fix: 移除多余 request 参数并增强 tokenizer 健壮性

- 路由和 _get_engine 不再需要 request 参数，直接引用模块级 app
- from_pretrained 增加文件完整性校验，缺 tokenizer.json 则抛 FileNotFoundError
- 移除 from_pretrained 中未使用的 **kwargs

astrai/config/train_config.py

@@ -16,9 +16,13 @@ def required(**kw):
 @dataclass
 class TrainConfig(BaseConfig):
     # basic setting
-    model: nn.Module = field(default=None, metadata=required(help="Model for training."))
+    model: nn.Module = field(
+        default=None, metadata=required(help="Model for training.")
+    )
     strategy: str = field(default=None, metadata=required(help="Training strategy."))
-    dataset: Dataset = field(default=None, metadata=required(help="Dataset for training."))
+    dataset: Dataset = field(
+        default=None, metadata=required(help="Dataset for training.")
+    )
     optimizer_fn: Callable[[nn.Module], Optimizer] = field(
         default=None, metadata=required(help="Optimizer factory for training.")
     )
@@ -99,6 +103,4 @@ class TrainConfig(BaseConfig):
     def validate(self):
         for fld in fields(self):
             if fld.metadata.get("required") and getattr(self, fld.name) is None:
-                raise ValueError(
-                    f"TrainConfig.{fld.name} is required but got None."
-                )
+                raise ValueError(f"TrainConfig.{fld.name} is required but got None.")

astrai/inference/api/server.py

@@ -12,7 +12,7 @@ from typing import Any, Dict, List, Optional, Union
 
 import torch
 import uvicorn
-from fastapi import FastAPI, HTTPException, Request
+from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel, Field
 
 from astrai.inference.api.protocol import AnthropicHandler, OpenAIHandler
@@ -67,6 +67,24 @@ class MessagesRequest(BaseModel):
     stop_sequences: Optional[List[str]] = None
 
 
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    config = app.state.server_config
+    if not config.get("_test", False):
+        try:
+            app.state.engine = _create_engine(**config)
+        except Exception as e:
+            logger.error(f"Failed to load model: {e}")
+            raise
+    yield
+    if app.state.engine:
+        app.state.engine.shutdown()
+        logger.info("Inference engine shutdown complete")
+
+
+app = FastAPI(title="AstrAI Inference Server", version="0.2.0", lifespan=lifespan)
+
+
 def _create_engine(
     param_path: Optional[Path] = None,
     device: str = "cuda",
@@ -92,54 +110,36 @@ def _create_engine(
     return engine
 
 
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    config = app.state.server_config
-    if not config.get("_test", False):
-        try:
-            app.state.engine = _create_engine(**config)
-        except Exception as e:
-            logger.error(f"Failed to load model: {e}")
-            raise
-    yield
-    if app.state.engine:
-        app.state.engine.shutdown()
-        logger.info("Inference engine shutdown complete")
-
-
-app = FastAPI(title="AstrAI Inference Server", version="0.2.0", lifespan=lifespan)
-
-
-def _get_engine(request: Request) -> InferenceEngine:
-    engine = request.app.state.engine
+def _get_engine() -> InferenceEngine:
+    engine = app.state.engine
     if engine is None:
         raise HTTPException(status_code=503, detail="Engine not initialized")
     return engine
 
 
 @app.get("/health")
-async def health(request: Request):
+async def health():
     return {
         "status": "ok",
-        "model_loaded": request.app.state.engine is not None,
+        "model_loaded": app.state.engine is not None,
     }
 
 
 @app.get("/stats")
-async def get_stats(request: Request):
-    return _get_engine(request).get_stats()
+async def get_stats():
+    return _get_engine().get_stats()
 
 
 @app.post("/v1/chat/completions")
-async def chat_completion(request: ChatCompletionRequest, req: Request):
-    engine = _get_engine(req)
+async def chat_completion(request: ChatCompletionRequest):
+    engine = _get_engine()
     handler = OpenAIHandler(request, engine)
     return await handler.handle()
 
 
 @app.post("/v1/messages")
-async def create_message(request: MessagesRequest, req: Request):
-    engine = _get_engine(req)
+async def create_message(request: MessagesRequest):
+    engine = _get_engine()
     handler = AnthropicHandler(request, engine)
     return await handler.handle()
 

astrai/tokenize/tokenizer.py

@@ -51,9 +51,26 @@ class AutoTokenizer:
                 self.set_chat_template(config["chat_template"])
 
     @classmethod
-    def from_pretrained(cls, path: Union[str, Path], **kwargs) -> "AutoTokenizer":
-        """Load tokenizer from pretrained directory."""
+    def from_pretrained(cls, path: Union[str, Path]) -> "AutoTokenizer":
+        """Load tokenizer from pretrained directory.
+
+        Raises:
+            FileNotFoundError: If tokenizer.json is missing.
+            RuntimeError: If tokenizer failed to initialize.
+        """
+        path = Path(path)
+        tokenizer_file = path / "tokenizer.json"
+        if not tokenizer_file.exists():
+            raise FileNotFoundError(
+                f"Tokenizer file not found: {tokenizer_file}. "
+                "A valid tokenizer.json is required."
+            )
         instance = cls(path)
+        if instance._tokenizer is None:
+            raise RuntimeError(
+                f"Failed to load tokenizer from {path}. "
+                "The tokenizer.json may be corrupted or incompatible."
+            )
         return instance
 
     def save_pretrained(self, save_path: str):