eigen/unsupported/test/cxx11_tensor_contract_cuda.cu

// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
// Copyright (C) 2014 Navdeep Jaitly <ndjaitly@google.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_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX
#define EIGEN_TEST_FUNC cxx11_tensor_cuda
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
#define EIGEN_USE_GPU


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

using Eigen::Tensor;
typedef Tensor<float, 1>::DimensionPair DimPair;

template<int DataLayout>
static void test_cuda_contraction(int m_size, int k_size, int n_size)
{
  std::cout << "Calling with (" << m_size << "," << k_size << "," << n_size << ")" << std::endl;
  // with these dimensions, the output has 300 * 140 elements, which is
  // more than 30 * 1024, which is the number of threads in blocks on
  // a 15 SM GK110 GPU
  Tensor<float, 2, DataLayout> t_left(Eigen::array<int, 2>(m_size, k_size));
  Tensor<float, 2, DataLayout> t_right(Eigen::array<int, 2>(k_size, n_size));
  Tensor<float, 2, DataLayout> t_result(Eigen::array<int, 2>(m_size, n_size));
  Tensor<float, 2, DataLayout> t_result_gpu(Eigen::array<int, 2>(m_size, n_size));
  Eigen::array<DimPair, 1> dims(DimPair(1, 0));

  t_left.setRandom();
  t_right.setRandom();

  std::size_t t_left_bytes = t_left.size()  * sizeof(float);
  std::size_t t_right_bytes = t_right.size() * sizeof(float);
  std::size_t t_result_bytes = t_result.size() * sizeof(float);

  float* d_t_left;
  float* d_t_right;
  float* d_t_result;

  cudaMalloc((void**)(&d_t_left), t_left_bytes);
  cudaMalloc((void**)(&d_t_right), t_right_bytes);
  cudaMalloc((void**)(&d_t_result), t_result_bytes);

  cudaMemcpy(d_t_left, t_left.data(), t_left_bytes, cudaMemcpyHostToDevice);
  cudaMemcpy(d_t_right, t_right.data(), t_right_bytes, cudaMemcpyHostToDevice);

  Eigen::CudaStreamDevice stream;
  Eigen::GpuDevice gpu_device(&stream);

  Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> >
      gpu_t_left(d_t_left, Eigen::array<int, 2>(m_size, k_size));
  Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> >
      gpu_t_right(d_t_right, Eigen::array<int, 2>(k_size, n_size));
  Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> >
      gpu_t_result(d_t_result, Eigen::array<int, 2>(m_size, n_size));


  gpu_t_result.device(gpu_device) = gpu_t_left.contract(gpu_t_right, dims);
  t_result = t_left.contract(t_right, dims);

  cudaMemcpy(t_result_gpu.data(), d_t_result, t_result_bytes, cudaMemcpyDeviceToHost);
  for (size_t i = 0; i < t_result.size(); i++) {
    if (fabs(t_result(i) - t_result_gpu(i)) < 1e-4f) {
      continue;
    }
    if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), 1e-4f)) {
      continue;
    }
    std::cout << "mismatch detected at index " << i << ": " << t_result(i)
              << " vs " <<  t_result_gpu(i) << std::endl;
    assert(false);
  }

  cudaFree((void*)d_t_left);
  cudaFree((void*)d_t_right);
  cudaFree((void*)d_t_result);
}


void test_cxx11_tensor_cuda()
{
  std::cout << "Calling contraction tests" << std::endl;
  CALL_SUBTEST(test_cuda_contraction<ColMajor>(128, 128, 128));
  CALL_SUBTEST(test_cuda_contraction<RowMajor>(128, 128, 128));
  for (int k = 32; k < 256; k++) {
    CALL_SUBTEST(test_cuda_contraction<ColMajor>(128, k, 128));
    CALL_SUBTEST(test_cuda_contraction<RowMajor>(128, k, 128));
  }
  for (int k = 32; k < 256; k++) {
    CALL_SUBTEST(test_cuda_contraction<ColMajor>(128, 128, k));
    CALL_SUBTEST(test_cuda_contraction<RowMajor>(128, 128, k));
  }
  for (int k = 32; k < 256; k++) {
    CALL_SUBTEST(test_cuda_contraction<ColMajor>(k, 128, 128));
    CALL_SUBTEST(test_cuda_contraction<RowMajor>(k, 128, 128));
  }

  int m_sizes[] = {31,   39,   63,   64,  65,
                   127, 129,  255,  257, 511,
                   512, 513, 1023, 1024, 1025 };
  int n_sizes[] = {31,   39,   63,   64,  65,
                   127, 129,  255,  257, 511,
                   512, 513, 1023, 1024, 1025 };

  int k_sizes[] = { 31,  39,  63, 64,    65,
                    95,  96, 127, 129,  255,
                   257, 511, 512, 513, 1023,
                  1024, 1025};

  for (int i = 0; i <15; i++)
    for (int j = 0; j < 15; j++)
      for (int k = 0; k < 17; k++) {
        CALL_SUBTEST(test_cuda_contraction<ColMajor>(m_sizes[i], n_sizes[j], k_sizes[k]));
        CALL_SUBTEST(test_cuda_contraction<RowMajor>(m_sizes[i], n_sizes[j], k_sizes[k]));
      }
}