contrib/test/tensor_chipping.cpp

// 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/.
// SPDX-License-Identifier: MPL-2.0

#include "main.h"

#include <Eigen/Tensor>

using Eigen::Tensor;

template <int DataLayout>
static void test_simple_chip() {
  Tensor<float, 5, DataLayout> tensor(2, 3, 5, 7, 11);
  tensor.setRandom();

  Tensor<float, 4, DataLayout> chip1;
  chip1 = tensor.template chip<0>(1);

  VERIFY_IS_EQUAL(chip1.dimension(0), 3);
  VERIFY_IS_EQUAL(chip1.dimension(1), 5);
  VERIFY_IS_EQUAL(chip1.dimension(2), 7);
  VERIFY_IS_EQUAL(chip1.dimension(3), 11);

  for (int i = 0; i < 3; ++i) {
    for (int j = 0; j < 5; ++j) {
      for (int k = 0; k < 7; ++k) {
        for (int l = 0; l < 11; ++l) {
          VERIFY_IS_EQUAL(chip1(i, j, k, l), tensor(1, i, j, k, l));
        }
      }
    }
  }

  Tensor<float, 4, DataLayout> chip2 = tensor.template chip<1>(1);
  VERIFY_IS_EQUAL(chip2.dimension(0), 2);
  VERIFY_IS_EQUAL(chip2.dimension(1), 5);
  VERIFY_IS_EQUAL(chip2.dimension(2), 7);
  VERIFY_IS_EQUAL(chip2.dimension(3), 11);
  for (int i = 0; i < 2; ++i) {
    for (int j = 0; j < 5; ++j) {
      for (int k = 0; k < 7; ++k) {
        for (int l = 0; l < 11; ++l) {
          VERIFY_IS_EQUAL(chip2(i, j, k, l), tensor(i, 1, j, k, l));
        }
      }
    }
  }

  Tensor<float, 4, DataLayout> chip3 = tensor.template chip<2>(2);
  VERIFY_IS_EQUAL(chip3.dimension(0), 2);
  VERIFY_IS_EQUAL(chip3.dimension(1), 3);
  VERIFY_IS_EQUAL(chip3.dimension(2), 7);
  VERIFY_IS_EQUAL(chip3.dimension(3), 11);
  for (int i = 0; i < 2; ++i) {
    for (int j = 0; j < 3; ++j) {
      for (int k = 0; k < 7; ++k) {
        for (int l = 0; l < 11; ++l) {
          VERIFY_IS_EQUAL(chip3(i, j, k, l), tensor(i, j, 2, k, l));
        }
      }
    }
  }

  Tensor<float, 4, DataLayout> chip4(tensor.template chip<3>(5));
  VERIFY_IS_EQUAL(chip4.dimension(0), 2);
  VERIFY_IS_EQUAL(chip4.dimension(1), 3);
  VERIFY_IS_EQUAL(chip4.dimension(2), 5);
  VERIFY_IS_EQUAL(chip4.dimension(3), 11);
  for (int i = 0; i < 2; ++i) {
    for (int j = 0; j < 3; ++j) {
      for (int k = 0; k < 5; ++k) {
        for (int l = 0; l < 11; ++l) {
          VERIFY_IS_EQUAL(chip4(i, j, k, l), tensor(i, j, k, 5, l));
        }
      }
    }
  }

  Tensor<float, 4, DataLayout> chip5(tensor.template chip<4>(7));
  VERIFY_IS_EQUAL(chip5.dimension(0), 2);
  VERIFY_IS_EQUAL(chip5.dimension(1), 3);
  VERIFY_IS_EQUAL(chip5.dimension(2), 5);
  VERIFY_IS_EQUAL(chip5.dimension(3), 7);
  for (int i = 0; i < 2; ++i) {
    for (int j = 0; j < 3; ++j) {
      for (int k = 0; k < 5; ++k) {
        for (int l = 0; l < 7; ++l) {
          VERIFY_IS_EQUAL(chip5(i, j, k, l), tensor(i, j, k, l, 7));
        }
      }
    }
  }
}

template <int DataLayout>
static void test_dynamic_chip() {
  Tensor<float, 5, DataLayout> tensor(2, 3, 5, 7, 11);
  tensor.setRandom();

  Tensor<float, 4, DataLayout> chip1;
  chip1 = tensor.chip(1, 0);
  VERIFY_IS_EQUAL(chip1.dimension(0), 3);
  VERIFY_IS_EQUAL(chip1.dimension(1), 5);
  VERIFY_IS_EQUAL(chip1.dimension(2), 7);
  VERIFY_IS_EQUAL(chip1.dimension(3), 11);
  for (int i = 0; i < 3; ++i) {
    for (int j = 0; j < 5; ++j) {
      for (int k = 0; k < 7; ++k) {
        for (int l = 0; l < 11; ++l) {
          VERIFY_IS_EQUAL(chip1(i, j, k, l), tensor(1, i, j, k, l));
        }
      }
    }
  }

  Tensor<float, 4, DataLayout> chip2 = tensor.chip(1, 1);
  VERIFY_IS_EQUAL(chip2.dimension(0), 2);
  VERIFY_IS_EQUAL(chip2.dimension(1), 5);
  VERIFY_IS_EQUAL(chip2.dimension(2), 7);
  VERIFY_IS_EQUAL(chip2.dimension(3), 11);
  for (int i = 0; i < 2; ++i) {
    for (int j = 0; j < 5; ++j) {
      for (int k = 0; k < 7; ++k) {
        for (int l = 0; l < 11; ++l) {
          VERIFY_IS_EQUAL(chip2(i, j, k, l), tensor(i, 1, j, k, l));
        }
      }
    }
  }

  Tensor<float, 4, DataLayout> chip3 = tensor.chip(2, 2);
  VERIFY_IS_EQUAL(chip3.dimension(0), 2);
  VERIFY_IS_EQUAL(chip3.dimension(1), 3);
  VERIFY_IS_EQUAL(chip3.dimension(2), 7);
  VERIFY_IS_EQUAL(chip3.dimension(3), 11);
  for (int i = 0; i < 2; ++i) {
    for (int j = 0; j < 3; ++j) {
      for (int k = 0; k < 7; ++k) {
        for (int l = 0; l < 11; ++l) {
          VERIFY_IS_EQUAL(chip3(i, j, k, l), tensor(i, j, 2, k, l));
        }
      }
    }
  }

  Tensor<float, 4, DataLayout> chip4(tensor.chip(5, 3));
  VERIFY_IS_EQUAL(chip4.dimension(0), 2);
  VERIFY_IS_EQUAL(chip4.dimension(1), 3);
  VERIFY_IS_EQUAL(chip4.dimension(2), 5);
  VERIFY_IS_EQUAL(chip4.dimension(3), 11);
  for (int i = 0; i < 2; ++i) {
    for (int j = 0; j < 3; ++j) {
      for (int k = 0; k < 5; ++k) {
        for (int l = 0; l < 11; ++l) {
          VERIFY_IS_EQUAL(chip4(i, j, k, l), tensor(i, j, k, 5, l));
        }
      }
    }
  }

  Tensor<float, 4, DataLayout> chip5(tensor.chip(7, 4));
  VERIFY_IS_EQUAL(chip5.dimension(0), 2);
  VERIFY_IS_EQUAL(chip5.dimension(1), 3);
  VERIFY_IS_EQUAL(chip5.dimension(2), 5);
  VERIFY_IS_EQUAL(chip5.dimension(3), 7);
  for (int i = 0; i < 2; ++i) {
    for (int j = 0; j < 3; ++j) {
      for (int k = 0; k < 5; ++k) {
        for (int l = 0; l < 7; ++l) {
          VERIFY_IS_EQUAL(chip5(i, j, k, l), tensor(i, j, k, l, 7));
        }
      }
    }
  }
}

template <int DataLayout>
static void test_chip_in_expr() {
  Tensor<float, 5, DataLayout> input1(2, 3, 5, 7, 11);
  input1.setRandom();
  Tensor<float, 4, DataLayout> input2(3, 5, 7, 11);
  input2.setRandom();

  Tensor<float, 4, DataLayout> result = input1.template chip<0>(0) + input2;
  for (int i = 0; i < 3; ++i) {
    for (int j = 0; j < 5; ++j) {
      for (int k = 0; k < 7; ++k) {
        for (int l = 0; l < 11; ++l) {
          float expected = input1(0, i, j, k, l) + input2(i, j, k, l);
          VERIFY_IS_EQUAL(result(i, j, k, l), expected);
        }
      }
    }
  }

  Tensor<float, 3, DataLayout> input3(3, 7, 11);
  input3.setRandom();
  Tensor<float, 3, DataLayout> result2 = input1.template chip<0>(0).template chip<1>(2) + input3;
  for (int i = 0; i < 3; ++i) {
    for (int j = 0; j < 7; ++j) {
      for (int k = 0; k < 11; ++k) {
        float expected = input1(0, i, 2, j, k) + input3(i, j, k);
        VERIFY_IS_EQUAL(result2(i, j, k), expected);
      }
    }
  }
}

template <int DataLayout>
static void test_chip_as_lvalue() {
  Tensor<float, 5, DataLayout> input1(2, 3, 5, 7, 11);
  input1.setRandom();

  Tensor<float, 4, DataLayout> input2(3, 5, 7, 11);
  input2.setRandom();
  Tensor<float, 5, DataLayout> tensor = input1;
  tensor.template chip<0>(1) = input2;
  for (int i = 0; i < 2; ++i) {
    for (int j = 0; j < 3; ++j) {
      for (int k = 0; k < 5; ++k) {
        for (int l = 0; l < 7; ++l) {
          for (int m = 0; m < 11; ++m) {
            if (i != 1) {
              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input1(i, j, k, l, m));
            } else {
              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input2(j, k, l, m));
            }
          }
        }
      }
    }
  }

  Tensor<float, 4, DataLayout> input3(2, 5, 7, 11);
  input3.setRandom();
  tensor = input1;
  tensor.template chip<1>(1) = input3;
  for (int i = 0; i < 2; ++i) {
    for (int j = 0; j < 3; ++j) {
      for (int k = 0; k < 5; ++k) {
        for (int l = 0; l < 7; ++l) {
          for (int m = 0; m < 11; ++m) {
            if (j != 1) {
              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input1(i, j, k, l, m));
            } else {
              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input3(i, k, l, m));
            }
          }
        }
      }
    }
  }

  Tensor<float, 4, DataLayout> input4(2, 3, 7, 11);
  input4.setRandom();
  tensor = input1;
  tensor.template chip<2>(3) = input4;
  for (int i = 0; i < 2; ++i) {
    for (int j = 0; j < 3; ++j) {
      for (int k = 0; k < 5; ++k) {
        for (int l = 0; l < 7; ++l) {
          for (int m = 0; m < 11; ++m) {
            if (k != 3) {
              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input1(i, j, k, l, m));
            } else {
              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input4(i, j, l, m));
            }
          }
        }
      }
    }
  }

  Tensor<float, 4, DataLayout> input5(2, 3, 5, 11);
  input5.setRandom();
  tensor = input1;
  tensor.template chip<3>(4) = input5;
  for (int i = 0; i < 2; ++i) {
    for (int j = 0; j < 3; ++j) {
      for (int k = 0; k < 5; ++k) {
        for (int l = 0; l < 7; ++l) {
          for (int m = 0; m < 11; ++m) {
            if (l != 4) {
              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input1(i, j, k, l, m));
            } else {
              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input5(i, j, k, m));
            }
          }
        }
      }
    }
  }

  Tensor<float, 4, DataLayout> input6(2, 3, 5, 7);
  input6.setRandom();
  tensor = input1;
  tensor.template chip<4>(5) = input6;
  for (int i = 0; i < 2; ++i) {
    for (int j = 0; j < 3; ++j) {
      for (int k = 0; k < 5; ++k) {
        for (int l = 0; l < 7; ++l) {
          for (int m = 0; m < 11; ++m) {
            if (m != 5) {
              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input1(i, j, k, l, m));
            } else {
              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input6(i, j, k, l));
            }
          }
        }
      }
    }
  }

  Tensor<float, 5, DataLayout> input7(2, 3, 5, 7, 11);
  input7.setRandom();
  tensor = input1;
  tensor.chip(0, 0) = input7.chip(0, 0);
  for (int i = 0; i < 2; ++i) {
    for (int j = 0; j < 3; ++j) {
      for (int k = 0; k < 5; ++k) {
        for (int l = 0; l < 7; ++l) {
          for (int m = 0; m < 11; ++m) {
            if (i != 0) {
              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input1(i, j, k, l, m));
            } else {
              VERIFY_IS_EQUAL(tensor(i, j, k, l, m), input7(i, j, k, l, m));
            }
          }
        }
      }
    }
  }
}

static void test_chip_raw_data_col_major() {
  Tensor<float, 5, ColMajor> tensor(2, 3, 5, 7, 11);
  tensor.setRandom();

  typedef TensorEvaluator<decltype(tensor.chip<4>(3)), DefaultDevice> Evaluator4;
  auto chip = Evaluator4(tensor.chip<4>(3), DefaultDevice());
  for (int i = 0; i < 2; ++i) {
    for (int j = 0; j < 3; ++j) {
      for (int k = 0; k < 5; ++k) {
        for (int l = 0; l < 7; ++l) {
          int chip_index = i + 2 * (j + 3 * (k + 5 * l));
          VERIFY_IS_EQUAL(chip.data()[chip_index], tensor(i, j, k, l, 3));
        }
      }
    }
  }

  typedef TensorEvaluator<decltype(tensor.chip<0>(0)), DefaultDevice> Evaluator0;
  auto chip0 = Evaluator0(tensor.chip<0>(0), DefaultDevice());
  VERIFY_IS_EQUAL(chip0.data(), static_cast<float*>(0));

  typedef TensorEvaluator<decltype(tensor.chip<1>(0)), DefaultDevice> Evaluator1;
  auto chip1 = Evaluator1(tensor.chip<1>(0), DefaultDevice());
  VERIFY_IS_EQUAL(chip1.data(), static_cast<float*>(0));

  typedef TensorEvaluator<decltype(tensor.chip<2>(0)), DefaultDevice> Evaluator2;
  auto chip2 = Evaluator2(tensor.chip<2>(0), DefaultDevice());
  VERIFY_IS_EQUAL(chip2.data(), static_cast<float*>(0));

  typedef TensorEvaluator<decltype(tensor.chip<3>(0)), DefaultDevice> Evaluator3;
  auto chip3 = Evaluator3(tensor.chip<3>(0), DefaultDevice());
  VERIFY_IS_EQUAL(chip3.data(), static_cast<float*>(0));
}

static void test_chip_raw_data_row_major() {
  Tensor<float, 5, RowMajor> tensor(11, 7, 5, 3, 2);
  tensor.setRandom();

  typedef TensorEvaluator<decltype(tensor.chip<0>(3)), DefaultDevice> Evaluator0;
  auto chip = Evaluator0(tensor.chip<0>(3), DefaultDevice());
  for (int i = 0; i < 7; ++i) {
    for (int j = 0; j < 5; ++j) {
      for (int k = 0; k < 3; ++k) {
        for (int l = 0; l < 2; ++l) {
          int chip_index = l + 2 * (k + 3 * (j + 5 * i));
          VERIFY_IS_EQUAL(chip.data()[chip_index], tensor(3, i, j, k, l));
        }
      }
    }
  }

  typedef TensorEvaluator<decltype(tensor.chip<1>(0)), DefaultDevice> Evaluator1;
  auto chip1 = Evaluator1(tensor.chip<1>(0), DefaultDevice());
  VERIFY_IS_EQUAL(chip1.data(), static_cast<float*>(0));

  typedef TensorEvaluator<decltype(tensor.chip<2>(0)), DefaultDevice> Evaluator2;
  auto chip2 = Evaluator2(tensor.chip<2>(0), DefaultDevice());
  VERIFY_IS_EQUAL(chip2.data(), static_cast<float*>(0));

  typedef TensorEvaluator<decltype(tensor.chip<3>(0)), DefaultDevice> Evaluator3;
  auto chip3 = Evaluator3(tensor.chip<3>(0), DefaultDevice());
  VERIFY_IS_EQUAL(chip3.data(), static_cast<float*>(0));

  typedef TensorEvaluator<decltype(tensor.chip<4>(0)), DefaultDevice> Evaluator4;
  auto chip4 = Evaluator4(tensor.chip<4>(0), DefaultDevice());
  VERIFY_IS_EQUAL(chip4.data(), static_cast<float*>(0));
}

template <int Rank, int Layout>
EIGEN_DONT_INLINE Tensor<double, Rank - 1, Layout> runtime_axis_chip(const Tensor<double, Rank, Layout>& input,
                                                                     Index offset, Index axis) {
  return input.chip(offset, axis);
}

template <int Rank, int Layout, int Axis>
void test_runtime_axis_block_chip() {
  DSizes<Index, Rank> dimensions;
  for (int i = 0; i < Rank; ++i) dimensions[i] = 3 + i;
  Tensor<double, Rank, Layout> input(dimensions);
  for (Index i = 0; i < input.size(); ++i) input.data()[i] = double(i);
  for (Index offset : {Index(0), dimensions[Axis] - 1}) {
    const Tensor<double, Rank - 1, Layout> result = runtime_axis_chip(input, offset, Axis);
    const Tensor<double, Rank - 1, Layout> expected = input.template chip<Axis>(offset);
    VERIFY_IS_EQUAL(result.size(), expected.size());
    for (Index i = 0; i < result.size(); ++i) VERIFY_IS_EQUAL(result.data()[i], expected.data()[i]);

    Tensor<double, Rank, Layout> actual = input;
    Tensor<double, Rank, Layout> reference = input;
    actual.chip(offset, Axis) = result + result;
    reference.template chip<Axis>(offset) = expected + expected;
    for (Index i = 0; i < actual.size(); ++i) VERIFY_IS_EQUAL(actual.data()[i], reference.data()[i]);
  }
}

template <int Layout>
void test_runtime_axis_block_chips() {
  test_runtime_axis_block_chip<3, Layout, 0>();
  test_runtime_axis_block_chip<3, Layout, 1>();
  test_runtime_axis_block_chip<3, Layout, 2>();
  test_runtime_axis_block_chip<4, Layout, 0>();
  test_runtime_axis_block_chip<4, Layout, 1>();
  test_runtime_axis_block_chip<4, Layout, 2>();
  test_runtime_axis_block_chip<4, Layout, 3>();
}

EIGEN_DECLARE_TEST(tensor_chipping) {
  CALL_SUBTEST(test_runtime_axis_block_chips<ColMajor>());
  CALL_SUBTEST(test_runtime_axis_block_chips<RowMajor>());
  CALL_SUBTEST(test_simple_chip<ColMajor>());
  CALL_SUBTEST(test_simple_chip<RowMajor>());
  CALL_SUBTEST(test_dynamic_chip<ColMajor>());
  CALL_SUBTEST(test_dynamic_chip<RowMajor>());
  CALL_SUBTEST(test_chip_in_expr<ColMajor>());
  CALL_SUBTEST(test_chip_in_expr<RowMajor>());
  CALL_SUBTEST(test_chip_as_lvalue<ColMajor>());
  CALL_SUBTEST(test_chip_as_lvalue<RowMajor>());
  CALL_SUBTEST(test_chip_raw_data_col_major());
  CALL_SUBTEST(test_chip_raw_data_row_major());
}