add DLA support：C++&&Python api (#30165)

* add dla

* add dla done

* add python api
Co-authored-by: Nshangzhizhou <root@szth-rp-fanyi-opera49.szth.baidu.com>

paddle/fluid/inference/analysis/argument.h

@@ -199,6 +199,8 @@ struct Argument {
   DECL_ARGUMENT_FIELD(disable_trt_plugin_fp16, CloseTrtPluginFp16, bool);
 
   DECL_ARGUMENT_FIELD(use_tensorrt, UseTensorRT, bool);
+  DECL_ARGUMENT_FIELD(tensorrt_use_dla, TensorRtUseDLA, bool);
+  DECL_ARGUMENT_FIELD(tensorrt_dla_core, TensorRtDLACore, int);
   DECL_ARGUMENT_FIELD(tensorrt_max_batch_size, TensorRtMaxBatchSize, int);
   DECL_ARGUMENT_FIELD(tensorrt_workspace_size, TensorRtWorkspaceSize, int);
   DECL_ARGUMENT_FIELD(tensorrt_min_subgraph_size, TensorRtMinSubgraphSize, int);

paddle/fluid/inference/analysis/ir_pass_manager.cc

@@ -143,6 +143,8 @@ void IRPassManager::CreatePasses(Argument *argument,
                     argument->optim_input_shape()));
       pass->Set("trt_disabled_ops", new std::vector<std::string>(
                                         argument->tensorrt_disabled_ops()));
+      pass->Set("trt_use_dla", new bool(argument->tensorrt_use_dla()));
+      pass->Set("trt_dla_core", new int(argument->tensorrt_dla_core()));
       // Setting the disable_trt_plugin_fp16 to true means that TRT plugin will
       // not
       // run fp16.

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

@@ -320,6 +320,8 @@ void TensorRtSubgraphPass::CreateTensorRTOp(
                   min_input_shape, max_input_shape, opt_input_shape,
                   disable_trt_plugin_fp16);
   trt_engine->SetUseOSS(Get<bool>("use_oss"));
+  trt_engine->SetUseDLA(Get<bool>("trt_use_dla"));
+  trt_engine->SetDLACore(Get<int>("trt_dla_core"));
 
   trt_engine->SetWithErnie(
       graph->Has(framework::ir::kEmbEltwiseLayernormPass) &&

paddle/fluid/inference/api/analysis_config.cc

@@ -126,6 +126,8 @@ AnalysisConfig::AnalysisConfig(const AnalysisConfig &other) {
   CP_MEMBER(tensorrt_min_subgraph_size_);
   CP_MEMBER(tensorrt_precision_mode_);
   CP_MEMBER(trt_disabled_ops_);
+  CP_MEMBER(trt_use_dla_);
+  CP_MEMBER(trt_dla_core_);
   CP_MEMBER(trt_use_static_engine_);
   CP_MEMBER(trt_use_calib_mode_);
   CP_MEMBER(trt_use_oss_);
@@ -305,6 +307,11 @@ void AnalysisConfig::SetTRTDynamicShapeInfo(
   disable_trt_plugin_fp16_ = disable_trt_plugin_fp16;
 }
 
+void AnalysisConfig::EnableTensorRtDLA(int dla_core) {
+  trt_use_dla_ = true;
+  trt_dla_core_ = dla_core;
+}
+
 void AnalysisConfig::Exp_DisableTensorRtOPs(
     const std::vector<std::string> &ops) {
   trt_disabled_ops_.insert(trt_disabled_ops_.end(), ops.begin(), ops.end());
@@ -452,6 +459,9 @@ std::string AnalysisConfig::SerializeInfoCache() {
   for (auto &op : trt_disabled_ops_) ss << op.c_str();
   ss << ";";
 
+  ss << trt_use_dla_;
+  ss << trt_dla_core_;
+
   ss << enable_memory_optim_;
 
   ss << use_mkldnn_;

paddle/fluid/inference/api/analysis_predictor.cc

@@ -477,6 +477,8 @@ void AnalysisPredictor::PrepareArgument() {
     argument_.SetTensorRtMaxBatchSize(config_.tensorrt_max_batchsize_);
     argument_.SetTensorRtMinSubgraphSize(config_.tensorrt_min_subgraph_size_);
     argument_.SetTensorRtDisabledOPs(config_.trt_disabled_ops_);
+    argument_.SetTensorRtUseDLA(config_.trt_use_dla_);
+    argument_.SetTensorRtDLACore(config_.trt_dla_core_);
     argument_.SetTensorRtPrecisionMode(config_.tensorrt_precision_mode_);
     argument_.SetTensorRtUseStaticEngine(config_.trt_use_static_engine_);
     argument_.SetTensorRtUseCalibMode(config_.trt_use_calib_mode_);

paddle/fluid/inference/api/paddle_analysis_config.h

@@ -326,6 +326,7 @@ struct PD_INFER_DECL AnalysisConfig {
   /// V7.2.1 is needed.
   ///
   void EnableTensorRtOSS();
+
   ///
   /// \brief A boolean state telling whether to use the TensorRT OSS.
   ///
@@ -333,6 +334,20 @@ struct PD_INFER_DECL AnalysisConfig {
   ///
   bool tensorrt_oss_enabled() { return trt_use_oss_; }
 
+  ///
+  /// \brief Enable TensorRT DLA
+  /// \param dla_core ID of DLACore, which should be 0, 1,
+  ///        ..., IBuilder.getNbDLACores() - 1
+  ///
+  void EnableTensorRtDLA(int dla_core = 0);
+
+  ///
+  /// \brief A boolean state telling whether to use the TensorRT DLA.
+  ///
+  /// \return bool Whether to use the TensorRT DLA.
+  ///
+  bool tensorrt_dla_enabled() { return trt_use_dla_; }
+
   ///
   /// \brief Turn on the usage of Lite sub-graph engine.
   ///
@@ -591,6 +606,8 @@ struct PD_INFER_DECL AnalysisConfig {
   bool trt_use_static_engine_{false};
   bool trt_use_calib_mode_{true};
   bool trt_use_oss_{false};
+  bool trt_use_dla_{false};
+  int trt_dla_core_{0};
   std::map<std::string, std::vector<int>> min_input_shape_{};
   std::map<std::string, std::vector<int>> max_input_shape_{};
   std::map<std::string, std::vector<int>> optim_input_shape_{};

paddle/fluid/inference/tensorrt/engine.cc

@@ -176,6 +176,29 @@ void TensorRTEngine::FreezeNetwork() {
     }
   }
 
+  if (use_dla_) {
+    if (!enable_int8 && !enable_fp16) {
+      LOG(WARNING) << "TensorRT DLA must be used with int8 or fp16, but you "
+                      "set float32, so DLA is not used.";
+    } else if (infer_builder_->getNbDLACores() == 0) {
+      LOG(WARNING)
+          << "TensorRT DLA is set by config, but your device does not have "
+             "DLA, so DLA is not used.";
+    } else {
+      if (dla_core_ < 0 || dla_core_ >= infer_builder_->getNbDLACores()) {
+        dla_core_ = 0;
+        LOG(WARNING) << "Invalid DLACore, must be 0 < DLACore < "
+                     << infer_builder_->getNbDLACores() << ", but got "
+                     << dla_core_ << ", so use use 0 as default.";
+      }
+      infer_builder_->setDefaultDeviceType(nvinfer1::DeviceType::kDLA);
+      infer_builder_->setDLACore(dla_core_);
+      infer_builder_->allowGPUFallback(true);
+      LOG(INFO) << "TensorRT DLA enabled in FreezeNetwork(), DLACore "
+                << dla_core_;
+    }
+  }
+
   if (with_dynamic_shape_) {
 #if IS_TRT_VERSION_GE(6000)
     LOG(INFO) << "Run Paddle-TRT Dynamic Shape mode.";

paddle/fluid/inference/tensorrt/engine.h

@@ -220,6 +220,29 @@ class TensorRTEngine {
   void Deserialize(const std::string& engine_serialized_data) {
     freshDeviceId();
     infer_ptr<nvinfer1::IRuntime> runtime(createInferRuntime(&logger_));
+
+    if (use_dla_) {
+      if (precision_ != AnalysisConfig::Precision::kInt8 &&
+          precision_ != AnalysisConfig::Precision::kHalf) {
+        LOG(WARNING) << "TensorRT DLA must be used with int8 or fp16, but you "
+                        "set float32, so DLA is not used.";
+      } else if (runtime->getNbDLACores() == 0) {
+        LOG(WARNING)
+            << "TensorRT DLA is set by config, but your device does not have "
+               "DLA, so DLA is not used.";
+      } else {
+        if (dla_core_ < 0 || dla_core_ >= runtime->getNbDLACores()) {
+          dla_core_ = 0;
+          LOG(WARNING) << "Invalid DLACore, must be 0 < DLACore < "
+                       << runtime->getNbDLACores() << ", but got " << dla_core_
+                       << ", so use use 0 as default.";
+        }
+        runtime->setDLACore(dla_core_);
+        LOG(INFO) << "TensorRT DLA enabled in Deserialize(), DLACore "
+                  << dla_core_;
+      }
+    }
+
     if (with_dynamic_shape_) {
 #if IS_TRT_VERSION_GE(6000)
       infer_engine_.reset(runtime->deserializeCudaEngine(
@@ -287,6 +310,8 @@ class TensorRTEngine {
   }
 
   void SetUseOSS(bool use_oss) { use_oss_ = use_oss; }
+  void SetUseDLA(bool use_dla) { use_dla_ = use_dla; }
+  void SetDLACore(int dla_core) { dla_core_ = dla_core; }
   void SetWithErnie(bool with_ernie) { with_ernie_ = with_ernie; }
 
   void ClearWeights() {
@@ -316,8 +341,8 @@ class TensorRTEngine {
   ShapeMapType min_input_shape() { return min_input_shape_; }
   ShapeMapType max_input_shape() { return max_input_shape_; }
   ShapeMapType optim_input_shape() { return optim_input_shape_; }
-  bool use_oss() { return use_oss_; };
-  bool with_ernie() { return with_ernie_; };
+  bool use_oss() { return use_oss_; }
+  bool with_ernie() { return with_ernie_; }
   bool disable_trt_plugin_fp16() { return disable_trt_plugin_fp16_; }
   bool with_dynamic_shape() { return with_dynamic_shape_; }
 
@@ -354,6 +379,8 @@ class TensorRTEngine {
   ShapeMapType optim_input_shape_;
   bool disable_trt_plugin_fp16_{false};
   bool use_oss_{false};
+  bool use_dla_{false};
+  int dla_core_{0};
   bool with_ernie_{false};
   nvinfer1::ILogger& logger_;
 

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

@@ -58,6 +58,7 @@ TEST(PredictorPool, use_gpu) {
   config.SetModel(model_dir);
   config.EnableTensorRtEngine();
   config.Exp_DisableTensorRtOPs({"fc"});
+  config.EnableTensorRtDLA(0);
   services::PredictorPool pred_pool(config, 1);
 
   auto predictor = pred_pool.Retrive(0);

paddle/fluid/pybind/inference_api.cc

@@ -504,6 +504,9 @@ void BindAnalysisConfig(py::module *m) {
            py::arg("disable_trt_plugin_fp16") = false)
       .def("enable_tensorrt_oss", &AnalysisConfig::EnableTensorRtOSS)
       .def("tensorrt_oss_enabled", &AnalysisConfig::tensorrt_oss_enabled)
+      .def("enable_tensorrt_dla", &AnalysisConfig::EnableTensorRtDLA,
+           py::arg("dla_core") = 0)
+      .def("tensorrt_dla_enabled", &AnalysisConfig::tensorrt_dla_enabled)
       .def("tensorrt_engine_enabled", &AnalysisConfig::tensorrt_engine_enabled)
       .def("enable_lite_engine", &AnalysisConfig::EnableLiteEngine,
            py::arg("precision_mode") = AnalysisConfig::Precision::kFloat32,