fix : 并行训练 state_dict 收集与训练/推理并发缺陷

- FSDPExecutor: unwrap_model 返回全量 state_dict (state_dict_type FULL)；use_orig_params=True
- DDPExecutor/BaseExecutor: unwrap_model 统一返回 model.module.state_dict() / model.state_dict()
- CheckpointCallback: 走 executor.unwrap_model 拿完整 state_dict
- strategy.py: 移除 FSDP/DDp 依赖；create_ref_model(model_fn, state_dict) 纯函数
- TrainContextBuilder: 传递 model_fn + executor 到 strategy
- GRPOStrategy.sync_ref_model: 通过 executor.unwrap_model 获取完整权重
- TaskManager.wait_for_tasks: 锁内检查队列，消除 clear/set 竞态
- ProtocolHandler: stop token 不再计入 completion_tokens（流式/非流式）

astrai/trainer/train_callback.py

@@ -146,8 +146,7 @@ class CheckpointCallback(TrainCallback):
         self.last_ckpt_iter = 0
 
     def _save_checkpoint(self, context: TrainContext):
-        unwrapped = context.executor.unwrap_model(context.model)
-        state_dict = unwrapped.state_dict()
+        state_dict = context.executor.unwrap_model(context.model)
         self.last_ckpt_iter = context.iteration
 
         if get_rank() == 0: