perf: replace per-element .item() GPU syncs with batch .tolist() in TTS code predictor (#1614)

Signed-off-by: dubin555 <dubin555@gmail.com>
Signed-off-by: linyueqian <linyueqian@outlook.com>
Co-authored-by: linyueqian <linyueqian@outlook.com>
Co-authored-by: Yueqian Lin <70319226+linyueqian@users.noreply.github.com>

vllm_omni/model_executor/models/qwen3_tts/qwen3_tts_code_predictor_vllm.py

@@ -120,11 +120,17 @@ class _LocalPredictorKVCache:
         if num_tokens <= 0:
             return {}, torch.empty((0,), dtype=torch.int64, device=self.device), {}
 
+        # Convert tensors to Python lists once to avoid per-element .item()
+        # calls (each .item() on a GPU tensor forces a GPU→CPU sync).
+        query_lens_list = query_lens_i32[:num_reqs].tolist()
+        seq_lens_list = seq_lens_i32[:num_reqs].tolist()
+        block_table_cpu = self._block_table[:num_reqs].cpu().tolist()
+
         # positions: for each request i, emit positions [seq_len-query_len .. seq_len-1]
         pos_list: list[torch.Tensor] = []
         for i in range(num_reqs):
-            ql = int(query_lens_i32[i].item())
-            sl = int(seq_lens_i32[i].item())
+            ql = query_lens_list[i]
+            sl = seq_lens_list[i]
             start = sl - ql
             pos_list.append(torch.arange(start, sl, dtype=torch.int64))
         positions_cpu = torch.cat(pos_list, dim=0)
@@ -134,13 +140,13 @@ class _LocalPredictorKVCache:
         slot_mapping = torch.empty((num_tokens,), dtype=torch.int64, device="cpu")
         cursor = 0
         for i in range(num_reqs):
-            ql = int(query_lens_i32[i].item())
-            sl = int(seq_lens_i32[i].item())
+            ql = query_lens_list[i]
+            sl = seq_lens_list[i]
             start = sl - ql
             for p in range(start, sl):
                 block_idx = p // self.block_size
                 offset = p % self.block_size
-                block_id = int(self._block_table[i, block_idx].item())
+                block_id = block_table_cpu[i][block_idx]
                 slot_mapping[cursor] = block_id * self.block_size + offset
                 cursor += 1