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:
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.
The example program [1] generates a Transformer that executes an RPN expression, using "breadcrumb" tokens to track its progress. The output looks like:
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...