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brevitas-0.9.1
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مشاهده بیشترتوضیحات
Quantization-aware training in PyTorch
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | brevitas-0.9.1 |
| نام | brevitas |
| نسخه کتابخانه | 0.9.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Alessandro Pappalardo |
| ایمیل نویسنده | alessand@amd.com |
| آدرس صفحه اصلی | https://github.com/Xilinx/brevitas |
| آدرس اینترنتی | https://pypi.org/project/brevitas/ |
| مجوز | - |
# 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 >= 3.7 .
* [Pytorch](https://pytorch.org) >= 1.5.1 .
* 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). Sample accuracy results are available [here](https://github.com/Xilinx/brevitas/blob/master/src/brevitas_examples/imagenet_classification/ptq/RESULTS_TORCHVISION_BEST_CONFIGS.csv) for a selection of three reference topologies (ResNet18, MobileNet V2, ViT), under a variety of different quantization settings.
For more info, checkout 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 = {2022},
publisher = {Zenodo},
doi = {10.5281/zenodo.3333552},
url = {https://doi.org/10.5281/zenodo.3333552}
}
```
## History
- *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.
نیازمندی
| مقدار | نام |
|---|---|
| ==2.0.1 | dependencies |
| - | packaging |
| - | setuptools |
| >=1.5.1 | torch |
| >=3.7.4 | typing-extensions |
| - | pre-commit |
| - | m2r2 |
| - | nbsphinx |
| - | nbsphinx-link |
| - | pydata-sphinx-theme |
| ==5.3.0 | sphinx |
| - | sphinx-autodoc-typehints |
| ==0.10.1 | sphinx-gallery |
| - | sphinxcontrib-napoleon |
| - | sphinxemoji |
| - | onnx |
| - | onnxoptimizer |
| - | bitstring |
| - | onnx |
| - | onnxoptimizer |
| - | onnxruntime |
| - | qonnx |
| - | toposort |
| - | scipy |
| - | jupyter |
| - | nbmake |
| - | onnx |
| - | onnxoptimizer |
| - | onnxruntime |
| - | inflect |
| - | librosa |
| - | numba |
| >=4.3.0 | pillow |
| - | requests |
| - | ruamel.yaml |
| - | soundfile |
| - | sox |
| - | torch-stft |
| - | tqdm |
| - | unidecode |
| - | hypothesis |
| - | mock |
| - | psutil |
| - | pytest |
| - | pytest-mock |
| - | pytest-xdist |
| - | pytest-cases |
| - | scipy |
| - | torchvision |
| - | librosa |
| - | numpy |
| - | pillow |
| - | pyyaml |
| - | scipy |
| - | soundfile |
| - | tqdm |
| - | torchvision |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl brevitas-0.9.1:
pip install brevitas-0.9.1.whl
نصب پکیج tar.gz brevitas-0.9.1:
pip install brevitas-0.9.1.tar.gz