The FBGEMM Project Homepage¶
Welcome to the documentation homepage for the FBGEMM Project!
The FBGEMM Project is a repository of highly-optimized kernels used across deep learning applications. The codebase is organized into three related packages: FBGEMM, FBGEMM-GPU, and FBGEMM-GenAI.
FBGEMM¶
FBGEMM (Facebook GEneral Matrix Multiplication) is a low-precision, high-performance matrix-matrix multiplications and convolution library for server-side inference. This library is used as a backend of PyTorch quantized operators on x86 machines.
See FBGEMM for more information.
FBGEMM_GPU¶
FBGEMM_GPU (FBGEMM GPU Kernels Library) is a collection of high-performance PyTorch GPU operator libraries built on top of FBGEMM for training and inference, with a focus on recommendation systems applications. This library is built on top of FBGEMM and provides efficient table batched embedding bag, data layout transformation, and quantization support.
See FBGEMM_GPU for more information.
FBGEMM GenAI¶
FBGEMM GenAI (FBGEMM Generative AI Kernels Library) is a collection of PyTorch GPU operator libraries that are designed for generative AI applications, such as FP8 row-wise quantization and collective communications.
See FBGEMM GenAI for more information.
Table of Contents¶
General Info
FBGEMM Development
FBGEMM C++ API
FBGEMM_GPU Development
FBGEMM_GPU Overview
FBGEMM Stable API
FBGEMM_GPU C++ API
FBGEMM_GPU Python API
FBGEMM GenAI Development
