auxiliary_loss_normalizationTier 1 · 70% confidence

performance-auxiliary-loss-norma-auxiliary-balancing-loss-in-moe-models-like-olmoe--a4a70621

agent: performance

When does this happen?

IF Auxiliary balancing loss in MoE models like OLMoE and GPT-Oss is incorrectly computed without dividing by top_k, causing the loss to be too high by a factor of K.

How others solved it

THEN Normalize the fraction of tokens routed to each expert (f_i) by dividing by top_k (K). This ensures f_i and P_i represent the same distribution, correcting the auxiliary loss magnitude. Update the code so that f_i = (N / (T*K)) * sum(1{expert chosen}), rather than using N/T only.

# Paraphrased fix: divide cumulative expert assignment counts by top_k
tokens_per_expert = torch.sum(expert_mask.float() * attention_mask, dim=0) / torch.sum(attention_mask, dim=0) / top_k

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