refactor: 重构 inference 模块架构，引入设计模式并分组文件

- 新增 protocol.py 协议层，Template Method 模式消除流/非流分支 45% 重复
- SSEBuilder 统一 SSE 构造，StopChecker 独立 stop_sequence 检测
- AnthropicHandler 追踪已产出文本，修复 stop 时重复 delta
- server.py 路由从约 100 行缩减至 3 行
- 拆分为 core/（cache/executor/scheduler/task）和 api/（protocol/server）
- 外部保持二级导入路径（from astrai.inference import Name）
- 删除所有分隔线注释，代码按语义自然分组

astrai/inference/api/server.py

@@ -0,0 +1,167 @@
+"""
+OpenAI / Anthropic-compatible chat completion server backed by continuous-batching inference.
+
+Protocol-specific formatting is delegated to ``astrai.inference.protocol``.
+This module owns the FastAPI app, request/response schemas, and dependency wiring.
+"""
+
+import logging
+from contextlib import asynccontextmanager
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Union
+
+import torch
+import uvicorn
+from fastapi import FastAPI, HTTPException, Request
+from pydantic import BaseModel, Field
+
+from astrai.inference.api.protocol import AnthropicHandler, OpenAIHandler
+from astrai.inference.engine import InferenceEngine
+from astrai.model import AutoModel
+from astrai.tokenize import AutoTokenizer
+
+logger = logging.getLogger(__name__)
+
+_project_root = Path(__file__).parent.parent.parent
+
+
+class ChatMessage(BaseModel):
+    role: str
+    content: str
+
+
+class ChatCompletionRequest(BaseModel):
+    """OpenAI Chat Completion API request body."""
+
+    model: str = "astrai"
+    messages: List[ChatMessage]
+    temperature: Optional[float] = Field(default=1.0, ge=0.0, le=2.0)
+    top_p: Optional[float] = Field(default=1.0, ge=0.0, le=1.0)
+    top_k: Optional[int] = Field(default=50, ge=1)
+    stream: Optional[bool] = False
+    stop: Optional[Union[str, List[str]]] = None
+    max_tokens: Optional[int] = Field(default=2048, ge=1)
+    n: Optional[int] = Field(default=1, ge=1)
+    presence_penalty: Optional[float] = Field(default=0.0, ge=-2.0, le=2.0)
+    frequency_penalty: Optional[float] = Field(default=0.0, ge=-2.0, le=2.0)
+    logit_bias: Optional[Dict[int, float]] = None
+    user: Optional[str] = None
+
+
+class AnthropicMessage(BaseModel):
+    role: str
+    content: Union[str, List[Dict[str, Any]]]
+
+
+class MessagesRequest(BaseModel):
+    """Anthropic Messages API request body."""
+
+    model: str = "astrai"
+    max_tokens: int = Field(default=1024, ge=1)
+    messages: List[AnthropicMessage]
+    system: Optional[str] = None
+    temperature: Optional[float] = Field(default=1.0, ge=0.0, le=2.0)
+    top_p: Optional[float] = Field(default=1.0, ge=0.0, le=1.0)
+    top_k: Optional[int] = Field(default=50, ge=1)
+    stream: Optional[bool] = False
+    stop_sequences: Optional[List[str]] = None
+
+
+def _create_engine(
+    param_path: Optional[Path] = None,
+    device: str = "cuda",
+    dtype: torch.dtype = torch.bfloat16,
+    max_batch_size: int = 16,
+) -> InferenceEngine:
+    if param_path is None:
+        param_path = _project_root / "params"
+    if not param_path.exists():
+        raise FileNotFoundError(f"Parameter directory not found: {param_path}")
+
+    tokenizer = AutoTokenizer.from_pretrained(param_path)
+    model = AutoModel.from_pretrained(param_path)
+    model.to(device=device, dtype=dtype)
+    logger.info(f"Model loaded on {device} with dtype {dtype}")
+
+    engine = InferenceEngine(
+        model=model,
+        tokenizer=tokenizer,
+        max_batch_size=max_batch_size,
+    )
+    logger.info(f"Inference engine initialized with max_batch_size={max_batch_size}")
+    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
+    if engine is None:
+        raise HTTPException(status_code=503, detail="Engine not initialized")
+    return engine
+
+
+@app.get("/health")
+async def health(request: Request):
+    return {
+        "status": "ok",
+        "model_loaded": request.app.state.engine is not None,
+    }
+
+
+@app.get("/stats")
+async def get_stats(request: Request):
+    return _get_engine(request).get_stats()
+
+
+@app.post("/v1/chat/completions")
+async def chat_completion(request: ChatCompletionRequest, req: Request):
+    engine = _get_engine(req)
+    handler = OpenAIHandler(request, engine)
+    return await handler.handle()
+
+
+@app.post("/v1/messages")
+async def create_message(request: MessagesRequest, req: Request):
+    engine = _get_engine(req)
+    handler = AnthropicHandler(request, engine)
+    return await handler.handle()
+
+
+def run_server(
+    host: str = "0.0.0.0",
+    port: int = 8000,
+    reload: bool = False,
+    device: str = "cuda",
+    dtype: torch.dtype = torch.bfloat16,
+    param_path: Optional[Path] = None,
+    max_batch_size: int = 16,
+):
+    app.state.server_config = {
+        "device": device,
+        "dtype": dtype,
+        "param_path": param_path,
+        "max_batch_size": max_batch_size,
+    }
+    uvicorn.run(
+        "astrai.inference.server:app",
+        host=host,
+        port=port,
+        reload=reload,
+    )