import math import torch from torch.optim.optimizer import Optimizer class AdaBelief(Optimizer): r"""Implements AdaBelief algorithm. Modified from Adam in PyTorch 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-16) weight_decay (float, optional): weight decay (L2 penalty) (default: 0) amsgrad (boolean, optional): whether to use the AMSGrad variant of this algorithm from the paper `On the Convergence of Adam and Beyond`_ (default: False) weight_decouple (boolean, optional): ( default: True) If set as True, then the optimizer uses decoupled weight decay as in AdamW fixed_decay (boolean, optional): (default: False) This is used when weight_decouple is set as True. When fixed_decay == True, the weight decay is performed as $W_{new} = W_{old} - W_{old} \times decay$. When fixed_decay == False, the weight decay is performed as $W_{new} = W_{old} - W_{old} \times decay \times lr$. Note that in this case, the weight decay ratio decreases with learning rate (lr). rectify (boolean, optional): (default: True) If set as True, then perform the rectified update similar to RAdam degenerated_to_sgd (boolean, optional) (default:True) If set as True, then perform SGD update when variance of gradient is high reference: AdaBelief Optimizer, adapting stepsizes by the belief in observed gradients, NeurIPS 2020 For a complete table of recommended hyperparameters, see https://github.com/juntang-zhuang/Adabelief-Optimizer' For example train/args for EfficientNet see these gists - link to train_scipt: https://gist.github.com/juntang-zhuang/0a501dd51c02278d952cf159bc233037 - link to args.yaml: https://gist.github.com/juntang-zhuang/517ce3c27022b908bb93f78e4f786dc3 """ def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-16, weight_decay=0, amsgrad=False, weight_decouple=True, fixed_decay=False, rectify=True, degenerated_to_sgd=True): if not 0.0 <= lr: raise ValueError("Invalid learning rate: {}".format(lr)) if not 0.0 <= eps: raise ValueError("Invalid epsilon value: {}".format(eps)) if not 0.0 <= betas[0] < 1.0: raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) if not 0.0 <= betas[1] < 1.0: raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) self.degenerated_to_sgd = degenerated_to_sgd if isinstance(params, (list, tuple)) and len(params) > 0 and isinstance(params[0], dict): for param in params: if 'betas' in param and (param['betas'][0] != betas[0] or param['betas'][1] != betas[1]): param['buffer'] = [[None, None, None] for _ in range(10)] defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad, buffer=[[None, None, None] for _ in range(10)]) super(AdaBelief, self).__init__(params, defaults) self.degenerated_to_sgd = degenerated_to_sgd self.weight_decouple = weight_decouple self.rectify = rectify self.fixed_decay = fixed_decay def __setstate__(self, state): super(AdaBelief, self).__setstate__(state) for group in self.param_groups: group.setdefault('amsgrad', False) def reset(self): for group in self.param_groups: for p in group['params']: state = self.state[p] amsgrad = group['amsgrad'] # State initialization state['step'] = 0 # Exponential moving average of gradient values state['exp_avg'] = torch.zeros_like(p.data) # Exponential moving average of squared gradient values state['exp_avg_var'] = torch.zeros_like(p.data) if amsgrad: # Maintains max of all exp. moving avg. of sq. grad. values state['max_exp_avg_var'] = torch.zeros_like(p.data) 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 # cast data type half_precision = False if p.data.dtype == torch.float16: half_precision = True p.data = p.data.float() p.grad = p.grad.float() grad = p.grad.data if grad.is_sparse: raise RuntimeError( 'AdaBelief does not support sparse gradients, please consider SparseAdam instead') amsgrad = group['amsgrad'] state = self.state[p] beta1, beta2 = group['betas'] # State initialization if len(state) == 0: state['step'] = 0 # Exponential moving average of gradient values state['exp_avg'] = torch.zeros_like(p.data) # Exponential moving average of squared gradient values state['exp_avg_var'] = torch.zeros_like(p.data) if amsgrad: # Maintains max of all exp. moving avg. of sq. grad. values state['max_exp_avg_var'] = torch.zeros_like(p.data) # perform weight decay, check if decoupled weight decay if self.weight_decouple: if not self.fixed_decay: p.data.mul_(1.0 - group['lr'] * group['weight_decay']) else: p.data.mul_(1.0 - group['weight_decay']) else: if group['weight_decay'] != 0: grad.add_(p.data, alpha=group['weight_decay']) # get current state variable exp_avg, exp_avg_var = state['exp_avg'], state['exp_avg_var'] state['step'] += 1 bias_correction1 = 1 - beta1 ** state['step'] bias_correction2 = 1 - beta2 ** state['step'] # Update first and second moment running average exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) grad_residual = grad - exp_avg exp_avg_var.mul_(beta2).addcmul_( grad_residual, grad_residual, value=1 - beta2) if amsgrad: max_exp_avg_var = state['max_exp_avg_var'] # Maintains the maximum of all 2nd moment running avg. till now torch.max(max_exp_avg_var, exp_avg_var.add_(group['eps']), out=max_exp_avg_var) # Use the max. for normalizing running avg. of gradient denom = (max_exp_avg_var.sqrt() / math.sqrt(bias_correction2)).add_(group['eps']) else: denom = (exp_avg_var.add_(group['eps']).sqrt() / math.sqrt(bias_correction2)).add_(group['eps']) # update if not self.rectify: # Default update step_size = group['lr'] / bias_correction1 p.data.addcdiv_( exp_avg, denom, value=-step_size) else: # Rectified update, forked from RAdam buffered = group['buffer'][int(state['step'] % 10)] if state['step'] == buffered[0]: N_sma, step_size = buffered[1], buffered[2] else: buffered[0] = state['step'] beta2_t = beta2 ** state['step'] N_sma_max = 2 / (1 - beta2) - 1 N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t) buffered[1] = N_sma # more conservative since it's an approximated value if N_sma >= 5: step_size = math.sqrt( (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / ( N_sma_max - 2)) / (1 - beta1 ** state['step']) elif self.degenerated_to_sgd: step_size = 1.0 / (1 - beta1 ** state['step']) else: step_size = -1 buffered[2] = step_size if N_sma >= 5: denom = exp_avg_var.sqrt().add_(group['eps']) p.data.addcdiv_(exp_avg, denom, value=-step_size * group['lr']) elif step_size > 0: p.data.add_( exp_avg, alpha=-step_size * group['lr']) if half_precision: p.data = p.data.half() p.grad = p.grad.half() return loss