testbed/huggingface__trl/trl/trainer/model_config.py

# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# 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
from typing import List, Literal, Optional


@dataclass
class ModelConfig:
    """
    Configuration class for the models.

    Using [`~transformers.HfArgumentParser`] we can turn this class into
    [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
    command line.

    Parameters:
        model_name_or_path (`Optional[str]`, *optional*, defaults to `None`):
            Model checkpoint for weights initialization.
        model_revision (`str`, *optional*, defaults to `"main"`):
            Specific model version to use. It can be a branch name, a tag name, or a commit id.
        torch_dtype (`Optional[Literal["auto", "bfloat16", "float16", "float32"]]`, *optional*, defaults to `None`):
            Override the default `torch.dtype` and load the model under this dtype. Possible values are

                - `"bfloat16"`: `torch.bfloat16`
                - `"float16"`: `torch.float16`
                - `"float32"`: `torch.float32`
                - `"auto"`: Automatically derive the dtype from the model's weights.

        trust_remote_code (`bool`, *optional*, defaults to `False`):
            Whether to allow for custom models defined on the Hub in their own modeling files. This option should only
            be set to `True` for repositories you trust and in which you have read the code, as it will execute code
            present on the Hub on your local machine.
        attn_implementation (`Optional[str]`, *optional*, defaults to `None`):
            Which attention implementation to use. You can run `--attn_implementation=flash_attention_2`, in which case
            you must install this manually by running `pip install flash-attn --no-build-isolation`.
        use_peft (`bool`, *optional*, defaults to `False`):
            Whether to use PEFT for training.
        lora_r (`int`, *optional*, defaults to `16`):
            LoRA R value.
        lora_alpha (`int`, *optional*, defaults to `32`):
            LoRA alpha.
        lora_dropout (`float`, *optional*, defaults to `0.05`):
            LoRA dropout.
        lora_target_modules (`Optional[Union[str, List[str]]]`, *optional*, defaults to `None`):
            LoRA target modules.
        lora_modules_to_save (`Optional[List[str]]`, *optional*, defaults to `None`):
            Model layers to unfreeze & train.
        lora_task_type (`str`, *optional*, defaults to `"CAUSAL_LM"`):
            Task type to pass for LoRA (use `"SEQ_CLS"` for reward modeling).
        use_rslora (`bool`, *optional*, defaults to `False`):
            Whether to use Rank-Stabilized LoRA, which sets the adapter scaling factor to `lora_alpha/√r`, instead of
            the original default value of `lora_alpha/r`.
        load_in_8bit (`bool`, *optional*, defaults to `False`):
            Whether to use 8 bit precision for the base model. Works only with LoRA.
        load_in_4bit (`bool`, *optional*, defaults to `False`):
            Whether to use 4 bit precision for the base model. Works only with LoRA.
        bnb_4bit_quant_type (`str`, *optional*, defaults to `"nf4"`):
            Quantization type (`"fp4"` or `"nf4"`).
        use_bnb_nested_quant (`bool`, *optional*, defaults to `False`):
            Whether to use nested quantization.
    """

    model_name_or_path: Optional[str] = None
    model_revision: str = "main"
    torch_dtype: Optional[Literal["auto", "bfloat16", "float16", "float32"]] = None
    trust_remote_code: bool = False
    attn_implementation: Optional[str] = None
    use_peft: bool = False
    lora_r: int = 16
    lora_alpha: int = 32
    lora_dropout: float = 0.05
    lora_target_modules: Optional[List[str]] = None
    lora_modules_to_save: Optional[List[str]] = None
    lora_task_type: str = "CAUSAL_LM"
    use_rslora: bool = False
    load_in_8bit: bool = False
    load_in_4bit: bool = False
    bnb_4bit_quant_type: Literal["fp4", "nf4"] = "nf4"
    use_bnb_nested_quant: bool = False

    def __post_init__(self):
        if self.load_in_8bit and self.load_in_4bit:
            raise ValueError("You can't use 8 bit and 4 bit precision at the same time")

        if isinstance(self.lora_target_modules, list) and len(self.lora_target_modules) == 1:
            self.lora_target_modules = self.lora_target_modules[0]