<div class="doc_text">
<p>Welcome to Chapter 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
+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. This shows how to get nice efficient code for your
-language.</p>
+compiler support. These additions will demonstrate how to get nice, efficient code
+for the Kaleidoscope language.</p>
</div>
</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
+<p>This code is a very, very literal transcription of the AST built by parsing
+the input. As such, this transcription 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
+<p>With LLVM, you don't need this support in the AST. 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.
</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>
+<tt>LLVMFoldingBuilder</tt>. Though we change no other code, we now have all of our
+instructions implicitly constant folded without us having to do anything
+about it. For example, the input above now compiles to:</p>
<div class="doc_code">
<pre>
</pre>
</div>
-<p>Well, that was easy. :) In practice, we recommend always using
+<p>Well, that was easy :). In practice, we recommend always using
<tt>LLVMFoldingBuilder</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
<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
</pre>
</div>
-<p>This code defines two objects, a <tt>ExistingModuleProvider</tt> and a
+<p>This code defines two objects, an <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>
+that we're not going to take advantage of here, so I won't dive into any details
+about it.</p>
-<p>The meat of the matter is the definition of the "<tt>OurFPM</tt>". It
+<p>The meat of the matter here, is the definition of "<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
<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>
</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. Some <a href="../Passes.html">documentation about the various
passes</a> is available, but it isn't very complete. Another good source of
-ideas is to look at the passes that <tt>llvm-gcc</tt> or
+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.
+ TheExecutionEngine = ExecutionEngine::create(TheModule);</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
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());
+ 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>
</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>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.</p>
-<p>What actually happened here is that the anonymous function is
+<p>What actually happened here is that the anonymous function was
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
+making the call to 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>
+it returns and the code re-executes the call.</p>
-<p>In summary, the JIT will lazily JIT code on the fly as it is needed. The
+<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
</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>
<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
+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>
// Call.
getNextToken(); // eat (
std::vector<ExprAST*> Args;
- while (1) {
- ExprAST *Arg = ParseExpression();
- if (!Arg) return 0;
- Args.push_back(Arg);
+ if (CurTok != ')') {
+ while (1) {
+ ExprAST *Arg = ParseExpression();
+ if (!Arg) return 0;
+ Args.push_back(Arg);
- if (CurTok == ')') break;
+ if (CurTok == ')') break;
- if (CurTok != ',')
- return Error("Expected ')'");
- getNextToken();
+ if (CurTok != ',')
+ return Error("Expected ')'");
+ getNextToken();
+ }
}
// Eat the ')'.
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");
default: return ErrorV("invalid binary operator");
projects / oota-llvm.git / blobdiff