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[MLInliner] Support training that doesn't require partial rewards
If we use training algorithms that don't need partial rewards, we don't need to worry about an ir2native model. In that case, training logs won't contain a 'delta_size' feature either (since that's the partial reward). Differential Revision: https://reviews.llvm.org/D86481
This commit is contained in:
Mircea Trofin
parent
cf617b95ee
commit
9c71d4e1d1
@ -232,7 +232,9 @@ public:
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size_t getTotalSizeEstimate();
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virtual ~DevelopmentModeMLInlineAdvisor();
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void updateNativeSizeEstimate(int64_t Change) { CurrentNativeSize += Change; }
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void updateNativeSizeEstimate(int64_t Change) {
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*CurrentNativeSize += Change;
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}
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void resetNativeSize(Function *F) {
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FAM.invalidate<InlineSizeEstimatorAnalysis>(*F);
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}
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@ -242,7 +244,7 @@ public:
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std::unique_ptr<MLInlineAdvice>
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getAdviceFromModel(CallBase &CB, OptimizationRemarkEmitter &ORE) override;
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size_t getNativeSizeEstimate(const Function &F) const;
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Optional<size_t> getNativeSizeEstimate(const Function &F) const;
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private:
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bool isLogging() const { return !!Logger; }
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@ -251,8 +253,8 @@ private:
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const bool IsDoingInference;
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std::unique_ptr<TrainingLogger> Logger;
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const int32_t InitialNativeSize;
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int32_t CurrentNativeSize = 0;
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const Optional<int32_t> InitialNativeSize;
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Optional<int32_t> CurrentNativeSize;
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};
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/// A variant of MLInlineAdvice that tracks all non-trivial inlining
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@ -261,9 +263,10 @@ class LoggingMLInlineAdvice : public MLInlineAdvice {
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public:
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LoggingMLInlineAdvice(DevelopmentModeMLInlineAdvisor *Advisor, CallBase &CB,
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OptimizationRemarkEmitter &ORE, bool Recommendation,
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TrainingLogger &Logger, size_t CallerSizeEstimateBefore,
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size_t CalleeSizeEstimateBefore, bool DefaultDecision,
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bool Mandatory = false)
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TrainingLogger &Logger,
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Optional<size_t> CallerSizeEstimateBefore,
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Optional<size_t> CalleeSizeEstimateBefore,
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bool DefaultDecision, bool Mandatory = false)
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: MLInlineAdvice(Advisor, CB, ORE, Recommendation), Logger(Logger),
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CallerSizeEstimateBefore(CallerSizeEstimateBefore),
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CalleeSizeEstimateBefore(CalleeSizeEstimateBefore),
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@ -279,11 +282,12 @@ private:
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MLInlineAdvice::recordInliningImpl();
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getAdvisor()->resetNativeSize(Caller);
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int Reward = std::numeric_limits<int>::max();
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if (!getAdvisor()->isForcedToStop()) {
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int NativeSizeAfter = getAdvisor()->getNativeSizeEstimate(*Caller) +
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CalleeSizeEstimateBefore;
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if (InlineSizeEstimatorAnalysis::isEvaluatorRequested() &&
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!getAdvisor()->isForcedToStop()) {
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int NativeSizeAfter = *getAdvisor()->getNativeSizeEstimate(*Caller) +
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*CalleeSizeEstimateBefore;
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Reward = NativeSizeAfter -
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(CallerSizeEstimateBefore + CalleeSizeEstimateBefore);
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(*CallerSizeEstimateBefore + *CalleeSizeEstimateBefore);
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getAdvisor()->updateNativeSizeEstimate(Reward);
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}
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log(Reward, /*Success=*/true);
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@ -292,10 +296,11 @@ private:
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void recordInliningWithCalleeDeletedImpl() override {
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MLInlineAdvice::recordInliningWithCalleeDeletedImpl();
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getAdvisor()->resetNativeSize(Caller);
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if (!getAdvisor()->isForcedToStop()) {
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int NativeSizeAfter = getAdvisor()->getNativeSizeEstimate(*Caller);
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if (InlineSizeEstimatorAnalysis::isEvaluatorRequested() &&
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!getAdvisor()->isForcedToStop()) {
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int NativeSizeAfter = *getAdvisor()->getNativeSizeEstimate(*Caller);
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int Reward = NativeSizeAfter -
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(CallerSizeEstimateBefore + CalleeSizeEstimateBefore);
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(*CallerSizeEstimateBefore + *CalleeSizeEstimateBefore);
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getAdvisor()->updateNativeSizeEstimate(Reward);
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log(Reward, /*Success=*/true);
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}
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@ -324,8 +329,8 @@ private:
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static const int64_t NoReward = 0;
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TrainingLogger &Logger;
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const size_t CallerSizeEstimateBefore;
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const size_t CalleeSizeEstimateBefore;
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const Optional<size_t> CallerSizeEstimateBefore;
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const Optional<size_t> CalleeSizeEstimateBefore;
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const bool DefaultDecision;
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const bool Mandatory;
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};
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@ -448,9 +453,11 @@ void TrainingLogger::print() {
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writeRawTensorsAsFeatureLists(
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OutFile, TensorSpec::createSpec<int64_t>(DecisionName, {1}),
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Outputs[0].data(), NumberOfRecords);
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writeTensorsAsFeatureLists(OutFile,
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TensorSpec::createSpec<int64_t>(RewardName, {1}),
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Rewards.data(), NumberOfRecords);
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if (InlineSizeEstimatorAnalysis::isEvaluatorRequested())
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writeTensorsAsFeatureLists(OutFile,
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TensorSpec::createSpec<int64_t>(RewardName, {1}),
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Rewards.data(), NumberOfRecords);
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for (size_t I = 1; I < Outputs.size(); ++I)
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writeRawTensorsAsFeatureLists(OutFile, MUTR->outputSpecs()[I],
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@ -479,8 +486,10 @@ DevelopmentModeMLInlineAdvisor::~DevelopmentModeMLInlineAdvisor() {
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Logger->print();
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}
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size_t
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Optional<size_t>
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DevelopmentModeMLInlineAdvisor::getNativeSizeEstimate(const Function &F) const {
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if (!InlineSizeEstimatorAnalysis::isEvaluatorRequested())
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return None;
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auto &R =
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FAM.getResult<InlineSizeEstimatorAnalysis>(const_cast<Function &>(F));
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if (!R) {
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@ -496,6 +505,7 @@ DevelopmentModeMLInlineAdvisor::getMandatoryAdvice(
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CallBase &CB, OptimizationRemarkEmitter &ORE) {
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if (!isLogging())
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return MLInlineAdvisor::getMandatoryAdvice(CB, ORE);
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return std::make_unique<LoggingMLInlineAdvice>(
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/*Advisor=*/this,
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/*CB=*/CB, /*ORE=*/ORE, /*Recommendation=*/true, /*Logger=*/*Logger,
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@ -524,13 +534,15 @@ DevelopmentModeMLInlineAdvisor::getAdviceFromModel(
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}
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size_t DevelopmentModeMLInlineAdvisor::getTotalSizeEstimate() {
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if (!InlineSizeEstimatorAnalysis::isEvaluatorRequested())
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return 0;
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size_t Ret = 0;
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for (auto &F : M) {
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if (F.isDeclaration())
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continue;
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if (isFunctionDeleted(&F))
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continue;
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Ret += getNativeSizeEstimate(F);
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Ret += *getNativeSizeEstimate(F);
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}
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return Ret;
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}
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@ -642,14 +654,6 @@ std::unique_ptr<InlineAdvisor> llvm::getDevelopmentModeAdvisor(
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Module &M, ModuleAnalysisManager &MAM,
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std::function<bool(CallBase &)> GetDefaultAdvice) {
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auto &Ctx = M.getContext();
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if (TrainingLog.empty() !=
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!InlineSizeEstimatorAnalysis::isEvaluatorRequested()) {
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Ctx.emitError("For development mode, if training logs are requested, then "
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"a size estimator must be available; either that, or neither "
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"are specified.");
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return nullptr;
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}
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std::unique_ptr<MLModelRunner> Runner;
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ModelUnderTrainingRunner *MUTRPtr = nullptr;
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bool IsDoingInference = false;
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@ -1,8 +1,10 @@
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; Test that we can produce a log if we have or do not have a model, in development mode.
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; REQUIRES: have_tf_api
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; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=- -ml-inliner-model-under-training=%S/../../../../lib/Analysis/models/inliner -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -S < %s | FileCheck %s
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; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=- -ml-inliner-model-under-training=%S/../../../../lib/Analysis/models/inliner -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -ml-inliner-output-spec-override=%S/Inputs/test_output_spec.json -S < %s | FileCheck %s --check-prefix=EXTRA-OUTPUTS
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; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=- -ml-inliner-model-under-training=%S/../../../../lib/Analysis/models/inliner -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -ml-inliner-output-spec-override=%S/Inputs/test_output_spec.json -S < %s | FileCheck %s --check-prefixes=EXTRA-OUTPUTS,CHECK
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; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=- -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -S < %s | FileCheck %s
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; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=- -ml-inliner-model-under-training=%S/../../../../lib/Analysis/models/inliner -S < %s | FileCheck %s --check-prefix=NOREWARD
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; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=- -S < %s | FileCheck %s --check-prefix=NOREWARD
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target datalayout = "e-m:e-i64:64-f80:128-n8:16:32:64-S128"
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target triple = "x86_64-pc-linux-gnu"
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@ -50,6 +52,7 @@ define dso_local i32 @top() {
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; CHECK-NEXT: feature: { int64_list: { value: [0] } }
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; CHECK-NEXT: }
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; CHECK-NEXT: }
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; NOREWARD-NOT: key: "delta_size" value: {
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; CHECK-NOT: fake_extra_output
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; EXTRA-OUTPUTS: key: "fake_extra_output" value: {
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; EXTRA-OUTPUTS-NEXT: feature: { int64_list: { value: [1] } }
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