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// 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_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
#include <stdint.h>
#include <iostream>
template <typename DataType, int DataLayout, typename IndexType>
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;
IndexType sizeDim1 = 100;
array<IndexType, 1> tensorRange = {{sizeDim1}};
Tensor<DataType, 1, DataLayout,IndexType> in(tensorRange);
Tensor<DataType, 1, DataLayout,IndexType> 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 (IndexType i=0; i<in.size(); i++) {
VERIFY_IS_EQUAL(in(i), in1(i));
}
sycl_device.deallocate(gpu_in_data);
}
template <typename DataType, int DataLayout, typename IndexType>
void test_device_exceptions(const Eigen::SyclDevice &sycl_device) {
VERIFY(sycl_device.ok());
IndexType sizeDim1 = 100;
array<IndexType, 1> tensorDims = {{sizeDim1}};
DataType* gpu_data = static_cast<DataType*>(sycl_device.allocate(sizeDim1*sizeof(DataType)));
sycl_device.memset(gpu_data, 1, sizeDim1*sizeof(DataType));
TensorMap<Tensor<DataType, 1, DataLayout,IndexType>> in(gpu_data, tensorDims);
TensorMap<Tensor<DataType, 1, DataLayout,IndexType>> out(gpu_data, tensorDims);
out.device(sycl_device) = in / in.constant(0);
sycl_device.synchronize();
VERIFY(!sycl_device.ok());
sycl_device.deallocate(gpu_data);
}
template<typename DataType> void sycl_device_test_per_device(const cl::sycl::device& d){
std::cout << "Running on " << d.template get_info<cl::sycl::info::device::name>() << std::endl;
QueueInterface queueInterface(d);
auto sycl_device = Eigen::SyclDevice(&queueInterface);
test_device_memory<DataType, RowMajor, int64_t>(sycl_device);
test_device_memory<DataType, ColMajor, int64_t>(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);
}
EIGEN_DECLARE_TEST(cxx11_tensor_device_sycl) {
for (const auto& device :Eigen::get_sycl_supported_devices()) {
CALL_SUBTEST(sycl_device_test_per_device<float>(device));
}
}
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