FBGEMM

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 Caffe2 and PyTorch quantized operators for x86 machines:

• Caffe2: https://github.com/pytorch/pytorch/tree/master/caffe2/quantization/server
• PyTorch: https://github.com/pytorch/pytorch/tree/master/aten/src/ATen/native/quantized/cpu

See the full Documentation 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 (January, 2020)

Citation

For a high-level overview, design philosophy and brief descriptions of various
parts of FBGEMM please see our blog post.

For those looking for the appropriate article to cite regarding FBGEMM, we
recommend citing our paper:

@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
• Post a discussion in GitHub Discussions
• Reach out to us on the #fbgemm channel in PyTorch Slack

For contributions, please see the CONTRIBUTING file for
ways to help out.

License

FBGEMM is BSD licensed, as found in the LICENSE file.