Google Brain新提出的优化器“Lion”，效果要比Adam(W)更好_1

"""PyTorch implementation of the Lion optimizer."""
import torch
from torch.optim.optimizer import Optimizer


class Lion(Optimizer):
  r"""Implements Lion algorithm."""

  def __init__(self, params, lr=1e-4, betas=(0.9, 0.99), weight_decay=0.0):
    """Initialize the hyperparameters.
    Args:
      params (iterable): iterable of parameters to optimize or dicts defining
        parameter groups
      lr (float, optional): learning rate (default: 1e-4)
      betas (Tuple[float, float], optional): coefficients used for computing
        running averages of gradient and its square (default: (0.9, 0.99))
      weight_decay (float, optional): weight decay coefficient (default: 0)
    """

    if not 0.0 <=lr:
      raise ValueError('Invalid learning rate:{}'.format(lr))
    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]))
    defaults=dict(lr=lr, betas=betas, weight_decay=weight_decay)
    super().__init__(params, defaults)

  @torch.no_grad()
  def step(self, closure=None):
    """Performs a single optimization step.
    Args:
      closure (callable, optional): A closure that reevaluates the model
        and returns the loss.
    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

        # Perform stepweight decay
        p.data.mul_(1 - group['lr']* group['weight_decay'])

        grad=p.grad
        state=self.state[p]
        # State initialization
        if len(state)==0:
          # Exponential moving average of gradient values
          state['exp_avg']=torch.zeros_like(p)

        exp_avg=state['exp_avg']
        beta1, beta2=group['betas']

        # Weight update
        update=exp_avg * beta1 + grad * (1 - beta1)
        p.add_(torch.sign(update), alpha=-group['lr'])
        # Decay the momentum running average coefficient
        exp_avg.mul_(beta2).add_(grad, alpha=1 - beta2)

    return loss