// This file is part of Eigen, a lightweight C++ template library // for linear algebra. // // Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com> // // This Source Code Form is subject to the terms of the Mozilla // Public License v. 2.0. If a copy of the MPL was not distributed // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. #include "main.h" #include <Eigen/CXX11/Tensor>using Eigen::DefaultDevice;using Eigen::Tensor;typedef Tensor<float , 1>::DimensionPair DimPair;template <int DataLayout>static void test_evals()float , 2, DataLayout> mat1(2, 3);float , 2, DataLayout> mat2(2, 3);float , 2, DataLayout> mat3(3, 2);float , 2, DataLayout> mat4(3,3);typedef TensorEvaluator<decltype(mat1.contract(mat2, dims3)), DefaultDevice> Evaluator;float , 2, DataLayout> mat5(2,2);typedef TensorEvaluator<decltype(mat1.contract(mat2, dims4)), DefaultDevice> Evaluator2;2(0,2));2(1,2));2(0,2));2(1,2));float , 2, DataLayout> mat6(2,2);typedef TensorEvaluator<decltype(mat1.contract(mat3, dims6)), DefaultDevice> Evaluator3;3(2,0));3(2,1));3(2,0));3(2,1));template <int DataLayout>static void test_scalar()float , 1, DataLayout> vec1({6});float , 1, DataLayout> vec2({6});float , 0, DataLayout> scalar = vec1.contract(vec2, dims);float expected = 0.0f;for (int i = 0; i < 6; ++i) {template <int DataLayout>static void test_multidims()float , 3, DataLayout> mat1(2, 2, 2);float , 4, DataLayout> mat2(2, 2, 2, 2);float , 3, DataLayout> mat3(2, 2, 2);typedef TensorEvaluator<decltype(mat1.contract(mat2, dims)), DefaultDevice> Evaluator;++++++++float , 2, DataLayout> mat4(2, 2);float , 3, DataLayout> mat5(2, 2, 2);float , 1, DataLayout> mat6(2);typedef TensorEvaluator<decltype(mat4.contract(mat5, dims2)), DefaultDevice> Evaluator2;template <int DataLayout>static void test_holes() {float , 4, DataLayout> t1(2, 5, 7, 3);float , 5, DataLayout> t2(2, 7, 11, 13, 3);float , 5, DataLayout> result = t1.contract(t2, dims);for (int i = 0; i < 5; ++i) {for (int j = 0; j < 5; ++j) {for (int k = 0; k < 5; ++k) {for (int l = 0; l < 5; ++l) {for (int m = 0; m < 5; ++m) {template <int DataLayout>static void test_full_redux()float , 2, DataLayout> t1(2, 2);float , 3, DataLayout> t2(2, 2, 2);float , 1, DataLayout> result = t1.contract(t2, dims);template <int DataLayout>static void test_contraction_of_contraction()float , 2, DataLayout> t1(2, 2);float , 2, DataLayout> t2(2, 2);float , 2, DataLayout> t3(2, 2);float , 2, DataLayout> t4(2, 2);auto contract1 = t1.contract(t2, dims);auto diff = t3 - contract1;auto contract2 = t1.contract(t4, dims);float , 2, DataLayout> result = contract2.contract(diff, dims);float , Dynamic, Dynamic, DataLayout>>float , Dynamic, Dynamic, DataLayout>template <int DataLayout>static void test_expr()float , 2, DataLayout> mat1(2, 3);float , 2, DataLayout> mat2(3, 2);float , 2, DataLayout> mat3(2,2);2(2,0));2(2,1));2(2,0));2(2,1));template <int DataLayout>static void test_out_of_order_contraction()float , 3, DataLayout> mat1(2, 2, 2);float , 3, DataLayout> mat2(2, 2, 2);float , 2, DataLayout> mat3(2, 2);template <int DataLayout>static void test_consistency()// this does something like testing (A*B)^T = (B^T * A^T) float , 3, DataLayout> mat1(4, 3, 5);float , 5, DataLayout> mat2(3, 2, 1, 5, 4);float , 4, DataLayout> mat3(5, 2, 1, 5);float , 4, DataLayout> mat4(2, 1, 5, 5);// contract on dimensions of size 4 and 3 // check that these are equal except for ordering of dimensions if (DataLayout == ColMajor) {for (size_t i = 0; i < 5; i++) {for (size_t j = 0; j < 10; j++) {else {// Row major for (size_t i = 0; i < 5; i++) {for (size_t j = 0; j < 10; j++) {template <int DataLayout>static void test_large_contraction()float , 4, DataLayout> t_left(30, 50, 8, 31);float , 5, DataLayout> t_right(8, 31, 7, 20, 10);float , 5, DataLayout> t_result(30, 50, 7, 20, 10);// Add a little offset so that the results won't be close to zero. typedef Map<Eigen::Matrix<float , Dynamic, Dynamic, DataLayout>> MapXf;float , Dynamic, Dynamic, DataLayout> m_result(1500, 1400);// this contraction should be equivalent to a single matrix multiplication // compute results by separate methods for (int i = 0; i < t_result.dimensions().TotalSize(); i++) {template <int DataLayout>static void test_matrix_vector()float , 2, DataLayout> t_left(30, 50);float , 1, DataLayout> t_right(50);float , 1, DataLayout> t_result(30);typedef Map<Eigen::Matrix<float , Dynamic, Dynamic, DataLayout>> MapXf;float , Dynamic, Dynamic, DataLayout> m_result(30, 1);// this contraction should be equivalent to a single matrix multiplication // compute results by separate methods for (int i = 0; i < t_result.dimensions().TotalSize(); i++) {template <int DataLayout>static void test_tensor_vector()float , 3, DataLayout> t_left(7, 13, 17);float , 2, DataLayout> t_right(1, 7);typedef typename Tensor<float , 1, DataLayout>::DimensionPair DimensionPair;float , 3, DataLayout> t_result = t_left.contract(t_right, dim_pair01);typedef Map<Eigen::Matrix<float , Dynamic, Dynamic, DataLayout>> MapXf;float , Dynamic, Dynamic, DataLayout> m_result = m_left.transpose() * m_right.transpose();for (int i = 0; i < t_result.dimensions().TotalSize(); i++) {template <int DataLayout>static void test_small_blocking_factors()float , 4, DataLayout> t_left(30, 5, 3, 31);float , 5, DataLayout> t_right(3, 31, 7, 20, 1);// Add a little offset so that the results won't be close to zero. // Force the cache sizes, which results in smaller blocking factors. // this contraction should be equivalent to a single matrix multiplication float , 5, DataLayout> t_result;// compute result using a simple eigen matrix product float , Dynamic, Dynamic, DataLayout>> m_left(t_left.data(), 150, 93);float , Dynamic, Dynamic, DataLayout>> m_right(t_right.data(), 93, 140);float , Dynamic, Dynamic, DataLayout> m_result = m_left * m_right;for (int i = 0; i < t_result.dimensions().TotalSize(); i++) {template <int DataLayout>static void test_tensor_product()float , 2, DataLayout> mat1(2, 3);float , 2, DataLayout> mat2(4, 1);float , 4, DataLayout> result = mat1.contract(mat2, dims);for (int i = 0; i < result.dimension(0); ++i) {for (int j = 0; j < result.dimension(1); ++j) {for (int k = 0; k < result.dimension(2); ++k) {for (int l = 0; l < result.dimension(3); ++l) {template <int DataLayout>static void test_const_inputs()float , 2, DataLayout> in1(2, 3);float , 2, DataLayout> in2(3, 2);const float , 2, DataLayout> > mat1(in1.data(), 2, 3);const float , 2, DataLayout> > mat2(in2.data(), 3, 2);float , 2, DataLayout> mat3(2,2);2(2,0));2(2,1));2(2,0));2(2,1));// Apply Sqrt to all output elements. struct SqrtOutputKernel {template <typename Index, typename Scalar>void operator ()(const internal::blas_data_mapper<Scalar, Index, ColMajor>& output_mapper,const TensorContractionParams&, Index, Index, Index num_rows,const {for (int i = 0; i < num_rows; ++i) {for (int j = 0; j < num_cols; ++j) {template <int DataLayout>static void test_large_contraction_with_output_kernel() {float , 4, DataLayout> t_left(30, 50, 8, 31);float , 5, DataLayout> t_right(8, 31, 7, 20, 10);float , 5, DataLayout> t_result(30, 50, 7, 20, 10);// Put trash in mat4 to verify contraction clears output memory. // Add a little offset so that the results won't be close to zero. typedef Map<Eigen::Matrix<float , Dynamic, Dynamic, DataLayout>> MapXf;float , Dynamic, Dynamic, DataLayout> m_result(1500, 1400);// this contraction should be equivalent to a single matrix multiplication // compute results by separate methods for (std::ptrdiff_t i = 0; i < t_result.dimensions().TotalSize(); i++) {// Force CMake to split this test. // EIGEN_SUFFIXES;1;2;3;4;5;6;7;8 quality 80%
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