Merge remote-tracking branch 'upstream/develop' into windows/build

AUTHORS.md

@@ -43,6 +43,7 @@
 | qingqing01 | Qing-Qing Dang |
 | reyoung | Yang Yu |
 | Superjom | Chun-Wei Yan |
+| tensor-tang | Jian Tang |
 | tianbingsz | Tian-Bing Xu |
 | tpatejko | Tomasz Patejko |
 | typhoonzero | Yi Wu |

cmake/inference_lib.cmake

@@ -185,10 +185,9 @@ endif ()
 set(module "inference")
 copy(inference_lib DEPS ${inference_deps}
   SRCS ${src_dir}/${module}/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/inference/libpaddle_fluid.*
-        ${src_dir}/${module}/api/paddle_inference_api.h
+       ${src_dir}/${module}/api/paddle_*.h
        ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h
   DSTS ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module}
-        )
 
 set(module "platform")
 copy(platform_lib DEPS profiler_py_proto
@@ -223,12 +222,12 @@ copy(third_party DEPS fluid_lib_dist
         DSTS ${FLUID_INFERENCE_INSTALL_DIR} ${FLUID_INFERENCE_INSTALL_DIR}
         )
 
-# only need libpaddle_fluid.so/a and paddle_inference_api.h for inference-only library
+# only need libpaddle_fluid.so/a and paddle_*.h for inference-only library
 copy(inference_api_lib DEPS fluid_lib_dist
   SRCS ${FLUID_INSTALL_DIR}/paddle/fluid/inference/libpaddle_fluid.*
-        ${FLUID_INSTALL_DIR}/paddle/fluid/inference/paddle_inference_api.h
+       ${FLUID_INSTALL_DIR}/paddle/fluid/inference/paddle_*.h
   DSTS ${FLUID_INFERENCE_INSTALL_DIR}/paddle/lib ${FLUID_INFERENCE_INSTALL_DIR}/paddle/include
-        )
+)
 
 add_custom_target(inference_lib_dist DEPENDS third_party inference_api_lib)
 

cmake/tensorrt.cmake

@@ -34,4 +34,5 @@ if(TENSORRT_FOUND)
         "Current TensorRT version is v${TENSORRT_MAJOR_VERSION}. ")
     include_directories(${TENSORRT_INCLUDE_DIR})
     list(APPEND EXTERNAL_LIBS ${TENSORRT_LIBRARY})
+    add_definitions(-DPADDLE_WITH_TENSORRT)
 endif()

paddle/fluid/API.spec

@@ -103,7 +103,7 @@ paddle.fluid.layers.beam_search ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 's
 paddle.fluid.layers.row_conv ArgSpec(args=['input', 'future_context_size', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.layers.multiplex ArgSpec(args=['inputs', 'index'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.layer_norm ArgSpec(args=['input', 'scale', 'shift', 'begin_norm_axis', 'epsilon', 'param_attr', 'bias_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(True, True, 1, 1e-05, None, None, None, None))
-paddle.fluid.layers.softmax_with_cross_entropy ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index', 'numeric_stable_mode'], varargs=None, keywords=None, defaults=(False, -100, False))
+paddle.fluid.layers.softmax_with_cross_entropy ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index', 'numeric_stable_mode', 'return_softmax'], varargs=None, keywords=None, defaults=(False, -100, False, False))
 paddle.fluid.layers.smooth_l1 ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.layers.one_hot ArgSpec(args=['input', 'depth'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.autoincreased_step_counter ArgSpec(args=['counter_name', 'begin', 'step'], varargs=None, keywords=None, defaults=(None, 1, 1))
@@ -184,6 +184,7 @@ paddle.fluid.layers.hash ArgSpec(args=['input', 'hash_size', 'num_hash', 'name']
 paddle.fluid.layers.grid_sampler ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.log_loss ArgSpec(args=['input', 'label', 'epsilon', 'name'], varargs=None, keywords=None, defaults=(0.0001, None))
 paddle.fluid.layers.add_position_encoding ArgSpec(args=['input', 'alpha', 'beta', 'name'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.layers.bilinear_tensor_product ArgSpec(args=['x', 'y', 'size', 'act', 'name', 'param_attr', 'bias_attr'], varargs=None, keywords=None, defaults=(None, None, None, None))
 paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
 paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
 paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
@@ -273,6 +274,7 @@ paddle.fluid.layers.hard_shrink ArgSpec(args=['x', 'threshold'], varargs=None, k
 paddle.fluid.layers.cumsum ArgSpec(args=['x', 'axis', 'exclusive', 'reverse'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.layers.thresholded_relu ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.prior_box ArgSpec(args=['input', 'image', 'min_sizes', 'max_sizes', 'aspect_ratios', 'variance', 'flip', 'clip', 'steps', 'offset', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, [1.0], [0.1, 0.1, 0.2, 0.2], False, False, [0.0, 0.0], 0.5, None, False))
+paddle.fluid.layers.density_prior_box ArgSpec(args=['input', 'image', 'densities', 'fixed_sizes', 'fixed_ratios', 'variance', 'clip', 'steps', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, None, [0.1, 0.1, 0.2, 0.2], False, [0.0, 0.0], 0.5, None))
 paddle.fluid.layers.multi_box_head ArgSpec(args=['inputs', 'image', 'base_size', 'num_classes', 'aspect_ratios', 'min_ratio', 'max_ratio', 'min_sizes', 'max_sizes', 'steps', 'step_w', 'step_h', 'offset', 'variance', 'flip', 'clip', 'kernel_size', 'pad', 'stride', 'name', 'min_max_aspect_ratios_order'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None, 0.5, [0.1, 0.1, 0.2, 0.2], True, False, 1, 0, 1, None, False))
 paddle.fluid.layers.bipartite_match ArgSpec(args=['dist_matrix', 'match_type', 'dist_threshold', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.layers.target_assign ArgSpec(args=['input', 'matched_indices', 'negative_indices', 'mismatch_value', 'name'], varargs=None, keywords=None, defaults=(None, None, None))

paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc

@@ -30,8 +30,8 @@ FastThreadedSSAGraphExecutor::FastThreadedSSAGraphExecutor(
       local_scopes_(local_scopes),
       places_(places),
       graph_(std::move(graph)),
-      pool_(strategy.num_threads_ +
-            1),  // add one more thread for generate op_deps
+      pool_(strategy.num_threads_),
+      prepare_pool_(1),  // add one more thread for generate op_deps
       fetch_ctxs_(places) {
   for (auto &op : ir::FilterByNodeWrapper<OpHandleBase>(*graph_)) {
     int dep = static_cast<int>(op->NotReadyInputSize());
@@ -160,7 +160,7 @@ void FastThreadedSSAGraphExecutor::RunOpAsync(
   });
 }
 void FastThreadedSSAGraphExecutor::PrepareAtomicOpDeps() {
-  atomic_op_deps_ = pool_.enqueue([&] {
+  atomic_op_deps_ = prepare_pool_.enqueue([&] {
     auto *op_deps = new std::unordered_map<OpHandleBase *, std::atomic<int>>;
     for (auto &pair : op_deps_) {
       (*op_deps)[pair.first] = pair.second;

paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h

@@ -46,6 +46,7 @@ class FastThreadedSSAGraphExecutor : public SSAGraphExecutor {
   std::vector<OpHandleBase *> bootstrap_ops_;
 
   ::ThreadPool pool_;
+  ::ThreadPool prepare_pool_;
   platform::DeviceContextPool fetch_ctxs_;
   std::atomic<int> remaining_;
 

paddle/fluid/framework/executor.cc

@@ -359,6 +359,7 @@ std::vector<std::shared_ptr<ExecutorPrepareContext>> Executor::Prepare(
 void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
                                   bool create_local_scope, bool create_vars,
                                   bool keep_kids) {
+  PADDLE_ENFORCE_NOT_NULL(scope);
   Scope* local_scope = scope;
   if (create_vars) {
     if (create_local_scope) {

paddle/fluid/framework/ir/CMakeLists.txt

@@ -5,6 +5,7 @@ file(APPEND ${pass_file} "\#include \"paddle/fluid/framework/ir/pass.h\"\n")
 
 
 # Usage: pass_library(target inference) will append to paddle_inference_pass.h
+unset(INFER_IR_PASSES CACHE) # clear the global variable
 function(pass_library TARGET DEST)
     set(options "")
     set(oneValueArgs "")
@@ -15,10 +16,11 @@ function(pass_library TARGET DEST)
     if (${DEST} STREQUAL "base" OR ${DEST} STREQUAL "inference")
         message(STATUS "add pass ${TARGET} ${DEST}")
         file(APPEND ${pass_file} "USE_PASS(${TARGET});\n")
-        set(PASS_LIBRARY ${TARGET} ${PASS_LIBRARY} PARENT_SCOPE)
+        set(INFER_IR_PASSES ${INFER_IR_PASSES} ${TARGET} CACHE INTERNAL "")
     endif()
 endfunction()
 
+
 cc_library(node SRCS node.cc DEPS proto_desc)
 cc_library(graph SRCS graph.cc DEPS node pretty_log)
 cc_library(graph_helper SRCS graph_helper.cc DEPS graph)

paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc

@@ -91,10 +91,10 @@ void FindWhileOp(Graph* graph) {
 #undef OP_SET_IN
 #undef OP_SET_OUT
 
-  auto* X = graph->RetriveNode(34);
-  auto* LSTMOUT = graph->RetriveNode(81);
-  auto* cell_init = graph->RetriveNode(6);
-  auto* hidden_init = graph->RetriveNode(8);
+  auto* X = graph->RetrieveNode(34);
+  auto* LSTMOUT = graph->RetrieveNode(81);
+  auto* cell_init = graph->RetrieveNode(6);
+  auto* hidden_init = graph->RetrieveNode(8);
 
   auto* lstm_op = graph->CreateOpNode(&op_desc);
   PrepareParameters(graph, param);
@@ -212,11 +212,11 @@ void PrepareLSTMWeight(const LoDTensor& W_forget_w0,
 
   float* out_data = out->mutable_data<float>(platform::CPUPlace());
   std::array<const float*, 4> tensors{
-      {W_forget_w0.data<float>(), W_input_w0.data<float>(),
-        W_output_w0.data<float>(), W_cell_w0.data<float>()}};
+      W_forget_w0.data<float>(), W_input_w0.data<float>(),
+        W_output_w0.data<float>(), W_cell_w0.data<float>()};
   std::array<const float*, 4> tensors1{
-      {W_forget_w1.data<float>(), W_input_w1.data<float>(),
-        W_output_w1.data<float>(), W_cell_w1.data<float>()}};
+      W_forget_w1.data<float>(), W_input_w1.data<float>(),
+        W_output_w1.data<float>(), W_cell_w1.data<float>()};
 
   for (int row = 0; row < D; row++) {
     for (int col = 0; col < 4; col++) {
@@ -239,8 +239,8 @@ void PrepareLSTMBias(const LoDTensor& B_forget, const LoDTensor& B_input,
                      const LoDTensor& B_output, const LoDTensor& B_cell,
                      LoDTensor* out) {
   std::array<const float*, 4> tensors{
-      {B_forget.data<float>(), B_input.data<float>(), B_output.data<float>(),
-        B_cell.data<float>()}};
+      B_forget.data<float>(), B_input.data<float>(), B_output.data<float>(),
+        B_cell.data<float>()};
 
   PADDLE_ENFORCE_EQ(B_forget.dims().size(), 1);
   int D = B_forget.dims()[0];

paddle/fluid/framework/ir/graph.cc

@@ -84,8 +84,6 @@ void CheckProgram(const ProgramDesc &program) {
 
 Graph::Graph(const ProgramDesc &program) : program_(program) {
   CheckProgram(program_);
-  // Make the nodes id start from 0.
-  Node::ResetId();
   auto var_nodes = InitFromProgram(program_);
   ResolveHazard(var_nodes);
 }

paddle/fluid/framework/ir/graph.h

@@ -116,13 +116,17 @@ 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));
+    auto *x = AddNode(new ir::Node(var_desc));
+    x->SetId(num_node_created_++);
+    return x;
   }
 
   // Create a normal runnable operator with OpDesc.
   ir::Node *CreateOpNode(OpDesc *op_desc) {
     PADDLE_ENFORCE(op_desc);
-    return AddNode(new ir::Node(op_desc));
+    auto *x = AddNode(new ir::Node(op_desc));
+    x->SetId(num_node_created_++);
+    return x;
   }
 
   // Create a control dependency var that connects 2 operations. The
@@ -132,13 +136,17 @@ class Graph {
     // TODO(panyx0718): control var name should be really unique.
     const std::string name = string::Sprintf(
         "%s@%llu", ir::Node::kControlDepVarName, node_set_.size());
-    return AddNode(new ir::Node(name, ir::Node::Type::kVariable));
+    auto *x = AddNode(new ir::Node(name, ir::Node::Type::kVariable));
+    x->SetId(num_node_created_++);
+    return x;
   }
 
   // A more free style way of creating a graph node. Mostly use for test
   // or "copy" from another node. Avoid using it if possible.
   ir::Node *CreateEmptyNode(const std::string &name, ir::Node::Type type) {
-    return AddNode(new ir::Node(name, type));
+    auto *x = AddNode(new ir::Node(name, type));
+    x->SetId(num_node_created_++);
+    return x;
   }
 
   // Clear all node information of the graph and return the ownership of the
@@ -160,7 +168,7 @@ class Graph {
   }
 
   // NOTE low performance, but simple and secure.
-  Node *RetriveNode(int id) {
+  Node *RetrieveNode(int id) {
     for (auto &node : nodes_) {
       if (node.second->id() == id) {
         return node.second.get();
@@ -169,6 +177,7 @@ class Graph {
     return nullptr;
   }
 
+  const ProgramDesc &program() const { return program_; }
   std::map<std::string, std::vector<ir::Node *>> InitFromProgram(
       const ProgramDesc &program);
 
@@ -190,6 +199,7 @@ class Graph {
   std::map<std::string, std::function<void(void)>> attr_dels_;
   std::map<ir::Node *, std::unique_ptr<ir::Node>> nodes_;
   std::unordered_set<ir::Node *> node_set_;
+  size_t num_node_created_{0};  // help to generate a unique node id.
 };
 
 bool IsControlDepVar(const ir::Node &var);

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -167,10 +167,12 @@ struct HitGroup {
 
   bool Match(Node *node, PDNode *pat) {
     if (nodes_.count(node)) {
-      if (!roles.count(pat)) return false;
-      return roles[pat] == node;
+      if (roles.count(pat) && roles[pat] == node) return true;
+      return false;
+    } else {
+      if (roles.count(pat) && roles[pat] != node) return false;
+      return true;
     }
-    return !roles.count(pat) || roles.at(pat) == node;
   }
 
   void Register(Node *node, PDNode *pat) {
@@ -198,7 +200,6 @@ GraphPatternDetector::DetectPatterns() {
   std::vector<GraphPatternDetector::subgraph_t> result;
   std::vector<HitGroup> init_groups;
   std::array<std::vector<HitGroup>, 2> bi_records;
-  // PADDLE_ENFORCE(!pattern_.edges().empty(), "At least one edge is needed");
   auto *first_pnode = pattern_.edges().empty() ? pattern().nodes().front().get()
                                                : pattern_.edges().front().first;
   if (!pdnodes2nodes_.count(first_pnode)) return result;
@@ -228,11 +229,12 @@ GraphPatternDetector::DetectPatterns() {
         VLOG(80) << "check " << source->id() << " -- " << target->id();
         // TODO(Superjomn) add some prune strategies.
         for (const auto &group : pre_groups) {
+          if (IsNodesLink(source, target)) {
             HitGroup new_group = group;
-          if (IsNodesLink(source, target) &&
-              new_group.Match(source, edge.first)) {
+            bool flag = new_group.Match(source, edge.first) &&
+                        new_group.Match(target, edge.second);
+            if (flag) {
               new_group.Register(source, edge.first);
-            if (new_group.Match(target, edge.second)) {
               new_group.Register(target, edge.second);
               cur_groups.push_back(new_group);
               // TODO(Superjomn) need to unique
@@ -261,14 +263,16 @@ GraphPatternDetector::DetectPatterns() {
   return result;
 }
 
-bool GraphItemCMP(const std::pair<PDNode *, Node *> &a,
+struct GraphItemLessThan {
+  bool operator()(const std::pair<PDNode *, Node *> &a,
                   const std::pair<PDNode *, Node *> &b) {
     if (a.first != b.first) {
       return a.first < b.first;
     } else {
       return a.second < b.second;
     }
-}
+  }
+};
 
 // TODO(Superjomn) enhance the function as it marks unique unique as duplicates
 // see https://github.com/PaddlePaddle/Paddle/issues/13550
@@ -282,7 +286,7 @@ void GraphPatternDetector::UniquePatterns(
   for (auto &g : *subgraphs) {
     // Sort the items in the sub-graph, and transform to a string key.
     std::vector<std::pair<PDNode *, Node *>> sorted_keys(g.begin(), g.end());
-    std::sort(sorted_keys.begin(), sorted_keys.end(), GraphItemCMP);
+    std::sort(sorted_keys.begin(), sorted_keys.end(), GraphItemLessThan());
     std::stringstream ss;
     for (auto &item : sorted_keys) {
       ss << item.first << ":" << item.second;

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -310,8 +310,8 @@ void GraphSafeRemoveNodes(Graph* graph,
                           const std::unordered_set<const Node*>& nodes);
 
 // Some pre-defined patterns those can be reused in multiple passes.
-// The related Fluid Layer or Op should be one pattern here for better reusage
-// accross different fusion.
+// The related Fluid Layer or Op should be one pattern here for better re-usage
+// across different fusion.
 namespace patterns {
 
 struct KeyCounter {

paddle/fluid/framework/ir/graph_to_program_pass.cc

@@ -35,10 +35,11 @@ std::unique_ptr<Graph> GraphToProgramPass::ApplyImpl(
       new proto::ProgramDesc(*program.Proto()));
 
   auto block = program_pb->mutable_blocks(kRootBlockIndex);
+  block->set_idx(kRootBlockIndex);
   block->clear_vars();
   std::unordered_set<std::string> visited_vars;
   for (ir::Node* n : graph->Nodes()) {
-    if (n->NodeType() == ir::Node::Type::kVariable) {
+    if (n->IsVar()) {
       if (n->Var() && visited_vars.count(n->Var()->Name()) == 0) {
         visited_vars.insert(n->Var()->Name());
         block->add_vars()->MergeFrom(*n->Var()->Proto());

paddle/fluid/framework/ir/graph_traits.cc

@@ -66,6 +66,76 @@ NodesDFSIterator &NodesDFSIterator::operator=(const NodesDFSIterator &other) {
 }
 Node *NodesDFSIterator::operator->() { return stack_.top(); }
 
+inline bool CheckNodeIndegreeEquals(const Node &node, size_t n) {
+  return node.inputs.size() == n;
+}
+
+NodesTSIterator::NodesTSIterator(const std::vector<Node *> &source) {
+  PADDLE_ENFORCE(!source.empty(),
+                 "Start points of topological sorting should not be empty!");
+  // CHECK all the inputs' in-degree is 0
+  for (auto *node : source) {
+    PADDLE_ENFORCE(CheckNodeIndegreeEquals(*node, 0));
+  }
+
+  std::unordered_set<Node *> visited;
+  std::unordered_set<Node *> to_visit{source.begin(), source.end()};
+
+  std::vector<Node *> inlink_visited;
+  while (!to_visit.empty()) {
+    std::vector<Node *> queue(to_visit.begin(), to_visit.end());
+    for (auto *p : queue) {
+      inlink_visited.clear();
+
+      std::copy_if(p->inputs.begin(), p->inputs.end(),
+                   std::back_inserter(inlink_visited),
+                   [&](Node *x) -> bool { return visited.count(x) != 0; });
+
+      if (inlink_visited.size() == p->inputs.size()) {
+        sorted_.push_back(p);
+        for (auto *_ : p->outputs) {
+          if (!visited.count(_)) {
+            to_visit.insert(_);
+          }
+        }
+
+        to_visit.erase(p);
+        visited.insert(p);
+      }
+    }
+  }
+}
+
+NodesTSIterator::NodesTSIterator(const NodesTSIterator &other)
+    : sorted_(other.sorted_), cursor_(other.cursor_) {}
+
+Node &NodesTSIterator::operator*() {
+  PADDLE_ENFORCE_LT(cursor_, sorted_.size());
+  return *sorted_[cursor_];
+}
+
+NodesTSIterator &NodesTSIterator::operator++() {
+  if (++cursor_ >= sorted_.size()) {
+    sorted_.clear();
+    cursor_ = 0;
+  }
+  return *this;
+}
+NodesTSIterator &NodesTSIterator::operator=(const NodesTSIterator &other) {
+  cursor_ = other.cursor_;
+  sorted_ = other.sorted_;
+  return *this;
+}
+
+bool NodesTSIterator::operator==(const NodesTSIterator &other) {
+  return sorted_ == other.sorted_ && cursor_ == other.cursor_;
+}
+
+Node *NodesTSIterator::operator->() {
+  PADDLE_ENFORCE_LT(cursor_, sorted_.size());
+  return sorted_[cursor_];
+}
+
 }  // namespace ir
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/ir/graph_traits.h

@@ -62,6 +62,32 @@ struct NodesDFSIterator
   std::unordered_set<Node *> visited_;
 };
 
+// Topological sorting iterator on nodes.
+struct NodesTSIterator
+    : public std::iterator<std::forward_iterator_tag, Node *> {
+  NodesTSIterator() = default;
+  NodesTSIterator(const std::vector<Node *> &source);
+  NodesTSIterator(NodesTSIterator &&other)
+      : sorted_(std::move(other.sorted_)), cursor_(other.cursor_) {
+    other.cursor_ = 0;
+  }
+  NodesTSIterator(const NodesTSIterator &other);
+
+  Node &operator*();
+  NodesTSIterator &operator++();
+  // TODO(Superjomn) current implementation just compare the first
+  // element, need to compare the graph and all the elements in the queue and
+  // set.
+  NodesTSIterator &operator=(const NodesTSIterator &other);
+  bool operator==(const NodesTSIterator &other);
+  bool operator!=(const NodesTSIterator &other) { return !(*this == other); }
+  Node *operator->();
+
+ private:
+  std::vector<Node *> sorted_;
+  size_t cursor_{0};
+};
+
 /*
  * GraphTraits contains some graph traversal algorithms.
  *
@@ -76,6 +102,14 @@ struct GraphTraits {
                                             NodesDFSIterator());
   }
 
+  static iterator_range<NodesTSIterator> TS(const Graph &g) {
+    auto start_points = ExtractStartPoints(g);
+    PADDLE_ENFORCE(!start_points.empty());
+    NodesTSIterator x(start_points);
+    return iterator_range<NodesTSIterator>(NodesTSIterator(start_points),
+                                           NodesTSIterator());
+  }
+
  private:
   // The nodes those have no input will be treated as start points.
   static std::vector<Node *> ExtractStartPoints(const Graph &g) {

paddle/fluid/framework/ir/node.h

@@ -119,37 +119,30 @@ class Node {
   int id_;
 
  private:
+  // ID can only set by a Graph.
+  void SetId(int id) { id_ = id; }
+
   friend class Graph;
   friend std::unique_ptr<Node> CreateNodeForTest(const std::string& name,
                                                  Node::Type type);
 
   explicit Node(const std::string& name, Type type)
-      : name_(name),
-        var_desc_(nullptr),
-        op_desc_(nullptr),
-        type_(type),
-        id_(count_++) {}
+      : name_(name), var_desc_(nullptr), op_desc_(nullptr), type_(type) {}
 
   explicit Node(VarDesc* var_desc)
       : name_(var_desc->Name()),
         var_desc_(new VarDesc(*var_desc)),
         op_desc_(nullptr),
-        type_(Type::kVariable),
-        id_(count_++) {}
+        type_(Type::kVariable) {}
 
   explicit Node(OpDesc* op_desc)
       : name_(op_desc->Type()),
         var_desc_(nullptr),
         op_desc_(new OpDesc(*op_desc, op_desc->Block())),
-        type_(Type::kOperation),
-        id_(count_++) {}
+        type_(Type::kOperation) {}
 
   Node() = delete;
 
-  static int count_;
-  // Please don't use this API or make this public.
-  static void ResetId() { count_ = 0; }
-
   boost::any wrapper_;
   std::function<void(void)> wrapper_deleter_;
   std::type_index wrapper_type_ = std::type_index(typeid(void));

paddle/fluid/framework/ir/pass.h

@@ -93,6 +93,7 @@ class Pass {
  protected:
   virtual std::unique_ptr<Graph> ApplyImpl(std::unique_ptr<Graph> graph) const {
     LOG(FATAL) << "Calling virtual Pass not implemented.";
+    return graph;
   }
 
  private:

paddle/fluid/framework/naive_executor.cc

@@ -57,59 +57,57 @@ static void InitializeVariable(Variable *var, proto::VarType::Type var_type) {
   }
 }
 
-void NaiveExecutor::Prepare(Scope *parent_scope,
-                            const ProgramDesc &program_desc, int block_id,
-                            bool with_feed_fetch_ops) {
-  if (!parent_scope) {
+void NaiveExecutor::Prepare(Scope *scope, const ProgramDesc &program_desc,
+                            int block_id, bool with_feed_fetch_ops) {
+  if (!scope) {
     scope_ = new framework::Scope;
   } else {
-    scope_ = &parent_scope->NewScope();
+    scope_ = scope;
   }
-  CreateVariables(program_desc, scope_, block_id);
+
+  VLOG(3) << "NaiveExecutor init with scope " << scope;
   CreateOps(program_desc, block_id, with_feed_fetch_ops);
 }
 
 void NaiveExecutor::Run() {
   for (auto &op : ops_) {
-    VLOG(40) << "run " << op->Type();
+    VLOG(3) << std::this_thread::get_id() << " run " << op->Type()
+            << " on scope " << scope_;
     op->Run(*scope_, place_);
   }
 }
 
-void NaiveExecutor::CreateVariables(const ProgramDesc &desc, Scope *scope,
-                                    int block_id) {
-  PADDLE_ENFORCE(scope);
+void NaiveExecutor::CreateVariables(const ProgramDesc &desc, int block_id,
+                                    bool persistable, Scope *scope) {
+  PADDLE_ENFORCE_NOT_NULL(scope);
+
   auto &global_block = desc.Block(block_id);
 
-  const Scope *ancestor_scope = scope;
-  while (ancestor_scope->parent()) {
-    ancestor_scope = ancestor_scope->parent();
+  const auto *anc = scope;
+  PADDLE_ENFORCE(anc->parent() != anc);
+  while (anc->parent()) {
+    anc = anc->parent();
   }
 
-  if (ancestor_scope != scope) {
   for (auto &var : global_block.AllVars()) {
     if (var->Name() == framework::kEmptyVarName) {
       continue;
     }
-      // Create persistable vars in ancestor scope.
-      if (var->Persistable()) {
-        auto *ptr = const_cast<Scope *>(ancestor_scope)->Var(var->Name());
-        InitializeVariable(ptr, var->GetType());
-        VLOG(30) << "Create Variable " << var->Name()
-                 << " global, which pointer is " << ptr;
-      } else {  // Create temporary variables in local scope.
-        auto *ptr = scope->Var(var->Name());
+
+    if (persistable == var->Persistable()) {
+      if (persistable) {
+        if (!anc->FindVar(var->Name())) {
+          auto *ptr = const_cast<Scope *>(anc)->Var(var->Name());
+          VLOG(3) << scope << " Create persistable variable " << var->Name()
+                  << ", which pointer is " << ptr;
           InitializeVariable(ptr, var->GetType());
-        VLOG(30) << "Create Variable " << var->Name()
-                 << " locally, which pointer is " << ptr;
-      }
         }
       } else {
-    for (auto &var : global_block.AllVars()) {
-      auto *ptr = scope->Var(var->Name());
+        auto *ptr = const_cast<Scope *>(scope)->Var(var->Name());
+        VLOG(3) << scope << " Create variable " << var->Name()
+                << ", which pointer is " << ptr;
         InitializeVariable(ptr, var->GetType());
-      VLOG(30) << "Create variable " << var->Name() << ", which pointer is "
-               << ptr;
+      }
     }
   }
 }

paddle/fluid/framework/naive_executor.h

@@ -35,8 +35,14 @@ class NaiveExecutor {
   // Create child scope.
   // Create variables.
   // @with_feed_fetch_ops: whether to work with the feed and fetch operators.
-  void Prepare(Scope* parent_scope, const ProgramDesc& program_desc,
-               int block_id, bool with_feed_fetch_ops);
+  void Prepare(Scope* scope, const ProgramDesc& program_desc, int block_id,
+               bool with_feed_fetch_ops);
+
+  // Create variables before head.
+  // Create parameters if persistable is ture, or create the temporary variables
+  // instead.
+  void CreateVariables(const ProgramDesc& desc, int block_id, bool persistable,
+                       Scope* scope);
 
   // Run all the operators.
   void Run();
@@ -49,8 +55,6 @@ class NaiveExecutor {
   void CleanFeedFetchOps();
 
  protected:
-  void CreateVariables(const ProgramDesc& desc, Scope* scope, int block_id);
-
   void CreateOps(const ProgramDesc& desc, int block_id,
                  bool with_feed_fetch_ops);
 

paddle/fluid/framework/naive_executor_test.cc

@@ -39,7 +39,7 @@ TEST(NaiveExecutor, Basic) {
 
   auto place = platform::CPUPlace();
   NaiveExecutor exe(place);
-  exe.Prepare(nullptr, program, 0, false /*with feed fetch ops*/);
+  exe.Prepare(nullptr, program, 0, false);
   auto* a_tensor = exe.FindTensor("a");
   auto* b_tensor = exe.FindTensor("b");
   auto* c_tensor = exe.FindTensor("c");

paddle/fluid/framework/scope.cc

@@ -15,7 +15,9 @@ limitations under the License. */
 #include "paddle/fluid/framework/scope.h"
 
 #include <memory>  // for unique_ptr
+#include <queue>
 #include <set>
+#include <unordered_set>
 #include "glog/logging.h"
 #include "paddle/fluid/framework/threadpool.h"
 #include "paddle/fluid/string/printf.h"
@@ -36,6 +38,16 @@ DEFINE_double(
     "Memory size threshold (GB) when the garbage collector clear tensors."
     "Disabled when this value is less than 0");
 
+// When in inference scenario, the scopes will not be written by two threads in
+// a mean time, but a scope may be read by multiple threads concurrently, and
+// the mutex will cause serious performance issue.
+// So the mutex is disabled when `ON_INFER`.
+#ifdef ON_INFER
+#define SCOPE_LOCK_GUARD
+#else
+#define SCOPE_LOCK_GUARD std::lock_guard<std::mutex> lock(mutex_);
+#endif
+
 namespace paddle {
 namespace framework {
 
@@ -49,18 +61,18 @@ int64_t GetEagerDeletionThreshold() {
 Scope::~Scope() { DropKids(); }
 
 Scope& Scope::NewScope() const {
-  std::lock_guard<std::mutex> lock(mutex_);
+  SCOPE_LOCK_GUARD
   kids_.push_back(new Scope(this));
   return *kids_.back();
 }
 
 Variable* Scope::Var(const std::string& name) {
-  std::lock_guard<std::mutex> lock(mutex_);
+  SCOPE_LOCK_GUARD
   return VarInternal(name);
 }
 
 Variable* Scope::Var(std::string* name) {
-  std::lock_guard<std::mutex> lock(mutex_);
+  SCOPE_LOCK_GUARD
   auto new_name = string::Sprintf("%p.%d", this, vars_.size());
   if (name != nullptr) {
     *name = new_name;
@@ -69,34 +81,34 @@ Variable* Scope::Var(std::string* name) {
 }
 
 Variable* Scope::FindVar(const std::string& name) const {
-  std::lock_guard<std::mutex> lock(mutex_);
+  SCOPE_LOCK_GUARD
   return FindVarInternal(name);
 }
 
 Variable* Scope::FindLocalVar(const std::string& name) const {
-  std::lock_guard<std::mutex> lock(mutex_);
+  SCOPE_LOCK_GUARD
   return FindVarLocally(name);
 }
 
 const Scope* Scope::FindScope(const Variable* var) const {
-  std::lock_guard<std::mutex> lock(mutex_);
+  SCOPE_LOCK_GUARD
   return FindScopeInternal(var);
 }
 
 void Scope::DropKids() {
-  std::lock_guard<std::mutex> lock(mutex_);
+  SCOPE_LOCK_GUARD
   for (Scope* s : kids_) delete s;
   kids_.clear();
 }
 
 bool Scope::HasKid(const Scope* scope) const {
-  std::lock_guard<std::mutex> lock(mutex_);
+  SCOPE_LOCK_GUARD
   auto it = std::find(this->kids_.begin(), this->kids_.end(), scope);
   return it != this->kids_.end();
 }
 
 std::vector<std::string> Scope::LocalVarNames() const {
-  std::lock_guard<std::mutex> lock(mutex_);
+  SCOPE_LOCK_GUARD
   std::vector<std::string> known_vars;
   known_vars.reserve(this->vars_.size());
   for (auto& p : vars_) {
@@ -106,9 +118,10 @@ std::vector<std::string> Scope::LocalVarNames() const {
 }
 
 void Scope::DeleteScope(Scope* scope) const {
-  std::lock_guard<std::mutex> lock(mutex_);
+  SCOPE_LOCK_GUARD
   auto it = std::find(this->kids_.begin(), this->kids_.end(), scope);
-  PADDLE_ENFORCE(it != this->kids_.end(), "Cannot find %p as kid scope", scope);
+  PADDLE_ENFORCE(it != this->kids_.end(), "%p Cannot find %p as kid scope",
+                 this, scope);
   this->kids_.erase(it);
   // When making memory benchmark on Fluid, we have to delete scope sync.
   if (FLAGS_benchmark || FLAGS_eager_delete_scope) {
@@ -119,7 +132,7 @@ void Scope::DeleteScope(Scope* scope) const {
 }
 
 void Scope::EraseVars(const std::vector<std::string>& var_names) {
-  std::lock_guard<std::mutex> lock(mutex_);
+  SCOPE_LOCK_GUARD
   std::set<std::string> var_set(var_names.begin(), var_names.end());
   for (auto it = vars_.begin(); it != vars_.end();) {
     if (var_set.find(it->first) != var_set.end()) {
@@ -132,12 +145,12 @@ void Scope::EraseVars(const std::vector<std::string>& var_names) {
 
 void Scope::Rename(const std::string& origin_name,
                    const std::string& new_name) const {
-  std::lock_guard<std::mutex> lock(mutex_);
+  SCOPE_LOCK_GUARD
   RenameInternal(origin_name, new_name);
 }
 
 std::string Scope::Rename(const std::string& origin_name) const {
-  std::lock_guard<std::mutex> lock(mutex_);
+  SCOPE_LOCK_GUARD
   auto new_name = string::Sprintf("%p.%d", this, vars_.size());
   RenameInternal(origin_name, new_name);
   return new_name;
@@ -189,5 +202,46 @@ Variable* Scope::FindVarLocally(const std::string& name) const {
   return nullptr;
 }
 
+std::string GenScopeTreeDebugInfo(Scope* root) {
+  std::stringstream os;
+
+  if (!root) return "";
+
+  // level traversal
+  std::queue<Scope*> queue;
+  queue.push(root);
+
+  std::vector<Scope*> scopes;
+
+  while (!queue.empty()) {
+    auto* end = queue.back();
+    Scope* q = nullptr;
+    while (q != end) {
+      q = queue.front();
+      queue.pop();
+      os << q << " ";
+      scopes.push_back(q);
+
+      for (auto* c : q->kids()) {
+        queue.push(c);
+      }
+    }
+    // end of a level
+    os << "\n------------------------------------------\n";
+  }
+
+  os << "\nDetails:\n\n";
+
+  for (Scope* q : scopes) {
+    os << "====\n";
+    os << q << ":\n";
+    for (auto& var : q->LocalVarNames()) {
+      os << "  - " << var << "\n";
+    }
+  }
+
+  return os.str();
+}
+
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/scope.h

@@ -78,11 +78,11 @@ class Scope {
   /// Drop all kids scopes belonged to this scope.
   void DropKids();
 
-  std::list<Scope*>& kids() const { return kids_; }
-
   /// Find if a scope exists in the kid scopes
   bool HasKid(const Scope* scope) const;
 
+  const std::list<Scope*>& kids() const { return kids_; }
+
   // enumerate all the variables current contains.
   std::vector<std::string> LocalVarNames() const;
 
@@ -118,12 +118,17 @@ class Scope {
 
   // Scope in `kids_` are owned by this class.
   mutable std::list<Scope*> kids_;
-  Scope const* parent_{nullptr};
+  const Scope* parent_{nullptr};
 
   DISABLE_COPY_AND_ASSIGN(Scope);
 
  private:
   mutable std::mutex mutex_;
 };
+
+// Generate some debug string about the inherience structure of scope, quite
+// naive.
+std::string GenScopeTreeDebugInfo(Scope*);
+
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/selected_rows.cc

@@ -63,6 +63,26 @@ struct TensorCopyVisitor {
   int64_t size_;
 };
 
+struct TensorFillVisitor {
+  TensorFillVisitor(framework::Tensor* dst, int64_t dst_offset, int64_t size,
+                    float value)
+      : dst_(dst), dst_offset_(dst_offset), size_(size) {}
+
+  template <typename T>
+  void apply() const {
+    // TODO(qiao): support other place
+    platform::CPUPlace cpu;
+    auto* tensor_data = dst_->mutable_data<T>(cpu);
+    auto* start = tensor_data + dst_offset_;
+    auto* end = start + size_;
+    std::fill(start, end, static_cast<T>(0.0));
+  }
+
+  framework::Tensor* dst_;
+  int64_t dst_offset_;
+  int64_t size_;
+};
+
 void SerializeToStream(std::ostream& os, const SelectedRows& selected_rows,
                        const platform::DeviceContext& dev_ctx) {
   {  // the 1st field, uint32_t version
@@ -120,7 +140,17 @@ bool SelectedRows::HasKey(int64_t key) const {
                                                                    : true;
 }
 
-int64_t SelectedRows::AutoGrownIndex(int64_t key, bool auto_grown) {
+int64_t SelectedRows::AutoGrownIndex(int64_t key, bool auto_grown,
+                                     bool is_test) {
+  if (is_test) {
+    auto iter = id_to_index_.find(key);
+    if (iter == id_to_index_.end()) {
+      return -1;
+    } else {
+      return iter->second;
+    }
+  }
+
   rwlock_->RDLock();
   auto iter = id_to_index_.find(key);
   if (iter == id_to_index_.end()) {
@@ -172,7 +202,7 @@ void SelectedRows::SyncIndex() {
 }
 
 void SelectedRows::Get(const framework::Tensor& ids, framework::Tensor* value,
-                       bool auto_grown) {
+                       bool auto_grown, bool is_test) {
   PADDLE_ENFORCE(value->IsInitialized(),
                  "The value tensor should be initialized.");
   if (ids.numel() == 0) {
@@ -183,13 +213,21 @@ void SelectedRows::Get(const framework::Tensor& ids, framework::Tensor* value,
                       "output tensor should have the same shape with table "
                       "except the dims[0].");
     for (int i = 0; i < ids.numel(); ++i) {
-      int64_t index = AutoGrownIndex(ids.data<int64_t>()[i], auto_grown);
+      auto id = ids.data<int64_t>()[i];
+      int64_t index = AutoGrownIndex(id, auto_grown, is_test);
+      if (index < 0) {
+        VLOG(5) << "id " << id << " not in the table, return 0";
+        framework::VisitDataType(
+            framework::ToDataType(value_->type()),
+            TensorFillVisitor(value, i * value_width, value_width, 0.0));
+      } else {
         framework::VisitDataType(
             framework::ToDataType(value_->type()),
             TensorCopyVisitor(value, i * value_width, *value_.get(),
                               index * value_width, value_width));
       }
     }
+  }
 }
 
 }  // namespace framework

paddle/fluid/framework/selected_rows.h

@@ -105,7 +105,7 @@ class SelectedRows {
    * the value
    */
   void Get(const framework::Tensor& ids, framework::Tensor* value,
-           bool auto_grown = false);
+           bool auto_grown = false, bool is_test = false);
 
   /*
    * @brief Get the index of the key from id_to_index_ map. If the key not
@@ -118,7 +118,7 @@ class SelectedRows {
    *
    * @return index of the key.
    */
-  int64_t AutoGrownIndex(int64_t key, bool auto_grown);
+  int64_t AutoGrownIndex(int64_t key, bool auto_grown, bool is_test = false);
 
   void SyncIndex();
 

paddle/fluid/framework/selected_rows_test.cc

@@ -84,10 +84,14 @@ TEST(SelectedRows, SparseTable) {
       data[i * embedding_width + j] = static_cast<float>(i);
     }
   }
-  ASSERT_EQ(table.AutoGrownIndex(10, true), 0);
-  ASSERT_EQ(table.AutoGrownIndex(8, true), 1);
-  ASSERT_EQ(table.AutoGrownIndex(8, true), 1);
-  ASSERT_EQ(table.AutoGrownIndex(6, true), 2);
+  ASSERT_EQ(table.AutoGrownIndex(10, true, false), 0);
+  ASSERT_EQ(table.AutoGrownIndex(8, true, false), 1);
+  ASSERT_EQ(table.AutoGrownIndex(8, true, false), 1);
+  ASSERT_EQ(table.AutoGrownIndex(6, true, false), 2);
+  for (int64_t i = 11; i < 20; i++) {
+    ASSERT_EQ(table.AutoGrownIndex(i, true, true), -1);
+    ASSERT_TRUE(!table.HasKey(i));
+  }
   ASSERT_TRUE(table.HasKey(10));
   ASSERT_TRUE(table.HasKey(8));
   ASSERT_TRUE(table.HasKey(6));

paddle/fluid/inference/CMakeLists.txt

@@ -39,19 +39,16 @@ set(SHARED_INFERENCE_SRCS
     io.cc ${CMAKE_CURRENT_SOURCE_DIR}/api/api.cc ${CMAKE_CURRENT_SOURCE_DIR}/api/api_impl.cc
     ${CMAKE_CURRENT_SOURCE_DIR}/api/analysis_predictor.cc
     ${CMAKE_CURRENT_SOURCE_DIR}/api/details/zero_copy_tensor.cc)
-if (WITH_GPU AND TENSORRT_FOUND)
-  set(STATIC_INFERENCE_APIS ${STATIC_INFERENCE_APIS} paddle_inference_tensorrt_subgraph_engine)
-  set(SHARED_INFERENCE_SRCS ${SHARED_INFERENCE_SRCS} ${CMAKE_CURRENT_SOURCE_DIR}/api/api_tensorrt_subgraph_engine.cc)
-endif()
 
-# Create static library
 if(WIN32)
-  sep_library(paddle_fluid DEPS ${fluid_modules} ${STATIC_INFERENCE_APIS} zero_copy_tensor reset_tensor_array)
+  sep_library(paddle_fluid DEPS ${fluid_modules} ${STATIC_INFERENCE_APIS} zero_copy_tensor reset_tensor_array
+    analysis_config paddle_pass_builder)
   if(WITH_GPU AND NOT WITH_DSO)
     target_link_libraries(paddle_fluid ${cuda_modules})
   endif(WITH_GPU AND NOT WITH_DSO)
 else(WIN32)
-  cc_library(paddle_fluid DEPS ${fluid_modules} ${STATIC_INFERENCE_APIS} zero_copy_tensor reset_tensor_array)
+  cc_library(paddle_fluid DEPS ${fluid_modules} ${STATIC_INFERENCE_APIS} zero_copy_tensor reset_tensor_array
+    analysis_config paddle_pass_builder)
 endif(WIN32)
 
 if(NOT APPLE)
@@ -63,14 +60,14 @@ endif()
 # Create shared library
 if(WIN32)
   sep_library(paddle_fluid_shared SHARED SRCS ${SHARED_INFERENCE_SRCS}
-          DEPS ${fluid_modules} paddle_fluid_api reset_tensor_array)
+          DEPS ${fluid_modules} paddle_fluid_api reset_tensor_array analysis_config paddle_pass_builder)
   target_link_libraries(paddle_fluid_shared shlwapi)
   if(WITH_GPU AND NOT WITH_DSO)
     target_link_libraries(paddle_fluid_origin ${cuda_modules})
   endif(WITH_GPU AND NOT WITH_DSO)
 else(WIN32)
   cc_library(paddle_fluid_shared SHARED SRCS ${SHARED_INFERENCE_SRCS}
-      DEPS ${fluid_modules} paddle_fluid_api reset_tensor_array)
+      DEPS ${fluid_modules} paddle_fluid_api reset_tensor_array analysis_config paddle_pass_builder)
 endif()
 
 set_target_properties(paddle_fluid_shared PROPERTIES OUTPUT_NAME paddle_fluid)

paddle/fluid/inference/analysis/CMakeLists.txt

-cc_library(ir_pass_manager SRCS ir_pass_manager.cc DEPS graph pass)
-set(analysis_deps
-        framework_proto proto_desc ir_pass_manager graph pass paddle_fluid_api executor pretty_log)
+unset(analysis_deps CACHE)
+set(analysis_deps # analysis_deps can be extended accross the project
+        framework_proto proto_desc graph pass paddle_fluid_api executor pretty_log
+        ir_pass_manager
+        CACHE INTERNAL "")
 
-cc_library(analysis SRCS pass_manager.cc node.cc data_flow_graph.cc graph_traits.cc subgraph_splitter.cc
+add_subdirectory(ir_passes)
+add_subdirectory(passes)
+
+cc_library(ir_pass_manager SRCS ir_pass_manager.cc DEPS graph pass ${INFER_IR_PASSES})
+
+cc_library(argument SRCS argument.cc DEPS scope proto_desc)
+cc_library(analysis_pass SRCS analysis_pass.cc DEPS proto_desc)
+
+cc_library(analysis SRCS
   analyzer.cc
   helper.cc
-  # passes
-  analysis_pass.cc
-  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
-  fluid_to_ir_pass.cc
-  model_store_pass.cc
-  DEPS ${analysis_deps})
-
-cc_test(test_node SRCS node_tester.cc DEPS analysis)
+  analysis_pass
+  DEPS ${analysis_deps}
+  )
+
 cc_test(test_dot SRCS dot_tester.cc DEPS analysis)
-cc_binary(inference_analyzer SRCS analyzer_main.cc DEPS analysis paddle_fluid)
 
 if(WIN32)
     target_link_libraries(inference_analyzer shlwapi)
@@ -43,13 +44,3 @@ function (inference_analysis_test TARGET)
 endfunction(inference_analysis_test)
 
 inference_analysis_test(test_analyzer SRCS analyzer_tester.cc EXTRA_DEPS paddle_inference_api)
-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_model_store_pass SRCS model_store_pass_tester.cc)

paddle/fluid/inference/analysis/analysis_pass.h

@@ -19,42 +19,36 @@ limitations under the License. */
 #include <string>
 
 #include "paddle/fluid/framework/framework.pb.h"
+#include "paddle/fluid/framework/ir/graph.h"
 #include "paddle/fluid/inference/analysis/argument.h"
-#include "paddle/fluid/inference/analysis/data_flow_graph.h"
 #include "paddle/fluid/inference/analysis/helper.h"
-#include "paddle/fluid/inference/analysis/node.h"
 
 namespace paddle {
 namespace inference {
 namespace analysis {
 
+/*
+ * AnalysisPass is a pass used to control the IR passes.
+ */
 class AnalysisPass {
  public:
   AnalysisPass() = default;
   virtual ~AnalysisPass() = default;
-  // Mutable Pass.
-  virtual bool Initialize(Argument *argument) { return false; }
-  // Readonly Pass.
-  virtual bool Initialize(const Argument &argument) { return false; }
 
-  // Virtual method overriden by subclasses to do any necessary clean up after
-  // all passes have run.
-  virtual bool Finalize() { return false; }
-
-  // Create a debugger Pass that draw the DFG by graphviz toolkit.
-  virtual AnalysisPass *CreateGraphvizDebugerPass() const { return nullptr; }
-
-  // Run on a single DataFlowGraph.
-  virtual void Run(DataFlowGraph *x) = 0;
+  // Run on a single Graph.
+  void Run(Argument* argument) { RunImpl(argument); }
 
   // Human-readable short representation.
   virtual std::string repr() const = 0;
   // Human-readable long description.
   virtual std::string description() const { return "No DOC"; }
-};
 
-// GraphPass processes on any GraphType.
-class DataFlowGraphPass : public AnalysisPass {};
+ protected:
+  // User should implement these.
+  virtual void RunImpl(Argument* argument) = 0;
+
+  Argument* argument_{nullptr};
+};
 
 }  // namespace analysis
 }  // namespace inference

paddle/fluid/inference/analysis/analyzer.cc

@@ -15,138 +15,23 @@
 #include "paddle/fluid/inference/analysis/analyzer.h"
 #include <string>
 #include <vector>
-
-#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"
-#include "paddle/fluid/inference/analysis/tensorrt_subgraph_pass.h"
-
-DEFINE_bool(IA_enable_tensorrt_subgraph_engine, false,
-            "Enable subgraph to TensorRT engine for acceleration");
-
-DEFINE_bool(IA_enable_ir, false, "Turn on IR support");
-
-DEFINE_string(IA_graphviz_log_root, "./",
-              "Graphviz debuger for data flow graphs.");
-
-DEFINE_string(IA_output_storage_path, "", "optimized model output path");
+#include "paddle/fluid/inference/analysis/passes/ir_analysis_compose_pass.h"
+#include "paddle/fluid/inference/analysis/passes/passes.h"
 
 namespace paddle {
 namespace inference {
 namespace analysis {
 
-class DfgPassManagerImpl final : public DfgPassManager {
- public:
-  DfgPassManagerImpl() {
-    // TODO(Superjomn) set the key with pass reprs.
-    if (!FLAGS_IA_enable_ir) {
-      AddPass("fluid-to-data-flow-graph", new FluidToDataFlowGraphPass);
-    } else {
-      AddPass("fluid-to-ir-pass", new FluidToIrPass);
-    }
-    TryAddTensorRtPass();
-    AddPass("data-flow-graph-to-fluid", new DataFlowGraphToFluidPass);
-    if (!FLAGS_IA_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, AnalysisPass* pass) {
-    VLOG(30) << "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(
-            {"mul", "conv2d", "pool2d", "relu", "softmax", "sigmoid",
-             "depthwise_conv2d", "batch_norm", "concat", "tanh", "pad",
-             "elementwise_add", "dropout"});
-        if (!node->IsFunction()) return false;
+Analyzer::Analyzer() {}
 
-        const auto* func = static_cast<const Function*>(node);
-        if (teller_set.count(func->func_type())) {
-          return true;
-        } else {
-          return false;
-        }
-      };
+void Analyzer::Run(Argument *argument) { RunIrAnalysis(argument); }
 
-      AddPass("tensorrt-subgraph-marker",
-              new TensorRTSubgraphNodeMarkPass(trt_teller));
-      AddPass("tensorrt-subgraph", new TensorRTSubGraphPass(trt_teller));
-    }
-  }
+void Analyzer::RunIrAnalysis(Argument *argument) {
+  std::vector<std::string> passes({"ir_analysis_compose_pass"});
 
-  // Add the graphviz debuger pass if the parent pass has one.
-  void AddGraphvizDebugerPass(AnalysisPass* pass) {
-    auto* debuger_pass = pass->CreateGraphvizDebugerPass();
-    if (debuger_pass) {
-      Register(debuger_pass->repr(), debuger_pass);
-    }
+  for (auto &pass : passes) {
+    PassRegistry::Global().Retreive(pass)->Run(argument);
   }
-};
-
-Analyzer::Analyzer() { Register("manager1", new DfgPassManagerImpl); }
-
-void Analyzer::Run(Argument* argument) {
-  std::vector<std::string> passes;
-  passes.push_back("graph_viz_pass");  // add graphviz for debug.
-#ifdef PADDLE_WITH_MKLDNN
-  if (use_mkldnn_) {
-    VLOG(30) << "Adding MKL-DNN placement pass";
-    passes.push_back("mkldnn_placement_pass");
-  }
-#endif
-  // infer_clean_graph_pass should be the first default pass
-  // after mkldnn_placement_pass.
-  passes.push_back("infer_clean_graph_pass");
-  passes.push_back("graph_viz_pass");  // add graphviz for debug.
-  for (auto& pass : ir_passes_) {
-    // skip mkldnn pass when use_mkldnn_ = false;
-    bool skip_pass = (!use_mkldnn_) && pass.find("mkldnn") != std::string::npos;
-    if (!disabled_ir_passes_.count(pass) && !skip_pass) {
-      passes.push_back(pass);
-      passes.push_back("graph_viz_pass");  // add graphviz for debug.
-    }
-  }
-  argument->Set(kFluidToIrPassesAttr, new std::vector<std::string>(passes));
-
-  for (auto& x : data_) {
-    PADDLE_ENFORCE(x->Initialize(argument));
-    x->RunAll();
-    PADDLE_ENFORCE(x->Finalize());
-  }
-}
-
-Analyzer& Analyzer::IncludeAllIrPasses() {
-  ir_passes_ = all_ir_passes_;
-  return *this;
-}
-
-Analyzer& Analyzer::DisableIrPasses(const std::vector<std::string>& passes) {
-  disabled_ir_passes_.insert(passes.begin(), passes.end());
-  return *this;
-}
-
-Analyzer& Analyzer::IncludeIrPasses(const std::vector<std::string>& passes) {
-  ir_passes_ = passes;
-  return *this;
-}
-
-Analyzer& Analyzer::SetUseMkldnn(bool use_mkldnn) {
-  use_mkldnn_ = use_mkldnn;
-  return *this;
 }
 
 }  // namespace analysis

paddle/fluid/inference/analysis/analyzer.h

@@ -40,56 +40,21 @@ limitations under the License. */
 #include <vector>
 #include "paddle/fluid/inference/analysis/analysis_pass.h"
 #include "paddle/fluid/inference/analysis/flags.h"
-#include "paddle/fluid/inference/analysis/pass_manager.h"
 
 namespace paddle {
 namespace inference {
 namespace analysis {
 
-class Analyzer : public OrderedRegistry<PassManager> {
+class Analyzer final {
  public:
-  // Register all the pass-managers.
   Analyzer();
 
   void Run(Argument* argument);
 
-  Analyzer& DisableIrPasses(const std::vector<std::string>& passes);
-  Analyzer& IncludeIrPasses(const std::vector<std::string>& passes);
-  Analyzer& IncludeAllIrPasses();
-  Analyzer& SetUseMkldnn(bool use_mkldnn);
-
   DISABLE_COPY_AND_ASSIGN(Analyzer);
 
- private:
-  // All avaiable IR passes.
-  // The bigger fuse comes first, so that the small operators prefer to be
-  // merged in a larger fuse op. The small fusion will not break the pattern of
-  // larger fusion.
-  const std::vector<std::string> all_ir_passes_{{
-      // Manual update the passes here.
-      "attention_lstm_fuse_pass",       //
-      "seqconv_eltadd_relu_fuse_pass",  //
-      "embedding_fc_lstm_fuse_pass",    //
-      "fc_lstm_fuse_pass",              //
-      "mul_lstm_fuse_pass",             //
-      "fc_gru_fuse_pass",               //
-      "mul_gru_fuse_pass",              //
-      "seq_concat_fc_fuse_pass",        //
-      "fc_fuse_pass",                   //
-      "conv_bn_fuse_pass",              //
-      "conv_eltwiseadd_bn_fuse_pass",   //
-#ifdef PADDLE_WITH_MKLDNN
-      "depthwise_conv_mkldnn_pass",             //
-      "conv_bias_mkldnn_fuse_pass",             //
-      "conv_relu_mkldnn_fuse_pass",             //
-      "conv_elementwise_add_mkldnn_fuse_pass",  //
-#endif
-  }};
-
-  std::unordered_set<std::string> disabled_ir_passes_;
-  // Ir passes to run
-  std::vector<std::string> ir_passes_;
-  bool use_mkldnn_;
+ protected:
+  void RunIrAnalysis(Argument* argument);
 };
 
 }  // namespace analysis

paddle/fluid/inference/analysis/analyzer_tester.cc

@@ -27,21 +27,21 @@ namespace analysis {
 using namespace framework;  // NOLINT
 
 TEST(Analyzer, analysis_without_tensorrt) {
-  FLAGS_IA_enable_tensorrt_subgraph_engine = false;
   Argument argument;
-  argument.fluid_model_dir.reset(new std::string(FLAGS_inference_model_dir));
+  argument.SetModelDir(FLAGS_inference_model_dir);
+  argument.SetIrAnalysisPasses({"infer_clean_graph_pass"});
+
   Analyzer analyser;
   analyser.Run(&argument);
 }
 
 TEST(Analyzer, analysis_with_tensorrt) {
-  FLAGS_IA_enable_tensorrt_subgraph_engine = true;
   Argument argument;
-  argument.Set<int>("minimum_subgraph_size", new int(0));
-  argument.Set<int>("max_batch_size", new int(3));
-  argument.Set<int>("workspace_size", new int(1 << 20));
-  argument.Set<std::string>("precision_mode", new std::string("FP32"));
-  argument.fluid_model_dir.reset(new std::string(FLAGS_inference_model_dir));
+  argument.SetTensorRtMaxBatchSize(3);
+  argument.SetTensorRtWorkspaceSize(1 << 20);
+  argument.SetModelDir(FLAGS_inference_model_dir);
+  argument.SetIrAnalysisPasses({"infer_clean_graph_pass"});
+
   Analyzer analyser;
   analyser.Run(&argument);
 }

paddle/fluid/inference/analysis/argument.h

@@ -24,13 +24,16 @@
 #pragma once
 
 #include <string>
+#include <vector>
+#include "paddle/fluid/framework/ir/graph.h"
 #include "paddle/fluid/framework/program_desc.h"
-#include "paddle/fluid/inference/analysis/data_flow_graph.h"
+#include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/platform/variant.h"
 
 namespace paddle {
 namespace inference {
 namespace analysis {
+using framework::ir::Graph;
 
 /*
  * The argument definition of both Pass and PassManagers.
@@ -39,75 +42,99 @@ namespace analysis {
  */
 struct Argument {
   Argument() = default;
-  explicit Argument(const std::string& fluid_model_dir)
-      : fluid_model_dir(new std::string(fluid_model_dir)) {}
-  // The directory of the trained model.
-  std::unique_ptr<std::string> fluid_model_dir;
-  // The path of `__model__` and `param`, this is used when the file name of
-  // model and param is changed.
-  std::unique_ptr<std::string> fluid_model_program_path;
-  std::unique_ptr<std::string> fluid_model_param_path;
-
-  // The graph that process by the Passes or PassManagers.
-  std::unique_ptr<DataFlowGraph> main_dfg;
-
-  // The original program desc.
-  std::unique_ptr<framework::proto::ProgramDesc> origin_program_desc;
-
-  // The processed program desc.
-  std::unique_ptr<framework::proto::ProgramDesc> transformed_program_desc;
-
-  // The output storage path of ModelStorePass.
-  std::unique_ptr<std::string> model_output_store_path;
-
-  // Support for any other attributes.
-  template <typename T>
-  void Set(const std::string& key, T* data) {
-    PADDLE_ENFORCE_NOT_NULL(data);
-    PADDLE_ENFORCE(!attrs_.count(key), "Duplicate set Argument's attr [%s]",
-                   key);
-    attrs_[key] = data;
-    attr_deleters_[key] = [data, key]() {
-      VLOG(30) << "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx";
-      VLOG(30) << "argument delete attr: " << key;
-      delete data;
-    };
-  }
-
-  bool Has(const std::string& name) const { return attrs_.count(name); }
-
-  template <typename T>
-  T* Release(const std::string& key) {
-    PADDLE_ENFORCE(attrs_.count(key));
-    auto* res = boost::any_cast<T*>(attrs_.at(key));
-    attrs_.erase(key);
-    attr_deleters_.erase(key);
-    return res;
-  }
-
-  template <typename T>
-  T& Get(const std::string& key) {
-    PADDLE_ENFORCE(Has(key));
-    return *boost::any_cast<T*>(attrs_.at(key));
-  }
-
-  ~Argument() {
-    for (auto& item : attr_deleters_) {
-      item.second();
-    }
-  }
+  explicit Argument(const std::string& model_dir) { SetModelDir(model_dir); }
+
+  using unique_ptr_t = std::unique_ptr<void, std::function<void(void*)>>;
+  using fusion_statis_t = std::unordered_map<std::string, int>;
+
+  bool Has(const std::string& key) const { return valid_fields_.count(key); }
+
+#define DECL_ARGUMENT_FIELD(field__, Field, type__) \
+ public:                                            \
+  type__& field__() {                               \
+    PADDLE_ENFORCE(Has(#field__));                  \
+    return field__##_;                              \
+  }                                                 \
+  void Set##Field(const type__& x) {                \
+    field__##_ = x;                                 \
+    valid_fields_.insert(#field__);                 \
+  }                                                 \
+  DECL_ARGUMENT_FIELD_VALID(field__);               \
+  type__* field__##_ptr() { return &field__##_; }   \
+                                                    \
+ private:                                           \
+  type__ field__##_;
+
+#define DECL_ARGUMENT_FIELD_VALID(field__) \
+  bool field__##_valid() { return Has(#field__); }
+
+#define DECL_ARGUMENT_UNIQUE_FIELD(field__, Field, type__)                \
+ public:                                                                  \
+  type__& field__() {                                                     \
+    PADDLE_ENFORCE_NOT_NULL(field__##_);                                  \
+    PADDLE_ENFORCE(Has(#field__));                                        \
+    return *static_cast<type__*>(field__##_.get());                       \
+  }                                                                       \
+  void Set##Field(type__* x) {                                            \
+    field__##_ =                                                          \
+        unique_ptr_t(x, [](void* x) { delete static_cast<type__*>(x); }); \
+    valid_fields_.insert(#field__);                                       \
+  }                                                                       \
+  void Set##Field##NotOwned(type__* x) {                                  \
+    valid_fields_.insert(#field__);                                       \
+    field__##_ = unique_ptr_t(x, [](void* x) {});                         \
+  }                                                                       \
+  DECL_ARGUMENT_FIELD_VALID(field__);                                     \
+  type__* field__##_ptr() {                                               \
+    PADDLE_ENFORCE(Has(#field__));                                        \
+    return static_cast<type__*>(field__##_.get());                        \
+  }                                                                       \
+  type__* Release##Field() {                                              \
+    PADDLE_ENFORCE(Has(#field__));                                        \
+    valid_fields_.erase(#field__);                                        \
+    return static_cast<type__*>(field__##_.release());                    \
+  }                                                                       \
+                                                                          \
+ private:                                                                 \
+  unique_ptr_t field__##_;
+
+  // Model path
+  DECL_ARGUMENT_FIELD(model_dir, ModelDir, std::string);
+  // Model specified with program and parameters files.
+  DECL_ARGUMENT_FIELD(model_program_path, ModelProgramPath, std::string);
+  DECL_ARGUMENT_FIELD(model_params_path, ModelParamsPath, std::string);
+
+  // The overall graph to work on.
+  DECL_ARGUMENT_UNIQUE_FIELD(main_graph, MainGraph, framework::ir::Graph);
+  // The overall Scope to work on.
+  DECL_ARGUMENT_UNIQUE_FIELD(scope, Scope, framework::Scope);
+
+  DECL_ARGUMENT_UNIQUE_FIELD(main_program, MainProgram, framework::ProgramDesc);
+
+  // The ir passes to perform in analysis phase.
+  DECL_ARGUMENT_FIELD(ir_analysis_passes, IrAnalysisPasses,
+                      std::vector<std::string>);
+
+  DECL_ARGUMENT_FIELD(use_gpu, UseGPU, bool);
+  DECL_ARGUMENT_FIELD(use_tensorrt, UseTensorRT, bool);
+  DECL_ARGUMENT_FIELD(tensorrt_node_teller, TensorRtNodeTeller,
+                      std::function<bool(const framework::ir::Node*)>);
+  DECL_ARGUMENT_FIELD(tensorrt_max_batch_size, TensorRtMaxBatchSize, int);
+  DECL_ARGUMENT_FIELD(tensorrt_workspace_size, TensorRtWorkspaceSize, int);
+
+  // The program transformed by IR analysis phase.
+  DECL_ARGUMENT_UNIQUE_FIELD(ir_analyzed_program, IrAnalyzedProgram,
+                             framework::proto::ProgramDesc);
+
+  DECL_ARGUMENT_FIELD(fusion_statis, FusionStatis, fusion_statis_t);
 
  private:
-  std::unordered_map<std::string, boost::any> attrs_;
-  std::unordered_map<std::string, std::function<void()>> attr_deleters_;
+  std::unordered_set<std::string> valid_fields_;
 };
 
-#define UNLIKELY(condition) __builtin_expect(static_cast<bool>(condition), 0)
-#define ANALYSIS_ARGUMENT_CHECK_FIELD(field__)               \
-  if (UNLIKELY(!(field__))) {                                \
-    LOG(ERROR) << "field " << #field__ << " should be set."; \
-    return false;                                            \
-  }
+#define ARGUMENT_CHECK_FIELD(argument__, fieldname__) \
+  PADDLE_ENFORCE(argument__->Has(#fieldname__),       \
+                 "the argument field [%s] should be set", #fieldname__);
 
 }  // namespace analysis
 }  // namespace inference

paddle/fluid/inference/analysis/data_flow_graph.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/data_flow_graph.h"
-#include "paddle/fluid/inference/analysis/dot.h"
-#include "paddle/fluid/inference/analysis/node.h"
-
-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();
-  std::unordered_set<Node *> ins;
-  std::unordered_set<Node *> outs;
-  for (auto &node : nodes.nodes()) {
-    for (auto *in : node->inlinks) {
-      ins.insert(in);
-    }
-    for (auto *out : node->outlinks) {
-      outs.insert(out);
-    }
-  }
-
-  // The nodes that in ins but not in outs is the graph's inputs
-  // 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);
-    }
-  }
-  for (auto *out : outs) {
-    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);
-        unique_written_vars.insert(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);
-      }
-    }
-  }
-  // 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(40) << "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(40) << "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(40) << "finish creating Nodes";
-
-  VLOG(40) << "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(30) << "finished build from IR";
-}
-
-void DataFlowGraph::Clean() {
-  for (auto &node : nodes.nodes()) {
-    std::unordered_set<Node *> inlinks_set(node->inlinks.begin(),
-                                           node->inlinks.end());
-    std::unordered_set<Node *> outlinks_set(node->outlinks.begin(),
-                                            node->outlinks.end());
-    if (inlinks_set.size() < node->inlinks.size()) {
-      node->inlinks.assign(inlinks_set.begin(), inlinks_set.end());
-    }
-    if (outlinks_set.size() < node->outlinks.size()) {
-      node->outlinks.assign(outlinks_set.begin(), outlinks_set.end());
-    }
-  }
-}
-
-std::string DataFlowGraph::DotString() const {
-  Dot dot;
-
-  // Add nodes
-  for (size_t i = 0; i < nodes.size(); i++) {
-    const Node &node = nodes.Get(i);
-    dot.AddNode(node.repr(), node.dot_attrs());
-  }
-
-  // Add edges
-  for (size_t i = 0; i < nodes.size(); i++) {
-    const Node &node = nodes.Get(i);
-    for (auto &in : node.inlinks) {
-      dot.AddEdge(in->repr(), node.repr(), {});
-    }
-  }
-  return dot.Build();
-}
-
-std::string DataFlowGraph::HumanReadableInfo(bool show_values,
-                                             bool show_functions) const {
-  std::stringstream values, functions;
-  for (auto &n : nodes.nodes()) {
-    if (show_values && n->IsValue()) {
-      values << n->repr() << "\n";
-    }
-    if (show_functions && n->IsFunction()) {
-      functions << n->repr() << "\n";
-    }
-  }
-  return "Values:\n" + values.str() + "\n\n" + "Functions:\n" + functions.str();
-}
-
-//
-// NodesBFSIterator
-//
-
-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(
-    const GraphTraits<DataFlowGraph>::NodesBFSIterator &other)
-    : queue_(other.queue_), visited_(other.visited_) {}
-
-Node &GraphTraits<DataFlowGraph>::NodesBFSIterator::operator*() {
-  PADDLE_ENFORCE(!queue_.empty());
-  return *queue_.front();
-}
-
-Node *GraphTraits<DataFlowGraph>::NodesBFSIterator::operator->() {
-  PADDLE_ENFORCE(!queue_.empty());
-  return queue_.front();
-}
-
-GraphTraits<DataFlowGraph>::NodesBFSIterator &
-GraphTraits<DataFlowGraph>::NodesBFSIterator::operator=(
-    const GraphTraits<DataFlowGraph>::NodesBFSIterator &other) {
-  queue_ = other.queue_;
-  visited_ = other.visited_;
-  return *this;
-}
-
-GraphTraits<DataFlowGraph>::NodesBFSIterator
-    &GraphTraits<DataFlowGraph>::NodesBFSIterator::operator++() {
-  PADDLE_ENFORCE(!queue_.empty());
-  auto *cur = queue_.front();
-  visited_.insert(cur);
-  queue_.pop_front();
-  for (auto *output : cur->outlinks) {
-    if (!visited_.count(output)) {
-      queue_.push_back(output);
-      visited_.insert(output);
-    }
-  }
-  return *this;
-}
-
-bool GraphTraits<DataFlowGraph>::NodesBFSIterator::operator==(
-    const GraphTraits<DataFlowGraph>::NodesBFSIterator &other) {
-  if (queue_.empty()) return other.queue_.empty();
-  if ((!queue_.empty()) && (!other.queue_.empty())) {
-    return queue_.front() == other.queue_.front() &&
-           visited_.size() == other.visited_.size();
-    // equality of queue and
-    // visited. Just a light but week implementation.
-  }
-  return false;
-}
-
-//
-// NodesDFSIterator
-//
-GraphTraits<DataFlowGraph>::NodesDFSIterator::NodesDFSIterator(
-    const std::vector<Node *> &source) {
-  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(
-    const GraphTraits<DataFlowGraph>::NodesDFSIterator &other)
-    : stack_(other.stack_), visited_(other.visited_) {}
-
-Node &GraphTraits<DataFlowGraph>::NodesDFSIterator::operator*() {
-  PADDLE_ENFORCE(!stack_.empty());
-  return *stack_.top();
-}
-
-GraphTraits<DataFlowGraph>::NodesDFSIterator
-    &GraphTraits<DataFlowGraph>::NodesDFSIterator::operator++() {
-  if (stack_.empty()) return *this;
-  visited_.insert(stack_.top());
-  auto *cur = stack_.top();
-  stack_.pop();
-  for (auto *x : cur->outlinks) {
-    if (!visited_.count(x)) {
-      stack_.push(x);
-      visited_.insert(x);
-    }
-  }
-  return *this;
-}
-bool GraphTraits<DataFlowGraph>::NodesDFSIterator::operator==(
-    const GraphTraits<DataFlowGraph>::NodesDFSIterator &other) {
-  if (stack_.empty()) return other.stack_.empty();
-  if ((!stack_.empty()) && (!other.stack_.empty())) {
-    return stack_.top() == other.stack_.top();
-  }
-  return false;
-}
-
-GraphTraits<DataFlowGraph>::NodesDFSIterator &
-GraphTraits<DataFlowGraph>::NodesDFSIterator::operator=(
-    const GraphTraits<DataFlowGraph>::NodesDFSIterator &other) {
-  stack_ = other.stack_;
-  visited_ = other.visited_;
-  return *this;
-}
-Node *GraphTraits<DataFlowGraph>::NodesDFSIterator::operator->() {
-  return stack_.top();
-}
-
-inline bool CheckNodeIndegreeEquals(const Node &node, size_t n) {
-  return node.inlinks.size() == n;
-}
-
-GraphTraits<DataFlowGraph>::NodesTSIterator::NodesTSIterator(
-    const std::vector<Node *> &source) {
-  PADDLE_ENFORCE(!source.empty(),
-                 "Start points of topological sorting should not be empty!");
-  // CHECK all the inputs' in-degree is 0
-  for (auto *node : source) {
-    PADDLE_ENFORCE(CheckNodeIndegreeEquals(*node, 0));
-  }
-
-  std::unordered_set<Node *> visited;
-  std::unordered_set<Node *> to_visit{source.begin(), source.end()};
-
-  std::vector<Node *> inlink_visited;
-  while (!to_visit.empty()) {
-    std::vector<Node *> queue(to_visit.begin(), to_visit.end());
-    for (auto *p : queue) {
-      if (p->deleted()) {
-        visited.insert(p);
-        to_visit.erase(p);
-        continue;
-      }
-      inlink_visited.clear();
-
-      std::copy_if(p->inlinks.begin(), p->inlinks.end(),
-                   std::back_inserter(inlink_visited),
-                   [&](Node *x) { return visited.count(x); });
-
-      if (inlink_visited.size() == p->inlinks.size()) {
-        sorted_.push_back(p);
-        for (auto *_ : p->outlinks) {
-          if (!visited.count(_)) {
-            to_visit.insert(_);
-          }
-        }
-
-        to_visit.erase(p);
-        visited.insert(p);
-      }
-    }
-  }
-}
-
-GraphTraits<DataFlowGraph>::NodesTSIterator::NodesTSIterator(
-    const paddle::inference::analysis::GraphTraits<
-        DataFlowGraph>::NodesTSIterator &other)
-    : sorted_(other.sorted_), cursor_(other.cursor_) {}
-
-Node &GraphTraits<DataFlowGraph>::NodesTSIterator::operator*() {
-  PADDLE_ENFORCE_LT(cursor_, sorted_.size());
-  return *sorted_[cursor_];
-}
-
-paddle::inference::analysis::GraphTraits<DataFlowGraph>::NodesTSIterator
-    &GraphTraits<DataFlowGraph>::NodesTSIterator::operator++() {
-  if (++cursor_ >= sorted_.size()) {
-    sorted_.clear();
-    cursor_ = 0;
-  }
-  return *this;
-}
-paddle::inference::analysis::GraphTraits<DataFlowGraph>::NodesTSIterator &
-GraphTraits<DataFlowGraph>::NodesTSIterator::operator=(
-    const paddle::inference::analysis::GraphTraits<
-        DataFlowGraph>::NodesTSIterator &other) {
-  cursor_ = other.cursor_;
-  sorted_ = other.sorted_;
-  return *this;
-}
-
-bool GraphTraits<DataFlowGraph>::NodesTSIterator::operator==(
-    const paddle::inference::analysis::GraphTraits<
-        DataFlowGraph>::NodesTSIterator &other) {
-  return sorted_ == other.sorted_ && cursor_ == other.cursor_;
-}
-
-Node *GraphTraits<DataFlowGraph>::NodesTSIterator::operator->() {
-  PADDLE_ENFORCE_LT(cursor_, sorted_.size());
-  return sorted_[cursor_];
-}
-
-std::pair<std::vector<Node *>, std::vector<Node *>>
-ExtractInputAndOutputOfSubGraph(std::vector<Node *> &graph) {  // NOLINT
-  std::unordered_set<Node *> nodes(graph.begin(), graph.end());
-  std::unordered_set<Node *> inputs;
-  std::unordered_set<Node *> outputs;
-  // Input a Value, check whether its inlink is in the subgraph.
-  auto inlink_in_subgraph = [&](Node *n) {
-    for (auto *in : n->inlinks) {
-      if (nodes.count(in)) return true;
-    }
-    return false;
-  };
-
-  for (auto &node : graph) {
-    for (auto *in : node->inlinks) {
-      // The Value that is written by nodes inside a sub-graph shouldn't be the
-      // input of the sub-graph.
-      if (!nodes.count(in) && in->type() == Node::Type::kValue &&
-          !inlink_in_subgraph(in)) {
-        inputs.insert(in);
-      }
-    }
-    for (auto *out : node->outlinks) {
-      if (!nodes.count(out) && out->type() == Node::Type::kValue) {
-        outputs.insert(out);
-      }
-    }
-  }
-  return std::make_pair(std::vector<Node *>(inputs.begin(), inputs.end()),
-                        std::vector<Node *>(outputs.begin(), outputs.end()));
-}
-
-// Filter the Intermediate results of the subgraph node.
-void FilterRedundantOutputOfSubGraph(DataFlowGraph *graph) {
-  std::vector<Node *> op_nodes;
-  for (auto &node : GraphTraits<DataFlowGraph>(*graph).nodes_in_TS()) {
-    if (node.type() == Node::Type::kValue || node.deleted()) {
-      continue;
-    }
-    op_nodes.push_back(&node);
-  }
-  size_t op_num = op_nodes.size();
-  for (size_t i = 0; i < op_num; i++) {
-    if (op_nodes[i]->type() == Node::Type::kFunction) continue;
-    std::unordered_set<std::string> follow_up_input_names;
-    for (size_t j = i + 1; j < op_num; j++) {
-      for (auto *in : op_nodes[j]->inlinks) {
-        follow_up_input_names.insert(in->name());
-      }
-    }
-    std::vector<Node *> filtered_subgraph_outlinks;
-    for (auto *out : op_nodes[i]->outlinks) {
-      if (follow_up_input_names.count(out->name())) {
-        filtered_subgraph_outlinks.push_back(out);
-      } else {
-        out->SetDeleted();
-      }
-    }
-    // The filtered_subgraph_outlinks may be empty.
-    op_nodes[i]->outlinks = filtered_subgraph_outlinks;
-  }
-}
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/data_flow_graph.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. */
-
-/*
- * Data flow graph is an pass that build the basic graph. It contains a graph
- * and the iterators that enable the iteration over the graph.
- */
-
-#pragma once
-
-#include <deque>
-#include <stack>
-#include <string>
-#include <unordered_set>
-#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"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-/*
- * DataFlowGraph - A container of Value and Function Nodes.
- *
- * This is the base graph for any other type of graphs, such as SSA or CFG.
- */
-struct DataFlowGraph {
-  NodeMap nodes;
-  // 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();
-};
-
-/*
- * An graph trait help to traverse the graph using BFS.
- * The BFS start from a graph's inputs, the graph should be fully-connected, so
- * that the iterator can reach the end.
- */
-template <>
-struct GraphTraits<DataFlowGraph> {
-  // BFS iterator on nodes.
-  struct NodesBFSIterator
-      : public std::iterator<std::forward_iterator_tag, Node *> {
-    NodesBFSIterator() = default;
-    explicit NodesBFSIterator(const std::vector<Node *> &source);
-    NodesBFSIterator(NodesBFSIterator &&other) noexcept;
-    // NOTE Heavy to use.
-    NodesBFSIterator(const NodesBFSIterator &other);
-
-    Node &operator*();
-    NodesBFSIterator &operator++();
-    Node *operator->();
-    // TODO(Superjomn) current implementation just compare the first
-    // element, need to compare the graph and all the elements in the queue and
-    // set.
-    NodesBFSIterator &operator=(const NodesBFSIterator &other);
-    bool operator==(const NodesBFSIterator &other);
-    bool operator!=(const NodesBFSIterator &other) { return !(*this == other); }
-
-   private:
-    std::deque<Node *> queue_;
-    std::unordered_set<Node *> visited_;
-  };
-
-  // DFS iterator on nodes.
-  struct NodesDFSIterator
-      : public std::iterator<std::forward_iterator_tag, Node *> {
-    NodesDFSIterator() = default;
-    NodesDFSIterator(const std::vector<Node *> &source);
-    NodesDFSIterator(NodesDFSIterator &&other) noexcept;
-    NodesDFSIterator(const NodesDFSIterator &other);
-
-    Node &operator*();
-    NodesDFSIterator &operator++();
-    // TODO(Superjomn) current implementation just compare the first
-    // element, need to compare the graph and all the elements in the queue and
-    // set.
-    NodesDFSIterator &operator=(const NodesDFSIterator &other);
-    bool operator==(const NodesDFSIterator &other);
-    bool operator!=(const NodesDFSIterator &other) { return !(*this == other); }
-    Node *operator->();
-
-   private:
-    std::stack<Node *> stack_;
-    std::unordered_set<Node *> visited_;
-  };
-
-  // Topological sorting iterator on nodes.
-  struct NodesTSIterator
-      : public std::iterator<std::forward_iterator_tag, Node *> {
-    NodesTSIterator() = default;
-    NodesTSIterator(const std::vector<Node *> &source);
-    NodesTSIterator(NodesTSIterator &&other)
-        : sorted_(std::move(other.sorted_)), cursor_(other.cursor_) {
-      other.cursor_ = 0;
-    }
-    NodesTSIterator(const NodesTSIterator &other);
-
-    Node &operator*();
-    NodesTSIterator &operator++();
-    // TODO(Superjomn) current implementation just compare the first
-    // element, need to compare the graph and all the elements in the queue and
-    // set.
-    NodesTSIterator &operator=(const NodesTSIterator &other);
-    bool operator==(const NodesTSIterator &other);
-    bool operator!=(const NodesTSIterator &other) { return !(*this == other); }
-    Node *operator->();
-
-   private:
-    std::vector<Node *> sorted_;
-    size_t cursor_{0};
-  };
-
-  explicit GraphTraits(const DataFlowGraph &graph) : graph_(graph) {}
-
-  // default use BFS to visit the nodes.
-  iterator_range<NodesBFSIterator> nodes() {
-    return iterator_range<NodesBFSIterator>(nodes_bfs_begin(), nodes_bfs_end());
-  }
-  iterator_range<NodesBFSIterator> nodes_in_BFS() {
-    return iterator_range<NodesBFSIterator>(nodes_bfs_begin(), nodes_bfs_end());
-  }
-  iterator_range<NodesDFSIterator> nodes_in_DFS() {
-    return iterator_range<NodesDFSIterator>(nodes_dfs_begin(), nodes_dfs_end());
-  }
-  iterator_range<NodesTSIterator> nodes_in_TS() {
-    return iterator_range<NodesTSIterator>(nodes_ts_begin(), nodes_ts_end());
-  }
-
- private:
-  NodesBFSIterator nodes_bfs_begin() {
-    return NodesBFSIterator(graph_.inputs());
-  }
-  NodesBFSIterator nodes_bfs_end() { return NodesBFSIterator(); }
-
-  NodesDFSIterator nodes_dfs_begin() {
-    return NodesDFSIterator(graph_.inputs());
-  }
-  NodesDFSIterator nodes_dfs_end() { return NodesDFSIterator(); }
-
-  NodesTSIterator nodes_ts_begin() { return NodesTSIterator(graph_.inputs()); }
-  NodesTSIterator nodes_ts_end() { return NodesTSIterator(); }
-
- private:
-  const DataFlowGraph &graph_;
-};
-
-// Extract the inputs and outputs of a graph. The inputs and outputs of a
-// sub-graph is the inputs nodes and output nodes that doesn't inside the
-// sub-graph.
-std::pair<std::vector<Node *>, std::vector<Node *>>
-ExtractInputAndOutputOfSubGraph(std::vector<Node *> &graph);  // NOLINT
-
-void FilterRedundantOutputOfSubGraph(DataFlowGraph *graph);
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/data_flow_graph_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/data_flow_graph.h"
-#include "paddle/fluid/framework/op_proto_maker.h"
-#include "paddle/fluid/framework/program_desc.h"
-#include "paddle/fluid/inference/analysis/ut_helper.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-TEST(DataFlowGraph, BFS) {
-  auto desc = LoadProgramDesc(FLAGS_inference_model_dir + "/__model__");
-  auto dfg = ProgramDescToDFG(desc);
-  dfg.Build();
-
-  for (auto* in : dfg.inputs()) {
-    LOG(INFO) << "inputs: " << in->name() << " "
-              << static_cast<int>(in->type());
-  }
-  for (auto* out : dfg.outputs()) {
-    LOG(INFO) << "outputs: " << out->name() << " "
-              << static_cast<int>(out->type());
-  }
-
-  size_t count = 0;
-  for (auto& node : GraphTraits<DataFlowGraph>(dfg).nodes()) {
-    LOG(INFO) << "visiting " << node.name();
-    ++count;
-  }
-  ASSERT_EQ(count, dfg.nodes.size());
-}
-
-TEST(DataFlowGraph, DFS) {
-  auto desc = LoadProgramDesc(FLAGS_inference_model_dir + "/__model__");
-  DataFlowGraph dfg;
-  dfg.Build(desc);
-  size_t count = 0;
-  for (auto& node : GraphTraits<DataFlowGraph>(dfg).nodes_in_DFS()) {
-    LOG(INFO) << "visiting " << node.name();
-    ++count;
-  }
-  ASSERT_EQ(count, dfg.nodes.size());
-}
-
-// Topological sorting.
-/*
- * Graph topology
- * inputs: 0, 1, 2
- * 0 -> 4
- * 0 -> 5
- * 1 -> 6
- * 2 -> 7
- * 4 -> 5
- * 4 -> 7
- * 4 -> 3
- * 7 -> 3
- */
-TEST(DataFlowGraph, TS) {
-  DataFlowGraph graph;
-
-  for (int i = 0; i < 8; i++) {
-    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);
-    target->inlinks.push_back(source);
-    source->outlinks.push_back(target);
-  };
-
-  add_link(0, 4);
-  add_link(0, 5);
-  add_link(1, 6);
-  add_link(2, 7);
-  add_link(4, 5);
-  add_link(4, 7);
-  add_link(4, 3);
-  add_link(7, 3);
-  graph.Build();
-
-  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();
-    sorted_ids.push_back(it->id());
-  }
-
-  // Assert a occurs prior to b in the sorted_ids.
-  auto assert_positive_sequence_pair = [&](int a, int b) {
-    auto a_offset = std::find(sorted_ids.begin(), sorted_ids.end(), a);
-    auto b_offset = std::find(sorted_ids.begin(), sorted_ids.end(), b);
-    ASSERT_LT(a_offset, b_offset);
-  };
-
-  assert_positive_sequence_pair(2, 7);
-  assert_positive_sequence_pair(7, 3);
-  assert_positive_sequence_pair(4, 3);
-  assert_positive_sequence_pair(0, 4);
-  assert_positive_sequence_pair(0, 5);
-  assert_positive_sequence_pair(1, 6);
-  assert_positive_sequence_pair(4, 5);
-  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);
-  op->SetAttr(framework::OpProtoAndCheckerMaker::OpRoleAttrName(),
-              static_cast<int>(framework::OpRole::kForward));
-}
-
-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.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 implements the transformation from fluid ProgramDesc to data flow
- * graph.
- */
-
-#pragma once
-
-#include <string>
-#include "paddle/fluid/framework/program_desc.h"
-#include "paddle/fluid/inference/analysis/analysis_pass.h"
-#include "paddle/fluid/inference/analysis/data_flow_graph.h"
-
-namespace paddle {
-namespace inference {
-
-namespace analysis {
-class DataFlowGraphToFluidPass final : public DataFlowGraphPass {
- public:
-  DataFlowGraphToFluidPass() = default;
-
-  bool Initialize(Argument *argument) override;
-  bool Finalize() override;
-
-  void Run(DataFlowGraph *graph) override;
-
-  std::string repr() const override { return "DFG to fluid"; }
-  std::string description() const override {
-    return "Transform a DFG to a Fluid ProgramDesc";
-  }
-
-  AnalysisPass *CreateGraphvizDebugerPass() const override;
-
- protected:
-  // Add a Fluid Op into the ProgramDesc.
-  void AddFluidOp(Node *node);
-  // Add a EngineOp into the ProgramDesc.
-  void AddEngineOp(Node *node);
-
- private:
-  framework::proto::ProgramDesc *desc_;
-  Argument *argument_;
-};
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/data_flow_graph_to_fluid_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/data_flow_graph_to_fluid_pass.h"
-
-#include <glog/logging.h>
-#include <google/protobuf/text_format.h>
-#include <gtest/gtest.h>
-#include "paddle/fluid/framework/executor.h"
-#include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
-#include "paddle/fluid/inference/analysis/ut_helper.h"
-#include "paddle/fluid/inference/io.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-TEST(DataFlowGraph, Test) {
-  Argument argument(FLAGS_inference_model_dir);
-
-  FluidToDataFlowGraphPass pass0;
-  DataFlowGraphToFluidPass pass1;
-  ASSERT_TRUE(pass0.Initialize(&argument));
-  ASSERT_TRUE(pass1.Initialize(&argument));
-
-  pass0.Run(argument.main_dfg.get());
-  pass1.Run(argument.main_dfg.get());
-
-  pass0.Finalize();
-  pass1.Finalize();
-
-  LOG(INFO) << argument.main_dfg->nodes.size();
-}
-
-};  // namespace analysis
-};  // namespace inference
-};  // namespace paddle

paddle/fluid/inference/analysis/dfg_graphviz_draw_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/dfg_graphviz_draw_pass.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-int DFG_GraphvizDrawPass::counter_{0};
-
-void DFG_GraphvizDrawPass::Run(DataFlowGraph *graph) {
-  auto content = Draw(graph);
-  auto dot_path = GenDotPath();
-  std::ofstream file(dot_path);
-  file.write(content.c_str(), content.size());
-  file.close();
-
-  auto png_path = dot_path.substr(0, dot_path.size() - 4) + ".png";
-  std::string message;
-  VLOG(30) << "draw to " << png_path;
-  ExecShellCommand("dot -Tpng " + dot_path + " -o " + png_path, &message);
-}
-
-std::string DFG_GraphvizDrawPass::Draw(DataFlowGraph *graph) {
-  Dot dot;
-  // Add nodes
-  for (size_t i = 0; i < graph->nodes.size(); i++) {
-    const Node &node = graph->nodes.Get(i);
-    if (config_.display_deleted_node || !node.deleted()) {
-      dot.AddNode(node.repr(), node.dot_attrs());
-    }
-  }
-  // Add edges
-  for (size_t i = 0; i < graph->nodes.size(); i++) {
-    const Node &node = graph->nodes.Get(i);
-    if (!config_.display_deleted_node && node.deleted()) continue;
-    for (auto &out : node.outlinks) {
-      if (!config_.display_deleted_node && out->deleted()) continue;
-      dot.AddEdge(node.repr(), out->repr(), {});
-    }
-  }
-  return dot.Build();
-}
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/dfg_graphviz_draw_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. */
-
-/*
- * This file create an DFG_GraphvizDrawPass which helps to draw a data flow
- * graph's structure using graphviz.
- */
-
-#pragma once
-
-#include <fstream>
-#include <string>
-#include "paddle/fluid/inference/analysis/analysis_pass.h"
-#include "paddle/fluid/inference/analysis/dot.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-/*
- * Output a dot file and write to some place.
- */
-class DFG_GraphvizDrawPass : public DataFlowGraphPass {
- public:
-  struct Config {
-    Config(const std::string &dir, const std::string &id,
-           bool display_deleted_node = false)
-        : dir(dir), id(id), display_deleted_node(display_deleted_node) {}
-
-    // The directory to store the .dot or .png files.
-    const std::string dir;
-    // The identifier for this dot file.
-    const std::string id;
-    // Whether to display deleted nodes, default false.
-    const bool display_deleted_node;
-  };
-
-  explicit DFG_GraphvizDrawPass(const Config &config) : config_(config) {}
-
-  bool Initialize(Argument *argument) override { return true; }
-  void Run(DataFlowGraph *graph) override;
-  bool Finalize() override { return true; }
-
-  std::string repr() const override { return "DFG graphviz drawer"; }
-  std::string description() const override {
-    return "Debug a DFG by draw with graphviz";
-  }
-
- protected:
-  // A counter to add a number prefix to the debugger image output so that they
-  // will sort in the triggered order.
-  static int counter_;
-
-  // Path of the dot file to output.
-  std::string GenDotPath() const {
-    return config_.dir + "/" + std::to_string(counter_++) + "-graph_" +
-           config_.id + ".dot";
-  }
-
-  virtual std::string Draw(DataFlowGraph *graph);
-
-  Config config_;
-};
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/dfg_graphviz_draw_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/dfg_graphviz_draw_pass.h"
-
-#include <gtest/gtest.h>
-#include <fstream>
-#include <string>
-#include "paddle/fluid/inference/analysis/ut_helper.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-TEST(DFG_GraphvizDrawPass, dfg_graphviz_draw_pass_tester) {
-  Argument argument(FLAGS_inference_model_dir);
-  FluidToDataFlowGraphPass pass0;
-  ASSERT_TRUE(pass0.Initialize(&argument));
-  pass0.Run(argument.main_dfg.get());
-
-  // auto dfg = ProgramDescToDFG(*argument.origin_program_desc);
-
-  DFG_GraphvizDrawPass::Config config("./", "test");
-  DFG_GraphvizDrawPass pass(config);
-  pass.Initialize(&argument);
-  pass.Run(argument.main_dfg.get());
-
-  // test content
-  std::ifstream file("./0-graph_test.dot");
-  ASSERT_TRUE(file.is_open());
-
-  std::string line;
-  int no{0};
-  while (std::getline(file, line)) {
-    no++;
-  }
-  // DFG is sensitive to ProgramDesc, be careful to change the existing models.
-  ASSERT_EQ(no, 83);
-}
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/dot_tester.cc

@@ -16,7 +16,6 @@
 
 #include <gtest/gtest.h>
 #include <memory>
-#include "paddle/fluid/inference/analysis/data_flow_graph.h"
 
 namespace paddle {
 namespace inference {

paddle/fluid/inference/analysis/fluid_to_data_flow_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 <glog/logging.h>
-#include <string>
-#include <vector>
-
-#include "paddle/fluid/inference/analysis/analyzer.h"
-#include "paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h"
-#include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-bool FluidToDataFlowGraphPass::Initialize(Argument *argument) {
-  ANALYSIS_ARGUMENT_CHECK_FIELD(argument);
-  if (argument->origin_program_desc) {
-    LOG(WARNING) << "argument's origin_program_desc is already set, might "
-                    "duplicate called";
-  }
-  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);
-  auto program = LoadProgramDesc(*argument->fluid_model_program_path);
-  argument->origin_program_desc.reset(
-      new framework::proto::ProgramDesc(program));
-
-  if (!argument->main_dfg) {
-    argument->main_dfg.reset(new DataFlowGraph);
-  }
-  desc_ = argument->origin_program_desc.get();
-  return true;
-}
-
-bool FluidToDataFlowGraphPass::Finalize() { return true; }
-
-void FluidToDataFlowGraphPass::Run(DataFlowGraph *graph) {
-  PADDLE_ENFORCE(graph);
-  PADDLE_ENFORCE(desc_);
-  graph->Build(*desc_);
-}
-
-namespace {
-class DFG_DebuggerPass : public DFG_GraphvizDrawPass {
- public:
-  using Config = DFG_GraphvizDrawPass::Config;
-  explicit DFG_DebuggerPass(const Config &config)
-      : DFG_GraphvizDrawPass(config) {}
-  std::string repr() const override { return "fluid-to-dfg-debuger-pass"; }
-  bool Finalize() override { return true; }
-};
-}
-
-AnalysisPass *FluidToDataFlowGraphPass::CreateGraphvizDebugerPass() const {
-  return new DFG_DebuggerPass(DFG_GraphvizDrawPass::Config(
-      FLAGS_IA_graphviz_log_root, "fluid-to-dfg-debuger"));
-}
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/fluid_to_data_flow_graph_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. */
-
-/*
- * This file implements the transformation from data flow graph to fluid
- * ProgramDesc.
- */
-
-#pragma once
-
-#include <string>
-
-#include "paddle/fluid/framework/program_desc.h"
-#include "paddle/fluid/inference/analysis/analysis_pass.h"
-#include "paddle/fluid/inference/analysis/data_flow_graph.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-/*
- * Transform a FluidDesc to a SSA.
- */
-class FluidToDataFlowGraphPass final : public DataFlowGraphPass {
- public:
-  FluidToDataFlowGraphPass() = default;
-
-  bool Initialize(Argument *argument) override;
-  bool Finalize() override;
-
-  void Run(DataFlowGraph *graph) override;
-
-  std::string repr() const override { return "fluid-to-data-flow-graph"; }
-  std::string description() const override {
-    return "transform a fluid ProgramDesc to a data flow graph.";
-  }
-
-  AnalysisPass *CreateGraphvizDebugerPass() const override;
-
- private:
-  framework::proto::ProgramDesc const *desc_;
-};
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

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 <string>
-#include <vector>
-
-#include "paddle/fluid/framework/ir/fuse_pass_base.h"
-#include "paddle/fluid/inference/analysis/analysis_pass.h"
-#include "paddle/fluid/inference/analysis/flags.h"
-#include "paddle/fluid/inference/analysis/ir_pass_manager.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-static const char kFluidToIrPassesAttr[] = "__fluid_to_ir_passes__";
-
-class FluidToIrPass final : public DataFlowGraphPass {
- public:
-  FluidToIrPass() = default;
-
-  bool Initialize(Argument *argument) override {
-    ANALYSIS_ARGUMENT_CHECK_FIELD(argument);
-    PADDLE_ENFORCE(argument->Has(kFluidToIrPassesAttr),
-                   "argument need the attr %s", kFluidToIrPassesAttr);
-    argument_ = 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);
-    }
-    argument->Set("ir_program_desc", new ProgramDesc(program));
-
-    LOG(INFO) << "Loading parameters";
-    // Load parameters to argument if needed.
-    if (argument->fluid_model_dir || (argument->fluid_model_program_path &&
-                                      argument->fluid_model_param_path)) {
-#define SAFE_GET(ATTR) std::string ATTR = argument->ATTR ? *argument->ATTR : "";
-      SAFE_GET(fluid_model_dir);
-      SAFE_GET(fluid_model_program_path);
-      SAFE_GET(fluid_model_param_path);
-#undef SAFE_GET
-      EnableParamModify(fluid_model_dir, fluid_model_program_path,
-                        fluid_model_param_path);
-    }
-
-    return true;
-  }
-
-  bool Finalize() override { return true; }
-
-  void Run(DataFlowGraph *graph) override {
-    // Call all the IR Passes
-    IRPassManager ir_passes(argument_->Get<ProgramDesc>("ir_program_desc"),
-                            nullptr);
-    // Pass the scope from analysis to IR if needed.
-    if (argument_->Has(framework::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(
-          framework::ir::kParamScopeAttr,
-          new framework::Scope *(&argument_->Get<framework::Scope>(
-              framework::ir::kParamScopeAttr)));
-    }
-
-    if (FLAGS_IA_enable_ir) {
-      const auto &ir_passes_to_apply =
-          argument_->Get<std::vector<std::string>>(kFluidToIrPassesAttr);
-      ir_passes.Apply(ir_passes_to_apply);
-    }
-
-    PADDLE_ENFORCE(argument_->main_dfg.get());
-    argument_->main_dfg->Build(ir_passes.graph());
-    // inherit the arguments from ir.
-    if (ir_passes.graph().Has(framework::ir::kFuseStatisAttr)) {
-      argument_->Set(
-          framework::ir::kFuseStatisAttr,
-          new std::unordered_map<std::string, int>(
-              ir_passes.graph().Get<std::unordered_map<std::string, int>>(
-                  framework::ir::kFuseStatisAttr)));
-    }
-  }
-
-  void EnableParamModify(const std::string &model_dir,
-                         const std::string &prog_file,
-                         const std::string &param_file);
-
-  std::string repr() const override { return "fluid-to-ir-pass"; }
-
- private:
-  // Load parameters from a single file or from a directory.
-  bool LoadParams(framework::Scope *scope, const std::string &dir,
-                  const std::string &prog_file, const std::string &param_file);
-
- private:
-  Argument *argument_{nullptr};
-};
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/graph_traits.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/graph_traits.h"

paddle/fluid/inference/analysis/graph_traits.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 the GraphTraits<X> template class that should be specified
- * by classes that want to be iteratable by generic graph iterators.
- *
- * This file also defines the marker class Inverse that is used to iterate over
- * graphs in a graph defined, inverse ordering...
- */
-
-#pragma once
-
-#include "paddle/fluid/inference/analysis/helper.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-/*
- * This class should be specialized by different graph types...
- * That's why the base class is empty.
- */
-template <typename GraphType>
-struct GraphTraits {
-  // using NodesBFSIterator = xxx
-
-  // NodesBFSIterator nodes_begin();
-  // NodesBFSIterator nodes_end();
-};
-
-/*
- * Inverse - This class is used as a marker class to tell the graph iterator to
- * iterate in a graph defined Inverse order.
- */
-template <typename GraphType>
-struct Inverse {
-  const GraphType &graph;
-
-  explicit Inverse(const GraphType &graph) : graph(graph) {}
-};
-
-/*
- * Provide a partial specialization of GraphTraits so that the inverse of an
- * inverse turns into the original graph.
- */
-template <typename GraphType>
-struct GraphTraits<Inverse<Inverse<GraphType>>> : GraphTraits<GraphType> {};
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/helper.h

@@ -102,20 +102,20 @@ class OrderedRegistry {
  public:
   T *Register(const std::string &name, T *x) {
     PADDLE_ENFORCE(!dic_.count(name), "duplicate key [%s]", name);
-    dic_[name] = data_.size();
-    data_.emplace_back(std::unique_ptr<T>(x));
-    return data_.back().get();
+    dic_[name] = elements_.size();
+    elements_.emplace_back(std::unique_ptr<T>(x));
+    return elements_.back().get();
   }
 
   T *Lookup(const std::string &name) {
     auto it = dic_.find(name);
     if (it == dic_.end()) return nullptr;
-    return data_[it->second].get();
+    return elements_[it->second].get();
   }
 
  protected:
   std::unordered_map<std::string, int> dic_;
-  std::vector<std::unique_ptr<T>> data_;
+  std::vector<std::unique_ptr<T>> elements_;
 };
 
 template <typename T>

paddle/fluid/inference/analysis/ir_pass_manager.cc

@@ -18,6 +18,8 @@
 #include "paddle/fluid/framework/ir/fuse_pass_base.h"
 #include "paddle/fluid/framework/ir/graph.h"
 #include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/inference/analysis/argument.h"
+#include "paddle/fluid/inference/analysis/ir_passes/subgraph_detector.h"
 #include "paddle/fluid/string/pretty_log.h"
 
 namespace paddle {
@@ -27,21 +29,33 @@ using string::PrettyLogEndl;
 using string::PrettyLog;
 using string::Style;
 
-IRPassManager::IRPassManager(const ProgramDesc &program,
-                             framework::Scope *scope)
-    : program_(program) {
-  graph_.reset(new framework::ir::Graph(program));
-  if (scope)
-    graph_->Set(framework::ir::kParamScopeAttr, new framework::Scope *(scope));
+IRPassManager::IRPassManager(Argument *argument) {
+  ARGUMENT_CHECK_FIELD(argument, main_program);
+  graph_ = std::unique_ptr<Graph>(new Graph(argument->main_program()));
+  if (argument->Has("scope")) {
+    graph_->Set(framework::ir::kParamScopeAttr,
+                new framework::Scope *(
+                    const_cast<framework::Scope *>(&argument->scope())));
+  }
+
+  ARGUMENT_CHECK_FIELD(argument, ir_analysis_passes);
+  CreatePasses(argument, argument->ir_analysis_passes());
 }
 
-void IRPassManager::Apply(const std::vector<std::string> &passes) {
-  // Apply all the passes
+void IRPassManager::CreatePasses(Argument *argument,
+                                 const std::vector<std::string> &passes) {
   std::string pre_pass;
   int pass_num = 0;
   for (const std::string &pass_name : passes) {
-    PrettyLogEndl(Style::H2(), "--- Running IR pass [%s]", pass_name);
     auto pass = framework::ir::PassRegistry::Instance().Get(pass_name);
+
+    // Set some pass attributes.
+    if (pass_name == "ir_analysis_pass") {
+      pass->Set("tensorrt_node_teller",
+                new SubgraphDetector::NodeInsideSubgraphTeller(
+                    argument->tensorrt_node_teller()));
+    }
+
     if (pass_name == "graph_viz_pass") {
       std::string dot_file_path = std::to_string(pass_num) + "_ir_" +
                                   (pre_pass.empty() ? "origin" : pre_pass) +
@@ -49,11 +63,47 @@ void IRPassManager::Apply(const std::vector<std::string> &passes) {
       pass->Set("graph_viz_path", new std::string(std::move(dot_file_path)));
       pass_num++;
     }
-    graph_ = pass->Apply(std::move(graph_));
+
+    if (pass_name == "tensorrt_subgraph_pass") {
+      PADDLE_ENFORCE(argument->tensorrt_node_teller_valid());
+      pass->SetNotOwned("tensorrt_node_teller",
+                        argument->tensorrt_node_teller_ptr());
+      pass->Set("workspace_size", new int(argument->tensorrt_workspace_size()));
+      pass->Set("max_batch_size", new int(argument->tensorrt_max_batch_size()));
+    }
+
+    // graph_ = pass->Apply(std::move(graph_));
     pre_pass = pass_name;
+
+    passes_.emplace_back(std::move(pass));
   }
 }
 
+std::unique_ptr<Graph> IRPassManager::Apply(std::unique_ptr<Graph> graph) {
+  if (passes_.empty()) {
+    return graph;
+  }
+  PADDLE_ENFORCE(graph.get());
+  // Apply all the passes
+  for (const auto &pass : passes_) {
+    PrettyLogEndl(Style::H2(), "--- Running IR pass [%s]", pass->Type());
+    graph = pass->Apply(std::move(graph));
+  }
+  return std::move(graph);
+}
+
+framework::proto::ProgramDesc IRPassManager::AcquireProgram(
+    std::unique_ptr<Graph> *graph, const ProgramDesc &program) const {
+  auto pass =
+      framework::ir::PassRegistry::Instance().Get("graph_to_program_pass");
+
+  ProgramDesc desc(program);
+  pass->SetNotOwned("program", &desc);
+  auto *the_graph = graph->release();
+  *graph = pass->Apply(std::unique_ptr<Graph>(the_graph));
+  return *desc.Proto();
+}
+
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/analysis/ir_pass_manager.h

@@ -20,27 +20,38 @@
  * for inference.
  */
 
+#pragma once
+
+#include <string>
+#include <vector>
 #include "paddle/fluid/framework/ir/graph.h"
 #include "paddle/fluid/framework/ir/pass.h"
 #include "paddle/fluid/framework/program_desc.h"
 #include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/inference/analysis/argument.h"
 
 namespace paddle {
 namespace inference {
 namespace analysis {
 using framework::ProgramDesc;
+using framework::ir::Graph;
 
 class IRPassManager final {
  public:
-  IRPassManager(const ProgramDesc &program, framework::Scope *scope);
+  explicit IRPassManager(Argument *argument);
+
+  std::unique_ptr<Graph> Apply(std::unique_ptr<Graph> graph);
 
-  void Apply(const std::vector<std::string> &passes);
+  framework::proto::ProgramDesc AcquireProgram(
+      std::unique_ptr<Graph> *graph, const ProgramDesc &program) const;
 
   framework::ir::Graph &graph() const { return *graph_; }
 
  private:
-  std::unique_ptr<framework::ir::Graph> graph_;
-  ProgramDesc program_;
+  void CreatePasses(Argument *argument, const std::vector<std::string> &passes);
+
+  std::unique_ptr<Graph> graph_;
+  std::vector<std::unique_ptr<framework::ir::Pass>> passes_;
 };
 
 }  // namespace analysis

paddle/fluid/inference/analysis/ir_passes/CMakeLists.txt

+cc_library(subgraph_detector SRCS subgraph_detector.cc DEPS proto_desc)
+cc_library(tensorrt_subgraph_pass SRCS tensorrt_subgraph_pass.cc DEPS subgraph_detector)
+set(analysis_deps ${analysis_deps}
+        subgraph_detector tensorrt_subgraph_pass
+        CACHE INTERNAL "")
+
+set(INFER_IR_PASSES ${INFER_IR_PASSES} tensorrt_subgraph_pass CACHE INTERNAL "")

paddle/fluid/inference/analysis/subgraph_splitter.cc → paddle/fluid/inference/analysis/ir_passes/subgraph_detector.cc

@@ -12,46 +12,110 @@ 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/subgraph_splitter.h"
+#include "paddle/fluid/inference/analysis/ir_passes/subgraph_detector.h"
+#include <string>
+#include <utility>
+#include "paddle/fluid/framework/ir/graph_helper.h"
+#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
+#include "paddle/fluid/framework/ir/node.h"
 
 namespace paddle {
 namespace inference {
 namespace analysis {
 
-const char *SubGraphSplitter::kMarkerAttrName =
-    "_sub_graph_splitter_inside_sub_graph";
+using framework::ir::Node;
 
-std::vector<std::vector<Node *>> SubGraphSplitter::operator()() {
+std::pair<std::vector<Node *>, std::vector<Node *>>
+ExtractInputAndOutputOfSubGraph(std::vector<Node *> &graph) {  // NOLINT
+  std::unordered_set<Node *> nodes(graph.begin(), graph.end());
+  std::unordered_set<Node *> inputs;
+  std::unordered_set<Node *> outputs;
+  // Input a Value, check whether its inlink is in the subgraph.
+  auto inlink_in_subgraph = [&](Node *n) {
+    for (auto *in : n->inputs) {
+      if (nodes.count(in)) return true;
+    }
+    return false;
+  };
+
+  for (auto &node : graph) {
+    for (auto *in : node->inputs) {
+      // The Value that is written by nodes inside a sub-graph shouldn't be the
+      // input of the sub-graph.
+      if (!nodes.count(in) && in->IsVar() && !inlink_in_subgraph(in)) {
+        inputs.insert(in);
+      }
+    }
+    for (auto *out : node->outputs) {
+      if (!nodes.count(out) && out->IsVar()) {
+        outputs.insert(out);
+      }
+    }
+  }
+  return std::make_pair(std::vector<Node *>(inputs.begin(), inputs.end()),
+                        std::vector<Node *>(outputs.begin(), outputs.end()));
+}
+
+// Filter the Intermediate results of the subgraph node.
+void FilterRedundantOutputOfSubGraph(Graph *graph) {
+  std::vector<Node *> op_nodes;
+  for (auto &node : TopologicalSort(*graph)) {
+    if (node.IsVar() || Agent(&node).deleted()) {
+      continue;
+    }
+    op_nodes.push_back(&node);
+  }
+  size_t op_num = op_nodes.size();
+  for (size_t i = 0; i < op_num; i++) {
+    if (op_nodes[i]->IsOp()) continue;
+    std::unordered_set<std::string> follow_up_input_names;
+    for (size_t j = i + 1; j < op_num; j++) {
+      for (auto *in : op_nodes[j]->inputs) {
+        follow_up_input_names.insert(in->Name());
+      }
+    }
+    std::vector<Node *> filtered_subgraph_outlinks;
+    for (auto *out : op_nodes[i]->outputs) {
+      if (follow_up_input_names.count(out->Name())) {
+        filtered_subgraph_outlinks.push_back(out);
+      } else {
+        Agent(out).set_deleted(true);
+      }
+    }
+    // The filtered_subgraph_outlinks may be empty.
+    op_nodes[i]->outputs = filtered_subgraph_outlinks;
+  }
+}
+
+std::vector<std::vector<Node *>> SubgraphDetector::operator()() {
   MarkNodesInsideSubGraph();
   return ExtractSubGraphs();
 }
 
 // Mark the output variables inside a subgraph with the func.
-inline void MarkOutLinksInSubGraph(const Function *func) {
-  for (auto *var : func->outlinks) {
-    var->attr(SubGraphSplitter::kMarkerAttrName).Bool() = true;
+inline void MarkOutLinksInSubGraph(const Node *func) {
+  for (auto *var : func->outputs) {
+    Agent(var).set_marked(true);
   }
 }
 
-void SubGraphSplitter::MarkNodesInsideSubGraph() {
-  for (auto &node : GraphTraits<DataFlowGraph>(*graph_).nodes()) {
+void SubgraphDetector::MarkNodesInsideSubGraph() {
+  for (auto &node : framework::ir::GraphTraits::DFS(*graph_)) {
     if (node_inside_subgraph_teller_(&node)) {
-      node.attr(kMarkerAttrName).Bool() = true;
-      if (node.type() == Node::Type::kFunction) {
+      Agent(&node).set_marked(true);
+      if (node.IsOp()) {
         // If a function is inside the sub-graph, mark all the output variables
         // to be inside too, so that two marked functions will be inside a same
         // sub-graph, lets take a example:  A_function->var->B_function, if
         // A_function is marked, var should also be marked, so that B_function
         // will be in the same sub-graph with A_function if B_function is
         // marked.
-        MarkOutLinksInSubGraph(static_cast<const Function *>(&node));
+        MarkOutLinksInSubGraph(&node);
       }
     }
   }
 }
 
-const char *kUnionFindParent = "_sub_graph_splitter_union_find_parent_";
-
 // Use the Union Find(UF) algorithm to find fully connected sub-graphs, if node
 // a's output is node b, that is a and b is in the same sub-graph. The UF
 // algorithm will group them to the same cluster.
@@ -60,8 +124,8 @@ using node_map_t = std::unordered_map<int, Node *>;
 int UnionFindGetAncestor(const node_map_t &node_map, size_t id) {
   int tmp = id;
   do {
-    tmp = node_map.at(tmp)->attr(kUnionFindParent).Int32();
-  } while (node_map.at(tmp)->attr(kUnionFindParent).Int32() != tmp);
+    tmp = Agent(node_map.at(tmp)).union_find_parent();
+  } while (Agent(node_map.at(tmp)).union_find_parent() != tmp);
   return tmp;
 }
 // Make this two node share the same ancestor.
@@ -69,9 +133,9 @@ int UnionFindGetAncestor(const node_map_t &node_map, size_t id) {
 void UnionFindCombine(const node_map_t &node_map, size_t a, size_t b) {
   int a_ancestor = UnionFindGetAncestor(node_map, a);
   int b_ancestor = UnionFindGetAncestor(node_map, b);
-  node_map.at(b_ancestor)->attr(kUnionFindParent).Int32() = a_ancestor;
-  node_map.at(a)->attr(kUnionFindParent).Int32() = a_ancestor;
-  node_map.at(b)->attr(kUnionFindParent).Int32() = a_ancestor;
+  Agent(node_map.at(b_ancestor)).set_union_find_parent(a_ancestor);
+  Agent(node_map.at(a)).set_union_find_parent(a_ancestor);
+  Agent(node_map.at(b)).set_union_find_parent(a_ancestor);
 }
 
 // This is a simple representation of a graph.
@@ -195,16 +259,21 @@ void FlexibleDFS(const std::vector<BriefNode *> &source, bool reverse,
   }
 }
 
-std::vector<std::vector<Node *>> SubGraphSplitter::ExtractSubGraphs() {
+std::vector<std::vector<Node *>> SubgraphDetector::ExtractSubGraphs() {
   // Run the Extract algorithm to find all subgraphs.
   std::vector<Node *> marked_nodes;
   //  We use brief_node_map to represent the original graph in order to avoid
   //  changing the original graph.
   std::unordered_map<int, BriefNode *> brief_node_map;
 
-  for (auto &node : GraphTraits<DataFlowGraph>(*graph_).nodes_in_TS()) {
+  std::unordered_set<int32_t> valid_node_ids;
+  for (auto *node : graph_->Nodes()) {
+    valid_node_ids.insert(node->id());
+  }
+
+  for (auto &node : framework::ir::GraphTraits::TS(*graph_)) {
     brief_node_map[node.id()] = new BriefNode(&node);
-    if (node.attr(kMarkerAttrName).Bool()) {
+    if (Agent(&node).marked()) {
       marked_nodes.push_back(&node);
     }
   }
@@ -213,26 +282,34 @@ std::vector<std::vector<Node *>> SubGraphSplitter::ExtractSubGraphs() {
   node_map_t node_map;  // id to ptr
   for (auto *n : marked_nodes) {
     // n's parent == n.id means it is the ancestor
-    n->attr(kUnionFindParent).Int32() = n->id();
+    Agent(n).set_union_find_parent(n->id());
     node_map[n->id()] = n;
   }
 
   // create breif node map
   for (auto &itr : brief_node_map) {
-    for (Node *node : itr.second->node->inlinks) {
-      itr.second->inlinks.push_back(brief_node_map[node->id()]);
+    for (Node *node : itr.second->node->inputs) {
+      if (!valid_node_ids.count(node->id())) {
+        LOG(INFO) << "invalid node id " << node->id();
+        continue;
+      }
+      itr.second->inlinks.push_back(brief_node_map.at(node->id()));
     }
 
-    for (Node *node : itr.second->node->outlinks) {
-      itr.second->outlinks.push_back(brief_node_map[node->id()]);
+    for (Node *node : itr.second->node->outputs) {
+      if (!valid_node_ids.count(node->id())) {
+        LOG(INFO) << "invalid node id " << node->id();
+        continue;
+      }
+      itr.second->outlinks.push_back(brief_node_map.at(node->id()));
     }
   }
 
   for (auto &itr : brief_node_map) {
     BriefNode *brief_node = itr.second;
 
-    if (!brief_node->node->attr(kMarkerAttrName).Bool()) {
-      VLOG(40) << brief_node->node->id() << " node not a trt candicate.";
+    if (!Agent(brief_node->node).marked()) {
+      VLOG(4) << brief_node->node->id() << " node not a trt candidate.";
       continue;
     }
 
@@ -254,7 +331,7 @@ std::vector<std::vector<Node *>> SubGraphSplitter::ExtractSubGraphs() {
       std::unordered_set<BriefNode *> contract_nodes;
       for (auto *out : brief_node->outlinks) {
         // must be an trt candidate
-        if (!out->node->attr(kMarkerAttrName).Bool()) continue;
+        if (!Agent(out->node).marked()) continue;
         // get all dst input nodes except src.
         std::vector<BriefNode *> source_nodes;
         for (auto *n : out->inlinks) {
@@ -289,9 +366,8 @@ std::vector<std::vector<Node *>> SubGraphSplitter::ExtractSubGraphs() {
 
   std::unordered_map<int /*ancestor*/, std::vector<Node *>> clusters;
   for (auto *n : marked_nodes) {
-    if (n->type() == Node::Type::kFunction) {
-      clusters[UnionFindGetAncestor(node_map,
-                                    n->attr(kUnionFindParent).Int32())]
+    if (n->IsOp()) {
+      clusters[UnionFindGetAncestor(node_map, Agent(n).union_find_parent())]
           .push_back(n);
     }
   }
@@ -304,28 +380,59 @@ std::vector<std::vector<Node *>> SubGraphSplitter::ExtractSubGraphs() {
   return result;
 }
 
-void SubGraphFuse::operator()() { ReplaceNodesWithSubGraphs(); }
+void SubGraphFuser::operator()() { ReplaceNodesWithSubGraphs(); }
+
+void RemoveIntermediateOutputInSubgraph(const std::vector<Node *> &subgraph,
+                                        Graph *graph,
+                                        std::vector<Node *> *outputs) {
+  std::unordered_set<Node *> subgraph_set(subgraph.begin(), subgraph.end());
+  std::unordered_set<Node *> valid_output;
+
+  for (auto *output : *outputs) {
+    int num_used = 0;
+    for (auto *node : output->outputs) {
+      if (!subgraph_set.count(node)) ++num_used;
+      if (num_used > 0) valid_output.insert(output);
+    }
+  }
+
+  outputs->assign(valid_output.begin(), valid_output.end());
+}
+
+void DetachDeletedNodes(framework::ir::Graph *graph) {
+  std::unordered_set<const Node *> nodes;
+  for (auto *node : graph->Nodes()) {
+    if (Agent(node).deleted()) {
+      node->inputs.clear();
+      node->outputs.clear();
+    }
+  }
+}
 
-void SubGraphFuse::ReplaceNodesWithSubGraphs() {
-  auto subgraphs = SubGraphSplitter(graph_, node_inside_subgraph_teller_)();
+void SubGraphFuser::ReplaceNodesWithSubGraphs() {
+  auto subgraphs = SubgraphDetector(graph_, node_inside_subgraph_teller_)();
   for (auto &subgraph : subgraphs) {
-    if (subgraph.size() <= argument_->Get<int>("minimum_subgraph_size"))
-      continue;
+    if (subgraph.size() <= min_subgraph_size_) continue;
+    LOG(INFO) << "detect a subgraph size " << subgraph.size();
     std::unordered_set<Node *> subgraph_uniq(subgraph.begin(), subgraph.end());
     // replace this sub-graph with the first node. Two steps: 1. Create a Block
     // Node that contains this subgraph 2. Mark the nodes inside the sub-graph
     // as deleted. 3. Replace the deleted node with the new Block Node.
-    auto *block_node = static_cast<FunctionBlock *>(
-        graph_->nodes.Create(Node::Type::kFunctionBlock));
+    framework::OpDesc empty_desc;
+    empty_desc.SetType("tensorrt_engine");
+    auto *block_node = graph_->CreateOpNode(&empty_desc);
+    Agent(block_node).set_subgraph({});
     auto io = ExtractInputAndOutputOfSubGraph(subgraph);
-    block_node->inlinks = std::move(io.first);
-    block_node->outlinks = std::move(io.second);
+    block_node->inputs = std::move(io.first);
+    block_node->outputs = std::move(io.second);
+
+    RemoveIntermediateOutputInSubgraph(subgraph, graph_, &block_node->outputs);
 
     for (auto *node : subgraph) {
       // TODO(Superjomn) need a unified mechanism to treat deleted node in each
       // pass.
-      node->SetDeleted();
-      block_node->subgraph.push_back(node);
+      Agent(node).set_deleted(true);
+      Agent(block_node).subgraph()->push_back(node);
     }
 
     // Change all the sub-graph's inputs and outputs corresponding inlink and
@@ -339,16 +446,92 @@ void SubGraphFuse::ReplaceNodesWithSubGraphs() {
       std::unordered_set<Node *> uniq(nodes.begin(), nodes.end());
       nodes.assign(uniq.begin(), uniq.end());
     };
-    for (auto *i : block_node->inlinks) {
-      inlink_or_outlink_cleaner(i->outlinks);
+    for (auto *i : block_node->inputs) {
+      inlink_or_outlink_cleaner(i->outputs);
     }
-    for (auto *&o : block_node->outlinks) {
-      inlink_or_outlink_cleaner(o->inlinks);
+    for (auto *&o : block_node->outputs) {
+      inlink_or_outlink_cleaner(o->inputs);
     }
   }
+  // DetachDeletedNodes(graph_);
   FilterRedundantOutputOfSubGraph(graph_);
 }
 
+inline bool CheckNodeIndegreeEquals(const Node &node, size_t n) {
+  return node.inputs.size() == n;
+}
+
+NodesTSIterator::NodesTSIterator(const std::vector<Node *> &source) {
+  PADDLE_ENFORCE(!source.empty(),
+                 "Start points of topological sorting should not be empty!");
+  // CHECK all the inputs' in-degree is 0
+  for (auto *node : source) {
+    PADDLE_ENFORCE(CheckNodeIndegreeEquals(*node, 0));
+  }
+
+  std::unordered_set<Node *> visited;
+  std::unordered_set<Node *> to_visit{source.begin(), source.end()};
+
+  std::vector<Node *> inlink_visited;
+  while (!to_visit.empty()) {
+    std::vector<Node *> queue(to_visit.begin(), to_visit.end());
+    for (auto *p : queue) {
+      if (Agent(p).deleted()) {
+        visited.insert(p);
+        to_visit.erase(p);
+      }
+
+      inlink_visited.clear();
+
+      std::copy_if(p->inputs.begin(), p->inputs.end(),
+                   std::back_inserter(inlink_visited),
+                   [&](Node *x) -> bool { return visited.count(x) != 0; });
+
+      if (inlink_visited.size() == p->inputs.size()) {
+        sorted_.push_back(p);
+        for (auto *_ : p->outputs) {
+          if (!visited.count(_)) {
+            to_visit.insert(_);
+          }
+        }
+
+        to_visit.erase(p);
+        visited.insert(p);
+      }
+    }
+  }
+}
+
+NodesTSIterator::NodesTSIterator(const NodesTSIterator &other)
+    : sorted_(other.sorted_), cursor_(other.cursor_) {}
+
+Node &NodesTSIterator::operator*() {
+  PADDLE_ENFORCE_LT(cursor_, sorted_.size());
+  return *sorted_[cursor_];
+}
+
+NodesTSIterator &NodesTSIterator::operator++() {
+  if (++cursor_ >= sorted_.size()) {
+    sorted_.clear();
+    cursor_ = 0;
+  }
+  return *this;
+}
+NodesTSIterator &NodesTSIterator::operator=(const NodesTSIterator &other) {
+  cursor_ = other.cursor_;
+  sorted_ = other.sorted_;
+  return *this;
+}
+
+bool NodesTSIterator::operator==(const NodesTSIterator &other) {
+  return sorted_ == other.sorted_ && cursor_ == other.cursor_;
+}
+
+Node *NodesTSIterator::operator->() {
+  PADDLE_ENFORCE_LT(cursor_, sorted_.size());
+  return sorted_[cursor_];
+}
+
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/analysis/ir_passes/subgraph_detector.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 the the class to partition a graph.
+ */
+
+#pragma once
+
+#include <vector>
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/graph_traits.h"
+#include "paddle/fluid/framework/ir/node.h"
+#include "paddle/fluid/inference/analysis/argument.h"
+#include "paddle/fluid/inference/analysis/helper.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+using framework::ir::Graph;
+
+const char kIsFunctionNode[] = "__is_function_node__";
+const char kFunctionNodeSubGraph[] = "__function_node_sub_graph__";
+const char kSubgraphSplitterMarkerAttrName[] =
+    "_sub_graph_splitter_inside_sub_graph";
+
+/*
+ * Detect the nodes in a sub-graph that meet some conditions. This class doesn't
+ * modify the graph.
+ */
+class SubgraphDetector {
+ public:
+  // Tell whether a node is inside a sub-graph.
+  using NodeInsideSubgraphTeller =
+      std::function<bool(const framework::ir::Node *)>;
+
+  SubgraphDetector(Graph *graph, const NodeInsideSubgraphTeller &teller)
+      : graph_(graph), node_inside_subgraph_teller_(teller) {}
+
+  std::vector<std::vector<framework::ir::Node *>> operator()();
+
+ protected:
+  // Mark the nodes inside the accepted sub-graph using
+  // node_inside_subgraph_teller.
+  void MarkNodesInsideSubGraph();
+
+  // Merge the marked nodes into sub-graphs and return the sub-graphs.
+  std::vector<std::vector<framework::ir::Node *>> ExtractSubGraphs();
+
+ private:
+  Graph *graph_;
+  NodeInsideSubgraphTeller node_inside_subgraph_teller_;
+};
+
+/*
+ * SubGraphFuser - Replace some nodes with the sub-graph node they are inside.
+ * To some extent, the TensorRT engine is just a fusion op for a model.
+ */
+class SubGraphFuser {
+ public:
+  using NodeInsideSubgraphTeller = SubgraphDetector::NodeInsideSubgraphTeller;
+
+  SubGraphFuser(Graph *graph, const NodeInsideSubgraphTeller &teller,
+                int min_subgraph_size)
+      : graph_(graph),
+        node_inside_subgraph_teller_(teller),
+        min_subgraph_size_{min_subgraph_size} {}
+
+  // The main method which run all the logic.
+  void operator()();
+
+ protected:
+  // Remove the nodes inside sub-graphs and replace with the SubGraphNode.
+  void ReplaceNodesWithSubGraphs();
+
+ private:
+  Graph *graph_;
+  NodeInsideSubgraphTeller node_inside_subgraph_teller_;
+  int min_subgraph_size_;
+};
+
+struct NodeWrapper {
+  bool deleted{false};
+  bool marked{false};
+  int union_find_parent{-1};
+  std::vector<framework::ir::Node *> subgraph;
+};
+
+/*
+ * ir::Node agent for subgraph detector.
+ */
+struct Agent {
+  explicit Agent(framework::ir::Node *x) : x_(x) {}
+
+  NodeWrapper &wrapper() {
+    if (!x_->IsWrappedBy<NodeWrapper>()) {
+      x_->WrappedBy<NodeWrapper>(new NodeWrapper);
+    }
+    return x_->template Wrapper<NodeWrapper>();
+  }
+
+  bool deleted() { return wrapper().deleted; }
+  void set_deleted(bool x) { wrapper().deleted = x; }
+
+  bool marked() { return wrapper().marked; }
+  void set_marked(bool x) { wrapper().marked = x; }
+
+  void set_subgraph(const std::vector<framework::ir::Node *> &x) {
+    wrapper().subgraph = x;
+  }
+
+  int union_find_parent() { return wrapper().union_find_parent; }
+  void set_union_find_parent(int v) { wrapper().union_find_parent = v; }
+
+  std::vector<framework::ir::Node *> *subgraph() { return &wrapper().subgraph; }
+  std::vector<framework::ir::Node *> &inputs() { return x_->inputs; }
+  std::vector<framework::ir::Node *> &outputs() { return x_->outputs; }
+
+ private:
+  framework::ir::Node *x_;
+};
+
+// Topological sorting iterator on nodes.
+struct NodesTSIterator
+    : public std::iterator<std::forward_iterator_tag, framework::ir::Node *> {
+  NodesTSIterator() = default;
+  explicit NodesTSIterator(const std::vector<framework::ir::Node *> &source);
+  NodesTSIterator(NodesTSIterator &&other)
+      : sorted_(std::move(other.sorted_)), cursor_(other.cursor_) {
+    other.cursor_ = 0;
+  }
+  NodesTSIterator(const NodesTSIterator &other);
+
+  framework::ir::Node &operator*();
+  NodesTSIterator &operator++();
+  // TODO(Superjomn) current implementation just compare the first
+  // element, need to compare the graph and all the elements in the queue and
+  // set.
+  NodesTSIterator &operator=(const NodesTSIterator &other);
+  bool operator==(const NodesTSIterator &other);
+  bool operator!=(const NodesTSIterator &other) { return !(*this == other); }
+  framework::ir::Node *operator->();
+
+ private:
+  std::vector<framework::ir::Node *> sorted_;
+  size_t cursor_{0};
+};
+
+// The nodes those have no input will be treated as start points.
+static std::vector<framework::ir::Node *> ExtractStartPoints(const Graph &g) {
+  std::vector<framework::ir::Node *> result;
+  for (auto *node : g.Nodes()) {
+    if (node->inputs.empty()) {
+      result.push_back(node);
+    }
+  }
+  return result;
+}
+
+static iterator_range<NodesTSIterator> TopologicalSort(const Graph &g) {
+  auto start_points = ExtractStartPoints(g);
+  PADDLE_ENFORCE(!start_points.empty());
+  NodesTSIterator x(start_points);
+  return iterator_range<NodesTSIterator>(NodesTSIterator(start_points),
+                                         NodesTSIterator());
+}
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.cc → paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc

@@ -12,120 +12,91 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.h"
+#include "paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.h"
+#include <string>
 #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"
-#include "paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h"
-#include "paddle/fluid/inference/io.h"
+#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
+#include "paddle/fluid/inference/analysis/helper.h"
+#include "paddle/fluid/inference/analysis/ir_passes/subgraph_detector.h"
 
 namespace paddle {
 namespace inference {
-
 namespace analysis {
 
-using framework::proto::ProgramDesc;
+using framework::ir::Node;
 
 std::vector<std::string> ExtractParameters(
-    const std::vector<std::unique_ptr<Node>> &nodes);
+    const std::unordered_set<Node *> &nodes);
 
-bool DataFlowGraphToFluidPass::Initialize(Argument *argument) {
-  ANALYSIS_ARGUMENT_CHECK_FIELD(argument)
-  ANALYSIS_ARGUMENT_CHECK_FIELD(argument->origin_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.
-  argument->transformed_program_desc.reset(
-      new ProgramDesc(*argument->origin_program_desc));
-  argument->transformed_program_desc->mutable_blocks(framework::kRootBlockIndex)
-      ->clear_ops();
-  desc_ = argument->transformed_program_desc.get();
-  argument_ = argument;
-  return true;
-}
+std::unique_ptr<framework::ir::Graph> analysis::TensorRtSubgraphPass::ApplyImpl(
+
+    std::unique_ptr<framework::ir::Graph> graph) const {
+  framework::ir::FusePassBase::Init("tensorrt_subgraph_pass", graph.get());
+
+  auto teller =
+      Get<SubgraphDetector::NodeInsideSubgraphTeller>("tensorrt_node_teller");
 
-bool DataFlowGraphToFluidPass::Finalize() { return true; }
+  SubGraphFuser fuser(graph.get(), teller, 2 /*min subgraph size*/);
+  fuser();
 
-void DataFlowGraphToFluidPass::Run(DataFlowGraph *graph) {
-  // FilterRedundantOutputOfSubGraph(graph);
-  for (auto &node : GraphTraits<DataFlowGraph>(*graph).nodes_in_TS()) {
-    if (node.deleted()) continue;
+  for (auto *node : graph->Nodes()) {
+    if (node->IsOp() && !Agent(node).subgraph()->empty()) {
+      CreateTensorRTOp(node, graph.get());
 
-    switch (node.type()) {
-      case Node::Type::kFunction: {
-        AddFluidOp(&node);
-      } break;
-      case Node::Type::kFunctionBlock: {
-        AddEngineOp(&node);
-      } break;
-      default:
-        continue;
+      std::unordered_set<const Node *> nodes2remove(
+          Agent(node).subgraph()->begin(), Agent(node).subgraph()->end());
+      framework::ir::GraphSafeRemoveNodes(graph.get(), nodes2remove);
     }
   }
 
-  if (argument_->Has(framework::ir::kParamScopeAttr)) {
-    LOG(WARNING) << "parameter changes in the scope takes effect";
+  std::unordered_set<const Node *> nodes2remove;
+  for (auto *node : graph->Nodes()) {
+    if (node->IsOp() && Agent(node).deleted()) {
+      nodes2remove.insert(node);
     }
+  }
+  framework::ir::GraphSafeRemoveNodes(graph.get(), nodes2remove);
 
-  PADDLE_ENFORCE(argument_->transformed_program_desc.get());
+  return graph;
 }
 
-void DataFlowGraphToFluidPass::AddFluidOp(Node *node) {
-  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();
+void TensorRtSubgraphPass::CreateTensorRTOp(framework::ir::Node *node,
+                                            Graph *graph) const {
+  auto *op_desc = node->Op();
+  static int counter{0};
+  auto &subgraph = *Agent(node).subgraph();
+  PADDLE_ENFORCE(!subgraph.empty());
 
-  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());
+  // An fake block desc.
+  framework::proto::BlockDesc block_proto;
+  framework::BlockDesc block_desc(nullptr, &block_proto);
+  block_desc.Proto()->set_parent_idx(-1);
+  block_desc.Proto()->set_idx(0);
+  for (auto *node : subgraph) {
+    auto *op = block_desc.AppendOp();
+    *op->Proto() = *node->Op()->Proto();
   }
-}
-
-void CreateTrtEngineOp(Node *node, Argument *argument,
-                       framework::proto::BlockDesc *block) {
-  PADDLE_ENFORCE(argument->main_dfg.get());
-  const DataFlowGraph &graph = *(argument->main_dfg);
-  static int counter{0};
-  PADDLE_ENFORCE(node->IsFunctionBlock());
-  framework::OpDesc desc;
-  auto *func = static_cast<FunctionBlock *>(node);
 
   // collect inputs
   std::unordered_set<std::string> input_names;
   std::unordered_set<std::string> input_names_with_id;
-  for (auto *x : func->inlinks) {
-    input_names.insert(x->name());
-    input_names_with_id.insert(x->name() + std::to_string(x->id()));
+  for (auto *x : node->inputs) {
+    input_names.insert(x->Name());
+    input_names_with_id.insert(x->Name() + std::to_string(x->id()));
   }
-  desc.SetInput(
+  op_desc->SetInput(
       "Xs", std::vector<std::string>(input_names.begin(), input_names.end()));
 
   std::unordered_set<std::string> output_names;
   std::unordered_set<std::string> output_names_with_id;
-  for (auto *x : func->outlinks) {
-    output_names.insert(x->name());
-    output_names_with_id.insert(x->name() + std::to_string(x->id()));
+  for (auto *x : node->outputs) {
+    output_names.insert(x->Name());
+    output_names_with_id.insert(x->Name() + std::to_string(x->id()));
   }
 
-  desc.SetOutput(
+  op_desc->SetOutput(
       "Ys", std::vector<std::string>(output_names.begin(), output_names.end()));
-  desc.SetType("tensorrt_engine");
+  op_desc->SetType("tensorrt_engine");
 
   std::unordered_map<std::string, std::string> output_name_map;
 
@@ -134,7 +105,7 @@ void CreateTrtEngineOp(Node *node, Argument *argument,
   // Why we do this?
   // During the transition from fluid OP to tensorrt OP, we map
   // the input and output Tensor(fluid data structure) of fluid OP
-  // to the correspondin ITensor (trt data structure) through the
+  // to the corresponding ITensor (trt data structure) through the
   // Tensor name. When we set up ITensor for an variable, we must
   // ensure that it has not been set before.
   // If there is variable in the fluid graph, which is not only the
@@ -142,21 +113,22 @@ void CreateTrtEngineOp(Node *node, Argument *argument,
   // So we have to rename the variable in the subgraph to make sure
   // it is either an OP's input or an OP's output.
 
-  auto subgraph_nodes = func->subgraph;
-  for (int index = 0; index < block->ops_size(); index++) {
-    framework::proto::OpDesc *op = block->mutable_ops(index);
+  auto &subgraph_nodes = *Agent(node).subgraph();
+  for (int index = 0; index < block_desc.OpSize(); index++) {
+    framework::proto::OpDesc *op = block_desc.Op(index)->Proto();
     auto correspond_node = subgraph_nodes[index];
-    PADDLE_ENFORCE_EQ(correspond_node->name(), op->type());
+    PADDLE_ENFORCE_EQ(correspond_node->Name(), op->type());
 
     std::unordered_map<std::string, size_t> var2id;
-    for (auto *in_var : correspond_node->inlinks) {
-      var2id[in_var->name()] = in_var->id();
+    for (auto *in_var : correspond_node->inputs) {
+      var2id[in_var->Name()] = in_var->id();
     }
     // rename for the input variables of op inside subgraph
     for (int i = 0; i < op->inputs_size(); i++) {
-      framework::proto::OpDesc_Var *in_var = op->mutable_inputs(i);
+      // one input
+      auto *in_var = op->mutable_inputs(i);
       std::vector<std::string> replaced_names;
-      for (int k = 0; k < in_var->arguments_size(); k++) {
+      for (int k = 0; k < in_var->arguments_size(); k++) {  // all the arguments
         std::string arg_value = in_var->arguments(k);
         std::string arg_value_with_id =
             arg_value + std::to_string(var2id[arg_value]);
@@ -172,8 +144,8 @@ void CreateTrtEngineOp(Node *node, Argument *argument,
       }
     }
     var2id.clear();
-    for (auto out_var : correspond_node->outlinks) {
-      var2id[out_var->name()] = out_var->id();
+    for (auto out_var : correspond_node->outputs) {
+      var2id[out_var->Name()] = out_var->id();
     }
 
     // rename for the output variables of op inside subgraph
@@ -195,91 +167,54 @@ void CreateTrtEngineOp(Node *node, Argument *argument,
       }
     }
   }
+
   // When tensorrt engine runs at the end of the operation,
   // output_mapping help us copy the data from the renamed ITensor
   // to Tensor.
   std::vector<std::string> output_mapping;
   for (auto name : output_names) {
+    // LOG(INFO) << name << " " << output_name_map.size();
     PADDLE_ENFORCE(output_name_map.count(name) != 0);
     output_mapping.push_back(output_name_map[name]);
   }
 
-  PADDLE_ENFORCE(!block->vars().empty(), "the block has no var-desc");
+  *block_desc.Proto()->mutable_vars() =
+      const_cast<framework::ProgramDesc *>(&graph->program())
+          ->Proto()
+          ->blocks(0)
+          .vars();
+  PADDLE_ENFORCE(!block_desc.Proto()->vars().empty(),
+                 "the block has no var-desc");
+  PADDLE_ENFORCE(!output_mapping.empty());
   // Set attrs
-
-  SetAttr(desc.Proto(), "subgraph", block->SerializeAsString());
-  SetAttr(desc.Proto(), "max_batch_size", argument->Get<int>("max_batch_size"));
-  SetAttr(desc.Proto(), "workspace_size", argument->Get<int>("workspace_size"));
-  SetAttr(desc.Proto(), "engine_uniq_key", "trt-" + std::to_string(counter++));
-  SetAttr(desc.Proto(), "parameters", ExtractParameters(graph.nodes.nodes()));
-  SetAttr(desc.Proto(), "output_name_mapping", output_mapping);
-  node->SetPbMsg(desc.Proto()->SerializeAsString());
+  SetAttr(op_desc->Proto(), "subgraph",
+          block_desc.Proto()->SerializeAsString());
+  SetAttr(op_desc->Proto(), "max_batch_size", Get<int>("max_batch_size"));
+  SetAttr(op_desc->Proto(), "workspace_size", Get<int>("workspace_size"));
+  SetAttr(op_desc->Proto(), "engine_uniq_key",
+          "trt-" + std::to_string(counter++));
+  SetAttr(op_desc->Proto(), "parameters", ExtractParameters(graph->Nodes()));
+  SetAttr(op_desc->Proto(), "output_name_mapping", output_mapping);
 }
 
 std::vector<std::string> ExtractParameters(
-    const std::vector<std::unique_ptr<Node>> &nodes) {
+    const std::unordered_set<Node *> &nodes) {
   std::vector<std::string> parameters;
   for (const auto &node : nodes) {
-    if (!node->IsValue()) continue;
-    PADDLE_ENFORCE(!node->pb_msg().empty(), "pb_msg should be set first");
-    framework::proto::VarDesc var;
-    var.ParseFromString(node->pb_msg());
-    if (var.persistable()) {
-      parameters.push_back(var.name());
+    if (!node->IsVar()) continue;
+    if (node->Var()->Persistable()) {
+      parameters.push_back(node->Name());
     }
   }
   return parameters;
 }
 
-void DataFlowGraphToFluidPass::AddEngineOp(Node *node) {
-  // TODO(Superjomn) Here need to expose some arguments for default setting.
-  PADDLE_ENFORCE(node->IsFunctionBlock());
-  auto *block_node = static_cast<FunctionBlock *>(node);
-  framework::proto::BlockDesc proto;
-  framework::BlockDesc block_desc(nullptr, &proto);
-  block_desc.Proto()->set_parent_idx(-1);
-  block_desc.Proto()->set_idx(0);
-  VLOG(40) << "origin variable size: "
-           << argument_->origin_program_desc->blocks(0).vars().size();
-  VLOG(40) << "transformed variable size: "
-           << block_desc.Proto()->vars().size();
-  // copy ops.
-
-  for (auto *node : block_node->subgraph) {
-    auto *op = block_desc.AppendOp();
-    PADDLE_ENFORCE(!node->pb_msg().empty());
-    op->Proto()->ParseFromString(node->pb_msg());
-  }
-
-  *block_desc.Proto()->mutable_vars() =
-      argument_->origin_program_desc->blocks(0).vars();
-  PADDLE_ENFORCE(!block_desc.Proto()->vars().empty());
-  CreateTrtEngineOp(node, argument_, block_desc.Proto());
-  auto *main_block = desc_->mutable_blocks(framework::kRootBlockIndex);
-  auto *op = main_block->add_ops();
-  PADDLE_ENFORCE(!node->pb_msg().empty(), "failed to set desc for block");
-  op->ParseFromString(node->pb_msg());
-}
-
-namespace {
-class DFG_DebuggerPass : public DFG_GraphvizDrawPass {
- public:
-  using Config = DFG_GraphvizDrawPass::Config;
-  explicit DFG_DebuggerPass(const Config &config)
-      : DFG_GraphvizDrawPass(config) {}
-
-  std::string repr() const override { return "dfg-to-fluid-debuger-pass"; }
-
-  bool Finalize() override { return true; }
-};
-}  // namespace
-
-AnalysisPass *DataFlowGraphToFluidPass::CreateGraphvizDebugerPass() const {
-  return new DFG_DebuggerPass(DFG_GraphvizDrawPass::Config(
-      FLAGS_IA_graphviz_log_root,
-      "data_flow_graph_to_fluid_graphviz_debugger"));
-}
-
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle
+
+REGISTER_PASS(tensorrt_subgraph_pass,
+              paddle::inference::analysis::TensorRtSubgraphPass)
+    .RequirePassAttr("tensorrt_node_teller")
+    .RequirePassAttr("max_batch_size")
+    .RequirePassAttr("workspace_size");

paddle/fluid/inference/analysis/model_store_pass_tester.cc → paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.h

@@ -12,31 +12,24 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/inference/analysis/model_store_pass.h"
-
-#include <gflags/gflags.h>
-#include <gtest/gtest.h>
-#include "paddle/fluid/inference/analysis/analyzer.h"
+#pragma once
+#include <paddle/fluid/framework/ir/fuse_pass_base.h>
+#include "paddle/fluid/framework/ir/pass.h"
 
 namespace paddle {
 namespace inference {
 namespace analysis {
 
-DEFINE_string(inference_model_dir, "", "Model path");
-
-TEST(DFG_StorePass, test) {
-  Analyzer analyzer;
-  Argument argument(FLAGS_inference_model_dir);
-  argument.model_output_store_path.reset(
-      new std::string("./_dfg_store_pass_tmp"));
-  // disable storage in alalyzer
-  FLAGS_IA_output_storage_path = "";
-  analyzer.Run(&argument);
+class TensorRtSubgraphPass : public framework::ir::FusePassBase {
+ public:
+  std::unique_ptr<framework::ir::Graph> ApplyImpl(
+      std::unique_ptr<framework::ir::Graph> graph) const override;
 
-  ModelStorePass pass;
-  pass.Initialize(&argument);
-  pass.Run(argument.main_dfg.get());
-}
+ private:
+  void CreateTensorRTOp(framework::ir::Node *x,
+                        framework::ir::Graph *graph) const;
+  void CleanIntermediateOutputs(framework::ir::Node *node);
+};
 
 }  // namespace analysis
 }  // namespace inference

paddle/fluid/inference/analysis/model_store_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 <stdio.h>
-#include <stdlib.h>
-#include <string>
-
-#include "paddle/fluid/inference/analysis/analyzer.h"
-#include "paddle/fluid/inference/analysis/argument.h"
-#include "paddle/fluid/inference/analysis/model_store_pass.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-void ModelStorePass::Run(DataFlowGraph *x) {
-  if (!argument_->fluid_model_param_path) {
-    PADDLE_ENFORCE_NOT_NULL(argument_->fluid_model_dir);
-    argument_->fluid_model_param_path.reset(
-        new std::string(*argument_->fluid_model_dir + "param"));
-  }
-  PADDLE_ENFORCE_NOT_NULL(argument_->model_output_store_path);
-  // Directly copy param file to destination.
-  std::stringstream ss;
-  // NOTE these commands only works on linux.
-  ss << "mkdir -p " << *argument_->model_output_store_path;
-  VLOG(30) << "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;
-  VLOG(30) << "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(30) << "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);
-  PADDLE_ENFORCE(file.is_open(), "failed to open %s to write.",
-                 program_output_path);
-  const std::string serialized_message =
-      argument_->transformed_program_desc->SerializeAsString();
-  file.write(serialized_message.c_str(), serialized_message.size());
-}
-
-bool ModelStorePass::Finalize() { return true; }
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/node.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/node.h"
-#include "glog/logging.h"
-#include "paddle/fluid/platform/enforce.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-std::vector<Dot::Attr> Value::dot_attrs() const {
-  return std::vector<Dot::Attr>({Dot::Attr("style", "filled,rounded"),
-                                 Dot::Attr("shape", "box"),
-                                 Dot::Attr("fillcolor", "red")});
-}
-
-std::vector<Dot::Attr> Function::dot_attrs() const {
-  return std::vector<Dot::Attr>({Dot::Attr("style", "filled,rounded"),
-                                 Dot::Attr("shape", "diamond"),
-                                 Dot::Attr("fillcolor", "yellow")});
-}
-
-Node *NodeMap::Create(Node::Type type) {
-  switch (type) {
-    case Node::Type::kFunction:
-      nodes_.emplace_back(new Function);
-      break;
-    case Node::Type::kValue:
-      nodes_.emplace_back(new Value);
-      break;
-    case Node::Type::kFunctionBlock:
-      nodes_.emplace_back(new FunctionBlock);
-      break;
-    default:
-      PADDLE_THROW("Not supported node type.");
-  }
-  nodes_.back()->id_ = size() - 1;
-  return nodes_.back().get();
-}
-
-Node *NodeMap::GetMutable(size_t id) {
-  PADDLE_ENFORCE_GT(size(), id);
-  return nodes_[id].get();
-}
-
-const Node &NodeMap::Get(size_t id) const {
-  PADDLE_ENFORCE_GT(size(), id);
-  return *nodes_[id].get();
-}
-
-void NodeMap::Delete(size_t id) {
-  PADDLE_ENFORCE_LT(id, size());
-  nodes_[id]->SetDeleted();
-}
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/node.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 the Node class and its subclasses. A Node is the basis
- * analysis element in a computation graph.
- * There are basically two kinds of nodes, the function node and value node.
- */
-#pragma once
-
-#include <limits>
-#include <memory>
-#include <string>
-#include <unordered_map>
-#include <vector>
-
-#include "paddle/fluid/framework/var_type.h"
-#include "paddle/fluid/inference/analysis/device.h"
-#include "paddle/fluid/inference/analysis/dot.h"
-#include "paddle/fluid/inference/analysis/helper.h"
-#include "paddle/fluid/platform/variant.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-class NodeMap;
-
-// A helper class to maintain the status from Pass.
-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
-  // types.
-  // NOTE the STL containers should not use here.
-  // Some usages
-  //   Attr attr;
-  //   attr.Bool() = true;
-  bool &Bool() { return As<bool>(); }
-  float &Float() { return As<float>(); }
-  int32_t &Int32() { return As<int32_t>(); }
-  int64_t &Int64() { return As<int64_t>(); }
-  void *&Pointer() { return As<void *>(); }
-  std::string &String() { return As<std::string>(); }
-
-  template <typename T>
-  T &As() {
-    if (type_index_ == typeid(AnyAttr)) {
-      type_index_ = typeid(T);
-      any_data_ = T();
-    } else {
-      PADDLE_ENFORCE(type_index_ == typeid(T), "fetch error type");
-    }
-    return boost::get<T>(any_data_);
-  }
-
- private:
-  any_t any_data_;
-  std::type_index type_index_{typeid(AnyAttr)};
-};
-
-/*
- * Node Representation.
- *
- * This is a very important class for analysis. It is the base class of all
- * nodes computed by a program that may be used as operands to other nodes.
- * Node is the super class of other important classes such as Function and
- * Value, some nodes can have a name.
- */
-class Node {
- public:
-  // Node type. NOTE the new node types should add here.
-  enum class Type { kNone = -1, kFunction, kValue, kFunctionBlock };
-
-  Node() = default;
-
-  // Cast to a subclass type, Function for example.
-  template <typename Subclass>
-  Subclass &As() {
-    return *dynamic_cast<Subclass *>(this);
-  }
-
-  // Formatted representation of this Node.
-  virtual std::string repr() const {
-    return name() + "(" + std::to_string(id()) + ")";
-  }
-
-  // DOT node representation. One Node type can customize its own node
-  // representation.
-  virtual std::vector<Dot::Attr> dot_attrs() const {
-    return std::vector<Dot::Attr>({Dot::Attr("style", "filled")});
-  }
-
-  // Get an additional attribute and convert it to T data type. NOTE this will
-  // silently create a new attribute if not exists.
-  AnyAttr &attr(const std::string &name) const { return attrs_[name]; }
-
-  int id() const { return id_; }
-
-  // The Protobuf description is set/get with a void* to decouple Node interface
-  // from a specific kind of Protobuf message.
-  void SetPbDesc(void *pb) { attr("pb_desc").Pointer() = pb; }
-  void *pb_desc() const { return attr("pb_desc").Pointer(); }
-
-  void SetPbMsg(const std::string &s) { attr("pb_msg").String() = s; }
-  const std::string &pb_msg() const { return attr("pb_msg").String(); }
-
-  void SetDeleted() { deleted_ = true; }
-  bool deleted() const { return deleted_; }
-
-  void SetName(const std::string &name) { name_ = name; }
-  const std::string &name() const { return name_; }
-
-  void SetType(Type type) { type_ = type; }
-  Type type() const { return type_; }
-
-  // Input links.
-  std::vector<Node *> inlinks;
-  // Output links.
-  std::vector<Node *> outlinks;
-
-  // Type checks.
-  bool IsFunction() const { return type_ == Node::Type::kFunction; }
-  bool IsValue() const { return type_ == Node::Type::kValue; }
-  bool IsFunctionBlock() const { return type_ == Node::Type::kFunctionBlock; }
-
-  virtual ~Node() {}
-
-  friend class NodeMap;
-
-  PADDLE_DISALLOW_COPY_AND_ASSIGN(Node);
-
- protected:
-  // The id number not the name is a node's unique identifier in the computation
-  // graph.
-  int id_{-1};
-  std::string name_;
-  Type type_{Type::kNone};
-  // Mark this node is deleted by some pass.
-  bool deleted_{false};
-  mutable std::unordered_map<std::string, AnyAttr> attrs_;
-};
-
-class Function;
-/*
- * Value represents a value node, it has some attributes including dims, data
- * type and so on.
- */
-class Value : public Node {
- public:
-  enum class DataType { kInt32, kInt64, kFloat32, kFloat64 };
-  using Dims = std::vector<int>;
-
-  void SetDataType(DataType data_type) { data_type_ = data_type; }
-  DataType data_type() const { return data_type_; }
-
-  void SetDims(const Dims &dims) { dims_ = dims; }
-  const Dims &dims() const { return dims_; }
-
-  Device device() const { return device_; }
-  void SetDevice(Device device) { device_ = device; }
-
-  std::vector<Dot::Attr> dot_attrs() const override;
-
-  PADDLE_DISALLOW_COPY_AND_ASSIGN(Value);
-
- protected:
-  Value() { SetType(Node::Type::kValue); }
-  friend class NodeMap;
-
- private:
-  DataType data_type_;
-  Dims dims_;
-  Device device_;
-};
-
-/*
- * Function represents any kind of executable concepts that takes several Values
- * as input, and outputs several Values.
- */
-class Function : public Node {
- public:
-  std::vector<Dot::Attr> dot_attrs() const override;
-
-  // Get the operator's type from Desc.
-  const std::string &func_type() const { return func_type_; }
-  // Set the operator's type.
-  void SetFuncType(const std::string &func_type) { func_type_ = func_type; }
-
-  PADDLE_DISALLOW_COPY_AND_ASSIGN(Function);
-
- protected:
-  std::string func_type_;
-  Function() { SetType(Node::Type::kFunction); }
-  friend class NodeMap;
-};
-
-/*
- * FunctionBlock is a Node that contains a sub-graph multiple Node.
- */
-struct FunctionBlock : public Node {
-  std::string repr() const override { return "block-" + std::to_string(id()); }
-  std::vector<Node *> subgraph;
-
- protected:
-  FunctionBlock() { SetType(Node::Type::kFunctionBlock); }
-  friend class NodeMap;
-};
-
-class NodeMap {
- public:
-  // Create a new node with type.
-  Node *Create(Node::Type type);
-
-  // Get a node by its id.
-  Node *GetMutable(size_t id);
-
-  const Node &Get(size_t id) const;
-
-  void Delete(size_t id);
-
-  const std::vector<std::unique_ptr<Node>> &nodes() const { return nodes_; }
-
-  size_t size() const { return nodes_.size(); }
-
- private:
-  std::vector<std::unique_ptr<Node>> nodes_;
-  std::unordered_map<std::string, Node *> map_;
-};
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/node_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/node.h"
-
-#include <gtest/gtest.h>
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-TEST(NodeAttr, bool) {
-  AnyAttr x;
-  x.Bool() = true;
-  ASSERT_EQ(x.Bool(), true);
-}
-
-TEST(NodeAttr, int32) {
-  AnyAttr x;
-  x.Int32() = 32;
-  ASSERT_EQ(x.Int32(), 32);
-}
-
-TEST(NodeAttr, string) {
-  AnyAttr x;
-  x.String() = "Hello";
-  ASSERT_EQ(x.String(), "Hello");
-}
-
-TEST(Node, Attr) {
-  // Node is an abstract class, use Value instead for they share the same Attr
-  // logic.
-  NodeMap nodes;
-  auto* node = nodes.Create(Node::Type::kValue);
-  node->attr("v0").Int32() = 2008;
-  ASSERT_EQ(node->attr("v0").Int32(), 2008);
-
-  node->attr("str").String() = "hello world";
-  ASSERT_EQ(node->attr("str").String(), "hello world");
-}
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/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/pass_manager.h"
-#include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
-#include "paddle/fluid/string/pretty_log.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-bool PassManager::Initialize(Argument* argument) {
-  argument_ = argument;
-  for (auto& pass : data_) {
-    VLOG(30) << "Initializing pass [" << pass->repr() << "]";
-    if (!pass->Initialize(argument)) {
-      LOG(ERROR) << "Failed to initialize pass [" << pass->repr() << "]";
-      return false;
-    }
-  }
-  return true;
-}
-
-void DfgPassManager::RunAll() {
-  PADDLE_ENFORCE(argument_);
-  VLOG(30) << "Total " << data_.size() << " Analysys passes";
-  for (auto& pass : data_) {
-    string::PrettyLogEndl(string::Style::H1(), "* Running Analysis pass [%s]",
-                          pass->repr());
-    pass->Run(argument_->main_dfg.get());
-  }
-}
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/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 the logic of pass management. The analysis for inference is
- * a pipeline of Passes, a PassManager is a agency that helps to manage the
- * executation of the Passes.
- *
- * There are two modes of Passes, the first one is called NodePass and takes
- * an Node as input and output; the second one is called DFGPass and takes a
- * DFG(Data Flow Graph) as input and output. It is hard to put all the passes in
- * the same pipeline, there are two kinds of PassManagers, both takes a DFG as
- * input and output a DFG, but the Passes inside are different:
- *
- *   1. NodePassManager: the passes inside are all NodePasses, it can have
- *      different graph trivial algorithm, for example, DFS_NodePassManager will
- *      trigger the passes in depth first order;
- *   2. DfgPassManager: the passes inside are all DfgPasses.
- */
-
-#pragma once
-
-#include <string>
-#include "paddle/fluid/framework/program_desc.h"
-#include "paddle/fluid/inference/analysis/analysis_pass.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-/*
- * PassManager is the base class for all pass managers, a pass manager has
- * several Pass-es registered, and execute them in the linear order.
- */
-class PassManager : public OrderedRegistry<AnalysisPass> {
- public:
-  PassManager() = default;
-  // Call all the passes' Initialize methods. The desc and data_flow_graph are
-  // globally shared, so pass them as the arguemnts for all the pass managers.
-  virtual bool Initialize(const Argument& argument) { return false; }
-
-  virtual bool Initialize(Argument* argument);
-
-  // Call all the passes' Finalize methods.
-  virtual bool Finalize() {
-    for (auto& pass : data_) {
-      if (!pass->Finalize()) {
-        LOG(ERROR) << "Failed to finalize pass [" << pass->repr() << "]";
-        return false;
-      }
-    }
-    return true;
-  }
-
-  // Run all the passes.
-  virtual void RunAll() = 0;
-
-  // Short identifier.
-  virtual std::string repr() const = 0;
-  // Long description.
-  virtual std::string description() const = 0;
-
-  virtual ~PassManager() = default;
-
- protected:
-  Argument* argument_{nullptr};
-};
-
-/*
- * A pass manager that process a DFG.
- */
-class DfgPassManager : public PassManager {
- public:
-  DfgPassManager() = default;
-
-  void RunAll() override;
-
-  virtual ~DfgPassManager() = default;
-};
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/pass_manager_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 <gtest/gtest.h>
-
-#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/pass_manager.h"
-#include "paddle/fluid/inference/analysis/ut_helper.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-class TestDfgPassManager final : public DfgPassManager {
- public:
-  TestDfgPassManager() = default;
-  virtual ~TestDfgPassManager() = default;
-  // Short identifier.
-  std::string repr() const override { return "test-pass-manager"; }
-  // Long description.
-  std::string description() const override { return "test doc"; }
-};
-
-TEST(PassManager, DFG_pass_manager) {
-  TestDfgPassManager manager;
-  DFG_GraphvizDrawPass::Config config("./", "dfg.dot");
-
-  manager.Register("fluid-to-flow-graph", new FluidToDataFlowGraphPass);
-  manager.Register("graphviz", new DFG_GraphvizDrawPass(config));
-  manager.Register("dfg-to-fluid", new DataFlowGraphToFluidPass);
-
-  Argument argument(FLAGS_inference_model_dir);
-
-  ASSERT_TRUE(&argument);
-  ASSERT_TRUE(manager.Initialize(&argument));
-  manager.RunAll();
-}
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/passes/CMakeLists.txt

+cc_library(ir_graph_build_pass SRCS ir_graph_build_pass.cc DEPS analysis_pass argument ir_pass_manager)
+cc_library(ir_analysis_pass SRCS ir_analysis_pass.cc DEPS analysis_pass argument ir_pass_manager)
+cc_library(analysis_passes SRCS passes.cc DEPS ir_graph_build_pass ir_analysis_pass)
+
+set(analysis_deps ${analysis_deps}
+        ir_graph_build_pass
+        ir_analysis_pass
+        analysis_passes
+        CACHE INTERNAL "")

paddle/fluid/inference/analysis/passes/ir_analysis_compose_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/passes/ir_analysis_compose_pass.h"
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/framework/ir/pass.h"
+#include "paddle/fluid/inference/analysis/ir_pass_manager.h"
+#include "paddle/fluid/inference/analysis/ir_passes/subgraph_detector.h"
+#include "paddle/fluid/string/pretty_log.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+void IrAnalysisComposePass::RunImpl(Argument *argument) {
+  ARGUMENT_CHECK_FIELD(argument, ir_analysis_passes);
+  if (argument->use_tensorrt_valid() && argument->use_tensorrt()) {
+    InitTensorRTAttrs(argument);
+  }
+  ApplyIrPasses(argument);
+  CollectFusionStatis(argument);
+}
+
+std::string IrAnalysisComposePass::repr() const {
+  return "ir-analysis-compose-pass";
+}
+
+void IrAnalysisComposePass::InitTensorRTAttrs(Argument *argument) {
+  if (argument->use_tensorrt_valid() && argument->use_tensorrt()) {
+    LOG(INFO) << "Initing TensorRT pass";
+    argument->SetTensorRtNodeTeller([](const framework::ir::Node *node) {
+      std::unordered_set<std::string> teller_set(
+          {"mul", "conv2d", "pool2d", "relu", "softmax", "sigmoid",
+           "depthwise_conv2d", "batch_norm", "concat", "tanh", "pad",
+           "elementwise_add", "dropout"});
+      if (!node->IsOp()) return false;
+
+      if (teller_set.count(node->Op()->Type())) {
+        return true;
+      } else {
+        return false;
+      }
+    });
+  }
+}
+
+void IrAnalysisComposePass::ApplyIrPasses(Argument *argument) {
+  std::vector<std::string> passes({
+      "ir_graph_build_pass", "ir_analysis_pass",
+  });
+  for (const auto &pass : passes) {
+    VLOG(2) << "Run pass " << pass;
+    auto *the_pass = PassRegistry::Global().Retreive(pass);
+    the_pass->Run(argument);
+  }
+}
+
+void IrAnalysisComposePass::CollectFusionStatis(Argument *argument) {
+  if (!argument->main_graph().Has(framework::ir::kFuseStatisAttr)) {
+    LOG(INFO) << "argument has no fuse statis";
+    return;
+  }
+  argument->SetFusionStatis(
+      argument->main_graph().Get<Argument::fusion_statis_t>(
+          framework::ir::kFuseStatisAttr));
+}
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/model_store_pass.h → paddle/fluid/inference/analysis/passes/ir_analysis_compose_pass.h

@@ -12,42 +12,35 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-/*
- * This file defines ModelStorePass, which store the runtime DFG to a Paddle
- * model in the disk, and that model can be reloaded for prediction.
- */
-
 #pragma once
+
 #include <string>
+#include <vector>
 #include "paddle/fluid/inference/analysis/analysis_pass.h"
+#include "paddle/fluid/inference/analysis/passes/passes.h"
 
 namespace paddle {
 namespace inference {
 namespace analysis {
 
-class ModelStorePass : public DataFlowGraphPass {
+/*
+ * The analysis pass to run a list of IR passes (like a function call).
+ * Currently, it should be the first pass of analysis phase.
+ */
+class IrAnalysisComposePass : public AnalysisPass {
  public:
-  bool Initialize(Argument* argument) override {
-    if (!argument) {
-      LOG(ERROR) << "invalid argument";
-      return false;
-    }
-    argument_ = argument;
-    return true;
-  }
+  void RunImpl(Argument* argument) override;
+  std::string repr() const override;
 
-  void Run(DataFlowGraph* x) override;
+ private:
+  void InitTensorRTAttrs(Argument* argument);
 
-  std::string repr() const override { return "DFG-store-pass"; }
-  std::string description() const override {
-    return R"DD(This file defines ModelStorePass, which store the runtime DFG to a Paddle
-    model in the disk, and that model can be reloaded for prediction again.)DD";
-  }
+  void ApplyIrPasses(Argument* argument);
 
-  bool Finalize() override;
+  void CollectFusionStatis(Argument* argument);
 
- private:
-  Argument* argument_{nullptr};
+  // Assign a Scope for IR passes to modify the weights.
+  void AssignScopeToModify(Argument* argument);
 };
 
 }  // namespace analysis

paddle/fluid/inference/analysis/passes/ir_analysis_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/passes/ir_analysis_pass.h"
+#include "paddle/fluid/inference/analysis/ir_pass_manager.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+void IrAnalysisPass::RunImpl(Argument* argument) {
+  ARGUMENT_CHECK_FIELD(argument, ir_analysis_passes);
+  ARGUMENT_CHECK_FIELD(argument, main_program);
+  ARGUMENT_CHECK_FIELD(argument, scope);
+
+  auto* the_graph = argument->ReleaseMainGraph();
+  auto graph = std::unique_ptr<Graph>(the_graph);
+
+  // Apply passes.
+  IRPassManager the_ir_manager(argument);
+  graph = the_ir_manager.Apply(std::move(graph));
+  PADDLE_ENFORCE_GT(graph->Nodes().size(), 0);
+  argument->SetIrAnalyzedProgram(new framework::proto::ProgramDesc(
+      the_ir_manager.AcquireProgram(&graph, argument->main_program())));
+  argument->SetMainGraph(graph.release());
+}
+
+std::string IrAnalysisPass::repr() const { return "ir-analysis-pass"; }
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/node_attr_flags.h → paddle/fluid/inference/analysis/passes/ir_analysis_pass.h

@@ -12,20 +12,25 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-/*
- * This file contains all the flags that declared in Node::Attr.
- *
- * The Node::Attr is designed to share information between different passes, one
- * can get other's attributes in a Node by the flags in this file.
- */
 #pragma once
+
+#include <string>
+#include "paddle/fluid/inference/analysis/analysis_pass.h"
+
 namespace paddle {
 namespace inference {
 namespace analysis {
 
-#define DECLARE_NODE_ATTR(flag__) const char ATTR_##flag__[] = #flag__;
-
-DECLARE_NODE_ATTR(supported_by_tensorrt)  // bool
+/*
+ * Perform IR analysis passes.
+ *
+ * It is used to fuse some
+ */
+class IrAnalysisPass : public AnalysisPass {
+ public:
+  void RunImpl(Argument* argument) override;
+  std::string repr() const override;
+};
 
 }  // namespace analysis
 }  // namespace inference

paddle/fluid/inference/api/README.md

@@ -2,25 +2,15 @@
 
 Paddle inference offers the APIs in `C` and `C++` languages.
 
-One can easily deploy a model trained by Paddle following the steps as below:
+You can easily deploy a model trained by Paddle following the steps as below:
 
 1. Optimize the native model;
 2. Write some codes for deployment.
 
+## The APIs
 
-Let's explain the steps in detail.
-
-## Optimize the native Fluid Model
-
-The native model that get from the training phase needs to be optimized for that.
-
-- Clean the noise such as the cost operators that do not need inference;
-- Prune unnecessary computation fork that has nothing to do with the output;
-- Remove extraneous variables;
-- Memory reuse for native Fluid executor;
-- Translate the model storage format to some third-party engine's, so that the inference API can utilize the engine for acceleration;
-
-We have an official tool to do the optimization, call `paddle_inference_optimize --help` for more information.
+All the released APIs are located in the `paddle_inference_api.h` header file. 
+The stable APIs are wrapped by `namespace paddle`, the unstable APIs are protected by `namespace paddle::contrib`.
 
 ## Write some codes