Unfortunately, it is pretty difficult to do it outside of my model because there are many computations performed with the data before actually handing them to torch.cholesky, but as I said, if I comment out the line with the torch.cholesky call, I dont have these memory issues…
But still, maybe you re right and these issues stem from something else but only get revealed somehow through the usage of torch.cholesky.
When you say “Are you sure that you don’t hold on to some memory across iterations?” what do you mean by that? What kind of functions or coding could lead to holding onto memory?