Merge remote-tracking branch 'upstream/master' into debian-packages

.ci/azure/windows_docker.yml

@@ -1,24 +0,0 @@
-jobs:
-- job: Win_docker
-  timeoutInMinutes: 60
-
-  pool:
-    name: WIN_DOCKER_VMSS_VENV_F4S_WU2
-
-  variables:
-    system.debug: true
-
-  steps:
-  - script: |
-      powershell -command "Invoke-RestMethod -Headers @{\"Metadata\"=\"true\"} -Method GET -Uri http://169.254.169.254/metadata/instance/compute?api-version=2019-06-01 | format-custom"
-      where python3
-      python3 --version
-      where python
-      python --version
-      wmic computersystem get TotalPhysicalMemory
-      wmic cpu list
-      wmic logicaldisk get description,name
-      wmic VOLUME list
-      set
-    displayName: 'System info'
-

docs/template_plugin/backend/int_executable.cpp

@@ -5,7 +5,6 @@
 #include "int_executable.hpp"
 
 #include <cstring>
-#include <openvino/op/util/variable_context.hpp>
 
 #include "evaluates_map.hpp"
 #include "ngraph/except.hpp"
@@ -88,10 +87,6 @@ bool runtime::interpreter::INTExecutable::call(const vector<shared_ptr<runtime::
         results_map[output] = output_count;
     }
 
-    EvaluationContext eval_context;
-    ov::op::util::VariableContext variable_context;
-    eval_context.emplace("VariableContext", variable_context);
-
     // for each ordered op in the graph
     for (const auto& op : m_nodes) {
         if (dynamic_pointer_cast<op::Parameter>(op) != nullptr) {
@@ -148,20 +143,8 @@ bool runtime::interpreter::INTExecutable::call(const vector<shared_ptr<runtime::
         if (m_performance_counters_enabled) {
             m_timer_map[op].start();
         }
-
-        if (auto var_extension = std::dynamic_pointer_cast<ov::op::util::VariableExtension>(cloned_node)) {
-            auto variable = var_extension->get_variable();
-            if (!variable_context.get_variable_value(variable)) {
-                auto h_tensor = std::make_shared<ngraph::HostTensor>(cloned_node->get_input_element_type(0),
-                                                                     cloned_node->get_input_shape(0));
-                std::vector<float> data(ov::shape_size(cloned_node->get_input_shape(0)), 0);
-                h_tensor->write(data.data(), data.size() * sizeof(float));
-                variable_context.set_variable_value(variable, std::make_shared<VariableValue>(h_tensor));
-            }
-        }
-
         // Call evaluate for cloned_node with static shapes
-        if (!cloned_node->evaluate(op_outputs, op_inputs, eval_context)) {
+        if (!cloned_node->evaluate(op_outputs, op_inputs)) {
             evaluate_node(cloned_node, op_outputs, op_inputs);
         }
         if (m_performance_counters_enabled) {

docs/template_plugin/backend/opset_int_tbl.hpp

@@ -85,7 +85,6 @@ NGRAPH_OP(NonMaxSuppression, op::v5)
 NGRAPH_OP(RNNSequence, op::v5)
 NGRAPH_OP(Round, op::v5)
 
-NGRAPH_OP(Assign, ngraph::op::v6)
 NGRAPH_OP(CTCGreedyDecoderSeqLen, op::v6)
 NGRAPH_OP(ExperimentalDetectronDetectionOutput, op::v6)
 NGRAPH_OP(ExperimentalDetectronGenerateProposalsSingleImage, op::v6)
@@ -94,7 +93,6 @@ NGRAPH_OP(ExperimentalDetectronROIFeatureExtractor, op::v6)
 NGRAPH_OP(ExperimentalDetectronTopKROIs, op::v6)
 NGRAPH_OP(GatherElements, op::v6)
 NGRAPH_OP(MVN, ngraph::op::v6)
-NGRAPH_OP(ReadValue, ngraph::op::v6)
 
 NGRAPH_OP(DFT, op::v7)
 NGRAPH_OP(Einsum, op::v7)

inference-engine/thirdparty/CMakeLists.txt

@@ -16,7 +16,3 @@ endif()
 if(ENABLE_LTO)
     set(CMAKE_INTERPROCEDURAL_OPTIMIZATION_RELEASE ON)
 endif()
-
-if (ENABLE_INTEL_GPU)
-    add_subdirectory(clDNN)
-endif()

inference-engine/thirdparty/clDNN/CMakeLists.txt

@@ -1,26 +0,0 @@
-# Copyright (C) 2018-2021 Intel Corporation
-# SPDX-License-Identifier: Apache-2.0
-#
-
-if(ENABLE_GPU_DEBUG_CAPS)
-  add_definitions(-DGPU_DEBUG_CONFIG=1)
-endif()
-
-set(MAIN_DIR "${CMAKE_CURRENT_SOURCE_DIR}")
-set(INCLUDE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/api")
-
-find_package(PythonInterp 3 QUIET)
-if(NOT PYTHONINTERP_FOUND)
-  message(WARNING "[clDNN] Project requires Python 3.x interpreter to build (with python loader). CMake could not detect it correctly.
-      If you have installed this interpreter, please disregard this warning or specify PYTHON_EXECUTABLE in CMake command-line."
-    )
-endif()
-
-add_subdirectory(runtime)
-add_subdirectory(kernel_selector)
-add_subdirectory(src)
-add_subdirectory(utils)
-
-if(ENABLE_TESTS)
-  add_subdirectory(tests)
-endif()

src/common/low_precision_transformations/include/low_precision/assign_and_read_value.hpp

@@ -1,27 +0,0 @@
-// Copyright (C) 2018-2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#pragma once
-
-#include <ngraph/ngraph.hpp>
-#include "layer_transformation.hpp"
-
-namespace ngraph {
-namespace pass {
-namespace low_precision {
-
-class LP_TRANSFORMATIONS_API AssignAndReadValueTransformation : public LayerTransformation {
-public:
-    NGRAPH_RTTI_DECLARATION;
-    AssignAndReadValueTransformation(const std::shared_ptr<ngraph::Function> function, const Params& params = Params());
-    bool transform(TransformationContext& context, ngraph::pattern::Matcher& m) override;
-    bool canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> op) const override;
-    bool isPrecisionPreserved(std::shared_ptr<Node> layer) const noexcept override;
-private:
-    std::shared_ptr<ngraph::Function> function;
-};
-
-} // namespace low_precision
-} // namespace pass
-} // namespace ngraph

src/common/low_precision_transformations/include/low_precision/common/fake_quantize_dequantization.hpp

@@ -33,7 +33,6 @@ public:
     bool multiplyHasZeroOrDenormal() const;
     bool isShared() const;
     bool isLowPrecision() const;
-    std::shared_ptr<Node> copyWithNewInput(const std::shared_ptr<Node>& input) const;
 
     static bool checkElementwise(const std::shared_ptr<ngraph::Node>& elementwise);
 

src/common/low_precision_transformations/include/low_precision/fake_quantize.hpp

@@ -22,10 +22,11 @@ public:
 
     static bool checkElementwise(const std::shared_ptr<Node>& eltwise);
 
-    static std::shared_ptr<opset1::FakeQuantize> fuseElementwise(
+private:
+    std::shared_ptr<opset1::FakeQuantize> fuseElementwise(
             TransformationContext& context,
             MatcherPass* matcherPass,
-            const std::shared_ptr<opset1::FakeQuantize>& fakeQuantize);
+            const std::shared_ptr<opset1::FakeQuantize>& fakeQuantize) const;
 };
 
 } // namespace low_precision

src/common/low_precision_transformations/include/low_precision/network_helper.hpp

@@ -41,12 +41,17 @@ public:
 
     static std::vector<Input<Node>> consumer_inputs(std::shared_ptr<Node> node);
 
+    // returns true if at least one child is not FQ
+    static bool notAllChildrensAreFQ(const NodeVector& layer);
+
     // Collect and return a vector with all nodes that consumes any of the `node` output
     static std::vector<std::shared_ptr<Node>> consumers(std::shared_ptr<Node> node);
 
     // return true if op is on a constant path
     static bool isConstantPath(const std::shared_ptr<Node>& op);
 
+    static Shape alignShapeForChannelDim(const Shape& shape, Rank rank);
+
     template <typename OperationType>
     static std::shared_ptr<Node> setOutDataPrecisionForTypeRelaxed(std::shared_ptr<OperationType> operation, const element::Type& precision);
 
@@ -210,6 +215,87 @@ public:
         const std::shared_ptr<Node>& dequantization,
         const std::shared_ptr<Node>& newNode);
 
+    static void replaceAttributeInNodes(
+        std::shared_ptr<ngraph::Function> f,
+        const std::string& name,
+        const ov::Any& newAttribute,
+        const ov::Any& oldAttribute,
+        const std::shared_ptr<ngraph::Node>& initialNode) {
+        std::set<std::shared_ptr<Node>> visited;
+        std::deque<std::shared_ptr<Node>> nodes;
+        nodes.emplace_back(initialNode);
+
+        while (!nodes.empty()) {
+            auto node = nodes.front();
+            nodes.pop_front();
+
+            if (visited.count(node) || ov::is_type<op::Constant>(node)) {
+                continue;
+            }
+
+            visited.insert(node);
+
+            bool handleConnectedNodes = false;
+            if (NetworkHelper::isPrecisionPreserved(node) || ov::is_type<opset1::FakeQuantize>(node)) {
+                auto& rt = node->get_rt_info();
+
+                if (node == initialNode) {
+                    rt[name] = newAttribute;
+                    handleConnectedNodes = true;
+                } else {
+                    auto it = rt.find(name);
+                    if (it != rt.end()) {
+                        const auto currentAttribute = it->second;
+                        if (oldAttribute == currentAttribute) {
+                            rt[name] = newAttribute;
+                        }
+                        handleConnectedNodes = true;
+                    }
+                }
+            }
+
+            if (!handleConnectedNodes) {
+                continue;
+            }
+
+            if (!ov::is_type<opset1::FakeQuantize>(node)) {
+                for (size_t index = 0ul; index < node->get_input_size(); ++index) {
+                    auto getInput = [](const std::shared_ptr<ngraph::Node>& node, const size_t index) {
+                        const auto dequantization = NetworkHelper::getDequantization(node, index);
+                        if (!dequantization.empty() &&
+                            (ov::is_type<opset1::Convert>(dequantization.data.get_node())) &&
+                            ov::is_type<opset1::FakeQuantize>(dequantization.data.get_node()->get_input_node_ptr(0))) {
+                            const auto input = dequantization.data.get_node()->input(0);
+                            return input;
+                        }
+                        return node->input(index);
+                    };
+
+                    const auto& input = getInput(node, index);
+                    const auto& input_node = input.get_source_output().get_node_shared_ptr();
+
+                    //const auto& input_node = input.get_source_output().get_node_shared_ptr();
+                    if (visited.count(input_node) || ov::is_type<op::Constant>(input_node)) {
+                        continue;
+                    }
+
+                    nodes.push_front(input_node);
+                }
+            }
+
+            for (auto& output : node->outputs()) {
+                for (auto& input_value : output.get_target_inputs()) {
+                    const auto& output_node = input_value.get_node()->shared_from_this();
+                    if (visited.count(output_node) || ov::is_type<op::Constant>(output_node)) {
+                        continue;
+                    }
+
+                    nodes.push_front(output_node);
+                }
+            }
+        }
+    }
+
     template <typename SharedAttribute>
     static void reassign(
         const std::shared_ptr<typename SharedAttribute::SharedValueAttribute::SharedValue>& sharedValue,
@@ -284,6 +370,14 @@ std::shared_ptr<Node> make_op_pattern(const ngraph::NodeVector& args) {
     return std::make_shared<ngraph::pattern::op::Any>(element::undefined, PartialShape{}, [](std::shared_ptr<Node> n) {return !!ov::as_type_ptr<T>(n); }, args);
 }
 
+template <typename T>
+std::shared_ptr<Node> make_op_label() {
+    return std::make_shared<ngraph::pattern::op::Label>(
+            element::undefined,
+            PartialShape{},
+            [](std::shared_ptr<Node> n) {return !!ov::as_type_ptr<T>(n); });
+}
+
 template <typename T, typename... Args>
 std::shared_ptr<Node> fold(Args&&... args) {
     auto node = std::make_shared<T>(std::forward<Args>(args)...);

src/common/low_precision_transformations/src/assign_and_read_value.cpp

@@ -1,132 +0,0 @@
-// Copyright (C) 2021 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-
-#include "low_precision/assign_and_read_value.hpp"
-#include <ngraph/ngraph.hpp>
-
-#include <ngraph/pattern/op/wrap_type.hpp>
-#include "low_precision/network_helper.hpp"
-#include <ngraph/opsets/opset6.hpp>
-#include <ngraph/pattern/op/or.hpp>
-#include <openvino/op/util/assign_base.hpp>
-#include "low_precision/fake_quantize.hpp"
-
-namespace ngraph {
-namespace pass {
-namespace low_precision {
-
-NGRAPH_RTTI_DEFINITION(ngraph::pass::low_precision::AssignAndReadValueTransformation, "AssignAndReadValueTransformation", 0);
-
-AssignAndReadValueTransformation::AssignAndReadValueTransformation(const std::shared_ptr<ngraph::Function> function, const Params& params) :
-    LayerTransformation(params), function(function) {
-    auto assign3 = pattern::wrap_type<opset3::Assign>({ pattern::wrap_type<opset1::Multiply>() });
-    auto assign6 = pattern::wrap_type<opset6::Assign>({ pattern::wrap_type<opset1::Multiply>() });
-
-    ngraph::graph_rewrite_callback callback = [=](pattern::Matcher& m) {
-        const auto& opsMap = m.get_pattern_value_map();
-        auto op = m.get_match_root();
-        auto assignIt = opsMap.find(assign3);
-        if (assignIt == opsMap.end()) {
-            assignIt = opsMap.find(assign6);
-        }
-        const auto assign = assignIt->second.get_node_shared_ptr();
-        // check that we have ReadValue as the first dependency
-        if (assign->get_control_dependencies().empty()) {
-            return false;
-        }
-
-        if (transformation_callback(op)) {
-            return false;
-        }
-        return transform(*context, m);
-    };
-
-    auto m = std::make_shared<ngraph::pattern::Matcher>(
-        std::make_shared<pattern::op::Or>(OutputVector{ assign3, assign6 }),
-        "AssignAndReadValueTransformation");
-    this->register_matcher(m, callback);
-}
-
-bool AssignAndReadValueTransformation::transform(TransformationContext& context, ngraph::pattern::Matcher& m) {
-    if (!canBeTransformed(context, m.get_match_root())) {
-        return false;
-    }
-
-    const auto oldAssign = m.get_match_root();
-    const auto readValue = oldAssign->get_control_dependencies()[0];
-    oldAssign->remove_control_dependency(readValue);
-
-    const auto assign = NetworkHelper::separateInStandaloneBranch(oldAssign);
-    const auto dequantization = NetworkHelper::getDequantization(assign);
-
-    auto oldVar = ov::as_type_ptr<op::ReadValueBase>(readValue)->get_variable();
-    auto variableInfo = oldVar->get_info();
-    // set new precision for oldVar to update precision in newReadValue
-    oldVar->update({variableInfo.data_shape, dequantization.data.get_element_type(), variableInfo.variable_id});
-    // transform ReadValue part
-    const auto newConstant = foldConvert(readValue->get_input_node_shared_ptr(0), dequantization.data.get_element_type());
-    const auto newReadValue = readValue->copy_with_new_inputs({newConstant});
-    const auto newDequantization = dequantization.copyWithNewInput(newReadValue);
-    replace_node(readValue, newDequantization);
-
-    // transform Assign part
-
-    const auto newAssign = assign->copy_with_new_inputs({dequantization.data});
-    function->remove_sink(as_type_ptr<op::Sink>(oldAssign));
-    function->add_sinks({as_type_ptr<op::Sink>(newAssign)});
-
-    NetworkHelper::copyInfo(assign, newAssign);
-    replace_node(assign, newAssign);
-    newAssign->add_control_dependency(newReadValue);
-
-    // fuse dequantization multiply with FQ after ReadValue if possible
-    const auto nextLayers = newDequantization->get_output_target_inputs(0);
-    if (nextLayers.size() > 1) {
-        return true;
-    }
-    const auto fakeQuantize = as_type_ptr<opset1::FakeQuantize>(nextLayers.begin()->get_node()->shared_from_this());
-
-    if (fakeQuantize == nullptr) {
-        return true;
-    }
-    auto fakeQuantizeInputs = fakeQuantize->input_values();
-
-    const auto inputLow = as_type_ptr<opset1::Constant>(fakeQuantizeInputs[1].get_node_shared_ptr());
-    const auto inputHigh = as_type_ptr<opset1::Constant>(fakeQuantizeInputs[2].get_node_shared_ptr());
-
-    if (inputLow == nullptr || inputHigh == nullptr) {
-        return true;
-    }
-
-    FakeQuantizeTransformation::fuseElementwise(context, this, fakeQuantize);
-
-    return true;
-}
-
-bool AssignAndReadValueTransformation::canBeTransformed(const TransformationContext& context, std::shared_ptr<Node> op) const {
-    if (!LayerTransformation::canBeTransformed(context, op)) {
-        return false;
-    }
-
-    const auto readValue = std::dynamic_pointer_cast<op::ReadValueBase>(op->get_control_dependencies()[0]);
-    if (!readValue) {
-        return false;
-    }
-
-    // TODO: remove this limitation and change the transformation when this constant will be accepted to be non-zero
-    if (!NetworkHelper::isZeroConst(readValue->get_input_node_shared_ptr(0))) {
-        return false;
-    }
-
-    const auto dequantization = NetworkHelper::getDequantization(op);
-    return dequantization.subtract == nullptr && dequantization.multiply != nullptr;
-}
-
-bool AssignAndReadValueTransformation::isPrecisionPreserved(std::shared_ptr<Node> layer) const noexcept {
-    return false;
-}
-
-} // namespace low_precision
-} // namespace pass
-} // namespace ngraph

src/common/low_precision_transformations/src/fake_quantize.cpp

@@ -129,15 +129,17 @@ bool FakeQuantizeTransformation::checkElementwise(const std::shared_ptr<Node>& e
 std::shared_ptr<opset1::FakeQuantize> FakeQuantizeTransformation::fuseElementwise(
     TransformationContext& context,
     MatcherPass* matcherPass,
-    const std::shared_ptr<opset1::FakeQuantize>& fakeQuantize) {
+    const std::shared_ptr<opset1::FakeQuantize>& fakeQuantize) const {
     const std::shared_ptr<Node> eltwise = fakeQuantize->get_input_node_shared_ptr(0);
 
-    std::shared_ptr<Node> inputLowConst_f32 = foldConvert(fakeQuantize->input_value(1), element::f32);
-    std::shared_ptr<Node> inputHighConst_f32 = foldConvert(fakeQuantize->input_value(2), element::f32);
+    std::shared_ptr<Node> inputLowConst_f32 = foldConvert(fakeQuantize->input_value(1), deqPrecision);
+    std::shared_ptr<Node> inputHighConst_f32 = foldConvert(fakeQuantize->input_value(2), deqPrecision);
 
     std::shared_ptr<opset1::Constant> constant = fq::getConstant(eltwise);
     if (ov::is_type<opset1::Multiply>(eltwise) && checkElementwise(eltwise)) {
-        const auto value = foldConvert(constant, element::f32);
+        const auto value = constant->get_output_element_type(0) == deqPrecision ?
+            constant :
+            foldConvert(constant, deqPrecision);
 
         const auto valueVec = ov::as_type_ptr<opset1::Constant>(value)->cast_vector<float>();
 
@@ -157,7 +159,9 @@ std::shared_ptr<opset1::FakeQuantize> FakeQuantizeTransformation::fuseElementwis
         inputLowConst_f32 = fq::updateShape(inputLowConst_f32, fakeQuantize->get_output_partial_shape(0));
         inputHighConst_f32 =  fq::updateShape(inputHighConst_f32, fakeQuantize->get_output_partial_shape(0));
     } else if (ov::is_type<opset1::Subtract>(eltwise) && checkElementwise(eltwise)) {
-        const auto value = foldConvert(constant, element::f32);
+        const auto value = constant->get_output_element_type(0) == deqPrecision ?
+            constant :
+            foldConvert(constant, deqPrecision);
 
         inputLowConst_f32 = fq::updateShape(fold<opset1::Add>(inputLowConst_f32, value), fakeQuantize->get_output_partial_shape(0));
         inputHighConst_f32 = fq::updateShape(fold<opset1::Add>(inputHighConst_f32, value), fakeQuantize->get_output_partial_shape(0));
@@ -169,7 +173,9 @@ std::shared_ptr<opset1::FakeQuantize> FakeQuantizeTransformation::fuseElementwis
             return nullptr;
         }
 
-        const auto value = foldConvert(constant, element::f32);
+        const auto value = constant->get_output_element_type(0) == deqPrecision ?
+            constant :
+            foldConvert(constant, deqPrecision);
 
         inputLowConst_f32 = fq::updateShape(fold<opset1::Subtract>(inputLowConst_f32, value), fakeQuantize->get_output_partial_shape(0));
         inputHighConst_f32 = fq::updateShape(fold<opset1::Subtract>(inputHighConst_f32, value), fakeQuantize->get_output_partial_shape(0));
@@ -190,8 +196,8 @@ std::shared_ptr<opset1::FakeQuantize> FakeQuantizeTransformation::fuseElementwis
         data->output(outputIdx),
         inputLowConst_f32,
         inputHighConst_f32,
-        foldConvert(fakeQuantize->input_value(3), element::f32),
-        foldConvert(fakeQuantize->input_value(4), element::f32) }));
+        foldConvert(fakeQuantize->input_value(3), deqPrecision),
+        foldConvert(fakeQuantize->input_value(4), deqPrecision) }));
 
     matcherPass->register_new_node(newFakeQuantize);
 

src/common/low_precision_transformations/src/fake_quantize_dequantization.cpp

@@ -155,26 +155,6 @@ bool FakeQuantizeDequantization::checkElementwise(const std::shared_ptr<ngraph::
     return true;
 }
 
-std::shared_ptr<Node> FakeQuantizeDequantization::copyWithNewInput(const std::shared_ptr<Node>& input) const {
-    auto lastNode = input;
-    if (convert) {
-        lastNode = convert->copy_with_new_inputs({lastNode});
-    }
-    if (subtract) {
-        std::shared_ptr<Node> input1 = nullptr;
-        if (subtractConvert) {
-            input1 = subtractConvert;
-        } else {
-            input1 = subtractConstant;
-        }
-        lastNode = subtract->copy_with_new_inputs({lastNode, input1});
-    }
-    if (multiply) {
-        lastNode = multiply->copy_with_new_inputs({lastNode, multiplyConstant});
-    }
-    return lastNode;
-}
-
 int FakeQuantizeDequantization::fillDequantizationParams(
     const std::shared_ptr<ngraph::Node>& elementwise,
     std::shared_ptr<ngraph::opset1::Convert>& convert,

src/common/low_precision_transformations/src/low_precision.cpp

@@ -38,7 +38,6 @@
 
 // general transformations
 #include "low_precision/add.hpp"
-#include "low_precision/assign_and_read_value.hpp"
 #include "low_precision/avg_pool.hpp"
 #include "low_precision/clamp.hpp"
 #include "low_precision/convolution.hpp"
@@ -208,7 +207,6 @@ bool ngraph::pass::low_precision::LowPrecision::run_on_model(const std::shared_p
 
     std::shared_ptr<ngraph::pass::GraphRewrite> common = manager.register_pass<ngraph::pass::GraphRewrite>();
     common->add_matcher<ngraph::pass::low_precision::AddTransformation>(params);
-    common->add_matcher<ngraph::pass::low_precision::AssignAndReadValueTransformation>(f, params);
     common->add_matcher<ngraph::pass::low_precision::AvgPoolTransformation>(params);
     common->add_matcher<ngraph::pass::low_precision::ClampTransformation>(params);
     common->add_matcher<ngraph::pass::low_precision::ConcatTransformation>(params);

src/common/low_precision_transformations/src/network_helper.cpp

@@ -37,6 +37,17 @@ bool NetworkHelper::is_castable_to_one_of(NodeTypeInfo type, const std::unordere
     return false;
 }
 
+bool NetworkHelper::notAllChildrensAreFQ(const NodeVector& childrens) {
+    // NOTE: This check was added for models that don't have FQ after AvgPool
+    //       They will have transparent precision as it was in old LPT.
+    for (const auto& child : childrens) {
+        if (!ov::is_type<opset1::FakeQuantize>(child)) {
+            return true;
+        }
+    }
+    return false;
+}
+
 // Collect and return a vector with all nodes that consumes any of the `node` output
 std::vector<Input<Node>> NetworkHelper::consumer_inputs(std::shared_ptr<Node> node) {
     std::vector<Input<Node>> result;
@@ -188,6 +199,15 @@ size_t NetworkHelper::getGroupsCount(std::shared_ptr<Node> layer) {
     }
 }
 
+// Assumin tensor in NC... layout, append necessary number of 1s to shape to align it to a give rank
+Shape NetworkHelper::alignShapeForChannelDim(const Shape& shape, Rank rank) {
+    assert(shape.size() == 1);
+    assert(rank.is_static());
+    Shape result = shape;
+    result.resize(rank.get_length() - 1, 1);
+    return result;
+}
+
 void NetworkHelper::removeLayer(std::shared_ptr<Node> layer) {
     ngraph::replace_output_update_name(layer->output(0), layer->input_value(0));
 }
@@ -1339,12 +1359,9 @@ FakeQuantizeDequantization NetworkHelper::getDequantization(const std::shared_pt
 
     const std::shared_ptr<opset1::Convert> convert = ov::as_type_ptr<opset1::Convert>(dataNode.get_node_shared_ptr());
     if (convert != nullptr) {
-        auto defaultPrecisions = LayerTransformation::getDefaultPrecisions();
-        auto el_type = convert->input(0).get_element_type();
-        auto foundIt = std::find(defaultPrecisions.begin(), defaultPrecisions.end(), el_type);
-        if (foundIt == defaultPrecisions.end() &&
-            el_type != element::i4  && el_type != element::u4 &&
-            el_type != element::f32 && el_type != element::f16) {
+        if ((convert->input(0).get_element_type() != element::i8) && (convert->input(0).get_element_type() != element::u8) &&
+            (convert->input(0).get_element_type() != element::i4) && (convert->input(0).get_element_type() != element::u4) &&
+            (convert->output(0).get_element_type() != element::f32)) {
             return FakeQuantizeDequantization(dataNode, nullptr, subtract, subtractConvert, subtractConstant, multiply, multiplyConstant);
         }
         dataNode = convert->get_input_source_output(0);

src/plugins/intel_gpu/CMakeLists.txt

@@ -5,7 +5,7 @@
 set (TARGET_NAME "ov_intel_gpu_plugin")
 
 if(CMAKE_COMPILER_IS_GNUCXX)
-    ie_add_compiler_flags(-Wno-all)
+    ie_add_compiler_flags(-Wno-all -Wno-missing-declarations)
     ie_add_compiler_flags(-msse4.1 -msse4.2)
     if (CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 7.0)
         ie_add_compiler_flags(-Wno-error=terminate)
@@ -16,6 +16,14 @@ if(ENABLE_GPU_DEBUG_CAPS)
   add_definitions(-DGPU_DEBUG_CONFIG=1)
 endif()
 
+set(MAIN_DIR "${CMAKE_CURRENT_SOURCE_DIR}")
+set(INCLUDE_DIR "${CMAKE_CURRENT_SOURCE_DIR}/include")
+
+add_subdirectory(src/runtime)
+add_subdirectory(src/kernel_selector)
+add_subdirectory(src/graph)
+add_subdirectory(thirdparty)
+
 file(GLOB_RECURSE PLUGIN_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/src/plugin/*.cpp ${CMAKE_CURRENT_SOURCE_DIR}/include/intel_gpu/plugin/*.hpp)
 
 addVersionDefines(src/plugin/plugin.cpp CI_BUILD_NUMBER)
@@ -41,5 +49,9 @@ if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
     set_target_properties(${TARGET_NAME} PROPERTIES LINK_FLAGS_RELEASE "-Wno-error=maybe-uninitialized -Wno-maybe-uninitialized")
 endif()
 
+if(ENABLE_TESTS)
+  add_subdirectory(tests)
+endif()
+
 # Failed because of OpenCL
 # ie_add_api_validator_post_build_step(TARGET ${TARGET_NAME})

src/plugins/intel_gpu/README.md

@@ -1,21 +1,6 @@
 
-# Compute Library for Deep Neural Networks (clDNN)
-[![Apache License Version 2.0](https://img.shields.io/badge/license-Apache_2.0-green.svg)](LICENSE)
-![v1.0](https://img.shields.io/badge/1.0-RC1-green.svg)
-
-*Compute Library for Deep Neural Networks* (*clDNN*) is an open source performance
-library for Deep Learning (DL) applications intended for acceleration of
-DL Inference on Intel® Processor Graphics – including HD Graphics and
-Iris® Graphics.
-*clDNN* includes highly optimized building blocks for implementation of
-convolutional neural networks (CNN) with C++ interface.
-
-## License
-clDNN is licensed is licensed under
-[Apache License Version 2.0](http://www.apache.org/licenses/LICENSE-2.0).
-
 ### Attached licenses
-clDNN uses 3<sup>rd</sup>-party components licensed under following licenses:
+GPU plugin uses 3<sup>rd</sup>-party components licensed under following licenses:
 - *googletest* under [Google\* License](https://github.com/google/googletest/blob/master/googletest/LICENSE)
 - *OpenCL™ ICD and C++ Wrapper* under [Khronos™ License](https://github.com/KhronosGroup/OpenCL-CLHPP/blob/master/LICENSE.txt)
 - *RapidJSON* under [Tencent\* License](https://github.com/Tencent/rapidjson/blob/master/license.txt)
@@ -25,7 +10,7 @@ Please report issues and suggestions
 [GitHub issues](https://github.com/openvinotoolkit/openvino/issues).
 
 ## How to Contribute
-We welcome community contributions to clDNN. If you have an idea how to improve the library:
+We welcome community contributions to GPU plugin. If you have an idea how to improve the library:
 
 - Share your proposal via
  [GitHub issues](https://github.com/openvinotoolkit/openvino/issues)
@@ -38,11 +23,11 @@ are necessary, may provide feedback to guide you. When accepted, your pull
 request will be merged into our GitHub repository.
 
 ## System Requirements
-clDNN supports Intel® HD Graphics and Intel® Iris® Graphics and is optimized for Gen9-Gen12LP architectures
+GPU plugin supports Intel® HD Graphics and Intel® Iris® Graphics and is optimized for Gen9-Gen12LP architectures
 
-clDNN currently uses OpenCL™ with multiple Intel OpenCL™ extensions and requires Intel® Graphics Driver to run.
+GPU plugin currently uses OpenCL™ with multiple Intel OpenCL™ extensions and requires Intel® Graphics Driver to run.
 
-clDNN requires CPU with Intel® SSE/Intel® AVX support.
+GPU plugin requires CPU with Intel® SSE/Intel® AVX support.
 
 ---