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[CI/Build] Refactor processing tests (#27470)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>
Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>
This commit is contained in:
Cyrus Leung 2025-10-26 00:14:30 +08:00 committed by GitHub
parent a99564ac5b
commit 66a168a197
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GPG Key ID: B5690EEEBB952194
4 changed files with 174 additions and 230 deletions
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277
tests/models/multimodal/processing/test_common.py
View File

@ -1,6 +1,7 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Set as AbstractSet
from functools import partial
import numpy as np
@ -22,14 +23,17 @@ from vllm.multimodal.cache import MultiModalProcessorOnlyCache
from vllm.multimodal.inputs import MultiModalInputs
from vllm.multimodal.processing import BaseMultiModalProcessor, InputProcessingContext
from vllm.transformers_utils.tokenizer import (
AnyTokenizer,
MistralTokenizer,
cached_tokenizer_from_config,
encode_tokens,
)
from ....multimodal.utils import random_audio, random_image, random_video
from ...registry import HF_EXAMPLE_MODELS
from ...registry import (
_MULTIMODAL_EXAMPLE_MODELS,
_TRANSFORMERS_BACKEND_MODELS,
HF_EXAMPLE_MODELS,
)
def glm4_1v_patch_mm_data(mm_data: MultiModalDataDict) -> MultiModalDataDict:
@ -83,6 +87,119 @@ def qwen3_vl_patch_mm_data(mm_data: MultiModalDataDict) -> MultiModalDataDict:
return mm_data
# For some multimodal models, tokenizer will always add bos_token
# at the beginning of prompt by default, causing hf_processor outputs
# incorrect token ids. So we need use `add_special_tokens=False` here
# to leave bos_token to be added by the processor.
_ADD_SPECIAL_TOKENS_OVERRIDES = {
"ovis": False,
"ovis2_5": False,
"paligemma": False,
"ultravox": False,
"whisper": False,
}
_IGNORE_MM_KEYS = {
# In Ultravox, the audio_features can be different depending on padding
# The slight difference should not be a problem though, since
# attention_mask lets us ignore the difference.
"ultravox": {"audio_features"},
}
MM_DATA_PATCHES = {
# GLM4.1V and Qwen3-VL requires video metadata to be included in the input
"glm4v": glm4_1v_patch_mm_data,
"glm4v_moe": glm4_1v_patch_mm_data,
"qwen3_vl": qwen3_vl_patch_mm_data,
"qwen3_vl_moe": qwen3_vl_patch_mm_data,
}
def _iter_model_ids_to_test(model_arch_list: AbstractSet[str]):
for model_arch in model_arch_list:
model_info = HF_EXAMPLE_MODELS.get_hf_info(model_arch)
yield model_info.default
for extra_type, extra_model_id in model_info.extras.items():
if "fp" in extra_type:
continue # Redundant to test quantized models
yield extra_model_id
def _get_model_ids_to_test(model_arch_list: AbstractSet[str]):
return list(_iter_model_ids_to_test(model_arch_list))
def get_model_ids_to_test():
transformers_arch_ids = {
model_id
for info in _TRANSFORMERS_BACKEND_MODELS.values()
for model_id in (info.default, *info.extras.values())
}
vllm_only_archs = {
arch
for arch, info in _MULTIMODAL_EXAMPLE_MODELS.items()
if not any(
model_id in transformers_arch_ids
for model_id in (info.default, *info.extras.values())
)
}
return _get_model_ids_to_test(vllm_only_archs)
def get_text_token_prompts(
processor: BaseMultiModalProcessor,
mm_data: MultiModalDataDict,
):
dummy_inputs = processor.dummy_inputs
tokenizer = processor.info.get_tokenizer()
model_config = processor.info.ctx.model_config
model_type = model_config.hf_config.model_type
if model_type in MM_DATA_PATCHES:
mm_data = MM_DATA_PATCHES[model_type](mm_data)
parsed_data = processor.data_parser.parse_mm_data(mm_data)
mm_counts = {k: len(vs) for k, vs in parsed_data.items()}
text_prompt: str | None
token_prompt: list[int]
if isinstance(tokenizer, MistralTokenizer):
images = parsed_data.get("image", [])
request = ChatCompletionRequest(
messages=[
UserMessage(
content=[
TextChunk(text=""),
*(ImageChunk(image=image) for image in images),
]
),
]
)
res = tokenizer.mistral.encode_chat_completion(request)
# Mistral does not support decode_tokens with skip_special_tokens=False
text_prompt = None
token_prompt = res.tokens
else:
inputs = dummy_inputs.get_dummy_processor_inputs(
model_config.max_model_len,
mm_counts,
)
assert isinstance(inputs.prompt, str)
text_prompt = inputs.prompt
token_prompt = encode_tokens(
tokenizer,
text_prompt,
add_special_tokens=_ADD_SPECIAL_TOKENS_OVERRIDES.get(model_type),
)
return text_prompt, token_prompt
def _test_processing_correctness(
model_id_or_arch: str,
hit_rate: float,
@ -148,8 +265,6 @@ def _test_processing_correctness(
baseline_processor = factories.build_processor(ctx, cache=None)
cached_processor = factories.build_processor(ctx, cache=cache)
dummy_inputs = baseline_processor.dummy_inputs
tokenizer = baseline_processor.info.get_tokenizer()
rng = np.random.RandomState(0)
@ -175,29 +290,6 @@ def _test_processing_correctness(
for k, limit in limit_mm_per_prompt_ints.items()
}
mm_counts = {k: len(vs) for k, vs in mm_data.items()}
# Mistral chat outputs tokens directly, rather than text prompts
if isinstance(tokenizer, MistralTokenizer):
images = mm_data.get("image", [])
request = ChatCompletionRequest(
messages=[
UserMessage(
content=[
TextChunk(text=""),
*(ImageChunk(image=image) for image in images),
]
),
]
)
res = tokenizer.mistral.encode_chat_completion(request)
prompt = res.tokens
else:
prompt = dummy_inputs.get_dummy_processor_inputs(
model_config.max_model_len,
mm_counts,
).prompt
# Drop unnecessary keys and test single -> multi conversion
if rng.rand() < simplify_rate:
for k in list(mm_data.keys()):
@ -208,8 +300,6 @@ def _test_processing_correctness(
_test_processing_correctness_one(
model_config,
tokenizer,
prompt,
mm_data,
baseline_processor,
cached_processor,
@ -217,59 +307,17 @@ def _test_processing_correctness(
)
# For some multimodal models, tokenizer will always add bos_token
# at the beginning of prompt by default, causing hf_processor outputs
# incorrect token ids. So we need use `add_special_tokens=False` here
# to leave bos_token to be added by the processor.
_ADD_SPECIAL_TOKENS_OVERRIDES = {
"ovis": False,
"ovis2_5": False,
"paligemma": False,
"ultravox": False,
"whisper": False,
}
_IGNORE_MM_KEYS = {
# In Ultravox, the audio_features can be different depending on padding
# The slight difference should not be a problem though, since
# attention_mask lets us ignore the difference.
"ultravox": {"audio_features"},
}
MM_DATA_PATCHES = {
# GLM4.1V and Qwen3-VL requires video metadata to be included in the input
"glm4v": glm4_1v_patch_mm_data,
"glm4v_moe": glm4_1v_patch_mm_data,
"qwen3_vl": qwen3_vl_patch_mm_data,
"qwen3_vl_moe": qwen3_vl_patch_mm_data,
}
def _test_processing_correctness_one(
model_config: ModelConfig,
tokenizer: AnyTokenizer,
prompt: str | list[int],
mm_data: MultiModalDataDict,
baseline_processor: BaseMultiModalProcessor,
cached_processor: BaseMultiModalProcessor,
batch_idx: int,
):
model_type = model_config.hf_config.model_type
ignore_mm_keys = _IGNORE_MM_KEYS.get(model_type, set[str]())
if model_type in MM_DATA_PATCHES:
mm_data = MM_DATA_PATCHES[model_type](mm_data)
if isinstance(prompt, str):
text_prompt = prompt
token_prompt = encode_tokens(
tokenizer,
prompt,
add_special_tokens=_ADD_SPECIAL_TOKENS_OVERRIDES.get(model_type),
)
else:
# Mistral does not support decode_tokens with skip_special_tokens=False
text_prompt = None
token_prompt = prompt
text_prompt, token_prompt = get_text_token_prompts(baseline_processor, mm_data)
ignore_mm_keys = _IGNORE_MM_KEYS.get(model_type, set[str]())
baseline_tokenized_result = baseline_processor.apply(
token_prompt,
@ -324,81 +372,7 @@ def _test_processing_correctness_one(
)
@pytest.mark.parametrize(
"model_id",
[
"rhymes-ai/Aria",
"CohereForAI/aya-vision-8b",
"Open-Bee/Bee-8B-RL",
"Salesforce/blip2-opt-2.7b",
"facebook/chameleon-7b",
"CohereLabs/command-a-vision-07-2025",
"deepseek-ai/deepseek-vl2-tiny",
"deepseek-ai/DeepSeek-OCR",
"baidu/ERNIE-4.5-VL-28B-A3B-PT",
"adept/fuyu-8b",
"google/gemma-3-4b-it",
"google/gemma-3n-E2B-it",
"zai-org/glm-4v-9b",
"zai-org/GLM-4.1V-9B-Thinking",
"zai-org/GLM-4.5V",
"ibm-granite/granite-speech-3.3-2b",
"h2oai/h2ovl-mississippi-800m",
"naver-hyperclovax/HyperCLOVAX-SEED-Vision-Instruct-3B",
"HuggingFaceM4/Idefics3-8B-Llama3",
"internlm/Intern-S1",
"OpenGVLab/InternVL2-1B",
"OpenGVLab/InternVL3-1B",
"OpenGVLab/InternVL3_5-1B",
"OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview",
"OpenGVLab/InternVL3_5-30B-A3B",
"Kwai-Keye/Keye-VL-8B-Preview",
"Kwai-Keye/Keye-VL-1_5-8B",
"moonshotai/Kimi-VL-A3B-Instruct",
"meta-llama/Llama-4-Scout-17B-16E-Instruct",
"llava-hf/llava-1.5-7b-hf",
"llava-hf/llava-v1.6-mistral-7b-hf",
"llava-hf/LLaVA-NeXT-Video-7B-hf",
"llava-hf/llava-onevision-qwen2-0.5b-ov-hf",
"TIGER-Lab/Mantis-8B-siglip-llama3",
"mispeech/midashenglm-7b",
"openbmb/MiniCPM-Llama3-V-2_5",
"openbmb/MiniCPM-o-2_6",
"openbmb/MiniCPM-V-2_6",
"MiniMaxAI/MiniMax-VL-01",
"allenai/Molmo-7B-D-0924",
"allenai/Molmo-7B-O-0924",
"nvidia/NVLM-D-72B",
"nvidia/Llama-3.1-Nemotron-Nano-VL-8B-V1",
"AIDC-AI/Ovis1.6-Gemma2-9B",
"AIDC-AI/Ovis1.6-Llama3.2-3B",
"AIDC-AI/Ovis2-1B",
"AIDC-AI/Ovis2.5-2B",
"google/paligemma-3b-mix-224",
"google/paligemma2-3b-ft-docci-448",
"microsoft/Phi-3.5-vision-instruct",
"microsoft/Phi-4-multimodal-instruct",
"mistralai/Pixtral-12B-2409",
"mistral-community/pixtral-12b",
"Qwen/Qwen-VL-Chat",
"Qwen/Qwen2-VL-2B-Instruct",
"Qwen/Qwen2.5-VL-3B-Instruct",
"Qwen/Qwen2-Audio-7B-Instruct",
"Qwen/Qwen2.5-Omni-3B",
"Qwen/Qwen3-VL-4B-Instruct",
"Qwen/Qwen3-VL-30B-A3B-Instruct",
"Qwen/Qwen3-Omni-30B-A3B-Instruct",
"YannQi/R-4B",
"Skywork/Skywork-R1V-38B",
"HuggingFaceTB/SmolVLM2-2.2B-Instruct",
"stepfun-ai/step3",
"fixie-ai/ultravox-v0_5-llama-3_2-1b",
"openai/whisper-large-v3",
"omni-research/Tarsier-7b",
"omni-research/Tarsier2-Recap-7b",
"mistralai/Voxtral-Mini-3B-2507",
],
)
@pytest.mark.parametrize("model_id", get_model_ids_to_test())
@pytest.mark.parametrize("hit_rate", [0.3, 0.5, 1.0])
@pytest.mark.parametrize("num_batches", [32])
@pytest.mark.parametrize("simplify_rate", [1.0])
@ -409,7 +383,12 @@ def test_processing_correctness(
simplify_rate: float,
):
if model_id == "google/gemma-3n-E2B-it":
pytest.skip("Skipping gemma-3n-E2B-it due to transformers #39911 bug.")
pytest.skip("Fix later")
if model_id == "OpenGVLab/InternVL2-2B":
pytest.skip("Fix later")
if model_id == "jinaai/jina-reranker-m0":
pytest.skip("Fix later")
_test_processing_correctness(
model_id,
hit_rate=hit_rate,

97
tests/models/multimodal/processing/test_tensor_schema.py
View File

@ -9,9 +9,6 @@ from typing import Any, TypeAlias
import numpy as np
import pytest
import torch.nn as nn
from mistral_common.protocol.instruct.chunk import ImageChunk, TextChunk
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from PIL import Image
from vllm.config import ModelConfig, VllmConfig, set_current_vllm_config
@ -37,22 +34,9 @@ from vllm.transformers_utils.tokenizer import cached_tokenizer_from_config
from vllm.utils.collection_utils import is_list_of
from vllm.utils.torch_utils import set_default_torch_dtype
from ...registry import _MULTIMODAL_EXAMPLE_MODELS, HF_EXAMPLE_MODELS
from ...registry import HF_EXAMPLE_MODELS
from ...utils import dummy_hf_overrides
ARCH_TO_SKIP = {
"MolmoForCausalLM": "incompatible requirements",
}
ARCH_NEEDS_EXTRAS = [
"InternVLChatModel",
"Idefics3ForConditionalGeneration",
"LlavaForConditionalGeneration",
"MiniCPMV",
"PaliGemmaForConditionalGeneration",
]
REPO_ID_TO_SKIP = {
"nm-testing/pixtral-12b-FP8-dynamic": "duplicated test",
}
from .test_common import get_model_ids_to_test, get_text_token_prompts
ImageInput = list[Image.Image]
VideoInput: TypeAlias = (
@ -61,6 +45,18 @@ VideoInput: TypeAlias = (
AudioInput = list[tuple[np.ndarray, int]]
MM_OPTIONS_OVERRIDES = {
# Qwen3-VL's default profiling video size (64x64) can cause trouble
# after resizing, so we override it here for testing.
"qwen3_vl": dict(
video=VideoDummyOptions(num_frames=128, width=256, height=256),
),
"qwen3_vl_moe": dict(
video=VideoDummyOptions(num_frames=128, width=256, height=256),
),
}
def _resize_data(
_data: Image.Image | np.ndarray, size_factor: float
) -> Image.Image | np.ndarray:
@ -94,7 +90,7 @@ def resize_mm_data(
if is_list_of(data, (Image.Image, np.ndarray, list)):
return [_resize_data(d, s) for d, s in zip(data, size_factors)]
elif is_list_of(data, tuple):
return [(_resize_data(d, s), meta) for (d, meta), s in zip(data, size_factors)]
return [_resize_data(d, s) for (d, _), s in zip(data, size_factors)]
raise ValueError("Unsupported multimodal data type.")
@ -104,6 +100,8 @@ def create_batched_mm_kwargs(
processor: BaseMultiModalProcessor,
size_factors: tuple[float, ...] = (1.0, 0.5, 0.25),
) -> Iterable[tuple[str, int, BatchedTensorInputs]]:
model_type = model_config.hf_config.model_type
processing_info = processor.info
dummy_inputs = processor.dummy_inputs
supported_mm_limits = processing_info.get_supported_mm_limits()
@ -114,32 +112,19 @@ def create_batched_mm_kwargs(
processor_inputs = dummy_inputs.get_dummy_processor_inputs(
seq_len=model_config.max_model_len,
mm_counts=mm_counts,
mm_options=MM_OPTIONS_OVERRIDES.get(model_type),
)
mm_data = processor_inputs.mm_data
resized_mm_data = {
modality: resize_mm_data(data, size_factors)
for modality, data in mm_data.items()
}
# Mistral chat outputs tokens directly, rather than text prompts
if model_config.tokenizer_mode == "mistral":
images = resized_mm_data.get("image", [])
request = ChatCompletionRequest(
messages=[
UserMessage(
content=[
TextChunk(text=""),
*(ImageChunk(image=image) for image in images),
]
),
]
)
tokenizer = processing_info.get_tokenizer()
res = tokenizer.mistral.encode_chat_completion(request)
prompt = res.tokens
else:
prompt = processor_inputs.prompt
# video metadata will be added back to the resized video data here.
text_prompt, token_prompt = get_text_token_prompts(processor, resized_mm_data)
mm_kwargs = processor.apply(
prompt=prompt,
prompt=token_prompt if text_prompt is None else text_prompt,
mm_data=resized_mm_data,
hf_processor_mm_kwargs=processor_inputs.hf_processor_mm_kwargs,
tokenization_kwargs=processor_inputs.tokenization_kwargs,
@ -175,35 +160,15 @@ def initialize_dummy_model(
cleanup_dist_env_and_memory()
def get_model_id_to_test(model_arch_list: Iterable[str]) -> list[tuple[str, str]]:
filtered_results = []
for model_arch in model_arch_list:
model_info = HF_EXAMPLE_MODELS.get_hf_info(model_arch)
if model_info.extras and model_arch in ARCH_NEEDS_EXTRAS:
available_repos = list(
map(
lambda model_id: (model_arch, model_id),
[model_info.default, *model_info.extras.values()],
)
)
filtered_results.extend(available_repos)
else:
filtered_results.append((model_arch, model_info.default))
return filtered_results
@pytest.mark.parametrize(
"model_arch, model_id", get_model_id_to_test(_MULTIMODAL_EXAMPLE_MODELS.keys())
)
def test_model_tensor_schema(model_arch: str, model_id: str):
if model_arch in ARCH_TO_SKIP:
pytest.skip(f"Skipping {model_arch} due to {ARCH_TO_SKIP[model_arch]}")
if model_id in REPO_ID_TO_SKIP:
pytest.skip(f"Skipping {model_id} due to {REPO_ID_TO_SKIP[model_id]}")
model_info = HF_EXAMPLE_MODELS.get_hf_info(model_arch)
@pytest.mark.parametrize("model_id", get_model_ids_to_test())
def test_model_tensor_schema(model_id: str):
model_info = HF_EXAMPLE_MODELS.find_hf_info(model_id)
model_info.check_available_online(on_fail="skip")
model_info.check_transformers_version(on_fail="skip", check_max_version=False)
model_info.check_transformers_version(on_fail="skip")
model_arch = next(
arch for arch, info in HF_EXAMPLE_MODELS.hf_models.items() if info == model_info
)
hf_overrides_fn = partial(
dummy_hf_overrides,

20
vllm/model_executor/models/qwen3_omni_moe_thinker.py
View File

@ -733,17 +733,21 @@ class Qwen3OmniMoeThinkerMultiModalProcessor(
else (pad_to_hop_length(audio[0], hop_length), audio[1])
for audio in audios
]
mm_kwargs = dict(
**mm_kwargs,
)
# TODO(Isotr0py): Remove this patch after upstream fix PR
# released and Transformers version update:
# https://github.com/huggingface/transformers/pull/41473
if (
Version(TRANSFORMERS_VERSION) < Version("4.58.0")
and "truncation" not in mm_kwargs
):
mm_kwargs["truncation"] = False
mm_kwargs = dict(mm_kwargs)
tok_kwargs = dict(tok_kwargs)
if Version(TRANSFORMERS_VERSION) < Version("4.58.0"):
# move truncation to audio_kwargs level to avoid conflict
# with tok_kwargs
mm_kwargs["audio_kwargs"] = {
"truncation": mm_kwargs.pop("truncation", False)
}
mm_kwargs["text_kwargs"] = {
"truncation": tok_kwargs.pop("truncation", False)
}
hf_inputs = super()._call_hf_processor(
prompt=prompt,

10
vllm/model_executor/models/qwen3_vl.py
View File

@ -898,16 +898,12 @@ class Qwen3VLMultiModalProcessor(BaseMultiModalProcessor[Qwen3VLProcessingInfo])
processor = self.info.get_hf_processor(**mm_kwargs)
# Separate video processing from image processing. Because the videos
# are processed into serval image patches
if (
"videos" in mm_data
and isinstance(mm_data["videos"], list)
and len(mm_data["videos"]) > 0
):
# are processed into several image patches
if videos := mm_data.pop("videos", []):
video_grid_thw_lst = []
pixel_values_videos_lst = []
for item_idx, item in enumerate(mm_data.pop("videos", [])):
for item in videos:
video_array, metadata = item
# NOTE: @JJJYmmm new attr metadata.frames_indices indicates