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00f32752f7
* 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
101 lines
3.5 KiB
C++
101 lines
3.5 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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template <typename DataType, int DataLayout, typename IndexType>
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static void test_sycl_random_uniform(const Eigen::SyclDevice& sycl_device)
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{
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Tensor<DataType, 2,DataLayout, IndexType> out(72,97);
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out.setZero();
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std::size_t out_bytes = out.size() * sizeof(DataType);
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IndexType sizeDim0 = 72;
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IndexType sizeDim1 = 97;
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array<IndexType, 2> tensorRange = {{sizeDim0, sizeDim1}};
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DataType* d_out = static_cast<DataType*>(sycl_device.allocate(out_bytes));
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TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu_out(d_out, tensorRange);
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gpu_out.device(sycl_device)=gpu_out.random();
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sycl_device.memcpyDeviceToHost(out.data(), d_out,out_bytes);
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for(IndexType i=1; i<sizeDim0; i++)
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for(IndexType j=1; j<sizeDim1; j++)
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{
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VERIFY_IS_NOT_EQUAL(out(i,j), out(i-1,j));
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VERIFY_IS_NOT_EQUAL(out(i,j), out(i,j-1));
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VERIFY_IS_NOT_EQUAL(out(i,j), out(i-1,j-1)); }
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// For now we just check thes code doesn't crash.
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// TODO: come up with a valid test of randomness
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sycl_device.deallocate(d_out);
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}
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template <typename DataType, int DataLayout, typename IndexType>
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void test_sycl_random_normal(const Eigen::SyclDevice& sycl_device)
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{
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Tensor<DataType, 2,DataLayout,IndexType> out(72,97);
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out.setZero();
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std::size_t out_bytes = out.size() * sizeof(DataType);
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IndexType sizeDim0 = 72;
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IndexType sizeDim1 = 97;
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array<IndexType, 2> tensorRange = {{sizeDim0, sizeDim1}};
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DataType* d_out = static_cast<DataType*>(sycl_device.allocate(out_bytes));
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TensorMap<Tensor<DataType, 2, DataLayout, IndexType>> gpu_out(d_out, tensorRange);
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Eigen::internal::NormalRandomGenerator<DataType> gen(true);
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gpu_out.device(sycl_device)=gpu_out.random(gen);
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sycl_device.memcpyDeviceToHost(out.data(), d_out,out_bytes);
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for(IndexType i=1; i<sizeDim0; i++)
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for(IndexType j=1; j<sizeDim1; j++)
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{
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VERIFY_IS_NOT_EQUAL(out(i,j), out(i-1,j));
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VERIFY_IS_NOT_EQUAL(out(i,j), out(i,j-1));
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VERIFY_IS_NOT_EQUAL(out(i,j), out(i-1,j-1));
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}
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// For now we just check thes code doesn't crash.
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// TODO: come up with a valid test of randomness
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sycl_device.deallocate(d_out);
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}
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template<typename DataType, typename dev_Selector> void sycl_random_test_per_device(dev_Selector s){
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QueueInterface queueInterface(s);
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auto sycl_device = Eigen::SyclDevice(&queueInterface);
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test_sycl_random_uniform<DataType, RowMajor, int64_t>(sycl_device);
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test_sycl_random_uniform<DataType, ColMajor, int64_t>(sycl_device);
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test_sycl_random_normal<DataType, RowMajor, int64_t>(sycl_device);
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test_sycl_random_normal<DataType, ColMajor, int64_t>(sycl_device);
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}
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EIGEN_DECLARE_TEST(cxx11_tensor_random_sycl)
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{
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for (const auto& device :Eigen::get_sycl_supported_devices()) {
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CALL_SUBTEST(sycl_random_test_per_device<float>(device));
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#ifdef EIGEN_SYCL_DOUBLE_SUPPORT
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CALL_SUBTEST(sycl_random_test_per_device<double>(device));
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#endif
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}
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}
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