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

Pan Bowen, Shen Yikang, Liu Haokun, Mishra Mayank, Zhang Gaoyuan, Oliva Aude, Raffel Colin, Panda Rameswar. Arxiv 2024

[Paper]    
Efficiency And Optimization GPT Pretraining Methods Tools Training Techniques

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.

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