o h0O$@sdZddlmZmZmZddlZddlmZddlmZm Z m Z m Z m Z m Z mZmZmZmZmZmZmZddgZGd ddeZd d ed ed e d ed e d e_deedeedeedeedeedeededededededededededef dd Zdeedeedeedeedeedeededededededededededef d!d"Ze ed#  $ $ $ $d'deedeedeedeedeedeed%eededededededededededef"d&dZdS)(z)Implementation for the RMSprop algorithm.)castOptionalUnionN)Tensor) _capturable_doc_default_to_fused_or_foreach_differentiable_doc_disable_dynamo_if_unsupported _foreach_doc!_get_capturable_supported_devices_get_scalar_dtype _maximize_doc _params_doc_use_grad_for_differentiable _view_as_real OptimizerParamsTRMSproprmspropcseZdZ          ddedeeefd ed ed ed ed ededeededeffdd Z fddZ ddZ e dddZ ZS)r{Gz?Gz?:0yE>rFNparamslralphaeps weight_decaymomentumcentered capturableforeachmaximizedifferentiablec st|tr|dkrtdd|kstd|d|ks%td|d|ks0td|d|ks;td|d|ksFtd|t|||||||| | | d } t|| dS) NrzTensor lr must be 1-elementgzInvalid learning rate: zInvalid epsilon value: zInvalid momentum value: zInvalid weight_decay value: zInvalid alpha value: ) rrrrrrr r!r"r#) isinstancernumel ValueErrordictsuper__init__) selfrrrrrrrr r!r"r#defaults __class__g/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/torch/optim/rmsprop.pyr)s2 zRMSprop.__init__cst||jD]_}|dd|dd|dd|dd|dd|dd|d D]4}|j|g}t|dkrgt|d sgt |d }|dr]tj |t |j d ntj |t d |d <q3q dS) NrrrFr!r"r#r rstepdtypedevicer2) r( __setstate__ param_groups setdefaultstategetlentorch is_tensorfloattensorr r3)r*r8grouppp_statestep_valr,r.r/r5Fs*           zRMSprop.__setstate__c Cs8d}|dD]} | jdurq|t| O}|| | jjr"td|| j|j| } t| dkrs|drAtjdt | j dntjdt d| d <tj | tj d | d <|d dkretj | tj d | d <|drstj | tj d | d<|| d || d |d dkr|| d |dr|| dq|S)NFrz)RMSprop does not support sparse gradientsrr r.r1r4r0) memory_format square_avgrmomentum_bufferrgrad_avg) gradr; is_complexappend is_sparse RuntimeErrorr8r:zerosr r3 zeros_likepreserve_format) r*r?params_with_gradgrads square_avgsmomentum_buffer_list grad_avgs state_steps has_complexr@r8r.r.r/ _init_group[sD          zRMSprop._init_groupc Cs|d}|dur!t |}Wdn1swY|jD]B}g}g}g}g}g}g} |||||||| } t|||||| |d|d|d|d|d|d|d|d |d |d | d q$|S) zPerform a single optimization step. Args: closure (Callable, optional): A closure that reevaluates the model and returns the loss. Nrrrrrrr!r"r#r ) rrrrrrr!r"r#r rU) _cuda_graph_capture_health_checkr; enable_gradr6rVr) r*closurelossr?rOrPrQrSrRrTrUr.r.r/r0sT   z RMSprop.step) rrrrrFFNFFN)__name__ __module__ __qualname__rrr=rboolrr)r5rVrr0 __classcell__r.r.r,r/rsL      )3aj 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, \: \epsilon \text{ (epsilon)} \\ &\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. z Args: a0 lr (float, Tensor, optional): learning rate (default: 1e-2) alpha (float, optional): smoothing constant (default: 0.99) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-8) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) momentum (float, optional): momentum factor (default: 0) centered (bool, optional) : if ``True``, compute the centered RMSProp, the gradient is normalized by an estimation of its variance z z rrPrQrSrRrTrrrrrrr"r#r rUc Cst|D]\}}||}tjs,|r,t}|jj|jjkr$|jj|vs,Jd|d||}| s4|n| }||}|d7}| dkrJ|j|| d}t|}|r`t |}t |}t |}| |j ||d|d| r||}|rzt |}| |d||j ||dd}n|}| r||}n||}| dkr||}|rt |}| | |||j|| dq|j||| dqdS)NIIf capturable=True, params and state_steps must be on supported devices: .rrrvalue) enumerater;compiler is_compilingr r3typeaddrH view_as_realmul_addcmul_lerp_addcmulsqrt_sqrtadd_addcdiv_)rrPrQrSrRrTrrrrrrr"r#r rUiparamr0capturable_supported_devicesrGrDis_complex_paramrFavgbufr.r.r/_single_tensor_rmspropsL         r{c !st|dkrdS| rJdtjs.|r.ttfddt||Ds.Jddt||||||g}| D]0\\}}}}}}}t t t |}t t t |}t t t |}t t t |}|r||g}| dkrzt t t |}| || rt t t |}| |t|g|R| rt|}tjs|djrtj|tjddd dd nt|d | dkr| rtj||| d ntj||| d }t||tj|||d |d | rt t t |}t||d |tj|||d d }t|t||n t|}t||| dkrKt t t |}t|| t||||rAt|tj rAt|| } t|| q=tj||| d q=|rdt|tj rdt|| t|||q=tj|||| d q=dS)Nrz#_foreach ops don't support autogradc3s0|]\}}|jj|jjko|jjvVqdSr[)r3rj).0r@r0rwr.r/ is   z(_multi_tensor_rmsprop..rarbg?cpu)r3rcrrdrf)r:r;rhrir allzipr"_group_tensors_by_device_and_dtypevaluesrlistrrIr _foreach_negis_cpu _foreach_add_r> _foreach_add _foreach_mul__foreach_addcmul__foreach_lerp__foreach_addcmul_foreach_sqrt_ _foreach_sqrt_foreach_addcdiv_r$ _foreach_mul _foreach_div_)!rrPrQrSrRrTrrrrrrr"r#r rUgrouped_tensorsgrouped_params_grouped_grads_grouped_square_avgs_grouped_grad_avgs_grouped_momentum_buffer_list_grouped_state_steps__grouped_params grouped_gradsgrouped_square_avgsgrouped_state_stepsstate_and_gradsgrouped_momentum_buffer_listgrouped_grad_avgsry momentum_lrr.r}r/_multi_tensor_rmspropNs                r)single_tensor_fnFr!c Cstjstdd|Dstd|durt||dd\}}|r*tjr*td|r4tjs4t}nt }|||||||| | | ||||| || ddS) ztFunctional API that performs rmsprop algorithm computation. See :class:`~torch.optim.RMSProp` for details. css|] }t|tjVqdSr[)r$r;r)r|tr.r.r/r~s  zrmsprop..zPAPI has changed, `state_steps` argument must contain a list of singleton tensorsNF) use_fusedz6torch.jit.script not supported with foreach optimizers) rrrrrrr"r r#rU) r;rhrirrKrjit is_scriptingrr{)rrPrQrSrRrTr!r"r#r rUrrrrrrrfuncr.r.r/rsB  )NFFFF)__doc__typingrrrr;r optimizerrrr r r r r rrrrrr__all__rrr=r_r{rrr.r.r.r/s <,,   A      G