diff --git a/paddlenlp/trainer/trainer.py b/paddlenlp/trainer/trainer.py
index 75e643c0caa3..2b0fce84bb82 100644
--- a/paddlenlp/trainer/trainer.py
+++ b/paddlenlp/trainer/trainer.py
@@ -2427,12 +2427,10 @@ def _save(self, output_dir: Optional[str] = None, state_dict=None, merge_tensor_
         # Save a trained model and configuration using `save_pretrained()`.
         # They can then be reloaded using `from_pretrained()`
 
-        local_rank = int(os.getenv("PADDLE_RANK_IN_NODE", 0))
         if (
             strtobool(os.getenv("FLAG_LLM_PDC", "False"))
-            and local_rank == 0
+            and paddle.distributed.get_rank() == 0
             and self.args.unified_checkpoint
-            and "async_save" in self.args.unified_checkpoint_config
         ):
             os.makedirs(self.args.logging_dir, exist_ok=True)
             world_size = paddle.distributed.get_world_size()
@@ -2441,9 +2439,8 @@ def _save(self, output_dir: Optional[str] = None, state_dict=None, merge_tensor_
                 "ignore_save_lr_and_optim": self.args.ignore_save_lr_and_optim,
                 "skip_save_model_weight": "skip_save_model_weight" in self.args.unified_checkpoint_config,
             }
-            if not os.path.exists(os.path.join(self.args.logging_dir, "async_save_info.json")):
-                with open(os.path.join(self.args.logging_dir, "async_save_info.json"), "w") as f:
-                    json.dump(save_info, f)
+            with open(os.path.join(self.args.logging_dir, "async_save_info.json"), "w") as f:
+                json.dump(save_info, f)
 
         if self.args.should_save:
             if self.tokenizer is not None: