# Copyright (C) 2019-2022, François-Guillaume Fernandez.
# This program is licensed under the Apache License 2.0.
# See LICENSE or go to <https://www.apache.org/licenses/LICENSE-2.0> for full license details.
from typing import Callable, Dict, Iterable, Optional, Tuple
import torch
from torch.optim.optimizer import Optimizer
from . import functional as F
[docs]
class TAdam(Optimizer):
"""Implements the TAdam optimizer from `"TAdam: A Robust Stochastic Gradient Optimizer"
<https://arxiv.org/pdf/2003.00179.pdf>`_.
Args:
params (iterable): iterable of parameters to optimize or dicts defining parameter groups
lr (float, optional): learning rate
betas (Tuple[float, float], optional): coefficients used for running averages (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 (L2 penalty) (default: 0)
dof (int, optional): degrees of freedom
"""
def __init__(
self,
params: Iterable[torch.nn.Parameter], # type: ignore[name-defined]
lr: float = 1e-3,
betas: Tuple[float, float] = (0.9, 0.999),
eps: float = 1e-8,
weight_decay: float = 0.0,
amsgrad: bool = False,
dof: Optional[float] = None,
) -> None:
if lr < 0.0:
raise ValueError(f"Invalid learning rate: {lr}")
if eps < 0.0:
raise ValueError(f"Invalid epsilon value: {eps}")
if not 0.0 <= betas[0] < 1.0:
raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
if not 0.0 <= betas[1] < 1.0:
raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
if not 0.0 <= weight_decay:
raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad, dof=dof)
super().__init__(params, defaults)
def __setstate__(self, state: Dict[str, torch.Tensor]) -> None:
super().__setstate__(state)
for group in self.param_groups:
group.setdefault("amsgrad", False)
@torch.no_grad()
def step(self, closure: Optional[Callable[[], float]] = None) -> Optional[float]:
"""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:
params_with_grad = []
grads = []
exp_avgs = []
exp_avg_sqs = []
W_ts = []
max_exp_avg_sqs = []
state_steps = []
beta1, beta2 = group["betas"]
for p in group["params"]:
if p.grad is not None:
params_with_grad.append(p)
if p.grad.is_sparse:
raise RuntimeError(f"{self.__class__.__name__} 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)
if group["amsgrad"]:
# 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)
# Tadam specific
state["W_t"] = beta1 / (1 - beta1) * torch.ones(1, dtype=p.data.dtype, device=p.data.device)
exp_avgs.append(state["exp_avg"])
exp_avg_sqs.append(state["exp_avg_sq"])
W_ts.append(state["W_t"])
if group["amsgrad"]:
max_exp_avg_sqs.append(state["max_exp_avg_sq"])
# update the steps for each param group update
state["step"] += 1
# record the step after step update
state_steps.append(state["step"])
F.tadam(
params_with_grad,
grads,
exp_avgs,
exp_avg_sqs,
max_exp_avg_sqs,
W_ts,
state_steps,
group["amsgrad"],
beta1,
beta2,
group["lr"],
group["weight_decay"],
group["eps"],
group["dof"],
)
return loss