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446 lines
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==============================================
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Kaleidoscope: Adding JIT and Optimizer Support
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==============================================
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.. contents::
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:local:
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Chapter 4 Introduction
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======================
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Welcome to Chapter 4 of the "`Implementing a language with
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LLVM <index.html>`_" tutorial. Chapters 1-3 described the implementation
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of a simple language and added support for generating LLVM IR. This
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chapter describes two new techniques: adding optimizer support to your
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language, and adding JIT compiler support. These additions will
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demonstrate how to get nice, efficient code for the Kaleidoscope
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language.
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Trivial Constant Folding
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========================
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Our demonstration for Chapter 3 is elegant and easy to extend.
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Unfortunately, it does not produce wonderful code. The IRBuilder,
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however, does give us obvious optimizations when compiling simple code:
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::
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ready> def test(x) 1+2+x;
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Read function definition:
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define double @test(double %x) {
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entry:
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%addtmp = fadd double 3.000000e+00, %x
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ret double %addtmp
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}
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This code is not a literal transcription of the AST built by parsing the
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input. That would be:
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::
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ready> def test(x) 1+2+x;
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Read function definition:
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define double @test(double %x) {
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entry:
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%addtmp = fadd double 2.000000e+00, 1.000000e+00
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%addtmp1 = fadd double %addtmp, %x
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ret double %addtmp1
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}
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Constant folding, as seen above, in particular, is a very common and
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very important optimization: so much so that many language implementors
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implement constant folding support in their AST representation.
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With LLVM, you don't need this support in the AST. Since all calls to
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build LLVM IR go through the LLVM IR builder, the builder itself checked
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to see if there was a constant folding opportunity when you call it. If
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so, it just does the constant fold and return the constant instead of
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creating an instruction.
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Well, that was easy :). In practice, we recommend always using
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``IRBuilder`` when generating code like this. It has no "syntactic
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overhead" for its use (you don't have to uglify your compiler with
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constant checks everywhere) and it can dramatically reduce the amount of
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LLVM IR that is generated in some cases (particular for languages with a
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macro preprocessor or that use a lot of constants).
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On the other hand, the ``IRBuilder`` is limited by the fact that it does
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all of its analysis inline with the code as it is built. If you take a
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slightly more complex example:
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::
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ready> def test(x) (1+2+x)*(x+(1+2));
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ready> Read function definition:
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define double @test(double %x) {
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entry:
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%addtmp = fadd double 3.000000e+00, %x
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%addtmp1 = fadd double %x, 3.000000e+00
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%multmp = fmul double %addtmp, %addtmp1
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ret double %multmp
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}
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In this case, the LHS and RHS of the multiplication are the same value.
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We'd really like to see this generate "``tmp = x+3; result = tmp*tmp;``"
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instead of computing "``x+3``" twice.
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Unfortunately, no amount of local analysis will be able to detect and
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correct this. This requires two transformations: reassociation of
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expressions (to make the add's lexically identical) and Common
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Subexpression Elimination (CSE) to delete the redundant add instruction.
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Fortunately, LLVM provides a broad range of optimizations that you can
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use, in the form of "passes".
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LLVM Optimization Passes
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========================
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LLVM provides many optimization passes, which do many different sorts of
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things and have different tradeoffs. Unlike other systems, LLVM doesn't
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hold to the mistaken notion that one set of optimizations is right for
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all languages and for all situations. LLVM allows a compiler implementor
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to make complete decisions about what optimizations to use, in which
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order, and in what situation.
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As a concrete example, LLVM supports both "whole module" passes, which
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look across as large of body of code as they can (often a whole file,
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but if run at link time, this can be a substantial portion of the whole
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program). It also supports and includes "per-function" passes which just
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operate on a single function at a time, without looking at other
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functions. For more information on passes and how they are run, see the
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`How to Write a Pass <../WritingAnLLVMPass.html>`_ document and the
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`List of LLVM Passes <../Passes.html>`_.
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For Kaleidoscope, we are currently generating functions on the fly, one
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at a time, as the user types them in. We aren't shooting for the
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ultimate optimization experience in this setting, but we also want to
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catch the easy and quick stuff where possible. As such, we will choose
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to run a few per-function optimizations as the user types the function
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in. If we wanted to make a "static Kaleidoscope compiler", we would use
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exactly the code we have now, except that we would defer running the
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optimizer until the entire file has been parsed.
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In order to get per-function optimizations going, we need to set up a
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`FunctionPassManager <../WritingAnLLVMPass.html#passmanager>`_ to hold
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and organize the LLVM optimizations that we want to run. Once we have
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that, we can add a set of optimizations to run. The code looks like
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this:
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.. code-block:: c++
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FunctionPassManager OurFPM(TheModule);
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// Set up the optimizer pipeline. Start with registering info about how the
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// target lays out data structures.
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OurFPM.add(new DataLayout(*TheExecutionEngine->getDataLayout()));
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// Provide basic AliasAnalysis support for GVN.
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OurFPM.add(createBasicAliasAnalysisPass());
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// Do simple "peephole" optimizations and bit-twiddling optzns.
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OurFPM.add(createInstructionCombiningPass());
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// Reassociate expressions.
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OurFPM.add(createReassociatePass());
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// Eliminate Common SubExpressions.
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OurFPM.add(createGVNPass());
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// Simplify the control flow graph (deleting unreachable blocks, etc).
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OurFPM.add(createCFGSimplificationPass());
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OurFPM.doInitialization();
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// Set the global so the code gen can use this.
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TheFPM = &OurFPM;
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// Run the main "interpreter loop" now.
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MainLoop();
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This code defines a ``FunctionPassManager``, "``OurFPM``". It requires a
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pointer to the ``Module`` to construct itself. Once it is set up, we use
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a series of "add" calls to add a bunch of LLVM passes. The first pass is
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basically boilerplate, it adds a pass so that later optimizations know
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how the data structures in the program are laid out. The
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"``TheExecutionEngine``" variable is related to the JIT, which we will
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get to in the next section.
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In this case, we choose to add 4 optimization passes. The passes we
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chose here are a pretty standard set of "cleanup" optimizations that are
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useful for a wide variety of code. I won't delve into what they do but,
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believe me, they are a good starting place :).
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Once the PassManager is set up, we need to make use of it. We do this by
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running it after our newly created function is constructed (in
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``FunctionAST::Codegen``), but before it is returned to the client:
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.. code-block:: c++
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if (Value *RetVal = Body->Codegen()) {
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// Finish off the function.
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Builder.CreateRet(RetVal);
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// Validate the generated code, checking for consistency.
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verifyFunction(*TheFunction);
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// Optimize the function.
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TheFPM->run(*TheFunction);
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return TheFunction;
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}
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As you can see, this is pretty straightforward. The
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``FunctionPassManager`` optimizes and updates the LLVM Function\* in
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place, improving (hopefully) its body. With this in place, we can try
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our test above again:
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::
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ready> def test(x) (1+2+x)*(x+(1+2));
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ready> Read function definition:
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define double @test(double %x) {
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entry:
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%addtmp = fadd double %x, 3.000000e+00
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%multmp = fmul double %addtmp, %addtmp
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ret double %multmp
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}
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As expected, we now get our nicely optimized code, saving a floating
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point add instruction from every execution of this function.
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LLVM provides a wide variety of optimizations that can be used in
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certain circumstances. Some `documentation about the various
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passes <../Passes.html>`_ is available, but it isn't very complete.
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Another good source of ideas can come from looking at the passes that
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``Clang`` runs to get started. The "``opt``" tool allows you to
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experiment with passes from the command line, so you can see if they do
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anything.
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Now that we have reasonable code coming out of our front-end, lets talk
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about executing it!
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Adding a JIT Compiler
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=====================
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Code that is available in LLVM IR can have a wide variety of tools
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applied to it. For example, you can run optimizations on it (as we did
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above), you can dump it out in textual or binary forms, you can compile
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the code to an assembly file (.s) for some target, or you can JIT
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compile it. The nice thing about the LLVM IR representation is that it
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is the "common currency" between many different parts of the compiler.
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In this section, we'll add JIT compiler support to our interpreter. The
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basic idea that we want for Kaleidoscope is to have the user enter
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function bodies as they do now, but immediately evaluate the top-level
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expressions they type in. For example, if they type in "1 + 2;", we
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should evaluate and print out 3. If they define a function, they should
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be able to call it from the command line.
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In order to do this, we first declare and initialize the JIT. This is
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done by adding a global variable and a call in ``main``:
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.. code-block:: c++
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static ExecutionEngine *TheExecutionEngine;
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...
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int main() {
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..
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// Create the JIT. This takes ownership of the module.
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TheExecutionEngine = EngineBuilder(TheModule).create();
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..
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}
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This creates an abstract "Execution Engine" which can be either a JIT
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compiler or the LLVM interpreter. LLVM will automatically pick a JIT
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compiler for you if one is available for your platform, otherwise it
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will fall back to the interpreter.
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Once the ``ExecutionEngine`` is created, the JIT is ready to be used.
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There are a variety of APIs that are useful, but the simplest one is the
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"``getPointerToFunction(F)``" method. This method JIT compiles the
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specified LLVM Function and returns a function pointer to the generated
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machine code. In our case, this means that we can change the code that
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parses a top-level expression to look like this:
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.. code-block:: c++
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static void HandleTopLevelExpression() {
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// Evaluate a top-level expression into an anonymous function.
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if (FunctionAST *F = ParseTopLevelExpr()) {
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if (Function *LF = F->Codegen()) {
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LF->dump(); // Dump the function for exposition purposes.
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// JIT the function, returning a function pointer.
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void *FPtr = TheExecutionEngine->getPointerToFunction(LF);
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// Cast it to the right type (takes no arguments, returns a double) so we
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// can call it as a native function.
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double (*FP)() = (double (*)())(intptr_t)FPtr;
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fprintf(stderr, "Evaluated to %f\n", FP());
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}
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Recall that we compile top-level expressions into a self-contained LLVM
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function that takes no arguments and returns the computed double.
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Because the LLVM JIT compiler matches the native platform ABI, this
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means that you can just cast the result pointer to a function pointer of
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that type and call it directly. This means, there is no difference
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between JIT compiled code and native machine code that is statically
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linked into your application.
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With just these two changes, lets see how Kaleidoscope works now!
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::
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ready> 4+5;
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Read top-level expression:
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define double @0() {
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entry:
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ret double 9.000000e+00
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}
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Evaluated to 9.000000
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Well this looks like it is basically working. The dump of the function
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shows the "no argument function that always returns double" that we
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synthesize for each top-level expression that is typed in. This
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demonstrates very basic functionality, but can we do more?
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::
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ready> def testfunc(x y) x + y*2;
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Read function definition:
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define double @testfunc(double %x, double %y) {
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entry:
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%multmp = fmul double %y, 2.000000e+00
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%addtmp = fadd double %multmp, %x
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ret double %addtmp
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}
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ready> testfunc(4, 10);
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Read top-level expression:
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define double @1() {
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entry:
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%calltmp = call double @testfunc(double 4.000000e+00, double 1.000000e+01)
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ret double %calltmp
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}
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Evaluated to 24.000000
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This illustrates that we can now call user code, but there is something
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a bit subtle going on here. Note that we only invoke the JIT on the
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anonymous functions that *call testfunc*, but we never invoked it on
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*testfunc* itself. What actually happened here is that the JIT scanned
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for all non-JIT'd functions transitively called from the anonymous
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function and compiled all of them before returning from
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``getPointerToFunction()``.
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The JIT provides a number of other more advanced interfaces for things
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like freeing allocated machine code, rejit'ing functions to update them,
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etc. However, even with this simple code, we get some surprisingly
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powerful capabilities - check this out (I removed the dump of the
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anonymous functions, you should get the idea by now :) :
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::
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ready> extern sin(x);
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Read extern:
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declare double @sin(double)
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ready> extern cos(x);
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Read extern:
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declare double @cos(double)
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ready> sin(1.0);
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Read top-level expression:
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define double @2() {
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entry:
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ret double 0x3FEAED548F090CEE
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}
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Evaluated to 0.841471
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ready> def foo(x) sin(x)*sin(x) + cos(x)*cos(x);
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Read function definition:
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define double @foo(double %x) {
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entry:
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%calltmp = call double @sin(double %x)
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%multmp = fmul double %calltmp, %calltmp
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%calltmp2 = call double @cos(double %x)
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%multmp4 = fmul double %calltmp2, %calltmp2
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%addtmp = fadd double %multmp, %multmp4
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ret double %addtmp
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}
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ready> foo(4.0);
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Read top-level expression:
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define double @3() {
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entry:
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%calltmp = call double @foo(double 4.000000e+00)
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ret double %calltmp
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}
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Evaluated to 1.000000
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Whoa, how does the JIT know about sin and cos? The answer is
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surprisingly simple: in this example, the JIT started execution of a
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function and got to a function call. It realized that the function was
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not yet JIT compiled and invoked the standard set of routines to resolve
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the function. In this case, there is no body defined for the function,
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so the JIT ended up calling "``dlsym("sin")``" on the Kaleidoscope
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process itself. Since "``sin``" is defined within the JIT's address
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space, it simply patches up calls in the module to call the libm version
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of ``sin`` directly.
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The LLVM JIT provides a number of interfaces (look in the
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``ExecutionEngine.h`` file) for controlling how unknown functions get
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resolved. It allows you to establish explicit mappings between IR
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objects and addresses (useful for LLVM global variables that you want to
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map to static tables, for example), allows you to dynamically decide on
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the fly based on the function name, and even allows you to have the JIT
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compile functions lazily the first time they're called.
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One interesting application of this is that we can now extend the
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language by writing arbitrary C++ code to implement operations. For
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example, if we add:
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.. code-block:: c++
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/// putchard - putchar that takes a double and returns 0.
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extern "C"
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double putchard(double X) {
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putchar((char)X);
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return 0;
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}
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Now we can produce simple output to the console by using things like:
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"``extern putchard(x); putchard(120);``", which prints a lowercase 'x'
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on the console (120 is the ASCII code for 'x'). Similar code could be
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used to implement file I/O, console input, and many other capabilities
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in Kaleidoscope.
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This completes the JIT and optimizer chapter of the Kaleidoscope
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tutorial. At this point, we can compile a non-Turing-complete
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programming language, optimize and JIT compile it in a user-driven way.
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Next up we'll look into `extending the language with control flow
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constructs <LangImpl5.html>`_, tackling some interesting LLVM IR issues
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along the way.
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Full Code Listing
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=================
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Here is the complete code listing for our running example, enhanced with
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the LLVM JIT and optimizer. To build this example, use:
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.. code-block:: bash
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# Compile
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clang++ -g toy.cpp `llvm-config --cppflags --ldflags --libs core jit native` -O3 -o toy
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# Run
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./toy
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If you are compiling this on Linux, make sure to add the "-rdynamic"
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option as well. This makes sure that the external functions are resolved
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properly at runtime.
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Here is the code:
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.. literalinclude:: ../../examples/Kaleidoscope/Chapter4/toy.cpp
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:language: c++
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`Next: Extending the language: control flow <LangImpl5.html>`_
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