diff --git a/paddle/fluid/inference/analysis/analysis_pass.h b/paddle/fluid/inference/analysis/analysis_pass.h
index 299f235a74ae0ffb663be61079607d8ac1105a97..d5a972fab3beae4d4e2e512d1ccda3f0b8356682 100644
--- a/paddle/fluid/inference/analysis/analysis_pass.h
+++ b/paddle/fluid/inference/analysis/analysis_pass.h
@@ -46,8 +46,6 @@ class AnalysisPass {
  protected:
   // User should implement these.
   virtual void RunImpl(Argument* argument) = 0;
-
-  Argument* argument_{nullptr};
 };
 
 }  // namespace analysis
diff --git a/paddle/fluid/inference/api/analysis_predictor.cc b/paddle/fluid/inference/api/analysis_predictor.cc
index 1862f61f0f4b94c9fa9636e876e943113d9aebd4..391330a7c0f2dda731fe8455fdab81b276e3f272 100644
--- a/paddle/fluid/inference/api/analysis_predictor.cc
+++ b/paddle/fluid/inference/api/analysis_predictor.cc
@@ -190,9 +190,13 @@ bool AnalysisPredictor::Run(const std::vector<PaddleTensor> &inputs,
   }
   VLOG(3) << "predict cost: " << timer.toc() << "ms";
 
-  // Fix TensorArray reuse not cleaned bug.
-  tensor_array_batch_cleaner_.CollectTensorArrays(scope_.get());
-  tensor_array_batch_cleaner_.ResetTensorArray();
+  // All the containers in the scope will be hold in inference, but the
+  // operators assume that the container will be reset after each batch.
+  // Here is a bugfix, collect all the container variables, and reset then to a
+  // bool; the next time, the operator will call MutableData and construct a new
+  // container again, so that the container will be empty for each batch.
+  tensor_array_batch_cleaner_.CollectNoTensorVars(sub_scope_);
+  tensor_array_batch_cleaner_.ResetNoTensorVars();
   return true;
 }
 
@@ -417,7 +421,7 @@ std::unique_ptr<ZeroCopyTensor> AnalysisPredictor::GetOutputTensor(
 bool AnalysisPredictor::ZeroCopyRun() {
   executor_->Run();
   // Fix TensorArray reuse not cleaned bug.
-  tensor_array_batch_cleaner_.CollectTensorArrays(scope_.get());
+  tensor_array_batch_cleaner_.CollectTensorArrays(sub_scope_);
   tensor_array_batch_cleaner_.ResetTensorArray();
   return true;
 }
diff --git a/paddle/fluid/inference/api/api_impl.cc b/paddle/fluid/inference/api/api_impl.cc
index 74369e886692fef3172d24c637b03a5bcf81a6c2..4c5b412a2c1717b8edbb17c238caaa11aeccebd3 100644
--- a/paddle/fluid/inference/api/api_impl.cc
+++ b/paddle/fluid/inference/api/api_impl.cc
@@ -154,9 +154,9 @@ bool NativePaddlePredictor::Run(const std::vector<PaddleTensor> &inputs,
   }
   VLOG(3) << "predict cost: " << timer.toc() << "ms";
 
-  // Fix TensorArray reuse not cleaned bug.
-  tensor_array_batch_cleaner_.CollectTensorArrays(scope_.get());
-  tensor_array_batch_cleaner_.ResetTensorArray();
+  // For some other vector like containers not cleaned after each batch.
+  tensor_array_batch_cleaner_.CollectNoTensorVars(scope_.get());
+  tensor_array_batch_cleaner_.ResetNoTensorVars();
   return true;
 }
 
diff --git a/paddle/fluid/inference/api/details/reset_tensor_array.cc b/paddle/fluid/inference/api/details/reset_tensor_array.cc
index 4ae6c6dc9f44650c1c62f5be5448864d817513b1..569a487328e2f1febe2ca5014b232dbd51d28079 100644
--- a/paddle/fluid/inference/api/details/reset_tensor_array.cc
+++ b/paddle/fluid/inference/api/details/reset_tensor_array.cc
@@ -46,5 +46,28 @@ void TensorArrayBatchCleaner::ResetTensorArray() {
   }
 }
 
+void TensorArrayBatchCleaner::CollectNoTensorVars(framework::Scope *scope) {
+  if (no_tensor_flag_) {
+    for (auto &var_name : scope->LocalVarNames()) {
+      auto *var = scope->FindVar(var_name);
+      if (!var->IsInitialized()) continue;
+      if (!valid_types_.count(var->Type())) {
+        no_tensor_vars_.insert(var);
+      }
+    }
+
+    for (auto *kid : scope->kids()) {
+      CollectTensorArrays(kid);
+    }
+    no_tensor_flag_ = false;  // Only collect one time.
+  }
+}
+
+void TensorArrayBatchCleaner::ResetNoTensorVars() {
+  for (auto *var : no_tensor_vars_) {
+    var->Clear();
+  }
+}
+
 }  // namespace details
 }  // namespace paddle
diff --git a/paddle/fluid/inference/api/details/reset_tensor_array.h b/paddle/fluid/inference/api/details/reset_tensor_array.h
index a39449ff0e67786815dfb8d2d30d79dcdba757d7..6a5ea64de66fcac44117d0d8f7798e8875703ec6 100644
--- a/paddle/fluid/inference/api/details/reset_tensor_array.h
+++ b/paddle/fluid/inference/api/details/reset_tensor_array.h
@@ -14,9 +14,11 @@
 
 #pragma once
 
+#include <unordered_set>
 #include <vector>
 #include "paddle/fluid/framework/lod_tensor_array.h"
 #include "paddle/fluid/framework/scope.h"
+#include "paddle/fluid/framework/variable.h"
 
 namespace paddle {
 namespace details {
@@ -24,13 +26,28 @@ namespace details {
 // Clean the TensorArray each batch to make the behavior the same with the
 // training phase.
 struct TensorArrayBatchCleaner {
+  TensorArrayBatchCleaner() {
+    valid_types_.insert(typeid(framework::Tensor));
+    valid_types_.insert(typeid(framework::LoDTensor));
+  }
+  // Collect the variables that are not Tensor or LoDTensor, and reset them to a
+  // bool(trick), because some of them are containers, and some operators just
+  // keep inserting new items without clearing the containers first; So the
+  // memory grow larger and larger in inference service deployed online.
+  void CollectNoTensorVars(framework::Scope *scope);
+  void ResetNoTensorVars();
+
   // Fix the tensor array not clear in the inference scenarios.
   void CollectTensorArrays(framework::Scope *scope);
   void ResetTensorArray();
 
  private:
   bool flag_{true};
+  bool no_tensor_flag_{true};
   std::vector<framework::LoDTensorArray *> arrays_;
+
+  std::unordered_set<std::type_index> valid_types_;
+  std::unordered_set<framework::Variable *> no_tensor_vars_;
 };
 
 }  // namespace details