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import torch
import torch.nn as nn
class RMSNorm(nn.Module):
def __init__(
self,
dim: int,
elementwise_affine=True,
eps: float = 1e-6,
device=None,
dtype=None,
):
"""
Initialize the RMSNorm normalization layer.
Args:
dim (int): The dimension of the input tensor.
eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
Attributes:
eps (float): A small value added to the denominator for numerical stability.
weight (nn.Parameter): Learnable scaling parameter.
"""
factory_kwargs = {"device": device, "dtype": dtype}
super().__init__()
self.eps = eps
if elementwise_affine:
self.weight = nn.Parameter(torch.ones(dim, **factory_kwargs))
def _norm(self, x):
"""
Apply the RMSNorm normalization to the input tensor.
Args:
x (torch.Tensor): The input tensor.
Returns:
torch.Tensor: The normalized tensor.
"""
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
"""
Forward pass through the RMSNorm layer.
Args:
x (torch.Tensor): The input tensor.
Returns:
torch.Tensor: The output tensor after applying RMSNorm.
"""
output = self._norm(x.float()).type_as(x)
if hasattr(self, "weight"):
output = output * self.weight
return output
def get_norm_layer(norm_layer):
"""
Get the normalization layer.
Args:
norm_layer (str): The type of normalization layer.
Returns:
norm_layer (nn.Module): The normalization layer.
"""
if norm_layer == "layer":
return nn.LayerNorm
elif norm_layer == "rms":
return RMSNorm
else:
raise NotImplementedError(f"Norm layer {norm_layer} is not implemented")
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