Mini-Sequence Transformer: Optimizing Intermediate Memory for Long Sequences Training

• URL: http://arxiv.org/abs/2407.15892v3
• Date: Thu, 31 Oct 2024 16:36:27 GMT
• Title: Mini-Sequence Transformer: Optimizing Intermediate Memory for Long Sequences Training
• Authors: Cheng Luo, Jiawei Zhao, Zhuoming Chen, Beidi Chen, Anima Anandkumar,
• Abstract summary: Mini-Sequence Transformer (MsT) is a 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 the huggingface library, MsT successfully extends the maximum context length of Qwen, Mistral, and Gemma-2 by 12-24x.
• Score: 78.93900796545523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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