test_pixtral.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import json
from dataclasses import asdict
from typing import TYPE_CHECKING, Any

import pytest
from mistral_common.multimodal import download_image
from mistral_common.protocol.instruct.chunk import ImageURLChunk
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.tokens.tokenizers.multimodal import image_from_chunk
from transformers import AutoProcessor

from vllm import SamplingParams, TextPrompt, TokensPrompt
from vllm.inputs import MultiModalDataBuiltins
from vllm.logprobs import Logprob, SampleLogprobs
from vllm.platforms import current_platform

from ....utils import VLLM_PATH, large_gpu_test
from ...utils import check_logprobs_close

if TYPE_CHECKING:
    from _typeshed import StrPath

PIXTRAL_ID = "mistralai/Pixtral-12B-2409"
MISTRAL_SMALL_3_1_ID = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
MINISTRAL_3B_ID = "mistralai/Ministral-3-3B-Instruct-2512"

MODELS = [PIXTRAL_ID, MISTRAL_SMALL_3_1_ID]

IMG_URLS = [
    "237-400x300.jpg",  # "https://huggingface.co/datasets/Isotr0py/mistral-test-images/resolve/main/237-400x300.jpg",
    "231-200x300.jpg",  # "https://huggingface.co/datasets/Isotr0py/mistral-test-images/resolve/main/237-400x300.jpg",
    "27-500x500.jpg",  # "https://huggingface.co/datasets/Isotr0py/mistral-test-images/resolve/main/237-400x300.jpg",
    "17-150x600.jpg",  # "https://huggingface.co/datasets/Isotr0py/mistral-test-images/resolve/main/237-400x300.jpg",
]
PROMPT = "Describe each image in one short sentence."


def _create_msg_format(urls: list[str]) -> list[dict[str, Any]]:
    return [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": PROMPT,
                }
            ]
            + [{"type": "image_url", "image_url": {"url": url}} for url in urls],
        }
    ]


def _create_msg_format_hf(urls: list[str]) -> list[dict[str, Any]]:
    return [
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "content": PROMPT,
                },
                *({"type": "image", "image": download_image(url)} for url in urls),
            ],
        }
    ]


def _create_engine_inputs(urls: list[str]) -> TokensPrompt:
    msg = _create_msg_format(urls)

    tokenizer = MistralTokenizer.from_model("pixtral")

    request = ChatCompletionRequest(messages=msg)  # type: ignore[type-var]
    tokenized = tokenizer.encode_chat_completion(request)

    engine_inputs = TokensPrompt(prompt_token_ids=tokenized.tokens)

    images = []
    for chunk in request.messages[0].content:
        if isinstance(chunk, ImageURLChunk):
            images.append(image_from_chunk(chunk))

    mm_data = MultiModalDataBuiltins(image=images)
    engine_inputs["multi_modal_data"] = mm_data

    return engine_inputs


def _create_engine_inputs_hf(urls: list[str]) -> TextPrompt:
    msg = _create_msg_format_hf(urls)

    tokenizer = AutoProcessor.from_pretrained("mistral-community/pixtral-12b")
    prompt = tokenizer.apply_chat_template(msg)

    images = []
    for chunk in msg[0]["content"]:
        if chunk["type"] == "image":
            images.append(chunk["image"])

    mm_data = MultiModalDataBuiltins(image=images)
    engine_inputs = TextPrompt(prompt=prompt, multi_modal_data=mm_data)

    return engine_inputs


SAMPLING_PARAMS = SamplingParams(max_tokens=512, temperature=0.0, logprobs=5)
LIMIT_MM_PER_PROMPT = dict(image=4)

MAX_MODEL_LEN = [8192, 65536]

FIXTURES_PATH = VLLM_PATH / "tests/models/fixtures"
assert FIXTURES_PATH.exists()

FIXTURE_LOGPROBS_CHAT = {
    PIXTRAL_ID: FIXTURES_PATH / "pixtral_chat.json",
    MISTRAL_SMALL_3_1_ID: FIXTURES_PATH / "mistral_small_3_chat.json",
    MINISTRAL_3B_ID: FIXTURES_PATH / "ministral_3b_chat.json",
}

OutputsLogprobs = list[tuple[list[int], str, SampleLogprobs | None]]


# For the test author to store golden output in JSON
def _dump_outputs_w_logprobs(
    outputs: OutputsLogprobs,
    filename: "StrPath",
) -> None:
    json_data = [
        (
            tokens,
            text,
            [
                {k: asdict(v) for k, v in token_logprobs.items()}
                for token_logprobs in (logprobs or [])
            ],
        )
        for tokens, text, logprobs in outputs
    ]

    with open(filename, "w") as f:
        json.dump(json_data, f)


def load_outputs_w_logprobs(filename: "StrPath") -> OutputsLogprobs:
    with open(filename, "rb") as f:
        json_data = json.load(f)

    return [
        (
            tokens,
            text,
            [
                {int(k): Logprob(**v) for k, v in token_logprobs.items()}
                for token_logprobs in logprobs
            ],
        )
        for tokens, text, logprobs in json_data
    ]


@large_gpu_test(min_gb=80)
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("max_model_len", MAX_MODEL_LEN)
@pytest.mark.parametrize("dtype", ["bfloat16"])
def test_chat(
    vllm_runner, max_model_len: int, model: str, dtype: str, local_asset_server
) -> None:
    if (
        model == MISTRAL_SMALL_3_1_ID
        and max_model_len == 65536
        and current_platform.is_rocm()
    ):
        pytest.skip(
            "OOM on ROCm: 24B model with 65536 context length exceeds GPU memory"
        )

    EXPECTED_CHAT_LOGPROBS = load_outputs_w_logprobs(FIXTURE_LOGPROBS_CHAT[model])
    with vllm_runner(
        model,
        dtype=dtype,
        tokenizer_mode="mistral",
        load_format="mistral",
        config_format="mistral",
        max_model_len=max_model_len,
        limit_mm_per_prompt=LIMIT_MM_PER_PROMPT,
    ) as vllm_model:
        outputs = []

        urls_all = [local_asset_server.url_for(u) for u in IMG_URLS]
        msgs = [
            _create_msg_format(urls_all[:1]),
            _create_msg_format(urls_all[:2]),
            _create_msg_format(urls_all),
        ]
        for msg in msgs:
            output = vllm_model.llm.chat(msg, sampling_params=SAMPLING_PARAMS)

            outputs.extend(output)

    logprobs = vllm_runner._final_steps_generate_w_logprobs(outputs)
    # Remove last `None` prompt_logprobs to compare with fixture
    for i in range(len(logprobs)):
        assert logprobs[i][-1] is None
        logprobs[i] = logprobs[i][:-1]
    check_logprobs_close(
        outputs_0_lst=EXPECTED_CHAT_LOGPROBS,
        outputs_1_lst=logprobs,
        name_0="h100_ref",
        name_1="output",
    )


@large_gpu_test(min_gb=16)
@pytest.mark.parametrize("dtype", ["bfloat16"])
def test_chat_consolidated(vllm_runner, dtype: str, local_asset_server) -> None:
    EXPECTED_CHAT_LOGPROBS = load_outputs_w_logprobs(
        FIXTURE_LOGPROBS_CHAT[MINISTRAL_3B_ID]
    )
    with vllm_runner(
        MINISTRAL_3B_ID,
        dtype=dtype,
        tokenizer_mode="mistral",
        load_format="mistral",
        config_format="mistral",
        max_model_len=8192,
        limit_mm_per_prompt=LIMIT_MM_PER_PROMPT,
    ) as vllm_model:
        outputs = []
        urls_all = [local_asset_server.url_for(u) for u in IMG_URLS]
        msgs = [
            _create_msg_format(urls_all[:1]),
            _create_msg_format(urls_all[:2]),
            _create_msg_format(urls_all),
        ]
        for msg in msgs:
            output = vllm_model.llm.chat(msg, sampling_params=SAMPLING_PARAMS)
            outputs.extend(output)

    logprobs = vllm_runner._final_steps_generate_w_logprobs(outputs)
    for i in range(len(logprobs)):
        assert logprobs[i][-1] is None
        logprobs[i] = logprobs[i][:-1]
    check_logprobs_close(
        outputs_0_lst=EXPECTED_CHAT_LOGPROBS,
        outputs_1_lst=logprobs,
        name_0="h100_ref",
        name_1="output",
    )