U Mh@]@s|ddlmZmZmZmZmZddlZddlmZddlm Z m Z m Z m Z m Z mZmZmZmZmZmZmZmZmZddgZGdddeZd d ed ed e d e d e_eeeeeeeeeeeeeeeeeeeed ddZeeeeeeeeeeeeeeeeeeeed ddZe eddeeeeeeeeeeeeeeeeeeeeeedddZdS))castListOptionalTupleUnionN)Tensor)_capturable_doc_default_to_fused_or_foreach_differentiable_doc_disable_dynamo_if_unsupported_dispatch_sqrt _foreach_doc!_get_capturable_supported_devices_get_scalar_dtype _get_value _maximize_doc_use_grad_for_differentiable _view_as_real OptimizerParamsTRAdamradamc speZdZddddddeeeeefeeeeeeeed fd d Zfd d Z d dZ e dddZ Z S)rMbP?g?g+?:0yE>rFN)foreachmaximize capturabledifferentiable) paramslrbetaseps weight_decaydecoupled_weight_decayrrrrc sd|kstd|d|ks,td|d|dkrDdksXntd|dd|dkrpdksntd|dd|kstd |t||||||| || d } t|| dS) NzInvalid learning rate: zInvalid epsilon value: r?z#Invalid beta parameter at index 0: rz#Invalid beta parameter at index 1: zInvalid weight_decay value: ) r!r"r#r$rrrr%r) ValueErrordictsuper__init__) selfr r!r"r#r$r%rrrrdefaults __class__C/var/www/html/venv/lib/python3.8/site-packages/torch/optim/radam.pyr+s, zRAdam.__init__cst||jD]}|dd|dd|dd|dd|dd|dD]h}|j|g}t|dkrZt|d sZt |d }|drtj |t |j d ntj |t d |d <qZqdS) NrrFrr%rr rstepdtypedevicer4) r* __setstate__ param_groups setdefaultstategetlentorchZ is_tensorfloattensorrr5)r,r:grouppZp_stateZstep_valr.r0r1r7Bs&         zRAdam.__setstate__c Csd}|dD]}|jdk r |t|O}|||jjrBtd||j|j|} t| dkr|drtjdt |j dntj dt d | d <tj |tj d | d <tj |tj d | d <|| d || d || d q |S)NFr z'RAdam does not support sparse gradientsrrr0r3r&r6r2)Z memory_formatexp_avg exp_avg_sq)gradr= is_complexappendZ is_sparse RuntimeErrorr:r<Zzerosrr5r?Z zeros_likeZpreserve_format) r,r@params_with_gradgradsexp_avgs exp_avg_sqs state_steps has_complexrAr:r0r0r1 _init_groupVs4        zRAdam._init_groupc Cs|d}|dk r.t |}W5QRX|jD]}g}g}g}g}g}ttttf|d\} } |||||||} t|||||| | |d|d|d|d|d|d|d |d | d q4|S) zPerforms a single optimization step. Args: closure (Callable, optional): A closure that reevaluates the model and returns the loss. Nr"r!r$r#rrrrr%) beta1beta2r!r$r#rrrrr%rM) Z _cuda_graph_capture_health_checkr=Z enable_gradr8rrr>rNr) r,closureZlossr@rHrIrJrKrLrOrPrMr0r0r1r2ysN  z RAdam.step)rrrrF)N)__name__ __module__ __qualname__rr>rboolrr+r7rNrr2 __classcell__r0r0r.r1rs4  & #aImplements RAdam algorithm. .. math:: \begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \gamma \text{ (lr)}, \: \beta_1, \beta_2 \text{ (betas)}, \: \theta_0 \text{ (params)}, \:f(\theta) \text{ (objective)}, \: \lambda \text{ (weightdecay)}, \:\textit{maximize} \\ &\hspace{13mm} \epsilon \text{ (epsilon)}, \textit{decoupled\_weight\_decay} \\ &\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)}, v_0 \leftarrow 0 \text{ ( second moment)}, \\ &\hspace{18mm} \rho_{\infty} \leftarrow 2/(1-\beta_2) -1 \\[-1.ex] &\rule{110mm}{0.4pt} \\ &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\ &\hspace{6mm}\textbf{if} \: \textit{maximize}: \\ &\hspace{12mm}g_t \leftarrow -\nabla_{\theta} f_t (\theta_{t-1}) \\ &\hspace{6mm}\textbf{else} \\ &\hspace{12mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\ &\hspace{6mm} \theta_t \leftarrow \theta_{t-1} \\ &\hspace{6mm} \textbf{if} \: \lambda \neq 0 \\ &\hspace{12mm}\textbf{if} \: \textit{decoupled\_weight\_decay} \\ &\hspace{18mm} \theta_t \leftarrow \theta_{t} - \gamma \lambda \theta_{t} \\ &\hspace{12mm}\textbf{else} \\ &\hspace{18mm} g_t \leftarrow g_t + \lambda \theta_{t} \\ &\hspace{6mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\ &\hspace{6mm}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\ &\hspace{6mm}\widehat{m_t} \leftarrow m_t/\big(1-\beta_1^t \big) \\ &\hspace{6mm}\rho_t \leftarrow \rho_{\infty} - 2 t \beta^t_2 /\big(1-\beta_2^t \big) \\[0.1.ex] &\hspace{6mm}\textbf{if} \: \rho_t > 5 \\ &\hspace{12mm} l_t \leftarrow \frac{\sqrt{ (1-\beta^t_2) }}{ \sqrt{v_t} +\epsilon } \\ &\hspace{12mm} r_t \leftarrow \sqrt{\frac{(\rho_t-4)(\rho_t-2)\rho_{\infty}}{(\rho_{\infty}-4)(\rho_{\infty}-2) \rho_t}} \\ &\hspace{12mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t} r_t l_t \\ &\hspace{6mm}\textbf{else} \\ &\hspace{12mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_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 `On the variance of the adaptive learning rate and beyond`_. This implementation provides an option to use either the original weight_decay implementation as in Adam (where the weight_decay is applied to the gradient) or the one from AdamW (where weight_decay is applied to the weight) through the decoupled_weight_decay option. When decoupled_weight_decay is set to False (default), it uses the original Adam style weight decay, otherwise, it uses the AdamW style which corresponds more closely to the `author's implementation`_ in the RAdam paper. Further information about decoupled weight decay can be found in `Decoupled Weight Decay Regularization`_. a Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 1e-3) betas (Tuple[float, float], optional): coefficients used for computing running averages of gradient and its square (default: (0.9, 0.999)) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-8) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) decoupled_weight_decay (bool, optional): whether to use decoupled weight decay as in AdamW to obtain RAdamW (default: False) z a .. _On the variance of the adaptive learning rate and beyond: https://arxiv.org/abs/1908.03265 .. _author's implementation: https://github.com/LiyuanLucasLiu/RAdam .. _Decoupled Weight Decay Regularization: https://arxiv.org/abs/1711.05101 )r rIrJrKrLrOrPr!r$r#r%rrrrMc st|D]\}}| s||n|| }||}||||}tjs| rt}|jj|jjkrp|jj|kstd|dt|rt |}t |}t |}t |d7}| r|nt |}|dkr| r| d||n|j ||d}| |d| |j||d|dd||}d||||}dd|dd|||fdd }fd d }| rtd k||d }|j|||ddqd kr|j||||ddq|j||ddqdS)NIIf capturable=True, params and state_steps must be on supported devices: .rralpha)valuecs,dddddS)Nr\?r0r0)rho_infrho_tr0r1 _compute_rect9sz+_single_tensor_radam.._compute_rectcs.}r|}n |}d|S)Nr^)sqrtaddadd_)Zexp_avg_sq_sqrt)bias_correction2rr#rCr0r1_compute_adaptive_lrAs   z2_single_tensor_radam.._compute_adaptive_lr@r'g) enumerater=_utils is_compilingrr5typeAssertionErrorrEZ view_as_realrZmul_rcZlerp_Zaddcmul_whererd)r rIrJrKrLrOrPr!r$r#r%rrrrMiparamrDrBZstep_tcapturable_supported_devicesr2bias_correction1Zbias_corrected_exp_avgrarfupdater0)rerr#rCr_r`r1_single_tensor_radamsh            rsc st|dkrdS| rtdtjs`| r`tddtfddt||Ds`tddt |||||g}| D]`\\}}}}}}|dj rtj |tj d d d d d n t |d |rt||||| rt|}dd d | rft|}t|t |d t|}t||t|dt||t|t ||}nfdd|D}|dkr| rt|d |n(| rtj |||d ntj|||d }t||d t|t|||d ~| r2t|d}t|d}t||~t|ddt|}t||~t|ddt||D}~~dd|D}t|t|}t|t |d t||t|t|}t|t |d t|t|t||~t|t||~nffdd|D}dd|D}fdd|D}fddt||D}fddt|||D}t|}t || t||t|t ||t|||q|dS)Nrz#_foreach ops don't support autogradF)Z supports_xlac3s.|]&\}}|jj|jjko$|jjkVqdSN)r5rk).0rAr2)rpr0r1 ysz&_multi_tensor_radam..rWrXr'cpu)r5rYrr\cs8g|]0}dt|t|dt|qS)r\rrrur2)rPr_r0r1 s z'_multi_tensor_radam..r]cSs"g|]\}}t|dk|dqS)rgr&r=rm)runr`r0r0r1rzscSsg|]}t|dkddqS)rr&r'r{rurectr0r0r1rzscsDg|]<}|dkr.zPAPI has changed, `state_steps` argument must contain a list of singleton tensorsNF)Z use_fusedz6torch.jit.script not supported with foreach optimizers) rOrPr!r$r#rr%rrrM)rrGr r=ZjitZ is_scriptingrrs)r rIrJrKrLr%rrrrMrrOrPr!r$r#rfuncr0r0r1rs@ )FNFFFF)typingrrrrrr=rZ optimizerr r r r r rrrrrrrrr__all__r__doc__r>rUrsrrr0r0r0r1s @3  Q d =