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[SYCL] Rebasing the SYCL support branch on top of the Einge upstream master branch.

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* 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
This commit is contained in:
Mehdi Goli
2019-11-28 10:08:54 +00:00
parent ea51a9eace
commit 00f32752f7
56 changed files with 7336 additions and 4826 deletions
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103
unsupported/test/cxx11_tensor_image_op_sycl.cpp Normal file
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// 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>
// Benoit Steiner <benoit.steiner.goog@gmail.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>
using Eigen::array;
using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;
using Eigen::Tensor;
using Eigen::RowMajor;
template <typename DataType, int DataLayout, typename IndexType>
static void test_image_op_sycl(const Eigen::SyclDevice &sycl_device)
{
IndexType sizeDim1 = 245;
IndexType sizeDim2 = 343;
IndexType sizeDim3 = 577;
array<IndexType, 3> input_range ={{sizeDim1, sizeDim2, sizeDim3}};
array<IndexType, 3> slice_range ={{sizeDim1-1, sizeDim2, sizeDim3}};
Tensor<DataType, 3,DataLayout, IndexType> tensor1(input_range);
Tensor<DataType, 3,DataLayout, IndexType> tensor2(input_range);
Tensor<DataType, 3, DataLayout, IndexType> tensor3(slice_range);
Tensor<DataType, 3, DataLayout, IndexType> tensor3_cpu(slice_range);
typedef Eigen::DSizes<IndexType, 3> Index3;
Index3 strides1(1L,1L, 1L);
Index3 indicesStart1(1L, 0L, 0L);
Index3 indicesStop1(sizeDim1, sizeDim2, sizeDim3);
Index3 strides2(1L,1L, 1L);
Index3 indicesStart2(0L, 0L, 0L);
Index3 indicesStop2(sizeDim1-1, sizeDim2, sizeDim3);
Eigen::DSizes<IndexType, 3> sizes(sizeDim1-1,sizeDim2,sizeDim3);
tensor1.setRandom();
tensor2.setRandom();
DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor1.size()*sizeof(DataType)));
DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(tensor2.size()*sizeof(DataType)));
DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(tensor3.size()*sizeof(DataType)));
TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu1(gpu_data1, input_range);
TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu2(gpu_data2, input_range);
TensorMap<Tensor<DataType, 3, DataLayout, IndexType>> gpu3(gpu_data3, slice_range);
sycl_device.memcpyHostToDevice(gpu_data1, tensor1.data(),(tensor1.size())*sizeof(DataType));
sycl_device.memcpyHostToDevice(gpu_data2, tensor2.data(),(tensor2.size())*sizeof(DataType));
gpu3.device(sycl_device)= gpu1.slice(indicesStart1, sizes) - gpu2.slice(indicesStart2, sizes);
sycl_device.memcpyDeviceToHost(tensor3.data(), gpu_data3,(tensor3.size())*sizeof(DataType));
tensor3_cpu = tensor1.stridedSlice(indicesStart1,indicesStop1,strides1) - tensor2.stridedSlice(indicesStart2,indicesStop2,strides2);
for (IndexType i = 0; i <slice_range[0] ; ++i) {
for (IndexType j = 0; j < slice_range[1]; ++j) {
for (IndexType k = 0; k < slice_range[2]; ++k) {
VERIFY_IS_EQUAL(tensor3_cpu(i,j,k), tensor3(i,j,k));
}
}
}
sycl_device.deallocate(gpu_data1);
sycl_device.deallocate(gpu_data2);
sycl_device.deallocate(gpu_data3);
}
template<typename DataType, typename dev_Selector> void sycl_computing_test_per_device(dev_Selector s){
QueueInterface queueInterface(s);
auto sycl_device = Eigen::SyclDevice(&queueInterface);
test_image_op_sycl<DataType, RowMajor, int64_t>(sycl_device);
}
EIGEN_DECLARE_TEST(cxx11_tensor_image_op_sycl) {
for (const auto& device :Eigen::get_sycl_supported_devices()) {
CALL_SUBTEST(sycl_computing_test_per_device<float>(device));
#ifdef EIGEN_SYCL_DOUBLE_SUPPORT
CALL_SUBTEST(sycl_computing_test_per_device<double>(device));
#endif
}
}