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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>
template <typename DataType, int DataLayout, typename IndexType>
static void test_sycl_random_uniform(const Eigen::SyclDevice& sycl_device)
{
Tensor<DataType, 2,DataLayout, IndexType> out(72,97);
out.setZero();
std::size_t out_bytes = out.size() * sizeof(DataType);
IndexType sizeDim0 = 72;
IndexType sizeDim1 = 97;
array<IndexType, 2> tensorRange = {{sizeDim0, sizeDim1}};
DataType* d_out = static_cast<DataType*>(sycl_device.allocate(out_bytes));
TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu_out(d_out, tensorRange);
gpu_out.device(sycl_device)=gpu_out.random();
sycl_device.memcpyDeviceToHost(out.data(), d_out,out_bytes);
for(IndexType i=1; i<sizeDim0; i++)
for(IndexType j=1; j<sizeDim1; j++)
{
VERIFY_IS_NOT_EQUAL(out(i,j), out(i-1,j));
VERIFY_IS_NOT_EQUAL(out(i,j), out(i,j-1));
VERIFY_IS_NOT_EQUAL(out(i,j), out(i-1,j-1)); }
// For now we just check thes code doesn't crash.
// TODO: come up with a valid test of randomness
sycl_device.deallocate(d_out);
}
template <typename DataType, int DataLayout, typename IndexType>
void test_sycl_random_normal(const Eigen::SyclDevice& sycl_device)
{
Tensor<DataType, 2,DataLayout,IndexType> out(72,97);
out.setZero();
std::size_t out_bytes = out.size() * sizeof(DataType);
IndexType sizeDim0 = 72;
IndexType sizeDim1 = 97;
array<IndexType, 2> tensorRange = {{sizeDim0, sizeDim1}};
DataType* d_out = static_cast<DataType*>(sycl_device.allocate(out_bytes));
TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu_out(d_out, tensorRange);
Eigen::internal::NormalRandomGenerator<DataType> gen(true);
gpu_out.device(sycl_device)=gpu_out.random(gen);
sycl_device.memcpyDeviceToHost(out.data(), d_out,out_bytes);
for(IndexType i=1; i<sizeDim0; i++)
for(IndexType j=1; j<sizeDim1; j++)
{
VERIFY_IS_NOT_EQUAL(out(i,j), out(i-1,j));
VERIFY_IS_NOT_EQUAL(out(i,j), out(i,j-1));
VERIFY_IS_NOT_EQUAL(out(i,j), out(i-1,j-1));
}
// For now we just check thes code doesn't crash.
// TODO: come up with a valid test of randomness
sycl_device.deallocate(d_out);
}
template<typename DataType, typename dev_Selector> void sycl_random_test_per_device(dev_Selector s){
QueueInterface queueInterface(s);
auto sycl_device = Eigen::SyclDevice(&queueInterface);
test_sycl_random_uniform<DataType, RowMajor, int64_t>(sycl_device);
test_sycl_random_uniform<DataType, ColMajor, int64_t>(sycl_device);
test_sycl_random_normal<DataType, RowMajor, int64_t>(sycl_device);
test_sycl_random_normal<DataType, ColMajor, int64_t>(sycl_device);
}
EIGEN_DECLARE_TEST(cxx11_tensor_random_sycl)
{
for (const auto& device :Eigen::get_sycl_supported_devices()) {
CALL_SUBTEST(sycl_random_test_per_device<float>(device));
#ifdef EIGEN_SYCL_DOUBLE_SUPPORT
CALL_SUBTEST(sycl_random_test_per_device<double>(device));
#endif
}
}
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