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修改了一些错误 (#254)

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* Update sse.ts

解决chatglm2控制台输出一半不输出的问题

* 解决知识库相似度搜索结果超过1

* Update sse.ts

---------

Co-authored-by: Archer <545436317@qq.com>
This commit is contained in:
qing hua
2023-09-05 18:28:59 +08:00
committed by GitHub
parent ea65d9b34b
commit 95389e31f7
2 changed files with 70 additions and 53 deletions
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121
files/models/ChatGLM2/openai_api.py
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@@ -1,25 +1,25 @@
# coding=utf-8
import argparse
import time
import torch
import uvicorn
from pydantic import BaseModel, Field
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from contextlib import asynccontextmanager
from typing import List, Literal, Optional, Union
from transformers import AutoTokenizer, AutoModel
from sse_starlette.sse import EventSourceResponse
from fastapi import Depends, HTTPException, Request
from starlette.status import HTTP_401_UNAUTHORIZED
import argparse
import tiktoken
import numpy as np
import tiktoken
import torch
import uvicorn
from fastapi import Depends, FastAPI, HTTPException, Request
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field
from sentence_transformers import SentenceTransformer
from sklearn.preprocessing import PolynomialFeatures
from sse_starlette.sse import EventSourceResponse
from starlette.status import HTTP_401_UNAUTHORIZED
from transformers import AutoModel, AutoTokenizer
@asynccontextmanager
async def lifespan(app: FastAPI): # collects GPU memory
async def lifespan(app: FastAPI): # collects GPU memory
yield
if torch.cuda.is_available():
torch.cuda.empty_cache()
@@ -71,36 +71,46 @@ class ChatCompletionResponseStreamChoice(BaseModel):
class ChatCompletionResponse(BaseModel):
model: str
object: Literal["chat.completion", "chat.completion.chunk"]
choices: List[Union[ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice]]
choices: List[
Union[ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice]
]
created: Optional[int] = Field(default_factory=lambda: int(time.time()))
async def verify_token(request: Request):
auth_header = request.headers.get('Authorization')
if auth_header:
token_type, _, token = auth_header.partition(' ')
if token_type.lower() == "bearer" and token == "sk-aaabbbcccdddeeefffggghhhiiijjjkkk": # 这里配置你的token
if (
token_type.lower() == "bearer"
and token == "sk-aaabbbcccdddeeefffggghhhiiijjjkkk"
): # 这里配置你的token
return True
raise HTTPException(
status_code=HTTP_401_UNAUTHORIZED,
detail="Invalid authorization credentials",
)
class EmbeddingRequest(BaseModel):
input: List[str]
model: str
class EmbeddingResponse(BaseModel):
data: list
model: str
object: str
usage: dict
def num_tokens_from_string(string: str) -> int:
"""Returns the number of tokens in a text string."""
encoding = tiktoken.get_encoding('cl100k_base')
num_tokens = len(encoding.encode(string))
return num_tokens
def expand_features(embedding, target_length):
poly = PolynomialFeatures(degree=2)
expanded_embedding = poly.fit_transform(embedding.reshape(1, -1))
@@ -110,12 +120,16 @@ def expand_features(embedding, target_length):
expanded_embedding = expanded_embedding[:target_length]
elif len(expanded_embedding) < target_length:
# 如果扩展后的特征少于目标长度，可以通过填充或其他方法来增加维度
expanded_embedding = np.pad(expanded_embedding, (0, target_length - len(expanded_embedding)))
expanded_embedding = np.pad(
expanded_embedding, (0, target_length - len(expanded_embedding))
)
return expanded_embedding
@app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
async def create_chat_completion(request: ChatCompletionRequest, token: bool = Depends(verify_token)):
async def create_chat_completion(
request: ChatCompletionRequest, token: bool = Depends(verify_token)
):
global model, tokenizer
if request.messages[-1].role != "user":
@@ -129,8 +143,11 @@ async def create_chat_completion(request: ChatCompletionRequest, token: bool = D
history = []
if len(prev_messages) % 2 == 0:
for i in range(0, len(prev_messages), 2):
if prev_messages[i].role == "user" and prev_messages[i+1].role == "assistant":
history.append([prev_messages[i].content, prev_messages[i+1].content])
if (
prev_messages[i].role == "user"
and prev_messages[i + 1].role == "assistant"
):
history.append([prev_messages[i].content, prev_messages[i + 1].content])
if request.stream:
generate = predict(query, history, request.model)
@@ -140,21 +157,23 @@ async def create_chat_completion(request: ChatCompletionRequest, token: bool = D
choice_data = ChatCompletionResponseChoice(
index=0,
message=ChatMessage(role="assistant", content=response),
finish_reason="stop"
finish_reason="stop",
)
return ChatCompletionResponse(model=request.model, choices=[choice_data], object="chat.completion")
return ChatCompletionResponse(
model=request.model, choices=[choice_data], object="chat.completion"
)
async def predict(query: str, history: List[List[str]], model_id: str):
global model, tokenizer
choice_data = ChatCompletionResponseStreamChoice(
index=0,
delta=DeltaMessage(role="assistant"),
finish_reason=None
index=0, delta=DeltaMessage(role="assistant"), finish_reason=None
)
chunk = ChatCompletionResponse(
model=model_id, choices=[choice_data], object="chat.completion.chunk"
)
chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
current_length = 0
@@ -167,60 +186,58 @@ async def predict(query: str, history: List[List[str]], model_id: str):
current_length = len(new_response)
choice_data = ChatCompletionResponseStreamChoice(
index=0,
delta=DeltaMessage(content=new_text),
finish_reason=None
index=0, delta=DeltaMessage(content=new_text), finish_reason=None
)
chunk = ChatCompletionResponse(
model=model_id, choices=[choice_data], object="chat.completion.chunk"
)
chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
choice_data = ChatCompletionResponseStreamChoice(
index=0,
delta=DeltaMessage(),
finish_reason="stop"
index=0, delta=DeltaMessage(), finish_reason="stop"
)
chunk = ChatCompletionResponse(
model=model_id, choices=[choice_data], object="chat.completion.chunk"
)
chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
yield '[DONE]'
@app.post("/v1/embeddings", response_model=EmbeddingResponse)
async def get_embeddings(request: EmbeddingRequest, token: bool = Depends(verify_token)):
# 计算嵌入向量和tokens数量
async def get_embeddings(
request: EmbeddingRequest, token: bool = Depends(verify_token)
):
# 计算嵌入向量和tokens数量
embeddings = [embeddings_model.encode(text) for text in request.input]
# 如果嵌入向量的维度不为1536，则使用插值法扩展至1536维度
embeddings = [expand_features(embedding, 1536) if len(embedding) < 1536 else embedding for embedding in embeddings]
# 如果嵌入向量的维度不为1536，则使用插值法扩展至1536维度
embeddings = [
expand_features(embedding, 1536) if len(embedding) < 1536 else embedding
for embedding in embeddings
]
# Min-Max normalization
embeddings = [(embedding - np.min(embedding)) / (np.max(embedding) - np.min(embedding)) if np.max(embedding) != np.min(embedding) else embedding for embedding in embeddings]
# Min-Max normalization 归一化
embeddings = [embedding / np.linalg.norm(embedding) for embedding in embeddings]
# 将numpy数组转换为列表
embeddings = [embedding.tolist() for embedding in embeddings]
prompt_tokens = sum(len(text.split()) for text in request.input)
total_tokens = sum(num_tokens_from_string(text) for text in request.input)
response = {
"data": [
{
"embedding": embedding,
"index": index,
"object": "embedding"
} for index, embedding in enumerate(embeddings)
{"embedding": embedding, "index": index, "object": "embedding"}
for index, embedding in enumerate(embeddings)
],
"model": request.model,
"object": "list",
"usage": {
"prompt_tokens": prompt_tokens,
"total_tokens": total_tokens,
}
},
}
return response
return response
if __name__ == "__main__":
@@ -231,13 +248,13 @@ if __name__ == "__main__":
model_dict = {
"4": "THUDM/chatglm2-6b-int4",
"8": "THUDM/chatglm2-6b-int8",
"16": "THUDM/chatglm2-6b"
"16": "THUDM/chatglm2-6b",
}
model_name = model_dict.get(args.model_name, "THUDM/chatglm2-6b")
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModel.from_pretrained(model_name, trust_remote_code=True).cuda()
embeddings_model = SentenceTransformer('moka-ai/m3e-large',device='cpu')
embeddings_model = SentenceTransformer('moka-ai/m3e-large', device='cpu')
uvicorn.run(app, host='0.0.0.0', port=6006, workers=1)