import torch
from torch.optim.optimizer import Optimizer
class Lars(Optimizer):
r"""Implements the LARS optimizer from https://arxiv.org/pdf/1708.03888.pdf
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float): learning rate
momentum (float, optional): momentum factor (default: 0)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
dampening (float, optional): dampening for momentum (default: 0)
nesterov (bool, optional): enables Nesterov momentum (default: False)
scale_clip (tuple, optional): the lower and upper bounds for the weight norm in local LR of LARS
Example:
>>> model = ResNet()
>>> optimizer = Lars(model.parameters(), lr=1e-2, weight_decay=1e-5)
"""
def __init__(self, params, lr, momentum=0, dampening=0,
weight_decay=0, nesterov=False, scale_clip=None):
if 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))
defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
weight_decay=weight_decay, nesterov=nesterov)
if nesterov and (momentum <= 0 or dampening != 0):
raise ValueError("Nesterov momentum requires a momentum and zero dampening")
super(Lars, self).__init__(params, defaults)
# LARS arguments
self.scale_clip = scale_clip
if self.scale_clip is None:
self.scale_clip = (0, 10)
def __setstate__(self, state):
super(Lars, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('nesterov', False)
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:
loss = closure()
for group in self.param_groups:
weight_decay = group['weight_decay']
momentum = group['momentum']
dampening = group['dampening']
nesterov = group['nesterov']
for p in group['params']:
if p.grad is None:
continue
d_p = p.grad.data
if weight_decay != 0:
d_p.add_(weight_decay, p.data)
if momentum != 0:
param_state = self.state[p]
if 'momentum_buffer' not in param_state:
buf = param_state['momentum_buffer'] = torch.clone(d_p).detach()
else:
buf = param_state['momentum_buffer']
buf.mul_(momentum).add_(1 - dampening, d_p)
if nesterov:
d_p = d_p.add(momentum, buf)
else:
d_p = buf
# LARS
p_norm = p.data.pow(2).sum().sqrt()
update_norm = d_p.pow(2).sum().sqrt()
# Compute the local LR
if p_norm == 0 or update_norm == 0:
local_lr = 1
else:
local_lr = p_norm / update_norm
p.data.add_(-group['lr'] * local_lr, d_p)
return loss
