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# Function Types

In prior chapters we have defined an interpreter over a language with only numbers, added Booleans, and showed how to handle and avoid type errors. With functions, we don’t need to add Booleans to see the same problem emerge again. Consider the following expression:

This expression simply applys inc to itself, something that made sense when we discussed untyped recursion. However, in this context trying to increment an increment function makes little sense. The value of inc is a lambda specifying an increment function over numbers. In the body of the bind, inc is applied to itself. There is of course nothing wrong with applying a function to itself, but in this case the body of inc attempts to add 1 to its argument. Adding 1 to a function is not defined and causes any of our interpreters to crash.

We can fix this problem as we have in the past - add run-time or predictive type checking. Said using terminology from our discussion of scoping, we can add dynamic or static type checking. As usual, dynamic performed at run-time and static performed before run-time.

## Typing Bind

Before diving into types of functions, let’s take a quick aside into finding the type of a bind remembering that like lambda and app, bind defines new identifiers and associates them with values. To start with, let’s examine a trivial bind that creates an identifier, binds it to a value, and returns the resulting identifier:

As always, bind evaluates its body and returns the result. The type of that result in turn becomes the type of the bind. If we can find the type of 1+1 we can find the type of what evaluating bind returns. In this case, that’s quite simple using typing rules we already have. Specifically, any term of the form t1+t2 is of type TNum if both t1 and t2 are also of type TNum. The type of the bind is clearly TNum.

Things are much more interesting if we use the bound identifier in the body of the bind. Defining new identifiers and giving them values is what bind is all about. Let’s change the body of the bind to use the identifier:

The same type rule applies to the body. If x:TNum and 1:TNum then x+1:TNum. The second term is simple as 1:TNum by definition. The first term requires some thought, but is still rather obvious. By definition x is bound to 1 in the body of the bind and 1 is of type TNum. It follows that in the body of the bind the identifier x has type TNum. We will implement this intuition using a context that behaves much like an environment does in an evaluator.

Thinking back to how environments work reveals the following simple annotation for the bind:

Following the x = 1 binding instance the environment contains a binding of x to 1. When x appears in the bind body, it is looked up in the enviornment when evaluated. A context will serve the same role, but for types rather than values. In the same way an environment stores identifiers and values during evaluation, a context will store identifiers an types during type checking.

The context is maintained exactly like an environment. When an identifier is bound, the type of the bound value is associated with the identifier in the context. Using a similar notation, we can decorate the type inference process for bind:

Now when x is used in the term x+1, its type is looked up in the context maintained by the typeof function. Thus, x+1 is known to have type TNum because both x and 1 are type TNum.

Let’s define the necessary type operations used by the typeof implementation that will perform the type inference operation. First, the case for an identifier:

The code is virtually identical to looking up the value of an identifier in an environment. The function uses the identifier’s name to look up the identifier’s type in the context, cont.

The type of a bind instance is again similar to evaluation:

The type of the bound value, v, is first determined using the current context, cont. Then type of the bind body is determined using a new context resulting from the original context with i bound to the bound value’s type. This looks just like evaluation, except we’re generating types rather than values. Hold that thought.

Now the formal rules. First for identifiers:

If $(i,T)$ is in the current context, then type of $i$ is $T$. This is a trivial definition, but necessary for completeness. Now the rule for bind:

Finding the type of bind gives us all the tools needed to talk about the type of app and lambda. We’ve defined what a context is and how to add an identifier to the context in the scope of a bind. As we move on to functions, keep these definitions in mind as finding their types will use quite similar techniques.

## Typing Functions

After a diversion into bind, let’s think about how to determine the type of a lambda. A function is at its essence a mapping from an input value to an output value. For example:

takes a value and returns that value plus 1. It is also true that this function will only work correctly on numbers. Applying (lambda x in x+1) to a Boolean or another function always results in an error. We can express this by saying that x must be of type TNum using the notation x:TNum. When applied to an argument of the correct type this lambda should result in a good value. For example:

Furthermore, the type of the resulting value can be predicted. In this case, the app results in a value of type TNum. Putting everything together, we can say that this lambda expression applied to any TNum will result in TNum should it terminate. To represent this we use a function type. Specifically, we extend our current type definition with a new function type in much the same way we defined binary operators over values like + or - by using a recursive construction:

This new definition is quite different from our other types. It allows constructing new types form existing types such as $\tnum \rightarrow \tnum$, $\tnum \rightarrow (\tnum \rightarrow \tnum)$, and $(\tnum \rightarrow \tnum) \rightarrow \tnum$. An informal name for the $\rightarrow$ operation is a type former or a type function that takes two types and generates a new type. Just like + takes two numbers and generates a new number, $\rightarrow$ will take two types and generate a new type. The left type is called the domain and the right type the range of the function type. If no parentheses are included, we assume that $\rightarrow$ is right associative. We will often use D for the domain type and the R for the range type.

Now that we have a means for defining function types, we can find types for lambda expressions like we did bind. Let’s try to define type inference iteratively by thinking about how we derive the type of a trivial lambda. Let’s start with the simple increment function and see if we can find its type. Specifically, we would like to find T in the following expression:

All lambda expressions must have a function type, thus T must have the form D->R where D is the domain type and R is the range type. We don’t know what either D or R is yet, but we do know that this particular function should have a function type:

Now we need to find D and R and will appeal to how we calculated the type of bind. Like the bind, x is the newly boudn identifier and x+1 is the body where x is bound. Just like bind if we can find the type of x+1 we will know R, but to find R we must know the type of x. bind uses a context to track defined identifiers and their types during type checking like evaluation uses an environment to track identifiers and their values during execution. If we can add a binding of x to its type to the context, its type will be known when we calculate tye type of x+1. Specifically using the same notation to track context:

Unfortunately, we have no way of inferring D in the general case. Look carefully at a similar bind:

Here we do know D because x is given a value that has an associated type. We do know know what value might be given to x until the lambda is applied to an argument. In the specific case of x+1 we can know D because + takes two numbers. But consider this trivial lambda instance:

There is no way to infer D without knowing the value for x and that value will not be known until app evaluates the lambda. It simply isn’t possible to infer the input type in every case.

The way forward is changing the lambda definition and ascribing a type to the function’s input parameter declaring what type D should be. In this case, TNum:

The notation (x:TNum) declares that x must be of type TNum and thus D becomes TNum. Because we now know the type of x we can find R by adding (x,TNum) to the context and finding the type of x+1. When calculating the type of x+1 using the definition for the type of + we simply look up x in the current context. Finally, type for the lambda becomes:

Generalizing this example, the case for funding the type of a lambda in typeof looks like this:

D is given by the lambda declaration. Find R by adding (x,D) to the context and finding the type of b. Given D and R, the type of the lambda is D->R.

The formal type rule can be written:

## Typing Applications

Knowing how to type lambda, we now turn to app. The two arguments to app should be a function and an argument to the function. Let’s consider applying the increment function from the lambda example and finding the resulting type:

The first argument to app must be a fucntion or it cannot be applied to anything. Thus we know that it must have the form D->R. In this example, (lambda (x:TNum) in x+1) has type TNum -> TNum and both D and R are TNum. For the function application to be successful, we also know the the argument type must be D. If we are going to evaluate app f a, then a must be of a type that f accepts. In this case, 5 must have type TNum as it does. The type of the app itself is then R because the function type D->R says the output will be of type R if the input is of type D. In this case, the type of the app is TNum because the type of x+1 in the context of x:TNum is TNum.

Let’s make this way simpler. Given some f:D->R and a:D, then (app f a):R. Simple as that. Here’s the type rule that captures this:

What does this say about the problem that motivated the chapter? Specifically, what is the type of:

Let’s walk through the type derivation. First, we add a type to the function’s formal parameter. It has to be TNum. (Think carefully about why.) The expression now becomes:

To find the type of inc, we must find the type of the lambda bound to it. D is given to be TNum, thus we add (D,TNum) to the context and determine the type of x + 1 to get R:

x is TNum thus x+1 is TNum by the definition of the type of t1+t2. The type of the lambda now becomes TNum -> TNum giving the type for inc. Now add the type of inc to the context and look at the type of app, noting that the type of x drops from the context when the lamdba’s scope closes:

Finding the type of app requires finding the type of inc to be TNum -> TNum by looking it up in the context. Now we apply a function of type TNum -> TNum to an argument of type TNum -> TNum. Clearly this is a problem because the type of the argument and the type of function’s domain do not match. The type inference function will throw and error and we will know before runtime that this program will crash.

## Static Type Checking

Now let’s build eval and typeof for FBAE. First thing we need to do is update the concrete syntax to include a parameter type for lambda and a new type constructor for function types:

No news here, we simply need to make new constructs available in the concrete syntax.

The abstract syntax follows from the concrete syntax, again adding a type parameter to Lambda:

and a function type constructor to FBAETy’:

Note the constructor for function types uses the Haskell infix constructor notation allowing them to have the form T :->: T similar to how they are written in rules. Just a bit of Haskell trickery, nothing significant.

Let’s write the typeof function first using old-fashioned let notation to construct cases for each term type. The signature of typeof is a context and term to a type:

There is nothing new in how types for constants and operations:

After all our work defining type rules, the new cases for typeof follow quickly. For Lambda, typeof is called on the Lambda body with the argument name bound to its type added to the original context. D is known from the lambda and R is learned by calling typeof:

The final type becomes td :->: tr as defined by our previous type rule.

App is a bit more involved, but nothing too dramatic. First, the App’s argument type is found and bound to and tyY respectively. A case statement finds the type of the App’s function element. If it is a function type, the domain type is compared to the argument type and the range type returned if they match. If they don’t, a mismatch occurs and an error results:

If the type of the App’s function argument is anything but a function, an error is thrown immediately.

That’s it for typeof over FBAE. Do we now need a new eval? Other than accounting for the new abstract syntax for lambda, no changes are needed for the eval function for FBAE. For brevity, we’ll skip that small update.

## Dynamic Type Checking

The simplest way to do dynamic type checking on this new language with function types is to do nothing. Literally. We have an interpreter that performs dynamic checking on terms other than lambda and app. While we could use the argument type from lambda, the simplest thing is to do what Lisp variants to and perform substitution and throw errors when expressions are evaluated.

## Discussion

In our discussion of static type checking for FBAE we did not discuss scoping. Specifically, now that we’ve identified dynamic and static scoping as different approaches, do we need different type inference capabilities for each?

The difference between static and dynamic scoping is whether the static declaration environment or the runtime environment is used to find symbol values. Obviously, we don’t know the runtime environment until, well, runtime. Do you see the problem? If we don’t know what symbol is being referenced until runtime, we can’t statically check it’s type. Look at this definition that slightly modifies an example from our discussion of static and dynamic scoping:

Using static scoping, we know the n referenced in the lambda is the first n that is a number. Using dynamic scoping, the second n gets used and it is a Boolean. Delete that binding, and dynamic scoping matching static scoping. The same function called in different places has different types, one legit and one not.

It may be too strong to say we can’t statically check this expression. However, we would need to do something akin to evaluation statically. That makes no sense. Regardless, this is yet another argument against using dynamic scoping.

An important observation is that typeof is simply an evaluation function that returns types rather than values. Or, types are values in this particular interpretation. Programs are simply data structures and writing interpreters for them - regardless of the interpretation type - has the same form. A great exercise to do now is rewrite the interpreter to use the values odd and even rather than numbers. You will discover that you can reuse almost all your code from eval or typeof` targeting these new values.

## Definitions

• Context - A list of identifiers and their types currently in scope.
• Static Type Checking - Type checking performed before interpretation.
• Dynamic Type Checking - Type checking performed at run-time.