Pathfinder

Hi, I built a JAX implementation of pathfinder here; since it’s in the list of #154 I can try to merge it. Any requirement for the API? Shall it fit the

new_state, info =  kernel(rng_key, state)

pattern, even if it’s not a MCMC kernel?

Michele

[junpenglao]

We had put off general VI implementation because it requires a bit more design decision.

In general we need:

target_density: callable = pytree -> scalar tensor
approx_density: callable = pytree -> scalar tensor
approx_sample: callable = PRNGKey -> pytree
KL_divergence: callable = pytree -> scalar tensor

which are much easier with a PPL.

I think for “standard” approximation that are multivariate Gaussian (meanfield and full rank ADVI, Pathfinder, Laplace approximation), we can start off with stand alone implementation and do abstraction later on. Also, even for “standard” approximation listed above, Pathfinder is still a bit different as it use a minimizer, which means user dont need to worry about number of approximation step etc.

[miclegr]

Hi guys, sorry it took this long 😄 got busy with interviewing then took some time off.

I’ve opened the pull request now, an introduction to the method and its implementation in blackjax here.

Some design decision/open points:

• basically pathfinder works by running L-BGFS optimization and storing the steps of the optimization path. Then for each of those steps the “implied” ELBO to the target distribution is estimated. Finally samples are drawn from the multi-normal distribution with the highest ELBO. In order to fit this schema into the init/step interface I have put the L-BFGS run and the estimation of the “implied” ELBOs in the init phase, and the sampling from the multi-normal distribution with highest ELBO into the step phase. This unnaturally makes all the computational heavy processing in the init, while the step function is just a draw of a single sample from a multi-normal distribution. The init function is jittable, and an helper function for making multiple samples from the multi-normal is available as well.

• Unfortunately the L-BFGS optimizer is not working well when optimizing model’s (negative) log-lilekihood while working in jax’s default float32 mode, usually convergence fails. I’ve noticed that by simply dividing model’s likelihood by number of observations (hence optimizing model’s average likelihood) optimization converges. Unfortunately given blackjax design it’s quite unnatural to ask for average likelihood. So in the end it’s recommended to turn on double precision mode

• A consequence of this is that, for running pathfinder tests, double precision mode is needed. Since double precision mode needs to be set at jax initialization time (see here), test suite should support some test in float32 mode and some in float64 mode (e.g. by running them in separate processes). It’s feasible (see here for example) but not implemented at the moment. Right know i’ve just set the float64 mode for that tests:

https://github.com/miclegr/blackjax/blob/128ce00bd2b28e06f79d126c9d3c097b6378ccc8/tests/test_pathfinder.py#L2-L3

But this wont work when running the full suite of tests. Happy to spend some time to get the multi float mode tests in place, if that’s the solution.

• Here I’ve introduced an adaptation scheme where the inverse mass matrix estimation it taken from pathfinder and then the dual averaging adapatation is run for estimating step size. This is NOT discussed in the pathfinder paper (here, section IV), but it makes sense to me. So it’s a bit experimental and for sure needs some more validation.

Happy to discuss feedbacks.

Michele