@inproceedings{elhoushi-etal-2024-layerskip,
title = "{L}ayer{S}kip: Enabling Early Exit Inference and Self-Speculative Decoding",
author = "Elhoushi, Mostafa and
Shrivastava, Akshat and
Liskovich, Diana and
Hosmer, Basil and
Wasti, Bram and
Lai, Liangzhen and
Mahmoud, Anas and
Acun, Bilge and
Agarwal, Saurabh and
Roman, Ahmed and
Aly, Ahmed and
Chen, Beidi and
Wu, Carole-Jean",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.681",
doi = "10.18653/v1/2024.acl-long.681",
pages = "12622--12642",
abstract = "We present LayerSkip, an end-to-end solution to speed-up inference of large language models (LLMs). First, during training we apply layer dropout, with low dropout rates for earlier layers and higher dropout rates for later layers, and an early exit loss where all transformer layers share the same exit. Second, during inference, we show that this training recipe increases the accuracy of early exit at earlier layers, without adding any auxiliary layers or modules to the model. Third, we present a novel self-speculative decoding solution where we exit at early layers and verify and correct with remaining layers of the model. Our proposed self-speculative decoding approach has less memory footprint than other speculative decoding approaches and benefits from shared compute and activations of the draft and verification stages. We run experiments on different Llama model sizes on different types of training: pretraining from scratch, continual pretraining, finetuning on specific data domain, and finetuning on specific task. We implement our inference solution and show speedups of up to 2.16x on summarization for CNN/DM documents, 1.82x on coding, and 2.0x on TOPv2 semantic parsing task. We open source our code at https://github.com/facebookresearch/LayerSkip.",
}
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<abstract>We present LayerSkip, an end-to-end solution to speed-up inference of large language models (LLMs). First, during training we apply layer dropout, with low dropout rates for earlier layers and higher dropout rates for later layers, and an early exit loss where all transformer layers share the same exit. Second, during inference, we show that this training recipe increases the accuracy of early exit at earlier layers, without adding any auxiliary layers or modules to the model. Third, we present a novel self-speculative decoding solution where we exit at early layers and verify and correct with remaining layers of the model. Our proposed self-speculative decoding approach has less memory footprint than other speculative decoding approaches and benefits from shared compute and activations of the draft and verification stages. We run experiments on different Llama model sizes on different types of training: pretraining from scratch, continual pretraining, finetuning on specific data domain, and finetuning on specific task. We implement our inference solution and show speedups of up to 2.16x on summarization for CNN/DM documents, 1.82x on coding, and 2.0x on TOPv2 semantic parsing task. We open source our code at https://github.com/facebookresearch/LayerSkip.</abstract>
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%0 Conference Proceedings
%T LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding
%A Elhoushi, Mostafa
%A Shrivastava, Akshat
%A Liskovich, Diana
%A Hosmer, Basil
%A Wasti, Bram
%A Lai, Liangzhen
%A Mahmoud, Anas
%A Acun, Bilge
%A Agarwal, Saurabh
%A Roman, Ahmed
%A Aly, Ahmed
%A Chen, Beidi
%A Wu, Carole-Jean
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F elhoushi-etal-2024-layerskip
%X We present LayerSkip, an end-to-end solution to speed-up inference of large language models (LLMs). First, during training we apply layer dropout, with low dropout rates for earlier layers and higher dropout rates for later layers, and an early exit loss where all transformer layers share the same exit. Second, during inference, we show that this training recipe increases the accuracy of early exit at earlier layers, without adding any auxiliary layers or modules to the model. Third, we present a novel self-speculative decoding solution where we exit at early layers and verify and correct with remaining layers of the model. Our proposed self-speculative decoding approach has less memory footprint than other speculative decoding approaches and benefits from shared compute and activations of the draft and verification stages. We run experiments on different Llama model sizes on different types of training: pretraining from scratch, continual pretraining, finetuning on specific data domain, and finetuning on specific task. We implement our inference solution and show speedups of up to 2.16x on summarization for CNN/DM documents, 1.82x on coding, and 2.0x on TOPv2 semantic parsing task. We open source our code at https://github.com/facebookresearch/LayerSkip.
%R 10.18653/v1/2024.acl-long.681
%U https://aclanthology.org/2024.acl-long.681
%U https://doi.org/10.18653/v1/2024.acl-long.681
%P 12622-12642
Markdown (Informal)
[LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding](https://aclanthology.org/2024.acl-long.681) (Elhoushi et al., ACL 2024)
ACL
- Mostafa Elhoushi, Akshat Shrivastava, Diana Liskovich, Basil Hosmer, Bram Wasti, Liangzhen Lai, Anas Mahmoud, Bilge Acun, Saurabh Agarwal, Ahmed Roman, Ahmed Aly, Beidi Chen, and Carole-Jean Wu. 2024. LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12622–12642, Bangkok, Thailand. Association for Computational Linguistics.
