a
    8SicXC                     @   sH   d dl Z d dlmZ ddlmZ dddZdddZG dd deZdS )    N)reduce   )	Optimizerc                 C   s   |d ur|\}}n| |kr"| |fn|| f\}}|| d||  | |   }	|	d ||  }
|
dkr|
  }| |kr|||  || |	 || d|     }n(| | | || |	 || d|     }tt|||S || d S d S )N      r   g       @)sqrtminmax)x1f1g1x2f2Zg2boundsZ
xmin_boundZ
xmax_boundd1Z	d2_squared2min_pos r   M/var/www/html/django/DPS/env/lib/python3.9/site-packages/torch/optim/lbfgs.py_cubic_interpolate   s    
	*(r   -C6??&.>   c           !   	   C   s   |   }|jtjd}| |||\}}d}||}d|||f\}}}}d}d}||
k r|||| |  ks|dkr||kr||g}||g}||jtjdg}||g}qt || | kr|g}|g}|g}d}q|dkr||g}||g}||jtjdg}||g}q|d||   }|d }|}t||||||||fd}|}|}|jtjd}|}| |||\}}|d7 }||}|d7 }qT||
krd|g}||g}||g}d}|d |d	 krd
nd\}}|s||
k rt |d |d  | |	k rqt|d |d |d |d |d |d }dt|t|  } tt|| |t| | k r|s|t|ks|t|krt |t| t |t| k rt||  }nt||  }d}nd}nd}| |||\}}|d7 }||}|d7 }|||| |  ks ||| krj|||< |||< |jtjd||< |||< |d |d kr`d
nd\}}nt || | krd}nJ||| ||   dkr|| ||< || ||< || ||< || ||< |||< |||< |jtjd||< |||< q|| }|| }|| }||||fS )Nmemory_formatr   r   FTg{Gz?
   )r   )r   r   )r   r   g?)absr	   clonetorchcontiguous_formatdotr   r   )!obj_funcxtdfggtdc1c2tolerance_changeZmax_lsZd_normZf_newZg_newls_func_evalsZgtd_newZt_prevZf_prevZg_prevZgtd_prevdoneZls_iterZbracketZ	bracket_fZ	bracket_gZbracket_gtdZmin_stepZmax_steptmpZinsuf_progressZlow_posZhigh_posepsr   r   r   _strong_wolfe"   s    

$





 ""
$ r1   c                       sb   e Zd ZdZd fdd		Zd
d Zdd Zdd Zdd Zdd Z	dd Z
e dd Z  ZS )LBFGSa  Implements L-BFGS algorithm, heavily inspired by `minFunc
    <https://www.cs.ubc.ca/~schmidtm/Software/minFunc.html>`_.

    .. warning::
        This optimizer doesn't support per-parameter options and parameter
        groups (there can be only one).

    .. warning::
        Right now all parameters have to be on a single device. This will be
        improved in the future.

    .. note::
        This is a very memory intensive optimizer (it requires additional
        ``param_bytes * (history_size + 1)`` bytes). If it doesn't fit in memory
        try reducing the history size, or use a different algorithm.

    Args:
        lr (float): learning rate (default: 1)
        max_iter (int): maximal number of iterations per optimization step
            (default: 20)
        max_eval (int): maximal number of function evaluations per optimization
            step (default: max_iter * 1.25).
        tolerance_grad (float): termination tolerance on first order optimality
            (default: 1e-5).
        tolerance_change (float): termination tolerance on function
            value/parameter changes (default: 1e-9).
        history_size (int): update history size (default: 100).
        line_search_fn (str): either 'strong_wolfe' or None (default: None).
    r      NHz>r   d   c	           
   	      sl   |d u r|d d }t |||||||d}	tt| ||	 t| jdkrRtd| jd d | _d | _d S )N      )lrmax_itermax_evaltolerance_gradr,   history_sizeline_search_fnr   z>LBFGS doesn't support per-parameter options (parameter groups)r   params)	dictsuperr2   __init__lenparam_groups
ValueError_params_numel_cache)
selfr>   r8   r9   r:   r;   r,   r<   r=   defaults	__class__r   r   rA      s     	zLBFGS.__init__c                 C   s$   | j d u rtdd | jd| _ | j S )Nc                 S   s   | |   S N)numel)totalpr   r   r   <lambda>       zLBFGS._numel.<locals>.<lambda>r   )rF   r   rE   rG   r   r   r   _numel   s    
zLBFGS._numelc                 C   sj   g }| j D ]R}|jd u r,||  }n&|jjrF|j d}n|jd}|| q
t	
|dS )Nr   r   )rE   gradnewrL   zero_	is_sparseto_denseviewappendr    cat)rG   viewsrN   rX   r   r   r   _gather_flat_grad   s    

zLBFGS._gather_flat_gradc                 C   sT   d}| j D ]4}| }|j||||  ||d ||7 }q
||  ksPJ d S )Nr   alpha)rE   rL   add_view_asrR   )rG   	step_sizeupdateoffsetrN   rL   r   r   r   	_add_grad  s    
 
zLBFGS._add_gradc                 C   s   dd | j D S )Nc                 S   s   g | ]}|j tjd qS )r   )r   r    r!   ).0rN   r   r   r   
<listcomp>  rP   z&LBFGS._clone_param.<locals>.<listcomp>)rE   rQ   r   r   r   _clone_param  s    zLBFGS._clone_paramc                 C   s$   t | j|D ]\}}|| qd S rK   )ziprE   copy_)rG   Zparams_datarN   Zpdatar   r   r   
_set_param  s    zLBFGS._set_paramc                 C   s0   |  || t| }|  }| | ||fS rK   )rd   floatr\   rj   )rG   closurer$   r%   r&   loss	flat_gradr   r   r   _directional_evaluate  s
    

zLBFGS._directional_evaluatec           &         s  t jdksJ t   jd }|d }|d }|d }|d }|d }|d }|d	 }	jjd  }
|
d
d |
dd   }t|}d}|
d
  d7  <  }|	 
 |k}|r|S |
d}|
d}|
d}|
d}|
d}|
d}|
d}|
d}d}||k rt|d7 }|
d  d7  < |
d dkrj| }g }g }g }d}nN||}||}||}|dkrt ||	kr|d |d |d || || |d|  ||| }t |}d|
vrdg|	 |
d< |
d }| }t|d ddD ]8}|| |||  ||< |j|| ||  d q.t|| }} t|D ]6}|| | ||  }!| j|| || |! d q|du r|jtjd}n
|| |}|
d dkr
tdd|	   | }n|}||}"|"| kr(qtd}#|dur|dkrJtdn2 }$ fdd}%t|%|$|||||"\}}}}#|| |	 
 |k}nf|| ||kr t  t  }W d   n1 s0    Y   }|	 
 |k}d}#||#7 }|
d
  |#7  < ||kr&qt||kr4qt|r>qt||	 
 |krZqtt	|| |k rqtq||
d< ||
d< ||
d< ||
d< ||
d< ||
d< ||
d< ||
d< |S )zPerforms a single optimization step.

        Args:
            closure (callable): A closure that reevaluates the model
                and returns the loss.
        r   r   r8   r9   r:   r;   r,   r=   r<   Z
func_evalsn_iterr&   r%   old_dirsold_stpsroH_diagprev_flat_grad	prev_lossg|=g      ?alNr   r]   r   Zstrong_wolfez only 'strong_wolfe' is supportedc                    s     | ||S rK   )ro   )r$   r%   r&   rl   rG   r   r   r#     s    zLBFGS.step.<locals>.obj_func)rB   rC   r    enable_gradstaterE   
setdefaultrk   r\   r   r	   getnegsubmulr"   poprY   ranger_   r   r!   ri   r   sumRuntimeErrorrg   r1   rd   )&rG   rl   groupr8   r9   r:   r;   r,   r=   r<   rz   Z	orig_lossrm   Zcurrent_evalsrn   Zopt_condr&   r%   rq   rr   rs   rt   ru   rv   rp   ysZysZnum_oldrw   qirZbe_ir)   r-   Zx_initr#   r   rx   r   step  s    



























*

z
LBFGS.step)r   r3   Nr4   r   r5   N)__name__
__module____qualname____doc__rA   rR   r\   rd   rg   rj   ro   r    no_gradr   __classcell__r   r   rI   r   r2      s"           	r2   )N)r   r   r   r   )r    	functoolsr   	optimizerr   r   r1   r2   r   r   r   r   <module>   s   
#    
 