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372 lines
18 KiB
C++
372 lines
18 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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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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} 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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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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} 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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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_as_rhs_sycl(const Eigen::SyclDevice& sycl_device) {
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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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typedef Eigen::DSizes<IndexType, 5> Index5;
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Index5 strides(1L, 1L, 1L, 1L, 1L);
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Index5 indicesStart(1L, 2L, 3L, 4L, 5L);
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Index5 indicesStop(2L, 3L, 4L, 5L, 6L);
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Index5 lengths(1L, 1L, 1L, 1L, 1L);
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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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Tensor<DataType, 5, DataLayout, IndexType> slice_stride1(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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DataType* gpu_data_stride2 = static_cast<DataType*>(sycl_device.allocate(slice_stride1.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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TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_stride2(gpu_data_stride2, 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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gpu_stride2.device(sycl_device) = gpu1.stridedSlice(indicesStart, indicesStop, strides);
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sycl_device.memcpyDeviceToHost(slice_stride1.data(), gpu_data_stride2, (slice_stride1.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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VERIFY_IS_EQUAL(slice_stride1(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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Tensor<DataType, 5, DataLayout, IndexType> strideSlice2(slice2_range);
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DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(slice2.size() * sizeof(DataType)));
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DataType* gpu_data_stride3 = static_cast<DataType*>(sycl_device.allocate(strideSlice2.size() * sizeof(DataType)));
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TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu3(gpu_data3, slice2_range);
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TensorMap<Tensor<DataType, 5, DataLayout, IndexType>> gpu_stride3(gpu_data_stride3, 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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Index5 strides2(1L, 1L, 1L, 1L, 1L);
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Index5 indicesStart2(1L, 1L, 3L, 4L, 5L);
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Index5 indicesStop2(2L, 2L, 5L, 6L, 8L);
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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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gpu_stride3.device(sycl_device) = gpu1.stridedSlice(indicesStart2, indicesStop2, strides2);
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sycl_device.memcpyDeviceToHost(strideSlice2.data(), gpu_data_stride3, (strideSlice2.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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VERIFY_IS_EQUAL(strideSlice2(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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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 OutIndex, typename DSizes>
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Eigen::array<OutIndex, DSizes::count> To32BitDims(const DSizes& in) {
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Eigen::array<OutIndex, DSizes::count> out;
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for (int i = 0; i < DSizes::count; ++i) {
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out[i] = in[i];
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}
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return out;
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}
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template <class DataType, int DataLayout, typename IndexType, typename ConvertedIndexType>
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int run_eigen(const SyclDevice& sycl_device) {
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using TensorI64 = Tensor<DataType, 5, DataLayout, IndexType>;
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using TensorI32 = Tensor<DataType, 5, DataLayout, ConvertedIndexType>;
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using TensorMI64 = TensorMap<TensorI64>;
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using TensorMI32 = TensorMap<TensorI32>;
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Eigen::array<IndexType, 5> tensor_range{{4, 1, 1, 1, 6}};
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Eigen::array<IndexType, 5> slice_range{{4, 1, 1, 1, 3}};
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TensorI64 out_tensor_gpu(tensor_range);
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TensorI64 out_tensor_cpu(tensor_range);
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out_tensor_cpu.setRandom();
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TensorI64 sub_tensor(slice_range);
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sub_tensor.setRandom();
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DataType* out_gpu_data = static_cast<DataType*>(sycl_device.allocate(out_tensor_cpu.size() * sizeof(DataType)));
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DataType* sub_gpu_data = static_cast<DataType*>(sycl_device.allocate(sub_tensor.size() * sizeof(DataType)));
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TensorMI64 out_gpu(out_gpu_data, tensor_range);
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TensorMI64 sub_gpu(sub_gpu_data, slice_range);
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sycl_device.memcpyHostToDevice(out_gpu_data, out_tensor_cpu.data(), out_tensor_cpu.size() * sizeof(DataType));
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sycl_device.memcpyHostToDevice(sub_gpu_data, sub_tensor.data(), sub_tensor.size() * sizeof(DataType));
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Eigen::array<ConvertedIndexType, 5> slice_offset_32{{0, 0, 0, 0, 3}};
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Eigen::array<ConvertedIndexType, 5> slice_range_32{{4, 1, 1, 1, 3}};
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TensorMI32 out_cpu_32(out_tensor_cpu.data(), To32BitDims<ConvertedIndexType>(out_tensor_cpu.dimensions()));
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TensorMI32 sub_cpu_32(sub_tensor.data(), To32BitDims<ConvertedIndexType>(sub_tensor.dimensions()));
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TensorMI32 out_gpu_32(out_gpu.data(), To32BitDims<ConvertedIndexType>(out_gpu.dimensions()));
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TensorMI32 sub_gpu_32(sub_gpu.data(), To32BitDims<ConvertedIndexType>(sub_gpu.dimensions()));
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out_gpu_32.slice(slice_offset_32, slice_range_32).device(sycl_device) = sub_gpu_32;
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out_cpu_32.slice(slice_offset_32, slice_range_32) = sub_cpu_32;
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sycl_device.memcpyDeviceToHost(out_tensor_gpu.data(), out_gpu_data, out_tensor_cpu.size() * sizeof(DataType));
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|
int has_err = 0;
|
|
for (IndexType i = 0; i < out_tensor_cpu.size(); ++i) {
|
|
auto exp = out_tensor_cpu(i);
|
|
auto val = out_tensor_gpu(i);
|
|
if (val != exp) {
|
|
std::cout << "#" << i << " got " << val << " but expected " << exp << std::endl;
|
|
has_err = 1;
|
|
}
|
|
}
|
|
sycl_device.deallocate(out_gpu_data);
|
|
sycl_device.deallocate(sub_gpu_data);
|
|
return has_err;
|
|
}
|
|
|
|
template <typename DataType, typename dev_Selector>
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|
void sycl_morphing_test_per_device(dev_Selector s) {
|
|
QueueInterface queueInterface(s);
|
|
auto sycl_device = Eigen::SyclDevice(&queueInterface);
|
|
test_simple_slice<DataType, RowMajor, int64_t>(sycl_device);
|
|
test_simple_slice<DataType, ColMajor, int64_t>(sycl_device);
|
|
test_simple_reshape<DataType, RowMajor, int64_t>(sycl_device);
|
|
test_simple_reshape<DataType, ColMajor, int64_t>(sycl_device);
|
|
test_reshape_as_lvalue<DataType, RowMajor, int64_t>(sycl_device);
|
|
test_reshape_as_lvalue<DataType, ColMajor, int64_t>(sycl_device);
|
|
test_strided_slice_write_sycl<DataType, ColMajor, int64_t>(sycl_device);
|
|
test_strided_slice_write_sycl<DataType, RowMajor, int64_t>(sycl_device);
|
|
test_strided_slice_as_rhs_sycl<DataType, ColMajor, int64_t>(sycl_device);
|
|
test_strided_slice_as_rhs_sycl<DataType, RowMajor, int64_t>(sycl_device);
|
|
run_eigen<float, RowMajor, long, int>(sycl_device);
|
|
}
|
|
EIGEN_DECLARE_TEST(cxx11_tensor_morphing_sycl) {
|
|
for (const auto& device : Eigen::get_sycl_supported_devices()) {
|
|
CALL_SUBTEST(sycl_morphing_test_per_device<half>(device));
|
|
CALL_SUBTEST(sycl_morphing_test_per_device<float>(device));
|
|
}
|
|
}
|