I am very happy to announce that the latest release candidate of Cmdstan is now available on Github! This release cycle brings you some new functions, performance improvements and profiling. You can find the release candidate for cmdstan here.
Please test the release candidate with your models and report if you experience any problem. We also kindly invite you to test the new features and provide feedback. If you feel some of the new features could be improved or should be changed before the release, please do not hesitate to comment.
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 2.26 version will be released next Tuesday.
Below are some of the highlights of the new release.
New functions
-
chol2inv(L)- This function is still missing proper documentation but in short it returns the inverse of an input matrix which is a Cholesky factor. It follows the Rchol2invfunction. Its signature ismatrix chol2inv(matrix L). -
discrete_rangedistribution - documentation -
generalized_inverse- documentation -
linspaced_int_array- documentation -
svd_Uandsvd_V- documentation -
symmetrize_from_lower_tri- documentation
Profiling
Users can now profile the execution time of their models using profiling statements as shown in this example:
model {
matrix[N, N] cov;
matrix[N, N] L_cov;
profile("building_cov") {
cov = gp_exp_quad_cov(x, alpha, rho)
+ diag_matrix(rep_vector(sigma, N));
}
profile("cholesky_cov"){
L_cov = cholesky_decompose(cov);
}
profile("priors"){
rho ~ gamma(25, 4);
alpha ~ normal(0, 2);
sigma ~ normal(0, 1);
}
profile("likelihood") {
y ~ multi_normal_cholesky(rep_vector(0, N), L_cov);
}
}
Stan will report the execution time and memory consumption of automatic differentiation (AD) for the statements in the profile. The two main motivations for using profiling is to identify bottlenecks in a model and to evaluate different approaches for a section of a model.
Without profiling, users had to have a good and deep understanding of the underlying AD implementation in Math for all the functions in order to try and optimize a section of the model using different approaches (using other functions or reduce_sum for example). But even with a deep uderstanding of the AD, it was very hard to measure the effect of a change to a section of the model as the effect was hard to identify in the execution time reported for a model’s overall execution, which can be very noisy due to randomness of sampling as well as input/output.
See here for a preliminary profiling example. More documentation will be ready for the release.
Backend performance improvements
-
improved use of Eigen expressions
All functions in Stan Math
primas well as user-defined Stan functions now accept and use Eigen expressions.
Where applicable functions also return Eigen expressions. This change has been in the works for multiple release cycles now and is now finalized.
Users should experience increased performance of generated quantites for statements with multiple vector/row_vector/matrix operations and increased performance of some distributions in all blocks of a Stan model. -
increased and more robust performance of
reduce_sumWe have optimized memory handling and copies when using automatic differentiation with
reduce_sum.
Improved error messaging in the compiler and deprecation warnings
All functions and language features that have been deprecated now properly raise a warning during
compilation. You can use deprecated features/functions as normal until the next major release of Stan, but we advise you move to using the suggested non-deprecated function/language feature as soon as possible.
The Stan-to-C++ transpiler also now has an improved syntax/semantic error reporting.
OpenCL support for additional distributions
In addition to the GLM lpdf/lpmf functions, there are now 32 additional distributions that can utilize OpenCL on the GPU or CPU to speedup execution. The speedups vary between distributions and argument types (whether they are data or parameters). We have observed speedups in the range of 2- to 50-fold compared to sequential execution. We anticipate the next Stan release in April to have a fully-fledged GPU-enabled backend support for most Stan functions and any feedback at this point would be more than welcome.
List of OpenCL-enabled distributions for 2.26:
- bernoulli_lpmf, bernoulli_logit_lpmf, bernoulli_logit_glm_lpmf
- beta_lpdf, beta_proportion_lpdf
- binomial_lpmf
- categorical_logit_glm_lpmf
- cauchy_lpdf
- chi_square_lpdf
- double_exponential_lpdf
- exp_mod_normal_lpdf
- exponential_lpdf
- frechet_lpdf
- gamma_lpdf
- gumbel_lpdf
- inv_chi_square_lpdf
- inv_gamma_lpdf
- logistic_lpdf
- lognormal_lpdf
- neg_binomial_lpmf, neg_binomial_2_lpmf, neg_binomial_2_log_lpmf, neg_binomial_2_log_glm_lpmf
- normal_lpdf, normal_id_glm_lpdf
- ordered_logistic_glm_lpmf
- pareto_lpdf, pareto_type_2_lpdf
- poisson_lpmf, poisson_log_lpmf, poisson_log_glm_lpmf
- rayleigh_lpdf
- scaled_inv_chi_square_lpdf
- skew_normal_lpdf
- std_normal_lpdf
- student_t_lpdf
- uniform_lpdf
- weibull_lpdf
See here for instructions on how to setup and use OpenCL with CmdStan. Official Stan documentation will be available before the release.
We have also upgraded our OpenCL support for CPUs and integrated GPUs, limiting transfers when those devices are used.
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 have an online Cmdstan guide available at CmdStan User’s 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(version = "2.26.0-rc1", cores = 4)
And then select the RC with
set_cmdstan_path(file.path(Sys.getenv("HOME"), ".cmdstanr", "cmdstan-2.26.0-rc1"))
Be advised that cmdstanr/py do not yet handle profiling CSV’s automatically.