JThSf&HSrSSKJrJrJr SSKrSSKJr SSKJrJ r J r J r J r J r JrJrJrJrJrJrJrJrJr SS/r"S S\5rS S \S \ S \S \S \ S3 -\lS\\S\\S\\S\\S\\S\\S\S\S\S\S\S\S\S\S\S\S\4"S jrS\\S\\S\\S\\S\\S\\S\S\S\S\S\S\S\S\S\S\S\4"S!jr\ "\S"9S%S\\S\\S\\S\\S\\S\\S\S#\\S\S\S\S\S\S\S\S\S\S\4$S$jj5rg)&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 OptimizerParamsTNAdamnadamc^\rSrSrSSSSSS.S\S\\\4S\\\4S\S \S \S \ S \ \ S \ S\ S\ 4U4Sjjjjr U4Sjr Sr \SSj5rSrU=r$)rFN)foreachmaximize capturabledifferentiableparamslrbetaseps weight_decaymomentum_decaydecoupled_weight_decayrrrrc >[U[5(aUR5S:wa [S5eSU::d[SU35eSU::d[SU35eSUSs=::aS:dO [SUS35eSUSs=::aS:dO [S US35eSU::d[S U35eSU::d[S U35e[ UUUUUUU UU U S 9 n [ T U]X5 g) NrzTensor lr must be 1-elementzInvalid learning rate: zInvalid epsilon value: r?z#Invalid beta parameter at index 0: z#Invalid beta parameter at index 1: zInvalid weight_decay value: zInvalid momentum_decay value: ) r r!r"r#r$r%rrrr) isinstancernumel ValueErrordictsuper__init__)selfrr r!r"r#r$r%rrrrdefaults __class__s I/var/www/auris/envauris/lib/python3.13/site-packages/torch/optim/nadam.pyr.NAdam.__init__s b& ! !bhhjAo:; ;by6rd;< <cz6se<= =eAh$$B58*MN NeAh$$B58*MN Nl";L>JK Kn$=n=MNO O%)#9!)   *cb>[TU]U5 URGHnURSS5 URSS5 URSS5 URSS5 URSS5 USGH&nURR U/5n[ U5S:wdM1[R"US 5(d`[US 5nUS(a([R"U[5URS 9O[R"U[5S 9US '[R"US 5(aMUS nUS(a([R"U[5URS 9O[R"U[5S 9US 'GM) GM g) NrFrrrr%rrstepdtypedevicer8 mu_product) r- __setstate__ param_groups setdefaultstategetlentorch is_tensorfloattensorrr9)r/r?grouppp_statestep_val mu_prod_valr1s r2r<NAdam.__setstate__Jsj U#&&E   Z /   Y -   \5 1   -u 5   5u =8_**..B/w<1$ ??76?;;#(#9 %\2"LL (0A0CAHH"'h>O>Q!R  !??7<+@AA&-l&; %\2"LL +3D3Fqxx"'kARAT!U  -% 'r4cSnUSGHn U RcMU[R"U 5-nURU 5 U RR(a [ S5eURU R5 UR U n [U 5S:XaUS(a([R"S[5U RS9O[R"S[5S 9U S 'US(a([R"S[5U RS9O[R"S [5S 9U S '[R"U [RS 9U S'[R"U [RS 9U S'URU S5 URU S5 URU S 5 URU S 5 GM U$)NFrz'NAdam does not support sparse gradientsrrr7r'r:r6r(r;) memory_formatexp_avg exp_avg_sq)gradrB is_complexappend is_sparse RuntimeErrorr?rAzerosrr9rEones zeros_likepreserve_format) r/rFparams_with_gradgradsexp_avgs exp_avg_sqs mu_products state_steps has_complexrGr?s r2 _init_groupNAdam._init_grouphs xAvv!u//22  ''*66##&'PQQ QVV$ 1 u:? !. B.?.A!((S"\\#5F5HI&M!. 2->-@R"\\#5F5HI,' (-'7'7)>)>(E)$+0*:*:)>)>+E,'i 01""5#67""5#67""5=1I!Jr4cUR5 SnUb%[R"5 U"5nSSS5 URH}n/n/n/n/n/n/n [ [ [ [ 4US5upURUUUUUUU 5n [UUUUUU U U USUSUSUSUSUSUS US US U S 9 M U$!,(df  N=f) 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_checkrB enable_gradr=rtuplerDrar) r/closurelossrFrZr[r\r]r^r_rdrer`s r2r6 NAdam.steps$ --/  ""$y%&&E-/ "$E%'H(*K(*K(*KeUl 3U7^DLE** K  ;">2$%56%Lz*',-E'Fi( .$%56'% ''P W%$s C  CrM)gMb`?)g?g+?g:0yE>rgMbp?FN)__name__ __module__ __qualname____firstlineno__rrrDrrhboolrr.r<rarr6__static_attributes__ __classcell__)r1s@r2rrs$(%1 $',)+#' $)+)+ %- )+UE\" )+  )+  )+)+!%)+$)+)+)+)+)+V<0d"6"6r4a 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. If True, the algorithm does not accumulate weight decay in the momentum nor variance. (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 rr[r\r]r^r_rdrer r#r$r"r%rrrr`c [U5GHunnU (dUUOUU*nUUnUUnUUnUUn[R"U5(aX[R"U5n[R"U5n[R"U5n[R"U5n[RR 5(dU(a{[ 5nURRURRs=:XaURR:XaO OURRU;d SUS35eUS- nU(aUnO [U5nSUU-- nU S:wa.U (aURSX-- 5 OURUU S9nUSSSUU ---- -nUSSSUS-U ---- -nUU-nURUSU- 5 URU5RUUSU- S 9 URU5R5nU(dU(a^URU 5nUU-nUU*SU- -SU- - -nUU*U-SU- - -nUR!UU5 UR!UU5 GM[U5U-nUR#U 5 UR!UUU*SU- -S[U5- - S 9 UR!UUU*U-SU- - S 9 GM g) NzVIf capturable=True, params, mu_products and state_steps must be on supported devices: .rralphar(?Q?)value) enumeraterBrR view_as_realcompiler is_compilingr r9typermul_addlerp_addcmul_divsqrtaddcdiv_add_)rr[r\r]r^r_rdrer r#r$r"r%rrrr`iparamrQrOrPr;step_tcapturable_supported_devicesr6bias_correction2mumu_nextdenommu_product_nexts r2_single_tensor_nadamrs(f%5'uQxeAhY1+ ^  ^ Q   E " "&&u-E%%d+D((1G++J7J~~**,,+L+N ( !!Z%6%6%;%;Qv}}?Q?QQLL%%)EE ))E(FaI  F !  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S )   R (&&':C@E    &   U +!O&&':HEE   " -   s +   % 0' **?MJI  # #I/?AQ R  # #NI O1+..A3*G*G B ^sRx0C*Z:P4PQUWW*GO 3033F/Q0R+ G #2!"J!7'!AAC  0R    # #    # # 0/CS y %`$b s=Z0!$Z5 'Z:40Z? 0[ )single_tensor_fnrc [SU55(d [S5e[SU55(d [S5eUc [X SS9unnU(a.[RR 5(a [S5eU(a*[RR 5(d[ nO[nU"UUUUUUU U UUUU UUUU U S 9 g) zhFunctional API that performs NAdam algorithm computation. See :class:`~torch.optim.NAdam` for details. c3V# UHn[U[R5v M! g7frlr)rBrrts r2rnadam..`@Kqz!U\\**K')zPAPI has changed, `state_steps` argument must contain a list of singleton tensorsc3V# UHn[U[R5v M! g7frlrrs r2rrerrzPAPI has changed, `mu_products` argument must contain a list of singleton tensorsNF) use_fusedz6torch.jit.script not supported with foreach optimizers) rdrer r#r$rr%r"rrr`)rrUr rBjit is_scriptingrr)rr[r\r]r^r_r%rrrr`rrdrer r#r$r"rfuncs r2rrDs8 @K@ @ @ ^   @K@ @ @ ^  1 e 7599))++STTuyy--//"#  !%5 %#r4)FNFFFF)__doc__typingrrrrBr optimizerrr r r r r rrrrrrrrr__all__rrrDrqrrrrMr4r2rsW.(( & 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* +DGDr4