// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #pragma once #include #include #include "paddle/fluid/framework/lod_tensor_array.h" #include "paddle/fluid/framework/scope.h" #include "paddle/fluid/framework/variable.h" namespace paddle { namespace details { // Clean the TensorArray each batch to make the behavior the same with the // training phase. struct TensorArrayBatchCleaner { TensorArrayBatchCleaner() { valid_types_.insert(typeid(framework::Tensor)); valid_types_.insert(typeid(framework::LoDTensor)); } // Collect the variables that are not Tensor or LoDTensor, and reset them to a // bool(trick), because some of them are containers, and some operators just // keep inserting new items without clearing the containers first; So the // memory grow larger and larger in inference service deployed online. void CollectNoTensorVars(framework::Scope *scope); void ResetNoTensorVars(); // Fix the tensor array not clear in the inference scenarios. void CollectTensorArrays(framework::Scope *scope); void ResetTensorArray(); private: bool flag_{true}; bool no_tensor_flag_{true}; std::vector arrays_; std::unordered_set valid_types_; std::unordered_set no_tensor_vars_; }; } // namespace details } // namespace paddle