o yi@s(ddlZddlmZGdddeZdS)N) OptimizercsHeZdZdZ        dfd d Zdd dZeddZZS)RangerQHapImplements the QHAdam optimization algorithm `(Ma and Yarats, 2019)`_. Along with Hinton/Zhang Lookahead. Args: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (:math:`\alpha` from the paper) (default: 1e-3) betas (Tuple[float, float], optional): coefficients used for computing running averages of the gradient and its square (default: (0.9, 0.999)) nus (Tuple[float, float], optional): immediate discount factors used to estimate the gradient and its square (default: (1.0, 1.0)) eps (float, optional): term added to the denominator to improve numerical stability (default: 1e-8) weight_decay (float, optional): weight decay (default: 0.0) decouple_weight_decay (bool, optional): whether to decouple the weight decay from the gradient-based optimization step (default: False) Example: >>> optimizer = qhoptim.pyt.QHAdam( ... model.parameters(), ... lr=3e-4, nus=(0.8, 1.0), betas=(0.99, 0.999)) >>> optimizer.zero_grad() >>> loss_fn(model(input), target).backward() >>> optimizer.step() .. _`(Ma and Yarats, 2019)`: https://arxiv.org/abs/1810.06801 MbP?g?g+?gffffff???F:0yE>c sd|ks td|d| kstd| d|dkr"dks,ntd|dd|dkr8dksBntd|d|dkrMtd ||||||| d } t|| ||_||_dS) NrzInvalid learning rate: {}zInvalid epsilon value: {}rrz%Invalid beta parameter at index 0: {}z%Invalid beta parameter at index 1: {}zInvalid weight_decay value: {})lrbetasnus weight_decaydecouple_weight_decayeps) ValueErrorformatsuper__init__alphak) selfparamsr rrrrrrrdefaults __class__K/home/ubuntu/.local/lib/python3.10/site-packages/pytorch_ranger/rangerqh.pyr4s(  zRangerQH.__init__NcCsd}|dur |}|jD]1}|d}|d\}}|d\}}|d} |d} |d} |dD] } | jdur8q/| jj} | jrCtd | d kr[| rT| jd || n| | | j| | }|j| }t |d krd |d <d |d<d |d<t | j|d<t | j|d<t | j|d<|d | j|dd 7<d||d |d <d||d|d<|d }|d}|d}|d}dd|}dd|}||d|| ||d||||}|dkr|d|| ||}|dkr|d|||| d kr|| | j| |||d|jd kr=|d}||j| j|| j |q/q |S)zPerforms a single optimization step. Args: closure (callable, optional): A closure that reevaluates the model and returns the loss. Nr rrrrrrz(QHAdam does not support sparse gradientsrr r beta1_weight beta2_weightstepexp_avg exp_avg_sq slow_bufferr) param_groupsgraddata is_sparse RuntimeErrormul_add_mulstatelentorch zeros_like empty_likecopy_sqrt_addcdiv_rr)rclosurelossgroupr beta1beta2nu1nu2rrrpd_pd_p_sq param_stater r!r#r$ beta1_adj beta2_adjavg_grad avg_grad_rmsslow_prrrr"Zst               Dz RangerQH.stepcCs|j|j|jf|j|jfdS)N)r rr)rr;r<r9r:)clsrrrr_params_to_dictszRangerQH._params_to_dict)rrrrr r Fr )N) __name__ __module__ __qualname____doc__rr" classmethodrG __classcell__rrrrrs" &Xr)r0torch.optim.optimizerrrrrrrs