.ipynb_checkpoints/Text_Encoder-checkpoint.py

import torch 
import torch.nn as nn
import math
from torch.nn.attention import SDPBackend, sdpa_kernel
from torch.nn import functional as F


class PrefixEncoder(torch.nn.Module):
    def __init__(self,config):
        super(PrefixEncoder,self).__init__()
        self.config=config
        self.device=config.device
        self.dtype=config.dtype
        self.num_virtual_tokens=config.num_virtual_tokens
        self.embedding=torch.nn.Embedding(config.num_virtual_tokens,config.token_dim,device=config.device,dtype=config.dtype)
        self.token_dim=config.token_dim
        self.encoder_hidden_size=config.encoder_hidden_size
        self.num_layers=config.num_layers
        self.transformer=torch.nn.Sequential(
            torch.nn.Linear(self.token_dim,self.encoder_hidden_size,device=self.device,dtype=self.dtype),
            torch.nn.Tanh(),
            torch.nn.Linear(self.encoder_hidden_size,self.num_layers*2*self.token_dim,device=self.device,dtype=self.dtype),
        )
    def forward(self,input_ids,batch_size):
        input_ids=input_ids.unsqueeze(0)
        prefix_embedding=self.embedding(input_ids)
        prefix_embedding=self.transformer(prefix_embedding) 
        self.register_parameter("prefix_embedding",nn.Parameter(prefix_embedding,requires_grad=False))
        prefix_embedding=prefix_embedding.expand(batch_size,self.num_virtual_tokens,self.num_layers*2*self.token_dim)
        prefix_embedding=prefix_embedding.reshape(batch_size,self.num_virtual_tokens,self.num_layers,2,self.token_dim)
        prefix_embedding=prefix_embedding.permute(3,2,0,1,4)
        del self.embedding
        del self.transformer
        k,v=prefix_embedding.chunk(2,dim=0)
        return (k.squeeze(0),v.squeeze(0))


class Transformer(nn.Module):
    def __init__(self,config):
        super(Transformer,self).__init__()
        self.resblocks=nn.ModuleList([ResidualAttentionBlock(config) for _ in range(config.num_layers)])
        self.prefix=PrefixEncoder(config)
        prefix_tokens=torch.arange(0,config.num_virtual_tokens,device=config.device,dtype=torch.long)
        self.register_buffer("prefix_tokens",prefix_tokens)
    def forward(self,hidden_state,use_emotion):
        if use_emotion:
            #print("激活text transformer prefix.")
            b,n,h=hidden_state.shape
            prefix_k,prefix_v=self.prefix(self.prefix_tokens,b)
            for index,resblock in enumerate(self.resblocks):
                hidden_state=resblock(hidden_state,prefix_k[index],prefix_v[index])
            return hidden_state
        else:
            for index,resblock in enumerate(self.resblocks):
                hidden_state=resblock(hidden_state)
            return hidden_state
            





class ResidualAttentionBlock(nn.Module):
    def __init__(self,config):
        super(ResidualAttentionBlock,self).__init__()
        self.ln_1=nn.LayerNorm(config.hidden_size,eps=config.norm_eps,elementwise_affine=True,device=config.device,dtype=config.dtype)
        self.ln_2=nn.LayerNorm(config.hidden_size,eps=config.norm_eps,elementwise_affine=True,device=config.device,dtype=config.dtype)
        #self.attn=nn.MultiheadAttention(config.hidden_size,config.num_heads,device=config.device,dtype=config.dtype)
        self.attn=MultiHeadAttention(config)
        self.mlp=MLP(config)
    def forward(self,hidden_state,prefix_k=None,prefix_v=None):
        residual=hidden_state
        hidden_state=self.ln_1(hidden_state)
        hidden_state=self.attn(hidden_state,prefix_k,prefix_v)
        hidden_state=residual+hidden_state
        residual=hidden_state
        hidden_state=self.ln_2(hidden_state)
        hidden_state=self.mlp(hidden_state)
        hidden_state=residual+hidden_state
        return hidden_state

class MultiHeadAttention(nn.Module):
    def __init__(self,config):
        super(MultiHeadAttention,self).__init__()
        self.hidden_size=config.hidden_size
        self.num_heads=config.num_heads
        self.head_size=self.hidden_size//self.num_heads
        #nn.Parameter包含weight和bias可训练参数
        self.in_proj_weight=nn.Parameter(torch.empty(3*config.hidden_size,config.hidden_size,device=config.device,dtype=config.dtype),requires_grad=False)
        self.in_proj_bias=nn.Parameter(torch.empty(3*config.hidden_size,device=config.device,dtype=config.dtype),requires_grad=False)
        #self.q_linear=nn.Linear(self.hidden_size,self.hidden_size,bias=True,device=config.device)
        #self.k_linear=nn.Linear(self.hidden_size,self.hidden_size,bias=True,device=config.device)
        #self.v_linear=nn.Linear(self.hidden_size,self.hidden_size,bias=True,device=config.device)
        self.out_proj=nn.Linear(self.hidden_size,self.hidden_size,bias=True,device=config.device,dtype=config.dtype)
    def forward(self,hidden_state,prefix_k=None,prefix_v=None):
        b,n,c=hidden_state.shape
        #q=self.q_linear(hidden_state).view(b,n,self.num_heads,self.head_size).permute(0,2,1,3)
        #k=self.k_linear(hidden_state).view(b,n,self.num_heads,self.head_size).permute(0,2,3,1)
        #v=self.v_linear(hidden_state).view(b,n,self.num_heads,self.head_size).permute(0,2,1,3)
        q,k,v=(torch.matmul(hidden_state,self.in_proj_weight.T)+self.in_proj_bias.expand(b,n,-1)).chunk(3,dim=-1)
        if prefix_k is not None and prefix_v is not None:
            #将前缀插入到序列之前
            k=torch.cat((prefix_k,k),dim=1)
            v=torch.cat((prefix_v,v),dim=1)
            #print("model origin k :",k[:,0,0])
        bk,nk,hk=k.shape
        bq,nq,hq=q.shape
        q=q.view(bq,nq,self.num_heads,self.head_size).permute(0,2,1,3)
        k=k.view(bk,nk,self.num_heads,self.head_size).permute(0,2,1,3)
        v=v.view(bk,nk,self.num_heads,self.head_size).permute(0,2,1,3)
        attention_logits=F.scaled_dot_product_attention(q, k, v)
        attention_logits=attention_logits.permute(0,2,1,3).contiguous().view(bk,nq,self.hidden_size)
        attention_output=self.out_proj(attention_logits)
        return attention_output


class GELU(nn.Module):
    """
    误差函数erf:
    erf(x)=2/sqrt(pi)*integral(exp(-t^2),t=0,x)
    其中t是一个虚拟变量，用于表示从0到x的积分范围内的每一个点，具体来说：
    x是误差函数的输入参数，表示积分的上限
    t是积分变量，它从0变化到x,在每个点上计算e-t^2的值
    e-t^2是被积函数，表示每个t点上的高斯分布的概率密度。
    通过积分，误差函数计算了从0到x的高斯分布的概率累积值，具体来说，误差函数的积分部分计算的是区间[0,x]内高斯分布的概率密度的积分
    """
    def forward(self,x):
        return 0.5*x*(1.0+torch.erf(x/torch.sqrt(2.0)))
    
class QuickGELU(nn.Module):
    def __init__(self):
        super(QuickGELU,self).__init__()
    def forward(self,x):
        old_dtype=x.dtype
        x=x.to(torch.float32)
        return (x*torch.sigmoid(1.702*x)).to(old_dtype)
    

class MLP(nn.Module):
    def __init__(self,config):
        super(MLP,self).__init__()
        self.hidden_size=config.hidden_size
        self.c_fc=nn.Linear(self.hidden_size,4*self.hidden_size,device=config.device,bias=True,dtype=config.dtype)
        self.gelu=QuickGELU()
        self.c_proj=nn.Linear(self.hidden_size*4,self.hidden_size,device=config.device,bias=True,dtype=config.dtype)
    def forward(self,hidden_state):
        hidden_state=self.c_fc(hidden_state)
        hidden_state=self.gelu(hidden_state)
        hidden_state=self.c_proj(hidden_state)
        return hidden_state
    
class Config:
    def __init__(self,vocab_size,max_position_embeddings,hidden_size,num_layers,num_heads,device,dtype):
        self.vocab_size=vocab_size
        self.max_position_embeddings=max_position_embeddings
        self.hidden_size=hidden_size
        self.num_layers=num_layers
        self.num_heads=num_heads
        self.device=device
        self.dtype=dtype
        self.norm_eps=1e-5
        self.num_virtual_tokens=20
        self.token_dim=hidden_size
        self.encoder_hidden_size=hidden_size
config=Config(
    vocab_size=49408,
    max_position_embeddings=77,
    hidden_size=512,
    num_layers=12,
    num_heads=8,
    device=torch.device('cuda:0'),
    dtype=torch.float16
)
class TextEncoder(nn.Module):
    def __init__(self,config):
        super(TextEncoder,self).__init__()
        self.token_embedding=nn.Embedding(config.vocab_size,config.hidden_size,device=config.device,dtype=config.dtype)
        self.positional_embedding=nn.Parameter(torch.zeros(config.max_position_embeddings,config.hidden_size,device=config.device,dtype=config.dtype),requires_grad=False)
        self.transformer=Transformer(config)
        self.ln_final=nn.LayerNorm(config.hidden_size,eps=config.norm_eps,elementwise_affine=True,device=config.device,dtype=config.dtype)
    def forward(self,input_ids):
        b,n=input_ids.shape
        prompt_embedding,token_embeddings=self.token_embedding(input_ids)
        position_ids=torch.arange(n,device=config.device,dtype=config.dtype).unsqueeze(0).expand(b,n)
        position_embeddings=self.positional_embedding[position_ids]
        embeddings=token_embeddings+position_embeddings
        embeddings=torch.cat((prompt_embedding,embeddings),dim=1)
        embeddings=self.transformer(embeddings)
        embeddings=self.ln_final(embeddings)
        return embeddings
    
text_encoder=Transformer(config)