Related papers: Mixture of A Million Experts

Mixture of A Million Experts

URL: http://arxiv.org/abs/2407.04153v1
Date: Thu, 4 Jul 2024 20:59:20 GMT
Title: Mixture of A Million Experts
Authors: Xu Owen He,
Abstract summary: This paper introduces PEER, a novel layer design that utilizes the product key technique for sparse retrieval from a vast pool of experts. Experiments on language modeling tasks demonstrate that PEER layers outperform dense FFWs and coarse-grained MoEs in terms of performance-compute trade-off.
Score: 1.240096657086732
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The feedforward (FFW) layers in standard transformer architectures incur a linear increase in computational costs and activation memory as the hidden layer width grows. Sparse mixture-of-experts (MoE) architectures have emerged as a viable approach to address this issue by decoupling model size from computational cost. The recent discovery of the fine-grained MoE scaling law shows that higher granularity leads to better performance. However, existing MoE models are limited to a small number of experts due to computational and optimization challenges. This paper introduces PEER (parameter efficient expert retrieval), a novel layer design that utilizes the product key technique for sparse retrieval from a vast pool of tiny experts (over a million). Experiments on language modeling tasks demonstrate that PEER layers outperform dense FFWs and coarse-grained MoEs in terms of performance-compute trade-off. By enabling efficient utilization of a massive number of experts, PEER unlocks the potential for further scaling of transformer models while maintaining computational efficiency.

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