perf: eliminate GPU syncs in contiguous cache write/gather hot paths

- Replace .tolist() calls with _total_len in gather(); move _slot_len updates from per-layer write to once-per-step bind_tasks

- Use torch.as_tensor instead of torch.tensor in decode penalty history construction

astrai/inference/core/executor.py

@@ -104,28 +104,25 @@ class Executor:
         )
 
         history_lists = []
-        mask_lists = []
+        history_lens = []
         for t in tasks:
             window = t.rep_window
             prompt_part = t.prompt_ids[-window:]
             ids = prompt_part + t.output_ids
             history_lists.append(ids)
-            mask_lists.append([True] * len(ids))
+            history_lens.append(len(ids))
 
-        max_len = max(len(h) for h in history_lists)
+        max_len = max(history_lens) if history_lens else 0
         padded_ids = torch.zeros(
             len(tasks), max_len, dtype=torch.long, device=self.device
         )
         padded_mask = torch.zeros(
             len(tasks), max_len, dtype=torch.bool, device=self.device
         )
-        for i, (h, m) in enumerate(zip(history_lists, mask_lists)):
-            padded_ids[i, : len(h)] = torch.tensor(
-                h, dtype=torch.long, device=self.device
-            )
-            padded_mask[i, : len(m)] = torch.tensor(
-                m, dtype=torch.bool, device=self.device
-            )
+        for i, h in enumerate(history_lists):
+            L = history_lens[i]
+            padded_ids[i, :L] = torch.as_tensor(h, dtype=torch.long, device=self.device)
+            padded_mask[i, :L] = True
 
         with torch.inference_mode():
             outputs = self.model(