diff --git a/python/paddle/jit/dy2static/partial_program.py b/python/paddle/jit/dy2static/partial_program.py
index a1fc67d04e6c60b975a4be6c9122fb5051412f00..0264d645a0676b41aba816cf4dcf3c4a6a56115d 100644
--- a/python/paddle/jit/dy2static/partial_program.py
+++ b/python/paddle/jit/dy2static/partial_program.py
@@ -33,6 +33,7 @@ from .utils import (
     RETURN_NO_VALUE_MAGIC_NUM,
     backend_guard,
     construct_grad_names,
+    tensor_name_guard,
 )
 
 __all__ = []
@@ -223,30 +224,25 @@ class PartialProgramLayer:
         Execute static graph by Interpreter and Return dynamic Tensors.
         """
         with UniqueNameGuard(self._name_generator):
-            in_vars, out_vars, in_var_names, resume_name_record = self._prepare(
-                inputs
-            )
+            in_vars, out_vars, in_var_names = self._prepare(inputs)
             self._cast_fp16_if_pure_fp16(in_vars)
             attrs = self._prepare_attributes()
             attrs.extend(["x_names", in_var_names])
 
             self._sync_lr_value_with_scheduler()
 
-            _legacy_C_ops.run_program(
-                self._valid_vars(in_vars),
-                self._valid_vars(self._params),
-                self._valid_vars(out_vars),
-                self._create_scope_vec(
-                    program_id=self.program_id, use_scope_cache=True
-                ),
-                self._double_grads,
-                self._cuda_graph_vec,
-                *attrs
-            )
-
-            for var in in_vars:
-                if var.name in resume_name_record:
-                    var.name = resume_name_record[var.name]
+            with tensor_name_guard(in_vars, in_var_names):
+                _legacy_C_ops.run_program(
+                    self._valid_vars(in_vars),
+                    self._valid_vars(self._params),
+                    self._valid_vars(out_vars),
+                    self._create_scope_vec(
+                        program_id=self.program_id, use_scope_cache=True
+                    ),
+                    self._double_grads,
+                    self._cuda_graph_vec,
+                    *attrs
+                )
 
             self._update_stop_gradient(out_vars)
             restored_nest_out = self._restore_out(out_vars)
@@ -905,7 +901,6 @@ class PartialProgramLayer:
         # Convert variable into Tensor and feed in training data.
         input_vars = []
         input_var_names = []
-        resume_name_record = {}
         expected_place = framework._current_expected_place()
         for i, value in enumerate(flatten_inputs):
             if isinstance(value, np.ndarray):
@@ -928,8 +923,6 @@ class PartialProgramLayer:
                     var.stop_gradient = True
                 else:
                     var = value
-                resume_name_record[self._inputs[i].desc.name()] = var.name
-                var.name = self._inputs[i].desc.name()
             else:
                 continue
             input_var_names.append(self._inputs[i].desc.name())
@@ -960,7 +953,7 @@ class PartialProgramLayer:
         # Create Tensor to receive output data.
         out_vars = list(map(create_out, self._outputs.var_ids))
 
-        return input_vars, out_vars, input_var_names, resume_name_record
+        return input_vars, out_vars, input_var_names
 
     def _create_scope_vec(self, program_id=None, use_scope_cache=False):
         # Hold forward variables
diff --git a/python/paddle/jit/dy2static/utils.py b/python/paddle/jit/dy2static/utils.py
index eaf808fbf4f075f7cc321d08825e9319351f1637..eaa47258b10742634126f69e6db52e693998c9e1 100644
--- a/python/paddle/jit/dy2static/utils.py
+++ b/python/paddle/jit/dy2static/utils.py
@@ -1527,3 +1527,16 @@ def construct_grad_names(grad_info_map, x_vars, param_vars, out_vars):
     out_grad_vars = backward._get_grad_vars(grad_info_map, out_vars)
     grad_var_names['out'] = list(map(fn, out_grad_vars))
     return grad_var_names
+
+
+@signature_safe_contextmanager
+def tensor_name_guard(tensors, names):
+    try:
+        assert len(tensors) == len(names)
+        origin_names = [t.name for t in tensors]
+        for t, name in zip(tensors, names):
+            t.name = name
+        yield
+    finally:
+        for t, name in zip(tensors, origin_names):
+            t.name = name