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Diffstat (limited to 'tests/validation/fixtures/MatMulKernelFixture.h')
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diff --git a/tests/validation/fixtures/MatMulKernelFixture.h b/tests/validation/fixtures/MatMulKernelFixture.h new file mode 100644 index 0000000000..26072dff65 --- /dev/null +++ b/tests/validation/fixtures/MatMulKernelFixture.h @@ -0,0 +1,390 @@ +/* + * 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_H +#define ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE_H + +#include "arm_compute/core/KernelDescriptors.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" + +#include "tests/CL/CLAccessor.h" +#include "tests/CL/Helper.h" +#include "tests/framework/Asserts.h" // Required for ARM_COMPUTE_ASSERT +#include "tests/framework/Fixture.h" +#include "tests/validation/Helpers.h" +#include "tests/validation/Validation.h" +#include "tests/validation/reference/GEMM.h" +#include "tests/validation/reference/GEMMLowp.h" +#include "tests/validation/reference/Permute.h" +#include "tests/validation/reference/ReshapeLayer.h" +#include <cmath> +#include <random> + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +using namespace arm_compute::opencl::kernels; + +template <typename T, typename KernelType, bool use_mmul = false> +class MatMulKernelGenericValidationFixture : public framework::Fixture +{ +public: + void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type, + bool enable_bias) + { + // This hash is used by random generators. There may be hash collisions but + // this is intentional as it's a very easy way to make the the current + // random generation process almost different for many test configurations, + // which were using the same set of values before. + _hash = M0 + N0 + K0 + shape_a[0] + shape_a[1] + shape_b[0] + shape_b[1] + enable_bias + export_rhs_to_cl_image; + + // Flag to create a bias + _enable_bias = enable_bias; + + // For brevity, the input shapes are assumed to be not-transposed for both Lhs and Rhs matrices. + QuantizationInfo lhs_q_info; + QuantizationInfo rhs_q_info; + QuantizationInfo dst_q_info; + + if(is_data_type_quantized(data_type)) + { + const int32_t t_max = static_cast<int32_t>(std::numeric_limits<T>::max()); + const int32_t t_min = static_cast<int32_t>(std::numeric_limits<T>::min()); + + std::mt19937 generator(library->seed() + _hash); + std::uniform_real_distribution<float> distribution_float(-5.0f, 3.0f); + std::uniform_int_distribution<int32_t> distribution_t(t_min, t_max); + + const float scale_lhs = pow(2, distribution_float(generator)); // [2^-5, 2^3] + const float scale_rhs = pow(2, distribution_float(generator)); // [2^-5, 2^3] + + const int32_t offset_lhs = distribution_t(generator); + const int32_t offset_rhs = distribution_t(generator); + + lhs_q_info = QuantizationInfo(scale_lhs, offset_lhs); + rhs_q_info = QuantizationInfo(scale_rhs, offset_rhs); + + const int m = shape_a.y(); + const int n = shape_b.x(); + const int k = shape_a.x(); + + const float bias_fraction = enable_bias ? 0.5f : 0.f; + + QuantizationHint q_hint = suggest_matmul_dst_q_info_and_bias(lhs_q_info, rhs_q_info, m, n, k, data_type, bias_fraction); + dst_q_info = q_hint.q_info; + _min_bias = q_hint.bias_min; + _max_bias = q_hint.bias_max; + } + + if(pretranspose_a) + { + permute(shape_a, PermutationVector(1U, 0U)); + } + + if(pretranspose_b) + { + permute(shape_b, 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. + } + + _device_supports_mmul = arm_matrix_multiply_supported(CLKernelLibrary::get().get_device()); + if(!_device_supports_mmul && use_mmul) + { + ARM_COMPUTE_TEST_INFO("cl_arm_matrix_multiply 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(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type, lhs_q_info, rhs_q_info, dst_q_info); + _reference = compute_reference(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, data_type, lhs_q_info, rhs_q_info, dst_q_info); + } + +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); + } + } + + template <typename U> + void fill_bias_s32(U &&tensor, int i, int32_t min, int32_t max) + { + std::uniform_int_distribution<int32_t> distribution(min, max); + library->fill(tensor, distribution, i); + } + + template <typename U, typename D> + void fill_constant(U &&tensor, D value) + { + library->fill_tensor_value(tensor, value); + } + + 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, + bool export_rhs_to_cl_image, DataType data_type, const QuantizationInfo &lhs_q_info, const QuantizationInfo &rhs_q_info, const QuantizationInfo &dst_q_info) + { + CLSynthetizeOperator<KernelType> 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_info.export_rhs_to_cl_image = export_rhs_to_cl_image; + + bool is_quantized = is_data_type_quantized(data_type); + + // Create tensors + CLTensor a = create_tensor<CLTensor>(shape_a, data_type, 1, lhs_q_info); + CLTensor b = create_tensor<CLTensor>(shape_b, data_type, 1, rhs_q_info); + CLTensor bias = create_tensor<CLTensor>(output_shape[0], (is_quantized) ? DataType::S32 : data_type, 1, dst_q_info); + CLTensor dst = create_tensor<CLTensor>(output_shape, data_type, 1, dst_q_info); + + matMul.configure(a.info(), b.info(), (_enable_bias) ? bias.info() : nullptr, 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), _hash + 1); + fill(CLAccessor(b), _hash + 2); + + // Compute matMul kernel + ITensorPack tensors_pack({ { ACL_SRC_0, &a }, + { ACL_SRC_1, &b }, + { ACL_DST, &dst } + }); + + if(_enable_bias) + { + // Allocate, fill and add bias to TensorPack obj + bias.allocator()->allocate(); + if(is_quantized) + { + fill_bias_s32(CLAccessor(bias), _hash + 3, _min_bias, _max_bias); + } + else + { + fill(CLAccessor(bias), _hash + 3); + } + tensors_pack.add_tensor(ACL_SRC_2, &bias); + } + + matMul.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, + const QuantizationInfo &lhs_q_info, const QuantizationInfo &rhs_q_info, const QuantizationInfo &dst_q_info) + { + // 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, lhs_q_info }; + SimpleTensor<T> b{ shape_b_collapsed, data_type, 1, rhs_q_info }; + SimpleTensor<T> c{ output_shape_collapsed, data_type, 1, dst_q_info }; + + // Fill reference + fill(a, _hash + 1); + fill(b, _hash + 2); + + /* 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 = gemm_reference<T>((pretranspose_a) ? a_transposed : a, (pretranspose_b) ? b_transposed : b, c); + + // 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; + } + + template <typename U = T> + typename std::enable_if < std::is_same<U, float>::value || std::is_same<U, half>::value, SimpleTensor<U >>::type gemm_reference(SimpleTensor<U> &a, SimpleTensor<U> &b, SimpleTensor<U> &c) + { + // Fill bias, then copy first dimension into subsequent dimensions to mimic broadcast + // of bias tensor from shape [dst.dimension(0)] to [dst.tensor_shape()] in target kernel + if(_enable_bias) + { + fill(c, _hash + 3); + const int n = c.shape().x(); + const int other_dims = c.shape().collapsed_from(1)[1]; + for(int i = 1; i < other_dims; ++i) // For all data, copy first n elements into remaining batches + { + memcpy(c.data() + i * n, c.data(), n * sizeof(T)); + } + } + // Setting beta to 0 will effectively disable C for the + // computation of the reference: alpha * A * B + 0 * C + return reference::gemm<U>(a, b, c, 1.0f, (_enable_bias) ? 1.0f : 0.f); + } + + template <typename U = T> + typename std::enable_if < std::is_same<U, int8_t>::value || std::is_same<U, uint8_t>::value, SimpleTensor<U >>::type gemm_reference(SimpleTensor<U> &a, SimpleTensor<U> &b, SimpleTensor<U> &c) + { + const UniformQuantizationInfo aq = a.quantization_info().uniform(); + const UniformQuantizationInfo bq = b.quantization_info().uniform(); + const UniformQuantizationInfo cq = c.quantization_info().uniform(); + + const SimpleTensor<int32_t> result = reference::gemmlowp_matrix_multiply_core<int32_t, U, U>(a, b, c.shape(), -aq.offset, -bq.offset); + + std::vector<int32_t> gemmlowp_multipliers{ 1 }; + std::vector<int32_t> gemmlowp_shifts{ 1 }; + const int gemmlowp_offset = cq.offset; + const float scale = aq.scale * bq.scale / cq.scale; + + quantization::calculate_quantized_multiplier(scale, &gemmlowp_multipliers[0], &gemmlowp_shifts[0]); + constexpr int32_t gemmlowp_min_bound = std::numeric_limits<int32_t>::min(); + constexpr int32_t gemmlowp_max_bound = std::numeric_limits<int32_t>::max(); + + SimpleTensor<int> bias{ c.shape(), DataType::S32 }; + if(_enable_bias) + { + // Identical to float implementation, fill and copy values of bias first dimension + fill_bias_s32(bias, _hash + 3, _min_bias, _max_bias); + const int n = bias.shape().x(); + const int other_dims = bias.shape().collapsed_from(1)[1]; + const unsigned int dt_size = sizeof(int32_t); + for(int i = 1; i < other_dims; ++i) + { + memcpy(bias.data() + i * n, bias.data(), n * dt_size); + } + } + else + { + fill_constant(bias, static_cast<int32_t>(0)); // effectively disable bias + } + + const SimpleTensor<U> final_result = reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, U>(result, bias, + gemmlowp_multipliers, gemmlowp_shifts, gemmlowp_offset, gemmlowp_min_bound, gemmlowp_max_bound); + + return final_result; + } + + CLTensor _target{}; + SimpleTensor<T> _reference{}; + bool _enable_bias{ false }; + bool _device_supports_export_to_cl_image{ true }; + bool _device_supports_mmul{ true }; + int32_t _min_bias{ 0 }; + int32_t _max_bias{ 0 }; + int32_t _hash{ 0 }; +}; + +template <typename T, typename KernelType, bool use_mmul = false> +class MatMulKernelValidationFixture : public MatMulKernelGenericValidationFixture<T, KernelType, use_mmul> +{ +public: + void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type) + { + MatMulKernelGenericValidationFixture<T, KernelType, use_mmul>::setup(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type, + false /* enable bias */); + } +}; + +template <typename T, typename KernelType, bool use_mmul = false> +class MatMulKernelWithBiasValidation : public MatMulKernelGenericValidationFixture<T, KernelType, use_mmul> +{ +public: + void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type) + { + MatMulKernelGenericValidationFixture<T, KernelType, use_mmul>::setup(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type, + true /* enable bias */); + } +}; +} // namespace validation +} // namespace test +} // namespace arm_compute +#endif // ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE_H |