text: // 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));
  }
  
  // Apply Sqrt to all output elements.
  struct SqrtOutputKernel {
    template <typename Index, typename Scalar>
    EIGEN_ALWAYS_INLINE void operator()(
        const OutputKernel::OutputMapper<Index, Scalar>& output_mapper,
        const TensorContractionParams&, Index, Index, Index num_rows,
        Index num_cols) const {
      for (int i = 0; i < num_rows; ++i) {
        for (int j = 0; j < num_cols; ++j) {
          output_mapper(i, j) = std::sqrt(output_mapper(i, j));
        }
      }
    }
  };
  
  template <int DataLayout>
  static void test_large_contraction_with_output_kernel() {
    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();
    // Put trash in mat4 to verify contraction clears output memory.
    t_result.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, SqrtOutputKernel());
  
    m_result = m_left * m_right;
  
    for (size_t i = 0; i < t_result.dimensions().TotalSize(); i++) {
      VERIFY(&t_result.data()[i] != &m_result.data()[i]);
      VERIFY_IS_APPROX(t_result.data()[i], std::sqrt(m_result.data()[i]));
    }
  }
  
  EIGEN_DECLARE_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>());
    CALL_SUBTEST(test_large_contraction_with_output_kernel<ColMajor>());
    CALL_SUBTEST(test_large_contraction_with_output_kernel<RowMajor>());
  }
  