diff --git a/paddle/fluid/inference/tensorrt/convert/op_converter.h b/paddle/fluid/inference/tensorrt/convert/op_converter.h
index 5b442c763c02c..cdd6345c48441 100644
--- a/paddle/fluid/inference/tensorrt/convert/op_converter.h
+++ b/paddle/fluid/inference/tensorrt/convert/op_converter.h
@@ -482,10 +482,18 @@ class OpConverter {
   template <typename T>
   // Create and add Multi-D constant float/int32 layer
   nvinfer1::ITensor* AddConstantLayer(const T* data,
-                                      const std::vector<int32_t>& weight_dims,
-                                      const std::string& weight_name) {
+                                      nvinfer1::Dims shape,
+                                      const std::string& weight_name = "") {
+    if (!(std::is_same<T, float>::value ||
+          std::is_same<T, platform::float16>::value ||
+          std::is_same<T, int32_t>::value)) {
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Unsupported data type (%s) for TensorRT AddConstantLayer, only "
+          "supports float, half or int32_t."));
+    }
+
     int data_size = std::accumulate(
-        weight_dims.begin(), weight_dims.end(), 1, std::multiplies<int>());
+        shape.d, shape.d + shape.nbDims, 1, std::multiplies<int>());
     std::unique_ptr<framework::Tensor> tmp_tensor(new framework::Tensor());
     tmp_tensor->Resize({data_size});
     auto* tmp_data = tmp_tensor->mutable_data<T>(platform::CPUPlace());
@@ -502,12 +510,9 @@ class OpConverter {
     TensorRTEngine::Weight weight{trt_dtype,
                                   static_cast<void*>(tmp_data),
                                   static_cast<size_t>(data_size)};
-    nvinfer1::Dims trt_dims;
-    trt_dims.nbDims = weight_dims.size();
-    for (size_t i = 0; i < weight_dims.size(); i++)
-      trt_dims.d[i] = weight_dims[i];
+
     auto const_layer =
-        TRT_ENGINE_ADD_LAYER(engine_, Constant, trt_dims, weight.get());
+        TRT_ENGINE_ADD_LAYER(engine_, Constant, shape, weight.get());
     return const_layer->getOutput(0);
   }
 
@@ -516,6 +521,14 @@ class OpConverter {
   nvinfer1::ITensor* Add1DConstantLayer(const std::vector<T>& data,
                                         const std::string& weight_name = "",
                                         bool scalar = false) {
+    if (!(std::is_same<T, float>::value ||
+          std::is_same<T, platform::float16>::value ||
+          std::is_same<T, int32_t>::value)) {
+      PADDLE_THROW(platform::errors::InvalidArgument(
+          "Unsupported data type (%s) for TensorRT AddConstantLayer, only "
+          "supports float, half or int32_t."));
+    }
+
     std::unique_ptr<framework::Tensor> tmp_tensor(new framework::Tensor());
     int data_size = data.size();
     tmp_tensor->Resize({data_size});
@@ -549,12 +562,13 @@ class OpConverter {
     return Add1DConstantLayer(tmp_data, weight_name, scalar);
   }
 
-  nvinfer1::ITensor* Add1DConstantLayer(int32_t data,
+  template <typename T>
+  nvinfer1::ITensor* Add1DConstantLayer(T data,
                                         const std::string& weight_name = "",
                                         bool scalar = false) {
-    std::vector<int> tmp_data;
-    tmp_data.push_back(data);
-    return Add1DConstantLayer(tmp_data, weight_name, scalar);
+    std::vector<T> input_data;
+    input_data.push_back(data);
+    return Add1DConstantLayer(input_data, weight_name, scalar);
   }
 
   // For cases when input is not middle-tensor , but persistable tensor
diff --git a/paddle/fluid/inference/tensorrt/convert/split_op.cc b/paddle/fluid/inference/tensorrt/convert/split_op.cc
index 8ad4158bd04dd..be21161893af2 100644
--- a/paddle/fluid/inference/tensorrt/convert/split_op.cc
+++ b/paddle/fluid/inference/tensorrt/convert/split_op.cc
@@ -91,7 +91,7 @@ class SplitOpConverter : public OpConverter {
           start_point += output_lengths[i];
         } else {
           this_len_tensor = avg_len_tensor;
-          auto* i_tensor = Add1DConstantLayer(i);
+          auto* i_tensor = Add1DConstantLayer(static_cast<int>(i));
           start_point_tensor = Prod(i_tensor, avg_len_tensor);
         }
 
diff --git a/paddle/fluid/inference/tensorrt/convert/swish_op.cc b/paddle/fluid/inference/tensorrt/convert/swish_op.cc
index 722b42bd895e4..4caf45c8d0ce5 100644
--- a/paddle/fluid/inference/tensorrt/convert/swish_op.cc
+++ b/paddle/fluid/inference/tensorrt/convert/swish_op.cc
@@ -63,17 +63,22 @@ class SwishOpConverter : public OpConverter {
 
     nvinfer1::ILayer* layer = nullptr;
     if (engine_->with_dynamic_shape()) {
-#if IS_TRT_VERSION_GE(6000)
-      bool with_fp16 =
-          engine_->WithFp16() && !engine_->disable_trt_plugin_fp16();
-      plugin::SwishPluginDynamic* plugin =
-          new plugin::SwishPluginDynamic(beta, with_fp16);
-      layer = engine_->AddDynamicPlugin(&input, input_num, plugin);
-#else
-      PADDLE_THROW(platform::errors::Fatal(
-          "You are running the TRT Dynamic Shape mode, need to confirm that "
-          "your TRT version is no less than 6.0"));
-#endif
+      int32_t rank = input->getDimensions().nbDims;
+      nvinfer1::Dims constant_shape;
+      constant_shape.nbDims = rank;
+      std::fill(constant_shape.d, constant_shape.d + rank, 1);
+      std::vector<float> weight_data{beta};
+      auto* beta_data = AddConstantLayer(weight_data.data(), constant_shape);
+      auto* input_mul_with_beta = Prod(beta_data, input);
+      auto* sigmoid = TRT_ENGINE_ADD_LAYER(engine_,
+                                           Activation,
+                                           *input_mul_with_beta,
+                                           nvinfer1::ActivationType::kSIGMOID);
+      layer = TRT_ENGINE_ADD_LAYER(engine_,
+                                   ElementWise,
+                                   *input,
+                                   *(sigmoid->getOutput(0)),
+                                   nvinfer1::ElementWiseOperation::kPROD);
     } else {
       bool with_fp16 =
           engine_->WithFp16() && !engine_->disable_trt_plugin_fp16();