tests/validation/fixtures/dynamic_fusion/gpu/cl/MatMulKernelFixture.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_MATMULKERNELFIXTURE_H
 #define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_MATMULKERNELFIXTURE_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/MatMulAttributes.h"
 #include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
 #include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuMatMul.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/Helpers.h"
 #include "tests/validation/reference/GEMM.h"
 #include "tests/validation/reference/Permute.h"
 #include "tests/validation/reference/ReshapeLayer.h"
 #include "tests/validation/Validation.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
 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class DynamicFusionGpuMatMulValidationGenericFixture : public framework::Fixture
 {
 public:
     void setup(TensorShape lhs_shape,
                TensorShape rhs_shape,
                TensorShape output_shape,
                bool        transpose_a,
                bool        transpose_b,
                int         M0,
                int         N0,
                int         K0,
                bool        export_rhs_to_cl_image,
                DataType    data_type)
     {
         //For brevity, the input shapes are assumed to be not-transposed for both a and b matrices.
         if (transpose_a)
         {
             permute(lhs_shape, PermutationVector(1U, 0U));
         }
         if (transpose_b)
         {
             permute(rhs_shape, PermutationVector(1U, 0U));
         }

         // Skip configurations unsupported by the device.
         _device_supports_export_to_cl_image = image2d_from_buffer_supported(CLKernelLibrary::get().get_device());
         if (!_device_supports_export_to_cl_image && export_rhs_to_cl_image)
         {
             ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped");
             framework::ARM_COMPUTE_PRINT_INFO();
             return; // Note: Also need to skip the validate in corresponding FIXTURE_DATA_TEST_CASEs.
         }

         _target    = compute_target(lhs_shape, rhs_shape, transpose_a, transpose_b, M0, N0, K0, export_rhs_to_cl_image,
                                     data_type);
         _reference = compute_reference(lhs_shape, rhs_shape, output_shape, transpose_a, transpose_b, data_type);
     }

 protected:
     TensorType compute_target(TensorShape &shape_a,
                               TensorShape &shape_b,
                               bool         transpose_a,
                               bool         transpose_b,
                               int          M0,
                               int          N0,
                               int          K0,
                               bool         export_rhs_to_cl_image,
                               DataType     data_type)
     {
         ARM_COMPUTE_UNUSED(export_rhs_to_cl_image);
         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
         ITensorInfo *lhs_info = context.create_tensor_info(TensorInfo(shape_a, 1, data_type));
         ITensorInfo *rhs_info = context.create_tensor_info(TensorInfo(shape_b, 1, data_type));
         ITensorInfo *dst_info = context.create_tensor_info();

         MatMulAttributes matmul_attr{};
         matmul_attr.adj_lhs(transpose_a);
         matmul_attr.adj_rhs(transpose_b);

         GpuMatMulSettings matmul_settings{};
         matmul_settings.m0(M0);
         matmul_settings.n0(N0);
         matmul_settings.k0(K0);

         ITensorInfo *ans_info = FunctionType::create_op(sketch, lhs_info, rhs_info, matmul_attr, matmul_settings);
         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_lhs{};
         TensorType t_rhs{};
         TensorType t_dst{};

         // Initialize user tensors
         t_lhs.allocator()->init(*lhs_info);
         t_rhs.allocator()->init(*rhs_info);
         t_dst.allocator()->init(*dst_info);

         ARM_COMPUTE_ASSERT(t_lhs.info()->is_resizable());
         ARM_COMPUTE_ASSERT(t_rhs.info()->is_resizable());
         ARM_COMPUTE_ASSERT(t_dst.info()->is_resizable());

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

         ARM_COMPUTE_ASSERT(!t_lhs.info()->is_resizable());
         ARM_COMPUTE_ASSERT(!t_rhs.info()->is_resizable());
         ARM_COMPUTE_ASSERT(!t_dst.info()->is_resizable());

         fill(AccessorType(t_lhs), 0);
         fill(AccessorType(t_rhs), 1);

         // Run runtime
         runtime.run({&t_lhs, &t_rhs, &t_dst});

         return t_dst;
     }

     SimpleTensor<T> compute_reference(const TensorShape &shape_a,
                                       const TensorShape &shape_b,
                                       const TensorShape &output_shape,
                                       bool               pretranspose_a,
                                       bool               pretranspose_b,
                                       DataType           data_type)
     {
         // We collapse dimensions > 3 onto dimension 3, i.e. 5D+ tensors will look like 4D
         // This is necessary unless we choose to extend gemm reference for 5D+ tensors
         TensorShape output_shape_collapsed = output_shape.collapsed_from(Window::DimZ);
         TensorShape shape_a_collapsed      = shape_a.collapsed_from(Window::DimZ);
         TensorShape shape_b_collapsed      = shape_b.collapsed_from(Window::DimZ);

         // Create reference
         SimpleTensor<T> a{shape_a_collapsed, data_type, 1};
         SimpleTensor<T> b{shape_b_collapsed, data_type, 1};
         SimpleTensor<T> c{output_shape_collapsed, data_type, 1};

         // Fill reference
         fill(a, 0);
         fill(b, 1);

         /* Note: Assuming the usual batch matmul dimensions A = (B x M x K), B = (B x K x N), if pretranspose_A is set to true, then A is assumed to be (B x K x M),
            therefore, A must be pre-transposed before passing it to the fixture. And, we transpose A again in the fixture to make it (B x M x K)
            in order to be able to call reference implementation that works with (B x M x K) input.
            Similarly, if pretranspose_B is set to true, then B is assumed to be (B x N x K), B must be pre-transposed before passing it to the fixture. */

         // Define transposed shapes
         TensorShape a_transposed_shape(a.shape());
         a_transposed_shape.set(0, a.shape().y());
         a_transposed_shape.set(1, a.shape().x());

         TensorShape b_transposed_shape(b.shape());
         b_transposed_shape.set(0, b.shape().y());
         b_transposed_shape.set(1, b.shape().x());

         // Define transposed tensors
         SimpleTensor<T> a_transposed{a_transposed_shape, data_type};
         SimpleTensor<T> b_transposed{b_transposed_shape, data_type};

         //pretranspose a if necessary
         if (pretranspose_a)
         {
             a_transposed = reference::permute<T>(a, PermutationVector(1U, 0U));
         }

         // pretranspose b if necessary
         if (pretranspose_b)
         {
             b_transposed = reference::permute<T>(b, PermutationVector(1U, 0U));
         }

         // Use transposed tensors if boolean enabled else use original tensors
         SimpleTensor<T> result =
             reference::gemm<T>((pretranspose_a) ? a_transposed : a, (pretranspose_b) ? b_transposed : b, c, 1.0f, 0.f);

         // We reshape the gemm output back if the tensor is high dimensional
         if (output_shape_collapsed != output_shape)
         {
             // std::cout << "called reshape: \n";
             result = reference::reshape_layer(result, output_shape);
         }

         return result;
     }

     CLTensor        _target{};
     SimpleTensor<T> _reference{};
     bool            _device_supports_export_to_cl_image{false};
     bool            _device_supports_mmul{false};
 };

 template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
 class DynamicFusionGpuMatMulValidationFixture
     : public DynamicFusionGpuMatMulValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
 {
 public:
     void setup(TensorShape lhs_shape,
                TensorShape rhs_shape,
                TensorShape output_shape,
                bool        transpose_a,
                bool        transpose_b,
                int         M0,
                int         N0,
                int         K0,
                bool        export_rhs_to_cl_image,
                DataType    data_type)
     {
         ARM_COMPUTE_UNUSED(export_rhs_to_cl_image);
         DynamicFusionGpuMatMulValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
             lhs_shape, rhs_shape, output_shape, transpose_a, transpose_b, M0, N0, K0,
             false /* export_rhs_to_cl_image bias */, data_type);
     }
 };

 } // namespace validation
 } // namespace test
 } // namespace arm_compute
 #endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_MATMULKERNELFIXTURE_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_MATMULKERNELFIXTURE_H
	#define ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_MATMULKERNELFIXTURE_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/MatMulAttributes.h"
	#include "arm_compute/dynamic_fusion/sketch/gpu/GpuWorkloadSketch.h"
	#include "arm_compute/dynamic_fusion/sketch/gpu/operators/GpuMatMul.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/Helpers.h"
	#include "tests/validation/reference/GEMM.h"
	#include "tests/validation/reference/Permute.h"
	#include "tests/validation/reference/ReshapeLayer.h"
	#include "tests/validation/Validation.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
	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class DynamicFusionGpuMatMulValidationGenericFixture : public framework::Fixture
	{
	public:
	void setup(TensorShape lhs_shape,
	TensorShape rhs_shape,
	TensorShape output_shape,
	bool transpose_a,
	bool transpose_b,
	int M0,
	int N0,
	int K0,
	bool export_rhs_to_cl_image,
	DataType data_type)
	{
	//For brevity, the input shapes are assumed to be not-transposed for both a and b matrices.
	if (transpose_a)
	{
	permute(lhs_shape, PermutationVector(1U, 0U));
	}
	if (transpose_b)
	{
	permute(rhs_shape, PermutationVector(1U, 0U));
	}

	// Skip configurations unsupported by the device.
	_device_supports_export_to_cl_image = image2d_from_buffer_supported(CLKernelLibrary::get().get_device());
	if (!_device_supports_export_to_cl_image && export_rhs_to_cl_image)
	{
	ARM_COMPUTE_TEST_INFO("cl_khr_image2d_from_buffer not supported. TEST skipped");
	framework::ARM_COMPUTE_PRINT_INFO();
	return; // Note: Also need to skip the validate in corresponding FIXTURE_DATA_TEST_CASEs.
	}

	_target = compute_target(lhs_shape, rhs_shape, transpose_a, transpose_b, M0, N0, K0, export_rhs_to_cl_image,
	data_type);
	_reference = compute_reference(lhs_shape, rhs_shape, output_shape, transpose_a, transpose_b, data_type);
	}

	protected:
	TensorType compute_target(TensorShape &shape_a,
	TensorShape &shape_b,
	bool transpose_a,
	bool transpose_b,
	int M0,
	int N0,
	int K0,
	bool export_rhs_to_cl_image,
	DataType data_type)
	{
	ARM_COMPUTE_UNUSED(export_rhs_to_cl_image);
	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
	ITensorInfo *lhs_info = context.create_tensor_info(TensorInfo(shape_a, 1, data_type));
	ITensorInfo *rhs_info = context.create_tensor_info(TensorInfo(shape_b, 1, data_type));
	ITensorInfo *dst_info = context.create_tensor_info();

	MatMulAttributes matmul_attr{};
	matmul_attr.adj_lhs(transpose_a);
	matmul_attr.adj_rhs(transpose_b);

	GpuMatMulSettings matmul_settings{};
	matmul_settings.m0(M0);
	matmul_settings.n0(N0);
	matmul_settings.k0(K0);

	ITensorInfo *ans_info = FunctionType::create_op(sketch, lhs_info, rhs_info, matmul_attr, matmul_settings);
	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_lhs{};
	TensorType t_rhs{};
	TensorType t_dst{};

	// Initialize user tensors
	t_lhs.allocator()->init(*lhs_info);
	t_rhs.allocator()->init(*rhs_info);
	t_dst.allocator()->init(*dst_info);

	ARM_COMPUTE_ASSERT(t_lhs.info()->is_resizable());
	ARM_COMPUTE_ASSERT(t_rhs.info()->is_resizable());
	ARM_COMPUTE_ASSERT(t_dst.info()->is_resizable());

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

	ARM_COMPUTE_ASSERT(!t_lhs.info()->is_resizable());
	ARM_COMPUTE_ASSERT(!t_rhs.info()->is_resizable());
	ARM_COMPUTE_ASSERT(!t_dst.info()->is_resizable());

	fill(AccessorType(t_lhs), 0);
	fill(AccessorType(t_rhs), 1);

	// Run runtime
	runtime.run({&t_lhs, &t_rhs, &t_dst});

	return t_dst;
	}

	SimpleTensor<T> compute_reference(const TensorShape &shape_a,
	const TensorShape &shape_b,
	const TensorShape &output_shape,
	bool pretranspose_a,
	bool pretranspose_b,
	DataType data_type)
	{
	// We collapse dimensions > 3 onto dimension 3, i.e. 5D+ tensors will look like 4D
	// This is necessary unless we choose to extend gemm reference for 5D+ tensors
	TensorShape output_shape_collapsed = output_shape.collapsed_from(Window::DimZ);
	TensorShape shape_a_collapsed = shape_a.collapsed_from(Window::DimZ);
	TensorShape shape_b_collapsed = shape_b.collapsed_from(Window::DimZ);

	// Create reference
	SimpleTensor<T> a{shape_a_collapsed, data_type, 1};
	SimpleTensor<T> b{shape_b_collapsed, data_type, 1};
	SimpleTensor<T> c{output_shape_collapsed, data_type, 1};

	// Fill reference
	fill(a, 0);
	fill(b, 1);

	/* Note: Assuming the usual batch matmul dimensions A = (B x M x K), B = (B x K x N), if pretranspose_A is set to true, then A is assumed to be (B x K x M),
	therefore, A must be pre-transposed before passing it to the fixture. And, we transpose A again in the fixture to make it (B x M x K)
	in order to be able to call reference implementation that works with (B x M x K) input.
	Similarly, if pretranspose_B is set to true, then B is assumed to be (B x N x K), B must be pre-transposed before passing it to the fixture. */

	// Define transposed shapes
	TensorShape a_transposed_shape(a.shape());
	a_transposed_shape.set(0, a.shape().y());
	a_transposed_shape.set(1, a.shape().x());

	TensorShape b_transposed_shape(b.shape());
	b_transposed_shape.set(0, b.shape().y());
	b_transposed_shape.set(1, b.shape().x());

	// Define transposed tensors
	SimpleTensor<T> a_transposed{a_transposed_shape, data_type};
	SimpleTensor<T> b_transposed{b_transposed_shape, data_type};

	//pretranspose a if necessary
	if (pretranspose_a)
	{
	a_transposed = reference::permute<T>(a, PermutationVector(1U, 0U));
	}

	// pretranspose b if necessary
	if (pretranspose_b)
	{
	b_transposed = reference::permute<T>(b, PermutationVector(1U, 0U));
	}

	// Use transposed tensors if boolean enabled else use original tensors
	SimpleTensor<T> result =
	reference::gemm<T>((pretranspose_a) ? a_transposed : a, (pretranspose_b) ? b_transposed : b, c, 1.0f, 0.f);

	// We reshape the gemm output back if the tensor is high dimensional
	if (output_shape_collapsed != output_shape)
	{
	// std::cout << "called reshape: \n";
	result = reference::reshape_layer(result, output_shape);
	}

	return result;
	}

	CLTensor _target{};
	SimpleTensor<T> _reference{};
	bool _device_supports_export_to_cl_image{false};
	bool _device_supports_mmul{false};
	};

	template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
	class DynamicFusionGpuMatMulValidationFixture
	: public DynamicFusionGpuMatMulValidationGenericFixture<TensorType, AccessorType, FunctionType, T>
	{
	public:
	void setup(TensorShape lhs_shape,
	TensorShape rhs_shape,
	TensorShape output_shape,
	bool transpose_a,
	bool transpose_b,
	int M0,
	int N0,
	int K0,
	bool export_rhs_to_cl_image,
	DataType data_type)
	{
	ARM_COMPUTE_UNUSED(export_rhs_to_cl_image);
	DynamicFusionGpuMatMulValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(
	lhs_shape, rhs_shape, output_shape, transpose_a, transpose_b, M0, N0, K0,
	false /* export_rhs_to_cl_image bias */, data_type);
	}
	};

	} // namespace validation
	} // namespace test
	} // namespace arm_compute
	#endif // ACL_TESTS_VALIDATION_FIXTURES_DYNAMIC_FUSION_GPU_CL_MATMULKERNELFIXTURE_H