mirror of
https://gitlab.com/libeigen/eigen.git
synced 2024-12-27 07:29:52 +08:00
89 lines
2.4 KiB
Plaintext
89 lines
2.4 KiB
Plaintext
// 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_TEST_NO_LONGDOUBLE
|
|
#define EIGEN_TEST_NO_COMPLEX
|
|
#define EIGEN_TEST_FUNC cxx11_tensor_random_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 <Eigen/CXX11/Tensor>
|
|
|
|
|
|
void test_cuda_random_uniform()
|
|
{
|
|
Tensor<float, 2> out(72,97);
|
|
out.setZero();
|
|
|
|
std::size_t out_bytes = out.size() * sizeof(float);
|
|
|
|
float* d_out;
|
|
cudaMalloc((void**)(&d_out), out_bytes);
|
|
|
|
Eigen::CudaStreamDevice stream;
|
|
Eigen::GpuDevice gpu_device(&stream);
|
|
|
|
Eigen::TensorMap<Eigen::Tensor<float, 2> > gpu_out(d_out, 72,97);
|
|
|
|
gpu_out.device(gpu_device) = gpu_out.random();
|
|
|
|
assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess);
|
|
assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess);
|
|
|
|
// For now we just check thes code doesn't crash.
|
|
// TODO: come up with a valid test of randomness
|
|
}
|
|
|
|
|
|
void test_cuda_random_normal()
|
|
{
|
|
Tensor<float, 2> out(72,97);
|
|
out.setZero();
|
|
|
|
std::size_t out_bytes = out.size() * sizeof(float);
|
|
|
|
float* d_out;
|
|
cudaMalloc((void**)(&d_out), out_bytes);
|
|
|
|
Eigen::CudaStreamDevice stream;
|
|
Eigen::GpuDevice gpu_device(&stream);
|
|
|
|
Eigen::TensorMap<Eigen::Tensor<float, 2> > gpu_out(d_out, 72,97);
|
|
|
|
Eigen::internal::NormalRandomGenerator<float> gen(true);
|
|
gpu_out.device(gpu_device) = gpu_out.random(gen);
|
|
|
|
assert(cudaMemcpyAsync(out.data(), d_out, out_bytes, cudaMemcpyDeviceToHost, gpu_device.stream()) == cudaSuccess);
|
|
assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess);
|
|
}
|
|
|
|
static void test_complex()
|
|
{
|
|
Tensor<std::complex<float>, 1> vec(6);
|
|
vec.setRandom();
|
|
|
|
// Fixme: we should check that the generated numbers follow a uniform
|
|
// distribution instead.
|
|
for (int i = 1; i < 6; ++i) {
|
|
VERIFY_IS_NOT_EQUAL(vec(i), vec(i-1));
|
|
}
|
|
}
|
|
|
|
|
|
void test_cxx11_tensor_random_cuda()
|
|
{
|
|
CALL_SUBTEST(test_cuda_random_uniform());
|
|
CALL_SUBTEST(test_cuda_random_normal());
|
|
CALL_SUBTEST(test_complex());
|
|
}
|