# Substructural Types

Substructural type system: arguments standard type abstraction mechanisms with the ability to control the number and order of uses of a data structure or operation.

## Structural Properties

1. Exchange:
2. Weakening:
3. Contraction:

As a result, a substructural type system is any type system designed in which one or more of the structural properties do not hold.

• Linear type systems ensure that every variable is used exactly once by allowing exchange but not weakening or contraction.
• Affine type systems ensure that every variable is used at most once by allowing exchange and weakening, but not contraction.
• Relevant type systems ensure that every variable is used at least once by allowing exchange and contraction, but not weakening.
• Ordered type systems ensure that every variable is used exactly once and in the order in which it is introduced by not allowing any of the structural properties.

## Linear TS

Problem: Allocation & Deallocation

Two invariants:

1. Linear variables are used exactly once along every control-flow path.
2. Unrestricted data structures may not contain linear data structures. More generally, data structures with less restrictive type may not contain data structures with more restrictive type.

We maintain the first invariant through context management. We split the linear variables between the different subterms to ensure each variable is used exactly once.

To check the second invariant, we define the predicate $q(T)$ (and its extension to contexts $q(\Gamma)$) to express the types $T$ that can appear in a $q$-qualified data structure.

• $q(T)$ if and only if $T=q'(P)$ and $q \sqsubseteq q'$
• $q(\Gamma)$ if and only if $\forall x:T \in \Gamma, q(T)$

NOTE: these rules are constructed anticipating a call-by-value operational semantics.

To avoid the non-deterministic problem introduces by splitting, we can have a algorithmic version of typing rules:

There, we introduced a "context difference" operator ($\div$)

Here, some properties are listed. $\mathcal L(\Gamma)$ and $\mathcal U(\Gamma)$ is correspondingly about linear and unrestricted assumptions in $\Gamma$ perspective.

Lemmas:

• Algorithmic monotonicity: If $\Gamma \vdash t :T; \Gamma'$, then $\mathcal U(\Gamma') = \mathcal U(\Gamma)$ and $\mathcal L(\Gamma') \subseteq \mathcal L(\Gamma)$
• Algorithmic exchange: If $\Gamma_1, x_1 : T_1, x_2 : T_2, \Gamma_2 \vdash t: T; \Gamma_3$ then $\Gamma_1, x_2 : T_2, x_1 : T_1, \Gamma_2 \vdash t: T; \Gamma_3'$, in which $\Gamma_3$ and $\Gamma_3'$ is the same up to transposition of the bindinds for $x_1$ and $x_2$.
• Algorithmic weakening: If $\Gamma \vdash t : T; \Gamma'$ then $\Gamma, x:T' \vdash t: T; \Gamma', x: T'$
• Algorithmic strengthening: If $\Gamma, x : lin \, P \vdash t: T; \Gamma', x : lin \, P$ then $\Gamma \vdash t : T; \Gamma'$.

### Operational Semantics

To make the memory management properties clear, we will evaluate terms in an abstract machine with an explicit store.

The operation of abstract machine is defined using a context-based, small-step semantics.

Context, represented with $E$, are terms with a single hole. Contexts define the order of evaluation of terms -- the places in a term where a computation can occur. And in the above definition, the order is left-to-right since $E \, t$ means $E$ in the function position so the function term can be firstly reduced. While $x \, E$ indicates that if the term in argument position $E$ is to be reduced, then the function position must have a pointer.

Let's see the operational semantics:

Thus, we can prove the standard progress and preservation theorems.

## Extensions & Variations

Practical extensions to the simple linear lambda calculus

### Sum & Recursive Types

Standard introduction elimination forms.

Injections: $q \, inl_P \, t$, $q \, inr_P \, t$, $P \triangleq T_1 + T_2$

Recursive: $roll_P \, t$, $P$ is the recursive pretype the expression will assume.

Since the free variables in a recursive function closure will be used on each recursive invocation of the function, we cannot allow the closure to contain linear variables.

Example: linear list of Ts:

type T llist = rec a. lin (unit + lin (T * lin a))


Fully unrestricted list:

type T list = rec a. unit + T * a


Let's define the map for linear list:

fun map(f: T1 -> T2, xs: T1 llist): T2 llist =
case unroll xs of
inl _  => nil()
inr xs =>
split xs as hd, tl in
cons(f hd, map lin <f, tl>)


Overhead: the f hd is accumulated on the stack in recursive calling.

If the last operation in a function is itself a function call, then this tail-call can be eliminated, i.e., the current stack frame can be deallocated before calling the new function.

The new implementation:

fun map(f: T1 -> T2, input: T1 llist): T2 llist =
reverse(mapRev(f, input, nil()), nil())

and mapRev(f: T1 -> T2,
input: T1 llist,
output: T2 llist): T2 llist =
case unroll input of
inl _  => output
inr xs =>
split xs as hd, tl in
mapRev (f, tl, cons(f hd, output)))

and reverse(input: T2 llist, output: T2 llist)
case unroll input of
inl _  => output
inr xs =>
split xs as hd, tl in
reverse(tl, cons(hd, output)))


### Polymorphism

• Poly over shape of data
• Poly over memory management of data (linear vs. unrestricted)
type (p1, p2, a) list =
rec a. p1 (unit + p1 (p2 a * (p1, p2, a) list))


Observe: p2 is about a, while p1 is about the structure. Here are two nested substructure + and *, and they both use p1. And here the way of qualifying is really explicit.

Now, the map is written like this:

val map =
Λa,b. Λ pa, pb.
fun aux (f: (pa a -> pb b).
xs: (lin, pa, a) list)
) : (lin, pb, b) list =
case unroll xs of
inl _  => nil [b, pb] ()
inr xs => split xs as hd, tl in
cons [b, pb] (pa < f hd
, map(lin <f, tl>) >
)


• Ensure that we propagate contexts containing abstract qualifiers safely through the other typing rules in the system. We can't risk duplicating unknown qualifiers which might turn out to be linear.
• Conservatively extend the relation on type qualifiers. $lin \sqsubseteq p \sqsubseteq un$.
• Type context $\Delta$: keep track of free type variables.

### Arrays

Problem: It is difficult to reflect the linearity of individual element in an array's type.

Possible solutions

• like tuple, which extracts all elements at the same time. BAD SOLUTION, since length of an array should be unknown at compiled time.
• Add a built-in iterator. BAD SOLUTION, since the merits of array, such as random-access, are sacrificed.

Solution: swap(a[i], t). By preserving the number of pointers to the array and the number to each of its elements, there will be no changed to the array that needs to be reflected in the type system. swap replaced the $i^{th}$ element $t'$ of the array $a$ with $t$, and returns a pair containing the new array and $t'$. (Note: that make me think about reference counting).

We will have special syntax for array allocation, query length and reallocation.

Exercise 1.3.4: The typing rule for array allocation (T-Array) contains the standard containment check to ensure that unrestricted arrays cannot contain linear objects. What kinds of errors can occur if this check is omitted?

Note that "T-Swap" rule. Only $q_1$ is specified. So, by swapping, the linear object will be swapped out, but becomes unrestricted.

SOLUTION: f an unrestricted array can contain a linear object, the linear object might never be used because the programmer might forget to use the entire array. Due to our swapping operational semantics for arrays, even though an unrestricted array (containing linear objects) can be used many times, the linear objects themselves can never be used more than once. In short, the supposedly linear objects would actually be affine.

### Reference Counting

New qualifier rc, explicit operation on count inc and dec. inc will turn one pointer into a linear pair of two copies, with pointed object's ref count incremented. dec will selectively the passed-in finalizer if the count is 1, and if not simply decrement the count.

Linear typing will ensure the number of references to an object is properly preserved.

## An Ordered Type System

• Linear TS: Heap allocation
• Ordered TS: Stack allocation

The idea: By banning the exchange property, we can guarantee that certain values, those values allocated on the stack, are used in a first-in/last-out order.

Sequencing construct: $let \, x = t_1 \, in \, t_2$. It gives programmer explicit control over the order of evaluation of terms. (NOTE: Normal form?).

New qualifier ord: marks data allocated on the stack. Functions can't be allocated on heap.

The first step in the development of the type system is to determine how assumptions will be used.

The second step in the development of the type system is to determine the containment rules for ordered data structures.

The third step in the development of the type system is to call the PhD student to type in LaTeX.

-- Benjamin C. Pierce

Example: function taking a boolean and a pair allocated sequentially at the top of the stack. If the boolean is true, leave the pair the same; if false, it swaps them.

fun (x : ord (ord (int * int) * bool).
split x as p, b in
if b
then p
else
split p as i1, i2 in
ord <i2, i2>


Exercise 1.4.1: Write a program that demonstrates what can happen if the syntax of pair formation is changed to allow programmers to write nested subexpressions (i.e., we allow the term ord <t1,t2> rather than the term ord <x,y>).

The SOLUTION by Benjamin is a bit weird. He said it would be bad if typing rules are not changed.

let x = ord <true,true> in
let y = ord <ord <3,2>,x> in
split y as z1,z2 in
split z2 as b1,b2 in
if b1 then... (* using an int as if it was a bool *)


Stack: ..., \, <<3, 2>, px>

z2 is ord <3, 2>, it is not typed. So we can add another type rule, similar to

Comment: Is there anything special about what can we store on stack? Or should there be?

## Further Applications

### Controlling Temporal Resources

Problems:

1. Iterators can increase the size of their arguments, cause an exponential blow-up
2. HOF make it even easier

Solution: Non-size-increasing functions + preventing the construction of HOF.

1. Demand all user-defined objects to have affine types, which can be used zero or one time but no more.
2. Altering the cons constructor so it can only be applied when it has access to a special resource with type $R$. operator cons : (R, T, T list) -> T list

The resource we acquire from destructing the list during iteration can be used to rebuild the list later.

val append : T list -> T list -> T list =
iter of
stop                => fun l : T list -> l
hd with rest and r  => fun l : T list . cons (r, hd, rest l)


If with extra credits

val double : (T * R) list -> T list =
iter of
stop                => nil
hd with rest and r1 =>
split hd as x, r2 in cons(r1, hd, cons(r2, hd, rest))


### Compiler Optimization

Use usage information:

• Affine (use <= 1)
• Relevant (use >= 1)

GHC optimization:

#### Floating in bindings

let x = e in (λy....x...) becomes λy.let x = e in (...x...). So we can avoid computing e and will not compute it more than once.

#### Inlining Expressions

• Used once: inline the expression
• No use: avoid computing at all

#### Thunk update avoidance

If the computed value will be used at most once, than the update can be avoided.

#### Eagerness

If some Haskell expression is used at least once, we can strictly evaluate it can avoid creating a thunk altogether.