Closed tmabraham closed 3 years ago
@tmabraham Hi, quickly skimming over the code, I noticed that you enabled fp16. Note that this might cause error with AdaBelief and Adam, if I remember correctly, float16 rounds number blow 1e-7 to 0, this means if eps is smaller than that then it would be rounded to 0. In this case, the division could explode. AdaBelief uses eps=1e-16 or 1e-12, this might be the reason. Please let me know if you have specified the full parameters to reproduce best baseline results, or try float32 format.
@tmabraham I'm closing this issue now since it seems to be solved. Please leave me a message if you have other comments.
@tmabraham Hi, please try this code to deal with mixed precision training. A by-pass is to cast weight and grad to float32., then update, and cast back to float16.
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
As we have discussed earlier, for 5-epoch Imagenette training, I am not achieving better results with AdaBelief/RangerAdaBelief compared to Ranger, Adam, or SGD. I have attached two notebooks with my baselines. Even after playing around with LR schedule, eps, wd, etc., I still wasn't able to reach similar performance to Ranger (80% vs. 83%) for 5-epoch run on Imagenette. Any tips to improve AdaBelief performance?
imagenette_baseline.ipynb imagenette_adabelief.ipynb