refactor: checkpoint 按 HF 方式存独立 .pt 文件，callback 接管恢复

- Checkpoint.save/load: extra 逐 key 写为 {key}.pt 而非单个 extra.pt
- meta.json 新增 timestamp
- CheckpointCallback: save_extra/load_extra 静态方法 + extra_keys 类属性
- on_train_begin 接管 optimizer/scheduler 恢复，TrainContextBuilder 不再传 load_extra_fn

astrai/trainer/train_context.py

@@ -1,5 +1,5 @@
 from dataclasses import dataclass, field
-from typing import Callable, Optional, Self
+from typing import Optional, Self
 
 import torch.nn as nn
 from torch.optim import Optimizer
@@ -35,11 +35,9 @@ class TrainContextBuilder:
     def __init__(
         self,
         config: TrainConfig,
-        load_extra_fn: Optional[Callable[[dict, "TrainContext"], None]] = None,
     ):
         self.config = config
         self._checkpoint: Optional[Checkpoint] = None
-        self._load_extra_fn = load_extra_fn
 
     def with_checkpoint(self, checkpoint: Optional[Checkpoint]) -> Self:
         self._checkpoint = checkpoint
@@ -71,9 +69,6 @@ class TrainContextBuilder:
         context.optimizer = self.config.optimizer_fn(context.model)
         context.scheduler = self.config.scheduler_fn(context.optimizer)
 
-        if self._checkpoint and self._checkpoint.extra and self._load_extra_fn:
-            self._load_extra_fn(self._checkpoint.extra, context)
-
         cfg = self.config
         sampler_offset = context.iteration * cfg.batch_size
         sampler = ResumableDistributedSampler(