ColossalAI/colossalai/nn/optimizer/lars.py

93 lines
3.6 KiB
Python

"""Adapted from https://github.com/NUS-HPC-AI-Lab/LARS-ImageNet-PyTorch/blob/main/lars.py"""
from typing import Iterable
import torch
from torch.optim import Optimizer
class Lars(Optimizer):
r"""Implements the LARS optimizer from `"Large batch training of convolutional networks"
<https://arxiv.org/pdf/1708.03888.pdf>`_.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
momentum (float, optional): momentum factor (default: 0)
eeta (float, optional): LARS coefficient as used in the paper (default: 1e-3)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
"""
def __init__(self,
params: Iterable[torch.nn.Parameter],
lr=1e-3,
momentum=0,
eeta=1e-3,
weight_decay=0,
epsilon=0.0) -> None:
if not isinstance(lr, float) or lr < 0.0:
raise ValueError("Invalid learning rate: {}".format(lr))
if momentum < 0.0:
raise ValueError("Invalid momentum value: {}".format(momentum))
if weight_decay < 0.0:
raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
if eeta <= 0 or eeta > 1:
raise ValueError("Invalid eeta value: {}".format(eeta))
if epsilon < 0:
raise ValueError("Invalid epsilon value: {}".format(epsilon))
defaults = dict(lr=lr, momentum=momentum, weight_decay=weight_decay, eeta=eeta, epsilon=epsilon, lars=True)
super().__init__(params, defaults)
@torch.no_grad()
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
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:
weight_decay = group['weight_decay']
momentum = group['momentum']
eeta = group['eeta']
lr = group['lr']
lars = group['lars']
eps = group['epsilon']
for p in group['params']:
if p.grad is None:
continue
decayed_grad = p.grad
scaled_lr = lr
if lars:
w_norm = torch.norm(p)
g_norm = torch.norm(p.grad)
trust_ratio = torch.where(w_norm > 0 and g_norm > 0,
eeta * w_norm / (g_norm + weight_decay * w_norm + eps),
torch.ones_like(w_norm))
trust_ratio.clamp_(0.0, 50)
scaled_lr *= trust_ratio.item()
if weight_decay != 0:
decayed_grad = decayed_grad.add(p, alpha=weight_decay)
decayed_grad = torch.clamp(decayed_grad, -10.0, 10.0)
if momentum != 0:
param_state = self.state[p]
if 'momentum_buffer' not in param_state:
buf = param_state['momentum_buffer'] = torch.clone(decayed_grad).detach()
else:
buf = param_state['momentum_buffer']
buf.mul_(momentum).add_(decayed_grad)
decayed_grad = buf
p.add_(decayed_grad, alpha=-scaled_lr)
return loss