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[Misc] Rename assets for testing (#17575)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung 2025-05-02 18:29:25 +08:00 committed by GitHub
parent c777df79f7
commit d7543862bd
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GPG Key ID: B5690EEEBB952194
28 changed files with 145 additions and 131 deletions
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4
examples/offline_inference/qwen2_5_omni/only_thinker.py
View File

@ -47,7 +47,7 @@ def get_mixed_modalities_query() -> QueryResult:
"image":
ImageAsset("cherry_blossom").pil_image.convert("RGB"),
"video":
VideoAsset(name="sample_demo_1", num_frames=16).np_ndarrays,
VideoAsset(name="baby_reading", num_frames=16).np_ndarrays,
},
},
limit_mm_per_prompt={
@ -65,7 +65,7 @@ def get_use_audio_in_video_query() -> QueryResult:
"<|im_start|>user\n<|vision_bos|><|VIDEO|><|vision_eos|>"
f"{question}<|im_end|>\n"
f"<|im_start|>assistant\n")
asset = VideoAsset(name="sample_demo_1", num_frames=16)
asset = VideoAsset(name="baby_reading", num_frames=16)
audio = asset.get_audio(sampling_rate=16000)
assert not envs.VLLM_USE_V1, ("V1 does not support use_audio_in_video. "
"Please launch this example with "

2
examples/offline_inference/vision_language.py
View File

@ -1109,7 +1109,7 @@ def get_multi_modal_input(args):
if args.modality == "video":
# Input video and question
video = VideoAsset(name="sample_demo_1",
video = VideoAsset(name="baby_reading",
num_frames=args.num_frames).np_ndarrays
vid_questions = ["Why is this video funny?"]

56
tests/conftest.py
View File

@ -1,9 +1,7 @@
# SPDX-License-Identifier: Apache-2.0
import json
import os
import tempfile
from collections import UserList
from enum import Enum
from typing import Any, Callable, Optional, TypedDict, TypeVar, Union
@ -58,16 +56,12 @@ def _read_prompts(filename: str) -> list[str]:
return prompts
class _ImageAssetPrompts(TypedDict):
class ImageAssetPrompts(TypedDict):
stop_sign: str
cherry_blossom: str
class _ImageAssetsBase(UserList[ImageAsset]):
pass
class _ImageAssets(_ImageAssetsBase):
class ImageTestAssets(list[ImageAsset]):
def __init__(self) -> None:
super().__init__([
@ -75,7 +69,7 @@ class _ImageAssets(_ImageAssetsBase):
ImageAsset("cherry_blossom"),
])
def prompts(self, prompts: _ImageAssetPrompts) -> list[str]:
def prompts(self, prompts: ImageAssetPrompts) -> list[str]:
"""
Convenience method to define the prompt for each test image.
@ -85,35 +79,27 @@ class _ImageAssets(_ImageAssetsBase):
return [prompts["stop_sign"], prompts["cherry_blossom"]]
class _VideoAssetPrompts(TypedDict):
sample_demo_1: str
class VideoAssetPrompts(TypedDict):
baby_reading: str
class _VideoAssetsBase(UserList[VideoAsset]):
pass
class _VideoAssets(_VideoAssetsBase):
class VideoTestAssets(list[VideoAsset]):
def __init__(self) -> None:
super().__init__([
VideoAsset("sample_demo_1"),
VideoAsset("baby_reading"),
])
def prompts(self, prompts: _VideoAssetPrompts) -> list[str]:
return [prompts["sample_demo_1"]]
def prompts(self, prompts: VideoAssetPrompts) -> list[str]:
return [prompts["baby_reading"]]
class _AudioAssetPrompts(TypedDict):
class AudioAssetPrompts(TypedDict):
mary_had_lamb: str
winning_call: str
class _AudioAssetsBase(UserList[AudioAsset]):
pass
class _AudioAssets(_AudioAssetsBase):
class AudioTestAssets(list[AudioAsset]):
def __init__(self) -> None:
super().__init__([
@ -121,16 +107,16 @@ class _AudioAssets(_AudioAssetsBase):
AudioAsset("winning_call"),
])
def prompts(self, prompts: _AudioAssetPrompts) -> list[str]:
def prompts(self, prompts: AudioAssetPrompts) -> list[str]:
return [prompts["mary_had_lamb"], prompts["winning_call"]]
IMAGE_ASSETS = _ImageAssets()
"""Singleton instance of :class:`_ImageAssets`."""
VIDEO_ASSETS = _VideoAssets()
"""Singleton instance of :class:`_VideoAssets`."""
AUDIO_ASSETS = _AudioAssets()
"""Singleton instance of :class:`_AudioAssets`."""
IMAGE_ASSETS = ImageTestAssets()
"""Singleton instance of :class:`ImageTestAssets`."""
VIDEO_ASSETS = VideoTestAssets()
"""Singleton instance of :class:`VideoTestAssets`."""
AUDIO_ASSETS = AudioTestAssets()
"""Singleton instance of :class:`AudioTestAssets`."""
@pytest.fixture(scope="function", autouse=True)
@ -278,17 +264,17 @@ def example_long_prompts() -> list[str]:
@pytest.fixture(scope="session")
def image_assets() -> _ImageAssets:
def image_assets() -> ImageTestAssets:
return IMAGE_ASSETS
@pytest.fixture(scope="session")
def video_assets() -> _VideoAssets:
def video_assets() -> VideoTestAssets:
return VIDEO_ASSETS
@pytest.fixture(scope="session")
def audio_assets() -> _AudioAssets:
def audio_assets() -> AudioTestAssets:
return AUDIO_ASSETS

21
tests/models/multimodal/generation/test_common.py
View File

@ -13,8 +13,8 @@ from transformers import AutoModelForImageTextToText, AutoModelForVision2Seq
from vllm.platforms import current_platform
from vllm.utils import identity
from ....conftest import (IMAGE_ASSETS, HfRunner, VllmRunner, _ImageAssets,
_VideoAssets)
from ....conftest import (IMAGE_ASSETS, HfRunner, ImageTestAssets,
VideoTestAssets, VllmRunner)
from ....utils import (create_new_process_for_each_test, large_gpu_mark,
multi_gpu_marks)
from ...utils import check_outputs_equal
@ -691,7 +691,7 @@ def test_single_image_models(tmp_path: PosixPath, model_type: str,
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
image_assets: _ImageAssets, monkeypatch):
image_assets: ImageTestAssets, monkeypatch):
if model_type in REQUIRES_V0_MODELS:
monkeypatch.setenv("VLLM_USE_V1", "0")
model_test_info = VLM_TEST_SETTINGS[model_type]
@ -716,7 +716,7 @@ def test_multi_image_models(tmp_path: PosixPath, model_type: str,
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
image_assets: _ImageAssets, monkeypatch):
image_assets: ImageTestAssets, monkeypatch):
if model_type in REQUIRES_V0_MODELS:
monkeypatch.setenv("VLLM_USE_V1", "0")
model_test_info = VLM_TEST_SETTINGS[model_type]
@ -741,7 +741,7 @@ def test_image_embedding_models(model_type: str,
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
image_assets: _ImageAssets, monkeypatch):
image_assets: ImageTestAssets, monkeypatch):
if model_type in REQUIRES_V0_MODELS:
monkeypatch.setenv("VLLM_USE_V1", "0")
model_test_info = VLM_TEST_SETTINGS[model_type]
@ -763,7 +763,7 @@ def test_image_embedding_models(model_type: str,
))
def test_video_models(model_type: str, test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner], vllm_runner: type[VllmRunner],
video_assets: _VideoAssets, monkeypatch):
video_assets: VideoTestAssets, monkeypatch):
if model_type in REQUIRES_V0_MODELS:
monkeypatch.setenv("VLLM_USE_V1", "0")
model_test_info = VLM_TEST_SETTINGS[model_type]
@ -814,7 +814,7 @@ def test_single_image_models_heavy(tmp_path: PosixPath, model_type: str,
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
image_assets: _ImageAssets, monkeypatch):
image_assets: ImageTestAssets, monkeypatch):
if model_type in REQUIRES_V0_MODELS:
monkeypatch.setenv("VLLM_USE_V1", "0")
model_test_info = VLM_TEST_SETTINGS[model_type]
@ -840,7 +840,7 @@ def test_multi_image_models_heavy(tmp_path: PosixPath, model_type: str,
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
image_assets: _ImageAssets, monkeypatch):
image_assets: ImageTestAssets, monkeypatch):
if model_type in REQUIRES_V0_MODELS:
monkeypatch.setenv("VLLM_USE_V1", "0")
model_test_info = VLM_TEST_SETTINGS[model_type]
@ -866,7 +866,8 @@ def test_image_embedding_models_heavy(model_type: str,
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
image_assets: _ImageAssets, monkeypatch):
image_assets: ImageTestAssets,
monkeypatch):
if model_type in REQUIRES_V0_MODELS:
monkeypatch.setenv("VLLM_USE_V1", "0")
model_test_info = VLM_TEST_SETTINGS[model_type]
@ -889,7 +890,7 @@ def test_image_embedding_models_heavy(model_type: str,
def test_video_models_heavy(model_type: str, test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
video_assets: _VideoAssets, monkeypatch):
video_assets: VideoTestAssets, monkeypatch):
if model_type in REQUIRES_V0_MODELS:
monkeypatch.setenv("VLLM_USE_V1", "0")
model_test_info = VLM_TEST_SETTINGS[model_type]

4
tests/models/multimodal/generation/test_florence2.py
View File

@ -9,7 +9,7 @@ from vllm.inputs.data import ExplicitEncoderDecoderPrompt, TextPrompt
from vllm.multimodal.image import rescale_image_size
from vllm.sequence import SampleLogprobs
from ....conftest import IMAGE_ASSETS, HfRunner, VllmRunner, _ImageAssets
from ....conftest import IMAGE_ASSETS, HfRunner, ImageTestAssets, VllmRunner
from ...utils import check_logprobs_close
MODELS = ["microsoft/Florence-2-base"]
@ -118,7 +118,7 @@ def run_test(
@pytest.mark.parametrize("max_tokens", [64])
@pytest.mark.parametrize("num_logprobs", [5])
def test_models(hf_runner: type[HfRunner], vllm_runner: type[VllmRunner],
image_assets: _ImageAssets, model: str,
image_assets: ImageTestAssets, model: str,
size_factors: list[int], dtype: str, max_tokens: int,
num_logprobs: int) -> None:
images = [asset.pil_image for asset in image_assets]

9
tests/models/multimodal/generation/test_granite_speech.py
View File

@ -9,7 +9,8 @@ from transformers import AutoModelForSpeechSeq2Seq
from vllm.lora.request import LoRARequest
from vllm.sequence import SampleLogprobs
from ....conftest import HfRunner, PromptAudioInput, VllmRunner, _AudioAssets
from ....conftest import (AudioTestAssets, HfRunner, PromptAudioInput,
VllmRunner)
from ...registry import HF_EXAMPLE_MODELS
from ...utils import check_logprobs_close
@ -116,9 +117,9 @@ def run_test(
@pytest.mark.parametrize("max_model_len", [2048])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [10])
def test_models(hf_runner, vllm_runner, model: str, audio_assets: _AudioAssets,
dtype: str, max_model_len: int, max_tokens: int,
num_logprobs: int) -> None:
def test_models(hf_runner, vllm_runner, model: str,
audio_assets: AudioTestAssets, dtype: str, max_model_len: int,
max_tokens: int, num_logprobs: int) -> None:
model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
model_info.check_available_online(on_fail="skip")
model_info.check_transformers_version(on_fail="skip")

2
tests/models/multimodal/generation/test_interleaved.py
View File

@ -29,7 +29,7 @@ def test_models(vllm_runner, model, dtype: str, max_tokens: int) -> None:
image_cherry = ImageAsset("cherry_blossom").pil_image.convert("RGB")
image_stop = ImageAsset("stop_sign").pil_image.convert("RGB")
images = [image_cherry, image_stop]
video = VideoAsset(name="sample_demo_1", num_frames=16).np_ndarrays
video = VideoAsset(name="baby_reading", num_frames=16).np_ndarrays
inputs = [
(

16
tests/models/multimodal/generation/test_mllama.py
View File

@ -14,8 +14,8 @@ from vllm.model_executor.models.mllama import MllamaForConditionalGeneration
from vllm.multimodal.image import rescale_image_size
from vllm.sequence import SampleLogprobs
from ....conftest import (IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner,
_ImageAssets)
from ....conftest import (IMAGE_ASSETS, HfRunner, ImageTestAssets,
PromptImageInput, VllmRunner)
from ....quantization.utils import is_quant_method_supported
from ....utils import (create_new_process_for_each_test, large_gpu_test,
multi_gpu_test)
@ -90,7 +90,7 @@ def vllm_to_hf_output(vllm_output: tuple[list[int], str,
def _get_inputs(
image_assets: _ImageAssets,
image_assets: ImageTestAssets,
*,
size_factors: Optional[list[float]] = None,
sizes: Optional[list[tuple[int, int]]] = None,
@ -126,7 +126,7 @@ def _get_inputs(
def run_test(
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
image_assets: _ImageAssets,
image_assets: ImageTestAssets,
model: str,
*,
size_factors: list[float],
@ -143,7 +143,7 @@ def run_test(
def run_test(
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
image_assets: _ImageAssets,
image_assets: ImageTestAssets,
model: str,
*,
sizes: list[tuple[int, int]],
@ -159,7 +159,7 @@ def run_test(
def run_test(
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
image_assets: _ImageAssets,
image_assets: ImageTestAssets,
model: str,
*,
size_factors: Optional[list[float]] = None,
@ -433,7 +433,7 @@ def test_models_distributed(
@pytest.mark.skipif(not is_quant_method_supported("bitsandbytes"),
reason='bitsandbytes is not supported on this GPU type.')
def test_bnb_regression(
image_assets: _ImageAssets,
image_assets: ImageTestAssets,
model: str,
dtype: str,
max_tokens: int,
@ -473,7 +473,7 @@ def test_bnb_regression(
@pytest.mark.parametrize("dtype", ["bfloat16"])
@pytest.mark.parametrize("max_tokens", [32])
def test_explicit_implicit_prompt(
image_assets: _ImageAssets,
image_assets: ImageTestAssets,
model: str,
dtype: str,
max_tokens: int,

2
tests/models/multimodal/generation/test_qwen2_vl.py
View File

@ -50,7 +50,7 @@ IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
})
VIDEO_PROMPTS = VIDEO_ASSETS.prompts({
"sample_demo_1":
"baby_reading":
qwen2_vl_chat_template(
VIDEO_PLACEHOLDER,
"Describe this video with a short sentence ",

44
tests/models/multimodal/generation/test_ultravox.py
View File

@ -11,13 +11,22 @@ from transformers import AutoModel, AutoTokenizer
from vllm.multimodal.audio import resample_audio_librosa
from vllm.sequence import SampleLogprobs
from ....conftest import HfRunner, VllmRunner, _AudioAssets
from ....conftest import AUDIO_ASSETS, AudioTestAssets, HfRunner, VllmRunner
from ....utils import RemoteOpenAIServer
from ...registry import HF_EXAMPLE_MODELS
from ...utils import check_logprobs_close
MODEL_NAME = "fixie-ai/ultravox-v0_5-llama-3_2-1b"
AUDIO_PROMPTS = AUDIO_ASSETS.prompts({
"mary_had_lamb":
"Transcribe this into English.",
"winning_call":
"What is happening in this audio clip?",
})
MULTI_AUDIO_PROMPT = "Describe each of the audios above."
AudioTuple = tuple[np.ndarray, int]
VLLM_PLACEHOLDER = "<|audio|>"
@ -31,12 +40,6 @@ CHUNKED_PREFILL_KWARGS = {
}
@pytest.fixture(scope="module", params=("mary_had_lamb", "winning_call"))
def audio(request):
from vllm.assets.audio import AudioAsset
return AudioAsset(request.param)
def params_kwargs_to_cli_args(params_kwargs: dict[str, Any]) -> list[str]:
"""Convert kwargs to CLI args."""
args = []
@ -53,7 +56,7 @@ def params_kwargs_to_cli_args(params_kwargs: dict[str, Any]) -> list[str]:
pytest.param({}, marks=pytest.mark.cpu_model),
pytest.param(CHUNKED_PREFILL_KWARGS),
])
def server(request, audio_assets: _AudioAssets):
def server(request, audio_assets: AudioTestAssets):
args = [
"--dtype", "bfloat16", "--max-model-len", "4096", "--enforce-eager",
"--limit-mm-per-prompt",
@ -199,15 +202,19 @@ def run_multi_audio_test(
pytest.param({}, marks=pytest.mark.cpu_model),
pytest.param(CHUNKED_PREFILL_KWARGS),
])
def test_models(hf_runner, vllm_runner, audio, dtype: str, max_tokens: int,
num_logprobs: int, vllm_kwargs: dict) -> None:
def test_models(hf_runner, vllm_runner, audio_assets: AudioTestAssets,
dtype: str, max_tokens: int, num_logprobs: int,
vllm_kwargs: dict) -> None:
audio_inputs = [(
_get_prompt(1, audio, VLLM_PLACEHOLDER),
_get_prompt(1, audio, HF_PLACEHOLDER),
audio.audio_and_sample_rate,
) for audio in audio_assets]
vllm_prompt = _get_prompt(1, "Describe the audio above.", VLLM_PLACEHOLDER)
hf_prompt = _get_prompt(1, "Describe the audio above.", HF_PLACEHOLDER)
run_test(
hf_runner,
vllm_runner,
[(vllm_prompt, hf_prompt, audio.audio_and_sample_rate)],
audio_inputs,
MODEL_NAME,
dtype=dtype,
max_tokens=max_tokens,
@ -224,13 +231,12 @@ def test_models(hf_runner, vllm_runner, audio, dtype: str, max_tokens: int,
pytest.param({}, marks=pytest.mark.cpu_model),
pytest.param(CHUNKED_PREFILL_KWARGS),
])
def test_models_with_multiple_audios(vllm_runner, audio_assets: _AudioAssets,
dtype: str, max_tokens: int,
num_logprobs: int,
def test_models_with_multiple_audios(vllm_runner,
audio_assets: AudioTestAssets, dtype: str,
max_tokens: int, num_logprobs: int,
vllm_kwargs: dict) -> None:
vllm_prompt = _get_prompt(len(audio_assets),
"Describe each of the audios above.",
vllm_prompt = _get_prompt(len(audio_assets), MULTI_AUDIO_PROMPT,
VLLM_PLACEHOLDER)
run_multi_audio_test(
vllm_runner,
@ -245,7 +251,7 @@ def test_models_with_multiple_audios(vllm_runner, audio_assets: _AudioAssets,
@pytest.mark.asyncio
async def test_online_serving(client, audio_assets: _AudioAssets):
async def test_online_serving(client, audio_assets: AudioTestAssets):
"""Exercises online serving with/without chunked prefill enabled."""
messages = [{

10
tests/models/multimodal/generation/vlm_utils/builders.py
View File

@ -11,7 +11,7 @@ from vllm.multimodal.image import rescale_image_size
from vllm.multimodal.video import (rescale_video_size, resize_video,
sample_frames_from_video)
from .....conftest import _ImageAssets, _VideoAssets
from .....conftest import ImageTestAssets, VideoTestAssets
from .types import (SINGLE_IMAGE_BASE_PROMPTS, TEST_IMG_PLACEHOLDER,
TEST_VIDEO_PLACEHOLDER, VIDEO_BASE_PROMPT,
ImageSizeWrapper, SizeType, VLMTestInfo)
@ -69,7 +69,7 @@ def get_model_prompts(base_prompts: Iterable[str],
def build_single_image_inputs_from_test_info(
test_info: VLMTestInfo,
image_assets: _ImageAssets,
image_assets: ImageTestAssets,
size_wrapper: ImageSizeWrapper,
tmp_path: Optional[PosixPath] = None):
if test_info.prompt_formatter is None:
@ -116,7 +116,7 @@ def build_single_image_inputs(images, model_prompts,
def build_multi_image_inputs_from_test_info(
test_info: VLMTestInfo,
image_assets: _ImageAssets,
image_assets: ImageTestAssets,
size_wrapper: ImageSizeWrapper,
tmp_path: Optional[PosixPath] = None):
if test_info.prompt_formatter is None:
@ -159,7 +159,7 @@ def build_multi_image_inputs(image_lists, model_prompts,
def build_embedding_inputs_from_test_info(
test_info: VLMTestInfo,
image_assets: _ImageAssets,
image_assets: ImageTestAssets,
size_wrapper: ImageSizeWrapper,
):
# These conditions will always be true if invoked through filtering,
@ -192,7 +192,7 @@ def build_embedding_inputs_from_test_info(
def build_video_inputs_from_test_info(
test_info: VLMTestInfo,
video_assets: _VideoAssets,
video_assets: VideoTestAssets,
size_wrapper: ImageSizeWrapper,
num_frames: int,
):

8
tests/models/multimodal/generation/vlm_utils/model_utils.py
View File

@ -16,7 +16,7 @@ from transformers import (AutoConfig, AutoTokenizer, BatchFeature,
from vllm.sequence import SampleLogprobs
from vllm.transformers_utils.tokenizer import patch_padding_side
from .....conftest import HfRunner, ImageAsset, _ImageAssets
from .....conftest import HfRunner, ImageAsset, ImageTestAssets
from .types import RunnerOutput
@ -238,14 +238,14 @@ def minimax_vl_01_hf_output(hf_output: RunnerOutput,
####### Functions for converting image assets to embeddings
def get_llava_embeddings(image_assets: _ImageAssets):
def get_llava_embeddings(image_assets: ImageTestAssets):
return [asset.image_embeds for asset in image_assets]
####### Prompt path encoders for models that need models on disk
def qwen_prompt_path_encoder(
tmp_path: PosixPath, prompt: str, assets: Union[list[ImageAsset],
_ImageAssets]) -> str:
tmp_path: PosixPath, prompt: str,
assets: Union[list[ImageAsset], ImageTestAssets]) -> 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 in its

11
tests/models/multimodal/generation/vlm_utils/runners.py
View File

@ -4,7 +4,8 @@ types / modalities.
"""
from pathlib import PosixPath
from .....conftest import HfRunner, VllmRunner, _ImageAssets, _VideoAssets
from .....conftest import (HfRunner, ImageTestAssets, VideoTestAssets,
VllmRunner)
from . import builders, core
from .types import ExpandableVLMTestArgs, VLMTestInfo
@ -14,7 +15,7 @@ def run_single_image_test(*, tmp_path: PosixPath, model_test_info: VLMTestInfo,
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
image_assets: _ImageAssets):
image_assets: ImageTestAssets):
assert test_case.size_wrapper is not None
inputs = builders.build_single_image_inputs_from_test_info(
model_test_info, image_assets, test_case.size_wrapper, tmp_path)
@ -37,7 +38,7 @@ def run_multi_image_test(*, tmp_path: PosixPath, model_test_info: VLMTestInfo,
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
image_assets: _ImageAssets):
image_assets: ImageTestAssets):
assert test_case.size_wrapper is not None
inputs = builders.build_multi_image_inputs_from_test_info(
model_test_info, image_assets, test_case.size_wrapper, tmp_path)
@ -60,7 +61,7 @@ def run_embedding_test(*, model_test_info: VLMTestInfo,
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
image_assets: _ImageAssets):
image_assets: ImageTestAssets):
assert test_case.size_wrapper is not None
inputs, vllm_embeddings = builders.build_embedding_inputs_from_test_info(
model_test_info, image_assets, test_case.size_wrapper)
@ -86,7 +87,7 @@ def run_video_test(
test_case: ExpandableVLMTestArgs,
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
video_assets: _VideoAssets,
video_assets: VideoTestAssets,
):
assert test_case.size_wrapper is not None
assert test_case.num_video_frames is not None

6
tests/models/multimodal/generation/vlm_utils/types.py
View File

@ -15,7 +15,7 @@ from vllm.config import TaskOption
from vllm.sequence import SampleLogprobs
from vllm.transformers_utils.tokenizer import AnyTokenizer
from .....conftest import IMAGE_ASSETS, HfRunner, ImageAsset, _ImageAssets
from .....conftest import IMAGE_ASSETS, HfRunner, ImageAsset, ImageTestAssets
from ....utils import check_logprobs_close
# meta image tag; will be replaced by the appropriate tag for the model
@ -85,7 +85,7 @@ class VLMTestInfo(NamedTuple):
# Function for converting ImageAssets to image embeddings;
# We need to define this explicitly for embedding tests
convert_assets_to_embeddings: Optional[Callable[[_ImageAssets],
convert_assets_to_embeddings: Optional[Callable[[ImageTestAssets],
torch.Tensor]] = None
# Exposed options for vLLM runner; we change these in a several tests,
@ -141,7 +141,7 @@ class VLMTestInfo(NamedTuple):
# for Qwen-VL, which requires encoding the image path / url into the prompt
# for HF runner
prompt_path_encoder: Optional[
Callable[[PosixPath, str, Union[list[ImageAsset], _ImageAssets]],
Callable[[PosixPath, str, Union[list[ImageAsset], ImageTestAssets]],
str]] = None # noqa: E501
# Allows configuring a test to run with custom inputs

4
tests/models/multimodal/pooling/test_intern_vit.py
View File

@ -7,7 +7,7 @@ from transformers import AutoConfig, AutoModel, CLIPImageProcessor
from vllm.distributed import cleanup_dist_env_and_memory
from ....conftest import _ImageAssets
from ....conftest import ImageTestAssets
# we use snapshot_download to prevent conflicts between
# dynamic_module and trust_remote_code for hf_runner
@ -15,7 +15,7 @@ DOWNLOAD_PATTERN = ["*.json", "*.py", "*.safetensors", "*.txt", "*.model"]
def run_intern_vit_test(
image_assets: _ImageAssets,
image_assets: ImageTestAssets,
model_id: str,
*,
dtype: str,

4
tests/models/multimodal/processing/test_h2ovl.py
View File

@ -11,7 +11,7 @@ from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.image import rescale_image_size
from vllm.multimodal.processing import BaseMultiModalProcessor
from ....conftest import _ImageAssets
from ....conftest import ImageTestAssets
from ...utils import build_model_context
@ -137,7 +137,7 @@ def _run_check(
@pytest.mark.parametrize("kwargs_on_init", [True, False])
def test_processor_override(
model_id: str,
image_assets: _ImageAssets,
image_assets: ImageTestAssets,
size_factors: list[int],
min_dynamic_patch: int,
max_dynamic_patch: int,

4
tests/models/multimodal/processing/test_idefics3.py
View File

@ -5,7 +5,7 @@ from transformers import Idefics3Config
from vllm.multimodal import MULTIMODAL_REGISTRY
from ....conftest import _ImageAssets
from ....conftest import ImageTestAssets
from ...utils import build_model_context
@ -21,7 +21,7 @@ from ...utils import build_model_context
@pytest.mark.parametrize("num_imgs", [1, 2])
@pytest.mark.parametrize("kwargs_on_init", [True, False])
def test_processor_override(
image_assets: _ImageAssets,
image_assets: ImageTestAssets,
model_id: str,
mm_processor_kwargs: dict[str, object],
expected_toks_per_img: int,

4
tests/models/multimodal/processing/test_internvl.py
View File

@ -11,7 +11,7 @@ from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.image import rescale_image_size
from vllm.multimodal.processing import BaseMultiModalProcessor
from ....conftest import _ImageAssets
from ....conftest import ImageTestAssets
from ...utils import build_model_context
@ -94,7 +94,7 @@ def _run_check(
@pytest.mark.parametrize("kwargs_on_init", [True, False])
def test_processor_override(
model_id: str,
image_assets: _ImageAssets,
image_assets: ImageTestAssets,
size_factors: list[int],
min_dynamic_patch: int,
max_dynamic_patch: int,

4
tests/models/multimodal/processing/test_llama4.py
View File

@ -6,7 +6,7 @@ import pytest
from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.transformers_utils.tokenizer import encode_tokens
from ....conftest import _ImageAssets
from ....conftest import ImageTestAssets
from ...utils import build_model_context
@ -17,7 +17,7 @@ from ...utils import build_model_context
@pytest.mark.parametrize("disable_mm_preprocessor_cache", [True, False])
@pytest.mark.parametrize("tokenized_prompt", [True, False])
def test_processor_override(
image_assets: _ImageAssets,
image_assets: ImageTestAssets,
model_id: str,
mm_processor_kwargs: dict,
num_imgs: int,

4
tests/models/multimodal/processing/test_minimax_vl_01.py
View File

@ -7,14 +7,14 @@ from vllm.multimodal import MULTIMODAL_REGISTRY
from vllm.multimodal.parse import ImageSize
from vllm.multimodal.processing import BaseMultiModalProcessor
from ....conftest import _ImageAssets
from ....conftest import ImageTestAssets
from ...utils import build_model_context
@pytest.mark.parametrize("model_id", ["MiniMaxAI/MiniMax-VL-01"])
@pytest.mark.parametrize("num_imgs", [1, 2])
def test_processor_override(
image_assets: _ImageAssets,
image_assets: ImageTestAssets,
model_id: str,
num_imgs: int,
):

4
tests/models/multimodal/processing/test_phi3v.py
View File

@ -4,7 +4,7 @@ import pytest
from vllm.multimodal import MULTIMODAL_REGISTRY
from ....conftest import _ImageAssets
from ....conftest import ImageTestAssets
from ...utils import build_model_context
@ -22,7 +22,7 @@ from ...utils import build_model_context
@pytest.mark.parametrize("num_imgs", [1, 2])
@pytest.mark.parametrize("kwargs_on_init", [True, False])
def test_processor_override(
image_assets: _ImageAssets,
image_assets: ImageTestAssets,
model_id: str,
mm_processor_kwargs: dict[str, int],
expected_toks_per_img: int,

4
tests/models/multimodal/processing/test_phi4mm.py
View File

@ -4,7 +4,7 @@ import pytest
from vllm.multimodal import MULTIMODAL_REGISTRY
from ....conftest import _ImageAssets
from ....conftest import ImageTestAssets
from ...utils import build_model_context
@ -22,7 +22,7 @@ from ...utils import build_model_context
@pytest.mark.parametrize("num_imgs", [1, 2])
@pytest.mark.parametrize("kwargs_on_init", [True, False])
def test_processor_override(
image_assets: _ImageAssets,
image_assets: ImageTestAssets,
model_id: str,
mm_processor_kwargs: dict[str, int],
expected_toks_per_img: int,

4
tests/models/multimodal/processing/test_qwen2_vl.py
View File

@ -4,7 +4,7 @@ import pytest
from vllm.multimodal import MULTIMODAL_REGISTRY
from ....conftest import _ImageAssets
from ....conftest import ImageTestAssets
from ...utils import build_model_context
@ -19,7 +19,7 @@ from ...utils import build_model_context
@pytest.mark.parametrize("num_imgs", [1, 2])
@pytest.mark.parametrize("kwargs_on_init", [True, False])
def test_processor_override(
image_assets: _ImageAssets,
image_assets: ImageTestAssets,
model_id: str,
mm_processor_kwargs: dict[str, object],
expected_toks_per_img: int,

4
tests/models/multimodal/processing/test_smolvlm.py
View File

@ -5,7 +5,7 @@ from transformers import SmolVLMConfig
from vllm.multimodal import MULTIMODAL_REGISTRY
from ....conftest import _ImageAssets
from ....conftest import ImageTestAssets
from ...utils import build_model_context
@ -21,7 +21,7 @@ from ...utils import build_model_context
@pytest.mark.parametrize("num_imgs", [1, 2])
@pytest.mark.parametrize("kwargs_on_init", [True, False])
def test_processor_override(
image_assets: _ImageAssets,
image_assets: ImageTestAssets,
model_id: str,
mm_processor_kwargs: dict[str, object],
expected_toks_per_img: int,

4
tests/models/quantization/test_awq.py
View File

@ -7,7 +7,7 @@ import torch
from vllm.multimodal.image import rescale_image_size
from ...conftest import IMAGE_ASSETS, VllmRunner, _ImageAssets
from ...conftest import IMAGE_ASSETS, ImageTestAssets, VllmRunner
from ..utils import check_logprobs_close
HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
@ -20,7 +20,7 @@ HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
def run_awq_test(
vllm_runner: type[VllmRunner],
image_assets: _ImageAssets,
image_assets: ImageTestAssets,
source_model: str,
quant_model: str,
*,

12
vllm/assets/audio.py
View File

@ -18,19 +18,25 @@ except ImportError:
ASSET_DIR = "multimodal_asset"
AudioAssetName = Literal["winning_call", "mary_had_lamb"]
@dataclass(frozen=True)
class AudioAsset:
name: Literal["winning_call", "mary_had_lamb"]
name: AudioAssetName
@property
def filename(self) -> str:
return f"{self.name}.ogg"
@property
def audio_and_sample_rate(self) -> tuple[npt.NDArray, float]:
audio_path = get_vllm_public_assets(filename=f"{self.name}.ogg",
audio_path = get_vllm_public_assets(filename=self.filename,
s3_prefix=ASSET_DIR)
return librosa.load(audio_path, sr=None)
def get_local_path(self) -> Path:
return get_vllm_public_assets(filename=f"{self.name}.ogg",
return get_vllm_public_assets(filename=self.filename,
s3_prefix=ASSET_DIR)
@property

4
vllm/assets/image.py
View File

@ -10,10 +10,12 @@ from .base import get_vllm_public_assets
VLM_IMAGES_DIR = "vision_model_images"
ImageAssetName = Literal["stop_sign", "cherry_blossom"]
@dataclass(frozen=True)
class ImageAsset:
name: Literal["stop_sign", "cherry_blossom"]
name: ImageAssetName
@property
def pil_image(self) -> Image.Image:

21
vllm/assets/video.py
View File

@ -2,7 +2,7 @@
from dataclasses import dataclass
from functools import lru_cache
from typing import Literal, Optional
from typing import ClassVar, Literal, Optional
import cv2
import numpy as np
@ -76,20 +76,31 @@ def video_to_pil_images_list(path: str,
]
VideoAssetName = Literal["baby_reading"]
@dataclass(frozen=True)
class VideoAsset:
name: Literal["sample_demo_1"]
name: VideoAssetName
num_frames: int = -1
_NAME_TO_FILE: ClassVar[dict[VideoAssetName, str]] = {
"baby_reading": "sample_demo_1.mp4",
}
@property
def filename(self) -> str:
return self._NAME_TO_FILE[self.name]
@property
def pil_images(self) -> list[Image.Image]:
video_path = download_video_asset(self.name + ".mp4")
video_path = download_video_asset(self.filename)
ret = video_to_pil_images_list(video_path, self.num_frames)
return ret
@property
def np_ndarrays(self) -> npt.NDArray:
video_path = download_video_asset(self.name + ".mp4")
video_path = download_video_asset(self.filename)
ret = video_to_ndarrays(video_path, self.num_frames)
return ret
@ -99,5 +110,5 @@ class VideoAsset:
See also: examples/offline_inference/qwen2_5_omni/only_thinker.py
"""
video_path = download_video_asset(self.name + ".mp4")
video_path = download_video_asset(self.filename)
return librosa.load(video_path, sr=sampling_rate)[0]