tests/validation/fixtures/dynamic_fusion/gpu/cl/Pool2dFixture.h - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2023-2024 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 ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_POOL2DFIXTURE_H
 #define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_POOL2DFIXTURE_H

 #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/Pool2dAttributes.h"
 #include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
 #include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h"
 #include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuPool2d.h"

 #include "src/dynamic_fusion/utils/Utils.h"
 #include "tests/CL/CLAccessor.h"
 #include "tests/framework/Fixture.h"
 #include "tests/validation/reference/PoolingLayer.h"

 using namespace arm_compute::experimental::dynamic_fusion;

 namespace arm_compute
 {
 namespace test
 {
 namespace validation
 {
 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class DynamicFusionGpuPool2dValidationGenericFixture : public framework::Fixture
 {
 public:
     void setup(TensorShape input_shape, const Pool2dAttributes &pool_attr, DataType data_type, bool mixed_precision)
     {
         _target = compute_target(input_shape, pool_attr, data_type, mixed_precision);
         _reference =
             compute_reference(input_shape, convert_pool_attr_to_pool_info(pool_attr, mixed_precision), data_type);
     }

 protected:
     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);
         }
     }

     // Given input is in nchw format
     TensorType compute_target(TensorShape             input_shape,
                               const Pool2dAttributes &pool_attr,
                               const DataType          data_type,
                               bool                    mixed_precision)
     {
         CLScheduler::get().default_reinit();

         // Change shape due to NHWC data layout, test shapes are NCHW
         permute(input_shape, PermutationVector(2U, 0U, 1U));

         // 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
         auto input_info = context.create_tensor_info(TensorInfo(input_shape, 1, data_type, DataLayout::NHWC));
         auto dst_info   = context.create_tensor_info();

         // Create Pool2dSettings
         GpuPool2dSettings pool_settings = GpuPool2dSettings().mixed_precision(mixed_precision);

         ITensorInfo *ans_info = FunctionType::create_op(sketch, input_info, pool_attr, pool_settings);
         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_dst{};

         // Initialize user tensors
         t_input.allocator()->init(*input_info);
         t_dst.allocator()->init(*dst_info);

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

         fill(AccessorType(t_input), 0);

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

     SimpleTensor<T> compute_reference(TensorShape shape, PoolingLayerInfo pool_info, DataType data_type)
     {
         // Create reference
         SimpleTensor<T> src(shape, data_type, 1, QuantizationInfo());
         // Fill reference
         fill(src, 0);
         return reference::pooling_layer<T>(src, pool_info, QuantizationInfo(), nullptr, DataLayout::NCHW);
     }

     TensorType      _target{};
     SimpleTensor<T> _reference{};
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class DynamicFusionGpuPool2dValidationFixture
     : public DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     void setup(TensorShape input_shape,
                PoolingType pool_type,
                Size2D      pool_size,
                Padding2D   pad,
                Size2D      stride,
                bool        exclude_padding,
                DataType    data_type)
     {
         DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
             input_shape,
             Pool2dAttributes().pool_type(pool_type).pool_size(pool_size).pad(pad).stride(stride).exclude_padding(
                 exclude_padding),
             data_type, false);
     }
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class DynamicFusionGpuPool2dMixedPrecisionValidationFixture
     : public DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     void setup(TensorShape input_shape,
                PoolingType pool_type,
                Size2D      pool_size,
                Padding2D   pad,
                Size2D      stride,
                bool        exclude_padding,
                DataType    data_type,
                bool        mixed_precision)
     {
         DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
             input_shape,
             Pool2dAttributes().pool_type(pool_type).pool_size(pool_size).pad(pad).stride(stride).exclude_padding(
                 exclude_padding),
             data_type, mixed_precision);
     }
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class DynamicFusionGpuPool2dSpecialValidationFixture
     : public DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     void setup(TensorShape input_shape, Pool2dAttributes pool_attr, DataType data_type)
     {
         DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
             input_shape, pool_attr, data_type, false);
     }
 };

 } // namespace validation
 } // namespace test
 } // namespace arm_compute

 #endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_POOL2DFIXTURE_H
	/*
	* Copyright (c) 2023-2024 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 ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_POOL2DFIXTURE_H
	#define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_POOL2DFIXTURE_H

	#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/Pool2dAttributes.h"
	#include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
	#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuOutput.h"
	#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuPool2d.h"

	#include "src/dynamic_fusion/utils/Utils.h"
	#include "tests/CL/CLAccessor.h"
	#include "tests/framework/Fixture.h"
	#include "tests/validation/reference/PoolingLayer.h"

	using namespace arm_compute::experimental::dynamic_fusion;

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class DynamicFusionGpuPool2dValidationGenericFixture : public framework::Fixture
	{
	public:
	void setup(TensorShape input_shape, const Pool2dAttributes &pool_attr, DataType data_type, bool mixed_precision)
	{
	_target = compute_target(input_shape, pool_attr, data_type, mixed_precision);
	_reference =
	compute_reference(input_shape, convert_pool_attr_to_pool_info(pool_attr, mixed_precision), data_type);
	}

	protected:
	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);
	}
	}

	// Given input is in nchw format
	TensorType compute_target(TensorShape input_shape,
	const Pool2dAttributes &pool_attr,
	const DataType data_type,
	bool mixed_precision)
	{
	CLScheduler::get().default_reinit();

	// Change shape due to NHWC data layout, test shapes are NCHW
	permute(input_shape, PermutationVector(2U, 0U, 1U));

	// 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
	auto input_info = context.create_tensor_info(TensorInfo(input_shape, 1, data_type, DataLayout::NHWC));
	auto dst_info = context.create_tensor_info();

	// Create Pool2dSettings
	GpuPool2dSettings pool_settings = GpuPool2dSettings().mixed_precision(mixed_precision);

	ITensorInfo *ans_info = FunctionType::create_op(sketch, input_info, pool_attr, pool_settings);
	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_dst{};

	// Initialize user tensors
	t_input.allocator()->init(*input_info);
	t_dst.allocator()->init(*dst_info);

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

	fill(AccessorType(t_input), 0);

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

	SimpleTensor<T> compute_reference(TensorShape shape, PoolingLayerInfo pool_info, DataType data_type)
	{
	// Create reference
	SimpleTensor<T> src(shape, data_type, 1, QuantizationInfo());
	// Fill reference
	fill(src, 0);
	return reference::pooling_layer<T>(src, pool_info, QuantizationInfo(), nullptr, DataLayout::NCHW);
	}

	TensorType _target{};
	SimpleTensor<T> _reference{};
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class DynamicFusionGpuPool2dValidationFixture
	: public DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	void setup(TensorShape input_shape,
	PoolingType pool_type,
	Size2D pool_size,
	Padding2D pad,
	Size2D stride,
	bool exclude_padding,
	DataType data_type)
	{
	DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
	input_shape,
	Pool2dAttributes().pool_type(pool_type).pool_size(pool_size).pad(pad).stride(stride).exclude_padding(
	exclude_padding),
	data_type, false);
	}
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class DynamicFusionGpuPool2dMixedPrecisionValidationFixture
	: public DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	void setup(TensorShape input_shape,
	PoolingType pool_type,
	Size2D pool_size,
	Padding2D pad,
	Size2D stride,
	bool exclude_padding,
	DataType data_type,
	bool mixed_precision)
	{
	DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
	input_shape,
	Pool2dAttributes().pool_type(pool_type).pool_size(pool_size).pad(pad).stride(stride).exclude_padding(
	exclude_padding),
	data_type, mixed_precision);
	}
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class DynamicFusionGpuPool2dSpecialValidationFixture
	: public DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	void setup(TensorShape input_shape, Pool2dAttributes pool_attr, DataType data_type)
	{
	DynamicFusionGpuPool2dValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
	input_shape, pool_attr, data_type, false);
	}
	};

	} // namespace validation
	} // namespace test
	} // namespace arm_compute

	#endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_POOL2DFIXTURE_H