o cf&@s4dZddlZddlmZddlZGdddeZdS)zLamb optimizer.N) Optimizercs.eZdZdZ d fdd Zd d d ZZS)LambaImplements Lamb algorithm. It has been proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`_. Arguments: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 1e-3) betas (Tuple[float, float], optional): coefficients used for computing running averages of gradient and its square (default: (0.9, 0.999)) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-8) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) adam (bool, optional): always use trust ratio = 1, which turns this into Adam. Useful for comparison purposes. .. _Large Batch Optimization for Deep Learning: Training BERT in 76 minutes: https://arxiv.org/abs/1904.00962 MbP?g?g+?:0yE>rFcsd|ks td|d|kstd|d|dkr"dks,ntd|dd|dkr8dksBntd|dt||||d }||_tt|||dS) NgzInvalid learning rate: {}zInvalid epsilon value: {}rg?z%Invalid beta parameter at index 0: {}z%Invalid beta parameter at index 1: {})lrbetaseps weight_decay) ValueErrorformatdictadamsuperr__init__)selfparamsrr r r rdefaults __class__5/home/dufour/Documents/diff_plonk/utils/optimizers.pyrsz Lamb.__init__Nc Csd}|dur |}|jD]}|dD]}|jdurq|jj}|jr%td|j|}t|dkrDd|d<t|j|d<t|j|d<|d|d}}|d\} } |dd 7<| | j |d | d | | j ||d | d d | |d} d | |d} || } || }|d }d |vr|d n|ddk}| | |d}|ddkr|j |j|dd |r|jjdd}|jdd}t|dt|d||d d }|js|sd }|jj || |d qq |S)zPerforms a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. NrzCLamb does not support sparse gradients, consider SparseAdam instad.rstepexp_avg exp_avg_sqr r)alpha)valuerlayer_adaptationr r )p) param_groupsgraddata is_sparse RuntimeErrorstatelentorch zeros_likemul_add_addcmul_sqrtaddnormwherener)rclosurelossgroupr r"r&rrbeta1beta2bias_correction1bias_correction2Z exp_avg_hatZexp_avg_sq_hat step_sizeZdo_layer_adaptationZ adam_step weight_normZ adam_normZ trust_ratiorrrr)s^          ;z Lamb.step)rrrrF)N)__name__ __module__ __qualname____doc__rr __classcell__rrrrrs r)r>r(Z torch.optimrmathrrrrrs