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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
249 lines
11 KiB
C++
249 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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static void test_simple_reshape(const Eigen::SyclDevice& sycl_device)
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{
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typename Tensor<DataType, 5 ,DataLayout, IndexType>::Dimensions dim1(2,3,1,7,1);
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typename Tensor<DataType, 3 ,DataLayout, IndexType>::Dimensions dim2(2,3,7);
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typename Tensor<DataType, 2 ,DataLayout, IndexType>::Dimensions dim3(6,7);
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typename Tensor<DataType, 2 ,DataLayout, IndexType>::Dimensions dim4(2,21);
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Tensor<DataType, 5, DataLayout, IndexType> tensor1(dim1);
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Tensor<DataType, 3, DataLayout, IndexType> tensor2(dim2);
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Tensor<DataType, 2, DataLayout, IndexType> tensor3(dim3);
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Tensor<DataType, 2, DataLayout, IndexType> tensor4(dim4);
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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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DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(tensor3.size()*sizeof(DataType)));
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DataType* gpu_data4 = static_cast<DataType*>(sycl_device.allocate(tensor4.size()*sizeof(DataType)));
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TensorMap<Tensor<DataType, 5,DataLayout, IndexType>> gpu1(gpu_data1, dim1);
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TensorMap<Tensor<DataType, 3,DataLayout, IndexType>> gpu2(gpu_data2, dim2);
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TensorMap<Tensor<DataType, 2,DataLayout, IndexType>> gpu3(gpu_data3, dim3);
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TensorMap<Tensor<DataType, 2,DataLayout, IndexType>> gpu4(gpu_data4, dim4);
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sycl_device.memcpyHostToDevice(gpu_data1, tensor1.data(),(tensor1.size())*sizeof(DataType));
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gpu2.device(sycl_device)=gpu1.reshape(dim2);
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sycl_device.memcpyDeviceToHost(tensor2.data(), gpu_data2,(tensor1.size())*sizeof(DataType));
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gpu3.device(sycl_device)=gpu1.reshape(dim3);
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sycl_device.memcpyDeviceToHost(tensor3.data(), gpu_data3,(tensor3.size())*sizeof(DataType));
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gpu4.device(sycl_device)=gpu1.reshape(dim2).reshape(dim4);
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sycl_device.memcpyDeviceToHost(tensor4.data(), gpu_data4,(tensor4.size())*sizeof(DataType));
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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,0,k,0), tensor2(i,j,k)); ///ColMajor
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if (static_cast<int>(DataLayout) == static_cast<int>(ColMajor)) {
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VERIFY_IS_EQUAL(tensor1(i,j,0,k,0), tensor3(i+2*j,k)); ///ColMajor
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VERIFY_IS_EQUAL(tensor1(i,j,0,k,0), tensor4(i,j+3*k)); ///ColMajor
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}
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else{
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//VERIFY_IS_EQUAL(tensor1(i,j,0,k,0), tensor2(i,j,k)); /// RowMajor
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VERIFY_IS_EQUAL(tensor1(i,j,0,k,0), tensor4(i,j*7 +k)); /// RowMajor
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VERIFY_IS_EQUAL(tensor1(i,j,0,k,0), tensor3(i*3 +j,k)); /// RowMajor
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}
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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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sycl_device.deallocate(gpu_data4);
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}
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template<typename DataType, int DataLayout, typename IndexType>
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static void test_reshape_as_lvalue(const Eigen::SyclDevice& sycl_device)
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{
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typename Tensor<DataType, 3, DataLayout, IndexType>::Dimensions dim1(2,3,7);
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typename Tensor<DataType, 2, DataLayout, IndexType>::Dimensions dim2(6,7);
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typename Tensor<DataType, 5, DataLayout, IndexType>::Dimensions dim3(2,3,1,7,1);
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Tensor<DataType, 3, DataLayout, IndexType> tensor(dim1);
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Tensor<DataType, 2, DataLayout, IndexType> tensor2d(dim2);
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Tensor<DataType, 5, DataLayout, IndexType> tensor5d(dim3);
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tensor.setRandom();
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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(tensor2d.size()*sizeof(DataType)));
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DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(tensor5d.size()*sizeof(DataType)));
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TensorMap< Tensor<DataType, 3, DataLayout, IndexType> > gpu1(gpu_data1, dim1);
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TensorMap< Tensor<DataType, 2, DataLayout, IndexType> > gpu2(gpu_data2, dim2);
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TensorMap< Tensor<DataType, 5, DataLayout, IndexType> > gpu3(gpu_data3, dim3);
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sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(),(tensor.size())*sizeof(DataType));
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gpu2.reshape(dim1).device(sycl_device)=gpu1;
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sycl_device.memcpyDeviceToHost(tensor2d.data(), gpu_data2,(tensor2d.size())*sizeof(DataType));
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gpu3.reshape(dim1).device(sycl_device)=gpu1;
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sycl_device.memcpyDeviceToHost(tensor5d.data(), gpu_data3,(tensor5d.size())*sizeof(DataType));
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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(tensor5d(i,j,0,k,0), tensor(i,j,k));
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if (static_cast<int>(DataLayout) == static_cast<int>(ColMajor)) {
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VERIFY_IS_EQUAL(tensor2d(i+2*j,k), tensor(i,j,k)); ///ColMajor
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}
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else{
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VERIFY_IS_EQUAL(tensor2d(i*3 +j,k),tensor(i,j,k)); /// RowMajor
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}
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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, int DataLayout, typename IndexType>
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static void test_simple_slice(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 = 5;
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IndexType sizeDim4 = 7;
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IndexType sizeDim5 = 11;
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array<IndexType, 5> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
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Tensor<DataType, 5,DataLayout, IndexType> tensor(tensorRange);
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tensor.setRandom();
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array<IndexType, 5> slice1_range ={{1, 1, 1, 1, 1}};
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Tensor<DataType, 5,DataLayout, IndexType> 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, IndexType>> gpu1(gpu_data1, tensorRange);
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TensorMap<Tensor<DataType, 5,DataLayout, IndexType>> gpu2(gpu_data2, slice1_range);
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Eigen::DSizes<IndexType, 5> indices(1,2,3,4,5);
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Eigen::DSizes<IndexType, 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<IndexType, 5> slice2_range ={{1,1,2,2,3}};
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Tensor<DataType, 5,DataLayout, IndexType> 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, IndexType>> gpu3(gpu_data3, slice2_range);
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Eigen::DSizes<IndexType, 5> indices2(1,1,3,4,5);
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Eigen::DSizes<IndexType, 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 (IndexType i = 0; i < 2; ++i) {
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for (IndexType j = 0; j < 2; ++j) {
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for (IndexType 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, int DataLayout, typename IndexType>
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static void test_strided_slice_write_sycl(const Eigen::SyclDevice& sycl_device)
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{
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typedef Tensor<DataType, 2, DataLayout, IndexType> Tensor2f;
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typedef Eigen::DSizes<IndexType, 2> Index2;
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IndexType sizeDim1 = 7L;
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IndexType sizeDim2 = 11L;
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array<IndexType, 2> tensorRange = {{sizeDim1, sizeDim2}};
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Tensor<DataType, 2, DataLayout, IndexType> tensor(tensorRange),tensor2(tensorRange);
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IndexType sliceDim1 = 2;
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IndexType sliceDim2 = 3;
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array<IndexType, 2> sliceRange = {{sliceDim1, sliceDim2}};
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Tensor2f slice(sliceRange);
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Index2 strides(1L,1L);
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Index2 indicesStart(3L,4L);
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Index2 indicesStop(5L,7L);
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Index2 lengths(2L,3L);
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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(tensor2.size()*sizeof(DataType)));
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DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(slice.size()*sizeof(DataType)));
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TensorMap<Tensor<DataType, 2,DataLayout,IndexType>> gpu1(gpu_data1, tensorRange);
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TensorMap<Tensor<DataType, 2,DataLayout,IndexType>> gpu2(gpu_data2, tensorRange);
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TensorMap<Tensor<DataType, 2,DataLayout,IndexType>> gpu3(gpu_data3, sliceRange);
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tensor.setRandom();
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sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(),(tensor.size())*sizeof(DataType));
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gpu2.device(sycl_device)=gpu1;
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slice.setRandom();
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sycl_device.memcpyHostToDevice(gpu_data3, slice.data(),(slice.size())*sizeof(DataType));
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gpu1.slice(indicesStart,lengths).device(sycl_device)=gpu3;
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gpu2.stridedSlice(indicesStart,indicesStop,strides).device(sycl_device)=gpu3;
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sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data1,(tensor.size())*sizeof(DataType));
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sycl_device.memcpyDeviceToHost(tensor2.data(), gpu_data2,(tensor2.size())*sizeof(DataType));
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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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VERIFY_IS_EQUAL(tensor(i,j), tensor2(i,j));
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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_morphing_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, int64_t>(sycl_device);
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test_simple_slice<DataType, ColMajor, int64_t>(sycl_device);
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test_simple_reshape<DataType, RowMajor, int64_t>(sycl_device);
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test_simple_reshape<DataType, ColMajor, int64_t>(sycl_device);
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test_reshape_as_lvalue<DataType, RowMajor, int64_t>(sycl_device);
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test_reshape_as_lvalue<DataType, ColMajor, int64_t>(sycl_device);
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test_strided_slice_write_sycl<DataType, ColMajor, int64_t>(sycl_device);
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test_strided_slice_write_sycl<DataType, RowMajor, int64_t>(sycl_device);
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}
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EIGEN_DECLARE_TEST(cxx11_tensor_morphing_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_morphing_test_per_device<float>(device));
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}
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}
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