pytorch - 💡(How to fix) Fix float32 cumsum CPU exactly matches float64; GPU drifts [1 participants]

print(f"CPU error vs float64: {cpu_err:.4e} ") print(f"GPU error vs float64: {gpu_err:.4e} ") CPU error vs float64: 0.0000e+00 GPU error vs float64: 6.2242e+06

TL;DR

The issue can be mitigated by using a higher precision data type, such as torch.float64, for the cumulative sum calculation on the GPU.

Guidance

• The large error on the GPU suggests a precision issue, likely due to the use of torch.float32 for the cumulative sum calculation.
• To verify the issue, compare the results of the cumulative sum calculation on the CPU and GPU using torch.float64 instead of torch.float32.
• The error on the GPU can be reduced by casting the input tensor to torch.float64 before performing the cumulative sum calculation, like this: gpu = torch.cumsum(x.cuda().double(), dim=0).cpu().float().
• The version of PyTorch being used (2.9.0) may also be a factor, but without more information, it's unclear if upgrading to a newer version would resolve the issue.

Example

gpu = torch.cumsum(x.cuda().double(), dim=0).cpu().float()

Notes

The issue may be specific to the GPU architecture or the version of PyTorch being used. Further testing would be needed to determine the root cause and the most effective solution.

Recommendation

Apply workaround: the code change suggested in the example above can reduce the error on the GPU, but it may come at the cost of increased memory usage and computation time due to the use of torch.float64.