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author | Stanislaw Halik <sthalik@misaki.pl> | 2017-03-25 14:17:07 +0100 |
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committer | Stanislaw Halik <sthalik@misaki.pl> | 2017-03-25 14:17:07 +0100 |
commit | 35f7829af10c61e33dd2e2a7a015058e11a11ea0 (patch) | |
tree | 7135010dcf8fd0a49f3020d52112709bcb883bd6 /eigen/unsupported/test/cxx11_tensor_broadcast_sycl.cpp | |
parent | 6e8724193e40a932faf9064b664b529e7301c578 (diff) |
update
Diffstat (limited to 'eigen/unsupported/test/cxx11_tensor_broadcast_sycl.cpp')
-rw-r--r-- | eigen/unsupported/test/cxx11_tensor_broadcast_sycl.cpp | 144 |
1 files changed, 144 insertions, 0 deletions
diff --git a/eigen/unsupported/test/cxx11_tensor_broadcast_sycl.cpp b/eigen/unsupported/test/cxx11_tensor_broadcast_sycl.cpp new file mode 100644 index 0000000..21fdfca --- /dev/null +++ b/eigen/unsupported/test/cxx11_tensor_broadcast_sycl.cpp @@ -0,0 +1,144 @@ +// 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_broadcast_sycl +#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t +#define EIGEN_USE_SYCL + +#include "main.h" +#include <unsupported/Eigen/CXX11/Tensor> + +using Eigen::array; +using Eigen::SyclDevice; +using Eigen::Tensor; +using Eigen::TensorMap; + +template <typename DataType, int DataLayout, typename IndexType> +static void test_broadcast_sycl_fixed(const Eigen::SyclDevice &sycl_device){ + + // BROADCAST test: + IndexType inDim1=2; + IndexType inDim2=3; + IndexType inDim3=5; + IndexType inDim4=7; + IndexType bDim1=2; + IndexType bDim2=3; + IndexType bDim3=1; + IndexType bDim4=4; + array<IndexType, 4> in_range = {{inDim1, inDim2, inDim3, inDim4}}; + array<IndexType, 4> broadcasts = {{bDim1, bDim2, bDim3, bDim4}}; + array<IndexType, 4> out_range; // = in_range * broadcasts + for (size_t i = 0; i < out_range.size(); ++i) + out_range[i] = in_range[i] * broadcasts[i]; + + Tensor<DataType, 4, DataLayout, IndexType> input(in_range); + Tensor<DataType, 4, DataLayout, IndexType> out(out_range); + + for (size_t i = 0; i < in_range.size(); ++i) + VERIFY_IS_EQUAL(out.dimension(i), out_range[i]); + + + for (IndexType i = 0; i < input.size(); ++i) + input(i) = static_cast<DataType>(i); + + DataType * gpu_in_data = static_cast<DataType*>(sycl_device.allocate(input.dimensions().TotalSize()*sizeof(DataType))); + DataType * gpu_out_data = static_cast<DataType*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(DataType))); + + TensorMap<TensorFixedSize<DataType, Sizes<2, 3, 5, 7>, DataLayout, IndexType>> gpu_in(gpu_in_data, in_range); + TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_out(gpu_out_data, out_range); + sycl_device.memcpyHostToDevice(gpu_in_data, input.data(),(input.dimensions().TotalSize())*sizeof(DataType)); + gpu_out.device(sycl_device) = gpu_in.broadcast(broadcasts); + sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(DataType)); + + for (IndexType i = 0; i < inDim1*bDim1; ++i) { + for (IndexType j = 0; j < inDim2*bDim2; ++j) { + for (IndexType k = 0; k < inDim3*bDim3; ++k) { + for (IndexType l = 0; l < inDim4*bDim4; ++l) { + VERIFY_IS_APPROX(input(i%2,j%3,k%5,l%7), out(i,j,k,l)); + } + } + } + } + printf("Broadcast Test with fixed size Passed\n"); + sycl_device.deallocate(gpu_in_data); + sycl_device.deallocate(gpu_out_data); +} + +template <typename DataType, int DataLayout, typename IndexType> +static void test_broadcast_sycl(const Eigen::SyclDevice &sycl_device){ + + // BROADCAST test: + IndexType inDim1=2; + IndexType inDim2=3; + IndexType inDim3=5; + IndexType inDim4=7; + IndexType bDim1=2; + IndexType bDim2=3; + IndexType bDim3=1; + IndexType bDim4=4; + array<IndexType, 4> in_range = {{inDim1, inDim2, inDim3, inDim4}}; + array<IndexType, 4> broadcasts = {{bDim1, bDim2, bDim3, bDim4}}; + array<IndexType, 4> out_range; // = in_range * broadcasts + for (size_t i = 0; i < out_range.size(); ++i) + out_range[i] = in_range[i] * broadcasts[i]; + + Tensor<DataType, 4, DataLayout, IndexType> input(in_range); + Tensor<DataType, 4, DataLayout, IndexType> out(out_range); + + for (size_t i = 0; i < in_range.size(); ++i) + VERIFY_IS_EQUAL(out.dimension(i), out_range[i]); + + + for (IndexType i = 0; i < input.size(); ++i) + input(i) = static_cast<DataType>(i); + + DataType * gpu_in_data = static_cast<DataType*>(sycl_device.allocate(input.dimensions().TotalSize()*sizeof(DataType))); + DataType * gpu_out_data = static_cast<DataType*>(sycl_device.allocate(out.dimensions().TotalSize()*sizeof(DataType))); + + TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_in(gpu_in_data, in_range); + TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu_out(gpu_out_data, out_range); + sycl_device.memcpyHostToDevice(gpu_in_data, input.data(),(input.dimensions().TotalSize())*sizeof(DataType)); + gpu_out.device(sycl_device) = gpu_in.broadcast(broadcasts); + sycl_device.memcpyDeviceToHost(out.data(), gpu_out_data,(out.dimensions().TotalSize())*sizeof(DataType)); + + for (IndexType i = 0; i < inDim1*bDim1; ++i) { + for (IndexType j = 0; j < inDim2*bDim2; ++j) { + for (IndexType k = 0; k < inDim3*bDim3; ++k) { + for (IndexType l = 0; l < inDim4*bDim4; ++l) { + VERIFY_IS_APPROX(input(i%inDim1,j%inDim2,k%inDim3,l%inDim4), out(i,j,k,l)); + } + } + } + } + printf("Broadcast Test Passed\n"); + sycl_device.deallocate(gpu_in_data); + sycl_device.deallocate(gpu_out_data); +} + +template<typename DataType> void sycl_broadcast_test_per_device(const cl::sycl::device& d){ + std::cout << "Running on " << d.template get_info<cl::sycl::info::device::name>() << std::endl; + QueueInterface queueInterface(d); + auto sycl_device = Eigen::SyclDevice(&queueInterface); + test_broadcast_sycl<DataType, RowMajor, int64_t>(sycl_device); + test_broadcast_sycl<DataType, ColMajor, int64_t>(sycl_device); + test_broadcast_sycl_fixed<DataType, RowMajor, int64_t>(sycl_device); + test_broadcast_sycl_fixed<DataType, ColMajor, int64_t>(sycl_device); +} + +void test_cxx11_tensor_broadcast_sycl() { + for (const auto& device :Eigen::get_sycl_supported_devices()) { + CALL_SUBTEST(sycl_broadcast_test_per_device<float>(device)); + } +} |