mirror of
https://gitlab.com/libeigen/eigen.git
synced 2024-12-21 07:19:46 +08:00
78 lines
3.1 KiB
C++
78 lines
3.1 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
|
|
// for linear algebra.
|
|
//
|
|
// Copyright (C) 2016
|
|
// Mehdi Goli Codeplay Software Ltd.
|
|
// Ralph Potter Codeplay Software Ltd.
|
|
// Luke Iwanski Codeplay Software Ltd.
|
|
// Contact: <eigen@codeplay.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_device_sycl
|
|
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
|
|
#define EIGEN_USE_SYCL
|
|
|
|
#include "main.h"
|
|
#include <unsupported/Eigen/CXX11/Tensor>
|
|
#include <stdint.h>
|
|
#include <iostream>
|
|
|
|
template <typename DataType, int DataLayout>
|
|
void test_device_memory(const Eigen::SyclDevice &sycl_device) {
|
|
std::cout << "Running on : "
|
|
<< sycl_device.sycl_queue().get_device(). template get_info<cl::sycl::info::device::name>()
|
|
<<std::endl;
|
|
int sizeDim1 = 100;
|
|
array<int, 1> tensorRange = {{sizeDim1}};
|
|
Tensor<DataType, 1, DataLayout> in(tensorRange);
|
|
Tensor<DataType, 1, DataLayout> in1(tensorRange);
|
|
memset(in1.data(), 1,in1.size()*sizeof(DataType));
|
|
DataType * gpu_in_data = static_cast<DataType*>(sycl_device.allocate(in.size()*sizeof(DataType)));
|
|
sycl_device.memset(gpu_in_data, 1,in.size()*sizeof(DataType) );
|
|
sycl_device.memcpyDeviceToHost(in.data(), gpu_in_data, in.size()*sizeof(DataType) );
|
|
for (int i=0; i<in.size(); i++) {
|
|
VERIFY_IS_EQUAL(in(i), in1(i));
|
|
}
|
|
sycl_device.deallocate(gpu_in_data);
|
|
}
|
|
|
|
template <typename DataType, int DataLayout>
|
|
void test_device_exceptions(const Eigen::SyclDevice &sycl_device) {
|
|
VERIFY(sycl_device.ok());
|
|
int sizeDim1 = 100;
|
|
array<int, 1> tensorDims = {{sizeDim1}};
|
|
DataType* gpu_data = static_cast<DataType*>(sycl_device.allocate(sizeDim1*sizeof(DataType)));
|
|
TensorMap<Tensor<DataType, 1,DataLayout>> in(gpu_data, tensorDims);
|
|
TensorMap<Tensor<DataType, 1,DataLayout>> out(gpu_data, tensorDims);
|
|
|
|
out.device(sycl_device) = in / in.constant(0);
|
|
VERIFY(!sycl_device.ok());
|
|
sycl_device.deallocate(gpu_data);
|
|
}
|
|
|
|
template<typename DataType, typename dev_Selector> void sycl_device_test_per_device(dev_Selector s){
|
|
QueueInterface queueInterface(s);
|
|
auto sycl_device = Eigen::SyclDevice(&queueInterface);
|
|
test_device_memory<DataType, RowMajor>(sycl_device);
|
|
test_device_memory<DataType, ColMajor>(sycl_device);
|
|
/// this test throw an exception. enable it if you want to see the exception
|
|
//test_device_exceptions<DataType, RowMajor>(sycl_device);
|
|
/// this test throw an exception. enable it if you want to see the exception
|
|
//test_device_exceptions<DataType, ColMajor>(sycl_device);
|
|
}
|
|
|
|
void test_cxx11_tensor_device_sycl() {
|
|
printf("Test on GPU: OpenCL\n");
|
|
CALL_SUBTEST(sycl_device_test_per_device<int>((cl::sycl::gpu_selector())));
|
|
// printf("repeating the test on CPU: OpenCL\n");
|
|
// CALL_SUBTEST(sycl_device_test_per_device<int>((cl::sycl::cpu_selector())));
|
|
printf("repeating the test on CPU: HOST\n");
|
|
CALL_SUBTEST(sycl_device_test_per_device<int>((cl::sycl::host_selector())));
|
|
printf("Test Passed******************\n" );
|
|
}
|