U Mh>@sdddlmZmZmZmZddlZddlmZddlmZm Z m Z m Z m Z m Z mZmZmZmZmZmZddgZGdddeZd d e d ed ed e d e_eeeeeeeeeeeeeeeeeed ddZeeeeeeeeeeeeeeeeeed ddZe eddeeeeeeeeeeeeeeeeeeeedddZdS))AnyDictListOptionalN)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 OptimizerParamsTAdadeltaadadeltac seZdZdddddeeeeeeeeeed fd d Zfd d Ze e e fe e e e e e e e e e d ddZedddZZS)r??ư>rNF) capturablemaximizedifferentiable) paramslrrhoeps weight_decayforeachrrrc sd|kstd|d|kr*dks:ntd|d|ksPtd|d|ksftd|t|||||||| d} t|| dS)NgzInvalid learning rate: rzInvalid rho value: zInvalid epsilon value: zInvalid weight_decay value: )rrrr rrr!r) ValueErrordictsuper__init__) selfrrrrr r!rrrdefaults __class__F/var/www/html/venv/lib/python3.8/site-packages/torch/optim/adadelta.pyr%s&  zAdadelta.__init__cst||jD]}|dd|dd|dd|dd|dD]h}|j|g}t|dkrNt|dsNt |d}|drtj |t |j d ntj |t d |d<qNqdS) Nr!rFrrrrstepdtypedevicer.) r$ __setstate__ param_groups setdefaultstategetlentorchZ is_tensorfloattensorrr/)r&r4grouppZp_stateZstep_valr(r*r+r1<s$        zAdadelta.__setstate__)r:params_with_gradgrads square_avgs acc_deltas state_stepsc Csd}|dD]}|jdkrq |t|O}|||jjrDtd||j|j|} t| dkr|drtjdt |j dntjdt d| d <tj |tj d | d <tj |tj d | d <|| d || d || d q |S) NFrz*Adadelta does not support sparse gradientsrrr*r-r0r,)Z memory_format square_avg acc_delta) gradr7 is_complexappendZ is_sparse RuntimeErrorr4r6Zzerosrr/Z zeros_likeZpreserve_format) r&r:r<r=r>r?r@ has_complexr;r4r*r*r+ _init_groupOs6         zAdadelta._init_groupcCs|d}|dk r.t |}W5QRX|jD]}g}g}g}g}g}|d|d|d|d|d|d|d|d f\} } } } } }}}|||||||}t|||||| | | | | ||||d q4|S) zPerform a single optimization step. Args: closure (Callable, optional): A closure that reevaluates the model and returns the loss. Nrrrr r!rrr) rrrr r!rrrrG)Z _cuda_graph_capture_health_checkr7Z enable_gradr2rHr)r&closureZlossr:r<r=r>r?r@rrrr r!rrrrGr*r*r+r,zsl    z Adadelta.step)rrrrN)N)__name__ __module__ __qualname__rr8rboolr%r1rstrrrrrHrr, __classcell__r*r*r(r+rs< "   +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). 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