Meta Programming

Notes taken during the learning process



I have not used metaprogramming in any real program myself, but I would like to see its usage.

Understanding metaprogramming is not easy, so don't panic if it doesn't come naturally from the start. One of the reasons for this is that it takes place at a level of abstraction higher than what we're used to with regular programming. We will start with symbols, which makes the introduction less abstract. They look like this –- :x or :scientific or :Celsius. As you may have noticed, a symbol represents an identifier and we use it very much like a constant. However, it's more than that. It represents a piece of code that, instead of being evaluated as the variable, is used to refer to a variable itself.

A good analogy for understanding the relationship between a symbol and a variable has to do with the words in a phrase. Take for example the sentence: Richard is tall. Here, we understand that Richard is the name of a person, most likely a man. And Richard, the person, is tall. However, in the sentence: Richard has seven letters, it is obvious that now we aren't talking about Richard the person. It wouldn't make too much sense to assume that Richard the person has seven letters. We are talking about the word Richard itself.

The equivalent, in Julia, of the first sentence (Richard is tall) would be julia> x. Here, x is immediately evaluated in order to produce its value. If it hasn't been defined, it will result in an error, shown as follows:

x # ERROR: UndefVarError: x not defined

Julia's symbols mimic the second sentence, where we talk about the word itself. In English, we wrap the word in single quotes, 'Richard', to indicate that we're not referring to a person but to the word itself. In the same way, in Julia, we prefix the variable name with a column, :x:

typeof(:x) # Symbol

Hence, the column : prefix is an operator that stops the evaluation. An unevaluated expression can be evaluated on demand by using the eval() function or the @eval macro, as follows:

eval(:x) # ERROR: UndefVarError: x not defined

But we can go beyond symbols. We can write more complex symbol-like statements, for example, :(x = 2). This works a lot like a symbol but it is, in fact, an Expr type, which stands for expression. The expression, like any other type, can be referenced through variable names and, like symbols, they can be evaluated:

assign = :(x = 2)
x # 2

The preceding snippet demonstrates that we can reference an Expr type with the assign variable and then eval it. Evaluation produces side effects, the actual value of the variable x now being 2.

Even more powerful, since Expr is a type, it has properties that expose its internal structure:

fieldnames(typeof(assign)) # (:head, :args)

Every Expr object has two fields –- head representing its kind and args standing for the arguments. We can view the internals of Expr by using the dump() function:


which shows

head: Symbol =
args: Array{Any}((2,))
1: Symbol x
2: Int64 2

This leads us to even more important discoveries. First, it means that we can programmatically manipulate Expr through its properties:

assign.args[2] = 3 
x # 3

Our expression is no longer :(x = 2); it's now :(x = 3). By manipulating the args of the assign expression, the value of x is now 3.

Second, we can programmatically create new instances of Expr using the type's constructor:

assign4 = Expr(:(=), :x, 4)
x # 4

Please notice here that we wrapped the equals sign (=) in parenthesis to designate an expression, as Julia gets confused otherwise, thinking we want to perform an assignment right there.

Quoting Expressions

The previous procedure, in which we wrap an expression within :(...) in order to create Expr objects, is called quoting. It can also be done using quote blocks. Quote blocks make quoting easier as we can pass regular-looking code into them (as opposed to translating everything in to symbols), and supports quoting multiple lines of code in order to build randomly complex expressions:

    y = 42 
    x = 10 
eval(ans) # 10
y # 42
x # 10

Interpolating strings

Just like with string interpolation, we can reference variables within the expressions:

name = "Dan"
greet = :("Hello " * $name)
eval(greet) # "Hello Dan"


Now, we finally have the knowledge to understand macros. They are language constructs, which are executed after the code is parsed, but before it is evaluated. It can optionally accept a tuple of arguments and must return an Expr. The resulting Expression is directly compiled, so we don't need to call eval() on it.

For example, we can implement a configurable version of the previous greet expression as a macro:

macro greet(name)
    :("Hello " * $name)
@greet("Adrian") # "Hello Adrian"

As per the snippet, macros are defined using the macro keyword and are invoked using the @... syntax. The brackets are optional when invoking macros, so we could also use @greet "Adrian".

Macros are very powerful language constructs that allow parts of the code to be customized before the full program is run. The official Julia documentation has a great example to illustrate this behavior:

macro twostep(arg)
    println("I execute at parse time. The argument is: ", arg)
    return :(println("I execute at runtime. The argument is: ", $arg))

We define a macro called twostep, which has a body that calls the println function to output text to the console. It returns an expression which, when evaluated, will also output a piece of text via the same println function.

Now we can see it in action:

ex = macroexpand(@__MODULE__, :(@twostep :(1, 2, 3)))
# I execute at parse time. The argument is: $(Expr(:quote, :((1, 2, 3))))

The snippet shows a call to macroexpand, which takes as an argument the module in which to expand the expression (in our case, @__MODULE__ stands for the current module) and an expression that represents a macro invocation. The call to macroexpand converts (expands) the macro into its resulting expressions. The output of the macroexpand call is suppressed by appending ; at the end of the line, but the resulting expression is still safely stored in ex. Then, we can see that the expanding of the macro (its parsing) takes place because the "I execute at parse time" message is output. Now look what happens when we evaluate the expression, ex:

eval(ex) # I execute at runtime. The argument is: (1, 2, 3)

The "I execute at runtime" message is outputted, but not the "I execute at parse time" message. This is a very powerful thing. Imagine that output instead of a simple text output if we'd had some very computationally intensive or time-consuming operations. In a simple function, we'd have to run this code every time, but with a macro, this is done only once, at parse time.

Closing Words about Macros

Besides they're very powerful, macros are also very convenient. They can provide a lot of functionality with minimal overhead and can simplify the invocation of functions that take expressions as arguments. For example, @time is a very useful macro that executes an Expression while measuring the execution time. And the great thing is that we can pass the argument expression as regular code, instead of building the Expr by hand:

@time rand(1000); # 0.000007 seconds (5 allocations: 8.094 KiB)

However, most of the time, don't do metaprogramming! The primary purpose of macros is not performance: it is to allow syntax transformations. Don't try to think that you can easily beat a modern compiler! More in depth explanations can be found in this post and Steve Johnson's talk at JuliaCon2019.

Macros –- and metaprogramming in general –- are powerful concepts that require whole books to discuss at length. For more, going over the official documentation.

Loop Unrolling

Surprisingly at first, metaprogramming can help unrolling loops with @generated when the size of the loop is known at compile time.

using BenchmarkTools

struct Vector2{N,T}

function avg2(vals::Vector) 
   sum = vals[1]
   for i in 2:length(vals)
      sum += vals[i]
   sum / length(vals) 

@generated function avgg(els::Vector2{N,T}) where {N,T}
   code = :(els.vals[1])
   for i=2:N
      code = :($code + els.vals[$i])

s = Vector2{4,Int64}([1,2,3,4])
@btime avgg($s)
@btime avg2($s.vals)

In the above example, the generated function can be 3x faster (which is affected by the size of the vector). As a side effect, the compiling time significantly increases. At about length(s)=20, the two give equal timings. However, I don't quite understand why the @time macro shows different trend compared with @btime?