373 lines
19 KiB
ReStructuredText
373 lines
19 KiB
ReStructuredText
=======================================================
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Building a JIT: Starting out with KaleidoscopeJIT
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=======================================================
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.. contents::
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:local:
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Chapter 1 Introduction
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======================
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Welcome to Chapter 1 of the "Building an ORC-based JIT in LLVM" tutorial. This
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tutorial runs through the implementation of a JIT compiler using LLVM's
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On-Request-Compilation (ORC) APIs. It begins with a simplified version of the
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KaleidoscopeJIT class used in the
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`Implementing a language with LLVM <LangImpl1.html>`_ tutorials and then
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introduces new features like optimization, lazy compilation and remote
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execution.
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The goal of this tutorial is to introduce you to LLVM's ORC JIT APIs, show how
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these APIs interact with other parts of LLVM, and to teach you how to recombine
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them to build a custom JIT that is suited to your use-case.
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The structure of the tutorial is:
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- Chapter #1: Investigate the simple KaleidoscopeJIT class. This will
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introduce some of the basic concepts of the ORC JIT APIs, including the
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idea of an ORC *Layer*.
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- `Chapter #2 <BuildingAJIT2.html>`_: Extend the basic KaleidoscopeJIT by adding
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a new layer that will optimize IR and generated code.
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- `Chapter #3 <BuildingAJIT3.html>`_: Further extend the JIT by adding a
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Compile-On-Demand layer to lazily compile IR.
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- `Chapter #4 <BuildingAJIT4.html>`_: Improve the laziness of our JIT by
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replacing the Compile-On-Demand layer with a custom layer that uses the ORC
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Compile Callbacks API directly to defer IR-generation until functions are
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called.
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- `Chapter #5 <BuildingAJIT5.html>`_: Add process isolation by JITing code into
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a remote process with reduced privileges using the JIT Remote APIs.
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To provide input for our JIT we will use the Kaleidoscope REPL from
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`Chapter 7 <LangImpl7.html>`_ of the "Implementing a language in LLVM tutorial",
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with one minor modification: We will remove the FunctionPassManager from the
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code for that chapter and replace it with optimization support in our JIT class
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in Chapter #2.
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Finally, a word on API generations: ORC is the 3rd generation of LLVM JIT API.
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It was preceded by MCJIT, and before that by the (now deleted) legacy JIT.
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These tutorials don't assume any experience with these earlier APIs, but
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readers acquainted with them will see many familiar elements. Where appropriate
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we will make this connection with the earlier APIs explicit to help people who
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are transitioning from them to ORC.
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JIT API Basics
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==============
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The purpose of a JIT compiler is to compile code "on-the-fly" as it is needed,
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rather than compiling whole programs to disk ahead of time as a traditional
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compiler does. To support that aim our initial, bare-bones JIT API will be:
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1. Handle addModule(Module &M) -- Make the given IR module available for
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execution.
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2. JITSymbol findSymbol(const std::string &Name) -- Search for pointers to
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symbols (functions or variables) that have been added to the JIT.
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3. void removeModule(Handle H) -- Remove a module from the JIT, releasing any
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memory that had been used for the compiled code.
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A basic use-case for this API, executing the 'main' function from a module,
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will look like:
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.. code-block:: c++
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std::unique_ptr<Module> M = buildModule();
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JIT J;
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Handle H = J.addModule(*M);
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int (*Main)(int, char*[]) =
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(int(*)(int, char*[])J.findSymbol("main").getAddress();
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int Result = Main();
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J.removeModule(H);
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The APIs that we build in these tutorials will all be variations on this simple
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theme. Behind the API we will refine the implementation of the JIT to add
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support for optimization and lazy compilation. Eventually we will extend the
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API itself to allow higher-level program representations (e.g. ASTs) to be
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added to the JIT.
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KaleidoscopeJIT
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===============
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In the previous section we described our API, now we examine a simple
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implementation of it: The KaleidoscopeJIT class [1]_ that was used in the
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`Implementing a language with LLVM <LangImpl1.html>`_ tutorials. We will use
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the REPL code from `Chapter 7 <LangImpl7.html>`_ of that tutorial to supply the
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input for our JIT: Each time the user enters an expression the REPL will add a
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new IR module containing the code for that expression to the JIT. If the
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expression is a top-level expression like '1+1' or 'sin(x)', the REPL will also
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use the findSymbol method of our JIT class find and execute the code for the
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expression, and then use the removeModule method to remove the code again
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(since there's no way to re-invoke an anonymous expression). In later chapters
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of this tutorial we'll modify the REPL to enable new interactions with our JIT
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class, but for now we will take this setup for granted and focus our attention on
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the implementation of our JIT itself.
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Our KaleidoscopeJIT class is defined in the KaleidoscopeJIT.h header. After the
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usual include guards and #includes [2]_, we get to the definition of our class:
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.. code-block:: c++
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#ifndef LLVM_EXECUTIONENGINE_ORC_KALEIDOSCOPEJIT_H
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#define LLVM_EXECUTIONENGINE_ORC_KALEIDOSCOPEJIT_H
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#include "llvm/ExecutionEngine/ExecutionEngine.h"
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#include "llvm/ExecutionEngine/RTDyldMemoryManager.h"
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#include "llvm/ExecutionEngine/Orc/CompileUtils.h"
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#include "llvm/ExecutionEngine/Orc/IRCompileLayer.h"
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#include "llvm/ExecutionEngine/Orc/LambdaResolver.h"
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#include "llvm/ExecutionEngine/Orc/ObjectLinkingLayer.h"
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#include "llvm/IR/Mangler.h"
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#include "llvm/Support/DynamicLibrary.h"
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namespace llvm {
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namespace orc {
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class KaleidoscopeJIT {
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private:
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std::unique_ptr<TargetMachine> TM;
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const DataLayout DL;
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ObjectLinkingLayer<> ObjectLayer;
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IRCompileLayer<decltype(ObjectLayer)> CompileLayer;
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public:
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typedef decltype(CompileLayer)::ModuleSetHandleT ModuleHandleT;
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Our class begins with four members: A TargetMachine, TM, which will be used
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to build our LLVM compiler instance; A DataLayout, DL, which will be used for
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symbol mangling (more on that later), and two ORC *layers*: an
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ObjectLinkingLayer and a IRCompileLayer. We'll be talking more about layers in
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the next chapter, but for now you can think of them as analogous to LLVM
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Passes: they wrap up useful JIT utilities behind an easy to compose interface.
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The first layer, ObjectLinkingLayer, is the foundation of our JIT: it takes
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in-memory object files produced by a compiler and links them on the fly to make
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them executable. This JIT-on-top-of-a-linker design was introduced in MCJIT,
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however the linker was hidden inside the MCJIT class. In ORC we expose the
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linker so that clients can access and configure it directly if they need to. In
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this tutorial our ObjectLinkingLayer will just be used to support the next layer
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in our stack: the IRCompileLayer, which will be responsible for taking LLVM IR,
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compiling it, and passing the resulting in-memory object files down to the
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object linking layer below.
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That's it for member variables, after that we have a single typedef:
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ModuleHandleT. This is the handle type that will be returned from our JIT's
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addModule method, and can be passed to the removeModule method to remove a
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module. The IRCompileLayer class already provides a convenient handle type
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(IRCompileLayer::ModuleSetHandleT), so we just alias our ModuleHandleT to this.
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.. code-block:: c++
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KaleidoscopeJIT()
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: TM(EngineBuilder().selectTarget()), DL(TM->createDataLayout()),
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CompileLayer(ObjectLayer, SimpleCompiler(*TM)) {
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llvm::sys::DynamicLibrary::LoadLibraryPermanently(nullptr);
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}
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TargetMachine &getTargetMachine() { return *TM; }
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Next up we have our class constructor. We begin by initializing TM using the
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EngineBuilder::selectTarget helper method, which constructs a TargetMachine for
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the current process. Next we use our newly created TargetMachine to initialize
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DL, our DataLayout. Then we initialize our IRCompileLayer. Our IRCompile layer
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needs two things: (1) A reference to our object linking layer, and (2) a
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compiler instance to use to perform the actual compilation from IR to object
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files. We use the off-the-shelf SimpleCompiler instance for now. Finally, in
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the body of the constructor, we call the DynamicLibrary::LoadLibraryPermanently
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method with a nullptr argument. Normally the LoadLibraryPermanently method is
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called with the path of a dynamic library to load, but when passed a null
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pointer it will 'load' the host process itself, making its exported symbols
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available for execution.
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.. code-block:: c++
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ModuleHandle addModule(std::unique_ptr<Module> M) {
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// Build our symbol resolver:
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// Lambda 1: Look back into the JIT itself to find symbols that are part of
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// the same "logical dylib".
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// Lambda 2: Search for external symbols in the host process.
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auto Resolver = createLambdaResolver(
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[&](const std::string &Name) {
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if (auto Sym = CompileLayer.findSymbol(Name, false))
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return Sym;
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return JITSymbol(nullptr);
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},
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[](const std::string &S) {
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if (auto SymAddr =
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RTDyldMemoryManager::getSymbolAddressInProcess(Name))
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return JITSymbol(SymAddr, JITSymbolFlags::Exported);
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return JITSymbol(nullptr);
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});
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// Build a singleton module set to hold our module.
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std::vector<std::unique_ptr<Module>> Ms;
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Ms.push_back(std::move(M));
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// Add the set to the JIT with the resolver we created above and a newly
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// created SectionMemoryManager.
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return CompileLayer.addModuleSet(std::move(Ms),
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make_unique<SectionMemoryManager>(),
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std::move(Resolver));
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}
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Now we come to the first of our JIT API methods: addModule. This method is
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responsible for adding IR to the JIT and making it available for execution. In
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this initial implementation of our JIT we will make our modules "available for
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execution" by adding them straight to the IRCompileLayer, which will
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immediately compile them. In later chapters we will teach our JIT to be lazier
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and instead add the Modules to a "pending" list to be compiled if and when they
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are first executed.
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To add our module to the IRCompileLayer we need to supply two auxiliary objects
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(as well as the module itself): a memory manager and a symbol resolver. The
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memory manager will be responsible for managing the memory allocated to JIT'd
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machine code, setting memory permissions, and registering exception handling
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tables (if the JIT'd code uses exceptions). For our memory manager we will use
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the SectionMemoryManager class: another off-the-shelf utility that provides all
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the basic functionality we need. The second auxiliary class, the symbol
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resolver, is more interesting for us. It exists to tell the JIT where to look
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when it encounters an *external symbol* in the module we are adding. External
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symbols are any symbol not defined within the module itself, including calls to
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functions outside the JIT and calls to functions defined in other modules that
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have already been added to the JIT. It may seem as though modules added to the
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JIT should "know about one another" by default, but since we would still have to
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supply a symbol resolver for references to code outside the JIT it turns out to
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be easier to just re-use this one mechanism for all symbol resolution. This has
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the added benefit that the user has full control over the symbol resolution
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process. Should we search for definitions within the JIT first, then fall back
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on external definitions? Or should we prefer external definitions where
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available and only JIT code if we don't already have an available
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implementation? By using a single symbol resolution scheme we are free to choose
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whatever makes the most sense for any given use case.
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Building a symbol resolver is made especially easy by the *createLambdaResolver*
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function. This function takes two lambdas [3]_ and returns a JITSymbolResolver
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instance. The first lambda is used as the implementation of the resolver's
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findSymbolInLogicalDylib method, which searches for symbol definitions that
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should be thought of as being part of the same "logical" dynamic library as this
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Module. If you are familiar with static linking: this means that
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findSymbolInLogicalDylib should expose symbols with common linkage and hidden
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visibility. If all this sounds foreign you can ignore the details and just
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remember that this is the first method that the linker will use to try to find a
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symbol definition. If the findSymbolInLogicalDylib method returns a null result
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then the linker will call the second symbol resolver method, called findSymbol,
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which searches for symbols that should be thought of as external to (but
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visibile from) the module and its logical dylib. In this tutorial we will adopt
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the following simple scheme: All modules added to the JIT will behave as if they
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were linked into a single, ever-growing logical dylib. To implement this our
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first lambda (the one defining findSymbolInLogicalDylib) will just search for
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JIT'd code by calling the CompileLayer's findSymbol method. If we don't find a
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symbol in the JIT itself we'll fall back to our second lambda, which implements
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findSymbol. This will use the RTDyldMemoryManager::getSymbolAddressInProcess
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method to search for the symbol within the program itself. If we can't find a
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symbol definition via either of these paths, the JIT will refuse to accept our
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module, returning a "symbol not found" error.
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Now that we've built our symbol resolver, we're ready to add our module to the
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JIT. We do this by calling the CompileLayer's addModuleSet method [4]_. Since
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we only have a single Module and addModuleSet expects a collection, we will
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create a vector of modules and add our module as the only member. Since we
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have already typedef'd our ModuleHandleT type to be the same as the
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CompileLayer's handle type, we can return the handle from addModuleSet
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directly from our addModule method.
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.. code-block:: c++
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JITSymbol findSymbol(const std::string Name) {
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std::string MangledName;
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raw_string_ostream MangledNameStream(MangledName);
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Mangler::getNameWithPrefix(MangledNameStream, Name, DL);
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return CompileLayer.findSymbol(MangledNameStream.str(), true);
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}
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void removeModule(ModuleHandle H) {
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CompileLayer.removeModuleSet(H);
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}
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Now that we can add code to our JIT, we need a way to find the symbols we've
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added to it. To do that we call the findSymbol method on our IRCompileLayer,
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but with a twist: We have to *mangle* the name of the symbol we're searching
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for first. The reason for this is that the ORC JIT components use mangled
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symbols internally the same way a static compiler and linker would, rather
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than using plain IR symbol names. The kind of mangling will depend on the
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DataLayout, which in turn depends on the target platform. To allow us to
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remain portable and search based on the un-mangled name, we just re-produce
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this mangling ourselves.
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We now come to the last method in our JIT API: removeModule. This method is
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responsible for destructing the MemoryManager and SymbolResolver that were
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added with a given module, freeing any resources they were using in the
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process. In our Kaleidoscope demo we rely on this method to remove the module
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representing the most recent top-level expression, preventing it from being
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treated as a duplicate definition when the next top-level expression is
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entered. It is generally good to free any module that you know you won't need
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to call further, just to free up the resources dedicated to it. However, you
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don't strictly need to do this: All resources will be cleaned up when your
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JIT class is destructed, if they haven't been freed before then.
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This brings us to the end of Chapter 1 of Building a JIT. You now have a basic
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but fully functioning JIT stack that you can use to take LLVM IR and make it
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executable within the context of your JIT process. In the next chapter we'll
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look at how to extend this JIT to produce better quality code, and in the
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process take a deeper look at the ORC layer concept.
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`Next: Extending the KaleidoscopeJIT <BuildingAJIT2.html>`_
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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. To build this
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example, use:
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.. code-block:: bash
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# Compile
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clang++ -g toy.cpp `llvm-config --cxxflags --ldflags --system-libs --libs core orc native` -O3 -o toy
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# Run
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./toy
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Here is the code:
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.. literalinclude:: ../../examples/Kaleidoscope/BuildingAJIT/Chapter1/KaleidoscopeJIT.h
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:language: c++
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.. [1] Actually we use a cut-down version of KaleidoscopeJIT that makes a
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simplifying assumption: symbols cannot be re-defined. This will make it
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impossible to re-define symbols in the REPL, but will make our symbol
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lookup logic simpler. Re-introducing support for symbol redefinition is
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left as an exercise for the reader. (The KaleidoscopeJIT.h used in the
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original tutorials will be a helpful reference).
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.. [2] +-----------------------+-----------------------------------------------+
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| File | Reason for inclusion |
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+=======================+===============================================+
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| ExecutionEngine.h | Access to the EngineBuilder::selectTarget |
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| | method. |
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+-----------------------+-----------------------------------------------+
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| | Access to the |
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| RTDyldMemoryManager.h | RTDyldMemoryManager::getSymbolAddressInProcess|
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| | method. |
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+-----------------------+-----------------------------------------------+
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| CompileUtils.h | Provides the SimpleCompiler class. |
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+-----------------------+-----------------------------------------------+
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| IRCompileLayer.h | Provides the IRCompileLayer class. |
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+-----------------------+-----------------------------------------------+
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| | Access the createLambdaResolver function, |
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| LambdaResolver.h | which provides easy construction of symbol |
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| | resolvers. |
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+-----------------------+-----------------------------------------------+
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| ObjectLinkingLayer.h | Provides the ObjectLinkingLayer class. |
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+-----------------------+-----------------------------------------------+
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| Mangler.h | Provides the Mangler class for platform |
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| | specific name-mangling. |
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+-----------------------+-----------------------------------------------+
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| DynamicLibrary.h | Provides the DynamicLibrary class, which |
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| | makes symbols in the host process searchable. |
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+-----------------------+-----------------------------------------------+
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.. [3] Actually they don't have to be lambdas, any object with a call operator
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will do, including plain old functions or std::functions.
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.. [4] ORC layers accept sets of Modules, rather than individual ones, so that
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all Modules in the set could be co-located by the memory manager, though
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this feature is not yet implemented.
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