aphrodite-engine/tests/models/test_qwen.py

import pathlib
from typing import Dict, List, Optional, Tuple, Type, Union

import pytest
import torch
from PIL.Image import Image

from aphrodite.common.config import ModelConfig
from aphrodite.inputs import InputContext, LLMInputs
from aphrodite.multimodal.base import MultiModalInputs
from aphrodite.multimodal.utils import cached_get_tokenizer, rescale_image_size

from ..conftest import (IMAGE_ASSETS, AphroditeRunner, HfRunner, ImageAsset,
                        PromptImageInput, _ImageAssets)
from .utils import check_logprobs_close

pytestmark = pytest.mark.vlm

text_only_models = [
    "Qwen/Qwen-7B-Chat"  # Has no visual component
]

multimodal_models = ["Qwen/Qwen-VL"]

HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
    "stop_sign":
    "Picture 1: <img></img>\nWhat's the content of the image?: ",
    "cherry_blossom":
    "Picture 1: <img></img>\nWhat is the season?: ",
})

HF_MULTIIMAGE_IMAGE_PROMPT = "Picture 1: <img></img>\nPicture 2: <img></img>\nCan you compare these images?\n"  # noqa: E501
HF_MULTIIMAGE_IMAGE_PROMPT = "Picture 1: <img></img>\nPicture 2: <img></img>\nDescribe the two images in detail.\n"  # noqa: E501
### Multimodal preprocessing tests
SAMPLE_IMAGE = IMAGE_ASSETS[0].pil_image
# These values are specific to Qwen-VL/Chat; we can get these from the model
# config also, but they are hardcoded here to keep the parameterize/fixtures
# easy to read.
IMG_START_ID = 151857
IMG_END_ID = 151858
IMG_PAD_ID = 151859
TOKS_PER_IMG = 256
VIS_ENC_DIM = 4096
IMG_SIZE = 448
def build_model_context(model_name: str,
                        tokenizer_name: Optional[str] = None,
                        trust_remote_code: bool = False):
    """Creates an InputContext for a given model.
    
    Args:
        model_name: Name of the model being considered.
        tokenizer_name: Name of the tokenizer being considered.
        trust_remote_code: Whether or not to allow loading remote code.
    Returns:
        InputContext for the model being considered.
    """
    if tokenizer_name is None:
        tokenizer_name = model_name
    model_config = ModelConfig(
        model_name,
        tokenizer_name,
        tokenizer_mode="auto",
        trust_remote_code=trust_remote_code,
        dtype="float32",
        seed=0,
    )
    return InputContext(model_config)
@pytest.fixture()
def input_mapper_for_qwen():
    # Lazy import to avoid initializing CUDA during test collection
    from aphrodite.modeling.models.qwen import input_mapper_for_qwen
    return input_mapper_for_qwen
@pytest.fixture()
def input_processor_for_qwen():
    # Lazy import to avoid initializing CUDA during test collection
    from aphrodite.modeling.models.qwen import input_processor_for_qwen
    return input_processor_for_qwen
@pytest.fixture()
def qwen_vl_context() -> InputContext:
    """Get an InputContext for Qwen-VL."""
    return build_model_context(model_name="Qwen/Qwen-VL",
                               trust_remote_code=True)
# Happy path tests for single/multi-image scenarios for the multimodal
# input processor and mapper, respectively
@pytest.mark.parametrize("num_images", [1, 2])
def test_input_processor_valid_mm_data(input_processor_for_qwen,
                                       qwen_vl_context: InputContext,
                                       num_images: int):
    """Happy cases for image inputs to Qwen's multimodal input processor."""
    prompt = "".join(
        [f"Picture {num}: <img></img>\n" for num in range(1, num_images + 1)])
    inputs = LLMInputs(
        prompt=prompt,
        # When processing multimodal data for a multimodal model, the qwen
        # input processor will overwrite the provided prompt_token_ids with
        # the image prompts
        prompt_token_ids=None,
        multi_modal_data={"image": torch.rand(num_images, TOKS_PER_IMG, 4096)},
    )
    proc_inputs = input_processor_for_qwen(qwen_vl_context, inputs)
    assert isinstance(proc_inputs, dict)
    # Each image should have one start / stop and a fixed context of 256
    proc_tokens = proc_inputs["prompt_token_ids"]
    assert proc_tokens.count(IMG_START_ID) == num_images
    assert proc_tokens.count(IMG_END_ID) == num_images
    assert proc_tokens.count(IMG_PAD_ID) == num_images * TOKS_PER_IMG
@pytest.mark.parametrize(
    "img_data,expected_shape",
    [
        # single / multi-image
        (SAMPLE_IMAGE, (1, 3, IMG_SIZE, IMG_SIZE)),
        (2 * [SAMPLE_IMAGE], (2, 3, IMG_SIZE, IMG_SIZE)),
        # single / multi-image embeddings
        (torch.rand(
            (TOKS_PER_IMG, VIS_ENC_DIM)), (1, TOKS_PER_IMG, VIS_ENC_DIM)),
        (torch.rand(
            (1, TOKS_PER_IMG, VIS_ENC_DIM)), (1, TOKS_PER_IMG, VIS_ENC_DIM)),
        (torch.rand(
            (2, TOKS_PER_IMG, VIS_ENC_DIM)), (2, TOKS_PER_IMG, VIS_ENC_DIM)),
    ])
def test_input_mapper_valid_mm_data(input_mapper_for_qwen,
                                    qwen_vl_context: InputContext,
                                    img_data: Union[torch.Tensor, List[Image],
                                                    Image],
                                    expected_shape: List[int]):
    """Happy cases for image inputs to Qwen's multimodal input mapper."""
    mapped_img_data = input_mapper_for_qwen(qwen_vl_context, img_data)
    # Ensure that we get the appropriately shaped pixel_values
    # for images and image embeddings, respectively.
    assert isinstance(mapped_img_data, MultiModalInputs)
    assert "pixel_values" in mapped_img_data
    assert mapped_img_data["pixel_values"].shape == expected_shape
# Sad path tests for the multimodal input processor and mapper, respectively
@pytest.mark.parametrize("mm_data", [
    {
        "image": torch.rand((5))
    },
    {
        "image": torch.rand((5, 5, 5, 5, 5))
    },
])
def test_input_processor_invalid_mm_data(input_processor_for_qwen,
                                         qwen_vl_context: InputContext,
                                         mm_data: Dict[str, torch.Tensor]):
    """Test sad cases validated in Qwen's multimodal input processor."""
    tokenizer = cached_get_tokenizer(qwen_vl_context.model_config.tokenizer,
                                     trust_remote_code=True)
    prompt = "Picture 1: <img></img>\n"
    prompt_token_ids = tokenizer.encode(prompt)
    inputs = LLMInputs(prompt=prompt,
                       prompt_token_ids=prompt_token_ids,
                       multi_modal_data=mm_data)
    # Should fail since we have too many or too few dimensions for embeddings
    with pytest.raises(ValueError):
        input_processor_for_qwen(qwen_vl_context, inputs)
@pytest.mark.parametrize(
    "img_data",
    [
        # Wrong context length
        torch.rand((1, TOKS_PER_IMG + 10, VIS_ENC_DIM)),
        # Wrong visual encoder output size
        torch.rand((1, TOKS_PER_IMG, VIS_ENC_DIM + 10)),
    ])
def test_input_mapper_invalid_mm_data(
    input_mapper_for_qwen,
    qwen_vl_context: InputContext,
    img_data: Union[torch.Tensor, List[Image], Image],
):
    """Sad cases validated in Qwen VL's multimodal input mapper."""
    with pytest.raises(ValueError):
        input_mapper_for_qwen(qwen_vl_context, img_data)
### End-to-end generation tests
def get_prompt_with_path(tmp_path: pathlib.PosixPath, prompt: str,
                         assets: Union[_ImageAssets, List[ImageAsset]]) -> str:
    """Given a temporary dir path, export one or more image assets into the
    tempdir & replace its contents with the local path to the string so that
    the HF version of Qwen-VL can resolve the path and load the image ni its
    forward() call.
    Args:
        tmp_path: Tempdir for test under consideration.
        prompt: Prompt with image placeholders.
        assets: List of image assets whose len equals the num placeholders.
    """
    # Ensure that the number of placeholders matches the number of assets;
    # If this is not true, the test is probably written incorrectly.
    assert prompt.count("<img></img>") == len(assets)
    # Replace the placeholders with local paths to the exported assets
    for asset in assets:
        image_tmp_path = tmp_path / f"{asset.name}.jpg"
        asset.pil_image.save(image_tmp_path)
        prompt = prompt.replace(
            "<img></img>",
            f"<img>{image_tmp_path}</img>",
            1,
        )
    return prompt

def run_test(
    hf_runner: Type[HfRunner],
    aphrodite_runner: Type[AphroditeRunner],
    inputs: List[Tuple[List[str], PromptImageInput]],
    model: str,
    *,
    dtype: str,
    max_tokens: int,
    num_logprobs: int,
    mm_limit: int,
    tensor_parallel_size: int,
    distributed_executor_backend: Optional[str] = None,
):
    """Inference result should be the same between hf and aphrodite.
    All the image fixtures for the test is under tests/images.
    For huggingface runner, we provide the PIL images as input.
    For aphrodite runner, we provide MultiModalDataDict objects
    and corresponding MultiModalConfig as input.
    Note, the text input is also adjusted to abide by aphrodite contract.
    The text output is sanitized to be able to compare with hf.
    """

    # NOTE: take care of the order. run Aphrodite first, and then run HF.
    # Aphrodite needs a fresh new process without cuda initialization.
    # if we run HF first, the cuda initialization will be done and it
    # will hurt multiprocessing backend with fork method (the default method).

    # max_model_len should be greater than image_feature_size
    # Qwen encodes each image into a fixed content size of 256
    with aphrodite_runner(model,
                     max_model_len=1024,
                     max_num_seqs=1,
                     dtype=dtype,
                     limit_mm_per_prompt={"image": mm_limit},
                     tensor_parallel_size=tensor_parallel_size,
                     distributed_executor_backend=distributed_executor_backend,
                     enforce_eager=True) as aphrodite_model:
        aphrodite_outputs_per_image = [
            aphrodite_model.generate_greedy_logprobs(prompts,
                                                max_tokens,
                                                num_logprobs=num_logprobs,
                                                images=images)
            for prompts, images in inputs
        ]

    with hf_runner(model, dtype=dtype) as hf_model:
        hf_outputs_per_image = [
            hf_model.generate_greedy_logprobs_limit(prompts,
                                                    max_tokens,
                                                    num_logprobs=num_logprobs,
                                                    images=images)
            for prompts, images in inputs
        ]

    for hf_outputs, aphrodite_outputs in zip(hf_outputs_per_image,
                                        aphrodite_outputs_per_image):

        check_logprobs_close(
            outputs_0_lst=hf_outputs,
            outputs_1_lst=aphrodite_outputs,
            name_0="hf",
            name_1="aphrodite",
        )


@pytest.mark.parametrize("model", multimodal_models)
@pytest.mark.parametrize(
    "size_factors",
    [
        # No image
        [],
        # Single-scale
        [1.0],
        # Single-scale, batched
        [1.0, 1.0, 1.0],
        # Multi-scale
        [0.25, 0.5, 1.0],
    ],
)
@pytest.mark.parametrize("dtype", ["bfloat16"])
@pytest.mark.parametrize("max_tokens", [8])
@pytest.mark.parametrize("num_logprobs", [5])
def test_multimodal_models_single_image(tmp_path: pathlib.PosixPath,
                                        hf_runner: Type[HfRunner],
                                        aphrodite_runner: Type[AphroditeRunner],
                                        image_assets: _ImageAssets, model: str,
                                        size_factors: List[float], dtype: str,
                                        max_tokens: int,
                                        num_logprobs: int) -> None:
    """Tests multimodal models with single image prompts."""
    images = [asset.pil_image for asset in image_assets]
    prompts = [
        get_prompt_with_path(tmp_path, prompt, [asset])
        for prompt, asset in zip(HF_IMAGE_PROMPTS, image_assets)
    ]
    inputs = [(
        [prompt for _ in size_factors],
        [rescale_image_size(image, factor) for factor in size_factors],
    ) for image, prompt in zip(images, prompts)]
    run_test(
        hf_runner,
        aphrodite_runner,
        inputs,
        model,
        dtype=dtype,
        max_tokens=max_tokens,
        num_logprobs=num_logprobs,
        mm_limit=1,
        tensor_parallel_size=1,
    )
@pytest.mark.parametrize("model", multimodal_models)
@pytest.mark.parametrize(
    "size_factors",
    [
        # No image
        [],
        # Single-scale
        [1.0],
        # Single-scale, batched
        [1.0, 1.0, 1.0],
        # Multi-scale
        [0.25, 0.5, 1.0],
    ],
)
@pytest.mark.parametrize("dtype", ["bfloat16"])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [5])
def test_multimodal_models_multi_image(tmp_path: pathlib.PosixPath,
                                       hf_runner: Type[HfRunner],
                                       aphrodite_runner: Type[AphroditeRunner],
                                       image_assets: _ImageAssets, model: str,
                                       size_factors: List[float], dtype: str,
                                       max_tokens: int,
                                       num_logprobs: int) -> None:
    """Tests multimodal models with multi-image prompts."""
    images = [asset.pil_image for asset in image_assets]
    # Put all of the images into one prompt.
    prompt = get_prompt_with_path(tmp_path, HF_MULTIIMAGE_IMAGE_PROMPT,
                                  image_assets)
    inputs = [([prompt for _ in size_factors],
               [[rescale_image_size(image, factor) for image in images]
                for factor in size_factors])]
    run_test(
        hf_runner,
        aphrodite_runner,
        inputs,
        model,
        dtype=dtype,
        max_tokens=max_tokens,
        num_logprobs=num_logprobs,
        mm_limit=2,
        tensor_parallel_size=1,
    )


# Ensure that a text-only Qwen model can still be loaded and
# used for inference in Aphrodite without throwing.
@pytest.mark.parametrize("model", text_only_models)
@pytest.mark.parametrize("dtype", ["bfloat16"])
@pytest.mark.parametrize("max_tokens", [32])
@pytest.mark.parametrize("num_logprobs", [5])
def test_text_only_qwen_model_can_be_loaded_and_run(
    aphrodite_runner: Type[AphroditeRunner],
    example_prompts: List[str],
    model: str,
    *,
    dtype: str,
    max_tokens: int,
    num_logprobs: int,
):
    with aphrodite_runner(model, dtype=dtype) as aphrodite_model:
        aphrodite_model.generate_greedy_logprobs(
            example_prompts,
            max_tokens,
            num_logprobs=num_logprobs,
        )