apply _validate_input to MistralTokenizer token-id chat prompts (#32448)

Signed-off-by: Vanshil Shah <vanshilshah@gmail.com>

tests/entrypoints/openai/test_serving_chat.py

@@ -731,6 +731,101 @@ async def test_serving_chat_should_set_correct_max_tokens():
     assert mock_engine.generate.call_args.args[1].max_tokens == 5
 
 
+@pytest.mark.asyncio
+async def test_serving_chat_mistral_token_ids_prompt_is_validated(monkeypatch_module):
+    """Regression test: when the Mistral tokenizer path returns token IDs
+    directly, we must still apply input length + max_tokens validation.
+    """
+
+    mock_engine = MagicMock(spec=AsyncLLM)
+    mock_engine.errored = False
+    mock_engine.model_config = MockModelConfig()
+    mock_engine.input_processor = MagicMock()
+    mock_engine.io_processor = MagicMock()
+
+    class DummyMistralTokenizer:
+        def decode(self, token_ids):
+            # Only used for logging/validation error messages.
+            return "dummy"
+
+    dummy_tokenizer = DummyMistralTokenizer()
+    mock_engine.get_tokenizer.return_value = dummy_tokenizer
+
+    # Patch the OpenAI engine serving module to treat our dummy tokenizer
+    # as a MistralTokenizer. This forces the code path where chat template
+    # rendering can return a list[int] (token IDs).
+    import vllm.entrypoints.openai.engine.serving as engine_serving
+
+    monkeypatch_module.setattr(
+        engine_serving, "MistralTokenizer", DummyMistralTokenizer
+    )
+
+    serving_chat = _build_serving_chat(mock_engine)
+
+    # Force the Mistral chat template renderer to return token IDs.
+    # Choose a prompt length that is < max_model_len, but large enough that
+    # adding max_tokens should exceed the model context window.
+    serving_chat._apply_mistral_chat_template_async = AsyncMock(
+        return_value=list(range(95))
+    )
+
+    req = ChatCompletionRequest(
+        model=MODEL_NAME,
+        messages=[{"role": "user", "content": "what is 1+1?"}],
+        max_tokens=10,
+    )
+
+    resp = await serving_chat.create_chat_completion(req)
+    assert isinstance(resp, ErrorResponse)
+    assert "max_tokens" in resp.error.message
+
+
+@pytest.mark.asyncio
+async def test_serving_chat_mistral_token_ids_prompt_too_long_is_rejected(
+    monkeypatch_module,
+):
+    """Regression test: MistralTokenizer token-id prompts must still enforce
+    the max context length for the input itself (token_num >= max_model_len).
+    """
+
+    mock_engine = MagicMock(spec=AsyncLLM)
+    mock_engine.errored = False
+    mock_engine.model_config = MockModelConfig()
+    mock_engine.input_processor = MagicMock()
+    mock_engine.io_processor = MagicMock()
+
+    class DummyMistralTokenizer:
+        def decode(self, token_ids):
+            return "dummy"
+
+    dummy_tokenizer = DummyMistralTokenizer()
+    mock_engine.get_tokenizer.return_value = dummy_tokenizer
+
+    import vllm.entrypoints.openai.engine.serving as engine_serving
+
+    monkeypatch_module.setattr(
+        engine_serving, "MistralTokenizer", DummyMistralTokenizer
+    )
+
+    serving_chat = _build_serving_chat(mock_engine)
+
+    # prompt_token_ids length == max_model_len should be rejected for
+    # completion-like requests (ChatCompletionRequest).
+    serving_chat._apply_mistral_chat_template_async = AsyncMock(
+        return_value=list(range(mock_engine.model_config.max_model_len))
+    )
+
+    req = ChatCompletionRequest(
+        model=MODEL_NAME,
+        messages=[{"role": "user", "content": "what is 1+1?"}],
+        max_tokens=1,
+    )
+
+    resp = await serving_chat.create_chat_completion(req)
+    assert isinstance(resp, ErrorResponse)
+    assert "maximum context length" in resp.error.message
+
+
 @pytest.mark.asyncio
 async def test_serving_chat_could_load_correct_generation_config():
     mock_model_config = MockModelConfig()

vllm/entrypoints/openai/engine/serving.py

@@ -1277,9 +1277,11 @@ class OpenAIServing:
             assert is_list_of(request_prompt, int), (
                 "Prompt has to be either a string or a list of token ids"
             )
-            prompt_inputs = TokensPrompt(
-                prompt=tokenizer.decode(request_prompt),
-                prompt_token_ids=request_prompt,
+            input_text = tokenizer.decode(request_prompt)
+            prompt_inputs = self._validate_input(
+                request=request,
+                input_ids=request_prompt,
+                input_text=input_text,
             )
 
         engine_prompt = TokensPrompt(prompt_token_ids=prompt_inputs["prompt_token_ids"])