| // 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)); |
| } |
| } |
