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import torch
import os
import sys
import traceback
import gradio as gr
from PIL import Image
from transformers import AutoModel, CLIPImageProcessor
print("=" * 50)
print("SIMPLE INTERNVIT-6B MODEL LOADING TEST")
print("=" * 50)
# System information
print(f"Python version: {sys.version}")
print(f"PyTorch version: {torch.__version__}")
print(f"CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
print(f"CUDA version: {torch.version.cuda}")
print(f"GPU count: {torch.cuda.device_count()}")
for i in range(torch.cuda.device_count()):
print(f"GPU {i}: {torch.cuda.get_device_name(i)}")
# Memory info
print(f"Total GPU memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB")
print(f"Allocated GPU memory: {torch.cuda.memory_allocated() / 1e9:.2f} GB")
print(f"Reserved GPU memory: {torch.cuda.memory_reserved() / 1e9:.2f} GB")
else:
print("CUDA is not available. This is a critical issue for model loading.")
# Create a function to load and test the model
def load_and_test_model():
try:
print("\nLoading model with bfloat16 precision and low_cpu_mem_usage=True...")
model = AutoModel.from_pretrained(
'OpenGVLab/InternViT-6B-224px',
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True)
if torch.cuda.is_available():
print("Moving model to CUDA...")
model = model.cuda()
model.eval()
print("✓ Model loaded successfully!")
# Now try to process a test image
print("\nLoading image processor...")
image_processor = CLIPImageProcessor.from_pretrained('OpenGVLab/InternViT-6B-224px')
print("✓ Image processor loaded successfully!")
# Create a simple test image
print("\nCreating test image...")
test_image = Image.new('RGB', (224, 224), color='red')
# Process the test image
print("Processing test image...")
pixel_values = image_processor(images=test_image, return_tensors='pt').pixel_values
if torch.cuda.is_available():
pixel_values = pixel_values.to(torch.bfloat16).cuda()
# Get model parameters
params = sum(p.numel() for p in model.parameters())
print(f"Model parameters: {params:,}")
# Forward pass
print("Running forward pass...")
with torch.no_grad():
outputs = model(pixel_values)
print("✓ Forward pass successful!")
print(f"Output shape: {outputs.last_hidden_state.shape}")
return f"SUCCESS: Model loaded and test passed!\nParameters: {params:,}\nOutput shape: {outputs.last_hidden_state.shape}"
except Exception as e:
print(f"\n❌ ERROR: {str(e)}")
traceback.print_exc()
return f"FAILED: Error loading model or processing image\nError: {str(e)}"
# Create a simple Gradio interface
def create_interface():
with gr.Blocks(title="InternViT-6B Test") as demo:
gr.Markdown("# InternViT-6B Model Loading Test")
with gr.Row():
test_btn = gr.Button("Test Model Loading")
output = gr.Textbox(label="Test Results", lines=10)
test_btn.click(fn=load_and_test_model, inputs=[], outputs=output)
return demo
# Main function
if __name__ == "__main__":
# Print environment variables
print("\nEnvironment variables:")
relevant_vars = ["CUDA_VISIBLE_DEVICES", "NVIDIA_VISIBLE_DEVICES",
"TRANSFORMERS_CACHE", "HF_HOME", "PYTORCH_CUDA_ALLOC_CONF"]
for var in relevant_vars:
print(f"{var}: {os.environ.get(var, 'Not set')}")
# Set environment variable for better GPU memory management
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"
# Create and launch the interface
demo = create_interface()
demo.launch(share=False, server_name="0.0.0.0") |