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Adding TensorShuffling backend for sycl; adding TensorReshaping backend for sycl; cleaning up the sycl backend.

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This commit is contained in:
Mehdi Goli
2016-11-29 15:30:42 +00:00
parent 02080e2b67
commit 577ce78085
15 changed files with 626 additions and 301 deletions
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1
unsupported/test/CMakeLists.txt
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@@ -147,6 +147,7 @@ if(EIGEN_TEST_CXX11)
ei_add_test_sycl(cxx11_tensor_device_sycl "-std=c++11")
ei_add_test_sycl(cxx11_tensor_reduction_sycl "-std=c++11")
ei_add_test_sycl(cxx11_tensor_morphing_sycl "-std=c++11")
ei_add_test_sycl(cxx11_tensor_shuffling_sycl "-std=c++11")
ei_add_test_sycl(cxx11_tensor_builtins_sycl "-std=c++11")
endif(EIGEN_TEST_SYCL)
# It should be safe to always run these tests as there is some fallback code for

114
unsupported/test/cxx11_tensor_morphing_sycl.cpp
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@@ -28,6 +28,112 @@ using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;
template <typename DataType, int DataLayout>
static void test_simple_reshape(const Eigen::SyclDevice& sycl_device)
{
typename Tensor<DataType, 5 ,DataLayout>::Dimensions dim1(2,3,1,7,1);
typename Tensor<DataType, 3 ,DataLayout>::Dimensions dim2(2,3,7);
typename Tensor<DataType, 2 ,DataLayout>::Dimensions dim3(6,7);
typename Tensor<DataType, 2 ,DataLayout>::Dimensions dim4(2,21);
Tensor<DataType, 5, DataLayout> tensor1(dim1);
Tensor<DataType, 3, DataLayout> tensor2(dim2);
Tensor<DataType, 2, DataLayout> tensor3(dim3);
Tensor<DataType, 2, DataLayout> tensor4(dim4);
tensor1.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)));
DataType* gpu_data4 = static_cast<DataType*>(sycl_device.allocate(tensor4.size()*sizeof(DataType)));
TensorMap<Tensor<DataType, 5,DataLayout>> gpu1(gpu_data1, dim1);
TensorMap<Tensor<DataType, 3,DataLayout>> gpu2(gpu_data2, dim2);
TensorMap<Tensor<DataType, 2,DataLayout>> gpu3(gpu_data3, dim3);
TensorMap<Tensor<DataType, 2,DataLayout>> gpu4(gpu_data4, dim4);
sycl_device.memcpyHostToDevice(gpu_data1, tensor1.data(),(tensor1.size())*sizeof(DataType));
gpu2.device(sycl_device)=gpu1.reshape(dim2);
sycl_device.memcpyDeviceToHost(tensor2.data(), gpu_data2,(tensor1.size())*sizeof(DataType));
gpu3.device(sycl_device)=gpu1.reshape(dim3);
sycl_device.memcpyDeviceToHost(tensor3.data(), gpu_data3,(tensor3.size())*sizeof(DataType));
gpu4.device(sycl_device)=gpu1.reshape(dim2).reshape(dim4);
sycl_device.memcpyDeviceToHost(tensor4.data(), gpu_data4,(tensor4.size())*sizeof(DataType));
for (int i = 0; i < 2; ++i){
for (int j = 0; j < 3; ++j){
for (int k = 0; k < 7; ++k){
VERIFY_IS_EQUAL(tensor1(i,j,0,k,0), tensor2(i,j,k)); ///ColMajor
if (static_cast<int>(DataLayout) == static_cast<int>(ColMajor)) {
VERIFY_IS_EQUAL(tensor1(i,j,0,k,0), tensor3(i+2*j,k)); ///ColMajor
VERIFY_IS_EQUAL(tensor1(i,j,0,k,0), tensor4(i,j+3*k)); ///ColMajor
}
else{
//VERIFY_IS_EQUAL(tensor1(i,j,0,k,0), tensor2(i,j,k)); /// RowMajor
VERIFY_IS_EQUAL(tensor1(i,j,0,k,0), tensor4(i,j*7 +k)); /// RowMajor
VERIFY_IS_EQUAL(tensor1(i,j,0,k,0), tensor3(i*3 +j,k)); /// RowMajor
}
}
}
}
sycl_device.deallocate(gpu_data1);
sycl_device.deallocate(gpu_data2);
sycl_device.deallocate(gpu_data3);
sycl_device.deallocate(gpu_data4);
}
template<typename DataType, int DataLayout>
static void test_reshape_as_lvalue(const Eigen::SyclDevice& sycl_device)
{
typename Tensor<DataType, 3, DataLayout>::Dimensions dim1(2,3,7);
typename Tensor<DataType, 2, DataLayout>::Dimensions dim2(6,7);
typename Tensor<DataType, 5, DataLayout>::Dimensions dim3(2,3,1,7,1);
Tensor<DataType, 3, DataLayout> tensor(dim1);
Tensor<DataType, 2, DataLayout> tensor2d(dim2);
Tensor<DataType, 5, DataLayout> tensor5d(dim3);
tensor.setRandom();
DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor.size()*sizeof(DataType)));
DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(tensor2d.size()*sizeof(DataType)));
DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(tensor5d.size()*sizeof(DataType)));
TensorMap< Tensor<DataType, 3, DataLayout> > gpu1(gpu_data1, dim1);
TensorMap< Tensor<DataType, 2, DataLayout> > gpu2(gpu_data2, dim2);
TensorMap< Tensor<DataType, 5, DataLayout> > gpu3(gpu_data3, dim3);
sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(),(tensor.size())*sizeof(DataType));
gpu2.reshape(dim1).device(sycl_device)=gpu1;
sycl_device.memcpyDeviceToHost(tensor2d.data(), gpu_data2,(tensor2d.size())*sizeof(DataType));
gpu3.reshape(dim1).device(sycl_device)=gpu1;
sycl_device.memcpyDeviceToHost(tensor5d.data(), gpu_data3,(tensor5d.size())*sizeof(DataType));
for (int i = 0; i < 2; ++i){
for (int j = 0; j < 3; ++j){
for (int k = 0; k < 7; ++k){
VERIFY_IS_EQUAL(tensor5d(i,j,0,k,0), tensor(i,j,k));
if (static_cast<int>(DataLayout) == static_cast<int>(ColMajor)) {
VERIFY_IS_EQUAL(tensor2d(i+2*j,k), tensor(i,j,k)); ///ColMajor
}
else{
VERIFY_IS_EQUAL(tensor2d(i*3 +j,k),tensor(i,j,k)); /// RowMajor
}
}
}
}
sycl_device.deallocate(gpu_data1);
sycl_device.deallocate(gpu_data2);
sycl_device.deallocate(gpu_data3);
}
template <typename DataType, int DataLayout>
static void test_simple_slice(const Eigen::SyclDevice &sycl_device)
{
@@ -74,15 +180,19 @@ static void test_simple_slice(const Eigen::SyclDevice &sycl_device)
sycl_device.deallocate(gpu_data3);
}
template<typename DataType, typename dev_Selector> void sycl_slicing_test_per_device(dev_Selector s){
template<typename DataType, typename dev_Selector> void sycl_morphing_test_per_device(dev_Selector s){
QueueInterface queueInterface(s);
auto sycl_device = Eigen::SyclDevice(&queueInterface);
test_simple_slice<DataType, RowMajor>(sycl_device);
test_simple_slice<DataType, ColMajor>(sycl_device);
test_simple_reshape<DataType, RowMajor>(sycl_device);
test_simple_reshape<DataType, ColMajor>(sycl_device);
test_reshape_as_lvalue<DataType, RowMajor>(sycl_device);
test_reshape_as_lvalue<DataType, ColMajor>(sycl_device);
}
void test_cxx11_tensor_morphing_sycl()
{
for (const auto& device :Eigen::get_sycl_supported_devices()) {
CALL_SUBTEST(sycl_slicing_test_per_device<float>(device));
CALL_SUBTEST(sycl_morphing_test_per_device<float>(device));
}
}

120
unsupported/test/cxx11_tensor_shuffling_sycl.cpp Normal file
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@@ -0,0 +1,120 @@
// 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_TEST_FUNC cxx11_tensor_shuffling_sycl
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
#define EIGEN_USE_SYCL
#include "main.h"
#include <unsupported/Eigen/CXX11/Tensor>
using Eigen::array;
using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;
template <typename DataType, int DataLayout, typename IndexTypes>
static void test_simple_shuffling_sycl(const Eigen::SyclDevice& sycl_device)
{
IndexTypes sizeDim1 = 2;
IndexTypes sizeDim2 = 3;
IndexTypes sizeDim3 = 5;
IndexTypes sizeDim4 = 7;
array<IndexTypes, 4> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
Tensor<DataType, 4, DataLayout,IndexTypes> tensor(tensorRange);
Tensor<DataType, 4, DataLayout,IndexTypes> no_shuffle(tensorRange);
tensor.setRandom();
const size_t buffSize =tensor.size()*sizeof(DataType);
array<IndexTypes, 4> shuffles;
shuffles[0] = 0;
shuffles[1] = 1;
shuffles[2] = 2;
shuffles[3] = 3;
DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(buffSize));
DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(buffSize));
TensorMap<Tensor<DataType, 4, DataLayout,IndexTypes>> gpu1(gpu_data1, tensorRange);
TensorMap<Tensor<DataType, 4, DataLayout,IndexTypes>> gpu2(gpu_data2, tensorRange);
sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(), buffSize);
gpu2.device(sycl_device)=gpu1.shuffle(shuffles);
sycl_device.memcpyDeviceToHost(no_shuffle.data(), gpu_data2, buffSize);
VERIFY_IS_EQUAL(no_shuffle.dimension(0), sizeDim1);
VERIFY_IS_EQUAL(no_shuffle.dimension(1), sizeDim2);
VERIFY_IS_EQUAL(no_shuffle.dimension(2), sizeDim3);
VERIFY_IS_EQUAL(no_shuffle.dimension(3), sizeDim4);
for (int i = 0; i < sizeDim1; ++i) {
for (int j = 0; j < sizeDim2; ++j) {
for (int k = 0; k < sizeDim3; ++k) {
for (int l = 0; l < sizeDim4; ++l) {
VERIFY_IS_EQUAL(tensor(i,j,k,l), no_shuffle(i,j,k,l));
}
}
}
}
shuffles[0] = 2;
shuffles[1] = 3;
shuffles[2] = 1;
shuffles[3] = 0;
array<IndexTypes, 4> tensorrangeShuffle = {{sizeDim3, sizeDim4, sizeDim2, sizeDim1}};
Tensor<DataType, 4, DataLayout,IndexTypes> shuffle(tensorrangeShuffle);
DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(buffSize));
TensorMap<Tensor<DataType, 4,DataLayout,IndexTypes>> gpu3(gpu_data3, tensorrangeShuffle);
gpu3.device(sycl_device)=gpu1.shuffle(shuffles);
sycl_device.memcpyDeviceToHost(shuffle.data(), gpu_data3, buffSize);
VERIFY_IS_EQUAL(shuffle.dimension(0), sizeDim3);
VERIFY_IS_EQUAL(shuffle.dimension(1), sizeDim4);
VERIFY_IS_EQUAL(shuffle.dimension(2), sizeDim2);
VERIFY_IS_EQUAL(shuffle.dimension(3), sizeDim1);
for (int i = 0; i < sizeDim1; ++i) {
for (int j = 0; j < sizeDim2; ++j) {
for (int k = 0; k < sizeDim3; ++k) {
for (int l = 0; l < sizeDim4; ++l) {
VERIFY_IS_EQUAL(tensor(i,j,k,l), shuffle(k,l,j,i));
}
}
}
}
}
template<typename DataType, typename dev_Selector> void sycl_shuffling_test_per_device(dev_Selector s){
QueueInterface queueInterface(s);
auto sycl_device = Eigen::SyclDevice(&queueInterface);
test_simple_shuffling_sycl<DataType, RowMajor, int>(sycl_device);
test_simple_shuffling_sycl<DataType, ColMajor, int>(sycl_device);
test_simple_shuffling_sycl<DataType, RowMajor, int64_t>(sycl_device);
test_simple_shuffling_sycl<DataType, ColMajor, int64_t>(sycl_device);
}
void test_cxx11_tensor_shuffling_sycl()
{
for (const auto& device :Eigen::get_sycl_supported_devices()) {
CALL_SUBTEST(sycl_shuffling_test_per_device<float>(device));
}
}

1
unsupported/test/cxx11_tensor_sycl.cpp
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@@ -197,7 +197,6 @@ template<typename DataType, typename dev_Selector> void sycl_computing_test_per_
test_sycl_computations<DataType, ColMajor>(sycl_device);
}
void test_cxx11_tensor_sycl() {
auto devices =Eigen::get_sycl_supported_devices();
for (const auto& device :Eigen::get_sycl_supported_devices()) {
CALL_SUBTEST(sycl_computing_test_per_device<float>(device));
}