a h@sjUddlmZddlZddlmmZddlmZddlm Z gZ e e e d<ejjGdddZdS))OptionalN)Tensor)2_scripted_functional_optimizer_deprecation_warning__all__c@sNeZdZdeeeeeeeeeeeeeed dd Zeeed d d Z d S)_FunctionalAdagrad{Gz??绽|=TF) paramslrlr_decay weight_decayinitial_accumulator_valuewarmup_lr_multiplierwarmup_num_iterseps coalesce_gradforeachfusedmaximize_allow_empty_param_listcCstdd|||||||d|_| |_| |_| |_| |_tjt tj t t tj ffi|_ t |dkrt| sttdd|i|_|jdD]$}t|j|tdd|j |<qdS) N) stacklevel)r r rrrrrrz%optimizer got an empty parameter listr r)sumstep)rdefaultsrrrrtorchjitZannotatedictrstrstatelen ValueError param_groupZ full_likedataZtensor)selfr r r rrrrrrrrrrpr(X/var/www/auris/lib/python3.9/site-packages/torch/distributed/optim/functional_adagrad.py__init__s*  $  z_FunctionalAdagrad.__init__) gradientsc Cs@|jd}g}g}g}g}t|t|krPtddt|ddt|d\}}t|jd|D]b\} } | durh|| jO}|t| O}|| || |j| } || d|| dqht Vt j |||||j d |j d |j d |j d ||j |j||jddd Wdn1s20YdS)Nr zEthe gradients passed in does not equal to the size of the parameters!zParams length: z. zGradients length: )FFrrr rr r) r rr rhas_sparse_gradrr has_complexrZ grad_scaleZ found_inf)r$r"r#zipZ is_sparserZ is_complexappendr!Zno_gradFZadagradrrrr) r&r+r Zparams_with_gradZgradsZ state_sumsZ state_stepsr,r-paramZgradientr!r(r(r)rIsR       z_FunctionalAdagrad.stepN) rrrrr rr TFFFF) __name__ __module__ __qualname__listrfloatboolr*rrr(r(r(r)rs8 /r)typingrrZtorch.optim._functionalZoptimZ _functionalr0rZ,torch.distributed.optim._deprecation_warningrrr5r __annotations__rscriptrr(r(r(r)s