This is great, thank you. I have to teach this stuff on Friday so perfect timing. It's hard to explain the attention mechanism in a way that becomes intuitive because the weighting scheme does not help much with the intuition. Having a visualization like this helps a lot. Don't move that page please since I'll link to it!
This is great, I've read multiple books and watched videos about the attention mechanism. Now that I understand it, this is the clearest example I've seen on how attention works.
INSANE
neat, combining info from two phrases is hard to see without such a tool.<p>are you worried later-layer attention gets drowned out by earlier layers just because there are more of them contributing to the sum?
I like the visualisation. Pretty cool
I highly question this simplistic idea of high vector magnitude = high influence.
You get what you pay for. If you want to think harder and get more, <a href="https://transformer-circuits.pub/2025/attention-qk/index.html" rel="nofollow">https://transformer-circuits.pub/2025/attention-qk/index.htm...</a>
I don't disagree with that. I did add an entire caveat paragraph there.<p>To me, it's more of a neat visualization, not something that can be used to interpret LLM behavior. Even with a lot of simplification, it can show some interesting patterns.
Same I dont get it just, could you clarify it
I don't know much about LLMs but does that mean you have N^2 computation with the context size since every token needs to track how it relates to every other token?
Yes, except no with the KV cache. Because tokens aren't modified by future tokens you can cache the meaning of previous tokens. This makes the total effort linear over the entire context (or constant per forward pass).
> This makes the total effort linear over the entire context (or constant per forward pass).<p>This is incorrect. The compute required per forward pass to generate <i>each additional token</i> during decode will scales as O(N), even with a KV cache (without a KV cache, it would scale as O(N^2)). Over generating N tokens, it's O(N^2) with the cache (and O(N^3) without).<p>It's O(N) for a forward pass because that new token still has to "attend to" to each previous token. That requires N dot products: between the cached key vectors and the new query vector for the new position. You also have N reads from memory (K and V) which is probably gonna be your actual bottleneck. (Decode is memory-bound.)<p>This is why you should avoid long contexts, if you can, even with a warm cache. You will get charged more, in "cache read" tokens.
I see and is there only 1 layer of relations?<p>Or does it accumulate the relations like A relates to B, so also add in B's relations
For full self attention yes
I am curious what's the actual formula.<p>I mean, there so many headers and layers, it is tricky to make a choice that will resonate with our intuition . Is it some weighted average? Or maybe ablation test?
It's really simple, basically just the magnitude of the value vector, weighted by QK dot product, summed across all attention heads and layers.<p>When I started, I expected I'd have to experiment a lot to find something comprehensible. But this simple computation can already show some patterns.
If you want quick access look at google images for "transformer attention formula" there are some interesting depictions
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