pytorch - 💡(How to fix) Fix float16 cumsum GPU 11–15x less accurate [1 participants]

print(f"CPU error vs float64 reference: {cpu_err:.4e} (CPU promotes f16→f32 internally)") print(f"GPU error vs float64 reference: {gpu_err:.4e} <-- BUG (GPU stays in f16)") CPU error vs float64 reference: 5.0529e-01 GPU error vs float64 reference: 5.5936e+00

TL;DR

The issue can be mitigated by promoting the data type of the tensor on the GPU to a higher precision, such as torch.float32, to reduce the error in the cumulative sum calculation.

Guidance

• The error is likely due to the lower precision of torch.float16 on the GPU, which can be verified by checking the data type of the tensor after it is moved to the GPU.
• To mitigate this issue, the data type of the tensor can be promoted to torch.float32 before performing the cumulative sum calculation on the GPU.
• The cpu_err and gpu_err values can be used to verify the effectiveness of the mitigation.
• The ratio of gpu_err to cpu_err can be used to quantify the improvement in accuracy.

Example

gpu = torch.cumsum(x.cuda().to(torch.float32), dim=0).cpu().numpy()

Notes

The issue may still exist even after promoting the data type, due to the inherent limitations of floating-point precision. However, promoting to torch.float32 should significantly reduce the error.

Recommendation

Apply workaround: promote the data type of the tensor on the GPU to torch.float32, as this should improve the accuracy of the cumulative sum calculation without requiring a version upgrade.