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import os
import torch
from typing import TYPE_CHECKING
from peft import (
PeftModel,
TaskType,
LoraConfig,
get_peft_model
)
from peft.utils import CONFIG_NAME, WEIGHTS_NAME
from llmtuner.extras.logging import get_logger
from llmtuner.tuner.core.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,
is_mergeable: bool
) -> "PreTrainedModel":
r"""
Initializes the adapters.
Support full-parameter, freeze and LoRA training.
Note that the trainable parameters must be cast to float32.
"""
if finetuning_args.finetuning_type == "none" and is_trainable:
raise ValueError("You cannot use finetuning_type=none while training.")
if finetuning_args.finetuning_type == "full" and is_trainable:
logger.info("Fine-tuning method: Full")
model = model.float()
if finetuning_args.finetuning_type == "freeze":
logger.info("Fine-tuning method: Freeze")
for name, param in model.named_parameters():
if not any(trainable_layer in name for trainable_layer in finetuning_args.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")
latest_checkpoint = None
if model_args.checkpoint_dir is not None:
assert os.path.exists(os.path.join(model_args.checkpoint_dir[0], WEIGHTS_NAME)), \
"Provided path ({}) does not contain a LoRA weight.".format(model_args.checkpoint_dir[0])
assert os.path.exists(os.path.join(model_args.checkpoint_dir[0], CONFIG_NAME)), \
"The given checkpoint may be not a LoRA checkpoint, please specify `--finetuning_type full/freeze` instead."
if (is_trainable and finetuning_args.resume_lora_training) or (not is_mergeable): # continually fine-tuning
checkpoints_to_merge, latest_checkpoint = 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 latest_checkpoint is not None: # resume lora training or quantized inference
model = PeftModel.from_pretrained(model, latest_checkpoint, is_trainable=is_trainable)
if is_trainable and latest_checkpoint 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, model_args.quantization_bit)
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
)
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
model.base_model.peft_config = model.peft_config
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
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