Dolphin/deployment/vllm/api_server.py
2025-06-27 15:01:22 +08:00

216 lines
7.0 KiB
Python

# SPDX-License-Identifier: Apache-2.0
"""
NOTE: This API server is used only for demonstrating usage of AsyncEngine
and simple performance benchmarks. It is not intended for production use.
For production use, we recommend using our OpenAI compatible server.
We are also not going to accept PRs modifying this file, please
change `vllm/entrypoints/openai/api_server.py` instead.
"""
import asyncio
import base64
import json
import io
import ssl
from argparse import Namespace
from collections.abc import AsyncGenerator
from PIL import Image
from typing import Any, Optional
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.entrypoints.launcher import serve_http
from vllm.entrypoints.utils import with_cancellation
from vllm.inputs import ExplicitEncoderDecoderPrompt, TextPrompt, TokensPrompt
from vllm.logger import init_logger
from vllm.sampling_params import SamplingParams
from vllm.usage.usage_lib import UsageContext
from vllm.utils import FlexibleArgumentParser, random_uuid, set_ulimit
from vllm.version import __version__ as VLLM_VERSION
logger = init_logger("api_server")
TIMEOUT_KEEP_ALIVE = 5 # seconds.
app = FastAPI()
engine = None
@app.get("/health")
async def health() -> Response:
"""Health check."""
return Response(status_code=200)
@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()
return await _generate(request_dict, raw_request=request)
async def decode_image(image_base64: str) -> Image.Image:
image_data = base64.b64decode(image_base64)
image = Image.open(io.BytesIO(image_data))
return image
async def custom_process_prompt(encoder_prompt: str, decoder_prompt: str,
image_base64: str) -> ExplicitEncoderDecoderPrompt:
assert engine is not None
tokenizer = engine.engine.get_tokenizer_group().tokenizer
image = await decode_image(image_base64)
if encoder_prompt == "":
encoder_prompt = "0" * 783 # For Dolphin
if decoder_prompt == "":
decoder_prompt_ids = tokenizer.bos_token_id
else:
decoder_prompt = f"<s>{decoder_prompt.strip()} <Answer/>"
decoder_prompt_ids = tokenizer(decoder_prompt, add_special_tokens=False)["input_ids"]
enc_dec_prompt = ExplicitEncoderDecoderPrompt(
encoder_prompt=TextPrompt(prompt=encoder_prompt, multi_modal_data={"image": image}),
decoder_prompt=TokensPrompt(prompt_token_ids=decoder_prompt_ids),
)
return enc_dec_prompt
@with_cancellation
async def _generate(request_dict: dict, raw_request: Request) -> Response:
encoder_prompt = request_dict.pop("encoder_prompt", "")
decoder_prompt = request_dict.pop("decoder_prompt", "")
image_base64 = request_dict.pop("image_base64", "")
stream = request_dict.pop("stream", False)
sampling_params = SamplingParams(**request_dict)
request_id = random_uuid()
assert engine is not None
enc_dec_prompt = await custom_process_prompt(encoder_prompt, decoder_prompt, image_base64)
results_generator = engine.generate(enc_dec_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
assert prompt is not None
text_outputs = [
prompt + output.text for output in request_output.outputs
]
ret = {"text": text_outputs}
yield (json.dumps(ret) + "\n").encode("utf-8")
if stream:
return StreamingResponse(stream_results())
# Non-streaming case
final_output = None
try:
async for request_output in results_generator:
final_output = request_output
except asyncio.CancelledError:
return Response(status_code=499)
assert final_output is not None
prompt = final_output.prompt
assert prompt is not None
text_outputs = [prompt + output.text.strip() for output in final_output.outputs]
ret = {"text": text_outputs}
return JSONResponse(ret)
def build_app(args: Namespace) -> FastAPI:
global app
app.root_path = args.root_path
return app
async def init_app(
args: Namespace,
llm_engine: Optional[AsyncLLMEngine] = None,
) -> FastAPI:
app = build_app(args)
global engine
engine_args = AsyncEngineArgs.from_cli_args(args)
engine = (llm_engine
if llm_engine is not None else AsyncLLMEngine.from_engine_args(
engine_args, usage_context=UsageContext.API_SERVER))
app.state.engine_client = engine
return app
async def run_server(args: Namespace,
llm_engine: Optional[AsyncLLMEngine] = None,
**uvicorn_kwargs: Any) -> None:
logger.info("vLLM API server version %s", VLLM_VERSION)
logger.info("args: %s", args)
set_ulimit()
app = await init_app(args, llm_engine)
assert engine is not None
shutdown_task = await serve_http(
app,
sock=None,
enable_ssl_refresh=args.enable_ssl_refresh,
host=args.host,
port=args.port,
log_level=args.log_level,
timeout_keep_alive=TIMEOUT_KEEP_ALIVE,
ssl_keyfile=args.ssl_keyfile,
ssl_certfile=args.ssl_certfile,
ssl_ca_certs=args.ssl_ca_certs,
ssl_cert_reqs=args.ssl_cert_reqs,
**uvicorn_kwargs,
)
await shutdown_task
if __name__ == "__main__":
parser = FlexibleArgumentParser()
parser.add_argument("--host", type=str, default=None)
parser.add_argument("--port", type=parser.check_port, default=8000)
parser.add_argument("--ssl-keyfile", type=str, default=None)
parser.add_argument("--ssl-certfile", type=str, default=None)
parser.add_argument("--ssl-ca-certs",
type=str,
default=None,
help="The CA certificates file")
parser.add_argument(
"--enable-ssl-refresh",
action="store_true",
default=False,
help="Refresh SSL Context when SSL certificate files change")
parser.add_argument(
"--ssl-cert-reqs",
type=int,
default=int(ssl.CERT_NONE),
help="Whether client certificate is required (see stdlib ssl module's)"
)
parser.add_argument(
"--root-path",
type=str,
default=None,
help="FastAPI root_path when app is behind a path based routing proxy")
parser.add_argument("--log-level", type=str, default="debug")
parser = AsyncEngineArgs.add_cli_args(parser)
args = parser.parse_args()
asyncio.run(run_server(args))