0cf477cb0fc956527b63d11638372c03dce1d1e6,gpytorch/utils/pivoted_cholesky.py,,woodbury_factor,#Any#Any#,102

Before Change

    shifted_mat = shifted_mat + shifted_mat.new(k).fill_(1).diag()

    if low_rank_mat.ndimension() == 3:
        R = torch.cat(
            [
                torch.potrs(low_rank_mat[i], shifted_mat[i].potrf()).unsqueeze(0)
                for i in range(shifted_mat.size(0))
            ]
        )
    else:
        R = torch.potrs(low_rank_mat, shifted_mat.potrf())

    return R

After Change

    shifted_mat = shifted_mat + shifted_mat.new(k).fill_(1).diag()

    if low_rank_mat.ndimension() == 3:
        R = batch_potrs(low_rank_mat, batch_potrf(shifted_mat))
    else:
        R = torch.potrs(low_rank_mat, shifted_mat.potrf())

    return R

Instances