// 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 #ifdef PADDLE_WITH_TESTING #include #endif #include #include #include #include #include #include #include #include #include #include "paddle/fluid/framework/framework.pb.h" #include "paddle/fluid/framework/ir/graph.h" #include "paddle/fluid/framework/ir/node.h" #include "paddle/fluid/inference/analysis/dot.h" namespace paddle { namespace framework { namespace ir { class Graph; class Node; } // namespace ir } // namespace framework } // namespace paddle namespace paddle { namespace framework { namespace ir { class PDPattern; // Some basic terminologies: // - PDPattern: a pattern defined as a data flow graph. // - PDNode: the node in the pattern, each PDNode represents an `ir::Node` // that meets some conditions defined in `PDNode.teller`. // - A pattern is defined with PDNodes with edges. // Pattern detector node. This node helps to build a pattern. struct PDNode { // tell whether an ir::Node* is a candidation for a PDNode. using teller_t = std::function; enum class Type { kOp, kVar }; enum class Role { kUnknown, // No role, kInput, // an input and will be retained, kOutput, // an output and will be retained, kIntermediate // will be removed after handler. }; // this link to others PDNode& LinksTo(const std::vector& others); PDNode& LinksFrom(const std::vector& others); bool Tell(Node* node) const { if (teller_) return teller_(node); for (auto& asrt : asserts_) { if (!asrt(node)) return false; } return true; } bool IsOp() const { return type_ == Type::kOp; } bool IsVar() const { return type_ == Type::kVar; } const std::string& name() const { return name_; } PDNode& operator=(const PDNode&) = delete; PDNode(const PDNode&) = delete; // Mark this node is an Input of a subgraph and will be retained. PDNode* AsInput() { role_ = Role::kInput; return this; } // Mark this node is an Output of a subgraph and will be retained. PDNode* AsOutput() { role_ = Role::kOutput; return this; } // Mark this node will be removed, so all the links should be inside a matched // sub-graph. PDNode* AsIntermediate() { role_ = Role::kIntermediate; return this; } bool IsIntermediate() const { return role_ == Role::kIntermediate; } bool IsInput() const { return role_ == Role::kInput; } bool IsOutput() const { return role_ == Role::kOutput; } // Assertions, helper functions to simplify the pattern definition. PDNode* assert_is_op(); PDNode* assert_is_op(const std::string& op_type); PDNode* assert_is_var(); PDNode* assert_var_dtype(proto::VarType::Type dtype); PDNode* assert_is_not_ctrl_var(); PDNode* assert_var_not_persistable(); PDNode* assert_is_persistable_var(); PDNode* assert_is_op_output(const std::string& op_type); PDNode* assert_is_op_output(const std::string& op_type, const std::string& argument); PDNode* assert_is_op_input(const std::string& op_type); PDNode* assert_is_op_input(const std::string& op_type, const std::string& argument); PDNode* assert_is_op_nth_input(const std::string& op_type, const std::string& argument, int nth); PDNode* assert_is_not_op_input(const std::string& argument); PDNode* assert_is_op_nth_output(const std::string& op_type, const std::string& argument, int nth); PDNode* assert_is_only_input_of_op(const std::string& op_type); PDNode* assert_is_only_output_of_op(const std::string& op_type); PDNode* assert_op_has_n_inputs(const std::string& op_type, size_t n); PDNode* assert_op_has_n_outputs(const std::string& op_type, size_t n); PDNode* assert_more(teller_t&& teller); PDNode* assert_is_ops_output(const std::unordered_set& op_types); PDNode* assert_is_ops(const std::unordered_set& op_types); PDNode* assert_is_ops_output(const std::unordered_set& op_types, const std::string& argument); PDNode* assert_is_ops_nth_input( const std::unordered_set& op_types, const std::string& argument, int nth); PDNode* assert_is_ops_input(const std::unordered_set& op_types); PDNode* assert_is_ops_input(const std::unordered_set& op_types, const std::string& argument); PDNode* assert_is_ops_nth_output( const std::unordered_set& op_types, const std::string& argument, int nth); PDNode* assert_has_n_inputs(size_t n); PDNode* assert_has_n_outputs(size_t n); template PDNode* assert_op_attr(const std::string& attr_name, const T& attr) { asserts_.emplace_back([=](Node* x) { return x && x->IsOp() && x->Op()->HasAttr(attr_name) && BOOST_GET_CONST(T, x->Op()->GetAttr(attr_name)) == attr; }); return this; } private: PDNode(PDPattern* pattern, const std::string& name = "", Type type = Type::kVar) : pattern_(pattern), name_(name), type_(type) {} PDNode(teller_t&& teller, PDPattern* pattern, const std::string& name = "", Type type = Type::kVar) : teller_(std::move(teller)), pattern_(pattern), name_(name), type_(type) { PADDLE_ENFORCE_NOT_NULL( teller_, platform::errors::NotFound("invalid teller is set, teller is null")); } PDNode(PDNode&& other) = default; friend class PDPattern; // Will removed latter. teller_t teller_; std::vector asserts_; PDPattern* pattern_; std::string name_; Type type_; Role role_{Role::kUnknown}; }; /* * A pattern in a graph, which defined with PDNode and edges. Most graph * patterns can be divided into PDNodes and link relations between them. * * For example, the FC fusion need to filter the MUL and ELEMENTWISE_ADD * operators from the computation graph, the MUL's output should have only one * consumer which is the ELEMENTWISE_ADD. * This pattern can be defined as with the following pseudo codes * * // Create two operator PDNodes. * MUL = PDPattern.NewNode().assert_is_op("mul"); * ELE = PDPattern.NewNode().assert_is_op("elementwise_add"); * // Create the variable PDNodes. * MUL_out = PDPattern.NewNode().assert_is_op_output("mul") \ * .assert_is_op_input("elementwise_add") \ * .AsIntermediate(); * // Add relations. * MUL->LinksTo({MUL_out}); * MUL_out->LinksTo({ELE}); * * One can add more specific asserts for PDNodes or edges, both the Operator * and Variable Nodes can be ruled in PDNode.assert_more(...). * * PDPattern can record the general patterns, such as the pattern represents * - Op in CPU -> Op in GPU -> Op in CPU, to findout the IO abnormal place. * - Ops whose inputs and outputs share the same variables */ class PDPattern { public: using edge_t = std::pair; void AddEdge(PDNode* a, PDNode* b); PDNode* NewNode(PDNode::teller_t&& teller, const std::string& name = NewID()); PDNode* NewNode(const std::string& name = NewID()); PDNode* NewNode(const std::string& prefix, const std::string& name) { return NewNode(prefix + "/" + name); } PDNode* RetrieveNode(const std::string& id) const; const std::vector>& nodes() const { return nodes_; } const std::vector& edges() const { return edges_; } std::string DotString() const; private: #ifdef PADDLE_WITH_TESTING FRIEND_TEST(PDPattern, AddEdge); FRIEND_TEST(PDPattern, NewNode); #endif static std::string NewID() { return "pdnode-" + std::to_string(id_++); } std::vector> nodes_; std::vector edges_; std::map node_map_; static size_t id_; }; /* * GraphPatternDetector helps to detect the specific patterns in the graph. * Input a pattern, output a list of the matched subgraphs/nodes. * This helper can be used to support fuse(conv+batchnorm => batchnorm e.g.). * * The algorithm has three phases: * 1. Mark the nodes that match the defined PDNodes in a PDPattern, * 2. Extend a PDNode to subgraphs by deducing the connection relation defined * in PAPattern(the edges), * 3. Get the filtered subgraphs and treat them with a pre-defined handler. * * Usage: * // Create a detector * GraphPatternDetector detector; * // Define the detector's pattern, by adding PDNode and define the edges. * auto* node0 = detector.mutable_pattern().AddNode(...) * auto* node1 = detector.mutable_pattern().AddNode(...) * node0->teller = some lambda. * node1->teller = some lambda. * detector.mutable_pattern().AddEdge(node0, node1); * // Create an handler, to define the behavior of treating the filtered * // subgraphs that comply with the patterns. * GraphPatternDetector::handle_t handler = some labmda * // Execute the detector. * detector(&graph, handler); */ class GraphPatternDetector { public: using subgraph_t = std::map; // Operate on the detected pattern. using handle_t = std::function; void operator()(Graph* graph, handle_t handler); const PDPattern& pattern() const { return pattern_; } PDPattern* mutable_pattern() { return &pattern_; } private: // Mark the nodes that fits the pattern. bool MarkPDNodesInGraph(const ir::Graph& graph); // Detect all the pattern and output the hit records. std::vector DetectPatterns(); // Remove duplicate patterns. void UniquePatterns(std::vector* subgraphs); // Sort subgraphs, sort subgraphs by the specified node so that // the removed forward and backward subgraphs are corresponding // when two subgraphs are overlapped. Note: this function is // currently only used for bn_add_act, refer to PR28196 for details. void SortSubgraphs(std::vector* subgraphs); // Remove overlapped match subgraphs, when overlapped, keep the previous one. // The intermediate PDNodes will be removed, so can't shared by multiple // patterns. void RemoveOverlappedMatch(std::vector* subgraphs); // Validate whether the intermediate nodes are linked by external nodes. void ValidateByNodeRole(std::vector* subgraphs); #ifdef PADDLE_WITH_TESTING FRIEND_TEST(GraphPatternDetecter, MarkPDNodesInGraph); FRIEND_TEST(GraphPatternDetecter, DetectPatterns); #endif private: using hit_rcd_t = std::pair; PDPattern pattern_; std::map> pdnodes2nodes_; }; // some helper methods. // Tell if a var links to an Op bool VarLinksToOp(Node* node, const std::string& op_type); // Tell if an op links to a var bool VarLinksFromOp(Node* node, const std::string& op_type); // Check whether a var node is a op node's nth input. bool IsNthInput(Node* var, Node* op, const std::string& argument, size_t nth); // Check whether the op node has input of given name. bool HasInput(Node* op, const std::string& argument); // Check whether the op node has output of given name. bool HasOutput(Node* op, const std::string& argument); // Tell whether a var node is a op node's nth output. bool IsNthOutput(Node* var, Node* op, const std::string& argument, size_t nth); // Graph safely remove some nodes, will automatically clean up the edges. void GraphSafeRemoveNodes(Graph* graph, const std::unordered_set& nodes); // Some pre-defined patterns those can be reused in multiple passes. // The related Fluid Layer or Op should be one pattern here for better re-usage // across different fusion. namespace patterns { struct KeyCounter { static KeyCounter& Instance() { static KeyCounter x; return x; } int IncCounter(const std::string& key) { return dic_[key]++; } private: std::unordered_map dic_; }; // Generate a unique PDNode's name with name_scope and id. // The format is {name_scope}/{repr}/{id}/{name} static std::string PDNodeName(const std::string& name_scope, const std::string& repr, size_t id, const std::string& name) { return string::Sprintf("%s/%s/%d/%s", name_scope, repr, id, name); } // Generate a unique PDNode's name. // The format is {name_scope}/{repr}/{id} static std::string PDNodeName(const std::string& name_scope, const std::string& repr) { return string::Sprintf("%s/%s/%d", name_scope, repr, KeyCounter::Instance().IncCounter(repr)); } // Generate a unique key. It can be used for a universally unique temporary // name. // The format is {repr}/{id} static std::string UniqueKey(const std::string& repr) { return string::Sprintf("%s/%d", repr, KeyCounter::Instance().IncCounter(repr)); } // Declare a PDNode in a pattern, will create two methods: // std::string xxx_repr(); return this PDNode's string id. // PDNode* xxx_n(); return the corresponding PDNode. #define PATTERN_DECL_NODE(name__) \ std::string name__##_repr() const { \ return PDNodeName(name_scope_, repr_, id_, #name__); \ } \ PDNode* name__##_n() const { return pattern->RetrieveNode(name__##_repr()); } // Get an ir::Node* from the matched subgraph. // var: variable. // arg: the argument declared by PATTERN_DECL_NODE in a pattern definition. // pat: the pattern object. #define GET_IR_NODE_FROM_SUBGRAPH(var, arg, pat) \ PADDLE_ENFORCE_NE(subgraph.count(pat.arg##_n()), 0UL, \ platform::errors::NotFound("Node not found for PDNode %s", \ pat.arg##_repr())); \ Node* var = subgraph.at(pat.arg##_n()); \ PADDLE_ENFORCE_NOT_NULL( \ var, platform::errors::NotFound("node %s not exists in the sub-graph", \ #arg)); // The base class of all the patterns. struct PatternBase { PatternBase(PDPattern* pattern, const std::string& name_scope, const std::string& repr) : pattern(pattern), name_scope_(name_scope), repr_(repr), id_(KeyCounter::Instance().IncCounter(repr)) {} PDPattern* pattern; protected: std::string name_scope_; std::string repr_; size_t id_; }; // Conv with batch norm // op: conv + (elementwise_add +) batch_norm // named nodes: // conv_weight, conv_out, conv, // bn_x, bn_scale, bn_bias, bn_mean, bn_variance, // bn_batch_norm, bn_y, bn_mean_out, bn_variance_out, // bn_saved_mean, bn_saved_variance struct ConvBN : public PatternBase { ConvBN(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "conv_bn") {} PDNode* operator()(PDNode* conv_input, const std::string& conv_type, bool with_eltwise_add); // declare operator node's name PATTERN_DECL_NODE(conv); PATTERN_DECL_NODE(batch_norm); PATTERN_DECL_NODE(eltwise); // ELEMENTWISE_ADD // CONV inputs PATTERN_DECL_NODE(conv_weight); // Filter // CONV outputs PATTERN_DECL_NODE(conv_out); // tmp // ELTWISE inputs PATTERN_DECL_NODE(eltwise_y_in); // ELTWISE outputs PATTERN_DECL_NODE(eltwise_out); // tmp // BN inputs PATTERN_DECL_NODE(bn_scale); PATTERN_DECL_NODE(bn_bias); PATTERN_DECL_NODE(bn_mean); PATTERN_DECL_NODE(bn_variance); // BN outputs PATTERN_DECL_NODE(bn_out); // Out PATTERN_DECL_NODE(bn_mean_out); PATTERN_DECL_NODE(bn_variance_out); PATTERN_DECL_NODE(bn_saved_mean); PATTERN_DECL_NODE(bn_saved_variance); }; // Conv with Activation // op: conv + activation // named nodes: // conv_input, conv_weight, // conv_out, conv, // activation_out, activation struct ConvActivation : public PatternBase { ConvActivation(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "conv_activation") {} PDNode* operator()(PDNode* conv_input, std::string conv_type = "conv2d", std::string activation_type = "relu"); // declare operator node's name PATTERN_DECL_NODE(conv); PATTERN_DECL_NODE(activation); // declare variable node's name PATTERN_DECL_NODE(conv_weight); PATTERN_DECL_NODE(conv_out); PATTERN_DECL_NODE(activation_out); }; // SEQCONV with Elementwise_Add ReLU // op: seqconv + elementwise_add + relu // named nodes: // seqconv_input, seqconv_weight, // seqconv_out, seqconv, // elementwise_add_bias, elementwise_add_out, elementwise_add // relu_out, relu struct SeqConvEltAddRelu : public PatternBase { SeqConvEltAddRelu(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "seqconv_eltadd_relu") {} PDNode* operator()(PDNode* seqconv_input); // declare operator node's name PATTERN_DECL_NODE(seqconv); PATTERN_DECL_NODE(eltadd); PATTERN_DECL_NODE(relu); // declare variable node's name PATTERN_DECL_NODE(seqconv_weight); PATTERN_DECL_NODE(seqconv_out); PATTERN_DECL_NODE(eltadd_bias); PATTERN_DECL_NODE(eltadd_out); PATTERN_DECL_NODE(relu_out); }; // FC with bias // op: mul + elementwise_add // named nodes: // mul, elementwise_add // w, mul_out, bias, fc_out struct FC : public PatternBase { FC(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "fc") {} PDNode* operator()(PDNode* x, bool with_bias, bool with_relu); // declare operator node's name PATTERN_DECL_NODE(fc); PATTERN_DECL_NODE(mul); PATTERN_DECL_NODE(elementwise_add); PATTERN_DECL_NODE(relu); // declare variable node's name PATTERN_DECL_NODE(w); PATTERN_DECL_NODE(mul_out); // (x,w) -> mul_out PATTERN_DECL_NODE(bias); PATTERN_DECL_NODE(elementwise_add_out); PATTERN_DECL_NODE(relu_out); }; // MKL-DNN's FC with bias // op: fc // named node: // fc // w, bias, output struct FCMKLDNN : public PatternBase { FCMKLDNN(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "fc_mkldnn") {} PDNode* operator()(PDNode* x, bool with_bias); // declare operator node's name PATTERN_DECL_NODE(fc); // declare variable node's name PATTERN_DECL_NODE(input); PATTERN_DECL_NODE(weights); PATTERN_DECL_NODE(bias); PATTERN_DECL_NODE(output); }; // Embedding struct Embedding : public PatternBase { Embedding(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "embedding") {} PDNode* operator()(PDNode* x); // declare operator node's name PATTERN_DECL_NODE(lookup_table); // Inputs // PATTERN_DECL_NODE(Ids); PATTERN_DECL_NODE(W); // embeddings // Outputs PATTERN_DECL_NODE(Out); }; struct LSTM : public PatternBase { LSTM(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "lstm") {} PDNode* operator()(PDNode* x); // Operators PATTERN_DECL_NODE(lstm); // Inputs PATTERN_DECL_NODE(Input); PATTERN_DECL_NODE(H0); PATTERN_DECL_NODE(C0); PATTERN_DECL_NODE(Weight); PATTERN_DECL_NODE(Bias); // Outputs PATTERN_DECL_NODE(Hidden); PATTERN_DECL_NODE(Cell); PATTERN_DECL_NODE(BatchGate); PATTERN_DECL_NODE(BatchCellPreAct); }; struct GRU : public PatternBase { GRU(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "gru") {} PDNode* operator()(PDNode* x); // Operators PATTERN_DECL_NODE(gru); // Inputs PATTERN_DECL_NODE(Bias); PATTERN_DECL_NODE(Weight); // Outputs PATTERN_DECL_NODE(BatchGate); PATTERN_DECL_NODE(BatchResetHiddenPrev); PATTERN_DECL_NODE(BatchHidden); PATTERN_DECL_NODE(Hidden); }; // The following pattern is used to fuse batch_norm and act // formula: act(bn(x)) // op: batch_norm + act struct BatchNormAct : public PatternBase { BatchNormAct(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "bn_act") {} PDNode* operator()(PDNode* x, std::unordered_set acts); // declare operator node's name PATTERN_DECL_NODE(batch_norm); PATTERN_DECL_NODE(act); // declare variable node's name // BN inputs PATTERN_DECL_NODE(bn_scale); PATTERN_DECL_NODE(bn_bias); PATTERN_DECL_NODE(bn_variance); PATTERN_DECL_NODE(bn_mean); // BN outputs PATTERN_DECL_NODE(bn_mean_out); PATTERN_DECL_NODE(bn_variance_out); PATTERN_DECL_NODE(bn_saved_variance); PATTERN_DECL_NODE(bn_saved_mean); PATTERN_DECL_NODE(bn_reserve_space); PATTERN_DECL_NODE(bn_out); // ACT output PATTERN_DECL_NODE(act_out); }; // the backward of act(bn(x)) // op: batch_norm_grad + act_grad struct BatchNormActGrad : public PatternBase { BatchNormActGrad(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "bn_act_grad") {} // act_grad: in["Out", "Out@GRAD"], out["X@GRAD"] // bn_grad: in["X", "Y@GRAD", "Scale", "Bias", "SavedMean", "SavedVariance", // "ReserveSpace"], // out["X@GRAD", "Scale@GRAD", "Bias@GRAD"] PDNode* operator()(PDNode* x, std::unordered_set act_grad_types); // declare operator node's name PATTERN_DECL_NODE(act_grad); PATTERN_DECL_NODE(batch_norm_grad); // declare variable node's name PATTERN_DECL_NODE(act_out); PATTERN_DECL_NODE(d_itermediate_out); PATTERN_DECL_NODE(bn_x); PATTERN_DECL_NODE(bn_scale); PATTERN_DECL_NODE(bn_bias); PATTERN_DECL_NODE(bn_saved_mean); PATTERN_DECL_NODE(bn_saved_variance); PATTERN_DECL_NODE(bn_reserve_space); PATTERN_DECL_NODE(d_bn_x); PATTERN_DECL_NODE(d_bn_scale); PATTERN_DECL_NODE(d_bn_bias); }; // // \brief Pattern looking for batch_norm and a directly following activation // operator. // // \note Currently only ReLU is supported as an activation function. // Formula: act(bn(x)) // Op: batch_norm + act struct BatchNormActOneDNN : public PatternBase { BatchNormActOneDNN(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "bn_act_onednn") {} PDNode* operator()(const std::string& act_type); // declare operator node's name PATTERN_DECL_NODE(bn_in); PATTERN_DECL_NODE(batch_norm); PATTERN_DECL_NODE(act); PATTERN_DECL_NODE(bn_out); PATTERN_DECL_NODE(act_out); }; // The following pattern is used to fuse batch_norm, elewise_add, and act // formula: act(bn(x) + z) // op: batch_norm + elewise_add + act struct BatchNormAddAct : public PatternBase { BatchNormAddAct(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "bn_add_act") {} PDNode* operator()(PDNode* x, std::unordered_set acts); // declare operator node's name PATTERN_DECL_NODE(batch_norm); PATTERN_DECL_NODE(elewise_add); PATTERN_DECL_NODE(act); // declare variable node's name // BN inputs PATTERN_DECL_NODE(bn_scale); PATTERN_DECL_NODE(bn_bias); // BN outputs PATTERN_DECL_NODE(bn_mean_out); PATTERN_DECL_NODE(bn_variance_out); PATTERN_DECL_NODE(bn_saved_variance); PATTERN_DECL_NODE(bn_saved_mean); PATTERN_DECL_NODE(bn_reserve_space); PATTERN_DECL_NODE(bn_out); // Elewise_Add input PATTERN_DECL_NODE(elewise_add_in); // Elewise_Add output PATTERN_DECL_NODE(elewise_add_out); // ACT output PATTERN_DECL_NODE(act_out); }; // the backward of act(bn(x) + z) // op: batch_norm_grad + elewise_add_grad + act_grad struct BatchNormAddActGrad : public PatternBase { BatchNormAddActGrad(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "bn_add_act_grad") {} // act_grad: in["Out", "Out@GRAD"], out["X@GRAD"] // elewise_add_grad: in["Out@GRAD"], out["X@GRAD", "Y@GRAD"] // bn_grad: in["X", "Z", "Y@GRAD", "Scale", "Bias", "SavedMean", // "SavedVariance", // "ReserveSpace"], // out["X@GRAD", "Z@GRAD", "Scale@GRAD", "Bias@GRAD"] PDNode* operator()(PDNode* x, std::unordered_set act_grad_types); // declare operator node's name PATTERN_DECL_NODE(act_grad); PATTERN_DECL_NODE(elewise_add_grad); PATTERN_DECL_NODE(batch_norm_grad); // declare variable node's name PATTERN_DECL_NODE(act_out); PATTERN_DECL_NODE(d_act_x); PATTERN_DECL_NODE(d_elewise_add_in); PATTERN_DECL_NODE(d_bn_out); PATTERN_DECL_NODE(bn_x); PATTERN_DECL_NODE(bn_scale); PATTERN_DECL_NODE(bn_bias); PATTERN_DECL_NODE(bn_saved_mean); PATTERN_DECL_NODE(bn_saved_variance); PATTERN_DECL_NODE(bn_reserve_space); PATTERN_DECL_NODE(d_bn_x); PATTERN_DECL_NODE(d_bn_scale); PATTERN_DECL_NODE(d_bn_bias); }; // The following patterns are used to fuse elewise_add and act // formula: act(ele_add(x, y)) // op: elementwise_add + act // named nodes: elementwise_add, act // ele_x, ele_y, elewise_add_out, act_out struct ElewiseAddAct : public PatternBase { ElewiseAddAct(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "elewise_add_act") {} PDNode* operator()(PDNode* x, std::unordered_set acts); // declare operator node's name PATTERN_DECL_NODE(ele_add); PATTERN_DECL_NODE(act); // declare variable node's name PATTERN_DECL_NODE(elewise_add_out); PATTERN_DECL_NODE(ele_y); PATTERN_DECL_NODE(act_out); }; // formula: ele_add(x, act(y)) // op: elementwise_add + act // named nodes: elementwise_add, act // act_in, act_out, ele_x, elewise_add_out struct ActElewiseAdd : public PatternBase { ActElewiseAdd(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "act_elewise_add") {} PDNode* operator()(PDNode* x, std::unordered_set acts); // declare operator node's name PATTERN_DECL_NODE(act); PATTERN_DECL_NODE(ele_add); // declare variable node's name PATTERN_DECL_NODE(act_out); PATTERN_DECL_NODE(ele_x); PATTERN_DECL_NODE(elewise_add_out); }; // the backward of act(ele_add(x, y)) // the act is inplace. // op: elementwise_add_grad + act_grad // named nodes: elementwise_add_grad, act_grad // act_out, act_out_g, ele_y, d_itermediate_out, d_ele_x, d_ele_y struct ElewiseAddActInplaceGrad : public PatternBase { ElewiseAddActInplaceGrad(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "elewise_add_act_grad1") {} // act_grad: in["Out", "Out@GRAD"], out["X@GRAD"] // ele_add_grad: in["Y", "Out@GRAD"], out["X@GRAD", "Y@GRAD"] PDNode* operator()(PDNode* x, std::unordered_set acts); // declare operator node's name PATTERN_DECL_NODE(act_grad); PATTERN_DECL_NODE(ele_add_grad); // declare variable node's name PATTERN_DECL_NODE(act_out); PATTERN_DECL_NODE(d_itermediate_out); PATTERN_DECL_NODE(d_ele_x); PATTERN_DECL_NODE(d_ele_y); PATTERN_DECL_NODE(ele_y); }; // Conv with Elementwise_add as bias // op: conv + elementwise_add // named nodes: // conv_input, conv_weight, // conv_out, conv, // eltwise_bias, eltwise_out, // elementwise_add struct ConvBias : public PatternBase { ConvBias(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "conv_bias") {} PDNode* operator()(PDNode* conv_input, std::string conv_type = "conv2d"); // declare operator node's name PATTERN_DECL_NODE(conv); PATTERN_DECL_NODE(eltwise); // declare variable node's name PATTERN_DECL_NODE(conv_weight); PATTERN_DECL_NODE(conv_out); PATTERN_DECL_NODE(eltwise_bias); PATTERN_DECL_NODE(eltwise_out); }; // Convolution op // Forward pass for convolution. // conv_input, conv_bias and conv_filter are inputs. // conv_output is a result of the operator. // residual_data is data used by skip connection. // If residual connection fusion is on, the formula is: // conv_output = conv_op(conv_filter, conv_input, conv_bias) // + conv_residual_data // If the fusion is off, conv_residual_data is not added. struct Conv : public PatternBase { Conv(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "convolution") {} PDNode* operator()(); PATTERN_DECL_NODE(conv_op); PATTERN_DECL_NODE(conv_input); PATTERN_DECL_NODE(conv_filter); PATTERN_DECL_NODE(conv_residual_data); PATTERN_DECL_NODE(conv_output); }; // Convolution op with residual data struct ConvResidual : public PatternBase { ConvResidual(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "conv_residual") {} PDNode* operator()(bool with_residual_data); PATTERN_DECL_NODE(conv_op); PATTERN_DECL_NODE(conv_input); PATTERN_DECL_NODE(conv_filter); PATTERN_DECL_NODE(conv_residual_data); PATTERN_DECL_NODE(conv_output); }; // Pool op // Forward pass for pooling. // pool_input is the input. // pool_output is a result of the operator. struct Pool : public PatternBase { Pool(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "pooling") {} PDNode* operator()(); PATTERN_DECL_NODE(pool_op); PATTERN_DECL_NODE(pool_input); PATTERN_DECL_NODE(pool_output); }; // ElementwiseAdd used in residual connections. // y_var is used and convolution output. // The operator is removed, when residual // connection fusion is on. struct ElementwiseAdd : public PatternBase { ElementwiseAdd(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "elementwise_add") {} PDNode* operator()(PDNode* x_var, PDNode* y_var); PATTERN_DECL_NODE(elementwise_add_op); PATTERN_DECL_NODE(elementwise_add_x); PATTERN_DECL_NODE(elementwise_add_y); PATTERN_DECL_NODE(elementwise_add_out); }; // Transpose op // Forward pass for transpose. // transpose_out is a result of the operator. struct Transpose : public PatternBase { Transpose(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "transpose2") {} PDNode* operator()(); PATTERN_DECL_NODE(prev_op); PATTERN_DECL_NODE(transpose_in); PATTERN_DECL_NODE(transpose_op); PATTERN_DECL_NODE(transpose_out); PATTERN_DECL_NODE(next_op); }; // Reshape op // Forward pass for reshape. // reshape_out is a result of the operator. struct Reshape : public PatternBase { Reshape(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "reshape2") {} PDNode* operator()(); PATTERN_DECL_NODE(prev_op); PATTERN_DECL_NODE(reshape_in); PATTERN_DECL_NODE(reshape_op); PATTERN_DECL_NODE(reshape_out); PATTERN_DECL_NODE(next_op); }; // Matmul op // Forward pass for matmul. // matmul_out is a result of the operator. struct Matmul : public PatternBase { Matmul(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "reshape2") {} PDNode* operator()(); PATTERN_DECL_NODE(prev_op_x); PATTERN_DECL_NODE(prev_op_y); PATTERN_DECL_NODE(matmul_in_x); PATTERN_DECL_NODE(matmul_in_y); PATTERN_DECL_NODE(matmul_op); PATTERN_DECL_NODE(matmul_out); }; // Concat op // Forward pass for concat. // concat_out is a result of the operator. struct Concat : public PatternBase { Concat(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "concat") {} PDNode* operator()(); PATTERN_DECL_NODE(concat_op); PATTERN_DECL_NODE(concat_out); }; // Concat + ReLU // named nodes: // concat_op, concat_out, relu_op, relu_out struct ConcatReLU : public PatternBase { ConcatReLU(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "concat_relu") {} PDNode* operator()(); PATTERN_DECL_NODE(concat_op); PATTERN_DECL_NODE(concat_out); PATTERN_DECL_NODE(relu_op); PATTERN_DECL_NODE(relu_out); }; // Conv + Concat + ReLU // named nodes: // conv_op, conv_out // concat_op, concat_out, relu_op, relu_out struct ConvConcatReLU : public PatternBase { ConvConcatReLU(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "conv_concat_relu") {} PDNode* operator()(); PATTERN_DECL_NODE(conv_op); PATTERN_DECL_NODE(conv_out); PATTERN_DECL_NODE(concat_op); PATTERN_DECL_NODE(concat_out); PATTERN_DECL_NODE(relu_op); PATTERN_DECL_NODE(relu_out); }; // Op + Requant // named nodes: // any_op, any_out // requant_op, requant_out struct OpRequant : public PatternBase { OpRequant(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "op_requant") {} PDNode* operator()(); PATTERN_DECL_NODE(any_op); PATTERN_DECL_NODE(requant_in); PATTERN_DECL_NODE(requant_op); PATTERN_DECL_NODE(requant_out); }; // Requant + Op // named nodes: // requant_in, requant_op, // requant_out, any_op struct RequantOp : public PatternBase { RequantOp(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "requant_op") {} PDNode* operator()(); PATTERN_DECL_NODE(any_op); PATTERN_DECL_NODE(requant_in); PATTERN_DECL_NODE(requant_op); PATTERN_DECL_NODE(requant_out); }; // Op + Dequant // named nodes: // any_op, dequant_in // dequant_op, dequant_out struct OpDequant : public PatternBase { OpDequant(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "op_dequant") {} PDNode* operator()(); PATTERN_DECL_NODE(any_op); PATTERN_DECL_NODE(dequant_in); PATTERN_DECL_NODE(dequant_op); PATTERN_DECL_NODE(dequant_out); }; // Dequantize + Scale struct DequantScale : public PatternBase { DequantScale(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "dequant_scale") {} PDNode* operator()(); PATTERN_DECL_NODE(dequant_op); PATTERN_DECL_NODE(dequant_out); PATTERN_DECL_NODE(scale_op); PATTERN_DECL_NODE(scale_out); }; // Scale + Matmul struct ScaleMatmul : public PatternBase { ScaleMatmul(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "scale_matmul") {} PDNode* operator()(); PATTERN_DECL_NODE(scale_in); PATTERN_DECL_NODE(scale_op); PATTERN_DECL_NODE(scale_out); PATTERN_DECL_NODE(matmul_op); }; // PriorBox operator // operator: prior_box_op // inputs: prior_box_input, prior_box_image // outputs: prior_box_boxes, prior_box_variances struct PriorBox : public PatternBase { PriorBox(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "PriorBox") {} PDNode* operator()(); PATTERN_DECL_NODE(prior_box_op); PATTERN_DECL_NODE(prior_box_input); PATTERN_DECL_NODE(prior_box_image); PATTERN_DECL_NODE(prior_box_boxes); PATTERN_DECL_NODE(prior_box_variances); }; // Conv + ElementwiseAdd + an activation // This pattern can futher fuse the conv related ops after the conv+bn fusion. struct ConvElementwiseaddAct : public PatternBase { ConvElementwiseaddAct(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "conv_elementwiseadd_act") {} PDNode* operator()(PDNode* conv_in); PATTERN_DECL_NODE(conv_op); PATTERN_DECL_NODE(conv_out); PATTERN_DECL_NODE(conv_filter); PATTERN_DECL_NODE(elementwise_add_op); PATTERN_DECL_NODE(elementwise_add_in_y); // input PATTERN_DECL_NODE(elementwise_add_out); PATTERN_DECL_NODE(act_op); PATTERN_DECL_NODE(act_out); }; // Conv + ElementwiseAdd + ElementwiseAdd + Activation struct ConvElementwiseadd2Act : public PatternBase { ConvElementwiseadd2Act(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "conv_elementwiseadd2_elementwiseadd_act") {} PDNode* operator()(PDNode* conv_in); PATTERN_DECL_NODE(conv_op); PATTERN_DECL_NODE(conv_filter); PATTERN_DECL_NODE(conv_out); PATTERN_DECL_NODE(elementwise_add_op); PATTERN_DECL_NODE(elementwise_add_in_y); // input PATTERN_DECL_NODE(elementwise_add_out); PATTERN_DECL_NODE(elementwise_add_op_1); PATTERN_DECL_NODE(elementwise_add_in_y_1); // input PATTERN_DECL_NODE(elementwise_add_out_1); PATTERN_DECL_NODE(act_op); PATTERN_DECL_NODE(act_out); }; // Conv + ElementwiseAdd // This pattern should be used after ConvElementwiseadd2Act or // ConvElementwiseadd pass struct ConvElementwiseadd : public PatternBase { ConvElementwiseadd(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "conv_elementwiseadd") {} PDNode* operator()(PDNode* conv_in); PATTERN_DECL_NODE(conv_op); PATTERN_DECL_NODE(conv_out); PATTERN_DECL_NODE(conv_filter); PATTERN_DECL_NODE(elementwise_add_op); PATTERN_DECL_NODE(elementwise_add_in_y); PATTERN_DECL_NODE(elementwise_add_out); }; // Conv with affine_channel // op: conv + (elementwise_add +) affine_channel // named nodes: // conv_weight, conv_out, conv, // ac_x, ac_scale, ac_bias // affine_channel, ac_out struct ConvAffineChannel : public PatternBase { ConvAffineChannel(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "conv_affine_channel") {} PDNode* operator()(PDNode* conv_input, bool with_eltwise_add); // declare operator node's name PATTERN_DECL_NODE(conv); PATTERN_DECL_NODE(affine_channel); PATTERN_DECL_NODE(eltwise); // ELEMENTWISE_ADD // CONV inputs PATTERN_DECL_NODE(conv_weight); // Filter // CONV outputs PATTERN_DECL_NODE(conv_out); // tmp // ELTWISE inputs PATTERN_DECL_NODE(eltwise_y_in); // ELTWISE outputs PATTERN_DECL_NODE(eltwise_out); // tmp // AC(Affine_Channel) inputs PATTERN_DECL_NODE(ac_scale); PATTERN_DECL_NODE(ac_bias); // AC outputs PATTERN_DECL_NODE(ac_out); // Out }; // Dequantize + Quantize + anyOP // This pattern is used for squashing the dequantize-quantize pairs. struct DequantQuantAny : public PatternBase { DequantQuantAny(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "dequant_quant_any") {} PDNode* operator()(); PATTERN_DECL_NODE(dequant_in); PATTERN_DECL_NODE(dequant_op); PATTERN_DECL_NODE(dequant_out); PATTERN_DECL_NODE(quant_op); PATTERN_DECL_NODE(quant_out); PATTERN_DECL_NODE(next_op); }; // Dequantize + anyOP // This quantize is used for getting number of ops the Dequantize's // output is an input to. struct DequantAny : public PatternBase { DequantAny(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "dequant_any") {} PDNode* operator()(); PATTERN_DECL_NODE(dequant_op); PATTERN_DECL_NODE(dequant_out); PATTERN_DECL_NODE(next_op); }; // anyOp + more then one quantize op // This pattern is used for squashing multiple quantize with the same scale. struct MultipleQuantize : public PatternBase { MultipleQuantize(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "multiple_quantize") {} PDNode* operator()(); PATTERN_DECL_NODE(prev_out); }; struct QuantizePlacement : public PatternBase { QuantizePlacement(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "quantize_placement") {} PDNode* operator()( const std::unordered_set& quantize_enabled_op_types); PATTERN_DECL_NODE(op); }; struct Bfloat16Placement : public PatternBase { Bfloat16Placement(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "bfloat16_placement") {} PDNode* operator()( const std::unordered_set& bfloat16_enabled_op_types); PATTERN_DECL_NODE(op); }; struct OrphanedBfloat16 : public PatternBase { OrphanedBfloat16(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "orphaned_bfloat16") {} PDNode* operator()(); PATTERN_DECL_NODE(prev_op); PATTERN_DECL_NODE(prev_out); PATTERN_DECL_NODE(op); PATTERN_DECL_NODE(op_out); PATTERN_DECL_NODE(next_op); }; struct LastBfloat16Ops : public PatternBase { LastBfloat16Ops(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "last_bfloat16_ops") {} PDNode* operator()(); PATTERN_DECL_NODE(op); PATTERN_DECL_NODE(op_out); PATTERN_DECL_NODE(next_op); }; struct FirstBfloat16Ops : public PatternBase { FirstBfloat16Ops(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "first_bfloat16_ops") {} PDNode* operator()(); PATTERN_DECL_NODE(prev_op); PATTERN_DECL_NODE(op_in); PATTERN_DECL_NODE(op); }; // Pattern used for enforcing inplace computation for in-place computation // supporting DNNL ops. softmax, batch_norm and layer_norm struct MKLDNNInPlace : public PatternBase { MKLDNNInPlace(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "mkldnn_inplace") {} PDNode* operator()(); // MKL-DNN's in-place ops: BatchNorm, Softmax, Elementwise_add PATTERN_DECL_NODE(inplace_to_be_op); PATTERN_DECL_NODE(inplace_to_be_op_in); PATTERN_DECL_NODE(inplace_to_be_op_out); PATTERN_DECL_NODE(next_op); PATTERN_DECL_NODE(next_op_out); }; struct TransposeFlattenConcat : public PatternBase { TransposeFlattenConcat(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "transpose_flatten_concat") {} PDNode* operator()(std::vector conv_inputs, int times); std::string GetNodeName(const std::string& op_type) { return PDNodeName(name_scope_, repr_, id_, op_type); } PDNode* GetPDNode(const std::string& op_type) { return pattern->RetrieveNode(GetNodeName(op_type)); } }; struct DeleteQuantOpFuse : public PatternBase { DeleteQuantOpFuse(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "delete_quant_fuse") {} void operator()(PDNode* input_act_node, const std::string& quant_type); std::string GetNodeName(const std::string& op_type) { return PDNodeName(name_scope_, repr_, id_, op_type); } PDNode* GetPDNode(const std::string& op_type) { return pattern->RetrieveNode(GetNodeName(op_type)); } }; struct DequantOpFuse : public PatternBase { DequantOpFuse(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "dequant_fuse") {} void operator()(PDNode* quant_op_input, const std::string& quantized_op_type, const std::string& dequant_type, const std::string& weight_name); std::string GetNodeName(const std::string& op_type) { return PDNodeName(name_scope_, repr_, id_, op_type); } PDNode* GetPDNode(const std::string& op_type) { return pattern->RetrieveNode(GetNodeName(op_type)); } }; struct ShuffleChannelPattern : public PatternBase { ShuffleChannelPattern(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "shufflechannel_pattern") {} void operator()(PDNode* reshape1_in); PATTERN_DECL_NODE(reshape1_op); PATTERN_DECL_NODE(reshape1_out); PATTERN_DECL_NODE(transpose_op); PATTERN_DECL_NODE(transpose_out); PATTERN_DECL_NODE(reshape2_op); PATTERN_DECL_NODE(reshape2_out); }; struct DeleteQuantDequantOpPattern : public PatternBase { DeleteQuantDequantOpPattern(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "delete_quantdequant_op_pattern") {} void operator()(); PATTERN_DECL_NODE(any_op_out); PATTERN_DECL_NODE(quant_dequant_op_inscale); PATTERN_DECL_NODE(quant_dequant_op); PATTERN_DECL_NODE(quant_dequant_op_outscale); PATTERN_DECL_NODE(quant_dequant_op_out); PATTERN_DECL_NODE(any_op2); }; // Reshape + Transpose + Matmul // named nodes: // reshape_op, reshape_out, reshape_xshape, // transpose_op, transpose_out, transpose_xshape, // matmul_op, matmul_out struct ReshapeTransposeMatmulPattern : public PatternBase { ReshapeTransposeMatmulPattern(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "reshape_transpose_matmul") {} PDNode* operator()(bool with_reshape_xshape, bool with_transpose_xshape); PATTERN_DECL_NODE(reshape_in); PATTERN_DECL_NODE(reshape_op); PATTERN_DECL_NODE(reshape_out); PATTERN_DECL_NODE(reshape_xshape); PATTERN_DECL_NODE(transpose_op); PATTERN_DECL_NODE(transpose_out); PATTERN_DECL_NODE(transpose_xshape); PATTERN_DECL_NODE(matmul_op); PATTERN_DECL_NODE(matmul_out); }; // Matmul + Transpose + Reshape struct MatmulTransposeReshapePattern : public PatternBase { MatmulTransposeReshapePattern(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "matmul_transpose_reshape") {} PDNode* operator()(); PATTERN_DECL_NODE(matmul_op); PATTERN_DECL_NODE(matmul_out); PATTERN_DECL_NODE(transpose_op); PATTERN_DECL_NODE(transpose_out); PATTERN_DECL_NODE(transpose_out_xshape); PATTERN_DECL_NODE(reshape_op); PATTERN_DECL_NODE(reshape_out); PATTERN_DECL_NODE(reshape_out_xshape); }; // fusion_gru op // Forward pass for fusion_gru. // fusion_gru out is a result of the operator. struct FusionGru : public PatternBase { FusionGru(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "fusion_gru") {} PDNode* operator()(); PATTERN_DECL_NODE(op); PATTERN_DECL_NODE(x); PATTERN_DECL_NODE(weight_h); PATTERN_DECL_NODE(weight_x); PATTERN_DECL_NODE(out); }; // two subsequent bi_fusion_gru ops // Forward pass for fusion of two subsequent fusion_gru ops. // Hidden of the last fusion_gru op is a result of the operator(). struct MultiGruSeq : public PatternBase { MultiGruSeq(PDPattern* pattern, const std::string& name_scope) : PatternBase(pattern, name_scope, "multi_gru_seq") {} PDNode* operator()(); PATTERN_DECL_NODE(x); PATTERN_DECL_NODE(gru1); PATTERN_DECL_NODE(wx11); PATTERN_DECL_NODE(wx12); PATTERN_DECL_NODE(wh11); PATTERN_DECL_NODE(wh12); PATTERN_DECL_NODE(b11); PATTERN_DECL_NODE(b12); PATTERN_DECL_NODE(h1); PATTERN_DECL_NODE(gru2); PATTERN_DECL_NODE(wx21); PATTERN_DECL_NODE(wx22); PATTERN_DECL_NODE(wh21); PATTERN_DECL_NODE(wh22); PATTERN_DECL_NODE(b21); PATTERN_DECL_NODE(b22); PATTERN_DECL_NODE(h2); }; } // namespace patterns // Link two ir::Nodes from each other. #define IR_NODE_LINK_TO(a, b) \ a->outputs.push_back(b); \ b->inputs.push_back(a); // Set the out_var as the output of the op #define IR_OP_VAR_LINK(op, out_var) \ op->outputs.push_back(out_var); \ out_var->inputs.clear(); \ out_var->inputs.push_back(op); } // namespace ir } // namespace framework } // namespace paddle