I think this is inherent in the way pruning is implemented using reparametrization. 
One way to overcome the error message could be to have a workflow where you do pruning and then remove the reparametrization.
The other option is to make it work with non-flattened inputs, i.e. either

to silence or avoid the warning using the Python warnings mechanism or
to disable CuDNN, if the flattening and Python warning overhead is larger than the savings by CuDNN optimizations,
rolling your own JIT fused RNN might be another option, but sadly it has never become as much the standard as one might have hoped.

I can see how neither option is terribly attractive but it seems that things are not working together as well as they should.
Best regards
Thomas