diff --git a/dicp/dicp/vendor/AscendGraph/codegen/ascend.py b/dicp/dicp/vendor/AscendGraph/codegen/ascend.py
index 9e7c3c67c..579616351 100644
--- a/dicp/dicp/vendor/AscendGraph/codegen/ascend.py
+++ b/dicp/dicp/vendor/AscendGraph/codegen/ascend.py
@@ -997,7 +997,7 @@ def Cast(name, x, ascend_dtype, device=None):
 
 
     @staticmethod
-    def CastCpu(name, x, ascend_dtype):
+    def CastCpu(name, x, ascend_dtype, device='cpu'):
         cast_op = OP(name, "Cast")
         cast_op.set_input("x", x)
         cast_op.set_attr_int("dst_type", get_ascend_dtype_num(ascend_dtype))
diff --git a/dicp/dicp/vendor/AscendGraph/conversion.py b/dicp/dicp/vendor/AscendGraph/conversion.py
index a9cfa885e..a7b3f64fb 100644
--- a/dicp/dicp/vendor/AscendGraph/conversion.py
+++ b/dicp/dicp/vendor/AscendGraph/conversion.py
@@ -215,7 +215,7 @@ def add_scalar(self, x, y):
     def _to_copy(self, x, dtype=None, layout=torch.strided, device=None):
         if dtype:
             if device == torch.device(type='cpu'):
-                return self.get_proxy(ascend_op.CastCpu, (x, get_ascend_dtype(dtype)))
+                return self.get_proxy(ascend_op.CastCpu, (x, get_ascend_dtype(dtype), device))
             else:
                 return self.get_proxy(ascend_op.Cast, (x, get_ascend_dtype(dtype)))
         else:
diff --git a/dicp/dicp/vendor/AscendGraph/opset_convert.py b/dicp/dicp/vendor/AscendGraph/opset_convert.py
index 34d8d0ec9..1e4dd7e0a 100644
--- a/dicp/dicp/vendor/AscendGraph/opset_convert.py
+++ b/dicp/dicp/vendor/AscendGraph/opset_convert.py
@@ -38,9 +38,11 @@ def transform(self, gm: torch.fx.graph_module):
             if n.op != 'call_function':
                 continue
             if type(n.target) == CastCpu:
-                self.cpu_tensor.append(n.name)
-            elif type(n.target) == IdentityInp and len(n.args) == 2 and n.args[1] is not None and str(n.args[1]) in input_names:
-                self.assign_args.append((n.name, input_names.index(str(n.args[1]))))
+                if len(n.args) == 3 and n.args[2] is not None and n.args[2] == torch.device(type='cpu'):
+                    self.cpu_tensor.append(n.name)
+            elif type(n.target) == IdentityInp:
+                if len(n.args) == 2 and n.args[1] is not None and str(n.args[1]) in input_names:
+                    self.assign_args.append((n.name, input_names.index(str(n.args[1]))))
 
         for n in gm.graph.nodes:
             if n.op == 'call_function':