o <&iu)@sddlmZmZmZmZddlZddlmZddlmZm Z m Z m Z m Z m Z mZmZmZmZmZmZmZddlmZddgZGd ddeZd d ed ed ed ed ed e_       d+deedeedeedeedeedeedeedededeedeedeedededededeeefd ed!ed"ef(d#dZdeedeedeedeedeedeedeedeedededededeeefd ed!ed"ededef$d$d%Zdeedeedeedeedeedeedeedeedededededeeefd ed!ed"ededef$d&d'Zdeedeedeedeedeedeedeedeedededededeeefd ed!ed"ededed(df&d)d*ZdS),)ListOptionalUnionTupleN)Tensor) OptimizerParamsT_use_grad_for_differentiable _get_value_stack_if_compiling_dispatch_sqrt_default_to_fused_or_foreach_capturable_doc_differentiable_doc _foreach_doc _fused_doc _maximize_doc _view_as_real)$_get_fused_kernels_supported_devicesAdamadamcseZdZ     dddddddded eeefd eeefd ed ed ede edededede effddZ fddZ ddZ e dddZZS)rMbP?g?g+?:0yE>rFN)foreachmaximize capturabledifferentiablefusedparamslrbetaseps weight_decayamsgradrrrrrc s6d|ks td|t|tr|r| stdd|ks#td|d|dkr/dks9ntd|dd|dkrEdksOntd |dd|ksZtd |t|||||||| | | d } t|| | r| rwtd d |_tt fdd|j Dstdd|rtddSdS)NzInvalid learning rate: Elr as a Tensor is not supported for capturable=False and foreach=TruezInvalid 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$r%rrrrrz)`fused` does not support `differentiable`Tc3s4|]}|dD]}|jjvot|VqqdS)r N)devicetypetorchZis_floating_point).0ZpgpZfused_supported_devices;C:\wamp64\www\opt\env\Lib\site-packages\torch/optim/adam.py 8s  z Adam.__init__..zX`fused=True` requires all the params to be floating point Tensors of supported devices: .z0`fused` and `foreach` cannot be `True` together.) ValueError isinstancerdictsuper__init__ RuntimeErrorZ_step_supports_amp_scalingrall param_groups) selfr r!r"r#r$r%rrrrrdefaults __class__r.r0r7sB   z Adam.__init__cst||jD]&}|dd|dd|dd|dd|dd|ddq t|j}t|dkoEt |dd }|s[|D]}tj t |d tj d |d <qJdSdS) Nr%Frrrrrrstepdtype) r6 __setstate__r: setdefaultliststatevalueslenr+ is_tensortensorfloatfloat32)r;rEgroupZ state_valuesZstep_is_tensorsr=r/r0rBAs       zAdam.__setstate__c Csfd}|dD]} | jdur|t| O}|| | jjr!td|| j|j| } t| dkrp|ds:|drDtjdtj | j dntj d tj d | d <tj | tj d | d <tj | tj d | d<|drptj | tj d | d<|| d || d|dr|| d|dr| d jrtd|drt|dr|dstd|| d q|S)NFr zJAdam does not support sparse gradients, please consider SparseAdam insteadrrrr/)rAr)r&r@r?)Z memory_formatexp_avg exp_avg_sqr%max_exp_avg_sqrzB`requires_grad` is not supported for `step` in differentiable moderr!r')gradr+ is_complexappendZ is_sparser8rErGZzerosrKr)rIZ zeros_likeZpreserve_formatZ requires_gradrH) r;rLparams_with_gradgradsexp_avgs exp_avg_sqsmax_exp_avg_sqs state_steps has_complexr-rEr/r/r0 _init_groupPs>      zAdam._init_groupc Cs|d}|dur!t |}Wdn1swY|jD]S}g}g}g}g}g}g} |d\} } |||||||| } t|||||| f|d| | | |d|d|d|d|d|d |d |d t|d dt|d ddq$|S)zPerform a single optimization step. Args: closure (Callable, optional): A closure that reevaluates the model and returns the loss. Nr"r%r!r$r#rrrrr grad_scale found_inf)r%rZbeta1beta2r!r$r#rrrrrr\r])Z _cuda_graph_capture_health_checkr+Z enable_gradr:r[rgetattr) r;closureZlossrLrTrUrVrWrXrYr^r_rZr/r/r0r?s^       z Adam.step)rrrrFN)__name__ __module__ __qualname__r rrJrrboolrr7rBr[r r? __classcell__r/r/r=r0rsN       25a Implements Adam 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)} \\ &\hspace{13mm} \lambda \text{ (weight decay)}, \: \textit{amsgrad}, \:\textit{maximize} \\ &\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)}, v_0\leftarrow 0 \text{ (second moment)},\: \widehat{v_0}^{max}\leftarrow 0\\[-1.ex] &\rule{110mm}{0.4pt} \\ &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\ &\hspace{5mm}\textbf{if} \: \textit{maximize}: \\ &\hspace{10mm}g_t \leftarrow -\nabla_{\theta} f_t (\theta_{t-1}) \\ &\hspace{5mm}\textbf{else} \\ &\hspace{10mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\ &\hspace{5mm}\textbf{if} \: \lambda \neq 0 \\ &\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\ &\hspace{5mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\ &\hspace{5mm}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\ &\hspace{5mm}\widehat{m_t} \leftarrow m_t/\big(1-\beta_1^t \big) \\ &\hspace{5mm}\widehat{v_t} \leftarrow v_t/\big(1-\beta_2^t \big) \\ &\hspace{5mm}\textbf{if} \: amsgrad \\ &\hspace{10mm}\widehat{v_t}^{max} \leftarrow \mathrm{max}(\widehat{v_t}^{max}, \widehat{v_t}) \\ &\hspace{10mm}\theta_t \leftarrow \theta_{t-1} - \gamma \widehat{m_t}/ \big(\sqrt{\widehat{v_t}^{max}} + \epsilon \big) \\ &\hspace{5mm}\textbf{else} \\ &\hspace{10mm}\theta_t \leftarrow \theta_{t-1} - \gamma \widehat{m_t}/ \big(\sqrt{\widehat{v_t}} + \epsilon \big) \\ &\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 `Adam: A Method for Stochastic Optimization`_. a Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, Tensor, optional): learning rate (default: 1e-3). 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