mirror of
https://github.com/immich-app/immich.git
synced 2024-11-28 09:33:27 +02:00
258b98c262
* load models in thread * set clip mode logs to debug level * updated tests * made fixtures slightly less ugly * moved responses to json file * formatting
116 lines
3.6 KiB
Python
116 lines
3.6 KiB
Python
import asyncio
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import threading
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from concurrent.futures import ThreadPoolExecutor
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from typing import Any
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from zipfile import BadZipFile
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import orjson
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from fastapi import FastAPI, Form, HTTPException, UploadFile
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from fastapi.responses import ORJSONResponse
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from onnxruntime.capi.onnxruntime_pybind11_state import InvalidProtobuf, NoSuchFile # type: ignore
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from starlette.formparsers import MultiPartParser
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from app.models.base import InferenceModel
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from .config import log, settings
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from .models.cache import ModelCache
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from .schemas import (
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MessageResponse,
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ModelType,
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TextResponse,
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)
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MultiPartParser.max_file_size = 2**24 # spools to disk if payload is 16 MiB or larger
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app = FastAPI()
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def init_state() -> None:
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app.state.model_cache = ModelCache(ttl=settings.model_ttl, revalidate=settings.model_ttl > 0)
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log.info(
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(
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"Created in-memory cache with unloading "
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f"{f'after {settings.model_ttl}s of inactivity' if settings.model_ttl > 0 else 'disabled'}."
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)
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)
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# asyncio is a huge bottleneck for performance, so we use a thread pool to run blocking code
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app.state.thread_pool = ThreadPoolExecutor(settings.request_threads) if settings.request_threads > 0 else None
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app.state.locks = {model_type: threading.Lock() for model_type in ModelType}
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log.info(f"Initialized request thread pool with {settings.request_threads} threads.")
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@app.on_event("startup")
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async def startup_event() -> None:
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init_state()
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@app.get("/", response_model=MessageResponse)
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async def root() -> dict[str, str]:
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return {"message": "Immich ML"}
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@app.get("/ping", response_model=TextResponse)
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def ping() -> str:
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return "pong"
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@app.post("/predict")
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async def predict(
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model_name: str = Form(alias="modelName"),
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model_type: ModelType = Form(alias="modelType"),
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options: str = Form(default="{}"),
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text: str | None = Form(default=None),
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image: UploadFile | None = None,
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) -> Any:
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if image is not None:
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inputs: str | bytes = await image.read()
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elif text is not None:
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inputs = text
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else:
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raise HTTPException(400, "Either image or text must be provided")
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try:
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kwargs = orjson.loads(options)
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except orjson.JSONDecodeError:
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raise HTTPException(400, f"Invalid options JSON: {options}")
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model = await load(await app.state.model_cache.get(model_name, model_type, **kwargs))
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model.configure(**kwargs)
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outputs = await run(model, inputs)
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return ORJSONResponse(outputs)
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async def run(model: InferenceModel, inputs: Any) -> Any:
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if app.state.thread_pool is None:
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return model.predict(inputs)
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return await asyncio.get_running_loop().run_in_executor(app.state.thread_pool, model.predict, inputs)
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async def load(model: InferenceModel) -> InferenceModel:
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if model.loaded:
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return model
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def _load() -> None:
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with app.state.locks[model.model_type]:
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model.load()
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loop = asyncio.get_running_loop()
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try:
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if app.state.thread_pool is None:
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model.load()
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else:
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await loop.run_in_executor(app.state.thread_pool, _load)
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return model
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except (OSError, InvalidProtobuf, BadZipFile, NoSuchFile):
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log.warn(
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(
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f"Failed to load {model.model_type.replace('_', ' ')} model '{model.model_name}'."
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"Clearing cache and retrying."
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)
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)
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model.clear_cache()
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if app.state.thread_pool is None:
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model.load()
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else:
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await loop.run_in_executor(app.state.thread_pool, _load)
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return model
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