wehub-resource-sync
52 lines
2.1 KiB
Python
52 lines
2.1 KiB
Python
"""
|
|
PEFT (Parameter-Efficient Fine-Tuning) is a library for efficiently adapting large pretrained
|
|
models without fine-tuning all of model parameters but only a small number of (extra) parameters.
|
|
Users can define a PEFT model that wraps a Transformer model to apply a thin adapter layer on
|
|
top of the base model. The PEFT model provides almost the same APIs as the original model such
|
|
as from_pretrained(), save_pretrained().
|
|
"""
|
|
|
|
_PEFT_ADAPTOR_DIR_NAME = "peft"
|
|
|
|
|
|
def is_peft_model(model) -> bool:
|
|
try:
|
|
from peft import PeftModel
|
|
except ImportError:
|
|
return False
|
|
|
|
return isinstance(model, PeftModel)
|
|
|
|
|
|
def get_peft_base_model(model):
|
|
"""Extract the base model from a PEFT model."""
|
|
peft_config = model.peft_config.get(model.active_adapter) if model.peft_config else None
|
|
|
|
# PEFT usually wraps the base model with two additional classes, one is PeftModel class
|
|
# and the other is the adaptor specific class, like LoraModel class, so the class hierarchy
|
|
# looks like PeftModel -> LoraModel -> BaseModel
|
|
# However, when the PEFT config is the one for "prompt learning", there is not adaptor class
|
|
# and the PeftModel class directly wraps the base model.
|
|
if peft_config and not peft_config.is_prompt_learning:
|
|
return model.base_model.model
|
|
|
|
return model.base_model
|
|
|
|
|
|
def get_model_with_peft_adapter(base_model, peft_adapter_path):
|
|
"""
|
|
Apply the PEFT adapter to the base model to create a PEFT model.
|
|
|
|
NB: The alternative way to load PEFT adapter is to use load_adapter API like
|
|
`base_model.load_adapter(peft_adapter_path)`, as it injects the adapter weights
|
|
into the model in-place hence reducing the memory footprint. However, doing so
|
|
returns the base model class and not the PEFT model, losing some properties
|
|
such as peft_config. This is not preferable because load_model API should
|
|
return the exact same object that was saved. Hence we construct the PEFT model
|
|
instead of in-place injection, for consistency over the memory saving which
|
|
should be small in most cases.
|
|
"""
|
|
from peft import PeftModel
|
|
|
|
return PeftModel.from_pretrained(base_model, peft_adapter_path)
|