AquaSense translates a StormIR or Stan probabilistic program to PyTorch code for the purpose of sensitivity analysis

Prerequisites:

• Java

  Check installation with java -version. If not installed, on Ubuntu you may try sudo apt -y update; sudo apt install openjdk-8-jdk

  Make sure the location of your java binary is added to PATH in your command-line environment, as java ... is used by AquaSense's helper scripts

• Maven

  Check installation with mvn --version. If not installed, on Ubuntu you may try sudo apt -y update; sudo apt install maven

• PyTorch (for running the generated code)

  To install PyTorch with your version of CUDA and preferred package manager, check out https://pytorch.org/get-started/locally/

Install dependencies and build AquaSense:

In the root directory of this repo, run

mvn package -DskipTests=true

For Apple silicon users, run

mvn package -DskipTests=true -U -Djavacpp.platform=macosx-x86_64

Blocked mirror link error refer

Refer to the following for other installation-related errors specific to Apple Silicon link

In the end it should print BUILD SUCCESS.

./benchmark.py

./aquasense.py benchmarks/stan_bench/neural

We provide a helper script aquasense.py to translate, run, and visualize the sensitivity of a probabilistic program

The script automatically increases the granularity of quantization (think of it as a accuracy hyperparameter) to approximate the true sensitivity, until the approximation is deemed to have converged; the results are shown in a plot

Usage:

./aquasense.py <path_to_model_dir> [-h] [-v RANDVAR] [-p PARAMETER] [-b BOUNDS] [-s SPLITS] [-m M] [-c C]

E.g.

./aquasense.py benchmarks/stan_bench/neural

E.g.

./aquasense.py benchmarks/stan_bench/neural -v "w[0]" -p 0 -b -0.1 0.1 -m expdist1

E.g. the sensitivity interpolations using different #splits

Or alternatively, one can manually translate the probabilistic program, then perform sensitivity analysis, see 2) and 3)

AquaSense works as a source-to-source translator that takes as input either

• a program in Storm IR (<prog_name>.template). Example: benchmarks/psense_bench/coins/coins.template
• a directory <prog_name>/ containing Stan file (<prog_name>.stan) and data (<prog_name>.data.R). Example: benchmarks/stan_bench/neural/

and output a python script, such as, benchmarks/stan_bench/neural/neural.py, to be used for sensitivity analysis

Usage:

java -cp "target/aqua-1.0.jar:lib/storm-1.0.jar" aqua.analyses.PyCompilerRunner <path_to_input_template_file>

E.g.:

java -cp "target/aqua-1.0.jar:lib/storm-1.0.jar" aqua.analyses.PyCompilerRunner ./benchmarks/psense_bench/coins/coins.template

The path_to_input_dir must contain a stan file (<prog_name>.stan) and a data file (<prog_name>.data.R) with the same name as the directory.

Usage:

java -cp "target/aqua-1.0.jar:lib/storm-1.0.jar" aqua.analyses.PyCompilerRunner <path_to_input_dir>

E.g.:

java -cp "target/aqua-1.0.jar:lib/storm-1.0.jar" aqua.analyses.PyCompilerRunner ./benchmarks/stan_bench/anova_radon_nopred

The directory ./benchmarks/stan_bench/anova_radon_nopred contains anova_radon_nopred.stan and anova_radon_nopred.data.R.

For each probabilistic program, there will be a <prog_name>.py containing the translated Pytorch code. It is under the same directory as the input <prog_name>.template or <prog_name>.stan file.

By default, AquaSense uses GPU for tensor computations in PyTorch, and analyzes the sensitivity of the first parameter of the first identified random variable within a range.

E.g.

python3 benchmarks/stan_bench/neural/neural.py

To specify a random variable, use the option -v; to specify the parameter (index), use the option -p; to specify the noise bound, use the option -b. e.g.

E.g. this command analyzes the sensitivity of parameter 0 (lower bound) of the random variable "w[0]" with noise in between -0.1 and 0.1

python3 benchmarks/stan_bench/neural/neural.py -v "w[0]" -p 0 -b -0.1 0.1

For more details on the usage, use the -h option

• AquaSense is shown empiricially to be exact on discrete probabilistic models, therefore there is no need to approximate the true sensitivity of models like coins using the helper script. One can simply perform step 2) and 3) and observe/visualize the outputs
• On certain discrete models, the injection of arbitrary noise into parameters can make certain distributions ill-defined, e.g. UniformInt(0.314, 5), leading to numerical issues. It is recommended to choose the noise vector manually.

    .  
    ├── benchmarks/                                         # All benchmarks
    │     ├── stan_bench/                                   # Benchmarks in Stan
    │     └── psense_bench/                                 # Benchmarks in Storm IR
    │
    ├── src/                                                # AQUA source code in Java
    │     ├── main/                  
    │     │     ├── java/                       
    │     │     │   └──  aqua/ 
    │     │     │        ├── analyses/                      # AQUA Analysis code
    │     │     │        │    ├── PyCompilerRunner.java     # Program entry point. Translates file, constructs CFG, and run compiler
    │     │     │        │    ├── PytorchCompiler.java      # Generate Pytorch code
    │     │     │        │    └── PytorchVisitor.java       # Used in PytorchCompiler to generates code for statements and expressions
    │     │     │        └── cfg/CFGBuilder.java            # CFG constructor for Storm IR
    │     │     └── resources/                              # Json files for properties of distributions and the config of Storm IR
    │     └── test/java/tests/                              # Unit tests in the development
    │ 
    ├── lib/grammar-1.0.jar                                 # Storm IR jar 
    ├── aquasense.py                                        # Helper script to translate, run and visualize 
    ├── converge.py                                         # Define convergence criteria
    ├── metrics.py                                          # Define distance metrics
    |
    ├── README.md                                           # README for basic info  
    ├── antlr-4.7.1-complete.jar                            # ANTLR jar used for parsing Stan / Storm IR files
    └── pom.xml                                             # POM file in maven for project configuration and dependency