Low-Rank Kronecker Product (LoKr), is a LoRA-variant method that approximates the large weight matrix with two low-rank matrices and combines them with the Kronecker product. LoKr also provides an optional third low-rank matrix to provide better control during fine-tuning.
( peft_type: Union = None auto_mapping: Optional = None base_model_name_or_path: Optional = None revision: Optional = None task_type: Union = None inference_mode: bool = False rank_pattern: Optional[dict] = <factory> alpha_pattern: Optional[dict] = <factory> r: int = 8 alpha: int = 8 rank_dropout: float = 0.0 module_dropout: float = 0.0 use_effective_conv2d: bool = False decompose_both: bool = False decompose_factor: int = -1 target_modules: Union = None init_weights: bool = True layers_to_transform: Union = None layers_pattern: Optional = None modules_to_save: Optional = None )
Parameters
int
) —
LoKr rank. int
) —
The alpha parameter for LoKr scaling. float
) —
The dropout probability for rank dimension during training. float
) —
The dropout probability for disabling LoKr modules during training. bool
) —
Use parameter effective decomposition for Conv2d with ksize > 1 (“Proposition 3” from FedPara paper). bool
) —
Perform rank decomposition of left kronecker product matrix. int
) —
Kronecker product decomposition factor. 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/Conv1D 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. bool
) —
Whether to perform initialization of adapter weights. This defaults to True
, passing False
is
discouraged. 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. str
) —
The layer pattern name, used only if layers_to_transform
is different from None
. dict
) —
The mapping from layer names or regexp expression to ranks which are different from the default rank
specified by r
. dict
) —
The mapping from layer names or regexp expression to alphas which are different from the default alpha
specified by alpha
. Optional[List[str]]
) —
List of modules apart from adapter layers to be set as trainable and saved in the final checkpoint. Configuration class of LoKrModel.
( model config adapter_name low_cpu_mem_usage: bool = False ) → torch.nn.Module
Parameters
torch.nn.Module
) — The model to which the adapter tuner layers will be attached. str
) — The name of the adapter, defaults to "default"
. bool
, optional
, defaults to False
) —
Create empty adapter weights on meta device. Useful to speed up the loading process. Returns
torch.nn.Module
The LoKr model.
Creates Low-Rank Kronecker Product model from a pretrained model. The original method is partially described in https://arxiv.org/abs/2108.06098 and in https://arxiv.org/abs/2309.14859 Current implementation heavily borrows from https://github.com/KohakuBlueleaf/LyCORIS/blob/eb460098187f752a5d66406d3affade6f0a07ece/lycoris/modules/lokr.py
Example:
>>> from diffusers import StableDiffusionPipeline
>>> from peft import LoKrModel, LoKrConfig
>>> config_te = LoKrConfig(
... r=8,
... lora_alpha=32,
... target_modules=["k_proj", "q_proj", "v_proj", "out_proj", "fc1", "fc2"],
... rank_dropout=0.0,
... module_dropout=0.0,
... init_weights=True,
... )
>>> config_unet = LoKrConfig(
... r=8,
... lora_alpha=32,
... target_modules=[
... "proj_in",
... "proj_out",
... "to_k",
... "to_q",
... "to_v",
... "to_out.0",
... "ff.net.0.proj",
... "ff.net.2",
... ],
... rank_dropout=0.0,
... module_dropout=0.0,
... init_weights=True,
... use_effective_conv2d=True,
... )
>>> model = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> model.text_encoder = LoKrModel(model.text_encoder, config_te, "default")
>>> model.unet = LoKrModel(model.unet, config_unet, "default")
Attributes:
~torch.nn.Module
) — The model to be adapted.