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126 lines
4.6 KiB
126 lines
4.6 KiB
// 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 <Eigen/CXX11/Tensor>
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using Eigen::Tensor;
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template <typename DataType, typename IndexType>
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static void test_simple_swap_sycl(const Eigen::SyclDevice& sycl_device)
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{
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IndexType sizeDim1 = 2;
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IndexType sizeDim2 = 3;
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IndexType sizeDim3 = 7;
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array<IndexType, 3> tensorColRange = {{sizeDim1, sizeDim2, sizeDim3}};
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array<IndexType, 3> tensorRowRange = {{sizeDim3, sizeDim2, sizeDim1}};
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Tensor<DataType, 3, ColMajor, IndexType> tensor1(tensorColRange);
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Tensor<DataType, 3, RowMajor, IndexType> tensor2(tensorRowRange);
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tensor1.setRandom();
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DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor1.size()*sizeof(DataType)));
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DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(tensor2.size()*sizeof(DataType)));
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TensorMap<Tensor<DataType, 3, ColMajor, IndexType>> gpu1(gpu_data1, tensorColRange);
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TensorMap<Tensor<DataType, 3, RowMajor, IndexType>> gpu2(gpu_data2, tensorRowRange);
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sycl_device.memcpyHostToDevice(gpu_data1, tensor1.data(),(tensor1.size())*sizeof(DataType));
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gpu2.device(sycl_device)=gpu1.swap_layout();
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sycl_device.memcpyDeviceToHost(tensor2.data(), gpu_data2,(tensor2.size())*sizeof(DataType));
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// Tensor<float, 3, ColMajor> tensor(2,3,7);
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//tensor.setRandom();
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// Tensor<float, 3, RowMajor> tensor2 = tensor.swap_layout();
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VERIFY_IS_EQUAL(tensor1.dimension(0), tensor2.dimension(2));
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VERIFY_IS_EQUAL(tensor1.dimension(1), tensor2.dimension(1));
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VERIFY_IS_EQUAL(tensor1.dimension(2), tensor2.dimension(0));
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for (IndexType i = 0; i < 2; ++i) {
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for (IndexType j = 0; j < 3; ++j) {
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for (IndexType k = 0; k < 7; ++k) {
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VERIFY_IS_EQUAL(tensor1(i,j,k), tensor2(k,j,i));
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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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}
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template <typename DataType, typename IndexType>
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static void test_swap_as_lvalue_sycl(const Eigen::SyclDevice& sycl_device)
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{
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IndexType sizeDim1 = 2;
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IndexType sizeDim2 = 3;
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IndexType sizeDim3 = 7;
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array<IndexType, 3> tensorColRange = {{sizeDim1, sizeDim2, sizeDim3}};
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array<IndexType, 3> tensorRowRange = {{sizeDim3, sizeDim2, sizeDim1}};
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Tensor<DataType, 3, ColMajor, IndexType> tensor1(tensorColRange);
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Tensor<DataType, 3, RowMajor, IndexType> tensor2(tensorRowRange);
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tensor1.setRandom();
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DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor1.size()*sizeof(DataType)));
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DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(tensor2.size()*sizeof(DataType)));
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TensorMap<Tensor<DataType, 3, ColMajor, IndexType>> gpu1(gpu_data1, tensorColRange);
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TensorMap<Tensor<DataType, 3, RowMajor, IndexType>> gpu2(gpu_data2, tensorRowRange);
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sycl_device.memcpyHostToDevice(gpu_data1, tensor1.data(),(tensor1.size())*sizeof(DataType));
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gpu2.swap_layout().device(sycl_device)=gpu1;
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sycl_device.memcpyDeviceToHost(tensor2.data(), gpu_data2,(tensor2.size())*sizeof(DataType));
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// Tensor<float, 3, ColMajor> tensor(2,3,7);
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// tensor.setRandom();
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//Tensor<float, 3, RowMajor> tensor2(7,3,2);
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// tensor2.swap_layout() = tensor;
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VERIFY_IS_EQUAL(tensor1.dimension(0), tensor2.dimension(2));
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VERIFY_IS_EQUAL(tensor1.dimension(1), tensor2.dimension(1));
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VERIFY_IS_EQUAL(tensor1.dimension(2), tensor2.dimension(0));
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for (IndexType i = 0; i < 2; ++i) {
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for (IndexType j = 0; j < 3; ++j) {
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for (IndexType k = 0; k < 7; ++k) {
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VERIFY_IS_EQUAL(tensor1(i,j,k), tensor2(k,j,i));
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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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}
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template<typename DataType, typename dev_Selector> void sycl_tensor_layout_swap_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_swap_sycl<DataType, int64_t>(sycl_device);
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test_swap_as_lvalue_sycl<DataType, int64_t>(sycl_device);
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}
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EIGEN_DECLARE_TEST(cxx11_tensor_layout_swap_sycl)
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{
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for (const auto& device :Eigen::get_sycl_supported_devices()) {
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CALL_SUBTEST(sycl_tensor_layout_swap_test_per_device<float>(device));
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}
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}
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