Rasmus Munk LarsenandRasmus Munk Larsen
8a3d61c790
libeigen/eigen!2509 Closes #2868 Co-authored-by: Rasmus Munk Larsen <rmlarsen@gmail.com>
80 lines
3.4 KiB
C++
80 lines
3.4 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2016
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// Mehdi Goli Codeplay Software Ltd.
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// Ralph Potter Codeplay Software Ltd.
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// Luke Iwanski Codeplay Software Ltd.
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// Contact: <eigen@codeplay.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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// SPDX-License-Identifier: MPL-2.0
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#define EIGEN_TEST_NO_LONGDOUBLE
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#define EIGEN_TEST_NO_COMPLEX
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#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
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#define EIGEN_USE_SYCL
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#include "main.h"
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#include <unsupported/Eigen/Tensor>
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using Eigen::Tensor;
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template <typename DataType, int DataLayout, typename IndexType>
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void test_forced_eval_sycl(const Eigen::SyclDevice& sycl_device) {
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IndexType sizeDim1 = 100;
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IndexType sizeDim2 = 20;
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IndexType sizeDim3 = 20;
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Eigen::array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
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Eigen::Tensor<DataType, 3, DataLayout, IndexType> in1(tensorRange);
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Eigen::Tensor<DataType, 3, DataLayout, IndexType> in2(tensorRange);
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Eigen::Tensor<DataType, 3, DataLayout, IndexType> out(tensorRange);
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DataType* gpu_in1_data =
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static_cast<DataType*>(sycl_device.allocate(in1.dimensions().TotalSize() * sizeof(DataType)));
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DataType* gpu_in2_data =
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static_cast<DataType*>(sycl_device.allocate(in2.dimensions().TotalSize() * sizeof(DataType)));
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DataType* gpu_out_data =
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static_cast<DataType*>(sycl_device.allocate(out.dimensions().TotalSize() * sizeof(DataType)));
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in1 = in1.random() + in1.constant(static_cast<DataType>(10.0f));
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in2 = in2.random() + in2.constant(static_cast<DataType>(10.0f));
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// creating TensorMap from tensor
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Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in1(gpu_in1_data, tensorRange);
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Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_in2(gpu_in2_data, tensorRange);
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Eigen::TensorMap<Eigen::Tensor<DataType, 3, DataLayout, IndexType>> gpu_out(gpu_out_data, tensorRange);
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sycl_device.memcpyHostToDevice(gpu_in1_data, in1.data(), (in1.dimensions().TotalSize()) * sizeof(DataType));
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sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(), (in2.dimensions().TotalSize()) * sizeof(DataType));
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/// c=(a+b)*b
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gpu_out.device(sycl_device) = (gpu_in1 + gpu_in2).eval() * gpu_in2;
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sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, (out.dimensions().TotalSize()) * sizeof(DataType));
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for (IndexType i = 0; i < sizeDim1; ++i) {
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for (IndexType j = 0; j < sizeDim2; ++j) {
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for (IndexType k = 0; k < sizeDim3; ++k) {
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VERIFY_IS_APPROX(out(i, j, k), (in1(i, j, k) + in2(i, j, k)) * in2(i, j, k));
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}
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}
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}
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printf("(a+b)*b Test Passed\n");
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sycl_device.deallocate(gpu_in1_data);
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sycl_device.deallocate(gpu_in2_data);
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sycl_device.deallocate(gpu_out_data);
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}
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template <typename DataType, typename Dev_selector>
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void tensorForced_evalperDevice(Dev_selector s) {
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QueueInterface queueInterface(s);
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auto sycl_device = Eigen::SyclDevice(&queueInterface);
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test_forced_eval_sycl<DataType, RowMajor, int64_t>(sycl_device);
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test_forced_eval_sycl<DataType, ColMajor, int64_t>(sycl_device);
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}
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EIGEN_DECLARE_TEST(tensor_forced_eval_sycl) {
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for (const auto& device : Eigen::get_sycl_supported_devices()) {
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CALL_SUBTEST(tensorForced_evalperDevice<float>(device));
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CALL_SUBTEST(tensorForced_evalperDevice<half>(device));
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}
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}
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