pytorch - 💡(How to fix) Fix CUDA torch.signbit returns false for negative float16 NaN [1 participants]

TL;DR

The inconsistency in torch.signbit results between CPU and CUDA for negative float16 NaN values can be addressed by ensuring consistent handling of NaN values across devices.

Guidance

• Verify that the input values are correctly interpreted as NaN with the sign bit set on both CPU and CUDA.
• Check the documentation for torch.signbit to see if there are any known issues or limitations related to NaN handling on CUDA devices.
• Consider filing a bug report or seeking assistance from the PyTorch community if the issue persists.
• Test the behavior with different types of input values to determine if the issue is specific to float16 NaN values.

Example

import torch

# Create a tensor with NaN values on CPU and CUDA
cpu_tensor = torch.tensor([float('nan')], dtype=torch.float16)
cuda_tensor = cpu_tensor.cuda()

# Test the behavior of torch.signbit on CPU and CUDA
cpu_result = torch.signbit(cpu_tensor)
cuda_result = torch.signbit(cuda_tensor).cpu()

print("CPU result:", cpu_result)
print("CUDA result:", cuda_result)

Notes

The issue may be related to differences in how NaN values are handled on CPU and CUDA devices. Further investigation is needed to determine the root cause of the inconsistency.

Recommendation

Apply a workaround by using a custom implementation to handle NaN values consistently across devices, as the current behavior of torch.signbit on CUDA devices appears to be incorrect.