'}hX#ddlZddlmZddlmZmZmZmZmZmZm Z m Z m Z m Z m Z ddlmZmZddgZGddeZd d e d e d e d ze_ d!d eedeedeedeedeedeededeedededededededededef"dZd eedeedeedeedeedeededededededededededef dZd eedeedeedeedeedeededededededededededef d Zy)"N)Tensor) Optimizer_use_grad_for_differentiable _get_value_dispatch_sqrt_stack_if_compiling_get_scalar_dtype_default_to_fused_or_foreach _view_as_real_capturable_doc_differentiable_doc _foreach_doc)ListOptionalNAdamnadamc heZdZ d dddddedeededeffdZfd Zd Zed d Z xZ S)rFN)foreach capturabledifferentiabledecoupled_weight_decayrrrc jd|kstd|d|kstd|d|dcxkrdksntd|dd|dcxkrdksntd|dd|kstd |d|kstd |t|||||||| | } t | || y) NzInvalid learning rate: zInvalid epsilon value: r?z#Invalid beta parameter at index 0: rz#Invalid beta parameter at index 1: zInvalid weight_decay value: zInvalid momentum_decay value: ) lrbetaseps weight_decaymomentum_decayrrrr) ValueErrordictsuper__init__) selfparamsrrrrr rrrrdefaults __class__s P/var/www/html/test/engine/venv/lib/python3.12/site-packages/torch/optim/nadam.pyr$zNAdam.__init__ sby6rd;< <cz6se<= =eAh$$B58*MN NeAh$$B58*MN Nl";L>JK Kn$=n=MNO O2U%1./E 'J~_ *ct|||jD]h}|jdd|jdd|jdd|jdd|dD]}|jj |g}t |dk7s/tj|ds_t|d}|dr*tj|t|j ntj|t |d<tj|d r|d }|dr*tj|t|j ntj|t |d <ky) NrrFrrr&rstepdtypedevicer. mu_product) r# __setstate__ param_groups setdefaultstategetlentorch is_tensorfloattensorr r/)r%r5grouppp_statestep_val mu_prod_valr(s r)r2zNAdam.__setstate__!sb U#&& CE   Y -   \5 1   -u 5   5u =8_ C**..B/w<1$ ??76?;#(#9.3L.A,1<<HYH[dedldl+mGL||T\dudwGx  ??7<+@A&-l&; 49,4G27kQbQdmnmumu1vMR\\Zem~nANB - C  Cr*cd}|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)NFr&z'NAdam does not support sparse gradientsrrr-rr0r,rr1) memory_formatexp_avg exp_avg_sq)gradr8 is_complexappend is_sparse RuntimeErrorr5r7zerosr r/r;ones zeros_likepreserve_format) r%r<params_with_gradgradsexp_avgs exp_avg_sqs mu_products state_steps has_complexr=r5s r) _init_groupzNAdam._init_group5s x 2Avv!u//22  ''*66##&'PQQ QVV$ 1 u:? !. B.?.A!((S49LLL]L_4`&M !. 2->-@R49LLL]L_4`,' (-'7'7I^I^'_E)$*/*:*:1ELaLa*bE,'i 01""5#67""5#67""5=1= 2>r*cn|jd}|$tj5|}ddd|jD]a}g}g}g}g}g}g} |d\} } |j ||||||| } t |||||| | | |d|d|d|d|d|d|d |d | c|S#1swY{xYw) zPerforms a single optimization step. Args: closure (Callable, optional): A closure that reevaluates the model and returns the loss. Nrrrr rrrrr) beta1beta2rrr rrrrrrU) _cuda_graph_capture_health_checkr8 enable_gradr3rVr) r%closurelossr<rOrPrQrRrSrTrXrYrUs r)r,z NAdam.stepXs --/  ""$ !y !&& +E! EHKKK >LE5**52BE8U`bmoz{K "4[$^4!&'7!8El)./G)H *"<0!&'7!8)! + +: A ! !s B++B4)gMb`?)g?g+?g:0yE>rgMbp?FN) __name__ __module__ __qualname__boolrr$r2rVrr, __classcell__)r(s@r)rr sa@DUZ+.2u(-+NR+%d^+@D+"&+,C(!F"+"+r*a 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} \\ &\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}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`_. a Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, 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 use decoupled weight decay as in AdamW to obtain NAdamW (default: False) z z .. _Incorporating Nesterov Momentum into Adam: https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ .. _Decoupled Weight Decay Regularization: https://arxiv.org/abs/1711.05101 r&rPrQrRrSrTrrrrrUrXrYrrr rc ~td|Ds tdtd|Ds td|t|| d\}}|r)tjj r td|r%tjj st }nt}|||||||| | | |||||| | y) zpFunctional API that performs NAdam algorithm computation. 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