TopoChains.jl provides a new data structure, a TopoChain, which picks up where Flux.jl's Chain left off. Unlike a Chain, which is designed for a sequential single-input single-output use case, a TopoChain can handle multi-input multi-output compositions of layers and functions. We achieve this by seperately specifying the topology (that is, the structure) of a model from the actual layers of a model. Check the docs for more!

The TopoChain was originally designed as the Stack, as part of Transformers.jl by Peter Cheng, and is repackaged here into a standalone package for general purpose use.

Suppose you want to define the following model:

First, we define the structure of the model:

topo = @functopo x:x => a:x => b:(a, b) => c => o

Then we define the model itself:

model = TopoChain(topo,
            Dense(32, 64),
            Dense(32, 64),
            (x, y) -> x .* y, 
            Dropout(0.1))

And that's it, you're done! You can see that the model both contains your layers, as well as information on how to run the layers:

model
# TopoChain(Dense(32, 64), Dense(32, 64), #7, Dropout(0.1)) representing the following function composition: 
# function(x)
#     a = Dense(32, 64)(x)
#     b = Dense(32, 64)(x)
#     c = #7(a, b)
#     o = Dropout(0.1)(c)
#     o
# end

TopoChain is a drop-in replacement for a Chain, which means all the features of Chain, such as parameter collection, indexing, layer slicing, etc. will work as intended.

You can get started by installing TopoChains.jl with the following command:

] add TopoChains