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

tests/unittest/_torch/multimodal/test_find_num_image_tokens.py

@@ -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 @@
     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
 

tests/unittest/_torch/multimodal/test_mm_encoder_standalone.py

@@ -2,6 +2,7 @@
 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