@inproceedings{tuli-etal-2024-dynamo,
title = "{D}yna{M}o: Accelerating Language Model Inference with Dynamic Multi-Token Sampling",
author = "Tuli, Shikhar and
Lin, Chi-Heng and
Hsu, Yen-Chang and
Jha, Niraj and
Shen, Yilin and
Jin, Hongxia",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.182",
pages = "3322--3345",
abstract = "Traditional language models operate autoregressively, i.e., they predict one token at a time. Rapid explosion in model sizes has resulted in high inference times. In this work, we propose DynaMo, a suite of multi-token prediction language models that reduce net inference times. Our models *dynamically* predict multiple tokens based on their confidence in the predicted joint probability distribution. We propose a lightweighttechnique to train these models, leveraging the weights of traditional autoregressive counterparts. Moreover, we propose novel ways to enhance the estimated joint probability to improve text generation quality, namely co-occurrence weighted masking and adaptive thresholding. We also propose systematic qualitative and quantitative methods to rigorously test the quality of generated text for non-autoregressive generation. One of the models in our suite, DynaMo-7.3B-T3, achieves same-quality generated text as the baseline (Pythia-6.9B) while achieving 2.57$\times$ speed-up with only 5.87{\%} and 2.67{\%} parameter and training time overheads, respectively.",
}
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%0 Conference Proceedings
%T DynaMo: Accelerating Language Model Inference with Dynamic Multi-Token Sampling
%A Tuli, Shikhar
%A Lin, Chi-Heng
%A Hsu, Yen-Chang
%A Jha, Niraj
%A Shen, Yilin
%A Jin, Hongxia
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F tuli-etal-2024-dynamo
%X Traditional language models operate autoregressively, i.e., they predict one token at a time. Rapid explosion in model sizes has resulted in high inference times. In this work, we propose DynaMo, a suite of multi-token prediction language models that reduce net inference times. Our models *dynamically* predict multiple tokens based on their confidence in the predicted joint probability distribution. We propose a lightweighttechnique to train these models, leveraging the weights of traditional autoregressive counterparts. Moreover, we propose novel ways to enhance the estimated joint probability to improve text generation quality, namely co-occurrence weighted masking and adaptive thresholding. We also propose systematic qualitative and quantitative methods to rigorously test the quality of generated text for non-autoregressive generation. One of the models in our suite, DynaMo-7.3B-T3, achieves same-quality generated text as the baseline (Pythia-6.9B) while achieving 2.57\times speed-up with only 5.87% and 2.67% parameter and training time overheads, respectively.
%U https://aclanthology.org/2024.naacl-long.182
%P 3322-3345
Markdown (Informal)
[DynaMo: Accelerating Language Model Inference with Dynamic Multi-Token Sampling](https://aclanthology.org/2024.naacl-long.182) (Tuli et al., NAACL 2024)
ACL
- Shikhar Tuli, Chi-Heng Lin, Yen-Chang Hsu, Niraj Jha, Yilin Shen, and Hongxia Jin. 2024. DynaMo: Accelerating Language Model Inference with Dynamic Multi-Token Sampling. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 3322–3345, Mexico City, Mexico. Association for Computational Linguistics.