DŽg@ddlmZmZmZmZmZmZddlZddlmZddl m Z m Z m Z m Z mZmZmZmZmZmZmZmZddgZGddeZd d ed e d ed e d ze_d eedeedeedeedeededededededededefdZd eedeedeedeedeededededededededefdZe e dd eedeedeedeedeededeedededededededefdZy) )AnycastDictListOptionalUnionN)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_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) Nr zTensor lr must be 1-elementgzInvalid learning rate: ?zInvalid rho value: zInvalid epsilon value: zInvalid weight_decay value: )rrr r!rrr"r) isinstancer numel ValueErrordictsuper__init__) selfrrrr r!r"rrrdefaults __class__s \/home/mcse/projects/flask_80/flask-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"rFrrrrstepdtypedevicer3) r) __setstate__ param_groups setdefaultstategetlentorch is_tensorfloattensorrr4)r+r9grouppp_statestep_valr-s r.r6zAdadelta.__setstate__@s U#&& E   Y -   Z /   -u 5   \5 18_ **..B/w<1$U__WV_-M$WV_5H !. $,=,?#\\(:K:MN FO   r/r@params_with_gradgrads square_avgs acc_deltas state_stepscd}|dD]o}|j|tj|z}|j||jjr t d|j|j|j |} t| dk(r|dr*tjdt|jntjdt| d <tj|tj | d <tj|tj | d <|j| d |j| d |j| d r|S) NFrz*Adadelta does not support sparse gradientsrrr2r5r1) memory_format square_avg acc_delta) gradr< is_complexappend is_sparse RuntimeErrorr9r;zerosrr4 zeros_likepreserve_format) r+r@rDrErFrGrH has_complexrAr9s r. _init_groupzAdadelta._init_groupSsS x .Avv~ 5++A. .K  # #A &vv"#OPP LL JJqME5zQ\*KK*;*=ahhOR/@/BCf ',&6&6U%:%:'l#&+%5%5U%:%:&k"   u\2 3   eK0 1   uV} -9 .<r/cx|jd}|$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. Nrrr r!r"rrr) rrr r!r"rrrrV) _cuda_graph_capture_health_checkr< enable_gradr7rWr)r+closurelossr@rDrErFrGrHrrr r!r"rrrrVs r.r1z 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>r rboolr*r6rstrrrrWrr1 __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`_. a Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups 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). lr (float, Tensor, optional): coefficient that scale delta before it is applied to the parameters (default: 1.0) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) z zd .. _ADADELTA\: An Adaptive Learning Rate Method: https://arxiv.org/abs/1212.5701 rrErFrGrHrrr r!rrrrVctjjs7| 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]r4type.0rAr1capturable_supported_devicess r. z*_single_tensor_adadelta.. Q 4 HHMMT[[-- - > !== > AAIIf capturable=True, params and state_steps must be on supported devices: .r ralphavalue)r<_utils is_compilingrallzipaddrO view_as_realmul_addcmul_sqrt_clonediv_view_as_complexadd_)rrErFrGrHrrr r!rrrrVparamrNrLrMr1stddeltarls @r._single_tensor_adadeltars" << $ $ &:'H( $ v{3   wWWsVttu v  w 58{J 5%0tZD  #t$ 1 88E86D   E "++J7J**95I%%d+D%%dDC%@nnS!'') c"((* KKME 3T"s$$UES$A   E "))%0E 5 $1%r/cn| rJdtjjs7| r5tdt fdt ||Ds Jddt |dk(rytj|||||g} | jD]\\}}}}}}ttt|}ttt|}ttt|}ttt|}ttt|}| rt||||tjjs=|djr.tj|tj dd d ntj|d | rtj"|}|dk7r3| rtj||| ntj$||| }tj&||tj(|||d |z tj$||}tj*|tj$||}tj*|tj,||tj&||tj&||tj(|||d |z | rIt/|tjr/tj&|| tj||tj||| y)Nz#_foreach ops don't support autogradFrec3K|]N\}}|jj|jjk(xr|jjvPywr]rhrjs r.rmz)_multi_tensor_adadelta..Brnrorprqrr$cpu)r4rrr rt)r<rvrwrrxryr;r"_group_tensors_by_device_and_dtypevaluesrrr ris_cpu _foreach_add_r? _foreach_neg _foreach_add _foreach_mul__foreach_addcmul__foreach_sqrt_ _foreach_div_r%)rrErFrGrHrrr r!rrrrVgrouped_tensorsdevice_params_ device_grads_device_square_avgs_device_acc_deltas_device_state_steps__ device_params device_gradsdevice_square_avgsdevice_acc_deltasdevice_state_stepsrdeltasrls @r._multi_tensor_adadeltar+s DDD  << $ $ &:'H( $ v{3   wWWsVttu v  w  6{aBB  Z=O  " " $ >B  T&\>: DL-8 !$v,0CD f/AB!$v,0CD  |-?AR ||((*/A!/D/K/K   "ELLU$C3     2A 6  --l;L 1 ##L-|T$11 -|   .4   l!c'   !3S9 S!##$5s; V$ FC( FL1 -s3  166SQ *R6    ,    v 6    vbS A}>Br/)single_tensor_fnr"c ztjjstd|Ds t d|t ||d\}}|r)tj jr t d|r%tj jst}nt}||||||| | | | | ||| y)zvFunctional API that performs Adadelta algorithm computation. See :class:`~torch.optim.Adadelta` for details. c3PK|]}t|tj ywr])r%r<r )rkts r.rmzadadelta..s 3() 1ell#3s$&zPAPI has changed, `state_steps` argument must contain a list of singleton tensorsNF) use_fusedz6torch.jit.script not supported with foreach optimizers)rrr r!rrrrV) r<rvrwrxrRr jit is_scriptingrr)rrErFrGrHrr"rrVrrr r!rrfuncs r.rrs6 << $ $ &s3-830 ^  1 Ne 7599))+STTuyy--/%&   !%r/)FNFF)typingrrrrrrr<r optimizerr r r rrrrrrrrr__all__r__doc__r>rarrrrJr/r.rs:9      z "ayaJ8     !91 l3% L3% <3%f3%V 3% f 3% 3% 3% 3%3%3%3%3%3%laB LaB <aBfaBV aB f aB aB aB aBaBaBaBaBaBH 1HI" = L= <=f=V = f ==d^=== = = = !="#=J=r/