o 闦inj*@sddlmZmZmZmZmZddlZddlmZddlm Z m Z m Z m Z m Z mZmZmZmZmZmZmZmZmZmZmZmZmZmZddgZGdddeZd d ed ed ed e d e d ed e_deedeedeedeedeedeedeedeedededee efdee efdee efde de dededef$d d!Z!deedeedeedeedeedeedeedeedededee efdee efdee efde de dededef$d"d#Z"deedeedeedeedeedeedeedeededede de dee efde de dededed$df&d%d&Z#e e!d'  ( (    (d,deedeedeedeedeedeed)eededed*eedeedeededede de dee efde de def(d+dZ$dS)-)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 OptimizerParamsTAdamadamcseZdZ     dddddddded eeefd eeeefeeeffd ed ed ede edededede effddZ fddZ ddZ e dddZZS)rMbP?g?g+?:0yE>rFN)foreachmaximize capturabledifferentiablefusedparamslrbetaseps weight_decayamsgradr!r"r#r$r%c st|tr|r | s td|dkrtdd|ks"td|d|ks-td|d|dkr9dksCntd |dd|dkrOdksYntd |dd|ksdtd |t|dtrrt|dtst|dtrt|dtstd t|dtr| s|rtd |ddkrtdt|dtr| s|rtd|ddkrtdt|||||||| | | d } t|| | r| rtdd|_ |rtddSdS)NElr as a Tensor is not supported for capturable=False and foreach=TruerzTensor lr must be 1-elementzInvalid learning rate: zInvalid epsilon value: r?z#Invalid beta parameter at index 0: z#Invalid beta parameter at index 1: zInvalid weight_decay value: z0betas must be either both floats or both TensorszKbetas[0] as a Tensor is not supported for capturable=False and foreach=Truez!Tensor betas[0] must be 1-elementzKbetas[1] as a Tensor is not supported for capturable=False and foreach=Truez!Tensor betas[1] must be 1-element) r'r(r)r*r+r"r!r#r$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$r%defaults __class__N/home/ubuntu/transcripts/venv/lib/python3.10/site-packages/torch/optim/adam.pyr5#sz       z Adam.__init__cst||jD]e}|dd|dd|dd|dd|dd|dd}|dD]:}|j|g}t|d krmt|d smt |d }|dsW|drctj |t |d |j d ntj |t d |d <q3q dS)Nr+Fr"r!r#r$r%r&rstepis_fuseddtypedevicerB) r4 __setstate__ param_groups setdefaultstategetlentorch is_tensorr2tensorrrC)r8rHgroupr%pp_statestep_valr:r<r=rEps2          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?rAr-rDr>) memory_formatexp_avg exp_avg_sqr+max_exp_avg_sqr$zB`requires_grad` is not supported for `step` in differentiable moder!r'r,)gradrK is_complexappend is_sparser6rHrJr zerosrrCrM zeros_likepreserve_format requires_gradrL) r8rNparams_with_gradgradsexp_avgs exp_avg_sqsmax_exp_avg_sqs state_steps has_complexrOrHr<r<r= _init_groupsl             zAdam._init_groupc Cs|d}|dur!t |}Wdn1swY|jD]S}g}g}g}g}g}g} |d\} } |||||||| } t|||||| f|d| | | |d|d|d|d|d|d |d |d t|d dt|d ddq$|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"r!r#r$r% grad_scale found_inf)r+rdbeta1beta2r'r*r)r"r!r#r$r%rfrg) _cuda_graph_capture_health_checkrK enable_gradrFrergetattr) r8closurelossrNr^r_r`rarbrcrhrirdr<r<r=r>s^       z Adam.step)rrr rFN)__name__ __module__ __qualname__rrr2rrboolrr5rErerr> __classcell__r<r<r:r=r"sN        MKa 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= .. 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&r_r`rarbrcrfrgr+rdrhrir'r*r)r"r#r$c &Cs8|dur|dus Jtjr$t| tsJt| tsJt| ts$Jt| tr2| j| jf| i}nd}t|D]`\}}|sC||n|| }||}||}||}tj st|rtt }|jj |jj krl|jj |vstJd|d|d7}| dkr|j || d}t|rt|}t|}t|}|rt||||<t|}|j}|dur|j}||f}||vr| j||dd||<||}n| }||d||| j||d| d|s|r@|}d| |}d| |} | |}!|!}"| }#|r,|r ||}$n||}$||t|$||||#|"||"}%n ||#|"||"}%|||%nEt|}d| |}d| |} | |}!| d }#|rstj|||||d |||#|}%n ||#|}%|j||%|! d|rt||rt||||<q8dS) NIIf capturable=True, params and state_steps must be on supported devices: .rralphaT)rCrB non_blocking)value?)out)rKjit is_scriptingr/r2rrCrB enumeratecompiler is_compilingrtypeaddrW view_as_realtolerp_mul_addcmul_conjnegsqrtclonecopy_maximumadd_addcdiv_rview_as_complex)&r&r_r`rarbrcrfrgr+rdrhrir'r*r)r"r#r$ beta1_dictiparamrVrSrTstep_tcapturable_supported_devicesrCrBkey device_beta1r>bias_correction1bias_correction2 step_size step_size_negbias_correction2_sqrtrUdenomr<r<r=_single_tensor_adamTs                rc *sPt|dkrdSttr|stdttr(|stddkr(tdttr=|s3tddkr=tdtjs_|r_t dd t fd d t ||Ds_Jd d |durg|dusiJ|roJdt ||||||g}ttrtjdkrjind}|D]\\}}}}}}}ttt|}ttt|}ttt|}ttt|}ttt|}|dj} |dur| |vrՈj| dd|| <|r|| n}!| r|rttt|}"t|||||"nt|||||rt|}tjs|djrtj|tjdddddnt|d| dkr8|r0tj||| dntj||| d}t||d|!t|ttjrYt|d}#d}$n|}#d}$t||#||$~~#|rt|}%t|}&t|%dt|&dt |&t!|%t"|%t#|&|%}'|&}(|rttt|}"t$|"|t%|"})nt%|})t!|)|(t|)|t!|)|'t&|||)qfdd|D}%fdd|D}&t'fdd|%D}'dd|&D}(|r ttt|}"t$|"|t%|"})nt%|})t!|)|(t|)|t&|||)|'qdS)Nrr,zHbeta1 as a Tensor is not supported for capturable=False and foreach=TruerzTensor beta1 must be 1-elementzHbeta2 as a Tensor is not supported for capturable=False and foreach=TruezTensor beta2 must be 1-elementF) supports_xlac3s0|]\}}|jj|jjko|jjvVqdSro)rCr).0rOr>)rr<r= s   z%_multi_tensor_adam..rurvz#_foreach ops don't support autogradcpuTrCryr.)rCrwcg|] }dt|qSrrrr>)rhr<r= z&_multi_tensor_adam..crrrr)rir<r=rrcsg|]}|dqS)r<rbc)r'r<r=rscSsg|]}|dqS)r{r<rr<r<r=rs)(rJr/rr6r0r1rKrrrallzipr"_group_tensors_by_device_and_dtypestrrCvaluesrrrr _foreach_negis_cpu _foreach_add_rM _foreach_add_foreach_lerp_ _foreach_mul_ _foreach_mul_foreach_addcmul_ _foreach_pow _foreach_sub_ _foreach_neg_ _foreach_div__foreach_reciprocal__foreach_sqrt__foreach_maximum_ _foreach_sqrt_foreach_addcdiv_r)*r&r_r`rarbrcrfrgr+rdrhrir'r*r)r"r#r$grouped_tensorsrdevice_params_ device_grads_device_exp_avgs_device_exp_avg_sqs_device_max_exp_avg_sqs_device_state_steps__ device_params device_gradsdevice_exp_avgsdevice_exp_avg_sqsdevice_state_stepsrCrdevice_max_exp_avg_sqsscaled_device_gradsrzrrrrexp_avg_sq_sqrtr<)rhrirr'r=_multi_tensor_adams                                    rreturnc %Cs|sdS|r td|dur|j|ini}|dur|j|ini}t| tr1t| jdkr1| j| ind}t||||||g}|D]\\}}\\}}}}}}}tt t|}tt t|}tt t|} tt t|}!tt t|}"|j dkr|dur|dusJd\}#}$|dur| ||j |dd}#|dur| ||j |dd}$|dur||vr| j |dd||<||} t |"dt j||| |!||"|| | | | |||#|$d |$durt |"|$gt|"qBdS) Nz9Adam with fused=True does not support differentiable=Truermps)NNT)ryrr) r+r'rhrir*r)r"rfrg)r6rCr/rrrritemsrrrrGrrKr _fused_adam_rrJ)%r&r_r`rarbrcrfrgr+rdrhrir'r*r)r"r#r$grad_scale_dictfound_inf_dictlr_dictrrCrrrrrrrrrrrrdevice_grad_scaledevice_found_infr<r<r= _fused_adams$   r)single_tensor_fnFr!r%c Cs| dur|durt||dd\}}|rt|tr|sd}| dur"d} |dur(d}tjs:tdd|Ds:td|rEtj rEtd| rPtj rPtd| rZtj sZt }n |rdtj sdt }nt }|||||||| | ||||||||| | d dS) znFunctional API that performs Adam algorithm computation. 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