reafactor: 统一并增强项目中的工厂模式实现

khaosz/config/__init__.py

@@ -1,16 +1,26 @@
 from khaosz.config.model_config import ModelConfig
 from khaosz.config.param_config import BaseModelIO, ModelParameter
-from khaosz.config.schedule_config import ScheduleConfig, CosineScheduleConfig, SGDRScheduleConfig
+from khaosz.config.schedule_config import (
+    ScheduleConfig, 
+    CosineScheduleConfig, 
+    SGDRScheduleConfig,
+    ScheduleConfigFactory
+)
 from khaosz.config.train_config import TrainConfig
 
 
 __all__ = [
+    # Base I/O
     "BaseModelIO",
     "ModelParameter",
+    
+    # Model configuration
     "ModelConfig",
     "TrainConfig",
     
+    # Schedule configuration
     "ScheduleConfig",
     "CosineScheduleConfig",
     "SGDRScheduleConfig",
+    "ScheduleConfigFactory",
 ]

khaosz/config/schedule_config.py

@@ -1,10 +1,15 @@
-from typing import Any, Dict
+from typing import Any, Dict, Type
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, field
 
 
 @dataclass
 class ScheduleConfig(ABC):
+    """Base configuration class for learning rate schedulers.
+    
+    Provides common validation and interface for all schedule types.
+    """
+    
     schedule_type: str = field(
         default="cosine",
         metadata={
@@ -23,6 +28,7 @@ class ScheduleConfig(ABC):
     
     @abstractmethod
     def get_kwargs(self) -> Dict[str, Any]:
+        """Get configuration kwargs for scheduler creation."""
         raise NotImplementedError
     
     def validate(self) -> None:
@@ -35,6 +41,8 @@ class ScheduleConfig(ABC):
 
 @dataclass
 class CosineScheduleConfig(ScheduleConfig):
+    """Cosine annealing learning rate schedule configuration."""
+    
     total_steps: int = field(
         default=None,
         metadata={"help": "Total training steps for cosine schedule."}
@@ -63,6 +71,8 @@ class CosineScheduleConfig(ScheduleConfig):
 
 @dataclass
 class SGDRScheduleConfig(ScheduleConfig):
+    """Stochastic Gradient Descent with Warm Restarts schedule configuration."""
+    
     cycle_length: int = field(
         default=1000,
         metadata={"help": "Length of the first cycle in steps."}
@@ -91,3 +101,50 @@ class SGDRScheduleConfig(ScheduleConfig):
             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}")
+
+
+class ScheduleConfigFactory:
+    """Factory class for creating ScheduleConfig instances.
+    
+    Supports both direct instantiation and factory creation methods.
+    
+    Example usage:
+        # Direct creation
+        config = CosineScheduleConfig(total_steps=10000)
+        
+        # Factory method
+        config = ScheduleConfigFactory.create("cosine", total_steps=10000)
+    """
+    
+    CONFIG_MAP: Dict[str, Type[ScheduleConfig]] = {
+        "cosine": CosineScheduleConfig,
+        "sgdr": SGDRScheduleConfig,
+    }
+    
+    @classmethod
+    def create(cls, schedule_type: str, **kwargs) -> ScheduleConfig:
+        """Create a schedule config instance.
+        
+        Args:
+            schedule_type: Type of schedule ("cosine", "sgdr")
+            **kwargs: Arguments passed to the config constructor
+            
+        Returns:
+            ScheduleConfig instance
+            
+        Raises:
+            ValueError: If schedule_type is not supported
+        """
+        if schedule_type not in cls.CONFIG_MAP:
+            raise ValueError(
+                f"Unknown schedule type: '{schedule_type}'. "
+                f"Supported types: {sorted(cls.CONFIG_MAP.keys())}"
+            )
+        
+        config_cls = cls.CONFIG_MAP[schedule_type]
+        return config_cls(**kwargs)
+    
+    @classmethod
+    def available_types(cls) -> list:
+        """Return list of available schedule type names."""
+        return list(cls.CONFIG_MAP.keys())

khaosz/data/__init__.py

@@ -5,20 +5,31 @@ from khaosz.data.dataset import (
     SFTDataset, 
     GRPODataset,
     MultiSegmentFetcher,
-    DatasetLoader
+    DatasetLoader,
+    DatasetFactory
 )
 
 from khaosz.data.tokenizer import BpeTokenizer
 from khaosz.data.sampler import ResumableDistributedSampler
 
 __all__ = [
+    # Base classes
     "BaseDataset",
+    
+    # Dataset implementations
     "SEQDataset",
     "SFTDataset",
     "DPODataset",
     "GRPODataset",
+    
+    # Fetchers
     "MultiSegmentFetcher",
+    
+    # Factory (DatasetLoader is alias for backward compatibility)
     "DatasetLoader",
+    "DatasetFactory",
+    
+    # Tokenizer and sampler
     "BpeTokenizer",
     "ResumableDistributedSampler"
 ]

khaosz/data/dataset.py

@@ -1,3 +1,5 @@
+"""Dataset implementations with factory pattern for training."""
+
 import torch
 import bisect
 
@@ -8,8 +10,13 @@ from khaosz.data.serialization import load_h5
 from typing import Callable, List, Dict, Literal, Optional, Union
 
 
-
 class BaseSegmentFetcher:
+    """Fetches data segments across multiple tensor segments.
+    
+    Maintains cumulative lengths for efficient range queries across
+    multiple discontinuous segments.
+    """
+    
     def __init__(self, segments: List[Tensor]):
         self.segments = segments
         self.cum_lengths = []
@@ -25,12 +32,21 @@ class BaseSegmentFetcher:
         return self.total_length
 
     def fetch_data(self, begin_idx: int, end_idx: int) -> Tensor:
+        """Fetch data in the range [begin_idx, end_idx).
+        
+        Args:
+            begin_idx: Starting index (inclusive)
+            end_idx: Ending index (exclusive)
+            
+        Returns:
+            Concatenated tensor of data in the specified range
+        """
         if not (0 <= begin_idx < self.total_length and 0 <= end_idx <= self.total_length):
             raise ValueError("begin_idx or end_idx out of bounds")
         if begin_idx >= end_idx:
             return torch.tensor([], dtype=torch.long)
         
-        # fix the range index bug
+        # Find segment boundaries for the range
         seg_start_idx = bisect.bisect_right(self.cum_lengths, begin_idx)
         seg_end_idx = bisect.bisect_left(self.cum_lengths, end_idx)
 
@@ -47,6 +63,11 @@ class BaseSegmentFetcher:
 
 
 class MultiSegmentFetcher:
+    """Manages multiple segment fetchers for different data keys.
+    
+    Each key corresponds to a different type of data (e.g., "sequence", "mask").
+    """
+    
     def __init__(self, muti_segments: Dict):
         self.muti_keys = list(muti_segments.keys())
         self.muti_fetchers = {
@@ -55,10 +76,21 @@ class MultiSegmentFetcher:
         }
     
     def __len__(self) -> int:
+        """Returns the minimum length across all fetchers."""
         len_list = [len(seg) for seg in self.muti_fetchers.values()]
         return min(len_list)
         
     def key_fetch(self, begin_idx: int, end_idx: int, keys: Union[str, List[str]]) -> Dict:
+        """Fetch data for specific keys.
+        
+        Args:
+            begin_idx: Starting index
+            end_idx: Ending index
+            keys: Single key or list of keys to fetch
+            
+        Returns:
+            Dictionary of tensors if multiple keys, single tensor if one key
+        """
         fetch_dict = {} 
         keys = [keys] if isinstance(keys, str) else keys
         
@@ -70,23 +102,43 @@ class MultiSegmentFetcher:
         return fetch_dict if len(keys) > 1 else fetch_dict[keys[0]]
     
     def fetch_data(self, begin_idx: int, end_idx: int) -> Dict:
+        """Fetch all keys."""
         return self.key_fetch(begin_idx, end_idx, self.muti_keys)
 
 
 class BaseDataset(Dataset, ABC):
+    """Abstract base class for all dataset types.
+    
+    Implements common functionality for window-based data fetching.
+    """
+    
     def __init__(self, window_size: int, stride: int):
         super().__init__()
         self.segments = {}
         self.window_size = window_size
         self.stride = stride
         self.total_samples = None
+        self.fetcher: Optional[MultiSegmentFetcher] = None
 
     def load(self, load_path: str):
+        """Load dataset from HDF5 file.
+        
+        Args:
+            load_path: Path to the HDF5 data file
+        """
         self.segments = load_h5(load_path)
         self.fetcher = MultiSegmentFetcher(self.segments)
         self.total_samples = len(self.fetcher)
         
-    def get_index(self, index: int) -> int:
+    def get_index(self, index: int) -> tuple:
+        """Calculate begin and end indices for a sample.
+        
+        Args:
+            index: Sample index
+            
+        Returns:
+            Tuple of (begin_idx, end_idx)
+        """
         assert self.total_samples > self.window_size
         
         begin_idx = min(index * self.stride, self.total_samples - 1 - self.window_size)
@@ -96,6 +148,10 @@ class BaseDataset(Dataset, ABC):
     
     @abstractmethod
     def __getitem__(self, index: int) -> Dict[str, Tensor]:
+        """Get a single sample by index.
+        
+        Must be implemented by subclasses.
+        """
         raise NotImplementedError
         
     def __len__(self) -> int:
@@ -105,16 +161,109 @@ class BaseDataset(Dataset, ABC):
         return (self.total_samples - 1 - self.window_size) // self.stride + 1
 
 
+class DatasetFactory:
+    """Factory class for creating dataset instances.
+    
+    Supports decorator-based registration for extensible dataset types.
+    All default dataset types (seq, sft, dpo, grpo) are registered automatically
+    when their classes are defined with the decorator.
+    
+    Example usage:
+        @DatasetFactory.register("custom")
+        class CustomDataset(BaseDataset):
+            ...
+        
+        dataset = DatasetFactory.create("custom", window_size, stride)
+    """
+    
+    SUPPORTED_TYPES = frozenset({"seq", "sft", "dpo", "grpo"})
+    DATASET_MAP: Dict[str, type] = {}
+    
+    @classmethod
+    def register(cls, name: str):
+        """Decorator to register a new dataset class.
+        
+        Args:
+            name: Registration name for the dataset type
+            
+        Returns:
+            Decorator function that registers the dataset class
+        """
+        def decorator(dataset_cls: type) -> type:
+            if not issubclass(dataset_cls, BaseDataset):
+                raise TypeError(f"{dataset_cls.__name__} must inherit from BaseDataset")
+            cls.DATASET_MAP[name] = dataset_cls
+            return dataset_cls
+        return decorator
+    
+    @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
+        """
+        if train_type not in cls.SUPPORTED_TYPES:
+            raise ValueError(
+                f"Unknown dataset type: '{train_type}'. "
+                f"Supported types: {sorted(cls.SUPPORTED_TYPES)}"
+            )
+        
+        if train_type not in cls.DATASET_MAP:
+            raise NotImplementedError(
+                f"Dataset type '{train_type}' is supported but not yet implemented."
+            )
+        
+        dataset_cls = cls.DATASET_MAP[train_type]
+        return dataset_cls(window_size, stride)
+    
+    @classmethod
+    def load(cls, train_type: str, load_path: str, window_size: int, stride: Optional[int] = None) -> BaseDataset:
+        """Create and load a dataset in one step.
+        
+        Args:
+            train_type: Type of training dataset
+            load_path: Path to the data file
+            window_size: Window size for data sampling
+            stride: Stride between consecutive samples (default: same as window_size)
+            
+        Returns:
+            Loaded dataset instance
+        """
+        if stride is None:
+            stride = window_size
+        
+        dataset = cls.create(train_type, window_size, stride)
+        dataset.load(load_path)
+        
+        return dataset
+    
+    @classmethod
+    def available_types(cls) -> list:
+        """Return list of registered dataset type names."""
+        return list(cls.DATASET_MAP.keys())
+
+
+# ============== Dataset Classes ==============
+# All dataset classes are registered at class definition time using the decorator
+
+
+@DatasetFactory.register("seq")
 class SEQDataset(BaseDataset):
+    """Dataset for sequential next-token prediction training."""
+    
     def __init__(self, window_size: int, stride: int):
         super().__init__(window_size, stride)
-        self.fetcher = MultiSegmentFetcher(self.segments)
 
     def _fetch_data(self, begin_idx: int, end_idx: int) -> Tensor:
         return self.fetcher.key_fetch(begin_idx, end_idx, "sequence")
     
     def __getitem__(self, index):
-        # fix the range index bug
         begin_idx, end_idx = self.get_index(index)
         
         x = self._fetch_data(begin_idx, end_idx).to(dtype=torch.long)
@@ -123,10 +272,12 @@ class SEQDataset(BaseDataset):
         return {"input_ids": x, "target_ids": y}
 
 
+@DatasetFactory.register("sft")
 class SFTDataset(BaseDataset):
+    """Dataset for supervised fine-tuning with loss masking."""
+    
     def __init__(self, window_size: int, stride: int):
         super().__init__(window_size, stride)
-        self.fetcher = MultiSegmentFetcher(self.segments)
 
     def _fetch_data(self, begin_idx: int, end_idx: int, key: str) -> Tensor:
         return self.fetcher.key_fetch(begin_idx, end_idx, key)
@@ -141,10 +292,12 @@ class SFTDataset(BaseDataset):
         return {"input_ids": x, "target_ids": y, "loss_mask": loss_mask}
 
 
+@DatasetFactory.register("dpo")
 class DPODataset(BaseDataset):
+    """Dataset for Direct Preference Optimization training."""
+    
     def __init__(self, window_size: int, stride: int):
         super().__init__(window_size, stride)
-        self.fetcher = MultiSegmentFetcher(self.segments)
 
     def _fetch_data(self, begin_idx: int, end_idx: int, key: str) -> Tensor:
         return self.fetcher.key_fetch(begin_idx, end_idx, key)
@@ -160,10 +313,12 @@ class DPODataset(BaseDataset):
         return {"chosen": chosen, "rejected": rejected, "chosen_mask": chosen_mask, "rejected_mask": rejected_mask}
 
 
+@DatasetFactory.register("grpo")
 class GRPODataset(BaseDataset):
+    """Dataset for Group Relative Policy Optimization training."""
+    
     def __init__(self, window_size: int, stride: int):
         super().__init__(window_size, stride)
-        self.fetcher = MultiSegmentFetcher(self.segments)
 
     def _fetch_data(self, begin_idx: int, end_idx: int, key: str) -> Tensor:
         return self.fetcher.key_fetch(begin_idx, end_idx, key)
@@ -179,24 +334,5 @@ class GRPODataset(BaseDataset):
         return {"prompts": prompts, "responses": responses, "masks": masks, "rewards": rewards}
 
 
-class DatasetLoader:
-    @staticmethod       
-    def load(
-        train_type: Literal["seq", "sft", "dpo"],
-        load_path: str,
-        window_size: int, 
-        stride: Optional[int] = None,
-        ) -> BaseDataset:
-        if stride is None:
-            stride = window_size
-        
-        dataset_router: Dict[str, Callable[[int], BaseDataset]] = {
-            "seq": lambda window_size: SEQDataset(window_size, stride),
-            "sft": lambda window_size: SFTDataset(window_size, stride),
-            "dpo": lambda window_size: DPODataset(window_size, stride),
-            "grpo": lambda window_size: GRPODataset(window_size, stride),
-        }
-        dataset = dataset_router[train_type](window_size)
-        dataset.load(load_path)
-        
-        return dataset
+# Backward compatibility alias
+DatasetLoader = DatasetFactory

khaosz/inference/generator.py

@@ -77,6 +77,7 @@ class GenerationRequest:
         query: Input query (string or list of strings for batch).
         history: Conversation history.
         system_prompt: System prompt for the conversation.
+        stream: Whether to use streaming generation.
     """
     top_k: int
     top_p: float
@@ -86,6 +87,7 @@ class GenerationRequest:
     query: Union[str, List[str]]
     history: Optional[Union[HistoryType, List[HistoryType]]] = None
     system_prompt: Optional[str] = None
+    stream: bool = False
 
     def __post_init__(self):
         if not isinstance(self.top_k, int) or self.top_k < 0:
@@ -233,33 +235,62 @@ class EmbeddingEncoder(EmbeddingEncoderCore):
 
 
 class GeneratorFactory:
-    """Factory class for creating appropriate generator instances based on request features."""
+    """Factory class for creating generator instances.
+    
+    Provides smart generator selection based on request characteristics:
+    - Streaming: Use StreamGenerator for streaming output
+    - Batch: Use BatchGenerator when query is a list
+    - Single: Use LoopGenerator for single query non-streaming
+    
+    Example usage:
+        generator = GeneratorFactory.create_generator(parameter, request)
+        result = generator.generate(request)
+    """
     
     @staticmethod
-    def create_generator(parameter: ModelParameter, request: GenerationRequest):
+    def create_generator(parameter: ModelParameter, request: GenerationRequest) -> GeneratorCore:
+        """Create a generator based on request characteristics.
+        
+        Args:
+            parameter: Model parameters containing model, tokenizer, config
+            request: Generation request with query, options, etc.
+            
+        Returns:
+            Appropriate GeneratorCore subclass instance
         """
-        Create a generator based on the characteristics of GenerationRequest.
+        # Streaming generation: check stream field first
+        if request.stream:
+            return StreamGenerator(parameter)
+        
+        # Batch generation: query is a list of strings
+        if isinstance(request.query, list):
+            return BatchGenerator(parameter)
+        
+        # Default: single query non-streaming
+        return LoopGenerator(parameter)
+    
+    @staticmethod
+    def create_encoder(parameter: ModelParameter) -> EmbeddingEncoderCore:
+        """Create an embedding encoder instance.
+        
+        Args:
+            parameter: Model parameters
+            
+        Returns:
+            EmbeddingEncoderCore instance
+        """
+        return EmbeddingEncoder(parameter)
+    
+    @classmethod
+    def create(cls, parameter: ModelParameter, request: GenerationRequest) -> GeneratorCore:
+        """Convenience method that delegates to create_generator.
+        
         Args:
             parameter: Model parameters
             request: Generation request
             
         Returns:
-            Subclass instance of GeneratorCore
+            Generator instance
         """
-
-        # Streaming generation detection: check stream field
-        if request.stream:
-            return StreamGenerator(parameter)
-        
-        # Batch generation detection: query is a list
-        if isinstance(request.query, list):
-            return BatchGenerator(parameter)
-        
-        # Default return LoopGenerator
-        return LoopGenerator(parameter)
-    
-    @staticmethod
-    def create_encoder(parameter: ModelParameter):
-        """Create an EmbeddingEncoder instance"""
-        return EmbeddingEncoder(parameter)
+        return cls.create_generator(parameter, request)
         

khaosz/parallel/setup.py

@@ -134,8 +134,8 @@ def spawn_parallel_fn(
     
     if world_size == 1:
         device_ids = device_ids or [0]
-        deice_id = torch.device(device_type, device_ids[0])
-        os.environ["LOCAL_DEVICE"] = str(deice_id)
+        device_id = torch.device(device_type, device_ids[0])
+        os.environ["LOCAL_DEVICE"] = str(device_id)
         
         func(**kwargs)
         return

khaosz/trainer/__init__.py

@@ -1,29 +1,33 @@
 from khaosz.trainer.trainer import Trainer
-from khaosz.trainer.strategy import StrategyFactory
-from khaosz.trainer.schedule import SchedulerFactory
+from khaosz.trainer.strategy import StrategyFactory, BaseStrategy
+from khaosz.trainer.schedule import SchedulerFactory, BaseScheduler
 
 from khaosz.trainer.train_callback import (
     TrainCallback,
-    ProgressBarCallback,
-    CheckpointCallback,
-    TrainCallback,
+    GradientClippingCallback,
     SchedulerCallback,
-    MetricLoggerCallback
+    CheckpointCallback,
+    ProgressBarCallback,
+    MetricLoggerCallback,
 )
 
 __all__ = [
-    # trainer
+    # Main trainer
     "Trainer",
     
-    # factory
+    # Strategy factory
     "StrategyFactory",
-    "SchedulerFactory",
+    "BaseStrategy",
     
-    # callback
-    "TrainCallback",
-    "ProgressBarCallback",
-    "CheckpointCallback",
+    # Scheduler factory
+    "SchedulerFactory",
+    "BaseScheduler",
+    
+    # Callbacks
     "TrainCallback",
+    "GradientClippingCallback",
     "SchedulerCallback",
-    "MetricLoggerCallback"
+    "CheckpointCallback",
+    "ProgressBarCallback",
+    "MetricLoggerCallback",
 ]

khaosz/trainer/schedule.py

@@ -1,20 +1,21 @@
+"""Learning rate scheduler implementations with factory pattern."""
+
 import math
 from abc import abstractmethod, ABC
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Type
 from torch.optim.lr_scheduler import LRScheduler
 from khaosz.config.schedule_config import ScheduleConfig
 
 
 class BaseScheduler(LRScheduler, ABC):
-    """
-    Base scheduler class for all other schedulers.
-    """
+    """Base scheduler class for all other schedulers."""
     
     def __init__(self, optimizer, last_epoch: int = -1):
         super().__init__(optimizer, last_epoch)
     
     @abstractmethod
     def get_lr(self) -> List[float]:
+        """Calculate the current learning rate."""
         raise NotImplementedError
     
     def state_dict(self) -> Dict[str, Any]:
@@ -24,10 +25,95 @@ class BaseScheduler(LRScheduler, ABC):
         super().load_state_dict(state_dict)
 
 
+class SchedulerFactory:
+    """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")
+        class CustomScheduler(BaseScheduler):
+            ...
+        
+        scheduler = SchedulerFactory.create(optimizer, "custom", **kwargs)
+        
+        # Or from config
+        config = CosineScheduleConfig(total_steps=10000)
+        scheduler = SchedulerFactory.load(optimizer, config)
+    """
+    
+    SCHEDULER_MAP: Dict[str, Type[BaseScheduler]] = {}
+    
+    @classmethod
+    def register(cls, name: str):
+        """Decorator to register a new scheduler class.
+        
+        Args:
+            name: Registration name for the scheduler
+            
+        Returns:
+            Decorator function that registers the scheduler class
+        """
+        def decorator(scheduler_cls: Type[BaseScheduler]) -> Type[BaseScheduler]:
+            if not issubclass(scheduler_cls, BaseScheduler):
+                raise TypeError(f"{scheduler_cls.__name__} must inherit from BaseScheduler")
+            cls.SCHEDULER_MAP[name] = scheduler_cls
+            return scheduler_cls
+        return decorator
+    
+    @classmethod
+    def create(cls, optimizer, schedule_type: str, **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
+            
+        Raises:
+            ValueError: If schedule_type is not supported
+        """
+        if schedule_type not in cls.SCHEDULER_MAP:
+            raise ValueError(
+                f"Unknown schedule type: '{schedule_type}'. "
+                f"Supported types: {sorted(cls.SCHEDULER_MAP.keys())}"
+            )
+        
+        scheduler_cls = cls.SCHEDULER_MAP[schedule_type]
+        return scheduler_cls(optimizer, **kwargs)
+    
+    @staticmethod
+    def load(optimizer, schedule_config: ScheduleConfig) -> BaseScheduler:
+        """Create a scheduler from a ScheduleConfig object.
+        
+        Args:
+            optimizer: PyTorch optimizer
+            schedule_config: ScheduleConfig instance
+            
+        Returns:
+            Scheduler instance
+        """
+        kwargs = schedule_config.get_kwargs()
+        schedule_type = kwargs.pop("schedule_type")
+        return SchedulerFactory.create(optimizer, schedule_type, **kwargs)
+    
+    @classmethod
+    def available_types(cls) -> list:
+        """Return list of registered scheduler type names."""
+        return list(cls.SCHEDULER_MAP.keys())
+
+
+# ============== Scheduler Classes ==============
+# All scheduler classes are registered at class definition time using the decorator
+
+
+@SchedulerFactory.register("cosine")
 class CosineScheduler(BaseScheduler):
-    """
-    Cosine decay scheduler with warmup, implemented as PyTorch LRScheduler.
-    """
+    """Cosine decay scheduler with warmup, implemented as PyTorch LRScheduler."""
     
     def __init__(
         self, 
@@ -75,10 +161,9 @@ class CosineScheduler(BaseScheduler):
         super().load_state_dict(state_dict)
 
 
+@SchedulerFactory.register("sgdr")
 class SGDRScheduler(BaseScheduler):
-    """
-    SGDR (Stochastic Gradient Descent with Warm Restarts) scheduler, 
-    """
+    """SGDR (Stochastic Gradient Descent with Warm Restarts) scheduler."""
     
     def __init__(
         self, 
@@ -142,23 +227,3 @@ class SGDRScheduler(BaseScheduler):
         self.min_rate = state_dict.pop('min_rate')
         self.t_mult = state_dict.pop('t_mult')
         super().load_state_dict(state_dict)
-
-
-    
-class SchedulerFactory:
-    """
-    Factory class for creating learning rate schedulers.
-    """
-
-    @staticmethod
-    def load(optimizer, schedule_config: ScheduleConfig) -> BaseScheduler:
-        kwargs = schedule_config.get_kwargs()
-        schedule_type = kwargs.pop("schedule_type")
-        
-        if schedule_type == "cosine":
-            return CosineScheduler(optimizer, **kwargs)
-        elif schedule_type == "sgdr":
-            return SGDRScheduler(optimizer, **kwargs)
-        else:
-            raise ValueError(f"Unsupported schedule type: {schedule_type}")
-        

khaosz/trainer/strategy.py

@@ -1,3 +1,5 @@
+"""Training strategy implementations with factory pattern."""
+
 import copy
 import torch
 import torch.nn as nn
@@ -17,9 +19,10 @@ def unwrap_model(model: nn.Module) -> nn.Module:
 
 
 def create_ref_model(model: nn.Module) -> nn.Module:
-    """
-    Create a reference model for DPO/GRPO training.
-    Handles DDP-wrapped models safely.
+    """Create a reference model for DPO/GRPO training.
+    
+    Handles DDP-wrapped models safely by unwrapping first,
+    then creating a deep copy with frozen gradients.
     """
     original_model = unwrap_model(model)
     ref_model = copy.deepcopy(original_model)
@@ -28,17 +31,18 @@ def create_ref_model(model: nn.Module) -> nn.Module:
     return ref_model
 
 
-def move_to_device(batch:Dict[str, Tensor], device: str) -> Any:
+def move_to_device(batch: Dict[str, Tensor], device: str) -> Any:
+    """Move batch tensors to specified device with non-blocking transfer."""
     return {key: value.to(device, non_blocking=True) for key, value in batch.items()}
 
+
 def get_logprobs(
     model: Union[nn.Module, Callable[..., Dict[str, Tensor]]], 
     input_ids: Tensor, 
     mask: Tensor, 
     reduction: str,
 ):
-    """
-    Compute token-wise log probabilities from model outputs.
+    """Compute token-wise log probabilities from model outputs.
     
     Args:
         model: The language model
@@ -49,7 +53,6 @@ def get_logprobs(
     Returns:
         Log probabilities with reduction applied over sequence dimension
     """
-    # reduction on seq_len dim
     allowed_reductions = ["mean", "sum", "none"]
     if reduction not in allowed_reductions:
         raise ValueError(f"reduction must be one of {allowed_reductions}, got '{reduction}'")
@@ -60,7 +63,6 @@ def get_logprobs(
     logits = model(input_ids[:, :-1], mask[:, :-1])["logits"]
     log_probs = torch.log_softmax(logits.float(), dim=-1)
 
-    # [batch_size, seq_len - 1]
     token_logprobs = torch.gather(
         log_probs, 
         dim=-1, 
@@ -76,20 +78,112 @@ def get_logprobs(
 
 
 class BaseStrategy(ABC):
+    """Abstract base class for training strategies."""
+    
     def __init__(self, model: Union[nn.Module, Callable[..., Dict[str, Tensor]]], device: str):
         self.model = model
         self.device = device
     
     @abstractmethod
     def compute_loss(self, batch: Dict[str, Tensor]) -> Tensor:
+        """Compute loss for the given batch.
+        
+        Args:
+            batch: Dictionary containing batch tensors
+            
+        Returns:
+            Computed loss tensor
+        """
         raise NotImplementedError
     
     def __call__(self, batch: Dict[str, Tensor]) -> Tensor:
+        """Allow calling strategy directly as a callable."""
         return self.compute_loss(batch)
 
 
+class StrategyFactory:
+    """Factory class for creating training strategy instances.
+    
+    Supports decorator-based registration for extensible strategy types.
+    All default strategies (seq, sft, dpo, grpo) are automatically registered.
+    
+    Example usage:
+        @StrategyFactory.register("custom")
+        class CustomStrategy(BaseStrategy):
+            ...
+        
+        strategy = StrategyFactory.create(model, "custom", device)
+    """
+    
+    SUPPORTED_STRATEGIES = frozenset({"seq", "sft", "dpo", "grpo"})
+    STRATEGY_MAP: Dict[str, type] = {}
+    
+    @classmethod
+    def register(cls, name: str):
+        """Decorator to register a new strategy class.
+        
+        Args:
+            name: Registration name for the strategy
+            
+        Returns:
+            Decorator function that registers the strategy class
+        """
+        def decorator(strategy_cls: type) -> type:
+            if not issubclass(strategy_cls, BaseStrategy):
+                raise TypeError(f"{strategy_cls.__name__} must inherit from BaseStrategy")
+            cls.STRATEGY_MAP[name] = strategy_cls
+            return strategy_cls
+        return decorator
+    
+    @classmethod
+    def create(cls, model, train_type: str, device: str, **kwargs) -> BaseStrategy:
+        """Create a strategy instance based on training type.
+        
+        Args:
+            model: Model instance for the strategy
+            train_type: Type of training ("seq", "sft", "dpo", "grpo")
+            device: Device to run the strategy on
+            **kwargs: Additional arguments passed to strategy constructor
+            
+        Returns:
+            Strategy instance
+            
+        Raises:
+            ValueError: If train_type is not supported
+            NotImplementedError: If train_type is in supported list but not implemented
+        """
+        if train_type not in cls.SUPPORTED_STRATEGIES:
+            raise ValueError(
+                f"Unknown training strategy: '{train_type}'. "
+                f"Supported strategies: {sorted(cls.SUPPORTED_STRATEGIES)}"
+            )
+        
+        if train_type not in cls.STRATEGY_MAP:
+            raise NotImplementedError(
+                f"Strategy '{train_type}' is supported but not yet implemented."
+            )
+        
+        strategy_cls = cls.STRATEGY_MAP[train_type]
+        return strategy_cls(model, device, **kwargs)
+    
+    @classmethod
+    def available_strategies(cls) -> list:
+        """Return list of registered strategy names."""
+        return list(cls.STRATEGY_MAP.keys())
+
+
+# ============== Strategy Classes ==============
+# All strategies are registered at class definition time using the decorator
+
+
+@StrategyFactory.register("seq")
 class SEQStrategy(BaseStrategy):
-    def __init__(self, model, device, label_smoothing):
+    """Standard next-token prediction training strategy.
+    
+    Computes cross-entropy loss for next token prediction.
+    """
+    
+    def __init__(self, model, device, label_smoothing: float = 0.0):
         super().__init__(model, device)
         self.label_smoothing = label_smoothing
     
@@ -99,15 +193,22 @@ class SEQStrategy(BaseStrategy):
         logits = self.model(input_ids=input_ids)["logits"]
         
         loss = F.cross_entropy(
-           input=logits.flatten(0, 1).float(),
-            target=target_ids.flatten()
+            input=logits.flatten(0, 1).float(),
+            target=target_ids.flatten(),
+            label_smoothing=self.label_smoothing
         )
         
         return loss
 
 
+@StrategyFactory.register("sft")
 class SFTStrategy(BaseStrategy):
-    def __init__(self, model, device, label_smoothing):
+    """Supervised Fine-tuning strategy with loss masking.
+    
+    Applies cross-entropy loss only to tokens where loss_mask is True.
+    """
+    
+    def __init__(self, model, device, label_smoothing: float = 0.0):
         super().__init__(model, device)
         self.label_smoothing = label_smoothing
     
@@ -122,19 +223,27 @@ class SFTStrategy(BaseStrategy):
         loss = F.cross_entropy(
             input=logits.flatten(0, 1).float(),
             target=target_ids.flatten(),
-            ignore_index=ignore_index
+            ignore_index=ignore_index,
+            label_smoothing=self.label_smoothing
         )
         
         return loss
 
 
+@StrategyFactory.register("dpo")
 class DPOStrategy(BaseStrategy):
+    """Direct Preference Optimization strategy.
+    
+    Implements the DPO loss from the paper "Direct Preference Optimization".
+    Uses a reference model to compute KL divergence penalty.
+    """
+    
     def __init__(
             self, 
             model: nn.Module, 
             device: str, 
-            beta: float,
-            reduction: str,
+            beta: float = 0.1,
+            reduction: str = "mean",
         ):
         super().__init__(model, device)
         self.ref_model = create_ref_model(model)
@@ -168,16 +277,21 @@ class DPOStrategy(BaseStrategy):
         return dpo_loss
 
 
+@StrategyFactory.register("grpo")
 class GRPOStrategy(BaseStrategy):
+    """Group Relative Policy Optimization strategy.
+    
+    Implements GRPO with clipping and KL penalty.
+    """
     
     def __init__(
         self, 
         model: nn.Module, 
         device: str, 
-        clip_eps: float,
-        kl_coef: float,
-        group_size: int,
-        reduction: str,
+        clip_eps: float = 0.2,
+        kl_coef: float = 0.01,
+        group_size: int = 4,
+        reduction: str = "mean",
     ):
         super().__init__(model, device)
         self.ref_model = create_ref_model(model)
@@ -209,16 +323,14 @@ class GRPOStrategy(BaseStrategy):
             log_probs_ref = get_logprobs(self.ref_model, full_sequences, full_masks, self.reduction)
             log_probs_ref = log_probs_ref.view(batch_size, group_size)
         
-        # Compute advantages from rewards
+        # Compute advantages from rewards with normalization
         eps = torch.finfo(log_probs_policy.dtype).eps
         mean = rewards.mean(dim=-1, keepdim=True)
         std = rewards.std(dim=-1, keepdim=True)
         advantages = (rewards - mean) / (std + eps)
         
-        # log_ratio = log_probs_policy - log_probs_old
-        # ratio = torch.exp(log_ratio)
-        # off policy: policy_model = old_model, then ratio = 1
-        ratio = torch.exp(0)
+        # PPO-style clipped surrogate objective
+        ratio = torch.exp(0)  # Off-policy: policy_model = old_model
         surr1 = ratio * advantages
         surr2 = torch.clamp(ratio, 1 - self.clip_eps, 1 + self.clip_eps) * advantages
         
@@ -227,36 +339,3 @@ class GRPOStrategy(BaseStrategy):
         total_loss = policy_loss + kl_penalty
         
         return total_loss
-
-
-class StrategyFactory:
-    
-    def load(model, train_type, device, **kwargs):
-        train_strategy: Dict[str, Callable[[], BaseStrategy]] = {
-            "seq": lambda: SEQStrategy(
-                model, 
-                device,
-                kwargs.get("label_smoothing", 0.0)
-            ),
-            "sft": lambda: SFTStrategy(
-                model, 
-                device,
-                kwargs.get("label_smoothing", 0.0)
-            ),
-            "dpo": lambda: DPOStrategy(
-                model,
-                device,
-                kwargs.get("dpo_beta"),
-                kwargs.get("reduction", "mean")
-            ),
-            "grpo": lambda: GRPOStrategy(
-                model,
-                device,
-                kwargs.get("grpo_clip_eps"),
-                kwargs.get("grpo_kl_coef"),
-                kwargs.get("grpo_group_size"),
-                kwargs.get("reduction", "mean")
-            )
-        }
-        strategy = train_strategy[train_type]()
-        return strategy