constraintflow 0.1.1

A DSL and tools for neural network certification

ConstraintFlow

ConstraintFlow is a domain-specific language (DSL) and toolchain for specifying, verifying, and compiling neural network certifiers. It bridges the gap between high-level formal specifications and efficient tensor-based runtimes, enabling precise and verifiable DNN analysis.

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📚 Features

ConstraintFlow allows you to:

• Specify certifiers declaratively using .cf files.
• Verify certifiers automatically for soundness.
• Compile high-level specifications into optimized tensor-based code.
• Execute compiled certifiers on neural network models.

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🚀 Quick Start

Installation

Clone the repository and install in editable mode:

git clone https://github.com/your-username/constraintflow.git
cd constraintflow
pip install -e .

Prepare Models

Create a directory for neural networks:

mkdir nets/

Download pretrained DNNs from ERAN and place them inside nets/.

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CLI Usage

ConstraintFlow provides the following commands with optional flags:

provesound

constraintflow provesound example.cf [OPTIONS]

Options:

Flag	Description	Default
--nprev	Number of previous states to consider	1
--nsymb	Number of symbols to track	1

compile

constraintflow compile example.cf [OPTIONS]

Options:

Flag	Description	Default
--output-path	Directory for generated code	output/

run

constraintflow run example.cf [OPTIONS]

Options:

Flag	Description	Default
--network	Network name	mnist_relu_3_50
--network-format	Format of the network file	onnx
--dataset	Dataset to use (mnist or cifar)	mnist
--batch-size	Batch size	1
--eps	Epsilon	0.01
--train	Use training dataset	False
--print-intermediate-results	Print intermediate results during execution	False
--no-sparsity	Disable sparsity optimizations	False
--output-path	Path where compiled program is stored	output/
--compile	Compile the program before running	False

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📄 Citations

If you use ConstraintFlow in your research, please cite the following papers:

@InProceedings{constraintflow,
  author = {Avaljot Singh and Yasmin Sarita and Charith Mendis and Gagandeep Singh},
  title = {ConstraintFlow: A DSL for Specification and Verification of Neural Network Analyses},
  booktitle = {Static Analysis},
  year = {2024},
  publisher = {Springer Nature Switzerland},
}

@InProceedings{provesound,
  author = {Avaljot Singh and Yasmin Sarita and Charith Mendis and Gagandeep Singh},
  title = {Automated Verification of Soundness of DNN Certifiers},
  booktitle = {OOPSLA},
  year = {2025},
}

@Article{compiler,
  author = {Avaljot Singh and Yasmin Sarita and Aditya Mishra and Ishaan Goyal and Gagandeep Singh and Charith Mendis},
  title = {A Tensor-Based Compiler and Runtime for Neuron-Level DNN Certifier Specifications},
  journal = {arXiv},
  year = {2025},
}

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🛠 Development

Requirements

• Python 3.9+
• antlr4-python3-runtime==4.9.2

Running Locally

Install the requirements:

pip install -r requirements.txt

You can then run, compile, or verify any .cf file using the CLI.

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📄 License

MIT License. See LICENSE for details.