eigen/unsupported/test/cxx11_tensor_thread_pool.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/.

#define EIGEN_USE_THREADS


#include "main.h"
#include <iostream>
#include <Eigen/CXX11/Tensor>

using Eigen::Tensor;


static 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);
      }
    }
  }
}


static 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>
static 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.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>
static 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>
static 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>
static 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, 1, DataLayout> full_redux(1);
  full_redux = t1.sum();

  Tensor<float, 1, DataLayout> full_redux_tp(1);
  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(0), full_redux_tp(0));
}


static 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]);
    }
  }
}


static 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>>();
}


void test_cxx11_tensor_thread_pool()
{
  CALL_SUBTEST(test_multithread_elementwise());
  CALL_SUBTEST(test_multithread_compound_assignment());

  CALL_SUBTEST(test_multithread_contraction<ColMajor>());
  CALL_SUBTEST(test_multithread_contraction<RowMajor>());

  CALL_SUBTEST(test_multithread_contraction_agrees_with_singlethread<ColMajor>());
  CALL_SUBTEST(test_multithread_contraction_agrees_with_singlethread<RowMajor>());

  // Exercise various cases that have been problematic in the past.
  CALL_SUBTEST(test_contraction_corner_cases<ColMajor>());
  CALL_SUBTEST(test_contraction_corner_cases<RowMajor>());

  CALL_SUBTEST(test_multithreaded_reductions<ColMajor>());
  CALL_SUBTEST(test_multithreaded_reductions<RowMajor>());

  CALL_SUBTEST(test_memcpy());

  CALL_SUBTEST(test_multithread_random());
}