U Mh8K@szddlmZmZddlZddlmZddlmZmZmZm Z m Z m Z m Z m Z mZmZmZmZddgZGdddeZd d e d e d ed ed e_eeeeeeeeeeeeeeeeeeeeeed ddZeeeeeeeeeeeeeeeeeeeeeed ddZe eddeeeeeeeeeeeeeeeeeeeeeeeedddZdS))ListOptionalN)Tensor) _capturable_doc_default_to_fused_or_foreach_differentiable_doc_disable_dynamo_if_unsupported _foreach_doc!_get_capturable_supported_devices_get_scalar_dtype _maximize_doc_use_grad_for_differentiable _view_as_real OptimizerParamsTRMSproprmspropc sZeZdZdeeeeeeeeeed fdd Zfd d Zd d Z e dddZ Z S)r{Gz?Gz?:0yE>rFN) paramslralphaeps weight_decaymomentumforeachmaximizedifferentiablec sd|kstd|d|ks,td|d|ksBtd|d|ksXtd|d|ksntd|t|||||||| | | d } t|| dS)NgzInvalid learning rate: zInvalid epsilon value: zInvalid momentum value: zInvalid weight_decay value: zInvalid alpha value: ) rrrrcenteredr capturablerrr) ValueErrordictsuper__init__) selfrrrrrrr r!rrrdefaults __class__E/var/www/html/venv/lib/python3.8/site-packages/torch/optim/rmsprop.pyr%s. zRMSprop.__init__cst||jD]}|dd|dd|dd|dd|dd|dd|d D]h}|j|g}t|dkrft|d sft |d }|drtj |t |j d ntj |t d |d <qfqdS) Nrrr Frrrr!rstepdtypedevicer.) r$ __setstate__ param_groups setdefaultstategetlentorchZ is_tensorfloattensorr r/)r&r4grouppZp_stateZstep_valr(r*r+r1@s(          zRMSprop.__setstate__c Cs<d}|dD](} | jdkrq |t| O}|| | jjrFtd|| j|j| } t| dkr|drtjdt | j dntjdt d| d <tj | tj d | d <|d dkrtj | tj d | d <|drtj | tj d | d<|| d || d |d dkr || d |dr || dq |S)NFrz)RMSprop does not support sparse gradientsrr!r*r-r0r,)Z memory_format square_avgrZmomentum_bufferr grad_avg) gradr7 is_complexappendZ is_sparse RuntimeErrorr4r6Zzerosr r/Z zeros_likeZpreserve_format) r&r:params_with_gradgrads square_avgsmomentum_buffer_list grad_avgs state_steps has_complexr;r4r*r*r+ _init_groupUsH          zRMSprop._init_groupc Cs|d}|dk r.t |}W5QRX|jD]}g}g}g}g}g}g} |||||||| } t|||||| |d|d|d|d|d|d|d|d |d |d | d q4|S) zPerforms a single optimization step. Args: closure (Callable, optional): A closure that reevaluates the model and returns the loss. Nrrrrrr rrrr!) rrrrrr rrrr!rH)Z _cuda_graph_capture_health_checkr7Z enable_gradr2rIr) r&closureZlossr:rBrCrDrFrErGrHr*r*r+r,sR   z RMSprop.step) rrrrrFFNFF)N) __name__ __module__ __qualname__rr8rboolr%r1rIrr, __classcell__r*r*r(r+rs2' 3a Implements RMSprop algorithm. .. math:: \begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \alpha \text{ (alpha)},\: \gamma \text{ (lr)}, \: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)} \\ &\hspace{13mm} \lambda \text{ (weight decay)},\: \mu \text{ (momentum)},\: centered\\ &\textbf{initialize} : v_0 \leftarrow 0 \text{ (square average)}, \: \textbf{b}_0 \leftarrow 0 \text{ (buffer)}, \: g^{ave}_0 \leftarrow 0 \\[-1.ex] &\rule{110mm}{0.4pt} \\ &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\ &\hspace{5mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\ &\hspace{5mm}if \: \lambda \neq 0 \\ &\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\ &\hspace{5mm}v_t \leftarrow \alpha v_{t-1} + (1 - \alpha) g^2_t \hspace{8mm} \\ &\hspace{5mm} \tilde{v_t} \leftarrow v_t \\ &\hspace{5mm}if \: centered \\ &\hspace{10mm} g^{ave}_t \leftarrow g^{ave}_{t-1} \alpha + (1-\alpha) g_t \\ &\hspace{10mm} \tilde{v_t} \leftarrow \tilde{v_t} - \big(g^{ave}_{t} \big)^2 \\ &\hspace{5mm}if \: \mu > 0 \\ &\hspace{10mm} \textbf{b}_t\leftarrow \mu \textbf{b}_{t-1} + g_t/ \big(\sqrt{\tilde{v_t}} + \epsilon \big) \\ &\hspace{10mm} \theta_t \leftarrow \theta_{t-1} - \gamma \textbf{b}_t \\ &\hspace{5mm} else \\ &\hspace{10mm}\theta_t \leftarrow \theta_{t-1} - \gamma g_t/ \big(\sqrt{\tilde{v_t}} + \epsilon \big) \hspace{3mm} \\ &\rule{110mm}{0.4pt} \\[-1.ex] &\bf{return} \: \theta_t \\[-1.ex] &\rule{110mm}{0.4pt} \\[-1.ex] \end{aligned} For further details regarding the algorithm we refer to `lecture notes `_ by G. 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