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import math
import torch
from torch.optim import Optimizer
class RAdamW(Optimizer):
r"""Implements RAdamW algorithm.
RAdam from `On the Variance of the Adaptive Learning Rate and Beyond
<https://arxiv.org/abs/1908.03265v1>`_
* `Adam: A Method for Stochastic Optimization
<https://arxiv.org/abs/1412.6980>`_
* `Decoupled Weight Decay Regularization
<https://arxiv.org/abs/1711.05101>`_
* `On the Convergence of Adam and Beyond
<https://openreview.net/forum?id=ryQu7f-RZ>`_
* `On the Variance of the Adaptive Learning Rate and Beyond
<https://arxiv.org/abs/1908.03265v1>`_
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay (float, optional): weight decay coefficient (default: 1e-2)
"""
def __init__(
self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=1e-2
):
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)
super(RAdamW, 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
# Perform optimization step
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError(
"Adam does not support sparse gradients, please consider SparseAdam instead"
)
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)
exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
beta1, beta2 = group["betas"]
eps = group["eps"]
lr = group["lr"]
if "rho_inf" not in group:
group["rho_inf"] = 2 / (1 - beta2) - 1
rho_inf = group["rho_inf"]
state["step"] += 1
t = state["step"]
# 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)
rho_t = rho_inf - ((2 * t * (beta2**t)) / (1 - beta2**t))
# Perform stepweight decay
p.data.mul_(1 - lr * group["weight_decay"])
if rho_t >= 5:
var = exp_avg_sq.sqrt().add_(eps)
r = math.sqrt(
(1 - beta2**t)
* ((rho_t - 4) * (rho_t - 2) * rho_inf)
/ ((rho_inf - 4) * (rho_inf - 2) * rho_t)
)
p.data.addcdiv_(exp_avg, var, value=-lr * r / (1 - beta1**t))
else:
p.data.add_(exp_avg, alpha=-lr / (1 - beta1**t))
return loss
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