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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>
// 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)
{
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);
cudaStream_t stream;
assert(cudaStreamCreate(&stream) == cudaSuccess);
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.dimensions().TotalSize(); i++) {
if (fabs(t_result.data()[i] - t_result_gpu.data()[i]) >= 1e-4) {
cout << "mismatch detected at index " << i << ": " << t_result.data()[i]
<< " vs " << t_result_gpu.data()[i] << endl;
assert(false);
}
}
cudaFree((void*)d_t_left);
cudaFree((void*)d_t_right);
cudaFree((void*)d_t_result);
}
void test_cxx11_tensor_cuda()
{
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]));
}
}
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