tests/validation/fixtures/BatchMatMulFixture.h - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 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 ACL_TESTS_VALIDATION_FIXTURES_BATCHMATMULFIXTURE
 #define ACL_TESTS_VALIDATION_FIXTURES_BATCHMATMULFIXTURE

 #include "arm_compute/core/KernelDescriptors.h"
 #include "src/gpu/cl/kernels/ClNativeMatMulKernel.h"
 #include "tests/CL/CLAccessor.h"
 #include "tests/CL/Helper.h"
 #include "tests/framework/Fixture.h"
 #include "tests/validation/reference/GEMM.h"
 #include "tests/validation/reference/Permute.h"
 #include "tests/validation/reference/ReshapeLayer.h"

 #include <random>

 namespace arm_compute
 {
 namespace test
 {
 namespace validation
 {
 using namespace arm_compute::opencl::kernels;

 template <typename T>
 class BatchMatMulValidationFixture : public framework::Fixture
 {
 public:
     template <typename...>
     void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, const int M0, const int N0, const int K0, DataType data_type)
     {
         // For brevity, the input shapes are assumed to be not-transposed for both Lhs and Rhs matrices.
         if(pretranspose_a)
         {
             permute(shape_a, PermutationVector(1U, 0U));
         }

         if(pretranspose_b)
         {
             permute(shape_b, PermutationVector(1U, 0U));
         }

         _target    = compute_target(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, data_type);
         _reference = compute_reference(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, data_type);
     }

 protected:
     template <typename U>
     void fill(U &&tensor, int i, float lo = -1.f, float hi = 1.f)
     {
         switch(tensor.data_type())
         {
             case DataType::F16:
             {
                 arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ float(lo), float(hi) };
                 library->fill(tensor, distribution, i);
                 break;
             }
             case DataType::F32:
             {
                 std::uniform_real_distribution<float> distribution(lo, hi);
                 library->fill(tensor, distribution, i);
                 break;
             }
             default:
                 library->fill_tensor_uniform(tensor, i);
         }
     }

     CLTensor compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &output_shape, bool pretranspose_a, bool pretranspose_b, const int M0, const int N0, const int K0,
                             DataType data_type)
     {
         // Create tensors
         CLTensor a   = create_tensor<CLTensor>(shape_a, data_type, 1);
         CLTensor b   = create_tensor<CLTensor>(shape_b, data_type, 1);
         CLTensor dst = create_tensor<CLTensor>(output_shape, data_type, 1);

         CLSynthetizeOperator<ClNativeMatMulKernel> batchMatMul{};
         MatMulKernelInfo                           matmul_info;
         matmul_info.adj_lhs = pretranspose_a;
         matmul_info.adj_rhs = pretranspose_b;
         matmul_info.m0      = M0;
         matmul_info.n0      = N0;
         matmul_info.k0      = K0;

         batchMatMul.configure(a.info(), b.info(), dst.info(), matmul_info);
         ARM_COMPUTE_ASSERT(a.info()->is_resizable());
         ARM_COMPUTE_ASSERT(b.info()->is_resizable());
         ARM_COMPUTE_ASSERT(dst.info()->is_resizable());

         // Allocate tensors
         a.allocator()->allocate();
         b.allocator()->allocate();
         dst.allocator()->allocate();

         ARM_COMPUTE_ASSERT(!a.info()->is_resizable());
         ARM_COMPUTE_ASSERT(!b.info()->is_resizable());
         ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());

         // Fill tensors
         fill(CLAccessor(a), 0);
         fill(CLAccessor(b), 1);

         // Compute batchMatMul kernel
         ITensorPack tensors_pack({ { ACL_SRC_0, &a },
             { ACL_SRC_1, &b },
             { ACL_DST, &dst }
         });
         batchMatMul.run(tensors_pack);

         return 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::DimW);
         TensorShape shape_a_collapsed      = shape_a.collapsed_from(Window::DimW);
         TensorShape shape_b_collapsed      = shape_b.collapsed_from(Window::DimW);

         // 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));
         }

         // Setting beta to 0 will effectively disable C for the
         // computation of the reference: alpha * A * B + 0 * C
         // 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)
         {
             result = reference::reshape_layer(result, output_shape);
         }

         return result;
     }

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

 } // namespace validation
 } // namespace test
 } // namespace arm_compute
 #endif /* ACL_TESTS_VALIDATION_FIXTURES_BATCHMATMULFIXTURE */
	/*
	* Copyright (c) 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 ACL_TESTS_VALIDATION_FIXTURES_BATCHMATMULFIXTURE
	#define ACL_TESTS_VALIDATION_FIXTURES_BATCHMATMULFIXTURE

	#include "arm_compute/core/KernelDescriptors.h"
	#include "src/gpu/cl/kernels/ClNativeMatMulKernel.h"
	#include "tests/CL/CLAccessor.h"
	#include "tests/CL/Helper.h"
	#include "tests/framework/Fixture.h"
	#include "tests/validation/reference/GEMM.h"
	#include "tests/validation/reference/Permute.h"
	#include "tests/validation/reference/ReshapeLayer.h"

	#include <random>

	namespace arm_compute
	{
	namespace test
	{
	namespace validation
	{
	using namespace arm_compute::opencl::kernels;

	template <typename T>
	class BatchMatMulValidationFixture : public framework::Fixture
	{
	public:
	template <typename...>
	void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, const int M0, const int N0, const int K0, DataType data_type)
	{
	// For brevity, the input shapes are assumed to be not-transposed for both Lhs and Rhs matrices.
	if(pretranspose_a)
	{
	permute(shape_a, PermutationVector(1U, 0U));
	}

	if(pretranspose_b)
	{
	permute(shape_b, PermutationVector(1U, 0U));
	}

	_target = compute_target(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, data_type);
	_reference = compute_reference(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, data_type);
	}

	protected:
	template <typename U>
	void fill(U &&tensor, int i, float lo = -1.f, float hi = 1.f)
	{
	switch(tensor.data_type())
	{
	case DataType::F16:
	{
	arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ float(lo), float(hi) };
	library->fill(tensor, distribution, i);
	break;
	}
	case DataType::F32:
	{
	std::uniform_real_distribution<float> distribution(lo, hi);
	library->fill(tensor, distribution, i);
	break;
	}
	default:
	library->fill_tensor_uniform(tensor, i);
	}
	}

	CLTensor compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &output_shape, bool pretranspose_a, bool pretranspose_b, const int M0, const int N0, const int K0,
	DataType data_type)
	{
	// Create tensors
	CLTensor a = create_tensor<CLTensor>(shape_a, data_type, 1);
	CLTensor b = create_tensor<CLTensor>(shape_b, data_type, 1);
	CLTensor dst = create_tensor<CLTensor>(output_shape, data_type, 1);

	CLSynthetizeOperator<ClNativeMatMulKernel> batchMatMul{};
	MatMulKernelInfo matmul_info;
	matmul_info.adj_lhs = pretranspose_a;
	matmul_info.adj_rhs = pretranspose_b;
	matmul_info.m0 = M0;
	matmul_info.n0 = N0;
	matmul_info.k0 = K0;

	batchMatMul.configure(a.info(), b.info(), dst.info(), matmul_info);
	ARM_COMPUTE_ASSERT(a.info()->is_resizable());
	ARM_COMPUTE_ASSERT(b.info()->is_resizable());
	ARM_COMPUTE_ASSERT(dst.info()->is_resizable());

	// Allocate tensors
	a.allocator()->allocate();
	b.allocator()->allocate();
	dst.allocator()->allocate();

	ARM_COMPUTE_ASSERT(!a.info()->is_resizable());
	ARM_COMPUTE_ASSERT(!b.info()->is_resizable());
	ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());

	// Fill tensors
	fill(CLAccessor(a), 0);
	fill(CLAccessor(b), 1);

	// Compute batchMatMul kernel
	ITensorPack tensors_pack({ { ACL_SRC_0, &a },
	{ ACL_SRC_1, &b },
	{ ACL_DST, &dst }
	});
	batchMatMul.run(tensors_pack);

	return 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::DimW);
	TensorShape shape_a_collapsed = shape_a.collapsed_from(Window::DimW);
	TensorShape shape_b_collapsed = shape_b.collapsed_from(Window::DimW);

	// 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));
	}

	// Setting beta to 0 will effectively disable C for the
	// computation of the reference: alpha * A * B + 0 * C
	// 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)
	{
	result = reference::reshape_layer(result, output_shape);
	}

	return result;
	}

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

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