936 lines
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ReStructuredText
936 lines
32 KiB
ReStructuredText
*************************************************
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Chapter 4: Adding JIT and Optimizer Support
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*************************************************
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Written by `Chris Lattner <mailto:sabre@nondot.org>`_ and `Max
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Shawabkeh <http://max99x.com>`_
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Introduction
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=======================
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Welcome to Chapter 4 of the `Implementing a language with
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LLVM <http://www.llvm.org/docs/tutorial/index.html>`_ tutorial. Chapters
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1-3 described the implementation of a simple language and added support
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for generating LLVM IR. This chapter describes two new techniques:
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adding optimizer support to your language, and adding JIT compiler
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support. These additions will demonstrate how to get nice, efficient
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code for the Kaleidoscope language.
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--------------
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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 LLVM Builder,
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however, does give us obvious optimizations when compiling simple code:
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.. code-block:: bash
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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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.. code-block:: bash
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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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``llvm.core.Builder`` when generating code like this. It has no
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"syntactic overhead" for its use (you don't have to uglify your compiler
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with constant checks everywhere) and it can dramatically reduce the
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amount of LLVM IR that is generated in some cases (particular for
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languages with a macro preprocessor or that use a lot of constants).
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On the other hand, the ``Builder`` 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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.. code-block:: bash
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ready> def test(x) (1+2+x)*(x+(1+2))
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Read a 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 ; <double> [#uses=1]
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%addtmp1 = fadd double %x, 3.000000e+00 ; <double> [#uses=1]
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%multmp = fmul double %addtmp, %addtmp1 ; <double> [#uses=1]
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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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--------------
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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 <http://www.llvm.org/docs/WritingAnLLVMPass.html>`_
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document and the `List of LLVM
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Passes <http://www.llvm.org/docs/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 <http://www.llvm.org/docs/WritingAnLLVMPass.html#passmanager>`_
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to hold and organize the LLVM optimizations that we want to run. Once we
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have 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:: python
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# The function optimization passes manager.
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g_llvm_pass_manager = FunctionPassManager.new(g_llvm_module)
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# The LLVM execution engine.
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g_llvm_executor = ExecutionEngine.new(g_llvm_module)
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...
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def main():
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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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g_llvm_pass_manager.add(g_llvm_executor.target_data)
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# Do simple "peephole" optimizations and bit-twiddling optzns.
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g_llvm_pass_manager.add(PASS_INSTRUCTION_COMBINING)
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# Reassociate expressions.
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g_llvm_pass_manager.add(PASS_REASSOCIATE)
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# Eliminate Common SubExpressions.
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g_llvm_pass_manager.add(PASS_GVN)
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# Simplify the control flow graph (deleting unreachable blocks, etc).
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g_llvm_pass_manager.add(PASS_CFG_SIMPLIFICATION)
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g_llvm_pass_manager.initialize()
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This code defines a ``FunctionPassManager``, ``g_llvm_pass_manager``.
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Once it is set up, we use a series of "add" calls to add a bunch of LLVM
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passes. The first pass is basically boilerplate, it adds a pass so that
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later optimizations know how the data structures in the program are laid
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out. (The "``g_llvm_executor``\ " variable is related to the JIT, which
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we will get to in the next section.) In this case, we choose to add 4
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optimization passes. The passes we chose here are a pretty standard set
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of "cleanup" optimizations that are useful for a wide variety of code. I
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won't delve into what they do but, believe me, they are a good starting
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place :).
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Once the pass manager is set up, we need to make use of it. We do this
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by running it after our newly created function is constructed (in
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``FunctionNode.CodeGen``), but before it is returned to the client:
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.. code-block:: python
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return_value = self.body.CodeGen()
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g_llvm_builder.ret(return_value)
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# Validate the generated code, checking for consistency.
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function.verify()
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# Optimize the function.
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g_llvm_pass_manager.run(function)
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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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.. code-block:: bash
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ready> def test(x) (1+2+x)*(x+(1+2))
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Read a 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 ; <double> [#uses=2]
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%multmp = fmul double %addtmp, %addtmp ; <double> [#uses=1]
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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 <http://www.llvm.org/docs/Passes.html>`_ is available, but it
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isn't very complete. Another good source of ideas can come from looking
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at the passes that ``llvm-gcc`` or ``llvm-ld`` run to get started. The
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``opt`` tool allows you to experiment with passes from the command line,
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so you can see if they do 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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--------------
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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 and initializing a global variable:
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.. code-block:: python
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# The LLVM execution engine.
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g_llvm_executor = ExecutionEngine.new(g_llvm_module)
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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. We
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can use the ``run_function`` method of the execution engine to execute a
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compiled function and get its return value. In our case, this means that
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we can change the code that parses a top-level expression to look like
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this:
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.. code-block:: python
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def HandleTopLevelExpression(self):
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try:
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function = self.ParseTopLevelExpr().CodeGen()
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result = g_llvm_executor.run_function(function, [])
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print 'Evaluated to:', result.as_real(Type.double())
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except Exception, e:
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print 'Error:', e
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try:
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self.Next() # Skip for error recovery.
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except:
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pass
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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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With just these two changes, lets see how Kaleidoscope works now!
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.. code-block:: bash
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ready> 4+5
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Read a 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: 9.0
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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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.. code-block:: bash
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ready> def testfunc(x y) x + y*2
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Read a 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 ; <double> [#uses=1]
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%addtmp = fadd double %multmp, %x ; <double> [#uses=1]
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ret double %addtmp
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}
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ready> testfunc(4, 10)
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Read a top level expression:
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define double @0() {
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entry:
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%calltmp = call double @testfunc(double 4.000000e+00, double 1.000000e+01) ; <double> [#uses=1]
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ret double %calltmp
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}
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*Evaluated to: 24.0*
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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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``run_function()``.
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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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.. code-block:: bash
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ready> extern sin(x)
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Read an extern:
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declare double @sin(double)
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ready> extern cos(x)
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Read an extern:
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declare double @cos(double)
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ready> sin(1.0)
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*Evaluated to: 0.841470984808*
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ready> def foo(x) sin(x) *sin(x) + cos(x)* cos(x)
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Read a 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) ; <double> [#uses=1]
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%calltmp1 = call double @sin(double %x) ; <double> [#uses=1]
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%multmp = fmul double %calltmp, %calltmp1 ; <double> [#uses=1]
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%calltmp2 = call double @cos(double %x) ; <double> [#uses=1]
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%calltmp3 = call double @cos(double %x) ; <double> [#uses=1]
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%multmp4 = fmul double %calltmp2, %calltmp3 ; <double> [#uses=1]
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%addtmp = fadd double %multmp, %multmp4 ; <double> [#uses=1]
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ret double %addtmp
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}
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ready> foo(4.0)
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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 Python process that
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is hosting our Kaleidoscope prompt. Since ``sin`` is defined within the
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JIT's address space, it simply patches up calls in the module to call
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the libm version of ``sin`` directly.
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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, we can create a C file with the following simple function:
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.. code-block:: c
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#include <stdio.h>
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double putchard(double x) {
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putchar((char)x); return 0;
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}
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We can then compile this into a shared library with GCC::
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gcc -shared -fPIC -o putchard.so putchard.c
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Now we can load this library into the Python process using
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``llvm.core.load_library_permanently`` and access it from Kaleidoscope
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to produce simple output to the console::
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>>> import llvm.core
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>>> llvm.core.load_library_permanently('/home/max/llvmpy-tutorial/putchard.so')
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>>> import kaleidoscope
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>>> kaleidoscope.main()
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ready> extern putchard(x)
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Read an extern:
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declare double @putchard(double)
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ready> putchard(65) + putchard(66) + putchard(67) + putchard(10)
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*ABC*
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Evaluated to: 0.0
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Similar code could be used to implement file I/O, console input, and
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many other capabilities 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 <PythonLangImpl5.html>`_, tackling some interesting LLVM IR
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issues along the way.
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--------------
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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:
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.. code-block:: python
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#!/usr/bin/env python
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import re
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from llvm.core import Module, Constant, Type, Function, Builder, FCMP_ULT
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from llvm.ee import ExecutionEngine, TargetData
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from llvm.passes import FunctionPassManager
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from llvm.passes import (PASS_INSTRUCTION_COMBINING,
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PASS_REASSOCIATE,
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PASS_GVN,
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PASS_CFG_SIMPLIFICATION)
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Globals
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-------
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.. code-block:: python
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# The LLVM module, which holds all the IR code.
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g_llvm_module = Module.new('my cool jit')
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# The LLVM instruction builder. Created whenever a new function is entered.
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g_llvm_builder = None
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# A dictionary that keeps track of which values are defined in the current scope
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# and what their LLVM representation is.
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g_named_values = {}
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# The function optimization passes manager.
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g_llvm_pass_manager = FunctionPassManager.new(g_llvm_module)
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# The LLVM execution engine.
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g_llvm_executor = ExecutionEngine.new(g_llvm_module)
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Lexer
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-----
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.. code-block:: python
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# The lexer yields one of these types for each token.
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class EOFToken(object):
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pass
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class DefToken(object):
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pass
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class ExternToken(object):
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pass
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class IdentifierToken(object):
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def __init__(self, name):
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self.name = name
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class NumberToken(object):
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def __init__(self, value):
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self.value = value
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class CharacterToken(object):
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def __init__(self, char):
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self.char = char
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def __eq__(self, other):
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return isinstance(other, CharacterToken) and self.char == other.char
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def __ne__(self, other):
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return not self == other
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# Regular expressions that tokens and comments of our language.
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REGEX_NUMBER = re.compile('[0-9]+(?:\.[0-9]+)?')
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REGEX_IDENTIFIER = re.compile('[a-zA-Z][a-zA-Z0-9]*')
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REGEX_COMMENT = re.compile('#.*')
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def Tokenize(string):
|
|
while string:
|
|
# Skip whitespace.
|
|
if string[0].isspace():
|
|
string = string[1:]
|
|
continue
|
|
|
|
# Run regexes.
|
|
comment_match = REGEX_COMMENT.match(string)
|
|
number_match = REGEX_NUMBER.match(string)
|
|
identifier_match = REGEX_IDENTIFIER.match(string)
|
|
|
|
# Check if any of the regexes matched and yield the appropriate result.
|
|
if comment_match:
|
|
comment = comment_match.group(0)
|
|
string = string[len(comment):]
|
|
elif number_match:
|
|
number = number_match.group(0)
|
|
yield NumberToken(float(number))
|
|
string = string[len(number):]
|
|
elif identifier_match:
|
|
identifier = identifier_match.group(0)
|
|
# Check if we matched a keyword.
|
|
if identifier == 'def':
|
|
yield DefToken()
|
|
elif identifier == 'extern':
|
|
yield ExternToken()
|
|
else:
|
|
yield IdentifierToken(identifier)
|
|
string = string[len(identifier):]
|
|
else:
|
|
# Yield the ASCII value of the unknown character.
|
|
yield CharacterToken(string[0])
|
|
string = string[1:]
|
|
|
|
yield EOFToken()
|
|
|
|
Abstract Syntax Tree (aka Parse Tree)
|
|
-------------------------------------
|
|
|
|
.. code-block:: python
|
|
|
|
# Base class for all expression nodes.
|
|
class ExpressionNode(object):
|
|
pass
|
|
|
|
# Expression class for numeric literals like "1.0".
|
|
class NumberExpressionNode(ExpressionNode):
|
|
|
|
def __init__(self, value):
|
|
self.value = value
|
|
|
|
def CodeGen(self):
|
|
return Constant.real(Type.double(), self.value)
|
|
|
|
# Expression class for referencing a variable, like "a".
|
|
class VariableExpressionNode(ExpressionNode):
|
|
|
|
def __init__(self, name):
|
|
self.name = name
|
|
|
|
def CodeGen(self):
|
|
if self.name in g_named_values:
|
|
return g_named_values[self.name]
|
|
else:
|
|
raise RuntimeError('Unknown variable name: ' + self.name)
|
|
|
|
# Expression class for a binary operator.
|
|
class BinaryOperatorExpressionNode(ExpressionNode):
|
|
|
|
def __init__(self, operator, left, right):
|
|
self.operator = operator
|
|
self.left = left
|
|
self.right = right
|
|
|
|
def CodeGen(self):
|
|
left = self.left.CodeGen()
|
|
right = self.right.CodeGen()
|
|
|
|
if self.operator == '+':
|
|
return g_llvm_builder.fadd(left, right, 'addtmp')
|
|
elif self.operator == '-':
|
|
return g_llvm_builder.fsub(left, right, 'subtmp')
|
|
elif self.operator == '*':
|
|
return g_llvm_builder.fmul(left, right, 'multmp')
|
|
elif self.operator == '<':
|
|
result = g_llvm_builder.fcmp(FCMP_ULT, left, right, 'cmptmp')
|
|
# Convert bool 0 or 1 to double 0.0 or 1.0.
|
|
return g_llvm_builder.uitofp(result, Type.double(), 'booltmp')
|
|
else:
|
|
raise RuntimeError('Unknown binary operator.')
|
|
|
|
# Expression class for function calls.
|
|
class CallExpressionNode(ExpressionNode):
|
|
|
|
def __init__(self, callee, args):
|
|
self.callee = callee
|
|
self.args = args
|
|
|
|
def CodeGen(self):
|
|
# Look up the name in the global module table.
|
|
callee = g_llvm_module.get_function_named(self.callee)
|
|
|
|
# Check for argument mismatch error.
|
|
if len(callee.args) != len(self.args):
|
|
raise RuntimeError('Incorrect number of arguments passed.')
|
|
|
|
arg_values = [i.CodeGen() for i in self.args]
|
|
|
|
return g_llvm_builder.call(callee, arg_values, 'calltmp')
|
|
|
|
# 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 PrototypeNode(object):
|
|
|
|
def __init__(self, name, args):
|
|
self.name = name
|
|
self.args = args
|
|
|
|
def CodeGen(self):
|
|
# Make the function type, eg. double(double,double).
|
|
funct_type = Type.function(
|
|
Type.double(), [Type.double()] * len(self.args), False)
|
|
|
|
function = Function.new(g_llvm_module, funct_type, self.name)
|
|
|
|
# If the name conflicted, there was already something with the same name.
|
|
# If it has a body, don't allow redefinition or reextern.
|
|
if function.name != self.name:
|
|
function.delete()
|
|
function = g_llvm_module.get_function_named(self.name)
|
|
|
|
# If the function already has a body, reject this.
|
|
if not function.is_declaration:
|
|
raise RuntimeError('Redefinition of function.')
|
|
|
|
# If F took a different number of args, reject.
|
|
if len(callee.args) != len(self.args):
|
|
raise RuntimeError('Redeclaration of a function with different number '
|
|
'of args.')
|
|
|
|
# Set names for all arguments and add them to the variables symbol table.
|
|
for arg, arg_name in zip(function.args, self.args):
|
|
arg.name = arg_name
|
|
# Add arguments to variable symbol table.
|
|
g_named_values[arg_name] = arg
|
|
|
|
return function
|
|
|
|
# This class represents a function definition itself.
|
|
class FunctionNode(object):
|
|
|
|
def __init__(self, prototype, body):
|
|
self.prototype = prototype
|
|
self.body = body
|
|
|
|
def CodeGen(self):
|
|
# Clear scope.
|
|
g_named_values.clear()
|
|
|
|
# Create a function object.
|
|
function = self.prototype.CodeGen()
|
|
|
|
# Create a new basic block to start insertion into.
|
|
block = function.append_basic_block('entry')
|
|
global g_llvm_builder
|
|
g_llvm_builder = Builder.new(block)
|
|
|
|
# Finish off the function.
|
|
try:
|
|
return_value = self.body.CodeGen()
|
|
g_llvm_builder.ret(return_value)
|
|
|
|
# Validate the generated code, checking for consistency.
|
|
function.verify()
|
|
|
|
# Optimize the function.
|
|
g_llvm_pass_manager.run(function)
|
|
except:
|
|
function.delete()
|
|
raise
|
|
|
|
return function
|
|
|
|
Parser
|
|
------
|
|
|
|
.. code-block:: python
|
|
|
|
class Parser(object):
|
|
|
|
def __init__(self, tokens, binop_precedence):
|
|
self.tokens = tokens
|
|
self.binop_precedence = binop_precedence
|
|
self.Next()
|
|
|
|
# Provide a simple token buffer. Parser.current is the current token the
|
|
# parser is looking at. Parser.Next() reads another token from the lexer and
|
|
# updates Parser.current with its results.
|
|
def Next(self):
|
|
self.current = self.tokens.next()
|
|
|
|
# Gets the precedence of the current token, or -1 if the token is not a
|
|
binary # operator.
|
|
def GetCurrentTokenPrecedence(self):
|
|
if isinstance(self.current, CharacterToken):
|
|
return self.binop_precedence.get(self.current.char, -1)
|
|
else:
|
|
return -1
|
|
|
|
# identifierexpr ::= identifier | identifier '(' expression* ')'
|
|
def ParseIdentifierExpr(self):
|
|
identifier_name = self.current.name
|
|
self.Next() # eat identifier.
|
|
|
|
if self.current != CharacterToken('('): # Simple variable reference.
|
|
return VariableExpressionNode(identifier_name)
|
|
|
|
# Call.
|
|
self.Next() # eat '('.
|
|
args = []
|
|
if self.current != CharacterToken(')'):
|
|
while True:
|
|
args.append(self.ParseExpression())
|
|
if self.current == CharacterToken(')'):
|
|
break
|
|
elif self.current != CharacterToken(','):
|
|
raise RuntimeError('Expected ")" or "," in argument list.')
|
|
self.Next()
|
|
|
|
self.Next() # eat ')'.
|
|
return CallExpressionNode(identifier_name, args)
|
|
|
|
# numberexpr ::= number
|
|
def ParseNumberExpr(self):
|
|
result = NumberExpressionNode(self.current.value)
|
|
self.Next() # consume the number.
|
|
return result
|
|
|
|
# parenexpr ::= '(' expression ')'
|
|
def ParseParenExpr(self):
|
|
self.Next() # eat '('.
|
|
|
|
contents = self.ParseExpression()
|
|
|
|
if self.current != CharacterToken(')'):
|
|
raise RuntimeError('Expected ")".')
|
|
self.Next() # eat ')'.
|
|
|
|
return contents
|
|
|
|
# primary ::= identifierexpr | numberexpr | parenexpr
|
|
def ParsePrimary(self):
|
|
if isinstance(self.current, IdentifierToken):
|
|
return self.ParseIdentifierExpr()
|
|
elif isinstance(self.current, NumberToken):
|
|
return self.ParseNumberExpr()
|
|
elif self.current == CharacterToken('('):
|
|
return self.ParseParenExpr()
|
|
else: raise RuntimeError('Unknown token when expecting an expression.')
|
|
|
|
# binoprhs ::= (operator primary)*
|
|
def ParseBinOpRHS(self, left, left_precedence):
|
|
# If this is a binary operator, find its precedence.
|
|
while True:
|
|
precedence = self.GetCurrentTokenPrecedence()
|
|
|
|
# If this is a binary operator that binds at least as tightly as the
|
|
# current one, consume it; otherwise we are done.
|
|
if precedence < left_precedence:
|
|
return left
|
|
|
|
binary_operator = self.current.char
|
|
self.Next() # eat the operator.
|
|
|
|
# Parse the primary expression after the binary operator.
|
|
right = self.ParsePrimary()
|
|
|
|
# If binary_operator binds less tightly with right than the operator after
|
|
# right, let the pending operator take right as its left.
|
|
next_precedence = self.GetCurrentTokenPrecedence()
|
|
if precedence < next_precedence:
|
|
right = self.ParseBinOpRHS(right, precedence + 1)
|
|
|
|
# Merge left/right.
|
|
left = BinaryOperatorExpressionNode(binary_operator, left, right)
|
|
|
|
# expression ::= primary binoprhs
|
|
def ParseExpression(self):
|
|
left = self.ParsePrimary()
|
|
return self.ParseBinOpRHS(left, 0)
|
|
|
|
# prototype ::= id '(' id* ')'
|
|
def ParsePrototype(self):
|
|
if not isinstance(self.current, IdentifierToken):
|
|
raise RuntimeError('Expected function name in prototype.')
|
|
|
|
function_name = self.current.name
|
|
self.Next() # eat function name.
|
|
|
|
if self.current != CharacterToken('('):
|
|
raise RuntimeError('Expected "(" in prototype.')
|
|
self.Next() # eat '('.
|
|
|
|
arg_names = []
|
|
while isinstance(self.current, IdentifierToken):
|
|
arg_names.append(self.current.name)
|
|
self.Next()
|
|
|
|
if self.current != CharacterToken(')'):
|
|
raise RuntimeError('Expected ")" in prototype.')
|
|
|
|
# Success.
|
|
self.Next() # eat ')'.
|
|
|
|
return PrototypeNode(function_name, arg_names)
|
|
|
|
# definition ::= 'def' prototype expression
|
|
def ParseDefinition(self):
|
|
self.Next() # eat def.
|
|
proto = self.ParsePrototype()
|
|
body = self.ParseExpression()
|
|
return FunctionNode(proto, body)
|
|
|
|
# toplevelexpr ::= expression
|
|
def ParseTopLevelExpr(self):
|
|
proto = PrototypeNode('', [])
|
|
return FunctionNode(proto, self.ParseExpression())
|
|
|
|
# external ::= 'extern' prototype
|
|
def ParseExtern(self):
|
|
self.Next() # eat extern.
|
|
return self.ParsePrototype()
|
|
|
|
# Top-Level parsing
|
|
def HandleDefinition(self):
|
|
self.Handle(self.ParseDefinition, 'Read a function definition:')
|
|
|
|
def HandleExtern(self):
|
|
self.Handle(self.ParseExtern, 'Read an extern:')
|
|
|
|
def HandleTopLevelExpression(self):
|
|
try:
|
|
function = self.ParseTopLevelExpr().CodeGen()
|
|
result = g_llvm_executor.run_function(function, [])
|
|
print 'Evaluated to:', result.as_real(Type.double())
|
|
except Exception, e:
|
|
print 'Error:', e
|
|
try:
|
|
self.Next() # Skip for error recovery.
|
|
except:
|
|
pass
|
|
|
|
def Handle(self, function, message):
|
|
try:
|
|
print message, function().CodeGen()
|
|
except Exception, e:
|
|
print 'Error:', e
|
|
try:
|
|
self.Next() # Skip for error recovery.
|
|
except:
|
|
pass
|
|
|
|
Main driver code.
|
|
-----------------
|
|
|
|
.. code-block:: python
|
|
|
|
def main():
|
|
# Set up the optimizer pipeline. Start with registering info about how the
|
|
# target lays out data structures.
|
|
g_llvm_pass_manager.add(g_llvm_executor.target_data)
|
|
# Do simple "peephole" optimizations and bit-twiddling optzns.
|
|
g_llvm_pass_manager.add(PASS_INSTRUCTION_COMBINING)
|
|
# Reassociate expressions.
|
|
g_llvm_pass_manager.add(PASS_REASSOCIATE)
|
|
# Eliminate Common SubExpressions.
|
|
g_llvm_pass_manager.add(PASS_GVN)
|
|
# Simplify the control flow graph (deleting unreachable blocks, etc).
|
|
g_llvm_pass_manager.add(PASS_CFG_SIMPLIFICATION)
|
|
|
|
g_llvm_pass_manager.initialize()
|
|
|
|
# Install standard binary operators.
|
|
# 1 is lowest possible precedence. 40 is the highest.
|
|
operator_precedence = {
|
|
'<': 10,
|
|
'+': 20,
|
|
'-': 20,
|
|
'*': 40
|
|
}
|
|
|
|
# Run the main "interpreter loop".
|
|
while True:
|
|
print 'ready>',
|
|
try:
|
|
raw = raw_input()
|
|
except KeyboardInterrupt:
|
|
break
|
|
|
|
parser = Parser(Tokenize(raw), operator_precedence)
|
|
while True:
|
|
# top ::= definition | external | expression | EOF
|
|
if isinstance(parser.current, EOFToken):
|
|
break
|
|
if isinstance(parser.current, DefToken):
|
|
parser.HandleDefinition()
|
|
elif isinstance(parser.current, ExternToken):
|
|
parser.HandleExtern()
|
|
else:
|
|
parser.HandleTopLevelExpression()
|
|
|
|
# Print out all of the generated code.
|
|
print '', g_llvm_module
|
|
|
|
if __name__ == '__main__':
|
|
main()
|