perf: merge decode batch for 10x throughput

- merge all active decode tasks into single forward pass (was grouped by next_pos)
- add per-task write_positions to ContiguousCacheView for correct KV writes
- override ContiguousCache.task_cached (base returned 0, caused prefill loops)
- add --cache_len/--frequency_penalty/--rep_window to generate.py
- chunked batch processing with tqdm progress

bench (1.2B model, 128 prompts, 64 tok, batch=128):
  before: 77.2s, ~111 tok/s
  after:   7.1s, ~1210 tok/s (10.9x)

astrai/inference/core/executor.py

@@ -106,7 +106,12 @@ class Executor:
         with torch.inference_mode():
             outputs = self.model(
                 input_ids.unsqueeze(1),
-                paged_cache=self.kv_cache.bind_tasks(task_ids, total_len, self.device),
+                paged_cache=self.kv_cache.bind_tasks(
+                    task_ids,
+                    total_len,
+                    self.device,
+                    write_positions=position_ids,
+                ),
                 position_ids=position_ids.unsqueeze(1),
             )
             logits = outputs["logits"][:, -1, :]