Skip to main content

"Automatic DNN generation for fuzzing and more."

Project description



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

Support Table

Models tvm onnxruntime tensorrt tflite xla torchjit
TensorFlow 🔨
PyTorch 🔨 🔨

✅: Supported; 🔨: Coming soon;

Quick Start

Install latest code (GitHub HEAD):

pip install "git+[torch,onnx]" --upgrade
# [optional] add more front- and back-ends such as [tensorflow] and [tvm,onnxruntime,xla,...] 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


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

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

nnsmith-0.1.0.tar.gz (131.4 kB view hashes)

Uploaded source

Built Distribution

nnsmith-0.1.0-py3-none-any.whl (90.5 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page