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radam.py
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radam.py
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# Original source taken from https://github.com/LiyuanLucasLiu/RAdam
#
# Copyright 2019 Liyuan Liu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import torch
# pylint: disable=no-name-in-module
from torch.optim.optimizer import Optimizer
class RAdam(Optimizer):
"""RAdam optimizer"""
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
weight_decay=0):
"""
Init
:param params: parameters to optimize
:param lr: learning rate
:param betas: beta
:param eps: numerical precision
:param weight_decay: weight decay weight
"""
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
self.buffer = [[None, None, None] for _ in range(10)]
super().__init__(params, defaults)
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 = self.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 = (
group['lr'] *
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'])
)
else:
step_size = group['lr'] / (1 - beta1 ** state['step'])
buffered[2] = step_size
if group['weight_decay'] != 0:
p_data_fp32.add_(
-group['weight_decay'] * group['lr'], p_data_fp32
)
# more conservative since it's an approximated value
if N_sma >= 5:
denom = exp_avg_sq.sqrt().add_(group['eps'])
p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
else:
p_data_fp32.add_(-step_size, exp_avg)
p.data.copy_(p_data_fp32)
return loss