<div class="doc_title">Kaleidoscope: Adding JIT and Optimizer Support</div>
+<ul>
+<li><a href="index.html">Up to Tutorial Index</a></li>
+<li>Chapter 4
+ <ol>
+ <li><a href="#intro">Chapter 4 Introduction</a></li>
+ <li><a href="#trivialconstfold">Trivial Constant Folding</a></li>
+ <li><a href="#optimizerpasses">LLVM Optimization Passes</a></li>
+ <li><a href="#jit">Adding a JIT Compiler</a></li>
+ <li><a href="#code">Full Code Listing</a></li>
+ </ol>
+</li>
+<li><a href="LangImpl5.html">Chapter 5</a>: Extending the Language: Control
+Flow</li>
+</ul>
+
<div class="doc_author">
<p>Written by <a href="mailto:sabre@nondot.org">Chris Lattner</a></p>
</div>
<!-- *********************************************************************** -->
-<div class="doc_section"><a name="intro">Part 4 Introduction</a></div>
+<div class="doc_section"><a name="intro">Chapter 4 Introduction</a></div>
<!-- *********************************************************************** -->
<div class="doc_text">
-<p>Welcome to part 4 of the "<a href="index.html">Implementing a language with
-LLVM</a>" tutorial. Parts 1-3 described the implementation of a simple language
-and included support for generating LLVM IR. This chapter describes two new
-techniques: adding optimizer support to your language, and adding JIT compiler
-support. This shows how to get nice efficient code for your language.</p>
+<p>Welcome to Chapter 4 of the "<a href="index.html">Implementing a language
+with LLVM</a>" 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.</p>
</div>
<p>
Our demonstration for Chapter 3 is elegant and easy to extend. Unfortunately,
-it does not produce wonderful code. For example, when compiling simple code,
-we don't get obvious optimizations:</p>
+it does not produce wonderful code. The IRBuilder, however, does give us
+obvious optimizations when compiling simple code:</p>
<div class="doc_code">
<pre>
Read function definition:
define double @test(double %x) {
entry:
- %addtmp = add double 1.000000e+00, 2.000000e+00
- %addtmp1 = add double %addtmp, %x
- ret double %addtmp1
+ %addtmp = fadd double 3.000000e+00, %x
+ ret double %addtmp
}
</pre>
</div>
-<p>This code is a very very literal transcription of the AST built by parsing
-our code, and as such, lacks optimizations like constant folding (we'd like to
-get "<tt>add x, 3.0</tt>" in the example above) as well as other more important
-optimizations. Constant folding 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.</p>
-
-<p>With LLVM, you don't need to. Since all calls to build LLVM IR go through
-the LLVM builder, it would be nice if the builder itself checked to see if there
-was a constant folding opportunity when you call it. If so, it could just do
-the constant fold and return the constant instead of creating an instruction.
-This is exactly what the <tt>LLVMFoldingBuilder</tt> class does. Lets make one
-change:
-
-<div class="doc_code">
-<pre>
-static LLVMFoldingBuilder Builder;
-</pre>
-</div>
-
-<p>All we did was switch from <tt>LLVMBuilder</tt> to
-<tt>LLVMFoldingBuilder</tt>. Though we change no other code, now all of our
-instructions are implicitly constant folded without us having to do anything
-about it. For example, our example above now compiles to:</p>
+<p>This code is not a literal transcription of the AST built by parsing the
+input. That would be:
<div class="doc_code">
<pre>
Read function definition:
define double @test(double %x) {
entry:
- %addtmp = add double 3.000000e+00, %x
- ret double %addtmp
+ %addtmp = fadd double 2.000000e+00, 1.000000e+00
+ %addtmp1 = fadd double %addtmp, %x
+ ret double %addtmp1
}
</pre>
</div>
-<p>Well, that was easy. :) In practice, we recommend always using
-<tt>LLVMConstantBuilder</tt> when generating code like this. It has no
+<p>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.</p>
+
+<p>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.
+
+<p>Well, that was easy :). In practice, we recommend always using
+<tt>IRBuilder</tt> 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).</p>
-<p>On the other hand, the <tt>LLVMFoldingBuilder</tt> is limited by the fact
+<p>On the other hand, the <tt>IRBuilder</tt> 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:</p>
ready> Read function definition:
define double @test(double %x) {
entry:
- %addtmp = add double 3.000000e+00, %x
- %addtmp1 = add double %x, 3.000000e+00
- %multmp = mul double %addtmp, %addtmp1
+ %addtmp = fadd double 3.000000e+00, %x
+ %addtmp1 = fadd double %x, 3.000000e+00
+ %multmp = fmul double %addtmp, %addtmp1
ret double %multmp
}
</pre>
<p>In this case, the LHS and RHS of the multiplication are the same value. We'd
really like to see this generate "<tt>tmp = x+3; result = tmp*tmp;</tt>" instead
-of computing "<tt>x*3</tt>" twice.</p>
+of computing "<tt>x+3</tt>" twice.</p>
<p>Unfortunately, no amount of local analysis will be able to detect and correct
this. This requires two transformations: reassociation of expressions (to
<div class="doc_text">
-<p>LLVM provides many optimization passes which do many different sorts of
+<p>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
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 the get run, see the <a href="../WritingAnLLVMPass.html">How
-to Write a Pass</a> document.</p>
+on passes and how they are run, see the <a href="../WritingAnLLVMPass.html">How
+to Write a Pass</a> document and the <a href="../Passes.html">List of LLVM
+Passes</a>.</p>
<p>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
<div class="doc_code">
<pre>
- ExistingModuleProvider OurModuleProvider(TheModule);
- FunctionPassManager OurFPM(&OurModuleProvider);
-
- // Set up the optimizer pipeline. Start with registering info about how the
- // target lays out data structures.
- OurFPM.add(new TargetData(*TheExecutionEngine->getTargetData()));
- // Do simple "peephole" optimizations and bit-twiddling optzns.
- OurFPM.add(createInstructionCombiningPass());
- // Reassociate expressions.
- OurFPM.add(createReassociatePass());
- // Eliminate Common SubExpressions.
- OurFPM.add(createGVNPass());
- // Simplify the control flow graph (deleting unreachable blocks, etc).
- OurFPM.add(createCFGSimplificationPass());
-
- // Set the global so the code gen can use this.
- TheFPM = &OurFPM;
-
- // Run the main "interpreter loop" now.
- MainLoop();
+ FunctionPassManager OurFPM(TheModule);
+
+ // Set up the optimizer pipeline. Start with registering info about how the
+ // target lays out data structures.
+ OurFPM.add(new TargetData(*TheExecutionEngine->getTargetData()));
+ // Do simple "peephole" optimizations and bit-twiddling optzns.
+ OurFPM.add(createInstructionCombiningPass());
+ // Reassociate expressions.
+ OurFPM.add(createReassociatePass());
+ // Eliminate Common SubExpressions.
+ OurFPM.add(createGVNPass());
+ // Simplify the control flow graph (deleting unreachable blocks, etc).
+ OurFPM.add(createCFGSimplificationPass());
+
+ OurFPM.doInitialization();
+
+ // Set the global so the code gen can use this.
+ TheFPM = &OurFPM;
+
+ // Run the main "interpreter loop" now.
+ MainLoop();
</pre>
</div>
-<p>This code defines two objects, a <tt>ExistingModuleProvider</tt> and a
-<tt>FunctionPassManager</tt>. The former is basically a wrapper around our
-<tt>Module</tt> that the PassManager requires. It provides certain flexibility
-that we're not going to take advantage of here, so I won't dive into what it is
-all about.</p>
-
-<p>The meat of the matter is the definition of the "<tt>OurFPM</tt>". It
-requires a pointer to the <tt>Module</tt> (through the <tt>ModuleProvider</tt>)
-to construct itself. Once it is set up, we use a series of "add" calls to add
-a bunch of LLVM passes. The first pass is basically boilerplate, it adds a pass
-so that later optimizations know how the data structures in the program are
-layed out. The "<tt>TheExecutionEngine</tt>" variable is related to the JIT,
-which we will get to in the next section.</p>
+<p>This code defines a <tt>FunctionPassManager</tt>, "<tt>OurFPM</tt>". It
+requires a pointer to the <tt>Module</tt> to construct itself. Once it is set
+up, we use a series of "add" calls to add a bunch of LLVM passes. The first
+pass is basically boilerplate, it adds a pass so that later optimizations know
+how the data structures in the program are laid out. The
+"<tt>TheExecutionEngine</tt>" variable is related to the JIT, which we will get
+to in the next section.</p>
<p>In this case, we choose to add 4 optimization passes. The passes we chose
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 that they
-are a good starting place.</p>
+a wide variety of code. I won't delve into what they do but, believe me,
+they are a good starting place :).</p>
-<p>Once the passmanager, is set up, we need to make use of it. We do this by
+<p>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
<tt>FunctionAST::Codegen</tt>), but before it is returned to the client:</p>
// Validate the generated code, checking for consistency.
verifyFunction(*TheFunction);
- // Optimize the function.
- TheFPM->run(*TheFunction);
+ <b>// Optimize the function.
+ TheFPM->run(*TheFunction);</b>
return TheFunction;
}
</pre>
</div>
-<p>As you can see, this is pretty straight-forward. The
+<p>As you can see, this is pretty straightforward. The
<tt>FunctionPassManager</tt> optimizes and updates the LLVM Function* in place,
improving (hopefully) its body. With this in place, we can try our test above
again:</p>
ready> Read function definition:
define double @test(double %x) {
entry:
- %addtmp = add double %x, 3.000000e+00
- %multmp = mul double %addtmp, %addtmp
+ %addtmp = fadd double %x, 3.000000e+00
+ %multmp = fmul double %addtmp, %addtmp
ret double %multmp
}
</pre>
</div>
<p>As expected, we now get our nicely optimized code, saving a floating point
-add from the program.</p>
+add instruction from every execution of this function.</p>
<p>LLVM provides a wide variety of optimizations that can be used in certain
-circumstances. Unfortunately we don't have a good centralized description of
-what every pass does, but you can check out the ones that <tt>llvm-gcc</tt> or
+circumstances. Some <a href="../Passes.html">documentation about the various
+passes</a> is available, but it isn't very complete. Another good source of
+ideas can come from looking at the passes that <tt>llvm-gcc</tt> or
<tt>llvm-ld</tt> run to get started. The "<tt>opt</tt>" tool allows you to
experiment with passes from the command line, so you can see if they do
anything.</p>
<div class="doc_text">
-<p>Once the code is available in LLVM IR form a wide variety of tools can be
+<p>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.
+about the LLVM IR representation is that it is the "common currency" between
+many different parts of the compiler.
</p>
-<p>In this chapter, we'll add JIT compiler support to our interpreter. The
+<p>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
<div class="doc_code">
<pre>
-static ExecutionEngine *TheExecutionEngine;
+<b>static ExecutionEngine *TheExecutionEngine;</b>
...
int main() {
..
- // Create the JIT.
- TheExecutionEngine = ExecutionEngine::create(TheModule);
+ <b>// Create the JIT. This takes ownership of the module.
+ TheExecutionEngine = EngineBuilder(TheModule).create();</b>
..
}
</pre>
the interpreter.</p>
<p>Once the <tt>ExecutionEngine</tt> is created, the JIT is ready to be used.
-There are a variety of APIs that are useful, but the most simple one is the
+There are a variety of APIs that are useful, but the simplest one is the
"<tt>getPointerToFunction(F)</tt>" method. This method JIT compiles the
specified LLVM Function and returns a function pointer to the generated machine
code. In our case, this means that we can change the code that parses a
<div class="doc_code">
<pre>
static void HandleTopLevelExpression() {
- // Evaluate a top level expression into an anonymous function.
+ // Evaluate a top-level expression into an anonymous function.
if (FunctionAST *F = ParseTopLevelExpr()) {
if (Function *LF = F->Codegen()) {
LF->dump(); // Dump the function for exposition purposes.
- // JIT the function, returning a function pointer.
+ <b>// JIT the function, returning a function pointer.
void *FPtr = TheExecutionEngine->getPointerToFunction(LF);
// Cast it to the right type (takes no arguments, returns a double) so we
// can call it as a native function.
- double (*FP)() = (double (*)())FPtr;
- fprintf(stderr, "Evaluated to %f\n", FP());
+ double (*FP)() = (double (*)())(intptr_t)FPtr;
+ fprintf(stderr, "Evaluated to %f\n", FP());</b>
}
</pre>
</div>
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.
-As such, there is no difference between JIT compiled code and native machine
+This means, there is no difference between JIT compiled code and native machine
code that is statically linked into your application.</p>
<p>With just these two changes, lets see how Kaleidoscope works now!</p>
<p>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 it. This demonstrates very basic
+for each top-level expression that is typed in. This demonstrates very basic
functionality, but can we do more?</p>
<div class="doc_code">
<pre>
-ready> def testfunc(x y) x + y*2; </b>
+ready> <b>def testfunc(x y) x + y*2; </b>
Read function definition:
define double @testfunc(double %x, double %y) {
entry:
- %multmp = mul double %y, 2.000000e+00
- %addtmp = add double %multmp, %x
+ %multmp = fmul double %y, 2.000000e+00
+ %addtmp = fadd double %multmp, %x
ret double %addtmp
}
</pre>
</div>
-<p>This illustrates that we can now call user code, but it is a bit subtle what
-is going on here. Note that we only invoke the JIT on the anonymous functions
-that <em>calls testfunc</em>, but we never invoked it on <em>testfunc
-itself</em>.</p>
-
-<p>What actually happened here is that the anonymous function is
-JIT'd when requested. When the Kaleidoscope app calls through the function
-pointer that is returned, the anonymous function starts executing. It ends up
-making the call for the "testfunc" function, and ends up in a stub that invokes
-the JIT, lazily, on testfunc. Once the JIT finishes lazily compiling testfunc,
-it returns and the code reexecutes the call.</p>
-
-<p>In summary, the JIT will lazily JIT code on the fly as it is needed. The
-JIT provides a number of other more advanced interfaces for things like freeing
-allocated machine code, rejit'ing functions to update them, etc. However, even
-with this simple code, we get some surprisingly powerful capabilities - check
-this out (I removed the dump of the anonymous functions, you should get the idea
-by now :) :</p>
+<p>This illustrates that we can now call user code, but there is something a bit
+subtle going on here. Note that we only invoke the JIT on the anonymous
+functions that <em>call testfunc</em>, but we never invoked it
+on <em>testfunc</em> itself. What actually happened here is that the JIT
+scanned for all non-JIT'd functions transitively called from the anonymous
+function and compiled all of them before returning
+from <tt>getPointerToFunction()</tt>.</p>
+
+<p>The JIT provides a number of other more advanced interfaces for things like
+freeing allocated machine code, rejit'ing functions to update them, etc.
+However, even with this simple code, we get some surprisingly powerful
+capabilities - check this out (I removed the dump of the anonymous functions,
+you should get the idea by now :) :</p>
<div class="doc_code">
<pre>
ready> <b>sin(1.0);</b>
<em>Evaluated to 0.841471</em>
+
ready> <b>def foo(x) sin(x)*sin(x) + cos(x)*cos(x);</b>
Read function definition:
define double @foo(double %x) {
entry:
%calltmp = call double @sin( double %x )
- %multmp = mul double %calltmp, %calltmp
+ %multmp = fmul double %calltmp, %calltmp
%calltmp2 = call double @cos( double %x )
- %multmp4 = mul double %calltmp2, %calltmp2
- %addtmp = add double %multmp, %multmp4
+ %multmp4 = fmul double %calltmp2, %calltmp2
+ %addtmp = fadd double %multmp, %multmp4
ret double %addtmp
}
</pre>
</div>
-<p>Whoa, how does the JIT know about sin and cos? The answer is simple: in this
+<p>Whoa, how does the JIT know about sin and cos? The answer is surprisingly
+simple: in this
example, the JIT started execution of a function and got to a function call. It
realized that the function was not yet JIT compiled and invoked the standard set
of routines to resolve the function. In this case, there is no body defined
-for the function, so the JIT ended up calling "<tt>dlsym("sin")</tt>" on itself.
+for the function, so the JIT ended up calling "<tt>dlsym("sin")</tt>" on the
+Kaleidoscope process itself.
Since "<tt>sin</tt>" is defined within the JIT's address space, it simply
patches up calls in the module to call the libm version of <tt>sin</tt>
directly.</p>
resolved. It allows you to establish explicit mappings between IR objects and
addresses (useful for LLVM global variables that you want to map to static
tables, for example), allows you to dynamically decide on the fly based on the
-function name, and even allows you to have the JIT abort itself if any lazy
-compilation is attempted.</p>
+function name, and even allows you to have the JIT compile functions lazily the
+first time they're called.</p>
+
+<p>One interesting application of this is that we can now extend the language
+by writing arbitrary C++ code to implement operations. For example, if we add:
+</p>
+
+<div class="doc_code">
+<pre>
+/// putchard - putchar that takes a double and returns 0.
+extern "C"
+double putchard(double X) {
+ putchar((char)X);
+ return 0;
+}
+</pre>
+</div>
+
+<p>Now we can produce simple output to the console by using things like:
+"<tt>extern putchard(x); putchard(120);</tt>", 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.</p>
<p>This completes the JIT and optimizer chapter of the Kaleidoscope tutorial. At
this point, we can compile a non-Turing-complete programming language, optimize
</pre>
</div>
+<p>
+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.</p>
+
<p>Here is the code:</p>
<div class="doc_code">
<pre>
#include "llvm/DerivedTypes.h"
#include "llvm/ExecutionEngine/ExecutionEngine.h"
+#include "llvm/ExecutionEngine/JIT.h"
+#include "llvm/LLVMContext.h"
#include "llvm/Module.h"
-#include "llvm/ModuleProvider.h"
#include "llvm/PassManager.h"
#include "llvm/Analysis/Verifier.h"
#include "llvm/Target/TargetData.h"
+#include "llvm/Target/TargetSelect.h"
#include "llvm/Transforms/Scalar.h"
-#include "llvm/Support/LLVMBuilder.h"
+#include "llvm/Support/IRBuilder.h"
#include <cstdio>
#include <string>
#include <map>
tok_def = -2, tok_extern = -3,
// primary
- tok_identifier = -4, tok_number = -5,
+ tok_identifier = -4, tok_number = -5
};
static std::string IdentifierStr; // Filled in if tok_identifier
if (LastChar == '#') {
// Comment until end of line.
do LastChar = getchar();
- while (LastChar != EOF && LastChar != '\n' & LastChar != '\r');
+ while (LastChar != EOF && LastChar != '\n' && LastChar != '\r');
if (LastChar != EOF)
return gettok();
};
/// PrototypeAST - This class represents the "prototype" for a function,
-/// which captures its argument names as well as if it is an operator.
+/// 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;
//===----------------------------------------------------------------------===//
/// CurTok/getNextToken - Provide a simple token buffer. CurTok is the current
-/// token the parser it looking at. getNextToken reads another token from the
+/// 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() {
static ExprAST *ParseExpression();
/// identifierexpr
-/// ::= identifer
-/// ::= identifer '(' expression* ')'
+/// ::= identifier
+/// ::= identifier '(' expression* ')'
static ExprAST *ParseIdentifierExpr() {
std::string IdName = IdentifierStr;
- getNextToken(); // eat identifer.
+ getNextToken(); // eat identifier.
if (CurTok != '(') // Simple variable ref.
return new VariableExprAST(IdName);
// Call.
getNextToken(); // eat (
std::vector<ExprAST*> Args;
- while (1) {
- ExprAST *Arg = ParseExpression();
- if (!Arg) return 0;
- Args.push_back(Arg);
-
- if (CurTok == ')') break;
-
- if (CurTok != ',')
- return Error("Expected ')'");
- getNextToken();
+ if (CurTok != ')') {
+ while (1) {
+ ExprAST *Arg = ParseExpression();
+ if (!Arg) return 0;
+ Args.push_back(Arg);
+
+ if (CurTok == ')') break;
+
+ if (CurTok != ',')
+ return Error("Expected ')' or ',' in argument list");
+ getNextToken();
+ }
}
// Eat the ')'.
//===----------------------------------------------------------------------===//
static Module *TheModule;
-static LLVMFoldingBuilder Builder;
+static IRBuilder<> Builder(getGlobalContext());
static std::map<std::string, Value*> NamedValues;
static FunctionPassManager *TheFPM;
Value *ErrorV(const char *Str) { Error(Str); return 0; }
Value *NumberExprAST::Codegen() {
- return ConstantFP::get(Type::DoubleTy, APFloat(Val));
+ return ConstantFP::get(getGlobalContext(), APFloat(Val));
}
Value *VariableExprAST::Codegen() {
case '-': return Builder.CreateSub(L, R, "subtmp");
case '*': return Builder.CreateMul(L, R, "multmp");
case '<':
- L = Builder.CreateFCmpULT(L, R, "multmp");
+ L = Builder.CreateFCmpULT(L, R, "cmptmp");
// Convert bool 0/1 to double 0.0 or 1.0
- return Builder.CreateUIToFP(L, Type::DoubleTy, "booltmp");
+ return Builder.CreateUIToFP(L, Type::getDoubleTy(getGlobalContext()),
+ "booltmp");
default: return ErrorV("invalid binary operator");
}
}
Function *PrototypeAST::Codegen() {
// Make the function type: double(double,double) etc.
- std::vector<const Type*> Doubles(Args.size(), Type::DoubleTy);
- FunctionType *FT = FunctionType::get(Type::DoubleTy, Doubles, false);
+ std::vector<const Type*> Doubles(Args.size(),
+ Type::getDoubleTy(getGlobalContext()));
+ FunctionType *FT = FunctionType::get(Type::getDoubleTy(getGlobalContext()),
+ Doubles, false);
- Function *F = new Function(FT, Function::ExternalLinkage, Name, TheModule);
+ Function *F = Function::Create(FT, Function::ExternalLinkage, Name, TheModule);
// If F conflicted, there was already something named 'Name'. If it has a
// body, don't allow redefinition or reextern.
return 0;
// Create a new basic block to start insertion into.
- BasicBlock *BB = new BasicBlock("entry", TheFunction);
+ BasicBlock *BB = BasicBlock::Create(getGlobalContext(), "entry", TheFunction);
Builder.SetInsertPoint(BB);
if (Value *RetVal = Body->Codegen()) {
}
static void HandleTopLevelExpression() {
- // Evaluate a top level expression into an anonymous function.
+ // Evaluate a top-level expression into an anonymous function.
if (FunctionAST *F = ParseTopLevelExpr()) {
if (Function *LF = F->Codegen()) {
// JIT the function, returning a function pointer.
// Cast it to the right type (takes no arguments, returns a double) so we
// can call it as a native function.
- double (*FP)() = (double (*)())FPtr;
+ double (*FP)() = (double (*)())(intptr_t)FPtr;
fprintf(stderr, "Evaluated to %f\n", FP());
}
} else {
fprintf(stderr, "ready> ");
switch (CurTok) {
case tok_eof: return;
- case ';': getNextToken(); break; // ignore top level semicolons.
+ case ';': getNextToken(); break; // ignore top-level semicolons.
case tok_def: HandleDefinition(); break;
case tok_extern: HandleExtern(); break;
default: HandleTopLevelExpression(); break;
}
}
-
-
//===----------------------------------------------------------------------===//
// "Library" functions that can be "extern'd" from user code.
//===----------------------------------------------------------------------===//
//===----------------------------------------------------------------------===//
int main() {
+ InitializeNativeTarget();
+ LLVMContext &Context = getGlobalContext();
+
// Install standard binary operators.
// 1 is lowest precedence.
BinopPrecedence['<'] = 10;
getNextToken();
// Make the module, which holds all the code.
- TheModule = new Module("my cool jit");
-
- // Create the JIT.
- TheExecutionEngine = ExecutionEngine::create(TheModule);
+ TheModule = new Module("my cool jit", Context);
+
+ // Create the JIT. This takes ownership of the module.
+ std::string ErrStr;
+ TheExecutionEngine = EngineBuilder(TheModule).setErrorStr(&ErrStr).create();
+ if (!TheExecutionEngine) {
+ fprintf(stderr, "Could not create ExecutionEngine: %s\n", ErrStr.c_str());
+ exit(1);
+ }
+
+ FunctionPassManager OurFPM(TheModule);
+
+ // Set up the optimizer pipeline. Start with registering info about how the
+ // target lays out data structures.
+ OurFPM.add(new TargetData(*TheExecutionEngine->getTargetData()));
+ // Do simple "peephole" optimizations and bit-twiddling optzns.
+ OurFPM.add(createInstructionCombiningPass());
+ // Reassociate expressions.
+ OurFPM.add(createReassociatePass());
+ // Eliminate Common SubExpressions.
+ OurFPM.add(createGVNPass());
+ // Simplify the control flow graph (deleting unreachable blocks, etc).
+ OurFPM.add(createCFGSimplificationPass());
+
+ OurFPM.doInitialization();
+
+ // Set the global so the code gen can use this.
+ TheFPM = &OurFPM;
+
+ // Run the main "interpreter loop" now.
+ MainLoop();
+
+ TheFPM = 0;
- {
- ExistingModuleProvider OurModuleProvider(TheModule);
- FunctionPassManager OurFPM(&OurModuleProvider);
-
- // Set up the optimizer pipeline. Start with registering info about how the
- // target lays out data structures.
- OurFPM.add(new TargetData(*TheExecutionEngine->getTargetData()));
- // Do simple "peephole" optimizations and bit-twiddling optzns.
- OurFPM.add(createInstructionCombiningPass());
- // Reassociate expressions.
- OurFPM.add(createReassociatePass());
- // Eliminate Common SubExpressions.
- OurFPM.add(createGVNPass());
- // Simplify the control flow graph (deleting unreachable blocks, etc).
- OurFPM.add(createCFGSimplificationPass());
-
- // Set the global so the code gen can use this.
- TheFPM = &OurFPM;
-
- // Run the main "interpreter loop" now.
- MainLoop();
-
- TheFPM = 0;
- } // Free module provider and pass manager.
-
-
// Print out all of the generated code.
TheModule->dump();
+
return 0;
}
</pre>
</div>
+<a href="LangImpl5.html">Next: Extending the language: control flow</a>
</div>
<!-- *********************************************************************** -->
<a href="mailto:sabre@nondot.org">Chris Lattner</a><br>
<a href="http://llvm.org">The LLVM Compiler Infrastructure</a><br>
- Last modified: $Date: 2007-10-17 11:05:13 -0700 (Wed, 17 Oct 2007) $
+ Last modified: $Date$
</address>
</body>
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