attention-dimension-equality-constraints

IN premise — summaries/2026-08-24/wiki-Transformer_deep_learning_architecture-chunk-2-chunk-2.md

Created 2026-08-24T17:11:25+00:00

Required dimension equalities for valid attention computation are d_query = d_key, d_value = d_head, and sequence lengths ℓ_seq,key = ℓ_seq,value; violating any of these makes the matrix multiplication ill-defined.

Summary

For an attention mechanism to compute at all, the input shapes have to line up in specific ways: queries and keys must share the same feature dimension, values must match the output head size, and the key and value sequences must be the same length. If any of those match up incorrectly, the core matrix multiplication has no valid result, so the whole computation breaks at the shape level before any learning can even be discussed.