o zi @s@ddlZddlmZddlmZmZmZdZGdddeZdS)N) Optimizer)OptFloatOptLossClosureParams) CoolMomentumc sTeZdZdZ    ddedededed ef fd d Zdd edefddZ Z S)ra!Implements CoolMomentum Optimizer Algorithm. It has been proposed in `CoolMomentum: A Method for Stochastic Optimization by Langevin Dynamics with Simulated Annealing`__. Example: >>> import torch_optimizer as optim >>> optimizer = optim.CoolMomentum(model.parameters(), lr=0.01) >>> optimizer.zero_grad() >>> loss_fn(model(input), target).backward() >>> optimizer.step() __ https://arxiv.org/abs/2005.14605 Note: Reference code: https://github.com/borbysh/coolmomentum MbP?r?paramslrmomentum weight_decaybetacs|dkr td||dkrtd||dkr!td||dkr,td|t|||||d}tt|||dS)NgzInvalid learning rate: {}zInvalid momentum value: {}rzInvalid weight_decay value: {}zInvalid beta parameter: {})r r r r beta_power) ValueErrorformatdictsuperr__init__)selfr r r r rdefaults __class__P/home/ubuntu/.local/lib/python3.10/site-packages/torch_optimizer/coolmomentum.pyrs$zCoolMomentum.__init__NclosurereturncCsd}|dur |}|jD]x}|d}|d}|d}|d}|d||d<dd||}t|d}|dd|d } |d D]B} | jdurIqA| j} |dkrW| j| |d } |j| } d | vrlt| } | d <n| d } | |j | | d | } | j | qAq |S) zPerforms a single optimization step. Arguments: closure: A closure that reevaluates the model and returns the loss. Nr r rrrrr r )alphamomentum_buffer) param_groupsmaxgradaddstatetorchclonedetachmul_add_data)rrlossgroupr r rrrolrnpd_p param_statebufrrrstep7s>      zCoolMomentum.step)rrrr )N) __name__ __module__ __qualname____doc__rfloatrrrr3 __classcell__rrrrr s$r) r%torch.optim.optimizerrtypesrrr__all__rrrrrs