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.
121 lines
5.0 KiB
121 lines
5.0 KiB
""" AdamW Optimizer
|
|
Impl copied from PyTorch master
|
|
|
|
NOTE: Builtin optim.AdamW is used by the factory, this impl only serves as a Python based reference, will be removed
|
|
someday
|
|
"""
|
|
import math
|
|
import torch
|
|
from torch.optim.optimizer import Optimizer
|
|
|
|
|
|
class AdamW(Optimizer):
|
|
r"""Implements AdamW algorithm.
|
|
|
|
The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_.
|
|
The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_.
|
|
|
|
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-8)
|
|
weight_decay (float, optional): weight decay coefficient (default: 1e-2)
|
|
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
|
|
algorithm from the paper `On the Convergence of Adam and Beyond`_
|
|
(default: False)
|
|
|
|
.. _Adam\: A Method for Stochastic Optimization:
|
|
https://arxiv.org/abs/1412.6980
|
|
.. _Decoupled Weight Decay Regularization:
|
|
https://arxiv.org/abs/1711.05101
|
|
.. _On the Convergence of Adam and Beyond:
|
|
https://openreview.net/forum?id=ryQu7f-RZ
|
|
"""
|
|
|
|
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
|
|
weight_decay=1e-2, amsgrad=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]))
|
|
defaults = dict(lr=lr, betas=betas, eps=eps,
|
|
weight_decay=weight_decay, amsgrad=amsgrad)
|
|
super(AdamW, self).__init__(params, defaults)
|
|
|
|
def __setstate__(self, state):
|
|
super(AdamW, self).__setstate__(state)
|
|
for group in self.param_groups:
|
|
group.setdefault('amsgrad', 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 in self.param_groups:
|
|
for p in group['params']:
|
|
if p.grad is None:
|
|
continue
|
|
|
|
# Perform stepweight decay
|
|
p.data.mul_(1 - group['lr'] * group['weight_decay'])
|
|
|
|
# Perform optimization step
|
|
grad = p.grad.data
|
|
if grad.is_sparse:
|
|
raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
|
|
amsgrad = group['amsgrad']
|
|
|
|
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 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']
|
|
|
|
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.mul_(beta1).add_(grad, alpha=1 - beta1)
|
|
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
|
|
if amsgrad:
|
|
# 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() / math.sqrt(bias_correction2)).add_(group['eps'])
|
|
else:
|
|
denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
|
|
|
|
step_size = group['lr'] / bias_correction1
|
|
|
|
p.data.addcdiv_(exp_avg, denom, value=-step_size)
|
|
|
|
return loss
|