support create_checkpoint_symlink (#2975)

docs/source/Instruction/命令行参数.md

@@ -295,6 +295,7 @@ Vera使用`target_modules`, `target_regex`, `modules_to_save`三个参数.
 - add_version: 在output_dir上额外增加目录`'<版本号>-<时间戳>'`防止权重覆盖，默认为True
 - resume_only_model: 如果resume_from_checkpoint，仅resume模型权重，默认为False
 - check_model: 检查本地模型文件有损坏或修改并给出提示，默认为True。如果是断网环境，请设置为False
+- create_checkpoint_symlink: 额外创建checkpoint软链接。best_model和last_model分别为f'{output_dir}/best'和f'{output_dir}/last'
 - loss_type: loss类型，默认使用模型自带损失函数
 
 - packing: 是否使用packing，默认为False

docs/source_en/Instruction/Command-line-parameters.md

@@ -299,6 +299,7 @@ Training arguments include the [base arguments](#base-arguments), [Seq2SeqTraine
 - add_version: Add directory to output_dir with `'<version>-<timestamp>'` to prevent weight overwrite, default is True.
 - resume_only_model: If resume_from_checkpoint, only resume model weights, default is False.
 - check_model: Check local model files for corruption or modification and give a prompt, default is True. If in an offline environment, please set to False.
+- create_checkpoint_symlink: Create additional checkpoint symlinks. best_model and last_model are f'{output_dir}/best' and f'{output_dir}/last', respectively.
 - loss_type: Type of loss, default uses the model's built-in loss function.
 - packing: Whether to use packing, default is False.
 - 🔥lazy_tokenize: Whether to use lazy_tokenize, default is False during LLM training, default is True during MLLM training.

swift/llm/argument/train_args.py

@@ -109,6 +109,7 @@ class TrainArguments(TorchAccArguments, TunerArguments, Seq2SeqTrainingOverrideA
     add_version: bool = True
     resume_only_model: bool = False
     check_model: bool = True
+    create_checkpoint_symlink: bool = False
 
     # dataset
     packing: bool = False

swift/llm/dataset/utils.py

@@ -188,6 +188,9 @@ def __getitem__(self, idx: int) -> Dict[str, Any]:
                                    'and another piece of data will be randomly selected.')
                     self._traceback_counter += 1
 
+        raise ValueError('Failed to retrieve the dataset. You can avoid this issue by increasing `max_length` or '
+                         'modifying the `truncation_strategy`.')
+
     def __len__(self) -> int:
         return len(self.dataset)
 

swift/llm/export/merge_lora.py

@@ -14,8 +14,7 @@ def merge_lora(args: ExportArguments, device_map=None, replace_if_exists=False)
     output_dir = getattr(args, 'output_dir', None) or f'{args.adapters[0]}-merged'
     if os.path.exists(output_dir) and not replace_if_exists:
         logger.info(f'The weight directory for the merged LoRA already exists in {output_dir}, '
-                    'skipping the saving process. '
-                    'you can pass `replace_if_exists=True` to overwrite it.')
+                    'skipping the saving process.')
     else:
         origin_device_map = args.device_map
         args.device_map = device_map or args.device_map

swift/llm/train/sft.py

@@ -158,6 +158,13 @@ def _save_trainer_state(self, trainer):
         training_args = trainer.args
         state = trainer.state
 
+        if self.args.create_checkpoint_symlink:
+            last_checkpoint = os.path.join(self.args.output_dir, 'last')
+            best_checkpoint = os.path.join(self.args.output_dir, 'best')
+            os.symlink(state.last_model_checkpoint, last_checkpoint)
+            os.symlink(state.best_model_checkpoint, best_checkpoint)
+            state.last_model_checkpoint = last_checkpoint
+            state.best_model_checkpoint = best_checkpoint
         logger.info(f'last_model_checkpoint: {state.last_model_checkpoint}')
         logger.info(f'best_model_checkpoint: {state.best_model_checkpoint}')