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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
118 lines
4.2 KiB
C++
118 lines
4.2 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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template <typename DataType, int DataLayout, typename IndexType>
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static void test_simple_shuffling_sycl(const Eigen::SyclDevice& sycl_device) {
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IndexType sizeDim1 = 2;
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IndexType sizeDim2 = 3;
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IndexType sizeDim3 = 5;
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IndexType sizeDim4 = 7;
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array<IndexType, 4> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
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Tensor<DataType, 4, DataLayout, IndexType> tensor(tensorRange);
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Tensor<DataType, 4, DataLayout, IndexType> no_shuffle(tensorRange);
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tensor.setRandom();
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const size_t buffSize = tensor.size() * sizeof(DataType);
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array<IndexType, 4> shuffles;
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shuffles[0] = 0;
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shuffles[1] = 1;
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shuffles[2] = 2;
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shuffles[3] = 3;
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DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(buffSize));
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DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(buffSize));
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TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu1(gpu_data1,
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tensorRange);
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TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu2(gpu_data2,
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tensorRange);
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sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(), buffSize);
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gpu2.device(sycl_device) = gpu1.shuffle(shuffles);
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sycl_device.memcpyDeviceToHost(no_shuffle.data(), gpu_data2, buffSize);
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sycl_device.synchronize();
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VERIFY_IS_EQUAL(no_shuffle.dimension(0), sizeDim1);
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VERIFY_IS_EQUAL(no_shuffle.dimension(1), sizeDim2);
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VERIFY_IS_EQUAL(no_shuffle.dimension(2), sizeDim3);
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VERIFY_IS_EQUAL(no_shuffle.dimension(3), sizeDim4);
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for (IndexType i = 0; i < sizeDim1; ++i) {
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for (IndexType j = 0; j < sizeDim2; ++j) {
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for (IndexType k = 0; k < sizeDim3; ++k) {
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for (IndexType l = 0; l < sizeDim4; ++l) {
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VERIFY_IS_EQUAL(tensor(i, j, k, l), no_shuffle(i, j, k, l));
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}
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}
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}
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}
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shuffles[0] = 2;
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shuffles[1] = 3;
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shuffles[2] = 1;
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shuffles[3] = 0;
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array<IndexType, 4> tensorrangeShuffle = {
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{sizeDim3, sizeDim4, sizeDim2, sizeDim1}};
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Tensor<DataType, 4, DataLayout, IndexType> shuffle(tensorrangeShuffle);
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DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(buffSize));
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TensorMap<Tensor<DataType, 4, DataLayout, IndexType>> gpu3(
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gpu_data3, tensorrangeShuffle);
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gpu3.device(sycl_device) = gpu1.shuffle(shuffles);
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sycl_device.memcpyDeviceToHost(shuffle.data(), gpu_data3, buffSize);
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sycl_device.synchronize();
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VERIFY_IS_EQUAL(shuffle.dimension(0), sizeDim3);
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VERIFY_IS_EQUAL(shuffle.dimension(1), sizeDim4);
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VERIFY_IS_EQUAL(shuffle.dimension(2), sizeDim2);
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VERIFY_IS_EQUAL(shuffle.dimension(3), sizeDim1);
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for (IndexType i = 0; i < sizeDim1; ++i) {
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for (IndexType j = 0; j < sizeDim2; ++j) {
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for (IndexType k = 0; k < sizeDim3; ++k) {
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for (IndexType l = 0; l < sizeDim4; ++l) {
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VERIFY_IS_EQUAL(tensor(i, j, k, l), shuffle(k, l, j, i));
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}
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}
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}
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}
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}
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template <typename DataType, typename dev_Selector>
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void sycl_shuffling_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_simple_shuffling_sycl<DataType, RowMajor, int64_t>(sycl_device);
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test_simple_shuffling_sycl<DataType, ColMajor, int64_t>(sycl_device);
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}
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EIGEN_DECLARE_TEST(cxx11_tensor_shuffling_sycl) {
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for (const auto& device : Eigen::get_sycl_supported_devices()) {
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CALL_SUBTEST(sycl_shuffling_test_per_device<float>(device));
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}
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}
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