Source code for machina.optims.adamw

import math
from torch.optim.optimizer import Optimizer


[docs]class AdamW(Optimizer): """Implements AdamW algorithm. It has been proposed in `Fixing Weight Decay Regularization in Adam`. Arguments: params (iterable): iterable of parameters to optimize or dicts defining parameter groups lr (float, optional): learning rate (default: 1e-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 (not L2 penalty) (default: 0) """ def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0): defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) super(AdamW, self).__init__(params, defaults)
[docs] def step(self, closure=None): """Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: loss = closure() for group in self.param_groups: for p in group['params']: if p.grad is None: continue grad = p.grad.data state = self.state[p] # State initialization if len(state) == 0: state['step'] = 0 # Exponential moving average of gradient values state['exp_avg'] = grad.new().resize_as_(grad).zero_() # Exponential moving average of squared gradient values state['exp_avg_sq'] = grad.new().resize_as_(grad).zero_() exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] beta1, beta2 = group['betas'] state['step'] += 1 # Decay the first and second moment running average coefficient exp_avg.mul_(beta1).add_(1 - beta1, grad) exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) denom = exp_avg_sq.sqrt().add_(group['eps']) bias_correction1 = 1 - beta1 ** state['step'] bias_correction2 = 1 - beta2 ** state['step'] step_size = group['lr'] * \ math.sqrt(bias_correction2) / bias_correction1 if group['weight_decay'] != 0: p.data.add_(-group['weight_decay'], p.data) p.data.addcdiv_(-step_size, exp_avg, denom) return loss