Functors.jl provides a set of tools to represent functors. Functors are a powerful means to apply functions to generic objects without changing their structure.

The most straightforward use is to traverse a complicated nested structure as a tree, and apply a function f to every field it encounters along the way.

For large models it can be cumbersome or inefficient to work with parameters as one big, flat vector, and structs help manage complexity; but it may be desirable to easily operate over all parameters at once, e.g. for changing precision or applying an optimiser update step.

Basic Usage and Implementation

When one marks a structure as @functor it means that Functors.jl is allowed to look into the fields of the instances of the struct and modify them. This is achieved through Functors.fmap.

The workhorse of fmap is actually a lower level function, functor:

julia> using Functors

julia> struct Foo

julia> @functor Foo

julia> foo = Foo(1, [1, 2, 3]) # notice all the elements are integers

julia> xs, re = Functors.functor(foo)
((x = 1, y = [1, 2, 3]), var"#21#22"())

julia> re(map(float, xs)) # element types have been switched out for floating point numbers
Foo(1.0, [1.0, 2.0, 3.0])

functor returns the parts of the object that can be inspected, as well as a reconstruction function (shown as re) that takes those values and restructures them back into an object of the original type.

To include only certain fields of a struct, one can pass a tuple of field names to @functor:

julia> struct Baz

julia> @functor Baz (x,)

julia> model = Baz(1, 2)
Baz(1, 2)

julia> fmap(float, model)
Baz(1.0, 2)

Any field not in the list will be passed through as-is during reconstruction. This is done by invoking the default constructor, so structs that define custom inner constructors are expected to provide one that acts like the default.

Appropriate Use

Not everything should be a functor!

Due to its generic nature it is very attractive to mark several structures as @functor when it may not be quite safe to do so.

Typically, since any function f is applied to the leaves of the tree, but it is possible for some functions to require dispatching on the specific type of the fields causing some methods to be missed entirely.

Examples of this include element types of arrays which typically have their own mathematical operations defined. Adding a @functor to such a type would end up missing methods such as +(::MyElementType, ::MyElementType). Think RGB from Colors.jl.