o h ,@sddlmZmZmZddlZddlmZddlmZmZm Z m Z m Z m Z m Z mZmZmZmZmZmZmZmZmZmZmZmZddgZGdddeZd d ed e d ed ed e d e d e_deedeedeedeedeedeedeedeedededeeefdeeefdeeefdededededed ef&d!d"Z deedeedeedeedeedeedeedeedededeeefdeeefdeeefdededededed ef&d#d$Z!deedeedeedeedeedeedeedeedededededeeefdededededed ed%df(d&d'Z"e e d(  ) )    ) )d-deedeedeedeedeedeed*eededed+eedeedeeded ededededeeefdededef*d,dZ#dS).)castOptionalUnionN)Tensor)_capturable_doc_default_to_fused_or_foreach_device_dtype_check_for_fused_differentiable_doc_disable_dynamo_if_unsupported _foreach_doc _fused_doc!_get_capturable_supported_devices_get_scalar_dtype _get_value _maximize_doc _params_doc_stack_if_compiling_use_grad_for_differentiable _view_as_real DeviceDictDeviceDtypeDict OptimizerParamsTAdamadamcseZdZ     ddddddddded eeefd eeeefeeeffd ed ed ede edededede edeffddZ fddZ ddZ e dddZZS)rMbP?g?g+?:0yE>rFN)foreachmaximize capturabledifferentiablefuseddecoupled_weight_decayparamslrbetaseps weight_decayamsgradrr r!r"r#r$c st|tr|r | s td|dkrtdd|ks"td|d|ks-td|d|dkr9dksCntd |dd|dkrOdksYntd |dd|ksdtd |t|dtrrt|dtst|dtrt|dtstd t|dtr| s|rtd |ddkrtdt|dtr| s|rtd|ddkrtdt|||||||| | | | d } t|| | r| rtdd|_ |rtddSdS)NElr as a Tensor is not supported for capturable=False and foreach=TruerzTensor 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 rr!r"r#r$z)`fused` does not support `differentiable`Tz0`fused` and `foreach` cannot be `True` together.) isinstancer ValueErrornumelfloatdictsuper__init__ RuntimeError_step_supports_amp_scaling)selfr%r&r'r(r)r*rr r!r"r#r$defaults __class__d/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/torch/optim/adam.pyr4"s|       z Adam.__init__cst||jD]k}|dd|dd|dd|dd|dd|dd|dd}|d D]:}|j|g}t|d krst|d sst |d }|ds]|dritj |t |d |j d ntj |t d|d <q9q dS)Nr*Fr rr!r"r$r#r%rstepis_fuseddtypedevicerA) r3 __setstate__ param_groups setdefaultstategetlentorch is_tensorr1tensorrrB)r7rGgroupr#pp_statestep_valr9r;r<rDqs4           zAdam.__setstate__c Cs~d}|dD]} | jdur|t| O}|| | jjr!td|| j|j| } t| dkr||dr:t| |dsB|drPtj dt |dd| j d ntj d t d | d <tj | tjd | d<tj | tjd | d<|dr|tj | tjd | d<|| d|| d|dr|| d|dr| d jrtd|drt|dr|dstd|| d q|S)NFr%zJAdam does not support sparse gradients, please consider SparseAdam insteadrr#r!r;r>r@r,rCr=) memory_formatexp_avg exp_avg_sqr*max_exp_avg_sqr"zB`requires_grad` is not supported for `step` in differentiable moderr&r+)gradrJ is_complexappend is_sparser5rGrIr zerosrrBrL zeros_likepreserve_format requires_gradrK) r7rMparams_with_gradgradsexp_avgs exp_avg_sqsmax_exp_avg_sqs state_steps has_complexrNrGr;r;r< _init_groupsl             zAdam._init_groupc Cs|d}|dur!t |}Wdn1swY|jD]V}g}g}g}g}g}g} |d\} } |||||||| } t|||||| f|d| | | |d|d|d|d|d|d |d |d t|d dt|d d|ddq$|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 rr!r"r# grad_scale found_infr$)r*rcbeta1beta2r&r)r(r rr!r"r#rerfr$) _cuda_graph_capture_health_checkrJ enable_gradrErdrgetattr) r7closurelossrMr]r^r_r`rarbrgrhrcr;r;r<r=s`       z Adam.step)rrrrFN)__name__ __module__ __qualname__rrr1rtupleboolrr4rDrdrr= __classcell__r;r;r9r<r!sT        OKaf 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: 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) 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= .. 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