I am very happy to announce that the latest release candidate of Cmdstan is now available on Github! This release cycle is dedicated to some nice “slice of life” features for Stan users and developers which I’ll go over below. You can find the release candidate for cmdstan here.
Vectorized binary functions
First, for users, we’ve started adding vectorized binary functions to the language. This means that users can now write code such as
matrix[17, 93] u[12];
matrix[17, 93] z[12];
z = pow(u, 2.0);
which provides the same results as calling
for (k in 1:12) {
for (j in 1:93) {
for (i in 1:17) {
z[i, j, k] = pow(u[i, j, k], 2.0);
}
}
}
The official docs are not updated from 2.24 until the full release of 2.25. Until then, the list of vectorized functions should suffice:
- bessel_first_kind, bessel_second_kind
- beta, lbeta
- binary_log_loss
- binomial_coefficient_log
- choose
- falling_factorial, rising_factorial, log_falling_factorial, log_rising_factorial
- fdim, fmax, fmin, fmod
- gamma_p, gamma_q
- hypot
- ldexp
- lmgamma
- log_diff_exp, log_inv_logit_diff
- log_modified_bessel_first_kind, modified_bessel_first_kind, modified_bessel_second_kind
- multiply_log
- owens_t
- pow
Improved reliability and minor cmdstan user-facing improvements
- Allowing
C0ingaussian_dlm_obs_lpdfandgaussian_dlp_obs_rngto now be a positive semidefinite matrix. -
binomial_lpmfnow works more reliably when the probability parameter is 0.0 or 1.0. - We’ve added an option to control the number of significant figures in the Cmdstan output CSV as well as when working with
stansummary. - Users can now download a specific version of stanc3, not only the most recent one.
- We fixed a bug when building the Boost library on MacOS.
User controlled unnormalized distribution syntax for the target +=
As you are probably aware
target += normal_lpdf(x| mu, sigma);
and
x ~ normal(mu, sigma);
behave differently. The functional form and hence target += includes normalizing constants (like log√2π in normal_lpdf). The sampling statement form (with ~ ) drops normalizing constants and everything else not relevant for computing the autodiff gradient in the samplers and optimizers.
We have now added the option of using unnormalized distribution with the target += syntax as well. This can be done by using the _lupdf or _lupmf suffix. So for example
target += normal_lupdf(x| mu, sigma);
is now equivalent to the sampling statement above. Official documentation for this feature is still a work in progress, but in the meantime you can read more on this here.
This feature will especially be useful with reduce_sum where the sampling statements can not be used.
Simplified makefile acces to C++ compiler optimizations
The backend Stan Math library is in the middle of a large refactor. The details are given below. Due to some of the changes in the backend, users who utilize the ODE solvers in Stan may see a small performance decrease in some cases. To fix that you can add the STAN_COMPILER_OPTIMS flag to the make/local to turn on link-time optimization for Stan which should remove any performance issues. Turning these optimizations can actually lead to speedups in other models as well. We are still investigating where and when this is beneficial in order to handle these optimizations automatically in the next releases.
OpenCL support
Users can now use GLM functions with OpenCL on GPUs for cases where any argument is a parameter, we’ve rewritten them to accept parameters or data for any of their input arguments. With the newest release of brms which can use the cmdstan backend it should be easier for users to access these methods.
Changes in the Stan backend
The Stan Math backend is undergoing a lot of changes at the moment (we’ve had 99 PRs since the last release!). There are three larger projects that are in lead by Ben Bales, Steve Bronder and Tadej Ciglarič. These are:
- Better handling and use of Eigen expressions
Almost all functions in the Stan Math library were refactored to handle Eigen expressions and use Eigen expressions internally. This will lead to better efficiency in the future but for some functions we have already observed significant speedups now.
- More efficient matrix algebra
We have reworked some major parts of Stan so that we can be way more efficient at matrix algebra. This is still a work in progress, but you can read more on that in this thread. While this has not been exposed to Stan, we had to make some changes in the backend that are used in current Stan programs as well. We made sure there was not a serious performance hit to current Stan programs and that the fast stuff we are writing now gives the same numeric answers from our current methods.
- Refactored reverse mode autodiff functions
Tadej figured out a wonderfully nice pattern for writing reverse mode autodiff functions which we call reverse_pass_callback(). reverse_pass_callback() breaks up the fact that reverse mode autodiff is
- Running the regular function
- Saving the data
- Adding a callback to a stack to calculate the adjoints in the reverse pass.
The pattern leads to some rather pretty code. It also leads to 15% speedup or so in some cases which is nice.
We would also like to note that we have put a lot of effort into testing these backend changes. We are running function level performance tests and also check all Math functions for leaks with an address sanitizer. But we still need your help in making sure none of these refactors affected your Stan models. So please try your models and report if you see any improvements or more importantly any performance regressions.
Please test the release candidate with your models and report back your findings. The Stan development team appreciates your time and help in making Stan more efficient while maintaining a high level of reliability.
If everything goes according to plan, the 2.25 version will be released next Thursday.
How to install?
Download the tar.gz file from the link above, extract it and use it the way you use any Cmdstan release. We also now have an online Cmdstan guide available at https://mc-stan.org/docs/2_24/cmdstan-guide/
If you are using cmdstanpy, make sure you point to the folder where you extracted the tar.gz file with
set_cmdstan_path(os.path.join('path','to','cmdstan'))
With cmdstanr you can install the release candidate using
install_cmdstan(release_url = "https://github.com/stan-dev/cmdstan/releases/download/v2.25.0-rc1/cmdstan-2.25.0-rc1.tar.gz", cores = 4)
And then select the RC with
set_cmdstan_path(file.path(Sys.getenv("HOME"), ".cmdstanr", "cmdstan-2.25.0-rc1"))