feat: 启用 Apple Silicon MPS 加速 + 兼容 transformers 5.x + 本地运行配置

nvidia_router.py 变更:
- device 默认值从 'cpu' 改为 'auto'，自动检测 MPS/CUDA/CPU
- AutoConfig 替换为 DebertaV2Config + 手动解析 config.json
  (nvidia/prompt-task-and-complexity-classifier 的 config.json 无 model_type，
   transformers 5.x 的 AutoConfig 会直接报错)
- MPS 设备自动转换 float16，修复 MPS 矩阵乘法数据类型冲突崩溃
  (MPS NDArrayMatrixMultiplication 要求 dst/accumulator 同类型)
- 日志增加设备和精度信息输出

docker-compose.yml 变更:
- 端口映射改为 402:8000 (本地开发端口)
- volume 从 named volume 改为 ./data 本地目录映射
- API Key 改回环境变量引用 (密钥存 .env 文件，已在 .gitignore 中)

测试环境: Mac Mini M4 Pro / 64GB / macOS 15.3.1
运行方式: .venv/bin/python -m uvicorn main:app --host 0.0.0.0 --port 402
测试结果:
- MPS + FP16 分类器正常工作，稳态路由延迟 ~53ms
- NVIDIA 3-tier 路由决策正确 (simple/medium/complex)
- OpenAI 兼容 API 正常响应，DashScope Qwen 模型调用正常

docker-compose.yml

@@ -3,11 +3,11 @@ services:
     build: .
     container_name: llm-compass
     ports:
-      - "8000:8000"
+      - "402:8000"
     environment:
       - DASHSCOPE_API_KEY=${DASHSCOPE_API_KEY}
     volumes:
-      - compass-data:/app/data
+      - ./data:/app/data
     restart: unless-stopped
     healthcheck:
       test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"]

nvidia_router.py

@@ -10,11 +10,12 @@ NVIDIA Prompt Task & Complexity Classifier Router
 
 import torch
 import torch.nn as nn
-from transformers import AutoTokenizer, DebertaV2Model, AutoConfig
+from transformers import AutoTokenizer, DebertaV2Model, DebertaV2Config
 from safetensors.torch import load_file
 from huggingface_hub import hf_hub_download
 from typing import Dict, Optional
 import logging
+import json
 
 logger = logging.getLogger(__name__)
 
@@ -115,7 +116,16 @@ class NvidiaComplexityRouter:
     # Creativity 映射
     CREATIVITY_MAP = {0: "High", 1: "Low", 2: "No"}
     
-    def __init__(self, device: str = "cpu"):
+    def __init__(self, device: str = "auto"):
+        if device == "auto":
+            if torch.backends.mps.is_available():
+                device = "mps"
+                logger.info("MPS (Metal GPU) detected, using MPS acceleration")
+            elif torch.cuda.is_available():
+                device = "cuda"
+            else:
+                device = "cpu"
+                logger.info("No GPU detected, using CPU")
         self.device = device
         self.tokenizer = None
         self.model = None
@@ -129,8 +139,18 @@ class NvidiaComplexityRouter:
         
         logger.info(f"Loading NVIDIA classifier: {self.MODEL_NAME}")
         
-        # 1. 加载 config
+        # 1. 手动加载自定义 config.json（该模型无 model_type，AutoConfig 不兼容）
-        self.config = AutoConfig.from_pretrained(self.MODEL_NAME)
+        config_path = hf_hub_download(self.MODEL_NAME, "config.json")
+        with open(config_path, "r") as f:
+            custom_config = json.load(f)
+        
+        # 构建 backbone 的 DeBERTa config（从 base_model 加载）
+        base_model = custom_config.get("base_model", "microsoft/DeBERTa-v3-base")
+        self.config = DebertaV2Config.from_pretrained(base_model)
+        # 保存自定义分类头参数
+        self.config.target_sizes = custom_config["target_sizes"]
+        self.config.fc_dropout = custom_config.get("fc_dropout", 0.2)
+        self.config.base_model = base_model
         
         # 2. 加载 tokenizer (slow模式，兼容性好)
         self.tokenizer = AutoTokenizer.from_pretrained(self.MODEL_NAME, use_fast=False)
@@ -143,9 +163,13 @@ class NvidiaComplexityRouter:
         self.model.load_state_dict(state_dict, strict=False)
         
         self.model.to(self.device)
+        # MPS 需要 float16 以避免矩阵乘法数据类型冲突
+        if self.device == "mps":
+            self.model.half()
         self.model.eval()
         self._initialized = True
-        logger.info("NVIDIA classifier loaded successfully")
+        dtype = "float16" if self.device == "mps" else "float32"
+        logger.info(f"NVIDIA classifier loaded successfully on {self.device} ({dtype})")
     
     def predict(self, query: str) -> Dict:
         """