Preliminary notes on Julia’s macros

First things first, I’m entirely unqualified to write a review, or even an email or a tweet, discussing macros in Julia. I don’t know anything. That said, I’m scheduled to give a 50 minute talk on Wednesday about macros in Julia, so I need to do some research and organize my thoughts. And you, dear reader, are reading the result of that research and the mechanism for organizing my thoughts. So be skeptical of everything you read here: it’s just my opinion, and my opinion isn’t especially well-informed. A better resource is Julia’s documentation on metaprogramming.

What are macros? How are they different than functions?

Julia’s functions, like functions in most languages, take a collection of arguments, run some instructions on them, and return a value. Julia’s macros, on the other hand, take a collection of expressions, run some transformations on them, and return Julia code. That new code is run in place of the original macro.

Now, what does that mean? I’ll go back to my first love in Julia, Dahua Lin’s @devec macro. Let x and y be numeric vectors that have the same length and suppose that we want to do some simple math on their elements and store the results in a new vector r. Like this:

r = exp(-abs(x - y))                      # basic version

You might disagree, but this is clearly the way we want to write these operations: our intent is unambiguous. But this isn’t the way we want the computer to execute these operations. As Dahua points out, it’s slow and uses more memory than necessary, because each operation is executed sequentially: first x-y is calculated and stored in a temporary array (call it temp1); then abs(temp1) is executed and stored in an array we can call temp2; then -temp2 is executed and stored in an array temp3; and finally exp(temp3) is executed and stored in r. Each intermediate step traverses its input, which takes time, and stores its output in a new array, which takes memory.

Dahua argues that we’d like to run the code

r = similar(x)                                # version 2
for i = 1:length(x)
    r[i] = exp(-abs(x[i] - y[i]))

which is faster and more memory efficient. The operations still create temporary variables, but those variables are now scalars and not vectors, and we only traverse the array once. Potentially noticable gains.

I’d go even further. The first line of that code frightens me, because x can be a vector of integers but we’d still exect that r would be floats (and, indeed, when you run this code with x as an integer, it fails with an error). Second, x and y can have different lengths. The code as written implicitly checks that x[i] and y[i] are legitimate indexes, but it does it at each step of the loop. It would be faster to check before the loop starts, and turn off checking inside the loop. (There’s another macro, @inbounds, that turns it off.) So I’d prefer to use the code

@assert length(x) == length(y)                # version 3
firstval = exp(-abs(x[1] - y[1]))
if length(x) > 1
    r = similar(x, typeof(firstval))
    r[1] = firstval
    @inbounds for i = 2:length(x)
        r[i] = exp(-abs(x[i] - y[i]))
    r = firstval

This code does the following:

  1. Checks that x and y have the same length.
  2. Calculates the first element of exp(-abs(x - y)) so that we can learn its type.
  3. If x and y only have one element, set r equal to firstval and stop. Otherwise, allocate an array for r, store firstval as its first element, and then iterate our calculations down the rest of x and y, assuming that the indexes are valid for each i.

Running all three options on my laptop with 80,000,000 elements gives the timings listed in the table below (I wrapped the code in trivial functions for the timings; this Gist has executable code.)

Version     Elapsed time (sec)     Memory allocated (MB)
1   7.21   2,560
2   5.03   640
3   4.84   640

We obviously want to run the last version of the code. (The second version is essentially as efficient, but will throw an error if x is an integer.) But, just as obviously, the last version of the code is hideous; so much so that I felt like it needed external documentation! So we clearly want to write the first version, but execute the second version.

Baby steps towards writing a simple devectorization macro

This is my first macro. Fun!

I’ll walk through the steps I took. First, as recommended in Paul Graham’s On Lisp, we’ll write out a representative call to the macro and the expression that we actually want evaluated. And let’s start with a baby version of the expression first:

@ourdevec r = exp(-abs(x - y))     # representative call
firstval = exp(-abs(x[1] - y[1]))  # baby expression goal

So now we want to write code that converts the first expression to the second. Julia expressions are represented as Expr objects and they have a head and args field. (This is being revisted and eventually may change, though.) Here, we have

julia> :(r = exp(-abs(x - y))).head
:(=)                                     # value returned

julia> :(r = exp(-abs(x - y))).args
2-element Array{Any,1}:                  # value returned
 :(exp(-(abs(x - y))))

And looking at the type of each of the argumets gives

julia> map(typeof, :(r = exp(-abs(x - y))).args)
2-element Array{Any,1}:                  # value returned

So the left side is just a Symbol (the variable type Julia uses for variable names) and the right side is another Expr. We could keep on going sub-expression by sub-expression, but fortunately there are several functions that present everything at once for us:

julia> Base.Meta.show_sexpr(:(r = exp(-abs(x - y))))
(:(=), :r, (:call, :exp, (:call, :-, (:call, :abs, (:call, :-, :x, :y)))))

which tells us that each sub-expression is a function call, until we finally get to the symbols :x and :y. It also shows us that the first element of args for a function call is the symbol that represents the function, and the rest of the elements are the expressions or symbols passed to that function as arguments.

To produce our baby expression, we want to replace :x and :y in our original expression with :(x[1]) and :(y[1]). More generally, we want to take every symbol that’s not a function, and replace it with a reference to its first element. (Important realization: what if we want to mix in scalars? Then this might not work! Oh crap! Let’s disregard that problem for now and worry about it tomorrow. This post is hard enough as it is.)

This replacement is something we can do pretty easily with recursion, since :(x[1]) is just another type of expression (a :ref). We’ll define two functions that help:

getindex(s::Symbol, i) = Expr(:ref, s, i)
getindex(e::Expr, i) = Expr(e.head, e.args[1],
              [getindex(a, i) for a in e.args[2:end]]...)


getindex already exists and is the function that’s called when you write x[i] for arrays, so it seems reasonable to extend its methods like this.

The baby macro is dead simple now

macro our_devec_p1(e)                # Ver 1 (baby macro)
    :($(e.args[1]) = $(getindex(e.args[2], 1)))

and we can verify that it “does the right thing” by using macroexpand.

julia> macroexpand(:(@our_devec_p1 r = exp(-abs(x - y))))
:(_38_firstval = exp(-(abs(x[1] - y[1]))))  # Value returned

A note on what happened to firstval: since macros write out code that’s executed in-place, there’s a chance that it could clobber variables that already exist. Macros, unlike functions, don’t define their own scope. To avoid overwriting existing variables, variables in macros are given unique names. (The function that does this is gensym.) The _38_ represents the unique identifier added to firstval to make it unique. (The _ really displays as # and screws up our code highlighting; I’ve manually replaced the symbol.)

Two more tiny steps towards a devectorization macro

If we look back at the expanded code, we can see three distinct parts:

  1. Evaluate the vectorized expression for the first element. We’ve already done this part.
  2. Check that the vectors have the same length, and check that x has more than one element.
  3. Loop over the elements of x and y and assign the results of each calculation to r.

This parallels our initial description, but now we’re going to implement the second and third parts in the macro.

First add the length check. Setting up the problem just like we did for the baby macro earlier, we start with the expression

r = exp(-abs(x - y))

and we need to produce

length(x) == length(y)

We can write similar functions as before (which is a sign that we might want to think about generalizing this as utility code, but nevermind that for now…)

getvecs(s::Symbol) = s
getvecs(e::Expr) = [[getvecs(a) for a in e.args[2:end]]...]

which will recurse down a nested expression until it finds the symbols at the bottom, then return a single array that lists them all. (Putting ... at the end of the Expr method flattens the results.)

It’s clumsy, but we can assemble the expression we need by hand, as the next macro shows.

macro our_devec_p2(e)
  vecs = getvecs(e.args[2])
  check_lengths = Expr(:comparison)
  check_lengths.args = Array(Any, 2 * length(vecs) - 1)
  check_lengths.args[1:2:end] = map(s -> Expr(:call, :length, s), vecs)
  check_lengths.args[2:2:end] = :(==)

(I’m sure that there’s a slick, functional way to assemble the check_lengths expression, but I don’t know it.) Using macroexpand to verify that we’re on the right track gives

julia> macroexpand(:(@our_devec_p2 r = exp(-abs(x - y))))
:(length(x) == length(y))                # value returned

And now the loop. Again, the expression we start with is

r = exp(-abs(x - y))

and now the expression we want to return is

if length(x) > 1
    r = similar(x, typeof(firstval))
    r[1] = firstval
    for i = 2:length(x)
        r[i] = exp(-abs(x[i] - y[i]))
end ### Ignore the `else` part for now

The body of the loop is almost what we did before when we calculated the first element, but now we don’t want gensym to create a new variable name, we want to clobber r. Julia, forntunately, offers a mechansim for “unhygenic” macros through the function, esc, which works as follows

macro our_devec_part3(e)
    vecs = getvecs(e.args[2])
        if length($(vecs[1])) > 1
            $(esc(e.args[1])) = similar($(vecs[1]), typeof(firstval))
            $(esc(e.args[1]))[1] = firstval
            for i = 2:length($(vecs[1]))
                $(esc(e.args[1]))[i] = $(getindex(e.args[2], :i))

You should expand this part on your own to verify that it works. (It does, though.)

Finishing the macro

We can put the three steps together for our final devectorization macro:

macro our_devec(e)                        # Final version
    vecs = getvecs(e.args[2])

    check_lengths = Expr(:comparison)            # Part 2
    check_lengths.args = Array(Any, 2 * length(vecs) - 1)
    check_lengths.args[1:2:end] = map(s -> Expr(:call, :length, s), vecs)
    check_lengths.args[2:2:end] = :(==)

        @assert $check_lengths
        firstval = $(getindex(e.args[2], 1))     # Part 1
        if length($(vecs[1])) > 1                # Part 3
            $(esc(e.args[1])) = similar($(vecs[1]), typeof(firstval))
            $(esc(e.args[1]))[1] = firstval
            @inbounds for i = 2:length($(vecs[1]))
                $(esc(e.args[1]))[i] = $(getindex(e.args[2], :i))
            $(esc(e.args[1])) = firstval   # new but easy

And one final expansion to verify that it does what we’d hoped:

julia> macroexpand(:(@our_devec r = exp(-abs(x - y))))
quote  # none, line 10:
    if length(x) == length(y)
        Base.error("assertion failed: length(x) == length(y)")
    end # line 11:
    _51_firstval = exp(-(abs(x[1] - y[1]))) # line 12:
    if length(x) > 1 # line 13:
        r = similar(x,typeof(_51_firstval)) # line 14:
        r[1] = _51_firstval # line 15:
            $(Expr(:boundscheck, false))
                for _52_i = 2:length(x) # line 16:
                    r[_52_i] = exp(-(abs(x[_52_i] - y[_52_i])))
                $(Expr(:boundscheck, :(Base.pop)))
    else  # line 19:
        r = _51_firstval

(I was happily surprised that the index of the for loop is taken care of automatically.)

So now the code

@our_devec r = exp(-abs(x - y))

runs as fast and with the same memory allocation as the explicitly devectorized code from before. You can download all of the code for this post here. (Obvious caveats are that the code is probably pretty fragile and you’d probably need to make it more robust if you use it for real.)

Are macros just about performance?

The devectorize macro we defined is representative of a class of macros: it takes Julia code that would run fine otherwise, and it rewrites the code to use the computer more efficiently. (The Julia manual calls these “Performance Annotations”). Some other macros like this are

I’m sure there are a few others, but these are, as far as I can tell, unrepresentative. Most macros are not written just to translate blocks of Julia code into blocks of faster Julia code. Dahua’s blog post was the first thing I read that got me really excited about Julia, though, so if I’m going to talk about macros…. But the main use of macros, at least historically (and “historically” here means “in Lisp, as far as I can tell.” Julia’s not old enough for any usage to be “historic”) is extending the language by adding new features.

First, a small example of language extensions. Perl, if you haven’t used it, is a programming language that excels at text manipulation. It does other things well too, and it was probably as important to web programming during the dot-com bubble as JavaScript has been for the whole Ajax/Web 2.0 [bubble]. One of the reasons why Perl is great at text manipulations is because it has excellent Regular Expressions (“regexes”), giving it an entire mini-language for finding and replacing patterns in strings.

Perl’s regexes are built into the language—large parts of the language are built around them—so they’re fast and immediately available. Julia has regexes as well, but they’re implemented as a macro. (My understanding is that this is how Common Lisp implements them as well.) Since these macro calls are translated before any code is actually run, the regex is parsed once, in advance, and they’re fast too.

So both languages have fast regexes. But the implementation methods are very different and the difference matters when you or I want to add a feature like regexes to the language; say (hypothetically) a regression modeling language like R’s, or a different text-processing minilanguage. With Perl, new features will never have the same performance or support that the built-in features do. With Julia, your macros are on equal footing with the native ones, so these new features have exactly the same support and potential for performance as the “built in” regex.

For flashier examples, there are at least two packages that support pattern matching through the macro system. (Match.jl and PatternDispatch.jl; you’ll have to look at the packages to really understand what they enable, but these are like powerful switch statements.) And support for DocStrings is (finally) being added to Base Julia, as a macro that was originally developed in a separate package (Docile.jl.)

Alternatives to macros

Another question: could we have written @our_devec as a function? Can we write macros as functions in general? Julia functions can take expressions as arguments and can return unevaluated expressions, and Julia provides an eval function that evaluates expressions on the fly. Specifically, would

eval(our_devec(:(r = exp(-abs(x - y)))))

do the same thing as

@our_devec r = exp(-abs(x - y))

As implemented in Julia, the answer is clearly “no.” eval has weird scoping issues: it evaluates expressions in the global scope, so if it’s called inside a function it wouldn’t have access to x and y. (I’ve read several places that this is for performance.)

But, even if it’s not the way Julia implements it, we can certainly imagine a “local eval” function. And we can certainly imagine people who don’t care about performance. So, without those constraints, do macros provide anything that we can’t get from evaluating expressions returned from functions?

I don’t think so, but I could be wrong. One reason is an R-News article by Thomas Lumley (2001), “Overcoming R’s Virtues,” that describes how to implement Lisp-style macros through R’s functions. (Unlike Julia, R lets functions evaluate expressions in essentially whatever environment you want.) So, from a conceptual standpoint, macros are just one mechanism for transforming expressions and choosing where to evaluate them, and there are potentially many other conceptually equivalent ways to do that.

“Conceptual equivalence” and “practical equivalence” are very different things, of course. The hypothetical eval version of macro execution would have to parse the expression on the fly each time it’s called, which we’d expect would give it a performance penalty compared to a “native” implementation and is going to make them less appealing in real applications. One of the driving principles in Julia’s development has been the idea that user-added functionality should be just as good as built-in functionality. This was part of the reason that Julia’s numeric types are implemented in Julia, and not as specialized C code, and it’s part of the reason that macros are used to implement fundamental objects like regexes: putting them on equal footing forces the language to support all of these extensions really well.

And obviously, performance-oriented macros like @our_devec are off the table without a real macro system.

Other things to read

— Gray Calhoun, 13 Oct 2014

Copyright (c) 2014–2015 Gray Calhoun. This document is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License and any source code listed in this document is also licensed under the MIT License.

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