From fde45d836cf753a94915ac42d8a13da7edc52221 Mon Sep 17 00:00:00 2001 From: Adnan AlSinan Date: Tue, 24 Oct 2023 12:03:21 +0100 Subject: Extend CKW MatMul with nt_t - Add the kernel variant: (nt_t) to GpuCKWMatMul. - Extend CKW MatMul validation test with nt_t. - Fixes a bug in CKW where z-dim = 1. Resolves: COMPMID-6435 Signed-off-by: Adnan AlSinan Change-Id: I4c5e8791e55f21ffff3c11eca7802c51a4259977 Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10525 Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins Reviewed-by: Gian Marco Iodice Benchmark: Arm Jenkins --- .../dynamic_fusion/gpu/cl/MatMulKernelFixture.h | 267 +++++++++++++++++++++ 1 file changed, 267 insertions(+) create mode 100644 tests/validation/fixtures/dynamic_fusion/gpu/cl/MatMulKernelFixture.h (limited to 'tests/validation/fixtures/dynamic_fusion/gpu') diff --git a/tests/validation/fixtures/dynamic_fusion/gpu/cl/MatMulKernelFixture.h b/tests/validation/fixtures/dynamic_fusion/gpu/cl/MatMulKernelFixture.h new file mode 100644 index 0000000000..c6ac4b91db --- /dev/null +++ b/tests/validation/fixtures/dynamic_fusion/gpu/cl/MatMulKernelFixture.h @@ -0,0 +1,267 @@ +/* + * 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_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/Validation.h" +#include "tests/validation/reference/GEMM.h" +#include "tests/validation/reference/Permute.h" +#include "tests/validation/reference/ReshapeLayer.h" + +using namespace arm_compute::experimental::dynamic_fusion; + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace +{ +template +void fill(U &&tensor, int i) +{ + switch(tensor.data_type()) + { + case DataType::F16: + { + arm_compute::utils::uniform_real_distribution_16bit distribution{ -1.0f, 1.0f }; + library->fill(tensor, distribution, i); + break; + } + case DataType::F32: + { + std::uniform_real_distribution distribution(-1.0f, 1.0f); + library->fill(tensor, distribution, i); + break; + } + default: + library->fill_tensor_uniform(tensor, i); + } +} + +} // namespace +template +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 + TensorInfo lhs_info = context.create_tensor_info(TensorInfo(shape_a, 1, data_type)); + TensorInfo rhs_info = context.create_tensor_info(TensorInfo(shape_b, 1, data_type)); + TensorInfo 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 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 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)); + } + + // 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) + { + // std::cout << "called reshape: \n"; + result = reference::reshape_layer(result, output_shape); + } + + return result; + } + + CLTensor _target{}; + SimpleTensor _reference{}; + bool _device_supports_export_to_cl_image{ false }; + bool _device_supports_mmul{ false }; +}; + +template +class DynamicFusionGpuMatMulValidationFixture : public DynamicFusionGpuMatMulValidationGenericFixture +{ + 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::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 -- cgit v1.2.1