o
    ,i<                  "   @   s  d dl mZ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 ddgZG dd deZd	e d
e d
e
 d
e d	e_dee dee dee dee dee 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e dee dee dee dee 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eed		 	 	 	 d#dee dee dee dee dee dee 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S )$    )ListOptionalTupleUnionN)Tensor   )_capturable_doc_default_to_fused_or_foreach_differentiable_doc_disable_dynamo_if_unsupported_foreach_doc!_get_capturable_supported_devices_get_scalar_dtype
_get_value_maximize_doc_use_grad_for_differentiable_view_as_real	OptimizerParamsTASGDasgdc                       s   e Zd Z									dded	ed
ededededee dededef fddZ fddZdd Z	e
dddZ  ZS )r   {Gz?-C6?      ?    .Ar   NFparamslrlambdalphat0weight_decayforeachmaximizedifferentiable
capturablec                    sX   d|kst d| d|kst d| t||||||||	|
d	}t || d S )Ng        zInvalid learning rate: zInvalid weight_decay value: )	r   r   r   r   r    r!   r"   r#   r$   )
ValueErrordictsuper__init__)selfr   r   r   r   r   r    r!   r"   r#   r$   defaults	__class__ N/home/ubuntu/SoloSpeech/.venv/lib/python3.10/site-packages/torch/optim/asgd.pyr(      s    zASGD.__init__c                    s   t  | | jD ]q}|dd  |dd |dd |dd |d D ]R}| j|g }t|dkryt|d sOt	|d }tj
|t |jd	|d< t|d
 sdtj
|d
 t |jd	|d
< t|d sytj
|d t |jd	|d< q'q	d S )Nr!   r"   Fr#   r$   r   r   step)dtypedeviceetamu)r'   __setstate__param_groups
setdefaultstategetlentorch	is_tensorfloattensorr   r1   )r)   r7   grouppp_statestep_valr+   r-   r.   r4   :   s2   




zASGD.__setstate__c                 C   s
  d}|d D ]|}	|	j d ur|t|	O }||	 |	j jr!td||	j  | j|	 }
t|
dkrftjd|	j	t
 d|
d< tj|d |	j	t
 d  |
d	< tjd|	j	t
 d|
d
< tj|	tjd|
d< ||
d
  ||
d  ||
d	  ||
d  q|S )NFr   z&ASGD does not support sparse gradientsr   r-   )r1   r0   r/   r   r2   r3   )memory_formatax)gradr:   
is_complexappend	is_sparseRuntimeErrorr7   r9   zerosr1   r   	as_tensorclonedetachones
zeros_likepreserve_format)r)   r>   params_with_gradgradsmusaxsetasstate_stepshas_complexr?   r7   r-   r-   r.   _init_groupR   s>   







zASGD._init_groupc                 C   s   |    d}|dur!t  | }W d   n1 sw   Y  | jD ]?}g }g }g }g }g }g }	| |||||||	}
t||||||	|d |d |d |d |d |d |d |d	 |d
 |
d q$|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    r!   r"   r#   r$   )
r   r   r   r   r    r!   r"   r#   r$   rV   ) _cuda_graph_capture_health_checkr:   enable_gradr5   rW   r   )r)   closurelossr>   rP   rQ   rR   rS   rT   rU   rV   r-   r-   r.   r/   v   sF   

z	ASGD.step)	r   r   r   r   r   NFFFN)__name__
__module____qualname__r   r<   r   boolr(   r4   rW   r   r/   __classcell__r-   r-   r+   r.   r      sF    	
$ah  Implements Averaged Stochastic Gradient Descent.

    It has been proposed in `Acceleration of stochastic approximation by
    averaging`_.

    Args:
        params (iterable): iterable of parameters to optimize or dicts defining
            parameter groups
        lr (float, optional): learning rate (default: 1e-2)
        lambd (float, optional): decay term (default: 1e-4)
        alpha (float, optional): power for eta update (default: 0.75)
        t0 (float, optional): point at which to start averaging (default: 1e6)
        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
        z	
        zx

    .. _Acceleration of stochastic approximation by averaging:
        https://dl.acm.org/citation.cfm?id=131098

    r   rQ   rS   rR   rT   rU   r   r   r   r   r    r"   r#   r$   rV   c       	      
   C   s  t | D ] \}}|| }|s|n| }|| }|| }|| }|| }tj sU|rUt }|jj|jj  krE|jj  krE|jjkrMn n|jj|v sUJ d| dt|rit|}t|}t|}|d7 }|
dkrx|j	||
d}|r|
d||   |j||dd nt|}|
d||   |j|| d |s| dkr|||
| n|| |r||d|| |  |	   |dt|| t|  qt|}t|d|| |  |	  }|| tdtd||  }|| qd S )NUIf capturable=True, params, mus, etas, and state_steps must be on supported devices: .r   r   r   value)	enumerater:   _utilsis_compilingr   r1   typerE   view_as_realaddmul_addcmul_r   add_itemsubcopy_maximum	ones_likerJ   max)r   rQ   rS   rR   rT   rU   r   r   r   r   r    r"   r#   r$   rV   iparamrD   r3   rC   r2   step_tcapturable_supported_devices	eta_valuer/   new_etanew_mur-   r-   r.   _single_tensor_asgd   s\   





"
r~   c       	            sD  t | dkrd S |rJ dtj s2|r2tddtfddt| |||D s2J d dt| |||||g}|	 D ]\\}\\}}}}}}}|rWt
||| |r^t|}|d jrqtj|tjd	d
dd	d nt|d |
dkr|rtj|||
d |}ntj|||
d}tj||d ntj||d}tj|||dd ~t||}t||| ~|rt|}t|d	 t| t|| ~t|}t| t|d t|  t| t| t|| qA fdd|D }fdd|D }t|| t|| qAd S )Nr   z#_foreach ops don't support autogradF)supports_xlac                 3   sV    | ]&\}}}}|j j|j j  ko|j j  ko|j jkn  o&|j j v V  qd S r\   )r1   rk   ).0r?   r3   r2   r/   )rz   r-   r.   	<genexpr>'  s    
2

z%_multi_tensor_asgd.<locals>.<genexpr>rb   rc   g      ?cpur1   rd   r   re   rf   c                    s.   g | ]}t jd  |     dqS r   r   )r:   rJ   r   r/   )r   r1   r   r   r-   r.   
<listcomp>  s     z&_multi_tensor_asgd.<locals>.<listcomp>c                    s,   g | ]}t jd td t|   dqS r   )r:   rJ   rv   r   r   )r1   r   r-   r.   r     s    )r9   r:   ri   rj   r   allzipr   "_group_tensors_by_device_and_dtypeitemsr   _foreach_negis_cpu_foreach_add_r=   _foreach_add_foreach_addcmul__foreach_sub_foreach_maximum__foreach_reciprocal__foreach_copy__foreach_mul_foreach_mul__foreach_pow_)r   rQ   rS   rR   rT   rU   r   r   r   r   r    r"   r#   r$   rV   grouped_tensors_grouped_paramsgrouped_gradsgrouped_axsgrouped_musgrouped_etasgrouped_state_stepsintermediatenew_musnew_etasr-   )r   rz   r1   r   r   r   r.   _multi_tensor_asgd  s   






r   )single_tensor_fnFr!   c                C   sr   |du rt | |dd\}}|rtj rtd|r"tj s"t}nt}|| |||||||||||||	|
d dS )znFunctional API that performs asgd algorithm computation.

    See :class:`~torch.optim.ASGD` for details.
    NF)	use_fusedz6torch.jit.script not supported with foreach optimizers)	r   r   r   r   r    r"   r#   r$   rV   )r	   r:   jitis_scriptingrH   r   r~   )r   rQ   rS   rR   rT   rU   r!   r"   r#   r$   rV   r   r   r   r   r    r   funcr-   r-   r.   r     s4   

)NFFFF)typingr   r   r   r   r:   r   	optimizerr   r	   r
   r   r   r   r   r   r   r   r   r   r   __all__r   __doc__r<   r`   r~   r   r   r-   r-   r-   r.   <module>   s   < 
	

L	

 
	
