Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding

• URL: http://arxiv.org/abs/2310.05424v1
• Date: Mon, 9 Oct 2023 05:53:05 GMT
• Title: Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding
• Authors: Sangmin Bae, Jongwoo Ko, Hwanjun Song, Se-Young Yun
• Abstract summary: We propose a Fast and Robust Early-Exiting framework, which incorporates a shallow-deep module and a synchronized parallel decoding. Our framework enables faster inference by synchronizing the decoding process of the current token with previously stacked early-exited tokens. As parallel decoding allows us to observe predictions from both shallow and deep models, we present a novel adaptive threshold estimator.
• Score: 43.659680579686544
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To tackle the high inference latency exhibited by autoregressive language models, previous studies have proposed an early-exiting framework that allocates adaptive computation paths for each token based on the complexity of generating the subsequent token. However, we observed several shortcomings, including performance degradation caused by a state copying mechanism or numerous exit paths, and sensitivity to exit confidence thresholds. Consequently, we propose a Fast and Robust Early-Exiting (FREE) framework, which incorporates a shallow-deep module and a synchronized parallel decoding. Our framework enables faster inference by synchronizing the decoding process of the current token with previously stacked early-exited tokens. Furthermore, as parallel decoding allows us to observe predictions from both shallow and deep models, we present a novel adaptive threshold estimator that exploits a Beta mixture model to determine suitable confidence thresholds. We empirically demonstrated the superiority of our proposed framework on extensive generation tasks.

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