import math
import torch
from torch.optim.optimizer import Optimizer
from .types import Betas2, OptFloat, OptLossClosure, Params
__all__ = ('AdamP',)
[docs]class AdamP(Optimizer):
r"""Implements AdamP algorithm.
It has been proposed in `Slowing Down the Weight Norm Increase in
Momentum-based Optimizers`__
Arguments:
params: iterable of parameters to optimize or dicts defining
parameter groups
lr: learning rate (default: 1e-3)
betas: coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
eps: term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay: weight decay (L2 penalty) (default: 0)
delta: threhold that determines whether a set of parameters is scale
invariant or not (default: 0.1)
wd_ratio: relative weight decay applied on scale-invariant parameters
compared to that applied on scale-variant parameters (default: 0.1)
nesterov: enables Nesterov momentum (default: False)
Example:
>>> import torch_optimizer as optim
>>> optimizer = optim.AdamP(model.parameters(), lr=0.1)
>>> optimizer.zero_grad()
>>> loss_fn(model(input), target).backward()
>>> optimizer.step()
__ https://arxiv.org/abs/2006.08217
Note:
Reference code: https://github.com/clovaai/AdamP
"""
def __init__(
self,
params: Params,
lr: float = 1e-3,
betas: Betas2 = (0.9, 0.999),
eps: float = 1e-8,
weight_decay: float = 0,
delta: float = 0.1,
wd_ratio: float = 0.1,
nesterov: bool = False,
) -> None:
if lr <= 0.0:
raise ValueError('Invalid learning rate: {}'.format(lr))
if eps < 0.0:
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])
)
if weight_decay < 0:
raise ValueError(
'Invalid weight_decay value: {}'.format(weight_decay)
)
if delta < 0:
raise ValueError('Invalid delta value: {}'.format(delta))
if wd_ratio < 0:
raise ValueError('Invalid wd_ratio value: {}'.format(wd_ratio))
defaults = dict(
lr=lr,
betas=betas,
eps=eps,
weight_decay=weight_decay,
delta=delta,
wd_ratio=wd_ratio,
nesterov=nesterov,
)
super(AdamP, self).__init__(params, defaults)
@staticmethod
def _channel_view(x):
return x.view(x.size(0), -1)
@staticmethod
def _layer_view(x):
return x.view(1, -1)
@staticmethod
def _cosine_similarity(x, y, eps, view_func):
x = view_func(x)
y = view_func(y)
x_norm = x.norm(dim=1).add_(eps)
y_norm = y.norm(dim=1).add_(eps)
dot = (x * y).sum(dim=1)
return dot.abs() / x_norm / y_norm
def _projection(self, p, grad, perturb, delta, wd_ratio, eps):
wd = 1
expand_size = [-1] + [1] * (len(p.shape) - 1)
for view_func in [self._channel_view, self._layer_view]:
cosine_sim = self._cosine_similarity(grad, p.data, eps, view_func)
if cosine_sim.max() < delta / math.sqrt(view_func(p.data).size(1)):
p_n = p.data / view_func(p.data).norm(dim=1).view(
expand_size
).add_(eps)
perturb -= p_n * view_func(p_n * perturb).sum(dim=1).view(
expand_size
)
wd = wd_ratio
return perturb, wd
return perturb, wd
[docs] def step(self, closure: OptLossClosure = None) -> OptFloat:
r"""Performs a single optimization step.
Arguments:
closure: 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
beta1, beta2 = group['betas']
nesterov = group['nesterov']
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
state['exp_avg'] = torch.zeros_like(p.data)
state['exp_avg_sq'] = torch.zeros_like(p.data)
# Adam
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
state['step'] += 1
bias_correction1 = 1 - beta1 ** state['step']
bias_correction2 = 1 - beta2 ** state['step']
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(
group['eps']
)
step_size = group['lr'] / bias_correction1
if nesterov:
perturb = (beta1 * exp_avg + (1 - beta1) * grad) / denom
else:
perturb = exp_avg / denom
# Projection
wd_ratio = 1
if len(p.shape) > 1:
perturb, wd_ratio = self._projection(
p,
grad,
perturb,
group['delta'],
group['wd_ratio'],
group['eps'],
)
# Weight decay
if group['weight_decay'] > 0:
p.data.mul_(
1 - group['lr'] * group['weight_decay'] * wd_ratio
)
# Step
p.data.add_(perturb, alpha=-step_size)
return loss
