[Core] Performance: Use list[np.ndarray] instead of list[list[int]] for output tokens for GC optimization (#26368)

Signed-off-by: Jialin Ouyang <Jialin.Ouyang@gmail.com>

vllm/v1/sample/rejection_sampler.py

@@ -3,6 +3,7 @@
 
 from dataclasses import replace
 
+import numpy as np
 import torch
 import torch.nn as nn
 
@@ -204,7 +205,7 @@ class RejectionSampler(nn.Module):
     def parse_output(
         output_token_ids: torch.Tensor,
         vocab_size: int,
-    ) -> list[list[int]]:
+    ) -> list[np.ndarray]:
         """Parse the output of the rejection sampler.
         Args:
             output_token_ids: The sampled token IDs in shape
@@ -220,10 +221,7 @@ class RejectionSampler(nn.Module):
         valid_mask = (output_token_ids_np != PLACEHOLDER_TOKEN_ID) & (
             output_token_ids_np < vocab_size
         )
-        outputs = [
-            row[valid_mask[i]].tolist() for i, row in enumerate(output_token_ids_np)
-        ]
-        return outputs
+        return [row[valid_mask[i]] for i, row in enumerate(output_token_ids_np)]
 
     def apply_logits_processors(
         self,