contrib/test/tensor_ref.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::RowMajor;
using Eigen::Tensor;

static void test_simple_lvalue_ref() {
  Tensor<int, 1> input(6);
  input.setRandom();

  TensorRef<Tensor<int, 1>> ref3(input);
  TensorRef<Tensor<int, 1>> ref4 = input;

  VERIFY_IS_EQUAL(ref3.data(), input.data());
  VERIFY_IS_EQUAL(ref4.data(), input.data());

  for (int i = 0; i < 6; ++i) {
    VERIFY_IS_EQUAL(ref3(i), input(i));
    VERIFY_IS_EQUAL(ref4(i), input(i));
  }

  for (int i = 0; i < 6; ++i) {
    ref3.coeffRef(i) = i;
  }
  for (int i = 0; i < 6; ++i) {
    VERIFY_IS_EQUAL(input(i), i);
  }
  for (int i = 0; i < 6; ++i) {
    ref4.coeffRef(i) = -i * 2;
  }
  for (int i = 0; i < 6; ++i) {
    VERIFY_IS_EQUAL(input(i), -i * 2);
  }
}

static void test_simple_rvalue_ref() {
  Tensor<int, 1> input1(6);
  setRandomDataInRange(input1, -1000, 1000);
  Tensor<int, 1> input2(6);
  setRandomDataInRange(input2, -1000, 1000);

  TensorRef<const Tensor<int, 1>> ref3(input1 + input2);
  TensorRef<const Tensor<int, 1>> ref4 = input1 + input2;

  VERIFY_IS_NOT_EQUAL(ref3.data(), input1.data());
  VERIFY_IS_NOT_EQUAL(ref4.data(), input1.data());
  VERIFY_IS_NOT_EQUAL(ref3.data(), input2.data());
  VERIFY_IS_NOT_EQUAL(ref4.data(), input2.data());

  for (int i = 0; i < 6; ++i) {
    VERIFY_IS_EQUAL(ref3(i), input1(i) + input2(i));
    VERIFY_IS_EQUAL(ref4(i), input1(i) + input2(i));
  }
}

static void test_multiple_dims() {
  Tensor<float, 3> input(3, 5, 7);
  input.setRandom();

  TensorRef<Tensor<float, 3>> ref(input);
  VERIFY_IS_EQUAL(ref.data(), input.data());
  VERIFY_IS_EQUAL(ref.dimension(0), 3);
  VERIFY_IS_EQUAL(ref.dimension(1), 5);
  VERIFY_IS_EQUAL(ref.dimension(2), 7);

  for (int i = 0; i < 3; ++i) {
    for (int j = 0; j < 5; ++j) {
      for (int k = 0; k < 7; ++k) {
        VERIFY_IS_EQUAL(ref(i, j, k), input(i, j, k));
      }
    }
  }
}

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

  Eigen::DSizes<ptrdiff_t, 5> indices(1, 2, 3, 4, 5);
  Eigen::DSizes<ptrdiff_t, 5> sizes(1, 1, 1, 1, 1);
  TensorRef<Tensor<float, 5>> slice = tensor.slice(indices, sizes);
  VERIFY_IS_EQUAL(slice(0, 0, 0, 0, 0), tensor(1, 2, 3, 4, 5));

  Eigen::DSizes<ptrdiff_t, 5> indices2(1, 1, 3, 4, 5);
  Eigen::DSizes<ptrdiff_t, 5> sizes2(1, 1, 2, 2, 3);
  slice = tensor.slice(indices2, sizes2);
  for (int i = 0; i < 2; ++i) {
    for (int j = 0; j < 2; ++j) {
      for (int k = 0; k < 3; ++k) {
        VERIFY_IS_EQUAL(slice(0, 0, i, j, k), tensor(1, 1, 3 + i, 4 + j, 5 + k));
      }
    }
  }

  Eigen::DSizes<ptrdiff_t, 5> indices3(0, 0, 0, 0, 0);
  Eigen::DSizes<ptrdiff_t, 5> sizes3(2, 3, 1, 1, 1);
  slice = tensor.slice(indices3, sizes3);
  VERIFY_IS_EQUAL(slice.data(), tensor.data());
}

static void test_ref_of_trace() {
  Tensor<int, 2> input(6, 6);
  setRandomDataInRange(input, -1000, 1000);
  int trace = 0;
  for (int i = 0; i < 6; ++i) {
    trace += input(i, i);
  }
  TensorRef<const Tensor<int, 0>> ref(input.trace());
  VERIFY_IS_EQUAL(ref.coeff(0), trace);
}

static void test_fixed_size_scalar_ref() {
  Tensor<Vector2d, 2> input(3, 3);
  TensorRef<Tensor<Vector2d, 2>> ref(input);

  VERIFY_IS_EQUAL(ref.data(), input.data());
  VERIFY_IS_EQUAL(ref.dimension(0), 3);
  VERIFY_IS_EQUAL(ref.dimension(1), 3);

  ref.coeffRef(0) = Vector2d(1.0, 2.0);
  VERIFY_IS_APPROX(input.coeff(0), Vector2d(1.0, 2.0));

  // Four doubles require over-aligned evaluator storage when AVX is enabled in C++14.
  Tensor<Vector4d, 1> wide_input(2);
  TensorRef<Tensor<Vector4d, 1>> wide_ref(wide_input);
  wide_ref.coeffRef(0) = Vector4d::Ones();
  VERIFY_IS_EQUAL(wide_input.coeff(0), Vector4d::Ones());
}

static void test_ref_of_ref() {
  Tensor<float, 3> input(3, 5, 7);
  input.setRandom();

  TensorRef<Tensor<float, 3>> ref(input);
  TensorRef<Tensor<float, 3>> ref_of_ref(ref);
  TensorRef<Tensor<float, 3>> ref_of_ref2;
  ref_of_ref2 = ref;

  VERIFY_IS_EQUAL(ref_of_ref.data(), input.data());
  VERIFY_IS_EQUAL(ref_of_ref.dimension(0), 3);
  VERIFY_IS_EQUAL(ref_of_ref.dimension(1), 5);
  VERIFY_IS_EQUAL(ref_of_ref.dimension(2), 7);

  VERIFY_IS_EQUAL(ref_of_ref2.data(), input.data());
  VERIFY_IS_EQUAL(ref_of_ref2.dimension(0), 3);
  VERIFY_IS_EQUAL(ref_of_ref2.dimension(1), 5);
  VERIFY_IS_EQUAL(ref_of_ref2.dimension(2), 7);

  for (int i = 0; i < 3; ++i) {
    for (int j = 0; j < 5; ++j) {
      for (int k = 0; k < 7; ++k) {
        VERIFY_IS_EQUAL(ref_of_ref(i, j, k), input(i, j, k));
        VERIFY_IS_EQUAL(ref_of_ref2(i, j, k), input(i, j, k));
      }
    }
  }
}

static void test_ref_in_expr() {
  Tensor<float, 3> input(3, 5, 7);
  input.setRandom();
  TensorRef<Tensor<float, 3>> input_ref(input);

  Tensor<float, 3> result(3, 5, 7);
  result.setRandom();
  TensorRef<const Tensor<float, 3>> result_ref(result);

  Tensor<float, 3> bias(3, 5, 7);
  bias.setRandom();

  result_ref = input_ref + bias;
  for (int i = 0; i < 3; ++i) {
    for (int j = 0; j < 5; ++j) {
      for (int k = 0; k < 7; ++k) {
        VERIFY_IS_EQUAL(result_ref(i, j, k), input(i, j, k) + bias(i, j, k));
        VERIFY_IS_NOT_EQUAL(result(i, j, k), input(i, j, k) + bias(i, j, k));
      }
    }
  }

  result = result_ref;
  for (int i = 0; i < 3; ++i) {
    for (int j = 0; j < 5; ++j) {
      for (int k = 0; k < 7; ++k) {
        VERIFY_IS_EQUAL(result(i, j, k), input(i, j, k) + bias(i, j, k));
      }
    }
  }
}

static void test_coeff_ref() {
  Tensor<float, 5> tensor(2, 3, 5, 7, 11);
  tensor.setRandom();
  Tensor<float, 5> original = tensor;

  TensorRef<Tensor<float, 4>> slice = tensor.chip(7, 4);
  slice.coeffRef(0, 0, 0, 0) = 1.0f;
  slice.coeffRef(1, 0, 0, 0) += 2.0f;

  VERIFY_IS_EQUAL(tensor(0, 0, 0, 0, 7), 1.0f);
  VERIFY_IS_EQUAL(tensor(1, 0, 0, 0, 7), original(1, 0, 0, 0, 7) + 2.0f);
}

static void test_nested_ops_with_ref() {
  Tensor<float, 4> t(2, 3, 5, 7);
  t.setRandom();
  TensorMap<Tensor<const float, 4>> m(t.data(), 2, 3, 5, 7);
  array<std::pair<ptrdiff_t, ptrdiff_t>, 4> paddings;
  paddings[0] = std::make_pair(0, 0);
  paddings[1] = std::make_pair(2, 1);
  paddings[2] = std::make_pair(3, 4);
  paddings[3] = std::make_pair(0, 0);
  DSizes<Eigen::DenseIndex, 4> shuffle_dims(0, 1, 2, 3);
  TensorRef<const Tensor<const float, 4>> ref(m.pad(paddings));
  array<std::pair<ptrdiff_t, ptrdiff_t>, 4> trivial;
  trivial[0] = std::make_pair(0, 0);
  trivial[1] = std::make_pair(0, 0);
  trivial[2] = std::make_pair(0, 0);
  trivial[3] = std::make_pair(0, 0);
  Tensor<float, 4> padded = ref.shuffle(shuffle_dims).pad(trivial);
  VERIFY_IS_EQUAL(padded.dimension(0), 2 + 0);
  VERIFY_IS_EQUAL(padded.dimension(1), 3 + 3);
  VERIFY_IS_EQUAL(padded.dimension(2), 5 + 7);
  VERIFY_IS_EQUAL(padded.dimension(3), 7 + 0);

  for (int i = 0; i < 2; ++i) {
    for (int j = 0; j < 6; ++j) {
      for (int k = 0; k < 12; ++k) {
        for (int l = 0; l < 7; ++l) {
          if (j >= 2 && j < 5 && k >= 3 && k < 8) {
            VERIFY_IS_EQUAL(padded(i, j, k, l), t(i, j - 2, k - 3, l));
          } else {
            VERIFY_IS_EQUAL(padded(i, j, k, l), 0.0f);
          }
        }
      }
    }
  }
}

// Regression test for issue #1616: unsigned index types must not trigger
// narrowing errors in the variadic accessors.
static void test_unsigned_indices() {
  Tensor<float, 3> tensor(2, 3, 7);
  tensor.setRandom();

  TensorRef<Tensor<float, 3>> ref(tensor);
  VERIFY_IS_EQUAL(ref(1u, std::size_t(2), 6ul), tensor(1, 2, 6));
  ref.coeffRef(1u, 2ul, std::size_t(6)) = 5.0f;
  VERIFY_IS_EQUAL(tensor(1, 2, 6), 5.0f);
}

// TensorRef's accessors must take the plain object's index type. Unqualified Index in the class body does not find
// the base's typedef, so coeffRef used to take Eigen::Index while operator() took the plain object's index type.
static void test_index_type() {
  using RefType = Eigen::TensorRef<Tensor<float, 3, Eigen::ColMajor, int>>;
  VERIFY((std::is_same<RefType::Index, int>::value));

  Tensor<float, 3, Eigen::ColMajor, int> tensor(2, 3, 7);
  tensor.setZero();
  RefType ref(tensor);

  float& (RefType::*coeff_ref)(RefType::Index) = &RefType::coeffRef;
  (ref.*coeff_ref)(3) = 5.0f;
  VERIFY_IS_EQUAL(tensor.coeff(3), 5.0f);
}

// An index the ref's index type cannot represent must assert rather than silently truncate. size_t(-1) is
// representable by no signed index type, so this holds whichever index type the accessor takes.
static void test_narrowing_indices() {
  Tensor<float, 3> tensor(2, 3, 7);
  tensor.setRandom();

  TensorRef<Tensor<float, 3>> ref(tensor);
  VERIFY_RAISES_ASSERT(ref(0, 0, std::size_t(-1)));
  VERIFY_RAISES_ASSERT(ref.coeffRef(0, 0, std::size_t(-1)) = 1.0f);
}

static void test_copy_of_empty_ref() {
  // Copying a default-constructed (empty) ref must not dereference its null evaluator.
  TensorRef<Tensor<float, 2>> empty;
  TensorRef<Tensor<float, 2>> copy(empty);
  Tensor<float, 2> input(2, 3);
  input.setRandom();
  TensorRef<Tensor<float, 2>> assigned(input);
  assigned = empty;
  copy = TensorRef<Tensor<float, 2>>(input);
  VERIFY_IS_EQUAL(copy(1, 2), input(1, 2));
}

EIGEN_DECLARE_TEST(tensor_ref) {
  CALL_SUBTEST(test_copy_of_empty_ref());
  CALL_SUBTEST(test_simple_lvalue_ref());
  CALL_SUBTEST(test_simple_rvalue_ref());
  CALL_SUBTEST(test_multiple_dims());
  CALL_SUBTEST(test_slice());
  CALL_SUBTEST(test_ref_of_trace());
  CALL_SUBTEST(test_fixed_size_scalar_ref());
  CALL_SUBTEST(test_ref_of_ref());
  CALL_SUBTEST(test_ref_in_expr());
  CALL_SUBTEST(test_coeff_ref());
  CALL_SUBTEST(test_nested_ops_with_ref());
  CALL_SUBTEST(test_unsigned_indices());
  CALL_SUBTEST(test_index_type());
  CALL_SUBTEST(test_narrowing_indices());
}