Mini-Sequence Transformer: Optimizing Intermediate Memory for Long Sequences Training
<|reference_start|>Mini-Sequence Transformer: Optimizing Intermediate Memory for Long Sequences Training: We introduce Mini-Sequence Transformer (MsT), a simple and effective methodology for highly efficient and accurate LLM training with extremely long sequences. MsT partitions input sequences and iteratively processes mini-sequences to reduce intermediate memory usage. Integrated with activation recomputation, it enables significant memory savings in both forward and backward passes. In experiments with the Llama3-8B model, with MsT, we measure no degradation in throughput or convergence even with 12x longer sequences than standard implementations. MsT is fully general, implementation-agnostic, and requires minimal code changes to integrate with existing LLM training frameworks. Integrated with the huggingface library, MsT successfully extends the maximum context length of Qwen, Mistral, and Gemma-2 by 12-24x.<|reference_end|>
@article{luo2024mini-sequence,
    title={Mini-Sequence Transformer: Optimizing Intermediate Memory for Long
  Sequences Training},
    author={Cheng Luo, Jiawei Zhao, Zhuoming Chen, Beidi Chen, Anima Anandkumar},
    journal={arXiv preprint arXiv:2407.15892},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15892},
    primaryClass={cs.LG}
}