# type: ignore
# from https://github.com/nanowell/AdEMAMix-Optimizer-Pytorch. Thanks a lot!
import math
import torch
from torch.optim import Optimizer
[docs]class AdEMAMix(Optimizer):
"""AdEMAMix optimizer."""
def __init__(
self,
params={},
lr=1e-3,
betas=(0.9, 0.999, 0.9999),
eps=1e-8,
weight_decay=0,
alpha=5.0,
T_alpha_beta3=None,
):
if not 0.0 <= lr:
raise ValueError(f"Invalid learning rate: {lr}")
if not 0.0 <= eps:
raise ValueError(f"Invalid epsilon value: {eps}")
assert len(betas) == 3, f"Invalid beta parameters: {betas}, expected 3"
assert all(
0.0 <= beta < 1.0 for beta in betas
), f"Invalid beta parameters: {betas}"
if not 0.0 <= weight_decay:
raise ValueError(f"Invalid weight_decay value: {weight_decay}")
defaults = dict(
lr=lr,
betas=betas,
eps=eps,
weight_decay=weight_decay,
alpha=alpha,
T_alpha_beta3=T_alpha_beta3,
)
super(AdEMAMix, self).__init__(params, defaults)
def __setstate__(self, state):
super(AdEMAMix, self).__setstate__(state)
[docs] @torch.no_grad()
def step(self, closure=None):
"""Run one optimizer step."""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
params_with_grad = []
grads = []
exp_avgs = []
exp_avg_sqs = []
exp_avg_slow = []
state_steps = []
for p in group["params"]:
if p.grad is not None:
params_with_grad.append(p)
if p.grad.is_sparse:
raise RuntimeError("AdEMAMix does not support sparse gradients")
grads.append(p.grad)
state = self.state[p]
# Lazy 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
)
# Slow exponential moving average
state["exp_avg_slow"] = torch.zeros_like(
p, memory_format=torch.preserve_format
)
exp_avgs.append(state["exp_avg"])
exp_avg_sqs.append(state["exp_avg_sq"])
exp_avg_slow.append(state["exp_avg_slow"])
state["step"] += 1
state_steps.append(state["step"])
beta1, beta2, beta3 = group["betas"]
alpha = group["alpha"]
T_alpha_beta3 = group["T_alpha_beta3"]
self._update_adamemix(
params_with_grad,
grads,
exp_avgs,
exp_avg_sqs,
exp_avg_slow,
state_steps,
beta1=beta1,
beta2=beta2,
beta3=beta3,
alpha=alpha,
T_alpha_beta3=T_alpha_beta3,
learning_rate=group["lr"],
weight_decay=group["weight_decay"],
eps=group["eps"],
)
return loss
def _update_adamemix(
self,
params,
grads,
exp_avgs,
exp_avg_sqs,
exp_avg_slow,
state_steps,
beta1,
beta2,
beta3,
alpha,
T_alpha_beta3,
learning_rate,
weight_decay,
eps,
):
"""Update one parameter group in place."""
for i, param in enumerate(params):
grad = grads[i]
exp_avg = exp_avgs[i]
exp_avg_sq = exp_avg_sqs[i]
exp_avg_slow_i = exp_avg_slow[i]
step = state_steps[i]
bias_correction1 = 1 - beta1**step
bias_correction2 = 1 - beta2**step
if T_alpha_beta3 is not None:
alpha_t = min(step * alpha / T_alpha_beta3, alpha)
beta3_t = min(
math.exp(
math.log(beta1)
* math.log(beta3)
/ (
(1 - step / T_alpha_beta3) * math.log(beta3)
+ (step / T_alpha_beta3) * math.log(beta1)
)
),
beta3,
)
else:
alpha_t = alpha
beta3_t = beta3
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
exp_avg_slow_i.mul_(beta3_t).add_(grad, alpha=1 - beta3_t)
denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(eps)
step_size = learning_rate / bias_correction1
if weight_decay != 0:
param.add_(param, alpha=-weight_decay * learning_rate)
param.addcdiv_(exp_avg + alpha_t * exp_avg_slow_i, denom, value=-step_size)