replica
/
hanchenye-llvm-project
2
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

replace the old kaleidoscope tutorial files with orphaned pages that forward to the new copy. 

llvm-svn: 366529
This commit is contained in:
Chris Lattner 2019-07-19 05:23:17 +00:00
parent 8ef8e5686e
commit 2e418e16dd
13 changed files with 47 additions and 5638 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

294
llvm/docs/tutorial/LangImpl01.rst
View File

@ -1,293 +1,7 @@
=================================================
Kaleidoscope: Tutorial Introduction and the Lexer
=================================================
:orphan:
.. contents::
:local:
Tutorial Introduction
=====================
Kaleidoscope Tutorial
=====================
Welcome to the "Implementing a language with LLVM" tutorial. This
tutorial runs through the implementation of a simple language, showing
how fun and easy it can be. This tutorial will get you up and started as
well as help to build a framework you can extend to other languages. The
code in this tutorial can also be used as a playground to hack on other
LLVM specific things.
The goal of this tutorial is to progressively unveil our language,
describing how it is built up over time. This will let us cover a fairly
broad range of language design and LLVM-specific usage issues, showing
and explaining the code for it all along the way, without overwhelming
you with tons of details up front.
It is useful to point out ahead of time that this tutorial is really
about teaching compiler techniques and LLVM specifically, *not* about
teaching modern and sane software engineering principles. In practice,
this means that we'll take a number of shortcuts to simplify the
exposition. For example, the code uses global variables
all over the place, doesn't use nice design patterns like
`visitors <http://en.wikipedia.org/wiki/Visitor_pattern>`_, etc... but
it is very simple. If you dig in and use the code as a basis for future
projects, fixing these deficiencies shouldn't be hard.
I've tried to put this tutorial together in a way that makes chapters
easy to skip over if you are already familiar with or are uninterested
in the various pieces. The structure of the tutorial is:
- `Chapter #1 <#language>`_: Introduction to the Kaleidoscope
language, and the definition of its Lexer - This shows where we are
going and the basic functionality that we want it to do. In order to
make this tutorial maximally understandable and hackable, we choose
to implement everything in C++ instead of using lexer and parser
generators. LLVM obviously works just fine with such tools, feel free
to use one if you prefer.
- `Chapter #2 <LangImpl02.html>`_: Implementing a Parser and AST -
With the lexer in place, we can talk about parsing techniques and
basic AST construction. This tutorial describes recursive descent
parsing and operator precedence parsing. Nothing in Chapters 1 or 2
is LLVM-specific, the code doesn't even link in LLVM at this point.
:)
- `Chapter #3 <LangImpl03.html>`_: Code generation to LLVM IR - With
the AST ready, we can show off how easy generation of LLVM IR really
is.
- `Chapter #4 <LangImpl04.html>`_: Adding JIT and Optimizer Support
- Because a lot of people are interested in using LLVM as a JIT,
we'll dive right into it and show you the 3 lines it takes to add JIT
support. LLVM is also useful in many other ways, but this is one
simple and "sexy" way to show off its power. :)
- `Chapter #5 <LangImpl05.html>`_: Extending the Language: Control
Flow - With the language up and running, we show how to extend it
with control flow operations (if/then/else and a 'for' loop). This
gives us a chance to talk about simple SSA construction and control
flow.
- `Chapter #6 <LangImpl06.html>`_: Extending the Language:
User-defined Operators - This is a silly but fun chapter that talks
about extending the language to let the user program define their own
arbitrary unary and binary operators (with assignable precedence!).
This lets us build a significant piece of the "language" as library
routines.
- `Chapter #7 <LangImpl07.html>`_: Extending the Language: Mutable
Variables - This chapter talks about adding user-defined local
variables along with an assignment operator. The interesting part
about this is how easy and trivial it is to construct SSA form in
LLVM: no, LLVM does *not* require your front-end to construct SSA
form!
- `Chapter #8 <LangImpl08.html>`_: Compiling to Object Files - This
chapter explains how to take LLVM IR and compile it down to object
files.
- `Chapter #9 <LangImpl09.html>`_: Extending the Language: Debug
Information - Having built a decent little programming language with
control flow, functions and mutable variables, we consider what it
takes to add debug information to standalone executables. This debug
information will allow you to set breakpoints in Kaleidoscope
functions, print out argument variables, and call functions - all
from within the debugger!
- `Chapter #10 <LangImpl10.html>`_: Conclusion and other useful LLVM
tidbits - This chapter wraps up the series by talking about
potential ways to extend the language, but also includes a bunch of
pointers to info about "special topics" like adding garbage
collection support, exceptions, debugging, support for "spaghetti
stacks", and a bunch of other tips and tricks.
By the end of the tutorial, we'll have written a bit less than 1000 lines
of non-comment, non-blank, lines of code. With this small amount of
code, we'll have built up a very reasonable compiler for a non-trivial
language including a hand-written lexer, parser, AST, as well as code
generation support with a JIT compiler. While other systems may have
interesting "hello world" tutorials, I think the breadth of this
tutorial is a great testament to the strengths of LLVM and why you
should consider it if you're interested in language or compiler design.
A note about this tutorial: we expect you to extend the language and
play with it on your own. Take the code and go crazy hacking away at it,
compilers don't need to be scary creatures - it can be a lot of fun to
play with languages!
The Basic Language
==================
This tutorial will be illustrated with a toy language that we'll call
"`Kaleidoscope <http://en.wikipedia.org/wiki/Kaleidoscope>`_" (derived
from "meaning beautiful, form, and view"). Kaleidoscope is a procedural
language that allows you to define functions, use conditionals, math,
etc. Over the course of the tutorial, we'll extend Kaleidoscope to
support the if/then/else construct, a for loop, user defined operators,
JIT compilation with a simple command line interface, etc.
Because we want to keep things simple, the only datatype in Kaleidoscope
is a 64-bit floating point type (aka 'double' in C parlance). As such,
all values are implicitly double precision and the language doesn't
require type declarations. This gives the language a very nice and
simple syntax. For example, the following simple example computes
`Fibonacci numbers: <http://en.wikipedia.org/wiki/Fibonacci_number>`_
::
# Compute the x'th fibonacci number.
def fib(x)
if x < 3 then
1
else
fib(x-1)+fib(x-2)
# This expression will compute the 40th number.
fib(40)
We also allow Kaleidoscope to call into standard library functions (the
LLVM JIT makes this completely trivial). This means that you can use the
'extern' keyword to define a function before you use it (this is also
useful for mutually recursive functions). For example:
::
extern sin(arg);
extern cos(arg);
extern atan2(arg1 arg2);
atan2(sin(.4), cos(42))
A more interesting example is included in Chapter 6 where we write a
little Kaleidoscope application that `displays a Mandelbrot
Set <LangImpl06.html#kicking-the-tires>`_ at various levels of magnification.
Lets dive into the implementation of this language!
The Lexer
=========
When it comes to implementing a language, the first thing needed is the
ability to process a text file and recognize what it says. The
traditional way to do this is to use a
"`lexer <http://en.wikipedia.org/wiki/Lexical_analysis>`_" (aka
'scanner') to break the input up into "tokens". Each token returned by
the lexer includes a token code and potentially some metadata (e.g. the
numeric value of a number). First, we define the possibilities:
.. code-block:: c++
// The lexer returns tokens [0-255] if it is an unknown character, otherwise one
// of these for known things.
enum Token {
tok_eof = -1,
// commands
tok_def = -2,
tok_extern = -3,
// primary
tok_identifier = -4,
tok_number = -5,
};
static std::string IdentifierStr; // Filled in if tok_identifier
static double NumVal; // Filled in if tok_number
Each token returned by our lexer will either be one of the Token enum
values or it will be an 'unknown' character like '+', which is returned
as its ASCII value. If the current token is an identifier, the
``IdentifierStr`` global variable holds the name of the identifier. If
the current token is a numeric literal (like 1.0), ``NumVal`` holds its
value. Note that we use global variables for simplicity, this is not the
best choice for a real language implementation :).
The actual implementation of the lexer is a single function named
``gettok``. The ``gettok`` function is called to return the next token
from standard input. Its definition starts as:
.. code-block:: c++
/// gettok - Return the next token from standard input.
static int gettok() {
static int LastChar = ' ';
// Skip any whitespace.
while (isspace(LastChar))
LastChar = getchar();
``gettok`` works by calling the C ``getchar()`` function to read
characters one at a time from standard input. It eats them as it
recognizes them and stores the last character read, but not processed,
in LastChar. The first thing that it has to do is ignore whitespace
between tokens. This is accomplished with the loop above.
The next thing ``gettok`` needs to do is recognize identifiers and
specific keywords like "def". Kaleidoscope does this with this simple
loop:
.. code-block:: c++
if (isalpha(LastChar)) { // identifier: [a-zA-Z][a-zA-Z0-9]*
IdentifierStr = LastChar;
while (isalnum((LastChar = getchar())))
IdentifierStr += LastChar;
if (IdentifierStr == "def")
return tok_def;
if (IdentifierStr == "extern")
return tok_extern;
return tok_identifier;
}
Note that this code sets the '``IdentifierStr``' global whenever it
lexes an identifier. Also, since language keywords are matched by the
same loop, we handle them here inline. Numeric values are similar:
.. code-block:: c++
if (isdigit(LastChar) || LastChar == '.') { // Number: [0-9.]+
std::string NumStr;
do {
NumStr += LastChar;
LastChar = getchar();
} while (isdigit(LastChar) || LastChar == '.');
NumVal = strtod(NumStr.c_str(), 0);
return tok_number;
}
This is all pretty straight-forward code for processing input. When
reading a numeric value from input, we use the C ``strtod`` function to
convert it to a numeric value that we store in ``NumVal``. Note that
this isn't doing sufficient error checking: it will incorrectly read
"1.23.45.67" and handle it as if you typed in "1.23". Feel free to
extend it :). Next we handle comments:
.. code-block:: c++
if (LastChar == '#') {
// Comment until end of line.
do
LastChar = getchar();
while (LastChar != EOF && LastChar != '\n' && LastChar != '\r');
if (LastChar != EOF)
return gettok();
}
We handle comments by skipping to the end of the line and then return
the next token. Finally, if the input doesn't match one of the above
cases, it is either an operator character like '+' or the end of the
file. These are handled with this code:
.. code-block:: c++
// Check for end of file. Don't eat the EOF.
if (LastChar == EOF)
return tok_eof;
// Otherwise, just return the character as its ascii value.
int ThisChar = LastChar;
LastChar = getchar();
return ThisChar;
}
With this, we have the complete lexer for the basic Kaleidoscope
language (the `full code listing <LangImpl02.html#full-code-listing>`_ for the Lexer
is available in the `next chapter <LangImpl02.html>`_ of the tutorial).
Next we'll `build a simple parser that uses this to build an Abstract
Syntax Tree <LangImpl02.html>`_. When we have that, we'll include a
driver so that you can use the lexer and parser together.
`Next: Implementing a Parser and AST <LangImpl02.html>`_
The Kaleidoscope Tutorial has `moved to another location <MyFirstLanguageFrontend/index>`_ .

740
llvm/docs/tutorial/LangImpl02.rst
View File

@ -1,737 +1,7 @@
===========================================
Kaleidoscope: Implementing a Parser and AST
===========================================
:orphan:
.. contents::
:local:
Chapter 2 Introduction
======================
Welcome to Chapter 2 of the "`Implementing a language with
LLVM <index.html>`_" tutorial. This chapter shows you how to use the
lexer, built in `Chapter 1 <LangImpl01.html>`_, to build a full
`parser <http://en.wikipedia.org/wiki/Parsing>`_ for our Kaleidoscope
language. Once we have a parser, we'll define and build an `Abstract
Syntax Tree <http://en.wikipedia.org/wiki/Abstract_syntax_tree>`_ (AST).
The parser we will build uses a combination of `Recursive Descent
Parsing <http://en.wikipedia.org/wiki/Recursive_descent_parser>`_ and
`Operator-Precedence
Parsing <http://en.wikipedia.org/wiki/Operator-precedence_parser>`_ to
parse the Kaleidoscope language (the latter for binary expressions and
the former for everything else). Before we get to parsing though, let's
talk about the output of the parser: the Abstract Syntax Tree.
The Abstract Syntax Tree (AST)
==============================
The AST for a program captures its behavior in such a way that it is
easy for later stages of the compiler (e.g. code generation) to
interpret. We basically want one object for each construct in the
language, and the AST should closely model the language. In
Kaleidoscope, we have expressions, a prototype, and a function object.
We'll start with expressions first:
.. code-block:: c++
/// ExprAST - Base class for all expression nodes.
class ExprAST {
public:
virtual ~ExprAST() {}
};
/// NumberExprAST - Expression class for numeric literals like "1.0".
class NumberExprAST : public ExprAST {
double Val;
public:
NumberExprAST(double Val) : Val(Val) {}
};
The code above shows the definition of the base ExprAST class and one
subclass which we use for numeric literals. The important thing to note
about this code is that the NumberExprAST class captures the numeric
value of the literal as an instance variable. This allows later phases
of the compiler to know what the stored numeric value is.
Right now we only create the AST, so there are no useful accessor
methods on them. It would be very easy to add a virtual method to pretty
print the code, for example. Here are the other expression AST node
definitions that we'll use in the basic form of the Kaleidoscope
language:
.. code-block:: c++
/// VariableExprAST - Expression class for referencing a variable, like "a".
class VariableExprAST : public ExprAST {
std::string Name;
public:
VariableExprAST(const std::string &Name) : Name(Name) {}
};
/// BinaryExprAST - Expression class for a binary operator.
class BinaryExprAST : public ExprAST {
char Op;
std::unique_ptr<ExprAST> LHS, RHS;
public:
BinaryExprAST(char op, std::unique_ptr<ExprAST> LHS,
std::unique_ptr<ExprAST> RHS)
: Op(op), LHS(std::move(LHS)), RHS(std::move(RHS)) {}
};
/// CallExprAST - Expression class for function calls.
class CallExprAST : public ExprAST {
std::string Callee;
std::vector<std::unique_ptr<ExprAST>> Args;
public:
CallExprAST(const std::string &Callee,
std::vector<std::unique_ptr<ExprAST>> Args)
: Callee(Callee), Args(std::move(Args)) {}
};
This is all (intentionally) rather straight-forward: variables capture
the variable name, binary operators capture their opcode (e.g. '+'), and
calls capture a function name as well as a list of any argument
expressions. One thing that is nice about our AST is that it captures
the language features without talking about the syntax of the language.
Note that there is no discussion about precedence of binary operators,
lexical structure, etc.
For our basic language, these are all of the expression nodes we'll
define. Because it doesn't have conditional control flow, it isn't
Turing-complete; we'll fix that in a later installment. The two things
we need next are a way to talk about the interface to a function, and a
way to talk about functions themselves:
.. code-block:: c++
/// PrototypeAST - This class represents the "prototype" for a function,
/// which captures its name, and its argument names (thus implicitly the number
/// of arguments the function takes).
class PrototypeAST {
std::string Name;
std::vector<std::string> Args;
public:
PrototypeAST(const std::string &name, std::vector<std::string> Args)
: Name(name), Args(std::move(Args)) {}
const std::string &getName() const { return Name; }
};
/// FunctionAST - This class represents a function definition itself.
class FunctionAST {
std::unique_ptr<PrototypeAST> Proto;
std::unique_ptr<ExprAST> Body;
public:
FunctionAST(std::unique_ptr<PrototypeAST> Proto,
std::unique_ptr<ExprAST> Body)
: Proto(std::move(Proto)), Body(std::move(Body)) {}
};
In Kaleidoscope, functions are typed with just a count of their
arguments. Since all values are double precision floating point, the
type of each argument doesn't need to be stored anywhere. In a more
aggressive and realistic language, the "ExprAST" class would probably
have a type field.
With this scaffolding, we can now talk about parsing expressions and
function bodies in Kaleidoscope.
Parser Basics
=============
Now that we have an AST to build, we need to define the parser code to
build it. The idea here is that we want to parse something like "x+y"
(which is returned as three tokens by the lexer) into an AST that could
be generated with calls like this:
.. code-block:: c++
auto LHS = llvm::make_unique<VariableExprAST>("x");
auto RHS = llvm::make_unique<VariableExprAST>("y");
auto Result = std::make_unique<BinaryExprAST>('+', std::move(LHS),
std::move(RHS));
In order to do this, we'll start by defining some basic helper routines:
.. code-block:: c++
/// CurTok/getNextToken - Provide a simple token buffer. CurTok is the current
/// token the parser is looking at. getNextToken reads another token from the
/// lexer and updates CurTok with its results.
static int CurTok;
static int getNextToken() {
return CurTok = gettok();
}
This implements a simple token buffer around the lexer. This allows us
to look one token ahead at what the lexer is returning. Every function
in our parser will assume that CurTok is the current token that needs to
be parsed.
.. code-block:: c++
/// LogError* - These are little helper functions for error handling.
std::unique_ptr<ExprAST> LogError(const char *Str) {
fprintf(stderr, "LogError: %s\n", Str);
return nullptr;
}
std::unique_ptr<PrototypeAST> LogErrorP(const char *Str) {
LogError(Str);
return nullptr;
}
The ``LogError`` routines are simple helper routines that our parser will
use to handle errors. The error recovery in our parser will not be the
best and is not particular user-friendly, but it will be enough for our
tutorial. These routines make it easier to handle errors in routines
that have various return types: they always return null.
With these basic helper functions, we can implement the first piece of
our grammar: numeric literals.
Basic Expression Parsing
========================
We start with numeric literals, because they are the simplest to
process. For each production in our grammar, we'll define a function
which parses that production. For numeric literals, we have:
.. code-block:: c++
/// numberexpr ::= number
static std::unique_ptr<ExprAST> ParseNumberExpr() {
auto Result = llvm::make_unique<NumberExprAST>(NumVal);
getNextToken(); // consume the number
return std::move(Result);
}
This routine is very simple: it expects to be called when the current
token is a ``tok_number`` token. It takes the current number value,
creates a ``NumberExprAST`` node, advances the lexer to the next token,
and finally returns.
There are some interesting aspects to this. The most important one is
that this routine eats all of the tokens that correspond to the
production and returns the lexer buffer with the next token (which is
not part of the grammar production) ready to go. This is a fairly
standard way to go for recursive descent parsers. For a better example,
the parenthesis operator is defined like this:
.. code-block:: c++
/// parenexpr ::= '(' expression ')'
static std::unique_ptr<ExprAST> ParseParenExpr() {
getNextToken(); // eat (.
auto V = ParseExpression();
if (!V)
return nullptr;
if (CurTok != ')')
return LogError("expected ')'");
getNextToken(); // eat ).
return V;
}
This function illustrates a number of interesting things about the
parser:
1) It shows how we use the LogError routines. When called, this function
expects that the current token is a '(' token, but after parsing the
subexpression, it is possible that there is no ')' waiting. For example,
if the user types in "(4 x" instead of "(4)", the parser should emit an
error. Because errors can occur, the parser needs a way to indicate that
they happened: in our parser, we return null on an error.
2) Another interesting aspect of this function is that it uses recursion
by calling ``ParseExpression`` (we will soon see that
``ParseExpression`` can call ``ParseParenExpr``). This is powerful
because it allows us to handle recursive grammars, and keeps each
production very simple. Note that parentheses do not cause construction
of AST nodes themselves. While we could do it this way, the most
important role of parentheses are to guide the parser and provide
grouping. Once the parser constructs the AST, parentheses are not
needed.
The next simple production is for handling variable references and
function calls:
.. code-block:: c++
/// identifierexpr
/// ::= identifier
/// ::= identifier '(' expression* ')'
static std::unique_ptr<ExprAST> ParseIdentifierExpr() {
std::string IdName = IdentifierStr;
getNextToken(); // eat identifier.
if (CurTok != '(') // Simple variable ref.
return llvm::make_unique<VariableExprAST>(IdName);
// Call.
getNextToken(); // eat (
std::vector<std::unique_ptr<ExprAST>> Args;
if (CurTok != ')') {
while (1) {
if (auto Arg = ParseExpression())
Args.push_back(std::move(Arg));
else
return nullptr;
if (CurTok == ')')
break;
if (CurTok != ',')
return LogError("Expected ')' or ',' in argument list");
getNextToken();
}
}
// Eat the ')'.
getNextToken();
return llvm::make_unique<CallExprAST>(IdName, std::move(Args));
}
This routine follows the same style as the other routines. (It expects
to be called if the current token is a ``tok_identifier`` token). It
also has recursion and error handling. One interesting aspect of this is
that it uses *look-ahead* to determine if the current identifier is a
stand alone variable reference or if it is a function call expression.
It handles this by checking to see if the token after the identifier is
a '(' token, constructing either a ``VariableExprAST`` or
``CallExprAST`` node as appropriate.
Now that we have all of our simple expression-parsing logic in place, we
can define a helper function to wrap it together into one entry point.
We call this class of expressions "primary" expressions, for reasons
that will become more clear `later in the
tutorial <LangImpl6.html#user-defined-unary-operators>`_. In order to parse an arbitrary
primary expression, we need to determine what sort of expression it is:
.. code-block:: c++
/// primary
/// ::= identifierexpr
/// ::= numberexpr
/// ::= parenexpr
static std::unique_ptr<ExprAST> ParsePrimary() {
switch (CurTok) {
default:
return LogError("unknown token when expecting an expression");
case tok_identifier:
return ParseIdentifierExpr();
case tok_number:
return ParseNumberExpr();
case '(':
return ParseParenExpr();
}
}
Now that you see the definition of this function, it is more obvious why
we can assume the state of CurTok in the various functions. This uses
look-ahead to determine which sort of expression is being inspected, and
then parses it with a function call.
Now that basic expressions are handled, we need to handle binary
expressions. They are a bit more complex.
Binary Expression Parsing
=========================
Binary expressions are significantly harder to parse because they are
often ambiguous. For example, when given the string "x+y\*z", the parser
can choose to parse it as either "(x+y)\*z" or "x+(y\*z)". With common
definitions from mathematics, we expect the later parse, because "\*"
(multiplication) has higher *precedence* than "+" (addition).
There are many ways to handle this, but an elegant and efficient way is
to use `Operator-Precedence
Parsing <http://en.wikipedia.org/wiki/Operator-precedence_parser>`_.
This parsing technique uses the precedence of binary operators to guide
recursion. To start with, we need a table of precedences:
.. code-block:: c++
/// BinopPrecedence - This holds the precedence for each binary operator that is
/// defined.
static std::map<char, int> BinopPrecedence;
/// GetTokPrecedence - Get the precedence of the pending binary operator token.
static int GetTokPrecedence() {
if (!isascii(CurTok))
return -1;
// Make sure it's a declared binop.
int TokPrec = BinopPrecedence[CurTok];
if (TokPrec <= 0) return -1;
return TokPrec;
}
int main() {
// Install standard binary operators.
// 1 is lowest precedence.
BinopPrecedence['<'] = 10;
BinopPrecedence['+'] = 20;
BinopPrecedence['-'] = 20;
BinopPrecedence['*'] = 40; // highest.
...
}
For the basic form of Kaleidoscope, we will only support 4 binary
operators (this can obviously be extended by you, our brave and intrepid
reader). The ``GetTokPrecedence`` function returns the precedence for
the current token, or -1 if the token is not a binary operator. Having a
map makes it easy to add new operators and makes it clear that the
algorithm doesn't depend on the specific operators involved, but it
would be easy enough to eliminate the map and do the comparisons in the
``GetTokPrecedence`` function. (Or just use a fixed-size array).
With the helper above defined, we can now start parsing binary
expressions. The basic idea of operator precedence parsing is to break
down an expression with potentially ambiguous binary operators into
pieces. Consider, for example, the expression "a+b+(c+d)\*e\*f+g".
Operator precedence parsing considers this as a stream of primary
expressions separated by binary operators. As such, it will first parse
the leading primary expression "a", then it will see the pairs [+, b]
[+, (c+d)] [\*, e] [\*, f] and [+, g]. Note that because parentheses are
primary expressions, the binary expression parser doesn't need to worry
about nested subexpressions like (c+d) at all.
To start, an expression is a primary expression potentially followed by
a sequence of [binop,primaryexpr] pairs:
.. code-block:: c++
/// expression
/// ::= primary binoprhs
///
static std::unique_ptr<ExprAST> ParseExpression() {
auto LHS = ParsePrimary();
if (!LHS)
return nullptr;
return ParseBinOpRHS(0, std::move(LHS));
}
``ParseBinOpRHS`` is the function that parses the sequence of pairs for
us. It takes a precedence and a pointer to an expression for the part
that has been parsed so far. Note that "x" is a perfectly valid
expression: As such, "binoprhs" is allowed to be empty, in which case it
returns the expression that is passed into it. In our example above, the
code passes the expression for "a" into ``ParseBinOpRHS`` and the
current token is "+".
The precedence value passed into ``ParseBinOpRHS`` indicates the
*minimal operator precedence* that the function is allowed to eat. For
example, if the current pair stream is [+, x] and ``ParseBinOpRHS`` is
passed in a precedence of 40, it will not consume any tokens (because
the precedence of '+' is only 20). With this in mind, ``ParseBinOpRHS``
starts with:
.. code-block:: c++
/// binoprhs
/// ::= ('+' primary)*
static std::unique_ptr<ExprAST> ParseBinOpRHS(int ExprPrec,
std::unique_ptr<ExprAST> LHS) {
// If this is a binop, find its precedence.
while (1) {
int TokPrec = GetTokPrecedence();
// If this is a binop that binds at least as tightly as the current binop,
// consume it, otherwise we are done.
if (TokPrec < ExprPrec)
return LHS;
This code gets the precedence of the current token and checks to see if
if is too low. Because we defined invalid tokens to have a precedence of
-1, this check implicitly knows that the pair-stream ends when the token
stream runs out of binary operators. If this check succeeds, we know
that the token is a binary operator and that it will be included in this
expression:
.. code-block:: c++
// Okay, we know this is a binop.
int BinOp = CurTok;
getNextToken(); // eat binop
// Parse the primary expression after the binary operator.
auto RHS = ParsePrimary();
if (!RHS)
return nullptr;
As such, this code eats (and remembers) the binary operator and then
parses the primary expression that follows. This builds up the whole
pair, the first of which is [+, b] for the running example.
Now that we parsed the left-hand side of an expression and one pair of
the RHS sequence, we have to decide which way the expression associates.
In particular, we could have "(a+b) binop unparsed" or "a + (b binop
unparsed)". To determine this, we look ahead at "binop" to determine its
precedence and compare it to BinOp's precedence (which is '+' in this
case):
.. code-block:: c++
// If BinOp binds less tightly with RHS than the operator after RHS, let
// the pending operator take RHS as its LHS.
int NextPrec = GetTokPrecedence();
if (TokPrec < NextPrec) {
If the precedence of the binop to the right of "RHS" is lower or equal
to the precedence of our current operator, then we know that the
parentheses associate as "(a+b) binop ...". In our example, the current
operator is "+" and the next operator is "+", we know that they have the
same precedence. In this case we'll create the AST node for "a+b", and
then continue parsing:
.. code-block:: c++
... if body omitted ...
}
// Merge LHS/RHS.
LHS = llvm::make_unique<BinaryExprAST>(BinOp, std::move(LHS),
std::move(RHS));
} // loop around to the top of the while loop.
}
In our example above, this will turn "a+b+" into "(a+b)" and execute the
next iteration of the loop, with "+" as the current token. The code
above will eat, remember, and parse "(c+d)" as the primary expression,
which makes the current pair equal to [+, (c+d)]. It will then evaluate
the 'if' conditional above with "\*" as the binop to the right of the
primary. In this case, the precedence of "\*" is higher than the
precedence of "+" so the if condition will be entered.
The critical question left here is "how can the if condition parse the
right hand side in full"? In particular, to build the AST correctly for
our example, it needs to get all of "(c+d)\*e\*f" as the RHS expression
variable. The code to do this is surprisingly simple (code from the
above two blocks duplicated for context):
.. code-block:: c++
// If BinOp binds less tightly with RHS than the operator after RHS, let
// the pending operator take RHS as its LHS.
int NextPrec = GetTokPrecedence();
if (TokPrec < NextPrec) {
RHS = ParseBinOpRHS(TokPrec+1, std::move(RHS));
if (!RHS)
return nullptr;
}
// Merge LHS/RHS.
LHS = llvm::make_unique<BinaryExprAST>(BinOp, std::move(LHS),
std::move(RHS));
} // loop around to the top of the while loop.
}
At this point, we know that the binary operator to the RHS of our
primary has higher precedence than the binop we are currently parsing.
As such, we know that any sequence of pairs whose operators are all
higher precedence than "+" should be parsed together and returned as
"RHS". To do this, we recursively invoke the ``ParseBinOpRHS`` function
specifying "TokPrec+1" as the minimum precedence required for it to
continue. In our example above, this will cause it to return the AST
node for "(c+d)\*e\*f" as RHS, which is then set as the RHS of the '+'
expression.
Finally, on the next iteration of the while loop, the "+g" piece is
parsed and added to the AST. With this little bit of code (14
non-trivial lines), we correctly handle fully general binary expression
parsing in a very elegant way. This was a whirlwind tour of this code,
and it is somewhat subtle. I recommend running through it with a few
tough examples to see how it works.
This wraps up handling of expressions. At this point, we can point the
parser at an arbitrary token stream and build an expression from it,
stopping at the first token that is not part of the expression. Next up
we need to handle function definitions, etc.
Parsing the Rest
================
The next thing missing is handling of function prototypes. In
Kaleidoscope, these are used both for 'extern' function declarations as
well as function body definitions. The code to do this is
straight-forward and not very interesting (once you've survived
expressions):
.. code-block:: c++
/// prototype
/// ::= id '(' id* ')'
static std::unique_ptr<PrototypeAST> ParsePrototype() {
if (CurTok != tok_identifier)
return LogErrorP("Expected function name in prototype");
std::string FnName = IdentifierStr;
getNextToken();
if (CurTok != '(')
return LogErrorP("Expected '(' in prototype");
// Read the list of argument names.
std::vector<std::string> ArgNames;
while (getNextToken() == tok_identifier)
ArgNames.push_back(IdentifierStr);
if (CurTok != ')')
return LogErrorP("Expected ')' in prototype");
// success.
getNextToken(); // eat ')'.
return llvm::make_unique<PrototypeAST>(FnName, std::move(ArgNames));
}
Given this, a function definition is very simple, just a prototype plus
an expression to implement the body:
.. code-block:: c++
/// definition ::= 'def' prototype expression
static std::unique_ptr<FunctionAST> ParseDefinition() {
getNextToken(); // eat def.
auto Proto = ParsePrototype();
if (!Proto) return nullptr;
if (auto E = ParseExpression())
return llvm::make_unique<FunctionAST>(std::move(Proto), std::move(E));
return nullptr;
}
In addition, we support 'extern' to declare functions like 'sin' and
'cos' as well as to support forward declaration of user functions. These
'extern's are just prototypes with no body:
.. code-block:: c++
/// external ::= 'extern' prototype
static std::unique_ptr<PrototypeAST> ParseExtern() {
getNextToken(); // eat extern.
return ParsePrototype();
}
Finally, we'll also let the user type in arbitrary top-level expressions
and evaluate them on the fly. We will handle this by defining anonymous
nullary (zero argument) functions for them:
.. code-block:: c++
/// toplevelexpr ::= expression
static std::unique_ptr<FunctionAST> ParseTopLevelExpr() {
if (auto E = ParseExpression()) {
// Make an anonymous proto.
auto Proto = llvm::make_unique<PrototypeAST>("", std::vector<std::string>());
return llvm::make_unique<FunctionAST>(std::move(Proto), std::move(E));
}
return nullptr;
}
Now that we have all the pieces, let's build a little driver that will
let us actually *execute* this code we've built!
The Driver
==========
The driver for this simply invokes all of the parsing pieces with a
top-level dispatch loop. There isn't much interesting here, so I'll just
include the top-level loop. See `below <#full-code-listing>`_ for full code in the
"Top-Level Parsing" section.
.. code-block:: c++
/// top ::= definition | external | expression | ';'
static void MainLoop() {
while (1) {
fprintf(stderr, "ready> ");
switch (CurTok) {
case tok_eof:
return;
case ';': // ignore top-level semicolons.
getNextToken();
break;
case tok_def:
HandleDefinition();
break;
case tok_extern:
HandleExtern();
break;
default:
HandleTopLevelExpression();
break;
}
}
}
The most interesting part of this is that we ignore top-level
semicolons. Why is this, you ask? The basic reason is that if you type
"4 + 5" at the command line, the parser doesn't know whether that is the
end of what you will type or not. For example, on the next line you
could type "def foo..." in which case 4+5 is the end of a top-level
expression. Alternatively you could type "\* 6", which would continue
the expression. Having top-level semicolons allows you to type "4+5;",
and the parser will know you are done.
Conclusions
===========
With just under 400 lines of commented code (240 lines of non-comment,
non-blank code), we fully defined our minimal language, including a
lexer, parser, and AST builder. With this done, the executable will
validate Kaleidoscope code and tell us if it is grammatically invalid.
For example, here is a sample interaction:
.. code-block:: bash
$ ./a.out
ready> def foo(x y) x+foo(y, 4.0);
Parsed a function definition.
ready> def foo(x y) x+y y;
Parsed a function definition.
Parsed a top-level expr
ready> def foo(x y) x+y );
Parsed a function definition.
Error: unknown token when expecting an expression
ready> extern sin(a);
ready> Parsed an extern
ready> ^D
$
There is a lot of room for extension here. You can define new AST nodes,
extend the language in many ways, etc. In the `next
installment <LangImpl03.html>`_, we will describe how to generate LLVM
Intermediate Representation (IR) from the AST.
Full Code Listing
=================
Here is the complete code listing for our running example. Because this
uses the LLVM libraries, we need to link them in. To do this, we use the
`llvm-config <http://llvm.org/cmds/llvm-config.html>`_ tool to inform
our makefile/command line about which options to use:
.. code-block:: bash
# Compile
clang++ -g -O3 toy.cpp `llvm-config --cxxflags`
# Run
./a.out
Here is the code:
.. literalinclude:: ../../examples/Kaleidoscope/Chapter2/toy.cpp
:language: c++
`Next: Implementing Code Generation to LLVM IR <LangImpl03.html>`_
=====================
Kaleidoscope Tutorial
=====================
The Kaleidoscope Tutorial has `moved to another location <MyFirstLanguageFrontend/index>`_ .

569
llvm/docs/tutorial/LangImpl03.rst
View File

@ -1,568 +1,7 @@
========================================
Kaleidoscope: Code generation to LLVM IR
========================================
:orphan:
.. contents::
:local:
Chapter 3 Introduction
======================
Welcome to Chapter 3 of the "`Implementing a language with
LLVM <index.html>`_" tutorial. This chapter shows you how to transform
the `Abstract Syntax Tree <LangImpl02.html>`_, built in Chapter 2, into
LLVM IR. This will teach you a little bit about how LLVM does things, as
well as demonstrate how easy it is to use. It's much more work to build
a lexer and parser than it is to generate LLVM IR code. :)
**Please note**: the code in this chapter and later require LLVM 3.7 or
later. LLVM 3.6 and before will not work with it. Also note that you
need to use a version of this tutorial that matches your LLVM release:
If you are using an official LLVM release, use the version of the
documentation included with your release or on the `llvm.org releases
page <http://llvm.org/releases/>`_.
Code Generation Setup
=====================
Kaleidoscope Tutorial
=====================
In order to generate LLVM IR, we want some simple setup to get started.
First we define virtual code generation (codegen) methods in each AST
class:
.. code-block:: c++
/// ExprAST - Base class for all expression nodes.
class ExprAST {
public:
virtual ~ExprAST() {}
virtual Value *codegen() = 0;
};
/// NumberExprAST - Expression class for numeric literals like "1.0".
class NumberExprAST : public ExprAST {
double Val;
public:
NumberExprAST(double Val) : Val(Val) {}
virtual Value *codegen();
};
...
The codegen() method says to emit IR for that AST node along with all
the things it depends on, and they all return an LLVM Value object.
"Value" is the class used to represent a "`Static Single Assignment
(SSA) <http://en.wikipedia.org/wiki/Static_single_assignment_form>`_
register" or "SSA value" in LLVM. The most distinct aspect of SSA values
is that their value is computed as the related instruction executes, and
it does not get a new value until (and if) the instruction re-executes.
In other words, there is no way to "change" an SSA value. For more
information, please read up on `Static Single
Assignment <http://en.wikipedia.org/wiki/Static_single_assignment_form>`_
- the concepts are really quite natural once you grok them.
Note that instead of adding virtual methods to the ExprAST class
hierarchy, it could also make sense to use a `visitor
pattern <http://en.wikipedia.org/wiki/Visitor_pattern>`_ or some other
way to model this. Again, this tutorial won't dwell on good software
engineering practices: for our purposes, adding a virtual method is
simplest.
The second thing we want is an "LogError" method like we used for the
parser, which will be used to report errors found during code generation
(for example, use of an undeclared parameter):
.. code-block:: c++
static LLVMContext TheContext;
static IRBuilder<> Builder(TheContext);
static std::unique_ptr<Module> TheModule;
static std::map<std::string, Value *> NamedValues;
Value *LogErrorV(const char *Str) {
LogError(Str);
return nullptr;
}
The static variables will be used during code generation. ``TheContext``
is an opaque object that owns a lot of core LLVM data structures, such as
the type and constant value tables. We don't need to understand it in
detail, we just need a single instance to pass into APIs that require it.
The ``Builder`` object is a helper object that makes it easy to generate
LLVM instructions. Instances of the
`IRBuilder <http://llvm.org/doxygen/IRBuilder_8h-source.html>`_
class template keep track of the current place to insert instructions
and has methods to create new instructions.
``TheModule`` is an LLVM construct that contains functions and global
variables. In many ways, it is the top-level structure that the LLVM IR
uses to contain code. It will own the memory for all of the IR that we
generate, which is why the codegen() method returns a raw Value\*,
rather than a unique_ptr<Value>.
The ``NamedValues`` map keeps track of which values are defined in the
current scope and what their LLVM representation is. (In other words, it
is a symbol table for the code). In this form of Kaleidoscope, the only
things that can be referenced are function parameters. As such, function
parameters will be in this map when generating code for their function
body.
With these basics in place, we can start talking about how to generate
code for each expression. Note that this assumes that the ``Builder``
has been set up to generate code *into* something. For now, we'll assume
that this has already been done, and we'll just use it to emit code.
Expression Code Generation
==========================
Generating LLVM code for expression nodes is very straightforward: less
than 45 lines of commented code for all four of our expression nodes.
First we'll do numeric literals:
.. code-block:: c++
Value *NumberExprAST::codegen() {
return ConstantFP::get(TheContext, APFloat(Val));
}
In the LLVM IR, numeric constants are represented with the
``ConstantFP`` class, which holds the numeric value in an ``APFloat``
internally (``APFloat`` has the capability of holding floating point
constants of Arbitrary Precision). This code basically just creates
and returns a ``ConstantFP``. Note that in the LLVM IR that constants
are all uniqued together and shared. For this reason, the API uses the
"foo::get(...)" idiom instead of "new foo(..)" or "foo::Create(..)".
.. code-block:: c++
Value *VariableExprAST::codegen() {
// Look this variable up in the function.
Value *V = NamedValues[Name];
if (!V)
LogErrorV("Unknown variable name");
return V;
}
References to variables are also quite simple using LLVM. In the simple
version of Kaleidoscope, we assume that the variable has already been
emitted somewhere and its value is available. In practice, the only
values that can be in the ``NamedValues`` map are function arguments.
This code simply checks to see that the specified name is in the map (if
not, an unknown variable is being referenced) and returns the value for
it. In future chapters, we'll add support for `loop induction
variables <LangImpl5.html#for-loop-expression>`_ in the symbol table, and for `local
variables <LangImpl7.html#user-defined-local-variables>`_.
.. code-block:: c++
Value *BinaryExprAST::codegen() {
Value *L = LHS->codegen();
Value *R = RHS->codegen();
if (!L || !R)
return nullptr;
switch (Op) {
case '+':
return Builder.CreateFAdd(L, R, "addtmp");
case '-':
return Builder.CreateFSub(L, R, "subtmp");
case '*':
return Builder.CreateFMul(L, R, "multmp");
case '<':
L = Builder.CreateFCmpULT(L, R, "cmptmp");
// Convert bool 0/1 to double 0.0 or 1.0
return Builder.CreateUIToFP(L, Type::getDoubleTy(TheContext),
"booltmp");
default:
return LogErrorV("invalid binary operator");
}
}
Binary operators start to get more interesting. The basic idea here is
that we recursively emit code for the left-hand side of the expression,
then the right-hand side, then we compute the result of the binary
expression. In this code, we do a simple switch on the opcode to create
the right LLVM instruction.
In the example above, the LLVM builder class is starting to show its
value. IRBuilder knows where to insert the newly created instruction,
all you have to do is specify what instruction to create (e.g. with
``CreateFAdd``), which operands to use (``L`` and ``R`` here) and
optionally provide a name for the generated instruction.
One nice thing about LLVM is that the name is just a hint. For instance,
if the code above emits multiple "addtmp" variables, LLVM will
automatically provide each one with an increasing, unique numeric
suffix. Local value names for instructions are purely optional, but it
makes it much easier to read the IR dumps.
`LLVM instructions <../LangRef.html#instruction-reference>`_ are constrained by strict
rules: for example, the Left and Right operators of an `add
instruction <../LangRef.html#add-instruction>`_ must have the same type, and the
result type of the add must match the operand types. Because all values
in Kaleidoscope are doubles, this makes for very simple code for add,
sub and mul.
On the other hand, LLVM specifies that the `fcmp
instruction <../LangRef.html#fcmp-instruction>`_ always returns an 'i1' value (a
one bit integer). The problem with this is that Kaleidoscope wants the
value to be a 0.0 or 1.0 value. In order to get these semantics, we
combine the fcmp instruction with a `uitofp
instruction <../LangRef.html#uitofp-to-instruction>`_. This instruction converts its
input integer into a floating point value by treating the input as an
unsigned value. In contrast, if we used the `sitofp
instruction <../LangRef.html#sitofp-to-instruction>`_, the Kaleidoscope '<' operator
would return 0.0 and -1.0, depending on the input value.
.. code-block:: c++
Value *CallExprAST::codegen() {
// Look up the name in the global module table.
Function *CalleeF = TheModule->getFunction(Callee);
if (!CalleeF)
return LogErrorV("Unknown function referenced");
// If argument mismatch error.
if (CalleeF->arg_size() != Args.size())
return LogErrorV("Incorrect # arguments passed");
std::vector<Value *> ArgsV;
for (unsigned i = 0, e = Args.size(); i != e; ++i) {
ArgsV.push_back(Args[i]->codegen());
if (!ArgsV.back())
return nullptr;
}
return Builder.CreateCall(CalleeF, ArgsV, "calltmp");
}
Code generation for function calls is quite straightforward with LLVM. The code
above initially does a function name lookup in the LLVM Module's symbol table.
Recall that the LLVM Module is the container that holds the functions we are
JIT'ing. By giving each function the same name as what the user specifies, we
can use the LLVM symbol table to resolve function names for us.
Once we have the function to call, we recursively codegen each argument
that is to be passed in, and create an LLVM `call
instruction <../LangRef.html#call-instruction>`_. Note that LLVM uses the native C
calling conventions by default, allowing these calls to also call into
standard library functions like "sin" and "cos", with no additional
effort.
This wraps up our handling of the four basic expressions that we have so
far in Kaleidoscope. Feel free to go in and add some more. For example,
by browsing the `LLVM language reference <../LangRef.html>`_ you'll find
several other interesting instructions that are really easy to plug into
our basic framework.
Function Code Generation
========================
Code generation for prototypes and functions must handle a number of
details, which make their code less beautiful than expression code
generation, but allows us to illustrate some important points. First,
let's talk about code generation for prototypes: they are used both for
function bodies and external function declarations. The code starts
with:
.. code-block:: c++
Function *PrototypeAST::codegen() {
// Make the function type: double(double,double) etc.
std::vector<Type*> Doubles(Args.size(),
Type::getDoubleTy(TheContext));
FunctionType *FT =
FunctionType::get(Type::getDoubleTy(TheContext), Doubles, false);
Function *F =
Function::Create(FT, Function::ExternalLinkage, Name, TheModule.get());
This code packs a lot of power into a few lines. Note first that this
function returns a "Function\*" instead of a "Value\*". Because a
"prototype" really talks about the external interface for a function
(not the value computed by an expression), it makes sense for it to
return the LLVM Function it corresponds to when codegen'd.
The call to ``FunctionType::get`` creates the ``FunctionType`` that
should be used for a given Prototype. Since all function arguments in
Kaleidoscope are of type double, the first line creates a vector of "N"
LLVM double types. It then uses the ``Functiontype::get`` method to
create a function type that takes "N" doubles as arguments, returns one
double as a result, and that is not vararg (the false parameter
indicates this). Note that Types in LLVM are uniqued just like Constants
are, so you don't "new" a type, you "get" it.
The final line above actually creates the IR Function corresponding to
the Prototype. This indicates the type, linkage and name to use, as
well as which module to insert into. "`external
linkage <../LangRef.html#linkage>`_" means that the function may be
defined outside the current module and/or that it is callable by
functions outside the module. The Name passed in is the name the user
specified: since "``TheModule``" is specified, this name is registered
in "``TheModule``"s symbol table.
.. code-block:: c++
// Set names for all arguments.
unsigned Idx = 0;
for (auto &Arg : F->args())
Arg.setName(Args[Idx++]);
return F;
Finally, we set the name of each of the function's arguments according to the
names given in the Prototype. This step isn't strictly necessary, but keeping
the names consistent makes the IR more readable, and allows subsequent code to
refer directly to the arguments for their names, rather than having to look up
them up in the Prototype AST.
At this point we have a function prototype with no body. This is how LLVM IR
represents function declarations. For extern statements in Kaleidoscope, this
is as far as we need to go. For function definitions however, we need to
codegen and attach a function body.
.. code-block:: c++
Function *FunctionAST::codegen() {
// First, check for an existing function from a previous 'extern' declaration.
Function *TheFunction = TheModule->getFunction(Proto->getName());
if (!TheFunction)
TheFunction = Proto->codegen();
if (!TheFunction)
return nullptr;
if (!TheFunction->empty())
return (Function*)LogErrorV("Function cannot be redefined.");
For function definitions, we start by searching TheModule's symbol table for an
existing version of this function, in case one has already been created using an
'extern' statement. If Module::getFunction returns null then no previous version
exists, so we'll codegen one from the Prototype. In either case, we want to
assert that the function is empty (i.e. has no body yet) before we start.
.. code-block:: c++
// Create a new basic block to start insertion into.
BasicBlock *BB = BasicBlock::Create(TheContext, "entry", TheFunction);
Builder.SetInsertPoint(BB);
// Record the function arguments in the NamedValues map.
NamedValues.clear();
for (auto &Arg : TheFunction->args())
NamedValues[Arg.getName()] = &Arg;
Now we get to the point where the ``Builder`` is set up. The first line
creates a new `basic block <http://en.wikipedia.org/wiki/Basic_block>`_
(named "entry"), which is inserted into ``TheFunction``. The second line
then tells the builder that new instructions should be inserted into the
end of the new basic block. Basic blocks in LLVM are an important part
of functions that define the `Control Flow
Graph <http://en.wikipedia.org/wiki/Control_flow_graph>`_. Since we
don't have any control flow, our functions will only contain one block
at this point. We'll fix this in `Chapter 5 <LangImpl05.html>`_ :).
Next we add the function arguments to the NamedValues map (after first clearing
it out) so that they're accessible to ``VariableExprAST`` nodes.
.. code-block:: c++
if (Value *RetVal = Body->codegen()) {
// Finish off the function.
Builder.CreateRet(RetVal);
// Validate the generated code, checking for consistency.
verifyFunction(*TheFunction);
return TheFunction;
}
Once the insertion point has been set up and the NamedValues map populated,
we call the ``codegen()`` method for the root expression of the function. If no
error happens, this emits code to compute the expression into the entry block
and returns the value that was computed. Assuming no error, we then create an
LLVM `ret instruction <../LangRef.html#ret-instruction>`_, which completes the function.
Once the function is built, we call ``verifyFunction``, which is
provided by LLVM. This function does a variety of consistency checks on
the generated code, to determine if our compiler is doing everything
right. Using this is important: it can catch a lot of bugs. Once the
function is finished and validated, we return it.
.. code-block:: c++
// Error reading body, remove function.
TheFunction->eraseFromParent();
return nullptr;
}
The only piece left here is handling of the error case. For simplicity,
we handle this by merely deleting the function we produced with the
``eraseFromParent`` method. This allows the user to redefine a function
that they incorrectly typed in before: if we didn't delete it, it would
live in the symbol table, with a body, preventing future redefinition.
This code does have a bug, though: If the ``FunctionAST::codegen()`` method
finds an existing IR Function, it does not validate its signature against the
definition's own prototype. This means that an earlier 'extern' declaration will
take precedence over the function definition's signature, which can cause
codegen to fail, for instance if the function arguments are named differently.
There are a number of ways to fix this bug, see what you can come up with! Here
is a testcase:
::
extern foo(a); # ok, defines foo.
def foo(b) b; # Error: Unknown variable name. (decl using 'a' takes precedence).
Driver Changes and Closing Thoughts
===================================
For now, code generation to LLVM doesn't really get us much, except that
we can look at the pretty IR calls. The sample code inserts calls to
codegen into the "``HandleDefinition``", "``HandleExtern``" etc
functions, and then dumps out the LLVM IR. This gives a nice way to look
at the LLVM IR for simple functions. For example:
::
ready> 4+5;
Read top-level expression:
define double @0() {
entry:
ret double 9.000000e+00
}
Note how the parser turns the top-level expression into anonymous
functions for us. This will be handy when we add `JIT
support <LangImpl4.html#adding-a-jit-compiler>`_ in the next chapter. Also note that the
code is very literally transcribed, no optimizations are being performed
except simple constant folding done by IRBuilder. We will `add
optimizations <LangImpl4.html#trivial-constant-folding>`_ explicitly in the next
chapter.
::
ready> def foo(a b) a*a + 2*a*b + b*b;
Read function definition:
define double @foo(double %a, double %b) {
entry:
%multmp = fmul double %a, %a
%multmp1 = fmul double 2.000000e+00, %a
%multmp2 = fmul double %multmp1, %b
%addtmp = fadd double %multmp, %multmp2
%multmp3 = fmul double %b, %b
%addtmp4 = fadd double %addtmp, %multmp3
ret double %addtmp4
}
This shows some simple arithmetic. Notice the striking similarity to the
LLVM builder calls that we use to create the instructions.
::
ready> def bar(a) foo(a, 4.0) + bar(31337);
Read function definition:
define double @bar(double %a) {
entry:
%calltmp = call double @foo(double %a, double 4.000000e+00)
%calltmp1 = call double @bar(double 3.133700e+04)
%addtmp = fadd double %calltmp, %calltmp1
ret double %addtmp
}
This shows some function calls. Note that this function will take a long
time to execute if you call it. In the future we'll add conditional
control flow to actually make recursion useful :).
::
ready> extern cos(x);
Read extern:
declare double @cos(double)
ready> cos(1.234);
Read top-level expression:
define double @1() {
entry:
%calltmp = call double @cos(double 1.234000e+00)
ret double %calltmp
}
This shows an extern for the libm "cos" function, and a call to it.
.. TODO:: Abandon Pygments' horrible `llvm` lexer. It just totally gives up
on highlighting this due to the first line.
::
ready> ^D
; ModuleID = 'my cool jit'
define double @0() {
entry:
%addtmp = fadd double 4.000000e+00, 5.000000e+00
ret double %addtmp
}
define double @foo(double %a, double %b) {
entry:
%multmp = fmul double %a, %a
%multmp1 = fmul double 2.000000e+00, %a
%multmp2 = fmul double %multmp1, %b
%addtmp = fadd double %multmp, %multmp2
%multmp3 = fmul double %b, %b
%addtmp4 = fadd double %addtmp, %multmp3
ret double %addtmp4
}
define double @bar(double %a) {
entry:
%calltmp = call double @foo(double %a, double 4.000000e+00)
%calltmp1 = call double @bar(double 3.133700e+04)
%addtmp = fadd double %calltmp, %calltmp1
ret double %addtmp
}
declare double @cos(double)
define double @1() {
entry:
%calltmp = call double @cos(double 1.234000e+00)
ret double %calltmp
}
When you quit the current demo (by sending an EOF via CTRL+D on Linux
or CTRL+Z and ENTER on Windows), it dumps out the IR for the entire
module generated. Here you can see the big picture with all the
functions referencing each other.
This wraps up the third chapter of the Kaleidoscope tutorial. Up next,
we'll describe how to `add JIT codegen and optimizer
support <LangImpl04.html>`_ to this so we can actually start running
code!
Full Code Listing
=================
Here is the complete code listing for our running example, enhanced with
the LLVM code generator. Because this uses the LLVM libraries, we need
to link them in. To do this, we use the
`llvm-config <http://llvm.org/cmds/llvm-config.html>`_ tool to inform
our makefile/command line about which options to use:
.. code-block:: bash
# Compile
clang++ -g -O3 toy.cpp `llvm-config --cxxflags --ldflags --system-libs --libs core` -o toy
# Run
./toy
Here is the code:
.. literalinclude:: ../../examples/Kaleidoscope/Chapter3/toy.cpp
:language: c++
`Next: Adding JIT and Optimizer Support <LangImpl04.html>`_
The Kaleidoscope Tutorial has `moved to another location <MyFirstLanguageFrontend/index>`_ .

660
llvm/docs/tutorial/LangImpl04.rst
View File

@ -1,659 +1,7 @@
==============================================
Kaleidoscope: Adding JIT and Optimizer Support
==============================================
:orphan:
.. contents::
:local:
Chapter 4 Introduction
======================
Welcome to Chapter 4 of the "`Implementing a language with
LLVM <index.html>`_" tutorial. Chapters 1-3 described the implementation
of a simple language and added support for generating LLVM IR. This
chapter describes two new techniques: adding optimizer support to your
language, and adding JIT compiler support. These additions will
demonstrate how to get nice, efficient code for the Kaleidoscope
language.
Trivial Constant Folding
========================
Our demonstration for Chapter 3 is elegant and easy to extend.
Unfortunately, it does not produce wonderful code. The IRBuilder,
however, does give us obvious optimizations when compiling simple code:
::
ready> def test(x) 1+2+x;
Read function definition:
define double @test(double %x) {
entry:
%addtmp = fadd double 3.000000e+00, %x
ret double %addtmp
}
This code is not a literal transcription of the AST built by parsing the
input. That would be:
::
ready> def test(x) 1+2+x;
Read function definition:
define double @test(double %x) {
entry:
%addtmp = fadd double 2.000000e+00, 1.000000e+00
%addtmp1 = fadd double %addtmp, %x
ret double %addtmp1
}
Constant folding, as seen above, in particular, is a very common and
very important optimization: so much so that many language implementors
implement constant folding support in their AST representation.
With LLVM, you don't need this support in the AST. Since all calls to
build LLVM IR go through the LLVM IR builder, the builder itself checked
to see if there was a constant folding opportunity when you call it. If
so, it just does the constant fold and return the constant instead of
creating an instruction.
Well, that was easy :). In practice, we recommend always using
``IRBuilder`` when generating code like this. It has no "syntactic
overhead" for its use (you don't have to uglify your compiler with
constant checks everywhere) and it can dramatically reduce the amount of
LLVM IR that is generated in some cases (particular for languages with a
macro preprocessor or that use a lot of constants).
On the other hand, the ``IRBuilder`` is limited by the fact that it does
all of its analysis inline with the code as it is built. If you take a
slightly more complex example:
::
ready> def test(x) (1+2+x)*(x+(1+2));
ready> Read function definition:
define double @test(double %x) {
entry:
%addtmp = fadd double 3.000000e+00, %x
%addtmp1 = fadd double %x, 3.000000e+00
%multmp = fmul double %addtmp, %addtmp1
ret double %multmp
}
In this case, the LHS and RHS of the multiplication are the same value.
We'd really like to see this generate "``tmp = x+3; result = tmp*tmp;``"
instead of computing "``x+3``" twice.
Unfortunately, no amount of local analysis will be able to detect and
correct this. This requires two transformations: reassociation of
expressions (to make the add's lexically identical) and Common
Subexpression Elimination (CSE) to delete the redundant add instruction.
Fortunately, LLVM provides a broad range of optimizations that you can
use, in the form of "passes".
LLVM Optimization Passes
========================
.. warning::
Due to the transition to the new PassManager infrastructure this tutorial
is based on ``llvm::legacy::FunctionPassManager`` which can be found in
`LegacyPassManager.h <http://llvm.org/doxygen/classllvm_1_1legacy_1_1FunctionPassManager.html>`_.
For the purpose of the this tutorial the above should be used until
the pass manager transition is complete.
LLVM provides many optimization passes, which do many different sorts of
things and have different tradeoffs. Unlike other systems, LLVM doesn't
hold to the mistaken notion that one set of optimizations is right for
all languages and for all situations. LLVM allows a compiler implementor
to make complete decisions about what optimizations to use, in which
order, and in what situation.
As a concrete example, LLVM supports both "whole module" passes, which
look across as large of body of code as they can (often a whole file,
but if run at link time, this can be a substantial portion of the whole
program). It also supports and includes "per-function" passes which just
operate on a single function at a time, without looking at other
functions. For more information on passes and how they are run, see the
`How to Write a Pass <../WritingAnLLVMPass.html>`_ document and the
`List of LLVM Passes <../Passes.html>`_.
For Kaleidoscope, we are currently generating functions on the fly, one
at a time, as the user types them in. We aren't shooting for the
ultimate optimization experience in this setting, but we also want to
catch the easy and quick stuff where possible. As such, we will choose
to run a few per-function optimizations as the user types the function
in. If we wanted to make a "static Kaleidoscope compiler", we would use
exactly the code we have now, except that we would defer running the
optimizer until the entire file has been parsed.
In order to get per-function optimizations going, we need to set up a
`FunctionPassManager <../WritingAnLLVMPass.html#what-passmanager-doesr>`_ to hold
and organize the LLVM optimizations that we want to run. Once we have
that, we can add a set of optimizations to run. We'll need a new
FunctionPassManager for each module that we want to optimize, so we'll
write a function to create and initialize both the module and pass manager
for us:
.. code-block:: c++
void InitializeModuleAndPassManager(void) {
// Open a new module.
TheModule = llvm::make_unique<Module>("my cool jit", TheContext);
// Create a new pass manager attached to it.
TheFPM = llvm::make_unique<FunctionPassManager>(TheModule.get());
// Do simple "peephole" optimizations and bit-twiddling optzns.
TheFPM->add(createInstructionCombiningPass());
// Reassociate expressions.
TheFPM->add(createReassociatePass());
// Eliminate Common SubExpressions.
TheFPM->add(createGVNPass());
// Simplify the control flow graph (deleting unreachable blocks, etc).
TheFPM->add(createCFGSimplificationPass());
TheFPM->doInitialization();
}
This code initializes the global module ``TheModule``, and the function pass
manager ``TheFPM``, which is attached to ``TheModule``. Once the pass manager is
set up, we use a series of "add" calls to add a bunch of LLVM passes.
In this case, we choose to add four optimization passes.
The passes we choose here are a pretty standard set
of "cleanup" optimizations that are useful for a wide variety of code. I won't
delve into what they do but, believe me, they are a good starting place :).
Once the PassManager is set up, we need to make use of it. We do this by
running it after our newly created function is constructed (in
``FunctionAST::codegen()``), but before it is returned to the client:
.. code-block:: c++
if (Value *RetVal = Body->codegen()) {
// Finish off the function.
Builder.CreateRet(RetVal);
// Validate the generated code, checking for consistency.
verifyFunction(*TheFunction);
// Optimize the function.
TheFPM->run(*TheFunction);
return TheFunction;
}
As you can see, this is pretty straightforward. The
``FunctionPassManager`` optimizes and updates the LLVM Function\* in
place, improving (hopefully) its body. With this in place, we can try
our test above again:
::
ready> def test(x) (1+2+x)*(x+(1+2));
ready> Read function definition:
define double @test(double %x) {
entry:
%addtmp = fadd double %x, 3.000000e+00
%multmp = fmul double %addtmp, %addtmp
ret double %multmp
}
As expected, we now get our nicely optimized code, saving a floating
point add instruction from every execution of this function.
LLVM provides a wide variety of optimizations that can be used in
certain circumstances. Some `documentation about the various
passes <../Passes.html>`_ is available, but it isn't very complete.
Another good source of ideas can come from looking at the passes that
``Clang`` runs to get started. The "``opt``" tool allows you to
experiment with passes from the command line, so you can see if they do
anything.
Now that we have reasonable code coming out of our front-end, let's talk
about executing it!
Adding a JIT Compiler
=====================
Kaleidoscope Tutorial
=====================
Code that is available in LLVM IR can have a wide variety of tools
applied to it. For example, you can run optimizations on it (as we did
above), you can dump it out in textual or binary forms, you can compile
the code to an assembly file (.s) for some target, or you can JIT
compile it. The nice thing about the LLVM IR representation is that it
is the "common currency" between many different parts of the compiler.
In this section, we'll add JIT compiler support to our interpreter. The
basic idea that we want for Kaleidoscope is to have the user enter
function bodies as they do now, but immediately evaluate the top-level
expressions they type in. For example, if they type in "1 + 2;", we
should evaluate and print out 3. If they define a function, they should
be able to call it from the command line.
In order to do this, we first prepare the environment to create code for
the current native target and declare and initialize the JIT. This is
done by calling some ``InitializeNativeTarget\*`` functions and
adding a global variable ``TheJIT``, and initializing it in
``main``:
.. code-block:: c++
static std::unique_ptr<KaleidoscopeJIT> TheJIT;
...
int main() {
InitializeNativeTarget();
InitializeNativeTargetAsmPrinter();
InitializeNativeTargetAsmParser();
// Install standard binary operators.
// 1 is lowest precedence.
BinopPrecedence['<'] = 10;
BinopPrecedence['+'] = 20;
BinopPrecedence['-'] = 20;
BinopPrecedence['*'] = 40; // highest.
// Prime the first token.
fprintf(stderr, "ready> ");
getNextToken();
TheJIT = llvm::make_unique<KaleidoscopeJIT>();
// Run the main "interpreter loop" now.
MainLoop();
return 0;
}
We also need to setup the data layout for the JIT:
.. code-block:: c++
void InitializeModuleAndPassManager(void) {
// Open a new module.
TheModule = llvm::make_unique<Module>("my cool jit", TheContext);
TheModule->setDataLayout(TheJIT->getTargetMachine().createDataLayout());
// Create a new pass manager attached to it.
TheFPM = llvm::make_unique<FunctionPassManager>(TheModule.get());
...
The KaleidoscopeJIT class is a simple JIT built specifically for these
tutorials, available inside the LLVM source code
at llvm-src/examples/Kaleidoscope/include/KaleidoscopeJIT.h.
In later chapters we will look at how it works and extend it with
new features, but for now we will take it as given. Its API is very simple:
``addModule`` adds an LLVM IR module to the JIT, making its functions
available for execution; ``removeModule`` removes a module, freeing any
memory associated with the code in that module; and ``findSymbol`` allows us
to look up pointers to the compiled code.
We can take this simple API and change our code that parses top-level expressions to
look like this:
.. code-block:: c++
static void HandleTopLevelExpression() {
// Evaluate a top-level expression into an anonymous function.
if (auto FnAST = ParseTopLevelExpr()) {
if (FnAST->codegen()) {
// JIT the module containing the anonymous expression, keeping a handle so
// we can free it later.
auto H = TheJIT->addModule(std::move(TheModule));
InitializeModuleAndPassManager();
// Search the JIT for the __anon_expr symbol.
auto ExprSymbol = TheJIT->findSymbol("__anon_expr");
assert(ExprSymbol && "Function not found");
// Get the symbol's address and cast it to the right type (takes no
// arguments, returns a double) so we can call it as a native function.
double (*FP)() = (double (*)())(intptr_t)ExprSymbol.getAddress();
fprintf(stderr, "Evaluated to %f\n", FP());
// Delete the anonymous expression module from the JIT.
TheJIT->removeModule(H);
}
If parsing and codegen succeeed, the next step is to add the module containing
the top-level expression to the JIT. We do this by calling addModule, which
triggers code generation for all the functions in the module, and returns a
handle that can be used to remove the module from the JIT later. Once the module
has been added to the JIT it can no longer be modified, so we also open a new
module to hold subsequent code by calling ``InitializeModuleAndPassManager()``.
Once we've added the module to the JIT we need to get a pointer to the final
generated code. We do this by calling the JIT's findSymbol method, and passing
the name of the top-level expression function: ``__anon_expr``. Since we just
added this function, we assert that findSymbol returned a result.
Next, we get the in-memory address of the ``__anon_expr`` function by calling
``getAddress()`` on the symbol. Recall that we compile top-level expressions
into a self-contained LLVM function that takes no arguments and returns the
computed double. Because the LLVM JIT compiler matches the native platform ABI,
this means that you can just cast the result pointer to a function pointer of
that type and call it directly. This means, there is no difference between JIT
compiled code and native machine code that is statically linked into your
application.
Finally, since we don't support re-evaluation of top-level expressions, we
remove the module from the JIT when we're done to free the associated memory.
Recall, however, that the module we created a few lines earlier (via
``InitializeModuleAndPassManager``) is still open and waiting for new code to be
added.
With just these two changes, let's see how Kaleidoscope works now!
::
ready> 4+5;
Read top-level expression:
define double @0() {
entry:
ret double 9.000000e+00
}
Evaluated to 9.000000
Well this looks like it is basically working. The dump of the function
shows the "no argument function that always returns double" that we
synthesize for each top-level expression that is typed in. This
demonstrates very basic functionality, but can we do more?
::
ready> def testfunc(x y) x + y*2;
Read function definition:
define double @testfunc(double %x, double %y) {
entry:
%multmp = fmul double %y, 2.000000e+00
%addtmp = fadd double %multmp, %x
ret double %addtmp
}
ready> testfunc(4, 10);
Read top-level expression:
define double @1() {
entry:
%calltmp = call double @testfunc(double 4.000000e+00, double 1.000000e+01)
ret double %calltmp
}
Evaluated to 24.000000
ready> testfunc(5, 10);
ready> LLVM ERROR: Program used external function 'testfunc' which could not be resolved!
Function definitions and calls also work, but something went very wrong on that
last line. The call looks valid, so what happened? As you may have guessed from
the API a Module is a unit of allocation for the JIT, and testfunc was part
of the same module that contained anonymous expression. When we removed that
module from the JIT to free the memory for the anonymous expression, we deleted
the definition of ``testfunc`` along with it. Then, when we tried to call
testfunc a second time, the JIT could no longer find it.
The easiest way to fix this is to put the anonymous expression in a separate
module from the rest of the function definitions. The JIT will happily resolve
function calls across module boundaries, as long as each of the functions called
has a prototype, and is added to the JIT before it is called. By putting the
anonymous expression in a different module we can delete it without affecting
the rest of the functions.
In fact, we're going to go a step further and put every function in its own
module. Doing so allows us to exploit a useful property of the KaleidoscopeJIT
that will make our environment more REPL-like: Functions can be added to the
JIT more than once (unlike a module where every function must have a unique
definition). When you look up a symbol in KaleidoscopeJIT it will always return
the most recent definition:
::
ready> def foo(x) x + 1;
Read function definition:
define double @foo(double %x) {
entry:
%addtmp = fadd double %x, 1.000000e+00
ret double %addtmp
}
ready> foo(2);
Evaluated to 3.000000
ready> def foo(x) x + 2;
define double @foo(double %x) {
entry:
%addtmp = fadd double %x, 2.000000e+00
ret double %addtmp
}
ready> foo(2);
Evaluated to 4.000000
To allow each function to live in its own module we'll need a way to
re-generate previous function declarations into each new module we open:
.. code-block:: c++
static std::unique_ptr<KaleidoscopeJIT> TheJIT;
...
Function *getFunction(std::string Name) {
// First, see if the function has already been added to the current module.
if (auto *F = TheModule->getFunction(Name))
return F;
// If not, check whether we can codegen the declaration from some existing
// prototype.
auto FI = FunctionProtos.find(Name);
if (FI != FunctionProtos.end())
return FI->second->codegen();
// If no existing prototype exists, return null.
return nullptr;
}
...
Value *CallExprAST::codegen() {
// Look up the name in the global module table.
Function *CalleeF = getFunction(Callee);
...
Function *FunctionAST::codegen() {
// Transfer ownership of the prototype to the FunctionProtos map, but keep a
// reference to it for use below.
auto &P = *Proto;
FunctionProtos[Proto->getName()] = std::move(Proto);
Function *TheFunction = getFunction(P.getName());
if (!TheFunction)
return nullptr;
To enable this, we'll start by adding a new global, ``FunctionProtos``, that
holds the most recent prototype for each function. We'll also add a convenience
method, ``getFunction()``, to replace calls to ``TheModule->getFunction()``.
Our convenience method searches ``TheModule`` for an existing function
declaration, falling back to generating a new declaration from FunctionProtos if
it doesn't find one. In ``CallExprAST::codegen()`` we just need to replace the
call to ``TheModule->getFunction()``. In ``FunctionAST::codegen()`` we need to
update the FunctionProtos map first, then call ``getFunction()``. With this
done, we can always obtain a function declaration in the current module for any
previously declared function.
We also need to update HandleDefinition and HandleExtern:
.. code-block:: c++
static void HandleDefinition() {
if (auto FnAST = ParseDefinition()) {
if (auto *FnIR = FnAST->codegen()) {
fprintf(stderr, "Read function definition:");
FnIR->print(errs());
fprintf(stderr, "\n");
TheJIT->addModule(std::move(TheModule));
InitializeModuleAndPassManager();
}
} else {
// Skip token for error recovery.
getNextToken();
}
}
static void HandleExtern() {
if (auto ProtoAST = ParseExtern()) {
if (auto *FnIR = ProtoAST->codegen()) {
fprintf(stderr, "Read extern: ");
FnIR->print(errs());
fprintf(stderr, "\n");
FunctionProtos[ProtoAST->getName()] = std::move(ProtoAST);
}
} else {
// Skip token for error recovery.
getNextToken();
}
}
In HandleDefinition, we add two lines to transfer the newly defined function to
the JIT and open a new module. In HandleExtern, we just need to add one line to
add the prototype to FunctionProtos.
With these changes made, let's try our REPL again (I removed the dump of the
anonymous functions this time, you should get the idea by now :) :
::
ready> def foo(x) x + 1;
ready> foo(2);
Evaluated to 3.000000
ready> def foo(x) x + 2;
ready> foo(2);
Evaluated to 4.000000
It works!
Even with this simple code, we get some surprisingly powerful capabilities -
check this out:
::
ready> extern sin(x);
Read extern:
declare double @sin(double)
ready> extern cos(x);
Read extern:
declare double @cos(double)
ready> sin(1.0);
Read top-level expression:
define double @2() {
entry:
ret double 0x3FEAED548F090CEE
}
Evaluated to 0.841471
ready> def foo(x) sin(x)*sin(x) + cos(x)*cos(x);
Read function definition:
define double @foo(double %x) {
entry:
%calltmp = call double @sin(double %x)
%multmp = fmul double %calltmp, %calltmp
%calltmp2 = call double @cos(double %x)
%multmp4 = fmul double %calltmp2, %calltmp2
%addtmp = fadd double %multmp, %multmp4
ret double %addtmp
}
ready> foo(4.0);
Read top-level expression:
define double @3() {
entry:
%calltmp = call double @foo(double 4.000000e+00)
ret double %calltmp
}
Evaluated to 1.000000
Whoa, how does the JIT know about sin and cos? The answer is surprisingly
simple: The KaleidoscopeJIT has a straightforward symbol resolution rule that
it uses to find symbols that aren't available in any given module: First
it searches all the modules that have already been added to the JIT, from the
most recent to the oldest, to find the newest definition. If no definition is
found inside the JIT, it falls back to calling "``dlsym("sin")``" on the
Kaleidoscope process itself. Since "``sin``" is defined within the JIT's
address space, it simply patches up calls in the module to call the libm
version of ``sin`` directly. But in some cases this even goes further:
as sin and cos are names of standard math functions, the constant folder
will directly evaluate the function calls to the correct result when called
with constants like in the "``sin(1.0)``" above.
In the future we'll see how tweaking this symbol resolution rule can be used to
enable all sorts of useful features, from security (restricting the set of
symbols available to JIT'd code), to dynamic code generation based on symbol
names, and even lazy compilation.
One immediate benefit of the symbol resolution rule is that we can now extend
the language by writing arbitrary C++ code to implement operations. For example,
if we add:
.. code-block:: c++
#ifdef _WIN32
#define DLLEXPORT __declspec(dllexport)
#else
#define DLLEXPORT
#endif
/// putchard - putchar that takes a double and returns 0.
extern "C" DLLEXPORT double putchard(double X) {
fputc((char)X, stderr);
return 0;
}
Note, that for Windows we need to actually export the functions because
the dynamic symbol loader will use GetProcAddress to find the symbols.
Now we can produce simple output to the console by using things like:
"``extern putchard(x); putchard(120);``", which prints a lowercase 'x'
on the console (120 is the ASCII code for 'x'). Similar code could be
used to implement file I/O, console input, and many other capabilities
in Kaleidoscope.
This completes the JIT and optimizer chapter of the Kaleidoscope
tutorial. At this point, we can compile a non-Turing-complete
programming language, optimize and JIT compile it in a user-driven way.
Next up we'll look into `extending the language with control flow
constructs <LangImpl05.html>`_, tackling some interesting LLVM IR issues
along the way.
Full Code Listing
=================
Here is the complete code listing for our running example, enhanced with
the LLVM JIT and optimizer. To build this example, use:
.. code-block:: bash
# Compile
clang++ -g toy.cpp `llvm-config --cxxflags --ldflags --system-libs --libs core mcjit native` -O3 -o toy
# Run
./toy
If you are compiling this on Linux, make sure to add the "-rdynamic"
option as well. This makes sure that the external functions are resolved
properly at runtime.
Here is the code:
.. literalinclude:: ../../examples/Kaleidoscope/Chapter4/toy.cpp
:language: c++
`Next: Extending the language: control flow <LangImpl05.html>`_
The Kaleidoscope Tutorial has `moved to another location <MyFirstLanguageFrontend/index>`_ .

BIN
llvm/docs/tutorial/LangImpl05-cfg.png
View File

Binary file not shown.
Side by Side

Before

Width:  |  Height:  |  Size: 38 KiB

815
llvm/docs/tutorial/LangImpl05.rst
View File

@ -1,814 +1,7 @@
==================================================
Kaleidoscope: Extending the Language: Control Flow
==================================================
:orphan:
.. contents::
:local:
Chapter 5 Introduction
======================
Welcome to Chapter 5 of the "`Implementing a language with
LLVM <index.html>`_" tutorial. Parts 1-4 described the implementation of
the simple Kaleidoscope language and included support for generating
LLVM IR, followed by optimizations and a JIT compiler. Unfortunately, as
presented, Kaleidoscope is mostly useless: it has no control flow other
than call and return. This means that you can't have conditional
branches in the code, significantly limiting its power. In this episode
of "build that compiler", we'll extend Kaleidoscope to have an
if/then/else expression plus a simple 'for' loop.
If/Then/Else
============
Extending Kaleidoscope to support if/then/else is quite straightforward.
It basically requires adding support for this "new" concept to the
lexer, parser, AST, and LLVM code emitter. This example is nice, because
it shows how easy it is to "grow" a language over time, incrementally
extending it as new ideas are discovered.
Before we get going on "how" we add this extension, let's talk about
"what" we want. The basic idea is that we want to be able to write this
sort of thing:
::
def fib(x)
if x < 3 then
1
else
fib(x-1)+fib(x-2);
In Kaleidoscope, every construct is an expression: there are no
statements. As such, the if/then/else expression needs to return a value
like any other. Since we're using a mostly functional form, we'll have
it evaluate its conditional, then return the 'then' or 'else' value
based on how the condition was resolved. This is very similar to the C
"?:" expression.
The semantics of the if/then/else expression is that it evaluates the
condition to a boolean equality value: 0.0 is considered to be false and
everything else is considered to be true. If the condition is true, the
first subexpression is evaluated and returned, if the condition is
false, the second subexpression is evaluated and returned. Since
Kaleidoscope allows side-effects, this behavior is important to nail
down.
Now that we know what we "want", let's break this down into its
constituent pieces.
Lexer Extensions for If/Then/Else
---------------------------------
The lexer extensions are straightforward. First we add new enum values
for the relevant tokens:
.. code-block:: c++
// control
tok_if = -6,
tok_then = -7,
tok_else = -8,
Once we have that, we recognize the new keywords in the lexer. This is
pretty simple stuff:
.. code-block:: c++
...
if (IdentifierStr == "def")
return tok_def;
if (IdentifierStr == "extern")
return tok_extern;
if (IdentifierStr == "if")
return tok_if;
if (IdentifierStr == "then")
return tok_then;
if (IdentifierStr == "else")
return tok_else;
return tok_identifier;
AST Extensions for If/Then/Else
-------------------------------
To represent the new expression we add a new AST node for it:
.. code-block:: c++
/// IfExprAST - Expression class for if/then/else.
class IfExprAST : public ExprAST {
std::unique_ptr<ExprAST> Cond, Then, Else;
public:
IfExprAST(std::unique_ptr<ExprAST> Cond, std::unique_ptr<ExprAST> Then,
std::unique_ptr<ExprAST> Else)
: Cond(std::move(Cond)), Then(std::move(Then)), Else(std::move(Else)) {}
Value *codegen() override;
};
The AST node just has pointers to the various subexpressions.
Parser Extensions for If/Then/Else
----------------------------------
Now that we have the relevant tokens coming from the lexer and we have
the AST node to build, our parsing logic is relatively straightforward.
First we define a new parsing function:
.. code-block:: c++
/// ifexpr ::= 'if' expression 'then' expression 'else' expression
static std::unique_ptr<ExprAST> ParseIfExpr() {
getNextToken(); // eat the if.
// condition.
auto Cond = ParseExpression();
if (!Cond)
return nullptr;
if (CurTok != tok_then)
return LogError("expected then");
getNextToken(); // eat the then
auto Then = ParseExpression();
if (!Then)
return nullptr;
if (CurTok != tok_else)
return LogError("expected else");
getNextToken();
auto Else = ParseExpression();
if (!Else)
return nullptr;
return llvm::make_unique<IfExprAST>(std::move(Cond), std::move(Then),
std::move(Else));
}
Next we hook it up as a primary expression:
.. code-block:: c++
static std::unique_ptr<ExprAST> ParsePrimary() {
switch (CurTok) {
default:
return LogError("unknown token when expecting an expression");
case tok_identifier:
return ParseIdentifierExpr();
case tok_number:
return ParseNumberExpr();
case '(':
return ParseParenExpr();
case tok_if:
return ParseIfExpr();
}
}
LLVM IR for If/Then/Else
------------------------
Now that we have it parsing and building the AST, the final piece is
adding LLVM code generation support. This is the most interesting part
of the if/then/else example, because this is where it starts to
introduce new concepts. All of the code above has been thoroughly
described in previous chapters.
To motivate the code we want to produce, let's take a look at a simple
example. Consider:
::
extern foo();
extern bar();
def baz(x) if x then foo() else bar();
If you disable optimizations, the code you'll (soon) get from
Kaleidoscope looks like this:
.. code-block:: llvm
declare double @foo()
declare double @bar()
define double @baz(double %x) {
entry:
%ifcond = fcmp one double %x, 0.000000e+00
br i1 %ifcond, label %then, label %else
then: ; preds = %entry
%calltmp = call double @foo()
br label %ifcont
else: ; preds = %entry
%calltmp1 = call double @bar()
br label %ifcont
ifcont: ; preds = %else, %then
%iftmp = phi double [ %calltmp, %then ], [ %calltmp1, %else ]
ret double %iftmp
}
To visualize the control flow graph, you can use a nifty feature of the
LLVM '`opt <http://llvm.org/cmds/opt.html>`_' tool. If you put this LLVM
IR into "t.ll" and run "``llvm-as < t.ll | opt -analyze -view-cfg``", `a
window will pop up <../ProgrammersManual.html#viewing-graphs-while-debugging-code>`_ and you'll
see this graph:
.. figure:: LangImpl05-cfg.png
:align: center
:alt: Example CFG
Example CFG
Another way to get this is to call "``F->viewCFG()``" or
"``F->viewCFGOnly()``" (where F is a "``Function*``") either by
inserting actual calls into the code and recompiling or by calling these
in the debugger. LLVM has many nice features for visualizing various
graphs.
Getting back to the generated code, it is fairly simple: the entry block
evaluates the conditional expression ("x" in our case here) and compares
the result to 0.0 with the "``fcmp one``" instruction ('one' is "Ordered
and Not Equal"). Based on the result of this expression, the code jumps
to either the "then" or "else" blocks, which contain the expressions for
the true/false cases.
Once the then/else blocks are finished executing, they both branch back
to the 'ifcont' block to execute the code that happens after the
if/then/else. In this case the only thing left to do is to return to the
caller of the function. The question then becomes: how does the code
know which expression to return?
The answer to this question involves an important SSA operation: the
`Phi
operation <http://en.wikipedia.org/wiki/Static_single_assignment_form>`_.
If you're not familiar with SSA, `the wikipedia
article <http://en.wikipedia.org/wiki/Static_single_assignment_form>`_
is a good introduction and there are various other introductions to it
available on your favorite search engine. The short version is that
"execution" of the Phi operation requires "remembering" which block
control came from. The Phi operation takes on the value corresponding to
the input control block. In this case, if control comes in from the
"then" block, it gets the value of "calltmp". If control comes from the
"else" block, it gets the value of "calltmp1".
At this point, you are probably starting to think "Oh no! This means my
simple and elegant front-end will have to start generating SSA form in
order to use LLVM!". Fortunately, this is not the case, and we strongly
advise *not* implementing an SSA construction algorithm in your
front-end unless there is an amazingly good reason to do so. In
practice, there are two sorts of values that float around in code
written for your average imperative programming language that might need
Phi nodes:
#. Code that involves user variables: ``x = 1; x = x + 1;``
#. Values that are implicit in the structure of your AST, such as the
Phi node in this case.
In `Chapter 7 <LangImpl07.html>`_ of this tutorial ("mutable variables"),
we'll talk about #1 in depth. For now, just believe me that you don't
need SSA construction to handle this case. For #2, you have the choice
of using the techniques that we will describe for #1, or you can insert
Phi nodes directly, if convenient. In this case, it is really
easy to generate the Phi node, so we choose to do it directly.
Okay, enough of the motivation and overview, let's generate code!
Code Generation for If/Then/Else
--------------------------------
In order to generate code for this, we implement the ``codegen`` method
for ``IfExprAST``:
.. code-block:: c++
Value *IfExprAST::codegen() {
Value *CondV = Cond->codegen();
if (!CondV)
return nullptr;
// Convert condition to a bool by comparing non-equal to 0.0.
CondV = Builder.CreateFCmpONE(
CondV, ConstantFP::get(TheContext, APFloat(0.0)), "ifcond");
This code is straightforward and similar to what we saw before. We emit
the expression for the condition, then compare that value to zero to get
a truth value as a 1-bit (bool) value.
.. code-block:: c++
Function *TheFunction = Builder.GetInsertBlock()->getParent();
// Create blocks for the then and else cases. Insert the 'then' block at the
// end of the function.
BasicBlock *ThenBB =
BasicBlock::Create(TheContext, "then", TheFunction);
BasicBlock *ElseBB = BasicBlock::Create(TheContext, "else");
BasicBlock *MergeBB = BasicBlock::Create(TheContext, "ifcont");
Builder.CreateCondBr(CondV, ThenBB, ElseBB);
This code creates the basic blocks that are related to the if/then/else
statement, and correspond directly to the blocks in the example above.
The first line gets the current Function object that is being built. It
gets this by asking the builder for the current BasicBlock, and asking
that block for its "parent" (the function it is currently embedded
into).
Once it has that, it creates three blocks. Note that it passes
"TheFunction" into the constructor for the "then" block. This causes the
constructor to automatically insert the new block into the end of the
specified function. The other two blocks are created, but aren't yet
inserted into the function.
Once the blocks are created, we can emit the conditional branch that
chooses between them. Note that creating new blocks does not implicitly
affect the IRBuilder, so it is still inserting into the block that the
condition went into. Also note that it is creating a branch to the
"then" block and the "else" block, even though the "else" block isn't
inserted into the function yet. This is all ok: it is the standard way
that LLVM supports forward references.
.. code-block:: c++
// Emit then value.
Builder.SetInsertPoint(ThenBB);
Value *ThenV = Then->codegen();
if (!ThenV)
return nullptr;
Builder.CreateBr(MergeBB);
// Codegen of 'Then' can change the current block, update ThenBB for the PHI.
ThenBB = Builder.GetInsertBlock();
After the conditional branch is inserted, we move the builder to start
inserting into the "then" block. Strictly speaking, this call moves the
insertion point to be at the end of the specified block. However, since
the "then" block is empty, it also starts out by inserting at the
beginning of the block. :)
Once the insertion point is set, we recursively codegen the "then"
expression from the AST. To finish off the "then" block, we create an
unconditional branch to the merge block. One interesting (and very
important) aspect of the LLVM IR is that it `requires all basic blocks
to be "terminated" <../LangRef.html#functionstructure>`_ with a `control
flow instruction <../LangRef.html#terminators>`_ such as return or
branch. This means that all control flow, *including fall throughs* must
be made explicit in the LLVM IR. If you violate this rule, the verifier
will emit an error.
The final line here is quite subtle, but is very important. The basic
issue is that when we create the Phi node in the merge block, we need to
set up the block/value pairs that indicate how the Phi will work.
Importantly, the Phi node expects to have an entry for each predecessor
of the block in the CFG. Why then, are we getting the current block when
we just set it to ThenBB 5 lines above? The problem is that the "Then"
expression may actually itself change the block that the Builder is
emitting into if, for example, it contains a nested "if/then/else"
expression. Because calling ``codegen()`` recursively could arbitrarily change
the notion of the current block, we are required to get an up-to-date
value for code that will set up the Phi node.
.. code-block:: c++
// Emit else block.
TheFunction->getBasicBlockList().push_back(ElseBB);
Builder.SetInsertPoint(ElseBB);
Value *ElseV = Else->codegen();
if (!ElseV)
return nullptr;
Builder.CreateBr(MergeBB);
// codegen of 'Else' can change the current block, update ElseBB for the PHI.
ElseBB = Builder.GetInsertBlock();
Code generation for the 'else' block is basically identical to codegen
for the 'then' block. The only significant difference is the first line,
which adds the 'else' block to the function. Recall previously that the
'else' block was created, but not added to the function. Now that the
'then' and 'else' blocks are emitted, we can finish up with the merge
code:
.. code-block:: c++
// Emit merge block.
TheFunction->getBasicBlockList().push_back(MergeBB);
Builder.SetInsertPoint(MergeBB);
PHINode *PN =
Builder.CreatePHI(Type::getDoubleTy(TheContext), 2, "iftmp");
PN->addIncoming(ThenV, ThenBB);
PN->addIncoming(ElseV, ElseBB);
return PN;
}
The first two lines here are now familiar: the first adds the "merge"
block to the Function object (it was previously floating, like the else
block above). The second changes the insertion point so that newly
created code will go into the "merge" block. Once that is done, we need
to create the PHI node and set up the block/value pairs for the PHI.
Finally, the CodeGen function returns the phi node as the value computed
by the if/then/else expression. In our example above, this returned
value will feed into the code for the top-level function, which will
create the return instruction.
Overall, we now have the ability to execute conditional code in
Kaleidoscope. With this extension, Kaleidoscope is a fairly complete
language that can calculate a wide variety of numeric functions. Next up
we'll add another useful expression that is familiar from non-functional
languages...
'for' Loop Expression
=====================
Kaleidoscope Tutorial
=====================
Now that we know how to add basic control flow constructs to the
language, we have the tools to add more powerful things. Let's add
something more aggressive, a 'for' expression:
::
extern putchard(char);
def printstar(n)
for i = 1, i < n, 1.0 in
putchard(42); # ascii 42 = '*'
# print 100 '*' characters
printstar(100);
This expression defines a new variable ("i" in this case) which iterates
from a starting value, while the condition ("i < n" in this case) is
true, incrementing by an optional step value ("1.0" in this case). If
the step value is omitted, it defaults to 1.0. While the loop is true,
it executes its body expression. Because we don't have anything better
to return, we'll just define the loop as always returning 0.0. In the
future when we have mutable variables, it will get more useful.
As before, let's talk about the changes that we need to Kaleidoscope to
support this.
Lexer Extensions for the 'for' Loop
-----------------------------------
The lexer extensions are the same sort of thing as for if/then/else:
.. code-block:: c++
... in enum Token ...
// control
tok_if = -6, tok_then = -7, tok_else = -8,
tok_for = -9, tok_in = -10
... in gettok ...
if (IdentifierStr == "def")
return tok_def;
if (IdentifierStr == "extern")
return tok_extern;
if (IdentifierStr == "if")
return tok_if;
if (IdentifierStr == "then")
return tok_then;
if (IdentifierStr == "else")
return tok_else;
if (IdentifierStr == "for")
return tok_for;
if (IdentifierStr == "in")
return tok_in;
return tok_identifier;
AST Extensions for the 'for' Loop
---------------------------------
The AST node is just as simple. It basically boils down to capturing the
variable name and the constituent expressions in the node.
.. code-block:: c++
/// ForExprAST - Expression class for for/in.
class ForExprAST : public ExprAST {
std::string VarName;
std::unique_ptr<ExprAST> Start, End, Step, Body;
public:
ForExprAST(const std::string &VarName, std::unique_ptr<ExprAST> Start,
std::unique_ptr<ExprAST> End, std::unique_ptr<ExprAST> Step,
std::unique_ptr<ExprAST> Body)
: VarName(VarName), Start(std::move(Start)), End(std::move(End)),
Step(std::move(Step)), Body(std::move(Body)) {}
Value *codegen() override;
};
Parser Extensions for the 'for' Loop
------------------------------------
The parser code is also fairly standard. The only interesting thing here
is handling of the optional step value. The parser code handles it by
checking to see if the second comma is present. If not, it sets the step
value to null in the AST node:
.. code-block:: c++
/// forexpr ::= 'for' identifier '=' expr ',' expr (',' expr)? 'in' expression
static std::unique_ptr<ExprAST> ParseForExpr() {
getNextToken(); // eat the for.
if (CurTok != tok_identifier)
return LogError("expected identifier after for");
std::string IdName = IdentifierStr;
getNextToken(); // eat identifier.
if (CurTok != '=')
return LogError("expected '=' after for");
getNextToken(); // eat '='.
auto Start = ParseExpression();
if (!Start)
return nullptr;
if (CurTok != ',')
return LogError("expected ',' after for start value");
getNextToken();
auto End = ParseExpression();
if (!End)
return nullptr;
// The step value is optional.
std::unique_ptr<ExprAST> Step;
if (CurTok == ',') {
getNextToken();
Step = ParseExpression();
if (!Step)
return nullptr;
}
if (CurTok != tok_in)
return LogError("expected 'in' after for");
getNextToken(); // eat 'in'.
auto Body = ParseExpression();
if (!Body)
return nullptr;
return llvm::make_unique<ForExprAST>(IdName, std::move(Start),
std::move(End), std::move(Step),
std::move(Body));
}
And again we hook it up as a primary expression:
.. code-block:: c++
static std::unique_ptr<ExprAST> ParsePrimary() {
switch (CurTok) {
default:
return LogError("unknown token when expecting an expression");
case tok_identifier:
return ParseIdentifierExpr();
case tok_number:
return ParseNumberExpr();
case '(':
return ParseParenExpr();
case tok_if:
return ParseIfExpr();
case tok_for:
return ParseForExpr();
}
}
LLVM IR for the 'for' Loop
--------------------------
Now we get to the good part: the LLVM IR we want to generate for this
thing. With the simple example above, we get this LLVM IR (note that
this dump is generated with optimizations disabled for clarity):
.. code-block:: llvm
declare double @putchard(double)
define double @printstar(double %n) {
entry:
; initial value = 1.0 (inlined into phi)
br label %loop
loop: ; preds = %loop, %entry
%i = phi double [ 1.000000e+00, %entry ], [ %nextvar, %loop ]
; body
%calltmp = call double @putchard(double 4.200000e+01)
; increment
%nextvar = fadd double %i, 1.000000e+00
; termination test
%cmptmp = fcmp ult double %i, %n
%booltmp = uitofp i1 %cmptmp to double
%loopcond = fcmp one double %booltmp, 0.000000e+00
br i1 %loopcond, label %loop, label %afterloop
afterloop: ; preds = %loop
; loop always returns 0.0
ret double 0.000000e+00
}
This loop contains all the same constructs we saw before: a phi node,
several expressions, and some basic blocks. Let's see how this fits
together.
Code Generation for the 'for' Loop
----------------------------------
The first part of codegen is very simple: we just output the start
expression for the loop value:
.. code-block:: c++
Value *ForExprAST::codegen() {
// Emit the start code first, without 'variable' in scope.
Value *StartVal = Start->codegen();
if (!StartVal)
return nullptr;
With this out of the way, the next step is to set up the LLVM basic
block for the start of the loop body. In the case above, the whole loop
body is one block, but remember that the body code itself could consist
of multiple blocks (e.g. if it contains an if/then/else or a for/in
expression).
.. code-block:: c++
// Make the new basic block for the loop header, inserting after current
// block.
Function *TheFunction = Builder.GetInsertBlock()->getParent();
BasicBlock *PreheaderBB = Builder.GetInsertBlock();
BasicBlock *LoopBB =
BasicBlock::Create(TheContext, "loop", TheFunction);
// Insert an explicit fall through from the current block to the LoopBB.
Builder.CreateBr(LoopBB);
This code is similar to what we saw for if/then/else. Because we will
need it to create the Phi node, we remember the block that falls through
into the loop. Once we have that, we create the actual block that starts
the loop and create an unconditional branch for the fall-through between
the two blocks.
.. code-block:: c++
// Start insertion in LoopBB.
Builder.SetInsertPoint(LoopBB);
// Start the PHI node with an entry for Start.
PHINode *Variable = Builder.CreatePHI(Type::getDoubleTy(TheContext),
2, VarName.c_str());
Variable->addIncoming(StartVal, PreheaderBB);
Now that the "preheader" for the loop is set up, we switch to emitting
code for the loop body. To begin with, we move the insertion point and
create the PHI node for the loop induction variable. Since we already
know the incoming value for the starting value, we add it to the Phi
node. Note that the Phi will eventually get a second value for the
backedge, but we can't set it up yet (because it doesn't exist!).
.. code-block:: c++
// Within the loop, the variable is defined equal to the PHI node. If it
// shadows an existing variable, we have to restore it, so save it now.
Value *OldVal = NamedValues[VarName];
NamedValues[VarName] = Variable;
// Emit the body of the loop. This, like any other expr, can change the
// current BB. Note that we ignore the value computed by the body, but don't
// allow an error.
if (!Body->codegen())
return nullptr;
Now the code starts to get more interesting. Our 'for' loop introduces a
new variable to the symbol table. This means that our symbol table can
now contain either function arguments or loop variables. To handle this,
before we codegen the body of the loop, we add the loop variable as the
current value for its name. Note that it is possible that there is a
variable of the same name in the outer scope. It would be easy to make
this an error (emit an error and return null if there is already an
entry for VarName) but we choose to allow shadowing of variables. In
order to handle this correctly, we remember the Value that we are
potentially shadowing in ``OldVal`` (which will be null if there is no
shadowed variable).
Once the loop variable is set into the symbol table, the code
recursively codegen's the body. This allows the body to use the loop
variable: any references to it will naturally find it in the symbol
table.
.. code-block:: c++
// Emit the step value.
Value *StepVal = nullptr;
if (Step) {
StepVal = Step->codegen();
if (!StepVal)
return nullptr;
} else {
// If not specified, use 1.0.
StepVal = ConstantFP::get(TheContext, APFloat(1.0));
}
Value *NextVar = Builder.CreateFAdd(Variable, StepVal, "nextvar");
Now that the body is emitted, we compute the next value of the iteration
variable by adding the step value, or 1.0 if it isn't present.
'``NextVar``' will be the value of the loop variable on the next
iteration of the loop.
.. code-block:: c++
// Compute the end condition.
Value *EndCond = End->codegen();
if (!EndCond)
return nullptr;
// Convert condition to a bool by comparing non-equal to 0.0.
EndCond = Builder.CreateFCmpONE(
EndCond, ConstantFP::get(TheContext, APFloat(0.0)), "loopcond");
Finally, we evaluate the exit value of the loop, to determine whether
the loop should exit. This mirrors the condition evaluation for the
if/then/else statement.
.. code-block:: c++
// Create the "after loop" block and insert it.
BasicBlock *LoopEndBB = Builder.GetInsertBlock();
BasicBlock *AfterBB =
BasicBlock::Create(TheContext, "afterloop", TheFunction);
// Insert the conditional branch into the end of LoopEndBB.
Builder.CreateCondBr(EndCond, LoopBB, AfterBB);
// Any new code will be inserted in AfterBB.
Builder.SetInsertPoint(AfterBB);
With the code for the body of the loop complete, we just need to finish
up the control flow for it. This code remembers the end block (for the
phi node), then creates the block for the loop exit ("afterloop"). Based
on the value of the exit condition, it creates a conditional branch that
chooses between executing the loop again and exiting the loop. Any
future code is emitted in the "afterloop" block, so it sets the
insertion position to it.
.. code-block:: c++
// Add a new entry to the PHI node for the backedge.
Variable->addIncoming(NextVar, LoopEndBB);
// Restore the unshadowed variable.
if (OldVal)
NamedValues[VarName] = OldVal;
else
NamedValues.erase(VarName);
// for expr always returns 0.0.
return Constant::getNullValue(Type::getDoubleTy(TheContext));
}
The final code handles various cleanups: now that we have the "NextVar"
value, we can add the incoming value to the loop PHI node. After that,
we remove the loop variable from the symbol table, so that it isn't in
scope after the for loop. Finally, code generation of the for loop
always returns 0.0, so that is what we return from
``ForExprAST::codegen()``.
With this, we conclude the "adding control flow to Kaleidoscope" chapter
of the tutorial. In this chapter we added two control flow constructs,
and used them to motivate a couple of aspects of the LLVM IR that are
important for front-end implementors to know. In the next chapter of our
saga, we will get a bit crazier and add `user-defined
operators <LangImpl06.html>`_ to our poor innocent language.
Full Code Listing
=================
Here is the complete code listing for our running example, enhanced with
the if/then/else and for expressions. To build this example, use:
.. code-block:: bash
# Compile
clang++ -g toy.cpp `llvm-config --cxxflags --ldflags --system-libs --libs core mcjit native` -O3 -o toy
# Run
./toy
Here is the code:
.. literalinclude:: ../../examples/Kaleidoscope/Chapter5/toy.cpp
:language: c++
`Next: Extending the language: user-defined operators <LangImpl06.html>`_
The Kaleidoscope Tutorial has `moved to another location <MyFirstLanguageFrontend/index>`_ .

771
llvm/docs/tutorial/LangImpl06.rst
View File

@ -1,768 +1,7 @@
============================================================
Kaleidoscope: Extending the Language: User-defined Operators
============================================================
:orphan:
.. contents::
:local:
Chapter 6 Introduction
======================
Welcome to Chapter 6 of the "`Implementing a language with
LLVM <index.html>`_" tutorial. At this point in our tutorial, we now
have a fully functional language that is fairly minimal, but also
useful. There is still one big problem with it, however. Our language
doesn't have many useful operators (like division, logical negation, or
even any comparisons besides less-than).
This chapter of the tutorial takes a wild digression into adding
user-defined operators to the simple and beautiful Kaleidoscope
language. This digression now gives us a simple and ugly language in
some ways, but also a powerful one at the same time. One of the great
things about creating your own language is that you get to decide what
is good or bad. In this tutorial we'll assume that it is okay to use
this as a way to show some interesting parsing techniques.
At the end of this tutorial, we'll run through an example Kaleidoscope
application that `renders the Mandelbrot set <#kicking-the-tires>`_. This gives an
example of what you can build with Kaleidoscope and its feature set.
User-defined Operators: the Idea
================================
The "operator overloading" that we will add to Kaleidoscope is more
general than in languages like C++. In C++, you are only allowed to
redefine existing operators: you can't programmatically change the
grammar, introduce new operators, change precedence levels, etc. In this
chapter, we will add this capability to Kaleidoscope, which will let the
user round out the set of operators that are supported.
The point of going into user-defined operators in a tutorial like this
is to show the power and flexibility of using a hand-written parser.
Thus far, the parser we have been implementing uses recursive descent
for most parts of the grammar and operator precedence parsing for the
expressions. See `Chapter 2 <LangImpl02.html>`_ for details. By
using operator precedence parsing, it is very easy to allow
the programmer to introduce new operators into the grammar: the grammar
is dynamically extensible as the JIT runs.
The two specific features we'll add are programmable unary operators
(right now, Kaleidoscope has no unary operators at all) as well as
binary operators. An example of this is:
::
# Logical unary not.
def unary!(v)
if v then
0
else
1;
# Define > with the same precedence as <.
def binary> 10 (LHS RHS)
RHS < LHS;
# Binary "logical or", (note that it does not "short circuit")
def binary| 5 (LHS RHS)
if LHS then
1
else if RHS then
1
else
0;
# Define = with slightly lower precedence than relationals.
def binary= 9 (LHS RHS)
!(LHS < RHS | LHS > RHS);
Many languages aspire to being able to implement their standard runtime
library in the language itself. In Kaleidoscope, we can implement
significant parts of the language in the library!
We will break down implementation of these features into two parts:
implementing support for user-defined binary operators and adding unary
operators.
User-defined Binary Operators
=============================
Adding support for user-defined binary operators is pretty simple with
our current framework. We'll first add support for the unary/binary
keywords:
.. code-block:: c++
enum Token {
...
// operators
tok_binary = -11,
tok_unary = -12
};
...
static int gettok() {
...
if (IdentifierStr == "for")
return tok_for;
if (IdentifierStr == "in")
return tok_in;
if (IdentifierStr == "binary")
return tok_binary;
if (IdentifierStr == "unary")
return tok_unary;
return tok_identifier;
This just adds lexer support for the unary and binary keywords, like we
did in `previous chapters <LangImpl5.html#lexer-extensions-for-if-then-else>`_. One nice thing
about our current AST, is that we represent binary operators with full
generalisation by using their ASCII code as the opcode. For our extended
operators, we'll use this same representation, so we don't need any new
AST or parser support.
On the other hand, we have to be able to represent the definitions of
these new operators, in the "def binary\| 5" part of the function
definition. In our grammar so far, the "name" for the function
definition is parsed as the "prototype" production and into the
``PrototypeAST`` AST node. To represent our new user-defined operators
as prototypes, we have to extend the ``PrototypeAST`` AST node like
this:
.. code-block:: c++
/// PrototypeAST - This class represents the "prototype" for a function,
/// which captures its argument names as well as if it is an operator.
class PrototypeAST {
std::string Name;
std::vector<std::string> Args;
bool IsOperator;
unsigned Precedence; // Precedence if a binary op.
public:
PrototypeAST(const std::string &name, std::vector<std::string> Args,
bool IsOperator = false, unsigned Prec = 0)
: Name(name), Args(std::move(Args)), IsOperator(IsOperator),
Precedence(Prec) {}
Function *codegen();
const std::string &getName() const { return Name; }
bool isUnaryOp() const { return IsOperator && Args.size() == 1; }
bool isBinaryOp() const { return IsOperator && Args.size() == 2; }
char getOperatorName() const {
assert(isUnaryOp() || isBinaryOp());
return Name[Name.size() - 1];
}
unsigned getBinaryPrecedence() const { return Precedence; }
};
Basically, in addition to knowing a name for the prototype, we now keep
track of whether it was an operator, and if it was, what precedence
level the operator is at. The precedence is only used for binary
operators (as you'll see below, it just doesn't apply for unary
operators). Now that we have a way to represent the prototype for a
user-defined operator, we need to parse it:
.. code-block:: c++
/// prototype
/// ::= id '(' id* ')'
/// ::= binary LETTER number? (id, id)
static std::unique_ptr<PrototypeAST> ParsePrototype() {
std::string FnName;
unsigned Kind = 0; // 0 = identifier, 1 = unary, 2 = binary.
unsigned BinaryPrecedence = 30;
switch (CurTok) {
default:
return LogErrorP("Expected function name in prototype");
case tok_identifier:
FnName = IdentifierStr;
Kind = 0;
getNextToken();
break;
case tok_binary:
getNextToken();
if (!isascii(CurTok))
return LogErrorP("Expected binary operator");
FnName = "binary";
FnName += (char)CurTok;
Kind = 2;
getNextToken();
// Read the precedence if present.
if (CurTok == tok_number) {
if (NumVal < 1 || NumVal > 100)
return LogErrorP("Invalid precedence: must be 1..100");
BinaryPrecedence = (unsigned)NumVal;
getNextToken();
}
break;
}
if (CurTok != '(')
return LogErrorP("Expected '(' in prototype");
std::vector<std::string> ArgNames;
while (getNextToken() == tok_identifier)
ArgNames.push_back(IdentifierStr);
if (CurTok != ')')
return LogErrorP("Expected ')' in prototype");
// success.
getNextToken(); // eat ')'.
// Verify right number of names for operator.
if (Kind && ArgNames.size() != Kind)
return LogErrorP("Invalid number of operands for operator");
return llvm::make_unique<PrototypeAST>(FnName, std::move(ArgNames), Kind != 0,
BinaryPrecedence);
}
This is all fairly straightforward parsing code, and we have already
seen a lot of similar code in the past. One interesting part about the
code above is the couple lines that set up ``FnName`` for binary
operators. This builds names like "binary@" for a newly defined "@"
operator. It then takes advantage of the fact that symbol names in the
LLVM symbol table are allowed to have any character in them, including
embedded nul characters.
The next interesting thing to add, is codegen support for these binary
operators. Given our current structure, this is a simple addition of a
default case for our existing binary operator node:
.. code-block:: c++
Value *BinaryExprAST::codegen() {
Value *L = LHS->codegen();
Value *R = RHS->codegen();
if (!L || !R)
return nullptr;
switch (Op) {
case '+':
return Builder.CreateFAdd(L, R, "addtmp");
case '-':
return Builder.CreateFSub(L, R, "subtmp");
case '*':
return Builder.CreateFMul(L, R, "multmp");
case '<':
L = Builder.CreateFCmpULT(L, R, "cmptmp");
// Convert bool 0/1 to double 0.0 or 1.0
return Builder.CreateUIToFP(L, Type::getDoubleTy(TheContext),
"booltmp");
default:
break;
}
// If it wasn't a builtin binary operator, it must be a user defined one. Emit
// a call to it.
Function *F = getFunction(std::string("binary") + Op);
assert(F && "binary operator not found!");
Value *Ops[2] = { L, R };
return Builder.CreateCall(F, Ops, "binop");
}
As you can see above, the new code is actually really simple. It just
does a lookup for the appropriate operator in the symbol table and
generates a function call to it. Since user-defined operators are just
built as normal functions (because the "prototype" boils down to a
function with the right name) everything falls into place.
The final piece of code we are missing, is a bit of top-level magic:
.. code-block:: c++
Function *FunctionAST::codegen() {
// Transfer ownership of the prototype to the FunctionProtos map, but keep a
// reference to it for use below.
auto &P = *Proto;
FunctionProtos[Proto->getName()] = std::move(Proto);
Function *TheFunction = getFunction(P.getName());
if (!TheFunction)
return nullptr;
// If this is an operator, install it.
if (P.isBinaryOp())
BinopPrecedence[P.getOperatorName()] = P.getBinaryPrecedence();
// Create a new basic block to start insertion into.
BasicBlock *BB = BasicBlock::Create(TheContext, "entry", TheFunction);
...
Basically, before codegening a function, if it is a user-defined
operator, we register it in the precedence table. This allows the binary
operator parsing logic we already have in place to handle it. Since we
are working on a fully-general operator precedence parser, this is all
we need to do to "extend the grammar".
Now we have useful user-defined binary operators. This builds a lot on
the previous framework we built for other operators. Adding unary
operators is a bit more challenging, because we don't have any framework
for it yet - let's see what it takes.
User-defined Unary Operators
============================
Since we don't currently support unary operators in the Kaleidoscope
language, we'll need to add everything to support them. Above, we added
simple support for the 'unary' keyword to the lexer. In addition to
that, we need an AST node:
.. code-block:: c++
/// UnaryExprAST - Expression class for a unary operator.
class UnaryExprAST : public ExprAST {
char Opcode;
std::unique_ptr<ExprAST> Operand;
public:
UnaryExprAST(char Opcode, std::unique_ptr<ExprAST> Operand)
: Opcode(Opcode), Operand(std::move(Operand)) {}
Value *codegen() override;
};
This AST node is very simple and obvious by now. It directly mirrors the
binary operator AST node, except that it only has one child. With this,
we need to add the parsing logic. Parsing a unary operator is pretty
simple: we'll add a new function to do it:
.. code-block:: c++
/// unary
/// ::= primary
/// ::= '!' unary
static std::unique_ptr<ExprAST> ParseUnary() {
// If the current token is not an operator, it must be a primary expr.
if (!isascii(CurTok) || CurTok == '(' || CurTok == ',')
return ParsePrimary();
// If this is a unary operator, read it.
int Opc = CurTok;
getNextToken();
if (auto Operand = ParseUnary())
return llvm::make_unique<UnaryExprAST>(Opc, std::move(Operand));
return nullptr;
}
The grammar we add is pretty straightforward here. If we see a unary
operator when parsing a primary operator, we eat the operator as a
prefix and parse the remaining piece as another unary operator. This
allows us to handle multiple unary operators (e.g. "!!x"). Note that
unary operators can't have ambiguous parses like binary operators can,
so there is no need for precedence information.
The problem with this function, is that we need to call ParseUnary from
somewhere. To do this, we change previous callers of ParsePrimary to
call ParseUnary instead:
.. code-block:: c++
/// binoprhs
/// ::= ('+' unary)*
static std::unique_ptr<ExprAST> ParseBinOpRHS(int ExprPrec,
std::unique_ptr<ExprAST> LHS) {
...
// Parse the unary expression after the binary operator.
auto RHS = ParseUnary();
if (!RHS)
return nullptr;
...
}
/// expression
/// ::= unary binoprhs
///
static std::unique_ptr<ExprAST> ParseExpression() {
auto LHS = ParseUnary();
if (!LHS)
return nullptr;
return ParseBinOpRHS(0, std::move(LHS));
}
With these two simple changes, we are now able to parse unary operators
and build the AST for them. Next up, we need to add parser support for
prototypes, to parse the unary operator prototype. We extend the binary
operator code above with:
.. code-block:: c++
/// prototype
/// ::= id '(' id* ')'
/// ::= binary LETTER number? (id, id)
/// ::= unary LETTER (id)
static std::unique_ptr<PrototypeAST> ParsePrototype() {
std::string FnName;
unsigned Kind = 0; // 0 = identifier, 1 = unary, 2 = binary.
unsigned BinaryPrecedence = 30;
switch (CurTok) {
default:
return LogErrorP("Expected function name in prototype");
case tok_identifier:
FnName = IdentifierStr;
Kind = 0;
getNextToken();
break;
case tok_unary:
getNextToken();
if (!isascii(CurTok))
return LogErrorP("Expected unary operator");
FnName = "unary";
FnName += (char)CurTok;
Kind = 1;
getNextToken();
break;
case tok_binary:
...
As with binary operators, we name unary operators with a name that
includes the operator character. This assists us at code generation
time. Speaking of, the final piece we need to add is codegen support for
unary operators. It looks like this:
.. code-block:: c++
Value *UnaryExprAST::codegen() {
Value *OperandV = Operand->codegen();
if (!OperandV)
return nullptr;
Function *F = getFunction(std::string("unary") + Opcode);
if (!F)
return LogErrorV("Unknown unary operator");
return Builder.CreateCall(F, OperandV, "unop");
}
This code is similar to, but simpler than, the code for binary
operators. It is simpler primarily because it doesn't need to handle any
predefined operators.
Kicking the Tires
=================
It is somewhat hard to believe, but with a few simple extensions we've
covered in the last chapters, we have grown a real-ish language. With
this, we can do a lot of interesting things, including I/O, math, and a
bunch of other things. For example, we can now add a nice sequencing
operator (printd is defined to print out the specified value and a
newline):
::
ready> extern printd(x);
Read extern:
declare double @printd(double)
ready> def binary : 1 (x y) 0; # Low-precedence operator that ignores operands.
...
ready> printd(123) : printd(456) : printd(789);
123.000000
456.000000
789.000000
Evaluated to 0.000000
We can also define a bunch of other "primitive" operations, such as:
::
# Logical unary not.
def unary!(v)
if v then
0
else
1;
# Unary negate.
def unary-(v)
0-v;
# Define > with the same precedence as <.
def binary> 10 (LHS RHS)
RHS < LHS;
# Binary logical or, which does not short circuit.
def binary| 5 (LHS RHS)
if LHS then
1
else if RHS then
1
else
0;
# Binary logical and, which does not short circuit.
def binary& 6 (LHS RHS)
if !LHS then
0
else
!!RHS;
# Define = with slightly lower precedence than relationals.
def binary = 9 (LHS RHS)
!(LHS < RHS | LHS > RHS);
# Define ':' for sequencing: as a low-precedence operator that ignores operands
# and just returns the RHS.
def binary : 1 (x y) y;
Given the previous if/then/else support, we can also define interesting
functions for I/O. For example, the following prints out a character
whose "density" reflects the value passed in: the lower the value, the
denser the character:
::
ready> extern putchard(char);
...
ready> def printdensity(d)
if d > 8 then
putchard(32) # ' '
else if d > 4 then
putchard(46) # '.'
else if d > 2 then
putchard(43) # '+'
else
putchard(42); # '*'
...
ready> printdensity(1): printdensity(2): printdensity(3):
printdensity(4): printdensity(5): printdensity(9):
putchard(10);
**++.
Evaluated to 0.000000
Based on these simple primitive operations, we can start to define more
interesting things. For example, here's a little function that determines
the number of iterations it takes for a certain function in the complex
plane to diverge:
::
# Determine whether the specific location diverges.
# Solve for z = z^2 + c in the complex plane.
def mandelconverger(real imag iters creal cimag)
if iters > 255 | (real*real + imag*imag > 4) then
iters
else
mandelconverger(real*real - imag*imag + creal,
2*real*imag + cimag,
iters+1, creal, cimag);
# Return the number of iterations required for the iteration to escape
def mandelconverge(real imag)
mandelconverger(real, imag, 0, real, imag);
This "``z = z2 + c``" function is a beautiful little creature that is
the basis for computation of the `Mandelbrot
Set <http://en.wikipedia.org/wiki/Mandelbrot_set>`_. Our
``mandelconverge`` function returns the number of iterations that it
takes for a complex orbit to escape, saturating to 255. This is not a
very useful function by itself, but if you plot its value over a
two-dimensional plane, you can see the Mandelbrot set. Given that we are
limited to using putchard here, our amazing graphical output is limited,
but we can whip together something using the density plotter above:
::
# Compute and plot the mandelbrot set with the specified 2 dimensional range
# info.
def mandelhelp(xmin xmax xstep ymin ymax ystep)
for y = ymin, y < ymax, ystep in (
(for x = xmin, x < xmax, xstep in
printdensity(mandelconverge(x,y)))
: putchard(10)
)
# mandel - This is a convenient helper function for plotting the mandelbrot set
# from the specified position with the specified Magnification.
def mandel(realstart imagstart realmag imagmag)
mandelhelp(realstart, realstart+realmag*78, realmag,
imagstart, imagstart+imagmag*40, imagmag);
Given this, we can try plotting out the mandelbrot set! Lets try it out:
::
ready> mandel(-2.3, -1.3, 0.05, 0.07);
*******************************+++++++++++*************************************
*************************+++++++++++++++++++++++*******************************
**********************+++++++++++++++++++++++++++++****************************
*******************+++++++++++++++++++++.. ...++++++++*************************
*****************++++++++++++++++++++++.... ...+++++++++***********************
***************+++++++++++++++++++++++..... ...+++++++++*********************
**************+++++++++++++++++++++++.... ....+++++++++********************
*************++++++++++++++++++++++...... .....++++++++*******************
************+++++++++++++++++++++....... .......+++++++******************
***********+++++++++++++++++++.... ... .+++++++*****************
**********+++++++++++++++++....... .+++++++****************
*********++++++++++++++........... ...+++++++***************
********++++++++++++............ ...++++++++**************
********++++++++++... .......... .++++++++**************
*******+++++++++..... .+++++++++*************
*******++++++++...... ..+++++++++*************
*******++++++....... ..+++++++++*************
*******+++++...... ..+++++++++*************
*******.... .... ...+++++++++*************
*******.... . ...+++++++++*************
*******+++++...... ...+++++++++*************
*******++++++....... ..+++++++++*************
*******++++++++...... .+++++++++*************
*******+++++++++..... ..+++++++++*************
********++++++++++... .......... .++++++++**************
********++++++++++++............ ...++++++++**************
*********++++++++++++++.......... ...+++++++***************
**********++++++++++++++++........ .+++++++****************
**********++++++++++++++++++++.... ... ..+++++++****************
***********++++++++++++++++++++++....... .......++++++++*****************
************+++++++++++++++++++++++...... ......++++++++******************
**************+++++++++++++++++++++++.... ....++++++++********************
***************+++++++++++++++++++++++..... ...+++++++++*********************
*****************++++++++++++++++++++++.... ...++++++++***********************
*******************+++++++++++++++++++++......++++++++*************************
*********************++++++++++++++++++++++.++++++++***************************
*************************+++++++++++++++++++++++*******************************
******************************+++++++++++++************************************
*******************************************************************************
*******************************************************************************
*******************************************************************************
Evaluated to 0.000000
ready> mandel(-2, -1, 0.02, 0.04);
**************************+++++++++++++++++++++++++++++++++++++++++++++++++++++
***********************++++++++++++++++++++++++++++++++++++++++++++++++++++++++
*********************+++++++++++++++++++++++++++++++++++++++++++++++++++++++++.
*******************+++++++++++++++++++++++++++++++++++++++++++++++++++++++++...
*****************+++++++++++++++++++++++++++++++++++++++++++++++++++++++++.....
***************++++++++++++++++++++++++++++++++++++++++++++++++++++++++........
**************++++++++++++++++++++++++++++++++++++++++++++++++++++++...........
************+++++++++++++++++++++++++++++++++++++++++++++++++++++..............
***********++++++++++++++++++++++++++++++++++++++++++++++++++........ .
**********++++++++++++++++++++++++++++++++++++++++++++++.............
********+++++++++++++++++++++++++++++++++++++++++++..................
*******+++++++++++++++++++++++++++++++++++++++.......................
******+++++++++++++++++++++++++++++++++++...........................
*****++++++++++++++++++++++++++++++++............................
*****++++++++++++++++++++++++++++...............................
****++++++++++++++++++++++++++...... .........................
***++++++++++++++++++++++++......... ...... ...........
***++++++++++++++++++++++............
**+++++++++++++++++++++..............
**+++++++++++++++++++................
*++++++++++++++++++.................
*++++++++++++++++............ ...
*++++++++++++++..............
*+++....++++................
*.......... ...........
*
*.......... ...........
*+++....++++................
*++++++++++++++..............
*++++++++++++++++............ ...
*++++++++++++++++++.................
**+++++++++++++++++++................
**+++++++++++++++++++++..............
***++++++++++++++++++++++............
***++++++++++++++++++++++++......... ...... ...........
****++++++++++++++++++++++++++...... .........................
*****++++++++++++++++++++++++++++...............................
*****++++++++++++++++++++++++++++++++............................
******+++++++++++++++++++++++++++++++++++...........................
*******+++++++++++++++++++++++++++++++++++++++.......................
********+++++++++++++++++++++++++++++++++++++++++++..................
Evaluated to 0.000000
ready> mandel(-0.9, -1.4, 0.02, 0.03);
*******************************************************************************
*******************************************************************************
*******************************************************************************
**********+++++++++++++++++++++************************************************
*+++++++++++++++++++++++++++++++++++++++***************************************
+++++++++++++++++++++++++++++++++++++++++++++**********************************
++++++++++++++++++++++++++++++++++++++++++++++++++*****************************
++++++++++++++++++++++++++++++++++++++++++++++++++++++*************************
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++**********************
+++++++++++++++++++++++++++++++++.........++++++++++++++++++*******************
+++++++++++++++++++++++++++++++.... ......+++++++++++++++++++****************
+++++++++++++++++++++++++++++....... ........+++++++++++++++++++**************
++++++++++++++++++++++++++++........ ........++++++++++++++++++++************
+++++++++++++++++++++++++++......... .. ...+++++++++++++++++++++**********
++++++++++++++++++++++++++........... ....++++++++++++++++++++++********
++++++++++++++++++++++++............. .......++++++++++++++++++++++******
+++++++++++++++++++++++............. ........+++++++++++++++++++++++****
++++++++++++++++++++++........... ..........++++++++++++++++++++++***
++++++++++++++++++++........... .........++++++++++++++++++++++*
++++++++++++++++++............ ...........++++++++++++++++++++
++++++++++++++++............... .............++++++++++++++++++
++++++++++++++................. ...............++++++++++++++++
++++++++++++.................. .................++++++++++++++
+++++++++.................. .................+++++++++++++
++++++........ . ......... ..++++++++++++
++............ ...... ....++++++++++
.............. ...++++++++++
.............. ....+++++++++
.............. .....++++++++
............. ......++++++++
........... .......++++++++
......... ........+++++++
......... ........+++++++
......... ....+++++++
........ ...+++++++
....... ...+++++++
....+++++++
.....+++++++
....+++++++
....+++++++
....+++++++
Evaluated to 0.000000
ready> ^D
At this point, you may be starting to realize that Kaleidoscope is a
real and powerful language. It may not be self-similar :), but it can be
used to plot things that are!
With this, we conclude the "adding user-defined operators" chapter of
the tutorial. We have successfully augmented our language, adding the
ability to extend the language in the library, and we have shown how
this can be used to build a simple but interesting end-user application
in Kaleidoscope. At this point, Kaleidoscope can build a variety of
applications that are functional and can call functions with
side-effects, but it can't actually define and mutate a variable itself.
Strikingly, variable mutation is an important feature of some languages,
and it is not at all obvious how to `add support for mutable
variables <LangImpl07.html>`_ without having to add an "SSA construction"
phase to your front-end. In the next chapter, we will describe how you
can add variable mutation without building SSA in your front-end.
Full Code Listing
=================
Here is the complete code listing for our running example, enhanced with
the support for user-defined operators. To build this example, use:
.. code-block:: bash
# Compile
clang++ -g toy.cpp `llvm-config --cxxflags --ldflags --system-libs --libs core mcjit native` -O3 -o toy
# Run
./toy
On some platforms, you will need to specify -rdynamic or
-Wl,--export-dynamic when linking. This ensures that symbols defined in
the main executable are exported to the dynamic linker and so are
available for symbol resolution at run time. This is not needed if you
compile your support code into a shared library, although doing that
will cause problems on Windows.
Here is the code:
.. literalinclude:: ../../examples/Kaleidoscope/Chapter6/toy.cpp
:language: c++
`Next: Extending the language: mutable variables / SSA
construction <LangImpl07.html>`_
=====================
Kaleidoscope Tutorial
=====================
The Kaleidoscope Tutorial has `moved to another location <MyFirstLanguageFrontend/index>`_ .

886
llvm/docs/tutorial/LangImpl07.rst
View File

@ -1,883 +1,7 @@
=======================================================
Kaleidoscope: Extending the Language: Mutable Variables
=======================================================
:orphan:
.. contents::
:local:
Chapter 7 Introduction
======================
Welcome to Chapter 7 of the "`Implementing a language with
LLVM <index.html>`_" tutorial. In chapters 1 through 6, we've built a
very respectable, albeit simple, `functional programming
language <http://en.wikipedia.org/wiki/Functional_programming>`_. In our
journey, we learned some parsing techniques, how to build and represent
an AST, how to build LLVM IR, and how to optimize the resultant code as
well as JIT compile it.
While Kaleidoscope is interesting as a functional language, the fact
that it is functional makes it "too easy" to generate LLVM IR for it. In
particular, a functional language makes it very easy to build LLVM IR
directly in `SSA
form <http://en.wikipedia.org/wiki/Static_single_assignment_form>`_.
Since LLVM requires that the input code be in SSA form, this is a very
nice property and it is often unclear to newcomers how to generate code
for an imperative language with mutable variables.
The short (and happy) summary of this chapter is that there is no need
for your front-end to build SSA form: LLVM provides highly tuned and
well tested support for this, though the way it works is a bit
unexpected for some.
Why is this a hard problem?
===========================
To understand why mutable variables cause complexities in SSA
construction, consider this extremely simple C example:
.. code-block:: c
int G, H;
int test(_Bool Condition) {
int X;
if (Condition)
X = G;
else
X = H;
return X;
}
In this case, we have the variable "X", whose value depends on the path
executed in the program. Because there are two different possible values
for X before the return instruction, a PHI node is inserted to merge the
two values. The LLVM IR that we want for this example looks like this:
.. code-block:: llvm
@G = weak global i32 0 ; type of @G is i32*
@H = weak global i32 0 ; type of @H is i32*
define i32 @test(i1 %Condition) {
entry:
br i1 %Condition, label %cond_true, label %cond_false
cond_true:
%X.0 = load i32* @G
br label %cond_next
cond_false:
%X.1 = load i32* @H
br label %cond_next
cond_next:
%X.2 = phi i32 [ %X.1, %cond_false ], [ %X.0, %cond_true ]
ret i32 %X.2
}
In this example, the loads from the G and H global variables are
explicit in the LLVM IR, and they live in the then/else branches of the
if statement (cond\_true/cond\_false). In order to merge the incoming
values, the X.2 phi node in the cond\_next block selects the right value
to use based on where control flow is coming from: if control flow comes
from the cond\_false block, X.2 gets the value of X.1. Alternatively, if
control flow comes from cond\_true, it gets the value of X.0. The intent
of this chapter is not to explain the details of SSA form. For more
information, see one of the many `online
references <http://en.wikipedia.org/wiki/Static_single_assignment_form>`_.
The question for this article is "who places the phi nodes when lowering
assignments to mutable variables?". The issue here is that LLVM
*requires* that its IR be in SSA form: there is no "non-ssa" mode for
it. However, SSA construction requires non-trivial algorithms and data
structures, so it is inconvenient and wasteful for every front-end to
have to reproduce this logic.
Memory in LLVM
==============
The 'trick' here is that while LLVM does require all register values to
be in SSA form, it does not require (or permit) memory objects to be in
SSA form. In the example above, note that the loads from G and H are
direct accesses to G and H: they are not renamed or versioned. This
differs from some other compiler systems, which do try to version memory
objects. In LLVM, instead of encoding dataflow analysis of memory into
the LLVM IR, it is handled with `Analysis
Passes <../WritingAnLLVMPass.html>`_ which are computed on demand.
With this in mind, the high-level idea is that we want to make a stack
variable (which lives in memory, because it is on the stack) for each
mutable object in a function. To take advantage of this trick, we need
to talk about how LLVM represents stack variables.
In LLVM, all memory accesses are explicit with load/store instructions,
and it is carefully designed not to have (or need) an "address-of"
operator. Notice how the type of the @G/@H global variables is actually
"i32\*" even though the variable is defined as "i32". What this means is
that @G defines *space* for an i32 in the global data area, but its
*name* actually refers to the address for that space. Stack variables
work the same way, except that instead of being declared with global
variable definitions, they are declared with the `LLVM alloca
instruction <../LangRef.html#alloca-instruction>`_:
.. code-block:: llvm
define i32 @example() {
entry:
%X = alloca i32 ; type of %X is i32*.
...
%tmp = load i32* %X ; load the stack value %X from the stack.
%tmp2 = add i32 %tmp, 1 ; increment it
store i32 %tmp2, i32* %X ; store it back
...
This code shows an example of how you can declare and manipulate a stack
variable in the LLVM IR. Stack memory allocated with the alloca
instruction is fully general: you can pass the address of the stack slot
to functions, you can store it in other variables, etc. In our example
above, we could rewrite the example to use the alloca technique to avoid
using a PHI node:
.. code-block:: llvm
@G = weak global i32 0 ; type of @G is i32*
@H = weak global i32 0 ; type of @H is i32*
define i32 @test(i1 %Condition) {
entry:
%X = alloca i32 ; type of %X is i32*.
br i1 %Condition, label %cond_true, label %cond_false
cond_true:
%X.0 = load i32* @G
store i32 %X.0, i32* %X ; Update X
br label %cond_next
cond_false:
%X.1 = load i32* @H
store i32 %X.1, i32* %X ; Update X
br label %cond_next
cond_next:
%X.2 = load i32* %X ; Read X
ret i32 %X.2
}
With this, we have discovered a way to handle arbitrary mutable
variables without the need to create Phi nodes at all:
#. Each mutable variable becomes a stack allocation.
#. Each read of the variable becomes a load from the stack.
#. Each update of the variable becomes a store to the stack.
#. Taking the address of a variable just uses the stack address
directly.
While this solution has solved our immediate problem, it introduced
another one: we have now apparently introduced a lot of stack traffic
for very simple and common operations, a major performance problem.
Fortunately for us, the LLVM optimizer has a highly-tuned optimization
pass named "mem2reg" that handles this case, promoting allocas like this
into SSA registers, inserting Phi nodes as appropriate. If you run this
example through the pass, for example, you'll get:
.. code-block:: bash
$ llvm-as < example.ll | opt -mem2reg | llvm-dis
@G = weak global i32 0
@H = weak global i32 0
define i32 @test(i1 %Condition) {
entry:
br i1 %Condition, label %cond_true, label %cond_false
cond_true:
%X.0 = load i32* @G
br label %cond_next
cond_false:
%X.1 = load i32* @H
br label %cond_next
cond_next:
%X.01 = phi i32 [ %X.1, %cond_false ], [ %X.0, %cond_true ]
ret i32 %X.01
}
The mem2reg pass implements the standard "iterated dominance frontier"
algorithm for constructing SSA form and has a number of optimizations
that speed up (very common) degenerate cases. The mem2reg optimization
pass is the answer to dealing with mutable variables, and we highly
recommend that you depend on it. Note that mem2reg only works on
variables in certain circumstances:
#. mem2reg is alloca-driven: it looks for allocas and if it can handle
them, it promotes them. It does not apply to global variables or heap
allocations.
#. mem2reg only looks for alloca instructions in the entry block of the
function. Being in the entry block guarantees that the alloca is only
executed once, which makes analysis simpler.
#. mem2reg only promotes allocas whose uses are direct loads and stores.
If the address of the stack object is passed to a function, or if any
funny pointer arithmetic is involved, the alloca will not be
promoted.
#. mem2reg only works on allocas of `first
class <../LangRef.html#first-class-types>`_ values (such as pointers,
scalars and vectors), and only if the array size of the allocation is
1 (or missing in the .ll file). mem2reg is not capable of promoting
structs or arrays to registers. Note that the "sroa" pass is
more powerful and can promote structs, "unions", and arrays in many
cases.
All of these properties are easy to satisfy for most imperative
languages, and we'll illustrate it below with Kaleidoscope. The final
question you may be asking is: should I bother with this nonsense for my
front-end? Wouldn't it be better if I just did SSA construction
directly, avoiding use of the mem2reg optimization pass? In short, we
strongly recommend that you use this technique for building SSA form,
unless there is an extremely good reason not to. Using this technique
is:
- Proven and well tested: clang uses this technique
for local mutable variables. As such, the most common clients of LLVM
are using this to handle a bulk of their variables. You can be sure
that bugs are found fast and fixed early.
- Extremely Fast: mem2reg has a number of special cases that make it
fast in common cases as well as fully general. For example, it has
fast-paths for variables that are only used in a single block,
variables that only have one assignment point, good heuristics to
avoid insertion of unneeded phi nodes, etc.
- Needed for debug info generation: `Debug information in
LLVM <../SourceLevelDebugging.html>`_ relies on having the address of
the variable exposed so that debug info can be attached to it. This
technique dovetails very naturally with this style of debug info.
If nothing else, this makes it much easier to get your front-end up and
running, and is very simple to implement. Let's extend Kaleidoscope with
mutable variables now!
Mutable Variables in Kaleidoscope
=================================
Now that we know the sort of problem we want to tackle, let's see what
this looks like in the context of our little Kaleidoscope language.
We're going to add two features:
#. The ability to mutate variables with the '=' operator.
#. The ability to define new variables.
While the first item is really what this is about, we only have
variables for incoming arguments as well as for induction variables, and
redefining those only goes so far :). Also, the ability to define new
variables is a useful thing regardless of whether you will be mutating
them. Here's a motivating example that shows how we could use these:
::
# Define ':' for sequencing: as a low-precedence operator that ignores operands
# and just returns the RHS.
def binary : 1 (x y) y;
# Recursive fib, we could do this before.
def fib(x)
if (x < 3) then
1
else
fib(x-1)+fib(x-2);
# Iterative fib.
def fibi(x)
var a = 1, b = 1, c in
(for i = 3, i < x in
c = a + b :
a = b :
b = c) :
b;
# Call it.
fibi(10);
In order to mutate variables, we have to change our existing variables
to use the "alloca trick". Once we have that, we'll add our new
operator, then extend Kaleidoscope to support new variable definitions.
Adjusting Existing Variables for Mutation
=========================================
The symbol table in Kaleidoscope is managed at code generation time by
the '``NamedValues``' map. This map currently keeps track of the LLVM
"Value\*" that holds the double value for the named variable. In order
to support mutation, we need to change this slightly, so that
``NamedValues`` holds the *memory location* of the variable in question.
Note that this change is a refactoring: it changes the structure of the
code, but does not (by itself) change the behavior of the compiler. All
of these changes are isolated in the Kaleidoscope code generator.
At this point in Kaleidoscope's development, it only supports variables
for two things: incoming arguments to functions and the induction
variable of 'for' loops. For consistency, we'll allow mutation of these
variables in addition to other user-defined variables. This means that
these will both need memory locations.
To start our transformation of Kaleidoscope, we'll change the
NamedValues map so that it maps to AllocaInst\* instead of Value\*. Once
we do this, the C++ compiler will tell us what parts of the code we need
to update:
.. code-block:: c++
static std::map<std::string, AllocaInst*> NamedValues;
Also, since we will need to create these allocas, we'll use a helper
function that ensures that the allocas are created in the entry block of
the function:
.. code-block:: c++
/// CreateEntryBlockAlloca - Create an alloca instruction in the entry block of
/// the function. This is used for mutable variables etc.
static AllocaInst *CreateEntryBlockAlloca(Function *TheFunction,
const std::string &VarName) {
IRBuilder<> TmpB(&TheFunction->getEntryBlock(),
TheFunction->getEntryBlock().begin());
return TmpB.CreateAlloca(Type::getDoubleTy(TheContext), 0,
VarName.c_str());
}
This funny looking code creates an IRBuilder object that is pointing at
the first instruction (.begin()) of the entry block. It then creates an
alloca with the expected name and returns it. Because all values in
Kaleidoscope are doubles, there is no need to pass in a type to use.
With this in place, the first functionality change we want to make belongs to
variable references. In our new scheme, variables live on the stack, so
code generating a reference to them actually needs to produce a load
from the stack slot:
.. code-block:: c++
Value *VariableExprAST::codegen() {
// Look this variable up in the function.
Value *V = NamedValues[Name];
if (!V)
return LogErrorV("Unknown variable name");
// Load the value.
return Builder.CreateLoad(V, Name.c_str());
}
As you can see, this is pretty straightforward. Now we need to update
the things that define the variables to set up the alloca. We'll start
with ``ForExprAST::codegen()`` (see the `full code listing <#id1>`_ for
the unabridged code):
.. code-block:: c++
Function *TheFunction = Builder.GetInsertBlock()->getParent();
// Create an alloca for the variable in the entry block.
AllocaInst *Alloca = CreateEntryBlockAlloca(TheFunction, VarName);
// Emit the start code first, without 'variable' in scope.
Value *StartVal = Start->codegen();
if (!StartVal)
return nullptr;
// Store the value into the alloca.
Builder.CreateStore(StartVal, Alloca);
...
// Compute the end condition.
Value *EndCond = End->codegen();
if (!EndCond)
return nullptr;
// Reload, increment, and restore the alloca. This handles the case where
// the body of the loop mutates the variable.
Value *CurVar = Builder.CreateLoad(Alloca);
Value *NextVar = Builder.CreateFAdd(CurVar, StepVal, "nextvar");
Builder.CreateStore(NextVar, Alloca);
...
This code is virtually identical to the code `before we allowed mutable
variables <LangImpl5.html#code-generation-for-the-for-loop>`_. The big difference is that we
no longer have to construct a PHI node, and we use load/store to access
the variable as needed.
To support mutable argument variables, we need to also make allocas for
them. The code for this is also pretty simple:
.. code-block:: c++
Function *FunctionAST::codegen() {
...
Builder.SetInsertPoint(BB);
// Record the function arguments in the NamedValues map.
NamedValues.clear();
for (auto &Arg : TheFunction->args()) {
// Create an alloca for this variable.
AllocaInst *Alloca = CreateEntryBlockAlloca(TheFunction, Arg.getName());
// Store the initial value into the alloca.
Builder.CreateStore(&Arg, Alloca);
// Add arguments to variable symbol table.
NamedValues[Arg.getName()] = Alloca;
}
if (Value *RetVal = Body->codegen()) {
...
For each argument, we make an alloca, store the input value to the
function into the alloca, and register the alloca as the memory location
for the argument. This method gets invoked by ``FunctionAST::codegen()``
right after it sets up the entry block for the function.
The final missing piece is adding the mem2reg pass, which allows us to
get good codegen once again:
.. code-block:: c++
// Promote allocas to registers.
TheFPM->add(createPromoteMemoryToRegisterPass());
// Do simple "peephole" optimizations and bit-twiddling optzns.
TheFPM->add(createInstructionCombiningPass());
// Reassociate expressions.
TheFPM->add(createReassociatePass());
...
It is interesting to see what the code looks like before and after the
mem2reg optimization runs. For example, this is the before/after code
for our recursive fib function. Before the optimization:
.. code-block:: llvm
define double @fib(double %x) {
entry:
%x1 = alloca double
store double %x, double* %x1
%x2 = load double, double* %x1
%cmptmp = fcmp ult double %x2, 3.000000e+00
%booltmp = uitofp i1 %cmptmp to double
%ifcond = fcmp one double %booltmp, 0.000000e+00
br i1 %ifcond, label %then, label %else
then: ; preds = %entry
br label %ifcont
else: ; preds = %entry
%x3 = load double, double* %x1
%subtmp = fsub double %x3, 1.000000e+00
%calltmp = call double @fib(double %subtmp)
%x4 = load double, double* %x1
%subtmp5 = fsub double %x4, 2.000000e+00
%calltmp6 = call double @fib(double %subtmp5)
%addtmp = fadd double %calltmp, %calltmp6
br label %ifcont
ifcont: ; preds = %else, %then
%iftmp = phi double [ 1.000000e+00, %then ], [ %addtmp, %else ]
ret double %iftmp
}
Here there is only one variable (x, the input argument) but you can
still see the extremely simple-minded code generation strategy we are
using. In the entry block, an alloca is created, and the initial input
value is stored into it. Each reference to the variable does a reload
from the stack. Also, note that we didn't modify the if/then/else
expression, so it still inserts a PHI node. While we could make an
alloca for it, it is actually easier to create a PHI node for it, so we
still just make the PHI.
Here is the code after the mem2reg pass runs:
.. code-block:: llvm
define double @fib(double %x) {
entry:
%cmptmp = fcmp ult double %x, 3.000000e+00
%booltmp = uitofp i1 %cmptmp to double
%ifcond = fcmp one double %booltmp, 0.000000e+00
br i1 %ifcond, label %then, label %else
then:
br label %ifcont
else:
%subtmp = fsub double %x, 1.000000e+00
%calltmp = call double @fib(double %subtmp)
%subtmp5 = fsub double %x, 2.000000e+00
%calltmp6 = call double @fib(double %subtmp5)
%addtmp = fadd double %calltmp, %calltmp6
br label %ifcont
ifcont: ; preds = %else, %then
%iftmp = phi double [ 1.000000e+00, %then ], [ %addtmp, %else ]
ret double %iftmp
}
This is a trivial case for mem2reg, since there are no redefinitions of
the variable. The point of showing this is to calm your tension about
inserting such blatent inefficiencies :).
After the rest of the optimizers run, we get:
.. code-block:: llvm
define double @fib(double %x) {
entry:
%cmptmp = fcmp ult double %x, 3.000000e+00
%booltmp = uitofp i1 %cmptmp to double
%ifcond = fcmp ueq double %booltmp, 0.000000e+00
br i1 %ifcond, label %else, label %ifcont
else:
%subtmp = fsub double %x, 1.000000e+00
%calltmp = call double @fib(double %subtmp)
%subtmp5 = fsub double %x, 2.000000e+00
%calltmp6 = call double @fib(double %subtmp5)
%addtmp = fadd double %calltmp, %calltmp6
ret double %addtmp
ifcont:
ret double 1.000000e+00
}
Here we see that the simplifycfg pass decided to clone the return
instruction into the end of the 'else' block. This allowed it to
eliminate some branches and the PHI node.
Now that all symbol table references are updated to use stack variables,
we'll add the assignment operator.
New Assignment Operator
=======================
With our current framework, adding a new assignment operator is really
simple. We will parse it just like any other binary operator, but handle
it internally (instead of allowing the user to define it). The first
step is to set a precedence:
.. code-block:: c++
int main() {
// Install standard binary operators.
// 1 is lowest precedence.
BinopPrecedence['='] = 2;
BinopPrecedence['<'] = 10;
BinopPrecedence['+'] = 20;
BinopPrecedence['-'] = 20;
Now that the parser knows the precedence of the binary operator, it
takes care of all the parsing and AST generation. We just need to
implement codegen for the assignment operator. This looks like:
.. code-block:: c++
Value *BinaryExprAST::codegen() {
// Special case '=' because we don't want to emit the LHS as an expression.
if (Op == '=') {
// Assignment requires the LHS to be an identifier.
VariableExprAST *LHSE = dynamic_cast<VariableExprAST*>(LHS.get());
if (!LHSE)
return LogErrorV("destination of '=' must be a variable");
Unlike the rest of the binary operators, our assignment operator doesn't
follow the "emit LHS, emit RHS, do computation" model. As such, it is
handled as a special case before the other binary operators are handled.
The other strange thing is that it requires the LHS to be a variable. It
is invalid to have "(x+1) = expr" - only things like "x = expr" are
allowed.
.. code-block:: c++
// Codegen the RHS.
Value *Val = RHS->codegen();
if (!Val)
return nullptr;
// Look up the name.
Value *Variable = NamedValues[LHSE->getName()];
if (!Variable)
return LogErrorV("Unknown variable name");
Builder.CreateStore(Val, Variable);
return Val;
}
...
Once we have the variable, codegen'ing the assignment is
straightforward: we emit the RHS of the assignment, create a store, and
return the computed value. Returning a value allows for chained
assignments like "X = (Y = Z)".
Now that we have an assignment operator, we can mutate loop variables
and arguments. For example, we can now run code like this:
::
# Function to print a double.
extern printd(x);
# Define ':' for sequencing: as a low-precedence operator that ignores operands
# and just returns the RHS.
def binary : 1 (x y) y;
def test(x)
printd(x) :
x = 4 :
printd(x);
test(123);
When run, this example prints "123" and then "4", showing that we did
actually mutate the value! Okay, we have now officially implemented our
goal: getting this to work requires SSA construction in the general
case. However, to be really useful, we want the ability to define our
own local variables, let's add this next!
User-defined Local Variables
============================
Adding var/in is just like any other extension we made to
Kaleidoscope: we extend the lexer, the parser, the AST and the code
generator. The first step for adding our new 'var/in' construct is to
extend the lexer. As before, this is pretty trivial, the code looks like
this:
.. code-block:: c++
enum Token {
...
// var definition
tok_var = -13
...
}
...
static int gettok() {
...
if (IdentifierStr == "in")
return tok_in;
if (IdentifierStr == "binary")
return tok_binary;
if (IdentifierStr == "unary")
return tok_unary;
if (IdentifierStr == "var")
return tok_var;
return tok_identifier;
...
The next step is to define the AST node that we will construct. For
var/in, it looks like this:
.. code-block:: c++
/// VarExprAST - Expression class for var/in
class VarExprAST : public ExprAST {
std::vector<std::pair<std::string, std::unique_ptr<ExprAST>>> VarNames;
std::unique_ptr<ExprAST> Body;
public:
VarExprAST(std::vector<std::pair<std::string, std::unique_ptr<ExprAST>>> VarNames,
std::unique_ptr<ExprAST> Body)
: VarNames(std::move(VarNames)), Body(std::move(Body)) {}
Value *codegen() override;
};
var/in allows a list of names to be defined all at once, and each name
can optionally have an initializer value. As such, we capture this
information in the VarNames vector. Also, var/in has a body, this body
is allowed to access the variables defined by the var/in.
With this in place, we can define the parser pieces. The first thing we
do is add it as a primary expression:
.. code-block:: c++
/// primary
/// ::= identifierexpr
/// ::= numberexpr
/// ::= parenexpr
/// ::= ifexpr
/// ::= forexpr
/// ::= varexpr
static std::unique_ptr<ExprAST> ParsePrimary() {
switch (CurTok) {
default:
return LogError("unknown token when expecting an expression");
case tok_identifier:
return ParseIdentifierExpr();
case tok_number:
return ParseNumberExpr();
case '(':
return ParseParenExpr();
case tok_if:
return ParseIfExpr();
case tok_for:
return ParseForExpr();
case tok_var:
return ParseVarExpr();
}
}
Next we define ParseVarExpr:
.. code-block:: c++
/// varexpr ::= 'var' identifier ('=' expression)?
// (',' identifier ('=' expression)?)* 'in' expression
static std::unique_ptr<ExprAST> ParseVarExpr() {
getNextToken(); // eat the var.
std::vector<std::pair<std::string, std::unique_ptr<ExprAST>>> VarNames;
// At least one variable name is required.
if (CurTok != tok_identifier)
return LogError("expected identifier after var");
The first part of this code parses the list of identifier/expr pairs
into the local ``VarNames`` vector.
.. code-block:: c++
while (1) {
std::string Name = IdentifierStr;
getNextToken(); // eat identifier.
// Read the optional initializer.
std::unique_ptr<ExprAST> Init;
if (CurTok == '=') {
getNextToken(); // eat the '='.
Init = ParseExpression();
if (!Init) return nullptr;
}
VarNames.push_back(std::make_pair(Name, std::move(Init)));
// End of var list, exit loop.
if (CurTok != ',') break;
getNextToken(); // eat the ','.
if (CurTok != tok_identifier)
return LogError("expected identifier list after var");
}
Once all the variables are parsed, we then parse the body and create the
AST node:
.. code-block:: c++
// At this point, we have to have 'in'.
if (CurTok != tok_in)
return LogError("expected 'in' keyword after 'var'");
getNextToken(); // eat 'in'.
auto Body = ParseExpression();
if (!Body)
return nullptr;
return llvm::make_unique<VarExprAST>(std::move(VarNames),
std::move(Body));
}
Now that we can parse and represent the code, we need to support
emission of LLVM IR for it. This code starts out with:
.. code-block:: c++
Value *VarExprAST::codegen() {
std::vector<AllocaInst *> OldBindings;
Function *TheFunction = Builder.GetInsertBlock()->getParent();
// Register all variables and emit their initializer.
for (unsigned i = 0, e = VarNames.size(); i != e; ++i) {
const std::string &VarName = VarNames[i].first;
ExprAST *Init = VarNames[i].second.get();
Basically it loops over all the variables, installing them one at a
time. For each variable we put into the symbol table, we remember the
previous value that we replace in OldBindings.
.. code-block:: c++
// Emit the initializer before adding the variable to scope, this prevents
// the initializer from referencing the variable itself, and permits stuff
// like this:
// var a = 1 in
// var a = a in ... # refers to outer 'a'.
Value *InitVal;
if (Init) {
InitVal = Init->codegen();
if (!InitVal)
return nullptr;
} else { // If not specified, use 0.0.
InitVal = ConstantFP::get(TheContext, APFloat(0.0));
}
AllocaInst *Alloca = CreateEntryBlockAlloca(TheFunction, VarName);
Builder.CreateStore(InitVal, Alloca);
// Remember the old variable binding so that we can restore the binding when
// we unrecurse.
OldBindings.push_back(NamedValues[VarName]);
// Remember this binding.
NamedValues[VarName] = Alloca;
}
There are more comments here than code. The basic idea is that we emit
the initializer, create the alloca, then update the symbol table to
point to it. Once all the variables are installed in the symbol table,
we evaluate the body of the var/in expression:
.. code-block:: c++
// Codegen the body, now that all vars are in scope.
Value *BodyVal = Body->codegen();
if (!BodyVal)
return nullptr;
Finally, before returning, we restore the previous variable bindings:
.. code-block:: c++
// Pop all our variables from scope.
for (unsigned i = 0, e = VarNames.size(); i != e; ++i)
NamedValues[VarNames[i].first] = OldBindings[i];
// Return the body computation.
return BodyVal;
}
The end result of all of this is that we get properly scoped variable
definitions, and we even (trivially) allow mutation of them :).
With this, we completed what we set out to do. Our nice iterative fib
example from the intro compiles and runs just fine. The mem2reg pass
optimizes all of our stack variables into SSA registers, inserting PHI
nodes where needed, and our front-end remains simple: no "iterated
dominance frontier" computation anywhere in sight.
Full Code Listing
=================
Here is the complete code listing for our running example, enhanced with
mutable variables and var/in support. To build this example, use:
.. code-block:: bash
# Compile
clang++ -g toy.cpp `llvm-config --cxxflags --ldflags --system-libs --libs core mcjit native` -O3 -o toy
# Run
./toy
Here is the code:
.. literalinclude:: ../../examples/Kaleidoscope/Chapter7/toy.cpp
:language: c++
`Next: Compiling to Object Code <LangImpl08.html>`_
=====================
Kaleidoscope Tutorial
=====================
The Kaleidoscope Tutorial has `moved to another location <MyFirstLanguageFrontend/index>`_ .

221
llvm/docs/tutorial/LangImpl08.rst
View File

@ -1,218 +1,7 @@
========================================
Kaleidoscope: Compiling to Object Code
========================================
:orphan:
.. contents::
:local:
=====================
Kaleidoscope Tutorial
=====================
Chapter 8 Introduction
======================
Welcome to Chapter 8 of the "`Implementing a language with LLVM
<index.html>`_" tutorial. This chapter describes how to compile our
language down to object files.
Choosing a target
=================
LLVM has native support for cross-compilation. You can compile to the
architecture of your current machine, or just as easily compile for
other architectures. In this tutorial, we'll target the current
machine.
To specify the architecture that you want to target, we use a string
called a "target triple". This takes the form
``<arch><sub>-<vendor>-<sys>-<abi>`` (see the `cross compilation docs
<http://clang.llvm.org/docs/CrossCompilation.html#target-triple>`_).
As an example, we can see what clang thinks is our current target
triple:
::
$ clang --version | grep Target
Target: x86_64-unknown-linux-gnu
Running this command may show something different on your machine as
you might be using a different architecture or operating system to me.
Fortunately, we don't need to hard-code a target triple to target the
current machine. LLVM provides ``sys::getDefaultTargetTriple``, which
returns the target triple of the current machine.
.. code-block:: c++
auto TargetTriple = sys::getDefaultTargetTriple();
LLVM doesn't require us to link in all the target
functionality. For example, if we're just using the JIT, we don't need
the assembly printers. Similarly, if we're only targeting certain
architectures, we can only link in the functionality for those
architectures.
For this example, we'll initialize all the targets for emitting object
code.
.. code-block:: c++
InitializeAllTargetInfos();
InitializeAllTargets();
InitializeAllTargetMCs();
InitializeAllAsmParsers();
InitializeAllAsmPrinters();
We can now use our target triple to get a ``Target``:
.. code-block:: c++
std::string Error;
auto Target = TargetRegistry::lookupTarget(TargetTriple, Error);
// Print an error and exit if we couldn't find the requested target.
// This generally occurs if we've forgotten to initialise the
// TargetRegistry or we have a bogus target triple.
if (!Target) {
errs() << Error;
return 1;
}
Target Machine
==============
We will also need a ``TargetMachine``. This class provides a complete
machine description of the machine we're targeting. If we want to
target a specific feature (such as SSE) or a specific CPU (such as
Intel's Sandylake), we do so now.
To see which features and CPUs that LLVM knows about, we can use
``llc``. For example, let's look at x86:
::
$ llvm-as < /dev/null | llc -march=x86 -mattr=help
Available CPUs for this target:
amdfam10 - Select the amdfam10 processor.
athlon - Select the athlon processor.
athlon-4 - Select the athlon-4 processor.
...
Available features for this target:
16bit-mode - 16-bit mode (i8086).
32bit-mode - 32-bit mode (80386).
3dnow - Enable 3DNow! instructions.
3dnowa - Enable 3DNow! Athlon instructions.
...
For our example, we'll use the generic CPU without any additional
features, options or relocation model.
.. code-block:: c++
auto CPU = "generic";
auto Features = "";
TargetOptions opt;
auto RM = Optional<Reloc::Model>();
auto TargetMachine = Target->createTargetMachine(TargetTriple, CPU, Features, opt, RM);
Configuring the Module
======================
We're now ready to configure our module, to specify the target and
data layout. This isn't strictly necessary, but the `frontend
performance guide <../Frontend/PerformanceTips.html>`_ recommends
this. Optimizations benefit from knowing about the target and data
layout.
.. code-block:: c++
TheModule->setDataLayout(TargetMachine->createDataLayout());
TheModule->setTargetTriple(TargetTriple);
Emit Object Code
================
We're ready to emit object code! Let's define where we want to write
our file to:
.. code-block:: c++
auto Filename = "output.o";
std::error_code EC;
raw_fd_ostream dest(Filename, EC, sys::fs::F_None);
if (EC) {
errs() << "Could not open file: " << EC.message();
return 1;
}
Finally, we define a pass that emits object code, then we run that
pass:
.. code-block:: c++
legacy::PassManager pass;
auto FileType = TargetMachine::CGFT_ObjectFile;
if (TargetMachine->addPassesToEmitFile(pass, dest, nullptr, FileType)) {
errs() << "TargetMachine can't emit a file of this type";
return 1;
}
pass.run(*TheModule);
dest.flush();
Putting It All Together
=======================
Does it work? Let's give it a try. We need to compile our code, but
note that the arguments to ``llvm-config`` are different to the previous chapters.
::
$ clang++ -g -O3 toy.cpp `llvm-config --cxxflags --ldflags --system-libs --libs all` -o toy
Let's run it, and define a simple ``average`` function. Press Ctrl-D
when you're done.
::
$ ./toy
ready> def average(x y) (x + y) * 0.5;
^D
Wrote output.o
We have an object file! To test it, let's write a simple program and
link it with our output. Here's the source code:
.. code-block:: c++
#include <iostream>
extern "C" {
double average(double, double);
}
int main() {
std::cout << "average of 3.0 and 4.0: " << average(3.0, 4.0) << std::endl;
}
We link our program to output.o and check the result is what we
expected:
::
$ clang++ main.cpp output.o -o main
$ ./main
average of 3.0 and 4.0: 3.5
Full Code Listing
=================
.. literalinclude:: ../../examples/Kaleidoscope/Chapter8/toy.cpp
:language: c++
`Next: Adding Debug Information <LangImpl09.html>`_
The Kaleidoscope Tutorial has `moved to another location <MyFirstLanguageFrontend/index>`_ .

468
llvm/docs/tutorial/LangImpl09.rst
View File

@ -1,465 +1,7 @@
======================================
Kaleidoscope: Adding Debug Information
======================================
:orphan:
.. contents::
:local:
Chapter 9 Introduction
======================
Welcome to Chapter 9 of the "`Implementing a language with
LLVM <index.html>`_" tutorial. In chapters 1 through 8, we've built a
decent little programming language with functions and variables.
What happens if something goes wrong though, how do you debug your
program?
Source level debugging uses formatted data that helps a debugger
translate from binary and the state of the machine back to the
source that the programmer wrote. In LLVM we generally use a format
called `DWARF <http://dwarfstd.org>`_. DWARF is a compact encoding
that represents types, source locations, and variable locations.
The short summary of this chapter is that we'll go through the
various things you have to add to a programming language to
support debug info, and how you translate that into DWARF.
Caveat: For now we can't debug via the JIT, so we'll need to compile
our program down to something small and standalone. As part of this
we'll make a few modifications to the running of the language and
how programs are compiled. This means that we'll have a source file
with a simple program written in Kaleidoscope rather than the
interactive JIT. It does involve a limitation that we can only
have one "top level" command at a time to reduce the number of
changes necessary.
Here's the sample program we'll be compiling:
.. code-block:: python
def fib(x)
if x < 3 then
1
else
fib(x-1)+fib(x-2);
fib(10)
Why is this a hard problem?
===========================
Debug information is a hard problem for a few different reasons - mostly
centered around optimized code. First, optimization makes keeping source
locations more difficult. In LLVM IR we keep the original source location
for each IR level instruction on the instruction. Optimization passes
should keep the source locations for newly created instructions, but merged
instructions only get to keep a single location - this can cause jumping
around when stepping through optimized programs. Secondly, optimization
can move variables in ways that are either optimized out, shared in memory
with other variables, or difficult to track. For the purposes of this
tutorial we're going to avoid optimization (as you'll see with one of the
next sets of patches).
Ahead-of-Time Compilation Mode
==============================
To highlight only the aspects of adding debug information to a source
language without needing to worry about the complexities of JIT debugging
we're going to make a few changes to Kaleidoscope to support compiling
the IR emitted by the front end into a simple standalone program that
you can execute, debug, and see results.
First we make our anonymous function that contains our top level
statement be our "main":
.. code-block:: udiff
- auto Proto = llvm::make_unique<PrototypeAST>("", std::vector<std::string>());
+ auto Proto = llvm::make_unique<PrototypeAST>("main", std::vector<std::string>());
just with the simple change of giving it a name.
Then we're going to remove the command line code wherever it exists:
.. code-block:: udiff
@@ -1129,7 +1129,6 @@ static void HandleTopLevelExpression() {
/// top ::= definition | external | expression | ';'
static void MainLoop() {
while (1) {
- fprintf(stderr, "ready> ");
switch (CurTok) {
case tok_eof:
return;
@@ -1184,7 +1183,6 @@ int main() {
BinopPrecedence['*'] = 40; // highest.
// Prime the first token.
- fprintf(stderr, "ready> ");
getNextToken();
Lastly we're going to disable all of the optimization passes and the JIT so
that the only thing that happens after we're done parsing and generating
code is that the LLVM IR goes to standard error:
.. code-block:: udiff
@@ -1108,17 +1108,8 @@ static void HandleExtern() {
static void HandleTopLevelExpression() {
// Evaluate a top-level expression into an anonymous function.
if (auto FnAST = ParseTopLevelExpr()) {
- if (auto *FnIR = FnAST->codegen()) {
- // We're just doing this to make sure it executes.
- TheExecutionEngine->finalizeObject();
- // JIT the function, returning a function pointer.
- void *FPtr = TheExecutionEngine->getPointerToFunction(FnIR);
-
- // Cast it to the right type (takes no arguments, returns a double) so we
- // can call it as a native function.
- double (*FP)() = (double (*)())(intptr_t)FPtr;
- // Ignore the return value for this.
- (void)FP;
+ if (!F->codegen()) {
+ fprintf(stderr, "Error generating code for top level expr");
}
} else {
// Skip token for error recovery.
@@ -1439,11 +1459,11 @@ int main() {
// target lays out data structures.
TheModule->setDataLayout(TheExecutionEngine->getDataLayout());
OurFPM.add(new DataLayoutPass());
+#if 0
OurFPM.add(createBasicAliasAnalysisPass());
// Promote allocas to registers.
OurFPM.add(createPromoteMemoryToRegisterPass());
@@ -1218,7 +1210,7 @@ int main() {
OurFPM.add(createGVNPass());
// Simplify the control flow graph (deleting unreachable blocks, etc).
OurFPM.add(createCFGSimplificationPass());
-
+ #endif
OurFPM.doInitialization();
// Set the global so the code gen can use this.
This relatively small set of changes get us to the point that we can compile
our piece of Kaleidoscope language down to an executable program via this
command line:
.. code-block:: bash
Kaleidoscope-Ch9 < fib.ks | & clang -x ir -
which gives an a.out/a.exe in the current working directory.
Compile Unit
============
The top level container for a section of code in DWARF is a compile unit.
This contains the type and function data for an individual translation unit
(read: one file of source code). So the first thing we need to do is
construct one for our fib.ks file.
DWARF Emission Setup
====================
Similar to the ``IRBuilder`` class we have a
`DIBuilder <http://llvm.org/doxygen/classllvm_1_1DIBuilder.html>`_ class
that helps in constructing debug metadata for an LLVM IR file. It
corresponds 1:1 similarly to ``IRBuilder`` and LLVM IR, but with nicer names.
Using it does require that you be more familiar with DWARF terminology than
you needed to be with ``IRBuilder`` and ``Instruction`` names, but if you
read through the general documentation on the
`Metadata Format <http://llvm.org/docs/SourceLevelDebugging.html>`_ it
should be a little more clear. We'll be using this class to construct all
of our IR level descriptions. Construction for it takes a module so we
need to construct it shortly after we construct our module. We've left it
as a global static variable to make it a bit easier to use.
Next we're going to create a small container to cache some of our frequent
data. The first will be our compile unit, but we'll also write a bit of
code for our one type since we won't have to worry about multiple typed
expressions:
.. code-block:: c++
static DIBuilder *DBuilder;
struct DebugInfo {
DICompileUnit *TheCU;
DIType *DblTy;
DIType *getDoubleTy();
} KSDbgInfo;
DIType *DebugInfo::getDoubleTy() {
if (DblTy)
return DblTy;
DblTy = DBuilder->createBasicType("double", 64, dwarf::DW_ATE_float);
return DblTy;
}
And then later on in ``main`` when we're constructing our module:
.. code-block:: c++
DBuilder = new DIBuilder(*TheModule);
KSDbgInfo.TheCU = DBuilder->createCompileUnit(
dwarf::DW_LANG_C, DBuilder->createFile("fib.ks", "."),
"Kaleidoscope Compiler", 0, "", 0);
There are a couple of things to note here. First, while we're producing a
compile unit for a language called Kaleidoscope we used the language
constant for C. This is because a debugger wouldn't necessarily understand
the calling conventions or default ABI for a language it doesn't recognize
and we follow the C ABI in our LLVM code generation so it's the closest
thing to accurate. This ensures we can actually call functions from the
debugger and have them execute. Secondly, you'll see the "fib.ks" in the
call to ``createCompileUnit``. This is a default hard coded value since
we're using shell redirection to put our source into the Kaleidoscope
compiler. In a usual front end you'd have an input file name and it would
go there.
One last thing as part of emitting debug information via DIBuilder is that
we need to "finalize" the debug information. The reasons are part of the
underlying API for DIBuilder, but make sure you do this near the end of
main:
.. code-block:: c++
DBuilder->finalize();
before you dump out the module.
Functions
=========
Now that we have our ``Compile Unit`` and our source locations, we can add
function definitions to the debug info. So in ``PrototypeAST::codegen()`` we
add a few lines of code to describe a context for our subprogram, in this
case the "File", and the actual definition of the function itself.
So the context:
.. code-block:: c++
DIFile *Unit = DBuilder->createFile(KSDbgInfo.TheCU.getFilename(),
KSDbgInfo.TheCU.getDirectory());
giving us an DIFile and asking the ``Compile Unit`` we created above for the
directory and filename where we are currently. Then, for now, we use some
source locations of 0 (since our AST doesn't currently have source location
information) and construct our function definition:
.. code-block:: c++
DIScope *FContext = Unit;
unsigned LineNo = 0;
unsigned ScopeLine = 0;
DISubprogram *SP = DBuilder->createFunction(
FContext, P.getName(), StringRef(), Unit, LineNo,
CreateFunctionType(TheFunction->arg_size(), Unit),
false /* internal linkage */, true /* definition */, ScopeLine,
DINode::FlagPrototyped, false);
TheFunction->setSubprogram(SP);
and we now have an DISubprogram that contains a reference to all of our
metadata for the function.
Source Locations
================
The most important thing for debug information is accurate source location -
this makes it possible to map your source code back. We have a problem though,
Kaleidoscope really doesn't have any source location information in the lexer
or parser so we'll need to add it.
.. code-block:: c++
struct SourceLocation {
int Line;
int Col;
};
static SourceLocation CurLoc;
static SourceLocation LexLoc = {1, 0};
static int advance() {
int LastChar = getchar();
if (LastChar == '\n' || LastChar == '\r') {
LexLoc.Line++;
LexLoc.Col = 0;
} else
LexLoc.Col++;
return LastChar;
}
In this set of code we've added some functionality on how to keep track of the
line and column of the "source file". As we lex every token we set our current
current "lexical location" to the assorted line and column for the beginning
of the token. We do this by overriding all of the previous calls to
``getchar()`` with our new ``advance()`` that keeps track of the information
and then we have added to all of our AST classes a source location:
.. code-block:: c++
class ExprAST {
SourceLocation Loc;
public:
ExprAST(SourceLocation Loc = CurLoc) : Loc(Loc) {}
virtual ~ExprAST() {}
virtual Value* codegen() = 0;
int getLine() const { return Loc.Line; }
int getCol() const { return Loc.Col; }
virtual raw_ostream &dump(raw_ostream &out, int ind) {
return out << ':' << getLine() << ':' << getCol() << '\n';
}
that we pass down through when we create a new expression:
.. code-block:: c++
LHS = llvm::make_unique<BinaryExprAST>(BinLoc, BinOp, std::move(LHS),
std::move(RHS));
giving us locations for each of our expressions and variables.
To make sure that every instruction gets proper source location information,
we have to tell ``Builder`` whenever we're at a new source location.
We use a small helper function for this:
.. code-block:: c++
void DebugInfo::emitLocation(ExprAST *AST) {
DIScope *Scope;
if (LexicalBlocks.empty())
Scope = TheCU;
else
Scope = LexicalBlocks.back();
Builder.SetCurrentDebugLocation(
DebugLoc::get(AST->getLine(), AST->getCol(), Scope));
}
This both tells the main ``IRBuilder`` where we are, but also what scope
we're in. The scope can either be on compile-unit level or be the nearest
enclosing lexical block like the current function.
To represent this we create a stack of scopes:
.. code-block:: c++
std::vector<DIScope *> LexicalBlocks;
and push the scope (function) to the top of the stack when we start
generating the code for each function:
.. code-block:: c++
KSDbgInfo.LexicalBlocks.push_back(SP);
Also, we may not forget to pop the scope back off of the scope stack at the
end of the code generation for the function:
.. code-block:: c++
// Pop off the lexical block for the function since we added it
// unconditionally.
KSDbgInfo.LexicalBlocks.pop_back();
Then we make sure to emit the location every time we start to generate code
for a new AST object:
.. code-block:: c++
KSDbgInfo.emitLocation(this);
Variables
=========
Now that we have functions, we need to be able to print out the variables
we have in scope. Let's get our function arguments set up so we can get
decent backtraces and see how our functions are being called. It isn't
a lot of code, and we generally handle it when we're creating the
argument allocas in ``FunctionAST::codegen``.
.. code-block:: c++
// Record the function arguments in the NamedValues map.
NamedValues.clear();
unsigned ArgIdx = 0;
for (auto &Arg : TheFunction->args()) {
// Create an alloca for this variable.
AllocaInst *Alloca = CreateEntryBlockAlloca(TheFunction, Arg.getName());
// Create a debug descriptor for the variable.
DILocalVariable *D = DBuilder->createParameterVariable(
SP, Arg.getName(), ++ArgIdx, Unit, LineNo, KSDbgInfo.getDoubleTy(),
true);
DBuilder->insertDeclare(Alloca, D, DBuilder->createExpression(),
DebugLoc::get(LineNo, 0, SP),
Builder.GetInsertBlock());
// Store the initial value into the alloca.
Builder.CreateStore(&Arg, Alloca);
// Add arguments to variable symbol table.
NamedValues[Arg.getName()] = Alloca;
}
Here we're first creating the variable, giving it the scope (``SP``),
the name, source location, type, and since it's an argument, the argument
index. Next, we create an ``lvm.dbg.declare`` call to indicate at the IR
level that we've got a variable in an alloca (and it gives a starting
location for the variable), and setting a source location for the
beginning of the scope on the declare.
One interesting thing to note at this point is that various debuggers have
assumptions based on how code and debug information was generated for them
in the past. In this case we need to do a little bit of a hack to avoid
generating line information for the function prologue so that the debugger
knows to skip over those instructions when setting a breakpoint. So in
``FunctionAST::CodeGen`` we add some more lines:
.. code-block:: c++
// Unset the location for the prologue emission (leading instructions with no
// location in a function are considered part of the prologue and the debugger
// will run past them when breaking on a function)
KSDbgInfo.emitLocation(nullptr);
and then emit a new location when we actually start generating code for the
body of the function:
.. code-block:: c++
KSDbgInfo.emitLocation(Body.get());
With this we have enough debug information to set breakpoints in functions,
print out argument variables, and call functions. Not too bad for just a
few simple lines of code!
Full Code Listing
=================
Here is the complete code listing for our running example, enhanced with
debug information. To build this example, use:
.. code-block:: bash
# Compile
clang++ -g toy.cpp `llvm-config --cxxflags --ldflags --system-libs --libs core mcjit native` -O3 -o toy
# Run
./toy
Here is the code:
.. literalinclude:: ../../examples/Kaleidoscope/Chapter9/toy.cpp
:language: c++
`Next: Conclusion and other useful LLVM tidbits <LangImpl10.html>`_
=====================
Kaleidoscope Tutorial
=====================
The Kaleidoscope Tutorial has `moved to another location <MyFirstLanguageFrontend/index>`_ .

257
llvm/docs/tutorial/LangImpl10.rst
View File

@ -1,254 +1,7 @@
======================================================
Kaleidoscope: Conclusion and other useful LLVM tidbits
======================================================
:orphan:
.. contents::
:local:
Tutorial Conclusion
===================
Welcome to the final chapter of the "`Implementing a language with
LLVM <index.html>`_" tutorial. In the course of this tutorial, we have
grown our little Kaleidoscope language from being a useless toy, to
being a semi-interesting (but probably still useless) toy. :)
It is interesting to see how far we've come, and how little code it has
taken. We built the entire lexer, parser, AST, code generator, an
interactive run-loop (with a JIT!), and emitted debug information in
standalone executables - all in under 1000 lines of (non-comment/non-blank)
code.
Our little language supports a couple of interesting features: it
supports user defined binary and unary operators, it uses JIT
compilation for immediate evaluation, and it supports a few control flow
constructs with SSA construction.
Part of the idea of this tutorial was to show you how easy and fun it
can be to define, build, and play with languages. Building a compiler
need not be a scary or mystical process! Now that you've seen some of
the basics, I strongly encourage you to take the code and hack on it.
For example, try adding:
- **global variables** - While global variables have questional value
in modern software engineering, they are often useful when putting
together quick little hacks like the Kaleidoscope compiler itself.
Fortunately, our current setup makes it very easy to add global
variables: just have value lookup check to see if an unresolved
variable is in the global variable symbol table before rejecting it.
To create a new global variable, make an instance of the LLVM
``GlobalVariable`` class.
- **typed variables** - Kaleidoscope currently only supports variables
of type double. This gives the language a very nice elegance, because
only supporting one type means that you never have to specify types.
Different languages have different ways of handling this. The easiest
way is to require the user to specify types for every variable
definition, and record the type of the variable in the symbol table
along with its Value\*.
- **arrays, structs, vectors, etc** - Once you add types, you can start
extending the type system in all sorts of interesting ways. Simple
arrays are very easy and are quite useful for many different
applications. Adding them is mostly an exercise in learning how the
LLVM `getelementptr <../LangRef.html#getelementptr-instruction>`_ instruction
works: it is so nifty/unconventional, it `has its own
FAQ <../GetElementPtr.html>`_!
- **standard runtime** - Our current language allows the user to access
arbitrary external functions, and we use it for things like "printd"
and "putchard". As you extend the language to add higher-level
constructs, often these constructs make the most sense if they are
lowered to calls into a language-supplied runtime. For example, if
you add hash tables to the language, it would probably make sense to
add the routines to a runtime, instead of inlining them all the way.
- **memory management** - Currently we can only access the stack in
Kaleidoscope. It would also be useful to be able to allocate heap
memory, either with calls to the standard libc malloc/free interface
or with a garbage collector. If you would like to use garbage
collection, note that LLVM fully supports `Accurate Garbage
Collection <../GarbageCollection.html>`_ including algorithms that
move objects and need to scan/update the stack.
- **exception handling support** - LLVM supports generation of `zero
cost exceptions <../ExceptionHandling.html>`_ which interoperate with
code compiled in other languages. You could also generate code by
implicitly making every function return an error value and checking
it. You could also make explicit use of setjmp/longjmp. There are
many different ways to go here.
- **object orientation, generics, database access, complex numbers,
geometric programming, ...** - Really, there is no end of crazy
features that you can add to the language.
- **unusual domains** - We've been talking about applying LLVM to a
domain that many people are interested in: building a compiler for a
specific language. However, there are many other domains that can use
compiler technology that are not typically considered. For example,
LLVM has been used to implement OpenGL graphics acceleration,
translate C++ code to ActionScript, and many other cute and clever
things. Maybe you will be the first to JIT compile a regular
expression interpreter into native code with LLVM?
Have fun - try doing something crazy and unusual. Building a language
like everyone else always has, is much less fun than trying something a
little crazy or off the wall and seeing how it turns out. If you get
stuck or want to talk about it, feel free to email the `llvm-dev mailing
list <http://lists.llvm.org/mailman/listinfo/llvm-dev>`_: it has lots
of people who are interested in languages and are often willing to help
out.
Before we end this tutorial, I want to talk about some "tips and tricks"
for generating LLVM IR. These are some of the more subtle things that
may not be obvious, but are very useful if you want to take advantage of
LLVM's capabilities.
Properties of the LLVM IR
=========================
We have a couple of common questions about code in the LLVM IR form -
let's just get these out of the way right now, shall we?
Target Independence
-------------------
Kaleidoscope is an example of a "portable language": any program written
in Kaleidoscope will work the same way on any target that it runs on.
Many other languages have this property, e.g. lisp, java, haskell,
javascript, python, etc (note that while these languages are portable,
not all their libraries are).
One nice aspect of LLVM is that it is often capable of preserving target
independence in the IR: you can take the LLVM IR for a
Kaleidoscope-compiled program and run it on any target that LLVM
supports, even emitting C code and compiling that on targets that LLVM
doesn't support natively. You can trivially tell that the Kaleidoscope
compiler generates target-independent code because it never queries for
any target-specific information when generating code.
The fact that LLVM provides a compact, target-independent,
representation for code gets a lot of people excited. Unfortunately,
these people are usually thinking about C or a language from the C
family when they are asking questions about language portability. I say
"unfortunately", because there is really no way to make (fully general)
C code portable, other than shipping the source code around (and of
course, C source code is not actually portable in general either - ever
port a really old application from 32- to 64-bits?).
The problem with C (again, in its full generality) is that it is heavily
laden with target specific assumptions. As one simple example, the
preprocessor often destructively removes target-independence from the
code when it processes the input text:
.. code-block:: c
#ifdef __i386__
int X = 1;
#else
int X = 42;
#endif
While it is possible to engineer more and more complex solutions to
problems like this, it cannot be solved in full generality in a way that
is better than shipping the actual source code.
That said, there are interesting subsets of C that can be made portable.
If you are willing to fix primitive types to a fixed size (say int =
32-bits, and long = 64-bits), don't care about ABI compatibility with
existing binaries, and are willing to give up some other minor features,
you can have portable code. This can make sense for specialized domains
such as an in-kernel language.
Safety Guarantees
-----------------
Many of the languages above are also "safe" languages: it is impossible
for a program written in Java to corrupt its address space and crash the
process (assuming the JVM has no bugs). Safety is an interesting
property that requires a combination of language design, runtime
support, and often operating system support.
It is certainly possible to implement a safe language in LLVM, but LLVM
IR does not itself guarantee safety. The LLVM IR allows unsafe pointer
casts, use after free bugs, buffer over-runs, and a variety of other
problems. Safety needs to be implemented as a layer on top of LLVM and,
conveniently, several groups have investigated this. Ask on the `llvm-dev
mailing list <http://lists.llvm.org/mailman/listinfo/llvm-dev>`_ if
you are interested in more details.
Language-Specific Optimizations
-------------------------------
One thing about LLVM that turns off many people is that it does not
solve all the world's problems in one system. One specific
complaint is that people perceive LLVM as being incapable of performing
high-level language-specific optimization: LLVM "loses too much
information". Here are a few observations about this:
First, you're right that LLVM does lose information. For example, as of
this writing, there is no way to distinguish in the LLVM IR whether an
SSA-value came from a C "int" or a C "long" on an ILP32 machine (other
than debug info). Both get compiled down to an 'i32' value and the
information about what it came from is lost. The more general issue
here, is that the LLVM type system uses "structural equivalence" instead
of "name equivalence". Another place this surprises people is if you
have two types in a high-level language that have the same structure
(e.g. two different structs that have a single int field): these types
will compile down into a single LLVM type and it will be impossible to
tell what it came from.
Second, while LLVM does lose information, LLVM is not a fixed target: we
continue to enhance and improve it in many different ways. In addition
to adding new features (LLVM did not always support exceptions or debug
info), we also extend the IR to capture important information for
optimization (e.g. whether an argument is sign or zero extended,
information about pointers aliasing, etc). Many of the enhancements are
user-driven: people want LLVM to include some specific feature, so they
go ahead and extend it.
Third, it is *possible and easy* to add language-specific optimizations,
and you have a number of choices in how to do it. As one trivial
example, it is easy to add language-specific optimization passes that
"know" things about code compiled for a language. In the case of the C
family, there is an optimization pass that "knows" about the standard C
library functions. If you call "exit(0)" in main(), it knows that it is
safe to optimize that into "return 0;" because C specifies what the
'exit' function does.
In addition to simple library knowledge, it is possible to embed a
variety of other language-specific information into the LLVM IR. If you
have a specific need and run into a wall, please bring the topic up on
the llvm-dev list. At the very worst, you can always treat LLVM as if it
were a "dumb code generator" and implement the high-level optimizations
you desire in your front-end, on the language-specific AST.
Tips and Tricks
===============
There is a variety of useful tips and tricks that you come to know after
working on/with LLVM that aren't obvious at first glance. Instead of
letting everyone rediscover them, this section talks about some of these
issues.
Implementing portable offsetof/sizeof
-------------------------------------
One interesting thing that comes up, if you are trying to keep the code
generated by your compiler "target independent", is that you often need
to know the size of some LLVM type or the offset of some field in an
llvm structure. For example, you might need to pass the size of a type
into a function that allocates memory.
Unfortunately, this can vary widely across targets: for example the
width of a pointer is trivially target-specific. However, there is a
`clever way to use the getelementptr
instruction <http://nondot.org/sabre/LLVMNotes/SizeOf-OffsetOf-VariableSizedStructs.txt>`_
that allows you to compute this in a portable way.
Garbage Collected Stack Frames
------------------------------
Some languages want to explicitly manage their stack frames, often so
that they are garbage collected or to allow easy implementation of
closures. There are often better ways to implement these features than
explicit stack frames, but `LLVM does support
them, <http://nondot.org/sabre/LLVMNotes/ExplicitlyManagedStackFrames.txt>`_
if you want. It requires your front-end to convert the code into
`Continuation Passing
Style <http://en.wikipedia.org/wiki/Continuation-passing_style>`_ and
the use of tail calls (which LLVM also supports).
=====================
Kaleidoscope Tutorial
=====================
The Kaleidoscope Tutorial has `moved to another location <MyFirstLanguageFrontend/index>`_ .

2
llvm/docs/tutorial/MyFirstLanguageFrontend/index.rst
View File

@ -1,5 +1,3 @@
:orphan:
=============================================
My First Language Frontend with LLVM Tutorial
=============================================

2
llvm/docs/tutorial/index.rst
View File

@ -10,7 +10,7 @@ Kaleidoscope: Implementing a Language with LLVM
:glob:
:numbered:
#. `My First Language Frontend with LLVM Tutorial <https://llvm.org/docs/tutorial/MyFirstLanguageFrontend/>`_
#. `My First Language Frontend with LLVM Tutorial <MyFirstLanguageFrontend/index>`_
This is the "Kaleidoscope" Language tutorial, showing how to implement
a simple language using LLVM components in C++.