Attention Head Output with Grouped Queries and Causal Masking
This formula calculates the output for a single attention head, head_j, in a transformer model that implements Grouped-Query Attention (GQA) with causal masking. The formula is: $\text{head}_j = \text{Att}_{\text{qkv}}(\mathbf{q}_i^{[j]}, \mathbf{K}_{\le i}^{[g(j)]}, \mathbf{V}_{\le i}^{[g(j)]})$. In this equation, $\text{Att}_{\text{qkv}}$ represents the attention function, and $\mathbf{q}_i^{[j]}$ is the query vector for the current token i and head j. The keys $\mathbf{K}$ and values $\mathbf{V}$ are shared among a group of query heads, as determined by the function $g(j)$. The subscript ${\le i}$ signifies that the attention is causal, meaning it only considers tokens up to the current position i.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Learn After
A transformer model calculates the output for a single attention head
jat token positioniusing the formula:$\text{head}_j = \text{Att}_{\text{qkv}}(\mathbf{q}_i^{[j]}, \mathbf{K}_{\le i}^{[g(j)]}, \mathbf{V}_{\le i}^{[g(j)]})$, whereg(j)maps the query headjto a specific group. What is the primary consequence of using the group-indexed key$\mathbf{K}_{\le i}^{[g(j)]}$instead of a head-specific key$\mathbf{K}_{\le i}^{[j]}$?Applying Grouped-Query Attention with Causal Masking
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