The Emergent Features of JuliaLang: Part I

Re-posted from: https://invenia.github.io/blog/2019/10/30/julialang-features-part-1/

Introduction

This blog post is based on a talk originally given at a Cambridge PyData Meetup, and also at a London Julia Users Meetup.

Julia is known to be a very expressive language with many interesting features.
It is worth considering that some features were not planned,
but simply emerged from combinations of other features.
This post will describe how several interesting features are implemented:

Unit syntactic sugar,
Pseudo-OO objects with public/private methods,
Dynamic Source Tranformation / Custom Compiler Passes (Cassette),
Traits.

Some of these (1 and 4) should be used when appropriate,
while others (2 and 3) are rarely appropriate, but are instructive.

Part I of this post (which you are reading now) will focus on the first three.
Part II of this post is about traits, which deserve there own section
because they are one of the most powerful and interesting Julia features.
They emerge from types, multiple dispatch,
and the ability to dispatch on the types themselves (rather than just instances).
We will review both the common use of traits on types,
and also the very interesting—but less common—use of traits on functions.

There are many other emergent features which are not discussed in this post.
For example, creating a vector using Int[] is an overload of
getindex, and constructors are overloads of ::Type{<:T}().

Juxtaposition Multiplication: Convenient Syntax for Units

Using units in certain kinds of calculations can be very helpful for protecting against arithmetic mistakes.
The package Unitful provides the ability
to do calculations using units in a very natural way, and makes it easy to write
things like “2 meters” as 2m. Here is an example of using Unitful units:

julia> using Unitful.DefaultSymbols

julia> 1m * 2m
2 m^2

julia> 10kg * 15m / 1s^2
150.0 kg m s^-2

julia> 150N == 10kg * 15m / 1s^2
true

How does this work? The answer is Juxtaposition Multiplication—a literal number
placed before an expression results in multiplication:

julia> x = 0.5π
1.5707963267948966

julia> 2x
3.141592653589793

julia> 2sin(x)
2.0

To make this work, we need to overload multiplication to invoke the constructor of the unit type.
Below is a simplified version of what goes on under the hood of Unitful.jl:

abstract type Unit<:Real end
struct Meter{T} <: Unit
    val::T
end

Base.:*(x::Any, U::Type{<:Unit}) = U(x)

Here we are overloading multiplication with a unit subtype, not an
instance of a unit subtype (Meter(2)), but with the subtype itself (Meter).
This is what ::Type{<:Unit} refers to. We can see this if we write:

julia> 5Meter
Meter{Int64}(5)

This shows that we create a Meter object with val=5.

To get to a full units system, we then need to define methods for everything that numbers need to work with,
such as addition and multiplication. The final result is units-style syntactic sugar.

Closures give us “Classic Object-Oriented Programming”

First, it is important to emphasize: don’t do this in real Julia code.
It is unidiomatic, and likely to hit edge-cases the compiler doesn’t optimize well
(see for example the infamous closure boxing bug).
This is all the more important because it often requires boxing (see below).

“Classic OO” has classes with member functions (methods) that can see all the fields and methods,
but that outside the methods of the class, only public fields and methods can be seen.
This idea was originally posted for Julia in one of Jeff Bezanson’s rare
Stack Overflow posts,
which uses a classic functional programming trick.

Let’s use closures to define a Duck type as follows:

function newDuck(name)
    # Declare fields:
    age=0

    # Declare methods:
    get_age() = age
    inc_age() = age+=1

    quack() = println("Quack!")
    speak() = quack()

    #Declare public:
    ()->(get_age, inc_age, speak, name)
end

This implements various things we would expect from “classic OO” encapsulation.
We can construct an object and call public methods:

julia> bill_the_duck = newDuck("Bill")
#7 (generic function with 1 method)

julia> bill_the_duck.get_age()
0

Public methods can change private fields:

julia> bill_the_duck.inc_age()
1

julia> bill_the_duck.get_age()
1

Outside the class we can’t access private fields:

julia> bill_the_duck.age
ERROR: type ##7#12 has no field age

We also can’t access private methods:

julia> bill_the_duck.quack()
ERROR: type ##7#12 has no field quack

However, accessing their functionality via public methods works:

julia> bill_the_duck.speak()
Quack!

How does this work?

Closures return singleton objects, with directly-referenced, closed variables as fields.
All the public fields/methods are directly referenced,
but the private fields (e.g age, quack) are not—they are closed over other methods that use them.
We can see those private methods and fields via accessing the public method closures:

julia> bill_the_duck.speak.quack
(::var"#quack#10") (generic function with 1 method)

julia> bill_the_duck.speak.quack()
Quack!

julia> bill_the_duck.inc_age.age
Core.Box(1)

An Aside on Boxing

The Box type is similar to the Ref type in function and purpose.
Box is the type Julia uses for variables that are closed over, but which might be rebound to a new value.
This is the case for primitives (like Int) which are rebound whenever they are incremented.
It is important to be clear on the difference between mutating the contents of a variable,
and rebinding that variable name.

julia> x = [1, 2, 3, 4];

julia> objectid(x)
0x79eedc509237c203

julia> x .= [10, 20, 30, 40];  # mutating contents

julia> objectid(x)
0x79eedc509237c203

julia> x = [100, 200, 300, 400];  # rebinding the variable name

julia> objectid(x)
0xfa2c022285c148ed

In closures, boxing applies only to rebinding, though the closure bug
means Julia will sometimes over-eagerly box variables because it believes that they might be rebound.
This has no bearing on what the code does, but it does impact performance.

While this kind of code itself should never be used (since Julia has a perfectly
functional system for dispatch and works well without “Classic OO”-style encapsulation),
knowing how closures work opens other opportunities to see how they can be used.
In our ChainRules.jl project,
we are considering the use of closures as callable named tuples as part of
a difficult problem
in extensibility and defaults.

Cassette, etc.

Cassette is built around a notable Julia feature, which goes by several names:
Custom Compiler Passes, Contextual Dispatch, Dynamically-scoped Macros or Dynamic Source Rewriting.
This same trick is used in IRTools.jl,
which is at the heart of the Zygote automatic differentiation package.
This is an incredibly powerful and general feature, and was the result of a very specific
Issue #21146 and very casual
PR #22440 suggestion that it might be useful for one particular case.
These describe everything about Cassette, but only in passing.

The Custom Compiler Pass feature is essential for:

AutoDiff tools (ForwardDiff2, Zygote, Yota),
Mocking tools (SimpleMock.jl),
Debuggers (MagneticReadHead, MixedModeDebugger.jl),
Code-proof related tools (ConcolicFuzzer.jl),
Generally rewriting all the code (GPUifyLoops.jl).

Call Overloading

One of the core uses of Cassette is the ability to effectively overload what it means to call a function.
Call overloading is much more general than operator overloading, as it applies to every call (special-cased as appropriate), whereas operator overloading applies to just one function call and just one set of types.

To give a concrete example of how call overloading is more general,
operator overloading/dispatch (multiple or otherwise) would allow us to
to overload, for example, sin(::T) for different types T.
This way sin(::DualNumber) could be specialized to be different from sin(::Float64),
so that with DualNumber as input it could be set to calculate the nonreal part using the cos (which is the derivative of sin). This is exactly how
DualNumbers operate.
However, operator overloading can’t express the notion that, for all functions f, if the input type is DualNumber, that f should calculate the dual part using the deriviative of f.
Call overloading allows for much more expressivity and massively simplifies the implementation of automatic differentiation.

The above written out in code:

function Cassette.overdub(::AutoDualCtx, f, d::DualNumber)
    res = ChainRules.frule(f, val(d))
    if res !== nothing
        f_val, pushforward = res
        f_diff = pushforward(partial(f))
        return Dual(f_val, extern(f_diff))
    else
        return f(d)
    end
end

That is just one of the more basic things that can be done with Cassette.
Before we can explain how Cassette works,
we need to understand how generated functions work,
and before that we need to understand the different ways code is presented in Julia,
as it is compiled.

Layers of Representation

Julia has many representations of the code it moves through during compilation.

Source code (CodeTracking.definition(String,....)),
Abstract Syntax Tree (AST): (CodeTracking.definition(Expr, ...),
Untyped Intermidate Represention (IR): @code_lowered,
Typed Intermidate Represention: @code_typed,
LLVM: @code_llvm,
ASM: @code_native.
We can retrieve the different representations using the functions/macros indicated.

The Untyped IR is of particular interest as this is what is needed for Cassette.
This is a linearization of the AST, with the following properties:

Only 1 operation per statement (nested expressions get broken up);
the return values for each statement are accessed as SSAValue(index);
variables become Slots;
control-flow becomes jumps (like Goto);
function names become qualified as GlobalRef(mod, func).
Untyped IR is stored in a CodeInfo object.
It is not particularly pleasent to write, though for a intermidate representation it is fairly readable.
The particularly hard part is that expression return values are SSAValues which are defined by position.
So if code is added, return values need to be renumbered.
Core.Compiler, Cassette and IRTools define various helpers to make this easier, but for out simple example we don’t need them.

Generated Functions

Generated functions
take types as inputs and return the AST (Abstract Syntax Tree) for what code should run, based on information in the types. This is a kind of metaprogramming.
Take for example a function f with input an N-dimensional array (type AbstractArray{T, N}).
Then a generated function for f might construct code with N nested loops to process each element.
It is then possible to generate code that only accesses the fields we want, which gives substantial performance improvements.

For a more detailed example lets consider merging NamedTuples.
This could be done with a regular function:

function merge(a::NamedTuple{an}, b::NamedTuple{bn}) where {an, bn}
    names = merge_names(an, bn)
    types = merge_types(names, typeof(a), typeof(b))
    NamedTuple{names,types}(map(n->getfield(sym_in(n, bn) ? b : a, n), names))
end

This checks at runtime what fields each NamedTuple has, in order to decide what will be in the merge.
However, we actually know all this information based on the types alone,
because a list of fields is stored as part of the type (that is the an and bn type-parameters.)

@generated function merge(a::NamedTuple{an}, b::NamedTuple{bn}) where {an, bn}
    names = merge_names(an, bn)
    types = merge_types(names, a, b)
    vals = Any[ :(getfield($(sym_in(n, bn) ? :b : :a), $(QuoteNode(n)))) for n in names ]
    :( NamedTuple{$names,$types}(($(vals...),)) )
end

Recall that a generated function returns an AST, which is the case in the examples above.
However, it is also allowed to return a CodeInfo for Untyped IR.

Making Cassette

There are two key facts mentioned above:

Generated Functions are allowed to return CodeInfo for Untyped IR.
@code_lowered f(args...) provided the ability to retrieve the CodeInfo for the given method of f.

The core of Cassete it to use a generated function
to call code defined in a new CodeInfo of Untyped IR,
which is based on a modified copy of code that is returned by @code_lowered.
To properly understand how it works, lets run through a manual example (originally from a JuliaCon talk on MagneticReadHead.jl).

We define a generated function rewritten that makes a copy of the Untyped IR (a CodeInfo object that it gets back from @code_lowered) and then mutates it, replacing each call with a call to the function call_and_print.
Finally, this returns the new CodeInfo to be run when it is called.

call_and_print(f, args...) = (println(f, " ", args); f(args...))

@generated function rewritten(f)
    ci = deepcopy(@code_lowered f.instance())
    for ii in eachindex(ci.code)
        if ci.code[ii] isa Expr && ci.code[ii].head==:call
            func = GlobalRef(Main, :call_and_print)
            ci.code[ii] = Expr(:call, func, ci.code[ii].args...)
        end
    end
    return ci
end

We can see that this works:

julia> foo() = 2*(1+1)
foo (generic function with 1 method)

julia> rewritten(foo)
+ (1, 1)
* (2, 2)
4

Rather than replacing each call with call_and_print,
we could instead call a function that does the work we are interested in,
and then calls rewritten on the function that would have been called:

function work_and_recurse(f, args...)
    println(f, " ", args)
    rewritten(f, args...)
end

So, not only does the function we call get rewritten, but so does every function it calls, all the way down.

This is how Cassette and IRTools work.
There are a few complexities and special cases that need to be taken care of,
but this is the core of it: recursive invocation of generated functions that rewrite the IR, similar to what is returned by @code_lowered.

Wrapping up Part I

In this part we covered the basics of:

Unit syntactic sugar,
Pseudo-OO objects with public/private methods,
Dynamic Source Tranformation / Custom Compiler Passes (Cassette).

In Part II, we will discuss Traits!

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