[Misc] Rename assets for testing (#17575)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

examples/offline_inference/qwen2_5_omni/only_thinker.py

@@ -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 "

examples/offline_inference/vision_language.py

@@ -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?"]
 

tests/conftest.py

@@ -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]):
+class ImageTestAssets(list[ImageAsset]):
-    pass
-
-
-class _ImageAssets(_ImageAssetsBase):
 
     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):
+class VideoAssetPrompts(TypedDict):
-    sample_demo_1: str
+    baby_reading: str
 
 
-class _VideoAssetsBase(UserList[VideoAsset]):
+class VideoTestAssets(list[VideoAsset]):
-    pass
-
-
-class _VideoAssets(_VideoAssetsBase):
 
     def __init__(self) -> None:
         super().__init__([
-            VideoAsset("sample_demo_1"),
+            VideoAsset("baby_reading"),
         ])
 
-    def prompts(self, prompts: _VideoAssetPrompts) -> list[str]:
+    def prompts(self, prompts: VideoAssetPrompts) -> list[str]:
-        return [prompts["sample_demo_1"]]
+        return [prompts["baby_reading"]]
 
 
-class _AudioAssetPrompts(TypedDict):
+class AudioAssetPrompts(TypedDict):
     mary_had_lamb: str
     winning_call: str
 
 
-class _AudioAssetsBase(UserList[AudioAsset]):
+class AudioTestAssets(list[AudioAsset]):
-    pass
-
-
-class _AudioAssets(_AudioAssetsBase):
 
     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()
+IMAGE_ASSETS = ImageTestAssets()
-"""Singleton instance of :class:`_ImageAssets`."""
+"""Singleton instance of :class:`ImageTestAssets`."""
-VIDEO_ASSETS = _VideoAssets()
+VIDEO_ASSETS = VideoTestAssets()
-"""Singleton instance of :class:`_VideoAssets`."""
+"""Singleton instance of :class:`VideoTestAssets`."""
-AUDIO_ASSETS = _AudioAssets()
+AUDIO_ASSETS = AudioTestAssets()
-"""Singleton instance of :class:`_AudioAssets`."""
+"""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
 
 

tests/models/multimodal/generation/test_common.py

@@ -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,
+from ....conftest import (IMAGE_ASSETS, HfRunner, ImageTestAssets,
-                          _VideoAssets)
+                          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]

tests/models/multimodal/generation/test_florence2.py

@@ -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]

tests/models/multimodal/generation/test_granite_speech.py

@@ -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,
+def test_models(hf_runner, vllm_runner, model: str,
-                dtype: str, max_model_len: int, max_tokens: int,
+                audio_assets: AudioTestAssets, dtype: str, max_model_len: int,
-                num_logprobs: int) -> None:
+                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")

tests/models/multimodal/generation/test_interleaved.py

@@ -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 = [
         (

tests/models/multimodal/generation/test_mllama.py

@@ -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,
+from ....conftest import (IMAGE_ASSETS, HfRunner, ImageTestAssets,
-                          _ImageAssets)
+                          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,

tests/models/multimodal/generation/test_qwen2_vl.py

@@ -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 ",

tests/models/multimodal/generation/test_ultravox.py

@@ -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,
+def test_models(hf_runner, vllm_runner, audio_assets: AudioTestAssets,
-                num_logprobs: int, vllm_kwargs: dict) -> None:
+                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,
+def test_models_with_multiple_audios(vllm_runner,
-                                     dtype: str, max_tokens: int,
+                                     audio_assets: AudioTestAssets, dtype: str,
-                                     num_logprobs: int,
+                                     max_tokens: int, num_logprobs: int,
                                      vllm_kwargs: dict) -> None:
 
-    vllm_prompt = _get_prompt(len(audio_assets),
+    vllm_prompt = _get_prompt(len(audio_assets), MULTI_AUDIO_PROMPT,
-                              "Describe each of the audios above.",
                               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 = [{

tests/models/multimodal/generation/vlm_utils/builders.py

@@ -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,
 ):

tests/models/multimodal/generation/vlm_utils/model_utils.py

@@ -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],
+        tmp_path: PosixPath, prompt: str,
-                                                        _ImageAssets]) -> 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

tests/models/multimodal/generation/vlm_utils/runners.py

@@ -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

tests/models/multimodal/generation/vlm_utils/types.py

@@ -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

tests/models/multimodal/pooling/test_intern_vit.py

@@ -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,

tests/models/multimodal/processing/test_h2ovl.py

@@ -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,

tests/models/multimodal/processing/test_idefics3.py

@@ -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,

tests/models/multimodal/processing/test_internvl.py

@@ -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,

tests/models/multimodal/processing/test_llama4.py

@@ -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,

tests/models/multimodal/processing/test_minimax_vl_01.py

@@ -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,
 ):

tests/models/multimodal/processing/test_phi3v.py

@@ -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,

tests/models/multimodal/processing/test_phi4mm.py

@@ -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,

tests/models/multimodal/processing/test_qwen2_vl.py

@@ -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,

tests/models/multimodal/processing/test_smolvlm.py

@@ -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,

tests/models/quantization/test_awq.py

@@ -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,
     *,

vllm/assets/audio.py

@@ -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

vllm/assets/image.py

@@ -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:

vllm/assets/video.py

@@ -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]