o oi2@sDddlZddlmZddlmZmZmZmZdZGdddeZ dS)N) Optimizer)Betas2OptFloatOptLossClosureParams)NovoGradcsveZdZdZ      ddeded ed ed ed ed effdd Zde ddffdd Z dde de fddZ ZS)raImplements Novograd optimization algorithm. It has been proposed in `Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks`__. 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: (0.95, 0)) eps: term added to the denominator to improve numerical stability (default: 1e-8) weight_decay: weight decay (L2 penalty) (default: 0) grad_averaging: gradient averaging (default: False) amsgrad: whether to use the AMSGrad variant of this algorithm from the paper `On the Convergence of Adam and Beyond` (default: False) Example: >>> import torch_optimizer as optim >>> optimizer = optim.Yogi(model.parameters(), lr=0.1) >>> optimizer.zero_grad() >>> loss_fn(model(input), target).backward() >>> scheduler = StepLR(optimizer, step_size=1, gamma=0.7) >>> optimizer.step() >>> scheduler.step() __ https://arxiv.org/abs/1905.11286 Note: Reference code: https://github.com/NVIDIA/DeepLearningExamples MbP?gffffff?r:0yE>rFparamslrbetaseps weight_decaygrad_averagingamsgradc s|dkr td||dkrtd|d|dkr"dks,ntd|dd|dkr8dksBntd|d|dkrMtd |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: {})r rrrrr) ValueErrorformatdictsuperr__init__) selfr r rrrrrdefaults __class__V/home/ubuntu/SoloSpeech/.venv/lib/python3.10/site-packages/torch_optimizer/novograd.pyr,s2    zNovoGrad.__init__statereturnNcs,tt|||jD]}|ddq dS)NrF)rr __setstate__ param_groups setdefault)rrgrouprrrr Qs zNovoGrad.__setstate__closurecCsd}|dur |}|jD]}|dD]}|jdurq|jj}|jr'd}t||d}|j|}t|dkr^d|d<t|j|d<t g |dj |d<|r^t g |dj |d <|d|d} } |rm|d } |d \} } |dd 7<t t |d }| dkr| |n | | j|d | d |rtj| | | d| |d}n | |d}|||ddkr|j|j|dd |dr|d | | | ||jj| |d d qq |S)zPerforms a single optimization step. Arguments: closure: A closure that reevaluates the model and returns the loss. Nr zNNovoGrad does not support sparse gradients, please consider SparseAdam insteadrrstepexp_avg exp_avg_sqmax_exp_avg_sqrr)alpha)outrrrr )r!graddata is_sparse RuntimeErrorrlentorch zeros_likezerostodevicesumpowcopy_mul_add_maxsqrtdiv_)rr$lossr#pr,msgrrr&r'r(beta1beta2normdenomrrrr%Vs\           >z NovoGrad.step)r r r rFF)N)__name__ __module__ __qualname____doc__rfloatrboolrrr rrr% __classcell__rrrrr s2%%r) r1torch.optim.optimizerrtypesrrrr__all__rrrrrs