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.
122 lines
5.6 KiB
122 lines
5.6 KiB
import math
|
|
import torch
|
|
from torch.optim import Optimizer
|
|
|
|
|
|
class AdaBound(Optimizer):
|
|
"""Implements AdaBound algorithm.
|
|
It has been proposed in `Adaptive Gradient Methods with Dynamic Bound of Learning Rate`_.
|
|
Arguments:
|
|
params (iterable): iterable of parameters to optimize or dicts defining
|
|
parameter groups
|
|
lr (float, optional): Adam learning rate (default: 1e-3)
|
|
betas (Tuple[float, float], optional): coefficients used for computing
|
|
running averages of gradient and its square (default: (0.9, 0.999))
|
|
final_lr (float, optional): final (SGD) learning rate (default: 0.1)
|
|
gamma (float, optional): convergence speed of the bound functions (default: 1e-3)
|
|
eps (float, optional): term added to the denominator to improve
|
|
numerical stability (default: 1e-8)
|
|
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
|
|
amsbound (boolean, optional): whether to use the AMSBound variant of this algorithm
|
|
.. Adaptive Gradient Methods with Dynamic Bound of Learning Rate:
|
|
https://openreview.net/forum?id=Bkg3g2R9FX
|
|
|
|
Originally taken from https://github.com/Luolc/AdaBound
|
|
NOTE: Has not provided good (or even decent) results on large datasets like ImageNet
|
|
"""
|
|
|
|
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), final_lr=0.1, gamma=1e-3,
|
|
eps=1e-8, weight_decay=0, amsbound=False):
|
|
if not 0.0 <= lr:
|
|
raise ValueError("Invalid learning rate: {}".format(lr))
|
|
if not 0.0 <= eps:
|
|
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 not 0.0 <= final_lr:
|
|
raise ValueError("Invalid final learning rate: {}".format(final_lr))
|
|
if not 0.0 <= gamma < 1.0:
|
|
raise ValueError("Invalid gamma parameter: {}".format(gamma))
|
|
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 = list(map(lambda group: group['lr'], self.param_groups))
|
|
|
|
def __setstate__(self, state):
|
|
super(AdaBound, self).__setstate__(state)
|
|
for group in self.param_groups:
|
|
group.setdefault('amsbound', 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, 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:
|
|
raise RuntimeError(
|
|
'Adam does not support sparse gradients, please consider SparseAdam instead')
|
|
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.data)
|
|
# Exponential moving average of squared gradient values
|
|
state['exp_avg_sq'] = torch.zeros_like(p.data)
|
|
if amsbound:
|
|
# Maintains max of all exp. moving avg. of sq. grad. values
|
|
state['max_exp_avg_sq'] = torch.zeros_like(p.data)
|
|
|
|
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(group['weight_decay'], p.data)
|
|
|
|
# Decay the first and second moment running average coefficient
|
|
exp_avg.mul_(beta1).add_(1 - beta1, grad)
|
|
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
|
|
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
|