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https://github.com/Xilinx/brevitas
Brevitas: neural network quantization in PyTorch
https://github.com/Xilinx/brevitas
brevitas deep-learning fpga hardware-acceleration neural-networks ptq pytorch qat quantization xilinx
Last synced: about 2 months ago
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Brevitas: neural network quantization in PyTorch
- Host: GitHub
- URL: https://github.com/Xilinx/brevitas
- Owner: Xilinx
- License: other
- Created: 2018-07-10T22:37:01.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2024-07-31T10:30:37.000Z (about 2 months ago)
- Last Synced: 2024-07-31T15:22:17.331Z (about 2 months ago)
- Topics: brevitas, deep-learning, fpga, hardware-acceleration, neural-networks, ptq, pytorch, qat, quantization, xilinx
- Language: Python
- Homepage: https://xilinx.github.io/brevitas/
- Size: 19.1 MB
- Stars: 1,135
- Watchers: 34
- Forks: 186
- Open Issues: 164
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Brevitas
[](https://pepy.tech/project/brevitas)
[](https://zenodo.org/badge/latestdoi/140494324)
Brevitas is a PyTorch library for neural network quantization, with support for both *post-training quantization (PTQ)* and *quantization-aware training (QAT)*.
**Please note that Brevitas is a research project and not an official Xilinx product.**
If you like this project please consider ⭐ this repo, as it is the simplest and best way to support it.
## Requirements
* Python >= 3.8.
* [Pytorch](https://pytorch.org) >= 1.9.1, <= 2.1 (more recent versions would be untested).
* Windows, Linux or macOS.
* GPU training-time acceleration (*Optional* but recommended).
## Installation
You can install the latest release from PyPI:
```bash
pip install brevitas
```
## Getting Started
Brevitas currently offers quantized implementations of the most common PyTorch layers used in DNN under `brevitas.nn`, such as `QuantConv1d`, `QuantConv2d`, `QuantConvTranspose1d`, `QuantConvTranspose2d`, `QuantMultiheadAttention`, `QuantRNN`, `QuantLSTM` etc., for adoption within PTQ and/or QAT.
For each one of these layers, quantization of different tensors (inputs, weights, bias, outputs, etc) can be individually tuned according to a wide range of quantization settings.
As a reference for PTQ, Brevitas provides an example user flow for ImageNet classification models under [`brevitas_examples.imagenet_classification.ptq`](https://github.com/Xilinx/brevitas/blob/master/src/brevitas_examples/imagenet_classification/ptq/ptq_evaluate.py) that quantizes an input torchvision model using PTQ under different quantization configurations (e.g. bit-width, granularity of scale, etc).
For more info, checkout our [getting started guide](https://xilinx.github.io/brevitas/getting_started).
## Cite as
If you adopt Brevitas in your work, please cite it as:
```
@software{brevitas,
author = {Alessandro Pappalardo},
title = {Xilinx/brevitas},
year = {2023},
publisher = {Zenodo},
doi = {10.5281/zenodo.3333552},
url = {https://doi.org/10.5281/zenodo.3333552}
}
```
## History
- *2024/02/19* - Minor release version 0.10.2, see the [release notes](https://github.com/Xilinx/brevitas/releases/tag/v0.10.2).
- *2024/02/15* - Minor release version 0.10.1, see the [release notes](https://github.com/Xilinx/brevitas/releases/tag/v0.10.1).
- *2023/12/08* - Release version 0.10.0, see the [release notes](https://github.com/Xilinx/brevitas/releases/tag/v0.10.0).
- *2023/04/28* - Minor release version 0.9.1, see the [release notes](https://github.com/Xilinx/brevitas/releases/tag/v0.9.1).
- *2023/04/21* - Release version 0.9.0, see the [release notes](https://github.com/Xilinx/brevitas/releases/tag/v0.9.0).
- *2023/01/10* - Release version 0.8.0, see the [release notes](https://github.com/Xilinx/brevitas/releases/tag/v0.8.0).
- *2021/12/13* - Release version 0.7.1, fix a bunch of issues. Added TVMCon 2021 tutorial notebook.
- *2021/11/03* - Re-release version 0.7.0 (build 1) on PyPI to fix a packaging issue.
- *2021/10/29* - Release version 0.7.0, see the [release notes](https://github.com/Xilinx/brevitas/releases/tag/v0.7.0).
- *2021/06/04* - Release version 0.6.0, see the [release notes](https://github.com/Xilinx/brevitas/releases/tag/v0.6.0).
- *2021/05/24* - Release version 0.5.1, fix a bunch of minor issues. See [release notes](https://github.com/Xilinx/brevitas/releases/tag/v0.5.1).
- *2021/05/06* - Release version 0.5.0, see the [release notes](https://github.com/Xilinx/brevitas/releases/tag/v0.5.0).
- *2021/03/15* - Release version 0.4.0, add support for \_\_torch_function\_\_ to QuantTensor.
- *2021/03/04* - Release version 0.3.1, fix bug w/ act initialization from statistics w/ IGNORE_MISSING_KEYS=1.
- *2021/03/01* - Release version 0.3.0, implements enum and shape solvers within extended dependency injectors. This allows declarative quantizers to be self-contained.
- *2021/02/04* - Release version 0.2.1, includes various bugfixes of QuantTensor w/ zero-point.
- *2021/01/30* - First release version 0.2.0 on PyPI.