ChatGLM2-6B/openai_api.py

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# coding=utf-8
# Implements API for ChatGLM2-6B in OpenAI's format. (https://platform.openai.com/docs/api-reference/chat)
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# Usage: python openai_api.py
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# Visit http://localhost:8000/docs for documents.
import time
import torch
import uvicorn
from pydantic import BaseModel, Field
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
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from contextlib import asynccontextmanager
from starlette.responses import StreamingResponse
from typing import Any, Dict, List, Literal, Optional, Union
from transformers import AutoTokenizer, AutoModel
@asynccontextmanager
async def lifespan(app: FastAPI): # collects GPU memory
yield
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
app = FastAPI(lifespan=lifespan)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
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class ModelCard(BaseModel):
id: str
object: str = "model"
created: int = Field(default_factory=lambda: int(time.time()))
owned_by: str = "owner"
root: Optional[str] = None
parent: Optional[str] = None
permission: Optional[list] = None
class ModelList(BaseModel):
object: str = "list"
data: List[ModelCard] = []
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class ChatMessage(BaseModel):
role: Literal["user", "assistant", "system"]
content: str
class DeltaMessage(BaseModel):
role: Optional[Literal["user", "assistant", "system"]] = None
content: Optional[str] = None
class ChatCompletionRequest(BaseModel):
model: str
messages: List[ChatMessage]
temperature: Optional[float] = None
top_p: Optional[float] = None
max_length: Optional[int] = None
stream: Optional[bool] = False
class ChatCompletionResponseChoice(BaseModel):
index: int
message: ChatMessage
finish_reason: Literal["stop", "length"]
class ChatCompletionResponseStreamChoice(BaseModel):
index: int
delta: DeltaMessage
finish_reason: Optional[Literal["stop", "length"]]
class ChatCompletionResponse(BaseModel):
model: str
object: Literal["chat.completion", "chat.completion.chunk"]
choices: List[Union[ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice]]
created: Optional[int] = Field(default_factory=lambda: int(time.time()))
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@app.get("/v1/models", response_model=ModelList)
async def list_models():
global model_args
model_card = ModelCard(id="gpt-3.5-turbo")
return ModelList(data=[model_card])
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@app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
async def create_chat_completion(request: ChatCompletionRequest):
global model, tokenizer
if request.messages[-1].role != "user":
raise HTTPException(status_code=400, detail="Invalid request")
query = request.messages[-1].content
prev_messages = request.messages[:-1]
if len(prev_messages) > 0 and prev_messages[0].role == "system":
query = prev_messages.pop(0).content + query
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 request.stream:
generate = predict(query, history, request.model)
return StreamingResponse(generate, media_type="text/event-stream")
response, _ = model.chat(tokenizer, query, history=history)
choice_data = ChatCompletionResponseChoice(
index=0,
message=ChatMessage(role="assistant", content=response),
finish_reason="stop"
)
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
)
chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
yield "data: {}\n\n".format(chunk.json(exclude_unset=True, ensure_ascii=False))
current_length = 0
for new_response, _ in model.stream_chat(tokenizer, query, history):
if len(new_response) == current_length:
continue
new_text = new_response[current_length:]
current_length = len(new_response)
choice_data = ChatCompletionResponseStreamChoice(
index=0,
delta=DeltaMessage(content=new_text),
finish_reason=None
)
chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
yield "data: {}\n\n".format(chunk.json(exclude_unset=True, ensure_ascii=False))
choice_data = ChatCompletionResponseStreamChoice(
index=0,
delta=DeltaMessage(),
finish_reason="stop"
)
chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
yield "data: {}\n\n".format(chunk.json(exclude_unset=True, ensure_ascii=False))
if __name__ == "__main__":
tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
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model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).cuda()
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# 多显卡支持使用下面两行代替上面一行将num_gpus改为你实际的显卡数量
# from utils import load_model_on_gpus
# model = load_model_on_gpus("THUDM/chatglm2-6b", num_gpus=2)
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model.eval()
uvicorn.run(app, host='0.0.0.0', port=8000, workers=1)