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96 lines
3.7 KiB
C++
96 lines
3.7 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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// 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_morphing_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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template <typename DataType, int DataLayout>
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static void test_simple_slice(const Eigen::SyclDevice &sycl_device)
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{
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int sizeDim1 = 2;
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int sizeDim2 = 3;
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int sizeDim3 = 5;
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int sizeDim4 = 7;
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int sizeDim5 = 11;
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array<int, 5> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
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Tensor<DataType, 5,DataLayout> tensor(tensorRange);
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tensor.setRandom();
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array<int, 5> slice1_range ={{1, 1, 1, 1, 1}};
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Tensor<DataType, 5,DataLayout> slice1(slice1_range);
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DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor.size()*sizeof(DataType)));
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DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(slice1.size()*sizeof(DataType)));
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TensorMap<Tensor<DataType, 5,DataLayout>> gpu1(gpu_data1, tensorRange);
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TensorMap<Tensor<DataType, 5,DataLayout>> gpu2(gpu_data2, slice1_range);
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Eigen::DSizes<ptrdiff_t, 5> indices(1,2,3,4,5);
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Eigen::DSizes<ptrdiff_t, 5> sizes(1,1,1,1,1);
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sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(),(tensor.size())*sizeof(DataType));
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gpu2.device(sycl_device)=gpu1.slice(indices, sizes);
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sycl_device.memcpyDeviceToHost(slice1.data(), gpu_data2,(slice1.size())*sizeof(DataType));
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VERIFY_IS_EQUAL(slice1(0,0,0,0,0), tensor(1,2,3,4,5));
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array<int, 5> slice2_range ={{1,1,2,2,3}};
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Tensor<DataType, 5,DataLayout> slice2(slice2_range);
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DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(slice2.size()*sizeof(DataType)));
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TensorMap<Tensor<DataType, 5,DataLayout>> gpu3(gpu_data3, slice2_range);
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Eigen::DSizes<ptrdiff_t, 5> indices2(1,1,3,4,5);
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Eigen::DSizes<ptrdiff_t, 5> sizes2(1,1,2,2,3);
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gpu3.device(sycl_device)=gpu1.slice(indices2, sizes2);
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sycl_device.memcpyDeviceToHost(slice2.data(), gpu_data3,(slice2.size())*sizeof(DataType));
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for (int i = 0; i < 2; ++i) {
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for (int j = 0; j < 2; ++j) {
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for (int k = 0; k < 3; ++k) {
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VERIFY_IS_EQUAL(slice2(0,0,i,j,k), tensor(1,1,3+i,4+j,5+k));
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}
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}
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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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sycl_device.deallocate(gpu_data3);
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}
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template<typename DataType, typename dev_Selector> void sycl_slicing_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_simple_slice<DataType, RowMajor>(sycl_device);
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test_simple_slice<DataType, ColMajor>(sycl_device);
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}
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void test_cxx11_tensor_morphing_sycl()
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{
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/// Currentlly it only works on cpu. Adding GPU cause LLVM ERROR in cunstructing OpenCL Kernel at runtime.
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// printf("Test on GPU: OpenCL\n");
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// CALL_SUBTEST(sycl_device_test_per_device((cl::sycl::gpu_selector())));
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printf("repeating the test on CPU: OpenCL\n");
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CALL_SUBTEST(sycl_slicing_test_per_device<float>((cl::sycl::cpu_selector())));
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printf("repeating the test on CPU: HOST\n");
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CALL_SUBTEST(sycl_slicing_test_per_device<float>((cl::sycl::host_selector())));
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printf("Test Passed******************\n" );
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}
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