o zi@sLddlZddlZddlmZddlmZmZmZmZdZ GdddeZ dS)N) Optimizer)Betas2OptFloatOptLossClosureParams)AdaModcs^eZdZdZ     ddeded ed ed ed ed dffdd Zdded e fddZ Z S)raImplements AdaMod algorithm. It has been proposed in `Adaptive and Momental Bounds for Adaptive Learning Rate Methods`__. 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)) beta3: smoothing coefficient for adaptive learning rates (default: 0.9999) eps: term added to the denominator to improve numerical stability (default: 1e-8) weight_decay: weight decay (L2 penalty) (default: 0) Example: >>> import torch_optimizer as optim >>> optimizer = optim.AdaMod(model.parameters(), lr=0.1) >>> optimizer.zero_grad() >>> loss_fn(model(input), target).backward() >>> optimizer.step() __ https://arxiv.org/abs/1910.12249 Note: Reference code: https://github.com/lancopku/AdaMod MbP?g?+?r :0yE>rparamslrbetasbeta3eps weight_decayreturnNcs|dkr td||dkrtd|d|dkr"dks,ntd|dd|dkr8dksBntd|dd|krLdksTntd ||dkr_td |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 beta3 parameter: {}zInvalid weight_decay value: {})rrrrr) ValueErrorformatdictsuperr__init__)selfr rrrrrdefaults __class__J/home/ubuntu/.local/lib/python3.10/site-packages/torch_optimizer/adamod.pyr*s,    zAdaMod.__init__closurecCsd}|dur |}|jD]}|dD]}|jdurq|jj}|jr'd}t||j|}t|dkrKd|d<t||d<t||d<t||d<|d|d|d}} } |d \} } |dd 7<| | j |d | d | | j ||d | d | |d } d | |d}d | |d}|dt ||}|ddkr|jj |j|d |dd t| |}|| | |dj |d |dd t|| }| ||j | qq |S)zPerforms a single optimization step. Arguments: closure: A closure that reevaluates the model and returns the loss. Nr z(AdaMod does not support sparse gradientsrstepexp_avg exp_avg_sq exp_avg_lrrr)alpha)valuerrrr) param_groupsgraddata is_sparse RuntimeErrorstatelentorch zeros_likemul_add_addcmul_sqrtmath full_likediv_min)rrlossgrouppr'msgr+r!r"r#beta1beta2denombias_correction1bias_correction2 step_sizerrrr Js`            ;z AdaMod.step)r r r r r)N) __name__ __module__ __qualname____doc__rfloatrrrrr __classcell__rrrrr s.! r) r3r-torch.optim.optimizerrtypesrrrr__all__rrrrrs