Beyond Distillation: Task-level Mixture-of-Experts for Efficient Inference

Sneha Kudugunta, Yanping Huang, Ankur Bapna, Maxim Krikun, Dmitry Lepikhin, Minh-Thang Luong, Orhan Firat


Abstract
Sparse Mixture-of-Experts (MoE) has been a successful approach for scaling multilingual translation models to billions of parameters without a proportional increase in training computation. However, MoE models are prohibitively large and practitioners often resort to methods such as distillation for serving. In this work, we investigate routing strategies at different granularity (token, sentence, task) in MoE models to bypass distillation. Experiments on WMT and a web-scale dataset suggest that task-level routing (task-MoE) enables us to extract smaller, ready-to-deploy sub-networks from large sparse models. On WMT, our task-MoE with 32 experts (533M parameters) outperforms the best performing token-level MoE model (token-MoE) by +1.0 BLEU on average across 30 language pairs. The peak inference throughput is also improved by a factor of 1.9x when we route by tasks instead of tokens. While distilling a token-MoE to a smaller dense model preserves only 32% of the BLEU gains, our sub-network task-MoE, by design, preserves all the gains with the same inference cost as the distilled student model. Finally, when scaling up to 200 language pairs, our 128-expert task-MoE (13B parameters) performs competitively with a token-level counterpart, while improving the peak inference throughput by a factor of 2.6x.
Anthology ID:
2021.findings-emnlp.304
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2021
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
EMNLP | Findings
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3577–3599
Language:
URL:
https://aclanthology.org/2021.findings-emnlp.304
DOI:
10.18653/v1/2021.findings-emnlp.304
Bibkey:
Cite (ACL):
Sneha Kudugunta, Yanping Huang, Ankur Bapna, Maxim Krikun, Dmitry Lepikhin, Minh-Thang Luong, and Orhan Firat. 2021. Beyond Distillation: Task-level Mixture-of-Experts for Efficient Inference. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3577–3599, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Beyond Distillation: Task-level Mixture-of-Experts for Efficient Inference (Kudugunta et al., Findings 2021)
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PDF:
https://aclanthology.org/2021.findings-emnlp.304.pdf