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 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).

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

A_dtype	W_dtype	Accum_dtype	Out_dtype	BitBLAS Support	Tested Platform
FP16	FP16	FP16	FP16	√	V100(SM_70)/A100(SM_80)/A6000(SM_86)/RTX 4090(SM_89)
FP16	FP4_E2M1	FP16	FP16	√	V100(SM_70)/A100(SM_80)/A6000(SM_86)/RTX 4090(SM_89)
FP16	INT8	FP16	FP16	√	V100(SM_70)/A100(SM_80)/A6000(SM_86)/RTX 4090(SM_89)
FP16	UINT4/INT4	FP16	FP16	√	V100(SM_70)/A100(SM_80)/A6000(SM_86)/RTX 4090(SM_89)
FP16	UINT2/INT2	FP16	FP16	√	V100(SM_70)/A100(SM_80)/A6000(SM_86)/RTX 4090(SM_89)
FP16	UINT1	FP16	FP16	√	V100(SM_70)/A100(SM_80)/A6000(SM_86)/RTX 4090(SM_89)
FP16	NF4	FP16	FP16	√	V100(SM_70)/A100(SM_80)/A6000(SM_86)/RTX 4090(SM_89)
INT8	INT8	INT32	FP32/INT32/FP16/INT8	√	V100(SM_70)/A100(SM_80)/A6000(SM_86)/RTX 4090(SM_89)
INT8	UINT4/INT4	INT32	FP32/INT32/FP16/INT8	√	V100(SM_70)/A100(SM_80)/A6000(SM_86)/RTX 4090(SM_89)
INT8	UINT2/INT2	INT32	FP32/INT32/FP16/INT8	√	V100(SM_70)/A100(SM_80)/A6000(SM_86)/RTX 4090(SM_89)
INT8	UINT1	INT32	FP32/INT32/FP16/INT8	√	V100(SM_70)/A100(SM_80)/A6000(SM_86)/RTX 4090(SM_89)

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.
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).

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.