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[https://nvbugs/5520490][fix] Fix intermittent test failures by avoiding external web data pulls (#7879)

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Signed-off-by: Chang Liu (Enterprise Products) <9713593+chang-l@users.noreply.github.com>
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Chang Liu 2025-09-19 17:24:13 -07:00 committed by GitHub
parent 8adaf0bb78
commit 2e317a7db6
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2 changed files with 28 additions and 27 deletions
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44
tests/unittest/_torch/multimodal/test_find_num_image_tokens.py
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@ -1,9 +1,9 @@
import io
import os
from pathlib import Path
import pytest
import requests
from PIL import Image
from transformers import AutoConfig, AutoTokenizer
from utils.llm_data import llm_models_root
from tensorrt_llm import MultimodalEncoder
from tensorrt_llm._torch.models.modeling_llava_next import \
@ -12,38 +12,34 @@ from tensorrt_llm._torch.models.modeling_qwen2vl import \
Qwen2VLInputProcessorBase
from tensorrt_llm._torch.shared_tensor import SharedTensorContainer
from tensorrt_llm.inputs import default_multimodal_input_loader
from tensorrt_llm.inputs.utils import load_video
from tensorrt_llm.inputs.utils import load_image, load_video
test_data_root = Path(
os.path.join(llm_models_root(), "multimodals", "test_data"))
example_images = [
"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/seashore.png",
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png",
"https://huggingface.co/datasets/Sayali9141/traffic_signal_images/resolve/main/61.jpg",
str(test_data_root / "seashore.png"),
str(test_data_root / "inpaint.png"),
str(test_data_root / "61.jpg"),
]
example_videos = [
"https://huggingface.co/datasets/Efficient-Large-Model/VILA-inference-demos/resolve/main/OAI-sora-tokyo-walk.mp4",
"https://huggingface.co/datasets/Efficient-Large-Model/VILA-inference-demos/resolve/main/world.mp4",
str(test_data_root / "OAI-sora-tokyo-walk.mp4"),
str(test_data_root / "world.mp4"),
]
def download_image(url: str) -> Image.Image:
"""Download image from URL and return as PIL Image."""
response = requests.get(url, timeout=30)
response.raise_for_status()
img = Image.open(io.BytesIO(response.content))
return img.convert("RGB")
@pytest.fixture(scope="function")
def multimodal_model_configs():
"""Get multimodal model configurations for testing."""
model_configs = {
'llava-v1.6-mistral-7b-hf': {
'hf_model_dir': 'llava-hf/llava-v1.6-mistral-7b-hf',
'model_dir':
llm_models_root() / "multimodals" / "llava-v1.6-mistral-7b-hf",
'model_type': 'llava_next',
},
'qwen2.5-vl': {
'hf_model_dir': 'Qwen/Qwen2.5-VL-3B-Instruct',
'model_dir': llm_models_root() / "Qwen2.5-VL-3B-Instruct",
'model_type': 'qwen2_5_vl',
},
}
@ -66,7 +62,7 @@ def test_get_num_tokens_per_image(model_key, multimodal_model_configs):
pytest.skip(f"Skipping test for {model_key} - model not available")
model_config = multimodal_model_configs[model_key]
encoder_model_dir = model_config['hf_model_dir']
encoder_model_dir = model_config['model_dir']
model_type = model_config['model_type']
# Test configuration
@ -119,10 +115,10 @@ def test_get_num_tokens_per_image(model_key, multimodal_model_configs):
example_images
), f"Expected {len(example_images)} encoder outputs, got {len(encoder_outputs)}"
for image_idx, test_image_url in enumerate(example_images):
for image_idx, test_image in enumerate(example_images):
# Get test image dimensions
test_image = download_image(test_image_url)
test_image = load_image(test_image, format="pil")
image_width, image_height = test_image.size
# Get actual embedding tensor for this image
@ -173,7 +169,7 @@ def test_get_num_tokens_per_video(model_key, multimodal_model_configs):
pytest.skip(f"Skipping test for {model_key} - model not available")
model_config = multimodal_model_configs[model_key]
encoder_model_dir = model_config['hf_model_dir']
encoder_model_dir = model_config['model_dir']
model_type = model_config['model_type']
# Test configuration
@ -226,10 +222,10 @@ def test_get_num_tokens_per_video(model_key, multimodal_model_configs):
example_videos
), f"Expected {len(example_videos)} encoder outputs, got {len(encoder_outputs)}"
for video_idx, test_video_url in enumerate(example_videos):
for video_idx, test_video in enumerate(example_videos):
# Get test video dimensions
test_video = load_video(test_video_url, num_frames=8, format="pil")
test_video = load_video(test_video, num_frames=8, format="pil")
# load_video returns a list of frames, we only have one video
video_width, video_height = test_video[0].size

11
tests/unittest/_torch/multimodal/test_mm_encoder_standalone.py
View File

@ -2,6 +2,7 @@ import json
import os
import pytest
from utils.llm_data import llm_models_root
from tensorrt_llm import MultimodalEncoder
from tensorrt_llm._torch.shared_tensor import SharedTensorContainer
@ -22,8 +23,12 @@ def multimodal_model_config():
# You can extend this to support multiple models or get from environment
model_configs = {
'llava-v1.6-mistral-7b-hf': {
'model_name': 'llava-v1.6-mistral-7b-hf',
'hf_model_dir': 'llava-hf/llava-v1.6-mistral-7b-hf',
'model_name':
'llava-v1.6-mistral-7b-hf',
'hf_model_dir':
'llava-hf/llava-v1.6-mistral-7b-hf',
'model_dir':
llm_models_root() / "multimodals" / "llava-v1.6-mistral-7b-hf",
}
}
@ -47,7 +52,7 @@ def test_single_image_chat(model_key, multimodal_model_config):
)
# Extract model information from config
encoder_model_dir = multimodal_model_config['hf_model_dir']
encoder_model_dir = multimodal_model_config['model_dir']
# Test configuration
max_tokens = 64