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ROCM_FIXES.md

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ROCm/AMD and PyTorch 2.7+ Compatibility Fixes

Issues Fixed

1. PyTorch 2.7 weights_only=True Security Feature Conflict

Problem: PyTorch 2.7.0+rocm6.3 changed the default weights_only parameter to True, blocking YOLO model loading due to getattr function restriction.

Error:

WeightsUnpickler error: Unsupported global: GLOBAL getattr was not an allowed global by default

Solution: Added getattr to PyTorch safe globals during module initialization in subcore.py:

# Add getattr to safe globals for PyTorch 2.6+ compatibility
if hasattr(torch.serialization, 'add_safe_globals'):
    torch.serialization.add_safe_globals([getattr])
    logging.info("[Impact Pack/Subpack] Added getattr to PyTorch safe globals for YOLO model compatibility")

2. ROCm Device Auto-Detection

Enhancement: Added automatic ROCm/CUDA device detection for inference functions.

Changes:

  • inference_bbox(): Auto-detects and uses CUDA/ROCm when available
  • inference_segm(): Auto-detects and uses CUDA/ROCm when available
# If device is empty and CUDA/ROCm is available, use it
if not device and torch.cuda.is_available():
    device = "cuda"

Verification

Test 1: Model Loading

python3 -c "
from modules import subcore
model = subcore.load_yolo('/path/to/model.pt')
print('Model loaded successfully!')
"

Test 2: ROCm Detection

python3 -c "
import torch
print(f'PyTorch: {torch.__version__}')
print(f'CUDA/ROCm available: {torch.cuda.is_available()}')
print(f'Device: {torch.cuda.get_device_name() if torch.cuda.is_available() else \"CPU\"}')
"

Environment

  • OS: Linux
  • Python: 3.12.3
  • PyTorch: 2.7.0+rocm6.3
  • GPU: AMD Radeon RX 7900 XTX
  • ComfyUI: 0.3.44

Files Modified

  1. /modules/subcore.py:
    • Added getattr to safe globals during initialization
    • Enhanced device auto-detection in inference functions

Testing Status

✅ All YOLO models load successfully
✅ ROCm device detection working
✅ No security warnings
✅ Backward compatibility maintained

Notes

  • The fix maintains PyTorch's security features while allowing trusted YOLO models
  • ROCm is treated as CUDA in PyTorch, so device="cuda" works for both
  • No changes needed to existing workflows or user code