| // This file is part of Eigen, a lightweight C++ template library | |
| // for linear algebra. | |
| // | |
| // Copyright (C) 2016 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_TEST_NO_LONGDOUBLE | |
| #define EIGEN_TEST_NO_COMPLEX | |
| #define EIGEN_TEST_FUNC cxx11_tensor_scan_cuda | |
| #define EIGEN_DEFAULT_DENSE_INDEX_TYPE int | |
| #define EIGEN_USE_GPU | |
| #if defined __CUDACC_VER__ && __CUDACC_VER__ >= 70500 | |
| #include <cuda_fp16.h> | |
| #endif | |
| #include "main.h" | |
| #include <unsupported/Eigen/CXX11/Tensor> | |
| using Eigen::Tensor; | |
| typedef Tensor<float, 1>::DimensionPair DimPair; | |
| template<int DataLayout> | |
| void test_cuda_cumsum(int m_size, int k_size, int n_size) | |
| { | |
| std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size << ")" << std::endl; | |
| Tensor<float, 3, DataLayout> t_input(m_size, k_size, n_size); | |
| Tensor<float, 3, DataLayout> t_result(m_size, k_size, n_size); | |
| Tensor<float, 3, DataLayout> t_result_gpu(m_size, k_size, n_size); | |
| t_input.setRandom(); | |
| std::size_t t_input_bytes = t_input.size() * sizeof(float); | |
| std::size_t t_result_bytes = t_result.size() * sizeof(float); | |
| float* d_t_input; | |
| float* d_t_result; | |
| cudaMalloc((void**)(&d_t_input), t_input_bytes); | |
| cudaMalloc((void**)(&d_t_result), t_result_bytes); | |
| cudaMemcpy(d_t_input, t_input.data(), t_input_bytes, cudaMemcpyHostToDevice); | |
| Eigen::CudaStreamDevice stream; | |
| Eigen::GpuDevice gpu_device(&stream); | |
| Eigen::TensorMap<Eigen::Tensor<float, 3, DataLayout> > | |
| gpu_t_input(d_t_input, Eigen::array<int, 3>(m_size, k_size, n_size)); | |
| Eigen::TensorMap<Eigen::Tensor<float, 3, DataLayout> > | |
| gpu_t_result(d_t_result, Eigen::array<int, 3>(m_size, k_size, n_size)); | |
| gpu_t_result.device(gpu_device) = gpu_t_input.cumsum(1); | |
| t_result = t_input.cumsum(1); | |
| cudaMemcpy(t_result_gpu.data(), d_t_result, t_result_bytes, cudaMemcpyDeviceToHost); | |
| for (DenseIndex 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_input); | |
| cudaFree((void*)d_t_result); | |
| } | |
| void test_cxx11_tensor_scan_cuda() | |
| { | |
| CALL_SUBTEST_1(test_cuda_cumsum<ColMajor>(128, 128, 128)); | |
| CALL_SUBTEST_2(test_cuda_cumsum<RowMajor>(128, 128, 128)); | |
| } |