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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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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));
}
}