test_run_batch.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import json
import subprocess
import tempfile

import pytest

from vllm.assets.audio import AudioAsset
from vllm.entrypoints.openai.run_batch import BatchRequestOutput

CHAT_MODEL_NAME = "hmellor/tiny-random-LlamaForCausalLM"
EMBEDDING_MODEL_NAME = "intfloat/multilingual-e5-small"
RERANKER_MODEL_NAME = "BAAI/bge-reranker-v2-m3"
REASONING_MODEL_NAME = "Qwen/Qwen3-0.6B"
SPEECH_LARGE_MODEL_NAME = "openai/whisper-large-v3"
SPEECH_SMALL_MODEL_NAME = "openai/whisper-small"

INPUT_BATCH = "\n".join(
    json.dumps(req)
    for req in [
        {
            "custom_id": "request-1",
            "method": "POST",
            "url": "/v1/chat/completions",
            "body": {
                "model": CHAT_MODEL_NAME,
                "messages": [
                    {
                        "role": "system",
                        "content": "You are a helpful assistant.",
                    },
                    {"role": "user", "content": "Hello world!"},
                ],
                "max_tokens": 1000,
            },
        },
        {
            "custom_id": "request-2",
            "method": "POST",
            "url": "/v1/chat/completions",
            "body": {
                "model": CHAT_MODEL_NAME,
                "messages": [
                    {
                        "role": "system",
                        "content": "You are an unhelpful assistant.",
                    },
                    {"role": "user", "content": "Hello world!"},
                ],
                "max_tokens": 1000,
            },
        },
        {
            "custom_id": "request-3",
            "method": "POST",
            "url": "/v1/chat/completions",
            "body": {
                "model": "NonExistModel",
                "messages": [
                    {
                        "role": "system",
                        "content": "You are an unhelpful assistant.",
                    },
                    {"role": "user", "content": "Hello world!"},
                ],
                "max_tokens": 1000,
            },
        },
        {
            "custom_id": "request-4",
            "method": "POST",
            "url": "/bad_url",
            "body": {
                "model": CHAT_MODEL_NAME,
                "messages": [
                    {
                        "role": "system",
                        "content": "You are an unhelpful assistant.",
                    },
                    {"role": "user", "content": "Hello world!"},
                ],
                "max_tokens": 1000,
            },
        },
        {
            "custom_id": "request-5",
            "method": "POST",
            "url": "/v1/chat/completions",
            "body": {
                "stream": "True",
                "model": CHAT_MODEL_NAME,
                "messages": [
                    {
                        "role": "system",
                        "content": "You are an unhelpful assistant.",
                    },
                    {"role": "user", "content": "Hello world!"},
                ],
                "max_tokens": 1000,
            },
        },
    ]
)

INVALID_INPUT_BATCH = "\n".join(
    json.dumps(req)
    for req in [
        {
            "invalid_field": "request-1",
            "method": "POST",
            "url": "/v1/chat/completions",
            "body": {
                "model": CHAT_MODEL_NAME,
                "messages": [
                    {"role": "system", "content": "You are a helpful assistant."},
                    {"role": "user", "content": "Hello world!"},
                ],
                "max_tokens": 1000,
            },
        },
        {
            "custom_id": "request-2",
            "method": "POST",
            "url": "/v1/chat/completions",
            "body": {
                "model": CHAT_MODEL_NAME,
                "messages": [
                    {"role": "system", "content": "You are an unhelpful assistant."},
                    {"role": "user", "content": "Hello world!"},
                ],
                "max_tokens": 1000,
            },
        },
    ]
)

INPUT_EMBEDDING_BATCH = "\n".join(
    json.dumps(req)
    for req in [
        {
            "custom_id": "request-1",
            "method": "POST",
            "url": "/v1/embeddings",
            "body": {
                "model": EMBEDDING_MODEL_NAME,
                "input": "You are a helpful assistant.",
            },
        },
        {
            "custom_id": "request-2",
            "method": "POST",
            "url": "/v1/embeddings",
            "body": {
                "model": EMBEDDING_MODEL_NAME,
                "input": "You are an unhelpful assistant.",
            },
        },
        {
            "custom_id": "request-3",
            "method": "POST",
            "url": "/v1/embeddings",
            "body": {
                "model": EMBEDDING_MODEL_NAME,
                "input": "Hello world!",
            },
        },
        {
            "custom_id": "request-4",
            "method": "POST",
            "url": "/v1/embeddings",
            "body": {
                "model": "NonExistModel",
                "input": "Hello world!",
            },
        },
    ]
)

_SCORE_RERANK_DOCUMENTS = [
    "The capital of Brazil is Brasilia.",
    "The capital of France is Paris.",
]

INPUT_SCORE_BATCH = "\n".join(
    json.dumps(req)
    for req in [
        {
            "custom_id": "request-1",
            "method": "POST",
            "url": "/score",
            "body": {
                "model": RERANKER_MODEL_NAME,
                "queries": "What is the capital of France?",
                "documents": _SCORE_RERANK_DOCUMENTS,
            },
        },
        {
            "custom_id": "request-2",
            "method": "POST",
            "url": "/v1/score",
            "body": {
                "model": RERANKER_MODEL_NAME,
                "queries": "What is the capital of France?",
                "documents": _SCORE_RERANK_DOCUMENTS,
            },
        },
    ]
)

INPUT_RERANK_BATCH = "\n".join(
    json.dumps(req)
    for req in [
        {
            "custom_id": "request-1",
            "method": "POST",
            "url": "/rerank",
            "body": {
                "model": RERANKER_MODEL_NAME,
                "query": "What is the capital of France?",
                "documents": _SCORE_RERANK_DOCUMENTS,
            },
        },
        {
            "custom_id": "request-2",
            "method": "POST",
            "url": "/v1/rerank",
            "body": {
                "model": RERANKER_MODEL_NAME,
                "query": "What is the capital of France?",
                "documents": _SCORE_RERANK_DOCUMENTS,
            },
        },
        {
            "custom_id": "request-2",
            "method": "POST",
            "url": "/v2/rerank",
            "body": {
                "model": RERANKER_MODEL_NAME,
                "query": "What is the capital of France?",
                "documents": _SCORE_RERANK_DOCUMENTS,
            },
        },
    ]
)

INPUT_REASONING_BATCH = "\n".join(
    json.dumps(req)
    for req in [
        {
            "custom_id": "request-1",
            "method": "POST",
            "url": "/v1/chat/completions",
            "body": {
                "model": REASONING_MODEL_NAME,
                "messages": [
                    {"role": "system", "content": "You are a helpful assistant."},
                    {"role": "user", "content": "Solve this math problem: 2+2=?"},
                ],
            },
        },
        {
            "custom_id": "request-2",
            "method": "POST",
            "url": "/v1/chat/completions",
            "body": {
                "model": REASONING_MODEL_NAME,
                "messages": [
                    {"role": "system", "content": "You are a helpful assistant."},
                    {"role": "user", "content": "What is the capital of France?"},
                ],
            },
        },
    ]
)

MINIMAL_WAV_BASE64 = "UklGRigAAABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YQQAAAAAAP9/"
INPUT_TRANSCRIPTION_BATCH = (
    json.dumps(
        {
            "custom_id": "request-1",
            "method": "POST",
            "url": "/v1/audio/transcriptions",
            "body": {
                "model": SPEECH_LARGE_MODEL_NAME,
                "file_url": f"data:audio/wav;base64,{MINIMAL_WAV_BASE64}",
                "response_format": "json",
            },
        }
    )
    + "\n"
)

INPUT_TRANSCRIPTION_HTTP_BATCH = (
    json.dumps(
        {
            "custom_id": "request-1",
            "method": "POST",
            "url": "/v1/audio/transcriptions",
            "body": {
                "model": SPEECH_LARGE_MODEL_NAME,
                "file_url": AudioAsset("mary_had_lamb").url,
                "response_format": "json",
            },
        }
    )
    + "\n"
)

INPUT_TRANSLATION_BATCH = (
    json.dumps(
        {
            "custom_id": "request-1",
            "method": "POST",
            "url": "/v1/audio/translations",
            "body": {
                "model": SPEECH_SMALL_MODEL_NAME,
                "file_url": AudioAsset("mary_had_lamb").url,
                "response_format": "text",
                "language": "it",
                "to_language": "en",
                "temperature": 0.0,
            },
        }
    )
    + "\n"
)

WEATHER_TOOL = {
    "type": "function",
    "function": {
        "name": "get_current_weather",
        "description": "Get the current weather in a given location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "The city and state, e.g. San Francisco, CA",
                },
                "unit": {
                    "type": "string",
                    "enum": ["celsius", "fahrenheit"],
                },
            },
            "required": ["location"],
        },
    },
}

INPUT_TOOL_CALLING_BATCH = json.dumps(
    {
        "custom_id": "request-1",
        "method": "POST",
        "url": "/v1/chat/completions",
        "body": {
            "model": REASONING_MODEL_NAME,
            "messages": [
                {"role": "user", "content": "What is the weather in San Francisco?"},
            ],
            "tools": [WEATHER_TOOL],
            "tool_choice": "required",
            "max_tokens": 1000,
        },
    }
)


def test_empty_file():
    with (
        tempfile.NamedTemporaryFile("w") as input_file,
        tempfile.NamedTemporaryFile("r") as output_file,
    ):
        input_file.write("")
        input_file.flush()
        proc = subprocess.Popen(
            [
                "vllm",
                "run-batch",
                "-i",
                input_file.name,
                "-o",
                output_file.name,
                "--model",
                EMBEDDING_MODEL_NAME,
            ],
        )
        proc.communicate()
        proc.wait()
        assert proc.returncode == 0, f"{proc=}"

        contents = output_file.read()
        assert contents.strip() == ""


def test_completions():
    with (
        tempfile.NamedTemporaryFile("w") as input_file,
        tempfile.NamedTemporaryFile("r") as output_file,
    ):
        input_file.write(INPUT_BATCH)
        input_file.flush()
        proc = subprocess.Popen(
            [
                "vllm",
                "run-batch",
                "-i",
                input_file.name,
                "-o",
                output_file.name,
                "--model",
                CHAT_MODEL_NAME,
            ],
        )
        proc.communicate()
        proc.wait()
        assert proc.returncode == 0, f"{proc=}"

        contents = output_file.read()
        for line in contents.strip().split("\n"):
            # Ensure that the output format conforms to the openai api.
            # Validation should throw if the schema is wrong.
            BatchRequestOutput.model_validate_json(line)


def test_completions_invalid_input():
    """
    Ensure that we fail when the input doesn't conform to the openai api.
    """
    with (
        tempfile.NamedTemporaryFile("w") as input_file,
        tempfile.NamedTemporaryFile("r") as output_file,
    ):
        input_file.write(INVALID_INPUT_BATCH)
        input_file.flush()
        proc = subprocess.Popen(
            [
                "vllm",
                "run-batch",
                "-i",
                input_file.name,
                "-o",
                output_file.name,
                "--model",
                CHAT_MODEL_NAME,
            ],
        )
        proc.communicate()
        proc.wait()
        assert proc.returncode != 0, f"{proc=}"


def test_embeddings():
    with (
        tempfile.NamedTemporaryFile("w") as input_file,
        tempfile.NamedTemporaryFile("r") as output_file,
    ):
        input_file.write(INPUT_EMBEDDING_BATCH)
        input_file.flush()
        proc = subprocess.Popen(
            [
                "vllm",
                "run-batch",
                "-i",
                input_file.name,
                "-o",
                output_file.name,
                "--model",
                EMBEDDING_MODEL_NAME,
            ],
        )
        proc.communicate()
        proc.wait()
        assert proc.returncode == 0, f"{proc=}"

        contents = output_file.read()
        for line in contents.strip().split("\n"):
            # Ensure that the output format conforms to the openai api.
            # Validation should throw if the schema is wrong.
            BatchRequestOutput.model_validate_json(line)


@pytest.mark.parametrize("input_batch", [INPUT_SCORE_BATCH, INPUT_RERANK_BATCH])
def test_score(input_batch):
    with (
        tempfile.NamedTemporaryFile("w") as input_file,
        tempfile.NamedTemporaryFile("r") as output_file,
    ):
        input_file.write(input_batch)
        input_file.flush()
        proc = subprocess.Popen(
            [
                "vllm",
                "run-batch",
                "-i",
                input_file.name,
                "-o",
                output_file.name,
                "--model",
                RERANKER_MODEL_NAME,
            ],
        )
        proc.communicate()
        proc.wait()
        assert proc.returncode == 0, f"{proc=}"

        contents = output_file.read()
        for line in contents.strip().split("\n"):
            # Ensure that the output format conforms to the openai api.
            # Validation should throw if the schema is wrong.
            BatchRequestOutput.model_validate_json(line)

            # Ensure that there is no error in the response.
            line_dict = json.loads(line)
            assert isinstance(line_dict, dict)
            assert line_dict["error"] is None


def test_reasoning_parser():
    """
    Test that reasoning_parser parameter works correctly in run_batch.
    """
    with (
        tempfile.NamedTemporaryFile("w") as input_file,
        tempfile.NamedTemporaryFile("r") as output_file,
    ):
        input_file.write(INPUT_REASONING_BATCH)
        input_file.flush()
        proc = subprocess.Popen(
            [
                "vllm",
                "run-batch",
                "-i",
                input_file.name,
                "-o",
                output_file.name,
                "--model",
                REASONING_MODEL_NAME,
                "--reasoning-parser",
                "qwen3",
            ],
        )
        proc.communicate()
        proc.wait()
        assert proc.returncode == 0, f"{proc=}"

        contents = output_file.read()
        for line in contents.strip().split("\n"):
            # Ensure that the output format conforms to the openai api.
            # Validation should throw if the schema is wrong.
            BatchRequestOutput.model_validate_json(line)

            # Ensure that there is no error in the response.
            line_dict = json.loads(line)
            assert isinstance(line_dict, dict)
            assert line_dict["error"] is None

            # Check that reasoning is present and not empty
            reasoning = line_dict["response"]["body"]["choices"][0]["message"][
                "reasoning"
            ]
            assert reasoning is not None
            assert len(reasoning) > 0


def test_transcription():
    with (
        tempfile.NamedTemporaryFile("w") as input_file,
        tempfile.NamedTemporaryFile("r") as output_file,
    ):
        input_file.write(INPUT_TRANSCRIPTION_BATCH)
        input_file.flush()
        proc = subprocess.Popen(
            [
                "vllm",
                "run-batch",
                "-i",
                input_file.name,
                "-o",
                output_file.name,
                "--model",
                SPEECH_LARGE_MODEL_NAME,
            ],
        )
        proc.communicate()
        proc.wait()
        assert proc.returncode == 0, f"{proc=}"

        contents = output_file.read()
        print(f"\n\ncontents: {contents}\n\n")
        for line in contents.strip().split("\n"):
            BatchRequestOutput.model_validate_json(line)

            line_dict = json.loads(line)
            assert isinstance(line_dict, dict)
            assert line_dict["error"] is None

            response_body = line_dict["response"]["body"]
            assert response_body is not None
            assert "text" in response_body
            assert "usage" in response_body


def test_transcription_http_url():
    with (
        tempfile.NamedTemporaryFile("w") as input_file,
        tempfile.NamedTemporaryFile("r") as output_file,
    ):
        input_file.write(INPUT_TRANSCRIPTION_HTTP_BATCH)
        input_file.flush()
        proc = subprocess.Popen(
            [
                "vllm",
                "run-batch",
                "-i",
                input_file.name,
                "-o",
                output_file.name,
                "--model",
                SPEECH_LARGE_MODEL_NAME,
            ],
        )
        proc.communicate()
        proc.wait()
        assert proc.returncode == 0, f"{proc=}"

        contents = output_file.read()
        for line in contents.strip().split("\n"):
            BatchRequestOutput.model_validate_json(line)

            line_dict = json.loads(line)
            assert isinstance(line_dict, dict)
            assert line_dict["error"] is None

            response_body = line_dict["response"]["body"]
            assert response_body is not None
            assert "text" in response_body
            assert "usage" in response_body

            transcription_text = response_body["text"]
            assert "Mary had a little lamb" in transcription_text


def test_translation():
    with (
        tempfile.NamedTemporaryFile("w") as input_file,
        tempfile.NamedTemporaryFile("r") as output_file,
    ):
        input_file.write(INPUT_TRANSLATION_BATCH)
        input_file.flush()
        proc = subprocess.Popen(
            [
                "vllm",
                "run-batch",
                "-i",
                input_file.name,
                "-o",
                output_file.name,
                "--model",
                SPEECH_SMALL_MODEL_NAME,
            ],
        )
        proc.communicate()
        proc.wait()
        assert proc.returncode == 0, f"{proc=}"

        contents = output_file.read()
        for line in contents.strip().split("\n"):
            BatchRequestOutput.model_validate_json(line)

            line_dict = json.loads(line)
            assert isinstance(line_dict, dict)
            assert line_dict["error"] is None

            response_body = line_dict["response"]["body"]
            assert response_body is not None
            assert "text" in response_body

            translation_text = response_body["text"]
            translation_text_lower = str(translation_text).strip().lower()
            assert "mary" in translation_text_lower or "lamb" in translation_text_lower


def test_tool_calling():
    """
    Test that tool calling works correctly in run_batch.
    Verifies that requests with tools return tool_calls in the response.
    """
    with (
        tempfile.NamedTemporaryFile("w") as input_file,
        tempfile.NamedTemporaryFile("r") as output_file,
    ):
        input_file.write(INPUT_TOOL_CALLING_BATCH)
        input_file.flush()
        proc = subprocess.Popen(
            [
                "vllm",
                "run-batch",
                "-i",
                input_file.name,
                "-o",
                output_file.name,
                "--model",
                REASONING_MODEL_NAME,
                "--enable-auto-tool-choice",
                "--tool-call-parser",
                "hermes",
            ],
        )
        proc.communicate()
        proc.wait()
        assert proc.returncode == 0, f"{proc=}"

        contents = output_file.read()
        for line in contents.strip().split("\n"):
            if not line.strip():  # Skip empty lines
                continue
            # Ensure that the output format conforms to the openai api.
            # Validation should throw if the schema is wrong.
            BatchRequestOutput.model_validate_json(line)

            # Ensure that there is no error in the response.
            line_dict = json.loads(line)
            assert isinstance(line_dict, dict)
            assert line_dict["error"] is None

            # Check that tool_calls are present in the response
            # With tool_choice="required", the model must call a tool
            response_body = line_dict["response"]["body"]
            assert response_body is not None
            message = response_body["choices"][0]["message"]
            assert "tool_calls" in message
            tool_calls = message.get("tool_calls")
            # With tool_choice="required", tool_calls must be present and non-empty
            assert tool_calls is not None
            assert isinstance(tool_calls, list)
            assert len(tool_calls) > 0
            # Verify tool_calls have the expected structure
            for tool_call in tool_calls:
                assert "id" in tool_call
                assert "type" in tool_call
                assert tool_call["type"] == "function"
                assert "function" in tool_call
                assert "name" in tool_call["function"]
                assert "arguments" in tool_call["function"]
                # Verify the tool name matches our tool definition
                assert tool_call["function"]["name"] == "get_current_weather"