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refactor adapter hparam

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Former-commit-id: f82aece9ebd6df83a7a005cc7cbbcec07fa6e14d
This commit is contained in:
hiyouga
2023-12-15 20:53:11 +08:00
parent 27ef5b1aa7
commit f902b0d420
21 changed files with 302 additions and 311 deletions
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36
src/llmtuner/model/adapter.py
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@@ -27,8 +27,8 @@ def init_adapter(
Note that the trainable parameters must be cast to float32.
"""
if (not is_trainable) and model_args.checkpoint_dir is None:
logger.info("Checkpoint is not found at evaluation, load the original model.")
if (not is_trainable) and model_args.adapter_name_or_path is None:
logger.info("Adapter is not found at evaluation, load the base model.")
return model
if finetuning_args.finetuning_type == "full" and is_trainable:
@@ -44,6 +44,7 @@ def init_adapter(
)
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
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
@@ -62,30 +63,31 @@ def init_adapter(
if finetuning_args.finetuning_type == "lora":
logger.info("Fine-tuning method: LoRA")
checkpoint_to_resume = None
adapter_to_resume = None
if model_args.checkpoint_dir is not None:
if model_args.adapter_name_or_path is not None:
is_mergeable = True
if getattr(model, "quantization_method", None): # merge lora in quantized model is unstable
assert len(model_args.checkpoint_dir) == 1, "Quantized model only accepts a single checkpoint."
assert len(model_args.adapter_name_or_path) == 1, "Quantized model only accepts a single adapter."
is_mergeable = False
if (is_trainable and finetuning_args.resume_lora_training) or (not is_mergeable):
checkpoints_to_merge, checkpoint_to_resume = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1]
if (is_trainable and not finetuning_args.create_new_adapter) or (not is_mergeable):
adapter_to_merge = model_args.adapter_name_or_path[:-1]
adapter_to_resume = model_args.adapter_name_or_path[-1]
else:
checkpoints_to_merge = model_args.checkpoint_dir
adapter_to_merge = model_args.adapter_name_or_path
for checkpoint in checkpoints_to_merge:
model = PeftModel.from_pretrained(model, checkpoint)
for adapter in adapter_to_merge:
model = PeftModel.from_pretrained(model, adapter)
model = model.merge_and_unload()
if len(checkpoints_to_merge) > 0:
logger.info("Merged {} model checkpoint(s).".format(len(checkpoints_to_merge)))
if len(adapter_to_merge) > 0:
logger.info("Merged {} adapter(s).".format(len(adapter_to_merge)))
if checkpoint_to_resume is not None: # resume lora training
model = PeftModel.from_pretrained(model, checkpoint_to_resume, is_trainable=is_trainable)
if adapter_to_resume is not None: # resume lora training
model = PeftModel.from_pretrained(model, adapter_to_resume, is_trainable=is_trainable)
if is_trainable and checkpoint_to_resume is None: # create new lora weights while training
if is_trainable and adapter_to_resume is None: # create new lora weights while training
if len(finetuning_args.lora_target) == 1 and finetuning_args.lora_target[0] == "all":
target_modules = find_all_linear_modules(model)
else:
@@ -105,7 +107,7 @@ def init_adapter(
for param in filter(lambda p: p.requires_grad, model.parameters()):
param.data = param.data.to(torch.float32)
if model_args.checkpoint_dir is not None:
logger.info("Loaded fine-tuned model from checkpoint(s): {}".format(",".join(model_args.checkpoint_dir)))
if model_args.adapter_name_or_path is not None:
logger.info("Loaded adapter(s): {}".format(",".join(model_args.adapter_name_or_path)))
return model