// 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 #include #include #include #include #include #include #include "paddle/fluid/framework/data_type.h" #include "paddle/fluid/framework/ir/graph.h" namespace paddle { namespace framework { namespace details { std::vector SortOpLikeDescOrder(const ir::Graph& graph); // NOTE(dzh): A ordered set for node reuse in memory optimize. // the orderedset sort node in ascend order(by node bytes size). // in fluid, -1 means the batch_size, which is determined in runtime. // So the reuse happens between nodes who's batch_size both are -1 // simultaneously or not. // // sort rule: // rule 0 : smaller node ranking in front. // rule 1 : batch_size equal -1 ranking in the front than the node not. // // For example, // node0[-1, 1] node1[-1, 1, 1], node2[1,1], node3[1,1024], .. class OrderedSet { public: // nodes with same name exists in pool. using NodeVector = std::vector; using Iter = typename std::list::iterator; using ConstIter = typename std::list::const_iterator; void Insert(ir::Node* var); void Erase(ir::Node* var); bool Has(ir::Node* var) const; void Clear() { mark_table_.clear(); nodes_.clear(); } // find the bestfit shape node block with var. ir::Node* FindBestFitNode(ir::Node* var) const; // map store non-const iterator, can not promise const int GetNodeIndexInPool(ir::Node* var); // pool all node to string std::string ToString() const; Iter begin() { return nodes_.begin(); } Iter end() { return nodes_.end(); } ConstIter begin() const { return nodes_.begin(); } ConstIter end() const { return nodes_.end(); } size_t size() const { return nodes_.size(); } private: // for searching. std::unordered_map mark_table_; // node pool std::list nodes_; }; class ControlFlowGraph { public: ControlFlowGraph() = default; // IR Graph explicit ControlFlowGraph(const ir::Graph& graph); void LiveVariableAnalysis(); void RenameVarInCFGGraph(const std::string& old_node, const std::string& new_node, int begin_idx); const std::set LiveIn(ir::Node* op) const; const std::set LiveOut(ir::Node* op) const; const std::set Use(ir::Node* op) const; const std::vector Ops() const; std::vector& Ops(); // for ssa-graph nodes ir::Node* GetNodeByName(const std::string& name, ir::Node* op) const; private: void BuildCFGGraph(); void ConnectNodes(); using NodeListMap = std::unordered_map>; using VarSetMap = std::map>; // successors ops use the output variables. NodeListMap successors_; // predecessors ops generated input variables. NodeListMap predecessors_; // variables lived before run current op. VarSetMap live_in_; // variables lived after run current op. VarSetMap live_out_; VarSetMap uses_; // op inputs VarSetMap defs_; // op outputs std::vector ops_; // op sequence by topology sort }; // valid a tensor can be reuse or not bool NodeCanReused(ir::Node* node); // valid a tensor can be reuse or not. bool NodeCanReused(const VarDesc& node); // check op has subblock or not bool OpHasSubBlock(OpDesc* desc); // node memory size in bytes size_t NodeSize(ir::Node* n); // node memory size in bytes size_t NodeSize(const VarDesc&); std::string DebugString(ir::Node* var); // NOTE(dzhwinter) // after node reuse, the replaced node shape is // different with its VarDesc. So need to find the // correct VarDesc in Block. VarDesc* FindVarDescInBlock(ir::Node* n); static inline bool IsSameDesc(OpDesc* op1, OpDesc* op2) { return op1->Type() == op2->Type() && op1->Inputs() == op2->Inputs() && op1->Outputs() == op2->Outputs(); } template class FilterVariableImpl { public: void operator()(const Container& nodes, Callback callback) { for (auto* node : nodes) { callback(node); } } }; // filter var node for op->inputs/outputs template class FilterVariableImpl, Callback> { public: void operator()(const std::vector& nodes, Callback callback) { for (auto* var : nodes) { if (var->IsVar() && !var->IsCtrlVar()) { callback(var); } } } }; template void FilterVariables(const Container& nodes, Callback callback) { FilterVariableImpl()(nodes, callback); } } // namespace details } // namespace framework } // namespace paddle