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Miking DPPL

Miking DPPL is a framework for developing probabilistic programming languages (PPLs) using Miking. Currently, the framework includes the PPLs CorePPL and RootPPL. There is also a convenience Python plotting script available named dppl-plot.


Miking DPPL currently depends on:

  • Miking
  • Owl. Essentially, opam install owl.
  • A reasonably recent version of GCC.

Optional dependencies for RootPPL are:

  • A CUDA NVIDIA GPU and the CUDA Toolkit to run RootPPL in parallel on the GPU.
  • OpenMP to run RootPPL in parallel on the CPU (included in most recent versions of GCC).

Building, Installing, and Running Tests​

To build the CorePPL compiler cppl, simply run make in the project root, which produces the binary in build/cppl. To remove the binary, run make clean. The RootPPL compiler rppl is a g++/nvcc wrapper sh script and requires no build.

In addition to building cppl, there are three additional make targets:

make install​

Installs cppl, rppl, and scripts (e.g., dppl-plot) to $HOME/.local/bin, and also source dependencies to $HOME/.local/src (according to the systemd file system hierarchy). Install individual components using the install-coreppl, install-rootppl, and install-scripts targets.

make uninstall​

Uninstalls everything installed by make install.

make test​

Runs a comprehensive test suite for CorePPL. Currently, make test does not run any RootPPL tests.


CorePPL extends MExpr (see Miking) with probabilistic constructs for defining random variables and for likelihood updating. For example, the program

let x = assume (Beta 10.0 5.0) in
observe true (Bernoulli x);

encodes a simple distribution for the bias of a coin, by setting a Beta prior for the probability of observing heads (i.e., true) for a single flip of the coin.

You define random variables in CorePPL by providing a probability distribution to the assume construct. Currently, there is no generated documentation for available distributions (you have to look at the source code).

You can update the likelihood through the weight and observe constructs. With weight, you update the logarithm of the likelihood directly (e.g., weight (log 2) multiplies the likelihood with 2). With observe, you update the likelihood with the value of the pmf or pdf for the given distribution at the given observation. For example observe true (Bernoulli 0.5) updates the likelihood with a factor of 0.5.

The default option for inferring the distribution encoded by a CorePPL program is to compile it to MExpr (which then compiles to OCaml). You compile a CorePPL program using the command cppl -m <method>, where <method> is an inference algorithm (run the command cppl without any arguments to see the current list of available algorithms). For example, cppl -m is-lw compiles to a binary file out which you can subsequently run to produce likelihood-weighted samples from the distribution encoded by

$ ./out 10
0.80022937428 -0.222856874559
0.843424730606 -0.170284615588
0.80463734988 -0.217363600111
0.884065007231 -0.123224681456
0.660101541887 -0.415361604451
0.83498403869 -0.180342669655
0.430312010842 -0.84324472681
0.721170725777 -0.326879379468
0.675965445901 -0.391613319777
0.826919003807 -0.190048528529

The argument to out is the number of samples. The first row prints the log of the normalizing constant, and the subsequent rows the samples. The first column is the sample, and the second its log-weight. To visualize the distribution induced by the samples, pipe the output to dppl-plot: ./out 10 | dppl-plot (currently only supports float samples). Run dppl-plot --help for more advice.

For more help and options, run the cppl command without any arguments.

Example Models​

The directory coreppl/models contains a set of example CorePPL programs. A brief overview:

  • The "Hello, world!" of probabilistic programming (similar to the example above)
  • The same example implemented with the higher-order iter function.
  • The classical sprinkler model often used to illustrate Bayesian inference.
  • Bayesian linear regression for a simple data set.
  • A fairly simple state-space positioning model for a single data set.
  • diversification-models/crbd*.mc: Constant rate birth-death model from evolutionary biology for two data sets.
  • diversification-models/clads*.mc: Cladogenetic diversification rate shift model from evolutionary biology for the same two data sets.
  • latent-dirichlet-allocation/lda*.mc: Latent dirichlet allocation for some simple synthetic data sets.
  • vector-borne-disease/ An SEIR model for a single data set.

You compile and run the above models with the cppl command, and the models are also the basis for the test suite run as part of make test. See the test files under coreppl/test for suitable inference algorithms and parameters.

The infer Keyword and its Limitations​

The experimental infer keyword in CorePPL allows users to apply inference algorithms within CorePPL programs. For examples showing how to use infer, see coreppl/models/ and coreppl/models/ To see the available inference algorithms and their parameters for use with infer, you must currently consult the source code under coreppl/src/inference.

If a CorePPL program contains no applications of infer, the entire program encodes one single inference problem. However, if the program contains one or more infer applications, the program may encode one or more inference problems simultaneously.

The infer keyword currently has a few limitations on how it can be used:

  • The second argument to infer must be provided inline. That is, we cannot store the argument in a let-binding to reuse it. We have to write it out explicitly for each case:

    let d = infer (Importance {particles = 1000}) model in -- OK

    let args = Importance {particles = 1000} in
    let d = infer args model in -- ERROR
  • The definition of the model function must be visible from the infer. We cannot use a higher-order function as the model, as the lambda variable hides the definition from our transformation.

    let f = lam. ... in
    let d = infer (Importance {particles = 1000}) f in -- OK

    let d = infer (Importance {particles = 1000}) (lam. ...) in -- OK

    let g = lam f.
    let d = infer (Importance {particles = 1000}) f in -- ERROR

Compiling CorePPL to RootPPL​

By default, cppl compiles CorePPL to MExprβ€”the language of the Miking framework. Another option is to compile CorePPL to RootPPL. This option potentially results in more efficient inference due to the efficiency of RootPPL, but also has quite a lot of limitations. For example, RootPPL does not support automatic memory management or higher-order functions. A CorePPL program in a file can be compiled to a RootPPL file using the command cppl -t rootppl Currently, the only supported inference algorithm in RootPPL is -m smc-bpf (the SMC bootstrap particle filter).

Implementing New Inference Algorithms​

The CorePPL to MExpr compiler is organized to make it easy to implement new inference algorithms. Currently, the best way to understand the framework is to look at the source code. There are also some slides available at coreppl/docs/coreppl-to-mexpr.pdf


RootPPL is an intermediate language for representing probabilistic models and comes with a framework that performs inference on the GPU in these models. See examples in the folder rootppl/models. The idea is that high-level Miking probabilistic programming languages should be compiled to this intermediate language. An experimental CorePPL-to-RootPPL compiler is currently available (see Compiling CorePPL to RootPPL).

Getting Started​

The instructions below are tested on Ubuntu 18.04 but should work for other Unix-like systems.

Using rppl​

To compile a model and build an executable:

rppl path/to/

This will compile the model along with the inference framework for CPU. The inference framework build products are placed under build/. The rppl command wraps g++/nvcc, and you may supply options used for g++ and/or nvcc to rppl. These options will be used when compiling the supplied models, but not when compiling the inference framework. One limitation is that it is currently only possible to supply source file(s) to rppl. Object/library files are not supported (for this, you must write your own custom build script).

To compile for GPU, add your GPU:s compute capability to the --target option. You can find your GPU:s compute capability in the Wikipedia table. Here is an example that will compile the airplane example for a GPU with a minimum compute capability of 7.5 (simply remove --target sm_75 to compile for CPU):

rppl models/airplane/ --target sm_75 -j 5

The optional argument -j x speeds up the compilation process by spawning x jobs, allowing for parallel compilation. The corresponding parallel CPU (OpenMP) example is:

rppl models/airplane/ --target omp -j 5

Alternatively, the c++ compiler can be specified with CXX. This is often required on Mac OS to enable OpenMP, by using g++ instead of the default clang. On Mac OS, g++ can be installed with e.g. brew install gcc. Then, assuming the version installed was gcc-10:

rppl models/airplane/ --target omp -j 5 --cxx g++-10

The first build will compile the entire inference framework and can take 20 seconds or so when building for GPU. (Run rppl with the -j num_threads to use multiple threads and speed up the build). The inference framework is recompiled whenever options such as --target are modified between runs of rppl.

Any of the above commands should generate an executable named a.out (add -o <exec_name> to name it differently). Execute it with ./a.out num_particles. For example:

./a.out 1000

An example output of this:

./a.out 1000
Num particles close to target: 98.9%, MinX: 56.2349, MaxX: 91.1572

First is the command that is executed, it executes the executable a.out with argument 1000. The first row is the normalizing constant. The second row is a print statement within the model.

You can supply a second argument to a.out, which is the number of sweeps:

./a.out 1000 3
Num particles close to target: 89.7%, MinX: 51.5005, MaxX: 88.2039
Num particles close to target: 90.5%, MinX: 53.0577, MaxX: 90.0298
Num particles close to target: 88.7%, MinX: 48.1773, MaxX: 89.0957

Creating a simple model​

Models are divided into fragments to enable pausing the execution within models. These fragments are functions referred to as basic blocks (BBLOCK). To control the program execution flow, a program counter (PC) can be modified. If it remains unchanged in when the basic block returns, resampling will be done, and then the same block will be executed again. The program counter corresponds to the index of the basic block to be executed. So, incrementing it means that the next block will be executed after resampling. An example of this can be seen in the example below. However, if no following blocks are defined, the inference will terminate as the model program has been executed.

Any interesting model will contain random choices, i.e. sampling from distributions. Below is a coin flip example which flips a biased coin. SAMPLE is a macro that can take variable number of arguments. First is the distribution name, followed by the distribution specific arguments.

Sampling is done differently on the CPU and the GPU,but these differences are hidden in the model with the help of this SAMPLE macro. All of these RootPPL keywords are macros similar to SAMPLE, they provide higher-level constructs that can be used in the same way regardless of if the model will be executed on the CPU or the GPU.

BBLOCK(coinFlip, {
int coinSample = SAMPLE(bernoulli, 0.6);

In the coin flip example above, there is one problem, however. The sample is only stored in a local variable and is never used. To store data that remains when the next block is executed, the program state (PSTATE) should be used. Before defining the blocks, the model must be initialized with the macro INIT_MODEL that takes two arguments. First the type of the program state (this could be any type, e.g. int or a structure), then the number of basic blocks in the program. So, adding it to the above example:

INIT_MODEL(int, 1)

BBLOCK(coinFlip, {
PSTATE = SAMPLE(bernoulli, 0.6);

Now to run this program, only one thing remains to be defined, the main function. This is done with the MAIN macro, taking a code block as argument. The code below shows what we need in order to run the program and perform SMC.



First, the block must be added to the array of blocks to be executed. The order in which blocks are added with ADD_BLOCK, defines their order in the array and thus defines the order of execution together with the program counter. Secondly, the SMC macro (its parameter is explained below) starts the inference.

Now the model can be compiled and executed! However, the result of the coin flips is stored, but never used. To aggregate the results of all the particles' samples, a callback function (CALLBACK) can be used. The callback will be called after inference, but before clean up of the particles. This way, the results can be used to generate desired distributions or values before particles are deleted. Within the CALLBACK macro, the array of program states is accessed with PSTATES and the number of particles is accessed with the parameter N (which is hidden within the macro). Also, the log-weights array can be accessed with the WEIGHTS macro. These weights are normalised (so that the exponent of these log-weights sum to 1). Here is an example callback, called "sampleMean" that calculates and prints the mean of the samples.

CALLBACK(sampleMean, {
double sum = 0;
for(int i = 0; i < N; i++)
sum += PSTATES[i];
double mean = sum / N;
printf("Sample mean: %f\n", mean);

For this to be used, the SMC macro call in main must be changed to: SMC(sampleMean);

This example can be found in rootppl/models/simple-examples/ and, being in the rootppl directory, compiled with:

rppl models/simple-examples/

Then it can be executed with the executable followed by the number of particles, for example: ./a.out 1000.

An example output is then:

Sample mean: 0.608000
log normalization constant = 0.000000

First we see our callback function's output. Then on the next line, is the logarithm of the normalization constant approximated by the inference. This is simply 0 here, since the model contains no statements that alter the weights of the particles.

Supplementary Examples​

Below follows some examples, these are all models that are defined within one single block.

Coin Flip Posterior​

Full example: rootppl/models/simple-examples/

In this model, a bias for a coin is sampled from the prior beta distribution. Then we observe that the coin flip is true. This model thus infers the posterior distribution of the bias, conditioned on the observation.

BBLOCK(coinFlip, {
double x = SAMPLE(beta, 2, 2);
OBSERVE(bernoulli, x, true);

Gaussian Mixture Model​

Full example: rootppl/models/simple-examples/

This model demonstrates an example of stochastic branching, meaning that different code is executed depending on the outcome of the sample.

BBLOCK(mixture, {
double x;
if(SAMPLE(bernoulli, 0.7))
x = SAMPLE(normal, -2, 1);
x = SAMPLE(normal, 3, 1);

Geometric Distribution (Recursive)​

Full example: rootppl/models/simple-examples/

This model combines stochastic branching with recursion. Basic blocks do not fully support recursion themselves, as they take no custom arguments or return values. Instead, a helper function is used to express the recursive model:

BBLOCK_HELPER(geometricRecursive, {
if(SAMPLE(bernoulli, p))
return 1;
return BBLOCK_CALL(geometricRecursive, p) + 1;

}, int, double p)
BBLOCK(geometric, {
PSTATE = BBLOCK_CALL(geometricRecursive, 0.6);

Note that the helper function takes its return value and parameters comma-separated after the function body.

While recursive functions is supported by CUDA, iterative solutions are encouraged. Below is the same model, implemented with a loop instead.

Geometric Distribution (Iterative)​

Full example: rootppl/models/simple-examples/

BBLOCK(geometric, {
int numFlips = 1;
while(! SAMPLE(bernoulli, 0.6))
PSTATE = numFlips;

Phylogenetic Models​

More sophisticated models can be found in the phylogenetics directory. These probabilistic models to inference on observed phylogenetic trees. These observed trees can be found in phylogenetics/tree-utils/trees.cuh. The correct phylogenetic models contain a link in the top of the file to the WebPPL source code used as reference when implementing them. These models contain a number of new things, e.g.:

  • Multiple basic blocks
  • Structures as program states
  • Recursive simulations
  • Global data used by the model
  • Resampling throughout the traversal of the observed tree
Constant-rate birth-death​

In phylogenetics/crbd, the constant-rate birth-death models can be found. The most interesting file here is as it is a correct model used by evolutionary biologists. This model uses a pre-processed DFS traversal path over the observed tree, rather than using a call stack.

Further phylogenetic models​

Further phylogenetic models are found in the package phyrppl.

The RootPPL Architecture​

The architecture's main goal is to decouple the inference from the probabilistic models, this is the case for probabilistic programming languages. All inference engine code is located under src/. In the inference directory, the code for inference methods can be found. Currently, only SMC is supported. Within the smc directory, the resampling directory contain resampling strategies. Currently only Systematic Resampling is supported. The resampling has a parallel implementation for the GPU, and a sequential and parallel implementation for the CPU.

  • The dists directory contains distributions and scoring functions. Both models and inference can rely on these.
  • The macros directory contains all the macros/constructs that are mostly used by models but also in inference code.
  • The utils directory contains code that RootPPL uses, but can be used by models as well.
  • The models directory contains example models that use the inference.

Compile with the built-in stack​

To compile with the progStateStack_t program state, the macro INIT_MODEL_STACK(num_bblocks) should be used instead of INIT_MODEL(progState_t, num_bblocks). Then the stack size is specified in bytes with --stack_size num_bytes, e.g. rppl --stack_size 10000.

Building a more interesting model​

TODO, stuff not yet demonstrated explicitly in README:

  • WEIGHT macro
  • Multiple BBLOCK models
  • Global data accessible by bblocks on GPU
  • Program States containing more than a primitive datatype, e.g. structs.