Revert "[Core] Performance: Use list[np.ndarray] instead of list[list… (#28773)

vllm/v1/sample/rejection_sampler.py

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