Sure - but on pytorch they suffer the kernel launch overhead each time through the loop, whereas on tensorflow and theano they do not. Which really impacts the kinds of algorithms that work well on each platform. Does that seem like a reasonable assessment to you?
Currently not many frameworks have actual fusion of kernels (to avoid launching many GPU kernels). If you look underneath a theano.scan or TF.scan, GPU kernels are still being launched individually (but are likely stream-overlapped where appropriate).
With TF's XLA compiler, they are slowly getting towards kernel fusion, which will then reduce launch overheads.
We have similar things in the works for pytorch: to quickly JIT at runtime the dynamic graph that is getting executed. More news on this will come when time-appropriate.
I WANT to use pytorch, but no bayesian learning or stochastic nodes like in edward. Any chance there are plans to for a compatibility layer with Edward or roll your own bayesian stuff?
Also, have you looked at Numba to do the jitting? Probably best not to have yet another separately maintained python JIT.
