Add openai_api
hanpeng
1 year ago
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9b00721a66
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# coding=utf-8
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# Implements API for Qwen-7B in OpenAI's format. (https://platform.openai.com/docs/api-reference/chat)
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# Usage: python openai_api.py
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# Visit http://localhost:8000/docs for documents.
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from argparse import ArgumentParser
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import time
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import torch
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import uvicorn
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from pydantic import BaseModel, Field
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from fastapi import FastAPI, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from contextlib import asynccontextmanager
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from typing import Any, Dict, List, Literal, Optional, Union
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from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM
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from transformers.generation import GenerationConfig
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from sse_starlette.sse import ServerSentEvent, EventSourceResponse
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@asynccontextmanager
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async def lifespan(app: FastAPI): # collects GPU memory
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yield
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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app = FastAPI(lifespan=lifespan)
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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class ModelCard(BaseModel):
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id: str
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object: str = "model"
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created: int = Field(default_factory=lambda: int(time.time()))
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owned_by: str = "owner"
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root: Optional[str] = None
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parent: Optional[str] = None
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permission: Optional[list] = None
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class ModelList(BaseModel):
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object: str = "list"
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data: List[ModelCard] = []
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class ChatMessage(BaseModel):
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role: Literal["user", "assistant", "system"]
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content: str
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class DeltaMessage(BaseModel):
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role: Optional[Literal["user", "assistant", "system"]] = None
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content: Optional[str] = None
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class ChatCompletionRequest(BaseModel):
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model: str
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messages: List[ChatMessage]
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temperature: Optional[float] = None
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top_p: Optional[float] = None
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max_length: Optional[int] = None
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stream: Optional[bool] = False
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class ChatCompletionResponseChoice(BaseModel):
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index: int
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message: ChatMessage
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finish_reason: Literal["stop", "length"]
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class ChatCompletionResponseStreamChoice(BaseModel):
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index: int
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delta: DeltaMessage
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finish_reason: Optional[Literal["stop", "length"]]
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class ChatCompletionResponse(BaseModel):
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model: str
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object: Literal["chat.completion", "chat.completion.chunk"]
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choices: List[Union[ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice]]
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created: Optional[int] = Field(default_factory=lambda: int(time.time()))
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@app.get("/v1/models", response_model=ModelList)
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async def list_models():
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global model_args
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model_card = ModelCard(id="gpt-3.5-turbo")
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return ModelList(data=[model_card])
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@app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
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async def create_chat_completion(request: ChatCompletionRequest):
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global model, tokenizer
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if request.messages[-1].role != "user":
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raise HTTPException(status_code=400, detail="Invalid request")
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query = request.messages[-1].content
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prev_messages = request.messages[:-1]
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if len(prev_messages) > 0 and prev_messages[0].role == "system":
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query = prev_messages.pop(0).content + query
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history = []
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if len(prev_messages) % 2 == 0:
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for i in range(0, len(prev_messages), 2):
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if prev_messages[i].role == "user" and prev_messages[i+1].role == "assistant":
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history.append([prev_messages[i].content, prev_messages[i+1].content])
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if request.stream:
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generate = predict(query, history, request.model)
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return EventSourceResponse(generate, media_type="text/event-stream")
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response, _ = model.chat_stream(tokenizer, query, history=history)
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choice_data = ChatCompletionResponseChoice(
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index=0,
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message=ChatMessage(role="assistant", content=response),
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finish_reason="stop"
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)
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return ChatCompletionResponse(model=request.model, choices=[choice_data], object="chat.completion")
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async def predict(query: str, history: List[List[str]], model_id: str):
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global model, tokenizer
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choice_data = ChatCompletionResponseStreamChoice(
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index=0,
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delta=DeltaMessage(role="assistant"),
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finish_reason=None
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)
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chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
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#yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
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yield "{}".format(chunk.model_dump_json(exclude_unset=True))
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current_length = 0
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for new_response in model.chat_stream(tokenizer, query, history):
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if len(new_response) == current_length:
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continue
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new_text = new_response[current_length:]
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current_length = len(new_response)
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choice_data = ChatCompletionResponseStreamChoice(
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index=0,
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delta=DeltaMessage(content=new_text),
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finish_reason=None
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)
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chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
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#yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
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yield "{}".format(chunk.model_dump_json(exclude_unset=True))
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choice_data = ChatCompletionResponseStreamChoice(
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index=0,
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delta=DeltaMessage(),
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finish_reason="stop"
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)
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chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
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#yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
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yield "{}".format(chunk.model_dump_json(exclude_unset=True))
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yield '[DONE]'
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def _get_args():
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parser = ArgumentParser()
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parser.add_argument("-c", "--checkpoint-path", type=str, default=DEFAULT_CKPT_PATH,
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help="Checkpoint name or path, default to %(default)r")
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parser.add_argument("--cpu-only", action="store_true", help="Run demo with CPU only")
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parser.add_argument("--server-port", type=int, default=8000,
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help="Demo server port.")
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parser.add_argument("--server-name", type=str, default="127.0.0.1",
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help="Demo server name.")
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args = parser.parse_args()
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return args
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DEFAULT_CKPT_PATH = 'QWen/QWen-7B-Chat'
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if __name__ == "__main__":
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args = _get_args()
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tokenizer = AutoTokenizer.from_pretrained(
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args.checkpoint_path, trust_remote_code=True, resume_download=True,
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)
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if args.cpu_only:
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device_map = "cpu"
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else:
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device_map = "auto"
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model = AutoModelForCausalLM.from_pretrained(
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args.checkpoint_path,
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device_map=device_map,
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trust_remote_code=True,
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resume_download=True,
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).eval()
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model.generation_config = GenerationConfig.from_pretrained(
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args.checkpoint_path, trust_remote_code=True, resume_download=True,
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)
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uvicorn.run(app, host=args.server_name, port=args.server_port, workers=1)
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