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"""Normalization layers for transfromers.
Copied from lucidrains:
https://github.com/lucidrains/x-transformers/blob/main/x_transformers/x_transformers.py
"""
from typing import Dict, Optional, Type
import torch
from torch import nn
from torch import Tensor
class RMSNorm(nn.Module):
"""Root mean square layer normalization."""
def __init__(self, dim: int, eps: float = 1e-8) -> None:
super().__init__()
self.scale = dim ** -0.5
self.eps = eps
self.g = nn.Parameter(torch.ones(dim))
def forward(self, x: Tensor) -> Tensor:
"""Applies normalization."""
norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
return x / norm.clamp(min=self.eps) * self.g
class PreNorm(nn.Module):
"""Applies layer normalization then function."""
def __init__(
self,
normalized_shape: int,
fn: Type[nn.Module],
context_dim: Optional[int] = None,
) -> None:
super().__init__()
self.norm = nn.LayerNorm(normalized_shape)
self.fn = fn
self.norm_context = (
nn.LayerNorm(context_dim) if context_dim is not None else None
)
def forward(self, x: Tensor, **kwargs) -> Tensor:
"""Applies pre norm."""
x = self.norm(x)
if self.norm_context is not None:
context = kwargs["context"]
normed_context = self.norm_context(context)
kwargs.update(context=normed_context)
return self.fn(x, **kwargs)
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