test_mistral.py

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

import pytest
import os

from vllm.entrypoints.openai.tool_parsers.mistral_tool_parser import (
    MistralToolCall,
    MistralToolParser,
)
from vllm.sampling_params import SamplingParams
from vllm.tokenizers import MistralTokenizer

from ...utils import check_logprobs_close
from ....utils import models_path_prefix

MODELS = [
    os.path.join(models_path_prefix, "mistralai/Mistral-7B-Instruct-v0.3"),
]

MISTRAL_FORMAT_MODELS = [
    os.path.join(models_path_prefix, "mistralai/Mistral-7B-Instruct-v0.3"),
    # uses the v3-Tekken tokenizer
    os.path.join(models_path_prefix, "mistralai/Ministral-8B-Instruct-2410"),
    # Mistral-Nemo is too big for CI, but passes locally
    # "mistralai/Mistral-Nemo-Instruct-2407"
]

SAMPLING_PARAMS = SamplingParams(max_tokens=512, temperature=0.0, logprobs=5)
SYMBOLIC_LANG_PROMPTS = [
    "勇敢な船乗りについての詩を書く",  # japanese
    "寫一首關於勇敢的水手的詩",  # chinese
    "ပုံပြင်လေးပြောပြပါ်:\n",  # burmese
    "Repeat the phrase 'URGENCY🌶️':\nURGENCY🌶️\nURGENCY🌶️\n",  # see https://github.com/vllm-project/vllm/pull/9625
]

# for function calling
TOOLS = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {
                        "type": "string",
                        "description": "The city to find the weather for, e.g. "
                        "'San Francisco'",
                    },
                    "state": {
                        "type": "string",
                        "description": "the two-letter abbreviation for the state that "
                        "the city is in, e.g. 'CA' which would mean 'California'",
                    },
                    "unit": {
                        "type": "string",
                        "description": "The unit to fetch the temperature in",
                        "enum": ["celsius", "fahrenheit"],
                    },
                },
                "required": ["city", "state", "unit"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "rewrite",
            "description": "Rewrites text",
            "parameters": {
                "type": "object",
                "required": [],
                "properties": {
                    "text": {
                        "type": "string",
                        "description": "The input text to rewrite.",
                    }
                },
            },
        },
    },
]
MSGS = [
    {"role": "system", "content": "You are an assistant."},
    {
        "role": "user",
        "content": "Could you please rewrite the below article? \n\n My English needs "
        "improvving, maybe I make errors.",
    },
    {
        "role": "assistant",
        "content": "",
        "tool_calls": [
            {
                "id": "bbc5b7ede",
                "type": "function",
                "function": {
                    "name": "rewrite",
                    "arguments": '{"text":"My English needs improvving, maybe '
                    'I make errors."}',
                },
            }
        ],
    },
    {
        "role": "tool",
        "content": '{"action":"rewrite","outcome":"My English needs improving, maybe '
        'I make errors."}',
        "tool_call_id": "bbc5b7ede",
        "name": "rewrite",
    },
    {
        "role": "assistant",
        "content": "---\n\nMy English needs improving, maybe I make errors",
    },
    {
        "role": "user",
        "content": (
            "Can you tell me what the temperate will be in Dallas, in fahrenheit?"
        ),
    },
]

SAMPLE_JSON_SCHEMA = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age": {"type": "integer"},
        "skills": {
            "type": "array",
            "items": {"type": "string", "maxLength": 10},
            "minItems": 3,
        },
        "work_history": {
            "type": "array",
            "items": {
                "type": "object",
                "properties": {
                    "company": {"type": "string"},
                    "duration": {"type": "number"},
                    "position": {"type": "string"},
                },
                "required": ["company", "position"],
            },
        },
    },
    "required": ["name", "age", "skills", "work_history"],
}


@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["bfloat16"])
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models(
    hf_runner,
    vllm_runner,
    example_prompts,
    model: str,
    dtype: str,
    max_tokens: int,
    num_logprobs: int,
) -> None:
    # TODO(sang): Sliding window should be tested separately.
    with hf_runner(model, dtype=dtype) as hf_model:
        hf_outputs = hf_model.generate_greedy_logprobs_limit(
            example_prompts, max_tokens, num_logprobs
        )

    with vllm_runner(model, dtype=dtype, tokenizer_mode="mistral") as vllm_model:
        vllm_outputs = vllm_model.generate_greedy_logprobs(
            example_prompts, max_tokens, num_logprobs
        )

    check_logprobs_close(
        outputs_0_lst=hf_outputs,
        outputs_1_lst=vllm_outputs,
        name_0="hf",
        name_1="vllm",
    )


@pytest.mark.parametrize("model", MISTRAL_FORMAT_MODELS)
@pytest.mark.parametrize("dtype", ["bfloat16"])
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
def test_mistral_format(
    vllm_runner,
    example_prompts,
    model: str,
    dtype: str,
    max_tokens: int,
    num_logprobs: int,
) -> None:
    with vllm_runner(
        model,
        dtype=dtype,
        tokenizer_mode="mistral",
        load_format="mistral",
        config_format="mistral",
    ) as mistral_format_model:
        mistral_format_outputs = mistral_format_model.generate_greedy_logprobs(
            example_prompts, max_tokens, num_logprobs
        )

    with vllm_runner(
        model,
        dtype=dtype,
        tokenizer_mode="hf",
        load_format="safetensors",
        config_format="hf",
    ) as hf_format_model:
        hf_format_outputs = hf_format_model.generate_greedy_logprobs(
            example_prompts, max_tokens, num_logprobs
        )

    check_logprobs_close(
        outputs_0_lst=hf_format_outputs,
        outputs_1_lst=mistral_format_outputs,
        name_0="hf",
        name_1="mistral",
    )


@pytest.mark.parametrize("model", MISTRAL_FORMAT_MODELS)
@pytest.mark.parametrize("dtype", ["bfloat16"])
def test_mistral_symbolic_languages(vllm_runner, model: str, dtype: str) -> None:
    with vllm_runner(
        model,
        dtype=dtype,
        max_model_len=8192,
        tokenizer_mode="mistral",
        config_format="mistral",
        load_format="mistral",
    ) as vllm_model:
        for prompt in SYMBOLIC_LANG_PROMPTS:
            msg = {"role": "user", "content": prompt}
            outputs = vllm_model.llm.chat([msg], sampling_params=SAMPLING_PARAMS)
            assert "�" not in outputs[0].outputs[0].text.strip()


@pytest.mark.parametrize("model", MISTRAL_FORMAT_MODELS)
@pytest.mark.parametrize("dtype", ["bfloat16"])
def test_mistral_function_calling(vllm_runner, model: str, dtype: str) -> None:
    with vllm_runner(
        model,
        dtype=dtype,
        tokenizer_mode="mistral",
        config_format="mistral",
        load_format="mistral",
    ) as vllm_model:
        msgs = copy.deepcopy(MSGS)
        outputs = vllm_model.llm.chat(
            msgs, tools=TOOLS, sampling_params=SAMPLING_PARAMS
        )

        tokenizer = vllm_model.llm.get_tokenizer()
        tool_parser = MistralToolParser(tokenizer)

        model_output = outputs[0].outputs[0].text.strip()
        assert model_output.startswith(tool_parser.bot_token), model_output
        parsed_message = tool_parser.extract_tool_calls(model_output, None)

        assert parsed_message.tools_called

        assert MistralToolCall.is_valid_id(parsed_message.tool_calls[0].id)
        assert parsed_message.tool_calls[0].function.name == "get_current_weather"
        assert (
            parsed_message.tool_calls[0].function.arguments
            == '{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}'
        )  # noqa
        assert parsed_message.content is None


def test_mistral_function_call_nested_json():
    """Ensure that the function-name regex captures the entire outermost
    JSON block, including nested braces."""

    # Create a minimal stub tokenizer that provides the few attributes the
    # parser accesses (`version` and `get_vocab`).
    class _StubMistralTokenizer(MistralTokenizer):
        version = 11  # Satisfy the version check

        def __init__(self):
            pass

        @staticmethod
        def get_vocab():
            # Provide the special TOOL_CALLS token expected by the parser.
            return {"[TOOL_CALLS]": 0}

    tokenizer = _StubMistralTokenizer()
    parser = MistralToolParser(tokenizer)

    # Craft a model output featuring nested JSON inside the arguments.
    args_dict = {
        "city": "Dallas",
        "state": "TX",
        "unit": "fahrenheit",
        "sub_dict": {"foo": "bar", "inner": {"x": 1, "y": 2}},
    }

    model_output = f"{parser.bot_token}get_current_weather{json.dumps(args_dict)}"

    parsed = parser.extract_tool_calls(model_output, None)

    # Assertions: the tool call is detected and the full nested JSON is parsed
    # without truncation.
    assert parsed.tools_called

    assert MistralToolCall.is_valid_id(parsed.tool_calls[0].id)
    assert parsed.tool_calls[0].function.name == "get_current_weather"
    assert json.loads(parsed.tool_calls[0].function.arguments) == args_dict
    # No additional content outside the tool call should be returned.
    assert parsed.content is None

    # multiple calls
    multiple_args_dict = [
        {
            "city": "Dallas",
            "state": "TX",
            "unit": "fahrenheit",
            "sub_dict": {"foo": "bar", "inner": {"x": 1, "y": 2}},
        },
        {},
        {"a": 0},
        {"a": 1, "b": "c"},
    ]
    names = ["get_current_weather", "get_current_weather_2", "random", "random_2"]

    model_output = "".join(
        [
            f"{parser.bot_token}{name}{json.dumps(args)}"
            for name, args in zip(names, multiple_args_dict)
        ]
    )

    parsed = parser.extract_tool_calls(model_output, None)

    # Assertions: the tool call is detected and the full nested JSON is parsed
    # without truncation.
    assert parsed.tools_called
    assert len(parsed.tool_calls) == len(multiple_args_dict)

    for i, tool_call in enumerate(parsed.tool_calls):
        assert MistralToolCall.is_valid_id(tool_call.id)
        assert tool_call.function.name == names[i]
        assert json.loads(tool_call.function.arguments) == multiple_args_dict[i]
        # No additional content outside the tool call should be returned.
        assert parsed.content is None