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# https://pytorch.org/docs/stable/_modules/torch/nn/modules/normalization.html#LayerNorm
import numbers
from typing import Tuple
import torch
from torch import Tensor, nn
[docs]class BiaslessLayerNorm(nn.Module):
r"""Applies Layer Normalization without a bias (beta) like in PaLM. Note
that this is not the same as RMSNorm which also doesn't shift the
distribution by considering the mean.
.. math::
y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma
Args:
normalized_shape (int or list or torch.Size): input shape from an expected input
of size
.. math::
[* \times \text{normalized\_shape}[0] \times \text{normalized\_shape}[1]
\times \ldots \times \text{normalized\_shape}[-1]]
If a single integer is used, it is treated as a singleton list, and this module will
normalize over the last dimension which is expected to be of that specific size.
eps: a value added to the denominator for numerical stability. Default: 1e-5
Attributes:
weight: the learnable weights of the module of shape
:math:`\text{normalized\_shape}`.
The values are initialized to 1.
Shape:
- Input: :math:`(N, *)`
- Output: :math:`(N, *)` (same shape as input)
"""
[docs] def __init__(
self,
normalized_shape: Tuple[int, ...],
eps: float = 1e-5,
device=None,
dtype=None,
) -> None:
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__()
if isinstance(normalized_shape, numbers.Integral):
normalized_shape = (normalized_shape,)
self.normalized_shape = tuple(normalized_shape)
self.eps = eps
self.weight = nn.Parameter(
torch.empty(self.normalized_shape, **factory_kwargs)
)
self.reset_parameters()
def reset_parameters(self) -> None:
self.weight.data.fill_(1.0)
def forward(self, input: Tensor) -> Tensor:
return nn.functional.layer_norm(
input, self.normalized_shape, self.weight, None, self.eps
)