// 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() { Tensor<float, 2, DataLayout> mat1(2, 3); Tensor<float, 2, DataLayout> mat2(2, 3); Tensor<float, 2, DataLayout> mat3(3, 2); mat1.setRandom(); mat2.setRandom(); mat3.setRandom(); Tensor<float, 2, DataLayout> mat4(3,3); mat4.setZero(); Eigen::array<DimPair, 1> dims3 = {{DimPair(0, 0)}}; typedef TensorEvaluator<decltype(mat1.contract(mat2, dims3)), DefaultDevice> Evaluator; Evaluator eval(mat1.contract(mat2, dims3), DefaultDevice()); eval.evalTo(mat4.data()); EIGEN_STATIC_ASSERT(Evaluator::NumDims==2ul, YOU_MADE_A_PROGRAMMING_MISTAKE); VERIFY_IS_EQUAL(eval.dimensions()[0], 3); VERIFY_IS_EQUAL(eval.dimensions()[1], 3); VERIFY_IS_APPROX(mat4(0,0), mat1(0,0)*mat2(0,0) + mat1(1,0)*mat2(1,0)); VERIFY_IS_APPROX(mat4(0,1), mat1(0,0)*mat2(0,1) + mat1(1,0)*mat2(1,1)); VERIFY_IS_APPROX(mat4(0,2), mat1(0,0)*mat2(0,2) + mat1(1,0)*mat2(1,2)); VERIFY_IS_APPROX(mat4(1,0), mat1(0,1)*mat2(0,0) + mat1(1,1)*mat2(1,0)); VERIFY_IS_APPROX(mat4(1,1), mat1(0,1)*mat2(0,1) + mat1(1,1)*mat2(1,1)); VERIFY_IS_APPROX(mat4(1,2), mat1(0,1)*mat2(0,2) + mat1(1,1)*mat2(1,2)); VERIFY_IS_APPROX(mat4(2,0), mat1(0,2)*mat2(0,0) + mat1(1,2)*mat2(1,0)); VERIFY_IS_APPROX(mat4(2,1), mat1(0,2)*mat2(0,1) + mat1(1,2)*mat2(1,1)); VERIFY_IS_APPROX(mat4(2,2), mat1(0,2)*mat2(0,2) + mat1(1,2)*mat2(1,2)); Tensor<float, 2, DataLayout> mat5(2,2); mat5.setZero(); Eigen::array<DimPair, 1> dims4 = {{DimPair(1, 1)}}; typedef TensorEvaluator<decltype(mat1.contract(mat2, dims4)), DefaultDevice> Evaluator2; Evaluator2 eval2(mat1.contract(mat2, dims4), DefaultDevice()); eval2.evalTo(mat5.data()); EIGEN_STATIC_ASSERT(Evaluator2::NumDims==2ul, YOU_MADE_A_PROGRAMMING_MISTAKE); VERIFY_IS_EQUAL(eval2.dimensions()[0], 2); VERIFY_IS_EQUAL(eval2.dimensions()[1], 2); VERIFY_IS_APPROX(mat5(0,0), mat1(0,0)*mat2(0,0) + mat1(0,1)*mat2(0,1) + mat1(0,2)*mat2(0,2)); VERIFY_IS_APPROX(mat5(0,1), mat1(0,0)*mat2(1,0) + mat1(0,1)*mat2(1,1) + mat1(0,2)*mat2(1,2)); VERIFY_IS_APPROX(mat5(1,0), mat1(1,0)*mat2(0,0) + mat1(1,1)*mat2(0,1) + mat1(1,2)*mat2(0,2)); VERIFY_IS_APPROX(mat5(1,1), mat1(1,0)*mat2(1,0) + mat1(1,1)*mat2(1,1) + mat1(1,2)*mat2(1,2)); Tensor<float, 2, DataLayout> mat6(2,2); mat6.setZero(); Eigen::array<DimPair, 1> dims6 = {{DimPair(1, 0)}}; typedef TensorEvaluator<decltype(mat1.contract(mat3, dims6)), DefaultDevice> Evaluator3; Evaluator3 eval3(mat1.contract(mat3, dims6), DefaultDevice()); eval3.evalTo(mat6.data()); EIGEN_STATIC_ASSERT(Evaluator3::NumDims==2ul, YOU_MADE_A_PROGRAMMING_MISTAKE); VERIFY_IS_EQUAL(eval3.dimensions()[0], 2); VERIFY_IS_EQUAL(eval3.dimensions()[1], 2); VERIFY_IS_APPROX(mat6(0,0), mat1(0,0)*mat3(0,0) + mat1(0,1)*mat3(1,0) + mat1(0,2)*mat3(2,0)); VERIFY_IS_APPROX(mat6(0,1), mat1(0,0)*mat3(0,1) + mat1(0,1)*mat3(1,1) + mat1(0,2)*mat3(2,1)); VERIFY_IS_APPROX(mat6(1,0), mat1(1,0)*mat3(0,0) + mat1(1,1)*mat3(1,0) + mat1(1,2)*mat3(2,0)); VERIFY_IS_APPROX(mat6(1,1), mat1(1,0)*mat3(0,1) + mat1(1,1)*mat3(1,1) + mat1(1,2)*mat3(2,1)); } template<int DataLayout> static void test_scalar() { Tensor<float, 1, DataLayout> vec1({6}); Tensor<float, 1, DataLayout> vec2({6}); vec1.setRandom(); vec2.setRandom(); Eigen::array<DimPair, 1> dims = {{DimPair(0, 0)}}; Tensor<float, 0, DataLayout> scalar = vec1.contract(vec2, dims); float expected = 0.0f; for (int i = 0; i < 6; ++i) { expected += vec1(i) * vec2(i); } VERIFY_IS_APPROX(scalar(), expected); } template<int DataLayout> static void test_multidims() { Tensor<float, 3, DataLayout> mat1(2, 2, 2); Tensor<float, 4, DataLayout> mat2(2, 2, 2, 2); mat1.setRandom(); mat2.setRandom(); Tensor<float, 3, DataLayout> mat3(2, 2, 2); mat3.setZero(); Eigen::array<DimPair, 2> dims = {{DimPair(1, 2), DimPair(2, 3)}}; typedef TensorEvaluator<decltype(mat1.contract(mat2, dims)), DefaultDevice> Evaluator; Evaluator eval(mat1.contract(mat2, dims), DefaultDevice()); eval.evalTo(mat3.data()); EIGEN_STATIC_ASSERT(Evaluator::NumDims==3ul, YOU_MADE_A_PROGRAMMING_MISTAKE); VERIFY_IS_EQUAL(eval.dimensions()[0], 2); VERIFY_IS_EQUAL(eval.dimensions()[1], 2); VERIFY_IS_EQUAL(eval.dimensions()[2], 2); VERIFY_IS_APPROX(mat3(0,0,0), mat1(0,0,0)*mat2(0,0,0,0) + mat1(0,1,0)*mat2(0,0,1,0) + mat1(0,0,1)*mat2(0,0,0,1) + mat1(0,1,1)*mat2(0,0,1,1)); VERIFY_IS_APPROX(mat3(0,0,1), mat1(0,0,0)*mat2(0,1,0,0) + mat1(0,1,0)*mat2(0,1,1,0) + mat1(0,0,1)*mat2(0,1,0,1) + mat1(0,1,1)*mat2(0,1,1,1)); VERIFY_IS_APPROX(mat3(0,1,0), mat1(0,0,0)*mat2(1,0,0,0) + mat1(0,1,0)*mat2(1,0,1,0) + mat1(0,0,1)*mat2(1,0,0,1) + mat1(0,1,1)*mat2(1,0,1,1)); VERIFY_IS_APPROX(mat3(0,1,1), mat1(0,0,0)*mat2(1,1,0,0) + mat1(0,1,0)*mat2(1,1,1,0) + mat1(0,0,1)*mat2(1,1,0,1) + mat1(0,1,1)*mat2(1,1,1,1)); VERIFY_IS_APPROX(mat3(1,0,0), mat1(1,0,0)*mat2(0,0,0,0) + mat1(1,1,0)*mat2(0,0,1,0) + mat1(1,0,1)*mat2(0,0,0,1) + mat1(1,1,1)*mat2(0,0,1,1)); VERIFY_IS_APPROX(mat3(1,0,1), mat1(1,0,0)*mat2(0,1,0,0) + mat1(1,1,0)*mat2(0,1,1,0) + mat1(1,0,1)*mat2(0,1,0,1) + mat1(1,1,1)*mat2(0,1,1,1)); VERIFY_IS_APPROX(mat3(1,1,0), mat1(1,0,0)*mat2(1,0,0,0) + mat1(1,1,0)*mat2(1,0,1,0) + mat1(1,0,1)*mat2(1,0,0,1) + mat1(1,1,1)*mat2(1,0,1,1)); VERIFY_IS_APPROX(mat3(1,1,1), mat1(1,0,0)*mat2(1,1,0,0) + mat1(1,1,0)*mat2(1,1,1,0) + mat1(1,0,1)*mat2(1,1,0,1) + mat1(1,1,1)*mat2(1,1,1,1)); Tensor<float, 2, DataLayout> mat4(2, 2); Tensor<float, 3, DataLayout> mat5(2, 2, 2); mat4.setRandom(); mat5.setRandom(); Tensor<float, 1, DataLayout> mat6(2); mat6.setZero(); Eigen::array<DimPair, 2> dims2({{DimPair(0, 1), DimPair(1, 0)}}); typedef TensorEvaluator<decltype(mat4.contract(mat5, dims2)), DefaultDevice> Evaluator2; Evaluator2 eval2(mat4.contract(mat5, dims2), DefaultDevice()); eval2.evalTo(mat6.data()); EIGEN_STATIC_ASSERT(Evaluator2::NumDims==1ul, YOU_MADE_A_PROGRAMMING_MISTAKE); VERIFY_IS_EQUAL(eval2.dimensions()[0], 2); VERIFY_IS_APPROX(mat6(0), mat4(0,0)*mat5(0,0,0) + mat4(1,0)*mat5(0,1,0) + mat4(0,1)*mat5(1,0,0) + mat4(1,1)*mat5(1,1,0)); VERIFY_IS_APPROX(mat6(1), mat4(0,0)*mat5(0,0,1) + mat4(1,0)*mat5(0,1,1) + mat4(0,1)*mat5(1,0,1) + mat4(1,1)*mat5(1,1,1)); } template<int DataLayout> static void test_holes() { Tensor<float, 4, DataLayout> t1(2, 5, 7, 3); Tensor<float, 5, DataLayout> t2(2, 7, 11, 13, 3); t1.setRandom(); t2.setRandom(); Eigen::array<DimPair, 2> dims = {{DimPair(0, 0), DimPair(3, 4)}}; Tensor<float, 5, DataLayout> result = t1.contract(t2, dims); VERIFY_IS_EQUAL(result.dimension(0), 5); VERIFY_IS_EQUAL(result.dimension(1), 7); VERIFY_IS_EQUAL(result.dimension(2), 7); VERIFY_IS_EQUAL(result.dimension(3), 11); VERIFY_IS_EQUAL(result.dimension(4), 13); 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) { VERIFY_IS_APPROX(result(i, j, k, l, m), t1(0, i, j, 0) * t2(0, k, l, m, 0) + t1(1, i, j, 0) * t2(1, k, l, m, 0) + t1(0, i, j, 1) * t2(0, k, l, m, 1) + t1(1, i, j, 1) * t2(1, k, l, m, 1) + t1(0, i, j, 2) * t2(0, k, l, m, 2) + t1(1, i, j, 2) * t2(1, k, l, m, 2)); } } } } } } template<int DataLayout> static void test_full_redux() { Tensor<float, 2, DataLayout> t1(2, 2); Tensor<float, 3, DataLayout> t2(2, 2, 2); t1.setRandom(); t2.setRandom(); Eigen::array<DimPair, 2> dims = {{DimPair(0, 0), DimPair(1, 1)}}; Tensor<float, 1, DataLayout> result = t1.contract(t2, dims); VERIFY_IS_EQUAL(result.dimension(0), 2); VERIFY_IS_APPROX(result(0), t1(0, 0) * t2(0, 0, 0) + t1(1, 0) * t2(1, 0, 0) + t1(0, 1) * t2(0, 1, 0) + t1(1, 1) * t2(1, 1, 0)); VERIFY_IS_APPROX(result(1), t1(0, 0) * t2(0, 0, 1) + t1(1, 0) * t2(1, 0, 1) + t1(0, 1) * t2(0, 1, 1) + t1(1, 1) * t2(1, 1, 1)); dims[0] = DimPair(1, 0); dims[1] = DimPair(2, 1); result = t2.contract(t1, dims); VERIFY_IS_EQUAL(result.dimension(0), 2); VERIFY_IS_APPROX(result(0), t1(0, 0) * t2(0, 0, 0) + t1(1, 0) * t2(0, 1, 0) + t1(0, 1) * t2(0, 0, 1) + t1(1, 1) * t2(0, 1, 1)); VERIFY_IS_APPROX(result(1), t1(0, 0) * t2(1, 0, 0) + t1(1, 0) * t2(1, 1, 0) + t1(0, 1) * t2(1, 0, 1) + t1(1, 1) * t2(1, 1, 1)); } template<int DataLayout> static void test_contraction_of_contraction() { Tensor<float, 2, DataLayout> t1(2, 2); Tensor<float, 2, DataLayout> t2(2, 2); Tensor<float, 2, DataLayout> t3(2, 2); Tensor<float, 2, DataLayout> t4(2, 2); t1.setRandom(); t2.setRandom(); t3.setRandom(); t4.setRandom(); Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}}; auto contract1 = t1.contract(t2, dims); auto diff = t3 - contract1; auto contract2 = t1.contract(t4, dims); Tensor<float, 2, DataLayout> result = contract2.contract(diff, dims); VERIFY_IS_EQUAL(result.dimension(0), 2); VERIFY_IS_EQUAL(result.dimension(1), 2); Eigen::Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> m1(t1.data(), 2, 2), m2(t2.data(), 2, 2), m3(t3.data(), 2, 2), m4(t4.data(), 2, 2); Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> expected = (m1 * m4) * (m3 - m1 * m2); VERIFY_IS_APPROX(result(0, 0), expected(0, 0)); VERIFY_IS_APPROX(result(0, 1), expected(0, 1)); VERIFY_IS_APPROX(result(1, 0), expected(1, 0)); VERIFY_IS_APPROX(result(1, 1), expected(1, 1)); } template<int DataLayout> static void test_expr() { Tensor<float, 2, DataLayout> mat1(2, 3); Tensor<float, 2, DataLayout> mat2(3, 2); mat1.setRandom(); mat2.setRandom(); Tensor<float, 2, DataLayout> mat3(2,2); Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}}; mat3 = mat1.contract(mat2, dims); VERIFY_IS_APPROX(mat3(0,0), mat1(0,0)*mat2(0,0) + mat1(0,1)*mat2(1,0) + mat1(0,2)*mat2(2,0)); VERIFY_IS_APPROX(mat3(0,1), mat1(0,0)*mat2(0,1) + mat1(0,1)*mat2(1,1) + mat1(0,2)*mat2(2,1)); VERIFY_IS_APPROX(mat3(1,0), mat1(1,0)*mat2(0,0) + mat1(1,1)*mat2(1,0) + mat1(1,2)*mat2(2,0)); VERIFY_IS_APPROX(mat3(1,1), mat1(1,0)*mat2(0,1) + mat1(1,1)*mat2(1,1) + mat1(1,2)*mat2(2,1)); } template<int DataLayout> static void test_out_of_order_contraction() { Tensor<float, 3, DataLayout> mat1(2, 2, 2); Tensor<float, 3, DataLayout> mat2(2, 2, 2); mat1.setRandom(); mat2.setRandom(); Tensor<float, 2, DataLayout> mat3(2, 2); Eigen::array<DimPair, 2> dims = {{DimPair(2, 0), DimPair(0, 2)}}; mat3 = mat1.contract(mat2, dims); VERIFY_IS_APPROX(mat3(0, 0), mat1(0,0,0)*mat2(0,0,0) + mat1(1,0,0)*mat2(0,0,1) + mat1(0,0,1)*mat2(1,0,0) + mat1(1,0,1)*mat2(1,0,1)); VERIFY_IS_APPROX(mat3(1, 0), mat1(0,1,0)*mat2(0,0,0) + mat1(1,1,0)*mat2(0,0,1) + mat1(0,1,1)*mat2(1,0,0) + mat1(1,1,1)*mat2(1,0,1)); VERIFY_IS_APPROX(mat3(0, 1), mat1(0,0,0)*mat2(0,1,0) + mat1(1,0,0)*mat2(0,1,1) + mat1(0,0,1)*mat2(1,1,0) + mat1(1,0,1)*mat2(1,1,1)); VERIFY_IS_APPROX(mat3(1, 1), mat1(0,1,0)*mat2(0,1,0) + mat1(1,1,0)*mat2(0,1,1) + mat1(0,1,1)*mat2(1,1,0) + mat1(1,1,1)*mat2(1,1,1)); Eigen::array<DimPair, 2> dims2 = {{DimPair(0, 2), DimPair(2, 0)}}; mat3 = mat1.contract(mat2, dims2); VERIFY_IS_APPROX(mat3(0, 0), mat1(0,0,0)*mat2(0,0,0) + mat1(1,0,0)*mat2(0,0,1) + mat1(0,0,1)*mat2(1,0,0) + mat1(1,0,1)*mat2(1,0,1)); VERIFY_IS_APPROX(mat3(1, 0), mat1(0,1,0)*mat2(0,0,0) + mat1(1,1,0)*mat2(0,0,1) + mat1(0,1,1)*mat2(1,0,0) + mat1(1,1,1)*mat2(1,0,1)); VERIFY_IS_APPROX(mat3(0, 1), mat1(0,0,0)*mat2(0,1,0) + mat1(1,0,0)*mat2(0,1,1) + mat1(0,0,1)*mat2(1,1,0) + mat1(1,0,1)*mat2(1,1,1)); VERIFY_IS_APPROX(mat3(1, 1), mat1(0,1,0)*mat2(0,1,0) + mat1(1,1,0)*mat2(0,1,1) + mat1(0,1,1)*mat2(1,1,0) + mat1(1,1,1)*mat2(1,1,1)); } template<int DataLayout> static void test_consistency() { // this does something like testing (A*B)^T = (B^T * A^T) Tensor<float, 3, DataLayout> mat1(4, 3, 5); Tensor<float, 5, DataLayout> mat2(3, 2, 1, 5, 4); mat1.setRandom(); mat2.setRandom(); Tensor<float, 4, DataLayout> mat3(5, 2, 1, 5); Tensor<float, 4, DataLayout> mat4(2, 1, 5, 5); // contract on dimensions of size 4 and 3 Eigen::array<DimPair, 2> dims1 = {{DimPair(0, 4), DimPair(1, 0)}}; Eigen::array<DimPair, 2> dims2 = {{DimPair(4, 0), DimPair(0, 1)}}; mat3 = mat1.contract(mat2, dims1); mat4 = mat2.contract(mat1, dims2); // 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++) { VERIFY_IS_APPROX(mat3.data()[i + 5 * j], mat4.data()[j + 10 * i]); } } } else { // Row major for (size_t i = 0; i < 5; i++) { for (size_t j = 0; j < 10; j++) { VERIFY_IS_APPROX(mat3.data()[10 * i + j], mat4.data()[i + 5 * j]); } } } } template<int DataLayout> static void test_large_contraction() { Tensor<float, 4, DataLayout> t_left(30, 50, 8, 31); Tensor<float, 5, DataLayout> t_right(8, 31, 7, 20, 10); Tensor<float, 5, DataLayout> t_result(30, 50, 7, 20, 10); t_left.setRandom(); t_right.setRandom(); // Add a little offset so that the results won't be close to zero. t_left += t_left.constant(1.0f); t_right += t_right.constant(1.0f); typedef Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf; MapXf m_left(t_left.data(), 1500, 248); MapXf m_right(t_right.data(), 248, 1400); Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result(1500, 1400); // this contraction should be equivalent to a single matrix multiplication Eigen::array<DimPair, 2> dims = {{DimPair(2, 0), DimPair(3, 1)}}; // compute results by separate methods t_result = t_left.contract(t_right, dims); m_result = m_left * m_right; for (int i = 0; i < t_result.dimensions().TotalSize(); i++) { VERIFY(&t_result.data()[i] != &m_result.data()[i]); VERIFY_IS_APPROX(t_result.data()[i], m_result.data()[i]); } } template<int DataLayout> static void test_matrix_vector() { Tensor<float, 2, DataLayout> t_left(30, 50); Tensor<float, 1, DataLayout> t_right(50); Tensor<float, 1, DataLayout> t_result(30); t_left.setRandom(); t_right.setRandom(); typedef Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf; MapXf m_left(t_left.data(), 30, 50); MapXf m_right(t_right.data(), 50, 1); Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result(30, 1); // this contraction should be equivalent to a single matrix multiplication Eigen::array<DimPair, 1> dims{{DimPair(1, 0)}}; // compute results by separate methods t_result = t_left.contract(t_right, dims); m_result = m_left * m_right; for (int i = 0; i < t_result.dimensions().TotalSize(); i++) { VERIFY(internal::isApprox(t_result(i), m_result(i, 0), 1)); } } template<int DataLayout> static void test_tensor_vector() { Tensor<float, 3, DataLayout> t_left(7, 13, 17); Tensor<float, 2, DataLayout> t_right(1, 7); t_left.setRandom(); t_right.setRandom(); typedef typename Tensor<float, 1, DataLayout>::DimensionPair DimensionPair; Eigen::array<DimensionPair, 1> dim_pair01{{{0, 1}}}; Tensor<float, 3, DataLayout> t_result = t_left.contract(t_right, dim_pair01); typedef Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf; MapXf m_left(t_left.data(), 7, 13*17); MapXf m_right(t_right.data(), 1, 7); Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result = m_left.transpose() * m_right.transpose(); for (int i = 0; i < t_result.dimensions().TotalSize(); i++) { VERIFY(internal::isApprox(t_result(i), m_result(i, 0), 1)); } } template<int DataLayout> static void test_small_blocking_factors() { Tensor<float, 4, DataLayout> t_left(30, 5, 3, 31); Tensor<float, 5, DataLayout> t_right(3, 31, 7, 20, 1); t_left.setRandom(); t_right.setRandom(); // Add a little offset so that the results won't be close to zero. t_left += t_left.constant(1.0f); t_right += t_right.constant(1.0f); // Force the cache sizes, which results in smaller blocking factors. Eigen::setCpuCacheSizes(896, 1920, 2944); // this contraction should be equivalent to a single matrix multiplication Eigen::array<DimPair, 2> dims = {{DimPair(2, 0), DimPair(3, 1)}}; Tensor<float, 5, DataLayout> t_result; t_result = t_left.contract(t_right, dims); // compute result using a simple eigen matrix product Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> m_left(t_left.data(), 150, 93); Map<Eigen::Matrix<float, Dynamic, Dynamic, DataLayout>> m_right(t_right.data(), 93, 140); Eigen::Matrix<float, Dynamic, Dynamic, DataLayout> m_result = m_left * m_right; for (int i = 0; i < t_result.dimensions().TotalSize(); i++) { VERIFY_IS_APPROX(t_result.data()[i], m_result.data()[i]); } } template<int DataLayout> static void test_tensor_product() { Tensor<float, 2, DataLayout> mat1(2, 3); Tensor<float, 2, DataLayout> mat2(4, 1); mat1.setRandom(); mat2.setRandom(); Tensor<float, 4, DataLayout> result = mat1.contract(mat2, Eigen::array<DimPair, 0>{{}}); VERIFY_IS_EQUAL(result.dimension(0), 2); VERIFY_IS_EQUAL(result.dimension(1), 3); VERIFY_IS_EQUAL(result.dimension(2), 4); VERIFY_IS_EQUAL(result.dimension(3), 1); 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) { VERIFY_IS_APPROX(result(i, j, k, l), mat1(i, j) * mat2(k, l) ); } } } } } template<int DataLayout> static void test_const_inputs() { Tensor<float, 2, DataLayout> in1(2, 3); Tensor<float, 2, DataLayout> in2(3, 2); in1.setRandom(); in2.setRandom(); TensorMap<Tensor<const float, 2, DataLayout> > mat1(in1.data(), 2, 3); TensorMap<Tensor<const float, 2, DataLayout> > mat2(in2.data(), 3, 2); Tensor<float, 2, DataLayout> mat3(2,2); Eigen::array<DimPair, 1> dims = {{DimPair(1, 0)}}; mat3 = mat1.contract(mat2, dims); VERIFY_IS_APPROX(mat3(0,0), mat1(0,0)*mat2(0,0) + mat1(0,1)*mat2(1,0) + mat1(0,2)*mat2(2,0)); VERIFY_IS_APPROX(mat3(0,1), mat1(0,0)*mat2(0,1) + mat1(0,1)*mat2(1,1) + mat1(0,2)*mat2(2,1)); VERIFY_IS_APPROX(mat3(1,0), mat1(1,0)*mat2(0,0) + mat1(1,1)*mat2(1,0) + mat1(1,2)*mat2(2,0)); VERIFY_IS_APPROX(mat3(1,1), mat1(1,0)*mat2(0,1) + mat1(1,1)*mat2(1,1) + mat1(1,2)*mat2(2,1)); } void test_cxx11_tensor_contraction() { CALL_SUBTEST(test_evals<ColMajor>()); CALL_SUBTEST(test_evals<RowMajor>()); CALL_SUBTEST(test_scalar<ColMajor>()); CALL_SUBTEST(test_scalar<RowMajor>()); CALL_SUBTEST(test_multidims<ColMajor>()); CALL_SUBTEST(test_multidims<RowMajor>()); CALL_SUBTEST(test_holes<ColMajor>()); CALL_SUBTEST(test_holes<RowMajor>()); CALL_SUBTEST(test_full_redux<ColMajor>()); CALL_SUBTEST(test_full_redux<RowMajor>()); CALL_SUBTEST(test_contraction_of_contraction<ColMajor>()); CALL_SUBTEST(test_contraction_of_contraction<RowMajor>()); CALL_SUBTEST(test_expr<ColMajor>()); CALL_SUBTEST(test_expr<RowMajor>()); CALL_SUBTEST(test_out_of_order_contraction<ColMajor>()); CALL_SUBTEST(test_out_of_order_contraction<RowMajor>()); CALL_SUBTEST(test_consistency<ColMajor>()); CALL_SUBTEST(test_consistency<RowMajor>()); CALL_SUBTEST(test_large_contraction<ColMajor>()); CALL_SUBTEST(test_large_contraction<RowMajor>()); CALL_SUBTEST(test_matrix_vector<ColMajor>()); CALL_SUBTEST(test_matrix_vector<RowMajor>()); CALL_SUBTEST(test_tensor_vector<ColMajor>()); CALL_SUBTEST(test_tensor_vector<RowMajor>()); CALL_SUBTEST(test_small_blocking_factors<ColMajor>()); CALL_SUBTEST(test_small_blocking_factors<RowMajor>()); CALL_SUBTEST(test_tensor_product<ColMajor>()); CALL_SUBTEST(test_tensor_product<RowMajor>()); CALL_SUBTEST(test_const_inputs<ColMajor>()); CALL_SUBTEST(test_const_inputs<RowMajor>()); }