o ,i8K$@sddlmZmZddlZddlmZddlmZmZmZm Z m Z m Z m Z m Z mZmZmZmZddgZGdddeZd d e d e d ed ed e_d eedeedeedeedeedeededededededededededef ddZd eedeedeedeedeedeededededededededededef dd Ze ed!  " " " "d%d eedeedeedeedeedeed#eededededededededededef"d$dZdS)&)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 OptimizerParamsTRMSproprmspropcs~eZdZ          ddeded ed ed ed ed eededeffdd ZfddZddZ e dddZ Z S)r{Gz?Gz?:0yE>rFNparamslralphaeps weight_decaymomentumforeachmaximizedifferentiablec sd|ks td|d|kstd|d|ks!td|d|ks,td|d|ks7td|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__Q/home/ubuntu/SoloSpeech/.venv/lib/python3.10/site-packages/torch/optim/rmsprop.pyr%s. zRMSprop.__init__cst||jD]_}|dd|dd|dd|dd|dd|dd|d D]4}|j|g}t|dkrgt|d sgt |d }|dr]tj |t |j d ntj |t d |d <q3q dS) Nrrr Frrrr!rstepdtypedevicer.) r$ __setstate__ param_groups setdefaultstategetlentorch is_tensorfloattensorr r/)r&r4grouppp_statestep_valr(r*r+r1@s*           zRMSprop.__setstate__c Cs8d}|dD]} | jdurq|t| O}|| | jjr"td|| j|j| } t| dkrs|drAtjdt | j dntjdt d| d <tj | tj d | d <|d dkretj | tj d | d <|drstj | tj d | d<|| d || d |d dkr|| d |dr|| dq|S)NFrz)RMSprop does not support sparse gradientsrr!r*r-r0r,) memory_format square_avgrmomentum_bufferr grad_avg) gradr7 is_complexappend is_sparse RuntimeErrorr4r6zerosr r/ zeros_likepreserve_format) r&r;params_with_gradgrads square_avgsmomentum_buffer_list grad_avgs state_steps has_complexr<r4r*r*r+ _init_groupUsD          zRMSprop._init_groupc Cs|d}|dur!t |}Wdn1swY|jD]B}g}g}g}g}g}g} |||||||| } t|||||| |d|d|d|d|d|d|d|d |d |d | d q$|S) zPerforms a single optimization step. Args: closure (Callable, optional): A closure that reevaluates the model and returns the loss. Nrrrrrr rrrr!) rrrrrr rrrr!rQ) _cuda_graph_capture_health_checkr7 enable_gradr2rRr) r&closurelossr;rKrLrMrOrNrPrQr*r*r+r,sT   z RMSprop.step) rrrrrFFNFFN) __name__ __module__ __qualname__rr9rboolr%r1rRrr, __classcell__r*r*r(r+rsD    '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. Hinton. and centered version `Generating Sequences With Recurrent Neural Networks `_. The implementation here takes the square root of the gradient average before adding epsilon (note that TensorFlow interchanges these two operations). The effective learning rate is thus :math:`\gamma/(\sqrt{v} + \epsilon)` where :math:`\gamma` is the scheduled learning rate and :math:`v` is the weighted moving average of the squared gradient. a Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 1e-2) momentum (float, optional): momentum factor (default: 0) alpha (float, optional): smoothing constant (default: 0.99) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-8) centered (bool, optional) : if ``True``, compute the centered RMSProp, the gradient is normalized by an estimation of its variance weight_decay (float, optional): weight decay (L2 penalty) (default: 0) z z rrLrMrOrNrPrrrrrr rrr!rQc Cst|D]\}}||}tjs,|r,t}|jj|jjkr$|jj|vs,Jd|d||}| s4|n| }||}|d7}| dkrJ|j|| d}t|}|r`t |}t |}t |}| |j ||d|d| r||}|rzt |}| |d||j ||dd}n|}| r||}n||}| dkr||}|rt |}| | |||j|| dq|j||| dqdS)NIIf capturable=True, params and state_steps must be on supported devices: .rrrvalue) enumerater7_utils is_compilingr r/typeaddrD view_as_realmul_addcmul_lerp_addcmulsqrt_sqrtadd_addcdiv_)rrLrMrOrNrPrrrrrr rrr!rQiparamr,capturable_supported_devicesrCr@is_complex_paramrBavgbufr*r*r+_single_tensor_rmspropsL         rwc sdt|dkrdS| rJdtjs.|r.ttfddt||Ds.Jddt||||||g}| D]\\}}}}}}}|re||g}| dkrV| || r]| |t |g|R| rlt |}|dj rtj|tjddd dd nt|d | dkr| rtj||| d ntj||| d }t||tj|||d |d | rt||d |tj|||d d }t|t||n t|}t||| dkr t|| t||||rt|tjrt|| }t||q=tj||| d q=|r%t|tjr%t|| t|||q=tj|||| d q=dS)Nrz#_foreach ops don't support autogradc3s0|]\}}|jj|jjko|jjvVqdSrW)r/rf).0r<r,rsr*r+ cs   z(_multi_tensor_rmsprop..r]r^g?cpu)r/r_rr`rb)r6r7rdrer allzipr"_group_tensors_by_device_and_dtypevaluesrEr _foreach_negis_cpu _foreach_add_r: _foreach_add _foreach_mul__foreach_addcmul__foreach_lerp__foreach_addcmul_foreach_sqrt_ _foreach_sqrt_foreach_addcdiv_ isinstancer _foreach_mul _foreach_div_)rrLrMrOrNrPrrrrrr rrr!rQgrouped_tensorsgrouped_params grouped_gradsgrouped_square_avgsgrouped_grad_avgsgrouped_momentum_buffer_listgrouped_state_steps_state_and_gradsru momentum_lrr*ryr+_multi_tensor_rmspropHs                 r)single_tensor_fnFrc Cstjstdd|Dstd|durt||dd\}}|r*tjr*td|r4tjs4t}nt }|||||||| | | ||||| || ddS) zsFunctional API that performs rmsprop algorithm computation. See :class:`~torch.optim.RMSProp` for details. css|] }t|tjVqdSrW)rr7r)rxtr*r*r+rzs  zrmsprop..zPAPI has changed, `state_steps` argument must contain a list of singleton tensorsNF) use_fusedz6torch.jit.script not supported with foreach optimizers) rrrrrr rr!rrQ) r7rdrer|rGrjit is_scriptingrrw)rrLrMrOrNrPrrrr!rQrrrrrr rfuncr*r*r+rsB  )NFFFF)typingrrr7r optimizerrrrr r r r r rrrr__all__r__doc__r9r[rwrrr*r*r*r+s 8*+  A      G      s