fix: use batch_per_device for rollout scheduler batch sizing

- train_context.py referenced non-existent cfg.batch_size, replaced with cfg.batch_per_device
- default group_size lowered from 8 to 1: without a group concept (DPO), scheduler batch equals batch_per_device; rollout-based DPO can opt in via extra_kwargs['group_size']>=2
- inline expressions (rollout_batch_size, max_seq_len) extracted for readability
- add tests/trainer/test_online_e2e.py: end-to-end online_dpo via Trainer.train, exercising KV-cache-backed rollout path

astrai/trainer/train_context.py

@@ -238,20 +238,23 @@ class TrainContextBuilder:
             tokenizer = AutoTokenizer.from_pretrained(self._param_path)
             reward_model = cfg.reward_model_fn()
 
+            group_size = strategy_kwargs.get("group_size", 1)
+            rollout_batch_size = group_size * max(1, cfg.batch_per_device)
+            max_seq_len = getattr(context.model.config, "max_len", None)
+
             scheduler = InferenceScheduler(
                 model=context.model,
                 tokenizer=tokenizer,
-                max_batch_size=strategy_kwargs.get("group_size", 8)
-                * max(1, cfg.batch_size or 1),
-                max_seq_len=getattr(context.model.config, "max_len", None),
-                max_prompt_len=getattr(context.model.config, "max_len", 4096),
+                max_batch_size=rollout_batch_size,
+                max_seq_len=max_seq_len,
+                max_prompt_len=max_seq_len or 4096,
             )
 
             generator = RolloutGenerator(
                 scheduler=scheduler,
                 tokenizer=tokenizer,
                 max_tokens=cfg.rollout_max_tokens,
-                group_size=strategy_kwargs.get("group_size", 8),
+                group_size=group_size,
                 temperature=cfg.rollout_temperature,
                 top_k=cfg.rollout_top_k,
                 top_p=cfg.rollout_top_p,