
    Th?              "       $   S SK JrJrJr  S SKrS SKJr  SSKJrJrJ	r	J
r
JrJrJrJrJrJrJrJrJrJrJr  SS/r " S S\5      rS	\ S
\ S\ S\	 S\ S3\l        S\\   S\\   S\\   S\\   S\\   S\\   S\S\S\S\S\S\S\S\S\4S jrS\\   S\\   S\\   S\\   S\\   S\\   S\S\S\S\S\S\S\S\S\4S jr\
" \S9     S!S\\   S\\   S\\   S\\   S\\   S\\   S\\   S\S\S\S\S\S\S\S\S\4 S  jj5       rg)"    )castOptionalUnionN)Tensor   )_capturable_doc_default_to_fused_or_foreach_differentiable_doc_disable_dynamo_if_unsupported_foreach_doc!_get_capturable_supported_devices_get_scalar_dtype
_get_value_maximize_doc_params_doc
_to_scalar_use_grad_for_differentiable_view_as_real	OptimizerParamsTASGDasgdc                      ^  \ rS rSr         SS\S\\\4   S\S\S\S\S\\	   S	\	S
\	S\	4U 4S jjjr
U 4S jrS r\SS j5       rSrU =r$ )r      paramslrlambdalphat0weight_decayforeachmaximizedifferentiable
capturablec                    > [        U[        5      (       a  UR                  5       S:w  a  [        S5      eSU::  d  [        SU 35      eSU::  d  [        SU 35      e[	        UUUUUUUU	U
S9	n[
        TU ]  X5        g )Nr   zTensor lr must be 1-elementg        zInvalid learning rate: zInvalid weight_decay value: )	r   r   r   r   r    r!   r"   r#   r$   )
isinstancer   numel
ValueErrordictsuper__init__)selfr   r   r   r   r   r    r!   r"   r#   r$   defaults	__class__s               J/var/www/fran/franai/venv/lib/python3.13/site-packages/torch/optim/asgd.pyr+   ASGD.__init__   s     b&!!bhhjAo:;;by6rd;<<l";L>JKK%)!

 	*    c                 6  > [         TU ]  U5        U R                   GHx  nUR                  SS 5        UR                  SS5        UR                  SS5        UR                  SS5        US    GH"  nU R                  R                  U/ 5      n[        U5      S:w  d  M1  [        R                  " US   5      (       d9  [        US   5      n[        R                  " U[        5       UR                  S	9US'   [        R                  " US
   5      (       d.  [        R                  " US
   [        5       UR                  S	9US
'   [        R                  " US   5      (       a  M  [        R                  " US   [        5       UR                  S	9US'   GM%     GM{     g )Nr!   r"   Fr#   r$   r   r   step)dtypedeviceetamu)r*   __setstate__param_groups
setdefaultstategetlentorch	is_tensorfloattensorr   r5   )r,   r;   grouppp_statestep_valr.   s         r/   r8   ASGD.__setstate__?   sE   U#&&EY-Z/-u5\518_**..B/w<1$ ??76?;;#(#9*/,,$,=,?+ !??75>::).#EN2C2Eahh* !??74=99(-#DM1B1DQXX) % 'r1   c                    SnUS    GH  n	U	R                   c  M  U[        R                  " U	5      -  nUR                  U	5        U	R                   R                  (       a  [        S5      eUR                  U	R                   5        U R                  U	   n
[        U
5      S:X  a  [        R                  " SU	R                  [        5       S9U
S'   [        R                  " [        US   5      U	R                  [        5       S9R                  5       R                  5       U
S	'   [        R                  " SU	R                  [        5       S9U
S
'   [        R                   " U	[        R"                  S9U
S'   UR                  U
S
   5        UR                  U
S   5        UR                  U
S	   5        UR                  U
S   5        GM     U$ )NFr   z&ASGD does not support sparse gradientsr    )r5   r4   r3   r   r6   r7   )memory_formatax)gradr>   
is_complexappend	is_sparseRuntimeErrorr;   r=   zerosr5   r   	as_tensorr   clonedetachones
zeros_likepreserve_format)r,   rB   params_with_gradgradsmusaxsetasstate_stepshas_complexrC   r;   s              r/   _init_groupASGD._init_groupW   sv   xAvv!u//22 ''*66##&'OPPQVV$

1u:?$)KK1883D3F%E&M &uT{3#$88"3"5
  %L #(**1883D3F#E$K #("2"2)>)>#E$K 

5;'

5;'E%L)""5=1C !D r1   c                 h   U R                  5         SnUb%  [        R                  " 5          U" 5       nSSS5        U R                   HV  n/ n/ n/ n/ n/ n/ n	U R	                  X4XVXxU	5      n
[        UUUUUU	US   US   US   US   US   US   US   US	   US
   U
S9  MX     U$ ! , (       d  f       Nv= f)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$   )
r   r   r   r   r    r!   r"   r#   r$   r]   ) _cuda_graph_capture_health_checkr>   enable_gradr9   r^   r   )r,   closurelossrB   rW   rX   rY   rZ   r[   r\   r]   s              r/   r3   	ASGD.step}   s     	--/""$y % &&E-/"$E "C "C!#D(*K**SK  Gn;;Gn">2i(z*$%56 .'! '> E %$s   B##
B1rH   )	g{Gz?g-C6?g      ?g    .Ar   NFFFN)__name__
__module____qualname____firstlineno__r   r   r@   r   r   boolr+   r8   r^   r   r3   __static_attributes____classcell__)r.   s   @r/   r   r      s     $("&$ ++ %- + 	+
 + + + $+ + + + +B0$L "- "-r1   zImplements Averaged Stochastic Gradient Descent.

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

    Args:
        am  
        lr (float, Tensor, 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	
        z

    .. _Acceleration of stochastic approximation by averaging:
        https://meyn.ece.ufl.edu/wp-content/uploads/sites/77/archive/spm_files/Courses/ECE555-2011/555media/poljud92.pdf

    r   rX   rZ   rY   r[   r\   r   r   r   r   r    r"   r#   r$   r]   c       	   
      |   [         R                  R                  5       (       d  [        U5      n[	        U 5       GH  u  nnX   nU(       d  UOU* nX?   nX/   nXO   nX_   n[         R
                  R                  5       (       d  U(       a  [        5       nUR                  R                  UR                  R                  s=:X  a5  UR                  R                  s=:X  a  UR                  R                  :X  a  O  OUR                  R                  U;   d   SU S35       e[         R                  " U5      (       aB  [         R                  " U5      n[         R                  " U5      n[         R                  " U5      nUS-  nU
S:w  a  UR                  UU
S9nU(       a)  UR                  SUU-  -
  5        UR                  UUSS9  O3[        U5      nUR                  SUU-  -
  5        UR!                  UU* S9  U(       d  UR#                  5       S:w  a0  UR!                  UR%                  U5      R                  U5      5        OUR'                  U5        U(       ab  UR'                  USXg-  U-  -   U	-  -  5        UR'                  S[         R(                  " UU-
  [         R*                  " U5      5      -  5        GM  [        U5      n[         R,                  " USXg-  U-  -   U	-  -  5      nUR'                  U5        [         R,                  " S[/        SUU-
  5      -  5      nUR'                  U5        GM     g )NUIf capturable=True, params, mus, etas, and state_steps must be on supported devices: .r   r   r   value)r>   jitis_scriptingr   	enumeratecompileris_compilingr   r5   typerL   view_as_realaddmul_addcmul_r   add_itemsubcopy_maximum	ones_likerQ   max)r   rX   rZ   rY   r[   r\   r   r   r   r   r    r"   r#   r$   r]   iparamrK   r7   rJ   r6   step_tcapturable_supported_devices	eta_valuer3   new_etanew_mus                              r/   _single_tensor_asgdr      s   $ 99!!##^f%5x#t$VVg ~~**,,+L+N(!!99>>&::??& ==%%& LL%%)EE	))E(FaI	F E""%%d+D&&u-E##B'B 	!188E86DJJq53;'NN4BN/"3IJJq59,,-JJtI:J. aGGEIIbM&&r*+HHUOIIbQf!44>?@HHQv{EOOF4KLLMf%DoobQd1B-Bu,L&MNGIIg__QQr	):%:;FHHVo &r1   c       	           ^" [        U 5      S:X  a  g U(       a   S5       e[        R                  R                  5       (       d?  U(       a8  [	        SS9m"[        U"4S j[        XXE5       5       5      (       d   ST" S35       e[        U5      n[        R                  " XX#XE/5      nUR                  5        GH  u  u  nnu  u  nnnnnnn[        [        [           U5      n[        [        [           U5      n[        [        [           U5      n[        [        [           U5      n[        [        [           U5      n[        [        [           U5      nU(       a  [        UUU5        U(       a  [        R                  " U5      n[        R                  R                  5       (       d>  US   R                   (       a*  [        R"                  " U[        R$                  " SS	S
9SS9  O[        R"                  " US5        U
S:w  aM  U(       a  [        R"                  " UUU
S9  UnO[        R&                  " UUU
S9n[        R"                  " UUUS9  O[        R&                  " UUUS9n[        R(                  " UUUSS9  A[        R*                  " UU5      n[        R(                  " UUU5        AU(       a  [        R*                  " UU5      n[        R,                  " US5        [        R.                  " U5        [        R0                  " UU5        A[        R2                  " UU5      n [        R4                  " U U5        [        R"                  " U S5        [        R6                  " U U	5        [        R.                  " U 5        [        R4                  " U U5        [        R0                  " UU 5        GM  U V!s/ s H&  n![        R8                  " USXg-  U!-  -   U	-  -  US
9PM(     n n!U V!s/ s H1  n![        R8                  " S[;        S[=        U!5      U-
  5      -  US
9PM3     nn![        R0                  " UU 5        [        R0                  " UU5        GM     g s  sn!f s  sn!f )Nr   z#_foreach ops don't support autogradF)supports_xlac              3   N  >#    U  H  u  pp4UR                   R                  UR                   R                  s=:H  =(       a:    UR                   R                  s=:H  =(       a    UR                   R                  :H  Os  =(       a    UR                   R                  T;   v   M     g 7frf   )r5   rz   ).0rC   r7   r6   r3   r   s        r/   	<genexpr>%_multi_tensor_asgd.<locals>.<genexpr>0  sr      
 %H s HHMMRYY^^RRszzRR$++BRBRR >!==>$Gs   B"B%ro   rp   g      ?cpu)r5   rq   r   rr   rs   )r=   r>   rx   ry   r   allzipr   r   "_group_tensors_by_device_and_dtypeitemsr   listr   r   _foreach_negis_cpu_foreach_add_rA   _foreach_add_foreach_addcmul__foreach_sub_foreach_maximum__foreach_reciprocal__foreach_copy__foreach_mul_foreach_mul__foreach_pow_rQ   r   r   )#r   rX   rZ   rY   r[   r\   r   r   r   r   r    r"   r#   r$   r]   grouped_tensorsr5   _grouped_params_grouped_grads_grouped_axs_grouped_mus_grouped_etas_grouped_state_steps_grouped_paramsgrouped_gradsgrouped_axsgrouped_musgrouped_etasgrouped_state_stepsintermediatenew_musnew_etasr3   r   s#                                     @r/   _multi_tensor_asgdr     s   $ 6{aDDD >>&&((Z'H(
$  
 %(T$G
 
 
 	

 d  eA  dB  BC  D	
 
 
BBBB	$4O 
			 
	 
	
 	d6lO<T&\>:4<64<6DL-8"4<1EF.-E!..}=M ~~**,,1DQ1G1N1N#U\\#e%DC  3Q7 1##M>V,$11!>  nEJ --~UL 	lRTU )).+F\;G (()<bAG##GS1&&w/  g6 ))*=uEH"-!,%0&&x0"-  x8 0/D q5:+<'<&F GPVW/   0/D C:d+;b+@$A A&Q/     x8  g6s 
!`s   8-Q+8Q)single_tensor_fnr!   c                $   Uc  [        XSS9u  nnU(       a.  [        R                  R                  5       (       a  [	        S5      eU(       a*  [        R                  R                  5       (       d  [
        nO[        nU" U UUUUUUUUUUUUU	U
S9  g)zfFunctional 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$   r]   )r	   r>   ru   rv   rO   r   r   )r   rX   rZ   rY   r[   r\   r!   r"   r#   r$   r]   r   r   r   r   r    r   funcs                     r/   r   r     s    4 1e

7 599))++STTuyy--//!"!%r1   )NFFFF)typingr   r   r   r>   r   	optimizerr   r	   r
   r   r   r   r   r   r   r   r   r   r   r   r   __all__r   __doc__r   r@   rk   r   r   r   rH   r1   r/   <module>r      s   ( (      & 6
N9 Nb	 
 	 
 		 		 		 .LLL<L 
fL 
f	L
 v,L fL L 	L 	L L L L L L  !L^L7LL7<L7 
fL7 
f	L7
 v,L7 fL7 L7 	L7 	L7 L7 L7 L7 L7 L7  !L7^  1DE # 6L6<6 
f6 
f	6
 v,6 f6 d^6 6 6 6 6 6  	!6" 	#6$ %6& '6 F6r1   