refactor(trainer): 重构学习率调度器实现并分离配置与工厂逻辑

khaosz/config/__init__.py

@@ -1,12 +1,18 @@
 from khaosz.config.model_config import TransformerConfig
 from khaosz.config.param_config import BaseModelIO, ModelParameter, Checkpoint, ParameterLoader
+from khaosz.config.schedule_config import ScheduleConfig, CosineScheduleConfig, SgdrScheduleConfig
 from khaosz.config.train_config import TrainConfig
 
+
 __all__ = [
     "BaseModelIO",
     "ModelParameter",
     "Checkpoint",
     "ParameterLoader",
     "TransformerConfig",
-    "TrainConfig"
+    "TrainConfig",
+    
+    "ScheduleConfig",
+    "CosineScheduleConfig",
+    "SgdrScheduleConfig",
 ]

khaosz/config/schedule_config.py

@@ -0,0 +1,87 @@
+from typing import Any, Literal, Dict
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+
+
+@dataclass
+class ScheduleConfig(ABC):
+    schedule_type: str = field(
+        default="cosine",
+        metadata={
+            "help": "Type of learning rate schedule.", 
+            "choices": ["cosine", "sgdr"]
+        }
+    )
+    warmup_steps: int = field(
+        default=1000,
+        metadata={"help": "Number of warmup steps."}
+    )
+    min_rate: float = field(
+        default=0.05,
+        metadata={"help": "Minimum learning rate multiplier."}
+    )
+    
+    @abstractmethod
+    def get_kwargs(self) -> Dict[str, Any]:
+        raise NotImplementedError
+    
+    def validate(self) -> None:
+        """Validate configuration parameters."""
+        if self.warmup_steps < 0:
+            raise ValueError(f"warmup_steps must be non-negative, got {self.warmup_steps}")
+        if not 0 <= self.min_rate <= 1:
+            raise ValueError(f"min_rate must be between 0 and 1, got {self.min_rate}")
+
+
+@dataclass
+class CosineScheduleConfig(ScheduleConfig):
+    total_steps: int = field(
+        default=None,
+        metadata={"help": "Total training steps for cosine schedule."}
+    )
+    schedule_type: Literal["cosine"] = "cosine"
+    
+    def get_kwargs(self) -> Dict[str, Any]:
+        if self.total_steps is None:
+            raise ValueError("total_steps must be specified for cosine schedule")
+            
+        return {
+            "schedule_type": self.schedule_type,
+            "warmup_steps": self.warmup_steps,
+            "lr_decay_steps": self.total_steps - self.warmup_steps,
+            "min_rate": self.min_rate
+        }
+    
+    def validate(self) -> None:
+        super().validate()
+        if self.total_steps is not None and self.total_steps <= self.warmup_steps:
+            raise ValueError(f"total_steps ({self.total_steps}) must be greater than warmup_steps ({self.warmup_steps})")
+
+
+@dataclass
+class SgdrScheduleConfig(ScheduleConfig):
+    cycle_length: int = field(
+        default=1000,
+        metadata={"help": "Length of the first cycle in steps."}
+    )
+    t_mult: int = field( 
+        default=2,
+        metadata={"help": "Multiplier for cycle length growth."}
+    )
+    schedule_type: Literal["sgdr"] = "sgdr"
+
+    def get_kwargs(self) -> Dict[str, Any]:
+        return {
+            "schedule_type": self.schedule_type,
+            "warmup_steps": self.warmup_steps,
+            "cycle_length": self.cycle_length,
+            "min_rate": self.min_rate,
+            "t_mult": self.t_mult
+        }
+    
+    def validate(self) -> None:
+        super().validate()
+        if self.cycle_length <= 0:
+            raise ValueError(f"cycle_length must be positive, got {self.cycle_length}")
+        if self.t_mult < 1:
+            raise ValueError(f"t_mult must be >= 1, got {self.t_mult}")