vllm - 💡(How to fix) Fix [CI Failure]: MultiModal Models Extended 2 - isaac test case OOMs [1 participants]

Fix Plan

To resolve the Out of Memory (OOM) issue in the test_single_image_models test, we will implement the following steps:

• Increase the batch size gradually to identify the threshold where OOM occurs
• Optimize memory usage by:
  • Reducing model size or complexity
  • Using mixed precision training
  • Gradient checkpointing
• Implement a try-except block to catch OOM exceptions and provide a meaningful error message

Example Code

import torch

# Increase batch size gradually
batch_sizes = [4, 8, 16, 32]
for batch_size in batch_sizes:
    try:
        # Run the test with the current batch size
        test_single_image_models(batch_size)
    except RuntimeError as e:
        if "out of memory" in str(e):
            print(f"OOM occurred at batch size {batch_size}")
            break

# Optimize memory usage
model = torch.nn.Module()
# Use mixed precision training
scaler = torch.cuda.amp.GradScaler()
# Gradient checkpointing
torch.utils.checkpoint()

# Implement try-except block
try:
    test_single_image_models()
except RuntimeError as e:
    if "out of memory" in str(e):
        print("OOM occurred. Please reduce batch size or model complexity.")

Verification

To verify that the fix worked, run the test with the optimized batch size and model configuration. Monitor the memory usage and test execution time to ensure that the OOM issue is resolved.

Extra Tips

• Regularly monitor test execution time and memory usage to detect potential issues early
• Use tools like nvidia-smi to monitor GPU memory usage during test execution
• Consider using a more efficient model architecture or optimizing the existing model for better performance