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https://github.com/ise-uiuc/nnsmith

Automatic DNN generation for fuzzing and more
https://github.com/ise-uiuc/nnsmith

compiler deep-learning fuzzing machine-learning pytorch tensorflow

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Automatic DNN generation for fuzzing and more

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# NNSmith

[![](https://github.com/ise-uiuc/nnsmith/actions/workflows/ci.yaml/badge.svg)](https://github.com/ise-uiuc/nnsmith/actions/workflows/ci.yaml)
[![](https://img.shields.io/pypi/v/nnsmith?color=g)](https://pypi.org/project/nnsmith/)
[![](https://static.pepy.tech/badge/nnsmith)](https://pepy.tech/project/nnsmith)
[![](https://img.shields.io/pypi/l/nnsmith)](https://github.com/ise-uiuc/nnsmith/blob/main/LICENSE)

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

## Support Table

| Models | [`tvm`](https://github.com/apache/tvm) | [`pt2`](https://pytorch.org/get-started/pytorch-2.0/) | [`torchjit`](https://pytorch.org/docs/stable/jit.html) | [`tensorrt`](https://github.com/NVIDIA/TensorRT) | [`onnxruntime`](https://github.com/microsoft/onnxruntime) | [`xla`](https://www.tensorflow.org/xla) | [`tflite`](https://www.tensorflow.org/lite) |
| ------------ | ------------------------------------ | ----------------------------------------------- | ---------------------------------------------- | ----------------------------------------- | ------------------------------------- | ----------------------------------------------------- | ------------ |
| ONNX | βœ… | | | βœ… | βœ… | | |
| PyTorch | | βœ…πŸ“ˆ | βœ…πŸ“ˆ | | | | |
| TensorFlow | | | | | | βœ… | βœ… |

βœ…: Supported; πŸ“ˆ: Supports gradient check;

## Quick Start

**Install latest code (GitHub HEAD):**

```shell
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]

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

Install latest pre-release [click]

```shell
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:

```shell
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](https://pygraphviz.github.io/documentation/stable/install.html).

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

## Learning More

- πŸ› [**Uncovered bugs**](doc/bugs.md).
- πŸ“š [**Documentation**](doc/): [CLI](doc/cli.md), [concept](doc/concept.md), [logging](doc/log-and-err.md), and [known issues](doc/known-issues.md).
- πŸ€— [**Contributing to NNSmith**](doc/CONTRIBUTING.md)
- πŸ“ We use [hydra](https://hydra.cc/) to manage configurations. See `nnsmith/config/main.yaml`.

## Papers

πŸ“œ NeuRI: Diversifying DNN Generation via Inductive Rule Inference [click :: citation]

```bibtex
@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},
}
```






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

```bibtex
@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}
}
```