Summary of Flashattention-3: Fast and Accurate Attention with Asynchrony and Low-precision, by Jay Shah et al.
FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision
by Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, Tri Dao
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper addresses the limitations of large language models by optimizing the attention mechanism, a crucial component of transformer architectures. The authors propose three techniques to speed up attention processing on Hopper GPUs: exploiting asynchrony, interleaving operations, and block quantization. These techniques enable efficient memory access, overlap computation and data movement, and leverage hardware support for low-precision floating-point calculations. The proposed method, FlashAttention-3, achieves a significant speedup of 1.5-2.0 times on H100 GPUs with FP16 precision, reaching up to 740 TFLOPs/s (75% utilization), and with FP8 precision, achieving close to 1.2 PFLOPs/s. Additionally, the authors demonstrate that FP8 FlashAttention-3 achieves lower numerical error than a baseline FP8 attention. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make big language models faster and more efficient. The researchers figured out ways to speed up the “attention” part of transformers, which is important for understanding long sentences or texts. They came up with three new techniques that work well on special computer chips called Hopper GPUs. These techniques help computers use memory better, do things at the same time, and use less precise calculations. The new method, FlashAttention-3, makes computers go faster by 50-100%, depending on how they’re doing calculations. |
Keywords
* Artificial intelligence * Attention * Precision * Quantization * Transformer
