| /* |
| * 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 "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/Fixture.h" |
| #include "tests/validation/Helpers.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 <random> |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| using namespace arm_compute::opencl::kernels; |
| |
| template <typename T, typename KernelType> |
| class MatMulKernelValidationFixture : public framework::Fixture |
| { |
| public: |
| template <typename...> |
| 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) |
| { |
| // 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()); |
| 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(); |
| |
| dst_q_info = calculate_mat_mul_dst_q_info(lhs_q_info, rhs_q_info, m, n, k, data_type); |
| } |
| |
| if(pretranspose_a) |
| { |
| permute(shape_a, PermutationVector(1U, 0U)); |
| } |
| |
| if(pretranspose_b) |
| { |
| permute(shape_b, PermutationVector(1U, 0U)); |
| } |
| |
| _device_supports_export_to_cl_image = image2d_from_buffer_supported(CLKernelLibrary::get().get_device()); |
| |
| if(!export_rhs_to_cl_image || _device_supports_export_to_cl_image) |
| { |
| _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, 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; |
| |
| // 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 dst = create_tensor<CLTensor>(output_shape, data_type, 1, dst_q_info); |
| |
| 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<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, 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)); |
| } |
| |
| // 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) |
| { |
| // 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, 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 }; |
| fill_constant(bias, static_cast<int32_t>(0)); |
| |
| 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 _device_supports_export_to_cl_image{ true }; |
| }; |
| |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
| #endif /* ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE */ |