Summary of Enhancing Training Efficiency Using Packing with Flash Attention, by Achintya Kundu et al.
Enhancing Training Efficiency Using Packing with Flash Attention
by Achintya Kundu, Rhui Dih Lee, Laura Wynter, Raghu Kiran Ganti, Mayank Mishra
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 discusses an issue with padding in Large Language Model (LLM) models during training, where special tokens are added to shorter sequences to match the length of the longest sequence in each batch. While this ensures uniformity for batch processing, it wastes GPU resources and includes irrelevant padding tokens in computations. The Hugging Face SFT trainer offers packing as a solution to maximize GPU resource utilization, but this feature did not previously provide proper masking for packed training examples. In Transformers 4.44, the capability for proper masking has been added, and this paper analyzes the benefits of this new feature across different packing variations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper talks about how padding can be a problem when training language models. Padding is like adding extra spaces to make all the text examples the same length. This helps with processing, but it also uses up computer resources and includes unnecessary information in calculations. The Hugging Face SFT trainer has an option called packing that makes the most of computer resources. However, this feature didn’t properly hide each packed example from calculation. In the latest version of Transformers 4.44, the ability to properly hide these examples has been added, and this paper looks at how this new feature works. |
Keywords
* Artificial intelligence * Large language model
