| // This file is part of Eigen, a lightweight C++ template library |
| // for linear algebra. |
| // |
| // Copyright (C) 2016 |
| // Mehdi Goli Codeplay Software Ltd. |
| // Ralph Potter Codeplay Software Ltd. |
| // Luke Iwanski Codeplay Software Ltd. |
| // Contact: <eigen@codeplay.com> |
| // |
| // This Source Code Form is subject to the terms of the Mozilla |
| // Public License v. 2.0. If a copy of the MPL was not distributed |
| // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. |
| |
| #define EIGEN_TEST_NO_LONGDOUBLE |
| #define EIGEN_TEST_NO_COMPLEX |
| |
| #define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t |
| #define EIGEN_USE_SYCL |
| |
| #include <iostream> |
| #include <chrono> |
| #include <ctime> |
| |
| #include "main.h" |
| #include <unsupported/Eigen/CXX11/Tensor> |
| |
| using Eigen::array; |
| using Eigen::SyclDevice; |
| using Eigen::Tensor; |
| using Eigen::TensorMap; |
| |
| |
| template <typename DataType, int DataLayout, typename IndexType> |
| static void test_simple_striding(const Eigen::SyclDevice& sycl_device) |
| { |
| |
| Eigen::array<IndexType, 4> tensor_dims = {{2,3,5,7}}; |
| Eigen::array<IndexType, 4> stride_dims = {{1,1,3,3}}; |
| |
| |
| Tensor<DataType, 4, DataLayout, IndexType> tensor(tensor_dims); |
| Tensor<DataType, 4, DataLayout,IndexType> no_stride(tensor_dims); |
| Tensor<DataType, 4, DataLayout,IndexType> stride(stride_dims); |
| |
| |
| std::size_t tensor_bytes = tensor.size() * sizeof(DataType); |
| std::size_t no_stride_bytes = no_stride.size() * sizeof(DataType); |
| std::size_t stride_bytes = stride.size() * sizeof(DataType); |
| DataType * d_tensor = static_cast<DataType*>(sycl_device.allocate(tensor_bytes)); |
| DataType * d_no_stride = static_cast<DataType*>(sycl_device.allocate(no_stride_bytes)); |
| DataType * d_stride = static_cast<DataType*>(sycl_device.allocate(stride_bytes)); |
| |
| Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_tensor(d_tensor, tensor_dims); |
| Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_no_stride(d_no_stride, tensor_dims); |
| Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_stride(d_stride, stride_dims); |
| |
| |
| tensor.setRandom(); |
| array<IndexType, 4> strides; |
| strides[0] = 1; |
| strides[1] = 1; |
| strides[2] = 1; |
| strides[3] = 1; |
| sycl_device.memcpyHostToDevice(d_tensor, tensor.data(), tensor_bytes); |
| gpu_no_stride.device(sycl_device)=gpu_tensor.stride(strides); |
| sycl_device.memcpyDeviceToHost(no_stride.data(), d_no_stride, no_stride_bytes); |
| |
| //no_stride = tensor.stride(strides); |
| |
| VERIFY_IS_EQUAL(no_stride.dimension(0), 2); |
| VERIFY_IS_EQUAL(no_stride.dimension(1), 3); |
| VERIFY_IS_EQUAL(no_stride.dimension(2), 5); |
| VERIFY_IS_EQUAL(no_stride.dimension(3), 7); |
| |
| for (IndexType i = 0; i < 2; ++i) { |
| for (IndexType j = 0; j < 3; ++j) { |
| for (IndexType k = 0; k < 5; ++k) { |
| for (IndexType l = 0; l < 7; ++l) { |
| VERIFY_IS_EQUAL(tensor(i,j,k,l), no_stride(i,j,k,l)); |
| } |
| } |
| } |
| } |
| |
| strides[0] = 2; |
| strides[1] = 4; |
| strides[2] = 2; |
| strides[3] = 3; |
| //Tensor<float, 4, DataLayout> stride; |
| // stride = tensor.stride(strides); |
| |
| gpu_stride.device(sycl_device)=gpu_tensor.stride(strides); |
| sycl_device.memcpyDeviceToHost(stride.data(), d_stride, stride_bytes); |
| |
| VERIFY_IS_EQUAL(stride.dimension(0), 1); |
| VERIFY_IS_EQUAL(stride.dimension(1), 1); |
| VERIFY_IS_EQUAL(stride.dimension(2), 3); |
| VERIFY_IS_EQUAL(stride.dimension(3), 3); |
| |
| for (IndexType i = 0; i < 1; ++i) { |
| for (IndexType j = 0; j < 1; ++j) { |
| for (IndexType k = 0; k < 3; ++k) { |
| for (IndexType l = 0; l < 3; ++l) { |
| VERIFY_IS_EQUAL(tensor(2*i,4*j,2*k,3*l), stride(i,j,k,l)); |
| } |
| } |
| } |
| } |
| |
| sycl_device.deallocate(d_tensor); |
| sycl_device.deallocate(d_no_stride); |
| sycl_device.deallocate(d_stride); |
| } |
| |
| template <typename DataType, int DataLayout, typename IndexType> |
| static void test_striding_as_lvalue(const Eigen::SyclDevice& sycl_device) |
| { |
| |
| Eigen::array<IndexType, 4> tensor_dims = {{2,3,5,7}}; |
| Eigen::array<IndexType, 4> stride_dims = {{3,12,10,21}}; |
| |
| |
| Tensor<DataType, 4, DataLayout, IndexType> tensor(tensor_dims); |
| Tensor<DataType, 4, DataLayout,IndexType> no_stride(stride_dims); |
| Tensor<DataType, 4, DataLayout,IndexType> stride(stride_dims); |
| |
| |
| std::size_t tensor_bytes = tensor.size() * sizeof(DataType); |
| std::size_t no_stride_bytes = no_stride.size() * sizeof(DataType); |
| std::size_t stride_bytes = stride.size() * sizeof(DataType); |
| |
| DataType * d_tensor = static_cast<DataType*>(sycl_device.allocate(tensor_bytes)); |
| DataType * d_no_stride = static_cast<DataType*>(sycl_device.allocate(no_stride_bytes)); |
| DataType * d_stride = static_cast<DataType*>(sycl_device.allocate(stride_bytes)); |
| |
| Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_tensor(d_tensor, tensor_dims); |
| Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_no_stride(d_no_stride, stride_dims); |
| Eigen::TensorMap<Eigen::Tensor<DataType, 4, DataLayout, IndexType> > gpu_stride(d_stride, stride_dims); |
| |
| //Tensor<float, 4, DataLayout> tensor(2,3,5,7); |
| tensor.setRandom(); |
| array<IndexType, 4> strides; |
| strides[0] = 2; |
| strides[1] = 4; |
| strides[2] = 2; |
| strides[3] = 3; |
| |
| // Tensor<float, 4, DataLayout> result(3, 12, 10, 21); |
| // result.stride(strides) = tensor; |
| sycl_device.memcpyHostToDevice(d_tensor, tensor.data(), tensor_bytes); |
| gpu_stride.stride(strides).device(sycl_device)=gpu_tensor; |
| sycl_device.memcpyDeviceToHost(stride.data(), d_stride, stride_bytes); |
| |
| for (IndexType i = 0; i < 2; ++i) { |
| for (IndexType j = 0; j < 3; ++j) { |
| for (IndexType k = 0; k < 5; ++k) { |
| for (IndexType l = 0; l < 7; ++l) { |
| VERIFY_IS_EQUAL(tensor(i,j,k,l), stride(2*i,4*j,2*k,3*l)); |
| } |
| } |
| } |
| } |
| |
| array<IndexType, 4> no_strides; |
| no_strides[0] = 1; |
| no_strides[1] = 1; |
| no_strides[2] = 1; |
| no_strides[3] = 1; |
| // Tensor<float, 4, DataLayout> result2(3, 12, 10, 21); |
| // result2.stride(strides) = tensor.stride(no_strides); |
| |
| gpu_no_stride.stride(strides).device(sycl_device)=gpu_tensor.stride(no_strides); |
| sycl_device.memcpyDeviceToHost(no_stride.data(), d_no_stride, no_stride_bytes); |
| |
| for (IndexType i = 0; i < 2; ++i) { |
| for (IndexType j = 0; j < 3; ++j) { |
| for (IndexType k = 0; k < 5; ++k) { |
| for (IndexType l = 0; l < 7; ++l) { |
| VERIFY_IS_EQUAL(tensor(i,j,k,l), no_stride(2*i,4*j,2*k,3*l)); |
| } |
| } |
| } |
| } |
| sycl_device.deallocate(d_tensor); |
| sycl_device.deallocate(d_no_stride); |
| sycl_device.deallocate(d_stride); |
| } |
| |
| |
| template <typename Dev_selector> void tensorStridingPerDevice(Dev_selector& s){ |
| QueueInterface queueInterface(s); |
| auto sycl_device=Eigen::SyclDevice(&queueInterface); |
| test_simple_striding<float, ColMajor, int64_t>(sycl_device); |
| test_simple_striding<float, RowMajor, int64_t>(sycl_device); |
| test_striding_as_lvalue<float, ColMajor, int64_t>(sycl_device); |
| test_striding_as_lvalue<float, RowMajor, int64_t>(sycl_device); |
| } |
| |
| EIGEN_DECLARE_TEST(cxx11_tensor_striding_sycl) { |
| for (const auto& device :Eigen::get_sycl_supported_devices()) { |
| CALL_SUBTEST(tensorStridingPerDevice(device)); |
| } |
| } |