Do you have some examples where this causes a memory issue? Usually, the factorizations of sparse matrices have fill in, which gets worse with larger matrices - and so this extra copy is not really a problem.
I suppose you can decrement! in place yourself, call the solver API directly and then increment! again afterwards - but be sure it is worthwhile to do that.
Writing supernodal or multifrontal sparse solvers is a lot of effort - instead just doing what I suggested above is trivial, if necessary. While it is likely a pure Julia left looking solver may come up soon (and is valuable in many situations), it only makes sense to use if your problems have very little structure.