llvm/docs/tutorial/BuildingAJIT2.rst - toolchain/llvm-project - Gitiles

 =====================================================================
 Building a JIT: Adding Optimizations -- An introduction to ORC Layers
 =====================================================================

 .. contents::
    :local:

 **This tutorial is under active development. It is incomplete and details may
 change frequently.** Nonetheless we invite you to try it out as it stands, and
 we welcome any feedback.

 Chapter 2 Introduction
 ======================

 **Warning: This text is currently out of date due to ORC API updates.**

 **The example code has been updated and can be used. The text will be updated
 once the API churn dies down.**

 Welcome to Chapter 2 of the "Building an ORC-based JIT in LLVM" tutorial. In
 `Chapter 1 <BuildingAJIT1.html>`_ of this series we examined a basic JIT
 class, KaleidoscopeJIT, that could take LLVM IR modules as input and produce
 executable code in memory. KaleidoscopeJIT was able to do this with relatively
 little code by composing two off-the-shelf *ORC layers*: IRCompileLayer and
 ObjectLinkingLayer, to do much of the heavy lifting.

 In this layer we'll learn more about the ORC layer concept by using a new layer,
 IRTransformLayer, to add IR optimization support to KaleidoscopeJIT.

 Optimizing Modules using the IRTransformLayer
 =============================================

 In `Chapter 4 <LangImpl04.html>`_ of the "Implementing a language with LLVM"
 tutorial series the llvm *FunctionPassManager* is introduced as a means for
 optimizing LLVM IR. Interested readers may read that chapter for details, but
 in short: to optimize a Module we create an llvm::FunctionPassManager
 instance, configure it with a set of optimizations, then run the PassManager on
 a Module to mutate it into a (hopefully) more optimized but semantically
 equivalent form. In the original tutorial series the FunctionPassManager was
 created outside the KaleidoscopeJIT and modules were optimized before being
 added to it. In this Chapter we will make optimization a phase of our JIT
 instead. For now this will provide us a motivation to learn more about ORC
 layers, but in the long term making optimization part of our JIT will yield an
 important benefit: When we begin lazily compiling code (i.e. deferring
 compilation of each function until the first time it's run), having
 optimization managed by our JIT will allow us to optimize lazily too, rather
 than having to do all our optimization up-front.

 To add optimization support to our JIT we will take the KaleidoscopeJIT from
 Chapter 1 and compose an ORC *IRTransformLayer* on top. We will look at how the
 IRTransformLayer works in more detail below, but the interface is simple: the
 constructor for this layer takes a reference to the layer below (as all layers
 do) plus an *IR optimization function* that it will apply to each Module that
 is added via addModule:

 .. code-block:: c++

   class KaleidoscopeJIT {
   private:
     std::unique_ptr<TargetMachine> TM;
     const DataLayout DL;
     RTDyldObjectLinkingLayer<> ObjectLayer;
     IRCompileLayer<decltype(ObjectLayer)> CompileLayer;

     using OptimizeFunction =
         std::function<std::shared_ptr<Module>(std::shared_ptr<Module>)>;

     IRTransformLayer<decltype(CompileLayer), OptimizeFunction> OptimizeLayer;

   public:
     using ModuleHandle = decltype(OptimizeLayer)::ModuleHandleT;

     KaleidoscopeJIT()
         : TM(EngineBuilder().selectTarget()), DL(TM->createDataLayout()),
           ObjectLayer([]() { return std::make_shared<SectionMemoryManager>(); }),
           CompileLayer(ObjectLayer, SimpleCompiler(*TM)),
           OptimizeLayer(CompileLayer,
                         [this](std::unique_ptr<Module> M) {
                           return optimizeModule(std::move(M));
                         }) {
       llvm::sys::DynamicLibrary::LoadLibraryPermanently(nullptr);
     }

 Our extended KaleidoscopeJIT class starts out the same as it did in Chapter 1,
 but after the CompileLayer we introduce a typedef for our optimization function.
 In this case we use a std::function (a handy wrapper for "function-like" things)
 from a single unique_ptr<Module> input to a std::unique_ptr<Module> output. With
 our optimization function typedef in place we can declare our OptimizeLayer,
 which sits on top of our CompileLayer.

 To initialize our OptimizeLayer we pass it a reference to the CompileLayer
 below (standard practice for layers), and we initialize the OptimizeFunction
 using a lambda that calls out to an "optimizeModule" function that we will
 define below.

 .. code-block:: c++

   // ...
   auto Resolver = createLambdaResolver(
       [&](const std::string &Name) {
         if (auto Sym = OptimizeLayer.findSymbol(Name, false))
           return Sym;
         return JITSymbol(nullptr);
       },
   // ...

 .. code-block:: c++

   // ...
   return cantFail(OptimizeLayer.addModule(std::move(M),
                                           std::move(Resolver)));
   // ...

 .. code-block:: c++

   // ...
   return OptimizeLayer.findSymbol(MangledNameStream.str(), true);
   // ...

 .. code-block:: c++

   // ...
   cantFail(OptimizeLayer.removeModule(H));
   // ...

 Next we need to replace references to 'CompileLayer' with references to
 OptimizeLayer in our key methods: addModule, findSymbol, and removeModule. In
 addModule we need to be careful to replace both references: the findSymbol call
 inside our resolver, and the call through to addModule.

 .. code-block:: c++

   std::shared_ptr<Module> optimizeModule(std::shared_ptr<Module> M) {
     // Create a function pass manager.
     auto FPM = llvm::make_unique<legacy::FunctionPassManager>(M.get());

     // Add some optimizations.
     FPM->add(createInstructionCombiningPass());
     FPM->add(createReassociatePass());
     FPM->add(createGVNPass());
     FPM->add(createCFGSimplificationPass());
     FPM->doInitialization();

     // Run the optimizations over all functions in the module being added to
     // the JIT.
     for (auto &F : *M)
       FPM->run(F);

     return M;
   }

 At the bottom of our JIT we add a private method to do the actual optimization:
 *optimizeModule*. This function sets up a FunctionPassManager, adds some passes
 to it, runs it over every function in the module, and then returns the mutated
 module. The specific optimizations are the same ones used in
 `Chapter 4 <LangImpl04.html>`_ of the "Implementing a language with LLVM"
 tutorial series. Readers may visit that chapter for a more in-depth
 discussion of these, and of IR optimization in general.

 And that's it in terms of changes to KaleidoscopeJIT: When a module is added via
 addModule the OptimizeLayer will call our optimizeModule function before passing
 the transformed module on to the CompileLayer below. Of course, we could have
 called optimizeModule directly in our addModule function and not gone to the
 bother of using the IRTransformLayer, but doing so gives us another opportunity
 to see how layers compose. It also provides a neat entry point to the *layer*
 concept itself, because IRTransformLayer turns out to be one of the simplest
 implementations of the layer concept that can be devised:

 .. code-block:: c++

   template <typename BaseLayerT, typename TransformFtor>
   class IRTransformLayer {
   public:
     using ModuleHandleT = typename BaseLayerT::ModuleHandleT;

     IRTransformLayer(BaseLayerT &BaseLayer,
                      TransformFtor Transform = TransformFtor())
       : BaseLayer(BaseLayer), Transform(std::move(Transform)) {}

     Expected<ModuleHandleT>
     addModule(std::shared_ptr<Module> M,
               std::shared_ptr<JITSymbolResolver> Resolver) {
       return BaseLayer.addModule(Transform(std::move(M)), std::move(Resolver));
     }

     void removeModule(ModuleHandleT H) { BaseLayer.removeModule(H); }

     JITSymbol findSymbol(const std::string &Name, bool ExportedSymbolsOnly) {
       return BaseLayer.findSymbol(Name, ExportedSymbolsOnly);
     }

     JITSymbol findSymbolIn(ModuleHandleT H, const std::string &Name,
                            bool ExportedSymbolsOnly) {
       return BaseLayer.findSymbolIn(H, Name, ExportedSymbolsOnly);
     }

     void emitAndFinalize(ModuleHandleT H) {
       BaseLayer.emitAndFinalize(H);
     }

     TransformFtor& getTransform() { return Transform; }

     const TransformFtor& getTransform() const { return Transform; }

   private:
     BaseLayerT &BaseLayer;
     TransformFtor Transform;
   };

 This is the whole definition of IRTransformLayer, from
 ``llvm/include/llvm/ExecutionEngine/Orc/IRTransformLayer.h``, stripped of its
 comments. It is a template class with two template arguments: ``BaesLayerT`` and
 ``TransformFtor`` that provide the type of the base layer and the type of the
 "transform functor" (in our case a std::function) respectively. This class is
 concerned with two very simple jobs: (1) Running every IR Module that is added
 with addModule through the transform functor, and (2) conforming to the ORC
 layer interface. The interface consists of one typedef and five methods:

 +------------------+-----------------------------------------------------------+
 |     Interface    |                         Description                       |
 +==================+===========================================================+
 |                  | Provides a handle that can be used to identify a module   |
 | ModuleHandleT    | set when calling findSymbolIn, removeModule, or           |
 |                  | emitAndFinalize.                                          |
 +------------------+-----------------------------------------------------------+
 |                  | Takes a given set of Modules and makes them "available    |
 |                  | for execution". This means that symbols in those modules  |
 |                  | should be searchable via findSymbol and findSymbolIn, and |
 |                  | the address of the symbols should be read/writable (for   |
 |                  | data symbols), or executable (for function symbols) after |
 |                  | JITSymbol::getAddress() is called. Note: This means that  |
 |   addModule      | addModule doesn't have to compile (or do any other        |
 |                  | work) up-front. It *can*, like IRCompileLayer, act        |
 |                  | eagerly, but it can also simply record the module and     |
 |                  | take no further action until somebody calls               |
 |                  | JITSymbol::getAddress(). In IRTransformLayer's case       |
 |                  | addModule eagerly applies the transform functor to        |
 |                  | each module in the set, then passes the resulting set     |
 |                  | of mutated modules down to the layer below.               |
 +------------------+-----------------------------------------------------------+
 |                  | Removes a set of modules from the JIT. Code or data       |
 |  removeModule    | defined in these modules will no longer be available, and |
 |                  | the memory holding the JIT'd definitions will be freed.   |
 +------------------+-----------------------------------------------------------+
 |                  | Searches for the named symbol in all modules that have    |
 |                  | previously been added via addModule (and not yet          |
 |    findSymbol    | removed by a call to removeModule). In                    |
 |                  | IRTransformLayer we just pass the query on to the layer   |
 |                  | below. In our REPL this is our default way to search for  |
 |                  | function definitions.                                     |
 +------------------+-----------------------------------------------------------+
 |                  | Searches for the named symbol in the module set indicated |
 |                  | by the given ModuleHandleT. This is just an optimized     |
 |                  | search, better for lookup-speed when you know exactly     |
 |                  | a symbol definition should be found. In IRTransformLayer  |
 |   findSymbolIn   | we just pass this query on to the layer below. In our     |
 |                  | REPL we use this method to search for functions           |
 |                  | representing top-level expressions, since we know exactly |
 |                  | where we'll find them: in the top-level expression module |
 |                  | we just added.                                            |
 +------------------+-----------------------------------------------------------+
 |                  | Forces all of the actions required to make the code and   |
 |                  | data in a module set (represented by a ModuleHandleT)     |
 |                  | accessible. Behaves as if some symbol in the set had been |
 |                  | searched for and JITSymbol::getSymbolAddress called. This |
 | emitAndFinalize  | is rarely needed, but can be useful when dealing with     |
 |                  | layers that usually behave lazily if the user wants to    |
 |                  | trigger early compilation (for example, to use idle CPU   |
 |                  | time to eagerly compile code in the background).          |
 +------------------+-----------------------------------------------------------+

 This interface attempts to capture the natural operations of a JIT (with some
 wrinkles like emitAndFinalize for performance), similar to the basic JIT API
 operations we identified in Chapter 1. Conforming to the layer concept allows
 classes to compose neatly by implementing their behaviors in terms of the these
 same operations, carried out on the layer below. For example, an eager layer
 (like IRTransformLayer) can implement addModule by running each module in the
 set through its transform up-front and immediately passing the result to the
 layer below. A lazy layer, by contrast, could implement addModule by
 squirreling away the modules doing no other up-front work, but applying the
 transform (and calling addModule on the layer below) when the client calls
 findSymbol instead. The JIT'd program behavior will be the same either way, but
 these choices will have different performance characteristics: Doing work
 eagerly means the JIT takes longer up-front, but proceeds smoothly once this is
 done. Deferring work allows the JIT to get up-and-running quickly, but will
 force the JIT to pause and wait whenever some code or data is needed that hasn't
 already been processed.

 Our current REPL is eager: Each function definition is optimized and compiled as
 soon as it's typed in. If we were to make the transform layer lazy (but not
 change things otherwise) we could defer optimization until the first time we
 reference a function in a top-level expression (see if you can figure out why,
 then check out the answer below [1]_). In the next chapter, however we'll
 introduce fully lazy compilation, in which function's aren't compiled until
 they're first called at run-time. At this point the trade-offs get much more
 interesting: the lazier we are, the quicker we can start executing the first
 function, but the more often we'll have to pause to compile newly encountered
 functions. If we only code-gen lazily, but optimize eagerly, we'll have a slow
 startup (which everything is optimized) but relatively short pauses as each
 function just passes through code-gen. If we both optimize and code-gen lazily
 we can start executing the first function more quickly, but we'll have longer
 pauses as each function has to be both optimized and code-gen'd when it's first
 executed. Things become even more interesting if we consider interproceedural
 optimizations like inlining, which must be performed eagerly. These are
 complex trade-offs, and there is no one-size-fits all solution to them, but by
 providing composable layers we leave the decisions to the person implementing
 the JIT, and make it easy for them to experiment with different configurations.

 `Next: Adding Per-function Lazy Compilation <BuildingAJIT3.html>`_

 Full Code Listing
 =================

 Here is the complete code listing for our running example with an
 IRTransformLayer added to enable optimization. To build this example, use:

 .. code-block:: bash

     # Compile
     clang++ -g toy.cpp `llvm-config --cxxflags --ldflags --system-libs --libs core orcjit native` -O3 -o toy
     # Run
     ./toy

 Here is the code:

 .. literalinclude:: ../../examples/Kaleidoscope/BuildingAJIT/Chapter2/KaleidoscopeJIT.h
    :language: c++

 .. [1] When we add our top-level expression to the JIT, any calls to functions
        that we defined earlier will appear to the RTDyldObjectLinkingLayer as
        external symbols. The RTDyldObjectLinkingLayer will call the SymbolResolver
        that we defined in addModule, which in turn calls findSymbol on the
        OptimizeLayer, at which point even a lazy transform layer will have to
        do its work.
	=====================================================================
	Building a JIT: Adding Optimizations -- An introduction to ORC Layers
	=====================================================================

	.. contents::
	:local:

	**This tutorial is under active development. It is incomplete and details may
	change frequently.** Nonetheless we invite you to try it out as it stands, and
	we welcome any feedback.

	Chapter 2 Introduction
	======================

	Warning: This text is currently out of date due to ORC API updates.

	**The example code has been updated and can be used. The text will be updated
	once the API churn dies down.**

	Welcome to Chapter 2 of the "Building an ORC-based JIT in LLVM" tutorial. In
	`Chapter 1 <BuildingAJIT1.html>`_ of this series we examined a basic JIT
	class, KaleidoscopeJIT, that could take LLVM IR modules as input and produce
	executable code in memory. KaleidoscopeJIT was able to do this with relatively
	little code by composing two off-the-shelf ORC layers: IRCompileLayer and
	ObjectLinkingLayer, to do much of the heavy lifting.

	In this layer we'll learn more about the ORC layer concept by using a new layer,
	IRTransformLayer, to add IR optimization support to KaleidoscopeJIT.

	Optimizing Modules using the IRTransformLayer
	=============================================

	In `Chapter 4 <LangImpl04.html>`_ of the "Implementing a language with LLVM"
	tutorial series the llvm FunctionPassManager is introduced as a means for
	optimizing LLVM IR. Interested readers may read that chapter for details, but
	in short: to optimize a Module we create an llvm::FunctionPassManager
	instance, configure it with a set of optimizations, then run the PassManager on
	a Module to mutate it into a (hopefully) more optimized but semantically
	equivalent form. In the original tutorial series the FunctionPassManager was
	created outside the KaleidoscopeJIT and modules were optimized before being
	added to it. In this Chapter we will make optimization a phase of our JIT
	instead. For now this will provide us a motivation to learn more about ORC
	layers, but in the long term making optimization part of our JIT will yield an
	important benefit: When we begin lazily compiling code (i.e. deferring
	compilation of each function until the first time it's run), having
	optimization managed by our JIT will allow us to optimize lazily too, rather
	than having to do all our optimization up-front.

	To add optimization support to our JIT we will take the KaleidoscopeJIT from
	Chapter 1 and compose an ORC IRTransformLayer on top. We will look at how the
	IRTransformLayer works in more detail below, but the interface is simple: the
	constructor for this layer takes a reference to the layer below (as all layers
	do) plus an IR optimization function that it will apply to each Module that
	is added via addModule:

	.. code-block:: c++

	class KaleidoscopeJIT {
	private:
	std::unique_ptr<TargetMachine> TM;
	const DataLayout DL;
	RTDyldObjectLinkingLayer<> ObjectLayer;
	IRCompileLayer<decltype(ObjectLayer)> CompileLayer;

	using OptimizeFunction =
	std::function<std::shared_ptr<Module>(std::shared_ptr<Module>)>;

	IRTransformLayer<decltype(CompileLayer), OptimizeFunction> OptimizeLayer;

	public:
	using ModuleHandle = decltype(OptimizeLayer)::ModuleHandleT;

	KaleidoscopeJIT()
	: TM(EngineBuilder().selectTarget()), DL(TM->createDataLayout()),
	ObjectLayer([]() { return std::make_shared<SectionMemoryManager>(); }),
	CompileLayer(ObjectLayer, SimpleCompiler(*TM)),
	OptimizeLayer(CompileLayer,
	[this](std::unique_ptr<Module> M) {
	return optimizeModule(std::move(M));
	}) {
	llvm::sys::DynamicLibrary::LoadLibraryPermanently(nullptr);
	}

	Our extended KaleidoscopeJIT class starts out the same as it did in Chapter 1,
	but after the CompileLayer we introduce a typedef for our optimization function.
	In this case we use a std::function (a handy wrapper for "function-like" things)
	from a single unique_ptr<Module> input to a std::unique_ptr<Module> output. With
	our optimization function typedef in place we can declare our OptimizeLayer,
	which sits on top of our CompileLayer.

	To initialize our OptimizeLayer we pass it a reference to the CompileLayer
	below (standard practice for layers), and we initialize the OptimizeFunction
	using a lambda that calls out to an "optimizeModule" function that we will
	define below.

	.. code-block:: c++

	// ...
	auto Resolver = createLambdaResolver(
	[&](const std::string &Name) {
	if (auto Sym = OptimizeLayer.findSymbol(Name, false))
	return Sym;
	return JITSymbol(nullptr);
	},
	// ...

	.. code-block:: c++

	// ...
	return cantFail(OptimizeLayer.addModule(std::move(M),
	std::move(Resolver)));
	// ...

	.. code-block:: c++

	// ...
	return OptimizeLayer.findSymbol(MangledNameStream.str(), true);
	// ...

	.. code-block:: c++

	// ...
	cantFail(OptimizeLayer.removeModule(H));
	// ...

	Next we need to replace references to 'CompileLayer' with references to
	OptimizeLayer in our key methods: addModule, findSymbol, and removeModule. In
	addModule we need to be careful to replace both references: the findSymbol call
	inside our resolver, and the call through to addModule.

	.. code-block:: c++

	std::shared_ptr<Module> optimizeModule(std::shared_ptr<Module> M) {
	// Create a function pass manager.
	auto FPM = llvm::make_unique<legacy::FunctionPassManager>(M.get());

	// Add some optimizations.
	FPM->add(createInstructionCombiningPass());
	FPM->add(createReassociatePass());
	FPM->add(createGVNPass());
	FPM->add(createCFGSimplificationPass());
	FPM->doInitialization();

	// Run the optimizations over all functions in the module being added to
	// the JIT.
	for (auto &F : *M)
	FPM->run(F);

	return M;
	}

	At the bottom of our JIT we add a private method to do the actual optimization:
	optimizeModule. This function sets up a FunctionPassManager, adds some passes
	to it, runs it over every function in the module, and then returns the mutated
	module. The specific optimizations are the same ones used in
	`Chapter 4 <LangImpl04.html>`_ of the "Implementing a language with LLVM"
	tutorial series. Readers may visit that chapter for a more in-depth
	discussion of these, and of IR optimization in general.

	And that's it in terms of changes to KaleidoscopeJIT: When a module is added via
	addModule the OptimizeLayer will call our optimizeModule function before passing
	the transformed module on to the CompileLayer below. Of course, we could have
	called optimizeModule directly in our addModule function and not gone to the
	bother of using the IRTransformLayer, but doing so gives us another opportunity
	to see how layers compose. It also provides a neat entry point to the layer
	concept itself, because IRTransformLayer turns out to be one of the simplest
	implementations of the layer concept that can be devised:

	.. code-block:: c++

	template <typename BaseLayerT, typename TransformFtor>
	class IRTransformLayer {
	public:
	using ModuleHandleT = typename BaseLayerT::ModuleHandleT;

	IRTransformLayer(BaseLayerT &BaseLayer,
	TransformFtor Transform = TransformFtor())
	: BaseLayer(BaseLayer), Transform(std::move(Transform)) {}

	Expected<ModuleHandleT>
	addModule(std::shared_ptr<Module> M,
	std::shared_ptr<JITSymbolResolver> Resolver) {
	return BaseLayer.addModule(Transform(std::move(M)), std::move(Resolver));
	}

	void removeModule(ModuleHandleT H) { BaseLayer.removeModule(H); }

	JITSymbol findSymbol(const std::string &Name, bool ExportedSymbolsOnly) {
	return BaseLayer.findSymbol(Name, ExportedSymbolsOnly);
	}

	JITSymbol findSymbolIn(ModuleHandleT H, const std::string &Name,
	bool ExportedSymbolsOnly) {
	return BaseLayer.findSymbolIn(H, Name, ExportedSymbolsOnly);
	}

	void emitAndFinalize(ModuleHandleT H) {
	BaseLayer.emitAndFinalize(H);
	}

	TransformFtor& getTransform() { return Transform; }

	const TransformFtor& getTransform() const { return Transform; }

	private:
	BaseLayerT &BaseLayer;
	TransformFtor Transform;
	};

	This is the whole definition of IRTransformLayer, from
	``llvm/include/llvm/ExecutionEngine/Orc/IRTransformLayer.h``, stripped of its
	comments. It is a template class with two template arguments: ``BaesLayerT`` and
	``TransformFtor`` that provide the type of the base layer and the type of the
	"transform functor" (in our case a std::function) respectively. This class is
	concerned with two very simple jobs: (1) Running every IR Module that is added
	with addModule through the transform functor, and (2) conforming to the ORC
	layer interface. The interface consists of one typedef and five methods:

	+------------------+-----------------------------------------------------------+
	\| Interface \| Description \|
	+==================+===========================================================+
	\| \| Provides a handle that can be used to identify a module \|
	\| ModuleHandleT \| set when calling findSymbolIn, removeModule, or \|
	\| \| emitAndFinalize. \|
	+------------------+-----------------------------------------------------------+
	\| \| Takes a given set of Modules and makes them "available \|
	\| \| for execution". This means that symbols in those modules \|
	\| \| should be searchable via findSymbol and findSymbolIn, and \|
	\| \| the address of the symbols should be read/writable (for \|
	\| \| data symbols), or executable (for function symbols) after \|
	\| \| JITSymbol::getAddress() is called. Note: This means that \|
	\| addModule \| addModule doesn't have to compile (or do any other \|
	\| \| work) up-front. It can, like IRCompileLayer, act \|
	\| \| eagerly, but it can also simply record the module and \|
	\| \| take no further action until somebody calls \|
	\| \| JITSymbol::getAddress(). In IRTransformLayer's case \|
	\| \| addModule eagerly applies the transform functor to \|
	\| \| each module in the set, then passes the resulting set \|
	\| \| of mutated modules down to the layer below. \|
	+------------------+-----------------------------------------------------------+
	\| \| Removes a set of modules from the JIT. Code or data \|
	\| removeModule \| defined in these modules will no longer be available, and \|
	\| \| the memory holding the JIT'd definitions will be freed. \|
	+------------------+-----------------------------------------------------------+
	\| \| Searches for the named symbol in all modules that have \|
	\| \| previously been added via addModule (and not yet \|
	\| findSymbol \| removed by a call to removeModule). In \|
	\| \| IRTransformLayer we just pass the query on to the layer \|
	\| \| below. In our REPL this is our default way to search for \|
	\| \| function definitions. \|
	+------------------+-----------------------------------------------------------+
	\| \| Searches for the named symbol in the module set indicated \|
	\| \| by the given ModuleHandleT. This is just an optimized \|
	\| \| search, better for lookup-speed when you know exactly \|
	\| \| a symbol definition should be found. In IRTransformLayer \|
	\| findSymbolIn \| we just pass this query on to the layer below. In our \|
	\| \| REPL we use this method to search for functions \|
	\| \| representing top-level expressions, since we know exactly \|
	\| \| where we'll find them: in the top-level expression module \|
	\| \| we just added. \|
	+------------------+-----------------------------------------------------------+
	\| \| Forces all of the actions required to make the code and \|
	\| \| data in a module set (represented by a ModuleHandleT) \|
	\| \| accessible. Behaves as if some symbol in the set had been \|
	\| \| searched for and JITSymbol::getSymbolAddress called. This \|
	\| emitAndFinalize \| is rarely needed, but can be useful when dealing with \|
	\| \| layers that usually behave lazily if the user wants to \|
	\| \| trigger early compilation (for example, to use idle CPU \|
	\| \| time to eagerly compile code in the background). \|
	+------------------+-----------------------------------------------------------+

	This interface attempts to capture the natural operations of a JIT (with some
	wrinkles like emitAndFinalize for performance), similar to the basic JIT API
	operations we identified in Chapter 1. Conforming to the layer concept allows
	classes to compose neatly by implementing their behaviors in terms of the these
	same operations, carried out on the layer below. For example, an eager layer
	(like IRTransformLayer) can implement addModule by running each module in the
	set through its transform up-front and immediately passing the result to the
	layer below. A lazy layer, by contrast, could implement addModule by
	squirreling away the modules doing no other up-front work, but applying the
	transform (and calling addModule on the layer below) when the client calls
	findSymbol instead. The JIT'd program behavior will be the same either way, but
	these choices will have different performance characteristics: Doing work
	eagerly means the JIT takes longer up-front, but proceeds smoothly once this is
	done. Deferring work allows the JIT to get up-and-running quickly, but will
	force the JIT to pause and wait whenever some code or data is needed that hasn't
	already been processed.

	Our current REPL is eager: Each function definition is optimized and compiled as
	soon as it's typed in. If we were to make the transform layer lazy (but not
	change things otherwise) we could defer optimization until the first time we
	reference a function in a top-level expression (see if you can figure out why,
	then check out the answer below [1]_). In the next chapter, however we'll
	introduce fully lazy compilation, in which function's aren't compiled until
	they're first called at run-time. At this point the trade-offs get much more
	interesting: the lazier we are, the quicker we can start executing the first
	function, but the more often we'll have to pause to compile newly encountered
	functions. If we only code-gen lazily, but optimize eagerly, we'll have a slow
	startup (which everything is optimized) but relatively short pauses as each
	function just passes through code-gen. If we both optimize and code-gen lazily
	we can start executing the first function more quickly, but we'll have longer
	pauses as each function has to be both optimized and code-gen'd when it's first
	executed. Things become even more interesting if we consider interproceedural
	optimizations like inlining, which must be performed eagerly. These are
	complex trade-offs, and there is no one-size-fits all solution to them, but by
	providing composable layers we leave the decisions to the person implementing
	the JIT, and make it easy for them to experiment with different configurations.

	`Next: Adding Per-function Lazy Compilation <BuildingAJIT3.html>`_

	Full Code Listing
	=================

	Here is the complete code listing for our running example with an
	IRTransformLayer added to enable optimization. To build this example, use:

	.. code-block:: bash

	# Compile
	clang++ -g toy.cpp `llvm-config --cxxflags --ldflags --system-libs --libs core orcjit native` -O3 -o toy
	# Run
	./toy

	Here is the code:

	.. literalinclude:: ../../examples/Kaleidoscope/BuildingAJIT/Chapter2/KaleidoscopeJIT.h
	:language: c++

	.. [1] When we add our top-level expression to the JIT, any calls to functions
	that we defined earlier will appear to the RTDyldObjectLinkingLayer as
	external symbols. The RTDyldObjectLinkingLayer will call the SymbolResolver
	that we defined in addModule, which in turn calls findSymbol on the
	OptimizeLayer, at which point even a lazy transform layer will have to
	do its work.