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82f0ce2726
This provide several advantages: - more flexibility in designing unit tests - unit tests can be glued to speed up compilation - unit tests are compiled with same predefined macros, which is a requirement for zapcc
277 lines
11 KiB
C++
277 lines
11 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_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/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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template <typename DataType, int DataLayout, typename IndexType>
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void test_sycl_mem_transfers(const Eigen::SyclDevice &sycl_device) {
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IndexType sizeDim1 = 100;
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IndexType sizeDim2 = 10;
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IndexType sizeDim3 = 20;
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array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
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Tensor<DataType, 3, DataLayout, IndexType> in1(tensorRange);
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Tensor<DataType, 3, DataLayout, IndexType> out1(tensorRange);
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Tensor<DataType, 3, DataLayout, IndexType> out2(tensorRange);
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Tensor<DataType, 3, DataLayout, IndexType> out3(tensorRange);
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in1 = in1.random();
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DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(in1.size()*sizeof(DataType)));
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DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(out1.size()*sizeof(DataType)));
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TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
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TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu2(gpu_data2, tensorRange);
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sycl_device.memcpyHostToDevice(gpu_data1, in1.data(),(in1.size())*sizeof(DataType));
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sycl_device.memcpyHostToDevice(gpu_data2, in1.data(),(in1.size())*sizeof(DataType));
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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(DataType));
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sycl_device.memcpyDeviceToHost(out2.data(), gpu_data1,(out2.size())*sizeof(DataType));
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sycl_device.memcpyDeviceToHost(out3.data(), gpu_data2,(out3.size())*sizeof(DataType));
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sycl_device.synchronize();
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for (IndexType 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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template <typename DataType, int DataLayout, typename IndexType>
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void test_sycl_mem_sync(const Eigen::SyclDevice &sycl_device) {
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IndexType size = 20;
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array<IndexType, 1> tensorRange = {{size}};
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Tensor<DataType, 1, DataLayout, IndexType> in1(tensorRange);
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Tensor<DataType, 1, DataLayout, IndexType> in2(tensorRange);
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Tensor<DataType, 1, DataLayout, IndexType> out(tensorRange);
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in1 = in1.random();
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in2 = in1;
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DataType* gpu_data = static_cast<DataType*>(sycl_device.allocate(in1.size()*sizeof(DataType)));
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TensorMap<Tensor<DataType, 1, DataLayout, IndexType>> gpu1(gpu_data, tensorRange);
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sycl_device.memcpyHostToDevice(gpu_data, in1.data(),(in1.size())*sizeof(DataType));
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sycl_device.synchronize();
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in1.setZero();
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sycl_device.memcpyDeviceToHost(out.data(), gpu_data, out.size()*sizeof(DataType));
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sycl_device.synchronize();
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for (IndexType i = 0; i < in1.size(); ++i) {
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VERIFY_IS_APPROX(out(i), in2(i));
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}
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sycl_device.deallocate(gpu_data);
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}
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template <typename DataType, int DataLayout, typename IndexType>
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void test_sycl_computations(const Eigen::SyclDevice &sycl_device) {
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IndexType sizeDim1 = 100;
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IndexType sizeDim2 = 10;
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IndexType sizeDim3 = 20;
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array<IndexType, 3> tensorRange = {{sizeDim1, sizeDim2, sizeDim3}};
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Tensor<DataType, 3,DataLayout, IndexType> in1(tensorRange);
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Tensor<DataType, 3,DataLayout, IndexType> in2(tensorRange);
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Tensor<DataType, 3,DataLayout, IndexType> in3(tensorRange);
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Tensor<DataType, 3,DataLayout, IndexType> out(tensorRange);
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in2 = in2.random();
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in3 = in3.random();
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DataType * gpu_in1_data = static_cast<DataType*>(sycl_device.allocate(in1.size()*sizeof(DataType)));
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DataType * gpu_in2_data = static_cast<DataType*>(sycl_device.allocate(in2.size()*sizeof(DataType)));
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DataType * gpu_in3_data = static_cast<DataType*>(sycl_device.allocate(in3.size()*sizeof(DataType)));
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DataType * gpu_out_data = static_cast<DataType*>(sycl_device.allocate(out.size()*sizeof(DataType)));
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TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_in1(gpu_in1_data, tensorRange);
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TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_in2(gpu_in2_data, tensorRange);
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TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_in3(gpu_in3_data, tensorRange);
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TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> 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(DataType));
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sycl_device.synchronize();
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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(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(DataType));
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sycl_device.synchronize();
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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),
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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(DataType));
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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(DataType));
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sycl_device.synchronize();
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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),
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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(DataType));
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sycl_device.synchronize();
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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),
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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(DataType));
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sycl_device.synchronize();
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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),
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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(DataType));
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sycl_device.synchronize();
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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),
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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(DataType));
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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(DataType));
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sycl_device.synchronize();
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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) > 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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template<typename Scalar1, typename Scalar2, int DataLayout, typename IndexType>
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static void test_sycl_cast(const Eigen::SyclDevice& sycl_device){
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IndexType size = 20;
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array<IndexType, 1> tensorRange = {{size}};
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Tensor<Scalar1, 1, DataLayout, IndexType> in(tensorRange);
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Tensor<Scalar2, 1, DataLayout, IndexType> out(tensorRange);
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Tensor<Scalar2, 1, DataLayout, IndexType> out_host(tensorRange);
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in = in.random();
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Scalar1* gpu_in_data = static_cast<Scalar1*>(sycl_device.allocate(in.size()*sizeof(Scalar1)));
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Scalar2 * gpu_out_data = static_cast<Scalar2*>(sycl_device.allocate(out.size()*sizeof(Scalar2)));
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TensorMap<Tensor<Scalar1, 1, DataLayout, IndexType>> gpu_in(gpu_in_data, tensorRange);
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TensorMap<Tensor<Scalar2, 1, DataLayout, IndexType>> gpu_out(gpu_out_data, tensorRange);
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sycl_device.memcpyHostToDevice(gpu_in_data, in.data(),(in.size())*sizeof(Scalar1));
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gpu_out.device(sycl_device) = gpu_in. template cast<Scalar2>();
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sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data, out.size()*sizeof(Scalar2));
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out_host = in. template cast<Scalar2>();
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for(IndexType i=0; i< size; i++)
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{
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VERIFY_IS_APPROX(out(i), out_host(i));
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}
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printf("cast Test Passed\n");
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sycl_device.deallocate(gpu_in_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> void sycl_computing_test_per_device(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_sycl_mem_transfers<DataType, RowMajor, int64_t>(sycl_device);
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test_sycl_computations<DataType, RowMajor, int64_t>(sycl_device);
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test_sycl_mem_sync<DataType, RowMajor, int64_t>(sycl_device);
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test_sycl_mem_transfers<DataType, ColMajor, int64_t>(sycl_device);
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test_sycl_computations<DataType, ColMajor, int64_t>(sycl_device);
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test_sycl_mem_sync<DataType, ColMajor, int64_t>(sycl_device);
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test_sycl_cast<DataType, int, RowMajor, int64_t>(sycl_device);
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test_sycl_cast<DataType, int, ColMajor, int64_t>(sycl_device);
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}
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EIGEN_DECLARE_TEST(cxx11_tensor_sycl) {
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for (const auto& device :Eigen::get_sycl_supported_devices()) {
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CALL_SUBTEST(sycl_computing_test_per_device<float>(device));
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}
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}
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