diff --git a/python/paddle/fluid/trainer.py b/python/paddle/fluid/trainer.py
index fbdd28f53efcd4c443a3c0e1508ca92f2cbaf95a..4ffc2064581b77f52e40f42c876950fb0577cde7 100644
--- a/python/paddle/fluid/trainer.py
+++ b/python/paddle/fluid/trainer.py
@@ -188,7 +188,7 @@ class Trainer(object):
             if not self.checkpoint.is_pserver:
                 epoch_id, step_id = io.load_trainer_args(
                     self.checkpoint.checkpoint_dir, self.checkpoint.load_serial,
-                    self.trainer_id, ["epoch_id", "step_id"])
+                    self.trainer_id, self._get_checkpoint_load_args())
                 self.checkpoint.epoch_id = int(epoch_id)
                 self.checkpoint.step_id = int(step_id)
 
@@ -432,22 +432,33 @@ class Trainer(object):
             return
         io.clean_checkpoint(checkpoint_dir=self.checkpoint.checkpoint_dir)
 
+    def _get_checkpoint_load_args(self):
+        """
+        epoch_id and step_id are runtime arguments, they are not variables, will load them independently.
+        """
+        return ["epoch_id", "step_id"]
+
+    def _get_checkpoint_save_args(self, epoch_id, step_id):
+        """
+        epoch_id and step_id are runtime arguments, they are not variables, will save them independently.
+        """
+        trainer_args = {}
+        trainer_args["epoch_id"] = epoch_id
+        trainer_args["step_id"] = step_id
+        return trainer_args
+
     def _save_checkpoint(self, epoch_id, step_id):
         if not self.checkpoint:
             return
 
         if epoch_id % self.checkpoint.epoch_interval == 0 and step_id % self.checkpoint.step_interval == 0:
-            trainer_args = {}
-            trainer_args["epoch_id"] = epoch_id
-            trainer_args["step_id"] = step_id
-
             exe = executor.Executor(self.place)
             io.save_checkpoint(
                 executor=exe,
                 checkpoint_dir=self.checkpoint.checkpoint_dir,
                 trainer_id=self.trainer_id,
                 is_chief=self.chief,
-                trainer_args=trainer_args,
+                trainer_args=self._get_checkpoint_save_args(epoch_id, step_id),
                 main_program=self.train_program,
                 max_num_checkpoints=self.checkpoint.max_num_checkpoints)