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""" PyTorch impl of LaProp optimizer
Code simplified from https://github.com/Z-T-WANG/LaProp-Optimizer, MIT License
Paper: LaProp: Separating Momentum and Adaptivity in Adam, https://arxiv.org/abs/2002.04839
@article{ziyin2020laprop,
title={LaProp: a Better Way to Combine Momentum with Adaptive Gradient},
author={Ziyin, Liu and Wang, Zhikang T and Ueda, Masahito},
journal={arXiv preprint arXiv:2002.04839},
year={2020}
}
"""
from typing import Tuple
from torch.optim import Optimizer
import torch
from ._types import ParamsT
class LaProp(Optimizer):
""" LaProp Optimizer
Paper: LaProp: Separating Momentum and Adaptivity in Adam, https://arxiv.org/abs/2002.04839
"""
def __init__(
self,
params: ParamsT,
lr: float = 4e-4,
betas: Tuple[float, float] = (0.9, 0.999),
eps: float = 1e-15,
weight_decay: float = 0.,
caution: bool = 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,
caution=caution,
)
super(LaProp, self).__init__(params, defaults)
@torch.no_grad()
def step(self, closure=None):
"""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:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad
if grad.is_sparse:
raise RuntimeError('LaProp does not support sparse gradients')
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)
# Exponential moving average of learning rates
state['exp_avg_lr_1'] = 0.
state['exp_avg_lr_2'] = 0.
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
beta1, beta2 = group['betas']
state['step'] += 1
one_minus_beta2 = 1 - beta2
one_minus_beta1 = 1 - beta1
# Decay the first and second moment running average coefficient
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=one_minus_beta2)
state['exp_avg_lr_1'] = state['exp_avg_lr_1'] * beta1 + one_minus_beta1 * group['lr']
state['exp_avg_lr_2'] = state['exp_avg_lr_2'] * beta2 + one_minus_beta2
# 1 - beta1 ** state['step']
bias_correction1 = state['exp_avg_lr_1'] / group['lr'] if group['lr'] != 0. else 1.
bias_correction2 = state['exp_avg_lr_2']
step_size = 1 / bias_correction1
denom = exp_avg_sq.div(bias_correction2).sqrt_().add_(group['eps'])
step_of_this_grad = grad / denom
exp_avg.mul_(beta1).add_(step_of_this_grad, alpha=group['lr'] * one_minus_beta1)
if group['caution']:
# Apply caution as per 'Cautious Optimizers' - https://arxiv.org/abs/2411.16085
mask = (exp_avg * grad > 0).to(grad.dtype)
mask.div_(mask.mean().clamp_(min=1e-3))
exp_avg = exp_avg * mask
p.add_(exp_avg, alpha=-step_size)
if group['weight_decay'] != 0:
p.add_(p, alpha=-(group['lr'] * group['weight_decay']))
return loss |