BitBLAS

BitBLAS is a library to support mixed-precision BLAS operations on GPUs, for example, the $W{wdtype}A{adtype}$ mixed-precision matrix multiplication where $C{cdtype}[M, N] = A{adtype}[M, K] \times W{wdtype}[N, K]$. BitBLAS aims to support efficient mixed-precision DNN model deployment, especially the $W{wdtype}A{adtype}$ quantization in large language models (LLMs), for example, the $W{UINT4}A{FP16}$ in GPTQ, the $W{INT2}A{FP16}$ in BitDistiller, the $W{INT2}A_{INT8}$ in BitNet-b1.58. BitBLAS is based on techniques from our accepted submission "Ladder: Enabling Efficient Low-Precision Deep Learning Computing through Hardware-aware Tensor Transformation" at OSDI'24.

Some of the key features of BitBLAS include:

• High performance matrix multiplication for both GEMV (e.g., the single batch auto-regressive decode phase in LLM) and GEMM (e.g., the batched auto-regressive decode phase and the prefill phase in LLM):
  • $W{wdtype}A{adtype}$ mixed-precision matrix multiplication including FP16xINT4/2/1, INT8xINT4/2/1, etc. Please checkout support matrix for detailed data types support.
  • Matrix multiplication like FP16xFP16 and INT8xINT8.
• Auto-Tensorization for TensorCore-like hardware instructions.
• Implemented integration to PyTorch, AutoGPTQ, vLLM and BitNet-b1.58 for LLM deployment. Please checkout benchmark summary for detailed end2end LLM inference performance.
• BitBLAS first implemented $W{INT2}A{INT8}$ GEMV/GEMM in BitNet-b1.58 with 8x/2x speedup over cuBLAS $W{FP16}A{FP16}$ on A100, please checkout op_benchmark_a100_int2_scaling for detailed benchmark results. Please checkout BitNet-b1.58 integration for the integration with the 3rdparty reproduced BitNet-b1.58 model.
• Support customizing mixed-precision DNN operations for your specific scenarios via the flexible DSL (TIR Script).

Latest News

• 2024.04.19: BitBLAS is now open source! We are excited to announce that BitBLAS, a high-performance library for mixed-precision DNN model deployment, is now available to the public.
• 2024.04.30: BitBLAS now supports FP8 TensorCore!

Integration Example of FasterTransformer with BitBLAS

Benchmark Summary

BitBLAS achieves exceptional performance across a variety of computational patterns. Below are selected results showcasing its capabilities:

• End2End Integration with Quantize Inference Kernel for AutoGPTQ and vLLM.

  

• Weight Only Matmul performance on A100

  

• TensorCore FP16/INT8 GEMM Performance Vs. Vendor Library on A100 and RTX4090

  

For more detailed information on benchmark sets with other formats (NF4/FP4) and other devices (RTX 3090), please refer to the benchmark.

Support Matrix

We are continuously expanding the support matrix. If you have any specific requirements, please feel free to open an issue or PR.

Getting Started

• Installation: To install BitBLAS, please checkout the document installation. Also Make sure you already have the cuda toolkit (version >= 11) installed in the system. Or you can easily install from pip install bitblas from PyPi. Currently we only provide whl files for CUDA>=12.1 and Ubuntu>=20.04 with Python>=3.8, if you are using a different version of CUDA or OS System, you may need to build BitBLAS from source.

• QuickStart: BitBLAS provides two Python APIs to perform mixed-precision matrix multiplication:

  • bitblas.Matmul implements the $W{wdtype}A{adtype}$ mixed-precision matrix multiplication of $C{cdtype}[M, N] = A{adtype}[M, K] \times W_{wdtype}[N, K]$.
  • bitblas.Linear is a PyTorch nn.Linear-like module to support a Linear of mixed-precision.

• Python API: The Python API doc of BitBLAS.

• Integration: Explore how BitBLAS seamlessly integrates with LLM deployment frameworks through our examples. Discover the ease of integrating BitBLAS with PyTorch, AutoGPTQ, and vLLM in the 3rd-party integration examples.

• Customization: BitBLAS supports implementing customized mixed-precision DNN operations rather than matrix multiplication with the flexible DSL (TIR Script).

Reference

Please cite BitBLAS/Ladder in your publications if it helps your research:

@inproceedings {ladder-osdi24,
author = {Lei Wang and Lingxiao Ma and Shijie Cao and Quanlu Zhang and Jilong Xue and Yining Shi and Ningxin Zheng and Ziming Miao and Fan Yang and Ting Cao and Yuqing Yang and Mao Yang},
title = {Ladder: Enabling Efficient Low-Precision Deep Learning Computing through Hardware-aware Tensor Transformation},
booktitle = {18th USENIX Symposium on Operating Systems Design and Implementation (OSDI 24)},
year = {2024},
url = {https://www.usenix.org/conference/osdi24/presentation/wang-lei},
}

Contributing

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