o Ni @sLdZddlmZddlZddlmZd dejdedeedejfdd Z dS) ad This module is intended to store mask functions for use inside SHiRA construction. The mask functions are required to have a specific signature as shown below. Required positional arguments: base_layer - This is the linear layer where the shira adapter will be attached. r - This parameter is used to determine the number of parameters in the shira adapter in a way that is consistent with LoRA sizing. SHiRA is a high rank adapter. Setting this parameter does not restrict the adapter rank. Keyword arguments can be provided as needed by the particular mask function implementation. Return: mask - this is a torch.tensor of the same shape as base_layer.weight that contains 0s and 1s with the same dtype and device as base_layer.weight If you would like to attach SHiRA adapters to a model using PEFT methods (such as get_peft_model()), using more arguments than the provided positional arguments, you can create the mask function reference like the following: ``` def create_mask_function_reference(**my_kwargs): def mask_fn(base_layer, r): ... your implementation here that might use my_kwargs ... return mask return mask_fn ``` Then, you can create your peft model with custom SHiRA mask as follows: ``` model = ... my_kwargs = ... mask_fn = create_mask_function_reference(**my_kwargs) peft_config = ShiraConfig(r=4, mask_type='my_custom_mask') peft_config.mask_fn = mask_fn peft_model = get_peft_model(model, peft_config) ``` Complete training examples are provided in the examples/shira/ directory. )OptionalN base_layerr random_seedreturnc Ks|jj}||d|d}t}|dur||tj|j|dd||jj}t | |jj }t |j dd} | d|d|d |} | S)Nr) generator)weightshapetorch Generator manual_seedrandpermnumeltodevice ones_liketypedtype zeros_likeviewscatter_ unsqueeze) rrrkwargsr num_shira_weightsrandom_generatoridxvalmaskr T/home/ubuntu/.local/lib/python3.10/site-packages/peft/tuners/shira/mask_functions.py random_mask;s  r")N) __doc__typingrr torch.nnnnModuleinttensorr"r r r r!s  & (