Summary of Mini-sequence Transformer: Optimizing Intermediate Memory For Long Sequences Training, by Cheng Luo et al.
Mini-Sequence Transformer: Optimizing Intermediate Memory for Long Sequences Training
by Cheng Luo, Jiawei Zhao, Zhuoming Chen, Beidi Chen, Anima Anandkumar
First submitted to arxiv on: 22 Jul 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 A new methodology for efficient language model (LLM) training is introduced in this paper, called Mini-Sequence Transformer (MsT). MsT addresses the challenge of processing extremely long sequences by partitioning them into smaller mini-sequences and iteratively processing these mini-sequences. This approach reduces intermediate memory usage and enables significant memory savings during both forward and backward passes. The authors demonstrate the effectiveness of MsT by measuring no degradation in throughput or convergence when training the Llama3-8B model on 12x longer sequences than standard implementations. MsT is a fully general, implementation-agnostic, and requires minimal code changes to integrate with existing LLM training frameworks. In addition, the authors successfully extend the maximum context length of Qwen, Mistral, and Gemma-2 by 12-24x using the huggingface library. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to train language models that can handle very long pieces of text. The method is called Mini-Sequence Transformer (MsT) and it works by breaking down the long sequences into smaller parts, then processing each part separately. This makes it much more efficient and allows for longer sequences than before. The authors tested MsT on a large model and found that it worked just as well as the usual way of training models, but with much longer sequences. |
Keywords
* Artificial intelligence * Context length * Language model * Transformer
