feat: BaseModelConfig + DeepSeekMoE + 工厂模式替代 if/else

- BaseModelConfig: fields() 精确字段匹配 + 类型矫正 + 未知key警告
- DeepSeekMoE: 共享专家 + 路由专家 + top-K 门控
- AttnFactory/FFNFactory: 装饰器注册，DecoderBlock 零分支
- config 用 attn_type/ffn_type 驱动组件选择

astrai/config/model_config.py

@@ -1,12 +1,92 @@
 import json
-from dataclasses import asdict, dataclass
-from typing import Optional, Self
+import sys
+from dataclasses import dataclass, fields
+from typing import Any, Dict, Optional, Self, get_type_hints
 
 
 @dataclass
-class ModelConfig:
-    # basic config
+class BaseModelConfig:
+    """Field-aware JSON load/save for dataclass configs.
+
+    Subclass with additional fields. The base ``model_type`` field
+    enables ``AutoModel`` to pick the correct subclass.
+    """
+
     model_type: Optional[str] = None
+
+    def load(self, config_path: str) -> Self:
+        raw: Dict[str, Any] = {}
+        with open(config_path, "r") as f:
+            raw.update(json.load(f))
+
+        hints = get_type_hints(type(self))
+        valid = {fld.name for fld in fields(self)}
+        for key, value in raw.items():
+            if key not in valid:
+                sys.stderr.write(f"WARNING: unknown config key '{key}'\n")
+                continue
+
+            target_type = self._unwrap_optional(hints.get(key))
+            if target_type is None:
+                continue
+
+            try:
+                value = self._coerce(value, target_type)
+            except (TypeError, ValueError):
+                sys.stderr.write(
+                    f"WARNING: cannot coerce '{key}' = {value!r} to {target_type}\n"
+                )
+                continue
+
+            setattr(self, key, value)
+
+        return self
+
+    def save(self, config_path: str):
+        config_dict: Dict[str, Any] = {}
+        for fld in fields(self):
+            v = getattr(self, fld.name)
+            if v is not None:
+                config_dict[fld.name] = v
+        with open(config_path, "w") as f:
+            json.dump(config_dict, f, indent=4)
+
+    @staticmethod
+    def _unwrap_optional(tp: type) -> Optional[type]:
+        if tp is None:
+            return None
+        origin = getattr(tp, "__origin__", None)
+        if origin is not None:
+            args = getattr(tp, "__args__", ())
+            non_none = [a for a in args if a is not type(None)]
+            return non_none[0] if non_none else None
+        return tp
+
+    @staticmethod
+    def _coerce(value: Any, target_type: type) -> Any:
+        if target_type is bool and isinstance(value, bool):
+            return value
+        if (
+            target_type is int
+            and isinstance(value, (int, float))
+            and not isinstance(value, bool)
+        ):
+            return int(value)
+        if (
+            target_type is float
+            and isinstance(value, (int, float))
+            and not isinstance(value, bool)
+        ):
+            return float(value)
+        if target_type is str and isinstance(value, str):
+            return value
+        if isinstance(value, target_type):
+            return value
+        raise TypeError
+
+
+@dataclass
+class ModelConfig(BaseModelConfig):
     vocab_size: Optional[int] = None
     dim: Optional[int] = None
 
@@ -19,24 +99,16 @@ class ModelConfig:
     max_len: Optional[int] = None
     rope_theta: Optional[float] = None
 
-    # GQA
+    # attention
+    attn_type: str = "gqa"
     n_heads: Optional[int] = None
     n_kv_heads: Optional[int] = None
     use_qk_norm: Optional[bool] = None
     use_gated_attention: Optional[bool] = None
 
-    def load(self, config_path: str) -> Self:
-        config = {}
-        with open(config_path, "r") as f:
-            config.update(json.load(f))
-
-        for key, value in config.items():
-            if hasattr(self, key):
-                setattr(self, key, value)
-
-        return self
-
-    def save(self, config_path: str):
-        config_dict = {k: v for k, v in asdict(self).items() if v is not None}
-        with open(config_path, "w") as f:
-            json.dump(config_dict, f, indent=4)
+    # MoE
+    ffn_type: str = "mlp"
+    n_routed_experts: Optional[int] = None
+    n_shared_experts: Optional[int] = None
+    n_activated_experts: Optional[int] = None
+    moe_topk_method: Optional[str] = None