[Bugfix] Fix pooling non-determinism from pinned prompt_lens aliasing

tests/v1/worker/test_gpu_input_batch.py

@@ -378,3 +378,65 @@ def test_swap_states_in_input_batch(device: str, batch_size: int, swap_list: lis
     ref_input_batch.refresh_metadata()
 
     _compare_objs(input_batch, ref_input_batch)
+
+
+def _construct_pooling_request(req_id_suffix: int):
+    from vllm.pooling_params import PoolingParams
+
+    prompt_token_ids = [
+        np.random.randint(0, VOCAB_SIZE)
+        for _ in range(np.random.randint(10, MAX_PROMPT_SIZE))
+    ]
+    return CachedRequestState(
+        req_id=f"pool_req_{req_id_suffix}",
+        prompt_token_ids=prompt_token_ids,
+        sampling_params=None,
+        pooling_params=PoolingParams(task="classify"),
+        mm_features=[],
+        block_ids=([],),
+        generator=None,
+        num_computed_tokens=0,
+        output_token_ids=[],
+    )
+
+
+@pytest.mark.parametrize("device", CUDA_DEVICES)
+def test_pooling_prompt_lens_not_aliased(device: str):
+    """Verify that prompt_lens in PoolingMetadata does not share memory
+    with the internal num_prompt_tokens pinned buffer. Guards against possible
+    non-determinism in pooling metadata due to mutations to the internal buffer.
+    """
+    batch_size = 4
+    input_batch = InputBatch(
+        max_num_reqs=batch_size * 2,
+        max_model_len=MAX_PROMPT_SIZE + NUM_OUTPUT_TOKENS,
+        max_num_batched_tokens=batch_size * (MAX_PROMPT_SIZE + NUM_OUTPUT_TOKENS),
+        device=torch.device(device),
+        pin_memory=is_pin_memory_available(),
+        vocab_size=VOCAB_SIZE,
+        block_sizes=[16],
+        kernel_block_sizes=[16],
+        is_pooling_model=True,
+    )
+
+    reqs = []
+    # Add requests
+    for i in range(batch_size):
+        req = _construct_pooling_request(i)
+        input_batch.add_request(req)
+        reqs.append(req)
+    input_batch.refresh_metadata()
+
+    # prompt_lens must be a snapshot
+    metadata = input_batch.get_pooling_metadata()
+    prompt_lens_snapshot = metadata.prompt_lens.clone()
+
+    # Mutate the internal buffer (simulates next batch adding new requests)
+    input_batch.num_prompt_tokens_cpu_tensor.fill_(999)
+
+    # prompt_lens must be unaffected by the mutation
+    assert torch.equal(metadata.prompt_lens, prompt_lens_snapshot), (
+        "prompt_lens shares memory with internal pinned buffer; "
+        "mutations to num_prompt_tokens_cpu_tensor corrupted prompt_lens. "
+        f"Expected {prompt_lens_snapshot}, got {metadata.prompt_lens}"
+    )

vllm/v1/worker/gpu_input_batch.py

@@ -892,7 +892,7 @@ def get_pooling_metadata(self) -> PoolingMetadata:
         pooling_states = self.get_pooling_states()
 
         return PoolingMetadata(
-            prompt_lens=torch.from_numpy(self.num_prompt_tokens[: self.num_reqs]),
+            prompt_lens=self.num_prompt_tokens_cpu_tensor[: self.num_reqs].clone(),
             prompt_token_ids=self.sampling_metadata.prompt_token_ids,
             pooling_params=pooling_params,
             pooling_states=pooling_states,