#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import math
import torch
from pytext.optimizer.optimizers import Optimizer
from torch.optim import Optimizer as PT_Optimizer
[docs]class RAdam(Optimizer, PT_Optimizer):
"""Implements rectified adam as derived in the following paper:
"On the Variance of the Adaptive Learning Rate and Beyond"
(https://arxiv.org/abs/1908.03265)
This code is mostly a direct copy-paste of the code provided by the authors here:
https://github.com/LiyuanLucasLiu/RAdam/blob/master/radam.py
"""
[docs] class Config(Optimizer.Config):
lr: float = 0.001
weight_decay: float = 0.00001
eps: float = 1e-8
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
defaults = {"lr": lr, "betas": betas, "eps": eps, "weight_decay": weight_decay}
self.buffer = [[None, None, None] for ind in range(10)]
PT_Optimizer.__init__(self, params, defaults)
def __setstate__(self, state):
super(RAdam, self).__setstate__(state)
[docs] def step(self, closure=None, **kwargs):
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
[docs] @classmethod
def from_config(cls, config: Config, model: torch.nn.Module):
return cls(
model.parameters(),
lr=config.lr,
weight_decay=config.weight_decay,
eps=config.eps,
)