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fix bug in freeze tuning

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Former-commit-id: ff52b1779c909819d0aef83d3f7ea663199cbe54
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hiyouga 2023-11-16 14:25:11 +08:00
parent 627212e48b
commit 0ed0b8f9c5
2 changed files with 8 additions and 1 deletions
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8
src/llmtuner/model/adapter.py
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@ -37,7 +37,13 @@ def init_adapter(
if finetuning_args.finetuning_type == "freeze" and is_trainable: if finetuning_args.finetuning_type == "freeze" and is_trainable:
logger.info("Fine-tuning method: Freeze") logger.info("Fine-tuning method: Freeze")
num_layers = getattr(model.config, "num_layers") num_layers = (
getattr(model.config, "num_hidden_layers", None)
or getattr(model.config, "num_layers", None)
or getattr(model.config, "n_layer", None)
)
if not num_layers:
raise ValueError("Current model does not support freeze tuning.")
if finetuning_args.num_layer_trainable > 0: # fine-tuning the last n layers if num_layer_trainable > 0 if finetuning_args.num_layer_trainable > 0: # fine-tuning the last n layers if num_layer_trainable > 0
trainable_layer_ids = [num_layers - k - 1 for k in range(finetuning_args.num_layer_trainable)] trainable_layer_ids = [num_layers - k - 1 for k in range(finetuning_args.num_layer_trainable)]
else: # fine-tuning the first n layers if num_layer_trainable < 0 else: # fine-tuning the first n layers if num_layer_trainable < 0

1
src/llmtuner/train/utils.py
View File

@ -76,4 +76,5 @@ def create_reward_model(
reward_finetuning_args = FinetuningArguments(finetuning_type="lora") reward_finetuning_args = FinetuningArguments(finetuning_type="lora")
reward_model, _ = load_model_and_tokenizer(reward_model_args, reward_finetuning_args, is_trainable=False, stage="ppo") reward_model, _ = load_model_and_tokenizer(reward_model_args, reward_finetuning_args, is_trainable=False, stage="ppo")
logger.info("Load full weights of reward model from {}".format(finetuning_args.reward_model)) logger.info("Load full weights of reward model from {}".format(finetuning_args.reward_model))
logger.warning("Please ensure the ppo model and reward model share SAME tokenizer and vocabulary.")
return reward_model return reward_model