Update grid for CUDA #233
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Update grid for CUDA #233
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Hi Ziming!
I noticed using "update_grid_from_samples", which really improves the training on CPU, leads to NaN in the network and gradient when used on CUDA. The problem seems to be coming from the fit of the B spline coefficients in spline.py:
coef = torch.linalg.lstsq(mat.to(device), y_eval.unsqueeze(dim=2).to(device),
driver='gelsy' if device == 'cpu' else 'gels').solution[:, :, 0]
Here "mat" is the B spline function which are not a full rank matrix depending on the samples. It seems that the driver 'gels' cannot handle degenerate matrices. So I just sent that operation to the CPU, which allows to use 'gelsy' and handle degenerate matrices. Perhaps there is a better solution but I'm committing it just in case, since it worked for me on both CUDA and MPS.