o zi@s`ddlZddlmZmZddlZddlmZddlmZm Z m Z m Z e Z dZ GdddeZdS) N)ListOptional) Optimizer)Betas2OptFloatOptLossClosureParams) AdahessiancseZdZdZ      dded ed ed ed ed edeeddffdd Z dede de e j fddZddedefddZZS)r aImplements Adahessian Algorithm. It has been proposed in `ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning`. Arguments: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 0.15) 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-4) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) hessian_power (float, optional): Hessian power (default: 0.5) seed (int, optional): Random number generator seed (default: None) Example: >>> import torch_optimizer as optim >>> optimizer = optim.Adahessian(model.parameters(), lr = 1.0) >>> optimizer.zero_grad() >>> loss_fn(model(input), target).backward(create_graph=True) >>> optimizer.step() __ https://arxiv.org/abs/2006.00719 Note: Reference code: https://github.com/amirgholami/adahessian 333333?g?g+?-C6?r?Nparamslrbetaseps weight_decay hessian_powerseedreturnc s|dkr td||dkrtd|d|dkr"dks,ntd|dd|dkr8dksBntd|dd|krLdksTntd ||dur]t|t|||||d }tt|||dS) NgzInvalid learning rate: {}zInvalid epsilon value: {}rg?z%Invalid beta parameter at index 0: {}rz%Invalid beta parameter at index 1: {}zInvalid Hessian power value: {})rrrrr) ValueErrorformattorch manual_seeddictsuperr __init__) selfrrrrrrrdefaults __class__N/home/ubuntu/.local/lib/python3.10/site-packages/torch_optimizer/adahessian.pyr,s4    zAdahessian.__init__gradsc Cst|D]\}}|jdurd}t||qdd|D}tjj|||ddd}g}|D](} | } t| dkr>| } nt| dkrPtj | dd gdd } | | q-|S) aJGet an estimate of Hessian Trace. This is done by computing the Hessian vector product with a random vector v at the current gradient point, to estimate Hessian trace by computing the gradient of . :param gradsH: a list of torch variables :return: a list of torch tensors NzzGradient tensor {:} does not have grad_fn. When calling loss.backward(), make sure the option create_graph is set to True.cSs&g|]}dtj|dtjddqS))high memory_formatr)r randint_likepreserve_format).0pr"r"r# dsz(Adahessian.get_trace..T) grad_outputs only_inputs retain_graphr%)dimkeepdim) enumerategrad_fn RuntimeErrorrrautogradgradsizelenabsmeanappend) rrr$ir8msgvhvshutchinson_tracehv param_size tmp_outputr"r"r# get_traceQs*      zAdahessian.get_traceclosurecCsd}|dur |}g}g}g}|jD]}|dD]}|jdur/||||||jqq|||}t||||D]\}}} } |j|} t| dkrcd| d<t|j | d<t|j | d<| d| d} } |d\}}| dd7<| |j | d|d | |j | | d|d d|| d}d|| d}|d }| |t|| |d }|j |d | |||d|j |_ q>|S)zPerform a single optimization step. Arguments: closure: A closure that reevaluates the model and returns the loss. Nrrstepexp_avgexp_hessian_diag_sqrr)alpha)valuerrrr) param_groupsr8r=rFzipstater:r zeros_likedatamul_add_detach_addcmul_sqrtmath)rrGlossrgroupsr$groupr+ hut_tracesr8 hut_tracerOrIrJbeta1beta2bias_correction1bias_correction2kdenomr"r"r#rHs\                   zAdahessian.step)r r r rrN)N)__name__ __module__ __qualname____doc__r floatrrintrGradsrrTensorrFrrrH __classcell__r"r"r r#r s6  %3r )rWtypingrrrtorch.optim.optimizerrtypesrrrr ri__all__r r"r"r"r#s