# ==============================================================================
# This code is adapted from
# https://github.com/google/automl/blob/master/lion/lion_pytorch.py
#
# Copyright 2016-2023 Cerebras Systems
# SPDX-License-Identifier: Apache-2.0
#
# Copyright 2023 Google Research. All Rights Reserved.
# Modifications Copyright 2023 Cerebras.
#
# 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.
# ==============================================================================
from typing import Iterable, Tuple
import torch
import cerebras.pytorch as cstorch
from .optimizer import Optimizer
[docs]class Lion(Optimizer):
r"""Implements Lion algorithm.
As proposed in `Symbolic Discovery of Optimization Algorithms`_.
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)
.. _Symbolic Discovery of Optimization Algorithms: https://arxiv.org/pdf/2302.06675.pdf
"""
[docs] def __init__(
self,
params: Iterable[torch.nn.parameter.Parameter],
lr: float = 1e-4,
betas: Tuple[float, float] = (0.9, 0.99),
weight_decay: float = 0.0,
):
if lr < 0.0:
raise ValueError(f"Invalid learning rate: {lr}")
if not 0.0 <= betas[0] < 1.0:
raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
if not 0.0 <= betas[1] < 1.0:
raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
if weight_decay < 0.0:
raise ValueError(f"Invalid weight decay value: {weight_decay}")
defaults = dict(lr=lr, betas=betas, weight_decay=weight_decay)
super().__init__(params, defaults)
[docs] def preinitialize(self):
"""
Allocates tensors for the optimizer state to allow direct compilation
of the model before the first step.
"""
for group in self.param_groups:
for p in group["params"]:
self.state[p]["exp_avg"] = cstorch.zeros_like(p)
@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.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
lr = group["lr"]
beta1, beta2 = group["betas"]
weight_decay = group["weight_decay"]
for p in group["params"]:
if p.grad is not None:
grad = p.grad
state = self.state[p]
exp_avg = state['exp_avg']
# Perform weight decay
if weight_decay != 0:
p.mul_(1 - lr * weight_decay)
# Perform weight update
update = (
exp_avg.clone()
.mul_(beta1)
.add(grad, alpha=1 - beta1)
.sign_()
)
p.add_(-lr * update)
# Update exponential moving average
exp_avg.mul_(beta2).add_(grad, alpha=1 - beta2)
return loss