bi,dZddlmZddlZddlmZddlmZddlZddl m Z ddl m Z ddlm Z ddlmZd d lmZd d lmZGd d eZ d ddZy)zC This module contains the implementation of the LoRA-FA optimizer. ) annotationsN)Iterable)Callable)is_bf16_available)autocast) Optimizer) PeftModel) infer_devicecxeZdZdZ d dfd Zej dddZxZS)LoraFAOptimizera; Implements the LoRA-FA optimizer designed specifically for training Low-Rank Adaptation (LoRA) parameters efficiently. Note that LoraFAOptimizer is based on adamw-hf in transformers, with only LoRA part modified. Without LoRA it will fall back to adamw-hf. Args: params (Iterable[nn.parameter.Parameter]): Parameters to optimize. lr (float, optional): Learning rate (default: 1e-3). betas (Tuple[float, float], optional): Coefficients for computing running averages of gradient and squared gradient (default: (0.9, 0.999)). eps (float, optional): Term added to denominator to improve numerical stability (default: 1e-6). weight_decay (float, optional): Weight decay (L2 penalty) (default: 0.0). correct_bias (bool, optional): Whether to apply bias correction as in original Adam (default: True). Args in sub-function step: closure (Callable, optional): A closure that reevaluates the model and returns the loss. Reference: - LoRA-FA: https://huggingface.co/papers/2308.03303 c|dkrtd|dd|dcxkrdksntd|ddd|dcxkrdksntd|ddd|kstd |d|||||d }t| ||y) NzInvalid learning rate: z - should be >= 0.0r?zInvalid beta parameter: z - should be in [0.0, 1.0)zInvalid epsilon value: )lrbetaseps weight_decay correct_bias) ValueErrorsuper__init__) selfparamsrrrrrdefaults __class__s Q/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/peft/optimizers/lorafa.pyrzLoraFAOptimizer.__init__9s 86rd:MNO OeAh$$7azA[\] ]eAh$$7azA[\] ]cz6se;NOP P((   *c H d}||}|jD]y}|d}g}g}t|d|dD]W\}}d|vr |j|j} d|vrW|j||j|t |dk(r|d|j ddz} nt |dk(r|} |j  } t | dk(rt |dk(r) device_typedtyperr)alpha)valuerrrrr) param_groupszipgradappendlenfindstatetorch zeros_likeTlinalgpinveyeshapetodevicer rrbfloat16r*mul_add_addcmul_sqrtmathaddcdiv_)rclosurelossgroupr! param_list name_listpnr/namer3AB grad_B_orindeltaAA_TAA_T_invr)grad_Br%r&beta1beta2denom_B step_sizebias_correction1bias_correction2r'r(denoms rr$zLoraFAOptimizer.stepSs  9D&&s PE"#34NJIE(OU7^<o P1?qvv~vvQ;%%a($$Q':!+ !1166&>2V;ZA- D 4(u:?:!+()f -2-=-=jm-Lk*050@0@A0On-()f +0+;+;A+>i(.3.>.>q.Al+z?a'"1 A"1 A"#&&K!Eqss7D$||00 !''RS*@U@X@XYZYaYa@b8b1bcH"..K(*%+U^^TX&'.!*;&; h@V%WFXX#$na&7"7K(LE5&MQ&MNN5)..vcEk.K %%e,55ffCRWK5X*//166uU|DG %d I^,+.%-1G+G(+.%-1G+G($- :J0K$KN^$^ JJy')JD^,s2q%+n8M)MO!#J "I+0 *:E,LE5&MQ&MLL',,T#+,GOOE*33D$cEk3R&OO-225<@E %d I^,+.%-1G+G(+.%-1G+G($- :J0K$KN^$^ JJwiZJ@^,s2q%+n8M)MO_o P s Pj uXXs RR! )gMbP?g?g+?gư>rT) rz Iterable[nn.parameter.Parameter]rfloatrztuple[float, float]rr[rr[rbool)N)rDr) __name__ __module__ __qualname____doc__rr4no_gradr$ __classcell__)rs@rr r #sv0%1!!+0+ +# +  +  ++4U]]_@@rr c0|jD]\}}d|vs |jd|r|tj|z n||z }|j ||jD cgc]\}} | c} }|d|dg} t | Scc} }w)a+ Helper function to instantiate a lorafa optimizer specifically configured for a given model using the LoRA method. This function will: - Disable gradient updates for the "lora_A" parameters (these are typically frozen during LoRA training). - Compute the scaling factor based on provided `lora_alpha` and rank `r` for proper gradient projection. - Create and configure parameter groups for the optimizer including specified learning rate, weight decay, and additional optimizer options. For hyper-params, LoRA-FA uses the same hyper-params as AdamW, except for the LoRA hyper-params (r, lora_alpha, use_rslora). One can always use the same hyper-params such as lr and weight_decay, as AdamW in LoRA tuning. Args: model (PeftModel): The model containing LoRA-adapted parameters. r (int): Rank of the LoRA decomposition. lora_alpha (int): Scaling factor for LoRA parameterization. lr (float): Learning rate for optimizer updates. weight_decay (float): Weight decay for AdamW. use_rslora (bool): whether to use rslora. In rslora, the lora scaling factor becomes to lora_alpha / math.sqrt(r) instead of lora_alpha / r. Returns: Optimizer: Configured lorafa optimizer instance ready for training. lora_AFrZ)rrr"r!rr)named_parametersrequires_grad_rBrA parametersr ) modelr lora_alpharr use_rslorarKparam lora_scaling_r-s rcreate_lorafa_optimizerros8--/( e t    '(1;: ! , QL&&(*/*@*@*BCwtQdC*!(   L < (( Ds1 B )rF)rhr riintrjrprr[rr[rkr\returnr)r` __future__rrBcollections.abcrtypingrr4torch.nnnnaccelerate.utils.importsrr torch.optimr peft_modelr utils.otherr r rorrr|s# $ 6!"&qiqjin*) *)*)*-*)38*)HM*)ae*)*)r