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Compiling to VampIR through Geb

Before, we discussed the standard normalization approach to compiling Juvix programs to VampIR. An alternative backend is provided by the Geb project currently implemented in Lisp showcasing a categorical view of the needed translations.

The post will be devoted to discussing the core ideas of Geb, the practical steps to take in order to use the corresponding pipeline, as well as the current limitations the backend faces.

We assume basic familiarity with categorical concepts and VampIR.

Geb Pipeline

Geb is a developing language providing a useful interface for specifying categorical constructs and helping define compilers in terms of functors. The approach is inspired by a classical observation that instead of dealing with 'good' compilation procedures one can instead specify functors between appropriate categories representing the languages in question.

In the current state of development, Geb is a fairly simple category in which one can do "basic arithmetic." It is a category freely spanned by terminal/initial objects and finite (co)products with additional constructs for objects representing n-bit natural numbers and the distributivity axiom. The pipeline itself is a string of functors between categories:

\[ \text {Lambda} \to \text {Geb} \to \text {Seq} \mathbb N \]

which are all implemented in Lisp. The morphisms in the final category have a fairly direct interpretation as VampIR programs.

Here, Lambda stands for an extension of STLC with natural numbers types of different bit-width and some arithmetic, while Seq\(\mathbb N\) is a category of finite natural number sequences with morphism $$f: (x_1,\dots,x_n) \to (y_1,\dots,y_m) $$ a VampIR function which takes in \(n\) inputs with \(i\)-th input of bit-length \(x_i\) and spits out an \(m\)-tuple with \(i\)-th outputs of bit-length \(y_i\).

The Practice

In this section, we describe all the practical steps one needs to take in order to compile Juvix code to VampIR using the Geb binary.

From Juvix to Lambda

The first step to the compilation is getting Juvix code to compile to Lambda. The translation can be done inside of Juvix itself.

Let us create an example using one of the VampIR tests in the codebase. We create a file NotZero.juvix with body

module NotZero;

import Stdlib.Data.Bool open;
import Stdlib.Data.Nat open;
import Stdlib.Trait open;

-- We need to name the module exactly the same name as the file
intToBool (x : Nat) : Bool := not (x == 0);

-- We need to include a function named main
-- for the compilation to succeed
main : Nat -> Bool := intToBool;

and we want to compile it to code that is readable by the Lisp implementation of Geb. We run

juvix compile -t geb NotZero.juvix

which generates a NotZero.lisp file with the following code:

(defpackage #:NotZero
  (:shadowing-import-from :geb.lambda.spec #:pair #:right #:left)
  (:shadowing-import-from :geb.spec #:case)
  (:shadowing-import-from :geb.lambda.trans #:int)
  (:use #:common-lisp #:geb.lambda.spec #:geb)
  (:export :*entry*))

(in-package :NotZero)

(defparameter *entry*
        (index 0)
        (bit-choice 0))
      (bit-choice 1)
      (bit-choice 0))))

The file generated will be used by the Geb binary where *entry* is a parameter for a term to be compiled. The Lambda code produced is similar to STLC with certain variations. In the above code, we have:

  1. int a stand-in for a type of 24-bit numbers
  2. lamb is lambda abstraction
  3. case-on is sum type induction
  4. lamb-eq predicate for checking number equality
  5. index n is an n-th DeBruijn index
  6. bit-choice n is a constructor for number n

From Lambda to VampIR

After generating the file, one needs to go into the Geb repository, make a binary, creating geb.image file inside the build folder. Next, copy the path of the NotZero.lisp file created, e.g. /home/Juvix_Files/NotZero.lisp. Afterwards, go to the build folder in Geb and feed in:

./geb.image -i "/home/Juvix_Files/NotZero.lisp" -e "NotZero:*entry*" -l -p -o "NotZero.pir"

This will generate a NotZero.pir file in build with the inserted Lambda code compiled to VampIR through Geb.

One can remove -o "NotZero.pir" in order for the terminal to print the relevant code instead of putting it in a separate file.

In the build directory type

./geb.image -i "NotZero.lisp" -e "NotZero:*entry*" -l -p

Which returns

(def entry x1 x2 = {
   (1 - (iszero x1))

Note that the last variable in the function is unused. This will be the case for any compiled function and is due to a compilation side-effect when we go from Lambda to Geb.

For further instructions on the use of the binary, consult the Geb documentation.

Interactive Mode

If one is interested in using more features than the direct compilation to VampIR, one may use the Geb codebase interactively.

To do so, load the system as per the Geb documentation, then go a package preferably using all the relevant lambda code such as geb.lambda.trans by typing

CL-USER> (in-package geb.lambda.trans)

Here one can compile the relevant code to several targets. to-circuit takes a lambda term in an empty context and a name.

If we want to compile the identity function on natural numbers (lamb (list int) (index 0)) with name id we type

TRANS> (to-circuit (lamb (list int) (index 0)) :id)

Moreover, we can compile this not to a circuit but to Geb in a nil context (which we need to specify explicitly), e.g. by typing

TRANS> (to-cat nil (lamb (list int) (index 0)))

or to Seq\(\mathbb N\) in the nil context by

TRANS> (to-seqn (lamb (list int) (index 0)))

Hence instead of using the binary, we could instead go to the REPL, enter geb.lambda.trans and type in the body of the compiled term:

 (to-circuit (lamb
        (index 0)
        (bit-choice 0))
      (bit-choice 1)
      (bit-choice 0))) :entry)

and get the same output:

(def entry x1 x2 = {
   (1 - (iszero x1))

For further utility function discription, please consult the relevant documentation.

The Theory

This section is devoted to describing the core ideas of how the compilation occurs step-by-step.

From Juvix to Lambda

The transformation of Juvix to Lambda is a pretty easy one compared to the normalization approach partially due to the limitations we will discuss later.

The Juvix code gets transformed by

  1. Transforming type names and rescuing needed type signatures, e.g. Nat becomes primitive Int
  2. Unrolling recursion
  3. Lifting out let-statements
  4. Inlining type synonyms
  5. Presenting finite indictive datatypes using sum/product type structures

Afterwards, a direct translation to STLC follows.

From Lambda to Geb

The compilation of Lambda to Geb is actually a canonical one. Geb is secretly a category equivalent to FinSet and in particular, is a Cartesian Closed Category. By a classical result of Lambek-Scott, there is a unique compilation of STLC to Geb preserving STLC structure. All other datatypes are compiled primitively: e.g. plus in Lambda gets compiled to the primitive nat-plus.

From Geb to Seq\(\mathbb N\)

The transformation to the finite-sequence category representing VampIR operations is a bit tricky, yet the point is that each object in Geb has an intuitive notion of bit-width. The object of n-bit numbers has n-width, while bool, the coproduct of two terminal objects, has width 1. Products should just append the widths of its parts while coproducts should mark the left bit entries with 0 while right ones with 1 to differentiate between the two. Hence all the objects get encoded in this manner with morphisms following a similar fashion.

From Seq\(\mathbb N\) to VampIR

The compilation to VampIR is a straightforward one as the morphisms in Seq\(\mathbb N\) have been chosen in such a way that the category can be directly seen as a model of VampIR. For more detailed info on the compilation, consult the recent report on the pipeline by the Juvix team.


After describing the Geb backend, we also need to mention current limitations and challenges that the pipeline faces.

Limited Interpretation Power

Currently, not all Juvix programs can be interpreted in Geb. As we have mentioned, Geb is just a variation of FinSet, a fairly weak category. We currently do not have support for interpreting any polymorphic type or any infinite datatype.

Moreover, as there are no primitive function types in Geb, we also do not have a way to make internal function definitions via constraints in VampIR the way the normalizer compilation does.

However, these issues are to be addressed in a newer version of Geb, which will significantly enhance the expressive power of the language.

Heap Exhaustion for Functions of Natural Number

While Geb seems like a good candidate to do "categorical arithmetic" in and hence to compile to arithmetic circuits, the fact that the primitive constructions are very minimal serves as a reason for an exponential blow-up. An object representing n-bit natural number nat-width n is analyzed as a 2^n coproduct of terminal objects. So even if we have primitives for natural numbers, for curry and uncurry operations the functions get analyzed bit-by-bit, causing a blow-up.

If one compiles complicated functions, even working with 2-bit numbers can cause a heap exhaust.

However, that is a problem that occurs when one wants to use Lambda separately and compile arbitrary terms. All the Juvix code that we compile become \(\beta\)-reduced with no function application or lambdas outside of the top of the term remaining. This solves our problem for using Geb as a Juvix backend.

Heap Exhaustion for Recursive Functions

The way that we interpret recursive Juvix functions (similarly to the strategy of compiling directly to circuits) comes from unrolling recursion. Currently this creates big Lambda terms and due to current memory-managing constraints, can create a heap exhaust during compilation.