diff --git a/debug/accuracy_tools/api_accuracy_checker/dump/api_info.py b/debug/accuracy_tools/api_accuracy_checker/dump/api_info.py
index 0e9edec8a6d8a2a3e661354e428324f7b3cd7330..d33ed21d8134afc0a8e90453754dd53da38899eb 100644
--- a/debug/accuracy_tools/api_accuracy_checker/dump/api_info.py
+++ b/debug/accuracy_tools/api_accuracy_checker/dump/api_info.py
@@ -13,7 +13,8 @@ class APIInfo:
         self.rank = os.getpid()
         self.api_name = api_name
         self.save_real_data = msCheckerConfig.real_data
-
+        self.torch_object_key = {'device' : self.analyze_device_in_kwargs, 'dtype' : self.analyze_dtype_in_kwargs}
+        
     def analyze_element(self, element):
         if isinstance(element, (list, tuple)):
             out = []
@@ -22,7 +23,11 @@ class APIInfo:
         elif isinstance(element, dict):
             out = {}
             for key, value in element.items():
-                out[key] = self.analyze_element(value)
+                if key in self.torch_object_key.keys():
+                    fun = self.torch_object_key[key]
+                    out[key] = fun(value)
+                else:
+                    out[key] = self.analyze_element(value)
 
         elif isinstance(element, torch.Tensor):
             out = self.analyze_tensor(element, self.save_real_data)
@@ -78,7 +83,26 @@ class APIInfo:
         if element is None or isinstance(element, (bool,int,float,str,slice)):
             return True
         return False
-
+        
+    def analyze_device_in_kwargs(self, element):
+        single_arg = {}
+        single_arg.update({'type' : 'torch.device'})
+        if not isinstance(element, str):
+            
+            if hasattr(element, "index"):
+                device_value = element.type + ":" + str(element.index)
+                single_arg.update({'value' : device_value})
+            else:
+                device_value = element.type
+        else:
+            single_arg.update({'value' : element})
+        return single_arg
+    
+    def analyze_dtype_in_kwargs(self, element):
+        single_arg = {}
+        single_arg.update({'type' : 'torch.dtype'})
+        single_arg.update({'value' : str(element)})
+        return single_arg
     
     def get_tensor_extremum(self, data, operator):
         if data.dtype is torch.bool: