1.Lion优化器介绍
论文：https://arxiv.org/abs/2302.06675
代码：automl/lion at master · google/automl · GitHub
1.1 简单、内存高效、运行速度更快
1）与 AdamW 和各种自适应优化器需要同时保存一阶和二阶矩相比，Lion 只需要动量，将额外的内存占用减半；
2）由于 Lion 的简单性，Lion 在我们的实验中具有更快的运行时间（step/s），通常比 AdamW 和 Adafactor 提速 2-15%；
1.2 Lion优化器在各种模型、任务和领域上的优越性能
1.2.1 图像分类
- Lion 在 ImageNet 上从头开始训练或在 ImageNet-21K 上预训练的各种网络模型上优于 AdamW。
1.3 Lion 超参数和批量大小选择
1)通过对 Lion 的分析表明，它的性能增益随着训练批量的增加而增加。它还需要比Adam更小的学习率，因为符号函数产生的更新范数更大。
2)优化器的另一个潜在限制——批量大小（batch size）。通过实验，论文指出 Lion 在小batch_size（小于64）的时候效果不如AdamW。
2. Lion优化器导入Yolov8
2.1 修改ultralytics/yolo/engine/trainer.py
核心代码：
代码语言：javascript复制# Copyright 2023 Google Research. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""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
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