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vLLM FastAPI server with very basic auth
# Adapted from
# https://github.com/lm-sys/FastChat/blob/168ccc29d3f7edc50823016105c024fe2282732a/fastchat/serve/openai_api_server.py
import argparse
import asyncio
import json
import os
import time
from http import HTTPStatus
from typing import AsyncGenerator, Dict, List, Optional, Tuple, Union
import fastapi
import uvicorn
from fastapi import Request
from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse, StreamingResponse, Response
from packaging import version
from pydantic import BaseModel, Field
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.engine.async_llm_engine import AsyncLLMEngine
from vllm.entrypoints.openai.protocol import (
CompletionResponse, CompletionResponseChoice,
CompletionResponseStreamChoice, CompletionStreamResponse,
ChatCompletionResponse,
ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice,
ChatCompletionStreamResponse, ChatMessage, DeltaMessage, ErrorResponse,
LogProbs, ModelCard, ModelList, ModelPermission, UsageInfo)
from vllm.logger import init_logger
from vllm.outputs import RequestOutput
from vllm.sampling_params import SamplingParams
from vllm.transformers_utils.tokenizer import get_tokenizer
from vllm.utils import random_uuid
try:
import fastchat
from fastchat.conversation import Conversation, SeparatorStyle
from fastchat.model.model_adapter import get_conversation_template
_fastchat_available = True
except ImportError:
_fastchat_available = False
TIMEOUT_KEEP_ALIVE = 5 # seconds
API_KEY = os.environ.get("API_KEY", None)
logger = init_logger(__name__)
served_model = None
app = fastapi.FastAPI()
engine = None
class ChatCompletionRequest(BaseModel):
model: str
api_key: str
messages: Union[str, List[Dict[str, str]]]
temperature: Optional[float] = 0.7
top_p: Optional[float] = 1.0
n: Optional[int] = 1
max_tokens: Optional[int] = None
stop: Optional[Union[str, List[str]]] = Field(default_factory=list)
stream: Optional[bool] = False
presence_penalty: Optional[float] = 0.0
frequency_penalty: Optional[float] = 0.0
logit_bias: Optional[Dict[str, float]] = None
user: Optional[str] = None
best_of: Optional[int] = None
top_k: Optional[int] = -1
ignore_eos: Optional[bool] = False
use_beam_search: Optional[bool] = False
stop_token_ids: Optional[List[int]] = Field(default_factory=list)
skip_special_tokens: Optional[bool] = True
spaces_between_special_tokens: Optional[bool] = True
class CompletionRequest(BaseModel):
model: str
api_key: str
prompt: Union[List[int], List[List[int]], str, List[str]]
suffix: Optional[str] = None
max_tokens: Optional[int] = 16
temperature: Optional[float] = 1.0
top_p: Optional[float] = 1.0
n: Optional[int] = 1
stream: Optional[bool] = False
logprobs: Optional[int] = None
echo: Optional[bool] = False
stop: Optional[Union[str, List[str]]] = Field(default_factory=list)
presence_penalty: Optional[float] = 0.0
frequency_penalty: Optional[float] = 0.0
best_of: Optional[int] = None
logit_bias: Optional[Dict[str, float]] = None
user: Optional[str] = None
# Additional parameters supported by vLLM
top_k: Optional[int] = -1
ignore_eos: Optional[bool] = False
use_beam_search: Optional[bool] = False
stop_token_ids: Optional[List[int]] = Field(default_factory=list)
skip_special_tokens: Optional[bool] = True
spaces_between_special_tokens: Optional[bool] = True
def create_error_response(status_code: HTTPStatus,
message: str) -> JSONResponse:
return JSONResponse(ErrorResponse(message=message,
type="invalid_request_error").dict(),
status_code=status_code.value)
@app.exception_handler(RequestValidationError)
async def validation_exception_handler(request, exc): # pylint: disable=unused-argument
return create_error_response(HTTPStatus.BAD_REQUEST, str(exc))
async def check_model(request) -> Optional[JSONResponse]:
if request.model == served_model:
return
ret = create_error_response(
HTTPStatus.NOT_FOUND,
f"The model `{request.model}` does not exist.",
)
return ret
async def get_gen_prompt(request) -> str:
if not _fastchat_available:
raise ModuleNotFoundError(
"fastchat is not installed. Please install fastchat to use "
"the chat completion and conversation APIs: `$ pip install fschat`"
)
if version.parse(fastchat.__version__) < version.parse("0.2.23"):
raise ImportError(
f"fastchat version is low. Current version: {fastchat.__version__} "
"Please upgrade fastchat to use: `$ pip install -U fschat`")
conv = get_conversation_template(request.model)
conv = Conversation(
name=conv.name,
system_template=conv.system_template,
system_message=conv.system_message,
roles=conv.roles,
messages=list(conv.messages), # prevent in-place modification
offset=conv.offset,
sep_style=SeparatorStyle(conv.sep_style),
sep=conv.sep,
sep2=conv.sep2,
stop_str=conv.stop_str,
stop_token_ids=conv.stop_token_ids,
)
if isinstance(request.messages, str):
prompt = request.messages
else:
for message in request.messages:
msg_role = message["role"]
if msg_role == "system":
conv.system_message = 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)
prompt = conv.get_prompt()
return prompt
async def check_length(
request: Union[ChatCompletionRequest, CompletionRequest],
prompt: Optional[str] = None,
prompt_ids: Optional[List[int]] = None
) -> Tuple[List[int], Optional[JSONResponse]]:
assert (not (prompt is None and prompt_ids is None)
and not (prompt is not None and prompt_ids is not None)
), "Either prompt or prompt_ids should be provided."
if prompt_ids is not None:
input_ids = prompt_ids
else:
input_ids = tokenizer(prompt).input_ids
token_num = len(input_ids)
if request.max_tokens is None:
request.max_tokens = max_model_len - token_num
if token_num + request.max_tokens > max_model_len:
return input_ids, create_error_response(
HTTPStatus.BAD_REQUEST,
f"This model's maximum context length is {max_model_len} tokens. "
f"However, you requested {request.max_tokens + token_num} tokens "
f"({token_num} in the messages, "
f"{request.max_tokens} in the completion). "
f"Please reduce the length of the messages or completion.",
)
else:
return input_ids, None
@app.get("/health")
async def health() -> Response:
"""Health check."""
return Response(status_code=200)
@app.get("/models")
async def show_available_models():
"""Show available models. Right now we only have one model."""
model_cards = [
ModelCard(id=served_model,
root=served_model,
permission=[ModelPermission()])
]
return ModelList(data=model_cards)
def create_logprobs(token_ids: List[int],
id_logprobs: List[Dict[int, float]],
initial_text_offset: int = 0) -> LogProbs:
"""Create OpenAI-style logprobs."""
logprobs = LogProbs()
last_token_len = 0
for token_id, id_logprob in zip(token_ids, id_logprobs):
token = tokenizer.convert_ids_to_tokens(token_id)
logprobs.tokens.append(token)
logprobs.token_logprobs.append(id_logprob[token_id])
if len(logprobs.text_offset) == 0:
logprobs.text_offset.append(initial_text_offset)
else:
logprobs.text_offset.append(logprobs.text_offset[-1] +
last_token_len)
last_token_len = len(token)
logprobs.top_logprobs.append({
tokenizer.convert_ids_to_tokens(i): p
for i, p in id_logprob.items()
})
return logprobs
@app.post("/chat/completions")
async def create_chat_completion(request: ChatCompletionRequest,
raw_request: Request):
"""Completion API similar to OpenAI's API.
See https://platform.openai.com/docs/api-reference/chat/create
for the API specification. This API mimics the OpenAI ChatCompletion API.
NOTE: Currently we do not support the following features:
- function_call (Users should implement this by themselves)
- logit_bias (to be supported by vLLM engine)
"""
if request.api_key != API_KEY or API_KEY is None:
return create_error_response(HTTPStatus.UNAUTHORIZED,
"Invalid API key")
logger.info(f"Received chat completion request: {request}")
error_check_ret = await check_model(request)
if error_check_ret is not None:
return error_check_ret
if request.logit_bias is not None and len(request.logit_bias) > 0:
# TODO: support logit_bias in vLLM engine.
return create_error_response(HTTPStatus.BAD_REQUEST,
"logit_bias is not currently supported")
prompt = await get_gen_prompt(request)
token_ids, error_check_ret = await check_length(request, prompt=prompt)
if error_check_ret is not None:
return error_check_ret
model_name = request.model
request_id = f"cmpl-{random_uuid()}"
created_time = int(time.monotonic())
try:
spaces_between_special_tokens = request.spaces_between_special_tokens
sampling_params = SamplingParams(
n=request.n,
presence_penalty=request.presence_penalty,
frequency_penalty=request.frequency_penalty,
temperature=request.temperature,
top_p=request.top_p,
stop=request.stop,
stop_token_ids=request.stop_token_ids,
max_tokens=request.max_tokens,
best_of=request.best_of,
top_k=request.top_k,
ignore_eos=request.ignore_eos,
use_beam_search=request.use_beam_search,
skip_special_tokens=request.skip_special_tokens,
spaces_between_special_tokens=spaces_between_special_tokens,
)
except ValueError as e:
return create_error_response(HTTPStatus.BAD_REQUEST, str(e))
result_generator = engine.generate(prompt, sampling_params, request_id,
token_ids)
def create_stream_response_json(
index: int,
text: str,
finish_reason: Optional[str] = None,
usage: Optional[UsageInfo] = None,
) -> str:
choice_data = ChatCompletionResponseStreamChoice(
index=index,
delta=DeltaMessage(content=text),
finish_reason=finish_reason,
)
response = ChatCompletionStreamResponse(
id=request_id,
created=created_time,
model=model_name,
choices=[choice_data],
)
if usage is not None:
response.usage = usage
# exclude unset to leave details out of each sse
response_json = response.json(exclude_unset=True, ensure_ascii=False)
return response_json
async def completion_stream_generator() -> AsyncGenerator[str, None]:
# First chunk with role
for i in range(request.n):
choice_data = ChatCompletionResponseStreamChoice(
index=i,
delta=DeltaMessage(role="assistant"),
finish_reason=None,
)
chunk = ChatCompletionStreamResponse(id=request_id,
choices=[choice_data],
model=model_name)
data = chunk.json(exclude_unset=True, ensure_ascii=False)
yield f"data: {data}\n\n"
previous_texts = [""] * request.n
previous_num_tokens = [0] * request.n
async for res in result_generator:
res: RequestOutput
for output in res.outputs:
i = output.index
delta_text = output.text[len(previous_texts[i]):]
previous_texts[i] = output.text
completion_tokens = len(output.token_ids)
previous_num_tokens[i] = completion_tokens
response_json = create_stream_response_json(
index=i,
text=delta_text,
)
yield f"data: {response_json}\n\n"
if output.finish_reason is not None:
prompt_tokens = len(res.prompt_token_ids)
final_usage = UsageInfo(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
response_json = create_stream_response_json(
index=i,
text="",
finish_reason=output.finish_reason,
usage=final_usage,
)
yield f"data: {response_json}\n\n"
yield "data: [DONE]\n\n"
# Streaming response
if request.stream:
return StreamingResponse(completion_stream_generator(),
media_type="text/event-stream")
# Non-streaming response
final_res: RequestOutput = None
async for res in result_generator:
if await raw_request.is_disconnected():
# Abort the request if the client disconnects.
await engine.abort(request_id)
return create_error_response(HTTPStatus.BAD_REQUEST,
"Client disconnected")
final_res = res
assert final_res is not None
choices = []
for output in final_res.outputs:
choice_data = ChatCompletionResponseChoice(
index=output.index,
message=ChatMessage(role="assistant", content=output.text),
finish_reason=output.finish_reason,
)
choices.append(choice_data)
num_prompt_tokens = len(final_res.prompt_token_ids)
num_generated_tokens = sum(
len(output.token_ids) for output in final_res.outputs)
usage = UsageInfo(
prompt_tokens=num_prompt_tokens,
completion_tokens=num_generated_tokens,
total_tokens=num_prompt_tokens + num_generated_tokens,
)
response = ChatCompletionResponse(
id=request_id,
created=created_time,
model=model_name,
choices=choices,
usage=usage,
)
if request.stream:
# When user requests streaming but we don't stream, we still need to
# return a streaming response with a single event.
response_json = response.json(ensure_ascii=False)
async def fake_stream_generator() -> AsyncGenerator[str, None]:
yield f"data: {response_json}\n\n"
yield "data: [DONE]\n\n"
return StreamingResponse(fake_stream_generator(),
media_type="text/event-stream")
return response
@app.post("/completions")
async def create_completion(request: CompletionRequest, raw_request: Request):
"""Completion API similar to OpenAI's API.
See https://platform.openai.com/docs/api-reference/completions/create
for the API specification. This API mimics the OpenAI Completion API.
NOTE: Currently we do not support the following features:
- echo (since the vLLM engine does not currently support
getting the logprobs of prompt tokens)
- suffix (the language models we currently support do not support
suffix)
- logit_bias (to be supported by vLLM engine)
"""
if request.api_key != API_KEY or API_KEY is None:
return create_error_response(HTTPStatus.UNAUTHORIZED,
"Invalid API key")
logger.info(f"Received completion request: {request}")
error_check_ret = await check_model(request)
if error_check_ret is not None:
return error_check_ret
if request.echo:
# We do not support echo since the vLLM engine does not
# currently support getting the logprobs of prompt tokens.
return create_error_response(HTTPStatus.BAD_REQUEST,
"echo is not currently supported")
if request.suffix is not None:
# The language models we currently support do not support suffix.
return create_error_response(HTTPStatus.BAD_REQUEST,
"suffix is not currently supported")
if request.logit_bias is not None and len(request.logit_bias) > 0:
# TODO: support logit_bias in vLLM engine.
return create_error_response(HTTPStatus.BAD_REQUEST,
"logit_bias is not currently supported")
model_name = request.model
request_id = f"cmpl-{random_uuid()}"
use_token_ids = False
if isinstance(request.prompt, list):
if len(request.prompt) == 0:
return create_error_response(HTTPStatus.BAD_REQUEST,
"please provide at least one prompt")
first_element = request.prompt[0]
if isinstance(first_element, int):
use_token_ids = True
prompt = request.prompt
elif isinstance(first_element, (str, list)):
# TODO: handles multiple prompt case in list[list[int]]
if len(request.prompt) > 1:
return create_error_response(
HTTPStatus.BAD_REQUEST,
"multiple prompts in a batch is not currently supported")
use_token_ids = not isinstance(first_element, str)
prompt = request.prompt[0]
else:
prompt = request.prompt
if use_token_ids:
_, error_check_ret = await check_length(request, prompt_ids=prompt)
else:
token_ids, error_check_ret = await check_length(request, prompt=prompt)
if error_check_ret is not None:
return error_check_ret
created_time = int(time.monotonic())
try:
spaces_between_special_tokens = request.spaces_between_special_tokens
sampling_params = SamplingParams(
n=request.n,
best_of=request.best_of,
presence_penalty=request.presence_penalty,
frequency_penalty=request.frequency_penalty,
temperature=request.temperature,
top_p=request.top_p,
top_k=request.top_k,
stop=request.stop,
stop_token_ids=request.stop_token_ids,
ignore_eos=request.ignore_eos,
max_tokens=request.max_tokens,
logprobs=request.logprobs,
use_beam_search=request.use_beam_search,
skip_special_tokens=request.skip_special_tokens,
spaces_between_special_tokens=spaces_between_special_tokens,
)
except ValueError as e:
return create_error_response(HTTPStatus.BAD_REQUEST, str(e))
if use_token_ids:
result_generator = engine.generate(None,
sampling_params,
request_id,
prompt_token_ids=prompt)
else:
result_generator = engine.generate(prompt, sampling_params, request_id,
token_ids)
# Similar to the OpenAI API, when n != best_of, we do not stream the
# results. In addition, we do not stream the results when use beam search.
stream = (request.stream
and (request.best_of is None or request.n == request.best_of)
and not request.use_beam_search)
def create_stream_response_json(
index: int,
text: str,
logprobs: Optional[LogProbs] = None,
finish_reason: Optional[str] = None,
usage: Optional[UsageInfo] = None,
) -> str:
choice_data = CompletionResponseStreamChoice(
index=index,
text=text,
logprobs=logprobs,
finish_reason=finish_reason,
)
response = CompletionStreamResponse(
id=request_id,
created=created_time,
model=model_name,
choices=[choice_data],
)
if usage is not None:
response.usage = usage
response_json = response.json(exclude_unset=True, ensure_ascii=False)
return response_json
async def completion_stream_generator() -> AsyncGenerator[str, None]:
previous_texts = [""] * request.n
previous_num_tokens = [0] * request.n
async for res in result_generator:
res: RequestOutput
for output in res.outputs:
i = output.index
delta_text = output.text[len(previous_texts[i]):]
if request.logprobs is not None:
logprobs = create_logprobs(
output.token_ids[previous_num_tokens[i]:],
output.logprobs[previous_num_tokens[i]:],
len(previous_texts[i]))
else:
logprobs = None
previous_texts[i] = output.text
previous_num_tokens[i] = len(output.token_ids)
response_json = create_stream_response_json(
index=i,
text=delta_text,
logprobs=logprobs,
)
yield f"data: {response_json}\n\n"
if output.finish_reason is not None:
logprobs = (LogProbs()
if request.logprobs is not None else None)
prompt_tokens = len(res.prompt_token_ids)
completion_tokens = len(output.token_ids)
final_usage = UsageInfo(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
response_json = create_stream_response_json(
index=i,
text="",
logprobs=logprobs,
finish_reason=output.finish_reason,
usage=final_usage,
)
yield f"data: {response_json}\n\n"
yield "data: [DONE]\n\n"
# Streaming response
if stream:
return StreamingResponse(completion_stream_generator(),
media_type="text/event-stream")
# Non-streaming response
final_res: RequestOutput = None
async for res in result_generator:
if await raw_request.is_disconnected():
# Abort the request if the client disconnects.
await engine.abort(request_id)
return create_error_response(HTTPStatus.BAD_REQUEST,
"Client disconnected")
final_res = res
assert final_res is not None
choices = []
for output in final_res.outputs:
if request.logprobs is not None:
logprobs = create_logprobs(output.token_ids, output.logprobs)
else:
logprobs = None
choice_data = CompletionResponseChoice(
index=output.index,
text=output.text,
logprobs=logprobs,
finish_reason=output.finish_reason,
)
choices.append(choice_data)
num_prompt_tokens = len(final_res.prompt_token_ids)
num_generated_tokens = sum(
len(output.token_ids) for output in final_res.outputs)
usage = UsageInfo(
prompt_tokens=num_prompt_tokens,
completion_tokens=num_generated_tokens,
total_tokens=num_prompt_tokens + num_generated_tokens,
)
response = CompletionResponse(
id=request_id,
created=created_time,
model=model_name,
choices=choices,
usage=usage,
)
if request.stream:
# When user requests streaming but we don't stream, we still need to
# return a streaming response with a single event.
response_json = response.json(ensure_ascii=False)
async def fake_stream_generator() -> AsyncGenerator[str, None]:
yield f"data: {response_json}\n\n"
yield "data: [DONE]\n\n"
return StreamingResponse(fake_stream_generator(),
media_type="text/event-stream")
return response
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="vLLM OpenAI-Compatible RESTful API server.")
parser.add_argument("--host", type=str, default=None, 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")
parser.add_argument("--served-model-name",
type=str,
default=None,
help="The model name used in the API. If not "
"specified, the model name will be the same as "
"the huggingface name.")
parser = AsyncEngineArgs.add_cli_args(parser)
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.info(f"args: {args}")
if args.served_model_name is not None:
served_model = args.served_model_name
else:
served_model = args.model
engine_args = AsyncEngineArgs.from_cli_args(args)
engine = AsyncLLMEngine.from_engine_args(engine_args)
engine_model_config = asyncio.run(engine.get_model_config())
max_model_len = engine_model_config.max_model_len
# A separate tokenizer to map token IDs to strings.
tokenizer = get_tokenizer(engine_args.tokenizer,
tokenizer_mode=engine_args.tokenizer_mode,
trust_remote_code=engine_args.trust_remote_code)
uvicorn.run(app,
host=args.host,
port=args.port,
log_level="info",
timeout_keep_alive=TIMEOUT_KEEP_ALIVE)
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