# Are there idioms in Julia for fast Algebraic Data Types (ADT)?

The main thing that makes this painful in julia is that performance tends to be quite dependent on type inference because our dynamic dispatches are so costly (due to everything being generic functions => huge method table). However, clever use of things like function barriers and `@nospecialize` can help a lot.

Concretely, I want the following function to be as fast as possible:

``````function evaluate_sum(es::Vector{Expression})
s = 0
for e in es
s += evaluate(e)
end
return s
end
``````

In a functional language where `Expression` is a closed ADT, the “dispatch” that happens at every call to `evaluate` costs almost nothing (just making a switch on an integer tag). I am worried that this may not be true in Julia. I guess I should run concrete benchmarks to see how problematic it is in practice.

1 Like

I am worried about the cost of dispatch, exactly. Any idea on how to make it as small as possible in my `evaluate_sum` example?

How fast are you looking for this to be? It’s already quite fast:

``````abstract type Expression end
struct Const{T} <: Expression
value :: T
end
lhs::L
rhs::R
end

evaluate(e::Const) = e.value

function evaluate_sum(exprs::Vector{Expression})
s = 0
for e in exprs
s += evaluate(e)
end
s
end
``````
``````julia> es = [Const(1)
Const(40)]
4-element Array{Expression,1}:
Const{Int64}(1)
Const{Int64}(40)

julia> @btime evaluate_sum(es)
127.472 ns (0 allocations: 0 bytes)
52
``````

I’m not really sure what to compare it to though.

1 Like

Turns out it’s much better to just go with your original idea and have abstract storage:

``````abstract type Expression end
struct Const <: Expression
value :: Int
end
lhs :: Expression
rhs :: Expression
end

evaluate(e::Const) = e.value

function evaluate_sum(exprs::Vector{Expression})
s = 0
for e in exprs
x = let e = e
evaluate(e)
end
s += x
end
s
end

es = [Const(1)
Const(40)]
``````
``````julia> @btime evaluate_sum(es);
15.159 ns (0 allocations: 0 bytes)
``````

(note you’ll need to restart julia to run this due to type redefinitions)

2 Likes

I ran the following benchmark to compare the cost of doing dispatch on closed unions with the cost of doing dispatch on abstract types:

``````using BenchmarkTools

abstract type SignedInteger end

struct Pos <: SignedInteger
abs :: UInt64
end

struct Neg <: SignedInteger
abs :: UInt64
end

const AnySignedInteger = Union{Pos, Neg}

const posvec = [Pos(i) for i in 1:100]
const negvec = [Neg(i) for i in 1:100]

value(x::Pos) = Int64(x.abs)
value(x::Neg) = -Int64(x.abs)

function test_open()
return sum(value(x) for x in SignedInteger[posvec; negvec])
end

function test_closed()
return sum(value(x) for x in AnySignedInteger[posvec; negvec])
end

println("Testing open version")
@btime test_open()
println("Testing closed version")
@btime test_closed()

``````

The result:

``````Testing open version
1.792 μs (202 allocations: 4.92 KiB)
Testing closed version
697.020 ns (2 allocations: 2.02 KiB)
``````

Conclusion: it is about 2.3x faster to do dispatch on a closed union type. I actually expected more of a difference, which makes me think that my naive solution would actually not be prohibitively slow compared to something smarter.

Interesting. This means that abstract storage is not as slow as I would have thought.
Do you have any idea why this is faster than the version where you add type parameters to `Add` and `Const`?

No, I’m actually a bit perplexed as this code:

``````abstract type Expression end
struct Const <: Expression
value :: Int
end
lhs :: L
rhs :: R
end

evaluate(e::Const) = e.value

function evaluate_sum(exprs::Vector{Expression})
s = 0
for e in exprs
s += evaluate(e)
end
s
end

es = [Const(1)
Const(40)]
``````
``````julia> @btime evaluate_sum(\$es);
49.006 ns (0 allocations: 0 bytes)
``````

produces identical `@code_warntype` as the other version, but is slower. This suggests that perhaps the problem is on the LLVM side.

Update: I found a way to encode closed recursive ADTs in Julia and benchmarked this solution against a naive solution. Unfortunately, it performs worse (4.7μs vs 3.3μs for the naive solution), probably due to having to do more allocations.

If anyone finds a better way, please tell me!

### Naive solution

``````abstract type Expression end

struct Const <: Expression
value :: Int
end

struct Var <: Expression
varname ::String
end

lhs :: Expression
rhs :: Expression
end

evaluate(e::Const, env) = e.value
evaluate(e::Var, env) = env[e.varname]
evaluate(e::Add, env) = evaluate(e.lhs, env) + evaluate(e.rhs, env)

function sum_of_ints(n)
if n == 1
return Const(1)
else
end
end

using BenchmarkTools
@btime evaluate(sum_of_ints(100), Dict{String, Int}())
``````
``````3.228 μs (202 allocations: 5.23 KiB)
``````

### Solution that encodes recursive closed ADTs

``````struct Const
value :: Int
end

struct Var
varname ::String
end

lhs :: E
rhs :: E
end

struct Expression
end

mkConst(value) = Expression(Const(value))
mkVar(var) = Expression(Var(var))

evaluate(e::Expression, env) = evaluate(e.ctor, env)
evaluate(e::Const, env) = e.value
evaluate(e::Var, env) = env[e.varname]
evaluate(e::Add, env) = evaluate(e.lhs, env) + evaluate(e.rhs, env)

function sum_of_ints(n)
if n == 1
return mkConst(1)
else
end
end

using BenchmarkTools
@btime evaluate(sum_of_ints(100), Dict{String, Int}())
``````
``````4.681 μs (396 allocations: 8.25 KiB)
``````

Maybe I’m missing something, but isn’t it nice that the simple solution is faster? At any rate, I don’t think the performance difference between your two solutions is particularly large.

@CameronBieganek What this experiment indicates, in my opinion, is that the compiler misses an opportunity to optimize the second version. In a perfect world, the second version should be faster as the compiler would leverage the fact that an expression can be nothing else other than a `Const`, a `Var` or an `Add`, and make dynamic dispatch very fast based on this.

So a more interesting comparison would be to compare the time it takes to evaluate the naive version in Julia with an equivalent program written in a language with native ADTs such as OCaml, Haskell or Rust. I am going to try this now.

So I did the experiment in OCaml, which is about twice as fast as the Julia version (1.61μs vs 3.23μs). This is actually not too bad for Julia and this makes me feel better about using ADTs in Julia.

### Benchmark Code

``````type expr =
| Const of int
| Var of string
| Add of expr * expr

let rec evaluate expr env =
match expr with
| Const v -> v
| Var x -> List.Assoc.find_exn env ~equal:String.equal x
| Add (lhs, rhs) -> evaluate lhs env + evaluate rhs env

let rec sum_of_ints = function
| 1 -> Const 1
| n -> Add (Const n, sum_of_ints (n - 1))

let profile n =
let acc = ref 0 in
let t = Caml.Sys.time () in
for i = 1 to n do
acc := !acc + evaluate (sum_of_ints 100) []
done;
let dt = (Caml.Sys.time () -. t) /. (Float.of_int n) in
Stdio.printf "Average time: %.3f μs" (dt *. 1e6);
acc

let _ = profile 1000000
``````
``````Average time: 1.653 μs
``````
3 Likes

This is similar to open types, and static exhaustive checking wouldn’t get affected if your analyzer can walk through the whole program.

I’m sorry that MLStyle didn’t address this performance issue.

Actually I did consider the questions you raised here, and due to the restrictions of Julia I don’t really find out an approach.

1 Like

Also, there is a technique to alter ADTs, called tagless final.

ADT approach is called initial approach in some context, and tagless final is called the final approach in this scope.

For your code, we can use tagless final, to achieve stably typed Julia program:

``````struct SYM{F1, F2}
constant :: F1
end

function constant(v)
function (sym::SYM)
sym.constant(v)
end
end

function (sym::SYM)
end
end

# self algebra

evaluate =
let constant(v::Int) = v,
add(l::Int, r::Int) = l + r
end

``````

There’re no red points, try above codes in your Julia shell

``````5
Variables
#self#::var"#17#18"{var"#15#16"{Int64},var"#15#16"{Int64}}

Body::Int64
│   %2 = Core.getfield(#self#, :term1)::var"#15#16"{Int64}
│   %3 = (%2)(sym)::Int64
│   %4 = Core.getfield(#self#, :term2)::var"#15#16"{Int64}
│   %5 = (%4)(sym)::Int64
│   %6 = (%1)(%3, %5)::Int64
└──      return %6
``````
6 Likes

Thanks @thautwarm! I’ve heard FP people talk a lot about final tagless, and I’ve explored it a bit. But I still don’t really understand its full potential, or any limitations. Are there situations you would definitely reach for this, or any where you would avoid it?

BTW for a nice interface for your example, you can do

``````julia> (sym::SYM)(term) = term(sym)

5
``````
2 Likes

Like @cscherrer, I am still not sure that I can see the full potential of “final tagless ADTs”.

Also, I do not see how it improves on the simple solution from @Mason:

``````abstract type Expression end
struct Const{T} <: Expression
value :: T
end
lhs::L
rhs::R
end

evaluate(e::Const) = e.value
``````

In both case, some specialized code is generated to evaluate a single, specific expression (or, more rigorously, a small family of expressions sharing the exact same tree structure) and so no tags are needed. Dispatch does not happen at runtime but during JIT compilation.

However, I have a hard time finding a lot of situations where this is really what you want. When working with a large number of expressions, you probably do not want to compile one version of the evaluation function per expression to evaluate. And even when working with a small number of expressions, can the savings that result from avoiding dynamic dispatch outweigh the increased cost of JIT compilation?

1 Like

One pain point that I’ve never seen solved in Julia is building trees top-down, for example for a decision tree. You might try something like

``````abstract type Tree

struct Branch{L,R} <: Tree
left :: L
right :: R
end

struct Leaf{T} <: Tree
value :: T
end
``````

But the `L` and `R` values aren’t known until the tree is done. Could final tagless be better for this?

3 Likes

@cscherrer Thanks for the wrapping!

Tagless final can do everything ADTs and GADTs can do, and IIRC there shall be some menchanical methods to transform your code from ADT approach to tagless final approach.

When you’re using ADTs, things’re totally straightforward because you see concrete data

ADTs(tagged unions) are signals, data and descriptors.

When you want to use them, you’re supposed to write interpreters/evaluators for your ADTs, like writing pattern matching to decide constructor-specific behaviors.

When you’re using tagless final, you manipulate operations extracted from the ADT data, and the data turns out to be unnecessary

Tagless final

• avoids the use of too many tags(hence, tagless). It catches the observation that, what you’ll finally do(operations) with your ADTs can live without ADTs.

• encodes the ADTs to post-order visiting functions, which can be composed to make bigger operations, just like how we compose ADTs to construct recursive data.

• has many other advantages, say, it can be used to achieve pattern matching without metaprogramming libraries like MLStyle.jl or Match.jl, and the implemented pattern matching is naturally first-class.

tagless final is usually more concise because it contains a post-order visiting, which you shall manually implement when you’re using ADTs.

Unfortunately, above code to transform ADTs to tagless final does not directly apply to all Julia code, because Julia is a strict programming language.

Given a term `Add(left, right)`, you might want to visit `Add` node firstly, and might ignore the visiting of `left` and `right`, i.e, you want pre-order visiting.

However, with tagless final, the sub-components in `Add(left, right)` are always firstly visited, so, to support pre-order visiting or other visiting strategies, things can be a little disturbing.

Hi, I agree with you.

I just want to post some thing here to give your a point of view, that, tags are in fact unnecessary, because we just match tags and then perform operations among them.

The final operations are always what we actually need.

For example, if we just come to your example, tagless final encoding is also concise(my previous reply just shows many underlying structures):

``````struct HowWeWorkWithExpr{F1, F2}
constant :: F1
end

evaluate =
let constant(v::Int) = v,
add(l, r) = l + r
end
``````

It’s equivalent to Mason’s code, because the initial approach is actually almost equivalent to the final approach.

Hence I have to say there’re no improvements.

However, I personally prefer tagless final because in this way

• we don’t need too many type parameters for each constructors
• we don’t need abstract types
• fewer global variables
• fewer data types

Further, if you try to make a larger ADT and its evaluation, you might find some interesting convenience that tagless final brings about:

you always do not need to write code for picking fields/contents from data, which makes me feel good for my shoulders…

5 Likes

Thanks Chad, this is really a good example!

See this:

``````struct Tree{C1, C2}
branch :: C1
leaf :: C2
end

# polymorphic branch/ `branch` constructor
branch(left, right) =
function run(mod)
mod.branch(left(mod), right(mod))
end

# polymorphic leaf/ `leaf` constructor
leaf(value) =
function run(mod)
mod.leaf(value)
end

# eval to Int
tree_sum_eval = Tree(
+, # how to evaluate branch
identity # how to evaluate leaf
)

# eval to a function (indent::String)::String
tree_print = Tree(
function (left, right)
function run(indent)
println(indent, "-")
left(indent * "  ")
right(indent * "  ")
end
end,
function (value)
function run(indent)
println(indent, value)
end
end
)

# a tree

tree =
branch(
branch(leaf(1), leaf(2)),
branch(
leaf(2),
branch(leaf(5), leaf(2))
)
)

tree(tree_print)("")

println(tree(tree_sum_eval))
``````

run it:

``````λ julia a.jl
-
-
1
2
-
2
-
5
2
12
``````
2 Likes

Thanks for your detailed answer and for your great work on MLStyle.jl. 1 Like