File size: 2,713 Bytes
188f4d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | """本地 vLLM 进程内推理：``from vllm import LLM`` 加载一个本地模型路径。
适用场景：用户提供一个本地权重路径（``--api_type local_vllm --model_path ...``），
"""
from __future__ import annotations
import threading
from pathlib import Path
from PIL import Image
from .base import APIBase
DEFAULT_MAX_TOKENS = 4096
DEFAULT_TEMPERATURE = 0.0
class LocalVLLMAPI(APIBase):
"""vLLM 进程内推理。线程安全：同一个 ``LLM`` 实例可多线程并发调用 ``generate``。"""
def __init__(
self,
model_path: str,
tensor_parallel_size: int = 1,
max_model_len: int | None = None,
dtype: str = "auto",
gpu_memory_utilization: float = 0.9,
trust_remote_code: bool = True,
max_tokens: int = DEFAULT_MAX_TOKENS,
temperature: float = DEFAULT_TEMPERATURE,
max_try: int = 1,
**engine_kwargs,
):
from vllm import LLM, SamplingParams # 延迟导入：openai_compat 用户可能没装 vllm
self.model_path = model_path
self.max_try = max_try
self.sampling_params = SamplingParams(
temperature=temperature,
max_tokens=max_tokens,
)
engine_args = dict(
model=model_path,
tensor_parallel_size=tensor_parallel_size,
dtype=dtype,
gpu_memory_utilization=gpu_memory_utilization,
trust_remote_code=trust_remote_code,
)
if max_model_len is not None:
engine_args["max_model_len"] = max_model_len
engine_args.update(engine_kwargs)
self.llm = LLM(**engine_args)
self._lock = threading.Lock() # vLLM 自带异步引擎，但部分版本对 generate 调用串行更稳
self._model_name = Path(model_path).name
def __call__(self, img_path: str | None, question: str, **kwargs):
try:
inputs = self._build_inputs(img_path, question)
with self._lock:
outputs = self.llm.generate([inputs], self.sampling_params)
text = outputs[0].outputs[0].text or ""
return True, "", text.strip()
except Exception as e:
print(f"[LocalVLLMAPI] inference 失败: {e}")
return False, "", ""
def _build_inputs(self, img_path: str | None, question: str) -> dict:
if not img_path:
return {"prompt": question}
# vLLM 多模态格式：使用 ``multi_modal_data``
image = Image.open(img_path).convert("RGB")
prompt = f"<|im_start|>user\n<image>\n{question}<|im_end|>\n<|im_start|>assistant\n"
return {"prompt": prompt, "multi_modal_data": {"image": image}}
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