a 8Sic@s4ddlZddlmZddlmZGdddeZdS)N) _functional) Optimizercs4eZdZdZd fdd Zed dd ZZS) SparseAdama Implements lazy version of Adam algorithm suitable for sparse tensors. In this variant, only moments that show up in the gradient get updated, and only those portions of the gradient get applied to the parameters. Args: 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) .. _Adam\: A Method for Stochastic Optimization: https://arxiv.org/abs/1412.6980 MbP?g?g+?:0yE>c s&d|kstd|d|ks,td|d|dkrDdksXntd|dd|dkrpdksntd|dt|}g}t|D]R\}}t|trt|d gD]\}} | jr|||gqq|jr||q|rtd |d t|||d } t t | || dS) NgzInvalid learning rate: {}zInvalid epsilon value: {}rg?z%Invalid beta parameter at index 0: {}rz%Invalid beta parameter at index 1: {}paramszSparse params at indices z-: SparseAdam requires dense parameter tensors)lrbetaseps) ValueErrorformatlist enumerate isinstancedictget is_sparseappendsuperr__init__) selfr r r r Z sparse_paramsindexparamZd_indexZd_paramdefaults __class__S/var/www/html/django/DPS/env/lib/python3.9/site-packages/torch/optim/sparse_adam.pyrs.   zSparseAdam.__init__Nc Cs^d}|dur:t|}Wdn1s00Y|jD]}g}g}g}g}g}|d} |d} |d\} } |dD]} | jdur~|| | jjstd|| j|j| }t|dkrd|d<tj | tj d |d <tj | tj d |d <||d ||d |dd 7<||dq~t j |||||| | |d|dd q@|S)zPerforms a single optimization step. Args: closure (callable, optional): A closure that reevaluates the model and returns the loss. Nr r r r zISparseAdam does not support dense gradients, please consider Adam insteadrstep) memory_formatexp_avg exp_avg_sqr)beta1beta2r r ) torch enable_grad param_groupsgradrr RuntimeErrorstatelen zeros_likepreserve_formatF sparse_adam)rclosurelossgroupparams_with_gradgradsexp_avgs exp_avg_sqs state_stepsr r r$r%pr+rrrr 5sN $         zSparseAdam.step)rrr)N) __name__ __module__ __qualname____doc__rr&no_gradr __classcell__rrrrrsr)r&rr/ optimizerrrrrrrs