#hnj*pddlmZmZmZmZmZddlZddlmZddlm Z m Z m Z m Z m Z mZmZmZmZmZmZmZmZmZmZmZmZmZmZddgZGddeZd d ed ed ed e d e d ed ze_deedeedeedeedeedeedeedeedededee efdee efdee efde de dededef$d Z!deedeedeedeedeedeedeedeedededee efdee efdee efde de dededef$d!Z"deedeedeedeedeedeedeedeededede de dee efde de dededed"df&d#Z#e e!$ d(deedeedeedeedeedeed%eededed&eedeedeededede de dee efde de def(d'Z$y)))castListOptionalTupleUnionN)Tensor)_capturable_doc_default_to_fused_or_foreach_device_dtype_check_for_fused_differentiable_doc_disable_dynamo_if_unsupported _foreach_doc _fused_doc!_get_capturable_supported_devices_get_scalar_dtype _get_value _maximize_doc _params_doc_stack_if_compiling_use_grad_for_differentiable _view_as_real DeviceDictDeviceDtypeDict OptimizerParamsTAdamadamceZdZ ddddddddedeeefdeeeefeeeffdeded ed e ed ed ed ede effdZ fdZ dZ e ddZxZS)rFN)foreachmaximize capturabledifferentiablefusedparamslrbetaseps weight_decayamsgradr r!r"r#r$c t|tr-|r | s td|jdk7r tdd|kstd|d|kstd|d|dcxkrdksntd |dd|dcxkrdksntd |dd|kstd |t|dtrt|dts1t|dtrt|dts td t|dtr0| s |r td |djdk7r tdt|dtr0| s |r td|djdk7r tdt |||||||| | |  } t ||| | r"| r tdd|_ |r tdyy)NElr as a Tensor is not supported for capturable=False and foreach=Truer zTensor 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*r!r r"r#r$z)`fused` does not support `differentiable`Tz0`fused` and `foreach` cannot be `True` together.) isinstancer ValueErrornumelfloatdictsuper__init__ RuntimeError_step_supports_amp_scaling)selfr%r&r'r(r)r*r r!r"r#r$defaults __class__s M/var/www/html/sandstorm/venv/lib/python3.12/site-packages/torch/optim/adam.pyr5z Adam.__init__#s b& !z [xxzQ !>??by6rd;< <cz6se<= =eAh$$B58*MN NeAh$$B58*MN Nl";L>JK K a% (Za%-H58V,E!Hf1MOP P eAh '' aQx~~1$ !DEE eAh '' aQx~~1$ !DEE%!)   * "#NOO.2D + "#UVV ct|||jD]#}|jdd|jdd|jdd|jdd|jdd|jdd}|dD]}|jj |g}t |d k7s.tj|d rGt|d }|ds|dr,tj|t| |j ntj|t |d <&y)Nr*Fr!r r"r#r$r%rstepis_fuseddtypedevicerB) r4 __setstate__ param_groups setdefaultstategetlentorch is_tensorr2tensorrrC)r8rHgroupr$pp_statestep_valr:s r;rEzAdam.__setstate__ps% U#&& E   Y .   Z /   Y -   \5 1   -u 5$$Wd3E8_ **..B/w<1$U__WV_-M$WV_5H!.%.  $"3U"C#$88 #\\(:K:MNFO  r<cRd}|dD]} | j|tj| z}|j| | jjr t d|j| j|j | } t| dk(r|dr t| |ds|dr/tjdt|d| j ntjd t | d <tj| tj | d<tj| tj | d<|dr(tj| tj | d<|j| d|j| d|dr|j| d|dr| d jr t d|dr(tj |dr|ds t d|j| d |S)NFr%zJAdam does not support sparse gradients, please consider SparseAdam insteadrr$r"r?rAr-rDr>) memory_formatexp_avg exp_avg_sqr*max_exp_avg_sqr#zB`requires_grad` is not supported for `step` in differentiable moder r&r,)gradrK is_complexappend is_sparser6rHrJr zerosrrCrM zeros_likepreserve_format requires_gradrL) r8rNparams_with_gradgradsexp_avgs exp_avg_sqsmax_exp_avg_sqs state_steps has_complexrOrHs r; _init_groupzAdam._init_groups x= 2Avv!u//22  ''*66##&d QVV$ 1 u:?W~5a8!.%.  "3U7^"L#$88 #\\#5F5HI&M(-'7'7)>)>(E)$+0*:*:)>)>+E,'Y'272B2BU-B-B3./i 01""5#67##**51A+BC)*uV}/J/J&\ )$d 4!,/&_""5=1{= 2|r<c|jd}|$tj5|}ddd|jD]|}g}g}g}g}g}g} |d\} } |j ||||||| } t |||||| f|d| | | |d|d|d|d|d|d |d |d t |d dt |d dd~|S#1swYxYw)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$ grad_scale found_inf)r*rfbeta1beta2r&r)r(r!r r"r#r$rirj) _cuda_graph_capture_health_checkrK enable_gradrFrgrgetattr) r8closurelossrNr`rarbrcrdrerkrlrfs r;r>z Adam.steps> --/  ""$ !y !&&( E-/ "$E%'H(*K,.O(*K >LE5** K    i(';">2%Lz*i( .$%56Gn"4t<!$ T:) '( T [ ! !s CC)gMbP?)g?g+?g:0yE>rFN)__name__ __module__ __qualname__rrr2rrboolrr5rErgrr> __classcell__)r:s@r;rr"s$(COKW#' $ $KWKW %- KWU5&=)5+??@ KW  KW  KWKW$KWKWKWKW~KWZ.IV"8"8r<a 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)},\: \widehat{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}\widehat{v_t} \leftarrow v_t/\big(1-\beta_2^t \big) \\ &\hspace{5mm}\textbf{if} \: amsgrad \\ &\hspace{10mm}\widehat{v_t}^{max} \leftarrow \mathrm{max}(\widehat{v_{t-1}}^{max}, \widehat{v_t}) \\ &\hspace{10mm}\theta_t \leftarrow \theta_{t-1} - \gamma \widehat{m_t}/ \big(\sqrt{\widehat{v_t}^{max}} + \epsilon \big) \\ &\hspace{5mm}\textbf{else} \\ &\hspace{10mm}\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: a 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[float, float], optional): coefficients used for computing running averages of gradient and its square (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) 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= .. 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See :class:`~torch.optim.Adam` for details. NF) use_fusedc3PK|]}t|tj ywrr)r/rKr)rts r;rzadam..Ys 5() 1ell#5s$&zPAPI has changed, `state_steps` argument must contain a list of singleton tensorsz6torch.jit.script not supported with foreach optimizersz4torch.jit.script not supported with fused optimizers) r*rfrkrlr&r)r(r!r"r#rirj) r r/rrKrrrr6rrrrr)r%rarbrcrdrer r"r#r$rirjrfr*rkrlr&r)r(r!rfuncs r;rr)s3D }1 Ne 7 z"f-jG } >> & & (5-852 ^  599))+STT '')QRR UYY++- //1!"  ! %%r<)NFFNNNF)%typingrrrrrrKr optimizerr r r r rrrrrrrrrrrrrrr__all__r__doc__rvr2rrrrrSr<r;rs65 . 6 i9iZ&N         %OA LNK LNK <NK6lNKf NK &\ NK f NK NKNKNKNK  NK  NK eVmNKNK !NK"#NK$%NK&'NKb` L` <`6l`f ` &\ ` f ` ````  `  ` eVm`` !`"#`$%`&'`F^ L^ <^6l^f ^ &\ ^ f ^ ^^^^ ^ ^ eVm^^ !^"#^$%^&'^( )^B 1DE#  #'"&U LU <U6lUf U &\ U f Ud^UUU D>U UUU"#U$ %U& 'U( eVm)U*+U, -U./UFUr<