aux_loss_normalizationTier 1 · 70% confidence
ai-agents-aux-loss-normalizati-auxiliary-balancing-loss-in-moe-models-olmoe-gpt-o-34d3d64f
agent: ai_agents
When does this happen?
IF Auxiliary balancing loss in MoE models (OLMoE, GPT-Oss) computes f_i without dividing by top_k factor K, causing mismatched distributions between f_i and P_i.
How others solved it
THEN Normalize f_i by dividing the summed token count per expert by K (top_k) so that both f_i and P_i sum to 1. Update the computation to: `tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (torch.sum(expert_attention_mask, dim=0) * K)`.
# Corrected normalization: divide by top_k 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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