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diff --git a/tests/validation/fixtures/MatMulFixture.h b/tests/validation/fixtures/MatMulFixture.h new file mode 100644 index 0000000000..ffd12e56d0 --- /dev/null +++ b/tests/validation/fixtures/MatMulFixture.h @@ -0,0 +1,612 @@ +/* + * Copyright (c) 2023-2024 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_MATMULFIXTURE_H +#define ACL_TESTS_VALIDATION_FIXTURES_MATMULFIXTURE_H + +#include "arm_compute/core/Types.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/utils/quantization/AsymmHelpers.h" + +#include "src/core/utils/quantization/AsymmHelpers.h" +#include "tests/framework/Asserts.h" // Required for ARM_COMPUTE_ASSERT +#include "tests/framework/Fixture.h" +#include "tests/validation/reference/ActivationLayer.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 "tests/validation/Validation.h" + +#include <limits> +#include <random> +#include <type_traits> + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +template <typename TensorType, typename AccessorType, typename FunctionType, typename Settings, typename T> +class MatMulGenericValidationFixture : public framework::Fixture +{ +public: + void setup(TensorShape shape_a, + TensorShape shape_b, + TensorShape output_shape, + bool transpose_a, + bool transpose_b, + DataType data_type, + ActivationLayerInfo act_info, + int num_extra_runs, + Settings settings, + QuantizationInfo a_qinfo = QuantizationInfo(), + QuantizationInfo b_qinfo = QuantizationInfo(), + QuantizationInfo o_qinfo = QuantizationInfo()) + { + // For brevity, the input shapes are assumed to be not-transposed for both a and b matrices. + if (transpose_a) + { + permute(shape_a, PermutationVector(1U, 0U)); + } + if (transpose_b) + { + permute(shape_b, PermutationVector(1U, 0U)); + } + + _target = compute_target(shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, + num_extra_runs, settings, a_qinfo, b_qinfo, o_qinfo); + _reference = compute_reference(shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, + a_qinfo, b_qinfo, o_qinfo); + } + +protected: + template <typename U> + void fill(U &&tensor, int i, float lo = -1.f, float hi = 1.f) + { + switch (tensor.data_type()) + { + case DataType::BFLOAT16: + { + arm_compute::utils::uniform_real_distribution_16bit<bfloat16> distribution{float(lo), float(hi)}; + library->fill(tensor, distribution, i); + break; + } + 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; + } + case DataType::QASYMM8: + case DataType::QASYMM8_SIGNED: + { + library->fill_tensor_uniform(tensor, i); + break; + } + default: + { + ARM_COMPUTE_ERROR("Unsupported data type."); + } + } + } + + virtual TensorType compute_target(const TensorShape &shape_a, + const TensorShape &shape_b, + const TensorShape &output_shape, + bool transpose_a, + bool transpose_b, + DataType data_type, + ActivationLayerInfo act_info, + int num_extra_runs, + const Settings &settings, + QuantizationInfo a_qinfo, + QuantizationInfo b_qinfo, + QuantizationInfo o_qinfo) + { + // 1. Create Classes and configure function + // ---------------------------------------------------- + // Create tensors + // Configure relevant classes and matmul function + TensorType a = create_tensor<TensorType>(shape_a, data_type, 1, a_qinfo); + TensorType b = create_tensor<TensorType>(shape_b, data_type, 1, b_qinfo); + TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, o_qinfo); + + FunctionType matmul; + + // Configure MatMulInfo class + MatMulInfo mm_info; + mm_info.adj_lhs(transpose_a).adj_rhs(transpose_b); + + // Ensure values are dynamic + a.info()->set_are_values_constant(false); + b.info()->set_are_values_constant(false); + + // Configure operator + matmul.configure(&a, &b, &dst, mm_info, settings, act_info); + + // Assertions + 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()); + + // For multiple runs. + for (int i = 0; i < num_extra_runs; i++) + { + // Stress dynamic tensors by running multiple times. + // -------------------------------------------------------- + // Fill tensors with new seed + // Run function + const int seed_offset = num_extra_runs * 100; + fill(AccessorType(a), seed_offset); + fill(AccessorType(b), seed_offset + 1); + + matmul.run(); + } + + // 2. Final Run for reference comparison + // -------------------------------------------------------- + // Re-fill tensors same seed as reference run + // Compute MatMul operation + fill(AccessorType(a), 2); + fill(AccessorType(b), 3); + + matmul.run(); + + return dst; + } + + template <typename TT> + typename std::enable_if < !std::is_integral<TT>::value, SimpleTensor<TT >>::type + compute_reference_gemm(const SimpleTensor<TT> &a, + const SimpleTensor<TT> &b, + const SimpleTensor<TT> &c, + float alpha, + float beta, + const QuantizationInfo &o_qinfo) + { + ARM_COMPUTE_UNUSED(o_qinfo); + + return reference::gemm(a, b, c, alpha, beta); + } + + template <typename TT> + typename std::enable_if<std::is_integral<TT>::value, SimpleTensor<TT>>::type + compute_reference_gemm(const SimpleTensor<TT> &a, + const SimpleTensor<TT> &b, + const SimpleTensor<TT> &c, + float alpha, + float beta, + const QuantizationInfo &o_qinfo) + { + ARM_COMPUTE_UNUSED(alpha, beta); + + const auto aq = a.quantization_info().uniform(); + const auto bq = b.quantization_info().uniform(); + const auto oq = o_qinfo.uniform(); + + const auto multiplier = aq.scale * bq.scale / oq.scale; + + int32_t output_multiplier = 0; + int32_t output_shift = 0; + quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift); + std::vector<int32_t> output_multipliers{output_multiplier}; + std::vector<int32_t> output_shifts{output_shift}; + + //The lhs and rhs offsets are negated here to keep the reference aligned with the function implementation where the lhs and rhs offsets are also negated. + const auto tmp = reference::gemmlowp_matrix_multiply_core<int32_t>(a, b, c.shape(), -aq.offset, -bq.offset); + + auto output = reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, TT>( + tmp, output_multipliers, output_shifts, oq.offset, std::numeric_limits<int32_t>::lowest(), + std::numeric_limits<int32_t>::max()); + output.quantization_info(o_qinfo); + + return output; + } + + SimpleTensor<T> compute_reference(const TensorShape &a_shape, + const TensorShape &b_shape, + const TensorShape &output_shape, + bool transpose_a, + bool transpose_b, + DataType data_type, + ActivationLayerInfo act_info, + QuantizationInfo a_qinfo, + QuantizationInfo b_qinfo, + QuantizationInfo o_qinfo) + { + // We collapse dimensions > 2 onto dimension 2, i.e. 4D+ tensors will look like 3D + // This is necessary unless we choose to extend gemm reference for 4D+ tensors + TensorShape output_shape_collapsed = output_shape.collapsed_from(Window::DimZ); + TensorShape a_shape_collapsed = a_shape.collapsed_from(Window::DimZ); + TensorShape b_shape_collapsed = b_shape.collapsed_from(Window::DimZ); + + // Create reference + SimpleTensor<T> a{a_shape_collapsed, data_type, 1, a_qinfo}; + SimpleTensor<T> b{b_shape_collapsed, data_type, 1, b_qinfo}; + SimpleTensor<T> c{output_shape_collapsed, data_type, 1}; + + // Fill reference + fill(a, 2); + fill(b, 3); + + /* Note: Assuming the usual batch matmul dimensions A = (B x M x K), B = (B x K x N), if transpose_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 transpose_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 (transpose_a) + { + a_transposed = reference::permute<T>(a, PermutationVector(1U, 0U)); + } + // pretranspose b if necessary + if (transpose_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 + auto result = compute_reference_gemm<T>((transpose_a) ? a_transposed : a, (transpose_b) ? b_transposed : b, c, + 1.0f, 0.f, o_qinfo); + + result = reference::activation_layer<T>(result, act_info, o_qinfo); + + // 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; + } + + TensorType _target{}; + SimpleTensor<T> _reference{}; +}; + +/// TODO: (ONCPUML-1451) The current state of this fixture is interim and a longer-term testing method will be implemented later. +/// @note: Currently we support only a 2x2 test due to the lack of reorder ref. implementation. +template <typename TensorType, typename AccessorType, typename FunctionType, typename Settings, typename T> +class MatMulFixedFormatFixture + : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T> +{ +public: + TensorType compute_target(const TensorShape &shape_a, + const TensorShape &shape_b, + const TensorShape &output_shape, + bool transpose_a, + bool transpose_b, + DataType data_type, + ActivationLayerInfo act_info, + int num_extra_runs, + const Settings &settings, + QuantizationInfo a_qinfo, + QuantizationInfo b_qinfo, + QuantizationInfo o_qinfo) override + { + // 1. Create Classes and configure function + // ---------------------------------------------------- + // Create tensors + // Configure relevant classes and matmul function + TensorType a = create_tensor<TensorType>(shape_a, data_type, 1, a_qinfo); + TensorType b = create_tensor<TensorType>(shape_b, data_type, 1, b_qinfo); + TensorType dst = create_tensor<TensorType>(output_shape, data_type, 1, o_qinfo); + + const auto weight_tensor_info = TensorInfo(*b.info()); + const TensorInfo new_tensor_info = prepare_weights(weight_tensor_info); + TensorType weights_transformed = create_tensor<TensorType>(new_tensor_info); + + // Configure MatMulInfo class + MatMulInfo mm_info; + mm_info.adj_lhs(transpose_a).adj_rhs(transpose_b); + + // Ensure values are dynamic + a.info()->set_are_values_constant(false); + b.info()->set_are_values_constant(false); + weights_transformed.info()->set_are_values_constant(false); + + FunctionType matmul; + + // Configure operator + matmul.configure(&a, &weights_transformed, &dst, mm_info, settings, act_info); + + // Assertions + ARM_COMPUTE_ASSERT(a.info()->is_resizable()); + ARM_COMPUTE_ASSERT(b.info()->is_resizable()); + ARM_COMPUTE_ASSERT(dst.info()->is_resizable()); + ARM_COMPUTE_ASSERT(weights_transformed.info()->is_resizable()); + + // Allocate tensors + a.allocator()->allocate(); + b.allocator()->allocate(); + dst.allocator()->allocate(); + weights_transformed.allocator()->allocate(); + + ARM_COMPUTE_ASSERT(!a.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!b.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!dst.info()->is_resizable()); + ARM_COMPUTE_ASSERT(!weights_transformed.info()->is_resizable()); + + // For multiple runs. + for (int i = 0; i < num_extra_runs; i++) + { + // Stress dynamic tensors by running multiple times. + // -------------------------------------------------------- + // Fill tensors with new seed + // Run function + const int seed_offset = num_extra_runs * 100; + this->fill(AccessorType(a), seed_offset); + this->fill(AccessorType(b), seed_offset + 1); + + matmul.run(); + } + + // 2. Final Run for reference comparison + // -------------------------------------------------------- + // Re-fill tensors same seed as reference run + // Compute MatMul operation + this->fill(AccessorType(a), 2); + this->fill(AccessorType(b), 3); + + rearrange_data(AccessorType(b), AccessorType(weights_transformed)); + + matmul.run(); + + return dst; + } + + void setup(TensorShape shape_a, + TensorShape shape_b, + TensorShape output_shape, + bool transpose_a, + bool transpose_b, + DataType data_type, + ActivationLayerInfo act_info, + int num_extra_runs, + Settings settings, + QuantizationInfo a_qinfo, + QuantizationInfo b_qinfo, + QuantizationInfo o_qinfo) + { + if (CPUInfo::get().has_bf16()) + { + MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup( + shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, num_extra_runs, settings, + a_qinfo, b_qinfo, o_qinfo); + } + } + +private: + TensorInfo prepare_weights(const TensorInfo tensor_info) + { + const DataLayout data_layout = tensor_info.data_layout(); + ARM_COMPUTE_EXPECT(data_layout == DataLayout::NCHW, framework::LogLevel::ERRORS); + const DataType data_type = tensor_info.data_type(); + const TensorShape tensor_shape = tensor_info.tensor_shape(); + const int H = tensor_shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT)]; + const int W = tensor_shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH)]; + ARM_COMPUTE_EXPECT(H <= 2 && W <= 2, framework::LogLevel::ERRORS); + + arm_compute::Strides strides_in_bytes = tensor_info.strides_in_bytes(); + strides_in_bytes.set(1, 32); + strides_in_bytes.set(2, 32); + + const size_t offset_first_element_in_bytes = tensor_info.offset_first_element_in_bytes(); + const size_t total_size_in_bytes = 32; + + const TensorShape TS(H, W); + + TensorInfo new_tensor_info = tensor_info; + new_tensor_info.init(TS, tensor_info.num_channels(), data_type, strides_in_bytes, offset_first_element_in_bytes, + total_size_in_bytes); + + return new_tensor_info; + } + + void rearrange_data(const AccessorType src, AccessorType dst) + { + const TensorShape src_tensor_shape = src.shape(); + const DataLayout data_layout = src.data_layout(); + ARM_COMPUTE_EXPECT(data_layout == DataLayout::NCHW, framework::LogLevel::ERRORS); + const unsigned int O = + src_tensor_shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES)]; // N=O + const unsigned int H = + src_tensor_shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT)]; + const unsigned int W = + src_tensor_shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH)]; + const unsigned int I = + src_tensor_shape[get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL)]; // C=I + ARM_COMPUTE_EXPECT(H <= 2 && W <= 2, framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(I == 1 && O == 1, framework::LogLevel::ERRORS); + ARM_COMPUTE_EXPECT(src.num_elements() <= dst.num_elements(), framework::LogLevel::ERRORS); + + const T *src_ptr = reinterpret_cast<const T *>(src.data()); + T *dst_ptr = reinterpret_cast<T *>(dst.data()); + + // rearrange indexes for 2x2 input and weight + int dst_idx[] = {0, 4, 1, 5}; + for (int i = 0; i < 4; i++) + { + dst_ptr[dst_idx[i]] = src_ptr[i]; + } + } +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename Settings, typename T> +class MatMulValidationFixture + : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T> +{ +public: + void setup(TensorShape shape_a, + TensorShape shape_b, + TensorShape output_shape, + bool transpose_a, + bool transpose_b, + DataType data_type) + { + MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup( + shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, ActivationLayerInfo(), 0, Settings()); + } +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename Settings, typename T> +class MatMulValidationWithDynamicTensorsFixture + : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T> +{ +public: + void setup(TensorShape shape_a, + TensorShape shape_b, + TensorShape output_shape, + bool transpose_a, + bool transpose_b, + DataType data_type, + ActivationLayerInfo act_info, + int num_extra_runs) + { + MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup( + shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, num_extra_runs, Settings()); + } +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename Settings, typename T> +class QuantizedMatMulValidationFixture + : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T> +{ +public: + void setup(TensorShape shape_a, + TensorShape shape_b, + TensorShape output_shape, + bool transpose_a, + bool transpose_b, + DataType data_type, + ActivationLayerInfo act_info, + int num_extra_runs, + QuantizationInfo a_qinfo, + QuantizationInfo b_qinfo, + QuantizationInfo o_qinfo) + { + MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup( + shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, num_extra_runs, Settings(), + a_qinfo, b_qinfo, o_qinfo); + } +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename Settings, typename T> +class MatMulValidationWithActivationFixture + : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T> +{ +public: + void setup(TensorShape shape_a, + TensorShape shape_b, + TensorShape output_shape, + bool transpose_a, + bool transpose_b, + DataType data_type, + ActivationLayerInfo act_info) + { + MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup( + shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, 0, Settings()); + } +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename Settings, typename T> +class MatMulValidationWithActivationAlphaBetaFixture + : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T> +{ +public: + void setup(TensorShape shape_a, + TensorShape shape_b, + TensorShape output_shape, + bool transpose_a, + bool transpose_b, + DataType data_type, + ActivationLayerInfo::ActivationFunction function, + float alpha_beta) + { + ActivationLayerInfo act_info(function, alpha_beta, alpha_beta); + MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup( + shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, 0, Settings()); + } +}; + +template <typename TensorType, typename AccessorType, typename FunctionType, typename Settings, typename T> +class QuantizedMatMulValidationWithActivationFixture + : public MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T> +{ +public: + void setup(TensorShape shape_a, + TensorShape shape_b, + TensorShape output_shape, + bool transpose_a, + bool transpose_b, + DataType data_type, + ActivationLayerInfo::ActivationFunction function, + float alpha_beta, + int num_extra_runs, + QuantizationInfo a_qinfo, + QuantizationInfo b_qinfo, + QuantizationInfo o_qinfo) + { + ActivationLayerInfo act_info(function, alpha_beta, alpha_beta); + MatMulGenericValidationFixture<TensorType, AccessorType, FunctionType, Settings, T>::setup( + shape_a, shape_b, output_shape, transpose_a, transpose_b, data_type, act_info, num_extra_runs, Settings(), + a_qinfo, b_qinfo, o_qinfo); + } +}; + +} // namespace validation +} // namespace test +} // namespace arm_compute +#endif // ACL_TESTS_VALIDATION_FIXTURES_MATMULFIXTURE_H |