Lab 07: Macros

A little reminder from the lecture, a macro in its essence is a function, which

takes as an input an expression (parsed input)
modifies the expressions in arguments
inserts the modified expression at the same place as the one that is parsed.

In this lab we are going to use what we have learned about manipulation of expressions and explore avenues of where macros can be useful

convenience (@repeat, @show)
performance critical code generation (@poly)
alleviate tedious code generation (@species, @eats)
just as a syntactic sugar (@ecosystem)

Show macro

Let's start with dissecting "simple" @show macro, which allows us to demonstrate advanced concepts of macros and expression manipulation.

julia> x = 11
julia> @show x + 1x + 1 = 2
2
julia> let y = x + 1       # creates a temporary local variable
           println("x + 1 = ", y)
           y               # show macro also returns the result
       endx + 1 = 2
2
julia> # assignments should create the variable
       @show x = 3x = 3 = 3
3
julia> let y = x = 2
           println("x = 2 = ", y)
           y
       endx = 2 = 2
2
julia> x                   # should be equal to 22

The original Julia's implementation is not dissimilar to the following macro definition:

macro myshow(ex)
    quote
        println($(QuoteNode(ex)), " = ", repr(begin local value = $(esc(ex)) end))
        value
    end
end

@myshow (macro with 1 method)

Testing it gives us the expected behavior

julia> @myshow xx = 1 + 1xx = 1 + 1 = 2
2
julia> xx                  # should be defined2

In this "simple" example, we had to use the following concepts mentioned already in the lecture:

QuoteNode(ex) is used to wrap the expression inside another layer of quoting, such that when it is interpolated into :() it stays being a piece of code instead of the value it represents - TRUE QUOTING
esc(ex) is used in case that the expression contains an assignment, that has to be evaluated in the top level module Main (we are escaping the local context) - ESCAPING
$(QuoteNode(ex)) and $(esc(ex)) is used to evaluate an expression into another expression. INTERPOLATION
local value = is used in order to return back the result after evaluation

Lastly, let's mention that we can use @macroexpand to see how the code is manipulated in the @myshow macro

julia> @macroexpand @show x + 1quote
    Base.println("x + 1 = ", Base.repr(begin
                #= show.jl:1128 =#
                local var"#315#value" = x + 1
            end))
    var"#315#value"
end

In the profiling/performance labs we have sometimes needed to run some code multiple times in order to gather some samples and we have tediously written out simple for loops inside functions such as this

function run_polynomial(n, a, x)
    for _ in 1:n
        polynomial(a, x)
    end
end

We can remove this boilerplate code by creating a very simple macro that does this for us.

Exercise

Define macro @repeat that takes two arguments, first one being the number of times a code is to be run and the other being the actual code.

julia> @repeat 3 println("Hello!")
Hello!
Hello!
Hello!

Before defining the macro, it is recommended to write the code manipulation functionality into a helper function _repeat, which helps in organization and debugging of macros.

_repeat(3, :(println("Hello!"))) # testing "macro" without defining it

HINTS:

use $ interpolation into a for loop expression; for example given ex = :(1+x) we can interpolate it into another expression :($ex + y) -> :(1 + x + y)
if unsure what gets interpolated use round brackets :($(ex) + y)
macro is a function that creates code that does what we want

BONUS: What happens if we call @repeat 3 x = 2? Is x defined?

Solution:

julia> macro repeat(n::Int, ex)
           return _repeat(n, ex)
       end@repeat (macro with 1 method)
julia> function _repeat(n::Int, ex)
           :(for _ in 1:$n
               $ex
            end)
       end_repeat (generic function with 1 method)
julia> _repeat(3, :(println("Hello!"))):(for _ = 1:3
      #= REPL[2]:3 =#
      println("Hello!")
      #= REPL[2]:4 =#
  end)
julia> @repeat 3 println("Hello!")Hello!
Hello!
Hello!

Even if we had used escaping the expression x = 2 won't get evaluated properly due to the induced scope of the for loop. In order to resolve this we would have to specially match that kind of expression and generate a proper syntax withing the for loop global $ex. However we may just warn the user in the docstring that the usage is disallowed.

Note that this kind of repeat macro is also defined in the Flux.jl machine learning framework, wherein it's called @epochs and is used for creating training loop.

Polynomial macro

This is probably the last time we are rewriting the polynomial function, though not quite in the same way. We have seen in the last lab, that some optimizations occur automatically, when the compiler can infer the length of the coefficient array, however with macros we can generate optimized code directly (not on the same level - we are essentially preparing already unrolled/inlined code).

Ideally we would like to write some macro @poly that takes a polynomial in a mathematical notation and spits out an anonymous function for its evaluation, where the loop is unrolled.

Example usage:

p = @poly x 3x^2+2x^1+10x^0  # the first argument being the independent variable to match
p(2) # return the value

However in order to make this happen, let's first consider much simpler case of creating the same but without the need for parsing the polynomial as a whole and employ the fact that macro can have multiple arguments separated by spaces.

p = @poly 3 2 10
p(2)

Exercise

Create macro @poly that takes multiple arguments and creates an anonymous function that constructs the unrolled code. Instead of directly defining the macro inside the macro body, create helper function _poly with the same signature that can be reused outside of it.

Recall Horner's method polynomial evaluation from previous labs:

function polynomial(a, x)
    accumulator = a[end] * one(x)
    for i in length(a)-1:-1:1
        accumulator = accumulator * x + a[i]
        #= accumulator = muladd(x, accumulator, a[i]) =# # equivalent
    end
    accumulator  
end

HINTS:

you can use muladd function as replacement for ac * x + a[i]
think of the accumulator variable as the mathematical expression that is incrementally built (try to write out the Horner's method^[1] to see it)
you can nest expression arbitrarily
the order of coefficients has different order than in previous labs (going from high powers of x last to them being first)
use evalpoly to check the correctness

using Test
p = @poly 3 2 10
@test p(2) == evalpoly(2, [10,2,3]) # reversed coefficients

Solution:


julia> macro poly(a...)
           return _poly(a...)
       end@poly (macro with 1 method)
julia> function _poly(a...)
           N = length(a)
           ex = :($(a[1]))
           for i in 2:N
               ex = :(muladd(x, $ex, $(a[i]))) # equivalent of :(x * $ex + $(a[i]))
           end
           :(x -> $ex)
       end_poly (generic function with 1 method)
julia> p = @poly 3 2 10#1 (generic function with 1 method)
julia> p(2) == evalpoly(2, [10,2,3])true
julia> @code_lowered p(2) # can show the generated codeCodeInfo(
1 ─ %1 = Main.muladd(x, 3, 2)
│   %2 = Main.muladd(x, %1, 10)
└──      return %2
)

Moving on to the first/harder case, where we need to parse the mathematical expression.

Exercise

Create macro @poly that takes two arguments first one being the independent variable and second one being the polynomial written in mathematical notation. As in the previous case this macro should define an anonymous function that constructs the unrolled code.

julia> p = @poly x 3x^2+2x^1+10x^0  # the first argument being the independent variable to match

HINTS:

though in general we should be prepared for some edge cases, assume that we are really strict with the syntax allowed (e.g. we really require spelling out x^0, even though it is mathematically equivalent to 1)
reuse the _poly function from the previous exercise
use the MacroTools.jl to match/capture a_*$v^(n_), where v is the symbol of independent variable, this is going to be useful in the following steps
1. get maximal rank of the polynomial
2. get coefficient for each power

`MacroTools.jl`

Though not the most intuitive, MacroTools.jl pkg help us with writing custom macros. We will use two utilities

`@capture`

This macro is used to match a pattern in a single expression and return values of particular spots. For example

julia> using MacroTools
julia> @capture(:[1, 2, 3, 4, 5, 6, 7], [1, a_, 3, b__, c_])
true

julia> a, b, c
(2,[4,5,6],7)

`postwalk`/`prewalk`

In order to extend @capture to more complicated expression trees, we can used either postwalk or prewalk to walk the AST and match expression along the way. For example

julia> using MacroTools: prewalk, postwalk
julia> ex = quote
     x = f(y, g(z))
     return h(x)
   end

julia> postwalk(ex) do x
        @capture(x, fun_(arg_)) && println("Function: ", fun, " with argument: ", arg)
        x
    end;
Function: g with argument: z
Function: h with argument: x

Note that the x or the iteration is required, because by default postwalk/prewalk replaces currently read expression with the output of the body of do block.

Solution:

using MacroTools
using MacroTools: postwalk, prewalk

macro poly(v::Symbol, p::Expr)
    a = Tuple(reverse(_get_coeffs(v, p)))
    return _poly(a...)
end

function _max_rank(v, p)
    mr = 0
    postwalk(p) do x
        if @capture(x, a_*$v^(n_))
            mr = max(mr, n)
        end
        x
    end
    mr
end

function _get_coeffs(v, p)
    N = _max_rank(v, p) + 1
    coefficients = zeros(N)
    postwalk(p) do x
        if @capture(x, a_*$v^(n_))
            coefficients[n+1] = a
        end
        x
    end
    coefficients
end

_get_coeffs (generic function with 1 method)

Let's test it.

julia> p = @poly x 3x^2+2x^1+10x^0#7 (generic function with 1 method)
julia> p(2) == evalpoly(2, [10,2,3])true
julia> @code_lowered p(2) # can show the generated codeCodeInfo(
1 ─ %1 = Main.muladd(x, 3.0, 2.0)
│   %2 = Main.muladd(x, %1, 10.0)
└──      return %2
)

Ecosystem macros

There are at least two ways how we can make our life simpler when using our Ecosystem and EcosystemCore pkgs. Firstly, recall that in order to test our simulation we always had to write something like this:

function create_world()
    n_grass       = 500
    regrowth_time = 17.0

    n_sheep         = 100
    Δenergy_sheep   = 5.0
    sheep_reproduce = 0.5
    sheep_foodprob  = 0.4

    n_wolves       = 8
    Δenergy_wolf   = 17.0
    wolf_reproduce = 0.03
    wolf_foodprob  = 0.02

    gs = [Grass(id, regrowth_time) for id in 1:n_grass];
    ss = [Sheep(id, 2*Δenergy_sheep, Δenergy_sheep, sheep_reproduce, sheep_foodprob) for id in n_grass+1:n_grass+n_sheep];
    ws = [Wolf(id, 2*Δenergy_wolf, Δenergy_wolf, wolf_reproduce, wolf_foodprob) for id in n_grass+n_sheep+1:n_grass+n_sheep+n_wolves];
    World(vcat(gs, ss, ws))
end
world = create_world();

which includes the tedious process of defining the agent counts, their parameters and last but not least the unique id manipulation. As part of the HW for this lecture you will be tasked to define a simple DSL, which can be used to define a world in a few lines.

Secondly, the definition of a new Animal or Plant, that did not have any special behavior currently requires quite a bit of repetitive code. For example defining a new plant type Broccoli goes as follows

abstract type Broccoli <: PlantSpecies end
Base.show(io::IO,::Type{Broccoli}) = print(io,"🥦")

EcosystemCore.eats(::Animal{Sheep},::Plant{Broccoli}) = true

and definition of a new animal like a Rabbit looks very similar

abstract type Rabbit <: AnimalSpecies end
Base.show(io::IO,::Type{Rabbit}) = print(io,"🐇")

EcosystemCore.eats(::Animal{Rabbit},p::Plant{Grass}) = size(p) > 0
EcosystemCore.eats(::Animal{Rabbit},p::Plant{Broccoli}) = size(p) > 0

In order to make this code "clearer" (depends on your preference) we will create two macros, which can be called at one place to construct all the relations.

New Animal/Plant definition

Our goal is to be able to define new plants and animal species, while having a clear idea about their relations. For this we have proposed the following macros/syntax:

@species Plant Broccoli 🥦
@species Animal Rabbit 🐇
@eats Rabbit [Grass => 0.5, Broccoli => 1.0, Mushroom => -1.0]

Unfortunately the current version of Ecosystem and EcosystemCore, already contains some definitions of species such as Sheep, Wolf and Mushroom, which may collide with definitions during prototyping, therefore we have created a modified version of those pkgs, which will be provided in the lab.

Testing relations

We can test the current definition with the following code that constructs "eating matrix"

using Ecosystem
using Ecosystem.EcosystemCore

function eating_matrix()
    _init(ps::Type{<:PlantSpecies}) = ps(1, 10.0)
    _init(as::Type{<:AnimalSpecies}) = as(1, 10.0, 1.0, 0.8, 0.7)
    function _check(s1, s2)
        try
            if s1 !== s2
                EcosystemCore.eats(_init(s1), _init(s2)) ? "✅" : "❌"
            else
                return "❌"
            end
        catch e
            if e isa MethodError
                return "❔"
            else
                throw(e)
            end
        end
    end

    animal_species = subtypes(AnimalSpecies)
    plant_species = subtypes(PlantSpecies)
    species = vcat(animal_species, plant_species)
    em = [_check(s, ss) for (s,ss) in Iterators.product(animal_species, species)]
    string.(hcat(["🌍", animal_species...], vcat(permutedims(species), em)))
end
eating_matrix()
 🌍  🐑  🐺  🌿  🍄
 🐑  ❌  ❌  ✅  ✅
 🐺  ✅  ❌  ❌  ❌

Exercise

Based on the following example syntax,

@species Plant Broccoli 🥦
@species Animal Rabbit 🐇

write macro @species inside Ecosystem pkg, which defines the abstract type, its show function and exports the type. For example @species Plant Broccoli 🥦 should generate code:

abstract type Broccoli <: PlantSpecies end
Base.show(io::IO,::Type{Broccoli}) = print(io,"🥦")
export Broccoli

Define first helper function _species to inspect the macro's output. This is indispensable, as we are defining new types/constants and thus we may otherwise encounter errors during repeated evaluation (though only if the type signature changed).

_species(:Plant, :Broccoli, :🥦)
_species(:Animal, :Rabbit, :🐇)

HINTS:

use QuoteNode in the show function just like in the @myshow example
escaping esc is needed for the returned in order to evaluate in the top most module (Ecosystem/Main)
ideally these changes should be made inside the modified Ecosystem pkg provided in the lab (though not everything can be refreshed with Revise) - there is a file ecosystem_macros.jl just for this purpose
multiple function definitions can be included into a quote end block
interpolation works with any expression, e.g. $(typ == :Animal ? AnimalSpecies : PlantSpecies)

BONUS: Based on @species define also macros @animal and @plant with two arguments instead of three, where the species type is implicitly carried in the macro's name.

Solution:

Macro @species

macro species(typ, name, icon)
    esc(_species(typ, name, icon))
end

function _species(typ, name, icon)
    quote
        abstract type $name <: $(typ == :Animal ? AnimalSpecies : PlantSpecies) end
        Base.show(io::IO, ::Type{$name}) = print(io, $(QuoteNode(icon)))
        export $name
    end
end

_species(:Plant, :Broccoli, :🥦)
_species(:Animal, :Rabbit, :🐇)

And the bonus macros @plant and @animal

macro plant(name, icon)
    return :(@species Plant $name $icon)
end

macro animal(name, icon)
    return :(@species Animal $name $icon)
end

The next exercise applies macros to the agents eating behavior.

Exercise

Define macro @eats inside Ecosystem pkg that assigns particular species their eating habits via eat! and eats functions. The macro should process the following example syntax

@eats Rabbit [Grass => 0.5, Broccoli => 1.0],

where Grass => 0.5 defines the behavior of the eat! function. The coefficient is used here as a multiplier for the energy balance, in other words the Rabbit should get only 0.5 of energy for a piece of Grass.

HINTS:

ideally these changes should be made inside the modified Ecosystem pkg provided in the lab (though not everything can be refreshed with Revise) - there is a file ecosystem_macros.jl just for this purpose
escaping esc is needed for the returned in order to evaluate in the top most module (Ecosystem/Main)
you can create an empty quote end block with code = Expr(:block) and push new expressions into its args incrementally
use dispatch to create specific code for the different combinations of agents eating other agents (there may be catch in that we have to first eval the symbols before calling in order to know if they are animals or plants)

Reminder of `EcosystemCore` `eat!` and `eats` functionality

In order to define that an Wolf eats Sheep, we have to define two methods

EcosystemCore.eats(::Animal{Wolf}, ::Animal{Sheep}) = true

function EcosystemCore.eat!(ae::Animal{Wolf}, af::Animal{Sheep}, w::World)
    incr_energy!(ae, $(multiplier)*energy(af)*Δenergy(ae))
    kill_agent!(af, w)
end

In order to define that an Sheep eats Grass, we have to define two methods

EcosystemCore.eats(::Animal{Sheep}, p::Plant{Grass}) = size(p)>0

function EcosystemCore.eat!(a::Animal{Sheep}, p::Plant{Grass}, w::World)
    incr_energy!(a, $(multiplier)*size(p)*Δenergy(a))
    p.size = 0
end

BONUS: You can try running the simulation with the newly added agents.

Solution:

macro eats(species::Symbol, foodlist::Expr)
    return esc(_eats(species, foodlist))
end


function _generate_eat(eater::Type{<:AnimalSpecies}, food::Type{<:PlantSpecies}, multiplier)
    quote
        EcosystemCore.eats(::Animal{$(eater)}, p::Plant{$(food)}) = size(p)>0
        function EcosystemCore.eat!(a::Animal{$(eater)}, p::Plant{$(food)}, w::World)
            incr_energy!(a, $(multiplier)*size(p)*Δenergy(a))
            p.size = 0
        end
    end
end

function _generate_eat(eater::Type{<:AnimalSpecies}, food::Type{<:AnimalSpecies}, multiplier)
    quote
        EcosystemCore.eats(::Animal{$(eater)}, ::Animal{$(food)}) = true
        function EcosystemCore.eat!(ae::Animal{$(eater)}, af::Animal{$(food)}, w::World)
            incr_energy!(ae, $(multiplier)*energy(af)*Δenergy(ae))
            kill_agent!(af, w)
        end
    end
end

_parse_eats(ex) = Dict(arg.args[2] => arg.args[3] for arg in ex.args if arg.head == :call && arg.args[1] == :(=>))

function _eats(species, foodlist)
    cfg = _parse_eats(foodlist)
    code = Expr(:block)
    for (k,v) in cfg
        push!(code.args, _generate_eat(eval(species), eval(k), v))
    end
    code
end

species = :Rabbit 
foodlist = :([Grass => 0.5, Broccoli => 1.0])
_eats(species, foodlist)

Resources

macros in Julia documentation

`Type{T}` type selectors

We have used ::Type{T} signature^[2] at few places in the Ecosystem family of packages (and it will be helpful in the HW as well), such as in the show methods

Base.show(io::IO,::Type{World}) = print(io,"🌍")

This particular example defines a method where the second argument is the World type itself and not an instance of a World type. As a result we are able to dispatch on specific types as values.

Furthermore we can use subtyping operator to match all types in a hierarchy, e.g. ::Type{<:AnimalSpecies} matches all animal species

1Explanation of the Horner schema can be found on https://en.wikipedia.org/wiki/Horner%27s_method.
2 https://docs.julialang.org/en/v1/manual/types/#man-typet-type