Recently I got interested in how to "compile" a program definition into the weights of a Transformer. I settled for distilling the MLPs individually, but the attention weights are fully "calculated".
The example program [1] generates a Transformer that executes an RPN expression, using "breadcrumb" tokens to track its progress. The output looks like:
Prompt: 3 4 + EXEC
Output: c2 c1 c0 7
Prompt: 3 4 + 3 3 + * EXEC
Output: c2 c1 c0 7 c5 c4 c3 6 c6 c5 c2 42
Prompt: 10 2 3 * + 2 + EXEC
Output: c3 c2 c1 6 c4 c3 c0 16 c6 c5 c4 18
I think there's still a lot that could be improved but I wanted to document what I have done so far. It turned out very interesting and made me think about transformers, attention and particularly the structure of the residual stream in a new way.
[1]: https://github.com/radarsat1/rpn_transformer/blob/main/src/p...
