chore: 修改类名，优化导入顺序

astrai/__init__.py

@@ -5,14 +5,14 @@ from astrai.config import (
     ModelConfig,
     TrainConfig,
 )
-from astrai.factory import BaseFactory
 from astrai.dataset import DatasetFactory
-from astrai.tokenize import BpeTokenizer
+from astrai.factory import BaseFactory
 from astrai.inference import (
     GenerationRequest,
     InferenceEngine,
 )
 from astrai.model import AutoModel, Transformer
+from astrai.tokenize import BpeTokenizer
 from astrai.trainer import SchedulerFactory, StrategyFactory, Trainer
 
 __all__ = [

astrai/dataset/__init__.py

@@ -1,7 +1,7 @@
 from astrai.dataset.dataset import (
     BaseDataset,
-    DatasetFactory,
     BaseSegmentFetcher,
+    DatasetFactory,
     MultiSegmentFetcher,
 )
 from astrai.dataset.sampler import ResumableDistributedSampler

astrai/factory.py

@@ -1,7 +1,7 @@
 """Base factory class for extensible component registration."""
 
 from abc import ABC
-from typing import Callable, Dict, Generic, Type, TypeVar, Optional, List, Tuple
+from typing import Callable, Dict, Generic, List, Optional, Tuple, Type, TypeVar
 
 T = TypeVar("T")
 

astrai/inference/engine.py

@@ -8,8 +8,8 @@ from typing import Any, Dict, Generator, List, Optional, Union
 import torch
 import torch.nn as nn
 
-from astrai.tokenize.tokenizer import TextTokenizer
 from astrai.inference.scheduler import InferenceScheduler
+from astrai.tokenize import AutoTokenizer
 
 logger = logging.getLogger(__name__)
 
@@ -109,7 +109,7 @@ class InferenceEngine:
     def __init__(
         self,
         model: nn.Module,
-        tokenizer: TextTokenizer,
+        tokenizer: AutoTokenizer,
         max_batch_size: int = 1,
         max_seq_len: Optional[int] = None,
     ):

astrai/inference/scheduler.py

@@ -9,7 +9,7 @@ import torch
 from torch import Tensor
 
 from astrai.model.automodel import AutoModel
-from astrai.tokenize.tokenizer import TextTokenizer
+from astrai.tokenize import AutoTokenizer
 
 
 class TaskStatus:
@@ -101,7 +101,7 @@ class InferenceScheduler:
     def __init__(
         self,
         model: AutoModel,
-        tokenizer: TextTokenizer,
+        tokenizer: AutoTokenizer,
         max_batch_size: int = 16,
         max_seq_len: Optional[int] = None,
         device: str = "cuda",

astrai/inference/server.py

@@ -19,7 +19,7 @@ from pydantic import BaseModel, Field
 
 from astrai.inference.engine import InferenceEngine
 from astrai.model import AutoModel
-from astrai.tokenize import TextTokenizer
+from astrai.tokenize import AutoTokenizer
 
 logger = logging.getLogger(__name__)
 
@@ -96,7 +96,7 @@ def load_model(
         raise FileNotFoundError(f"Parameter directory not found: {param_path}")
 
     # Load tokenizer separately
-    tokenizer = TextTokenizer.from_pretrained(param_path)
+    tokenizer = AutoTokenizer.from_pretrained(param_path)
     _model_param = AutoModel.from_pretrained(param_path)
     _model_param.to(device=device, dtype=dtype)
     logger.info(f"Model loaded on {device} with dtype {dtype}")

astrai/model/__init__.py

@@ -1,3 +1,4 @@
+from astrai.model.automodel import AutoModel
 from astrai.model.module import (
     GQA,
     MLP,
@@ -6,8 +7,6 @@ from astrai.model.module import (
     RMSNorm,
 )
 from astrai.model.transformer import Transformer
-from astrai.model.automodel import AutoModel
-
 
 __all__ = [
     # Modules

astrai/model/automodel.py

@@ -2,12 +2,12 @@
 AutoModel base class for model loading and saving.
 """
 
-import torch.nn as nn
-import safetensors.torch as st
-
-from pathlib import Path
 from contextlib import contextmanager
-from typing import Self, Union, Dict, Type
+from pathlib import Path
+from typing import Dict, Self, Type, Union
+
+import safetensors.torch as st
+import torch.nn as nn
 
 from astrai.config import ModelConfig
 

astrai/tokenize/__init__.py

@@ -1,23 +1,15 @@
+from astrai.tokenize.chat_template import ChatTemplate, MessageType
 from astrai.tokenize.tokenizer import (
-    TextTokenizer,
+    AutoTokenizer,
     BpeTokenizer,
 )
 from astrai.tokenize.trainer import BpeTrainer
-from astrai.tokenize.chat_template import (
-    ChatTemplate,
-    HistoryType,
-    MessageType,
-)
-
-# Alias for compatibility
-AutoTokenizer = TextTokenizer
 
 __all__ = [
-    "TextTokenizer",
     "AutoTokenizer",
     "BpeTokenizer",
     "BpeTrainer",
     "ChatTemplate",
-    "HistoryType",
     "MessageType",
+    "HistoryType",
 ]

astrai/tokenize/chat_template.py

@@ -1,9 +1,10 @@
-from typing import Dict, List, Optional, Tuple, Any
-from jinja2 import Template
 from dataclasses import dataclass
+from typing import Any, Dict, List, Optional
 
-HistoryType = List[Tuple[str, str]]
-MessageType = Dict[str, str]
+from jinja2 import Template
+
+# Message type for chat messages
+type MessageType = Dict[str, Any]
 
 
 @dataclass

astrai/tokenize/tokenizer.py

@@ -8,10 +8,11 @@ from typing import Dict, List, Optional, Union
 
 from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
 from tokenizers.models import BPE
+
 from astrai.tokenize.chat_template import ChatTemplate
 
 
-class TextTokenizer:
+class AutoTokenizer:
     """Base tokenizer class with automatic loading support"""
 
     TOKENIZER_CLASSES = {}  # Registry for auto-loading
@@ -51,7 +52,7 @@ class TextTokenizer:
                 self.set_chat_template(config["chat_template"])
 
     @classmethod
-    def from_pretrained(cls, path: Union[str, Path], **kwargs) -> "TextTokenizer":
+    def from_pretrained(cls, path: Union[str, Path], **kwargs) -> "AutoTokenizer":
         """Load tokenizer from pretrained directory."""
         instance = cls(path)
         return instance
@@ -235,7 +236,7 @@ class TextTokenizer:
         return rendered
 
 
-class BpeTokenizer(TextTokenizer):
+class BpeTokenizer(AutoTokenizer):
     """BPE tokenizer implementation."""
 
     def __init__(

astrai/trainer/__init__.py

@@ -1,8 +1,8 @@
 from astrai.trainer.schedule import BaseScheduler, SchedulerFactory
 from astrai.trainer.strategy import BaseStrategy, StrategyFactory
 from astrai.trainer.train_callback import (
-    TrainCallback,
     CallbackFactory,
+    TrainCallback,
 )
 from astrai.trainer.trainer import Trainer
 

astrai/trainer/train_callback.py

@@ -8,8 +8,9 @@ import torch.nn as nn
 from torch.nn.utils import clip_grad_norm_
 from tqdm import tqdm
 
-from astrai.serialization import Checkpoint
+from astrai.factory import BaseFactory
 from astrai.parallel import only_on_rank
+from astrai.serialization import Checkpoint
 from astrai.trainer.metric_util import (
     ctx_get_grad_max,
     ctx_get_grad_mean,
@@ -21,7 +22,6 @@ from astrai.trainer.metric_util import (
     ctx_get_lr,
 )
 from astrai.trainer.train_context import TrainContext
-from astrai.factory import BaseFactory
 
 
 @runtime_checkable

astrai/trainer/train_context.py

@@ -8,8 +8,8 @@ from torch.utils.data import DataLoader
 
 from astrai.config.train_config import TrainConfig
 from astrai.dataset import ResumableDistributedSampler
-from astrai.serialization import Checkpoint
 from astrai.parallel.setup import get_current_device, get_rank, get_world_size
+from astrai.serialization import Checkpoint
 from astrai.trainer.strategy import BaseStrategy, StrategyFactory
 
 

astrai/trainer/trainer.py

@@ -2,11 +2,11 @@ import logging
 from typing import List, Optional
 
 from astrai.config import TrainConfig
-from astrai.serialization import Checkpoint
 from astrai.parallel.setup import spawn_parallel_fn
+from astrai.serialization import Checkpoint
 from astrai.trainer.train_callback import (
-    TrainCallback,
     CallbackFactory,
+    TrainCallback,
 )
 from astrai.trainer.train_context import TrainContext, TrainContextBuilder