| import math |
|
|
| import torch |
| from torch.optim.optimizer import Optimizer |
|
|
|
|
| class NAdamLegacy(Optimizer): |
| """Implements Nadam algorithm (a variant of Adam based on Nesterov momentum). |
| |
| NOTE: This impl has been deprecated in favour of torch.optim.NAdam and remains as a reference |
| |
| It has been proposed in `Incorporating Nesterov Momentum into Adam`__. |
| |
| Arguments: |
| params (iterable): iterable of parameters to optimize or dicts defining |
| parameter groups |
| lr (float, optional): learning rate (default: 2e-3) |
| betas (Tuple[float, float], optional): coefficients used for computing |
| running averages of gradient and its square |
| eps (float, optional): term added to the denominator to improve |
| numerical stability (default: 1e-8) |
| weight_decay (float, optional): weight decay (L2 penalty) (default: 0) |
| schedule_decay (float, optional): momentum schedule decay (default: 4e-3) |
| |
| __ http://cs229.stanford.edu/proj2015/054_report.pdf |
| __ http://www.cs.toronto.edu/~fritz/absps/momentum.pdf |
| |
| Originally taken from: https://github.com/pytorch/pytorch/pull/1408 |
| NOTE: Has potential issues but does work well on some problems. |
| """ |
|
|
| def __init__( |
| self, |
| params, |
| lr=2e-3, |
| betas=(0.9, 0.999), |
| eps=1e-8, |
| weight_decay=0, |
| schedule_decay=4e-3, |
| ): |
| if not 0.0 <= lr: |
| raise ValueError("Invalid learning rate: {}".format(lr)) |
| defaults = dict( |
| lr=lr, |
| betas=betas, |
| eps=eps, |
| weight_decay=weight_decay, |
| schedule_decay=schedule_decay, |
| ) |
| super(NAdamLegacy, self).__init__(params, defaults) |
|
|
| @torch.no_grad() |
| 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: |
| with torch.enable_grad(): |
| loss = closure() |
|
|
| for group in self.param_groups: |
| for p in group['params']: |
| if p.grad is None: |
| continue |
| grad = p.grad |
| state = self.state[p] |
|
|
| |
| if len(state) == 0: |
| state['step'] = 0 |
| state['m_schedule'] = 1. |
| state['exp_avg'] = torch.zeros_like(p) |
| state['exp_avg_sq'] = torch.zeros_like(p) |
|
|
| |
| m_schedule = state['m_schedule'] |
| schedule_decay = group['schedule_decay'] |
| exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] |
| beta1, beta2 = group['betas'] |
| eps = group['eps'] |
| state['step'] += 1 |
| t = state['step'] |
| bias_correction2 = 1 - beta2 ** t |
|
|
| if group['weight_decay'] != 0: |
| grad = grad.add(p, alpha=group['weight_decay']) |
|
|
| momentum_cache_t = beta1 * (1. - 0.5 * (0.96 ** (t * schedule_decay))) |
| momentum_cache_t_1 = beta1 * (1. - 0.5 * (0.96 ** ((t + 1) * schedule_decay))) |
| m_schedule_new = m_schedule * momentum_cache_t |
| m_schedule_next = m_schedule * momentum_cache_t * momentum_cache_t_1 |
| state['m_schedule'] = m_schedule_new |
|
|
| |
| exp_avg.mul_(beta1).add_(grad, alpha=1. - beta1) |
| exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1. - beta2) |
|
|
| denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps) |
| p.addcdiv_(grad, denom, value=-group['lr'] * (1. - momentum_cache_t) / (1. - m_schedule_new)) |
| p.addcdiv_(exp_avg, denom, value=-group['lr'] * momentum_cache_t_1 / (1. - m_schedule_next)) |
|
|
| return loss |
|
|
