model/fastchat/serve/api.py

"""This module provides a ChatGPT-compatible Restful API for chat completion.

Usage:

python3 -m fastchat.serve.api

Reference: https://platform.openai.com/docs/api-reference/chat/create
"""
import asyncio
from typing import Union, Dict, List, Any

import argparse
import json
import logging

import fastapi
from fastapi.middleware.cors import CORSMiddleware
import httpx
import uvicorn
from pydantic import BaseSettings

from fastchat.protocol.chat_completion import (
    ChatCompletionRequest,
    ChatCompletionResponse,
    ChatMessage,
    ChatCompletionResponseChoice,
)
from fastchat.conversation import get_default_conv_template, SeparatorStyle
from fastchat.serve.inference import compute_skip_echo_len

logger = logging.getLogger(__name__)


class AppSettings(BaseSettings):
    # The address of the model controller.
    FASTCHAT_CONTROLLER_URL: str = "http://localhost:21001"


app_settings = AppSettings()
app = fastapi.FastAPI()
headers = {"User-Agent": "FastChat API Server"}


@app.get("/v1/models")
async def show_available_models():
    controller_url = app_settings.FASTCHAT_CONTROLLER_URL
    async with httpx.AsyncClient() as client:
        ret = await client.post(controller_url + "/refresh_all_workers")
        ret = await client.post(controller_url + "/list_models")
    models = ret.json()["models"]
    models.sort()
    return {"data": [{"id": m} for m in models], "object": "list"}


@app.post("/v1/chat/completions")
async def create_chat_completion(request: ChatCompletionRequest):
    """Creates a completion for the chat message"""
    payload, skip_echo_len = generate_payload(
        request.model,
        request.messages,
        temperature=request.temperature,
        max_tokens=request.max_tokens,
        stop=request.stop,
    )

    choices = []
    # TODO: batch the requests. maybe not necessary if using CacheFlow worker
    chat_completions = []
    for i in range(request.n):
        content = asyncio.create_task(chat_completion(request.model, payload, skip_echo_len))
        chat_completions.append(content)

    for i, content_task in enumerate(chat_completions):
        content = await content_task
        choices.append(
            ChatCompletionResponseChoice(
                index=i,
                message=ChatMessage(role="assistant", content=content),
                # TODO: support other finish_reason
                finish_reason="stop",
            )
        )

    # TODO: support usage field
    # "usage": {
    #     "prompt_tokens": 9,
    #     "completion_tokens": 12,
    #     "total_tokens": 21
    # }
    return ChatCompletionResponse(choices=choices)


def generate_payload(
    model_name: str,
    messages: List[Dict[str, str]],
    *,
    temperature: float,
    max_tokens: int,
    stop: Union[str, None],
):
    is_chatglm = "chatglm" in model_name.lower()
    # TODO(suquark): The template is currently a reference. Here we have to make a copy.
    # We use create a template factory to avoid this.
    conv = get_default_conv_template(model_name).copy()

    # TODO(suquark): Conv.messages should be a list. But it is a tuple now.
    #  We should change it to a list.
    conv.messages = list(conv.messages)

    for message in messages:
        msg_role = message["role"]
        if msg_role == "system":
            conv.system = message["content"]
        elif msg_role == "user":
            conv.append_message(conv.roles[0], message["content"])
        elif msg_role == "assistant":
            conv.append_message(conv.roles[1], message["content"])
        else:
            raise ValueError(f"Unknown role: {msg_role}")

    # Add a blank message for the assistant.
    conv.append_message(conv.roles[1], None)

    if is_chatglm:
        prompt = conv.messages[conv.offset :]
    else:
        prompt = conv.get_prompt()
    skip_echo_len = compute_skip_echo_len(model_name, conv, prompt)

    if stop is None:
        stop = conv.sep if conv.sep_style == SeparatorStyle.SINGLE else conv.sep2

    # TODO(suquark): We should get the default `max_new_tokens`` from the model.
    if max_tokens is None:
        max_tokens = 512

    payload = {
        "model": model_name,
        "prompt": prompt,
        "temperature": temperature,
        "max_new_tokens": max_tokens,
        "stop": stop,
    }

    logger.debug(f"==== request ====\n{payload}")
    return payload, skip_echo_len


async def chat_completion(model_name: str, payload: Dict[str, Any], skip_echo_len: int):
    controller_url = app_settings.FASTCHAT_CONTROLLER_URL
    async with httpx.AsyncClient() as client:
        ret = await client.post(
            controller_url + "/get_worker_address", json={"model": model_name}
        )
        worker_addr = ret.json()["address"]
        # No available worker
        if worker_addr == "":
            raise ValueError(f"No available worker for {model_name}")

        logger.debug(f"model_name: {model_name}, worker_addr: {worker_addr}")

        output = ""
        delimiter = b"\0"
        async with client.stream(
            "POST",
            worker_addr + "/worker_generate_stream",
            headers=headers,
            json=payload,
            timeout=20,
        ) as response:
            content = await response.aread()

        for chunk in content.split(delimiter):
            if not chunk:
                continue
            data = json.loads(chunk.decode())
            if data["error_code"] == 0:
                output = data["text"][skip_echo_len:].strip()

        return output


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="FastChat ChatGPT-compatible Restful API server."
    )
    parser.add_argument("--host", type=str, default="localhost", help="host name")
    parser.add_argument("--port", type=int, default=8000, help="port number")
    parser.add_argument("--allow-credentials", action="store_true", help="allow credentials")
    parser.add_argument("--allowed-origins", type=json.loads, default=["*"], help="allowed origins")
    parser.add_argument("--allowed-methods", type=json.loads, default=["*"], help="allowed methods")
    parser.add_argument("--allowed-headers", type=json.loads, default=["*"], help="allowed headers")

    args = parser.parse_args()

    app.add_middleware(
        CORSMiddleware,
        allow_origins=args.allowed_origins,
        allow_credentials=args.allow_credentials,
        allow_methods=args.allowed_methods,
        allow_headers=args.allowed_headers,
    )

    logger.debug(f"==== args ====\n{args}")

    uvicorn.run("fastchat.serve.api:app", host=args.host, port=args.port, reload=True)