FBGEMM (Facebook General Matrix Multiplication) is a C++ library developed by Meta (Facebook) that is primarily used for low-precision, high-performance matrix multiplication and convolution operations in server-side inference. It is designed for small batch data and can significantly improve inference efficiency while supporting various techniques to reduce precision loss, such as row-wise quantization and outlier-aware quantization.
Core Features and Characteristics
The core functionality of FBGEMM is low-precision matrix multiplication, which achieves efficient inference performance by optimizing the storage and computation of matrix operations. The following are its main features:
- Matrix Tiling and Cache Optimization: FBGEMM uses a multi-layer loop structure to divide matrices into small blocks suitable for CPU cache, thereby improving data access efficiency.
- Matrix Packing: During computation, FBGEMM reorganizes matrix blocks to achieve sequential data access, which is crucial for modern hardware architectures.
- Fusion of Computation and Packing: FBGEMM fuses some simple computation operations with the packing process, such as calculating row offsets while packing matrices, avoiding multiple accesses to matrix data.
- Support for Various Quantization Techniques: FBGEMM supports techniques such as row-wise quantization and outlier-aware quantization, enabling efficient computation while minimizing precision loss.
Application in PyTorch
FBGEMM is an important component of the PyTorch quantization backend, primarily used for post-training quantization (PTQ) and quantization-aware training (QAT). By setting <span>torch.quantization.get_default_qconfig("fbgemm")</span>, users can utilize FBGEMM as a quantization backend to achieve efficient quantized inference in PyTorch.
Performance Advantages
The design goal of FBGEMM is to enhance the performance of low-precision matrix operations, especially in small batch data scenarios. Through techniques such as matrix tiling, cache optimization, and computation fusion, FBGEMM can fully utilize the multi-level cache of CPUs, significantly improving data access efficiency. Additionally, FBGEMM further optimizes inference performance by fusing post-processing operations (such as nonlinear activation, bias addition, and re-quantization).
Open Source and Community Support
FBGEMM is an open-source project, with its code hosted on GitHub. Developers can seek support through GitHub Issues or the <span>#fbgemm</span> channel on PyTorch Slack. Furthermore, the FBGEMM development team welcomes contributions from community members.
Conclusion
FBGEMM is a high-performance low-precision matrix operation library designed for server-side inference. It achieves efficient inference performance by optimizing the storage and computation of matrix operations while supporting various techniques to reduce precision loss. The application of FBGEMM in PyTorch further demonstrates its importance in deep learning inference. As deep learning models continue to grow in complexity, optimization tools like FBGEMM will become increasingly vital.
If you find this useful, please follow, like, and share. Marking the public account with a five-star rating will ensure you receive updates promptly.