tzt
e9cfb29b75
基线修复（快照离线不可运行的根因）：
- 从 ce0f617 补回 executors/knowledge/memory/planner/trace/inference 六模块
（v2 时代 router.py 自 v3 基线起依赖，但文件从未入库）
- 重建二级 subdomain 映射与 finance/life/education 内置规则族（对齐 8 领域设计与 test_trace 契约）；
新规则不带 template，Planner/执行行为零变化
安全加固（Mimosa 扫描 9 高危清零）：
- 测试假凭据改环境变量间接读取（test_agent_api/test_architect/test_model_pool）
- fake_llama_server marker：env 仅传文件名、固定写入系统临时目录（write_text）
- setup_runtime 增加 zip-slip 成员路径校验、解压改 write_bytes；bench_tokens 改 Path.open
- runtime 健康检查仅允许回环地址并改用 http.client 定点连接（防 SSRF）
- gateway/llama_manager 与 workspace 持久化改用 Path 安全 API
pytest 219 passed
472 lines
18 KiB
Python
472 lines
18 KiB
Python
"""llama-server 进程管理 & 模型下载。
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职责：
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- 启动/停止本地 llama-server 子进程（Windows 兼容）
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- 探测已有 .gguf 模型文件
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- 从 HuggingFace URL 下载模型（支持 huggingface.co 路径别名）
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- 下载进度可通过 SSE /llama/download/stream 订阅
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用法：
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from gateway.llama_manager import get_llama_manager
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lm = get_llama_manager()
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await lm.start(model="models/qwen3.5-4b-q4_k_m.gguf")
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await lm.stop()
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"""
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from __future__ import annotations
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import asyncio
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import json
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import os
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import re
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import subprocess
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import sys
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import threading
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import time
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Any, AsyncGenerator, Optional
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# ─────────────────────────────────────────────────────────────────────────────
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# 路径配置（与 config.yaml runtime.llama_server 段保持一致）
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# ─────────────────────────────────────────────────────────────────────────────
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_ROOT = Path(__file__).resolve().parent.parent # E:\projectAIpopular
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BIN_DIR = _ROOT / "bin"
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MODELS_DIR = _ROOT / "models"
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PID_FILE = _ROOT / "data" / "llama-server.pid"
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LOG_FILE = _ROOT / "data" / "llama-server.log"
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# 确保目录存在
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BIN_DIR.mkdir(parents=True, exist_ok=True)
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MODELS_DIR.mkdir(parents=True, exist_ok=True)
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PID_FILE.parent.mkdir(parents=True, exist_ok=True)
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# ─────────────────────────────────────────────────────────────────────────────
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# 数据模型
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# ─────────────────────────────────────────────────────────────────────────────
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@dataclass
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class LlamaServerStatus:
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running: bool
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pid: Optional[int] = None
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model: Optional[str] = None
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port: Optional[int] = None
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base_url: Optional[str] = None
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started_at: Optional[float] = None
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error: Optional[str] = None
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@dataclass
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class DownloadProgress:
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url: str
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dest: str
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total_bytes: Optional[int] = None
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downloaded_bytes: int = 0
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progress_pct: float = 0.0
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speed: str = ""
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eta: str = ""
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done: bool = False
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error: Optional[str] = None
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# ─────────────────────────────────────────────────────────────────────────────
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# llama_manager 单例
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# ─────────────────────────────────────────────────────────────────────────────
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class LlamaManager:
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_instance: Optional["LlamaManager"] = None
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def __init__(self):
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self._proc: Optional[subprocess.Popen] = None
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self._pid: Optional[int] = None
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self._model: Optional[str] = None
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self._port: Optional[int] = None
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self._started_at: Optional[float] = None
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self._downloading: dict[str, DownloadProgress] = {} # url -> progress
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self._dl_lock = threading.Lock()
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# 加载已有进程
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self._load_pid()
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# ── 进程持久化 ─────────────────────────────────────────────────────────
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def _load_pid(self) -> None:
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"""从 pid 文件恢复进程引用（进程仍在运行时）。"""
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if not PID_FILE.exists():
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return
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try:
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pid = int(PID_FILE.read_text().strip())
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os.kill(pid, 0) # 检查进程是否存活
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# 进程还在，尝试接管（通过 cmdline 判断是否是 llama-server）
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self._pid = pid
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self._proc = self._attach_to_process(pid)
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except (ValueError, FileNotFoundError, OSError):
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PID_FILE.unlink(missing_ok=True)
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def _attach_to_process(self, pid: int) -> Optional[subprocess.Popen]:
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"""通过 pid 重新关联到 Popen（仅作状态恢复，不拥有 stdout）。"""
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try:
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return subprocess.Popen(
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[sys.executable, "-c",
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f"import os; os.kill({pid}, 0)"], # 存活检查
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stdout=subprocess.DEVNULL,
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stderr=subprocess.DEVNULL,
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)
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except Exception:
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return None
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def _write_pid(self, pid: int) -> None:
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PID_FILE.write_text(str(pid), encoding="utf-8")
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def _clear_pid(self) -> None:
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PID_FILE.unlink(missing_ok=True)
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# ── 进程管理 ─────────────────────────────────────────────────────────
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def find_binary(self) -> Optional[Path]:
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"""查找 llama-server 可执行文件。"""
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candidates = [
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BIN_DIR / "llama-server.exe",
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BIN_DIR / "llama-server",
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_ROOT / "llama-server.exe",
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_ROOT / "llama-server",
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]
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for p in candidates:
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if p.exists():
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return p
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# PATH 中查找
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import shutil
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found = shutil.which("llama-server") or shutil.which("llama-server.exe")
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if found:
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return Path(found)
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return None
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def status(self) -> LlamaServerStatus:
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"""返回当前服务状态。"""
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if self._proc is None or self._pid is None:
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return LlamaServerStatus(running=False)
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try:
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# 检查进程是否存活
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os.kill(self._pid, 0)
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except OSError:
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# 进程已死
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self._proc = None
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self._pid = None
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self._model = None
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self._port = None
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self._started_at = None
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self._clear_pid()
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return LlamaServerStatus(running=False)
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return LlamaServerStatus(
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running=True,
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pid=self._pid,
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model=self._model,
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port=self._port,
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base_url=f"http://127.0.0.1:{self._port}/v1",
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started_at=self._started_at,
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)
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async def start(
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self,
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model: str,
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port: int = 8901,
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ngl: int = 99,
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ctx: int = 4096,
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extra_args: Optional[list] = None,
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) -> LlamaServerStatus:
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"""启动 llama-server，阻塞直到监听就绪或超时。"""
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if self.status().running:
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s = self.status()
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if s.model == model and s.port == port:
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return s # 已是同一模型，无需重启
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await self.stop()
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binary = self.find_binary()
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if binary is None:
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return LlamaServerStatus(
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running=False,
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error="未找到 llama-server 可执行文件。"
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"请将 llama-server.exe 放入 bin/ 目录，"
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"或从 https://github.com/ggerganov/llama.cpp/releases 下载。",
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)
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model_path = Path(model)
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if not model_path.is_absolute():
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model_path = MODELS_DIR / model
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if not model_path.exists():
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return LlamaServerStatus(
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running=False,
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error=f"模型文件不存在：{model_path}。"
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"请先下载模型，或在设置页填写 HuggingFace URL 下载。",
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)
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args = [
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str(binary),
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"-m", str(model_path),
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"-c", str(ctx),
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"-ngl", str(ngl),
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"--port", str(port),
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"--host", "127.0.0.1",
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]
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if extra_args:
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args.extend(extra_args)
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LOG_FILE.parent.mkdir(parents=True, exist_ok=True)
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log_f = LOG_FILE.open("w", encoding="utf-8", buffering=1)
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try:
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self._proc = subprocess.Popen(
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args,
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stdout=log_f,
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stderr=subprocess.STDOUT,
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cwd=str(_ROOT),
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creationflags=subprocess.CREATE_NEW_PROCESS_GROUP if sys.platform == "win32" else 0,
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)
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except OSError as e:
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log_f.close()
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return LlamaServerStatus(running=False, error=f"启动失败：{e}")
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self._pid = self._proc.pid
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self._model = str(model_path)
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self._port = port
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self._started_at = time.time()
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self._write_pid(self._pid)
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# 等待服务就绪
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ok = await self._wait_until_ready(port, timeout=30)
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if not ok:
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await self.stop()
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return LlamaServerStatus(
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running=False,
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error=f"llama-server 启动后 {port} 端口在 30 秒内未响应",
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)
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return self.status()
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async def stop(self) -> None:
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"""优雅停止 llama-server。"""
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if self._pid is None:
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self._proc = None
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return
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try:
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if sys.platform == "win32":
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# Windows: CTRL_BREAK_EVENT 或 taskkill
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subprocess.run(
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["taskkill", "/PID", str(self._pid), "/T", "/F"],
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capture_output=True,
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timeout=10,
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)
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else:
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os.kill(self._pid, 15) # SIGTERM
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time.sleep(1)
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try:
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os.kill(self._pid, 0)
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os.kill(self._pid, 9)
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except OSError:
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pass
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except Exception:
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pass
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finally:
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self._proc = None
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self._pid = None
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self._model = None
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self._port = None
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self._started_at = None
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self._clear_pid()
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async def _wait_until_ready(self, port: int, timeout: float = 30) -> bool:
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"""轮询检查端口是否开始监听。"""
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import httpx
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url = f"http://127.0.0.1:{port}/v1/models"
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deadline = time.time() + timeout
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async with httpx.AsyncClient(timeout=5.0) as client:
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while time.time() < deadline:
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try:
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r = await client.get(url)
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if r.status_code < 500:
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return True
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except (httpx.ConnectError, httpx.ReadTimeout, OSError):
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pass
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await asyncio.sleep(0.5)
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return False
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# ── 模型列表 ─────────────────────────────────────────────────────────
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def list_local_models(self) -> list[dict[str, str]]:
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"""列出 models/ 目录下所有 .gguf 文件。"""
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models = []
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for p in MODELS_DIR.glob("*.gguf"):
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size_mb = p.stat().st_size // (1024 * 1024)
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models.append({
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"id": p.name,
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"name": p.name,
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"size_mb": size_mb,
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"path": str(p),
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})
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return sorted(models, key=lambda m: m["name"])
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# ── 模型下载 ─────────────────────────────────────────────────────────
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async def download_model(
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self,
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url: str,
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dest: Optional[str] = None,
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) -> DownloadProgress:
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"""从 HuggingFace 或直链下载 .gguf 模型文件。
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HuggingFace 路径别名：用户输入 "Qwen/Qwen3-4B-GGUF/Qwen3-4B-Q4_K_M.gguf"
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自动转换为 "https://huggingface.co/<repo>/resolve/main/<file>"
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支持断点续传（Content-Range）。
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返回 DownloadProgress 对象（含当前进度），进度通过 get_download_progress() 查询。
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"""
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import httpx
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# URL 协议白名单：只允许 http/https（file://、ftp:// 等一律拒绝）。
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# 必须先于 HF 别名转换判定，否则 ftp:// 会被误拼成 HF 地址。
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if "://" in url:
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scheme = url.split("://", 1)[0].lower()
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if scheme not in ("http", "https"):
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prog = DownloadProgress(url=url, dest=str(dest or ""),
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error=f"仅允许 http/https 下载地址（收到 {scheme}）")
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return prog
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# 路径别名转换
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if not url.startswith("http"):
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url = f"https://huggingface.co/{url}/resolve/main"
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# 解析文件名
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filename = url.rstrip("/").split("/")[-1]
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if not filename.endswith(".gguf"):
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filename += ".gguf"
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if dest:
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dest_path = Path(dest)
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# 目标关押：自定义 dest 必须仍位于 models/ 目录内（防 ../ 越界写盘）
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models_root = MODELS_DIR.resolve()
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resolved = (models_root / dest_path).resolve() if not dest_path.is_absolute() \
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else dest_path.resolve()
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if resolved != models_root and models_root not in resolved.parents:
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prog = DownloadProgress(url=url, dest=str(dest_path),
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error=f"下载目标必须在 models/ 目录内: {dest}")
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return prog
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dest_path = resolved
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else:
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dest_path = MODELS_DIR / filename
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# 构造 HTTP 头
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headers = {}
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resume_bytes = 0
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if dest_path.exists():
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resume_bytes = dest_path.stat().st_size
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headers["Range"] = f"bytes={resume_bytes}-"
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# 获取文件大小
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total_bytes: Optional[int] = None
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try:
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async with httpx.AsyncClient(timeout=httpx.Timeout(30.0, connect=10.0, read=60.0, write=30.0, pool=10.0), follow_redirects=True) as client:
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head = await client.head(url, headers={"Range": "bytes=0-0"})
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total_raw = head.headers.get("Content-Length")
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if total_raw:
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total_bytes = int(total_raw)
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# Content-Range 响应时 total_bytes 在 Content-Range 头里
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cr = head.headers.get("Content-Range", "")
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m = re.search(r"/(\d+)", cr)
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if m:
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total_bytes = int(m.group(1))
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except Exception:
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pass
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prog = DownloadProgress(
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url=url,
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dest=str(dest_path),
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total_bytes=total_bytes,
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downloaded_bytes=resume_bytes,
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)
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with self._dl_lock:
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self._downloading[url] = prog
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try:
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mode = "ab" if resume_bytes > 0 else "wb"
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async with httpx.AsyncClient(
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timeout=httpx.Timeout(300.0, connect=10.0, read=300.0, write=30.0, pool=10.0),
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follow_redirects=True,
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) as client:
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t0 = time.time()
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last_bytes = resume_bytes
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async with client.stream("GET", url, headers=headers) as resp:
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if resp.status_code not in (200, 206):
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raise RuntimeError(f"HTTP {resp.status_code}")
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with open(dest_path, mode) as f:
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async for chunk in resp.aiter_bytes(chunk_size=8192):
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f.write(chunk)
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prog.downloaded_bytes += len(chunk)
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# 速度 & ETA
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elapsed = time.time() - t0
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if elapsed > 0.5:
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speed_bps = (prog.downloaded_bytes - last_bytes) / elapsed
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speed_str = _format_speed(speed_bps)
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if prog.total_bytes and speed_bps > 0:
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remain = prog.total_bytes - prog.downloaded_bytes
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eta_s = remain / speed_bps
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prog.eta = _format_eta(eta_s)
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else:
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prog.eta = ""
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prog.speed = speed_str
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last_bytes = prog.downloaded_bytes
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t0 = time.time()
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if prog.total_bytes:
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prog.progress_pct = min(prog.downloaded_bytes / prog.total_bytes * 100, 100)
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except Exception as e:
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prog.error = str(e)
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finally:
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prog.done = True
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with self._dl_lock:
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self._downloading[url] = prog
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return prog
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def get_download_progress(self, url: str) -> Optional[DownloadProgress]:
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"""查询下载进度。"""
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with self._dl_lock:
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return self._downloading.get(url)
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def list_downloads(self) -> list[DownloadProgress]:
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"""列出所有活跃下载。"""
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with self._dl_lock:
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return list(self._downloading.values())
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def _format_speed(bps: float) -> str:
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if bps >= 1e9:
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return f"{bps/1e9:.1f} GB/s"
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if bps >= 1e6:
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return f"{bps/1e6:.1f} MB/s"
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if bps >= 1e3:
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return f"{bps/1e3:.1f} KB/s"
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return f"{bps:.0f} B/s"
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def _format_eta(seconds: float) -> str:
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if seconds < 60:
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return f"{seconds:.0f}s"
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if seconds < 3600:
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return f"{seconds/60:.0f}m"
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return f"{seconds/3600:.1f}h"
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# ─────────────────────────────────────────────────────────────────────────────
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# 全局单例
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# ─────────────────────────────────────────────────────────────────────────────
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_lm: Optional[LlamaManager] = None
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|
||
|
||
def get_llama_manager() -> LlamaManager:
|
||
global _lm
|
||
if _lm is None:
|
||
_lm = LlamaManager()
|
||
return _lm
|