add trt int8 dynamic support (#44800)

* add trt int8 dynamic support

* just support trt7+

* just for trt7.1.3.a

* Update tensorrt_subgraph_pass.cc

* delete trt_engine when it not use

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

@@ -40,21 +40,24 @@ void analysis::TensorRtSubgraphPass::ApplyImpl(
   auto with_dynamic_shape = Get<bool>("with_dynamic_shape");
   auto teller = [&](const framework::ir::Node *node) {
     if (!node->IsOp() || !node->Op()) return false;
-    if (find(trt_disabled_ops.begin(), trt_disabled_ops.end(),
+    if (find(trt_disabled_ops.begin(),
+             trt_disabled_ops.end(),
              node->Op()->Type()) != trt_disabled_ops.end()) {
       VLOG(3) << node->Op()->Type().c_str()
               << " is diabled by config in TensorRT";
       return false;
     }
-    bool is_ok = tensorrt::OpTeller::Global().Tell(node, no_calib_int8,
-                                                   with_dynamic_shape);
+    bool is_ok = tensorrt::OpTeller::Global().Tell(
+        node, no_calib_int8, with_dynamic_shape);
     if (!is_ok)
       VLOG(3) << node->Op()->Type().c_str() << " op is not in TensorRT";
     return is_ok;
   };
 
   framework::ir::SubGraphFuser fuser(
-      graph, teller, Get<int>("min_subgraph_size") /*min subgraph size*/,
+      graph,
+      teller,
+      Get<int>("min_subgraph_size") /*min subgraph size*/,
       "tensorrt_engine");
   fuser();
 
@@ -102,26 +105,27 @@ std::string GenerateEngineKey(const std::set<std::string> &engine_inputs,
     engine_hash_key += "#";
   }
   engine_hash_key += predictor_id;
-  if (!for_calibration) {
-    engine_hash_key += "#";
-    engine_hash_key += max_batch_size;
-  }
   engine_hash_key += "#";
   engine_hash_key += precision;
 
   auto engine_key = std::to_string(std::hash<std::string>()(engine_hash_key));
+  if (!for_calibration) {
+    engine_key += max_batch_size;
+  }
   VLOG(2) << "TRT engine hash key: " << engine_hash_key;
   VLOG(2) << "TRT engine key: " << engine_key;
   return engine_key;
 }
 
 void TensorRtSubgraphPass::CreateTensorRTOp(
-    framework::ir::Node *node, framework::ir::Graph *graph,
+    framework::ir::Node *node,
+    framework::ir::Graph *graph,
     const std::vector<std::string> &graph_params,
     std::vector<std::string> *repetitive_params) const {
   auto *op_desc = node->Op();
   auto &subgraph = *framework::ir::Agent(node).subgraph();
-  PADDLE_ENFORCE_EQ(subgraph.empty(), false,
+  PADDLE_ENFORCE_EQ(subgraph.empty(),
+                    false,
                     platform::errors::PreconditionNotMet(
                         "The subgraph should not be empty."));
 
@@ -208,7 +212,8 @@ void TensorRtSubgraphPass::CreateTensorRTOp(
   if (trt_tuned_dynamic_shape) {
     VLOG(1) << "trt dynamic_shape deserialize from " << shape_range_info_path;
     inference::DeserializeShapeRangeInfo(shape_range_info_path,
-                                         &min_input_shape, &max_input_shape,
+                                         &min_input_shape,
+                                         &max_input_shape,
                                          &opt_input_shape);
   }
 
@@ -224,9 +229,14 @@ void TensorRtSubgraphPass::CreateTensorRTOp(
   // input of a OP, but also the output of a Op, there will be problems.
   // 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.
-  RenameAndGetOutputs(subgraph_nodes, &block_desc, input_names_with_id,
-                      &output_names_with_id, &output_names, &output_name_map,
-                      graph_var_map, !enable_int8);
+  RenameAndGetOutputs(subgraph_nodes,
+                      &block_desc,
+                      input_names_with_id,
+                      &output_names_with_id,
+                      &output_names,
+                      &output_name_map,
+                      graph_var_map,
+                      !enable_int8);
 
   // When tensorrt engine runs at the end of the operation,
   // output_mapping help us copy the data from the renamed ITensor
@@ -234,17 +244,20 @@ void TensorRtSubgraphPass::CreateTensorRTOp(
   std::vector<std::string> output_mapping;
   std::vector<int> renamed_output_dims;
   for (auto name : output_names) {
-    PADDLE_ENFORCE_NE(output_name_map.count(name), 0,
+    PADDLE_ENFORCE_NE(output_name_map.count(name),
+                      0,
                       platform::errors::PreconditionNotMet(
                           "The output_name_map should have %s", name));
     output_mapping.push_back(output_name_map[name]);
     renamed_output_dims.push_back(origin_name_output_dims[name]);
   }
-  PADDLE_ENFORCE_EQ(output_mapping.empty(), false,
+  PADDLE_ENFORCE_EQ(output_mapping.empty(),
+                    false,
                     platform::errors::PreconditionNotMet(
                         "The output_mapping should not be empty."));
   PADDLE_ENFORCE_EQ(
-      !block_desc.Proto()->vars().empty(), true,
+      !block_desc.Proto()->vars().empty(),
+      true,
       platform::errors::PreconditionNotMet("the block has no var-desc"));
 
   // Set attrs
@@ -287,14 +300,20 @@ void TensorRtSubgraphPass::CreateTensorRTOp(
   // when running in the 'use_serialize' mode, there is a bug.
   // serialization is affected by max_batch_size, but calibration is not.
   // So we use seperate engine keys in serialization and calibration.
-  auto engine_key = GenerateEngineKey(
-      input_names_with_id, output_names_with_id, std::to_string(0),
+  auto engine_key =
+      GenerateEngineKey(input_names_with_id,
+                        output_names_with_id,
+                        std::to_string(0),
                         std::to_string(max_batch_size),
-      std::to_string(static_cast<int>(precision_mode)), false);
+                        std::to_string(static_cast<int>(precision_mode)),
+                        false);
   auto calibration_engine_key =
-      GenerateEngineKey(input_names_with_id, output_names_with_id,
-                        std::to_string(0), std::to_string(max_batch_size),
-                        std::to_string(static_cast<int>(precision_mode)), true);
+      GenerateEngineKey(input_names_with_id,
+                        output_names_with_id,
+                        std::to_string(0),
+                        std::to_string(max_batch_size),
+                        std::to_string(static_cast<int>(precision_mode)),
+                        true);
   auto predictor_id = Get<int>("predictor_id");
 
   // Get "" when there is no cached calibration table data.
@@ -302,7 +321,8 @@ void TensorRtSubgraphPass::CreateTensorRTOp(
   if (enable_int8 && use_calib_mode) {
     calibration_data =
         GetTrtCalibTableData(Get<std::string>("model_opt_cache_dir"),
-                             calibration_engine_key, enable_int8);
+                             calibration_engine_key,
+                             enable_int8);
   }
   op_desc->SetAttr("calibration_data", calibration_data);
   op_desc->SetAttr("enable_int8", enable_int8);
@@ -325,13 +345,12 @@ void TensorRtSubgraphPass::CreateTensorRTOp(
   // calibration table data.
   bool calibration_mode =
       (enable_int8 && calibration_data.size() == 0 && use_calib_mode);
-  if (calibration_mode) {
-    // calibraion mode means generate int8 calibration table data process.
-    return;
-  }
 
-  std::copy(params_not_shared.begin(), params_not_shared.end(),
+  if (!calibration_mode) {
+    std::copy(params_not_shared.begin(),
+              params_not_shared.end(),
               std::back_inserter(*repetitive_params));
+  }
 
   // Check trt version for dynamic shape input.
 
@@ -368,10 +387,16 @@ void TensorRtSubgraphPass::CreateTensorRTOp(
   bool disable_trt_plugin_fp16 = Get<bool>("disable_trt_plugin_fp16");
   tensorrt::TensorRTEngine *trt_engine =
       inference::Singleton<inference::tensorrt::TRTEngineManager>::Global()
-          .Create(engine_key + std::to_string(predictor_id), max_batch_size,
-                  Get<int>("workspace_size"), precision_mode, calibrator.get(),
-                  Get<int>("gpu_device_id"), min_input_shape, max_input_shape,
-                  opt_input_shape, disable_trt_plugin_fp16);
+          .Create(engine_key + std::to_string(predictor_id),
+                  max_batch_size,
+                  Get<int>("workspace_size"),
+                  precision_mode,
+                  calibrator.get(),
+                  Get<int>("gpu_device_id"),
+                  min_input_shape,
+                  max_input_shape,
+                  opt_input_shape,
+                  disable_trt_plugin_fp16);
   trt_engine->SetUseOSS(Get<bool>("use_oss"));
   trt_engine->SetWithInterleaved(Get<bool>("with_interleaved"));
   trt_engine->SetUseDLA(Get<bool>("trt_use_dla"));
@@ -384,6 +409,11 @@ void TensorRtSubgraphPass::CreateTensorRTOp(
       (graph->Has(framework::ir::kPrelnEmbEltwiseLayernormPass) &&
        graph->Has(framework::ir::kMultiheadMatmulPass)));
 
+  if (calibration_mode) {
+    // calibraion mode means generate int8 calibration table data process.
+    return;
+  }
+
   if (use_static_engine) {
     trt_engine_serialized_data = GetTrtEngineSerializedData(
         Get<std::string>("model_opt_cache_dir"), engine_key);
@@ -408,9 +438,12 @@ void TensorRtSubgraphPass::CreateTensorRTOp(
   std::unordered_set<std::string> param_set(params.begin(), params.end());
   inference::Singleton<inference::tensorrt::OpConverter>::Global()
       .ConvertBlockToTRTEngine(
-          &block_desc_temp, *scope,
+          &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);
 
   if (use_static_engine) {
     nvinfer1::IHostMemory *serialized_engine_data = trt_engine->Serialize();

paddle/fluid/inference/tensorrt/engine.cc

@@ -254,6 +254,11 @@ void TensorRTEngine::FreezeNetwork() {
             nvinfer1::OptProfileSelector::kOPT,
             Vec2TRT_Dims(optim_input_shape_[input.first], input.first, true));
       }
+#if IS_TRT_VERSION_GE(7130)
+      if (enable_int8) {
+        infer_builder_config_->setCalibrationProfile(optim_profiles_[i]);
+      }
+#endif
       infer_builder_config_->addOptimizationProfile(optim_profiles_[i]);
     }
     if (WithFp16() && disable_trt_plugin_fp16()) {

paddle/fluid/inference/tensorrt/engine.h

@@ -614,6 +614,10 @@ class TensorRTEngine {
 
   void SetUseInspector(bool use_inspector) { use_inspector_ = use_inspector; }
 
+  void SetCalibrator(TRTInt8Calibrator* calibrator) {
+    calibrator_ = calibrator;
+  }
+
  private:
   // Each ICudaEngine object is bound to a specific GPU when it is instantiated,
   // ensure that the thread is associated with the correct device by calling

paddle/fluid/operators/tensorrt/tensorrt_engine_op.h

@@ -52,23 +52,28 @@ namespace operators {
 
 using inference::Singleton;
 using inference::tensorrt::TensorRTEngine;
-using inference::tensorrt::TRTInt8Calibrator;
 using inference::tensorrt::TRTCalibratorEngine;
 using inference::tensorrt::TRTCalibratorEngineManager;
+using inference::tensorrt::TRTInt8Calibrator;
 
 static void RuntimeStaticShapeCheck(std::vector<int64_t> runtime_input_shape,
                                     std::vector<int64_t> model_input_shape) {
   auto comma_fold = [](std::string a, int b) {
     return std::move(a) + ", " + std::to_string(b);
   };
-  std::string model_input_shape_str = std::accumulate(
-      std::next(model_input_shape.begin()), model_input_shape.end(),
-      std::to_string(model_input_shape[0]), comma_fold);
-  std::string runtime_input_shape_str = std::accumulate(
-      std::next(runtime_input_shape.begin()), runtime_input_shape.end(),
-      std::to_string(runtime_input_shape[0]), comma_fold);
+  std::string model_input_shape_str =
+      std::accumulate(std::next(model_input_shape.begin()),
+                      model_input_shape.end(),
+                      std::to_string(model_input_shape[0]),
+                      comma_fold);
+  std::string runtime_input_shape_str =
+      std::accumulate(std::next(runtime_input_shape.begin()),
+                      runtime_input_shape.end(),
+                      std::to_string(runtime_input_shape[0]),
+                      comma_fold);
   PADDLE_ENFORCE_EQ(
-      model_input_shape == runtime_input_shape, true,
+      model_input_shape == runtime_input_shape,
+      true,
       platform::errors::InvalidArgument(
           "Input shapes are inconsistent with the model. Expect [%s] in "
           "model description, but got [%s] in runtime. TRT 5 "
@@ -76,7 +81,8 @@ static void RuntimeStaticShapeCheck(std::vector<int64_t> runtime_input_shape,
           "does not support dynamic input shapes. Please check and "
           "modify "
           "your input shapes.",
-          model_input_shape_str, runtime_input_shape_str));
+          model_input_shape_str,
+          runtime_input_shape_str));
 }
 
 static paddle::experimental::DataType TRT2FluidDataType(
@@ -102,7 +108,8 @@ static paddle::experimental::DataType TRT2FluidDataType(
 }
 
 static void RuntimeDynamicShapeCheck(
-    const std::string &x, const std::vector<int32_t> &runtime_input_shape,
+    const std::string &x,
+    const std::vector<int32_t> &runtime_input_shape,
     const std::vector<int32_t> &min_input_shape,
     const std::vector<int32_t> &max_input_shape) {
   // PADDLE_ENFORCE_EQ(
@@ -111,8 +118,8 @@ static void RuntimeDynamicShapeCheck(
   //         "TRT engine runtime input %s dims size(%d) inconsistent "
   //         "with the dynamic shape size(%d)",
   //         x, runtime_input_shape.size(), min_input_shape.size()));
-  auto is_input_shape_valid = [&](
-      const std::vector<int32_t> &runtime_input_shape,
+  auto is_input_shape_valid =
+      [&](const std::vector<int32_t> &runtime_input_shape,
           const std::vector<int32_t> &min_input_shape,
           const std::vector<int32_t> &max_input_shape) -> bool {
     for (size_t i = 0; i < runtime_input_shape.size(); i++) {
@@ -128,17 +135,23 @@ static void RuntimeDynamicShapeCheck(
   auto comma_fold = [](std::string a, int b) {
     return std::move(a) + ", " + std::to_string(b);
   };
-  std::string runtime_input_shape_str = std::accumulate(
-      std::next(runtime_input_shape.begin()), runtime_input_shape.end(),
-      std::to_string(runtime_input_shape[0]), comma_fold);
+  std::string runtime_input_shape_str =
+      std::accumulate(std::next(runtime_input_shape.begin()),
+                      runtime_input_shape.end(),
+                      std::to_string(runtime_input_shape[0]),
+                      comma_fold);
   std::string min_input_shape_str =
-      std::accumulate(std::next(min_input_shape.begin()), min_input_shape.end(),
-                      std::to_string(min_input_shape[0]), comma_fold);
+      std::accumulate(std::next(min_input_shape.begin()),
+                      min_input_shape.end(),
+                      std::to_string(min_input_shape[0]),
+                      comma_fold);
   std::string max_input_shape_str =
-      std::accumulate(std::next(max_input_shape.begin()), max_input_shape.end(),
-                      std::to_string(max_input_shape[0]), comma_fold);
-  PADDLE_ENFORCE_EQ(is_input_shape_valid(runtime_input_shape, min_input_shape,
-                                         max_input_shape),
+      std::accumulate(std::next(max_input_shape.begin()),
+                      max_input_shape.end(),
+                      std::to_string(max_input_shape[0]),
+                      comma_fold);
+  PADDLE_ENFORCE_EQ(is_input_shape_valid(
+                        runtime_input_shape, min_input_shape, max_input_shape),
                     true,
                     platform::errors::InvalidArgument(
                         "TRT runtime input shape of %s is invalid. Expect "
@@ -146,7 +159,9 @@ static void RuntimeDynamicShapeCheck(
                         "configured in SetTRTDynamicShapeInfo(),"
                         "but got runtime input shape = [%s], min input shape = "
                         "[%s], max input shape = [%s].",
-                        x, runtime_input_shape_str, min_input_shape_str,
+                        x,
+                        runtime_input_shape_str,
+                        min_input_shape_str,
                         max_input_shape_str));
 }
 
@@ -158,7 +173,7 @@ class TensorRTEngineOp : public framework::OperatorBase {
   mutable TensorRTEngine *trt_engine_{nullptr};
   int max_batch_size_;
   int workspace_size_;
-  std::unique_ptr<TRTInt8Calibrator> calibrator_;
+  mutable std::unique_ptr<TRTInt8Calibrator> calibrator_;
   bool enable_int8_;
   bool enable_fp16_;
   bool use_calib_mode_;
@@ -260,7 +275,7 @@ class TensorRTEngineOp : public framework::OperatorBase {
         inference::Singleton<inference::tensorrt::TRTEngineManager>::Global()
             .Has(engine_key_ + std::to_string(predictor_id_));
 
-    if (!calibration_mode_ && has_engine) {
+    if (has_engine) {
       trt_engine_ =
           inference::Singleton<inference::tensorrt::TRTEngineManager>::Global()
               .Get(engine_key_ + std::to_string(predictor_id_));
@@ -287,8 +302,8 @@ class TensorRTEngineOp : public framework::OperatorBase {
         Attr<std::vector<std::string>>("output_name_mapping");
 
     inference::Singleton<inference::tensorrt::OpConverter>::Global()
-        .ConvertBlockToTRTEngine(&block_desc, scope, inputs, param_names_,
-                                 outputs, engine);
+        .ConvertBlockToTRTEngine(
+            &block_desc, scope, inputs, param_names_, outputs, engine);
   }
 
  protected:
@@ -304,11 +319,14 @@ class TensorRTEngineOp : public framework::OperatorBase {
 
   void RunImpl(const framework::Scope &scope,
                const platform::Place &dev_place) const override {
+    auto *trt_engine = GetEngine(scope, dev_place);
     if (calibration_mode_ == true) {
-      RunCalibration(scope, dev_place);
+      RunCalibration(scope, dev_place, trt_engine);
+      paddle::inference::Singleton<
+          inference::tensorrt::TRTEngineManager>::Global()
+          .DeleteKey(engine_key_ + std::to_string(predictor_id_));
       return;
     }
-    auto *trt_engine = GetEngine(scope, dev_place);
     if (use_inspector_) {
       trt_engine->GetEngineInfo();
     }
@@ -331,15 +349,19 @@ class TensorRTEngineOp : public framework::OperatorBase {
             trt_engine->max_input_shape();
         for (auto &x : runtime_input_names_) {
           PADDLE_ENFORCE_EQ(
-              min_input_shape.count(x), true,
+              min_input_shape.count(x),
+              true,
               platform::errors::InvalidArgument(
                   "Input %s not found in TRT engine min_input_shape.", x));
           PADDLE_ENFORCE_EQ(
-              max_input_shape.count(x), true,
+              max_input_shape.count(x),
+              true,
               platform::errors::InvalidArgument(
                   "Input %s not found in TRT engine max_input_shape.", x));
-          RuntimeDynamicShapeCheck(x, runtime_input_shape[x],
-                                   min_input_shape[x], max_input_shape[x]);
+          RuntimeDynamicShapeCheck(x,
+                                   runtime_input_shape[x],
+                                   min_input_shape[x],
+                                   max_input_shape[x]);
         }
       } else {
         // compare runtime_input_shape and trt_engine dynamic shapes.
@@ -357,12 +379,17 @@ class TensorRTEngineOp : public framework::OperatorBase {
           if (anc == nullptr) {
             anc = &scope;
           }
+          if (enable_int8_ && calibration_data_.size()) {
+            calibrator_.reset(new TRTInt8Calibrator(calibration_data_));
+            trt_engine->SetCalibrator(calibrator_.get());
+          }
           PrepareTRTEngine(*anc, trt_engine);
           // update shape_range_info_pbtxt
           if (!shape_range_info_path_.empty()) {
-            inference::UpdateShapeRangeInfo(
-                shape_range_info_path_, trt_engine->min_input_shape(),
-                trt_engine->max_input_shape(), trt_engine->optim_input_shape(),
+            inference::UpdateShapeRangeInfo(shape_range_info_path_,
+                                            trt_engine->min_input_shape(),
+                                            trt_engine->max_input_shape(),
+                                            trt_engine->optim_input_shape(),
                                             shape_changed_name);
           }
 
@@ -387,7 +414,8 @@ class TensorRTEngineOp : public framework::OperatorBase {
   }
 
   void RunCalibration(const framework::Scope &scope,
-                      const platform::Place &dev_place) const {
+                      const platform::Place &dev_place,
+                      TensorRTEngine *trt_engine) const {
     // This process will builds a 32-bit trt engine, runs it on the calibration
     // set, and records a histogram for each
     // tensor of the distribution of activation values.
@@ -412,9 +440,15 @@ class TensorRTEngineOp : public framework::OperatorBase {
       calib_res->calib_.reset(new TRTInt8Calibrator(
           calib_buffers, runtime_batch, calibration_engine_key_, dev_place));
       calib_res->thr_.reset(new std::thread([&]() {
-        calib_res->engine_.reset(new TensorRTEngine(
-            max_batch_size_, workspace_size_, precision_mode_,
-            calib_res->calib_.get(), dev_place.device));
+        calib_res->engine_.reset(
+            new TensorRTEngine(max_batch_size_,
+                               workspace_size_,
+                               precision_mode_,
+                               calib_res->calib_.get(),
+                               dev_place.device,
+                               trt_engine->min_input_shape(),
+                               trt_engine->max_input_shape(),
+                               trt_engine->optim_input_shape()));
         VLOG(3) << "start the calib trt engine thread";
         PrepareTRTEngine(scope, calib_res->engine_.get());
       }));
@@ -436,7 +470,8 @@ class TensorRTEngineOp : public framework::OperatorBase {
     RunNativeImpl(scope, dev_place);
   }
 
-  void RunTrt(const framework::Scope &scope, const platform::Place &dev_place,
+  void RunTrt(const framework::Scope &scope,
+              const platform::Place &dev_place,
               TensorRTEngine *engine) const {
     int runtime_batch = -1;
     platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
@@ -445,7 +480,8 @@ class TensorRTEngineOp : public framework::OperatorBase {
         reinterpret_cast<const platform::CUDADeviceContext &>(dev_ctx).stream();
 
     PADDLE_ENFORCE_EQ(
-        runtime_input_names_.empty(), false,
+        runtime_input_names_.empty(),
+        false,
         platform::errors::PreconditionNotMet(
             "TensorRT engine needs at least one input, but no input is found. "
             "Please check if you set the input correctly."));
@@ -488,13 +524,15 @@ class TensorRTEngineOp : public framework::OperatorBase {
       const int bind_index =
           engine->engine()->getBindingIndex(x.c_str()) + binding_offset;
       PADDLE_ENFORCE_LT(
-          bind_index, num_bindings,
+          bind_index,
+          num_bindings,
           platform::errors::InvalidArgument(
               "Wrong TRT engine input binding index. Expected The "
               "binding index of TRT engine input to be less than "
               "the number of inputs and outputs. Received binding "
               "index=%d >= total inputs and outputs=%d",
-              bind_index, num_bindings));
+              bind_index,
+              num_bindings));
       if (!engine->with_dynamic_shape()) {
         // check if the input shapes are consistent with model.
         if (HasAttr(x + "_shape")) {
@@ -507,7 +545,8 @@ class TensorRTEngineOp : public framework::OperatorBase {
           RuntimeStaticShapeCheck(runtime_input_shape, model_input_shape);
           if (runtime_batch != -1) {
             PADDLE_ENFORCE_EQ(
-                runtime_batch, t_shape[0],
+                runtime_batch,
+                t_shape[0],
                 platform::errors::InvalidArgument(
                     "Inputs of trt subgraphs has different batchsize. "
                     "It's not allowed in static shape mode. "
@@ -589,12 +628,14 @@ class TensorRTEngineOp : public framework::OperatorBase {
       auto *fluid_t = fluid_v->GetMutable<framework::LoDTensor>();
       fluid_t->Resize(phi::make_ddim(ddim));
 
-      PADDLE_ENFORCE_LT(bind_index, num_bindings,
+      PADDLE_ENFORCE_LT(bind_index,
+                        num_bindings,
                         platform::errors::InvalidArgument(
                             "The binding index in TRT engine should be less "
                             "than the number of bindings, but got binding "
                             "index = %d, number of bindings = %d.",
-                            bind_index, num_bindings));
+                            bind_index,
+                            num_bindings));
       auto trt_type = engine->engine()->getBindingDataType(bind_index);
       // get adr and set type
       buffers[bind_index] = static_cast<void *>(
@@ -604,7 +645,8 @@ class TensorRTEngineOp : public framework::OperatorBase {
 
     if (!engine->with_dynamic_shape()) {
       PADDLE_ENFORCE_LE(
-          runtime_batch, max_batch_size_,
+          runtime_batch,
+          max_batch_size_,
           platform::errors::InvalidArgument(
               "The runtime batch size (%d) is greater than the max batch "
               "size(%d).\n"
@@ -623,7 +665,8 @@ class TensorRTEngineOp : public framework::OperatorBase {
               "\tThe min_subgraph_size shouble to be greater than the number "
               "of "
               "nodes in the inconsistent subgraph.\n",
-              runtime_batch, max_batch_size_));
+              runtime_batch,
+              max_batch_size_));
     }
     // Execute the engine.
     engine->Execute(runtime_batch, &buffers, stream);
@@ -635,9 +678,14 @@ class TensorRTEngineOp : public framework::OperatorBase {
       trt_engine_ =
           inference::Singleton<inference::tensorrt::TRTEngineManager>::Global()
               .Create(engine_key_ + std::to_string(predictor_id_),
-                      max_batch_size_, workspace_size_, precision_mode_,
-                      calibrator_.get(), device_id_, min_input_shape_,
-                      max_input_shape_, opt_input_shape_);
+                      max_batch_size_,
+                      workspace_size_,
+                      precision_mode_,
+                      calibrator_.get(),
+                      device_id_,
+                      min_input_shape_,
+                      max_input_shape_,
+                      opt_input_shape_);
       PrepareTRTEngine(scope, trt_engine_);
     }
     return trt_engine_;