3KigP&:SrSSKJr SSKrSSKJr SSKJrJrJrJ r J r J r J r J r JrJrJrJrJrJr 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\SS4"S jrS\\S\\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\S\SS4$S$jj5rg)&z)Implementation for the RMSprop 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 _maximize_doc _params_doc _to_scalar_use_grad_for_differentiable _view_as_real OptimizerParamsTRMSproprmspropc^\rSrSrSS\S\\-S\S\S\S\S \S \S \S-S \S \SS4U4SjjjrU4Sjr Sr \ SSj5r Sr U=r$)rNparamslralphaeps weight_decaymomentumcentered capturableforeachmaximizedifferentiablereturnc n>[U[5(aUR5S:wa [S5eSU::d[SU35eSU::d[SU35eSU::d[SU35eSU::d[SU35eSU::d[SU35eUUUUUUUU U U S . n [T U]X5 g) NrzTensor lr must be 1-elementgzInvalid learning rate: zInvalid epsilon value: zInvalid momentum value: zInvalid weight_decay value: zInvalid alpha value: ) rrrrrrrr r!r") isinstancernumel ValueErrorsuper__init__)selfrrrrrrrrr r!r"defaults __class__s U/var/www/html/dynamic-report/venv/lib/python3.13/site-packages/torch/optim/rmsprop.pyr)RMSprop.__init__s b& ! !bhhjAo:; ;by6rd;< <cz6se<= =h7zBC Cl";L>JK Ke|4UG<= =  ($ ,   *c>[TU]U5 URGH)nURSS5 URSS5 URSS5 URSS5 URSS5 URSS5 US HnURR U/5n[ U5S:wdM0[R"US 5(aMP[US 5nUS(a([R"U[5URS 9O[R"U[5S 9US 'M GM, g) NrrrFr r!r"rrstepdtypedevicer3) r( __setstate__ param_groups setdefaultstategetlentorch is_tensorfloattensorr r4)r*r9grouppp_statestep_valr,s r-r6RMSprop.__setstate__Hs U#&&E   Z +   Z /   Y -   Z /   -u 5   \5 18_**..B/w<1$U__WV_-M-M$WV_5H !. $,=,?#\\(:K:MN FO %'r/cSnUSGHn U RcMU[R"U 5-nURU 5 U RR(a [ S5eURU R5 UR U n [U 5S:XaUS(a([R"S[5U RS9O[R"S[5S9U S '[R"U [RS 9U S 'US S:a&[R"U [RS 9U S 'US(a&[R"U [RS 9U S'URU S 5 URU S 5 US S:aURU S 5 US(dGMURU S5 GM U$)NFrz)RMSprop does not support sparse gradientsrrr2r5r1) memory_format square_avgrmomentum_bufferrgrad_avg) gradr< is_complexappend is_sparse RuntimeErrorr9r;zerosr r4 zeros_likepreserve_format) r*r@params_with_gradgrads square_avgsmomentum_buffer_list grad_avgs state_steps has_complexrAr9s r- _init_groupRMSprop._init_group]s xAvv~ 5++A. .K  # #A &vv"#NOO LL JJqME5zQ\*KK*;*=ahhOR/@/BCf ',&6&6U%:%:'l#$q(/4/?/?)>)>0E+,$(-(8(8)>)>)E*%   u\2 3   uV} -Z 1$$++E2C,DEZ    z!23I!Lr/cvUR5 SnUb%[R"5 U"5nSSS5 URH]n/n/n/n/n/n/n UR UUUUUUU 5n [ UUUUUU USUSUSUSUSUSUSUS 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. Nrrrrrrr r!r"r) rrrrrrr r!r"rrY) _cuda_graph_capture_health_checkr< enable_gradr7rZr) r*closurelossr@rSrTrUrWrVrXrYs r-r1 RMSprop.steps  --/  ""$y%&&E-/ "$E(*K&(I13 (*K** $K  $;Gn%L">2z*z*i(z*$%56 .'# %'L S%$s B** B8rF) g{Gz?gGz?g:0yE>rrFFNFFN)__name__ __module__ __qualname____firstlineno__rr>rboolr)r6rZrr1__static_attributes__ __classcell__)r,s@r-rrs" #$'+'+ FN'+ '+  '+  '+'+'+'+'+'+'+ '+'+R*1f"4"4r/aj Implements RMSprop algorithm. .. math:: \begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \alpha \text{ (alpha)}, \: \gamma \text{ (lr)}, \: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)} \\ &\hspace{13mm} \lambda \text{ (weight decay)},\: \mu \text{ (momentum)}, \: centered, \: \epsilon \text{ (epsilon)} \\ &\textbf{initialize} : v_0 \leftarrow 0 \text{ (square average)}, \: \textbf{b}_0 \leftarrow 0 \text{ (buffer)}, \: g^{ave}_0 \leftarrow 0 \\[-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}if \: \lambda \neq 0 \\ &\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\ &\hspace{5mm}v_t \leftarrow \alpha v_{t-1} + (1 - \alpha) g^2_t \hspace{8mm} \\ &\hspace{5mm} \tilde{v_t} \leftarrow v_t \\ &\hspace{5mm}if \: centered \\ &\hspace{10mm} g^{ave}_t \leftarrow g^{ave}_{t-1} \alpha + (1-\alpha) g_t \\ &\hspace{10mm} \tilde{v_t} \leftarrow \tilde{v_t} - \big(g^{ave}_{t} \big)^2 \\ &\hspace{5mm}if \: \mu > 0 \\ &\hspace{10mm} \textbf{b}_t\leftarrow \mu \textbf{b}_{t-1} + g_t/ \big(\sqrt{\tilde{v_t}} + \epsilon \big) \\ &\hspace{10mm} \theta_t \leftarrow \theta_{t-1} - \gamma \textbf{b}_t \\ &\hspace{5mm} else \\ &\hspace{10mm}\theta_t \leftarrow \theta_{t-1} - \gamma g_t/ \big(\sqrt{\tilde{v_t}} + \epsilon \big) \hspace{3mm} \\ &\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 `lecture notes `_ by G. Hinton. and centered version `Generating Sequences With Recurrent Neural Networks `_. The implementation here takes the square root of the gradient average before adding epsilon (note that TensorFlow interchanges these two operations). The effective learning rate is thus :math:`\gamma/(\sqrt{v} + \epsilon)` where :math:`\gamma` is the scheduled learning rate and :math:`v` is the weighted moving average of the squared gradient. z Args: a0 lr (float, Tensor, optional): learning rate (default: 1e-2) alpha (float, optional): smoothing constant (default: 0.99) 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 (float, optional): momentum factor (default: 0) centered (bool, optional) : if ``True``, compute the centered RMSProp, the gradient is normalized by an estimation of its variance z z rrTrUrWrVrXrrrrrrr!r"rrYr#c "[RR5(d [U5n[ U5GHQunnUUn[R R 5(dhU(aa[5nURRURR:XaURRU;d[SUS35eUUnU (dUOU*nUUnUS- nU S:waURUU S9n[R"U5nU(aB[R"U5n[R"U5n[R"U5nURU5RUUSU- S9 U (aWUUnU(a[R"U5nUR!USU- 5 UR#UUSS9R%5nOUR'5nU (aURU5nOUR)U5nU S:aWUUnU(a[R"U5nURU 5R+UU5 UR)UU*S9 GM?UR+UUU*S9 GMT g)NIIf capturable=True, params and state_steps must be on supported devices: .rrrvalue)r<jit is_scriptingr enumeratecompiler is_compilingr r4typeAssertionErroraddrL view_as_realmul_addcmul_lerp_addcmulsqrt_sqrtadd_addcdiv_)rrTrUrWrVrXrrrrrrr!r"rrYiparamr1capturable_supported_devicesrKrHis_complex_paramrJavgbufs r-_single_tensor_rmspropr s-& 99 ! ! # # ^f%51~~~**,,+L+N ( !!T[[%5%55LL%%)EE$_`|_}}~Qx#t$ ^    1 88E86D ++E2 &&u-E%%d+D++J7J''d!e)'D  |H --h7 NN4U +$$Xxr$BHHJC//#C ''#,C((3-C a<&q)C((- HHX  ' 'c 2 JJs2#J & NN4RCN 0i&r/c ^![U5S:XagU (a [S5e[RR 5(dBU(a;[ 5m![ U!4Sj[XSS955(d[ST!S35e[U5n[R"XX#XE/5nUR5GHuunnnnnnn[[[U5n[[[U5n[[[U5n[[[U5nU(amUU/nU S:a([[[U5nURU5 U (a([[[U5nURU5 [!U/UQ76 U (a[R""U5n[RR 5(d>USR$(a*[R&"U[R("SS S 9SS 9 O[R&"US 5 U S:wa4U (a[R&"UUU S 9 O[R*"UUU S 9n[R,"UU5 [R."UUUS U- S 9 U (aw[[[U5n[R0"UUS U- 5 [R2"UUUSS 9n[R4"U5 [R&"UU5 O-[R6"U5n[R&"UU5 U S:a[[[U5n[R,"UU 5 [R8"UUU5 U(aQ[;U[R5(a2[R<"UU*5n [R&"UU 5 GM-[R&"UUU*S 9 GMGU(aR[;U[R5(a3[R>"UU*5 [R8"UUU5 GM[R8"UUUU*S 9 GM g)Nrz#_foreach ops don't support autogradc3># UHTupURRURR:H=(a URRT;v MV g7frb)r4rv).0rAr1rs r- (_multi_tensor_rmsprop..rsO A HHMMT[[-- - > !== >@sAAT)strictrkrlg?cpu)r4rmrrnrp) r;rwr<rtrur allziprr"_group_tensors_by_device_and_dtypevaluesrlistrrMr _foreach_negis_cpu _foreach_add_r? _foreach_add _foreach_mul__foreach_addcmul__foreach_lerp__foreach_addcmul_foreach_sqrt_ _foreach_sqrt_foreach_addcdiv_r% _foreach_mul _foreach_div_)"rrTrUrWrVrXrrrrrrr!r"rrYgrouped_tensorsgrouped_params_grouped_grads_grouped_square_avgs_grouped_grad_avgs_grouped_momentum_buffer_list_grouped_state_steps__grouped_params grouped_gradsgrouped_square_avgsgrouped_state_stepsstate_and_gradsgrouped_momentum_buffer_listgrouped_grad_avgsr momentum_lrrs" @r-_multi_tensor_rmsproprVs& 6{aBCC >> & & ( (Z'H'J$ v4@   ![\x[yyz{  BBBB  0DRO  " " $     ) d6lO<T&\>: "4<1EF"4<1EF ,.ABO!|/3L"?0, &&'CD$(f7I$J!&&'89 . ;? ; !..}=M ~~**,,1DQ1G1N1N   #U\\#e%DC     3Q 7 1 ##M>V % 2 2!>!  /7   QY   $T&\3E F   !2M1u9 M((#%68IQSC   %   S )%%&9:C   S ) a<+/V ;, (    .s!5-8 1ell##[s')zPAPI has changed, `state_steps` argument must contain a list of singleton tensorsNF) use_fusedz6torch.jit.script not supported with foreach optimizers) rrrrrrr!rr"rY) r<rtrurrOrrqrrrr)rrTrUrWrVrXr r!r"rrYrrrrrrrfuncs r-rrs: >> & & ( (5-8522 ^  1 e 7599))++STTuyy--//$%   !%!r/)NFFFF)__doc__typingrr<r optimizerrrrr r r r r rrrrrr__all__rrr>rgrrrrFr/r-rs^0 $ i gigV+X       Y<BJ1 LJ1 <J1fJ1F| J1 v, J1 f J1 J1 J1 J1J1J1J1J1J1 !J1"#J1$ %J1ZCW LCW <CWfCWF| CW v, CW f CW CW CW CWCWCWCWCWCW !CW"#CW$ %CWL 1GH  A LA <AfAF| A v, A f AD[AAAAA A !A" #A$%A&'A()A* +AIAr/