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"""https://github.com/Z-T-WANG/LaProp-Optimizer/blob/master/laprop.py"""
from torch.optim import Optimizer
import math
import torch
class LaProp(Optimizer):
def __init__(
self,
params,
lr=4e-4,
betas=(0.9, 0.999),
eps=1e-15,
weight_decay=0,
amsgrad=False,
centered=False,
):
self.steps_before_using_centered = 10
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,
centered=centered,
)
super(LaProp, self).__init__(params, defaults)
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:
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(
"Adam does not support sparse gradients, please consider SparseAdam instead"
)
amsgrad = group["amsgrad"]
centered = group["centered"]
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 learning rates
state["exp_avg_lr_1"] = 0.0
state["exp_avg_lr_2"] = 0.0
# Exponential moving average of squared gradient values
state["exp_avg_sq"] = torch.zeros_like(p.data)
if centered:
# Exponential moving average of gradient values as calculated by beta2
state["exp_mean_avg_beta2"] = torch.zeros_like(p.data)
if amsgrad:
# 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 centered:
exp_mean_avg_beta2 = state["exp_mean_avg_beta2"]
if amsgrad:
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_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
state["exp_avg_lr_1"] = (
state["exp_avg_lr_1"] * beta1 + (1 - beta1) * group["lr"]
)
state["exp_avg_lr_2"] = state["exp_avg_lr_2"] * beta2 + (1 - beta2)
bias_correction1 = (
state["exp_avg_lr_1"] / group["lr"] if group["lr"] != 0.0 else 1.0
) # 1 - beta1 ** state['step']
step_size = 1 / bias_correction1
bias_correction2 = state["exp_avg_lr_2"]
denom = exp_avg_sq
if centered:
exp_mean_avg_beta2.mul_(beta2).add_(1 - beta2, grad)
if state["step"] > self.steps_before_using_centered:
mean = exp_mean_avg_beta2**2
denom = denom - mean
if amsgrad:
if not (
centered and state["step"] <= self.steps_before_using_centered
):
# Maintains the maximum of all (centered) 2nd moment running avg. till now
torch.max(max_exp_avg_sq, denom, out=max_exp_avg_sq)
# Use the max. for normalizing running avg. of gradient
denom = max_exp_avg_sq
denom = denom.div(bias_correction2).sqrt_().add_(group["eps"])
step_of_this_grad = grad / denom
exp_avg.mul_(beta1).add_((1 - beta1) * group["lr"], step_of_this_grad)
p.data.add_(-step_size, exp_avg)
if group["weight_decay"] != 0:
p.data.add_(-group["weight_decay"], p.data)
return loss
|
