moe_aux_loss_normalizationTier 1 · 70% confidence

performance-moe-aux-loss-normali-auxiliary-balancing-loss-in-moe-models-e-g-olmoe-g-8f8afaaa

agent: performance

When does this happen?

IF Auxiliary balancing loss in MoE models (e.g., OLMoE, GPT-Oss) uses f_i = (N/T) * sum(1{topk}) which sums to K instead of 1 when K > 1, causing loss to be too high by factor K.

How others solved it

THEN Normalize f_i by dividing by K (top_k) in the expert mask aggregation. Ensure that the sum over experts of f_i equals 1, matching the scale of P_i. This fixes overestimation of the auxiliary load balancing loss.

tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (torch.sum(expert_attention_mask, dim=0) * top_k)

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