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[Misc] Remove unnecessary Transformers version guards (#57556)

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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
2026-09-18 15:01:35 +00:00
committed by GitHub
parent 1cdf1689e5
commit dbf4b89cf2
11 changed files with 15 additions and 146 deletions
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tests/basic_correctness/test_basic_correctness.py
-11
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@@ -11,8 +11,6 @@ from unittest.mock import Mock
import pytest
import torch
from packaging.version import Version
from transformers import __version__ as TRANSFORMERS_VERSION
import vllm.envs as envs
from vllm import LLM
@@ -149,15 +147,6 @@ def test_models(
if enable_prompt_embeds:
with torch.no_grad():
prompt_embeds = hf_model.get_prompt_embeddings(example_prompts)
if model == "hmellor/tiny-random-Gemma2ForCausalLM" and (
Version(TRANSFORMERS_VERSION) < Version("5.3.0.dev0")
):
# For Gemma 1/2 models with Transformers 5.4.0+, the prompt embeddings
# are normalised in `get_prompt_embeddings`, like Gemma 3.
# For older versions, we need to manually normalise.
embed_scale = hf_model.config.hidden_size**0.5
normalizer = torch.tensor(embed_scale, dtype=prompt_embeds[0].dtype)
prompt_embeds = [p_e * normalizer for p_e in prompt_embeds]
with VllmRunner(
model,
tests/lora/test_qwenvl.py
+5 -15
View File
@@ -3,8 +3,6 @@
from dataclasses import dataclass
import pytest
from packaging.version import Version
from transformers import __version__ as TRANSFORMERS_VERSION
import vllm
from tests.conftest import VllmRunner
@@ -29,20 +27,12 @@ class TestConfig:
def __post_init__(self):
if self.mm_processor_kwargs is None:
# There is a bug in transformers v4 where size is ignored by
# `Qwen2VLProcessor.__call__`
if Version(TRANSFORMERS_VERSION) < Version("5.2.0"):
self.mm_processor_kwargs = {
"min_pixels": 28 * 28,
"max_pixels": 1280 * 28 * 28,
}
else:
self.mm_processor_kwargs = {
"size": {
"shortest_edge": 28 * 28,
"longest_edge": 1280 * 28 * 28,
}
self.mm_processor_kwargs = {
"size": {
"shortest_edge": 28 * 28,
"longest_edge": 1280 * 28 * 28,
}
}
class Qwen2VLTester:
tests/models/language/generation/test_common.py
-12
View File
@@ -3,8 +3,6 @@
import pytest
import torch
from packaging.version import Version
from transformers import __version__ as TRANSFORMERS_VERSION
from vllm.platforms import current_platform
@@ -162,16 +160,6 @@ def test_models(
if prompt_embeds is not None:
embed = hf_model.model.get_input_embeddings()(token_ids)
if "gemma" in model.lower() and (
Version(TRANSFORMERS_VERSION) < Version("5.3.0.dev0")
):
# For Gemma 1/2 models with Transformers 5.4.0+, the prompt
# embeddings are normalised in `get_prompt_embeddings`,
# like Gemma 3. For older versions, we need to manually normalise.
embed_scale = hf_model.config.hidden_size**0.5
normalizer = torch.tensor(embed_scale, dtype=embed.dtype)
embed *= normalizer
# MiniCPM models apply scale_emb to embeddings internally.
# vLLM expects pre-scaled embeddings when using inputs_embeds.
if model in EMBED_SCALING_MODELS:
tests/models/multimodal/generation/test_common.py
-6
View File
@@ -317,8 +317,6 @@ VLM_TEST_SETTINGS = {
img_idx_to_prompt=lambda idx: "",
auto_cls=AutoModelForImageTextToText,
vllm_output_post_proc=model_utils.blip2_vllm_to_hf_output,
# FIXME: https://github.com/huggingface/transformers/pull/38510
marks=[pytest.mark.skip("Model is broken")],
),
"cosmos3": VLMTestInfo(
models=["nvidia/Cosmos3-Nano"],
@@ -819,10 +817,6 @@ VLM_TEST_SETTINGS = {
patch_hf_runner=model_utils.paddleocr_vl_patch_hf_runner,
image_size_factors=[(0.25,)],
marks=[
pytest.mark.skipif(
Version(TRANSFORMERS_VERSION) == Version("4.57.3"),
reason="This model is broken in Transformers v4.57.3",
),
pytest.mark.skipif(
Version(TRANSFORMERS_VERSION) >= Version("5.0.0"),
reason="Model's custom code uses ROPE_INIT_FUNCTIONS"
tests/models/multimodal/generation/vlm_utils/model_utils.py
-10
View File
@@ -308,16 +308,6 @@ def gemma3_patch_hf_runner(hf_model: HfRunner) -> HfRunner:
hf_model.processor = processor
orig_generate = hf_model.model.generate
def _generate(self, *args, **kwargs):
# FIXME: https://github.com/huggingface/transformers/issues/38333
kwargs["disable_compile"] = True
return orig_generate(*args, **kwargs)
hf_model.model.generate = types.MethodType(_generate, hf_model.model)
return hf_model
tests/models/multimodal/pooling/test_jinavl_reranker.py
-22
View File
@@ -2,8 +2,6 @@
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
import transformers
from packaging import version
from transformers import AutoModel
from vllm.assets.base import VLLM_S3_BUCKET_URL
@@ -246,10 +244,6 @@ def _run_test(
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.skipif(
version.parse(transformers.__version__) == version.parse("4.57.5"),
reason="Skipped for transformers==4.57.5, https://github.com/huggingface/transformers/issues/43295",
)
def test_model_text_image(
hf_runner,
vllm_runner,
@@ -269,10 +263,6 @@ def test_model_text_image(
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.skipif(
version.parse(transformers.__version__) == version.parse("4.57.5"),
reason="Skipped for transformers==4.57.5, https://github.com/huggingface/transformers/issues/43295",
)
def test_model_text_text(
hf_runner,
vllm_runner,
@@ -292,10 +282,6 @@ def test_model_text_text(
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.skipif(
version.parse(transformers.__version__) == version.parse("4.57.5"),
reason="Skipped for transformers==4.57.5, https://github.com/huggingface/transformers/issues/43295",
)
def test_model_image_text(
hf_runner,
vllm_runner,
@@ -315,10 +301,6 @@ def test_model_image_text(
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.skipif(
version.parse(transformers.__version__) == version.parse("4.57.5"),
reason="Skipped for transformers==4.57.5, https://github.com/huggingface/transformers/issues/43295",
)
def test_model_image_image(
hf_runner,
vllm_runner,
@@ -338,10 +320,6 @@ def test_model_image_image(
@pytest.mark.parametrize("model", MODELS)
@pytest.mark.parametrize("dtype", ["half"])
@pytest.mark.skipif(
version.parse(transformers.__version__) == version.parse("4.57.5"),
reason="Skipped for transformers==4.57.5, https://github.com/huggingface/transformers/issues/43295",
)
def test_model_text_mixed_documents(
hf_runner,
vllm_runner,
tests/models/multimodal/processing/test_idefics3.py
-6
View File
@@ -3,9 +3,7 @@
"""Tests for Idefics3's multimodal preprocessing kwargs."""
import pytest
from packaging.version import Version
from transformers import Idefics3Config
from transformers import __version__ as TRANSFORMERS_VERSION
from vllm.multimodal import MULTIMODAL_REGISTRY
@@ -13,10 +11,6 @@ from ....conftest import ImageTestAssets
from ...utils import build_model_context
@pytest.mark.skipif(
Version(TRANSFORMERS_VERSION) < Version("5.2.0"),
reason="See https://github.com/huggingface/transformers/pull/43948",
)
@pytest.mark.parametrize("model_id", ["HuggingFaceM4/Idefics3-8B-Llama3"])
@pytest.mark.parametrize(
("mm_processor_kwargs", "expected_toks_per_img"),
tests/models/multimodal/processing/test_qwen2_vl.py
-14
View File
@@ -3,9 +3,7 @@
import pytest
import torch
from packaging.version import Version
from PIL import Image
from transformers import __version__ as TRANSFORMERS_VERSION
from vllm.model_executor.models.vision import FusedInputNorm
from vllm.multimodal import MULTIMODAL_REGISTRY
@@ -133,12 +131,6 @@ def test_processor_override(
kwargs_on_init: bool,
):
"""Ensure Qwen2VLMultiModalProcessor handles min/max pixels properly."""
if (
Version(TRANSFORMERS_VERSION) < Version("5.2.0")
and "size" in mm_processor_kwargs
):
pytest.skip("`size` ignored by `Qwen2VLProcessor.__call__`")
ctx = build_model_context(
model_id,
mm_processor_kwargs=mm_processor_kwargs if kwargs_on_init else None,
@@ -184,12 +176,6 @@ def test_get_image_size_with_most_features(
model_id: str,
mm_processor_kwargs: dict[str, object],
):
if (
Version(TRANSFORMERS_VERSION) < Version("5.2.0")
and "size" in mm_processor_kwargs
):
pytest.skip("`size` ignored by `Qwen2VLProcessor.__call__`")
ctx = build_model_context(
model_id,
mm_processor_kwargs=mm_processor_kwargs,
tests/models/multimodal/processing/test_smolvlm.py
-6
View File
@@ -3,9 +3,7 @@
"""Tests for smolvlm's multimodal preprocessing kwargs."""
import pytest
from packaging.version import Version
from transformers import SmolVLMConfig
from transformers import __version__ as TRANSFORMERS_VERSION
from vllm.multimodal import MULTIMODAL_REGISTRY
@@ -13,10 +11,6 @@ from ....conftest import ImageTestAssets
from ...utils import build_model_context
@pytest.mark.skipif(
Version(TRANSFORMERS_VERSION) < Version("5.2.0"),
reason="See https://github.com/huggingface/transformers/pull/43948",
)
@pytest.mark.parametrize("model_id", ["HuggingFaceTB/SmolVLM2-2.2B-Instruct"])
@pytest.mark.parametrize(
("mm_processor_kwargs", "expected_toks_per_img"),
vllm/model_executor/models/glm4_1v.py
+10 -32
View File
@@ -36,9 +36,7 @@ import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import transformers
from einops import rearrange
from packaging.version import Version
from transformers import BatchFeature, Glm4vProcessor
from transformers.image_processing_base import ImageProcessingMixin
from transformers.models.glm4v.configuration_glm4v import (
@@ -132,8 +130,6 @@ logger = init_logger(__name__)
# For profile run
_MAX_FRAMES_PER_VIDEO = 600
TRANSFORMERS_WITH_GA = Version(transformers.__version__) >= Version("5.10.0.dev0")
def _to_video_metadata(metadata: Mapping[str, Any]) -> VideoMetadata:
return VideoMetadata(
@@ -1389,17 +1385,6 @@ class Glm4vProcessingInfo(BaseProcessingInfo):
timestamps_list = full_second_idxs[::2]
return list(timestamps_list)
def _get_video_frame_embed_token_id(self, hf_processor: object) -> int:
attr = (
"image_token_id"
if isinstance(hf_processor, Glm4vProcessor) or TRANSFORMERS_WITH_GA
else "video_token_id"
)
token_id = getattr(hf_processor, attr, None)
if not isinstance(token_id, int):
raise ValueError(f"Processor has no valid {attr}")
return token_id
def _construct_video_placeholder(
self,
video_array: np.ndarray,
@@ -1437,7 +1422,7 @@ class Glm4vProcessingInfo(BaseProcessingInfo):
num_tokens_per_frame = int(H * W) // merge_length
placeholder = []
placeholder.append(bov_token_id)
frame_embed_token_id = self._get_video_frame_embed_token_id(hf_processor)
frame_embed_token_id = hf_processor.image_token_id
for frame_idx in frames_idx_token:
placeholder.append(boi_token_id)
placeholder.extend([frame_embed_token_id] * num_tokens_per_frame)
@@ -1601,10 +1586,7 @@ class Glm4vMultiModalProcessor(BaseMultiModalProcessor[Glm4vProcessingInfo]):
processor = self.info.get_hf_processor(**hf_kwargs)
use_direct_path = (
not isinstance(processor, Glm4vProcessor) and TRANSFORMERS_WITH_GA
)
if use_direct_path:
if not isinstance(processor, Glm4vProcessor):
prepared_data, prepared_kwargs = self._get_direct_path_inputs(
hf_data, hf_kwargs
)
@@ -1627,8 +1609,6 @@ class Glm4vMultiModalProcessor(BaseMultiModalProcessor[Glm4vProcessingInfo]):
del hf_data["videos"]
video_grid_thw_lst = []
pixel_values_videos_lst = []
frame_embed_token_id = self.info._get_video_frame_embed_token_id(processor)
swap_video_frame_tokens = frame_embed_token_id == processor.image_token_id
for item in videos:
video_array, metadata = item
@@ -1651,10 +1631,10 @@ class Glm4vMultiModalProcessor(BaseMultiModalProcessor[Glm4vProcessingInfo]):
video_mm_kwargs,
)
input_ids = video_outputs.pop("input_ids")
if swap_video_frame_tokens:
input_ids[input_ids == processor.image_token_id] = (
processor.video_token_id
)
input_ids[input_ids == processor.image_token_id] = (
processor.video_token_id
)
video_placeholder = processor.tokenizer.batch_decode(input_ids)[0]
prompt_text = prompt_text.replace(
"<|begin_of_video|><|video|><|end_of_video|>",
@@ -1670,17 +1650,15 @@ class Glm4vMultiModalProcessor(BaseMultiModalProcessor[Glm4vProcessingInfo]):
)
else:
video_outputs = dict()
swap_video_frame_tokens = False
processed_data = self.info.ctx.call_hf_processor(
self.info.get_hf_processor(**hf_kwargs),
dict(text=prompt_text, **hf_data),
hf_kwargs,
)
if swap_video_frame_tokens:
input_ids = processed_data["input_ids"]
input_ids[input_ids == processor.video_token_id] = processor.image_token_id
processed_data["input_ids"] = input_ids
input_ids = processed_data["input_ids"]
input_ids[input_ids == processor.video_token_id] = processor.image_token_id
processed_data["input_ids"] = input_ids
processed_data.update(video_outputs)
return self._finalize_hf_mm_data(
@@ -1728,7 +1706,7 @@ class Glm4vMultiModalProcessor(BaseMultiModalProcessor[Glm4vProcessingInfo]):
)
return PromptUpdateDetails.select_token_id(
placeholder,
embed_token_id=self.info._get_video_frame_embed_token_id(hf_processor),
embed_token_id=hf_processor.image_token_id,
)
return [
vllm/model_executor/models/qwen3_omni_moe_thinker.py
-12
View File
@@ -1228,18 +1228,6 @@ class Qwen3OmniMoeThinkerMultiModalProcessor(
) -> HFMultiModalInputs:
hf_inputs = super()._get_hf_mm_inputs(mm_items, hf_kwargs)
if mm_items.get_count("audio", strict=False):
# TODO(Isotr0py): Remove this patch after upstream fix PR
# released and Transformers version update:
# https://github.com/huggingface/transformers/pull/41473
hf_inputs = hf_inputs._replace(
hf_kwargs={
**hf_inputs.hf_kwargs,
"audio_kwargs": dict(hf_inputs.hf_kwargs.get("audio_kwargs") or {}),
"text_kwargs": dict(hf_inputs.hf_kwargs.get("text_kwargs") or {}),
}
)
audios = hf_inputs.hf_data.get("audio")
if audios:
feature_extractor = self.info.get_feature_extractor()