Fix: save model/model.module

examples/lm_finetuning/finetune_on_pregenerated.py

@@ -155,12 +155,12 @@ def main():
                         help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
                         "0 (default value): dynamic loss scaling.\n"
                         "Positive power of 2: static loss scaling value.\n")
-    parser.add_argument("--warmup_steps", 
-                        default=0, 
+    parser.add_argument("--warmup_steps",
+                        default=0,
                         type=int,
                         help="Linear warmup over warmup_steps.")
-    parser.add_argument("--adam_epsilon", 
-                        default=1e-8, 
+    parser.add_argument("--adam_epsilon",
+                        default=1e-8,
                         type=float,
                         help="Epsilon for Adam optimizer.")
     parser.add_argument("--learning_rate",
@@ -322,7 +322,8 @@ def main():
     # Save a trained model
     if args.local_rank == -1 or torch.distributed.get_rank() == 0:
         logging.info("** ** * Saving fine-tuned model ** ** * ")
-        model.save_pretrained(args.output_dir)
+        model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
+        model_to_save.save_pretrained(args.output_dir)
         tokenizer.save_pretrained(args.output_dir)
 
 

examples/lm_finetuning/simple_lm_finetuning.py

@@ -610,7 +610,8 @@ def main():
         # Save a trained model
         if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
             logger.info("** ** * Saving fine - tuned model ** ** * ")
-            model.save_pretrained(args.output_dir)
+            model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
+            model_to_save.save_pretrained(args.output_dir)
             tokenizer.save_pretrained(args.output_dir)