
    n
qiE                      p   d Z ddl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 ddgZ G d	 de          Zd
de de de
 de de dz   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dfdZ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dfdZ e	e          	 	 	 	 	 d#dee         dee         dee         dee         dee         d!edz  dededededededededdfd"            ZdS )$z1Implementation for the Resilient backpropagation.    )castN)Tensor   )_capturable_doc_default_to_fused_or_foreach_differentiable_doc_disable_dynamo_if_unsupported_foreach_doc!_get_capturable_supported_devices_get_scalar_dtype_maximize_doc_params_doc
_to_scalar_use_grad_for_differentiable_view_as_real	OptimizerParamsTRproprpropc                        e Zd Z	 	 	 dddddddedeez  d	eeef         d
eeef         dededz  dededdf fdZ fdZ	d Z
edd            Z xZS )r   {Gz?g      ?g333333?gư>2   FN)
capturableforeachmaximizedifferentiableparamslretas
step_sizesr   r   r   r   returnc                   t          |t                    r'|                                dk    rt          d          d|k    st          d|           d|d         cxk     rdcxk     r|d         k     s#n t          d|d          d|d                    |||||||d	}	t	                                          ||	           d S )
Nr   zTensor lr must be 1-elementg        zInvalid learning rate: r         ?zInvalid eta values: z, )r    r!   r"   r   r   r   r   )
isinstancer   numel
ValueErrorsuper__init__)selfr   r    r!   r"   r   r   r   r   defaults	__class__s             q/var/www/html/bestrading.cuttalo.com/services/ml-inference/venv/lib/python3.11/site-packages/torch/optim/rprop.pyr*   zRprop.__init__   s     b&!! 	<bhhjjAoo:;;;byy;r;;<<<T!W,,,,s,,,,T!W,,,,HDGHHtAwHHIII $ ,$
 
 	*****    c                    t                                          |           | j        D ]}|                    dd            |                    dd           |                    dd           |                    dd           |d         D ]}| j                            |g           }t          |          dk    rt          j        |d                   sjt          |d                   }|d         r(t          j
        |t                      |j        	          n!t          j
        |t                      
          |d<   d S )Nr   r   Fr   r   r   r   stepdtypedevicer3   )r)   __setstate__param_groups
setdefaultstategetlentorch	is_tensorfloattensorr   r4   )r+   r9   grouppp_statestep_valr-   s         r.   r6   zRprop.__setstate__=   sO   U###& 	 	EY---Z///-u555\51118_ 
 
*..B//w<<1$$U_WV_-M-M$$WV_55H
 !.O$,=,?,?    #\(:K:M:MNNN FO	
	 	r/   c           	         d}|d         D ]}|j         |t          j        |          z  }|                    |           |j         }	|	j        rt          d          |                    |	           | j        |         }
t          |
          dk    r|d         r(t          j        dt                      |j
                  n!t          j        dt                                |
d	<   t          j        |t          j        
          |
d<   |j        j        r3t          j        |	t          |d         |d                             |
d<   n+t          j        |	t!          |d                             |
d<   |                    |
d                    |                    |
d                    |                    |
d	                    |S )NFr   z'Rprop does not support sparse gradientsr   r    r2   r5   r1   memory_formatprevr    	step_size)gradr<   
is_complexappend	is_sparseRuntimeErrorr9   r;   zerosr   r4   
zeros_likepreserve_formatr3   	full_likecomplexr   )r+   r@   r   gradsprevsr"   state_stepshas_complexrA   rJ   r9   s              r.   _init_groupzRprop._init_groupP   s   x  	.  	.Av~5+A...KMM!6D~ N"#LMMMLLJqME 5zzQ \*DEK*;*=*=ahOOOOR/@/B/BCCC f !& 0%BW X X Xf7% X */geDk5;??* *E+&& */z%PT+?V?V)W)WE+&LLv'''eK0111uV}----r/   c                    |                                   d}|5t          j                    5   |            }ddd           n# 1 swxY w Y   | j        D ]u}g }g }g }g }g }|d         \  }	}
|d         \  }}|d         }|d         }|                     ||||||          }t          ||||||||	|
|||d         |d         |           v|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   )	step_size_minstep_size_maxetaminusetaplusr   r   r   r   rW   ) _cuda_graph_capture_health_checkr<   enable_gradr7   rX   r   )r+   closurelossr@   r   rT   rU   r"   rV   r\   r]   rZ   r[   r   r   rW   s                   r.   r1   z
Rprop.stepv   sj    	--///"$$ ! !wyy! ! ! ! ! ! ! ! ! ! ! ! ! ! ! & 	 	E#%F"$E"$E')J(*K %fHg+0+>(M=I&GZ(H**vueZ K ++!!$%56 .'    " s   AA
A)r   r   r   N)__name__
__module____qualname__r   r>   r   tupleboolr*   r6   rX   r   r1   __classcell__)r-   s   @r.   r   r      s!        "$.*4+ !#$+ + ++ FN+ E5L!	+
 %,'+ + + + + 
+ + + + + +<    &$ $ $L "/ / / "!/ / / / /r/   a
  Implements the resilient backpropagation algorithm.

    .. math::
       \begin{aligned}
            &\rule{110mm}{0.4pt}                                                                 \\
            &\textbf{input}      : \theta_0 \in \mathbf{R}^d \text{ (params)},f(\theta)
                \text{ (objective)},                                                             \\
            &\hspace{13mm}      \eta_{+/-} \text{ (etaplus, etaminus)}, \Gamma_{max/min}
                \text{ (step sizes)}                                                             \\
            &\textbf{initialize} :   g^0_{prev} \leftarrow 0,
                \: \eta_0 \leftarrow \text{lr (learning rate)}                                   \\
            &\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} \textbf{for} \text{  } i = 0, 1, \ldots, d-1 \: \mathbf{do}            \\
            &\hspace{10mm}  \textbf{if} \:   g^i_{prev} g^i_t  > 0                               \\
            &\hspace{15mm}  \eta^i_t \leftarrow \mathrm{min}(\eta^i_{t-1} \eta_{+},
                \Gamma_{max})                                                                    \\
            &\hspace{10mm}  \textbf{else if}  \:  g^i_{prev} g^i_t < 0                           \\
            &\hspace{15mm}  \eta^i_t \leftarrow \mathrm{max}(\eta^i_{t-1} \eta_{-},
                \Gamma_{min})                                                                    \\
            &\hspace{15mm}  g^i_t \leftarrow 0                                                   \\
            &\hspace{10mm}  \textbf{else}  \:                                                    \\
            &\hspace{15mm}  \eta^i_t \leftarrow \eta^i_{t-1}                                     \\
            &\hspace{5mm}\theta_t \leftarrow \theta_{t-1}- \eta_t \mathrm{sign}(g_t)             \\
            &\hspace{5mm}g_{prev} \leftarrow  g_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 the paper
    `A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm
    <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1417>`_.z

    Args:
        a{  
        lr (float, optional): learning rate (default: 1e-2)
        etas (Tuple[float, float], optional): pair of (etaminus, etaplus), that
            are multiplicative increase and decrease factors
            (default: (0.5, 1.2))
        step_sizes (Tuple[float, float], optional): a pair of minimal and
            maximal allowed step sizes (default: (1e-6, 50))
        z	
        z

    r   rT   rU   r"   rV   rZ   r[   r\   r]   r   r   r   rW   r#   c                   t          |           D ]@\  }}||         }|	s|n| }||         }||         }||         }t          j                                        sK|
rIt	                      }|j        j        |j        j        k    r|j        j        |v st          d| d          |dz  }t          j        |          rPt          j	        |          }t          j	        |          }t          j	        |          }t          j	        |          }|r:|
                    |                                                                          }n'|
                    |                                          }|
r|                    t          j        |                    d          ||                     |                    t          j        |                    d          ||                     |                    t          j        |                    d          d|                     nH|||                    d          <   |||                    d          <   d||                    d          <   |                    |                              ||           |                    t          j                  }|
r=|                    t          j        |                    |          d|                     nd||                    |          <   |                    |                                |d           |                    |           Bd S )NIIf capturable=True, params and state_steps must be on supported devices: .r   r   rF   value)	enumerater<   compileris_compilingr   r4   typeAssertionErrorrK   view_as_realmulclonesigncopy_wheregtlteqmul_clamp_rQ   addcmul_)r   rT   rU   r"   rV   rZ   r[   r\   r]   r   r   r   rW   iparamrJ   rH   rI   r1   capturable_supported_devicesrw   s                        r.   _single_tensor_rpropr      s     f%% 4 45Qx#.tt$QxqM	1~ ~**,, 	 	+L+N+N(!T[%555L%)EEE$`|   		E"" 	6%d++D%d++D&u--E*955I 	)88DJJLL))..00DD88D>>&&((D 	!JJu{4771::w==>>>JJu{4771::x>>???JJu{4771::q$778888&D'D D 	t##M=AAA zz(=z>> 	(JJu{4778#4#4a>>????&'D""# 	tyy{{IR888

4i4 4r/   c          
      n   t          |           dk    rd S |rt          d          t          j                                        sN|
rLt                      t          fdt          | |d          D                       st          d d          t          j	        | ||||g          }|
                                D ]r\  \  }}}}}}t          t          t                   |          }t          t          t                   |          }t          t          t                   |          }t          t          t                   |          }t          t          t                   |          }t          j                                        s9|d         j        r,t          j        |t          j        dd	
          d           nt          j        |d           |rt#          ||||           t          j        ||          }|	rt          j        |           t          j        ||           |	rt          j        |           |}t          j        |           |
r|D ]}|                    t          j        |                    d          ||                     |                    t          j        |                    d          ||                     |                    t          j        |                    d          d|                     nM|D ]J}|||                    d          <   |||                    d          <   d||                    d          <   Kt          j        ||           |D ]}|                    ||           t          |          }t;          t          |                    D ]P}||                             t          j        ||                             |          d||                              Q~d |D             }t          j        |||d           td S )Nr   z#_foreach ops don't support autogradc              3   n   K   | ]/\  }}|j         j        |j         j        k    o|j         j        v V  0d S rb   )r4   rr   ).0rA   r1   r   s      r.   	<genexpr>z&_multi_tensor_rprop.<locals>.<genexpr>?  s]       
 
 4 HMT[-- >!==
 
 
 
 
 
r/   T)strictrj   rk   r%   cpu)r4   )alphar   c                 6    g | ]}|                                 S rE   )rw   )r   rJ   s     r.   
<listcomp>z'_multi_tensor_rprop.<locals>.<listcomp>  s     <<<ddiikk<<<r/   rl   rm   )r;   rs   r<   rp   rq   r   allzipr   "_group_tensors_by_device_and_dtypevaluesr   listr   is_cpu_foreach_add_r?   r   _foreach_mul_foreach_neg__foreach_copy__foreach_sign_rx   ry   rz   r{   r|   _foreach_mul_r~   range_foreach_addcmul_)r   rT   rU   r"   rV   rZ   r[   r\   r]   r   r   r   rW   grouped_tensorsgrouped_params_grouped_grads_grouped_prevs_grouped_step_sizes_grouped_state_steps__grouped_paramsgrouped_gradsgrouped_prevsgrouped_step_sizesgrouped_state_stepssignsrw   rI   r   
grad_signsr   s                                 @r.   _multi_tensor_rpropr   &  s     6{{a DBCCC >&&(( 	Z 	'H'J'J$ 
 
 
 
 v{4@@@
 
 
 
 
 	
 !{\x{{{    B	z;7 O ""$$J
 J
 		 	d6lO<<T&\>::T&\>::!$v,0CDD"4<1EFF ~**,, 	81DQ1G1N 	8#U\#e%D%D%DC      3Q777  	}>P   "=-@@ 	'&&&
 	]M::: 	/...%U### 		% = =

5;twwqzz7DAABBB

5;twwqzz8TBBCCC

5;twwqzz1d;;<<<<=
  % %#*TWWQZZ #+TWWQZZ #$TWWQZZ   	.666+ 	; 	;I]M:::: ]++s=))** 	 	A!""E!HKK111mA6FGG   
  =<m<<<
J(:"	
 	
 	
 	
 	
QJ
 J
r/   )single_tensor_fnFr   c
                   t           j                                        s(t          d |D                       st	          d          |t          | |d          \  }}|r-t           j                                        rt	          d          |r&t           j                                        st          }nt          } || |||||
|||||||	           dS )zpFunctional API that performs rprop algorithm computation.

    See :class:`~torch.optim.Rprop` for details.
    c              3   J   K   | ]}t          |t          j                  V  d S rb   )r&   r<   r   )r   ts     r.   r   zrprop.<locals>.<genexpr>  s?       5 5()
1el##5 5 5 5 5 5r/   zPAPI has changed, `state_steps` argument must contain a list of singleton tensorsNF)	use_fusedz6torch.jit.script not supported with foreach optimizers)rZ   r[   r\   r]   r   r   r   rW   )
r<   rp   rq   r   rN   r   jitis_scriptingr   r   )r   rT   rU   r"   rV   r   r   r   r   rW   rZ   r[   r\   r]   r   funcs                   r.   r   r     s'   4 >&&(( 
 5 5-85 5 5 2 2 
 ^
 
 	
 1Ne
 
 

7  U59))++ USTTT $uy--// $"#D##%     r/   )NFFFF)__doc__typingr   r<   r   	optimizerr   r   r   r	   r
   r   r   r   r   r   r   r   r   r   __all__r   r   r>   rg   r   r   r   rE   r/   r.   <module>r      s   8 8                                            $ G
H H H H HI H H HX!LD 
  
  
  
  
  E1 lDLD<D <D V	D
 fD D D D D D D D D 
D D D DNo
Lo
<o
 <o
 V	o

 fo
 o
 o
 o
 o
 o
 o
 o
 o
 
o
 o
 o
 o
l  1EFFF   ; ;L;<; <; V	;
 f; D[; ; ; ; ; ; ;  !;" #;$ 
%; ; ; GF; ; ;r/   