mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-04 12:42:51 +08:00
112 lines
4.6 KiB
Python
112 lines
4.6 KiB
Python
import torch
|
|
from typing import TYPE_CHECKING
|
|
from peft import PeftModel, TaskType, LoraConfig, get_peft_model
|
|
|
|
from llmtuner.extras.logging import get_logger
|
|
from llmtuner.model.utils import find_all_linear_modules
|
|
|
|
if TYPE_CHECKING:
|
|
from transformers.modeling_utils import PreTrainedModel
|
|
from llmtuner.hparams import ModelArguments, FinetuningArguments
|
|
|
|
|
|
logger = get_logger(__name__)
|
|
|
|
|
|
def init_adapter(
|
|
model: "PreTrainedModel",
|
|
model_args: "ModelArguments",
|
|
finetuning_args: "FinetuningArguments",
|
|
is_trainable: bool
|
|
) -> "PreTrainedModel":
|
|
r"""
|
|
Initializes the adapters.
|
|
|
|
Support full-parameter, freeze and LoRA training.
|
|
|
|
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.")
|
|
return model
|
|
|
|
if finetuning_args.finetuning_type == "full" and is_trainable:
|
|
logger.info("Fine-tuning method: Full")
|
|
model = model.float()
|
|
|
|
if finetuning_args.finetuning_type == "freeze" and is_trainable:
|
|
logger.info("Fine-tuning method: Freeze")
|
|
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
|
|
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 = []
|
|
for module_name in finetuning_args.name_module_trainable:
|
|
for idx in trainable_layer_ids:
|
|
trainable_layers.append("{:d}.{}".format(idx, module_name))
|
|
|
|
for name, param in model.named_parameters():
|
|
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)
|
|
|
|
if finetuning_args.finetuning_type == "lora":
|
|
logger.info("Fine-tuning method: LoRA")
|
|
checkpoint_to_resume = None
|
|
|
|
if model_args.checkpoint_dir 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."
|
|
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]
|
|
else:
|
|
checkpoints_to_merge = model_args.checkpoint_dir
|
|
|
|
for checkpoint in checkpoints_to_merge:
|
|
model = PeftModel.from_pretrained(model, checkpoint)
|
|
model = model.merge_and_unload()
|
|
|
|
if len(checkpoints_to_merge) > 0:
|
|
logger.info("Merged {} model checkpoint(s).".format(len(checkpoints_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 is_trainable and checkpoint_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:
|
|
target_modules = finetuning_args.lora_target
|
|
|
|
lora_config = LoraConfig(
|
|
task_type=TaskType.CAUSAL_LM,
|
|
inference_mode=False,
|
|
r=finetuning_args.lora_rank,
|
|
lora_alpha=finetuning_args.lora_alpha,
|
|
lora_dropout=finetuning_args.lora_dropout,
|
|
target_modules=target_modules,
|
|
modules_to_save=finetuning_args.additional_target
|
|
)
|
|
model = get_peft_model(model, lora_config)
|
|
|
|
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)))
|
|
|
|
return model
|