LayerNorm介绍请参考：【AI知识】归一化、批量归一化 、 层归一化 和 实例归一化

RMSNorm介绍请参考：【大模型知识点】RMSNorm（Root Mean Square Normalization）均方根归一化

LayerNorm实现：

import torch 
import torch.nn as nn


class LayerNorm(nn.Module):
    def __init__(self,dim,eps=1e-5,bias=False):
        super().__init__()
        self.dim = dim
        self.eps = eps
        # 可训练的缩放参数
        self.gamma = nn.Parameter(torch.ones(dim))

        self.bias = nn.Parameter(torch.zeros(dim)) if bias else None
    
    def forward(self,x):
        # x: (batch_size,seq_len,dim)
        # 计算均值 x_mean ： (batch_size,seq_len,dim)
        x_mean = x.mean(-1,keepdim=True)
        # 计算均方根 rms :  (batch_size,seq_len,dim)
        rms = torch.sqrt(x.pow(2).mean(-1,keepdim=True)+self.eps)

        if self.bias:
            return self.gamma*((x-x_mean)/rms)+self.bias
        else:
            return self.gamma*((x-x_mean)/rms)

RMSNorm实现：

import torch 
import torch.nn as nn

class RMSNorm(nn.Module):
    def __init__(self,dim,eps=1e-5,bias=False):
   		super().__init__()
        self.dim = dim 
        self.eps = eps
        # 可训练的缩放参数
        self.gamma = nn.Parameter(torch.ones(dim))
        self.bias = nn.Parameter(torch.zeros(dim)) if bias else None
    def forward(self,x):
        # 计算输入的均方根
        # x: (batch_size,seq_len,dim)
        # .mean(-1,keepdim=True) : 在最后一个维度（特征维度）上计算平均值，并保持维度不变
        # rms : (batch_size,seq_len,1)
        rms = torch.sqrt(x.pow(2).mean(-1,keepdim=True)+self.eps)

        if self.bias:
            return self.gamma*(x/rms) + self.bias
        else:
            return self.gamma*(x/rms)