https://github.com/pytorch/FBGEMM

FB (Facebook) + GEMM (General Matrix-Matrix Multiplication) - https://code.fb.com/ml-applications/fbgemm/
https://github.com/pytorch/FBGEMM

Last synced: 10 days ago
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FB (Facebook) + GEMM (General Matrix-Matrix Multiplication) - https://code.fb.com/ml-applications/fbgemm/

• Host: GitHub
• URL: https://github.com/pytorch/FBGEMM
• Owner: pytorch
• License: other
• Created: 2018-09-24T19:07:42.000Z (about 6 years ago)
• Default Branch: main
• Last Pushed: 2024-10-29T00:14:29.000Z (11 days ago)
• Last Synced: 2024-10-29T09:17:01.063Z (10 days ago)
• Language: C++
• Homepage:
• Size: 20 MB
• Stars: 1,198
• Watchers: 66
• Forks: 494
• Open Issues: 374
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

• awesome-list - FBGEMM - A low-precision, high-performance matrix-matrix multiplications and convolution library for server-side inference. (Linear Algebra / Statistics Toolkit / General Purpose Tensor Library)

README

        
# FBGEMM

[![FBGEMM CI](https://github.com/pytorch/FBGEMM/actions/workflows/fbgemm_ci.yml/badge.svg)](https://github.com/pytorch/FBGEMM/actions/workflows/fbgemm_ci.yml)

FBGEMM (Facebook GEneral Matrix Multiplication) is a low-precision,

high-performance matrix-matrix multiplications and convolution library for

server-side inference.

The library provides efficient low-precision general matrix multiplication for

small batch sizes and support for accuracy-loss minimizing techniques such as

row-wise quantization and outlier-aware quantization. FBGEMM also exploits

fusion opportunities in order to overcome the unique challenges of matrix

multiplication at lower precision with bandwidth-bound operations.

FBGEMM is used as a backend of PyTorch quantized operators for x86 machines:

  * PyTorch: https://github.com/pytorch/pytorch/tree/master/aten/src/ATen/native/quantized/cpu

See the full [Documentation](https://pytorch.org/FBGEMM) for more information

on building, installing, and developing with FBGEMM, as well as the most

up-to-date support matrix and API documentation for this library.

### What's New?

* [New Features and Recent Improvements](https://github.com/pytorch/FBGEMM/wiki/Recent-feature-additions-and-improvements-in-FBGEMM) (January, 2020)

### Citation

For a high-level overview, design philosophy and brief descriptions of various

parts of FBGEMM please see [our blog post](https://code.fb.com/ml-applications/fbgemm).

For those looking for the appropriate article to cite regarding FBGEMM, we

recommend citing our [paper](https://arxiv.org/pdf/2101.05615.pdf):

```

@article{fbgemm,

  title={FBGEMM: Enabling High-Performance Low-Precision Deep Learning Inference},

  author={Khudia, Daya and Huang, Jianyu and Basu, Protonu and Deng, Summer and Liu, Haixin and Park, Jongsoo and Smelyanskiy, Mikhail},

  journal={arXiv preprint arXiv:2101.05615},

  year={2021}

}

```

## Join the FBGEMM community

For questions, support, news updates, or feature requests, please feel free to:

* File a ticket in [GitHub Issues](https://github.com/pytorch/FBGEMM/issues)

* Post a discussion in [GitHub Discussions](https://github.com/pytorch/FBGEMM/discussions)

* Reach out to us on the `#fbgemm` channel in [PyTorch Slack](https://bit.ly/ptslack)

For contributions, please see the [`CONTRIBUTING`](./CONTRIBUTING.md) file for

ways to help out.

## License

FBGEMM is BSD licensed, as found in the [`LICENSE`](LICENSE) file.