| // 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 "main.h" |
| #include <unsupported/Eigen/CXX11/Tensor> |
| |
| using Eigen::Tensor; |
| |
| // Inflation Definition for each dimension the inflated val would be |
| //((dim-1)*strid[dim] +1) |
| |
| // for 1 dimension vector of size 3 with value (4,4,4) with the inflated stride value of 3 would be changed to |
| // tensor of size (2*3) +1 = 7 with the value of |
| // (4, 0, 0, 4, 0, 0, 4). |
| |
| template <typename DataType, int DataLayout, typename IndexType> |
| void test_simple_inflation_sycl(const Eigen::SyclDevice &sycl_device) { |
| |
| |
| IndexType sizeDim1 = 2; |
| IndexType sizeDim2 = 3; |
| IndexType sizeDim3 = 5; |
| IndexType sizeDim4 = 7; |
| array<IndexType, 4> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}}; |
| Tensor<DataType, 4, DataLayout,IndexType> tensor(tensorRange); |
| Tensor<DataType, 4, DataLayout,IndexType> no_stride(tensorRange); |
| tensor.setRandom(); |
| |
| array<IndexType, 4> strides; |
| strides[0] = 1; |
| strides[1] = 1; |
| strides[2] = 1; |
| strides[3] = 1; |
| |
| |
| const size_t tensorBuffSize =tensor.size()*sizeof(DataType); |
| DataType* gpu_data_tensor = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize)); |
| DataType* gpu_data_no_stride = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize)); |
| |
| TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_tensor(gpu_data_tensor, tensorRange); |
| TensorMap<Tensor<DataType, 4, DataLayout,IndexType>> gpu_no_stride(gpu_data_no_stride, tensorRange); |
| |
| sycl_device.memcpyHostToDevice(gpu_data_tensor, tensor.data(), tensorBuffSize); |
| gpu_no_stride.device(sycl_device)=gpu_tensor.inflate(strides); |
| sycl_device.memcpyDeviceToHost(no_stride.data(), gpu_data_no_stride, tensorBuffSize); |
| |
| VERIFY_IS_EQUAL(no_stride.dimension(0), sizeDim1); |
| VERIFY_IS_EQUAL(no_stride.dimension(1), sizeDim2); |
| VERIFY_IS_EQUAL(no_stride.dimension(2), sizeDim3); |
| VERIFY_IS_EQUAL(no_stride.dimension(3), sizeDim4); |
| |
| 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; |
| |
| IndexType inflatedSizeDim1 = 3; |
| IndexType inflatedSizeDim2 = 9; |
| IndexType inflatedSizeDim3 = 9; |
| IndexType inflatedSizeDim4 = 19; |
| array<IndexType, 4> inflatedTensorRange = {{inflatedSizeDim1, inflatedSizeDim2, inflatedSizeDim3, inflatedSizeDim4}}; |
| |
| Tensor<DataType, 4, DataLayout, IndexType> inflated(inflatedTensorRange); |
| |
| const size_t inflatedTensorBuffSize =inflated.size()*sizeof(DataType); |
| DataType* gpu_data_inflated = static_cast<DataType*>(sycl_device.allocate(inflatedTensorBuffSize)); |
| TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_inflated(gpu_data_inflated, inflatedTensorRange); |
| gpu_inflated.device(sycl_device)=gpu_tensor.inflate(strides); |
| sycl_device.memcpyDeviceToHost(inflated.data(), gpu_data_inflated, inflatedTensorBuffSize); |
| |
| VERIFY_IS_EQUAL(inflated.dimension(0), inflatedSizeDim1); |
| VERIFY_IS_EQUAL(inflated.dimension(1), inflatedSizeDim2); |
| VERIFY_IS_EQUAL(inflated.dimension(2), inflatedSizeDim3); |
| VERIFY_IS_EQUAL(inflated.dimension(3), inflatedSizeDim4); |
| |
| for (IndexType i = 0; i < inflatedSizeDim1; ++i) { |
| for (IndexType j = 0; j < inflatedSizeDim2; ++j) { |
| for (IndexType k = 0; k < inflatedSizeDim3; ++k) { |
| for (IndexType l = 0; l < inflatedSizeDim4; ++l) { |
| if (i % strides[0] == 0 && |
| j % strides[1] == 0 && |
| k % strides[2] == 0 && |
| l % strides[3] == 0) { |
| VERIFY_IS_EQUAL(inflated(i,j,k,l), |
| tensor(i/strides[0], j/strides[1], k/strides[2], l/strides[3])); |
| } else { |
| VERIFY_IS_EQUAL(0, inflated(i,j,k,l)); |
| } |
| } |
| } |
| } |
| } |
| sycl_device.deallocate(gpu_data_tensor); |
| sycl_device.deallocate(gpu_data_no_stride); |
| sycl_device.deallocate(gpu_data_inflated); |
| } |
| |
| template<typename DataType, typename dev_Selector> void sycl_inflation_test_per_device(dev_Selector s){ |
| QueueInterface queueInterface(s); |
| auto sycl_device = Eigen::SyclDevice(&queueInterface); |
| test_simple_inflation_sycl<DataType, RowMajor, int64_t>(sycl_device); |
| test_simple_inflation_sycl<DataType, ColMajor, int64_t>(sycl_device); |
| } |
| EIGEN_DECLARE_TEST(cxx11_tensor_inflation_sycl) |
| { |
| for (const auto& device :Eigen::get_sycl_supported_devices()) { |
| CALL_SUBTEST(sycl_inflation_test_per_device<half>(device)); |
| CALL_SUBTEST(sycl_inflation_test_per_device<float>(device)); |
| } |
| } |