mirror of https://github.com/THUDM/ChatGLM2-6B
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
141 lines
4.6 KiB
141 lines
4.6 KiB
# coding=utf-8 |
|
# Implements API for ChatGLM2-6B in OpenAI's format. (https://platform.openai.com/docs/api-reference/chat) |
|
# Usage: python openai_api.py |
|
# Visit http://localhost:8000/docs for documents. |
|
|
|
|
|
import time |
|
import torch |
|
import uvicorn |
|
from pydantic import BaseModel, Field |
|
from fastapi import FastAPI, HTTPException |
|
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) |
|
|
|
|
|
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())) |
|
|
|
|
|
@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) |
|
model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True, device="cuda") |
|
model.eval() |
|
|
|
uvicorn.run(app, host='0.0.0.0', port=8000, workers=1)
|
|
|