+++ /dev/null
-//===- 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 std::vector<std::pair<size_t, size_t>>
- ImportantInstructionSuccessions;
- 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);
-
-private:
- /// Sort once the feature tuples.
- struct SortFeatureTuples {
- bool IsSorted = false;
- SortFeatureTuples() {
- std::sort(FunctionFeatures::ImportantInstructionSuccessions.begin(),
- FunctionFeatures::ImportantInstructionSuccessions.end());
- IsSorted = true;
- }
- };
-
- static llvm::ManagedStatic<SortFeatureTuples> TupleSorter;
-
- static bool ensureSortedTuples() { return TupleSorter->IsSorted; }
-};
-llvm::ManagedStatic<IRToNativeSizeLearning::SortFeatureTuples>
- IRToNativeSizeLearning::TupleSorter;
-
-// 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 vector is given in opcode pairs rather than labels because 1) labels
-// weren't readily available, and 2) the successions were hand - extracted
-std::vector<std::pair<size_t, size_t>>
- IRToNativeSizeLearning::FunctionFeatures::ImportantInstructionSuccessions =
- {{1, 34}, {15, 27}, {53, 53}, {53, 34}, {1, 11}, {32, 2}, {2, 48},
- {28, 48}, {1, 45}, {49, 32}, {57, 56}, {55, 53}, {1, 28}, {57, 34},
- {1, 1}, {32, 28}, {32, 15}, {49, 28}, {53, 1}, {2, 53}, {48, 34},
- {28, 53}, {2, 32}, {1, 40}, {32, 48}, {29, 56}, {56, 32}, {55, 56},
- {48, 56}, {1, 31}, {33, 34}, {2, 28}, {1, 12}, {55, 1}, {31, 31},
- {65, 1}, {33, 56}, {32, 32}, {13, 13}, {1, 26}, {13, 26}, {2, 1},
- {1, 33}, {47, 49}, {64, 1}, {2, 38}, {34, 53}, {48, 2}, {55, 34},
- {34, 32}, {1, 5}, {56, 13}, {2, 2}, {2, 49}, {33, 2}, {49, 39},
- {56, 49}, {33, 49}, {32, 39}, {39, 57}, {29, 33}, {31, 34}, {32, 29},
- {47, 15}, {13, 34}, {2, 33}, {32, 49}, {49, 34}, {56, 33}, {1, 30},
- {33, 33}, {31, 33}, {2, 29}, {56, 7}, {32, 13}, {2, 55}, {56, 56},
- {2, 34}, {1, 42}, {34, 49}, {1, 20}, {32, 33}, {1, 25}, {53, 28},
- {1, 14}, {31, 49}, {28, 2}, {2, 13}, {2, 56}, {1, 32}, {56, 53},
- {65, 65}, {33, 53}, {64, 64}, {13, 2}, {34, 33}, {1, 4}, {49, 2},
- {1, 9}, {56, 1}, {33, 1}, {53, 57}, {32, 53}, {13, 56}, {32, 56},
- {55, 55}, {1, 18}, {49, 56}, {34, 34}, {1, 7}, {56, 64}, {32, 1},
- {13, 33}, {55, 28}, {49, 33}, {57, 57}, {56, 34}, {34, 56}, {33, 32},
- {32, 40}, {1, 29}, {53, 2}, {34, 1}, {32, 34}, {49, 49}, {1, 24},
- {40, 34}, {1, 13}, {38, 34}, {29, 2}, {34, 2}, {1, 39}, {1, 22},
- {1, 27}, {49, 1}, {1, 8}, {56, 2}};
-
-// 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 =
- IRToNativeSizeLearning::FunctionFeatures::ImportantInstructionSuccessions
- .size() +
- getMaxInstructionID() + 1 + IRToNativeSizeLearning::NumNamedFeatures;
-
-size_t getSize(Function &F, TargetTransformInfo &TTI) {
- size_t Ret = 0;
- for (auto &BB : F)
- for (auto &I : BB)
- Ret += TTI.getInstructionCost(
- &I, TargetTransformInfo::TargetCostKind::TCK_CodeSize);
- 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 (auto &BB : F)
- if (auto *TN = Tree.getNode(&BB))
- Ret = std::max(Ret, TN->getLevel());
- return Ret;
-}
-} // namespace
-
-IRToNativeSizeLearning::FunctionFeatures
-IRToNativeSizeLearning::getFunctionFeatures(Function &F,
- FunctionAnalysisManager &FAM) {
- assert(ensureSortedTuples() && "expected lazy initialization");
-
- auto &DomTree = FAM.getResult<DominatorTreeAnalysis>(F);
- FunctionFeatures FF;
- size_t InstrCount = getMaxInstructionID() + 1;
- FF.InstructionHistogram.resize(InstrCount);
-
- FF.InstructionPairHistogram.resize(
- FunctionFeatures::ImportantInstructionSuccessions.size());
-
- auto StartID = 0;
- auto LastID = StartID;
- auto getPairIndex = [](size_t a, size_t b) {
- auto I =
- std::find(FunctionFeatures::ImportantInstructionSuccessions.begin(),
- FunctionFeatures::ImportantInstructionSuccessions.end(),
- std::make_pair(a, b));
- if (I == FunctionFeatures::ImportantInstructionSuccessions.end())
- return -1;
- return static_cast<int>(std::distance(
- FunctionFeatures::ImportantInstructionSuccessions.begin(), I));
- };
-
- // We don't want debug calls, because they'd just add noise.
- for (auto &BB : F) {
- for (auto I = BB.instructionsWithoutDebug().begin(),
- E = BB.instructionsWithoutDebug().end();
- I != E; ++I) {
- 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<std::string> InputNames{"serving_default_input_1"};
- std::vector<std::string> OutputName{"StatefulPartitionedCall"};
- Evaluator = std::make_unique<TFModelEvaluator>(
- TFIR2NativeModelPath.getValue().c_str(), InputNames, OutputName);
- if (!Evaluator || !Evaluator->isValid()) {
- Evaluator.reset();
- return;
- }
- static const std::vector<int64_t> Dim{
- 1, static_cast<int64_t>(
- IRToNativeSizeLearning::FunctionFeatures::FeatureCount)};
-
- Evaluator->initInput(0, TF_INT32, Dim);
-}
-
-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 = static_cast<int32_t *>(TF_TensorData(Evaluator->getInput()[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
\ No newline at end of file
+++ /dev/null
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