vllm/tests/models/test_pixtral.py

"""Compare the outputs of HF and vLLM for Mistral models using greedy sampling.

Run `pytest tests/models/test_mistral.py`.
"""
import json
import uuid
from dataclasses import asdict
from typing import Any, Dict, List, Optional, Tuple

import pytest
from mistral_common.protocol.instruct.messages 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 vllm import EngineArgs, LLMEngine, SamplingParams, TokensPrompt
from vllm.multimodal import MultiModalDataBuiltins
from vllm.sequence import Logprob, SampleLogprobs

from .utils import check_logprobs_close

pytestmark = pytest.mark.vlm

MODELS = ["mistralai/Pixtral-12B-2409"]
IMG_URLS = [
    "https://picsum.photos/id/237/400/300",
    "https://picsum.photos/id/231/200/300",
    "https://picsum.photos/id/27/500/500",
    "https://picsum.photos/id/17/150/600",
]
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_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


MSGS = [
    _create_msg_format(IMG_URLS[:1]),
    _create_msg_format(IMG_URLS[:2]),
    _create_msg_format(IMG_URLS),
]
ENGINE_INPUTS = [
    _create_engine_inputs(IMG_URLS[:1]),
    _create_engine_inputs(IMG_URLS[:2]),
    _create_engine_inputs(IMG_URLS),
]

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

MAX_MODEL_LEN = [8192, 65536]
FIXTURE_LOGPROBS_CHAT = "tests/models/fixtures/pixtral_chat.json"
FIXTURE_LOGPROBS_ENGINE = "tests/models/fixtures/pixtral_chat_engine.json"

OutputsLogprobs = List[Tuple[List[int], str, Optional[SampleLogprobs]]]


# For the test author to store golden output in JSON
def _dump_outputs_w_logprobs(outputs: OutputsLogprobs, filename: str) -> 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: str) -> 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]


@pytest.mark.skip(
    reason=
    "Model is too big, test passed on A100 locally but will OOM on CI machine."
)
@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,
) -> None:
    EXPECTED_CHAT_LOGPROBS = load_outputs_w_logprobs(FIXTURE_LOGPROBS_CHAT)
    with vllm_runner(
            model,
            dtype=dtype,
            tokenizer_mode="mistral",
            enable_chunked_prefill=False,
            max_model_len=max_model_len,
            limit_mm_per_prompt=LIMIT_MM_PER_PROMPT,
    ) as vllm_model:
        outputs = []
        for msg in MSGS:
            output = vllm_model.model.chat(msg,
                                           sampling_params=SAMPLING_PARAMS)

            outputs.extend(output)

    logprobs = vllm_runner._final_steps_generate_w_logprobs(outputs)
    check_logprobs_close(outputs_0_lst=EXPECTED_CHAT_LOGPROBS,
                         outputs_1_lst=logprobs,
                         name_0="h100_ref",
                         name_1="output")


@pytest.mark.skip(
    reason=
    "Model is too big, test passed on A100 locally but will OOM on CI machine."
)
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["bfloat16"])
def test_model_engine(vllm_runner, model: str, dtype: str) -> None:
    EXPECTED_ENGINE_LOGPROBS = load_outputs_w_logprobs(FIXTURE_LOGPROBS_ENGINE)
    args = EngineArgs(
        model=model,
        tokenizer_mode="mistral",
        enable_chunked_prefill=False,
        limit_mm_per_prompt=LIMIT_MM_PER_PROMPT,
        dtype=dtype,
    )
    engine = LLMEngine.from_engine_args(args)

    engine.add_request(uuid.uuid4().hex, ENGINE_INPUTS[0], SAMPLING_PARAMS)
    engine.add_request(uuid.uuid4().hex, ENGINE_INPUTS[1], SAMPLING_PARAMS)

    outputs = []
    count = 0
    while True:
        out = engine.step()
        count += 1
        for request_output in out:
            if request_output.finished:
                outputs.append(request_output)

        if count == 2:
            engine.add_request(uuid.uuid4().hex, ENGINE_INPUTS[2],
                               SAMPLING_PARAMS)
        if not engine.has_unfinished_requests():
            break

    logprobs = vllm_runner._final_steps_generate_w_logprobs(outputs)
    check_logprobs_close(outputs_0_lst=EXPECTED_ENGINE_LOGPROBS,
                         outputs_1_lst=logprobs,
                         name_0="h100_ref",
                         name_1="output")