o
    ‡ZŽh2  ã                   @   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ƒƒZdS )é    )ÚOptionalN)ÚTensor)Ú2_scripted_functional_optimizer_deprecation_warningÚ__all__c                   @   s‚   e Zd Z									ddee dedededed	ed
edededefdd„Zdedee fdd„Z	deee  fdd„Z
dS )Ú_FunctionalSGDç{®Gáz„?ç        FÚparamsÚlrÚmomentumÚ	dampeningÚweight_decayÚnesterovÚmaximizeÚforeachÚfusedÚ_allow_empty_param_listc                 C   s|   t dd ||||dœ| _|| _|| _|| _|	| _tj t	tj
t	ttj
f f i ¡| _t|ƒdkr7|
s7tdƒ‚d|i| _d S )Né   )Ú
stacklevel)r
   r   r   r   r   z%optimizer got an empty parameter listr	   )r   Údefaultsr   r   r   r   ÚtorchÚjitZannotateÚdictr   ÚstrÚstateÚlenÚ
ValueErrorÚparam_group)Úselfr	   r
   r   r   r   r   r   r   r   r   © r   úU/var/www/auris/lib/python3.10/site-packages/torch/distributed/optim/functional_sgd.pyÚ__init__   s   
ü$z_FunctionalSGD.__init__ÚparamÚgradc                 C   s  | j d }| j d }| j d }| j d }|g}g }g }	d}
|durK|	 |¡ |jr+d}
|| jvr5i | j|< | j| }d|vrD| d¡ n| |d ¡ t ¡  tj||	|||||| j| j	|
| j
| jddd	 W d  ƒ n1 sqw   Y  | j| }|d
 }|dur‰||d< dS dS )z[Similar to self.step, but operates on a single parameter and
        its gradient.
        r   r   r   r
   FNTÚmomentum_buffer©r   r   r
   r   r   r   Úhas_sparse_gradr   r   Z
grad_scaleZ	found_infr   )r   ÚappendÚ	is_sparser   r   Úno_gradÚFÚsgdr   r   r   r   )r   r"   r#   r   r   r   r
   r	   Úmomentum_buffer_listÚgradsr&   r   r$   r   r   r    Ú
step_param;   sR   








òÿ
ÿz_FunctionalSGD.step_paramÚ	gradientsc                 C   s€  | j d }g }g }g }| jd }| jd }| jd }| jd }	t|ƒt|ƒkr:tddt|ƒ› d d	t|ƒ›  ƒ‚d
}
t||ƒD ]7\}}|d urx| |¡ | |¡ |jrXd}
|| jvrbi | j|< | j| }d|vrq| d ¡ qA| |d ¡ qAt 	¡  t
j|||||||	| j| j|
| j| jd d d W d   ƒ n1 sŸw   Y  t|ƒD ]\}}| j| }|| }|d ur½||d< q¨d S )Nr	   r
   r   r   r   zEthe gradients passed in does not equal to the size of the parameters!zParams length: z. zGradients length: FTr$   r%   )r   r   r   r   Úzipr'   r(   r   r   r)   r*   r+   r   r   r   r   Ú	enumerate)r   r/   r	   Zparams_with_gradr-   r,   r
   r   r   r   r&   r"   Zgradientr   ÚiÚpr$   r   r   r    Ústepm   sn   




ÿþÿ




€
òÿ
€üz_FunctionalSGD.stepN)	r   r   r   r   FFFFF)Ú__name__Ú
__module__Ú__qualname__Úlistr   ÚfloatÚboolr!   r   r.   r4   r   r   r   r    r      sB    õþýüûúùø	÷
ö
õ!2r   )Útypingr   r   Ztorch.optim._functionalZoptimZ_functionalr*   r   Z,torch.distributed.optim._deprecation_warningr   r   r8   r   Ú__annotations__r   Úscriptr   r   r   r   r    Ú<module>   s   