Here is what I normally use: the major difference being that I do not pass the noise to GPU at every call which should speed things up 
class GaussianNoise(nn.Module):
"""Gaussian noise regularizer.
Args:
sigma (float, optional): relative standard deviation used to generate the
noise. Relative means that it will be multiplied by the magnitude of
the value your are adding the noise to. This means that sigma can be
the same regardless of the scale of the vector.
is_relative_detach (bool, optional): whether to detach the variable before
computing the scale of the noise. If `False` then the scale of the noise
won't be seen as a constant but something to optimize: this will bias the
network to generate vectors with smaller values.
"""
def __init__(self, sigma=0.1, is_relative_detach=True):
super().__init__()
self.sigma = sigma
self.is_relative_detach = is_relative_detach
self.noise = torch.tensor(0).to(device)
def forward(self, x):
if self.training and self.sigma != 0:
scale = self.sigma * x.detach() if self.is_relative_detach else self.sigma * x
sampled_noise = self.noise.repeat(*x.size()).normal_() * scale
x = x + sampled_noise
return x