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[BuildingAJIT] Update chapter 2 to use the ORCv2 APIs.

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docs/tutorial/BuildingAJIT2.rst
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@ -12,10 +12,11 @@ we welcome any feedback.
Chapter 2 Introduction
======================
**Warning: This text is currently out of date due to ORC API updates.**
**Warning: This tutorial is currently being updated to account for ORC API
changes. Only Chapters 1 and 2 are up-to-date.**
**The example code has been updated and can be used. The text will be updated
once the API churn dies down.**
**Example code from Chapters 3 to 5 will compile and run, but has not been
updated**
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
@ -42,67 +43,49 @@ 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
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:
constructor for this layer takes a reference to the execution session and 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;
ExecutionSession ES;
RTDyldObjectLinkingLayer ObjectLayer;
IRCompileLayer CompileLayer;
IRTransformLayer TransformLayer;
using OptimizeFunction =
std::function<std::shared_ptr<Module>(std::shared_ptr<Module>)>;
IRTransformLayer<decltype(CompileLayer), OptimizeFunction> OptimizeLayer;
DataLayout DL;
MangleAndInterner Mangle;
ThreadSafeContext Ctx;
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);
KaleidoscopeJIT(JITTargetMachineBuilder JTMB, DataLayout DL)
: ObjectLayer(ES,
[]() { return llvm::make_unique<SectionMemoryManager>(); }),
CompileLayer(ES, ObjectLayer, ConcurrentIRCompiler(std::move(JTMB))),
TransformLayer(ES, CompileLayer, optimizeModule),
DL(std::move(DL)), Mangle(ES, this->DL),
Ctx(llvm::make_unique<LLVMContext>()) {
ES.getMainJITDylib().setGenerator(
cantFail(DynamicLibrarySearchGenerator::GetForCurrentProcess(DL)));
}
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);
},
// ...
but after the CompileLayer we introduce a new member, TransformLayer, which sits
on top of our CompileLayer. We initialize our OptimizeLayer with a reference to
the ExecutionSession and output layer (standard practice for layers), along with
a *transform function*. For our transform function we supply our classes
optimizeModule static method.
.. code-block:: c++
@ -111,26 +94,13 @@ define below.
std::move(Resolver)));
// ...
.. code-block:: c++
// ...
return OptimizeLayer.findSymbol(MangledNameStream.str(), true);
// ...
Next we need to update our addModule method to replace the call to
``CompileLayer::add`` with a call to ``OptimizeLayer::add`` instead.
.. 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) {
ThreadSafeModule optimizeModule(ThreadSafeModule M,
const MaterializationResponsibility &R) {
// Create a function pass manager.
auto FPM = llvm::make_unique<legacy::FunctionPassManager>(M.get());
@ -150,12 +120,18 @@ inside our resolver, and the call through to addModule.
}
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.
*optimizeModule*. This function takes the module to be transformed as input (as
a ThreadSafeModule) along with a reference to a reference to a new class:
``MaterializationResponsibility``. The MaterializationResponsibility argument
can be used to query JIT state for the module being transformed, such as the set
of definitions in the module that JIT'd code is actively trying to call/access.
For now we will ignore this argument and use a standard optimization
pipeline. To do this we set up a FunctionPassManager, add some passes to it, run
it over every function in the module, and then return 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
@ -163,148 +139,122 @@ 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:
concept itself, because IRTransformLayer is one of the simplest layers that
can be implemented.
.. code-block:: c++
template <typename BaseLayerT, typename TransformFtor>
class IRTransformLayer {
// From IRTransformLayer.h:
class IRTransformLayer : public IRLayer {
public:
using ModuleHandleT = typename BaseLayerT::ModuleHandleT;
using TransformFunction = std::function<Expected<ThreadSafeModule>(
ThreadSafeModule, const MaterializationResponsibility &R)>;
IRTransformLayer(BaseLayerT &BaseLayer,
TransformFtor Transform = TransformFtor())
: BaseLayer(BaseLayer), Transform(std::move(Transform)) {}
IRTransformLayer(ExecutionSession &ES, IRLayer &BaseLayer,
TransformFunction Transform = identityTransform);
Expected<ModuleHandleT>
addModule(std::shared_ptr<Module> M,
std::shared_ptr<JITSymbolResolver> Resolver) {
return BaseLayer.addModule(Transform(std::move(M)), std::move(Resolver));
void setTransform(TransformFunction Transform) {
this->Transform = std::move(Transform);
}
void removeModule(ModuleHandleT H) { BaseLayer.removeModule(H); }
JITSymbol findSymbol(const std::string &Name, bool ExportedSymbolsOnly) {
return BaseLayer.findSymbol(Name, ExportedSymbolsOnly);
static ThreadSafeModule
identityTransform(ThreadSafeModule TSM,
const MaterializationResponsibility &R) {
return TSM;
}
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; }
void emit(MaterializationResponsibility R, ThreadSafeModule TSM) override;
private:
BaseLayerT &BaseLayer;
TransformFtor Transform;
IRLayer &BaseLayer;
TransformFunction Transform;
};
// From IRTransfomrLayer.cpp:
IRTransformLayer::IRTransformLayer(ExecutionSession &ES,
IRLayer &BaseLayer,
TransformFunction Transform)
: IRLayer(ES), BaseLayer(BaseLayer), Transform(std::move(Transform)) {}
void IRTransformLayer::emit(MaterializationResponsibility R,
ThreadSafeModule TSM) {
assert(TSM.getModule() && "Module must not be null");
if (auto TransformedTSM = Transform(std::move(TSM), R))
BaseLayer.emit(std::move(R), std::move(*TransformedTSM));
else {
R.failMaterialization();
getExecutionSession().reportError(TransformedTSM.takeError());
}
}
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:
``llvm/include/llvm/ExecutionEngine/Orc/IRTransformLayer.h`` and
``llvm/lib/ExecutionEngine/Orc/IRTransformLayer.cpp``. This class is concerned
with two very simple jobs: (1) Running every IR Module that is emitted via this
layer through the transform function object, and (2) implementing the ORC
``IRLayer`` interface (which itself conforms to the general ORC Layer concept,
more on that below). Most of the class is straightforward: a typedef for the
transform function, a constructor to initialize the members, a setter for the
transform function value, and a default no-op transform. The most important
method is ``emit`` as this is half of our IRLayer interface. The emit method
applies our transform to each module that it is called on and, if the transform
succeeds, passes the transformed module to the base layer. If the transform
fails, our emit function calls
``MaterializationResponsibility::failMaterialization`` (this JIT clients who
may be waiting on other threads know that the code they were waiting for has
failed to compile) and logs the error with the execution session before bailing
out.
+------------------+-----------------------------------------------------------+
| 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). |
+------------------+-----------------------------------------------------------+
The other half of the IRLayer interface we inherit unmodified from the IRLayer
class:
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.
.. code-block:: c++
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
Error IRLayer::add(JITDylib &JD, ThreadSafeModule TSM, VModuleKey K) {
return JD.define(llvm::make_unique<BasicIRLayerMaterializationUnit>(
*this, std::move(K), std::move(TSM)));
}
This code, from ``llvm/lib/ExecutionEngine/Orc/Layer.cpp``, adds a
ThreadSafeModule to a given JITDylib by wrapping it up in a
``MaterializationUnit`` (in this case a ``BasicIRLayerMaterializationUnit``).
Most layers that derived from IRLayer can rely on this default implementation
of the ``add`` method.
These two operations, ``add`` and ``emit``, together constitute the layer
concept: A layer is a way to wrap a portion of a compiler pipeline (in this case
the "opt" phase of an LLVM compiler) whose API is is opaque to ORC in an
interface that allows ORC to invoke it when needed. The add method takes an
module in some input program representation (in this case an LLVM IR module) and
stores it in the target JITDylib, arranging for it to be passed back to the
Layer's emit method when any symbol defined by that module is requested. Layers
can compose neatly by calling the 'emit' method of a base layer to complete
their work. For example, in this tutorial our IRTransformLayer calls through to
our IRCompileLayer to compile the transformed IR, and our IRCompileLayer in turn
calls our ObjectLayer to link the object file produced by our compiler.
So far we have learned how to optimize and compile our LLVM IR, but we have not
focused on when compilation happens. Our current REPL is eager: Each function
definition is optimized and compiled as soon as it is referenced by any other
code, regardless of whether it is ever called at runtime. In the next chapter we
will introduce fully lazy compilation, in which functions are not compiled until
they are 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.
function, but the more often we will have to pause to compile newly encountered
functions. If we only code-gen lazily, but optimize eagerly, we will have a
longer startup time (as 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 will have
longer pauses as each function has to be both optimized and code-gen'd when it
is 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>`_
@ -325,10 +275,3 @@ 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.

109
examples/Kaleidoscope/BuildingAJIT/Chapter2/KaleidoscopeJIT.h
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@ -14,29 +14,23 @@
#ifndef LLVM_EXECUTIONENGINE_ORC_KALEIDOSCOPEJIT_H
#define LLVM_EXECUTIONENGINE_ORC_KALEIDOSCOPEJIT_H
#include "llvm/ADT/STLExtras.h"
#include "llvm/ExecutionEngine/ExecutionEngine.h"
#include "llvm/ADT/StringRef.h"
#include "llvm/ExecutionEngine/JITSymbol.h"
#include "llvm/ExecutionEngine/Orc/CompileUtils.h"
#include "llvm/ExecutionEngine/Orc/Core.h"
#include "llvm/ExecutionEngine/Orc/ExecutionUtils.h"
#include "llvm/ExecutionEngine/Orc/IRCompileLayer.h"
#include "llvm/ExecutionEngine/Orc/IRTransformLayer.h"
#include "llvm/ExecutionEngine/Orc/LambdaResolver.h"
#include "llvm/ExecutionEngine/Orc/JITTargetMachineBuilder.h"
#include "llvm/ExecutionEngine/Orc/RTDyldObjectLinkingLayer.h"
#include "llvm/ExecutionEngine/RTDyldMemoryManager.h"
#include "llvm/ExecutionEngine/SectionMemoryManager.h"
#include "llvm/IR/DataLayout.h"
#include "llvm/IR/LLVMContext.h"
#include "llvm/IR/LegacyPassManager.h"
#include "llvm/IR/Mangler.h"
#include "llvm/Support/DynamicLibrary.h"
#include "llvm/Support/raw_ostream.h"
#include "llvm/Target/TargetMachine.h"
#include "llvm/Transforms/InstCombine/InstCombine.h"
#include "llvm/Transforms/Scalar.h"
#include "llvm/Transforms/Scalar/GVN.h"
#include <algorithm>
#include <memory>
#include <string>
#include <vector>
namespace llvm {
namespace orc {
@ -44,69 +38,60 @@ namespace orc {
class KaleidoscopeJIT {
private:
ExecutionSession ES;
std::shared_ptr<SymbolResolver> Resolver;
std::unique_ptr<TargetMachine> TM;
const DataLayout DL;
LegacyRTDyldObjectLinkingLayer ObjectLayer;
LegacyIRCompileLayer<decltype(ObjectLayer), SimpleCompiler> CompileLayer;
RTDyldObjectLinkingLayer ObjectLayer;
IRCompileLayer CompileLayer;
IRTransformLayer OptimizeLayer;
using OptimizeFunction =
std::function<std::unique_ptr<Module>(std::unique_ptr<Module>)>;
LegacyIRTransformLayer<decltype(CompileLayer), OptimizeFunction> OptimizeLayer;
DataLayout DL;
MangleAndInterner Mangle;
ThreadSafeContext Ctx;
public:
KaleidoscopeJIT()
: Resolver(createLegacyLookupResolver(
ES,
[this](const std::string &Name) -> JITSymbol {
if (auto Sym = OptimizeLayer.findSymbol(Name, false))
return Sym;
else if (auto Err = Sym.takeError())
return std::move(Err);
if (auto SymAddr =
RTDyldMemoryManager::getSymbolAddressInProcess(Name))
return JITSymbol(SymAddr, JITSymbolFlags::Exported);
return nullptr;
},
[](Error Err) { cantFail(std::move(Err), "lookupFlags failed"); })),
TM(EngineBuilder().selectTarget()), DL(TM->createDataLayout()),
ObjectLayer(ES,
[this](VModuleKey) {
return LegacyRTDyldObjectLinkingLayer::Resources{
std::make_shared<SectionMemoryManager>(), Resolver};
}),
CompileLayer(ObjectLayer, SimpleCompiler(*TM)),
OptimizeLayer(CompileLayer, [this](std::unique_ptr<Module> M) {
return optimizeModule(std::move(M));
}) {
llvm::sys::DynamicLibrary::LoadLibraryPermanently(nullptr);
KaleidoscopeJIT(JITTargetMachineBuilder JTMB, DataLayout DL)
: ObjectLayer(ES,
[]() { return llvm::make_unique<SectionMemoryManager>(); }),
CompileLayer(ES, ObjectLayer, ConcurrentIRCompiler(std::move(JTMB))),
OptimizeLayer(ES, CompileLayer, optimizeModule),
DL(std::move(DL)), Mangle(ES, this->DL),
Ctx(llvm::make_unique<LLVMContext>()) {
ES.getMainJITDylib().setGenerator(
cantFail(DynamicLibrarySearchGenerator::GetForCurrentProcess(DL)));
}
TargetMachine &getTargetMachine() { return *TM; }
const DataLayout &getDataLayout() const { return DL; }
VModuleKey addModule(std::unique_ptr<Module> M) {
// Add the module to the JIT with a new VModuleKey.
auto K = ES.allocateVModule();
cantFail(OptimizeLayer.addModule(K, std::move(M)));
return K;
LLVMContext &getContext() { return *Ctx.getContext(); }
static Expected<std::unique_ptr<KaleidoscopeJIT>> Create() {
auto JTMB = JITTargetMachineBuilder::detectHost();
if (!JTMB)
return JTMB.takeError();
auto DL = JTMB->getDefaultDataLayoutForTarget();
if (!DL)
return DL.takeError();
return llvm::make_unique<KaleidoscopeJIT>(std::move(*JTMB), std::move(*DL));
}
JITSymbol findSymbol(const std::string Name) {
std::string MangledName;
raw_string_ostream MangledNameStream(MangledName);
Mangler::getNameWithPrefix(MangledNameStream, Name, DL);
return OptimizeLayer.findSymbol(MangledNameStream.str(), true);
Error addModule(std::unique_ptr<Module> M) {
return OptimizeLayer.add(ES.getMainJITDylib(),
ThreadSafeModule(std::move(M), Ctx));
}
void removeModule(VModuleKey K) {
cantFail(OptimizeLayer.removeModule(K));
Expected<JITEvaluatedSymbol> lookup(StringRef Name) {
return ES.lookup({&ES.getMainJITDylib()}, Mangle(Name.str()));
}
private:
std::unique_ptr<Module> optimizeModule(std::unique_ptr<Module> M) {
static Expected<ThreadSafeModule>
optimizeModule(ThreadSafeModule TSM,
const MaterializationResponsibility &R) {
// Create a function pass manager.
auto FPM = llvm::make_unique<legacy::FunctionPassManager>(M.get());
auto FPM = llvm::make_unique<legacy::FunctionPassManager>(TSM.getModule());
// Add some optimizations.
FPM->add(createInstructionCombiningPass());
@ -117,10 +102,10 @@ private:
// Run the optimizations over all functions in the module being added to
// the JIT.
for (auto &F : *M)
for (auto &F : *TSM.getModule())
FPM->run(F);
return M;
return TSM;
}
};

150
examples/Kaleidoscope/BuildingAJIT/Chapter2/toy.cpp
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@ -676,10 +676,11 @@ static std::unique_ptr<FunctionAST> ParseDefinition() {
}
/// toplevelexpr ::= expression
static std::unique_ptr<FunctionAST> ParseTopLevelExpr() {
static std::unique_ptr<FunctionAST> ParseTopLevelExpr(unsigned ExprCount) {
if (auto E = ParseExpression()) {
// Make an anonymous proto.
auto Proto = llvm::make_unique<PrototypeAST>("__anon_expr",
auto Proto = llvm::make_unique<PrototypeAST>(("__anon_expr" +
Twine(ExprCount)).str(),
std::vector<std::string>());
return llvm::make_unique<FunctionAST>(std::move(Proto), std::move(E));
}
@ -696,12 +697,13 @@ static std::unique_ptr<PrototypeAST> ParseExtern() {
// Code Generation
//===----------------------------------------------------------------------===//
static LLVMContext TheContext;
static IRBuilder<> Builder(TheContext);
static std::unique_ptr<KaleidoscopeJIT> TheJIT;
static LLVMContext *TheContext;
static std::unique_ptr<IRBuilder<>> Builder;
static std::unique_ptr<Module> TheModule;
static std::map<std::string, AllocaInst *> NamedValues;
static std::unique_ptr<KaleidoscopeJIT> TheJIT;
static std::map<std::string, std::unique_ptr<PrototypeAST>> FunctionProtos;
static ExitOnError ExitOnErr;
Value *LogErrorV(const char *Str) {
LogError(Str);
@ -729,11 +731,11 @@ static AllocaInst *CreateEntryBlockAlloca(Function *TheFunction,
const std::string &VarName) {
IRBuilder<> TmpB(&TheFunction->getEntryBlock(),
TheFunction->getEntryBlock().begin());
return TmpB.CreateAlloca(Type::getDoubleTy(TheContext), nullptr, VarName);
return TmpB.CreateAlloca(Type::getDoubleTy(*TheContext), nullptr, VarName);
}
Value *NumberExprAST::codegen() {
return ConstantFP::get(TheContext, APFloat(Val));
return ConstantFP::get(*TheContext, APFloat(Val));
}
Value *VariableExprAST::codegen() {
@ -743,7 +745,7 @@ Value *VariableExprAST::codegen() {
return LogErrorV("Unknown variable name");
// Load the value.
return Builder.CreateLoad(V, Name.c_str());
return Builder->CreateLoad(V, Name.c_str());
}
Value *UnaryExprAST::codegen() {
@ -755,7 +757,7 @@ Value *UnaryExprAST::codegen() {
if (!F)
return LogErrorV("Unknown unary operator");
return Builder.CreateCall(F, OperandV, "unop");
return Builder->CreateCall(F, OperandV, "unop");
}
Value *BinaryExprAST::codegen() {
@ -778,7 +780,7 @@ Value *BinaryExprAST::codegen() {
if (!Variable)
return LogErrorV("Unknown variable name");
Builder.CreateStore(Val, Variable);
Builder->CreateStore(Val, Variable);
return Val;
}
@ -789,15 +791,15 @@ Value *BinaryExprAST::codegen() {
switch (Op) {
case '+':
return Builder.CreateFAdd(L, R, "addtmp");
return Builder->CreateFAdd(L, R, "addtmp");
case '-':
return Builder.CreateFSub(L, R, "subtmp");
return Builder->CreateFSub(L, R, "subtmp");
case '*':
return Builder.CreateFMul(L, R, "multmp");
return Builder->CreateFMul(L, R, "multmp");
case '<':
L = Builder.CreateFCmpULT(L, R, "cmptmp");
L = Builder->CreateFCmpULT(L, R, "cmptmp");
// Convert bool 0/1 to double 0.0 or 1.0
return Builder.CreateUIToFP(L, Type::getDoubleTy(TheContext), "booltmp");
return Builder->CreateUIToFP(L, Type::getDoubleTy(*TheContext), "booltmp");
default:
break;
}
@ -808,7 +810,7 @@ Value *BinaryExprAST::codegen() {
assert(F && "binary operator not found!");
Value *Ops[] = {L, R};
return Builder.CreateCall(F, Ops, "binop");
return Builder->CreateCall(F, Ops, "binop");
}
Value *CallExprAST::codegen() {
@ -828,7 +830,7 @@ Value *CallExprAST::codegen() {
return nullptr;
}
return Builder.CreateCall(CalleeF, ArgsV, "calltmp");
return Builder->CreateCall(CalleeF, ArgsV, "calltmp");
}
Value *IfExprAST::codegen() {
@ -837,46 +839,46 @@ Value *IfExprAST::codegen() {
return nullptr;
// Convert condition to a bool by comparing equal to 0.0.
CondV = Builder.CreateFCmpONE(
CondV, ConstantFP::get(TheContext, APFloat(0.0)), "ifcond");
CondV = Builder->CreateFCmpONE(
CondV, ConstantFP::get(*TheContext, APFloat(0.0)), "ifcond");
Function *TheFunction = Builder.GetInsertBlock()->getParent();
Function *TheFunction = Builder->GetInsertBlock()->getParent();
// Create blocks for the then and else cases. Insert the 'then' block at the
// end of the function.
BasicBlock *ThenBB = BasicBlock::Create(TheContext, "then", TheFunction);
BasicBlock *ElseBB = BasicBlock::Create(TheContext, "else");
BasicBlock *MergeBB = BasicBlock::Create(TheContext, "ifcont");
BasicBlock *ThenBB = BasicBlock::Create(*TheContext, "then", TheFunction);
BasicBlock *ElseBB = BasicBlock::Create(*TheContext, "else");
BasicBlock *MergeBB = BasicBlock::Create(*TheContext, "ifcont");
Builder.CreateCondBr(CondV, ThenBB, ElseBB);
Builder->CreateCondBr(CondV, ThenBB, ElseBB);
// Emit then value.
Builder.SetInsertPoint(ThenBB);
Builder->SetInsertPoint(ThenBB);
Value *ThenV = Then->codegen();
if (!ThenV)
return nullptr;
Builder.CreateBr(MergeBB);
Builder->CreateBr(MergeBB);
// Codegen of 'Then' can change the current block, update ThenBB for the PHI.
ThenBB = Builder.GetInsertBlock();
ThenBB = Builder->GetInsertBlock();
// Emit else block.
TheFunction->getBasicBlockList().push_back(ElseBB);
Builder.SetInsertPoint(ElseBB);
Builder->SetInsertPoint(ElseBB);
Value *ElseV = Else->codegen();
if (!ElseV)
return nullptr;
Builder.CreateBr(MergeBB);
Builder->CreateBr(MergeBB);
// Codegen of 'Else' can change the current block, update ElseBB for the PHI.
ElseBB = Builder.GetInsertBlock();
ElseBB = Builder->GetInsertBlock();
// Emit merge block.
TheFunction->getBasicBlockList().push_back(MergeBB);
Builder.SetInsertPoint(MergeBB);
PHINode *PN = Builder.CreatePHI(Type::getDoubleTy(TheContext), 2, "iftmp");
Builder->SetInsertPoint(MergeBB);
PHINode *PN = Builder->CreatePHI(Type::getDoubleTy(*TheContext), 2, "iftmp");
PN->addIncoming(ThenV, ThenBB);
PN->addIncoming(ElseV, ElseBB);
@ -903,7 +905,7 @@ Value *IfExprAST::codegen() {
// br endcond, loop, endloop
// outloop:
Value *ForExprAST::codegen() {
Function *TheFunction = Builder.GetInsertBlock()->getParent();
Function *TheFunction = Builder->GetInsertBlock()->getParent();
// Create an alloca for the variable in the entry block.
AllocaInst *Alloca = CreateEntryBlockAlloca(TheFunction, VarName);
@ -914,17 +916,17 @@ Value *ForExprAST::codegen() {
return nullptr;
// Store the value into the alloca.
Builder.CreateStore(StartVal, Alloca);
Builder->CreateStore(StartVal, Alloca);
// Make the new basic block for the loop header, inserting after current
// block.
BasicBlock *LoopBB = BasicBlock::Create(TheContext, "loop", TheFunction);
BasicBlock *LoopBB = BasicBlock::Create(*TheContext, "loop", TheFunction);
// Insert an explicit fall through from the current block to the LoopBB.
Builder.CreateBr(LoopBB);
Builder->CreateBr(LoopBB);
// Start insertion in LoopBB.
Builder.SetInsertPoint(LoopBB);
Builder->SetInsertPoint(LoopBB);
// Within the loop, the variable is defined equal to the PHI node. If it
// shadows an existing variable, we have to restore it, so save it now.
@ -945,7 +947,7 @@ Value *ForExprAST::codegen() {
return nullptr;
} else {
// If not specified, use 1.0.
StepVal = ConstantFP::get(TheContext, APFloat(1.0));
StepVal = ConstantFP::get(*TheContext, APFloat(1.0));
}
// Compute the end condition.
@ -955,23 +957,23 @@ Value *ForExprAST::codegen() {
// Reload, increment, and restore the alloca. This handles the case where
// the body of the loop mutates the variable.
Value *CurVar = Builder.CreateLoad(Alloca, VarName.c_str());
Value *NextVar = Builder.CreateFAdd(CurVar, StepVal, "nextvar");
Builder.CreateStore(NextVar, Alloca);
Value *CurVar = Builder->CreateLoad(Alloca, VarName.c_str());
Value *NextVar = Builder->CreateFAdd(CurVar, StepVal, "nextvar");
Builder->CreateStore(NextVar, Alloca);
// Convert condition to a bool by comparing equal to 0.0.
EndCond = Builder.CreateFCmpONE(
EndCond, ConstantFP::get(TheContext, APFloat(0.0)), "loopcond");
EndCond = Builder->CreateFCmpONE(
EndCond, ConstantFP::get(*TheContext, APFloat(0.0)), "loopcond");
// Create the "after loop" block and insert it.
BasicBlock *AfterBB =
BasicBlock::Create(TheContext, "afterloop", TheFunction);
BasicBlock::Create(*TheContext, "afterloop", TheFunction);
// Insert the conditional branch into the end of LoopEndBB.
Builder.CreateCondBr(EndCond, LoopBB, AfterBB);
Builder->CreateCondBr(EndCond, LoopBB, AfterBB);
// Any new code will be inserted in AfterBB.
Builder.SetInsertPoint(AfterBB);
Builder->SetInsertPoint(AfterBB);
// Restore the unshadowed variable.
if (OldVal)
@ -980,13 +982,13 @@ Value *ForExprAST::codegen() {
NamedValues.erase(VarName);
// for expr always returns 0.0.
return Constant::getNullValue(Type::getDoubleTy(TheContext));
return Constant::getNullValue(Type::getDoubleTy(*TheContext));
}
Value *VarExprAST::codegen() {
std::vector<AllocaInst *> OldBindings;
Function *TheFunction = Builder.GetInsertBlock()->getParent();
Function *TheFunction = Builder->GetInsertBlock()->getParent();
// Register all variables and emit their initializer.
for (unsigned i = 0, e = VarNames.size(); i != e; ++i) {
@ -1004,11 +1006,11 @@ Value *VarExprAST::codegen() {
if (!InitVal)
return nullptr;
} else { // If not specified, use 0.0.
InitVal = ConstantFP::get(TheContext, APFloat(0.0));
InitVal = ConstantFP::get(*TheContext, APFloat(0.0));
}
AllocaInst *Alloca = CreateEntryBlockAlloca(TheFunction, VarName);
Builder.CreateStore(InitVal, Alloca);
Builder->CreateStore(InitVal, Alloca);
// Remember the old variable binding so that we can restore the binding when
// we unrecurse.
@ -1033,9 +1035,9 @@ Value *VarExprAST::codegen() {
Function *PrototypeAST::codegen() {
// Make the function type: double(double,double) etc.
std::vector<Type *> Doubles(Args.size(), Type::getDoubleTy(TheContext));
std::vector<Type *> Doubles(Args.size(), Type::getDoubleTy(*TheContext));
FunctionType *FT =
FunctionType::get(Type::getDoubleTy(TheContext), Doubles, false);
FunctionType::get(Type::getDoubleTy(*TheContext), Doubles, false);
Function *F =
Function::Create(FT, Function::ExternalLinkage, Name, TheModule.get());
@ -1062,8 +1064,8 @@ Function *FunctionAST::codegen() {
BinopPrecedence[P.getOperatorName()] = P.getBinaryPrecedence();
// Create a new basic block to start insertion into.
BasicBlock *BB = BasicBlock::Create(TheContext, "entry", TheFunction);
Builder.SetInsertPoint(BB);
BasicBlock *BB = BasicBlock::Create(*TheContext, "entry", TheFunction);
Builder->SetInsertPoint(BB);
// Record the function arguments in the NamedValues map.
NamedValues.clear();
@ -1072,7 +1074,7 @@ Function *FunctionAST::codegen() {
AllocaInst *Alloca = CreateEntryBlockAlloca(TheFunction, Arg.getName());
// Store the initial value into the alloca.
Builder.CreateStore(&Arg, Alloca);
Builder->CreateStore(&Arg, Alloca);
// Add arguments to variable symbol table.
NamedValues[Arg.getName()] = Alloca;
@ -1080,7 +1082,7 @@ Function *FunctionAST::codegen() {
if (Value *RetVal = Body->codegen()) {
// Finish off the function.
Builder.CreateRet(RetVal);
Builder->CreateRet(RetVal);
// Validate the generated code, checking for consistency.
verifyFunction(*TheFunction);
@ -1102,8 +1104,11 @@ Function *FunctionAST::codegen() {
static void InitializeModule() {
// Open a new module.
TheModule = llvm::make_unique<Module>("my cool jit", TheContext);
TheModule->setDataLayout(TheJIT->getTargetMachine().createDataLayout());
TheModule = llvm::make_unique<Module>("my cool jit", *TheContext);
TheModule->setDataLayout(TheJIT->getDataLayout());
// Create a new builder for the module.
Builder = llvm::make_unique<IRBuilder<>>(*TheContext);
}
static void HandleDefinition() {
@ -1112,7 +1117,7 @@ static void HandleDefinition() {
fprintf(stderr, "Read function definition:");
FnIR->print(errs());
fprintf(stderr, "\n");
TheJIT->addModule(std::move(TheModule));
ExitOnErr(TheJIT->addModule(std::move(TheModule)));
InitializeModule();
}
} else {
@ -1136,25 +1141,27 @@ static void HandleExtern() {
}
static void HandleTopLevelExpression() {
static unsigned ExprCount = 0;
// Update ExprCount. This number will be added to anonymous expressions to
// prevent them from clashing.
++ExprCount;
// Evaluate a top-level expression into an anonymous function.
if (auto FnAST = ParseTopLevelExpr()) {
if (auto FnAST = ParseTopLevelExpr(ExprCount)) {
if (FnAST->codegen()) {
// JIT the module containing the anonymous expression, keeping a handle so
// we can free it later.
auto H = TheJIT->addModule(std::move(TheModule));
ExitOnErr(TheJIT->addModule(std::move(TheModule)));
InitializeModule();
// Search the JIT for the __anon_expr symbol.
auto ExprSymbol = TheJIT->findSymbol("__anon_expr");
assert(ExprSymbol && "Function not found");
// Get the anonymous expression's JITSymbol.
auto Sym =
ExitOnErr(TheJIT->lookup(("__anon_expr" + Twine(ExprCount)).str()));
// Get the symbol's address and cast it to the right type (takes no
// arguments, returns a double) so we can call it as a native function.
double (*FP)() = (double (*)())(intptr_t)cantFail(ExprSymbol.getAddress());
auto *FP = (double (*)())(intptr_t)Sym.getAddress();
assert(FP && "Failed to codegen function");
fprintf(stderr, "Evaluated to %f\n", FP());
// Delete the anonymous expression module from the JIT.
TheJIT->removeModule(H);
}
} else {
// Skip token for error recovery.
@ -1222,7 +1229,8 @@ int main() {
fprintf(stderr, "ready> ");
getNextToken();
TheJIT = llvm::make_unique<KaleidoscopeJIT>();
TheJIT = ExitOnErr(KaleidoscopeJIT::Create());
TheContext = &TheJIT->getContext();
InitializeModule();

3
lib/ExecutionEngine/Orc/Core.cpp
View File

@ -1947,7 +1947,8 @@ ExecutionSession::lookup(ArrayRef<JITDylib *> SearchOrder,
SymbolStringPtr Name) {
SymbolNameSet Names({Name});
JITDylibSearchList FullSearchOrder(SearchOrder.size());
JITDylibSearchList FullSearchOrder;
FullSearchOrder.reserve(SearchOrder.size());
for (auto *JD : SearchOrder)
FullSearchOrder.push_back({JD, false});