tests/validation/fixtures/dynamic_fusion/gpu/cl/DirectConv2dFixture.h - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2022-2023 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to
  * deal in the Software without restriction, including without limitation the
  * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
  * sell copies of the Software, and to permit persons to whom the Software is
  * furnished to do so, subject to the following conditions:
  *
  * The above copyright notice and this permission notice shall be included in all
  * copies or substantial portions of the Software.
  *
  * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
 #ifndef TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DIRECTCONV2DFIXTURE
 #define TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DIRECTCONV2DFIXTURE

 #include "arm_compute/core/CL/CLKernelLibrary.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "arm_compute/core/Types.h"

 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h"
 #include "arm_compute/dynamic_fusion/sketch/attributes/Conv2dAttributes.h"
 #include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
 #include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuConv2d.h"
 #include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h"

 #include "tests/CL/CLAccessor.h"
 #include "tests/framework/Fixture.h"
 #include "tests/framework/Macros.h"
 #include "tests/validation/Validation.h"
 #include "tests/validation/reference/ConvolutionLayer.h"
 #include "tests/validation/reference/Permute.h"

 using namespace arm_compute::experimental::dynamic_fusion;

 namespace arm_compute
 {
 namespace test
 {
 namespace validation
 {
 namespace
 {
 template <typename U>
 void fill(U &&tensor, int i)
 {
     switch(tensor.data_type())
     {
         case DataType::F16:
         {
             arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f };
             library->fill(tensor, distribution, i);
             break;
         }
         case DataType::F32:
         {
             std::uniform_real_distribution<float> distribution(-1.0f, 1.0f);
             library->fill(tensor, distribution, i);
             break;
         }
         default:
             library->fill_tensor_uniform(tensor, i);
     }
 }

 } // namespace

 /** General Conv2d fixture
  *  Adapted from tests/validation/fixtures/ConvolutionLayerFixture.h
  *  TODO: Parameterize to be fully backend agnostic: COMPMID-5760; remove Gpu from name
  */
 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class DynamicFusionGpuConv2dValidationGenericFixture : public framework::Fixture
 {
 public:
     using TBias = typename std::conditional < std::is_same<typename std::decay<T>::type, uint8_t>::value
                   || std::is_same<typename std::decay<T>::type, int8_t>::value,
                   int32_t, T >::type; // If T: uint8_t or int8_t then TBias: int32_t, otherwise TBias: T

     template <typename...>
     void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, const PadStrideInfo &info, const Size2D &dilation, DataType data_type,
                DataLayout data_layout, QuantizationInfo quantization_info, QuantizationInfo weight_quantization_info)
     {
         ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC); // Dynamic fusion conv2d only supports NHWC layout
         const Conv2dAttributes conv2d_attr = convert_pad_stride_info_to_conv_attr(info, dilation);
         _data_type                         = data_type;
         _data_layout                       = data_layout;
         _is_quantized                      = is_data_type_quantized_asymmetric(data_type);
         _quantization_info                 = quantization_info;
         _weight_quantization_info          = weight_quantization_info;
         _bias_data_type                    = _is_quantized ? DataType::S32 : data_type;
         _target                            = compute_target(input_shape, weights_shape, bias_shape, conv2d_attr);
         _reference                         = compute_reference(input_shape, weights_shape, bias_shape, output_shape, conv2d_attr);
     }

 protected:
     // Given input is in nchw format
     TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape &bias_shape, Conv2dAttributes conv2d_attr)
     {
         ARM_COMPUTE_ERROR_ON(_data_layout != DataLayout::NHWC);
         permute(input_shape, PermutationVector(2U, 0U, 1U));
         permute(weights_shape, PermutationVector(2U, 0U, 1U));
         CLScheduler::get().default_reinit();

         // Create a new workload sketch
         auto              cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
         auto              context        = GpuWorkloadContext{ &cl_compile_ctx };
         GpuWorkloadSketch sketch{ &context };

         // Create sketch tensors
         TensorInfo input_info  = context.create_tensor_info(TensorInfo(input_shape, 1, _data_type, _data_layout));
         TensorInfo weight_info = context.create_tensor_info(TensorInfo(weights_shape, 1, _data_type, _data_layout));
         TensorInfo bias_info   = context.create_tensor_info(TensorInfo(bias_shape, 1, _data_type, _data_layout));
         TensorInfo dst_info    = context.create_tensor_info();

         ITensorInfo *ans_info = FunctionType::create_op(sketch, &input_info, &weight_info, &bias_info, conv2d_attr);
         GpuOutput::create_op(sketch, ans_info, &dst_info);

         // Configure runtime
         ClWorkloadRuntime runtime;
         runtime.configure(sketch);
         // (Important) Allocate auxiliary tensor memory if there are any
         for(auto &data : runtime.get_auxiliary_tensors())
         {
             CLTensor     *tensor      = std::get<0>(data);
             TensorInfo    info        = std::get<1>(data);
             AuxMemoryInfo aux_mem_req = std::get<2>(data);
             tensor->allocator()->init(info, aux_mem_req.alignment);
             tensor->allocator()->allocate(); // Use ACL allocated memory
         }
         // Construct user tensors
         TensorType t_input{};
         TensorType t_weight{};
         TensorType t_bias{};
         TensorType t_dst{};

         // Initialize user tensors
         t_input.allocator()->init(input_info);
         t_weight.allocator()->init(weight_info);
         t_bias.allocator()->init(bias_info);
         t_dst.allocator()->init(dst_info);

         // Allocate and fill user tensors
         t_input.allocator()->allocate();
         t_weight.allocator()->allocate();
         t_bias.allocator()->allocate();
         t_dst.allocator()->allocate();

         fill(AccessorType(t_input), 0);
         fill(AccessorType(t_weight), 1);
         fill(AccessorType(t_bias), 2);

         // Run runtime
         runtime.run({ &t_input, &t_weight, &t_bias, &t_dst });
         return t_dst;
     }

     SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape,
                                       const TensorShape &output_shape, Conv2dAttributes conv2d_attr)
     {
         // Create reference
         SimpleTensor<T>     src{ input_shape, _data_type, 1, _quantization_info };
         SimpleTensor<T>     weight{ weights_shape, _data_type, 1, _weight_quantization_info };
         SimpleTensor<TBias> bias{ bias_shape, _data_type, 1, _quantization_info };

         fill(src, 0);
         fill(weight, 1);
         fill(bias, 2);

         auto src_nchw          = src;
         auto weights_nchw      = weight;
         auto bias_nchw         = bias;
         auto output_shape_nchw = output_shape;

         PadStrideInfo legacy_pad_stride(conv2d_attr.stride().x(), conv2d_attr.stride().y(), conv2d_attr.pad().left, conv2d_attr.pad().right, conv2d_attr.pad().top, conv2d_attr.pad().bottom,
                                         DimensionRoundingType{});
         auto dst_nchw = reference::convolution_layer(src_nchw, weights_nchw, bias_nchw, output_shape_nchw, legacy_pad_stride, conv2d_attr.dilation());
         return dst_nchw;
     }

     TensorType       _target{};
     SimpleTensor<T>  _reference{};
     DataType         _data_type{};
     DataType         _bias_data_type{};
     DataLayout       _data_layout{};
     QuantizationInfo _quantization_info{};
     QuantizationInfo _weight_quantization_info{};
     bool             _is_quantized = false;
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class DynamicFusionGpuConv2dValidationFixture : public DynamicFusionGpuConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     template <typename...>
     void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape output_shape, TensorShape bias_shape,
                const PadStrideInfo &info, const Size2D &dialation, DataType data_type, DataLayout data_layout, QuantizationInfo quantization_info)
     {
         DynamicFusionGpuConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, output_shape, bias_shape, info, dialation,
                                                                                                          data_type, data_layout, quantization_info, quantization_info);
     }
 };

 /** Specific Conv2d method: Direct Conv2d fixture
  *  Adapted from tests/validation/fixtures/DirectConvolutionLayerFixture.h
  *  TODO: Parameterize to be fully backend agnostic: COMPMID-5760
  */
 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class DynamicFusionDirectConv2dValidationGenericFixture : public framework::Fixture
 {
 public:
     using TBias = typename std::conditional < std::is_same<T, uint8_t>::value || std::is_same<T, int8_t>::value, int32_t, T >::type;

     template <typename...>
     void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels,
                DataType data_type, QuantizationInfo quantization_info, DataLayout data_layout)
     {
         ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC); // Dynamic fusion conv2d only supports NHWC layout

         TensorShape         weights_shape(kernel_size, kernel_size, input_shape.z(), num_kernels);
         const TensorShape   bias_shape(num_kernels);
         const PadStrideInfo info(stride_x, stride_y, pad_x, pad_y, DimensionRoundingType::FLOOR);
         const DataType      bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;

         const Conv2dAttributes conv2d_attr = convert_pad_stride_info_to_conv_attr(info, { 1U, 1U } /* dilation */);

         TensorInfo input_info   = TensorInfo(input_shape, 1, data_type);
         TensorInfo weights_info = TensorInfo(weights_shape, 1, data_type);

         const TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_info, weights_info, info);

         _target    = compute_target(input_shape, weights_shape, bias_shape, output_shape, conv2d_attr, data_type, bias_data_type, quantization_info, data_layout);
         _reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, quantization_info);
     }

 protected:
     TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape &bias_shape, TensorShape output_shape, const Conv2dAttributes &conv2d_attr,
                               DataType data_type, DataType bias_data_type, QuantizationInfo quantization_info, const DataLayout &data_layout)
     {
         ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC);
         ARM_COMPUTE_UNUSED(quantization_info);
         // Dataset shapes are in NCHW layout
         permute(input_shape, PermutationVector(2U, 0U, 1U));
         permute(weights_shape, PermutationVector(2U, 0U, 1U));
         permute(output_shape, PermutationVector(2U, 0U, 1U));

         auto              cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
         auto              context        = GpuWorkloadContext{ &cl_compile_ctx };
         GpuWorkloadSketch sketch{ &context };

         // Create sketch tensors
         auto input_info  = context.create_tensor_info(TensorInfo(input_shape, 1, data_type, data_layout));
         auto weight_info = context.create_tensor_info(TensorInfo(weights_shape, 1, data_type, data_layout));
         auto bias_info   = context.create_tensor_info(TensorInfo(bias_shape, 1, bias_data_type, data_layout));
         auto dst_info    = context.create_tensor_info();

         ITensorInfo *ans_info = FunctionType::create_op(sketch, &input_info, &weight_info, &bias_info, conv2d_attr);
         GpuOutput::create_op(sketch, ans_info, &dst_info);

         // Configure runtime
         ClWorkloadRuntime runtime;
         runtime.configure(sketch);

         for(auto &data : runtime.get_auxiliary_tensors())
         {
             CLTensor     *tensor      = std::get<0>(data);
             TensorInfo    info        = std::get<1>(data);
             AuxMemoryInfo aux_mem_req = std::get<2>(data);
             tensor->allocator()->init(info, aux_mem_req.alignment);
             tensor->allocator()->allocate(); // Use ACL allocated memory
         }
         // Construct user tensors
         TensorType t_input{};
         TensorType t_weight{};
         TensorType t_bias{};
         TensorType t_dst{};

         // Initialize user tensors
         t_input.allocator()->init(input_info);
         t_weight.allocator()->init(weight_info);
         t_bias.allocator()->init(bias_info);
         t_dst.allocator()->init(dst_info);

         ARM_COMPUTE_ASSERT(t_input.info()->is_resizable());
         ARM_COMPUTE_ASSERT(t_weight.info()->is_resizable());
         ARM_COMPUTE_ASSERT(t_bias.info()->is_resizable());
         ARM_COMPUTE_ASSERT(t_dst.info()->is_resizable());

         // Allocate and fill user tensors
         t_input.allocator()->allocate();
         t_weight.allocator()->allocate();
         t_bias.allocator()->allocate();
         t_dst.allocator()->allocate();

         ARM_COMPUTE_ASSERT(!t_input.info()->is_resizable());
         ARM_COMPUTE_ASSERT(!t_weight.info()->is_resizable());
         ARM_COMPUTE_ASSERT(!t_bias.info()->is_resizable());
         ARM_COMPUTE_ASSERT(!t_dst.info()->is_resizable());

         fill(AccessorType(t_input), 0);
         fill(AccessorType(t_weight), 1);
         fill(AccessorType(t_bias), 2);

         // Run runtime
         runtime.run({ &t_input, &t_weight, &t_bias, &t_dst });
         return t_dst;
     }

     SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info,
                                       DataType data_type, DataType bias_data_type, QuantizationInfo quantization_info)
     {
         // Create reference
         SimpleTensor<T>     src{ input_shape, data_type, 1, quantization_info };
         SimpleTensor<T>     weights{ weights_shape, data_type, 1, quantization_info };
         SimpleTensor<TBias> bias{ bias_shape, bias_data_type, 1, quantization_info };

         // Fill reference
         fill(src, 0);
         fill(weights, 1);
         fill(bias, 2);

         SimpleTensor<T> dst = reference::convolution_layer<T>(src, weights, bias, output_shape, info);
         return dst;
     }
     TensorType      _target{};
     SimpleTensor<T> _reference{};
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class DynamicFusionDirectConv2dValidationFixture : public DynamicFusionDirectConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     template <typename...>
     void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type,
                DataLayout data_layout)
     {
         DynamicFusionDirectConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type,
                                                                                                             QuantizationInfo(),
                                                                                                             data_layout);
     }
 };

 } // namespace validation
 } // namespace test
 } // namespace arm_compute
 #endif /* TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DIRECTCONV2DFIXTURE */
	/*
	* Copyright (c) 2022-2023 Arm Limited.
	*
	* SPDX-License-Identifier: MIT
	*
	* Permission is hereby granted, free of charge, to any person obtaining a copy
	* of this software and associated documentation files (the "Software"), to
	* deal in the Software without restriction, including without limitation the
	* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
	* sell copies of the Software, and to permit persons to whom the Software is
	* furnished to do so, subject to the following conditions:
	*
	* The above copyright notice and this permission notice shall be included in all
	* copies or substantial portions of the Software.
	*
	* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
	* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
	* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
	* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
	* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
	* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
	* SOFTWARE.
	*/
	#ifndef TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DIRECTCONV2DFIXTURE
	#define TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DIRECTCONV2DFIXTURE

	#include "arm_compute/core/CL/CLKernelLibrary.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "arm_compute/core/Types.h"

	#include "arm_compute/core/utils/misc/ShapeCalculator.h"
	#include "arm_compute/dynamic_fusion/runtime/gpu/cl/ClWorkloadRuntime.h"
	#include "arm_compute/dynamic_fusion/sketch/attributes/Conv2dAttributes.h"
	#include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
	#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuConv2d.h"
	#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h"

	#include "tests/CL/CLAccessor.h"
	#include "tests/framework/Fixture.h"
	#include "tests/framework/Macros.h"
	#include "tests/validation/Validation.h"
	#include "tests/validation/reference/ConvolutionLayer.h"
	#include "tests/validation/reference/Permute.h"

	using namespace arm_compute::experimental::dynamic_fusion;

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	namespace
	{
	template <typename U>
	void fill(U &&tensor, int i)
	{
	switch(tensor.data_type())
	{
	case DataType::F16:
	{
	arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ -1.0f, 1.0f };
	library->fill(tensor, distribution, i);
	break;
	}
	case DataType::F32:
	{
	std::uniform_real_distribution<float> distribution(-1.0f, 1.0f);
	library->fill(tensor, distribution, i);
	break;
	}
	default:
	library->fill_tensor_uniform(tensor, i);
	}
	}

	} // namespace

	/** General Conv2d fixture
	* Adapted from tests/validation/fixtures/ConvolutionLayerFixture.h
	* TODO: Parameterize to be fully backend agnostic: COMPMID-5760; remove Gpu from name
	*/
	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class DynamicFusionGpuConv2dValidationGenericFixture : public framework::Fixture
	{
	public:
	using TBias = typename std::conditional < std::is_same<typename std::decay<T>::type, uint8_t>::value
	\|\| std::is_same<typename std::decay<T>::type, int8_t>::value,
	int32_t, T >::type; // If T: uint8_t or int8_t then TBias: int32_t, otherwise TBias: T

	template <typename...>
	void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, const PadStrideInfo &info, const Size2D &dilation, DataType data_type,
	DataLayout data_layout, QuantizationInfo quantization_info, QuantizationInfo weight_quantization_info)
	{
	ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC); // Dynamic fusion conv2d only supports NHWC layout
	const Conv2dAttributes conv2d_attr = convert_pad_stride_info_to_conv_attr(info, dilation);
	_data_type = data_type;
	_data_layout = data_layout;
	_is_quantized = is_data_type_quantized_asymmetric(data_type);
	_quantization_info = quantization_info;
	_weight_quantization_info = weight_quantization_info;
	_bias_data_type = _is_quantized ? DataType::S32 : data_type;
	_target = compute_target(input_shape, weights_shape, bias_shape, conv2d_attr);
	_reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, conv2d_attr);
	}

	protected:
	// Given input is in nchw format
	TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape &bias_shape, Conv2dAttributes conv2d_attr)
	{
	ARM_COMPUTE_ERROR_ON(_data_layout != DataLayout::NHWC);
	permute(input_shape, PermutationVector(2U, 0U, 1U));
	permute(weights_shape, PermutationVector(2U, 0U, 1U));
	CLScheduler::get().default_reinit();

	// Create a new workload sketch
	auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
	auto context = GpuWorkloadContext{ &cl_compile_ctx };
	GpuWorkloadSketch sketch{ &context };

	// Create sketch tensors
	TensorInfo input_info = context.create_tensor_info(TensorInfo(input_shape, 1, _data_type, _data_layout));
	TensorInfo weight_info = context.create_tensor_info(TensorInfo(weights_shape, 1, _data_type, _data_layout));
	TensorInfo bias_info = context.create_tensor_info(TensorInfo(bias_shape, 1, _data_type, _data_layout));
	TensorInfo dst_info = context.create_tensor_info();

	ITensorInfo *ans_info = FunctionType::create_op(sketch, &input_info, &weight_info, &bias_info, conv2d_attr);
	GpuOutput::create_op(sketch, ans_info, &dst_info);

	// Configure runtime
	ClWorkloadRuntime runtime;
	runtime.configure(sketch);
	// (Important) Allocate auxiliary tensor memory if there are any
	for(auto &data : runtime.get_auxiliary_tensors())
	{
	CLTensor *tensor = std::get<0>(data);
	TensorInfo info = std::get<1>(data);
	AuxMemoryInfo aux_mem_req = std::get<2>(data);
	tensor->allocator()->init(info, aux_mem_req.alignment);
	tensor->allocator()->allocate(); // Use ACL allocated memory
	}
	// Construct user tensors
	TensorType t_input{};
	TensorType t_weight{};
	TensorType t_bias{};
	TensorType t_dst{};

	// Initialize user tensors
	t_input.allocator()->init(input_info);
	t_weight.allocator()->init(weight_info);
	t_bias.allocator()->init(bias_info);
	t_dst.allocator()->init(dst_info);

	// Allocate and fill user tensors
	t_input.allocator()->allocate();
	t_weight.allocator()->allocate();
	t_bias.allocator()->allocate();
	t_dst.allocator()->allocate();

	fill(AccessorType(t_input), 0);
	fill(AccessorType(t_weight), 1);
	fill(AccessorType(t_bias), 2);

	// Run runtime
	runtime.run({ &t_input, &t_weight, &t_bias, &t_dst });
	return t_dst;
	}

	SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape,
	const TensorShape &output_shape, Conv2dAttributes conv2d_attr)
	{
	// Create reference
	SimpleTensor<T> src{ input_shape, _data_type, 1, _quantization_info };
	SimpleTensor<T> weight{ weights_shape, _data_type, 1, _weight_quantization_info };
	SimpleTensor<TBias> bias{ bias_shape, _data_type, 1, _quantization_info };

	fill(src, 0);
	fill(weight, 1);
	fill(bias, 2);

	auto src_nchw = src;
	auto weights_nchw = weight;
	auto bias_nchw = bias;
	auto output_shape_nchw = output_shape;

	PadStrideInfo legacy_pad_stride(conv2d_attr.stride().x(), conv2d_attr.stride().y(), conv2d_attr.pad().left, conv2d_attr.pad().right, conv2d_attr.pad().top, conv2d_attr.pad().bottom,
	DimensionRoundingType{});
	auto dst_nchw = reference::convolution_layer(src_nchw, weights_nchw, bias_nchw, output_shape_nchw, legacy_pad_stride, conv2d_attr.dilation());
	return dst_nchw;
	}

	TensorType _target{};
	SimpleTensor<T> _reference{};
	DataType _data_type{};
	DataType _bias_data_type{};
	DataLayout _data_layout{};
	QuantizationInfo _quantization_info{};
	QuantizationInfo _weight_quantization_info{};
	bool _is_quantized = false;
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class DynamicFusionGpuConv2dValidationFixture : public DynamicFusionGpuConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	template <typename...>
	void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape output_shape, TensorShape bias_shape,
	const PadStrideInfo &info, const Size2D &dialation, DataType data_type, DataLayout data_layout, QuantizationInfo quantization_info)
	{
	DynamicFusionGpuConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, output_shape, bias_shape, info, dialation,
	data_type, data_layout, quantization_info, quantization_info);
	}
	};

	/** Specific Conv2d method: Direct Conv2d fixture
	* Adapted from tests/validation/fixtures/DirectConvolutionLayerFixture.h
	* TODO: Parameterize to be fully backend agnostic: COMPMID-5760
	*/
	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class DynamicFusionDirectConv2dValidationGenericFixture : public framework::Fixture
	{
	public:
	using TBias = typename std::conditional < std::is_same<T, uint8_t>::value \|\| std::is_same<T, int8_t>::value, int32_t, T >::type;

	template <typename...>
	void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels,
	DataType data_type, QuantizationInfo quantization_info, DataLayout data_layout)
	{
	ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC); // Dynamic fusion conv2d only supports NHWC layout

	TensorShape weights_shape(kernel_size, kernel_size, input_shape.z(), num_kernels);
	const TensorShape bias_shape(num_kernels);
	const PadStrideInfo info(stride_x, stride_y, pad_x, pad_y, DimensionRoundingType::FLOOR);
	const DataType bias_data_type = is_data_type_quantized_asymmetric(data_type) ? DataType::S32 : data_type;

	const Conv2dAttributes conv2d_attr = convert_pad_stride_info_to_conv_attr(info, { 1U, 1U } /* dilation */);

	TensorInfo input_info = TensorInfo(input_shape, 1, data_type);
	TensorInfo weights_info = TensorInfo(weights_shape, 1, data_type);

	const TensorShape output_shape = misc::shape_calculator::compute_deep_convolution_shape(input_info, weights_info, info);

	_target = compute_target(input_shape, weights_shape, bias_shape, output_shape, conv2d_attr, data_type, bias_data_type, quantization_info, data_layout);
	_reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, data_type, bias_data_type, quantization_info);
	}

	protected:
	TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape &bias_shape, TensorShape output_shape, const Conv2dAttributes &conv2d_attr,
	DataType data_type, DataType bias_data_type, QuantizationInfo quantization_info, const DataLayout &data_layout)
	{
	ARM_COMPUTE_ERROR_ON(data_layout != DataLayout::NHWC);
	ARM_COMPUTE_UNUSED(quantization_info);
	// Dataset shapes are in NCHW layout
	permute(input_shape, PermutationVector(2U, 0U, 1U));
	permute(weights_shape, PermutationVector(2U, 0U, 1U));
	permute(output_shape, PermutationVector(2U, 0U, 1U));

	auto cl_compile_ctx = CLKernelLibrary::get().get_compile_context();
	auto context = GpuWorkloadContext{ &cl_compile_ctx };
	GpuWorkloadSketch sketch{ &context };

	// Create sketch tensors
	auto input_info = context.create_tensor_info(TensorInfo(input_shape, 1, data_type, data_layout));
	auto weight_info = context.create_tensor_info(TensorInfo(weights_shape, 1, data_type, data_layout));
	auto bias_info = context.create_tensor_info(TensorInfo(bias_shape, 1, bias_data_type, data_layout));
	auto dst_info = context.create_tensor_info();

	ITensorInfo *ans_info = FunctionType::create_op(sketch, &input_info, &weight_info, &bias_info, conv2d_attr);
	GpuOutput::create_op(sketch, ans_info, &dst_info);

	// Configure runtime
	ClWorkloadRuntime runtime;
	runtime.configure(sketch);

	for(auto &data : runtime.get_auxiliary_tensors())
	{
	CLTensor *tensor = std::get<0>(data);
	TensorInfo info = std::get<1>(data);
	AuxMemoryInfo aux_mem_req = std::get<2>(data);
	tensor->allocator()->init(info, aux_mem_req.alignment);
	tensor->allocator()->allocate(); // Use ACL allocated memory
	}
	// Construct user tensors
	TensorType t_input{};
	TensorType t_weight{};
	TensorType t_bias{};
	TensorType t_dst{};

	// Initialize user tensors
	t_input.allocator()->init(input_info);
	t_weight.allocator()->init(weight_info);
	t_bias.allocator()->init(bias_info);
	t_dst.allocator()->init(dst_info);

	ARM_COMPUTE_ASSERT(t_input.info()->is_resizable());
	ARM_COMPUTE_ASSERT(t_weight.info()->is_resizable());
	ARM_COMPUTE_ASSERT(t_bias.info()->is_resizable());
	ARM_COMPUTE_ASSERT(t_dst.info()->is_resizable());

	// Allocate and fill user tensors
	t_input.allocator()->allocate();
	t_weight.allocator()->allocate();
	t_bias.allocator()->allocate();
	t_dst.allocator()->allocate();

	ARM_COMPUTE_ASSERT(!t_input.info()->is_resizable());
	ARM_COMPUTE_ASSERT(!t_weight.info()->is_resizable());
	ARM_COMPUTE_ASSERT(!t_bias.info()->is_resizable());
	ARM_COMPUTE_ASSERT(!t_dst.info()->is_resizable());

	fill(AccessorType(t_input), 0);
	fill(AccessorType(t_weight), 1);
	fill(AccessorType(t_bias), 2);

	// Run runtime
	runtime.run({ &t_input, &t_weight, &t_bias, &t_dst });
	return t_dst;
	}

	SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info,
	DataType data_type, DataType bias_data_type, QuantizationInfo quantization_info)
	{
	// Create reference
	SimpleTensor<T> src{ input_shape, data_type, 1, quantization_info };
	SimpleTensor<T> weights{ weights_shape, data_type, 1, quantization_info };
	SimpleTensor<TBias> bias{ bias_shape, bias_data_type, 1, quantization_info };

	// Fill reference
	fill(src, 0);
	fill(weights, 1);
	fill(bias, 2);

	SimpleTensor<T> dst = reference::convolution_layer<T>(src, weights, bias, output_shape, info);
	return dst;
	}
	TensorType _target{};
	SimpleTensor<T> _reference{};
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class DynamicFusionDirectConv2dValidationFixture : public DynamicFusionDirectConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	template <typename...>
	void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type,
	DataLayout data_layout)
	{
	DynamicFusionDirectConv2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type,
	QuantizationInfo(),
	data_layout);
	}
	};

	} // namespace validation
	} // namespace test
	} // namespace arm_compute
	#endif /* TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_DIRECTCONV2DFIXTURE */