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gadam.py
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gadam.py
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"""
Generalization of Adam, AdaMax, AMSGrad algorithms (GAdam)
Based on PyTorch Adam optimizer
Alexander Penkin, sss13594@gmail.com
"""
import math
import torch
import numpy as np
from torch.optim.optimizer import Optimizer
class GAdam(Optimizer):
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), optimism=0.0, avg_sq_mode='weight',
amsgrad_decay=1, weight_decay=0, l1_decay=0, late_weight_decay=True, eps=1e-8):
"""Implements generalization of Adam, AdaMax, AMSGrad algorithms.
Adam and AdaMax has been proposed in `Adam: A Method for Stochastic Optimization`_.
With `betas` = (beta1, 0) and `amsgrad_decay` = beta2 it will become AdaMax.
With `amsgrad_decay` = 0 it will become AMSGrad.
I've found it's better to use something in-between.
`betas` = (0.9, 0.99) and `amsgrad_decay` = (0.0001) or
`betas` = (0.9, 0.95) and `amsgrad_decay` = (0.05)
worked best for me, but I've seen good results with wide range of settings.
Args:
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))
optimism (float, optional): Look-ahead factor proposed in `Training GANs with Optimism`_.
Must be in [0, 1) range. Value of 0.5 corresponds to paper,
0 disables it, 0.9 is 5x stronger than 0.5 (default: 0)
avg_sq_mode (str, optional): Specifies how square gradient term should be calculated. Valid values are
'weight' will calculate it per-weight as in vanilla Adam (default)
'output' will average it over 0 dim of each tensor,
i.e. shape[0] average squares will be used for each tensor
'tensor' will average it over each tensor
'global' will take average of average over each tensor,
i.e. only one avg sq value will be used
amsgrad_decay (float, optional): Decay factor for maximum running square of gradient.
Should be in [0, 1] range.
0 will instantly update it to current running mean square (default)
1 will behave as proposed in `On the Convergence of Adam and Beyond`_
Values between 0 and 1 will pull maximum mean square closer to current mean square on each step
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
l1_decay (float, optional): L1 penalty (default: 0)
late_weight_decay (boolean, optional): Whether L1 and L2 penalty should be
applied before (as proposed in 'Fixing Weight Decay Regularization in Adam'_)
or after (vanilla Adam) normalization with gradient average squares (default: True)
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
.. _Adam\: A Method for Stochastic Optimization:
https://arxiv.org/abs/1412.6980
.. _On the Convergence of Adam and Beyond:
https://openreview.net/forum?id=ryQu7f-RZ
.. _Training GANs with Optimism:
https://arxiv.org/abs/1711.00141
.. _Fixing Weight Decay Regularization in Adam:
https://arxiv.org/abs/1711.05101
"""
defaults = dict(lr=lr, betas=betas, optimism=optimism, amsgrad_decay=amsgrad_decay,
weight_decay=weight_decay, l1_decay=l1_decay, late_weight_decay=late_weight_decay, eps=eps)
self.avg_sq_mode = avg_sq_mode
super().__init__(params, defaults)
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()
if self.avg_sq_mode == 'global':
exp_avg_sq_list = []
for group in self.param_groups:
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')
state = self.state[p]
amsgrad_decay = group['amsgrad_decay']
amsgrad = amsgrad_decay != 1
# State initialization
if len(state) == 0:
state['step'] = 0
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p.data)
state['prev_shift'] = torch.zeros_like(p.data)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p.data)
if amsgrad:
# 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 amsgrad:
max_exp_avg_sq = state['max_exp_avg_sq']
beta1, beta2 = group['betas']
exp_avg.mul_(beta1).add_(1 - beta1, grad)
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
if amsgrad:
torch.max(max_exp_avg_sq * (1 - amsgrad_decay), exp_avg_sq, out=max_exp_avg_sq)
if self.avg_sq_mode == 'global':
exp_avg_sq_list.append(max_exp_avg_sq.mean())
else:
if self.avg_sq_mode == 'global':
exp_avg_sq_list.append(exp_avg_sq.mean())
if self.avg_sq_mode == 'global':
global_exp_avg_sq = np.mean(exp_avg_sq_list)
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
state = self.state[p]
amsgrad_decay = group['amsgrad_decay']
amsgrad = amsgrad_decay != 1
exp_avg = state['exp_avg']
if self.avg_sq_mode == 'weight':
exp_avg_sq = state['max_exp_avg_sq'] if amsgrad else state['exp_avg_sq']
elif self.avg_sq_mode == 'tensor':
exp_avg_sq = (state['max_exp_avg_sq'] if amsgrad else state['exp_avg_sq']).mean()
elif self.avg_sq_mode == 'output':
exp_avg_sq = (state['max_exp_avg_sq'] if amsgrad else state['exp_avg_sq'])
exp_avg_sq = exp_avg_sq.view(exp_avg_sq.shape[0], -1).mean(-1)\
.view(exp_avg_sq.shape[0], *((exp_avg_sq.dim() - 1) * [1]))
elif self.avg_sq_mode == 'global':
exp_avg_sq = global_exp_avg_sq
else:
raise ValueError()
beta1, beta2 = group['betas']
state['step'] += 1
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
# Decay the first and second moment running average coefficient
exp_avg = exp_avg / bias_correction1
exp_avg_sq = exp_avg_sq / bias_correction2
if self.avg_sq_mode == 'weight' or self.avg_sq_mode == 'output':
denom = exp_avg_sq.sqrt().add_(group['eps'])
else:
denom = math.sqrt(exp_avg_sq) + group['eps']
late_weight_decay = group['late_weight_decay']
if late_weight_decay:
exp_avg = exp_avg.div(denom)
weight_decay = group['weight_decay']
l1_decay = group['l1_decay']
if weight_decay != 0:
exp_avg.add_(weight_decay, p.data)
if l1_decay != 0:
exp_avg.add_(l1_decay, p.data.sign())
if not late_weight_decay:
exp_avg = exp_avg.div(denom)
lr = group['lr']
optimism = group['optimism']
if optimism != 0:
prev_shift = state['prev_shift']
p.data.sub_(optimism, prev_shift)
cur_shift = (-lr / (1 - optimism)) * exp_avg
prev_shift.copy_(cur_shift)
p.data.add_(cur_shift)
else:
grad = exp_avg
p.data.add_(-lr, grad)
return loss