text: // Copyright (C) 2021 Intel Corporation
  // SPDX-License-Identifier: Apache-2.0
  //
  
  #include "layer_transformation.hpp"
  
  #include <string>
  #include <sstream>
  #include <memory>
  
  #include <gtest/gtest.h>
  
  #include <transformations/utils/utils.hpp>
  #include <transformations/init_node_info.hpp>
  #include <low_precision/convolution_backprop_data.hpp>
  #include <low_precision/network_helper.hpp>
  
  #include "common_test_utils/ngraph_test_utils.hpp"
  #include "simple_low_precision_transformer.hpp"
  #include "lpt_ngraph_functions/convolution_backprop_data_function.hpp"
  
  namespace {
  using namespace testing;
  using namespace ngraph;
  using namespace ngraph::pass;
  
  using const_node_ptr = const std::shared_ptr<const ngraph::Node>;
  using callback_function_type = std::function<bool(const_node_ptr&)>;
  
  bool empty_callback(const std::shared_ptr<const ngraph::Node>& node) {
      return false;
  }
  
  class ConvolutionBackpropDataTransformationTestValues {
  public:
      class Actual {
      public:
          ngraph::element::Type precisionBeforeDequantization;
          ngraph::builder::subgraph::DequantizationOperations dequantizationOnActivations;
          builder::subgraph::FakeQuantizeOnWeights fakeQuantizeOnWeights;
          builder::subgraph::DequantizationOperations dequantizationOnWeights;
          std::shared_ptr<ngraph::opset1::Constant> weights;
          callback_function_type callback;
  
          Actual() = default;
          Actual(
              const ngraph::element::Type& precisionBeforeDequantization,
              const ngraph::builder::subgraph::DequantizationOperations& dequantizationOnActivations,
              const builder::subgraph::FakeQuantizeOnWeights& fakeQuantizeOnWeights,
              const std::shared_ptr<ngraph::opset1::Constant>& weights,
              const callback_function_type& callback = empty_callback) :
                  precisionBeforeDequantization(precisionBeforeDequantization),
                  dequantizationOnActivations(dequantizationOnActivations),
                  fakeQuantizeOnWeights(fakeQuantizeOnWeights),
                  weights(weights),
                  callback(callback) {}
          Actual(
              const  ngraph::element::Type& precisionBeforeDequantization,
              const  ngraph::builder::subgraph::DequantizationOperations& dequantizationOnActivations,
              const  builder::subgraph::DequantizationOperations& dequantizationOnWeights,
              const std::shared_ptr<ngraph::opset1::Constant>& weights,
              const callback_function_type& callback = empty_callback) :
              precisionBeforeDequantization(precisionBeforeDequantization),
              dequantizationOnActivations(dequantizationOnActivations),
              dequantizationOnWeights(dequantizationOnWeights),
              weights(weights),
              callback(callback) {}
      };
  
      class Expected {
      public:
          ngraph::element::Type precisionBeforeDequantization;
          ngraph::builder::subgraph::DequantizationOperations dequantizationOnActivations;
          builder::subgraph::DequantizationOperations dequantizationOnWeights;
          ngraph::builder::subgraph::DequantizationOperations dequantizationAfter;
          std::shared_ptr<ngraph::opset1::Constant> weights;
          bool transformed;
      };
  
      TestTransformationParams params;
      Actual actual;
      Expected expected;
  };
  
  typedef std::tuple<
          element::Type,
          ngraph::PartialShape,
          ConvolutionBackpropDataTransformationTestValues> ConvolutionBackpropDataTransformationParams;
  
  class ConvolutionBackpropDataTransformation : public LayerTransformation, public testing::WithParamInterface<ConvolutionBackpropDataTransformationParams> {
  public:
      void SetUp() override {
          const auto netPrecision = std::get<0>(GetParam());
          const auto inputShape = std::get<1>(GetParam());
          auto testValues = std::get<2>(GetParam());
  
          ngraph::Shape outputShape;
          if (inputShape.is_static()) {
              outputShape = inputShape.to_shape();
          } else {
              outputShape = ngraph::Shape({ 1, 8, 16, 16 });
          }
          outputShape[1] /= 4;
          outputShape[2] *= 2;
          outputShape[3] *= 2;
  
          bool channelsIsDynamic = inputShape.rank().is_dynamic() || inputShape[1].is_dynamic();
          const size_t inputChannels = channelsIsDynamic ? 8ul : static_cast<size_t>(inputShape[1].get_length());
  
          std::shared_ptr<Node> actualWeights = pass::low_precision::fold<opset1::Broadcast>(
                  testValues.actual.weights,
                  opset1::Constant::create(
                          element::i64,
                          Shape{ outputShape.size() },
                          Shape{ inputChannels, outputShape[1], 1, 1 }));
          if (!testValues.actual.fakeQuantizeOnWeights.empty()) {
              actualWeights = ngraph::builder::subgraph::ConvolutionBackpropDataFunction::getWeights(
                      outputShape,
                      netPrecision,
                      testValues.actual.fakeQuantizeOnWeights,
                      ov::as_type_ptr<opset1::Constant>(actualWeights));
          } else {
              actualWeights = ngraph::builder::subgraph::ConvolutionBackpropDataFunction::getWeights(
                      outputShape,
                      netPrecision,
                      testValues.actual.dequantizationOnWeights,
                      ov::as_type_ptr<opset1::Constant>(actualWeights));
          }
  
          actualFunction = ngraph::builder::subgraph::ConvolutionBackpropDataFunction::getOriginal(
                  testValues.actual.precisionBeforeDequantization,
                  netPrecision,
                  inputShape,
                  outputShape,
                  testValues.actual.dequantizationOnActivations,
                  actualWeights);
  
          SimpleLowPrecisionTransformer transform;
          transform.add<low_precision::ConvolutionBackpropDataTransformation, opset1::ConvolutionBackpropData>(testValues.params);
          transform.get_pass_config()->set_callback<low_precision::ConvolutionBackpropDataTransformation>(testValues.actual.callback);
          transform.transform(actualFunction);
  
          std::shared_ptr<Node> refWeights = low_precision::fold<opset1::Broadcast>(
                  testValues.expected.weights,
                  opset1::Constant::create(
                          element::i64,
                          Shape{ outputShape.size() },
                          Shape{ inputChannels, outputShape[1], 1, 1 }));
  
          if (!testValues.expected.transformed) {
              refWeights = ngraph::builder::subgraph::ConvolutionBackpropDataFunction::getWeights(
                  outputShape,
                  netPrecision,
                  testValues.actual.fakeQuantizeOnWeights,
                  ov::as_type_ptr<opset1::Constant>(refWeights));
          } else {
              refWeights = ngraph::builder::subgraph::ConvolutionBackpropDataFunction::getWeights(
                  outputShape,
                  netPrecision,
                  testValues.expected.dequantizationOnWeights,
                  ov::as_type_ptr<opset1::Constant>(refWeights));
          }
  
          referenceFunction = ngraph::builder::subgraph::ConvolutionBackpropDataFunction::getReference(
                  testValues.expected.precisionBeforeDequantization,
                  netPrecision,
                  inputShape,
                  outputShape,
                  testValues.expected.dequantizationOnActivations,
                  refWeights,
                  testValues.expected.dequantizationAfter);
      }
  
      static std::string getTestCaseName(testing::TestParamInfo<ConvolutionBackpropDataTransformationParams> obj) {
          const auto netPrecision = std::get<0>(obj.param);
          auto inputShape = std::get<1>(obj.param);
          ConvolutionBackpropDataTransformationTestValues testValues = std::get<2>(obj.param);
  
          std::ostringstream result;
          result << toString(testValues.params) << "_" <<
                 netPrecision << "_" <<
                 inputShape << "_" <<
                 testValues.actual.precisionBeforeDequantization << "_" <<
                 testValues.actual.dequantizationOnActivations << "_" <<
                 testValues.actual.dequantizationOnWeights << "_" <<
                 testValues.actual.fakeQuantizeOnWeights << "_" <<"_weights_" <<
                 testValues.actual.weights->get_element_type() << "_" << "{ " <<
                 testValues.actual.weights->cast_vector<float>()[0] << " }_";
          return result.str();
      }
  };
  
  TEST_P(ConvolutionBackpropDataTransformation, CompareFunctions) {
      actualFunction->validate_nodes_and_infer_types();
      auto res = compare_functions(referenceFunction, actualFunction, true, true, false);
      ASSERT_TRUE(res.first) << res.second;
  
      ASSERT_TRUE(LayerTransformation::allNamesAreUnique(actualFunction)) << "Not all names are unique";
  }
  
  const std::vector<element::Type> netPrecisions = {
      element::f32,
      element::f16
  };
  
  namespace testValues1 {
  const std::vector<ngraph::PartialShape> shapes = {
      { 1, 8, 16, 16 },
      { Dimension::dynamic(), 8, Dimension::dynamic(), Dimension::dynamic() }
  };
  
  const std::vector<ConvolutionBackpropDataTransformationTestValues> testValues = {
      // with zero point
      {
          LayerTransformation::createParamsU8I8(),
          // ActualValues
          {
              ngraph::element::u8,
              {{ngraph::element::f32}, { 128.f }, { 0.02f }},
              { 255ul, Shape({ 1, 1, 1, 1 }), { 0.f }, { 254.f }, { -1.27f }, { 1.27f } },
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f })
          },
          // ExpectedValues
          {
              ngraph::element::u8,
              {{}, { { 128.f }, ngraph::element::f32, {}, false }, {}},
              {},
              {{}, {}, {{ 0.0002f }, ngraph::element::f32, {}}},
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ -125.f }),
              true
          }
      },
      // with zero point
      {
          LayerTransformation::createParamsU8I8(),
          // ActualValues
          {
              ngraph::element::u8,
              {{ngraph::element::f32}, { 128.f }, { 0.02f }},
              { 255ul, Shape({}), { 0.f }, { 254.f }, { -1.27f }, { 1.27f } },
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f })
          },
          // ExpectedValues
          {
              ngraph::element::u8,
              {{}, { { 128.f }, ngraph::element::f32, {}, false }, {}},
              {},
              {{}, {}, {{ 0.0002f }, ngraph::element::f32, {}}},
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ -125.f }),
              true
          }
      },
      // updatePrecisions = false
      {
          LayerTransformation::createParamsU8I8().setUpdatePrecisions(false),
          // ActualValues
          {
              ngraph::element::u8,
              {{ngraph::element::f32}, { 128.f }, { 0.02f }},
              { 255ul, Shape({ 1, 1, 1, 1 }), { 0.f }, { 254.f }, { -1.27f }, { 1.27f } },
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f })
          },
          // ExpectedValues
          {
              ngraph::element::u8,
              {{}, { { 128.f }, ngraph::element::f32, {}, false }, {}},
              {},
              {{}, {}, {{ 0.0002f }, ngraph::element::f32, {}}},
              op::Constant::create(ngraph::element::f32, ngraph::Shape{}, std::vector<float>{ -125.f }),
              true
          }
      },
      // QDq version
      {
          LayerTransformation::createParamsU8I8(),
          // ActualValues
          {
              ngraph::element::u8,
              {{ngraph::element::f32}, { 128.f }, { 0.02f }},
              {{ngraph::element::f32}, { 2.f }, { 0.01f }},
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f })
          },
          // ExpectedValues
          {
              ngraph::element::u8,
              {{}, { { 128.f }, ngraph::element::f32, {}, false }, {}},
              {{}, { { 2.f }, ngraph::element::f32, {1, 2, 1, 1}, true, 1ul, element::i8, false, { "DISABLED_CONSTANT_FOLDING" }  }, {}},
              {{}, {}, {{ 0.0002f }, ngraph::element::f32, {}}},
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f }),
              true
          }
      },
      // without zero point
      {
          LayerTransformation::createParamsU8I8(),
          // ActualValues
          {
              ngraph::element::u8,
              {{ngraph::element::f32}, {}, { 0.02f }},
              { 255ul, Shape({ 1, 1, 1, 1 }), { 0.f }, { 254.f }, { -1.27f }, { 1.27f } },
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f })
          },
          // ExpectedValues
          {
              ngraph::element::u8,
              {},
              {},
              {{}, {}, {{ 0.0002f }, ngraph::element::f32, {}}},
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ -125.f }),
              true
          }
      },
      // without zero point
      {
          LayerTransformation::createParamsU8I8(),
          // ActualValues
          {
              ngraph::element::u8,
              {{ngraph::element::f32}, {}, { 0.02f }},
              { 255ul, Shape({}), { 0.f }, { 254.f }, { -1.27f }, { 1.27f } },
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f })
          },
          // ExpectedValues
          {
              ngraph::element::u8,
              {},
              {},
              {{}, {}, {{ 0.0002f }, ngraph::element::f32, {}}},
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ -125.f }),
              true
          }
      },
      // QDq version
      {
          LayerTransformation::createParamsU8I8(),
          // ActualValues
          {
              ngraph::element::u8,
              {{ngraph::element::f32}, {}, { 0.02f }},
              {{ngraph::element::f32}, {}, { 0.01f }},
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f })
          },
          // ExpectedValues
          {
              ngraph::element::u8,
              {},
              {},
              {{}, {}, {{ 0.0002f }, ngraph::element::f32, {}}},
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f }),
              true
          }
      },
      // per-channel dequantization with the same values
      {
          LayerTransformation::createParamsU8I8(),
          // ActualValues
          {
              ngraph::element::u8,
              {{ngraph::element::f32}, {}, { std::vector<float>{0.02f, 0.02f, 0.02f, 0.02f, 0.02f, 0.02f, 0.02f, 0.02f}  }},
              { 255ul, Shape({ 1, 1, 1, 1 }), { 0.f }, { 254.f }, { -1.27f }, { 1.27f } },
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f })
          },
          // ExpectedValues
          {
              ngraph::element::u8,
              {},
              {},
              {{}, {}, {{ 0.0002f }, ngraph::element::f32, {}}},
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ -125.f }),
              true
          }
      },
      // per-channel dequantization with different values
      {
          LayerTransformation::createParamsU8I8(),
          // ActualValues
          {
              ngraph::element::u8,
              {{ngraph::element::f32}, {}, { std::vector<float>{0.02f, 0.01f, 0.02f, 0.01f, 0.02f, 0.01f, 0.02f, 0.01f} }},
              { 255ul, Shape({ 1, 1, 1, 1 }), { 0.f }, { 254.f }, { -1.27f }, { 1.27f } },
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f })
          },
          // ExpectedValues
          {
              ngraph::element::u8,
              {{ngraph::element::f32}, {}, { std::vector<float>{0.02f, 0.01f, 0.02f, 0.01f, 0.02f, 0.01f, 0.02f, 0.01f} }},
              {},
              {},
              op::Constant::create(ngraph::element::f32, ngraph::Shape{}, std::vector<float>{ -1.25f }),
              true
          }
      },
      // per-channel dequantization on weights
      {
          LayerTransformation::createParamsU8I8(),
          // ActualValues
          {
              ngraph::element::u8,
              {{ngraph::element::f32}, {}, { 0.02f }},
              { 255ul, Shape({ 1, 2, 1, 1 }), { 0.f }, { 254.f }, { -1.27f }, { 1.27f } },
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f })
          },
          // ExpectedValues
          {
              ngraph::element::u8,
              {},
              {},
              {{}, {}, {{ 0.0002f }, ngraph::element::f32, {}}},
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ -125.f }),
              true
          }
      },
      // QDq version
      {
          LayerTransformation::createParamsU8I8(),
          // ActualValues
          {
              ngraph::element::u8,
              {{ngraph::element::f32}, {}, { 0.02f }},
              {{ngraph::element::f32}, {}, { std::vector<float>{0.01f, 0.01f} }},
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f })
          },
          // ExpectedValues
          {
              ngraph::element::u8,
              {},
              {},
              {{}, {}, {{ 0.0002f }, ngraph::element::f32, {}}},
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f }),
              true
          }
      },
      // issue #56886: unsupported per-batch dequantization on weights
      {
          LayerTransformation::createParamsU8I8(),
          // ActualValues
          {
              ngraph::element::u8,
              {{ngraph::element::f32}, {}, { 0.02f }},
              { 255ul, Shape({ 8, 1, 1, 1 }), { 0.f }, { 254.f }, { -1.27f }, { 1.27f } },
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f })
          },
          // ExpectedValues
          {
              ngraph::element::u8,
              {{ngraph::element::f32}, {}, { 0.02f }},
              {},
              {},
              op::Constant::create(ngraph::element::f32, ngraph::Shape{}, std::vector<float>{ -1.25f }),
              true
          }
      },
      // QDq version
      {
          LayerTransformation::createParamsU8I8(),
          // ActualValues
          {
              ngraph::element::u8,
              {{ngraph::element::f32}, {}, { 0.02f }},
              {{ngraph::element::f32}, {}, { std::vector<float>{0.01f}, ngraph::element::f32, {8, 1, 1, 1} }},
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f })
          },
          // ExpectedValues
          {
              ngraph::element::u8,
              {{ngraph::element::f32}, {}, { 0.02f }},
              {},
              {},
              op::Constant::create(ngraph::element::f32, ngraph::Shape{}, std::vector<float>{ 0.02f }),
              true
          }
      },
      //  issue #59593: subtract on activations, non-asymmetric
      {
          LayerTransformation::createParamsU8I8(),
          // ActualValues
          {
              ngraph::element::u8,
              {{ngraph::element::f32}, {128.f}, {0.01f}},
              { 255ul, Shape({ 1, 2, 1, 1 }), { 0.f }, { 254.f }, { 0.f }, { 25.4f } },
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f }),
              low_precision::LayerTransformation::isAsymmetricQuantization
          },
          // ExpectedValues
          {
              ngraph::element::u8,
              {{ngraph::element::f32}, {128.f}, {0.01f}},
              {},
              {},
              op::Constant::create(ngraph::element::f32, ngraph::Shape{}, std::vector<float>{ 2.f }),
              false // weights are not folded because of callback returning true
          }
      },
  };
  
  INSTANTIATE_TEST_SUITE_P(
      smoke_LPT,
      ConvolutionBackpropDataTransformation,
      ::testing::Combine(
      ::testing::ValuesIn(netPrecisions),
      ::testing::ValuesIn(shapes),
      ::testing::ValuesIn(testValues)),
      ConvolutionBackpropDataTransformation::getTestCaseName);
  } // namespace testValues1
  
  namespace testValues2 {
  const std::vector<ngraph::PartialShape> shapesWithDynamicChannels = {
      { Dimension::dynamic(), Dimension::dynamic(), Dimension::dynamic(), Dimension::dynamic() },
      PartialShape::dynamic()
  };
  
  const std::vector<ConvolutionBackpropDataTransformationTestValues> testValues = {
      {
          LayerTransformation::createParamsU8I8(),
          // ActualValues
          {
              ngraph::element::u8,
              {{ngraph::element::f32}, {}, { 0.02f }},
              {{ngraph::element::f32}, {}, { 0.01f }},
              op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f })
          },
          // ExpectedValues
          {
              ngraph::element::u8,
              {{ngraph::element::f32}, {}, { 0.02f }},
              {},
              {},
              op::Constant::create(ngraph::element::f32, ngraph::Shape{}, std::vector<float>{ 0.02f }),
              true
          }
      },
  };
  
  INSTANTIATE_TEST_SUITE_P(
      smoke_LPT,
      ConvolutionBackpropDataTransformation,
      ::testing::Combine(
      ::testing::ValuesIn(netPrecisions),
      ::testing::ValuesIn(shapesWithDynamicChannels),
      ::testing::ValuesIn(testValues)),
      ConvolutionBackpropDataTransformation::getTestCaseName);
  } // namespace testValues2
  } // namespace
  