An endpoint server for efficiently serving quantized open-source LLMs for code.
import argparse
import json
from typing import AsyncGenerator

import uvicorn
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse, Response, StreamingResponse
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.engine.async_llm_engine import AsyncLLMEngine
from vllm.sampling_params import SamplingParams
from vllm.utils import random_uuid

TIMEOUT_KEEP_ALIVE = 5  # seconds.
TIMEOUT_TO_PREVENT_DEADLOCK = 1  # seconds.
app = FastAPI()
engine = None


@app.post("/generate")
async def generate(request: Request) -> Response:
    """Generate completion for the request.

    The request should be a JSON object with the following fields:
    - prompt: the prompt to use for the generation.
    - stream: whether to stream the results or not.
    - other fields: the sampling parameters (See `SamplingParams` for details).
    """
    request_dict = await request.json()
    prompt = request_dict.pop("inputs")
    parameters = request_dict.pop("parameters")
    max_new_tokens = parameters.pop("max_new_tokens")
    do_sample = parameters.pop("do_sample", True)
    stream = request_dict.pop("stream", False)
    sampling_params = SamplingParams(max_tokens=max_new_tokens,
                                     use_beam_search=not do_sample,
                                     **parameters)
    request_id = random_uuid()

    results_generator = engine.generate(prompt, sampling_params, request_id)

    # Streaming case
    async def stream_results() -> AsyncGenerator[bytes, None]:
        async for request_output in results_generator:
            prompt = request_output.prompt
            text_outputs = [
                prompt + output.text for output in request_output.outputs
            ]
            ret = {"text": text_outputs}
            yield (json.dumps(ret) + "\0").encode("utf-8")

    if stream:
        return StreamingResponse(stream_results())

    # Non-streaming case
    final_output = None
    async for request_output in results_generator:
        if await request.is_disconnected():
            # Abort the request if the client disconnects.
            await engine.abort(request_id)
            return Response(status_code=499)
        final_output = request_output

    assert final_output is not None
    prompt = final_output.prompt
    text_outputs = [prompt + output.text for output in final_output.outputs]
    ret = {"generated_text": text_outputs[0], "status": 200}
    return JSONResponse(ret)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--host", type=str, default="localhost")
    parser.add_argument("--port", type=int, default=8000)
    parser = AsyncEngineArgs.add_cli_args(parser)
    args = parser.parse_args()

    engine_args = AsyncEngineArgs.from_cli_args(args)
    engine = AsyncLLMEngine.from_engine_args(engine_args)

    uvicorn.run(app,
                host=args.host,
                port=args.port,
                log_level="debug",
                timeout_keep_alive=TIMEOUT_KEEP_ALIVE)

api_server.py
wangcx18/llm-vscode-inference-server