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aarne
2004-09-20 08:51:48 +00:00
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12
examples/gfcc/Imper.gf
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@@ -14,7 +14,7 @@ abstract Imper = {
fun
Empty : Program ;
Funct : (AS : ListTyp) -> (V : Typ) ->
(Body AS) -> (Fun AS V -> Program) -> Program ;
Body AS -> (Fun AS V -> Program) -> Program ;
BodyNil : Stm -> Body NilTyp ;
BodyCons : (A : Typ) -> (AS : ListTyp) ->
@@ -31,16 +31,12 @@ abstract Imper = {
EVar : (A : Typ) -> Var A -> Exp A ;
EInt : Int -> Exp TInt ;
EFloat : Int -> Int -> Exp TFloat ;
EAddI : Exp TInt -> Exp TInt -> Exp TInt ;
EAddF : Exp TFloat -> Exp TFloat -> Exp TFloat ;
ESubI : Exp TInt -> Exp TInt -> Exp TInt ;
ESubF : Exp TFloat -> Exp TFloat -> Exp TFloat ;
EMulI : Exp TInt -> Exp TInt -> Exp TInt ;
EMulF : Exp TFloat -> Exp TFloat -> Exp TFloat ;
ELtI : Exp TInt -> Exp TInt -> Exp TInt ;
ELtF : Exp TFloat -> Exp TFloat -> Exp TInt ;
EApp : (AS : ListTyp) -> (V : Typ) -> Fun AS V -> ListExp AS -> Exp V ;
EAddI, EMulI, ESubI : Exp TInt -> Exp TInt -> Exp TInt ;
EAddF, EMulF, ESubF : Exp TFloat -> Exp TFloat -> Exp TFloat ;
TInt : Typ ;
TFloat : Typ ;

265
examples/gfcc/complin.tex
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@@ -46,27 +46,31 @@ The challenge was to use dependent types to write a compiler from
C to bytecode. This paper does not meet the challenge quite literally,
since our compiler is for a different subset of C than Augustsson's
specification, and since the bytecode that we generate is JVM instead
of his format.
of his format. But it definitely makes use of dependent types.
Augustsson's challenge did not specify \textit{how} dependent
types are to be used, and the first of the two points we make in this
paper is how we interpreted the use of dependent types:
we use dependent types in combination with higher-order abstract syntax (HOAS)
paper (and its title) reflects our interpretation:
we use dependent types, in combination with higher-order abstract syntax (HOAS),
to define the grammar of the source language (here, the fragment of C).
This grammar constitutes a single, declarative source, from which
The grammar constitutes the single, declarative source from which
the compiler front end is derived, comprising both parser and type
checker.
The second point, code generation by linearization, means that the
back end is likewise implemented by a grammar of the target
language (in this case, a fragment of JVM). This grammar is the
declarative source from which the compiler back end is derived.
The complete code of the compiler is 300 lines. It is found in
the appendices of this paper.
\section{The Grammatical Framework}
The second point mentioned in the title,
code generation as linearization, was suggested by
the tool we have used for implementing the grammar:
the \empha{Grammatical Framework} \cite{gf-jfp}. GF
The tool we have used for implementing the compiler is
GF, the \empha{Grammatical Framework} \cite{gf-jfp}. GF
is similar to a Logical Framework (LF) extended with
a notation for defining concrete syntax. GF was originally
designed to help building multilingual
@@ -74,25 +78,29 @@ translation systems for natural languages and also
between formal and natural languages. The translation model
implemented by GF is very simple:
\begin{verbatim}
linearization
--------------->
Abstract Syntax Language_i
<---------------
parsing
parsing linearization
------------> ------------>
Language_1 Abstract Syntax Language_2
<------------ <------------
linearization parsing
\end{verbatim}
An abstract syntax is similar to a \empha{theory} ,or a
An abstract syntax is similar to a \empha{theory}, or a
\empha{signature} in a logical framework. A
concrete syntax defines, in a declarative way,
a translation of abstract syntax trees (well-formed terms)
into concrete language structures, and this definition can
be used for both linearization and parsing.
into concrete language structures, and from this definition, one can
derive both both linearization and parsing.
The number of languages related to one abstract syntax in
this way is unlimited. If just one language is involved, GF
works much the same way as any grammar formalism. The largest
application known to us links 88 languages and translates
numeral expressions between them.
this way is of course not limited to two. Sometimes just just one
language is involved;
GF then works much the same way as any grammar formalism or parser
generator.
The largest application known to us has 88 languages and translates
numeral expressions from 1 to 999,999 between them \cite{gf-homepage}.
From the GF point of view, the goal of the experiment
From the GF point of view, the goal of the compiler experiment
is to investigate if GF is capable of implementing
compilers using the ideas of single-source language definition
and code generation as linearization. The working hypothesis
@@ -123,7 +131,7 @@ JVM code back into C code.
The language has an interactive editor that also supports incremental
compilation.
\enqu
The problems that we have encountered and their causes will be explained in
The problems that we encountered and their causes will be explained in
the relevant sections of this report. To summarize,
\bequ
The scoping conditions resulting from HOAS are slightly different
@@ -131,29 +139,43 @@ from the standard ones of C.
Our JVM syntax is forced to be slightly different from original.
Using HOAS to encode bindings of functions is somewhat cumbersome.
Using HOAS to encode all bindings is sometimes cumbersome.
The C parser derived from the GF grammar does not recognize all
valid programs.
\enqu
The first two shortcomings seem to be inevitable with the technique
we use. The real JVM syntax, however, is easy to obtain by simple
string processing from our one. The latter two shortcomings
we use. The best we can do with the JVM syntax is to use simple
postprocessing, on string level, to obtain valid JVM. The latter
two shortcomings have to do with the current implementation of GF
rather than its core. They
suggest that GF should be fine-tuned to give better support
to compiler construction, which, after all, is not an intended
use of GF as it is now.
\section{The abstract syntax}
An abstract syntax in GF consists of \texttt{cat} judgements
declaring basic types, and \texttt{fun} judgements declaring
functions. Syntax trees are well-formed terms of basic
An \empha{abstract syntax} in GF consists of \texttt{cat} judgements
\[
\mbox{\texttt{cat}} \; C \; \Gamma
\]
declaring basic types (depending on a context $\Gamma$),
and \texttt{fun} judgements
\[
\mbox{\texttt{fun}} \; f \; \mbox{\texttt{:}} \; A
\]
declaring functions $f$ of any type $A$, which can be a basic type or
a function type.
\empha{Syntax trees} are well-formed terms of basic
types. As for notation, each judgement form is recognized by
its keyword, and the same keyword governs all judgements
its keyword (\texttt{cat}, \texttt{fun}, etc),
and the same keyword governs all judgements
until the next keyword is encountered.
\subsection{Statements}
Statements in C may involve variables, expressions, and
@@ -168,8 +190,9 @@ that are needed to construct statements
Var Typ ;
\end{verbatim}
The type \texttt{Typ} is the type of C's datatypes.
Expressions (\texttt{Exp}) is a dependent type: we
will give rules to construct well-typed expressions of
Expressions (\texttt{Exp}) is a dependent type, since it has a
nonempty context. The rules for \texttt{Exp}
will thus be rules to construct well-typed expressions of
a given type. \texttt{Var}\ is the type of variables,
of a given type, that get bound in C's variable
declarations.
@@ -185,7 +208,7 @@ rules define their abstract syntax:
Dependent types are used in \texttt{Assign} to
control that a variable is always assigned a value of proper
type. The \texttt{Decl}\ function captures the rule that
a variable must be declared before it can be assigned to:
a variable must be declared before it can be used or assigned to:
its second argument is a \empha{continuation}, which is
the sequence of statements that depend on (= may refer to)
the declared variable.
@@ -194,7 +217,7 @@ We will treat all statements, except
\texttt{return}s, in terms of continuations. A sequence of
statements (which always has the type \texttt{Stm}) thus
always ends in a \texttt{return}, or, abruptly, in
an empty statement. Here are rules for some other
an empty statement, \texttt{End}. Here are rules for some other
statement forms:
\begin{verbatim}
Return : (A : Typ) -> Exp A -> Stm ;
@@ -209,7 +232,8 @@ Here is an example of a piece of code and its abstract syntax.
x = 5 ; Assign TInt x (EInt 5) (
return x ; Return TInt (EVar TInt x)))
\end{verbatim}
(Expression syntax is explained in the next section.)
(Expression syntax will be explained in the next section.)
\subsection{Expressions and types}
@@ -225,14 +249,10 @@ from variables, and by means of binary operations.
EVar : (A : Typ) -> Var A -> Exp A ;
EInt : Int -> Exp TInt ;
EFloat : Int -> Int -> Exp TFloat ;
EAddI : Exp TInt -> Exp TInt -> Exp TInt ;
EAddF : Exp TFloat -> Exp TFloat -> Exp TFloat ;
ESubI : Exp TInt -> Exp TInt -> Exp TInt ;
ESubF : Exp TFloat -> Exp TFloat -> Exp TFloat ;
EMulI : Exp TInt -> Exp TInt -> Exp TInt ;
EMulF : Exp TFloat -> Exp TFloat -> Exp TFloat ;
ELtI : Exp TInt -> Exp TInt -> Exp TInt ;
ELtF : Exp TFloat -> Exp TFloat -> Exp TInt ;
EAddI, EMulI, ESubI : Exp TInt -> Exp TInt -> Exp TInt ;
EAddF, EMulF, ESubF : Exp TFloat -> Exp TFloat -> Exp TFloat ;
\end{verbatim}
Notice that GF has a built-in type \texttt{Int} of
integer literals, but floats are constructed by hand.
@@ -277,14 +297,14 @@ of variables in statements, using a continuation.
fun
Empty : Program ;
Funct : (AS : ListTyp) -> (V : Typ) ->
(Body AS) -> (Fun AS V -> Program) -> Program ;
Body AS -> (Fun AS V -> Program) -> Program ;
\end{verbatim}
However, here we must also account for the binding of
a function's parameters in its body. We could use
vectors of variables, in the same way as vectors of
expressions are used to give arguments to functions.
However, this would lead to the need of cumbersome
projection functions when dereferencing the parameters
projection functions when using the parameters
in the function body. A more elegant solution is
to use HOAS to build function bodies:
\begin{verbatim}
@@ -313,16 +333,36 @@ name of the function appears in the syntax trees.
A more serious shortcoming of our way of defining functions
is that it does not allow recursion. The reason is simple:
the function symbol is only bound in the continuation
of the program, not in the function body.
of the program, not in the function body. It seems we could
save recursive functions with the following variant:
\begin{verbatim}
cat
BodyProgram ListTyp ;
Body ListTyp ;
fun
mkBodyProgram : (AS : ListTyp) ->
Body AS -> Program -> BodyProgram ListTyp ;
RecFunct : (AS : ListTyp) -> (V : Typ) ->
(Fun AS V -> BodyProgram AS) -> Program ;
\end{verbatim}
but we have not yet investigated this.
\section{The concrete syntax of C}
A concrete syntax, for a given abstract syntax,
consists of \texttt{lincat} judgements
defining the \empha{linearization types} of each category,
\[
\mbox{\texttt{lincat}} \; C \; \mbox{\texttt{=}} \; T
\]
defining the \empha{linearization types} $T$ of each category $C$,
and \texttt{lin} judgements
defining the \empha{linearization functions} of each function
\[
\mbox{\texttt{lin}} \; f \; \mbox{\texttt{=}} \; t
\]
defining the \empha{linearization functions} $t$ of each function $f$
in the abstract syntax. The linearization functions are
checked to be well-typed with respect the \texttt{lincat}
definitions, and the syntax of GF forces them to be \empha{compositional}
@@ -348,9 +388,9 @@ linearization functions, and linearizations of trees.
Because of compositionality, no case analysis on expressions
is possible in linearization rules. The values of linearization
therefore have to carry information on how they are used in
different situations, which means that linearization
types are record types instead of just the string type.
The simplest record type that is used is
different situations. Therefore linearization
types are generally record types instead of just the string type.
The simplest record type that is used in GF is
\begin{verbatim}
{s : Str}
\end{verbatim}
@@ -365,13 +405,23 @@ used by default.
Resource modules define auxiliary notions that can be
used in concrete syntax. These notions include
\empha{parameter types} defined by \texttt{param}
judgements, and \empha{operations} defined by
\texttt{oper} judgements. These judgements are
judgements
\[
\mbox{\texttt{param}} \; P \; \mbox{\texttt{=}}
\; C_{1} \; \Gamma_{1} \; \mid \; \cdots \; \mid \;
\; C_{n} \; \Gamma_{n}
\]
and \empha{operations} defined by
\texttt{oper} judgements
\[
\mbox{\texttt{oper}} \; f \; \mbox{\texttt{:}} \; T \; \mbox{\texttt{=}} \; t
\]
These judgements are
similar to datatype and function definitions
in functional programming, with the restriction
that parameter types must be finite and operations
may not be recursive. It is due to these restrictions that
the parsing algorithms are possible to derive from
we can always derive a parsing algorithm from a set of
linearization rules.
The following string operations are useful in almost
@@ -435,6 +485,7 @@ of superfluous parentheses, which is not allowed by
the present grammar.
\subsection{Statements}
Statements in C have
@@ -448,7 +499,7 @@ the use of semicolons on a high level.
statement : Str -> SS = \s -> ss (s ++ ";");
\end{verbatim}
As for declarations, which bind variables, we notice the
special projection \verb6.$06 to refet to the bound variable.
special projection \verb6.$06 to refer to the bound variable.
Technically, terms that are linearized must be in $\eta$-long
form, which guarantees that a variable symbol can always be
found, and that a field representing it can be added to the
@@ -465,6 +516,7 @@ linearization record.
\end{verbatim}
\subsection{Functions}
The only new problem presented by functions is the proper
@@ -475,7 +527,7 @@ Since compositionality prevents taking cases of list forms, e.g.
-- ConsExp _ _ e NilExp = e ;
-- ConsExp _ _ e es = ss (e.s ++ "," ++ es.s) ;
\end{verbatim}
we have to encode the size of a list in a parameter:
we have to encode the size of a list (zero, one, or more) in a parameter:
\begin{verbatim}
param
Size = Zero | One | More ;
@@ -526,40 +578,56 @@ component.
JVM syntax is, linguistically, more straightforward than
the syntax of C, and could be described by a regular
expression. The transition of our abstract syntax to JVM,
expression. The translation from our abstract syntax to JVM,
however, is tricky because variables are replaced by
their addresses (relative to the frame pointer), and
linearization must maintain a symbol table that permits
linearization must therefore maintain a symbol table that permits
the lookup of a variable address. As shown in the code
in Appendix C, we have not quite succeeded to do this
in the generated code. Instead, we generate (non-JVM)
\texttt{alloc} instructions and use another pass,
written in Haskell (Appendix E), to replace variable
symbols by their addresses.
in the code generated by linearization.
Instead, we use variable symbols instead of addresses
in \texttt{load} and \texttt{store} instructions, and
generate \texttt{alloc} pseudoinstructions from declarations.
Then we use another pass, written in Haskell (Appendix E),
to replace variable symbols by their addresses. The following example
shows how the three representations (C, pseudo-JVM, JVM) look like
for a piece of code.
\begin{verbatim}
int x ; alloc i x ; x has address 0
int y ; alloc i y ; y has address 1
x = 5 ; i _push 5 ipush 5
i _store x istore 0
y = x ; i _load x iload 0
i _store y istore 1
\end{verbatim}
A related problem is the generation of fresh labels for
jumps. We solve this by maintaining a growing label
jumps. We solve this by maintaining a growing label suffix
as a field of the linearization of statements into
instructions. The problem remains that the two branches
in an \texttt{if-else} statement can use the same
labels. Making them unique will have to be
added to the post-processing pass. This is, however,
added to the post-processing pass. This is
always possible, because labels are nested in a
disciplined way.
disciplined way, and jumps can never go to remote labels.
As it turned out too laborious to thread the label counter
to expressions, we decided to compile comparison
expressions into method calls, which should be provided
by a run-time library.
As it turned out laborious to thread the label counter
to expressions, we decided to compile comparison \verb6x < y6
expressions into function calls, which should be provided
by a run-time library. This would no more work for the
conjunction \verb6x && y6
and disjunction \verb6x || y6, if we want to keep their semantics
lazy, since function calls are strict in their arguments.
The JVM syntax used is from the Jasmin assembler
\cite{jasmin}, with small deviation which will
be removed shortly.
\subsection{A code example}
Here is a C source program, the JVM code obtained by linearization, and
Here is a complete C source program, the JVM code obtained by linearization, and
the postprocessed JVM code.
\small
\begin{verbatim}
@@ -594,6 +662,7 @@ int main () { call ilt ; call ilt ;
\normalsize
\section{Related work}
The theoretical ideas behind our compiler experiment
@@ -603,22 +672,23 @@ dependent types and higher-order abstract syntax
has been studied in various logical frameworks
\cite{harper-honsell,magnusson-nordstr,twelf}.
The idea of using a common abstract syntax for different
languages was clearly exposed in \cite{landin}. The view of
languages was clearly exposed by Landin \cite{landin}. The view of
code generation as linearization is a central aspect of
the classic compiler textbook \cite{aho-ullman}. The use
of the same grammar both for parsing and linearization
the classic compiler textbook by Aho, Sethi, and Ullman
\cite{aho-ullman}.
The use of the same grammar both for parsing and linearization
is a guiding principle of unification-based linguistic grammar
formalisms \cite{pereira}. Interactive editors derived from
formalisms \cite{pereira-shieber}. Interactive editors derived from
grammars have been used in various programming and proof
assistants \cite{teitelbaum,metal,ALF}.
assistants \cite{teitelbaum,metal,magnusson-nordstr}.
Even though the different ideas are well-known, they are
applied less in practice than in theory. In particular,
we have not seen them used together to construct a complete
compiler. In our view, putting these ideas together is
an attractive approach to compiling, since a compiler written
in this way is completely declarative, and therefore concise,
and therefore easy to modify and extend. For instance, if
in this way is completely declarative, hence concise,
and therefore easy to modify and extend. What is more, if
a new language construct is added, the GF type checker
verifies that the addition is propagated to all components
of the compiler. As the implementation is declarative,
@@ -631,6 +701,51 @@ semantics that is actually used in the implementation.
We managed to compile a large subset of C, and growing it
does not necessarily pose any new kinds of problems.
Using HOAS and dependent types to describe the abstract
syntax of C works fine, and defining the concrete syntax
of C on top of this using GF linearization machinery is
already possible, even though more support could be
desired for things like literals and precedences.
The parser generated by GF is not able to parse all
source programs, because of cyclic context-free
rules (of the form $C ::= C$). Recovery from cyclic
rules is ongoing work in GF independently of this
experiment.
The most serious problem is that the idea of compilation
as linearization does not quite work in the generation
of JVM code, if linearization is understood in the
sense of GF. In GF, linearization rules must be
compositional, and can only depend on parameters from
finite parameter sets. Hence it is not possible to encode
linearization with updates to and lookups from a symbol table,
as is usual in code generation.
Compositionality also prevents optimizations during linearization
by clever instruction selection, elimination of superfluous
labels and jumps, etc.
It would of course be possible to implement the compiler
back end in GF in the traditional way, as a noncompositional
function from the abstract syntax of C to a different abstract
syntax of JVM. The abstract syntax notation of GF permits
definitions of functions, and the GF interpreter can be used
for evaluating terms into normal form. Thus one could write
\begin{verbatim}
fun
transStm : Env -> Stm -> EnvInstr ;
def
transStm env (Decl typ rest) = ...
transStm env (Assign typ var exp rest) = ...
\end{verbatim}
This would be cumbersome in practice, because
GF does not have facilities like built-in lists and tuples,
or monads. Of course, the compiler could no longer be
inverted into a decompiler, in the way true linearization
can be inverted into a parser.
\bibliographystyle{plain}
@@ -778,7 +893,7 @@ concrete ImperJVM of Imper = open ResImper in {
BodyCons a as body = instrb a.s [] (body ** {s3 = []});
Decl typ cont = instrb typ.s (
"alloc_" ++ typ.s ++ cont.$0
"alloc" ++ typ.s ++ cont.$0
) cont ;
Assign t x exp = instrc (
exp.s ++