o ,ivy*@sddlmZmZmZmZddlZddlmZddlmZddl m Z m Z m Z m Z mZmZmZmZmZmZmZmZmZmZmZmZmZddgZGd ddeZd d ed ed e d e d ed e_deedeedeedeedeedeedeedeedededededeeefdededededef$d d!Z deedeedeedeedeedeedeedeedededededeeefdededededef$d"d#Z!deedeedeedeedeedeedeedeedededededeeefdededededed$df&d%d&Z"e e d'  ( (    (d,deedeedeedeedeedeed)eededed*eedeedeedededededeeefdededef(d+dZ#dS)-)ListOptionalTupleUnionN)Tensor)$_get_fused_kernels_supported_devices)_capturable_doc_default_to_fused_or_foreach_differentiable_doc_disable_dynamo_if_unsupported_dispatch_sqrt _foreach_doc _fused_doc!_get_capturable_supported_devices_get_scalar_dtype _get_value _maximize_doc_stack_if_compiling_use_grad_for_differentiable _view_as_real DeviceDict OptimizerParamsTAdamadamcseZdZ     dddddddded eeefd eeefd ed ed ede edededede effddZ fddZ ddZ e dddZZS)rMbP?g?g+?:0yE>rFN)foreachmaximize capturabledifferentiablefusedparamslrbetaseps weight_decayamsgradrr r!r"r#c s6d|ks td|t|tr|r| stdd|ks#td|d|dkr/dks9ntd|dd|dkrEdksOntd |dd|ksZtd |t|||||||| | | d } t|| | r| rwtd d |_tt fdd|j Dstdd|rtddSdS)NzInvalid learning rate: Elr as a Tensor is not supported for capturable=False and foreach=TruezInvalid epsilon value: r?z#Invalid beta parameter at index 0: rz#Invalid beta parameter at index 1: zInvalid weight_decay value: ) r%r&r'r(r)r rr!r"r#z)`fused` does not support `differentiable`Tc3s4|]}|dD]}|jjvot|VqqdS)r$N)devicetypetorchis_floating_point).0pgpfused_supported_devicesN/home/ubuntu/SoloSpeech/.venv/lib/python3.10/site-packages/torch/optim/adam.py Ts z Adam.__init__..zX`fused=True` requires all the params to be floating point Tensors of supported devices: .z0`fused` and `foreach` cannot be `True` together.) ValueError isinstancerdictsuper__init__ RuntimeError_step_supports_amp_scalingrall param_groups) selfr$r%r&r'r(r)rr r!r"r#defaults __class__r4r7r>sX  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 rr!r"r#r$rstepis_fuseddtyper-rK) r= __setstate__rB setdefaultstategetlenr/ is_tensorfloattensorrr-)rCrOgroupr#r3p_statestep_valrEr6r7rM`s2          zAdam.__setstate__c Csnd}|dD]} | jdur|t| O}|| | jjr!td|| j|j| } t| dkrt|ds:|drHtjdt |dd| j d ntj d t d | d <tj | tj d | d<tj | tj d | d<|drttj | tj d | d<|| d|| d|dr|| d|dr| d jrtd|drt|dr|dstd|| d q|S)NFr$zJAdam does not support sparse gradients, please consider SparseAdam insteadrr!r#r6rHrJr*rLrG) memory_formatexp_avg exp_avg_sqr)max_exp_avg_sqr"zB`requires_grad` is not supported for `step` in differentiable moderr%r+)gradr/ is_complexappend is_sparser?rOrQzerosrr-rT zeros_likepreserve_format requires_gradrR) rCrUparams_with_gradgradsexp_avgs exp_avg_sqsmax_exp_avg_sqs state_steps has_complexr3rOr6r6r7 _init_groupwsh             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 rr!r"r# grad_scale found_inf)r)rjbeta1beta2r%r(r'r rr!r"r#rlrm) _cuda_graph_capture_health_checkr/ enable_gradrBrkrgetattr) rCclosurelossrUrdrerfrgrhrirnrorjr6r6r7rGs^       z Adam.step)rrrrFN)__name__ __module__ __qualname__rrrSrrboolrr>rMrkrrG __classcell__r6r6rEr7rsN         AIa 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} \\ &\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}^{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`_. a Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups 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 z .. _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$rerfrgrhrirlrmr)rjrnror%r(r'r r!r"c !Cs|dur|dus Jtjrt| tsJt|D]:\}}|s%||n|| }||}||}||}tjsV|rVt}|j j |j j krN|j j |vsVJd|d|d7}| dkre|j || d}t |rt |}t |}t |}|rt ||||<t |}||d| || j||d| d|s|r|}d| |}d| |}| |}|}|}|r|r||}n||}||t||||||||} n |||||} ||| nEt|}d| |}d| |}| |}t|}|r/tj|||||d||||} n |||} |j|| | d|rUt ||rUt||||<qdS)NIIf capturable=True, params and state_steps must be on supported devices: r9rralpha)value)out)r/jit is_scriptingr;rS enumerate_utils is_compilingrr-r.addr] view_as_reallerp_mul_addcmul_conjnegsqrtclonecopy_maximumadd_addcdiv_rr view_as_complex)!r$rerfrgrhrirlrmr)rjrnror%r(r'r r!r"iparamr\rYrZstep_tcapturable_supported_devicesrGbias_correction1bias_correction2 step_size step_size_negbias_correction2_sqrtr[denomr6r6r7_single_tensor_adamAsv             rc st|dkrdSttr|stdtjs5|r5tddtfddt ||Ds5Jdd|dur=|dus?J|rEJd t ||||||g}| D]\\}}}}}}}| rs|rlt |||||nt |||||rzt|}|djrtj|tjd d d d d nt|d| dkr|rtj||| d ntj||| d }t||dt|t|||d~|r&t|}t|}t|dt|dt|t|t|t||}|}|rt||t|}nt|}t||t||t||t|||qTfdd|D}fdd|D}tfdd|D}dd|D}|rYt||t|}nt|}t||t||t||||qTdS)Nrr+F) supports_xlac3s0|]\}}|jj|jjko|jjvVqdSru)r-r.)r1r3rG)rr6r7r8s   z%_multi_tensor_adam..r{r9z#_foreach ops don't support autogradr,cpu)r-r|rcg|] }dt|qSrrr1rG)rnr6r7 Hz&_multi_tensor_adam..crrrr)ror6r7rKrcsg|]}|dqS)r6r1bc)r%r6r7rOscSsg|]}t|qSr6)r rr6r6r7rQs) rQr;rr?r/rrrrAzipr"_group_tensors_by_device_and_dtypevaluesr _foreach_negis_cpu _foreach_add_rT _foreach_add_foreach_lerp_ _foreach_mul__foreach_addcmul_ _foreach_pow _foreach_sub_ _foreach_neg_ _foreach_div__foreach_reciprocal__foreach_sqrt__foreach_maximum_ _foreach_sqrt_foreach_addcdiv_r)r$rerfrgrhrirlrmr)rjrnror%r(r'r r!r"grouped_tensors device_params device_gradsdevice_exp_avgsdevice_exp_avg_sqsdevice_max_exp_avg_sqsdevice_state_steps_rrrrexp_avg_sq_sqrtr6)rnrorr%r7_multi_tensor_adams                               rreturnc Csf|sdS|r td|dur|j|ini}|dur|j|ini}t| tr1t| jdkr1| j| ind}t||||||g}|D]n\\}}\\}}}}}}}d\}}|durc|||j |dd}|durr|||j |dd}|dur||vr| j |dd||<||} t |dt j |||||||| | | | ||||d|durt ||gt|qBdS) Nz9Adam with fused=True does not support differentiable=Truer)NNT) non_blocking)r-rr) r)r%rnror(r'r rlrm)r?r-r;rstrrritemsrNtor/r _fused_adam_rrQ) r$rerfrgrhrirlrmr)rjrnror%r(r'r r!r"grad_scale_dictfound_inf_dictlr_dictrr-rrrrrrrdevice_grad_scaledevice_found_infr6r6r7 _fused_adamcsv$  r)single_tensor_fnFrr#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. See :class:`~torch.optim.Adam` for details. NF) use_fusedcss|] }t|tjVqdSru)r;r/r)r1tr6r6r7r8s  zadam..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)rjrnror%r(r'r r!r"rlrm) r r;rr/rrrAr?rrrrr)r$rerfrgrhrirr!r"r#rlrmrjr)rnror%r(r'r rfuncr6r6r7rsZ"  )NFFNNNF)$typingrrrrr/rtorch.utils._foreach_utilsr optimizerr r r r r rrrrrrrrrrrr__all__r__doc__ryrSrrrrr6r6r6r7sr  L`'E      w      ,      X