From 8918b23073851417e8be6e5e53c6380dbdedf201 Mon Sep 17 00:00:00 2001 From: Gunes Bayir Date: Fri, 17 Mar 2023 13:52:21 +0000 Subject: Implement OpenCL MatMul for Lhs T Rhs T/NT FP32/16 - Implement opencl kernel for LHS transposed and RHS non-transposed - Implement opencl kernel for LHS transposed and RHS transposed - Add validation tests Resolves: COMPMID-5953, COMPMID-5955 Change-Id: I55589acbffe86c44e29807574975978a1ec09bad Signed-off-by: Gunes Bayir Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/9345 Tested-by: Arm Jenkins Reviewed-by: Gian Marco Iodice Comments-Addressed: Arm Jenkins --- tests/validation/fixtures/BatchMatMulFixture.h | 203 ------------------------ tests/validation/fixtures/MatMulKernelFixture.h | 203 ++++++++++++++++++++++++ 2 files changed, 203 insertions(+), 203 deletions(-) delete mode 100644 tests/validation/fixtures/BatchMatMulFixture.h create mode 100644 tests/validation/fixtures/MatMulKernelFixture.h (limited to 'tests/validation/fixtures') diff --git a/tests/validation/fixtures/BatchMatMulFixture.h b/tests/validation/fixtures/BatchMatMulFixture.h deleted file mode 100644 index 9fb2dcc1b7..0000000000 --- a/tests/validation/fixtures/BatchMatMulFixture.h +++ /dev/null @@ -1,203 +0,0 @@ -/* - * 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 - -namespace arm_compute -{ -namespace test -{ -namespace validation -{ -using namespace arm_compute::opencl::kernels; - -template -class BatchMatMulValidationFixture : public framework::Fixture -{ -public: - template - 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 - 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 distribution{ float(lo), float(hi) }; - library->fill(tensor, distribution, i); - break; - } - case DataType::F32: - { - std::uniform_real_distribution 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(shape_a, data_type, 1); - CLTensor b = create_tensor(shape_b, data_type, 1); - CLTensor dst = create_tensor(output_shape, data_type, 1); - - CLSynthetizeOperator 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 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 a{ shape_a_collapsed, data_type, 1 }; - SimpleTensor b{ shape_b_collapsed, data_type, 1 }; - SimpleTensor 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 a_transposed{ a_transposed_shape, data_type }; - SimpleTensor b_transposed{ b_transposed_shape, data_type }; - - // pretranspose a if necessary - if(pretranspose_a) - { - a_transposed = reference::permute(a, PermutationVector(1U, 0U)); - } - - // pretranspose b if necessary - if(pretranspose_b) - { - b_transposed = reference::permute(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 result = reference::gemm((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 _reference{}; -}; - -} // namespace validation -} // namespace test -} // namespace arm_compute -#endif /* ACL_TESTS_VALIDATION_FIXTURES_BATCHMATMULFIXTURE */ diff --git a/tests/validation/fixtures/MatMulKernelFixture.h b/tests/validation/fixtures/MatMulKernelFixture.h new file mode 100644 index 0000000000..459564618f --- /dev/null +++ b/tests/validation/fixtures/MatMulKernelFixture.h @@ -0,0 +1,203 @@ +/* + * 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_MATMULKERNELFIXTURE +#define ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE + +#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 + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +using namespace arm_compute::opencl::kernels; + +template +class MatMulKernelValidationFixture : public framework::Fixture +{ +public: + template + 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 + 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 distribution{ float(lo), float(hi) }; + library->fill(tensor, distribution, i); + break; + } + case DataType::F32: + { + std::uniform_real_distribution 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(shape_a, data_type, 1); + CLTensor b = create_tensor(shape_b, data_type, 1); + CLTensor dst = create_tensor(output_shape, data_type, 1); + + CLSynthetizeOperator matMul{}; + 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; + + matMul.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 matMul kernel + ITensorPack tensors_pack({ { ACL_SRC_0, &a }, + { ACL_SRC_1, &b }, + { ACL_DST, &dst } + }); + matMul.run(tensors_pack); + + return dst; + } + + SimpleTensor 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 a{ shape_a_collapsed, data_type, 1 }; + SimpleTensor b{ shape_b_collapsed, data_type, 1 }; + SimpleTensor 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 a_transposed{ a_transposed_shape, data_type }; + SimpleTensor b_transposed{ b_transposed_shape, data_type }; + + // pretranspose a if necessary + if(pretranspose_a) + { + a_transposed = reference::permute(a, PermutationVector(1U, 0U)); + } + + // pretranspose b if necessary + if(pretranspose_b) + { + b_transposed = reference::permute(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 result = reference::gemm((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 _reference{}; +}; + +} // namespace validation +} // namespace test +} // namespace arm_compute +#endif /* ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE */ -- cgit v1.2.1