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201 lines
7.1 KiB
C++
201 lines
7.1 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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// Benoit Steiner <benoit.steiner.goog@gmail.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_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::array;
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using Eigen::SyclDevice;
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using Eigen::Tensor;
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using Eigen::TensorMap;
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void test_sycl_mem_transfers(const Eigen::SyclDevice &sycl_device) {
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int sizeDim1 = 100;
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int sizeDim2 = 100;
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int sizeDim3 = 100;
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array<int, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
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Tensor<float, 3> in1(tensorRange);
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Tensor<float, 3> out1(tensorRange);
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Tensor<float, 3> out2(tensorRange);
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Tensor<float, 3> out3(tensorRange);
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in1 = in1.random();
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float* gpu_data1 = static_cast<float*>(sycl_device.allocate(in1.size()*sizeof(float)));
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float* gpu_data2 = static_cast<float*>(sycl_device.allocate(out1.size()*sizeof(float)));
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//float* gpu_data = static_cast<float*>(sycl_device.allocate(out2.size()*sizeof(float)));
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TensorMap<Tensor<float, 3>> gpu1(gpu_data1, tensorRange);
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TensorMap<Tensor<float, 3>> gpu2(gpu_data2, tensorRange);
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//TensorMap<Tensor<float, 3>> gpu_out2(gpu_out2_data, tensorRange);
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sycl_device.memcpyHostToDevice(gpu_data1, in1.data(),(in1.size())*sizeof(float));
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sycl_device.memcpyHostToDevice(gpu_data2, in1.data(),(in1.size())*sizeof(float));
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gpu1.device(sycl_device) = gpu1 * 3.14f;
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gpu2.device(sycl_device) = gpu2 * 2.7f;
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sycl_device.memcpyDeviceToHost(out1.data(), gpu_data1,(out1.size())*sizeof(float));
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sycl_device.memcpyDeviceToHost(out2.data(), gpu_data1,(out2.size())*sizeof(float));
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sycl_device.memcpyDeviceToHost(out3.data(), gpu_data2,(out3.size())*sizeof(float));
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// sycl_device.Synchronize();
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for (int i = 0; i < in1.size(); ++i) {
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VERIFY_IS_APPROX(out1(i), in1(i) * 3.14f);
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VERIFY_IS_APPROX(out2(i), in1(i) * 3.14f);
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VERIFY_IS_APPROX(out3(i), in1(i) * 2.7f);
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}
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sycl_device.deallocate(gpu_data1);
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sycl_device.deallocate(gpu_data2);
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}
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void test_sycl_computations(const Eigen::SyclDevice &sycl_device) {
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int sizeDim1 = 100;
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int sizeDim2 = 100;
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int sizeDim3 = 100;
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array<int, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
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Tensor<float, 3> in1(tensorRange);
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Tensor<float, 3> in2(tensorRange);
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Tensor<float, 3> in3(tensorRange);
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Tensor<float, 3> out(tensorRange);
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in2 = in2.random();
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in3 = in3.random();
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float * gpu_in1_data = static_cast<float*>(sycl_device.allocate(in1.size()*sizeof(float)));
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float * gpu_in2_data = static_cast<float*>(sycl_device.allocate(in2.size()*sizeof(float)));
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float * gpu_in3_data = static_cast<float*>(sycl_device.allocate(in3.size()*sizeof(float)));
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float * gpu_out_data = static_cast<float*>(sycl_device.allocate(out.size()*sizeof(float)));
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TensorMap<Tensor<float, 3>> gpu_in1(gpu_in1_data, tensorRange);
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TensorMap<Tensor<float, 3>> gpu_in2(gpu_in2_data, tensorRange);
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TensorMap<Tensor<float, 3>> gpu_in3(gpu_in3_data, tensorRange);
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TensorMap<Tensor<float, 3>> gpu_out(gpu_out_data, tensorRange);
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/// a=1.2f
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gpu_in1.device(sycl_device) = gpu_in1.constant(1.2f);
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sycl_device.memcpyDeviceToHost(in1.data(), gpu_in1_data ,(in1.size())*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(in1(i,j,k), 1.2f);
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}
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}
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}
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printf("a=1.2f Test passed\n");
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/// a=b*1.2f
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gpu_out.device(sycl_device) = gpu_in1 * 1.2f;
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sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data ,(out.size())*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) * 1.2f);
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}
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}
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}
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printf("a=b*1.2f Test Passed\n");
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/// c=a*b
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sycl_device.memcpyHostToDevice(gpu_in2_data, in2.data(),(in2.size())*sizeof(float));
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gpu_out.device(sycl_device) = gpu_in1 * gpu_in2;
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sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.size())*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) *
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in2(i,j,k));
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}
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}
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}
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printf("c=a*b Test Passed\n");
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/// c=a+b
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gpu_out.device(sycl_device) = gpu_in1 + gpu_in2;
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sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.size())*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) +
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in2(i,j,k));
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}
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}
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}
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printf("c=a+b Test Passed\n");
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/// c=a*a
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gpu_out.device(sycl_device) = gpu_in1 * gpu_in1;
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sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.size())*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) *
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in1(i,j,k));
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}
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}
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}
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printf("c= a*a Test Passed\n");
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//a*3.14f + b*2.7f
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gpu_out.device(sycl_device) = gpu_in1 * gpu_in1.constant(3.14f) + gpu_in2 * gpu_in2.constant(2.7f);
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sycl_device.memcpyDeviceToHost(out.data(),gpu_out_data,(out.size())*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) * 3.14f
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+ in2(i,j,k) * 2.7f);
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}
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}
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}
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printf("a*3.14f + b*2.7f Test Passed\n");
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///d= (a>0.5? b:c)
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sycl_device.memcpyHostToDevice(gpu_in3_data, in3.data(),(in3.size())*sizeof(float));
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gpu_out.device(sycl_device) =(gpu_in1 > gpu_in1.constant(0.5f)).select(gpu_in2, gpu_in3);
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sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.size())*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), (in1(i, j, k) > 0.5f)
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? in2(i, j, k)
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: in3(i, j, k));
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}
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}
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}
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printf("d= (a>0.5? b:c) 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_in3_data);
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sycl_device.deallocate(gpu_out_data);
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}
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void test_cxx11_tensor_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_sycl_mem_transfers(sycl_device));
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CALL_SUBTEST(test_sycl_computations(sycl_device));
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}
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