save_pretrained: mkdir(exist_ok=True) (#5258)

* all save_pretrained methods mkdir if not os.path.exists

examples/question-answering/run_squad.py

@@ -240,8 +240,6 @@ def train(args, train_dataset, model, tokenizer):
                 # Save model checkpoint
                 if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
                     output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step))
-                    if not os.path.exists(output_dir):
-                        os.makedirs(output_dir)
                     # Take care of distributed/parallel training
                     model_to_save = model.module if hasattr(model, "module") else model
                     model_to_save.save_pretrained(output_dir)
@@ -768,10 +766,6 @@ def main():
 
     # Save the trained model and the tokenizer
     if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
-        # Create output directory if needed
-        if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
-            os.makedirs(args.output_dir)
-
         logger.info("Saving model checkpoint to %s", args.output_dir)
         # Save a trained model, configuration and tokenizer using `save_pretrained()`.
         # They can then be reloaded using `from_pretrained()`