6. delete useless predictor id

test=develop

paddle/fluid/inference/analysis/argument.h

@@ -99,10 +99,6 @@ struct Argument {
  private:                                                                 \
   unique_ptr_t field__##_;
 
-  // Each predictor has an unique id.
-  // For now, this attr will help us to get the right
-  // trt_engine for each trt_engine_op for each predictor when using trt.
-  DECL_ARGUMENT_FIELD(predictor_id, PredictorID, int);
   // Model path
   DECL_ARGUMENT_FIELD(model_dir, ModelDir, std::string);
   // Model specified with program and parameters files.

paddle/fluid/inference/analysis/ir_pass_manager.cc

@@ -81,7 +81,6 @@ void IRPassManager::CreatePasses(Argument *argument,
       pass->Set(
           "model_opt_cache_dir",
           new std::string(GetOrCreateModelOptCacheDir(model_opt_cache_dir)));
-      pass->Set("predictor_id", new int(argument->predictor_id()));
       pass->Set("gpu_device_id", new int(argument->gpu_device_id()));
     }
 

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

@@ -209,9 +209,8 @@ void TensorRtSubgraphPass::CreateTensorRTOp(
   SetAttr(op_desc->Proto(), "parameters", params);
 
   auto enable_int8 = Get<bool>("enable_int8");
-  int predictor_id = Get<int>("predictor_id");
   auto engine_key = GenerateEngineKey(input_names_with_id, output_names_with_id,
-                                      std::to_string(predictor_id));
+                                      std::to_string(0));
 
   // Get "" when there is no cached calibration table data.
   std::string calibration_data = GetTrtCalibTableData(
@@ -221,9 +220,6 @@ void TensorRtSubgraphPass::CreateTensorRTOp(
   SetAttr(op_desc->Proto(), "enable_int8", enable_int8);
   SetAttr(op_desc->Proto(), "engine_key", engine_key);
   SetAttr(op_desc->Proto(), "engine_serialized_data", std::string(""));
-  SetAttr(op_desc->Proto(), "engine_serialized_data_path",
-          GetTrtEngineSerializedPath(Get<std::string>("model_opt_cache_dir"),
-                                     engine_key));
 
   std::unique_ptr<tensorrt::TRTInt8Calibrator> calibrator;
   if (enable_int8 && calibration_data.size() != 0) {
@@ -239,13 +235,13 @@ void TensorRtSubgraphPass::CreateTensorRTOp(
     std::string trt_engine_serialized_data = GetTrtEngineSerializedData(
         Get<std::string>("model_opt_cache_dir"), engine_key);
 
-    tensorrt::TensorRTEngine *trt_engine =
-        inference::Singleton<tensorrt::TRTEngineManager>::Global().Create(
-            Get<int>("max_batch_size"), Get<int>("workspace_size"), enable_int8,
-            calibrator.get(), engine_key, Get<int>("gpu_device_id"));
     if (trt_engine_serialized_data.size() == 0) {
       LOG(INFO) << "Prepare TRT engine (Optimize model structure, Select OP "
                    "kernel etc). This process may cost a lot of time.";
+      std::unique_ptr<tensorrt::TensorRTEngine> trt_engine(
+          new tensorrt::TensorRTEngine(
+              Get<int>("max_batch_size"), Get<int>("workspace_size"),
+              enable_int8, calibrator.get(), Get<int>("gpu_device_id")));
       auto *scope = param_scope();
       framework::BlockDesc block_desc_temp(nullptr, block_desc.Proto());
       std::unordered_set<std::string> param_set(params.begin(), params.end());
@@ -253,7 +249,7 @@ void TensorRtSubgraphPass::CreateTensorRTOp(
           .ConvertBlockToTRTEngine(
               &block_desc_temp, *scope,
               std::vector<std::string>(input_names.begin(), input_names.end()),
-              param_set, output_mapping, trt_engine);
+              param_set, output_mapping, trt_engine.get());
       nvinfer1::IHostMemory *serialized_engine_data = trt_engine->Serialize();
       trt_engine_serialized_data =
           std::string((const char *)serialized_engine_data->data(),
@@ -263,11 +259,11 @@ void TensorRtSubgraphPass::CreateTensorRTOp(
                                      engine_key),
           trt_engine_serialized_data);
     } else {
-      LOG(INFO) << "Load TRT Engine from optimized serialized data : "
+      LOG(INFO) << "Load TRT Optimized Info from "
                 << GetTrtEngineSerializedPath(
                        Get<std::string>("model_opt_cache_dir"), engine_key);
-      trt_engine->Deserialize(trt_engine_serialized_data);
     }
+
     SetAttr(op_desc->Proto(), "engine_serialized_data",
             trt_engine_serialized_data);
   }

paddle/fluid/inference/api/analysis_predictor.cc

@@ -342,7 +342,6 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
       config_.static_memory_optim_force_update_);
   argument_.SetModelFromMemory(config_.model_from_memory_);
   // Analyze inference_program
-  argument_.SetPredictorID(predictor_id_);
   if (!config_.model_dir().empty()) {
     argument_.SetModelDir(config_.model_dir());
   } else {

paddle/fluid/inference/api/analysis_predictor.h

@@ -44,9 +44,7 @@ using framework::NaiveExecutor;
  */
 class AnalysisPredictor : public PaddlePredictor {
  public:
-  explicit AnalysisPredictor(const AnalysisConfig &config) : config_(config) {
-    predictor_id_ = inference::GetUniqueId();
-  }
+  explicit AnalysisPredictor(const AnalysisConfig &config) : config_(config) {}
   ~AnalysisPredictor();
 
   bool Init(const std::shared_ptr<framework::Scope> &parent_scope,
@@ -146,7 +144,6 @@ class AnalysisPredictor : public PaddlePredictor {
   const size_t max_shape_collect_count_{1000};
   int need_collect_var_shapes_{-1};  // -1 for default, 0 for false, 1 for true.
   std::vector<std::map<std::string, std::vector<int>>> batch_var_shapes_;
-  int predictor_id_;
 
  private:
   // Some status here that help to determine the status inside the predictor.

paddle/fluid/inference/tensorrt/engine.h

@@ -199,43 +199,6 @@ class TensorRTEngine {
 #define TRT_ENGINE_ADD_LAYER(engine__, layer__, ARGS...) \
   engine__->network()->add##layer__(ARGS);
 
-/*
- * Helper to control the TensorRT engine's creation and deletion.
- */
-class TRTEngineManager {
- public:
-  bool HasEngine(const std::string& name) const {
-    if (engines_.count(name) == 0) return false;
-    return engines_.at(name).get() != nullptr;
-  }
-
-  // Get an engine called `name`.
-  TensorRTEngine* Get(const std::string& name) const {
-    return engines_.at(name).get();
-  }
-
-  // Create or get an engine called `name`
-  TensorRTEngine* Create(int max_batch, int max_workspace, bool enable_int8,
-                         TRTInt8Calibrator* calibrator,
-                         const std::string& engine_name, int device_id = 0) {
-    std::unique_lock<std::mutex> lk(mut_);
-    auto* p = new TensorRTEngine(max_batch, max_workspace, enable_int8,
-                                 calibrator, device_id);
-    engines_[engine_name].reset(p);
-    return p;
-  }
-
-  void DeleteALL() {
-    for (auto& item : engines_) {
-      item.second.reset(nullptr);
-    }
-  }
-
- private:
-  std::unordered_map<std::string, std::unique_ptr<TensorRTEngine>> engines_;
-  std::mutex mut_;
-};
-
 }  // namespace tensorrt
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/inference/tensorrt/plugin/trt_plugin_factory.h

@@ -31,7 +31,8 @@ namespace inference {
 namespace tensorrt {
 namespace plugin {
 
-class PluginFactoryTensorRT : public nvinfer1::IPluginFactory {
+class PluginFactoryTensorRT : public nvinfer1::IPluginFactory,
+                              public DeleteHelper {
  public:
   // Deserialization method
   PluginTensorRT* createPlugin(const char* layer_name, const void* serial_data,

paddle/fluid/inference/tensorrt/plugin/trt_plugin_utils.h

@@ -24,6 +24,13 @@ namespace inference {
 namespace tensorrt {
 namespace plugin {
 
+// Some trt base classes lack of the destructor.
+// We use a assisted class to fix this.
+struct DeleteHelper {
+ protected:
+  virtual ~DeleteHelper() {}
+};
+
 template <typename T>
 inline void SerializeValue(void** buffer, T const& value);
 

paddle/fluid/operators/tensorrt/tensorrt_engine_op.h

@@ -41,7 +41,7 @@ class TensorRTEngineOp : public framework::OperatorBase {
  private:
   std::vector<std::string> input_names_;
   std::unordered_set<std::string> param_names_;
-  mutable TensorRTEngine *trt_engine_;
+  mutable std::unique_ptr<TensorRTEngine> trt_engine_;
   int max_batch_size_;
   int workspace_size_;
   std::unique_ptr<TRTInt8Calibrator> calibrator_;
@@ -64,7 +64,6 @@ class TensorRTEngineOp : public framework::OperatorBase {
     calibration_data_ = Attr<std::string>("calibration_data");
     engine_key_ = Attr<std::string>("engine_key");
     engine_serialized_data_ = Attr<std::string>("engine_serialized_data");
-    trt_engine_ = nullptr;
 
     auto params = Attr<std::vector<std::string>>("parameters");
     for (const auto &param : params) {
@@ -78,16 +77,6 @@ class TensorRTEngineOp : public framework::OperatorBase {
     if (enable_int8_ && calibration_data_.size()) {
       calibrator_.reset(new TRTInt8Calibrator(calibration_data_));
     }
-
-    // we will create an engine here.
-    if (!calibration_mode_) {
-      if (inference::Singleton<inference::tensorrt::TRTEngineManager>::Global()
-              .HasEngine(engine_key_)) {
-        trt_engine_ = inference::Singleton<
-                          inference::tensorrt::TRTEngineManager>::Global()
-                          .Get(engine_key_);
-      }
-    }
   }
 
  protected:
@@ -231,15 +220,17 @@ class TensorRTEngineOp : public framework::OperatorBase {
 
   TensorRTEngine *GetEngine(const framework::Scope &scope,
                             const platform::Place &dev_place) const {
-    if (trt_engine_ == nullptr) {
-      trt_engine_ =
-          inference::Singleton<inference::tensorrt::TRTEngineManager>::Global()
-              .Create(max_batch_size_, workspace_size_, enable_int8_,
-                      calibrator_.get(), engine_key_,
-                      boost::get<platform::CUDAPlace>(dev_place).device);
-      PrepareTRTEngine(scope, trt_engine_);
+    if (trt_engine_.get() == nullptr) {
+      trt_engine_.reset(new inference::tensorrt::TensorRTEngine(
+          max_batch_size_, workspace_size_, enable_int8_, calibrator_.get(),
+          boost::get<platform::CUDAPlace>(dev_place).device));
+      if (engine_serialized_data_.size() > 0) {
+        trt_engine_->Deserialize(engine_serialized_data_);
+      } else {
+        PrepareTRTEngine(scope, trt_engine_.get());
+      }
     }
-    return trt_engine_;
+    return trt_engine_.get();
   }
 
   void PrepareTRTEngine(const framework::Scope &scope,