Source code for lmflow.optim.adabound
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
import torch
from torch.optim.optimizer import Optimizer
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class AdaBound(Optimizer):
r"""Implements AdaBound algorithm.
It has been proposed in `Adaptive Gradient Methods with Dynamic Bound of
Learning Rate
https://arxiv.org/abs/1902.09843
Note:
Reference code: https://github.com/Luolc/AdaBound
"""
def __init__(
self,
params,
lr: float = 1e-3,
betas = (0.9, 0.999),
final_lr: float = 0.1,
gamma: float = 1e-3,
eps: float = 1e-8,
weight_decay: float = 0,
amsbound: bool = False,
) -> None:
if lr <= 0.0:
raise ValueError("Invalid learning rate: {}".format(lr))
if eps < 0.0:
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 final_lr < 0.0:
raise ValueError(
"Invalid final learning rate: {}".format(final_lr)
)
if not 0.0 <= gamma < 1.0:
raise ValueError("Invalid gamma parameter: {}".format(gamma))
if weight_decay < 0:
raise ValueError(
"Invalid weight_decay value: {}".format(weight_decay)
)
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)
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self.base_lrs = [group["lr"] for group in self.param_groups]
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def __setstate__(self, state) -> None:
super(AdaBound, self).__setstate__(state)
for group in self.param_groups:
group.setdefault("amsbound", False)
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def step(self, closure = None):
r"""Performs a single optimization step.
Arguments:
closure: 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:
msg = (
"AdaBound does not support sparse gradients, "
"please consider SparseAdam instead"
)
raise RuntimeError(msg)
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, memory_format=torch.preserve_format
)
# Exponential moving average of squared gradient values
state["exp_avg_sq"] = torch.zeros_like(
p, memory_format=torch.preserve_format
)
if amsbound:
# Maintains max of all exp. moving avg. of
# sq. grad. values
state["max_exp_avg_sq"] = torch.zeros_like(
p, memory_format=torch.preserve_format
)
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(p.data, alpha=group["weight_decay"])
# 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