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refine conv cudnn enforce (#12353)

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* refine conv cudnn enforce

* update

* update all cudnn ops

* fix
bugfix/anakin-compile
Wu Yi 7 years ago committed by GitHub
parent 91b70da81a
commit 73fcfc06ec
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 26 additions and 26 deletions
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26
paddle/fluid/operators/conv_cudnn_op.cu.cc
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@ -77,7 +77,7 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
// cudnn 7 can support groups, no need to do it mannually // cudnn 7 can support groups, no need to do it mannually
// FIXME(typhoonzero): find a better way to disable groups // FIXME(typhoonzero): find a better way to disable groups
// rather than setting it to 1. // rather than setting it to 1.
PADDLE_ENFORCE(platform::dynload::cudnnSetConvolutionGroupCount( CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionGroupCount(
cudnn_conv_desc, groups)); cudnn_conv_desc, groups));
groups = 1; groups = 1;
#endif #endif
@ -129,7 +129,7 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>(); auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto handle = dev_ctx.cudnn_handle(); auto handle = dev_ctx.cudnn_handle();
PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm( CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc, handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT, cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo)); workspace_size_limit, &algo));
@ -140,18 +140,18 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
if (dev_ctx.GetComputeCapability() >= 70 && if (dev_ctx.GetComputeCapability() >= 70 &&
std::type_index(typeid(T)) == std::type_index(typeid(T)) ==
std::type_index(typeid(platform::float16))) { std::type_index(typeid(platform::float16))) {
PADDLE_ENFORCE(platform::dynload::cudnnSetConvolutionMathType( CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
cudnn_conv_desc, CUDNN_TENSOR_OP_MATH)); cudnn_conv_desc, CUDNN_TENSOR_OP_MATH));
// Currently tensor core is only enabled using this algo // Currently tensor core is only enabled using this algo
algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM; algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
} else { } else {
PADDLE_ENFORCE(platform::dynload::cudnnSetConvolutionMathType( CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
cudnn_conv_desc, CUDNN_DEFAULT_MATH)); cudnn_conv_desc, CUDNN_DEFAULT_MATH));
} }
#endif #endif
// get workspace size able to allocate // get workspace size able to allocate
PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize( CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc, handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_output_desc, algo, &workspace_size_in_bytes)); cudnn_output_desc, algo, &workspace_size_in_bytes));
// It is possible for float16 on Volta GPU to allocate more memory than // It is possible for float16 on Volta GPU to allocate more memory than
@ -165,7 +165,7 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
// ------------------- cudnn conv forward --------------------- // ------------------- cudnn conv forward ---------------------
ScalingParamType<T> alpha = 1.0f, beta = 0.0f; ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
for (int i = 0; i < groups; i++) { for (int i = 0; i < groups; i++) {
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionForward( CUDNN_ENFORCE(platform::dynload::cudnnConvolutionForward(
handle, &alpha, cudnn_input_desc, input_data + i * group_offset_in, handle, &alpha, cudnn_input_desc, input_data + i * group_offset_in,
cudnn_filter_desc, filter_data + i * group_offset_filter, cudnn_filter_desc, filter_data + i * group_offset_filter,
cudnn_conv_desc, algo, cudnn_workspace, workspace_size_in_bytes, cudnn_conv_desc, algo, cudnn_workspace, workspace_size_in_bytes,
@ -218,7 +218,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
// cudnn 7 can support groups, no need to do it mannually // cudnn 7 can support groups, no need to do it mannually
// FIXME(typhoonzero): find a better way to disable groups // FIXME(typhoonzero): find a better way to disable groups
// rather than setting it to 1. // rather than setting it to 1.
PADDLE_ENFORCE(platform::dynload::cudnnSetConvolutionGroupCount( CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionGroupCount(
cudnn_conv_desc, groups)); cudnn_conv_desc, groups));
groups = 1; groups = 1;
#endif #endif
@ -273,7 +273,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
auto handle = dev_ctx.cudnn_handle(); auto handle = dev_ctx.cudnn_handle();
if (input_grad) { if (input_grad) {
if (FLAGS_cudnn_deterministic) { if (FLAGS_cudnn_deterministic) {
PADDLE_ENFORCE( CUDNN_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm( platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
handle, cudnn_filter_desc, handle, cudnn_filter_desc,
// dyDesc: Handle to the previously initialized input // dyDesc: Handle to the previously initialized input
@ -289,7 +289,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
data_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1; data_algo = CUDNN_CONVOLUTION_BWD_DATA_ALGO_1;
} }
PADDLE_ENFORCE( CUDNN_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize( platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
handle, cudnn_filter_desc, cudnn_output_grad_desc, handle, cudnn_filter_desc, cudnn_output_grad_desc,
cudnn_conv_desc, cudnn_input_desc, data_algo, &tmp_size)); cudnn_conv_desc, cudnn_input_desc, data_algo, &tmp_size));
@ -298,7 +298,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
if (filter_grad) { if (filter_grad) {
if (FLAGS_cudnn_deterministic) { if (FLAGS_cudnn_deterministic) {
PADDLE_ENFORCE( CUDNN_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm( platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
handle, cudnn_input_desc, cudnn_output_grad_desc, handle, cudnn_input_desc, cudnn_output_grad_desc,
cudnn_conv_desc, cudnn_filter_desc, cudnn_conv_desc, cudnn_filter_desc,
@ -308,7 +308,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
filter_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1; filter_algo = CUDNN_CONVOLUTION_BWD_FILTER_ALGO_1;
} }
PADDLE_ENFORCE( CUDNN_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize( platform::dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize(
handle, cudnn_input_desc, cudnn_output_grad_desc, cudnn_conv_desc, handle, cudnn_input_desc, cudnn_output_grad_desc, cudnn_conv_desc,
cudnn_filter_desc, filter_algo, &tmp_size)); cudnn_filter_desc, filter_algo, &tmp_size));
@ -326,7 +326,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
// Because beta is zero, it is unnecessary to reset input_grad. // Because beta is zero, it is unnecessary to reset input_grad.
for (int i = 0; i < groups; i++) { for (int i = 0; i < groups; i++) {
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardData( CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardData(
handle, &alpha, cudnn_filter_desc, handle, &alpha, cudnn_filter_desc,
filter_data + i * group_offset_filter, cudnn_output_grad_desc, filter_data + i * group_offset_filter, cudnn_output_grad_desc,
output_grad_data + i * group_offset_out, cudnn_conv_desc, data_algo, output_grad_data + i * group_offset_out, cudnn_conv_desc, data_algo,
@ -339,7 +339,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
T* filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace()); T* filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace());
// Because beta is zero, it is unnecessary to reset filter_grad. // Because beta is zero, it is unnecessary to reset filter_grad.
for (int i = 0; i < groups; i++) { for (int i = 0; i < groups; i++) {
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter( CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
handle, &alpha, cudnn_input_desc, input_data + i * group_offset_in, handle, &alpha, cudnn_input_desc, input_data + i * group_offset_in,
cudnn_output_grad_desc, output_grad_data + i * group_offset_out, cudnn_output_grad_desc, output_grad_data + i * group_offset_out,
cudnn_conv_desc, filter_algo, cudnn_workspace, cudnn_conv_desc, filter_algo, cudnn_workspace,

18
paddle/fluid/operators/conv_transpose_cudnn_op.cu.cc
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@ -87,7 +87,7 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>(); auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto handle = dev_ctx.cudnn_handle(); auto handle = dev_ctx.cudnn_handle();
// Get the algorithm // Get the algorithm
PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm( CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
handle, cudnn_filter_desc, cudnn_input_desc, cudnn_conv_desc, handle, cudnn_filter_desc, cudnn_input_desc, cudnn_conv_desc,
// dxDesc: Handle to the previously initialized output tensor // dxDesc: Handle to the previously initialized output tensor
// descriptor. // descriptor.
@ -95,7 +95,7 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
workspace_size_limit, &algo)); workspace_size_limit, &algo));
// get workspace size able to allocate // get workspace size able to allocate
PADDLE_ENFORCE( CUDNN_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize( platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
handle, cudnn_filter_desc, cudnn_input_desc, cudnn_conv_desc, handle, cudnn_filter_desc, cudnn_input_desc, cudnn_conv_desc,
cudnn_output_desc, algo, &workspace_size_in_bytes)); cudnn_output_desc, algo, &workspace_size_in_bytes));
@ -110,7 +110,7 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
int filter_offset = filter->numel() / groups; int filter_offset = filter->numel() / groups;
T alpha = 1.0f, beta = 0.0f; T alpha = 1.0f, beta = 0.0f;
for (int g = 0; g < groups; g++) { for (int g = 0; g < groups; g++) {
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardData( CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardData(
handle, &alpha, cudnn_filter_desc, filter_data + filter_offset * g, handle, &alpha, cudnn_filter_desc, filter_data + filter_offset * g,
cudnn_input_desc, input_data + input_offset * g, cudnn_conv_desc, cudnn_input_desc, input_data + input_offset * g, cudnn_conv_desc,
algo, cudnn_workspace, workspace_size_in_bytes, &beta, algo, cudnn_workspace, workspace_size_in_bytes, &beta,
@ -178,11 +178,11 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
auto handle = dev_ctx.cudnn_handle(); auto handle = dev_ctx.cudnn_handle();
if (input_grad) { if (input_grad) {
// choose backward algorithm for data // choose backward algorithm for data
PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm( CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
handle, cudnn_output_desc, cudnn_filter_desc, cudnn_conv_desc, handle, cudnn_output_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_input_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT, cudnn_input_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &data_algo)); workspace_size_limit, &data_algo));
PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize( CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
handle, cudnn_output_desc, cudnn_filter_desc, cudnn_conv_desc, handle, cudnn_output_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_input_desc, data_algo, &fwd_ws_size)); cudnn_input_desc, data_algo, &fwd_ws_size));
workspace_size_in_bytes = std::max(workspace_size_in_bytes, fwd_ws_size); workspace_size_in_bytes = std::max(workspace_size_in_bytes, fwd_ws_size);
@ -190,7 +190,7 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
if (filter_grad) { if (filter_grad) {
// choose backward algorithm for filter // choose backward algorithm for filter
PADDLE_ENFORCE( CUDNN_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm( platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
handle, cudnn_output_desc, cudnn_input_desc, cudnn_conv_desc, handle, cudnn_output_desc, cudnn_input_desc, cudnn_conv_desc,
cudnn_filter_desc, cudnn_filter_desc,
@ -198,7 +198,7 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
workspace_size_limit, &filter_algo)); workspace_size_limit, &filter_algo));
// get workspace for backwards filter algorithm // get workspace for backwards filter algorithm
PADDLE_ENFORCE( CUDNN_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize( platform::dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize(
handle, cudnn_output_desc, cudnn_input_desc, cudnn_conv_desc, handle, cudnn_output_desc, cudnn_input_desc, cudnn_conv_desc,
cudnn_filter_desc, filter_algo, &bwd_filter_ws_size)); cudnn_filter_desc, filter_algo, &bwd_filter_ws_size));
@ -222,7 +222,7 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace()); T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
// Because beta is zero, it is unnecessary to reset input_grad. // Because beta is zero, it is unnecessary to reset input_grad.
for (int g = 0; g < groups; g++) { for (int g = 0; g < groups; g++) {
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionForward( CUDNN_ENFORCE(platform::dynload::cudnnConvolutionForward(
handle, &alpha, cudnn_output_desc, handle, &alpha, cudnn_output_desc,
output_grad_data + output_grad_offset * g, cudnn_filter_desc, output_grad_data + output_grad_offset * g, cudnn_filter_desc,
filter_data + filter_offset * g, cudnn_conv_desc, data_algo, filter_data + filter_offset * g, cudnn_conv_desc, data_algo,
@ -237,7 +237,7 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
// Because beta is zero, it is unnecessary to reset filter_grad. // Because beta is zero, it is unnecessary to reset filter_grad.
// Gradient with respect to the filter // Gradient with respect to the filter
for (int g = 0; g < groups; g++) { for (int g = 0; g < groups; g++) {
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter( CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
handle, &alpha, cudnn_output_desc, handle, &alpha, cudnn_output_desc,
output_grad_data + output_grad_offset * g, cudnn_input_desc, output_grad_data + output_grad_offset * g, cudnn_input_desc,
input_data + input_offset * g, cudnn_conv_desc, filter_algo, input_data + input_offset * g, cudnn_conv_desc, filter_algo,

4
paddle/fluid/operators/math/softmax.cu
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@ -52,7 +52,7 @@ void SoftmaxCUDNNFunctor<T>::operator()(
xDesc.descriptor<T>(layout, cudnn_tensor_dims); xDesc.descriptor<T>(layout, cudnn_tensor_dims);
cudnnTensorDescriptor_t cudnn_y_desc = cudnnTensorDescriptor_t cudnn_y_desc =
xDesc.descriptor<T>(layout, cudnn_tensor_dims); xDesc.descriptor<T>(layout, cudnn_tensor_dims);
PADDLE_ENFORCE(platform::dynload::cudnnSoftmaxForward( CUDNN_ENFORCE(platform::dynload::cudnnSoftmaxForward(
context.cudnn_handle(), CUDNN_SOFTMAX_ACCURATE, context.cudnn_handle(), CUDNN_SOFTMAX_ACCURATE,
CUDNN_SOFTMAX_MODE_INSTANCE, CudnnDataType<T>::kOne(), cudnn_x_desc, CUDNN_SOFTMAX_MODE_INSTANCE, CudnnDataType<T>::kOne(), cudnn_x_desc,
X->data<T>(), CudnnDataType<T>::kZero(), cudnn_y_desc, X->data<T>(), CudnnDataType<T>::kZero(), cudnn_y_desc,
@ -83,7 +83,7 @@ void SoftmaxGradCUDNNFunctor<T>::operator()(
dxDesc.descriptor<T>(layout, cudnn_tensor_dims); dxDesc.descriptor<T>(layout, cudnn_tensor_dims);
cudnnTensorDescriptor_t cudnn_ygrad_desc = cudnnTensorDescriptor_t cudnn_ygrad_desc =
dyDesc.descriptor<T>(layout, cudnn_tensor_dims); dyDesc.descriptor<T>(layout, cudnn_tensor_dims);
PADDLE_ENFORCE(platform::dynload::cudnnSoftmaxBackward( CUDNN_ENFORCE(platform::dynload::cudnnSoftmaxBackward(
context.cudnn_handle(), CUDNN_SOFTMAX_ACCURATE, context.cudnn_handle(), CUDNN_SOFTMAX_ACCURATE,
CUDNN_SOFTMAX_MODE_INSTANCE, CudnnDataType<T>::kOne(), cudnn_y_desc, CUDNN_SOFTMAX_MODE_INSTANCE, CudnnDataType<T>::kOne(), cudnn_y_desc,
Y->data<T>(), cudnn_ygrad_desc, YGrad->data<T>(), Y->data<T>(), cudnn_ygrad_desc, YGrad->data<T>(),

4
paddle/fluid/operators/pool_cudnn_op.cu.cc
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@ -81,7 +81,7 @@ class PoolCUDNNOpKernel : public framework::OpKernel<T> {
// ------------------- cudnn pool algorithm --------------------- // ------------------- cudnn pool algorithm ---------------------
auto handle = ctx.cuda_device_context().cudnn_handle(); auto handle = ctx.cuda_device_context().cudnn_handle();
ScalingParamType<T> alpha = 1.0f, beta = 0.0f; ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
PADDLE_ENFORCE(platform::dynload::cudnnPoolingForward( CUDNN_ENFORCE(platform::dynload::cudnnPoolingForward(
handle, cudnn_pool_desc, &alpha, cudnn_input_desc, input_data, &beta, handle, cudnn_pool_desc, &alpha, cudnn_input_desc, input_data, &beta,
cudnn_output_desc, output_data)); cudnn_output_desc, output_data));
} }
@ -154,7 +154,7 @@ class PoolCUDNNGradOpKernel : public framework::OpKernel<T> {
T *input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace()); T *input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
// Because beta is zero, it is unnecessary to reset input_grad. // Because beta is zero, it is unnecessary to reset input_grad.
PADDLE_ENFORCE(platform::dynload::cudnnPoolingBackward( CUDNN_ENFORCE(platform::dynload::cudnnPoolingBackward(
handle, cudnn_pool_desc, &alpha, cudnn_output_desc, output_data, handle, cudnn_pool_desc, &alpha, cudnn_output_desc, output_data,
cudnn_output_desc, output_grad_data, cudnn_input_desc, input_data, cudnn_output_desc, output_grad_data, cudnn_input_desc, input_data,
&beta, cudnn_input_desc, input_grad_data)); &beta, cudnn_input_desc, input_grad_data));

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