theory Introduction

imports Setup

begin

section {* Introduction and Overview *}

text {*

  This tutorial introduces a generic code generator for the

  @{text Isabelle} system.

  Generic in the sense that the

  \qn{target language} for which code shall ultimately be

  generated is not fixed but may be an arbitrary state-of-the-art

  functional programming language (currently, the implementation

  supports @{text SML} \cite{SML}, @{text OCaml} \cite{OCaml} and @{text Haskell}

  \cite{haskell-revised-report}).

  Conceptually the code generator framework is part

  of Isabelle's @{theory Pure} meta logic framework; the logic

  @{theory HOL} which is an extension of @{theory Pure}

  already comes with a reasonable framework setup and thus provides

  a good working horse for raising code-generation-driven

  applications.  So, we assume some familiarity and experience

  with the ingredients of the @{theory HOL} distribution theories.

  (see also \cite{isa-tutorial}).

  The code generator aims to be usable with no further ado

  in most cases while allowing for detailed customisation.

  This manifests in the structure of this tutorial: after a short

  conceptual introduction with an example (\secref{sec:intro}),

  we discuss the generic customisation facilities (\secref{sec:program}).

  A further section (\secref{sec:adaption}) is dedicated to the matter of

  \qn{adaption} to specific target language environments.  After some

  further issues (\secref{sec:further}) we conclude with an overview

  of some ML programming interfaces (\secref{sec:ml}).

  \begin{warn}

    Ultimately, the code generator which this tutorial deals with

    is supposed to replace the existing code generator

    by Stefan Berghofer \cite{Berghofer-Nipkow:2002}.

    So, for the moment, there are two distinct code generators

    in Isabelle.  In case of ambiguity, we will refer to the framework

    described here as @{text "generic code generator"}, to the

    other as @{text "SML code generator"}.

    Also note that while the framework itself is

    object-logic independent, only @{theory HOL} provides a reasonable

    framework setup.    

  \end{warn}

*}

subsection {* Code generation via shallow embedding \label{sec:intro} *}

text {*

  The key concept for understanding @{text Isabelle}'s code generation is

  \emph{shallow embedding}, i.e.~logical entities like constants, types and

  classes are identified with corresponding concepts in the target language.

  Inside @{theory HOL}, the @{command datatype} and

  @{command definition}/@{command primrec}/@{command fun} declarations form

  the core of a functional programming language.  The default code generator setup

  allows to turn those into functional programs immediately.

  This means that \qt{naive} code generation can proceed without further ado.

  For example, here a simple \qt{implementation} of amortised queues:

*}

datatype %quote 'a queue = AQueue "'a list" "'a list"

definition %quote empty :: "'a queue" where

  "empty = AQueue [] []"

primrec %quote enqueue :: "'a \<Rightarrow> 'a queue \<Rightarrow> 'a queue" where

  "enqueue x (AQueue xs ys) = AQueue (x # xs) ys"

fun %quote dequeue :: "'a queue \<Rightarrow> 'a option \<times> 'a queue" where

    "dequeue (AQueue [] []) = (None, AQueue [] [])"

  | "dequeue (AQueue xs (y # ys)) = (Some y, AQueue xs ys)"

  | "dequeue (AQueue xs []) =

      (case rev xs of y # ys \<Rightarrow> (Some y, AQueue [] ys))"

text {* \noindent Then we can generate code e.g.~for @{text SML} as follows: *}

export_code %quote empty dequeue enqueue in SML

  module_name Example file "examples/example.ML"

text {* \noindent resulting in the following code: *}

text %quote {*@{code_stmts empty enqueue dequeue (SML)}*}

text {*

  \noindent The @{command export_code} command takes a space-separated list of

  constants for which code shall be generated;  anything else needed for those

  is added implicitly.  Then follows a target language identifier

  (@{text SML}, @{text OCaml} or @{text Haskell}) and a freely chosen module name.

  A file name denotes the destination to store the generated code.  Note that

  the semantics of the destination depends on the target language:  for

  @{text SML} and @{text OCaml} it denotes a \emph{file}, for @{text Haskell}

  it denotes a \emph{directory} where a file named as the module name

  (with extension @{text ".hs"}) is written:

*}

export_code %quote empty dequeue enqueue in Haskell

  module_name Example file "examples/"

text {*

  \noindent This is how the corresponding code in @{text Haskell} looks like:

*}

text %quote {*@{code_stmts empty enqueue dequeue (Haskell)}*}

text {*

  \noindent This demonstrates the basic usage of the @{command export_code} command;

  for more details see \secref{sec:further}.

*}

subsection {* Code generator architecture \label{sec:concept} *}

text {*

  What you have seen so far should be already enough in a lot of cases.  If you

  are content with this, you can quit reading here.  Anyway, in order to customise

  and adapt the code generator, it is inevitable to gain some understanding

  how it works.

  \begin{figure}[h]

    \includegraphics{architecture}

    \caption{Code generator architecture}

    \label{fig:arch}

  \end{figure}

  The code generator employs a notion of executability

  for three foundational executable ingredients known

  from functional programming:

  \emph{code equations}, \emph{datatypes}, and

  \emph{type classes}.  A code equation as a first approximation

  is a theorem of the form @{text "f t\<^isub>1 t\<^isub>2 \<dots> t\<^isub>n \<equiv> t"}

  (an equation headed by a constant @{text f} with arguments

  @{text "t\<^isub>1 t\<^isub>2 \<dots> t\<^isub>n"} and right hand side @{text t}).

  Code generation aims to turn code equations

  into a functional program.  This is achieved by three major

  components which operate sequentially, i.e. the result of one is

  the input

  of the next in the chain,  see figure \ref{fig:arch}:

  \begin{itemize}

    \item Starting point is a collection of raw code equations in a

      theory; due to proof irrelevance it is not relevant where they

      stem from but typically they are either descendant of specification

      tools or explicit proofs by the user.

    \item Before these raw code equations are continued

      with, they can be subjected to theorem transformations.  This

      \qn{preprocessor} is an interface which allows to apply the full

      expressiveness of ML-based theorem transformations to code

      generation.  The result of the preprocessing step is a

      structured collection of code equations.

    \item These code equations are \qn{translated} to a program in an

      abstract intermediate language.  Think of it as a kind

      of \qt{Mini-Haskell} with four \qn{statements}: @{text data}

      (for datatypes), @{text fun} (stemming from code equations),

      also @{text class} and @{text inst} (for type classes).

    \item Finally, the abstract program is \qn{serialised} into concrete

      source code of a target language.

      This step only produces concrete syntax but does not change the

      program in essence; all conceptual transformations occur in the

      translation step.

  \end{itemize}

  \noindent From these steps, only the two last are carried out outside the logic;  by

  keeping this layer as thin as possible, the amount of code to trust is

  kept to a minimum.

*}

end