a
    h                     @   sj   U d dl mZ d dlZd dlm  mZ d dlmZ d dlm	Z	 g Z
ee ed< ejjG dd dZdS )    )OptionalN)Tensor)2_scripted_functional_optimizer_deprecation_warning__all__c                   @   sd   e Zd Zdee eeeef eeeeeeed
ddZee	e d	d
dZ
ee	e  dddZdS )_FunctionalAdamMbP?g?g+?:0yE>        F)
paramslrbetasepsweight_decayamsgradmaximizeforeachfused_allow_empty_param_listc                 C   s$  t dd d|ks td| d|ks6td| d|d   krNdk sbn td|d  d|d	   krzdk sn td
|d	  d|kstd| |||d |d	 |d| _|| _|| _|| _|	| _tj	t
tjt
ttjf f i | _t|dkr|
stdd|i| _d S )N   )
stacklevelr
   zInvalid learning rate: zInvalid epsilon value: r   g      ?z#Invalid beta parameter at index 0:    z#Invalid beta parameter at index 1: zInvalid weight_decay value: )r   r   beta1beta2r   z%optimizer got an empty parameter listr   )r   
ValueErrordefaultsr   r   r   r   torchjitZannotatedictr   strstatelenparam_group)selfr   r   r   r   r   r   r   r   r   r    r$   U/var/www/auris/lib/python3.9/site-packages/torch/distributed/optim/functional_adam.py__init__   s2    
$z_FunctionalAdam.__init__)paramgradc                 C   sv  g }g }g }g }g }g }t |}	|dur>|| || || jvri | j|< | j| }
t d|
d< t j|t jd|
d< t j|t jd|
d< | jrt j|t jd|
d< | j| }
||
d  ||
d  | jr||
d  ||
d  t  d t	j
||||||| j|	| j| jd | jd	 | jd
 | jd | jd | j| jddd W d   n1 sh0    Y  dS )zo
        Similar to step, but operates on a single parameter and optionally a
        gradient tensor.
        Nr
   stepZmemory_formatexp_avg
exp_avg_sqmax_exp_avg_sqr   r   r   r   r   r   has_complexr   r   r   r   r   r   r   r   Z
grad_scaleZ	found_inf)r   
is_complexappendr    tensor
zeros_likepreserve_formatr   no_gradFadamr   r   r   r   )r#   r'   r(   params_with_gradgradsexp_avgsexp_avg_sqsmax_exp_avg_sqsstate_stepsr/   r    r$   r$   r%   
step_paramG   sf    










z_FunctionalAdam.step_param)	gradientsc                 C   s  | j d }g }g }g }g }g }g }d}	t|t|kr\tddt| d dt|  t| j d |D ]\}
}|d url|	t|
O }	||
 || |
| jvri | j|
< | j|
 }td|d< tj	|
tj
d	|d
< tj	|
tj
d	|d< | jrtj	|
tj
d	|d< | j|
 }||d
  ||d  | jrL||d  ||d  qlt d tj||||||| j|	| j| jd | jd | jd | jd | jd | j| jd d d W d    n1 s0    Y  d S )Nr   FzEthe gradients passed in does not equal to the size of the parameters!zParams length: z. zGradients length: r
   r)   r*   r+   r,   r-   r   r   r   r   r   r.   )r"   r!   r   zipr   r0   r1   r    r2   r3   r4   r   r5   r6   r7   r   r   r   r   )r#   r?   r   r8   r9   r:   r;   r<   r=   r/   r'   Zgradientr    r$   r$   r%   r)      s|    









z_FunctionalAdam.stepN)	r   r   r	   r
   FFFFF)__name__
__module____qualname__listr   floattupleboolr&   r   r>   r)   r$   r$   r$   r%   r      s.            
-<r   )typingr   r   Ztorch.optim._functionalZoptimZ_functionalr6   r   Z,torch.distributed.optim._deprecation_warningr   r   rD   r   __annotations__r   scriptr   r$   r$   r$   r%   <module>   s   