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https://gitlab.com/libeigen/eigen.git
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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
104 lines
3.8 KiB
C++
104 lines
3.8 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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// Benoit Steiner <benoit.steiner.goog@gmail.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::array;
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using Eigen::SyclDevice;
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using Eigen::Tensor;
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using Eigen::TensorMap;
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using Eigen::Tensor;
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using Eigen::RowMajor;
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template <typename DataType, int DataLayout, typename IndexType>
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static void test_image_op_sycl(const Eigen::SyclDevice &sycl_device)
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{
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IndexType sizeDim1 = 245;
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IndexType sizeDim2 = 343;
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IndexType sizeDim3 = 577;
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array<IndexType, 3> input_range ={{sizeDim1, sizeDim2, sizeDim3}};
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array<IndexType, 3> slice_range ={{sizeDim1-1, sizeDim2, sizeDim3}};
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Tensor<DataType, 3,DataLayout, IndexType> tensor1(input_range);
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Tensor<DataType, 3,DataLayout, IndexType> tensor2(input_range);
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Tensor<DataType, 3, DataLayout, IndexType> tensor3(slice_range);
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Tensor<DataType, 3, DataLayout, IndexType> tensor3_cpu(slice_range);
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typedef Eigen::DSizes<IndexType, 3> Index3;
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Index3 strides1(1L,1L, 1L);
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Index3 indicesStart1(1L, 0L, 0L);
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Index3 indicesStop1(sizeDim1, sizeDim2, sizeDim3);
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Index3 strides2(1L,1L, 1L);
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Index3 indicesStart2(0L, 0L, 0L);
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Index3 indicesStop2(sizeDim1-1, sizeDim2, sizeDim3);
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Eigen::DSizes<IndexType, 3> sizes(sizeDim1-1,sizeDim2,sizeDim3);
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tensor1.setRandom();
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tensor2.setRandom();
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DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor1.size()*sizeof(DataType)));
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DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(tensor2.size()*sizeof(DataType)));
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DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(tensor3.size()*sizeof(DataType)));
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TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu1(gpu_data1, input_range);
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TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu2(gpu_data2, input_range);
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TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu3(gpu_data3, slice_range);
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sycl_device.memcpyHostToDevice(gpu_data1, tensor1.data(),(tensor1.size())*sizeof(DataType));
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sycl_device.memcpyHostToDevice(gpu_data2, tensor2.data(),(tensor2.size())*sizeof(DataType));
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gpu3.device(sycl_device)= gpu1.slice(indicesStart1, sizes) - gpu2.slice(indicesStart2, sizes);
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sycl_device.memcpyDeviceToHost(tensor3.data(), gpu_data3,(tensor3.size())*sizeof(DataType));
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tensor3_cpu = tensor1.stridedSlice(indicesStart1,indicesStop1,strides1) - tensor2.stridedSlice(indicesStart2,indicesStop2,strides2);
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for (IndexType i = 0; i <slice_range[0] ; ++i) {
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for (IndexType j = 0; j < slice_range[1]; ++j) {
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for (IndexType k = 0; k < slice_range[2]; ++k) {
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VERIFY_IS_EQUAL(tensor3_cpu(i,j,k), tensor3(i,j,k));
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}
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}
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}
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sycl_device.deallocate(gpu_data1);
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sycl_device.deallocate(gpu_data2);
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sycl_device.deallocate(gpu_data3);
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}
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template<typename DataType, typename dev_Selector> void sycl_computing_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_image_op_sycl<DataType, RowMajor, int64_t>(sycl_device);
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}
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EIGEN_DECLARE_TEST(cxx11_tensor_image_op_sycl) {
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for (const auto& device :Eigen::get_sycl_supported_devices()) {
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CALL_SUBTEST(sycl_computing_test_per_device<float>(device));
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#ifdef EIGEN_SYCL_DOUBLE_SUPPORT
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CALL_SUBTEST(sycl_computing_test_per_device<double>(device));
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#endif
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}
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}
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