groq/nnsmith

groq/nnsmith

Language: Python

License: Apache-2.0

Stars: 0

Forks: 0

Open issues: 0

Created: 2025-01-31T15:54:48Z

Pushed: 2025-01-14T04:16:54Z

Default branch: main

Fork: yes

Parent repository: ise-uiuc/nnsmith

Archived: no

README:

NNSmith

 

🌟NNSmith🌟 is a random DNN generator and a fuzzing infrastructure, primarily designed for automatically validating deep-learning frameworks and compilers.

Support Table

Quick Start

Install latest code (GitHub HEAD):

pip install pip --upgrade
pip install "nnsmith[torch,onnx] @ git+https://github.com/ise-uiuc/nnsmith@main" --upgrade
# [optional] add more front- and back-ends such as [tensorflow] and [tvm,onnxruntime,...] in "[...]"

Install latest stable release [click]

pip install "nnsmith[torch,onnx]" --upgrade

Install latest pre-release [click]

pip install "nnsmith[torch,onnx]" --upgrade --pre

Setting up graphviz for debugging [click]

Graphviz provides dot for visualizing graphs in nice pictures. But it needs to be installed via the following methods:

sudo apt-get install graphviz graphviz-dev # Linux
brew install graphviz # MacOS
conda install --channel conda-forge pygraphviz # Conda
choco install graphviz # Windows

pip install pygraphviz # Final step.

Also see pygraphviz install guidance.

# Generate a random model in "nnsmith_outputs/*"
nnsmith.model_gen model.type=onnx debug.viz=true

Learning More

• 🐛 [Uncovered bugs](doc/bugs.md).
• 📚 [Documentation](doc/): [CLI](doc/cli.md), [concept](doc/concept.md), [logging](doc/log-and-err.md), and [known issues](doc/known-issues.md).
• 🤗 [Contributing to NNSmith](doc/CONTRIBUTING.md)
• 📝 We use hydra to manage configurations. See nnsmith/config/main.yaml.

Papers

📜 NeuRI: Diversifying DNN Generation via Inductive Rule Inference [click :: citation]

@article{liu2023neuri,
title = {NeuRI: Diversifying DNN Generation via Inductive Rule Inference},
author = {Liu, Jiawei and Peng, Jinjun and Wang, Yuyao and Zhang, Lingming},
journal = {arXiv preprint arXiv:2302.02261},
year = {2023},
}

📜 NNSmith: Generating Diverse and Valid Test Cases for Deep Learning Compilers [click :: citation]

@inproceedings{liu2023nnsmith,
title={Nnsmith: Generating diverse and valid test cases for deep learning compilers},
author={Liu, Jiawei and Lin, Jinkun and Ruffy, Fabian and Tan, Cheng and Li, Jinyang and Panda, Aurojit and Zhang, Lingming},
booktitle={Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2},
pages={530--543},
year={2023}
}