#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import math
import torch
from torch.optim.optimizer import Optimizer as PT_Optimizer
from .optimizers import Optimizer
[docs]class AdaBelief(Optimizer, PT_Optimizer):
"""
`AdaBelief Optimizer, adapting stepsizes by the belief in observed gradients`
Paper: https://arxiv.org/abs/2010.07468
Implementation has been copied over from the original author (https://github.com/juntang-zhuang/Adabelief-Optimizer)
"""
[docs] class Config(Optimizer.Config):
lr: float = 1e-3
beta_1: float = 0.9
beta_2: float = 0.999
eps: float = 1e-8
weight_decay: float = 0
amsgrad: bool = False
weight_decouple: bool = True
fixed_decay: bool = True
rectify: bool = False
r"""Implements AdaBelief algorithm. Modified from Adam in PyTorch
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
(default: False)
weight_decouple (boolean, optional): ( default: False) If set as True, then
the optimizer uses decoupled weight decay as in AdamW
fixed_decay (boolean, optional): (default: False) This is used when weight_decouple
is set as True.
When fixed_decay == True, the weight decay is performed as
$W_{new} = W_{old} - W_{old} \times decay$.
When fixed_decay == False, the weight decay is performed as
$W_{new} = W_{old} - W_{old} \times decay \times lr$. Note that in this case, the
weight decay ratio decreases with learning rate (lr).
rectify (boolean, optional): (default: False) If set as True, then perform the rectified
update similar to RAdam
reference: AdaBelief Optimizer, adapting stepsizes by the belief in observed gradients
NeurIPS 2020 Spotlight
"""
[docs] @classmethod
def from_config(cls, config: Config, model: torch.nn.Module):
return cls(
params=model.parameters(),
lr=config.lr,
betas=(config.beta_1, config.beta_2),
eps=config.eps,
weight_decay=config.weight_decay,
amsgrad=config.amsgrad,
weight_decouple=config.weight_decouple,
fixed_decay=config.fixed_decay,
rectify=config.rectify,
)
def __init__(
self,
params,
lr=1e-3,
betas=(0.9, 0.999),
eps=1e-8,
weight_decay=0,
amsgrad=False,
weight_decouple=False,
fixed_decay=False,
rectify=False,
):
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]))
defaults = dict(
lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad
)
PT_Optimizer.__init__(self, params, defaults)
self.weight_decouple = weight_decouple
self.rectify = rectify
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("AMS enabled in AdaBelief")
def __setstate__(self, state):
super(AdaBelief, self).__setstate__(state)
for group in self.param_groups:
group.setdefault("amsgrad", False)
[docs] 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, memory_format=torch.preserve_format
)
# Exponential moving average of squared gradient values
state["exp_avg_var"] = torch.zeros_like(
p.data, memory_format=torch.preserve_format
)
if amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state["max_exp_avg_var"] = torch.zeros_like(
p.data, memory_format=torch.preserve_format
)
[docs] def step(self, closure=None, **kwargs):
"""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(
"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["rho_inf"] = 2.0 / (1.0 - beta2) - 1.0
state["step"] = 0
# Exponential moving average of gradient values
state["exp_avg"] = torch.zeros_like(
p.data, memory_format=torch.preserve_format
)
# Exponential moving average of squared gradient values
state["exp_avg_var"] = torch.zeros_like(
p.data, memory_format=torch.preserve_format
)
if amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state["max_exp_avg_var"] = torch.zeros_like(
p.data, memory_format=torch.preserve_format
)
# 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"]
# 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_(group["weight_decay"], p.data)
# Update first and second moment running average
exp_avg.mul_(beta1).add_(1 - beta1, grad)
grad_residual = grad - exp_avg
exp_avg_var.mul_(beta2).addcmul_(
1 - beta2, grad_residual, grad_residual
)
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, out=max_exp_avg_var)
# Use the max. for normalizing running avg. of gradient
denom = (
max_exp_avg_var.add_(group["eps"]).sqrt()
/ math.sqrt(bias_correction2)
).add_(group["eps"])
else:
denom = (
exp_avg_var.add_(group["eps"]).sqrt()
/ math.sqrt(bias_correction2)
).add_(group["eps"])
if not self.rectify:
# Default update
step_size = group["lr"] / bias_correction1
p.data.addcdiv_(-step_size, exp_avg, denom)
else: # Rectified update
# calculate rho_t
state["rho_t"] = state["rho_inf"] - 2 * state[
"step"
] * beta2 ** state["step"] / (1.0 - beta2 ** state["step"])
if (
state["rho_t"] > 4
): # perform Adam style update if variance is small
rho_inf, rho_t = state["rho_inf"], state["rho_t"]
rt = (
(rho_t - 4.0)
* (rho_t - 2.0)
* rho_inf
/ (rho_inf - 4.0)
/ (rho_inf - 2.0)
/ rho_t
)
rt = math.sqrt(rt)
step_size = rt * group["lr"] / bias_correction1
p.data.addcdiv_(-step_size, exp_avg, denom)
else: # perform SGD style update
p.data.add_(-group["lr"], exp_avg)
return loss
[docs] def clip_grad_norm(self, max_norm, model=None):
return Optimizer.clip_grad_norm(self, max_norm, model)