refactor : 清理工厂和配置系统中的死代码与冗余抽象

- 删除 Registry 中未使用的 category/priority 字段，_entries 简化为直接存储类引用
- 修正 __init_subclass__ 避免叶子类（AutoRegressiveLM 等）创建空注册表
- 删除 5 个工厂的薄 create() 覆写，统一使用 BaseFactory.create(name, *args, **kwargs)
- 删除 3 处零调用的 available_types/available_strategies 别名死代码
- 删除零调用的 BaseModelConfig.to_file 死代码
- 将 BaseConfig.from_json/to_json 重命名为 from_file/to_file，消除与子类重复
- 移除两个 inference builder 中总是被覆写的 prompt_tokens=0

astrai/config/base.py

@@ -89,10 +89,10 @@ class BaseConfig:
         raise TypeError
 
     @classmethod
-    def from_json(cls, path: Union[str, Path]) -> Self:
+    def from_file(cls, path: Union[str, Path]) -> Self:
         with open(path, "r", encoding="utf-8") as f:
             return cls.from_dict(json.load(f))
 
-    def to_json(self, path: Union[str, Path]):
+    def to_file(self, path: Union[str, Path]):
         with open(path, "w", encoding="utf-8") as f:
             json.dump(self.to_dict(), f, indent=2, ensure_ascii=False)

astrai/config/model_config.py

@@ -1,6 +1,5 @@
-import json
 from dataclasses import dataclass
-from typing import Any, Dict, Optional, Self
+from typing import Any, Dict, Optional
 
 from astrai.config.base import BaseConfig
 from astrai.factory import BaseFactory
@@ -22,18 +21,6 @@ class BaseModelConfig(BaseConfig):
 
     model_type: Optional[str] = None
 
-    @classmethod
-    def from_file(cls, config_path: str) -> Self:
-        with open(config_path, "r") as f:
-            raw: Dict[str, Any] = json.load(f)
-        return cls.from_dict(raw)
-
-    def to_file(self, config_path: str):
-        d = self.to_dict()
-        config_dict = {k: v for k, v in d.items() if v is not None}
-        with open(config_path, "w") as f:
-            json.dump(config_dict, f, indent=4)
-
 
 @dataclass
 @ConfigFactory.register("autoregressive_lm")

astrai/dataset/dataset.py

@@ -136,20 +136,6 @@ class DatasetFactory(BaseFactory["BaseDataset"]):
         dataset = DatasetFactory.create("custom", window_size, stride)
     """
 
-    @classmethod
-    def create(cls, train_type: str, window_size: int, stride: int) -> "BaseDataset":
-        """Create a dataset instance.
-
-        Args:
-            train_type: Type of training ("seq", "sft", "dpo", "grpo")
-            window_size: Window size for data sampling
-            stride: Stride between consecutive samples
-
-        Returns:
-            Dataset instance
-        """
-        return super().create(train_type, window_size, stride)
-
     @classmethod
     def load(
         cls,
@@ -179,11 +165,6 @@ class DatasetFactory(BaseFactory["BaseDataset"]):
 
         return dataset
 
-    @classmethod
-    def available_types(cls) -> list:
-        """Return list of registered dataset type names."""
-        return cls.list_registered()
-
 
 @DatasetFactory.register("seq")
 class SEQDataset(BaseDataset):

astrai/factory.py

@@ -8,8 +8,6 @@ from typing import (
     Dict,
     ForwardRef,
     Generic,
-    Optional,
-    Tuple,
     Type,
     TypeVar,
 )
@@ -56,21 +54,19 @@ class BaseFactory(ABC, Generic[T]):
     unrelated parameters.
     """
 
-    _entries: Dict[str, Tuple[Type, Optional[str], int]]
+    _entries: Dict[str, Type[T]]
 
     def __init_subclass__(cls, **kwargs):
         super().__init_subclass__(**kwargs)
-        cls._entries = {}
         for orig_base in getattr(cls, "__orig_bases__", ()):
             if _get_origin(orig_base) is BaseFactory:
                 (arg,) = _get_args(orig_base)
+                cls._entries = {}
                 cls._component_base = _resolve_type(arg, cls)
                 return
 
     @classmethod
-    def register(
+    def register(cls, name: str) -> Callable[[Type[T]], Type[T]]:
-        cls, name: str, category: Optional[str] = None, priority: int = 0
-    ) -> Callable[[Type[T]], Type[T]]:
         """Decorator to register a component class.
 
         Validates that the decorated class inherits from the generic
@@ -81,7 +77,7 @@ class BaseFactory(ABC, Generic[T]):
             cls._validate_component(component_cls)
             if name in cls._entries:
                 raise ValueError(f"Component '{name}' is already registered")
-            cls._entries[name] = (component_cls, category, priority)
+            cls._entries[name] = component_cls
             return component_cls
 
         return decorator
@@ -96,7 +92,7 @@ class BaseFactory(ABC, Generic[T]):
             raise ValueError(
                 f"Unknown component: '{name}'. Supported types: {sorted(cls._entries)}"
             )
-        component_cls = entry[0]
+        component_cls = entry
         sig = inspect.signature(component_cls.__init__)
         has_var_kwargs = any(
             p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values()
@@ -130,7 +126,7 @@ class BaseFactory(ABC, Generic[T]):
             raise ValueError(
                 f"Unknown component: '{name}'. Supported types: {sorted(cls._entries)}"
             )
-        return entry[0]
+        return entry
 
     @classmethod
     def list_registered(cls) -> list:

astrai/inference/api/anthropic.py

@@ -42,7 +42,6 @@ class AnthropicResponseBuilder(ResponseBuilder):
             resp_id=f"msg_{uuid.uuid4().hex[:24]}",
             created=int(time.time()),
             model=request.model,
-            prompt_tokens=0,
         )
         stop_sequences = getattr(request, "stop_sequences", None) or []
         return prompt, ctx, stop_sequences

astrai/inference/api/openai.py

@@ -86,7 +86,6 @@ class OpenAIResponseBuilder(ResponseBuilder):
             resp_id=self._resp_id,
             created=int(time.time()),
             model=self._model,
-            prompt_tokens=0,
         )
         stop = request.stop
         stop_sequences = (

astrai/inference/api/protocol.py

@@ -35,7 +35,7 @@ class GenContext:
     resp_id: str
     created: int
     model: str
-    prompt_tokens: int
+    prompt_tokens: int = 0
     completion_tokens: int = 0
 
 

astrai/model/components/attention.py

@@ -24,9 +24,7 @@ def repeat_kv(x: Tensor, n_rep: int) -> Tensor:
 
 
 class AttnFactory(BaseFactory[nn.Module]):
-    @classmethod
+    pass
-    def create(cls, attn_type: str, **kwargs) -> nn.Module:
-        return super().create(attn_type, **kwargs)
 
 
 @AttnFactory.register("gqa")

astrai/model/components/mlp.py

@@ -8,9 +8,7 @@ from astrai.model.components.linear import Linear
 
 
 class FFNFactory(BaseFactory[nn.Module]):
-    @classmethod
+    pass
-    def create(cls, ffn_type: str, dim: int, dim_ffn: int, **kwargs) -> nn.Module:
-        return super().create(ffn_type, dim, dim_ffn, **kwargs)
 
 
 @FFNFactory.register("mlp")

astrai/preprocessing/pipeline.py

@@ -44,7 +44,7 @@ class Pipeline:
 
     Usage::
 
-        config = PipelineConfig.from_json("sft_pipeline.json")
+        config = PipelineConfig.from_file("sft_pipeline.json")
         Pipeline(config, ["data.jsonl"], output_dir="out", tokenizer_path="params").run()
     """
 

astrai/trainer/schedule.py

@@ -31,7 +31,6 @@ class SchedulerFactory(BaseFactory["BaseScheduler"]):
     """Factory class for creating learning rate schedulers.
 
     Supports decorator-based registration for extensible scheduler types.
-    Also supports creation from ScheduleConfig objects.
 
     Example usage:
         @SchedulerFactory.register("custom")
@@ -41,27 +40,6 @@ class SchedulerFactory(BaseFactory["BaseScheduler"]):
         scheduler = SchedulerFactory.create("custom", optimizer, **kwargs)
     """
 
-    @classmethod
-    def create(
-        cls, optimizer, schedule_type: str = "none", **kwargs
-    ) -> "BaseScheduler":
-        """Create a scheduler instance by type name.
-
-        Args:
-            optimizer: PyTorch optimizer
-            schedule_type: Type of scheduler ("cosine", "sgdr")
-            **kwargs: Arguments passed to the scheduler constructor
-
-        Returns:
-            Scheduler instance
-        """
-        return super().create(schedule_type, optimizer, **kwargs)
-
-    @classmethod
-    def available_types(cls) -> list:
-        """Return list of registered scheduler type names."""
-        return cls.list_registered()
-
 
 # ----------- Scheduler implementations -----------
 

astrai/trainer/strategy.py

@@ -127,26 +127,6 @@ class StrategyFactory(BaseFactory["BaseStrategy"]):
         strategy = StrategyFactory.create("custom", model, device)
     """
 
-    @classmethod
-    def create(cls, train_type: str, model, device: str, **kwargs) -> "BaseStrategy":
-        """Create a strategy instance based on training type.
-
-        Args:
-            train_type: Type of training ("seq", "sft", "dpo", "grpo")
-            model: Model instance for the strategy
-            device: Device to run the strategy on
-            **kwargs: Additional arguments passed to strategy constructor
-
-        Returns:
-            Strategy instance
-        """
-        return super().create(train_type, model, device, **kwargs)
-
-    @classmethod
-    def available_strategies(cls) -> list:
-        """Return list of registered strategy names."""
-        return cls.list_registered()
-
 
 # ============== Strategy Classes ==============
 # All strategies are registered at class definition time using the decorator

astrai/trainer/train_context.py

@@ -172,8 +172,8 @@ class TrainContextBuilder:
                         obj.load_state_dict(extra[name])
 
         context.strategy = StrategyFactory.create(
+            cfg.strategy,
             model=context.model,
-            train_type=cfg.strategy,
             device=device,
             executor=executor,
             model_fn=cfg.model_fn,

scripts/tools/preprocess.py

@@ -24,7 +24,7 @@ def main():
     )
     args = parser.parse_args()
 
-    config = PipelineConfig.from_json(args.config)
+    config = PipelineConfig.from_file(args.config)
 
     Pipeline(
         config=config,

scripts/tools/train.py

@@ -231,7 +231,8 @@ def create_optimizer(model, **kwargs) -> optim.Optimizer:
 def create_scheduler(
     optimizer: optim.Optimizer, **kwargs
 ) -> optim.lr_scheduler.LRScheduler:
-    return SchedulerFactory.create(optimizer, **kwargs)
+    schedule_type = kwargs.pop("schedule_type")
+    return SchedulerFactory.create(schedule_type, optimizer, **kwargs)
 
 
 def compute_total_steps(

tests/data/test_preprocess_config.py

@@ -53,15 +53,15 @@ def test_to_dict_roundtrip():
     assert config2.mask == {"prompt": "mask", "response": "train"}
 
 
-def test_to_json_from_json(temp_dir):
+def test_to_file_from_file(temp_dir):
     config = PipelineConfig(
         input=InputConfig(sections=_TEXT_SECTIONS),
         mask={"text": "train"},
         mask_default="mask",
     )
     path = os.path.join(temp_dir, "config.json")
-    config.to_json(path)
+    config.to_file(path)
-    loaded = PipelineConfig.from_json(path)
+    loaded = PipelineConfig.from_file(path)
     assert loaded.input.sections == _TEXT_SECTIONS
     assert loaded.mask == {"text": "train"}
 
@@ -69,8 +69,8 @@ def test_to_json_from_json(temp_dir):
 def test_dpo_config_roundtrip(temp_dir):
     config = make_dpo_chat_config()
     path = os.path.join(temp_dir, "config.json")
-    config.to_json(path)
+    config.to_file(path)
-    loaded = PipelineConfig.from_json(path)
+    loaded = PipelineConfig.from_file(path)
     assert loaded.input.sources is not None
     assert "chosen" in loaded.input.sources
     assert "rejected" in loaded.input.sources

tests/trainer/conftest.py

@@ -65,7 +65,7 @@ def create_train_config(
 
     def scheduler_fn(optim):
         return SchedulerFactory.create(
-            optim, "cosine", warmup_steps=10, lr_decay_steps=10, min_rate=0.05
+            "cosine", optim, warmup_steps=10, lr_decay_steps=10, min_rate=0.05
         )
 
     return TrainConfig(

tests/trainer/test_callbacks.py

@@ -102,7 +102,7 @@ def test_gradient_checkpointing_trainer_integration(base_test_env, random_datase
 
     def scheduler_fn(optim):
         return SchedulerFactory.create(
-            optim, "cosine", warmup_steps=10, lr_decay_steps=10, min_rate=0.05
+            "cosine", optim, warmup_steps=10, lr_decay_steps=10, min_rate=0.05
         )
 
     train_config = TrainConfig(
@@ -136,7 +136,7 @@ def test_callback_integration(base_test_env, random_dataset):
 
     def scheduler_fn(optim):
         return SchedulerFactory.create(
-            optim, "cosine", warmup_steps=10, lr_decay_steps=10, min_rate=0.05
+            "cosine", optim, warmup_steps=10, lr_decay_steps=10, min_rate=0.05
         )
 
     train_config = TrainConfig(

tests/trainer/test_early_stopping.py

@@ -16,7 +16,7 @@ def test_early_stopping_simulation(base_test_env, early_stopping_dataset):
 
     def scheduler_fn(optim):
         return SchedulerFactory.create(
-            optim, "cosine", warmup_steps=10, lr_decay_steps=10, min_rate=0.05
+            "cosine", optim, warmup_steps=10, lr_decay_steps=10, min_rate=0.05
         )
 
     train_config = TrainConfig(

tests/trainer/test_train_strategy.py

@@ -36,8 +36,8 @@ def test_schedule_factory_random_configs():
                 min_rate = params["min_rate"]
                 lr_decay_steps = total_steps - warmup_steps
                 scheduler = SchedulerFactory.create(
-                    optimizer,
                     schedule_type,
+                    optimizer,
                     warmup_steps=warmup_steps,
                     lr_decay_steps=lr_decay_steps,
                     min_rate=min_rate,
@@ -52,8 +52,8 @@ def test_schedule_factory_random_configs():
                 t_mult = params["t_mult"]
                 min_rate = params["min_rate"]
                 scheduler = SchedulerFactory.create(
-                    optimizer,
                     schedule_type,
+                    optimizer,
                     warmup_steps=warmup_steps,
                     cycle_length=cycle_length,
                     t_mult=t_mult,
@@ -103,8 +103,8 @@ def test_schedule_factory_edge_cases():
         min_rate = params["min_rate"]
         lr_decay_steps = total_steps - warmup_steps
         scheduler = SchedulerFactory.create(
-            optimizer,
             "cosine",
+            optimizer,
             warmup_steps=warmup_steps,
             lr_decay_steps=lr_decay_steps,
             min_rate=min_rate,
@@ -129,8 +129,8 @@ def test_schedule_factory_state_persistence():
     min_rate = 0.1
     lr_decay_steps = total_steps - warmup_steps
     scheduler = SchedulerFactory.create(
-        optimizer,
         "cosine",
+        optimizer,
         warmup_steps=warmup_steps,
         lr_decay_steps=lr_decay_steps,
         min_rate=min_rate,
@@ -146,8 +146,8 @@ def test_schedule_factory_state_persistence():
 
     # Create new scheduler with same parameters
     new_scheduler = SchedulerFactory.create(
-        optimizer,
         "cosine",
+        optimizer,
         warmup_steps=warmup_steps,
         lr_decay_steps=lr_decay_steps,
         min_rate=min_rate,