o zi@sLddlZddlZddlmZddlmZmZmZmZdZ GdddeZ dS)N) Optimizer)Betas2OptFloatOptLossClosureParams)DiffGradc sXeZdZdZ    ddededed ed ed d f fd d Zdded e fddZ Z S)raImplements DiffGrad algorithm. It has been proposed in `DiffGrad: An Optimization Method for Convolutional Neural Networks`__. Arguments: params: iterable of parameters to optimize or dicts defining parameter groups lr: learning rate (default: 1e-3) betas: coefficients used for computing running averages of gradient and its square (default: (0.9, 0.999)) eps: term added to the denominator to improve numerical stability (default: 1e-8) weight_decay: weight decay (L2 penalty) (default: 0) Example: >>> import torch_optimizer as optim >>> optimizer = optim.DiffGrad(model.parameters(), lr=0.1) >>> optimizer.zero_grad() >>> loss_fn(model(input), target).backward() >>> optimizer.step() __ https://arxiv.org/abs/1909.11015 Note: Reference code: https://github.com/shivram1987/diffGrad MbP?g?g+?:0yE>paramslrbetaseps weight_decayreturnNcs|dkr td||dkrtd|d|dkr"dks,ntd|dd|dkr8dksBntd|d|dkrMtd |t||||d }tt|||dS) Nr zInvalid learning rate: {}zInvalid epsilon value: {}r?z%Invalid beta parameter at index 0: {}rz%Invalid beta parameter at index 1: {}zInvalid weight_decay value: {})rrrr) ValueErrorformatdictsuperr__init__)selfr rrrrdefaults __class__L/home/ubuntu/.local/lib/python3.10/site-packages/torch_optimizer/diffgrad.pyr(s$  zDiffGrad.__init__closurec Csd}|dur |}|jD]}|d\}}|dD]}|jdur q|jj}|jr-d}t||j|} t| dkrQd| d<t|| d<t|| d<t|| d <| d| d| d } } } | dd 7<|d dkrw|j |j|d d | |j |d |d | |j ||d |d | |d} d || d}d || d}t | |}tddt| }|| d <| |}|dt ||}|jj|| | d qq |S)zPerforms a single optimization step. Arguments: closure: A closure that reevaluates the model and returns the loss. Nrr zNDiffGrad does not support sparse gradients, please consider SparseAdam insteadrstepexp_avg exp_avg_sq previous_gradrr)alpha)valuerrr) param_groupsgraddata is_sparse RuntimeErrorstatelentorch zeros_likeadd_mul_addcmul_sqrtabsdivexpclonemathaddcdiv_)rrlossgroupbeta1beta2pr'msgr+r!r"r#denombias_correction1bias_correction2diffdfcexp_avg1 step_sizerrrr DsX         :z DiffGrad.step)r r r r )N) __name__ __module__ __qualname____doc__rfloatrrrrr __classcell__rrrrr s(r) r7r-torch.optim.optimizerrtypesrrrr__all__rrrrrs