File size: 3,211 Bytes
9c6594c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
# 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, **kwargs):
"""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
|