YOLOv8优化策略： 谷歌强势推出优化器Lion，内存更小、效率更高，秒杀Adam(W)

# Copyright 2023 Google Research. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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# ==============================================================================
"""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