# Copyright (c) Alibaba, Inc. and its affiliates.
import torch
from torch.optim import * # noqa: F401,F403
from torch.optim.optimizer import Optimizer, required
from easycv.framework.errors import ValueError
[docs]class LARS(Optimizer):
r"""Implements layer-wise adaptive rate scaling for SGD.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float): base learning rate (\gamma_0)
momentum (float, optional): momentum factor (default: 0) ("m")
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
("\beta")
dampening (float, optional): dampening for momentum (default: 0)
eta (float, optional): LARS coefficient
nesterov (bool, optional): enables Nesterov momentum (default: False)
Based on Algorithm 1 of the following paper by You, Gitman, and Ginsburg.
Large Batch Training of Convolutional Networks:
https://arxiv.org/abs/1708.03888
Example:
>>> optimizer = LARS(model.parameters(), lr=0.1, momentum=0.9,
>>> weight_decay=1e-4, eta=1e-3)
>>> optimizer.zero_grad()
>>> loss_fn(model(input), target).backward()
>>> optimizer.step()
"""
[docs] def __init__(self,
params,
lr=required,
momentum=0,
dampening=0,
weight_decay=0,
eta=0.001,
nesterov=False):
if lr is not required and 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 eta < 0.0:
raise ValueError('Invalid LARS coefficient value: {}'.format(eta))
defaults = dict(
lr=lr,
momentum=momentum,
dampening=dampening,
weight_decay=weight_decay,
nesterov=nesterov,
eta=eta)
if nesterov and (momentum <= 0 or dampening != 0):
raise ValueError(
'Nesterov momentum requires a momentum and zero dampening')
super(LARS, self).__init__(params, defaults)
def __setstate__(self, state):
super(LARS, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('nesterov', False)
[docs] @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']
dampening = group['dampening']
eta = group['eta']
nesterov = group['nesterov']
lr = group['lr']
lars_exclude = group.get('lars_exclude', False)
for p in group['params']:
if p.grad is None:
continue
d_p = p.grad
if lars_exclude:
local_lr = 1.
else:
weight_norm = torch.norm(p).item()
grad_norm = torch.norm(d_p).item()
# Compute local learning rate for this layer
local_lr = eta * weight_norm / \
(grad_norm + weight_decay * weight_norm)
actual_lr = local_lr * lr
d_p = d_p.add(p, alpha=weight_decay).mul(actual_lr)
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_(d_p, alpha=1 - dampening)
if nesterov:
d_p = d_p.add(buf, alpha=momentum)
else:
d_p = buf
p.add_(-d_p)
return loss