peft/tuners/adalora/config.py

# Copyright 2023-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
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from dataclasses import dataclass, field
from typing import Optional

from peft.tuners.lora import LoraConfig
from peft.utils import PeftType


@dataclass
class AdaLoraConfig(LoraConfig):
    """
    This is the configuration class to store the configuration of a [`~peft.AdaLora`].

    Args:
        target_r (`int`): The target average rank of incremental matrix.
        init_r (`int`): The initial rank for each incremental matrix.
        tinit (`int`): The steps of initial fine-tuning warmup.
        tfinal (`int`): The step of final fine-tuning.
        deltaT (`int`): The time internval between two budget allocations.
        beta1 (`float`): The hyperparameter of EMA for sensitivity smoothing.
        beta2 (`float`): The hyperparameter of EMA for undertainty quantification.
        orth_reg_weight (`float`): The coefficient of orthogonal regularization.
        total_step (`int`): The total training steps that should be specified before training.
        rank_pattern (`list`): The allocated rank for each weight matrix by RankAllocator.
    """

    target_r: int = field(default=8, metadata={"help": "Target Lora matrix dimension."})
    init_r: int = field(default=12, metadata={"help": "Initial Lora matrix dimension."})
    tinit: int = field(default=0, metadata={"help": "The steps of initial warmup."})
    tfinal: int = field(default=0, metadata={"help": "The steps of final warmup."})
    deltaT: int = field(default=1, metadata={"help": "Step interval of rank allocation."})
    beta1: float = field(default=0.85, metadata={"help": "Hyperparameter of EMA."})
    beta2: float = field(default=0.85, metadata={"help": "Hyperparameter of EMA."})
    orth_reg_weight: float = field(default=0.5, metadata={"help": "The orthogonal regularization coefficient."})
    total_step: Optional[int] = field(default=None, metadata={"help": "The total training steps."})
    rank_pattern: Optional[dict] = field(default=None, metadata={"help": "The saved rank pattern."})

    def __post_init__(self):
        self.peft_type = PeftType.ADALORA

        if self.use_dora:
            raise ValueError(f"{self.peft_type} does not support DoRA.")

        if self.loftq_config:
            raise ValueError(f"{self.peft_type} does not support LOFTQ.")

        self.target_modules = (
            set(self.target_modules) if isinstance(self.target_modules, list) else self.target_modules
        )
        # if target_modules is a regex expression, then layers_to_transform should be None
        if isinstance(self.target_modules, str) and self.layers_to_transform is not None:
            raise ValueError("`layers_to_transform` cannot be used when `target_modules` is a str.")

        # if target_modules is a regex expression, then layers_pattern should be None
        if isinstance(self.target_modules, str) and self.layers_pattern is not None:
            raise ValueError("`layers_pattern` cannot be used when `target_modules` is a str.")