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[D]In transformer models, why is there a query and key matrix instead of just the product? (alien.top)

submitted 9 months ago by lildaemon@alien.top to c/machinelearning@academy.garden

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The only time that the query and key matrices are used is to compute the attention scores. That is $v_i^T \cdot W_q^T W_k v_j$ But what is used is the matrix $W_q^T W_k$. Why not just replace $W_q^T W_k$ with a single matrix $W_{qv}$, and learn the matrix that is the product of W_q^T W_k instead of the matrices themselves? How does it help to have two matrices instead of one? And if it helps, why is that not done when applying matrices between neuron layers?

Chatgpt tells me that the reason is that it allows the model to learn a different representation for the query and key. But because they are just dotted together, it seems to me that you can just use the original embedding as the query with no loss of generality.

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[–] Tensor_Devourer_56@alien.top 1 points 9 months ago (2 children)

If I'm understanding your question correctly, it probably doesn't make any differences computation wise. But if we have query dot key as one single input, then the attention layer would just have two inputs: 1.query dot key matrix; 2. value matrix. I think this would be a worse formulation thant the original paper altough they are the same computation wise. By allowing separate key and value matrices, the data flow is clearer. For example the Encoder-Decoder attention layer takes the result of Encoder block as key and value but the processed target sequence as value. This idea is very clear with the original attention layer formation.

[–] tdgros@alien.top 1 points 9 months ago (1 children)

It's the same mathematically but not computation wise, the tokens are projected to a smaller dimension. The complexity is 2Nd whereas it'd be N² if you'd fuse the weight matrices.

[–] lildaemon@alien.top 1 points 9 months ago

This.