[Bugfix] Validate logit biases to prevent out of vocab ids crashing engine (#16529)

Signed-off-by: Ryan McConville <ryan@ryanmcconville.com>

tests/entrypoints/openai/test_chat_logit_bias_validation.py

+# SPDX-License-Identifier: Apache-2.0
+
+import openai
+import pytest
+import pytest_asyncio
+
+from vllm.config import ModelConfig
+
+from ...utils import RemoteOpenAIServer
+
+MODEL_NAME = "Qwen/Qwen2.5-1.5B-Instruct"
+
+
+def get_vocab_size(model_name):
+    config = ModelConfig(
+        model=model_name,
+        task="auto",
+        tokenizer=model_name,
+        tokenizer_mode="auto",
+        trust_remote_code=False,
+        seed=0,
+        dtype="bfloat16",
+    )
+    return config.get_vocab_size()
+
+
+@pytest.fixture(scope="module")
+def server():
+    args = [
+        "--dtype",
+        "bfloat16",
+        "--max-model-len",
+        "1024",
+        "--enforce-eager",
+    ]
+
+    with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
+        yield remote_server
+
+
+@pytest_asyncio.fixture
+async def client(server):
+    async with server.get_async_client() as async_client:
+        yield async_client
+
+
+@pytest.mark.asyncio
+async def test_chat_logit_bias_valid(client):
+    """Test that valid logit_bias values are accepted in chat completions."""
+    vocab_size = get_vocab_size(MODEL_NAME)
+    valid_token_id = vocab_size - 1
+
+    completion = await client.chat.completions.create(
+        model=MODEL_NAME,
+        messages=[{
+            "role": "user",
+            "content": "Testing valid logit bias"
+        }],
+        max_tokens=5,
+        logit_bias={str(valid_token_id): 1.0},
+    )
+
+    assert completion.choices[0].message.content is not None
+
+
+@pytest.mark.asyncio
+async def test_chat_logit_bias_invalid(client):
+    """Test that invalid logit_bias values are rejected in chat completions."""
+    vocab_size = get_vocab_size(MODEL_NAME)
+    invalid_token_id = vocab_size + 1
+
+    with pytest.raises(openai.BadRequestError) as excinfo:
+        await client.chat.completions.create(
+            model=MODEL_NAME,
+            messages=[{
+                "role": "user",
+                "content": "Testing invalid logit bias"
+            }],
+            max_tokens=5,
+            logit_bias={str(invalid_token_id): 1.0},
+        )
+
+    error = excinfo.value
+    error_message = str(error)
+
+    assert error.status_code == 400
+    assert str(invalid_token_id) in error_message
+    assert str(vocab_size) in error_message

vllm/v1/engine/processor.py

@@ -77,6 +77,7 @@ class Processor:
         params: SamplingParams,
     ) -> None:
         self._validate_structured_output(params)
+        self._validate_logit_bias(params)
 
         if params.allowed_token_ids is None:
             return
@@ -87,6 +88,26 @@ class Processor:
             raise ValueError(
                 "allowed_token_ids contains out-of-vocab token id!")
 
+    def _validate_logit_bias(
+        self,
+        params: SamplingParams,
+    ) -> None:
+        """Validate logit_bias token IDs are within vocabulary range."""
+        if not params.logit_bias:
+            return
+
+        vocab_size = self.model_config.get_vocab_size()
+        invalid_token_ids = []
+
+        for token_id in params.logit_bias:
+            if token_id < 0 or token_id >= vocab_size:
+                invalid_token_ids.append(token_id)
+
+        if invalid_token_ids:
+            raise ValueError(
+                f"token_id(s) {invalid_token_ids} in logit_bias contain "
+                f"out-of-vocab token ids. Vocabulary size: {vocab_size}")
+
     def _validate_supported_sampling_params(
         self,
         params: SamplingParams,

vllm/v1/sample/sampler.py

@@ -230,9 +230,19 @@ class Sampler(nn.Module):
         # TODO(houseroad): this implementation is extremely inefficient.
         # One idea is implement this as a PyTorch C++ op, and we may
         # even optimize the logit_bias layout.
+
+        # Get vocabulary size from logits
+        vocab_size = logits.shape[-1]
+
         for i, logit_bias in enumerate(sampling_metadata.logit_bias):
             if logit_bias:
                 for token_id, bias in logit_bias.items():
+                    # Check token_id bounds to ensure within vocabulary
+                    if token_id < 0 or token_id >= vocab_size:
+                        raise ValueError(
+                            f"token_id {token_id} in logit_bias contains "
+                            f"out-of-vocab token id. Vocabulary size: "
+                            f"{vocab_size}")
                     logits[i, token_id] += bias
         return logits