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122 lines
4.5 KiB
C++
122 lines
4.5 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.com>
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// Copyright (C) 2014 Navdeep Jaitly <ndjaitly@google.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#define EIGEN_TEST_NO_LONGDOUBLE
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#define EIGEN_TEST_NO_COMPLEX
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#define EIGEN_TEST_FUNC cxx11_tensor_cuda
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#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
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#define EIGEN_USE_GPU
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#include "main.h"
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#include <unsupported/Eigen/CXX11/Tensor>
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using Eigen::Tensor;
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typedef Tensor<float, 1>::DimensionPair DimPair;
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template<int DataLayout>
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static void test_cuda_contraction(int m_size, int k_size, int n_size)
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{
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cout<<"Calling with ("<<m_size<<","<<k_size<<","<<n_size<<")"<<std::endl;
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// with these dimensions, the output has 300 * 140 elements, which is
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// more than 30 * 1024, which is the number of threads in blocks on
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// a 15 SM GK110 GPU
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Tensor<float, 2, DataLayout> t_left(Eigen::array<int, 2>(m_size, k_size));
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Tensor<float, 2, DataLayout> t_right(Eigen::array<int, 2>(k_size, n_size));
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Tensor<float, 2, DataLayout> t_result(Eigen::array<int, 2>(m_size, n_size));
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Tensor<float, 2, DataLayout> t_result_gpu(Eigen::array<int, 2>(m_size, n_size));
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Eigen::array<DimPair, 1> dims(DimPair(1, 0));
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t_left.setRandom();
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t_right.setRandom();
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std::size_t t_left_bytes = t_left.size() * sizeof(float);
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std::size_t t_right_bytes = t_right.size() * sizeof(float);
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std::size_t t_result_bytes = t_result.size() * sizeof(float);
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float* d_t_left;
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float* d_t_right;
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float* d_t_result;
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cudaMalloc((void**)(&d_t_left), t_left_bytes);
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cudaMalloc((void**)(&d_t_right), t_right_bytes);
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cudaMalloc((void**)(&d_t_result), t_result_bytes);
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cudaMemcpy(d_t_left, t_left.data(), t_left_bytes, cudaMemcpyHostToDevice);
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cudaMemcpy(d_t_right, t_right.data(), t_right_bytes, cudaMemcpyHostToDevice);
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cudaStream_t stream;
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assert(cudaStreamCreate(&stream) == cudaSuccess);
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Eigen::GpuDevice gpu_device(&stream);
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Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> >
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gpu_t_left(d_t_left, Eigen::array<int, 2>(m_size, k_size));
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Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> >
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gpu_t_right(d_t_right, Eigen::array<int, 2>(k_size, n_size));
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Eigen::TensorMap<Eigen::Tensor<float, 2, DataLayout> >
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gpu_t_result(d_t_result, Eigen::array<int, 2>(m_size, n_size));
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gpu_t_result.device(gpu_device) = gpu_t_left.contract(gpu_t_right, dims);
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t_result = t_left.contract(t_right, dims);
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cudaMemcpy(t_result_gpu.data(), d_t_result, t_result_bytes, cudaMemcpyDeviceToHost);
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for (size_t i = 0; i < t_result.dimensions().TotalSize(); i++) {
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if (fabs(t_result.data()[i] - t_result_gpu.data()[i]) >= 1e-4) {
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cout << "mismatch detected at index " << i << ": " << t_result.data()[i]
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<< " vs " << t_result_gpu.data()[i] << endl;
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assert(false);
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}
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}
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cudaFree((void*)d_t_left);
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cudaFree((void*)d_t_right);
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cudaFree((void*)d_t_result);
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}
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void test_cxx11_tensor_cuda()
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{
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cout<<"Calling contraction tests"<<std::endl;
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CALL_SUBTEST(test_cuda_contraction<ColMajor>(128, 128, 128));
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CALL_SUBTEST(test_cuda_contraction<RowMajor>(128, 128, 128));
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for (int k = 32; k < 256; k++) {
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CALL_SUBTEST(test_cuda_contraction<ColMajor>(128, k, 128));
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CALL_SUBTEST(test_cuda_contraction<RowMajor>(128, k, 128));
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}
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for (int k = 32; k < 256; k++) {
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CALL_SUBTEST(test_cuda_contraction<ColMajor>(128, 128, k));
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CALL_SUBTEST(test_cuda_contraction<RowMajor>(128, 128, k));
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}
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for (int k = 32; k < 256; k++) {
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CALL_SUBTEST(test_cuda_contraction<ColMajor>(k, 128, 128));
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CALL_SUBTEST(test_cuda_contraction<RowMajor>(k, 128, 128));
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}
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int m_sizes[] = {31, 39, 63, 64, 65,
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127, 129, 255, 257, 511,
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512, 513, 1023, 1024, 1025 };
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int n_sizes[] = {31, 39, 63, 64, 65,
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127, 129, 255, 257, 511,
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512, 513, 1023, 1024, 1025 };
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int k_sizes[] = { 31, 39, 63, 64, 65,
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95, 96, 127, 129, 255,
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257, 511, 512, 513, 1023,
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1024, 1025};
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for (int i = 0; i <15; i++)
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for (int j = 0; j < 15; j++)
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for (int k = 0; k < 17; k++) {
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CALL_SUBTEST(test_cuda_contraction<ColMajor>(m_sizes[i], n_sizes[j], k_sizes[k]));
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CALL_SUBTEST(test_cuda_contraction<RowMajor>(m_sizes[i], n_sizes[j], k_sizes[k]));
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}
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}
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