pytorch - 💡(How to fix) Fix float32/16/bf16 NaN/Inf → int: CPU and GPU disagree [1 participants]

TL;DR

The issue can be addressed by ensuring consistent handling of NaN and infinity values when converting between float and integer tensors on CPU and GPU.

Guidance

• Verify that the PyTorch version being used is up-to-date, as the issue might be related to a known bug in version 2.9.0.
• Check the documentation for torch.tensor() and to() methods to ensure correct usage, especially when dealing with NaN and infinity values.
• Consider using a try-except block to catch and handle potential errors when converting between data types.
• Test the code on both CPU and GPU to identify any inconsistencies in behavior.

Example

import torch

try:
    x = torch.tensor(float("nan"), dtype=torch.float32)
    cpu_v = x.to(torch.int32).item()
    gpu_v = x.cuda().to(torch.int32).cpu().item()
    print(f"CPU={cpu_v}  GPU={gpu_v}")
except RuntimeError as e:
    print(f"Error: {e}")

Notes

The provided code snippet and output suggest a potential issue with the conversion of NaN and infinity values between float and integer tensors on CPU and GPU. However, without further information about the expected behavior, it is difficult to provide a definitive solution.

Recommendation

Apply workaround: The code should be modified to handle NaN and infinity values explicitly, using try-except blocks or conditional statements to ensure consistent behavior on both CPU and GPU.