Transformer attention masks: Causal vs. padding mask necessity

A technical deep dive into the necessity of causal and padding masks in transformer models reveals that right padding makes the padding mask redundant, as causality already excludes padded tokens. However, left padding, commonly used in decoder-only generation, requires the padding mask to prevent attention leaks onto filler tokens. The author also highlights a common issue where masked rows can lead to meaningless uniform distributions after softmax, suggesting fixes like masking output at padded query positions or zeroing fully-masked rows. AI

IMPACT Clarifies a subtle but critical detail in transformer attention mechanisms, potentially improving model training and inference efficiency.

RANK_REASON Technical paper detailing a specific aspect of transformer model architecture. [lever_c_demoted from research: ic=1 ai=1.0]

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• Causal mask
• Padding mask
• Softmax

• paper

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