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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>
using Eigen::Tensor;
template <typename DataType, int DataLayout, typename IndexType>
void test_forced_eval_sycl(const Eigen::SyclDevice &sycl_device) {
IndexType sizeDim1 = 100;
IndexType sizeDim2 = 20;
IndexType sizeDim3 = 20;
Eigen::array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
Eigen::Tensor<DataType, 3, DataLayout, IndexType> in1(tensorRange);
Eigen::Tensor<DataType, 3, DataLayout, IndexType> in2(tensorRange);
Eigen::Tensor<DataType, 3, DataLayout, IndexType> out(tensorRange);
DataType * gpu_in1_data = static_cast<DataType*>(sycl_device.allocate(in1.dimensions().TotalSize()*sizeof(DataType)));
DataType * gpu_in2_data = static_cast<DataType*>(sycl_device.allocate(in2.dimensions().TotalSize()*sizeof(DataType)));
DataType * gpu_out_data = static_cast<DataType*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(DataType)));
in1 = in1.random() + in1.constant(static_cast<DataType>(10.0f));
in2 = in2.random() + in2.constant(static_cast<DataType>(10.0f));
// creating TensorMap from tensor
Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in1(gpu_in1_data, tensorRange);
Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in2(gpu_in2_data, tensorRange);
Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_out(gpu_out_data, tensorRange);
sycl_device.memcpyHostToDevice(gpu_in1_data, in1.data(),(in1.dimensions().TotalSize())*sizeof(DataType));
sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in2.dimensions().TotalSize())*sizeof(DataType));
/// c=(a+b)*b
gpu_out.device(sycl_device) =(gpu_in1 + gpu_in2).eval() * gpu_in2;
sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(DataType));
for (IndexType i = 0; i < sizeDim1; ++i) {
for (IndexType j = 0; j < sizeDim2; ++j) {
for (IndexType k = 0; k < sizeDim3; ++k) {
VERIFY_IS_APPROX(out(i, j, k),
(in1(i, j, k) + in2(i, j, k)) * in2(i, j, k));
}
}
}
printf("(a+b)*b Test Passed\n");
sycl_device.deallocate(gpu_in1_data);
sycl_device.deallocate(gpu_in2_data);
sycl_device.deallocate(gpu_out_data);
}
template <typename DataType, typename Dev_selector> void tensorForced_evalperDevice(Dev_selector s){
QueueInterface queueInterface(s);
auto sycl_device = Eigen::SyclDevice(&queueInterface);
test_forced_eval_sycl<DataType, RowMajor, int64_t>(sycl_device);
test_forced_eval_sycl<DataType, ColMajor, int64_t>(sycl_device);
}
EIGEN_DECLARE_TEST(cxx11_tensor_forced_eval_sycl) {
for (const auto& device :Eigen::get_sycl_supported_devices()) {
CALL_SUBTEST(tensorForced_evalperDevice<float>(device));
CALL_SUBTEST(tensorForced_evalperDevice<half>(device));
}
}
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