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Diffstat (limited to 'unsupported/test/cxx11_tensor_scan_sycl.cpp')
-rw-r--r-- | unsupported/test/cxx11_tensor_scan_sycl.cpp | 141 |
1 files changed, 141 insertions, 0 deletions
diff --git a/unsupported/test/cxx11_tensor_scan_sycl.cpp b/unsupported/test/cxx11_tensor_scan_sycl.cpp new file mode 100644 index 000000000..09c45fce5 --- /dev/null +++ b/unsupported/test/cxx11_tensor_scan_sycl.cpp @@ -0,0 +1,141 @@ +// 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; +typedef Tensor<float, 1>::DimensionPair DimPair; + +template <typename DataType, int DataLayout, typename IndexType> +void test_sycl_cumsum(const Eigen::SyclDevice& sycl_device, IndexType m_size, + IndexType k_size, IndexType n_size, int consume_dim, + bool exclusive) { + static const DataType error_threshold = 1e-4f; + std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size + << " consume_dim : " << consume_dim << ")" << std::endl; + Tensor<DataType, 3, DataLayout, IndexType> t_input(m_size, k_size, n_size); + Tensor<DataType, 3, DataLayout, IndexType> t_result(m_size, k_size, n_size); + Tensor<DataType, 3, DataLayout, IndexType> t_result_gpu(m_size, k_size, + n_size); + + t_input.setRandom(); + std::size_t t_input_bytes = t_input.size() * sizeof(DataType); + std::size_t t_result_bytes = t_result.size() * sizeof(DataType); + + DataType* gpu_data_in = + static_cast<DataType*>(sycl_device.allocate(t_input_bytes)); + DataType* gpu_data_out = + static_cast<DataType*>(sycl_device.allocate(t_result_bytes)); + + array<IndexType, 3> tensorRange = {{m_size, k_size, n_size}}; + TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_t_input( + gpu_data_in, tensorRange); + TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_t_result( + gpu_data_out, tensorRange); + sycl_device.memcpyHostToDevice(gpu_data_in, t_input.data(), t_input_bytes); + sycl_device.memcpyHostToDevice(gpu_data_out, t_input.data(), t_input_bytes); + + gpu_t_result.device(sycl_device) = gpu_t_input.cumsum(consume_dim, exclusive); + + t_result = t_input.cumsum(consume_dim, exclusive); + + sycl_device.memcpyDeviceToHost(t_result_gpu.data(), gpu_data_out, + t_result_bytes); + sycl_device.synchronize(); + + for (IndexType i = 0; i < t_result.size(); i++) { + if (static_cast<DataType>(std::fabs(static_cast<DataType>( + t_result(i) - t_result_gpu(i)))) < error_threshold) { + continue; + } + if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), + error_threshold)) { + continue; + } + std::cout << "mismatch detected at index " << i << " CPU : " << t_result(i) + << " vs SYCL : " << t_result_gpu(i) << std::endl; + assert(false); + } + sycl_device.deallocate(gpu_data_in); + sycl_device.deallocate(gpu_data_out); +} + +template <typename DataType, typename Dev> +void sycl_scan_test_exclusive_dim0_per_device(const Dev& sycl_device) { + test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 2049, 1023, 127, 0, + true); + test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 2049, 1023, 127, 0, + true); +} +template <typename DataType, typename Dev> +void sycl_scan_test_exclusive_dim1_per_device(const Dev& sycl_device) { + test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 1023, 2049, 127, 1, + true); + test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 1023, 2049, 127, 1, + true); +} +template <typename DataType, typename Dev> +void sycl_scan_test_exclusive_dim2_per_device(const Dev& sycl_device) { + test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 1023, 127, 2049, 2, + true); + test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 1023, 127, 2049, 2, + true); +} +template <typename DataType, typename Dev> +void sycl_scan_test_inclusive_dim0_per_device(const Dev& sycl_device) { + test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 2049, 1023, 127, 0, + false); + test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 2049, 1023, 127, 0, + false); +} +template <typename DataType, typename Dev> +void sycl_scan_test_inclusive_dim1_per_device(const Dev& sycl_device) { + test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 1023, 2049, 127, 1, + false); + test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 1023, 2049, 127, 1, + false); +} +template <typename DataType, typename Dev> +void sycl_scan_test_inclusive_dim2_per_device(const Dev& sycl_device) { + test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 1023, 127, 2049, 2, + false); + test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 1023, 127, 2049, 2, + false); +} +EIGEN_DECLARE_TEST(cxx11_tensor_scan_sycl) { + for (const auto& device : Eigen::get_sycl_supported_devices()) { + std::cout << "Running on " + << device.template get_info<cl::sycl::info::device::name>() + << std::endl; + QueueInterface queueInterface(device); + auto sycl_device = Eigen::SyclDevice(&queueInterface); + CALL_SUBTEST_1( + sycl_scan_test_exclusive_dim0_per_device<float>(sycl_device)); + CALL_SUBTEST_2( + sycl_scan_test_exclusive_dim1_per_device<float>(sycl_device)); + CALL_SUBTEST_3( + sycl_scan_test_exclusive_dim2_per_device<float>(sycl_device)); + CALL_SUBTEST_4( + sycl_scan_test_inclusive_dim0_per_device<float>(sycl_device)); + CALL_SUBTEST_5( + sycl_scan_test_inclusive_dim1_per_device<float>(sycl_device)); + CALL_SUBTEST_6( + sycl_scan_test_inclusive_dim2_per_device<float>(sycl_device)); + } +} |