bitblas 0.0.1.dev2

A light weight framework to generate high performance CUDA/HIP code for BLAS operators.

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

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