DŽg_f&DdZddlmZmZmZmZmZddlZddlmZddl m Z m Z m Z m Z mZmZmZmZmZmZmZmZmZmZddgZGd deZd d ed ed e d e d ze_deedeedeedeedeedeedededededededededededef"dZdeedeedeedeedeedeedededededededededededef"d Ze e! d$deedeedeedeedeedeeded"eededededededededededef$d#Zy)%z'Implementation for the NAdam algorithm.)castListOptionalTupleUnionN)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_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) Nr zTensor 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: ) r r!r"r#r$r%rrrr) isinstancernumel ValueErrordictsuper__init__)selfrr r!r"r#r$r%rrrrdefaults __class__s Y/home/mcse/projects/flask_80/flask-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$=n=MNO O%)#9!)   *c8t|||jD]z}|jdd|jdd|jdd|jdd|jdd|dD]}|jj |g}t |dk7s/tj|d s_t|d }|dr*tj|t|j ntj|t |d <tj|d r|d }|dr*tj|t|j ntj|t |d <}y) NrFrrrr%rrstepdtypedevicer7 mu_product) r- __setstate__ param_groups setdefaultstategetlentorch is_tensorfloattensorrr8)r/r>grouppp_statestep_val mu_prod_valr1s r2r;zNAdam.__setstate__Jsn U#&& E   Z /   Y -   \5 1   -u 5   5u =8_ **..B/w<1$ ??76?;#(#9 %\2"LL (0A0CAHH"'h>O>Q!R  !??7<+@A&-l&; %\2"LL +3D3Fqxx"'kARAT!U  -   r3cd}|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 gradientsrrr6r'r9r5r(r:) memory_formatexp_avg exp_avg_sq)gradrA is_complexappend is_sparse RuntimeErrorr>r@zerosrr8rDones zeros_likepreserve_format) r/rEparams_with_gradgradsexp_avgs exp_avg_sqs mu_products state_steps has_complexrFr>s r2 _init_groupzNAdam._init_grouphs x$ 2Avv!u//22  ''*66##&'PQQ QVV$ 1 u:? !. B.?.A!((S"\\#5F5HI&M!. 2->-@R"\\#5F5HI,' (-'7'7)>)>(E)$+0*:*:)>)>+E,'i 01""5#67""5#67""5=1I$ 2Jr3c|jd}|$tj5|}ddd|jD]}g}g}g}g}g}g} t t t t f|d\} } |j||||||| } t|||||| | | |d|d|d|d|d|d|d |d |d | |S#1swYxYw) zPerform a single optimization step. Args: closure (Callable, optional): A closure that reevaluates the model and returns the loss. Nr!r r#r$r"rr%rrr) beta1beta2r r#r$r"rr%rrrr^) _cuda_graph_capture_health_checkrA enable_gradr<rrrCr_r) r/closurelossrErXrYrZr[r\r]rarbr^s r2r5z NAdam.steps2 --/  ""$ !y !&&& E-/ "$E%'H(*K(*K(*KeUl 3U7^DLE5** K  ;">2$%56%Lz*',-E'Fi( .$%56'% '& P W ! !s C  C)gMb`?)g?g+?g:0yE>rgMbp?FN)__name__ __module__ __qualname__rrrCrrboolrr.r;r_rr5 __classcell__)r1s@r2rrs$(%1 $',)+#' $)+)+ %- )+UE\" )+  )+  )+)+!%)+$)+)+)+)+V<0d"6"6r3a 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`_. a  Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups 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 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 rrYrZr[r\r]rarbr r#r$r"r%rrrr^c t|D]\}}| s||n|| }||}||}||}||}tj|rTtj|}tj|}tj|}tj|}tjj sy|rwt }|jj|jjcxk(r|jjk(rnn|jj|vs Jd|d|dz }|r|}n t|}d||zz }| dk7r-| r|jd|| zz n|j|| }|ddd|| zzzz z}|ddd|dz| zzzz z}||z}|j|d|z |j|j||d|z |j|j}|s|r]|j| }||z}|| d|z zd|z z z}|| |zd|z z z}|j!|||j!||\t||z}|j#| |j!||| d|z zdt|z z |j!||| |zd|z z y) NzVIf capturable=True, params, mu_products and state_steps must be on supported devices: .r ralphar(?Q?)value) enumeraterArP view_as_real_utils is_compilingrr8typermul_addlerp_addcmul_divsqrtaddcdiv_add_)rrYrZr[r\r]rarbr r#r$r"r%rrrr^iparamrOrMrNr:step_tcapturable_supported_devicesr5bias_correction2mumu_nextdenommu_product_nexts r2_single_tensor_nadamrs(f%G5'uQxeAhY1+ ^  ^ Q   E "&&u-E%%d+D((1G++J7J||((*z+L+N ( !!Z%6%6%;%;Qv}}?Q?QQLL%%)EE ))E(FaI  F !  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See :class:`~torch.optim.NAdam` for details. c3PK|]}t|tj ywrgr)rArrts r2rznadam..`@qz!U\\*@$&zPAPI has changed, `state_steps` argument must contain a list of singleton tensorsc3PK|]}t|tj ywrgrrs r2rznadam..errzPAPI has changed, `mu_products` argument must contain a list of singleton tensorsNF) use_fusedz6torch.jit.script not supported with foreach optimizers) rarbr r#r$rr%r"rrr^)rrSr rAjit is_scriptingrr)rrYrZr[r\r]r%rrrr^rrarbr r#r$r"rfuncs r2rrDs8 @K@ @ ^   @K@ @ ^  1 Ne 7599))+STTuyy--/"#  !%5 %#r3)FNFFFF)__doc__typingrrrrrrAr optimizerr r r r rrrrrrrrrr__all__rrCrkrrrrKr3r2rsJ/55 $ 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* +DGDr3