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