#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Implementation of the `AdaBound optimizer
<https://github.com/Luolc/AdaBound/blob/master/adabound/adabound.py>`::
@inproceedings{Luo2019AdaBound,
author = {Luo, Liangchen and Xiong, Yuanhao and Liu, Yan and Sun, Xu},
title = {Adaptive Gradient Methods with Dynamic Bound of Learning Rate},
booktitle = {Proceedings of the 7th International Conference on Learning Representations},
month = {May},
year = {2019},
address = {New Orleans, Louisiana}
}
"""
import math
import torch
import torch.optim
[docs]class AdaBound(torch.optim.Optimizer):
"""Implements the AdaBound algorithm.
Parameters
----------
params : list
Iterable of parameters to optimize or dicts defining parameter groups
lr : :obj:`float`, optional
Adam learning rate
betas : :obj:`tuple`, optional
Coefficients (as a 2-tuple of floats) used for computing running
averages of gradient and its square
final_lr : :obj:`float`, optional
Final (SGD) learning rate
gamma : :obj:`float`, optional
Convergence speed of the bound functions
eps : :obj:`float`, optional
Term added to the denominator to improve numerical stability
weight_decay : :obj:`float`, optional
Weight decay (L2 penalty)
amsbound : :obj:`bool`, optional
Whether to use the AMSBound variant of this algorithm
"""
def __init__(
self,
params,
lr=1e-3,
betas=(0.9, 0.999),
final_lr=0.1,
gamma=1e-3,
eps=1e-8,
weight_decay=0,
amsbound=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])
)
if not 0.0 <= final_lr:
raise ValueError("Invalid final learning rate: {}".format(final_lr))
if not 0.0 <= gamma < 1.0:
raise ValueError("Invalid gamma parameter: {}".format(gamma))
defaults = dict(
lr=lr,
betas=betas,
final_lr=final_lr,
gamma=gamma,
eps=eps,
weight_decay=weight_decay,
amsbound=amsbound,
)
super(AdaBound, self).__init__(params, defaults)
self.base_lrs = list(map(lambda group: group["lr"], self.param_groups))
def __setstate__(self, state):
super(AdaBound, self).__setstate__(state)
for group in self.param_groups:
group.setdefault("amsbound", False)
[docs] def step(self, closure=None):
"""Performs a single optimization step.
Parameters
----------
closure : :obj:`callable`, optional
A closure that reevaluates the model and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group, base_lr in zip(self.param_groups, self.base_lrs):
for p in group["params"]:
if p.grad is None:
continue
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError(
"Adam does not support sparse gradients, please consider SparseAdam instead"
)
amsbound = group["amsbound"]
state = self.state[p]
# 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_sq"] = torch.zeros_like(p.data)
if amsbound:
# Maintains max of all exp. moving avg. of sq. grad. values
state["max_exp_avg_sq"] = torch.zeros_like(p.data)
exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
if amsbound:
max_exp_avg_sq = state["max_exp_avg_sq"]
beta1, beta2 = group["betas"]
state["step"] += 1
if group["weight_decay"] != 0:
grad = grad.add(group["weight_decay"], p.data)
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
if amsbound:
# Maintains the maximum of all 2nd moment running avg. till now
torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
# Use the max. for normalizing running avg. of gradient
denom = max_exp_avg_sq.sqrt().add_(group["eps"])
else:
denom = exp_avg_sq.sqrt().add_(group["eps"])
bias_correction1 = 1 - beta1 ** state["step"]
bias_correction2 = 1 - beta2 ** state["step"]
step_size = (
group["lr"] * math.sqrt(bias_correction2) / bias_correction1
)
# Applies bounds on actual learning rate
# lr_scheduler cannot affect final_lr, this is a workaround to apply lr decay
final_lr = group["final_lr"] * group["lr"] / base_lr
lower_bound = final_lr * (
1 - 1 / (group["gamma"] * state["step"] + 1)
)
upper_bound = final_lr * (
1 + 1 / (group["gamma"] * state["step"])
)
step_size = torch.full_like(denom, step_size)
step_size.div_(denom).clamp_(lower_bound, upper_bound).mul_(
exp_avg
)
p.data.add_(-step_size)
return loss
[docs]class AdaBoundW(torch.optim.Optimizer):
"""Implements AdaBound algorithm with Decoupled Weight Decay
(See https://arxiv.org/abs/1711.05101)
Parameters
----------
params : list
Iterable of parameters to optimize or dicts defining parameter groups
lr : :obj:`float`, optional
Adam learning rate
betas : :obj:`tuple`, optional
Coefficients (as a 2-tuple of floats) used for computing running
averages of gradient and its square
final_lr : :obj:`float`, optional
Final (SGD) learning rate
gamma : :obj:`float`, optional
Convergence speed of the bound functions
eps : :obj:`float`, optional
Term added to the denominator to improve numerical stability
weight_decay : :obj:`float`, optional
Weight decay (L2 penalty)
amsbound : :obj:`bool`, optional
Whether to use the AMSBound variant of this algorithm
"""
def __init__(
self,
params,
lr=1e-3,
betas=(0.9, 0.999),
final_lr=0.1,
gamma=1e-3,
eps=1e-8,
weight_decay=0,
amsbound=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])
)
if not 0.0 <= final_lr:
raise ValueError("Invalid final learning rate: {}".format(final_lr))
if not 0.0 <= gamma < 1.0:
raise ValueError("Invalid gamma parameter: {}".format(gamma))
defaults = dict(
lr=lr,
betas=betas,
final_lr=final_lr,
gamma=gamma,
eps=eps,
weight_decay=weight_decay,
amsbound=amsbound,
)
super(AdaBoundW, self).__init__(params, defaults)
self.base_lrs = list(map(lambda group: group["lr"], self.param_groups))
def __setstate__(self, state):
super(AdaBoundW, self).__setstate__(state)
for group in self.param_groups:
group.setdefault("amsbound", False)
[docs] def step(self, closure=None):
"""Performs a single optimization step.
Parameters
----------
closure : :obj:`callable`, optional
A closure that reevaluates the model and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group, base_lr in zip(self.param_groups, self.base_lrs):
for p in group["params"]:
if p.grad is None:
continue
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError(
"Adam does not support sparse gradients, please consider SparseAdam instead"
)
amsbound = group["amsbound"]
state = self.state[p]
# 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_sq"] = torch.zeros_like(p.data)
if amsbound:
# Maintains max of all exp. moving avg. of sq. grad. values
state["max_exp_avg_sq"] = torch.zeros_like(p.data)
exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
if amsbound:
max_exp_avg_sq = state["max_exp_avg_sq"]
beta1, beta2 = group["betas"]
state["step"] += 1
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
if amsbound:
# Maintains the maximum of all 2nd moment running avg. till now
torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
# Use the max. for normalizing running avg. of gradient
denom = max_exp_avg_sq.sqrt().add_(group["eps"])
else:
denom = exp_avg_sq.sqrt().add_(group["eps"])
bias_correction1 = 1 - beta1 ** state["step"]
bias_correction2 = 1 - beta2 ** state["step"]
step_size = (
group["lr"] * math.sqrt(bias_correction2) / bias_correction1
)
# Applies bounds on actual learning rate
# lr_scheduler cannot affect final_lr, this is a workaround to
# apply lr decay
final_lr = group["final_lr"] * group["lr"] / base_lr
lower_bound = final_lr * (
1 - 1 / (group["gamma"] * state["step"] + 1)
)
upper_bound = final_lr * (
1 + 1 / (group["gamma"] * state["step"])
)
step_size = torch.full_like(denom, step_size)
step_size.div_(denom).clamp_(lower_bound, upper_bound).mul_(
exp_avg
)
if group["weight_decay"] != 0:
decayed_weights = torch.mul(p.data, group["weight_decay"])
p.data.add_(-step_size)
p.data.sub_(decayed_weights)
else:
p.data.add_(-step_size)
return loss