Hi @dpernes,
the happy news is that between the last post and yours, we added documentations to make the Cholesky functions easier to find, so
torch.potrf(a).diag().prod()

gives you the determinant. (The functions are exactly the same as in 0.1.12, too, but be sure to use the master docs.)
If you want a differentiable version, you could make a Cholesky layer by combination with inverse (in lieu of having  triangular solving in autograd) and then do Cholesky.apply(v).diag().prod().
Although the notebook is not as finished as I would like, there is a Cholesky layer in my  notebook doing most basic Gaussian Process regression 187.
Best regards
Thomas