diff options
| -rw-r--r-- | unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h | 13 | ||||
| -rw-r--r-- | unsupported/test/CMakeLists.txt | 1 | ||||
| -rw-r--r-- | unsupported/test/cxx11_tensor_generator_sycl.cpp | 147 |
3 files changed, 158 insertions, 3 deletions
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h b/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h index eb1d4934e..ca87f493a 100644 --- a/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h @@ -97,10 +97,9 @@ struct TensorEvaluator<const TensorGeneratorOp<Generator, ArgType>, Device> }; EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) - : m_generator(op.generator()) + : m_generator(op.generator()), m_argImpl(op.expression(), device) { - TensorEvaluator<ArgType, Device> impl(op.expression(), device); - m_dimensions = impl.dimensions(); + m_dimensions = m_argImpl.dimensions(); if (static_cast<int>(Layout) == static_cast<int>(ColMajor)) { m_strides[0] = 1; @@ -155,6 +154,12 @@ struct TensorEvaluator<const TensorGeneratorOp<Generator, ArgType>, Device> EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } + /// required by sycl in order to extract the accessor + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const TensorEvaluator<ArgType, Device>& impl() const { return m_argImpl; } + /// required by sycl in order to extract the accessor + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Generator& functor() const { return m_generator; } + + protected: EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void extract_coordinates(Index index, array<Index, NumDims>& coords) const { @@ -178,6 +183,8 @@ struct TensorEvaluator<const TensorGeneratorOp<Generator, ArgType>, Device> Dimensions m_dimensions; array<Index, NumDims> m_strides; Generator m_generator; + // required by sycl + TensorEvaluator<ArgType, Device> m_argImpl; }; } // end namespace Eigen diff --git a/unsupported/test/CMakeLists.txt b/unsupported/test/CMakeLists.txt index 282f9eb55..69c892362 100644 --- a/unsupported/test/CMakeLists.txt +++ b/unsupported/test/CMakeLists.txt @@ -171,6 +171,7 @@ if(EIGEN_TEST_CXX11) ei_add_test_sycl(cxx11_tensor_layout_swap_sycl "-std=c++11") ei_add_test_sycl(cxx11_tensor_image_patchOP_sycl "-std=c++11") ei_add_test_sycl(cxx11_tensor_inflation_sycl "-std=c++11") + ei_add_test_sycl(cxx11_tensor_generator_sycl "-std=c++11") endif(EIGEN_TEST_SYCL) # It should be safe to always run these tests as there is some fallback code for # older compiler that don't support cxx11. diff --git a/unsupported/test/cxx11_tensor_generator_sycl.cpp b/unsupported/test/cxx11_tensor_generator_sycl.cpp new file mode 100644 index 000000000..f551c8d0c --- /dev/null +++ b/unsupported/test/cxx11_tensor_generator_sycl.cpp @@ -0,0 +1,147 @@ +// 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_TEST_FUNC cxx11_tensor_generator_sycl +#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t +#define EIGEN_USE_SYCL +static const float error_threshold =1e-8f; + +#include "main.h" +#include <unsupported/Eigen/CXX11/Tensor> + +using Eigen::Tensor; +struct Generator1D { + Generator1D() { } + + float operator()(const array<Eigen::DenseIndex, 1>& coordinates) const { + return coordinates[0]; + } +}; + +template <typename DataType, int DataLayout, typename IndexType> +static void test_1D_sycl(const Eigen::SyclDevice& sycl_device) +{ + + IndexType sizeDim1 = 6; + array<IndexType, 1> tensorRange = {{sizeDim1}}; + Tensor<DataType, 1, DataLayout,IndexType> vec(tensorRange); + Tensor<DataType, 1, DataLayout,IndexType> result(tensorRange); + + const size_t tensorBuffSize =vec.size()*sizeof(DataType); + DataType* gpu_data_vec = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize)); + DataType* gpu_data_result = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize)); + + TensorMap<Tensor<DataType, 1, DataLayout,IndexType>> gpu_vec(gpu_data_vec, tensorRange); + TensorMap<Tensor<DataType, 1, DataLayout,IndexType>> gpu_result(gpu_data_result, tensorRange); + + sycl_device.memcpyHostToDevice(gpu_data_vec, vec.data(), tensorBuffSize); + gpu_result.device(sycl_device)=gpu_vec.generate(Generator1D()); + sycl_device.memcpyDeviceToHost(result.data(), gpu_data_result, tensorBuffSize); + + for (IndexType i = 0; i < 6; ++i) { + VERIFY_IS_EQUAL(result(i), i); + } +} + + +struct Generator2D { + Generator2D() { } + + float operator()(const array<Eigen::DenseIndex, 2>& coordinates) const { + return 3 * coordinates[0] + 11 * coordinates[1]; + } +}; + +template <typename DataType, int DataLayout, typename IndexType> +static void test_2D_sycl(const Eigen::SyclDevice& sycl_device) +{ + IndexType sizeDim1 = 5; + IndexType sizeDim2 = 7; + array<IndexType, 2> tensorRange = {{sizeDim1, sizeDim2}}; + Tensor<DataType, 2, DataLayout,IndexType> matrix(tensorRange); + Tensor<DataType, 2, DataLayout,IndexType> result(tensorRange); + + const size_t tensorBuffSize =matrix.size()*sizeof(DataType); + DataType* gpu_data_matrix = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize)); + DataType* gpu_data_result = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize)); + + TensorMap<Tensor<DataType, 2, DataLayout,IndexType>> gpu_matrix(gpu_data_matrix, tensorRange); + TensorMap<Tensor<DataType, 2, DataLayout,IndexType>> gpu_result(gpu_data_result, tensorRange); + + sycl_device.memcpyHostToDevice(gpu_data_matrix, matrix.data(), tensorBuffSize); + gpu_result.device(sycl_device)=gpu_matrix.generate(Generator2D()); + sycl_device.memcpyDeviceToHost(result.data(), gpu_data_result, tensorBuffSize); + + for (IndexType i = 0; i < 5; ++i) { + for (IndexType j = 0; j < 5; ++j) { + VERIFY_IS_EQUAL(result(i, j), 3*i + 11*j); + } + } +} + +template <typename DataType, int DataLayout, typename IndexType> +static void test_gaussian_sycl(const Eigen::SyclDevice& sycl_device) +{ + IndexType rows = 32; + IndexType cols = 48; + array<DataType, 2> means; + means[0] = rows / 2.0f; + means[1] = cols / 2.0f; + array<DataType, 2> std_devs; + std_devs[0] = 3.14f; + std_devs[1] = 2.7f; + internal::GaussianGenerator<DataType, Eigen::DenseIndex, 2> gaussian_gen(means, std_devs); + + array<IndexType, 2> tensorRange = {{rows, cols}}; + Tensor<DataType, 2, DataLayout,IndexType> matrix(tensorRange); + Tensor<DataType, 2, DataLayout,IndexType> result(tensorRange); + + const size_t tensorBuffSize =matrix.size()*sizeof(DataType); + DataType* gpu_data_matrix = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize)); + DataType* gpu_data_result = static_cast<DataType*>(sycl_device.allocate(tensorBuffSize)); + + TensorMap<Tensor<DataType, 2, DataLayout,IndexType>> gpu_matrix(gpu_data_matrix, tensorRange); + TensorMap<Tensor<DataType, 2, DataLayout,IndexType>> gpu_result(gpu_data_result, tensorRange); + + sycl_device.memcpyHostToDevice(gpu_data_matrix, matrix.data(), tensorBuffSize); + gpu_result.device(sycl_device)=gpu_matrix.generate(gaussian_gen); + sycl_device.memcpyDeviceToHost(result.data(), gpu_data_result, tensorBuffSize); + + for (IndexType i = 0; i < rows; ++i) { + for (IndexType j = 0; j < cols; ++j) { + DataType g_rows = powf(rows/2.0f - i, 2) / (3.14f * 3.14f) * 0.5f; + DataType g_cols = powf(cols/2.0f - j, 2) / (2.7f * 2.7f) * 0.5f; + DataType gaussian = expf(-g_rows - g_cols); + Eigen::internal::isApprox(result(i, j), gaussian, error_threshold); + } + } +} + +template<typename DataType, typename dev_Selector> void sycl_generator_test_per_device(dev_Selector s){ + QueueInterface queueInterface(s); + auto sycl_device = Eigen::SyclDevice(&queueInterface); + test_1D_sycl<DataType, RowMajor, int64_t>(sycl_device); + test_1D_sycl<DataType, ColMajor, int64_t>(sycl_device); + test_2D_sycl<DataType, RowMajor, int64_t>(sycl_device); + test_2D_sycl<DataType, ColMajor, int64_t>(sycl_device); + test_gaussian_sycl<DataType, RowMajor, int64_t>(sycl_device); + test_gaussian_sycl<DataType, ColMajor, int64_t>(sycl_device); +} +void test_cxx11_tensor_generator_sycl() +{ + for (const auto& device :Eigen::get_sycl_supported_devices()) { + CALL_SUBTEST(sycl_generator_test_per_device<float>(device)); + } +} |