mirror of https://github.com/hpcaitech/ColossalAI
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
96 lines
3.6 KiB
96 lines
3.6 KiB
"""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
|
|
|
|
from colossalai.legacy.registry import OPTIMIZERS
|
|
|
|
|
|
@OPTIMIZERS.register_module
|
|
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
|