From 4a42d16f9559f0e8bfcdc69386bef9c9bff3a9d6 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 8 May 2018 22:57:35 -0700 Subject: [PATCH] Unifying argument documentation style in CudnnSupport. PiperOrigin-RevId: 195926489 --- tensorflow/stream_executor/cuda/cuda_dnn.cc | 132 ++++++++++++++-------------- 1 file changed, 66 insertions(+), 66 deletions(-) diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.cc b/tensorflow/stream_executor/cuda/cuda_dnn.cc index af78efe..a0640e1 100644 --- a/tensorflow/stream_executor/cuda/cuda_dnn.cc +++ b/tensorflow/stream_executor/cuda/cuda_dnn.cc @@ -1206,16 +1206,16 @@ CudnnRnnParamsDescriptor::CudnnRnnParamsDescriptor( int dims[] = {1, rnn_desc.input_size(), 1}; int strides[] = {dims[1] * dims[2], dims[2], 1}; status = cudnnSetTensorNdDescriptor( - /*tensorDesc=*/input_desc, rnn_desc.data_type() /*dataType*/, - sizeof(dims) / sizeof(dims[0]) /*nbDims*/, /*dimA=*/dims, + /*tensorDesc=*/input_desc, /*dataType=*/rnn_desc.data_type(), + /*nbDims=*/sizeof(dims) / sizeof(dims[0]), /*dimA=*/dims, /*strideA=*/strides); CUDNN_RETURN_IF_FAIL(status, "Cudnn fails to set tensor descriptor"); size_t params_size = 0; status = cudnnGetRNNParamsSize( - cudnn.handle() /*handle*/, rnn_desc.handle() /*rnnDesc*/, + /*handle=*/cudnn.handle(), /*rnnDesc=*/rnn_desc.handle(), /*xDesc=*/input_desc, /*sizeInBytes=*/¶ms_size, - rnn_desc.data_type() /*dataType*/); + /*dataType=*/rnn_desc.data_type()); CUDNN_RETURN_IF_FAIL(status, "Cudnn fails to get RNN parameter size"); params_size_in_bytes_ = static_cast(params_size); } @@ -1226,8 +1226,8 @@ CudnnRnnParamsDescriptor::CudnnRnnParamsDescriptor( CUDNN_RETURN_IF_FAIL(status, "Cudnn fails to create RNN filter descriptor"); int dims[] = {static_cast(params_size_in_bytes_), 1, 1}; status = cudnnSetFilterNdDescriptor( - /*filterDesc=*/handle_, rnn_desc.data_type() /*dataType*/, - /*format=*/CUDNN_TENSOR_NCHW, sizeof(dims) / sizeof(dims[0]) /*nbDims*/, + /*filterDesc=*/handle_, /*dataType=*/rnn_desc.data_type(), + /*format=*/CUDNN_TENSOR_NCHW, /*nbDims=*/sizeof(dims) / sizeof(dims[0]), /*filterDimA=*/dims); CUDNN_RETURN_IF_FAIL(status, "Cudnn fails to update RNN filter descriptor"); } @@ -1247,7 +1247,7 @@ CudnnRnnParamsDescriptor::CudnnRnnParamsDescriptor( void* offset = nullptr; if (type == 0) { status = cudnnGetRNNLinLayerMatrixParams( - cudnn.handle() /*handle*/, rnn_desc.handle() /*rnnDesc*/, + /*handle=*/cudnn.handle(), /*rnnDesc=*/rnn_desc.handle(), /*layer=*/layer, /*xDesc=*/input_desc, /*wDesc=*/handle_, /*w=*/nullptr, /*linLayerID=*/region, /*linLayerMatDesc=*/region_desc_handle, @@ -1256,7 +1256,7 @@ CudnnRnnParamsDescriptor::CudnnRnnParamsDescriptor( status, "Cudnn fails to call cudnnGetRNNLinLayerMatrixParams"); } else { status = cudnnGetRNNLinLayerBiasParams( - cudnn.handle() /*rnnDesc*/, rnn_desc.handle() /*rnnDesc*/, + /*handle=*/cudnn.handle(), /*rnnDesc=*/rnn_desc.handle(), /*layer=*/layer, /*xDesc=*/input_desc, /*wDesc=*/handle_, /*w=*/nullptr, /*linLayerID=*/region, /*linLayerBiasDesc=*/region_desc_handle, @@ -1270,7 +1270,7 @@ CudnnRnnParamsDescriptor::CudnnRnnParamsDescriptor( int n_dims; status = cudnnGetFilterNdDescriptor( /*filterDesc=*/region_desc_handle, - sizeof(dims) / sizeof(dims[0]) /*nbDimsRequested*/, + /*nbDimsRequested=*/sizeof(dims) / sizeof(dims[0]), /*dataType=*/&data_type, /*format=*/&tensor_format, /*nbDims=*/&n_dims, /*filterDimA=*/dims); CUDNN_RETURN_IF_FAIL(status, "Cudnn fails to get filter description"); @@ -1338,7 +1338,7 @@ class CudnnRnnSequenceTensorDescriptor int strides[] = {dims[1] * dims[2], dims[2], 1}; status = cudnnSetTensorNdDescriptor( /*tensorDesc=*/handle, /*dataType=*/data_type, - sizeof(dims) / sizeof(dims[0]) /*nbDims*/, /*dimA=*/dims, + /*nbDims=*/sizeof(dims) / sizeof(dims[0]), /*dimA=*/dims, /*strideA=*/strides); CUDNN_RETURN_IF_FAIL(status, "Failed to update tensor descriptor"); // Replicate handle across the number of steps. @@ -1390,7 +1390,7 @@ class CudnnRnnStateTensorDescriptor int strides[] = {dims[1] * dims[2], dims[2], 1}; status = cudnnSetTensorNdDescriptor( /*tensorDesc=*/handle_, /*dataType=*/data_type, - sizeof(dims) / sizeof(dims[0]) /*nbDims*/, /*dimA=*/dims, + /*nbDims=*/sizeof(dims) / sizeof(dims[0]), /*dimA=*/dims, /*strideA=*/strides); CUDNN_RETURN_IF_FAIL(status, "Failed to update tensor descriptor"); } @@ -1497,9 +1497,9 @@ bool CheckRNNParameterSize(const CudnnHandle& cudnn, const CudnnRnnSequenceTensorDescriptor& input_desc) { size_t params_size_in_bytes = 0; cudnnStatus_t status = cudnnGetRNNParamsSize( - /*handle=*/cudnn.handle(), rnn_desc.handle() /*rnnDesc*/, - input_desc.handles()[0] /*xDesc*/, /*sizeInBytes=*/¶ms_size_in_bytes, - rnn_desc.data_type() /*dataType*/); + /*handle=*/cudnn.handle(), /*rnnDesc=*/rnn_desc.handle(), + /*xDesc=*/input_desc.handles()[0], /*sizeInBytes=*/¶ms_size_in_bytes, + /*dataType=*/rnn_desc.data_type()); if (status != CUDNN_STATUS_SUCCESS) { LOG(ERROR) << "Unable to check RNN param size: " << ToString(status); return false; @@ -1592,8 +1592,8 @@ bool CudnnSupport::DoRnnForwardImpl( if (is_training) { size_t reserve_space_size_in_bytes = 0; cudnnStatus_t status = cudnnGetRNNTrainingReserveSize( - cudnn.handle() /*handle*/, rnn_desc.handle() /*rnnDesc*/, - /*seqLength=*/model_dims.seq_length, input_desc.handles() /*xDesc*/, + /*handle=*/cudnn.handle(), /*rnnDesc=*/rnn_desc.handle(), + /*seqLength=*/model_dims.seq_length, /*xDesc=*/input_desc.handles(), /*sizeInBytes=*/&reserve_space_size_in_bytes); if (status != CUDNN_STATUS_SUCCESS) { LOG(ERROR) << "Unable to query reserve space size: " << ToString(status); @@ -1630,30 +1630,30 @@ bool CudnnSupport::DoRnnForwardImpl( cudnnStatus_t status; if (!is_training) { status = cudnnRNNForwardInference( - cudnn.handle() /*handle*/, rnn_desc.handle() /*rnnDesc*/, - model_dims.seq_length /*seqLength*/, input_desc.handles() /*xDesc*/, - input_data.opaque() /*x*/, input_h_desc.handle() /*hxDesc*/, - input_h_data.opaque() /*hx*/, input_c_desc.handle() /*cxDesc*/, - input_c_data.opaque() /*cx*/, rnn_desc.params_handle() /*wDesc*/, - params.opaque() /*w*/, output_desc.handles() /*yDesc*/, - output_data->opaque() /*y*/, output_h_desc.handle() /*hyDesc*/, - output_h_data->opaque() /*hy*/, output_c_desc.handle() /*cyDesc*/, - output_c_data->opaque() /*cy*/, workspace.opaque() /*workspace*/, - workspace.size() /*workSpaceSizeInBytes*/); + /*handle=*/cudnn.handle(), /*rnnDesc=*/rnn_desc.handle(), + /*seqLength=*/model_dims.seq_length, /*xDesc=*/input_desc.handles(), + /*x=*/input_data.opaque(), /*hxDesc=*/input_h_desc.handle(), + /*hx=*/input_h_data.opaque(), /*cxDesc=*/input_c_desc.handle(), + /*cx=*/input_c_data.opaque(), /*wDesc=*/rnn_desc.params_handle(), + /*w=*/params.opaque(), /*yDesc=*/output_desc.handles(), + /*y=*/output_data->opaque(), /*hyDesc=*/output_h_desc.handle(), + /*hy=*/output_h_data->opaque(), /*cyDesc=*/output_c_desc.handle(), + /*cy=*/output_c_data->opaque(), /*workspace=*/workspace.opaque(), + /*workSpaceSizeInBytes=*/workspace.size()); } else { status = cudnnRNNForwardTraining( - cudnn.handle() /*handle*/, rnn_desc.handle() /*rnnDesc*/, - model_dims.seq_length /*seqLength*/, input_desc.handles() /*xDesc*/, - input_data.opaque() /*x*/, input_h_desc.handle() /*hxDesc*/, - input_h_data.opaque() /*hx*/, input_c_desc.handle() /*cxDesc*/, - input_c_data.opaque() /*cx*/, rnn_desc.params_handle() /*wDesc*/, - params.opaque() /*w*/, output_desc.handles() /*yDesc*/, - output_data->opaque() /*y*/, output_h_desc.handle() /*hyDesc*/, - output_h_data->opaque() /*hy*/, output_c_desc.handle() /*cyDesc*/, - output_c_data->opaque() /*cy*/, workspace.opaque() /*workspace*/, - workspace.size() /*workSpaceSizeInBytes*/, - reserve_space.opaque() /*reserveSpace*/, - reserve_space.size() /*reserveSpaceSizeInBytes*/); + /*handle=*/cudnn.handle(), /*rnnDesc=*/rnn_desc.handle(), + /*seqLength=*/model_dims.seq_length, /*xDesc=*/input_desc.handles(), + /*x=*/input_data.opaque(), /*hxDesc=*/input_h_desc.handle(), + /*hx=*/input_h_data.opaque(), /*cxDesc=*/input_c_desc.handle(), + /*cx=*/input_c_data.opaque(), /*wDesc=*/rnn_desc.params_handle(), + /*w=*/params.opaque(), /*yDesc=*/output_desc.handles(), + /*y=*/output_data->opaque(), /*hyDesc=*/output_h_desc.handle(), + /*hy=*/output_h_data->opaque(), /*cyDesc=*/output_c_desc.handle(), + /*cy=*/output_c_data->opaque(), /*workspace=*/workspace.opaque(), + /*workSpaceSizeInBytes=*/workspace.size(), + /*reserveSpace=*/reserve_space.opaque(), + /*reserveSpaceSizeInBytes=*/reserve_space.size()); } if (is_profiling) { if (!timer->Stop(AsCUDAStream(stream))) { @@ -1748,24 +1748,24 @@ bool CudnnSupport::DoRnnBackwardImpl( } // make the backward data call cudnnStatus_t status = cudnnRNNBackwardData( - cudnn.handle() /*handle*/, rnn_desc.handle() /*rnnDesc*/, - model_dims.seq_length /*seqLength*/, output_desc.handles() /*yDesc*/, - output_data.opaque() /*y*/, output_desc.handles() /*dyDesc*/, - output_backprop_data.opaque() /*dy*/, output_h_desc.handle() /*dhyDesc*/, - output_h_backprop_data.opaque() /*dhy*/, - output_c_desc.handle() /*dcyDesc*/, - output_c_backprop_data.opaque() /*dcy*/, - rnn_desc.params_handle() /*wDesc*/, params.opaque() /*w*/, - input_h_desc.handle() /*hxDesc*/, input_h_data.opaque() /*hx*/, - input_c_desc.handle() /*cxDesc*/, input_c_data.opaque() /*cx*/, - input_desc.handles() /*dxDesc*/, input_backprop_data->opaque() /*dx*/, - input_h_desc.handle() /*dhxDesc*/, - input_h_backprop_data->opaque() /*dhx*/, - input_c_desc.handle() /*dcxDesc*/, - input_c_backprop_data->opaque() /*dcx*/, workspace.opaque() /*workspace*/, - workspace.size() /*workSpaceSizeInBytes*/, - reserve_space_data->opaque() /*reserveSpace*/, - reserve_space_data->size() /*reserveSpaceSizeInBytes*/); + /*handle=*/cudnn.handle(), /*rnnDesc=*/rnn_desc.handle(), + /*seqLength=*/model_dims.seq_length, /*yDesc=*/output_desc.handles(), + /*y=*/output_data.opaque(), /*dyDesc=*/output_desc.handles(), + /*dy=*/output_backprop_data.opaque(), /*dhyDesc=*/output_h_desc.handle(), + /*dhy=*/output_h_backprop_data.opaque(), + /*dcyDesc=*/output_c_desc.handle(), + /*dcy=*/output_c_backprop_data.opaque(), + /*wDesc=*/rnn_desc.params_handle(), /*w=*/params.opaque(), + /*hxDesc=*/input_h_desc.handle(), /*hx=*/input_h_data.opaque(), + /*cxDesc=*/input_c_desc.handle(), /*cx=*/input_c_data.opaque(), + /*dxDesc=*/input_desc.handles(), /*dx=*/input_backprop_data->opaque(), + /*dhxDesc=*/input_h_desc.handle(), + /*dhx=*/input_h_backprop_data->opaque(), + /*dcxDesc=*/input_c_desc.handle(), + /*dcx=*/input_c_backprop_data->opaque(), /*workspace=*/workspace.opaque(), + /*workSpaceSizeInBytes=*/workspace.size(), + /*reserveSpace=*/reserve_space_data->opaque(), + /*reserveSpaceSizeInBytes=*/reserve_space_data->size()); if (status != CUDNN_STATUS_SUCCESS) { if (is_profiling) { @@ -1780,16 +1780,16 @@ bool CudnnSupport::DoRnnBackwardImpl( stream->ThenMemZero(params_backprop_data, params_backprop_data->size()); // make the backward weight call status = cudnnRNNBackwardWeights( - cudnn.handle() /*handle*/, rnn_desc.handle() /*rnnDesc*/, - model_dims.seq_length /*seqLength*/, input_desc.handles() /*xDesc*/, - input_data.opaque() /*x*/, input_h_desc.handle() /*hxDesc*/, - input_h_data.opaque() /*hx*/, output_desc.handles() /*yDesc*/, - output_data.opaque() /*y*/, workspace.opaque() /*workspace*/, - workspace.size() /*workSpaceSizeInBytes*/, - rnn_desc.params_handle() /*dwDesc*/, - params_backprop_data->opaque() /*dw*/, - reserve_space_data->opaque() /*reserveSpace*/, - reserve_space_data->size() /*reserveSpaceSizeInBytes*/); + /*handle=*/cudnn.handle(), /*rnnDesc=*/rnn_desc.handle(), + /*seqLength=*/model_dims.seq_length, /*xDesc=*/input_desc.handles(), + /*x=*/input_data.opaque(), /*hxDesc=*/input_h_desc.handle(), + /*hx=*/input_h_data.opaque(), /*yDesc=*/output_desc.handles(), + /*y=*/output_data.opaque(), /*workspace=*/workspace.opaque(), + /*workSpaceSizeInBytes=*/workspace.size(), + /*dwDesc=*/rnn_desc.params_handle(), + /*dw=*/params_backprop_data->opaque(), + /*reserveSpace=*/reserve_space_data->opaque(), + /*reserveSpaceSizeInBytes=*/reserve_space_data->size()); if (status != CUDNN_STATUS_SUCCESS) { if (is_profiling) { timer->Stop(AsCUDAStream(stream)); -- 2.7.4