mirror of
https://github.com/RPCS3/llvm-mirror.git
synced 2024-11-24 03:33:20 +01:00
284 lines
10 KiB
C++
284 lines
10 KiB
C++
//===- InlineSizeEstimatorAnalysis.cpp - IR to native size from ML model --===//
|
|
//
|
|
// The LLVM Compiler Infrastructure
|
|
//
|
|
// This file is distributed under the University of Illinois Open Source
|
|
// License. See LICENSE.TXT for details.
|
|
//
|
|
//===----------------------------------------------------------------------===//
|
|
//
|
|
// This implements feature and label extraction for offline supervised learning
|
|
// of a IR to native size model.
|
|
//
|
|
//===----------------------------------------------------------------------===//
|
|
#include "llvm/Analysis/InlineSizeEstimatorAnalysis.h"
|
|
|
|
#ifdef LLVM_HAVE_TF_API
|
|
#include "llvm/Analysis/Utils/TFUtils.h"
|
|
#endif
|
|
#include "llvm/Analysis/LoopInfo.h"
|
|
#include "llvm/Analysis/TargetLibraryInfo.h"
|
|
#include "llvm/Analysis/TargetTransformInfo.h"
|
|
#include "llvm/IR/BasicBlock.h"
|
|
#include "llvm/IR/Dominators.h"
|
|
#include "llvm/IR/Function.h"
|
|
#include "llvm/IR/Instructions.h"
|
|
#include "llvm/IR/PassManager.h"
|
|
#include "llvm/MC/MCAsmLayout.h"
|
|
#include "llvm/Support/Casting.h"
|
|
#include "llvm/Support/CommandLine.h"
|
|
#include "llvm/Support/raw_ostream.h"
|
|
|
|
#include <algorithm>
|
|
#include <deque>
|
|
|
|
using namespace llvm;
|
|
|
|
AnalysisKey InlineSizeEstimatorAnalysis::Key;
|
|
|
|
#define DEBUG_TYPE "inline-size-estimator"
|
|
|
|
#ifdef LLVM_HAVE_TF_API
|
|
cl::opt<std::string> TFIR2NativeModelPath(
|
|
"ml-inliner-ir2native-model", cl::Hidden,
|
|
cl::desc("Path to saved model evaluating native size from IR."));
|
|
|
|
namespace {
|
|
unsigned getMaxInstructionID() {
|
|
#define LAST_OTHER_INST(NR) return NR;
|
|
#include "llvm/IR/Instruction.def"
|
|
}
|
|
|
|
class IRToNativeSizeLearning {
|
|
public:
|
|
enum class NamedFeatureIndex : size_t {
|
|
InitialSize,
|
|
Blocks,
|
|
Calls,
|
|
IsLocal,
|
|
IsLinkOnceODR,
|
|
IsLinkOnce,
|
|
Loops,
|
|
MaxLoopDepth,
|
|
MaxDomTreeLevel,
|
|
|
|
NumNamedFeatures
|
|
};
|
|
static const size_t NumNamedFeatures =
|
|
static_cast<size_t>(NamedFeatureIndex::NumNamedFeatures);
|
|
struct FunctionFeatures {
|
|
static const size_t FeatureCount;
|
|
|
|
std::array<int32_t, NumNamedFeatures> NamedFeatures = {0};
|
|
std::vector<int32_t> InstructionHistogram;
|
|
std::vector<int32_t> InstructionPairHistogram;
|
|
|
|
void fillTensor(int32_t *Ptr) const;
|
|
int32_t &operator[](NamedFeatureIndex Pos) {
|
|
return NamedFeatures[static_cast<size_t>(Pos)];
|
|
}
|
|
};
|
|
IRToNativeSizeLearning() = default;
|
|
|
|
static FunctionFeatures getFunctionFeatures(Function &F,
|
|
FunctionAnalysisManager &FAM);
|
|
};
|
|
|
|
// This is a point in time - we determined including these pairs of
|
|
// consecutive instructions (in the IR layout available at inline time) as
|
|
// features improves the model performance. We want to move away from manual
|
|
// feature selection.
|
|
// The array is given in opcode pairs rather than labels because 1) labels
|
|
// weren't readily available, and 2) the successions were hand - extracted.
|
|
//
|
|
// This array must be sorted.
|
|
static const std::array<std::pair<size_t, size_t>, 137>
|
|
ImportantInstructionSuccessions{
|
|
{{1, 1}, {1, 4}, {1, 5}, {1, 7}, {1, 8}, {1, 9}, {1, 11},
|
|
{1, 12}, {1, 13}, {1, 14}, {1, 18}, {1, 20}, {1, 22}, {1, 24},
|
|
{1, 25}, {1, 26}, {1, 27}, {1, 28}, {1, 29}, {1, 30}, {1, 31},
|
|
{1, 32}, {1, 33}, {1, 34}, {1, 39}, {1, 40}, {1, 42}, {1, 45},
|
|
{2, 1}, {2, 2}, {2, 13}, {2, 28}, {2, 29}, {2, 32}, {2, 33},
|
|
{2, 34}, {2, 38}, {2, 48}, {2, 49}, {2, 53}, {2, 55}, {2, 56},
|
|
{13, 2}, {13, 13}, {13, 26}, {13, 33}, {13, 34}, {13, 56}, {15, 27},
|
|
{28, 2}, {28, 48}, {28, 53}, {29, 2}, {29, 33}, {29, 56}, {31, 31},
|
|
{31, 33}, {31, 34}, {31, 49}, {32, 1}, {32, 2}, {32, 13}, {32, 15},
|
|
{32, 28}, {32, 29}, {32, 32}, {32, 33}, {32, 34}, {32, 39}, {32, 40},
|
|
{32, 48}, {32, 49}, {32, 53}, {32, 56}, {33, 1}, {33, 2}, {33, 32},
|
|
{33, 33}, {33, 34}, {33, 49}, {33, 53}, {33, 56}, {34, 1}, {34, 2},
|
|
{34, 32}, {34, 33}, {34, 34}, {34, 49}, {34, 53}, {34, 56}, {38, 34},
|
|
{39, 57}, {40, 34}, {47, 15}, {47, 49}, {48, 2}, {48, 34}, {48, 56},
|
|
{49, 1}, {49, 2}, {49, 28}, {49, 32}, {49, 33}, {49, 34}, {49, 39},
|
|
{49, 49}, {49, 56}, {53, 1}, {53, 2}, {53, 28}, {53, 34}, {53, 53},
|
|
{53, 57}, {55, 1}, {55, 28}, {55, 34}, {55, 53}, {55, 55}, {55, 56},
|
|
{56, 1}, {56, 2}, {56, 7}, {56, 13}, {56, 32}, {56, 33}, {56, 34},
|
|
{56, 49}, {56, 53}, {56, 56}, {56, 64}, {57, 34}, {57, 56}, {57, 57},
|
|
{64, 1}, {64, 64}, {65, 1}, {65, 65}}};
|
|
|
|
// We have: 9 calculated features (the features here); 1 feature for each
|
|
// instruction opcode; and 1 feature for each manually-identified sequence.
|
|
// For the latter 2, we build a histogram: we count the number of
|
|
// occurrences of each instruction opcode or succession of instructions,
|
|
// respectively.
|
|
// Note that instruction opcodes start from 1. For convenience, we also have an
|
|
// always 0 feature for the '0' opcode, hence the extra 1.
|
|
const size_t IRToNativeSizeLearning::FunctionFeatures::FeatureCount =
|
|
ImportantInstructionSuccessions.size() + getMaxInstructionID() + 1 +
|
|
IRToNativeSizeLearning::NumNamedFeatures;
|
|
|
|
size_t getSize(Function &F, TargetTransformInfo &TTI) {
|
|
size_t Ret = 0;
|
|
for (const auto &BB : F)
|
|
for (const auto &I : BB)
|
|
Ret += *(TTI.getInstructionCost(
|
|
&I, TargetTransformInfo::TargetCostKind::TCK_CodeSize).getValue());
|
|
return Ret;
|
|
}
|
|
|
|
size_t getSize(Function &F, FunctionAnalysisManager &FAM) {
|
|
auto &TTI = FAM.getResult<TargetIRAnalysis>(F);
|
|
return getSize(F, TTI);
|
|
}
|
|
|
|
unsigned getMaxDominatorTreeDepth(const Function &F,
|
|
const DominatorTree &Tree) {
|
|
unsigned Ret = 0;
|
|
for (const auto &BB : F)
|
|
if (const auto *TN = Tree.getNode(&BB))
|
|
Ret = std::max(Ret, TN->getLevel());
|
|
return Ret;
|
|
}
|
|
} // namespace
|
|
|
|
IRToNativeSizeLearning::FunctionFeatures
|
|
IRToNativeSizeLearning::getFunctionFeatures(Function &F,
|
|
FunctionAnalysisManager &FAM) {
|
|
assert(llvm::is_sorted(ImportantInstructionSuccessions) &&
|
|
"expected function features are sorted");
|
|
|
|
auto &DomTree = FAM.getResult<DominatorTreeAnalysis>(F);
|
|
FunctionFeatures FF;
|
|
size_t InstrCount = getMaxInstructionID() + 1;
|
|
FF.InstructionHistogram.resize(InstrCount);
|
|
|
|
FF.InstructionPairHistogram.resize(ImportantInstructionSuccessions.size());
|
|
|
|
int StartID = 0;
|
|
int LastID = StartID;
|
|
auto getPairIndex = [](size_t a, size_t b) {
|
|
auto I = llvm::find(ImportantInstructionSuccessions, std::make_pair(a, b));
|
|
if (I == ImportantInstructionSuccessions.end())
|
|
return -1;
|
|
return static_cast<int>(
|
|
std::distance(ImportantInstructionSuccessions.begin(), I));
|
|
};
|
|
|
|
// We don't want debug calls, because they'd just add noise.
|
|
for (const auto &BB : F) {
|
|
for (const auto &I : BB.instructionsWithoutDebug()) {
|
|
auto ID = I.getOpcode();
|
|
|
|
++FF.InstructionHistogram[ID];
|
|
int PairIndex = getPairIndex(LastID, ID);
|
|
if (PairIndex >= 0)
|
|
++FF.InstructionPairHistogram[PairIndex];
|
|
LastID = ID;
|
|
if (isa<CallBase>(I))
|
|
++FF[NamedFeatureIndex::Calls];
|
|
}
|
|
}
|
|
|
|
FF[NamedFeatureIndex::InitialSize] = getSize(F, FAM);
|
|
FF[NamedFeatureIndex::IsLocal] = F.hasLocalLinkage();
|
|
FF[NamedFeatureIndex::IsLinkOnceODR] = F.hasLinkOnceODRLinkage();
|
|
FF[NamedFeatureIndex::IsLinkOnce] = F.hasLinkOnceLinkage();
|
|
FF[NamedFeatureIndex::Blocks] =
|
|
std::distance(F.getBasicBlockList().begin(), F.getBasicBlockList().end());
|
|
auto &LI = FAM.getResult<LoopAnalysis>(F);
|
|
FF[NamedFeatureIndex::Loops] = std::distance(LI.begin(), LI.end());
|
|
for (auto &L : LI)
|
|
FF[NamedFeatureIndex::MaxLoopDepth] =
|
|
std::max(FF[NamedFeatureIndex::MaxLoopDepth],
|
|
static_cast<int32_t>(L->getLoopDepth()));
|
|
FF[NamedFeatureIndex::MaxDomTreeLevel] = getMaxDominatorTreeDepth(F, DomTree);
|
|
return FF;
|
|
}
|
|
|
|
void IRToNativeSizeLearning::FunctionFeatures::fillTensor(int32_t *Ptr) const {
|
|
std::copy(NamedFeatures.begin(), NamedFeatures.end(), Ptr);
|
|
Ptr += NamedFeatures.size();
|
|
std::copy(InstructionHistogram.begin(), InstructionHistogram.end(), Ptr);
|
|
Ptr += InstructionHistogram.size();
|
|
std::copy(InstructionPairHistogram.begin(), InstructionPairHistogram.end(),
|
|
Ptr);
|
|
}
|
|
|
|
bool InlineSizeEstimatorAnalysis::isEvaluatorRequested() {
|
|
return !TFIR2NativeModelPath.empty();
|
|
}
|
|
|
|
InlineSizeEstimatorAnalysis::InlineSizeEstimatorAnalysis() {
|
|
if (!isEvaluatorRequested()) {
|
|
return;
|
|
}
|
|
std::vector<TensorSpec> InputSpecs{TensorSpec::createSpec<int32_t>(
|
|
"serving_default_input_1",
|
|
{1, static_cast<int64_t>(
|
|
IRToNativeSizeLearning::FunctionFeatures::FeatureCount)})};
|
|
std::vector<TensorSpec> OutputSpecs{
|
|
TensorSpec::createSpec<float>("StatefulPartitionedCall", {1})};
|
|
Evaluator = std::make_unique<TFModelEvaluator>(
|
|
TFIR2NativeModelPath.getValue().c_str(), InputSpecs, OutputSpecs);
|
|
if (!Evaluator || !Evaluator->isValid()) {
|
|
Evaluator.reset();
|
|
return;
|
|
}
|
|
}
|
|
|
|
InlineSizeEstimatorAnalysis::Result
|
|
InlineSizeEstimatorAnalysis::run(const Function &F,
|
|
FunctionAnalysisManager &FAM) {
|
|
if (!Evaluator)
|
|
return None;
|
|
auto Features = IRToNativeSizeLearning::getFunctionFeatures(
|
|
const_cast<Function &>(F), FAM);
|
|
int32_t *V = Evaluator->getInput<int32_t>(0);
|
|
Features.fillTensor(V);
|
|
auto ER = Evaluator->evaluate();
|
|
if (!ER)
|
|
return None;
|
|
float Ret = *ER->getTensorValue<float>(0);
|
|
if (Ret < 0.0)
|
|
Ret = 0.0;
|
|
return static_cast<size_t>(Ret);
|
|
}
|
|
|
|
InlineSizeEstimatorAnalysis::~InlineSizeEstimatorAnalysis() {}
|
|
InlineSizeEstimatorAnalysis::InlineSizeEstimatorAnalysis(
|
|
InlineSizeEstimatorAnalysis &&Other)
|
|
: Evaluator(std::move(Other.Evaluator)) {}
|
|
|
|
#else
|
|
namespace llvm {
|
|
class TFModelEvaluator {};
|
|
} // namespace llvm
|
|
InlineSizeEstimatorAnalysis::InlineSizeEstimatorAnalysis() {}
|
|
InlineSizeEstimatorAnalysis ::InlineSizeEstimatorAnalysis(
|
|
InlineSizeEstimatorAnalysis &&) {}
|
|
InlineSizeEstimatorAnalysis::~InlineSizeEstimatorAnalysis() {}
|
|
InlineSizeEstimatorAnalysis::Result
|
|
InlineSizeEstimatorAnalysis::run(const Function &F,
|
|
FunctionAnalysisManager &FAM) {
|
|
return None;
|
|
}
|
|
bool InlineSizeEstimatorAnalysis::isEvaluatorRequested() { return false; }
|
|
#endif
|
|
|
|
PreservedAnalyses
|
|
InlineSizeEstimatorAnalysisPrinterPass::run(Function &F,
|
|
FunctionAnalysisManager &AM) {
|
|
OS << "[InlineSizeEstimatorAnalysis] size estimate for " << F.getName()
|
|
<< ": " << AM.getResult<InlineSizeEstimatorAnalysis>(F) << "\n";
|
|
return PreservedAnalyses::all();
|
|
}
|