U ʹj@sbdZddlZddlmZmZmZmZddlZddlZerFddl m Z neZ Gdddej j Z dS)z~ PyTorch MADGRAD optimizer MADGRAD: https://arxiv.org/abs/2101.11075 Code from: https://github.com/facebookresearch/madgrad N) TYPE_CHECKINGAnyCallableOptional) _params_tcseZdZdZdeeeeeedfdd Zeed d d Z eed d dZ e de egefe edddZZS)MADGRADa MADGRAD_: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization. .. _MADGRAD: https://arxiv.org/abs/2101.11075 MADGRAD is a general purpose optimizer that can be used in place of SGD or Adam may converge faster and generalize better. Currently GPU-only. Typically, the same learning rate schedule that is used for SGD or Adam may be used. The overall learning rate is not comparable to either method and should be determined by a hyper-parameter sweep. MADGRAD requires less weight decay than other methods, often as little as zero. Momentum values used for SGD or Adam's beta1 should work here also. On sparse problems both weight_decay and momentum should be set to 0. Arguments: params (iterable): Iterable of parameters to optimize or dicts defining parameter groups. lr (float): Learning rate (default: 1e-2). momentum (float): Momentum value in the range [0,1) (default: 0.9). weight_decay (float): Weight decay, i.e. a L2 penalty (default: 0). eps (float): Term added to the denominator outside of the root operation to improve numerical stability. (default: 1e-6). {Gz??rư>F)paramslrmomentum weight_decayepsdecoupled_decaycs|dks|dkr td|d|dkr8td|d|dkrPtd|d|dkr`td t|||||d }t||dS) Nrz Momentum z must be in the range [0,1]zLearning rate z must be positivez Weight decay z must be non-negativezEps must be non-negative)r rr rr) ValueErrordictsuper__init__)selfr r r rrrdefaults __class__Q/var/www/html/bwave/python/venv/lib/python3.8/site-packages/timm/optim/madgrad.pyr7s zMADGRAD.__init__)returncCsdS)NFrrrrrsupports_memory_efficient_fp16Msz&MADGRAD.supports_memory_efficient_fp16cCsdS)NTrrrrrsupports_flat_paramsQszMADGRAD.supports_flat_paramsN)closurerc Csd}|dk r&t |}W5QRX|jD]}|d}|d|}|d}|d}d|}|dD]} | jdkrxqf| j} |dkr| jrtd |j| } t| d krd | d <t| | d <t| | d <|d krt |  | d<| d d7<| d } | d } |t | d }|d krd|drF| d|d|n| jrVtd| j| |d| jrT| } | }| | }| | }| | }|d|}|j||dd}| | }| j||d|j||d|d|}| j| |d|j||d|j||dd}|j|dd| j|ddqf|d kr| d|}| j| |dd}n| d}| j| | |d| d|}| j| |d|d kr| |j| |ddqf|j| |dd}| d|j||dqfq,|S)zPerforms a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. Nrr rr rr gz5momentum != 0 is not compatible with sparse gradientsrstep grad_sum_sqsx0rg?z;weight_decay option is not compatible with sparse gradients)alphagUUUUUU?)value)torchZ enable_gradZ param_groupsgradZ is_sparse RuntimeErrorstatelenZ zeros_likeclonedetachmathsqrtZmul_Zadd_ZcoalesceZ_valuesZ sparse_maskpowZaddcdivZpow_Zaddcmul_Zcopy_)rr Zlossgrouprr rr Zckpr)r+r"r#ZlambZgrad_valZp_maskedZgrad_sum_sq_maskedZs_maskedZrms_masked_valsZx0_masked_valsZgrad_sqZp_kp1_masked_valsZrmsr$zrrrr!Us|             z MADGRAD.step)rr rr F)N)__name__ __module__ __qualname____doc__rfloatboolrpropertyrrr(Zno_gradrrr! __classcell__rrrrrs(!r)r8r/typingrrrrr(Z torch.optimZtorch.optim.optimizerrZoptimZ Optimizerrrrrrs