bi/hddlmZddlmZmZddlmZmZddlm Z ddl m Z eGdde Z y) ) annotations) dataclassfield)OptionalUnion) PeftConfig)PeftTypeceZdZUdZedddiZded<eddd iZd ed <ed dd iZded<edddiZ ded<edddiZ ded<edddiZ ded<edddiZ ded<edddiZ ded <eddd!iZd"ed#<eddd$iZded%<eedd&i'Zd(ed)<eddd*iZded+<fd,ZxZS)-FourierFTConfiga This is the configuration class to store the configuration of a [`FourierFTModel`]. Args: n_frequency (`int`): Num of learnable frequencies for the Discrete Fourier Transform. 'n_frequency' is an integer that is greater than 0 and less than or equal to d^2 (assuming the weight W has dimensions of d by d). Additionally, it is the number of trainable parameters required to update each delta W weight. 'n_frequency' will affect the performance and efficiency for PEFT. Specifically, it has little impact on training speed, but higher values of it (typically) result in larger GPU memory costs and better accuracy. With the same `target_modules`, the number of parameters of LoRA is (2*d*r/n_frequency) times that of FourierFT. The following examples of settings regarding 'n_frequency' can be used as reference for users. For NLU tasks with the RoBERTa-large model, adopting 'n_frequency': 1000 can almost achieve similar results as 'r': 8 in LoRA. At this time, the number of parameters of LoRA is about 16 times that of FourierFT. For image classification tasks with Vit-large models, adopting 'n_frequency': 3000 can almost achieve similar results as 'r': 16 in LoRA, where the number of parameters of LoRA is about 11 times that of FourierFT. scaling (`float`): The scaling value for the delta W matrix. This is an important hyperparameter used for scaling, similar to the 'lora_alpha' parameter in the LoRA method. 'scaling' can be determined during the hyperparameter search process. However, if users want to skip this process, one can refer to the settings in the following scenarios. This parameter can be set to 100.0 or 150.0 for both RoBERTa-base and RoBERTa-large models across all NLU (GLUE) tasks. This parameter can be set to 300.0 for both LLaMA family models for all instruction tuning. This parameter can be set to 300.0 for both ViT-base and ViT-large models across all image classification tasks. random_loc_seed (`int`): Seed for the random location of the frequencies, i.e., the spectral entry matrix. target_modules (`Union[list[str],str]`): List of module names or regex expression of the module names to replace with FourierFT. For example, ['q', 'v'] or '.*decoder.*(SelfAttention|EncDecAttention).*(q|v)$'. Only linear layers are supported. 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. fan_in_fan_out (`bool`): Set this to True if the layer to replace stores weight like (fan_in, fan_out). bias (`str`): Bias type for FourierFT. Can be 'none', 'all' or 'fourier_only'. modules_to_save (`list[str]`): List of modules apart from FourierFT layers to be set as trainable and saved in the final checkpoint. For example, in Sequence Classification or Token Classification tasks, the final layer `classifier/score` are randomly initialized and as such need to be trainable and saved. layers_to_transform (`Union[list[int],int]`): The layer indexes to transform, is this argument is specified, PEFT will transform only the layers indexes that are specified inside this list. If a single integer is passed, PEFT will transform only the layer at this index. layers_pattern (`Optional[Union[List[str], str]]`): The layer pattern name, used only if `layers_to_transform` is different to None and if the layer pattern is not in the common layers pattern. This should target the `nn.ModuleList` of the model, which is often called `'layers'` or `'h'`. n_frequency_pattern (`dict`): The mapping from layer names or regexp expression to n_frequency which are different from the default specified. For example, `{model.decoder.layers.0.encoder_attn.k_proj: 1000`}. init_weights (`bool`): The initialization of the Fourier weights. Set this to False if the spectrum are initialized to a standard normal distribution. Set this to True if the spectrum are initialized to zeros. ihelpaeNum of learnable frequencies for the Discrete Fourier Transform. 'n_frequency' is an integer that isgreater than 0 and less than or equal to d^2 (assuming the weight W has dimensions of d by d).Additionally, it is the number of trainable parameters required to update each delta W weight.'n_frequency' will affect the performance and efficiency for PEFT. Specifically, it has little impact ontraining speed, but higher values of it (typically) result in larger GPU memory costs and better accuracy.With the same `target_modules`, the number of parameters of LoRA is (2*d*r/n_frequency) times that of FourierFT.The following examples of settings regarding 'n_frequency' can be used as reference for users. For NLUtasks with the RoBERTa-large model, adopting 'n_frequency': 1000 can almost achieve similar results as'r': 8 in LoRA. At this time, the number of parameters of LoRA is about 16 times that of FourierFT.For image classification tasks with Vit-large models, adopting 'n_frequency': 3000 can almost achievesimilar results as 'r': 16 in LoRA, where the number of parameters of LoRA is about 11 times that of FourierFT.)defaultmetadataint n_frequencygb@aThe scaling value for the delta W matrix. This is an important hyperparameter used for scaling, similar to the'lora_alpha' parameter in the LoRA method. 'scaling' can be determined during the hyperparameter search process.However, if users want to skip this process, one can refer to the settings in the following scenarios.This parameter can be set to 100.0 or 150.0 for both RoBERTa-base and RoBERTa-large models across all NLU (GLUE) tasks.This parameter can be set to 300.0 for both LLaMA family models for all instruction tuning.This parameter can be set to 300.0 for both ViT-base and ViT-large models across all image classification tasks.floatscalingi z0Seed for the random location of the frequencies.z Optional[int]random_loc_seedFzMSet this to True if the layer to replace stores weight like (fan_in, fan_out)boolfan_in_fan_outNzList of module names or regex expression of the module names to replace with FourierFT.For example, ['q', 'v'] or '.*decoder.*(SelfAttention|EncDecAttention).*(q|v)$'. Only linear layers are supported.zOptional[Union[list[str], str]]target_moduleszWList of module names or regex expression of the module names to exclude from fourierft.exclude_modulesnonez@Bias type for FourierFT. Can be 'none', 'all' or 'fourier_only'.strbiasaList of modules apart from FourierFT layers to be set as trainable and saved in the final checkpoint. For example, in Sequence Classification or Token Classification tasks, the final layer `classifier/score` are randomly initialized and as such need to be trainable and saved.zOptional[list[str]]modules_to_savezThe layer indexes to transform, is this argument is specified, PEFT will transform only the layers indexes that are specified inside this list. If a single integer is passed, PEFT will transform only the layer at this index.zOptional[Union[list[int], int]]layers_to_transformzThe layer pattern name, used only if `layers_to_transform` is different to None and if the layer pattern is not in the common layers pattern. This should target the `nn.ModuleList` of the model, which is often called `'layers'` or `'h'`.layers_patternzThe mapping from layer names or regexp expression to n_frequency which are different from the default specified.For example, `{model.decoder.layers.0.encoder_attn.k_proj: 500`}.)default_factoryrzOptional[dict]n_frequency_patternzThe initialization of the Fourier weights. Set this to False if the spectrum should be initialized to a standard normal distribution.Set this to True if the spectrum should be initialized to zeros. init_weightscXt|tj|_t |j trt|j n |j |_t |jtrt|jn |j|_ t |j tr|j tdt |j tr|j td|jr|js tdyy)NzD`layers_to_transform` cannot be used when `target_modules` is a str.z?`layers_pattern` cannot be used when `target_modules` is a str.zRWhen `layers_pattern` is specified, `layers_to_transform` must also be specified. )super __post_init__r FOURIERFT peft_type isinstancerlistsetrrr ValueErrorr)self __class__s W/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/peft/tuners/fourierft/config.pyr#zFourierFTConfig.__post_init__s !++(243F3F(MC## $SWSfSf *4D4H4H$)OC$$ %UYUiUi  d))3 /D4L4L4Xcd d d))3 /D4G4G4S^_ _   t'?'?qr r(@ )__name__ __module__ __qualname____doc__rr__annotations__rrrrrrrrrdictrr r# __classcell__)r+s@r,r r s7r  B K$ C   GU &+v'YZ&O]!ijND7< 4  7N3 8=st8O4&*l!mD#,1 k  ,O( r;s7#("" tsjts tsr-