o zi. @s@ddlZddlmZddlmZmZmZdZGdddeZdS)N) Optimizer)OptFloatOptLossClosureParams)AccSGDcs^eZdZdZ     ddeded ed ed ed ed dffdd Zdded efddZ Z S)raImplements AccSGD algorithm. It has been proposed in `On the insufficiency of existing momentum schemes for Stochastic Optimization`__ and `Accelerating Stochastic Gradient Descent For Least Squares Regression`__ Arguments: params: iterable of parameters to optimize or dicts defining parameter groups lr: learning rate (default: 1e-3) kappa: ratio of long to short step (default: 1000) xi: statistical advantage parameter (default: 10) small_const: any value <=1 (default: 0.7) weight_decay: weight decay (L2 penalty) (default: 0) Example: >>> import torch_optimizer as optim >>> optimizer = optim.AccSGD(model.parameters(), lr=0.1) >>> optimizer.zero_grad() >>> loss_fn(model(input), target).backward() >>> optimizer.step() __ https://arxiv.org/abs/1704.08227 __ https://arxiv.org/abs/1803.05591 Note: Reference code: https://github.com/rahulkidambi/AccSGD MbP?@@$@ffffff?rparamslrkappaxi small_const weight_decayreturnNcsT|dkr td||dkrtd|t|||||d}tt|||dS)NgzInvalid learning rate: {}rzInvalid weight_decay value: {})r rrrr) ValueErrorformatdictsuperr__init__)selfr r rrrrdefaults __class__J/home/ubuntu/.local/lib/python3.10/site-packages/torch_optimizer/accsgd.pyr(s zAccSGD.__init__closurec CsTd}|dur |}|jD]}|d}|d|d|d}d|d|d|d|d}d|}|d|d|}|dD]d} | jdurJqB| jj} |d krZ| j| j|d |j| } d | vrkt| j| d <| d } | d|d| j| | d | | j| || jj| |d d | j|| jj| d|d qBq |S) zPerforms a single optimization step. Arguments: closure: A closure that reevaluates the model and returns the loss. Nrr rrg?rr r)alphamomentum_buffer) param_groupsgraddataadd_statecopydeepcopymul_) rrlossgrouprlarge_lrrbetazetapd_p param_statebufrrrstep@s@       z AccSGD.step)rr r r r)N) __name__ __module__ __qualname____doc__rfloatrrrr2 __classcell__rrrrr s. r) r&torch.optim.optimizerrtypesrrr__all__rrrrrs