+ jY ^RIHt^RIt^RIHt^RIHtHtHtHtH t H t H t H t H t HtHtHtHtHtRR.t!RR]4tRR ] R ] R ] R ] R 2 ,]nRR RlltRtRRltRRltRRltR#))castN)Tensor)_default_to_fused_or_foreach_device_dtype_check_for_fused_differentiable_doc _foreach_doc_get_scalar_dtype _get_value _maximize_doc _params_doc _to_scalar_use_grad_for_differentiable _view_as_real DeviceDict OptimizerParamsTAdagradadagradcaa]tRt^toRRRRRRR/V3RlV3RlllltV3RltV3R lR ltR t]RR l4t R t Vt V;t #)rNmaximizeFdifferentiablefusedc<V^8dQhRS[RS[S[,RS[RS[RS[RS[RS[R,R S[R S[R S[R,R R/ #) paramslrlr_decay weight_decayinitial_accumulator_valueepsforeachNrrrreturn)rfloatrbool)format __classdict__s"T/home/luca/screen-checking/.venv/lib/python3.14/site-packages/torch/optim/adagrad.py __annotate__Adagrad.__annotate__sEEE FNE E  E $) EEEEEd{E Ec<\V\4'd!VP4^8wd \R4hRV8:g\RV 24hRV8:g\RV 24hRV8:g\RV 24hRV8:g\RV 24hRV8:g\RV 24hRVR VR VR VR VR VRVRV RV / p \SV`W4V 'd5V 'd \ R4hV'd \ R4hRVnRVnVPFp V R,Fp VPV ,pV R,'d5\P!R\V R,R7V PR7M\P!R\4R7VR&\P !V 4'd \#WU4MTp\P$!W\P&R7VR&K K R#)zTensor lr must be 1-elementzInvalid 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.Tris_fuseddtypedevicer1step memory_formatsumN) isinstancernumel ValueErrorsuper__init__ RuntimeError"_need_device_dtype_check_for_fused_step_supports_amp_scaling param_groupsstatetorchzerosr r2tensor is_complexcomplex full_likepreserve_format)selfrrrrrr r!rrrdefaultsgroupprB init_value __class__s&&&&&&&&$$$ r'r=Adagrad.__init__s b& ! !bhhjAo:; ;by6rd;< <h7zBC Cl";L>JK K//;>  $"3U"C#$88 #\\(:K:MNFO % '(DJJ--/0 l+q0  eoo OF #7 !!LL!F)$,=u,M& "r*c<V^8dQhRR/#)rr"Nr8)r%r&s"r'r(r)s--d-r*c VPF=pVR,F-pVPV,pVR,P4K/ K? R#)z6Calls tensor.share_memory_() on the state sum tensors.rr7N)rArB share_memory_)rJrLrMrBs& r' share_memoryAdagrad.share_memorys?&&E8__ 1 e **,%'r*cRRrvVR,EFpVPfKVR,'d&\VRR4'd\V4RVnWhPP,pV\ P !V4,pVPV4VPVP4VPV,p \V 4^8XdVR,'d \V4VR,'d5\ P!R \VR,R7VPR7M\ P!R\4R7V R &VPR ,p \ P !V4'd \W4MT p \ P !W\ P"R 7V R &VPV R ,4VPV R ,4EK Wg3#)Frrr?Tr.r0r-r3r4rr5r7r8)gradgetattrrr? is_sparserCrFappendrBrUrDr r2rErKrGrHrI) rJrLparams_with_gradgrads state_sums state_stepshas_sparse_grad has_complexrMrBrrNs &&&&&& r' _init_groupAdagrad._init_groups',exAvv!>>g8'' 2!4>CD;66#3#33u//22  ''* QVV$ 1 u:?W~~5a8!>>  "3U7^"L#$88 #\\#5F5HI&M15 31- !++A.. 9U6 $)??U5J5J$E%L!!%,/""5=1Q!T++r*c RpVe.\P!4;_uu_4V!4pRRR4VPFp.p.p.p.pVPW4WVV4wr\ VVVVVR,VR,VR,VR,VVR,VR,VR,V VR ,\ VR R4\ VR R4R 7K V# +'giL;i) zPerform a single optimization step. Args: closure (Callable, optional): A closure that reevaluates the model and returns the loss. Nrrrr r!rrr grad_scale found_inf) rrrr rmr!rrrnrrrrs)rC enable_gradrArorrf) rJclosurelossrLrirjrkrlrmrns && r'r4 Adagrad.steps  ""$$y%&&E-/ "$E')J(*K+/+;+;K, (O  ;">2z*%L /i(z*$%56'Gn"4t<!$ T:! ': A%$s C C )r?r@)g{Gz?rrrg|=NN) __name__ __module__ __qualname____firstlineno__r=rRrbrorr4__static_attributes____classdictcell__ __classcell__)rOr&s@@r'rrsbEE %E"EEN!F--,,\"*"**r*a[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 and CUDA only) is used. 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