Ross Wightman
3 years ago
committed by
GitHub
commit
779107b693
@ -0,0 +1,244 @@
|
||||
import math
|
||||
import torch
|
||||
from torch.optim.optimizer import Optimizer
|
||||
from tabulate import tabulate
|
||||
from colorama import Fore, Back, Style
|
||||
|
||||
version_higher = ( torch.__version__ >= "1.5.0" )
|
||||
|
||||
class AdaBelief(Optimizer):
|
||||
r"""Implements AdaBelief algorithm. Modified from Adam in PyTorch
|
||||
Arguments:
|
||||
params (iterable): iterable of parameters to optimize or dicts defining
|
||||
parameter groups
|
||||
lr (float, optional): 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))
|
||||
eps (float, optional): term added to the denominator to improve
|
||||
numerical stability (default: 1e-16)
|
||||
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
|
||||
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
|
||||
algorithm from the paper `On the Convergence of Adam and Beyond`_
|
||||
(default: False)
|
||||
weight_decouple (boolean, optional): ( default: True) If set as True, then
|
||||
the optimizer uses decoupled weight decay as in AdamW
|
||||
fixed_decay (boolean, optional): (default: False) This is used when weight_decouple
|
||||
is set as True.
|
||||
When fixed_decay == True, the weight decay is performed as
|
||||
$W_{new} = W_{old} - W_{old} \times decay$.
|
||||
When fixed_decay == False, the weight decay is performed as
|
||||
$W_{new} = W_{old} - W_{old} \times decay \times lr$. Note that in this case, the
|
||||
weight decay ratio decreases with learning rate (lr).
|
||||
rectify (boolean, optional): (default: True) If set as True, then perform the rectified
|
||||
update similar to RAdam
|
||||
degenerated_to_sgd (boolean, optional) (default:True) If set as True, then perform SGD update
|
||||
when variance of gradient is high
|
||||
print_change_log (boolean, optional) (default: True) If set as True, print the modifcation to
|
||||
default hyper-parameters
|
||||
reference: AdaBelief Optimizer, adapting stepsizes by the belief in observed gradients, NeurIPS 2020
|
||||
"""
|
||||
|
||||
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-16,
|
||||
weight_decay=0, amsgrad=False, weight_decouple=True, fixed_decay=False, rectify=True,
|
||||
degenerated_to_sgd=True, print_change_log = True):
|
||||
|
||||
# ------------------------------------------------------------------------------
|
||||
# Print modifications to default arguments
|
||||
if print_change_log:
|
||||
print(Fore.RED + 'Please check your arguments if you have upgraded adabelief-pytorch from version 0.0.5.')
|
||||
print(Fore.RED + 'Modifications to default arguments:')
|
||||
default_table = tabulate([
|
||||
['adabelief-pytorch=0.0.5','1e-8','False','False'],
|
||||
['>=0.1.0 (Current 0.2.0)','1e-16','True','True']],
|
||||
headers=['eps','weight_decouple','rectify'])
|
||||
print(Fore.RED + default_table)
|
||||
|
||||
recommend_table = tabulate([
|
||||
['Recommended eps = 1e-8', 'Recommended eps = 1e-16'],
|
||||
],
|
||||
headers=['SGD better than Adam (e.g. CNN for Image Classification)','Adam better than SGD (e.g. Transformer, GAN)'])
|
||||
print(Fore.BLUE + recommend_table)
|
||||
|
||||
print(Fore.BLUE +'For a complete table of recommended hyperparameters, see')
|
||||
print(Fore.BLUE + 'https://github.com/juntang-zhuang/Adabelief-Optimizer')
|
||||
|
||||
print(Fore.GREEN + 'You can disable the log message by setting "print_change_log = False", though it is recommended to keep as a reminder.')
|
||||
|
||||
print(Style.RESET_ALL)
|
||||
# ------------------------------------------------------------------------------
|
||||
|
||||
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]))
|
||||
|
||||
self.degenerated_to_sgd = degenerated_to_sgd
|
||||
if isinstance(params, (list, tuple)) and len(params) > 0 and isinstance(params[0], dict):
|
||||
for param in params:
|
||||
if 'betas' in param and (param['betas'][0] != betas[0] or param['betas'][1] != betas[1]):
|
||||
param['buffer'] = [[None, None, None] for _ in range(10)]
|
||||
|
||||
defaults = dict(lr=lr, betas=betas, eps=eps,
|
||||
weight_decay=weight_decay, amsgrad=amsgrad, buffer=[[None, None, None] for _ in range(10)])
|
||||
super(AdaBelief, self).__init__(params, defaults)
|
||||
|
||||
self.degenerated_to_sgd = degenerated_to_sgd
|
||||
self.weight_decouple = weight_decouple
|
||||
self.rectify = rectify
|
||||
self.fixed_decay = fixed_decay
|
||||
if self.weight_decouple:
|
||||
print('Weight decoupling enabled in AdaBelief')
|
||||
if self.fixed_decay:
|
||||
print('Weight decay fixed')
|
||||
if self.rectify:
|
||||
print('Rectification enabled in AdaBelief')
|
||||
if amsgrad:
|
||||
print('AMSGrad enabled in AdaBelief')
|
||||
|
||||
def __setstate__(self, state):
|
||||
super(AdaBelief, self).__setstate__(state)
|
||||
for group in self.param_groups:
|
||||
group.setdefault('amsgrad', False)
|
||||
|
||||
def reset(self):
|
||||
for group in self.param_groups:
|
||||
for p in group['params']:
|
||||
state = self.state[p]
|
||||
amsgrad = group['amsgrad']
|
||||
|
||||
# State initialization
|
||||
state['step'] = 0
|
||||
# Exponential moving average of gradient values
|
||||
state['exp_avg'] = torch.zeros_like(p.data,memory_format=torch.preserve_format) \
|
||||
if version_higher else torch.zeros_like(p.data)
|
||||
|
||||
# Exponential moving average of squared gradient values
|
||||
state['exp_avg_var'] = torch.zeros_like(p.data,memory_format=torch.preserve_format) \
|
||||
if version_higher else torch.zeros_like(p.data)
|
||||
|
||||
if amsgrad:
|
||||
# Maintains max of all exp. moving avg. of sq. grad. values
|
||||
state['max_exp_avg_var'] = torch.zeros_like(p.data,memory_format=torch.preserve_format) \
|
||||
if version_higher else torch.zeros_like(p.data)
|
||||
|
||||
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:
|
||||
for p in group['params']:
|
||||
if p.grad is None:
|
||||
continue
|
||||
|
||||
# cast data type
|
||||
half_precision = False
|
||||
if p.data.dtype == torch.float16:
|
||||
half_precision = True
|
||||
p.data = p.data.float()
|
||||
p.grad = p.grad.float()
|
||||
|
||||
grad = p.grad.data
|
||||
if grad.is_sparse:
|
||||
raise RuntimeError(
|
||||
'AdaBelief does not support sparse gradients, please consider SparseAdam instead')
|
||||
amsgrad = group['amsgrad']
|
||||
|
||||
state = self.state[p]
|
||||
|
||||
beta1, beta2 = group['betas']
|
||||
|
||||
# State initialization
|
||||
if len(state) == 0:
|
||||
state['step'] = 0
|
||||
# Exponential moving average of gradient values
|
||||
state['exp_avg'] = torch.zeros_like(p.data,memory_format=torch.preserve_format) \
|
||||
if version_higher else torch.zeros_like(p.data)
|
||||
# Exponential moving average of squared gradient values
|
||||
state['exp_avg_var'] = torch.zeros_like(p.data,memory_format=torch.preserve_format) \
|
||||
if version_higher else torch.zeros_like(p.data)
|
||||
if amsgrad:
|
||||
# Maintains max of all exp. moving avg. of sq. grad. values
|
||||
state['max_exp_avg_var'] = torch.zeros_like(p.data,memory_format=torch.preserve_format) \
|
||||
if version_higher else torch.zeros_like(p.data)
|
||||
|
||||
# perform weight decay, check if decoupled weight decay
|
||||
if self.weight_decouple:
|
||||
if not self.fixed_decay:
|
||||
p.data.mul_(1.0 - group['lr'] * group['weight_decay'])
|
||||
else:
|
||||
p.data.mul_(1.0 - group['weight_decay'])
|
||||
else:
|
||||
if group['weight_decay'] != 0:
|
||||
grad.add_(p.data, alpha=group['weight_decay'])
|
||||
|
||||
# get current state variable
|
||||
exp_avg, exp_avg_var = state['exp_avg'], state['exp_avg_var']
|
||||
|
||||
state['step'] += 1
|
||||
bias_correction1 = 1 - beta1 ** state['step']
|
||||
bias_correction2 = 1 - beta2 ** state['step']
|
||||
|
||||
# Update first and second moment running average
|
||||
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
|
||||
grad_residual = grad - exp_avg
|
||||
exp_avg_var.mul_(beta2).addcmul_( grad_residual, grad_residual, value=1 - beta2)
|
||||
|
||||
if amsgrad:
|
||||
max_exp_avg_var = state['max_exp_avg_var']
|
||||
# Maintains the maximum of all 2nd moment running avg. till now
|
||||
torch.max(max_exp_avg_var, exp_avg_var.add_(group['eps']), out=max_exp_avg_var)
|
||||
|
||||
# Use the max. for normalizing running avg. of gradient
|
||||
denom = (max_exp_avg_var.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
|
||||
else:
|
||||
denom = (exp_avg_var.add_(group['eps']).sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
|
||||
|
||||
# update
|
||||
if not self.rectify:
|
||||
# Default update
|
||||
step_size = group['lr'] / bias_correction1
|
||||
p.data.addcdiv_( exp_avg, denom, value=-step_size)
|
||||
|
||||
else: # Rectified update, forked from RAdam
|
||||
buffered = group['buffer'][int(state['step'] % 10)]
|
||||
if state['step'] == buffered[0]:
|
||||
N_sma, step_size = buffered[1], buffered[2]
|
||||
else:
|
||||
buffered[0] = state['step']
|
||||
beta2_t = beta2 ** state['step']
|
||||
N_sma_max = 2 / (1 - beta2) - 1
|
||||
N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
|
||||
buffered[1] = N_sma
|
||||
|
||||
# more conservative since it's an approximated value
|
||||
if N_sma >= 5:
|
||||
step_size = math.sqrt(
|
||||
(1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (
|
||||
N_sma_max - 2)) / (1 - beta1 ** state['step'])
|
||||
elif self.degenerated_to_sgd:
|
||||
step_size = 1.0 / (1 - beta1 ** state['step'])
|
||||
else:
|
||||
step_size = -1
|
||||
buffered[2] = step_size
|
||||
|
||||
if N_sma >= 5:
|
||||
denom = exp_avg_var.sqrt().add_(group['eps'])
|
||||
p.data.addcdiv_(exp_avg, denom, value=-step_size * group['lr'])
|
||||
elif step_size > 0:
|
||||
p.data.add_( exp_avg, alpha=-step_size * group['lr'])
|
||||
|
||||
if half_precision:
|
||||
p.data = p.data.half()
|
||||
p.grad = p.grad.half()
|
||||
|
||||
return loss
|
||||
Loading…
Reference in new issue