U d@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__;/tmp/pip-unpacked-wheel-ua33x9lu/torch/optim/sparse_adam.pyrs.   zSparseAdam.__init__Nc CsJd}|dk r&t |}W5QRX|jD]}g}g}g}g}g}|d} |d} |d\} } |dD]} | jdk rj|| | jjstd|| j|j| }t|dkrd|d<tj | tj d |d <tj | tj d |d <||d ||d |dd 7<||dqjt 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)Z memory_formatZexp_avgZ exp_avg_sqr)beta1beta2r r ) torchZ enable_gradZ param_groupsZgradrr RuntimeErrorstatelenZ zeros_likeZpreserve_formatFZ sparse_adam)rclosureZlossgroupZparams_with_gradZgradsZexp_avgsZ exp_avg_sqsZ state_stepsr r r!r"pr%rrrr 5sN          zSparseAdam.step)rrr)N) __name__ __module__ __qualname____doc__rr#Zno_gradr __classcell__rrrrrsr)r#rr'Z optimizerrrrrrrs