透過 Flash Attention 2 的 Packing 技術提升 Hugging Face 訓練效率
Original: Improving Hugging Face Training Efficiency Through Packing with Flash Attention 2
When fine-tuning or pre-training large language models (LLMs), the sequence lengths of input data are typically uneven. The traditional…
在 LLM 訓練中，傳統的 Padding 會浪費大量算力。Hugging Face 介紹了結合 Flash Attention 2 的 Packing（序列打包）技術，將多個短樣本拼接成固定長度，並利用 FA2 的變長注意力（varlen）避免樣本間干擾。這項優化能顯著提升訓練吞吐量並降低顯存佔用，已整合至 TRL 等工具中。
When fine-tuning or pre-training large language models (LLMs), the sequence lengths of input data are typically uneven. The traditional approach is to use padding to bring all sequences up to the maximum length, but this causes GPUs to spend significant time and memory computing on meaningless padding tokens — a serious waste of compute resources.
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