OSSForks/tensorflow
1
0
Fork 0
mirror of https://github.com/zebrajr/tensorflow.git synced 2026-01-15 12:15:41 +00:00
Code Issues Actions 5 Packages Projects Releases Wiki Activity

Cleanup codes

Browse Source
This commit is contained in:
Kaixi Hou
2021-03-01 11:45:44 -08:00
parent 76b4645547
commit 322e858bd8
7 changed files with 82 additions and 82 deletions
Download Patch File Download Diff File Expand all files Collapse all files

22
tensorflow/core/kernels/conv_grad_filter_ops.cc
View File

@@ -1072,21 +1072,21 @@ void LaunchConv2DBackpropFilterOp<Eigen::GpuDevice, T>::operator()(
filter_backprop_ptr, out_backprop_ptr, input_desc,
filter_desc, output_desc, conv_desc,
stream->parent(), results);
int idx_, idx_no_scratch_;
int idx, idx_no_scratch;
OP_REQUIRES_OK(ctx,
BestCudnnConvExecutionPlan(results, &idx_, &idx_no_scratch_));
BestCudnnConvExecutionPlan(results, &idx, &idx_no_scratch));
exec_plan_config.set_plan(
ExecutionPlanDesc(exec_plans[idx_]->getTag(),
exec_plans[idx_]->get_raw_desc()));
exec_plan_config.set_scratch_size(exec_plans[idx_]->getWorkspaceSize());
if (idx_no_scratch_ != -1) {
ExecutionPlanDesc(exec_plans[idx]->getTag(),
exec_plans[idx]->get_raw_desc()));
exec_plan_config.set_scratch_size(exec_plans[idx]->getWorkspaceSize());
if (idx_no_scratch != -1) {
exec_plan_config.set_plan_no_scratch(
ExecutionPlanDesc(exec_plans[idx_no_scratch_]->getTag(),
exec_plans[idx_no_scratch_]->get_raw_desc()));
ExecutionPlanDesc(exec_plans[idx_no_scratch]->getTag(),
exec_plans[idx_no_scratch]->get_raw_desc()));
}
selected_exec_plans.push_back(std::move(exec_plans[idx_]));
if (idx_no_scratch_ != idx_ and idx_no_scratch_ != -1) {
selected_exec_plans.push_back(std::move(exec_plans[idx_no_scratch_]));
selected_exec_plans.push_back(std::move(exec_plans[idx]));
if (idx_no_scratch != idx and idx_no_scratch != -1) {
selected_exec_plans.push_back(std::move(exec_plans[idx_no_scratch]));
}
AutoTuneConvBwdFilter::GetInstance()->Insert(conv_parameters,
selected_exec_plans);

22
tensorflow/core/kernels/conv_grad_input_ops.cc
View File

@@ -482,21 +482,21 @@ void LaunchConv2DBackpropInputOp<GPUDevice, T>::operator()(
se::dnn::ConvolutionKind::BACKWARD_DATA, se::dnn::ToDataType<T>::value,
in_backprop_ptr, filter_ptr, out_backprop_ptr, input_desc, filter_desc,
output_desc, conv_desc, stream->parent(), results);
int idx_, idx_no_scratch_;
int idx, idx_no_scratch;
OP_REQUIRES_OK(ctx,
BestCudnnConvExecutionPlan(results, &idx_, &idx_no_scratch_));
BestCudnnConvExecutionPlan(results, &idx, &idx_no_scratch));
exec_plan_config.set_plan(
ExecutionPlanDesc(exec_plans[idx_]->getTag(),
exec_plans[idx_]->get_raw_desc()));
exec_plan_config.set_scratch_size(exec_plans[idx_]->getWorkspaceSize());
if (idx_no_scratch_ != -1) {
ExecutionPlanDesc(exec_plans[idx]->getTag(),
exec_plans[idx]->get_raw_desc()));
exec_plan_config.set_scratch_size(exec_plans[idx]->getWorkspaceSize());
if (idx_no_scratch != -1) {
exec_plan_config.set_plan_no_scratch(
ExecutionPlanDesc(exec_plans[idx_no_scratch_]->getTag(),
exec_plans[idx_no_scratch_]->get_raw_desc()));
ExecutionPlanDesc(exec_plans[idx_no_scratch]->getTag(),
exec_plans[idx_no_scratch]->get_raw_desc()));
}
selected_exec_plans.push_back(std::move(exec_plans[idx_]));
if (idx_no_scratch_ != idx_ and idx_no_scratch_ != -1) {
selected_exec_plans.push_back(std::move(exec_plans[idx_no_scratch_]));
selected_exec_plans.push_back(std::move(exec_plans[idx]));
if (idx_no_scratch != idx and idx_no_scratch != -1) {
selected_exec_plans.push_back(std::move(exec_plans[idx_no_scratch]));
}
AutoTuneConvBwdData::GetInstance()->Insert(conv_parameters,
selected_exec_plans);

44
tensorflow/core/kernels/conv_grad_ops_3d.cc
View File

@@ -1510,21 +1510,21 @@ class Conv3DBackpropInputOp<GPUDevice, T> : public OpKernel {
se::dnn::ToDataType<T>::value, in_backprop_ptr, filter_ptr,
out_backprop_ptr, input_desc, filter_desc, output_desc, conv_desc,
stream->parent(), results);
int idx_, idx_no_scratch_;
int idx, idx_no_scratch;
OP_REQUIRES_OK(context,
BestCudnnConvExecutionPlan(results, &idx_, &idx_no_scratch_));
BestCudnnConvExecutionPlan(results, &idx, &idx_no_scratch));
exec_plan_config.set_plan(
ExecutionPlanDesc(exec_plans[idx_]->getTag(),
exec_plans[idx_]->get_raw_desc()));
exec_plan_config.set_scratch_size(exec_plans[idx_]->getWorkspaceSize());
if (idx_no_scratch_ != -1) {
ExecutionPlanDesc(exec_plans[idx]->getTag(),
exec_plans[idx]->get_raw_desc()));
exec_plan_config.set_scratch_size(exec_plans[idx]->getWorkspaceSize());
if (idx_no_scratch != -1) {
exec_plan_config.set_plan_no_scratch(
ExecutionPlanDesc(exec_plans[idx_no_scratch_]->getTag(),
exec_plans[idx_no_scratch_]->get_raw_desc()));
ExecutionPlanDesc(exec_plans[idx_no_scratch]->getTag(),
exec_plans[idx_no_scratch]->get_raw_desc()));
}
selected_exec_plans.push_back(std::move(exec_plans[idx_]));
if (idx_no_scratch_ != idx_ and idx_no_scratch_ != -1) {
selected_exec_plans.push_back(std::move(exec_plans[idx_no_scratch_]));
selected_exec_plans.push_back(std::move(exec_plans[idx]));
if (idx_no_scratch != idx and idx_no_scratch != -1) {
selected_exec_plans.push_back(std::move(exec_plans[idx_no_scratch]));
}
AutoTuneConv3dBwdData::GetInstance()->Insert(conv_parameters,
selected_exec_plans);
@@ -2106,21 +2106,21 @@ class Conv3DBackpropFilterOp<GPUDevice, T> : public OpKernel {
filter_backprop_ptr, out_backprop_ptr, input_desc,
filter_desc, output_desc, conv_desc,
stream->parent(), results);
int idx_, idx_no_scratch_;
int idx, idx_no_scratch;
OP_REQUIRES_OK(context,
BestCudnnConvExecutionPlan(results, &idx_, &idx_no_scratch_));
BestCudnnConvExecutionPlan(results, &idx, &idx_no_scratch));
exec_plan_config.set_plan(
ExecutionPlanDesc(exec_plans[idx_]->getTag(),
exec_plans[idx_]->get_raw_desc()));
exec_plan_config.set_scratch_size(exec_plans[idx_]->getWorkspaceSize());
if (idx_no_scratch_ != -1) {
ExecutionPlanDesc(exec_plans[idx]->getTag(),
exec_plans[idx]->get_raw_desc()));
exec_plan_config.set_scratch_size(exec_plans[idx]->getWorkspaceSize());
if (idx_no_scratch != -1) {
exec_plan_config.set_plan_no_scratch(
ExecutionPlanDesc(exec_plans[idx_no_scratch_]->getTag(),
exec_plans[idx_no_scratch_]->get_raw_desc()));
ExecutionPlanDesc(exec_plans[idx_no_scratch]->getTag(),
exec_plans[idx_no_scratch]->get_raw_desc()));
}
selected_exec_plans.push_back(std::move(exec_plans[idx_]));
if (idx_no_scratch_ != idx_ and idx_no_scratch_ != -1) {
selected_exec_plans.push_back(std::move(exec_plans[idx_no_scratch_]));
selected_exec_plans.push_back(std::move(exec_plans[idx]));
if (idx_no_scratch != idx and idx_no_scratch != -1) {
selected_exec_plans.push_back(std::move(exec_plans[idx_no_scratch]));
}
AutoTuneConv3dBwdFilter::GetInstance()->Insert(conv_parameters,
selected_exec_plans);

22
tensorflow/core/kernels/conv_ops.cc
View File

@@ -1077,21 +1077,21 @@ void LaunchConv2DOp<GPUDevice, T>::operator()(
se::dnn::ToDataType<T>::value, input_ptr, filter_ptr,
output_tensor, input_desc, filter_desc, output_desc,
conv_desc, stream->parent(), results);
int idx_, idx_no_scratch_;
int idx, idx_no_scratch;
OP_REQUIRES_OK(ctx,
BestCudnnConvExecutionPlan(results, &idx_, &idx_no_scratch_));
BestCudnnConvExecutionPlan(results, &idx, &idx_no_scratch));
exec_plan_config.set_plan(
ExecutionPlanDesc(exec_plans[idx_]->getTag(),
exec_plans[idx_]->get_raw_desc()));
exec_plan_config.set_scratch_size(exec_plans[idx_]->getWorkspaceSize());
if (idx_no_scratch_ != -1) {
ExecutionPlanDesc(exec_plans[idx]->getTag(),
exec_plans[idx]->get_raw_desc()));
exec_plan_config.set_scratch_size(exec_plans[idx]->getWorkspaceSize());
if (idx_no_scratch != -1) {
exec_plan_config.set_plan_no_scratch(
ExecutionPlanDesc(exec_plans[idx_no_scratch_]->getTag(),
exec_plans[idx_no_scratch_]->get_raw_desc()));
ExecutionPlanDesc(exec_plans[idx_no_scratch]->getTag(),
exec_plans[idx_no_scratch]->get_raw_desc()));
}
selected_exec_plans.push_back(std::move(exec_plans[idx_]));
if (idx_no_scratch_ != idx_ and idx_no_scratch_ != -1) {
selected_exec_plans.push_back(std::move(exec_plans[idx_no_scratch_]));
selected_exec_plans.push_back(std::move(exec_plans[idx]));
if (idx_no_scratch != idx and idx_no_scratch != -1) {
selected_exec_plans.push_back(std::move(exec_plans[idx_no_scratch]));
}
AutoTuneConv::GetInstance()->Insert(conv_parameters, selected_exec_plans);
}

22
tensorflow/core/kernels/conv_ops_3d.cc
View File

@@ -588,21 +588,21 @@ struct LaunchConvOp<GPUDevice, T> {
se::dnn::ToDataType<T>::value, input_ptr, filter_ptr, output_ptr_rz,
input_desc, filter_desc, output_desc, conv_desc, stream->parent(),
results);
int idx_, idx_no_scratch_;
int idx, idx_no_scratch;
OP_REQUIRES_OK(ctx,
BestCudnnConvExecutionPlan(results, &idx_, &idx_no_scratch_));
BestCudnnConvExecutionPlan(results, &idx, &idx_no_scratch));
exec_plan_config.set_plan(
ExecutionPlanDesc(exec_plans[idx_]->getTag(),
exec_plans[idx_]->get_raw_desc()));
exec_plan_config.set_scratch_size(exec_plans[idx_]->getWorkspaceSize());
if (idx_no_scratch_ != -1) {
ExecutionPlanDesc(exec_plans[idx]->getTag(),
exec_plans[idx]->get_raw_desc()));
exec_plan_config.set_scratch_size(exec_plans[idx]->getWorkspaceSize());
if (idx_no_scratch != -1) {
exec_plan_config.set_plan_no_scratch(
ExecutionPlanDesc(exec_plans[idx_no_scratch_]->getTag(),
exec_plans[idx_no_scratch_]->get_raw_desc()));
ExecutionPlanDesc(exec_plans[idx_no_scratch]->getTag(),
exec_plans[idx_no_scratch]->get_raw_desc()));
}
selected_exec_plans.push_back(std::move(exec_plans[idx_]));
if (idx_no_scratch_ != idx_ and idx_no_scratch_ != -1) {
selected_exec_plans.push_back(std::move(exec_plans[idx_no_scratch_]));
selected_exec_plans.push_back(std::move(exec_plans[idx]));
if (idx_no_scratch != idx and idx_no_scratch != -1) {
selected_exec_plans.push_back(std::move(exec_plans[idx_no_scratch]));
}
AutoTuneConv3d::GetInstance()->Insert(conv_parameters,
selected_exec_plans);

30
tensorflow/stream_executor/cuda/cuda_dnn.cc
View File

@@ -3170,31 +3170,31 @@ struct RnnDoFP32ComputationFP16Input {
namespace {
#if CUDNN_VERSION >= 8100
bool isNonDeterministic(cudnnBackendDescriptor_t engine_config) {
bool IsNonDeterministic(cudnnBackendDescriptor_t engine_config) {
return cudnn_frontend::hasNumericalNote<
CUDNN_NUMERICAL_NOTE_NONDETERMINISTIC>(engine_config);
}
bool isWinograd(cudnnBackendDescriptor_t engine_config) {
bool IsWinograd(cudnnBackendDescriptor_t engine_config) {
return cudnn_frontend::hasNumericalNote<
CUDNN_NUMERICAL_NOTE_WINOGRAD>(engine_config);
}
bool isDownConvertingInputs(cudnnBackendDescriptor_t engine_config) {
bool IsDownConvertingInputs(cudnnBackendDescriptor_t engine_config) {
if (CudnnEnvVar<WinogradNonfused>::IsEnabled()) {
return cudnn_frontend::hasNumericalNote<
CUDNN_NUMERICAL_NOTE_DOWN_CONVERT_INPUTS>(engine_config);
} else {
return isWinograd(engine_config) ||
return IsWinograd(engine_config) ||
cudnn_frontend::hasNumericalNote<
CUDNN_NUMERICAL_NOTE_DOWN_CONVERT_INPUTS>(engine_config);
}
}
bool isNonDeterministicOrIsDownConverting(
bool IsNonDeterministicOrIsDownConverting(
cudnnBackendDescriptor_t engine_config) {
return isNonDeterministic(engine_config) ||
isDownConvertingInputs(engine_config);
return IsNonDeterministic(engine_config) ||
IsDownConvertingInputs(engine_config);
}
#endif // CUDNN_VERSION >= 8100
@@ -3665,14 +3665,14 @@ port::Status CudnnSupport::DoConvolve(
cudnn_frontend::EngineConfigList filtered_configs;
if (RequireCudnnDeterminism()) {
cudnn_frontend::filter(engine_config, filtered_configs,
isNonDeterministicOrIsDownConverting);
IsNonDeterministicOrIsDownConverting);
cudnn_frontend::filter(fallback_list, filtered_configs,
isNonDeterministicOrIsDownConverting);
IsNonDeterministicOrIsDownConverting);
} else {
cudnn_frontend::filter(engine_config, filtered_configs,
isDownConvertingInputs);
IsDownConvertingInputs);
cudnn_frontend::filter(fallback_list, filtered_configs,
isDownConvertingInputs);
IsDownConvertingInputs);
}
for (int i = 0; i < filtered_configs.size(); i++) {
auto plan = cudnn_frontend::ExecutionPlanBuilder()
@@ -3939,14 +3939,14 @@ bool CudnnSupport::GetConvolveExecutionPlans(
cudnn_frontend::EngineConfigList filtered_configs;
if (RequireCudnnDeterminism()) {
cudnn_frontend::filter(heur_configs, filtered_configs,
isNonDeterministicOrIsDownConverting);
IsNonDeterministicOrIsDownConverting);
cudnn_frontend::filter(fallback_configs, filtered_configs,
isNonDeterministicOrIsDownConverting);
IsNonDeterministicOrIsDownConverting);
} else {
cudnn_frontend::filter(heur_configs, filtered_configs,
isDownConvertingInputs);
IsDownConvertingInputs);
cudnn_frontend::filter(fallback_configs, filtered_configs,
isDownConvertingInputs);
IsDownConvertingInputs);
}
VLOG(4) << "\nFiltered engine configs size: " << filtered_configs.size();

2
tensorflow/stream_executor/dnn.h
View File

@@ -779,7 +779,7 @@ class AlgorithmDesc {
AlgorithmProto proto_;
};
// Collects parameters for DNN execution plans
// Collects parameters for DNN execution plans.
class ExecutionPlanDesc {
public:
typedef std::string Index;