#!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
import torch
from torch.optim.optimizer import Optimizer
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class AdaBelief(Optimizer):
r"""Implements AdaBelief algorithm. Modified from Adam in PyTorch
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):
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]))
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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
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self.weight_decouple = weight_decouple
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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')
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def __setstate__(self, state):
super(AdaBelief, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('amsgrad', False)
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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)
# Exponential moving average of squared gradient values
state['exp_avg_var'] = 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)
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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)
# Exponential moving average of squared gradient values
state['exp_avg_var'] = 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)
# 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
