Pit One Against Many: Leveraging Attention-head Embeddings for Parameter-efficient Multi-head Attention
This addresses memory efficiency for scaling pre-trained language models, offering an incremental improvement over existing attention mechanisms.
The paper tackles the high memory cost of multi-head attention in transformers by proposing a parameter-efficient alternative using head embeddings, achieving substantial memory savings with minimal performance loss on downstream tasks.
Scaling pre-trained language models has resulted in large performance gains in various natural language processing tasks but comes with a large cost in memory requirements. Inspired by the position embeddings in transformers, we aim to simplify and reduce the memory footprint of the multi-head attention (MHA) mechanism. We propose an alternative module that uses only a single shared projection matrix and multiple head embeddings (MHE), i.e. one per head. We empirically demonstrate that our MHE attention is substantially more memory efficient compared to alternative attention mechanisms while achieving high predictive performance retention ratio to vanilla MHA on several downstream tasks. MHE attention only requires a negligible fraction of additional parameters ($3nd$, where $n$ is the number of attention heads and $d$ the size of the head embeddings) compared to a single-head attention, while MHA requires $(3n^2-3n)d^2-3nd$ additional parameters.