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author | Mehdi Goli <mehdi.goli@codeplay.com> | 2019-11-28 10:08:54 +0000 |
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committer | Mehdi Goli <mehdi.goli@codeplay.com> | 2019-11-28 10:08:54 +0000 |
commit | 00f32752f7d0b193c6788691c3cf0b76457a044d (patch) | |
tree | 792e46110f0751ea8802fa9d403d1472d5977ac3 /unsupported/test/cxx11_tensor_random_sycl.cpp | |
parent | ea51a9eace7e4f0ea839e61eb2df85ccfb94aee8 (diff) |
[SYCL] Rebasing the SYCL support branch on top of the Einge upstream master branch.
* Unifying all loadLocalTile from lhs and rhs to an extract_block function.
* Adding get_tensor operation which was missing in TensorContractionMapper.
* Adding the -D method missing from cmake for Disable_Skinny Contraction operation.
* Wrapping all the indices in TensorScanSycl into Scan parameter struct.
* Fixing typo in Device SYCL
* Unifying load to private register for tall/skinny no shared
* Unifying load to vector tile for tensor-vector/vector-tensor operation
* Removing all the LHS/RHS class for extracting data from global
* Removing Outputfunction from TensorContractionSkinnyNoshared.
* Combining the local memory version of tall/skinny and normal tensor contraction into one kernel.
* Combining the no-local memory version of tall/skinny and normal tensor contraction into one kernel.
* Combining General Tensor-Vector and VectorTensor contraction into one kernel.
* Making double buffering optional for Tensor contraction when local memory is version is used.
* Modifying benchmark to accept custom Reduction Sizes
* Disabling AVX optimization for SYCL backend on the host to allow SSE optimization to the host
* Adding Test for SYCL
* Modifying SYCL CMake
Diffstat (limited to 'unsupported/test/cxx11_tensor_random_sycl.cpp')
-rw-r--r-- | unsupported/test/cxx11_tensor_random_sycl.cpp | 100 |
1 files changed, 100 insertions, 0 deletions
diff --git a/unsupported/test/cxx11_tensor_random_sycl.cpp b/unsupported/test/cxx11_tensor_random_sycl.cpp new file mode 100644 index 000000000..6c83894a3 --- /dev/null +++ b/unsupported/test/cxx11_tensor_random_sycl.cpp @@ -0,0 +1,100 @@ +// 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> + +template <typename DataType, int DataLayout, typename IndexType> +static void test_sycl_random_uniform(const Eigen::SyclDevice& sycl_device) +{ + Tensor<DataType, 2,DataLayout, IndexType> out(72,97); + out.setZero(); + + std::size_t out_bytes = out.size() * sizeof(DataType); + + IndexType sizeDim0 = 72; + IndexType sizeDim1 = 97; + + array<IndexType, 2> tensorRange = {{sizeDim0, sizeDim1}}; + + DataType* d_out = static_cast<DataType*>(sycl_device.allocate(out_bytes)); + TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu_out(d_out, tensorRange); + + gpu_out.device(sycl_device)=gpu_out.random(); + sycl_device.memcpyDeviceToHost(out.data(), d_out,out_bytes); + for(IndexType i=1; i<sizeDim0; i++) + for(IndexType j=1; j<sizeDim1; j++) + { + VERIFY_IS_NOT_EQUAL(out(i,j), out(i-1,j)); + VERIFY_IS_NOT_EQUAL(out(i,j), out(i,j-1)); + VERIFY_IS_NOT_EQUAL(out(i,j), out(i-1,j-1)); } + + // For now we just check thes code doesn't crash. + // TODO: come up with a valid test of randomness + sycl_device.deallocate(d_out); +} + +template <typename DataType, int DataLayout, typename IndexType> +void test_sycl_random_normal(const Eigen::SyclDevice& sycl_device) +{ + Tensor<DataType, 2,DataLayout,IndexType> out(72,97); + out.setZero(); + std::size_t out_bytes = out.size() * sizeof(DataType); + + IndexType sizeDim0 = 72; + IndexType sizeDim1 = 97; + + array<IndexType, 2> tensorRange = {{sizeDim0, sizeDim1}}; + + DataType* d_out = static_cast<DataType*>(sycl_device.allocate(out_bytes)); + TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu_out(d_out, tensorRange); + Eigen::internal::NormalRandomGenerator<DataType> gen(true); + gpu_out.device(sycl_device)=gpu_out.random(gen); + sycl_device.memcpyDeviceToHost(out.data(), d_out,out_bytes); + for(IndexType i=1; i<sizeDim0; i++) + for(IndexType j=1; j<sizeDim1; j++) + { + VERIFY_IS_NOT_EQUAL(out(i,j), out(i-1,j)); + VERIFY_IS_NOT_EQUAL(out(i,j), out(i,j-1)); + VERIFY_IS_NOT_EQUAL(out(i,j), out(i-1,j-1)); + + } + + // For now we just check thes code doesn't crash. + // TODO: come up with a valid test of randomness + sycl_device.deallocate(d_out); +} + +template<typename DataType, typename dev_Selector> void sycl_random_test_per_device(dev_Selector s){ + QueueInterface queueInterface(s); + auto sycl_device = Eigen::SyclDevice(&queueInterface); + test_sycl_random_uniform<DataType, RowMajor, int64_t>(sycl_device); + test_sycl_random_uniform<DataType, ColMajor, int64_t>(sycl_device); + test_sycl_random_normal<DataType, RowMajor, int64_t>(sycl_device); + test_sycl_random_normal<DataType, ColMajor, int64_t>(sycl_device); + +} +EIGEN_DECLARE_TEST(cxx11_tensor_random_sycl) +{ + for (const auto& device :Eigen::get_sycl_supported_devices()) { + CALL_SUBTEST(sycl_random_test_per_device<float>(device)); +#ifdef EIGEN_SYCL_DOUBLE_SUPPORT + CALL_SUBTEST(sycl_random_test_per_device<double>(device)); +#endif + } +} |