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Diffstat (limited to 'tests/validation/fixtures/BatchMatMulFixture.h')
| -rw-r--r-- | tests/validation/fixtures/BatchMatMulFixture.h | 203 |
1 files changed, 203 insertions, 0 deletions
diff --git a/tests/validation/fixtures/BatchMatMulFixture.h b/tests/validation/fixtures/BatchMatMulFixture.h new file mode 100644 index 0000000000..9fb2dcc1b7 --- /dev/null +++ b/tests/validation/fixtures/BatchMatMulFixture.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_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 */ |