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tiny fix

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Former-commit-id: eae79707d31fd8be2cf4bee4d610557bbd49f6e7
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hiyouga 2023-05-29 09:42:29 +08:00
parent 83fc73c580
commit 35d04a2c05
5 changed files with 12 additions and 12 deletions
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2
src/train_ppo.py
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@ -69,7 +69,7 @@ def main():
ppo_trainer.ppo_train(max_target_length=data_args.max_target_length) ppo_trainer.ppo_train(max_target_length=data_args.max_target_length)
ppo_trainer.save_model() ppo_trainer.save_model()
ppo_trainer.save_state() # must be after save_model ppo_trainer.save_state() # must be after save_model
if ppo_trainer.is_world_process_zero() and finetuning_args.plot_loss: if ppo_trainer.is_world_process_zero() and model_args.plot_loss:
plot_loss(training_args, keys=["loss", "reward"]) plot_loss(training_args, keys=["loss", "reward"])

2
src/train_rm.py
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@ -55,7 +55,7 @@ def main():
trainer.save_metrics("train", train_result.metrics) trainer.save_metrics("train", train_result.metrics)
trainer.save_state() trainer.save_state()
trainer.save_model() trainer.save_model()
if trainer.is_world_process_zero() and finetuning_args.plot_loss: if trainer.is_world_process_zero() and model_args.plot_loss:
plot_loss(training_args, keys=["loss", "eval_loss"]) plot_loss(training_args, keys=["loss", "eval_loss"])
# Evaluation # Evaluation

2
src/train_sft.py
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@ -71,7 +71,7 @@ def main():
trainer.save_metrics("train", train_result.metrics) trainer.save_metrics("train", train_result.metrics)
trainer.save_state() trainer.save_state()
trainer.save_model() trainer.save_model()
if trainer.is_world_process_zero() and finetuning_args.plot_loss: if trainer.is_world_process_zero() and model_args.plot_loss:
plot_loss(training_args, keys=["loss", "eval_loss"]) plot_loss(training_args, keys=["loss", "eval_loss"])
# Evaluation # Evaluation

2
src/utils/common.py
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@ -91,7 +91,7 @@ def init_adapter(
lastest_checkpoint = None lastest_checkpoint = None
if model_args.checkpoint_dir is not None: if model_args.checkpoint_dir is not None:
if is_trainable and finetuning_args.resume_lora_training: # continually train on the lora weights if is_trainable and model_args.resume_lora_training: # continually train on the lora weights
checkpoints_to_merge, lastest_checkpoint = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1] checkpoints_to_merge, lastest_checkpoint = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1]
else: else:
checkpoints_to_merge = model_args.checkpoint_dir checkpoints_to_merge = model_args.checkpoint_dir

16
src/utils/config.py
View File

@ -51,6 +51,14 @@ class ModelArguments:
default=None, default=None,
metadata={"help": "Path to the directory containing the checkpoints of the reward model."} metadata={"help": "Path to the directory containing the checkpoints of the reward model."}
) )
resume_lora_training: Optional[bool] = field(
default=True,
metadata={"help": "Whether to resume training from the last LoRA weights or create new weights after merging them."}
)
plot_loss: Optional[bool] = field(
default=False,
metadata={"help": "Whether to plot the training loss after fine-tuning or not."}
)
def __post_init__(self): def __post_init__(self):
if self.checkpoint_dir is not None: # support merging lora weights if self.checkpoint_dir is not None: # support merging lora weights
@ -173,14 +181,6 @@ class FinetuningArguments:
default="q_proj,v_proj", default="q_proj,v_proj",
metadata={"help": "Name(s) of target modules to apply LoRA. Use comma to separate multiple modules."} metadata={"help": "Name(s) of target modules to apply LoRA. Use comma to separate multiple modules."}
) )
resume_lora_training: Optional[bool] = field(
default=True,
metadata={"help": "Whether to resume training from the last LoRA weights or create new weights after merging them."}
)
plot_loss: Optional[bool] = field(
default=False,
metadata={"help": "Whether to plot the training loss after fine-tuning or not."}
)
def __post_init__(self): def __post_init__(self):
if isinstance(self.lora_target, str): if isinstance(self.lora_target, str):