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Zero
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# Copyright 2024-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Optional, Union
from peft.config import PeftConfig
from peft.utils import PeftType
@dataclass
class LNTuningConfig(PeftConfig):
"""
This is the configuration class to store the configuration of a :class:`~peft.tuners.LNTuningModel`.
Args:
target_modules (`Optional[Union[List[str], str]]`):
List of module names or regex expression of the module names to replace with LNTuning. For example,
'.*decoder.*' or '.*encoder.*'. If this is not specified, modules will be chosen according to the model
architecture. If the architecture is not known, an error will be raised -- in this case, you should specify
the target modules manually.
modules_to_save (`Optional[Union[List[str], str]]`):
List of modules to be set as trainable and saved in the final checkpoint. For example, in Sequence
Classification or Token Classification tasks, the final layer `classifier/score` are randomly initialized
and as such need to be trainable and saved.
"""
target_modules: Optional[Union[list[str], str]] = field(
default=None,
metadata={
"help": (
"List of module names or regex expression of the module names to replace with LNTuning."
"For example, '.*decoder.*' or '.*encoder.*'. "
"If not specified, modules will be chosen according to the model architecture, If the architecture is "
"not known, an error will be raised -- in this case, you shoud specify the target modules manually."
),
},
)
modules_to_save: Optional[Union[list[str], str]] = field(
default=None,
metadata={
"help": "List of modules to be set as trainable and saved in the final checkpoint. "
"For example, in Sequence Classification or Token Classification tasks, "
"the final layer `classifier/score` are randomly initialized and as such need to be trainable and saved."
},
)
def __post_init__(self):
self.peft_type = PeftType.LN_TUNING
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