eigen/unsupported/test/cxx11_tensor_thread_pool.cpp
2016-01-30 10:56:47 -08:00

335 lines
11 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/.
#define EIGEN_USE_THREADS
#include "main.h"
#include <iostream>
#include <Eigen/CXX11/Tensor>
using Eigen::Tensor;
void test_multithread_elementwise()
{
Tensor<float, 3> in1(2,3,7);
Tensor<float, 3> in2(2,3,7);
Tensor<float, 3> out(2,3,7);
in1.setRandom();
in2.setRandom();
Eigen::ThreadPool tp(internal::random<int>(3, 11));
Eigen::ThreadPoolDevice thread_pool_device(&tp, internal::random<int>(3, 11));
out.device(thread_pool_device) = in1 + in2 * 3.14f;
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 3; ++j) {
for (int k = 0; k < 7; ++k) {
VERIFY_IS_APPROX(out(i,j,k), in1(i,j,k) + in2(i,j,k) * 3.14f);
}
}
}
}
void test_multithread_compound_assignment()
{
Tensor<float, 3> in1(2,3,7);
Tensor<float, 3> in2(2,3,7);
Tensor<float, 3> out(2,3,7);
in1.setRandom();
in2.setRandom();
Eigen::ThreadPool tp(internal::random<int>(3, 11));
Eigen::ThreadPoolDevice thread_pool_device(&tp, internal::random<int>(3, 11));
out.device(thread_pool_device) = in1;
out.device(thread_pool_device) += in2 * 3.14f;
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 3; ++j) {
for (int k = 0; k < 7; ++k) {
VERIFY_IS_APPROX(out(i,j,k), in1(i,j,k) + in2(i,j,k) * 3.14f);
}
}
}
}
template<int DataLayout>
void test_multithread_contraction()
{
Tensor<float, 4, DataLayout> t_left(30, 50, 37, 31);
Tensor<float, 5, DataLayout> t_right(37, 31, 70, 2, 10);
Tensor<float, 5, DataLayout> t_result(30, 50, 70, 2, 10);
t_left.setRandom();
t_right.setRandom();
// this contraction should be equivalent to a single matrix multiplication
typedef Tensor<float, 1>::DimensionPair DimPair;
Eigen::array<DimPair, 2> dims({{DimPair(2, 0), DimPair(3, 1)}});
typedef Map<Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf;
MapXf m_left(t_left.data(), 1500, 1147);
MapXf m_right(t_right.data(), 1147, 1400);
Matrix<float, Dynamic, Dynamic, DataLayout> m_result(1500, 1400);
Eigen::ThreadPool tp(4);
Eigen::ThreadPoolDevice thread_pool_device(&tp, 4);
// compute results by separate methods
t_result.device(thread_pool_device) = t_left.contract(t_right, dims);
m_result = m_left * m_right;
for (ptrdiff_t i = 0; i < t_result.size(); i++) {
VERIFY(&t_result.data()[i] != &m_result.data()[i]);
if (fabs(t_result(i) - m_result(i)) < 1e-4) {
continue;
}
if (Eigen::internal::isApprox(t_result(i), m_result(i), 1e-4f)) {
continue;
}
std::cout << "mismatch detected at index " << i << ": " << t_result(i)
<< " vs " << m_result(i) << std::endl;
assert(false);
}
}
template<int DataLayout>
void test_contraction_corner_cases()
{
Tensor<float, 2, DataLayout> t_left(32, 500);
Tensor<float, 2, DataLayout> t_right(32, 28*28);
Tensor<float, 2, DataLayout> t_result(500, 28*28);
t_left = (t_left.constant(-0.5f) + t_left.random()) * 2.0f;
t_right = (t_right.constant(-0.6f) + t_right.random()) * 2.0f;
t_result = t_result.constant(NAN);
// this contraction should be equivalent to a single matrix multiplication
typedef Tensor<float, 1>::DimensionPair DimPair;
Eigen::array<DimPair, 1> dims{{DimPair(0, 0)}};
typedef Map<Matrix<float, Dynamic, Dynamic, DataLayout>> MapXf;
MapXf m_left(t_left.data(), 32, 500);
MapXf m_right(t_right.data(), 32, 28*28);
Matrix<float, Dynamic, Dynamic, DataLayout> m_result(500, 28*28);
Eigen::ThreadPool tp(12);
Eigen::ThreadPoolDevice thread_pool_device(&tp, 12);
// compute results by separate methods
t_result.device(thread_pool_device) = t_left.contract(t_right, dims);
m_result = m_left.transpose() * m_right;
for (ptrdiff_t i = 0; i < t_result.size(); i++) {
assert(!(numext::isnan)(t_result.data()[i]));
if (fabs(t_result.data()[i] - m_result.data()[i]) >= 1e-4) {
std::cout << "mismatch detected at index " << i << " : " << t_result.data()[i] << " vs " << m_result.data()[i] << std::endl;
assert(false);
}
}
t_left.resize(32, 1);
t_left = (t_left.constant(-0.5f) + t_left.random()) * 2.0f;
t_result.resize (1, 28*28);
t_result = t_result.constant(NAN);
t_result.device(thread_pool_device) = t_left.contract(t_right, dims);
new(&m_left) MapXf(t_left.data(), 32, 1);
m_result = m_left.transpose() * m_right;
for (ptrdiff_t i = 0; i < t_result.size(); i++) {
assert(!(numext::isnan)(t_result.data()[i]));
if (fabs(t_result.data()[i] - m_result.data()[i]) >= 1e-4) {
std::cout << "mismatch detected: " << t_result.data()[i] << " vs " << m_result.data()[i] << std::endl;
assert(false);
}
}
t_left.resize(32, 500);
t_right.resize(32, 4);
t_left = (t_left.constant(-0.5f) + t_left.random()) * 2.0f;
t_right = (t_right.constant(-0.6f) + t_right.random()) * 2.0f;
t_result.resize (500, 4);
t_result = t_result.constant(NAN);
t_result.device(thread_pool_device) = t_left.contract(t_right, dims);
new(&m_left) MapXf(t_left.data(), 32, 500);
new(&m_right) MapXf(t_right.data(), 32, 4);
m_result = m_left.transpose() * m_right;
for (ptrdiff_t i = 0; i < t_result.size(); i++) {
assert(!(numext::isnan)(t_result.data()[i]));
if (fabs(t_result.data()[i] - m_result.data()[i]) >= 1e-4) {
std::cout << "mismatch detected: " << t_result.data()[i] << " vs " << m_result.data()[i] << std::endl;
assert(false);
}
}
t_left.resize(32, 1);
t_right.resize(32, 4);
t_left = (t_left.constant(-0.5f) + t_left.random()) * 2.0f;
t_right = (t_right.constant(-0.6f) + t_right.random()) * 2.0f;
t_result.resize (1, 4);
t_result = t_result.constant(NAN);
t_result.device(thread_pool_device) = t_left.contract(t_right, dims);
new(&m_left) MapXf(t_left.data(), 32, 1);
new(&m_right) MapXf(t_right.data(), 32, 4);
m_result = m_left.transpose() * m_right;
for (ptrdiff_t i = 0; i < t_result.size(); i++) {
assert(!(numext::isnan)(t_result.data()[i]));
if (fabs(t_result.data()[i] - m_result.data()[i]) >= 1e-4) {
std::cout << "mismatch detected: " << t_result.data()[i] << " vs " << m_result.data()[i] << std::endl;
assert(false);
}
}
}
template<int DataLayout>
void test_multithread_contraction_agrees_with_singlethread() {
int contract_size = internal::random<int>(1, 5000);
Tensor<float, 3, DataLayout> left(internal::random<int>(1, 80),
contract_size,
internal::random<int>(1, 100));
Tensor<float, 4, DataLayout> right(internal::random<int>(1, 25),
internal::random<int>(1, 37),
contract_size,
internal::random<int>(1, 51));
left.setRandom();
right.setRandom();
// add constants to shift values away from 0 for more precision
left += left.constant(1.5f);
right += right.constant(1.5f);
typedef Tensor<float, 1>::DimensionPair DimPair;
Eigen::array<DimPair, 1> dims({{DimPair(1, 2)}});
Eigen::ThreadPool tp(internal::random<int>(2, 11));
Eigen::ThreadPoolDevice thread_pool_device(&tp, internal::random<int>(2, 11));
Tensor<float, 5, DataLayout> st_result;
st_result = left.contract(right, dims);
Tensor<float, 5, DataLayout> tp_result(st_result.dimensions());
tp_result.device(thread_pool_device) = left.contract(right, dims);
VERIFY(dimensions_match(st_result.dimensions(), tp_result.dimensions()));
for (ptrdiff_t i = 0; i < st_result.size(); i++) {
// if both of the values are very small, then do nothing (because the test will fail
// due to numerical precision issues when values are small)
if (fabs(st_result.data()[i] - tp_result.data()[i]) >= 1e-4) {
VERIFY_IS_APPROX(st_result.data()[i], tp_result.data()[i]);
}
}
}
template<int DataLayout>
void test_multithreaded_reductions() {
const int num_threads = internal::random<int>(3, 11);
ThreadPool thread_pool(num_threads);
Eigen::ThreadPoolDevice thread_pool_device(&thread_pool, num_threads);
const int num_rows = internal::random<int>(13, 732);
const int num_cols = internal::random<int>(13, 732);
Tensor<float, 2, DataLayout> t1(num_rows, num_cols);
t1.setRandom();
Tensor<float, 0, DataLayout> full_redux;
full_redux = t1.sum();
Tensor<float, 0, DataLayout> full_redux_tp;
full_redux_tp.device(thread_pool_device) = t1.sum();
// Check that the single threaded and the multi threaded reductions return
// the same result.
VERIFY_IS_APPROX(full_redux(), full_redux_tp());
}
void test_memcpy() {
for (int i = 0; i < 5; ++i) {
const int num_threads = internal::random<int>(3, 11);
Eigen::ThreadPool tp(num_threads);
Eigen::ThreadPoolDevice thread_pool_device(&tp, num_threads);
const int size = internal::random<int>(13, 7632);
Tensor<float, 1> t1(size);
t1.setRandom();
std::vector<float> result(size);
thread_pool_device.memcpy(&result[0], t1.data(), size*sizeof(float));
for (int j = 0; j < size; j++) {
VERIFY_IS_EQUAL(t1(j), result[j]);
}
}
}
void test_multithread_random()
{
Eigen::ThreadPool tp(2);
Eigen::ThreadPoolDevice device(&tp, 2);
Tensor<float, 1> t(1 << 20);
t.device(device) = t.random<Eigen::internal::NormalRandomGenerator<float>>();
}
template<int DataLayout>
void test_multithread_shuffle()
{
Tensor<float, 4, DataLayout> tensor(17,5,7,11);
tensor.setRandom();
const int num_threads = internal::random<int>(2, 11);
ThreadPool threads(num_threads);
Eigen::ThreadPoolDevice device(&threads, num_threads);
Tensor<float, 4, DataLayout> shuffle(7,5,11,17);
array<ptrdiff_t, 4> shuffles = {{2,1,3,0}};
shuffle.device(device) = tensor.shuffle(shuffles);
for (int i = 0; i < 17; ++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(tensor(i,j,k,l), shuffle(k,j,l,i));
}
}
}
}
}
void test_cxx11_tensor_thread_pool()
{
CALL_SUBTEST_1(test_multithread_elementwise());
CALL_SUBTEST_1(test_multithread_compound_assignment());
CALL_SUBTEST_2(test_multithread_contraction<ColMajor>());
CALL_SUBTEST_2(test_multithread_contraction<RowMajor>());
CALL_SUBTEST_3(test_multithread_contraction_agrees_with_singlethread<ColMajor>());
CALL_SUBTEST_3(test_multithread_contraction_agrees_with_singlethread<RowMajor>());
// Exercise various cases that have been problematic in the past.
CALL_SUBTEST_4(test_contraction_corner_cases<ColMajor>());
CALL_SUBTEST_4(test_contraction_corner_cases<RowMajor>());
CALL_SUBTEST_5(test_multithreaded_reductions<ColMajor>());
CALL_SUBTEST_5(test_multithreaded_reductions<RowMajor>());
CALL_SUBTEST_6(test_memcpy());
CALL_SUBTEST_6(test_multithread_random());
CALL_SUBTEST_6(test_multithread_shuffle<ColMajor>());
CALL_SUBTEST_6(test_multithread_shuffle<RowMajor>());
}