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Fix aya_vision test (#38674)

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* fix 1: load_in_4bit=True,

* fix 2: decorateor

* fixfix 2: breakpoint

* fixfix 3: update

* fixfix 4: fast

* fixfix 5: cond

* fixfix 5: cond

* fixfix 6: cuda 8

* ruff

* breakpoint

* dtype

* a10

* a10

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar 2025-06-09 22:18:52 +02:00 committed by GitHub
parent b61c47f5a5
commit e55983e2b9
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2 changed files with 74 additions and 26 deletions
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97
tests/models/aya_vision/test_modeling_aya_vision.py
View File

@ -27,6 +27,7 @@ from transformers import (
from transformers.testing_utils import (
Expectations,
cleanup,
get_device_properties,
require_deterministic_for_xpu,
require_read_token,
require_torch,
@ -330,19 +331,39 @@ class AyaVisionModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTester
@require_read_token
@require_torch
class AyaVisionIntegrationTest(unittest.TestCase):
def setUp(self):
self.model_checkpoint = "CohereForAI/aya-vision-8b"
@classmethod
def setUpClass(cls):
cls.model_checkpoint = "CohereForAI/aya-vision-8b"
cls.model = None
@classmethod
def tearDownClass(cls):
del cls.model_checkpoint
cleanup(torch_device, gc_collect=True)
def tearDown(self):
cleanup(torch_device, gc_collect=True)
@classmethod
def get_model(cls):
# Use 4-bit on T4
load_in_4bit = get_device_properties()[0] == "cuda" and get_device_properties()[1] < 8
torch_dtype = None if load_in_4bit else torch.float16
if cls.model is None:
cls.model = AyaVisionForConditionalGeneration.from_pretrained(
cls.model_checkpoint,
device_map=torch_device,
torch_dtype=torch_dtype,
load_in_4bit=load_in_4bit,
)
return cls.model
@slow
@require_torch_accelerator
def test_small_model_integration_forward(self):
processor = AutoProcessor.from_pretrained(self.model_checkpoint)
model = AyaVisionForConditionalGeneration.from_pretrained(
self.model_checkpoint, device_map=torch_device, torch_dtype=torch.float16
)
model = self.get_model()
messages = [
{
"role": "user",
@ -361,7 +382,17 @@ class AyaVisionIntegrationTest(unittest.TestCase):
output = model(**inputs)
actual_logits = output.logits[0, -1, :5].cpu()
expected_logits = torch.tensor([0.4109, 0.1532, 0.8018, 2.1328, 0.5483], dtype=torch.float16)
EXPECTED_LOGITS = Expectations(
{
("xpu", 3): [0.4109, 0.1532, 0.8018, 2.1328, 0.5483],
# 4-bit
("cuda", 7): [0.1097, 0.3481, 3.8340, 9.7969, 2.0488],
("cuda", 8): [1.6396, 0.6094, 3.1992, 8.5234, 2.1875],
}
) # fmt: skip
expected_logits = torch.tensor(EXPECTED_LOGITS.get_expectation(), dtype=torch.float16)
self.assertTrue(
torch.allclose(actual_logits, expected_logits, atol=0.1),
f"Actual logits: {actual_logits}"
@ -374,9 +405,7 @@ class AyaVisionIntegrationTest(unittest.TestCase):
@require_deterministic_for_xpu
def test_small_model_integration_generate_text_only(self):
processor = AutoProcessor.from_pretrained(self.model_checkpoint)
model = AyaVisionForConditionalGeneration.from_pretrained(
self.model_checkpoint, device_map=torch_device, torch_dtype=torch.float16
)
model = self.get_model()
messages = [
{
"role": "user",
@ -398,7 +427,9 @@ class AyaVisionIntegrationTest(unittest.TestCase):
expected_outputs = Expectations(
{
("xpu", 3): "Whispers on the breeze,\nLeaves dance under moonlit sky,\nNature's quiet song.",
("cuda", 7): "Whispers on the breeze,\nLeaves dance under moonlit skies,\nNature's quiet song.",
# 4-bit
("cuda", 7): "Sure, here's a haiku for you:\n\nMorning dew sparkles,\nPetals unfold in sunlight,\n",
("cuda", 8): "Whispers on the breeze,\nLeaves dance under moonlit skies,\nNature's quiet song.",
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
@ -409,9 +440,7 @@ class AyaVisionIntegrationTest(unittest.TestCase):
@require_torch_accelerator
def test_small_model_integration_generate_chat_template(self):
processor = AutoProcessor.from_pretrained(self.model_checkpoint)
model = AyaVisionForConditionalGeneration.from_pretrained(
self.model_checkpoint, device_map=torch_device, torch_dtype=torch.float16
)
model = self.get_model()
messages = [
{
"role": "user",
@ -430,16 +459,24 @@ class AyaVisionIntegrationTest(unittest.TestCase):
decoded_output = processor.decode(
generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
)
expected_output = "The image depicts a cozy scene of two cats resting on a bright pink blanket. The cats," # fmt: skip
expected_outputs = Expectations(
{
("xpu", 3): "The image depicts a cozy scene of two cats resting on a bright pink blanket. The cats,",
# 4-bit
("cuda", 7): 'The image depicts two cats comfortably resting on a pink blanket spread across a sofa. The cats,',
("cuda", 8): 'The image depicts a cozy scene of two cats resting on a bright pink blanket. The cats,',
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
self.assertEqual(decoded_output, expected_output)
@slow
@require_torch_accelerator
def test_small_model_integration_batched_generate(self):
processor = AutoProcessor.from_pretrained(self.model_checkpoint)
model = AyaVisionForConditionalGeneration.from_pretrained(
self.model_checkpoint, device_map=torch_device, torch_dtype=torch.float16
)
model = self.get_model()
# Prepare inputs
messages = [
[
@ -472,7 +509,9 @@ class AyaVisionIntegrationTest(unittest.TestCase):
expected_outputs = Expectations(
{
("xpu", 3): "Wooden path to water,\nMountains echo in stillness,\nPeaceful forest lake.",
("cuda", 7): "Wooden path to water,\nMountains echo in stillness,\nPeaceful forest scene.",
# 4-bit
("cuda", 7): "Wooden bridge stretches\nMirrored lake below, mountains rise\nPeaceful, serene",
("cuda", 8): 'Wooden path to water,\nMountains echo in stillness,\nPeaceful forest scene.',
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
@ -485,7 +524,16 @@ class AyaVisionIntegrationTest(unittest.TestCase):
# Check second output
decoded_output = processor.decode(output[1, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
expected_output = 'This image captures a vibrant street scene in a bustling urban area, likely in an Asian city. The focal point is a' # fmt: skip
expected_outputs = Expectations(
{
("xpu", 3): 'This image captures a vibrant street scene in a bustling urban area, likely in an Asian city. The focal point is a',
# 4-bit
("cuda", 7): 'This vibrant image captures a bustling street scene in a multicultural urban area, featuring a traditional Chinese gate adorned with intricate red and',
("cuda", 8): 'This image captures a vibrant street scene in a bustling urban area, likely in an Asian city. The focal point is a',
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
self.assertEqual(
decoded_output,
@ -498,9 +546,7 @@ class AyaVisionIntegrationTest(unittest.TestCase):
@require_deterministic_for_xpu
def test_small_model_integration_batched_generate_multi_image(self):
processor = AutoProcessor.from_pretrained(self.model_checkpoint)
model = AyaVisionForConditionalGeneration.from_pretrained(
self.model_checkpoint, device_map=torch_device, torch_dtype=torch.float16
)
model = self.get_model()
# Prepare inputs
messages = [
[
@ -543,7 +589,8 @@ class AyaVisionIntegrationTest(unittest.TestCase):
expected_outputs = Expectations(
{
("xpu", 3): "Wooden path to water,\nMountains echo in stillness,\nPeaceful forest lake.",
("cuda", 7): "Wooden path to water,\nMountains echo in stillness,\nPeaceful forest scene.",
("cuda", 7): 'Wooden bridge stretches\nMirrored lake below, mountains rise\nPeaceful, serene',
("cuda", 8): 'Wooden path to water,\nMountains echo in stillness,\nPeaceful forest scene.',
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
@ -559,10 +606,12 @@ class AyaVisionIntegrationTest(unittest.TestCase):
expected_outputs = Expectations(
{
("xpu", 3): "The first image showcases the Statue of Liberty, a colossal neoclassical sculpture on Liberty Island in New York Harbor. Standing at ",
("cuda", 7): "The first image showcases the Statue of Liberty, a colossal neoclassical sculpture on Liberty Island in New York Harbor. Standing at a",
("cuda", 7): 'The first image showcases the Statue of Liberty, a monumental sculpture located on Liberty Island in New York Harbor. Standing atop a',
("cuda", 8): 'The first image showcases the Statue of Liberty, a colossal neoclassical sculpture on Liberty Island in New York Harbor. Standing at ',
}
) # fmt: skip
expected_output = expected_outputs.get_expectation()
self.assertEqual(
decoded_output,
expected_output,

3
tests/models/aya_vision/test_processor_aya_vision.py
View File

@ -17,7 +17,7 @@ import tempfile
import unittest
from transformers import AutoProcessor, AutoTokenizer, AyaVisionProcessor
from transformers.testing_utils import require_read_token, require_torch, require_vision
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_processing_common import ProcessorTesterMixin
@ -31,7 +31,6 @@ if is_vision_available():
from transformers import GotOcr2ImageProcessor
@require_read_token
@require_vision
class AyaVisionProcessorTest(ProcessorTesterMixin, unittest.TestCase):
processor_class = AyaVisionProcessor