Thanks for the feedback!

When I understand correctly, when removing the output projection layer, the results from using 1 large head (via theCausalAttention class) versus multiple smaller heads that add to the same size (via theMultiHeadAttention class) lead to approximately the same results?

I think this could be because we use a very simple dataset and short training here. In practice, on a larger-scale, I don't think that this will be true. I.e., I think the AI answer is incorrect.

That's because a single head with the same overall dimensionality is not equivalent to multi-head, because the single head has just one Q/K/V projection, while multi-head hasn_heads sets of them, and the attention matrices can differ between them then. I.e., the different heads can then learn to pay attention to different aspects.

Regarding theout_proj, it can be useful to "combine" the heads in the sense that it just adds a learned linear transformation that is useful here. But I was saying that it is not necessary becausethis paper found that it can be removed without affecting the loss.