[^(h@ddlmZmZmZmZddlZddlmZddlmZm Z m Z m Z m Z m Z mZmZmZmZmZmZmZddgZGddeZd d ed e d ed ed e d ze_deedeedeedeedeededededededededefdZdeedeedeedeedeededededededededefdZe e d deedeedeedeedeededeedededededededefdZy)!)AnycastOptionalUnionN)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_use_grad_for_differentiable _view_as_real OptimizerParamsTAdadeltaadadeltaceZdZ ddddddedeeefdedededeed ed ed effd Z fd Z de e e fdeedeedeedeedeef dZeddZxZS)rF) capturablemaximizedifferentiableparamslrrhoeps weight_decayforeachrrrc Pt|tr|jdk7r tdd|kstd|d|cxkrdksntd|d|kstd|d|kstd|t |||||||| } t ||| y) NrzTensor lr must be 1-elementgzInvalid learning rate: ?zInvalid rho value: zInvalid epsilon value: zInvalid weight_decay value: )rrrr rrr!r) isinstancernumel ValueErrordictsuper__init__) selfrrrrr r!rrrdefaults __class__s R/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/torch/optim/adadelta.pyr)zAdadelta.__init__s b& !bhhjAo:; ;by6rd;< <c S 23%89 9cz6se<= =l";L>JK K%!)   *c0t|||jD]}|jdd|jdd|jdd|jdd|dD]}|jj |g}t |dk7s.tj|drGt|d}|dr*tj|t|j ntj|t |d<y) Nr!rFrrrrstepdtypedevicer2) r( __setstate__ param_groups setdefaultstategetlentorch is_tensorfloattensorrr3)r*r8grouppp_statestep_valr,s r-r5zAdadelta.__setstate__@s U#&& E   Y -   Z /   -u 5   \5 18_ **..B/w<1$U__WV_-M$WV_5H !. $,=,?#\\(:K:MN FO   r.r?params_with_gradgrads square_avgs acc_deltas state_stepscd}|dD]o}|j|tj|z}|j||jjr t d|j|j|j |} t| dk(r|dr*tjdt|jntjdt| d <tj|tj | d <tj|tj | d <|j| d |j| d |j| d r|S) NFrz*Adadelta does not support sparse gradientsrrr1r4r0) memory_format square_avg acc_delta) gradr; is_complexappend is_sparse RuntimeErrorr8r:zerosrr3 zeros_likepreserve_format) r*r?rCrDrErFrG has_complexr@r8s r- _init_groupzAdadelta._init_groupSsS x .Avv~ 5++A. .K  # #A &vv"#OPP LL JJqME5zQ\*KK*;*=ahhOR/@/BCf ',&6&6U%:%:'l#&+%5%5U%:%:&k"   u\2 3   eK0 1   uV} -9 .<r.cx|jd}|$tj5|}ddd|jD]f}g}g}g}g}g}|d|d|d|d|d|d|d|d f\} } } } } }}}|j ||||||}t |||||| | | | | |||| h|S#1swYxYw) zPerform a single optimization step. Args: closure (Callable, optional): A closure that reevaluates the model and returns the loss. Nrrrr r!rrr) rrrr r!rrrrU) _cuda_graph_capture_health_checkr; enable_gradr6rVr)r*closurelossr?rCrDrErFrGrrrr r!rrrrUs r-r0z Adadelta.step~s3 --/  ""$ !y !&&- E-/ "$E(*K')J(*Kd e e n%i j!&'l#  **' ZK  )!-%' =- ^ e ! !s B00B9)r#g?gư>rNN)__name__ __module__ __qualname__rrr=rrboolr)r5r'strrlistrVrr0 __classcell__)r,s@r-rrs$'"&"+!$"+"+ %- "+ "+  "+  "+$"+"+"+"+H&)CH~)v,)F| ) &\ ) L )&\)V"="=r.aImplements Adadelta algorithm. .. math:: \begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)}, \: \rho \text{ (decay)}, \: \lambda \text{ (weight decay)} \\ &\textbf{initialize} : v_0 \leftarrow 0 \: \text{ (square avg)}, \: u_0 \leftarrow 0 \: \text{ (accumulate variables)} \\[-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 v_{t-1} \rho + g^2_t (1 - \rho) \\ &\hspace{5mm}\Delta x_t \leftarrow \frac{\sqrt{u_{t-1} + \epsilon }}{ \sqrt{v_t + \epsilon} }g_t \hspace{21mm} \\ &\hspace{5mm} u_t \leftarrow u_{t-1} \rho + \Delta x^2_t (1 - \rho) \\ &\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \gamma \Delta x_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 `ADADELTA: An Adaptive Learning Rate Method`_. z Args: ar lr (float, Tensor, optional): coefficient that scale delta before it is applied to the parameters (default: 1.0) rho (float, optional): coefficient used for computing a running average of squared gradients (default: 0.9). A higher value of `rho` will result in a slower average, which can be helpful for preventing oscillations in the learning process. eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-6). weight_decay (float, optional): weight decay (L2 penalty) (default: 0) z zd .. _ADADELTA\: An Adaptive Learning Rate Method: https://arxiv.org/abs/1212.5701 rrDrErFrGrrrr rrrrUctjjs7| r5tdt fdt ||Ds Jddt |||||D]{\} }}}}|dz }| s|n| }|dk7r|j | |}tj| r?tj|}tj|}tj|}|j|j||d|z |j |j}|j |j}| r|j}|j|j||j|j||d|z tj| rtj|}| j|| ~y) NF supports_xlac3K|]N\}}|jj|jjk(xr|jjvPywr\r3type.0r@r0capturable_supported_devicess r- z*_single_tensor_adadelta.. 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