o <&i[Y#@sddlZddlmZddlmZmZmZmZmZmZm Z m Z m Z m Z ddl mZmZddgZGdddeZd d e d ed e d e_  d$deedeedeedeedeedeededeedededededededededef"ddZdeedeedeedeedeedeededededededededededef d d!Zdeedeedeedeedeedeededededededededededef d"d#ZdS)%N)Tensor) Optimizer_use_grad_for_differentiable _get_value_dispatch_sqrt_stack_if_compiling_capturable_doc_differentiable_doc _foreach_doc_default_to_fused_or_foreach _view_as_real)ListOptionalNAdamnadamc sdeZdZ  dddddd ed eed ed effd dZfddZddZedddZ Z S)rMb`?g?g+?:0yE>rMbp?FN)foreach capturabledifferentiabledecoupled_weight_decayrrrc sd|ks td|d|kstd|d|dkr"dks,ntd|dd|dkr8dksBntd|dd|ksMtd |d|ksXtd |t|||||||| | d } t|| dS) NzInvalid learning rate: zInvalid epsilon value: r?z#Invalid beta parameter at index 0: rz#Invalid beta parameter at index 1: zInvalid weight_decay value: zInvalid momentum_decay value: ) lrbetaseps weight_decaymomentum_decayrrrr) ValueErrordictsuper__init__) selfparamsrrrrr rrrrdefaults __class__t|dko]t |dd }|sq|D]}tj |d tj d|d <qbdSdS) NrrFrrrstepdtype mu_product) r# __setstate__ param_groups setdefaultliststatevalueslentorchZ is_tensortensorfloatfloat32)r%r4groupZ state_valuesZstep_is_tensorsZmu_product_is_tensorr(r*r+r0 s"     zNAdam.__setstate__c Cs*d}|dD]} | jdur|t| O}|| | jjr!td|| j|j| } t| dkrv|dr@tjdtj | j dntj dtj d | d <|drXtj dtj | j dntj d tj d | d <tj | tjd | d<tj | tjd | d<|| d|| d|| d || d q|S)NFr&z'NAdam does not support sparse gradientsrrr*)r.devicerr-r,rr/)Z memory_formatexp_avg exp_avg_sq)gradr7 is_complexappendZ is_sparse RuntimeErrorr4r6Zzerosr:r=r8ZonesZ zeros_likeZpreserve_format) r%r;params_with_gradgradsexp_avgs exp_avg_sqs mu_products state_steps has_complexpr4r*r*r+ _init_group1s4      zNAdam._init_groupc Cs|d}|dur!t |}Wdn1swY|jD]D}g}g}g}g}g}g} |d\} } |||||||| } t|||||| | | |d|d|d|d|d|d|d |d | d q$|S) zPerforms a single optimization step. Args: closure (Callable, optional): A closure that reevaluates the model and returns the loss. Nrrrr rrrrr) beta1beta2rrr rrrrrrJ)Z _cuda_graph_capture_health_checkr7Z enable_gradr1rLr) r%closureZlossr;rDrErFrGrHrIrMrNrJr*r*r+r,TsD   z NAdam.step)rrrrrFN) __name__ __module__ __qualname__boolrr$r0rLrr, __classcell__r*r*r(r+r s& #a Implements NAdam algorithm. .. math:: \begin{aligned} &\rule{110mm}{0.4pt} \\ &\textbf{input} : \gamma_t \text{ (lr)}, \: \beta_1,\beta_2 \text{ (betas)}, \: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)} \\ &\hspace{13mm} \: \lambda \text{ (weight decay)}, \:\psi \text{ (momentum decay)} \\ &\hspace{13mm} \: \textit{decoupled\_weight\_decay} \\ &\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)}, v_0 \leftarrow 0 \text{ ( second moment)} \\[-1.ex] &\rule{110mm}{0.4pt} \\ &\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\ &\hspace{5mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\ &\hspace{5mm} \theta_t \leftarrow \theta_{t-1} \\ &\hspace{5mm} \textbf{if} \: \lambda \neq 0 \\ &\hspace{10mm}\textbf{if} \: \textit{decoupled\_weight\_decay} \\ &\hspace{15mm} \theta_t \leftarrow \theta_{t-1} - \gamma \lambda \theta_{t-1} \\ &\hspace{10mm}\textbf{else} \\ &\hspace{15mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\ &\hspace{5mm} \mu_t \leftarrow \beta_1 \big(1 - \frac{1}{2} 0.96^{t \psi} \big) \\ &\hspace{5mm} \mu_{t+1} \leftarrow \beta_1 \big(1 - \frac{1}{2} 0.96^{(t+1)\psi}\big)\\ &\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 \mu_{t+1} m_t/(1-\prod_{i=1}^{t+1}\mu_i)\\[-1.ex] & \hspace{11mm} + (1-\mu_t) g_t /(1-\prod_{i=1}^{t} \mu_{i}) \\ &\hspace{5mm}\widehat{v_t} \leftarrow v_t/\big(1-\beta_2^t \big) \\ &\hspace{5mm}\theta_t \leftarrow \theta_t - \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 `Incorporating Nesterov Momentum into Adam`_. a Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 2e-3) 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) momentum_decay (float, optional): momentum momentum_decay (default: 4e-3) decoupled_weight_decay (bool, optional): whether to use decoupled weight decay as in AdamW to obtain NAdamW (default: False) z z .. _Incorporating Nesterov Momentum into Adam: https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ .. _Decoupled Weight Decay Regularization: https://arxiv.org/abs/1711.05101 Fr&rErFrGrHrIrrrrrJrMrNrrr rc Cstdd|Ds tdtdd|Dstd|dur't|| dd\}}|r2tjr2td |rznadam..zPAPI has changed, `state_steps` argument must contain a list of singleton tensorscsrVrPrWrYr*r*r+r\r]zPAPI has changed, `mu_products` argument must contain a list of singleton tensorsNF)Z use_fusedz6torch.jit.script not supported with foreach optimizers) rMrNrrr rrrrrJ)allrCr r7ZjitZ is_scripting_multi_tensor_nadam_single_tensor_nadam)r&rErFrGrHrIrrrrrJrMrNrrr r_funcr*r*r+rs8 c Cs2t|D]\}}||}||}||}||}||}t|r6t|}t|}t|}t|}tjsS| rS|jrF|jrF|jsS|jrO|jrO|jsSJd|d7}| r\|}nt|}d||}| dkr}| rv| d|| n|j || d}|ddd|| }|ddd|d| }||9}| |d|| |j ||d|d| |}|s| r| | }||}|| d|d|}|| |d|}||||||qt||}|| |j||| d|dt|d|j||| |d|dqdS) NzUIf capturable=True, params, mu_products, and state_steps must be CUDA or XLA tensors.rralphar?Q?)value) enumerater7rAZ view_as_real_utils is_compilingis_cudaZis_xlarZmul_addZlerp_Zaddcmul_divsqrtZaddcdiv_Zadd_)r&rErFrGrHrIrMrNrrr rrrrrJiparamr@r>r?r/Zstep_tr,Zbias_correction2mumu_nextdenomZmu_product_nextr*r*r+r`sj          & r`c !sBt|dkrdS|rJdtjs&| r&tddt|||Ds&Jdt||||||g}|D]h\\}}}}}}}|rIt |||||dj r\tj |tj ddddd nt |d | dkr{| rst |d | ntj||| d }t||d t |t|||d t|}| rt|}td |}t |d t |dt |t |td |}t |d t |dt |~t|}t|dt|t|nfd d|D}fdd|D}fdd|D}t ||t||t || ~| rrt|dt |t|d}t|t|||}~t||}t |t|dt|||}~t||} t| ||t|| |q5tfddt||D}tfddt||D}t||||t||||q5dS)Nrz#_foreach ops don't support autogradcss(|]\}}}|jo|jo|jVqdSrP)rk)rZrKmpr,r*r*r+r\gsz&_multi_tensor_nadam..zNIf capturable=True, params, mu_products, and state_steps must be CUDA tensors.rcpu)r=rcrrfgcs g|] }tdt|qS)r)rrrZr,)rNr*r+ s z'_multi_tensor_nadam..cs(g|]}dddt|qS)rrerfrrvrMr r*r+rws(cs,g|]}dddt|dqS)rrerfrrxrvryr*r+rws$cs,g|]\}}d|dt|dqSrrx)rZr/rqrr*r+rw cs,g|]\}}|dt||dqSrzrx)rZr/rrr|r*r+rwr})r6r7rirjr^ziprZ"_group_tensors_by_device_and_dtyper5r Zis_cpuZ _foreach_add_r8Z _foreach_mul_Z _foreach_addZ_foreach_lerp_Z_foreach_addcmul_Z _foreach_sqrtZ _foreach_mulZ _foreach_powZ _foreach_sub_Z _foreach_neg_Z_foreach_sqrt_Z _foreach_div_Z _foreach_subZ_foreach_addcdiv_r)!r&rErFrGrHrIrMrNrrr rrrrrJZgrouped_tensorsZgrouped_paramsZ grouped_gradsZgrouped_exp_avgsZgrouped_exp_avg_sqsZgrouped_mu_productsZgrouped_state_stepsraZexp_avg_sq_sqrtexponentZmusZmu_nextsZbias_correction_sqrtrsZstep_size_gradsZstep_size_expavg numeratorr*)rMrNrr r+r_Ns                                      r_)FNFFF)r7rZ optimizerrrrrrr r r r r typingrr__all__r__doc__rTr9rr`r_r*r*r*r+s 0y# D      =      S