:jTQ!rddlmZmZmZddlZddlmZddlmZmZm Z m Z m Z m Z m Z mZmZmZmZmZmZddgZGddeZd d ed e d e d e d ze_ d$deedeedeedeedeedeedeededeedededededededef dZdZdeedeedeedeedeedeededededededededefd Zdeedeedeedeedeedeededededededededefd!Zdeedeedeedeedeedeededededededededed"dfd#Zy)%)castOptionalUnionN)Tensor) _default_to_fused_or_foreach_device_dtype_check_for_fused_differentiable_doc _foreach_doc_get_scalar_dtype _get_value _maximize_doc _params_doc _to_scalar_use_grad_for_differentiable _view_as_real OptimizerParamsTAdagradadagradceZdZ ddddddedeeefdededed ed eed ed ed eeffdZ fdZ dZ dZ e ddZxZS)rNF)maximizedifferentiablefusedparamslrlr_decay weight_decayinitial_accumulator_valueepsforeachrrrc Ft|tr|jdk7r tdd|kstd|d|kstd|d|kstd|d|kstd|d|kstd|t |||||||| | } t ||| | r!| r td |r td d |_|jD]} | d D]} |j| }| dr/tjdt| d| jntjdt|d<tj | r t#||n|}tj$| |tj&|d<y)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 h/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torch/optim/adagrad.pyr1zAdagrad.__init__s b& !bhhjAo:; ;by6rd;< <h7zBC Cl";L>JK K//;g8' 2!dK>CD;166#3#33u//22  ''* QVV$ 1 !!%,/""5=1 2" ++rDcbd}|$tj5|}ddd|jD]k}g}g}g}g}|j|||||\}} t |||||d|d|d|d||d|d|d| |d t |d dt |d d m|S#1swYxYw) zPerform a single optimization step. Args: closure (Callable, optional): A closure that reevaluates the model and returns the loss. Nrrrr r!rrr grad_scale found_inf) rrrr r]r!rrr^rrarb)r6 enable_gradr4r_rrV) r=closurelossr?rYrZr[r\r]r^s rCr)z Adagrad.steps  ""$ !y !&& E-/ "$E')J(*K+/+;+;' K, (O[  ;">2z*%L /i(z*$%56'Gn"4t<!$ T:!  : A ! !s B%%B.)g{Gz?rrrg|=NN)__name__ __module__ __qualname__rrrLrrboolr1rFrRr_rr) __classcell__)rBs@rCrrs$(+,"&D$ $DD %- D D  D $) DD$DDD~DL*- ,*"*"*rDa[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 rrZr[r\rrarbr]r!rr^rrrr rc td|Ds td||t|| d\}}|d}|d}|r)tjj r td|r)tjj r td|r%tjj st }n-|r%tjj st}nt}|||||| | | |||| | ||y) ztFunctional API that performs Adagrad algorithm computation. See :class:`~torch.optim.Adagrad` for details. c3PK|]}t|tj ywrf)r,r6r).0ts rC zadagrad.. s@qz!U\\*@s$&zPAPI has changed, `state_steps` argument must contain a list of singleton tensorsNF) use_fusedz6torch.jit.script not supported with foreach optimizersz4torch.jit.script not supported with fused optimizers rrrr r]rrr^rarb) allr2rr6jit is_scripting_fused_adagrad_multi_tensor_adagrad_single_tensor_adagrad)rrZr[r\rrarbr]r!rr^rrrr r_funcs rCrrs2 @K@ @ ^   }1 Ne 7 }599))+STT '')QRR UYY++- //1$%  ! 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