blob: e138e82c8b05bafd6fa40dbb53fd9c2a57aa1594 [file] [log] [blame]
//===- DevelopmentModeInlineAdvisor.cpp - runtime-loadable model runner --===//
// The LLVM Compiler Infrastructure
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
// This file implements a model runner using Tensorflow C APIs, allowing the
// loading of a model from a command line option.
#include "llvm/Config/config.h"
#if defined(LLVM_HAVE_TF_API)
#include "llvm/Analysis/CallGraph.h"
#include "llvm/Analysis/InlineSizeEstimatorAnalysis.h"
#include "llvm/Analysis/MLInlineAdvisor.h"
#include "llvm/Analysis/Utils/TFUtils.h"
#include "llvm/IR/LLVMContext.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/ManagedStatic.h"
#include <vector>
using namespace llvm;
static cl::opt<std::string> TrainingLog(
"training-log", cl::Hidden,
cl::desc("Path where the development - mode inlining log is saved."));
static cl::opt<std::string> TFModelUnderTrainingPath(
"ml-inliner-model-under-training", cl::Hidden,
cl::desc(R"(Path to SavedModel from the previous training iteration.
The directory is also expected to contain a JSON specification of the
outputs expected to be logged, where the first entry must be the
inlining decision. The file containing the specification should be
called output_spec.json. The expected JSON value is an array of
dictionaries. Each dictionary should have 2 keys:
- "tensor_spec, followed by the TensorSpec description of the
output; and
- "logging_name", a string indicating the name to use when
logging the output values.
"logging_name" : "some_name",
"tensor_spec" : {
"name" : "model_name",
"port" : 0,
"shape" : [2, 3],
"type" : "float"
The first value must always correspond to the decision.)"));
static cl::opt<std::string> TFOutputSpecOverride(
"ml-inliner-output-spec-override", cl::Hidden,
cl::desc("Override the path to the output spec json file. See "
"-ml-inliner-model-under-training documentation for the "
"specification of that file."));
static cl::opt<std::string> TFFeedPrefix("ml-inliner-trained-model-feed-prefix",
cl::Hidden, cl::init("action_"),
cl::desc("Prefix for feature names."));
namespace {
/// An InlineEvent, used by TrainingLogger.
struct InlineEvent {
/// What the default policy's decision would have been.
int64_t DefaultDecision = 0;
/// What we advised. When training off the default policy, this is the same as
/// DefaultDecision.
int64_t AdvisedDecision = 0;
/// What actually happened. This would be 'false' in the case of an inline
/// error, even if AdvisedDecision were true, otherwise it agrees with
/// AdvisedDecision.
bool Effect = false;
/// What the change in size was: size_after - size_before
int64_t Reward = 0;
/// Collect data we may use for training a model, and write it as a textual
/// Tensorflow SequenceExample
/// (
/// protobuf (
/// Because this is a protobuf, we cannot just stream the events as they come.
/// Internally, TrainingLogger stores data in column-major format, because that
/// lines up with how TF SequenceExample represents it.
class ModelUnderTrainingRunner;
class TrainingLogger final {
TrainingLogger(StringRef LogFileName, const ModelUnderTrainingRunner *MUTR);
/// Log one inlining event.
void logInlineEvent(const InlineEvent &Event,
const MLModelRunner &ModelRunner);
/// Print the stored tensors.
void print();
StringRef LogFileName;
const ModelUnderTrainingRunner *const MUTR;
std::unique_ptr<Logger> L;
std::vector<bool> Effects;
/// There's at least one output. We'll set this to a different value if MUTR
/// is avaliable.
size_t OutputCount = 1;
/// Set these 2 clearly OOB, to make sure we set them later.
size_t DefaultDecisionPos = std::numeric_limits<size_t>::max();
size_t DecisionPos = std::numeric_limits<size_t>::max();
/// An extension of the MLInlineAdvisor for the 'development' mode, targeting
/// the offline training scenario. Note that training happens outside of the
/// compiler, this facility is concerned with producing training data ("logs").
/// This InlineAdvisor can operate in the following modes:
/// 1) collect logs for the default policy. This is useful for bootstrapping
/// training, which will be considerably faster by starting from a reasonable
/// policy.
/// 2) collect logs for the ML policy, using a model from a previous
/// training. Potentially, that model uses internally some small random
/// perturbation of its weights, to induce exploration (setting this up is the
/// responsibility of the training algorithm). The logs would then be used to
/// retrain and improve on this model.
/// 3) use the provided model, with no logging. This is useful for end to end
/// validation - the model, in this case, is a release candidate and shouldn't
/// have random perturbations. It is a convenience feature: rather than needing
/// to take the release candidate model and compile it in 'release' mode,
/// validate it, then potentially discard it, it's easier to just pass the model
/// to the compiler, albeit compilation would be slower, as a one-off. Once the
/// model behaves satisfactorily, it can be compiled AOT, for efficiency, in
/// release mode. The expectation is that a well-trained model provides a good
/// policy over a sufficiently diverse codebase, over many changes (i.e.
/// training happens seldom).
class DevelopmentModeMLInlineAdvisor : public MLInlineAdvisor {
Module &M, ModuleAnalysisManager &MAM,
std::unique_ptr<MLModelRunner> ModelRunner,
std::function<bool(CallBase &)> GetDefaultAdvice, bool IsDoingInference,
std::unique_ptr<TrainingLogger> Logger);
size_t getTotalSizeEstimate();
virtual ~DevelopmentModeMLInlineAdvisor();
void updateNativeSizeEstimate(int64_t Change) {
*CurrentNativeSize += Change;
void resetNativeSize(Function *F) {
getAdviceFromModel(CallBase &CB, OptimizationRemarkEmitter &ORE) override;
Optional<size_t> getNativeSizeEstimate(const Function &F) const;
bool isLogging() const { return !!Logger; }
std::unique_ptr<MLInlineAdvice> getMandatoryAdviceImpl(CallBase &CB) override;
std::function<bool(CallBase &)> GetDefaultAdvice;
const bool IsDoingInference;
std::unique_ptr<TrainingLogger> Logger;
const Optional<int32_t> InitialNativeSize;
Optional<int32_t> CurrentNativeSize;
/// A variant of MLInlineAdvice that tracks all non-trivial inlining
/// decisions, for training/logging.
class LoggingMLInlineAdvice : public MLInlineAdvice {
LoggingMLInlineAdvice(DevelopmentModeMLInlineAdvisor *Advisor, CallBase &CB,
OptimizationRemarkEmitter &ORE, bool Recommendation,
TrainingLogger &Logger,
Optional<size_t> CallerSizeEstimateBefore,
Optional<size_t> CalleeSizeEstimateBefore,
bool DefaultDecision, bool Mandatory = false)
: MLInlineAdvice(Advisor, CB, ORE, Recommendation), Logger(Logger),
DefaultDecision(DefaultDecision), Mandatory(Mandatory) {}
virtual ~LoggingMLInlineAdvice() = default;
DevelopmentModeMLInlineAdvisor *getAdvisor() const {
return static_cast<DevelopmentModeMLInlineAdvisor *>(Advisor);
void recordInliningImpl() override {
int Reward = std::numeric_limits<int>::max();
if (InlineSizeEstimatorAnalysis::isEvaluatorRequested() &&
!getAdvisor()->isForcedToStop()) {
int NativeSizeAfter = *getAdvisor()->getNativeSizeEstimate(*Caller) +
Reward = NativeSizeAfter -
(*CallerSizeEstimateBefore + *CalleeSizeEstimateBefore);
log(Reward, /*Success=*/true);
void recordInliningWithCalleeDeletedImpl() override {
if (InlineSizeEstimatorAnalysis::isEvaluatorRequested() &&
!getAdvisor()->isForcedToStop()) {
int NativeSizeAfter = *getAdvisor()->getNativeSizeEstimate(*Caller);
int Reward = NativeSizeAfter -
(*CallerSizeEstimateBefore + *CalleeSizeEstimateBefore);
log(Reward, /*Success=*/true);
void recordUnsuccessfulInliningImpl(const InlineResult &Result) override {
log(NoReward, /*Success=*/false);
void recordUnattemptedInliningImpl() override {
log(NoReward, /*Success=*/false);
void log(int64_t Reward, bool Success) {
if (Mandatory)
InlineEvent Event;
Event.AdvisedDecision = isInliningRecommended();
Event.DefaultDecision = DefaultDecision;
Event.Effect = Success;
Event.Reward = Reward;
Logger.logInlineEvent(Event, getAdvisor()->getModelRunner());
static const int64_t NoReward = 0;
TrainingLogger &Logger;
const Optional<size_t> CallerSizeEstimateBefore;
const Optional<size_t> CalleeSizeEstimateBefore;
const int64_t DefaultDecision;
const int64_t Mandatory;
/// A pseudo model runner. We use it to store feature values when collecting
/// logs for the default policy, but never ask it to 'run'.
class NoInferenceModelRunner : public MLModelRunner {
NoInferenceModelRunner(LLVMContext &Ctx)
: MLModelRunner(Ctx), Features(NumberOfFeatures) {}
void setFeature(FeatureIndex Index, int64_t Value) override {
Features[static_cast<int>(Index)] = Value;
int64_t getFeature(int Index) const override { return Features[Index]; }
bool run() override {
llvm_unreachable("We shouldn't call run on this model runner.");
InlineFeatures Features;
/// ModelUnderTrainingRunner - training mode implementation. It uses TF C APIs
/// to dynamically load and evaluate a TF SavedModel
/// ( Runtime performance is
/// sacrificed for ease of use while training.
class ModelUnderTrainingRunner final : public MLModelRunner {
ModelUnderTrainingRunner(LLVMContext &Ctx, const std::string &ModelPath);
bool run() override;
// Disallows copy and assign.
ModelUnderTrainingRunner(const ModelUnderTrainingRunner &) = delete;
ModelUnderTrainingRunner &
operator=(const ModelUnderTrainingRunner &) = delete;
void setFeature(FeatureIndex Index, int64_t Value) override;
int64_t getFeature(int Index) const override;
bool isValid() const { return !!Evaluator; }
const std::vector<LoggedFeatureSpec> &outputLoggedFeatureSpecs() const {
return OutputSpecs;
const Optional<TFModelEvaluator::EvaluationResult> &
lastEvaluationResult() const {
return LastEvaluationResult;
std::unique_ptr<TFModelEvaluator> Evaluator;
std::vector<LoggedFeatureSpec> OutputSpecs;
Optional<TFModelEvaluator::EvaluationResult> LastEvaluationResult;
// The training framework needs some additional features.
const std::vector<TensorSpec> TrainingOnlyFeatures{
TensorSpec::createSpec<int64_t>(TFFeedPrefix + "inlining_default", {1}),
TensorSpec::createSpec<float>(TFFeedPrefix + "discount", {1}),
TensorSpec::createSpec<float>(TFFeedPrefix + "reward", {1}),
TensorSpec::createSpec<int32_t>(TFFeedPrefix + "step_type", {1})};
} // namespace
TrainingLogger::TrainingLogger(StringRef LogFileName,
const ModelUnderTrainingRunner *MUTR)
: LogFileName(LogFileName), MUTR(MUTR) {
// The first output is the inlining decision.
if (MUTR)
OutputCount = MUTR->outputLoggedFeatureSpecs().size();
std::vector<LoggedFeatureSpec> FT;
for (size_t I = 0; I < NumberOfFeatures; ++I)
{TensorSpec::createSpec<int64_t>(, {1}), None});
if (MUTR && MUTR->outputLoggedFeatureSpecs().size() > 1)
append_range(FT, drop_begin(MUTR->outputLoggedFeatureSpecs()));
DefaultDecisionPos = FT.size();
{TensorSpec::createSpec<int64_t>(DefaultDecisionName, {1}), None});
DecisionPos = FT.size();
FT.push_back({TensorSpec::createSpec<int64_t>(DecisionName, {1}), None});
L = std::make_unique<Logger>(
FT, TensorSpec::createSpec<int64_t>(RewardName, {1}),
/// Log one inlining event.
void TrainingLogger::logInlineEvent(const InlineEvent &Event,
const MLModelRunner &ModelRunner) {
size_t CurrentFeature = 0;
for (; CurrentFeature < NumberOfFeatures; ++CurrentFeature) {
int64_t F = ModelRunner.getFeature(CurrentFeature);
L->logTensorValue(CurrentFeature, &F);
for (size_t I = 1; I < OutputCount; ++I) {
const auto &Result = *MUTR->lastEvaluationResult();
auto &Spec = MUTR->outputLoggedFeatureSpecs()[I].Spec;
const char *RawData =
reinterpret_cast<const char *>(Result.getUntypedTensorValue(I));
L->logTensorValue(CurrentFeature, RawData,
Spec.getElementCount() * Spec.getElementByteSize());
assert(CurrentFeature == DefaultDecisionPos);
L->logTensorValue(DefaultDecisionPos, &Event.DefaultDecision);
L->logTensorValue(DecisionPos, &Event.AdvisedDecision);
if (InlineSizeEstimatorAnalysis::isEvaluatorRequested())
// For debugging / later use
void TrainingLogger::print() {
std::error_code EC;
raw_fd_ostream OutFile(LogFileName, EC);
Module &M, ModuleAnalysisManager &MAM,
std::unique_ptr<MLModelRunner> ModelRunner,
std::function<bool(CallBase &)> GetDefaultAdvice, bool IsDoingInference,
std::unique_ptr<TrainingLogger> Logger)
: MLInlineAdvisor(M, MAM, std::move(ModelRunner)),
GetDefaultAdvice(GetDefaultAdvice), IsDoingInference(IsDoingInference),
InitialNativeSize(isLogging() ? getTotalSizeEstimate() : 0),
CurrentNativeSize(InitialNativeSize) {
// We cannot have the case of neither inference nor logging.
assert(IsDoingInference || isLogging());
DevelopmentModeMLInlineAdvisor::~DevelopmentModeMLInlineAdvisor() {
if (isLogging())
DevelopmentModeMLInlineAdvisor::getNativeSizeEstimate(const Function &F) const {
if (!InlineSizeEstimatorAnalysis::isEvaluatorRequested())
return None;
auto &R =
FAM.getResult<InlineSizeEstimatorAnalysis>(const_cast<Function &>(F));
if (!R) {
"Native size estimator is not present.");
return 0;
return *R;
DevelopmentModeMLInlineAdvisor::getMandatoryAdviceImpl(CallBase &CB) {
return std::make_unique<LoggingMLInlineAdvice>(
/*CB=*/CB, /*ORE=*/getCallerORE(CB), /*Recommendation=*/true,
/*DefaultDecision=*/true, /*Mandatory*/ true);
CallBase &CB, OptimizationRemarkEmitter &ORE) {
if (IsDoingInference && !isLogging())
return MLInlineAdvisor::getAdviceFromModel(CB, ORE);
bool DefaultAdvice = GetDefaultAdvice(CB);
auto Recommendation = IsDoingInference ? ModelRunner->run() : DefaultAdvice;
return std::make_unique<LoggingMLInlineAdvice>(
/*CB=*/CB, /*ORE=*/ORE, /*Recommendation=*/Recommendation,
size_t DevelopmentModeMLInlineAdvisor::getTotalSizeEstimate() {
if (!InlineSizeEstimatorAnalysis::isEvaluatorRequested())
return 0;
size_t Ret = 0;
for (auto &F : M) {
if (F.isDeclaration())
if (isFunctionDeleted(&F))
Ret += *getNativeSizeEstimate(F);
return Ret;
ModelUnderTrainingRunner::ModelUnderTrainingRunner(LLVMContext &Ctx,
const std::string &ModelPath)
: MLModelRunner(Ctx) {
std::vector<TensorSpec> InputSpecs;
for (size_t I = 0; I < NumberOfFeatures; ++I)
TensorSpec::createSpec<int64_t>(TFFeedPrefix + FeatureNameMap[I], {1}));
append_range(InputSpecs, TrainingOnlyFeatures);
if (auto MaybeOutSpecs =
loadOutputSpecs(Ctx, DecisionName, ModelPath, TFOutputSpecOverride))
OutputSpecs = std::move(*MaybeOutSpecs);
Evaluator = std::make_unique<TFModelEvaluator>(
ModelPath, InputSpecs, [&](size_t I) { return OutputSpecs[I].Spec; },
if (!Evaluator || !Evaluator->isValid()) {
Ctx.emitError("Failed to create inliner saved model evaluator");
bool ModelUnderTrainingRunner::run() {
LastEvaluationResult = Evaluator->evaluate();
if (!LastEvaluationResult.hasValue()) {
Ctx.emitError("Error evaluating model.");
return false;
int64_t Decision = *LastEvaluationResult->getTensorValue<int64_t>(0);
return static_cast<bool>(Decision);
int64_t ModelUnderTrainingRunner::getFeature(int Index) const {
return *Evaluator->getInput<int64_t>(Index);
void ModelUnderTrainingRunner::setFeature(FeatureIndex Index, int64_t Value) {
size_t NumericIndex = static_cast<size_t>(Index);
*(Evaluator->getInput<int64_t>(NumericIndex)) = Value;
std::unique_ptr<InlineAdvisor> llvm::getDevelopmentModeAdvisor(
Module &M, ModuleAnalysisManager &MAM,
std::function<bool(CallBase &)> GetDefaultAdvice) {
auto &Ctx = M.getContext();
std::unique_ptr<MLModelRunner> Runner;
ModelUnderTrainingRunner *MUTRPtr = nullptr;
bool IsDoingInference = false;
if (TFModelUnderTrainingPath.empty())
Runner.reset(new NoInferenceModelRunner(Ctx));
else {
auto MUTR = std::make_unique<ModelUnderTrainingRunner>(
Ctx, TFModelUnderTrainingPath);
if (!MUTR || !MUTR->isValid()) {
Ctx.emitError("Could not load the policy model from the provided path");
return nullptr;
IsDoingInference = true;
MUTRPtr = MUTR.get();
Runner = std::move(MUTR);
std::unique_ptr<TrainingLogger> Logger;
if (!TrainingLog.empty())
Logger = std::make_unique<TrainingLogger>(TrainingLog, MUTRPtr);
return std::make_unique<DevelopmentModeMLInlineAdvisor>(
M, MAM, std::move(Runner), GetDefaultAdvice, IsDoingInference,
#endif // defined(LLVM_HAVE_TF_API)