"Eia$"SrSSKJr SSKrSSKJr SSKJrJrJrJ r J r J r J r J r JrJrJrJrJrJrJr SS/r"S S\5rS S \S \ S \S \S \S3 -\lS\\S\\S\\S\\S\\S\S\S\S\S\S\S\S\S\S\SS4 SjrS\\S\\S\\S\\S\\S\S\S\S\S\S\S\S\S\S\SS4 S jr\ "\S!9S$S\\S\\S\\S\\S\\S\S"\S-S\S\S\S\S\S\S\S\S\SS4"S#jj5rg)%z'Implementation for the RAdam algorithm.)castN)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 _to_scalar_use_grad_for_differentiable _view_as_real OptimizerParamsTRAdamradamc^\rSrSrSSSSSS.S\S\\-S\\\4S\S \S \S \S-S \S \S\SS4U4Sjjjjr U4Sjr Sr \ SSj5r SrU=r$)rFN)foreachmaximize capturabledifferentiableparamslrbetaseps weight_decaydecoupled_weight_decayrrrrreturnc >[U[5(aUR5S:wa [S5eSU::d[SU35eSU::d[SU35eSUSs=::aS:dO [SUS35eSUSs=::aS:dO [S US35eSU::d[S U35eUUUUUUU UU S . 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: ) rrr r!rrrr"r) isinstancernumel ValueErrorsuper__init__) selfrrrr r!r"rrrrdefaults __class__s N/home/mande/repo/quber/.venv/lib/python3.13/site-packages/torch/optim/radam.pyr+RAdam.__init__ s b& ! !bhhjAo:; ;by6rd;< <cz6se<= =eAh$$B58*MN NeAh$$B58*MN Nl";L>JK K( $&<,   *ct>[TU]U5 URGHnURSS5 URSS5 URSS5 URSS5 URSS5 USHnURR U/5n[ U5S:wdM0[R"US 5(aMP[US 5nUS(a([R"U[5URS 9O[R"U[5S 9US 'M GM g) NrrFrr"rrrstepdtypedevicer5) r* __setstate__ param_groups setdefaultstategetlentorch is_tensorfloattensorr r6)r,r;grouppp_statestep_valr.s r/r8RAdam.__setstate__Hs U#&&E   Y -   Z /   -u 5   5u =   \5 18_**..B/w<1$U__WV_-M-M$WV_5H !. $,=,?#\\(:K:MN FO % 'r1c SnUSGHvnURcMU[R"U5-nURU5 URR(a [ S5eURUR5 UR Un [U 5S:XaUS(a([R"S[5URS9O[R"S[5S 9U S '[R"U[RS 9U S '[R"U[RS 9U S 'URU S 5 URU S 5 URU S 5 GMy U$)NFrz'RAdam does not support sparse gradientsrrr4r%r7r3) memory_formatexp_avg exp_avg_sq)gradr> is_complexappend is_sparse RuntimeErrorr;r=zerosr r6rA zeros_likepreserve_format) r,rBparams_with_gradgradsexp_avgs exp_avg_sqs state_steps has_complexrCr;s r/ _init_groupRAdam._init_group\sH xAvv!u//22  ''*66##&'PQQ QVV$ 1 u:?!. B.?.A!((S"\\#5F5HI&M (-'7'7)>)>(E)$+0*:*:)>)>+E,'i 01""5#67""5=17!:r1cUR5 SnUb%[R"5 U"5nSSS5 URHrn/n/n/n/n/n[ [ [ [ 4US5upURX4XVXx5n [UUUUUU U USUSUSUSUSUSUS US U S 9 Mt U$!,(df  N=f) zPerform a single optimization step. Args: closure (Callable, optional): A closure that reevaluates the model and returns the loss. Nrrr!r rrrrr") beta1beta2rr!r rrrrr"rY) '_accelerator_graph_capture_health_checkr> enable_gradr9rtupler@rZr) r,closurelossrBrTrUrVrWrXr]r^rYs r/r3 RAdam.steps 446  ""$y%&&E-/ "$E%'H(*K(*KeUl 3U7^DLE**+K  ;">2%Lz*i( .$%56',-E'F'! '> E%$s B?? C rH)gMbP?)g?g+?g:0yE>rFN)__name__ __module__ __qualname____firstlineno__rr@rraboolr+r8rZrr3__static_attributes__ __classcell__)r.s@r/rrs"%1',&+ $ $&+&+ FN&+UE\" &+  &+  &+!%&+&+&+&+&+ &+&+P(!F"-"-r1aImplements RAdam algorithm. .. math:: \begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \gamma \text{ (lr)}, \: \beta_1, \beta_2 \text{ (betas)}, \: \theta_0 \text{ (params)}, \:f(\theta) \text{ (objective)}, \: \lambda \text{ (weightdecay)}, \:\textit{maximize} \\ &\hspace{13mm} \epsilon \text{ (epsilon)}, \textit{decoupled\_weight\_decay} \\ &\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)}, v_0 \leftarrow 0 \text{ ( second moment)}, \\ &\hspace{18mm} \rho_{\infty} \leftarrow 2/(1-\beta_2) -1 \\[-1.ex] &\rule{110mm}{0.4pt} \\ &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\ &\hspace{6mm}\textbf{if} \: \textit{maximize}: \\ &\hspace{12mm}g_t \leftarrow -\nabla_{\theta} f_t (\theta_{t-1}) \\ &\hspace{6mm}\textbf{else} \\ &\hspace{12mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\ &\hspace{6mm} \theta_t \leftarrow \theta_{t-1} \\ &\hspace{6mm} \textbf{if} \: \lambda \neq 0 \\ &\hspace{12mm}\textbf{if} \: \textit{decoupled\_weight\_decay} \\ &\hspace{18mm} \theta_t \leftarrow \theta_{t} - \gamma \lambda \theta_{t} \\ &\hspace{12mm}\textbf{else} \\ &\hspace{18mm} g_t \leftarrow g_t + \lambda \theta_{t} \\ &\hspace{6mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\ &\hspace{6mm}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\ &\hspace{6mm}\widehat{m_t} \leftarrow m_t/\big(1-\beta_1^t \big) \\ &\hspace{6mm}\rho_t \leftarrow \rho_{\infty} - 2 t \beta^t_2 /\big(1-\beta_2^t \big) \\[0.1.ex] &\hspace{6mm}\textbf{if} \: \rho_t > 5 \\ &\hspace{12mm} l_t \leftarrow \frac{\sqrt{ (1-\beta^t_2) }}{ \sqrt{v_t} +\epsilon } \\ &\hspace{12mm} r_t \leftarrow \sqrt{\frac{(\rho_t-4)(\rho_t-2)\rho_{\infty}}{(\rho_{\infty}-4)(\rho_{\infty}-2) \rho_t}} \\ &\hspace{12mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t} r_t l_t \\ &\hspace{6mm}\textbf{else} \\ &\hspace{12mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t} \\ &\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 `On the variance of the adaptive learning rate and beyond`_. This implementation provides an option to use either the original weight_decay implementation as in Adam (where the weight_decay is applied to the gradient) or the one from AdamW (where weight_decay is applied to the weight) through the decoupled_weight_decay option. When decoupled_weight_decay is set to False (default), it uses the original Adam style weight decay, otherwise, it uses the AdamW style which corresponds more closely to the `author's implementation`_ in the RAdam paper. Further information about decoupled weight decay can be found in `Decoupled Weight Decay Regularization`_. z Args: a lr (float, Tensor, optional): learning rate (default: 1e-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) decoupled_weight_decay (bool, optional): whether to decouple the weight decay as in AdamW to obtain RAdamW. If True, the algorithm does not accumulate weight decay in the momentum nor variance. (default: False) z a .. _On the variance of the adaptive learning rate and beyond: https://arxiv.org/abs/1908.03265 .. _author's implementation: https://github.com/LiyuanLucasLiu/RAdam .. _Decoupled Weight Decay Regularization: https://arxiv.org/abs/1711.05101 rrUrVrWrXr]r^rr!r r"rrrrYr#c h^ ^ ^^^^[RR5(d [U5n[ U5GHnunnU (dXOX*nX/nX?mXOn[R R 5(dhU (aa[5nURRURR:XaURRU;d[SUS35e[R"U5(aX[R"U5n[R"U5n[R"U5n[R"T5mUS- nU (aUO [U5nUS:wa.U (aURSXx-- 5 OURUUS9nUR!USU- 5 TRU5R#UUSU- S9 SUU-- nSUU-- mUU- nSSU- - S- mTSU-UU--T- - mUU4SjnUU U U4S jnU (aA[R$"TS :U"5U"5-S 5nUR'UU-U-S S9 GM/TS :a&UR'UU-U"5-U"5-S S9 GM[UR'UU-S S9 GMq g) NIIf capturable=True, params and state_steps must be on supported devices: .rralpha)valuecD>TS- TS- -T-TS- TS- -T-- S-$)Nrs?rH)rho_infrho_tsr/ _compute_rect+_single_tensor_radam.._compute_rectEsK19aKGaK058:  r1c>TR5nT(aURT5nOURT5nTS-U- $)Nrv)sqrtaddadd_)exp_avg_sq_sqrtbias_correction2rr rKs r/_compute_adaptive_lr2_single_tensor_radam.._compute_adaptive_lrNsD(oo/O"1"5"5c":"1"6"6s";%c)_< jit is_scriptingr enumeratecompiler is_compilingr r6typeAssertionErrorrM view_as_realr mul_r}lerp_addcmul_wherer~)rrUrVrWrXr]r^rr!r r"rrrrYiparamrLrJstep_tcapturable_supported_devicesr3bias_correction1bias_corrected_exp_avgryrupdaterrKrwrxs ` ` @@@@r/_single_tensor_radamrs$ 99 ! ! # # ^f%5'uxehY+ ^ ~~**,,+L+N ( !!V]]%7%77LL%%)EE$_`|_}}~   E " "&&u-E%%d+D((1G++J7J ! #vF); 1 % 1r001xx\x:  dAI&''d!e)'Dud{?ud{?")+;!;q5y/A%!d(eTk25EEE  = = [[ ]_/C/EEsF JJ-2V;4J Hs{ **,-$o&  1B6d Cg&r1c V^*[U5S:XagU (a [S5e[RR 5(dAU (a:[ SS9m*[ U*4Sj[XSS955(d[ST*S 35e[U5n[R"XX#U/5nUR5GHuunnnnnn[[[U5n[[[U5n[[[U5n[[[U5n[[[U5n[RR 5(d>USR(a*[R "U[R""S S S 9S S 9 O[R "US5 U(a[%UUUU5 U (a[R&"U5nSSU- - S- nU (a[R("UU5n[R*"U5 [R "US5 [R("UU5n[R,"UU5 [R,"US5 [R."UU5 [R*"U5 [R "UU5 UnOBUVs/sH5nUS[1U5-U[1U5--SU[1U5-- - - PM7 nnUS:waXU (a[R,"USXx-- 5 O4U (a[R "UUUS 9 O[R2"UUUS 9n[R4"UUSU- 5 [R,"UU5 [R6"UUUSU- 5 AU (GaS[R8"US5n [R8"US5n![R,"U U!5 A![R,"U U5 US- US- -n[R:"UU5n"[R."U U"5 A"[R<"U 5 [U USS9V#V$s/sH!un#n$[R>"U$S:U#S5PM# n%n#n$A AU%V%s/sHn%[R>"U%S:SS 5PM n&n%[R,"U&U5 [R("UU5n[R*"U5 [R "US5 [R."U&U5 [R*"U&5 [R("UU5n[R*"U5 [R "US5 [R<"U5 [R,"UU5 [R,"UW%5 A%[R*"U5 [R."UU5 AOUV$s/sH+n$U$S:a U$S- U$S- -U-US- US- -U$-- S-OSPM- n%n$U%V%s/sH n%U%S:aSOS PM n'n%UVs/sHnSU[1U5-- PM nn[U'USS9V%V(s/sHun%n(UU%-U(- S-PM n&n%n([UW%USS9VV%V(s/sH'unn%n(SU[1U5-- S-UU%-U(- -S-PM) nn%nn([R@"U5n)[R "U)U 5 [R."U)U5 [RB"U)5 [R "U)U&5 [R6"UUU)5 GM gs snfs sn$n#fs sn%fs sn$fs sn%fs snfs sn(n%fs sn(n%nf)Nrz#_foreach ops don't support autogradF) supports_xlac3># UHTupURRURR:H=(a URRT;v MV g7fre)r6r).0rCr3rs r/ &_multi_tensor_radam..sO A HHMMT[[-- - > !== >@sAAT)strictrnror&cpu)r6rprrsrurr%rv)"r=rr>rrr allziprr"_group_tensors_by_device_and_dtypevaluesrlistris_cpu _foreach_add_rAr _foreach_neg _foreach_pow _foreach_neg_ _foreach_mul_ _foreach_div_r _foreach_add_foreach_lerp__foreach_addcmul_ _foreach_sub _foreach_mul_foreach_sqrt_r _foreach_sqrt_foreach_reciprocal_)+rrUrVrWrXr]r^rr!r r"rrrrYgrouped_tensorsgrouped_params_grouped_grads_grouped_exp_avgs_grouped_exp_avg_sqs_grouped_state_steps__grouped_params grouped_gradsgrouped_exp_avgsgrouped_exp_avg_sqsgrouped_state_stepsrwrr rho_t_listr3numsub2denomnrxrectunrect_step_size unrectifiedbcbufferrs+ @r/_multi_tensor_radamrksc$ 6{aBCC >> & & ( (Z'H( $ v4@   ![\x[yyz{  BBBB {;O  " " $   d6lO<T&\>: V .?@"4<1EF"4<1EF ~~**,,1DQ1G1N1N   #U\\#e%DC     3Q 7   /?AT  !..}=Mq5y/A% $11%9LM     0 1    0! 4$11%9LM     02E F    0! 4    02B C    0 1    0' :)J0  0D T"#Jt,,.u 4 00022 0  1 %##NA8I4IJ''%~\%*$6$6%~\%M -}a%iH /7   q5y  $$Z3C%%j!4D   T *   W -{w{3G&&z7;E   U +   % !$CD A AHAu ECKC0 A LPQDD D1Hc3 ?D Q    0" 5$11%9LM     0 1    0! 4    02B C    0 1$11%9LM     0 1    0! 4  !1 2    0" 5    0$ 7    0 1    02B C ( (E19 QYqy" ! ! 4u<>  (  ?CCddq1c1dKC;N :M$EZ---:M   !$K1A$ O  OHD"dR2% O   '*'/?' 'ND$ez$///C7BINKbP'  $$%89 FC( F$45 ""6* F$45 0@&Is %X^ R* D   s0 <]?:(^+%^ 2^^(^^>.^$ )single_tensor_fnrc h[SU55(d [S5eUc [XSS9unnU(a.[RR 5(a [S5eU(a*[RR 5(d[ nO[nU"UUUUUU U U UUU UUUU S9 g)zhFunctional API that performs RAdam algorithm computation. See :class:`~torch.optim.RAdam` for details. c3V# UHn[U[R5v M! g7fre)r'r>r)rts r/rradam..Rs@Kqz!U\\**Ks')zPAPI has changed, `state_steps` argument must contain a list of singleton tensorsNF) use_fusedz6torch.jit.script not supported with foreach optimizers) r]r^rr!r rr"rrrY)rrPrr>rrrr)rrUrVrWrXr"rrrrYrr]r^rr!r rfuncs r/rr8s4 @K@ @ @ ^  1 e 7599))++STTuyy--//"#  ! 5%r1)FNFFFF)__doc__typingrr>r optimizerrrrr r r r r rrrrrrr__all__rrr@rjrrrrHr1r/rs7. & G NINd2f        gK `hD LhD <hD6lhDf hD f hD hD hD hDhD hD!hDhDhDhD !hD" #hDVJJ LJJ <JJ6lJJf JJ f JJ JJ JJ JJJJ JJ!JJJJJJJJ !JJ" #JJZ 1EF$) ; L; <;6l;f ; f ;!;D[;;;;; ; !;" #;$%;& ';( );G;r1