1//===- InlineSizeEstimatorAnalysis.cpp - IR to native size from ML model --===//
2//
3// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
4// See https://llvm.org/LICENSE.txt for license information.
5// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
6//
7//===----------------------------------------------------------------------===//
8//
9// This implements feature and label extraction for offline supervised learning
10// of a IR to native size model.
11//
12//===----------------------------------------------------------------------===//
13#include "llvm/Analysis/InlineSizeEstimatorAnalysis.h"
14
15#ifdef LLVM_HAVE_TFLITE
16#include "llvm/Analysis/Utils/TFUtils.h"
17#endif
18#include "llvm/IR/Function.h"
19#include "llvm/IR/PassManager.h"
20#include "llvm/Support/raw_ostream.h"
21
22using namespace llvm;
23
24AnalysisKey InlineSizeEstimatorAnalysis::Key;
25
26#ifdef LLVM_HAVE_TFLITE
27#include "llvm/Analysis/LoopInfo.h"
28#include "llvm/Analysis/TargetLibraryInfo.h"
29#include "llvm/Analysis/TargetTransformInfo.h"
30#include "llvm/IR/BasicBlock.h"
31#include "llvm/IR/Dominators.h"
32#include "llvm/IR/Instructions.h"
33#include "llvm/Support/Casting.h"
34#include "llvm/Support/CommandLine.h"
35#include <algorithm>
36#include <deque>
37#include <optional>
38
39static cl::opt<std::string> TFIR2NativeModelPath(
40 "ml-inliner-ir2native-model", cl::Hidden,
41 cl::desc("Path to saved model evaluating native size from IR."));
42
43#define DEBUG_TYPE "inline-size-estimator"
44namespace {
45unsigned getMaxInstructionID() {
46#define LAST_OTHER_INST(NR) return NR;
47#include "llvm/IR/Instruction.def"
48}
49
50class IRToNativeSizeLearning {
51public:
52 enum class NamedFeatureIndex : size_t {
53 InitialSize,
54 Blocks,
55 Calls,
56 IsLocal,
57 IsLinkOnceODR,
58 IsLinkOnce,
59 Loops,
60 MaxLoopDepth,
61 MaxDomTreeLevel,
62
63 NumNamedFeatures
64 };
65 static const size_t NumNamedFeatures =
66 static_cast<size_t>(NamedFeatureIndex::NumNamedFeatures);
67 struct FunctionFeatures {
68 static const size_t FeatureCount;
69
70 std::array<int32_t, NumNamedFeatures> NamedFeatures = {0};
71 std::vector<int32_t> InstructionHistogram;
72 std::vector<int32_t> InstructionPairHistogram;
73
74 void fillTensor(int32_t *Ptr) const;
75 int32_t &operator[](NamedFeatureIndex Pos) {
76 return NamedFeatures[static_cast<size_t>(Pos)];
77 }
78 };
79 IRToNativeSizeLearning() = default;
80
81 static FunctionFeatures getFunctionFeatures(Function &F,
82 FunctionAnalysisManager &FAM);
83};
84
85// This is a point in time - we determined including these pairs of
86// consecutive instructions (in the IR layout available at inline time) as
87// features improves the model performance. We want to move away from manual
88// feature selection.
89// The array is given in opcode pairs rather than labels because 1) labels
90// weren't readily available, and 2) the successions were hand - extracted.
91//
92// This array must be sorted.
93static const std::array<std::pair<size_t, size_t>, 137>
94 ImportantInstructionSuccessions{
95 {{1, 1}, {1, 4}, {1, 5}, {1, 7}, {1, 8}, {1, 9}, {1, 11},
96 {1, 12}, {1, 13}, {1, 14}, {1, 18}, {1, 20}, {1, 22}, {1, 24},
97 {1, 25}, {1, 26}, {1, 27}, {1, 28}, {1, 29}, {1, 30}, {1, 31},
98 {1, 32}, {1, 33}, {1, 34}, {1, 39}, {1, 40}, {1, 42}, {1, 45},
99 {2, 1}, {2, 2}, {2, 13}, {2, 28}, {2, 29}, {2, 32}, {2, 33},
100 {2, 34}, {2, 38}, {2, 48}, {2, 49}, {2, 53}, {2, 55}, {2, 56},
101 {13, 2}, {13, 13}, {13, 26}, {13, 33}, {13, 34}, {13, 56}, {15, 27},
102 {28, 2}, {28, 48}, {28, 53}, {29, 2}, {29, 33}, {29, 56}, {31, 31},
103 {31, 33}, {31, 34}, {31, 49}, {32, 1}, {32, 2}, {32, 13}, {32, 15},
104 {32, 28}, {32, 29}, {32, 32}, {32, 33}, {32, 34}, {32, 39}, {32, 40},
105 {32, 48}, {32, 49}, {32, 53}, {32, 56}, {33, 1}, {33, 2}, {33, 32},
106 {33, 33}, {33, 34}, {33, 49}, {33, 53}, {33, 56}, {34, 1}, {34, 2},
107 {34, 32}, {34, 33}, {34, 34}, {34, 49}, {34, 53}, {34, 56}, {38, 34},
108 {39, 57}, {40, 34}, {47, 15}, {47, 49}, {48, 2}, {48, 34}, {48, 56},
109 {49, 1}, {49, 2}, {49, 28}, {49, 32}, {49, 33}, {49, 34}, {49, 39},
110 {49, 49}, {49, 56}, {53, 1}, {53, 2}, {53, 28}, {53, 34}, {53, 53},
111 {53, 57}, {55, 1}, {55, 28}, {55, 34}, {55, 53}, {55, 55}, {55, 56},
112 {56, 1}, {56, 2}, {56, 7}, {56, 13}, {56, 32}, {56, 33}, {56, 34},
113 {56, 49}, {56, 53}, {56, 56}, {56, 64}, {57, 34}, {57, 56}, {57, 57},
114 {64, 1}, {64, 64}, {65, 1}, {65, 65}}};
115
116// We have: 9 calculated features (the features here); 1 feature for each
117// instruction opcode; and 1 feature for each manually-identified sequence.
118// For the latter 2, we build a histogram: we count the number of
119// occurrences of each instruction opcode or succession of instructions,
120// respectively.
121// Note that instruction opcodes start from 1. For convenience, we also have an
122// always 0 feature for the '0' opcode, hence the extra 1.
123const size_t IRToNativeSizeLearning::FunctionFeatures::FeatureCount =
124 ImportantInstructionSuccessions.size() + getMaxInstructionID() + 1 +
125 IRToNativeSizeLearning::NumNamedFeatures;
126
127size_t getSize(Function &F, TargetTransformInfo &TTI) {
128 size_t Ret = 0;
129 for (const auto &BB : F)
130 for (const auto &I : BB)
131 Ret += TTI.getInstructionCost(
132 &I, TargetTransformInfo::TargetCostKind::TCK_CodeSize)
133 .getValue();
134 return Ret;
135}
136
137size_t getSize(Function &F, FunctionAnalysisManager &FAM) {
138 auto &TTI = FAM.getResult<TargetIRAnalysis>(F);
139 return getSize(F, TTI);
140}
141
142unsigned getMaxDominatorTreeDepth(const Function &F,
143 const DominatorTree &Tree) {
144 unsigned Ret = 0;
145 for (const auto &BB : F)
146 if (const auto *TN = Tree.getNode(&BB))
147 Ret = std::max(Ret, TN->getLevel());
148 return Ret;
149}
150} // namespace
151
152IRToNativeSizeLearning::FunctionFeatures
153IRToNativeSizeLearning::getFunctionFeatures(Function &F,
154 FunctionAnalysisManager &FAM) {
155 assert(llvm::is_sorted(ImportantInstructionSuccessions) &&
156 "expected function features are sorted");
157
158 auto &DomTree = FAM.getResult<DominatorTreeAnalysis>(F);
159 FunctionFeatures FF;
160 size_t InstrCount = getMaxInstructionID() + 1;
161 FF.InstructionHistogram.resize(InstrCount);
162
163 FF.InstructionPairHistogram.resize(ImportantInstructionSuccessions.size());
164
165 int StartID = 0;
166 int LastID = StartID;
167 auto getPairIndex = [](size_t a, size_t b) {
168 auto I = llvm::find(ImportantInstructionSuccessions, std::make_pair(a, b));
169 if (I == ImportantInstructionSuccessions.end())
170 return -1;
171 return static_cast<int>(
172 std::distance(ImportantInstructionSuccessions.begin(), I));
173 };
174
175 // We don't want debug calls, because they'd just add noise.
176 for (const auto &BB : F) {
177 for (const auto &I : BB.instructionsWithoutDebug()) {
178 auto ID = I.getOpcode();
179
180 ++FF.InstructionHistogram[ID];
181 int PairIndex = getPairIndex(LastID, ID);
182 if (PairIndex >= 0)
183 ++FF.InstructionPairHistogram[PairIndex];
184 LastID = ID;
185 if (isa<CallBase>(I))
186 ++FF[NamedFeatureIndex::Calls];
187 }
188 }
189
190 FF[NamedFeatureIndex::InitialSize] = getSize(F, FAM);
191 FF[NamedFeatureIndex::IsLocal] = F.hasLocalLinkage();
192 FF[NamedFeatureIndex::IsLinkOnceODR] = F.hasLinkOnceODRLinkage();
193 FF[NamedFeatureIndex::IsLinkOnce] = F.hasLinkOnceLinkage();
194 FF[NamedFeatureIndex::Blocks] = F.size();
195 auto &LI = FAM.getResult<LoopAnalysis>(F);
196 FF[NamedFeatureIndex::Loops] = std::distance(LI.begin(), LI.end());
197 for (auto &L : LI)
198 FF[NamedFeatureIndex::MaxLoopDepth] =
199 std::max(FF[NamedFeatureIndex::MaxLoopDepth],
200 static_cast<int32_t>(L->getLoopDepth()));
201 FF[NamedFeatureIndex::MaxDomTreeLevel] = getMaxDominatorTreeDepth(F, DomTree);
202 return FF;
203}
204
205void IRToNativeSizeLearning::FunctionFeatures::fillTensor(int32_t *Ptr) const {
206 std::copy(NamedFeatures.begin(), NamedFeatures.end(), Ptr);
207 Ptr += NamedFeatures.size();
208 std::copy(InstructionHistogram.begin(), InstructionHistogram.end(), Ptr);
209 Ptr += InstructionHistogram.size();
210 std::copy(InstructionPairHistogram.begin(), InstructionPairHistogram.end(),
211 Ptr);
212}
213
214bool InlineSizeEstimatorAnalysis::isEvaluatorRequested() {
215 return !TFIR2NativeModelPath.empty();
216}
217
218InlineSizeEstimatorAnalysis::InlineSizeEstimatorAnalysis() {
219 if (!isEvaluatorRequested()) {
220 return;
221 }
222 std::vector<TensorSpec> InputSpecs{TensorSpec::createSpec<int32_t>(
223 "serving_default_input_1",
224 {1, static_cast<int64_t>(
225 IRToNativeSizeLearning::FunctionFeatures::FeatureCount)})};
226 std::vector<TensorSpec> OutputSpecs{
227 TensorSpec::createSpec<float>("StatefulPartitionedCall", {1})};
228 Evaluator = std::make_unique<TFModelEvaluator>(
229 TFIR2NativeModelPath.getValue().c_str(), InputSpecs, OutputSpecs);
230 if (!Evaluator || !Evaluator->isValid()) {
231 Evaluator.reset();
232 return;
233 }
234}
235
236InlineSizeEstimatorAnalysis::Result
237InlineSizeEstimatorAnalysis::run(const Function &F,
238 FunctionAnalysisManager &FAM) {
239 if (!Evaluator)
240 return std::nullopt;
241 auto Features = IRToNativeSizeLearning::getFunctionFeatures(
242 const_cast<Function &>(F), FAM);
243 int32_t *V = Evaluator->getInput<int32_t>(0);
244 Features.fillTensor(V);
245 auto ER = Evaluator->evaluate();
246 if (!ER)
247 return std::nullopt;
248 float Ret = *ER->getTensorValue<float>(0);
249 if (Ret < 0.0)
250 Ret = 0.0;
251 return static_cast<size_t>(Ret);
252}
253
254InlineSizeEstimatorAnalysis::~InlineSizeEstimatorAnalysis() {}
255InlineSizeEstimatorAnalysis::InlineSizeEstimatorAnalysis(
256 InlineSizeEstimatorAnalysis &&Other)
257 : Evaluator(std::move(Other.Evaluator)) {}
258
259#else
260namespace llvm {
261class TFModelEvaluator {};
262} // namespace llvm
263InlineSizeEstimatorAnalysis::InlineSizeEstimatorAnalysis() = default;
264InlineSizeEstimatorAnalysis ::InlineSizeEstimatorAnalysis(
265 InlineSizeEstimatorAnalysis &&) {}
266InlineSizeEstimatorAnalysis::~InlineSizeEstimatorAnalysis() = default;
267InlineSizeEstimatorAnalysis::Result
268InlineSizeEstimatorAnalysis::run(const Function &F,
269 FunctionAnalysisManager &FAM) {
270 return std::nullopt;
271}
272bool InlineSizeEstimatorAnalysis::isEvaluatorRequested() { return false; }
273#endif
274
275PreservedAnalyses
276InlineSizeEstimatorAnalysisPrinterPass::run(Function &F,
277 FunctionAnalysisManager &AM) {
278 OS << "[InlineSizeEstimatorAnalysis] size estimate for " << F.getName()
279 << ": " << AM.getResult<InlineSizeEstimatorAnalysis>(IR&: F) << "\n";
280 return PreservedAnalyses::all();
281}
282