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refactor finetuning Args

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Former-commit-id: be425a70a4c8f051717cf1e4464dbd79dae4c0b5
This commit is contained in:
hiyouga
2023-09-27 22:28:06 +08:00
parent ebb3901b05
commit 386d85ae72
2 changed files with 18 additions and 22 deletions
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11
src/llmtuner/tuner/core/adapter.py
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@@ -45,9 +45,15 @@ def init_adapter(
if finetuning_args.finetuning_type == "freeze":
logger.info("Fine-tuning method: Freeze")
num_layers = getattr(model.config, "num_layers")
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)]
else: # fine-tuning the first n layers if num_layer_trainable < 0
trainable_layer_ids = [k for k in range(-finetuning_args.num_layer_trainable)]
trainable_layers = ["{:d}.{}".format(idx, finetuning_args.name_module_trainable) for idx in trainable_layer_ids]
for name, param in model.named_parameters():
if not any(trainable_layer in name for trainable_layer in finetuning_args.trainable_layers):
if not any(trainable_layer in name for trainable_layer in trainable_layers):
param.requires_grad_(False)
else:
param.data = param.data.to(torch.float32)
@@ -89,7 +95,8 @@ def init_adapter(
r=finetuning_args.lora_rank,
lora_alpha=finetuning_args.lora_alpha,
lora_dropout=finetuning_args.lora_dropout,
target_modules=target_modules
target_modules=target_modules,
modules_to_save=finetuning_args.additional_target
)
model = get_peft_model(model, lora_config)
if id(model.peft_config) != id(model.base_model.peft_config): # https://github.com/huggingface/peft/issues/923