mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-10-17 17:18:10 +08:00
135 lines
4.3 KiB
Python
135 lines
4.3 KiB
Python
# Copyright 2024 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import asyncio
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import os
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from contextlib import asynccontextmanager
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from functools import partial
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from typing import Optional
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from typing_extensions import Annotated
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from ..chat import ChatModel
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from ..extras.misc import torch_gc
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from ..extras.packages import is_fastapi_available, is_starlette_available, is_uvicorn_available
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from .chat import (
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create_chat_completion_response,
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create_score_evaluation_response,
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create_stream_chat_completion_response,
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)
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from .protocol import (
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ChatCompletionRequest,
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ChatCompletionResponse,
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ModelCard,
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ModelList,
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ScoreEvaluationRequest,
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ScoreEvaluationResponse,
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)
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if is_fastapi_available():
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from fastapi import Depends, FastAPI, HTTPException, status
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.security.http import HTTPAuthorizationCredentials, HTTPBearer
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if is_starlette_available():
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from sse_starlette import EventSourceResponse
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if is_uvicorn_available():
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import uvicorn
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async def sweeper() -> None:
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while True:
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torch_gc()
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await asyncio.sleep(300)
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@asynccontextmanager
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async def lifespan(app: "FastAPI", chat_model: "ChatModel"): # collects GPU memory
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if chat_model.engine_type == "huggingface":
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asyncio.create_task(sweeper())
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yield
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torch_gc()
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def create_app(chat_model: "ChatModel") -> "FastAPI":
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root_path = os.getenv("FASTAPI_ROOT_PATH", "")
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app = FastAPI(lifespan=partial(lifespan, chat_model=chat_model), root_path=root_path)
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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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api_key = os.getenv("API_KEY")
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security = HTTPBearer(auto_error=False)
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async def verify_api_key(auth: Annotated[Optional[HTTPAuthorizationCredentials], Depends(security)]):
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if api_key and (auth is None or auth.credentials != api_key):
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raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid API key.")
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@app.get(
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"/v1/models",
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response_model=ModelList,
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status_code=status.HTTP_200_OK,
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dependencies=[Depends(verify_api_key)],
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)
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async def list_models():
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model_card = ModelCard(id=os.getenv("API_MODEL_NAME", "gpt-3.5-turbo"))
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return ModelList(data=[model_card])
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@app.post(
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"/v1/chat/completions",
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response_model=ChatCompletionResponse,
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status_code=status.HTTP_200_OK,
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dependencies=[Depends(verify_api_key)],
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)
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async def create_chat_completion(request: ChatCompletionRequest):
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if not chat_model.engine.can_generate:
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raise HTTPException(status_code=status.HTTP_405_METHOD_NOT_ALLOWED, detail="Not allowed")
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if request.stream:
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generate = create_stream_chat_completion_response(request, chat_model)
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return EventSourceResponse(generate, media_type="text/event-stream")
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else:
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return await create_chat_completion_response(request, chat_model)
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@app.post(
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"/v1/score/evaluation",
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response_model=ScoreEvaluationResponse,
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status_code=status.HTTP_200_OK,
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dependencies=[Depends(verify_api_key)],
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)
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async def create_score_evaluation(request: ScoreEvaluationRequest):
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if chat_model.engine.can_generate:
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raise HTTPException(status_code=status.HTTP_405_METHOD_NOT_ALLOWED, detail="Not allowed")
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return await create_score_evaluation_response(request, chat_model)
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return app
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def run_api() -> None:
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chat_model = ChatModel()
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app = create_app(chat_model)
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api_host = os.getenv("API_HOST", "0.0.0.0")
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api_port = int(os.getenv("API_PORT", "8000"))
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print(f"Visit http://localhost:{api_port}/docs for API document.")
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uvicorn.run(app, host=api_host, port=api_port)
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