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Bone

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Bone

Block-Affine Adaptation (Bone) This work, inspired by GQA and MQA, leverages the sparsity of LLM weights to design the Block-Affine Adaptation (Bone) structure. In Bone, the original weights are divided into multiple sub-spaces, all of which share a single low-rank matrix initialized to zero for updates. Extensive exper-iments demonstrate that Bone consistently outperforms LoRA and its variants across various tasks, while also offering superior computational efficiency.

The abstract from the paper is:

Low-Rank Adaptation (LoRA) has achieved remarkable training results by freezing the original weights and training only low-rank matrices, establishing itself as the predominant fine-tuning method for LLMs. In pursuit of performance closer to full-parameter training, a series of LoRA variants have emerged, such as LoRA+, PISSA, Olora, and LoRA-GA. This paper introduces a novel PEFT technique distinct from LoRA, called Block-Affine Adaptation (Bone). By dividing the original weights into multiple subspaces that share a single matrix for weight updates, Bone simplifies the process by requiring the trainable matrix to be initialized to zero, eliminating the need for complex initialization as in some LoRA variants. Compared to LoRA, Bone significantly reduces memory usage and achieves faster computation. Evaluation of both NLU and NLG tasks demonstrates that Bone substantially outperforms LoRA and its variants. Inspired by Pissa, we further proposed the ‘Weight Guide’ theory to better utilize the information from the original weights. By integrating ‘Weight Guide’ with Bone, we developed a new structure called Block-Affine Transformation (Bat), and ablation experiments confirmed the effectiveness of ‘Weight Guide’.

BoneConfig

class peft.BoneConfig

< >

( task_type: typing.Union[str, peft.utils.peft_types.TaskType, NoneType] = None peft_type: typing.Union[str, peft.utils.peft_types.PeftType, NoneType] = None auto_mapping: typing.Optional[dict] = None base_model_name_or_path: typing.Optional[str] = None revision: typing.Optional[str] = None inference_mode: bool = False r: int = 64 target_modules: Optional[Union[list[str], str]] = None exclude_modules: Optional[Union[list[str], str]] = None init_weights: bool | Literal['bat'] = True layers_to_transform: Optional[Union[list[int], int]] = None layers_pattern: Optional[str] = None bias: str = 'none' modules_to_save: Optional[list[str]] = None )

Parameters

  • r (int) — The rank of Bone across different layers. It is best to set ‘r’ to an even number; otherwise, the default initialization method will not work.
  • target_modules (Optional[Union[List[str], str]]) — The names of the modules to apply the adapter to. If this is specified, only the modules with the specified names will be replaced. When passing a string, a regex match will be performed. When passing a list of strings, either an exact match will be performed or it is checked if the name of the module ends with any of the passed strings. If this is specified as ‘all-linear’, then all linear modules are chosen, excluding the output layer. 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.
  • exclude_modules (Optional[Union[List[str], str]]) — The names of the modules to not apply the adapter. When passing a string, a regex match will be performed. When passing a list of strings, either an exact match will be performed or it is checked if the name of the module ends with any of the passed strings.
  • init_weights (bool | Literal[“bat”]) — Different initializations correspond to different Bone variants. By default, setting True uses the Bone structure, while “bat” selects the Bat structure.
  • layers_to_transform (Union[List[int], int]) — The layer indices to transform. If a list of ints is passed, it will apply the adapter to the layer indices that are specified in this list. If a single integer is passed, it will apply the transformations on the layer at this index.
  • layers_pattern (str) — The layer pattern name, used only if layers_to_transform is different from None.
  • rank_pattern (dict) — The mapping from layer names or regexp expression to ranks which are different from the default rank specified by r.
  • modules_to_save (List[str]) — List of modules apart from adapter layers to be set as trainable and saved in the final checkpoint.

This is the configuration class to store the configuration of a BoneModel.

BoneModel

class peft.BoneModel

< >

( model peft_config: Union[PeftConfig, dict[str, PeftConfig]] adapter_name: str low_cpu_mem_usage: bool = False ) → torch.nn.Module

Parameters

  • model (torch.nn.Module) — The model to which the adapter tuner layers will be attached.
  • config (BoneConfig) — The configuration of the Bone model.
  • adapter_name (str) — The name of the adapter, defaults to "default".
  • low_cpu_mem_usage (bool, optional, defaults to False) — Create empty adapter weights on meta device. Useful to speed up the loading process.

Returns

torch.nn.Module

The Bone model.

Creates Householder reflection adaptation (Bone) model from a pretrained model. The method is described in https://arxiv.org/abs/2409.15371

Example:

>>> from diffusers import StableDiffusionPipeline
>>> from peft import BoneModel, BoneConfig

>>> config_te = BoneConfig(
...     r=8,
...     target_modules=["k_proj", "q_proj", "v_proj", "out_proj", "fc1", "fc2"],
...     init_weights=True,
... )
>>> config_unet = BoneConfig(
...     r=8,
...     target_modules=[
...         "proj_in",
...         "proj_out",
...         "to_k",
...         "to_q",
...         "to_v",
...         "to_out.0",
...         "ff.net.0.proj",
...         "ff.net.2",
...     ],
...     init_weights=True,
... )

>>> model = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> model.text_encoder = BoneModel(model.text_encoder, config_te, "default")
>>> model.unet = BoneModel(model.unet, config_unet, "default")

Attributes:

  • model (~torch.nn.Module) — The model to be adapted.
  • peft_config (BoneConfig): The configuration of the Bone model.

delete_adapter

< >

( adapter_name: str )

Parameters

  • adapter_name (str) — Name of the adapter to be deleted.

Deletes an existing adapter.

merge_and_unload

< >

( progressbar: bool = False safe_merge: bool = False adapter_names: typing.Optional[typing.List[str]] = None )

Parameters

  • progressbar (bool) — whether to show a progressbar indicating the unload and merge process
  • safe_merge (bool) — whether to activate the safe merging check to check if there is any potential Nan in the adapter weights
  • adapter_names (List[str], optional) — The list of adapter names that should be merged. If None, all active adapters will be merged. Defaults to None.

This method merges the Bone layers into the base model. This is needed if someone wants to use the base model as a standalone model.

unload

< >

( )

Gets back the base model by removing all the bone modules without merging. This gives back the original base model.

< > Update on GitHub

←CPT Model merge→