[^(hSf&FdZddlmZmZmZddlZddlmZddlmZm Z m Z m Z m Z m Z mZmZmZmZmZmZmZmZmZddgZGd deZd d ed e d ed ed e d ze_deedeedeedeedeedeedededededededededededef"d Zdeedeedeedeedeedeedededededededededededef"d!Ze e" d%deedeedeedeedeedeeded#eededededededededededef$d$Zy)&z'Implementation for the NAdam 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 _get_value _maximize_doc _params_doc_stack_if_compiling_use_grad_for_differentiable _view_as_real OptimizerParamsTNAdamnadamceZdZ dddddddedeeefdeeefdeded ed ed e ed ed edeffdZ fdZ dZ e ddZxZS)rFN)foreachmaximize capturabledifferentiableparamslrbetaseps weight_decaymomentum_decaydecoupled_weight_decayrrrrc t|tr|jdk7r tdd|kstd|d|kstd|d|dcxkrdksntd|dd|dcxkrdksntd |dd|kstd |d|kstd |t ||||||| || | } t ||| y) NrzTensor lr must be 1-elementzInvalid learning rate: zInvalid epsilon value: r?z#Invalid beta parameter at index 0: z#Invalid beta parameter at index 1: zInvalid weight_decay value: zInvalid momentum_decay value: ) rr r!r"r#r$rrrr) isinstancernumel ValueErrordictsuper__init__)selfrrr r!r"r#r$rrrrdefaults __class__s O/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/torch/optim/nadam.pyr-zNAdam.__init__s b& !bhhjAo:; ;by6rd;< <cz6se<= =eAh$$B58*MN NeAh$$B58*MN Nl";L>JK Kn$=n=MNO O%)#9!)   *c8t|||jD]z}|jdd|jdd|jdd|jdd|jdd|dD]}|jj |g}t |dk7s/tj|d s_t|d }|dr*tj|t|j ntj|t |d <tj|d r|d }|dr*tj|t|j ntj|t |d <}y) NrFrrrr$rrstepdtypedevicer6 mu_product) r, __setstate__ param_groups setdefaultstategetlentorch is_tensorfloattensorrr7)r.r=grouppp_statestep_val mu_prod_valr0s r1r:zNAdam.__setstate__Jsn U#&& E   Z /   Y -   \5 1   -u 5   5u =8_ **..B/w<1$ ??76?;#(#9 %\2"LL (0A0CAHH"'h>O>Q!R  !??7<+@A&-l&; %\2"LL +3D3Fqxx"'kARAT!U  -   r2cd}|dD]} | j|tj| z}|j| | jjr t d|j| j|j | } t| dk(r|dr*tjdt| jntjdt | d <|dr*tjdt| jntjd t | d <tj| tj | d<tj| tj | d<|j| d|j| d|j| d |j| d |S)NFrz'NAdam does not support sparse gradientsrrr5r&r8r4r'r9) memory_formatexp_avg exp_avg_sq)gradr@ is_complexappend is_sparse RuntimeErrorr=r?zerosrr7rCones zeros_likepreserve_format) r.rDparams_with_gradgradsexp_avgs exp_avg_sqs mu_products state_steps has_complexrEr=s r1 _init_groupzNAdam._init_grouphs x$ 2Avv!u//22  ''*66##&'PQQ QVV$ 1 u:? !. B.?.A!((S"\\#5F5HI&M!. 2->-@R"\\#5F5HI,' (-'7'7)>)>(E)$+0*:*:)>)>+E,'i 01""5#67""5#67""5=1I$ 2Jr2c|jd}|$tj5|}ddd|jD]}g}g}g}g}g}g} t t t t f|d\} } |j||||||| } t|||||| | | |d|d|d|d|d|d|d |d |d | |S#1swYxYw) zPerform a single optimization step. Args: closure (Callable, optional): A closure that reevaluates the model and returns the loss. Nr rr"r#r!rr$rrr) beta1beta2rr"r#r!rr$rrrr]) _cuda_graph_capture_health_checkr@ enable_gradr;rtuplerBr^r) r.closurelossrDrWrXrYrZr[r\r`rar]s r1r4z NAdam.steps2 --/  ""$ !y !&&& E-/ "$E%'H(*K(*K(*KeUl 3U7^DLE5** K  ;">2$%56%Lz*',-E'Fi( .$%56'% '& P W ! !s C  C)gMb`?)g?g+?g:0yE>rgMbp?FN)__name__ __module__ __qualname__rrrBrrdboolrr-r:r^rr4 __classcell__)r0s@r1rrs$(%1 $',)+#' $)+)+ %- )+UE\" )+  )+  )+)+!%)+$)+)+)+)+V<0d"6"6r2a Implements NAdam algorithm. .. math:: \begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \gamma_t \text{ (lr)}, \: \beta_1,\beta_2 \text{ (betas)}, \: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)} \\ &\hspace{13mm} \: \lambda \text{ (weight decay)}, \:\psi \text{ (momentum decay)} \\ &\hspace{13mm} \: \textit{decoupled\_weight\_decay}, \:\textit{maximize} \\ &\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)}, v_0 \leftarrow 0 \text{ ( second moment)} \\[-1.ex] &\rule{110mm}{0.4pt} \\ &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\ &\hspace{5mm}\textbf{if} \: \textit{maximize}: \\ &\hspace{10mm}g_t \leftarrow -\nabla_{\theta} f_t (\theta_{t-1}) \\ &\hspace{5mm}\textbf{else} \\ &\hspace{10mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\ &\hspace{5mm} \theta_t \leftarrow \theta_{t-1} \\ &\hspace{5mm} \textbf{if} \: \lambda \neq 0 \\ &\hspace{10mm}\textbf{if} \: \textit{decoupled\_weight\_decay} \\ &\hspace{15mm} \theta_t \leftarrow \theta_{t-1} - \gamma \lambda \theta_{t-1} \\ &\hspace{10mm}\textbf{else} \\ &\hspace{15mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\ &\hspace{5mm} \mu_t \leftarrow \beta_1 \big(1 - \frac{1}{2} 0.96^{t \psi} \big) \\ &\hspace{5mm} \mu_{t+1} \leftarrow \beta_1 \big(1 - \frac{1}{2} 0.96^{(t+1)\psi}\big)\\ &\hspace{5mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\ &\hspace{5mm}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\ &\hspace{5mm}\widehat{m_t} \leftarrow \mu_{t+1} m_t/(1-\prod_{i=1}^{t+1}\mu_i)\\[-1.ex] & \hspace{11mm} + (1-\mu_t) g_t /(1-\prod_{i=1}^{t} \mu_{i}) \\ &\hspace{5mm}\widehat{v_t} \leftarrow v_t/\big(1-\beta_2^t \big) \\ &\hspace{5mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t}/ \big(\sqrt{\widehat{v_t}} + \epsilon \big) \\ &\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 `Incorporating Nesterov Momentum into Adam`_. z Args: a lr (float, Tensor, optional): learning rate (default: 2e-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) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) momentum_decay (float, optional): momentum momentum_decay (default: 4e-3) decoupled_weight_decay (bool, optional): whether to decouple the weight decay as in AdamW to obtain NAdamW. 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S )   R (&&':C@E    &   U +!O&&':HEE   " -   s +   % 0' **?MJI  # #I/?AQ R  # #NI O1+..A3*G& B ^sRx0C*Z:P4PQUWWO 3033F/Q, G #2!"J!7'!AAC     # #    # # 0/CS Gen$b s=Y!Y $Y-Y -Y )single_tensor_fnrc td|Ds tdtd|Ds td|t|| d\}}|r)tjj r td|r%tjj st }nt}|||||||| | |||| |||| | y) zpFunctional API that performs NAdam algorithm computation. See :class:`~torch.optim.NAdam` for details. c3PK|]}t|tj ywrgr(r@rrts r1rznadam..`@qz!U\\*@$&zPAPI has changed, `state_steps` argument must contain a list of singleton tensorsc3PK|]}t|tj ywrgrrs r1rznadam..errzPAPI has changed, `mu_products` argument must contain a list of singleton tensorsNF) use_fusedz6torch.jit.script not supported with foreach optimizers) r`rarr"r#rr$r!rrr])rrRr r@jit is_scriptingrr)rrXrYrZr[r\r$rrrr]rr`rarr"r#r!rfuncs r1rrDs8 @K@ @ ^   @K@ @ ^  1 Ne 7599))+STTuyy--/"#  !%5 %#r2)FNFFFF)__doc__typingrrrr@r optimizerrr r r r r rrrrrrrrr__all__rrrBrkrrrrJr2r1rsW.(( & G sIsn&N       !O> F[ L[ <[6l[f [ f [ f [ [ [ [[[ [![[ !["#[$%[|L LL <L6lLf L f L f L L L LLL L!LL !L"#L$%L^ 1EF$)" D LD <D6lDf D f D f D!Dd^DDDDD !D" #D$ %D&'D()D* +DGDr2