diff --git a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/pytorch_processor.py b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/pytorch_processor.py
index 566f5c6de75a3b441f0f472eb81459ff05ba3f6c..39675f492885520cd354739eb646fd7f31eea2ec 100644
--- a/debug/accuracy_tools/msprobe/core/data_dump/data_processor/pytorch_processor.py
+++ b/debug/accuracy_tools/msprobe/core/data_dump/data_processor/pytorch_processor.py
@@ -111,27 +111,28 @@ class PytorchDataProcessor(BaseDataProcessor):
         data_clone = data.detach()
         if not data_clone.numel() or not data_clone.data_ptr():
             return tensor_stat
-        if torch.is_complex(data):
+        if torch.is_complex(data_clone):
             if async_dump:
                 logger.warning("Async dump do not support complex data!")
                 return tensor_stat
-            data_np = data.cpu().numpy()
+            data_np = data_clone.cpu().numpy()
             data_abs = np.abs(data_np)
             tensor_stat.max = np.max(data_abs).item()
             tensor_stat.min = np.min(data_abs).item()
             tensor_stat.mean = np.mean(data_abs).item()
-        elif data.dtype == torch.bool:
-            tensor_stat.max = torch.any(data)
-            tensor_stat.min = torch.all(data)
-        elif not data.shape:
-            tensor_stat.max = tensor_stat.min = tensor_stat.mean = tensor_stat.norm = data.clone()
+        elif data_clone.dtype == torch.bool:
+            tensor_stat.max = torch.any(data_clone)
+            tensor_stat.min = torch.all(data_clone)
+        elif not data_clone.shape:
+            tensor_stat.max = tensor_stat.min = tensor_stat.mean = tensor_stat.norm = data_clone.clone()
         else:
-            if precision == Const.DUMP_PRECISION_HIGH or data.dtype == torch.float64 or not data.is_floating_point():
-                data = data.float()
-            tensor_stat.max = torch.max(data)
-            tensor_stat.min = torch.min(data)
-            tensor_stat.mean = torch.mean(data)
-            tensor_stat.norm = torch.norm(data)
+            if (precision == Const.DUMP_PRECISION_HIGH or data_clone.dtype == torch.float64
+                    or not data_clone.is_floating_point()):
+                data_clone = data_clone.float()
+            tensor_stat.max = torch.max(data_clone)
+            tensor_stat.min = torch.min(data_clone)
+            tensor_stat.mean = torch.mean(data_clone)
+            tensor_stat.norm = torch.norm(data_clone)
         return tensor_stat
 
     @staticmethod