import math
import torch
from torch.optim.optimizer import Optimizer
from flambe.compile import Component
[docs]class RAdam(Optimizer, Component):
'''
Rectified Adam optimizer.
Taken from https://github.com/LiyuanLucasLiu/RAdam.
'''
def __init__(self,
params,
lr=1e-3,
betas=(0.9, 0.999),
eps=1e-8,
weight_decay=0,
degenerated_to_sgd=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]))
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:
betas_diff = (param['betas'][0] != betas[0] or param['betas'][1] != betas[1])
if 'betas' in param and betas_diff:
param['buffer'] = [[None, None, None] for _ in range(10)]
defaults = dict(lr=lr,
betas=betas,
eps=eps,
weight_decay=weight_decay,
buffer=[[None, None, None] for _ in range(10)])
super(RAdam, self).__init__(params, defaults)
[docs] def __setstate__(self, state):
super(RAdam, self).__setstate__(state)
[docs] def state_dict(self):
state_dict = super().state_dict()
return state_dict
[docs] def step(self, closure=None):
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.float()
if grad.is_sparse:
raise RuntimeError('RAdam does not support sparse gradients')
p_data_fp32 = p.data.float()
state = self.state[p]
if len(state) == 0:
state['step'] = 0
state['exp_avg'] = torch.zeros_like(p_data_fp32)
state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
else:
state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
beta1, beta2 = group['betas']
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
exp_avg.mul_(beta1).add_(1 - beta1, grad)
state['step'] += 1
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:
term1 = (1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4)
term2 = (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)
step_size = math.sqrt(term1 * term2) / (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
# more conservative since it's an approximated value
if N_sma >= 5:
if group['weight_decay'] != 0:
p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
denom = exp_avg_sq.sqrt().add_(group['eps'])
p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom)
p.data.copy_(p_data_fp32)
elif step_size > 0:
if group['weight_decay'] != 0:
p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
p_data_fp32.add_(-step_size * group['lr'], exp_avg)
p.data.copy_(p_data_fp32)
return loss