[Responses][CI] Filter negative token IDs in schema fuzz test to avoid 500 errors

[AndreasKaratzas]

Adds server-side validation to reject negative token IDs in POST /v1/completions, which previously caused an unhandled 500 Internal Server Error.

Problem

When the prompt field contains negative token IDs (e.g. {"prompt": [[-1]]}), the server crashes with a 500 instead of returning a proper 400 validation error. This was discovered via schemathesis fuzz testing in test_openapi_stateless.

curl -X POST -H 'Content-Type: application/json' \
  -d '{"prompt": [[-1]]}' \
  http://localhost:8000/v1/completions
# Before: 500 Internal Server Error
# After:  400 Bad Request with descriptive error message

Fix

Added a model_validator in CompletionRequest (vllm/entrypoints/openai/completion/protocol.py) that checks all token IDs in prompt are non-negative, consistent with how other fields like logprobs, prompt_logprobs, and cache_salt are already validated on the same class.

Testing

• Existing test_openapi_stateless schemathesis fuzz test now passes for POST /v1/completions without needing a test-side filter (the server returns a 400 which schemathesis treats as valid behavior).
• Added explicit test cases in test_completion_error.py for both flat ([-1]) and nested ([[-1]]) negative token ID prompts, verifying the validator returns a proper validation error.

[gemini-code-assist]

Code Review

This pull request adds server-side validation to reject negative token IDs in completion requests, which previously caused 500 errors. The change is implemented via a model_validator in CompletionRequest and is accompanied by new tests. The implementation is correct, but I've suggested a simplification to the validation logic to improve readability and maintainability by removing a redundant check and a nested loop.

[gemini-code-assist]

The logic for validating prompt token IDs can be simplified to improve readability and maintainability. The current implementation has a redundant check for string types and a nested loop that can be replaced with a more concise any() expression. This also removes code duplication.

        if isinstance(prompt, list):
            for item in prompt:
                if isinstance(item, int):
                    if item < 0:
                        raise VLLMValidationError(
                            "Token IDs in `prompt` must be non-negative.",
                            parameter="prompt",
                        )
                elif isinstance(item, list):
                    if any(isinstance(x, int) and x < 0 for x in item):
                        raise VLLMValidationError(
                            "Token IDs in `prompt` must be non-negative.",
                            parameter="prompt",
                        )

[AndreasKaratzas]

Replaced the custom validator with a Pydantic-native Field(ge=0) constraint on the prompt type annotation. No custom validation code needed. Also added explicit test cases for both flat and nested negative token ID prompts in test_completion_error.py.

[DarkLight1337]

Thanks!