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// 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::Tensor;
template <int DataLayout>
static void test_simple_broadcasting()
{
Tensor<float, 4, DataLayout> tensor(2,3,5,7);
tensor.setRandom();
array<ptrdiff_t, 4> broadcasts;
broadcasts[0] = 1;
broadcasts[1] = 1;
broadcasts[2] = 1;
broadcasts[3] = 1;
Tensor<float, 4, DataLayout> no_broadcast;
no_broadcast = tensor.broadcast(broadcasts);
VERIFY_IS_EQUAL(no_broadcast.dimension(0), 2);
VERIFY_IS_EQUAL(no_broadcast.dimension(1), 3);
VERIFY_IS_EQUAL(no_broadcast.dimension(2), 5);
VERIFY_IS_EQUAL(no_broadcast.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(tensor(i,j,k,l), no_broadcast(i,j,k,l));
}
}
}
}
broadcasts[0] = 2;
broadcasts[1] = 3;
broadcasts[2] = 1;
broadcasts[3] = 4;
Tensor<float, 4, DataLayout> broadcast;
broadcast = tensor.broadcast(broadcasts);
VERIFY_IS_EQUAL(broadcast.dimension(0), 4);
VERIFY_IS_EQUAL(broadcast.dimension(1), 9);
VERIFY_IS_EQUAL(broadcast.dimension(2), 5);
VERIFY_IS_EQUAL(broadcast.dimension(3), 28);
for (int i = 0; i < 4; ++i) {
for (int j = 0; j < 9; ++j) {
for (int k = 0; k < 5; ++k) {
for (int l = 0; l < 28; ++l) {
VERIFY_IS_EQUAL(tensor(i%2,j%3,k%5,l%7), broadcast(i,j,k,l));
}
}
}
}
}
template <int DataLayout>
static void test_vectorized_broadcasting()
{
Tensor<float, 3, DataLayout> tensor(8,3,5);
tensor.setRandom();
array<ptrdiff_t, 3> broadcasts;
broadcasts[0] = 2;
broadcasts[1] = 3;
broadcasts[2] = 4;
Tensor<float, 3, DataLayout> broadcast;
broadcast = tensor.broadcast(broadcasts);
VERIFY_IS_EQUAL(broadcast.dimension(0), 16);
VERIFY_IS_EQUAL(broadcast.dimension(1), 9);
VERIFY_IS_EQUAL(broadcast.dimension(2), 20);
for (int i = 0; i < 16; ++i) {
for (int j = 0; j < 9; ++j) {
for (int k = 0; k < 20; ++k) {
VERIFY_IS_EQUAL(tensor(i%8,j%3,k%5), broadcast(i,j,k));
}
}
}
tensor.resize(11,3,5);
tensor.setRandom();
broadcast = tensor.broadcast(broadcasts);
VERIFY_IS_EQUAL(broadcast.dimension(0), 22);
VERIFY_IS_EQUAL(broadcast.dimension(1), 9);
VERIFY_IS_EQUAL(broadcast.dimension(2), 20);
for (int i = 0; i < 22; ++i) {
for (int j = 0; j < 9; ++j) {
for (int k = 0; k < 20; ++k) {
VERIFY_IS_EQUAL(tensor(i%11,j%3,k%5), broadcast(i,j,k));
}
}
}
}
template <int DataLayout>
static void test_static_broadcasting()
{
Tensor<float, 3, DataLayout> tensor(8,3,5);
tensor.setRandom();
#if EIGEN_HAS_CONSTEXPR
Eigen::IndexList<Eigen::type2index<2>, Eigen::type2index<3>, Eigen::type2index<4>> broadcasts;
#else
Eigen::array<int, 3> broadcasts;
broadcasts[0] = 2;
broadcasts[1] = 3;
broadcasts[2] = 4;
#endif
Tensor<float, 3, DataLayout> broadcast;
broadcast = tensor.broadcast(broadcasts);
VERIFY_IS_EQUAL(broadcast.dimension(0), 16);
VERIFY_IS_EQUAL(broadcast.dimension(1), 9);
VERIFY_IS_EQUAL(broadcast.dimension(2), 20);
for (int i = 0; i < 16; ++i) {
for (int j = 0; j < 9; ++j) {
for (int k = 0; k < 20; ++k) {
VERIFY_IS_EQUAL(tensor(i%8,j%3,k%5), broadcast(i,j,k));
}
}
}
tensor.resize(11,3,5);
tensor.setRandom();
broadcast = tensor.broadcast(broadcasts);
VERIFY_IS_EQUAL(broadcast.dimension(0), 22);
VERIFY_IS_EQUAL(broadcast.dimension(1), 9);
VERIFY_IS_EQUAL(broadcast.dimension(2), 20);
for (int i = 0; i < 22; ++i) {
for (int j = 0; j < 9; ++j) {
for (int k = 0; k < 20; ++k) {
VERIFY_IS_EQUAL(tensor(i%11,j%3,k%5), broadcast(i,j,k));
}
}
}
}
template <int DataLayout>
static void test_fixed_size_broadcasting()
{
// Need to add a [] operator to the Size class for this to work
#if 0
Tensor<float, 1, DataLayout> t1(10);
t1.setRandom();
TensorFixedSize<float, Sizes<1>, DataLayout> t2;
t2 = t2.constant(20.0f);
Tensor<float, 1, DataLayout> t3 = t1 + t2.broadcast(Eigen::array<int, 1>{{10}});
for (int i = 0; i < 10; ++i) {
VERIFY_IS_APPROX(t3(i), t1(i) + t2(0));
}
TensorMap<TensorFixedSize<float, Sizes<1>, DataLayout> > t4(t2.data(), {{1}});
Tensor<float, 1, DataLayout> t5 = t1 + t4.broadcast(Eigen::array<int, 1>{{10}});
for (int i = 0; i < 10; ++i) {
VERIFY_IS_APPROX(t5(i), t1(i) + t2(0));
}
#endif
}
void test_cxx11_tensor_broadcasting()
{
CALL_SUBTEST(test_simple_broadcasting<ColMajor>());
CALL_SUBTEST(test_simple_broadcasting<RowMajor>());
CALL_SUBTEST(test_vectorized_broadcasting<ColMajor>());
CALL_SUBTEST(test_vectorized_broadcasting<RowMajor>());
CALL_SUBTEST(test_static_broadcasting<ColMajor>());
CALL_SUBTEST(test_static_broadcasting<RowMajor>());
CALL_SUBTEST(test_fixed_size_broadcasting<ColMajor>());
CALL_SUBTEST(test_fixed_size_broadcasting<RowMajor>());
}
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