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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2407.15892 |
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| _version_ | 1866909381743869952 |
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| author | Luo, Cheng Zhao, Jiawei Chen, Zhuoming Chen, Beidi Anandkumar, Anima |
| author_facet | Luo, Cheng Zhao, Jiawei Chen, Zhuoming Chen, Beidi Anandkumar, Anima |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_15892 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mini-Sequence Transformer: Optimizing Intermediate Memory for Long Sequences Training Luo, Cheng Zhao, Jiawei Chen, Zhuoming Chen, Beidi Anandkumar, Anima Machine Learning 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. |
| title | Mini-Sequence Transformer: Optimizing Intermediate Memory for Long Sequences Training |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.15892 |