JThP!xSSKJrJrJr SSKrSSKJr SSKJrJrJ r J r J r J r J r JrJrJrJrJr SS/r"SS\5rS S \S \ S \ S \ S 3 -\lS$S\\S\\S\\S\\S\\S\\S\\S\S\\S\S\S\S\S\S\S\4 SjjrSrS\\S\\S\\S\\S\\S\\S\S\S\S\S\S\S\S\4S jrS\\S\\S\\S\\S\\S\\S\S\S\S\S\S\S\S\4S!jrS\\S\\S\\S\\S\\S\\S\S\S\S\S\S\S\S\S"S4S#jrg)%)castOptionalUnionN)Tensor) _default_to_fused_or_foreach_device_dtype_check_for_fused_differentiable_doc _foreach_doc_get_scalar_dtype _get_value _maximize_doc _params_doc_use_grad_for_differentiable _view_as_real OptimizerParamsTAdagradadagradc^\rSrSrSSSSS.S\S\\\4S\S\S \S \S \\ S \ S \ S\\ 4U4Sjjjjr U4Sjr Sr Sr \SSj5rSrU=r$)rNF)maximizedifferentiablefusedparamslrlr_decay weight_decayinitial_accumulator_valueepsforeachrrrc z>[U[5(aUR5S:wa [S5eSU::d[SU35eSU::d[SU35eSU::d[SU35eSU::d[SU35eSU::d[SU35e[ UUUUUUUU U S 9 n [ TU]X5 U (a+U (a [S 5eU(a [S 5eS UlURHn U S Hn URU nU S(a*[R"S[U SS9U RS9O[R"S[5S9US'[R "U 5(a [#XU5OUn[R$"X[R&S9US'M M g)NrzTensor lr must be 1-elementgzInvalid learning rate: zInvalid lr_decay value: zInvalid weight_decay value: z)Invalid initial_accumulator_value value: zInvalid epsilon value: ) rrr rrr!rrrz)`fused` does not support `differentiable`z0`fused` and `foreach` cannot be `True` together.Trris_fused)dtypedevicer&step) memory_formatsum) isinstancernumel ValueErrordictsuper__init__ RuntimeError"_need_device_dtype_check_for_fused param_groupsstatetorchzerosr r'tensor is_complexcomplex full_likepreserve_format)selfrrrrrr r!rrrdefaultsgrouppr5 init_value __class__s K/var/www/auris/envauris/lib/python3.13/site-packages/torch/optim/adagrad.pyr1Adagrad.__init__s b& ! !bhhjAo:; ;by6rd;< <h7zBC Cl";L>JK K//;[TU]U5 SnURHKnURSS5 URSS5 URSS5 URSS5nMM [ UR R 55n[U5S:g=(a [R"USS5nU(d5UH.n[R"[US5[US9S 9US'M0 gg) Nr!rFrrrr)r$r() r0 __setstate__r4 setdefaultlistr5valueslenr6 is_tensorr8floatr )r=r5rr? state_valuesstep_is_tensorsrBs rCrGAdagrad.__setstate__as U#&&E   Y -   Z /   -u 5$$Wd3E ' DJJ--/0 l+q0 eoo OF #7 !!LL!F)$,=u,M& "rEcURH1nUSH%nURUnUSR5 M' M3 g)Nrr+)r4r5 share_memory_)r=r?r@r5s rC share_memoryAdagrad.share_memoryvs=&&E8_ 1 e **,%'rEcSupgUSHnURcMUS(a#[USS5(a[USS9 SUlXhRR-nU[ R "U5-nURU5 URUR5 URUn URU S5 URU S 5 M Xg4$) N)FFrrr3T)cuda_unsupportedFr+r)) gradgetattrr r3 is_sparser6r9appendr5) r=r?params_with_gradgrads state_sums state_stepshas_sparse_grad has_complexr@r5s rC _init_groupAdagrad._init_group|s'3$xAvv!>g8'' 2!dK>CD;66#3#33u//22  ''* QVV$ 1 !!%,/""5=1!"++rEchSnUb%[R"5 U"5nSSS5 URHfn/n/n/n/nURX4XVU5up[ UUUUUSUSUSUSUUSUSUSU US [ US S5[ US S5S 9 Mh U$!,(df  N=f) zPerform a single optimization step. Args: closure (Callable, optional): A closure that reevaluates the model and returns the loss. Nrrrr r!rrr grad_scale found_inf) rrrr r`r!rrrarrerf)r6 enable_gradr4rbrrY) r=closurelossr?r\r]r^r_r`ras rCr) Adagrad.steps  ""$y%&&E-/ "$E')J(*K+/+;+;K, (O  ;">2z*%L /i(z*$%56'Gn"4t<!$ T:! ': A%$s B## B1)r3)g{Gz?rrrg|=NN)__name__ __module__ __qualname____firstlineno__rrrMrrboolr1rGrTrbrr)__static_attributes__ __classcell__)rBs@rCrrs$(+,"&D$ $DD %- D D  D $) DD$DDD~DDL*- ,*"*"*rEa[Implements Adagrad algorithm. .. math:: \begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)}, \: \lambda \text{ (weight decay)}, \\ &\hspace{12mm} \tau \text{ (initial accumulator value)}, \: \eta\text{ (lr decay)}\\ &\textbf{initialize} : state\_sum_0 \leftarrow \tau \\[-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} \tilde{\gamma} \leftarrow \gamma / (1 +(t-1) \eta) \\ &\hspace{5mm} \textbf{if} \: \lambda \neq 0 \\ &\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\ &\hspace{5mm}state\_sum_t \leftarrow state\_sum_{t-1} + g^2_t \\ &\hspace{5mm}\theta_t \leftarrow \theta_{t-1}- \tilde{\gamma} \frac{g_t}{\sqrt{state\_sum_t}+\epsilon} \\ &\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 `Adaptive Subgradient Methods for Online Learning and Stochastic Optimization`_. z Args: a lr (float, Tensor, optional): learning rate (default: 1e-2) lr_decay (float, optional): learning rate decay (default: 0) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) initial_accumulator_value (float, optional): initial value of the sum of squares of gradients (default: 0) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-10) z a fused (bool, optional): whether the fused implementation (CPU only) is used. Currently, `torch.float64`, `torch.float32`, `torch.float16`, and `torch.bfloat16` are supported. (default: None). Please note that the fused implementations does not support sparse or complex gradients. .. _Adaptive Subgradient Methods for Online Learning and Stochastic Optimization: http://jmlr.org/papers/v12/duchi11a.html rr]r^r_rrerfr`r!rrarrrr rc L[SU55(d [S5eUcUc [X SS9unnUcSnUcSnU(a.[RR 5(a [S5eU(a.[RR 5(a [S5eU(a*[RR 5(d[ nO7U(a*[RR 5(d[nO[nU"UUUUU U U UUUU U UUS9 g) zlFunctional API that performs Adagrad algorithm computation. See :class:`~torch.optim.Adagrad` for details. c3V# UHn[U[R5v M! g7frk)r,r6r).0ts rC adagrad.. s@Kqz!U\\**Ks')zPAPI has changed, `state_steps` argument must contain a list of singleton tensorsNF) use_fusedz6torch.jit.script not supported with foreach optimizersz4torch.jit.script not supported with fused optimizers rrrr r`rrrarerf) allr2rr6jit is_scripting_fused_adagrad_multi_tensor_adagrad_single_tensor_adagrad)rr]r^r_rrerfr`r!rrarrrr r_funcs rCrrs2 @K@ @ @ ^   }1 e 7 }599))++STT ''))QRR UYY++-- //11$%  ! '%rEcPUR5n[R"XU5$rk)sizer6sparse_coo_tensor)rX grad_indicesrJrs rC _make_sparser<s 99;D  " "< >>rEc vUcUbe[XX#5GH upnnUS- n[U5nU (dUOU*nUS:wa+UR(a [S5eUR XS9nUSUS- U--- nUR(aUR 5nUR 5nUR5nUR[UUURS555 URU5nUR5R5RU 5nUR[UUUU- 5U*S9 GM/[R"U5nU(aB[R"U5n[R"U5n[R"U5nUR!XSS9 U (aUR#5U -nOUR#5RU 5nUR%UUU*S9 U(dGM[R&"U5n[R&"U5nGM# g)Nrrz;weight_decay option is not compatible with sparse gradientsalphavalue)zipr rZr2addcoalesce_indices_valuesadd_rpow sparse_masksqrt_r6r9 view_as_realaddcmul_sqrtaddcdiv_view_as_complex)rr]r^r_rerfrrrr r`rrraparamrX state_sumstep_tr)clrr grad_valuesstd std_valuesr9s rCrrAs"  )"33 3*-fZ*U&Y! &!#t$ 1 ~~"Q88E86DAX--. >>==?D==?L,,.K NN<lKOOAUSR(a*[R"U[R "SSS9SS 9 O[R"US 5 US:wa4U (a[R"UUUS 9 O[R""UUUS 9nUVs/sHnU*S [%U5S - U--- PM nn[R&"UUUS S 9 [R("U5n[R"UU 5 US:wdU (a[R*"UU5 UnO[R,"UU5n[R."UUU5 GMJ gs snf) Nz#_foreach ops don't support autogradrc38# UHoRv M g7frk)rZ)rurXs rCrw(_multi_tensor_adagrad..s9 '3tNN|sTrzg?cpu)r'rrr)rKr"_group_tensors_by_device_and_dtyperJrrIranyrrr6 _foreach_negcompiler is_compilingis_cpu _foreach_add_r8 _foreach_addr _foreach_addcmul_ _foreach_sqrt _foreach_mul_ _foreach_mul_foreach_addcdiv_)rr]r^r_rerfrrrr r`rrragrouped_tensorlistsdevice_params_ device_grads_device_state_sums_device_state_steps_r device_params device_gradsdevice_state_sumsdevice_state_stepsdevice_has_sparse_gradr) minus_clrr numerators rCrr~s"DDD   )"33 3 6{a#FF  0 & & (   T&\>: DL-8  f/AB!$v,0CD!0" S9 '39 6  " "!")! $!-'%#    -7H I  --l;L ~~**,,1CA1F1M1M   "ELLU$C3     2A 6 1 ##L-|T$11 -|  GY FXdRC1 4(1,88 9FX    1<UVW!!"34 C% 1     i 8$I**<CI  y#>Q )p s"Jreturnc@U(dgU (dU (a [S5eU (a [S5eUbURU0OSnUbURU0OSn[R"XX#/5nUR 5GHuunnuunnnnn[ [ [U5n[ [ [U5n[ [ [U5n[ [ [U5nSunnUb!UbUU;aURUSS9UU'UUnUb UbX_;aURUSS9UU'UUn[R"US5 [R"UUUUUUUU U UUS9 UcM[R"UU/[U5-5 GM g)Nz5`fused` does not support sparse grad or complex paramz: DL-8  f/AB!$v,0CD.8++  !o&A_,*4--T-*R' / 7   ^%?.)2f4)Pv&-f5  .2     %(&   '   "%5$6=O9P$P = !rE)NNNFNFF)typingrrrr6r optimizerrr r r r r rrrrrr__all__r__doc__rIrprMrrrrr~r#rErCrs((      i bibL4      5.p!#'"&"" G LG <GV Gf G D> G  GGGd^GGG G !G"#G$ %G&'GT? := L:= <:=V :=f :=  :=  := :=:=:= :=:=:=:=:=zh? 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