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Remove unused unit tests #1923

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Remove unused unit tests #1923

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82 changes: 50 additions & 32 deletions test/python/test_qwen_fara_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,8 @@
import onnxruntime_genai as og
import pytest
from _test_utils import run_subprocess
from PIL import Image
from transformers import AutoProcessor

logging.basicConfig(format="%(asctime)s %(name)s [%(levelname)s] - %(message)s", level=logging.DEBUG)
log = logging.getLogger("qwen-fara-vision-tests")
Expand Down Expand Up @@ -70,35 +72,6 @@ def test_qwen_fara_vision_load_from_bytes(test_data_path, relative_model_path, r
assert "pixel_values" in inputs


@pytest.mark.parametrize("relative_model_path", [Path("qwen-vision-preprocessing")])
@pytest.mark.parametrize(
"relative_image_paths",
[[Path("images") / "australia.jpg", Path("images") / "landscape.jpg"]],
)
@pytest.mark.skip(reason="Multiple images not fully supported in dummy model - image_grid_thw shape issue")
def test_qwen_fara_vision_multiple_images(test_data_path, relative_model_path, relative_image_paths):
"""Test processing multiple images with Qwen/Fara models."""
model_path = os.fspath(Path(test_data_path) / relative_model_path)
model = og.Model(model_path)

processor = model.create_multimodal_processor()

image_paths = [
os.fspath(Path(test_data_path) / relative_image_path) for relative_image_path in relative_image_paths
]
images = og.Images.open(*image_paths)

# Add vision tokens for each image
prompt = ""
for _ in range(len(relative_image_paths)):
prompt += "<|vision_start|><|image_pad|><|vision_end|>"
prompt += "Describe these images"
inputs = processor(prompt, images=images)

assert inputs is not None
assert "pixel_values" in inputs


@pytest.mark.parametrize("relative_model_path", [Path("qwen-vision-preprocessing")])
def test_qwen_fara_text_only_generation(test_data_path, relative_model_path):
"""Test text-only generation without images."""
Expand Down Expand Up @@ -213,9 +186,54 @@ def test_qwen_fara_accuracy_comparison(test_data_path, relative_model_path, rela
pixel_max = pixel_array.max()
assert pixel_min >= -10.0 and pixel_max <= 10.0, f"Pixel values out of expected range: [{pixel_min}, {pixel_max}]"

log.debug(f"ONNX pixel_values shape: {pixel_array.shape}")
log.debug(f"ONNX pixel_values dtype: {pixel_array.dtype}")
log.debug(f"ONNX pixel_values range: [{pixel_min:.4f}, {pixel_max:.4f}]")
try:
pytorch_processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", trust_remote_code=True)

# Load and process image with PyTorch
pil_image = Image.open(image_path)
pytorch_inputs = pytorch_processor(text=prompt, images=pil_image, return_tensors="pt")

if "pixel_values" in pytorch_inputs:
pytorch_pixel_values = pytorch_inputs["pixel_values"].numpy()

# Check if shapes match
if pixel_array.shape == pytorch_pixel_values.shape:
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@tianleiwu tianleiwu Dec 18, 2025 •
edited
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Do we have a test case with shape match so that this branch is hit?

np.testing.assert_allclose(
pixel_array,
pytorch_pixel_values,
rtol=1e-5,
atol=1e-6,
err_msg="ONNX pixel_values should match PyTorch reference implementation",
)
log.debug("Accuracy validation passed: ONNX matches PyTorch reference")
else:
# Shape mismatch is expected due to preprocessing differences
log.warning(
f"Shape mismatch due to preprocessing differences: ONNX {pixel_array.shape} vs PyTorch {pytorch_pixel_values.shape}"
)
log.info(
"Skipping numerical comparison due to shape mismatch - this is expected with different preprocessing"
)

# Validate that both have same dtype and reasonable value ranges
assert pixel_array.dtype == pytorch_pixel_values.dtype, (
f"Data types should match: ONNX {pixel_array.dtype} vs PyTorch {pytorch_pixel_values.dtype}"
)

# Both should have similar value ranges even if shapes differ
onnx_range = (pixel_array.min(), pixel_array.max())
pytorch_range = (pytorch_pixel_values.min(), pytorch_pixel_values.max())
log.info(f"Value range validation: ONNX {onnx_range} vs PyTorch {pytorch_range}")

# Allow for reasonable variation in ranges
assert abs(onnx_range[0] - pytorch_range[0]) < 1.0, "Min values should be in similar range"
assert abs(onnx_range[1] - pytorch_range[1]) < 1.0, "Max values should be in similar range"
else:
pytest.fail("PyTorch processor did not return pixel_values")

except Exception as e:
log.error(f"Could not compare with PyTorch reference: {e}")
pytest.fail(f"PyTorch comparison failed: {e}")


@pytest.mark.parametrize("relative_model_path", [Path("qwen-vision-preprocessing")])
Expand Down
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