407 lines
15 KiB
Python
407 lines
15 KiB
Python
#!/usr/bin/env python3
|
||
# -*- coding: utf-8 -*-
|
||
"""
|
||
BGEM3 inference server (FastAPI) with robust GPU / CPU management.
|
||
|
||
Launch examples:
|
||
python server.py # 自动选卡 / 自动降级
|
||
python server.py --device 1 # 固定用第 1 张 GPU
|
||
CUDA_VISIBLE_DEVICES=0,1 python server.py
|
||
"""
|
||
|
||
import argparse
|
||
import logging
|
||
import os
|
||
from typing import List, Union
|
||
from threading import Lock
|
||
|
||
import torch
|
||
from fastapi import FastAPI, HTTPException
|
||
from pydantic import BaseModel
|
||
from transformers import AutoTokenizer
|
||
from FlagEmbedding import BGEM3FlagModel
|
||
|
||
# -----------------------------------------------------------------------------#
|
||
# Config
|
||
# -----------------------------------------------------------------------------#
|
||
MODEL_PATH = "model/bge-m3" # 按需改成你的权重路径
|
||
MODEL_VRAM_MB = int(os.getenv("MODEL_VRAM_MB", "8000")) # bge-m3-large fp32 ≈ 8 GiB
|
||
POST_LOAD_GAP_MB = 192
|
||
SAFE_MIN_FREE_MB = MODEL_VRAM_MB + POST_LOAD_GAP_MB # == 8192 MB
|
||
|
||
# 请求批次与单条最大长度上限（防御异常大 payload）
|
||
MAX_BATCH = int(os.getenv("MAX_BATCH", "1024"))
|
||
MAX_TEXT_LEN = int(os.getenv("MAX_TEXT_LEN", "200000"))
|
||
BATCH_SIZE = max(1, int(os.getenv("BATCH_SIZE", "32")))
|
||
|
||
# GPU 连续失败多少次后尝试重载（自愈）
|
||
GPU_MAX_CONSEC_FAILS = int(os.getenv("GPU_MAX_CONSEC_FAILS", "3"))
|
||
|
||
# CPU 兜底模型缓存
|
||
CPU_MODEL_CACHE = None
|
||
|
||
# 记录 GPU 连续失败次数；仅用于触发重载，不影响正常路径
|
||
_GPU_FAIL_COUNT = 0
|
||
_reload_lock = Lock()
|
||
|
||
_READY = False
|
||
|
||
class EmbeddingRequest(BaseModel):
|
||
input: Union[str, List[str]]
|
||
model: str = "text-embedding-bge-m3"
|
||
|
||
# -----------------------------------------------------------------------------#
|
||
# Logging
|
||
# -----------------------------------------------------------------------------#
|
||
logger = logging.getLogger("bge-m3-server")
|
||
logging.basicConfig(
|
||
level=logging.INFO,
|
||
format="%(asctime)s [%(levelname)s] %(message)s",
|
||
)
|
||
|
||
def _http_error(status: int, code: str, message: str):
|
||
"""统一错误体：便于日志/客户端识别。"""
|
||
raise HTTPException(status_code=status, detail={"code": code, "message": message})
|
||
|
||
# -----------------------------------------------------------------------------#
|
||
# GPU memory helpers (NVML → torch fallback)
|
||
# -----------------------------------------------------------------------------#
|
||
try:
|
||
import pynvml
|
||
|
||
pynvml.nvmlInit()
|
||
_USE_NVML = True
|
||
except Exception:
|
||
_USE_NVML = False
|
||
logger.warning("pynvml 不可用，将使用 torch.cuda.mem_get_info() 探测显存")
|
||
|
||
def _gpu_mem_info(idx: int) -> tuple[int, int]:
|
||
"""Return (free_bytes, total_bytes) for GPU `idx`（全局序号）."""
|
||
if _USE_NVML:
|
||
handle = pynvml.nvmlDeviceGetHandleByIndex(idx)
|
||
mem = pynvml.nvmlDeviceGetMemoryInfo(handle)
|
||
return mem.free, mem.total
|
||
# torch fallback（注意：若进程已屏蔽，仅能访问进程内索引）
|
||
torch.cuda.set_device(idx)
|
||
free, total = torch.cuda.mem_get_info(idx)
|
||
return free, total
|
||
|
||
# -----------------------------------------------------------------------------#
|
||
# Precision & model loader
|
||
# -----------------------------------------------------------------------------#
|
||
def _choose_precision_by_idx(idx: int) -> str:
|
||
"""Return 'fp16' or 'fp32' by compute capability."""
|
||
try:
|
||
major, _ = torch.cuda.get_device_capability(idx) # 这里 idx 为全局序号
|
||
if major >= 8: # Ampere/Hopper
|
||
return "fp16"
|
||
if major >= 7: # Volta/Turing
|
||
return "fp16"
|
||
return "fp32"
|
||
except Exception:
|
||
return "fp16" if torch.cuda.is_available() else "fp32"
|
||
|
||
def load_model(device: str):
|
||
"""
|
||
device: "cpu" 或 "cuda:{global_idx}"
|
||
屏蔽后在本进程内统一使用 "cuda:0" 加载，避免 invalid device ordinal。
|
||
"""
|
||
if device == "cpu":
|
||
os.environ["CUDA_VISIBLE_DEVICES"] = ""
|
||
precision = "fp32"
|
||
mdl = BGEM3FlagModel(MODEL_PATH, use_fp16=False, device="cpu")
|
||
logger.info("Loading BGEM3 on cpu (fp32)")
|
||
return mdl, precision
|
||
|
||
# 解析全局序号并在屏蔽前确定精度
|
||
idx = int(device.split(":")[1])
|
||
precision = _choose_precision_by_idx(idx)
|
||
use_fp16 = (precision == "fp16")
|
||
|
||
# 仅暴露这张卡；进程内映射为 cuda:0
|
||
os.environ["CUDA_VISIBLE_DEVICES"] = str(idx)
|
||
mapped = "cuda:0"
|
||
|
||
logger.info("Loading BGEM3 on %s (mapped=%s, %s)", device, mapped, precision)
|
||
mdl = BGEM3FlagModel(MODEL_PATH, use_fp16=use_fp16, device=mapped)
|
||
return mdl, precision
|
||
|
||
# -----------------------------------------------------------------------------#
|
||
# Auto-select device (startup)
|
||
# -----------------------------------------------------------------------------#
|
||
def auto_select_and_load() -> tuple:
|
||
"""
|
||
1. 过滤掉空闲显存 < MODEL_VRAM_MB 的 GPU
|
||
2. 按空闲显存降序依次尝试加载
|
||
3. 载入后再次检查：若剩余 < POST_LOAD_GAP_MB → 视为失败
|
||
4. 若全部 GPU 不满足 → CPU
|
||
"""
|
||
if not torch.cuda.is_available():
|
||
logger.info("No GPU detected → CPU")
|
||
mdl, prec = load_model("cpu")
|
||
return mdl, prec, "cpu"
|
||
|
||
# 收集候选卡 (free_MB, idx)
|
||
candidates = []
|
||
for idx in range(torch.cuda.device_count()):
|
||
free_mb = _gpu_mem_info(idx)[0] // 2**20
|
||
if free_mb >= MODEL_VRAM_MB:
|
||
candidates.append((free_mb, idx))
|
||
|
||
if not candidates:
|
||
logger.warning("All GPUs free_mem < %d MB → CPU", MODEL_VRAM_MB)
|
||
mdl, prec = load_model("cpu")
|
||
return mdl, prec, "cpu"
|
||
|
||
# 空闲显存从高到低
|
||
for free_mb, idx in sorted(candidates, reverse=True):
|
||
dev = f"cuda:{idx}"
|
||
try:
|
||
logger.info("Trying %s (free=%d MB)", dev, free_mb)
|
||
mdl, prec = load_model(dev)
|
||
|
||
# 载入后余量检查：NVML 用全局 idx；无 NVML 时，用进程内 0 号
|
||
if _USE_NVML:
|
||
remain_mb = _gpu_mem_info(idx)[0] // 2**20
|
||
else:
|
||
remain_mb = _gpu_mem_info(0)[0] // 2**20
|
||
|
||
if remain_mb < POST_LOAD_GAP_MB:
|
||
raise RuntimeError(f"post-load free {remain_mb} MB < {POST_LOAD_GAP_MB} MB")
|
||
return mdl, prec, dev
|
||
except RuntimeError as e:
|
||
logger.warning("%s unusable (%s) → next", dev, e)
|
||
torch.cuda.empty_cache()
|
||
|
||
logger.warning("No suitable GPU left → CPU fallback")
|
||
mdl, prec = load_model("cpu")
|
||
return mdl, prec, "cpu"
|
||
|
||
# -----------------------------------------------------------------------------#
|
||
# CLI
|
||
# -----------------------------------------------------------------------------#
|
||
FORCE_DEVICE = os.getenv("FORCE_DEVICE") # "cpu" 或 "0" / "1" / ...
|
||
|
||
parser = argparse.ArgumentParser()
|
||
parser.add_argument("--device", help="GPU index (e.g. 0) or 'cpu'; overrides auto-selection")
|
||
args, _ = parser.parse_known_args()
|
||
|
||
if FORCE_DEVICE is not None:
|
||
if FORCE_DEVICE.lower() == "cpu":
|
||
DEVICE = "cpu"
|
||
else:
|
||
DEVICE = f"cuda:{int(FORCE_DEVICE)}" if torch.cuda.is_available() else "cpu"
|
||
model, PRECISION = load_model(DEVICE)
|
||
elif args.device is not None:
|
||
if args.device.lower() == "cpu":
|
||
DEVICE = "cpu"
|
||
else:
|
||
DEVICE = f"cuda:{int(args.device)}" if torch.cuda.is_available() else "cpu"
|
||
model, PRECISION = load_model(DEVICE)
|
||
else:
|
||
model, PRECISION, DEVICE = auto_select_and_load()
|
||
|
||
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
|
||
|
||
|
||
# -----------------------------------------------------------------------------#
|
||
# FastAPI
|
||
# -----------------------------------------------------------------------------#
|
||
app = FastAPI()
|
||
logger.info("Using SAFE_MIN_FREE_MB = %d MB, BATCH_SIZE = %d", SAFE_MIN_FREE_MB, BATCH_SIZE)
|
||
|
||
|
||
@app.on_event("startup")
|
||
def _warmup():
|
||
global _READY
|
||
try:
|
||
# 尝试用 batch_size 预热；不支持就回退
|
||
try:
|
||
model.encode(["warmup sentence"], return_dense=True, batch_size=BATCH_SIZE)
|
||
except TypeError:
|
||
_ = _encode_chunked(model, ["warmup sentence"], max(1, min(BATCH_SIZE, 8)))
|
||
_READY = True
|
||
logger.info("Warm-up complete.")
|
||
except Exception as e:
|
||
_READY = False
|
||
logger.warning("Warm-up failed: %s", e)
|
||
|
||
@app.get("/ready")
|
||
def ready():
|
||
if not _READY:
|
||
# 503 让编排器/HEALTHCHECK 等待
|
||
raise HTTPException(status_code=503, detail={"code": "not_ready", "message": "warming up"})
|
||
return {"status": "ready"}
|
||
|
||
|
||
# ---------------------- GPU 自愈：连续失败后重载模型 ---------------------- #
|
||
def _maybe_reload_gpu_model():
|
||
"""
|
||
当 GPU 连挂达到阈值时尝试重载；失败不影响请求流程（继续用 CPU 兜底）。
|
||
"""
|
||
global _GPU_FAIL_COUNT, model, PRECISION, DEVICE
|
||
if not str(DEVICE).startswith("cuda"):
|
||
return
|
||
if _GPU_FAIL_COUNT < GPU_MAX_CONSEC_FAILS:
|
||
return
|
||
with _reload_lock:
|
||
# 双重检查，避免并发重复 reload
|
||
if _GPU_FAIL_COUNT < GPU_MAX_CONSEC_FAILS:
|
||
return
|
||
try:
|
||
logger.warning("GPU unhealthy (fail_count=%d) → reloading on %s", _GPU_FAIL_COUNT, DEVICE)
|
||
model, PRECISION = load_model(DEVICE)
|
||
_GPU_FAIL_COUNT = 0
|
||
logger.info("GPU model reloaded on %s", DEVICE)
|
||
except Exception as e:
|
||
logger.exception("GPU reload failed: %s", e)
|
||
# 保持 CPU 兜底路径继续工作
|
||
|
||
def _encode_chunked(model_obj, texts: List[str], chunk: int):
|
||
"""不依赖 batch_size 形参的手动分块编码；返回结构与 BGEM3 一致。"""
|
||
chunk = max(1, int(chunk)) # guard
|
||
dense_all = []
|
||
for i in range(0, len(texts), chunk):
|
||
part = texts[i:i+chunk]
|
||
out = model_obj.encode(part, return_dense=True) # 不传 batch_size
|
||
dense_all.extend(out["dense_vecs"])
|
||
return {"dense_vecs": dense_all}
|
||
|
||
def _encode(texts: List[str]):
|
||
"""
|
||
返回 (output_dict, served_device)
|
||
正常走 GPU；若 OOM/CUDA 错误，自动切 CPU 兜底并触发自愈计数。
|
||
"""
|
||
served = DEVICE
|
||
try:
|
||
try:
|
||
return model.encode(texts, return_dense=True, batch_size=BATCH_SIZE), served
|
||
except TypeError:
|
||
# encode() 不支持 batch_size 形参 → 手动分块
|
||
return _encode_chunked(model, texts, BATCH_SIZE), served
|
||
except RuntimeError as e:
|
||
emsg = str(e).lower()
|
||
if "out of memory" in emsg or "cuda error" in emsg:
|
||
logger.warning("GPU OOM/CUDA error → fallback to CPU: %s", str(e))
|
||
torch.cuda.empty_cache()
|
||
global CPU_MODEL_CACHE, _GPU_FAIL_COUNT
|
||
if CPU_MODEL_CACHE is None:
|
||
CPU_MODEL_CACHE, _ = load_model("cpu")
|
||
_GPU_FAIL_COUNT += 1 # 记录连续失败
|
||
_maybe_reload_gpu_model() # 达到阈值尝试自愈
|
||
served = "cpu"
|
||
# CPU 也先尝试 batch_size，再回退分块
|
||
try:
|
||
return CPU_MODEL_CACHE.encode(texts, return_dense=True, batch_size=BATCH_SIZE), served
|
||
except TypeError:
|
||
return _encode_chunked(CPU_MODEL_CACHE, texts, BATCH_SIZE), served
|
||
raise
|
||
|
||
# =============== 参数规范化（入口就拦住会炸栈的输入） =============== #
|
||
def _normalize_inputs(raw) -> List[str]:
|
||
"""
|
||
行为约束：
|
||
- 字符串：保持原行为，转为 [str]（允许 ""）
|
||
- 列表 ：要求 list[str]；不 strip、不丢弃空白（保持原行为）
|
||
- 空列表：直接 400（以前会让 HF tokenizer 抛 IndexError）
|
||
- 非字符串元素：400
|
||
- 批量/文本超大：413（防御性限制）
|
||
"""
|
||
if isinstance(raw, str):
|
||
texts = [raw]
|
||
elif isinstance(raw, list):
|
||
if len(raw) == 0:
|
||
_http_error(400, "empty_list", "`input` is an empty list.")
|
||
for i, x in enumerate(raw):
|
||
if not isinstance(x, str):
|
||
_http_error(400, "non_string_item", f"`input[{i}]` must be a string.")
|
||
texts = raw
|
||
else:
|
||
_http_error(400, "invalid_type", "`input` must be a string or list of strings.")
|
||
|
||
if len(texts) > MAX_BATCH:
|
||
_http_error(413, "batch_too_large", f"batch size {len(texts)} > MAX_BATCH={MAX_BATCH}.")
|
||
for i, t in enumerate(texts):
|
||
if t is not None and len(t) > MAX_TEXT_LEN:
|
||
_http_error(413, "text_too_long", f"`input[{i}]` exceeds MAX_TEXT_LEN={MAX_TEXT_LEN}.")
|
||
return texts
|
||
|
||
# ------------------------------- 主路由 --------------------------------------#
|
||
@app.post("/v1/embeddings")
|
||
def create_embedding(request: EmbeddingRequest):
|
||
# 1) 参数规范化（入口就拦住会炸栈的输入；不改变允许 "" 的原行为）
|
||
texts = _normalize_inputs(request.input)
|
||
|
||
# 2) Token 统计：失败不影响主流程（记 0）
|
||
try:
|
||
enc = tokenizer(
|
||
texts,
|
||
padding=True,
|
||
truncation=True,
|
||
max_length=8192,
|
||
return_tensors="pt",
|
||
)
|
||
prompt_tokens = int(enc["attention_mask"].sum().item())
|
||
except IndexError:
|
||
# 双保险（理论上不会再发生）
|
||
_http_error(400, "empty_after_tokenize", "`input` contains no encodable texts.")
|
||
except Exception as e:
|
||
logger.warning("token counting failed: %s", e)
|
||
prompt_tokens = 0
|
||
|
||
# 3) 编码（GPU→CPU 兜底；自愈计数与重载）
|
||
try:
|
||
output, served_device = _encode(texts)
|
||
embeddings = output["dense_vecs"]
|
||
except HTTPException:
|
||
raise
|
||
except Exception as e:
|
||
logger.exception("Embedding failed")
|
||
_http_error(500, "embedding_failed", str(e))
|
||
|
||
# 4) 原样返回（结构/字段保持一致）
|
||
return {
|
||
"object": "list",
|
||
"data": [
|
||
{
|
||
"object": "embedding",
|
||
"index": i,
|
||
"embedding": emb.tolist() if hasattr(emb, "tolist") else emb,
|
||
}
|
||
for i, emb in enumerate(embeddings)
|
||
],
|
||
"model": request.model,
|
||
"usage": {
|
||
"prompt_tokens": prompt_tokens,
|
||
"total_tokens": prompt_tokens,
|
||
},
|
||
"device": served_device, # 返回真实服务设备
|
||
"precision": "fp32" if served_device == "cpu" else PRECISION,
|
||
}
|
||
|
||
# --------------------------- 健康检查（可选） --------------------------------#
|
||
@app.get("/health")
|
||
def healthz():
|
||
return {
|
||
"status": "ok",
|
||
"device": DEVICE,
|
||
"precision": PRECISION,
|
||
"gpu_fail_count": _GPU_FAIL_COUNT,
|
||
"batch_size": BATCH_SIZE,
|
||
"ready": _READY,
|
||
}
|
||
|
||
# -----------------------------------------------------------------------------#
|
||
# Entry-point for `python server.py`
|
||
# -----------------------------------------------------------------------------#
|
||
if __name__ == "__main__":
|
||
import uvicorn
|
||
|
||
uvicorn.run(
|
||
"server:app",
|
||
host="0.0.0.0",
|
||
port=int(os.getenv("PORT", 8000)),
|
||
log_level="info",
|
||
workers=1, # multi-process → use gunicorn/uvicorn externally
|
||
)
|