71 lines
2.7 KiB
C++
71 lines
2.7 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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#define EIGEN_TEST_NO_LONGDOUBLE
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#define EIGEN_TEST_NO_COMPLEX
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#define EIGEN_TEST_FUNC cxx11_tensor_forced_eval_sycl
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#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
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#define EIGEN_USE_SYCL
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#include "main.h"
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#include <unsupported/Eigen/CXX11/Tensor>
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using Eigen::Tensor;
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void test_forced_eval_sycl(const Eigen::SyclDevice &sycl_device) {
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int sizeDim1 = 100;
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int sizeDim2 = 200;
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int sizeDim3 = 200;
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Eigen::array<int, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
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Eigen::Tensor<float, 3> in1(tensorRange);
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Eigen::Tensor<float, 3> in2(tensorRange);
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Eigen::Tensor<float, 3> out(tensorRange);
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float * gpu_in1_data = static_cast<float*>(sycl_device.allocate(in1.dimensions().TotalSize()*sizeof(float)));
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float * gpu_in2_data = static_cast<float*>(sycl_device.allocate(in2.dimensions().TotalSize()*sizeof(float)));
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float * gpu_out_data = static_cast<float*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(float)));
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in1 = in1.random() + in1.constant(10.0f);
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in2 = in2.random() + in2.constant(10.0f);
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// creating TensorMap from tensor
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Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in1(gpu_in1_data, tensorRange);
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Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_in2(gpu_in2_data, tensorRange);
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Eigen::TensorMap<Eigen::Tensor<float, 3>> gpu_out(gpu_out_data, tensorRange);
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sycl_device.memcpyHostToDevice(gpu_in1_data, in1.data(),(in1.dimensions().TotalSize())*sizeof(float));
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sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in1.dimensions().TotalSize())*sizeof(float));
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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(float));
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for (int i = 0; i < sizeDim1; ++i) {
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for (int j = 0; j < sizeDim2; ++j) {
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for (int k = 0; k < sizeDim3; ++k) {
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VERIFY_IS_APPROX(out(i, j, k),
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(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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void test_cxx11_tensor_forced_eval_sycl() {
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cl::sycl::gpu_selector s;
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Eigen::SyclDevice sycl_device(s);
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CALL_SUBTEST(test_forced_eval_sycl(sycl_device));
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}
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