diff --git a/paddle/fluid/inference/tensorrt/convert/emb_eltwise_layernorm.cc b/paddle/fluid/inference/tensorrt/convert/emb_eltwise_layernorm.cc
index 098df15b3e7ff0d2aa75d067dbd33c359f93f5d9..fbf49ece7551e0a6805970308b57953980c4bea1 100644
--- a/paddle/fluid/inference/tensorrt/convert/emb_eltwise_layernorm.cc
+++ b/paddle/fluid/inference/tensorrt/convert/emb_eltwise_layernorm.cc
@@ -72,11 +72,15 @@ class EmbEltwiseLayerNormOpConverter : public OpConverter {
       }
 
       auto* shape_tensor = Shape(mask_id_tensor);
+      std::vector<nvinfer1::ITensor*> start_vec_tensor;
       std::vector<nvinfer1::ITensor*> size_vec_tensor;
       for (int i = 0; i < mask_dims.nbDims; i++) {
+        start_vec_tensor.push_back(Add1DConstantLayer(0));
         size_vec_tensor.push_back(Add1DConstantLayer(1));
       }
       size_vec_tensor[1] = GetEleTensorOfShape(shape_tensor, 1);
+
+      auto start_tensor = Concat(start_vec_tensor);
       auto size_tensor = Concat(size_vec_tensor);
 
       auto slice_layer =
@@ -86,6 +90,7 @@ class EmbEltwiseLayerNormOpConverter : public OpConverter {
                                slice_start_dims,
                                slice_start_dims,
                                slice_stride_dims);  // unuseful slice_start_dims
+      slice_layer->setInput(1, *start_tensor);
       slice_layer->setInput(2, *size_tensor);
       slice_layer->setName(
           ("Embeltwise_slice_layer (Output: slice_max_seqlen " +
diff --git a/paddle/fluid/inference/tensorrt/engine.cc b/paddle/fluid/inference/tensorrt/engine.cc
index ebce2508c04325a0eb0c0ff6f1072b7c58c0a62d..e70a49c685e70a7fb86b75f4f233fd7d0ff045a1 100644
--- a/paddle/fluid/inference/tensorrt/engine.cc
+++ b/paddle/fluid/inference/tensorrt/engine.cc
@@ -89,9 +89,7 @@ void TensorRTEngine::Execute(int batch_size,
   if (!with_dynamic_shape()) {
     infer_context->enqueue(batch_size, buffers->data(), stream, nullptr);
   } else {
-#if IS_TRT_VERSION_GE(6000)
     infer_context->enqueueV2(buffers->data(), stream, nullptr);
-#endif
   }
   SetRuntimeBatch(batch_size);
 }
@@ -134,7 +132,6 @@ void TensorRTEngine::FreezeNetwork() {
     } else {
       infer_builder_config_->setInt8Calibrator(nullptr);
 
-#if IS_TRT_VERSION_GE(5000)
       for (auto &quant_range : quant_dynamic_range_) {
         auto tensor = quant_range.first;
         float range = quant_range.second;
@@ -160,72 +157,6 @@ void TensorRTEngine::FreezeNetwork() {
                   << ", this might be ok when trt does not need this range";
         }
       }
-
-#if IS_TRT_VERSION_GE(5122)
-      auto layer_int8_fallback = [&](nvinfer1::ILayer *layer) -> bool {
-        if (layer->getType() == nvinfer1::LayerType::kSHAPE) {
-          return false;
-        }
-        bool all_int = true;
-        for (int j = 0; j < layer->getNbInputs(); j++) {
-          auto *temp_in = layer->getInput(j);
-          if (temp_in->getType() != nvinfer1::DataType::kINT32) {
-            all_int = false;
-          }
-        }
-        for (int j = 0; j < layer->getNbOutputs(); j++) {
-          auto *temp_out = layer->getOutput(j);
-          if (temp_out->getType() != nvinfer1::DataType::kINT32) {
-            all_int = false;
-          }
-        }
-        if (all_int) return false;
-
-        for (int j = 0; j < layer->getNbInputs(); j++) {
-          auto *temp_in = layer->getInput(j);
-          if (!temp_in->dynamicRangeIsSet()) {
-            VLOG(1) << "Layer(Name: " << layer->getName()
-                    << ") is set to float32 because its input("
-                    << temp_in->getName() << ") doesn't have dynamic range.";
-            return true;
-          }
-        }
-        for (int j = 0; j < layer->getNbOutputs(); j++) {
-          auto *temp_out = layer->getOutput(j);
-          if (!temp_out->dynamicRangeIsSet()) {
-            VLOG(1) << "Layer(Name: " << layer->getName()
-                    << ") is set to float32 because its output("
-                    << temp_out->getName() << ") doesn't have dynamic range.";
-            return true;
-          }
-        }
-        return false;
-      };
-      // If a layer's output is the network's output, or not all of its inputs
-      // and outputs have scales,
-      // this layer's precision and output type are set to float32.
-      // This step has no effect if this layer is fused during TRT optimization.
-      int layers_no_int8 = 0;
-      for (int i = 0; i < network()->getNbLayers(); i++) {
-        auto layer = network()->getLayer(i);
-        if (layer_int8_fallback(layer)) {
-          layer->setPrecision(nvinfer1::DataType::kFLOAT);
-          ++layers_no_int8;
-        }
-      }
-      // Disable int8 or build engine failed if all layers aren't int8
-      if (layers_no_int8 == network()->getNbLayers()) {
-        nvinfer1::BuilderFlags flags = infer_builder_config_->getFlags();
-        flags = flags & ~(1U << static_cast<int>(nvinfer1::BuilderFlag::kINT8));
-        // reset flags
-        infer_builder_config_->setFlags(flags);
-      }
-#else
-      LOG(WARNING) << "If your TensorRT version is lower than 5.1.2.2, you "
-                      "must provide quantization scales for all tensors using "
-                      "TRT to run.";
-#endif
-#endif
     }
   }
 
@@ -265,7 +196,6 @@ void TensorRTEngine::FreezeNetwork() {
   }
 
   if (with_dynamic_shape_) {
-#if IS_TRT_VERSION_GE(6000)
     LOG(INFO) << "Run Paddle-TRT Dynamic Shape mode.";
     for (int i = 0; i < max_profile_num_; i++) {
       for (auto &input : min_input_shape_) {
@@ -310,7 +240,6 @@ void TensorRTEngine::FreezeNetwork() {
                    "'config.SetDynamicShapeInfo(min_shape, max_shape, "
                    "opt_shape, false /*disable_trt_plugin_fp16*/)'";
     }
-#endif
   }
 #if IS_TRT_VERSION_GE(8200)
   if (use_inspector_) {