pytorch - 💡(How to fix) Fix CPU promotes bf16→f32 for cumprod and11 GPU in bfloat16. [1 participants]

print(f"CPU error vs float64 reference: {cpu_err:.4e}") print(f"GPU error vs float64 reference: {gpu_err:.4e} ") CPU error vs float64 reference: 1.6893e-02 GPU error vs float64 reference: 5.6472e-01

TL;DR

The issue can be mitigated by using a higher precision data type for the GPU calculation.

Guidance

• The large difference in error between CPU and GPU calculations suggests a precision issue, likely due to the use of torch.bfloat16 on the GPU.
• To verify the cause, try converting the GPU tensor to a higher precision type, such as torch.float32, before performing the calculation.
• Consider using torch.cumprod with a higher precision data type, such as torch.float32 or torch.double, to reduce the error.
• If the issue persists, investigate the specific GPU architecture and its support for torch.bfloat16 to determine if there are any known limitations or workarounds.

Example

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

Notes

The issue may be specific to certain GPU architectures or drivers, and further investigation may be needed to determine the root cause.

Recommendation

Apply workaround: use a higher precision data type, such as torch.float32, for the GPU calculation to reduce the error. This is because the torch.bfloat16 type may not provide sufficient precision for the calculation, leading to the large error difference between CPU and GPU.