From 1ef41b83374ce5756e24746201d21432d7ecada0 Mon Sep 17 00:00:00 2001 From: wangfei <1140554608@qq.com> Date: Sun, 18 Aug 2019 11:03:12 +0800 Subject: [PATCH] Revert "Fix: save model/model.module" This reverts commit 00e9c4cc9616cab1666cab0a331b5d7e68946928. --- examples/lm_finetuning/finetune_on_pregenerated.py | 11 +++++------ examples/lm_finetuning/simple_lm_finetuning.py | 3 +-- 2 files changed, 6 insertions(+), 8 deletions(-) diff --git a/examples/lm_finetuning/finetune_on_pregenerated.py b/examples/lm_finetuning/finetune_on_pregenerated.py index 1177d84cd4..7c40342f18 100644 --- a/examples/lm_finetuning/finetune_on_pregenerated.py +++ b/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", @@ -322,8 +322,7 @@ def main(): # Save a trained model if args.local_rank == -1 or torch.distributed.get_rank() == 0: logging.info("** ** * Saving fine-tuned model ** ** * ") - 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) + model.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir) diff --git a/examples/lm_finetuning/simple_lm_finetuning.py b/examples/lm_finetuning/simple_lm_finetuning.py index 9633640faf..25333de0ed 100644 --- a/examples/lm_finetuning/simple_lm_finetuning.py +++ b/examples/lm_finetuning/simple_lm_finetuning.py @@ -610,8 +610,7 @@ 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_to_save = model.module if hasattr(model, 'module') else model # Take care of distributed/parallel training - model_to_save.save_pretrained(args.output_dir) + model.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir)