728f76a 100644
--- a/src/core/tests/type_prop/prior_box.cpp
+++ b/src/core/tests/type_prop/prior_box.cpp
@@ -10,19 +10,19 @@
 using namespace ngraph;
 
 TEST(type_prop, prior_box1) {
-    op::PriorBoxAttrs attrs;
+    op::v0::PriorBox::Attributes attrs;
     attrs.min_size = {2.0f, 3.0f};
     attrs.aspect_ratio = {1.5f, 2.0f, 2.5f};
     attrs.scale_all_sizes = false;
 
     auto layer_shape = op::Constant::create<int64_t>(element::i64, Shape{2}, {32, 32});
     auto image_shape = op::Constant::create<int64_t>(element::i64, Shape{2}, {300, 300});
-    auto pb = std::make_shared<op::PriorBox>(layer_shape, image_shape, attrs);
+    auto pb = std::make_shared<op::v0::PriorBox>(layer_shape, image_shape, attrs);
     ASSERT_EQ(pb->get_shape(), (Shape{2, 20480}));
 }
 
 TEST(type_prop, prior_box2) {
-    op::PriorBoxAttrs attrs;
+    op::v0::PriorBox::Attributes attrs;
     attrs.min_size = {2.0f, 3.0f};
     attrs.aspect_ratio = {1.5f, 2.0f, 2.5f};
     attrs.flip = true;
@@ -30,12 +30,12 @@ TEST(type_prop, prior_box2) {
 
     auto layer_shape = op::Constant::create<int64_t>(element::i64, Shape{2}, {32, 32});
     auto image_shape = op::Constant::create<int64_t>(element::i64, Shape{2}, {300, 300});
-    auto pb = std::make_shared<op::PriorBox>(layer_shape, image_shape, attrs);
+    auto pb = std::make_shared<op::v0::PriorBox>(layer_shape, image_shape, attrs);
     ASSERT_EQ(pb->get_shape(), (Shape{2, 32768}));
 }
 
 TEST(type_prop, prior_box3) {
-    op::PriorBoxAttrs attrs;
+    op::v0::PriorBox::Attributes attrs;
     attrs.min_size = {256.0f};
     attrs.max_size = {315.0f};
     attrs.aspect_ratio = {2.0f};
@@ -43,6 +43,33 @@ TEST(type_prop, prior_box3) {
 
     auto layer_shape = op::Constant::create<int64_t>(element::i64, Shape{2}, {1, 1});
     auto image_shape = op::Constant::create<int64_t>(element::i64, Shape{2}, {300, 300});
-    auto pb = std::make_shared<op::PriorBox>(layer_shape, image_shape, attrs);
+    auto pb = std::make_shared<op::v0::PriorBox>(layer_shape, image_shape, attrs);
     ASSERT_EQ(pb->get_shape(), (Shape{2, 16}));
+}
+
+TEST(type_prop, prior_box_v8_1) {
+    op::v8::PriorBox::Attributes attrs;
+    attrs.min_size = {2.0f, 3.0f};
+    attrs.aspect_ratio = {1.5f, 2.0f, 2.5f};
+    attrs.scale_all_sizes = false;
+    attrs.min_max_aspect_ratios_order = true;
+
+    auto layer_shape = op::Constant::create<int64_t>(element::i64, Shape{2}, {32, 32});
+    auto image_shape = op::Constant::create<int64_t>(element::i64, Shape{2}, {300, 300});
+    auto pb = std::make_shared<op::v8::PriorBox>(layer_shape, image_shape, attrs);
+    ASSERT_EQ(pb->get_shape(), (Shape{2, 20480}));
+}
+
+TEST(type_prop, prior_box_v8_2) {
+    op::v8::PriorBox::Attributes attrs;
+    attrs.min_size = {2.0f, 3.0f};
+    attrs.aspect_ratio = {1.5f, 2.0f, 2.5f};
+    attrs.flip = true;
+    attrs.scale_all_sizes = false;
+    attrs.min_max_aspect_ratios_order = false;
+
+    auto layer_shape = op::Constant::create<int64_t>(element::i64, Shape{2}, {32, 32});
+    auto image_shape = op::Constant::create<int64_t>(element::i64, Shape{2}, {300, 300});
+    auto pb = std::make_shared<op::v8::PriorBox>(layer_shape, image_shape, attrs);
+    ASSERT_EQ(pb->get_shape(), (Shape{2, 32768}));
 }
\ No newline at end of file
diff --git a/src/core/tests/visitors/op/prior_box.cpp b/src/core/tests/visitors/op/prior_box.cpp
index 7ff922c62ce..26f5000fc55 100644
--- a/src/core/tests/visitors/op/prior_box.cpp
+++ b/src/core/tests/visitors/op/prior_box.cpp
@@ -9,6 +9,7 @@
 #include "ngraph/opsets/opset3.hpp"
 #include "ngraph/opsets/opset4.hpp"
 #include "ngraph/opsets/opset5.hpp"
+#include "ngraph/opsets/opset8.hpp"
 #include "util/visitor.hpp"
 
 using namespace std;
@@ -21,7 +22,7 @@ TEST(attributes, prior_box_op) {
     const auto layer_shape = make_shared<op::Parameter>(element::i64, Shape{128, 128});
     const auto image_shape = make_shared<op::Parameter>(element::i64, Shape{32, 32});
 
-    op::PriorBoxAttrs attrs;
+    op::v0::PriorBox::Attributes attrs;
     attrs.min_size = vector<float>{16.f, 32.f};
     attrs.max_size = vector<float>{256.f, 512.f};
     attrs.aspect_ratio = vector<float>{0.66f, 1.56f};
@@ -57,3 +58,47 @@ TEST(attributes, prior_box_op) {
     EXPECT_EQ(g_prior_box_attrs.variance, prior_box_attrs.variance);
     EXPECT_EQ(g_prior_box_attrs.scale_all_sizes, prior_box_attrs.scale_all_sizes);
 }
+
+TEST(attributes, prior_box_v8_op) {
+    NodeBuilder::get_ops().register_factory<opset8::PriorBox>();
+    const auto layer_shape = make_shared<op::Parameter>(element::i64, Shape{128, 128});
+    const auto image_shape = make_shared<op::Parameter>(element::i64, Shape{32, 32});
+
+    op::v8::PriorBox::Attributes attrs;
+    attrs.min_size = vector<float>{16.f, 32.f};
+    attrs.max_size = vector<float>{256.f, 512.f};
+    attrs.aspect_ratio = vector<float>{0.66f, 1.56f};
+    attrs.density = vector<float>{0.55f};
+    attrs.fixed_ratio = vector<float>{0.88f};
+    attrs.fixed_size = vector<float>{1.25f};
+    attrs.clip = true;
+    attrs.flip = false;
+    attrs.step = 1.0f;
+    attrs.offset = 0.0f;
+    attrs.variance = vector<float>{2.22f, 3.14f};
+    attrs.scale_all_sizes = true;
+    attrs.min_max_aspect_ratios_order = false;
+
+    auto prior_box = make_shared<opset8::PriorBox>(layer_shape, image_shape, attrs);
+    NodeBuilder builder(prior_box);
+    auto g_prior_box = ov::as_type_ptr<opset8::PriorBox>(builder.create());
+
+    const auto prior_box_attrs = prior_box->get_attrs();
+    const auto g_prior_box_attrs = g_prior_box->get_attrs();
+
+    const auto expected_attr_count = 13;
+    EXPECT_EQ(builder.get_value_map_size(), expected_attr_count);
+    EXPECT_EQ(g_prior_box_attrs.min_size, prior_box_attrs.min_size);
+    EXPECT_EQ(g_prior_box_attrs.max_size, prior_box_attrs.max_size);
+    EXPECT_EQ(g_prior_box_attrs.aspect_ratio, prior_box_attrs.aspect_ratio);
+    EXPECT_EQ(g_prior_box_attrs.density, prior_box_attrs.density);
+    EXPECT_EQ(g_prior_box_attrs.fixed_ratio, prior_box_attrs.fixed_ratio);
+    EXPECT_EQ(g_prior_box_attrs.fixed_size, prior_box_attrs.fixed_size);
+    EXPECT_EQ(g_prior_box_attrs.clip, prior_box_attrs.clip);
+    EXPECT_EQ(g_prior_box_attrs.flip, prior_box_attrs.flip);
+    EXPECT_EQ(g_prior_box_attrs.step, prior_box_attrs.step);
+    EXPECT_EQ(g_prior_box_attrs.offset, prior_box_attrs.offset);
+    EXPECT_EQ(g_prior_box_attrs.variance, prior_box_attrs.variance);
+    EXPECT_EQ(g_prior_box_attrs.scale_all_sizes, prior_box_attrs.scale_all_sizes);
+    EXPECT_EQ(g_prior_box_attrs.min_max_aspect_ratios_order, prior_box_attrs.min_max_aspect_ratios_order);
+}
diff --git a/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/prior_box.cpp b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/prior_box.cpp
index 5b178eb96cc..52a28e525e8 100644
--- a/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/prior_box.cpp
+++ b/src/frontends/onnx/frontend/src/op/org.openvinotoolkit/prior_box.cpp
@@ -38,7 +38,7 @@ OutputVector prior_box(const Node& node) {
     auto output_shape_slice = detail::make_slice(output_shape, 2, 4);
     auto image_shape_slice = detail::make_slice(image_shape, 2, 4);
 
-    ngraph::op::PriorBoxAttrs attrs;
+    ngraph::op::v0::PriorBox::Attributes attrs;
     attrs.min_size = node.get_attribute_value<std::vector<float>>("min_size", {});
     attrs.max_size = node.get_attribute_value<std::vector<float>>("max_size", {});
     attrs.aspect_ratio = node.get_attribute_value<std::vector<float>>("aspect_ratio", {});
@@ -55,7 +55,7 @@ OutputVector prior_box(const Node& node) {
     auto axes = default_opset::Constant::create(element::i64, Shape{1}, std::vector<int64_t>{0});
 
     return {std::make_shared<default_opset::Unsqueeze>(
-        std::make_shared<default_opset::PriorBox>(output_shape_slice, image_shape_slice, attrs),
+        std::make_shared<ngraph::op::v0::PriorBox>(output_shape_slice, image_shape_slice, attrs),
         axes)};
 }
 
diff --git a/src/frontends/paddlepaddle/src/op/prior_box.cpp b/src/frontends/paddlepaddle/src/op/prior_box.cpp
index 4b18ad839f9..1801f8fd59f 100644
--- a/src/frontends/paddlepaddle/src/op/prior_box.cpp
+++ b/src/frontends/paddlepaddle/src/op/prior_box.cpp
@@ -40,12 +40,11 @@ NamedOutputs prior_box(const NodeContext& node) {
     attrs.flip = node.get_attribute<bool>("flip", false);
     attrs.clip = node.get_attribute<bool>("clip", false);
     attrs.step = node.get_attribute<float>("step_w", 0);
+    attrs.min_max_aspect_ratios_order = node.get_attribute<bool>("min_max_aspect_ratios_order", false);
 
     attrs.offset = node.get_attribute<float>("offset", 0.5);
     attrs.variance = node.get_attribute<std::vector<float>>("variances", {0.1, 0.1, 0.2, 0.2});
 
-    bool min_max_aspect_ratios_order = node.get_attribute<bool>("min_max_aspect_ratios_order", false);
-
     const auto ov_prior_box_node = std::make_shared<PriorBox>(output_shape_slice, image_shape_slice, attrs);
 
     const auto split_axis_node = Constant::create(i64, ov::Shape{}, {0});
@@ -60,47 +59,6 @@ NamedOutputs prior_box(const NodeContext& node) {
     auto node_boxes_reshape = std::make_shared<Reshape>(node_boxes_origin, out_shape, true);
     const auto node_variances_reshape = std::make_shared<Reshape>(node_variances_origin, out_shape, true);
 
-    int64_t total_aspect_ratios = PriorBox::normalized_aspect_ratio(attrs.aspect_ratio, attrs.flip).size();
-    if ((total_aspect_ratios > 1) && !attrs.min_size.empty() && !attrs.max_size.empty() &&
-        !min_max_aspect_ratios_order) {
-        std::vector<int64_t> mask{1, 1, 1, 0, 1};
-        int64_t min_size_len = static_cast<int64_t>(attrs.min_size.size());
-
-        const auto out_shape_div_numpri = std::make_shared<Concat>(
-            NodeVector{output_shape_slice, Constant::create<int64_t>(i64, {3}, {min_size_len, -1, 4})},
-            0);
-        const auto node_boxes_div_numpri = std::make_shared<Reshape>(node_boxes_reshape, out_shape_div_numpri, true);
-
-        const auto slice_begin_min = Constant::create(i64, Shape{5}, std::vector<int64_t>{0, 0, 0, 0, 0});
-        const auto slice_end_min = std::make_shared<Concat>(
-            NodeVector{output_shape_slice, Constant::create<int64_t>(i64, {3}, {min_size_len, 1, 4})},
-            0);
-        const auto slice_min_node =
-            std::make_shared<StridedSlice>(node_boxes_div_numpri, slice_begin_min, slice_end_min, mask, mask);
-
-        const auto slice_begin_max = Constant::create(i64, Shape{5}, std::vector<int64_t>{0, 0, 0, 1, 0});
-        const auto slice_end_max = std::make_shared<Concat>(
-            NodeVector{output_shape_slice, Constant::create<int64_t>(i64, {3}, {min_size_len, 2, 4})},
-            0);
-        const auto slice_max_node =
-            std::make_shared<StridedSlice>(node_boxes_div_numpri, slice_begin_max, slice_end_max, mask, mask);
-
-        const auto slice_begin_aspect_ratios = Constant::create(i64, Shape{5}, std::vector<int64_t>{0, 0, 0, 2, 0});
-        const auto slice_end_aspect_ratios = std::make_shared<Concat>(
-            NodeVector{output_shape_slice,
-                       Constant::create<int64_t>(i64, {3}, {min_size_len, 2 + (total_aspect_ratios - 1), 4})},
-            0);
-        const auto slice_aspect_ratios_node = std::make_shared<StridedSlice>(node_boxes_div_numpri,
-                                                                             slice_begin_aspect_ratios,
-                                                                             slice_end_aspect_ratios,
-                                                                             mask,
-                                                                             mask);
-
-        const auto node_boxes_div_numpri_reorder =
-            std::make_shared<Concat>(NodeVector{slice_min_node, slice_aspect_ratios_node, slice_max_node}, 3);
-        node_boxes_reshape = std::make_shared<Reshape>(node_boxes_div_numpri_reorder, out_shape, true);
-    }
-
     NamedOutputs outputs;
     outputs["Boxes"] = {node_boxes_reshape};
     outputs["Variances"] = {node_variances_reshape};

From a148e126b853e405bc15915b6b3065f5cb4492ac Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Mon, 29 Nov 2021 09:38:06 +0300
Subject: [PATCH 60/72] Fixed coverage paths (#8873)

---
 cmake/coverage.cmake | 44 +++++++++++++++++++-------------------------
 1 file changed, 19 insertions(+), 25 deletions(-)

diff --git a/cmake/coverage.cmake b/cmake/coverage.cmake
index 137acbb8993..9ef4617846c 100644
--- a/cmake/coverage.cmake
+++ b/cmake/coverage.cmake
@@ -12,88 +12,82 @@ ie_coverage_capture(INFO_FILE "openvino"
 
 # Generate reports
 
-ie_coverage_extract(INPUT "openvino" OUTPUT "inference_engine"
-                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/inference_engine/*"
-                             "${OV_COVERAGE_BASE_DIRECTORY}/plugin_api/*")
+ie_coverage_extract(INPUT "openvino" OUTPUT "inference"
+                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/src/inference/*")
+
 ie_coverage_genhtml(INFO_FILE "inference_engine"
                     PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
 
-ie_coverage_extract(INPUT "openvino" OUTPUT "inference_engine_ir_v10_reader"
-                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/readers/ir_reader/*"
-                             "${OV_COVERAGE_BASE_DIRECTORY}/readers/reader_api/*")
-ie_coverage_genhtml(INFO_FILE "inference_engine_ir_v10_reader"
-                    PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
-
 ie_coverage_extract(INPUT "openvino" OUTPUT "inference_engine_legacy"
-                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/legacy_api/*")
+                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/src/common/legacy/*")
 ie_coverage_genhtml(INFO_FILE "inference_engine_legacy"
                     PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
 
 ie_coverage_extract(INPUT "openvino" OUTPUT "hetero_plugin"
-                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/hetero_plugin/*")
+                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/src/plugins/hetero_plugin/*")
 ie_coverage_genhtml(INFO_FILE "hetero_plugin"
                     PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
 
 ie_coverage_extract(INPUT "openvino" OUTPUT "multi_device"
-                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/multi_device/*")
+                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/inference-engine/src/multi_device/*")
 ie_coverage_genhtml(INFO_FILE "multi_device"
                     PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
 
 ie_coverage_extract(INPUT "openvino" OUTPUT "preprocessing"
-                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/preprocessing/*")
+                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}src/common/preprocessing/*")
 ie_coverage_genhtml(INFO_FILE "preprocessing"
                     PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
 
 ie_coverage_extract(INPUT "openvino" OUTPUT "inference_engine_transformations"
-                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/inference_engine_transformations/*")
+                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/src/common/transformations/*")
 ie_coverage_genhtml(INFO_FILE "inference_engine_transformations"
                     PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
 
 ie_coverage_extract(INPUT "openvino" OUTPUT "inference_engine_snippets"
-                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/snippets/*")
+                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/src/common/snippets/*")
 ie_coverage_genhtml(INFO_FILE "inference_engine_snippets"
                     PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
 
 ie_coverage_extract(INPUT "openvino" OUTPUT "low_precision_transformations"
-                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/low_precision_transformations/*")
+                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/src/common/low_precision_transformations/*")
 ie_coverage_genhtml(INFO_FILE "low_precision_transformations"
                     PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
 
 ie_coverage_extract(INPUT "openvino" OUTPUT "template_plugin"
-                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/template_plugin/*")
+                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/docs/template_plugin/*")
 ie_coverage_genhtml(INFO_FILE "template_plugin"
                     PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
 
 if(ENABLE_MKL_DNN)
     ie_coverage_extract(INPUT "openvino" OUTPUT "mkldnn_plugin"
-                        PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/mkldnn_plugin/*")
+                        PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/inference-engine/src/mkldnn_plugin/*")
     ie_coverage_genhtml(INFO_FILE "mkldnn_plugin"
                         PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
 endif()
 
 if(ENABLE_CLDNN)
     ie_coverage_extract(INPUT "openvino" OUTPUT "cldnn_engine"
-                        PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/cldnn_engine/*")
+                        PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/inference-engine/src/cldnn_engine/*")
     ie_coverage_genhtml(INFO_FILE "cldnn_engine"
                         PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
 endif()
 
 if(ENABLE_GNA)
     ie_coverage_extract(INPUT "openvino" OUTPUT "gna_plugin"
-                        PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/gna_plugin/*")
+                        PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/inference-engine/src/gna_plugin/*")
     ie_coverage_genhtml(INFO_FILE "gna_plugin"
                         PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
 endif()
 
-ie_coverage_extract(INPUT "openvino" OUTPUT "ngraph"
-                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/ngraph/core/*")
-ie_coverage_genhtml(INFO_FILE "ngraph"
+ie_coverage_extract(INPUT "openvino" OUTPUT "core"
+                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/src/core/*")
+ie_coverage_genhtml(INFO_FILE "core"
                     PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
 
 if(NGRAPH_ONNX_FRONTEND_ENABLE)
     ie_coverage_extract(INPUT "openvino" OUTPUT "onnx"
-        PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/ngraph/frontend/onnx/*"
-        "${OV_COVERAGE_BASE_DIRECTORY}/ngraph/frontend/onnx/*")
+        PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/src/frontends/onnx/*"
+        "${OV_COVERAGE_BASE_DIRECTORY}/src/frontends/onnx/*")
     ie_coverage_genhtml(INFO_FILE "onnx"
         PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
 endif()

From f38fa4d845aa060a791a25dfeb05dbab354b488e Mon Sep 17 00:00:00 2001
From: Chen Xu <chen.xu@intel.com>
Date: Mon, 29 Nov 2021 15:28:11 +0800
Subject: [PATCH 61/72] [CPU] Reduce node dynamism support + perf optimizations
 (#8555)

* Reduce node feature extension

* Reduce node dynamism support
---
 .../mkldnn_plugin/mkldnn_graph_optimizer.cpp  |   49 +-
 .../mkldnn_plugin/mkldnn_graph_optimizer.h    |    1 +
 .../src/mkldnn_plugin/mkldnn_node.cpp         |   11 +-
 .../src/mkldnn_plugin/mkldnn_plugin.cpp       |    4 +
 .../nodes/mkldnn_reduce_node.cpp              | 1676 ++++++++++++-----
 .../mkldnn_plugin/nodes/mkldnn_reduce_node.h  |   68 +-
 .../reduce_max_transformation.cpp             |   12 +-
 .../reduce_mean_transformation.cpp            |   12 +-
 .../reduce_sum_transformation.cpp             |   12 +-
 .../skip_tests_config.cpp                     |    4 +
 .../cpu/single_layer_tests/reduce_ops.cpp     |  678 +++++--
 inference-engine/thirdparty/mkl-dnn           |    2 +-
 12 files changed, 1879 insertions(+), 650 deletions(-)

diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp
index 04360eafef7..7ea97eca345 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp
@@ -17,6 +17,7 @@
 #include "nodes/mkldnn_mvn_node.h"
 #include <nodes/mkldnn_transpose_node.h>
 #include "nodes/mkldnn_interpolate_node.h"
+#include "nodes/mkldnn_reduce_node.h"
 #include "nodes/mkldnn_input_node.h"
 #include "nodes/mkldnn_rnn.h"
 #include "nodes/common/cpu_convert.h"
@@ -137,6 +138,10 @@ void MKLDNNGraphOptimizer::ApplyCommonGraphOptimizations(MKLDNNGraph &graph) {
     FuseNormalizeL2AndSimpleOperation(graph);
     graph.RemoveDroppedNodes();
 
+    OV_ITT_SCOPE_NEXT(FIRST_INFERENCE, taskChain, "FuseReduceAndSimpleOperation");
+    FuseReduceAndSimpleOperation(graph);
+    graph.RemoveDroppedNodes();
+
     OV_ITT_SCOPE_NEXT(FIRST_INFERENCE, taskChain, "FuseEltwiseAndSimple");
     FuseEltwiseAndSimple(graph);
     graph.RemoveDroppedNodes();
@@ -1352,6 +1357,46 @@ void MKLDNNGraphOptimizer::FuseNormalizeL2AndSimpleOperation(MKLDNNGraph &graph)
     }
 }
 
+void MKLDNNGraphOptimizer::FuseReduceAndSimpleOperation(MKLDNNGraph &graph) {
+    auto& graphNodes = graph.GetNodes();
+
+    auto isSuitableParentNode = [](MKLDNNNodePtr node) {
+        return node->getType() == Reduce && node->getChildEdges().size() == 1;
+    };
+
+    auto parent = graphNodes.begin();
+    while (parent != graphNodes.end()) {
+        auto parentNode = *parent;
+        if (!isSuitableParentNode(parentNode)) {
+            parent++;
+            continue;
+        }
+
+        auto childNode = parentNode->getChildEdgeAt(0)->getChild();
+        if (!parentNode->canFuse(childNode)) {
+            parent++;
+            continue;
+        }
+
+        childNode->fuseInto(parentNode);
+
+        if (childNode->getType() == FakeQuantize || childNode->getType() == Eltwise) {
+            auto parentEdges = childNode->parentEdges;
+            for (auto &parentEdge : parentEdges) {
+                auto p_edge = parentEdge.lock();
+                if (p_edge == nullptr)
+                    IE_THROW() << "Cannot get parent edge " << childNode->getName();
+                if (p_edge->getParent()->getType() == Reduce)
+                    continue;
+
+                graph.RemoveEdge(p_edge);
+            }
+        }
+
+        graph.DropNode(childNode);
+    }
+}
+
 void MKLDNNGraphOptimizer::FuseEltwiseAndSimple(MKLDNNGraph &graph) {
     auto& graphNodes = graph.GetNodes();
 
@@ -1918,7 +1963,7 @@ void MKLDNNGraphOptimizer::MergeTransposeAndReorder(MKLDNNGraph &graph) {
 void MKLDNNGraphOptimizer::reshapeRnnSeq(MKLDNNGraph &graph) {
     auto& graphNodes = graph.GetNodes();
 
-    auto isSutableParentNode = [](MKLDNNNodePtr node) {
+    auto isSuitableParentNode = [](MKLDNNNodePtr node) {
         if (node->type != RNNSeq)
             return false;
         auto rnnNode = std::dynamic_pointer_cast<MKLDNNRNN>(node);
@@ -1927,7 +1972,7 @@ void MKLDNNGraphOptimizer::reshapeRnnSeq(MKLDNNGraph &graph) {
 
     for (size_t i = 0; i < graphNodes.size(); i++) {
         auto parentNode = graphNodes[i];
-        if (!isSutableParentNode(parentNode)) {
+        if (!isSuitableParentNode(parentNode)) {
             continue;
         }
 
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.h b/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.h
index e5615a1541b..0b896da6272 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.h
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.h
@@ -32,6 +32,7 @@ private:
     void FuseMVNAndSimpleOperation(MKLDNNGraph &graph);
     void FuseInterpolateAndSimpleOperation(MKLDNNGraph &graph);
     void FuseNormalizeL2AndSimpleOperation(MKLDNNGraph &graph);
+    void FuseReduceAndSimpleOperation(MKLDNNGraph &graph);
 
     void DropDoubleReorders(MKLDNNGraph& graph);
     void FuseConvolutionAndZeroPoints(MKLDNNGraph &graph);
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp
index 4db90997d19..8a0223a9687 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp
@@ -524,10 +524,17 @@ void MKLDNNNode::redefineOutputMemory(const std::vector<VectorDims> &newOutputSh
     }
     for (size_t i = 0; i < outputShapes.size(); i++) {
         const auto edges = getChildEdgesAtPort(i);
-        const auto memDesc = getBaseMemDescAtOutputPort(i)->cloneWithNewDims(newOutputShapes[i]);
+
+        // avoid 0D shape incompatible
+        auto newOutputShape = newOutputShapes[i];
+        if (newOutputShape.empty()) {
+            newOutputShape.push_back(1);
+        }
+
+        const auto memDesc = getBaseMemDescAtOutputPort(i)->cloneWithNewDims(newOutputShape);
 
         const auto &currDesc = edges[0]->getMemory().getDesc();
-        if (currDesc.getShape().isStatic() && currDesc.getShape().getStaticDims() == newOutputShapes[i])
+        if (currDesc.getShape().isStatic() && currDesc.getShape().getStaticDims() == newOutputShape)
             continue;
 
         // this path neccesary if there are several edges per one port
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
index c6b398de27c..e6c7f675efb 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_plugin.cpp
@@ -71,6 +71,7 @@
 #include <transformations/op_conversions/convert_deformable_conv_v8_to_v1.hpp>
 #include <transformations/smart_reshape/matmul_sr.hpp>
 #include <transformations/op_conversions/convert_minimum_to_power_and_max.hpp>
+#include <transformations/op_conversions/convert_reduce_to_pooling.hpp>
 #include <transformations/convert_precision.hpp>
 #include <transformations/init_node_info.hpp>
 #include <transformations/rt_info/fused_names_attribute.hpp>
@@ -367,6 +368,9 @@ static void TransformationUpToCPUSpecificOpSet(std::shared_ptr<ngraph::Function>
     pass_config->disable<ngraph::pass::ConvertGather7ToGather1>();
     pass_config->disable<ngraph::pass::ConvertMinimum>();
     pass_config->disable<ngraph::pass::ConvertBroadcastToTiles>();
+    pass_config->disable<ngraph::pass::ConvertReduceMeanToPooling>();
+    pass_config->disable<ngraph::pass::ConvertReduceMaxToPooling>();
+    pass_config->disable<ngraph::pass::ConvertReduceSumToPooling>();
 
     pass_config->enable<ngraph::pass::NormalizeL2Decomposition>();
     pass_config->enable<ngraph::pass::ConvertInterpolate1ToInterpolate4>();
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.cpp
index b9439b4a2ed..3ecc41eee7e 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.cpp
@@ -5,6 +5,7 @@
 #include "mkldnn_reduce_node.h"
 
 #include "mkldnn_fake_quantize_node.h"
+#include "mkldnn_eltwise_node.h"
 #include <mkldnn.hpp>
 #include <string>
 #include <vector>
@@ -44,6 +45,7 @@ using namespace Xbyak;
                                                                 OW = width;
 
 #define GET_OFF(field) offsetof(jit_reduce_call_args, field)
+#define GET_OFF_POST(field) offsetof(jit_reduce_post_call_args, field)
 
 #define GET_PTR_N_PLN              const uint8_t    *in_ptr_n      = in_ptr       + src_data_size * ib * IC * ID * IH * IW;               \
                                          uint8_t    *out_ptr_n     = out_ptr      + dst_data_size * ob * OC * OD * OH * OW;
@@ -86,17 +88,22 @@ struct jit_uni_reduce_kernel_f32 : public jit_uni_reduce_kernel, public jit_gene
     }
 
     void generate() override {
-        exp_injector.reset(new jit_uni_eltwise_injector_f32<isa>(this, alg_kind::eltwise_exp, 0.f, 0.f, 1));
+        if (jcp_.reduce_mode == ReduceLogSumExp) {
+            exp_injector = std::make_shared<jit_uni_eltwise_injector_f32<isa>>(this, alg_kind::eltwise_exp, 0.f, 0.f, 1);
+        }
 
         if (!mayiuse(avx512_core_bf16) && mayiuse(avx512_core))
-            emu_vcvtneps2bf16.reset(new jit_emu_vcvtneps2bf16(this, isa, nullptr));
+            emu_vcvtneps2bf16 = std::make_shared<jit_emu_vcvtneps2bf16>(this, isa, nullptr);
 
         this->preamble();
 
+        planar_layout = jcp_.layout == ReduceLayoutType::reduce_ncsp || jcp_.layout == ReduceLayoutType::reduce_nspc;
+
         mov(reg_src, ptr[reg_params + GET_OFF(src)]);
         mov(reg_dst, ptr[reg_params + GET_OFF(dst)]);
         mov(reg_work_amount, ptr[reg_params + GET_OFF(work_amount)]);
-        if (jcp_.planar_layout)
+        mov(reg_work_batch, ptr[reg_params + GET_OFF(work_batch)]);
+        if (planar_layout)
             mov(reg_reduce_w, ptr[reg_params + GET_OFF(reduce_w)]);
 
         if (jcp_.reduce_mode == ReduceAnd || jcp_.reduce_mode == ReduceL1 || jcp_.reduce_mode == ReduceMax ||
@@ -131,19 +138,26 @@ private:
     using Vmm = typename conditional3<isa == cpu::x64::sse41, Xbyak::Xmm, isa == cpu::x64::avx2,
             Xbyak::Ymm, Xbyak::Zmm>::type;
     size_t vlen = cpu_isa_traits<isa>::vlen;
+    bool planar_layout;
 
     Xbyak::Address table_val(int index) { return ptr[reg_table + index * vlen]; }
 
     Xbyak::Reg64 reg_src = r8;
     Xbyak::Reg64 reg_dst = r9;
+    Xbyak::Reg64 reg_idx = rdx;
     Xbyak::Reg64 reg_work_amount = r10;
     Xbyak::Reg64 reg_reduce_w = r11;
-    Xbyak::Reg64 reg_table = r12;
+    Xbyak::Reg64 reg_reduce_stride = r12;
+    Xbyak::Reg64 reg_work_batch = r13;
+    Xbyak::Reg64 reg_table = r14;
     Xbyak::Reg64 reg_params = abi_param1;
 
-    Xbyak::Reg8 reg_tmp_8 = r13b;
-    Xbyak::Reg32 reg_tmp_32 = r13d;
-    Xbyak::Reg64 reg_tmp_64 = r13;
+    Xbyak::Reg8 reg_tmp_8 = r15b;
+    Xbyak::Reg32 reg_tmp_32 = r15d;
+    Xbyak::Reg64 reg_tmp_64 = r15;
+
+    Xbyak::Reg64 reg_src_aux = rax;
+    Xbyak::Reg64 reg_work_batch_aux = rbx;
 
     Vmm vmm_aux = Vmm(0);
     Xmm xmm_aux = Xmm(0);
@@ -154,16 +168,17 @@ private:
     Vmm vmm_zero = Vmm(3);
     Xmm xmm_zero = Xmm(3);
     Vmm vmm_dst_aux = Vmm(4);
-    Xbyak::Xmm xmm_aux1 = Xbyak::Xmm(5);
-    Xbyak::Xmm xmm_aux2 = Xbyak::Xmm(6);
-    Xbyak::Xmm xmm_aux3 = Xbyak::Xmm(7);
+    Xmm xmm_aux1 = Xmm(5);
+    Xmm xmm_aux2 = Xmm(6);
+    Xmm xmm_aux3 = Xmm(7);
+    Vmm vmm_idx = Vmm(8);
+    Vmm vmm_mask = Vmm(9);
 
     const Xbyak::Opmask k_mask = Xbyak::Opmask(1);
 
-    std::unique_ptr<jit_emu_vcvtneps2bf16> emu_vcvtneps2bf16;
-
     Xbyak::Label l_table;
 
+    std::shared_ptr<jit_emu_vcvtneps2bf16> emu_vcvtneps2bf16;
     std::shared_ptr<jit_uni_eltwise_injector_f32<isa>> exp_injector;
 
     inline void reduce_main() {
@@ -225,8 +240,12 @@ private:
         // ================================================================
         Xbyak::Label reduce_to_vector_label;
         Xbyak::Label reduce_to_scalar_label;
+        Xbyak::Label reduce_to_gather_label;
         Xbyak::Label reduce_main_end_label;
-        if (jcp_.planar_layout) {
+        if (planar_layout) {
+            cmp(reg_work_batch, 0);
+            je(reduce_to_gather_label, T_NEAR);
+
             cmp(reg_reduce_w, 1); // planar layout reducing W
             je(reduce_to_scalar_label, T_NEAR);
         }
@@ -246,29 +265,8 @@ private:
             // load
             load_dst_vector();
 
-            Xbyak::Label reduce_loop_label;
-            Xbyak::Label reduce_loop_end_label;
-
             // reduce
-            L(reduce_loop_label);
-            {
-                cmp(reg_work_amount, step);
-                jl(reduce_loop_end_label, T_NEAR);
-
-                load_vector(vmm_src, ptr[reg_src], jcp_.src_dt);
-                reduce_kernel(vmm_src, vmm_dst);
-
-                if (isa == cpu::x64::sse41) {
-                    load_vector(vmm_src, ptr[reg_src + 4 * jcp_.src_data_size], jcp_.src_dt);
-                    reduce_kernel(vmm_src, vmm_dst_aux);
-                }
-
-                add(reg_src, step * jcp_.src_data_size);
-                sub(reg_work_amount, step);
-
-                jmp(reduce_loop_label, T_NEAR);
-            }
-            L(reduce_loop_end_label);
+            reduce_kernel();
 
             // store
             store_dst_vector();
@@ -317,16 +315,57 @@ private:
             // reduce
             reduce_main_loop();
             if (jcp_.reduce_mode == ReduceOr && isa != cpu::x64::avx512_common) {
-                if (isa == cpu::x64::avx2) {
-                    vcmpneqps(vmm_dst, vmm_dst, vmm_zero);
-                } else if (isa == cpu::x64::sse41) {
-                    cmpneqps(vmm_dst, vmm_zero);
-                }
+                uni_cmpneqps(vmm_dst, vmm_dst, vmm_zero);
                 uni_vandps(vmm_dst, vmm_dst, vmm_aux);
             }
             // store
             // store after horizontal calculation and calculation with loaded original ptr[reg_dst]
-            load_embedded_horiz_reduce_store(vmm_dst, jcp_.dst_dt);
+            horiz_reduce_store(vmm_dst, jcp_.dst_dt, true);
+
+            jmp(reduce_main_end_label, T_NEAR);
+        }
+
+        // load vmm_src with gather, then store vmm_dst directly into memory after reducing
+        // cases: [planar layout reducing small W]
+        L(reduce_to_gather_label);
+        {
+            int step = 1;
+            cmp(reg_work_amount, step);
+            jl(reduce_main_end_label, T_NEAR); //avoid illegal loading and storing
+
+            mov(reg_idx, ptr[reg_params + GET_OFF(idx)]);
+            uni_vmovdqu(vmm_idx, ptr[reg_idx]);
+
+            if (jcp_.reduce_mode == ReduceL1) {
+                uni_vmovups(vmm_aux, table_val(1));
+            }
+
+            // load
+            load_dst_vector();
+
+            // reduce
+            Xbyak::Label reduce_loop_label;
+            Xbyak::Label reduce_loop_end_label;
+            L(reduce_loop_label);
+            {
+                cmp(reg_work_amount, step);
+                jl(reduce_loop_end_label, T_NEAR);
+
+                reduce_gather(vmm_dst, 0);
+                if (isa == cpu::x64::sse41) {
+                    reduce_gather(vmm_dst_aux, 4 * jcp_.src_data_size);
+                }
+
+                add(reg_src, step * jcp_.src_data_size);
+                sub(reg_work_amount, step);
+                jmp(reduce_loop_label, T_NEAR);
+            }
+            L(reduce_loop_end_label);
+
+            // store
+            store_dst_vector();
+
+            jmp(reduce_main_end_label, T_NEAR);
         }
 
         L(reduce_main_end_label);
@@ -340,7 +379,7 @@ private:
         Xbyak::Label tail_dst_shifted_label;
         Xbyak::Label tail_dst_fixed_label;
         Xbyak::Label reduce_tail_end_label;
-        if (jcp_.planar_layout) {
+        if (planar_layout) {
             cmp(reg_reduce_w, 1);  // planar layout reducing W
             je(tail_dst_fixed_label, T_NEAR);
         }
@@ -349,40 +388,7 @@ private:
         // cases: [planar layout reducing other dimensions but W] [blocked layout concern padding]
         L(tail_dst_shifted_label);
         {
-            Xbyak::Label reduce_loop_label;
-            Xbyak::Label reduce_loop_end_label;
-
-            int step = 1;
-            L(reduce_loop_label);
-            {
-                cmp(reg_work_amount, step);
-                jl(reduce_loop_end_label, T_NEAR);
-
-                // load
-                load_scalar(xmm_dst, ptr[reg_dst], jcp_.dst_dt);
-                load_scalar(xmm_src, ptr[reg_src], jcp_.src_dt);
-
-                // reduce
-                reduce_kernel_scalar(xmm_src, xmm_dst);
-                if (jcp_.reduce_mode == ReduceOr) {
-                    if (isa == cpu::x64::sse41) {
-                        cmpneqps(xmm_dst, xmm_zero);
-                    } else {
-                        vcmpneqps(xmm_dst, xmm_dst, xmm_zero);
-                    }
-                    uni_vandps(xmm_dst, xmm_dst, xmm_aux);
-                }
-
-                // store
-                store_scalar(ptr[reg_dst], xmm_dst, jcp_.dst_dt);
-
-                add(reg_dst, step * jcp_.dst_data_size);
-                add(reg_src, step * jcp_.src_data_size);
-                sub(reg_work_amount, step);
-
-                jmp(reduce_loop_label, T_NEAR);
-            }
-            L(reduce_loop_end_label);
+            reduce_kernel_tail();
 
             jmp(reduce_tail_end_label, T_NEAR);
         }
@@ -408,11 +414,7 @@ private:
 
                 reduce_kernel_scalar(xmm_src, xmm_dst);
                 if (jcp_.reduce_mode == ReduceOr) {
-                    if (isa == cpu::x64::sse41) {
-                        cmpneqps(xmm_dst, xmm_zero);
-                    } else {
-                        vcmpneqps(xmm_dst, xmm_dst, xmm_zero);
-                    }
+                    uni_cmpneqps(xmm_dst, xmm_dst, xmm_zero);
                     uni_vandps(xmm_dst, xmm_dst, xmm_aux);
                 }
 
@@ -425,12 +427,254 @@ private:
 
             // store
             store_scalar(ptr[reg_dst], xmm_dst, jcp_.dst_dt);
-            add(reg_dst, step * jcp_.dst_data_size);
         }
 
         L(reduce_tail_end_label);
     }
 
+    inline void init_reg_reduce_stride() {
+        mov(reg_reduce_stride, ptr[reg_params + GET_OFF(reduce_stride)]);
+        mul_by_const(reg_reduce_stride, reg_tmp_64, jcp_.src_data_size);
+    }
+
+    inline void reduce_kernel() {
+        Xbyak::Label reduce_label;
+        Xbyak::Label reduce_end_label;
+        Xbyak::Label reduce_batch_label;
+        Xbyak::Label reduce_batch_end_label;
+
+        int step = vlen / sizeof(float) < 8 ? 8 : vlen / sizeof(float);
+        cmp(reg_work_batch, 1);
+        je(reduce_label, T_NEAR);
+
+        init_reg_reduce_stride();
+
+        L(reduce_batch_label);
+        {
+            cmp(reg_work_amount, step);
+            jl(reduce_end_label, T_NEAR);
+
+            reduce_batch();
+
+            add(reg_src, step * jcp_.src_data_size);
+            sub(reg_work_amount, step);
+            jmp(reduce_batch_label, T_NEAR);
+        }
+        L(reduce_batch_end_label);
+
+        L(reduce_label);
+        {
+            cmp(reg_work_amount, step);
+            jl(reduce_end_label, T_NEAR);
+
+            reduce_once();
+
+            add(reg_src, step * jcp_.src_data_size);
+            sub(reg_work_amount, step);
+            jmp(reduce_label, T_NEAR);
+        }
+        L(reduce_end_label);
+    }
+
+    inline void reduce_once() {
+        load_vector(vmm_src, ptr[reg_src], jcp_.src_dt);
+        reduce_kernel(vmm_src, vmm_dst);
+
+        if (isa == cpu::x64::sse41) {
+            load_vector(vmm_src, ptr[reg_src + 4 * jcp_.src_data_size], jcp_.src_dt);
+            reduce_kernel(vmm_src, vmm_dst_aux);
+        }
+    }
+
+    inline void reduce_batch() {
+        mov(reg_src_aux, reg_src);
+        mov(reg_work_batch_aux, reg_work_batch);
+
+        Xbyak::Label reduce_batch_loop_label;
+        Xbyak::Label reduce_batch_loop_end_label;
+        L(reduce_batch_loop_label);
+        {
+            cmp(reg_work_batch_aux, 1);
+            jl(reduce_batch_loop_end_label, T_NEAR);
+
+            load_vector(vmm_src, ptr[reg_src_aux], jcp_.src_dt);
+            reduce_kernel(vmm_src, vmm_dst);
+            if (isa == cpu::x64::sse41) {
+                load_vector(vmm_src, ptr[reg_src_aux + 4 * jcp_.src_data_size], jcp_.src_dt);
+                reduce_kernel(vmm_src, vmm_dst_aux);
+            }
+
+            add(reg_src_aux, reg_reduce_stride);
+            sub(reg_work_batch_aux, 1);
+            jmp(reduce_batch_loop_label, T_NEAR);
+        }
+        L(reduce_batch_loop_end_label);
+    }
+
+    inline void reduce_gather(Vmm vmm_dst, int offset) {
+        switch (jcp_.src_dt) {
+            case memory::data_type::f32:
+            case memory::data_type::s32:
+                if (isa == cpu::x64::avx512_common) {
+                    kxnord(k_mask, k_mask, k_mask);
+                    vgatherdps(vmm_src | k_mask, ptr[reg_src + offset + vmm_idx]);
+                } else if (isa == cpu::x64::avx2) {
+                    uni_vpcmpeqd(vmm_mask, vmm_mask, vmm_mask);
+                    vgatherdps(vmm_src, ptr[reg_src + offset + vmm_idx], vmm_mask);
+                } else {
+                    pack_gathered_vector(vmm_src, vmm_idx, offset, jcp_.src_dt);
+                }
+                break;
+            case memory::data_type::bf16:
+            case memory::data_type::s8:
+            case memory::data_type::u8:
+                pack_gathered_vector(vmm_src, vmm_idx, offset, jcp_.src_dt);
+                break;
+            default:
+                assert(!"unknown src_dt");
+        }
+        reduce_kernel(vmm_src, vmm_dst);
+    }
+
+    inline void pack_gathered_vector(Vmm vmm_val, Vmm vmm_index, int offset, memory::data_type src_dt) {
+        sub(rsp, vlen);
+        uni_vmovdqu(ptr[rsp], vmm_index);
+        int repeats = vlen / sizeof(float);
+        for (size_t i = 0; i < repeats; i++) {
+            mov(reg_tmp_64.cvt32(), ptr[rsp + i * sizeof(int)]);
+            Xbyak::Address table_idx = ptr[reg_src + offset + reg_tmp_64];
+            switch (src_dt) {
+                case memory::data_type::f32:
+                case memory::data_type::s32:
+                    mov(reg_tmp_64.cvt32(), table_idx);
+                    mov(ptr[rsp + i * sizeof(int)], reg_tmp_64.cvt32());
+                    break;
+                case memory::data_type::bf16:
+                    mov(reg_tmp_64.cvt16(), table_idx);
+                    mov(ptr[rsp + i * sizeof(MKLDNNPlugin::bfloat16_t)], reg_tmp_64.cvt16());
+                    break;
+                case memory::data_type::s8:
+                case memory::data_type::u8:
+                    mov(reg_tmp_64.cvt8(), table_idx);
+                    mov(ptr[rsp + i * sizeof(char)], reg_tmp_64.cvt8());
+                    break;
+                default:
+                    assert(!"unknown src_dt");
+            }
+        }
+
+        switch (src_dt) {
+            case memory::data_type::f32:
+            case memory::data_type::s32:
+                uni_vmovups(vmm_val, ptr[rsp]);
+                break;
+            case memory::data_type::bf16:
+                uni_vpmovzxwd(vmm_val, ptr[rsp]);
+                uni_vpslld(vmm_val, vmm_val, 16);
+            break;
+            case memory::data_type::s8:
+                uni_vpmovsxbd(vmm_val, ptr[rsp]);
+                break;
+            case memory::data_type::u8:
+                uni_vpmovzxbd(vmm_val, ptr[rsp]);
+                break;
+            default:
+                assert(!"unknown src_dt");
+        }
+        add(rsp, vlen);
+    }
+
+    inline void reduce_kernel_tail() {
+        Xbyak::Label reduce_label;
+        Xbyak::Label reduce_end_label;
+        Xbyak::Label reduce_batch_label;
+        Xbyak::Label reduce_batch_end_label;
+
+        int step = 1;
+        cmp(reg_work_batch, 1);
+        je(reduce_label, T_NEAR);
+
+        init_reg_reduce_stride();
+
+        L(reduce_batch_label);
+        {
+            cmp(reg_work_amount, step);
+            jl(reduce_end_label, T_NEAR);
+
+            // load
+            load_scalar(xmm_dst, ptr[reg_dst], jcp_.dst_dt);
+
+            // reduce
+            reduce_batch_tail();
+
+            // store
+            store_scalar(ptr[reg_dst], xmm_dst, jcp_.dst_dt);
+
+            add(reg_dst, step * jcp_.dst_data_size);
+            add(reg_src, step * jcp_.src_data_size);
+            sub(reg_work_amount, step);
+
+            jmp(reduce_batch_label, T_NEAR);
+        }
+        L(reduce_batch_end_label);
+
+        L(reduce_label);
+        {
+            cmp(reg_work_amount, step);
+            jl(reduce_end_label, T_NEAR);
+
+            // load
+            load_scalar(xmm_dst, ptr[reg_dst], jcp_.dst_dt);
+
+            // reduce
+            reduce_batch_tail();
+
+            // store
+            store_scalar(ptr[reg_dst], xmm_dst, jcp_.dst_dt);
+
+            add(reg_dst, step * jcp_.dst_data_size);
+            add(reg_src, step * jcp_.src_data_size);
+            sub(reg_work_amount, step);
+
+            jmp(reduce_label, T_NEAR);
+        }
+        L(reduce_end_label);
+    }
+
+    inline void reduce_once_tail() {
+        load_scalar(xmm_src, ptr[reg_src], jcp_.src_dt);
+        reduce_kernel_scalar(xmm_src, xmm_dst);
+        if (jcp_.reduce_mode == ReduceOr) {
+            uni_cmpneqps(xmm_dst, xmm_dst, xmm_zero);
+            uni_vandps(xmm_dst, xmm_dst, xmm_aux);
+        }
+    }
+
+    inline void reduce_batch_tail() {
+        mov(reg_src_aux, reg_src);
+        mov(reg_work_batch_aux, reg_work_batch);
+
+        Xbyak::Label reduce_batch_loop_label;
+        Xbyak::Label reduce_batch_loop_end_label;
+        L(reduce_batch_loop_label);
+        {
+            cmp(reg_work_batch_aux, 1);
+            jl(reduce_batch_loop_end_label, T_NEAR);
+
+            load_scalar(xmm_src, ptr[reg_src_aux], jcp_.src_dt);
+            reduce_kernel_scalar(xmm_src, xmm_dst);
+            if (jcp_.reduce_mode == ReduceOr) {
+                uni_cmpneqps(xmm_dst, xmm_dst, xmm_zero);
+                uni_vandps(xmm_dst, xmm_dst, xmm_aux);
+            }
+
+            add(reg_src_aux, reg_reduce_stride);
+            sub(reg_work_batch_aux, 1);
+            jmp(reduce_batch_loop_label, T_NEAR);
+        }
+        L(reduce_batch_loop_end_label);
+    }
+
     inline void reduce_main_loop() {
         Xbyak::Label reduce_loop_label;
         Xbyak::Label reduce_loop_end_label;
@@ -463,10 +707,8 @@ private:
                 if (isa == cpu::x64::avx512_common) {
                     vcmpps(k_mask, vmm_src, vmm_zero, _cmp_neq_uq);
                     vblendmps(vmm_src | k_mask, vmm_zero, vmm_aux);
-                } else if (isa == cpu::x64::avx2) {
-                    vcmpneqps(vmm_src, vmm_src, vmm_zero);
                 } else {
-                    cmpneqps(vmm_src, vmm_zero);
+                    uni_cmpneqps(vmm_src, vmm_src, vmm_zero);
                 }
                 uni_vandps(vmm_dst, vmm_dst, vmm_src);
                 break;
@@ -512,11 +754,7 @@ private:
     inline void reduce_kernel_scalar(Xmm xmm_src, Xmm xmm_dst) {
         switch (jcp_.reduce_mode) {
             case ReduceAnd:
-                if (isa == cpu::x64::sse41) {
-                    cmpneqps(xmm_src, xmm_zero);
-                } else {
-                    vcmpneqps(xmm_src, xmm_src, xmm_zero);
-                }
+                uni_cmpneqps(xmm_src, xmm_src, xmm_zero);
                 uni_vandps(xmm_dst, xmm_dst, xmm_src);
                 break;
             case ReduceL1:
@@ -562,15 +800,11 @@ private:
 
     inline void store_dst_vector() {
         if (jcp_.reduce_mode == ReduceOr && isa != cpu::x64::avx512_common) {
-            if (isa == cpu::x64::avx2) {
-                vcmpneqps(vmm_dst, vmm_dst, vmm_zero);
-            } else if (isa == cpu::x64::sse41) {
-                cmpneqps(vmm_dst, vmm_zero);
-            }
+            uni_cmpneqps(vmm_dst, vmm_dst, vmm_zero);
             uni_vandps(vmm_dst, vmm_dst, vmm_aux);
 
             if (isa == cpu::x64::sse41) {
-                cmpneqps(vmm_dst_aux, vmm_zero);
+                uni_cmpneqps(vmm_dst_aux, vmm_dst_aux, vmm_zero);
                 uni_vandps(vmm_dst_aux, vmm_dst_aux, vmm_aux);
             }
         }
@@ -607,19 +841,19 @@ private:
         switch (src_dt) {
             case memory::data_type::f32:
             case memory::data_type::s32:
-                movss(xmm_src, op);
+                uni_vmovss(xmm_src, op);
                 break;
             case memory::data_type::bf16:
-                pinsrw(xmm_src, op, 0x0);
+                uni_vpinsrw(xmm_src, xmm_src, op, 0x0);
                 uni_vpslld(xmm_src, xmm_src, 16);
                 break;
             case memory::data_type::s8:
                 movsx(reg_tmp_32, op);
-                movq(xmm_src, reg_tmp_64);
+                uni_vmovq(xmm_src, reg_tmp_64);
                 break;
             case memory::data_type::u8:
                 movzx(reg_tmp_32, op);
-                movq(xmm_src, reg_tmp_64);
+                uni_vmovq(xmm_src, reg_tmp_64);
                 break;
             default:
                 assert(!"unknown src_dt");
@@ -662,7 +896,7 @@ private:
                     if (isa != cpu::x64::sse41)
                         vmovq(op, xmm_dst);
                     else
-                        movd(op, xmm_dst);
+                        uni_vmovd(op, xmm_dst);
                 }
                 break;
             case memory::data_type::u8:
@@ -676,7 +910,7 @@ private:
                     if (isa != cpu::x64::sse41)
                         vmovq(op, xmm_dst);
                     else
-                        movd(op, xmm_dst);
+                        uni_vmovd(op, xmm_dst);
                 }
                 break;
             default:
@@ -692,22 +926,22 @@ private:
         switch (dst_dt) {
             case memory::data_type::f32:
             case memory::data_type::s32:
-                movss(op, xmm_dst);
+                uni_vmovss(op, xmm_dst);
                 break;
             case memory::data_type::bf16:
                 uni_vpsrld(xmm_dst, xmm_dst, 16);
-                pextrw(op, xmm_dst, 0x0);
+                uni_vpextrw(op, xmm_dst, 0x0);
                 break;
             case memory::data_type::s8:
                 uni_vpackssdw(xmm_dst, xmm_dst, xmm_dst);
                 uni_vpacksswb(xmm_dst, xmm_dst, xmm_dst);
-                movq(reg_tmp_64, xmm_dst);
+                uni_vmovq(reg_tmp_64, xmm_dst);
                 mov(op, reg_tmp_8);
                 break;
             case memory::data_type::u8:
                 uni_vpackusdw(xmm_dst, xmm_dst, xmm_dst);
                 uni_vpackuswb(xmm_dst, xmm_dst, xmm_dst);
-                movq(reg_tmp_64, xmm_dst);
+                uni_vmovq(reg_tmp_64, xmm_dst);
                 mov(op, reg_tmp_8);
                 break;
             default:
@@ -715,15 +949,15 @@ private:
         }
     }
 
-    inline void load_embedded_horiz_reduce_store(Vmm vmm_dst, memory::data_type dst_dt) {
+    inline void horiz_reduce_store(Vmm vmm_dst, memory::data_type dst_dt, bool load_embedded = false) {
         if (isa == cpu::x64::sse41) {
-            load_embedded_horiz_store(vmm_dst, dst_dt);
+            horiz_store(vmm_dst, dst_dt, load_embedded);
         } else if (isa == cpu::x64::avx2) {
             Xbyak::Ymm ymm_dst = Xbyak::Ymm(vmm_dst.getIdx());
             vextractf128(xmm_aux1, ymm_dst, 0);
             vextractf128(xmm_aux2, ymm_dst, 1);
             horiz_ps(xmm_aux1, xmm_aux2);
-            load_embedded_horiz_store(xmm_aux1, dst_dt);
+            horiz_store(xmm_aux1, dst_dt, load_embedded);
         } else {
             Xbyak::Zmm zmm_dst = Xbyak::Zmm(vmm_dst.getIdx());
             vextractf32x4(xmm_aux1, zmm_dst, 0);
@@ -733,51 +967,26 @@ private:
             vextractf32x4(xmm_aux3, zmm_dst, 3);
             horiz_ps(xmm_aux2, xmm_aux3);
             horiz_ps(xmm_aux1, xmm_aux2);
-            load_embedded_horiz_store(xmm_aux1, dst_dt);
+            horiz_store(xmm_aux1, dst_dt, load_embedded);
         }
     }
 
-    inline void load_embedded_horiz_store(Xbyak::Xmm xmm_dst, memory::data_type dst_dt) {
-        movshdup(xmm_aux3, xmm_dst); // dst:1,2,3,4; aux3:2,2,4,4
-        horiz_ps(xmm_dst, xmm_aux3); // dst:f(1,2),f(2,2),f(3,4),f(4,4)
-        movhlps(xmm_aux3, xmm_dst);  // aux3:f(3,4),f(4,4),4,4
-        horiz_ps(xmm_dst, xmm_aux3); // dst:f(1,2,3,4),...
-        load_scalar(xmm_aux3, ptr[reg_dst], dst_dt);
-
-        switch (dst_dt) {
-            case memory::data_type::f32:
-            case memory::data_type::bf16:
-                horiz_ps(xmm_dst, xmm_aux3);
-                store_scalar(ptr[reg_dst], xmm_dst, dst_dt);
-                break;
-            case memory::data_type::s32:
-                horiz_ps(xmm_dst, xmm_aux3);
-                uni_vcvtps2dq(xmm_dst, xmm_dst);
-                movss(ptr[reg_dst], xmm_dst);
-                break;
-            case memory::data_type::u8:
-                horiz_ps(xmm_dst, xmm_aux3);
-                uni_vcvtps2dq(xmm_dst, xmm_dst);
-                uni_vpackusdw(xmm_dst, xmm_dst, xmm_dst);
-                uni_vpackuswb(xmm_dst, xmm_dst, xmm_dst);
-                pextrb(ptr[reg_dst], xmm_dst, 0);
-                break;
-            case memory::data_type::s8:
-                horiz_ps(xmm_dst, xmm_aux3);
-                uni_vcvtps2dq(xmm_dst, xmm_dst);
-                uni_vpackssdw(xmm_dst, xmm_dst, xmm_dst);
-                uni_vpacksswb(xmm_dst, xmm_dst, xmm_dst);
-                pextrb(ptr[reg_dst], xmm_dst, 0);
-                break;
-            default:
-                assert(!"unknown dst_dt");
+    inline void horiz_store(Xbyak::Xmm xmm_dst, memory::data_type dst_dt, bool load_embedded) {
+        uni_movshdup(xmm_aux3, xmm_dst); // dst:1,2,3,4; aux3:2,2,4,4
+        horiz_ps(xmm_dst, xmm_aux3);     // dst:f(1,2),f(2,2),f(3,4),f(4,4)
+        uni_movhlps(xmm_aux3, xmm_dst);  // aux3:f(3,4),f(4,4),4,4
+        horiz_ps(xmm_dst, xmm_aux3);     // dst:f(1,2,3,4),...
+        if (load_embedded) {
+            load_scalar(xmm_aux3, ptr[reg_dst], dst_dt);
+            horiz_ps(xmm_dst, xmm_aux3);
         }
+        store_scalar(ptr[reg_dst], xmm_dst, dst_dt);
     }
 
     inline void horiz_ps(const Xmm& xmm, const Operand& op) {
         switch (jcp_.reduce_mode) {
             case ReduceAnd:
-                andps(xmm, op);
+                uni_vandps(xmm, xmm, op);
                 break;
             case ReduceL1:
             case ReduceL2:
@@ -786,19 +995,19 @@ private:
             case ReduceSum:
             case ReduceSumSquare:
             case ReduceLogSumExp:
-                addps(xmm, op);
+                uni_vaddps(xmm, xmm, op);
                 break;
             case ReduceMax:
-                maxps(xmm, op);
+                uni_vmaxps(xmm, xmm, op);
                 break;
             case ReduceMin:
-                minps(xmm, op);
+                uni_vminps(xmm, xmm, op);
                 break;
             case ReduceOr:
-                orps(xmm, op);
+                uni_vorps(xmm, xmm, op);
                 break;
             case ReduceProd:
-                mulps(xmm, op);
+                uni_vmulps(xmm, xmm, op);
                 break;
             default:
                 assert(!"unsupported reduce mode");
@@ -837,8 +1046,8 @@ template <cpu_isa_t isa>
 struct jit_uni_reduce_post_kernel_f32 : public jit_uni_reduce_post_kernel, public jit_generator {
     DECLARE_CPU_JIT_AUX_FUNCTIONS(jit_uni_reduce_post_kernel_f32)
 
-    explicit jit_uni_reduce_post_kernel_f32(jit_reduce_config_params jcp)
-    : jit_uni_reduce_post_kernel(jcp), jit_generator() {}
+    explicit jit_uni_reduce_post_kernel_f32(jit_reduce_config_params jcp, const mkldnn_primitive_attr &attr)
+    : jit_uni_reduce_post_kernel(jcp, attr), jit_generator() {}
 
     void create_ker() override {
         jit_generator::create_kernel();
@@ -846,25 +1055,69 @@ struct jit_uni_reduce_post_kernel_f32 : public jit_uni_reduce_post_kernel, publi
     }
 
     void generate() override {
-        log_injector.reset(new jit_uni_eltwise_injector_f32<isa>(this, alg_kind::eltwise_log, 0.f, 0.f, 1.f));
+        const auto &p = attr_.post_ops_;
+        for (int i = 0; i < p.len(); i++) {
+            auto &post_op = p.entry_[i];
+            if (post_op.is_eltwise()) {
+                eltwise_injectors.push_back(std::make_shared<jit_uni_eltwise_injector_f32<isa>>(
+                        this, post_op.eltwise.alg, post_op.eltwise.alpha, post_op.eltwise.beta, post_op.eltwise.scale));
+            } else if (post_op.is_depthwise()) {
+                depthwise_injectors.push_back(std::make_shared<jit_uni_depthwise_injector_f32<isa>>(
+                        this, post_op.depthwise.alg));
+            } else if (post_op.is_quantization()) {
+                quantization_injectors.push_back(std::make_shared<jit_uni_quantization_injector_f32<isa>>(
+                        this, post_op, vmm_d_weights, vmm_d_bias, reg_d_weights, reg_d_bias));
+            }
+        }
+
+        if (jcp_.reduce_mode == ReduceLogSum || jcp_.reduce_mode == ReduceLogSumExp) {
+            log_injector = std::make_shared<jit_uni_eltwise_injector_f32<isa>>(this, alg_kind::eltwise_log, 0.f, 0.f, 1.f);
+        }
 
         if (!mayiuse(avx512_core_bf16) && mayiuse(avx512_core))
-            emu_vcvtneps2bf16.reset(new jit_emu_vcvtneps2bf16(this, isa, nullptr));
+            emu_vcvtneps2bf16 = std::make_shared<jit_emu_vcvtneps2bf16>(this, isa, nullptr);
 
         this->preamble();
 
-        mov(reg_dst, ptr[reg_params + GET_OFF(dst)]);
-        mov(reg_work_amount, ptr[reg_params + GET_OFF(work_amount)]);
-        mov(reg_divisor, ptr[reg_params + GET_OFF(divisor)]);
-        if (!jcp_.planar_layout)
-            mov(reg_reduce_c, ptr[reg_params + GET_OFF(reduce_c)]);
+        planar_layout = jcp_.layout == ReduceLayoutType::reduce_ncsp || jcp_.layout == ReduceLayoutType::reduce_nspc;
+
+        mov(reg_dst, ptr[reg_params + GET_OFF_POST(dst)]);
+        mov(reg_work_amount, ptr[reg_params + GET_OFF_POST(work_amount)]);
+        mov(reg_channel_size, ptr[reg_params + GET_OFF_POST(channel_size)]);
+        mov(reg_divisor, ptr[reg_params + GET_OFF_POST(divisor)]);
+        if (!planar_layout)
+            mov(reg_reduce_c, ptr[reg_params + GET_OFF_POST(reduce_c)]);
+        if (attr_.post_ops_.len() != 0)
+            mov(reg_oc_off, ptr[reg_params + GET_OFF_POST(oc_off)]);
 
         if (isa == cpu::x64::avx512_common)
             uni_vpxor(vmm_zero, vmm_zero, vmm_zero);
 
-        reduce_post_main();
-        if (jcp_.planar_layout)
+        if (jcp_.layout == ReduceLayoutType::reduce_blocked) {
+            reduce_post_main();
+        } else if (jcp_.layout == ReduceLayoutType::reduce_nspc && attr_.post_ops_.len() != 0) {
+            // the tail of channel dimension should always be concerned during post ops fusing for nspc layout
+            Xbyak::Label reduce_nspc_loop_label;
+            Xbyak::Label reduce_nspc_loop_end_label;
+            mov(reg_total_work_amount, reg_work_amount);
+            L(reduce_nspc_loop_label);
+            {
+                cmp(reg_total_work_amount, 0);
+                jle(reduce_nspc_loop_end_label, T_NEAR);
+
+                mov(reg_oc_off, 0);
+                mov(reg_work_amount, reg_channel_size);
+                reduce_post_main();
+                reduce_post_tail();
+
+                sub(reg_total_work_amount, reg_channel_size);
+                jmp(reduce_nspc_loop_label, T_NEAR);
+            }
+            L(reduce_nspc_loop_end_label);
+        } else {
+            reduce_post_main();
             reduce_post_tail();
+        }
 
         this->postamble();
 
@@ -874,22 +1127,32 @@ struct jit_uni_reduce_post_kernel_f32 : public jit_uni_reduce_post_kernel, publi
         if (jcp_.reduce_mode == ReduceLogSum || jcp_.reduce_mode == ReduceLogSumExp) {
             log_injector->prepare_table();
         }
+
+        for (auto& inj : eltwise_injectors)
+            inj->prepare_table();
     }
 
 private:
     using Vmm = typename conditional3<isa == cpu::x64::sse41, Xbyak::Xmm, isa == cpu::x64::avx2,
             Xbyak::Ymm, Xbyak::Zmm>::type;
     size_t vlen = cpu_isa_traits<isa>::vlen;
+    bool planar_layout;
 
     Xbyak::Reg64 reg_dst = r8;
     Xbyak::Reg64 reg_work_amount = r9;
-    Xbyak::Reg64 reg_divisor = r10;
-    Xbyak::Reg64 reg_reduce_c = r11;
+    Xbyak::Reg64 reg_total_work_amount = r10;
+    Xbyak::Reg64 reg_channel_size = r11;
+    Xbyak::Reg64 reg_divisor = r12;
+    Xbyak::Reg64 reg_reduce_c = r13;
     Xbyak::Reg64 reg_params = abi_param1;
 
-    Xbyak::Reg8 reg_tmp_8 = r12b;
-    Xbyak::Reg32 reg_tmp_32 = r12d;
-    Xbyak::Reg64 reg_tmp_64 = r12;
+    Xbyak::Reg8 reg_tmp_8 = r14b;
+    Xbyak::Reg32 reg_tmp_32 = r14d;
+    Xbyak::Reg64 reg_tmp_64 = r14;
+
+    Xbyak::Reg64 reg_oc_off = rax;
+    Xbyak::Reg64 reg_d_weights = rbx;
+    Xbyak::Reg64 reg_d_bias = rdx;
 
     Vmm vmm_aux = Vmm(0);
     Xmm xmm_aux = Xmm(0);
@@ -901,14 +1164,20 @@ private:
     Xbyak::Xmm xmm_aux2 = Xbyak::Xmm(5);
     Xbyak::Xmm xmm_aux3 = Xbyak::Xmm(6);
 
-    std::unique_ptr<jit_emu_vcvtneps2bf16> emu_vcvtneps2bf16;
+    Vmm vmm_d_weights = Vmm(7);
+    Vmm vmm_d_bias = Vmm(8);
 
+    std::shared_ptr<jit_emu_vcvtneps2bf16> emu_vcvtneps2bf16;
     std::shared_ptr<jit_uni_eltwise_injector_f32<isa>> log_injector;
 
+    std::vector<std::shared_ptr<jit_uni_eltwise_injector_f32<isa>>> eltwise_injectors;
+    std::vector<std::shared_ptr<jit_uni_depthwise_injector_f32<isa>>> depthwise_injectors;
+    std::vector<std::shared_ptr<jit_uni_quantization_injector_f32<isa>>> quantization_injectors;
+
     inline void reduce_post_main() {
         Xbyak::Label reduce_channel_label;
         Xbyak::Label reduce_map_label;
-        if (jcp_.planar_layout) {
+        if (planar_layout) {
             jmp(reduce_map_label, T_NEAR);
         } else {
             cmp(reg_reduce_c, 1);
@@ -937,7 +1206,7 @@ private:
                 // reduce and store
                 horiz_reduce_store(vmm_dst, jcp_.dst_dt);
                 if (isa == cpu::x64::sse41)
-                    load_embedded_horiz_reduce_store(vmm_dst_aux, jcp_.dst_dt);
+                    horiz_reduce_store(vmm_dst_aux, jcp_.dst_dt, true);
 
                 add(reg_dst, step * jcp_.dst_data_size);
                 sub(reg_work_amount, step);
@@ -946,8 +1215,8 @@ private:
             }
             L(reduce_loop_end_label);
 
-            mov(reg_dst, ptr[reg_params + GET_OFF(dst)]);
-            mov(reg_work_amount, ptr[reg_params + GET_OFF(work_amount)]);
+            mov(reg_dst, ptr[reg_params + GET_OFF_POST(dst)]);
+            mov(reg_work_amount, ptr[reg_params + GET_OFF_POST(work_amount)]);
         }
 
         // reduce map for value in dst memory
@@ -968,27 +1237,67 @@ private:
                     cmp(reg_work_amount, step);
                     jl(reduce_loop_end_label, T_NEAR);
 
-                    // load
                     load_vector(vmm_dst, ptr[reg_dst], jcp_.dst_dt);
-                    if (isa == cpu::x64::sse41)
-                        load_vector(vmm_dst_aux, ptr[reg_dst + 4 * jcp_.dst_data_size], jcp_.dst_dt);
-
-                    // reduce
                     reduce_map_kernel(vmm_dst);
-                    if (isa == cpu::x64::sse41)
-                        reduce_map_kernel(vmm_dst_aux);
-
-                    // store
+                    if (attr_.post_ops_.len() != 0)
+                        apply_post_ops(jcp_.dst_dt, jcp_.layout == ReduceLayoutType::reduce_ncsp);
                     store_vector(ptr[reg_dst], vmm_dst, jcp_.dst_dt);
-                    if (isa == cpu::x64::sse41)
-                        store_vector(ptr[reg_dst + 4 * jcp_.dst_data_size], vmm_dst_aux, jcp_.dst_dt);
+
+                    if (isa == cpu::x64::sse41) {
+                        load_vector(vmm_dst, ptr[reg_dst + 4 * jcp_.dst_data_size], jcp_.dst_dt);
+                        reduce_map_kernel(vmm_dst);
+                        if (attr_.post_ops_.len() != 0) {
+                            if (jcp_.layout != ReduceLayoutType::reduce_ncsp)
+                                add(reg_oc_off, 4 * sizeof(float));
+                            apply_post_ops(jcp_.dst_dt, jcp_.layout == ReduceLayoutType::reduce_ncsp);
+                            if (jcp_.layout != ReduceLayoutType::reduce_ncsp)
+                                sub(reg_oc_off, 4 * sizeof(float));
+                        }
+                        store_vector(ptr[reg_dst + 4 * jcp_.dst_data_size], vmm_dst, jcp_.dst_dt);
+                    }
 
                     add(reg_dst, step * jcp_.dst_data_size);
+                    if (jcp_.layout == ReduceLayoutType::reduce_nspc && attr_.post_ops_.len() != 0)
+                        add(reg_oc_off, step * sizeof(float));
                     sub(reg_work_amount, step);
 
                     jmp(reduce_loop_label, T_NEAR);
                 }
                 L(reduce_loop_end_label);
+            } else {
+                if (attr_.post_ops_.len() != 0) {
+                    Xbyak::Label reduce_loop_label;
+                    Xbyak::Label reduce_loop_end_label;
+
+                    int step = vlen / sizeof(float) < 8 ? 8 : vlen / sizeof(float);
+                    L(reduce_loop_label);
+                    {
+                        cmp(reg_work_amount, step);
+                        jl(reduce_loop_end_label, T_NEAR);
+
+                        load_vector(vmm_dst, ptr[reg_dst], jcp_.dst_dt);
+                        apply_post_ops(jcp_.dst_dt, jcp_.layout == ReduceLayoutType::reduce_ncsp);
+                        store_vector(ptr[reg_dst], vmm_dst, jcp_.dst_dt);
+
+                        if (isa == cpu::x64::sse41) {
+                            load_vector(vmm_dst, ptr[reg_dst + 4 * jcp_.dst_data_size], jcp_.dst_dt);
+                            if (jcp_.layout != ReduceLayoutType::reduce_ncsp)
+                                add(reg_oc_off, 4 * sizeof(float));
+                            apply_post_ops(jcp_.dst_dt, jcp_.layout == ReduceLayoutType::reduce_ncsp);
+                            if (jcp_.layout != ReduceLayoutType::reduce_ncsp)
+                                sub(reg_oc_off, 4 * sizeof(float));
+                            store_vector(ptr[reg_dst + 4 * jcp_.dst_data_size], vmm_dst, jcp_.dst_dt);
+                        }
+
+                        add(reg_dst, step * jcp_.dst_data_size);
+                        if (jcp_.layout == ReduceLayoutType::reduce_nspc && attr_.post_ops_.len() != 0)
+                            add(reg_oc_off, step * sizeof(float));
+                        sub(reg_work_amount, step);
+
+                        jmp(reduce_loop_label, T_NEAR);
+                    }
+                    L(reduce_loop_end_label);
+                }
             }
         }
     }
@@ -1017,14 +1326,84 @@ private:
                 reduce_map_kernel_scalar(xmm_dst);
 
                 // store
+                if (attr_.post_ops_.len() != 0)
+                    apply_post_ops(jcp_.dst_dt, jcp_.layout == ReduceLayoutType::reduce_ncsp);
                 store_scalar(ptr[reg_dst], xmm_dst, jcp_.dst_dt);
 
                 add(reg_dst, step * jcp_.dst_data_size);
+                if (jcp_.layout == ReduceLayoutType::reduce_nspc && attr_.post_ops_.len() != 0)
+                    add(reg_oc_off, step * sizeof(float));
                 sub(reg_work_amount, step);
 
                 jmp(reduce_loop_label, T_NEAR);
             }
             L(reduce_loop_end_label);
+        } else {
+            if (attr_.post_ops_.len() != 0) {
+                Xbyak::Label reduce_loop_label;
+                Xbyak::Label reduce_loop_end_label;
+
+                int step = 1;
+                L(reduce_loop_label);
+                {
+                    cmp(reg_work_amount, step);
+                    jl(reduce_loop_end_label, T_NEAR);
+
+                    // load
+                    load_scalar(xmm_dst, ptr[reg_dst], jcp_.dst_dt);
+
+                    // store
+                    apply_post_ops(jcp_.dst_dt, jcp_.layout == ReduceLayoutType::reduce_ncsp);
+                    store_scalar(ptr[reg_dst], xmm_dst, jcp_.dst_dt);
+
+                    add(reg_dst, step * jcp_.dst_data_size);
+                    if (jcp_.layout == ReduceLayoutType::reduce_nspc && attr_.post_ops_.len() != 0)
+                        add(reg_oc_off, step * sizeof(float));
+                    sub(reg_work_amount, step);
+
+                    jmp(reduce_loop_label, T_NEAR);
+                }
+                L(reduce_loop_end_label);
+            }
+        }
+    }
+
+    void apply_post_ops(memory::data_type dst_dt, bool is_broadcast) {
+        const auto &p = attr_.post_ops_;
+        int eltwise_inj_idx = 0;
+        int depthwise_inj_idx = 0;
+        int quantization_inj_idx = 0;
+        for (int i = 0; i < p.len(); i++) {
+            auto& post_op = p.entry_[i];
+            if (post_op.is_eltwise()) {
+                eltwise_injectors[eltwise_inj_idx]->compute_vector_range(vmm_dst.getIdx(), vmm_dst.getIdx() + 1);
+                eltwise_inj_idx++;
+            } else if (post_op.is_depthwise()) {
+                mov(reg_d_weights, reinterpret_cast<size_t>(post_op.depthwise.weights_data));
+                mov(reg_d_bias, reinterpret_cast<size_t>(post_op.depthwise.biases_data));
+                add(reg_d_weights, reg_oc_off);
+                add(reg_d_bias, reg_oc_off);
+                depthwise_injectors[depthwise_inj_idx]->compute_vector_range(vmm_dst.getIdx(), vmm_dst.getIdx() + 1, reg_d_weights, reg_d_bias, is_broadcast);
+                depthwise_inj_idx++;
+            } else if (post_op.is_quantization()) {
+                bool do_dequantization = post_op.quantization.alg == alg_kind::quantization_quantize_dequantize;
+                bool do_rounding = do_dequantization || isFloatCompatible(dst_dt) || i != p.len() - 1;
+
+                int s_idx = vmm_dst.getIdx();
+
+                quantization_injectors[quantization_inj_idx]->init_crop_ptrs(reg_oc_off);
+                quantization_injectors[quantization_inj_idx]->compute_crop(s_idx, s_idx + 1, 0, 0, is_broadcast);
+
+                quantization_injectors[quantization_inj_idx]->init_input_scale_shift_ptrs(reg_oc_off);
+                quantization_injectors[quantization_inj_idx]->compute_input_scale_shift(s_idx, s_idx + 1, 0, do_rounding, 0, is_broadcast);
+
+                if (do_dequantization) {
+                    quantization_injectors[quantization_inj_idx]->init_output_scale_shift_ptrs(reg_oc_off);
+                    quantization_injectors[quantization_inj_idx]->compute_output_scale_shift(s_idx, s_idx + 1, 0, 0, is_broadcast);
+                }
+
+                quantization_inj_idx++;
+            }
         }
     }
 
@@ -1074,19 +1453,19 @@ private:
         switch (src_dt) {
             case memory::data_type::f32:
             case memory::data_type::s32:
-                movss(xmm_src, op);
+                uni_vmovss(xmm_src, op);
                 break;
             case memory::data_type::bf16:
-                pinsrw(xmm_src, op, 0x0);
+                uni_vpinsrw(xmm_src, xmm_src, op, 0x0);
                 uni_vpslld(xmm_src, xmm_src, 16);
                 break;
             case memory::data_type::s8:
                 movsx(reg_tmp_32, op);
-                movq(xmm_src, reg_tmp_64);
+                uni_vmovq(xmm_src, reg_tmp_64);
                 break;
             case memory::data_type::u8:
                 movzx(reg_tmp_32, op);
-                movq(xmm_src, reg_tmp_64);
+                uni_vmovq(xmm_src, reg_tmp_64);
                 break;
             default:
                 assert(!"unknown src_dt");
@@ -1129,7 +1508,7 @@ private:
                     if (isa != cpu::x64::sse41)
                         vmovq(op, xmm_dst);
                     else
-                        movd(op, xmm_dst);
+                        uni_vmovd(op, xmm_dst);
                 }
                 break;
             case memory::data_type::u8:
@@ -1143,7 +1522,7 @@ private:
                     if (isa != cpu::x64::sse41)
                         vmovq(op, xmm_dst);
                     else
-                        movd(op, xmm_dst);
+                        uni_vmovd(op, xmm_dst);
                 }
                 break;
             default:
@@ -1159,22 +1538,22 @@ private:
         switch (dst_dt) {
             case memory::data_type::f32:
             case memory::data_type::s32:
-                movss(op, xmm_dst);
+                uni_vmovss(op, xmm_dst);
                 break;
             case memory::data_type::bf16:
                 uni_vpsrld(xmm_dst, xmm_dst, 16);
-                pextrw(op, xmm_dst, 0x0);
+                uni_vpextrw(op, xmm_dst, 0x0);
                 break;
             case memory::data_type::s8:
                 uni_vpackssdw(xmm_dst, xmm_dst, xmm_dst);
                 uni_vpacksswb(xmm_dst, xmm_dst, xmm_dst);
-                movq(reg_tmp_64, xmm_dst);
+                uni_vmovq(reg_tmp_64, xmm_dst);
                 mov(op, reg_tmp_8);
                 break;
             case memory::data_type::u8:
                 uni_vpackusdw(xmm_dst, xmm_dst, xmm_dst);
                 uni_vpackuswb(xmm_dst, xmm_dst, xmm_dst);
-                movq(reg_tmp_64, xmm_dst);
+                uni_vmovq(reg_tmp_64, xmm_dst);
                 mov(op, reg_tmp_8);
                 break;
             default:
@@ -1182,15 +1561,15 @@ private:
         }
     }
 
-    inline void horiz_reduce_store(Vmm vmm_dst, memory::data_type dst_dt) {
+    inline void horiz_reduce_store(Vmm vmm_dst, memory::data_type dst_dt, bool load_embedded = false) {
         if (isa == cpu::x64::sse41) {
-            horize_store(vmm_dst, dst_dt);
+            horiz_store(vmm_dst, dst_dt, load_embedded);
         } else if (isa == cpu::x64::avx2) {
             Xbyak::Ymm ymm_dst = Xbyak::Ymm(vmm_dst.getIdx());
             vextractf128(xmm_aux1, ymm_dst, 0);
             vextractf128(xmm_aux2, ymm_dst, 1);
             horiz_ps(xmm_aux1, xmm_aux2);
-            horize_store(xmm_aux1, dst_dt);
+            horiz_store(xmm_aux1, dst_dt, load_embedded);
         } else {
             Xbyak::Zmm zmm_dst = Xbyak::Zmm(vmm_dst.getIdx());
             vextractf32x4(xmm_aux1, zmm_dst, 0);
@@ -1200,107 +1579,26 @@ private:
             vextractf32x4(xmm_aux3, zmm_dst, 3);
             horiz_ps(xmm_aux2, xmm_aux3);
             horiz_ps(xmm_aux1, xmm_aux2);
-            horize_store(xmm_aux1, dst_dt);
+            horiz_store(xmm_aux1, dst_dt, load_embedded);
         }
     }
 
-    inline void horize_store(Xbyak::Xmm xmm_dst, memory::data_type dst_dt) {
-        movshdup(xmm_aux3, xmm_dst); // dst:1,2,3,4; aux3:2,2,4,4
-        horiz_ps(xmm_dst, xmm_aux3); // dst:f(1,2),f(2,2),f(3,4),f(4,4)
-        movhlps(xmm_aux3, xmm_dst);  // aux3:f(3,4),f(4,4),4,4
-        horiz_ps(xmm_dst, xmm_aux3); // dst:f(1,2,3,4),...
-        switch (dst_dt) {
-            case memory::data_type::f32:
-                movss(ptr[reg_dst], xmm_dst);
-                break;
-            case memory::data_type::bf16:
-                uni_vpsrld(xmm_dst, xmm_dst, 16);
-                pextrw(ptr[reg_dst], xmm_dst, 0x0);
-                break;
-            case memory::data_type::s32:
-                uni_vcvtps2dq(xmm_dst, xmm_dst);
-                movss(ptr[reg_dst], xmm_dst);
-                break;
-            case memory::data_type::u8:
-                uni_vcvtps2dq(xmm_dst, xmm_dst);
-                uni_vpackusdw(xmm_dst, xmm_dst, xmm_dst);
-                uni_vpackuswb(xmm_dst, xmm_dst, xmm_dst);
-                pextrb(ptr[reg_dst], xmm_dst, 0);
-                break;
-            case memory::data_type::s8:
-                uni_vcvtps2dq(xmm_dst, xmm_dst);
-                uni_vpackssdw(xmm_dst, xmm_dst, xmm_dst);
-                uni_vpacksswb(xmm_dst, xmm_dst, xmm_dst);
-                pextrb(ptr[reg_dst], xmm_dst, 0);
-                break;
-            default:
-                assert(!"unknown dst_dt");
-        }
-    }
-
-    inline void load_embedded_horiz_reduce_store(Vmm vmm_dst, memory::data_type dst_dt) {
-        if (isa == cpu::x64::sse41) {
-            load_embedded_horiz_store(vmm_dst, dst_dt);
-        } else if (isa == cpu::x64::avx2) {
-            Xbyak::Ymm ymm_dst = Xbyak::Ymm(vmm_dst.getIdx());
-            vextractf128(xmm_aux1, ymm_dst, 0);
-            vextractf128(xmm_aux2, ymm_dst, 1);
-            horiz_ps(xmm_aux1, xmm_aux2);
-            load_embedded_horiz_store(xmm_aux1, dst_dt);
-        } else {
-            Xbyak::Zmm zmm_dst = Xbyak::Zmm(vmm_dst.getIdx());
-            vextractf32x4(xmm_aux1, zmm_dst, 0);
-            vextractf32x4(xmm_aux2, zmm_dst, 1);
-            horiz_ps(xmm_aux1, xmm_aux2);
-            vextractf32x4(xmm_aux2, zmm_dst, 2);
-            vextractf32x4(xmm_aux3, zmm_dst, 3);
-            horiz_ps(xmm_aux2, xmm_aux3);
-            horiz_ps(xmm_aux1, xmm_aux2);
-            load_embedded_horiz_store(xmm_aux1, dst_dt);
-        }
-    }
-
-    inline void load_embedded_horiz_store(Xbyak::Xmm xmm_dst, memory::data_type dst_dt) {
-        movshdup(xmm_aux3, xmm_dst); // dst:1,2,3,4; aux3:2,2,4,4
-        horiz_ps(xmm_dst, xmm_aux3); // dst:f(1,2),f(2,2),f(3,4),f(4,4)
-        movhlps(xmm_aux3, xmm_dst);  // aux3:f(3,4),f(4,4),4,4
-        horiz_ps(xmm_dst, xmm_aux3); // dst:f(1,2,3,4),...
-        load_scalar(xmm_aux3, ptr[reg_dst], dst_dt);
-
-        switch (dst_dt) {
-            case memory::data_type::f32:
-            case memory::data_type::bf16:
-                horiz_ps(xmm_dst, xmm_aux3);
-                store_scalar(ptr[reg_dst], xmm_dst, dst_dt);
-                break;
-            case memory::data_type::s32:
-                horiz_ps(xmm_dst, xmm_aux3);
-                uni_vcvtps2dq(xmm_dst, xmm_dst);
-                movss(ptr[reg_dst], xmm_dst);
-                break;
-            case memory::data_type::u8:
-                horiz_ps(xmm_dst, xmm_aux3);
-                uni_vcvtps2dq(xmm_dst, xmm_dst);
-                uni_vpackusdw(xmm_dst, xmm_dst, xmm_dst);
-                uni_vpackuswb(xmm_dst, xmm_dst, xmm_dst);
-                pextrb(ptr[reg_dst], xmm_dst, 0);
-                break;
-            case memory::data_type::s8:
-                horiz_ps(xmm_dst, xmm_aux3);
-                uni_vcvtps2dq(xmm_dst, xmm_dst);
-                uni_vpackssdw(xmm_dst, xmm_dst, xmm_dst);
-                uni_vpacksswb(xmm_dst, xmm_dst, xmm_dst);
-                pextrb(ptr[reg_dst], xmm_dst, 0);
-                break;
-            default:
-                assert(!"unknown dst_dt");
+    inline void horiz_store(Xbyak::Xmm xmm_dst, memory::data_type dst_dt, bool load_embedded) {
+        uni_movshdup(xmm_aux3, xmm_dst); // dst:1,2,3,4; aux3:2,2,4,4
+        horiz_ps(xmm_dst, xmm_aux3);     // dst:f(1,2),f(2,2),f(3,4),f(4,4)
+        uni_movhlps(xmm_aux3, xmm_dst);  // aux3:f(3,4),f(4,4),4,4
+        horiz_ps(xmm_dst, xmm_aux3);     // dst:f(1,2,3,4),...
+        if (load_embedded) {
+            load_scalar(xmm_aux3, ptr[reg_dst], dst_dt);
+            horiz_ps(xmm_dst, xmm_aux3);
         }
+        store_scalar(ptr[reg_dst], xmm_dst, dst_dt);
     }
 
     inline void horiz_ps(const Xmm& xmm, const Operand& op) {
         switch (jcp_.reduce_mode) {
             case ReduceAnd:
-                andps(xmm, op);
+                uni_vandps(xmm, xmm, op);
                 break;
             case ReduceL1:
             case ReduceL2:
@@ -1309,19 +1607,19 @@ private:
             case ReduceSum:
             case ReduceSumSquare:
             case ReduceLogSumExp:
-                addps(xmm, op);
+                uni_vaddps(xmm, xmm, op);
                 break;
             case ReduceMax:
-                maxps(xmm, op);
+                uni_vmaxps(xmm, xmm, op);
                 break;
             case ReduceMin:
-                minps(xmm, op);
+                uni_vminps(xmm, xmm, op);
                 break;
             case ReduceOr:
-                orps(xmm, op);
+                uni_vorps(xmm, xmm, op);
                 break;
             case ReduceProd:
-                mulps(xmm, op);
+                uni_vmulps(xmm, xmm, op);
                 break;
             default:
                 assert(!"unsupported reduce mode");
@@ -1361,15 +1659,25 @@ const std::map<const ngraph::DiscreteTypeInfo, std::function<void(const std::sha
 
 bool MKLDNNReduceNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
         if (std::dynamic_pointer_cast<const ngraph::op::util::ArithmeticReductionKeepDims>(op) == nullptr &&
                 std::dynamic_pointer_cast<const ngraph::op::util::LogicalReductionKeepDims>(op) == nullptr) {
             errorMessage = "Reduce node with name " + op->get_friendly_name() + " is not derived from ArithmeticReductionKeepDims or LogicalReductionKeepDims";
             return false;
         }
+        if (const auto reduce = std::dynamic_pointer_cast<const ngraph::op::util::ArithmeticReductionKeepDims>(op)) {
+            auto reduceConst = std::dynamic_pointer_cast<const ngraph::opset1::Constant>(reduce->get_input_node_shared_ptr(REDUCE_INDEXES));
+            if (!reduceConst) {
+                errorMessage = "Second tensor is not constant";
+                return false;
+            }
+        }
+        if (const auto reduce = std::dynamic_pointer_cast<const ngraph::op::util::LogicalReductionKeepDims>(op)) {
+            auto reduceConst = std::dynamic_pointer_cast<const ngraph::opset1::Constant>(reduce->get_input_node_shared_ptr(REDUCE_INDEXES));
+            if (!reduceConst) {
+                errorMessage = "Second tensor is not constant";
+                return false;
+            }
+        }
         if (initializers.find(op->get_type_info()) == initializers.end()) {
             errorMessage = "Doesn't support Reduce algorithm: " +  std::string(op->get_type_info().name);
             return false;
@@ -1392,9 +1700,18 @@ MKLDNNReduceNode::MKLDNNReduceNode(const std::shared_ptr<ngraph::Node>& op, cons
         initializers.at(op->get_type_info())(op, *this);
         if (const auto reduce = std::dynamic_pointer_cast<ngraph::op::util::ArithmeticReductionKeepDims>(op)) {
             keep_dims = reduce->get_keep_dims();
+            auto reduceConst = std::dynamic_pointer_cast<const ngraph::opset1::Constant>(reduce->get_input_node_shared_ptr(REDUCE_INDEXES));
+            if (!reduceConst)
+                IE_THROW() << errorPrefix << " second tensor is not constant!";
+            raw_axes = reduceConst->cast_vector<int>();
         } else if (const auto reduce = std::dynamic_pointer_cast<ngraph::op::util::LogicalReductionKeepDims>(op)) {
             keep_dims = reduce->get_keep_dims();
+            auto reduceConst = std::dynamic_pointer_cast<const ngraph::opset1::Constant>(reduce->get_input_node_shared_ptr(REDUCE_INDEXES));
+            if (!reduceConst)
+                IE_THROW() << errorPrefix << " second tensor is not constant!";
+            raw_axes = reduceConst->cast_vector<int>();
         }
+        setJITBeyond5D();
     } else {
         IE_THROW(NotImplemented) << errorMessage;
     }
@@ -1429,38 +1746,30 @@ void MKLDNNReduceNode::initSupportedPrimitiveDescriptors() {
     if (!supportedPrimitiveDescriptors.empty())
         return;
 
-    static const Precision supportedPrecisions[] = {
-            Precision::FP32,
-            Precision::BF16,
-            Precision::I32,
-            Precision::I8,
-            Precision::U8
-    };
+    input_prec = getOriginalInputPrecisionAtPort(REDUCE_DATA);
+    output_prec = getOriginalOutputPrecisionAtPort(0);
 
-    Precision inputPrecision = getOriginalInputPrecisionAtPort(REDUCE_DATA);
-    Precision outputPrecision = getOriginalOutputPrecisionAtPort(0);
+    if (!fusedWith.empty()) {
+        output_prec = fusedWith[fusedWith.size() - 1]->getOriginalOutputPrecisionAtPort(0);
+    }
 
-    jit_mode = (mayiuse(cpu::x64::sse41)) && getInputShapeAtPort(REDUCE_DATA).getRank() <= 5 &&
-               std::find(std::begin(supportedPrecisions), std::end(supportedPrecisions), inputPrecision) != std::end(supportedPrecisions) &&
-               std::find(std::begin(supportedPrecisions), std::end(supportedPrecisions), outputPrecision) != std::end(supportedPrecisions);
+    jit_mode = canApplyJIT(input_prec, output_prec);
 
     if (jit_mode) {
         // Since in jit mode we use the output memory as an intermediate accumulator for certain reduce modes, we can't use BF16 output precision due to
         // the possible accuracy loss. Therefore, for such mods, we will change the output precision to FP32.
-        if (Precision::BF16 == outputPrecision) {
+        if (Precision::BF16 == output_prec) {
             if (!mayiuse(avx512_core)) {
-                    outputPrecision = Precision::FP32;
+                    output_prec = Precision::FP32;
             } else if (algorithm != ReduceAnd && algorithm != ReduceOr &&
                        algorithm != ReduceMin && algorithm != ReduceMax) {
-                            outputPrecision = Precision::FP32;
+                            output_prec = Precision::FP32;
             }
         }
     }
 
-    input_prec = inputPrecision;
-    output_prec = outputPrecision;
-    src_data_size = inputPrecision.size();
-    dst_data_size = outputPrecision.size();
+    src_data_size = input_prec.size();
+    dst_data_size = output_prec.size();
 
     NodeConfig config;
     config.dynBatchSupport = false;
@@ -1475,12 +1784,12 @@ void MKLDNNReduceNode::initSupportedPrimitiveDescriptors() {
 
     auto& creatorsMap = BlockedDescCreator::getCommonCreators();
 
-    auto pushDesc = [&](LayoutType inFormat, LayoutType outFormat, InferenceEngine::Precision inDataType,
-            InferenceEngine::Precision outDataType, impl_desc_type impl_type) {
-        config.inConfs[REDUCE_DATA].desc = creatorsMap.at(inFormat)->createSharedDesc(inDataType, getInputShapeAtPort(REDUCE_DATA));
+    auto pushDesc = [&](LayoutType inFormat, LayoutType outFormat, InferenceEngine::Precision inPrecision,
+            InferenceEngine::Precision outPrecision, impl_desc_type impl_type) {
+        config.inConfs[REDUCE_DATA].desc = creatorsMap.at(inFormat)->createSharedDesc(inPrecision, getInputShapeAtPort(REDUCE_DATA));
         config.inConfs[REDUCE_INDEXES].desc = creatorsMap.at(LayoutType::ncsp)->createSharedDesc(InferenceEngine::Precision::I32,
                                                                                                  getInputShapeAtPort(REDUCE_INDEXES));
-        config.outConfs[0].desc = creatorsMap.at(outFormat)->createSharedDesc(outDataType, getOutputShapeAtPort(0));
+        config.outConfs[0].desc = creatorsMap.at(outFormat)->createSharedDesc(outPrecision, getOutputShapeAtPort(0));
         supportedPrimitiveDescriptors.push_back({config, impl_type});
     };
 
@@ -1492,14 +1801,24 @@ void MKLDNNReduceNode::initSupportedPrimitiveDescriptors() {
             impl_type = impl_desc_type::jit_avx2;
         }
 
-        pushDesc(LayoutType::ncsp, LayoutType::ncsp, inputPrecision, outputPrecision, impl_type);
-        if (keep_dims) {
-            if ((getInputShapeAtPort(REDUCE_DATA).getRank() == 4 || getInputShapeAtPort(REDUCE_DATA).getRank() == 5) &&
-                    getInputShapeAtPort(REDUCE_DATA).getStaticDims()[1] > 1) {
+        pushDesc(LayoutType::ncsp, LayoutType::ncsp, input_prec, output_prec, impl_type);
+        if ((getInputShapeAtPort(REDUCE_DATA).getRank() == 4 || getInputShapeAtPort(REDUCE_DATA).getRank() == 5) &&
+                getInputShapeAtPort(REDUCE_DATA).getMinDims()[1] > 1) {
+            if (keep_dims) {
                 if (mayiuse(cpu::x64::avx512_common)) {
-                    pushDesc(LayoutType::nCsp16c, LayoutType::nCsp16c, inputPrecision, outputPrecision, impl_type);
+                    pushDesc(LayoutType::nspc, LayoutType::nspc, input_prec, output_prec, impl_type);
+                    pushDesc(LayoutType::nCsp16c, LayoutType::nCsp16c, input_prec, output_prec, impl_type);
                 } else if (mayiuse(cpu::x64::avx2) || mayiuse(cpu::x64::sse41)) {
-                    pushDesc(LayoutType::nCsp8c, LayoutType::nCsp8c, inputPrecision, outputPrecision, impl_type);
+                    pushDesc(LayoutType::nspc, LayoutType::nspc, input_prec, output_prec, impl_type);
+                    pushDesc(LayoutType::nCsp8c, LayoutType::nCsp8c, input_prec, output_prec, impl_type);
+                }
+            } else {
+                if (mayiuse(cpu::x64::avx512_common)) {
+                    pushDesc(LayoutType::nspc, LayoutType::ncsp, input_prec, output_prec, impl_type);
+                    pushDesc(LayoutType::nCsp16c, LayoutType::ncsp, input_prec, output_prec, impl_type);
+                } else if (mayiuse(cpu::x64::avx2) || mayiuse(cpu::x64::sse41)) {
+                    pushDesc(LayoutType::nspc, LayoutType::ncsp, input_prec, output_prec, impl_type);
+                    pushDesc(LayoutType::nCsp8c, LayoutType::ncsp, input_prec, output_prec, impl_type);
                 }
             }
         }
@@ -1508,94 +1827,112 @@ void MKLDNNReduceNode::initSupportedPrimitiveDescriptors() {
     }
 }
 
+void MKLDNNReduceNode::prepareParams() {
+    src_dims = getParentEdgesAtPort(REDUCE_DATA)[0]->getMemory().getDesc().getShape().getDims();
+    std::vector<int> reduce_axes;
+    if (jit_mode && jit_beyond_5D) {
+        reduce_axes = update_src_dims();
+    } else {
+        reduce_axes = raw_axes;
+    }
+
+    auto &dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
+    const SizeVector &dst_dims = dstMemPtr->getDesc().getShape().getDims();
+    dst_size = dstMemPtr->GetSize();
+    calc_process_dst_dims(reduce_axes, dst_dims);
+    if (jit_mode) {
+        set_reduce_dim_flags();
+    }
+
+    if (compile_post_kernel) {
+        setPostOps(attr, dst_dims, true);
+        if (mayiuse(cpu::x64::avx512_common)) {
+            reduce_post_kernel.reset(new jit_uni_reduce_post_kernel_f32<cpu::x64::avx512_common>(jcp, *attr.get()));
+        } else if (mayiuse(cpu::x64::avx2)) {
+            reduce_post_kernel.reset(new jit_uni_reduce_post_kernel_f32<cpu::x64::avx2>(jcp, *attr.get()));
+        } else if (mayiuse(cpu::x64::sse41)) {
+            reduce_post_kernel.reset(new jit_uni_reduce_post_kernel_f32<cpu::x64::sse41>(jcp, *attr.get()));
+        }
+        if (reduce_post_kernel)
+            reduce_post_kernel->create_ker();
+        jit_mode = jit_mode && reduce_post_kernel;
+
+        if (!isDynamicNode() || (isDynamicNode() && attr.get()->post_ops_.len() == 0)) {
+            compile_post_kernel = false;
+        }
+    }
+}
+
 void MKLDNNReduceNode::createPrimitive() {
     auto &dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
-    auto &srcDataMemPtr = getParentEdgeAt(REDUCE_DATA)->getMemoryPtr();
-    auto &srcIndexesMemPtr = getParentEdgeAt(REDUCE_INDEXES)->getMemoryPtr();
+    auto &srcMemPtr = getParentEdgeAt(REDUCE_DATA)->getMemoryPtr();
     if (!dstMemPtr || !dstMemPtr->GetPrimitivePtr())
         IE_THROW() << errorPrefix << " has not allocated destination memory.";
-    if (!srcDataMemPtr || !srcDataMemPtr->GetPrimitivePtr() || !srcIndexesMemPtr || !srcIndexesMemPtr->GetPrimitivePtr())
+    if (!srcMemPtr || !srcMemPtr->GetPrimitivePtr())
         IE_THROW() << errorPrefix << " has not allocate input memory.";
     if (getSelectedPrimitiveDescriptor() == nullptr)
         IE_THROW() << errorPrefix << " has nullable preferable primitive descriptor";
 
-    auto selectedPD = getSelectedPrimitiveDescriptor();
-    planar_layout = getParentEdgeAt(REDUCE_DATA)->getMemory().getDesc().hasLayoutType(LayoutType::ncsp);
+    if (srcMemPtr->getDesc().hasLayoutType(LayoutType::ncsp)) {
+        layout = ReduceLayoutType::reduce_ncsp;
+    } else if (srcMemPtr->getDesc().hasLayoutType(LayoutType::nspc)) {
+        layout = ReduceLayoutType::reduce_nspc;
+    } else {
+        layout = ReduceLayoutType::reduce_blocked;
+    }
 
-    auto jcp = jit_reduce_config_params();
+    // hybrid layout: nspc/blocked layout for input and ncsp for output
+    // !keep_dims is needed to avoid hybrid layout for cases eg. (A, B, C, D) reduce to (A, 1, 1, 1)
+    if (!keep_dims && (layout == ReduceLayoutType::reduce_nspc || layout == ReduceLayoutType::reduce_blocked)) {
+        is_hybrid_layout = dstMemPtr->getDesc().hasLayoutType(LayoutType::ncsp);
+    }
+
+    auto selectedPD = getSelectedPrimitiveDescriptor();
+    jcp = jit_reduce_config_params();
     jcp.src_dt = MKLDNNExtensionUtils::IEPrecisionToDataType(selectedPD->getConfig().inConfs[REDUCE_DATA].desc->getPrecision());
     jcp.dst_dt = MKLDNNExtensionUtils::IEPrecisionToDataType(selectedPD->getConfig().outConfs[0].desc->getPrecision());
     jcp.src_data_size = MKLDNNExtensionUtils::sizeOfDataType(jcp.src_dt);
     jcp.dst_data_size = MKLDNNExtensionUtils::sizeOfDataType(jcp.dst_dt);
-    jcp.planar_layout = planar_layout;
+    jcp.layout = layout;
     jcp.reduce_mode = getAlgorithm();
 
+    compile_post_kernel = true;
+
+    if (inputShapesDefined()) {
+        if (needPrepareParams())
+            prepareParams();
+        updateLastInputDims();
+    }
+
     if (mayiuse(cpu::x64::avx512_common)) {
         reduce_kernel.reset(new jit_uni_reduce_kernel_f32<cpu::x64::avx512_common>(jcp));
-        reduce_post_kernel.reset(new jit_uni_reduce_post_kernel_f32<cpu::x64::avx512_common>(jcp));
         blk_size = 16;
     } else if (mayiuse(cpu::x64::avx2)) {
         reduce_kernel.reset(new jit_uni_reduce_kernel_f32<cpu::x64::avx2>(jcp));
-        reduce_post_kernel.reset(new jit_uni_reduce_post_kernel_f32<cpu::x64::avx2>(jcp));
         blk_size = 8;
     } else if (mayiuse(cpu::x64::sse41)) {
         reduce_kernel.reset(new jit_uni_reduce_kernel_f32<cpu::x64::sse41>(jcp));
-        reduce_post_kernel.reset(new jit_uni_reduce_post_kernel_f32<cpu::x64::sse41>(jcp));
         blk_size = 8;
     }
-
     if (reduce_kernel)
         reduce_kernel->create_ker();
-
-    if (reduce_post_kernel)
-        reduce_post_kernel->create_ker();
-
     jit_mode = jit_mode && reduce_kernel;
 }
 
 void MKLDNNReduceNode::execute(mkldnn::stream strm) {
     auto &dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
     auto &srcMemPtr = getParentEdgeAt(REDUCE_DATA)->getMemoryPtr();
-    auto &srcIndexesMemPtr = getParentEdgeAt(REDUCE_INDEXES)->getMemoryPtr();
-
-    const auto idx_data = reinterpret_cast<const int32_t *>(srcIndexesMemPtr->GetData());
-    size_t dst_size = dstMemPtr->GetSize();
-    src_dims = getParentEdgeAt(REDUCE_DATA)->getMemory().getStaticDims();
-    src_strides = getParentEdgeAt(REDUCE_DATA)->getMemory().GetDescWithType<BlockedMemoryDesc>()->getStrides();
-    dims_size = src_dims.size();
-    calc_process_dst_dims(idx_data);
-
-    if (dims_size <= 5) {
-        if (dims_size == 5) {
-            SET_SRC_DIM_VALUE(src_dims[0], src_dims[1], src_dims[2], src_dims[3], src_dims[4]);
-            SET_DST_DIM_VALUE(process_dst_dims[0], process_dst_dims[1], process_dst_dims[2], process_dst_dims[3], process_dst_dims[4]);
-        } else if (dims_size == 4) {
-            SET_SRC_DIM_VALUE(src_dims[0], src_dims[1], 1, src_dims[2], src_dims[3]);
-            SET_DST_DIM_VALUE(process_dst_dims[0], process_dst_dims[1], 1, process_dst_dims[2], process_dst_dims[3]);
-        } else if (dims_size == 3) {
-            SET_SRC_DIM_VALUE(1, src_dims[0], 1, src_dims[1], src_dims[2]);
-            SET_DST_DIM_VALUE(1, process_dst_dims[0], 1, process_dst_dims[1], process_dst_dims[2]);
-        } else if (dims_size == 2) {
-            SET_SRC_DIM_VALUE(1, 1, 1, src_dims[0], src_dims[1]);
-            SET_DST_DIM_VALUE(1, 1, 1, process_dst_dims[0], process_dst_dims[1]);
-        } else {
-            SET_SRC_DIM_VALUE(1, src_dims[0], 1, 1, 1);
-            SET_DST_DIM_VALUE(1, process_dst_dims[0], 1, 1, 1);
-        }
-
-        ReduceN = IB != OB && OB == 1;
-        ReduceC = IC != OC && OC == 1;
-        ReduceD = ID != OD && OD == 1;
-        ReduceH = IH != OH && OH == 1;
-        ReduceW = IW != OW && OW == 1;
-    }
 
     const uint8_t *src_data = reinterpret_cast<const uint8_t *>(srcMemPtr->GetPtr());
     uint8_t *dst_data = reinterpret_cast<uint8_t *>(dstMemPtr->GetPtr());
+
     if (jit_mode) {
+        if (is_hybrid_layout) {
+            dst_data = reinterpret_cast<uint8_t *>(prc_mem->get_data_handle());
+        }
         reduce_type(src_data, dst_data, dst_size);
     } else {
-        if (planar_layout) {
+        if (layout == ReduceLayoutType::reduce_ncsp) {
             auto in_ptr = reinterpret_cast<const float *>(src_data);
             auto out_ptr = reinterpret_cast<float *>(dst_data);
             reduce_ref(in_ptr, out_ptr);
@@ -1607,71 +1944,175 @@ void MKLDNNReduceNode::execute(mkldnn::stream strm) {
 
 void MKLDNNReduceNode::reduce_type(const uint8_t *in_ptr, uint8_t *out_ptr, size_t dst_size) {
     init_dst_data(out_ptr, dst_size);
+    reduce_stride = IW;
 
-    if (planar_layout) {
+    if (layout == ReduceLayoutType::reduce_ncsp || layout == ReduceLayoutType::reduce_nspc) {
         reduce_PLN(in_ptr, out_ptr);
     } else {
-        if ((algorithm == ReduceAnd || algorithm == ReduceLogSumExp || algorithm == ReduceMax ||
-             algorithm == ReduceMin || algorithm == ReduceProd) && ReduceC) {
+        if (ReduceC && (IC % blk_size)) {
             reduce_BLK_concern_padding(in_ptr, out_ptr);
         } else {
             reduce_BLK(in_ptr, out_ptr);
         }
     }
+
+    if (is_hybrid_layout) {
+        uint8_t *proc_ptr = out_ptr;
+        auto &dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
+        out_ptr = reinterpret_cast<uint8_t *>(dstMemPtr->GetPtr());
+        if (layout == ReduceLayoutType::reduce_nspc) {
+            nspc2ncsp(proc_ptr, out_ptr);
+        } else {
+            blocked2ncsp(proc_ptr, out_ptr);
+        }
+    }
 }
 
 void MKLDNNReduceNode::reduce_PLN(const uint8_t *in_ptr, uint8_t *out_ptr) {
-    for (size_t ib = 0; ib < IB; ib++) {
-        size_t ob = ReduceN ? 0 : ib; GET_PTR_N_PLN;
-        if (!ReduceC && !ReduceD && ReduceH && ReduceW) {
-            parallel_for2d(IC, ID, [&](size_t ic, size_t id) {
-                size_t oc = ic, od = id; GET_PTR_NCD_BASE_PTR_N_PLN;
-                reduce_kernel_process(in_ptr_ncd, out_ptr_ncd, IH * IW, 1);
-            });
-        } else if (ReduceH && ReduceW) {
-            for (size_t ic = 0; ic < IC; ic++) {
-                size_t oc = ReduceC ? 0 : ic; GET_PTR_NC_PLN;
-                for (size_t id = 0; id < ID; id++) {
-                    size_t od = ReduceD ? 0 : id; GET_PTR_NCD_PLN;
-                    reduce_kernel_process(in_ptr_ncd, out_ptr_ncd, IH * IW, 1);
-                }
-            }
-        } else if (!ReduceH && ReduceW) {
-            for (size_t ic = 0; ic < IC; ic++) {
-                size_t oc = ReduceC ? 0 : ic; GET_PTR_NC_PLN;
-                for (size_t id = 0; id < ID; id++) {
-                    size_t od = ReduceD ? 0 : id; GET_PTR_NCD_PLN;
-                    parallel_for(IH, [&](size_t ih){
-                        size_t oh = ih; GET_PTR_NCDH_PLN;
-                        reduce_kernel_process(in_ptr_ncdh, out_ptr_ncdh, IW, 1);
+    if (ReduceN && !ReduceC && !ReduceD && !ReduceH && !ReduceW) {
+        size_t IA = IC * ID * IH * IW;
+        reduce_stride = IA;
+        parallel_for(IA / blk_size, [&](size_t iba){
+            size_t oba = iba;
+            reduce_kernel_process(in_ptr + iba * blk_size * src_data_size, out_ptr + oba * blk_size * dst_data_size,
+                                  blk_size, 0, IB);
+        });
+
+        size_t tail_start = IA / blk_size * blk_size;
+        reduce_kernel_process(in_ptr + tail_start * src_data_size, out_ptr + tail_start * dst_data_size,
+                              IA - tail_start, 0, IB);
+    } else {
+        for (size_t ib = 0; ib < IB; ib++) {
+            size_t ob = ReduceN ? 0 : ib; GET_PTR_N_PLN;
+            if (!ReduceC && !ReduceD && ReduceW) {
+                size_t work_amount = ReduceH ? IH * IW : IW;
+                if (work_amount < blk_size && mayiuse(cpu::x64::avx2)) {
+                    size_t outer_size = ReduceH ? IC * ID : IC * ID * IH;
+                    size_t inner_size = ReduceH ? IH * IW : IW;
+                    size_t output_inner_size = ReduceH ? OH * OW : OW;
+                    size_t IK = outer_size / blk_size;
+                    std::vector<int> index_buf(blk_size);
+                    for (size_t i = 0; i < blk_size; i++) {
+                        index_buf[i] = i * work_amount * src_data_size;
+                    }
+                    parallel_for(IK, [&](size_t ik) {
+                        size_t ok = ik;
+                        reduce_kernel_process(in_ptr_n + ik * blk_size * inner_size * src_data_size,
+                                              out_ptr_n + ok * blk_size * output_inner_size * dst_data_size,
+                                              work_amount, 1, 0, static_cast<int *>(&index_buf[0]));
                     });
-                }
-            }
-        } else if (ReduceW) {
-            for (size_t ic = 0; ic < IC; ic++) {
-                size_t oc = ReduceC ? 0 : ic; GET_PTR_NC_PLN;
-                for (size_t id = 0; id < ID; id++) {
-                    size_t od = ReduceD ? 0 : id; GET_PTR_NCD_PLN;
-                    for (size_t ih = 0; ih < IH; ih++) {
-                        size_t oh = ReduceH ? 0 : ih; GET_PTR_NCDH_PLN;
-                        reduce_kernel_process(in_ptr_ncdh, out_ptr_ncdh, IW, 1);
+                    size_t tail_start = IK * blk_size;
+                    size_t IT = outer_size - tail_start;
+                    parallel_for(IT, [&](size_t it) {
+                        size_t ot = it;
+                        reduce_kernel_process(in_ptr_n + (tail_start + it) * inner_size * src_data_size,
+                                              out_ptr_n + (tail_start + ot) * output_inner_size * dst_data_size, work_amount, 1);
+                    });
+                } else {
+                    if (ReduceH) {
+                        parallel_for2d(IC, ID, [&](size_t ic, size_t id) {
+                            size_t oc = ic, od = id; GET_PTR_NCD_BASE_PTR_N_PLN;
+                            reduce_kernel_process(in_ptr_ncd, out_ptr_ncd, work_amount, 1);
+                        });
+                    } else {
+                        parallel_for3d(IC, ID, IH, [&](size_t ic, size_t id, size_t ih) {
+                            size_t oc = ic, od = id; GET_PTR_NCD_BASE_PTR_N_PLN;
+                            size_t oh = ih; GET_PTR_NCDH_PLN;
+                            reduce_kernel_process(in_ptr_ncdh, out_ptr_ncdh, work_amount, 1);
+                        });
                     }
                 }
-            }
-        } else {
-            for (size_t ic = 0; ic < IC; ic++) {
-                size_t oc = ReduceC ? 0 : ic; GET_PTR_NC_PLN;
-                for (size_t id = 0; id < ID; id++) {
-                    size_t od = ReduceD ? 0 : id; GET_PTR_NCD_PLN;
-                    for (size_t ih = 0; ih < IH; ih++) {
-                        size_t oh = ReduceH ? 0 : ih; GET_PTR_NCDH_PLN;
-                        for (size_t ibw = 0; ibw < IW / blk_size; ibw++) {
-                            size_t obw = ibw;
-                            reduce_kernel_process(in_ptr_ncdh + ibw * blk_size * src_data_size,
-                                                  out_ptr_ncdh + obw * blk_size * dst_data_size, blk_size, 0);
+            } else if (ReduceH && ReduceW) {
+                for (size_t ic = 0; ic < IC; ic++) {
+                    size_t oc = ReduceC ? 0 : ic; GET_PTR_NC_PLN;
+                    for (size_t id = 0; id < ID; id++) {
+                        size_t od = ReduceD ? 0 : id; GET_PTR_NCD_PLN;
+                        reduce_kernel_process(in_ptr_ncd, out_ptr_ncd, IH * IW, 1);
+                    }
+                }
+            } else if (!ReduceH && ReduceW) {
+                for (size_t ic = 0; ic < IC; ic++) {
+                    size_t oc = ReduceC ? 0 : ic; GET_PTR_NC_PLN;
+                    for (size_t id = 0; id < ID; id++) {
+                        size_t od = ReduceD ? 0 : id; GET_PTR_NCD_PLN;
+                        parallel_for(IH, [&](size_t ih){
+                            size_t oh = ih; GET_PTR_NCDH_PLN;
+                            reduce_kernel_process(in_ptr_ncdh, out_ptr_ncdh, IW, 1);
+                        });
+                    }
+                }
+            } else if (ReduceW) {
+                for (size_t ic = 0; ic < IC; ic++) {
+                    size_t oc = ReduceC ? 0 : ic; GET_PTR_NC_PLN;
+                    for (size_t id = 0; id < ID; id++) {
+                        size_t od = ReduceD ? 0 : id; GET_PTR_NCD_PLN;
+                        for (size_t ih = 0; ih < IH; ih++) {
+                            size_t oh = ReduceH ? 0 : ih; GET_PTR_NCDH_PLN;
+                            reduce_kernel_process(in_ptr_ncdh, out_ptr_ncdh, IW, 1);
+                        }
+                    }
+                }
+            } else if (!ReduceC && !ReduceD && ReduceH && !ReduceW) {
+                parallel_for2d(IC, ID, [&](size_t ic, size_t id) {
+                    size_t oc = ic, od = id; GET_PTR_NCD_BASE_PTR_N_PLN;
+                    parallel_for(IW / blk_size, [&](size_t ibw){
+                        size_t obw = ibw;
+                        reduce_kernel_process(in_ptr_ncd + ibw * blk_size * src_data_size, out_ptr_ncd + obw * blk_size * dst_data_size,
+                                              blk_size, 0, IH);
+                    });
+                    size_t tail_start = IW / blk_size * blk_size;
+                    reduce_kernel_process(in_ptr_ncd + tail_start * src_data_size, out_ptr_ncd + tail_start * dst_data_size,
+                                          IW - tail_start, 0, IH);
+                });
+            } else if (!ReduceC && ReduceD && ReduceH && !ReduceW) {
+                parallel_for(IC, [&](size_t ic) {
+                    size_t oc = ic; GET_PTR_NC_PLN;
+                    parallel_for(IW / blk_size, [&](size_t ibw){
+                        size_t obw = ibw;
+                        reduce_kernel_process(in_ptr_nc + ibw * blk_size * src_data_size, out_ptr_nc + obw * blk_size * dst_data_size,
+                                              blk_size, 0, ID * IH);
+                    });
+                    size_t tail_start = IW / blk_size * blk_size;
+                    reduce_kernel_process(in_ptr_nc + tail_start * src_data_size, out_ptr_nc + tail_start * dst_data_size,
+                                          IW - tail_start, 0, ID * IH);
+                });
+            } else if (ReduceC && ReduceD && ReduceH && !ReduceW) {
+                parallel_for(IW / blk_size, [&](size_t ibw){
+                    size_t obw = ibw;
+                    reduce_kernel_process(in_ptr_n + ibw * blk_size * src_data_size, out_ptr_n + obw * blk_size * dst_data_size,
+                                          blk_size, 0, IC * ID * IH);
+                });
+
+                size_t tail_start = IW / blk_size * blk_size;
+                reduce_kernel_process(in_ptr_n + tail_start * src_data_size, out_ptr_n + tail_start * dst_data_size,
+                                      IW - tail_start, 0, IC * ID * IH);
+            } else if (ReduceC && !ReduceD && !ReduceH && !ReduceW) {
+                size_t IS = ID * IH * IW;
+                reduce_stride = IS;
+                parallel_for(IS / blk_size, [&](size_t ibs){
+                    size_t obs = ibs;
+                    reduce_kernel_process(in_ptr_n + ibs * blk_size * src_data_size, out_ptr_n + obs * blk_size * dst_data_size,
+                                          blk_size, 0, IC);
+                });
+
+                size_t tail_start = IS / blk_size * blk_size;
+                reduce_kernel_process(in_ptr_n + tail_start * src_data_size, out_ptr_n + tail_start * dst_data_size,
+                                      IS - tail_start, 0, IC);
+            } else {
+                for (size_t ic = 0; ic < IC; ic++) {
+                    size_t oc = ReduceC ? 0 : ic; GET_PTR_NC_PLN;
+                    for (size_t id = 0; id < ID; id++) {
+                        size_t od = ReduceD ? 0 : id; GET_PTR_NCD_PLN;
+                        for (size_t ih = 0; ih < IH; ih++) {
+                            size_t oh = ReduceH ? 0 : ih; GET_PTR_NCDH_PLN;
+                            for (size_t ibw = 0; ibw < IW / blk_size; ibw++) {
+                                size_t obw = ibw;
+                                reduce_kernel_process(in_ptr_ncdh + ibw * blk_size * src_data_size,
+                                                      out_ptr_ncdh + obw * blk_size * dst_data_size, blk_size, 0);
+                            }
+                            size_t tail_start = IW / blk_size * blk_size;
+                            reduce_kernel_process(in_ptr_ncdh + tail_start * src_data_size, out_ptr_ncdh + tail_start * dst_data_size, IW - tail_start, 0);
                         }
-                        size_t tail_start = IW / blk_size * blk_size;
-                        reduce_kernel_process(in_ptr_ncdh + tail_start * src_data_size, out_ptr_ncdh + tail_start * dst_data_size, IW - tail_start, 0);
                     }
                 }
             }
@@ -1692,13 +2133,24 @@ void MKLDNNReduceNode::reduce_BLK(const uint8_t *in_ptr, uint8_t *out_ptr) {
                 size_t ocb = icb, od = id; GET_PTR_NCD_BASE_PTR_N_BLK;
                 reduce_kernel_process(in_ptr_ncd, out_ptr_ncd, IH * IW * blk_size);
             });
-        } else if (ReduceH && ReduceW) {
-            for (size_t icb = 0; icb < ICB; icb++) {
-                size_t ocb = ReduceC ? 0 : icb; GET_PTR_NC_BLK;
-                for (size_t id = 0; id < ID; id++) {
-                    size_t od = ReduceD ? 0 : id; GET_PTR_NCD_BLK;
-                    reduce_kernel_process(in_ptr_ncd, out_ptr_ncd, IH * IW * blk_size);
-                }
+        } else if (ReduceC && ReduceD && ReduceH && ReduceW) {
+            if (input_prec != output_prec || getAlgorithm() == ReduceL2 ||
+                 algorithm == ReduceLogSumExp || algorithm == ReduceSumSquare) {
+                reduce_kernel_process(in_ptr_n, out_ptr_n, ICB * ID * IH * IW * blk_size);
+            } else {
+                // reduce parallelly
+                // step1: !ReduceC && ReduceD && ReduceH && ReduceW
+                size_t prc_size = ICB * blk_size * dst_data_size;
+                std::vector<uint8_t> vec_prc(prc_size);
+                init_dst_data(vec_prc.data(), prc_size);
+                uint8_t *out_ptr_n_cp = out_ptr_n;
+                out_ptr_n = vec_prc.data();
+                parallel_for(ICB, [&](size_t icb) {
+                    size_t ocb = icb; GET_PTR_NC_BLK;
+                    reduce_kernel_process(in_ptr_nc, out_ptr_nc, ID * IH * IW * blk_size);
+                });
+                // step2: ReduceC
+                reduce_kernel_process(out_ptr_n, out_ptr_n_cp, ICB * blk_size);
             }
         } else if (ReduceW) {
             for (size_t icb = 0; icb < ICB; icb++) {
@@ -1711,6 +2163,15 @@ void MKLDNNReduceNode::reduce_BLK(const uint8_t *in_ptr, uint8_t *out_ptr) {
                     }
                 }
             }
+        } else if (ReduceC && !ReduceD && !ReduceH && !ReduceW) {
+            reduce_stride = ID * IH * IW * blk_size;
+            parallel_for3d(ID, IH, IW, [&](size_t id, size_t ih, size_t iw) {
+                size_t icb = 0, ocb = 0; GET_PTR_NC_BLK;
+                size_t od = id; GET_PTR_NCD_BLK;
+                size_t oh = ih; GET_PTR_NCDH_BLK;
+                size_t ow = iw; GET_PTR_NCDHW_BLK;
+                reduce_kernel_process(in_ptr_ncdhw, out_ptr_ncdhw, blk_size, 0, ICB);
+            });
         } else {
             for (size_t icb = 0; icb < ICB; icb++) {
                 size_t ocb = ReduceC ? 0 : icb; GET_PTR_NC_BLK;
@@ -1815,44 +2276,180 @@ void MKLDNNReduceNode::reduce_BLK_concern_padding(const uint8_t *in_ptr, uint8_t
     reduce_kernel_post_process(out_ptr);
 }
 
-inline void MKLDNNReduceNode::reduce_kernel_process(const uint8_t *in_p, uint8_t *out_p, size_t work_amount, size_t reduce_w) {
+inline void MKLDNNReduceNode::reduce_kernel_process(const uint8_t *in_p, uint8_t *out_p, size_t work_amount,
+                                                    size_t reduce_w, size_t work_batch, const int *tab_idx) {
     auto arg = jit_reduce_call_args();
     arg.src = static_cast<const void *>(in_p);
+    arg.idx = tab_idx;
     arg.dst = static_cast<void *>(out_p);
     arg.work_amount = work_amount;
+    arg.work_batch = work_batch;
     arg.reduce_w = reduce_w;
+    arg.reduce_stride = reduce_stride;
+
     (*reduce_kernel)(&arg);
 }
 
 inline void MKLDNNReduceNode::reduce_kernel_post_process(uint8_t *out_ptr) {
     const size_t integerDivisor = IB * IC * ID * IH * IW / (OB * OC * OD * OH * OW);
     const float divisor = static_cast<float>(integerDivisor);
-    if (planar_layout) {
-        size_t parallel_amount = OB * OC * OD;
-        parallel_for(parallel_amount, [&](size_t i) {
-            uint8_t *out_p = out_ptr + i * OH * OW * dst_data_size;
-            auto arg = jit_reduce_call_args();
+    if (layout == ReduceLayoutType::reduce_ncsp || layout == ReduceLayoutType::reduce_nspc) {
+        parallel_for2d(OB, OC, [&](size_t ob, size_t oc) {
+            uint8_t *out_p = out_ptr + (ob * OC + oc) * OD * OH * OW * dst_data_size;
+            auto arg = jit_reduce_post_call_args();
             arg.dst = static_cast<void *>(out_p);
-            arg.reduce_c = 2;
-            arg.work_amount = OH * OW;
+            arg.oc_off = layout == ReduceLayoutType::reduce_nspc ? 0 : oc * sizeof(float);
+            arg.channel_size = layout == ReduceLayoutType::reduce_nspc ? OW : OC; // OW is related to nspc-ncsp dimension reinterpret
+            arg.work_amount = OD * OH * OW;
             arg.divisor = &divisor;
             (*reduce_post_kernel)(&arg);
         });
     } else {
         size_t OCB = div_up(OC, blk_size);
-        size_t parallel_amount = OB * OCB * OD;
-        parallel_for(parallel_amount, [&](size_t i) {
-            uint8_t *out_p = out_ptr + i * OH * OW * blk_size * dst_data_size;
-            auto arg = jit_reduce_call_args();
+        parallel_for2d(OB, OCB, [&](size_t ob, size_t ocb) {
+            uint8_t *out_p = out_ptr + (ob * OCB + ocb) * OD * OH * OW * blk_size * dst_data_size;
+            auto arg = jit_reduce_post_call_args();
             arg.dst = static_cast<void *>(out_p);
             arg.reduce_c = ReduceC ? 1 : 0;
-            arg.work_amount = OH * OW * blk_size;
+            arg.oc_off = ocb * blk_size * sizeof(float);
+            arg.work_amount = OD * OH * OW * blk_size;
             arg.divisor = &divisor;
             (*reduce_post_kernel)(&arg);
         });
     }
 }
 
+void MKLDNNReduceNode::nspc2ncsp(uint8_t *proc_ptr, uint8_t *out_ptr) {
+    // dimension reinterpret after nspc reusing routine reduce_PLN
+    // demote -- nspc -- ncsp
+    //  DIM0  --   B  --  B
+    //  DIM1  --   C  --  W
+    //  DIM2  --   D  --  C
+    //  DIM3  --   H  --  D
+    //  DIM4  --   W  --  H
+    const size_t DIM0 = OB;
+    const size_t DIM1 = OW;
+    const size_t DIM2 = OC;
+    const size_t DIM3 = OD;
+    const size_t DIM4 = OH;
+    const size_t stride1 = DIM2 * DIM3 * DIM4;
+    const size_t stride0 = stride1 * DIM1;
+
+    if (dst_data_size == 4) {
+        auto src_data = reinterpret_cast<const float *>(proc_ptr);
+        auto dst_data = reinterpret_cast<float *>(out_ptr);
+        parallel_for2d(DIM0, stride1, [&](size_t b, size_t j) {
+            auto src_off = b * stride0 + j * DIM1;
+            auto dst_off = b * stride0 + j;
+            for (size_t dim1 = 0; dim1 < DIM1; dim1++) {
+                dst_data[dst_off] = src_data[src_off];
+                src_off++;
+                dst_off += stride1;
+            }
+        });
+    } else if (dst_data_size == 2) {
+        auto src_data = reinterpret_cast<const uint16_t *>(proc_ptr);
+        auto dst_data = reinterpret_cast<uint16_t *>(out_ptr);
+        parallel_for2d(DIM0, stride1, [&](size_t b, size_t j) {
+            auto src_off = b * stride0 + j * DIM1;
+            auto dst_off = b * stride0 + j;
+            for (size_t dim1 = 0; dim1 < DIM1; dim1++) {
+                dst_data[dst_off] = src_data[src_off];
+                src_off++;
+                dst_off += stride1;
+            }
+        });
+    } else {
+        auto src_data = reinterpret_cast<const uint8_t *>(proc_ptr);
+        auto dst_data = reinterpret_cast<uint8_t *>(out_ptr);
+        parallel_for2d(DIM0, stride1, [&](size_t b, size_t j) {
+            auto src_off = b * stride0 + j * DIM1;
+            auto dst_off = b * stride0 + j;
+            for (size_t dim1 = 0; dim1 < DIM1; dim1++) {
+                dst_data[dst_off] = src_data[src_off];
+                src_off++;
+                dst_off += stride1;
+            }
+        });
+    }
+}
+
+void MKLDNNReduceNode::blocked2ncsp(uint8_t *proc_ptr, uint8_t *out_ptr) {
+    const size_t DIM0 = OB;
+    const size_t DIM1 = OC;
+    const size_t DIM2 = OD;
+    const size_t DIM3 = OH;
+    const size_t DIM4 = OW;
+    const size_t stride1 = DIM2 * DIM3 * DIM4;
+    const size_t src_stride0 = stride1 * div_up(OC, blk_size) * blk_size;
+    const size_t dst_stride0 = stride1 * DIM1;
+
+    if (dst_data_size == 4) {
+        auto src_data = reinterpret_cast<const float *>(proc_ptr);
+        auto dst_data = reinterpret_cast<float *>(out_ptr);
+        parallel_for2d(DIM0, stride1, [&](size_t b, size_t j) {
+            auto src_off = b * src_stride0 + j * blk_size;
+            auto dst_off = b * dst_stride0 + j;
+            for (size_t dim1 = 0; dim1 + blk_size <= DIM1; dim1 += blk_size) {
+                for (size_t k = 0; k < blk_size; k++) {
+                    dst_data[dst_off] = src_data[src_off];
+                    src_off++;
+                    dst_off += stride1;
+                }
+                src_off += (stride1 - 1) * blk_size;
+            }
+            size_t tail = DIM1 % blk_size;
+            for (size_t k = 0; k < tail; k++) {
+                dst_data[dst_off] = src_data[src_off];
+                src_off++;
+                dst_off += stride1;
+            }
+        });
+    } else if (dst_data_size == 2) {
+        auto src_data = reinterpret_cast<const uint16_t *>(proc_ptr);
+        auto dst_data = reinterpret_cast<uint16_t *>(out_ptr);
+        parallel_for2d(DIM0, stride1, [&](size_t b, size_t j) {
+            auto src_off = b * src_stride0 + j * blk_size;
+            auto dst_off = b * dst_stride0 + j;
+            for (size_t dim1 = 0; dim1 + blk_size <= DIM1; dim1 += blk_size) {
+                for (size_t k = 0; k < blk_size; k++) {
+                    dst_data[dst_off] = src_data[src_off];
+                    src_off++;
+                    dst_off += stride1;
+                }
+                src_off += (stride1 - 1) * blk_size;
+            }
+            size_t tail = DIM1 % blk_size;
+            for (size_t k = 0; k < tail; k++) {
+                dst_data[dst_off] = src_data[src_off];
+                src_off++;
+                dst_off += stride1;
+            }
+        });
+    } else {
+        auto src_data = reinterpret_cast<const uint8_t *>(proc_ptr);
+        auto dst_data = reinterpret_cast<uint8_t *>(out_ptr);
+        parallel_for2d(DIM0, stride1, [&](size_t b, size_t j) {
+            auto src_off = b * src_stride0 + j * blk_size;
+            auto dst_off = b * dst_stride0 + j;
+            for (size_t dim1 = 0; dim1 + blk_size <= DIM1; dim1 += blk_size) {
+                for (size_t k = 0; k < blk_size; k++) {
+                    dst_data[dst_off] = src_data[src_off];
+                    src_off++;
+                    dst_off += stride1;
+                }
+                src_off += (stride1 - 1) * blk_size;
+            }
+            size_t tail = DIM1 % blk_size;
+            for (size_t k = 0; k < tail; k++) {
+                dst_data[dst_off] = src_data[src_off];
+                src_off++;
+                dst_off += stride1;
+            }
+        });
+    }
+}
+
 inline void MKLDNNReduceNode::init_dst_data(uint8_t *out_ptr, size_t dst_size) {
     switch (algorithm) {
         case ReduceL1:
@@ -1925,12 +2522,23 @@ inline void MKLDNNReduceNode::init_dst_data(uint8_t *out_ptr, size_t dst_size) {
     }
 }
 
-inline void MKLDNNReduceNode::calc_process_dst_dims(const int32_t *idx_data) {
-    SizeVector out_dims;
-    SizeVector dst_dims = getOutputShapeAtPort(0).getStaticDims();
+inline void MKLDNNReduceNode::create_working_memory() {
+    auto rank = getInputShapeAtPort(REDUCE_DATA).getRank();
+    memory::format_tag format = (layout == ReduceLayoutType::reduce_nspc) ? (rank == 4 ? memory::format_tag::nhwc : memory::format_tag::ndhwc)
+                                        : (rank == 4 ? (mayiuse(cpu::x64::avx512_common) ? memory::format_tag::nChw16c : memory::format_tag::nChw8c)
+                                                     : (mayiuse(cpu::x64::avx512_common) ? memory::format_tag::nCdhw16c : memory::format_tag::nCdhw8c));
+    auto prc_dims = rank == 4 ? std::vector<size_t>{OB, OC, OH, OW} : std::vector<size_t>{OB, OC, OD, OH, OW};
+    auto desc = mkldnn::memory::desc(MKLDNNExtensionUtils::convertToDnnlDims(prc_dims), MKLDNNExtensionUtils::IEPrecisionToDataType(output_prec), format);
+    prc_mem = std::make_shared<mkldnn::memory>(desc, getEngine());
+    dst_size = desc.get_size();
+}
+
+inline void MKLDNNReduceNode::calc_process_dst_dims(std::vector<int> &reduce_axes, const SizeVector &dst_dims) {
     std::set<size_t> axes;
-    for (size_t i = 0; i < getParentEdgeAt(REDUCE_INDEXES)->getMemory().getStaticDims()[0]; i++) {
-        int32_t axis = idx_data[i];
+    SizeVector out_dims;
+    process_dst_dims.clear();
+    axes_for_reduction.clear();
+    for (auto &axis : reduce_axes) {
         if (axis < 0)
             axis += src_dims.size();
         if (static_cast<size_t>(axis) > src_dims.size())
@@ -1954,9 +2562,66 @@ inline void MKLDNNReduceNode::calc_process_dst_dims(const int32_t *idx_data) {
             process_dst_dims.push_back(src_dims[i]);
         }
     }
-    for (size_t i = 0; i < std::min(out_dims.size(), dst_dims.size()); i++) {
-        if (out_dims[i] != dst_dims[i])
+    if (jit_mode && jit_beyond_5D) {
+        if (std::accumulate(out_dims.begin(), out_dims.end(), 1, std::multiplies<double>()) !=
+            std::accumulate(dst_dims.begin(), dst_dims.end(), 1, std::multiplies<double>()))
             IE_THROW() << errorPrefix << "gets incorrect number of output dimensions!";
+    } else {
+        for (size_t i = 0; i < std::min(out_dims.size(), dst_dims.size()); i++) {
+            if (out_dims[i] != dst_dims[i])
+                IE_THROW() << errorPrefix << "gets incorrect number of output dimensions!";
+        }
+    }
+}
+
+inline void MKLDNNReduceNode::set_reduce_dim_flags() {
+    size_t dims_size = src_dims.size();
+    if (dims_size == 5) {
+        SET_SRC_DIM_VALUE(src_dims[0], src_dims[1], src_dims[2], src_dims[3], src_dims[4]);
+        SET_DST_DIM_VALUE(process_dst_dims[0], process_dst_dims[1], process_dst_dims[2], process_dst_dims[3], process_dst_dims[4]);
+    } else if (dims_size == 4) {
+        SET_SRC_DIM_VALUE(src_dims[0], src_dims[1], 1, src_dims[2], src_dims[3]);
+        SET_DST_DIM_VALUE(process_dst_dims[0], process_dst_dims[1], 1, process_dst_dims[2], process_dst_dims[3]);
+    } else if (dims_size == 3) {
+        SET_SRC_DIM_VALUE(1, src_dims[0], 1, src_dims[1], src_dims[2]);
+        SET_DST_DIM_VALUE(1, process_dst_dims[0], 1, process_dst_dims[1], process_dst_dims[2]);
+    } else if (dims_size == 2) {
+        SET_SRC_DIM_VALUE(1, 1, 1, src_dims[0], src_dims[1]);
+        SET_DST_DIM_VALUE(1, 1, 1, process_dst_dims[0], process_dst_dims[1]);
+    } else {
+        SET_SRC_DIM_VALUE(1, src_dims[0], 1, 1, 1);
+        SET_DST_DIM_VALUE(1, process_dst_dims[0], 1, 1, 1);
+    }
+
+    // must be done before the following dimension change
+    if (is_hybrid_layout) {
+        create_working_memory();
+    }
+
+    // Reducing a dimesion in nspc layout can be treated as reducing another dimension in ncsp layout,
+    // eg. reducing C in nspc can be treated as reducing W in ncsp layout, so that the routine reduce_PLN can be reused.
+    // nspc -- ncsp
+    //    D -- C
+    //    H -- D
+    //    W -- H
+    //    C -- W
+    if (layout == ReduceLayoutType::reduce_nspc) {
+        size_t ITmp = IC; IC = ID; ID = IH; IH = IW; IW = ITmp;
+        size_t OTmp = OC; OC = OD; OD = OH; OH = OW; OW = OTmp;
+    }
+
+    ReduceN = IB != OB && OB == 1;
+    ReduceC = IC != OC && OC == 1;
+    ReduceD = ID != OD && OD == 1;
+    ReduceH = IH != OH && OH == 1;
+    ReduceW = IW != OW && OW == 1;
+
+    // suit for parallel
+    if (ReduceH && IW == 1) {
+        ReduceW = true;
+    }
+    if (ReduceC && ReduceH && ID == 1) {
+        ReduceD = true;
     }
 }
 
@@ -2013,6 +2678,7 @@ void MKLDNNReduceNode::reduce_ref_process(const float *in_ptr, float *out_ptr, f
         reduced_dims_work_amount *= src_dims[i];
     reduced_dims_work_amount /= work_amount_dst;
 
+    SizeVector src_strides = getParentEdgeAt(REDUCE_DATA)->getMemory().GetDescWithType<BlockedMemoryDesc>()->getStrides();
     parallel_nt(0, [&](const int ithr, const int nthr) {
         int j;
         size_t i, start = 0, end = 0;
@@ -2091,6 +2757,114 @@ inline void MKLDNNReduceNode::reduce_ref_map(float *out_ptr, size_t work_amount_
     }
 }
 
+void MKLDNNReduceNode::setPostOps(mkldnn::primitive_attr &attr, const VectorDims &postOpDims, bool initWeights) {
+    mkldnn::post_ops ops;
+    for (auto &node : fusedWith) {
+        auto* fakeQuantizeNode = dynamic_cast<MKLDNNFakeQuantizeNode *>(node.get());
+        if (fakeQuantizeNode) {
+            fakeQuantizeNode->appendPostOps(ops);
+            continue;
+        }
+
+        auto* eltwiseNode = dynamic_cast<MKLDNNEltwiseNode *>(node.get());
+        if (eltwiseNode) {
+            constexpr int align = 16;
+            eltwiseNode->appendPostOps(ops, postOpDims, align);
+            continue;
+        }
+        IE_THROW() << "Fusing of " << NameFromType(node->getType()) << " operation to " << NameFromType(this->getType()) << " node is not implemented";
+    }
+    attr.set_post_ops(ops);
+}
+
+void MKLDNNReduceNode::setJITBeyond5D() {
+    jit_beyond_5D = false;
+    if (getInputShapeAtPort(REDUCE_DATA).getRank() > 5) {
+        for (auto &axis : raw_axes) {
+            if (axis < 0)
+                axis += static_cast<int>(getInputShapeAtPort(REDUCE_DATA).getRank());
+        }
+
+        if (raw_axes.size() <= 1) {
+            jit_beyond_5D = true;
+        } else {
+            for (size_t i = 1; i < raw_axes.size(); i++) {
+                if (raw_axes[i] != raw_axes[i - 1] + 1) {
+                    jit_beyond_5D = false;
+                    break;
+                }
+                jit_beyond_5D = true;
+            }
+        }
+    }
+}
+
+std::vector<int> MKLDNNReduceNode::update_src_dims() {
+    std::vector<int> reduce_axes = raw_axes;
+
+    if (reduce_axes.size() < 1)
+        return reduce_axes;
+
+    size_t axis_dim = 1;
+    size_t outer_dim = 1;
+    size_t inner_dim = 1;
+    int outer_end = reduce_axes[0];
+    int inner_start = reduce_axes[reduce_axes.size() - 1];
+    for (size_t i = 0; i < src_dims.size(); i++) {
+        if (i < outer_end) {
+            outer_dim *= src_dims[i];
+        } else if (i > inner_start) {
+            inner_dim *= src_dims[i];
+        } else {
+            axis_dim *= src_dims[i];
+        }
+    }
+
+    reduce_axes.clear();
+    reduce_axes.push_back(1);
+
+    src_dims.clear();
+    src_dims.push_back(outer_dim);
+    src_dims.push_back(axis_dim);
+    src_dims.push_back(inner_dim);
+
+    return reduce_axes;
+}
+
+bool MKLDNNReduceNode::canApplyJIT(const Precision &input_prec, const Precision &output_prec) const {
+    static const Precision supportedPrecisions[] = {
+            Precision::FP32,
+            Precision::BF16,
+            Precision::I32,
+            Precision::I8,
+            Precision::U8
+    };
+
+    return (mayiuse(cpu::x64::sse41)) && (getInputShapeAtPort(REDUCE_DATA).getRank() <= 5 || jit_beyond_5D) &&
+           std::find(std::begin(supportedPrecisions), std::end(supportedPrecisions), input_prec) != std::end(supportedPrecisions) &&
+           std::find(std::begin(supportedPrecisions), std::end(supportedPrecisions), output_prec) != std::end(supportedPrecisions);
+}
+
+bool MKLDNNReduceNode::canFuse(const MKLDNNNodePtr& node) const {
+    Precision input_prec = getOriginalInputPrecisionAtPort(REDUCE_DATA);
+    Precision output_prec = getOriginalOutputPrecisionAtPort(0);
+    if (!canApplyJIT(input_prec, output_prec) || jit_beyond_5D || algorithm == ReduceAnd || algorithm == ReduceOr) {
+        return false;
+    }
+
+    // In jit mode we use the output memory as an intermediate accumulator for certain reduce modes.
+    // If the post ops node has a lower precision for such modes, post ops fusing won't be supposted, in order to avoid accuracy loss.
+    if (output_prec == Precision::FP32 &&
+        !node->getOriginalOutputPrecisions().empty() && node->getOriginalOutputPrecisionAtPort(0) != Precision::FP32) {
+        if (algorithm != ReduceAnd && algorithm != ReduceOr &&
+            algorithm != ReduceMin && algorithm != ReduceMax) {
+            return false;
+        }
+    }
+
+    return canFuseSimpleOperation(node);
+}
+
 bool MKLDNNReduceNode::created() const {
     return getType() == Reduce;
 }
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.h
index 202961e3e03..07effa0891f 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.h
@@ -12,8 +12,14 @@
 
 namespace MKLDNNPlugin {
 
+enum ReduceLayoutType {
+    reduce_ncsp,
+    reduce_nspc,
+    reduce_blocked
+};
+
 struct jit_reduce_config_params {
-    bool planar_layout;
+    ReduceLayoutType layout;
     Algorithm reduce_mode;
     mkldnn::memory::data_type src_dt;
     mkldnn::memory::data_type dst_dt;
@@ -23,11 +29,22 @@ struct jit_reduce_config_params {
 
 struct jit_reduce_call_args {
     const void *src;
+    const int *idx;
     void *dst;
     size_t work_amount;
-    size_t reduce_w = 2;  // only used in planar layout  [1: reduce width dimension]   [0: reduce other dimension] [other value: N/A]
-    size_t reduce_c = 2;  // only used in blocked layout [1: reduce channel dimension] [0: reduce other dimension] [other value: N/A]
-    const float *divisor; // mean = sum / divisor
+    size_t work_batch;
+    size_t reduce_w = 2;    // only used in planar layout  [1: reduce width dimension]   [0: reduce other dimension] [other value: N/A]
+    size_t reduce_stride;   // only used in planar layout while reducing dimensions except for width
+};
+
+struct jit_reduce_post_call_args {
+    const void *src;
+    void *dst;
+    size_t work_amount;
+    size_t reduce_c = 2;    // only used in blocked layout [1: reduce channel dimension] [0: reduce other dimension] [other value: N/A]
+    size_t oc_off;          // offset in byte along channel on output tensor
+    size_t channel_size;    // only for post ops fusion of nspc layout
+    const float *divisor;   // mean = sum / divisor
 };
 
 struct jit_uni_reduce_kernel {
@@ -47,19 +64,20 @@ struct jit_uni_reduce_kernel {
 };
 
 struct jit_uni_reduce_post_kernel {
-    void (*ker_)(const jit_reduce_call_args *);
+    void (*ker_)(const jit_reduce_post_call_args *);
 
-    void operator()(const jit_reduce_call_args *args) {
+    void operator()(const jit_reduce_post_call_args *args) {
         assert(ker_);
         ker_(args);
     }
 
-    virtual void create_ker() = 0;
-
-    explicit jit_uni_reduce_post_kernel(jit_reduce_config_params jcp) : ker_(nullptr), jcp_(jcp) {}
+    explicit jit_uni_reduce_post_kernel(jit_reduce_config_params jcp, const mkldnn_primitive_attr &attr) : ker_(nullptr), jcp_(jcp), attr_(attr) {}
     virtual ~jit_uni_reduce_post_kernel() {}
 
+    virtual void create_ker() = 0;
+
     jit_reduce_config_params jcp_;
+    const mkldnn_primitive_attr &attr_;
 };
 
 class MKLDNNReduceNode : public MKLDNNNode {
@@ -68,9 +86,12 @@ public:
 
     void getSupportedDescriptors() override;
     void initSupportedPrimitiveDescriptors() override;
+    void prepareParams() override;
     void createPrimitive() override;
     bool created() const override;
     void execute(mkldnn::stream strm) override;
+    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+    bool canFuse(const MKLDNNNodePtr& node) const override;
     bool canBeInPlace() const override {
         return false;
     }
@@ -82,30 +103,49 @@ private:
     void reduce_PLN(const uint8_t *in_ptr, uint8_t *out_ptr);
     void reduce_BLK(const uint8_t *in_ptr, uint8_t *out_ptr);
     void reduce_BLK_concern_padding(const uint8_t *in_ptr, uint8_t *out_ptr);
-    inline void reduce_kernel_process(const uint8_t *in_p, uint8_t *out_p, size_t work_amount, size_t reduce_w = 2);
+    inline void reduce_kernel_process(const uint8_t *in_p, uint8_t *out_p, size_t work_amount,
+                                      size_t reduce_w = 2, size_t work_batch = 1, const int *tab_idx = NULL);
     inline void reduce_kernel_post_process(uint8_t *out_ptr);
     inline void init_dst_data(uint8_t *out_ptr, size_t dst_size);
-    inline void calc_process_dst_dims(const int32_t *idx_data);
+    inline void create_working_memory();
+    inline void calc_process_dst_dims(std::vector<int> &reduce_axes, const InferenceEngine::SizeVector &dst_dim);
+    inline void set_reduce_dim_flags();
     inline void reduce_ref(const float *in_ptr, float *out_ptr);
     void reduce_ref_process(const float *in_ptr, float *out_ptr, float init_value, std::function<float(float, float)> func);
     inline void reduce_ref_map(float *out_ptr, size_t work_amount_dst, size_t reduced_dims_work_amount);
+    void nspc2ncsp(uint8_t *proc_ptr, uint8_t *out_ptr);
+    void blocked2ncsp(uint8_t *proc_ptr, uint8_t *out_ptr);
+    void setPostOps(mkldnn::primitive_attr &attr, const VectorDims &postOpDims, bool initWeights = false);
+    void setJITBeyond5D();
+    std::vector<int> update_src_dims();
+    bool canApplyJIT(const InferenceEngine::Precision &input_prec, const InferenceEngine::Precision &output_prec) const;
 
     size_t blk_size;
-    size_t dims_size;
+    size_t dst_size;
     static const size_t REDUCE_DATA = 0;
     static const size_t REDUCE_INDEXES = 1;
-    bool planar_layout = true;
+    bool jit_beyond_5D = false;
     bool jit_mode = true;
     bool keep_dims = true;
+    bool is_hybrid_layout = false;
+    bool compile_post_kernel = true;
     bool ReduceN, ReduceC, ReduceD, ReduceH, ReduceW;
     size_t IB, IC, ID, IH, IW;
     size_t OB, OC, OD, OH, OW;
     size_t src_data_size, dst_data_size;
+    size_t reduce_stride;
+    ReduceLayoutType layout;
     InferenceEngine::Precision input_prec, output_prec;
     InferenceEngine::SizeVector src_dims;
-    InferenceEngine::SizeVector src_strides;
     InferenceEngine::SizeVector process_dst_dims;
     InferenceEngine::SizeVector axes_for_reduction;
+    std::vector<int> raw_axes;
+
+    jit_reduce_config_params jcp;
+
+    mkldnn::primitive_attr attr;
+
+    std::shared_ptr<mkldnn::memory> prc_mem;
 
     std::shared_ptr<jit_uni_reduce_kernel> reduce_kernel;
     std::shared_ptr<jit_uni_reduce_post_kernel> reduce_post_kernel;
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/reduce_max_transformation.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/reduce_max_transformation.cpp
index ec5a659c72b..67e960de0ad 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/reduce_max_transformation.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/reduce_max_transformation.cpp
@@ -33,21 +33,21 @@ const std::vector<LayerTestsDefinitions::ReduceMaxTransformationParam> params =
         { 256ul, ngraph::Shape{ 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 127.f } },
         { 2, 3 },
         false,
-        "Output/pool",
+        "Output_original",
         "U8"
     },
     {
         { 256ul, ngraph::Shape{ 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 127.f } },
         { 1 },
         true,
-        "Output/pool",
+        "Output_original",
         "U8"
     },
     {
         { 256ul, ngraph::Shape{ 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 127.f } },
         { 1 },
         false,
-        "Output/pool",
+        "Output_original",
         "U8"
     },
     {
@@ -73,7 +73,7 @@ const std::vector<LayerTestsDefinitions::ReduceMaxTransformationParam> params =
         },
         { 2, 3 },
         false,
-        "Output/pool",
+        "Output_original",
         "U8"
     },
     {
@@ -86,7 +86,7 @@ const std::vector<LayerTestsDefinitions::ReduceMaxTransformationParam> params =
         },
         { 0, 1 },
         true,
-        "Output/pool",
+        "Output",
         "FP32"
     },
     {
@@ -99,7 +99,7 @@ const std::vector<LayerTestsDefinitions::ReduceMaxTransformationParam> params =
         },
         { 0, 1 },
         false,
-        "Output/pool",
+        "Output",
         "FP32"
     },
 };
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/reduce_mean_transformation.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/reduce_mean_transformation.cpp
index d77121a8e5c..284373d7748 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/reduce_mean_transformation.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/reduce_mean_transformation.cpp
@@ -33,21 +33,21 @@ const std::vector<LayerTestsDefinitions::ReduceMeanTransformationParam> params =
         { 256ul, ngraph::Shape{ 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 127.f } },
         { 2, 3 },
         false,
-        "Output/pool",
+        "Output_original",
         "U8"
     },
     {
         { 256ul, ngraph::Shape{ 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 127.f } },
         { 1 },
         true,
-        "Output/pool",
+        "Output_original",
         "U8"
     },
     {
         { 256ul, ngraph::Shape{ 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 127.f } },
         { 1 },
         false,
-        "Output/pool",
+        "Output_original",
         "U8"
     },
     {
@@ -73,7 +73,7 @@ const std::vector<LayerTestsDefinitions::ReduceMeanTransformationParam> params =
         },
         { 2, 3 },
         false,
-        "Output/pool",
+        "Output_original",
         "U8"
     },
     {
@@ -86,7 +86,7 @@ const std::vector<LayerTestsDefinitions::ReduceMeanTransformationParam> params =
         },
         { 0, 1 },
         true,
-        "Output/pool",
+        "Output",
         "FP32"
     },
     {
@@ -99,7 +99,7 @@ const std::vector<LayerTestsDefinitions::ReduceMeanTransformationParam> params =
         },
         { 0, 1 },
         false,
-        "Output/pool",
+        "Output",
         "FP32"
     },
 };
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/reduce_sum_transformation.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/reduce_sum_transformation.cpp
index a7ca938ddbe..bf9dac7876b 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/reduce_sum_transformation.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/low_precision_transformations/reduce_sum_transformation.cpp
@@ -26,14 +26,14 @@ const std::vector<LayerTestsDefinitions::ReduceSumTransformationParam> params =
         { 256ul, ngraph::Shape{ 1, 1, 1, 1 }, { 0.f }, { 255.f }, { 0.f }, { 127.f } },
         { 2, 3 },
         true,
-        "Output/pool",
+        "Output_original",
         "U8"
     },
     {
         { 256ul, ngraph::Shape{ 1, 1, 1, 1 }, { 2.f }, { 10.f }, { 2.f }, { 10.f } },
         { 2, 3 },
         false,
-        "Output/pool",
+        "Output_original",
         "U8"
     },
     {
@@ -46,7 +46,7 @@ const std::vector<LayerTestsDefinitions::ReduceSumTransformationParam> params =
         },
         { 2, 3 },
         true,
-        "Output/pool",
+        "Output_original",
         "U8"
     },
     {
@@ -59,7 +59,7 @@ const std::vector<LayerTestsDefinitions::ReduceSumTransformationParam> params =
         },
         { 2, 3 },
         false,
-        "Output/pool",
+        "Output_original",
         "U8"
     },
     {
@@ -72,7 +72,7 @@ const std::vector<LayerTestsDefinitions::ReduceSumTransformationParam> params =
         },
         { 0, 1 },
         true,
-        "Output/pool",
+        "Output",
         "FP32"
     },
     {
@@ -85,7 +85,7 @@ const std::vector<LayerTestsDefinitions::ReduceSumTransformationParam> params =
         },
         { 0, 1 },
         false,
-        "Output/pool",
+        "Output",
         "FP32"
     },
 };
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
index 3fb94dea712..bd1598d893e 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
@@ -84,6 +84,10 @@ std::vector<std::string> disabledTestPatterns() {
         R"(.*(Auto|Multi).*Behavior.*IncorrectConfigTests.*CanNotLoadNetworkWithIncorrectConfig.*)",
         R"(.*OVExecutableNetworkBaseTest.*(CanGetInputsInfoAndCheck|CanSetConfigToExecNet).*)",
         R"(.*Behavior.*CorrectConfigCheck.*(canSetConfigAndCheckGetConfig|canSetConfigTwiceAndCheckGetConfig).*CPU_BIND_THREAD=YES.*)",
+        // Issue: 62846 Here shape[1] is not the channel dimension size
+        R"(.*smoke_Reduce.*KeepNoDims.*(_axes=\((0.*|.*1.*)).*Fused=.*PerChannel.*)",
+        // Issue: 72021 Unreasonable abs_threshold for comparing bf16 results
+        R"(.*smoke_Reduce.*type=(Prod|Min).*netPRC=(BF|bf)16.*)",
         // TODO: 56520 Accuracy mismatch
         R"(.*ReduceOpsLayerTest.*type=Mean_.*netPRC=(I64|I32).*)",
         R"(.*ReduceOpsLayerTest.*type=Mean_.*netPRC=U64.*)",
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/reduce_ops.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/reduce_ops.cpp
index 222651e51ff..dbc3e8435c7 100644
--- a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/reduce_ops.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/reduce_ops.cpp
@@ -2,64 +2,114 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <shared_test_classes/single_layer/reduce_ops.hpp>
+#include "shared_test_classes/base/ov_subgraph.hpp"
 #include "ngraph_functions/builders.hpp"
 #include "test_utils/cpu_test_utils.hpp"
+#include "functional_test_utils/ov_tensor_utils.hpp"
+#include "test_utils/fusing_test_utils.hpp"
 
-using namespace InferenceEngine;
 using namespace CPUTestUtils;
-using namespace LayerTestsDefinitions;
+using namespace ov::test;
 
 namespace CPULayerTestsDefinitions {
 
-typedef std::tuple<reduceMeanParams, CPUSpecificParams> ReduceLayerCPUTestParamSet;
+typedef std::tuple<
+        std::vector<int>,               // Axis to reduce order
+        CommonTestUtils::OpType,        // Scalar or vector type axis
+        bool,                           // Keep dims
+        ngraph::helpers::ReductionType, // Reduce operation type
+        ElementType,                    // Net precision
+        ElementType,                    // Input precision
+        ElementType,                    // Output precision
+        std::vector<InputShape>         // Input shapes
+> basicReduceParams;
+
+typedef std::tuple<
+        basicReduceParams,
+        CPUSpecificParams,
+        fusingSpecificParams> ReduceLayerCPUTestParamSet;
 
 class ReduceCPULayerTest : public testing::WithParamInterface<ReduceLayerCPUTestParamSet>,
-                           virtual public LayerTestsUtils::LayerTestsCommon, public CPUTestsBase {
+                           virtual public SubgraphBaseTest, public CpuTestWithFusing {
 public:
     static std::string getTestCaseName(testing::TestParamInfo<ReduceLayerCPUTestParamSet> obj) {
-        reduceMeanParams basicParamsSet;
+        basicReduceParams basicParams;
         CPUSpecificParams cpuParams;
-        std::tie(basicParamsSet, cpuParams) = obj.param;
+        fusingSpecificParams fusingParams;
+        std::tie(basicParams, cpuParams, fusingParams) = obj.param;
+
+        std::vector<int> axes;
+        CommonTestUtils::OpType opType;
+        bool keepDims;
+        ngraph::helpers::ReductionType reductionType;
+        ElementType netPrecision, inPrc, outPrc;
+        std::vector<InputShape> inputShapes;
+
+        std::tie(axes, opType, keepDims, reductionType, netPrecision, inPrc, outPrc, inputShapes) = basicParams;
 
         std::ostringstream result;
-        result << LayerTestsDefinitions::ReduceOpsLayerTest::getTestCaseName(testing::TestParamInfo<reduceMeanParams>(
-                basicParamsSet, 0));
+        result << "IS=(";
+        for (const auto& shape : inputShapes) {
+            result << CommonTestUtils::partialShape2str({shape.first}) << "_";
+        }
+        result << ")_TS=(";
+        for (const auto& shape : inputShapes) {
+            for (const auto& item : shape.second) {
+                result << CommonTestUtils::vec2str(item) << "_";
+            }
+        }
+        result << ")_axes=" << CommonTestUtils::vec2str(axes) << "_";
+        result << "opType=" << opType << "_";
+        result << "type=" << reductionType << "_";
+        if (keepDims)
+            result << "KeepDims=true_";
+        else
+            result << "KeepDims=false_";
+        result << "netPRC=" << netPrecision << "_";
+        result << "inPRC=" << inPrc << "_";
+        result << "outPRC=" << outPrc << "_";
+
         result << CPUTestsBase::getTestCaseName(cpuParams);
+        result << CpuTestWithFusing::getTestCaseName(fusingParams);
 
         return result.str();
     }
 protected:
     void SetUp() override {
-        reduceMeanParams basicParamsSet;
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+
+        basicReduceParams basicParams;
         CPUSpecificParams cpuParams;
-        std::tie(basicParamsSet, cpuParams) = this->GetParam();
+        fusingSpecificParams fusingParams;
+        std::tie(basicParams, cpuParams, fusingParams) = this->GetParam();
 
         std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
+        std::tie(postOpMgrPtr, fusedOps) = fusingParams;
 
-        InferenceEngine::Precision netPrecision;
-        bool keepDims;
-        std::vector<size_t> inputShape;
         std::vector<int> axes;
         CommonTestUtils::OpType opType;
-        std::tie(axes, opType, keepDims, reductionType, netPrecision, inPrc, outPrc, inLayout, inputShape, targetDevice) = basicParamsSet;
+        bool keepDims;
+        ElementType inPrc, outPrc;
+        std::vector<InputShape> inputShapes;
+
+        std::tie(axes, opType, keepDims, reductionType, netPrecision, inPrc, outPrc, inputShapes) = basicParams;
         inPrc = outPrc = netPrecision;
-        auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
-        auto params = ngraph::builder::makeParams(ngPrc, {inputShape});
+
+        init_input_shapes(inputShapes);
+
+        auto params = ngraph::builder::makeDynamicParams(netPrecision, inputDynamicShapes);
         auto paramOuts = ngraph::helpers::convert2OutputVector(
                 ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
 
         std::vector<size_t> shapeAxes;
         switch (opType) {
-            case CommonTestUtils::OpType::SCALAR: {
+            case CommonTestUtils::OpType::SCALAR:
                 if (axes.size() > 1)
                     FAIL() << "In reduce op if op type is scalar, 'axis' input's must contain 1 element";
                 break;
-            }
-            case CommonTestUtils::OpType::VECTOR: {
+            case CommonTestUtils::OpType::VECTOR:
                 shapeAxes.push_back(axes.size());
                 break;
-            }
             default:
                 FAIL() << "Reduce op doesn't support operation type: " << opType;
         }
@@ -68,46 +118,75 @@ protected:
 
         const auto reduce = ngraph::builder::makeReduce(paramOuts[0], reductionAxesNode, keepDims, reductionType);
 
-        selectedType = getPrimitiveType() + "_" + (inPrc == Precision::BOOL ? "I8" : inPrc.name());
+        selectedType = getPrimitiveType() + "_" +
+                       (inPrc == ElementType::boolean ? "I8" : InferenceEngine::details::convertPrecision(inPrc).name());
 
-        reduce->get_rt_info() = getCPUInfo();
-
-        const ngraph::ResultVector results{std::make_shared<ngraph::opset3::Result>(reduce)};
-        function = std::make_shared<ngraph::Function>(results, params, "Reduce");
-    }
-    InferenceEngine::Blob::Ptr GenerateInput(const InferenceEngine::InputInfo &info) const override {
-        if (ngraph::helpers::ReductionType::Prod == reductionType) {
-            // We change the range of random values to avoid possible floating point overflow
-            auto blob = FuncTestUtils::createAndFillBlob(info.getTensorDesc(), 10, 5);
-            if (Precision::FP32 == info.getTensorDesc().getPrecision()) {
-                auto *rawBlobDataPtr = blob->buffer().as<float *>();
-                for (size_t i = 0; i < blob->size(); ++i) {
-                    rawBlobDataPtr[i] /= 10.f;
-                }
-            } else if (Precision::BF16 == info.getTensorDesc().getPrecision()) {
-                auto *rawBlobDataPtr = blob->buffer().as<ngraph::bfloat16 *>();
-                for (size_t i = 0; i < blob->size(); ++i) {
-                    rawBlobDataPtr[i] /= 10.f;
-                }
+        // hybrid layouts
+        if (inFmts.size() != 0 && outFmts.size() == 0) {
+            size_t outShapeSize = inputDynamicShapes[0].size() - axes.size();
+            switch (outShapeSize) {
+                case 0:
+                case 1:
+                    outFmts.push_back(x);
+                    break;
+                case 2:
+                    outFmts.push_back(nc);
+                    break;
+                case 3:
+                    outFmts.push_back(tnc);
+                    break;
+                case 4:
+                    outFmts.push_back(nchw);
+                    break;
+                default:
+                    FAIL() << "Invaid outShapeSize: " << outShapeSize;
             }
-            return blob;
-        } else {
-            return LayerTestsCommon::GenerateInput(info);
+        }
+
+        function = makeNgraphFunction(netPrecision, params, reduce, "Reduce");
+    }
+
+    void generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) override {
+        inputs.clear();
+        const auto& funcInputs = function->inputs();
+        for (int i = 0; i < funcInputs.size(); ++i) {
+            const auto& funcInput = funcInputs[i];
+            ov::runtime::Tensor tensor;
+            if (reductionType == ngraph::helpers::ReductionType::Prod) {
+                tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i], 10, 5);
+                if (netPrecision == ElementType::f32) {
+                    auto *rawBlobDataPtr = static_cast<float *>(tensor.data());
+                    for (size_t i = 0; i < tensor.get_size(); ++i) {
+                        rawBlobDataPtr[i] /= 10.f;
+                    }
+                } else if (netPrecision == ElementType::bf16) {
+                    auto *rawBlobDataPtr = static_cast<ngraph::bfloat16 *>(tensor.data());
+                    for (size_t i = 0; i < tensor.get_size(); ++i) {
+                        rawBlobDataPtr[i] /= 10.f;
+                    }
+                }
+            } else {
+                tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i]);
+            }
+
+            inputs.insert({funcInput.get_node_shared_ptr(), tensor});
         }
     }
 
 private:
     ngraph::helpers::ReductionType reductionType;
+    ElementType netPrecision;
 };
 
 TEST_P(ReduceCPULayerTest, CompareWithRefs) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
 
-    Run();
+    run();
+
     CheckPluginRelatedResults(executableNetwork, "Reduce");
 }
 namespace {
-std::vector<Precision> inpOutPrc = {Precision::BF16, Precision::FP32};
+const std::vector<ElementType> inpOutPrc = {ElementType::bf16, ElementType::f32};
 
 const std::vector<bool> keepDims = {
         true,
@@ -135,15 +214,44 @@ const std::vector<std::vector<int>> axesND = {
         {0, 1, 2, 3}
 };
 
+const std::vector<std::vector<int>> axes5D = {
+        {2, 4},
+        {0, 2, 4},
+        {0, 1, 2, 3, 4},
+};
+
+const std::vector<std::vector<int>> axes6D = {
+        {5},
+        {4, 5},
+        {3, 4, 5},
+        {2, 3, 4, 5},
+        {1, 2, 3, 4, 5},
+        {0, 1, 2, 3, 4, 5}
+};
+
+const std::vector<std::vector<int>> axesNDFusing = {
+        {0, 1},
+        {0, 2},
+        {0, 3},
+        {1, 2},
+        {1, 3},
+        {2, 3},
+};
+
+const std::vector<std::vector<int>> axes5DFusing = {
+        {2, 4},
+        {0, 2, 4},
+};
+
 std::vector<CommonTestUtils::OpType> opTypes = {
         CommonTestUtils::OpType::SCALAR,
         CommonTestUtils::OpType::VECTOR,
 };
 
 const std::vector<ngraph::helpers::ReductionType> reductionTypes = {
-//        ngraph::helpers::ReductionType::Mean, //optimized out during the graph transformations
-//        ngraph::helpers::ReductionType::Max, //optimized out during the graph transformations
-//        ngraph::helpers::ReductionType::Sum, //optimized out during the graph transformations
+        ngraph::helpers::ReductionType::Mean,
+        ngraph::helpers::ReductionType::Max,
+        ngraph::helpers::ReductionType::Sum,
         ngraph::helpers::ReductionType::Min,
         ngraph::helpers::ReductionType::Prod,
         ngraph::helpers::ReductionType::L1,
@@ -155,186 +263,432 @@ const std::vector<ngraph::helpers::ReductionType> reductionLogicalTypes = {
         ngraph::helpers::ReductionType::LogicalAnd
 };
 
-const std::vector<std::vector<size_t>> inputShapes = {
-        std::vector<size_t>{10, 5, 15, 12},
-        std::vector<size_t>{3, 5, 7, 9},
+std::vector<std::vector<ov::test::InputShape>> inputShapes = {
+    {{{}, {{2, 19, 2, 9}}}},
+    {{{{1, 5}, 19, {1, 5}, {1, 10}}, {{2, 19, 2, 2}, {2, 19, 2, 9}}}},
+};
+
+std::vector<std::vector<ov::test::InputShape>> inputShapes_5D = {
+    {{{}, {{2, 19, 2, 2, 9}}}},
+    {{{{1, 5}, 19, {1, 5}, {1, 5}, {1, 5}}, {{2, 19, 2, 2, 2}, {2, 19, 3, 2, 2}}}},
+};
+
+// TODO: should remove inputShapesFusingKeepNoDims and inputShapesFusingKeepNoDims_5D,
+//       and use inputShapes and inputShapes_5D directly,
+//       after Shape mismatching in fusing per channel[Issue: 62846] being fixed
+std::vector<std::vector<ov::test::InputShape>> inputShapesFusingKeepNoDims = {
+    {{{}, {{2, 19, 2, 9}}}},
+    {{{{1, 5}, 19, 2, 9}, {{2, 19, 2, 9}, {3, 19, 2, 9}}}},
+};
+
+std::vector<std::vector<ov::test::InputShape>> inputShapesFusingKeepNoDims_5D = {
+    {{{}, {{2, 19, 2, 2, 9}}}},
+    {{{{1, 5}, 19, 2, 2, 2}, {{2, 19, 2, 2, 2}, {3, 19, 2, 2, 2}}}},
+};
+
+std::vector<std::vector<ov::test::InputShape>> inputShapes_6D = {
+    {{{}, {{2, 19, 2, 2, 2, 2}}}},
+    {{{{1, 5}, 19, {1, 5}, {1, 5}, {1, 5}, {1, 5}}, {{2, 19, 2, 2, 2, 2}, {2, 19, 2, 2, 3, 2}}}},
 };
 
 std::vector<CPUSpecificParams> cpuParams_4D = {
         CPUSpecificParams({nChw16c}, {nChw16c}, {}, {}),
-        CPUSpecificParams({nchw}, {nchw}, {}, {})
+        CPUSpecificParams({nchw}, {nchw}, {}, {}),
+        CPUSpecificParams({nhwc}, {nhwc}, {}, {})
 };
 
 std::vector<CPUSpecificParams> cpuParams_5D = {
         CPUSpecificParams({nCdhw16c}, {nCdhw16c}, {}, {}),
-        CPUSpecificParams({ncdhw}, {ncdhw}, {}, {})
+        CPUSpecificParams({ncdhw}, {ncdhw}, {}, {}),
+        CPUSpecificParams({ndhwc}, {ndhwc}, {}, {})
 };
 
-const auto paramsOneAxis = ::testing::Combine(
+std::vector<CPUSpecificParams> cpuParams_HybridLayout_4D = {
+        CPUSpecificParams({nChw16c}, {}, {}, {}),
+        CPUSpecificParams({nhwc}, {}, {}, {})
+};
+
+std::vector<CPUSpecificParams> cpuParams_HybridLayout_5D = {
+        CPUSpecificParams({nCdhw16c}, {}, {}, {}),
+        CPUSpecificParams({ndhwc}, {}, {}, {})
+};
+
+const std::vector<fusingSpecificParams> fusingParamsSet {
+        /* activations */
+        fusingRelu,
+        fusingElu,
+        fusingTanh,
+        fusingSwish,
+
+        /* FQ */
+        fusingFakeQuantizePerChannel,
+        fusingFakeQuantizePerChannelRelu,
+        fusingFakeQuantizePerTensorRelu,
+        /* another patterns */
+        fusingScaleShift
+};
+
+/* ================================ 1.1 No fusion - Arithmetic ================================ */
+const auto params_OneAxis = testing::Combine(
         testing::Combine(
             testing::ValuesIn(axes),
             testing::ValuesIn(opTypes),
             testing::ValuesIn(keepDims),
             testing::ValuesIn(reductionTypes),
             testing::ValuesIn(inpOutPrc),
-            testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-            testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-            testing::Values(InferenceEngine::Layout::ANY),
-            testing::ValuesIn(inputShapes),
-            testing::Values(CommonTestUtils::DEVICE_CPU)),
-        testing::Values(emptyCPUSpec));
-
-const auto paramsOneAxisLogical = testing::Combine(
-        testing::Combine(
-            testing::ValuesIn(axes),
-            testing::ValuesIn(opTypes),
-            testing::ValuesIn(keepDims),
-            testing::ValuesIn(reductionLogicalTypes),
-            testing::Values(InferenceEngine::Precision::BOOL),
-            testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-            testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-            testing::Values(InferenceEngine::Layout::ANY),
-            testing::ValuesIn(inputShapes),
-            testing::Values(CommonTestUtils::DEVICE_CPU)),
-        testing::Values(emptyCPUSpec));
-
-const auto params_MultiAxis = testing::Combine(
-        testing::Combine(
-            testing::ValuesIn(axesND),
-            testing::Values(opTypes[1]),
-            testing::Values(false),
-            testing::ValuesIn(reductionTypes),
-            testing::ValuesIn(inpOutPrc),
-            testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-            testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-            testing::Values(InferenceEngine::Layout::ANY),
-            testing::Values(std::vector<size_t>{2, 9, 2, 9}),
-            testing::Values(CommonTestUtils::DEVICE_CPU)),
-        testing::Values(emptyCPUSpec));
+            testing::Values(ElementType::undefined),
+            testing::Values(ElementType::undefined),
+            testing::ValuesIn(inputShapes)),
+        testing::Values(emptyCPUSpec),
+        testing::Values(emptyFusingSpec));
 
 const auto params_MultiAxis_4D = testing::Combine(
         testing::Combine(
                 testing::ValuesIn(axesND),
-                testing::Values(opTypes[1]),
+                testing::Values(CommonTestUtils::OpType::VECTOR),
                 testing::Values(true),
                 testing::ValuesIn(reductionTypes),
                 testing::ValuesIn(inpOutPrc),
-                testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                testing::Values(InferenceEngine::Layout::ANY),
-                testing::Values(std::vector<size_t>{2, 19, 2, 9}),
-                testing::Values(CommonTestUtils::DEVICE_CPU)),
-        testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D)));
+                testing::Values(ElementType::undefined),
+                testing::Values(ElementType::undefined),
+                testing::ValuesIn(inputShapes)),
+        testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D)),
+        testing::Values(emptyFusingSpec));
 
 const auto params_MultiAxis_5D = testing::Combine(
         testing::Combine(
-                testing::ValuesIn(axesND),
-                testing::Values(opTypes[1]),
+                testing::ValuesIn(axes5D),
+                testing::Values(CommonTestUtils::OpType::VECTOR),
                 testing::Values(true),
                 testing::ValuesIn(reductionTypes),
                 testing::ValuesIn(inpOutPrc),
-                testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                testing::Values(InferenceEngine::Layout::ANY),
-                testing::Values(std::vector<size_t>{2, 19, 7, 2, 9}),
-                testing::Values(CommonTestUtils::DEVICE_CPU)),
-        testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D)));
+                testing::Values(ElementType::undefined),
+                testing::Values(ElementType::undefined),
+                testing::ValuesIn(inputShapes_5D)),
+        testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D)),
+        testing::Values(emptyFusingSpec));
 
-const auto params_MultiAxisLogical = testing::Combine(
+const auto params_MultiAxis_4D_Hybrid = testing::Combine(
         testing::Combine(
             testing::ValuesIn(axesND),
-            testing::Values(opTypes[1]),
+            testing::Values(CommonTestUtils::OpType::VECTOR),
             testing::Values(false),
-            testing::ValuesIn(reductionLogicalTypes),
-            testing::Values(InferenceEngine::Precision::BOOL),
-            testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-            testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-            testing::Values(InferenceEngine::Layout::ANY),
-            testing::Values(std::vector<size_t>{2, 9, 2, 9}),
-            testing::Values(CommonTestUtils::DEVICE_CPU)),
-        testing::Values(emptyCPUSpec));
+            testing::ValuesIn(reductionTypes),
+            testing::ValuesIn(inpOutPrc),
+            testing::Values(ElementType::undefined),
+            testing::Values(ElementType::undefined),
+            testing::ValuesIn(inputShapes)),
+        testing::ValuesIn(filterCPUSpecificParams(cpuParams_HybridLayout_4D)),
+        testing::Values(emptyFusingSpec));
 
-const auto params_MultiAxisLogical4D = testing::Combine(
+const auto params_MultiAxis_5D_Hybrid = testing::Combine(
         testing::Combine(
-                testing::ValuesIn(axesND),
-                testing::Values(opTypes[1]),
-                testing::Values(true),
-                testing::ValuesIn(reductionLogicalTypes),
-                testing::Values(InferenceEngine::Precision::BOOL),
-                testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                testing::Values(InferenceEngine::Layout::ANY),
-                testing::Values(std::vector<size_t>{2, 19, 2, 9}),
-                testing::Values(CommonTestUtils::DEVICE_CPU)),
-        testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D)));
+            testing::ValuesIn(axes5D),
+            testing::Values(CommonTestUtils::OpType::VECTOR),
+            testing::Values(false),
+            testing::ValuesIn(reductionTypes),
+            testing::ValuesIn(inpOutPrc),
+            testing::Values(ElementType::undefined),
+            testing::Values(ElementType::undefined),
+            testing::ValuesIn(inputShapes_5D)),
+        testing::ValuesIn(filterCPUSpecificParams(cpuParams_HybridLayout_5D)),
+        testing::Values(emptyFusingSpec));
 
-const auto params_MultiAxisLogical5D = testing::Combine(
+const auto params_MultiAxis_6D = testing::Combine(
         testing::Combine(
-                testing::ValuesIn(axesND),
-                testing::Values(opTypes[1]),
-                testing::Values(true),
-                testing::ValuesIn(reductionLogicalTypes),
-                testing::Values(InferenceEngine::Precision::BOOL),
-                testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                testing::Values(InferenceEngine::Layout::ANY),
-                testing::Values(std::vector<size_t>{2, 19, 7, 2, 9}),
-                testing::Values(CommonTestUtils::DEVICE_CPU)),
-        testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D)));
+                testing::ValuesIn(axes6D),
+                testing::Values(CommonTestUtils::OpType::VECTOR),
+                testing::ValuesIn(keepDims),
+                testing::ValuesIn(reductionTypes),
+                testing::ValuesIn(inpOutPrc),
+                testing::Values(ElementType::undefined),
+                testing::Values(ElementType::undefined),
+                testing::ValuesIn(inputShapes_6D)),
+        testing::Values(emptyCPUSpec),
+        testing::Values(emptyFusingSpec));
 
 INSTANTIATE_TEST_SUITE_P(
-        smoke_ReduceOneAxis_CPU,
+        smoke_Reduce_OneAxis_CPU,
         ReduceCPULayerTest,
-        paramsOneAxis,
+        params_OneAxis,
         ReduceCPULayerTest::getTestCaseName
 );
 
 INSTANTIATE_TEST_SUITE_P(
-        smoke_ReduceLogicalOneAxis_CPU,
-        ReduceCPULayerTest,
-        paramsOneAxisLogical,
-        ReduceCPULayerTest::getTestCaseName
-);
-
-INSTANTIATE_TEST_SUITE_P(
-        smoke_Reduce_ReductionTypes_CPU,
-        ReduceCPULayerTest,
-        params_MultiAxis,
-        ReduceCPULayerTest::getTestCaseName
-);
-
-INSTANTIATE_TEST_SUITE_P(
-        smoke_Reduce_ReductionTypes4D_CPU,
+        smoke_Reduce_MultiAxis_4D_CPU,
         ReduceCPULayerTest,
         params_MultiAxis_4D,
         ReduceCPULayerTest::getTestCaseName
 );
 
 INSTANTIATE_TEST_SUITE_P(
-        smoke_Reduce_ReductionTypes5D_CPU,
+        smoke_Reduce_MultiAxis_5D_CPU,
         ReduceCPULayerTest,
         params_MultiAxis_5D,
         ReduceCPULayerTest::getTestCaseName
 );
 
 INSTANTIATE_TEST_SUITE_P(
-        smoke_ReduceLogical_ReductionTypes_CPU,
+        smoke_Reduce_MultiAxis_4D_Hybrid_CPU,
         ReduceCPULayerTest,
-        params_MultiAxisLogical,
+        params_MultiAxis_4D_Hybrid,
         ReduceCPULayerTest::getTestCaseName
 );
 
 INSTANTIATE_TEST_SUITE_P(
-        smoke_ReduceLogical4D_ReductionTypes_CPU,
+        smoke_Reduce_MultiAxis_5D_Hybrid_CPU,
         ReduceCPULayerTest,
-        params_MultiAxisLogical4D,
+        params_MultiAxis_5D_Hybrid,
         ReduceCPULayerTest::getTestCaseName
 );
 
 INSTANTIATE_TEST_SUITE_P(
-        smoke_ReduceLogical5D_ReductionTypes_CPU,
+        smoke_Reduce_MultiAxis_6D_CPU,
         ReduceCPULayerTest,
-        params_MultiAxisLogical5D,
+        params_MultiAxis_6D,
+        ReduceCPULayerTest::getTestCaseName
+);
+
+/* ================================ 1.2 No fusion - Logical ================================ */
+const auto params_OneAxis_Logical = testing::Combine(
+        testing::Combine(
+            testing::ValuesIn(axes),
+            testing::ValuesIn(opTypes),
+            testing::ValuesIn(keepDims),
+            testing::ValuesIn(reductionLogicalTypes),
+            testing::Values(ElementType::boolean),
+            testing::Values(ElementType::undefined),
+            testing::Values(ElementType::undefined),
+            testing::ValuesIn(inputShapes)),
+        testing::Values(emptyCPUSpec),
+        testing::Values(emptyFusingSpec));
+
+const auto params_MultiAxis_4D_Logical = testing::Combine(
+        testing::Combine(
+                testing::ValuesIn(axesND),
+                testing::Values(CommonTestUtils::OpType::VECTOR),
+                testing::Values(true),
+                testing::ValuesIn(reductionLogicalTypes),
+                testing::Values(ElementType::boolean),
+                testing::Values(ElementType::undefined),
+                testing::Values(ElementType::undefined),
+                testing::ValuesIn(inputShapes)),
+        testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D)),
+        testing::Values(emptyFusingSpec));
+
+const auto params_MultiAxis_5D_Logical = testing::Combine(
+        testing::Combine(
+                testing::ValuesIn(axes5D),
+                testing::Values(CommonTestUtils::OpType::VECTOR),
+                testing::Values(true),
+                testing::ValuesIn(reductionLogicalTypes),
+                testing::Values(ElementType::boolean),
+                testing::Values(ElementType::undefined),
+                testing::Values(ElementType::undefined),
+                testing::ValuesIn(inputShapes_5D)),
+        testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D)),
+        testing::Values(emptyFusingSpec));
+
+const auto params_MultiAxis_4D_Hybrid_Logical = testing::Combine(
+        testing::Combine(
+            testing::ValuesIn(axesND),
+            testing::Values(CommonTestUtils::OpType::VECTOR),
+            testing::Values(false),
+            testing::ValuesIn(reductionLogicalTypes),
+            testing::Values(ElementType::boolean),
+            testing::Values(ElementType::undefined),
+            testing::Values(ElementType::undefined),
+            testing::ValuesIn(inputShapes)),
+        testing::ValuesIn(filterCPUSpecificParams(cpuParams_HybridLayout_4D)),
+        testing::Values(emptyFusingSpec));
+
+const auto params_MultiAxis_5D_Hybrid_Logical = testing::Combine(
+        testing::Combine(
+            testing::ValuesIn(axes5D),
+            testing::Values(CommonTestUtils::OpType::VECTOR),
+            testing::Values(false),
+            testing::ValuesIn(reductionLogicalTypes),
+            testing::Values(ElementType::boolean),
+            testing::Values(ElementType::undefined),
+            testing::Values(ElementType::undefined),
+            testing::ValuesIn(inputShapes_5D)),
+        testing::ValuesIn(filterCPUSpecificParams(cpuParams_HybridLayout_5D)),
+        testing::Values(emptyFusingSpec));
+
+const auto params_MultiAxis_6D_Logical = testing::Combine(
+        testing::Combine(
+                testing::ValuesIn(axes6D),
+                testing::Values(CommonTestUtils::OpType::VECTOR),
+                testing::ValuesIn(keepDims),
+                testing::ValuesIn(reductionLogicalTypes),
+                testing::Values(ElementType::boolean),
+                testing::Values(ElementType::undefined),
+                testing::Values(ElementType::undefined),
+                testing::ValuesIn(inputShapes_6D)),
+        testing::Values(emptyCPUSpec),
+        testing::Values(emptyFusingSpec));
+
+INSTANTIATE_TEST_SUITE_P(
+        smoke_Reduce_OneAxis_Logical_CPU,
+        ReduceCPULayerTest,
+        params_OneAxis_Logical,
+        ReduceCPULayerTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        smoke_Reduce_MultiAxis_4D_Logical_CPU,
+        ReduceCPULayerTest,
+        params_MultiAxis_4D_Logical,
+        ReduceCPULayerTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        smoke_Reduce_MultiAxis_5D_Logical_CPU,
+        ReduceCPULayerTest,
+        params_MultiAxis_5D_Logical,
+        ReduceCPULayerTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        smoke_Reduce_MultiAxis_4D_Hybrid_Logical_CPU,
+        ReduceCPULayerTest,
+        params_MultiAxis_4D_Hybrid_Logical,
+        ReduceCPULayerTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        smoke_Reduce_MultiAxis_5D_Hybrid_Logical_CPU,
+        ReduceCPULayerTest,
+        params_MultiAxis_5D_Hybrid_Logical,
+        ReduceCPULayerTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        smoke_Reduce_MultiAxis_6D_Logical_CPU,
+        ReduceCPULayerTest,
+        params_MultiAxis_6D_Logical,
+        ReduceCPULayerTest::getTestCaseName
+);
+
+/* ================================ 2.1 Fusion - KeepDims ================================ */
+const auto params_OneAxis_fusing = testing::Combine(
+        testing::Combine(
+            testing::ValuesIn(axes),
+            testing::ValuesIn(opTypes),
+            testing::Values(true),
+            testing::ValuesIn(reductionTypes),
+            testing::ValuesIn(inpOutPrc),
+            testing::Values(ElementType::undefined),
+            testing::Values(ElementType::undefined),
+            testing::ValuesIn(inputShapes)),
+        testing::Values(emptyCPUSpec),
+        testing::ValuesIn(fusingParamsSet));
+
+const auto params_MultiAxis_4D_fusing = testing::Combine(
+        testing::Combine(
+                testing::ValuesIn(axesND),
+                testing::Values(CommonTestUtils::OpType::VECTOR),
+                testing::Values(true),
+                testing::ValuesIn(reductionTypes),
+                testing::ValuesIn(inpOutPrc),
+                testing::Values(ElementType::undefined),
+                testing::Values(ElementType::undefined),
+                testing::ValuesIn(inputShapes)),
+        testing::ValuesIn(filterCPUSpecificParams(cpuParams_4D)),
+        testing::ValuesIn(fusingParamsSet));
+
+const auto params_MultiAxis_5D_fusing = testing::Combine(
+        testing::Combine(
+                testing::ValuesIn(axes5D),
+                testing::Values(CommonTestUtils::OpType::VECTOR),
+                testing::Values(true),
+                testing::ValuesIn(reductionTypes),
+                testing::ValuesIn(inpOutPrc),
+                testing::Values(ElementType::undefined),
+                testing::Values(ElementType::undefined),
+                testing::ValuesIn(inputShapes_5D)),
+        testing::ValuesIn(filterCPUSpecificParams(cpuParams_5D)),
+        testing::ValuesIn(fusingParamsSet));
+
+INSTANTIATE_TEST_SUITE_P(
+        smoke_Reduce_OneAxis_fusing_CPU,
+        ReduceCPULayerTest,
+        params_OneAxis_fusing,
+        ReduceCPULayerTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        smoke_Reduce_MultiAxis_4D_fusing_CPU,
+        ReduceCPULayerTest,
+        params_MultiAxis_4D_fusing,
+        ReduceCPULayerTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        smoke_Reduce_MultiAxis_5D_fusing_CPU,
+        ReduceCPULayerTest,
+        params_MultiAxis_5D_fusing,
+        ReduceCPULayerTest::getTestCaseName
+);
+
+/* ================================ 2.2 Fusion - KeepNoDims ================================ */
+const auto params_OneAxis_fusing_KeepNoDims = testing::Combine(
+        testing::Combine(
+            testing::ValuesIn(axes),
+            testing::ValuesIn(opTypes),
+            testing::Values(false),
+            testing::ValuesIn(reductionTypes),
+            testing::ValuesIn(inpOutPrc),
+            testing::Values(ElementType::undefined),
+            testing::Values(ElementType::undefined),
+            testing::ValuesIn(inputShapesFusingKeepNoDims)),
+        testing::Values(emptyCPUSpec),
+        testing::ValuesIn(fusingParamsSet));
+
+const auto params_MultiAxis_4D_Hybrid_fusing_KeepNoDims = testing::Combine(
+        testing::Combine(
+            testing::ValuesIn(axesNDFusing),
+            testing::Values(CommonTestUtils::OpType::VECTOR),
+            testing::Values(false),
+            testing::ValuesIn(reductionTypes),
+            testing::ValuesIn(inpOutPrc),
+            testing::Values(ElementType::undefined),
+            testing::Values(ElementType::undefined),
+            testing::ValuesIn(inputShapesFusingKeepNoDims)),
+        testing::ValuesIn(filterCPUSpecificParams(cpuParams_HybridLayout_4D)),
+        testing::ValuesIn(fusingParamsSet));
+
+const auto params_MultiAxis_5D_Hybrid_fusing_KeepNoDims = testing::Combine(
+        testing::Combine(
+            testing::ValuesIn(axes5DFusing),
+            testing::Values(CommonTestUtils::OpType::VECTOR),
+            testing::Values(false),
+            testing::ValuesIn(reductionTypes),
+            testing::ValuesIn(inpOutPrc),
+            testing::Values(ElementType::undefined),
+            testing::Values(ElementType::undefined),
+            testing::ValuesIn(inputShapesFusingKeepNoDims_5D)),
+        testing::ValuesIn(filterCPUSpecificParams(cpuParams_HybridLayout_5D)),
+        testing::ValuesIn(fusingParamsSet));
+
+INSTANTIATE_TEST_SUITE_P(
+        smoke_Reduce_OneAxis_fusing_KeepNoDims_CPU,
+        ReduceCPULayerTest,
+        params_OneAxis_fusing_KeepNoDims,
+        ReduceCPULayerTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        smoke_Reduce_MultiAxis_4D_Hybrid_fusing_KeepNoDims_CPU,
+        ReduceCPULayerTest,
+        params_MultiAxis_4D_Hybrid_fusing_KeepNoDims,
+        ReduceCPULayerTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        smoke_Reduce_MultiAxis_5D_Hybrid_fusing_KeepNoDims_CPU,
+        ReduceCPULayerTest,
+        params_MultiAxis_5D_Hybrid_fusing_KeepNoDims,
         ReduceCPULayerTest::getTestCaseName
 );
 } // namespace
diff --git a/inference-engine/thirdparty/mkl-dnn b/inference-engine/thirdparty/mkl-dnn
index cdc3fd7c168..deab85766ff 160000
--- a/inference-engine/thirdparty/mkl-dnn
+++ b/inference-engine/thirdparty/mkl-dnn
@@ -1 +1 @@
-Subproject commit cdc3fd7c1687a31b5db8eba6d46a44e0513b1e2d
+Subproject commit deab85766ff3fb547ae34eeff94fca4e73bd3053

From b10409da5bada91aef259383b7a6276bf1536884 Mon Sep 17 00:00:00 2001
From: Elizaveta Lobanova <elizaveta.lobanova@intel.com>
Date: Mon, 29 Nov 2021 10:55:31 +0300
Subject: [PATCH 62/72] [GNA] Support NHWC convolutions with 3d transposes
 (#8599)

* [GNA] Support NHWC convolutions with 3d transposes

* [GNA] Removed calculation of permutations for unsupported layouts

* [GNA] Added comments
---
 .../src/gna_plugin/gna_graph_patterns.hpp     | 102 ++++++--
 .../gna_plugin/optimizer/gna_pass_manager.cpp |  30 ++-
 .../remove_permutations_NHWC_to_NCHW_pass.cpp | 242 ++++++++++--------
 3 files changed, 235 insertions(+), 139 deletions(-)

diff --git a/inference-engine/src/gna_plugin/gna_graph_patterns.hpp b/inference-engine/src/gna_plugin/gna_graph_patterns.hpp
index 24109c5a8a8..bfc90890043 100644
--- a/inference-engine/src/gna_plugin/gna_graph_patterns.hpp
+++ b/inference-engine/src/gna_plugin/gna_graph_patterns.hpp
@@ -13,8 +13,62 @@
 namespace GNAPluginNS {
 
 /**
- * @brief searchs for a pattern: Permute(0,3,1,2) -> ... -> Convolution -> ... -> Permute(0,2,3,1) or
- *        Reshape -> ... -> Convolution -> ... -> Permute(0,2,3,1) if Convolution has only one input dimension not equal to 1
+ * @brief checks if it's a reshape from 4d to 3d tensor inserted after convolution
+ * @param layer Non-functional layer
+ */
+inline bool IsReshapeFrom4dTo3d(InferenceEngine::CNNLayerPtr layer) {
+    if (!LayerInfo(layer).isNonFunctional()) {
+        return false;
+    }
+
+    auto input_dims = layer->insData[0].lock()->getDims();
+    auto output_dims = layer->outData[0]->getDims();
+    // If H input dimension is not 1, it can't be just skipped during reshape to 3d
+    size_t h_dim = input_dims[2];
+    if (input_dims.size() != 4 || output_dims.size() != 3 || h_dim != 1) {
+        return false;
+    }
+
+    input_dims.erase(std::begin(input_dims) + 2);
+    if (input_dims != output_dims) {
+        return false;
+    }
+
+    return true;
+}
+
+/**
+ * @brief checks if it's a reshape from 3d to 4d tensor inserted before convolution
+ * @param layer Non-functional layer
+ */
+inline bool IsReshapeFrom3dTo4d(InferenceEngine::CNNLayerPtr layer) {
+    if (!LayerInfo(layer).isNonFunctional()) {
+        return false;
+    }
+
+    auto input_dims = layer->insData[0].lock()->getDims();
+    auto output_dims = layer->outData[0]->getDims();
+    if (input_dims.size() != 3 || output_dims.size() != 4) {
+        return false;
+    }
+
+    input_dims.insert(std::begin(input_dims) + 2, 1);
+    if (input_dims != output_dims) {
+        return false;
+    }
+
+    return true;
+}
+
+/**
+ * @brief searchs for a pattern: Permute(NHWC->NCHW) -> ... -> Convolution -> ... -> Permute(NCHW->NHWC) or
+ * Reshape(NHWC->NCHW) -> ... -> Convolution -> ... -> Reshape(NCHW->NHWC) if Convolution has only one input/output
+ * dimension not equal to 1,
+ * if the original convolution layout is 3d, 3d->4d/4d->3d reshapes will be inserted before/after the convolution,
+ * so the possible patterns will be:
+ * Permute(NWC->NCW) -> ... -> Reshape(NCW ->NCHW) -> Convolution -> Reshape(NCHW->NCW) ... -> Permute(NCW->NWC) or
+ * Reshape(NWC->NCW) -> ... -> Reshape(NCW ->NCHW) -> Convolution -> Reshape(NCHW->NCW) ... -> Reshape(NCW->NWC)
+ * if Convolution has only one input/output dimension not equal to 1.
  * @param layer convolution layer
  * @return the found permutations before and after convolution
  */
@@ -35,8 +89,10 @@ inline std::pair<InferenceEngine::CNNLayerPtr, InferenceEngine::CNNLayerPtr> Fin
 
     auto next = getInputTo(layer->outData.front()).begin()->second;
     // Permute is inserted before Reshape by MO in NHWC models, so we need to find either permute, or reshape, or output
-    while (!LayerInfo(next).isPermute() && !LayerInfo(next).isNonFunctional() && !LayerInfo(next).isOutput() &&
-           next->outData.size() == 1) {
+    while (!LayerInfo(next).isPermute() && !LayerInfo(next).isOutput() && next->outData.size() == 1) {
+        if (LayerInfo(next).isNonFunctional() && !IsReshapeFrom4dTo3d(next) && !IsReshapeFrom3dTo4d(next)) {
+            break;
+        }
         auto input_to = getInputTo(next->outData.front());
         if (input_to.size() != 1) break;
         next = input_to.begin()->second;
@@ -44,8 +100,11 @@ inline std::pair<InferenceEngine::CNNLayerPtr, InferenceEngine::CNNLayerPtr> Fin
 
     // Check if the found layer is NCHW to NHWC permute or has 1D data, if it's not just skip this convolution
     if (LayerInfo(next).isPermute()) {
-        if (next->outData[0]->getLayout() != InferenceEngine::Layout::NCHW ||
-            next->GetParamAsInts("order") != GetPermuteOrder(InferenceEngine::Layout::NCHW, InferenceEngine::Layout::NHWC)) {
+        const auto layout = next->outData[0]->getLayout();
+        const auto order = next->GetParamAsInts("order");
+        if (layout != InferenceEngine::Layout::NCHW && layout != InferenceEngine::Layout::CHW ||
+            order != GetPermuteOrder(InferenceEngine::Layout::NCHW, InferenceEngine::Layout::NHWC) &&
+            order != std::vector<int32_t>{0, 2, 1} /* NCW to NWC */) {
             return std::make_pair(nullptr, nullptr);
         }
     } else if (LayerInfo(next).isReshape()) {
@@ -54,9 +113,11 @@ inline std::pair<InferenceEngine::CNNLayerPtr, InferenceEngine::CNNLayerPtr> Fin
         }
         // Check if reshape is expected for this pattern:
         // the next layer has the both, height and width dimensions > 1
-        if (next->outData[0]->getDims().size() != 4 ||
-            GetDataDimSize(next->insData[0].lock(), InferenceEngine::DataDimName::H) != 1 ||
-            GetDataDimSize(next->insData[0].lock(), InferenceEngine::DataDimName::W) != 1) {
+        auto in_dim_size = next->insData[0].lock()->getDims().size();
+        IE_ASSERT(in_dim_size == 3 || in_dim_size == 4);
+        size_t height = in_dim_size == 3 ? 1 : GetDataDimSize(next->insData[0].lock(), InferenceEngine::DataDimName::H);
+        size_t width = GetDataDimSize(next->insData[0].lock(), InferenceEngine::DataDimName::W);
+        if (next->outData[0]->getDims().size() < 3 || height != 1 || width != 1) {
             return std::make_pair(nullptr, nullptr);
         }
     } else {
@@ -66,14 +127,19 @@ inline std::pair<InferenceEngine::CNNLayerPtr, InferenceEngine::CNNLayerPtr> Fin
     // Permute is inserted after Reshape by MO in NHWC models, so we need to find either permute, or reshape, or input
     auto parent = InferenceEngine::CNNNetPrevLayer(layer);
     auto prev = parent;
-    while (!LayerInfo(prev).isPermute() && !LayerInfo(prev).isNonFunctional() && !LayerInfo(prev).isInput() &&
-           InferenceEngine::CNNNetHasPrevLayer(prev.get())) {
+    while (!LayerInfo(prev).isPermute() && !LayerInfo(prev).isInput() && InferenceEngine::CNNNetHasPrevLayer(prev.get())) {
+        if (LayerInfo(prev).isNonFunctional() && !IsReshapeFrom4dTo3d(prev) && !IsReshapeFrom3dTo4d(prev)) {
+            break;
+        }
         prev = InferenceEngine::CNNNetPrevLayer(prev);
     }
     // Check if the found layer is NHWC to NCHW permute or has 1D data, if it's not just skip this convolution
     if (LayerInfo(prev).isPermute()) {
-        if (prev->outData[0]->getLayout() != InferenceEngine::Layout::NCHW ||
-            prev->GetParamAsInts("order") != GetPermuteOrder(InferenceEngine::Layout::NHWC, InferenceEngine::Layout::NCHW)) {
+        const auto layout = prev->outData[0]->getLayout();
+        const auto order = prev->GetParamAsInts("order");
+        if (layout != InferenceEngine::Layout::NCHW && layout != InferenceEngine::Layout::CHW ||
+            order != GetPermuteOrder(InferenceEngine::Layout::NHWC, InferenceEngine::Layout::NCHW) &&
+            order != std::vector<int32_t>{0, 2, 1} /* NWC to NCW */) {
             return std::make_pair(nullptr, nullptr);
         }
     } else if (LayerInfo(prev).isReshape())  {
@@ -82,10 +148,12 @@ inline std::pair<InferenceEngine::CNNLayerPtr, InferenceEngine::CNNLayerPtr> Fin
         }
         // Check if reshape is expected for this pattern:
         // the previous layer has number of channels > 1 and one of height/width dimensions is also > 1
-        if (parent->insData[0].lock()->getDims().size() != 4 ||
-            GetDataDimSize(parent->outData[0], InferenceEngine::DataDimName::C) != 1 &&
-            (GetDataDimSize(parent->outData[0], InferenceEngine::DataDimName::H) != 1 ||
-             GetDataDimSize(parent->outData[0], InferenceEngine::DataDimName::W) != 1)) {
+        size_t out_dims_size = parent->outData[0]->getDims().size();
+        IE_ASSERT(out_dims_size == 3 || out_dims_size == 4);
+        size_t channels = GetDataDimSize(parent->outData[0], out_dims_size - 1);
+        size_t height = out_dims_size == 3 ? 1 : GetDataDimSize(parent->outData[0], InferenceEngine::DataDimName::H);
+        size_t width = GetDataDimSize(parent->outData[0], InferenceEngine::DataDimName::W);
+        if (parent->insData[0].lock()->getDims().size() < 3 || channels != 1 && (height != 1 || width != 1)) {
             return std::make_pair(nullptr, nullptr);
         }
     } else {
diff --git a/inference-engine/src/gna_plugin/optimizer/gna_pass_manager.cpp b/inference-engine/src/gna_plugin/optimizer/gna_pass_manager.cpp
index 732d53dcfb9..7cc085151df 100644
--- a/inference-engine/src/gna_plugin/optimizer/gna_pass_manager.cpp
+++ b/inference-engine/src/gna_plugin/optimizer/gna_pass_manager.cpp
@@ -648,27 +648,39 @@ void RemovePermutationsNHWCToNCHWPass::run() {
         auto pattern_start = layers.first;
         auto pattern_end = layers.second;
 
-        auto setNHWCOrder = [](InferenceEngine::DataPtr data) {
-            if (data->getLayout() == Layout::NHWC) return;
+        auto getTransposedLayout = [](InferenceEngine::DataPtr data) {
+            size_t dims_size = data->getDims().size();
+            if (dims_size < 3 || dims_size > 4) {
+                THROW_GNA_EXCEPTION << data->getName() <<
+                    " unexpected dimensions size in Permute - Conv - Permute pattern";
+            }
+            return dims_size == 4 ? Layout::NHWC : Layout::HWC;
+        };
+
+        auto setTransposedOrder = [getTransposedLayout](InferenceEngine::DataPtr data) {
+            auto layout = getTransposedLayout(data);
+            if (data->getLayout() == layout) return;
+
             auto dims = data->getDims();
-            auto order = GetPermuteOrder(Layout::NCHW, Layout::NHWC);
+            auto order = dims.size() == 4 ? GetPermuteOrder(Layout::NCHW, Layout::NHWC) :
+                std::vector<int32_t>{0, 2, 1};
             InferenceEngine::SizeVector new_dims;
             for (int i = 0; i < dims.size(); ++i) {
                 new_dims.push_back(dims[order[i]]);
             }
             data->setDims(new_dims);
-            data->setLayout(Layout::NHWC);
+            data->setLayout(layout);
         };
 
         auto input_to = getInputTo(pattern_start->outData[0]);
         IE_ASSERT(!input_to.empty());
         auto current_layer = input_to.begin()->second;
-        setNHWCOrder(current_layer->input());
+        setTransposedOrder(current_layer->input());
         std::function<void(CNNLayerPtr)> propogateNHWCOrderRecursive =
-            [pattern_end, &propogateNHWCOrderRecursive, &setNHWCOrder](CNNLayerPtr current_layer) {
+            [pattern_end, &propogateNHWCOrderRecursive, &setTransposedOrder](CNNLayerPtr current_layer) {
             if (current_layer == pattern_end) return;
             for (size_t i = 0; i < current_layer->outData.size(); ++i) {
-                setNHWCOrder(current_layer->outData[i]);
+                setTransposedOrder(current_layer->outData[i]);
                 auto input_to = getInputTo(current_layer->outData[i]);
                 IE_ASSERT(!input_to.empty());
                 propogateNHWCOrderRecursive(input_to.begin()->second);
@@ -682,7 +694,7 @@ void RemovePermutationsNHWCToNCHWPass::run() {
             for (auto before_output : layer_before_permute->outData) {
                 if (areEqualDatas(pattern_start->input(), before_output)) {
                     output = before_output;
-                    output->setLayout(Layout::NHWC);
+                    output->setLayout(getTransposedLayout(output));
                     break;
                 }
             }
@@ -693,7 +705,7 @@ void RemovePermutationsNHWCToNCHWPass::run() {
 
         if (!pattern_end->outData.empty() && !getInputTo(pattern_end->outData.front()).empty()) {
             auto layer_after_permute = getInputTo(pattern_end->outData.front()).begin()->second;
-            layer_after_permute->input()->setLayout(Layout::NHWC);
+            layer_after_permute->input()->setLayout(getTransposedLayout(layer_after_permute->input()));
         }
     }
 
diff --git a/inference-engine/tests/functional/plugin/gna/pass_tests/remove_permutations_NHWC_to_NCHW_pass.cpp b/inference-engine/tests/functional/plugin/gna/pass_tests/remove_permutations_NHWC_to_NCHW_pass.cpp
index 5366f4c8ee6..379cbffdf01 100644
--- a/inference-engine/tests/functional/plugin/gna/pass_tests/remove_permutations_NHWC_to_NCHW_pass.cpp
+++ b/inference-engine/tests/functional/plugin/gna/pass_tests/remove_permutations_NHWC_to_NCHW_pass.cpp
@@ -36,9 +36,65 @@ typedef std::tuple<
 
 namespace LayerTestsDefinitions {
 
-std::vector<size_t> GetKernelShape(size_t height, size_t width, size_t kernel_size) {
-    return (height == 1 ? std::vector<size_t>{1, kernel_size} :
-           (width == 1 ? std::vector<size_t>{kernel_size, 1} : std::vector<size_t>{kernel_size, kernel_size}));
+std::vector<size_t> GetKernelShape(std::vector<size_t> input_shape, size_t kernel_size, bool output_1d = false) {
+    if (input_shape.size() == 3) {
+        return output_1d ? std::vector<size_t>{input_shape.back()} : std::vector<size_t>{kernel_size};
+    }
+
+    if (output_1d) {
+        return std::vector<size_t>{input_shape[1], input_shape[2]};
+    }
+
+    return (input_shape[1] == 1 ? std::vector<size_t>{1, kernel_size} :
+           (input_shape[2] == 1 ? std::vector<size_t>{kernel_size, 1} : std::vector<size_t>{kernel_size, kernel_size}));
+}
+
+std::shared_ptr<ngraph::Node> CreateTranspose(std::shared_ptr<ngraph::Node> input, size_t shape_size,
+                                                     bool before_conv) {
+    std::vector<int32_t> permute_order;
+    if (before_conv) {
+        permute_order = shape_size == 4 ? std::vector<int32_t>{ 0, 3, 1, 2 } : std::vector<int32_t>{ 0, 2, 1 };
+    } else {
+        permute_order = shape_size == 4 ? std::vector<int32_t>{ 0, 2, 3, 1 } : std::vector<int32_t>{ 0, 2, 1 };
+    }
+    return std::make_shared<ngraph::opset1::Transpose>(input,
+        ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ shape_size }, permute_order));
+}
+
+ngraph::Shape GetLayerTransposedOutputShape(std::shared_ptr<ngraph::Node> layer) {
+    auto out_shape = layer->get_output_shape(0);
+    auto perm = out_shape.size() == 4 ? std::vector<size_t>{0, 2, 3, 1} : std::vector<size_t>{0, 2, 1};
+    std::vector<size_t> nhwc_out_shape;
+    std::transform(std::begin(perm), std::end(perm), std::back_inserter(nhwc_out_shape),
+        [out_shape](int i) { return out_shape[i]; });
+    return nhwc_out_shape;
+}
+
+std::shared_ptr<ngraph::Node> CreateConvolution(std::shared_ptr<ngraph::Node> input,
+                                                const ov::element::Type& ngPrc,
+                                                const std::vector<size_t> &input_shape,
+                                                bool output1D = false,
+                                                bool withPool = false,
+                                                bool withActivation = false) {
+    const size_t num_out_channels = 12;
+    auto kernel_shape = GetKernelShape(input_shape, 8, output1D);
+    size_t filter_total_size = num_out_channels * input_shape.back() *
+        std::accumulate(std::begin(kernel_shape), std::end(kernel_shape), 1, std::multiplies<size_t>());
+    const std::vector<float> filter_weights = CommonTestUtils::generate_float_numbers(filter_total_size, -0.01f, 0.01f);
+    const auto shape_size = input_shape.size();
+    auto conv = ngraph::builder::makeConvolution(input, ngPrc, kernel_shape, std::vector<size_t>(shape_size - 2, 1),
+        std::vector<ptrdiff_t>(shape_size - 2, 0l), std::vector<ptrdiff_t>(shape_size - 2, 0l),
+        std::vector<size_t>(shape_size - 2, 1), ngraph::op::PadType::VALID, num_out_channels, false, filter_weights);
+
+    if (!withPool) {
+        return conv;
+    }
+
+    auto pool_kernal_shape = GetKernelShape(GetLayerTransposedOutputShape(conv), 2, false);
+    auto pool = ngraph::builder::makePooling(conv, pool_kernal_shape, std::vector<size_t>(shape_size - 2, 0),
+        std::vector<size_t>(shape_size - 2, 0), pool_kernal_shape, ngraph::op::RoundingType::FLOOR,
+        ngraph::op::PadType::VALID, false, ngraph::helpers::PoolingTypes::MAX);
+   return withActivation ? std::make_shared<ngraph::opset3::Relu>(pool) : pool;
 }
 
 class RemovePermutationsNHWCToNCHWPassTest : public testing::WithParamInterface<removePermutationsAddParamPassParams>,
@@ -80,25 +136,19 @@ class RemovePermutationsNHWCToNCHWPassTest : public testing::WithParamInterface<
             std::tie(netPrecision, targetDevice, configuration, inputShape, output1D) = this->GetParam();
             auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
 
+            size_t shape_size = inputShape.size();
+            ASSERT_GT(shape_size, 2);
+            ASSERT_LT(shape_size, 5);
             size_t in_total_dims_size = std::accumulate(std::begin(inputShape), std::end(inputShape), 1, std::multiplies<double>());
             auto params = ngraph::builder::makeParams(ngPrc, { {1, in_total_dims_size} });
 
-            auto pattern1 = std::make_shared<ngraph::opset1::Constant>(ngraph::element::Type_t::i64, ngraph::Shape{ 4 }, inputShape);
+            auto pattern1 = std::make_shared<ngraph::opset1::Constant>(ngraph::element::Type_t::i64,
+                ngraph::Shape{ shape_size }, inputShape);
             auto reshape1 = std::make_shared<ngraph::opset1::Reshape>(params[0], pattern1, false);
-            auto permute1 = std::make_shared<ngraph::opset1::Transpose>(reshape1,
-                ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 0, 3, 1, 2 }));
 
-            size_t num_out_channels = 12;
-            auto kernel_shape = output1D ? ngraph::Shape{inputShape[1], inputShape[2]} :
-                GetKernelShape(inputShape[1], inputShape[2], 8);
-            std::vector<float> filter_weights = CommonTestUtils::generate_float_numbers(num_out_channels * inputShape[3] *
-                                                                                        kernel_shape[0] * kernel_shape[1],
-                                                                                        -0.2f, 0.2f);
-            auto conv = ngraph::builder::makeConvolution(permute1, ngPrc, kernel_shape, { 1, 1 }, { 0, 0 }, { 0, 0 }, { 1, 1 },
-                ngraph::op::PadType::VALID, num_out_channels, false, filter_weights);
-
-            auto permute2 = std::make_shared<ngraph::opset1::Transpose>(conv,
-                ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 0, 2, 3, 1 }));
+            auto permute1 = CreateTranspose(reshape1, shape_size, true);
+            auto conv = CreateConvolution(permute1, ngPrc, inputShape, output1D);
+            auto permute2 = CreateTranspose(conv, shape_size, false);
 
             auto conv_out_size = std::accumulate(std::begin(conv->get_output_shape(0)), std::end(conv->get_output_shape(0)),
                 size_t(1), std::multiplies<size_t>());
@@ -111,7 +161,7 @@ class RemovePermutationsNHWCToNCHWPassTest : public testing::WithParamInterface<
         }
 };
 
-class RemovePermutationsNHWCToNCHWPass4DOutputTest : public testing::WithParamInterface<removePermutationsPassParams>,
+class RemovePermutationsNHWCToNCHWPassNoReshapesTest : public testing::WithParamInterface<removePermutationsPassParams>,
     public LayerTestsUtils::LayerTestsCommon {
 public:
     static std::string getTestCaseName(testing::TestParamInfo<removePermutationsPassParams> obj) {
@@ -138,19 +188,18 @@ protected:
         std::tie(netPrecision, targetDevice, configuration, inputShape) = this->GetParam();
         auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
 
+        size_t shape_size = inputShape.size();
+        ASSERT_GT(shape_size, 2);
+        ASSERT_LT(shape_size, 5);
+
         auto params = ngraph::builder::makeParams(ngPrc, { inputShape });
-        auto permute1 = std::make_shared<ngraph::opset1::Transpose>(params[0],
-                             ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 0, 3, 1, 2 }));
-
-        auto kernel_shape = GetKernelShape(inputShape[1], inputShape[2], 8);
-        auto conv1 = ngraph::builder::makeConvolution(permute1, ngPrc, kernel_shape, { 1, 1 }, { 0, 0 }, { 0, 0 }, { 1, 1 }, ngraph::op::PadType::VALID, 12);
-
-        auto permute2 = std::make_shared<ngraph::opset1::Transpose>(conv1,
-                             ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 0, 2, 3, 1 }));
+        auto permute1 = CreateTranspose(params[0], shape_size, true);
+        auto conv = CreateConvolution(permute1, ngPrc, inputShape);
+        auto permute2 = CreateTranspose(conv, shape_size, false);
 
         ngraph::ResultVector results{ std::make_shared<ngraph::opset1::Result>(permute2) };
 
-        function = std::make_shared<ngraph::Function>(results, params, "RemovePermutationPass4DOutput");
+        function = std::make_shared<ngraph::Function>(results, params, "RemovePermutationPassNoReshapes");
     }
 };
 
@@ -209,39 +258,24 @@ class RemovePermutationsWithPoolAndActTest : public testing::WithParamInterface<
             std::tie(netPrecision, targetDevice, configuration, inputShape, withActivation) = this->GetParam();
             auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
 
+            size_t shape_size = inputShape.size();
+            ASSERT_GT(shape_size, 2);
+            ASSERT_LT(shape_size, 5);
+
             size_t in_total_dims_size = std::accumulate(std::begin(inputShape), std::end(inputShape), 1, std::multiplies<double>());
             auto params = ngraph::builder::makeParams(ngPrc, { {1, in_total_dims_size} });
 
-            auto pattern1 = std::make_shared<ngraph::opset1::Constant>(ngraph::element::Type_t::i64, ngraph::Shape{ 4 }, inputShape);
+            auto pattern1 = std::make_shared<ngraph::opset1::Constant>(ngraph::element::Type_t::i64, ngraph::Shape{ shape_size }, inputShape);
             auto reshape1 = std::make_shared<ngraph::opset1::Reshape>(params[0], pattern1, false);
-            auto permute1 = std::make_shared<ngraph::opset1::Transpose>(reshape1,
-                ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 0, 3, 1, 2 }));
 
-            size_t num_out_channels = 12;
-            auto kernel_shape = GetKernelShape(inputShape[1], inputShape[2], 8);
-            std::vector<float> filter_weights = CommonTestUtils::generate_float_numbers(num_out_channels * inputShape[3] *
-                                                                                        kernel_shape[0] * kernel_shape[1],
-                                                                                        -0.2f, 0.2f);
-            auto conv1 = ngraph::builder::makeConvolution(permute1, ngPrc, kernel_shape, { 1, 1 }, { 0, 0 }, { 0, 0 }, { 1, 1 },
-                ngraph::op::PadType::VALID, num_out_channels, false, filter_weights);
-            auto pool_kernal_shape = (inputShape[1] == 1 ? std::vector<size_t>{1, 2} : std::vector<size_t>{2, 1});
-            auto pool = ngraph::builder::makePooling(conv1, pool_kernal_shape, {0, 0}, {0, 0}, pool_kernal_shape, ngraph::op::RoundingType::FLOOR,
-                                                     ngraph::op::PadType::VALID, false, ngraph::helpers::PoolingTypes::MAX);
+            auto permute1 = CreateTranspose(reshape1, shape_size, true);
+            auto conv = CreateConvolution(permute1, ngPrc, inputShape, false, true, true);
+            auto permute2 = CreateTranspose(conv, shape_size, false);
 
-            size_t out_width = ((inputShape[2] - kernel_shape[1]) + 1) / pool_kernal_shape[1];
-            size_t out_height = ((inputShape[1] - kernel_shape[0]) + 1) / pool_kernal_shape[0];
-
-            auto pool_output = pool;
-            if (withActivation) {
-                auto relu2 = std::make_shared<ngraph::opset3::Relu>(pool);
-                pool_output = relu2;
-            }
-
-            auto permute2 = std::make_shared<ngraph::opset1::Transpose>(pool_output,
-                ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 0, 2, 3, 1 }));
-
-            std::vector<size_t> outFormShapes = { 1, out_width * out_height * num_out_channels };
-            auto pattern2 = std::make_shared<ngraph::opset1::Constant>(ngraph::element::Type_t::i64, ngraph::Shape{ 2 }, outFormShapes);
+            auto conv_out_size = std::accumulate(std::begin(conv->get_output_shape(0)), std::end(conv->get_output_shape(0)),
+                size_t(1), std::multiplies<size_t>());
+            auto pattern2 = std::make_shared<ngraph::opset1::Constant>(ngraph::element::Type_t::i64,
+                ngraph::Shape{ 2 }, ngraph::Shape{ 1, conv_out_size });
             auto reshape2 = std::make_shared<ngraph::opset1::Reshape>(permute2, pattern2, false);
 
             ngraph::ResultVector results{ std::make_shared<ngraph::opset1::Result>(reshape2) };
@@ -299,39 +333,25 @@ class RemovePermutationsWithTwoConvTest : public testing::WithParamInterface<rem
             std::tie(netPrecision, targetDevice, configuration, inputShape) = this->GetParam();
             auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
 
+            size_t shape_size = inputShape.size();
+            ASSERT_GT(shape_size, 2);
+            ASSERT_LT(shape_size, 5);
+
             size_t in_total_dims_size = std::accumulate(std::begin(inputShape), std::end(inputShape), 1, std::multiplies<double>());
             auto params = ngraph::builder::makeParams(ngPrc, { {1, in_total_dims_size} });
 
-            auto pattern1 = std::make_shared<ngraph::opset1::Constant>(ngraph::element::Type_t::i64, ngraph::Shape{ 4 }, inputShape);
+            auto pattern1 = std::make_shared<ngraph::opset1::Constant>(ngraph::element::Type_t::i64, ngraph::Shape{ shape_size }, inputShape);
             auto reshape1 = std::make_shared<ngraph::opset1::Reshape>(params[0], pattern1, false);
-            auto permute1 = std::make_shared<ngraph::opset1::Transpose>(reshape1,
-                ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 0, 3, 1, 2 }));
 
-            size_t num_out_channels = 12;
-            size_t kernel_size = 8;
-            auto kernel_shape1 = GetKernelShape(inputShape[1], inputShape[2], kernel_size);
-            std::vector<float> filter_weights_1 = CommonTestUtils::generate_float_numbers(num_out_channels * inputShape[3] *
-                                                                                          kernel_shape1[0] * kernel_shape1[1],
-                                                                                          0.0f, 0.5f);
-            auto conv1 = ngraph::builder::makeConvolution(permute1, ngPrc, kernel_shape1, { 1, 1 }, { 0, 0 }, { 0, 0 }, { 1, 1 },
-                ngraph::op::PadType::VALID, num_out_channels, false, filter_weights_1);
-            size_t out_width = conv1->get_output_shape(0).at(3);
-            size_t out_height = conv1->get_output_shape(0).at(2);
+            auto permute1 = CreateTranspose(reshape1, shape_size, true);
+            auto conv1 = CreateConvolution(permute1, ngPrc, inputShape);
+            auto conv2 = CreateConvolution(conv1, ngPrc, GetLayerTransposedOutputShape(conv1));
+            auto permute2 = CreateTranspose(conv2, shape_size, false);
 
-            std::vector<size_t> kernel_shape2 = (out_height == 1 ? std::vector<size_t>{1, kernel_size} : std::vector<size_t>{kernel_size, 1});
-            std::vector<float> filter_weights_2 = CommonTestUtils::generate_float_numbers(num_out_channels * num_out_channels *
-                                                                                          kernel_size,
-                                                                                          -0.2f, 0.2f);
-            auto conv2 = ngraph::builder::makeConvolution(conv1, ngPrc, kernel_shape2, { 1, 1 }, { 0, 0 }, { 0, 0 }, { 1, 1 },
-                ngraph::op::PadType::VALID, num_out_channels, false, filter_weights_2);
-            out_width = conv2->get_output_shape(0).at(3);
-            out_height = conv2->get_output_shape(0).at(2);
-
-            auto permute2 = std::make_shared<ngraph::opset1::Transpose>(conv2,
-                ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 0, 2, 3, 1 }));
-
-            std::vector<size_t> outFormShapes = { 1, out_width * out_height * num_out_channels };
-            auto pattern2 = std::make_shared<ngraph::opset1::Constant>(ngraph::element::Type_t::i64, ngraph::Shape{ 2 }, outFormShapes);
+            auto conv_out_size = std::accumulate(std::begin(conv2->get_output_shape(0)), std::end(conv2->get_output_shape(0)),
+                size_t(1), std::multiplies<size_t>());
+            auto pattern2 = std::make_shared<ngraph::opset1::Constant>(ngraph::element::Type_t::i64,
+                ngraph::Shape{ 2 }, ngraph::Shape{ 1, conv_out_size });
             auto reshape2 = std::make_shared<ngraph::opset1::Reshape>(permute2, pattern2, false);
 
             ngraph::ResultVector results{ std::make_shared<ngraph::opset1::Result>(reshape2) };
@@ -390,46 +410,35 @@ class RemovePermutationsWithEltwiseTest : public testing::WithParamInterface<rem
             std::tie(netPrecision, targetDevice, configuration, inputShape) = this->GetParam();
             auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
 
+            size_t shape_size = inputShape.size();
+            ASSERT_GT(shape_size, 2);
+            ASSERT_LT(shape_size, 5);
+
             size_t in_total_dims_size = std::accumulate(std::begin(inputShape), std::end(inputShape), 1, std::multiplies<double>());
             auto params = ngraph::builder::makeParams(ngPrc, { {1, 2 * in_total_dims_size} });
             auto split = ngraph::builder::makeSplit(params[0], ngPrc, 2, 1);
-            auto in_width = inputShape[2];
-            auto in_height = inputShape[1];
-            auto in_channels = inputShape[3];
 
-            auto pattern1 = std::make_shared<ngraph::opset1::Constant>(ngraph::element::Type_t::i64, ngraph::Shape{ 4 }, inputShape);
+            auto pattern1 = std::make_shared<ngraph::opset1::Constant>(ngraph::element::Type_t::i64,
+                ngraph::Shape{ shape_size }, inputShape);
             auto reshape1 = std::make_shared<ngraph::opset1::Reshape>(split->output(0), pattern1, false);
-            auto permute1 = std::make_shared<ngraph::opset1::Transpose>(reshape1,
-                ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 0, 3, 1, 2 }));
 
-            size_t num_out_channels = 12;
-            auto kernel_shape = GetKernelShape(inputShape[1], inputShape[2], 8);
-            std::vector<float> filter_weights_1 = CommonTestUtils::generate_float_numbers(num_out_channels * in_channels *
-                                                                                          kernel_shape[0] * kernel_shape[1],
-                                                                                          -0.2f, 0.2f);
-            auto conv1 = ngraph::builder::makeConvolution(permute1, ngPrc, kernel_shape, { 1, 1 }, { 0, 0 }, { 0, 0 }, { 1, 1 },
-                ngraph::op::PadType::VALID, num_out_channels, false, filter_weights_1);
+            auto permute1 = CreateTranspose(reshape1, shape_size, true);
+            auto conv1 = CreateConvolution(permute1, ngPrc, inputShape);
 
-            auto pattern2 = std::make_shared<ngraph::opset1::Constant>(ngraph::element::Type_t::i64, ngraph::Shape{ 4 }, inputShape);
+            auto pattern2 = std::make_shared<ngraph::opset1::Constant>(ngraph::element::Type_t::i64,
+                ngraph::Shape{ shape_size }, inputShape);
             auto reshape2 = std::make_shared<ngraph::opset1::Reshape>(split->output(1), pattern2, false);
-            auto permute2 = std::make_shared<ngraph::opset1::Transpose>(reshape2,
-                ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 0, 3, 1, 2 }));
 
-            std::vector<float> filter_weights_2 = CommonTestUtils::generate_float_numbers(num_out_channels * in_channels *
-                                                                                          kernel_shape[0] * kernel_shape[1],
-                                                                                          -0.2f, 0.2f);
-            auto conv2 = ngraph::builder::makeConvolution(permute2, ngPrc, kernel_shape, { 1, 1 }, { 0, 0 }, { 0, 0 }, { 1, 1 },
-                ngraph::op::PadType::VALID, num_out_channels, false, filter_weights_2);
+            auto permute2 = CreateTranspose(reshape2, shape_size, true);
+            auto conv2 = CreateConvolution(permute2, ngPrc, inputShape);
 
             auto add = std::make_shared<ngraph::opset1::Add>(conv1, conv2);
+            auto permute3 = CreateTranspose(add, add->get_output_shape(0).size(), false);
 
-            auto permute3 = std::make_shared<ngraph::opset1::Transpose>(add,
-                ngraph::opset1::Constant::create(ngraph::element::i64, ngraph::Shape{ 4 }, { 0, 2, 3, 1 }));
-
-            size_t out_width = ((in_width - kernel_shape[1]) + 1);
-            size_t out_height = ((in_height - kernel_shape[0]) + 1);
-            std::vector<size_t> outFormShapes = { 1, out_width * out_height * num_out_channels };
-            auto pattern3 = std::make_shared<ngraph::opset1::Constant>(ngraph::element::Type_t::i64, ngraph::Shape{ 2 }, outFormShapes);
+            auto conv_out_size = std::accumulate(std::begin(add->get_output_shape(0)), std::end(add->get_output_shape(0)),
+                size_t(1), std::multiplies<size_t>());
+            auto pattern3 = std::make_shared<ngraph::opset1::Constant>(ngraph::element::Type_t::i64,
+                ngraph::Shape{ 2 }, ngraph::Shape{ 1, conv_out_size });
             auto reshape3 = std::make_shared<ngraph::opset1::Reshape>(permute3, pattern3, false);
 
             ngraph::ResultVector results{ std::make_shared<ngraph::opset1::Result>(reshape3) };
@@ -441,7 +450,7 @@ class RemovePermutationsWithEltwiseTest : public testing::WithParamInterface<rem
         Run();
     };
 
-    TEST_P(RemovePermutationsNHWCToNCHWPass4DOutputTest, CompareWithRefImpl) {
+    TEST_P(RemovePermutationsNHWCToNCHWPassNoReshapesTest, CompareWithRefImpl) {
         Run();
     };
 
@@ -487,7 +496,14 @@ class RemovePermutationsWithEltwiseTest : public testing::WithParamInterface<rem
         {1, 32, 1, 2},
         {1, 32, 1, 8},
         {1, 32, 1, 9},
-        {1, 16, 8, 1}
+        {1, 16, 8, 1},
+        {1, 168, 1},
+        {1, 168, 2},
+        {1, 168, 4},
+        {1, 32, 1},
+        {1, 32, 2},
+        {1, 32, 8},
+        {1, 32, 9}
     };
 
     INSTANTIATE_TEST_SUITE_P(smoke_PermutationPass, RemovePermutationsNHWCToNCHWPassTest,
@@ -499,13 +515,13 @@ class RemovePermutationsWithEltwiseTest : public testing::WithParamInterface<rem
             ::testing::ValuesIn(std::vector<bool>{false, true})), // with 1d output of convolution
         RemovePermutationsNHWCToNCHWPassTest::getTestCaseName);
 
-    INSTANTIATE_TEST_SUITE_P(smoke_PermutationPass, RemovePermutationsNHWCToNCHWPass4DOutputTest,
+    INSTANTIATE_TEST_SUITE_P(smoke_PermutationPass, RemovePermutationsNHWCToNCHWPassNoReshapesTest,
         ::testing::Combine(
             ::testing::ValuesIn(netPrecisions),
             ::testing::Values(CommonTestUtils::DEVICE_GNA),
             ::testing::ValuesIn(configs),
             ::testing::ValuesIn(inputShapes)),
-        RemovePermutationsNHWCToNCHWPass4DOutputTest::getTestCaseName);
+        RemovePermutationsNHWCToNCHWPassNoReshapesTest::getTestCaseName);
 
     INSTANTIATE_TEST_SUITE_P(smoke_PermutationPass, RemovePermutationsWithPoolAndActTest,
         ::testing::Combine(

From d50f20b977c5ac0c5e75dd57ec8018e275529d17 Mon Sep 17 00:00:00 2001
From: Yuan Hu <yuan2.hu@intel.com>
Date: Mon, 29 Nov 2021 16:41:48 +0800
Subject: [PATCH 63/72] Add log utils for Auto plugin (#8309)

* add log utils

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

* optimize

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

* remove env support in log util

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

* add IE LOG CONFIG support in auto model

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

* Add LOG

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

* fix compile error

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

* add test

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

* fix set config error

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

* fix code error

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

* add more log and support no arguments format string

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

* fix some review issue

change name from HDEBUG to LOG_DEBUG and so on
remove redundance code
move log config into log util

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

* modify log hpp name

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

* try to fix macos support issue

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

* enable env AUTO_LOG_LEVEL

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

* add comment for WaitActualNetworkReady()

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

* add error log when all device failed

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

* fix rebase issue

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

* fix add log in code cause auto_select_device_failed_test failed issue

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

* add test code

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

* change the Name from AUOT_LOG_LEVEL to OPENVINO_LOG_LEVEL

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

* use just GLOB_SOURCES in cmake

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

* add singleton test

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

* try to fix macos compile issue

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

* move printing config info to async loading

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

* keep CheckConfig as origin code

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

* fix compile issue caused by icore->GetMetric API change

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

* use confMutex instead of a new mutex

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

* try to fix windows compile issue

Signed-off-by: Hu, Yuan2 <yuan2.hu@intel.com>
---
 .../src/multi_device/CMakeLists.txt           |   5 +-
 .../multi_device_exec_network.cpp             |  32 ++-
 .../src/multi_device/multi_device_plugin.cpp  |  10 +
 .../src/multi_device/multi_device_plugin.hpp  |   1 +
 .../src/multi_device/utils/log.cpp            |  11 +
 .../src/multi_device/utils/log.hpp            | 228 ++++++++++++++++++
 .../src/multi_device/utils/log_util.hpp       |  58 +++++
 .../src/multi_device/utils/non_copyable.hpp   |  32 +++
 .../src/multi_device/utils/singleton.hpp      |  47 ++++
 .../src/multi_device/utils/thread_utils.cpp   | 107 ++++++++
 .../src/multi_device/utils/thread_utils.hpp   |  63 +++++
 .../src/multi_device/utils/time_utils.cpp     |  74 ++++++
 .../src/multi_device/utils/time_utils.hpp     |  58 +++++
 .../behavior/plugin/configuration_tests.cpp   |  48 +++-
 .../behavior/plugin/config.cpp                |  27 ++-
 .../tests/unit/multi/CMakeLists.txt           |   4 +-
 .../multi/auto_select_device_failed_test.cpp  |   4 +
 .../tests/unit/multi/log_utils_test.cpp       | 133 ++++++++++
 .../unit/multi/plugin/mock_log_utils.hpp      |  30 +++
 19 files changed, 959 insertions(+), 13 deletions(-)
 create mode 100644 inference-engine/src/multi_device/utils/log.cpp
 create mode 100644 inference-engine/src/multi_device/utils/log.hpp
 create mode 100644 inference-engine/src/multi_device/utils/log_util.hpp
 create mode 100644 inference-engine/src/multi_device/utils/non_copyable.hpp
 create mode 100644 inference-engine/src/multi_device/utils/singleton.hpp
 create mode 100644 inference-engine/src/multi_device/utils/thread_utils.cpp
 create mode 100644 inference-engine/src/multi_device/utils/thread_utils.hpp
 create mode 100644 inference-engine/src/multi_device/utils/time_utils.cpp
 create mode 100644 inference-engine/src/multi_device/utils/time_utils.hpp
 create mode 100644 inference-engine/tests/unit/multi/log_utils_test.cpp
 create mode 100644 inference-engine/tests/unit/multi/plugin/mock_log_utils.hpp

diff --git a/inference-engine/src/multi_device/CMakeLists.txt b/inference-engine/src/multi_device/CMakeLists.txt
index 5018ebcad2c..a23919651e2 100644
--- a/inference-engine/src/multi_device/CMakeLists.txt
+++ b/inference-engine/src/multi_device/CMakeLists.txt
@@ -4,12 +4,11 @@
 
 set (TARGET_NAME "MultiDevicePlugin")
 
-file(GLOB SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
-file(GLOB HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/*.hpp)
+file(GLOB SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp ${CMAKE_CURRENT_SOURCE_DIR}/utils/*.cpp)
 
 ie_add_plugin(NAME ${TARGET_NAME}
               DEVICE_NAME "MULTI"
-              SOURCES ${SOURCES} ${HEADERS}
+              SOURCES ${SOURCES} 
               VERSION_DEFINES_FOR multi_device_plugin.cpp)
 
 ie_add_plugin(NAME ${TARGET_NAME}
diff --git a/inference-engine/src/multi_device/multi_device_exec_network.cpp b/inference-engine/src/multi_device/multi_device_exec_network.cpp
index aae95da315c..c7dab36dfb7 100644
--- a/inference-engine/src/multi_device/multi_device_exec_network.cpp
+++ b/inference-engine/src/multi_device/multi_device_exec_network.cpp
@@ -20,6 +20,7 @@
 
 #include "ngraph/opsets/opset1.hpp"
 #include "transformations/utils/utils.hpp"
+#include "utils/log_util.hpp"
 
 #include "multi_itt.hpp"
 // ------------------------------MultiDeviceExecutableNetwork----------------------------
@@ -167,6 +168,7 @@ MultiDeviceExecutableNetwork::MultiDeviceExecutableNetwork(const std::string&
     _loadContext[ACTUALDEVICE].networkPrecision = GetNetworkPrecision(network);
     _loadContext[ACTUALDEVICE].metaDevices = metaDevices;
     _loadContext[ACTUALDEVICE].deviceInfo = _multiPlugin->SelectDevice(metaDevices, _loadContext[ACTUALDEVICE].networkPrecision);
+    LOG_INFO("[AUTOPLUGIN]:select device:%s", _loadContext[ACTUALDEVICE].deviceInfo.deviceName.c_str());
     bool isActualDevCPU =
         _loadContext[ACTUALDEVICE].deviceInfo.deviceName.find("CPU") != std::string::npos;
     // if Actual device is CPU, disabled _loadContext[CPU], only use _loadContext[ACTUALDEVICE]
@@ -179,6 +181,7 @@ MultiDeviceExecutableNetwork::MultiDeviceExecutableNetwork(const std::string&
         if (CPUIter != metaDevices.end()) {
             _loadContext[CPU].isEnabled = true;
             _loadContext[CPU].deviceInfo = *CPUIter;
+            LOG_INFO("[AUTOPLUGIN]:will load CPU for accelerator");
         } else {
             _loadContext[CPU].isEnabled = false;
         }
@@ -201,6 +204,21 @@ MultiDeviceExecutableNetwork::MultiDeviceExecutableNetwork(const std::string&
                                             contextPtr->deviceInfo.config.end());
                           }
                           contextPtr->isAlready = true;
+                          auto& deviceName = contextPtr->deviceInfo.deviceName;
+                          LOG_INFO("[AUTOPLUGIN]:device:%s loading Network finished",
+                                  deviceName.c_str());
+                          std::vector<std::string> supported_config_keys =
+                              _core->GetMetric(deviceName, METRIC_KEY(SUPPORTED_CONFIG_KEYS));
+                          // there is log mutex in LOG_DEBUG, add _configMutex just want to print them all together
+                          // toDo maybe neet to implement LOG_RUN(task, LOG_LEVEL) to run some debug code.
+                          std::lock_guard<std::mutex> lock(_confMutex);
+                          for (const auto& cfg : supported_config_keys) {
+                              try {
+                                  LOG_DEBUG("[AUTOPLUGIN]:device:%s, GetConfig:%s=%s", deviceName.c_str(),
+                                          cfg.c_str(), contextPtr->executableNetwork->GetConfig(cfg).as<std::string>().c_str());
+                              } catch (...) {
+                              }
+                          }
                       }
                       contextPtr->promise.set_value();
                       // the first load network process finished
@@ -285,6 +303,7 @@ void MultiDeviceExecutableNetwork::TryToLoadNetWork(AutoLoadContext& context,
         return;
     }
 
+    LOG_DEBUG("[AUTOPLUGIN] try to load %s", context.deviceInfo.deviceName.c_str());
     // try to load this candidate device
     TryToLoadNetWork(context, modelPath, network);
 }
@@ -313,8 +332,14 @@ void MultiDeviceExecutableNetwork::WaitFirstNetworkReady() {
         }
     }
 
-    // ToDo need to print failed error mesage
-    IE_THROW() << "[AUTO] load all devices failed";
+    //print errMessage
+    for (int i = CONTEXTNUM - 1; i >= 0; i--) {
+        if (_loadContext[i].isEnabled) {
+            LOG_ERROR("[AUTOPLUGIN] load failed, %s", _loadContext[i].errMessage.c_str());
+        }
+    }
+
+    IE_THROW() << "[AUTOPLUGIN] load all devices failed";
 }
 
 void MultiDeviceExecutableNetwork::WaitActualNetworkReady() const {
@@ -331,7 +356,7 @@ void MultiDeviceExecutableNetwork::WaitActualNetworkReady() const {
                    _loadContext[ACTUALDEVICE].deviceInfo = _loadContext[CPU].deviceInfo;
                    _loadContext[ACTUALDEVICE].isAlready = true;
                }
-            });
+               });
 }
 
 void MultiDeviceExecutableNetwork::ScheduleToWorkerInferRequest(Task inferPipelineTask, DeviceName preferred_device) {
@@ -347,6 +372,7 @@ void MultiDeviceExecutableNetwork::ScheduleToWorkerInferRequest(Task inferPipeli
             }
             devices.push_back(_loadContext[ACTUALDEVICE].deviceInfo);
         } else {
+            // _acceleratorDevice could be the same as _cpuDevice, such as AUTO:CPU
             if (_loadContext[ACTUALDEVICE].isAlready) {
                 devices.push_back(_loadContext[ACTUALDEVICE].deviceInfo);
             } else {
diff --git a/inference-engine/src/multi_device/multi_device_plugin.cpp b/inference-engine/src/multi_device/multi_device_plugin.cpp
index 8fc3fc7a621..95a421b2764 100644
--- a/inference-engine/src/multi_device/multi_device_plugin.cpp
+++ b/inference-engine/src/multi_device/multi_device_plugin.cpp
@@ -165,6 +165,7 @@ void MultiDeviceInferencePlugin::SetConfig(const std::map<std::string, std::stri
     bool needPerfCounters = false;
     std::map<std::string, std::string> filterConfig;
     CheckConfig(config, needPerfCounters, filterConfig);
+
     for (auto && kvp : config) {
         const auto& name = kvp.first;
         _config[name] = kvp.second;
@@ -259,6 +260,8 @@ IExecutableNetworkInternal::Ptr MultiDeviceInferencePlugin::LoadNetworkImpl(cons
              for (auto& config : configs) {
                  if (std::find(validConfigKey.begin(), validConfigKey.end(), config.first) != validConfigKey.end()) {
                      deviceConfig.insert({config.first, config.second});
+                     LOG_INFO("[AUTOPLUGIN]:device:%s, config:%s=%s", iter->deviceName.c_str(),
+                             config.first.c_str(), config.second.c_str());
                  }
              }
              iter->config = deviceConfig;
@@ -519,6 +522,7 @@ std::string MultiDeviceInferencePlugin::GetDeviceList(const std::map<std::string
 void MultiDeviceInferencePlugin::CheckConfig(const std::map<std::string, std::string>& config,
         bool& needPerfCounters, std::map<std::string, std::string>& filterConfig) {
     // TODO need to optimize this code, too much duplicated code
+
     const auto perf_hints_configs = PerfHintsConfig::SupportedKeys();
     for (auto&& kvp : config) {
         if (kvp.first.find("AUTO_") == 0) {
@@ -541,6 +545,12 @@ void MultiDeviceInferencePlugin::CheckConfig(const std::map<std::string, std::st
                 IE_THROW() << "Unsupported config value: " << kvp.second
                            << " for key: " << kvp.first;
             }
+        } else if (kvp.first == PluginConfigParams::KEY_LOG_LEVEL) {
+               auto success = MultiDevicePlugin::setLogLevel(kvp.second);
+               if (!success) {
+                   IE_THROW() << "Unsupported config value: " << kvp.second
+                              << " for key: " << kvp.first;
+               }
         } else if (std::find(perf_hints_configs.begin(), perf_hints_configs.end(), kvp.first) != perf_hints_configs.end()) {
             PerfHintsConfig::CheckConfigAndValue(kvp);
         } else if (supported_configKeys.end() == std::find(supported_configKeys.begin(), supported_configKeys.end(), kvp.first)) {
diff --git a/inference-engine/src/multi_device/multi_device_plugin.hpp b/inference-engine/src/multi_device/multi_device_plugin.hpp
index a9cd219628f..90063335eb4 100644
--- a/inference-engine/src/multi_device/multi_device_plugin.hpp
+++ b/inference-engine/src/multi_device/multi_device_plugin.hpp
@@ -12,6 +12,7 @@
 #include <cpp_interfaces/interface/ie_iplugin_internal.hpp>
 #include <cpp_interfaces/interface/ie_internal_plugin_config.hpp>
 #include "multi_device_exec_network.hpp"
+#include "utils/log_util.hpp"
 
 #ifdef  MULTIUNITTEST
 #define MOCKTESTMACRO virtual
diff --git a/inference-engine/src/multi_device/utils/log.cpp b/inference-engine/src/multi_device/utils/log.cpp
new file mode 100644
index 00000000000..8b2a094878b
--- /dev/null
+++ b/inference-engine/src/multi_device/utils/log.cpp
@@ -0,0 +1,11 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+#include "log.hpp"
+
+namespace MultiDevicePlugin {
+uint32_t Log::defaultLogLevel = static_cast<uint32_t>(LogLevel::LOG_NONE);
+} // namespace MultiDevicePlugin
diff --git a/inference-engine/src/multi_device/utils/log.hpp b/inference-engine/src/multi_device/utils/log.hpp
new file mode 100644
index 00000000000..02c0f1e2bc2
--- /dev/null
+++ b/inference-engine/src/multi_device/utils/log.hpp
@@ -0,0 +1,228 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+#ifndef MULTIDEVICEPLUGIN_LOG_H
+#define MULTIDEVICEPLUGIN_LOG_H
+
+#include <fstream>
+#include <iostream>
+#include <sstream>
+#include <string>
+#include <mutex>
+
+#include "singleton.hpp"
+#include "time_utils.hpp"
+#include "thread_utils.hpp"
+
+#ifdef  MULTIUNITTEST
+#define MOCKTESTMACRO virtual
+#define MultiDevicePlugin MockMultiDevicePlugin
+#else
+#define MOCKTESTMACRO
+#endif
+
+#ifdef COLOR_LOG
+#define COL(x) "\033[1;" #x ";40m"
+#define COL_END "\033[0m"
+#else
+#define COL(x) ""
+#define COL_END ""
+#endif
+
+#define RED COL(31)
+#define GREEN COL(32)
+#define YELLOW COL(33)
+#define BLUE COL(34)
+#define MAGENTA COL(35)
+#define CYAN COL(36)
+#define WHITE COL(0)
+#define DEFAULT_COLOR ""
+
+#ifdef ERROR
+#undef ERROR
+#endif
+
+namespace MultiDevicePlugin {
+inline int parseInteger(const char* str) {
+    std::string var(str ? str : "");
+    try {
+        return std::stoi(var);
+    } catch (...) {
+        return INT32_MAX;
+    }
+}
+
+inline std::string getFileName(const std::string& filePath) {
+    auto index = filePath.find_last_of("/\\");
+    if (std::string::npos == index) {
+        return filePath;
+    }
+    return filePath.substr(index + 1);
+}
+
+inline int getDebugLevel() {
+    return parseInteger(std::getenv("OPENVINO_LOG_LEVEL"));
+}
+const int debug_level = getDebugLevel();
+
+enum class LogLevel : uint32_t {
+    FREQUENT = 0x01,
+    PROCESS = 0x02,
+    DEBUG = 0x04,
+    INFO = 0x08,
+    WARN = 0x10,
+    ERROR = 0x40,
+    FATAL = 0x80,
+    LOG_NONE = 0,
+    LOG_FATAL = static_cast<uint32_t>(LogLevel::FATAL),
+    LOG_ERROR = static_cast<uint32_t>(LogLevel::ERROR) | static_cast<uint32_t>(LogLevel::LOG_FATAL),
+    LOG_WARNING = static_cast<uint32_t>(LogLevel::WARN) | static_cast<uint32_t>(LogLevel::LOG_ERROR),
+    LOG_INFO = static_cast<uint32_t>(LogLevel::INFO) | static_cast<uint32_t>(LogLevel::LOG_WARNING),
+    LOG_DEBUG = static_cast<uint32_t>(LogLevel::DEBUG) | static_cast<uint32_t>(LogLevel::LOG_INFO),
+    LOG_TRACE = static_cast<uint32_t>(LogLevel::PROCESS) | static_cast<uint32_t>(LogLevel::LOG_DEBUG),
+    LOG_FREQUENT = static_cast<uint32_t>(LogLevel::FREQUENT) | static_cast<uint32_t>(LogLevel::LOG_TRACE)
+};
+
+class Log : public Singleton<Log> {
+public:
+    void setPrefix(std::string prefix);
+    void setSuffix(std::string suffix);
+    void setLogLevel(LogLevel logLevel);
+
+    template <typename... Args>
+    void doLog(bool on, bool isTraceCallStack, LogLevel level, const char* levelStr, const char* file,
+        const char* func, long line, const char* tag, const char* fmt, Args... args);
+#ifdef MULTIUNITTEST
+    Log(std::string unittest):Log() {
+    }
+#endif
+
+private:
+    Log();
+    friend Singleton<Log>;
+    static std::string colorBegin(LogLevel logLevel);
+    static std::string colorEnd(LogLevel logLevel);
+    MOCKTESTMACRO void print(std::stringstream& stream);
+
+private:
+    std::mutex mutex;
+    std::string logName;
+    std::string logPath;
+    std::string prefix;
+    std::string suffix;
+    uint32_t logLevel;
+    static uint32_t defaultLogLevel;
+};
+
+inline Log::Log()
+    : logLevel(defaultLogLevel) {
+    switch (debug_level) {
+        case 0: {
+                    logLevel = static_cast<uint32_t>(LogLevel::LOG_NONE);
+                    break;
+                }
+        case 1: {
+                    logLevel = static_cast<uint32_t>(LogLevel::LOG_FATAL);
+                    break;
+                }
+        case 2: {
+                    logLevel = static_cast<uint32_t>(LogLevel::LOG_ERROR);
+                    break;
+                }
+        case 3: {
+                    logLevel = static_cast<uint32_t>(LogLevel::LOG_WARNING);
+                    break;
+                }
+        case 4: {
+                    logLevel = static_cast<uint32_t>(LogLevel::LOG_INFO);
+                    break;
+                }
+        case 5: {
+                    logLevel = static_cast<uint32_t>(LogLevel::LOG_DEBUG);
+                    break;
+                }
+        case 6: {
+                    logLevel = static_cast<uint32_t>(LogLevel::LOG_TRACE);
+                    break;
+                }
+        case 7: {
+                    logLevel = static_cast<uint32_t>(LogLevel::LOG_FREQUENT);
+                    break;
+                }
+        default:
+                break;
+    }
+}
+
+inline void Log::setPrefix(std::string prefix_) {
+    std::lock_guard<std::mutex> autoLock(mutex);
+    prefix = std::move(prefix_);
+}
+
+inline void Log::setSuffix(std::string suffix_) {
+    std::lock_guard<std::mutex> autoLock(mutex);
+    suffix = std::move(suffix_);
+}
+
+inline void Log::setLogLevel(LogLevel logLevel_) {
+    std::lock_guard<std::mutex> autoLock(mutex);
+    logLevel = static_cast<uint32_t>(logLevel_);
+}
+
+inline void Log::print(std::stringstream& stream) {
+    std::cout << stream.str() << std::endl;
+}
+template <typename... Args>
+inline void Log::doLog(bool on, bool isTraceCallStack, LogLevel level, const char* levelStr, const char* file,
+    const char* func, const long line, const char* tag, const char* fmt, Args... args) {
+
+    if (!(static_cast<uint32_t>(level) & static_cast<uint32_t>(logLevel)) || !on) {
+        return;
+    }
+
+    std::stringstream stream;
+    stream << colorBegin(level) << prefix << '[' << TimeUtils::getCurrentTime() << ']';
+
+    stream << '[' << ThreadUtils::getThreadId() << ']';
+    if (level < LogLevel::ERROR) {
+        stream << levelStr[0];
+    } else {
+        stream << levelStr;
+    }
+    stream << '[' << getFileName(file) << ':' << line << ']';
+
+    if (isTraceCallStack) {
+        stream << '[' << func << '(' << ')' << ']';
+    }
+    if (tag) {
+        stream << '[' << tag << ']';
+    }
+    char buffer[255];
+    std::string compatibleString =  "%s" + std::string(fmt);
+    std::snprintf (&buffer[0], sizeof(buffer), compatibleString.c_str(), "", args...);
+    stream << ' ' << buffer << suffix << colorEnd(level);
+    std::lock_guard<std::mutex> autoLock(mutex);
+    print(stream);
+}
+
+inline std::string Log::colorBegin(MultiDevicePlugin::LogLevel logLevel) {
+    if (logLevel == LogLevel::WARN) {
+        return std::string(CYAN);
+    }
+    if (logLevel == LogLevel::ERROR || logLevel == LogLevel::FATAL) {
+        return std::string(RED);
+    }
+    return std::string(DEFAULT_COLOR);
+}
+
+inline std::string Log::colorEnd(MultiDevicePlugin::LogLevel logLevel) {
+    if (logLevel == LogLevel::WARN || logLevel == LogLevel::ERROR || logLevel == LogLevel::FATAL) {
+        return std::string(COL_END);
+    }
+    return {};
+}
+} // namespace MultiDevicePlugin
+
+#endif //MULTIDEVICEPLUGIN_LOG_H
diff --git a/inference-engine/src/multi_device/utils/log_util.hpp b/inference-engine/src/multi_device/utils/log_util.hpp
new file mode 100644
index 00000000000..42a8b717213
--- /dev/null
+++ b/inference-engine/src/multi_device/utils/log_util.hpp
@@ -0,0 +1,58 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+#ifndef MULTIDEVICEPLUGIN_HLOG_H
+#define MULTIDEVICEPLUGIN_HLOG_H
+
+#include <cstdarg>
+
+#include "log.hpp"
+#include <ie_plugin_config.hpp>
+
+#ifdef  MULTIUNITTEST
+#include "plugin/mock_log_utils.hpp"
+#define MOCKTESTMACRO virtual
+#define MultiDevicePlugin MockMultiDevicePlugin
+#define HLogger MockMultiDevice::MockLog::GetInstance()
+#else
+#define MOCKTESTMACRO
+#define HLogger MultiDevicePlugin::Log::instance()
+#endif
+
+
+#define HLogPrint(isOn, isTraceCallStack, logLevel, level, tag, ...) \
+    HLogger->doLog(isOn, isTraceCallStack, logLevel, level, __FILE__, __func__, __LINE__, tag, __VA_ARGS__)
+
+// #define HFrequent(isOn, tag, ...) HLogPrint(isOn, MultiDevicePlugin::LogLevel::FREQUENT, "FREQ", tag, __VA_ARGS__)
+// #define HFatal(...) HLogPrint(true, false, MultiDevicePlugin::LogLevel::FATAL, "FATAL", nullptr, __VA_ARGS__)
+#define LOG_TRACE(isOn, tag, ...) HLogPrint(isOn, false, MultiDevicePlugin::LogLevel::PROCESS, "PROC", tag, __VA_ARGS__)
+#define LOG_DEBUG(...) HLogPrint(true, false, MultiDevicePlugin::LogLevel::DEBUG, "DEBUG", nullptr, __VA_ARGS__)
+#define LOG_INFO(...) HLogPrint(true, false, MultiDevicePlugin::LogLevel::INFO, "INFO", nullptr, __VA_ARGS__)
+#define LOG_WARNING(...) HLogPrint(true, false, MultiDevicePlugin::LogLevel::WARN, "WARN", nullptr, __VA_ARGS__)
+#define LOG_ERROR(...) HLogPrint(true, false, MultiDevicePlugin::LogLevel::ERROR, "ERROR", nullptr, __VA_ARGS__)
+
+#define TraceCallStacks(...) HLogPrint(true, true, MultiDevicePlugin::LogLevel::DEBUG, "DEBUG", nullptr, __VA_ARGS__)
+#define TraceCallStack() TraceCallStacks(" ")
+
+namespace MultiDevicePlugin {
+inline bool setLogLevel(std::string logLevel) {
+    static std::map<std::string, LogLevel> logValueMap = {{CONFIG_VALUE(LOG_NONE), LogLevel::LOG_NONE},
+        {CONFIG_VALUE(LOG_ERROR), LogLevel::LOG_ERROR},
+        {CONFIG_VALUE(LOG_WARNING), LogLevel::LOG_WARNING},
+        {CONFIG_VALUE(LOG_INFO), LogLevel::LOG_INFO},
+        {CONFIG_VALUE(LOG_DEBUG), LogLevel::LOG_DEBUG},
+        {CONFIG_VALUE(LOG_TRACE), LogLevel::LOG_TRACE}};
+    auto it = logValueMap.find(logLevel);
+    if (it != logValueMap.end()) {
+        HLogger->setLogLevel(it->second);
+        return true;
+    } else {
+        return false;
+    }
+}
+
+} // namespace MultiDevicePlugin
+
+#endif //MULTIDEVICEPLUGIN_HLOG_H
diff --git a/inference-engine/src/multi_device/utils/non_copyable.hpp b/inference-engine/src/multi_device/utils/non_copyable.hpp
new file mode 100644
index 00000000000..dfe4dbbe629
--- /dev/null
+++ b/inference-engine/src/multi_device/utils/non_copyable.hpp
@@ -0,0 +1,32 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+#ifndef MULTIDEVICEPLUGIN_NONCOPYABLE_H
+#define MULTIDEVICEPLUGIN_NONCOPYABLE_H
+
+#ifdef  MULTIUNITTEST
+#define MOCKTESTMACRO virtual
+#define MultiDevicePlugin MockMultiDevicePlugin
+#else
+#define MOCKTESTMACRO
+#endif
+
+namespace MultiDevicePlugin {
+class NonCopyable {
+public:
+    NonCopyable(const NonCopyable&) = delete;
+    NonCopyable(NonCopyable&&) = delete;
+
+    NonCopyable& operator=(const NonCopyable&) = delete;
+    NonCopyable& operator=(NonCopyable&&) = delete;
+
+protected:
+    NonCopyable() = default;
+    virtual ~NonCopyable() = default;
+};
+} // namespace MultiDevicePlugin
+
+#endif //MULTIDEVICEPLUGIN_NONCOPYABLE_H
diff --git a/inference-engine/src/multi_device/utils/singleton.hpp b/inference-engine/src/multi_device/utils/singleton.hpp
new file mode 100644
index 00000000000..e4f0b046cd5
--- /dev/null
+++ b/inference-engine/src/multi_device/utils/singleton.hpp
@@ -0,0 +1,47 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+#ifndef MULTIDEVICEPLUGIN_SINGLETON_H
+#define MULTIDEVICEPLUGIN_SINGLETON_H
+#include <cassert>
+#include <list>
+#include <memory>
+#include <mutex>
+
+#include "non_copyable.hpp"
+
+#ifdef  MULTIUNITTEST
+#define MOCKTESTMACRO virtual
+#define MultiDevicePlugin MockMultiDevicePlugin
+#else
+#define MOCKTESTMACRO
+#endif
+
+namespace MultiDevicePlugin {
+template <typename Type>
+class Singleton : public NonCopyable {
+public:
+    static std::shared_ptr<Type>& instance() {
+        static std::shared_ptr<Type> obj;
+        std::call_once(m_onceFlag, [&]() {
+            auto* objPtr = new Type();
+            assert(objPtr!= nullptr);
+            obj.reset(objPtr);
+        });
+        return obj;
+    }
+
+protected:
+    static std::once_flag m_onceFlag;
+    Singleton() = default;
+    virtual ~Singleton() = default;
+};
+
+template <typename Type>
+std::once_flag Singleton<Type>::m_onceFlag;
+} // namespace MultiDevicePlugin
+
+#endif //MULTIDEVICEPLUGIN_SINGLETON_H
diff --git a/inference-engine/src/multi_device/utils/thread_utils.cpp b/inference-engine/src/multi_device/utils/thread_utils.cpp
new file mode 100644
index 00000000000..970decb28b8
--- /dev/null
+++ b/inference-engine/src/multi_device/utils/thread_utils.cpp
@@ -0,0 +1,107 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+#include <fstream>
+#include <iomanip>
+
+#include "thread_utils.hpp"
+#include <mutex>
+
+namespace MultiDevicePlugin {
+namespace ThreadUtils {
+void setName(const std::string& name) {
+    setName(name.c_str());
+}
+
+std::string getName() {
+    char threadName[32] = { '\0' };
+    getName(threadName, 32);
+    return threadName;
+}
+
+void saveThreadInfo(const std::string& threadName, const std::string& saveFile) {
+    static std::mutex mutex;
+    static uint32_t threadCount = 0;
+
+    std::lock_guard<std::mutex> autoLock(mutex);
+
+    std::ofstream stream;
+    if (!threadCount) {
+        stream.open(saveFile, std::ios::out | std::ios::trunc);
+    } else {
+        stream.open(saveFile, std::ios::out | std::ios::app);
+    }
+
+    stream << "[" << std::setw(2) << std::setfill('0') << threadCount++ << "]"
+           << " threadId=" << getThreadId() << " threadName=" << threadName << std::endl;
+}
+
+#if (defined(_WIN32) || defined(_WIN64))
+DWORD getThreadId() {
+    return GetCurrentThreadId();
+}
+
+static void setThreadName(DWORD dwThreadID, const char* threadName) {
+    HANDLE handle = OpenThread(THREAD_SET_LIMITED_INFORMATION, true, dwThreadID);
+    auto len = MultiByteToWideChar(CP_ACP, 0, threadName, static_cast<int>(strlen(threadName)), NULL, 0);
+    PWSTR threadDesc = new WCHAR[len + 1];
+    MultiByteToWideChar(CP_ACP, 0, threadName, static_cast<int>(strlen(threadName)), threadDesc, len);
+    threadDesc[len] = '\0';
+    SetThreadDescription(handle, threadDesc);
+    delete[] threadDesc;
+    CloseHandle(handle);
+}
+
+void setName(const char* pname) {
+    setThreadName(GetCurrentThreadId(), pname);
+}
+
+void getName(char* name, size_t size) {
+    HANDLE handle = OpenThread(THREAD_QUERY_LIMITED_INFORMATION, true, GetCurrentThreadId());
+    PWSTR threadDesc;
+    if (SUCCEEDED(GetThreadDescription(handle, &threadDesc))) {
+        auto len = WideCharToMultiByte(CP_ACP, 0, threadDesc, static_cast<int>(wcslen(threadDesc)), NULL, 0, NULL, NULL);
+        if (len < size) {
+            WideCharToMultiByte(CP_ACP, 0, threadDesc, static_cast<int>(wcslen(threadDesc)), name, len, NULL, NULL);
+            name[len] = '\0';
+        }
+        LocalFree(threadDesc);
+    }
+    CloseHandle(handle);
+}
+#elif defined(__linux__)
+long getThreadId() {
+    return syscall(SYS_gettid);
+}
+
+void setName(const char* pname) {
+    prctl(PR_SET_NAME, pname);
+}
+
+void getName(char* name, size_t size) {
+    if (name && size >= 16) {
+        prctl(PR_GET_NAME, name);
+    }
+}
+
+#elif defined(__APPLE__)
+long getThreadId() {
+    return (long)pthread_self();
+}
+
+void setName(const char* pname) {
+    pthread_setname_np(pname);
+}
+
+void getName(char* name, size_t size) {
+    pthread_getname_np(pthread_self(), name, size);
+}
+
+#else
+#endif
+} // namespace ThreadUtils
+} // namespace MultiDevicePlugin
+
diff --git a/inference-engine/src/multi_device/utils/thread_utils.hpp b/inference-engine/src/multi_device/utils/thread_utils.hpp
new file mode 100644
index 00000000000..e22fa974015
--- /dev/null
+++ b/inference-engine/src/multi_device/utils/thread_utils.hpp
@@ -0,0 +1,63 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+#ifndef MULTIDEVICEPLUGIN_THREADUTILS_H
+#define MULTIDEVICEPLUGIN_THREADUTILS_H
+
+#include <cstdint>
+#include <string>
+
+#ifdef  MULTIUNITTEST
+#define MOCKTESTMACRO virtual
+#define MultiDevicePlugin MockMultiDevicePlugin
+#else
+#define MOCKTESTMACRO
+#endif
+
+#if (defined(_WIN32) || defined(_WIN64))
+#include <windows.h>
+#include <winsock.h>
+#include <intrin.h>
+namespace MultiDevicePlugin {
+namespace ThreadUtils {
+DWORD getThreadId();
+} // namespace ThreadUtils
+#elif defined(__linux__)
+#include <sys/types.h>
+#include <sys/prctl.h>
+#include <sys/syscall.h>
+#include <unistd.h>
+namespace MultiDevicePlugin {
+namespace ThreadUtils {
+long getThreadId();
+} // namespace ThreadUtils
+#elif defined(__APPLE__)
+#include <pthread.h>
+#include <sys/proc_info.h>
+namespace MultiDevicePlugin {
+namespace ThreadUtils {
+long getThreadId();
+} // namespace ThreadUtils
+#else
+namespace MultiDevicePlugin {
+namespace ThreadUtils {
+long getThreadId();
+} // namespace ThreadUtils
+#endif
+
+
+
+namespace ThreadUtils {
+uint64_t getRdtsc();
+std::string getName();
+
+void setName(const char* name);
+void setName(const std::string& name);
+void getName(char* name, size_t size);
+void saveThreadInfo(const std::string& threadName, const std::string& saveFile = "threads.info");
+} // namespace ThreadUtils
+} // namespace MultiDevicePlugin
+
+#endif //MULTIDEVICEPLUGIN_THREADUTILS_H
diff --git a/inference-engine/src/multi_device/utils/time_utils.cpp b/inference-engine/src/multi_device/utils/time_utils.cpp
new file mode 100644
index 00000000000..1ac43c57215
--- /dev/null
+++ b/inference-engine/src/multi_device/utils/time_utils.cpp
@@ -0,0 +1,74 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+#include <chrono>
+#include <cstring>
+#include <iomanip>
+#include <sstream>
+#include "time_utils.hpp"
+
+namespace MultiDevicePlugin {
+namespace TimeUtils {
+
+bool localtimeSafe(const time_t* time, struct tm* result) {
+    if (time && result) {
+#if (defined(_WIN32) || defined(_WIN64))
+        localtime_s(result, time);
+#else
+        localtime_r(time, result);
+#endif
+        return true;
+    }
+    return false;
+}
+
+std::string putTime(std::chrono::system_clock::time_point tp, const char* format) {
+    struct tm t = {};
+    time_t timeObj = std::chrono::system_clock::to_time_t(tp);
+
+    localtimeSafe(&timeObj, &t);
+
+    std::stringstream ss;
+
+#if (defined(__GNUC__) && (__GNUC__ < 5))
+    char time_str[24];
+    strftime(time_str, sizeof(time_str), format, &t);
+    ss << time_str;
+#else
+    ss << std::put_time(&t, format);
+#endif
+
+    return ss.str();
+}
+
+std::string formatTimeMilli(std::chrono::system_clock::time_point tp) {
+    std::stringstream ss;
+
+    auto milliseconds = (std::chrono::duration_cast<std::chrono::milliseconds>(tp.time_since_epoch()).count() % 1000);
+
+    ss << putTime(tp, "%T") << '.' << std::setfill('0') << std::setw(3) << milliseconds;
+
+    return ss.str();
+}
+
+std::string getCurrentTime() {
+    std::stringstream ss;
+
+#ifdef VERBOSE_LOG
+    const char* timeFormat = "%F %T";
+#else
+    const char* timeFormat = "%T";
+#endif
+
+    auto now = std::chrono::system_clock::now();
+
+    auto microseconds = (std::chrono::duration_cast<std::chrono::microseconds>(now.time_since_epoch()).count() % 1000000) / 100;
+
+    ss << putTime(now, timeFormat) << '.' << std::setfill('0') << std::setw(4) << microseconds;
+
+    return ss.str();
+}
+} // namespace TimeUtils
+} // namespace MultiDevicePlugin
diff --git a/inference-engine/src/multi_device/utils/time_utils.hpp b/inference-engine/src/multi_device/utils/time_utils.hpp
new file mode 100644
index 00000000000..6c2675d4de3
--- /dev/null
+++ b/inference-engine/src/multi_device/utils/time_utils.hpp
@@ -0,0 +1,58 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+#ifndef MULTIDEVICEPLUGIN_TIMEUTILS_H
+#define MULTIDEVICEPLUGIN_TIMEUTILS_H
+
+#include <chrono>
+#include <ctime>
+#include <string>
+
+#ifdef  MULTIUNITTEST
+#define MOCKTESTMACRO virtual
+#define MultiDevicePlugin MockMultiDevicePlugin
+#else
+#define MOCKTESTMACRO
+#endif
+
+namespace MultiDevicePlugin {
+namespace TimeUtils {
+class StopWatch {
+public:
+    explicit StopWatch(int seconds) {
+        m_start = std::chrono::steady_clock::now();
+        m_deadline = m_start + std::chrono::seconds(seconds);
+    }
+
+    void reset(int seconds) {
+        m_start = std::chrono::steady_clock::now();
+        m_deadline = m_start + std::chrono::seconds(seconds);
+    }
+
+    bool isExpired() const {
+        auto now = std::chrono::steady_clock::now();
+        return now > m_deadline;
+    }
+
+private:
+    std::chrono::steady_clock::time_point m_start;
+    std::chrono::steady_clock::time_point m_deadline;
+};
+
+template <class TimePoint>
+float getTimeDiffInMs(TimePoint tp1, TimePoint tp2) {
+    std::chrono::duration<float, std::milli> duration = (tp1 > tp2) ? tp1 - tp2 : tp2 - tp1;
+    return duration.count();
+}
+
+bool localtimeSafe(const time_t* time, struct tm* result);
+
+std::string getCurrentTime();
+std::string putTime(std::chrono::system_clock::time_point tp, const char* format);
+std::string formatTimeMilli(std::chrono::system_clock::time_point tp); // format tp to HH:MM:SS.mmm
+} // namespace TimeUtils
+} // namespace MultiDevicePlugin
+
+#endif //MULTIDEVICEPLUGIN_TIMEUTILS_H
diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/behavior/plugin/configuration_tests.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/behavior/plugin/configuration_tests.cpp
index a772c660d5b..ff240aa56ed 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/behavior/plugin/configuration_tests.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/behavior/plugin/configuration_tests.cpp
@@ -76,6 +76,29 @@ namespace {
                     {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT_NUM_REQUESTS, "1"}}
     };
 
+    const std::vector<std::map<std::string, std::string>> AutoConfigs = {
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+             {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT, InferenceEngine::PluginConfigParams::THROUGHPUT}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+             {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT, InferenceEngine::PluginConfigParams::LATENCY}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+             {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT, InferenceEngine::PluginConfigParams::LATENCY},
+             {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT_NUM_REQUESTS, "1"}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+             {InferenceEngine::PluginConfigParams::KEY_LOG_LEVEL, InferenceEngine::PluginConfigParams::LOG_NONE}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+             {InferenceEngine::PluginConfigParams::KEY_LOG_LEVEL, InferenceEngine::PluginConfigParams::LOG_ERROR}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+             {InferenceEngine::PluginConfigParams::KEY_LOG_LEVEL, InferenceEngine::PluginConfigParams::LOG_WARNING}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+             {InferenceEngine::PluginConfigParams::KEY_LOG_LEVEL, InferenceEngine::PluginConfigParams::LOG_INFO}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+             {InferenceEngine::PluginConfigParams::KEY_LOG_LEVEL, InferenceEngine::PluginConfigParams::LOG_DEBUG}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+             {InferenceEngine::PluginConfigParams::KEY_LOG_LEVEL, InferenceEngine::PluginConfigParams::LOG_TRACE}}
+    };
+
     INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, CorrectConfigTests,
             ::testing::Combine(
                 ::testing::Values(CommonTestUtils::DEVICE_CPU),
@@ -91,7 +114,7 @@ namespace {
     INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, CorrectConfigTests,
             ::testing::Combine(
                 ::testing::Values(CommonTestUtils::DEVICE_AUTO),
-                ::testing::ValuesIn(MultiConfigs)),
+                ::testing::ValuesIn(AutoConfigs)),
             CorrectConfigTests::getTestCaseName);
 
     const std::vector<std::map<std::string, std::string>> inconfigs = {
@@ -137,6 +160,25 @@ namespace {
             {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU}}
     };
 
+    const std::vector<std::map<std::string, std::string>> autoinconfigs = {
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+             {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT, "DOESN'T EXIST"}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+             {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT, InferenceEngine::PluginConfigParams::LATENCY},
+                    {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT_NUM_REQUESTS, "-1"}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+             {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT, InferenceEngine::PluginConfigParams::THROUGHPUT},
+                    {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT_NUM_REQUESTS, "should be int"}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+                    {InferenceEngine::PluginConfigParams::KEY_CPU_THROUGHPUT_STREAMS, "OFF"}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+                    {InferenceEngine::PluginConfigParams::KEY_CPU_BIND_THREAD, "OFF"}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+                    {InferenceEngine::PluginConfigParams::KEY_DYN_BATCH_LIMIT, "NAN"}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+                    {InferenceEngine::PluginConfigParams::KEY_LOG_LEVEL, "NAN"}}
+    };
+
     INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, IncorrectConfigTests,
             ::testing::Combine(
                 ::testing::Values(CommonTestUtils::DEVICE_CPU),
@@ -152,7 +194,7 @@ namespace {
     INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, IncorrectConfigTests,
             ::testing::Combine(
             ::testing::Values(CommonTestUtils::DEVICE_AUTO),
-            ::testing::ValuesIn(multiinconfigs)),
+            ::testing::ValuesIn(autoinconfigs)),
             IncorrectConfigTests::getTestCaseName);
 
     INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, IncorrectConfigAPITests,
@@ -170,7 +212,7 @@ namespace {
     INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, IncorrectConfigAPITests,
             ::testing::Combine(
             ::testing::Values(CommonTestUtils::DEVICE_AUTO),
-            ::testing::ValuesIn(multiinconfigs)),
+            ::testing::ValuesIn(autoinconfigs)),
             IncorrectConfigAPITests::getTestCaseName);
 
     const std::vector<std::map<std::string, std::string>> ConfigsCheck = {
diff --git a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/plugin/config.cpp b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/plugin/config.cpp
index 006d1aaeb7f..d46d0e3cc6f 100644
--- a/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/plugin/config.cpp
+++ b/inference-engine/tests/functional/plugin/gpu/shared_tests_instances/behavior/plugin/config.cpp
@@ -63,6 +63,8 @@ namespace {
                     {InferenceEngine::PluginConfigParams::KEY_TUNING_MODE, "TUNING_UNKNOWN_MODE"}},
             {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_GPU},
                     {InferenceEngine::PluginConfigParams::KEY_DEVICE_ID, "DEVICE_UNKNOWN"}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES , CommonTestUtils::DEVICE_CPU},
+                    {InferenceEngine::PluginConfigParams::KEY_LOG_LEVEL, "NAN"}},
             {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES ,
                  CommonTestUtils::DEVICE_GPU + std::string(",") + CommonTestUtils::DEVICE_CPU},
                     {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT, "DOESN'T EXIST"}},
@@ -84,7 +86,10 @@ namespace {
                     {InferenceEngine::PluginConfigParams::KEY_TUNING_MODE, "TUNING_UNKNOWN_MODE"}},
             {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES ,
                  CommonTestUtils::DEVICE_GPU + std::string(",") + CommonTestUtils::DEVICE_CPU},
-                    {InferenceEngine::PluginConfigParams::KEY_DEVICE_ID, "DEVICE_UNKNOWN"}}
+                    {InferenceEngine::PluginConfigParams::KEY_DEVICE_ID, "DEVICE_UNKNOWN"}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES ,
+                 CommonTestUtils::DEVICE_GPU + std::string(",") + CommonTestUtils::DEVICE_CPU},
+                    {InferenceEngine::PluginConfigParams::KEY_LOG_LEVEL, "NAN"}}
     };
     IE_SUPPRESS_DEPRECATED_END
 
@@ -178,7 +183,25 @@ namespace {
             {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES ,
                  CommonTestUtils::DEVICE_GPU + std::string(",") + CommonTestUtils::DEVICE_CPU},
              {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT, InferenceEngine::PluginConfigParams::LATENCY},
-             {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT_NUM_REQUESTS, "1"}}
+             {InferenceEngine::PluginConfigParams::KEY_PERFORMANCE_HINT_NUM_REQUESTS, "1"}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES ,
+                 CommonTestUtils::DEVICE_GPU + std::string(",") + CommonTestUtils::DEVICE_CPU},
+             {InferenceEngine::PluginConfigParams::KEY_LOG_LEVEL, InferenceEngine::PluginConfigParams::LOG_NONE}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES ,
+                 CommonTestUtils::DEVICE_GPU + std::string(",") + CommonTestUtils::DEVICE_CPU},
+             {InferenceEngine::PluginConfigParams::KEY_LOG_LEVEL, InferenceEngine::PluginConfigParams::LOG_ERROR}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES ,
+                 CommonTestUtils::DEVICE_GPU + std::string(",") + CommonTestUtils::DEVICE_CPU},
+             {InferenceEngine::PluginConfigParams::KEY_LOG_LEVEL, InferenceEngine::PluginConfigParams::LOG_WARNING}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES ,
+                 CommonTestUtils::DEVICE_GPU + std::string(",") + CommonTestUtils::DEVICE_CPU},
+             {InferenceEngine::PluginConfigParams::KEY_LOG_LEVEL, InferenceEngine::PluginConfigParams::LOG_INFO}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES ,
+                 CommonTestUtils::DEVICE_GPU + std::string(",") + CommonTestUtils::DEVICE_CPU},
+             {InferenceEngine::PluginConfigParams::KEY_LOG_LEVEL, InferenceEngine::PluginConfigParams::LOG_DEBUG}},
+            {{InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES ,
+                 CommonTestUtils::DEVICE_GPU + std::string(",") + CommonTestUtils::DEVICE_CPU},
+             {InferenceEngine::PluginConfigParams::KEY_LOG_LEVEL, InferenceEngine::PluginConfigParams::LOG_TRACE}}
     };
 
     INSTANTIATE_TEST_SUITE_P(smoke_BehaviorTests, DefaultValuesConfigTests,
diff --git a/inference-engine/tests/unit/multi/CMakeLists.txt b/inference-engine/tests/unit/multi/CMakeLists.txt
index 09990de09d2..4456c3100f9 100644
--- a/inference-engine/tests/unit/multi/CMakeLists.txt
+++ b/inference-engine/tests/unit/multi/CMakeLists.txt
@@ -11,9 +11,9 @@ add_definitions(-DMULTIUNITTEST)
 addIeTargetTest(
         NAME ${TARGET_NAME}
         ROOT ${CMAKE_CURRENT_SOURCE_DIR}
-        ADDITIONAL_SOURCE_DIRS ${IE_MAIN_SOURCE_DIR}/src/multi_device
+        ADDITIONAL_SOURCE_DIRS ${IE_MAIN_SOURCE_DIR}/src/multi_device ${IE_MAIN_SOURCE_DIR}/src/multi_device/utils
         INCLUDES
-            ${IE_MAIN_SOURCE_DIR}/src/multi_device
+            ${IE_MAIN_SOURCE_DIR}/src/multi_device ${CMAKE_CURRENT_SOURCE_DIR}
         LINK_LIBRARIES
             inference_engine_lp_transformations
             ngraphFunctions
diff --git a/inference-engine/tests/unit/multi/auto_select_device_failed_test.cpp b/inference-engine/tests/unit/multi/auto_select_device_failed_test.cpp
index 261af289ae5..98aefa36eb3 100644
--- a/inference-engine/tests/unit/multi/auto_select_device_failed_test.cpp
+++ b/inference-engine/tests/unit/multi/auto_select_device_failed_test.cpp
@@ -127,6 +127,10 @@ public:
        IE_SET_METRIC(OPTIMAL_NUMBER_OF_INFER_REQUESTS, optimalNum, 2);
        ON_CALL(*mockIExeNet.get(), GetMetric(StrEq(METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS))))
            .WillByDefault(Return(optimalNum));
+       IE_SET_METRIC(SUPPORTED_CONFIG_KEYS, supportConfigs, {});
+       ON_CALL(*core, GetMetric(_, StrEq(METRIC_KEY(SUPPORTED_CONFIG_KEYS)), _))
+           .WillByDefault(Return(supportConfigs));
+       EXPECT_CALL(*core, GetMetric(_, StrEq(METRIC_KEY(SUPPORTED_CONFIG_KEYS)), _)).Times(AnyNumber());
     }
 };
 
diff --git a/inference-engine/tests/unit/multi/log_utils_test.cpp b/inference-engine/tests/unit/multi/log_utils_test.cpp
new file mode 100644
index 00000000000..5c694b30d99
--- /dev/null
+++ b/inference-engine/tests/unit/multi/log_utils_test.cpp
@@ -0,0 +1,133 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+
+#include <gtest/gtest.h>
+#include <gmock/gmock.h>
+#include "utils/log_util.hpp"
+#include <future>
+using ::testing::_;
+using namespace MockMultiDevice;
+#if (defined(_WIN32) || defined(_WIN64))
+#include <winbase.h>
+#elif defined(__linux__)
+#include <stdlib.h>
+#elif defined(__APPLE__)
+#include <stdlib.h>
+#else
+#endif
+
+MockLog* MockLog::_mockLog = NULL;
+using ConfigParams = std::tuple<
+        std::string,     // logLevel
+        std::string,     // envlogLevel
+        int             //  expectCallNum
+        >;
+class LogUtilsTest : public ::testing::TestWithParam<ConfigParams> {
+public:
+    std::string _logLevel;
+    std::string _envLogLevel;
+    int _expectCallNum;
+
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<ConfigParams> obj) {
+        std::string logLevel;
+        std::string envLogLevel;
+        int expectCallNum;
+        std::tie(logLevel, envLogLevel, expectCallNum) = obj.param;
+        std::ostringstream result;
+        result << "logLevel_" << logLevel << "_expectCallNum_" << expectCallNum
+            << "envlogLevel" << envLogLevel;
+        return result.str();
+    }
+
+    void SetTestEnv(std::string key, std::string value) {
+#ifdef WIN32
+        SetEnvironmentVariable(key.c_str(), value.c_str());
+#elif defined(__linux__)
+        ::setenv(key.c_str(), value.c_str(), true);
+#elif defined(__APPLE__)
+        ::setenv(key.c_str(), value.c_str(), true);
+#else
+#endif
+    }
+
+    void SetUp() override {
+        std::tie(_logLevel, _envLogLevel, _expectCallNum) = this->GetParam();
+    }
+
+    void TearDown() override {
+        MockLog::Release();
+    }
+
+    void printLog() {
+        LOG_TRACE(true, "test", "TRACE");
+        LOG_DEBUG("DEBUG");
+        LOG_INFO("INFO");
+        LOG_WARNING("WARNING");
+        LOG_ERROR("ERROR");
+        LOG_TRACE(true, "test", "%s", "TRACE");
+        LOG_DEBUG("%s", "DEBUG");
+        LOG_INFO("%s", "INFO");
+        LOG_WARNING("%s", "WARNING");
+        LOG_ERROR("%s", "ERROR");
+    }
+};
+
+TEST_P(LogUtilsTest, setLogLevel) {
+    EXPECT_CALL(*(HLogger), print(_)).Times(_expectCallNum);
+    setLogLevel(_logLevel);
+    printLog();
+}
+
+TEST_P(LogUtilsTest, setEnvNotAffectSetLogLevel) {
+    EXPECT_CALL(*(HLogger), print(_)).Times(_expectCallNum);
+    setLogLevel(_logLevel);
+    SetTestEnv("OPENVINO_LOG_LEVEL", "3");
+    printLog();
+}
+
+//can not test ENV case. because of the ENV variable is readed at the
+//beginning of test application and modify it in runtime is not valid
+//still need to test it in different platform manully
+//TEST_P(LogUtilsTest, setEnvLogLevel) {
+//    SetTestEnv("AUTO_LOG_LEVEL", _envLogLevel);
+//    EXPECT_CALL(*(HLogger), print(_)).Times(_expectCallNum);
+//    printLog();
+//}
+//
+
+TEST(smoke_Auto_BehaviorTests, LogUtilsSingleton) {
+    std::vector<std::future<void>> futureVect;
+    std::shared_ptr<Log> instanceVector[20];
+    for (unsigned int i = 0; i < 20; i++) {
+        auto future = std::async(std::launch::async, [this, &instanceVector, i] {
+                instanceVector[i] = Log::instance();
+                });
+        futureVect.push_back(std::move(future));
+    }
+
+    for (auto& future : futureVect) {
+        future.wait();
+    }
+
+    for (unsigned int i = 0; i < 19; i++) {
+        EXPECT_NE(instanceVector[i].get(), nullptr);
+        EXPECT_EQ(instanceVector[i].get(), instanceVector[i+1].get());
+    }
+}
+
+const std::vector<ConfigParams> testConfigs =
+{ConfigParams {"LOG_NONE", "0", 0},
+    ConfigParams {"LOG_NONE", "1", 0},
+    ConfigParams {"LOG_ERROR", "2", 2},
+    ConfigParams {"LOG_WARNING", "3", 4},
+    ConfigParams {"LOG_INFO", "4", 6},
+    ConfigParams {"LOG_DEBUG", "5", 8},
+    ConfigParams {"LOG_TRACE", "6", 10}};
+
+INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, LogUtilsTest,
+                ::testing::ValuesIn(testConfigs),
+            LogUtilsTest::getTestCaseName);
+
diff --git a/inference-engine/tests/unit/multi/plugin/mock_log_utils.hpp b/inference-engine/tests/unit/multi/plugin/mock_log_utils.hpp
new file mode 100644
index 00000000000..99ff9f29e7e
--- /dev/null
+++ b/inference-engine/tests/unit/multi/plugin/mock_log_utils.hpp
@@ -0,0 +1,30 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+#include <gmock/gmock.h>
+#include "utils/log.hpp"
+using namespace MockMultiDevicePlugin;
+namespace MockMultiDevice {
+
+class MockLog : public Log {
+public:
+    MOCK_METHOD(void, print, (std::stringstream& stream),  (override));
+    MockLog(std::string unittest):Log(unittest) {
+    }
+    static MockLog* GetInstance() {
+        if (_mockLog == NULL) {
+            _mockLog = new MockLog("unittest");
+        }
+        return _mockLog;
+    }
+    static void Release() {
+        if (_mockLog) {
+            delete _mockLog;
+            _mockLog = NULL;
+        }
+    }
+    static MockLog* _mockLog;
+};
+}// namespace MockMultiDevice

From 2d996c135422773e61c53e1b4c74627a7f2b2140 Mon Sep 17 00:00:00 2001
From: Kelvin Choi <kelvin.choi@intel.com>
Date: Mon, 29 Nov 2021 17:51:19 +0900
Subject: [PATCH 64/72] [GPU] Update GatherND primitive (#8813)

* Cldnn output memory size at GatherND functional-test is aligned with TensorDesc of output blob
* Add param for rank of input data
* Update unittests to add rank of input data
* Update gpu fusing tests
---
 .../src/cldnn_engine/ops/gather_nd.cpp        |  4 ++++
 .../clDNN/api/cldnn/primitives/gather_nd.hpp  |  6 +++++
 .../thirdparty/clDNN/src/gather_nd.cpp        |  5 ++--
 .../tests/test_cases/fusings_gpu_test.cpp     | 23 +++++++++++++++++--
 .../tests/test_cases/gather_nd_gpu_test.cpp   | 14 ++++++++++-
 5 files changed, 46 insertions(+), 6 deletions(-)

diff --git a/inference-engine/src/cldnn_engine/ops/gather_nd.cpp b/inference-engine/src/cldnn_engine/ops/gather_nd.cpp
index 06760d291a8..ba29fb765fc 100644
--- a/inference-engine/src/cldnn_engine/ops/gather_nd.cpp
+++ b/inference-engine/src/cldnn_engine/ops/gather_nd.cpp
@@ -17,6 +17,7 @@ static void CreateGatherNDOp(Program& p, const std::shared_ptr<ngraph::op::v5::G
     auto inputPrimitives = p.GetInputPrimitiveIDs(op);
     std::string layerName = layer_type_name_ID(op);
 
+    int32_t input_rank = static_cast<int32_t>(op->get_input_shape(0).size());
     int32_t indices_rank = static_cast<int32_t>(op->get_input_shape(1).size());
 
     auto batch_dims = op->get_batch_dims();
@@ -24,6 +25,7 @@ static void CreateGatherNDOp(Program& p, const std::shared_ptr<ngraph::op::v5::G
     auto primitive = cldnn::gather_nd(layerName,
                                       inputPrimitives[0],
                                       inputPrimitives[1],
+                                      input_rank,
                                       indices_rank,
                                       batch_dims,
                                       true,
@@ -40,6 +42,7 @@ static void CreateGatherNDOp(Program& p, const std::shared_ptr<ngraph::op::v8::G
     auto inputPrimitives = p.GetInputPrimitiveIDs(op);
     std::string layerName = layer_type_name_ID(op);
 
+    int32_t input_rank = static_cast<int32_t>(op->get_input_shape(0).size());
     int32_t indices_rank = static_cast<int32_t>(op->get_input_shape(1).size());
 
     auto batch_dims = op->get_batch_dims();
@@ -47,6 +50,7 @@ static void CreateGatherNDOp(Program& p, const std::shared_ptr<ngraph::op::v8::G
     auto primitive = cldnn::gather_nd(layerName,
                                       inputPrimitives[0],
                                       inputPrimitives[1],
+                                      input_rank,
                                       indices_rank,
                                       batch_dims,
                                       false,
diff --git a/inference-engine/thirdparty/clDNN/api/cldnn/primitives/gather_nd.hpp b/inference-engine/thirdparty/clDNN/api/cldnn/primitives/gather_nd.hpp
index bb3cc10e915..58482455ef5 100644
--- a/inference-engine/thirdparty/clDNN/api/cldnn/primitives/gather_nd.hpp
+++ b/inference-engine/thirdparty/clDNN/api/cldnn/primitives/gather_nd.hpp
@@ -23,6 +23,7 @@ struct gather_nd : public primitive_base<gather_nd> {
     /// @param id                   This primitive id.
     /// @param data                 Input data primitive id.
     /// @param indices              Input indexes primitive id.
+    /// @param input_rank           Rank of input data.
     /// @param indices_rank         Rank of indices.
     /// @param batch_dims           batch_dims as an attribute of GatherND. Optional.
     /// @param batch_merged_output  batched output shape is merged as a dimention for v5.
@@ -32,16 +33,21 @@ struct gather_nd : public primitive_base<gather_nd> {
     gather_nd(const primitive_id& id,
               const primitive_id& data,
               const primitive_id& indices,
+              const uint8_t input_rank,
               const uint8_t indices_rank,
               const uint8_t batch_dims = 0,
               const bool batch_merged_output = true,
               const primitive_id& ext_prim_id = "",
               const padding& output_padding = padding())
         : primitive_base(id, {data, indices}, ext_prim_id, output_padding),
+                         input_rank(input_rank),
                          indices_rank(indices_rank),
                          batch_dims(batch_dims),
                          batch_merged_output(batch_merged_output) {}
 
+    /// @brief GatherND input_rank
+    uint8_t input_rank;
+
     /// @brief GatherND indices_rank
     uint8_t indices_rank;
 
diff --git a/inference-engine/thirdparty/clDNN/src/gather_nd.cpp b/inference-engine/thirdparty/clDNN/src/gather_nd.cpp
index 43ad9f028a5..01b664e1995 100644
--- a/inference-engine/thirdparty/clDNN/src/gather_nd.cpp
+++ b/inference-engine/thirdparty/clDNN/src/gather_nd.cpp
@@ -24,8 +24,7 @@ layout gather_nd_inst::calc_output_layout(gather_nd_node const& node) {
     auto input_layout = input_layout_origin.size.sizes(input_layout_origin.format);
     auto indices_layout = indices_layout_origin.size.sizes(indices_layout_origin.format);
 
-    const size_t input_dims = input_layout.size();
-
+    const auto input_rank = static_cast<size_t>(op->input_rank);
     const auto indices_rank = op->indices_rank;
     const auto batch_dims = op->batch_dims;
 
@@ -37,7 +36,7 @@ layout gather_nd_inst::calc_output_layout(gather_nd_node const& node) {
     }
 
     const size_t indices_last_dim = indices_layout[indices_rank - 1];
-    for (size_t x = static_cast<size_t>(batch_dims + indices_last_dim); x < input_dims; x++) {
+    for (size_t x = static_cast<size_t>(batch_dims + indices_last_dim); x < input_rank; x++) {
         output_sizes.push_back(input_layout[x]);
     }
 
diff --git a/inference-engine/thirdparty/clDNN/tests/test_cases/fusings_gpu_test.cpp b/inference-engine/thirdparty/clDNN/tests/test_cases/fusings_gpu_test.cpp
index 1b10eadb02f..f2defd6e2cb 100644
--- a/inference-engine/thirdparty/clDNN/tests/test_cases/fusings_gpu_test.cpp
+++ b/inference-engine/thirdparty/clDNN/tests/test_cases/fusings_gpu_test.cpp
@@ -8749,13 +8749,23 @@ public:
 class gather_nd_quantize : public GatherNDPrimitiveFusingTest {};
 TEST_P(gather_nd_quantize, basic) {
     auto p = GetParam();
+
+    auto input_rank = 0;
+    if (p.input_format == format::bfyx) {
+        input_rank = 4;
+    } else if (p.input_format == format::bfzyx) {
+        input_rank = 5;
+    } else if (p.input_format == format::bfwzyx) {
+        input_rank = 6;
+    }
+
     create_topologies(input_layout("input", get_input_layout(p)),
         data("gather_nd_indices", get_mem(get_indices_layout(p), 0, p.max_number_in_indices - 1)),
         data("in_lo", get_mem(get_per_channel_layout(p), min_random, 0)),
         data("in_hi", get_mem(get_per_channel_layout(p), 1, max_random)),
         data("out_lo", get_mem(get_single_element_layout(p), -127)),
         data("out_hi", get_mem(get_single_element_layout(p), 127)),
-        gather_nd("gather_nd_prim", "input", "gather_nd_indices", p.indices_rank, p.batch_dims),
+        gather_nd("gather_nd_prim", "input", "gather_nd_indices", input_rank, p.indices_rank, p.batch_dims),
         quantize("quantize", "gather_nd_prim", "in_lo", "in_hi", "out_lo", "out_hi", 255, data_types::i8),
         reorder("reorder_bfyx", "quantize", p.default_format, data_types::f32)
     );
@@ -8802,11 +8812,20 @@ class gather_nd_activation_scale_eltwise : public GatherNDPrimitiveFusingTest {}
 TEST_P(gather_nd_activation_scale_eltwise, basic) {
     auto p = GetParam();
 
+    auto input_rank = 0;
+    if (p.input_format == format::bfyx) {
+        input_rank = 4;
+    } else if (p.input_format == format::bfzyx) {
+        input_rank = 5;
+    } else if (p.input_format == format::bfwzyx) {
+        input_rank = 6;
+    }
+
     create_topologies(input_layout("input", get_input_layout(p)),
         data("gather_nd_indices", get_mem(get_indices_layout(p), 0, p.max_number_in_indices - 1)),
         data("scale_data", get_mem(get_per_channel_layout(p), 1.0f / 255)),
         data("eltwise_data", get_mem(get_output_layout(p))),
-        gather_nd("gather_nd_prim", "input", "gather_nd_indices", p.indices_rank, p.batch_dims),
+        gather_nd("gather_nd_prim", "input", "gather_nd_indices", input_rank, p.indices_rank, p.batch_dims),
         activation("activation", "gather_nd_prim", activation_func::abs),
         scale("scale", "activation", "scale_data"),
         eltwise("eltwise", { "scale", "eltwise_data" }, eltwise_mode::sum, p.data_type),
diff --git a/inference-engine/thirdparty/clDNN/tests/test_cases/gather_nd_gpu_test.cpp b/inference-engine/thirdparty/clDNN/tests/test_cases/gather_nd_gpu_test.cpp
index 7dbffa73b07..ec9b8332041 100644
--- a/inference-engine/thirdparty/clDNN/tests/test_cases/gather_nd_gpu_test.cpp
+++ b/inference-engine/thirdparty/clDNN/tests/test_cases/gather_nd_gpu_test.cpp
@@ -21,7 +21,19 @@ inline void DoTestBase(engine& engine,
     const tensor ts,
     const bool batch_merged_output) {
     topology topology;
-    auto gather_nd_inst = gather_nd("gather_nd", "InputData", "InputIndices", indices_rank, batch_dims, batch_merged_output);
+
+    int input_rank = 0;
+    if (input0->get_layout().format == format::bfyx) {
+        input_rank = 4;
+    } else if (input0->get_layout().format == format::bfzyx) {
+        input_rank = 5;
+    } else if (input0->get_layout().format == format::bfwzyx) {
+        input_rank = 6;
+    } else {
+        FAIL();
+    }
+
+    auto gather_nd_inst = gather_nd("gather_nd", "InputData", "InputIndices", input_rank, indices_rank, batch_dims, batch_merged_output);
     topology.add(input_layout("InputData", input0->get_layout()));
     topology.add(input_layout("InputIndices", input1->get_layout()));
     topology.add(gather_nd_inst);

From 541c8721c74b900e1fa0c97fdbef914c6f09fbfa Mon Sep 17 00:00:00 2001
From: Mateusz Tabaka <mateusz.tabaka@intel.com>
Date: Mon, 29 Nov 2021 09:53:08 +0100
Subject: [PATCH 65/72] =?UTF-8?q?[GPU]=20fix=20Constant=20handling=20when?=
 =?UTF-8?q?=20it=20has=20multiple=20users=20and=20one=20if=20it=20?=
 =?UTF-8?q?=E2=80=A6=20(#8832)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* [GPU] fix Constant handling when it has multiple users and one if it is bprop conv

When constant is connected to ConvolutionBackpropData or GroupConvolutionBackpropData weights,
we need to swap 'O' and 'I' dimensions. That can be problematic if the same constant
is also connected to other nodes - since after swap - the dimensions may not match
the other node's dimensions.
To handle that, we can create a copy of that constant, replace backprop convolution weights
with that copy and create additional (to the original constant) cldnn::data primitive with swapped dimensions.

* fix windows build

* address review comments
---
 .../src/cldnn_engine/ops/constant.cpp         | 150 +++++++++---------
 .../gpu/subgraph_tests/shared_constant.cpp    |  45 ++++++
 2 files changed, 121 insertions(+), 74 deletions(-)
 create mode 100644 inference-engine/tests/functional/plugin/gpu/subgraph_tests/shared_constant.cpp

diff --git a/inference-engine/src/cldnn_engine/ops/constant.cpp b/inference-engine/src/cldnn_engine/ops/constant.cpp
index 26e8c2845ee..3e8648ddc0a 100644
--- a/inference-engine/src/cldnn_engine/ops/constant.cpp
+++ b/inference-engine/src/cldnn_engine/ops/constant.cpp
@@ -22,35 +22,6 @@
 
 namespace CLDNNPlugin {
 
-struct ConstProperties {
-    bool isWeights;
-    bool hasGroupDimension;
-    bool reversedChannelsOrder;
-};
-
-static ConstProperties getConstProperties(const std::shared_ptr<ngraph::op::Constant>& op) {
-    for (size_t i = 0; i < op->get_output_size(); i++) {
-        auto outTensors = op->get_output_target_inputs(i);
-        for (auto& t : outTensors) {
-            auto outOp = t.get_node();
-            if (dynamic_cast<ngraph::op::v1::Convolution*>(outOp)) {
-                return {t.get_index() == 1, false, false};
-            } else if (dynamic_cast<ngraph::op::v1::BinaryConvolution*>(outOp)) {
-                return {t.get_index() == 1, false, false};
-            } else if (auto castedOp = dynamic_cast<ngraph::op::v1::DeformableConvolution*>(outOp)) {
-                return {t.get_index() == 2, castedOp->get_group() > 1, false};
-            } else if (dynamic_cast<ngraph::op::v1::GroupConvolution*>(outOp)) {
-                return {t.get_index() == 1, true, false};
-            } else if (dynamic_cast<ngraph::op::v1::ConvolutionBackpropData*>(outOp)) {
-                return {t.get_index() == 1, false, true};
-            } else if (dynamic_cast<ngraph::op::v1::GroupConvolutionBackpropData*>(outOp)) {
-                return {t.get_index() == 1, true, true};
-            }
-        }
-    }
-    return {false, false, false};
-}
-
 static cldnn::tensor getConstTensor(const ngraph::Shape constDims) {
     cldnn::tensor constTensor;
     switch (constDims.size()) {
@@ -78,71 +49,103 @@ static cldnn::tensor getConstTensor(const ngraph::Shape constDims) {
     return constTensor;
 }
 
+struct ConstProperties {
+    bool needsBatchInterpretation;
+    bool swapOI;
+    bool hasGroupDimension;
+};
+
+static void createClDnnConstant(Program& p, const ngraph::Shape& constDims, const std::shared_ptr<ngraph::op::v0::Constant>& op, const ConstProperties& props);
+
 static void CreateConstantOp(Program& p, const std::shared_ptr<ngraph::op::v0::Constant>& op) {
-    auto constDims = op->get_shape();
-    cldnn::tensor constTensor = getConstTensor(constDims);
+    const auto& constDims = op->get_shape();
+    auto constUsers = op->get_output_target_inputs(0);
+    size_t numConstUsers = constUsers.size();
+
+    std::unordered_map<std::shared_ptr<ngraph::op::v0::Constant>, ConstProperties> consts = {
+        {op, {false, false, false}}
+    };
+
+    // handleConvWeights function is executed when one of the constant users is ConvolutionBackpropData or GroupConvolutionBackpropData.
+    // In that case, we mark that constant's O and I dimensions need to be swapped.
+    auto handleConvWeights = [&op] (ngraph::Node* conv, std::unordered_map<std::shared_ptr<ngraph::op::v0::Constant>, ConstProperties>& consts,
+                                 size_t& numConstUsers, bool hasGroupDimension) {
+                                 // If constant has multiple users - create its copy and replace 'conv' weights with the copy.
+                                 // This is to make sure that dimension change doesn't break other users of the constant node.
+                                 // It is a shallow copy, but that's fine since in createClDnnConstant
+                                 // every constant created here, gets memcopied to a brand new cldnn::memory.
+                                 if (numConstUsers > 1) {
+                                     auto constant = std::make_shared<ngraph::op::v0::Constant>(*(op.get()));
+                                     conv->input(1).replace_source_output(constant);
+                                     consts.insert({constant, {false, true, hasGroupDimension}});
+                                     numConstUsers--;
+                                 } else {
+                                     consts[op].swapOI = true;
+                                     consts[op].hasGroupDimension = hasGroupDimension;
+                                 }
+                             };
 
     // WA to inconsistency between input and const 1d tensors
     // For Concat along batch we go with batch interpretation
     // For Gather input we go with batch interpretation
-    bool needsBatchInterpretation = false;
-    if (constDims.size() == 1) {
-        for (size_t i = 0; i < op->get_output_size(); i++) {
-            auto outTensors = op->get_output_target_inputs(i);
-
-            for (auto& t : outTensors) {
-                auto outOp = t.get_node();
-                if (auto castedOp = dynamic_cast<ngraph::op::v0::Concat*>(outOp)) {
-                    if (castedOp->get_axis() == 0) {
-                        needsBatchInterpretation = true;
-                        break;
-                    }
-                } else if (ngraph::op::is_binary_elementwise_arithmetic(outOp) ||
-                           ngraph::op::is_binary_elementwise_logical(outOp) ||
-                           ngraph::is_type<ngraph::op::v0::SquaredDifference>(outOp)) {
-                    bool all_inputs_1d = true;
-                    for (size_t j = 0; j < outOp->get_input_size(); j++) {
-                        auto& in_shape = outOp->get_input_shape(j);
-                        if (in_shape.size() > 1)
-                            all_inputs_1d = false;
-                    }
-                    needsBatchInterpretation = all_inputs_1d;
-                    break;
-                } else if (ngraph::is_type<ngraph::op::v1::Gather>(outOp) ||
-                           ngraph::is_type<ngraph::op::v1::Split>(outOp) ||
-                           ngraph::is_type<ngraph::op::v1::VariadicSplit>(outOp)) {
-                    needsBatchInterpretation = true;
-                    break;
-                }
+    // Also check if constant users is a backprop convolution - in that case O and I need to be swapped.
+    for (auto& node : constUsers) {
+        auto outOp = node.get_node();
+        if (auto castedOp = dynamic_cast<ngraph::op::v0::Concat*>(outOp)) {
+            if (castedOp->get_axis() == 0) {
+                consts[op].needsBatchInterpretation = constDims.size() == 1;
             }
+        } else if (ngraph::op::is_binary_elementwise_arithmetic(outOp) ||
+                   ngraph::op::is_binary_elementwise_logical(outOp) ||
+                   ngraph::is_type<ngraph::op::v0::SquaredDifference>(outOp)) {
+            bool all_inputs_1d = true;
+            for (size_t j = 0; j < outOp->get_input_size(); j++) {
+                auto& in_shape = outOp->get_input_shape(j);
+                if (in_shape.size() > 1)
+                    all_inputs_1d = false;
+            }
+            consts[op].needsBatchInterpretation = all_inputs_1d && constDims.size() == 1;
+        } else if (ngraph::is_type<ngraph::op::v1::Gather>(outOp) ||
+                   ngraph::is_type<ngraph::op::v1::Split>(outOp) ||
+                   ngraph::is_type<ngraph::op::v1::VariadicSplit>(outOp)) {
+            consts[op].needsBatchInterpretation = constDims.size() == 1;
+        } else if (ngraph::is_type<ngraph::op::v1::ConvolutionBackpropData>(outOp) && node.get_index() == 1) {
+            handleConvWeights(outOp, consts, numConstUsers, false);
+        } else if (ngraph::is_type<ngraph::op::v1::GroupConvolutionBackpropData>(outOp) && node.get_index() == 1) {
+            handleConvWeights(outOp, consts, numConstUsers, true);
         }
     }
 
-    if (needsBatchInterpretation) {
+    for (auto& it : consts) {
+        createClDnnConstant(p, constDims, it.first, it.second);
+    }
+}
+
+void createClDnnConstant(Program& p, const ngraph::Shape& constDims, const std::shared_ptr<ngraph::op::v0::Constant>& op, const ConstProperties& props) {
+    cldnn::tensor constTensor = getConstTensor(constDims);
+    auto constFormat = DefaultFormatForDims(constDims.size());
+
+    if (props.needsBatchInterpretation) {
         constTensor.batch[0] = constTensor.count();
         constTensor.feature[0] = 1;
     }
 
-    auto constFormat = DefaultFormatForDims(op->get_output_shape(0).size());
-    auto prop = getConstProperties(op);
-
     // If constDims has a dimension = 0, then create tensor with single value
     // TODO: check if dim=0 is a valid case
     if (std::accumulate(constDims.begin(), constDims.end(), 1, std::multiplies<size_t>()) == 0)
         constTensor = cldnn::tensor{1};
 
     // Swap O and I dimensions to match expected deconvolution weights format
-    bool swap_oi = prop.isWeights && prop.reversedChannelsOrder;
     size_t inputFeatureElements = 1;
     size_t outputFeatureElements = 1;
     size_t groups = 1;
-    if (swap_oi) {
-        size_t expected_min_rank = 2 + (prop.hasGroupDimension ? 1 : 0);
+    auto newDims = constDims;
+    if (props.swapOI) {
+        size_t expected_min_rank = 2 + (props.hasGroupDimension ? 1 : 0);
         if (expected_min_rank > constDims.size())
             IE_THROW() << "Invalid constant properties or shape";
 
-        auto newDims = constDims;
-        if (prop.hasGroupDimension) {
+        if (props.hasGroupDimension) {
             std::swap(newDims[2], newDims[1]);
             inputFeatureElements = newDims[2];
             outputFeatureElements = newDims[1];
@@ -164,8 +167,7 @@ static void CreateConstantOp(Program& p, const std::shared_ptr<ngraph::op::v0::C
     cldnn::primitive_id constPrimID;
     auto data = op->get_data_ptr<char>();
 
-
-    auto bufIter = p.blobMemCache.find(std::make_pair(data, constDims));
+    auto bufIter = p.blobMemCache.find(std::make_pair(data, newDims));
 
     if (bufIter != p.blobMemCache.end()) {
         constPrimID = bufIter->second;
@@ -181,9 +183,9 @@ static void CreateConstantOp(Program& p, const std::shared_ptr<ngraph::op::v0::C
         auto bufSize = constLayout.bytes_count();
 
         // Do actual weights reorder and change O and I channels order
-        if (swap_oi) {
+        if (props.swapOI) {
             auto elementSize = cldnn::data_type_traits::size_of(constLayout.data_type);
-            size_t spatial_dim_off = prop.hasGroupDimension ? 3 : 2;
+            size_t spatial_dim_off = props.hasGroupDimension ? 3 : 2;
             size_t featureSize = elementSize;
             for (size_t i = spatial_dim_off; i < constDims.size(); i++) {
                 featureSize *= constDims[i];
@@ -205,7 +207,7 @@ static void CreateConstantOp(Program& p, const std::shared_ptr<ngraph::op::v0::C
             std::memcpy(&buf[0], &data[0], bufSize);
         }
         p.AddPrimitive(cldnn::data(initialconstPrimID, mem, op->get_friendly_name()));
-        p.blobMemCache[std::make_pair(data, constDims)] = initialconstPrimID;
+        p.blobMemCache[std::make_pair(data, newDims)] = initialconstPrimID;
         constPrimID = initialconstPrimID;
     }
 
diff --git a/inference-engine/tests/functional/plugin/gpu/subgraph_tests/shared_constant.cpp b/inference-engine/tests/functional/plugin/gpu/subgraph_tests/shared_constant.cpp
new file mode 100644
index 00000000000..f106acae24f
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/gpu/subgraph_tests/shared_constant.cpp
@@ -0,0 +1,45 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <shared_test_classes/base/layer_test_utils.hpp>
+#include <ngraph/opsets/opset8.hpp>
+
+namespace {
+
+using namespace ngraph;
+
+// Validate scenario where a single Constant has multiple users (like one constant is used for Convolution, ConvolutionBackpropData, Multiply, etc.)
+class SharedConstant : virtual public LayerTestsUtils::LayerTestsCommon {
+protected:
+    void SetUp() override {
+        targetDevice = CommonTestUtils::DEVICE_GPU;
+        auto type = element::f32;
+        Shape constShape{4, 1, 3, 3};
+        Shape convInputShape{1, 1, 5, 5};
+        Shape convBackpropInputShape{1, 4, 5, 5};
+        Shape constGroupConvBackpropShape{2, 2, 3, 3, 3};
+        auto constant = opset8::Constant::create(type, constShape, {1});
+        auto input1 = std::make_shared<opset8::Parameter>(type, convInputShape);
+        auto conv = std::make_shared<opset8::Convolution>(input1, constant, Strides{1, 1}, CoordinateDiff{0, 0}, CoordinateDiff{0, 0}, Strides{1, 1});
+        auto input2 = std::make_shared<opset8::Parameter>(type, convBackpropInputShape);
+        auto convBprop = std::make_shared<opset8::ConvolutionBackpropData>(input2, constant, Strides{1, 1},
+                CoordinateDiff{0, 0}, CoordinateDiff{0, 0}, Strides{1, 1});
+        auto input3 = std::make_shared<opset8::Parameter>(type, convBackpropInputShape);
+        auto constantGroupConv = opset8::Constant::create(type, constGroupConvBackpropShape, {1});
+        auto groupConvBprop = std::make_shared<opset8::GroupConvolutionBackpropData>(input3, constantGroupConv, Strides{1, 1},
+                CoordinateDiff{0, 0}, CoordinateDiff{0, 0}, Strides{1, 1});
+        auto input4 = std::make_shared<opset8::Parameter>(type, constShape);
+        auto mul = std::make_shared<opset8::Multiply>(input4, constant);
+        auto input5 = std::make_shared<opset8::Parameter>(type, constGroupConvBackpropShape);
+        auto mul2 = std::make_shared<opset8::Multiply>(input5, constantGroupConv);
+        function = std::make_shared<ngraph::Function>(NodeVector{convBprop, conv, groupConvBprop, mul2, mul},
+                ParameterVector{input1, input2, input3, input4, input5});
+    }
+};
+
+TEST_F(SharedConstant, smoke_SharedConstant) {
+    Run();
+}
+
+}  // namespace

From 63822e0bdb6896132443fa5eec74403f68505a31 Mon Sep 17 00:00:00 2001
From: Vladislav Golubev <vladislav.golubev@intel.com>
Date: Mon, 29 Nov 2021 12:03:21 +0300
Subject: [PATCH 66/72] ROIAlign cpu tests disabled (#8887)

---
 .../plugin/cpu/shared_tests_instances/skip_tests_config.cpp     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
index bd1598d893e..d4b26dababb 100644
--- a/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/shared_tests_instances/skip_tests_config.cpp
@@ -166,7 +166,7 @@ std::vector<std::string> disabledTestPatterns() {
         R"(.*CTCLossLayerTest.*CMR=1.*)",
         R"(.*CTCLossLayerCPUTest.*ctcMergeRepeated=1.*)",
         // Issue: 72151
-        R"(.*smoke_ROIAlignLayoutTest.*bf16.*)",
+        R"(.*smoke_ROIAlignLayoutTest.*)",
     };
 
 #define FIX_62820 0

From c8d5b20c95a57d20f344250d847a6ec7396a3a6c Mon Sep 17 00:00:00 2001
From: Maxim Andronov <maxim.andronov@intel.com>
Date: Mon, 29 Nov 2021 12:16:14 +0300
Subject: [PATCH 67/72] [CPU] Interpolate dynamism support (#8717)

---
 .../src/mkldnn_plugin/mkldnn_node.cpp         |   13 +
 .../src/mkldnn_plugin/mkldnn_node.h           |    2 +
 .../nodes/mkldnn_interpolate_node.cpp         | 1661 +++++++++--------
 .../nodes/mkldnn_interpolate_node.h           |  175 +-
 .../mkldnn_plugin/nodes/mkldnn_pooling_node.h |    2 +-
 .../cpu/single_layer_tests/interpolate.cpp    |  657 ++++---
 6 files changed, 1479 insertions(+), 1031 deletions(-)

diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp
index 8a0223a9687..0fda2ab04fb 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_node.cpp
@@ -1303,6 +1303,19 @@ bool MKLDNNNode::inputShapesDefined() const {
     return true;
 }
 
+bool MKLDNNNode::outputShapesDefined() const {
+    for (size_t i = 0; i < outputShapes.size(); i++) {
+        if (!getChildEdgesAtPort(i)[0]->getMemory().getDesc().isDefined()) {
+            return false;
+        }
+    }
+    return true;
+}
+
+bool MKLDNNNode::shapesDefined() const {
+    return inputShapesDefined() && outputShapesDefined();
+}
+
 bool MKLDNNNode::needPrepareParams() const {
     return inputShapesModified();
 }
diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_node.h b/inference-engine/src/mkldnn_plugin/mkldnn_node.h
index 0747a642e40..6406101a878 100644
--- a/inference-engine/src/mkldnn_plugin/mkldnn_node.h
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_node.h
@@ -707,6 +707,8 @@ protected:
     bool isDynamic = false;
 
     bool inputShapesDefined() const;
+    bool outputShapesDefined() const;
+    bool shapesDefined() const;
     void updateLastInputDims();
 
     bool inputShapesModified() const;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_interpolate_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_interpolate_node.cpp
index 8949bf025d4..d58adc21d81 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_interpolate_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_interpolate_node.cpp
@@ -25,6 +25,9 @@
 
 #include <ngraph/opsets/opset1.hpp>
 #include <ngraph/opsets/opset4.hpp>
+#include <utils/shape_inference/static_shape.hpp>
+#include <utils/shape_inference/shape_inference.hpp>
+#include <ie_ngraph_utils.hpp>
 #include "utils/cpu_utils.hpp"
 
 using namespace mkldnn;
@@ -1605,7 +1608,7 @@ private:
 // shapeND: n     c     d     h    w
 // blockND: ncdhw cdhw  dhw   hw   w    1
 // index  : 0      1    2     3    4    5
-inline SizeVector getBlockND(SizeVector& shape) {
+inline SizeVector getBlockND(const SizeVector& shape) {
     int shapeRank = shape.size();
     SizeVector blockND(shapeRank + 1, 1);
     for (int i = shapeRank - 1; i >= 0; i--) {
@@ -1644,11 +1647,6 @@ using ngInterpShapeCalcMode = ngraph::opset4::Interpolate::ShapeCalcMode;
 
 bool MKLDNNInterpolateNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
-
         const auto interp = std::dynamic_pointer_cast<const ngraph::opset4::Interpolate>(op);
         if (!interp) {
             errorMessage = "Only opset4 Interpolate operation is supported";
@@ -1683,7 +1681,7 @@ bool MKLDNNInterpolateNode::isSupportedOperation(const std::shared_ptr<const ngr
             return false;
         }
 
-        const size_t dataRank = interp->get_input_shape(DATA_ID).size();
+        const size_t dataRank = interp->get_input_partial_shape(DATA_ID).rank().get_length();
         if (dataRank < 1 || dataRank > 5) {
             errorMessage = "Does not support input tensor of rank : " + std::to_string(dataRank);
             return false;
@@ -1694,8 +1692,9 @@ bool MKLDNNInterpolateNode::isSupportedOperation(const std::shared_ptr<const ngr
             return false;
         }
 
-        if (std::dynamic_pointer_cast<const ngraph::opset1::Constant>(interp->get_input_node_shared_ptr(SCALES_ID)) == nullptr) {
-            errorMessage = "Only const 'scales' input is supported";
+        if (!isDynamicNgraphNode(op) && interpShapeCalcMode == ngInterpShapeCalcMode::scales &&
+                !ngraph::is_type<ngraph::opset1::Constant>(op->get_input_node_ptr(2))) {
+            errorMessage = "Only const 'scales' input is supported for static shapes";
             return false;
         }
 
@@ -1717,74 +1716,61 @@ MKLDNNInterpolateNode::MKLDNNInterpolateNode(const std::shared_ptr<ngraph::Node>
 
         const auto interp = std::dynamic_pointer_cast<const ngraph::opset4::Interpolate>(op);
 
-        if (interp->get_input_size() != 3 && interp->get_input_size() != 4)
+        const auto numInputs = inputShapes.size();
+        if (numInputs != 3 && numInputs != 4)
             IE_THROW() << errorPrefix << " has incorrect number of input edges";
-        if (interp->get_output_size() != 1)
+        if (outputShapes.size() != 1)
             IE_THROW() << errorPrefix << " has incorrect number of output edges";
-        isAxesSpecified = interp->get_input_size() != 3;
+        isAxesSpecified = numInputs != 3;
 
         const auto &interpAttr = interp->get_attrs();
 
-        const size_t dataRank = interp->get_input_shape(DATA_ID).size();
+        const size_t dataRank = getInputShapeAtPort(DATA_ID).getRank();
         const auto &interpMode = interpAttr.mode;
         if (interpMode == ngInterpMode::nearest) {
-            mode = InterpolateMode::nearest;
+            interpAttrs.mode = InterpolateMode::nearest;
         } else if (interpMode == ngInterpMode::linear) {
             if (dataRank < 5) {
-                mode = InterpolateMode::linear_onnx;
+                interpAttrs.mode = InterpolateMode::linear_onnx;
             } else {
-                mode = InterpolateMode::linear;
+                interpAttrs.mode = InterpolateMode::linear;
             }
         } else if (interpMode == ngInterpMode::linear_onnx) {
-            mode = InterpolateMode::linear_onnx;
+            interpAttrs.mode = InterpolateMode::linear_onnx;
         } else if (interpMode == ngInterpMode::cubic) {
-            mode = InterpolateMode::cubic;
-        }
-
-        switch (dataRank) {
-            case 1:
-            case 3:
-                spatialDimSize = 1;
-                break;
-            case 2:
-            case 4:
-                spatialDimSize = 2;
-                break;
-            case 5:
-                spatialDimSize = 3;
-                break;
+            interpAttrs.mode = InterpolateMode::cubic;
         }
 
         const auto &interpCoordTransMode = interpAttr.coordinate_transformation_mode;
         if (interpCoordTransMode == ngInterpCoordTransf::half_pixel) {
-            coordTransMode = InterpolateCoordTransMode::half_pixel;
+            interpAttrs.coordTransMode = InterpolateCoordTransMode::half_pixel;
         } else if (interpCoordTransMode == ngInterpCoordTransf::pytorch_half_pixel) {
-            coordTransMode = InterpolateCoordTransMode::pytorch_half_pixel;
+            interpAttrs.coordTransMode = InterpolateCoordTransMode::pytorch_half_pixel;
         } else if (interpCoordTransMode == ngInterpCoordTransf::asymmetric) {
-            coordTransMode = InterpolateCoordTransMode::asymmetric;
+            interpAttrs.coordTransMode = InterpolateCoordTransMode::asymmetric;
         } else if (interpCoordTransMode == ngInterpCoordTransf::tf_half_pixel_for_nn) {
-            coordTransMode = InterpolateCoordTransMode::tf_half_pixel_for_nn;
+            interpAttrs.coordTransMode = InterpolateCoordTransMode::tf_half_pixel_for_nn;
         } else if (interpCoordTransMode == ngInterpCoordTransf::align_corners) {
-            coordTransMode = InterpolateCoordTransMode::align_corners;
+            interpAttrs.coordTransMode = InterpolateCoordTransMode::align_corners;
         }
 
-        if (mode == InterpolateMode::nearest) {
+        if (interpAttrs.mode == InterpolateMode::nearest) {
             const auto &interpNearestMode = interpAttr.nearest_mode;
             if (interpNearestMode == ngInterpNearMode::round_prefer_floor) {
-                nearestMode = InterpolateNearestMode::round_prefer_floor;
+                interpAttrs.nearestMode = InterpolateNearestMode::round_prefer_floor;
             } else if (interpNearestMode == ngInterpNearMode::round_prefer_ceil) {
-                nearestMode = InterpolateNearestMode::round_prefer_ceil;
+                interpAttrs.nearestMode = InterpolateNearestMode::round_prefer_ceil;
             } else if (interpNearestMode == ngInterpNearMode::floor) {
-                nearestMode = InterpolateNearestMode::floor;
+                interpAttrs.nearestMode = InterpolateNearestMode::floor;
             } else if (interpNearestMode == ngInterpNearMode::ceil) {
-                nearestMode = InterpolateNearestMode::ceil;
+                interpAttrs.nearestMode = InterpolateNearestMode::ceil;
             } else if (interpNearestMode == ngInterpNearMode::simple) {
-                nearestMode = InterpolateNearestMode::simple;
+                interpAttrs.nearestMode = InterpolateNearestMode::simple;
             }
-        } else if (mode == InterpolateMode::cubic) {
-            cubeCoeff = static_cast<float>(interpAttr.cube_coeff);
+        } else if (interpAttrs.mode == InterpolateMode::cubic) {
+            interpAttrs.cubeCoeff = static_cast<float>(interpAttr.cube_coeff);
         }
-        antialias = interpAttr.antialias;
+        interpAttrs.antialias = interpAttr.antialias;
 
         const auto &interpShapeCalcMode = interpAttr.shape_calculation_mode;
         if (interpShapeCalcMode == ngInterpShapeCalcMode::scales) {
@@ -1794,23 +1780,21 @@ MKLDNNInterpolateNode::MKLDNNInterpolateNode(const std::shared_ptr<ngraph::Node>
         }
 
         if (interpAttr.pads_begin.empty()) {
-            padBegin.resize(dataRank, 0);
+            interpAttrs.padBegin.resize(dataRank, 0);
         } else {
-            padBegin.resize(interpAttr.pads_begin.size());
+            interpAttrs.padBegin.resize(interpAttr.pads_begin.size());
             for (size_t i = 0; i < interpAttr.pads_begin.size(); i++)
-                padBegin[i] = static_cast<int>(interpAttr.pads_begin[i]);
+                interpAttrs.padBegin[i] = static_cast<int>(interpAttr.pads_begin[i]);
         }
 
         if (interpAttr.pads_end.empty()) {
-            padEnd.resize(dataRank, 0);
+            interpAttrs.padEnd.resize(dataRank, 0);
         } else {
-            padEnd.resize(interpAttr.pads_end.size());
+            interpAttrs.padEnd.resize(interpAttr.pads_end.size());
             for (size_t i = 0; i < interpAttr.pads_end.size(); i++)
-                padEnd[i] = static_cast<int>(interpAttr.pads_end[i]);
+                interpAttrs.padEnd[i] = static_cast<int>(interpAttr.pads_end[i]);
         }
 
-        scales = std::dynamic_pointer_cast<const ngraph::opset1::Constant>(interp->get_input_node_shared_ptr(SCALES_ID))->cast_vector<float>();
-
         if (isAxesSpecified) {
             axes = std::dynamic_pointer_cast<const ngraph::opset1::Constant>(interp->get_input_node_shared_ptr(AXES_ID))->cast_vector<int>();
         } else {
@@ -1819,10 +1803,6 @@ MKLDNNInterpolateNode::MKLDNNInterpolateNode(const std::shared_ptr<ngraph::Node>
                 axes[i] = i;
             }
         }
-
-        if (scales.size() != axes.size()) {
-            IE_THROW() << errorPrefix << " does not have the same number elements in scales as in axis.";
-        }
     } else {
         IE_THROW(NotImplemented) << errorMessage;
     }
@@ -1835,18 +1815,17 @@ void MKLDNNInterpolateNode::getSupportedDescriptors() {
     if (getChildEdges().empty())
         IE_THROW() << errorPrefix << " has incorrect number of output edges";
 
-    srcDim = getInputShapeAtPort(DATA_ID).getStaticDims();
-    int dataRank = srcDim.size();
+    int dataRank = getInputShapeAtPort(DATA_ID).getRank();
 
     // get pad
-    for (int i = 0; i < padBegin.size(); i++) {
-        if (padBegin[i] != 0) {
+    for (int i = 0; i < interpAttrs.padBegin.size(); i++) {
+        if (interpAttrs.padBegin[i] != 0) {
             hasPad = true;
             break;
         }
     }
-    for (int i = 0; i < padEnd.size(); i++) {
-        if (padEnd[i] != 0) {
+    for (int i = 0; i < interpAttrs.padEnd.size(); i++) {
+        if (interpAttrs.padEnd[i] != 0) {
             hasPad = true;
             break;
         }
@@ -1868,21 +1847,15 @@ void MKLDNNInterpolateNode::getSupportedDescriptors() {
             return result;
         };
 
-        padBegin = correctPad(padBegin, dataRank);
-        padEnd = correctPad(padEnd, dataRank);
-        srcDimPad = getPaddedInputShape();
-    } else {
-        srcDimPad = srcDim;
+        interpAttrs.padBegin = correctPad(interpAttrs.padBegin, dataRank);
+        interpAttrs.padEnd = correctPad(interpAttrs.padEnd, dataRank);
     }
-    dstDim = getOutputShapeAtPort(0).getStaticDims();
 }
 
 void MKLDNNInterpolateNode::initSupportedPrimitiveDescriptors() {
     if (!supportedPrimitiveDescriptors.empty())
         return;
 
-    setPostOps(attr, true);
-
     Precision inputPrecision = getOriginalInputPrecisionAtPort(DATA_ID);
     if ((inputPrecision != Precision::I8) && (inputPrecision != Precision::U8) && (inputPrecision != Precision::BF16)) {
         inputPrecision = Precision::FP32;
@@ -1900,12 +1873,6 @@ void MKLDNNInterpolateNode::initSupportedPrimitiveDescriptors() {
         inputPrecision = outputPrecision = Precision::FP32;
     }
 
-    srcDataSize = inputPrecision.size();
-    dstDataSize = outputPrecision.size();
-
-    inputPrec = inputPrecision;
-    outputPrec = outputPrecision;
-
     NodeConfig config;
     config.dynBatchSupport = false;
     if (isAxesSpecified) {
@@ -1932,36 +1899,117 @@ void MKLDNNInterpolateNode::initSupportedPrimitiveDescriptors() {
         supportedPrimitiveDescriptors.push_back({config, implDetail});
     };
 
-    auto channels = getInputShapeAtPort(DATA_ID).getRank() > 1 ? getInputShapeAtPort(DATA_ID).getStaticDims()[1] : 1;
+    const auto &dataMinDims = getInputShapeAtPort(DATA_ID).getMinDims();
+    bool isBlkApplied = getInputShapeAtPort(DATA_ID).getRank() > 1 && dataMinDims[1] != Shape::UNDEFINED_DIM && dataMinDims[1] > 1;
 
-    if (!mayiuse(cpu::x64::sse41) || mode == InterpolateMode::linear) {
+    if (!mayiuse(cpu::x64::sse41) || interpAttrs.mode == InterpolateMode::linear) {
         pushDesc(LayoutType::ncsp, ref);
     } else {
         // blk and by_channel JIT kernel on sse41 or above machine
-        if (getInputShapeAtPort(DATA_ID).getRank() == 4 || (getInputShapeAtPort(DATA_ID).getRank() == 5 && mode != InterpolateMode::cubic)) {
+        if (getInputShapeAtPort(DATA_ID).getRank() == 4 || (getInputShapeAtPort(DATA_ID).getRank() == 5 && interpAttrs.mode != InterpolateMode::cubic)) {
             if (mayiuse(cpu::x64::avx512_common)) {
                 pushDesc(LayoutType::nspc, jit_avx512);
-                if (channels != 1)
+                if (isBlkApplied)
                     pushDesc(LayoutType::nCsp16c, jit_avx512);
             } else if (mayiuse(cpu::x64::avx2)) {
                 pushDesc(LayoutType::nspc, jit_avx2);
-                if (channels != 1)
+                if (isBlkApplied)
                     pushDesc(LayoutType::nCsp8c, jit_avx2);
             } else {
                 pushDesc(LayoutType::nspc, jit_sse42);
-                if (channels != 1)
+                if (isBlkApplied)
                     pushDesc(LayoutType::nCsp8c, jit_sse42);
             }
         }
 
         // planar for 1.ref on machine without sse41(if no sse41, canFuse() is false). 2.JIT kernel for f32 && avx2(gather).(with fuse)
-        if (mayiuse(cpu::x64::avx2) && inputPrec == Precision::FP32) {
+        if (mayiuse(cpu::x64::avx2) && inputPrecision == Precision::FP32) {
             pushDesc(LayoutType::ncsp, jit_avx2);
         }
     }
 }
 
-void MKLDNNInterpolateNode::createPrimitive() {
+bool MKLDNNInterpolateNode::needShapeInfer() const {
+    if (MKLDNNNode::inputShapesModified()) {
+        return true;
+    }
+    if (shapeCalcMode == InterpolateShapeCalcMode::scales) {
+        if (lastScales.empty()) {
+            return true;
+        }
+        const float *scales = reinterpret_cast<const float *>(getParentEdgesAtPort(SCALES_ID)[0]->getMemory().GetPtr());
+        for (size_t i = 0; i < lastScales.size(); i++) {
+            if (lastScales[i] != scales[i]) {
+                return true;
+            }
+        }
+    } else {
+        if (lastSizes.empty()) {
+            return true;
+        }
+        const int32_t *sizes = reinterpret_cast<const int32_t *>(getParentEdgesAtPort(TARGET_SHAPE_ID)[0]->getMemory().GetPtr());
+        for (size_t i = 0; i < lastSizes.size(); i++) {
+            if (sizes[i] != lastSizes[i]) {
+                return true;
+            }
+        }
+    }
+    return false;
+}
+
+std::vector<VectorDims> MKLDNNInterpolateNode::shapeInfer() const {
+    std::vector<ov::StaticShape> input_shapes = {
+            getParentEdgesAtPort(DATA_ID)[0]->getMemory().GetShape().getStaticDims(),
+            getParentEdgesAtPort(TARGET_SHAPE_ID)[0]->getMemory().GetShape().getStaticDims(),
+            getParentEdgesAtPort(SCALES_ID)[0]->getMemory().GetShape().getStaticDims()
+    };
+
+    const size_t port = shapeCalcMode == InterpolateShapeCalcMode::sizes ? TARGET_SHAPE_ID : SCALES_ID;
+    const auto &memory = getParentEdgesAtPort(port)[0]->getMemory();
+    std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>> input_values = {
+            {port, std::make_shared<ngraph::runtime::HostTensor>(InferenceEngine::details::convertPrecision(memory.getDesc().getPrecision()),
+                                                                 memory.getStaticDims(), memory.GetPtr())}
+    };
+
+    if (getParentEdges().size() > AXES_ID) {
+        const auto &memory = getParentEdgesAtPort(AXES_ID)[0]->getMemory();
+        input_shapes.push_back(memory.getStaticDims());
+        input_values.insert({3, std::make_shared<ngraph::runtime::HostTensor>(InferenceEngine::details::convertPrecision(memory.getDesc().getPrecision()),
+                                                                              memory.getStaticDims(), memory.GetPtr())});
+    }
+
+    std::vector<ov::StaticShape> output_shapes;
+    shape_inference(opToShapeInfer.get(), input_shapes, output_shapes, input_values);
+
+    std::vector<VectorDims> result(output_shapes.size());
+    std::transform(output_shapes.begin(), output_shapes.end(), result.begin(), [](const ov::StaticShape& s){ return s.to_shape(); });
+
+    return result;
+}
+
+void MKLDNNInterpolateNode::executeDynamicImpl(mkldnn::stream strm) {
+    execute(strm);
+
+    const size_t port = shapeCalcMode == InterpolateShapeCalcMode::sizes ? TARGET_SHAPE_ID : SCALES_ID;
+    const auto &memory = getParentEdgesAtPort(port)[0]->getMemory();
+    if (shapeCalcMode == InterpolateShapeCalcMode::scales) {
+        const float *scales = reinterpret_cast<const float *>(memory.GetPtr());
+        lastScales.assign(scales, scales + memory.getDesc().getShape().getElementsCount());
+    } else {
+        const int32_t *sizes = reinterpret_cast<const int32_t *>(memory.GetPtr());
+        lastSizes.assign(sizes, sizes + memory.getDesc().getShape().getElementsCount());
+    }
+}
+
+bool MKLDNNInterpolateNode::needPrepareParams() const {
+    return (inputShapesModified() || lastOutputDims != getChildEdgesAtPort(0)[0]->getMemory().getStaticDims());
+}
+
+void MKLDNNInterpolateNode::prepareParams() {
+    if (!shapesDefined()) {
+        IE_THROW() << "Can't prepare params for Interpolate node with name: " << getName() << ", because input/output dims aren't defined";
+    }
+
     auto& dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
     auto& srcMemPtr = getParentEdgeAt(DATA_ID)->getMemoryPtr();
     auto& tsMemPtr = getParentEdgeAt(TARGET_SHAPE_ID)->getMemoryPtr();
@@ -1982,81 +2030,55 @@ void MKLDNNInterpolateNode::createPrimitive() {
     if (getSelectedPrimitiveDescriptor() == nullptr)
         IE_THROW() << errorPrefix << " did not set preferable primitive descriptor";
 
-    auto jcp = jit_interpolate_config_params();
-    jcp.mode = mode;
-    jcp.src_dt = getParentEdgeAt(0)->getMemory().GetDataType();
-    jcp.dst_dt = getChildEdgeAt(0)->getMemory().GetDataType();
-    jcp.src_data_size = MKLDNNExtensionUtils::sizeOfDataType(jcp.src_dt);
-    jcp.dst_data_size = MKLDNNExtensionUtils::sizeOfDataType(jcp.dst_dt);
-    jcp.indices_size = sizeof(int);
-    size_t dimSize = dstDim.size();
-    auto srcDimPad5d = to5Dim(srcDimPad);
-    auto dstDim5d = to5Dim(dstDim);
-    jcp.OW = dstDim5d[4];
-    jcp.OH = dstDim5d[3];
-    jcp.OD = dstDim5d[2];
-    jcp.IW = srcDimPad5d[4];
-    jcp.IH = srcDimPad5d[3];
-    jcp.ID = srcDimPad5d[2];
-    jcp.spatial_dim_size = spatialDimSize;
+    const auto &srcDims = srcMemPtr->getStaticDims();
+    const auto &dstDims = dstMemPtr->getStaticDims();
+    setPostOps(attr, dstDims, true);
 
-    if (getChildEdgeAt(0)->getMemory().getDesc().hasLayoutType(LayoutType::ncsp)) {
-        jcp.layout = InterpolateLayoutType::planar;
-    } else if (getChildEdgeAt(0)->getMemory().getDesc().hasLayoutType(LayoutType::nCsp8c) ||
-               getChildEdgeAt(0)->getMemory().getDesc().hasLayoutType(LayoutType::nCsp16c)) {
-        jcp.layout = InterpolateLayoutType::block;
-    } else {
-        jcp.layout = InterpolateLayoutType::by_channel;
-    }
-
-    configured_for_layout = jcp.layout;
-
-    if (mode == InterpolateMode::nearest || mode == InterpolateMode::linear_onnx || mode == InterpolateMode::cubic) {
-        if (jcp.layout != InterpolateLayoutType::planar) {
-            if (mayiuse(cpu::x64::avx512_common)) {
-                interpolateKernel.reset(new jit_uni_interpolate_kernel_f32<cpu::x64::avx512_common>(jcp, *attr.get()));
-            } else if (mayiuse(cpu::x64::avx2)) {
-                interpolateKernel.reset(new jit_uni_interpolate_kernel_f32<cpu::x64::avx2>(jcp, *attr.get()));
-            } else if (mayiuse(cpu::x64::sse41)) {
-                interpolateKernel.reset(new jit_uni_interpolate_kernel_f32<cpu::x64::sse41>(jcp, *attr.get()));
-            }
-        } else {
-            // gather ISA(for planar JIT kernel) for avx2 and fp32
-            if (mayiuse(cpu::x64::avx2) && inputPrec == Precision::FP32) {
-                interpolateKernel.reset(new jit_uni_interpolate_kernel_f32<cpu::x64::avx2>(jcp, *attr.get()));
-            }
-        }
-        if (interpolateKernel)
-            interpolateKernel->create_ker();
-    }
-
-    // build indices table
-    std::vector<float> dataScales = getScales();
-    if (dimSize > 2 && (dataScales[0] != 1.f || dataScales[1] != 1.f)) {
+    std::vector<float> dataScales = getScales(getPaddedInputShape(srcDims, interpAttrs.padBegin, interpAttrs.padEnd), dstDims);
+    if (getOutputShapeAtPort(0).getRank() > 2 && (dataScales[0] != 1.f || dataScales[1] != 1.f)) {
         IE_THROW() << "Interpolate layer only supports resize on spatial dimensions(depth, height and width)";
     }
+    if ((interpAttrs.mode == InterpolateMode::nearest || interpAttrs.mode == InterpolateMode::linear_onnx || interpAttrs.mode == InterpolateMode::cubic) &&
+        ((interpAttrs.layout != InterpolateLayoutType::planar && mayiuse(cpu::x64::sse41)) ||
+            (mayiuse(cpu::x64::avx2) && interpAttrs.inPrc == Precision::FP32))) {
+        execPtr = std::make_shared<InterpolateJitExecutor>(interpAttrs,
+                                                           srcDims,
+                                                           dstDims,
+                                                           dataScales,
+                                                           attr);
+    } else {
+        execPtr = std::make_shared<InterpolateRefExecutor>(interpAttrs,
+                                                           srcDims,
+                                                           dstDims,
+                                                           dataScales);
+    }
+    lastOutputDims = dstDims;
+}
 
-    switch (mode) {
-        case InterpolateMode::nearest: {
-            buildTblNN(srcDimPad5d, dstDim5d, dataScales, jcp.layout);
-            break;
-        }
-        case InterpolateMode::linear_onnx: {
-            buildTblLinearOnnx(srcDimPad5d, dstDim5d, dataScales, jcp.layout);
-            break;
-        }
-        case InterpolateMode::linear: {
-            buildTblLinear(srcDimPad5d, dstDim5d, dataScales, LINEAR_KERNEL, antialias);
-            break;
-        }
-        case InterpolateMode::cubic: {
-            buildTblCubic(srcDimPad5d, dstDim5d, dataScales, cubeCoeff, jcp.layout);
-            break;
-        }
-        default: {
-            IE_THROW() << errorPrefix << " does not support interpolate mode:" << mode;
-            break;
-        }
+void MKLDNNInterpolateNode::createPrimitive() {
+    auto& srcMemPtr = getParentEdgeAt(DATA_ID)->getMemoryPtr();
+    auto& dstMemPtr = getChildEdgesAtPort(0)[0]->getMemoryPtr();
+    if (!srcMemPtr || !srcMemPtr->GetPrimitivePtr())
+        IE_THROW() << errorPrefix << " did not allocate input memory";
+    if (!dstMemPtr || !dstMemPtr->GetPrimitivePtr())
+        IE_THROW() << errorPrefix << " did not allocate destination memory";
+
+    if (dstMemPtr->getDesc().hasLayoutType(LayoutType::ncsp)) {
+        interpAttrs.layout = InterpolateLayoutType::planar;
+    } else if (dstMemPtr->getDesc().hasLayoutType(LayoutType::nCsp8c) ||
+               dstMemPtr->getDesc().hasLayoutType(LayoutType::nCsp16c)) {
+        interpAttrs.layout = InterpolateLayoutType::block;
+    } else {
+        interpAttrs.layout = InterpolateLayoutType::by_channel;
+    }
+
+    interpAttrs.inPrc = srcMemPtr->getDesc().getPrecision();
+    interpAttrs.outPrc = dstMemPtr->getDesc().getPrecision();
+
+    if (shapesDefined()) {
+        if (needPrepareParams())
+            prepareParams();
+        updateLastInputDims();
     }
 }
 
@@ -2064,11 +2086,412 @@ inline int clipCoord(int pos, int length) {
     return std::max(static_cast<int>(0), std::min(pos, length - 1));
 }
 
+static inline float triangleCoeff(float x) {
+    return (std::max)(0.0f, 1 - std::abs(x));
+}
+
+void MKLDNNInterpolateNode::setPostOps(mkldnn::primitive_attr &attr, const VectorDims &dims, bool initWeights) {
+    mkldnn::post_ops ops;
+
+    for (auto &node : fusedWith) {
+        auto* fakeQuantizeNode = dynamic_cast<MKLDNNFakeQuantizeNode *>(node.get());
+        if (fakeQuantizeNode) {
+            fakeQuantizeNode->appendPostOps(ops);
+            continue;
+        }
+
+        auto* eltwiseNode = dynamic_cast<MKLDNNEltwiseNode *>(node.get());
+        if (eltwiseNode) {
+            constexpr int align = 16;
+            eltwiseNode->appendPostOps(ops, dims, align);
+            continue;
+        }
+
+        IE_THROW() << "Fusing of " << NameFromType(node->getType()) << " operation to " << NameFromType(this->getType()) << " node is not implemented";
+    }
+
+    attr.set_post_ops(ops);
+}
+
+SizeVector MKLDNNInterpolateNode::getPaddedInputShape(const VectorDims &srcDims,
+                                                      const std::vector<int> &padBegin,
+                                                      const std::vector<int> &padEnd) {
+    SizeVector paddedShape;
+    int dataRank = srcDims.size();
+    for (int i = 0; i < dataRank; i++) {
+        paddedShape.push_back(srcDims[i] + padBegin[i] + padEnd[i]);
+    }
+    return paddedShape;
+}
+
+// get scales of data rank size
+// if "scale" version: set scales with input scales, 1.f for other dims not in axis
+// if "size" version: scales = shape[target] / shape[input].pad, 1.f for other dims not in axis
+// scales is a required input, but should not use input scales when "size" case, which may added eps that lead to inaccurate result, recalculate scales instead.
+std::vector<float> MKLDNNInterpolateNode::getScales(const VectorDims &srcDimPad, const VectorDims &dstDim) {
+    const size_t dataRank = getInputShapeAtPort(DATA_ID).getRank();
+    const float *scales = reinterpret_cast<const float *>(getParentEdgesAtPort(SCALES_ID)[0]->getMemory().GetPtr());
+    std::vector<float> fullScales(dataRank, 1.f);
+    const size_t axesRank = axes.size();
+    for (size_t i = 0; i < axesRank; i++) {
+        int axis = axes[i];
+        fullScales[axis] = (shapeCalcMode == InterpolateShapeCalcMode::scales) ? scales[i] :
+                                                                                 static_cast<float>(dstDim[axis]) / static_cast<float>(srcDimPad[axis]);
+    }
+    return fullScales;
+}
+
+void MKLDNNInterpolateNode::execute(mkldnn::stream strm) {
+    if (!execPtr) {
+        IE_THROW() << "Can't execute Interpolate node. Primitive didn't created";
+    }
+
+    auto &dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
+    auto &srcMemPtr = getParentEdgeAt(DATA_ID)->getMemoryPtr();
+
+    uint8_t *dst_data = reinterpret_cast<uint8_t*>(dstMemPtr->GetPtr());
+    const uint8_t *src_data_origin = reinterpret_cast<uint8_t*>(srcMemPtr->GetData());
+
+    const auto &srcDim = srcMemPtr->getStaticDims();
+    const auto &dstDim = dstMemPtr->getStaticDims();
+    size_t dimSize = srcDim.size();
+    auto srcDimPad = execPtr->getSrcDimPad5d();
+
+    const auto srcDim5d = to5Dim(srcDim);
+    const auto srcDimPad5d = to5Dim(srcDimPad);
+    const auto dstDim5d = to5Dim(dstDim);
+    const auto srcDataSize = srcMemPtr->getDesc().getPrecision().size();
+
+    const uint8_t *src_data = nullptr;
+    std::vector<uint8_t> srcPadded;
+    if (hasPad) {
+        int padB0 = (dimSize > 2) ? interpAttrs.padBegin[0] : 0;
+        int padB1 = (dimSize > 2) ? interpAttrs.padBegin[1] : 0;
+        int padB2 = (dimSize == 5) ? interpAttrs.padBegin[dimSize - 3] : 0;
+        int padB3 = interpAttrs.padBegin[dimSize - 2];
+        int padB4 = interpAttrs.padBegin[dimSize - 1];
+
+        SizeVector inShapeBlock = getBlockND(srcDim5d);
+        SizeVector inShapePadBlock = getBlockND(srcDimPad5d);
+
+        if (interpAttrs.layout == InterpolateLayoutType::planar) {
+            srcPadded.resize(inShapePadBlock[0] * srcDataSize, 0);
+            uint8_t *src_data_pad = static_cast<uint8_t *>(&srcPadded[0]);
+            parallel_for4d(srcDim5d[0], srcDim5d[1], srcDim5d[2], srcDim5d[3], [&](int n, int c, int d, int h) {
+                const uint8_t *src = src_data_origin + (inShapeBlock[1] * n + inShapeBlock[2] * c + inShapeBlock[3] * d + inShapeBlock[4] * h) * srcDataSize;
+                uint8_t *srcPad = src_data_pad + (inShapePadBlock[1] * (n + padB0) + inShapePadBlock[2] * (c + padB1) +
+                               inShapePadBlock[3] * (d + padB2) + inShapePadBlock[4] * (h + padB3) + padB4) * srcDataSize;
+                cpu_memcpy(srcPad, src, srcDim5d[4] * srcDataSize);
+            });
+            src_data = src_data_pad;
+        } else if (interpAttrs.layout == InterpolateLayoutType::by_channel) {
+            srcPadded.resize(inShapePadBlock[0] * srcDataSize, 0);
+            uint8_t *src_data_pad = static_cast<uint8_t *>(&srcPadded[0]);
+            parallel_for4d(srcDim5d[0], srcDim5d[2], srcDim5d[3], srcDim5d[4], [&](int n, int d, int h, int w) {
+                const uint8_t *src = src_data_origin + (inShapeBlock[1] * n +
+                                (inShapeBlock[3] * d + inShapeBlock[4] * h + inShapeBlock[5] * w) * srcDim5d[1]) * srcDataSize;
+                uint8_t *srcPad = src_data_pad + (inShapePadBlock[1] * (n + padB0) + (inShapePadBlock[3] * (d + padB2) +
+                                inShapePadBlock[4] * (h + padB3) + inShapePadBlock[5] * (w + padB4)) * srcDimPad5d[1] + padB1) * srcDataSize;
+                cpu_memcpy(srcPad, src, srcDim5d[1] * srcDataSize);
+            });
+            src_data = src_data_pad;
+        } else if (interpAttrs.layout == InterpolateLayoutType::block) {
+            size_t blkSize = mayiuse(cpu::x64::avx512_common) ? 16 : 8;
+            size_t CB = div_up(srcDimPad5d[1], blkSize);
+            size_t eltsTotal = srcDimPad5d[0] * CB * srcDimPad5d[2] * srcDimPad5d[3] * srcDimPad5d[4] * blkSize;
+            srcPadded.resize(eltsTotal * srcDataSize, 0x0);
+            uint8_t *src_data_pad = static_cast<uint8_t *>(&srcPadded[0]);
+            if ((srcDim5d[0] != srcDimPad5d[0]) || (srcDim5d[1] != srcDimPad5d[1])) {
+                IE_THROW() << "Interpolate layer with name '" << getName() <<
+                "' does not support padding on batch and channel dimensions";
+            }
+            parallel_for5d(srcDim5d[0], CB, srcDim5d[2], srcDim5d[3], srcDim5d[4], [&](int n, int cb, int d, int h, int w) {
+                const uint8_t *src = src_data_origin + (n * CB * srcDim5d[2] * srcDim5d[3] * srcDim5d[4] * blkSize) * srcDataSize
+                                               + (cb * srcDim5d[2] * srcDim5d[3] * srcDim5d[4] * blkSize) * srcDataSize
+                                               + (d * srcDim5d[3] * srcDim5d[4] * blkSize) * srcDataSize
+                                               + (h * srcDim5d[4] * blkSize) * srcDataSize
+                                               + (w * blkSize) * srcDataSize;
+                uint8_t *srcPad = src_data_pad + (n * CB * srcDimPad5d[2] * srcDimPad5d[3] * srcDimPad5d[4] * blkSize) * srcDataSize
+                                               + (cb * srcDimPad5d[2] * srcDimPad5d[3] * srcDimPad5d[4] * blkSize) * srcDataSize
+                                               + ((d + padB2) * srcDimPad5d[3] * srcDimPad5d[4] * blkSize) * srcDataSize
+                                               + ((h + padB3) * srcDimPad5d[4] * blkSize) * srcDataSize
+                                               + ((w + padB4) * blkSize) * srcDataSize;
+                cpu_memcpy(srcPad, src, blkSize * srcDataSize);
+            });
+            src_data = src_data_pad;
+        }
+    } else {
+        src_data = src_data_origin;
+    }
+
+    execPtr->exec(src_data, dst_data);
+}
+
+// for ndhwc and nCdhw8c[16c]
+// input may be f32/bf16/int8, fused->output varies
+void MKLDNNInterpolateNode::InterpolateJitExecutor::NNCGathered(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C,
+                                                                int ID, int IH, int IW, int OD, int OH, int OW) {
+    int *index_d = static_cast<int*>(&indexTable[0]);
+    int *index_h = static_cast<int*>(&indexTable[OD]);
+    int *index_w = static_cast<int*>(&indexTable[OD + OH]);
+
+    bool is_nhwc = (configured_for_layout == by_channel);
+
+    for (int b = 0; b < B; b++) {
+        if (is_nhwc) {
+            const uint8_t *in_ptr = in_ptr_ + (IW * IH * ID * C * b) * srcDataSize;
+            uint8_t *out_ptr = out_ptr_ + (OW * OH * OD * C * b) * dstDataSize;
+            std::vector<int> index_w_kernel(OW);
+            for (int ox = 0; ox < OW; ox++) {
+                index_w_kernel[ox] = index_w[ox] * C * srcDataSize;
+            }
+            parallel_for2d(OD, OH, [&](size_t d, size_t h) {
+                // kernel for C * OW
+                uint8_t *out_ptr_dh = out_ptr + (C * OW * OH * d + C * OW * h) * dstDataSize;
+                const uint8_t *in_ptr_dh = in_ptr + (C * IW * IH * index_d[d] + C * IW * index_h[h]) * srcDataSize;
+                auto arg = jit_interpolate_call_args();
+                arg.dst = out_ptr_dh;
+                arg.src_ptr[0] = in_ptr_dh;
+                arg.index = static_cast<int*>(&(index_w_kernel[0]));
+                arg.work_amount = C;
+                arg.oc_off = 0;
+                (*interpolateKernel)(&arg);
+            });
+        } else {  // for blk
+            int blk_size = mayiuse(cpu::x64::avx512_common) ? 16 : 8;
+            int CB = div_up(C, blk_size);
+            const uint8_t *in_ptr = in_ptr_ + (IW * IH * ID * CB * blk_size * b) * srcDataSize;
+            uint8_t *out_ptr = out_ptr_ + (OW * OH * OD * CB * blk_size * b) * dstDataSize;
+            std::vector<int> index_w_kernel(OW);
+            for (int ox = 0; ox < OW; ox++) {
+                index_w_kernel[ox] = index_w[ox] * blk_size * srcDataSize;
+            }
+            parallel_for2d(CB, OD, [&](size_t cb, size_t d) {
+                uint8_t *out_ptr_cbd = out_ptr + (blk_size * OW * OH * OD * cb + blk_size * OW * OH * d) * dstDataSize;
+                const uint8_t *in_ptr_cbd = in_ptr + (blk_size * IW * IH * ID * cb + blk_size * IW * IH * index_d[d]) * srcDataSize;
+                auto arg = jit_interpolate_call_args();
+                for (int h = 0; h < OH; h++) {  // kernel for blk_size * OW
+                    arg.dst = out_ptr_cbd + blk_size * OW * h * dstDataSize;
+                    arg.src_ptr[0] = in_ptr_cbd + blk_size * IW * index_h[h] * srcDataSize;
+                    arg.index = static_cast<int*>(&(index_w_kernel[0]));
+                    arg.work_amount = static_cast<size_t>(OW);
+                    arg.oc_off = cb * blk_size * sizeof(float);
+                    (*interpolateKernel)(&arg);
+                }
+            });
+        }
+    }  // batch end
+}
+
+void MKLDNNInterpolateNode::InterpolateJitExecutor::NNPlanar(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW,
+                                                             int OD, int OH, int OW) {
+    int *index_d = static_cast<int*>(&indexTable[0]);
+    int *index_h = static_cast<int*>(&indexTable[OD]);
+    int *index_w = static_cast<int*>(&indexTable[OD + OH]);
+
+    std::vector<int> index_kernel(OH + OW);
+    // index_h * IW * srcDataSize to reduce and simplify redundant compute
+    for (int oh = 0; oh < OH; oh++) {
+        index_kernel[oh] = index_h[oh] * IW * srcDataSize;
+    }
+    // index_w * srcDataSize
+    for (int ow = 0; ow < OW; ow++) {
+        index_kernel[OH + ow] = index_w[ow] * srcDataSize;
+    }
+
+    parallel_for3d(B, C, OD, [&](size_t b, size_t c, size_t od) {
+        const uint8_t *in_ptr = in_ptr_ + (IW * IH * ID * C * b + IW * IH * ID * c + IW * IH * index_d[od]) * srcDataSize;
+        uint8_t *out_ptr = out_ptr_ + (OW * OH * OD * C * b + OW * OH * OD * c + OW * OH * od) * dstDataSize;
+
+        auto arg = jit_interpolate_call_args();
+        arg.src_ptr[0] = in_ptr;
+        arg.dst = out_ptr;
+        arg.index = static_cast<int*>(&index_kernel[0]);  // need index_h and index_w in kernel, it's in continous memory so one param
+        arg.oc_off = static_cast<size_t>(c * sizeof(float));
+        // work_amount is OH(out loop) and OW(inner loop), can get in kernel from jcp.
+        (*interpolateKernel)(&arg);
+    });
+}
+
+void MKLDNNInterpolateNode::InterpolateJitExecutor::linearOnnxPlanar(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C,
+                                                                     int ID, int IH, int IW, int OD, int OH, int OW) {
+    // FrontTopLeft:0, FrontTopRight:1, FrontBottomLeft:2, FrontBottomRight:3, EndTopLeft:4,   EndTopRight:5,   EndBottomLeft:6,   EndBottomRight:7
+    // weight: Left:0, ritht:1, top:2, bottom:3, front:4, end:5
+    int *index = static_cast<int*>(&indexTable[0]);
+    int eltInGrid = (spatialDimSize > 2) ? MAX_INPUT_INTERPOLATE : ((spatialDimSize > 1) ? 4 : 2);
+    int scratchLen = rnd_up(eltInGrid * OW * OH * OD, 16);
+    float *weight = reinterpret_cast<float*>(&indexTable[scratchLen]);
+
+    parallel_for2d(B, C, [&](size_t b, size_t c) {
+        uint8_t *out_ptr_nc = out_ptr_ + (OH * OW * OD * C * b + OH * OW * OD * c) * dstDataSize;
+        const uint8_t *in_ptr_nc = in_ptr_ + (IH * IW * ID * C * b + IH * IW * ID * c) * srcDataSize;
+        auto arg = jit_interpolate_call_args();
+        arg.src_ptr[0] = in_ptr_nc;
+        arg.index = static_cast<int*>(&index[0]);
+        arg.weight_ptr[0] = static_cast<float*>(&weight[0]);
+        arg.dst = out_ptr_nc;
+        arg.work_amount = OW * OH * OD;
+        arg.oc_off = static_cast<size_t>(c * sizeof(float));
+        (*interpolateKernel)(&arg);
+    });
+}
+
+void MKLDNNInterpolateNode::InterpolateJitExecutor::linearOnnxCGathered(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW,
+                                                                        int OD, int OH, int OW) {
+    // left:OW right:OW Top:OH Bottom:OH Front:OD End:OD
+    std::vector<int*> indexPtr(MAX_INPUT_INTERPOLATE, 0);
+    std::vector<float*> weightPtr(MAX_INPUT_INTERPOLATE, 0);
+    size_t scratchLen = rnd_up(OW + OW + OH + OH + OD + OD, 16);
+    indexPtr[0] = static_cast<int*>(&indexTable[0]);
+    indexPtr[1] = static_cast<int*>(&indexTable[OW]);
+    indexPtr[2] = static_cast<int*>(&indexTable[2 * OW]);
+    indexPtr[3] = static_cast<int*>(&indexTable[2 * OW + OH]);
+    indexPtr[4] = static_cast<int*>(&indexTable[2 * OW + 2 * OH]);
+    indexPtr[5] = static_cast<int*>(&indexTable[2 * OW + 2 * OH + OD]);
+
+    weightPtr[0] = reinterpret_cast<float*>(&indexTable[scratchLen]);
+    weightPtr[1] = reinterpret_cast<float*>(&indexTable[scratchLen + OW]);
+    weightPtr[2] = reinterpret_cast<float*>(&indexTable[scratchLen + 2 * OW]);
+    weightPtr[3] = reinterpret_cast<float*>(&indexTable[scratchLen + 2 * OW + OH]);
+    weightPtr[4] = reinterpret_cast<float*>(&indexTable[scratchLen + 2 * OW + 2 * OH]);
+    weightPtr[5] = reinterpret_cast<float*>(&indexTable[scratchLen + 2 * OW + 2 * OH + OD]);
+
+    bool isByChannel = (configured_for_layout == by_channel) ? true : false;
+
+    int blkSize = mayiuse(cpu::x64::avx512_common) ? 16 : 8;
+    int CB = isByChannel ? 1 : div_up(C, blkSize);
+    int CGatherLen = isByChannel ? C : blkSize;
+    int workAmount = isByChannel ? C : CB;
+    // n_CB(1)_d_h_w_8[16](c), () for by-channel
+    int C0 = OW * CGatherLen;
+    int C1 = OH * C0;
+    int C2 = OD * C1;
+    int C3 = CB * C2;
+    int I0 = IW * CGatherLen;
+    int I1 = IH * I0;
+    int I2 = ID * I1;
+    int I3 = CB * I2;
+    parallel_for3d(B, OD, OH, [&](size_t b, size_t d, size_t h) {
+        uint8_t *out_ptr_ndh = out_ptr_ + (C3 * b + C1 * d + C0 * h) * dstDataSize;
+
+        const uint8_t *in_ptr_n = in_ptr_ + (I3 * b) * srcDataSize;
+        const uint8_t *in_ptr_nf = in_ptr_n + (indexPtr[4][d] * I1) * srcDataSize;
+        const uint8_t *in_ptr_nft = in_ptr_nf + (indexPtr[2][h] * I0) * srcDataSize;
+        const uint8_t *in_ptr_nfb = in_ptr_nf + (indexPtr[3][h] * I0) * srcDataSize;
+        const uint8_t *in_ptr_ne = in_ptr_n + (indexPtr[5][d] * I1) * srcDataSize;
+        const uint8_t *in_ptr_net = in_ptr_ne + (indexPtr[2][h] * I0) * srcDataSize;
+        const uint8_t *in_ptr_neb = in_ptr_ne + (indexPtr[3][h] * I0) * srcDataSize;
+        auto arg = jit_interpolate_call_args();
+        for (int w = 0; w < OW; ++w) {
+            uint8_t *out_ptr_ndhw = out_ptr_ndh + CGatherLen * w * dstDataSize;
+
+            arg.src_ptr[0] = in_ptr_nft + (indexPtr[0][w] * CGatherLen) * srcDataSize;
+            arg.src_ptr[1] = in_ptr_nft + (indexPtr[1][w] * CGatherLen) * srcDataSize;
+            arg.src_ptr[2] = in_ptr_nfb + (indexPtr[0][w] * CGatherLen) * srcDataSize;
+            arg.src_ptr[3] = in_ptr_nfb + (indexPtr[1][w] * CGatherLen) * srcDataSize;
+            arg.src_ptr[4] = in_ptr_net + (indexPtr[0][w] * CGatherLen) * srcDataSize;
+            arg.src_ptr[5] = in_ptr_net + (indexPtr[1][w] * CGatherLen) * srcDataSize;
+            arg.src_ptr[6] = in_ptr_neb + (indexPtr[0][w] * CGatherLen) * srcDataSize;
+            arg.src_ptr[7] = in_ptr_neb + (indexPtr[1][w] * CGatherLen) * srcDataSize;
+            arg.weight_ptr[0] = static_cast<float*>(&weightPtr[0][w]);
+            arg.weight_ptr[1] = static_cast<float*>(&weightPtr[1][w]);
+            arg.weight_ptr[2] = static_cast<float*>(&weightPtr[2][h]);
+            arg.weight_ptr[3] = static_cast<float*>(&weightPtr[3][h]);
+            arg.weight_ptr[4] = static_cast<float*>(&weightPtr[4][d]);
+            arg.weight_ptr[5] = static_cast<float*>(&weightPtr[5][d]);
+            arg.dst = out_ptr_ndhw;
+            arg.work_amount = workAmount;
+            arg.oc_off = 0;
+            (*interpolateKernel)(&arg);
+        }
+    });
+}
+
+void MKLDNNInterpolateNode::InterpolateJitExecutor::cubicCGathered(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int IH, int IW, int OH, int OW) {
+    const int idxNum = 1;
+    int *xOrigin = static_cast<int*>(&indexTable[0]);
+    float *xFactor = reinterpret_cast<float*>(&indexTable[OW]);
+    int *yOrigin = static_cast<int*>(&indexTable[(CUBIC_GRID_LEN + idxNum) * OW]);
+    float *yFactor = reinterpret_cast<float*>(&indexTable[(CUBIC_GRID_LEN + idxNum) * OW + OH]);
+
+    int blkSize = mayiuse(cpu::x64::avx512_common) ? 16 : 8;
+    int CB = div_up(C, blkSize);
+    int CSize = configured_for_layout == InterpolateLayoutType::by_channel ? C : blkSize * CB;
+    int CGatherLen = configured_for_layout == InterpolateLayoutType::by_channel ? C : blkSize;
+    int workAmount = configured_for_layout == InterpolateLayoutType::by_channel ? C : CB;
+
+    parallel_for3d(B, OH, OW, [&](size_t b, size_t h, size_t w) {
+        uint8_t *out_ptr_nhw = out_ptr_ + (OH * OW * CSize * b + OW * CGatherLen * h + CGatherLen * w) * dstDataSize;
+        const uint8_t *in_ptr_n = in_ptr_ + (IH * IW * CSize * b) * srcDataSize;
+
+        std::vector<int> kernelIndex(CUBIC_GRID_LEN * CUBIC_GRID_LEN);  // 16 address offset to src(batch) or src(CB)
+        int iy = yOrigin[h];
+        int ix = xOrigin[w];
+        for (int y = iy - 1, i = 0; y <= iy + 2; y++, i++) {
+            int yInRange = std::max(0, std::min(y, IH - 1));
+            yInRange = yInRange * CGatherLen * IW * srcDataSize;
+            for (int x = ix - 1, j = 0; x <= ix + 2; x++, j++) {
+                int xInRange = std::max(0, std::min(x, IW - 1));
+                xInRange = yInRange + xInRange * CGatherLen * srcDataSize;
+                kernelIndex[i * CUBIC_GRID_LEN + j] = xInRange;
+            }
+        }
+        auto arg = jit_interpolate_call_args();
+            arg.dst = out_ptr_nhw;
+            arg.src_ptr[0] = in_ptr_n;
+            arg.index = static_cast<int*>(&kernelIndex[0]);
+            // 0 for weight_W, 1 for weight_H
+            arg.weight_ptr[0] = static_cast<float*>(&xFactor[w * CUBIC_GRID_LEN]);
+            arg.weight_ptr[1] = static_cast<float*>(&yFactor[h * CUBIC_GRID_LEN]);
+
+            // for by channel, src + step, dst + step, process next step on continuous memory
+            // for blk, src + IW*IH*blkSize, dst + OW*OH*blkSize, process the blkSize on next CB
+            arg.work_amount = workAmount;
+            arg.oc_off = 0;
+            (*interpolateKernel)(&arg);
+    });
+}
+
+void MKLDNNInterpolateNode::InterpolateJitExecutor::cubicPlanar(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int IH, int IW, int OH, int OW) {
+    int tblAdvance = 0;
+    int *xOrigin = static_cast<int*>(&indexTable[tblAdvance]);
+    tblAdvance += OW;
+    float *xFactor = reinterpret_cast<float*>(&indexTable[tblAdvance]);
+    tblAdvance += CUBIC_GRID_LEN * OW;
+    int *yOrigin = static_cast<int*>(&indexTable[tblAdvance]);
+    tblAdvance += OH;
+    float *yFactor = reinterpret_cast<float*>(&indexTable[tblAdvance]);
+
+    tblAdvance += CUBIC_GRID_LEN * OH;
+    int *sequenceOH = static_cast<int*>(&indexTable[tblAdvance]);
+    tblAdvance += OW * OH;
+    int *sequenceOW = static_cast<int*>(&indexTable[tblAdvance]);
+
+    parallel_for2d(B, C, [&](size_t n, size_t c) {
+        const uint8_t *in_ptr_nc = in_ptr_ + (IW * IH * C * n + IW * IH * c) * srcDataSize;
+        uint8_t *out_ptr_nc = out_ptr_ + (OW * OH * C * n + OW * OH * c) * dstDataSize;
+
+        auto arg = jit_interpolate_call_args();
+        arg.dst = out_ptr_nc;
+        arg.src_ptr[0] = in_ptr_nc;
+        arg.index = xOrigin;
+        arg.src_ptr[1] = yOrigin;
+        arg.src_ptr[2] = static_cast<int*>(&sequenceOH[0]);
+        arg.src_ptr[3] = static_cast<int*>(&sequenceOW[0]);
+        arg.weight_ptr[0] = xFactor;
+        arg.weight_ptr[1] = yFactor;
+        arg.work_amount = static_cast<size_t>(OW * OH);
+        arg.oc_off = static_cast<size_t>(c * sizeof(float));
+        (*interpolateKernel)(&arg);
+    });
+}
+
+// =====================================================================================================================
 // index layout:
 // d_0............d_OD-1, h_0..............h_OH-1, w_0................w_OW-1
-void MKLDNNInterpolateNode::buildTblNN(SizeVector& srcDimPad5d, SizeVector& dstDim5d,
-                                        std::vector<float>& dataScales, InterpolateLayoutType layout) {
-    int dimSize = srcDim.size();
+void MKLDNNInterpolateNode::InterpolateExecutor::buildTblNN(const SizeVector& srcDimPad5d, const SizeVector& dstDim5d,
+                                        const std::vector<float>& dataScales, InterpolateLayoutType layout, InterpolateNearestMode nearestMode) {
+    const int dimSize = dataRank;
     float fz = (dimSize == 5) ? dataScales[dimSize - 3] : 1.f;
     float fy = dataScales[dimSize - 2];
     float fx = dataScales[dimSize - 1];
@@ -2081,22 +2504,98 @@ void MKLDNNInterpolateNode::buildTblNN(SizeVector& srcDimPad5d, SizeVector& dstD
     bool isWDownsample = (fx < 1) ? true : false;
     for (int oz = 0; oz < OD; oz++) {
         float iz = coordTransToInput(oz, fz, ID, OD);
-        indexTable[oz] = nearestRound(iz, isDDownsample);
+        indexTable[oz] = nearestRound(iz, isDDownsample, nearestMode);
         indexTable[oz] = clipCoord(indexTable[oz], ID);
     }
     for (int oy = 0; oy < OH; oy++) {
         float iy = coordTransToInput(oy, fy, IH, OH);
-        indexTable[OD + oy] = nearestRound(iy, isHDownsample);
+        indexTable[OD + oy] = nearestRound(iy, isHDownsample, nearestMode);
         indexTable[OD + oy] = clipCoord(indexTable[OD + oy], IH);
     }
     for (int ox = 0; ox < OW; ox++) {
         float ix = coordTransToInput(ox, fx, IW, OW);
-        indexTable[OD + OH + ox] = nearestRound(ix, isWDownsample);
+        indexTable[OD + OH + ox] = nearestRound(ix, isWDownsample, nearestMode);
         indexTable[OD + OH + ox] = clipCoord(indexTable[OD + OH + ox], IW);
     }
 }
 
-void MKLDNNInterpolateNode::linearOnnxCF(int outCoord, float scale, int inShape, int outShape, int& index0, int& index1, float& weight0, float& weight1) {
+// scale is float(outShape) / float(inShape)
+// strictly consistent with onnx calc manner(div scale, not multiply inverse), given this is done offline
+// the slight precison diff can produce obvious wrong value due to "nearest round" behavior for NN mode
+float MKLDNNInterpolateNode::InterpolateExecutor::coordTransToInput(int outCoord, float scale, int inShape, int outShape) const {
+    if (scale == 1.0f || (inShape == outShape)) {
+        return outCoord;
+    }
+    switch (coordTransMode) {
+        case InterpolateCoordTransMode::half_pixel: {
+            return (outCoord + 0.5f) / scale - 0.5f;
+            break;
+        }
+        case InterpolateCoordTransMode::pytorch_half_pixel: {
+            if (outShape > 1)
+                return (outCoord + 0.5f) / scale - 0.5f;
+            else
+                return 0;
+            break;
+        }
+        case InterpolateCoordTransMode::asymmetric: {
+            return static_cast<float>(outCoord) / scale;
+            break;
+        }
+        case InterpolateCoordTransMode::tf_half_pixel_for_nn: {
+            return (outCoord + 0.5f) / scale;
+            break;
+        }
+        case InterpolateCoordTransMode::align_corners: {
+            if (outShape > 1)
+                return outCoord * (static_cast<float>(inShape - 1) / static_cast<float>(outShape - 1));
+            else
+                return 0;
+            break;
+        }
+        default: {
+            IE_THROW() << "errorPrefix" << " does not support specified coordinate transformation mode";
+            break;
+        }
+    }
+}
+
+int MKLDNNInterpolateNode::InterpolateExecutor::nearestRound(float originCoord, bool isDownsample, InterpolateNearestMode nearestMode) const {
+    switch (nearestMode) {
+        case InterpolateNearestMode::round_prefer_floor: {
+            if (originCoord == (static_cast<int>(originCoord) + 0.5f))
+                return static_cast<int>(std::floor(originCoord));
+            else
+                return static_cast<int>(std::round(originCoord));
+            break;
+        }
+        case InterpolateNearestMode::round_prefer_ceil: {
+            return static_cast<int>(std::round(originCoord));
+            break;
+        }
+        case InterpolateNearestMode::floor: {
+            return static_cast<int>(std::floor(originCoord));
+            break;
+        }
+        case InterpolateNearestMode::ceil: {
+            return static_cast<int>(std::ceil(originCoord));
+            break;
+        }
+        case InterpolateNearestMode::simple: {
+            if (isDownsample)
+                return static_cast<int>(std::ceil(originCoord));
+            else
+                return static_cast<int>(originCoord);
+        }
+        default: {
+            IE_THROW() << "errorPrefix" << " does not support specified nearest round mode";
+            break;
+        }
+    }
+}
+
+void MKLDNNInterpolateNode::InterpolateExecutor::linearOnnxCF(int outCoord, float scale, int inShape, int outShape,
+                int& index0, int& index1, float& weight0, float& weight1) {
     float inCoord = coordTransToInput(outCoord, scale, inShape, outShape);
     inCoord = std::max(0.0f, std::min(inCoord, static_cast<float>(inShape - 1)));
     index0 = std::min(static_cast<int>(inCoord), inShape - 1);
@@ -2110,9 +2609,9 @@ void MKLDNNInterpolateNode::linearOnnxCF(int outCoord, float scale, int inShape,
     }
 }
 
-void MKLDNNInterpolateNode::buildTblLinearOnnx(SizeVector& srcDimPad5d, SizeVector& dstDim5d,
-                                                std::vector<float>& dataScales, InterpolateLayoutType layout) {
-    int dimSize = srcDim.size();
+void MKLDNNInterpolateNode::InterpolateExecutor::buildTblLinearOnnx(const SizeVector& srcDimPad5d, const SizeVector& dstDim5d,
+                                                const std::vector<float>& dataScales, InterpolateLayoutType layout) {
+    int dimSize = dataRank;
     float fz = (spatialDimSize > 2) ? dataScales[dimSize - 3] : 1.f;
     float fy = (spatialDimSize > 1) ? dataScales[dimSize - 2] : 1.f;
     float fx = dataScales[dimSize - 1];
@@ -2219,17 +2718,13 @@ void MKLDNNInterpolateNode::buildTblLinearOnnx(SizeVector& srcDimPad5d, SizeVect
     }
 }
 
-static inline float triangleCoeff(float x) {
-    return (std::max)(0.0f, 1 - std::abs(x));
-}
-
 // table layout:
 // wd .........wd, wh............wh, ww.............ww, id...........id, ih............ih, iw..............iw
 //                        |                                                      |
 //                   wh0.....wh_diameter                                    ih0.....ih_diameter
-void MKLDNNInterpolateNode::buildTblLinear(SizeVector& srcDimPad5d, SizeVector& dstDim5d,
-                                            std::vector<float>& dataScales, int kernel_width, bool antialias) {
-    int dimSize = srcDim.size();
+void MKLDNNInterpolateNode::InterpolateExecutor::buildTblLinear(const SizeVector& srcDimPad5d, const SizeVector& dstDim5d,
+                                            const std::vector<float>& dataScales, int kernel_width, bool antialias) {
+    int dimSize = dataRank;
     float fz = (dimSize == 5) ? dataScales[dimSize - 3] : 1.f;
     float fy = dataScales[dimSize - 2];
     float fx = dataScales[dimSize - 1];
@@ -2304,7 +2799,7 @@ void MKLDNNInterpolateNode::buildTblLinear(SizeVector& srcDimPad5d, SizeVector&
     }
 }
 
-std::vector<float> MKLDNNInterpolateNode::getCubicCoeffs(float mantissa, float a) {
+std::vector<float> MKLDNNInterpolateNode::InterpolateExecutor::getCubicCoeffs(float mantissa, float a) {
     float m = std::fabs(mantissa);
     std::vector<float> coeffs(4, 0.f);
 
@@ -2318,9 +2813,9 @@ std::vector<float> MKLDNNInterpolateNode::getCubicCoeffs(float mantissa, float a
 // table layout:
 // OW      OW         OW         OW         OW          OH       OH           OH           OH           OH
 // x_idx   x_weight0  x_weight1  x_weight2  x_weight3   y_idx    y_weight0    y_weight1    y_weight2    y_weight3
-void MKLDNNInterpolateNode::buildTblCubic(SizeVector& srcDimPad5d, SizeVector& dstDim5d, std::vector<float>& dataScales,
+void MKLDNNInterpolateNode::InterpolateExecutor::buildTblCubic(const SizeVector& srcDimPad5d, const SizeVector& dstDim5d, const std::vector<float>& dataScales,
                                         float cubicCoeff, InterpolateLayoutType layout) {
-    int dimSize = srcDim.size();
+    int dimSize = dataRank;
     float fy = dataScales[dimSize - 2];
     float fx = dataScales[dimSize - 1];
     int IH = srcDimPad5d[3], IW = srcDimPad5d[4];
@@ -2383,276 +2878,8 @@ void MKLDNNInterpolateNode::buildTblCubic(SizeVector& srcDimPad5d, SizeVector& d
     }
 }
 
-void MKLDNNInterpolateNode::setPostOps(mkldnn::primitive_attr &attr, bool initWeights) {
-    mkldnn::post_ops ops;
-
-    for (auto &node : fusedWith) {
-        auto* fakeQuantizeNode = dynamic_cast<MKLDNNFakeQuantizeNode *>(node.get());
-        if (fakeQuantizeNode) {
-            fakeQuantizeNode->appendPostOps(ops);
-            continue;
-        }
-
-        auto* eltwiseNode = dynamic_cast<MKLDNNEltwiseNode *>(node.get());
-        if (eltwiseNode) {
-            constexpr int align = 16;
-            // TODO [DS]: change to shape from memory
-            eltwiseNode->appendPostOps(ops, getOutputShapeAtPort(0).getStaticDims(), align);
-            continue;
-        }
-
-        IE_THROW() << "Fusing of " << NameFromType(node->getType()) << " operation to " << NameFromType(this->getType()) << " node is not implemented";
-    }
-
-    attr.set_post_ops(ops);
-}
-
-SizeVector MKLDNNInterpolateNode::getPaddedInputShape() {
-    SizeVector paddedShape;
-    int dataRank = srcDim.size();
-    for (int i = 0; i < dataRank; i++) {
-        paddedShape.push_back(srcDim[i] + padBegin[i] + padEnd[i]);
-    }
-    return paddedShape;
-}
-
-// get scales of data rank size
-// if "scale" version: set scales with input scales, 1.f for other dims not in axis
-// if "size" version: scales = shape[target] / shape[input].pad, 1.f for other dims not in axis
-// scales is a required input, but should not use input scales when "size" case, which may added eps that lead to inaccurate result, recalculate scales instead.
-std::vector<float> MKLDNNInterpolateNode::getScales() {
-    int dataRank = srcDim.size();
-    std::vector<float> fullScales(dataRank, 1.f);
-    int axesRank = axes.size();
-    for (int i = 0; i < axesRank; i++) {
-        int axis = axes[i];
-        fullScales[axis] = (shapeCalcMode == InterpolateShapeCalcMode::scales) ? scales[i] :
-                                                                                 static_cast<float>(dstDim[axis]) / static_cast<float>(srcDimPad[axis]);
-    }
-    return fullScales;
-}
-
-void MKLDNNInterpolateNode::execute(mkldnn::stream strm) {
-    auto &dstMemPtr = getChildEdgeAt(0)->getMemoryPtr();
-    auto &srcMemPtr = getParentEdgeAt(DATA_ID)->getMemoryPtr();
-
-    uint8_t *dst_data = reinterpret_cast<uint8_t*>(dstMemPtr->GetPtr());
-    uint8_t *src_data_origin = reinterpret_cast<uint8_t*>(srcMemPtr->GetData());
-
-    size_t dimSize = srcDim.size();
-    SizeVector srcDimPad = getPaddedInputShape();
-
-    auto srcDim5d = to5Dim(srcDim);
-    auto srcDimPad5d = to5Dim(srcDimPad);
-    auto dstDim5d = to5Dim(dstDim);
-
-    uint8_t *src_data = nullptr;
-    std::vector<uint8_t> srcPadded;
-    if (hasPad) {
-        int padB0 = (dimSize > 2) ? padBegin[0] : 0;
-        int padB1 = (dimSize > 2) ? padBegin[1] : 0;
-        int padB2 = (dimSize == 5) ? padBegin[dimSize - 3] : 0;
-        int padB3 = padBegin[dimSize - 2];
-        int padB4 = padBegin[dimSize - 1];
-
-        SizeVector inShapeBlock = getBlockND(srcDim5d);
-        SizeVector inShapePadBlock = getBlockND(srcDimPad5d);
-
-        if (configured_for_layout == InterpolateLayoutType::planar) {
-            srcPadded.resize(inShapePadBlock[0] * srcDataSize, 0);
-            uint8_t *src_data_pad = static_cast<uint8_t *>(&srcPadded[0]);
-            parallel_for4d(srcDim5d[0], srcDim5d[1], srcDim5d[2], srcDim5d[3], [&](int n, int c, int d, int h) {
-                uint8_t *src = src_data_origin + (inShapeBlock[1] * n + inShapeBlock[2] * c + inShapeBlock[3] * d + inShapeBlock[4] * h) * srcDataSize;
-                uint8_t *srcPad = src_data_pad + (inShapePadBlock[1] * (n + padB0) + inShapePadBlock[2] * (c + padB1) +
-                               inShapePadBlock[3] * (d + padB2) + inShapePadBlock[4] * (h + padB3) + padB4) * srcDataSize;
-                cpu_memcpy(srcPad, src, srcDim5d[4] * srcDataSize);
-            });
-            src_data = src_data_pad;
-        } else if (configured_for_layout == InterpolateLayoutType::by_channel) {
-            srcPadded.resize(inShapePadBlock[0] * srcDataSize, 0);
-            uint8_t *src_data_pad = static_cast<uint8_t *>(&srcPadded[0]);
-            parallel_for4d(srcDim5d[0], srcDim5d[2], srcDim5d[3], srcDim5d[4], [&](int n, int d, int h, int w) {
-                uint8_t *src = src_data_origin + (inShapeBlock[1] * n +
-                                (inShapeBlock[3] * d + inShapeBlock[4] * h + inShapeBlock[5] * w) * srcDim5d[1]) * srcDataSize;
-                uint8_t *srcPad = src_data_pad + (inShapePadBlock[1] * (n + padB0) + (inShapePadBlock[3] * (d + padB2) +
-                                inShapePadBlock[4] * (h + padB3) + inShapePadBlock[5] * (w + padB4)) * srcDimPad5d[1] + padB1) * srcDataSize;
-                cpu_memcpy(srcPad, src, srcDim5d[1] * srcDataSize);
-            });
-            src_data = src_data_pad;
-        } else if (configured_for_layout == InterpolateLayoutType::block) {
-            size_t blkSize = mayiuse(cpu::x64::avx512_common) ? 16 : 8;
-            size_t CB = div_up(srcDimPad5d[1], blkSize);
-            size_t eltsTotal = srcDimPad5d[0] * CB * srcDimPad5d[2] * srcDimPad5d[3] * srcDimPad5d[4] * blkSize;
-            srcPadded.resize(eltsTotal * srcDataSize, 0x0);
-            uint8_t *src_data_pad = static_cast<uint8_t *>(&srcPadded[0]);
-            if ((srcDim5d[0] != srcDimPad5d[0]) || (srcDim5d[1] != srcDimPad5d[1])) {
-                IE_THROW() << "Interpolate layer with name '" << getName() <<
-                "' does not support padding on batch and channel dimensions";
-            }
-            parallel_for5d(srcDim5d[0], CB, srcDim5d[2], srcDim5d[3], srcDim5d[4], [&](int n, int cb, int d, int h, int w) {
-                uint8_t *src = src_data_origin + (n * CB * srcDim5d[2] * srcDim5d[3] * srcDim5d[4] * blkSize) * srcDataSize
-                                               + (cb * srcDim5d[2] * srcDim5d[3] * srcDim5d[4] * blkSize) * srcDataSize
-                                               + (d * srcDim5d[3] * srcDim5d[4] * blkSize) * srcDataSize
-                                               + (h * srcDim5d[4] * blkSize) * srcDataSize
-                                               + (w * blkSize) * srcDataSize;
-                uint8_t *srcPad = src_data_pad + (n * CB * srcDimPad5d[2] * srcDimPad5d[3] * srcDimPad5d[4] * blkSize) * srcDataSize
-                                               + (cb * srcDimPad5d[2] * srcDimPad5d[3] * srcDimPad5d[4] * blkSize) * srcDataSize
-                                               + ((d + padB2) * srcDimPad5d[3] * srcDimPad5d[4] * blkSize) * srcDataSize
-                                               + ((h + padB3) * srcDimPad5d[4] * blkSize) * srcDataSize
-                                               + ((w + padB4) * blkSize) * srcDataSize;
-                cpu_memcpy(srcPad, src, blkSize * srcDataSize);
-            });
-            src_data = src_data_pad;
-        }
-    } else {
-        src_data = src_data_origin;
-    }
-
-    size_t N = srcDimPad5d[0], C = srcDimPad5d[1], ID = srcDimPad5d[2], IH = srcDimPad5d[3], IW = srcDimPad5d[4];
-    size_t OD = dstDim5d[2], OH = dstDim5d[3], OW = dstDim5d[4];
-    std::vector<float> dataScales = getScales();
-    if (dimSize > 2 && (dataScales[0] != 1.f || dataScales[1] != 1.f)) {
-        IE_THROW() << "Interpolate layer only supports resize on spatial dimensions(depth, height and width)";
-    }
-
-    switch (mode) {
-        case InterpolateMode::nearest: {
-            if (interpolateKernel) {
-                if (configured_for_layout == InterpolateLayoutType::planar) {
-                    NNPlanar(src_data, dst_data, N, C, ID, IH, IW, OD, OH, OW);
-                } else {
-                    NNCGathered(src_data, dst_data, N, C, ID, IH, IW, OD, OH, OW);
-                }
-            } else {
-                NNRef(src_data, dst_data, N, C, ID, IH, IW, OD, OH, OW);
-            }
-            break;
-        }
-        case InterpolateMode::linear_onnx: {
-            if (interpolateKernel) {
-                if (configured_for_layout == InterpolateLayoutType::planar) {
-                    linearOnnxPlanar(src_data, dst_data, N, C, ID, IH, IW, OD, OH, OW);
-                } else {
-                    linearOnnxCGathered(src_data, dst_data, N, C, ID, IH, IW, OD, OH, OW);
-                }
-            } else {
-                linearOnnxRef(src_data, dst_data, N, C, ID, IH, IW, OD, OH, OW);
-            }
-            break;
-        }
-        case InterpolateMode::cubic: {
-            if (interpolateKernel) {
-                if (configured_for_layout == InterpolateLayoutType::planar) {
-                    cubicPlanar(src_data, dst_data, N, C, IH, IW, OH, OW);
-                } else {
-                    cubicCGathered(src_data, dst_data, N, C, IH, IW, OH, OW);
-                }
-            } else {
-                cubicRef(src_data, dst_data, N, C, IH, IW, OH, OW);
-            }
-            break;
-        }
-        case InterpolateMode::linear: {
-            float fz = (dimSize == 5) ? dataScales[dimSize - 3] : 1.f;
-            float fy = dataScales[dimSize - 2];
-            float fx = dataScales[dimSize - 1];
-
-            bool isDownsample = (fx < 1.f) || (fy < 1.f) || (fz < 1.f);
-            int kernel_width = 2;
-            linearInterpolation(src_data, dst_data, N, C, ID, IH, IW, fx, fy, fz, OD, OH, OW, kernel_width, isDownsample && antialias);
-            break;
-        }
-        default: {
-            IE_THROW() << "Interpolate layer has unsupported interpolate mode: " << mode;
-        }
-    }
-}
-
-// for ndhwc and nCdhw8c[16c]
-// input may be f32/bf16/int8, fused->output varies
-void MKLDNNInterpolateNode::NNCGathered(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW, int OD, int OH, int OW) {
-    int *index_d = static_cast<int*>(&indexTable[0]);
-    int *index_h = static_cast<int*>(&indexTable[OD]);
-    int *index_w = static_cast<int*>(&indexTable[OD + OH]);
-
-    bool is_nhwc = (configured_for_layout == by_channel);
-
-    for (int b = 0; b < B; b++) {
-        if (is_nhwc) {
-            const uint8_t *in_ptr = in_ptr_ + (IW * IH * ID * C * b) * srcDataSize;
-            uint8_t *out_ptr = out_ptr_ + (OW * OH * OD * C * b) * dstDataSize;
-            std::vector<int> index_w_kernel(OW);
-            for (int ox = 0; ox < OW; ox++) {
-                index_w_kernel[ox] = index_w[ox] * C * srcDataSize;
-            }
-            parallel_for2d(OD, OH, [&](size_t d, size_t h) {
-                // kernel for C * OW
-                uint8_t *out_ptr_dh = out_ptr + (C * OW * OH * d + C * OW * h) * dstDataSize;
-                const uint8_t *in_ptr_dh = in_ptr + (C * IW * IH * index_d[d] + C * IW * index_h[h]) * srcDataSize;
-                auto arg = jit_interpolate_call_args();
-                arg.dst = out_ptr_dh;
-                arg.src_ptr[0] = in_ptr_dh;
-                arg.index = static_cast<int*>(&(index_w_kernel[0]));
-                arg.work_amount = C;
-                arg.oc_off = 0;
-                (*interpolateKernel)(&arg);
-            });
-        } else {  // for blk
-            int blk_size = mayiuse(cpu::x64::avx512_common) ? 16 : 8;
-            int CB = div_up(C, blk_size);
-            const uint8_t *in_ptr = in_ptr_ + (IW * IH * ID * CB * blk_size * b) * srcDataSize;
-            uint8_t *out_ptr = out_ptr_ + (OW * OH * OD * CB * blk_size * b) * dstDataSize;
-            std::vector<int> index_w_kernel(OW);
-            for (int ox = 0; ox < OW; ox++) {
-                index_w_kernel[ox] = index_w[ox] * blk_size * srcDataSize;
-            }
-            parallel_for2d(CB, OD, [&](size_t cb, size_t d) {
-                uint8_t *out_ptr_cbd = out_ptr + (blk_size * OW * OH * OD * cb + blk_size * OW * OH * d) * dstDataSize;
-                const uint8_t *in_ptr_cbd = in_ptr + (blk_size * IW * IH * ID * cb + blk_size * IW * IH * index_d[d]) * srcDataSize;
-                auto arg = jit_interpolate_call_args();
-                for (int h = 0; h < OH; h++) {  // kernel for blk_size * OW
-                    arg.dst = out_ptr_cbd + blk_size * OW * h * dstDataSize;
-                    arg.src_ptr[0] = in_ptr_cbd + blk_size * IW * index_h[h] * srcDataSize;
-                    arg.index = static_cast<int*>(&(index_w_kernel[0]));
-                    arg.work_amount = static_cast<size_t>(OW);
-                    arg.oc_off = cb * blk_size * sizeof(float);
-                    (*interpolateKernel)(&arg);
-                }
-            });
-        }
-    }  // batch end
-}
-
-void MKLDNNInterpolateNode::NNPlanar(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW, int OD, int OH, int OW) {
-    int *index_d = static_cast<int*>(&indexTable[0]);
-    int *index_h = static_cast<int*>(&indexTable[OD]);
-    int *index_w = static_cast<int*>(&indexTable[OD + OH]);
-
-    std::vector<int> index_kernel(OH + OW);
-    // index_h * IW * srcDataSize to reduce and simplify redundant compute
-    for (int oh = 0; oh < OH; oh++) {
-        index_kernel[oh] = index_h[oh] * IW * srcDataSize;
-    }
-    // index_w * srcDataSize
-    for (int ow = 0; ow < OW; ow++) {
-        index_kernel[OH + ow] = index_w[ow] * srcDataSize;
-    }
-
-    parallel_for3d(B, C, OD, [&](size_t b, size_t c, size_t od) {
-        const uint8_t *in_ptr = in_ptr_ + (IW * IH * ID * C * b + IW * IH * ID * c + IW * IH * index_d[od]) * srcDataSize;
-        uint8_t *out_ptr = out_ptr_ + (OW * OH * OD * C * b + OW * OH * OD * c + OW * OH * od) * dstDataSize;
-
-        auto arg = jit_interpolate_call_args();
-        arg.src_ptr[0] = in_ptr;
-        arg.dst = out_ptr;
-        arg.index = static_cast<int*>(&index_kernel[0]);  // need index_h and index_w in kernel, it's in continous memory so one param
-        arg.oc_off = static_cast<size_t>(c * sizeof(float));
-        // work_amount is OH(out loop) and OW(inner loop), can get in kernel from jcp.
-        (*interpolateKernel)(&arg);
-    });
-}
-
-void MKLDNNInterpolateNode::NNRef(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW, int OD, int OH, int OW) {
+void MKLDNNInterpolateNode::InterpolateRefExecutor::NNRef(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW,
+                                                          int OD, int OH, int OW) {
     int *index_d = static_cast<int*>(&indexTable[0]);
     int *index_h = static_cast<int*>(&indexTable[OD]);
     int *index_w = static_cast<int*>(&indexTable[OD + OH]);
@@ -2673,99 +2900,8 @@ void MKLDNNInterpolateNode::NNRef(const uint8_t *in_ptr_, uint8_t *out_ptr_, int
     });
 }
 
-void MKLDNNInterpolateNode::linearOnnxPlanar(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW, int OD, int OH, int OW) {
-    // FrontTopLeft:0, FrontTopRight:1, FrontBottomLeft:2, FrontBottomRight:3, EndTopLeft:4,   EndTopRight:5,   EndBottomLeft:6,   EndBottomRight:7
-    // weight: Left:0, ritht:1, top:2, bottom:3, front:4, end:5
-    int *index = static_cast<int*>(&indexTable[0]);
-    int eltInGrid = (spatialDimSize > 2) ? MAX_INPUT_INTERPOLATE : ((spatialDimSize > 1) ? 4 : 2);
-    int scratchLen = rnd_up(eltInGrid * OW * OH * OD, 16);
-    float *weight = reinterpret_cast<float*>(&indexTable[scratchLen]);
-
-    parallel_for2d(B, C, [&](size_t b, size_t c) {
-        uint8_t *out_ptr_nc = out_ptr_ + (OH * OW * OD * C * b + OH * OW * OD * c) * dstDataSize;
-        const uint8_t *in_ptr_nc = in_ptr_ + (IH * IW * ID * C * b + IH * IW * ID * c) * srcDataSize;
-        auto arg = jit_interpolate_call_args();
-        arg.src_ptr[0] = in_ptr_nc;
-        arg.index = static_cast<int*>(&index[0]);
-        arg.weight_ptr[0] = static_cast<float*>(&weight[0]);
-        arg.dst = out_ptr_nc;
-        arg.work_amount = OW * OH * OD;
-        arg.oc_off = static_cast<size_t>(c * sizeof(float));
-        (*interpolateKernel)(&arg);
-    });
-}
-
-void MKLDNNInterpolateNode::linearOnnxCGathered(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW, int OD, int OH, int OW) {
-    // left:OW right:OW Top:OH Bottom:OH Front:OD End:OD
-    std::vector<int*> indexPtr(MAX_INPUT_INTERPOLATE, 0);
-    std::vector<float*> weightPtr(MAX_INPUT_INTERPOLATE, 0);
-    size_t scratchLen = rnd_up(OW + OW + OH + OH + OD + OD, 16);
-    indexPtr[0] = static_cast<int*>(&indexTable[0]);
-    indexPtr[1] = static_cast<int*>(&indexTable[OW]);
-    indexPtr[2] = static_cast<int*>(&indexTable[2 * OW]);
-    indexPtr[3] = static_cast<int*>(&indexTable[2 * OW + OH]);
-    indexPtr[4] = static_cast<int*>(&indexTable[2 * OW + 2 * OH]);
-    indexPtr[5] = static_cast<int*>(&indexTable[2 * OW + 2 * OH + OD]);
-
-    weightPtr[0] = reinterpret_cast<float*>(&indexTable[scratchLen]);
-    weightPtr[1] = reinterpret_cast<float*>(&indexTable[scratchLen + OW]);
-    weightPtr[2] = reinterpret_cast<float*>(&indexTable[scratchLen + 2 * OW]);
-    weightPtr[3] = reinterpret_cast<float*>(&indexTable[scratchLen + 2 * OW + OH]);
-    weightPtr[4] = reinterpret_cast<float*>(&indexTable[scratchLen + 2 * OW + 2 * OH]);
-    weightPtr[5] = reinterpret_cast<float*>(&indexTable[scratchLen + 2 * OW + 2 * OH + OD]);
-
-    bool isByChannel = (configured_for_layout == by_channel) ? true : false;
-
-    int blkSize = mayiuse(cpu::x64::avx512_common) ? 16 : 8;
-    int CB = isByChannel ? 1 : div_up(C, blkSize);
-    int CGatherLen = isByChannel ? C : blkSize;
-    int workAmount = isByChannel ? C : CB;
-    // n_CB(1)_d_h_w_8[16](c), () for by-channel
-    int C0 = OW * CGatherLen;
-    int C1 = OH * C0;
-    int C2 = OD * C1;
-    int C3 = CB * C2;
-    int I0 = IW * CGatherLen;
-    int I1 = IH * I0;
-    int I2 = ID * I1;
-    int I3 = CB * I2;
-    parallel_for3d(B, OD, OH, [&](size_t b, size_t d, size_t h) {
-        uint8_t *out_ptr_ndh = out_ptr_ + (C3 * b + C1 * d + C0 * h) * dstDataSize;
-
-        const uint8_t *in_ptr_n = in_ptr_ + (I3 * b) * srcDataSize;
-        const uint8_t *in_ptr_nf = in_ptr_n + (indexPtr[4][d] * I1) * srcDataSize;
-        const uint8_t *in_ptr_nft = in_ptr_nf + (indexPtr[2][h] * I0) * srcDataSize;
-        const uint8_t *in_ptr_nfb = in_ptr_nf + (indexPtr[3][h] * I0) * srcDataSize;
-        const uint8_t *in_ptr_ne = in_ptr_n + (indexPtr[5][d] * I1) * srcDataSize;
-        const uint8_t *in_ptr_net = in_ptr_ne + (indexPtr[2][h] * I0) * srcDataSize;
-        const uint8_t *in_ptr_neb = in_ptr_ne + (indexPtr[3][h] * I0) * srcDataSize;
-        auto arg = jit_interpolate_call_args();
-        for (int w = 0; w < OW; ++w) {
-            uint8_t *out_ptr_ndhw = out_ptr_ndh + CGatherLen * w * dstDataSize;
-
-            arg.src_ptr[0] = in_ptr_nft + (indexPtr[0][w] * CGatherLen) * srcDataSize;
-            arg.src_ptr[1] = in_ptr_nft + (indexPtr[1][w] * CGatherLen) * srcDataSize;
-            arg.src_ptr[2] = in_ptr_nfb + (indexPtr[0][w] * CGatherLen) * srcDataSize;
-            arg.src_ptr[3] = in_ptr_nfb + (indexPtr[1][w] * CGatherLen) * srcDataSize;
-            arg.src_ptr[4] = in_ptr_net + (indexPtr[0][w] * CGatherLen) * srcDataSize;
-            arg.src_ptr[5] = in_ptr_net + (indexPtr[1][w] * CGatherLen) * srcDataSize;
-            arg.src_ptr[6] = in_ptr_neb + (indexPtr[0][w] * CGatherLen) * srcDataSize;
-            arg.src_ptr[7] = in_ptr_neb + (indexPtr[1][w] * CGatherLen) * srcDataSize;
-            arg.weight_ptr[0] = static_cast<float*>(&weightPtr[0][w]);
-            arg.weight_ptr[1] = static_cast<float*>(&weightPtr[1][w]);
-            arg.weight_ptr[2] = static_cast<float*>(&weightPtr[2][h]);
-            arg.weight_ptr[3] = static_cast<float*>(&weightPtr[3][h]);
-            arg.weight_ptr[4] = static_cast<float*>(&weightPtr[4][d]);
-            arg.weight_ptr[5] = static_cast<float*>(&weightPtr[5][d]);
-            arg.dst = out_ptr_ndhw;
-            arg.work_amount = workAmount;
-            arg.oc_off = 0;
-            (*interpolateKernel)(&arg);
-        }
-    });
-}
-
-void MKLDNNInterpolateNode::linearOnnxRef(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW, int OD, int OH, int OW) {
+void MKLDNNInterpolateNode::InterpolateRefExecutor::linearOnnxRef(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW,
+                                                                  int OD, int OH, int OW) {
     std::vector<int*> indexPtr(MAX_INPUT_INTERPOLATE, 0);
     std::vector<float*> weightPtr(MAX_INPUT_INTERPOLATE, 0);
     // FrontTopLeft:0, FrontTopRight:1, FrontBottomLeft:2, FrontBottomRight:3,
@@ -2862,11 +2998,102 @@ void MKLDNNInterpolateNode::linearOnnxRef(const uint8_t *in_ptr_, uint8_t *out_p
     });
 }
 
-void MKLDNNInterpolateNode::linearInterpolation(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW,
+void MKLDNNInterpolateNode::InterpolateRefExecutor::cubicRef(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int IH, int IW, int OH, int OW) {
+    const int idxNum = 1;
+    int *xOrigin = static_cast<int*>(&indexTable[0]);
+    float *xFactor = reinterpret_cast<float*>(&indexTable[OW]);
+    int *yOrigin = static_cast<int*>(&indexTable[(CUBIC_GRID_LEN + idxNum) * OW]);
+    float *yFactor = reinterpret_cast<float*>(&indexTable[(CUBIC_GRID_LEN + idxNum) * OW + OH]);
+
+    const float *in_ptr_f32 = reinterpret_cast<const float *>(in_ptr_);
+    float *out_ptr_f32 = reinterpret_cast<float *>(out_ptr_);
+
+    parallel_for4d(B, C, OH, OW, [&](size_t n, size_t c, size_t oy, size_t ox) {
+        const float *in_ptr_nc = in_ptr_f32 + (IW * IH * C * n + IW * IH * c);
+        float *out_ptr_nc = out_ptr_f32 + (OW * OH * C * n + OW * OH * c);
+
+        int iy = yOrigin[oy];
+        int ix = xOrigin[ox];
+
+        float retY = 0.f;
+        for (int y = iy - 1, i = 0; y <= iy + 2; y++, i++) {
+            int yInRange = std::max(0, std::min(y, IH - 1));
+            const float *in_ptr_nch = in_ptr_nc + IW * yInRange;
+            float retX = 0.f;
+            for (int x = ix - 1, j = 0; x <= ix + 2; x++, j++) {
+                int xInRange = std::max(0, std::min(x, IW - 1));
+                retX += xFactor[ox * CUBIC_GRID_LEN + j] * in_ptr_nch[xInRange];
+            }
+            retY += yFactor[oy * CUBIC_GRID_LEN + i] * retX;
+        }
+        out_ptr_nc[oy * OW + ox] = retY;
+    });
+}
+
+float MKLDNNInterpolateNode::InterpolateRefExecutor::getValue(const uint8_t *base, size_t offset, InferenceEngine::Precision prec) {
+    const uint8_t *baseOffset = base + offset;
+    switch (prec) {
+        case Precision::U8: {
+            return static_cast<float>(*baseOffset);
+            break;
+        }
+        case Precision::I8: {
+            const int8_t *valuePtr = reinterpret_cast<const int8_t *>(baseOffset);
+            return static_cast<float>(*valuePtr);
+            break;
+        }
+        case Precision::BF16: {
+            const uint16_t *valuePtr = reinterpret_cast<const uint16_t *>(baseOffset);
+            return bfloat16_t::from_bits(*valuePtr);
+            break;
+        }
+        case Precision::FP32: {
+            const float *valuePtr = reinterpret_cast<const float *>(baseOffset);
+            return *valuePtr;
+            break;
+        }
+        default: {
+            IE_THROW() << "Interpolate layer does not support precision: " << prec;
+            break;
+        }
+    }
+}
+
+void MKLDNNInterpolateNode::InterpolateRefExecutor::setValue(uint8_t *base, size_t offset, float value, InferenceEngine::Precision prec) {
+    uint8_t *baseOffset = base + offset;
+    switch (prec) {
+        case Precision::U8: {
+            uint8_t data = static_cast<uint8_t>(value < 0 ? 0 : value);
+            cpu_memcpy(baseOffset, &data, 1);
+            break;
+        }
+        case Precision::I8: {
+            int8_t data = static_cast<int8_t>(value);
+            cpu_memcpy(baseOffset, &data, 1);
+            break;
+        }
+        case Precision::BF16: {
+            uint16_t data = bfloat16_t(value).to_bits();
+            cpu_memcpy(baseOffset, &data, 2);
+            break;
+        }
+        case Precision::FP32: {
+            cpu_memcpy(baseOffset, &value, sizeof(float));
+            break;
+        }
+        default: {
+            IE_THROW() << "Interpolate layer does not support precision: " << prec;
+            break;
+        }
+    }
+}
+
+void MKLDNNInterpolateNode::InterpolateRefExecutor::linearInterpolation(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW,
                                           float fx, float fy, float fz, int OD, int OH, int OW, int kernel_width, bool antialias) {
     if (IW == OW && IH == OH && ID == OD) {
         size_t spatialDimSize = IW * IH * ID;
-        if (fusedWith.empty() && inputPrec == outputPrec) {
+        // TODO: enable when fusing into interp with linear mode will support
+        if (/*fusedWith.empty() &&*/ inputPrec == outputPrec) {
             size_t size = B * C * spatialDimSize * srcDataSize;
             cpu_memcpy(out_ptr_, in_ptr_, size);
         } else {
@@ -2978,252 +3205,174 @@ void MKLDNNInterpolateNode::linearInterpolation(const uint8_t *in_ptr_, uint8_t
     });
 }
 
-void MKLDNNInterpolateNode::cubicCGathered(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int IH, int IW, int OH, int OW) {
-    const int idxNum = 1;
-    int *xOrigin = static_cast<int*>(&indexTable[0]);
-    float *xFactor = reinterpret_cast<float*>(&indexTable[OW]);
-    int *yOrigin = static_cast<int*>(&indexTable[(CUBIC_GRID_LEN + idxNum) * OW]);
-    float *yFactor = reinterpret_cast<float*>(&indexTable[(CUBIC_GRID_LEN + idxNum) * OW + OH]);
+MKLDNNInterpolateNode::InterpolateExecutor::InterpolateExecutor(const InterpolateAttrs& interpAttrs,
+                                                                const VectorDims &srcDims,
+                                                                const VectorDims &dstDims,
+                                                                const std::vector<float> &dataScales) :
+        mode(interpAttrs.mode), configured_for_layout(interpAttrs.layout), coordTransMode(interpAttrs.coordTransMode),
+        inputPrec(interpAttrs.inPrc), outputPrec(interpAttrs.outPrc) {
+    srcDimPad5d = to5Dim(getPaddedInputShape(srcDims, interpAttrs.padBegin, interpAttrs.padEnd));
+    dstDim5d = to5Dim(dstDims);
+    srcDataSize = interpAttrs.inPrc.size();
+    dstDataSize = interpAttrs.outPrc.size();
+    dataRank = srcDims.size();
+    spatialDimSize = getSpatialDimsNum(dataRank);
 
-    int blkSize = mayiuse(cpu::x64::avx512_common) ? 16 : 8;
-    int CB = div_up(C, blkSize);
-    int CSize = configured_for_layout == InterpolateLayoutType::by_channel ? C : blkSize * CB;
-    int CGatherLen = configured_for_layout == InterpolateLayoutType::by_channel ? C : blkSize;
-    int workAmount = configured_for_layout == InterpolateLayoutType::by_channel ? C : CB;
+    switch (mode) {
+        case InterpolateMode::nearest: {
+            buildTblNN(srcDimPad5d, dstDim5d, dataScales, interpAttrs.layout, interpAttrs.nearestMode);
+            break;
+        }
+        case InterpolateMode::linear_onnx: {
+            buildTblLinearOnnx(srcDimPad5d, dstDim5d, dataScales, interpAttrs.layout);
+            break;
+        }
+        case InterpolateMode::linear: {
+            static constexpr int LINEAR_KERNEL = 2;
+            buildTblLinear(srcDimPad5d, dstDim5d, dataScales, LINEAR_KERNEL, interpAttrs.antialias);
+            break;
+        }
+        case InterpolateMode::cubic: {
+            buildTblCubic(srcDimPad5d, dstDim5d, dataScales, interpAttrs.cubeCoeff, interpAttrs.layout);
+            break;
+        }
+        default: {
+            IE_THROW() << "Interpolate executor does not support interpolate mode: " << mode;
+            break;
+        }
+    }
+}
 
-    parallel_for3d(B, OH, OW, [&](size_t b, size_t h, size_t w) {
-        uint8_t *out_ptr_nhw = out_ptr_ + (OH * OW * CSize * b + OW * CGatherLen * h + CGatherLen * w) * dstDataSize;
-        const uint8_t *in_ptr_n = in_ptr_ + (IH * IW * CSize * b) * srcDataSize;
+MKLDNNInterpolateNode::InterpolateJitExecutor::InterpolateJitExecutor(const InterpolateAttrs& interpAttrs,
+                                                                      const VectorDims &srcDims,
+                                                                      const VectorDims &dstDims,
+                                                                      const std::vector<float> &dataScales,
+                                                                      const mkldnn::primitive_attr &attr) :
+        InterpolateExecutor(interpAttrs, srcDims, dstDims, dataScales) {
+    auto jcp = jit_interpolate_config_params();
+    jcp.mode = mode;
+    jcp.src_dt = MKLDNNExtensionUtils::IEPrecisionToDataType(interpAttrs.inPrc);
+    jcp.dst_dt = MKLDNNExtensionUtils::IEPrecisionToDataType(interpAttrs.outPrc);
+    jcp.src_data_size = MKLDNNExtensionUtils::sizeOfDataType(jcp.src_dt);
+    jcp.dst_data_size = MKLDNNExtensionUtils::sizeOfDataType(jcp.dst_dt);
+    jcp.indices_size = sizeof(int);
+    jcp.OW = dstDim5d[4];
+    jcp.OH = dstDim5d[3];
+    jcp.OD = dstDim5d[2];
+    jcp.IW = srcDimPad5d[4];
+    jcp.IH = srcDimPad5d[3];
+    jcp.ID = srcDimPad5d[2];
+    jcp.spatial_dim_size = getSpatialDimsNum(srcDims.size());
+    jcp.layout = interpAttrs.layout;
+    if (jcp.layout != InterpolateLayoutType::planar) {
+        if (mayiuse(cpu::x64::avx512_common)) {
+            interpolateKernel.reset(new jit_uni_interpolate_kernel_f32<cpu::x64::avx512_common>(jcp, *attr.get()));
+        } else if (mayiuse(cpu::x64::avx2)) {
+            interpolateKernel.reset(new jit_uni_interpolate_kernel_f32<cpu::x64::avx2>(jcp, *attr.get()));
+        } else if (mayiuse(cpu::x64::sse41)) {
+            interpolateKernel.reset(new jit_uni_interpolate_kernel_f32<cpu::x64::sse41>(jcp, *attr.get()));
+        }
+    } else if (mayiuse(cpu::x64::avx2) && interpAttrs.inPrc == InferenceEngine::Precision::FP32) {
+        // gather ISA(for planar JIT kernel) for avx2 and fp32
+        interpolateKernel.reset(new jit_uni_interpolate_kernel_f32<cpu::x64::avx2>(jcp, *attr.get()));
+    } else {
+        IE_THROW() << "Can't create InterpolateJitExecutor";
+    }
+    if (interpolateKernel) {
+        interpolateKernel->create_ker();
+    } else {
+        IE_THROW() << "Can't compile InterpolateJitExecutor";
+    }
+}
 
-        std::vector<int> kernelIndex(CUBIC_GRID_LEN * CUBIC_GRID_LEN);  // 16 address offset to src(batch) or src(CB)
-        int iy = yOrigin[h];
-        int ix = xOrigin[w];
-        for (int y = iy - 1, i = 0; y <= iy + 2; y++, i++) {
-            int yInRange = std::max(0, std::min(y, IH - 1));
-            yInRange = yInRange * CGatherLen * IW * srcDataSize;
-            for (int x = ix - 1, j = 0; x <= ix + 2; x++, j++) {
-                int xInRange = std::max(0, std::min(x, IW - 1));
-                xInRange = yInRange + xInRange * CGatherLen * srcDataSize;
-                kernelIndex[i * CUBIC_GRID_LEN + j] = xInRange;
+void MKLDNNInterpolateNode::InterpolateJitExecutor::exec(const uint8_t *in_ptr_, uint8_t *out_ptr_) {
+    size_t N = srcDimPad5d[0], C = srcDimPad5d[1], ID = srcDimPad5d[2], IH = srcDimPad5d[3], IW = srcDimPad5d[4];
+    size_t OD = dstDim5d[2], OH = dstDim5d[3], OW = dstDim5d[4];
+
+    if (!interpolateKernel) {
+        IE_THROW() << "Can't execute, kernel for Interpolate node is not compiled";
+    }
+    switch (mode) {
+        case InterpolateMode::nearest: {
+            if (configured_for_layout == InterpolateLayoutType::planar) {
+                NNPlanar(in_ptr_, out_ptr_, N, C, ID, IH, IW, OD, OH, OW);
+            } else {
+                NNCGathered(in_ptr_, out_ptr_, N, C, ID, IH, IW, OD, OH, OW);
             }
+            break;
         }
-        auto arg = jit_interpolate_call_args();
-            arg.dst = out_ptr_nhw;
-            arg.src_ptr[0] = in_ptr_n;
-            arg.index = static_cast<int*>(&kernelIndex[0]);
-            // 0 for weight_W, 1 for weight_H
-            arg.weight_ptr[0] = static_cast<float*>(&xFactor[w * CUBIC_GRID_LEN]);
-            arg.weight_ptr[1] = static_cast<float*>(&yFactor[h * CUBIC_GRID_LEN]);
-
-            // for by channel, src + step, dst + step, process next step on continuous memory
-            // for blk, src + IW*IH*blkSize, dst + OW*OH*blkSize, process the blkSize on next CB
-            arg.work_amount = workAmount;
-            arg.oc_off = 0;
-            (*interpolateKernel)(&arg);
-    });
-}
-
-void MKLDNNInterpolateNode::cubicPlanar(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int IH, int IW, int OH, int OW) {
-    int tblAdvance = 0;
-    int *xOrigin = static_cast<int*>(&indexTable[tblAdvance]);
-    tblAdvance += OW;
-    float *xFactor = reinterpret_cast<float*>(&indexTable[tblAdvance]);
-    tblAdvance += CUBIC_GRID_LEN * OW;
-    int *yOrigin = static_cast<int*>(&indexTable[tblAdvance]);
-    tblAdvance += OH;
-    float *yFactor = reinterpret_cast<float*>(&indexTable[tblAdvance]);
-
-    tblAdvance += CUBIC_GRID_LEN * OH;
-    int *sequenceOH = static_cast<int*>(&indexTable[tblAdvance]);
-    tblAdvance += OW * OH;
-    int *sequenceOW = static_cast<int*>(&indexTable[tblAdvance]);
-
-    parallel_for2d(B, C, [&](size_t n, size_t c) {
-        const uint8_t *in_ptr_nc = in_ptr_ + (IW * IH * C * n + IW * IH * c) * srcDataSize;
-        uint8_t *out_ptr_nc = out_ptr_ + (OW * OH * C * n + OW * OH * c) * dstDataSize;
-
-        auto arg = jit_interpolate_call_args();
-        arg.dst = out_ptr_nc;
-        arg.src_ptr[0] = in_ptr_nc;
-        arg.index = xOrigin;
-        arg.src_ptr[1] = yOrigin;
-        arg.src_ptr[2] = static_cast<int*>(&sequenceOH[0]);
-        arg.src_ptr[3] = static_cast<int*>(&sequenceOW[0]);
-        arg.weight_ptr[0] = xFactor;
-        arg.weight_ptr[1] = yFactor;
-        arg.work_amount = static_cast<size_t>(OW * OH);
-        arg.oc_off = static_cast<size_t>(c * sizeof(float));
-        (*interpolateKernel)(&arg);
-    });
-}
-
-void MKLDNNInterpolateNode::cubicRef(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int IH, int IW, int OH, int OW) {
-    const int idxNum = 1;
-    int *xOrigin = static_cast<int*>(&indexTable[0]);
-    float *xFactor = reinterpret_cast<float*>(&indexTable[OW]);
-    int *yOrigin = static_cast<int*>(&indexTable[(CUBIC_GRID_LEN + idxNum) * OW]);
-    float *yFactor = reinterpret_cast<float*>(&indexTable[(CUBIC_GRID_LEN + idxNum) * OW + OH]);
-
-    const float *in_ptr_f32 = reinterpret_cast<const float *>(in_ptr_);
-    float *out_ptr_f32 = reinterpret_cast<float *>(out_ptr_);
-
-    parallel_for4d(B, C, OH, OW, [&](size_t n, size_t c, size_t oy, size_t ox) {
-        const float *in_ptr_nc = in_ptr_f32 + (IW * IH * C * n + IW * IH * c);
-        float *out_ptr_nc = out_ptr_f32 + (OW * OH * C * n + OW * OH * c);
-
-        int iy = yOrigin[oy];
-        int ix = xOrigin[ox];
-
-        float retY = 0.f;
-        for (int y = iy - 1, i = 0; y <= iy + 2; y++, i++) {
-            int yInRange = std::max(0, std::min(y, IH - 1));
-            const float *in_ptr_nch = in_ptr_nc + IW * yInRange;
-            float retX = 0.f;
-            for (int x = ix - 1, j = 0; x <= ix + 2; x++, j++) {
-                int xInRange = std::max(0, std::min(x, IW - 1));
-                retX += xFactor[ox * CUBIC_GRID_LEN + j] * in_ptr_nch[xInRange];
+        case InterpolateMode::linear_onnx: {
+            if (configured_for_layout == InterpolateLayoutType::planar) {
+                linearOnnxPlanar(in_ptr_, out_ptr_, N, C, ID, IH, IW, OD, OH, OW);
+            } else {
+                linearOnnxCGathered(in_ptr_, out_ptr_, N, C, ID, IH, IW, OD, OH, OW);
             }
-            retY += yFactor[oy * CUBIC_GRID_LEN + i] * retX;
-        }
-        out_ptr_nc[oy * OW + ox] = retY;
-    });
-}
-
-float MKLDNNInterpolateNode::getValue(const uint8_t *base, size_t offset, InferenceEngine::Precision prec) {
-    const uint8_t *baseOffset = base + offset;
-    switch (prec) {
-        case Precision::U8: {
-            return static_cast<float>(*baseOffset);
             break;
         }
-        case Precision::I8: {
-            const int8_t *valuePtr = reinterpret_cast<const int8_t *>(baseOffset);
-            return static_cast<float>(*valuePtr);
-            break;
-        }
-        case Precision::BF16: {
-            const uint16_t *valuePtr = reinterpret_cast<const uint16_t *>(baseOffset);
-            return bfloat16_t::from_bits(*valuePtr);
-            break;
-        }
-        case Precision::FP32: {
-            const float *valuePtr = reinterpret_cast<const float *>(baseOffset);
-            return *valuePtr;
+        case InterpolateMode::cubic: {
+            if (configured_for_layout == InterpolateLayoutType::planar) {
+                cubicPlanar(in_ptr_, out_ptr_, N, C, IH, IW, OH, OW);
+            } else {
+                cubicCGathered(in_ptr_, out_ptr_, N, C, IH, IW, OH, OW);
+            }
             break;
         }
         default: {
-            IE_THROW() << "Interpolate layer does not support precision: " << prec;
-            break;
+            IE_THROW() << "InterpolateJitExecutor has unsupported interpolate mode: " << mode;
         }
     }
 }
 
-void MKLDNNInterpolateNode::setValue(uint8_t *base, size_t offset, float value, InferenceEngine::Precision prec) {
-    uint8_t *baseOffset = base + offset;
-    switch (prec) {
-        case Precision::U8: {
-            uint8_t data = static_cast<uint8_t>(value < 0 ? 0 : value);
-            cpu_memcpy(baseOffset, &data, 1);
+void MKLDNNInterpolateNode::InterpolateRefExecutor::exec(const uint8_t *in_ptr_, uint8_t *out_ptr_) {
+    size_t N = srcDimPad5d[0], C = srcDimPad5d[1], ID = srcDimPad5d[2], IH = srcDimPad5d[3], IW = srcDimPad5d[4];
+    size_t OD = dstDim5d[2], OH = dstDim5d[3], OW = dstDim5d[4];
+
+    switch (mode) {
+        case InterpolateMode::nearest: {
+            NNRef(in_ptr_, out_ptr_, N, C, ID, IH, IW, OD, OH, OW);
             break;
         }
-        case Precision::I8: {
-            int8_t data = static_cast<int8_t>(value);
-            cpu_memcpy(baseOffset, &data, 1);
+        case InterpolateMode::linear_onnx: {
+            linearOnnxRef(in_ptr_, out_ptr_, N, C, ID, IH, IW, OD, OH, OW);
             break;
         }
-        case Precision::BF16: {
-            uint16_t data = bfloat16_t(value).to_bits();
-            cpu_memcpy(baseOffset, &data, 2);
+        case InterpolateMode::cubic: {
+            cubicRef(in_ptr_, out_ptr_, N, C, IH, IW, OH, OW);
             break;
         }
-        case Precision::FP32: {
-            cpu_memcpy(baseOffset, &value, sizeof(float));
+        case InterpolateMode::linear: {
+            float fz = (dataRank == 5) ? dataScales[dataRank - 3] : 1.f;
+            float fy = dataScales[dataRank - 2];
+            float fx = dataScales[dataRank - 1];
+
+            bool isDownsample = (fx < 1.f) || (fy < 1.f) || (fz < 1.f);
+            int kernel_width = 2;
+            linearInterpolation(in_ptr_, out_ptr_, N, C, ID, IH, IW, fx, fy, fz, OD, OH, OW, kernel_width, isDownsample && antialias);
             break;
         }
         default: {
-            IE_THROW() << "Interpolate layer does not support precision: " << prec;
-            break;
+            IE_THROW() << "Interpolate layer has unsupported interpolate mode: " << mode;
         }
     }
 }
 
-// scale is float(outShape) / float(inShape)
-// strictly consistent with onnx calc manner(div scale, not multiply inverse), given this is done offline
-// the slight precison diff can produce obvious wrong value due to "nearest round" behavior for NN mode
-inline float MKLDNNInterpolateNode::coordTransToInput(int outCoord, float scale, int inShape, int outShape) {
-    if (scale == 1.0f || (inShape == outShape)) {
-        return outCoord;
-    }
-    switch (coordTransMode) {
-        case InterpolateCoordTransMode::half_pixel: {
-            return (outCoord + 0.5f) / scale - 0.5f;
-            break;
-        }
-        case InterpolateCoordTransMode::pytorch_half_pixel: {
-            if (outShape > 1)
-                return (outCoord + 0.5f) / scale - 0.5f;
-            else
-                return 0;
-            break;
-        }
-        case InterpolateCoordTransMode::asymmetric: {
-            return static_cast<float>(outCoord) / scale;
-            break;
-        }
-        case InterpolateCoordTransMode::tf_half_pixel_for_nn: {
-            return (outCoord + 0.5f) / scale;
-            break;
-        }
-        case InterpolateCoordTransMode::align_corners: {
-            if (outShape > 1)
-                return outCoord * (static_cast<float>(inShape - 1) / static_cast<float>(outShape - 1));
-            else
-                return 0;
-            break;
-        }
-        default: {
-            IE_THROW() << errorPrefix << " does not support specified coordinate transformation mode";
-            break;
-        }
-    }
-}
-
-inline int MKLDNNInterpolateNode::nearestRound(float originCoord, bool isDownsample) {
-    switch (nearestMode) {
-        case InterpolateNearestMode::round_prefer_floor: {
-            if (originCoord == (static_cast<int>(originCoord) + 0.5f))
-                return static_cast<int>(std::floor(originCoord));
-            else
-                return static_cast<int>(std::round(originCoord));
-            break;
-        }
-        case InterpolateNearestMode::round_prefer_ceil: {
-            return static_cast<int>(std::round(originCoord));
-            break;
-        }
-        case InterpolateNearestMode::floor: {
-            return static_cast<int>(std::floor(originCoord));
-            break;
-        }
-        case InterpolateNearestMode::ceil: {
-            return static_cast<int>(std::ceil(originCoord));
-            break;
-        }
-        case InterpolateNearestMode::simple: {
-            if (isDownsample)
-                return static_cast<int>(std::ceil(originCoord));
-            else
-                return static_cast<int>(originCoord);
-        }
-        default: {
-            IE_THROW() << errorPrefix << " does not support specified nearest round mode";
-            break;
-        }
+size_t MKLDNNInterpolateNode::getSpatialDimsNum(const Dim rank) {
+    switch (rank) {
+        case 1:
+        case 3:
+            return 1;
+        case 2:
+        case 4:
+            return 2;
+        case 5:
+            return 3;
+        default:
+            IE_THROW() << "Can't define number spatial";
     }
 }
 
 bool MKLDNNInterpolateNode::canFuse(const MKLDNNNodePtr& node) const {
-    if (!mayiuse(cpu::x64::sse41) || mode == InterpolateMode::linear) {
+    if (!mayiuse(cpu::x64::sse41) || interpAttrs.mode == InterpolateMode::linear) {
         return false;
     }
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_interpolate_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_interpolate_node.h
index b39960040a6..5c423711c7f 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_interpolate_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_interpolate_node.h
@@ -97,6 +97,7 @@ public:
     void createPrimitive() override;
     bool created() const override;
     void execute(mkldnn::stream strm) override;
+    void executeDynamicImpl(mkldnn::stream strm) override;
     bool canBeInPlace() const override {
         return false;
     }
@@ -104,83 +105,141 @@ public:
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
+    bool needShapeInfer() const override;
+    std::vector<VectorDims> shapeInfer() const override;
+    bool needPrepareParams() const override;
+    void prepareParams() override;
+
 private:
-    // nearest neighbor
-    void NNPlanar(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW, int OD, int OH, int OW);
-    void NNCGathered(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW, int OD, int OH, int OW);
-    void NNRef(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW, int OD, int OH, int OW);
+    struct InterpolateAttrs {
+        InterpolateMode mode;
+        InterpolateCoordTransMode coordTransMode;
+        InterpolateNearestMode nearestMode;
+        bool antialias;
+        float cubeCoeff;
+        std::vector<int> padBegin;
+        std::vector<int> padEnd;
+        InferenceEngine::Precision inPrc;
+        InferenceEngine::Precision outPrc;
+        InterpolateLayoutType layout;
+    } interpAttrs;
 
-    // onnx linear
-    void linearOnnxCF(int outCoord, float scale, int inShape, int outShape, int& index0, int& index1, float& weight0, float& weight1);
-    void linearOnnxPlanar(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW, int OD, int OH, int OW);
-    void linearOnnxCGathered(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW, int OD, int OH, int OW);
-    void linearOnnxRef(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW, int OD, int OH, int OW);
+    class InterpolateExecutor {
+        public:
+            InterpolateExecutor(const InterpolateAttrs& interpAttrs,
+                                const VectorDims &srcDims,
+                                const VectorDims &dstDims,
+                                const std::vector<float> &dataScales);
 
-    // cubic
-    std::vector<float> getCubicCoeffs(float mantissa, float a);
-    void cubicPlanar(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int IH, int IW, int OH, int OW);
-    void cubicCGathered(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int IH, int IW, int OH, int OW);
-    void cubicRef(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int IH, int IW, int OH, int OW);
+            virtual void exec(const uint8_t *in_ptr_, uint8_t *out_ptr_) = 0;
+            virtual ~InterpolateExecutor() = default;
+            VectorDims getSrcDimPad5d() const { return srcDimPad5d; }
 
-    // linear
-    void linearInterpolation(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW,
-                                          float fx, float fy, float fz, int OD, int OH, int OW, int kernel_width, bool antialias);
+        private:
+            void buildTblNN(const SizeVector& srcDimPad5d, const SizeVector& dstDim5d, const std::vector<float>& dataScales,
+                            InterpolateLayoutType layout, InterpolateNearestMode nearestMode);
+            void buildTblLinearOnnx(const SizeVector& srcDimPad5d, const SizeVector& dstDim5d, const std::vector<float>& dataScales,
+                                    InterpolateLayoutType layout);
+            void buildTblLinear(const SizeVector& srcDimPad5d, const SizeVector& dstDim5d, const std::vector<float>& dataScales, int kernel_width,
+                                bool antialias);
+            void buildTblCubic(const SizeVector& srcDimPad5d, const SizeVector& dstDim5d, const std::vector<float>& dataScales, float cubicCoeff,
+                               InterpolateLayoutType layout);
 
-    void buildTblNN(SizeVector& srcDimPad5d, SizeVector& dstDim5d, std::vector<float>& dataScales, InterpolateLayoutType layout);
-    void buildTblLinearOnnx(SizeVector& srcDimPad5d, SizeVector& dstDim5d, std::vector<float>& dataScales, InterpolateLayoutType layout);
-    void buildTblLinear(SizeVector& srcDimPad5d, SizeVector& dstDim5d, std::vector<float>& dataScales, int kernel_width, bool antialias);
-    void buildTblCubic(SizeVector& srcDimPad5d, SizeVector& dstDim5d, std::vector<float>& dataScales, float cubicCoeff, InterpolateLayoutType layout);
+            float coordTransToInput(int outCoord, float scale, int inShape, int outShape) const;
+            int nearestRound(float origin, bool isDownsample, InterpolateNearestMode nearestMode) const;
+            void linearOnnxCF(int outCoord, float scale, int inShape, int outShape, int& index0, int& index1, float& weight0, float& weight1);
+            std::vector<float> getCubicCoeffs(float mantissa, float a);
 
-    void setPostOps(mkldnn::primitive_attr &attr, bool initWeights = false);
+        protected:
+            InterpolateMode mode;
+            InterpolateCoordTransMode coordTransMode;
+            InterpolateLayoutType configured_for_layout;
+            VectorDims srcDimPad5d, dstDim5d;
+            InferenceEngine::Precision inputPrec, outputPrec;
+            size_t srcDataSize, dstDataSize;
+            int spatialDimSize;
+            size_t dataRank;
+            std::vector<int> indexTable;
+    };
+    std::shared_ptr<InterpolateExecutor> execPtr = nullptr;
 
-    inline float coordTransToInput(int outCoord, float scale, int inShape, int outShape);
-    inline int nearestRound(float origin, bool isDownsample);
-    float getValue(const uint8_t *base, size_t offset, InferenceEngine::Precision prec);
-    void setValue(uint8_t *base, size_t offset, float value, InferenceEngine::Precision prec);
+    class InterpolateJitExecutor : public InterpolateExecutor {
+        public:
+            InterpolateJitExecutor(const InterpolateAttrs& interpAttrs,
+                                   const VectorDims &srcDims,
+                                   const VectorDims &dstDims,
+                                   const std::vector<float> &dataScales,
+                                   const mkldnn::primitive_attr &attr);
 
-    SizeVector getPaddedInputShape();
-    std::vector<float> getScales();
+            void exec(const uint8_t *in_ptr_, uint8_t *out_ptr_) override;
 
-    static const size_t DATA_ID = 0;
-    static const size_t TARGET_SHAPE_ID = 1;
-    static const size_t SCALES_ID = 2;
-    static const size_t AXES_ID = 3;
-    const int LINEAR_KERNEL = 2;
-    const int CUBIC_GRID_LEN = 4;
+        private:
+            // nearest neighbor
+            void NNPlanar(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW, int OD, int OH, int OW);
+            void NNCGathered(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW, int OD, int OH, int OW);
+
+            // onnx linear
+            void linearOnnxPlanar(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW, int OD, int OH, int OW);
+            void linearOnnxCGathered(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW, int OD, int OH, int OW);
+
+            // cubic
+            void cubicPlanar(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int IH, int IW, int OH, int OW);
+            void cubicCGathered(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int IH, int IW, int OH, int OW);
+
+        private:
+            std::shared_ptr<jit_uni_interpolate_kernel> interpolateKernel = nullptr;
+    };
+
+    class InterpolateRefExecutor : public InterpolateExecutor {
+        public:
+            InterpolateRefExecutor(const InterpolateAttrs& interpAttrs,
+                                   const VectorDims &srcDims,
+                                   const VectorDims &dstDims,
+                                   const std::vector<float> &_dataScales) : dataScales(_dataScales), antialias(interpAttrs.antialias),
+                InterpolateExecutor(interpAttrs, srcDims, dstDims, _dataScales) {}
+
+            void exec(const uint8_t *in_ptr_, uint8_t *out_ptr_) override;
+
+        private:
+            void NNRef(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW, int OD, int OH, int OW);
+            void linearOnnxRef(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW, int OD, int OH, int OW);
+
+            void cubicRef(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int IH, int IW, int OH, int OW);
+            void linearInterpolation(const uint8_t *in_ptr_, uint8_t *out_ptr_, int B, int C, int ID, int IH, int IW,
+                                      float fx, float fy, float fz, int OD, int OH, int OW, int kernel_width, bool antialias);
+
+            static float getValue(const uint8_t *base, size_t offset, InferenceEngine::Precision prec);
+            static void setValue(uint8_t *base, size_t offset, float value, InferenceEngine::Precision prec);
+
+        private:
+            bool antialias;
+            std::vector<float> dataScales;
+    };
+
+    void setPostOps(mkldnn::primitive_attr &attr, const VectorDims &dims, bool initWeights = false);
+
+    static SizeVector getPaddedInputShape(const VectorDims &srcDims, const std::vector<int> &padBegin, const std::vector<int> &padEnd);
+    std::vector<float> getScales(const VectorDims &srcDimPad, const VectorDims &dstDim);
+    static size_t getSpatialDimsNum(const Dim rank);
+
+    static constexpr size_t DATA_ID = 0;
+    static constexpr size_t TARGET_SHAPE_ID = 1;
+    static constexpr size_t SCALES_ID = 2;
+    static constexpr size_t AXES_ID = 3;
+    static constexpr int CUBIC_GRID_LEN = 4;
 
-    InterpolateMode mode;
-    InterpolateCoordTransMode coordTransMode = InterpolateCoordTransMode::half_pixel;
-    bool antialias = false;
-    std::vector<int> padBegin;
-    std::vector<int> padEnd;
     bool hasPad = false;
-    InterpolateNearestMode nearestMode = InterpolateNearestMode::round_prefer_floor;
     InterpolateShapeCalcMode shapeCalcMode;
 
-    float cubeCoeff = -0.75;
-
     bool isAxesSpecified = false;
-    // axes and scales from buffer, partical size.
     std::vector<int> axes;
-    std::vector<float> scales;
-    // target shape is dst dim, full size.
-    SizeVector dstDim;
-    SizeVector srcDim;
-    SizeVector srcDimPad;
-    int spatialDimSize = 1;
 
     mkldnn::primitive_attr attr;
-    std::vector<MKLDNNMemoryPtr> PostOpsIntBlobMemory;
 
-    InferenceEngine::Precision inputPrec, outputPrec;
-    size_t srcDataSize = 0;
-    size_t dstDataSize = 0;
+    std::vector<float> lastScales;
+    std::vector<int32_t> lastSizes;
 
-    InterpolateLayoutType configured_for_layout = InterpolateLayoutType::planar;
-
-    std::vector<int> indexTable;
-
-    std::shared_ptr<jit_uni_interpolate_kernel> interpolateKernel = nullptr;
+    VectorDims lastOutputDims;
 
     std::string errorPrefix;
 };
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.h
index 104b6f65e0b..1d91199f95a 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.h
@@ -28,7 +28,7 @@ public:
         return false;
     }
 
-    void prepareParams() override;;
+    void prepareParams() override;
     void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/interpolate.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/interpolate.cpp
index 7392cbdb530..f3bbbe6d1b4 100644
--- a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/interpolate.cpp
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/interpolate.cpp
@@ -2,35 +2,95 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <shared_test_classes/single_layer/interpolate.hpp>
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "ngraph_functions/builders.hpp"
 #include "test_utils/cpu_test_utils.hpp"
 #include "test_utils/fusing_test_utils.hpp"
+#include "functional_test_utils/ov_tensor_utils.hpp"
+#include "openvino/core/preprocess/pre_post_process.hpp"
 
-using namespace InferenceEngine;
+using namespace ov::test;
 using namespace CPUTestUtils;
+using ngraph::helpers::operator<<;
 
 namespace CPULayerTestsDefinitions {
 
-typedef std::tuple<
-            LayerTestsDefinitions::InterpolateLayerTestParams,
-            CPUSpecificParams,
-            fusingSpecificParams,
-            std::map<std::string, std::string>
-        > InterpolateLayerCPUTestParamsSet;
+using InterpolateSpecificParams = std::tuple<ngraph::op::v4::Interpolate::InterpolateMode,          // InterpolateMode
+                                             ngraph::op::v4::Interpolate::CoordinateTransformMode,  // CoordinateTransformMode
+                                             ngraph::op::v4::Interpolate::NearestMode,              // NearestMode
+                                             bool,                                                  // AntiAlias
+                                             std::vector<size_t>,                                   // PadBegin
+                                             std::vector<size_t>,                                   // PadEnd
+                                             double>;                                               // Cube coef
+
+using ShapeParams = std::tuple<ngraph::op::v4::Interpolate::ShapeCalcMode, // ShapeCalculationMode
+                               InputShape,                                 // Input shapes
+                               // params describing input, choice of which depends on ShapeCalcMode
+                               ngraph::helpers::InputLayerType,            // input type
+                               std::vector<std::vector<float>>,            // scales or sizes values
+                               std::vector<int64_t>>;                      // axes
+
+using InterpolateLayerCPUTestParamsSet = std::tuple<InterpolateSpecificParams,
+                                                    ShapeParams,
+                                                    ElementType,
+                                                    CPUSpecificParams,
+                                                    fusingSpecificParams,
+                                                    std::map<std::string, std::string>>;
 
 class InterpolateLayerCPUTest : public testing::WithParamInterface<InterpolateLayerCPUTestParamsSet>,
-                                     virtual public LayerTestsUtils::LayerTestsCommon, public CpuTestWithFusing {
+                                virtual public SubgraphBaseTest, public CpuTestWithFusing {
 public:
     static std::string getTestCaseName(testing::TestParamInfo<InterpolateLayerCPUTestParamsSet> obj) {
-        LayerTestsDefinitions::InterpolateLayerTestParams basicParamsSet;
+        InterpolateSpecificParams specificParams;
+        ShapeParams shapeParams;
+        ElementType prec;
         CPUSpecificParams cpuParams;
         fusingSpecificParams fusingParams;
         std::map<std::string, std::string> additionalConfig;
-        std::tie(basicParamsSet, cpuParams, fusingParams, additionalConfig) = obj.param;
+        std::tie(specificParams, shapeParams, prec, cpuParams, fusingParams, additionalConfig) = obj.param;
+
+        ngraph::op::v4::Interpolate::InterpolateMode mode;
+        ngraph::op::v4::Interpolate::CoordinateTransformMode transfMode;
+        ngraph::op::v4::Interpolate::NearestMode nearMode;
+        bool antiAlias;
+        std::vector<size_t> padBegin;
+        std::vector<size_t> padEnd;
+        double cubeCoef;
+        std::tie(mode, transfMode, nearMode, antiAlias, padBegin, padEnd, cubeCoef) = specificParams;
+
+        ngraph::op::v4::Interpolate::ShapeCalcMode shapeCalcMode;
+        InputShape inputShapes;
+        ngraph::helpers::InputLayerType shapeInputType;
+        std::vector<std::vector<float>> shapeDataForInput;
+        std::vector<int64_t> axes;
+        std::tie(shapeCalcMode, inputShapes, shapeInputType, shapeDataForInput, axes) = shapeParams;
 
         std::ostringstream result;
-        result << LayerTestsDefinitions::InterpolateLayerTest::getTestCaseName(testing::TestParamInfo<LayerTestsDefinitions::InterpolateLayerTestParams>(
-                basicParamsSet, 0));
+        result << "ShapeCalcMode=" << shapeCalcMode << "_";
+        result << "IS=";
+        result << CommonTestUtils::partialShape2str({inputShapes.first}) << "_";
+        result << "TS=";
+        for (const auto& shape : inputShapes.second) {
+            result << CommonTestUtils::vec2str(shape) << "_";
+        }
+        if (shapeCalcMode == ngraph::op::v4::Interpolate::ShapeCalcMode::SCALES) {
+            result << "Scales=";
+        } else {
+            result << "Sizes=";
+        }
+        for (const auto &data : shapeDataForInput) {
+            result << CommonTestUtils::vec2str(data) << "_";
+        }
+        result << shapeInputType << "_";
+        result << "InterpolateMode=" << mode << "_";
+        result << "CoordinateTransformMode=" << transfMode << "_";
+        result << "NearestMode=" << nearMode << "_";
+        result << "CubeCoef=" << cubeCoef << "_";
+        result << "Antialias=" << antiAlias << "_";
+        result << "PB=" << CommonTestUtils::vec2str(padBegin) << "_";
+        result << "PE=" << CommonTestUtils::vec2str(padEnd) << "_";
+        result << "Axes=" << CommonTestUtils::vec2str(axes) << "_";
+        result << "PRC=" << prec << "_";
 
         result << CPUTestsBase::getTestCaseName(cpuParams);
         result << CpuTestWithFusing::getTestCaseName(fusingParams);
@@ -45,75 +105,168 @@ public:
         return result.str();
     }
 
+    void generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) override {
+        inputs.clear();
+        const auto& funcInputs = function->inputs();
+        for (int i = 0; i < funcInputs.size(); ++i) {
+            const auto& funcInput = funcInputs[i];
+            ov::runtime::Tensor tensor;
+
+            if (i == 1) {
+                if (shapeCalcMode == ngraph::op::v4::Interpolate::ShapeCalcMode::SIZES) {
+                    tensor = ov::runtime::Tensor(funcInput.get_element_type(), targetInputStaticShapes[i], sizes[inferRequestNum].data());
+                } else {
+                    tensor = ov::runtime::Tensor(funcInput.get_element_type(), targetInputStaticShapes[i], scales[inferRequestNum].data());
+                }
+            } else {
+                tensor = ov::test::utils::create_and_fill_tensor(funcInput.get_element_type(), targetInputStaticShapes[i], 2560, 0, 256);
+            }
+
+            inputs.insert({funcInput.get_node_shared_ptr(), tensor});
+        }
+        inferRequestNum++;
+    }
+
+    void configure_model() override {
+        ov::preprocess::PrePostProcessor p(function);
+        {
+            auto& params = function->get_parameters();
+            for (size_t i = 0; i < params.size(); i++) {
+                if (i > 0) {
+                    continue;
+                }
+                if (inType != ov::element::Type_t::undefined) {
+                    p.input(ov::preprocess::InputInfo(i)
+                            .tensor(ov::preprocess::InputTensorInfo().set_element_type(inType)));
+                }
+            }
+        }
+        {
+            auto results = function->get_results();
+            for (size_t i = 0; i < results.size(); i++) {
+                if (outType != ov::element::Type_t::undefined) {
+                    p.output(ov::preprocess::OutputInfo(i)
+                                 .tensor(ov::preprocess::OutputTensorInfo().set_element_type(outType)));
+                }
+            }
+        }
+        function = p.build();
+    }
+
 protected:
+    std::vector<std::vector<float>> scales;
+    std::vector<std::vector<int32_t>> sizes;
+    ngraph::op::v4::Interpolate::ShapeCalcMode shapeCalcMode;
+    size_t inferRequestNum = 0;
+
     void SetUp() override {
-        LayerTestsDefinitions::InterpolateLayerTestParams basicParamsSet;
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+
+        InterpolateSpecificParams specificParams;
+        ShapeParams shapeParams;
+        ElementType ngPrc;
         CPUSpecificParams cpuParams;
         fusingSpecificParams fusingParams;
         std::map<std::string, std::string> additionalConfig;
-        std::tie(basicParamsSet, cpuParams, fusingParams, additionalConfig) = this->GetParam();
+        std::tie(specificParams, shapeParams, ngPrc, cpuParams, fusingParams, additionalConfig) = this->GetParam();
 
         std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
         std::tie(postOpMgrPtr, fusedOps) = fusingParams;
-
-        LayerTestsDefinitions::InterpolateSpecificParams interpolateParams;
-        std::vector<size_t> inputShape;
-        std::vector<size_t> targetShape;
-        Precision netPrecision;
-        std::map<std::string, std::string> additional_config;
-        std::tie(interpolateParams, netPrecision, inPrc, outPrc, inLayout, outLayout, inputShape,
-                 targetShape, targetDevice, additional_config) = basicParamsSet;
+        configuration.insert(additionalConfig.begin(), additionalConfig.end());
 
         ngraph::op::v4::Interpolate::InterpolateMode mode;
-        ngraph::op::v4::Interpolate::ShapeCalcMode shapeCalcMode;
-        ngraph::op::v4::Interpolate::CoordinateTransformMode coordinateTransformMode;
-        ngraph::op::v4::Interpolate::NearestMode nearestMode;
-        bool antialias;
-        std::vector<size_t> padBegin, padEnd;
+        ngraph::op::v4::Interpolate::CoordinateTransformMode transfMode;
+        ngraph::op::v4::Interpolate::NearestMode nearMode;
+        bool antiAlias;
+        std::vector<size_t> padBegin;
+        std::vector<size_t> padEnd;
         double cubeCoef;
+        std::tie(mode, transfMode, nearMode, antiAlias, padBegin, padEnd, cubeCoef) = specificParams;
+
+        InputShape dataShape;
+        ngraph::helpers::InputLayerType shapeInputType;
+        std::vector<std::vector<float>> shapeDataForInput;
         std::vector<int64_t> axes;
-        std::vector<float> scales;
-        std::tie(mode, shapeCalcMode, coordinateTransformMode, nearestMode, antialias, padBegin, padEnd, cubeCoef, axes, scales) = interpolateParams;
-        inPrc = outPrc = netPrecision;
-        configuration.insert(additionalConfig.begin(), additionalConfig.end());
-        using ShapeCalcMode = ngraph::op::v4::Interpolate::ShapeCalcMode;
+        std::tie(shapeCalcMode, dataShape, shapeInputType, shapeDataForInput, axes) = shapeParams;
 
-        auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
-        auto params = ngraph::builder::makeParams(ngPrc, {inputShape});
+        if (shapeCalcMode == ngraph::op::v4::Interpolate::ShapeCalcMode::SCALES) {
+            scales = shapeDataForInput;
+            sizes.resize(scales.size(), std::vector<int32_t>(scales.front().size(), 0));
+        } else {
+            sizes.resize(shapeDataForInput.size());
+            for (size_t i = 0; i < shapeDataForInput.size(); i++) {
+                for (size_t j = 0; j < shapeDataForInput[i].size(); j++) {
+                    sizes[i].push_back(shapeDataForInput[i][j]);
+                }
+            }
+            scales.resize(sizes.size(), std::vector<float>(sizes.front().size(), 0));
+        }
 
-        auto constant = ngraph::opset3::Constant(ngraph::element::Type_t::i64, {targetShape.size()}, targetShape);
+        std::vector<InputShape> inputShapes;
+        inputShapes.push_back(dataShape);
+        if (shapeInputType == ngraph::helpers::InputLayerType::PARAMETER) {
+            inputShapes.push_back(InputShape({static_cast<int64_t>(axes.size())}, std::vector<ov::Shape>(dataShape.second.size(), {axes.size()})));
+        }
 
-        auto scales_const = ngraph::opset3::Constant(ngraph::element::Type_t::f32, {scales.size()}, scales);
+        if (additionalConfig[InferenceEngine::PluginConfigParams::KEY_ENFORCE_BF16] == InferenceEngine::PluginConfigParams::YES) {
+            inType = outType = ngPrc = ElementType::bf16;
+            rel_threshold = 1e-2f;
+        } else {
+            inType = outType = ngPrc;
+        }
 
-        auto scalesInput = std::make_shared<ngraph::opset3::Constant>(scales_const);
+        init_input_shapes(inputShapes);
 
-        auto secondaryInput = std::make_shared<ngraph::opset3::Constant>(constant);
+        auto params = ngraph::builder::makeDynamicParams(ngPrc, {inputDynamicShapes.front()});
+
+        std::shared_ptr<ov::Node> sizesInput, scalesInput;
+        if (shapeCalcMode == ngraph::op::v4::Interpolate::ShapeCalcMode::SCALES) {
+            if (shapeInputType == ngraph::helpers::InputLayerType::PARAMETER) {
+                auto paramNode = std::make_shared<ngraph::opset3::Parameter>(ngraph::element::Type_t::f32, ov::Shape{scales.front().size()});
+                params.push_back(paramNode);
+                scalesInput = paramNode;
+            } else {
+                scalesInput = std::make_shared<ngraph::opset3::Constant>(ngraph::element::Type_t::f32, ov::Shape{scales.front().size()}, scales.front());
+            }
+            sizesInput = std::make_shared<ngraph::opset3::Constant>(ngraph::element::Type_t::i32, ov::Shape{sizes.front().size()}, sizes.front());
+        } else {
+            if (shapeInputType == ngraph::helpers::InputLayerType::PARAMETER) {
+                auto paramNode = std::make_shared<ngraph::opset3::Parameter>(ngraph::element::Type_t::i32, ov::Shape{sizes.front().size()});
+                params.push_back(paramNode);
+                sizesInput = paramNode;
+            } else {
+                sizesInput = std::make_shared<ngraph::opset3::Constant>(ngraph::element::Type_t::i32, ov::Shape{sizes.front().size()}, sizes.front());
+            }
+            scalesInput = std::make_shared<ngraph::opset3::Constant>(ngraph::element::Type_t::f32, ov::Shape{scales.front().size()}, scales.front());
+        }
+        auto axesInput = std::make_shared<ngraph::opset3::Constant>(ngraph::element::Type_t::i64, ov::Shape{axes.size()}, axes);
+
+        for (size_t i = 0; i < params.size(); i++) {
+            params[i]->set_friendly_name(std::string("param_") + std::to_string(i));
+        }
+
+        ngraph::op::v4::Interpolate::InterpolateAttrs interpAttr{mode, shapeCalcMode, padBegin, padEnd, transfMode, nearMode,
+                                                                            antiAlias, cubeCoef};
 
-        auto axesConst = ngraph::opset3::Constant(ngraph::element::Type_t::i64, {axes.size()}, axes);
-        auto axesInput = std::make_shared<ngraph::opset3::Constant>(axesConst);
-        ngraph::op::v4::Interpolate::InterpolateAttrs interpolateAttributes{mode, shapeCalcMode, padBegin,
-            padEnd, coordinateTransformMode, nearestMode, antialias, cubeCoef};
         auto interpolate = std::make_shared<ngraph::op::v4::Interpolate>(params[0],
-                                                                         secondaryInput,
+                                                                         sizesInput,
                                                                          scalesInput,
                                                                          axesInput,
-                                                                         interpolateAttributes);
-        function = makeNgraphFunction(ngPrc, params, interpolate, "interpolate");
+                                                                         interpAttr);
+
+        function = makeNgraphFunction(ngPrc, params, interpolate, "InterpolateCPU");
+
         if (selectedType.empty()) {
             selectedType = getPrimitiveType();
         }
-        selectedType.push_back('_');
-        if (additionalConfig.count(PluginConfigParams::KEY_ENFORCE_BF16) && additionalConfig[PluginConfigParams::KEY_ENFORCE_BF16] == PluginConfigParams::YES)
-            selectedType += "BF16";
-        else
-            selectedType += netPrecision.name();
+        selectedType = makeSelectedTypeStr(selectedType, ngPrc);
     }
 };
 
 TEST_P(InterpolateLayerCPUTest, CompareWithRefs) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
 
-    Run();
+    run();
     CheckPluginRelatedResults(executableNetwork, "Interpolate");
 }
 
@@ -122,14 +275,14 @@ namespace {
 /* CPU PARAMS */
 std::vector<CPUSpecificParams> filterCPUInfoForDevice() {
     std::vector<CPUSpecificParams> resCPUParams;
-    if (with_cpu_x86_avx512f()) {
+    if (InferenceEngine::with_cpu_x86_avx512f()) {
         resCPUParams.push_back(CPUSpecificParams{{nChw16c, x, x, x}, {nChw16c}, {"jit_avx512"}, "jit_avx512"});
         resCPUParams.push_back(CPUSpecificParams{{nhwc, x, x, x}, {nhwc}, {"jit_avx512"}, "jit_avx512"});
-    } else if (with_cpu_x86_avx2()) {
+    } else if (InferenceEngine::with_cpu_x86_avx2()) {
         resCPUParams.push_back(CPUSpecificParams{{nChw8c, x, x, x}, {nChw8c}, {"jit_avx2"}, "jit_avx2"});
         resCPUParams.push_back(CPUSpecificParams{{nhwc, x, x, x}, {nhwc}, {"jit_avx2"}, "jit_avx2"});
         resCPUParams.push_back(CPUSpecificParams{{nchw, x, x, x}, {nchw}, {"jit_avx2"}, "jit_avx2"});
-    } else if (with_cpu_x86_sse42()) {
+    } else if (InferenceEngine::with_cpu_x86_sse42()) {
         resCPUParams.push_back(CPUSpecificParams{{nChw8c, x, x, x}, {nChw8c}, {"jit_sse42"}, "jit_sse42"});
         resCPUParams.push_back(CPUSpecificParams{{nhwc, x, x, x}, {nhwc}, {"jit_sse42"}, "jit_sse42"});
     } else {
@@ -138,11 +291,6 @@ std::vector<CPUSpecificParams> filterCPUInfoForDevice() {
     return resCPUParams;
 }
 /* ========== */
-
-const std::vector<InferenceEngine::Precision> netPrecisions = {
-    InferenceEngine::Precision::FP32
-};
-
 const std::vector<ngraph::op::v4::Interpolate::CoordinateTransformMode> coordinateTransformModes = {
         ngraph::op::v4::Interpolate::CoordinateTransformMode::TF_HALF_PIXEL_FOR_NN,
         ngraph::op::v4::Interpolate::CoordinateTransformMode::PYTORCH_HALF_PIXEL,
@@ -168,11 +316,6 @@ const std::vector<ngraph::op::v4::Interpolate::NearestMode> defNearestModes = {
         ngraph::op::v4::Interpolate::NearestMode::ROUND_PREFER_FLOOR,
 };
 
-const std::vector<std::vector<size_t>> pads = {
-        {0, 0, 0, 0},
-        {0, 0, 1, 1},
-};
-
 const std::vector<bool> antialias = {
         false,
 };
@@ -181,152 +324,178 @@ const std::vector<double> cubeCoefs = {
         -0.75f,
 };
 
-const std::vector<std::vector<int64_t>> defaultAxes = {
-    {0, 1, 2, 3}
-};
-
-const std::vector<std::vector<float>> defaultScales = {
-    {1.f, 1.f, 1.25f, 1.5f}
-};
-
-const auto interpolateCasesNN = ::testing::Combine(
-        ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::nearest),
-        ::testing::ValuesIn(shapeCalculationMode),
-        ::testing::ValuesIn(coordinateTransformModes),
-        ::testing::ValuesIn(nearestModes),
-        ::testing::ValuesIn(antialias),
-        ::testing::ValuesIn(pads),
-        ::testing::ValuesIn(pads),
-        ::testing::ValuesIn(cubeCoefs),
-        ::testing::ValuesIn(defaultAxes),
-        ::testing::ValuesIn(defaultScales));
-
-const auto interpolateCasesLinearOnnx = ::testing::Combine(
-        ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::linear_onnx),
-        ::testing::ValuesIn(shapeCalculationMode),
-        ::testing::ValuesIn(coordinateTransformModes),
-        ::testing::ValuesIn(defNearestModes),
-        ::testing::ValuesIn(antialias),
-        ::testing::ValuesIn(pads),
-        ::testing::ValuesIn(pads),
-        ::testing::ValuesIn(cubeCoefs),
-        ::testing::ValuesIn(defaultAxes),
-        ::testing::ValuesIn(defaultScales));
-
-const auto interpolateCasesLinear = ::testing::Combine(
-        ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::linear),
-        ::testing::ValuesIn(shapeCalculationMode),
-        ::testing::ValuesIn(coordinateTransformModes),
-        ::testing::ValuesIn(defNearestModes),
-        ::testing::ValuesIn(antialias),
-        ::testing::ValuesIn(pads),
-        ::testing::ValuesIn(pads),
-        ::testing::ValuesIn(cubeCoefs),
-        ::testing::ValuesIn(defaultAxes),
-        ::testing::ValuesIn(defaultScales));
-
-const auto interpolateCasesCubic = ::testing::Combine(
-        ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::cubic),
-        ::testing::ValuesIn(shapeCalculationMode),
-        ::testing::ValuesIn(coordinateTransformModes),
-        ::testing::ValuesIn(defNearestModes),
-        ::testing::ValuesIn(antialias),
-        ::testing::ValuesIn(pads),
-        ::testing::ValuesIn(pads),
-        ::testing::ValuesIn(cubeCoefs),
-        ::testing::ValuesIn(defaultAxes),
-        ::testing::ValuesIn(defaultScales));
-
 const std::vector<fusingSpecificParams> interpolateFusingParamsSet{
         emptyFusingSpec,
-        fusingRelu,
         fusingSwish,
-        fusingFakeQuantizePerChannelRelu,
+        fusingFakeQuantizePerTensorRelu,
 };
 
-std::map<std::string, std::string> additional_config = {};
-
 std::vector<std::map<std::string, std::string>> filterAdditionalConfig() {
-    if (with_cpu_x86_avx512f()) {
+    if (InferenceEngine::with_cpu_x86_avx512f()) {
         return {
-            {{PluginConfigParams::KEY_ENFORCE_BF16, PluginConfigParams::NO}},
-            {{PluginConfigParams::KEY_ENFORCE_BF16, PluginConfigParams::YES}}
+            {{InferenceEngine::PluginConfigParams::KEY_ENFORCE_BF16, InferenceEngine::PluginConfigParams::NO}},
+            {{InferenceEngine::PluginConfigParams::KEY_ENFORCE_BF16, InferenceEngine::PluginConfigParams::YES}}
         };
     } else {
         return {
             // default config as an stub for target without avx512, otherwise all tests with BF16 in its name are skipped
-            {{PluginConfigParams::KEY_PERF_COUNT, PluginConfigParams::NO}}
+            {{InferenceEngine::PluginConfigParams::KEY_PERF_COUNT, InferenceEngine::PluginConfigParams::NO}}
         };
     }
 }
 
+const std::vector<std::vector<size_t>> pads4D = {
+        {0, 0, 0, 0},
+        {0, 0, 1, 1},
+};
+
+const std::vector<std::vector<int64_t>> defaultAxes4D = {
+    {0, 1, 2, 3}
+};
+
+const std::vector<ShapeParams> shapeParams4D = {
+    ShapeParams{
+        ngraph::op::v4::Interpolate::ShapeCalcMode::SCALES,
+        InputShape{{}, {{1, 11, 4, 4}}},
+        ngraph::helpers::InputLayerType::CONSTANT,
+        {{1.f, 1.f, 1.25f, 1.5f}},
+        defaultAxes4D.front()
+    },
+    ShapeParams{
+        ngraph::op::v4::Interpolate::ShapeCalcMode::SIZES,
+        InputShape{{}, {{1, 11, 4, 4}}},
+        ngraph::helpers::InputLayerType::CONSTANT,
+        {{1, 11, 5, 6}},
+        defaultAxes4D.front()
+    },
+    ShapeParams{
+        ngraph::op::v4::Interpolate::ShapeCalcMode::SCALES,
+        InputShape{{-1, {2, 20}, -1, -1}, {{1, 11, 4, 4}, {2, 7, 6, 5}}},
+        ngraph::helpers::InputLayerType::CONSTANT,
+        {{1.f, 1.f, 1.25f, 1.5f}},
+        defaultAxes4D.front()
+    },
+    ShapeParams{
+        ngraph::op::v4::Interpolate::ShapeCalcMode::SIZES,
+        InputShape{{-1, {2, 20}, -1, -1}, {{1, 11, 4, 4}, {1, 11, 5, 5}}},
+        ngraph::helpers::InputLayerType::CONSTANT,
+        {{1, 11, 5, 6}},
+        defaultAxes4D.front()
+    },
+    ShapeParams{
+        ngraph::op::v4::Interpolate::ShapeCalcMode::SCALES,
+        InputShape{{-1, {2, 20}, -1, -1}, {{1, 11, 4, 4}, {2, 7, 6, 5}}},
+        ngraph::helpers::InputLayerType::PARAMETER,
+        {{1.f, 1.f, 1.25f, 1.5f}, {1.f, 1.f, 1.25f, 1.25f}},
+        defaultAxes4D.front()
+    },
+    ShapeParams{
+        ngraph::op::v4::Interpolate::ShapeCalcMode::SIZES,
+        InputShape{{-1, {2, 20}, -1, -1}, {{1, 11, 4, 4}, {2, 7, 6, 5}}},
+        ngraph::helpers::InputLayerType::PARAMETER,
+        {{1, 11, 6, 7}, {2, 7, 8, 7}},
+        defaultAxes4D.front()
+    }
+};
+
+const auto interpolateCasesNN = ::testing::Combine(
+        ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::nearest),
+        ::testing::ValuesIn(coordinateTransformModes),
+        ::testing::ValuesIn(nearestModes),
+        ::testing::ValuesIn(antialias),
+        ::testing::ValuesIn(pads4D),
+        ::testing::ValuesIn(pads4D),
+        ::testing::ValuesIn(cubeCoefs));
+
 INSTANTIATE_TEST_SUITE_P(smoke_InterpolateNN_Layout_Test, InterpolateLayerCPUTest,
         ::testing::Combine(
-            ::testing::Combine(
-                interpolateCasesNN,
-                ::testing::ValuesIn(netPrecisions),
-                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(std::vector<size_t>({1, 21, 4, 4})),
-                ::testing::Values(std::vector<size_t>({1, 21, 5, 6})),
-                ::testing::Values(CommonTestUtils::DEVICE_CPU),
-                ::testing::Values(additional_config)),
+            interpolateCasesNN,
+            ::testing::ValuesIn(shapeParams4D),
+            ::testing::Values(ElementType::f32),
             ::testing::ValuesIn(filterCPUInfoForDevice()),
             ::testing::ValuesIn(interpolateFusingParamsSet),
             ::testing::ValuesIn(filterAdditionalConfig())),
     InterpolateLayerCPUTest::getTestCaseName);
 
+const std::vector<ShapeParams> shapeParams4D_fixed_C = {
+    ShapeParams{
+        ngraph::op::v4::Interpolate::ShapeCalcMode::SCALES,
+        InputShape{{}, {{1, 11, 4, 4}}},
+        ngraph::helpers::InputLayerType::CONSTANT,
+        {{1.f, 1.f, 1.25f, 1.5f}},
+        defaultAxes4D.front()
+    },
+    ShapeParams{
+        ngraph::op::v4::Interpolate::ShapeCalcMode::SIZES,
+        InputShape{{-1, 16, -1, -1}, {{1, 16, 4, 4}, {1, 16, 6, 5}}},
+        ngraph::helpers::InputLayerType::CONSTANT,
+        {{1, 16, 6, 7}},
+        defaultAxes4D.front()
+    }
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_InterpolateNN_Layout_PerChannelFuse_Test, InterpolateLayerCPUTest,
+        ::testing::Combine(
+            interpolateCasesNN,
+            ::testing::ValuesIn(shapeParams4D_fixed_C),
+            ::testing::Values(ElementType::f32),
+            ::testing::ValuesIn(filterCPUInfoForDevice()),
+            ::testing::Values(fusingFakeQuantizePerChannelRelu),
+            ::testing::ValuesIn(filterAdditionalConfig())),
+    InterpolateLayerCPUTest::getTestCaseName);
+
+const auto interpolateCasesLinearOnnx = ::testing::Combine(
+        ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::linear_onnx),
+        ::testing::ValuesIn(coordinateTransformModes),
+        ::testing::ValuesIn(defNearestModes),
+        ::testing::ValuesIn(antialias),
+        ::testing::ValuesIn(pads4D),
+        ::testing::ValuesIn(pads4D),
+        ::testing::ValuesIn(cubeCoefs));
+
 INSTANTIATE_TEST_SUITE_P(smoke_InterpolateLinearOnnx_Layout_Test, InterpolateLayerCPUTest,
         ::testing::Combine(
-            ::testing::Combine(
-                interpolateCasesLinearOnnx,
-                ::testing::ValuesIn(netPrecisions),
-                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(std::vector<size_t>({1, 21, 4, 4})),
-                ::testing::Values(std::vector<size_t>({1, 21, 5, 6})),
-                ::testing::Values(CommonTestUtils::DEVICE_CPU),
-                ::testing::Values(additional_config)),
+            interpolateCasesLinearOnnx,
+            ::testing::ValuesIn(shapeParams4D),
+            ::testing::Values(ElementType::f32),
             ::testing::ValuesIn(filterCPUInfoForDevice()),
             ::testing::ValuesIn(interpolateFusingParamsSet),
             ::testing::ValuesIn(filterAdditionalConfig())),
     InterpolateLayerCPUTest::getTestCaseName);
 
+const auto interpolateCasesLinear = ::testing::Combine(
+        ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::linear),
+        ::testing::ValuesIn(coordinateTransformModes),
+        ::testing::ValuesIn(defNearestModes),
+        ::testing::ValuesIn(antialias),
+        ::testing::ValuesIn(pads4D),
+        ::testing::ValuesIn(pads4D),
+        ::testing::ValuesIn(cubeCoefs));
+
 INSTANTIATE_TEST_SUITE_P(smoke_InterpolateLinear_Layout_Test, InterpolateLayerCPUTest,
         ::testing::Combine(
-            ::testing::Combine(
-                interpolateCasesLinear,
-                ::testing::ValuesIn(netPrecisions),
-                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(std::vector<size_t>({1, 21, 4, 4})),
-                ::testing::Values(std::vector<size_t>({1, 21, 5, 6})),
-                ::testing::Values(CommonTestUtils::DEVICE_CPU),
-                ::testing::Values(additional_config)),
+            interpolateCasesLinear,
+            ::testing::ValuesIn(shapeParams4D),
+            ::testing::Values(ElementType::f32),
             ::testing::ValuesIn(filterCPUInfoForDevice()),
             ::testing::ValuesIn(interpolateFusingParamsSet),
             ::testing::ValuesIn(filterAdditionalConfig())),
     InterpolateLayerCPUTest::getTestCaseName);
 
+const auto interpolateCasesCubic = ::testing::Combine(
+        ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::cubic),
+        ::testing::ValuesIn(coordinateTransformModes),
+        ::testing::ValuesIn(defNearestModes),
+        ::testing::ValuesIn(antialias),
+        ::testing::ValuesIn(pads4D),
+        ::testing::ValuesIn(pads4D),
+        ::testing::ValuesIn(cubeCoefs));
+
 INSTANTIATE_TEST_SUITE_P(smoke_InterpolateCubic_Layout_Test, InterpolateLayerCPUTest,
         ::testing::Combine(
-            ::testing::Combine(
-                interpolateCasesCubic,
-                ::testing::ValuesIn(netPrecisions),
-                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(std::vector<size_t>({1, 21, 4, 4})),
-                ::testing::Values(std::vector<size_t>({1, 21, 5, 6})),
-                ::testing::Values(CommonTestUtils::DEVICE_CPU),
-                ::testing::Values(additional_config)),
+            interpolateCasesCubic,
+            ::testing::ValuesIn(shapeParams4D),
+            ::testing::Values(ElementType::f32),
             ::testing::ValuesIn(filterCPUInfoForDevice()),
             ::testing::ValuesIn(interpolateFusingParamsSet),
             ::testing::ValuesIn(filterAdditionalConfig())),
@@ -335,14 +504,14 @@ INSTANTIATE_TEST_SUITE_P(smoke_InterpolateCubic_Layout_Test, InterpolateLayerCPU
 ////////////////////////5D/////////////////////////////
 std::vector<CPUSpecificParams> filterCPUInfoForDevice5D() {
     std::vector<CPUSpecificParams> resCPUParams;
-    if (with_cpu_x86_avx512f()) {
+    if (InferenceEngine::with_cpu_x86_avx512f()) {
         resCPUParams.push_back(CPUSpecificParams{{nCdhw16c, x, x, x}, {nCdhw16c}, {"jit_avx512"}, "jit_avx512"});
         resCPUParams.push_back(CPUSpecificParams{{ndhwc, x, x, x}, {ndhwc}, {"jit_avx512"}, "jit_avx512"});
-    } else if (with_cpu_x86_avx2()) {
+    } else if (InferenceEngine::with_cpu_x86_avx2()) {
         resCPUParams.push_back(CPUSpecificParams{{nCdhw8c, x, x, x}, {nCdhw8c}, {"jit_avx2"}, "jit_avx2"});
         resCPUParams.push_back(CPUSpecificParams{{ndhwc, x, x, x}, {ndhwc}, {"jit_avx2"}, "jit_avx2"});
         resCPUParams.push_back(CPUSpecificParams{{ncdhw, x, x, x}, {ncdhw}, {"jit_avx2"}, "jit_avx2"});
-    } else if (with_cpu_x86_sse42()) {
+    } else if (InferenceEngine::with_cpu_x86_sse42()) {
         resCPUParams.push_back(CPUSpecificParams{{nCdhw8c, x, x, x}, {nCdhw8c}, {"jit_sse42"}, "jit_sse42"});
         resCPUParams.push_back(CPUSpecificParams{{ndhwc, x, x, x}, {ndhwc}, {"jit_sse42"}, "jit_sse42"});
     } else {
@@ -359,70 +528,126 @@ const std::vector<std::vector<int64_t>> defaultAxes5D = {
     {0, 1, 2, 3, 4}
 };
 
-const std::vector<std::vector<float>> defaultScales5D = {
-    {1.f, 1.f, 1.25f, 1.5f, 0.5f}
+const std::vector<ShapeParams> shapeParams5D = {
+    ShapeParams{
+        ngraph::op::v4::Interpolate::ShapeCalcMode::SCALES,
+        InputShape{{}, {{1, 11, 4, 4, 4}}},
+        ngraph::helpers::InputLayerType::CONSTANT,
+        {{1.f, 1.f, 1.25f, 1.5f, 0.5f}},
+        defaultAxes5D.front()
+    },
+    ShapeParams{
+        ngraph::op::v4::Interpolate::ShapeCalcMode::SIZES,
+        InputShape{{}, {{1, 11, 4, 4, 4}}},
+        ngraph::helpers::InputLayerType::CONSTANT,
+        {{1, 11, 5, 6, 2}},
+        defaultAxes5D.front()
+    },
+    ShapeParams{
+        ngraph::op::v4::Interpolate::ShapeCalcMode::SCALES,
+        InputShape{{-1, {2, 20}, -1, -1, -1}, {{1, 11, 4, 4, 4}, {2, 7, 6, 5, 8}}},
+        ngraph::helpers::InputLayerType::CONSTANT,
+        {{1.f, 1.f, 1.25f, 1.5f, 0.5f}},
+        defaultAxes5D.front()
+    },
+    ShapeParams{
+        ngraph::op::v4::Interpolate::ShapeCalcMode::SIZES,
+        InputShape{{-1, {2, 20}, -1, -1, -1}, {{1, 11, 4, 4, 4}, {1, 11, 5, 5, 8}}},
+        ngraph::helpers::InputLayerType::CONSTANT,
+        {{1, 11, 5, 6, 4}},
+        defaultAxes5D.front()
+    },
+    ShapeParams{
+        ngraph::op::v4::Interpolate::ShapeCalcMode::SCALES,
+        InputShape{{-1, {2, 20}, -1, -1, -1}, {{1, 11, 4, 4, 4}, {2, 7, 6, 5, 8}}},
+        ngraph::helpers::InputLayerType::PARAMETER,
+        {{1.f, 1.f, 1.25f, 1.5f, 0.5f}, {1.f, 1.f, 1.25f, 1.25f, 1.25f}},
+        defaultAxes5D.front()
+    },
+    ShapeParams{
+        ngraph::op::v4::Interpolate::ShapeCalcMode::SIZES,
+        InputShape{{-1, {2, 20}, -1, -1, -1}, {{1, 11, 4, 4, 4}, {2, 7, 6, 5, 8}}},
+        ngraph::helpers::InputLayerType::PARAMETER,
+        {{1, 11, 6, 7, 2}, {2, 7, 8, 7, 4}},
+        defaultAxes5D.front()
+    },
 };
 
 const auto interpolateCasesLinearOnnx5D = ::testing::Combine(
         ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::linear_onnx),
-        ::testing::ValuesIn(shapeCalculationMode),
         ::testing::ValuesIn(coordinateTransformModes),
         ::testing::ValuesIn(nearestModes),
         ::testing::ValuesIn(antialias),
         ::testing::ValuesIn(pads5D),
         ::testing::ValuesIn(pads5D),
-        ::testing::ValuesIn(cubeCoefs),
-        ::testing::ValuesIn(defaultAxes5D),
-        ::testing::ValuesIn(defaultScales5D));
-
-const auto interpolateCasesNN5D = ::testing::Combine(
-        ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::nearest),
-        ::testing::ValuesIn(shapeCalculationMode),
-        ::testing::ValuesIn(coordinateTransformModes),
-        ::testing::ValuesIn(defNearestModes),
-        ::testing::ValuesIn(antialias),
-        ::testing::ValuesIn(pads5D),
-        ::testing::ValuesIn(pads5D),
-        ::testing::ValuesIn(cubeCoefs),
-        ::testing::ValuesIn(defaultAxes5D),
-        ::testing::ValuesIn(defaultScales5D));
+        ::testing::ValuesIn(cubeCoefs));
 
 INSTANTIATE_TEST_SUITE_P(smoke_InterpolateLinearOnnx5D_Layout_Test, InterpolateLayerCPUTest,
         ::testing::Combine(
-            ::testing::Combine(
-                interpolateCasesLinearOnnx5D,
-                ::testing::ValuesIn(netPrecisions),
-                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(std::vector<size_t>({1, 21, 4, 4, 4})),
-                ::testing::Values(std::vector<size_t>({1, 21, 5, 6, 2})),
-                ::testing::Values(CommonTestUtils::DEVICE_CPU),
-                ::testing::Values(additional_config)),
+            interpolateCasesLinearOnnx5D,
+            ::testing::ValuesIn(shapeParams5D),
+            ::testing::Values(ElementType::f32),
             ::testing::ValuesIn(filterCPUInfoForDevice5D()),
             ::testing::ValuesIn(interpolateFusingParamsSet),
             ::testing::ValuesIn(filterAdditionalConfig())),
     InterpolateLayerCPUTest::getTestCaseName);
 
+const auto interpolateCasesNN5D = ::testing::Combine(
+        ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::nearest),
+        ::testing::ValuesIn(coordinateTransformModes),
+        ::testing::ValuesIn(defNearestModes),
+        ::testing::ValuesIn(antialias),
+        ::testing::ValuesIn(pads5D),
+        ::testing::ValuesIn(pads5D),
+        ::testing::ValuesIn(cubeCoefs));
+
 INSTANTIATE_TEST_SUITE_P(smoke_InterpolateNN5D_Layout_Test, InterpolateLayerCPUTest,
         ::testing::Combine(
-            ::testing::Combine(
-                interpolateCasesNN5D,
-                ::testing::ValuesIn(netPrecisions),
-                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(InferenceEngine::Layout::ANY),
-                ::testing::Values(std::vector<size_t>({1, 21, 4, 4, 4})),
-                ::testing::Values(std::vector<size_t>({1, 21, 5, 6, 2})),
-                ::testing::Values(CommonTestUtils::DEVICE_CPU),
-                ::testing::Values(additional_config)),
+            interpolateCasesNN5D,
+            ::testing::ValuesIn(shapeParams5D),
+            ::testing::Values(ElementType::f32),
             ::testing::ValuesIn(filterCPUInfoForDevice5D()),
             ::testing::ValuesIn(interpolateFusingParamsSet),
             ::testing::ValuesIn(filterAdditionalConfig())),
     InterpolateLayerCPUTest::getTestCaseName);
 
+// corner cases
+const std::vector<ShapeParams> shapeParams4D_corner = {
+    ShapeParams{
+        ngraph::op::v4::Interpolate::ShapeCalcMode::SCALES,
+        InputShape{{1, 11, 4, 4}, {{1, 11, 4, 4}, {1, 11, 4, 4}}},
+        ngraph::helpers::InputLayerType::PARAMETER,
+        {{1.f, 1.f, 1.25f, 1.5f}, {1.f, 1.f, 1.25f, 1.25f}},
+        defaultAxes4D.front()
+    },
+    ShapeParams{
+        ngraph::op::v4::Interpolate::ShapeCalcMode::SIZES,
+        InputShape{{1, 11, 4, 4}, {{1, 11, 4, 4}, {1, 11, 4, 4}}},
+        ngraph::helpers::InputLayerType::PARAMETER,
+        {{1, 11, 6, 7}, {1, 11, 8, 7}},
+        defaultAxes4D.front()
+    }
+};
+
+const auto interpolateCornerCases = ::testing::Combine(
+        ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::nearest),
+        ::testing::Values(ngraph::op::v4::Interpolate::CoordinateTransformMode::ASYMMETRIC),
+        ::testing::Values(ngraph::op::v4::Interpolate::NearestMode::SIMPLE),
+        ::testing::ValuesIn(antialias),
+        ::testing::Values(std::vector<size_t>{0, 0, 0, 0}),
+        ::testing::Values(std::vector<size_t>{0, 0, 0, 0}),
+        ::testing::ValuesIn(cubeCoefs));
+
+INSTANTIATE_TEST_SUITE_P(smoke_Interpolate_corner_Layout_Test, InterpolateLayerCPUTest,
+        ::testing::Combine(
+            interpolateCornerCases,
+            ::testing::ValuesIn(shapeParams4D_corner),
+            ::testing::Values(ElementType::f32),
+            ::testing::ValuesIn(filterCPUInfoForDevice()),
+            ::testing::ValuesIn(interpolateFusingParamsSet),
+            ::testing::ValuesIn(filterAdditionalConfig())),
+    InterpolateLayerCPUTest::getTestCaseName);
+
 } // namespace
 
 } // namespace CPULayerTestsDefinitions

From e2ed99a93e6e7cdcd18a005f7f1e3fbb7ad1b6a6 Mon Sep 17 00:00:00 2001
From: Edward Shogulin <edward.shogulin@intel.com>
Date: Mon, 29 Nov 2021 12:28:16 +0300
Subject: [PATCH 68/72] [CPU] Dynamism support for EmbeddingSegmentsSum &
 EmbeddingBagOffsetSum & EmbeddingBagPackedSum nodes (#8387)

---
 .../mkldnn_embedding_bag_offset_sum_node.cpp  |  29 +--
 .../mkldnn_embedding_bag_offset_sum_node.h    |   6 +-
 .../mkldnn_embedding_bag_packed_sum_node.cpp  |  24 ++-
 .../mkldnn_embedding_bag_packed_sum_node.h    |   6 +-
 .../nodes/mkldnn_embedding_bag_sum_node.cpp   |   7 +-
 .../nodes/mkldnn_embedding_bag_sum_node.h     |   2 +
 .../mkldnn_embedding_segments_sum_node.cpp    |  28 ++-
 .../mkldnn_embedding_segments_sum_node.h      |   6 +-
 .../embedding_bag_offsets_sum.cpp             | 168 +++++++++++++++++
 .../embedding_bag_packed_sum.cpp              | 152 +++++++++++++++
 .../embedding_segments_sum.cpp                | 173 ++++++++++++++++++
 11 files changed, 566 insertions(+), 35 deletions(-)
 create mode 100644 inference-engine/tests/functional/plugin/cpu/single_layer_tests/embedding_bag_offsets_sum.cpp
 create mode 100644 inference-engine/tests/functional/plugin/cpu/single_layer_tests/embedding_bag_packed_sum.cpp
 create mode 100644 inference-engine/tests/functional/plugin/cpu/single_layer_tests/embedding_segments_sum.cpp

diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_offset_sum_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_offset_sum_node.cpp
index 0f1c1da89de..86cb02d45d7 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_offset_sum_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_offset_sum_node.cpp
@@ -13,10 +13,6 @@ using namespace InferenceEngine;
 
 bool MKLDNNEmbeddingBagOffsetSumNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
         const auto embBagOffsetSumOp = ngraph::as_type_ptr<const ngraph::op::v3::EmbeddingBagOffsetsSum>(op);
         if (!embBagOffsetSumOp) {
             errorMessage = "Node is not an instance of the EmbeddingBagOffsetsSum operation from opset v3.";
@@ -35,14 +31,11 @@ MKLDNNEmbeddingBagOffsetSumNode::MKLDNNEmbeddingBagOffsetSumNode(const std::shar
         IE_THROW(NotImplemented) << errorMessage;
     }
 
-    if (op->get_input_shape(INDICES_IDX).size() != 1)
-        IE_THROW() << "'" << _layerName << "' layer has indices data with invalid shape.";
+    if (getInputShapeAtPort(INDICES_IDX).getRank() != 1ul)
+        IE_THROW() << "'" << _layerName << "' layer has indices data with invalid rank.";
 
-    if (op->get_input_shape(OFFSETS_IDX).size() != 1)
-        IE_THROW() << "'" << _layerName << "' layer's offsets data has invalid shape.";
-
-    _indicesLen = op->get_input_shape(INDICES_IDX)[0];
-    _offsetsLen = op->get_input_shape(OFFSETS_IDX)[0];
+    if (getInputShapeAtPort(OFFSETS_IDX).getRank() != 1ul)
+        IE_THROW() << "'" << _layerName << "' layer's offsets data has invalid rank.";
 }
 
 void MKLDNNEmbeddingBagOffsetSumNode::initSupportedPrimitiveDescriptors() {
@@ -77,6 +70,20 @@ void MKLDNNEmbeddingBagOffsetSumNode::initSupportedPrimitiveDescriptors() {
     addSupportedPrimDesc(inDataConfigurators, {{LayoutType::ncsp, inDataPrecision}}, impl_desc_type::ref_any);
 }
 
+void MKLDNNEmbeddingBagOffsetSumNode::createPrimitive() {
+    if (inputShapesDefined()) {
+        if (needPrepareParams())
+            prepareParams();
+        updateLastInputDims();
+    }
+}
+
+void MKLDNNEmbeddingBagOffsetSumNode::prepareParams() {
+    _indicesLen = getParentEdgesAtPort(INDICES_IDX)[0]->getMemory().getStaticDims()[0];
+    _offsetsLen = getParentEdgesAtPort(OFFSETS_IDX)[0]->getMemory().getStaticDims()[0];
+    MKLDNNEmbeddingBagSumNode::prepareParams(getParentEdgesAtPort(EMB_TABLE_IDX)[0]->getMemory().getStaticDims());
+}
+
 void MKLDNNEmbeddingBagOffsetSumNode::initFromInputs() {
     indicesData_ = reinterpret_cast<const int *>(getParentEdgeAt(INDICES_IDX)->getMemoryPtr()->GetPtr());
     offsetsData_ = reinterpret_cast<const int *>(getParentEdgeAt(OFFSETS_IDX)->getMemoryPtr()->GetPtr());
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_offset_sum_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_offset_sum_node.h
index 8827ad545d9..a7b3cac7a2e 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_offset_sum_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_offset_sum_node.h
@@ -19,12 +19,16 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
+    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
+protected:
+    void prepareParams() override;
+    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+
 private:
     void initFromInputs() override;
     void getIndices(int embIndex, const int*& indices, size_t& size, int& weightsIdx, bool& withWeight) override;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_packed_sum_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_packed_sum_node.cpp
index d95fbb5ca7a..9dbaad08d80 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_packed_sum_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_packed_sum_node.cpp
@@ -13,10 +13,6 @@ using namespace InferenceEngine;
 
 bool MKLDNNEmbeddingBagPackedSumNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
         const auto embBagPackedSumOp = ngraph::as_type_ptr<const ngraph::op::v3::EmbeddingBagPackedSum>(op);
         if (!embBagPackedSumOp) {
             errorMessage = "Node is not an instance of the EmbeddingBagPackedSum operation from opset v3.";
@@ -35,10 +31,8 @@ MKLDNNEmbeddingBagPackedSumNode::MKLDNNEmbeddingBagPackedSumNode(const std::shar
         IE_THROW(NotImplemented) << errorMessage;
     }
 
-    if (op->get_input_shape(INDICES_IDX).size() != 2)
-        IE_THROW() << "'" << _layerName << "' layer has indices data with invalid shape.";
-    _batch = op->get_input_shape(INDICES_IDX)[0];
-    _indicesPerBag = op->get_input_shape(INDICES_IDX)[1];
+    if (getInputShapeAtPort(INDICES_IDX).getRank() != 2ul)
+        IE_THROW() << "'" << _layerName << "' layer has indices data with invalid rank.";
 }
 
 void MKLDNNEmbeddingBagPackedSumNode::initSupportedPrimitiveDescriptors() {
@@ -70,6 +64,20 @@ void MKLDNNEmbeddingBagPackedSumNode::initSupportedPrimitiveDescriptors() {
     addSupportedPrimDesc(inDataConfigurators, {{LayoutType::ncsp, inDataPrecision}}, impl_desc_type::ref_any);
 }
 
+void MKLDNNEmbeddingBagPackedSumNode::createPrimitive() {
+    if (inputShapesDefined()) {
+        if (needPrepareParams())
+            prepareParams();
+        updateLastInputDims();
+    }
+}
+
+void MKLDNNEmbeddingBagPackedSumNode::prepareParams() {
+    _batch = getParentEdgesAtPort(INDICES_IDX)[0]->getMemory().getStaticDims()[0];
+    _indicesPerBag = getParentEdgesAtPort(INDICES_IDX)[0]->getMemory().getStaticDims()[1];
+    MKLDNNEmbeddingBagSumNode::prepareParams(getParentEdgesAtPort(EMB_TABLE_IDX)[0]->getMemory().getStaticDims());
+}
+
 void MKLDNNEmbeddingBagPackedSumNode::initFromInputs() {
     _indices = reinterpret_cast<const int *>(getParentEdgeAt(INDICES_IDX)->getMemoryPtr()->GetPtr());
 }
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_packed_sum_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_packed_sum_node.h
index 9e7053f3e96..fb01897debb 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_packed_sum_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_packed_sum_node.h
@@ -19,12 +19,16 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
+    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
+protected:
+    void prepareParams() override;
+    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+
 private:
     void initFromInputs() override;
     void getIndices(int embIndex, const int*& indices, size_t& size, int& weightsIdx, bool& withWeight) override;
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_sum_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_sum_node.cpp
index 853da79accf..a1d54d9db87 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_sum_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_sum_node.cpp
@@ -34,11 +34,12 @@ MKLDNNEmbeddingBagSumNode::MKLDNNEmbeddingBagSumNode(
         if (op->get_input_shape(PER_SAMPLE_WEIGHTS_IDX) != op->get_input_shape(INDICES_IDX))
              IE_THROW() << logPrefix << "must have equal shapes for indices and per_sample_weights inputs.";
     }
+}
 
-    const auto& inDataDims = op->get_input_shape(EMB_TABLE_IDX);
+void MKLDNNEmbeddingBagSumNode::prepareParams(const VectorDims& indexStaticShape) {
     _embDepth = 1lu;
-    for (size_t i = 1lu; i < inDataDims.size(); i++) {
-        _embDepth *= inDataDims[i];
+    for (size_t i = 1lu; i < indexStaticShape.size(); i++) {
+        _embDepth *= indexStaticShape[i];
     }
 }
 
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_sum_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_sum_node.h
index ef5e7ed9a2f..d8f6a6233a3 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_sum_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_sum_node.h
@@ -35,6 +35,8 @@ protected:
             int& weightsIdx,
             bool& withWeights) = 0;
 
+    void prepareParams(const VectorDims& indexStaticShape);
+
     template<typename T>
     void processData(const T* srcData, const T* weightsData, T* dstData,
                      const InferenceEngine::SizeVector& inDataDims, const InferenceEngine::SizeVector& outDataDims);
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_segments_sum_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_segments_sum_node.cpp
index 6196c6478ac..2dca1793df1 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_segments_sum_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_segments_sum_node.cpp
@@ -11,12 +11,16 @@
 using namespace MKLDNNPlugin;
 using namespace InferenceEngine;
 
+void MKLDNNEmbeddingSegmentsSumNode::createPrimitive() {
+    if (inputShapesDefined()) {
+        if (needPrepareParams())
+            prepareParams();
+        updateLastInputDims();
+    }
+}
+
 bool MKLDNNEmbeddingSegmentsSumNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
         const auto embBagSegSumOp = ngraph::as_type_ptr<const ngraph::op::v3::EmbeddingSegmentsSum>(op);
         if (!embBagSegSumOp) {
             errorMessage = "Node is not an instance of the EmbeddingSegmentsSum operation from opset v3.";
@@ -36,13 +40,13 @@ MKLDNNEmbeddingSegmentsSumNode::MKLDNNEmbeddingSegmentsSumNode(const std::shared
     }
 
     std::string errPrefix = std::string("EmbeddingSegmentsSum layer with name '") + _layerName + "' ";
-    if (op->get_input_shape(INDICES_IDX).size() != 1)
-        IE_THROW() << errPrefix << "has indices data with invalid shape: "
-                   << op->get_input_shape(INDICES_IDX).size();
+    if (getInputShapeAtPort(INDICES_IDX).getRank() != 1ul)
+        IE_THROW() << errPrefix << "has indices data with invalid rank: "
+                   << getInputShapeAtPort(INDICES_IDX).getRank();
 
-    if (op->get_input_shape(SEGMENT_ID_IDX).size() != 1)
-        IE_THROW() << errPrefix << "has invalid segmentID data shape: "
-                   << op->get_input_shape(SEGMENT_ID_IDX).size();
+    if (getInputShapeAtPort(SEGMENT_ID_IDX).getRank() != 1ul)
+        IE_THROW() << errPrefix << "has invalid segmentID data rank: "
+                   << getInputShapeAtPort(SEGMENT_ID_IDX).getRank();
 }
 
 void MKLDNNEmbeddingSegmentsSumNode::initSupportedPrimitiveDescriptors() {
@@ -78,6 +82,10 @@ void MKLDNNEmbeddingSegmentsSumNode::initSupportedPrimitiveDescriptors() {
     addSupportedPrimDesc(inDataConfigurators, {{LayoutType::ncsp, inDataPrecision}}, impl_desc_type::ref_any);
 }
 
+void MKLDNNEmbeddingSegmentsSumNode::prepareParams() {
+    MKLDNNEmbeddingBagSumNode::prepareParams(getParentEdgesAtPort(EMB_TABLE_IDX)[0]->getMemory().getStaticDims());
+}
+
 void MKLDNNEmbeddingSegmentsSumNode::initFromInputs() {
     indices_ = reinterpret_cast<const int *>(getParentEdgeAt(INDICES_IDX)->getMemoryPtr()->GetPtr());
     indicesSize_ = getParentEdgeAt(INDICES_IDX)->getMemory().GetShape().getElementsCount();
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_segments_sum_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_segments_sum_node.h
index 828d76cf1fb..942d337d099 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_segments_sum_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_segments_sum_node.h
@@ -19,12 +19,16 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
+    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
+protected:
+    void prepareParams() override;
+    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
+
 private:
     void initFromInputs() override;
     void getIndices(int embIndex, const int*& indices, size_t& size, int& weightsIdx, bool& withWeight) override;
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/embedding_bag_offsets_sum.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/embedding_bag_offsets_sum.cpp
new file mode 100644
index 00000000000..9ae91d53dc1
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/embedding_bag_offsets_sum.cpp
@@ -0,0 +1,168 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <string>
+#include <sstream>
+#include <vector>
+
+#include <openvino/core/partial_shape.hpp>
+#include "ngraph_functions/builders.hpp"
+#include "shared_test_classes/base/layer_test_utils.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "test_utils/cpu_test_utils.hpp"
+
+using namespace InferenceEngine;
+using namespace CPUTestUtils;
+using namespace ov::test;
+
+namespace CPULayerTestsDefinitions {
+
+typedef std::tuple<
+    InputShape, // input_shapes
+    std::vector<size_t>, // indices
+    std::vector<size_t>, // offsets
+    size_t,              // default_index
+    bool,                // with_weights
+    bool                 // with_def_index
+    > embeddingBagOffsetsSumParams;
+
+typedef std::tuple<
+    embeddingBagOffsetsSumParams,
+    ElementType, // embedding table
+    ElementType, // indices
+    LayerTestsUtils::TargetDevice> embeddingBagOffsetsSumLayerTestParamsSet;
+
+class EmbeddingBagOffsetsSumLayerCPUTest :
+        public testing::WithParamInterface<embeddingBagOffsetsSumLayerTestParamsSet>,
+        virtual public SubgraphBaseTest,
+        public CPUTestsBase {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<embeddingBagOffsetsSumLayerTestParamsSet>& obj) {
+        embeddingBagOffsetsSumParams params;
+        ElementType netPrecision, indPrecision;
+        std::string targetDevice;
+        std::tie(params, netPrecision, indPrecision, targetDevice) = obj.param;
+
+        InputShape inputShapes;
+        std::vector<size_t> indices, offsets;
+        size_t defaultIndex;
+        bool withWeights, withDefIndex;
+        std::tie(inputShapes, indices, offsets, defaultIndex, withWeights, withDefIndex) = params;
+
+        std::ostringstream result;
+        result << "IS=" << inputShapes << "_";
+        result << "I" << CommonTestUtils::vec2str(indices) << "_";
+        result << "O" << CommonTestUtils::vec2str(offsets) << "_";
+        result << "DI" << defaultIndex << "_";
+        result << "WW" << withWeights << "_";
+        result << "WDI" << withDefIndex << "_";
+        result << "netPRC=" << netPrecision << "_";
+        result << "indPRC=" << indPrecision << "_";
+        result << "targetDevice=" << targetDevice;
+        return result.str();
+    }
+
+    void SetUp() override {
+        embeddingBagOffsetsSumParams embParams;
+        ElementType indPrecision;
+        std::tie(embParams, inType, indPrecision, targetDevice) = this->GetParam();
+
+        InputShape inputShapes;
+        std::vector<size_t> indices, offsets;
+        bool withWeights, withDefIndex;
+        size_t defaultIndex;
+        std::tie(inputShapes, indices, offsets, defaultIndex, withWeights, withDefIndex) = embParams;
+
+        selectedType = makeSelectedTypeStr("ref", inType);
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+
+        init_input_shapes({ inputShapes });
+
+        auto emb_table_node = std::make_shared<ngraph::opset1::Parameter>(inType, inputShapes.first);
+        ngraph::ParameterVector params = {emb_table_node};
+
+        auto embBag = std::dynamic_pointer_cast<ngraph::opset3::EmbeddingBagOffsetsSum>(ngraph::builder::makeEmbeddingBagOffsetsSum(
+            inType,
+            indPrecision,
+            emb_table_node,
+            indices,
+            offsets,
+            defaultIndex,
+            withWeights,
+            withDefIndex));
+        ngraph::ResultVector results{std::make_shared<ngraph::opset1::Result>(embBag)};
+        function = std::make_shared<ngraph::Function>(results, params, "embeddingBagOffsetsSum");
+    }
+};
+
+TEST_P(EmbeddingBagOffsetsSumLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+    run();
+    CheckPluginRelatedResults(executableNetwork, "embeddingBagOffsetsSum");
+}
+
+namespace {
+
+const std::vector<ElementType> netPrecisions = {
+        ElementType::f32,
+        ElementType::i32,
+        ElementType::u8
+};
+
+const std::vector<ElementType> indPrecisions = {
+        ElementType::i64,
+        ElementType::i32
+};
+
+const std::vector<InputShape> input_shapes = {
+        // dynamic input shapes
+        {
+            // input model dynamic shapes
+            {ov::Dimension::dynamic(), ov::Dimension::dynamic()},
+            // input tensor shapes
+            {{5, 6}, {10, 35}}
+        },
+        {
+            // input model dynamic shapes
+            {ov::Dimension::dynamic(), ov::Dimension::dynamic(), ov::Dimension::dynamic()},
+            // input tensor shapes
+            {{5, 4, 16}, {10, 12, 8}}
+        },
+        {
+            // input model dynamic shapes with limits
+            {{5, 10}, {6, 35}, {4, 8}},
+            // input tensor shapes
+            {{5, 6, 4}, {10, 35, 8}, {5, 6, 4}}
+        },
+        // static shapes
+        {{5, 6}, {{5, 6}}},
+        {{10, 35}, {{10, 35}}},
+        {{5, 4, 16}, {{5, 4, 16}}},
+};
+
+const std::vector<std::vector<size_t>> indices =
+        {{0, 1, 2, 2, 3}, {4, 4, 3, 1, 0}, {1, 2, 1, 2, 1, 2, 1, 2, 1, 2}};
+const std::vector<std::vector<size_t>> offsets = {{0, 2}, {0, 0, 2, 2}, {2, 4}};
+const std::vector<size_t> default_index = {0, 4};
+const std::vector<bool> with_weights = {false, true};
+const std::vector<bool> with_default_index = {false, true};
+
+const auto embBagOffsetSumArgSet = ::testing::Combine(
+        ::testing::ValuesIn(input_shapes),
+        ::testing::ValuesIn(indices),
+        ::testing::ValuesIn(offsets),
+        ::testing::ValuesIn(default_index),
+        ::testing::ValuesIn(with_weights),
+        ::testing::ValuesIn(with_default_index)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke, EmbeddingBagOffsetsSumLayerCPUTest,
+        ::testing::Combine(
+                embBagOffsetSumArgSet,
+                ::testing::ValuesIn(netPrecisions),
+                ::testing::ValuesIn(indPrecisions),
+                ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+        EmbeddingBagOffsetsSumLayerCPUTest::getTestCaseName);
+}  // namespace
+}  // namespace CPULayerTestsDefinitions
\ No newline at end of file
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/embedding_bag_packed_sum.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/embedding_bag_packed_sum.cpp
new file mode 100644
index 00000000000..90b08799f73
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/embedding_bag_packed_sum.cpp
@@ -0,0 +1,152 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <string>
+#include <sstream>
+#include <vector>
+
+#include <openvino/core/partial_shape.hpp>
+#include "ngraph_functions/builders.hpp"
+#include "shared_test_classes/base/layer_test_utils.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "test_utils/cpu_test_utils.hpp"
+
+using namespace InferenceEngine;
+using namespace CPUTestUtils;
+using namespace ov::test;
+
+namespace CPULayerTestsDefinitions {
+
+typedef std::tuple<
+    InputShape, // input_shapes
+    std::vector<std::vector<size_t>>, // indices
+    bool                 // with_weights
+    > embeddingBagPackedSumParams;
+
+typedef std::tuple<
+    embeddingBagPackedSumParams,
+    ElementType, // embedding table
+    ElementType, // indices
+    LayerTestsUtils::TargetDevice> embeddingBagPackedSumLayerTestParamsSet;
+
+class EmbeddingBagPackedSumLayerCPUTest :
+        public testing::WithParamInterface<embeddingBagPackedSumLayerTestParamsSet>,
+        virtual public SubgraphBaseTest,
+        public CPUTestsBase {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<embeddingBagPackedSumLayerTestParamsSet>& obj) {
+        embeddingBagPackedSumParams params;
+        ElementType netPrecision, indPrecision;
+        std::string targetDevice;
+        std::tie(params, netPrecision, indPrecision, targetDevice) = obj.param;
+
+        InputShape inputShapes;
+        std::vector<std::vector<size_t>> indices;
+        bool withWeights;
+        std::tie(inputShapes, indices, withWeights) = params;
+
+        std::ostringstream result;
+        result << "IS=" << inputShapes << "_";
+        result << "I" << CommonTestUtils::vec2str(indices) << "_";
+        result << "WW" << withWeights << "_";
+        result << "netPRC=" << netPrecision << "_";
+        result << "indPRC=" << indPrecision << "_";
+        result << "targetDevice=" << targetDevice;
+        return result.str();
+    }
+
+protected:
+    void SetUp() override {
+        embeddingBagPackedSumParams embParams;
+        ElementType indPrecision;
+        std::tie(embParams, inType, indPrecision, targetDevice) = this->GetParam();
+
+        InputShape inputShapes;
+        std::vector<std::vector<size_t>> indices;
+        bool withWeights;
+        std::tie(inputShapes, indices, withWeights) = embParams;
+
+        selectedType = makeSelectedTypeStr("ref", inType);
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+
+        init_input_shapes({ inputShapes });
+
+        auto emb_table_node = std::make_shared<ngraph::opset1::Parameter>(inType, inputShapes.first);
+        ngraph::ParameterVector params = {emb_table_node};
+
+        auto embBag = std::dynamic_pointer_cast<ngraph::opset3::EmbeddingBagPackedSum>(ngraph::builder::makeEmbeddingBagPackedSum(
+            inType,
+            indPrecision,
+            emb_table_node,
+            indices,
+            withWeights));
+        ngraph::ResultVector results{std::make_shared<ngraph::opset1::Result>(embBag)};
+        function = std::make_shared<ngraph::Function>(results, params, "embeddingBagPackedSum");
+    }
+};
+
+TEST_P(EmbeddingBagPackedSumLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+    run();
+    CheckPluginRelatedResults(executableNetwork, "embeddingBagPackedSum");
+}
+
+namespace {
+
+const std::vector<ElementType> netPrecisions = {
+        ElementType::f32,
+        ElementType::i32,
+        ElementType::u8
+};
+
+const std::vector<ElementType> indPrecisions = {
+        ElementType::i64,
+        ElementType::i32
+};
+
+const std::vector<InputShape> input_shapes = {
+        // dynamic input shapes
+        {
+            // input model dynamic shapes
+            {ov::Dimension::dynamic(), ov::Dimension::dynamic()},
+            // input tensor shapes
+            {{{5, 6}}, {10, 35}}
+        },
+        {
+            // input model dynamic shapes
+            {ov::Dimension::dynamic(), ov::Dimension::dynamic(), ov::Dimension::dynamic()},
+            // input tensor shapes
+            {{5, 4, 16}, {10, 12, 8}}
+        },
+        {
+            // input model dynamic shapes with limits
+            {{5, 10}, {6, 35}, {4, 8}},
+            // input tensor shapes
+            {{5, 6, 4}, {10, 35, 8}, {5, 6, 4}}
+        },
+        // static shapes
+        {{5, 6}, {{5, 6}}},
+        {{10, 35}, {{10, 35}}},
+        {{5, 4, 16}, {{5, 4, 16}}},
+};
+
+const std::vector<std::vector<std::vector<size_t>>> indices =
+        {{{0, 1}, {2, 2}, {3, 4}}, {{4, 4, 3}, {1, 0, 2}}, {{1, 2, 1, 2}, {1, 2, 1, 2}}};
+const std::vector<bool> with_weights = {false, true};
+
+const auto embBagPackedSumArgSet = ::testing::Combine(
+        ::testing::ValuesIn(input_shapes),
+        ::testing::ValuesIn(indices),
+        ::testing::ValuesIn(with_weights)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke, EmbeddingBagPackedSumLayerCPUTest,
+        ::testing::Combine(
+                embBagPackedSumArgSet,
+                ::testing::ValuesIn(netPrecisions),
+                ::testing::ValuesIn(indPrecisions),
+                ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+        EmbeddingBagPackedSumLayerCPUTest::getTestCaseName);
+}  // namespace
+}  // namespace CPULayerTestsDefinitions
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/embedding_segments_sum.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/embedding_segments_sum.cpp
new file mode 100644
index 00000000000..49df7ba6b14
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/embedding_segments_sum.cpp
@@ -0,0 +1,173 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <string>
+#include <sstream>
+#include <vector>
+
+#include <openvino/core/partial_shape.hpp>
+#include "ngraph_functions/builders.hpp"
+#include "shared_test_classes/base/layer_test_utils.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "test_utils/cpu_test_utils.hpp"
+
+using namespace InferenceEngine;
+using namespace CPUTestUtils;
+using namespace ov::test;
+
+namespace CPULayerTestsDefinitions {
+
+typedef std::tuple<
+    InputShape, // input_shapes
+    std::vector<size_t>, // indices
+    std::vector<size_t>, // segment_ids
+    size_t,              // num_segments
+    size_t,              // default_index
+    bool,                // with_weights
+    bool                 // with_def_index
+    > embeddingSegmentsSumParams;
+
+typedef std::tuple<
+    embeddingSegmentsSumParams,
+    ElementType, // embedding table
+    ElementType, // indices
+    LayerTestsUtils::TargetDevice> embeddingSegmentsSumLayerTestParamsSet;
+
+class EmbeddingSegmentsSumLayerCPUTest :
+        public testing::WithParamInterface<embeddingSegmentsSumLayerTestParamsSet>,
+        virtual public SubgraphBaseTest,
+        public CPUTestsBase {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<embeddingSegmentsSumLayerTestParamsSet>& obj) {
+        embeddingSegmentsSumParams params;
+        ElementType netPrecision, indPrecision;
+        std::string targetDevice;
+        std::tie(params, netPrecision, indPrecision, targetDevice) = obj.param;
+
+        InputShape inputShapes;
+        std::vector<size_t> indices, segmentIds;
+        size_t numSegments, defaultIndex;
+        bool withWeights, withDefIndex;
+        std::tie(inputShapes, indices, segmentIds, numSegments, defaultIndex, withWeights, withDefIndex) = params;
+
+        std::ostringstream result;
+        result << "IS=" << inputShapes << "_";
+        result << "I"  << CommonTestUtils::vec2str(indices) << "_";
+        result << "SI" << CommonTestUtils::vec2str(segmentIds) << "_";
+        result << "NS" << numSegments << "_";
+        result << "DI" << defaultIndex << "_";
+        result << "WW" << withWeights << "_";
+        result << "WDI" << withDefIndex << "_";
+        result << "netPRC=" << netPrecision << "_";
+        result << "indPRC=" << indPrecision << "_";
+        result << "targetDevice=" << targetDevice;
+        return result.str();
+    }
+
+protected:
+    void SetUp() override {
+        embeddingSegmentsSumParams embParams;
+        ElementType indPrecision;
+        std::tie(embParams, inType, indPrecision, targetDevice) = this->GetParam();
+
+        InputShape inputShapes;
+        std::vector<size_t> indices, segmentIds;
+        bool withWeights, withDefIndex;
+        size_t numSegments, defaultIndex;
+        std::tie(inputShapes, indices, segmentIds, numSegments, defaultIndex, withWeights, withDefIndex) = embParams;
+
+        selectedType = makeSelectedTypeStr("ref", inType);
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+
+        init_input_shapes({ inputShapes });
+
+        auto emb_table_node = std::make_shared<ngraph::opset1::Parameter>(inType, inputShapes.first);
+        ngraph::ParameterVector params = {emb_table_node};
+
+        auto embBag = std::dynamic_pointer_cast<ngraph::opset3::EmbeddingSegmentsSum>(ngraph::builder::makeEmbeddingSegmentsSum(
+            inType,
+            indPrecision,
+            emb_table_node,
+            indices,
+            segmentIds,
+            numSegments,
+            defaultIndex,
+            withWeights,
+            withDefIndex));
+        ngraph::ResultVector results{std::make_shared<ngraph::opset1::Result>(embBag)};
+        function = std::make_shared<ngraph::Function>(results, params, "embeddingSegmentsSum");
+    }
+};
+
+TEST_P(EmbeddingSegmentsSumLayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+    run();
+    CheckPluginRelatedResults(executableNetwork, "embeddingSegmentsSum");
+}
+
+namespace {
+const std::vector<ElementType> netPrecisions = {
+        ElementType::f32,
+        ElementType::i32,
+        ElementType::u8
+};
+
+const std::vector<ElementType> indPrecisions = {
+        ElementType::i64,
+        ElementType::i32
+};
+
+const std::vector<InputShape> input_shapes = {
+    // dynamic input shapes
+    {
+        // input model dynamic shapes
+        {ov::Dimension::dynamic(), ov::Dimension::dynamic()},
+        // input tensor shapes
+        {{5, 6}, {10, 35}}
+    },
+    {
+        // input model dynamic shapes
+        {ov::Dimension::dynamic(), ov::Dimension::dynamic(), ov::Dimension::dynamic()},
+        // input tensor shapes
+        {{5, 4, 16}, {10, 12, 8}}
+    },
+    {
+        // input model dynamic shapes with limits
+        {{5, 10}, {6, 35}, {4, 8}},
+        // input tensor shapes
+        {{5, 6, 4}, {10, 35, 8}, {5, 6, 4}}
+    },
+    // static shapes
+    {{5, 6}, {{5, 6}}},
+    {{10, 35}, {{10, 35}}},
+    {{5, 4, 16}, {{5, 4, 16}}},
+};
+
+const std::vector<std::vector<size_t>> indices =
+    {{0, 1, 2, 2, 3}, {4, 4, 3, 1, 2}};
+const std::vector<std::vector<size_t>> segment_ids = {{0, 1, 2, 3, 4}, {0, 0, 2, 2, 4}};
+const std::vector<size_t> num_segments = {5, 7};
+const std::vector<size_t> default_index = {0, 4};
+const std::vector<bool> with_weights = {false, true};
+const std::vector<bool> with_default_index = {false, true};
+
+const auto embSegmentsSumArgSet = ::testing::Combine(
+    ::testing::ValuesIn(input_shapes),
+    ::testing::ValuesIn(indices),
+    ::testing::ValuesIn(segment_ids),
+    ::testing::ValuesIn(num_segments),
+    ::testing::ValuesIn(default_index),
+    ::testing::ValuesIn(with_weights),
+    ::testing::ValuesIn(with_default_index)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke, EmbeddingSegmentsSumLayerCPUTest,
+     ::testing::Combine(
+         embSegmentsSumArgSet,
+         ::testing::ValuesIn(netPrecisions),
+         ::testing::ValuesIn(indPrecisions),
+         ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+         EmbeddingSegmentsSumLayerCPUTest::getTestCaseName);
+}  // namespace
+}  // namespace CPULayerTestsDefinitions

From b0da652e46730101da257459a3e97274b70b7753 Mon Sep 17 00:00:00 2001
From: Tingqian Li <tingqian.li@intel.com>
Date: Mon, 29 Nov 2021 18:31:23 +0800
Subject: [PATCH 69/72] [CPU] Native dynamic shapes support in the ReorgYolo
 node (#8642)

---
 .../nodes/mkldnn_reorg_yolo_node.cpp          |  4 -
 .../nodes/mkldnn_reorg_yolo_node.h            |  4 +
 .../cpu/single_layer_tests/reorg_yolo.cpp     | 90 +++++++++++++++++++
 src/core/src/op/reorg_yolo.cpp                |  3 +-
 4 files changed, 96 insertions(+), 5 deletions(-)
 create mode 100644 inference-engine/tests/functional/plugin/cpu/single_layer_tests/reorg_yolo.cpp

diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorg_yolo_node.cpp b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorg_yolo_node.cpp
index 194ddca390c..3ebc977bc41 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorg_yolo_node.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorg_yolo_node.cpp
@@ -13,10 +13,6 @@ using namespace InferenceEngine;
 
 bool MKLDNNReorgYoloNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
         const auto reorgYolo = std::dynamic_pointer_cast<const ngraph::opset2::ReorgYolo>(op);
         if (!reorgYolo) {
             errorMessage = "Only opset2 ReorgYolo operation is supported";
diff --git a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorg_yolo_node.h b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorg_yolo_node.h
index d22147c48e0..c3be7362484 100644
--- a/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorg_yolo_node.h
+++ b/inference-engine/src/mkldnn_plugin/nodes/mkldnn_reorg_yolo_node.h
@@ -18,6 +18,10 @@ public:
     void createPrimitive() override {};
     void execute(mkldnn::stream strm) override;
     bool created() const override;
+    bool needPrepareParams() const override { return false; }
+    void executeDynamicImpl(mkldnn::stream strm) override {
+        execute(strm);
+    }
 
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
diff --git a/inference-engine/tests/functional/plugin/cpu/single_layer_tests/reorg_yolo.cpp b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/reorg_yolo.cpp
new file mode 100644
index 00000000000..f728d590038
--- /dev/null
+++ b/inference-engine/tests/functional/plugin/cpu/single_layer_tests/reorg_yolo.cpp
@@ -0,0 +1,90 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "functional_test_utils/ov_tensor_utils.hpp"
+#include "ngraph_functions/builders.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "test_utils/cpu_test_utils.hpp"
+
+using namespace InferenceEngine;
+using namespace CPUTestUtils;
+using namespace ngraph::opset3;
+using namespace ov::test;
+
+namespace CPULayerTestsDefinitions {
+
+using ReorgYoloCPUParamsTuple = typename std::tuple<InputShape,     // Input Shape
+                                                    size_t,         // stride
+                                                    ElementType,    // Network precision
+                                                    TargetDevice>;  // Device
+
+class ReorgYoloLayerCPUTest : public testing::WithParamInterface<ReorgYoloCPUParamsTuple>,
+                              virtual public ov::test::SubgraphBaseTest {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<ReorgYoloCPUParamsTuple>& obj) {
+        InputShape inputShape;
+        size_t stride;
+        ElementType netPrecision;
+        TargetDevice targetDev;
+        std::tie(inputShape, stride, netPrecision, targetDev) = obj.param;
+        std::ostringstream result;
+        result << "IS=" << CommonTestUtils::partialShape2str({inputShape.first}) << "_";
+        for (const auto& item : inputShape.second) {
+            result << CommonTestUtils::vec2str(item) << "_";
+        }
+        result << "stride=" << stride << "_";
+        result << "netPRC=" << netPrecision << "_";
+        result << "targetDevice=" << targetDev << "_";
+        return result.str();
+    }
+
+protected:
+    void SetUp() override {
+        InputShape inputShape;
+        size_t stride;
+        ElementType netPrecision;
+        std::tie(inputShape, stride, netPrecision, targetDevice) = this->GetParam();
+
+        init_input_shapes({inputShape});
+
+        auto param = std::make_shared<ngraph::op::Parameter>(ngraph::element::f32, inputDynamicShapes[0]);
+        auto reorg_yolo = std::make_shared<ngraph::op::v0::ReorgYolo>(param, stride);
+        function = std::make_shared<ngraph::Function>(std::make_shared<ngraph::opset1::Result>(reorg_yolo),
+                                                      ngraph::ParameterVector{param},
+                                                      "ReorgYolo");
+    }
+};
+
+TEST_P(ReorgYoloLayerCPUTest, CompareWithRefs) {
+    run();
+};
+
+const std::vector<ov::test::InputShape> inShapesDynamic = {
+    {{{1, 2}, -1, -1, -1}, {{1, 4, 4, 4}, {1, 8, 4, 4}, {2, 8, 4, 4}}}};
+
+const std::vector<size_t> strides = {2, 3};
+
+const std::vector<ov::test::InputShape> inShapesDynamic2 = {{{{1, 2}, -1, -1, -1}, {{1, 9, 3, 3}}}};
+
+const auto testCase_stride2_Dynamic = ::testing::Combine(::testing::ValuesIn(inShapesDynamic),
+                                                         ::testing::Values(strides[0]),
+                                                         ::testing::Values(ov::element::f32),
+                                                         ::testing::Values(CommonTestUtils::DEVICE_CPU));
+
+const auto testCase_stride3_Dynamic = ::testing::Combine(::testing::ValuesIn(inShapesDynamic2),
+                                                         ::testing::Values(strides[1]),
+                                                         ::testing::Values(ov::element::f32),
+                                                         ::testing::Values(CommonTestUtils::DEVICE_CPU));
+
+INSTANTIATE_TEST_SUITE_P(smoke_TestsReorgYolo_stride2_DynamicShape,
+                         ReorgYoloLayerCPUTest,
+                         testCase_stride2_Dynamic,
+                         ReorgYoloLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_TestsReorgYolo_stride3_DynamicShape,
+                         ReorgYoloLayerCPUTest,
+                         testCase_stride3_Dynamic,
+                         ReorgYoloLayerCPUTest::getTestCaseName);
+
+};  // namespace CPULayerTestsDefinitions
diff --git a/src/core/src/op/reorg_yolo.cpp b/src/core/src/op/reorg_yolo.cpp
index 68788d72ded..56739f3820f 100644
--- a/src/core/src/op/reorg_yolo.cpp
+++ b/src/core/src/op/reorg_yolo.cpp
@@ -50,7 +50,8 @@ void op::ReorgYolo::validate_and_infer_types() {
         }
         set_output_type(0, input_et, output_shape);
     } else {
-        set_output_type(0, input_et, ov::PartialShape::dynamic());
+        auto input_shape = get_input_partial_shape(0);
+        set_output_type(0, input_et, ov::PartialShape::dynamic(input_shape.rank()));
     }
 }
 

From 7678ab2b18f546b1f961080e0bea7cd51d5add5c Mon Sep 17 00:00:00 2001
From: Ilya Churaev <ilya.churaev@intel.com>
Date: Mon, 29 Nov 2021 14:05:08 +0300
Subject: [PATCH 70/72] Move hetero plugin (#8872)

---
 cmake/coverage.cmake                          |   6 +-
 ...Deep_Learning_Inference_Engine_DevGuide.md |   2 +-
 docs/IE_DG/inference_engine_intro.md          |   2 +-
 .../tests/functional/CMakeLists.txt           |   2 +-
 inference-engine/src/CMakeLists.txt           |   4 -
 .../hetero_executable_network.hpp             |  81 -----
 .../inference_engine/CMakeLists.txt           |   2 +-
 .../include/api_conformance_helpers.hpp       |   4 +-
 .../functional/plugin/shared/CMakeLists.txt   |   2 +-
 .../functional/gna/CMakeLists.txt             |   2 +-
 .../functional/shared_tests/CMakeLists.txt    |   2 +-
 .../functional/vpu/CMakeLists.txt             |   2 +-
 src/CMakeLists.txt                            |   1 +
 src/inference/CMakeLists.txt                  |   2 +-
 src/plugins/CMakeLists.txt                    |   7 +
 .../plugins/hetero}/CMakeLists.txt            |   5 +-
 .../plugins/hetero/async_infer_request.cpp    |  31 +-
 .../plugins/hetero/async_infer_request.hpp    |  12 +-
 .../plugins/hetero/executable_network.cpp     | 333 +++++++++---------
 src/plugins/hetero/executable_network.hpp     |  82 +++++
 .../plugins/hetero/infer_request.cpp          |  47 +--
 .../plugins/hetero/infer_request.hpp          |  28 +-
 .../plugins/hetero/itt.hpp                    |   8 +-
 .../plugins/hetero/plugin.cpp                 |  75 ++--
 .../plugins/hetero/plugin.hpp                 |  39 +-
 tools/deployment_manager/configs/darwin.json  |   2 +-
 tools/deployment_manager/configs/linux.json   |   2 +-
 tools/deployment_manager/configs/windows.json |   2 +-
 28 files changed, 398 insertions(+), 389 deletions(-)
 delete mode 100644 inference-engine/src/hetero_plugin/hetero_executable_network.hpp
 create mode 100644 src/plugins/CMakeLists.txt
 rename {inference-engine/src/hetero_plugin => src/plugins/hetero}/CMakeLists.txt (84%)
 rename inference-engine/src/hetero_plugin/hetero_async_infer_request.cpp => src/plugins/hetero/async_infer_request.cpp (66%)
 rename inference-engine/src/hetero_plugin/hetero_async_infer_request.hpp => src/plugins/hetero/async_infer_request.hpp (82%)
 rename inference-engine/src/hetero_plugin/hetero_executable_network.cpp => src/plugins/hetero/executable_network.cpp (76%)
 create mode 100644 src/plugins/hetero/executable_network.hpp
 rename inference-engine/src/hetero_plugin/hetero_infer_request.cpp => src/plugins/hetero/infer_request.cpp (77%)
 rename inference-engine/src/hetero_plugin/hetero_infer_request.hpp => src/plugins/hetero/infer_request.hpp (82%)
 rename inference-engine/src/hetero_plugin/hetero_itt.hpp => src/plugins/hetero/itt.hpp (72%)
 rename inference-engine/src/hetero_plugin/hetero_plugin.cpp => src/plugins/hetero/plugin.cpp (78%)
 rename inference-engine/src/hetero_plugin/hetero_plugin.hpp => src/plugins/hetero/plugin.hpp (52%)

diff --git a/cmake/coverage.cmake b/cmake/coverage.cmake
index 9ef4617846c..1bff5026081 100644
--- a/cmake/coverage.cmake
+++ b/cmake/coverage.cmake
@@ -23,9 +23,9 @@ ie_coverage_extract(INPUT "openvino" OUTPUT "inference_engine_legacy"
 ie_coverage_genhtml(INFO_FILE "inference_engine_legacy"
                     PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
 
-ie_coverage_extract(INPUT "openvino" OUTPUT "hetero_plugin"
-                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/src/plugins/hetero_plugin/*")
-ie_coverage_genhtml(INFO_FILE "hetero_plugin"
+ie_coverage_extract(INPUT "openvino" OUTPUT "ov_hetero_plugin"
+                    PATTERNS "${OV_COVERAGE_BASE_DIRECTORY}/src/plugins/hetero/*")
+ie_coverage_genhtml(INFO_FILE "ov_hetero_plugin"
                     PREFIX "${OV_COVERAGE_BASE_DIRECTORY}")
 
 ie_coverage_extract(INPUT "openvino" OUTPUT "multi_device"
diff --git a/docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md b/docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md
index 9abaca6c1c9..ff0367313f0 100644
--- a/docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md
+++ b/docs/IE_DG/Deep_Learning_Inference_Engine_DevGuide.md
@@ -70,7 +70,7 @@ The table below shows the plugin libraries and additional dependencies for Linux
 | MYRIAD | `libmyriadPlugin.so`        | `libusb.so`,                                                | `myriadPlugin.dll`       | `usb.dll`                                                                                              | `libmyriadPlugin.so`      | `libusb.dylib`                              |
 | HDDL   | `libHDDLPlugin.so`          | `libbsl.so`, `libhddlapi.so`, `libmvnc-hddl.so`             | `HDDLPlugin.dll`         | `bsl.dll`, `hddlapi.dll`, `json-c.dll`, `libcrypto-1_1-x64.dll`, `libssl-1_1-x64.dll`, `mvnc-hddl.dll` |  Is not supported            |  -                                          |
 | GNA    | `libGNAPlugin.so`           | `libgna.so`,                                                | `GNAPlugin.dll`          | `gna.dll`                                                                                              |  Is not supported            |  -                                          |
-| HETERO | `libHeteroPlugin.so`        | Same as for selected plugins                                | `HeteroPlugin.dll`       | Same as for selected plugins                                                                           | `libHeteroPlugin.so`      |  Same as for selected plugins               |
+| HETERO | `libov_hetero_plugin.so`        | Same as for selected plugins                                | `ov_hetero_plugin.dll`       | Same as for selected plugins                                                                           | `libov_hetero_plugin.so`      |  Same as for selected plugins               |
 | MULTI  | `libMultiDevicePlugin.so`   | Same as for selected plugins                                | `MultiDevicePlugin.dll`  | Same as for selected plugins                                                                           | `libMultiDevicePlugin.so` |  Same as for selected plugins               |
 
 > **NOTE**: All plugin libraries also depend on core Inference Engine libraries.
diff --git a/docs/IE_DG/inference_engine_intro.md b/docs/IE_DG/inference_engine_intro.md
index cd703b1a3ee..0ead43f773e 100644
--- a/docs/IE_DG/inference_engine_intro.md
+++ b/docs/IE_DG/inference_engine_intro.md
@@ -75,7 +75,7 @@ The table below shows the plugin libraries and additional dependencies for Linux
 | MYRIAD | `libmyriadPlugin.so`        | `libusb.so`,                                                | `myriadPlugin.dll`       | `usb.dll`                                                                                              | `libmyriadPlugin.so`      | `libusb.dylib`                              |
 | HDDL   | `libHDDLPlugin.so`          | `libbsl.so`, `libhddlapi.so`, `libmvnc-hddl.so`             | `HDDLPlugin.dll`         | `bsl.dll`, `hddlapi.dll`, `json-c.dll`, `libcrypto-1_1-x64.dll`, `libssl-1_1-x64.dll`, `mvnc-hddl.dll` |  Is not supported            |  -                                          |
 | GNA    | `libGNAPlugin.so`           | `libgna.so`,                                                | `GNAPlugin.dll`          | `gna.dll`                                                                                              |  Is not supported            |  -                                          |
-| HETERO | `libHeteroPlugin.so`        | Same as for selected plugins                                | `HeteroPlugin.dll`       | Same as for selected plugins                                                                           | `libHeteroPlugin.so`      |  Same as for selected plugins               |
+| HETERO | `libov_hetero_plugin.so`        | Same as for selected plugins                                | `ov_hetero_plugin.dll`       | Same as for selected plugins                                                                           | `libov_hetero_plugin.so`      |  Same as for selected plugins               |
 | MULTI  | `libMultiDevicePlugin.so`   | Same as for selected plugins                                | `MultiDevicePlugin.dll`  | Same as for selected plugins                                                                           | `libMultiDevicePlugin.so` |  Same as for selected plugins               |
 
 > **NOTE**: All plugin libraries also depend on core Inference Engine libraries.
diff --git a/docs/template_plugin/tests/functional/CMakeLists.txt b/docs/template_plugin/tests/functional/CMakeLists.txt
index 16db218346d..0a0309992b6 100644
--- a/docs/template_plugin/tests/functional/CMakeLists.txt
+++ b/docs/template_plugin/tests/functional/CMakeLists.txt
@@ -21,7 +21,7 @@ addIeTargetTest(
 )
 
 if(ENABLE_HETERO)
-    add_dependencies(${TARGET_NAME} HeteroPlugin)
+    add_dependencies(${TARGET_NAME} ov_hetero_plugin)
 endif()
 
 find_package(OpenCV QUIET COMPONENTS core imgproc)
diff --git a/inference-engine/src/CMakeLists.txt b/inference-engine/src/CMakeLists.txt
index 1090339f5c6..de3e3ea2035 100644
--- a/inference-engine/src/CMakeLists.txt
+++ b/inference-engine/src/CMakeLists.txt
@@ -25,10 +25,6 @@ if(ENABLE_GNA)
     add_subdirectory(gna_plugin)
 endif()
 
-if(ENABLE_HETERO)
-    add_subdirectory(hetero_plugin)
-endif()
-
 if(ENABLE_MULTI)
     add_subdirectory(multi_device)
 endif()
diff --git a/inference-engine/src/hetero_plugin/hetero_executable_network.hpp b/inference-engine/src/hetero_plugin/hetero_executable_network.hpp
deleted file mode 100644
index f7ecbe5ea46..00000000000
--- a/inference-engine/src/hetero_plugin/hetero_executable_network.hpp
+++ /dev/null
@@ -1,81 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-/**
- * @brief a header file for ExecutableNetwork
- * @file hetero_executable_network.hpp
- */
-#pragma once
-
-#include <memory>
-#include <string>
-#include <vector>
-#include <map>
-#include <unordered_map>
-#include <unordered_set>
-
-#include <ie_common.h>
-#include <cpp_interfaces/impl/ie_executable_network_thread_safe_default.hpp>
-
-#include "hetero_infer_request.hpp"
-#include "ie_icore.hpp"
-#include "hetero_async_infer_request.hpp"
-
-namespace HeteroPlugin {
-
-class Engine;
-
-/**
- * @class ExecutableNetwork
- * @brief Interface of executable network
- */
-class HeteroExecutableNetwork : public InferenceEngine::ExecutableNetworkThreadSafeDefault {
-public:
-    typedef std::shared_ptr<HeteroExecutableNetwork> Ptr;
-
-    /**
-    * @brief constructor
-    */
-    HeteroExecutableNetwork(const InferenceEngine::CNNNetwork&          network,
-                            const std::map<std::string, std::string>&   config,
-                            Engine*                                     plugin);
-    /**
-    * @brief Import from opened file constructor
-    */
-    HeteroExecutableNetwork(std::istream&                               heteroModel,
-                            const std::map<std::string, std::string>&   config,
-                            Engine*                                     plugin);
-
-    InferenceEngine::IInferRequestInternal::Ptr CreateInferRequestImpl(InferenceEngine::InputsDataMap networkInputs,
-                                                                       InferenceEngine::OutputsDataMap networkOutputs) override;
-    InferenceEngine::IInferRequestInternal::Ptr CreateInferRequestImpl(const std::vector<std::shared_ptr<const ov::Node>>& inputs,
-                                                                       const std::vector<std::shared_ptr<const ov::Node>>& outputs) override;
-
-
-    InferenceEngine::IInferRequestInternal::Ptr CreateInferRequest() override;
-
-    InferenceEngine::Parameter GetConfig(const std::string &name) const override;
-
-    InferenceEngine::Parameter GetMetric(const std::string &name) const override;
-
-    void Export(std::ostream& modelFile) override;
-
-private:
-    void InitCNNImpl(const InferenceEngine::CNNNetwork&    network);
-    void InitNgraph(const InferenceEngine::CNNNetwork&     network);
-
-    struct NetworkDesc {
-        std::string                                   _device;
-        InferenceEngine::CNNNetwork                   _clonedNetwork;
-        InferenceEngine::SoExecutableNetworkInternal  _network;
-    };
-
-    std::vector<NetworkDesc>                     _networks;
-    Engine*                                      _heteroPlugin;
-    std::string                                  _name;
-    std::map<std::string, std::string>           _config;
-    std::unordered_map<std::string, std::string> _blobNameMap;
-};
-
-}  // namespace HeteroPlugin
diff --git a/inference-engine/tests/functional/inference_engine/CMakeLists.txt b/inference-engine/tests/functional/inference_engine/CMakeLists.txt
index 11463cf8bcb..f5cb873d067 100644
--- a/inference-engine/tests/functional/inference_engine/CMakeLists.txt
+++ b/inference-engine/tests/functional/inference_engine/CMakeLists.txt
@@ -41,7 +41,7 @@ if(ENABLE_IR_V7_READER)
 endif()
 
 if(ENABLE_HETERO)
-    list(APPEND DEPENDENCIES HeteroPlugin)
+    list(APPEND DEPENDENCIES ov_hetero_plugin)
 endif()
 
 if(ENABLE_MULTI)
diff --git a/inference-engine/tests/functional/plugin/conformance/test_runner/api_conformance_runner/include/api_conformance_helpers.hpp b/inference-engine/tests/functional/plugin/conformance/test_runner/api_conformance_runner/include/api_conformance_helpers.hpp
index 36dc38cf3e9..9c83e12fd62 100644
--- a/inference-engine/tests/functional/plugin/conformance/test_runner/api_conformance_runner/include/api_conformance_helpers.hpp
+++ b/inference-engine/tests/functional/plugin/conformance/test_runner/api_conformance_runner/include/api_conformance_helpers.hpp
@@ -23,7 +23,7 @@ inline const std::string getPluginLibNameByDevice(const std::string& deviceName)
             { "CPU", "MKLDNNPlugin" },
             { "GNA", "GNAPlugin" },
             { "GPU", "clDNNPlugin" },
-            { "HETERO", "HeteroPlugin" },
+            { "HETERO", "ov_hetero_plugin" },
             { "MULTI", "MultiDevicePlugin" },
             { "MYRIAD", "myriadPlugin" },
             { "TEMPLATE", "templatePlugin" },
@@ -83,4 +83,4 @@ const std::vector<std::map<std::string, std::string>> emptyConfig = {
 
 }  // namespace conformance
 }  // namespace test
-}  // namespace ov
\ No newline at end of file
+}  // namespace ov
diff --git a/inference-engine/tests/functional/plugin/shared/CMakeLists.txt b/inference-engine/tests/functional/plugin/shared/CMakeLists.txt
index 825c0f66a2c..47a0700a820 100644
--- a/inference-engine/tests/functional/plugin/shared/CMakeLists.txt
+++ b/inference-engine/tests/functional/plugin/shared/CMakeLists.txt
@@ -9,7 +9,7 @@ set(PUBLIC_HEADERS_DIR "${CMAKE_CURRENT_SOURCE_DIR}/include")
 set(DEPENDENCIES mock_engine)
 
 if(ENABLE_HETERO)
-    list(APPEND DEPENDENCIES HeteroPlugin)
+    list(APPEND DEPENDENCIES ov_hetero_plugin)
 endif()
 
 if(ENABLE_MULTI)
diff --git a/inference-engine/tests_deprecated/functional/gna/CMakeLists.txt b/inference-engine/tests_deprecated/functional/gna/CMakeLists.txt
index d8a66f32830..06b8a48e12f 100644
--- a/inference-engine/tests_deprecated/functional/gna/CMakeLists.txt
+++ b/inference-engine/tests_deprecated/functional/gna/CMakeLists.txt
@@ -16,7 +16,7 @@ file(GLOB TEST_SRC
 list(APPEND DEPENDENCIES GNAPlugin)
 
 if(ENABLE_HETERO)
-    list(APPEND DEPENDENCIES HeteroPlugin)
+    list(APPEND DEPENDENCIES ov_hetero_plugin)
 endif()
 
 if(ENABLE_MKL_DNN)
diff --git a/inference-engine/tests_deprecated/functional/shared_tests/CMakeLists.txt b/inference-engine/tests_deprecated/functional/shared_tests/CMakeLists.txt
index 4584a6810c2..951a288c291 100644
--- a/inference-engine/tests_deprecated/functional/shared_tests/CMakeLists.txt
+++ b/inference-engine/tests_deprecated/functional/shared_tests/CMakeLists.txt
@@ -57,7 +57,7 @@ endif()
 target_link_libraries(${TARGET_NAME} PUBLIC ${SHARED_LIBRARIES})
 
 if(ENABLE_HETERO)
-    add_dependencies(${TARGET_NAME} HeteroPlugin)
+    add_dependencies(${TARGET_NAME} ov_hetero_plugin)
 endif()
 
 # developer package
diff --git a/inference-engine/tests_deprecated/functional/vpu/CMakeLists.txt b/inference-engine/tests_deprecated/functional/vpu/CMakeLists.txt
index c6fa691c53d..7e70aaf90dc 100644
--- a/inference-engine/tests_deprecated/functional/vpu/CMakeLists.txt
+++ b/inference-engine/tests_deprecated/functional/vpu/CMakeLists.txt
@@ -8,7 +8,7 @@ set(VPU_DEPENDENCIES
 if (ENABLE_CLDNN)
     list(APPEND VPU_DEPENDENCIES clDNNPlugin)
     if(ENABLE_HETERO)
-        list(APPEND VPU_DEPENDENCIES HeteroPlugin)
+        list(APPEND VPU_DEPENDENCIES ov_hetero_plugin)
     endif()
 endif()
 
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 0f7b4f529b0..a98635a36a3 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -21,6 +21,7 @@ if(CMAKE_COMPILER_IS_GNUCXX)
     ie_add_compiler_flags(-Wmissing-declarations)
 endif()
 
+add_subdirectory(plugins)
 add_subdirectory(inference)
 add_subdirectory(common)
 
diff --git a/src/inference/CMakeLists.txt b/src/inference/CMakeLists.txt
index fe587bd9912..9c044d8a7d9 100644
--- a/src/inference/CMakeLists.txt
+++ b/src/inference/CMakeLists.txt
@@ -200,7 +200,7 @@ endif()
 target_compile_definitions(${TARGET_NAME} PRIVATE IMPLEMENT_INFERENCE_ENGINE_API)
 
 ie_register_plugins(MAIN_TARGET ${TARGET_NAME}
-                    POSSIBLE_PLUGINS MultiDevicePlugin HeteroPlugin clDNNPlugin GNAPlugin MKLDNNPlugin myriadPlugin)
+                    POSSIBLE_PLUGINS MultiDevicePlugin ov_hetero_plugin clDNNPlugin GNAPlugin MKLDNNPlugin myriadPlugin)
 
 ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})
 
diff --git a/src/plugins/CMakeLists.txt b/src/plugins/CMakeLists.txt
new file mode 100644
index 00000000000..7743176ab42
--- /dev/null
+++ b/src/plugins/CMakeLists.txt
@@ -0,0 +1,7 @@
+# Copyright (C) 2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+if(ENABLE_HETERO)
+    add_subdirectory(hetero)
+endif()
diff --git a/inference-engine/src/hetero_plugin/CMakeLists.txt b/src/plugins/hetero/CMakeLists.txt
similarity index 84%
rename from inference-engine/src/hetero_plugin/CMakeLists.txt
rename to src/plugins/hetero/CMakeLists.txt
index 4f325769369..61c9eab2a94 100644
--- a/inference-engine/src/hetero_plugin/CMakeLists.txt
+++ b/src/plugins/hetero/CMakeLists.txt
@@ -2,7 +2,7 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-set (TARGET_NAME "HeteroPlugin")
+set (TARGET_NAME "ov_hetero_plugin")
 
 file(GLOB SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
 file(GLOB_RECURSE HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/*.hpp)
@@ -10,7 +10,8 @@ file(GLOB_RECURSE HEADERS ${CMAKE_CURRENT_SOURCE_DIR}/*.hpp)
 ie_add_plugin(NAME ${TARGET_NAME}
               DEVICE_NAME "HETERO"
               SOURCES ${SOURCES} ${HEADERS}
-              VERSION_DEFINES_FOR hetero_plugin.cpp)
+              VERSION_DEFINES_FOR plugin.cpp
+              ADD_CLANG_FORMAT)
 
 ie_faster_build(${TARGET_NAME}
     UNITY
diff --git a/inference-engine/src/hetero_plugin/hetero_async_infer_request.cpp b/src/plugins/hetero/async_infer_request.cpp
similarity index 66%
rename from inference-engine/src/hetero_plugin/hetero_async_infer_request.cpp
rename to src/plugins/hetero/async_infer_request.cpp
index 019332f0a62..8496ceb172b 100644
--- a/inference-engine/src/hetero_plugin/hetero_async_infer_request.cpp
+++ b/src/plugins/hetero/async_infer_request.cpp
@@ -2,24 +2,24 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include <utility>
+#include "async_infer_request.hpp"
+
 #include <memory>
-#include "hetero_async_infer_request.hpp"
+#include <utility>
 
 using namespace HeteroPlugin;
 using namespace InferenceEngine;
 
-HeteroAsyncInferRequest::HeteroAsyncInferRequest(const IInferRequestInternal::Ptr&  request,
-                                                 const ITaskExecutor::Ptr&          taskExecutor,
-                                                 const ITaskExecutor::Ptr&          callbackExecutor) :
-    AsyncInferRequestThreadSafeDefault(request, taskExecutor, callbackExecutor),
-    _heteroInferRequest(std::static_pointer_cast<HeteroInferRequest>(request)) {
+HeteroAsyncInferRequest::HeteroAsyncInferRequest(const IInferRequestInternal::Ptr& request,
+                                                 const ITaskExecutor::Ptr& taskExecutor,
+                                                 const ITaskExecutor::Ptr& callbackExecutor)
+    : AsyncInferRequestThreadSafeDefault(request, taskExecutor, callbackExecutor),
+      _heteroInferRequest(std::static_pointer_cast<HeteroInferRequest>(request)) {
     _pipeline.clear();
     for (std::size_t requestId = 0; requestId < _heteroInferRequest->_inferRequests.size(); ++requestId) {
         struct RequestExecutor : ITaskExecutor {
-            explicit RequestExecutor(SoIInferRequestInternal & inferRequest) : _inferRequest(inferRequest) {
-                _inferRequest->SetCallback(
-                [this] (std::exception_ptr exceptionPtr) mutable {
+            explicit RequestExecutor(SoIInferRequestInternal& inferRequest) : _inferRequest(inferRequest) {
+                _inferRequest->SetCallback([this](std::exception_ptr exceptionPtr) mutable {
                     _exceptionPtr = exceptionPtr;
                     auto capturedTask = std::move(_task);
                     capturedTask();
@@ -29,12 +29,13 @@ HeteroAsyncInferRequest::HeteroAsyncInferRequest(const IInferRequestInternal::Pt
                 _task = std::move(task);
                 _inferRequest->StartAsync();
             };
-            SoIInferRequestInternal &  _inferRequest;
-            std::exception_ptr         _exceptionPtr;
-            Task                       _task;
+            SoIInferRequestInternal& _inferRequest;
+            std::exception_ptr _exceptionPtr;
+            Task _task;
         };
 
-        auto requestExecutor = std::make_shared<RequestExecutor>(_heteroInferRequest->_inferRequests[requestId]._request);
+        auto requestExecutor =
+            std::make_shared<RequestExecutor>(_heteroInferRequest->_inferRequests[requestId]._request);
         _pipeline.emplace_back(requestExecutor, [requestExecutor] {
             if (nullptr != requestExecutor->_exceptionPtr) {
                 std::rethrow_exception(requestExecutor->_exceptionPtr);
@@ -47,7 +48,7 @@ StatusCode HeteroAsyncInferRequest::Wait(int64_t millis_timeout) {
     auto waitStatus = StatusCode::OK;
     try {
         waitStatus = AsyncInferRequestThreadSafeDefault::Wait(millis_timeout);
-    } catch(...) {
+    } catch (...) {
         for (auto&& requestDesc : _heteroInferRequest->_inferRequests) {
             requestDesc._request->Wait(InferRequest::RESULT_READY);
         }
diff --git a/inference-engine/src/hetero_plugin/hetero_async_infer_request.hpp b/src/plugins/hetero/async_infer_request.hpp
similarity index 82%
rename from inference-engine/src/hetero_plugin/hetero_async_infer_request.hpp
rename to src/plugins/hetero/async_infer_request.hpp
index 85d6a13afba..1f0befcc720 100644
--- a/inference-engine/src/hetero_plugin/hetero_async_infer_request.hpp
+++ b/src/plugins/hetero/async_infer_request.hpp
@@ -4,10 +4,11 @@
 
 #pragma once
 
-#include <vector>
 #include <memory>
+#include <vector>
+
 #include "cpp_interfaces/impl/ie_infer_async_request_thread_safe_default.hpp"
-#include "hetero_infer_request.hpp"
+#include "infer_request.hpp"
 
 namespace HeteroPlugin {
 
@@ -15,14 +16,13 @@ class HeteroAsyncInferRequest : public InferenceEngine::AsyncInferRequestThreadS
 public:
     using Ptr = std::shared_ptr<HeteroAsyncInferRequest>;
     HeteroAsyncInferRequest(const InferenceEngine::IInferRequestInternal::Ptr& request,
-                            const InferenceEngine::ITaskExecutor::Ptr&        taskExecutor,
-                            const InferenceEngine::ITaskExecutor::Ptr&        callbackExecutor);
+                            const InferenceEngine::ITaskExecutor::Ptr& taskExecutor,
+                            const InferenceEngine::ITaskExecutor::Ptr& callbackExecutor);
     ~HeteroAsyncInferRequest();
     InferenceEngine::StatusCode Wait(int64_t millis_timeout) override;
 
 private:
-    HeteroInferRequest::Ptr                     _heteroInferRequest;
+    HeteroInferRequest::Ptr _heteroInferRequest;
 };
 
 }  // namespace HeteroPlugin
-
diff --git a/inference-engine/src/hetero_plugin/hetero_executable_network.cpp b/src/plugins/hetero/executable_network.cpp
similarity index 76%
rename from inference-engine/src/hetero_plugin/hetero_executable_network.cpp
rename to src/plugins/hetero/executable_network.cpp
index c38ea9ed112..c79090d9c12 100644
--- a/inference-engine/src/hetero_plugin/hetero_executable_network.cpp
+++ b/src/plugins/hetero/executable_network.cpp
@@ -2,10 +2,11 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+// clang-format off
 #include "ie_metric_helpers.hpp"
-#include "hetero_executable_network.hpp"
-#include "hetero_async_infer_request.hpp"
-#include "hetero_itt.hpp"
+#include "executable_network.hpp"
+#include "async_infer_request.hpp"
+#include "itt.hpp"
 #include "ie_precision.hpp"
 #include "openvino/core/dimension.hpp"
 #include "openvino/core/except.hpp"
@@ -34,7 +35,7 @@
 #include "ie_plugin_config.hpp"
 #include "ie_algorithm.hpp"
 #include "cpp_interfaces/interface/ie_internal_plugin_config.hpp"
-#include "hetero_plugin.hpp"
+#include "plugin.hpp"
 #include <ie_algorithm.hpp>
 
 #include <ngraph/function.hpp>
@@ -45,6 +46,7 @@
 #include <ngraph/op/util/op_types.hpp>
 #include <ngraph/rt_info.hpp>
 #include <ngraph/pass/visualize_tree.hpp>
+// clang-format on
 
 using namespace InferenceEngine;
 using namespace details;
@@ -52,25 +54,25 @@ using namespace HeteroPlugin;
 using namespace InferenceEngine::PluginConfigParams;
 using namespace InferenceEngine::HeteroConfigParams;
 
-template<typename T>
+template <typename T>
 using NodeMap = std::unordered_map<ngraph::Node*, T>;
 
-HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::CNNNetwork&     network,
-                                                 const Engine::Configs&                 config,
-                                                 Engine*                                plugin):
-    InferenceEngine::ExecutableNetworkThreadSafeDefault(
-        nullptr, std::make_shared<InferenceEngine::ImmediateExecutor>()),
-    _heteroPlugin{plugin},
-    _name{network.getName()},
-    _config{config} {
+HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::CNNNetwork& network,
+                                                 const Engine::Configs& config,
+                                                 Engine* plugin)
+    : InferenceEngine::ExecutableNetworkThreadSafeDefault(nullptr,
+                                                          std::make_shared<InferenceEngine::ImmediateExecutor>()),
+      _heteroPlugin{plugin},
+      _name{network.getName()},
+      _config{config} {
     auto function = network.getFunction();
     IE_ASSERT(function != nullptr);
     auto clonedFunction = ngraph::clone_function(*function);
     auto itDumpDotFile = _config.find(HETERO_CONFIG_KEY(DUMP_GRAPH_DOT));
     bool dumpDotFile = itDumpDotFile != _config.end() ? (itDumpDotFile->second == YES) : false;
-//#ifndef NDEBUG
-//    dumpDotFile  = true;
-//#endif
+    //#ifndef NDEBUG
+    //    dumpDotFile  = true;
+    //#endif
 
     QueryNetworkResult queryNetworkResult;
     auto orderedOps = clonedFunction->get_ordered_ops();
@@ -101,7 +103,7 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::CNNNetwo
     using NodeSet = std::unordered_set<ngraph::Node*>;
     using InputSet = std::set<Input>;
 
-    auto InputNode  = [] (const ngraph::Input<ngraph::Node>& input) {
+    auto InputNode = [](const ngraph::Input<ngraph::Node>& input) {
         return input.get_source_output().get_node();
     };
 
@@ -109,13 +111,13 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::CNNNetwo
     for (auto&& node : clonedFunction->get_ops()) {
         if (ngraph::op::is_constant(node) || ngraph::op::is_output(node) || ngraph::op::is_parameter(node)) {
             if (!contains(queryNetworkResult.supportedLayersMap, node->get_friendly_name())) {
-                auto& nodeWithAffinityName = ngraph::op::is_output(node)
-                                           ? node->input_value(0).get_node()->get_friendly_name()
-                                           : node->output(0).get_target_inputs().begin()->get_node()->get_friendly_name();
+                auto& nodeWithAffinityName =
+                    ngraph::op::is_output(node)
+                        ? node->input_value(0).get_node()->get_friendly_name()
+                        : node->output(0).get_target_inputs().begin()->get_node()->get_friendly_name();
                 auto itAffinity = queryNetworkResult.supportedLayersMap.find(nodeWithAffinityName);
                 if (itAffinity == queryNetworkResult.supportedLayersMap.end()) {
-                    IE_THROW() << "Node " << nodeWithAffinityName <<
-                                        " was not assigned on any pointed device.";
+                    IE_THROW() << "Node " << nodeWithAffinityName << " was not assigned on any pointed device.";
                 }
                 queryNetworkResult.supportedLayersMap.emplace(node->get_friendly_name(), itAffinity->second);
             }
@@ -131,18 +133,18 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::CNNNetwo
             affinities[node.get()] = itAffinity->second;
             devices.emplace(itAffinity->second);
         } else if (allEmpty) {
-            IE_THROW() << "Hetero plugin used default fallback policy, but some layers eg: \n(Name:" <<
-                node->get_friendly_name() << ", Type: " << node->get_type_name() <<
-                ") were not able to be assigned on any pointed device.\n" <<
-                "It happened because these layers are not supported in plugins by default.\n" <<
-                "You need to implement custom layers to support them.";
+            IE_THROW() << "Hetero plugin used default fallback policy, but some layers eg: \n(Name:"
+                       << node->get_friendly_name() << ", Type: " << node->get_type_name()
+                       << ") were not able to be assigned on any pointed device.\n"
+                       << "It happened because these layers are not supported in plugins by default.\n"
+                       << "You need to implement custom layers to support them.";
         } else {
-            IE_THROW() << "Network passed to LoadNetwork has affinity assigned, but some layers eg: \n(Name:" <<
-                node->get_friendly_name() << ", Type: " << node->get_type_name() <<
-                ") were not assigned to any device.\n" <<
-                "It might happen if you assigned layers manually and missed some layers or\n" <<
-                "if you used some automatic assigning mode which decided that these layers are not\n" <<
-                "supported by any plugin";
+            IE_THROW() << "Network passed to LoadNetwork has affinity assigned, but some layers eg: \n(Name:"
+                       << node->get_friendly_name() << ", Type: " << node->get_type_name()
+                       << ") were not assigned to any device.\n"
+                       << "It might happen if you assigned layers manually and missed some layers or\n"
+                       << "if you used some automatic assigning mode which decided that these layers are not\n"
+                       << "supported by any plugin";
         }
     }
 
@@ -164,17 +166,21 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::CNNNetwo
     };
 
     if (dumpDotFile) {
-        ngraph::pass::VisualizeTree{"hetero_affinity_" + _name + ".dot",
-            [&] (const ngraph::Node& node, std::vector<std::string>& attributes) {
+        ngraph::pass::VisualizeTree{
+            "hetero_affinity_" + _name + ".dot",
+            [&](const ngraph::Node& node, std::vector<std::string>& attributes) {
                 auto nodeDevice = queryNetworkResult.supportedLayersMap.at(node.get_friendly_name());
                 int colorIndex = 0;
                 for (auto&& device : devices) {
                     if (device == nodeDevice) {
-                        attributes.push_back(std::string {"fillcolor="} + colors[colorIndex % colors.size()] + " style=filled");
-                        auto itLabel = std::find_if(std::begin(attributes), std::end(attributes), [] (const std::string& str) {
-                            return str.find("label") != std::string::npos;
-                        });
-                        auto label = "\\ndevice=" + queryNetworkResult.supportedLayersMap.at(node.get_friendly_name()) + '\"';
+                        attributes.push_back(std::string{"fillcolor="} + colors[colorIndex % colors.size()] +
+                                             " style=filled");
+                        auto itLabel =
+                            std::find_if(std::begin(attributes), std::end(attributes), [](const std::string& str) {
+                                return str.find("label") != std::string::npos;
+                            });
+                        auto label =
+                            "\\ndevice=" + queryNetworkResult.supportedLayersMap.at(node.get_friendly_name()) + '\"';
                         IE_ASSERT(itLabel != attributes.end());
                         itLabel->pop_back();
                         (*itLabel) += label;
@@ -182,10 +188,10 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::CNNNetwo
                     }
                     colorIndex++;
                 }
-            }}.run_on_function(ngraph::clone_function(*function));
+            }}
+            .run_on_function(ngraph::clone_function(*function));
     }
 
-
     NodeMap<InputSet> nodeInputDependencies;
     NodeSet graphInputNodes;
     InputSet subgraphInputs;
@@ -245,7 +251,8 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::CNNNetwo
     };
 
     // Split cyclic dependencies.
-    for (std::size_t prevSubgraphs = 0, cyclicSplitStep = 0; prevSubgraphs != subgraphInputs.size(); ++cyclicSplitStep) {
+    for (std::size_t prevSubgraphs = 0, cyclicSplitStep = 0; prevSubgraphs != subgraphInputs.size();
+         ++cyclicSplitStep) {
         IE_ASSERT(cyclicSplitStep < orderedOps.size());
         prevSubgraphs = subgraphInputs.size();
         auto subgraphIds = CollectSubgraphs();
@@ -282,10 +289,10 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::CNNNetwo
                     }
                 }
                 for (auto&& input : node->inputs()) {
-                    auto& inputNodeSubgraphCyclicInputDependency = nodeSubgraphCyclicInputDependencies[InputNode(input)];
+                    auto& inputNodeSubgraphCyclicInputDependency =
+                        nodeSubgraphCyclicInputDependencies[InputNode(input)];
                     auto& inputNodeSubgraphInputDependency = nodeSubgraphInputDependencies[InputNode(input)];
-                    if (!Intersects(nodeSubgraphCyclicInputDependency,
-                                    inputNodeSubgraphCyclicInputDependency) &&
+                    if (!Intersects(nodeSubgraphCyclicInputDependency, inputNodeSubgraphCyclicInputDependency) &&
                         Intersects(cyclicInputsDependencies, inputNodeSubgraphInputDependency)) {
                         subgraphInputs.insert(input);
                     }
@@ -303,30 +310,32 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::CNNNetwo
             auto output = input.get_source_output();
             output.remove_target_input(input);
             auto result = std::make_shared<ngraph::op::Result>(output);
-            result->set_friendly_name(output.get_node()->get_friendly_name()
-                + "_" + std::to_string(output.get_index()) + "_result");
+            result->set_friendly_name(output.get_node()->get_friendly_name() + "_" +
+                                      std::to_string(output.get_index()) + "_result");
             ngraph::copy_runtime_info(output.get_node_shared_ptr(), result);
-            auto parameter = std::make_shared<ngraph::op::Parameter>(output.get_element_type(), output.get_partial_shape());
-            parameter->set_friendly_name(input.get_node()->get_friendly_name()
-                + "_" + std::to_string(input.get_index()) + "_parameter");
+            auto parameter =
+                std::make_shared<ngraph::op::Parameter>(output.get_element_type(), output.get_partial_shape());
+            parameter->set_friendly_name(input.get_node()->get_friendly_name() + "_" +
+                                         std::to_string(input.get_index()) + "_parameter");
             ngraph::copy_runtime_info(input.get_node()->shared_from_this(), parameter);
             input.replace_source_output(parameter->output(0));
             results.push_back(result);
             subgraphIds.emplace(result.get(), subgraphIds[output.get_node()]);
             subgraphIds.emplace(parameter.get(), subgraphIds[input.get_node()]);
             subgraphParameterToPrevResult.emplace(parameter.get(), result.get());
-            _blobNameMap.emplace(parameter->get_friendly_name(),
-                                 output.get_node()->get_friendly_name() +
-                                 ((output.get_node()->get_output_size() != 1)
-                                 ? ("." + std::to_string(output.get_index())) : std::string{}));
+            _blobNameMap.emplace(
+                parameter->get_friendly_name(),
+                output.get_node()->get_friendly_name() + ((output.get_node()->get_output_size() != 1)
+                                                              ? ("." + std::to_string(output.get_index()))
+                                                              : std::string{}));
         }
     }
 
     struct Subgraph {
-        ngraph::ResultVector    _results;
+        ngraph::ResultVector _results;
         ngraph::ParameterVector _parameters;
-        ngraph::SinkVector      _sinks;
-        std::string             _affinity;
+        ngraph::SinkVector _sinks;
+        std::string _affinity;
     };
     std::unordered_map<int, Subgraph> subgraphs;
     // Extracts subgraph parameters, results and affinities
@@ -334,14 +343,12 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::CNNNetwo
         auto node = subgraphIdPtrValue.first;
         auto& subgraph = subgraphs[subgraphIdPtrValue.second];
         if (ngraph::op::is_output(node)) {
-            subgraph._results.emplace_back(
-                std::dynamic_pointer_cast<ngraph::op::v0::Result>(node->shared_from_this()));
+            subgraph._results.emplace_back(std::dynamic_pointer_cast<ngraph::op::v0::Result>(node->shared_from_this()));
         } else if (ngraph::op::is_parameter(node)) {
             subgraph._parameters.emplace_back(
                 std::dynamic_pointer_cast<ngraph::op::v0::Parameter>(node->shared_from_this()));
         } else if (ngraph::op::is_sink(node)) {
-            subgraph._sinks.emplace_back(
-                    std::dynamic_pointer_cast<ngraph::op::Sink>(node->shared_from_this()));
+            subgraph._sinks.emplace_back(std::dynamic_pointer_cast<ngraph::op::Sink>(node->shared_from_this()));
         }
         auto itAffinity = affinities.find(node);
         if (itAffinity != affinities.end()) {
@@ -362,20 +369,24 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::CNNNetwo
         IE_ASSERT(subgraphTopoSortsStep < subgraphs.size());
         ++subgraphTopoSortsStep;
         std::vector<Subgraph> nextSubgraphs;
-        auto IsNextSubGraph = [&] (const Subgraph& subgraph) {
+        auto IsNextSubGraph = [&](const Subgraph& subgraph) {
             auto& parameters = subgraph._parameters;
-            return std::all_of(parameters.begin(), parameters.end(),
-                    [&] (const ngraph::ParameterVector::value_type& parameter) {
-                    return contains(graphInputNodes, parameter.get()) ||
-                           contains(prevResults, subgraphParameterToPrevResult[parameter.get()]);});
+            return std::all_of(parameters.begin(),
+                               parameters.end(),
+                               [&](const ngraph::ParameterVector::value_type& parameter) {
+                                   return contains(graphInputNodes, parameter.get()) ||
+                                          contains(prevResults, subgraphParameterToPrevResult[parameter.get()]);
+                               });
         };
-        std::remove_copy_if(std::begin(allSubgraphs), std::end(allSubgraphs),
+        std::remove_copy_if(std::begin(allSubgraphs),
+                            std::end(allSubgraphs),
                             std::back_inserter(nextSubgraphs),
-                            [&] (const Subgraph& subgraph) { return !IsNextSubGraph(subgraph);});
-        allSubgraphs.erase(
-            std::remove_if(std::begin(allSubgraphs), std::end(allSubgraphs), IsNextSubGraph),
-            std::end(allSubgraphs));
-        for (auto&& subgraph :  nextSubgraphs) {
+                            [&](const Subgraph& subgraph) {
+                                return !IsNextSubGraph(subgraph);
+                            });
+        allSubgraphs.erase(std::remove_if(std::begin(allSubgraphs), std::end(allSubgraphs), IsNextSubGraph),
+                           std::end(allSubgraphs));
+        for (auto&& subgraph : nextSubgraphs) {
             for (auto&& result : subgraph._results) {
                 prevResults.insert(result.get());
             }
@@ -390,9 +401,10 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::CNNNetwo
     int id = 0;
     for (auto&& subgraph : orderedSubgraphs) {
         _networks[id]._device = subgraph._affinity;
-        subFunctions[id] =
-            std::make_shared<ngraph::Function>(subgraph._results, subgraph._sinks, subgraph._parameters,
-                                                     _name + '_' + std::to_string(id));
+        subFunctions[id] = std::make_shared<ngraph::Function>(subgraph._results,
+                                                              subgraph._sinks,
+                                                              subgraph._parameters,
+                                                              _name + '_' + std::to_string(id));
         _networks[id]._clonedNetwork = CNNNetwork{subFunctions[id]};
         // update of pre-processing info
         auto clonedInputs = _networks[id]._clonedNetwork.getInputsInfo();
@@ -414,9 +426,9 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::CNNNetwo
             }
         }
 
-        auto toLegacyType = [] (const ngraph::element::Type& ngraph_type) {
-            return (ngraph_type == ngraph::element::f16 || ngraph_type == ngraph::element::bf16) ?
-                ngraph::element::f32 : ngraph_type;
+        auto toLegacyType = [](const ngraph::element::Type& ngraph_type) {
+            return (ngraph_type == ngraph::element::f16 || ngraph_type == ngraph::element::bf16) ? ngraph::element::f32
+                                                                                                 : ngraph_type;
         };
 
         // CNNNetwork converts input and output types to preserve legacy behaviour
@@ -428,8 +440,7 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::CNNNetwo
                     auto name = parameter->get_friendly_name();
                     if (parameter->get_friendly_name() == input.first) {
                         input.second->setPrecision(
-                            InferenceEngine::details::convertPrecision(
-                                toLegacyType(parameter->get_element_type())));
+                            InferenceEngine::details::convertPrecision(toLegacyType(parameter->get_element_type())));
                     }
                 }
             }
@@ -441,8 +452,7 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::CNNNetwo
                     auto output_name = ngraph::op::util::create_ie_output_name(source_output);
                     if (output_name == output.first) {
                         output.second->setPrecision(
-                            InferenceEngine::details::convertPrecision(
-                                toLegacyType(source_output.get_element_type())));
+                            InferenceEngine::details::convertPrecision(toLegacyType(source_output.get_element_type())));
                     }
                 }
             }
@@ -450,37 +460,42 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(const InferenceEngine::CNNNetwo
         ++id;
     }
     if (dumpDotFile) {
-        ngraph::pass::VisualizeTree{"hetero_subgraphs_" + _name + ".dot",
-            [&] (const ngraph::Node& node, std::vector<std::string>& attributes) {
+        ngraph::pass::VisualizeTree{
+            "hetero_subgraphs_" + _name + ".dot",
+            [&](const ngraph::Node& node, std::vector<std::string>& attributes) {
                 for (size_t i = 0; i < subFunctions.size(); i++) {
                     for (auto&& nodeInSubfunction : subFunctions[i]->get_ops()) {
                         if (nodeInSubfunction->get_friendly_name() == node.get_friendly_name()) {
-                            attributes.push_back(std::string {"fillcolor="} + colors[i % colors.size()] + " style=filled");
-                            auto itLabel = std::find_if(std::begin(attributes), std::end(attributes), [] (const std::string& str) {
-                                return str.find("label") != std::string::npos;
-                            });
-                            auto label = "\\nsubgraph=" + std::to_string(i) + "\\n"
-                                       + "device=" + queryNetworkResult.supportedLayersMap.at(node.get_friendly_name()) + '\"';
+                            attributes.push_back(std::string{"fillcolor="} + colors[i % colors.size()] +
+                                                 " style=filled");
+                            auto itLabel =
+                                std::find_if(std::begin(attributes), std::end(attributes), [](const std::string& str) {
+                                    return str.find("label") != std::string::npos;
+                                });
+                            auto label = "\\nsubgraph=" + std::to_string(i) + "\\n" + "device=" +
+                                         queryNetworkResult.supportedLayersMap.at(node.get_friendly_name()) + '\"';
                             IE_ASSERT(itLabel != attributes.end());
                             itLabel->pop_back();
                             (*itLabel) += label;
                         }
                     }
                 }
-            }}.run_on_function(ngraph::clone_function(*function));
+            }}
+            .run_on_function(ngraph::clone_function(*function));
     }
     for (auto&& network : _networks) {
         auto metaDevices = _heteroPlugin->GetDevicePlugins(network._device, _config);
         metaDevices[network._device].emplace(CONFIG_KEY_INTERNAL(FORCE_DISABLE_CACHE), "");
         network._network = _heteroPlugin->GetCore()->LoadNetwork(network._clonedNetwork,
-            network._device, metaDevices[network._device]);
+                                                                 network._device,
+                                                                 metaDevices[network._device]);
     }
 }
 
-HeteroExecutableNetwork::HeteroExecutableNetwork(std::istream&                               heteroModel,
-                                                 const std::map<std::string, std::string>&   configs,
-                                                 Engine*                                     heteroPlugin) :
-    _heteroPlugin(heteroPlugin) {
+HeteroExecutableNetwork::HeteroExecutableNetwork(std::istream& heteroModel,
+                                                 const std::map<std::string, std::string>& configs,
+                                                 Engine* heteroPlugin)
+    : _heteroPlugin(heteroPlugin) {
     std::string heteroXmlStr;
     std::getline(heteroModel, heteroXmlStr);
 
@@ -498,24 +513,20 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(std::istream&
 
     std::unordered_set<std::string> networkInputs;
     pugi::xml_node inputsNode = heteroNode.child("inputs");
-    FOREACH_CHILD(inputNode, inputsNode, "input")  {
-        networkInputs.insert(GetStrAttr(inputNode, "name"));
-    }
+    FOREACH_CHILD (inputNode, inputsNode, "input") { networkInputs.insert(GetStrAttr(inputNode, "name")); }
 
     std::unordered_set<std::string> networkOutputs;
     pugi::xml_node outputsNode = heteroNode.child("outputs");
-    FOREACH_CHILD(outputNode, outputsNode, "output") {
-        networkOutputs.insert(GetStrAttr(outputNode, "name"));
-    }
+    FOREACH_CHILD (outputNode, outputsNode, "output") { networkOutputs.insert(GetStrAttr(outputNode, "name")); }
 
     Engine::Configs importedConfigs;
     auto configsNode = heteroNode.child("configs");
-    FOREACH_CHILD(configNode, configsNode, "config") {
+    FOREACH_CHILD (configNode, configsNode, "config") {
         importedConfigs.emplace(GetStrAttr(configNode, "key"), GetStrAttr(configNode, "value"));
     }
 
     auto blobNamesNode = heteroNode.child("blob_names_map");
-    FOREACH_CHILD(blobNameNode, blobNamesNode, "blob_name_map") {
+    FOREACH_CHILD (blobNameNode, blobNamesNode, "blob_name_map") {
         _blobNameMap.emplace(GetStrAttr(blobNameNode, "key"), GetStrAttr(blobNameNode, "value"));
     }
 
@@ -525,7 +536,7 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(std::istream&
 
     std::vector<NetworkDesc> descs;
     pugi::xml_node subnetworksNode = heteroNode.child("subnetworks");
-    FOREACH_CHILD(subnetworkNode, subnetworksNode, "subnetwork") {
+    FOREACH_CHILD (subnetworkNode, subnetworksNode, "subnetwork") {
         auto deviceName = GetStrAttr(subnetworkNode, "device");
 
         auto metaDevices = _heteroPlugin->GetDevicePlugins(deviceName, importedConfigs);
@@ -560,14 +571,14 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(std::istream&
             cnnnetwork = _heteroPlugin->GetCore()->ReadNetwork(xmlString, std::move(dataBlob));
             auto inputs = cnnnetwork.getInputsInfo();
             auto inputsNode = subnetworkNode.child("inputs");
-            FOREACH_CHILD(inputNode, inputsNode, "input") {
+            FOREACH_CHILD (inputNode, inputsNode, "input") {
                 auto inputName = GetStrAttr(inputNode, "name");
                 inputs[inputName]->setPrecision(Precision::FromStr(GetStrAttr(inputNode, "precision")));
             }
 
             auto outputs = cnnnetwork.getOutputsInfo();
             auto outputsNode = subnetworkNode.child("outputs");
-            FOREACH_CHILD(outputNode, outputsNode, "output") {
+            FOREACH_CHILD (outputNode, outputsNode, "output") {
                 auto outputName = GetStrAttr(outputNode, "name");
                 outputs[outputName]->setPrecision(Precision::FromStr(GetStrAttr(outputNode, "precision")));
             }
@@ -596,21 +607,19 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(std::istream&
         });
     }
 
-    const auto parseNode = [] (const pugi::xml_node & xml_node, bool is_param) ->
-        std::shared_ptr<const ov::Node> {
+    const auto parseNode = [](const pugi::xml_node& xml_node, bool is_param) -> std::shared_ptr<const ov::Node> {
         const std::string operation_name = GetStrAttr(xml_node, "operation_name");
-        const auto elementType =
-            ov::EnumNames<ov::element::Type_t>::as_enum(GetStrAttr(xml_node, "element_type"));
+        const auto elementType = ov::EnumNames<ov::element::Type_t>::as_enum(GetStrAttr(xml_node, "element_type"));
 
         std::vector<ov::Dimension> partialShape;
         pugi::xml_node partialShapeNode = xml_node.child("partial_shape");
-        FOREACH_CHILD(dimNode, partialShapeNode, "dim") {
+        FOREACH_CHILD (dimNode, partialShapeNode, "dim") {
             partialShape.emplace_back(ov::Dimension(GetInt64Attr(dimNode, "value")));
         }
 
         pugi::xml_node tensorNamesNode = xml_node.child("tensor_names");
         std::unordered_set<std::string> tensorNames;
-        FOREACH_CHILD(tensorNameNode, tensorNamesNode, "tensor_name") {
+        FOREACH_CHILD (tensorNameNode, tensorNamesNode, "tensor_name") {
             tensorNames.insert(GetStrAttr(tensorNameNode, "value"));
         }
 
@@ -625,14 +634,12 @@ HeteroExecutableNetwork::HeteroExecutableNetwork(std::istream&
     (void)parseNode;
 
     pugi::xml_node parametersNode = heteroNode.child("parameters");
-    FOREACH_CHILD(parameterNode, parametersNode, "parameter") {
+    FOREACH_CHILD (parameterNode, parametersNode, "parameter") {
         _parameters.emplace_back(parseNode(parameterNode, true));
     }
 
     pugi::xml_node resultsNode = heteroNode.child("results");
-    FOREACH_CHILD(resultNode, resultsNode, "result") {
-        _results.emplace_back(parseNode(resultNode, false));
-    }
+    FOREACH_CHILD (resultNode, resultsNode, "result") { _results.emplace_back(parseNode(resultNode, false)); }
 
     // save state
     this->_config = importedConfigs;
@@ -657,28 +664,26 @@ void HeteroExecutableNetwork::Export(std::ostream& heteroModel) {
         outputsNode.append_child("output").append_attribute("name").set_value(networkInput.first.c_str());
     }
 
-    const auto serializeNode = [&] (const std::shared_ptr<const ov::Node>& node,
-        pugi::xml_node & xml_node) {
+    const auto serializeNode = [&](const std::shared_ptr<const ov::Node>& node, pugi::xml_node& xml_node) {
         const bool is_result = ov::is_type<ov::op::v0::Result>(node);
-        const std::string name = is_result ?
-            ngraph::op::util::create_ie_output_name(node->input_value(0)) :
-            node->get_friendly_name();
+        const std::string name =
+            is_result ? ngraph::op::util::create_ie_output_name(node->input_value(0)) : node->get_friendly_name();
         xml_node.append_attribute("operation_name").set_value(name.c_str());
-        xml_node.append_attribute("element_type").set_value(
-            node->get_output_element_type(0).get_type_name().c_str());
+        xml_node.append_attribute("element_type").set_value(node->get_output_element_type(0).get_type_name().c_str());
 
-        const auto & pShape = node->get_output_partial_shape(0);
+        const auto& pShape = node->get_output_partial_shape(0);
         OPENVINO_ASSERT(pShape.rank().is_static(), "Serialization of shapes with dynamic rank is not supported");
         auto partialShapeNode = xml_node.append_child("partial_shape");
-        for (auto && dim : node->get_output_partial_shape(0)) {
+        for (auto&& dim : node->get_output_partial_shape(0)) {
             if (dim.is_dynamic())
                 partialShapeNode.append_child("dim").append_attribute("value").set_value("-1");
             else
-                partialShapeNode.append_child("dim").append_attribute("value").set_value(std::to_string(dim.get_length()).c_str());
+                partialShapeNode.append_child("dim").append_attribute("value").set_value(
+                    std::to_string(dim.get_length()).c_str());
         }
 
         auto tensorNamesNode = xml_node.append_child("tensor_names");
-        for (auto & tensorName : node->get_output_tensor(0).get_names()) {
+        for (auto& tensorName : node->get_output_tensor(0).get_names()) {
             tensorNamesNode.append_child("tensor_name").append_attribute("value").set_value(tensorName.c_str());
         }
     };
@@ -753,8 +758,7 @@ void HeteroExecutableNetwork::Export(std::ostream& heteroModel) {
 
             // Note: custom ngraph extensions are not supported
             std::stringstream xmlFile, binFile;
-            ov::pass::Serialize serializer(xmlFile, binFile,
-                ov::pass::Serialize::Version::IR_V10);
+            ov::pass::Serialize serializer(xmlFile, binFile, ov::pass::Serialize::Version::IR_V10);
             serializer.run_on_function(subnet.getFunction());
 
             auto m_constants = binFile.str();
@@ -784,15 +788,11 @@ IInferRequestInternal::Ptr HeteroExecutableNetwork::CreateInferRequestImpl(
         desc._profilingTask = openvino::itt::handle("Infer" + std::to_string(index++));
         inferRequests.push_back(desc);
     }
-    return std::make_shared<HeteroInferRequest>(inputs,
-                                                outputs,
-                                                inferRequests,
-                                                _blobNameMap);
+    return std::make_shared<HeteroInferRequest>(inputs, outputs, inferRequests, _blobNameMap);
 }
 
-IInferRequestInternal::Ptr HeteroExecutableNetwork::CreateInferRequestImpl(
-        InputsDataMap networkInputs,
-        OutputsDataMap networkOutputs) {
+IInferRequestInternal::Ptr HeteroExecutableNetwork::CreateInferRequestImpl(InputsDataMap networkInputs,
+                                                                           OutputsDataMap networkOutputs) {
     HeteroInferRequest::SubRequestsList inferRequests;
     int index = 0;
     for (auto&& subnetwork : _networks) {
@@ -801,17 +801,14 @@ IInferRequestInternal::Ptr HeteroExecutableNetwork::CreateInferRequestImpl(
         desc._profilingTask = openvino::itt::handle("Infer" + std::to_string(index++));
         inferRequests.push_back(desc);
     }
-    return std::make_shared<HeteroInferRequest>(networkInputs,
-                                                networkOutputs,
-                                                inferRequests,
-                                                _blobNameMap);
+    return std::make_shared<HeteroInferRequest>(networkInputs, networkOutputs, inferRequests, _blobNameMap);
 }
 
 IInferRequestInternal::Ptr HeteroExecutableNetwork::CreateInferRequest() {
     return CreateAsyncInferRequestFromSync<HeteroAsyncInferRequest>();
 }
 
-InferenceEngine::Parameter HeteroExecutableNetwork::GetConfig(const std::string &name) const {
+InferenceEngine::Parameter HeteroExecutableNetwork::GetConfig(const std::string& name) const {
     InferenceEngine::Parameter result;
     if (name == "TARGET_FALLBACK") {
         auto it = _config.find(name);
@@ -820,8 +817,7 @@ InferenceEngine::Parameter HeteroExecutableNetwork::GetConfig(const std::string
         } else {
             result = std::string{};
         }
-    } else if (name == HETERO_CONFIG_KEY(DUMP_GRAPH_DOT) ||
-               name == CONFIG_KEY(EXCLUSIVE_ASYNC_REQUESTS)) {
+    } else if (name == HETERO_CONFIG_KEY(DUMP_GRAPH_DOT) || name == CONFIG_KEY(EXCLUSIVE_ASYNC_REQUESTS)) {
         auto it = _config.find(name);
         IE_ASSERT(it != _config.end());
         result = it->second == YES ? true : false;
@@ -830,7 +826,7 @@ InferenceEngine::Parameter HeteroExecutableNetwork::GetConfig(const std::string
         for (auto&& desc : _networks) {
             auto execNetwork = desc._network;
             auto param = execNetwork->GetMetric(METRIC_KEY(SUPPORTED_CONFIG_KEYS));
-            for (auto && configKey : param.as<std::vector<std::string>>()) {
+            for (auto&& configKey : param.as<std::vector<std::string>>()) {
                 if (configKey == name) {
                     return execNetwork->GetConfig(configKey);
                 }
@@ -847,16 +843,14 @@ using Metrics = std::map<std::string, Parameter>;
 
 namespace {
 
-void collectPluginMetrics(std::vector<std::string> & baseMetrics,
-                          const std::vector<::Metrics> pluginMetrics) {
+void collectPluginMetrics(std::vector<std::string>& baseMetrics, const std::vector<::Metrics> pluginMetrics) {
     // check whether the metric has unique name and value among all the plugins
-    auto isMetricValueUnique = [&](const std::string & key,
-                                    const Parameter & value) -> bool {
-        if (std::find(baseMetrics.begin(), baseMetrics.end(), key) !=  baseMetrics.end())
+    auto isMetricValueUnique = [&](const std::string& key, const Parameter& value) -> bool {
+        if (std::find(baseMetrics.begin(), baseMetrics.end(), key) != baseMetrics.end())
             return false;
 
-        for (auto && metrics : pluginMetrics) {
-            for (auto && metric : metrics)
+        for (auto&& metrics : pluginMetrics) {
+            for (auto&& metric : metrics)
                 if (key == metric.first && value != metric.second)
                     return false;
         }
@@ -866,8 +860,8 @@ void collectPluginMetrics(std::vector<std::string> & baseMetrics,
 
     // collect only unique metrics
     std::vector<std::string> uniqueMetrics;
-    for (auto && metrics : pluginMetrics) {
-        for (auto && metric : metrics) {
+    for (auto&& metrics : pluginMetrics) {
+        for (auto&& metric : metrics) {
             if (isMetricValueUnique(metric.first, metric.second)) {
                 uniqueMetrics.push_back(metric.first);
             }
@@ -880,14 +874,12 @@ void collectPluginMetrics(std::vector<std::string> & baseMetrics,
 
 }  // namespace
 
-InferenceEngine::Parameter HeteroExecutableNetwork::GetMetric(const std::string &name) const {
+InferenceEngine::Parameter HeteroExecutableNetwork::GetMetric(const std::string& name) const {
     if (EXEC_NETWORK_METRIC_KEY(SUPPORTED_METRICS) == name) {
-        std::vector<std::string> heteroMetrics = {
-            METRIC_KEY(NETWORK_NAME),
-            METRIC_KEY(SUPPORTED_METRICS),
-            METRIC_KEY(SUPPORTED_CONFIG_KEYS),
-            METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS)
-        };
+        std::vector<std::string> heteroMetrics = {METRIC_KEY(NETWORK_NAME),
+                                                  METRIC_KEY(SUPPORTED_METRICS),
+                                                  METRIC_KEY(SUPPORTED_CONFIG_KEYS),
+                                                  METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS)};
 
         {
             std::vector<::Metrics> pluginMetrics;
@@ -895,7 +887,7 @@ InferenceEngine::Parameter HeteroExecutableNetwork::GetMetric(const std::string
                 auto execNetwork = desc._network;
                 auto param = execNetwork->GetMetric(METRIC_KEY(SUPPORTED_METRICS));
                 ::Metrics metrics;
-                for (auto && metricName : param.as<std::vector<std::string>>()) {
+                for (auto&& metricName : param.as<std::vector<std::string>>()) {
                     metrics[metricName] = execNetwork->GetMetric(metricName);
                 }
                 pluginMetrics.push_back(std::move(metrics));
@@ -906,11 +898,9 @@ InferenceEngine::Parameter HeteroExecutableNetwork::GetMetric(const std::string
 
         IE_SET_METRIC_RETURN(SUPPORTED_METRICS, heteroMetrics);
     } else if (EXEC_NETWORK_METRIC_KEY(SUPPORTED_CONFIG_KEYS) == name) {
-        std::vector<std::string> heteroConfigKeys = {
-            "TARGET_FALLBACK",
-            HETERO_CONFIG_KEY(DUMP_GRAPH_DOT),
-            CONFIG_KEY(EXCLUSIVE_ASYNC_REQUESTS)
-        };
+        std::vector<std::string> heteroConfigKeys = {"TARGET_FALLBACK",
+                                                     HETERO_CONFIG_KEY(DUMP_GRAPH_DOT),
+                                                     CONFIG_KEY(EXCLUSIVE_ASYNC_REQUESTS)};
 
         {
             std::vector<::Metrics> pluginConfigKeys;
@@ -918,7 +908,7 @@ InferenceEngine::Parameter HeteroExecutableNetwork::GetMetric(const std::string
                 auto execNetwork = desc._network;
                 auto param = execNetwork->GetMetric(METRIC_KEY(SUPPORTED_CONFIG_KEYS));
                 ::Metrics configKeys;
-                for (auto && metricName : param.as<std::vector<std::string>>()) {
+                for (auto&& metricName : param.as<std::vector<std::string>>()) {
                     configKeys[metricName] = execNetwork->GetConfig(metricName);
                 }
                 pluginConfigKeys.push_back(std::move(configKeys));
@@ -933,7 +923,8 @@ InferenceEngine::Parameter HeteroExecutableNetwork::GetMetric(const std::string
     } else if (EXEC_NETWORK_METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS) == name) {
         unsigned int value = 0u;
         for (auto&& desc : _networks) {
-            value = std::max(value, desc._network->GetMetric(METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS)).as<unsigned int>());
+            value = std::max(value,
+                             desc._network->GetMetric(METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS)).as<unsigned int>());
         }
         IE_SET_METRIC_RETURN(OPTIMAL_NUMBER_OF_INFER_REQUESTS, value);
     } else {
@@ -941,7 +932,7 @@ InferenceEngine::Parameter HeteroExecutableNetwork::GetMetric(const std::string
         for (auto&& desc : _networks) {
             auto execNetwork = desc._network;
             auto param = execNetwork->GetMetric(METRIC_KEY(SUPPORTED_METRICS));
-            for (auto && metricKey : param.as<std::vector<std::string>>()) {
+            for (auto&& metricKey : param.as<std::vector<std::string>>()) {
                 if (metricKey == name) {
                     return execNetwork->GetMetric(metricKey);
                 }
diff --git a/src/plugins/hetero/executable_network.hpp b/src/plugins/hetero/executable_network.hpp
new file mode 100644
index 00000000000..db8c3efb59f
--- /dev/null
+++ b/src/plugins/hetero/executable_network.hpp
@@ -0,0 +1,82 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+/**
+ * @brief a header file for ExecutableNetwork
+ * @file executable_network.hpp
+ */
+#pragma once
+
+#include <ie_common.h>
+
+#include <cpp_interfaces/impl/ie_executable_network_thread_safe_default.hpp>
+#include <map>
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+
+#include "async_infer_request.hpp"
+#include "ie_icore.hpp"
+#include "infer_request.hpp"
+
+namespace HeteroPlugin {
+
+class Engine;
+
+/**
+ * @class ExecutableNetwork
+ * @brief Interface of executable network
+ */
+class HeteroExecutableNetwork : public InferenceEngine::ExecutableNetworkThreadSafeDefault {
+public:
+    typedef std::shared_ptr<HeteroExecutableNetwork> Ptr;
+
+    /**
+     * @brief constructor
+     */
+    HeteroExecutableNetwork(const InferenceEngine::CNNNetwork& network,
+                            const std::map<std::string, std::string>& config,
+                            Engine* plugin);
+    /**
+     * @brief Import from opened file constructor
+     */
+    HeteroExecutableNetwork(std::istream& heteroModel,
+                            const std::map<std::string, std::string>& config,
+                            Engine* plugin);
+
+    InferenceEngine::IInferRequestInternal::Ptr CreateInferRequestImpl(
+        InferenceEngine::InputsDataMap networkInputs,
+        InferenceEngine::OutputsDataMap networkOutputs) override;
+    InferenceEngine::IInferRequestInternal::Ptr CreateInferRequestImpl(
+        const std::vector<std::shared_ptr<const ov::Node>>& inputs,
+        const std::vector<std::shared_ptr<const ov::Node>>& outputs) override;
+
+    InferenceEngine::IInferRequestInternal::Ptr CreateInferRequest() override;
+
+    InferenceEngine::Parameter GetConfig(const std::string& name) const override;
+
+    InferenceEngine::Parameter GetMetric(const std::string& name) const override;
+
+    void Export(std::ostream& modelFile) override;
+
+private:
+    void InitCNNImpl(const InferenceEngine::CNNNetwork& network);
+    void InitNgraph(const InferenceEngine::CNNNetwork& network);
+
+    struct NetworkDesc {
+        std::string _device;
+        InferenceEngine::CNNNetwork _clonedNetwork;
+        InferenceEngine::SoExecutableNetworkInternal _network;
+    };
+
+    std::vector<NetworkDesc> _networks;
+    Engine* _heteroPlugin;
+    std::string _name;
+    std::map<std::string, std::string> _config;
+    std::unordered_map<std::string, std::string> _blobNameMap;
+};
+
+}  // namespace HeteroPlugin
diff --git a/inference-engine/src/hetero_plugin/hetero_infer_request.cpp b/src/plugins/hetero/infer_request.cpp
similarity index 77%
rename from inference-engine/src/hetero_plugin/hetero_infer_request.cpp
rename to src/plugins/hetero/infer_request.cpp
index df8490df742..13a90b7e749 100644
--- a/inference-engine/src/hetero_plugin/hetero_infer_request.cpp
+++ b/src/plugins/hetero/infer_request.cpp
@@ -2,40 +2,45 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "hetero_infer_request.hpp"
-#include "hetero_itt.hpp"
+#include "infer_request.hpp"
+
 #include <ie_blob.h>
-#include <description_buffer.hpp>
 #include <ie_layouts.h>
-#include <ie_algorithm.hpp>
+
 #include <cassert>
+#include <description_buffer.hpp>
+#include <ie_algorithm.hpp>
 #include <map>
 #include <string>
 
+#include "itt.hpp"
+
 using namespace HeteroPlugin;
 using namespace InferenceEngine;
 using namespace InferenceEngine::details;
 
-HeteroInferRequest::HeteroInferRequest(const std::vector<std::shared_ptr<const ov::Node>>& inputs,
-                                       const std::vector<std::shared_ptr<const ov::Node>>& outputs,
-                                       const SubRequestsList& inferRequests,
-                                       const std::unordered_map<std::string, std::string>& subgraphInputToOutputBlobNames) :
-    IInferRequestInternal(inputs, outputs),
-    _inferRequests(inferRequests) {
+HeteroInferRequest::HeteroInferRequest(
+    const std::vector<std::shared_ptr<const ov::Node>>& inputs,
+    const std::vector<std::shared_ptr<const ov::Node>>& outputs,
+    const SubRequestsList& inferRequests,
+    const std::unordered_map<std::string, std::string>& subgraphInputToOutputBlobNames)
+    : IInferRequestInternal(inputs, outputs),
+      _inferRequests(inferRequests) {
     CreateInferRequest(subgraphInputToOutputBlobNames);
 }
 
-
-HeteroInferRequest::HeteroInferRequest(InferenceEngine::InputsDataMap networkInputs,
-                                       InferenceEngine::OutputsDataMap networkOutputs,
-                                       const SubRequestsList& inferRequests,
-                                       const std::unordered_map<std::string, std::string>& subgraphInputToOutputBlobNames) :
-    IInferRequestInternal(networkInputs, networkOutputs),
-    _inferRequests(inferRequests) {
+HeteroInferRequest::HeteroInferRequest(
+    InferenceEngine::InputsDataMap networkInputs,
+    InferenceEngine::OutputsDataMap networkOutputs,
+    const SubRequestsList& inferRequests,
+    const std::unordered_map<std::string, std::string>& subgraphInputToOutputBlobNames)
+    : IInferRequestInternal(networkInputs, networkOutputs),
+      _inferRequests(inferRequests) {
     CreateInferRequest(subgraphInputToOutputBlobNames);
 }
 
-void HeteroInferRequest::CreateInferRequest(const std::unordered_map<std::string, std::string>& subgraphInputToOutputBlobNames) {
+void HeteroInferRequest::CreateInferRequest(
+    const std::unordered_map<std::string, std::string>& subgraphInputToOutputBlobNames) {
     if (_networkOutputs.empty() || _networkInputs.empty()) {
         IE_THROW() << "Internal error: no information about network's output/input";
     }
@@ -112,9 +117,9 @@ const InferenceEngine::PreProcessInfo& HeteroInferRequest::GetPreProcess(const s
 }
 
 void HeteroInferRequest::InferImpl() {
-    for (auto &&desc : _inferRequests) {
+    for (auto&& desc : _inferRequests) {
         OV_ITT_SCOPED_TASK(itt::domains::HeteroPlugin, desc._profilingTask);
-        auto &r = desc._request;
+        auto& r = desc._request;
         assert(r);
         r->Infer();
     }
@@ -124,7 +129,7 @@ std::map<std::string, InferenceEngineProfileInfo> HeteroInferRequest::GetPerform
     std::map<std::string, InferenceEngineProfileInfo> perfMap;
     for (size_t i = 0; i < _inferRequests.size(); i++) {
         auto perfMapRequest = _inferRequests[i]._request->GetPerformanceCounts();
-        for (auto &&r : perfMapRequest) {
+        for (auto&& r : perfMapRequest) {
             perfMap[std::string("subgraph") + std::to_string(i) + ": " + r.first] = r.second;
         }
     }
diff --git a/inference-engine/src/hetero_plugin/hetero_infer_request.hpp b/src/plugins/hetero/infer_request.hpp
similarity index 82%
rename from inference-engine/src/hetero_plugin/hetero_infer_request.hpp
rename to src/plugins/hetero/infer_request.hpp
index 0d141231f33..fc08e786bd0 100644
--- a/inference-engine/src/hetero_plugin/hetero_infer_request.hpp
+++ b/src/plugins/hetero/infer_request.hpp
@@ -4,15 +4,16 @@
 
 #pragma once
 
-#include <map>
-#include <string>
-#include <vector>
-#include <memory>
-#include <unordered_map>
 #include <ie_common.h>
-#include <cpp_interfaces/interface/ie_iinfer_request_internal.hpp>
+
 #include <cpp_interfaces/interface/ie_iexecutable_network_internal.hpp>
+#include <cpp_interfaces/interface/ie_iinfer_request_internal.hpp>
+#include <map>
+#include <memory>
 #include <openvino/itt.hpp>
+#include <string>
+#include <unordered_map>
+#include <vector>
 
 namespace HeteroPlugin {
 
@@ -21,20 +22,20 @@ public:
     typedef std::shared_ptr<HeteroInferRequest> Ptr;
 
     struct SubRequestDesc {
-        InferenceEngine::SoExecutableNetworkInternal  _network;
-        InferenceEngine::SoIInferRequestInternal      _request;
-        openvino::itt::handle_t                       _profilingTask;
+        InferenceEngine::SoExecutableNetworkInternal _network;
+        InferenceEngine::SoIInferRequestInternal _request;
+        openvino::itt::handle_t _profilingTask;
     };
     using SubRequestsList = std::vector<SubRequestDesc>;
 
     HeteroInferRequest(InferenceEngine::InputsDataMap networkInputs,
                        InferenceEngine::OutputsDataMap networkOutputs,
-                       const SubRequestsList &inferRequests,
+                       const SubRequestsList& inferRequests,
                        const std::unordered_map<std::string, std::string>& blobNameMap);
 
     HeteroInferRequest(const std::vector<std::shared_ptr<const ov::Node>>& networkInputs,
                        const std::vector<std::shared_ptr<const ov::Node>>& networkOutputs,
-                       const SubRequestsList &inferRequests,
+                       const SubRequestsList& inferRequests,
                        const std::unordered_map<std::string, std::string>& blobNameMap);
 
     void InferImpl() override;
@@ -52,12 +53,11 @@ public:
     std::map<std::string, InferenceEngine::InferenceEngineProfileInfo> GetPerformanceCounts() const override;
 
     SubRequestsList _inferRequests;
-    std::map<std::string, InferenceEngine::Blob::Ptr>               _blobs;
-    std::map<std::string, InferenceEngine::IInferRequestInternal*>  _subRequestFromBlobName;
+    std::map<std::string, InferenceEngine::Blob::Ptr> _blobs;
+    std::map<std::string, InferenceEngine::IInferRequestInternal*> _subRequestFromBlobName;
 
 private:
     void CreateInferRequest(const std::unordered_map<std::string, std::string>& subgraphInputToOutputBlobNames);
 };
 
 }  // namespace HeteroPlugin
-
diff --git a/inference-engine/src/hetero_plugin/hetero_itt.hpp b/src/plugins/hetero/itt.hpp
similarity index 72%
rename from inference-engine/src/hetero_plugin/hetero_itt.hpp
rename to src/plugins/hetero/itt.hpp
index f14541b380a..68ad91551ec 100644
--- a/inference-engine/src/hetero_plugin/hetero_itt.hpp
+++ b/src/plugins/hetero/itt.hpp
@@ -4,7 +4,7 @@
 
 /**
  * @brief Defines openvino domains for tracing
- * @file hetero_itt.hpp
+ * @file itt.hpp
  */
 
 #pragma once
@@ -14,7 +14,7 @@
 namespace HeteroPlugin {
 namespace itt {
 namespace domains {
-    OV_ITT_DOMAIN(HeteroPlugin);
-}
-}
+OV_ITT_DOMAIN(HeteroPlugin);
 }
+}  // namespace itt
+}  // namespace HeteroPlugin
diff --git a/inference-engine/src/hetero_plugin/hetero_plugin.cpp b/src/plugins/hetero/plugin.cpp
similarity index 78%
rename from inference-engine/src/hetero_plugin/hetero_plugin.cpp
rename to src/plugins/hetero/plugin.cpp
index 6b376b93f6a..8ff68515290 100644
--- a/inference-engine/src/hetero_plugin/hetero_plugin.cpp
+++ b/src/plugins/hetero/plugin.cpp
@@ -2,8 +2,9 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
+// clang-format off
 #include "ie_metric_helpers.hpp"
-#include "hetero_plugin.hpp"
+#include "plugin.hpp"
 #include <memory>
 #include <vector>
 #include <map>
@@ -12,8 +13,9 @@
 #include <fstream>
 #include <unordered_set>
 #include "ie_plugin_config.hpp"
-#include "hetero_executable_network.hpp"
+#include "executable_network.hpp"
 #include <cpp_interfaces/interface/ie_internal_plugin_config.hpp>
+// clang-format on
 
 using namespace InferenceEngine;
 using namespace InferenceEngine::PluginConfigParams;
@@ -28,18 +30,17 @@ Engine::Engine() {
 
 namespace {
 
-Engine::Configs mergeConfigs(Engine::Configs config, const Engine::Configs & local) {
-    for (auto && kvp : local) {
+Engine::Configs mergeConfigs(Engine::Configs config, const Engine::Configs& local) {
+    for (auto&& kvp : local) {
         config[kvp.first] = kvp.second;
     }
     return config;
 }
 
-const std::vector<std::string> & getSupportedConfigKeys() {
-    static const std::vector<std::string> supported_configKeys = {
-        HETERO_CONFIG_KEY(DUMP_GRAPH_DOT),
-        "TARGET_FALLBACK",
-        CONFIG_KEY(EXCLUSIVE_ASYNC_REQUESTS) };
+const std::vector<std::string>& getSupportedConfigKeys() {
+    static const std::vector<std::string> supported_configKeys = {HETERO_CONFIG_KEY(DUMP_GRAPH_DOT),
+                                                                  "TARGET_FALLBACK",
+                                                                  CONFIG_KEY(EXCLUSIVE_ASYNC_REQUESTS)};
 
     return supported_configKeys;
 }
@@ -47,7 +48,7 @@ const std::vector<std::string> & getSupportedConfigKeys() {
 }  // namespace
 
 InferenceEngine::IExecutableNetworkInternal::Ptr Engine::LoadExeNetworkImpl(const InferenceEngine::CNNNetwork& network,
-                                                                            const Configs&                     config) {
+                                                                            const Configs& config) {
     if (GetCore() == nullptr) {
         IE_THROW() << "Please, work with HETERO device via InferencEngine::Core object";
     }
@@ -66,11 +67,13 @@ InferenceEngine::IExecutableNetworkInternal::Ptr Engine::LoadExeNetworkImpl(cons
     return std::make_shared<HeteroExecutableNetwork>(network, mergeConfigs(_config, config), this);
 }
 
-InferenceEngine::IExecutableNetworkInternal::Ptr Engine::ImportNetwork(std::istream& heteroModel, const std::map<std::string, std::string>& config) {
+InferenceEngine::IExecutableNetworkInternal::Ptr Engine::ImportNetwork(
+    std::istream& heteroModel,
+    const std::map<std::string, std::string>& config) {
     return std::make_shared<HeteroExecutableNetwork>(heteroModel, mergeConfigs(_config, config), this);
 }
 
-Engine::Configs Engine::GetSupportedConfig(const Engine::Configs& config, const std::string & deviceName) const {
+Engine::Configs Engine::GetSupportedConfig(const Engine::Configs& config, const std::string& deviceName) const {
     std::vector<std::string> supportedConfigKeys = GetCore()->GetMetric(deviceName, METRIC_KEY(SUPPORTED_CONFIG_KEYS));
     Engine::Configs supportedConfig;
     for (auto&& key : supportedConfigKeys) {
@@ -83,8 +86,8 @@ Engine::Configs Engine::GetSupportedConfig(const Engine::Configs& config, const
 }
 
 Engine::DeviceMetaInformationMap Engine::GetDevicePlugins(const std::string& targetFallback,
-                                                          const Configs & localConfig) const {
-    auto getDeviceConfig = [&](const std::string & deviceWithID) {
+                                                          const Configs& localConfig) const {
+    auto getDeviceConfig = [&](const std::string& deviceWithID) {
         DeviceIDParser deviceParser(deviceWithID);
         std::string deviceName = deviceParser.getDeviceName();
         Configs tconfig = mergeConfigs(_config, localConfig);
@@ -109,10 +112,10 @@ Engine::DeviceMetaInformationMap Engine::GetDevicePlugins(const std::string& tar
     return metaDevices;
 }
 
-void Engine::SetConfig(const Configs &configs) {
-    for (auto && kvp : configs) {
+void Engine::SetConfig(const Configs& configs) {
+    for (auto&& kvp : configs) {
         const auto& name = kvp.first;
-        const auto & supported_configKeys = getSupportedConfigKeys();
+        const auto& supported_configKeys = getSupportedConfigKeys();
         if (supported_configKeys.end() != std::find(supported_configKeys.begin(), supported_configKeys.end(), name))
             _config[name] = kvp.second;
         else
@@ -120,7 +123,7 @@ void Engine::SetConfig(const Configs &configs) {
     }
 }
 
-QueryNetworkResult Engine::QueryNetwork(const CNNNetwork &network, const Configs& config) const {
+QueryNetworkResult Engine::QueryNetwork(const CNNNetwork& network, const Configs& config) const {
     QueryNetworkResult qr;
 
     if (GetCore() == nullptr) {
@@ -164,12 +167,12 @@ QueryNetworkResult Engine::QueryNetwork(const CNNNetwork &network, const Configs
 
 Parameter Engine::GetMetric(const std::string& name, const std::map<std::string, Parameter>& options) const {
     if (METRIC_KEY(SUPPORTED_METRICS) == name) {
-        IE_SET_METRIC_RETURN(SUPPORTED_METRICS, std::vector<std::string>{
-            METRIC_KEY(SUPPORTED_METRICS),
-            METRIC_KEY(FULL_DEVICE_NAME),
-            METRIC_KEY(SUPPORTED_CONFIG_KEYS),
-            METRIC_KEY(DEVICE_ARCHITECTURE),
-            METRIC_KEY(IMPORT_EXPORT_SUPPORT)});
+        IE_SET_METRIC_RETURN(SUPPORTED_METRICS,
+                             std::vector<std::string>{METRIC_KEY(SUPPORTED_METRICS),
+                                                      METRIC_KEY(FULL_DEVICE_NAME),
+                                                      METRIC_KEY(SUPPORTED_CONFIG_KEYS),
+                                                      METRIC_KEY(DEVICE_ARCHITECTURE),
+                                                      METRIC_KEY(IMPORT_EXPORT_SUPPORT)});
     } else if (METRIC_KEY(SUPPORTED_CONFIG_KEYS) == name) {
         IE_SET_METRIC_RETURN(SUPPORTED_CONFIG_KEYS, getSupportedConfigKeys());
     } else if (METRIC_KEY(FULL_DEVICE_NAME) == name) {
@@ -195,29 +198,29 @@ std::string Engine::DeviceArchitecture(const std::string& targetFallback) const
     for (const auto& device : fallbackDevices) {
         InferenceEngine::DeviceIDParser parser(device);
 
-        std::vector<std::string> supportedMetricKeys = GetCore()->GetMetric(
-                parser.getDeviceName(), METRIC_KEY(SUPPORTED_METRICS));
-        auto it = std::find(supportedMetricKeys.begin(), supportedMetricKeys.end(),
-                            METRIC_KEY(DEVICE_ARCHITECTURE));
-        auto arch = (it != supportedMetricKeys.end()) ?
-                GetCore()->GetMetric(device, METRIC_KEY(DEVICE_ARCHITECTURE)).as<std::string>() : parser.getDeviceName();
+        std::vector<std::string> supportedMetricKeys =
+            GetCore()->GetMetric(parser.getDeviceName(), METRIC_KEY(SUPPORTED_METRICS));
+        auto it = std::find(supportedMetricKeys.begin(), supportedMetricKeys.end(), METRIC_KEY(DEVICE_ARCHITECTURE));
+        auto arch = (it != supportedMetricKeys.end())
+                        ? GetCore()->GetMetric(device, METRIC_KEY(DEVICE_ARCHITECTURE)).as<std::string>()
+                        : parser.getDeviceName();
         resArch += " " + arch;
     }
     return resArch;
 }
 
-Parameter Engine::GetConfig(const std::string& name, const std::map<std::string, Parameter> & /*options*/) const {
+Parameter Engine::GetConfig(const std::string& name, const std::map<std::string, Parameter>& /*options*/) const {
     if (name == HETERO_CONFIG_KEY(DUMP_GRAPH_DOT)) {
         auto it = _config.find(HETERO_CONFIG_KEY(DUMP_GRAPH_DOT));
         IE_ASSERT(it != _config.end());
         bool dump = it->second == YES;
-        return { dump };
+        return {dump};
     } else if (name == "TARGET_FALLBACK") {
         auto it = _config.find("TARGET_FALLBACK");
         if (it == _config.end()) {
             IE_THROW() << "Value for TARGET_FALLBACK is not set";
         } else {
-            return { it->second };
+            return {it->second};
         }
     } else {
         IE_THROW() << "Unsupported config key: " << name;
@@ -225,9 +228,9 @@ Parameter Engine::GetConfig(const std::string& name, const std::map<std::string,
 }
 
 static Version heteroPluginDescription = {
-        {2, 1},  // plugin API version
-        CI_BUILD_NUMBER,
-        "heteroPlugin"  // plugin description message
+    {2, 1},  // plugin API version
+    CI_BUILD_NUMBER,
+    "heteroPlugin"  // plugin description message
 };
 
 IE_DEFINE_PLUGIN_CREATE_FUNCTION(Engine, heteroPluginDescription)
diff --git a/inference-engine/src/hetero_plugin/hetero_plugin.hpp b/src/plugins/hetero/plugin.hpp
similarity index 52%
rename from inference-engine/src/hetero_plugin/hetero_plugin.hpp
rename to src/plugins/hetero/plugin.hpp
index fbc602116d1..7021005290e 100644
--- a/inference-engine/src/hetero_plugin/hetero_plugin.hpp
+++ b/src/plugins/hetero/plugin.hpp
@@ -4,15 +4,16 @@
 
 #pragma once
 
-#include "description_buffer.hpp"
-#include "ie_icore.hpp"
-#include "cpp_interfaces/interface/ie_iplugin_internal.hpp"
+#include <map>
 #include <memory>
 #include <string>
-#include <map>
 #include <unordered_map>
-#include <vector>
 #include <utility>
+#include <vector>
+
+#include "cpp_interfaces/interface/ie_iplugin_internal.hpp"
+#include "description_buffer.hpp"
+#include "ie_icore.hpp"
 
 namespace HeteroPlugin {
 
@@ -23,28 +24,30 @@ public:
 
     Engine();
 
-    InferenceEngine::IExecutableNetworkInternal::Ptr
-    LoadExeNetworkImpl(const InferenceEngine::CNNNetwork &network, const Configs &config) override;
+    InferenceEngine::IExecutableNetworkInternal::Ptr LoadExeNetworkImpl(const InferenceEngine::CNNNetwork& network,
+                                                                        const Configs& config) override;
 
-    void SetConfig(const Configs &config) override;
+    void SetConfig(const Configs& config) override;
 
-    InferenceEngine::QueryNetworkResult QueryNetwork(const InferenceEngine::CNNNetwork &network,
+    InferenceEngine::QueryNetworkResult QueryNetwork(const InferenceEngine::CNNNetwork& network,
                                                      const Configs& config) const override;
 
-    InferenceEngine::Parameter GetMetric(const std::string& name, const std::map<std::string,
-                                         InferenceEngine::Parameter> & options) const override;
+    InferenceEngine::Parameter GetMetric(
+        const std::string& name,
+        const std::map<std::string, InferenceEngine::Parameter>& options) const override;
 
-    InferenceEngine::Parameter GetConfig(const std::string& name, const std::map<std::string,
-                                         InferenceEngine::Parameter> & options) const override;
+    InferenceEngine::Parameter GetConfig(
+        const std::string& name,
+        const std::map<std::string, InferenceEngine::Parameter>& options) const override;
 
-    InferenceEngine::IExecutableNetworkInternal::Ptr
-    ImportNetwork(std::istream& heteroModel, const std::map<std::string, std::string>& config) override;
+    InferenceEngine::IExecutableNetworkInternal::Ptr ImportNetwork(
+        std::istream& heteroModel,
+        const std::map<std::string, std::string>& config) override;
 
-    DeviceMetaInformationMap GetDevicePlugins(const std::string& targetFallback,
-                                              const Configs & localConfig) const;
+    DeviceMetaInformationMap GetDevicePlugins(const std::string& targetFallback, const Configs& localConfig) const;
 
 private:
-    Configs GetSupportedConfig(const Configs& config, const std::string & deviceName) const;
+    Configs GetSupportedConfig(const Configs& config, const std::string& deviceName) const;
     std::string DeviceArchitecture(const std::string& targetFallback) const;
 };
 }  // namespace HeteroPlugin
diff --git a/tools/deployment_manager/configs/darwin.json b/tools/deployment_manager/configs/darwin.json
index 232d2f275c0..6efba1e28bb 100644
--- a/tools/deployment_manager/configs/darwin.json
+++ b/tools/deployment_manager/configs/darwin.json
@@ -20,7 +20,7 @@
         "runtime/lib/intel64/libinference_engine_transformations.dylib",
         "runtime/lib/intel64/libinference_engine_preproc.so",
         "runtime/lib/intel64/libinference_engine_c_api.dylib",
-        "runtime/lib/intel64/libHeteroPlugin.so",
+        "runtime/lib/intel64/libov_hetero_plugin.so",
         "runtime/lib/intel64/libMultiDevicePlugin.so",
         "runtime/lib/intel64/libngraph.dylib",
         "runtime/lib/intel64/libfrontend_common.dylib",
diff --git a/tools/deployment_manager/configs/linux.json b/tools/deployment_manager/configs/linux.json
index 4e4c530919c..d05f2f5a8fa 100644
--- a/tools/deployment_manager/configs/linux.json
+++ b/tools/deployment_manager/configs/linux.json
@@ -26,7 +26,7 @@
         "runtime/lib/intel64/libinference_engine_transformations.so",
         "runtime/lib/intel64/libinference_engine_preproc.so",
         "runtime/lib/intel64/libinference_engine_c_api.so",
-        "runtime/lib/intel64/libHeteroPlugin.so",
+        "runtime/lib/intel64/libov_hetero_plugin.so",
         "runtime/lib/intel64/libMultiDevicePlugin.so",
         "runtime/lib/intel64/libngraph.so",
         "runtime/lib/intel64/libfrontend_common.so",
diff --git a/tools/deployment_manager/configs/windows.json b/tools/deployment_manager/configs/windows.json
index cf1a64f04bb..e04ccd3094c 100644
--- a/tools/deployment_manager/configs/windows.json
+++ b/tools/deployment_manager/configs/windows.json
@@ -20,7 +20,7 @@
         "runtime/bin/intel64/Release/inference_engine_transformations.dll",
         "runtime/bin/intel64/Release/inference_engine_preproc.dll",
         "runtime/bin/intel64/Release/inference_engine_c_api.dll",
-        "runtime/bin/intel64/Release/HeteroPlugin.dll",
+        "runtime/bin/intel64/Release/ov_hetero_plugin.dll",
         "runtime/bin/intel64/Release/MultiDevicePlugin.dll",
         "runtime/bin/intel64/Release/ngraph.dll",
         "runtime/bin/intel64/Release/frontend_common.dll",

From c084f8aa427cf278b72b5726800187c8733fec6d Mon Sep 17 00:00:00 2001
From: Vitaliy Urusovskij <vitaliy.urusovskij@intel.com>
Date: Mon, 29 Nov 2021 15:24:17 +0300
Subject: [PATCH 71/72] Clean up evaluate of `boolean` (#8843)

Remove  evaluate of `boolean` for ops
inherited from `UnaryElementwiseArithmetic`
---
 src/core/src/op/abs.cpp     | 2 --
 src/core/src/op/ceiling.cpp | 2 --
 src/core/src/op/floor.cpp   | 2 --
 src/core/src/op/log.cpp     | 2 --
 src/core/src/op/relu.cpp    | 2 --
 src/core/src/op/sign.cpp    | 1 -
 6 files changed, 11 deletions(-)

diff --git a/src/core/src/op/abs.cpp b/src/core/src/op/abs.cpp
index 159850b650e..61f180d9934 100644
--- a/src/core/src/op/abs.cpp
+++ b/src/core/src/op/abs.cpp
@@ -36,7 +36,6 @@ bool evaluate_abs(const ngraph::HostTensorPtr& arg0, const ngraph::HostTensorPtr
     out->set_unary(arg0);
 
     switch (arg0->get_element_type()) {
-        NGRAPH_TYPE_CASE(evaluate_abs, boolean, arg0, out, count);
         NGRAPH_TYPE_CASE(evaluate_abs, i32, arg0, out, count);
         NGRAPH_TYPE_CASE(evaluate_abs, i64, arg0, out, count);
         NGRAPH_TYPE_CASE(evaluate_abs, u32, arg0, out, count);
@@ -68,7 +67,6 @@ bool ov::op::v0::Abs::has_evaluate() const {
     case ngraph::element::f16:
     case ngraph::element::f32:
     case ngraph::element::bf16:
-    case ngraph::element::boolean:
         return true;
     default:
         break;
diff --git a/src/core/src/op/ceiling.cpp b/src/core/src/op/ceiling.cpp
index 5a92ea19eae..c8bfccb07c3 100644
--- a/src/core/src/op/ceiling.cpp
+++ b/src/core/src/op/ceiling.cpp
@@ -47,7 +47,6 @@ bool evaluate_ceiling(const HostTensorPtr& arg0, const HostTensorPtr& out, const
     out->set_unary(arg0);
 
     switch (arg0->get_element_type()) {
-        NGRAPH_COPY_TENSOR(evaluate_ceiling, boolean, arg0, out, count);
         NGRAPH_COPY_TENSOR(evaluate_ceiling, i8, arg0, out, count);
         NGRAPH_COPY_TENSOR(evaluate_ceiling, i16, arg0, out, count);
         NGRAPH_COPY_TENSOR(evaluate_ceiling, i32, arg0, out, count);
@@ -75,7 +74,6 @@ bool op::Ceiling::evaluate(const HostTensorVector& outputs, const HostTensorVect
 bool op::Ceiling::has_evaluate() const {
     NGRAPH_OP_SCOPE(v0_Ceiling_has_evaluate);
     switch (get_input_element_type(0)) {
-    case ngraph::element::boolean:
     case ngraph::element::i8:
     case ngraph::element::i16:
     case ngraph::element::i32:
diff --git a/src/core/src/op/floor.cpp b/src/core/src/op/floor.cpp
index 14c75605558..c47153de03a 100644
--- a/src/core/src/op/floor.cpp
+++ b/src/core/src/op/floor.cpp
@@ -52,7 +52,6 @@ bool evaluate_floor(const HostTensorPtr& arg0, const HostTensorPtr& out, const s
     out->set_unary(arg0);
 
     switch (arg0->get_element_type()) {
-        NGRAPH_COPY_TENSOR(evaluate_floor, boolean, arg0, out, count);
         NGRAPH_COPY_TENSOR(evaluate_floor, i8, arg0, out, count);
         NGRAPH_COPY_TENSOR(evaluate_floor, i16, arg0, out, count);
         NGRAPH_COPY_TENSOR(evaluate_floor, i32, arg0, out, count);
@@ -80,7 +79,6 @@ bool op::Floor::evaluate(const HostTensorVector& outputs, const HostTensorVector
 bool op::Floor::has_evaluate() const {
     NGRAPH_OP_SCOPE(v0_Floor_has_evaluate);
     switch (get_input_element_type(0)) {
-    case ngraph::element::boolean:
     case ngraph::element::i8:
     case ngraph::element::i16:
     case ngraph::element::i32:
diff --git a/src/core/src/op/log.cpp b/src/core/src/op/log.cpp
index c1d7878ab8c..8d2173e96b9 100644
--- a/src/core/src/op/log.cpp
+++ b/src/core/src/op/log.cpp
@@ -43,7 +43,6 @@ bool evaluate_log(const HostTensorPtr& arg0, const HostTensorPtr& out, const siz
     out->set_unary(arg0);
 
     switch (arg0->get_element_type()) {
-        NGRAPH_TYPE_CASE(evaluate_log, boolean, arg0, out, count);
         NGRAPH_TYPE_CASE(evaluate_log, i32, arg0, out, count);
         NGRAPH_TYPE_CASE(evaluate_log, i64, arg0, out, count);
         NGRAPH_TYPE_CASE(evaluate_log, u32, arg0, out, count);
@@ -67,7 +66,6 @@ bool op::Log::evaluate(const HostTensorVector& outputs, const HostTensorVector&
 bool op::Log::has_evaluate() const {
     NGRAPH_OP_SCOPE(v0_Log_has_evaluate);
     switch (get_input_element_type(0)) {
-    case ngraph::element::boolean:
     case ngraph::element::i32:
     case ngraph::element::i64:
     case ngraph::element::u32:
diff --git a/src/core/src/op/relu.cpp b/src/core/src/op/relu.cpp
index 097a4713cca..0aefc0b8a21 100644
--- a/src/core/src/op/relu.cpp
+++ b/src/core/src/op/relu.cpp
@@ -41,7 +41,6 @@ bool evaluate_relu(const HostTensorPtr& arg0, const HostTensorPtr& out) {
     out->set_unary(arg0);
 
     switch (arg0->get_element_type()) {
-        NGRAPH_TYPE_CASE(evaluate_relu, boolean, arg0, out, count);
         NGRAPH_TYPE_CASE(evaluate_relu, i32, arg0, out, count);
         NGRAPH_TYPE_CASE(evaluate_relu, i64, arg0, out, count);
         NGRAPH_TYPE_CASE(evaluate_relu, u32, arg0, out, count);
@@ -66,7 +65,6 @@ bool op::Relu::evaluate(const HostTensorVector& outputs, const HostTensorVector&
 bool op::Relu::has_evaluate() const {
     NGRAPH_OP_SCOPE(v0_Relu_has_evaluate);
     switch (get_input_element_type(0)) {
-    case ngraph::element::boolean:
     case ngraph::element::i32:
     case ngraph::element::i64:
     case ngraph::element::u32:
diff --git a/src/core/src/op/sign.cpp b/src/core/src/op/sign.cpp
index 7ac52000eed..1a4a322b65d 100644
--- a/src/core/src/op/sign.cpp
+++ b/src/core/src/op/sign.cpp
@@ -67,7 +67,6 @@ bool op::Sign::evaluate(const HostTensorVector& outputs, const HostTensorVector&
 bool op::Sign::has_evaluate() const {
     NGRAPH_OP_SCOPE(v0_Sign_has_evaluate);
     switch (get_input_element_type(0)) {
-    case ngraph::element::boolean:
     case ngraph::element::i32:
     case ngraph::element::i64:
     case ngraph::element::u32:

From 980ad59ac43645c0c95d006dcb1bb5e0c52b6a19 Mon Sep 17 00:00:00 2001
From: Svetlana Dolinina <svetlana.a.dolinina@intel.com>
Date: Mon, 29 Nov 2021 15:29:00 +0300
Subject: [PATCH 72/72] Correct ReverseV2ToReverseSequence transformation
 (#8120)

* add subgraph instead of constant with fixed shape to allow model have undefined batch

* updated transformation (not checked yet)

* changed ReverseV2ToReverseSequence to support dynamic shapes/reshape;
added transformation to reverse_tensor_iterator to support new subgraph got from ReverseV2ToReverseSequence

* remove changes that should not be on this branch

* added tests;
fixed old transformation

* added delete of reversesequences to avoid run of transformation twice

* fixed pattern check for case with dynamic value for input of reversesequence

* Revert "fixed pattern check for case with dynamic value for input of reversesequence"

This reverts commit 0c04164e

* Revert "added delete of reversesequences to avoid run of transformation twice"

This reverts commit fcb7de9c

* reversed changes in reverse_tensorr_iterator for Squeeze case;
update reverse_tensor_iterator with shapeof subgraph
added permutations for attributes to pass layer test

* minor fix for dynamic shape

* updated test;
fixed backward compatibility in reverse_tensor_iterator transformation

* revew comments fixed:
added comments;
refactoring done;
fixed framework name saving for rank = 1

* minor review fixes

* small fix
---
 .../middle/ReverseV2ToReverseSequence.py      |  96 +++++++++-----
 .../middle/reverse_tensor_iterator.py         |  50 +++++---
 .../extensions/ops/reverse_sequence.py        |   5 +-
 model-optimizer/mo/ops/op.py                  |   2 +
 .../middle/reverse_tensor_iterator_test.py    | 117 ++++++++++++++++++
 .../tensorflow_tests/test_tf_ReverseV2.py     |  56 +++++++++
 6 files changed, 276 insertions(+), 50 deletions(-)
 create mode 100644 model-optimizer/unit_tests/extensions/middle/reverse_tensor_iterator_test.py
 create mode 100644 tests/layer_tests/tensorflow_tests/test_tf_ReverseV2.py

diff --git a/model-optimizer/extensions/middle/ReverseV2ToReverseSequence.py b/model-optimizer/extensions/middle/ReverseV2ToReverseSequence.py
index f9f9a4ade40..26e9e06af7f 100644
--- a/model-optimizer/extensions/middle/ReverseV2ToReverseSequence.py
+++ b/model-optimizer/extensions/middle/ReverseV2ToReverseSequence.py
@@ -1,16 +1,27 @@
 # Copyright (C) 2018-2021 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import numpy as np
-
 from extensions.ops.reverse_sequence import ReverseSequence
+from mo.front.common.partial_infer.utils import int64_array
 from mo.front.tf.graph_utils import create_op_node_with_second_input
 from mo.graph.graph import Graph, rename_node
 from mo.middle.replacement import MiddleReplacementPattern
-from mo.utils.error import Error
+from mo.ops.broadcast import Broadcast
+from mo.ops.shape import Shape
+from mo.ops.squeeze import Squeeze
+from mo.ops.unsqueeze import Unsqueeze
+from mo.utils.shape import node_to_get_shape_value_of_indices
 
 
 class ReverseToReverseSequence(MiddleReplacementPattern):
+    """
+    Transformation converts Reverse to ReverseSequence operation.
+    Parameters for ReverseSequence calculates in the following way:
+     * seq_axis - set axis value from Reverse operation
+     * batch_axis - set 0 if seq_axis is not 0 otherwise set 1
+     * seq_lengths - take from shape shape[seq_axis] value and broadcast it to vector with shape[batch_axis] length
+    If input is 1D tensor then we add one more dimension to set different seq_axis and batch_axis.
+    """
     enabled = True
 
     def run_after(self):
@@ -21,40 +32,57 @@ class ReverseToReverseSequence(MiddleReplacementPattern):
         from extensions.middle.reverse_tensor_iterator import ReverseTensorIteratorLSTM
         return [ReverseTensorIteratorLSTM]
 
-    @staticmethod
-    def pattern():
-        return dict(
-            nodes=[
-                ('reverse', dict(kind='op', op='Reverse'))
-            ],
-            edges=[]
-        )
+    def find_and_replace_pattern(self, graph: Graph):
+        reverse_nodes = graph.get_op_nodes(op='Reverse')
+        for reverse in reverse_nodes:
+            reverse_name = reverse.soft_get('name', reverse.id)
 
-    def replace_pattern(self, graph: Graph, match: dict):
-        reverse = match['reverse']
-        input_data_shape = reverse.in_node(0).shape
+            assert reverse.in_port(1).disconnected()
+            assert reverse.has_valid('axis')
 
-        if len(input_data_shape) == 1:
-            raise Error('Reverse operation name = {} is\'t supported because of 1D input.'.format(reverse.name))
+            in_shape_rank = len(reverse.in_port(0).data.get_shape())
+            # 1. Add new dimension as batch for rank = 1 to have batch != seq_axis
+            if in_shape_rank == 1:
+                unsq_node = create_op_node_with_second_input(graph, Unsqueeze, int64_array([0]),
+                                                             {'name': reverse_name+"/Unsqueeze"})
+                reverse.in_port(0).get_source().connect(unsq_node.in_port(0))
+                new_in = unsq_node.out_port(0)
+                batch_axis = 0
+                seq_axis = 1
+            else:
+                new_in = reverse.in_port(0).get_source()
+                seq_axis = reverse['axis']
+                batch_axis = 0 if seq_axis != 0 else 1
 
-        assert reverse.in_port(1).disconnected()
+            # 2. For ReverseSequence 1-port input is seq_lengths => create this input node as
+            # shape[seq_axis] broadcasted to shape[batch_axis]
+            # in ---> ShapeOf ----> Gather(seq_axis)  ----> Broadcast----->
+            #            |                                      |
+            #            | -------> Gather(batch_axis)----------|
+            shape_node = Shape(graph, {'name': reverse_name + "/Shape"}).create_node()
+            new_in.connect(shape_node.in_port(0))
+            seq_axis_node = node_to_get_shape_value_of_indices(shape_node, [seq_axis])
+            batch_node = node_to_get_shape_value_of_indices(shape_node, [batch_axis])
+            broadcast_node = Broadcast(graph, {'name': reverse_name + "/Broadcast"}).create_node()
+            broadcast_node.in_port(0).connect(seq_axis_node.out_port(0))
+            broadcast_node.in_port(1).connect(batch_node.out_port(0))
 
-        seq_axis = reverse['axis']
-        # We need to choose arbitrary batch_axis != sequence_axis
-        batch_axis = int(not seq_axis)
+            # 3. Create new ReverseSequence node and reconnect all inputs/outputs to it
+            rename_node(reverse, reverse_name + '/to_delete')
+            reverse_sequence = ReverseSequence(graph, {'name':  reverse_name, 'seq_axis': seq_axis,
+                                                       'batch_axis': batch_axis}).create_node()
+            reverse_sequence.in_port(0).connect(new_in)
+            reverse_sequence.in_port(1).connect(broadcast_node.out_port(0))
 
-        # 1. For ReverseSequence 1-port input is seq_lengths => create this input node
-        seq_lengths = np.ones(input_data_shape[batch_axis]) * input_data_shape[seq_axis]
+            # 4. remove added dimension for rank = 1
+            if in_shape_rank == 1:
+                rename_node(reverse_sequence, reverse_name + '/ReverseSequence')
+                squeeze_node = create_op_node_with_second_input(graph, Squeeze, int64_array([0]),
+                                                                {'name': reverse_name})
+                squeeze_node.in_port(0).connect(reverse_sequence.out_port(0))
+                reverse.out_port(0).get_connection().set_source(squeeze_node.out_port(0))
+            else:
+                reverse.out_port(0).get_connection().set_source(reverse_sequence.out_port(0))
 
-        reverse_name = reverse.soft_get('name',  reverse.id)
-        rename_node(reverse, reverse_name + '/to_delete')
-        # 2. Create new ReverseSequence node and reconnect all inputs/outputs to it
-        reverse_sequence = create_op_node_with_second_input(graph, ReverseSequence, seq_lengths,
-                                                            {'name':  reverse_name, 'seq_axis': seq_axis,
-                                                             'batch_axis': batch_axis})
-        rename_node(reverse_sequence, reverse_name)
-        reverse.in_port(0).get_connection().set_destination(reverse_sequence.in_port(0))
-        reverse.out_port(0).get_connection().set_source(reverse_sequence.out_port(0))
-
-        # 3. Delete old Reverse node
-        graph.remove_node(reverse.id)
+        # 5. Delete old Reverse node
+        graph.remove_nodes_from([reverse.id for reverse in reverse_nodes])
diff --git a/model-optimizer/extensions/middle/reverse_tensor_iterator.py b/model-optimizer/extensions/middle/reverse_tensor_iterator.py
index 558551ae899..2b7e99ef85f 100644
--- a/model-optimizer/extensions/middle/reverse_tensor_iterator.py
+++ b/model-optimizer/extensions/middle/reverse_tensor_iterator.py
@@ -5,6 +5,7 @@ import numpy as np
 
 from extensions.middle.ONNXRNNSequenceNormalize import ONNXRNNSequenceNormalize
 from extensions.middle.permute_tensor_iterator import TransposeTensorIteratorLSTM
+from mo.front.common.partial_infer.utils import is_fully_defined
 from mo.graph.graph import Graph, Node
 from mo.middle.passes.eliminate import remove_op_node_with_data_node
 from mo.middle.replacement import MiddleReplacementPattern
@@ -18,6 +19,7 @@ class ReverseTensorIteratorLSTM(MiddleReplacementPattern):
     """
 
     enabled = True
+    force_clean_up = True
 
     def run_after(self):
         return [
@@ -32,39 +34,55 @@ class ReverseTensorIteratorLSTM(MiddleReplacementPattern):
     @staticmethod
     def is_fusable_reverse_sequence(node: Node):
         sequence_lengths = node.in_port(1).data.get_value()
-        assert sequence_lengths is not None
         input_shape = node.in_port(0).data.get_shape()
         assert input_shape is not None
 
         seq_len = input_shape[node.seq_axis]
-        return np.all(sequence_lengths == seq_len)
+        if sequence_lengths is not None and is_fully_defined(sequence_lengths) and is_fully_defined(seq_len):
+            return np.all(sequence_lengths == seq_len)
+        else:
+            # check that we take sequence_length from input shape based on ReverseV2ToReverseSequence transformation
+            broadcast_node = node.in_port(1).get_source().node
+            if broadcast_node.op != 'Broadcast':
+                return False
+            gather_node = broadcast_node.in_port(0).get_source().node
+            if gather_node.op != "Gather" or \
+                    (np.all(gather_node.in_port(2).data.get_value() != [0]) or
+                     np.all(gather_node.in_port(1).data.get_value() != [node.seq_axis])):
+                return False
+            gather_node_2 = broadcast_node.in_port(1).get_source().node
+            if gather_node_2.op != "Gather" or \
+                    (np.all(gather_node_2.in_port(2).data.get_value() != [0]) or
+                     np.all(gather_node_2.in_port(1).data.get_value() != [node.batch_axis])):
+                return False
+            shape_node = gather_node.in_port(0).get_source().node
+            if shape_node.op != "ShapeOf":
+                return False
+            if shape_node.in_port(0).get_source().node != node.in_port(0).get_source().node:
+                return False
+
+            return True
 
     def pattern(self):
         return dict(
             nodes=[
                 ('input', dict(kind='data')),
 
-                ('const', dict(type='Const')),
-                ('const_d', dict(kind='data')),
-
+                ('direct_seq_len_d', dict(kind='data')),
                 ('direct_reverse', dict(op='ReverseSequence')),
                 ('input_reversed', dict(kind='data')),
                 ('init_hidden', dict(kind='data')),
 
                 ('ti', dict(kind='op', op='TensorIterator')),
-
                 ('output_reversed', dict(kind='data')),
 
-                ('const_1', dict(type='Const')),
-                ('const_1_d', dict(kind='data')),
-
+                ('inverse_seq_len_d', dict(kind='data')),
                 ('inverse_reverse', dict(op='ReverseSequence')),
                 ('output', dict(kind='data')),
             ],
             edges=[
                 ('input', 'direct_reverse', {'in': 0}),
-                ('const', 'const_d'),
-                ('const_d', 'direct_reverse', {'in': 1}),
+                ('direct_seq_len_d', 'direct_reverse', {'in': 1}),
                 ('direct_reverse', 'input_reversed'),
 
                 ('input_reversed', 'ti', {'in': 0}),
@@ -72,8 +90,7 @@ class ReverseTensorIteratorLSTM(MiddleReplacementPattern):
                 ('ti', 'output_reversed', {'out': 0}),
 
                 ('output_reversed', 'inverse_reverse', {'in': 0}),
-                ('const_1', 'const_1_d'),
-                ('const_1_d', 'inverse_reverse', {'in': 1}),
+                ('inverse_seq_len_d', 'inverse_reverse', {'in': 1}),
                 ('inverse_reverse', 'output'),
             ]
         )
@@ -106,9 +123,12 @@ class ReverseTensorIteratorLSTM(MiddleReplacementPattern):
                         port['start'] = -1
                         port['end'] = 0
                     elif port['stride'] == 1:
-                        port['start'] = None
-                        port['end'] = None
+                        port['start'] = 0
+                        port['end'] = -1
 
+        # disconnect subgraph for seq length calculation
+        direct_reverse.in_port(1).disconnect()
+        inverse_reverse.in_port(1).disconnect()
         # Remove reverses
         remove_op_node_with_data_node(graph, direct_reverse)
         remove_op_node_with_data_node(graph, inverse_reverse)
diff --git a/model-optimizer/extensions/ops/reverse_sequence.py b/model-optimizer/extensions/ops/reverse_sequence.py
index 06509ec0e59..3b3fe9550d7 100644
--- a/model-optimizer/extensions/ops/reverse_sequence.py
+++ b/model-optimizer/extensions/ops/reverse_sequence.py
@@ -2,7 +2,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 from mo.graph.graph import Graph
-from mo.ops.op import Op
+from mo.ops.op import Op, PermuteAttrs
 
 
 class ReverseSequence(Op):
@@ -35,3 +35,6 @@ class ReverseSequence(Op):
 
         assert len(node.out_nodes()) == 1
         node.out_port(0).data.set_shape(input_data_shape)
+
+        PermuteAttrs.create_permute_attrs(node, attrs=[('seq_axis', 'input:0')])
+        PermuteAttrs.create_permute_attrs(node, attrs=[('batch_axis', 'input:0')])
diff --git a/model-optimizer/mo/ops/op.py b/model-optimizer/mo/ops/op.py
index ff9f923d171..db3dabcc0ab 100644
--- a/model-optimizer/mo/ops/op.py
+++ b/model-optimizer/mo/ops/op.py
@@ -374,6 +374,8 @@ class PermuteAttrs:
             'ellipsis_mask': slice_permutation,
             'axes': common_permutation_inv,
             'axis': common_permutation_inv,
+            'seq_axis': common_permutation_inv,
+            'batch_axis': common_permutation_inv,
             'batch_dims': common_permutation_inv,
             'channel_dims': common_permutation_inv,
             'spatial_dims': common_permutation_inv,
diff --git a/model-optimizer/unit_tests/extensions/middle/reverse_tensor_iterator_test.py b/model-optimizer/unit_tests/extensions/middle/reverse_tensor_iterator_test.py
new file mode 100644
index 00000000000..1dd81ea2bd8
--- /dev/null
+++ b/model-optimizer/unit_tests/extensions/middle/reverse_tensor_iterator_test.py
@@ -0,0 +1,117 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import unittest
+
+import numpy as np
+
+from extensions.middle.reverse_tensor_iterator import ReverseTensorIteratorLSTM
+from mo.utils.ir_engine.compare_graphs import compare_graphs
+from unit_tests.utils.graph import build_graph, regular_op_with_shaped_data, connect, \
+    valued_const_with_data, regular_op_with_empty_data, result
+
+nodes = {
+    **regular_op_with_shaped_data('parameter', [1, 3, 227, 227],
+                                  {'type': 'Parameter', 'op': 'Parameter', 'shape': [1, 3, 227, 227]}),
+    **valued_const_with_data('seq_len', np.array([227])),
+    **regular_op_with_empty_data('shapeof', {'type': 'ShapeOf', 'op': 'ShapeOf'}),
+    **valued_const_with_data('gather_axis', np.array([0])),
+    **valued_const_with_data('gather_batch_ind', np.array([0])),
+    **valued_const_with_data('gather_seq_ind', np.array([2])),
+    **regular_op_with_empty_data('gather_batch', {'type': 'Gather', 'op': 'Gather'}),
+    **regular_op_with_empty_data('gather_seq', {'type': 'Gather', 'op': 'Gather'}),
+    **regular_op_with_empty_data('broadcast', {'type': 'Broadcast', 'op': 'Broadcast'}),
+    **regular_op_with_shaped_data('direct_reverse', [1, 3, 227, 227], {'type': 'ReverseSequence',
+                                                                       'op': 'ReverseSequence',
+                                                                       'seq_axis': 2, 'batch_axis': 0}),
+    **regular_op_with_empty_data('init_hidden', {'type': 'Init', 'op': 'Init'}),
+
+    **regular_op_with_shaped_data('ti', [1, 2, 34, 56], {'type': 'TensorIterator', 'op': 'TensorIterator',
+                                                         'output_port_map': [{'axis': 2, 'start': 0, 'end': -1,
+                                                                              'stride': 1, 'external_port_id': 0}],
+                                                         'input_port_map': [{'axis': 2, 'start': -1, 'end': 0,
+                                                                             'stride': -1, 'external_port_id': 0}]}),
+    **valued_const_with_data('inverse_seq_len', np.array([34])),
+    **regular_op_with_empty_data('inverse_shapeof', {'type': 'ShapeOf', 'op': 'ShapeOf'}),
+    **regular_op_with_empty_data('inverse_gather_batch', {'type': 'Gather', 'op': 'Gather'}),
+    **regular_op_with_empty_data('inverse_gather_seq', {'type': 'Gather', 'op': 'Gather'}),
+    **regular_op_with_empty_data('inverse_broadcast', {'type': 'Broadcast', 'op': 'Broadcast'}),
+    **regular_op_with_shaped_data('inverse_reverse', [1, 2, 34, 56], {'type': 'ReverseSequence',
+                                                                      'op': 'ReverseSequence',
+                                                                      'seq_axis': 2, 'batch_axis': 0}),
+    **regular_op_with_empty_data('some_op', {'op': 'SomeOp'}),
+    **result()
+}
+
+ref_nodes = {
+    **regular_op_with_shaped_data('parameter', [1, 3, 227, 227],
+                                  {'type': 'Parameter', 'op': 'Parameter', 'shape': [1, 3, 227, 227]}),
+    **regular_op_with_empty_data('init_hidden', {'type': 'Init', 'op': 'Init'}),
+    **regular_op_with_empty_data('ti', {'type': 'TensorIterator', 'op': 'TensorIterator',
+                                        'output_port_map': [{'axis': 2, 'start': -1, 'end': 0, 'stride': -1,
+                                                             'external_port_id': 0}],
+                                        'input_port_map': [{'axis': 2, 'start': 0, 'end': -1, 'stride': 1,
+                                                            'external_port_id': 0}]}),
+    **regular_op_with_empty_data('some_op', {'op': 'SomeOp'}),
+    **result()
+}
+
+
+class ReverseTensorIteratorTest(unittest.TestCase):
+    def test_ti_reverse(self):
+        graph = build_graph(nodes, [*connect('parameter:0', '0:direct_reverse'),
+                                    *connect('parameter:0', 'shapeof', skip_data=True),
+                                    *connect('shapeof:0', '0:gather_batch'),
+                                    *connect('gather_batch_ind', '1:gather_batch'),
+                                    *connect('gather_axis', '2:gather_batch'),
+                                    *connect('shapeof:0', '0:gather_seq', skip_data=True),
+                                    *connect('gather_seq_ind', '1:gather_seq'),
+                                    *connect('gather_axis', '2:gather_seq'),
+                                    *connect('gather_seq', '0:broadcast'),
+                                    *connect('gather_batch', '1:broadcast'),
+                                    *connect('broadcast', '1:direct_reverse'),
+                                    *connect('direct_reverse', '0:ti'),
+                                    *connect('init_hidden', '1:ti'),
+                                    *connect('ti', 'inverse_shapeof'),
+                                    *connect('inverse_shapeof:0', '0:inverse_gather_batch'),
+                                    *connect('gather_batch_ind', '1:inverse_gather_batch'),
+                                    *connect('gather_axis', '2:inverse_gather_batch'),
+                                    *connect('inverse_shapeof:0', '0:inverse_gather_seq', skip_data=True),
+                                    *connect('gather_seq_ind', '1:inverse_gather_seq'),
+                                    *connect('gather_axis', '2:inverse_gather_seq'),
+                                    *connect('inverse_gather_seq', '0:inverse_broadcast'),
+                                    *connect('inverse_gather_batch', '1:inverse_broadcast'),
+                                    *connect('ti', '0:inverse_reverse', skip_data=True),
+                                    *connect('inverse_broadcast', '1:inverse_reverse'),
+                                    *connect('inverse_reverse', 'some_op'),
+                                    *connect('some_op', 'output')], nodes_with_edges_only=True)
+
+        ReverseTensorIteratorLSTM().find_and_replace_pattern(graph)
+        graph.clean_up()
+
+        ref_graph = build_graph(ref_nodes, [*connect('parameter', '0:ti'),
+                                            *connect('init_hidden', '1:ti'),
+                                            *connect('ti', 'some_op'),
+                                            *connect('some_op', 'output')])
+        flag, resp = compare_graphs(graph, ref_graph, 'output', check_op_attrs=True)
+        self.assertTrue(flag, resp)
+
+    def test_ti_reverse_const(self):
+        graph = build_graph(nodes, [*connect('parameter:0', '0:direct_reverse'),
+                                    *connect('seq_len', '1:direct_reverse'),
+                                    *connect('direct_reverse', '0:ti'),
+                                    *connect('init_hidden', '1:ti'),
+                                    *connect('ti', '0:inverse_reverse'),
+                                    *connect('inverse_seq_len', '1:inverse_reverse'),
+                                    *connect('inverse_reverse', 'some_op'),
+                                    *connect('some_op', 'output')], nodes_with_edges_only=True)
+
+        ReverseTensorIteratorLSTM().find_and_replace_pattern(graph)
+        graph.clean_up()
+
+        ref_graph = build_graph(ref_nodes, [*connect('parameter', '0:ti'),
+                                            *connect('init_hidden', '1:ti'),
+                                            *connect('ti', 'some_op'),
+                                            *connect('some_op', 'output')])
+        flag, resp = compare_graphs(graph, ref_graph, 'output', check_op_attrs=True)
+        self.assertTrue(flag, resp)
diff --git a/tests/layer_tests/tensorflow_tests/test_tf_ReverseV2.py b/tests/layer_tests/tensorflow_tests/test_tf_ReverseV2.py
new file mode 100644
index 00000000000..ceb4b7ba34d
--- /dev/null
+++ b/tests/layer_tests/tensorflow_tests/test_tf_ReverseV2.py
@@ -0,0 +1,56 @@
+# Copyright (C) 2018-2021 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+import numpy as np
+
+from common.tf_layer_test_class import CommonTFLayerTest
+
+
+class TestReverseV2Ops(CommonTFLayerTest):
+    def _prepare_input(self, inputs_dict):
+        for input in inputs_dict.keys():
+            inputs_dict[input] = np.random.random(inputs_dict[input])
+        return inputs_dict
+
+    def create_reversev2_net(self, shape, keep_dims, axis, ir_version):
+        import tensorflow as tf
+        tf.compat.v1.reset_default_graph()
+        with tf.compat.v1.Session() as sess:
+            shapes = shape.copy()
+            if len(shapes) >= 4:
+                shapes.append(shapes.pop(1))
+
+            x = tf.compat.v1.placeholder(tf.float32, shapes, 'Input')
+            tf.compat.v1.reverse_v2(x, axis)
+            tf.compat.v1.global_variables_initializer()
+            tf_net = sess.graph_def
+
+        return tf_net, None
+
+    test_data = []
+    test_data.extend([
+                      dict(shape=[5], axis=[0]),
+                      dict(shape=[2, 3], axis=[1]),
+                      dict(shape=[2, 3, 5], axis=[-2]),
+                      dict(shape=[2, 3, 5, 7], axis=[0]),
+                      ])
+
+    @pytest.mark.parametrize("params", test_data)
+    @pytest.mark.parametrize("keep_dims", [True, False])
+    @pytest.mark.nightly
+    def test_reversev2(self, params, keep_dims, ie_device, precision, ir_version, temp_dir):
+        self._test(*self.create_reversev2_net(**params, keep_dims=keep_dims, ir_version=ir_version),
+                   ie_device, precision, ir_version, temp_dir=temp_dir)
+
+    test_data_pre_commit = []
+    test_data_pre_commit.extend([dict(shape=[5], axis=[0]),
+                                 dict(shape=[2, 3, 5], axis=[-2])
+                                ])
+
+    @pytest.mark.parametrize("params", test_data_pre_commit)
+    @pytest.mark.parametrize("keep_dims", [True])
+    @pytest.mark.precommit
+    def test_reversev2_precommit(self, params, keep_dims, ie_device, precision, ir_version, temp_dir):
+        self._test(*self.create_reversev2_net(**params, keep_dims=keep_dims, ir_version=ir_version),
+                   ie_device, precision, ir_version, temp_dir=temp_dir)