3KiΜ.~SSKJr SSKrSSKJr SSKJrJrJrJrJ r J r J r J r J r JrJrJrJrJrJrJrJrJrJrJr SS/r"SS\5rS S \S \ S \S \S \S \ S 3 -\lS\\S\\S\\S\\S\\S\\S\S-S\S-S\S\S\\-S\\-S\\-S\S\S\S\S\S \S!S4(S"jrS\\S\\S\\S\\S\\S\\S\S-S\S-S\S\S\\-S\\-S\\-S\S\S\S\S\S \S!S4(S#jr S\\S\\S\\S\\S\\S\\S\S-S\S-S\S\S\\-S\\-S\\-S\S\S\S\S\S \S!S4(S$jr!\ "\S%9S)S\\S\\S\\S\\S\\S\\S&\S-S\S\S'\S-S\S-S\S-S\S \S\S\\-S\\-S\\-S\S\S\S!S4,S(jj5r"g)*)castN)Tensor)_capturable_doc_default_to_fused_or_foreach_device_dtype_check_for_fused_differentiable_doc_disable_dynamo_if_unsupported _foreach_doc _fused_doc!_get_capturable_supported_devices_get_scalar_dtype _get_value _maximize_doc _params_doc_stack_if_compiling _to_scalar_use_grad_for_differentiable _view_as_real DeviceDictDeviceDtypeDict OptimizerParamsTAdamadamc^\rSrSrSSSSSSSS.S\S\\-S\\\-\\-4S\S \S \S \S-S \S \S\S\S-S\SS4U4Sjjjjr U4Sjr Sr \ SSj5r SrU=r$)r"FN)foreachmaximize capturabledifferentiablefuseddecoupled_weight_decayparamslrbetaseps weight_decayamsgradrrr r!r"r#returnc >[U[5(a8U(aU (d [S5eUR5S:wa [S5eSU::d[SU35eSU::d[SU35eSUSs=::aS:dO [S US35eSUSs=::aS:dO [S US35eSU::d[S U35e[US[5(a[US[5(d;[US[5(a[US[5(d [S 5e[US[5(a;U (dU(a [S 5eUSR5S:wa [S5e[US[5(a;U (dU(a [S5eUSR5S:wa [S5e[ [ [U55nUUUUUUUU U U U S. n [TU]%X5 U (a,U (a [S5eSUl U(a [S5egg)NElr as a Tensor is not supported for capturable=False and foreach=TruerTensor lr must be 1-elementzInvalid learning rate: zInvalid epsilon value: r?z#Invalid beta parameter at index 0: z#Invalid beta parameter at index 1: zInvalid weight_decay value: z0betas must be either both floats or both TensorszKbetas[0] as a Tensor is not supported for capturable=False and foreach=Truez!Tensor betas[0] must be 1-elementzKbetas[1] as a Tensor is not supported for capturable=False and foreach=Truez!Tensor betas[1] must be 1-element) r%r&r'r(r)rrr r!r"r#z)`fused` does not support `differentiable`Tz0`fused` and `foreach` cannot be `True` together.) isinstancer ValueErrornumelfloattuplemaprsuper__init__ RuntimeError_step_supports_amp_scaling)selfr$r%r&r'r(r)rrr r!r"r#defaults __class__s R/var/www/html/dynamic-report/venv/lib/python3.13/site-packages/torch/optim/adam.pyr7 Adam.__init__#s< b& ! !z [xxzQ !>??by6rd;< <cz6se<= =eAh$$B58*MN NeAh$$B58*MN Nl";L>JK K a% ( (Za%-H-H58V,,E!Hf1M1MOP P eAh ' '' aQx~~1$ !DEE eAh ' '' aQx~~1$ !DEEc*e,-( $,&<   * "#NOO.2D + "#UVV c >[TU]U5 URGHDnURSS5 URSS5 URSS5 URSS5 URSS5 URSS5 URSS5nUS HnURR U/5n[ U5S :wdM0[R"US 5(aMP[US 5nUS(d US(a'[R"U[US 9URS 9O[R"U[5S9US 'M GMG g)Nr)Frrr r!r#r"r$rstepis_fuseddtypedevicerE) r6 __setstate__ param_groups setdefaultstategetlentorch is_tensorr3tensorrrF)r:rKgroupr"pp_statestep_valr<s r=rHAdam.__setstate__ss/ U#&&E   Y .   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"3U7^"L#$88 #\\#5F5HI&M(-'7'7)>)>(E)$+0*:*:)>)>+E,'Y'272B2BU-B-B3./i 01""5#67##**51A+BC)*uV}/J/J&\ )$d 44!,/&_""5=1{!|r?cUR5 SnUb%[R"5 U"5nSSS5 URHn/n/n/n/n/n/n USupUR UUUUUUU 5n [ UUUUUU 4USU U U USUSUSUSUSUS US US [ US S5[ US S5USS.6 M U$!,(df  N=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'rrr r!r" grad_scale found_infr#)r)rjbeta1beta2r%r(r'rrr r!r"rnror#) _cuda_graph_capture_health_checkrN enable_gradrIrkrgetattr) r:closurelossrQrdrerfrgrhrirprqrjs r=rA Adam.steps9 --/  ""$y%&&E-/ "$E%'H(*K,.O(*K >LE** K    i(';">2%Lz*i( .$%56Gn"4t<!$ T:',-E'F+ ''V ]%$s C  C)r9)gMbP?)g?g+?g:0yE>rFN)__name__ __module__ __qualname____firstlineno__rr3rr4boolr7rHrkrrA__static_attributes__ __classcell__)r<s@r=rr"s"7CNW $ $!',NWNW FNNWUV^UV^34 NW  NW  NWNWNWNWNWNWd{NW!%NW NWNW`0IV"9"9r?af Implements Adam algorithm. .. math:: \begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \gamma \text{ (lr)}, \beta_1, \beta_2 \text{ (betas)},\theta_0 \text{ (params)},f(\theta) \text{ (objective)} \\ &\hspace{13mm} \lambda \text{ (weight decay)}, \: \textit{amsgrad}, \:\textit{maximize}, \: \epsilon \text{ (epsilon)} \\ &\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)}, v_0\leftarrow 0 \text{ (second moment)},\: v_0^{max}\leftarrow 0 \\[-1.ex] &\rule{110mm}{0.4pt} \\ &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\ &\hspace{5mm}\textbf{if} \: \textit{maximize}: \\ &\hspace{10mm}g_t \leftarrow -\nabla_{\theta} f_t (\theta_{t-1}) \\ &\hspace{5mm}\textbf{else} \\ &\hspace{10mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\ &\hspace{5mm}\textbf{if} \: \lambda \neq 0 \\ &\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\ &\hspace{5mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\ &\hspace{5mm}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\ &\hspace{5mm}\widehat{m_t} \leftarrow m_t/\big(1-\beta_1^t \big) \\ &\hspace{5mm}\textbf{if} \: amsgrad \\ &\hspace{10mm} v_t^{max} \leftarrow \mathrm{max}(v_{t-1}^{max},v_t) \\ &\hspace{10mm}\widehat{v_t} \leftarrow v_t^{max}/\big(1-\beta_2^t \big) \\ &\hspace{5mm}\textbf{else} \\ &\hspace{10mm}\widehat{v_t} \leftarrow v_t/\big(1-\beta_2^t \big) \\ &\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \gamma \widehat{m_t}/ \big(\sqrt{\widehat{v_t}} + \epsilon \big) \\ &\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 `Adam: A Method for Stochastic Optimization`_. z Args: aG lr (float, Tensor, optional): learning rate (default: 1e-3). A tensor LR is not yet supported for all our implementations. Please use a float LR if you are not also specifying fused=True or capturable=True. betas (tuple[Union[float, Tensor], Union[float, Tensor]], optional): coefficients used for computing running averages of gradient and its square. If a tensor is provided, must be 1-element. (default: (0.9, 0.999)) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-8) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) decoupled_weight_decay (bool, optional): if True, this optimizer is equivalent to AdamW and the algorithm will not accumulate weight decay in the momentum nor variance. (default: False) amsgrad (bool, optional): whether to use the AMSGrad variant of this algorithm from the paper `On the Convergence of Adam and Beyond`_ (default: False) z a= .. Note:: A prototype implementation of Adam and AdamW for MPS supports `torch.float32` and `torch.float16`. .. _Adam\: A Method for Stochastic Optimization: https://arxiv.org/abs/1412.6980 .. _On the Convergence of Adam and Beyond: https://openreview.net/forum?id=ryQu7f-RZ r$rerfrgrhrirnror)rjrprqr%r(r'rr r!r#r*c N UcUb [S5e[RR5(a[ U [ 5(d[S[ U 535e[ U [ 5(d[S[ U 535e[ U [ 5(d[S[ U 535eO![U 5n [U 5n [U 5n [ U [5(aU RU R4U 0nOSn[U5GHunnU(dUUOUU*nUUnUUnUUn[RR5(dhU(aa[5nURR URR :XaURR U;d[SUS35eUS- nU S:waU(aURSX-- 5 OoU(aX[ U [5(aCU R (a!UR#UR%5U 5nO!UR'UU S 9nOUR'UU S 9n[R("U5(a{[R*"U5n[R*"U5n[R*"U5nU(a[R*"UU5UU'[R*"U5nURnUb0URnUU4nUU;aU R-UUS S 9UU'UUnOU nUR/USU- 5 U(a[ U [5(akU R (a(UR/[R0"U5SU - S 9 OUURU 5R#UU[3[ SU - 5S 9 O#URU 5R#UUSU - S 9 U(dU(GaUnU(aF[ U [5(a1U R (aSU UR%5-- n OSU U-- n OSU U-- n U(aF[ U [5(a1U R (aSU UR%5-- n!OSU U-- n!OSU U-- n!U U - n"U"R55n#U!R75n$U(auU(aUUR%5n%OUUn%UUR9[R:"U%U55 UUR75U$U#-- R=UU#- 5n&O(UR75U$U#-- R=UU#- 5n&U(a!UR?UR%5U&5 OUR?UU&5 O[AU5nSU U-- n SU U-- n!U U - n"U!S-n$U(aB[R:"UUUUUS9 UUR75U$- R=U5n&O"UR75U$- R=U5n&UR?UU&U"*S 9 U(dGML[R("UU5(dGMm[RB"UU5UU'GM g)N,Expected grad_scale and found_inf to be Nonez#Expected lr to be a float, but got z&Expected beta1 to be a float, but got z&Expected beta2 to be a float, but got IIf capturable=True, params and state_steps must be on supported devices: .rralphaT)rFrE non_blocking)weight)value?)out)"AssertionErrorrNjit is_scriptingr0r3typerrrFrE enumeratecompiler is_compilingr mul_rcaddcmul_cloneaddr] view_as_realtolerp_squarernegsqrtcopy_maximumadd_addcdiv_rview_as_complex)'r$rerfrgrhrirnror)rjrprqr%r(r'rr r!r# beta1_dictiparamr\rYrZstep_tcapturable_supported_devicesrFrEkey device_beta1rAbias_correction1bias_correction2 step_size step_size_negbias_correction2_sqrtr[denoms' r=_single_tensor_adamr[s,!6KLL yy"e$$ #FtBxj!QR R%'' #I$u+!WX X%'' #I$u+!WX X(^5!5! %  /4||U[[.I5-Q  f%5'uQxeAhY1+ ^ Q~~**,,+L+N ( !!V]]%7%77LL%%)EE$_`|_}}~ !  1 % 1r001"jv&F&F#11#}}U[[]LI $xx\xB88E8>D   E " "%%d+D((1G++J7J%*%7%78J%K"&&u-E  !KKE5/C*$"'((!T#+# 3,6c?L L  dA ,- j77""  d!3AI F&//$d5!e)&<0 OOE " + +D$a%i + H D*UF";";&&'(5DJJL+@'@$'(5$;$#$ud{? 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   6#8 ee6eEGFOB .2)?u!!UEXEX     " %(& "  '   "%5$6=O9P$P O !r?)single_tensor_fnrr"cU c5Uc2[XSS9unnU(a[U[5(a U(dSnU cSn UcSn[RR 5(d"[ SU55(d [S5eU(a.[RR5(a [S5eU (a.[RR5(a [S5eU (a*[RR5(d[nO7U(a*[RR5(d[nO[nU"UUUUUU4UU UUUUUUUUU U U S. 6 g) zfFunctional API that performs Adam algorithm computation. 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