support W4A8 Marlin kernel #1113
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/1113
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 952f65e with merge base c87cc9b ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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can we do some comparisons between this and #880? |
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Summary
We inroduce a mixed precision GEMM kernel for INT4-Weight and INT8-Activation. We implemented the W4A8 GEMM based on Marlin GEMM. The kernel is designed to support our W4A8 quantization method QQQ. For more details on the kernel implementation, you can refer to our paper. The kernel demonstrates excellent performance and has been merged into the official vLLM project (see vllm-project/vllm#5218).
We hope the w4a8 GEMM can also provide a practical speedup for other W4A8 quantization methods in the community.
Additionally, since torchao is widely used in frameworks like SGLang, we can extend support for W4A8 once the kernel is integrated into torchao.
Performance
Here is the speedup over PyTorch FP16 GEMM (Calling CUTLASS) of all GEMMs under different numbers of input tokens. The weight matrix size is (N=8192, K=21760). You can reproduce the benchmark results using bench_w4a8.py in my repo.
