Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models

TL;DR

This paper introduces DS-MoE, a hybrid dense training and sparse inference approach for Mixture-of-Experts models, achieving high efficiency and performance comparable to dense models while reducing computational costs.

Contribution

Proposes a novel hybrid dense-sparse framework (DS-MoE) that improves parameter and computational efficiency during training and inference of MoE language models.

Findings

DS-MoE models are more parameter-efficient than standard sparse MoEs.

DS-MoE achieves performance comparable to dense models with fewer active parameters.

DS-MoE models run up to 1.86x faster than similar dense models.

Abstract

Mixture-of-Experts (MoE) language models can reduce computational costs by 2-4$\times$ compared to dense models without sacrificing performance, making them more efficient in computation-bounded scenarios. However, MoE models generally require 2-4$\times$ times more parameters to achieve comparable performance to a dense model, which incurs larger GPU memory requirements and makes MoE models less efficient in I/O-bounded scenarios like autoregressive generation. In this work, we propose a hybrid dense training and sparse inference framework for MoE models (DS-MoE) which achieves strong computation and parameter efficiency by employing dense computation across all experts during training and sparse computation during inference. Our experiments on training LLMs demonstrate that our DS-MoE models are more parameter-efficient than standard sparse MoEs and are on par with dense models in terms of total parameter size and performance while being computationally cheaper (activating 30-40% of the model's parameters). Performance tests using vLLM show that our DS-MoE-6B model runs up to $1.86\times$ faster than similar dense models like Mistral-7B, and between $1.50\times$ and $1.71\times$ faster than comparable MoEs, such as DeepSeekMoE-16B and Qwen1.5-MoE-A2.7B.