fea/link ir to inference analysis and fc fuse support (#12789)

* link IR graph to analysis graph

* add clean code and update

* add infer_clean_pass

* add ir_pass_manager

* support fc fuse executation

* fix ir circle

doc/fluid/design/others/graph_survey.md

@@ -28,7 +28,7 @@ def get_symbol(num_classes=10, **kwargs):
 
 
 
-Varible here is actually a Symbol. Every basic Symbol will correspond to one Node, and every Node has its own NodeAttr. There is a op field in NodeAttr class, when a Symbol represents Variable(often input data), the op field is null.
+Varible here is actually a Symbol. Every basic Symbol will correspond to one Node, and every Node has its own AnyAttr. There is a op field in AnyAttr class, when a Symbol represents Variable(often input data), the op field is null.
 
 Symbol contains a data member, std::vector<NodeEntry> outputs, and NodeEntry cantains a poniter to Node. We can follow the Node pointer to get all the Graph.
 

paddle/fluid/framework/ir/CMakeLists.txt

@@ -5,8 +5,12 @@ cc_library(pass SRCS pass.cc DEPS graph node graph_helper)
 cc_library(graph_viz_pass SRCS graph_viz_pass.cc DEPS graph pass graph_helper)
 cc_library(graph_traits SRCS graph_traits.cc DEPS graph)
 cc_library(graph_pattern_detecter SRCS graph_pattern_detecter.cc DEPS graph graph_helper graph_traits)
+cc_library(fc_fuse_pass SRCS fc_fuse_pass.cc DEPS graph graph_pattern_detecter)
+cc_library(infer_clean_graph_pass SRCS infer_clean_graph_pass.cc DEPS graph pass)
+
 
 cc_test(pass_test SRCS pass_test.cc DEPS graph pass graph_helper)
 cc_test(graph_test SRCS graph_test.cc DEPS graph graph_helper op_registry)
 cc_test(graph_helper_test SRCS graph_helper_test.cc DEPS graph graph_helper op_registry)
 cc_test(test_graph_pattern_detecter SRCS graph_pattern_detecter_tester.cc DEPS graph_pattern_detecter)
+cc_test(test_fc_fuse_pass SRCS fc_fuse_pass_tester.cc DEPS fc_fuse_pass graph_pattern_detecter graph pass graph_traits framework_proto)

paddle/fluid/framework/ir/fc_fuse_pass.cc

+// 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/framework/ir/fc_fuse_pass.h"
+#include <string>
+#include <vector>
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+bool VarOutLinksToOp(Node* node, const std::string& op_type) {
+  for (auto* out : node->outputs) {
+    if (out->IsOp() && out->Op()->Type() == op_type) {
+      return true;
+    }
+  }
+  return false;
+}
+
+void BuildFCPattern(PDPattern* pattern) {
+  // make sure the selected MUL op has one input argument is a parameter.
+  auto* mul_parameter_var = pattern->NewNode(
+      [](Node* node) {
+        return node->IsVar() && node->outputs.size() == 1UL &&
+               node->outputs.front()->Op()->Type() == "mul" && node->Var() &&
+               node->Var()->Persistable();  // check is a parameter
+      },
+      "mul_weight" /*name*/);
+
+  auto* mul_tmp_input_var = pattern->NewNode(
+      [](Node* node) {
+        bool result =
+            node->IsVar() && node->outputs.size() >= 1UL && node->Var() &&
+            !node->Var()->Persistable();  // this input is not an parameter.
+        if (!result) return false;
+        // check whether one output is MUL op.
+        for (auto* op : node->outputs) {
+          if (op->IsOp() && op->Op()->Type() == "mul") return true;
+        }
+        return false;
+      },
+      "mul_tmp_var" /*name*/);
+
+  // select a MUL op
+  auto* mul_op = pattern->NewNode(
+      [](Node* node) {
+        return node->IsOp() &&               // start from an Op
+               node->Op()->Type() == "mul";  // type is mul
+        // 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");
+
+  pattern->AddEdge(mul_parameter_var, mul_op);
+  pattern->AddEdge(mul_tmp_input_var, mul_op);
+  pattern->AddEdge(mul_op, mul_out_var);
+  pattern->AddEdge(mul_out_var, elementwise_add_op);
+  pattern->AddEdge(elementwise_add_tmp_var, elementwise_add_op);
+  pattern->AddEdge(elementwise_add_op, elementwise_add_out_var);
+}
+
+// Replace the node `from` in the links to `to`
+bool LinksReplace(std::vector<Node*>* links, Node* from, Node* to) {
+  for (auto*& n : *links) {
+    if (n == from) {
+      n = to;
+      return true;
+    }
+  }
+  return false;
+}
+
+std::unique_ptr<ir::Graph> FCFusePass::ApplyImpl(
+    std::unique_ptr<ir::Graph> graph) const {
+  PADDLE_ENFORCE(graph.get());
+
+  std::unordered_set<Node*> nodes2delete;
+
+  GraphPatternDetecter gpd;
+  BuildFCPattern(gpd.mutable_pattern());
+
+#define GET_NODE(id)                                             \
+  PADDLE_ENFORCE(subgraph.count(gpd.pattern().RetriveNode(#id)), \
+                 "pattern has no Node called %s", #id);          \
+  auto* id = subgraph.at(gpd.pattern().RetriveNode(#id));        \
+  PADDLE_ENFORCE_NOT_NULL(id, "subgraph has no node %s", #id);
+
+  auto handler = [&](const GraphPatternDetecter::subgraph_t& subgraph,
+                     Graph* g) {
+    VLOG(4) << "handle FC fuse";
+    // Currently, there is no FC op available, so I will just simulate the
+    // scenerio.
+    // FC's fusion is simple, just op fuse, no need to process the
+    // parameters.
+    GET_NODE(mul_tmp_var);             // x
+    GET_NODE(mul_weight);              // Y
+    GET_NODE(elementwise_add_tmpvar);  // bias
+    GET_NODE(elementwise_add_out);     // Out
+    GET_NODE(mul);                     // MUL op
+    GET_NODE(elementwise_add);         // ELEMENT_ADD op
+    GET_NODE(mul_out);                 // tmp
+#undef GET_NODE
+
+    // Create an FC Node.
+    OpDesc desc;
+    std::string fc_x_in = mul_tmp_var->Name();
+    std::string fc_Y_in = mul_weight->Name();
+    std::string fc_bias_in = elementwise_add_tmpvar->Name();
+    std::string fc_out = elementwise_add_out->Name();
+    desc.SetInput("Input", std::vector<std::string>({fc_x_in}));
+    desc.SetInput("W", std::vector<std::string>({fc_Y_in}));
+    desc.SetInput("Bias", std::vector<std::string>({fc_bias_in}));
+    desc.SetOutput("Out", std::vector<std::string>({fc_out}));
+    desc.SetType("fc");
+    auto fc_node = g->CreateOpNode(&desc);  // OpDesc will be copied.
+    fc_node->inputs =
+        std::vector<Node*>({mul_tmp_var, mul_weight, elementwise_add_tmpvar});
+    fc_node->outputs.push_back(elementwise_add_out);
+
+    // Update link relatons
+    PADDLE_ENFORCE(LinksReplace(&mul_tmp_var->outputs, mul, fc_node));
+    PADDLE_ENFORCE(LinksReplace(&mul_weight->outputs, mul, fc_node));
+    PADDLE_ENFORCE(LinksReplace(&elementwise_add_tmpvar->outputs,
+                                elementwise_add, fc_node));
+    PADDLE_ENFORCE(
+        LinksReplace(&elementwise_add_out->inputs, elementwise_add, fc_node));
+
+    // Drop old nodes
+    graph->RemoveNode(mul);
+    graph->RemoveNode(elementwise_add);
+    graph->RemoveNode(mul_out);  // tmp variable
+  };
+
+  gpd(graph.get(), handler);
+
+  return graph;
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(fc_fuse_pass, paddle::framework::ir::FCFusePass);

paddle/fluid/framework/ir/fc_fuse_pass.h

+// 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/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/graph_pattern_detecter.h"
+#include "paddle/fluid/framework/ir/pass.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+/*
+ * Fuse the MUL and ELEMENTWISE_ADD to a FCOp.
+ */
+class FCFusePass : public Pass {
+ public:
+  virtual ~FCFusePass() {}
+
+ protected:
+  std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/ir/fc_fuse_pass_tester.cc

+// 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/framework/ir/fc_fuse_pass.h"
+
+#include <gtest/gtest.h>
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+void SetOp(ProgramDesc* prog, const std::string& type,
+           const std::vector<std::string>& inputs,
+           const std::vector<std::string>& outputs) {
+  auto* op = prog->MutableBlock(0)->AppendOp();
+  op->SetType(type);
+  op->SetInput("Xs", inputs);
+  op->SetOutput("Ys", outputs);
+}
+
+// a->OP0->b
+// a->OP1->c
+// (b, c)->mul->d
+// (d, e)->elementwise_add->f
+ProgramDesc BuildProgramDesc() {
+  ProgramDesc prog;
+  for (auto& v : std::vector<std::string>({"a", "b", "c", "d", "e", "f"})) {
+    auto* var = prog.MutableBlock(0)->Var(v);
+    var->SetType(proto::VarType::SELECTED_ROWS);
+    if (v == "c") {
+      var->SetPersistable(true);
+    }
+  }
+
+  SetOp(&prog, "OP0", std::vector<std::string>({"a"}),
+        std::vector<std::string>({"b"}));
+  SetOp(&prog, "OP1", std::vector<std::string>({"a"}),
+        std::vector<std::string>({"c"}));
+  SetOp(&prog, "mul", std::vector<std::string>({"b", "c"}),
+        std::vector<std::string>({"d"}));
+  SetOp(&prog, "elementwise_add", std::vector<std::string>({"d", "e"}),
+        std::vector<std::string>({"f"}));
+
+  return prog;
+}
+
+TEST(FCFusePass, basic) {
+  auto prog = BuildProgramDesc();
+
+  std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
+
+  auto pass = PassRegistry::Instance().Get("fc_fuse_pass");
+
+  int pre_nodes = graph->Nodes().size();
+
+  graph = pass->Apply(std::move(graph));
+
+  int after_nodes = graph->Nodes().size();
+
+  // Remove 3 Nodes: MUL,ELEMENTWISE_ADD, mul_out
+  // Add 1 Node: FC
+  EXPECT_EQ(pre_nodes - 2, after_nodes);
+
+  // Assert fc op in newly generated graph
+  int fc_count = 0;
+
+  for (auto* node : graph->Nodes()) {
+    if (node->IsOp() && node->Op()->Type() == "fc") {
+      ++fc_count;
+    }
+  }
+  EXPECT_EQ(fc_count, 1);
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+USE_PASS(fc_fuse_pass);

paddle/fluid/framework/ir/graph.h

@@ -98,11 +98,13 @@ class Graph {
 
   // Create a normal variable with non-null VarDesc.
   ir::Node *CreateVarNode(VarDesc *var_desc) {
+    PADDLE_ENFORCE(var_desc);
     return AddNode(new ir::Node(var_desc));
   }
 
   // Create a normal runnable operator with OpDesc.
   ir::Node *CreateOpNode(OpDesc *op_desc) {
+    PADDLE_ENFORCE(op_desc);
     return AddNode(new ir::Node(op_desc));
   }
 
@@ -134,6 +136,14 @@ class Graph {
     return ret;
   }
 
+  void RemoveNode(ir::Node *node) {
+    PADDLE_ENFORCE(node_set_.find(node) != node_set_.end());
+    node_set_.erase(node);
+    nodes_.erase(node);
+  }
+
+  const ProgramDesc &program() const { return program_; }
+
  private:
   // This method takes ownership of `node`.
   ir::Node *AddNode(ir::Node *node) {
@@ -143,12 +153,6 @@ class Graph {
     return node;
   }
 
-  void RemoveNode(ir::Node *node) {
-    PADDLE_ENFORCE(node_set_.find(node) != node_set_.end());
-    node_set_.erase(node);
-    nodes_.erase(node);
-  }
-
   // NOTE: program_ shouldn't be exposed to user.
   const ProgramDesc &program_;
   std::map<std::string, boost::any> attrs_;

paddle/fluid/framework/ir/graph_pattern_detecter.cc

@@ -25,12 +25,30 @@ namespace paddle {
 namespace framework {
 namespace ir {
 
+size_t PDPattern::id_ = 0UL;
+
 PDNode* PDPattern::NewNode(PDNode::teller_t&& teller, 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(std::move(teller), name));
   auto* cur = nodes_.back().get();
+  node_map_[name] = cur;
   return cur;
 }
 
+PDNode* PDPattern::RetriveNode(const std::string& id) const {
+  auto it = node_map_.find(id);
+  if (it == node_map_.end()) {
+    return nullptr;
+  }
+
+  return it->second;
+}
+
 void PDPattern::AddEdge(PDNode* a, PDNode* b) {
   PADDLE_ENFORCE(a);
   PADDLE_ENFORCE(b);
@@ -51,15 +69,18 @@ void GraphPatternDetecter::operator()(Graph* graph,
 }
 
 bool GraphPatternDetecter::MarkPDNodesInGraph(const ir::Graph& graph) {
+  VLOG(4) << "mark pdnodes in graph";
   if (graph.Nodes().empty()) return false;
 
   for (auto& node : GraphTraits::DFS(graph)) {
     for (const auto& pdnode : pattern_.nodes()) {
       if (pdnode->Tell(&node)) {
+        VLOG(4) << "pdnode " << pdnode->name() << " marked";
         pdnodes2nodes_[pdnode.get()].insert(&node);
       }
     }
   }
+  VLOG(3) << pdnodes2nodes_.size() << " nodes marked";
   return !pdnodes2nodes_.empty();
 }
 
@@ -67,10 +88,20 @@ struct HitGroup {
   std::unordered_map<PDNode*, Node*> roles;
 
   bool Match(Node* node, PDNode* pat) {
+    if (nodes_.count(node)) {
+      if (!roles.count(pat)) return false;
+      return roles[pat] == node;
+    }
     return !roles.count(pat) || roles.at(pat) == node;
   }
 
-  void Register(Node* node, PDNode* pat) { roles[pat] = node; }
+  void Register(Node* node, PDNode* pat) {
+    roles[pat] = node;
+    nodes_.insert(node);
+  }
+
+ private:
+  std::unordered_set<Node*> nodes_;
 };
 
 // Tell whether Node a links to b.
@@ -104,6 +135,7 @@ GraphPatternDetecter::DetectPatterns() {
   // Extend a PDNode to subgraphs by deducing the connection relations defined
   // in edges of PDNodes.
   for (const auto& edge : pattern_.edges()) {
+    VLOG(4) << "check " << edge.first->name() << " -> " << edge.second->name();
     // 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];
@@ -127,6 +159,7 @@ GraphPatternDetecter::DetectPatterns() {
         }
       }
     }
+    VLOG(3) << "step " << step << " get records: " << cur_groups.size();
   }
 
   for (auto& group : bi_records[step % 2]) {

paddle/fluid/framework/ir/graph_pattern_detecter.h

@@ -96,7 +96,8 @@ class PDPattern {
 
   void AddEdge(PDNode* a, PDNode* b);
 
-  PDNode* NewNode(PDNode::teller_t&& teller, const std::string& name = "");
+  PDNode* NewNode(PDNode::teller_t&& teller, const std::string& name = NewID());
+  PDNode* RetriveNode(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_; }
@@ -107,8 +108,12 @@ class PDPattern {
   FRIEND_TEST(PDPattern, NewNode);
 #endif
 
+  static std::string NewID() { return "pdnode-" + std::to_string(id_++); }
+
   std::vector<std::unique_ptr<PDNode>> nodes_;
   std::vector<edge_t> edges_;
+  std::unordered_map<std::string, PDNode*> node_map_;
+  static size_t id_;
 };
 
 /*

paddle/fluid/framework/ir/graph_viz_pass.cc

@@ -25,6 +25,7 @@ static const char kGraphVizPath[] = "graph_viz_path";
 std::unique_ptr<ir::Graph> GraphVizPass::ApplyImpl(
     std::unique_ptr<ir::Graph> graph) const {
   const std::string graph_viz_path = Get<std::string>(kGraphVizPath);
+  VLOG(3) << "draw IR graph viz to " << graph_viz_path;
   std::unique_ptr<std::ostream> fout(new std::ofstream(graph_viz_path));
   PADDLE_ENFORCE(fout->good());
   std::ostream& sout = *fout;

paddle/fluid/framework/ir/infer_clean_graph_pass.cc

+// 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 <algorithm>
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/pass.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+class InferCleanGraphPass : public Pass {
+ public:
+  virtual ~InferCleanGraphPass() {}
+
+ protected:
+  std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const {
+    PADDLE_ENFORCE(graph.get());
+
+    auto is_valid_node = [](Node* x) {
+      return x && IsControlDepVar(*x) && x->IsVar() && !x->Var();
+    };
+
+    std::unordered_set<Node*> invalid_nodes;
+    for (auto* node : graph->Nodes()) {
+      if (is_valid_node(node)) {
+        invalid_nodes.insert(node);
+      }
+    }
+
+    // remove nodes from the graph.
+    for (auto* node : invalid_nodes) {
+      graph->RemoveNode(node);
+    }
+
+    // clean edges.
+    for (auto* node : graph->Nodes()) {
+      CleanEdges(&node->inputs, invalid_nodes);
+      CleanEdges(&node->outputs, invalid_nodes);
+    }
+
+    return graph;
+  }
+
+  void CleanEdges(std::vector<Node*>* nodes,
+                  const std::unordered_set<Node*>& to_remove) const {
+    auto it = std::remove_if(nodes->begin(), nodes->end(),
+                             [&](Node* x) { return to_remove.count(x); });
+    nodes->erase(it, nodes->end());
+  }
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(infer_clean_graph_pass,
+              paddle::framework::ir::InferCleanGraphPass);

paddle/fluid/framework/ir/node.h

@@ -34,14 +34,15 @@ class Node {
 
   explicit Node(VarDesc* var_desc)
       : name_(var_desc->Name()),
-        var_desc_(var_desc),
+        var_desc_(new VarDesc(*var_desc)),
         op_desc_(nullptr),
         type_(Type::kVariable) {}
 
   explicit Node(OpDesc* op_desc)
       : name_(op_desc->Type()),
         var_desc_(nullptr),
-        op_desc_(op_desc),
+        op_desc_(new OpDesc(*op_desc)),  // TODO(panyx0718) the pointer in the
+                                         // original OpDesc might go out.
         type_(Type::kOperation) {}
 
   Type NodeType() const { return type_; }
@@ -50,12 +51,12 @@ class Node {
 
   VarDesc* Var() {
     PADDLE_ENFORCE(type_ == Type::kVariable);
-    return var_desc_;
+    return var_desc_.get();
   }
 
   OpDesc* Op() {
-    PADDLE_ENFORCE(type_ == Type::kOperation);
-    return op_desc_;
+    PADDLE_ENFORCE(IsOp());
+    return op_desc_.get();
   }
 
   bool IsOp() const { return type_ == Type::kOperation; }
@@ -66,8 +67,8 @@ class Node {
 
  protected:
   const std::string name_;
-  VarDesc* var_desc_;
-  OpDesc* op_desc_;
+  std::unique_ptr<VarDesc> var_desc_;
+  std::unique_ptr<OpDesc> op_desc_;
   Type type_;
 
  private:

paddle/fluid/inference/analysis/CMakeLists.txt

+cc_library(ir_pass_manager SRCS ir_pass_manager.cc DEPS graph pass)
 cc_library(analysis SRCS pass_manager.cc dot.cc node.cc data_flow_graph.cc graph_traits.cc subgraph_splitter.cc
+  analyzer.cc
+  helper.cc
+  # passes
   fluid_to_data_flow_graph_pass.cc
   data_flow_graph_to_fluid_pass.cc
   dfg_graphviz_draw_pass.cc
   tensorrt_subgraph_pass.cc
   tensorrt_subgraph_node_mark_pass.cc
-  analyzer.cc
-  helper.cc
-        model_store_pass.cc
-  DEPS framework_proto proto_desc)
-cc_test(test_node SRCS node_tester.cc DEPS analysis gflags glog gtest)
+  fluid_to_ir_pass.cc
+  model_store_pass.cc
+  DEPS framework_proto proto_desc ir_pass_manager graph pass)
+
+cc_test(test_node SRCS node_tester.cc DEPS analysis)
 cc_test(test_dot SRCS dot_tester.cc DEPS analysis)
 cc_binary(inference_analyzer SRCS analyzer_main.cc DEPS analysis)
 
@@ -27,19 +31,30 @@ function (inference_analysis_test TARGET)
         endif()
         cc_test(${TARGET}
                 SRCS "${analysis_test_SRCS}"
-                DEPS analysis
+                DEPS analysis graph fc_fuse_pass graph_viz_pass infer_clean_graph_pass graph_pattern_detecter pass
                 ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model ${mem_opt})
         set_tests_properties(${TARGET} PROPERTIES DEPENDS test_word2vec)
     endif(WITH_TESTING)
 endfunction(inference_analysis_test)
 
+cc_test(test_analyzer SRCS analyzer_tester.cc DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis
+		  # ir
+		  fc_fuse_pass
+		  graph_viz_pass
+		  infer_clean_graph_pass
+		  graph_pattern_detecter
+		  pass
+        ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model)
+#set_tests_properties(test_analyzer PROPERTIES DEPENDS test_word2vec)
+#inference_api_test(test_analyzer SRC analyzer_tester.cc ARGS test_word2vec)
+
 inference_analysis_test(test_data_flow_graph SRCS data_flow_graph_tester.cc)
 inference_analysis_test(test_data_flow_graph_to_fluid_pass SRCS data_flow_graph_to_fluid_pass_tester.cc)
+inference_analysis_test(test_fluid_to_ir_pass SRCS fluid_to_ir_pass_tester.cc)
 inference_analysis_test(test_fluid_to_data_flow_graph_pass SRCS fluid_to_data_flow_graph_pass_tester.cc)
 inference_analysis_test(test_subgraph_splitter SRCS subgraph_splitter_tester.cc)
 inference_analysis_test(test_dfg_graphviz_draw_pass SRCS dfg_graphviz_draw_pass_tester.cc)
 inference_analysis_test(test_tensorrt_subgraph_pass SRCS tensorrt_subgraph_pass_tester.cc)
 inference_analysis_test(test_pass_manager SRCS pass_manager_tester.cc)
 inference_analysis_test(test_tensorrt_subgraph_node_mark_pass SRCS tensorrt_subgraph_node_mark_pass_tester.cc)
-inference_analysis_test(test_analyzer SRCS analyzer_tester.cc)
 inference_analysis_test(test_model_store_pass SRCS model_store_pass_tester.cc)

paddle/fluid/inference/analysis/analyzer.cc

@@ -17,6 +17,7 @@
 #include "paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.h"
 #include "paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h"
 #include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
+#include "paddle/fluid/inference/analysis/fluid_to_ir_pass.h"
 #include "paddle/fluid/inference/analysis/model_store_pass.h"
 #include "paddle/fluid/inference/analysis/pass_manager.h"
 #include "paddle/fluid/inference/analysis/tensorrt_subgraph_node_mark_pass.h"
@@ -24,14 +25,15 @@
 
 namespace paddle {
 
-DEFINE_bool(inference_analysis_enable_tensorrt_subgraph_engine, true,
+DEFINE_bool(IA_enable_tensorrt_subgraph_engine, false,
             "Enable subgraph to TensorRT engine for acceleration");
 
-DEFINE_string(inference_analysis_graphviz_log_root, "./",
+DEFINE_bool(IA_enable_ir, false, "Turn on IR support");
+
+DEFINE_string(IA_graphviz_log_root, "./",
               "Graphviz debuger for data flow graphs.");
 
-DEFINE_string(inference_analysis_output_storage_path, "",
-              "optimized model output path");
+DEFINE_string(IA_output_storage_path, "", "optimized model output path");
 
 namespace inference {
 namespace analysis {
@@ -40,8 +42,34 @@ class DfgPassManagerImpl final : public DfgPassManager {
  public:
   DfgPassManagerImpl() {
     // TODO(Superjomn) set the key with pass reprs.
-    AddPass("fluid-to-data-flow-graph", new FluidToDataFlowGraphPass);
-    if (FLAGS_inference_analysis_enable_tensorrt_subgraph_engine) {
+    LOG(INFO)
+        << "-----------------------------------------------------------------";
+    if (FLAGS_IA_enable_ir) {
+      AddPass("fluid-to-ir-pass", new FluidToIrPass);
+    } else {
+      AddPass("fluid-to-data-flow-graph", new FluidToDataFlowGraphPass);
+    }
+    TryAddTensorRtPass();
+    AddPass("data-flow-graph-to-fluid", new DataFlowGraphToFluidPass);
+    if (!FLAGS_IA_output_storage_path.empty()) {
+      AddPass("model-store-pass", new ModelStorePass);
+    }
+    LOG(INFO)
+        << "-----------------------------------------------------------------";
+  }
+
+  std::string repr() const override { return "dfg-pass-manager"; }
+  std::string description() const override { return "DFG pass manager."; }
+
+ private:
+  void AddPass(const std::string& name, Pass* pass) {
+    VLOG(3) << "Adding pass " << name;
+    Register(name, pass);
+    AddGraphvizDebugerPass(pass);
+  }
+
+  void TryAddTensorRtPass() {
+    if (FLAGS_IA_enable_tensorrt_subgraph_engine) {
       auto trt_teller = [&](const Node* node) {
         std::unordered_set<std::string> teller_set(
             {"elementwise_add", "mul", "conv2d", "pool2d", "relu", "softmax"});
@@ -59,20 +87,6 @@ class DfgPassManagerImpl final : public DfgPassManager {
               new TensorRTSubgraphNodeMarkPass(trt_teller));
       AddPass("tensorrt-subgraph", new TensorRTSubGraphPass(trt_teller));
     }
-    AddPass("data-flow-graph-to-fluid", new DataFlowGraphToFluidPass);
-    if (!FLAGS_inference_analysis_output_storage_path.empty()) {
-      AddPass("model-store-pass", new ModelStorePass);
-    }
-  }
-
-  std::string repr() const override { return "dfg-pass-manager"; }
-  std::string description() const override { return "DFG pass manager."; }
-
- private:
-  void AddPass(const std::string& name, Pass* pass) {
-    LOG(INFO) << "Adding pass " << name;
-    Register(name, pass);
-    AddGraphvizDebugerPass(pass);
   }
 
   // Add the graphviz debuger pass if the parent pass has one.

paddle/fluid/inference/analysis/analyzer.h

@@ -43,9 +43,10 @@ namespace paddle {
 
 // TODO(Superjomn) add a definition flag like PADDLE_WITH_TENSORRT and hide this
 // flag if not available.
-DECLARE_bool(inference_analysis_enable_tensorrt_subgraph_engine);
-DECLARE_string(inference_analysis_graphviz_log_root);
-DECLARE_string(inference_analysis_output_storage_path);
+DECLARE_bool(IA_enable_tensorrt_subgraph_engine);
+DECLARE_string(IA_graphviz_log_root);
+DECLARE_string(IA_output_storage_path);
+DECLARE_bool(IA_enable_ir);
 
 namespace inference {
 namespace analysis {

paddle/fluid/inference/analysis/analyzer_tester.cc

@@ -14,14 +14,16 @@
 
 #include "paddle/fluid/inference/analysis/analyzer.h"
 #include <google/protobuf/text_format.h>
+#include "paddle/fluid/framework/ir/pass.h"
 #include "paddle/fluid/inference/analysis/ut_helper.h"
+#include "paddle/fluid/inference/api/paddle_inference_api.h"
 
 namespace paddle {
 namespace inference {
 namespace analysis {
 
 TEST(Analyzer, analysis_without_tensorrt) {
-  FLAGS_inference_analysis_enable_tensorrt_subgraph_engine = false;
+  FLAGS_IA_enable_tensorrt_subgraph_engine = false;
   Argument argument;
   argument.fluid_model_dir.reset(new std::string(FLAGS_inference_model_dir));
   Analyzer analyser;
@@ -29,13 +31,73 @@ TEST(Analyzer, analysis_without_tensorrt) {
 }
 
 TEST(Analyzer, analysis_with_tensorrt) {
-  FLAGS_inference_analysis_enable_tensorrt_subgraph_engine = true;
+  FLAGS_IA_enable_tensorrt_subgraph_engine = true;
   Argument argument;
   argument.fluid_model_dir.reset(new std::string(FLAGS_inference_model_dir));
   Analyzer analyser;
   analyser.Run(&argument);
 }
 
+void TestWord2vecPrediction(const std::string& model_path) {
+  NativeConfig config;
+  config.model_dir = model_path;
+  config.use_gpu = false;
+  config.device = 0;
+  auto predictor =
+      ::paddle::CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(
+          config);
+
+  // One single batch
+
+  int64_t data[4] = {1, 2, 3, 4};
+  PaddleTensor tensor;
+  tensor.shape = std::vector<int>({4, 1});
+  tensor.data = PaddleBuf(data, sizeof(data));
+  tensor.dtype = PaddleDType::INT64;
+
+  // For simplicity, we set all the slots with the same data.
+  std::vector<PaddleTensor> slots(4, tensor);
+  std::vector<PaddleTensor> outputs;
+  CHECK(predictor->Run(slots, &outputs));
+
+  PADDLE_ENFORCE(outputs.size(), 1UL);
+  // Check the output buffer size and result of each tid.
+  PADDLE_ENFORCE(outputs.front().data.length(), 33168UL);
+  float result[5] = {0.00129761, 0.00151112, 0.000423564, 0.00108815,
+                     0.000932706};
+  const size_t num_elements = outputs.front().data.length() / sizeof(float);
+  // The outputs' buffers are in CPU memory.
+  for (size_t i = 0; i < std::min(5UL, num_elements); i++) {
+    LOG(INFO) << "data: "
+              << static_cast<float*>(outputs.front().data.data())[i];
+    PADDLE_ENFORCE(static_cast<float*>(outputs.front().data.data())[i],
+                   result[i]);
+  }
+}
+
+// Turn on the IR pass supportion, run a real inference and check the result.
+TEST(Analyzer, SupportIRPass) {
+  FLAGS_IA_enable_ir = true;
+  FLAGS_IA_enable_tensorrt_subgraph_engine = false;
+  FLAGS_IA_output_storage_path = "./analysis.out";
+
+  Argument argument(FLAGS_inference_model_dir);
+  argument.model_output_store_path.reset(new std::string("./analysis.out"));
+
+  Analyzer analyzer;
+  analyzer.Run(&argument);
+
+  // Should get the transformed model stored to ./analysis.out
+  ASSERT_TRUE(PathExists("./analysis.out"));
+
+  // Inference from this path.
+  TestWord2vecPrediction("./analysis.out");
+}
+
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle
+
+USE_PASS(fc_fuse_pass);
+USE_PASS(graph_viz_pass);
+USE_PASS(infer_clean_graph_pass);

paddle/fluid/inference/analysis/data_flow_graph.cc

@@ -19,14 +19,16 @@ limitations under the License. */
 namespace paddle {
 namespace inference {
 namespace analysis {
+using ir_node_t = framework::ir::Node;
+using ir_graph_t = framework::ir::Graph;
 
 // It is a better idea that the inputs and outputs of this graph is set manually
 // before, but there must be a Pass that helps to prune the unnecessary ops that
 // do not contribute to the given targets, so in this pass, analysis and get the
 // inputs and outputs is OK.
 void DataFlowGraph::Build() {
-  inputs.clear();
-  outputs.clear();
+  inputs_.clear();
+  outputs_.clear();
   std::unordered_set<Node *> ins;
   std::unordered_set<Node *> outs;
   for (auto &node : nodes.nodes()) {
@@ -42,18 +44,140 @@ void DataFlowGraph::Build() {
   // similarly, the nodes that in outs but not in ins is the graphs' outputs
   for (auto *in : ins) {
     if (!outs.count(in)) {
-      inputs.push_back(in);
+      inputs_.push_back(in);
     }
   }
   for (auto *out : outs) {
-    if (!outs.count(out)) {
-      outputs.push_back(out);
+    if (!ins.count(out)) {
+      outputs_.push_back(out);
     }
   }
 
   Clean();
 }
 
+void DataFlowGraph::Build(const framework::proto::ProgramDesc &prog) {
+  // insert vars
+  // The `var2id` keeps a map from a variable's name to its Node-id, the Node-id
+  // will keep updating to its latest alias during the graph-building.
+  std::unordered_map<std::string, size_t> var2id;
+  auto &main_block = prog.blocks(framework::kRootBlockIndex);
+  for (int i = 0; i < main_block.vars_size(); i++) {
+    const auto &var = main_block.vars(i);
+    auto *v = nodes.Create(Node::Type::kValue);
+    v->SetName(var.name());
+    v->SetPbDesc(const_cast<void *>(static_cast<const void *>(&var)));
+    v->SetPbMsg(var.SerializeAsString());
+    var2id[var.name()] = v->id();
+  }
+
+  // The variables in a SSA can only write once, so if a variable is written
+  // multiple times(quite common in our ProgramDesc design), multiple alias
+  // Nodes of this variable will be created, and each will just write once.
+
+  // An set that keep all the names of the variables(the original, not alias)
+  // that have been written(as outputs). Once an Op's output variable hit the
+  // set, it should create a new alias and update the global alias for this
+  // variable. And that make a Data Flow Graph a SSA.
+  std::unordered_set<Node *> unique_written_vars;
+  for (int i = 0; i < main_block.ops_size(); i++) {
+    const auto &op = main_block.ops(i);
+    auto *o = nodes.Create(Node::Type::kFunction);
+    o->SetName(op.type());
+    static_cast<Function *>(o)->SetFuncType(op.type());
+    // Link to the original protobuf message's memory, make it easier to
+    // generate from a data flow graph to fluid ProgramDesc.
+    o->SetPbDesc(const_cast<void *>(static_cast<const void *>(&op)));
+    o->SetPbMsg(op.SerializeAsString());
+
+    // set inputs and outputs
+    for (int j = 0; j < op.inputs_size(); j++) {
+      auto &in_var = op.inputs(j);
+      for (int k = 0; k < in_var.arguments_size(); k++) {
+        auto *in = nodes.GetMutable(var2id.at(in_var.arguments(k)));
+        in->outlinks.push_back(o);
+        o->inlinks.push_back(in);
+      }
+    }
+    for (int j = 0; j < op.outputs_size(); j++) {
+      auto &out_var = op.outputs(j);
+      for (int k = 0; k < out_var.arguments_size(); k++) {
+        auto *out = nodes.GetMutable(var2id[out_var.arguments(k)]);
+        if (unique_written_vars.count(out)) {
+          // Loop found, for example, a = op(a), use SSA, change to a1 = op(a).
+          auto *out_alias = nodes.Create(Node::Type::kValue);
+          out_alias->SetName(out->name());
+          out_alias->SetPbDesc(out->pb_desc());
+          out_alias->SetPbMsg(out->pb_msg());
+          var2id[out_alias->name()] =
+              out_alias->id();  // update variable's alias Node
+          LOG(INFO) << "loop found in graph, create SSA alias node ["
+                    << out_alias->repr() << "] for [" << out->repr() << "]";
+          out = out_alias;
+        }
+        out->inlinks.push_back(o);
+        o->outlinks.push_back(out);
+        unique_written_vars.insert(out);
+      }
+    }
+  }
+  // Analysis and extract the inputs and outputs of this graph.
+  Build();
+}
+
+void DataFlowGraph::Build(const framework::ir::Graph &graph) {
+  // Create nodes
+  std::unordered_map<ir_node_t *, Node *> ir_node_map;
+  for (auto *ir_node : graph.Nodes()) {
+    Node *x{nullptr};
+    if (ir_node->IsOp()) {
+      PADDLE_ENFORCE(ir_node->Op());
+      VLOG(4) << "get op " << ir_node << " " << ir_node->Name();
+      x = nodes.Create(Node::Type::kFunction);
+      x->attr("ir_node").Pointer() = ir_node;
+      PADDLE_ENFORCE(ir_node->Op()->Proto());
+      x->SetName(ir_node->Op()->Proto()->type());
+      x->SetPbMsg(ir_node->Op()->Proto()->SerializeAsString());
+    } else if (ir_node->IsVar()) {
+      // Not create a Node for IR ControlDepVar, considering Inference currently
+      // just used in single thread scenerio.
+      VLOG(4) << "get var " << ir_node->Name();
+      x = nodes.Create(Node::Type::kValue);
+      x->attr("ir_node").Pointer() = ir_node;
+      x->SetName(ir_node->Name());
+      // x->SetPbMsg(ir_node->Var()->Proto()->SerializeAsString());
+    } else {
+      PADDLE_THROW("Failed to create an Node from IR, unknown type");
+    }
+    ir_node_map.emplace(ir_node, x);
+  }
+  VLOG(4) << "finish creating Nodes";
+
+  VLOG(4) << "to create edge";
+  // Create links
+  for (auto *ir_node : graph.Nodes()) {
+    auto it = ir_node_map.find(ir_node);
+    // Skip ControlDepVar.
+    if (it == ir_node_map.end()) continue;
+    auto *node = it->second;
+    for (auto *x : ir_node->inputs) {
+      if (!ir_node_map.count(x)) continue;
+      node->inlinks.push_back(ir_node_map.at(x));
+    }
+    for (auto *x : ir_node->outputs) {
+      if (!ir_node_map.count(x)) continue;
+      node->outlinks.push_back(ir_node_map.at(x));
+    }
+  }
+
+  Build();
+  PADDLE_ENFORCE(!inputs_.empty(),
+                 "Can't deduce any inputs from the graph, Is the graph empty?");
+
+  ir_graph = &graph;
+  VLOG(3) << "finished build from IR";
+}
+
 void DataFlowGraph::Clean() {
   for (auto &node : nodes.nodes()) {
     std::unordered_set<Node *> inlinks_set(node->inlinks.begin(),
@@ -61,11 +185,9 @@ void DataFlowGraph::Clean() {
     std::unordered_set<Node *> outlinks_set(node->outlinks.begin(),
                                             node->outlinks.end());
     if (inlinks_set.size() < node->inlinks.size()) {
-      LOG(INFO) << "Clean: node " << node->repr() << " prune duplicate inputs";
       node->inlinks.assign(inlinks_set.begin(), inlinks_set.end());
     }
     if (outlinks_set.size() < node->outlinks.size()) {
-      LOG(INFO) << "Clean: node " << node->repr() << " prune duplicate inputs";
       node->outlinks.assign(outlinks_set.begin(), outlinks_set.end());
     }
   }
@@ -112,10 +234,10 @@ GraphTraits<DataFlowGraph>::NodesBFSIterator::NodesBFSIterator(
     const std::vector<Node *> &source)
     : queue_(source.begin(), source.end()) {}
 
-// GraphTraits<DataFlowGraph>::NodesBFSIterator::NodesBFSIterator(
-//     GraphTraits<DataFlowGraph>::NodesBFSIterator &&other) noexcept
-//     : queue_(std::move(other.queue_)),
-//       visited_(std::move(other.visited_)) {}
+GraphTraits<DataFlowGraph>::NodesBFSIterator::NodesBFSIterator(
+    GraphTraits<DataFlowGraph>::NodesBFSIterator &&other) noexcept
+    : queue_(std::move(other.queue_)),
+      visited_(std::move(other.visited_)) {}
 
 GraphTraits<DataFlowGraph>::NodesBFSIterator::NodesBFSIterator(
     const GraphTraits<DataFlowGraph>::NodesBFSIterator &other)
@@ -159,7 +281,7 @@ bool GraphTraits<DataFlowGraph>::NodesBFSIterator::operator==(
   if (queue_.empty()) return other.queue_.empty();
   if ((!queue_.empty()) && (!other.queue_.empty())) {
     return queue_.front() == other.queue_.front() &&
-           visited_.size() == other.visited_.size();  // here need to check the
+           visited_.size() == other.visited_.size();
     // equality of queue and
     // visited. Just a light but week implementation.
   }
@@ -174,10 +296,10 @@ GraphTraits<DataFlowGraph>::NodesDFSIterator::NodesDFSIterator(
   for (auto *x : source) stack_.push(x);
 }
 
-// GraphTraits<DataFlowGraph>::NodesDFSIterator::NodesDFSIterator(
-//     GraphTraits<DataFlowGraph>::NodesDFSIterator &&other) noexcept
-//     : stack_(std::move(other.stack_)),
-//       visited_(std::move(other.visited_)) {}
+GraphTraits<DataFlowGraph>::NodesDFSIterator::NodesDFSIterator(
+    GraphTraits<DataFlowGraph>::NodesDFSIterator &&other) noexcept
+    : stack_(std::move(other.stack_)),
+      visited_(std::move(other.visited_)) {}
 
 GraphTraits<DataFlowGraph>::NodesDFSIterator::NodesDFSIterator(
     const GraphTraits<DataFlowGraph>::NodesDFSIterator &other)
@@ -339,7 +461,7 @@ ExtractInputAndOutputOfSubGraph(std::vector<Node *> &graph) {  // NOLINT
 
 void FilterRedundantOutputOfSubGraph(DataFlowGraph *graph) {
   std::vector<Node *> op_nodes;
-  for (auto &node : GraphTraits<DataFlowGraph>(graph).nodes_in_TS()) {
+  for (auto &node : GraphTraits<DataFlowGraph>(*graph).nodes_in_TS()) {
     if (node.type() == Node::Type::kValue || node.deleted()) {
       continue;
     }

paddle/fluid/inference/analysis/data_flow_graph.h

@@ -26,6 +26,7 @@ limitations under the License. */
 #include <utility>
 #include <vector>
 
+#include "paddle/fluid/framework/ir/graph.h"
 #include "paddle/fluid/inference/analysis/graph_traits.h"
 #include "paddle/fluid/inference/analysis/node.h"
 #include "paddle/fluid/platform/enforce.h"
@@ -41,19 +42,43 @@ namespace analysis {
  */
 struct DataFlowGraph {
   NodeMap nodes;
-  std::vector<Node *> inputs;
-  std::vector<Node *> outputs;
+  // inputs and outputs are deduced from the graph.
+  // Used to interact with IR.
+  const framework::ir::Graph *ir_graph{nullptr};
 
   // Extract inputs and outputs of the graph.
   void Build();
 
+  void Build(const framework::proto::ProgramDesc &prog);
+
+  // Build a graph from ir::Graph.
+  void Build(const framework::ir::Graph &graph);
+
+  // Get an attribute.
+  AnyAttr &Attr(const std::string &key) { return attrs_[key]; }
+
   // Output a DOT graph file for debug.
   std::string DotString() const;
 
   std::string HumanReadableInfo(bool show_values = true,
                                 bool show_functions = true) const;
 
+  const std::vector<Node *> &inputs() const {
+    PADDLE_ENFORCE(!inputs_.empty(),
+                   "No inputs are deduced, need to Build() first.");
+    return inputs_;
+  }
+  const std::vector<Node *> &outputs() const {
+    PADDLE_ENFORCE(!outputs_.empty(),
+                   "No outputs are deduced, need to Build() first.");
+    return outputs_;
+  }
+
  private:
+  mutable std::vector<Node *> inputs_;
+  mutable std::vector<Node *> outputs_;
+  std::unordered_map<std::string, AnyAttr> attrs_;
+
   // Remove duplicate edges and so on.
   void Clean();
 };
@@ -70,7 +95,7 @@ struct GraphTraits<DataFlowGraph> {
       : public std::iterator<std::forward_iterator_tag, Node *> {
     NodesBFSIterator() = default;
     explicit NodesBFSIterator(const std::vector<Node *> &source);
-    // NodesBFSIterator(NodesBFSIterator &&other) noexcept;
+    NodesBFSIterator(NodesBFSIterator &&other) noexcept;
     // NOTE Heavy to use.
     NodesBFSIterator(const NodesBFSIterator &other);
 
@@ -93,8 +118,8 @@ struct GraphTraits<DataFlowGraph> {
   struct NodesDFSIterator
       : public std::iterator<std::forward_iterator_tag, Node *> {
     NodesDFSIterator() = default;
-    explicit NodesDFSIterator(const std::vector<Node *> &source);
-    // NodesDFSIterator(NodesDFSIterator &&other) noexcept;
+    NodesDFSIterator(const std::vector<Node *> &source);
+    NodesDFSIterator(NodesDFSIterator &&other) noexcept;
     NodesDFSIterator(const NodesDFSIterator &other);
 
     Node &operator*();
@@ -116,7 +141,7 @@ struct GraphTraits<DataFlowGraph> {
   struct NodesTSIterator
       : public std::iterator<std::forward_iterator_tag, Node *> {
     NodesTSIterator() = default;
-    explicit NodesTSIterator(const std::vector<Node *> &source);
+    NodesTSIterator(const std::vector<Node *> &source);
     NodesTSIterator(NodesTSIterator &&other)
         : sorted_(std::move(other.sorted_)), cursor_(other.cursor_) {
       other.cursor_ = 0;
@@ -138,7 +163,7 @@ struct GraphTraits<DataFlowGraph> {
     size_t cursor_{0};
   };
 
-  explicit GraphTraits(DataFlowGraph *graph) : graph_(graph) {}
+  explicit GraphTraits(const DataFlowGraph &graph) : graph_(graph) {}
 
   // default use BFS to visit the nodes.
   iterator_range<NodesBFSIterator> nodes() {
@@ -156,20 +181,20 @@ struct GraphTraits<DataFlowGraph> {
 
  private:
   NodesBFSIterator nodes_bfs_begin() {
-    return NodesBFSIterator(graph_->inputs);
+    return NodesBFSIterator(graph_.inputs());
   }
   NodesBFSIterator nodes_bfs_end() { return NodesBFSIterator(); }
 
   NodesDFSIterator nodes_dfs_begin() {
-    return NodesDFSIterator(graph_->inputs);
+    return NodesDFSIterator(graph_.inputs());
   }
   NodesDFSIterator nodes_dfs_end() { return NodesDFSIterator(); }
 
-  NodesTSIterator nodes_ts_begin() { return NodesTSIterator(graph_->inputs); }
+  NodesTSIterator nodes_ts_begin() { return NodesTSIterator(graph_.inputs()); }
   NodesTSIterator nodes_ts_end() { return NodesTSIterator(); }
 
  private:
-  DataFlowGraph *graph_;
+  const DataFlowGraph &graph_;
 };
 
 // Extract the inputs and outputs of a graph. The inputs and outputs of a

paddle/fluid/inference/analysis/data_flow_graph_tester.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/inference/analysis/data_flow_graph.h"
+#include "paddle/fluid/framework/program_desc.h"
 #include "paddle/fluid/inference/analysis/ut_helper.h"
 
 namespace paddle {
@@ -24,20 +25,18 @@ TEST(DataFlowGraph, BFS) {
   auto dfg = ProgramDescToDFG(desc);
   dfg.Build();
 
-  for (auto *in : dfg.inputs) {
+  for (auto* in : dfg.inputs()) {
     LOG(INFO) << "inputs: " << in->name() << " "
               << static_cast<int>(in->type());
   }
-  for (auto *out : dfg.outputs) {
+  for (auto* out : dfg.outputs()) {
     LOG(INFO) << "outputs: " << out->name() << " "
               << static_cast<int>(out->type());
   }
 
-  GraphTraits<DataFlowGraph> trait(&dfg);
-  auto nodes = trait.nodes();
   size_t count = 0;
-  for (auto it = nodes.begin(); it != nodes.end(); ++it) {
-    LOG(INFO) << "visiting " << it->name();
+  for (auto& node : GraphTraits<DataFlowGraph>(dfg).nodes()) {
+    LOG(INFO) << "visiting " << node.name();
     ++count;
   }
   ASSERT_EQ(count, dfg.nodes.size());
@@ -45,13 +44,11 @@ TEST(DataFlowGraph, BFS) {
 
 TEST(DataFlowGraph, DFS) {
   auto desc = LoadProgramDesc(FLAGS_inference_model_dir + "/__model__");
-  auto dfg = ProgramDescToDFG(desc);
-  dfg.Build();
-  GraphTraits<DataFlowGraph> trait(&dfg);
-  auto nodes = trait.nodes_in_DFS();
+  DataFlowGraph dfg;
+  dfg.Build(desc);
   size_t count = 0;
-  for (auto it = nodes.begin(); it != nodes.end(); ++it) {
-    LOG(INFO) << "visiting " << it->name();
+  for (auto& node : GraphTraits<DataFlowGraph>(dfg).nodes_in_DFS()) {
+    LOG(INFO) << "visiting " << node.name();
     ++count;
   }
   ASSERT_EQ(count, dfg.nodes.size());
@@ -74,21 +71,17 @@ TEST(DataFlowGraph, TS) {
   DataFlowGraph graph;
 
   for (int i = 0; i < 8; i++) {
-    auto *node = graph.nodes.Create(Node::Type::kValue);
+    auto* node = graph.nodes.Create(Node::Type::kValue);
     node->SetName("node-" + std::to_string(i));
   }
 
   auto add_link = [&](int i, int j) {
-    Node *source = graph.nodes.GetMutable(i);
-    Node *target = graph.nodes.GetMutable(j);
+    Node* source = graph.nodes.GetMutable(i);
+    Node* target = graph.nodes.GetMutable(j);
     target->inlinks.push_back(source);
     source->outlinks.push_back(target);
   };
 
-  graph.inputs.push_back(graph.nodes.GetMutable(0));
-  graph.inputs.push_back(graph.nodes.GetMutable(1));
-  graph.inputs.push_back(graph.nodes.GetMutable(2));
-
   add_link(0, 4);
   add_link(0, 5);
   add_link(1, 6);
@@ -97,8 +90,9 @@ TEST(DataFlowGraph, TS) {
   add_link(4, 7);
   add_link(4, 3);
   add_link(7, 3);
+  graph.Build();
 
-  auto its = GraphTraits<DataFlowGraph>(&graph).nodes_in_TS();
+  auto its = GraphTraits<DataFlowGraph>(graph).nodes_in_TS();
   std::vector<int> sorted_ids;
   for (auto it = its.begin(); it != its.end(); ++it) {
     LOG(INFO) << it->name();
@@ -122,6 +116,50 @@ TEST(DataFlowGraph, TS) {
   assert_positive_sequence_pair(4, 7);
 }
 
+TEST(DataFlowGraph, Build_ProgramDesc) {
+  auto desc = LoadProgramDesc(FLAGS_inference_model_dir + "/__model__");
+  DataFlowGraph graph;
+  graph.Build(desc);
+  ASSERT_EQ(graph.nodes.size(), 38UL);
+}
+
+void SetOp(framework::ProgramDesc* prog, const std::string& type,
+           const std::vector<std::string>& inputs,
+           const std::vector<std::string>& outputs) {
+  auto* op = prog->MutableBlock(0)->AppendOp();
+  op->SetType(type);
+  op->SetInput("Xs", inputs);
+  op->SetOutput("Xs", outputs);
+}
+
+TEST(DataFlowGraph, Build_IR_Graph) {
+  framework::ProgramDesc prog;
+  for (auto& v : std::vector<std::string>({"a", "b", "c", "d", "e", "f"})) {
+    auto* var = prog.MutableBlock(0)->Var(v);
+    var->SetType(framework::proto::VarType::SELECTED_ROWS);
+    if (v == "c") {
+      var->SetPersistable(true);
+    }
+  }
+
+  SetOp(&prog, "OP0", std::vector<std::string>({"a"}),
+        std::vector<std::string>({"b"}));
+  SetOp(&prog, "OP1", std::vector<std::string>({"a"}),
+        std::vector<std::string>({"c"}));
+  SetOp(&prog, "mul", std::vector<std::string>({"b", "c"}),
+        std::vector<std::string>({"d"}));
+  SetOp(&prog, "elementwise_add", std::vector<std::string>({"d", "e"}),
+        std::vector<std::string>({"f"}));
+
+  DataFlowGraph graph;
+
+  framework::ir::Graph ir_graph(prog);
+
+  graph.Build(ir_graph);
+
+  ASSERT_EQ(graph.nodes.size(), ir_graph.Nodes().size());
+}
+
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.cc

@@ -52,18 +52,15 @@ bool DataFlowGraphToFluidPass::Initialize(Argument *argument) {
 bool DataFlowGraphToFluidPass::Finalize() { return true; }
 
 void DataFlowGraphToFluidPass::Run(DataFlowGraph *graph) {
-  LOG(INFO) << "graph.inputs " << graph->inputs.size();
-  for (auto &node : GraphTraits<DataFlowGraph>(graph).nodes_in_TS()) {
+  // FilterRedundantOutputOfSubGraph(graph);
+  for (auto &node : GraphTraits<DataFlowGraph>(*graph).nodes_in_TS()) {
     if (node.deleted()) continue;
 
     switch (node.type()) {
       case Node::Type::kFunction: {
-        LOG(INFO) << "add function " << node.repr();
         AddFluidOp(&node);
       } break;
       case Node::Type::kFunctionBlock: {
-        LOG(INFO) << "add engine op " << node.repr() << " , "
-                  << static_cast<FunctionBlock *>(&node)->subgraph.size();
         AddEngineOp(&node);
       } break;
       default:
@@ -75,15 +72,27 @@ void DataFlowGraphToFluidPass::Run(DataFlowGraph *graph) {
 }
 
 void DataFlowGraphToFluidPass::AddFluidOp(Node *node) {
-  auto *ori_op = static_cast<framework::proto::OpDesc *>(node->pb_desc());
+  PADDLE_ENFORCE(node);
+  PADDLE_ENFORCE(node->IsFunction());
+  PADDLE_ENFORCE(node->pb_desc() || !node->pb_msg().empty(),
+                 "node has invalid protobuf repr.");
+
   // currently only the main block is analyzed.
+  PADDLE_ENFORCE(desc_);
   auto *main_block = desc_->mutable_blocks(framework::kRootBlockIndex);
   auto *op = main_block->add_ops();
-  *op = *ori_op;  // copy the attributes, by default, these will not be changed
-  // by analysis phrase.
-  // The inputs and outputs of the existing ops are not changed by tensorrt
-  // subgraph pass.
-  // NOTE It might be changed by other passes in the long run.
+
+  if (node->pb_desc()) {
+    auto *ori_op = static_cast<framework::proto::OpDesc *>(node->pb_desc());
+    *op =
+        *ori_op;  // copy the attributes, by default, these will not be changed
+    // by analysis phrase.
+    // The inputs and outputs of the existing ops are not changed by tensorrt
+    // subgraph pass.
+    // NOTE It might be changed by other passes in the long run.
+  } else {
+    op->ParseFromString(node->pb_msg());
+  }
 }
 
 void CreateTrtEngineOp(Node *node, const DataFlowGraph &graph,
@@ -220,10 +229,9 @@ void DataFlowGraphToFluidPass::AddEngineOp(Node *node) {
   framework::BlockDesc block_desc(nullptr, &proto);
   block_desc.Proto()->set_parent_idx(-1);
   block_desc.Proto()->set_idx(0);
-  LOG(INFO) << "origin variable size: "
-            << argument_->origin_program_desc->blocks(0).vars().size();
-  LOG(INFO) << "transformed variable size: "
-            << block_desc.Proto()->vars().size();
+  VLOG(4) << "origin variable size: "
+          << argument_->origin_program_desc->blocks(0).vars().size();
+  VLOG(4) << "transformed variable size: " << block_desc.Proto()->vars().size();
   // copy ops.
 
   for (auto *node : block_node->subgraph) {
@@ -257,7 +265,7 @@ class DFG_DebuggerPass : public DFG_GraphvizDrawPass {
 
 Pass *DataFlowGraphToFluidPass::CreateGraphvizDebugerPass() const {
   return new DFG_DebuggerPass(DFG_GraphvizDrawPass::Config(
-      FLAGS_inference_analysis_graphviz_log_root,
+      FLAGS_IA_graphviz_log_root,
       "data_flow_graph_to_fluid_graphviz_debugger"));
 }
 

paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.cc

@@ -29,7 +29,7 @@ void DFG_GraphvizDrawPass::Run(DataFlowGraph *graph) {
 
   auto png_path = dot_path.substr(0, dot_path.size() - 4) + ".png";
   std::string message;
-  LOG(INFO) << "draw to " << png_path;
+  VLOG(3) << "draw to " << png_path;
   ExecShellCommand("dot -Tpng " + dot_path + " -o " + png_path, &message);
 }
 

paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.cc

@@ -52,72 +52,7 @@ bool FluidToDataFlowGraphPass::Finalize() { return true; }
 void FluidToDataFlowGraphPass::Run(DataFlowGraph *graph) {
   PADDLE_ENFORCE(graph);
   PADDLE_ENFORCE(desc_);
-  // insert vars
-  // The `var2id` keeps a map from a variable's name to its Node-id, the Node-id
-  // will keep updating to its latest alias during the graph-building.
-  std::unordered_map<std::string, size_t> var2id;
-  auto &main_block = desc_->blocks(framework::kRootBlockIndex);
-  for (int i = 0; i < main_block.vars_size(); i++) {
-    const auto &var = main_block.vars(i);
-    auto *v = graph->nodes.Create(Node::Type::kValue);
-    v->SetName(var.name());
-    v->SetPbDesc(const_cast<void *>(static_cast<const void *>(&var)));
-    v->SetPbMsg(var.SerializeAsString());
-    var2id[var.name()] = v->id();
-  }
-
-  // The variables in a SSA can only write once, so if a variable is written
-  // multiple times(quite common in our ProgramDesc design), multiple alias
-  // Nodes of this variable will be created, and each will just write once.
-
-  // An set that keep all the names of the variables(the original, not alias)
-  // that have been written(as outputs). Once an Op's output variable hit the
-  // set, it should create a new alias and update the global alias for this
-  // variable. And that make a Data Flow Graph a SSA.
-  std::unordered_set<Node *> unique_written_vars;
-  for (int i = 0; i < main_block.ops_size(); i++) {
-    const auto &op = main_block.ops(i);
-    auto *o = graph->nodes.Create(Node::Type::kFunction);
-    o->SetName(op.type());
-    static_cast<Function *>(o)->SetFuncType(op.type());
-    // Link to the original protobuf message's memory, make it easier to
-    // generate from a data flow graph to fluid ProgramDesc.
-    o->SetPbDesc(const_cast<void *>(static_cast<const void *>(&op)));
-    o->SetPbMsg(op.SerializeAsString());
-
-    // set inputs and outputs
-    for (int j = 0; j < op.inputs_size(); j++) {
-      auto &in_var = op.inputs(j);
-      for (int k = 0; k < in_var.arguments_size(); k++) {
-        auto *in = graph->nodes.GetMutable(var2id.at(in_var.arguments(k)));
-        in->outlinks.push_back(o);
-        o->inlinks.push_back(in);
-      }
-    }
-    for (int j = 0; j < op.outputs_size(); j++) {
-      auto &out_var = op.outputs(j);
-      for (int k = 0; k < out_var.arguments_size(); k++) {
-        auto *out = graph->nodes.GetMutable(var2id[out_var.arguments(k)]);
-        if (unique_written_vars.count(out)) {
-          // Loop found, for example, a = op(a), use SSA, change to a1 = op(a).
-          auto *out_alias = graph->nodes.Create(Node::Type::kValue);
-          out_alias->SetName(out->name());
-          out_alias->SetPbDesc(out->pb_desc());
-          out_alias->SetPbMsg(out->pb_msg());
-          var2id[out_alias->name()] =
-              out_alias->id();  // update variable's alias Node
-          LOG(INFO) << "loop found in graph, create SSA alias node ["
-                    << out_alias->repr() << "] for [" << out->repr() << "]";
-          out = out_alias;
-        }
-        out->inlinks.push_back(o);
-        o->outlinks.push_back(out);
-        unique_written_vars.insert(out);
-      }
-    }
-  }
-  // Analysis and extract the inputs and outputs of this graph.
-  graph->Build();
+  graph->Build(*desc_);
 }
 
 namespace {
@@ -133,7 +68,7 @@ class DFG_DebuggerPass : public DFG_GraphvizDrawPass {
 
 Pass *FluidToDataFlowGraphPass::CreateGraphvizDebugerPass() const {
   return new DFG_DebuggerPass(DFG_GraphvizDrawPass::Config(
-      FLAGS_inference_analysis_graphviz_log_root, "fluid-to-dfg-debuger"));
+      FLAGS_IA_graphviz_log_root, "fluid-to-dfg-debuger"));
 }
 
 }  // namespace analysis

paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass_tester.cc

@@ -30,7 +30,7 @@ TEST(FluidToDataFlowGraphPass, Test) {
   ASSERT_EQ(argument.main_dfg->nodes.size(), 38UL);
   pass.Finalize();
   ASSERT_FALSE(argument.main_dfg->DotString().empty());
-  EXPECT_FALSE(argument.main_dfg->inputs.empty());
+  EXPECT_FALSE(argument.main_dfg->inputs().empty());
 }
 
 }  // namespace analysis

paddle/fluid/inference/analysis/fluid_to_ir_pass.cc

+// 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/fluid_to_ir_pass.h"

paddle/fluid/inference/analysis/fluid_to_ir_pass.h

+// 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 "paddle/fluid/inference/analysis/ir_pass_manager.h"
+#include "paddle/fluid/inference/analysis/pass.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+class FluidToIrPass final : public DataFlowGraphPass {
+ public:
+  FluidToIrPass() = default;
+
+  bool Initialize(Argument *argument) override {
+    ANALYSIS_ARGUMENT_CHECK_FIELD(argument);
+    if (argument->origin_program_desc) {
+      LOG(WARNING) << "argument's origin_program_desc is already set, might "
+                      "duplicate called";
+    }
+    // set fluid model program path
+    if (!argument->fluid_model_program_path) {
+      ANALYSIS_ARGUMENT_CHECK_FIELD(argument->fluid_model_dir);
+      argument->fluid_model_program_path.reset(
+          new std::string(*argument->fluid_model_dir + "/__model__"));
+    }
+    ANALYSIS_ARGUMENT_CHECK_FIELD(argument->fluid_model_program_path);
+    // Load program.
+    auto program = LoadProgramDesc(*argument->fluid_model_program_path);
+    argument->origin_program_desc.reset(
+        new framework::proto::ProgramDesc(program));
+    // Create main data flow graph.
+    if (!argument->main_dfg) {
+      argument->main_dfg.reset(new DataFlowGraph);
+    }
+    // Persist the ProgramDesc in graph's attribute. The IR graph just keep the
+    // address, will segfault if the original ProgramDesc destroys.
+    auto &ir_program_p = argument->main_dfg->Attr("ir_program_desc").Pointer();
+    ir_program_p = new framework::ProgramDesc(program);
+
+    argument_ = argument;
+    return true;
+  }
+
+  bool Finalize() override { return true; }
+
+  void Run(DataFlowGraph *graph) override {
+    // Call all the IR Passes
+    IRPassManager ir_passes(*static_cast<framework::ProgramDesc *>(
+        argument_->main_dfg->Attr("ir_program_desc").Pointer()));
+    ir_passes.Apply(std::vector<std::string>(
+        {// Manual update the passes here.
+         "graph_viz_pass", "infer_clean_graph_pass", "graph_viz_pass",
+         "fc_fuse_pass", "graph_viz_pass"}));
+
+    PADDLE_ENFORCE(argument_->main_dfg.get());
+    argument_->main_dfg->Build(ir_passes.graph());
+    // PADDLE_ENFORCE(argument_->main_dfg->IsFullyConnected());
+  }
+
+  std::string repr() const override { return "fluid-to-ir-pass"; }
+
+ private:
+  Argument *argument_{nullptr};
+};
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/fluid_to_ir_pass_tester.cc

+// 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/fluid_to_ir_pass.h"
+
+#include <gtest/gtest.h>
+#include "paddle/fluid/inference/analysis/ut_helper.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+TEST(FluidToIrPass, Test) {
+  FluidToIrPass pass;
+  Argument argument(FLAGS_inference_model_dir);
+  pass.Initialize(&argument);
+  pass.Run(argument.main_dfg.get());
+}
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle
+
+USE_PASS(fc_fuse_pass);
+USE_PASS(graph_viz_pass);
+USE_PASS(infer_clean_graph_pass);

paddle/fluid/inference/analysis/helper.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 
+#include <sys/stat.h>
 #include <cstdio>
 #include <fstream>
 #include <string>
@@ -151,6 +152,23 @@ static framework::proto::ProgramDesc LoadProgramDesc(
   return program_desc;
 }
 
+static bool FileExists(const std::string &filepath) {
+  std::ifstream file(filepath);
+  bool exists = file.is_open();
+  file.close();
+  return exists;
+}
+
+static bool PathExists(const std::string &path) {
+  struct stat statbuf;
+  if (stat(path.c_str(), &statbuf) != -1) {
+    if (S_ISDIR(statbuf.st_mode)) {
+      return true;
+    }
+  }
+  return false;
+}
+
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/analysis/ir_pass_manager.cc

+// 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/ir_pass_manager.h"
+#include <string>
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+IRPassManager::IRPassManager(const ProgramDesc& program) {
+  graph_.reset(new framework::ir::Graph(program));
+}
+
+void IRPassManager::Apply(const std::vector<std::string>& passes) {
+  graph_->Set("graph_viz_path", new std::string("./1.dot"));
+  // Apply all the passes
+  std::string pre_pass;
+  for (const std::string& pass_name : passes) {
+    LOG(WARNING) << "Running IR pass [" << pass_name << "]";
+    auto pass = framework::ir::PassRegistry::Instance().Get(pass_name);
+    if (pass_name == "graph_viz_pass") {
+      std::string dot_file_path =
+          "ir_" + (pre_pass.empty() ? "origin" : pre_pass) + ".dot";
+      pass->Set("graph_viz_path", new std::string(std::move(dot_file_path)));
+    }
+    graph_ = pass->Apply(std::move(graph_));
+    pre_pass = pass_name;
+  }
+}
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/ir_pass_manager.h

+// 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.
+
+/*
+ * This file defines IRPassManager, it helps control the passes in IR. Inference
+ * phrase will load the model program and parameters from disk, that is quite
+ * different from the training phase.
+ * This manager will control the Passes and make the passes in IR work smoothly
+ * for inference.
+ */
+
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/pass.h"
+#include "paddle/fluid/framework/program_desc.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+using framework::ProgramDesc;
+
+class IRPassManager final {
+ public:
+  IRPassManager(const ProgramDesc& program);
+
+  void Apply(const std::vector<std::string>& passes);
+
+  framework::ir::Graph& graph() const { return *graph_; }
+
+ private:
+  std::unique_ptr<framework::ir::Graph> graph_;
+};
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/model_store_pass.cc

@@ -35,19 +35,21 @@ void ModelStorePass::Run(DataFlowGraph *x) {
   std::stringstream ss;
   // NOTE these commands only works on linux.
   ss << "mkdir -p " << *argument_->model_output_store_path;
-  LOG(INFO) << "run command: " << ss.str();
+  VLOG(3) << "run command: " << ss.str();
   PADDLE_ENFORCE_EQ(system(ss.str().c_str()), 0);
   ss.str("");
 
   ss << "cp " << *argument_->fluid_model_dir << "/*"
      << " " << *argument_->model_output_store_path;
-  LOG(INFO) << "run command: " << ss.str();
+  VLOG(3) << "run command: " << ss.str();
   PADDLE_ENFORCE_EQ(system(ss.str().c_str()), 0);
 
   // Store program
   PADDLE_ENFORCE_NOT_NULL(argument_->transformed_program_desc,
                           "program desc is not transformed, should call "
                           "DataFlowGraphToFluidPass first.");
+  VLOG(3) << "store analyzed program to "
+          << *argument_->model_output_store_path;
   const std::string program_output_path =
       *argument_->model_output_store_path + "/__model__";
   std::ofstream file(program_output_path, std::ios::binary);
@@ -58,6 +60,8 @@ void ModelStorePass::Run(DataFlowGraph *x) {
   file.write(serialized_message.c_str(), serialized_message.size());
 }
 
+bool ModelStorePass::Finalize() { return true; }
+
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/analysis/model_store_pass.h

@@ -44,6 +44,8 @@ class ModelStorePass : public DataFlowGraphPass {
     model in the disk, and that model can be reloaded for prediction again.)DD";
   }
 
+  bool Finalize() override;
+
  private:
   Argument* argument_{nullptr};
 };

paddle/fluid/inference/analysis/model_store_pass_tester.cc

@@ -30,7 +30,7 @@ TEST(DFG_StorePass, test) {
   argument.model_output_store_path.reset(
       new std::string("./_dfg_store_pass_tmp"));
   // disable storage in alalyzer
-  FLAGS_inference_analysis_output_storage_path = "";
+  FLAGS_IA_output_storage_path = "";
   analyzer.Run(&argument);
 
   ModelStorePass pass;

paddle/fluid/inference/analysis/node.h

@@ -38,7 +38,7 @@ namespace analysis {
 class NodeMap;
 
 // A helper class to maintain the status from Pass.
-struct NodeAttr {
+struct AnyAttr {
   using any_t =
       boost::variant<bool, float, int32_t, int64_t, void *, std::string>;
   // NOTE T should be a primary type or a struct combined by several primary
@@ -54,10 +54,9 @@ struct NodeAttr {
   void *&Pointer() { return As<void *>(); }
   std::string &String() { return As<std::string>(); }
 
- private:
   template <typename T>
   T &As() {
-    if (type_index_ == typeid(NodeAttr)) {
+    if (type_index_ == typeid(AnyAttr)) {
       type_index_ = typeid(T);
       any_data_ = T();
     } else {
@@ -68,7 +67,7 @@ struct NodeAttr {
 
  private:
   any_t any_data_;
-  std::type_index type_index_{typeid(NodeAttr)};
+  std::type_index type_index_{typeid(AnyAttr)};
 };
 
 /*
@@ -105,7 +104,7 @@ class Node {
 
   // Get an additional attribute and convert it to T data type. NOTE this will
   // silently create a new attribute if not exists.
-  NodeAttr &attr(const std::string &name) const { return attrs_[name]; }
+  AnyAttr &attr(const std::string &name) const { return attrs_[name]; }
 
   int id() const { return id_; }
 
@@ -150,7 +149,7 @@ class Node {
   Type type_{Type::kNone};
   // Mark this node is deleted by some pass.
   bool deleted_{false};
-  mutable std::unordered_map<std::string, NodeAttr> attrs_;
+  mutable std::unordered_map<std::string, AnyAttr> attrs_;
 };
 
 class Function;

paddle/fluid/inference/analysis/node_tester.cc

@@ -21,19 +21,19 @@ namespace inference {
 namespace analysis {
 
 TEST(NodeAttr, bool) {
-  NodeAttr x;
+  AnyAttr x;
   x.Bool() = true;
   ASSERT_EQ(x.Bool(), true);
 }
 
 TEST(NodeAttr, int32) {
-  NodeAttr x;
+  AnyAttr x;
   x.Int32() = 32;
   ASSERT_EQ(x.Int32(), 32);
 }
 
 TEST(NodeAttr, string) {
-  NodeAttr x;
+  AnyAttr x;
   x.String() = "Hello";
   ASSERT_EQ(x.String(), "Hello");
 }

paddle/fluid/inference/analysis/pass.h

@@ -63,7 +63,7 @@ class Pass {
   // Human-readable short representation.
   virtual std::string repr() const = 0;
   // Human-readable long description.
-  virtual std::string description() const = 0;
+  virtual std::string description() const { return "No DOC"; }
 };
 
 // NodePass process on any Node types.

paddle/fluid/inference/analysis/pass_manager.cc

@@ -22,7 +22,7 @@ namespace analysis {
 bool PassManager::Initialize(Argument* argument) {
   argument_ = argument;
   for (auto& pass : data_) {
-    LOG(INFO) << "Initializing pass " << pass->repr();
+    LOG(WARNING) << "Initializing pass [" << pass->repr() << "]";
     if (!pass->Initialize(argument)) {
       LOG(ERROR) << "Failed to initialize pass [" << pass->repr() << "]";
       return false;
@@ -33,8 +33,9 @@ bool PassManager::Initialize(Argument* argument) {
 
 void DfgPassManager::RunAll() {
   PADDLE_ENFORCE(argument_);
+  LOG(INFO) << "Total " << data_.size() << " passes";
   for (auto& pass : data_) {
-    VLOG(4) << "Running pass [" << pass->repr() << "]";
+    LOG(WARNING) << "Running pass [" << pass->repr() << "]";
     pass->Run(argument_->main_dfg.get());
   }
 }
@@ -42,8 +43,7 @@ void DfgPassManager::RunAll() {
 void NodePassManager::RunAll() {
   PADDLE_ENFORCE(argument_);
   PADDLE_ENFORCE(argument_->main_dfg.get());
-  auto trait =
-      GraphTraits<DataFlowGraph>(argument_->main_dfg.get()).nodes_in_DFS();
+  auto trait = GraphTraits<DataFlowGraph>(*argument_->main_dfg).nodes_in_DFS();
   for (auto& node : trait) {
     for (auto& pass : data_) {
       pass->Run(&node);

paddle/fluid/inference/analysis/subgraph_splitter.cc

@@ -34,7 +34,7 @@ inline void MarkOutLinksInSubGraph(const Function *func) {
 }
 
 void SubGraphSplitter::MarkNodesInsideSubGraph() {
-  for (auto &node : GraphTraits<DataFlowGraph>(graph_).nodes()) {
+  for (auto &node : GraphTraits<DataFlowGraph>(*graph_).nodes()) {
     if (node_inside_subgraph_teller_(&node)) {
       node.attr(kMarkerAttrName).Bool() = true;
       if (node.type() == Node::Type::kFunction) {
@@ -76,7 +76,7 @@ void UnionFindCombine(const node_map_t &node_map, size_t a, size_t b) {
 
 std::vector<std::vector<Node *>> SubGraphSplitter::ExtractSubGraphs() {
   std::vector<Node *> marked_nodes;
-  for (auto &node : GraphTraits<DataFlowGraph>(graph_).nodes_in_TS()) {
+  for (auto &node : GraphTraits<DataFlowGraph>(*graph_).nodes_in_TS()) {
     if (node.attr(kMarkerAttrName).Bool()) {
       marked_nodes.push_back(&node);
     }

paddle/fluid/inference/analysis/tensorrt_subgraph_node_mark_pass.cc

@@ -69,8 +69,8 @@ class DfgDebuggerPass : public DFG_GraphvizDrawPass {
 };
 
 Pass *TensorRTSubgraphNodeMarkPass::CreateGraphvizDebugerPass() const {
-  DFG_GraphvizDrawPass::Config config(
-      FLAGS_inference_analysis_graphviz_log_root, "tensorrt_marked_node");
+  DFG_GraphvizDrawPass::Config config(FLAGS_IA_graphviz_log_root,
+                                      "tensorrt_marked_node");
   return new DfgDebuggerPass(config);
 }
 bool TensorRTSubgraphNodeMarkPass::Finalize() { return true; }

paddle/fluid/inference/api/api_tensorrt_subgraph_engine_tester.cc

@@ -23,7 +23,7 @@ namespace paddle {
 DEFINE_string(dirname, "", "Directory of the inference model.");
 
 void CompareTensorRTWithFluid(bool enable_tensorrt) {
-  FLAGS_inference_analysis_enable_tensorrt_subgraph_engine = enable_tensorrt;
+  FLAGS_IA_enable_tensorrt_subgraph_engine = enable_tensorrt;
 
   //# 1. Create PaddlePredictor with a config.
   NativeConfig config0;

paddle/fluid/operators/fc_op.cc

@@ -35,9 +35,14 @@ void FCOp::InferShape(framework::InferShapeContext* ctx) const {
 
   if (ctx->HasInput("Bias")) {
     auto bias_dims = ctx->GetInputDim("Bias");
-    PADDLE_ENFORCE_EQ(bias_dims[0], 1, "The shape of Bias must be [1, dim].");
-    PADDLE_ENFORCE_EQ(bias_dims[1], w_dims[1],
-                      "The shape of Bias must be [1, dim].");
+    if (bias_dims.size() == 2) {
+      PADDLE_ENFORCE_EQ(bias_dims[0], 1, "The shape of Bias must be [1, dim].");
+      PADDLE_ENFORCE_EQ(bias_dims[1], w_dims[1],
+                        "The shape of Bias must be [1, dim].");
+    } else if (bias_dims.size() == 1) {
+      PADDLE_ENFORCE_EQ(bias_dims[0], w_dims[1],
+                        "The shape of Bias must be [1, dim].");
+    }
   }
   PADDLE_ENFORCE(in_dims.size() == 2 || in_dims.size() == 4,
                  "Fully Connected input should be 2-D or 4-D tensor.");