update

paddle/fluid/operators/conv_cudnn_op.cu.cc

@@ -28,6 +28,8 @@ using ScopedTensorDescriptor = platform::ScopedTensorDescriptor;
 using ScopedFilterDescriptor = platform::ScopedFilterDescriptor;
 using ScopedConvolutionDescriptor = platform::ScopedConvolutionDescriptor;
 using DataLayout = platform::DataLayout;
+template <typename T>
+using ScalingParamType = typename platform::CudnnDataType<T>::ScalingParamType;
 
 static constexpr size_t kCONV_CUDNN_WORKSPACE_LIMIT_BYTES =
     static_cast<size_t>(1024) * 1024 * 1024;
@@ -134,8 +136,7 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
     platform::CUDAPlace gpu = boost::get<platform::CUDAPlace>(ctx.GetPlace());
     cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
     // ------------------- cudnn conv forward ---------------------
-    typename platform::CudnnDataType<T>::ScalingParamType alpha = 1.0f,
-                                                          beta = 0.0f;
+    ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
     for (int i = 0; i < groups; i++) {
       PADDLE_ENFORCE(platform::dynload::cudnnConvolutionForward(
           handle, &alpha, cudnn_input_desc, input_data + i * group_offset_in,
@@ -282,8 +283,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
     platform::CUDAPlace gpu = boost::get<platform::CUDAPlace>(ctx.GetPlace());
     cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
     // ------------------- cudnn conv backward data ---------------------
-    typename platform::CudnnDataType<T>::ScalingParamType alpha = 1.0f,
-                                                          beta = 0.0f;
+    ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
     if (input_grad) {
       T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
       // Because beta is zero, it is unnecessary to reset input_grad.

paddle/fluid/operators/pool_cudnn_op.cu.cc

@@ -24,6 +24,8 @@ using ScopedTensorDescriptor = platform::ScopedTensorDescriptor;
 using ScopedPoolingDescriptor = platform::ScopedPoolingDescriptor;
 using DataLayout = platform::DataLayout;
 using PoolingMode = platform::PoolingMode;
+template <typename T>
+using ScalingParamType = typename platform::CudnnDataType<T>::ScalingParamType;
 
 template <typename T>
 class PoolCUDNNOpKernel : public framework::OpKernel<T> {
@@ -78,9 +80,7 @@ class PoolCUDNNOpKernel : public framework::OpKernel<T> {
 
     // ------------------- cudnn pool algorithm ---------------------
     auto handle = ctx.cuda_device_context().cudnn_handle();
-    typename platform::CudnnDataType<T>::ScalingParamType alpha = 1.0f,
-                                                          beta = 0.0f;
-
+    ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
     PADDLE_ENFORCE(platform::dynload::cudnnPoolingForward(
         handle, cudnn_pool_desc, &alpha, cudnn_input_desc, input_data, &beta,
         cudnn_output_desc, output_data));
@@ -145,9 +145,7 @@ class PoolCUDNNGradOpKernel : public framework::OpKernel<T> {
 
     // ------------------- cudnn pool algorithm ---------------------
     auto handle = ctx.cuda_device_context().cudnn_handle();
-    typename platform::CudnnDataType<T>::ScalingParamType alpha = 1.0f,
-                                                          beta = 0.0f;
-
+    ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
     if (input_grad) {
       T *input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
       // Because beta is zero, it is unnecessary to reset input_grad.