fix: 修复 CLI 参数缺失/重复、device_ids 越界、generate 参数名不一致、scheduler 时序、非流式截断等 bug

- train.py: 补上 --batch_size、--grpo_clip_eps，删除 3 处重复 --group_size
- generate.py: --model_dir 改为 --param_path 对齐 README
- automodel.py: from_pretrained 新增 strict 参数（默认 True）
- parallel/setup.py: 修复 device_ids 索引越界
- train_callback.py: scheduler.step() 移至 on_step_end
- test_train_strategy.py: 测试中补 optimizer.step()
- engine.py: 非流式改为循环等待所有任务完成，补 remove_task 清理
- scheduler.py: Task 添加 _pages_freed 标志，杜绝双重释放
- trainer.py: accumulation_steps=0 时 clamp 为 1
- tokenizer.py: save_pretrained 添加 _tokenizer is None 检查
- benchmark.py: 修复 ModelConfig 过时 import 路径
- inference/__init__.py: 修复 stale docstring

astrai/trainer/trainer.py

@@ -68,8 +68,9 @@ class Trainer:
                 context.epoch = epoch
                 self._call_callbacks("on_epoch_begin", context)
 
+                accumulation_steps = max(self.train_config.accumulation_steps, 1)
                 for batch in context.dataloader:
-                    if context.iteration % self.train_config.accumulation_steps == 0:
+                    if context.iteration % accumulation_steps == 0:
                         # 2. step
                         self._call_callbacks("on_step_begin", context)
                         context.optimizer.step()
@@ -83,7 +84,7 @@ class Trainer:
                     context.iteration += 1
 
                     # to make the loss normalized by accumulation steps
-                    stand_loss = loss / self.train_config.accumulation_steps
+                    stand_loss = loss / accumulation_steps
                     stand_loss.backward()
 
                     self._call_callbacks("on_batch_end", context)