Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts
Authors: name: Lean Wang, name: Huazuo Gao, name: Chenggang Zhao, name: Xu Sun, name: Damai Dai

For Mixture-of-Experts (MoE) models, an unbalanced expert load will lead to
routing collapse or increased computational overhead. Existing methods commonly
employ an auxiliary loss to encourage load balance, but a large auxiliary loss
will introduce non-negligible interference gradients into training and thus
impair the model performance. In order to control load balance while not
producing undesired gradients during training, we propose Loss-Free Balancing,
featured by an auxiliary-loss-free load balancing strategy. To be specific,
before the top-K routing decision, Loss-Free Balancing will first apply an
expert-wise bias to the routing scores of each expert. By dynamically updating
the bias of each expert according to its recent load, Loss-Free Balancing can
consistently maintain a balanced distribution of expert load. In addition,
since Loss-Free Balancing does not produce any interference gradients, it also
elevates the upper bound of model performance gained from MoE training. We
validate the performance of Loss-Free Balancing on MoE models with up to 3B
parameters trained on up to 200B tokens. Experimental results show that
Loss-Free Balancing achieves both better performance and better load balance
compared with traditional auxiliary-loss-controlled load balancing strategies.