theory "ML"

imports Setup

begin

section {* ML system interfaces \label{sec:ml} *}

text {*

  Since the code generator framework not only aims to provide

  a nice Isar interface but also to form a base for

  code-generation-based applications, here a short

  description of the most important ML interfaces.

*}

subsection {* Executable theory content: @{text Code} *}

text {*

  This Pure module implements the core notions of

  executable content of a theory.

*}

subsubsection {* Managing executable content *}

text %mlref {*

  \begin{mldecls}

  @{index_ML Code.add_eqn: "thm -> theory -> theory"} \\

  @{index_ML Code.del_eqn: "thm -> theory -> theory"} \\

  @{index_ML Code_Preproc.map_pre: "(simpset -> simpset) -> theory -> theory"} \\

  @{index_ML Code_Preproc.map_post: "(simpset -> simpset) -> theory -> theory"} \\

  @{index_ML Code_Preproc.add_functrans: "string * (theory -> (thm * bool) list -> (thm * bool) list option)

    -> theory -> theory"} \\

  @{index_ML Code_Preproc.del_functrans: "string -> theory -> theory"} \\

  @{index_ML Code.add_datatype: "(string * typ) list -> theory -> theory"} \\

  @{index_ML Code.get_type: "theory -> string

    -> (string * sort) list * (string * typ list) list"} \\

  @{index_ML Code.get_type_of_constr_or_abstr: "theory -> string -> (string * bool) option"}

  \end{mldecls}

  \begin{description}

  \item @{ML Code.add_eqn}~@{text "thm"}~@{text "thy"} adds function

     theorem @{text "thm"} to executable content.

  \item @{ML Code.del_eqn}~@{text "thm"}~@{text "thy"} removes function

     theorem @{text "thm"} from executable content, if present.

  \item @{ML Code_Preproc.map_pre}~@{text "f"}~@{text "thy"} changes

     the preprocessor simpset.

  \item @{ML Code_Preproc.add_functrans}~@{text "(name, f)"}~@{text "thy"} adds

     function transformer @{text f} (named @{text name}) to executable content;

     @{text f} is a transformer of the code equations belonging

     to a certain function definition, depending on the

     current theory context.  Returning @{text NONE} indicates that no

     transformation took place;  otherwise, the whole process will be iterated

     with the new code equations.

  \item @{ML Code_Preproc.del_functrans}~@{text "name"}~@{text "thy"} removes

     function transformer named @{text name} from executable content.

  \item @{ML Code.add_datatype}~@{text cs}~@{text thy} adds

     a datatype to executable content, with generation

     set @{text cs}.

  \item @{ML Code.get_type_of_constr_or_abstr}~@{text "thy"}~@{text "const"}

     returns type constructor corresponding to

     constructor @{text const}; returns @{text NONE}

     if @{text const} is no constructor.

  \end{description}

*}

subsection {* Auxiliary *}

text %mlref {*

  \begin{mldecls}

  @{index_ML Code.read_const: "theory -> string -> string"}

  \end{mldecls}

  \begin{description}

  \item @{ML Code.read_const}~@{text thy}~@{text s}

     reads a constant as a concrete term expression @{text s}.

  \end{description}

*}

subsection {* Implementing code generator applications *}

text {*

  Implementing code generator applications on top

  of the framework set out so far usually not only

  involves using those primitive interfaces

  but also storing code-dependent data and various

  other things.

*}

subsubsection {* Data depending on the theory's executable content *}

text {*

  Due to incrementality of code generation, changes in the

  theory's executable content have to be propagated in a

  certain fashion.  Additionally, such changes may occur

  not only during theory extension but also during theory

  merge, which is a little bit nasty from an implementation

  point of view.  The framework provides a solution

  to this technical challenge by providing a functorial

  data slot @{ML_functor Code_Data}; on instantiation

  of this functor, the following types and operations

  are required:

  \medskip

  \begin{tabular}{l}

  @{text "type T"} \\

  @{text "val empty: T"} \\

  @{text "val purge: theory \<rightarrow> string list option \<rightarrow> T \<rightarrow> T"}

  \end{tabular}

  \begin{description}

  \item @{text T} the type of data to store.

  \item @{text empty} initial (empty) data.

  \item @{text purge}~@{text thy}~@{text consts} propagates changes in executable content;

    @{text consts} indicates the kind

    of change: @{ML NONE} stands for a fundamental change

    which invalidates any existing code, @{text "SOME consts"}

    hints that executable content for constants @{text consts}

    has changed.

  \end{description}

  \noindent An instance of @{ML_functor Code_Data} provides the following

  interface:

  \medskip

  \begin{tabular}{l}

  @{text "get: theory \<rightarrow> T"} \\

  @{text "change: theory \<rightarrow> (T \<rightarrow> T) \<rightarrow> T"} \\

  @{text "change_yield: theory \<rightarrow> (T \<rightarrow> 'a * T) \<rightarrow> 'a * T"}

  \end{tabular}

  \begin{description}

  \item @{text get} retrieval of the current data.

  \item @{text change} update of current data (cached!)

    by giving a continuation.

  \item @{text change_yield} update with side result.

  \end{description}

*}

text {*

  \bigskip

  \emph{Happy proving, happy hacking!}

*}

end