Summary of Fastattention: Extend Flashattention2 to Npus and Low-resource Gpus, by Haoran Lin et al.
FastAttention: Extend FlashAttention2 to NPUs and Low-resource GPUs
by Haoran Lin, Xianzhi Yu, Kang Zhao, Lu Hou, Zongyuan Zhan, Stanislav Kamenev, Han Bao, Ting Hu, Mingkai Wang, Qixin Chang, Siyue Sui, Weihao Sun, Jiaxin Hu, Jun Yao, Zekun Yin, Cheng Qian, Ying Zhang, Yinfei Pan, Yu Yang, Weiguo Liu
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed FastAttention mechanism adapts the FlashAttention series for inference on NPUs and low-resource GPUs, enhancing efficiency for large language models (LLMs). The innovation is twofold: it migrates FlashAttention to Ascend NPUs using a two-level tiling strategy for speedup, memory-saving tiling-mask strategy, and reduced communication overhead through tiling-AllReduce. Additionally, FastAttention is redesigned for Volta-based GPUs by optimizing operands layout in shared memory and introducing a CPU-GPU cooperative strategy for efficient memory utilization. Experimental results demonstrate significant performance gains: 10.7x speedup on Ascend NPUs, 5.16x higher throughput for Llama-7B within FastAttention, 1.43x speedup on Volta architecture GPUs, and 1.46x end-to-end speedup using FasterTransformer. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FastAttention is a new way to make large language models work better on special computers called NPUs and low-resource GPUs. These devices are not as powerful as high-level GPUs, but FastAttention helps them do tasks faster by making adjustments and optimizations. The idea works for two types of devices: Ascend NPUs and Volta-based GPUs. On these devices, FastAttention is 10 times faster than usual attention mechanisms. It also makes Llama-7B language models run 5.16 times faster and Pangu-38B models run 1.46 times faster. |
Keywords
» Artificial intelligence » Attention » Inference » Llama » Mask
