o oi}@sLddlZddlZddlmZddlmZmZmZmZdZ GdddeZ dS)N) Optimizer)Betas2OptFloatOptLossClosureParams)LambcsjeZdZdZ       dded ed ed ed ed edededdffdd Zdde de fddZ Z S)raImplements Lamb algorithm. It has been proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`__. Arguments: params: iterable of parameters to optimize or dicts defining parameter groups lr: learning rate (default: 1e-3) betas: coefficients used for computing running averages of gradient and its square (default: (0.9, 0.999)) eps: term added to the denominator to improve numerical stability (default: 1e-8) weight_decay: weight decay (L2 penalty) (default: 0) clamp_value: clamp weight_norm in (0,clamp_value) (default: 10) set to a high value to avoid it (e.g 10e3) adam: always use trust ratio = 1, which turns this into Adam. Useful for comparison purposes. (default: False) debias: debias adam by (1 - beta**step) (default: False) Example: >>> import torch_optimizer as optim >>> optimizer = optim.Lamb(model.parameters(), lr=0.1) >>> optimizer.zero_grad() >>> loss_fn(model(input), target).backward() >>> optimizer.step() __ https://arxiv.org/abs/1904.00962 Note: Reference code: https://github.com/cybertronai/pytorch-lamb MbP?g?g+?ư>r Fparamslrbetaseps weight_decay clamp_valueadamdebiasreturnNc s|dkr td||dkrtd|d|dkr"dks,ntd|dd|dkr8dksBntd|d|dkrMtd ||dkrXtd |t||||d } ||_||_||_tt||| dS) NgzInvalid learning rate: {}zInvalid epsilon value: {}rg?z%Invalid beta parameter at index 0: {}rz%Invalid beta parameter at index 1: {}zInvalid weight_decay value: {}zInvalid clamp value: {})rrrr) ValueErrorformatdictrrrsuperr__init__) selfr rrrrrrrdefaults __class__R/home/ubuntu/SoloSpeech/.venv/lib/python3.10/site-packages/torch_optimizer/lamb.pyr-s.   z Lamb.__init__closurecCsd}|dur |}|jD]}|dD]}|jdurq|jj}|jr'd}t||j|}t|dkrDd|d<t||d<t||d<|d|d}} |d\} } |dd 7<| | j |d | d | | j ||d | d |j rt d | |d} | d | |d} nd } |d | } t|jd|j}|| |d }|ddkr|j |j|dd t|}|dks|dkrd }n||}||d<||d<||d<|jrd }|jj || |d qq |S)zPerforms a single optimization step. Arguments: closure: A closure that reevaluates the model and returns the loss. Nr zJLamb does not support sparse gradients, please consider SparseAdam insteadrstepexp_avg exp_avg_sqrr)alpha)valuerrr weight_norm adam_norm trust_ratio) param_groupsgraddata is_sparse RuntimeErrorstatelentorch zeros_likemul_add_addcmul_rmathsqrtnormclampraddr)rr!lossgrouppr+msgr/r#r$beta1beta2bias_correction step_sizer' adam_stepr(r)rrr r"RsV         =z Lamb.step)r r r rr FF)N) __name__ __module__ __qualname____doc__rfloatrboolrrrr" __classcell__rrrr r s:$  %r) r6r1torch.optim.optimizerrtypesrrrr__all__rrrrr s