diff --git a/debug/accuracy_tools/atat/pytorch/functional/data_processor.py b/debug/accuracy_tools/atat/pytorch/functional/data_processor.py
index 79433ffff5ebabd99fde8fc5f4b24511efd59c1c..f41a96768de20b83868311b18a243f2c119bb650 100644
--- a/debug/accuracy_tools/atat/pytorch/functional/data_processor.py
+++ b/debug/accuracy_tools/atat/pytorch/functional/data_processor.py
@@ -278,7 +278,7 @@ class FullTensorDataProcessor(DataProcessor):
         return single_arg
 
 
-class OverflowTensorDataProcessor(FullTensorDataProcessor):
+class OverflowTensorDataProcessor(DataProcessor):
     __slots__ = ["cached_tensors_and_file_paths"]
 
     def __init__(self, config, data_writer):
@@ -293,26 +293,29 @@ class OverflowTensorDataProcessor(FullTensorDataProcessor):
         self.cached_tensors_and_file_paths.update({file_path: tensor})
         single_arg = super()._analyze_tensor(tensor, suffix)
         single_arg.update({"data_name": dump_data_name})
+        return single_arg
 
     def analyze_forward(self, name,
                         module_input_output: ModuleForwardInputsOutputs):
         self.has_overflow = False
         api_info_struct = super().analyze_forward(name, module_input_output)
+        self.maybe_save_overflow_data()
         if self.has_overflow:
-            self.save_overflow_data()
             return api_info_struct
-        return None
+        else:
+            return None
 
     def analyze_backward(self, name,
                         module_input_output: ModuleBackwardInputsOutputs):
         self.has_overflow = False
         api_info_struct = super().analyze_backward(name, module_input_output)
+        self.maybe_save_overflow_data()
         if self.has_overflow:
-            self.save_overflow_data()
             return api_info_struct
         return None
 
-    def save_overflow_data(self):
-        for file_path, tensor in self.cached_tensors_and_file_paths.items():
-            torch.save(tensor, file_path)
+    def maybe_save_overflow_data(self):
+        if self.has_overflow:
+            for file_path, tensor in self.cached_tensors_and_file_paths.items():
+                torch.save(tensor, file_path)
         self.cached_tensors_and_file_paths = {}