mirror of
https://gitlab.com/libeigen/eigen.git
synced 2024-12-15 07:10:37 +08:00
187 lines
6.5 KiB
Plaintext
187 lines
6.5 KiB
Plaintext
// 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_FUNC cxx11_tensor_complex
|
|
#define EIGEN_USE_GPU
|
|
|
|
#include "main.h"
|
|
#include <unsupported/Eigen/CXX11/Tensor>
|
|
|
|
using Eigen::Tensor;
|
|
|
|
void test_cuda_nullary() {
|
|
Tensor<std::complex<float>, 1, 0, int> in1(2);
|
|
Tensor<std::complex<float>, 1, 0, int> in2(2);
|
|
in1.setRandom();
|
|
in2.setRandom();
|
|
|
|
std::size_t float_bytes = in1.size() * sizeof(float);
|
|
std::size_t complex_bytes = in1.size() * sizeof(std::complex<float>);
|
|
|
|
std::complex<float>* d_in1;
|
|
std::complex<float>* d_in2;
|
|
float* d_out2;
|
|
cudaMalloc((void**)(&d_in1), complex_bytes);
|
|
cudaMalloc((void**)(&d_in2), complex_bytes);
|
|
cudaMalloc((void**)(&d_out2), float_bytes);
|
|
cudaMemcpy(d_in1, in1.data(), complex_bytes, cudaMemcpyHostToDevice);
|
|
cudaMemcpy(d_in2, in2.data(), complex_bytes, cudaMemcpyHostToDevice);
|
|
|
|
Eigen::CudaStreamDevice stream;
|
|
Eigen::GpuDevice gpu_device(&stream);
|
|
|
|
Eigen::TensorMap<Eigen::Tensor<std::complex<float>, 1, 0, int>, Eigen::Aligned> gpu_in1(
|
|
d_in1, 2);
|
|
Eigen::TensorMap<Eigen::Tensor<std::complex<float>, 1, 0, int>, Eigen::Aligned> gpu_in2(
|
|
d_in2, 2);
|
|
Eigen::TensorMap<Eigen::Tensor<float, 1, 0, int>, Eigen::Aligned> gpu_out2(
|
|
d_out2, 2);
|
|
|
|
gpu_in1.device(gpu_device) = gpu_in1.constant(std::complex<float>(3.14f, 2.7f));
|
|
gpu_out2.device(gpu_device) = gpu_in2.abs();
|
|
|
|
Tensor<std::complex<float>, 1, 0, int> new1(2);
|
|
Tensor<float, 1, 0, int> new2(2);
|
|
|
|
assert(cudaMemcpyAsync(new1.data(), d_in1, complex_bytes, cudaMemcpyDeviceToHost,
|
|
gpu_device.stream()) == cudaSuccess);
|
|
assert(cudaMemcpyAsync(new2.data(), d_out2, float_bytes, cudaMemcpyDeviceToHost,
|
|
gpu_device.stream()) == cudaSuccess);
|
|
|
|
assert(cudaStreamSynchronize(gpu_device.stream()) == cudaSuccess);
|
|
|
|
for (int i = 0; i < 2; ++i) {
|
|
VERIFY_IS_APPROX(new1(i), std::complex<float>(3.14f, 2.7f));
|
|
VERIFY_IS_APPROX(new2(i), std::abs(in2(i)));
|
|
}
|
|
|
|
cudaFree(d_in1);
|
|
cudaFree(d_in2);
|
|
cudaFree(d_out2);
|
|
}
|
|
|
|
|
|
static void test_cuda_sum_reductions() {
|
|
|
|
Eigen::CudaStreamDevice stream;
|
|
Eigen::GpuDevice gpu_device(&stream);
|
|
|
|
const int num_rows = internal::random<int>(1024, 5*1024);
|
|
const int num_cols = internal::random<int>(1024, 5*1024);
|
|
|
|
Tensor<std::complex<float>, 2> in(num_rows, num_cols);
|
|
in.setRandom();
|
|
|
|
Tensor<std::complex<float>, 0> full_redux;
|
|
full_redux = in.sum();
|
|
|
|
std::size_t in_bytes = in.size() * sizeof(std::complex<float>);
|
|
std::size_t out_bytes = full_redux.size() * sizeof(std::complex<float>);
|
|
std::complex<float>* gpu_in_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(in_bytes));
|
|
std::complex<float>* gpu_out_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(out_bytes));
|
|
gpu_device.memcpyHostToDevice(gpu_in_ptr, in.data(), in_bytes);
|
|
|
|
TensorMap<Tensor<std::complex<float>, 2> > in_gpu(gpu_in_ptr, num_rows, num_cols);
|
|
TensorMap<Tensor<std::complex<float>, 0> > out_gpu(gpu_out_ptr);
|
|
|
|
out_gpu.device(gpu_device) = in_gpu.sum();
|
|
|
|
Tensor<std::complex<float>, 0> full_redux_gpu;
|
|
gpu_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_ptr, out_bytes);
|
|
gpu_device.synchronize();
|
|
|
|
// Check that the CPU and GPU reductions return the same result.
|
|
VERIFY_IS_APPROX(full_redux(), full_redux_gpu());
|
|
|
|
gpu_device.deallocate(gpu_in_ptr);
|
|
gpu_device.deallocate(gpu_out_ptr);
|
|
}
|
|
|
|
static void test_cuda_mean_reductions() {
|
|
|
|
Eigen::CudaStreamDevice stream;
|
|
Eigen::GpuDevice gpu_device(&stream);
|
|
|
|
const int num_rows = internal::random<int>(1024, 5*1024);
|
|
const int num_cols = internal::random<int>(1024, 5*1024);
|
|
|
|
Tensor<std::complex<float>, 2> in(num_rows, num_cols);
|
|
in.setRandom();
|
|
|
|
Tensor<std::complex<float>, 0> full_redux;
|
|
full_redux = in.mean();
|
|
|
|
std::size_t in_bytes = in.size() * sizeof(std::complex<float>);
|
|
std::size_t out_bytes = full_redux.size() * sizeof(std::complex<float>);
|
|
std::complex<float>* gpu_in_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(in_bytes));
|
|
std::complex<float>* gpu_out_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(out_bytes));
|
|
gpu_device.memcpyHostToDevice(gpu_in_ptr, in.data(), in_bytes);
|
|
|
|
TensorMap<Tensor<std::complex<float>, 2> > in_gpu(gpu_in_ptr, num_rows, num_cols);
|
|
TensorMap<Tensor<std::complex<float>, 0> > out_gpu(gpu_out_ptr);
|
|
|
|
out_gpu.device(gpu_device) = in_gpu.mean();
|
|
|
|
Tensor<std::complex<float>, 0> full_redux_gpu;
|
|
gpu_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_ptr, out_bytes);
|
|
gpu_device.synchronize();
|
|
|
|
// Check that the CPU and GPU reductions return the same result.
|
|
VERIFY_IS_APPROX(full_redux(), full_redux_gpu());
|
|
|
|
gpu_device.deallocate(gpu_in_ptr);
|
|
gpu_device.deallocate(gpu_out_ptr);
|
|
}
|
|
|
|
static void test_cuda_product_reductions() {
|
|
|
|
Eigen::CudaStreamDevice stream;
|
|
Eigen::GpuDevice gpu_device(&stream);
|
|
|
|
const int num_rows = internal::random<int>(1024, 5*1024);
|
|
const int num_cols = internal::random<int>(1024, 5*1024);
|
|
|
|
Tensor<std::complex<float>, 2> in(num_rows, num_cols);
|
|
in.setRandom();
|
|
|
|
Tensor<std::complex<float>, 0> full_redux;
|
|
full_redux = in.prod();
|
|
|
|
std::size_t in_bytes = in.size() * sizeof(std::complex<float>);
|
|
std::size_t out_bytes = full_redux.size() * sizeof(std::complex<float>);
|
|
std::complex<float>* gpu_in_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(in_bytes));
|
|
std::complex<float>* gpu_out_ptr = static_cast<std::complex<float>*>(gpu_device.allocate(out_bytes));
|
|
gpu_device.memcpyHostToDevice(gpu_in_ptr, in.data(), in_bytes);
|
|
|
|
TensorMap<Tensor<std::complex<float>, 2> > in_gpu(gpu_in_ptr, num_rows, num_cols);
|
|
TensorMap<Tensor<std::complex<float>, 0> > out_gpu(gpu_out_ptr);
|
|
|
|
out_gpu.device(gpu_device) = in_gpu.prod();
|
|
|
|
Tensor<std::complex<float>, 0> full_redux_gpu;
|
|
gpu_device.memcpyDeviceToHost(full_redux_gpu.data(), gpu_out_ptr, out_bytes);
|
|
gpu_device.synchronize();
|
|
|
|
// Check that the CPU and GPU reductions return the same result.
|
|
VERIFY_IS_APPROX(full_redux(), full_redux_gpu());
|
|
|
|
gpu_device.deallocate(gpu_in_ptr);
|
|
gpu_device.deallocate(gpu_out_ptr);
|
|
}
|
|
|
|
|
|
void test_cxx11_tensor_complex()
|
|
{
|
|
CALL_SUBTEST(test_cuda_nullary());
|
|
CALL_SUBTEST(test_cuda_sum_reductions());
|
|
CALL_SUBTEST(test_cuda_mean_reductions());
|
|
CALL_SUBTEST(test_cuda_product_reductions());
|
|
}
|