Wednesday, July 20, 2011

Termination of guarded recursion

I will now give a termination proof for the guarded recursion calculus I sketched in the last two posts. This post got delayed because I tried to oversimplify the proof, and that didn't work -- I actually had to go back and look at Nakano's original proof to figure out where I was going wrong. It turns out the proof is still quite simple, but there's one really devious subtlety in it.

First, we recall the types, syntax and values.

A ::= N | A → B | •A
e ::= z | s(e) | case(e, z → e₀, s(x) → e₁) | λx.e | e e
| •e | let •x = e in e | μx.e | x
v ::= z | s(e) | λx.e | •e

The typing rules are in an earlier post, and I give some big-step evaluation rules at the end of this post. Now, the question is, given · ⊢ e : A[n], can we show that e ↝ v?

To do this, we'll turn to our old friend, Mrs. step-indexed logical relation. This is a Kripke logical relation in which the Kripke worlds are given by the natural numbers and the accessibility relation is given by ≤. So, we define a family of predicates on closed values indexed by type, and by a Kripke world (i.e., a natural number n).

V(•A)ⁿ = {•e | ∀j<n. e ∈ E(A)ʲ}
V(A ⇒ B)ⁿ = {λx.e | ∀j≤n, v ∈ V(A)ʲ. [v/x]e ∈ E(B)ʲ}
V(N)ⁿ = {z} ∪ { s(e) | ∀j<n. e ∈ E(N)ʲ}

E(A)ʲ = {e | ∃v. e ↝ v and v ∈ V(A)ʲ}

This follows the usual pattern of logical relations, where we give a relation defining values mutually recursively with a relation defining well-behaved expressions (i.e., expressions are ok if they terminate and reduce to a value in the relation at that type).

Note that as we expect, j ≤ n implies V(A)ⁿ ⊆ V(A)ʲ. (The apparent antitonicity comes from the fact that if v is in the n-relation, it's also in the j relation.) One critical feature of this definition is that at n = 0, the condition on V(•A)⁰ always holds, because of the strict less-than in the definition.

The fun happens in the interpretation of contexts:

Ctxⁿ(· :: j) = ()
Ctxⁿ(Γ,x:A[i] :: j) = {(γ,[v/x]) | γ ∈ Ctxⁿ(Γ) and v ∈ Vⁿ(A)}
when i ≤ j
Ctxⁿ(Γ,x:A[j+l] :: j) = {(γ,[e/x]) | γ ∈ Ctxⁿ(Γ) and •ˡe ∈ Vⁿ(•ˡA)}
when l > 0

The context interpretation has a strange dual nature. At times less than or equal to j, it is a familiar context of values. But at future times, it is a context of expressions. This is because the evaluation rules substitute values for variables at the current time, and expressions for variables at future times. We abuse the bullet value relation in the third clause, to more closely follow Nakano's proof.

On the one hand, the fixed point operator is μx.e at any type A, and unfolding this fixed point has to substitute an expression (the mu-term itself) for the variable x. So the fixed point rule tells us that there is something necessarily lazy going on.

One the other hand, the focusing behavior of this connective is quite bizarre. It is not apparently positive or negative, since it distribute neither through all positives (eg, •(A + B) ≄ •A + •B) nor is it the case that it distributes through all negatives (eg, •(A → B) ≄ •A → •B). (See Noam Zeilberger, The Logical Basis of Evaluation Order and Pattern Matching.)

I take this to mean that •A should probably be decomposed further. I have no present idea of how to do it, though.

Anyway, this is enough to let you prove the fundamental property of logical relations:

Theorem (Fundamental Property). If Γ ⊢ e : A[j], then for all n and γ ∈ Ctxⁿ(Γ :: j), we have that γ(e) ∈ Eⁿ(A).
The proof of this is a straightforward induction on typing derivations, with one nested induction at the fixed point rule. I'll sketch that case of the proof here, assuming an empty context Γ just to reduce the notation:

Case: · ⊢ μx.e : A[j]
By inversion: x:A[j+1] ⊢ e : A[j]
By induction, for all n,e'. if •e' ∈ Vⁿ(•A) then [e'/x]e ∈ Eⁿ(A)
By nested induction on n, we'll show that [μx.e/x]e ∈ Eⁿ(A)
Subcase n = 0:
We know if •μx.e ∈ V⁰(•A) then [μx.e/x]e ∈ E⁰(A)
We know •μx.e ∈ V⁰(•A) is true, since · ⊢ μx.e : A[j]
Hence [μx.e/x]e ∈ E⁰(A)
Hence μx.e ∈ E⁰(A)
Subcase n = x+1:
We know if •μx.e ∈ Vˣ⁺¹(•A) then [μx.e/x]e ∈ Eˣ⁺¹(A)
By induction, we know [μx.e/x]e ∈ Eˣ(A)
Hence μx.e ∈ Eˣ(A)
Hence •μx.e ∈ Vˣ⁺¹(A)
So [μx.e/x]e ∈ Eˣ⁺¹(A)
Hence μx.e ∈ Eˣ⁺¹(A)

Once we have the fundamental property of logical relations, the normalization theorem follows immediately.

Corollary (Termination). If · ⊢ e : A[n], then ∃v. e ↝ v.

Evaluation rules:

e₁ ↝ (λx.e) e₂ ↝ v [v/x]e ↝ v'
—————— ————————————————————————————————
v ↝ v e₁ e₂ ↝ v'



e ↝ z e₀ ↝ v e ↝ s(e) [e/x]e₁ ↝ v
—————————————————————————————— —————————————————————————————————
case(e, z → e₀, s(x) → e₁) ↝ v case(s(e), z → e₀, s(x) → e₁) ↝ v



e₁ ↝ •e [e/x]e' ↝ v [μx.e/x]e ↝ v
————————————————————— ——————————————
let •x = e₁ in e₂ ↝ v μx.e ↝ v

Friday, July 15, 2011

Semantics of a weak delay modality

In my previous post, I sketched some typing rules for a guarded recursion calculus. Now I'll give its categorical semantics. So, suppose we have a Cartesian closed category with a delay functor and the functorial action and natural transformations:

•(f : A → B) : •A → •B
δ : A → •A
ι : •A × •B → •(A × B)
ι⁻¹ : •(A × B) → •A × •B
fix : (•A ⇒ A) → A

I intend that the next modality is a Cartesian functor (ie, distributes through products) and furthermore we have a delay operator δ. We also have a fixed point operator for the language. However, I don't assume that the delay distributes through the exponential. Now, we can follow the usual pattern of categorical logic, and interpret contexts and types as objects, and terms as morphisms. So types are interpreted as follows:

〚A → B〛 = 〚A〛 ⇒ 〚B〛
〚•A〛 = •〚A〛

Note that we haven't done anything with time indices yet. They will start to appear with the interpretation of contexts, which is relativized by time:

〚·〛ⁿ = 1
〚Γ, x:A[j]〛ⁿ = 〚Γ〛 × •⁽ʲ⁻ⁿ⁾〚A〛 if j > n
〚Γ, x:A[j]〛ⁿ = 〚Γ〛 × 〚A〛 if j ≤ n

The idea is that we interpret a context at time n, and so all the indices are interpreted relative to that. If the index j is bigger than n, then we delay the hypothesis, and otherwise we don't. Then we can interpret morphisms at time n as 〚Γ ⊢ e : A[n]〛 ∈ 〚Γ〛ⁿ → 〚A〛, which we give below:

〚Γ ⊢ e : A[n]〛 ∈ 〚Γ〛ⁿ → 〚A〛

〚Γ ⊢ μx.e : A[n]〛 =
fix ○ λ(〚Γ, x:A[n+1] ⊢ e : A[n]〛)

〚Γ ⊢ x : A[n]〛 =
π(x) (where x:A[j] ∈ Γ)

〚Γ ⊢ λx.e : A → B[n]〛 =
λ(〚Γ, x:A[n] ⊢ e : B[n]〛)

〚Γ ⊢ e e' : B[n]〛 =
eval ○ ⟨〚Γ ⊢ e : A → B[n]〛, 〚Γ ⊢ e' : B[n]〛⟩

〚Γ ⊢ •e : •A[n]〛 =
•〚Γ ⊢ e : A[n+1]〛 ○ Nextⁿ(Γ)

〚Γ ⊢ let •x = e in e' : •B[n]〛 =
〚Γ, x:A[n+1] ⊢ e' : B[n]〛 ○ ⟨id(Γ), 〚Γ ⊢ e : •A[n]〛⟩

Most of these rules are standard, with the exception of the introduction rule for delays. We interpret the body •e at time n+1, and then use the functorial action to get an element of type •A. This means we need to take a context at time n and produce a delayed one interpreted at time n+1.

Nextⁿ(Γ) ∈ 〚Γ〛ⁿ → •〚Γ〛ⁿ⁺¹
Nextⁿ(·) = δ₁
Nextⁿ(Γ, x:A[j]) = ι ○ (Nextⁿ(Γ) × δʲ⁻ⁿ) if j > n
Nextⁿ(Γ, x:A[j]) = ι ○ (Nextⁿ(Γ) × δ) if j ≤ n


I think this is a pretty slick way of interpreting hybrid annotations, and a trick worth remembering for other type constructors that don't necessarily play nicely with implications.

Next up, if I find a proof small enough to blog, is a cut-elimination/normalization proof for this calculus.

Wednesday, July 13, 2011

Guarded recursion with a weaker-than-Nakano guard modality

We have a new draft paper up, on controlling the memory usage of FRP. I have to say that I really enjoy this line of work: there's a very strong interplay between theory and practice. For example, this paper --- which is chock full of type theory and denotational semantics --- is strongly motivated by questions that arose from thinking about how to make our implementation efficient.

In this post, I'm going to start spinning out some consequences of one minor point of our current draft, which we do not draw much attention to. (It's not really the point of the paper, and isn't really enough to be a paper on its own -- which makes it perfect for research blogging.) Namely, we have a new delay modality, which substantially differs from the original Nakano proposed in his LICS 2000 paper.

Recall that the delay modality $\bullet A$ is a type constructor for guarded recursion. I'll start by giving a small type theory for guarded recursion below.

$$
\begin{array}{lcl}
A & ::= & A \to B \;\;|\;\; \bullet A \;\;|\;\; \mathbb{N} \\
\Gamma & ::= & \cdot \;\;|\;\; \Gamma, x:A[i]
\end{array}
$$

As can be seen above, the types are delay types, functions, and natural numbers, and contexts come indexed with time indices $i$. So is the typing judgement $\Gamma \vdash e : A[i]$.

$$
\begin{array}{ll}
\frac{x:A[i] \in \Gamma \qquad i \leq j}
{\Gamma \vdash x : A[j]}
&
\frac{\begin{array}{l}
\Gamma \vdash e:A[i] &
\Gamma,x:A[i] \vdash e' : B[i]
\end{array}}
{\Gamma \vdash \mathsf{let}\; x = e \;\mathsf{in}\; e' : B[i]}
\\ & \\
\frac{\Gamma, x:A[i] \vdash e : B[i]}
{\Gamma \vdash \lambda x.\;e : A \to B}
&
\frac{\begin{array}{l}
\Gamma \vdash e : A \to B [i] &
\Gamma \vdash e' : A[i]
\end{array}}
{\Gamma \vdash e \; e' : B[i]}
\\ & \\

\frac{\Gamma \vdash e : A[i+1]}
{\Gamma \vdash \bullet e : A[i]}
&
\frac{\begin{array}{ll}
\Gamma \vdash e : \bullet A[i] &
\Gamma, x:A[i+1] \vdash e' : B[i]
\end{array}}
{\Gamma \vdash \mathsf{let}\; \bullet x = e \;\mathsf{in}\; e' : B[i]}

\\ & \\

\frac{}
{\Gamma \vdash \mathsf{z} : \mathbb{N}[i]}

&
\frac{\Gamma \vdash e : \mathbb{N}[i+1]}
{\Gamma \vdash \mathsf{s}(e) : \mathbb{N}[i]}
\\ & \\

\frac{\Gamma, x:A[i+1] \vdash e : A[i]}
{\Gamma \vdash \mu x.\;e : A[i]}
&
\frac{\begin{array}{l}
\Gamma \vdash e : \mathbb{N}[i] \\
\Gamma \vdash e_1 : A[i] \\
\Gamma, x:\mathbb{N}[i+1] \vdash e_2 : A[i]
\end{array}}
{\Gamma \vdash \mathsf{case}(e, \mathsf{z} \to e_1, \mathsf{s}(x) \to e_2) : A[i]}
\end{array}
$$

The $i+1$ in the successor rule for natural numbers pairs with the rule for case statements, and this is what allows the fixed point rule to do its magic. Fixed points are only well-typed if the recursion variable occurs at a later time, and the case statement for numbers gives the variable one step later. So by typing we guarantee well-founded recursion!

The intro and elim rules for delays internalize increasing the time index, so that an intro for $\bullet A$ at time $i$ takes an expression of type $A$ at time $i+1$. We have a let-binding elimination form for delays, which differs from our earlier LICS paper, which had a direct-style elimination for delays. The new elimination is weaker than the old one, in that it cannot prove the isomorphism of $\bullet(A \to B) \simeq \bullet A \to \bullet B$.

This is really quite great, since that isomorphism was really hard to implement! (It was maybe half the complexity of the logical relation.) The only question is whether or not we can still give a categorical semantics to this language. In fact, we can, and I'll
describe it in my next post.

Friday, June 17, 2011

The constructive lift monad

The constructive lift monad is the coinductive type $T(A) \equiv \nu\alpha.\; A + \alpha.$

The intuition is that an element of this type either tells you a value of type $A$, or tells you to compute some more and try again. Nontermination is modeled by the element which never returns a value, and always keeps telling you to compute some more.

Our goal is to construct a general fixed-point combinator $\mu(f : TA \to TA) : 1 \to TA$, which takes an $f$ and then produces a computation corresponding to the fixed point of $f$. To fix notation, we'll take the constructors to be:
$$
\begin{array}{lcl}
\mathsf{roll} & : & A + TA \to TA \\
\mathsf{unroll} & : & TA \to A + TA
\end{array}
$$
Since this is a coinductive type, we also have an unfold satisfying the following equation:
$$
\mathsf{unfold}(f : X \to A + X) : X \to TA \equiv \mathsf{roll} \circ (\mathit{id} + (\mathsf{unfold}\; f)) \circ f
$$
First, we will explicitly construct the bottom element, corresponding to the computation that runs forever, with the following definition:
$$
\bot : 1 \to TA \equiv \mathsf{unfold}(\mathsf{inr})
$$
This definition just keeps telling us to wait, over and over again. Now, we can define the fixed point operator:
$$
\begin{array}{l}
\mu(f : TA \to TA) : 1 \to TA \\
\mu(f) \equiv (\mathsf{unfold} (\mathsf{unroll} \circ f)) \circ \bot
\end{array}
$$
What this does is to pass bottom to $f$. If $f$ returns a value, then we're done. Otherwise, $f$ returns us another thunk, which we can pass back to $f$ again, and repeat.

Of course, this is exactly the intuition behind fixed points in domain theory. Lifting in domains is usually defined directly, and I don't know who invented the idea of defining it as a coinductive type. I do recall a 1999 paper by Martin Escardo which uses (a slight variant of) it, and he refers to it as a well-known construction in metric spaces, so probably the papers from the Dutch school of semantics are a good place to start the search. This construction has seen a renewed burst of interest in the last few years, since it offers a relatively convenient way to represent nonterminating functions in dependent type theory. It's also closely related to step-indexed models in operational semantics, since, given a terminating element of the lift monad, you can compute how many steps it takes to return a value.

It's stuff like this that makes me say I can't tell the difference between operational and denotational semantics any more.

Tuesday, May 31, 2011

ICFP 2011

I was just notified that the paper I wrote with Nick Benton, A Semantic Model of Graphical User Interfaces, was accepted to ICFP 2011!

This is my first paper at ICFP, so I'm quite excited. Also, I accidentally misread the submission guidelines and wrote a 10-page draft, instead of a 12-page draft. As a result, I have two whole pages to add examples, intuition, and motivation. This feels positively decadent. :)

Thursday, May 5, 2011

GUI Programming and MVC

This post was inspired by this post on William Cook's blog.

I've been thinking a lot about GUIs lately, and I think model-view-controller is a very good idea -- but one that existing languages do a poor job supporting. Smalltalk deserves credit for making these thoughts possible for Trygve Reenskaug to express, but it's been over 30 years; we really ought to have made it easy to express by now.

Now, the point of a controller is to take low-level events and turn them into high-level events. Note that "low-level" and "high-level" ought to be relative notions -- the toolkit might give us mouse and keyboard events, from which we write button widgets to turn them into click events, from which we write calculator button turns into application events (numbers and operations).

However, in reality new widgets tend to be extremely painful to write, since it usually involves mucking around with the guts of the event loop, and so people tend to stick with what the toolkit gives them. This way, they don't need to understand the deep implementation invariants of the GUI toolkit. As a result, they don't really build GUI abstractions, and so they don't need separate controllers. (A good example would be to think about how hard it would be to write a new text entry widget in your toolkit of choice.)

So I see the move from MVC to MV as a symptom of a problem. The C is there to let you build abstractions, but since it's really hard we don't. As a result new frameworks drop the C, and just grow by accretion -- the toolkit designers add some new widgets with each release, and everybody just uses them. I find this a little bit sad, honestly.

As you might guess from this blog, I find this quite an interesting problem. I think functional reactive programming offers a relatively convenient model (stream transducers) to write event processors with, but we have the problem that FRP systems tend towards the unusably inefficient. In some sense this is the opposite problem of MVC, which can be rather efficient, but can require very involved reasoning to get correct.

This basically leads to my current research program: can we compile functional reactive programming into model-view-controller code? Then you can combine the ease-of-use of FRP, with the relative efficiency[*] of the MVC design.

IMO, the system in our LICS paper is a good first step towards fixing this problem, but only a first step. It's quite efficient for many programs, but it's a bit too expressive: it's possible to write programs which leak rather a lot of memory without realizing it. Basically, the problem is that promoting streams across time requires buffering them, and it's possible to accidentally write programs which repeatedly buffer a stream at each tick, leading to unbounded memory use.

[*] As usual, "efficiency" is relative to the program architecture. MVC is a retained mode design, and if the UI is constantly changing a lot, you lose. For things like games, immediate mode GUIs seem like a better design to me. In the longer term, I'd like to try synthesizing these two designs, For example, even a live-document app like a spreadsheet or web page (the ideal cases for MVC) may want to embed video or 3D animations (which work better in immediate mode).

Tuesday, April 19, 2011

Models of Linear Logic: Length Spaces

One of the things I've wondered about is what the folk model of linear logic is. Basically, I've noticed that people use linear logic an awful lot, and they have very clear intuitions about the operational behavior they expect, but they are often not clear about what equational properties they expect to hold are.

Since asking that question, I've learned about a few models of linear logic, which are probably worth blogging about. Morally, I ought to start by showing how linear algebra is a model of linear logic, but I'll set that aside for now, and start with some examples that will be more familiar to computer scientists.

First, recall that the general model of simply-typed functional languages are cartesian closed categories, and that the naive model of functional programming is "sets and functions". That is, types are interpreted as sets, and terms are functions between sets, with the cartesian closed structure corresponding to function spaces on sets.

Next, recall that the general model of intuitionistic linear logic are symmetric monoidal closed categories. So the question to ask is, what are some natural symmetric monoidal closed category given by sets-with-structure and functions preserving that structure? There are actually many answers to this question, but one I like a lot I learned from a 1999 LICS paper by Martin Hofmann, and is called "length spaces". It is very simple, and I find it quite beautiful.

To understand the construction, let's first set out the problem: how can we write programs that respect some resource bounds? For example, we may wish to bound the memory usage of a language, so that every definable program is size-bounded: the output of a computation is no bigger than its input.

Now, let's proceed in a really simple-minded way. A length space is a pair $(A, \sigma_A)$, where $A$ is a set and $\sigma_A : A \to \N$ is a size function. Think of $A$ as the set of values in a type, and $\sigma_A$ as a function which tells you how big each element is. These pairs will be the objects of our category of length spaces.

Now, define a morphism $f : (A, \sigma) \to (B, \tau)$ to be a function on sets $A \to B$, with the property that $\forall a \in A.\; \tau(f\;a) \leq \sigma(a)$. That is, the size of the output is always less then or equal to the size of the input.

Here's the punchline: length spaces are a symmetric monoidal closed category.

Given two length spaces $(A, \sigma)$ and $(B, \tau)$, they have a monoidal product $(A, \sigma) \otimes (B, \tau) = (A \times B, \fun{(a,b)}{\sigma(a) + \tau(b)})$. The unit is $I = (\{*\}, \fun{*}{0})$, and the associators and unitors are inherited from the cartesian product. The intuition here is that strict pair, requires resources equal to the sum of the resources of the components.

The function space construction is pretty clever, too. The exponential $(B, \sigma) \multimap (C, \tau) = (B \to C, \fun{f}{\min\comprehend{a \in \N}{\forall b \in B.\;\tau(f\;b) \leq \sigma(b) + a}})$. The intuition here is that if you have a morphism $A \otimes B \to C$, when we curry it we want a morphism $A \to (B \multimap C)$. However, the returned function may have free variables in $A$, so the size of the value it can return should be bounded by the size of its input $b \in B$ plus the size of its environment.

We also have a cartesian product, corresponding to lazy pairs. We define $(A, \sigma) \times (B, \tau) = (A \times B, \fun{(a,b)}{\max(\sigma(a), \tau(b)})$. The intuition is that with a lazy pair, where we have the (linear) choice of either the first or second component, we can bound the space usage by maximum of the two alternatives.

Note how pretty this model is -- you can think about functions in a totally naive way, and that's fine. We just carry around a little extra data to model size information, and now we have a very pretty extensional model of an intensional property -- space usage. This also illustrates the general principle that increasing the observations you can do creates finer type distinctions.