Merge branch 'windows/build' into windows/online

test=develop

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

@@ -184,11 +184,10 @@ 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
-        ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h
-        DSTS ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module}
-        )
+  SRCS ${src_dir}/${module}/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/inference/libpaddle_fluid.*
+       ${src_dir}/${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
-        DSTS ${FLUID_INFERENCE_INSTALL_DIR}/paddle/lib ${FLUID_INFERENCE_INSTALL_DIR}/paddle/include
-        )
+  SRCS ${FLUID_INSTALL_DIR}/paddle/fluid/inference/libpaddle_fluid.*
+       ${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);

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) {
-          HitGroup new_group = group;
-          if (IsNodesLink(source, target) &&
-              new_group.Match(source, edge.first)) {
-            new_group.Register(source, edge.first);
-            if (new_group.Match(target, edge.second)) {
+          if (IsNodesLink(source, target)) {
+            HitGroup new_group = group;
+            bool flag = new_group.Match(source, edge.first) &&
+                        new_group.Match(target, edge.second);
+            if (flag) {
+              new_group.Register(source, edge.first);
               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;
+    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

@@ -57,60 +57,58 @@ 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());
+  for (auto &var : global_block.AllVars()) {
+    if (var->Name() == framework::kEmptyVarName) {
+      continue;
+    }
+
+    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());
+        }
+      } else {
+        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()
-                 << " locally, which pointer is " << ptr;
       }
     }
-  } else {
-    for (auto &var : global_block.AllVars()) {
-      auto *ptr = scope->Var(var->Name());
-      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) {
@@ -183,11 +213,19 @@ 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);
-      framework::VisitDataType(
-          framework::ToDataType(value_->type()),
-          TensorCopyVisitor(value, i * value_width, *value_.get(),
-                            index * value_width, value_width));
+      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));
+      }
     }
   }
 }

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);
-    }
-  }
+Analyzer::Analyzer() {}
 
-  std::string repr() const override { return "dfg-pass-manager"; }
-  std::string description() const override { return "DFG pass manager."; }
+void Analyzer::Run(Argument *argument) { RunIrAnalysis(argument); }
 
- private:
-  void AddPass(const std::string& name, AnalysisPass* pass) {
-    VLOG(30) << "Adding pass " << name;
-    Register(name, pass);
-    AddGraphvizDebugerPass(pass);
-  }
+void Analyzer::RunIrAnalysis(Argument *argument) {
+  std::vector<std::string> passes({"ir_analysis_compose_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;
-
-        const auto* func = static_cast<const Function*>(node);
-        if (teller_set.count(func->func_type())) {
-          return true;
-        } else {
-          return false;
-        }
-      };
-
-      AddPass("tensorrt-subgraph-marker",
-              new TensorRTSubgraphNodeMarkPass(trt_teller));
-      AddPass("tensorrt-subgraph", new TensorRTSubGraphPass(trt_teller));
-    }
-  }
-
-  // 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::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 *>> SubGraphSplitter::operator()() {
+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

-//   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+// 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.
@@ -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/analysis/fluid_to_ir_pass.cc → paddle/fluid/inference/analysis/passes/ir_graph_build_pass.cc

@@ -12,49 +12,62 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/inference/analysis/fluid_to_ir_pass.h"
+#include "paddle/fluid/inference/analysis/passes/ir_graph_build_pass.h"
+#include <paddle/fluid/framework/ir/fuse_pass_base.h>
+#include <string>
 #include "paddle/fluid/framework/executor.h"
-#include "paddle/fluid/framework/ir/fuse_pass_base.h"
 #include "paddle/fluid/inference/io.h"
-#include "paddle/fluid/platform/device_context.h"
-#include "paddle/fluid/platform/place.h"
+#include "paddle/fluid/platform/enforce.h"
 
 namespace paddle {
 namespace inference {
+
+extern void ReadBinaryFile(const std::string &filename, std::string *contents);
+
 namespace analysis {
 
-void FluidToIrPass::EnableParamModify(const std::string &model_dir,
-                                      const std::string &prog_file,
-                                      const std::string &param_file) {
-  PADDLE_ENFORCE(argument_);
-  argument_->Set(framework::ir::kParamScopeAttr, new framework::Scope);
-  // Load parameters.
-  VLOG(30) << "Loading parameters from " << model_dir;
-  LoadParams(&argument_->Get<framework::Scope>(framework::ir::kParamScopeAttr),
-             model_dir, prog_file, param_file);
-}
+void IrGraphBuildPass::RunImpl(Argument *argument) {
+  if (!argument->scope_valid()) {
+    argument->SetScope(new framework::Scope);
+  }
 
-bool FluidToIrPass::LoadParams(framework::Scope *scope, const std::string &dir,
-                               const std::string &prog_file,
-                               const std::string &param_file) {
-  platform::CPUPlace place;
-  platform::CPUDeviceContext ctx(place);
-  framework::Executor executor(place);
-  PADDLE_ENFORCE(argument_->origin_program_desc.get());
-  framework::ProgramDesc program(*argument_->origin_program_desc);
-  if ((!prog_file.empty()) && (!param_file.empty())) {
-    LOG(INFO) << "load single model file from " << prog_file;
-    Load(&executor, scope, prog_file, param_file);
-  } else if (!dir.empty()) {
-    LOG(INFO) << "load from dir " << dir;
-    Load(&executor, scope, dir);
+  if (argument->model_dir_valid()) {
+    auto program = LoadModel(argument->model_dir(), argument->scope_ptr());
+    argument->SetMainProgram(program.release());
+  } else if (argument->model_program_path_valid() &&
+             argument->model_params_path_valid()) {
+    auto program =
+        LoadModel(argument->model_program_path(), argument->model_params_path(),
+                  argument->scope_ptr());
+    argument->SetMainProgram(program.release());
   } else {
-    LOG(ERROR) << "failed to load parameters";
-    return false;
+    PADDLE_THROW(
+        "either model_dir or (program path and parameter path) should be set.");
   }
-  return true;
+
+  auto graph = std::unique_ptr<Graph>(new Graph(argument->main_program()));
+  argument->SetMainGraph(graph.release());
+  argument->main_graph().Set(framework::ir::kParamScopeAttr,
+                             new framework::Scope *(argument->scope_ptr()));
 }
 
+std::unique_ptr<framework::ProgramDesc> IrGraphBuildPass::LoadModel(
+    const std::string &path, framework::Scope *scope) {
+  platform::CPUPlace place;
+  framework::Executor exe(place);
+  return Load(&exe, scope, path);
+}
+
+std::unique_ptr<framework::ProgramDesc> IrGraphBuildPass::LoadModel(
+    const std::string &program_path, const std::string &params_path,
+    framework::Scope *scope) {
+  platform::CPUPlace place;
+  framework::Executor exe(place);
+  return Load(&exe, scope, program_path, params_path);
+}
+
+std::string IrGraphBuildPass::repr() const { return "ir-graph-build-pass"; }
+
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/analysis/passes/ir_graph_build_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 "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/inference/analysis/analysis_pass.h"
+
+namespace paddle {
+namespace inference {
+namespace analysis {
+
+/*
+ * Load program and parameter to memory from the disk.
+ */
+class IrGraphBuildPass : public AnalysisPass {
+ public:
+  void RunImpl(Argument *argument) override;
+
+  std::string repr() const override;
+
+ private:
+  std::unique_ptr<framework::ProgramDesc> LoadModel(const std::string &path,
+                                                    framework::Scope *scope);
+  std::unique_ptr<framework::ProgramDesc> LoadModel(
+      const std::string &program_path, const std::string &params_path,
+      framework::Scope *scope);
+
+  std::string model_binary_str_;
+};
+
+}  // namespace analysis
+}  // namespace inference
+}  // namespace paddle

paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass_tester.cc → paddle/fluid/inference/analysis/passes/passes.cc

-//   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+// 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.
@@ -12,25 +12,21 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
-
-#include <gtest/gtest.h>
-#include "paddle/fluid/inference/analysis/ut_helper.h"
+#include "paddle/fluid/inference/analysis/passes/passes.h"
+#include "paddle/fluid/inference/analysis/passes/ir_analysis_compose_pass.cc"
+#include "paddle/fluid/inference/analysis/passes/ir_analysis_pass.h"
+#include "paddle/fluid/inference/analysis/passes/ir_graph_build_pass.h"
 
 namespace paddle {
 namespace inference {
 namespace analysis {
-
-TEST(FluidToDataFlowGraphPass, Test) {
-  FluidToDataFlowGraphPass pass;
-  Argument argument(FLAGS_inference_model_dir);
-  pass.Initialize(&argument);
-  pass.Run(argument.main_dfg.get());
-  // Analysis is sensitive to ProgramDesc, careful to change the original model.
-  ASSERT_EQ(argument.main_dfg->nodes.size(), 38UL);
-  pass.Finalize();
-  ASSERT_FALSE(argument.main_dfg->DotString().empty());
-  EXPECT_FALSE(argument.main_dfg->inputs().empty());
+PassRegistry::PassRegistry() {
+  passes_.emplace("ir_analysis_pass",
+                  std::unique_ptr<AnalysisPass>(new IrAnalysisPass));
+  passes_.emplace("ir_graph_build_pass",
+                  std::unique_ptr<AnalysisPass>(new IrGraphBuildPass));
+  passes_.emplace("ir_analysis_compose_pass",
+                  std::unique_ptr<AnalysisPass>(new IrAnalysisComposePass));
 }
 
 }  // namespace analysis

paddle/fluid/inference/analysis/fluid_to_ir_pass_tester.cc → paddle/fluid/inference/analysis/passes/passes.h

@@ -12,24 +12,30 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/inference/analysis/fluid_to_ir_pass.h"
+#pragma once
 
-#include <gtest/gtest.h>
-#include "paddle/fluid/inference/analysis/ut_helper.h"
-#include "paddle/fluid/inference/api/paddle_inference_pass.h"
+#include <string>
+#include "paddle/fluid/inference/analysis/analysis_pass.h"
 
 namespace paddle {
 namespace inference {
 namespace analysis {
 
-TEST(FluidToIrPass, Test) {
-  FluidToIrPass pass;
-  Argument argument(FLAGS_inference_model_dir);
-  argument.Set(kFluidToIrPassesAttr,
-               new std::vector<std::string>({"infer_clean_graph_pass"}));
-  pass.Initialize(&argument);
-  pass.Run(argument.main_dfg.get());
-}
+struct PassRegistry {
+  PassRegistry();
+
+  AnalysisPass* Retreive(const std::string& pass_type) {
+    return passes_[pass_type].get();
+  }
+
+  static PassRegistry& Global() {
+    static auto* x = new PassRegistry;
+    return *x;
+  }
+
+ private:
+  std::unordered_map<std::string, std::unique_ptr<AnalysisPass>> passes_;
+};
 
 }  // namespace analysis
 }  // namespace inference

paddle/fluid/inference/analysis/subgraph_splitter.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/inference/analysis/argument.h"
-#include "paddle/fluid/inference/analysis/data_flow_graph.h"
-#include "paddle/fluid/inference/analysis/node.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-/*
- * Detect the nodes in a sub-graph that meet some conditions. This class doesn't
- * modify the graph.
- */
-class SubGraphSplitter {
- public:
-  static const char *kMarkerAttrName;
-  // Tell whether a node is inside a sub-graph.
-  using NodeInsideSubgraphTeller = std::function<bool(const Node *)>;
-
-  SubGraphSplitter(DataFlowGraph *graph, const NodeInsideSubgraphTeller &teller)
-      : graph_(graph), node_inside_subgraph_teller_(teller) {}
-
-  std::vector<std::vector<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<Node *>> ExtractSubGraphs();
-
- private:
-  DataFlowGraph *graph_;
-  NodeInsideSubgraphTeller node_inside_subgraph_teller_;
-};
-
-/*
- * SubGraphFuse - 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 SubGraphFuse {
- public:
-  using NodeInsideSubgraphTeller = SubGraphSplitter::NodeInsideSubgraphTeller;
-
-  SubGraphFuse(DataFlowGraph *graph, const NodeInsideSubgraphTeller &teller,
-               Argument *argument)
-      : graph_(graph),
-        node_inside_subgraph_teller_(teller),
-        argument_(argument) {}
-
-  // 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:
-  DataFlowGraph *graph_;
-  NodeInsideSubgraphTeller node_inside_subgraph_teller_;
-  Argument *argument_;
-};
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/subgraph_splitter_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/subgraph_splitter.h"
-#include "paddle/fluid/inference/analysis/ut_helper.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-SubGraphSplitter::NodeInsideSubgraphTeller teller = [](const Node* node) {
-  if (node->type() != Node::Type::kFunction) return false;
-  const auto* func = static_cast<const Function*>(node);
-  if (func->func_type() == "elementwise_add" || func->func_type() == "relu" ||
-      func->func_type() == "conv2d" || func->func_type() == "mul" ||
-      func->func_type() == "sigmoid" || func->func_type() == "softmax") {
-    LOG(INFO) << "sub-graph marked " << node->repr();
-    return true;
-  }
-  return false;
-};
-
-TEST(SubGraphSplitter, Split) {
-  auto desc = LoadProgramDesc(FLAGS_inference_model_dir + "/__model__");
-  auto dfg = ProgramDescToDFG(desc);
-  LOG(INFO) << "spliter\n" << dfg.DotString();
-
-  ASSERT_GT(dfg.nodes.size(), 5UL);
-
-  auto subgraphs = SubGraphSplitter(&dfg, teller)();
-
-  // Check the number of the marked nodes.
-  int marked_nodes = 0;
-  for (auto& node : dfg.nodes.nodes()) {
-    if (node->IsFunction() &&
-        node->attr(SubGraphSplitter::kMarkerAttrName).Bool()) {
-      ++marked_nodes;
-    }
-  }
-  EXPECT_EQ(marked_nodes, 6);
-
-  // For human debug.
-  for (auto& subgraph : subgraphs) {
-    LOG(INFO) << "subgraph size " << subgraph.size();
-    for (auto* node : subgraph) {
-      LOG(INFO) << "node " << node->repr();
-    }
-  }
-
-  ASSERT_EQ(subgraphs.size(), 1UL);
-  // The last sub-graph has 5 Functions.
-  ASSERT_EQ(subgraphs.back().size(), 6UL);
-}
-
-TEST(SubGraphSplitter, Fuse) {
-  auto desc = LoadProgramDesc(FLAGS_inference_model_dir + "/__model__");
-  auto dfg = ProgramDescToDFG(desc);
-  Argument argument;
-  argument.Set<int>("minimum_subgraph_size", new int(3));
-
-  size_t count0 = dfg.nodes.size();
-
-  SubGraphFuse fuse(&dfg, teller, &argument);
-  fuse();
-
-  int count1 = 0;
-  for (auto& node : dfg.nodes.nodes()) {
-    if (node->deleted()) {
-      LOG(INFO) << "deleted " << node->repr();
-    }
-    count1 += node->deleted();
-  }
-
-  // At least one nodes should be deleted.
-  ASSERT_EQ(dfg.nodes.size(), count0 + 1);  // added a new FunctionBlock
-  ASSERT_EQ(11, count1);
-}
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/tensorrt_subgraph_node_mark_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 <string>
-
-#include "paddle/fluid/inference/analysis/analyzer.h"
-#include "paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h"
-#include "paddle/fluid/inference/analysis/node_attr_flags.h"
-#include "paddle/fluid/inference/analysis/tensorrt_subgraph_node_mark_pass.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-void TensorRTSubgraphNodeMarkPass::Run(DataFlowGraph *graph) {
-  for (auto &node : graph->nodes.nodes()) {
-    node->attr(ATTR_supported_by_tensorrt).Bool() = teller_(node.get());
-  }
-}
-
-class DfgDebuggerPass : public DFG_GraphvizDrawPass {
- public:
-  explicit DfgDebuggerPass(const DFG_GraphvizDrawPass::Config &config)
-      : DFG_GraphvizDrawPass(config) {}
-
-  std::string repr() const override {
-    return "tensorrt-subgraph-node-mark-debugger";
-  }
-
-  bool Finalize() override { return true; }
-
- protected:
-  std::string Draw(DataFlowGraph *graph) override {
-    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()) {
-        auto dot_attr = node.dot_attrs();
-        if (node.attr(ATTR_supported_by_tensorrt).Bool()) {
-          dot_attr.assign(
-              {Dot::Attr{"color", "green"}, Dot::Attr{"style", "filled"}});
-        }
-        dot.AddNode(node.repr(), dot_attr);
-      }
-    }
-    // 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 &in : node.inlinks) {
-        if (!config_.display_deleted_node && in->deleted()) continue;
-        dot.AddEdge(in->repr(), node.repr(), {});
-      }
-    }
-    return dot.Build();
-  }
-};
-
-AnalysisPass *TensorRTSubgraphNodeMarkPass::CreateGraphvizDebugerPass() const {
-  DFG_GraphvizDrawPass::Config config(FLAGS_IA_graphviz_log_root,
-                                      "tensorrt_marked_node");
-  return new DfgDebuggerPass(config);
-}
-bool TensorRTSubgraphNodeMarkPass::Finalize() { return true; }
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/tensorrt_subgraph_node_mark_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/tensorrt_subgraph_node_mark_pass.h"
-
-#include <gtest/gtest.h>
-#include "paddle/fluid/inference/analysis/node_attr_flags.h"
-#include "paddle/fluid/inference/analysis/ut_helper.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-TEST(TensorRTSubgraphNodeMarkPass, test) {
-  // init
-  FluidToDataFlowGraphPass pass;
-  Argument argument(FLAGS_inference_model_dir);
-  ASSERT_TRUE(pass.Initialize(&argument));
-  pass.Run(argument.main_dfg.get());
-
-  TensorRTSubgraphNodeMarkPass::teller_t teller = [](const Node* node) {
-    return node->IsFunction() &&
-           static_cast<const Function*>(node)->func_type() == "mul";
-  };
-  TensorRTSubgraphNodeMarkPass pass1(teller);
-  ASSERT_TRUE(pass1.Initialize(&argument));
-  pass1.Run(argument.main_dfg.get());
-
-  int counter{0};
-  for (auto& node : argument.main_dfg->nodes.nodes()) {
-    counter += node->attr(ATTR_supported_by_tensorrt).Bool();
-  }
-  ASSERT_EQ(counter, 2);
-  LOG(INFO) << counter << " nodes marked";
-}
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/tensorrt_subgraph_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/tensorrt_subgraph_pass.h"
-#include "paddle/fluid/inference/analysis/subgraph_splitter.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-TensorRTSubGraphPass::TensorRTSubGraphPass(
-    const TensorRTSubGraphPass::NodeInsideSubgraphTeller &teller)
-    : node_inside_subgraph_teller_(teller) {}
-
-void TensorRTSubGraphPass::Run(DataFlowGraph *graph) {
-  SubGraphFuse(graph, node_inside_subgraph_teller_, argument_)();
-  VLOG(40) << "debug info "
-           << graph->HumanReadableInfo(false /*show_values*/,
-                                       true /*show_functions*/);
-}
-
-}  // namespace analysis
-}  // namespace inference
-
-}  // namespace paddle

paddle/fluid/inference/analysis/tensorrt_subgraph_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 "paddle/fluid/inference/analysis/analysis_pass.h"
-#include "paddle/fluid/inference/analysis/node.h"
-#include "paddle/fluid/inference/analysis/subgraph_splitter.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-/*
- * Parse the graph and replace TensorRT supported nodes with SubGraphNode
- */
-class TensorRTSubGraphPass : public DataFlowGraphPass {
- public:
-  // Tell whether to transform a sub-graph into TensorRT.
-  using NodeInsideSubgraphTeller = SubGraphFuse::NodeInsideSubgraphTeller;
-
-  explicit TensorRTSubGraphPass(const NodeInsideSubgraphTeller& teller);
-
-  bool Initialize(Argument* argument) override {
-    argument_ = argument;
-    return true;
-  }
-
-  // This class get a sub-graph as input and determine whether to transform this
-  // sub-graph into TensorRT.
-  void Run(DataFlowGraph* graph) override;
-
-  bool Finalize() override { return true; }
-
-  std::string repr() const override { return "tensorrt-sub-graph"; }
-  std::string description() const override { return "tensorrt sub graph pass"; }
-
- private:
-  NodeInsideSubgraphTeller node_inside_subgraph_teller_;
-  Argument* argument_;
-};
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/tensorrt_subgraph_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/tensorrt_subgraph_pass.h"
-
-#include <gflags/gflags.h>
-#include <gtest/gtest.h>
-#include "paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h"
-#include "paddle/fluid/inference/analysis/ut_helper.h"
-
-namespace paddle {
-namespace inference {
-namespace analysis {
-
-DEFINE_string(dot_dir, "./", "");
-
-TEST(TensorRTSubGraphPass, main) {
-  std::unordered_set<std::string> teller_set(
-      {"elementwise_add", "mul", "sigmoid"});
-  SubGraphSplitter::NodeInsideSubgraphTeller teller = [&](const Node* node) {
-    if (node->type() != Node::Type::kFunction) return false;
-    const auto* func = static_cast<const Function*>(node);
-    if (teller_set.count(func->func_type())) return true;
-    return false;
-  };
-
-  Argument argument(FLAGS_inference_model_dir);
-  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"));
-
-  DFG_GraphvizDrawPass::Config config{FLAGS_dot_dir, "origin"};
-  DFG_GraphvizDrawPass::Config config1{FLAGS_dot_dir, "fusion"};
-
-  DFG_GraphvizDrawPass dfg_pass(config);
-  DFG_GraphvizDrawPass dfg_pass1(config1);
-  FluidToDataFlowGraphPass pass0;
-  TensorRTSubGraphPass trt_pass(std::move(teller));
-
-  dfg_pass.Initialize(&argument);
-  dfg_pass1.Initialize(&argument);
-  pass0.Initialize(&argument);
-  trt_pass.Initialize(&argument);
-
-  argument.main_dfg.reset(new DataFlowGraph);
-  pass0.Run(argument.main_dfg.get());
-  dfg_pass.Run(argument.main_dfg.get());
-  trt_pass.Run(argument.main_dfg.get());
-  dfg_pass1.Run(argument.main_dfg.get());
-
-  // Check the TRT op's block desc
-  for (auto& node : argument.main_dfg->nodes.nodes()) {
-    if (node->IsFunctionBlock()) {
-      LOG(INFO) << "get function block";
-    }
-  }
-}
-
-}  // namespace analysis
-}  // namespace inference
-}  // namespace paddle

paddle/fluid/inference/analysis/ut_helper.h

@@ -18,8 +18,6 @@ limitations under the License. */
 #include <fstream>
 #include <string>
 #include "paddle/fluid/framework/executor.h"
-#include "paddle/fluid/inference/analysis/data_flow_graph.h"
-#include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
 #include "paddle/fluid/inference/analysis/helper.h"
 
 namespace paddle {
@@ -32,29 +30,6 @@ namespace analysis {
 
 DEFINE_string(inference_model_dir, "", "inference test model dir");
 
-static DataFlowGraph ProgramDescToDFG(
-    const framework::proto::ProgramDesc& desc) {
-  DataFlowGraph graph;
-  FluidToDataFlowGraphPass pass;
-  Argument argument;
-  argument.fluid_model_dir.reset(new std::string(FLAGS_inference_model_dir));
-  argument.origin_program_desc.reset(new framework::proto::ProgramDesc(desc));
-  pass.Initialize(&argument);
-  pass.Run(&graph);
-  pass.Finalize();
-  return graph;
-}
-
-class DFG_Tester : public ::testing::Test {
- protected:
-  void SetUp() override {
-    auto desc = LoadProgramDesc(FLAGS_inference_model_dir + "/__model__");
-    argument.origin_program_desc.reset(new framework::proto::ProgramDesc(desc));
-  }
-
-  Argument argument;
-};
-
 }  // namespace analysis
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/api/CMakeLists.txt

@@ -17,17 +17,22 @@ if(APPLE)
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-error=pessimizing-move")
 endif(APPLE)
 
-set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager naive_executor ${GLOB_PASS_LIB})
+
+set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager naive_executor analysis_predictor ${GLOB_PASS_LIB})
 
 if(WITH_GPU AND TENSORRT_FOUND)
-    set(inference_deps ${inference_deps} paddle_inference_tensorrt_subgraph_engine analysis_predictor)
+    set(inference_deps ${inference_deps} tensorrt_engine tensorrt_converter)
 endif()
 
 cc_library(reset_tensor_array SRCS details/reset_tensor_array.cc DEPS lod_tensor scope)
-cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS reset_tensor_array lod_tensor scope)
-cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis naive_executor zero_copy_tensor)
+cc_library(analysis_config SRCS analysis_config.cc DEPS lod_tensor paddle_pass_builder)
+cc_library(paddle_pass_builder SRCS paddle_pass_builder.cc)
+cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor scope paddle_pass_builder reset_tensor_array analysis_config analysis_config paddle_pass_builder)
+cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis naive_executor zero_copy_tensor reset_tensor_array analysis_config paddle_pass_builder)
 cc_library(zero_copy_tensor SRCS details/zero_copy_tensor.cc DEPS paddle_inference_api)
 cc_library(zero_copy_tensor_dummy SRCS details/zero_copy_tensor_dummy.cc DEPS paddle_inference_api)
+
+
 cc_test(test_paddle_inference_api
         SRCS api_tester.cc
         DEPS paddle_inference_api)
@@ -40,20 +45,10 @@ endif()
 cc_test(test_analysis_predictor SRCS analysis_predictor_tester.cc DEPS analysis_predictor ${inference_deps}
         ARGS --dirname=${WORD2VEC_MODEL_DIR})
 
-if(WITH_GPU AND TENSORRT_FOUND)
-cc_library(paddle_inference_tensorrt_subgraph_engine
-        SRCS api_tensorrt_subgraph_engine.cc
-        DEPS paddle_inference_api analysis tensorrt_engine paddle_inference_api paddle_fluid_api tensorrt_converter zero_copy_tensor_dummy)
-  if(WITH_TESTING)
-    inference_base_test(test_api_tensorrt_subgraph_engine SRCS api_tensorrt_subgraph_engine_tester.cc DEPS ${inference_deps}
-                      ARGS --dirname=${WORD2VEC_MODEL_DIR})
-  endif()
-endif()
-
 if (WITH_ANAKIN AND WITH_MKL) # only needed in CI
     # compile the libinference_anakin_api.a and anakin.so.
-    cc_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber mklml scope zero_copy_tensor_dummy)
-    cc_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber scope)
+    cc_library(inference_anakin_api SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber mklml zero_copy_tensor_dummy)
+    cc_library(inference_anakin_api_shared SHARED SRCS api.cc api_anakin_engine.cc DEPS anakin_shared anakin_saber zero_copy_tensor_dummy)
     function(anakin_target target_name)
       target_compile_options(${target_name} BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
     endfunction()

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
 

paddle/fluid/inference/api/analysis_config.cc

+// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/inference/api/paddle_inference_api.h"
+#include "paddle/fluid/platform/enforce.h"
+#include "paddle_pass_builder.h"  // NOLINT
+
+namespace paddle {
+
+PassStrategy *contrib::AnalysisConfig::pass_builder() const {
+  PADDLE_ENFORCE(
+      pass_builder_.get(),
+      "Should call constructor first, that will init the pass_builder_.");
+  return pass_builder_.get();
+}
+
+contrib::AnalysisConfig::AnalysisConfig(bool use_gpu) {
+  this->use_gpu = use_gpu;
+  if (use_gpu) {
+    pass_builder_.reset(new GpuPassStrategy);
+  } else {
+    pass_builder_.reset(new CpuPassStrategy);
+  }
+}
+
+contrib::AnalysisConfig::AnalysisConfig(const contrib::AnalysisConfig &other) {
+  // fields from Config
+  model_dir = other.model_dir;
+  // fields from NativeConfig
+  use_gpu = other.use_gpu;
+  device = other.device;
+  fraction_of_gpu_memory = other.fraction_of_gpu_memory;
+  prog_file = other.prog_file;
+  param_file = other.param_file;
+  specify_input_name = other.specify_input_name;
+  // fields from this.
+  enable_ir_optim = other.enable_ir_optim;
+  use_feed_fetch_ops = other.use_feed_fetch_ops;
+  use_tensorrt_ = other.use_tensorrt_;
+  tensorrt_max_batchsize_ = other.tensorrt_max_batchsize_;
+  tensorrt_workspace_size_ = other.tensorrt_workspace_size_;
+
+  if (use_gpu) {
+    pass_builder_.reset(new GpuPassStrategy(
+        *static_cast<GpuPassStrategy *>(other.pass_builder())));
+  } else {
+    pass_builder_.reset(new CpuPassStrategy(
+        *static_cast<CpuPassStrategy *>(other.pass_builder())));
+  }
+}
+
+contrib::AnalysisConfig::AnalysisConfig(contrib::AnalysisConfig &&other) {
+  // fields from Config
+  model_dir = other.model_dir;
+  // fields from NativeConfig
+  use_gpu = other.use_gpu;
+  device = other.device;
+  fraction_of_gpu_memory = other.fraction_of_gpu_memory;
+  prog_file = other.prog_file;
+  param_file = other.param_file;
+  specify_input_name = other.specify_input_name;
+  // fields from this.
+  enable_ir_optim = other.enable_ir_optim;
+  use_feed_fetch_ops = other.use_feed_fetch_ops;
+  use_tensorrt_ = other.use_tensorrt_;
+  tensorrt_max_batchsize_ = other.tensorrt_max_batchsize_;
+  tensorrt_workspace_size_ = other.tensorrt_workspace_size_;
+  pass_builder_ = std::move(other.pass_builder_);
+}
+
+void contrib::AnalysisConfig::EnableMKLDNN() {
+#ifdef PADDLE_WITH_MKLDNN
+  pass_builder()->EnableMKLDNN();
+  use_mkldnn_ = true;
+#else
+  LOG(ERROR) << "Please compile with MKLDNN first to use MKLDNN";
+  use_mkldnn_ = false;
+#endif
+}
+
+void contrib::AnalysisConfig::EnableTensorRtEngine(int workspace_size,
+                                                   int max_batch_size) {
+  use_tensorrt_ = true;
+  tensorrt_workspace_size_ = workspace_size;
+  tensorrt_max_batchsize_ = max_batch_size;
+  // Append after the infer_clean pass.
+  pass_builder()->InsertPass(1, "tensorrt_subgraph_pass");
+}
+
+}  // namespace paddle

paddle/fluid/inference/api/analysis_predictor.cc

@@ -13,10 +13,13 @@
 // limitations under the License.
 
 #include "paddle/fluid/inference/api/analysis_predictor.h"
+#include <glog/logging.h>
+#include <algorithm>
 #include <memory>
 #include <string>
 #include <vector>
 #include "paddle/fluid/framework/feed_fetch_method.h"
+#include "paddle/fluid/framework/feed_fetch_type.h"
 #include "paddle/fluid/framework/ir/fuse_pass_base.h"
 #include "paddle/fluid/framework/ir/pass.h"
 #include "paddle/fluid/framework/naive_executor.h"
@@ -24,6 +27,9 @@
 #include "paddle/fluid/inference/api/helper.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
 #include "paddle/fluid/inference/api/paddle_inference_pass.h"
+#if PADDLE_WITH_TENSORRT
+#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
+#endif
 #include "paddle/fluid/inference/utils/singleton.h"
 #include "paddle/fluid/platform/cpu_helper.h"
 #include "paddle/fluid/platform/profiler.h"
@@ -35,6 +41,17 @@ namespace paddle {
 
 using contrib::AnalysisConfig;
 
+namespace {
+bool IsPersistable(const framework::VarDesc *var) {
+  if (var->Persistable() &&
+      var->GetType() != framework::proto::VarType::FEED_MINIBATCH &&
+      var->GetType() != framework::proto::VarType::FETCH_LIST) {
+    return true;
+  }
+  return false;
+}
+}  // namespace
+
 bool AnalysisPredictor::Init(
     const std::shared_ptr<framework::Scope> &parent_scope,
     const std::shared_ptr<framework::ProgramDesc> &program) {
@@ -52,36 +69,93 @@ bool AnalysisPredictor::Init(
   // no matter with or without MKLDNN
   paddle::platform::SetNumThreads(FLAGS_paddle_num_threads);
 
-  if (config_.use_gpu) {
-    place_ = paddle::platform::CUDAPlace(config_.device);
-    LOG(WARNING) << "ir optimize only supports CPU currently, enable_ir_optim "
-                    "is turned false.";
-    config_.enable_ir_optim = false;
-  } else {
-    place_ = paddle::platform::CPUPlace();
+  if (!PrepareScope(parent_scope)) {
+    return false;
+  }
+  if (!CreateExecutor()) {
+    return false;
+  }
+  if (!PrepareProgram(program)) {
+    return false;
+  }
+
+  // Prepare executor, create local variables.
+  if (!PrepareExecutor()) {
+    return true;
   }
+
+  // Get the feed_target_names and fetch_target_names
+  PrepareFeedFetch();
+
+  return true;
+}
+
+bool AnalysisPredictor::PrepareScope(
+    const std::shared_ptr<framework::Scope> &parent_scope) {
   if (parent_scope) {
+    PADDLE_ENFORCE_NOT_NULL(
+        parent_scope,
+        "Both program and parent_scope should be set in Clone mode.");
     scope_ = parent_scope;
-    sub_scope_ = &(parent_scope->NewScope());
+    status_is_cloned_ = true;
   } else {
     paddle::framework::InitDevices(false);
     scope_.reset(new paddle::framework::Scope());
+    status_is_cloned_ = false;
   }
-
-  executor_.reset(new paddle::framework::NaiveExecutor(place_));
-
+  sub_scope_ = &scope_->NewScope();
+  return true;
+}
+bool AnalysisPredictor::PrepareProgram(
+    const std::shared_ptr<framework::ProgramDesc> &program) {
   if (!program) {
     if (!LoadProgramDesc()) return false;
-    OptimizeInferenceProgram();
+
+    // Optimize the program, and load parameters and modify them in the
+    // scope_.
+    // This will change the scope_ address.
+    if (config_.enable_ir_optim) {
+      status_ir_optim_enabled_ = true;
+      OptimizeInferenceProgram();
+    } else {
+      // If the parent_scope is passed, we assert that the persistable variables
+      // are already created, so just create the no persistable variables.
+
+      // If not cloned, the parameters should be loaded
+      // OptimizeInferenceProgram.
+      // So in both cases, just the local variables are needed to load, not the
+      // parematers.
+      executor_->CreateVariables(*inference_program_, 0, true, sub_scope_);
+
+      // Load parameters
+      LOG(INFO) << "load parameters ";
+      LoadParameters();
+    }
   } else {
+    // If the program is passed from external, no need to optimize it, this
+    // logic is used in the clone scenario.
     inference_program_ = program;
   }
 
-  executor_->Prepare(scope_.get(), *inference_program_, 0,
+  executor_->CreateVariables(*inference_program_, 0, false, sub_scope_);
+
+  return true;
+}
+bool AnalysisPredictor::CreateExecutor() {
+  if (config_.use_gpu) {
+    status_use_gpu_ = true;
+    place_ = paddle::platform::CUDAPlace(config_.device);
+  } else {
+    place_ = paddle::platform::CPUPlace();
+  }
+  executor_.reset(new paddle::framework::NaiveExecutor(place_));
+  return true;
+}
+bool AnalysisPredictor::PrepareExecutor() {
+  executor_->Prepare(sub_scope_, *inference_program_, 0,
                      config_.use_feed_fetch_ops);
 
-  // Get the feed_target_names and fetch_target_names
-  PrepareFeedFetch();
+  PADDLE_ENFORCE_NOT_NULL(sub_scope_);
 
   return true;
 }
@@ -206,54 +280,40 @@ bool AnalysisPredictor::GetFetch(std::vector<PaddleTensor> *outputs,
   return true;
 }
 
+// NOTE All the members in AnalysisConfig should be copied to Argument.
 void AnalysisPredictor::OptimizeInferenceProgram() {
-  LOG(INFO) << "optimize begin";
-  FLAGS_IA_enable_ir = config_.enable_ir_optim;
-  FLAGS_IA_enable_tensorrt_subgraph_engine = false;
-  FLAGS_IA_output_storage_path = "";  // Don't output the model.
+  status_program_optimized_ = true;
+
+  argument_.SetUseGPU(config_.use_gpu);
   // Analyze inference_program
   if (!config_.model_dir.empty()) {
-    argument_.fluid_model_dir.reset(new std::string(config_.model_dir));
+    argument_.SetModelDir(config_.model_dir);
   } else {
     PADDLE_ENFORCE(
         !config_.param_file.empty(),
         "Either model_dir or (param_file, prog_file) should be set.");
     PADDLE_ENFORCE(!config_.prog_file.empty());
-    argument_.fluid_model_program_path.reset(
-        new std::string(config_.prog_file));
-    argument_.fluid_model_param_path.reset(new std::string(config_.param_file));
+    argument_.SetModelProgramPath(config_.prog_file);
+    argument_.SetModelParamsPath(config_.param_file);
   }
 
-  argument_.origin_program_desc.reset(
-      new ProgramDesc(*inference_program_->Proto()));
-
-  switch (config_.ir_mode) {
-    case contrib::AnalysisConfig::IrPassMode::kExclude:
-      Analyzer()
-          .IncludeAllIrPasses()
-          .SetUseMkldnn(config_._use_mkldnn)
-          .DisableIrPasses(config_.ir_passes)
-          .Run(&argument_);
-      break;
-    case contrib::AnalysisConfig::IrPassMode::kInclude:
-      Analyzer()
-          .SetUseMkldnn(config_._use_mkldnn)
-          .IncludeIrPasses(config_.ir_passes)
-          .Run(&argument_);
-      break;
-    default:
-      LOG(ERROR) << "Only kExclude and kInclude modes are supoorted yet.";
+  if (config_.use_gpu && config_.use_tensorrt_) {
+    argument_.SetUseTensorRT(true);
+    argument_.SetTensorRtWorkspaceSize(config_.tensorrt_workspace_size_);
+    argument_.SetTensorRtMaxBatchSize(config_.tensorrt_max_batchsize_);
   }
 
-  CHECK(argument_.transformed_program_desc);
-  VLOG(50) << "to prepare executor";
+  auto passes = config_.pass_builder()->AllPasses();
+  if (!config_.enable_ir_optim) passes.clear();
+  argument_.SetIrAnalysisPasses(passes);
+  argument_.SetScopeNotOwned(const_cast<framework::Scope *>(scope_.get()));
+  Analyzer().Run(&argument_);
+
+  PADDLE_ENFORCE(argument_.scope_valid());
+  VLOG(5) << "to prepare executor";
+  ARGUMENT_CHECK_FIELD((&argument_), ir_analyzed_program);
   inference_program_.reset(
-      new framework::ProgramDesc(*argument_.transformed_program_desc));
-  if (argument_.Has(framework::ir::kParamScopeAttr)) {
-    // Update scope.
-    scope_.reset(
-        argument_.Release<framework::Scope>(framework::ir::kParamScopeAttr));
-  }
+      new framework::ProgramDesc(argument_.ir_analyzed_program()));
   LOG(INFO) << "== optimize end ==";
 }
 
@@ -283,10 +343,12 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
   if (!dynamic_cast<AnalysisPredictor *>(predictor.get())->Init(nullptr)) {
     return nullptr;
   }
-  return predictor;
+  return std::move(predictor);
 }
 
 void AnalysisPredictor::PrepareFeedFetch() {
+  PADDLE_ENFORCE_NOT_NULL(sub_scope_);
+  CreateFeedFetchVar(sub_scope_);
   for (auto *op : inference_program_->Block(0).AllOps()) {
     if (op->Type() == "feed") {
       int idx = boost::get<int>(op->GetAttr("col"));
@@ -305,6 +367,14 @@ void AnalysisPredictor::PrepareFeedFetch() {
   }
 }
 
+void AnalysisPredictor::CreateFeedFetchVar(framework::Scope *scope) {
+  PADDLE_ENFORCE_NOT_NULL(scope);
+  auto *var = scope->Var("feed");
+  var->GetMutable<framework::FeedFetchList>();
+  var = scope->Var("fetch");
+  var->GetMutable<framework::FeedFetchList>();
+}
+
 std::unique_ptr<ZeroCopyTensor> AnalysisPredictor::GetInputTensor(
     const std::string &name) {
   PADDLE_ENFORCE(executor_->scope()->FindVar(name), "no name called %s", name);
@@ -335,27 +405,98 @@ bool AnalysisPredictor::ZeroCopyRun() {
 
 bool AnalysisPredictor::LoadProgramDesc() {
   // Initialize the inference program
-  std::unique_ptr<framework::Executor> tmp_exe(
-      new framework::Executor(platform::CPUPlace()));
+  std::string filename;
   if (!config_.model_dir.empty()) {
-    // Parameters are saved in separate files sited in
-    // the specified `dirname`.
-    inference_program_ = paddle::inference::Load(
-        static_cast<framework::Executor *>(tmp_exe.get()), scope_.get(),
-        config_.model_dir);
+    filename = config_.model_dir + "/__model__";
   } else if (!config_.prog_file.empty() && !config_.param_file.empty()) {
     // All parameters are saved in a single file.
     // The file names should be consistent with that used
     // in Python API `fluid.io.save_inference_model`.
-    inference_program_ = paddle::inference::Load(
-        static_cast<framework::Executor *>(tmp_exe.get()), scope_.get(),
-        config_.prog_file, config_.param_file);
+    filename = config_.prog_file;
   } else {
+    if (config_.model_dir.empty() && config_.prog_file.empty()) {
+      LOG(ERROR)
+          << "Either model_dir or (prog_file, param_file) should be set.";
+      return false;
+    }
     LOG(ERROR) << string::Sprintf(
         "not valid model path '%s' or program path '%s'.", config_.model_dir,
         config_.param_file);
     return false;
   }
+
+  std::string pb_content;
+  // Read binary
+  std::ifstream fin(filename, std::ios::in | std::ios::binary);
+  PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot open file %s", filename);
+  fin.seekg(0, std::ios::end);
+
+  pb_content.resize(fin.tellg());
+  fin.seekg(0, std::ios::beg);
+  fin.read(&(pb_content.at(0)), pb_content.size());
+  fin.close();
+
+  // Create ProgramDesc
+  framework::proto::ProgramDesc proto;
+  proto.ParseFromString(pb_content);
+  inference_program_.reset(new framework::ProgramDesc(proto));
+  return true;
+}
+
+bool AnalysisPredictor::LoadParameters() {
+  PADDLE_ENFORCE_NOT_NULL(inference_program_.get(),
+                          "The inference program should be loaded first.");
+  const auto &global_block = inference_program_->MutableBlock(0);
+
+  // create a temporary program to load parameters.
+
+  std::unique_ptr<framework::ProgramDesc> load_program(
+      new framework::ProgramDesc());
+  framework::BlockDesc *load_block = load_program->MutableBlock(0);
+  std::vector<std::string> params;
+
+  for (auto *var : global_block->AllVars()) {
+    if (IsPersistable(var)) {
+      VLOG(3) << "persistable variable's name: " << var->Name();
+
+      framework::VarDesc *new_var = load_block->Var(var->Name());
+      new_var->SetShape(var->GetShape());
+      new_var->SetDataType(var->GetDataType());
+      new_var->SetType(var->GetType());
+      new_var->SetLoDLevel(var->GetLoDLevel());
+      new_var->SetPersistable(true);
+
+      if (!config_.param_file.empty()) {
+        params.push_back(new_var->Name());
+      } else {
+        // append_op
+        framework::OpDesc *op = load_block->AppendOp();
+        op->SetType("load");
+        op->SetOutput("Out", {new_var->Name()});
+        op->SetAttr("file_path", {config_.model_dir + "/" + new_var->Name()});
+        op->CheckAttrs();
+      }
+    }
+  }
+
+  if (!config_.param_file.empty()) {
+    // sort paramlist to have consistent ordering
+    std::sort(params.begin(), params.end());
+    // append just the load_combine op
+    framework::OpDesc *op = load_block->AppendOp();
+    op->SetType("load_combine");
+    op->SetOutput("Out", params);
+    op->SetAttr("file_path", {config_.param_file});
+    op->CheckAttrs();
+  }
+
+  // Use NaiveExecutor to Load parameters.
+  platform::CPUPlace place;
+  framework::NaiveExecutor e(place);
+  e.Prepare(scope_.get(), *load_program, 0, false);
+  e.Run();
+  VLOG(3) << "get " << scope_->LocalVarNames().size() << " vars after load";
+
   return true;
 }
 
@@ -385,3 +526,26 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<contrib::AnalysisConfig>(
 }
 
 }  // namespace paddle
+
+#if PADDLE_WITH_TENSORRT
+USE_TRT_CONVERTER(elementwise_add_weight);
+USE_TRT_CONVERTER(elementwise_add_tensor);
+USE_TRT_CONVERTER(elementwise_sub_tensor);
+USE_TRT_CONVERTER(elementwise_div_tensor);
+USE_TRT_CONVERTER(elementwise_mul_tensor);
+USE_TRT_CONVERTER(elementwise_max_tensor);
+USE_TRT_CONVERTER(elementwise_min_tensor);
+USE_TRT_CONVERTER(elementwise_pow_tensor);
+USE_TRT_CONVERTER(mul);
+USE_TRT_CONVERTER(conv2d);
+USE_TRT_CONVERTER(relu);
+USE_TRT_CONVERTER(sigmoid);
+USE_TRT_CONVERTER(tanh);
+USE_TRT_CONVERTER(fc);
+USE_TRT_CONVERTER(pool2d);
+USE_TRT_CONVERTER(softmax);
+USE_TRT_CONVERTER(batch_norm);
+USE_TRT_CONVERTER(concat);
+USE_TRT_CONVERTER(dropout);
+USE_TRT_CONVERTER(pad);
+#endif

paddle/fluid/inference/api/analysis_predictor.h

@@ -23,7 +23,10 @@
 #include "paddle/fluid/inference/api/details/reset_tensor_array.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
 #include "paddle/fluid/string/printf.h"
-
+#ifdef PADDLE_WITH_TESTING
+#include <gtest/gtest.h>
+#include <gtest/gtest_prod.h>
+#endif
 namespace paddle {
 
 using inference::analysis::Argument;
@@ -54,6 +57,7 @@ class AnalysisPredictor : public PaddlePredictor {
 
   bool ZeroCopyRun() override;
 
+  void CreateFeedFetchVar(framework::Scope *scope);
   void PrepareFeedFetch();
 
   void OptimizeInferenceProgram();
@@ -62,11 +66,17 @@ class AnalysisPredictor : public PaddlePredictor {
 
   std::unique_ptr<PaddlePredictor> Clone() override;
 
-  framework::Scope *scope() { return executor_->scope(); }
+  framework::Scope *scope() { return scope_.get(); }
   framework::ProgramDesc &program() { return *inference_program_; }
 
  protected:
+  bool PrepareProgram(const std::shared_ptr<framework::ProgramDesc> &program);
+  bool PrepareScope(const std::shared_ptr<framework::Scope> &parent_scope);
+  bool CreateExecutor();
+  bool PrepareExecutor();
+
   bool LoadProgramDesc();
+  bool LoadParameters();
 
   bool SetFeed(const std::vector<PaddleTensor> &input_datas,
                framework::Scope *scope);
@@ -77,6 +87,14 @@ class AnalysisPredictor : public PaddlePredictor {
                    PaddleTensor *output_data);
   ~AnalysisPredictor();
 
+// Some more detailed tests, they are made the friends of the predictor, so that
+// the all the details can be tested.
+#if PADDLE_WITH_TESTING
+  FRIEND_TEST(AnalysisPredictor, analysis_off);
+  FRIEND_TEST(AnalysisPredictor, analysis_on);
+  FRIEND_TEST(AnalysisPredictor, with_gpu);
+#endif
+
  private:
   contrib::AnalysisConfig config_;
   Argument argument_;
@@ -92,6 +110,13 @@ class AnalysisPredictor : public PaddlePredictor {
   // concurrency problems, so cache them.
   std::vector<framework::LoDTensor> feed_tensors_;
   details::TensorArrayBatchCleaner tensor_array_batch_cleaner_;
+
+ private:
+  // Some status here that help to determine the status inside the predictor.
+  bool status_program_optimized_{false};
+  bool status_is_cloned_{false};
+  bool status_use_gpu_{false};
+  bool status_ir_optim_enabled_{false};
 };
 
 }  // namespace paddle

paddle/fluid/inference/api/analysis_predictor_tester.cc

@@ -12,16 +12,85 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+#include "paddle/fluid/inference/api/analysis_predictor.h"
 #include <glog/logging.h>
 #include <gtest/gtest.h>
+#include <thread>
+#include "paddle/fluid/inference/api/helper.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
 
 DEFINE_string(dirname, "", "dirname to tests.");
 
 namespace paddle {
-namespace inference {
 using contrib::AnalysisConfig;
 
+TEST(AnalysisPredictor, analysis_off) {
+  AnalysisConfig config(false);
+  config.model_dir = FLAGS_dirname;
+  config.enable_ir_optim = false;
+
+  auto _predictor = CreatePaddlePredictor<AnalysisConfig>(config);
+  auto* predictor = static_cast<AnalysisPredictor*>(_predictor.get());
+
+  // Without analysis, the scope_ and sub_scope_ are created by predictor
+  // itself.
+  ASSERT_TRUE(predictor->scope_);
+  ASSERT_TRUE(predictor->sub_scope_);
+  ASSERT_EQ(predictor->scope_->parent(), nullptr);
+  ASSERT_EQ(predictor->sub_scope_->parent(), predictor->scope_.get());
+  // ir is turned off, so program shouldn't be optimized.
+  ASSERT_FALSE(predictor->status_program_optimized_);
+  LOG(INFO) << "scope parameters " << predictor->scope_->LocalVarNames().size();
+
+  // 2. Dummy Input Data
+  int64_t data[4] = {1, 2, 3, 4};
+  PaddleTensor tensor;
+  tensor.shape = std::vector<int>({4, 1});
+  tensor.data.Reset(data, sizeof(data));
+  tensor.dtype = PaddleDType::INT64;
+
+  std::vector<PaddleTensor> inputs(4, tensor);
+  std::vector<PaddleTensor> outputs;
+  ASSERT_TRUE(predictor->Run(inputs, &outputs));
+}
+
+TEST(AnalysisPredictor, analysis_on) {
+  AnalysisConfig config(false);
+  config.model_dir = FLAGS_dirname;
+  config.enable_ir_optim = true;
+
+  auto _predictor = CreatePaddlePredictor<AnalysisConfig>(config);
+  auto* predictor = static_cast<AnalysisPredictor*>(_predictor.get());
+
+  ASSERT_TRUE(predictor->scope_);
+  ASSERT_TRUE(predictor->sub_scope_);
+  ASSERT_EQ(predictor->scope_->parent(), nullptr);
+  ASSERT_EQ(predictor->sub_scope_->parent(), predictor->scope_.get());
+  // ir is turned on, so program should be optimized.
+  ASSERT_TRUE(predictor->status_program_optimized_);
+  // 2. Dummy Input Data
+  int64_t data[4] = {1, 2, 3, 4};
+  PaddleTensor tensor;
+  tensor.shape = std::vector<int>({4, 1});
+  tensor.data.Reset(data, sizeof(data));
+  tensor.dtype = PaddleDType::INT64;
+
+  std::vector<PaddleTensor> inputs(4, tensor);
+  std::vector<PaddleTensor> outputs;
+  ASSERT_TRUE(predictor->Run(inputs, &outputs));
+
+  for (auto& output : outputs) {
+    LOG(INFO) << inference::DescribeTensor(output);
+  }
+
+  // compare with NativePredictor
+  auto naive_predictor = CreatePaddlePredictor<NativeConfig>(config);
+  std::vector<PaddleTensor> naive_outputs;
+  ASSERT_TRUE(naive_predictor->Run(inputs, &naive_outputs));
+  ASSERT_EQ(naive_outputs.size(), 1UL);
+  inference::CompareTensor(outputs.front(), naive_outputs.front());
+}
+
 TEST(AnalysisPredictor, ZeroCopy) {
   AnalysisConfig config;
   config.model_dir = FLAGS_dirname;
@@ -61,5 +130,59 @@ TEST(AnalysisPredictor, ZeroCopy) {
   LOG(INFO) << "output_data: " << out_data;
 }
 
-}  // namespace inference
+TEST(AnalysisPredictor, Clone) {
+  AnalysisConfig config;
+  config.model_dir = FLAGS_dirname;
+  config.use_feed_fetch_ops = true;
+  config.enable_ir_optim = true;
+
+  std::vector<std::unique_ptr<PaddlePredictor>> predictors;
+  predictors.emplace_back(CreatePaddlePredictor(config));
+
+  LOG(INFO) << "************** to clone ************************";
+  const int num_threads = 3;
+  for (int i = 1; i < num_threads; i++) {
+    predictors.emplace_back(predictors.front()->Clone());
+  }
+
+  auto* root_scope =
+      static_cast<AnalysisPredictor*>(predictors[0].get())->scope();
+  ASSERT_FALSE(root_scope->kids().empty());
+  LOG(INFO) << "***** scope ******\n"
+            << framework::GenScopeTreeDebugInfo(root_scope);
+
+  // 2. Dummy Input Data
+  int64_t data[4] = {1, 2, 3, 4};
+  PaddleTensor tensor;
+  tensor.shape = std::vector<int>({4, 1});
+  tensor.data.Reset(data, sizeof(data));
+  tensor.dtype = PaddleDType::INT64;
+
+  std::vector<PaddleTensor> inputs(4, tensor);
+  std::vector<PaddleTensor> outputs;
+  predictors[0]->Run(inputs, &outputs);
+
+  LOG(INFO) << "Run with single thread";
+  for (int i = 0; i < num_threads; i++) {
+    LOG(INFO) << "run predictor " << i;
+    ASSERT_TRUE(predictors[i]->Run(inputs, &outputs));
+  }
+
+  LOG(INFO) << "Run with multiple threads";
+  std::vector<std::thread> threads;
+  for (int i = 0; i < num_threads; i++) {
+    threads.emplace_back([&predictors, &inputs, i] {
+      LOG(INFO) << "thread #" << i << " running";
+      std::vector<PaddleTensor> outputs;
+      for (int j = 0; j < 10; j++) {
+        ASSERT_TRUE(predictors[i]->Run(inputs, &outputs));
+      }
+    });
+  }
+
+  for (auto& t : threads) {
+    t.join();
+  }
+}
+
 }  // namespace paddle

paddle/fluid/inference/api/api.cc

@@ -15,6 +15,7 @@
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
+#include "paddle/fluid/inference/api/paddle_pass_builder.h"
 #include "paddle/fluid/platform/enforce.h"
 
 namespace paddle {

paddle/fluid/inference/api/api_anakin_engine.h

@@ -19,11 +19,13 @@ limitations under the License. */
 
 #pragma once
 
+#define WITH_ANAKIN
+
 #include <vector>
 
 #include "framework/core/net/net.h"
 #include "framework/graph/graph.h"
-#include "paddle/fluid/inference/api/paddle_inference_api.h"
+#include "paddle/fluid/inference/api/paddle_anakin_config.h"
 #include "saber/core/shape.h"
 #include "saber/saber_types.h"
 

paddle/fluid/inference/api/api_impl_tester.cc

@@ -292,7 +292,14 @@ TEST(inference_api_native, image_classification_gpu) {
 // TEST(inference_api_native, image_classification_gpu_threads) {
 //   MainThreadsImageClassification(true /*use_gpu*/);
 // }
-
 #endif
 
+TEST(PassBuilder, Delete) {
+  contrib::AnalysisConfig config(false);
+  config.pass_builder()->DeletePass("attention_lstm_fuse_pass");
+  const auto& passes = config.pass_builder()->AllPasses();
+  auto it = std::find(passes.begin(), passes.end(), "attention_lstm_fuse_pass");
+  ASSERT_EQ(it, passes.end());
+}
+
 }  // namespace paddle

paddle/fluid/inference/api/api_tensorrt_subgraph_engine.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/analyzer.h"
-#include "paddle/fluid/inference/api/api_impl.h"
-#include "paddle/fluid/inference/api/paddle_inference_api.h"
-#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
-#include "paddle/fluid/inference/utils/singleton.h"
-#include "paddle/fluid/operators/tensorrt_engine_op.h"
-
-namespace paddle {
-
-using inference::analysis::Argument;
-using inference::Singleton;
-using inference::analysis::Analyzer;
-using framework::proto::ProgramDesc;
-using paddle::contrib::MixedRTConfig;
-
-class TensorRTSubgraphPredictor : public NativePaddlePredictor {
- public:
-  explicit TensorRTSubgraphPredictor(const MixedRTConfig& config)
-      : NativePaddlePredictor(config), config_(config) {}
-
-  bool Init(const std::shared_ptr<framework::Scope>& parent_scope) {
-    FLAGS_IA_enable_tensorrt_subgraph_engine = true;
-    VLOG(30) << "Predictor::init()";
-    if (config_.use_gpu) {
-      place_ = paddle::platform::CUDAPlace(config_.device);
-    } else {
-      place_ = paddle::platform::CPUPlace();
-    }
-    if (parent_scope) {
-      scope_ = parent_scope;
-      sub_scope_ = &(parent_scope->NewScope());
-    } else {
-      paddle::framework::InitDevices(false);
-      scope_.reset(new paddle::framework::Scope());
-    }
-
-    executor_.reset(new paddle::framework::Executor(place_));
-
-    // Initialize the inference program
-    if (!config_.model_dir.empty()) {
-      // Parameters are saved in separate files sited in
-      // the specified `dirname`.
-      inference_program_ = paddle::inference::Load(
-          executor_.get(), scope_.get(), config_.model_dir);
-    } else if (!config_.prog_file.empty() && !config_.param_file.empty()) {
-      // All parameters are saved in a single file.
-      // The file names should be consistent with that used
-      // in Python API `fluid.io.save_inference_model`.
-      inference_program_ = paddle::inference::Load(
-          executor_.get(), scope_.get(), config_.prog_file, config_.param_file);
-    } else {
-      LOG(ERROR) << "fail to load inference model.";
-      return false;
-    }
-
-    OptimizeInferenceProgram();
-    ctx_ = executor_->Prepare(*inference_program_, 0);
-
-    VLOG(50) << "to create variables";
-    executor_->CreateVariables(*inference_program_,
-                               sub_scope_ ? sub_scope_ : scope_.get(), 0);
-    // Get the feed_target_names and fetch_target_names
-    PrepareFeedFetch();
-    return true;
-  }
-
-  bool Run(const std::vector<PaddleTensor>& inputs,
-           std::vector<PaddleTensor>* output_data,
-           int batch_size = -1) override {
-    PADDLE_ENFORCE_GT(batch_size, 0,
-                      "TensorRT engine needs the argument batch_size set");
-    FLAGS_tensorrt_engine_batch_size = batch_size;
-    return NativePaddlePredictor::Run(inputs, output_data, batch_size);
-  }
-
-  void OptimizeInferenceProgram() {
-    // Analyze inference_program
-    Argument argument;
-
-    argument.Set<int>("minimum_subgraph_size",
-                      new int(config_.minimum_subgraph_size));
-    argument.Set<int>("max_batch_size", new int(config_.max_batch_size));
-    argument.Set<int>("workspace_size", new int(config_.workspace_size));
-    argument.Set<std::string>("precision_mode",
-                              new std::string(config_.precision_mode));
-
-    if (!config_.model_dir.empty()) {
-      argument.fluid_model_dir.reset(new std::string(config_.model_dir));
-    } else {
-      PADDLE_ENFORCE(
-          !config_.param_file.empty(),
-          "Either model_dir or (param_file, prog_file) should be set.");
-      PADDLE_ENFORCE(!config_.prog_file.empty());
-      argument.fluid_model_program_path.reset(
-          new std::string(config_.prog_file));
-      argument.fluid_model_param_path.reset(
-          new std::string(config_.param_file));
-    }
-    argument.origin_program_desc.reset(
-        new ProgramDesc(*inference_program_->Proto()));
-    Singleton<Analyzer>::Global().Run(&argument);
-    CHECK(argument.transformed_program_desc);
-    VLOG(50) << "transformed program:\n"
-             << argument.transformed_program_desc->SerializeAsString();
-    VLOG(50) << "to prepare executor";
-    inference_program_.reset(
-        new framework::ProgramDesc(*argument.transformed_program_desc));
-  }
-
- private:
-  MixedRTConfig config_;
-};
-
-template <>
-std::unique_ptr<PaddlePredictor>
-CreatePaddlePredictor<MixedRTConfig, PaddleEngineKind::kAutoMixedTensorRT>(
-    const MixedRTConfig& config) {
-  VLOG(30) << "create TensorRTSubgraphPredictor";
-  if (config.use_gpu) {
-    // 1. GPU memeroy
-    PADDLE_ENFORCE_GT(
-        config.fraction_of_gpu_memory, 0.f,
-        "fraction_of_gpu_memory in the config should be set to range (0., 1.]");
-    PADDLE_ENFORCE_GE(config.device, 0, "Invalid device id %d", config.device);
-    std::vector<std::string> flags;
-    if (config.fraction_of_gpu_memory >= 0.0f ||
-        config.fraction_of_gpu_memory <= 0.95f) {
-      flags.push_back("dummpy");
-      std::string flag = "--fraction_of_gpu_memory_to_use=" +
-                         std::to_string(config.fraction_of_gpu_memory);
-      flags.push_back(flag);
-      VLOG(30) << "set flag: " << flag;
-      framework::InitGflags(flags);
-    }
-  }
-
-  std::unique_ptr<PaddlePredictor> predictor(
-      new TensorRTSubgraphPredictor(config));
-  if (!dynamic_cast<TensorRTSubgraphPredictor*>(predictor.get())
-           ->Init(nullptr)) {
-    return nullptr;
-  }
-  return std::move(predictor);
-}
-
-template <>
-std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<MixedRTConfig>(
-    const MixedRTConfig& config) {
-  return CreatePaddlePredictor<MixedRTConfig,
-                               PaddleEngineKind::kAutoMixedTensorRT>(config);
-}
-
-}  // namespace paddle
-
-USE_TRT_CONVERTER(elementwise_add_weight);
-USE_TRT_CONVERTER(elementwise_add_tensor);
-USE_TRT_CONVERTER(elementwise_sub_tensor);
-USE_TRT_CONVERTER(elementwise_div_tensor);
-USE_TRT_CONVERTER(elementwise_mul_tensor);
-USE_TRT_CONVERTER(elementwise_max_tensor);
-USE_TRT_CONVERTER(elementwise_min_tensor);
-USE_TRT_CONVERTER(elementwise_pow_tensor);
-USE_TRT_CONVERTER(mul);
-USE_TRT_CONVERTER(conv2d);
-USE_TRT_CONVERTER(relu);
-USE_TRT_CONVERTER(sigmoid);
-USE_TRT_CONVERTER(tanh);
-USE_TRT_CONVERTER(fc);
-USE_TRT_CONVERTER(pool2d);
-USE_TRT_CONVERTER(softmax);
-USE_TRT_CONVERTER(batch_norm);
-USE_TRT_CONVERTER(concat);
-USE_TRT_CONVERTER(dropout);
-USE_TRT_CONVERTER(pad);

paddle/fluid/inference/api/api_tensorrt_subgraph_engine_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 <gflags/gflags.h>
-#include <glog/logging.h>
-#include <gtest/gtest.h>
-#include "paddle/fluid/inference/analysis/analyzer.h"
-#include "paddle/fluid/inference/api/paddle_inference_api.h"
-
-namespace paddle {
-
-using contrib::MixedRTConfig;
-
-DEFINE_string(dirname, "", "Directory of the inference model.");
-
-void CompareTensorRTWithFluid(bool enable_tensorrt) {
-  FLAGS_IA_enable_tensorrt_subgraph_engine = enable_tensorrt;
-
-  //# 1. Create PaddlePredictor with a config.
-  NativeConfig config0;
-  config0.model_dir = FLAGS_dirname;
-  config0.use_gpu = true;
-  config0.fraction_of_gpu_memory = 0.3;
-  config0.device = 0;
-
-  MixedRTConfig config1;
-  config1.model_dir = FLAGS_dirname;
-  config1.use_gpu = true;
-  config1.fraction_of_gpu_memory = 0.3;
-  config1.device = 0;
-  config1.max_batch_size = 10;
-
-  auto predictor0 = CreatePaddlePredictor<NativeConfig>(config0);
-  auto predictor1 = CreatePaddlePredictor<MixedRTConfig>(config1);
-
-  for (int batch_id = 0; batch_id < 1; batch_id++) {
-    //# 2. Prepare input.
-    std::vector<int64_t> data(20);
-    for (int i = 0; i < 20; i++) data[i] = i;
-
-    PaddleTensor tensor;
-    tensor.shape = std::vector<int>({10, 1});
-    tensor.data = PaddleBuf(data.data(), data.size() * sizeof(int64_t));
-    tensor.dtype = PaddleDType::INT64;
-
-    // For simplicity, we set all the slots with the same data.
-    std::vector<PaddleTensor> slots(4, tensor);
-
-    //# 3. Run
-    std::vector<PaddleTensor> outputs0;
-    std::vector<PaddleTensor> outputs1;
-    CHECK(predictor0->Run(slots, &outputs0));
-    CHECK(predictor1->Run(slots, &outputs1, 10));
-
-    //# 4. Get output.
-    ASSERT_EQ(outputs0.size(), 1UL);
-    ASSERT_EQ(outputs1.size(), 1UL);
-
-    const size_t num_elements = outputs0.front().data.length() / sizeof(float);
-    const size_t num_elements1 = outputs1.front().data.length() / sizeof(float);
-    EXPECT_EQ(num_elements, num_elements1);
-
-    auto *data0 = static_cast<float *>(outputs0.front().data.data());
-    auto *data1 = static_cast<float *>(outputs1.front().data.data());
-
-    ASSERT_GT(num_elements, 0UL);
-    for (size_t i = 0; i < std::min(num_elements, num_elements1); i++) {
-      EXPECT_NEAR(data0[i], data1[i], 1e-3);
-    }
-  }
-}
-
-TEST(paddle_inference_api_tensorrt_subgraph_engine, without_tensorrt) {
-  CompareTensorRTWithFluid(false);
-}
-
-TEST(paddle_inference_api_tensorrt_subgraph_engine, with_tensorrt) {
-  CompareTensorRTWithFluid(true);
-}
-
-}  // namespace paddle

paddle/fluid/inference/api/demo_ci/simple_on_word2vec.cc

@@ -23,7 +23,7 @@ limitations under the License. */
 #include <memory>
 #include <thread>  //NOLINT
 
-#include "paddle/include/paddle_inference_api.h"
+#include "utils.h"
 
 DEFINE_string(dirname, "", "Directory of the inference model.");
 DEFINE_bool(use_gpu, false, "Whether use gpu.");

paddle/fluid/inference/api/demo_ci/trt_mobilenet_demo.cc

@@ -36,14 +36,13 @@ namespace demo {
  */
 void Main() {
   std::unique_ptr<PaddlePredictor> predictor;
-  paddle::contrib::MixedRTConfig config;
+  paddle::contrib::AnalysisConfig config(true);
   config.param_file = FLAGS_modeldir + "/__params__";
   config.prog_file = FLAGS_modeldir + "/__model__";
-  config.use_gpu = true;
   config.device = 0;
-  config.max_batch_size = 1;
+  config.EnableTensorRtEngine();
   config.fraction_of_gpu_memory = 0.1;  // set by yourself
-  predictor = CreatePaddlePredictor<paddle::contrib::MixedRTConfig>(config);
+  predictor = CreatePaddlePredictor(config);
 
   VLOG(30) << "begin to process data";
   // Just a single batch of data.

paddle/fluid/inference/api/demo_ci/vis_demo.cc

@@ -17,7 +17,7 @@ limitations under the License. */
  */
 
 #include <gflags/gflags.h>
-#include <glog/logging.h>  // use glog instead of CHECK to avoid importing other paddle header files.
+#include <glog/logging.h>
 #include "utils.h"  // NOLINT
 
 #ifdef PADDLE_WITH_CUDA
@@ -40,20 +40,17 @@ using contrib::AnalysisConfig;
  */
 void Main(bool use_gpu) {
   std::unique_ptr<PaddlePredictor> predictor, analysis_predictor;
-  AnalysisConfig config;
+  AnalysisConfig config(use_gpu);
   config.param_file = FLAGS_modeldir + "/__params__";
   config.prog_file = FLAGS_modeldir + "/__model__";
-  config.use_gpu = use_gpu;
   config.device = 0;
   if (FLAGS_use_gpu) {
     config.fraction_of_gpu_memory = 0.1;  // set by yourself
   }
 
-  VLOG(30) << "init predictor";
   predictor = CreatePaddlePredictor<NativeConfig>(config);
-  analysis_predictor = CreatePaddlePredictor<AnalysisConfig>(config);
+  analysis_predictor = CreatePaddlePredictor(config);
 
-  VLOG(30) << "begin to process data";
   // Just a single batch of data.
   std::string line;
   std::ifstream file(FLAGS_data);
@@ -68,13 +65,10 @@ void Main(bool use_gpu) {
       PaddleBuf(record.data.data(), record.data.size() * sizeof(float));
   input.dtype = PaddleDType::FLOAT32;
 
-  VLOG(30) << "run executor";
   std::vector<PaddleTensor> output, analysis_output;
   predictor->Run({input}, &output, 1);
 
-  VLOG(30) << "output.size " << output.size();
   auto& tensor = output.front();
-  VLOG(30) << "output: " << SummaryTensor(tensor);
 
   // compare with reference result
   CheckOutput(FLAGS_refer, tensor);

paddle/fluid/inference/api/details/zero_copy_tensor.cc

@@ -51,7 +51,7 @@ T *ZeroCopyTensor::mutable_data(PaddlePlace place) {
 }
 
 template <typename T>
-T *ZeroCopyTensor::data(PaddlePlace *place, int *size) {
+T *ZeroCopyTensor::data(PaddlePlace *place, int *size) const {
   auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
   auto *res = tensor->data<T>();
 
@@ -67,8 +67,10 @@ T *ZeroCopyTensor::data(PaddlePlace *place, int *size) {
   return res;
 }
 
-template float *ZeroCopyTensor::data<float>(PaddlePlace *place, int *size);
-template int64_t *ZeroCopyTensor::data<int64_t>(PaddlePlace *place, int *size);
+template float *ZeroCopyTensor::data<float>(PaddlePlace *place,
+                                            int *size) const;
+template int64_t *ZeroCopyTensor::data<int64_t>(PaddlePlace *place,
+                                                int *size) const;
 template float *ZeroCopyTensor::mutable_data<float>(PaddlePlace place);
 template int64_t *ZeroCopyTensor::mutable_data<int64_t>(PaddlePlace place);
 
@@ -84,7 +86,7 @@ void *ZeroCopyTensor::FindTensor() const {
   return tensor;
 }
 
-std::vector<int64_t> ZeroCopyTensor::shape() {
+std::vector<int64_t> ZeroCopyTensor::shape() const {
   auto *tensor = static_cast<framework::LoDTensor *>(FindTensor());
   PADDLE_ENFORCE(tensor, "not found tensor called %s in the scope", name_);
   return framework::vectorize(tensor->dims());

paddle/fluid/inference/api/details/zero_copy_tensor_dummy.cc

@@ -24,18 +24,20 @@ T *ZeroCopyTensor::mutable_data(PaddlePlace place) {
 }
 
 template <typename T>
-T *ZeroCopyTensor::data(PaddlePlace *place, int *size) {
+T *ZeroCopyTensor::data(PaddlePlace *place, int *size) const {
   return nullptr;
 }
 
-template float *ZeroCopyTensor::data<float>(PaddlePlace *place, int *size);
-template int64_t *ZeroCopyTensor::data<int64_t>(PaddlePlace *place, int *size);
+template float *ZeroCopyTensor::data<float>(PaddlePlace *place,
+                                            int *size) const;
+template int64_t *ZeroCopyTensor::data<int64_t>(PaddlePlace *place,
+                                                int *size) const;
 template float *ZeroCopyTensor::mutable_data(PaddlePlace place);
 template int64_t *ZeroCopyTensor::mutable_data(PaddlePlace place);
 
 void *ZeroCopyTensor::FindTensor() const { return nullptr; }
 
-std::vector<int64_t> ZeroCopyTensor::shape() { return {}; }
+std::vector<int64_t> ZeroCopyTensor::shape() const { return {}; }
 
 void ZeroCopyTensor::SetLoD(const std::vector<std::vector<size_t>> &x) {}
 

paddle/fluid/inference/api/helper.h

@@ -130,6 +130,51 @@ static int ZeroCopyTensorAssignData(ZeroCopyTensor *tensor,
   return size;
 }
 
+static bool CompareTensor(const PaddleTensor &a, const PaddleTensor &b) {
+  if (a.dtype != b.dtype) {
+    LOG(ERROR) << "dtype not match";
+    return false;
+  }
+
+  if (a.lod.size() != b.lod.size()) {
+    LOG(ERROR) << "lod not match";
+    return false;
+  }
+  for (size_t i = 0; i < a.lod.size(); i++) {
+    if (a.lod[i].size() != b.lod[i].size()) {
+      LOG(ERROR) << "lod not match";
+      return false;
+    }
+    for (size_t j = 0; j < a.lod[i].size(); j++) {
+      if (a.lod[i][j] != b.lod[i][j]) {
+        LOG(ERROR) << "lod not match";
+        return false;
+      }
+    }
+  }
+
+  if (a.shape.size() != b.shape.size()) {
+    LOG(INFO) << "shape not match";
+    return false;
+  }
+  for (size_t i = 0; i < a.shape.size(); i++) {
+    if (a.shape[i] != b.shape[i]) {
+      LOG(ERROR) << "shape not match";
+      return false;
+    }
+  }
+
+  auto *adata = static_cast<float *>(a.data.data());
+  auto *bdata = static_cast<float *>(b.data.data());
+  for (int i = 0; i < VecReduceToInt(a.shape); i++) {
+    if (adata[i] != bdata[i]) {
+      LOG(ERROR) << "data not match";
+      return false;
+    }
+  }
+  return true;
+}
+
 static std::string DescribeTensor(const PaddleTensor &tensor) {
   std::stringstream os;
   os << "Tensor [" << tensor.name << "]\n";
@@ -162,6 +207,26 @@ static std::string DescribeTensor(const PaddleTensor &tensor) {
   return os.str();
 }
 
+static std::string DescribeZeroCopyTensor(const ZeroCopyTensor &tensor) {
+  std::stringstream os;
+  os << "Tensor [" << tensor.name() << "]\n";
+
+  os << " - shape: " << to_string(tensor.shape()) << '\n';
+  os << " - lod: ";
+  for (auto &l : tensor.lod()) {
+    os << to_string(l) << "; ";
+  }
+  os << "\n";
+  os << " - data: ";
+  PaddlePlace place;
+  int size;
+  const auto *data = tensor.data<float>(&place, &size);
+  for (int i = 0; i < size; i++) {
+    os << data[i] << " ";
+  }
+  return os.str();
+}
+
 static void PrintTime(int batch_size, int repeat, int num_threads, int tid,
                       double latency, int epoch = 1) {
   LOG(INFO) << "====== batch_size: " << batch_size << ", repeat: " << repeat

paddle/fluid/inference/analysis/analyzer_main.cc → paddle/fluid/inference/api/paddle_anakin_config.h

@@ -11,23 +11,25 @@
 // 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
 
-/*
- * This file implements analysizer -- an executation help to analyze and
- * optimize trained model.
- */
-#include "paddle/fluid/inference/analysis/analyzer.h"
-#include <gflags/gflags.h>
-#include <glog/logging.h>
+#include <cassert>
+#include <memory>
+#include <string>
+#include <vector>
 
-int main(int argc, char** argv) {
-  google::ParseCommandLineFlags(&argc, &argv, true);
-  using paddle::inference::analysis::Analyzer;
-  using paddle::inference::analysis::Argument;
+#include "paddle_api.h"  // NOLINT
 
-  Argument argument;
-  Analyzer analyzer;
-  analyzer.Run(&argument);
+namespace paddle {
+namespace contrib {
+// Configurations for Anakin engine.
+struct AnakinConfig : public PaddlePredictor::Config {
+  enum TargetType { NVGPU = 0, X86 };
+  int device;
+  std::string model_file;
+  int max_batch_size{-1};
+  TargetType target_type;
+};
 
-  return 0;
-}
+}  // namespace contrib
+}  // namespace paddle

paddle/fluid/inference/api/paddle_analysis_config.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 <cassert>
+#include <memory>
+#include <string>
+#include <vector>
+
+// Here we include some header files with relative paths, for that in deploy,
+// the abstract path of this header file will be changed.
+#include "paddle_api.h"           // NOLINT
+#include "paddle_pass_builder.h"  // NOLINT
+
+namespace paddle {
+
+class AnalysisPredictor;
+// ==
+//
+// -----------------------------------------------------------------------------------
+// NOTE: The following APIs are not mature yet, we are still working on them.
+namespace contrib {
+
+// NOTE WIP, not stable yet.
+struct AnalysisConfig : public NativeConfig {
+  explicit AnalysisConfig(bool use_gpu = false);
+  explicit AnalysisConfig(const AnalysisConfig& other);
+  explicit AnalysisConfig(AnalysisConfig&& other);
+
+  // Determine whether to perform graph optimization.
+  bool enable_ir_optim = true;
+
+  // Get a pass builder for customize the passes in IR analysis phase.
+  PassStrategy* pass_builder() const;
+
+  // NOT stable yet.
+  bool use_feed_fetch_ops{true};
+
+  void EnableTensorRtEngine(int workspace_size = 1 << 20,
+                            int max_batch_size = 1);
+  // NOTE this is just for internal development, please not use it.
+  // NOT stable yet.
+  void EnableMKLDNN();
+  bool use_mkldnn() const { return use_mkldnn_; }
+
+  friend class ::paddle::AnalysisPredictor;
+
+ protected:
+  bool use_tensorrt_{false};
+  bool use_mkldnn_{false};
+  int tensorrt_workspace_size_;
+  int tensorrt_max_batchsize_;
+  std::unique_ptr<PassStrategy> pass_builder_;
+};
+
+// Configurations for Anakin engine.
+struct AnakinConfig : public PaddlePredictor::Config {
+  enum TargetType { NVGPU = 0, X86 };
+  int device;
+  std::string model_file;
+  int max_batch_size{-1};
+  TargetType target_type;
+};
+
+}  // namespace contrib
+}  // namespace paddle

paddle/fluid/inference/api/paddle_api.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 <cassert>
+#include <memory>
+#include <string>
+#include <vector>
+
+namespace paddle {
+
+// Data type.
+enum PaddleDType {
+  FLOAT32,
+  INT64,
+  // TODO(Superjomn) support more data types if needed.
+};
+
+/*
+ * Memory menage for PaddleTensor.
+ * The PaddleBuf holds a buffer for data input or output. The memory can be
+ * allocated by user or by PaddleBuf itself, but in any case, the PaddleBuf
+ * should be reused for better performance.
+ *
+ * For user allocated memory, the following API can be used:
+ * - PaddleBuf(void* data, size_t length) to set an external memory by
+ * specifying
+ *   the memory address and length.
+ * - Reset(void* data, size_t length) to reset the PaddleBuf with an external
+ * memory.
+ * ATTENTION, for user allocated memory, deallocation should be done by users
+ * externally after the program finished. The PaddleBuf won't do any allocation
+ * or deallocation.
+ *
+ * To have the PaddleBuf allocate and manage the memory:
+ * - PaddleBuf(size_t length) will allocate a memory of size `length`.
+ * - Resize(size_t length) resize the memory to no less than `length`, ATTENTION
+ *   if the allocated memory is larger than `length`, nothing will done.
+ */
+class PaddleBuf {
+ public:
+  // PaddleBuf allocate memory internally, and manage it.
+  explicit PaddleBuf(size_t length)
+      : data_(new char[length]), length_(length), memory_owned_(true) {}
+  // Set external memory, the PaddleBuf won't manage it.
+  PaddleBuf(void* data, size_t length)
+      : data_(data), length_(length), memory_owned_{false} {}
+  // Copy only available when memory is managed externally.
+  explicit PaddleBuf(const PaddleBuf&);
+
+  // Resize the memory.
+  void Resize(size_t length);
+  // Reset to external memory, with address and length set.
+  void Reset(void* data, size_t length);
+  // Tell whether the buffer is empty.
+  bool empty() const { return length_ == 0; }
+  // Get the memory address.
+  void* data() const { return data_; }
+  // Get the memory length.
+  size_t length() const { return length_; }
+
+  ~PaddleBuf() { Free(); }
+  PaddleBuf& operator=(const PaddleBuf&);
+  PaddleBuf& operator=(PaddleBuf&&);
+  PaddleBuf() = default;
+  PaddleBuf(PaddleBuf&& other);
+
+ private:
+  void Free();
+  void* data_{nullptr};  // pointer to the data memory.
+  size_t length_{0};     // number of memory bytes.
+  bool memory_owned_{true};
+};
+
+// Basic input and output data structure for PaddlePredictor.
+struct PaddleTensor {
+  PaddleTensor() = default;
+  std::string name;  // variable name.
+  std::vector<int> shape;
+  PaddleBuf data;  // blob of data.
+  PaddleDType dtype;
+  std::vector<std::vector<size_t>> lod;  // Tensor+LoD equals LoDTensor
+};
+
+enum class PaddlePlace { kUNK = -1, kCPU, kGPU };
+// Tensor without copy, currently only supports AnalysisPredictor.
+class ZeroCopyTensor {
+ public:
+  void Reshape(const std::vector<int>& shape);
+
+  // Get the memory in CPU or GPU with specific data type, should Reshape first
+  // to tell the data size.
+  // Once can directly call this data to feed the data.
+  // This is for write the input tensor.
+  template <typename T>
+  T* mutable_data(PaddlePlace place);
+  // Get the memory directly, will return the place and memory size by pointer.
+  // This is for reading the output tensor.
+  template <typename T>
+  T* data(PaddlePlace* place, int* size) const;
+
+  std::vector<int64_t> shape() const;
+
+  void SetLoD(const std::vector<std::vector<size_t>>& x);
+  std::vector<std::vector<size_t>> lod() const;
+  const std::string& name() const { return name_; }
+
+ protected:
+  explicit ZeroCopyTensor(void* scope) : scope_{scope} {}
+  void SetName(const std::string& name) { name_ = name; }
+  void* FindTensor() const;
+
+ private:
+  std::string name_;
+  bool input_or_output_;
+  friend class AnalysisPredictor;
+  void* scope_{nullptr};
+};
+
+/*
+ * A simple Inference API for Paddle.
+ */
+class PaddlePredictor {
+ public:
+  struct Config;
+  PaddlePredictor() = default;
+  PaddlePredictor(const PaddlePredictor&) = delete;
+  PaddlePredictor& operator=(const PaddlePredictor&) = delete;
+
+  // Predict an record.
+  // The caller should be responsible for allocating and releasing the memory of
+  // `inputs`. `inputs` should be available until Run returns. Caller should be
+  // responsible for the output tensor's buffer, either allocated or passed from
+  // outside.
+  virtual bool Run(const std::vector<PaddleTensor>& inputs,
+                   std::vector<PaddleTensor>* output_data,
+                   int batch_size = -1) = 0;
+
+  // Zero copy input and output optimization.
+  // Get the input or output tensors, and operate on their memory directly,
+  // without copy.
+  virtual std::unique_ptr<ZeroCopyTensor> GetInputTensor(
+      const std::string& name) {
+    return nullptr;
+  }
+  virtual std::unique_ptr<ZeroCopyTensor> GetOutputTensor(
+      const std::string& name) {
+    return nullptr;
+  }
+  virtual bool ZeroCopyRun() { return false; }
+
+  // Clone a predictor that share the model weights, the Cloned predictor should
+  // be thread-safe.
+  virtual std::unique_ptr<PaddlePredictor> Clone() = 0;
+
+  // Destroy the Predictor.
+  virtual ~PaddlePredictor() = default;
+
+  // The common configs for all the predictors.
+  struct Config {
+    std::string model_dir;  // path to the model directory.
+  };
+};
+
+struct NativeConfig : public PaddlePredictor::Config {
+  // GPU related fields.
+  bool use_gpu{false};
+  int device{0};
+  float fraction_of_gpu_memory{-1.f};  // Change to a float in (0,1] if needed.
+
+  // Specify the exact path of program and parameter files.
+  std::string prog_file;
+  std::string param_file;
+
+  // Specify the variable's name of each input if input tensors don't follow the
+  // `feeds` and `fetches` of the phase `save_inference_model`.
+  bool specify_input_name{false};
+};
+
+// A factory to help create different predictors.
+//
+// Usage:
+//
+// NativeConfig config;
+// ... // change the configs.
+// auto native_predictor = CreatePaddlePredictor(config);
+//
+// FOR EXTENSION DEVELOPER:
+// Different predictors are designated by config type. Similar configs can be
+// merged, but there shouldn't be a huge config containing different fields for
+// more than one kind of predictors.
+template <typename ConfigT>
+std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(const ConfigT& config);
+
+// NOTE The following APIs are too trivial, we will discard it in the following
+// versions.
+enum class PaddleEngineKind {
+  kNative = 0,         // Use the native Fluid facility.
+  kAutoMixedTensorRT,  // Automatically mix Fluid with TensorRT.
+  kAnalysis,           // More optimization.
+  kAnakin              // Use Anakin for inference, not mature yet.
+};
+
+template <typename ConfigT, PaddleEngineKind engine>
+std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(const ConfigT& config);
+
+int PaddleDtypeSize(PaddleDType dtype);
+
+}  // namespace paddle

paddle/fluid/inference/api/paddle_inference_api.h

@@ -26,265 +26,9 @@ limitations under the License. */
 #include <string>
 #include <vector>
 
-namespace paddle {
-
-// Data type.
-enum PaddleDType {
-  FLOAT32,
-  INT64,
-  // TODO(Superjomn) support more data types if needed.
-};
-
-/*
- * Memory menage for PaddleTensor.
- * The PaddleBuf holds a buffer for data input or output. The memory can be
- * allocated by user or by PaddleBuf itself, but in any case, the PaddleBuf
- * should be reused for better performance.
- *
- * For user allocated memory, the following API can be used:
- * - PaddleBuf(void* data, size_t length) to set an external memory by
- * specifying
- *   the memory address and length.
- * - Reset(void* data, size_t length) to reset the PaddleBuf with an external
- * memory.
- * ATTENTION, for user allocated memory, deallocation should be done by users
- * externally after the program finished. The PaddleBuf won't do any allocation
- * or deallocation.
- *
- * To have the PaddleBuf allocate and manage the memory:
- * - PaddleBuf(size_t length) will allocate a memory of size `length`.
- * - Resize(size_t length) resize the memory to no less than `length`, ATTENTION
- *   if the allocated memory is larger than `length`, nothing will done.
- */
-class PaddleBuf {
- public:
-  // PaddleBuf allocate memory internally, and manage it.
-  explicit PaddleBuf(size_t length)
-      : data_(new char[length]), length_(length), memory_owned_(true) {}
-  // Set external memory, the PaddleBuf won't manage it.
-  PaddleBuf(void* data, size_t length)
-      : data_(data), length_(length), memory_owned_{false} {}
-  // Copy only available when memory is managed externally.
-  explicit PaddleBuf(const PaddleBuf&);
-
-  // Resize the memory.
-  void Resize(size_t length);
-  // Reset to external memory, with address and length set.
-  void Reset(void* data, size_t length);
-  // Tell whether the buffer is empty.
-  bool empty() const { return length_ == 0; }
-  // Get the memory address.
-  void* data() const { return data_; }
-  // Get the memory length.
-  size_t length() const { return length_; }
-
-  ~PaddleBuf() { Free(); }
-  PaddleBuf& operator=(const PaddleBuf&);
-  PaddleBuf& operator=(PaddleBuf&&);
-  PaddleBuf() = default;
-  PaddleBuf(PaddleBuf&& other);
-
- private:
-  void Free();
-  void* data_{nullptr};  // pointer to the data memory.
-  size_t length_{0};     // number of memory bytes.
-  bool memory_owned_{true};
-};
-
-// Basic input and output data structure for PaddlePredictor.
-struct PaddleTensor {
-  PaddleTensor() = default;
-  std::string name;  // variable name.
-  std::vector<int> shape;
-  PaddleBuf data;  // blob of data.
-  PaddleDType dtype;
-  std::vector<std::vector<size_t>> lod;  // Tensor+LoD equals LoDTensor
-};
-
-enum class PaddlePlace { kUNK = -1, kCPU, kGPU };
-// Tensor without copy, currently only supports AnalysisPredictor.
-class ZeroCopyTensor {
- public:
-  void Reshape(const std::vector<int>& shape);
-
-  // Get the memory in CPU or GPU with specific data type, should Reshape first
-  // to tell the data size.
-  // Once can directly call this data to feed the data.
-  // This is for write the input tensor.
-  template <typename T>
-  T* mutable_data(PaddlePlace place);
-  // Get the memory directly, will return the place and memory size by pointer.
-  // This is for reading the output tensor.
-  template <typename T>
-  T* data(PaddlePlace* place, int* size);
-
-  std::vector<int64_t> shape();
-
-  void SetLoD(const std::vector<std::vector<size_t>>& x);
-  std::vector<std::vector<size_t>> lod() const;
-
- protected:
-  explicit ZeroCopyTensor(void* scope) : scope_{scope} {}
-  void SetName(const std::string& name) { name_ = name; }
-  void* FindTensor() const;
-
- private:
-  std::string name_;
-  bool input_or_output_;
-  friend class AnalysisPredictor;
-  void* scope_{nullptr};
-};
-
-/*
- * A simple Inference API for Paddle.
- */
-class PaddlePredictor {
- public:
-  struct Config;
-  PaddlePredictor() = default;
-  PaddlePredictor(const PaddlePredictor&) = delete;
-  PaddlePredictor& operator=(const PaddlePredictor&) = delete;
-
-  // Predict an record.
-  // The caller should be responsible for allocating and releasing the memory of
-  // `inputs`. `inputs` should be available until Run returns. Caller should be
-  // responsible for the output tensor's buffer, either allocated or passed from
-  // outside.
-  virtual bool Run(const std::vector<PaddleTensor>& inputs,
-                   std::vector<PaddleTensor>* output_data,
-                   int batch_size = -1) = 0;
-
-  // Zero copy input and output optimization.
-  // Get the input or output tensors, and operate on their memory directly,
-  // without copy.
-  virtual std::unique_ptr<ZeroCopyTensor> GetInputTensor(
-      const std::string& name) {
-    return nullptr;
-  }
-  virtual std::unique_ptr<ZeroCopyTensor> GetOutputTensor(
-      const std::string& name) {
-    return nullptr;
-  }
-  virtual bool ZeroCopyRun() { return false; }
-
-  // Clone a predictor that share the model weights, the Cloned predictor should
-  // be thread-safe.
-  virtual std::unique_ptr<PaddlePredictor> Clone() = 0;
-
-  // Destroy the Predictor.
-  virtual ~PaddlePredictor() = default;
-
-  // The common configs for all the predictors.
-  struct Config {
-    std::string model_dir;  // path to the model directory.
-  };
-};
-
-struct NativeConfig : public PaddlePredictor::Config {
-  // GPU related fields.
-  bool use_gpu{false};
-  int device{0};
-  float fraction_of_gpu_memory{-1.f};  // Change to a float in (0,1] if needed.
-
-  // Specify the exact path of program and parameter files.
-  std::string prog_file;
-  std::string param_file;
-
-  // Specify the variable's name of each input if input tensors don't follow the
-  // `feeds` and `fetches` of the phase `save_inference_model`.
-  bool specify_input_name{false};
-};
-
-// A factory to help create different predictors.
-//
-// Usage:
-//
-// NativeConfig config;
-// ... // change the configs.
-// auto native_predictor = CreatePaddlePredictor(config);
-//
-// FOR EXTENSION DEVELOPER:
-// Different predictors are designated by config type. Similar configs can be
-// merged, but there shouldn't be a huge config containing different fields for
-// more than one kind of predictors.
-template <typename ConfigT>
-std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(const ConfigT& config);
-
-// NOTE The following APIs are too trivial, we will discard it in the following
-// versions.
-enum class PaddleEngineKind {
-  kNative = 0,         // Use the native Fluid facility.
-  kAutoMixedTensorRT,  // Automatically mix Fluid with TensorRT.
-  kAnalysis,           // More optimization.
-  kAnakin              // Use Anakin for inference, not mature yet.
-};
-
-template <typename ConfigT, PaddleEngineKind engine>
-std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(const ConfigT& config);
-
-// ==
-//
-// -----------------------------------------------------------------------------------
-// NOTE: The following APIs are not mature yet, we are still working on them.
-
-namespace contrib {
-
-// Accelerate GPU computation with TensorRT engine.
-struct MixedRTConfig : public NativeConfig {
-  // Determine whether a subgraph will be executed by TRT.
-  int min_subgraph_size{1};
-  // While TensorRT allows an engine optimized for a given max batch size
-  // to run at any smaller size, the performance for those smaller
-  // sizes may not be as well-optimized. Therefore, Max batch is best
-  // equivalent to the runtime batch size.
-  int max_batch_size{1};
-  // For workspace_size, refer it from here:
-  // https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#troubleshooting
-  int workspace_size{1 << 30};
-  //  We transform the Ops that can be converted into TRT layer in the model,
-  //  and aggregate these Ops into subgraphs for TRT execution.
-  //  We set this variable to control the minimum number of nodes in the
-  //  subgraph, 3 as default value.
-  int minimum_subgraph_size = 3;
-  // Reserved configuration
-  // We just support "FP32" now, "FP16" and "INT8" will be supported.
-  std::string precision_mode = "FP32";
-};
-
-// NOTE WIP, not stable yet.
-struct AnalysisConfig : public NativeConfig {
-  enum class IrPassMode {
-    kSystem,   // Use system default passes, not customize.
-    kInclude,  // Specify the passes in `ir_passes`.
-    kExclude   // Specify the disabled passes in `ir_passes`.
-  };
-
-  // Determine whether to perform graph optimization.
-  bool enable_ir_optim = true;
-  // Manually determine the IR passes to run.
-  IrPassMode ir_mode{IrPassMode::kExclude};
-  // passes to be excluded/included
-  std::vector<std::string> ir_passes{"embedding_fc_lstm_fuse_pass"};
-
-  // NOT stable yet.
-  bool use_feed_fetch_ops{true};
-
-  // NOTE this is just for internal development, please not use it.
-  // NOT stable yet.
-  bool _use_mkldnn{false};
-};
-
-// Configurations for Anakin engine.
-struct AnakinConfig : public PaddlePredictor::Config {
-  enum TargetType { NVGPU = 0, X86 };
-  int device;
-  std::string model_file;
-  int max_batch_size{-1};
-  TargetType target_type;
-};
-
-}  // namespace contrib
-
-int PaddleDtypeSize(PaddleDType dtype);
-
-}  // namespace paddle
+#include "paddle_api.h"  // NOLINT
+#ifndef WITH_ANAKIN
+#include "paddle_analysis_config.h"  // NOLINT
+#else
+#include "paddle_anakin_config.h"  // NOLINT
+#endif

paddle/fluid/inference/analysis/tensorrt_subgraph_node_mark_pass.h → paddle/fluid/inference/api/paddle_pass_builder.cc

@@ -12,49 +12,57 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-/*
- * This file defines TensorRTSubgraphNodeMarkPass which helps to mark the ops
- * that supported by TensorRT engine.
- */
-
-#pragma once
-
-#include <string>
-#include "paddle/fluid/inference/analysis/analysis_pass.h"
-#include "paddle/fluid/inference/analysis/subgraph_splitter.h"
+#include "paddle/fluid/inference/api/paddle_pass_builder.h"
+#include <glog/logging.h>
 
 namespace paddle {
-namespace inference {
-namespace analysis {
-
-/*
- * Mark the operators that TensorRT engine supports.
- */
-class TensorRTSubgraphNodeMarkPass : public DataFlowGraphPass {
- public:
-  using teller_t = SubGraphSplitter::NodeInsideSubgraphTeller;
 
-  explicit TensorRTSubgraphNodeMarkPass(const teller_t& teller)
-      : teller_(teller) {}
+void PaddlePassBuilder::AppendPass(const std::string &pass_type) {
+  passes_.push_back(pass_type);
+}
 
-  bool Initialize(Argument* argument) override { return true; }
+void PaddlePassBuilder::TurnOnDebug() {
+  std::vector<std::string> passes;
+  auto it = std::begin(passes_);
+  while (it != std::end(passes_)) {
+    if (*it != "graph_viz_pass") {
+      it = passes_.insert(it + 1, "graph_viz_pass");
+    } else {
+      ++it;
+    }
+  }
+}
 
-  // This class get a sub-graph as input and determine whether to transform this
-  // sub-graph into TensorRT.
-  void Run(DataFlowGraph* graph) override;
+std::string PaddlePassBuilder::DebugString() {
+  std::stringstream ss;
+  ss << "Passes to apply:\n";
+  for (auto &pass : passes_) {
+    ss << "  - " << pass << '\n';
+  }
+  return ss.str();
+}
 
-  std::string repr() const override { return "tensorrt-sub-subgraph-mark"; }
-  std::string description() const override {
-    return "tensorrt sub-graph mark pass";
+void PaddlePassBuilder::DeletePass(const std::string &pass_type) {
+  auto it = std::begin(passes_);
+  while (it != std::end(passes_)) {
+    if (*it == pass_type) {
+      it = passes_.erase(it);
+    } else {
+      ++it;
+    }
   }
+}
+
+void PaddlePassBuilder::InsertPass(size_t idx, const std::string &pass_type) {
+  passes_.insert(std::begin(passes_) + idx, pass_type);
+}
 
-  AnalysisPass* CreateGraphvizDebugerPass() const override;
-  bool Finalize() override;
+void PaddlePassBuilder::DeletePass(size_t idx) {
+  passes_.erase(std::begin(passes_) + idx);
+}
 
- private:
-  teller_t teller_;
-};
+void GpuPassStrategy::EnableMKLDNN() {
+  LOG(ERROR) << "GPU not support MKLDNN yet";
+}
 
-}  // namespace analysis
-}  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/api/paddle_pass_builder.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 <sstream>
+#include <string>
+#include <vector>
+
+namespace paddle {
+/*
+ * This is a pass builder based on string. It is part of inference API.
+ */
+class PaddlePassBuilder {
+ public:
+  explicit PaddlePassBuilder(const std::vector<std::string> &passes)
+      : passes_(passes) {}
+
+  void AppendPass(const std::string &pass_type);
+
+  void InsertPass(size_t idx, const std::string &pass_type);
+
+  // Delete the `idx`-th pass.
+  void DeletePass(size_t idx);
+
+  // Delete all the passes that has type `pass_type`.
+  void DeletePass(const std::string &pass_type);
+
+  // Visualize the computation graph after each pass by generating a DOT
+  // language file, one can draw them with the Graphviz toolkit.
+  void TurnOnDebug();
+
+  // Human-readible information.
+  std::string DebugString();
+
+  const std::vector<std::string> &AllPasses() const { return passes_; }
+
+ protected:
+  std::vector<std::string> passes_;
+};
+
+/*
+ * Pass strategy to help control the IR passes.
+ */
+class PassStrategy : public PaddlePassBuilder {
+ public:
+  explicit PassStrategy(const std::vector<std::string> &passes)
+      : PaddlePassBuilder(passes) {}
+
+  // The MKLDNN control exists in both CPU and GPU mode, because there can be
+  // still some CPU kernels running in CPU mode.
+  virtual void EnableMKLDNN() = 0;
+
+  virtual ~PassStrategy() = default;
+};
+
+/*
+ * The CPU passes controller, it is used in AnalysisPredictor with CPU mode.
+ */
+class CpuPassStrategy : public PassStrategy {
+ public:
+  CpuPassStrategy() : PassStrategy({}) {
+    // NOTE the large fusions should be located in the front, so that they will
+    // not be damaged by smaller ones.
+    passes_.assign({
+        "infer_clean_graph_pass",         //
+        "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",  //
+    });
+  }
+
+  virtual ~CpuPassStrategy() = default;
+
+  virtual void EnableMKLDNN() override {
+// TODO(Superjomn) Consider the way to mix CPU with GPU.
+#ifdef PADDLE_WITH_MKLDNN
+    passes_.insert(passes_.begin(), "mkldnn_placement_pass");
+
+    for (auto &pass :
+         std::vector<std::string>({"depthwise_conv_mkldnn_pass",  //
+                                   "conv_bias_mkldnn_fuse_pass",  //
+                                   "conv_relu_mkldnn_fuse_pass",  //
+                                   "conv_elementwise_add_mkldnn_fuse_pass"})) {
+      passes_.push_back(pass);
+    }
+#endif
+  }
+
+  CpuPassStrategy(const CpuPassStrategy &other) : PassStrategy(other.passes_) {}
+};
+
+/*
+ * The GPU passes strategy, it is used in
+ */
+class GpuPassStrategy : public PassStrategy {
+ public:
+  GpuPassStrategy() : PassStrategy({}) {
+    passes_.assign({
+        "infer_clean_graph_pass", "conv_bn_fuse_pass",
+    });
+  }
+
+  GpuPassStrategy(const GpuPassStrategy &other)
+      : PassStrategy(other.AllPasses()) {}
+
+  virtual void EnableMKLDNN() override;
+
+  virtual ~GpuPassStrategy() = default;
+};
+
+}  // namespace paddle

paddle/fluid/inference/tensorrt/convert/activation_op.cc

@@ -27,7 +27,7 @@ class ActivationOpConverter : public OpConverter {
     // Here the two nullptr looks strange, that's because the
     // framework::OpDesc's constructor is strange.
     framework::OpDesc op_desc(op, nullptr);
-    LOG(INFO)
+    VLOG(3)
         << "convert a fluid Activation op to tensorrt activation layer whose "
            "type is "
         << op_type_;

paddle/fluid/inference/tensorrt/convert/batch_norm_op.cc

@@ -23,7 +23,7 @@ class BatchNormOpConverter : public OpConverter {
  public:
   void operator()(const framework::proto::OpDesc& op,
                   const framework::Scope& scope, bool test_mode) override {
-    LOG(INFO) << "convert a fluid batch norm op to tensorrt batch_norm";
+    VLOG(3) << "convert a fluid batch norm op to tensorrt batch_norm";
 
     framework::OpDesc op_desc(op, nullptr);
     PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);

paddle/fluid/inference/tensorrt/convert/concat_op.cc

@@ -25,7 +25,7 @@ class ConcatOpConverter : public OpConverter {
  public:
   void operator()(const framework::proto::OpDesc& op,
                   const framework::Scope& scope, bool test_mode) override {
-    VLOG(40) << "convert a fluid mul op to tensorrt mul layer without bias";
+    VLOG(3) << "convert a fluid mul op to tensorrt mul layer without bias";
 
     framework::OpDesc op_desc(op, nullptr);
     // Declare inputs

paddle/fluid/inference/tensorrt/convert/conv2d_op.cc

@@ -37,8 +37,7 @@ class Conv2dOpConverter : public OpConverter {
  public:
   void operator()(const framework::proto::OpDesc& op,
                   const framework::Scope& scope, bool test_mode) override {
-    LOG(INFO)
-        << "convert a fluid conv2d op to tensorrt conv layer without bias";
+    VLOG(3) << "convert a fluid conv2d op to tensorrt conv layer without bias";
 
     framework::OpDesc op_desc(op, nullptr);
     PADDLE_ENFORCE_EQ(op_desc.Input("Input").size(), 1);

paddle/fluid/inference/tensorrt/convert/dropout_op.cc

@@ -25,7 +25,7 @@ class DropoutOpConverter : public OpConverter {
  public:
   void operator()(const framework::proto::OpDesc& op,
                   const framework::Scope& scope, bool test_mode) override {
-    VLOG(40) << "convert a fluid dropout op to tensorrt dropout layer";
+    VLOG(3) << "convert a fluid dropout op to tensorrt dropout layer";
     framework::OpDesc op_desc(op, nullptr);
     // Declare inputs
     auto* input1 = engine_->GetITensor(op_desc.Input("X")[0]);

paddle/fluid/inference/tensorrt/convert/elementwise_op.cc

@@ -26,7 +26,7 @@ class ElementwiseWeightOpConverter : public OpConverter {
     // Here the two nullptr looks strange, that's because the
     // framework::OpDesc's constructor is strange.
     framework::OpDesc op_desc(op, nullptr);
-    LOG(INFO) << "convert a fluid elementwise op to tensorrt IScaleLayer";
+    VLOG(3) << "convert a fluid elementwise op to tensorrt IScaleLayer";
 
     PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
     PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1);  // Y is a weight
@@ -108,7 +108,7 @@ class ElementwiseTensorOpConverter : public OpConverter {
     // Here the two nullptr looks strange, that's because the
     // framework::OpDesc's constructor is strange.
     framework::OpDesc op_desc(op, nullptr);
-    LOG(INFO) << "convert a fluid elementwise op to tensorrt IScaleLayer";
+    VLOG(3) << "convert a fluid elementwise op to tensorrt IScaleLayer";
 
     PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
     PADDLE_ENFORCE_EQ(op_desc.Input("Y").size(), 1);  // Y is a weight

paddle/fluid/inference/tensorrt/convert/fc_op.cc

@@ -52,7 +52,7 @@ class FcOpConverter : public OpConverter {
  public:
   void operator()(const framework::proto::OpDesc& op,
                   const framework::Scope& scope, bool test_mode) override {
-    VLOG(40) << "convert a fluid fc op to tensorrt fc layer without bias";
+    VLOG(3) << "convert a fluid fc op to tensorrt fc layer without bias";
 
     framework::OpDesc op_desc(op, nullptr);
     PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);

paddle/fluid/inference/tensorrt/convert/mul_op.cc

@@ -25,7 +25,7 @@ class MulOpConverter : public OpConverter {
  public:
   void operator()(const framework::proto::OpDesc& op,
                   const framework::Scope& scope, bool test_mode) override {
-    VLOG(40) << "convert a fluid mul op to tensorrt mul layer without bias";
+    VLOG(3) << "convert a fluid mul op to tensorrt mul layer without bias";
 
     framework::OpDesc op_desc(op, nullptr);
     // Declare inputs

paddle/fluid/inference/tensorrt/convert/pad_op.cc

@@ -25,7 +25,7 @@ class PadOpConverter : public OpConverter {
  public:
   void operator()(const framework::proto::OpDesc& op,
                   const framework::Scope& scope, bool test_mode) override {
-    VLOG(40) << "convert a fluid transpose op to tensorrt tranpose layer";
+    VLOG(3) << "convert a fluid transpose op to tensorrt tranpose layer";
 
     framework::OpDesc op_desc(op, nullptr);
     // Declare inputs

paddle/fluid/inference/tensorrt/convert/pool2d_op.cc

@@ -25,7 +25,7 @@ class Pool2dOpConverter : public OpConverter {
  public:
   void operator()(const framework::proto::OpDesc& op,
                   const framework::Scope& scope, bool test_mode) override {
-    VLOG(40)
+    VLOG(3)
         << "convert a fluid pool2d op to tensorrt pool2d layer without bias";
     framework::OpDesc op_desc(op, nullptr);
     // Declare inputs

paddle/fluid/inference/tensorrt/convert/softmax_op.cc

@@ -25,7 +25,7 @@ class SoftMaxOpConverter : public OpConverter {
  public:
   void operator()(const framework::proto::OpDesc& op,
                   const framework::Scope& scope, bool test_mode) override {
-    VLOG(40)
+    VLOG(3)
         << "convert a fluid softmax op to tensorrt softmax layer without bias";
     framework::OpDesc op_desc(op, nullptr);
     // Declare inputs

paddle/fluid/inference/tensorrt/engine.cc

@@ -61,6 +61,7 @@ TensorRTEngine::~TensorRTEngine() {
 }
 
 void TensorRTEngine::FreezeNetwork() {
+  VLOG(3) << "TRT to freeze network";
   freshDeviceId();
   PADDLE_ENFORCE(infer_builder_ != nullptr,
                  "Call InitNetwork first to initialize network.");

paddle/fluid/inference/tensorrt/helper.h

@@ -52,7 +52,7 @@ class NaiveLogger : public nvinfer1::ILogger {
   void log(nvinfer1::ILogger::Severity severity, const char* msg) override {
     switch (severity) {
       case Severity::kINFO:
-        LOG(INFO) << msg;
+        VLOG(3) << msg;
         break;
       case Severity::kWARNING:
         LOG(WARNING) << msg;

paddle/fluid/inference/tests/api/CMakeLists.txt

@@ -108,7 +108,8 @@ if(WITH_GPU AND TENSORRT_FOUND)
    if (NOT EXISTS ${TRT_MODEL_INSTALL_DIR})
        inference_download_and_uncompress(${TRT_MODEL_INSTALL_DIR} ${INFERENCE_URL}/tensorrt_test "trt_test_models.tar.gz")
    endif()
-   cc_test(test_trt_models SRCS trt_models_tester.cc  
-     ARGS --dirname=${TRT_MODEL_INSTALL_DIR}/trt_test_models
-     DEPS paddle_inference_tensorrt_subgraph_engine)
+
+   inference_analysis_test(test_trt_models SRCS trt_models_tester.cc
+      EXTRA_DEPS ${INFERENCE_EXTRA_DEPS} analysis ${analysis_deps} ir_pass_manager analysis_predictor
+        ARGS --dirname=${TRT_MODEL_INSTALL_DIR}/trt_test_models SERIAL)
 endif()

paddle/fluid/inference/tests/api/analyzer_resnet50_tester.cc

@@ -37,7 +37,10 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
 void profile(bool use_mkldnn = false) {
   AnalysisConfig cfg;
   SetConfig(&cfg);
-  cfg._use_mkldnn = use_mkldnn;
+
+  if (use_mkldnn) {
+    cfg.EnableMKLDNN();
+  }
   std::vector<PaddleTensor> outputs;
 
   std::vector<std::vector<PaddleTensor>> input_slots_all;
@@ -65,7 +68,9 @@ TEST(Analyzer_resnet50, fuse_statis) {
 void compare(bool use_mkldnn = false) {
   AnalysisConfig cfg;
   SetConfig(&cfg);
-  cfg._use_mkldnn = use_mkldnn;
+  if (use_mkldnn) {
+    cfg.EnableMKLDNN();
+  }
 
   std::vector<std::vector<PaddleTensor>> input_slots_all;
   SetInput(&input_slots_all);

paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc

@@ -210,7 +210,6 @@ void SetConfig(AnalysisConfig *cfg) {
   cfg->device = 0;
   cfg->specify_input_name = true;
   cfg->enable_ir_optim = true;
-  cfg->ir_passes.clear();  // Do not exclude any pass.
 }
 
 void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
@@ -226,13 +225,15 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
 
 // Easy for profiling independently.
 TEST(Analyzer_rnn1, profile) {
-  contrib::AnalysisConfig cfg;
+  contrib::AnalysisConfig cfg(false);
   SetConfig(&cfg);
-  cfg.use_gpu = false;
+  cfg.fraction_of_gpu_memory = 0.1;
+  cfg.pass_builder()->TurnOnDebug();
   std::vector<PaddleTensor> outputs;
 
   std::vector<std::vector<PaddleTensor>> input_slots_all;
   SetInput(&input_slots_all);
+  LOG(INFO) << "to test prediction";
   TestPrediction(cfg, input_slots_all, &outputs, FLAGS_num_threads);
 }
 
@@ -274,31 +275,6 @@ TEST(Analyzer_rnn1, multi_thread) {
   TestPrediction(cfg, input_slots_all, &outputs, 4 /* multi_thread */);
 }
 
-bool CompareTensors(const framework::Scope &a_scope,
-                    const framework::Scope &b_scope,
-                    const std::vector<std::string> &tensors) {
-  for (auto &x : tensors) {
-    auto *a_var = a_scope.FindVar(x);
-    auto *b_var = b_scope.FindVar(x);
-    if (a_var && b_var) {
-      if (a_var->Type() == typeid(framework::LoDTensor) ||
-          a_var->Type() == typeid(framework::Tensor)) {
-        LOG(INFO) << "comparing tensor " << x;
-        auto &a_t = a_var->Get<framework::LoDTensor>();
-        auto &b_t = b_var->Get<framework::LoDTensor>();
-        if (!inference::CompareTensor(a_t, b_t)) {
-          LOG(ERROR) << string::Sprintf("tensor %s not match in two scopes", x);
-        }
-      } else {
-        LOG(INFO) << "skip no tensor " << x;
-      }
-    } else {
-      LOG(INFO) << "skip tensor " << x;
-    }
-  }
-  return true;
-}
-
 // Validate that the AnalysisPredictor + ZeroCopyTensor really works by testing
 // on the complex RNN1 model.
 TEST(Analyzer_rnn1, ZeroCopy) {
@@ -307,7 +283,6 @@ TEST(Analyzer_rnn1, ZeroCopy) {
   config.use_feed_fetch_ops = false;
 
   PaddlePlace place;
-  int output_size{0};
 
   auto predictor = CreatePaddlePredictor<AnalysisConfig>(config);
 
@@ -353,86 +328,22 @@ TEST(Analyzer_rnn1, ZeroCopy) {
 
   Timer timer;
   double total_time{0};
-  double native_total_time{0};
-  double analysis_total_time{0.};
-
   for (int i = 0; i < FLAGS_repeat; i++) {
     timer.tic();
     predictor->ZeroCopyRun();
     total_time += timer.toc();
   }
+  LOG(INFO) << "ZeroCopy output: " << DescribeZeroCopyTensor(*output_tensor);
 
-  auto *output_data = output_tensor->data<float>(&place, &output_size);
-  ASSERT_GT(output_size, 0);  // more than one output!
-
-  for (int i = 0; i < FLAGS_repeat; i++) {
-    // Run native predictor.
-    timer.tic();
-    ASSERT_TRUE(native_predictor->Run(native_inputs.front(), &native_outputs));
-    native_total_time += timer.toc();
-  }
-
-  for (int i = 0; i < FLAGS_repeat; i++) {
-    timer.tic();
-    ASSERT_TRUE(
-        analysis_predictor->Run(native_inputs.front(), &analysis_outputs));
-    analysis_total_time += timer.toc();
-  }
-
-  if (!FLAGS_with_precision_check) {
-    return;
-  }
-  int native_output_size = VecReduceToInt(native_outputs.front().shape);
-
-  EXPECT_EQ(native_output_size, output_size);
+  ASSERT_TRUE(native_predictor->Run(native_inputs.front(), &native_outputs));
+  LOG(INFO) << "native output " << DescribeTensor(native_outputs.front());
 
-  // Compare tensors between analysis and zerocopy
-  auto *p0 = static_cast<AnalysisPredictor *>(predictor.get());
-  auto *p1 = static_cast<AnalysisPredictor *>(analysis_predictor.get());
-  auto *p2 = static_cast<NativePaddlePredictor *>(native_predictor.get());
-
-  std::vector<std::string> tensor_names;
-  for (auto &var_desc : p0->program().Block(0).AllVars()) {
-    tensor_names.push_back(var_desc->Name());
-  }
-
-  LOG(INFO) << "Comparing tensors";
-  ASSERT_TRUE(
-      CompareTensors(*p0->scope(), *p1->scope(), {"final_output.tmp_1"}));
-  ASSERT_TRUE(
-      CompareTensors(*p0->scope(), *p2->scope(), {"final_output.tmp_1"}));
-
-  LOG(INFO) << "output1 " << inference::LoDTensorSummary<float>(
-                                 p0->scope()
-                                     ->FindVar("final_output.tmp_1")
-                                     ->Get<framework::LoDTensor>());
-  LOG(INFO) << "output2 " << inference::LoDTensorSummary<float>(
-                                 p1->scope()
-                                     ->FindVar("final_output.tmp_1")
-                                     ->Get<framework::LoDTensor>());
-  LOG(INFO) << "output3 " << inference::LoDTensorSummary<float>(
-                                 p2->scope()
-                                     ->FindVar("final_output.tmp_1")
-                                     ->Get<framework::LoDTensor>());
-
-  for (int i = 0; i < output_size; i++) {
-    LOG(INFO) << output_data[i] << " "
-              << static_cast<float *>(native_outputs.front().data.data())[i]
-              << " "
-              << static_cast<float *>(analysis_outputs.front().data.data())[i];
-    EXPECT_NEAR(output_data[i],
-                static_cast<float *>(native_outputs.front().data.data())[i],
-                1e-3);
+  int output_size{0};
+  auto *zero_copy_data = output_tensor->data<float>(&place, &output_size);
+  auto *native_data = static_cast<float *>(native_outputs.front().data.data());
+  for (size_t i = 0; i < output_size / sizeof(float); i++) {
+    EXPECT_NEAR(zero_copy_data[i], native_data[i], 1e-3);
   }
-
-  LOG(INFO) << "batch_size: " << FLAGS_batch_size;
-
-  LOG(INFO) << "zero average time: "
-            << total_time / (FLAGS_repeat * FLAGS_batch_size);
-  LOG(INFO) << "analysis average time: "
-            << analysis_total_time / (FLAGS_repeat * FLAGS_batch_size);
-  LOG(INFO) << "native average time: "
-            << native_total_time / (FLAGS_repeat * FLAGS_batch_size);
 }
 
 TEST(Analyzer_rnn1, ZeroCopyMultiThread) {

paddle/fluid/inference/tests/api/analyzer_text_classification_tester.cc

@@ -108,9 +108,7 @@ TEST(Analyzer_Text_Classification, compare_against_embedding_fc_lstm_fused) {
   AnalysisConfig cfg;
   SetConfig(&cfg);
   // Enable embedding_fc_lstm_fuse_pass (disabled by default)
-  auto it = std::find(cfg.ir_passes.begin(), cfg.ir_passes.end(),
-                      "embedding_fc_lstm_fuse_pass");
-  if (it != cfg.ir_passes.end()) cfg.ir_passes.erase(it);
+  cfg.pass_builder()->InsertPass(2, "embedding_fc_lstm_fuse_pass");
 
   std::vector<std::vector<PaddleTensor>> input_slots_all;
   SetInput(&input_slots_all);

paddle/fluid/inference/tests/api/analyzer_vis_tester.cc

@@ -58,7 +58,10 @@ void SetConfig(AnalysisConfig *cfg) {
   cfg->enable_ir_optim = true;
   cfg->specify_input_name = true;
   // TODO(TJ): fix fusion gru
-  cfg->ir_passes.push_back("fc_gru_fuse_pass");
+  cfg->pass_builder()->DeletePass("fc_gru_fuse_pass");
+#ifdef PADDLE_WITH_MKLDNN
+  cfg->EnableMKLDNN();
+#endif
 }
 
 void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
@@ -84,7 +87,9 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
 void profile(bool use_mkldnn = false) {
   AnalysisConfig cfg;
   SetConfig(&cfg);
-  cfg._use_mkldnn = use_mkldnn;
+  if (use_mkldnn) {
+    cfg.EnableMKLDNN();
+  }
   std::vector<PaddleTensor> outputs;
 
   std::vector<std::vector<PaddleTensor>> input_slots_all;
@@ -125,7 +130,9 @@ TEST(Analyzer_vis, fuse_statis) {
 void compare(bool use_mkldnn = false) {
   AnalysisConfig cfg;
   SetConfig(&cfg);
-  cfg._use_mkldnn = use_mkldnn;
+  if (use_mkldnn) {
+    cfg.EnableMKLDNN();
+  }
 
   std::vector<std::vector<PaddleTensor>> input_slots_all;
   SetInput(&input_slots_all);

paddle/fluid/inference/tests/api/tester_helper.h

@@ -20,6 +20,7 @@
 #include <thread>  // NOLINT
 #include <vector>
 #include "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/framework/scope.h"
 #include "paddle/fluid/inference/analysis/analyzer.h"
 #include "paddle/fluid/inference/analysis/ut_helper.h"
 #include "paddle/fluid/inference/api/analysis_predictor.h"
@@ -88,22 +89,25 @@ size_t GetSize(const PaddleTensor &out) { return VecReduceToInt(out.shape); }
 
 std::unordered_map<std::string, int> GetFuseStatis(PaddlePredictor *predictor,
                                                    int *num_ops) {
+  std::unordered_map<std::string, int> res;
   auto *analysis_predictor = static_cast<AnalysisPredictor *>(predictor);
-  auto &fuse_statis = analysis_predictor->analysis_argument()
-                          .Get<std::unordered_map<std::string, int>>(
-                              framework::ir::kFuseStatisAttr);
-  for (auto &item : fuse_statis) {
+  auto *fusion_status =
+      analysis_predictor->analysis_argument().fusion_statis_ptr();
+  if (!fusion_status) {
+    return res;
+  }
+  for (auto &item : *fusion_status) {
     LOG(INFO) << "fused " << item.first << " " << item.second;
   }
   int num = 0;
   for (auto &node :
-       analysis_predictor->analysis_argument().main_dfg->nodes.nodes()) {
-    if (node->IsFunction()) {
+       analysis_predictor->analysis_argument().main_graph().Nodes()) {
+    if (node->IsOp()) {
       ++num;
     }
   }
   *num_ops = num;
-  return fuse_statis;
+  return *fusion_status;
 }
 
 void SetFakeImageInput(std::vector<std::vector<PaddleTensor>> *inputs,
@@ -161,11 +165,12 @@ void TestMultiThreadPrediction(
   int num_times = FLAGS_repeat;
   std::vector<std::thread> threads;
   std::vector<std::unique_ptr<PaddlePredictor>> predictors;
-  // TODO(yanchunwei): Bug here, the analyzer phase can't be parallelled
-  // because AttentionLSTM's hard code nodeid will be damanged.
-  for (int tid = 0; tid < num_threads; ++tid) {
-    predictors.emplace_back(CreateTestPredictor(config, use_analysis));
+  predictors.emplace_back(CreateTestPredictor(config, use_analysis));
+  for (int tid = 1; tid < num_threads; ++tid) {
+    predictors.emplace_back(predictors.front()->Clone());
   }
+
+  size_t total_time{0};
   for (int tid = 0; tid < num_threads; ++tid) {
     threads.emplace_back([&, tid]() {
 #ifdef PADDLE_WITH_MKLDNN
@@ -173,17 +178,21 @@ void TestMultiThreadPrediction(
 #endif
       // Each thread should have local inputs and outputs.
       // The inputs of each thread are all the same.
-      std::vector<std::vector<PaddleTensor>> inputs_tid = inputs;
       std::vector<PaddleTensor> outputs_tid;
+      auto &predictor = predictors[tid];
+      LOG(INFO) << "running thread " << tid;
       Timer timer;
       timer.tic();
       for (int i = 0; i < num_times; i++) {
-        for (size_t j = 0; j < inputs_tid.size(); j++) {
-          predictors[tid]->Run(inputs_tid[j], &outputs_tid);
+        for (const auto &input : inputs) {
+          ASSERT_TRUE(predictor->Run(input, &outputs_tid));
         }
       }
-      PrintTime(batch_size, num_times, num_threads, tid,
-                timer.toc() / num_times, inputs_tid.size());
+
+      auto time = timer.toc();
+      total_time += time;
+      PrintTime(batch_size, num_times, num_threads, tid, time / num_times,
+                inputs.size());
     });
   }
   for (int i = 0; i < num_threads; ++i) {
@@ -196,7 +205,7 @@ void TestPrediction(const AnalysisConfig &config,
                     std::vector<PaddleTensor> *outputs, int num_threads,
                     bool use_analysis = FLAGS_use_analysis) {
   LOG(INFO) << "use_analysis: " << use_analysis
-            << ", use_mkldnn: " << config._use_mkldnn;
+            << ", use_mkldnn: " << config.use_mkldnn();
   if (num_threads == 1) {
     TestOneThreadPrediction(config, inputs, outputs, use_analysis);
   } else {
@@ -208,7 +217,7 @@ void TestPrediction(const AnalysisConfig &config,
 void CompareNativeAndAnalysis(
     const AnalysisConfig &config,
     const std::vector<std::vector<PaddleTensor>> &inputs) {
-  LOG(INFO) << "use_mkldnn: " << config._use_mkldnn;
+  LOG(INFO) << "use_mkldnn: " << config.use_mkldnn();
   std::vector<PaddleTensor> native_outputs, analysis_outputs;
   TestOneThreadPrediction(config, inputs, &native_outputs, false);
   TestOneThreadPrediction(config, inputs, &analysis_outputs, true);

paddle/fluid/inference/tests/api/trt_models_tester.cc

@@ -16,10 +16,13 @@
 #include <glog/logging.h>
 #include <gtest/gtest.h>
 #include "paddle/fluid/inference/analysis/analyzer.h"
+#include "paddle/fluid/inference/api/helper.h"
 #include "paddle/fluid/inference/api/paddle_inference_api.h"
+#include "paddle/fluid/inference/api/paddle_inference_pass.h"
+#include "paddle/fluid/inference/tests/api/tester_helper.h"
 
 namespace paddle {
-using paddle::contrib::MixedRTConfig;
+using paddle::contrib::AnalysisConfig;
 
 DEFINE_string(dirname, "", "Directory of the inference model.");
 
@@ -27,33 +30,24 @@ NativeConfig GetConfigNative() {
   NativeConfig config;
   config.model_dir = FLAGS_dirname;
   // LOG(INFO) << "dirname  " << config.model_dir;
-  config.fraction_of_gpu_memory = 0.45;
+  config.fraction_of_gpu_memory = 0.15;
   config.use_gpu = true;
   config.device = 0;
   return config;
 }
 
-MixedRTConfig GetConfigTRT() {
-  MixedRTConfig config;
-  config.model_dir = FLAGS_dirname;
-  config.use_gpu = true;
-  config.fraction_of_gpu_memory = 0.2;
-  config.device = 0;
-  config.max_batch_size = 3;
-  return config;
+void PrepareTRTConfig(AnalysisConfig *config) {
+  config->model_dir = FLAGS_dirname + "/" + "mobilenet";
+  config->fraction_of_gpu_memory = 0.15;
+  config->EnableTensorRtEngine(1 << 10, 5);
+  config->pass_builder()->DeletePass("conv_bn_fuse_pass");
+  config->pass_builder()->DeletePass("fc_fuse_pass");
+  config->pass_builder()->TurnOnDebug();
 }
 
-void CompareTensorRTWithFluid(int batch_size, std::string model_dirname) {
-  NativeConfig config0 = GetConfigNative();
-  config0.model_dir = model_dirname;
-
-  MixedRTConfig config1 = GetConfigTRT();
-  config1.model_dir = model_dirname;
-  config1.max_batch_size = batch_size;
-
-  auto predictor0 = CreatePaddlePredictor<NativeConfig>(config0);
-  auto predictor1 = CreatePaddlePredictor<MixedRTConfig>(config1);
-  // Prepare inputs
+void PrepareInputs(std::vector<PaddleTensor> *tensors, int batch_size) {
+  PADDLE_ENFORCE_EQ(tensors->size(), 1UL);
+  auto &tensor = tensors->front();
   int height = 224;
   int width = 224;
   float *data = new float[batch_size * 3 * height * width];
@@ -61,25 +55,34 @@ void CompareTensorRTWithFluid(int batch_size, std::string model_dirname) {
   data[0] = 1.0f;
 
   // Prepare inputs
-  PaddleTensor tensor;
   tensor.name = "input_0";
   tensor.shape = std::vector<int>({batch_size, 3, height, width});
   tensor.data = PaddleBuf(static_cast<void *>(data),
                           sizeof(float) * (batch_size * 3 * height * width));
   tensor.dtype = PaddleDType::FLOAT32;
-  std::vector<PaddleTensor> paddle_tensor_feeds(1, tensor);
+}
+
+void CompareTensorRTWithFluid(int batch_size, std::string model_dirname) {
+  auto config0 = GetConfigNative();
+  config0.model_dir = model_dirname;
+
+  AnalysisConfig config1(true);
+  PrepareTRTConfig(&config1);
+  config1.model_dir = model_dirname;
+
+  auto predictor0 = CreatePaddlePredictor<NativeConfig>(config0);
+  auto predictor1 = CreatePaddlePredictor(config1);
+
+  // Prepare inputs
+  std::vector<PaddleTensor> paddle_tensor_feeds(1);
+  PrepareInputs(&paddle_tensor_feeds, batch_size);
 
   // Prepare outputs
   std::vector<PaddleTensor> outputs0;
   std::vector<PaddleTensor> outputs1;
   CHECK(predictor0->Run(paddle_tensor_feeds, &outputs0));
-
   CHECK(predictor1->Run(paddle_tensor_feeds, &outputs1, batch_size));
 
-  // Get output.
-  ASSERT_EQ(outputs0.size(), 1UL);
-  ASSERT_EQ(outputs1.size(), 1UL);
-
   const size_t num_elements = outputs0.front().data.length() / sizeof(float);
   const size_t num_elements1 = outputs1.front().data.length() / sizeof(float);
   EXPECT_EQ(num_elements, num_elements1);
@@ -94,15 +97,52 @@ void CompareTensorRTWithFluid(int batch_size, std::string model_dirname) {
 }
 
 TEST(trt_models_test, mobilenet) {
-  CompareTensorRTWithFluid(1, FLAGS_dirname + "/mobilenet");
+  CompareTensorRTWithFluid(1, FLAGS_dirname + "/" + "mobilenet");
 }
-
 TEST(trt_models_test, resnet50) {
-  CompareTensorRTWithFluid(1, FLAGS_dirname + "/resnet50");
+  CompareTensorRTWithFluid(1, FLAGS_dirname + "/" + "resnet50");
 }
-
 TEST(trt_models_test, resnext50) {
-  CompareTensorRTWithFluid(1, FLAGS_dirname + "/resnext50");
+  CompareTensorRTWithFluid(1, FLAGS_dirname + "/" + "resnext50");
+}
+
+TEST(trt_models_test, raw_gpu) {
+  std::string model_dir = FLAGS_dirname + "/" + "mobilenet";
+  auto config0 = GetConfigNative();
+  config0.model_dir = model_dir;
+  int batch_size = 2;
+
+  AnalysisConfig config1(true);
+  config1.fraction_of_gpu_memory = 0.1;
+  config1.enable_ir_optim = true;
+  config1.model_dir = model_dir;
+
+  auto predictor0 = CreatePaddlePredictor<NativeConfig>(config0);
+  auto predictor1 = CreatePaddlePredictor(config1);
+
+  // Prepare inputs
+  std::vector<PaddleTensor> paddle_tensor_feeds(1);
+  PrepareInputs(&paddle_tensor_feeds, batch_size);
+
+  // Prepare outputs
+  std::vector<PaddleTensor> outputs0;
+  std::vector<PaddleTensor> outputs1;
+  CHECK(predictor0->Run(paddle_tensor_feeds, &outputs0));
+  CHECK(predictor1->Run(paddle_tensor_feeds, &outputs1, batch_size));
+
+  const size_t num_elements = outputs0.front().data.length() / sizeof(float);
+  const size_t num_elements1 = outputs1.front().data.length() / sizeof(float);
+  EXPECT_EQ(num_elements, num_elements1);
+
+  auto *data0 = static_cast<float *>(outputs0.front().data.data());
+  auto *data1 = static_cast<float *>(outputs1.front().data.data());
+
+  ASSERT_GT(num_elements, 0UL);
+  for (size_t i = 0; i < std::min(num_elements, num_elements1); i++) {
+    EXPECT_NEAR(data0[i], data1[i], 1e-3);
+  }
 }
 
 }  // namespace paddle
+
+USE_PASS(tensorrt_subgraph_pass);

paddle/fluid/operators/auc_op.cc

@@ -53,7 +53,7 @@ class AucOp : public framework::OperatorWithKernel {
       const framework::ExecutionContext &ctx) const override {
     return framework::OpKernelType(
         framework::ToDataType(ctx.Input<Tensor>("Predict")->type()),
-        ctx.device_context());
+        platform::CPUPlace());
   }
 };
 

paddle/fluid/operators/detection/CMakeLists.txt

@@ -22,6 +22,7 @@ iou_similarity_op.cu)
 detection_library(mine_hard_examples_op SRCS mine_hard_examples_op.cc)
 detection_library(multiclass_nms_op SRCS multiclass_nms_op.cc poly_util.cc gpc.cc)
 detection_library(prior_box_op SRCS prior_box_op.cc prior_box_op.cu)
+detection_library(density_prior_box_op SRCS density_prior_box_op.cc)
 detection_library(anchor_generator_op SRCS anchor_generator_op.cc
 anchor_generator_op.cu)
 detection_library(target_assign_op SRCS target_assign_op.cc

paddle/fluid/operators/detection/density_prior_box_op.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/operators/detection/density_prior_box_op.h"
+
+namespace paddle {
+namespace operators {
+
+class DensityPriorBoxOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Input"),
+                   "Input(Input) of DensityPriorBoxOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Image"),
+                   "Input(Image) of DensityPriorBoxOp should not be null.");
+
+    auto image_dims = ctx->GetInputDim("Image");
+    auto input_dims = ctx->GetInputDim("Input");
+    PADDLE_ENFORCE(image_dims.size() == 4, "The layout of image is NCHW.");
+    PADDLE_ENFORCE(input_dims.size() == 4, "The layout of input is NCHW.");
+
+    PADDLE_ENFORCE_LT(input_dims[2], image_dims[2],
+                      "The height of input must smaller than image.");
+
+    PADDLE_ENFORCE_LT(input_dims[3], image_dims[3],
+                      "The width of input must smaller than image.");
+    auto variances = ctx->Attrs().Get<std::vector<float>>("variances");
+
+    auto fixed_sizes = ctx->Attrs().Get<std::vector<float>>("fixed_sizes");
+    auto fixed_ratios = ctx->Attrs().Get<std::vector<float>>("fixed_ratios");
+    auto densities = ctx->Attrs().Get<std::vector<int>>("densities");
+
+    PADDLE_ENFORCE_EQ(fixed_sizes.size(), densities.size(),
+                      "The number of fixed_sizes and densities must be equal.");
+    size_t num_priors = 0;
+    if ((fixed_sizes.size() > 0) && (densities.size() > 0)) {
+      for (size_t i = 0; i < densities.size(); ++i) {
+        if (fixed_ratios.size() > 0) {
+          num_priors += (fixed_ratios.size()) * (pow(densities[i], 2));
+        }
+      }
+    }
+    std::vector<int64_t> dim_vec(4);
+    dim_vec[0] = input_dims[2];
+    dim_vec[1] = input_dims[3];
+    dim_vec[2] = num_priors;
+    dim_vec[3] = 4;
+    ctx->SetOutputDim("Boxes", framework::make_ddim(dim_vec));
+    ctx->SetOutputDim("Variances", framework::make_ddim(dim_vec));
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<framework::Tensor>("Input")->type()),
+        platform::CPUPlace());
+  }
+};
+
+class DensityPriorBoxOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput(
+        "Input",
+        "(Tensor, default Tensor<float>), "
+        "the input feature data of DensityPriorBoxOp, the layout is NCHW.");
+    AddInput("Image",
+             "(Tensor, default Tensor<float>), "
+             "the input image data of DensityPriorBoxOp, the layout is NCHW.");
+    AddOutput("Boxes",
+              "(Tensor, default Tensor<float>), the output prior boxes of "
+              "DensityPriorBoxOp. The layout is [H, W, num_priors, 4]. "
+              "H is the height of input, W is the width of input, num_priors "
+              "is the box count of each position.");
+    AddOutput("Variances",
+              "(Tensor, default Tensor<float>), the expanded variances of "
+              "DensityPriorBoxOp. The layout is [H, W, num_priors, 4]. "
+              "H is the height of input, W is the width of input, num_priors "
+              "is the box count of each position.");
+    AddAttr<std::vector<float>>("variances",
+                                "(vector<float>) List of variances to be "
+                                "encoded in density prior boxes.")
+        .AddCustomChecker([](const std::vector<float>& variances) {
+          PADDLE_ENFORCE_EQ(variances.size(), 4,
+                            "Must and only provide 4 variance.");
+          for (size_t i = 0; i < variances.size(); ++i) {
+            PADDLE_ENFORCE_GT(variances[i], 0.0,
+                              "variance[%d] must be greater than 0.", i);
+          }
+        });
+    AddAttr<bool>("clip", "(bool) Whether to clip out-of-boundary boxes.")
+        .SetDefault(true);
+
+    AddAttr<float>(
+        "step_w",
+        "Density prior boxes step across width, 0.0 for auto calculation.")
+        .SetDefault(0.0)
+        .AddCustomChecker([](const float& step_w) {
+          PADDLE_ENFORCE_GE(step_w, 0.0, "step_w should be larger than 0.");
+        });
+    AddAttr<float>(
+        "step_h",
+        "Density prior boxes step across height, 0.0 for auto calculation.")
+        .SetDefault(0.0)
+        .AddCustomChecker([](const float& step_h) {
+          PADDLE_ENFORCE_GE(step_h, 0.0, "step_h should be larger than 0.");
+        });
+
+    AddAttr<float>("offset",
+                   "(float) "
+                   "Density prior boxes center offset.")
+        .SetDefault(0.5);
+    AddAttr<std::vector<float>>("fixed_sizes",
+                                "(vector<float>) List of fixed sizes "
+                                "of generated density prior boxes.")
+        .SetDefault(std::vector<float>{})
+        .AddCustomChecker([](const std::vector<float>& fixed_sizes) {
+          for (size_t i = 0; i < fixed_sizes.size(); ++i) {
+            PADDLE_ENFORCE_GT(fixed_sizes[i], 0.0,
+                              "fixed_sizes[%d] should be larger than 0.", i);
+          }
+        });
+
+    AddAttr<std::vector<float>>("fixed_ratios",
+                                "(vector<float>) List of fixed ratios "
+                                "of generated density prior boxes.")
+        .SetDefault(std::vector<float>{})
+        .AddCustomChecker([](const std::vector<float>& fixed_ratios) {
+          for (size_t i = 0; i < fixed_ratios.size(); ++i) {
+            PADDLE_ENFORCE_GT(fixed_ratios[i], 0.0,
+                              "fixed_ratios[%d] should be larger than 0.", i);
+          }
+        });
+
+    AddAttr<std::vector<int>>("densities",
+                              "(vector<float>) List of densities "
+                              "of generated density prior boxes.")
+        .SetDefault(std::vector<int>{})
+        .AddCustomChecker([](const std::vector<int>& densities) {
+          for (size_t i = 0; i < densities.size(); ++i) {
+            PADDLE_ENFORCE_GT(densities[i], 0,
+                              "densities[%d] should be larger than 0.", i);
+          }
+        });
+    AddComment(R"DOC(
+        Density Prior box operator
+        Each position of the input produce N density prior boxes, N is determined by
+        the count of fixed_ratios, densities, the calculation of N is as follows:
+        for density in densities:
+        N += size(fixed_ratios)*density^2
+        )DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(density_prior_box, ops::DensityPriorBoxOp,
+                  ops::DensityPriorBoxOpMaker,
+                  paddle::framework::EmptyGradOpMaker);
+
+REGISTER_OP_CPU_KERNEL(density_prior_box, ops::DensityPriorBoxOpKernel<float>,
+                       ops::DensityPriorBoxOpKernel<double>);

paddle/fluid/operators/detection/density_prior_box_op.h

+/* Copyright (c) 2016 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 <algorithm>
+#include <vector>
+#include "paddle/fluid/operators/detection/prior_box_op.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input = ctx.Input<paddle::framework::Tensor>("Input");
+    auto* image = ctx.Input<paddle::framework::Tensor>("Image");
+    auto* boxes = ctx.Output<paddle::framework::Tensor>("Boxes");
+    auto* vars = ctx.Output<paddle::framework::Tensor>("Variances");
+
+    auto variances = ctx.Attr<std::vector<float>>("variances");
+    auto clip = ctx.Attr<bool>("clip");
+
+    auto fixed_sizes = ctx.Attr<std::vector<float>>("fixed_sizes");
+    auto fixed_ratios = ctx.Attr<std::vector<float>>("fixed_ratios");
+    auto densities = ctx.Attr<std::vector<int>>("densities");
+
+    T step_w = static_cast<T>(ctx.Attr<float>("step_w"));
+    T step_h = static_cast<T>(ctx.Attr<float>("step_h"));
+    T offset = static_cast<T>(ctx.Attr<float>("offset"));
+
+    auto img_width = image->dims()[3];
+    auto img_height = image->dims()[2];
+
+    auto feature_width = input->dims()[3];
+    auto feature_height = input->dims()[2];
+
+    T step_width, step_height;
+    if (step_w == 0 || step_h == 0) {
+      step_width = static_cast<T>(img_width) / feature_width;
+      step_height = static_cast<T>(img_height) / feature_height;
+    } else {
+      step_width = step_w;
+      step_height = step_h;
+    }
+    int num_priors = 0;
+    if (fixed_sizes.size() > 0 && densities.size() > 0) {
+      for (size_t i = 0; i < densities.size(); ++i) {
+        if (fixed_ratios.size() > 0) {
+          num_priors += (fixed_ratios.size()) * (pow(densities[i], 2));
+        }
+      }
+    }
+
+    boxes->mutable_data<T>(ctx.GetPlace());
+    vars->mutable_data<T>(ctx.GetPlace());
+    auto e_boxes = framework::EigenTensor<T, 4>::From(*boxes).setConstant(0.0);
+
+    int step_average = static_cast<int>((step_width + step_height) * 0.5);
+
+    for (int h = 0; h < feature_height; ++h) {
+      for (int w = 0; w < feature_width; ++w) {
+        T center_x = (w + offset) * step_width;
+        T center_y = (h + offset) * step_height;
+        int idx = 0;
+        // Generate density prior boxes with fixed sizes.
+        for (size_t s = 0; s < fixed_sizes.size(); ++s) {
+          auto fixed_size = fixed_sizes[s];
+          int density = densities[s];
+          // Generate density prior boxes with fixed ratios.
+          if (fixed_ratios.size() > 0) {
+            for (size_t r = 0; r < fixed_ratios.size(); ++r) {
+              float ar = fixed_ratios[r];
+              int shift = step_average / density;
+              float box_width_ratio = fixed_size * sqrt(ar);
+              float box_height_ratio = fixed_size / sqrt(ar);
+              for (int di = 0; di < density; ++di) {
+                for (int dj = 0; dj < density; ++dj) {
+                  float center_x_temp =
+                      center_x - step_average / 2. + shift / 2. + dj * shift;
+                  float center_y_temp =
+                      center_y - step_average / 2. + shift / 2. + di * shift;
+                  e_boxes(h, w, idx, 0) =
+                      (center_x_temp - box_width_ratio / 2.) / img_width >= 0
+                          ? (center_x_temp - box_width_ratio / 2.) / img_width
+                          : 0;
+                  e_boxes(h, w, idx, 1) =
+                      (center_y_temp - box_height_ratio / 2.) / img_height >= 0
+                          ? (center_y_temp - box_height_ratio / 2.) / img_height
+                          : 0;
+                  e_boxes(h, w, idx, 2) =
+                      (center_x_temp + box_width_ratio / 2.) / img_width <= 1
+                          ? (center_x_temp + box_width_ratio / 2.) / img_width
+                          : 1;
+                  e_boxes(h, w, idx, 3) =
+                      (center_y_temp + box_height_ratio / 2.) / img_height <= 1
+                          ? (center_y_temp + box_height_ratio / 2.) / img_height
+                          : 1;
+                  idx++;
+                }
+              }
+            }
+          }
+        }
+      }
+    }
+    if (clip) {
+      platform::Transform<platform::CPUDeviceContext> trans;
+      ClipFunctor<T> clip_func;
+      trans(ctx.template device_context<platform::CPUDeviceContext>(),
+            boxes->data<T>(), boxes->data<T>() + boxes->numel(),
+            boxes->data<T>(), clip_func);
+    }
+    framework::Tensor var_t;
+    var_t.mutable_data<T>(
+        framework::make_ddim({1, static_cast<int>(variances.size())}),
+        ctx.GetPlace());
+
+    auto var_et = framework::EigenTensor<T, 2>::From(var_t);
+
+    for (size_t i = 0; i < variances.size(); ++i) {
+      var_et(0, i) = variances[i];
+    }
+
+    int box_num = feature_height * feature_width * num_priors;
+    auto var_dim = vars->dims();
+    vars->Resize({box_num, static_cast<int>(variances.size())});
+
+    auto e_vars = framework::EigenMatrix<T, Eigen::RowMajor>::From(*vars);
+
+    e_vars = var_et.broadcast(Eigen::DSizes<int, 2>(box_num, 1));
+
+    vars->Resize(var_dim);
+  }
+};  // namespace operators
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/load_op.cc

@@ -40,8 +40,9 @@ class LoadOp : public framework::OperatorBase {
 
     auto out_var_name = Output("Out");
     auto *out_var = scope.FindVar(out_var_name);
-    PADDLE_ENFORCE(out_var != nullptr, "Output variable %s cannot be found",
-                   out_var_name);
+    PADDLE_ENFORCE(out_var != nullptr,
+                   "Output variable %s cannot be found in scope %p",
+                   out_var_name, &scope);
 
     if (out_var->IsType<framework::LoDTensor>()) {
       LoadLodTensor(fin, place, out_var);

paddle/fluid/operators/lookup_sparse_table_op.cc

@@ -45,6 +45,7 @@ class LookupSparseTableOp : public framework::OperatorBase {
     auto out_var = scope.FindVar(Output("Out"));
     auto w_var = scope.FindVar(Input("W"));
     auto ids_var = scope.FindVar(Input("Ids"));
+    auto is_test = Attr<bool>("is_test");
 
     PADDLE_ENFORCE(out_var->IsType<framework::LoDTensor>(),
                    "The type of Out var should be LodTensor.");
@@ -65,7 +66,7 @@ class LookupSparseTableOp : public framework::OperatorBase {
     PADDLE_ENFORCE_EQ(framework::ToDataType(w_t->value().type()),
                       framework::proto::VarType::FP32,
                       "The sparse table only support FP32");
-    w_t->Get(ids_t, out_t, true);
+    w_t->Get(ids_t, out_t, true, is_test);
   }
 };
 
@@ -91,6 +92,10 @@ class LookupSparseTableOpMaker : public framework::OpProtoAndCheckerMaker {
                   "(bool default false)"
                   "Whether create new value if for nonexistent key.")
         .SetDefault(true);
+    AddAttr<bool>("is_test",
+                  "In test mode, lookup_sparse_table will "
+                  "return a 0 for unknown id")
+        .SetDefault(false);
     AddComment(R"DOC(
 Lookup Sprase Tablel Operator.
 

paddle/fluid/operators/mul_op.cc

@@ -56,7 +56,8 @@ class MulOp : public framework::OperatorWithKernel {
 
     PADDLE_ENFORCE_EQ(x_mat_dims[1], y_mat_dims[0],
                       "First matrix's width must be equal with second matrix's "
-                      "height. %s, %s");
+                      "height. %s, %s",
+                      x_mat_dims[1], y_mat_dims[0]);
     std::vector<int64_t> output_dims;
     output_dims.reserve(
         static_cast<size_t>(x_num_col_dims + y_dims.size() - y_num_col_dims));

paddle/fluid/operators/nce_op.cc

@@ -69,7 +69,7 @@ class NCEOp : public framework::OperatorWithKernel {
       const framework::ExecutionContext& ctx) const override {
     return framework::OpKernelType(
         framework::ToDataType(ctx.Input<Tensor>("Input")->type()),
-        ctx.GetPlace());
+        platform::CPUPlace());
   }
 };
 
@@ -174,7 +174,7 @@ class NCEOpGrad : public framework::OperatorWithKernel {
       const framework::ExecutionContext& ctx) const override {
     return framework::OpKernelType(
         framework::ToDataType(ctx.Input<Tensor>("Input")->type()),
-        ctx.GetPlace());
+        platform::CPUPlace());
   }
 };
 

paddle/fluid/operators/sgd_op.h

@@ -109,8 +109,6 @@ class SGDOpKernel : public framework::OpKernel<T> {
       const auto *grad_data = grad.value().data<T>();
       auto *out_data = param_out->mutable_value()->data<T>();
       for (size_t i = 0; i < grad.rows().size(); i++) {
-        PADDLE_ENFORCE(grad.rows()[i] < grad.height(),
-                       "Input rows index should less than height");
         int64_t id_index = param_out->AutoGrownIndex(grad.rows()[i], false);
         PADDLE_ENFORCE_GE(id_index, static_cast<int64_t>(0),
                           "id should be in the table");

python/paddle/fluid/layers/detection.py

@@ -31,6 +31,7 @@ from functools import reduce
 
 __all__ = [
     'prior_box',
+    'density_prior_box',
     'multi_box_head',
     'bipartite_match',
     'target_assign',
@@ -1023,6 +1024,135 @@ def prior_box(input,
     return box, var
 
 
+def density_prior_box(input,
+                      image,
+                      densities=None,
+                      fixed_sizes=None,
+                      fixed_ratios=None,
+                      variance=[0.1, 0.1, 0.2, 0.2],
+                      clip=False,
+                      steps=[0.0, 0.0],
+                      offset=0.5,
+                      name=None):
+    """
+    **Density Prior Box Operator**
+
+    Generate density prior boxes for SSD(Single Shot MultiBox Detector) 
+    algorithm. Each position of the input produce N prior boxes, N is 
+    determined by the count of densities, fixed_sizes and fixed_ratios. 
+    Boxes center at grid points around each input position is generated by 
+    this operator, and the grid points is determined by densities and 
+    the count of density prior box is determined by fixed_sizes and fixed_ratios. 
+    Obviously, the number of fixed_sizes is equal to the number of densities.
+    For densities_i in densities:
+    N_density_prior_box =sum(N_fixed_ratios * densities_i^2),
+
+    Args:
+       input(Variable): The Input Variables, the format is NCHW.
+       image(Variable): The input image data of PriorBoxOp,
+            the layout is NCHW.
+       densities(list|tuple|None): the densities of generated density prior 
+            boxes, this attribute should be a list or tuple of integers. 
+            Default: None.
+       fixed_sizes(list|tuple|None): the fixed sizes of generated density
+            prior boxes, this attribute should a list or tuple of same 
+            length with :attr:`densities`. Default: None.
+       fixed_ratios(list|tuple|None): the fixed ratios of generated density
+            prior boxes, if this attribute is not set and :attr:`densities`
+            and :attr:`fix_sizes` is set, :attr:`aspect_ratios` will be used
+            to generate density prior boxes.
+       variance(list|tuple): the variances to be encoded in density prior boxes.
+            Default:[0.1, 0.1, 0.2, 0.2].
+       clip(bool): Whether to clip out-of-boundary boxes. Default: False.
+       step(list|turple): Prior boxes step across width and height, If
+            step[0] == 0.0/step[1] == 0.0, the density prior boxes step across
+            height/weight of the input will be automatically calculated.
+            Default: [0., 0.]
+       offset(float): Prior boxes center offset. Default: 0.5
+       name(str): Name of the density prior box op. Default: None.
+
+    Returns:
+        tuple: A tuple with two Variable (boxes, variances)
+
+        boxes: the output density prior boxes of PriorBox.
+        The layout is [H, W, num_priors, 4].
+        H is the height of input, W is the width of input,
+        num_priors is the total
+        box count of each position of input.
+
+        variances: the expanded variances of PriorBox.
+        The layout is [H, W, num_priors, 4].
+        H is the height of input, W is the width of input
+        num_priors is the total
+        box count of each position of input
+
+
+    Examples:
+        .. code-block:: python
+
+            box, var = fluid.layers.density_prior_box(
+                input=conv1,
+                image=images,
+                min_sizes=[100.],
+                max_sizes=[200.],
+                aspect_ratios=[1.0, 1.0 / 2.0, 2.0],
+                densities=[3, 4],
+                fixed_sizes=[50., 60.],
+                fixed_ratios=[1.0, 3.0, 1.0 / 3.0],
+                flip=True,
+                clip=True)
+    """
+    helper = LayerHelper("density_prior_box", **locals())
+    dtype = helper.input_dtype()
+
+    def _is_list_or_tuple_(data):
+        return (isinstance(data, list) or isinstance(data, tuple))
+
+    if not _is_list_or_tuple_(densities):
+        raise TypeError('densities should be a list or a tuple or None.')
+    if not _is_list_or_tuple_(fixed_sizes):
+        raise TypeError('fixed_sizes should be a list or a tuple or None.')
+    if not _is_list_or_tuple_(fixed_ratios):
+        raise TypeError('fixed_ratios should be a list or a tuple or None.')
+    if len(densities) != len(fixed_sizes):
+        raise ValueError('densities and fixed_sizes length should be euqal.')
+    if not (_is_list_or_tuple_(steps) and len(steps) == 2):
+        raise ValueError('steps should be a list or tuple ',
+                         'with length 2, (step_width, step_height).')
+
+    densities = list(map(int, densities))
+    fixed_sizes = list(map(float, fixed_sizes))
+    fixed_ratios = list(map(float, fixed_ratios))
+    steps = list(map(float, steps))
+
+    attrs = {
+        'variances': variance,
+        'clip': clip,
+        'step_w': steps[0],
+        'step_h': steps[1],
+        'offset': offset,
+    }
+    if densities is not None and len(densities) > 0:
+        attrs['densities'] = densities
+    if fixed_sizes is not None and len(fixed_sizes) > 0:
+        attrs['fixed_sizes'] = fixed_sizes
+    if fixed_ratios is not None and len(fixed_ratios) > 0:
+        attrs['fixed_ratios'] = fixed_ratios
+
+    box = helper.create_variable_for_type_inference(dtype)
+    var = helper.create_variable_for_type_inference(dtype)
+    helper.append_op(
+        type="density_prior_box",
+        inputs={"Input": input,
+                "Image": image},
+        outputs={"Boxes": box,
+                 "Variances": var},
+        attrs=attrs, )
+    box.stop_gradient = True
+    var.stop_gradient = True
+    return box, var
+
+
 def multi_box_head(inputs,
                    image,
                    base_size,

python/paddle/fluid/layers/nn.py

@@ -166,6 +166,7 @@ __all__ = [
     'grid_sampler',
     'log_loss',
     'add_position_encoding',
+    'bilinear_tensor_product',
 ]
 
 
@@ -4073,8 +4074,8 @@ def edit_distance(input, label, normalized=True, ignored_tokens=None):
     Examples:
         .. code-block:: python
 
-            x = fluid.layers.data(name='x', shape=[8], dtype='float32')
-            y = fluid.layers.data(name='y', shape=[7], dtype='float32')
+            x = fluid.layers.data(name='x', shape=[1], dtype='float32')
+            y = fluid.layers.data(name='y', shape=[1], dtype='float32')
             cost = fluid.layers.edit_distance(input=x,label=y)
     """
     helper = LayerHelper("edit_distance", **locals())
@@ -4748,7 +4749,8 @@ def softmax_with_cross_entropy(logits,
                                label,
                                soft_label=False,
                                ignore_index=-100,
-                               numeric_stable_mode=False):
+                               numeric_stable_mode=False,
+                               return_softmax=False):
     """
     **Softmax With Cross Entropy Operator.**
 
@@ -4812,9 +4814,15 @@ def softmax_with_cross_entropy(logits,
                                     the algorithm is always numerically stable. 
                                     Note that the speed may be slower when use 
                                     stable algorithm. Default: False
+        return_softmax (bool): A flag indicating whether to return the softmax 
+                               along with the cross entropy loss. Default: False
 
     Returns:
-        Variable: The cross entropy loss is a 2-D tensor with shape [N x 1].
+        Variable or Tuple of two Variables: Return the cross entropy loss if 
+                              `return_softmax` is False, otherwise the tuple 
+                              (loss, softmax), where the cross entropy loss is 
+                              a 2-D tensor with shape [N x 1], and softmax is a 
+                              2-D tensor with shape [N x K].
 
     Examples:
         .. code-block:: python
@@ -4839,6 +4847,10 @@ def softmax_with_cross_entropy(logits,
             'ignore_index': ignore_index,
             'numeric_stable_mode': numeric_stable_mode
         })
+
+    if return_softmax:
+        return loss, softmax
+
     return loss
 
 
@@ -8302,3 +8314,72 @@ def add_position_encoding(input, alpha, beta, name=None):
         attrs={"alpha": alpha,
                "beta": beta})
     return out
+
+
+def bilinear_tensor_product(x,
+                            y,
+                            size,
+                            act=None,
+                            name=None,
+                            param_attr=None,
+                            bias_attr=None):
+    """
+    **Add Bilinear Tensor Product Layer**
+
+    This layer performs bilinear tensor product on two inputs.
+    For example:
+
+    .. math::
+       out{i} = x * W_{i} * {y^\mathrm{T}}, i=0,1,...,size-1
+
+    In this formula:
+      - :math:`x`: the first input contains M elements, shape is [batch_size, M].
+      - :math:`y`: the second input contains N elements, shape is [batch_size, N].
+      - :math:`W_{i}`: the i-th learned weight, shape is [M, N]
+      - :math:`out{i}`: the i-th element of out, shape is [batch_size, size].
+      - :math:`y^\mathrm{T}`: the transpose of :math:`y_{2}`.
+
+    Args:
+        x (Variable): 2-D input tensor with shape [batch_size, M]
+        y (Variable): 2-D input tensor with shape [batch_size, N]
+        size (int): The dimension of this layer.
+        act (str, default None): Activation to be applied to the output of this layer.
+        name (str, default None): The name of this layer.
+        param_attr (ParamAttr, default None): The parameter attribute for the learnable w.
+            parameters/weights of this layer.
+        bias_attr (ParamAttr, default None): The parameter attribute for the bias
+            of this layer. If it is set to False, no bias will be added to the output units.
+            If it is set to None, the bias is initialized zero. Default: None.
+
+    Returns:
+        Variable: A 2-D Tensor of shape [batch_size, size].
+
+    Examples:
+        .. code-block:: python
+
+          tensor = bilinear_tensor_product(x=layer1, y=layer2, size=1000)
+    """
+    helper = LayerHelper('bilinear_tensor_product', **locals())
+    dtype = helper.input_dtype('x')
+
+    param_shape = [size, x.shape[1], y.shape[1]]
+
+    w = helper.create_parameter(
+        attr=helper.param_attr, shape=param_shape, dtype=dtype, is_bias=False)
+
+    if name is None:
+        out = helper.create_variable_for_type_inference(dtype=dtype)
+    else:
+        out = helper.create_variable(name=name, dtype=dtype, persistable=False)
+
+    inputs = {"X": x, "Y": y, "Weight": w}
+    if helper.bias_attr:
+        bias_size = [1, size]
+        bias = helper.create_parameter(
+            attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
+        inputs["Bias"] = bias
+    helper.append_op(
+        type="bilinear_tensor_product", inputs=inputs, outputs={"Out": out})
+
+    # add activation
+    return helper.append_activation(out)

python/paddle/fluid/layers/tensor.py

@@ -235,11 +235,11 @@ def tensor_array_to_tensor(input, axis=1, name=None):
 
            output, output_index = fluid.layers.tensor_array_to_tensor(input=tensor_array)
     """
-    helper = LayerHelper('tensor_array_concat', **locals())
+    helper = LayerHelper('tensor_array_to_tensor', **locals())
     out = helper.create_variable_for_type_inference(dtype=helper.input_dtype())
     out_index = helper.create_variable_for_type_inference(dtype="int32")
     helper.append_op(
-        type='tensor_array_concat',
+        type='tensor_array_to_tensor',
         inputs={'X': input},
         outputs={'Out': [out],
                  'OutIndex': [out_index]},

python/paddle/fluid/tests/test_detection.py

@@ -128,6 +128,24 @@ class TestPriorBox(unittest.TestCase):
         assert box.shape[3] == 4
 
 
+class TestDensityPriorBox(unittest.TestCase):
+    def test_density_prior_box(self):
+        data_shape = [3, 224, 224]
+        images = fluid.layers.data(
+            name='pixel', shape=data_shape, dtype='float32')
+        conv1 = fluid.layers.conv2d(images, 3, 3, 2)
+        box, var = layers.density_prior_box(
+            input=conv1,
+            image=images,
+            densities=[3, 4],
+            fixed_sizes=[50., 60.],
+            fixed_ratios=[1.0],
+            clip=True)
+        assert len(box.shape) == 4
+        assert box.shape == var.shape
+        assert box.shape[3] == 4
+
+
 class TestAnchorGenerator(unittest.TestCase):
     def test_anchor_generator(self):
         data_shape = [3, 224, 224]

python/paddle/fluid/tests/unittests/test_density_prior_box_op.py

+# 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import sys
+import math
+from op_test import OpTest
+
+
+class TestDensityPriorBoxOp(OpTest):
+    def set_data(self):
+        self.init_test_params()
+        self.init_test_input()
+        self.init_test_output()
+        self.inputs = {'Input': self.input, 'Image': self.image}
+
+        self.attrs = {
+            'variances': self.variances,
+            'clip': self.clip,
+            'step_w': self.step_w,
+            'step_h': self.step_h,
+            'offset': self.offset,
+            'densities': self.densities,
+            'fixed_sizes': self.fixed_sizes,
+            'fixed_ratios': self.fixed_ratios
+        }
+        self.outputs = {'Boxes': self.out_boxes, 'Variances': self.out_var}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def setUp(self):
+        self.op_type = "density_prior_box"
+        self.set_data()
+
+    def set_density(self):
+        self.densities = []
+        self.fixed_sizes = []
+        self.fixed_ratios = []
+
+    def init_test_params(self):
+        self.layer_w = 32
+        self.layer_h = 32
+
+        self.image_w = 40
+        self.image_h = 40
+
+        self.step_w = float(self.image_w) / float(self.layer_w)
+        self.step_h = float(self.image_h) / float(self.layer_h)
+
+        self.input_channels = 2
+        self.image_channels = 3
+        self.batch_size = 10
+
+        self.variances = [0.1, 0.1, 0.2, 0.2]
+        self.variances = np.array(self.variances, dtype=np.float).flatten()
+
+        self.set_density()
+
+        self.clip = True
+        self.num_priors = 0
+        if len(self.fixed_sizes) > 0 and len(self.densities) > 0:
+            for density in self.densities:
+                if len(self.fixed_ratios) > 0:
+                    self.num_priors += len(self.fixed_ratios) * (pow(density,
+                                                                     2))
+        self.offset = 0.5
+
+    def init_test_input(self):
+        self.image = np.random.random(
+            (self.batch_size, self.image_channels, self.image_w,
+             self.image_h)).astype('float32')
+
+        self.input = np.random.random(
+            (self.batch_size, self.input_channels, self.layer_w,
+             self.layer_h)).astype('float32')
+
+    def init_test_output(self):
+        out_dim = (self.layer_h, self.layer_w, self.num_priors, 4)
+        out_boxes = np.zeros(out_dim).astype('float32')
+        out_var = np.zeros(out_dim).astype('float32')
+
+        step_average = int((self.step_w + self.step_h) * 0.5)
+        for h in range(self.layer_h):
+            for w in range(self.layer_w):
+                idx = 0
+                c_x = (w + self.offset) * self.step_w
+                c_y = (h + self.offset) * self.step_h
+                # Generate density prior boxes with fixed size
+                for density, fixed_size in zip(self.densities,
+                                               self.fixed_sizes):
+                    if (len(self.fixed_ratios) > 0):
+                        for ar in self.fixed_ratios:
+                            shift = int(step_average / density)
+                            box_width_ratio = fixed_size * math.sqrt(ar)
+                            box_height_ratio = fixed_size / math.sqrt(ar)
+                            for di in range(density):
+                                for dj in range(density):
+                                    c_x_temp = c_x - step_average / 2.0 + shift / 2.0 + dj * shift
+                                    c_y_temp = c_y - step_average / 2.0 + shift / 2.0 + di * shift
+                                    out_boxes[h, w, idx, :] = [
+                                        max((c_x_temp - box_width_ratio / 2.0) /
+                                            self.image_w, 0),
+                                        max((c_y_temp - box_height_ratio / 2.0)
+                                            / self.image_h, 0),
+                                        min((c_x_temp + box_width_ratio / 2.0) /
+                                            self.image_w, 1),
+                                        min((c_y_temp + box_height_ratio / 2.0)
+                                            / self.image_h, 1)
+                                    ]
+                                    idx += 1
+        if self.clip:
+            out_boxes = np.clip(out_boxes, 0.0, 1.0)
+        out_var = np.tile(self.variances,
+                          (self.layer_h, self.layer_w, self.num_priors, 1))
+        self.out_boxes = out_boxes.astype('float32')
+        self.out_var = out_var.astype('float32')
+
+
+class TestDensityPriorBox(TestDensityPriorBoxOp):
+    def set_density(self):
+        self.densities = [3, 4]
+        self.fixed_sizes = [1.0, 2.0]
+        self.fixed_ratios = [1.0]
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_layers.py

@@ -369,6 +369,10 @@ class TestBook(unittest.TestCase):
         with program_guard(program):
             x = layers.data(name='x', shape=[16], dtype='float32')
             y = layers.data(name='label', shape=[1], dtype='int64')
+            loss, softmax = layers.softmax_with_cross_entropy(
+                x, y, return_softmax=True)
+            self.assertIsNotNone(loss)
+            self.assertIsNotNone(softmax)
             loss = layers.softmax_with_cross_entropy(x, y)
             self.assertIsNotNone(loss)
         print(str(program))
@@ -911,6 +915,16 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(data_1)
         print(str(program))
 
+    def test_bilinear_tensor_product_layer(self):
+        program = Program()
+        with program_guard(program):
+            data = layers.data(name='data', shape=[4], dtype="float32")
+
+            theta = layers.data(name="theta", shape=[5], dtype="float32")
+            out = layers.bilinear_tensor_product(data, theta, 6)
+
+        print(str(program))
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_lookup_sparse_table_op.py

@@ -80,6 +80,33 @@ class TestLookupSpraseTable(OpTest):
         assert (result_array2[3] == w_array[6]).all()
         assert (result_array2[4] == w_array[7]).all()
 
+        # create and run lookup_table operator
+        test_lookup_table = Operator(
+            "lookup_sparse_table",
+            W='W',
+            Ids='Ids',
+            Out='Out',
+            min=-5.0,
+            max=10.0,
+            seed=10,
+            is_test=True)
+
+        ids = scope.var("Ids").get_tensor()
+        unknown_id = [44, 22, 33]
+        ids_array2 = np.array([4, 2, 3, 7, 100000] + unknown_id).astype("int64")
+        ids.set(ids_array2, place)
+        test_lookup_table.run(scope, place)
+
+        result_array2 = np.array(out_tensor)
+        assert (result_array2[0] == w_array[5]).all()
+        assert (result_array2[1] == w_array[1]).all()
+        assert (result_array2[2] == w_array[2]).all()
+        assert (result_array2[3] == w_array[6]).all()
+        assert (result_array2[4] == w_array[7]).all()
+
+        for i in [5, 6, 7]:
+            assert np.all(result_array2[i] == 0)
+
     def test_w_is_selected_rows(self):
         places = [core.CPUPlace()]
         # currently only support CPU

python/paddle/fluid/tests/unittests/test_parallel_executor_dry_run.py

@@ -21,8 +21,8 @@ import six
 class TestBase(unittest.TestCase):
     def main(self,
              network_func,
-             iter=100,
-             iter_per_pe=100,
+             iter=10,
+             iter_per_pe=10,
              use_gpu=True,
              use_experimental_executor=False):
         if use_gpu and not fluid.core.is_compiled_with_cuda():
@@ -45,7 +45,7 @@ class TestBase(unittest.TestCase):
             exe_strategy._dry_run = True
             exe_strategy.use_experimental_executor = use_experimental_executor
             pe = fluid.ParallelExecutor(
-                use_cuda=True,
+                use_cuda=use_gpu,
                 loss_name=loss.name,
                 main_program=main_prog,
                 exec_strategy=exe_strategy)