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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
204 lines
6.9 KiB
C++
204 lines
6.9 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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//
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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 <iostream>
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#include <chrono>
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#include <ctime>
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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_striding(const Eigen::SyclDevice& sycl_device)
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{
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Eigen::array<IndexType, 4> tensor_dims = {{2,3,5,7}};
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Eigen::array<IndexType, 4> stride_dims = {{1,1,3,3}};
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Tensor<DataType, 4, DataLayout, IndexType> tensor(tensor_dims);
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Tensor<DataType, 4, DataLayout,IndexType> no_stride(tensor_dims);
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Tensor<DataType, 4, DataLayout,IndexType> stride(stride_dims);
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std::size_t tensor_bytes = tensor.size() * sizeof(DataType);
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std::size_t no_stride_bytes = no_stride.size() * sizeof(DataType);
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std::size_t stride_bytes = stride.size() * sizeof(DataType);
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DataType * d_tensor = static_cast<DataType*>(sycl_device.allocate(tensor_bytes));
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DataType * d_no_stride = static_cast<DataType*>(sycl_device.allocate(no_stride_bytes));
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DataType * d_stride = static_cast<DataType*>(sycl_device.allocate(stride_bytes));
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Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_tensor(d_tensor, tensor_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_no_stride(d_no_stride, tensor_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_stride(d_stride, stride_dims);
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tensor.setRandom();
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array<IndexType, 4> strides;
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strides[0] = 1;
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strides[1] = 1;
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strides[2] = 1;
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strides[3] = 1;
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sycl_device.memcpyHostToDevice(d_tensor, tensor.data(), tensor_bytes);
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gpu_no_stride.device(sycl_device)=gpu_tensor.stride(strides);
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sycl_device.memcpyDeviceToHost(no_stride.data(), d_no_stride, no_stride_bytes);
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//no_stride = tensor.stride(strides);
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VERIFY_IS_EQUAL(no_stride.dimension(0), 2);
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VERIFY_IS_EQUAL(no_stride.dimension(1), 3);
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VERIFY_IS_EQUAL(no_stride.dimension(2), 5);
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VERIFY_IS_EQUAL(no_stride.dimension(3), 7);
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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 < 5; ++k) {
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for (IndexType l = 0; l < 7; ++l) {
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VERIFY_IS_EQUAL(tensor(i,j,k,l), no_stride(i,j,k,l));
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}
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}
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}
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}
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strides[0] = 2;
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strides[1] = 4;
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strides[2] = 2;
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strides[3] = 3;
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//Tensor<float, 4, DataLayout> stride;
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// stride = tensor.stride(strides);
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gpu_stride.device(sycl_device)=gpu_tensor.stride(strides);
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sycl_device.memcpyDeviceToHost(stride.data(), d_stride, stride_bytes);
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VERIFY_IS_EQUAL(stride.dimension(0), 1);
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VERIFY_IS_EQUAL(stride.dimension(1), 1);
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VERIFY_IS_EQUAL(stride.dimension(2), 3);
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VERIFY_IS_EQUAL(stride.dimension(3), 3);
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for (IndexType i = 0; i < 1; ++i) {
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for (IndexType j = 0; j < 1; ++j) {
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for (IndexType k = 0; k < 3; ++k) {
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for (IndexType l = 0; l < 3; ++l) {
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VERIFY_IS_EQUAL(tensor(2*i,4*j,2*k,3*l), stride(i,j,k,l));
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}
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}
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}
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}
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sycl_device.deallocate(d_tensor);
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sycl_device.deallocate(d_no_stride);
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sycl_device.deallocate(d_stride);
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}
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template <typename DataType, int DataLayout, typename IndexType>
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static void test_striding_as_lvalue(const Eigen::SyclDevice& sycl_device)
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{
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Eigen::array<IndexType, 4> tensor_dims = {{2,3,5,7}};
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Eigen::array<IndexType, 4> stride_dims = {{3,12,10,21}};
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Tensor<DataType, 4, DataLayout, IndexType> tensor(tensor_dims);
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Tensor<DataType, 4, DataLayout,IndexType> no_stride(stride_dims);
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Tensor<DataType, 4, DataLayout,IndexType> stride(stride_dims);
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std::size_t tensor_bytes = tensor.size() * sizeof(DataType);
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std::size_t no_stride_bytes = no_stride.size() * sizeof(DataType);
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std::size_t stride_bytes = stride.size() * sizeof(DataType);
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DataType * d_tensor = static_cast<DataType*>(sycl_device.allocate(tensor_bytes));
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DataType * d_no_stride = static_cast<DataType*>(sycl_device.allocate(no_stride_bytes));
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DataType * d_stride = static_cast<DataType*>(sycl_device.allocate(stride_bytes));
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Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_tensor(d_tensor, tensor_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_no_stride(d_no_stride, stride_dims);
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Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_stride(d_stride, stride_dims);
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//Tensor<float, 4, DataLayout> tensor(2,3,5,7);
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tensor.setRandom();
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array<IndexType, 4> strides;
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strides[0] = 2;
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strides[1] = 4;
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strides[2] = 2;
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strides[3] = 3;
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// Tensor<float, 4, DataLayout> result(3, 12, 10, 21);
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// result.stride(strides) = tensor;
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sycl_device.memcpyHostToDevice(d_tensor, tensor.data(), tensor_bytes);
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gpu_stride.stride(strides).device(sycl_device)=gpu_tensor;
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sycl_device.memcpyDeviceToHost(stride.data(), d_stride, stride_bytes);
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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 < 5; ++k) {
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for (IndexType l = 0; l < 7; ++l) {
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VERIFY_IS_EQUAL(tensor(i,j,k,l), stride(2*i,4*j,2*k,3*l));
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}
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}
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}
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}
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array<IndexType, 4> no_strides;
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no_strides[0] = 1;
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no_strides[1] = 1;
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no_strides[2] = 1;
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no_strides[3] = 1;
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// Tensor<float, 4, DataLayout> result2(3, 12, 10, 21);
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// result2.stride(strides) = tensor.stride(no_strides);
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gpu_no_stride.stride(strides).device(sycl_device)=gpu_tensor.stride(no_strides);
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sycl_device.memcpyDeviceToHost(no_stride.data(), d_no_stride, no_stride_bytes);
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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 < 5; ++k) {
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for (IndexType l = 0; l < 7; ++l) {
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VERIFY_IS_EQUAL(tensor(i,j,k,l), no_stride(2*i,4*j,2*k,3*l));
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}
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}
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}
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}
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sycl_device.deallocate(d_tensor);
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sycl_device.deallocate(d_no_stride);
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sycl_device.deallocate(d_stride);
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}
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template <typename Dev_selector> void tensorStridingPerDevice(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_striding<float, ColMajor, int64_t>(sycl_device);
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test_simple_striding<float, RowMajor, int64_t>(sycl_device);
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test_striding_as_lvalue<float, ColMajor, int64_t>(sycl_device);
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test_striding_as_lvalue<float, RowMajor, int64_t>(sycl_device);
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}
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EIGEN_DECLARE_TEST(cxx11_tensor_striding_sycl) {
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for (const auto& device :Eigen::get_sycl_supported_devices()) {
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CALL_SUBTEST(tensorStridingPerDevice(device));
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}
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}
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