NNSmith
🌟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 |
|---|---|---|---|---|---|---|
| ONNX | ✅ | ✅ | ✅ | |||
| TensorFlow | 🔨 | ✅ | ✅ | |||
| PyTorch | 🔨 | 🔨 | ✅ |
✅: Supported; 🔨: Coming soon;
Quick Start
Install latest code (GitHub HEAD):
pip install "git+https://github.com/ise-uiuc/nnsmith@main#egg=nnsmith[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
- Bugs: links to reports.
- Documentation: CLI, concept, logging, and known issues.
- Contributions:
doc/CONTRIBUTING.md - We use hydra to manage configurations. See
nnsmith/config/main.yaml.
Papers
📜 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}
}
Metadata
Release files for nnsmith 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| nnsmith-0.1.0.tar.gz | 131.4 kB | Without provenance | Details for nnsmith-0.1.0.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| nnsmith-0.1.0-py3-none-any.whl | Python 3 | none | any | Details for nnsmith-0.1.0-py3-none-any.whl |
Total release size: 221.9 kB
Release files / nnsmith-0.1.0.tar.gz
| Download URL | nnsmith-0.1.0.tar.gz |
|---|---|
| Size | 131.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Release files / nnsmith-0.1.0-py3-none-any.whl
| Download URL | nnsmith-0.1.0-py3-none-any.whl |
|---|---|
| Size | 90.5 kB |
| Tags | Python 3 |
|
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| Uploaded via |
twine/4.0.2 CPython/3.8.13
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