fix: 断点续训恢复优化器/调度器状态及采样器剩余长度

- 使用Checkpoint.load()替代手动加载model.safetensors，恢复optimizer/scheduler状态
- TrainContextBuilder从checkpoint.extra恢复优化器和调度器state_dict
- ResumableDistributedSampler.__len__返回剩余样本数而非总数
- 训练前对state_dict置空避免mp.spawn pickle 7GB大对象

astrai/dataset/sampler.py

@@ -43,6 +43,7 @@ class ResumableDistributedSampler(Sampler[int]):
         offset = 0 if drop_last else self.num_replicas - 1
         self.num_samples_per_replica = (self.num_samples + offset) // self.num_replicas
         self.total_size = self.num_samples_per_replica * self.num_replicas
+        self.iter = self.iter % self.num_samples_per_replica
 
         self._indices = None
 
@@ -74,5 +75,10 @@ class ResumableDistributedSampler(Sampler[int]):
         self.epoch += 1
         self._indices = None
 
+    @property
+    def _remaining(self):
+        remaining = self.num_samples_per_replica - self.iter
+        return max(remaining, 0)
+
     def __len__(self):
-        return self.num_samples_per_replica
+        return self._remaining

astrai/trainer/train_context.py

@@ -71,6 +71,7 @@ class TrainContextBuilder:
         if self._checkpoint is not None:
             context.epoch = max(self._checkpoint.epoch, cfg.start_epoch)
             context.iteration = max(self._checkpoint.iteration, cfg.start_batch)
+            if self._checkpoint.state_dict:
                 context.model.load_state_dict(self._checkpoint.state_dict)
             context.checkpoint = self._checkpoint
         else:

scripts/tools/train.py

@@ -2,13 +2,13 @@ import argparse
 import os
 from functools import partial
 
-import safetensors.torch as st
 import torch
 import torch.optim as optim
 
 from astrai.config import AutoRegressiveLMConfig, TrainConfig
 from astrai.dataset import DatasetFactory
 from astrai.model import AutoRegressiveLM
+from astrai.serialization import Checkpoint
 from astrai.trainer import SchedulerFactory, Trainer
 
 
@@ -236,16 +236,14 @@ def train(
     if window_size is None:
         window_size = config.max_len
 
-    # Create bare AutoRegressiveLM (for training, no tokenizer needed)
-    model = AutoRegressiveLM(config)
+    # Create model and load full checkpoint (state_dict + optimizer + scheduler + meta)
+    checkpoint = Checkpoint.load(param_path)
+    model = AutoRegressiveLM(config).to(dtype=torch.bfloat16)
+    model.load_state_dict(checkpoint.state_dict, strict=False)
 
-    # Load weights if available
-    weights_path = os.path.join(param_path, "model.safetensors")
-    if os.path.exists(weights_path):
-        state_dict = st.load_file(weights_path)
-        model.load_state_dict(state_dict, strict=False)
-
-    model = model.to(dtype=torch.bfloat16)
+    # Strip state_dict to avoid pickling ~7GB through mp.spawn pipe
+    # (model weights already loaded into model above)
+    checkpoint.state_dict = {}
 
     strategy_kwargs = {
         "beta": dpo_beta,
@@ -319,7 +317,7 @@ def train(
     )
 
     trainer = Trainer(train_config)
-    trainer.train()
+    trainer.train(checkpoint=checkpoint)
 
 
 if __name__ == "__main__":