fea/refine fuse (#13076)

paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc

@@ -59,7 +59,7 @@ void FindWhileOp(Graph* graph) {
 
   auto handle = [&](const GraphPatternDetector::subgraph_t& subgraph,
                     Graph* g) {
-    auto* while_pat_node = gpd.pattern().RetriveNode("while");
+    auto* while_pat_node = gpd.pattern().RetrieveNode("while");
     auto* while_node = subgraph.at(while_pat_node);
     marked_nodes.insert(while_node);
   };

paddle/fluid/framework/ir/fc_fuse_pass.cc

@@ -31,77 +31,34 @@ bool VarOutLinksToOp(Node* node, const std::string& op_type) {
 }
 
 void BuildFCPattern(PDPattern* pattern) {
-  // make sure the selected MUL op has one input argument is a parameter.
+  // Create Operators
-  auto* mul_parameter_var = pattern->NewNode(
+  auto* mul_op = pattern->NewNode("mul")->assert_is_op("mul");
-      [](Node* node) {
+  auto* elementwise_add_op =
-        return node->IsVar() && node->outputs.size() == 1UL &&
+      pattern->NewNode("elementwise_add")->assert_is_op("elementwise_add");
-               node->outputs.front()->Op()->Type() == "mul" && node->Var() &&
+  // Create variables
-               node->Var()->Persistable();  // check is a parameter
+  // w
-      },
+  auto* mul_weight_var = pattern->NewNode("mul_weight")
-      "mul_weight" /*name*/);
+                             ->AsInput()
-
+                             ->assert_is_op_nth_input("mul", "Y", 0);
-  auto* mul_tmp_input_var = pattern->NewNode(
+  // x
-      [](Node* node) {
+  auto* mul_tmp_var = pattern->NewNode("mul_tmp_var")
-        bool result =
+                          ->AsInput()
-            node->IsVar() && node->outputs.size() >= 1UL && node->Var() &&
+                          ->assert_is_op_nth_input("mul", "X", 0);
-            !node->Var()->Persistable();  // this input is not an parameter.
+  // intermediate variable, will be removed in the IR after fuse.
-        if (!result) return false;
+  auto* mul_out_var = pattern->NewNode("mul_out")
-        // check whether one output is MUL op.
+                          ->AsIntermediate()
-        for (auto* op : node->outputs) {
+                          ->assert_is_only_output_of_op("mul")
-          if (op->IsOp() && op->Op()->Type() == "mul") return true;
+                          ->assert_is_op_input("elementwise_add");
-        }
+  // bias
-        return false;
+  auto* elementwise_add_tmp_var = pattern->NewNode("elementwise_add_tmpvar")
-      },
+                                      ->assert_is_op_input("elementwise_add")
-      "mul_tmp_var" /*name*/);
+                                      ->AsInput();
-
+  // output
-  // select a MUL op
+  auto* elementwise_add_out_var = pattern->NewNode("elementwise_add_out")
-  auto* mul_op = pattern->NewNode(
+                                      ->AsOutput()
-      [](Node* node) {
+                                      ->assert_is_op_output("elementwise_add");
-        return node->IsOp() &&               // start from an Op
+
-               node->Op()->Type() == "mul";  // type is mul
+  mul_op->LinksFrom({mul_weight_var, mul_tmp_var}).LinksTo({mul_out_var});
-        // the output should be consumed only by one element_add, that check
-        // leaves in a Var PDNode.
-      },
-      "mul" /*name*/);
-
-  // make sure the MUL op's output has only one consumer and links to an
-  // ELEMENTWISE_ADD op.
-  auto* mul_out_var = pattern->NewNode(
-      [](Node* node) {
-        return node->IsVar() &&                  // starts from a Var
-               node->outputs.size() == 1UL &&    // only has one consumer
-               node->outputs.front()->IsOp() &&  // check basic logic
-               node->Var() &&                    // not a ControlDepVar
-               node->outputs.front()->Op()->Type() ==
-                   "elementwise_add";  // a very strong validation
-      },
-      "mul_out");
-  // this check is not essential, just to make the corresponding variable Node
-  // retrival easier.
-  auto* elementwise_add_tmp_var = pattern->NewNode(
-      [](Node* node) {
-        return node->IsVar() && node->outputs.size() >= 1UL && node->Var() &&
-               VarOutLinksToOp(node, "elementwise_add");
-      },
-      "elementwise_add_tmpvar");
-
-  // select an ELEMENTWISE_ADD op
-  auto* elementwise_add_op = pattern->NewNode(
-      [](Node* node) {
-        return node->IsOp() && node->Op()->Type() == "elementwise_add";
-      },
-      "elementwise_add" /*name*/);
-
-  // get the ELEMENTWISE_ADD op's output
-  auto* elementwise_add_out_var = pattern->NewNode(
-      [](Node* node) {
-        return node->IsVar() && node->inputs.size() == 1UL && node->Var() &&
-               node->inputs.front()->Op()->Type() == "elementwise_add";
-      },
-      "elementwise_add_out");
-
-  mul_op->LinksFrom({mul_parameter_var, mul_tmp_input_var})
-      .LinksTo({mul_out_var});
   elementwise_add_op->LinksFrom({mul_out_var, elementwise_add_tmp_var})
       .LinksTo({elementwise_add_out_var});
 }
@@ -120,18 +77,20 @@ bool LinksReplace(std::vector<Node*>* links, Node* from, Node* to) {
 std::unique_ptr<ir::Graph> FCFusePass::ApplyImpl(
     std::unique_ptr<ir::Graph> graph) const {
   PADDLE_ENFORCE(graph.get());
+  FusePassBase::Init("fc", graph.get());
 
   std::unordered_set<Node*> nodes2delete;
 
   GraphPatternDetector gpd;
   BuildFCPattern(gpd.mutable_pattern());
 
-#define GET_NODE(id)                                             \
+#define GET_NODE(id)                                              \
-  PADDLE_ENFORCE(subgraph.count(gpd.pattern().RetriveNode(#id)), \
+  PADDLE_ENFORCE(subgraph.count(gpd.pattern().RetrieveNode(#id)), \
-                 "pattern has no Node called %s", #id);          \
+                 "pattern has no Node called %s", #id);           \
-  auto* id = subgraph.at(gpd.pattern().RetriveNode(#id));        \
+  auto* id = subgraph.at(gpd.pattern().RetrieveNode(#id));        \
   PADDLE_ENFORCE_NOT_NULL(id, "subgraph has no node %s", #id);
 
+  int found_fc_count = 0;
   auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
                      Graph* g) {
     VLOG(4) << "handle FC fuse";
@@ -176,10 +135,13 @@ std::unique_ptr<ir::Graph> FCFusePass::ApplyImpl(
     graph->RemoveNode(mul);
     graph->RemoveNode(elementwise_add);
     graph->RemoveNode(mul_out);  // tmp variable
+
+    found_fc_count++;
   };
 
   gpd(graph.get(), handler);
 
+  AddStatis(found_fc_count);
   return graph;
 }
 

paddle/fluid/framework/ir/fc_fuse_pass.h

@@ -12,6 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
 #include "paddle/fluid/framework/ir/graph.h"
 #include "paddle/fluid/framework/ir/graph_pattern_detector.h"
 #include "paddle/fluid/framework/ir/pass.h"
@@ -23,7 +24,7 @@ namespace ir {
 /*
  * Fuse the MUL and ELEMENTWISE_ADD to a FCOp.
  */
-class FCFusePass : public Pass {
+class FCFusePass : public FusePassBase {
  public:
   virtual ~FCFusePass() {}
 

paddle/fluid/framework/ir/fc_fuse_pass_tester.cc

@@ -25,8 +25,13 @@ void SetOp(ProgramDesc* prog, const std::string& type,
            const std::vector<std::string>& outputs) {
   auto* op = prog->MutableBlock(0)->AppendOp();
   op->SetType(type);
-  op->SetInput("Xs", inputs);
+  if (type == "mul") {
-  op->SetOutput("Ys", outputs);
+    op->SetInput("X", {inputs[0]});
+    op->SetInput("Y", {inputs[1]});
+  } else if (type == "elementwise_add") {
+    op->SetInput("X", inputs);
+  }
+  op->SetOutput("Out", outputs);
 }
 
 // a->OP0->b

paddle/fluid/framework/ir/fc_lstm_fuse_pass.cc

@@ -36,7 +36,7 @@ std::unique_ptr<ir::Graph> FCLstmFusePass::ApplyImpl(
   auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
                      Graph* g) {
 
-    auto* id = subgraph.at(gpd.pattern().RetriveNode("any_node"));
+    auto* id = subgraph.at(gpd.pattern().RetrieveNode("any_node"));
     marked_nodes.insert(id);
   };
   gpd(graph.get(), handler);
@@ -64,9 +64,9 @@ std::unique_ptr<ir::Graph> FCLstmFusePass::ApplyImpl(
 #undef GET_NODE
 #undef SET_IN
 
-    LOG(INFO) << "hidden_n: " << hidden_n->Name();
+    VLOG(4) << "hidden_n: " << hidden_n->Name();
-    LOG(INFO) << "cell: " << cell_n->Name();
+    VLOG(4) << "cell: " << cell_n->Name();
-    LOG(INFO) << "xx: " << xx_n->Name();
+    VLOG(4) << "xx: " << xx_n->Name();
 
     op_desc.SetInput("H0", {});
     op_desc.SetInput("C0", {});

paddle/fluid/framework/ir/fuse_pass_base.h

@@ -22,21 +22,37 @@ namespace paddle {
 namespace framework {
 namespace ir {
 
-static const char kParamScopeAttr[] = "param_scope";
+static const char kParamScopeAttr[] = "__param_scope__";
+static const char kFuseStatisAttr[] = "__fuse_statis__";
 
 class FusePassBase : public Pass {
  public:
-  void Init(Graph* graph) const { graph_ = graph; }
+  void Init(const std::string& repr, Graph* graph) const {
+    repr_ = repr;
+    graph_ = graph;
+  }
 
   Scope* param_scope() const {
     PADDLE_ENFORCE(graph_->Has(kParamScopeAttr));
     return graph_->Get<framework::Scope*>(kParamScopeAttr);
   }
 
+  void AddStatis(int count_of_fused) const {
+    PADDLE_ENFORCE(graph_);
+    PADDLE_ENFORCE(!repr_.empty());
+    if (!graph_->Has(kFuseStatisAttr)) {
+      graph_->Set(kFuseStatisAttr, new std::unordered_map<std::string, int>);
+    }
+    auto& info =
+        graph_->Get<std::unordered_map<std::string, int>>(kFuseStatisAttr);
+    info[repr_] = count_of_fused;
+  }
+
   virtual ~FusePassBase() {}
 
  protected:
   mutable Graph* graph_;
+  mutable std::string repr_;
 };
 
 }  // namespace ir

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -27,6 +27,19 @@ namespace ir {
 
 size_t PDPattern::id_ = 0UL;
 
+PDNode* PDPattern::NewNode(const std::string& name) {
+  if (!name.empty()) {
+    PADDLE_ENFORCE_EQ(node_map_.count(name), 0,
+                      "PDNode's name should be unique, get duplicate [%s]",
+                      name);
+  }
+
+  nodes_.emplace_back(new PDNode(this, name));
+  auto* cur = nodes_.back().get();
+  node_map_[name] = cur;
+  return cur;
+}
+
 PDNode* PDPattern::NewNode(PDNode::teller_t&& teller, const std::string& name) {
   if (!name.empty()) {
     PADDLE_ENFORCE_EQ(node_map_.count(name), 0,
@@ -40,7 +53,7 @@ PDNode* PDPattern::NewNode(PDNode::teller_t&& teller, const std::string& name) {
   return cur;
 }
 
-PDNode* PDPattern::RetriveNode(const std::string& id) const {
+PDNode* PDPattern::RetrieveNode(const std::string& id) const {
   auto it = node_map_.find(id);
   if (it == node_map_.end()) {
     return nullptr;
@@ -62,7 +75,9 @@ void GraphPatternDetector::operator()(Graph* graph,
   auto subgraphs = DetectPatterns();
   UniquePatterns(&subgraphs);
   RemoveOverlappedMatch(&subgraphs);
+  ValidateByNodeRole(&subgraphs);
 
+  if (subgraphs.empty()) return;
   LOG(INFO) << "detect " << subgraphs.size() << " subgraph matches the pattern";
   int id = 0;
   for (auto& g : subgraphs) {
@@ -83,10 +98,54 @@ bool GraphPatternDetector::MarkPDNodesInGraph(const ir::Graph& graph) {
       }
     }
   }
+  // Check to early stop if some PDNode can't find matched Node.
+  for (auto& pdnode : pattern_.nodes()) {
+    if (!pdnodes2nodes_.count(pdnode.get())) {
+      VLOG(4) << pdnode->name() << " can't find matched Node, early stop";
+
+      return false;
+    }
+  }
   VLOG(3) << pdnodes2nodes_.size() << " nodes marked";
   return !pdnodes2nodes_.empty();
 }
 
+// The intermediate Nodes can only link to the nodes inside the pattern, or this
+// subgraph will be droped.
+void GraphPatternDetector::ValidateByNodeRole(
+    std::vector<GraphPatternDetector::subgraph_t>* subgraphs) {
+  std::vector<GraphPatternDetector::subgraph_t> result;
+
+  subgraphs->erase(
+      std::remove_if(
+          subgraphs->begin(), subgraphs->end(),
+          [](const GraphPatternDetector::subgraph_t& subgraph) -> bool {
+            // Collect the inputs and outputs.
+            std::unordered_set<Node*> ios;
+            for (auto& item : subgraph) {
+              if (!item.first->IsIntermediate()) {
+                ios.insert(item.second);
+              }
+            }
+            for (auto& item : subgraph) {
+              if (item.first->IsIntermediate()) {
+                for (auto* x : item.second->inputs) {
+                  if (!ios.count(x)) {
+                    return true;
+                  }
+                }
+                for (auto* x : item.second->outputs) {
+                  if (!ios.count(x)) {
+                    return true;
+                  }
+                }
+              }
+            }
+            return false;
+          }),
+      subgraphs->end());
+}
+
 struct HitGroup {
   std::unordered_map<PDNode*, Node*> roles;
 
@@ -140,6 +199,7 @@ GraphPatternDetector::DetectPatterns() {
   // in edges of PDNodes.
   for (const auto& edge : pattern_.edges()) {
     VLOG(4) << "check " << edge.first->name() << " -> " << edge.second->name();
+    // TODO(Superjomn) Fix bug here, the groups might be duplicate here.
     // Each role has two PDNodes, which indicates two roles.
     // Detect two Nodes that can match these two roles and they are connected.
     auto& pre_groups = bi_records[step % 2];
@@ -149,6 +209,7 @@ GraphPatternDetector::DetectPatterns() {
     // source -> target
     for (Node* source : pdnodes2nodes_[edge.first]) {
       for (Node* target : pdnodes2nodes_[edge.second]) {
+        VLOG(8) << "check " << source->id() << " -- " << target->id();
         // TODO(Superjomn) add some prune strategies.
         for (const auto& group : pre_groups) {
           HitGroup new_group = group;
@@ -165,6 +226,12 @@ GraphPatternDetector::DetectPatterns() {
       }
     }
     VLOG(3) << "step " << step << " get records: " << cur_groups.size();
+    for (auto& group : cur_groups) {
+      for (auto& item : group.roles) {
+        VLOG(4) << "node " << item.second->id() << " as " << item.first->name();
+      }
+      VLOG(4) << "=========================================================";
+    }
   }
 
   for (auto& group : bi_records[step % 2]) {
@@ -260,6 +327,118 @@ PDNode& PDNode::LinksFrom(const std::vector<PDNode*>& others) {
   return *this;
 }
 
+PDNode* PDNode::assert_is_op() {
+  asserts_.emplace_back([this](Node* x) { return x && x->IsOp(); });
+  return this;
+}
+PDNode* PDNode::assert_is_op(const std::string& op_type) {
+  asserts_.emplace_back([this, op_type](Node* x) {
+    return x && x->IsOp() && x->Op()->Type() == op_type;
+  });
+  return this;
+}
+PDNode* PDNode::assert_is_var() {
+  asserts_.emplace_back([this](Node* x) { return x && x->IsVar(); });
+  return this;
+}
+PDNode* PDNode::assert_var_not_persistable() {
+  assert_is_var();
+  asserts_.emplace_back([this](Node* x) { return !x->Var()->Persistable(); });
+  return this;
+}
+PDNode* PDNode::assert_is_persistable_var() {
+  assert_is_var();
+  asserts_.emplace_back([=](Node* x) { return x->Var()->Persistable(); });
+  return this;
+}
+PDNode* PDNode::assert_is_op_nth_input(const std::string& op_type,
+                                       const std::string& argument, int nth) {
+  assert_is_var();
+  assert_is_op_input(op_type);
+  asserts_.emplace_back([=](Node* x) {
+    for (auto* op : x->outputs) {
+      if (IsNthInput(x, op, argument, nth)) return true;
+    }
+    return false;
+  });
+  return this;
+}
+PDNode* PDNode::assert_is_op_nth_output(const std::string& op_type,
+                                        const std::string& argument, int nth) {
+  assert_is_var();
+  asserts_.emplace_back([=](Node* x) {
+    for (auto* op : x->inputs) {
+      if (IsNthOutput(x, op, argument, nth)) return true;
+    }
+    return false;
+  });
+  return this;
+}
+PDNode* PDNode::assert_is_only_input_of_op(const std::string& op_type) {
+  assert_is_var();
+  asserts_.emplace_back([=](Node* x) {
+    for (auto* op : x->outputs) {
+      if (op && op->IsOp() && op->Op() && op->Op()->Type() == op_type &&
+          op->inputs.size() == 1) {
+        return true;
+      }
+    }
+    return false;
+  });
+  return this;
+}
+PDNode* PDNode::assert_is_only_output_of_op(const std::string& op_type) {
+  assert_is_var();
+  asserts_.emplace_back([=](Node* x) {
+    for (auto* op : x->inputs) {
+      if (op && op->IsOp() && op->Op() && op->Op()->Type() == op_type &&
+          op->outputs.size() == 1) {
+        return true;
+      }
+    }
+    return false;
+  });
+  return this;
+}
+PDNode* PDNode::assert_is_op_output(const std::string& op_type) {
+  assert_is_var();
+  asserts_.emplace_back([=](Node* x) {
+    for (auto* op : x->inputs) {
+      if (op && op->IsOp() && op->Op() && op->Op()->Type() == op_type) {
+        return true;
+      }
+    }
+    return false;
+  });
+  return this;
+}
+PDNode* PDNode::assert_is_op_input(const std::string& op_type) {
+  assert_is_var();
+  asserts_.emplace_back([=](Node* x) {
+    for (auto* op : x->outputs) {
+      if (op && op->IsOp() && op->Op() && op->Op()->Type() == op_type) {
+        return true;
+      }
+    }
+    return false;
+  });
+  return this;
+}
+PDNode* PDNode::assert_op_has_n_inputs(const std::string& op_type, size_t n) {
+  assert_is_op(op_type);
+  asserts_.emplace_back([=](Node* x) { return x->inputs.size() == n; });
+  return this;
+}
+PDNode* PDNode::assert_op_has_n_outputs(const std::string& op_type, size_t n) {
+  assert_is_op(op_type);
+  asserts_.emplace_back([=](Node* x) { return x->outputs.size() == n; });
+  return this;
+}
+PDNode* PDNode::assert_more(PDNode::teller_t&& teller) {
+  asserts_.emplace_back(std::move(teller));
+  return this;
+}
+
 }  // namespace ir
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -39,14 +39,24 @@ struct PDNode {
   // tell whether an ir::Node* is a candidation for a PDNode.
   using teller_t = std::function<bool(Node*)>;
   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<PDNode*>& others);
   PDNode& LinksFrom(const std::vector<PDNode*>& others);
 
   bool Tell(Node* node) const {
-    PADDLE_ENFORCE(teller_ != nullptr, "teller should be set for a PDNode");
+    if (teller_) return teller_(node);
-    return teller_(node);
+
+    for (auto& asrt : asserts_) {
+      if (!asrt(node)) return false;
+    }
+    return true;
   }
 
   bool IsOp() const { return type_ == Type::kOp; }
@@ -54,10 +64,52 @@ struct PDNode {
 
   const std::string& name() const { return name_; }
 
-  PDNode(const PDNode&) = delete;
   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_not_persistable();
+  PDNode* assert_is_persistable_var();
+  PDNode* assert_is_op_output(const std::string& op_type);
+  PDNode* assert_is_op_input(const std::string& op_type);
+  PDNode* assert_is_op_nth_input(const std::string& op_type,
+                                 const std::string& argument, int nth);
+  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);
 
  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)),
@@ -71,10 +123,13 @@ struct PDNode {
 
   friend class PDPattern;
 
+  // Will removed latter.
   teller_t teller_;
+  std::vector<teller_t> asserts_;
   PDPattern* pattern_;
   std::string name_;
   Type type_;
+  Role role_{Role::kUnknown};
 };
 
 /*
@@ -87,19 +142,18 @@ struct PDNode {
  * This pattern can be defined as with the following pseudo codes
  *
  *     // Create two operator PDNodes.
- *     MUL = PDPattern.NewNode()
+ *     MUL = PDPattern.NewNode().assert_is_op("mul");
- *     ELE = PDPattern.NewNode()
+ *     ELE = PDPattern.NewNode().assert_is_op("elementwise_add");
  *     // Create the variable PDNodes.
- *     MUL_out = PDPattern.NewNode()
+ *     MUL_out = PDPattern.NewNode().assert_is_op_output("mul") \
- *     // Add teller to define some rules that help to filter the target Nodes.
+ *                                  .assert_is_op_input("elementwise_add") \
- *     MUL.teller = lambda(node): node->IsOp() && node->Op()->Type == "mul";
+ *                                  .AsIntermediate();
- *     ELE.teller = lambda(node): \
+ *     // Add relations.
- *                        node->IsOp() && node->Op()->Type == "elementwise_add";
+ *     MUL->LinksTo({MUL_out});
- *     MUL_out.teller = lambda(node): node->IsVar() && (MUL in node->inputs)
+ *     MUL_out->LinksTo({ELE});
- *                                                  && (ELE in node->outputs)
  *
- * One can add more specific tellers for PDNodes or edges, both the Operator
+ * One can add more specific asserts for PDNodes or edges, both the Operator
- * and Variable Nodes can be ruled in PDNode.teller.
+ * 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.
@@ -112,7 +166,8 @@ class PDPattern {
   void AddEdge(PDNode* a, PDNode* b);
 
   PDNode* NewNode(PDNode::teller_t&& teller, const std::string& name = NewID());
-  PDNode* RetriveNode(const std::string& id) const;
+  PDNode* NewNode(const std::string& name = NewID());
+  PDNode* RetrieveNode(const std::string& id) const;
 
   const std::vector<std::unique_ptr<PDNode>>& nodes() const { return nodes_; }
   const std::vector<edge_t>& edges() const { return edges_; }
@@ -185,6 +240,9 @@ class GraphPatternDetector {
   // Remove overlapped match subgraphs, when overlapped, keep the previous one.
   void RemoveOverlappedMatch(std::vector<subgraph_t>* subgraphs);
 
+  // Validate whether the intermediate nodes are linked by external nodes.
+  void ValidateByNodeRole(std::vector<subgraph_t>* subgraphs);
+
 #ifdef PADDLE_WITH_TESTING
   FRIEND_TEST(GraphPatternDetecter, MarkPDNodesInGraph);
   FRIEND_TEST(GraphPatternDetecter, DetectPatterns);
@@ -228,6 +286,14 @@ static bool IsNthInput(Node* var, Node* op, const std::string& argument,
   return var->Name() == op->Op()->Input(argument)[nth];
 }
 
+static bool IsNthOutput(Node* var, Node* op, const std::string& argument,
+                        size_t nth) {
+  PADDLE_ENFORCE(var->IsVar());
+  PADDLE_ENFORCE(op->IsOp());
+  if (op->inputs.size() <= nth) return false;
+  return var->Name() == op->Op()->Output(argument)[nth];
+}
+
 static void GraphSafeRemoveNodes(Graph* graph,
                                  const std::unordered_set<const Node*>& nodes) {
   for (auto* node : nodes) {

paddle/fluid/framework/ir/graph_pattern_detector_tester.cc

@@ -167,6 +167,39 @@ TEST(GraphPatternDetecter, MultiSubgraph) {
   ASSERT_LE(count, 2);
 }
 
+TEST(GraphPatternDetector, IntermediateCheck) {
+  ProgramDesc program;
+  Graph graph(program);
+  BuildGraph(&graph);
+
+  // o2->v2->o3
+  // o2->v2->o4
+  // check o2+o3 fuse, should fail because v2 also link to o4.
+  GraphPatternDetector detector;
+  auto* op2 = detector.mutable_pattern()->NewNode(
+      [](Node* x) { return x && x->IsOp() && x->Name() == "op2"; }, "op2");
+  auto* op3 = detector.mutable_pattern()->NewNode(
+      [](Node* x) { return x && x->IsOp() && x->Name() == "op3"; }, "op3");
+  auto* v2 =
+      detector.mutable_pattern()
+          ->NewNode(
+              [](Node* x) { return x && x->IsVar() && x->Name() == "var2"; },
+              "var2")
+          ->AsIntermediate();
+  v2->LinksFrom({op2}).LinksTo({op3});
+
+  int count = 0;
+  detector(&graph, [&](const GraphPatternDetector::subgraph_t& g,
+                       Graph* graph) { ++count; });
+  EXPECT_EQ(count, 0);
+
+  count = 0;
+  v2->AsInput();
+  detector(&graph, [&](const GraphPatternDetector::subgraph_t& g,
+                       Graph* graph) { ++count; });
+  ASSERT_EQ(count, 1);
+}
+
 }  // namespace ir
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/ir/seq_concat_fc_fuse_pass.cc

@@ -180,16 +180,16 @@ PDNode* BuildFCPattern(PDPattern* pattern, PDNode* fc_x) {
 
 std::unique_ptr<ir::Graph> SeqConcatFcFusePass::ApplyImpl(
     std::unique_ptr<ir::Graph> graph) const {
-  FusePassBase::Init(graph.get());
+  FusePassBase::Init("seq_concat_fc_fuse", graph.get());
   GraphPatternDetector detector;
   auto* pattern = detector.mutable_pattern();
   auto* concat_out = BuildSeqExpandConcatPattern(pattern);
   BuildFCPattern(pattern, concat_out);
 
-#define GET_NODE(id, pattern)                              \
+#define GET_NODE(id, pattern)                               \
-  PADDLE_ENFORCE(subgraph.count(pattern.RetriveNode(#id)), \
+  PADDLE_ENFORCE(subgraph.count(pattern.RetrieveNode(#id)), \
-                 "pattern has no Node called %s", #id);    \
+                 "pattern has no Node called %s", #id);     \
-  auto* id = subgraph.at(pattern.RetriveNode(#id));        \
+  auto* id = subgraph.at(pattern.RetrieveNode(#id));        \
   PADDLE_ENFORCE_NOT_NULL(id, "subgraph has no node %s", #id);
 
   detector(graph.get(), [&](const GraphPatternDetector::subgraph_t& subgraph,

paddle/fluid/inference/analysis/analyzer.cc

@@ -93,7 +93,6 @@ class DfgPassManagerImpl final : public DfgPassManager {
   void AddGraphvizDebugerPass(Pass* pass) {
     auto* debuger_pass = pass->CreateGraphvizDebugerPass();
     if (debuger_pass) {
-      LOG(INFO) << " - register debug pass [" << debuger_pass->repr() << "]";
       Register(debuger_pass->repr(), debuger_pass);
     }
   }

paddle/fluid/inference/analysis/analyzer_tester.cc

@@ -16,10 +16,13 @@
 
 #include <google/protobuf/text_format.h>
 #include <gtest/gtest.h>
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
 #include "paddle/fluid/framework/ir/pass.h"
 #include "paddle/fluid/inference/analysis/ut_helper.h"
+#include "paddle/fluid/inference/api/analysis_predictor.h"
 #include "paddle/fluid/inference/api/helper.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
+#include "paddle/fluid/inference/utils/singleton.h"
 #include "paddle/fluid/platform/profiler.h"
 
 DEFINE_string(infer_ditu_rnn_model, "", "model path for ditu RNN");
@@ -31,6 +34,8 @@ namespace paddle {
 namespace inference {
 namespace analysis {
 
+using namespace framework;
+
 TEST(Analyzer, analysis_without_tensorrt) {
   FLAGS_IA_enable_tensorrt_subgraph_engine = false;
   Argument argument;
@@ -311,6 +316,20 @@ void TestDituRNNPrediction(const std::string &model_path,
       EXPECT_NEAR(data[i], base_data[i], 1e-3);
     }
   }
+
+  if (use_analysis && activate_ir) {
+    AnalysisPredictor *analysis_predictor =
+        dynamic_cast<AnalysisPredictor *>(predictor.get());
+    auto &fuse_statis = analysis_predictor->analysis_argument()
+                            .Get<std::unordered_map<std::string, int>>(
+                                framework::ir::kFuseStatisAttr);
+    for (auto &item : fuse_statis) {
+      LOG(INFO) << "fused " << item.first << " " << item.second;
+    }
+
+    ASSERT_TRUE(fuse_statis.count("fc"));
+    EXPECT_EQ(fuse_statis.at("fc"), 1);
+  }
 }
 
 // Directly infer with the original model.

paddle/fluid/inference/analysis/argument.h

@@ -64,7 +64,8 @@ struct Argument {
   template <typename T>
   void Set(const std::string& key, T* data) {
     PADDLE_ENFORCE_NOT_NULL(data);
-    PADDLE_ENFORCE(!attrs_.count(key), "duplicate attr called %s", key);
+    PADDLE_ENFORCE(!attrs_.count(key), "Duplicate set Argument's attr [%s]",
+                   key);
     attrs_[key] = data;
     attr_deleters_[key] = [data, key, this]() {
       VLOG(3) << "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx";

paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.cc

@@ -15,6 +15,7 @@
 #include "paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.h"
 #include <vector>
 #include "paddle/fluid/framework/block_desc.h"
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
 #include "paddle/fluid/framework/op_desc.h"
 #include "paddle/fluid/framework/proto_desc.h"
 #include "paddle/fluid/inference/analysis/analyzer.h"
@@ -34,7 +35,6 @@ std::vector<std::string> ExtractParameters(
 bool DataFlowGraphToFluidPass::Initialize(Argument *argument) {
   ANALYSIS_ARGUMENT_CHECK_FIELD(argument)
   ANALYSIS_ARGUMENT_CHECK_FIELD(argument->origin_program_desc)
-  PADDLE_ENFORCE(!argument->transformed_program_desc);
   // The transformed_program_desc should inherit all the VarDesc and BlockDesc
   // from the original program desc. The operators of the main block(the first
   // block) should rewritten by data flow graph.
@@ -66,7 +66,7 @@ void DataFlowGraphToFluidPass::Run(DataFlowGraph *graph) {
     }
   }
 
-  if (argument_->Has("param_scope")) {
+  if (argument_->Has(framework::ir::kParamScopeAttr)) {
     LOG(WARNING) << "parameter changes in the scope takes effect";
   }
 

paddle/fluid/inference/analysis/fluid_to_ir_pass.cc

@@ -14,6 +14,7 @@
 
 #include "paddle/fluid/inference/analysis/fluid_to_ir_pass.h"
 #include "paddle/fluid/framework/executor.h"
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
 #include "paddle/fluid/inference/io.h"
 #include "paddle/fluid/platform/device_context.h"
 #include "paddle/fluid/platform/place.h"
@@ -26,11 +27,11 @@ void FluidToIrPass::EnableParamModify(const std::string &model_dir,
                                       const std::string &prog_file,
                                       const std::string &param_file) {
   PADDLE_ENFORCE(argument_);
-  argument_->Set("param_scope", new framework::Scope);
+  argument_->Set(framework::ir::kParamScopeAttr, new framework::Scope);
   // Load parameters.
   VLOG(3) << "Loading parameters from " << model_dir;
-  LoadParams(&argument_->Get<framework::Scope>("param_scope"), model_dir,
+  LoadParams(&argument_->Get<framework::Scope>(framework::ir::kParamScopeAttr),
-             prog_file, param_file);
+             model_dir, prog_file, param_file);
 }
 
 bool FluidToIrPass::LoadParams(framework::Scope *scope, const std::string &dir,

paddle/fluid/inference/analysis/fluid_to_ir_pass.h

@@ -14,12 +14,14 @@
 
 #pragma once
 
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
 #include "paddle/fluid/inference/analysis/ir_pass_manager.h"
 #include "paddle/fluid/inference/analysis/pass.h"
 
 namespace paddle {
 namespace inference {
 namespace analysis {
+using namespace framework;
 
 static const char kFluidToIrPassesAttr[] = "__fluid_to_ir_passes__";
 
@@ -45,13 +47,12 @@ class FluidToIrPass final : public DataFlowGraphPass {
     ANALYSIS_ARGUMENT_CHECK_FIELD(argument->fluid_model_program_path);
     // Load program.
     auto program = LoadProgramDesc(*argument->fluid_model_program_path);
-    argument->origin_program_desc.reset(
+    argument->origin_program_desc.reset(new proto::ProgramDesc(program));
-        new framework::proto::ProgramDesc(program));
     // Create main data flow graph.
     if (!argument->main_dfg) {
       argument->main_dfg.reset(new DataFlowGraph);
     }
-    argument->Set("ir_program_desc", new framework::ProgramDesc(program));
+    argument->Set("ir_program_desc", new ProgramDesc(program));
 
     LOG(INFO) << "Loading parameters";
     // Load parameters to argument if needed.
@@ -73,15 +74,15 @@ class FluidToIrPass final : public DataFlowGraphPass {
 
   void Run(DataFlowGraph *graph) override {
     // Call all the IR Passes
-    IRPassManager ir_passes(
+    IRPassManager ir_passes(argument_->Get<ProgramDesc>("ir_program_desc"),
-        argument_->Get<framework::ProgramDesc>("ir_program_desc"), nullptr);
+                            nullptr);
     // Pass the scope from analysis to IR if needed.
-    if (argument_->Has("param_scope")) {
+    if (argument_->Has(ir::kParamScopeAttr)) {
       // Here the address is passed, attention that IR doesn't own the scope, so
       // the real scope in analysis should live during the IR phase.
       ir_passes.graph().Set(
-          "param_scope", new framework::Scope *(
+          ir::kParamScopeAttr,
-                             &argument_->Get<framework::Scope>("param_scope")));
+          new Scope *(&argument_->Get<Scope>(ir::kParamScopeAttr)));
     }
 
     const auto &ir_passes_to_apply =
@@ -90,6 +91,14 @@ class FluidToIrPass final : public DataFlowGraphPass {
 
     PADDLE_ENFORCE(argument_->main_dfg.get());
     argument_->main_dfg->Build(ir_passes.graph());
+    // inherit the arguments from ir.
+    if (ir_passes.graph().Has(ir::kFuseStatisAttr)) {
+      argument_->Set(
+          ir::kFuseStatisAttr,
+          new std::unordered_map<std::string, int>(
+              ir_passes.graph().Get<std::unordered_map<std::string, int>>(
+                  ir::kFuseStatisAttr)));
+    }
   }
 
   void EnableParamModify(const std::string &model_dir,
@@ -100,7 +109,7 @@ class FluidToIrPass final : public DataFlowGraphPass {
 
  private:
   // Load parameters from a single file or from a directory.
-  bool LoadParams(framework::Scope *scope, const std::string &dir,
+  bool LoadParams(Scope *scope, const std::string &dir,
                   const std::string &prog_file, const std::string &param_file);
 
  private:

paddle/fluid/inference/analysis/ir_pass_manager.cc

@@ -14,6 +14,7 @@
 
 #include "paddle/fluid/inference/analysis/ir_pass_manager.h"
 #include <string>
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
 #include "paddle/fluid/framework/ir/graph.h"
 #include "paddle/fluid/framework/scope.h"
 
@@ -25,7 +26,8 @@ IRPassManager::IRPassManager(const ProgramDesc &program,
                              framework::Scope *scope)
     : program_(program) {
   graph_.reset(new framework::ir::Graph(program));
-  if (scope) graph_->Set("param_scope", new framework::Scope *(scope));
+  if (scope)
+    graph_->Set(framework::ir::kParamScopeAttr, new framework::Scope *(scope));
 }
 
 void IRPassManager::Apply(const std::vector<std::string> &passes) {

paddle/fluid/inference/api/analysis_predictor.cc

@@ -12,121 +12,96 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include "paddle/fluid/inference/api/analysis_predictor.h"
 #include <memory>
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
 #include "paddle/fluid/framework/ir/pass.h"
 #include "paddle/fluid/framework/scope.h"
-#include "paddle/fluid/inference/analysis/analyzer.h"
-#include "paddle/fluid/inference/api/api_impl.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
 #include "paddle/fluid/inference/utils/singleton.h"
 
 namespace paddle {
 
-using inference::analysis::Argument;
+bool AnalysisPredictor::Init(
-using inference::Singleton;
+    const std::shared_ptr<framework::Scope>& parent_scope) {
-using inference::analysis::Analyzer;
+  VLOG(3) << "Predictor::init()";
-using framework::proto::ProgramDesc;
+  if (config_.use_gpu) {
-
+    place_ = paddle::platform::CUDAPlace(config_.device);
-/* This predictor is based on the original native predictor with IR and Analysis
+  } else {
- * support. It will optimize IR and Parameters in the runtime.
+    place_ = paddle::platform::CPUPlace();
- * TODO(Superjomn) Replace the Navive predictor?
- */
-class AnalysisPredictor : public NativePaddlePredictor {
- public:
-  explicit AnalysisPredictor(const NativeConfig& config)
-      : NativePaddlePredictor(config), config_(config) {}
-
-  bool Init(const std::shared_ptr<framework::Scope>& parent_scope) {
-    VLOG(3) << "Predictor::init()";
-    if (config_.use_gpu) {
-      place_ = paddle::platform::CUDAPlace(config_.device);
-    } else {
-      place_ = paddle::platform::CPUPlace();
-    }
-    PADDLE_ENFORCE(!parent_scope);
-    if (parent_scope) {
-      scope_ = parent_scope;
-      sub_scope_ = &(parent_scope->NewScope());
-    } else {
-      paddle::framework::InitDevices(false);
-      scope_.reset(new paddle::framework::Scope());
-    }
-
-    executor_.reset(new paddle::framework::Executor(place_));
-
-    // Initialize the inference program
-    if (!config_.model_dir.empty()) {
-      // Parameters are saved in separate files sited in
-      // the specified `dirname`.
-      inference_program_ = paddle::inference::Load(
-          executor_.get(), scope_.get(), config_.model_dir);
-    } else if (!config_.prog_file.empty() && !config_.param_file.empty()) {
-      // All parameters are saved in a single file.
-      // The file names should be consistent with that used
-      // in Python API `fluid.io.save_inference_model`.
-      inference_program_ = paddle::inference::Load(
-          executor_.get(), scope_.get(), config_.prog_file, config_.param_file);
-    } else {
-      LOG(ERROR) << "fail to load inference model.";
-      return false;
-    }
-
-    OptimizeInferenceProgram();
-    ctx_ = executor_->Prepare(*inference_program_, 0);
-
-    VLOG(5) << "to create variables";
-    PADDLE_ENFORCE(scope_.get());
-    executor_->CreateVariables(*inference_program_,
-                               sub_scope_ ? sub_scope_ : scope_.get(), 0);
-
-    // Get the feed_target_names and fetch_target_names
-    PrepareFeedFetch();
-    return true;
   }
-
+  PADDLE_ENFORCE(!parent_scope);
-  bool Run(const std::vector<PaddleTensor>& inputs,
+  if (parent_scope) {
-           std::vector<PaddleTensor>* output_data,
+    scope_ = parent_scope;
-           int batch_size = -1) override {
+    sub_scope_ = &(parent_scope->NewScope());
-    return NativePaddlePredictor::Run(inputs, output_data, batch_size);
+  } else {
+    paddle::framework::InitDevices(false);
+    scope_.reset(new paddle::framework::Scope());
   }
 
-  void OptimizeInferenceProgram() {
+  executor_.reset(new paddle::framework::Executor(place_));
-    LOG(INFO) << "optimize begin";
+
-    FLAGS_IA_enable_ir = true;
+  // Initialize the inference program
-    FLAGS_IA_enable_tensorrt_subgraph_engine = false;
+  if (!config_.model_dir.empty()) {
-    FLAGS_IA_output_storage_path = "";  // Don't output the model.
+    // Parameters are saved in separate files sited in
-    // Analyze inference_program
+    // the specified `dirname`.
-    Argument argument;
+    inference_program_ = paddle::inference::Load(executor_.get(), scope_.get(),
-    if (!config_.model_dir.empty()) {
+                                                 config_.model_dir);
-      argument.fluid_model_dir.reset(new std::string(config_.model_dir));
+  } else if (!config_.prog_file.empty() && !config_.param_file.empty()) {
-    } else {
+    // All parameters are saved in a single file.
-      PADDLE_ENFORCE(
+    // The file names should be consistent with that used
-          !config_.param_file.empty(),
+    // in Python API `fluid.io.save_inference_model`.
-          "Either model_dir or (param_file, prog_file) should be set.");
+    inference_program_ = paddle::inference::Load(
-      PADDLE_ENFORCE(!config_.prog_file.empty());
+        executor_.get(), scope_.get(), config_.prog_file, config_.param_file);
-      argument.fluid_model_program_path.reset(
+  } else {
-          new std::string(config_.prog_file));
+    LOG(ERROR) << "fail to load inference model.";
-      argument.fluid_model_param_path.reset(
+    return false;
-          new std::string(config_.param_file));
-    }
-    argument.origin_program_desc.reset(
-        new ProgramDesc(*inference_program_->Proto()));
-    Singleton<Analyzer>::Global().Run(&argument);
-    CHECK(argument.transformed_program_desc);
-    VLOG(5) << "to prepare executor";
-    // LOG(INFO) << "transformed_parogram_desc " <<
-    // argument.transformed_program_desc->DebugString();
-    inference_program_.reset(
-        new framework::ProgramDesc(*argument.transformed_program_desc));
-    PADDLE_ENFORCE(argument.Has("param_scope"));
-    // Update scope.
-    scope_.reset(argument.Release<framework::Scope>("param_scope"));
-    LOG(INFO) << "optimize end ==";
   }
 
- private:
+  OptimizeInferenceProgram();
-  NativeConfig config_;
+  ctx_ = executor_->Prepare(*inference_program_, 0);
-};
+
+  VLOG(5) << "to create variables";
+  PADDLE_ENFORCE(scope_.get());
+  executor_->CreateVariables(*inference_program_,
+                             sub_scope_ ? sub_scope_ : scope_.get(), 0);
+  // Get the feed_target_names and fetch_target_names
+  PrepareFeedFetch();
+  return true;
+}
+
+void AnalysisPredictor::OptimizeInferenceProgram() {
+  LOG(INFO) << "optimize begin";
+  FLAGS_IA_enable_ir = true;
+  FLAGS_IA_enable_tensorrt_subgraph_engine = false;
+  FLAGS_IA_output_storage_path = "";  // Don't output the model.
+  // Analyze inference_program
+  if (!config_.model_dir.empty()) {
+    argument_.fluid_model_dir.reset(new std::string(config_.model_dir));
+  } else {
+    PADDLE_ENFORCE(
+        !config_.param_file.empty(),
+        "Either model_dir or (param_file, prog_file) should be set.");
+    PADDLE_ENFORCE(!config_.prog_file.empty());
+    argument_.fluid_model_program_path.reset(
+        new std::string(config_.prog_file));
+    argument_.fluid_model_param_path.reset(new std::string(config_.param_file));
+  }
+  argument_.origin_program_desc.reset(
+      new ProgramDesc(*inference_program_->Proto()));
+  Analyzer().Run(&argument_);
+  CHECK(argument_.transformed_program_desc);
+  VLOG(5) << "to prepare executor";
+  // LOG(INFO) << "transformed_parogram_desc " <<
+  // argument.transformed_program_desc->DebugString();
+  inference_program_.reset(
+      new framework::ProgramDesc(*argument_.transformed_program_desc));
+  PADDLE_ENFORCE(argument_.Has(framework::ir::kParamScopeAttr));
+  // Update scope.
+  scope_.reset(
+      argument_.Release<framework::Scope>(framework::ir::kParamScopeAttr));
+  LOG(INFO) << "optimize end ==";
+}
 
 template <>
 std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<

paddle/fluid/inference/api/analysis_predictor.h

@@ -0,0 +1,51 @@
+// 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.
+
+#include "paddle/fluid/inference/analysis/analyzer.h"
+#include "paddle/fluid/inference/api/api_impl.h"
+#include "paddle/fluid/inference/api/paddle_inference_api.h"
+
+namespace paddle {
+
+using inference::analysis::Argument;
+using inference::analysis::Analyzer;
+using framework::proto::ProgramDesc;
+
+/* This predictor is based on the original native predictor with IR and Analysis
+ * support. It will optimize IR and Parameters in the runtime.
+ * TODO(Superjomn) Replace the Navive predictor?
+ */
+class AnalysisPredictor : public NativePaddlePredictor {
+ public:
+  explicit AnalysisPredictor(const NativeConfig& config)
+      : NativePaddlePredictor(config), config_(config) {}
+
+  bool Init(const std::shared_ptr<framework::Scope>& parent_scope);
+
+  bool Run(const std::vector<PaddleTensor>& inputs,
+           std::vector<PaddleTensor>* output_data,
+           int batch_size = -1) override {
+    return NativePaddlePredictor::Run(inputs, output_data, batch_size);
+  }
+
+  void OptimizeInferenceProgram();
+
+  Argument& analysis_argument() { return argument_; }
+
+ private:
+  NativeConfig config_;
+  Argument argument_;
+};
+
+}  // namespace paddle

paddle/fluid/inference/api/api_impl.cc

@@ -62,7 +62,7 @@ void NativePaddlePredictor::PrepareFeedFetch() {
   for (auto *op : inference_program_->Block(0).AllOps()) {
     if (op->Type() == "feed") {
       int idx = boost::get<int>(op->GetAttr("col"));
-      if (feeds_.size() <= idx) {
+      if (feeds_.size() <= static_cast<size_t>(idx)) {
         feeds_.resize(idx + 1);
       }
       feeds_[idx] = op;