refactor: Transformer更名为AutoRegressiveLM并新增EmbeddingEncoder

- AutoRegressiveLM 注册名改为 autoregressive_lm
- 新增 EmbeddingEncoder 支持 mean/cls/last pooling
- ModelConfig 增加 pooling_type / normalize_embeddings 字段
- 导入、注释、测试全部同步更新

astrai/config/model_config.py

@@ -1,18 +1,24 @@
 import json
-import warnings
-from dataclasses import dataclass, fields
+from dataclasses import dataclass
 from typing import Any, Dict, Optional, Self
 
 from astrai.config.base import BaseConfig
+from astrai.factory import BaseFactory
+
+
+class ConfigFactory(BaseFactory[BaseConfig]):
+    """Factory that dispatches config classes by ``model_type``."""
+
+    @classmethod
+    def load(cls, raw: Dict[str, Any]) -> BaseConfig:
+        model_type = raw.get("model_type") or "autoregressive_lm"
+        config_cls = cls.get_component_class(model_type)
+        return config_cls.from_dict(raw)
 
 
 @dataclass
 class BaseModelConfig(BaseConfig):
-    """Field-aware JSON from/to file for dataclass configs.
-
-    Subclass with additional fields. The base ``model_type`` field
-    enables ``AutoModel`` to pick the correct subclass.
-    """
+    """Base config with ``model_type`` dispatch and file I/O."""
 
     model_type: Optional[str] = None
 
@@ -20,13 +26,6 @@ class BaseModelConfig(BaseConfig):
     def from_file(cls, config_path: str) -> Self:
         with open(config_path, "r") as f:
             raw: Dict[str, Any] = json.load(f)
-
-        valid = {fld.name for fld in fields(cls)}
-        for key in list(raw):
-            if key not in valid:
-                warnings.warn(f"Unknown config key '{key}'")
-                del raw[key]
-
         return cls.from_dict(raw)
 
     def to_file(self, config_path: str):
@@ -37,34 +36,55 @@ class BaseModelConfig(BaseConfig):
 
 
 @dataclass
-class ModelConfig(BaseModelConfig):
+@ConfigFactory.register("autoregressive_lm")
+class AutoRegressiveLMConfig(BaseModelConfig):
+    """Configuration for autoregressive language model."""
+
     vocab_size: Optional[int] = None
     dim: Optional[int] = None
-
     n_layers: Optional[int] = None
     norm_eps: Optional[float] = None
     dim_ffn: Optional[int] = None
     tie_weight: Optional[bool] = None
 
-    # RoPE
     max_len: Optional[int] = None
     rope_theta: Optional[float] = None
 
-    # 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
 
-    # MLA
     kv_lora_rank: Optional[int] = None
     qk_nope_head_dim: Optional[int] = None
     qk_rope_head_dim: Optional[int] = None
 
-    # MoE
     ffn_type: str = "mlp"
     n_routed_experts: Optional[int] = None
     n_shared_experts: Optional[int] = None
     n_activated_experts: Optional[int] = None
     topk_method: Optional[str] = None
+
+
+@dataclass
+@ConfigFactory.register("embedding")
+class EncoderConfig(BaseModelConfig):
+    """Configuration for embedding encoder model."""
+
+    vocab_size: Optional[int] = None
+    dim: Optional[int] = None
+    n_layers: Optional[int] = None
+    norm_eps: Optional[float] = None
+    dim_ffn: Optional[int] = None
+
+    max_len: Optional[int] = None
+    rope_theta: Optional[float] = None
+
+    n_heads: Optional[int] = None
+    n_kv_heads: Optional[int] = None
+    use_qk_norm: Optional[bool] = None
+    use_gated_attention: Optional[bool] = None
+
+    pooling_type: Optional[str] = None
+    normalize_embeddings: Optional[bool] = None