The Extender: A Log-Structured Transformer

We introduce the Extender, a log-structured variant of the standard Transformer architecture. In a standard Transformer, each layer communicates with subsequent layers exclusively via the residual $\mathbf{h}$, a superposition channel. The Extender adds a concatenation channel $\mathbf{x}$: each layer $\ell$ emits both a residual update ${\delta }_{\ell }$ which is added to $\mathbf{h}$, and a much smaller \it extension ${ϵ}_{\ell }$ which is appended to $\mathbf{x}$. While both the FFN and $\mathbf{q}$ see $\mathbf{h}$, the attention $\mathbf{kv}$ projections take only $\mathbf{x}$ as input. As a result, the fully \it extended $\mathbf{x}$ contains the complete input for the $\mathbf{kv}$ projections of all layers, reducing the persistent attention memory footprint from $2L{d}_{model}$ to $\sum |{ϵ}_{\ell }|$. We find that with $|{ϵ}_{\ell }|=32$, the Extender matches Transformer accuracy on short-context (CORE) tasks at 199M-924M parameters, and exceeds Transformer accuracy on long-context (RULER) workloads, again at 924M parameters. For our 1664-wide, 924M model, the Extender's persistent attention memory footprint is $104\times$ smaller than MHA. The memory savings grow with model width.