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[MLInliner] Support training that doesn't require partial rewards

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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 2020-08-24 11:36:22 -07:00
parent cf617b95ee
commit 9c71d4e1d1
2 changed files with 37 additions and 30 deletions
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62
lib/Analysis/DevelopmentModeInlineAdvisor.cpp
View File

@ -232,7 +232,9 @@ public:
size_t getTotalSizeEstimate();
virtual ~DevelopmentModeMLInlineAdvisor();
void updateNativeSizeEstimate(int64_t Change) { CurrentNativeSize += Change; }
void updateNativeSizeEstimate(int64_t Change) {
*CurrentNativeSize += Change;
}
void resetNativeSize(Function *F) {
FAM.invalidate<InlineSizeEstimatorAnalysis>(*F);
}
@ -242,7 +244,7 @@ public:
std::unique_ptr<MLInlineAdvice>
getAdviceFromModel(CallBase &CB, OptimizationRemarkEmitter &ORE) override;
size_t getNativeSizeEstimate(const Function &F) const;
Optional<size_t> getNativeSizeEstimate(const Function &F) const;
private:
bool isLogging() const { return !!Logger; }
@ -251,8 +253,8 @@ private:
const bool IsDoingInference;
std::unique_ptr<TrainingLogger> Logger;
const int32_t InitialNativeSize;
int32_t CurrentNativeSize = 0;
const Optional<int32_t> InitialNativeSize;
Optional<int32_t> CurrentNativeSize;
};
/// A variant of MLInlineAdvice that tracks all non-trivial inlining
@ -261,9 +263,10 @@ class LoggingMLInlineAdvice : public MLInlineAdvice {
public:
LoggingMLInlineAdvice(DevelopmentModeMLInlineAdvisor *Advisor, CallBase &CB,
OptimizationRemarkEmitter &ORE, bool Recommendation,
TrainingLogger &Logger, size_t CallerSizeEstimateBefore,
size_t CalleeSizeEstimateBefore, bool DefaultDecision,
bool Mandatory = false)
TrainingLogger &Logger,
Optional<size_t> CallerSizeEstimateBefore,
Optional<size_t> CalleeSizeEstimateBefore,
bool DefaultDecision, bool Mandatory = false)
: MLInlineAdvice(Advisor, CB, ORE, Recommendation), Logger(Logger),
CallerSizeEstimateBefore(CallerSizeEstimateBefore),
CalleeSizeEstimateBefore(CalleeSizeEstimateBefore),
@ -279,11 +282,12 @@ private:
MLInlineAdvice::recordInliningImpl();
getAdvisor()->resetNativeSize(Caller);
int Reward = std::numeric_limits<int>::max();
if (!getAdvisor()->isForcedToStop()) {
int NativeSizeAfter = getAdvisor()->getNativeSizeEstimate(*Caller) +
CalleeSizeEstimateBefore;
if (InlineSizeEstimatorAnalysis::isEvaluatorRequested() &&
!getAdvisor()->isForcedToStop()) {
int NativeSizeAfter = *getAdvisor()->getNativeSizeEstimate(*Caller) +
*CalleeSizeEstimateBefore;
Reward = NativeSizeAfter -
(CallerSizeEstimateBefore + CalleeSizeEstimateBefore);
(*CallerSizeEstimateBefore + *CalleeSizeEstimateBefore);
getAdvisor()->updateNativeSizeEstimate(Reward);
}
log(Reward, /*Success=*/true);
@ -292,10 +296,11 @@ private:
void recordInliningWithCalleeDeletedImpl() override {
MLInlineAdvice::recordInliningWithCalleeDeletedImpl();
getAdvisor()->resetNativeSize(Caller);
if (!getAdvisor()->isForcedToStop()) {
int NativeSizeAfter = getAdvisor()->getNativeSizeEstimate(*Caller);
if (InlineSizeEstimatorAnalysis::isEvaluatorRequested() &&
!getAdvisor()->isForcedToStop()) {
int NativeSizeAfter = *getAdvisor()->getNativeSizeEstimate(*Caller);
int Reward = NativeSizeAfter -
(CallerSizeEstimateBefore + CalleeSizeEstimateBefore);
(*CallerSizeEstimateBefore + *CalleeSizeEstimateBefore);
getAdvisor()->updateNativeSizeEstimate(Reward);
log(Reward, /*Success=*/true);
}
@ -324,8 +329,8 @@ private:
static const int64_t NoReward = 0;
TrainingLogger &Logger;
const size_t CallerSizeEstimateBefore;
const size_t CalleeSizeEstimateBefore;
const Optional<size_t> CallerSizeEstimateBefore;
const Optional<size_t> CalleeSizeEstimateBefore;
const bool DefaultDecision;
const bool Mandatory;
};
@ -448,9 +453,11 @@ void TrainingLogger::print() {
writeRawTensorsAsFeatureLists(
OutFile, TensorSpec::createSpec<int64_t>(DecisionName, {1}),
Outputs[0].data(), NumberOfRecords);
writeTensorsAsFeatureLists(OutFile,
TensorSpec::createSpec<int64_t>(RewardName, {1}),
Rewards.data(), NumberOfRecords);
if (InlineSizeEstimatorAnalysis::isEvaluatorRequested())
writeTensorsAsFeatureLists(OutFile,
TensorSpec::createSpec<int64_t>(RewardName, {1}),
Rewards.data(), NumberOfRecords);
for (size_t I = 1; I < Outputs.size(); ++I)
writeRawTensorsAsFeatureLists(OutFile, MUTR->outputSpecs()[I],
@ -479,8 +486,10 @@ DevelopmentModeMLInlineAdvisor::~DevelopmentModeMLInlineAdvisor() {
Logger->print();
}
size_t
Optional<size_t>
DevelopmentModeMLInlineAdvisor::getNativeSizeEstimate(const Function &F) const {
if (!InlineSizeEstimatorAnalysis::isEvaluatorRequested())
return None;
auto &R =
FAM.getResult<InlineSizeEstimatorAnalysis>(const_cast<Function &>(F));
if (!R) {
@ -496,6 +505,7 @@ DevelopmentModeMLInlineAdvisor::getMandatoryAdvice(
CallBase &CB, OptimizationRemarkEmitter &ORE) {
if (!isLogging())
return MLInlineAdvisor::getMandatoryAdvice(CB, ORE);
return std::make_unique<LoggingMLInlineAdvice>(
/*Advisor=*/this,
/*CB=*/CB, /*ORE=*/ORE, /*Recommendation=*/true, /*Logger=*/*Logger,
@ -524,13 +534,15 @@ DevelopmentModeMLInlineAdvisor::getAdviceFromModel(
}
size_t DevelopmentModeMLInlineAdvisor::getTotalSizeEstimate() {
if (!InlineSizeEstimatorAnalysis::isEvaluatorRequested())
return 0;
size_t Ret = 0;
for (auto &F : M) {
if (F.isDeclaration())
continue;
if (isFunctionDeleted(&F))
continue;
Ret += getNativeSizeEstimate(F);
Ret += *getNativeSizeEstimate(F);
}
return Ret;
}
@ -642,14 +654,6 @@ std::unique_ptr<InlineAdvisor> llvm::getDevelopmentModeAdvisor(
Module &M, ModuleAnalysisManager &MAM,
std::function<bool(CallBase &)> GetDefaultAdvice) {
auto &Ctx = M.getContext();
if (TrainingLog.empty() !=
!InlineSizeEstimatorAnalysis::isEvaluatorRequested()) {
Ctx.emitError("For development mode, if training logs are requested, then "
"a size estimator must be available; either that, or neither "
"are specified.");
return nullptr;
}
std::unique_ptr<MLModelRunner> Runner;
ModelUnderTrainingRunner *MUTRPtr = nullptr;
bool IsDoingInference = false;

5
test/Transforms/Inline/ML/development-training-log.ll
View File

@ -1,8 +1,10 @@
; Test that we can produce a log if we have or do not have a model, in development mode.
; REQUIRES: have_tf_api
; 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
; 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
; 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
; 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
; 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
; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=- -S < %s | FileCheck %s --check-prefix=NOREWARD
target datalayout = "e-m:e-i64:64-f80:128-n8:16:32:64-S128"
target triple = "x86_64-pc-linux-gnu"
@ -50,6 +52,7 @@ define dso_local i32 @top() {
; CHECK-NEXT: feature: { int64_list: { value: [0] } }
; CHECK-NEXT: }
; CHECK-NEXT: }
; NOREWARD-NOT: key: "delta_size" value: {
; CHECK-NOT: fake_extra_output
; EXTRA-OUTPUTS: key: "fake_extra_output" value: {
; EXTRA-OUTPUTS-NEXT: feature: { int64_list: { value: [1] } }