285 lines
9.3 KiB
Python
285 lines
9.3 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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BGEM3 inference server (FastAPI) with robust GPU / CPU management.
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Launch examples:
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python server.py # 自动选卡 / 自动降级
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python server.py --device 1 # 固定用第 1 张 GPU
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CUDA_VISIBLE_DEVICES=0,1 python server.py
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"""
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import argparse
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import logging
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import os
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import sys
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import time
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from typing import List, Union
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import multiprocessing as mp
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import torch
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from fastapi import FastAPI, HTTPException
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from pydantic import BaseModel
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from transformers import AutoTokenizer
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from FlagEmbedding import BGEM3FlagModel
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# -----------------------------------------------------------------------------#
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# Config
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# -----------------------------------------------------------------------------#
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MODEL_PATH = "model/bge-m3" # 按需改成你的权重路径
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MODEL_VRAM_MB = int(os.getenv("MODEL_VRAM_MB", "16384")) # bge-m3-large fp16 ≈ 16 GiB
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POST_LOAD_GAP_MB = 512
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SAFE_MIN_FREE_MB = MODEL_VRAM_MB + POST_LOAD_GAP_MB # == 16896 MB
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# -----------------------------------------------------------------------------#
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# Logging
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# -----------------------------------------------------------------------------#
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logger = logging.getLogger("bge-m3-server")
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(message)s",
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)
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# -----------------------------------------------------------------------------#
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# GPU memory helpers (NVML → torch fallback)
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# -----------------------------------------------------------------------------#
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try:
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import pynvml
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pynvml.nvmlInit()
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_USE_NVML = True
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except Exception:
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_USE_NVML = False
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logger.warning("pynvml 不可用，将使用 torch.cuda.mem_get_info() 探测显存")
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def _gpu_mem_info(idx: int) -> tuple[int, int]:
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"""Return (free_bytes, total_bytes) for GPU `idx`."""
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if _USE_NVML:
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handle = pynvml.nvmlDeviceGetHandleByIndex(idx)
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mem = pynvml.nvmlDeviceGetMemoryInfo(handle)
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return mem.free, mem.total
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# torch fallback
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torch.cuda.set_device(idx)
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free, total = torch.cuda.mem_get_info(idx)
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return free, total
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# -----------------------------------------------------------------------------#
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# Precision & model loader
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# -----------------------------------------------------------------------------#
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def _choose_precision(device: str) -> str:
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"""Return 'fp16'|'bf16'|'fp32'."""
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if not device.startswith("cuda"):
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return "fp32"
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major, _ = torch.cuda.get_device_capability(device)
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if major >= 8: # Ampere/Hopper
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return "fp16"
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if major >= 7:
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return "bf16"
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return "fp32"
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def load_model(device: str):
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precision = _choose_precision(device)
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use_fp16 = precision == "fp16"
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logger.info("Loading BGEM3 on %s (%s)", device, precision)
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if device == "cpu":
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# CPU 路径：彻底摘掉 CUDA
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os.environ["CUDA_VISIBLE_DEVICES"] = ""
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torch.cuda.is_available = lambda: False
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torch.cuda.device_count = lambda: 0
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else:
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# GPU 路径：只暴露本 worker 选中的那张卡
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# 例：device == "cuda:3" → 只让当前进程看到 GPU 3
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idx = device.split(":")[1]
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os.environ["CUDA_VISIBLE_DEVICES"] = idx
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# device_count 现在返回 1，BGEM3 只会在这张卡上建 1 个子进程
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torch.cuda.device_count = lambda: 1
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mdl = BGEM3FlagModel(MODEL_PATH, use_fp16=use_fp16, device=device)
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# 不再包 DataParallel；每个 worker 单卡即可
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return mdl, precision
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# -----------------------------------------------------------------------------#
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# Auto-select device (startup)
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# -----------------------------------------------------------------------------#
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def auto_select_and_load() -> tuple:
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"""
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1. 过滤掉空闲显存 < MODEL_VRAM_MB 的 GPU
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2. 按空闲显存降序依次尝试加载
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3. 载入后再次检查：若剩余 < POST_LOAD_GAP_MB → 视为失败
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4. 若全部 GPU 不满足 → CPU
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"""
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if not torch.cuda.is_available():
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logger.info("No GPU detected → CPU")
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return (*load_model("cpu"), "cpu")
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# 收集候选卡 (free_MB, idx)
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candidates = []
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for idx in range(torch.cuda.device_count()):
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free_mb = _gpu_mem_info(idx)[0] // 2**20
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if free_mb >= MODEL_VRAM_MB: # 至少能放下权重
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candidates.append((free_mb, idx))
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if not candidates:
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logger.warning("All GPUs free_mem < %d MB → CPU", MODEL_VRAM_MB)
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return (*load_model("cpu"), "cpu")
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# 空闲显存从高到低
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for free_mb, idx in sorted(candidates, reverse=True):
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dev = f"cuda:{idx}"
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try:
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logger.info("Trying %s (free=%d MB)", dev, free_mb)
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mdl, prec = load_model(dev)
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remain_mb = _gpu_mem_info(idx)[0] // 2**20
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if remain_mb < POST_LOAD_GAP_MB:
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raise RuntimeError(
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f"post-load free {remain_mb} MB < {POST_LOAD_GAP_MB} MB")
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return mdl, prec, dev # 成功
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except RuntimeError as e:
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logger.warning("%s unusable (%s) → next", dev, e)
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torch.cuda.empty_cache()
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logger.warning("No suitable GPU left → CPU fallback")
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return (*load_model("cpu"), "cpu")
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# -----------------------------------------------------------------------------#
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# CLI
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# -----------------------------------------------------------------------------#
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--device", help="GPU index (e.g. 0) or 'cpu'; overrides auto-selection"
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)
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args, _ = parser.parse_known_args()
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if args.device is not None:
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# Forced path
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if args.device.lower() == "cpu":
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DEVICE = "cpu"
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else:
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DEVICE = f"cuda:{int(args.device)}" if torch.cuda.is_available() else "cpu"
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model, PRECISION = load_model(DEVICE)
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else:
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# Auto path with VRAM check
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model, PRECISION, DEVICE = auto_select_and_load()
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tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
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# -----------------------------------------------------------------------------#
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# FastAPI
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# -----------------------------------------------------------------------------#
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app = FastAPI()
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logger.info("Using SAFE_MIN_FREE_MB = %d MB", SAFE_MIN_FREE_MB)
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# ② -------- FastAPI 启动预热 --------
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@app.on_event("startup")
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async def warm_up():
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logger.info("Warm-up on %s", DEVICE)
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try:
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_ = model.encode(["warmup"], return_dense=True, num_processes=1)
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except Exception as e:
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logger.warning("Warm-up failed: %s — 首条请求时再退避", e)
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class EmbeddingRequest(BaseModel):
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input: Union[str, List[str]]
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model: str = "text-embedding-bge-m3"
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def _encode(texts: List[str]):
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"""
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单次请求：
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1. 子进程跑 GPU 推理；成功→返回
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2. 若子进程 OOM / CUDA Error → 同一次请求 fallback 到 CPU
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绝不改全局状态，其他并发请求不受影响
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"""
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# ③ -------- _encode() 里 worker 调用 --------
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def _worker(t, q):
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try:
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# out = model.encode(t, return_dense=True) # GPU or CPU 均安全
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out = model.encode(t, return_dense=True, num_processes=1)
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q.put(("ok", out))
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except Exception as e:
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q.put(("err", str(e)))
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q = mp.Queue()
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p = mp.Process(target=_worker, args=(texts, q))
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p.start()
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p.join(timeout=60)
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if not q.empty():
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status, payload = q.get()
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if status == "ok":
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return payload
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if "out of memory" in payload.lower() or "cuda error" in payload.lower():
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logger.warning("GPU OOM → 本次请求改走 CPU：%s", payload)
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torch.cuda.empty_cache()
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cpu_model, _ = load_model("cpu")
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return cpu_model.encode(texts, return_dense=True)
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raise RuntimeError(payload)
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raise RuntimeError("子进程异常退出，无返回")
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@app.post("/v1/embeddings")
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def create_embedding(request: EmbeddingRequest):
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texts = [request.input] if isinstance(request.input, str) else request.input
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# Token stats
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enc = tokenizer(
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texts,
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padding=True,
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truncation=True,
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max_length=8192,
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return_tensors="pt",
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)
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prompt_tokens = int(enc["attention_mask"].sum().item())
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try:
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output = _encode(texts)
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embeddings = output["dense_vecs"]
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except Exception as e:
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logger.exception("Embedding failed")
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raise HTTPException(status_code=500, detail=str(e))
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return {
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"object": "list",
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"data": [
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{
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"object": "embedding",
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"index": i,
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"embedding": emb.tolist() if hasattr(emb, "tolist") else emb,
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}
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for i, emb in enumerate(embeddings)
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],
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"model": request.model,
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"usage": {
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"prompt_tokens": prompt_tokens,
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"total_tokens": prompt_tokens,
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},
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"device": DEVICE,
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"precision": PRECISION,
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}
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# -----------------------------------------------------------------------------#
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# Entry-point for `python server.py`
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# -----------------------------------------------------------------------------#
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(
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"server:app",
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host="0.0.0.0",
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port=int(os.getenv("PORT", 8000)),
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log_level="info",
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workers=1, # multi-process → use gunicorn/uvicorn externally
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)
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