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[Misc] Update transformers version limits of multi-modal tests (#16381)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
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Cyrus Leung 2025-04-10 14:03:33 +08:00 committed by GitHub
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commit 3d4c87758e
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4 changed files with 20 additions and 270 deletions
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7
.buildkite/test-pipeline.yaml
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@ -429,7 +429,7 @@ steps:
- pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
- pytest -v -s models/multimodal
- pytest -v -s models/decoder_only/audio_language -m 'core_model or quant_model'
- pytest -v -s --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m 'core_model or quant_model'
- pytest -v -s models/decoder_only/vision_language -m 'core_model or quant_model'
- pytest -v -s models/embedding/vision_language -m core_model
- pytest -v -s models/encoder_decoder/audio_language -m core_model
- pytest -v -s models/encoder_decoder/language -m core_model
@ -448,10 +448,7 @@ steps:
- pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
- pytest -v -s models/decoder_only/audio_language -m 'not core_model and not quant_model'
- pytest -v -s models/decoder_only/vision_language/test_models.py -m 'split(group=0) and not core_model and not quant_model'
# HACK - run phi3v tests separately to sidestep this transformers bug
# https://github.com/huggingface/transformers/issues/34307
- pytest -v -s models/decoder_only/vision_language/test_phi3v.py
- pytest -v -s --ignore models/decoder_only/vision_language/test_models.py --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m 'not core_model and not quant_model'
- pytest -v -s --ignore models/decoder_only/vision_language/test_models.py models/decoder_only/vision_language -m 'not core_model and not quant_model'
- pytest -v -s models/embedding/vision_language -m 'not core_model'
- pytest -v -s models/encoder_decoder/language -m 'not core_model'
- pytest -v -s models/encoder_decoder/vision_language -m 'not core_model'

31
tests/models/decoder_only/vision_language/test_models.py
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@ -425,23 +425,20 @@ VLM_TEST_SETTINGS = {
max_num_seqs=2,
patch_hf_runner=model_utils.molmo_patch_hf_runner,
),
# Tests for phi3v currently live in another file because of a bug in
# transformers. Once this issue is fixed, we can enable them here instead.
# https://github.com/huggingface/transformers/issues/34307
# "phi3v": VLMTestInfo(
# models=["microsoft/Phi-3.5-vision-instruct"],
# test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
# prompt_formatter=lambda img_prompt: f"<|user|>\n{img_prompt}<|end|>\n<|assistant|>\n", # noqa: E501
# img_idx_to_prompt=lambda idx: f"<|image_{idx}|>\n",
# max_model_len=4096,
# max_num_seqs=2,
# task="generate",
# # use eager mode for hf runner since phi3v didn't work with flash_attn
# hf_model_kwargs={"_attn_implementation": "eager"},
# use_tokenizer_eos=True,
# vllm_output_post_proc=model_utils.phi3v_vllm_to_hf_output,
# num_logprobs=10,
# ),
"phi3v": VLMTestInfo(
models=["microsoft/Phi-3.5-vision-instruct"],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),
prompt_formatter=lambda img_prompt: f"<|user|>\n{img_prompt}<|end|>\n<|assistant|>\n", # noqa: E501
img_idx_to_prompt=lambda idx: f"<|image_{idx}|>\n",
max_model_len=4096,
max_num_seqs=2,
task="generate",
# use eager mode for hf runner since phi3v didn't work with flash_attn
hf_model_kwargs={"_attn_implementation": "eager"},
use_tokenizer_eos=True,
vllm_output_post_proc=model_utils.phi3v_vllm_to_hf_output,
num_logprobs=10,
),
"pixtral_hf": VLMTestInfo(
models=["nm-testing/pixtral-12b-FP8-dynamic"],
test_type=(VLMTestType.IMAGE, VLMTestType.MULTI_IMAGE),

245
tests/models/decoder_only/vision_language/test_phi3v.py
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@ -1,245 +0,0 @@
# SPDX-License-Identifier: Apache-2.0
import os
import re
from typing import Optional
import pytest
from packaging.version import Version
from transformers import AutoTokenizer
from transformers import __version__ as TRANSFORMERS_VERSION
from vllm.multimodal.image import rescale_image_size
from vllm.platforms import current_platform
from vllm.sequence import SampleLogprobs
from ....conftest import IMAGE_ASSETS, HfRunner, PromptImageInput, VllmRunner
from ...utils import check_logprobs_close
HF_IMAGE_PROMPTS = IMAGE_ASSETS.prompts({
"stop_sign":
"<|user|>\n<|image_1|>\nWhat's the content of the image?<|end|>\n<|assistant|>\n", # noqa: E501
"cherry_blossom":
"<|user|>\n<|image_1|>\nWhat is the season?<|end|>\n<|assistant|>\n",
})
HF_MULTIIMAGE_IMAGE_PROMPT = "<|user|>\n<|image_1|>\n<|image_2|>\nDescribe these images.<|end|>\n<|assistant|>\n" # noqa: E501
models = ["microsoft/Phi-3.5-vision-instruct"]
def vllm_to_hf_output(vllm_output: tuple[list[int], str,
Optional[SampleLogprobs]],
model: str):
"""Sanitize vllm output to be comparable with hf output."""
_, output_str, out_logprobs = vllm_output
output_str_without_image = re.sub(r"(<\|image_\d+\|>)+", "", output_str)
assert output_str_without_image[0] == " "
output_str_without_image = output_str_without_image[1:]
hf_output_str = output_str_without_image + "<|end|><|endoftext|>"
tokenizer = AutoTokenizer.from_pretrained(model)
hf_output_ids = tokenizer.encode(output_str_without_image)
assert hf_output_ids[0] == 1
hf_output_ids = hf_output_ids[1:]
return hf_output_ids, hf_output_str, out_logprobs
target_dtype = "half"
# ROCm Triton FA can run into shared memory issues with these models,
# use other backends in the meantime
# FIXME (mattwong, gshtrasb, hongxiayan)
if current_platform.is_rocm():
os.environ["VLLM_USE_TRITON_FLASH_ATTN"] = "0"
def run_test(
hf_runner: type[HfRunner],
vllm_runner: type[VllmRunner],
inputs: list[tuple[list[str], PromptImageInput]],
model: str,
*,
dtype: str,
max_tokens: int,
num_logprobs: int,
mm_limit: int,
tensor_parallel_size: int,
distributed_executor_backend: Optional[str] = None,
):
"""Inference result should be the same between hf and vllm.
All the image fixtures for the test are from IMAGE_ASSETS.
For huggingface runner, we provide the PIL images as input.
For vllm runner, we provide MultiModalDataDict objects
and corresponding MultiModalConfig as input.
Note, the text input is also adjusted to abide by vllm contract.
The text output is sanitized to be able to compare with hf.
"""
# HACK - this is an attempted workaround for the following bug
# https://github.com/huggingface/transformers/issues/34307
from transformers import AutoImageProcessor # noqa: F401
from transformers import AutoProcessor # noqa: F401
# Once the model repo is updated to 4.49, we should be able to run the
# test in `test_models.py` without the above workaround
if Version(TRANSFORMERS_VERSION) >= Version("4.49"):
pytest.skip(f"`transformers=={TRANSFORMERS_VERSION}` installed, "
"but `transformers<=4.49` is required to run this model. "
"Reason: Cannot run HF implementation")
# NOTE: take care of the order. run vLLM first, and then run HF.
# vLLM needs a fresh new process without cuda initialization.
# if we run HF first, the cuda initialization will be done and it
# will hurt multiprocessing backend with fork method (the default method).
# max_model_len should be greater than image_feature_size
with vllm_runner(model,
task="generate",
max_model_len=4096,
max_num_seqs=2,
dtype=dtype,
limit_mm_per_prompt={"image": mm_limit},
tensor_parallel_size=tensor_parallel_size,
distributed_executor_backend=distributed_executor_backend,
enforce_eager=True) as vllm_model:
vllm_outputs_per_case = [
vllm_model.generate_greedy_logprobs(prompts,
max_tokens,
num_logprobs=num_logprobs,
images=images)
for prompts, images in inputs
]
# use eager mode for hf runner, since phi3_v didn't work with flash_attn
hf_model_kwargs = {"_attn_implementation": "eager"}
with hf_runner(model, dtype=dtype,
model_kwargs=hf_model_kwargs) as hf_model:
eos_token_id = hf_model.processor.tokenizer.eos_token_id
hf_outputs_per_case = [
hf_model.generate_greedy_logprobs_limit(prompts,
max_tokens,
num_logprobs=num_logprobs,
images=images,
eos_token_id=eos_token_id)
for prompts, images in inputs
]
for hf_outputs, vllm_outputs in zip(hf_outputs_per_case,
vllm_outputs_per_case):
check_logprobs_close(
outputs_0_lst=hf_outputs,
outputs_1_lst=[
vllm_to_hf_output(vllm_output, model)
for vllm_output in vllm_outputs
],
name_0="hf",
name_1="vllm",
)
# Since we use _attn_implementation="eager" for hf_runner, there is more
# significant numerical difference. The basic `logprobs=5` fails to pass.
@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize(
"size_factors",
[
# No image
[],
# Single-scale
[1.0],
# Single-scale, batched
[1.0, 1.0, 1.0],
# Multi-scale
[0.25, 0.5, 1.0],
],
)
@pytest.mark.parametrize("dtype", [target_dtype])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [10])
def test_models(hf_runner, vllm_runner, image_assets, model, size_factors,
dtype: str, max_tokens: int, num_logprobs: int) -> None:
images = [asset.pil_image for asset in image_assets]
inputs_per_image = [(
[prompt for _ in size_factors],
[rescale_image_size(image, factor) for factor in size_factors],
) for image, prompt in zip(images, HF_IMAGE_PROMPTS)]
run_test(
hf_runner,
vllm_runner,
inputs_per_image,
model,
dtype=dtype,
max_tokens=max_tokens,
num_logprobs=num_logprobs,
mm_limit=1,
tensor_parallel_size=1,
)
@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize("dtype", [target_dtype])
def test_regression_7840(hf_runner, vllm_runner, image_assets, model,
dtype) -> None:
images = [asset.pil_image for asset in image_assets]
inputs_regresion_7840 = [
([prompt], [image]) for image, prompt in zip(images, HF_IMAGE_PROMPTS)
]
# Regression test for #7840.
run_test(
hf_runner,
vllm_runner,
inputs_regresion_7840,
model,
dtype=dtype,
max_tokens=128,
num_logprobs=10,
mm_limit=1,
tensor_parallel_size=1,
)
@pytest.mark.parametrize("model", models)
@pytest.mark.parametrize(
"size_factors",
[
# No image
[],
# Single-scale
[1.0],
# Single-scale, batched
[1.0, 1.0, 1.0],
# Multi-scale
[0.25, 0.5, 1.0],
],
)
@pytest.mark.parametrize("dtype", [target_dtype])
@pytest.mark.parametrize("max_tokens", [128])
@pytest.mark.parametrize("num_logprobs", [10])
def test_multi_images_models(hf_runner, vllm_runner, image_assets, model,
size_factors, dtype: str, max_tokens: int,
num_logprobs: int) -> None:
images = [asset.pil_image for asset in image_assets]
inputs_per_case = [
([HF_MULTIIMAGE_IMAGE_PROMPT for _ in size_factors],
[[rescale_image_size(image, factor) for image in images]
for factor in size_factors])
]
run_test(
hf_runner,
vllm_runner,
inputs_per_case,
model,
dtype=dtype,
max_tokens=max_tokens,
num_logprobs=num_logprobs,
mm_limit=2,
tensor_parallel_size=1,
)

7
tests/models/registry.py
View File

@ -326,7 +326,7 @@ _MULTIMODAL_EXAMPLE_MODELS = {
extras={"fp8": "nm-testing/Mistral-Small-3.1-24B-Instruct-2503-FP8-dynamic"}), # noqa: E501
"MolmoForCausalLM": _HfExamplesInfo("allenai/Molmo-7B-D-0924",
max_transformers_version="4.48",
transformers_version_reason="Use of private method which no longer exists.", # noqa: E501
transformers_version_reason="Incorrectly-detected `tensorflow` import.", # noqa: E501
extras={"olmo": "allenai/Molmo-7B-O-0924"}, # noqa: E501
trust_remote_code=True),
"NVLM_D": _HfExamplesInfo("nvidia/NVLM-D-72B",
@ -335,6 +335,8 @@ _MULTIMODAL_EXAMPLE_MODELS = {
extras={"v2": "google/paligemma2-3b-ft-docci-448"}), # noqa: E501
"Phi3VForCausalLM": _HfExamplesInfo("microsoft/Phi-3-vision-128k-instruct",
trust_remote_code=True,
max_transformers_version="4.48",
transformers_version_reason="Use of deprecated imports which have been removed.", # noqa: E501
extras={"phi3.5": "microsoft/Phi-3.5-vision-instruct"}), # noqa: E501
"Phi4MMForCausalLM": _HfExamplesInfo("microsoft/Phi-4-multimodal-instruct",
trust_remote_code=True),
@ -351,8 +353,7 @@ _MULTIMODAL_EXAMPLE_MODELS = {
"SkyworkR1VChatModel": _HfExamplesInfo("Skywork/Skywork-R1V-38B"),
"SmolVLMForConditionalGeneration": _HfExamplesInfo("HuggingFaceTB/SmolVLM2-2.2B-Instruct"), # noqa: E501
"UltravoxModel": _HfExamplesInfo("fixie-ai/ultravox-v0_5-llama-3_2-1b", # noqa: E501
trust_remote_code=True,
max_transformers_version="4.50"),
trust_remote_code=True),
# [Encoder-decoder]
# Florence-2 uses BartFastTokenizer which can't be loaded from AutoTokenizer
# Therefore, we borrow the BartTokenizer from the original Bart model