""" PyTorch LARS / LARC Optimizer
An implementation of LARS (SGD) + LARC in PyTorch
Based on:
* PyTorch SGD: https://github.com/pytorch/pytorch/blob/1.7/torch/optim/sgd.py#L100
* NVIDIA APEX LARC: https://github.com/NVIDIA/apex/blob/master/apex/parallel/LARC.py
Additional cleanup and modifications to properly support PyTorch XLA.
Copyright 2021 Ross Wightman
"""
import torch
from torch.optim.optimizer import Optimizer
[docs]class Lars(Optimizer):
""" LARS for PyTorch
Paper: `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: 1.0).
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)
trust_coeff (float): trust coefficient for computing adaptive lr / trust_ratio (default: 0.001)
eps (float): eps for division denominator (default: 1e-8)
trust_clip (bool): enable LARC trust ratio clipping (default: False)
always_adapt (bool): always apply LARS LR adapt, otherwise only when group weight_decay != 0 (default: False)
"""
def __init__(
self,
params,
lr=1.0,
momentum=0,
dampening=0,
weight_decay=0,
nesterov=False,
trust_coeff=0.001,
eps=1e-8,
trust_clip=False,
always_adapt=False,
):
if lr < 0.0:
raise ValueError(f"Invalid learning rate: {lr}")
if momentum < 0.0:
raise ValueError(f"Invalid momentum value: {momentum}")
if weight_decay < 0.0:
raise ValueError(f"Invalid weight_decay value: {weight_decay}")
if nesterov and (momentum <= 0 or dampening != 0):
raise ValueError("Nesterov momentum requires a momentum and zero dampening")
defaults = dict(
lr=lr,
momentum=momentum,
dampening=dampening,
weight_decay=weight_decay,
nesterov=nesterov,
trust_coeff=trust_coeff,
eps=eps,
trust_clip=trust_clip,
always_adapt=always_adapt,
)
super().__init__(params, defaults)
def __setstate__(self, state):
super().__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.
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()
device = self.param_groups[0]['params'][0].device
one_tensor = torch.tensor(1.0, device=device) # because torch.where doesn't handle scalars correctly
for group in self.param_groups:
weight_decay = group['weight_decay']
momentum = group['momentum']
dampening = group['dampening']
nesterov = group['nesterov']
trust_coeff = group['trust_coeff']
eps = group['eps']
for p in group['params']:
if p.grad is None:
continue
grad = p.grad
# apply LARS LR adaptation, LARC clipping, weight decay
# ref: https://github.com/NVIDIA/apex/blob/master/apex/parallel/LARC.py
if weight_decay != 0 or group['always_adapt']:
w_norm = p.norm(2.0)
g_norm = grad.norm(2.0)
trust_ratio = trust_coeff * w_norm / (g_norm + w_norm * weight_decay + eps)
# FIXME nested where required since logical and/or not working in PT XLA
trust_ratio = torch.where(
w_norm > 0,
torch.where(g_norm > 0, trust_ratio, one_tensor),
one_tensor,
)
if group['trust_clip']:
trust_ratio = torch.minimum(trust_ratio / group['lr'], one_tensor)
grad.add(p, alpha=weight_decay)
grad.mul_(trust_ratio)
# apply SGD update https://github.com/pytorch/pytorch/blob/1.7/torch/optim/sgd.py#L100
if momentum != 0:
param_state = self.state[p]
if 'momentum_buffer' not in param_state:
buf = param_state['momentum_buffer'] = torch.clone(grad).detach()
else:
buf = param_state['momentum_buffer']
buf.mul_(momentum).add_(grad, alpha=1. - dampening)
if nesterov:
grad = grad.add(buf, alpha=momentum)
else:
grad = buf
p.add_(grad, alpha=-group['lr'])
return loss