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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
4 changed files with 174 additions and 230 deletions
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277
tests/models/multimodal/processing/test_common.py
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@@ -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,