OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models

• URL: http://arxiv.org/abs/2402.01739v2
• Date: Wed, 27 Mar 2024 10:21:24 GMT
• Title: OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models
• Authors: Fuzhao Xue, Zian Zheng, Yao Fu, Jinjie Ni, Zangwei Zheng, Wangchunshu Zhou, Yang You,
• Abstract summary: We release OpenMoE, a series of fully open-sourced and reproducible decoder-only Mixture-of-Experts (MoE) based large language models (LLMs) Our investigation confirms that MoE-based LLMs can offer a more favorable cost-effectiveness trade-off than dense LLMs. We find that routing decisions in MoE models are predominantly based on token IDs, with minimal context relevance.
• Score: 44.848642930797155
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To help the open-source community have a better understanding of Mixture-of-Experts (MoE) based large language models (LLMs), we train and release OpenMoE, a series of fully open-sourced and reproducible decoder-only MoE LLMs, ranging from 650M to 34B parameters and trained on up to over 1T tokens. Our investigation confirms that MoE-based LLMs can offer a more favorable cost-effectiveness trade-off than dense LLMs, highlighting the potential effectiveness for future LLM development. One more important contribution of this study is an in-depth analysis of the routing mechanisms within our OpenMoE models, leading to three significant findings: Context-Independent Specialization, Early Routing Learning, and Drop-towards-the-End. We discovered that routing decisions in MoE models are predominantly based on token IDs, with minimal context relevance. The token-to-expert assignments are determined early in the pre-training phase and remain largely unchanged. This imperfect routing can result in performance degradation, particularly in sequential tasks like multi-turn conversations, where tokens appearing later in a sequence are more likely to be dropped. Finally, we rethink our design based on the above-mentioned observations and analysis. To facilitate future MoE LLM development, we propose potential strategies for mitigating the issues we found and further improving off-the-shelf MoE LLM designs.

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