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"""
AdamW optimizer (weight decay fix)
originally from hugginface (https://github.com/huggingface/transformers).
Copied from UNITER
(https://github.com/ChenRocks/UNITER)
"""
import math
import torch
from torch.optim import Optimizer
class AdamW(Optimizer):
""" Implements Adam algorithm with weight decay fix.
Parameters:
lr (float): learning rate. Default 1e-3.
betas (tuple of 2 floats): Adams beta parameters (b1, b2).
Default: (0.9, 0.999)
eps (float): Adams epsilon. Default: 1e-6
weight_decay (float): Weight decay. Default: 0.0
correct_bias (bool): can be set to False to avoid correcting bias
in Adam (e.g. like in Bert TF repository). Default True.
"""
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-6,
weight_decay=0.0, correct_bias=True):
if lr < 0.0:
raise ValueError(
"Invalid learning rate: {} - should be >= 0.0".format(lr))
if not 0.0 <= betas[0] < 1.0:
raise ValueError("Invalid beta parameter: {} - "
"should be in [0.0, 1.0[".format(betas[0]))
if not 0.0 <= betas[1] < 1.0:
raise ValueError("Invalid beta parameter: {} - "
"should be in [0.0, 1.0[".format(betas[1]))
if not 0.0 <= eps:
raise ValueError("Invalid epsilon value: {} - "
"should be >= 0.0".format(eps))
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
correct_bias=correct_bias)
super(AdamW, self).__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()
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]
# 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)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
beta1, beta2 = group['betas']
state['step'] += 1
# Decay the first and second moment running average coefficient
# In-place operations to update the averages at the same time
exp_avg.mul_(beta1).add_(grad , alpha=1.0 - beta1)
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1.0 - beta2)
denom = exp_avg_sq.sqrt().add_(group['eps'])
step_size = group['lr']
if group['correct_bias']: # No bias correction for Bert
bias_correction1 = 1.0 - beta1 ** state['step']
bias_correction2 = 1.0 - beta2 ** state['step']
step_size = (step_size * math.sqrt(bias_correction2)
/ bias_correction1)
p.data.addcdiv_(exp_avg, denom, value=-step_size)
# Just adding the square of the weights to the loss function is
# *not* the correct way of using L2 regularization/weight decay
# with Adam, since that will interact with the m and v
# parameters in strange ways.
#
# Instead we want to decay the weights in a manner that doesn't
# interact with the m/v parameters. This is equivalent to
# adding the square of the weights to the loss with plain
# (non-momentum) SGD.
# Add weight decay at the end (fixed version)
if group['weight_decay'] > 0.0:
p.data.add_(p.data, alpha=-group['lr'] * group['weight_decay'])
return loss
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