## zero-overhead C++17 currying & partial application

23 january 2017

As I mentioned in my previous article many features introduced in the latest C++ standards allow functional patterns to thrive in your codebase. Two ideas from that programming paradigm that I really like are currying and partial application.

• Introduce and briefly explain the two aforementioned concepts.

• Write a generic constexpr zero-overhead curry function in C++17.

• Analyze the generated assembly of the implementation to prove the lack of overhead.

### Currying

Let’s begin by explaining currying.

In mathematics and computer science, currying is the technique of translating the evaluation of a function that takes multiple arguments (or a tuple of arguments) into evaluating a sequence of functions, each with a single argument.

(from Wikipedia)

The following add3 function is an example of a non-curried function, as its arity is $$3$$.

auto add3(int a, int b, int c)
{
return a + b + c;
}

add3(1, 2, 3); // Returns 6.

We can curry it by returning nested lambda expressions.

{
return [a](int b)
{
return [a, b](int c)
{
return a + b + c;
};
};
}

curried_add3(1)(2)(3); // Returns 6.

wandbox example

As you can see, the arity of every lambda is $$1$$. This pattern is useful because it allows developers to intuitively bind arguments incrementally until the last one. If you’re finding yourself constantly using the same arguments except one in a series of function calls, currying avoids repetition and increases readability.

add1_three(3); // Returns 6.
add1_three(4); // Returns 7.
add1_three(5); // Returns 8.

wandbox example

A slightly more realistic example could involve std::find:

std::vector<std::string> names{/* ... */}

auto find_in_names =
curried_find(std::begin(names))(std::end(names));

auto jack = find_in_names("Jack");
auto rose = find_in_names("Rose");

In the above code snippet some repetition between std::find invocations is cleanly avoided thanks to currying.

(This short article by Ivan Čukić has some additional interesting examples of currying in C++.)

### Partial application

In computer science, partial application (or partial function application) refers to the process of fixing a number of arguments to a function, producing another function of smaller arity.

(from Wikipedia)

Despite them being two separate concepts, partial application is very similar to currying. Even though I couldn’t find a formal confirmation anywhere, I believe that thinking about partial application as a “generalized form of currying” can be helpful: instead of binding one argument and getting $$(arity - 1)$$ unary functions back, we can bind $$n$$ arguments at once and get another partially-applicable function with $$(arity - n)$$ arity.

partial_add3(1, 2, 3); // Returns 6.
partial_add3(1)(2, 3); // Returns 6.
partial_add3(1, 2)(3); // Returns 6.
partial_add3(1)(2)(3); // Returns 6. (Currying!)

As you can see, we can decide how many arguments to bind (including zero). We could easily implement this in C++17 using recursion, generic lambdas, if constexpr(...), and variadic templates. (We’ll also use a fold expression to compute the sum.)

template <typename... Ts>
{
static_assert(sizeof...(xs) <= 3);

if constexpr (sizeof...(xs) == 3)
{
// Base case: evaluate and return the sum.
return (0 + ... + xs);
}
else
{
// Recursive case: bind xs... and return another
return [xs...](auto... ys)
{
};
}
}

wandbox example

Writing code that enables currying and partial application for every function is cumbersome. Let’s write a generic curry function that, given a function object f, returns a curried/partially-applicable version of f!

### C++17 curry

As mentioned in the beginning of the article, these are the goals for our curry function:

• Given a generic function object f, invoking curry(f) returns a curried/partially-applicable version of f.

• If f is constexpr-friendly, the returned one will be as well.

• curry should not introduce any overhead compared to hand-written currying/partial application.

#### Credit where it’s due

Please note that the design and implementation of curry that I am going to cover is a heavily-modified version of this snippet that was tweeted by Julian Becker - in fact, it was that tweet that inspired me to write this article. Thanks!

(Julian also wrote an excellent answer on the StackOverflow question “How can currying be done in C++?” - make sure to check it out.)

#### Example usage

Before we analyze the declaration and definition of curry, let’s take a look at some usage examples.

• Nullary functions:

auto greet = []{ std::puts("hi!\n"); };

greet(); // Prints "hi!".
curry(greet); // Prints "hi!".

// Compile-time error:
/* curry(greet)(); */

As you can see, in the case of a nullary function object f, invoking curry(f) calls the original object immediately.

• Unary functions:

auto plus_one = [](auto x){ return x + 1; };

plus_one(0); // Returns 1.
curry(plus_one)(0); // Returns 1.

// Returns a wrapper around plus_one that enables
// currying/partial application.
// plus_one is "perfectly-captured" in the wrapper.
auto curried_plus_one = curry(plus_one);

curried_plus_one(1); // Returns 2.

What does perfectly-captured mean?

It means that if the captured object is an lvalue, it will be captured by reference. If the captured object is an rvalue, it will be captured by move*. I’ve written a comprehensive article on this topic: “capturing perfectly-forwarded objects in lambdas”.

• Binary functions:

auto add2 = [](auto a, auto b){ return a + b; };

// All of the invocations below return 3.

// Example of "binding" an argument:
add_one(2); // Returns 3.
add_one(3); // Returns 4.

You should be starting to see the pattern now…

• $$N$$-ary functions:

auto add3 = [](auto a, auto b, auto c)
{
return a + b + c;
};

// All of the invocations below return 6.

The example above shows that currying and partial application can be freely combined. Let’s see another example of that with a constexpr lambda of arity $$5$$.

auto add5 = [](auto a, auto b, auto c,
auto d, auto e) constexpr
{
return a + b + c + d + e;
};

constexpr auto csum5 = curry(sum5);

constexpr auto a = csum5(0, 1, 2, 3, 4, 5);
constexpr auto b = csum5(0)(1, 2, 3, 4, 5);
constexpr auto c = csum5(0, 1)(2, 3, 4, 5);
constexpr auto d = csum5(0, 1, 2)(3, 4, 5);
constexpr auto e = csum5(0, 1, 2, 3)(4, 5);
constexpr auto f = csum5(0, 1, 2, 3, 4)(5);

Note that the usages of curry(sum5) above are in no way exhaustive - more combinations such as curry(sum5)(0, 1)(2, 3)(4, 5) can be written, and every intermediate step can be given a name.

Now that you have an idea on how curry can be used, let’s dive into its declaration and definition.

#### Declaration

Given the constraints listed earlier, we can easily write down the declaration of curry.

template <typename TF>
constexpr decltype(auto) curry(TF&& f);

Because the final step of curry needs to return exactly what the original function object does. Example:

auto f = [](auto, auto) -> auto&
{
return some_global_variable;
};

// OK - can return an additional "curry wrapper" by value.
auto step0 = curry(f);

// Same as above.
auto step1 = step0('a');

// Now step1 has to return a reference!
auto& that_same_global = step1('b');

Additionally, the f parameter is taken by forwarding-reference. I will assume you’re familiar with move semantics, std::forward, and “forward captures” for the rest of the article.

#### Definition

I’ll show the complete definition of curry first, and then analyze all the parts one-by-one more closely.

template <typename TF>
constexpr decltype(auto) curry(TF&& f)
{
if constexpr (std::is_callable<TF&&()>{})
{
return FWD(f)();
}
else
{
return [xf = FWD_CAPTURE(f)](auto&&... partials) mutable constexpr
{
return curry
(
[
partial_pack = FWD_CAPTURE_PACK_AS_TUPLE(partials),
yf = std::move(xf)
]
(auto&&... xs) constexpr
-> decltype(forward_like<TF>(xf.get())(FWD(partials)...,
FWD(xs)...))
{
return apply_fwd_capture([&yf](auto&&... ys) constexpr
-> decltype(forward_like<TF>(yf.get())(FWD(ys)...))
{
return forward_like<TF>(yf.get())(FWD(ys)...);
}, partial_pack, FWD_CAPTURE_PACK_AS_TUPLE(xs));
}
);
};
}
}

The first thing to notice is the recursive structure of curry.

template <typename TF>
constexpr decltype(auto) curry(TF&& f)
{
if constexpr (std::is_callable<TF&&()>{})
{
return FWD(f)();
}
else
{
// ...
}
}

The base case branch is taken when std::is_callable<TF&&()>{} evaluates to true. std::is_callable is a new C++17 type trait that checks whether or not a particular object types can be called with a specific set of argument types.

• If std::is_callable<TF&&()>{} evaluates to false, then it means that TF needs some arguments in order to be called - those arguments can be curried/partially-applied.

• If it evaluates to true, it means that there are no more arguments to curry/partially-apply in f. Therefore, f can be invoked to get the final result:

return FWD(f)();

FWD is a macro that expands to std::forward<decltype(f)>(f). It’s being used as TF may have a ref-qualified operator() that behaves differently depending on f’s value category.

We will now focus on the recursive case of curry. The first step is allowing partial application of arguments - since we don’t know how many arguments will be bound in advance, a generic variadic lambda will be returned:

return [xf = FWD_CAPTURE(f)](auto&&... partials) mutable constexpr
{
return curry(/* ... */);
}

The returned lambda will:

• Capture f by forward capture into xf.

• Accept any amount of forwarding references in the partials... pack. These arguments will be bound for subsequent calls.

• Be marked as mutable: this is important as xf will be moved in the inner lambda’s capture list.

• Be marked as constexpr: this allows curry to be used as a constant expression where possible.

• Recursively call curry in its body, returning a new curried/partially-applicable function.

Let’s now focus on the return curry(/*...*/) statement. We want to return a curried version of a new intermediate function object where:

• The partials... pack values are bound for its invocation - these values will be captured by forward capture as partial_pack.

• The forward-captured xf from the “parent” lambda is captured by move into yf. xf doesn’t need to be forwarded as FWD_CAPTURE(f) returns a movable wrapper that either stores an lvalue reference or a value.

return curry
(
[
partial_pack = FWD_CAPTURE_PACK_AS_TUPLE(partials),
yf = std::move(xf)
]
(auto&&... xs) constexpr
-> decltype(forward_like<TF>(xf.get())(FWD(partials)...,
FWD(xs)...))
{
// ...
}
);

The lambda passed to curry will accept any number of forwarding references in the xs... pack that will be used alongside the captured partials... to call f. The expected function call can be easily understood by the lambda’s trailing return type:

//          Unwrap f from the xf FWD_CAPTURE wrapper and propagate
//          the original function object's value category.
//          vvvvvvvvvvvvvvvvvvvvvvvvv
-> decltype(forward_like<TF>(xf.get())(FWD(partials)..., FWD(xs)...))
//                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
//          Unpack both the partials and xs argument packs in a
//          single function call to f.

forward_like is an utility function in my vrm_core library that forwards the passed argument with the same value category of the potentially-unrelated specified type. It basically copies the “lvalue/rvalue-ness” of the user-provided template parameter and applies it to its argument.

The expression inside the above return type essentially means: “invoke the original function object by unpacking partials... and xs... one after another”.

Lastly, let’s analyze the body of the lambda.

return apply_fwd_capture([&yf](auto&&... ys) constexpr
-> decltype(forward_like<TF>(yf.get())(FWD(ys)...))
{
return forward_like<TF>(yf.get())(FWD(ys)...);
}, partial_pack, FWD_CAPTURE_PACK_AS_TUPLE(xs));

Remember: we’re trying to call f by unpacking both partials... and xs... at the same time. The partials... pack is stored in a special wrapper returned by FWD_CAPTURE_PACK_AS_TUPLE. The xs... pack contains the arguments passed to the lambda.

The apply_fwd_capture takes any number of wrapped forward-capture pack wrappers and uses them to invoke an user-provided function object. The wrappers are unpacked at the same time, preserving the original value category. Since xs... is not wrapped, we’re going to explicitly do so by using the FWD_CAPTURE_PACK_AS_TUPLE macro.

In short, apply_fwd_capture will invoke the constexpr variadic lambda by expanding partials... and xs... correctly - those values will be then forwarded to the wrapped callable object yf.

That’s it! Eventually the recursion will end as one of the steps will produce an intermediate function objects that satisfies std::is_callable<TF&&()>{}, giving back a “concrete” result to the caller.

### Generated assembly benchmarks

As I did in my previous “passing functions to functions” article, I will compare the number of generated assembly lines for different code snippets where curry is used. The point of these “benchmarks” is giving the readers an idea on how easy it is for the compiler to optimize curry out - they are in no way exhaustive or representative of a real-world situation. (The benchmarks were generated with this Python script, which also prints out the assembly.)

The compiler used for these measurements is g++ 7.0.0 20170113, compiled from the SVN repository.

#### constexpr variables

When curry is used in a constexpr context it is trivial to prove that it gets completely optimized out by the compiler. Regardless, here’s the snippet that’s going to be measured:

int main()
{
const auto sum = [](auto a, auto b, auto c, auto d, auto e, auto f, auto g,
auto h) constexpr
{
return a + b + c + d + e + f + g + h;
};

constexpr auto expected = sum(0, 1, 2, 3, 4, 5, 6, 7);

#if defined(VR_BASELINE)
constexpr auto s0 = sum(0, 1, 2, 3, 4, 5, 6, 7);
constexpr auto s1 = sum(0, 1, 2, 3, 4, 5, 6, 7);
constexpr auto s2 = sum(0, 1, 2, 3, 4, 5, 6, 7);
constexpr auto s3 = sum(0, 1, 2, 3, 4, 5, 6, 7);
constexpr auto s4 = sum(0, 1, 2, 3, 4, 5, 6, 7);
constexpr auto s5 = sum(0, 1, 2, 3, 4, 5, 6, 7);
constexpr auto s6 = sum(0, 1, 2, 3, 4, 5, 6, 7);
constexpr auto s7 = sum(0, 1, 2, 3, 4, 5, 6, 7);
#elif defined(VR_CURRY)
constexpr auto s0 = curry(sum)(0, 1, 2, 3, 4, 5, 6, 7);
constexpr auto s1 = curry(sum)(0)(1, 2, 3, 4, 5, 6, 7);
constexpr auto s2 = curry(sum)(0, 1)(2, 3, 4, 5, 6, 7);
constexpr auto s3 = curry(sum)(0, 1, 2)(3, 4, 5, 6, 7);
constexpr auto s4 = curry(sum)(0, 1, 2, 3)(4, 5, 6, 7);
constexpr auto s5 = curry(sum)(0, 1, 2, 3, 4)(5, 6, 7);
constexpr auto s6 = curry(sum)(0, 1, 2, 3, 4, 5)(6, 7);
constexpr auto s7 = curry(sum)(0, 1, 2, 3, 4, 5, 6)(7);
#endif

static_assert(s0 == expected);
static_assert(s1 == expected);
static_assert(s2 == expected);
static_assert(s3 == expected);
static_assert(s4 == expected);
static_assert(s5 == expected);
static_assert(s6 == expected);
static_assert(s7 == expected);

return s0 + s1 + s2 + s3 + s4 + s5 + s6 + s7;
}
• sum is a constexpr generic lambda with an arity of $$8$$.

• When measuring the baseline, the s0s7 constexpr variables are initialized by simply calling sum.

• When using curry, s0s7 are initialized by using various invocations of curry(sum).

• In the end, the expected sum result is statically asserted and returned from main.

Baseline

O0 O1 O2 O3 Ofast
14 2 2 2 2

Curry

O0 O1 O2 O3 Ofast
14 (+0.0%) 2 (+0.0%) 2 (+0.0%) 2 (+0.0%) 2 (+0.0%)

As shown by the tables above, using curry introduces no additional overhead when used in the initialization of constexpr variables.

You can find the complete snippet on GitHub.

#### volatile variables

Let’s now measure the eventual overhead of curry when initializing volatile variables. The snippet is almost identical to the previous one, except for a few differences:

• The s0s7 variables are now marked as volatile instead of constexpr.

• The static_assert(x) checks have been replaced with if(!x){ return -1; }.

Baseline

O0 O1 O2 O3 Ofast
68 56 42 42 42

Curry

O0 O1 O2 O3 Ofast
68 (+0.0%) 56 (+0.0%) 42 (+0.0%) 42 (+0.0%) 42 (+0.0%)

You can find the complete snippet on GitHub.

#### Intermediate curry steps

The above benchmarks never stored any intermediate curry return value - the entire expression was part of the s0s7 initializer expression. Let’s see what happens when those intermediate steps are stored as follows:

auto i0 = curry(sum);
auto i1 = curry(sum)(0);
auto i2 = curry(sum)(0, 1);
auto i3 = curry(sum)(0, 1, 2);
auto i4 = curry(sum)(0, 1, 2, 3);
auto i5 = curry(sum)(0, 1, 2, 3, 4);
auto i6 = curry(sum)(0, 1, 2, 3, 4, 5);
auto i7 = curry(sum)(0, 1, 2, 3, 4, 5, 6);

volatile auto s0 = i0(0, 1, 2, 3, 4, 5, 6, 7);
volatile auto s1 = i1(1, 2, 3, 4, 5, 6, 7);
volatile auto s2 = i2(2, 3, 4, 5, 6, 7);
volatile auto s3 = i3(3, 4, 5, 6, 7);
volatile auto s4 = i4(4, 5, 6, 7);
volatile auto s5 = i5(5, 6, 7);
volatile auto s6 = i6(6, 7);
volatile auto s7 = i7(7);

Baseline

O0 O1 O2 O3 Ofast
68 56 42 42 42

Curry

O0 O1 O2 O3 Ofast
19141 (+2804%) 56 (+0.0%) 42 (+0.0%) 42 (+0.0%) 42 (+0.0%)

From optimization level -O1 onwards everything is great: zero overhead! When using -O0, though, there is a quite noticeable overhead of $$+2804\%$$ extra generated assembly compared to the baseline.

You can find the complete snippet on GitHub.

(Some additional benchmarks with volatile lambda parameters and values are available on the GitHub repository.)

### Compiler bugs

curry looks great! Zero run-time overhead, partial application and currying all in one… what’s the catch?

Well, the hardest part is… getting curry to compile. As seen from these tweets between me and Julian Becker, it seems that both g++ and clang++ fail with internal compiler errors for different reasons.

I managed to compile curry and the snippets used for this article by cloning the latest version of gcc from SVN and compiling it on my machine - I assume that some of the crashes were fixed in on trunk and gcc.godbolt.org is still a little bit behind.

### Acknowledgments

Many thanks to Julian Becker and Jackie Kay for proofreading the article and providing very valuable feedback.