a
    hC                     @   sx  d dl mZmZmZ d dlZd dlmZ ddlmZmZm	Z	m
Z
mZmZmZmZmZmZmZmZmZmZmZ ddgZG dd deZd	d
e de de de	 de d e_ee ee ee ee ee eeeeeeeeedddZee ee ee ee ee eeeeeeeeedddZe
eddee ee ee ee ee ee eeeeeeeeedddZdS )    )castOptionalUnionN)Tensor   )_capturable_doc_default_to_fused_or_foreach_differentiable_doc_disable_dynamo_if_unsupported_foreach_doc!_get_capturable_supported_devices_get_scalar_dtype
_get_value_maximize_doc_params_doc
_to_scalar_use_grad_for_differentiable_view_as_real	OptimizerParamsTAdamaxadamaxc                       st   e Zd Zdddddeeeef eeef eeee	 e	e	e	d	 fd	d
Z
 fddZdd ZedddZ  ZS )r   Mb`?g?g+?:0yE>r   NF)maximizedifferentiable
capturable)	paramslrbetasepsweight_decayforeachr   r   r   c             
      s   t |tr| dkrtdd|ks4td| d|ksJtd| d|d   krbdk svn td|d  d|d   krdk sn td	|d  d|kstd
| t||||||||	d}
t ||
 d S )Nr   zTensor lr must be 1-element        zInvalid learning rate: zInvalid epsilon value: r         ?z#Invalid beta parameter at index 0: z#Invalid beta parameter at index 1: zInvalid weight_decay value: )r   r    r!   r"   r#   r   r   r   )
isinstancer   Znumel
ValueErrordictsuper__init__)selfr   r   r    r!   r"   r#   r   r   r   defaults	__class__ @/var/www/auris/lib/python3.9/site-packages/torch/optim/adamax.pyr*      s.    
zAdamax.__init__c                    s   t  | | jD ]}|dd  |dd |dd |dd |d D ]h}| j|g }t|dkrNt|d sNt	|d }|d rtj
|t |jd	ntj
|t d
|d< qNqd S )Nr#   r   Fr   r   r   r   stepdtypedevicer3   )r)   __setstate__param_groups
setdefaultstategetlentorchZ	is_tensorfloattensorr   r4   )r+   r9   grouppZp_stateZstep_valr-   r/   r0   r6   D   s     

zAdamax.__setstate__c           
      C   s   d}|d D ]}|j d u rq|t|O }|| |j jrDtd||j  | j| }	t|	dkr|d rtjdt	 |j
dntjdt	 d	|	d
< tj|tjd|	d< tj|tjd|	d< ||	d  ||	d  ||	d
  q|S )NFr   z(Adamax does not support sparse gradientsr   r   r/   r2   r$   r5   r1   )Zmemory_formatexp_avgexp_inf)gradr<   
is_complexappendZ	is_sparseRuntimeErrorr9   r;   zerosr   r4   r>   Z
zeros_likeZpreserve_format)
r+   r?   params_with_gradgradsexp_avgsexp_infsstate_stepshas_complexr@   r9   r/   r/   r0   _init_groupW   s2    




zAdamax._init_groupc                 C   s   |    d}|durBt  | }W d   n1 s80    Y  | jD ]}g }g }g }g }g }|d \}	}
|d }|d }|d }|d }|d }|d }|d	 }| ||||||}t|||||||	|
|||||||d
 qH|S )zPerforms a single optimization step.

        Args:
            closure (Callable, optional): A closure that reevaluates the model
                and returns the loss.
        Nr    r!   r   r"   r#   r   r   r   )
r!   beta1beta2r   r"   r#   r   r   r   rM   )Z _cuda_graph_capture_health_checkr<   Zenable_gradr7   rN   r   )r+   closureZlossr?   rH   rI   rJ   rK   rL   rO   rP   r!   r   r"   r#   r   r   r   rM   r/   r/   r0   r1   z   sP    
$
zAdamax.step)r   r   r   r   N)N)__name__
__module____qualname__r   r   r=   r   tupler   boolr*   r6   rN   r   r1   __classcell__r/   r/   r-   r0   r      s0        	

&#a  Implements Adamax algorithm (a variant of Adam based on infinity norm).

    .. 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{ (weight decay)},                                                \\
            &\hspace{13mm}    \epsilon \text{ (epsilon)}                                          \\
            &\textbf{initialize} :  m_0 \leftarrow 0 \text{ ( first moment)},
                u_0 \leftarrow 0 \text{ ( infinity norm)}                                 \\[-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}m_t      \leftarrow   \beta_1 m_{t-1} + (1 - \beta_1) g_t               \\
            &\hspace{5mm}u_t      \leftarrow   \mathrm{max}(\beta_2 u_{t-1}, |g_{t}|+\epsilon)   \\
            &\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \frac{\gamma m_t}{(1-\beta^t_1) u_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 `Adam: A Method for Stochastic Optimization`_.
    z
    Args:
        a  
        lr (float, Tensor, optional): learning rate (default: 2e-3)
        betas (Tuple[float, float], optional): coefficients used for computing
            running averages of gradient and its square
        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)
        z	
        zd

    .. _Adam\: A Method for Stochastic Optimization:
        https://arxiv.org/abs/1412.6980

    )r   rI   rJ   rK   rL   r!   rO   rP   r   r"   r   r   r   rM   c       	         C   s  t j st|}t| D ]\}}|| }|
s4|n| }|| }|| }|| }t j s|rt }|jj	|jj	kr|jj	|v sJ d| d|d7 }|	dkr|j
||	d}t |rt |}t |}t |}t |}||d|  |st j||| ||d nBt ||d| |dgd}|t j|ddd |r|| d }|| || }||| qd|t|  }|| }|j||| d	 qd S )
NIIf capturable=True, params and state_steps must be on supported devices: .r   r   alpha)outF)Zkeepdim)value)r<   jitis_scriptingr   	enumeratecompileris_compilingr   r4   typeaddrD   Zview_as_realZlerp_maximumZmul_absZadd_catZ	unsqueezeZ
unsqueeze_Zcopy_ZamaxZdiv_Zaddcdiv_r   )r   rI   rJ   rK   rL   r!   rO   rP   r   r"   r   r   r   rM   iparamrC   rA   rB   Zstep_tcapturable_supported_devicesZnorm_bufZneg_bias_correctiondenomZbias_correctionZclrr/   r/   r0   _single_tensor_adamax   sX    







"
rl   c       	            sL  |rJ dt | dkrd S tj s`|r`tddtfddt| |D s`J d dtt	| ||||g}|
 D ]\\}}}}}}ttt |}ttt |}ttt |}ttt |}ttt |}|rt|||| |
rt|}tj s2|d jr2tj|tjd	d
dd	d nt|d |	dkrp|
r`tj|||	d ntj|||	d}t||d   t|| |
s|	dkrt|}n
t| t|| t|| |rt |}t|d t| t||}t||| q fdd|D }fdd|D }t|||| qd S )Nz#_foreach ops don't support autogradr   F)Zsupports_xlac                 3   s.   | ]&\}}|j j|j jko$|j j v V  qd S N)r4   rc   ).0r@   r1   )rj   r/   r0   	<genexpr>L  s   z'_multi_tensor_adamax.<locals>.<genexpr>rX   rY   r%   cpu)r4   rZ   r   c                    s   g | ]}d  t |  qS )r   r   )rn   r1   )rO   r/   r0   
<listcomp>  s   z(_multi_tensor_adamax.<locals>.<listcomp>c                    s   g | ]}t  | d  qS )rq   )rn   Zbc)r   r/   r0   rr         )r;   r<   ra   rb   r   allzipr   r   Z"_group_tensors_by_device_and_dtypevaluesr   listr   r   Z_foreach_negZis_cpuZ_foreach_add_r>   Z_foreach_addZ_foreach_lerp_Z_foreach_mul_Z_foreach_absZ_foreach_abs_Z_foreach_maximum_Z_foreach_powZ_foreach_sub_Z_foreach_div_Z_foreach_mulZ_foreach_addcdiv_)r   rI   rJ   rK   rL   r!   rO   rP   r   r"   r   r   r   rM   Zgrouped_tensorsZgrouped_params_Zgrouped_grads_Zgrouped_exp_avgs_Zgrouped_exp_infs_Zgrouped_state_steps__Zgrouped_paramsZgrouped_gradsZgrouped_exp_avgsZgrouped_exp_infsZgrouped_state_stepsZbias_correctionsrk   Z	step_sizer/   )rO   rj   r   r0   _multi_tensor_adamax1  s    

	




rz   )Zsingle_tensor_fnF)r   rI   rJ   rK   rL   r#   r   r   r   rM   r!   rO   rP   r   r"   c
                C   s   t j s$tdd |D s$td|du r>t| |dd\}}|rTt j rTtd|rht j sht}nt	}|| |||||
|||||||	|d dS )	zrFunctional API that performs adamax algorithm computation.

    See :class:`~torch.optim.Adamax` for details.
    c                 s   s   | ]}t |tjV  qd S rm   )r&   r<   r   )rn   tr/   r/   r0   ro     s   zadamax.<locals>.<genexpr>zPAPI has changed, `state_steps` argument must contain a list of singleton tensorsNF)Z	use_fusedz6torch.jit.script not supported with foreach optimizers)	r!   rO   rP   r   r"   r   r   rM   r   )
r<   ra   rb   ru   rF   r   r^   r_   rz   rl   )r   rI   rJ   rK   rL   r#   r   r   r   rM   r!   rO   rP   r   r"   ry   funcr/   r/   r0   r     s>    
)NFFFF)typingr   r   r   r<   r   Z	optimizerr   r   r	   r
   r   r   r   r   r   r   r   r   r   r   r   __all__r   __doc__rx   r=   rV   rl   rz   r   r/   r/   r/   r0   <module>   s   D 	
1Pt	     