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Reducing warnings in Sycl backend.

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This commit is contained in:
Mehdi Goli
2017-02-01 15:29:53 +00:00
parent 48a20b7d95
commit bab29936a1
19 changed files with 666 additions and 672 deletions
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119
unsupported/test/cxx11_tensor_morphing_sycl.cpp
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@@ -16,7 +16,7 @@
#define EIGEN_TEST_NO_LONGDOUBLE
#define EIGEN_TEST_NO_COMPLEX
#define EIGEN_TEST_FUNC cxx11_tensor_morphing_sycl
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
#define EIGEN_USE_SYCL
@@ -28,18 +28,18 @@ using Eigen::SyclDevice;
using Eigen::Tensor;
using Eigen::TensorMap;
template <typename DataType, int DataLayout>
template <typename DataType, int DataLayout, typename IndexType>
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);
typename Tensor<DataType, 5 ,DataLayout, IndexType>::Dimensions dim1(2,3,1,7,1);
typename Tensor<DataType, 3 ,DataLayout, IndexType>::Dimensions dim2(2,3,7);
typename Tensor<DataType, 2 ,DataLayout, IndexType>::Dimensions dim3(6,7);
typename Tensor<DataType, 2 ,DataLayout, IndexType>::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);
Tensor<DataType, 5, DataLayout, IndexType> tensor1(dim1);
Tensor<DataType, 3, DataLayout, IndexType> tensor2(dim2);
Tensor<DataType, 2, DataLayout, IndexType> tensor3(dim3);
Tensor<DataType, 2, DataLayout, IndexType> tensor4(dim4);
tensor1.setRandom();
@@ -48,10 +48,10 @@ static void test_simple_reshape(const Eigen::SyclDevice& sycl_device)
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);
TensorMap<Tensor<DataType, 5,DataLayout, IndexType>> gpu1(gpu_data1, dim1);
TensorMap<Tensor<DataType, 3,DataLayout, IndexType>> gpu2(gpu_data2, dim2);
TensorMap<Tensor<DataType, 2,DataLayout, IndexType>> gpu3(gpu_data3, dim3);
TensorMap<Tensor<DataType, 2,DataLayout, IndexType>> gpu4(gpu_data4, dim4);
sycl_device.memcpyHostToDevice(gpu_data1, tensor1.data(),(tensor1.size())*sizeof(DataType));
@@ -63,9 +63,9 @@ static void test_simple_reshape(const Eigen::SyclDevice& sycl_device)
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){
for (IndexType i = 0; i < 2; ++i){
for (IndexType j = 0; j < 3; ++j){
for (IndexType 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
@@ -86,15 +86,15 @@ static void test_simple_reshape(const Eigen::SyclDevice& sycl_device)
}
template<typename DataType, int DataLayout>
template<typename DataType, int DataLayout, typename IndexType>
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);
typename Tensor<DataType, 3, DataLayout, IndexType>::Dimensions dim1(2,3,7);
typename Tensor<DataType, 2, DataLayout, IndexType>::Dimensions dim2(6,7);
typename Tensor<DataType, 5, DataLayout, IndexType>::Dimensions dim3(2,3,1,7,1);
Tensor<DataType, 3, DataLayout, IndexType> tensor(dim1);
Tensor<DataType, 2, DataLayout, IndexType> tensor2d(dim2);
Tensor<DataType, 5, DataLayout, IndexType> tensor5d(dim3);
tensor.setRandom();
@@ -102,9 +102,9 @@ static void test_reshape_as_lvalue(const Eigen::SyclDevice& sycl_device)
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);
TensorMap< Tensor<DataType, 3, DataLayout, IndexType> > gpu1(gpu_data1, dim1);
TensorMap< Tensor<DataType, 2, DataLayout, IndexType> > gpu2(gpu_data2, dim2);
TensorMap< Tensor<DataType, 5, DataLayout, IndexType> > gpu3(gpu_data3, dim3);
sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(),(tensor.size())*sizeof(DataType));
@@ -115,9 +115,9 @@ static void test_reshape_as_lvalue(const Eigen::SyclDevice& sycl_device)
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){
for (IndexType i = 0; i < 2; ++i){
for (IndexType j = 0; j < 3; ++j){
for (IndexType 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
@@ -134,43 +134,43 @@ static void test_reshape_as_lvalue(const Eigen::SyclDevice& sycl_device)
}
template <typename DataType, int DataLayout>
template <typename DataType, int DataLayout, typename IndexType>
static void test_simple_slice(const Eigen::SyclDevice &sycl_device)
{
int sizeDim1 = 2;
int sizeDim2 = 3;
int sizeDim3 = 5;
int sizeDim4 = 7;
int sizeDim5 = 11;
array<int, 5> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
Tensor<DataType, 5,DataLayout> tensor(tensorRange);
IndexType sizeDim1 = 2;
IndexType sizeDim2 = 3;
IndexType sizeDim3 = 5;
IndexType sizeDim4 = 7;
IndexType sizeDim5 = 11;
array<IndexType, 5> tensorRange = {{sizeDim1, sizeDim2, sizeDim3, sizeDim4, sizeDim5}};
Tensor<DataType, 5,DataLayout, IndexType> tensor(tensorRange);
tensor.setRandom();
array<int, 5> slice1_range ={{1, 1, 1, 1, 1}};
Tensor<DataType, 5,DataLayout> slice1(slice1_range);
array<IndexType, 5> slice1_range ={{1, 1, 1, 1, 1}};
Tensor<DataType, 5,DataLayout, IndexType> slice1(slice1_range);
DataType* gpu_data1 = static_cast<DataType*>(sycl_device.allocate(tensor.size()*sizeof(DataType)));
DataType* gpu_data2 = static_cast<DataType*>(sycl_device.allocate(slice1.size()*sizeof(DataType)));
TensorMap<Tensor<DataType, 5,DataLayout>> gpu1(gpu_data1, tensorRange);
TensorMap<Tensor<DataType, 5,DataLayout>> gpu2(gpu_data2, slice1_range);
Eigen::DSizes<ptrdiff_t, 5> indices(1,2,3,4,5);
Eigen::DSizes<ptrdiff_t, 5> sizes(1,1,1,1,1);
TensorMap<Tensor<DataType, 5,DataLayout, IndexType>> gpu1(gpu_data1, tensorRange);
TensorMap<Tensor<DataType, 5,DataLayout, IndexType>> gpu2(gpu_data2, slice1_range);
Eigen::DSizes<IndexType, 5> indices(1,2,3,4,5);
Eigen::DSizes<IndexType, 5> sizes(1,1,1,1,1);
sycl_device.memcpyHostToDevice(gpu_data1, tensor.data(),(tensor.size())*sizeof(DataType));
gpu2.device(sycl_device)=gpu1.slice(indices, sizes);
sycl_device.memcpyDeviceToHost(slice1.data(), gpu_data2,(slice1.size())*sizeof(DataType));
VERIFY_IS_EQUAL(slice1(0,0,0,0,0), tensor(1,2,3,4,5));
array<int, 5> slice2_range ={{1,1,2,2,3}};
Tensor<DataType, 5,DataLayout> slice2(slice2_range);
array<IndexType, 5> slice2_range ={{1,1,2,2,3}};
Tensor<DataType, 5,DataLayout, IndexType> slice2(slice2_range);
DataType* gpu_data3 = static_cast<DataType*>(sycl_device.allocate(slice2.size()*sizeof(DataType)));
TensorMap<Tensor<DataType, 5,DataLayout>> gpu3(gpu_data3, slice2_range);
Eigen::DSizes<ptrdiff_t, 5> indices2(1,1,3,4,5);
Eigen::DSizes<ptrdiff_t, 5> sizes2(1,1,2,2,3);
TensorMap<Tensor<DataType, 5,DataLayout, IndexType>> gpu3(gpu_data3, slice2_range);
Eigen::DSizes<IndexType, 5> indices2(1,1,3,4,5);
Eigen::DSizes<IndexType, 5> sizes2(1,1,2,2,3);
gpu3.device(sycl_device)=gpu1.slice(indices2, sizes2);
sycl_device.memcpyDeviceToHost(slice2.data(), gpu_data3,(slice2.size())*sizeof(DataType));
for (int i = 0; i < 2; ++i) {
for (int j = 0; j < 2; ++j) {
for (int k = 0; k < 3; ++k) {
for (IndexType i = 0; i < 2; ++i) {
for (IndexType j = 0; j < 2; ++j) {
for (IndexType k = 0; k < 3; ++k) {
VERIFY_IS_EQUAL(slice2(0,0,i,j,k), tensor(1,1,3+i,4+j,5+k));
}
}
@@ -219,7 +219,8 @@ static void test_strided_slice_write_sycl(const Eigen::SyclDevice& sycl_device)
sycl_device.memcpyDeviceToHost(tensor.data(), gpu_data1,(tensor.size())*sizeof(DataType));
sycl_device.memcpyDeviceToHost(tensor2.data(), gpu_data2,(tensor2.size())*sizeof(DataType));
for(int i=0;i<sizeDim1;i++) for(int j=0;j<sizeDim2;j++){
for(IndexType i=0;i<sizeDim1;i++)
for(IndexType j=0;j<sizeDim2;j++){
VERIFY_IS_EQUAL(tensor(i,j), tensor2(i,j));
}
sycl_device.deallocate(gpu_data1);
@@ -230,12 +231,12 @@ static void test_strided_slice_write_sycl(const Eigen::SyclDevice& sycl_device)
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);
test_simple_slice<DataType, RowMajor, int64_t>(sycl_device);
test_simple_slice<DataType, ColMajor, int64_t>(sycl_device);
test_simple_reshape<DataType, RowMajor, int64_t>(sycl_device);
test_simple_reshape<DataType, ColMajor, int64_t>(sycl_device);
test_reshape_as_lvalue<DataType, RowMajor, int64_t>(sycl_device);
test_reshape_as_lvalue<DataType, ColMajor, int64_t>(sycl_device);
test_strided_slice_write_sycl<DataType, ColMajor, int64_t>(sycl_device);
test_strided_slice_write_sycl<DataType, RowMajor, int64_t>(sycl_device);
}