Add debug logging to GPU detection

- Added debug output to see what CUDA device names are detected
- Will help identify why AMD GPU is still being counted as CUDA device
- Debug output shows device names and functional test results
- User can now see what devices PyTorch is detecting

vidai/compat.py

@@ -267,8 +267,11 @@ def detect_gpu_backends() -> dict:
                 try:
                     # Test if device is actually functional by trying a simple operation
                     device_name = torch.cuda.get_device_name(i).lower()
+                    print(f"CUDA Device {i}: {device_name}")  # Debug output
+
                     # Only consider NVIDIA GPUs
                     if not any(keyword in device_name for keyword in ['nvidia', 'geforce', 'quadro', 'tesla', 'rtx', 'gtx']):
+                        print(f"Skipping non-NVIDIA device {i}: {device_name}")
                         continue
 
                     # Test device functionality
@@ -277,12 +280,15 @@ def detect_gpu_backends() -> dict:
                             test_tensor = torch.tensor([1.0], device=f'cuda:{i}')
                             test_result = test_tensor + 1  # Simple operation
                             del test_tensor, test_result
+                        print(f"CUDA Device {i} is functional: {device_name}")
                         working_cuda_devices += 1
-                    except Exception:
+                    except Exception as e:
+                        print(f"CUDA Device {i} failed functional test: {device_name} - {e}")
                         # Device not functional, skip it
                         continue
 
-                except Exception:
+                except Exception as e:
+                    print(f"Error checking CUDA device {i}: {e}")
                     continue
 
             if working_cuda_devices > 0: