import math
import torch
from torch.optim.optimizer import Optimizer
from .types import Betas2, OptFloat, OptLossClosure, Params, State
__all__ = ('AdaBound',)
[docs]class AdaBound(Optimizer):
r"""Implements AdaBound algorithm.
It has been proposed in `Adaptive Gradient Methods with Dynamic Bound of
Learning Rate`__.
Arguments:
params: iterable of parameters to optimize or dicts defining
parameter groups
lr: learning rate (default: 1e-3)
betas: coefficients used for computing running averages of gradient
and its square (default: (0.9, 0.999))
final_lr: final (SGD) learning rate (default: 0.1)
gamma: convergence speed of the bound functions
(default: 1e-3)
eps: term added to the denominator to improve numerical stability
(default: 1e-8)
weight_decay: weight decay (L2 penalty) (default: 0)
amsbound: whether to use the AMSBound variant of this algorithm
Example:
>>> import torch_optimizer as optim
>>> optimizer = optim.AdaBound(model.parameters(), lr=0.1)
>>> optimizer.zero_grad()
>>> loss_fn(model(input), target).backward()
>>> optimizer.step()
__ https://arxiv.org/abs/1902.09843
Note:
Reference code: https://github.com/Luolc/AdaBound
"""
def __init__(
self,
params: Params,
lr: float = 1e-3,
betas: Betas2 = (0.9, 0.999),
final_lr: float = 0.1,
gamma: float = 1e-3,
eps: float = 1e-8,
weight_decay: float = 0,
amsbound: bool = False,
) -> None:
if lr <= 0.0:
raise ValueError('Invalid learning rate: {}'.format(lr))
if eps < 0.0:
raise ValueError('Invalid epsilon value: {}'.format(eps))
if not 0.0 <= betas[0] < 1.0:
raise ValueError(
'Invalid beta parameter at index 0: {}'.format(betas[0])
)
if not 0.0 <= betas[1] < 1.0:
raise ValueError(
'Invalid beta parameter at index 1: {}'.format(betas[1])
)
if final_lr < 0.0:
raise ValueError(
'Invalid final learning rate: {}'.format(final_lr)
)
if not 0.0 <= gamma < 1.0:
raise ValueError('Invalid gamma parameter: {}'.format(gamma))
if weight_decay < 0:
raise ValueError(
'Invalid weight_decay value: {}'.format(weight_decay)
)
defaults = dict(
lr=lr,
betas=betas,
final_lr=final_lr,
gamma=gamma,
eps=eps,
weight_decay=weight_decay,
amsbound=amsbound,
)
super(AdaBound, self).__init__(params, defaults)
self.base_lrs = [group['lr'] for group in self.param_groups]
def __setstate__(self, state: State) -> None:
super(AdaBound, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('amsbound', False)
[docs] def step(self, closure: OptLossClosure = None) -> OptFloat:
r"""Performs a single optimization step.
Arguments:
closure: A closure that reevaluates the model and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group, base_lr in zip(self.param_groups, self.base_lrs):
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data
if grad.is_sparse:
msg = (
'AdaBound does not support sparse gradients, '
'please consider SparseAdam instead'
)
raise RuntimeError(msg)
amsbound = group['amsbound']
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p)
if amsbound:
# Maintains max of all exp. moving avg. of
# sq. grad. values
state['max_exp_avg_sq'] = torch.zeros_like(p)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
if amsbound:
max_exp_avg_sq = state['max_exp_avg_sq']
beta1, beta2 = group['betas']
state['step'] += 1
if group['weight_decay'] != 0:
grad = grad.add(p.data, alpha=group['weight_decay'])
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
if amsbound:
# Maintains the maximum of all 2nd moment running
# avg. till now
torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
# Use the max. for normalizing running avg. of gradient
denom = max_exp_avg_sq.sqrt().add_(group['eps'])
else:
denom = exp_avg_sq.sqrt().add_(group['eps'])
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
step_size = (
group['lr']
* math.sqrt(bias_correction2)
/ bias_correction1
)
# Applies bounds on actual learning rate
# lr_scheduler cannot affect final_lr, this is a workaround
# to apply lr decay
final_lr = group['final_lr'] * group['lr'] / base_lr
lower_bound = final_lr * (
1 - 1 / (group['gamma'] * state['step'] + 1)
)
upper_bound = final_lr * (
1 + 1 / (group['gamma'] * state['step'])
)
step_size = torch.full_like(denom, step_size)
step_size.div_(denom).clamp_(lower_bound, upper_bound).mul_(
exp_avg
)
p.data.add_(-step_size)
return loss
