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Fix llava_onevision tests (#38791)

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* fix

* fix

* fix

* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
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Yih-Dar 2025-06-12 15:06:49 +02:00 committed by GitHub
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1 changed files with 20 additions and 2 deletions
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22
tests/models/llava_onevision/test_modeling_llava_onevision.py
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@ -29,6 +29,7 @@ from transformers import (
is_vision_available,
)
from transformers.testing_utils import (
Expectations,
cleanup,
require_bitsandbytes,
require_torch,
@ -194,6 +195,12 @@ class LlavaOnevisionForConditionalGenerationModelTest(ModelTesterMixin, Generati
)
test_pruning = False
test_head_masking = False
# MP works but offload doesn't work when the MultiheadAttention is offloaded
# TODO: One potential solution would be to add to set preload_module_classes = ["Siglip2MultiheadAttentionPoolingHead"]
# in the dispatch_model function
test_cpu_offload = False
test_disk_offload_safetensors = False
test_disk_offload_bin = False
_is_composite = True
def setUp(self):
@ -379,7 +386,15 @@ class LlavaOnevisionForConditionalGenerationIntegrationTest(unittest.TestCase):
# verify generation
output = model.generate(**inputs, max_new_tokens=100)
EXPECTED_DECODED_TEXT = 'user\n\nWhat do you see in this image?\nassistant\nThe image is a radar chart that compares the performance of different models in a specific task, likely related to natural language processing or machine learning. The chart is divided into several axes, each representing a different model or method. The models are color-coded and labeled with their respective names. The axes are labeled with terms such as "VQA," "GQA," "MQA," "VQAv2," "MM-Vet," "LLaVA-Bench," "LLaVA-1' # fmt: skip
EXPECTED_DECODED_TEXTS = Expectations(
{
("cuda", 7): 'user\n\nWhat do you see in this image?\nassistant\nThe image is a radar chart that compares the performance of different models in a specific task, likely related to natural language processing or machine learning. The chart is divided into several axes, each representing a different model or method. The models are color-coded and labeled with their respective names. The axes are labeled with terms such as "VQA," "GQA," "MQA," "VQAv2," "MM-Vet," "LLaVA-Bench," "LLaVA-1',
("cuda", 8): 'user\n\nWhat do you see in this image?\nassistant\nThe image is a radar chart that compares the performance of different models in a specific task, likely related to natural language processing or machine learning. The chart is divided into several axes, each representing a different model or method. The models are color-coded and labeled with their respective names. The axes are labeled with terms such as "VQA," "GQA," "MQA," "VIZ," "TextVQA," "SQA-IMG," and "MQE." The radar chart shows',
}
) # fmt: skip
EXPECTED_DECODED_TEXT = EXPECTED_DECODED_TEXTS.get_expectation()
self.assertEqual(
self.processor.decode(output[0], skip_special_tokens=True),
EXPECTED_DECODED_TEXT,
@ -532,7 +547,10 @@ class LlavaOnevisionForConditionalGenerationIntegrationTest(unittest.TestCase):
# verify generation
output = model.generate(**inputs, max_new_tokens=50)
EXPECTED_DECODED_TEXT = ['user\n\nWhat do you see in this image?\nassistant\nThe image shows a scene from a wildlife camera, likely a security camera, capturing a moment in a natural setting. It features two deer, one larger and one smaller, grazing on the grass. The environment is foggy, suggesting early morning or late', 'user\n\nWhat do you see in this image?\nassistant\nIn the tranquil setting of this image, two cats are enjoying a peaceful nap on a vibrant pink blanket. The cat on the left, with its gray and black striped fur, is lying on its side, its head comfortably resting on the blanket. Its'] # fmt: skip
EXPECTED_DECODED_TEXT = [
'user\n\nWhat do you see in this image?\nassistant\nThe image shows a scene of two deer in a grassy area with trees in the background. The weather appears to be foggy, giving the scene a misty and somewhat mysterious atmosphere. The deer are standing close to each other, possibly grazing or',
'user\n\nWhat do you see in this image?\nassistant\nIn the tranquil setting of this image, two cats are enjoying a peaceful nap on a vibrant pink blanket. The cat on the left, with its gray and black striped fur, is lying on its side, its head comfortably resting on the blanket. Its',
] # fmt: skip
self.assertEqual(
self.processor.batch_decode(output, skip_special_tokens=True),
EXPECTED_DECODED_TEXT,