mirror of
https://gitlab.com/libeigen/eigen.git
synced 2025-04-22 01:29:35 +08:00
6293ad3f39
1. Added new packet functions using SIMD for NByOne, OneByN cases 2. Modified existing packet functions to reduce index calculations when input stride is non-SIMD 3. Added 4 test cases to cover the new packet functions
257 lines
6.9 KiB
C++
257 lines
6.9 KiB
C++
// 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
|
|
}
|
|
|
|
template <int DataLayout>
|
|
static void test_simple_broadcasting_one_by_n()
|
|
{
|
|
Tensor<float, 4, DataLayout> tensor(1,13,5,7);
|
|
tensor.setRandom();
|
|
array<ptrdiff_t, 4> broadcasts;
|
|
broadcasts[0] = 9;
|
|
broadcasts[1] = 1;
|
|
broadcasts[2] = 1;
|
|
broadcasts[3] = 1;
|
|
Tensor<float, 4, DataLayout> broadcast;
|
|
broadcast = tensor.broadcast(broadcasts);
|
|
|
|
VERIFY_IS_EQUAL(broadcast.dimension(0), 9);
|
|
VERIFY_IS_EQUAL(broadcast.dimension(1), 13);
|
|
VERIFY_IS_EQUAL(broadcast.dimension(2), 5);
|
|
VERIFY_IS_EQUAL(broadcast.dimension(3), 7);
|
|
|
|
for (int i = 0; i < 9; ++i) {
|
|
for (int j = 0; j < 13; ++j) {
|
|
for (int k = 0; k < 5; ++k) {
|
|
for (int l = 0; l < 7; ++l) {
|
|
VERIFY_IS_EQUAL(tensor(i%1,j%13,k%5,l%7), broadcast(i,j,k,l));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
template <int DataLayout>
|
|
static void test_simple_broadcasting_n_by_one()
|
|
{
|
|
Tensor<float, 4, DataLayout> tensor(7,3,5,1);
|
|
tensor.setRandom();
|
|
array<ptrdiff_t, 4> broadcasts;
|
|
broadcasts[0] = 1;
|
|
broadcasts[1] = 1;
|
|
broadcasts[2] = 1;
|
|
broadcasts[3] = 19;
|
|
Tensor<float, 4, DataLayout> broadcast;
|
|
broadcast = tensor.broadcast(broadcasts);
|
|
|
|
VERIFY_IS_EQUAL(broadcast.dimension(0), 7);
|
|
VERIFY_IS_EQUAL(broadcast.dimension(1), 3);
|
|
VERIFY_IS_EQUAL(broadcast.dimension(2), 5);
|
|
VERIFY_IS_EQUAL(broadcast.dimension(3), 19);
|
|
|
|
for (int i = 0; i < 7; ++i) {
|
|
for (int j = 0; j < 3; ++j) {
|
|
for (int k = 0; k < 5; ++k) {
|
|
for (int l = 0; l < 19; ++l) {
|
|
VERIFY_IS_EQUAL(tensor(i%7,j%3,k%5,l%1), broadcast(i,j,k,l));
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
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>());
|
|
CALL_SUBTEST(test_simple_broadcasting_one_by_n<RowMajor>());
|
|
CALL_SUBTEST(test_simple_broadcasting_n_by_one<RowMajor>());
|
|
CALL_SUBTEST(test_simple_broadcasting_one_by_n<ColMajor>());
|
|
CALL_SUBTEST(test_simple_broadcasting_n_by_one<ColMajor>());
|
|
}
|