Delete Junk

.ipynb_checkpoints/EmotionCLIP-checkpoint.py

@@ -1,151 +0,0 @@
-""" 
-VIT的transformer结构没有因果掩码，因为任意一个位置都能访问其它位置，它们之间没有因果关系，或者说关系很弱
-
-文本生成仍然考虑因果掩码。
-"""
-import torch.nn.functional as F
-from VIT import model as VIT 
-from Text_Encoder import text_encoder as transformer
-import torch.nn as nn
-import torch
-from Text_Encoder import MLP
-
-class Prompt_block(nn.Module):
-    def __init__(self,config):
-        super(Prompt_block,self).__init__()
-        self.prompt_embedding=nn.Embedding(config.prompt_num,config.hidden_size,dtype=config.dtype,device=config.device)
-    def forward(self,text_embeddings):
-        b,_,_=text_embeddings.size()
-        n,dim=self.prompt_embedding.weight.size()
-        """
-        new_embeddings=[]
-        for batch,index_ in enumerate(index):
-            text_embedding=text_embeddings[0]
-            text_embedding=torch.cat((text_embedding[:index_,:],self.prompt_embedding.weight,text_embedding[index_:,:]),0)
-            new_embeddings.append(text_embedding)
-        stacked_embedding= torch.stack(new_embeddings, dim=0) 
-        return stacked_embedding
-        """
-        text_embeddings=torch.cat((text_embeddings[:,0:1,:],self.prompt_embedding.weight.expand(b,n,dim),text_embeddings[:,1:,:]),1)
-        return text_embeddings
-        
-        
-        
-        
-
-class CLIP(nn.Module):
-    def __init__(self,config):  
-        super().__init__()
-        self.visual=VIT
-        self.device=config.device
-        self.dtype=config.dtype
-        self.token_embedding=nn.Embedding(config.vocab_size,config.hidden_size,dtype=config.dtype,device=config.device)
-        self.max_position_embeddings=config.max_position_embeddings
-        self.prompt_num=config.prompt_num
-        self.transformer=transformer
-        #增加一个prompt block
-        self.prompt_block=Prompt_block(config)
-        self.positional_embedding=nn.Parameter(torch.empty(config.max_position_embeddings,config.hidden_size,device=config.device))
-        self.ln_final=nn.LayerNorm(config.hidden_size,eps=config.layer_norm_eps,dtype=config.dtype,device=config.device)
-        self.text_projection=nn.Parameter(torch.empty(config.hidden_size,config.hidden_size,device=config.device))
-        self.logit_scale=nn.Parameter(torch.empty([],dtype=config.dtype,device=config.device)*config.logit_scale_init,requires_grad=False)
-    def encode_image(self,img,use_emotion=True):
-        cls_embedding=self.visual(img,use_emotion)
-        #cls_embedding:[batch_size,1,512],image_embedding:[batch_size,7,512]
-        return cls_embedding
-    def encode_text(self,text,use_emotion=True):
-        #预留20token的位置
-        b,n=text.size()
-        index=text.argmax(dim=-1)
-        text_embedding=self.token_embedding(text)
-        #text_embedding=self.prompt_block(index,text_embedding)
-        if n==self.max_position_embeddings-self.prompt_num:
-            text_embedding=self.prompt_block(text_embedding)
-            index=index+torch.tensor(20,device=index.device,dtype=index.dtype)
-        position_embedding=self.positional_embedding[None,:text_embedding.shape[1],:].to(self.dtype)
-        text_embedding=position_embedding+text_embedding
-        text_embedding=self.transformer(text_embedding,use_emotion=use_emotion)
-        text_embedding=self.ln_final(text_embedding)
-        #传入的标记有
-        #print(index[0],index_new[0],text_embedding.shape)
-        text_embedding=text_embedding[torch.arange(text.shape[0]),index]
-        text_embedding=text_embedding@self.text_projection.to(self.dtype)
-
-        return text_embedding
-
-    def forward(self,image,text,use_emotion=True):
-        image_features=self.encode_image(image,use_emotion)
-        text_features=self.encode_text(text,use_emotion)
-        # normalized features
-        image_features=image_features/image_features.norm(dim=-1,keepdim=True)
-        text_features=text_features/text_features.norm(dim=-1,keepdim=True)
-        # cosine similarity as logits
-        logit_scale=self.logit_scale.exp()
-        logits_per_image=logit_scale*image_features@text_features.t()
-        logits_per_text=logits_per_image.t()
-        # shape = [global_batch_size, global_batch_size]
-        return logits_per_image,logits_per_text
-
-class Config:
-    def __init__(self):
-        self.vocab_size=49408
-        self.image_dim=768
-        self.num_patches=49
-        self.patch_size=32
-        self.hidden_size=512
-        self.prompt_num=20
-        self.max_position_embeddings=77
-        self.num_hidden_layers=12
-        self.num_attention_heads=8
-        self.head_size=64
-        self.layer_norm_eps=1e-5
-        self.activation_function="Quickgelu"
-        self.dtype=torch.float16
-        self.device=torch.device("cuda:0")
-        self.logit_scale_init=4.6052
-        self.num_virtual_tokens=20
-        self.token_dim=self.hidden_size
-        self.encoder_hidden_size=self.hidden_size
-        
-config=Config()
-model=CLIP(config)
-#加载预训练权重
-model.load_state_dict(torch.load(r'/root/autodl-tmp/true_Emoset/EmotionCLIP_v2.bin',weights_only=True,map_location='cpu'),strict=True)
-"""
-for name, param in model.named_parameters():
-    if 'prefix' not in name and 'prompt' not  in name and 'ln' not in name:  # 如果参数名中不包含'prefix'
-        print(name,"'s requires_grad turn off.")
-        param.requires_grad = False  # 冻结该参数
-    else:
-        print(name,"'s requires_grad turn on.")
-        param.requires_grad = True  # 允许该参数进行训练
-"""
-
-#编译模型
-#model=torch.compile(model)
-import pickle
-from PIL import Image
-import clip
-with open('./preprocess.pkl','rb') as f:
-    preprocess = pickle.load(f)
-with open('./tokenize.pkl','rb') as f:
-    tokenizer=pickle.load(f)
-device=config.device
-image = preprocess(Image.open("spider.jpg")).unsqueeze(0).to(device)
-text = tokenizer(["This picture conveys a sense of fear", "This picture conveys a sense of contentment", "This picture conveys a sense of anger","This picture conveys a sense of sadness","This picture conveys a sense of neutral","This picture conveys a sense of disgust","This picture conveys a sense of excitement","This picture conveys a sense of awe","This picture conveys a sense of amusement"],context_length=57).to(device)
-#context_length=57
-with torch.no_grad():
-    logits_per_image, logits_per_text = model(image.to(config.dtype), text)
-    probs = logits_per_image.softmax(dim=-1).cpu().numpy()
-print("情感识别：",probs)
-#保存合并前缀的权重
-import torch
-torch.save(model.state_dict(),'./upload/EmotionCLIP-V2.pth')
-#泛化性能
-"""
-text=tokenizer(['This is a spider.','This is a dog','This is a cat'],context_length=57).to(device)
-with torch.no_grad():
-    logits_per_image, logits_per_text = model(image.to(config.dtype), text,use_emotion=False)
-    probs = logits_per_image.softmax(dim=-1).cpu().numpy()
-print("泛化识别：",probs)
-"""

.ipynb_checkpoints/Text_Encoder-checkpoint.py

@@ -1,192 +0,0 @@
-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)

.ipynb_checkpoints/VIT-checkpoint.py

@@ -1,243 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import numpy as np
-import math
-import os
-import sys
-
-#huggingface实现的前缀微调
-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))
-
-
-import torch 
-import torch.nn as nn
-import math
-from torch.nn.attention import SDPBackend, sdpa_kernel
-from torch.nn import functional as F
-def position_embedding(x,position_ids):
-    hidden_size=x.size(2)
-    seq_len=x.size(1)
-    div_term=torch.exp(torch.arange(0,hidden_size,2,device=x.device).float()*(-math.log(10000.0)/hidden_size))
-    positional_encoding=torch.zeros(seq_len,hidden_size,device=x.device)
-    positional_encoding[:,0::2]=torch.sin(position_ids.float()[:,None]*div_term)
-    positional_encoding[:,1::2]=torch.cos(position_ids.float()[:,None]*div_term)
-    positional_encoding=positional_encoding.unsqueeze(0)
-    return positional_encoding
-
-
-
-
-class VisionTransformer(nn.Module):
-    def __init__(self,config):
-        super(VisionTransformer,self).__init__()
-        self.image_channel=config.image_channel
-        self.hidden_size=config.hidden_size
-        self.norm_eps=config.norm_eps
-        self.patch_size=config.patch_size
-        self.output_dim=config.output_dim
-        self.dtype=config.dtype
-        self.num_patches=config.num_patches
-        self.num_virtual_tokens=config.num_virtual_tokens if hasattr(config,"num_virtual_tokens") else None
-        self.conv1=nn.Conv2d(self.image_channel,self.hidden_size,self.patch_size,stride=self.patch_size,bias=False,device=config.device,dtype=config.dtype)
-        self.ln_pre=nn.LayerNorm(self.hidden_size,eps=self.norm_eps,elementwise_affine=True,device=config.device,dtype=config.dtype)
-        self.transformer=Transformer(config)
-        #self.position_ids=torch.arange(config.num_patches+1,dtype=torch.long,device=config.device)
-        #self.position_embeddings=nn.Parameter(torch.zeros(1,config.num_patches+1,config.hidden_size))
-        #nn.init.normal_(self.position_embeddings)
-        #clsToken，用于图像分类任务
-        #self.cls_token=nn.Parameter(torch.zeros(1,1,config.hidden_size,device=config.device))
-        #分类token不是可训练参数
-        self.class_embedding=nn.Parameter(torch.empty(config.hidden_size,device=config.device),requires_grad=False)
-        #很明显这里的position_embedding也是一个可学习参数
-        self.positional_embedding=nn.Parameter(torch.empty(config.num_patches+1,config.hidden_size,device=config.device),requires_grad=False)
-        #可训练参数
-        self.proj=nn.Parameter(torch.empty(config.hidden_size,config.output_dim,device=config.device,dtype=config.dtype),requires_grad=False)
-        self.ln_post=nn.LayerNorm(self.hidden_size,eps=self.norm_eps,elementwise_affine=True,device=config.device,dtype=config.dtype)
-    def forward(self,hidden_state,use_emotion):
-        b,c,h,w=hidden_state.shape
-        #获得embedding向量
-        hidden_state=self.conv1(hidden_state)
-        hidden_state=hidden_state.reshape(b,self.hidden_size,-1).transpose(1,2)
-        #添加cls token embedding
-        hidden_state=torch.cat((self.class_embedding.expand(b,1,-1).to(hidden_state.dtype),hidden_state),dim=1)
-        #使用transformer原论文中的固定位置嵌入
-        #hidden_state=hidden_state+position_embedding(hidden_state,self.position_ids)
-        hidden_state=hidden_state+self.positional_embedding.unsqueeze(0).to(hidden_state.dtype)
-        hidden_state=self.ln_pre(hidden_state)
-        hidden_state=self.transformer(hidden_state,use_emotion)
-        #提取cls token输出 与image patch输出
-        cls_state=hidden_state[:,0,:]
-        cls_state=self.ln_post(cls_state)
-        cls_state=torch.matmul(cls_state,self.proj)
-        #image_state=hidden_state[:,1:,:]
-        #image_state size (batch_size,49,768)
-        return cls_state
-    
-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:
-            b,n,h=hidden_state.shape
-            prefix_k,prefix_v=self.prefix(self.prefix_tokens,b)
-            for index,resblock in enumerate(self.resblocks):
-                #在每一层之前提取前缀向量输入到resblock中进行拼接
-                hidden_state=resblock(hidden_state,prefix_k[index],prefix_v[index])
-            return hidden_state
-        else:
-            for index,resblock in enumerate(self.resblocks):
-                #在每一层之前提取前缀向量输入到resblock中进行拼接
-                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,h=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
-            #print("origional k.shape",prefix_k.shape)
-            k=torch.cat((prefix_k,k),dim=1)
-            v=torch.cat((prefix_v,v),dim=1)
-            #print("model original 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���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):
-        old_dtype=x.dtype
-        x=x.to(torch.float32)
-        return (0.5*x*(1.0+torch.erf(x/torch.sqrt(2.0)))).to(old_dtype)
-    
-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 ViTConfig:
-    def __init__(self,image_channel,hidden_size,num_heads,num_layers,patch_size,num_patches,output_dim,norm_eps,device):
-        self.image_channel=image_channel
-        self.hidden_size=hidden_size
-        self.num_heads=num_heads
-        self.num_layers=num_layers
-        self.patch_size=patch_size
-        self.num_patches=num_patches
-        self.norm_eps=norm_eps
-        self.device=device
-        self.dtype=torch.float16
-        self.patch_token_num=self.hidden_size//self.patch_size**2+1
-        self.output_dim=output_dim
-        self.num_virtual_tokens=20
-        self.token_dim=self.hidden_size
-        self.encoder_hidden_size=self.hidden_size
-
-config=ViTConfig(3,768,12,12,32,49,512,1e-5,torch.device("cuda"))
-model=VisionTransformer(config)