o zi@sPddlZddlZddlmZddlmZmZmZmZm Z dZ GdddeZ dS)N) Optimizer)Betas2OptFloatOptLossClosureParamsState)AdaBoundcseZdZdZ       dded ed ed ed ed edededdffdd Zde ddffdd Z dde de fddZ ZS)r a{Implements AdaBound algorithm. It has been proposed in `Adaptive Gradient Methods with Dynamic Bound of Learning Rate`__. 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)) final_lr: final (SGD) learning rate (default: 0.1) gamma: convergence speed of the bound functions (default: 1e-3) eps: term added to the denominator to improve numerical stability (default: 1e-8) weight_decay: weight decay (L2 penalty) (default: 0) amsbound: whether to use the AMSBound variant of this algorithm Example: >>> import torch_optimizer as optim >>> optimizer = optim.AdaBound(model.parameters(), lr=0.1) >>> optimizer.zero_grad() >>> loss_fn(model(input), target).backward() >>> optimizer.step() __ https://arxiv.org/abs/1902.09843 Note: Reference code: https://github.com/Luolc/AdaBound MbP?g?g+?皙?:0yE>rFparamslrbetasfinal_lrgammaeps weight_decayamsboundreturnNc s|dkr td||dkrtd|d|dkr"dks,ntd|dd|dkr8dksBntd|d|dkrMtd |d|krWdks_ntd ||dkrjtd |t|||||||d } tt||| d d|jD|_dS)NgzInvalid learning rate: {}zInvalid epsilon value: {}rg?z%Invalid beta parameter at index 0: {}rz%Invalid beta parameter at index 1: {}zInvalid final learning rate: {}zInvalid gamma parameter: {}zInvalid weight_decay value: {})rrrrrrrcSsg|]}|dqS)r).0grouprrL/home/ubuntu/.local/lib/python3.10/site-packages/torch_optimizer/adabound.py Wsz%AdaBound.__init__..) ValueErrorformatdictsuperr __init__ param_groupsbase_lrs) selfrrrrrrrrdefaults __class__rrr ,sB    zAdaBound.__init__statecs,tt|||jD]}|ddq dS)NrF)rr __setstate__r! setdefault)r#r'rr%rrr(Ys zAdaBound.__setstate__closurecCs6d}|dur |}t|j|jD]\}}|dD]}|jdur!q|jj}|jr.d}t||d}|j|} t| dkrXd| d<t || d<t || d<|rXt || d <| d| d} } |rg| d } |d \} }| dd 7<|d dkr|j |j|d d }| | j |d | d | |j||d |d|rt j| | | d|  |d}n |  |d}d | | d}d || d}|dt||}|d|d|}|d d |d| dd }|d d |d| d}t ||}|||| | |j | qq|S)zPerforms a single optimization step. Arguments: closure: A closure that reevaluates the model and returns the loss. NrzNAdaBound does not support sparse gradients, please consider SparseAdam insteadrrstepexp_avg exp_avg_sqmax_exp_avg_sqrrr)alpha)value)outrrrr)zipr!r"graddata is_sparse RuntimeErrorr'lentorch zeros_likeaddmul_add_addcmul_maxsqrtmath full_likediv_clamp_)r#r*lossrbase_lrpr3msgrr'r,r-r.beta1beta2denombias_correction1bias_correction2 step_sizer lower_bound upper_boundrrrr+^sn        Hz AdaBound.step)r r r r r rF)N)__name__ __module__ __qualname____doc__rfloatrboolr rr(rrr+ __classcell__rrr%rr s<#  -r ) r@r8torch.optim.optimizerrtypesrrrrr__all__r rrrrs