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113 lines
5.4 KiB
C++
113 lines
5.4 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2016
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// Mehdi Goli Codeplay Software Ltd.
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// Ralph Potter Codeplay Software Ltd.
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// Luke Iwanski Codeplay Software Ltd.
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// Contact: <eigen@codeplay.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#define EIGEN_TEST_NO_LONGDOUBLE
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#define EIGEN_TEST_NO_COMPLEX
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#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
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#define EIGEN_USE_SYCL
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#include "main.h"
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#include <unsupported/Eigen/CXX11/Tensor>
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using Eigen::Tensor;
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typedef Tensor<float, 1>::DimensionPair DimPair;
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template <typename DataType, int DataLayout, typename IndexType>
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void test_sycl_cumsum(const Eigen::SyclDevice& sycl_device, IndexType m_size, IndexType k_size, IndexType n_size,
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int consume_dim, bool exclusive) {
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static const DataType error_threshold = 1e-4f;
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std::cout << "Testing for (" << m_size << "," << k_size << "," << n_size << " consume_dim : " << consume_dim << ")"
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<< std::endl;
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Tensor<DataType, 3, DataLayout, IndexType> t_input(m_size, k_size, n_size);
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Tensor<DataType, 3, DataLayout, IndexType> t_result(m_size, k_size, n_size);
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Tensor<DataType, 3, DataLayout, IndexType> t_result_gpu(m_size, k_size, n_size);
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t_input.setRandom();
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std::size_t t_input_bytes = t_input.size() * sizeof(DataType);
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std::size_t t_result_bytes = t_result.size() * sizeof(DataType);
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DataType* gpu_data_in = static_cast<DataType*>(sycl_device.allocate(t_input_bytes));
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DataType* gpu_data_out = static_cast<DataType*>(sycl_device.allocate(t_result_bytes));
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array<IndexType, 3> tensorRange = {{m_size, k_size, n_size}};
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TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_t_input(gpu_data_in, tensorRange);
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TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu_t_result(gpu_data_out, tensorRange);
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sycl_device.memcpyHostToDevice(gpu_data_in, t_input.data(), t_input_bytes);
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sycl_device.memcpyHostToDevice(gpu_data_out, t_input.data(), t_input_bytes);
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gpu_t_result.device(sycl_device) = gpu_t_input.cumsum(consume_dim, exclusive);
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t_result = t_input.cumsum(consume_dim, exclusive);
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sycl_device.memcpyDeviceToHost(t_result_gpu.data(), gpu_data_out, t_result_bytes);
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sycl_device.synchronize();
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for (IndexType i = 0; i < t_result.size(); i++) {
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if (static_cast<DataType>(std::fabs(static_cast<DataType>(t_result(i) - t_result_gpu(i)))) < error_threshold) {
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continue;
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}
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if (Eigen::internal::isApprox(t_result(i), t_result_gpu(i), error_threshold)) {
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continue;
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}
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std::cout << "mismatch detected at index " << i << " CPU : " << t_result(i) << " vs SYCL : " << t_result_gpu(i)
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<< std::endl;
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assert(false);
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}
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sycl_device.deallocate(gpu_data_in);
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sycl_device.deallocate(gpu_data_out);
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}
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template <typename DataType, typename Dev>
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void sycl_scan_test_exclusive_dim0_per_device(const Dev& sycl_device) {
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test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 2049, 1023, 127, 0, true);
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test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 2049, 1023, 127, 0, true);
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}
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template <typename DataType, typename Dev>
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void sycl_scan_test_exclusive_dim1_per_device(const Dev& sycl_device) {
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test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 1023, 2049, 127, 1, true);
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test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 1023, 2049, 127, 1, true);
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}
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template <typename DataType, typename Dev>
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void sycl_scan_test_exclusive_dim2_per_device(const Dev& sycl_device) {
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test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 1023, 127, 2049, 2, true);
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test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 1023, 127, 2049, 2, true);
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}
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template <typename DataType, typename Dev>
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void sycl_scan_test_inclusive_dim0_per_device(const Dev& sycl_device) {
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test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 2049, 1023, 127, 0, false);
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test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 2049, 1023, 127, 0, false);
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}
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template <typename DataType, typename Dev>
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void sycl_scan_test_inclusive_dim1_per_device(const Dev& sycl_device) {
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test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 1023, 2049, 127, 1, false);
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test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 1023, 2049, 127, 1, false);
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}
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template <typename DataType, typename Dev>
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void sycl_scan_test_inclusive_dim2_per_device(const Dev& sycl_device) {
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test_sycl_cumsum<DataType, ColMajor, int64_t>(sycl_device, 1023, 127, 2049, 2, false);
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test_sycl_cumsum<DataType, RowMajor, int64_t>(sycl_device, 1023, 127, 2049, 2, false);
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}
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EIGEN_DECLARE_TEST(cxx11_tensor_scan_sycl) {
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for (const auto& device : Eigen::get_sycl_supported_devices()) {
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std::cout << "Running on " << device.template get_info<cl::sycl::info::device::name>() << std::endl;
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QueueInterface queueInterface(device);
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auto sycl_device = Eigen::SyclDevice(&queueInterface);
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CALL_SUBTEST_1(sycl_scan_test_exclusive_dim0_per_device<float>(sycl_device));
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CALL_SUBTEST_2(sycl_scan_test_exclusive_dim1_per_device<float>(sycl_device));
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CALL_SUBTEST_3(sycl_scan_test_exclusive_dim2_per_device<float>(sycl_device));
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CALL_SUBTEST_4(sycl_scan_test_inclusive_dim0_per_device<float>(sycl_device));
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CALL_SUBTEST_5(sycl_scan_test_inclusive_dim1_per_device<float>(sycl_device));
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CALL_SUBTEST_6(sycl_scan_test_inclusive_dim2_per_device<float>(sycl_device));
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}
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}
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