## Sunday, February 21, 2016

### Pattern Matching: Is There Anything it Can't Do?

I recently read Joe Duffy’s excellent account of the error-handling mechanism in Midori. It can be viewed either as an exception-handling mechanism or as an error-return mechanism using pattern matching. This is cool because it means that exception raising is the fourth function-calling abstraction that can be eliminated in favor of pattern matching, so long as we have expressive-enough pattern matching. I’m going to take this opportunity to describe the four eliminations.

A pattern is an expression representing a value with variable elements. Some of the variable elements are named, representing variables. Pattern matching is the process of comparing a pattern against a value (called the subject) to try to find an assignment of values to the variable elements of the pattern which would make the resulting value equal to the subject. If such an assignment is found, then the match is said to succeed, and any variables in the pattern are left equal to the value they were assigned. In the kinds of patterns we are considering, there is never more than one way in which a pattern may match a subject.

## Multiple Return Values as Pattern Matching

Common Lisp has a concept called multiple values. A multiple-values “thing” is not a first-class value: one cannot insert one into a list, for example, nor is there any distinction between a single value and a multiple-values thing that has one value. This feature is really intended just to allow a function to return multiple values at once. The values are collected at the return site of the function:
(defun sum-and-product (x y)
; returns two values, x+y and x*y
(values (+ x y) (* x y))
and burst into individual values at the call site
(multiple-value-setq (sum, prod) (sum-and-product 2 3))
; here, sum=5 and prod=6
This is a useful concept because it is more efficient than creating a list of values and then taking the list apart. An implementation that returns values in registers would only have to set the first return register to 5 and the second to 6 in the above example, then those registers can be used at the call site without further effort.

Many languages have generalized this concept with tuples and pattern matching. For example in Python, a tuple is an immutable sequence of values created by a list of comma-separated expressions:
2, 3   # A tuple of two integers
An assignment with a tuple of variables on the left and a tuple on the right, will assign the corresponding values:
x, y = 2, 3  # Assigns x=2 and y=3
Naturally, a function that returns a tuple would work the same on the right hand side:
def SumAndProd(a, b): return a+b, a*b
x, y = SumAndProd(2, 3)  # Assigns x=5 and y=6
A Python tuple is different from a Common Lisp multiple value because a tuple is a data object on its own. In the above example,
result = SumAndProd(2, 3)
would assign to x a tuple of (5, 6), whereas the corresponding Common Lisp expression
(setq result (sum-and-product 2 3))
would only set result to the first element of the multiple result, 5.

Python has to construct the tuple at the return site and then access the elements of the tuple at the call site to assign them to x and y. Common Lisp can can put the values directly in registers at the return site and use them directly at the call site.

The reason Common Lisp can do this is because we can have a calling convention that deals directly with multiple values. If we have, say, 4 registers dedicated to return values, the callee can return up to four values in registers. Any expression that is not expecting multiple results will only look at the first register and only use the first of the multiple results, which is what the semantics says it should do. Everything works out (you also need to return an indication of how many results were returned--I assume there is a dedicated register for this in implementations that return values in registers).

In a statically-typed language that lets us declare that a function returns a tuple of a certain length, we can define a calling convention such that a function that returns a tuple of four elements or less returns the elements in the four result registers without constructing a tuple. The call site then has to know about this calling convention and handle it appropriately. In particular, if the caller actually wants a tuple rather than the individual values, then the caller must construct the tuple from the values in the registers.

## Argument Assignment as Pattern Matching

I believe I’ve seen this in another language, but I don’t recall where, so I’ll talk about this as a feature of Unobtainabol. In Unobtainabol, a tuple is a sequence of values. For example:
(‘a’, ‘b’, ‘c’, ‘d’)
represents a tuple of four elements numbered 1 to 4. The parenthesis are not required except for disambiguation.

A pattern is a tuple expression with possibly-typed variables in place of values. A pattern can be matched against a subject using the assignment statement:
a, b = 1, 2  # assigns a=1, b=2
a, b, c = 1, 2  # fails and raises an error
int a, float b = 1, 2.2  # assigns a=1, b=2.2
int a, int b = 1, 2.2  # fails and raises an error
It should be no surprise now to say that an Unobtainabol function call takes a single argument, which is a tuple, For example
f(‘a’, ‘b’, ‘c’, ‘d’)
The formal parameter list is a pattern which matches the argument tuple and assigns to the formal parameters. For example, the above call, with the function
void f(a, b, string c, string d)
would produce the formal parameter assignments: a=’a’, b=’b’, c=’c’, d=‘d’.

With suitable extensions to tuples and matching, we can handle default values, keyword arguments, variable argument lists, and the various parameter passing methods. The result is a parameter-passing mechanism that is identical to pattern-matching assignment in the language, so there is only one set of rules for the programmer to learn in order to use both features.

## Dispatch as Pattern Matching

For our purposes, a function signature is the list of argument types and possibly other information about the arguments. Many languages allow polymorphic functions: multiple functions with the same name but differing signatures:
int f(int i);
int f(int i, int j);
int f(float x);
The selection of which of a set of polymorphic functions to invoke is called function dispatch.

Dispatching is a fairly complex matching problem. Frequently, more than one function signature matches the actual parameters, so there must be rules about how to choose the best match. For example, in the presence of automatic casting, the call f(0) would match both the first and third signatures above because while 0 is an int, it can be cast to a float.

A pattern-matching case is a case expression or statement that uses pattern matching rather than mere equality to resolve the condition. For example:
match x {
case (int a, int b): ...
case (int a, float b): ...
}
This will select the branch that best matches x and will execute the associated clause with the indicated bindings.

Some functional languages allow functions to be declared as a set of clauses like this:
factorial(0) = 1
factorial(n) |n > 0| = n * factorial(n-1)
The expression inside |...| is called a guard; it is a way to extend patterns with general conditions. The pattern only matches when the guard is true.

The function above would be equivalent to one declared with a pattern-matching case:
factorial(n) = switch n {
case 0: 1
case i |i > 0|: factorial(i-1)
}
All of this suggests the following: a collection of polymorphic functions, all with the same name can be viewed as a single function where the body is a pattern-matching case over the formal parameter lists of the individual signatures. For example, given the functions above
int f(int i) {body1}
int f(int i, int j) {body2}
int f(float x) {body3}
These would be equivalent to the single function:
int f(Args) {
match Args {
case (int i): {body1}
case (int i, int j): {body2}
case (float x): {body3}
}
}
A call to f(actual-parameters) gets resolved by setting Args=actual-parameters and then executing the switch statement. This unifies the concepts of dynamic dispatch and pattern matching. Static dispatch is still an open issue.

In Unobtainabol we take this all the way: you don’t actually have to give a formal parameters list; you can just use Args in the body of the function. Args[1] is the first actual parameter, Args[2] is the second, and so on. For extra convenients, we define \$1 as an abbreviation for Args[1], \$2 as an abbreviation for Args[2], and so on. This makes for a nice syntax for small closures.

## Exception Handling as Pattern Matching

A continuation is a control point in the program. Technically, a continuation encodes both an address in the code and an environment (a stack frame), but when the continuation is just used as a function return and the function uses the normal stack discipline, the continuation can be encoded as just an address in the code because normal stack handling will take care of the environment. Because of this, I will sometimes conflate the notion of a continuation and a code address.

A basic function, when it has done its work just returns to the caller at the point after the call. In pseudo-assembly language it looks like this:
push arg 1
push arg 2
push labelA
goto function
labelA:
...
First we push the arguments and the continuation/return label on the stack, then we jump to the function. When the function is done, it will pull the return label off the stack and jump to it. The stack may be altered either before the return or after, depending on the calling convention. It’s better to pass arguments in registers than on the stack, but you usually need a stack anyway so I’ll ignore registers.

In some cases, you need two continuations. For example, suppose f is a generator function and the language has a looping feature that uses a generator function like this:
for x in f(y):
do something with x
go on with program
The code for this is pretty complicated and it doesn’t just use normal stack discipline, so I’ll spare you the details, but suffice it to say that we call f with two continuations: one for “do something with x” and one for “go on with the program”. The function will branch to the first one for each value it generates, and then branch to the second one when there are no more values.

Another use for multiple continuations is for functions that throw exceptions. One continuation would be for normal value return and another continuation for a thrown exception. Or there can be one continuation for each type of exception thrown by the function. Let’s assume a continuation for each type of exception, so
try {x = f();}
catch Exception1 e: {handler1}
catch Exception2 e: {handler2}
go on with program
represents a call to function f with three continuations: the first one jumps to “go on with program”. The others jump to handler1 or handler2.

Passing an arbitrarily long list of continuations could get expensive. Actually, just passing two continuations may be measurably more expensive than one, so instead of passing all of the continuations, we can pass in just one and have the others findable from the one. For the above code it would look like this:
call f
handler1_label:
code for handler1
handler2_label:
code for handler2
handler2_label
handler1_label
normal_label:
code for go on with program
So a normal exit just jumps to ret, the return point. A throw to handle1 jumps to the address found at ret[-8] where 8 is the word size. A throw to handle2 jumps to the address found at ret[-16].

In the normal execution case, there is practically no overhead for the existence of additional continuations. In the case of an exception, there is an extra indirection. While this technique is probably optimal for the normal case, it does have the disadvantage that it can’t pop off multiple frames at once--it has to branch to and clean up each individual stack frame from the point of the throw to the point of the exception. But I’m pretty sure most exception-handling protocols do that anyway.

Rust supports a discriminated union data type that it calls an enum. Rather than Rust syntax, which is a bit obscure, I’ll use C-like syntax and call the discriminated union a dunion instead of an enum:
dunion Foo {int Bar; float Baz;};
The typecase in Rust is a special case of their pattern-matching statement:
match foo {
case Foo::Bar(i): code to execute when foo is an int,
case Foo::Baz(f): code to execute when foo is a float
}
The name in parenthesis is assigned the unboxed value. For example if foo=5, then we will jump to the first branch and execute the body with i set to 5.

Rust error handling makes use of a discriminated union that returns either the result of the function or an error of some sort. For example, if if the normal return value is an integer, but it can throw a string exception, we might use this:
dunion Result {
int Value,
string Err,
}
The function is then:
Result f(...) {...}
and you call it in a match statement
match f(...) {
Result::Value i: code to execute on success
Result::Err s: code to execute on error
}
There is a similarity between this and the multiple-continuation example.

So why wasn’t this obvious? The reason is that with exceptions, you can have a single handler for multiple function calls:
try {
i = f1(x);
j = f2(y);
k = f3(z);
}
catch (Exception e) {...}
All of the functions could be called with the same handler. This isn’t usually good style--it’s better to keep your exception handling with the function that can throw the exception--but it’s occasionally useful and most exception mechanisms allow something like this.

Unfortunately this doesn’t map into the discriminated-union paradigm. The innovation of Midori is to break up the monolithic exception handler into two parts: a function-return mechanism that is equivalent to returning a discriminated union, and a strictly local control structure similar to a switch statement. With Midori-style exception handling we would write the above something like this:
try {
i = try f1(x);
j = f2(y);
k = try f3(z);
}
catch (Exception e) {handle exception}
This tells us that f1 and f3 can throw exceptions. The trys that come right before the functions will pass on an exception if the function raises one. try f1(...) is an abbreviation for code something like this:
match f1(...) {
Result::Value(temp_i): i = temp_i; break;
Result::Err(e): {goto handle exception}
}
The try with the brace after it acts similar to a switch statement where the catches replace cases.

With this change, we can replace exceptions with discriminated unions and still optimize as if it were multiple continuations. Of course you probably need for the optimizer to distinguish between discriminated unions that represent exceptions and those that don’t because the optimizations rely on the fact that one branch is a lot more likely than the others.

Still, with this change, Unobtainabol can have all the features and optimizations of a language with a complex function model, but only support non-polymorphic, single-argument functions that return a single value to a single continuation.