blob: 456d58660680d7daf3bf7a43185b9e475bae9861 [file] [log] [blame]
//===- DevelopmentModeInlineAdvisor.cpp - runtime-loadable model runner --===//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// This file implements a model runner using Tensorflow C APIs, allowing the
// loading of a model from a command line option.
#include "llvm/Analysis/TensorSpec.h"
#include "llvm/Config/config.h"
#if defined(LLVM_HAVE_TFLITE)
#include "llvm/ADT/BitVector.h"
#include "llvm/Analysis/CallGraph.h"
#include "llvm/Analysis/InlineSizeEstimatorAnalysis.h"
#include "llvm/Analysis/MLInlineAdvisor.h"
#include "llvm/Analysis/ModelUnderTrainingRunner.h"
#include "llvm/Analysis/NoInferenceModelRunner.h"
#include "llvm/Analysis/Utils/TFUtils.h"
#include "llvm/Analysis/Utils/TrainingLogger.h"
#include "llvm/IR/LLVMContext.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/ManagedStatic.h"
#include <vector>
#include <optional>
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.
class TrainingLogger final {
TrainingLogger(StringRef LogFileName, const ModelUnderTrainingRunner *MUTR);
/// Log one inlining event.
void logInlineEvent(const InlineEvent &Event,
const MLModelRunner &ModelRunner);
StringRef LogFileName;
const ModelUnderTrainingRunner *const MUTR;
std::unique_ptr<Logger> L;
BitVector Effects;
/// 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,
std::unique_ptr<TrainingLogger> Logger);
size_t getTotalSizeEstimate();
void updateNativeSizeEstimate(int64_t Change) {
*CurrentNativeSize += Change;
void resetNativeSize(Function *F) {
PreservedAnalyses PA = PreservedAnalyses::all();
FAM.invalidate(*F, PA);
getAdviceFromModel(CallBase &CB, OptimizationRemarkEmitter &ORE) override;
std::optional<size_t> getNativeSizeEstimate(const Function &F) const;
bool isLogging() const { return !!Logger; }
std::unique_ptr<MLInlineAdvice> getMandatoryAdviceImpl(CallBase &CB) override;
const bool IsDoingInference;
std::unique_ptr<TrainingLogger> Logger;
const std::optional<int32_t> InitialNativeSize;
std::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,
std::optional<size_t> CallerSizeEstimateBefore,
std::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);
} else {
log(NoReward, /*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 std::optional<size_t> CallerSizeEstimateBefore;
const std::optional<size_t> CalleeSizeEstimateBefore;
const int64_t DefaultDecision;
const int64_t Mandatory;
static 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})};
static const std::vector<TensorSpec> getInputFeatures() {
std::vector<TensorSpec> InputSpecs;
for (size_t I = 0; I < NumberOfFeatures; ++I)
TFFeedPrefix + FeatureMap[I].name(), FeatureMap[I].shape()));
append_range(InputSpecs, TrainingOnlyFeatures);
return InputSpecs;
} // namespace
TrainingLogger::TrainingLogger(StringRef LogFileName,
const ModelUnderTrainingRunner *MUTR)
: LogFileName(LogFileName), MUTR(MUTR) {
// The first output is the inlining decision.
std::vector<TensorSpec> FT(FeatureMap.begin(), FeatureMap.end());
if (MUTR)
append_range(FT, MUTR->extraOutputsForLoggingSpecs());
DefaultDecisionPos = FT.size();
DecisionPos = FT.size();
std::error_code EC;
auto OS = std::make_unique<raw_fd_ostream>(TrainingLog, EC);
if (EC)
dbgs() << (EC.message() + ":" + TrainingLog);
L = std::make_unique<Logger>(
std::move(OS), 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)
reinterpret_cast<const char *>(
if (MUTR)
for (size_t I = 0; I < MUTR->extraOutputsForLoggingSpecs().size(); ++I) {
const char *RawData =
reinterpret_cast<const char *>(MUTR->getUntypedExtraOutputValue(I));
L->logTensorValue(CurrentFeature, RawData);
assert(CurrentFeature == DefaultDecisionPos);
reinterpret_cast<const char *>(&Event.DefaultDecision));
reinterpret_cast<const char *>(&Event.AdvisedDecision));
if (InlineSizeEstimatorAnalysis::isEvaluatorRequested())
// For debugging / later use
Module &M, ModuleAnalysisManager &MAM,
std::unique_ptr<MLModelRunner> ModelRunner,
std::function<bool(CallBase &)> GetDefaultAdvice,
std::unique_ptr<TrainingLogger> Logger)
: MLInlineAdvisor(M, MAM, std::move(ModelRunner), GetDefaultAdvice),
InitialNativeSize(isLogging() ? getTotalSizeEstimate() : 0),
CurrentNativeSize(InitialNativeSize) {
// We cannot have the case of neither inference nor logging.
assert(IsDoingInference || isLogging());
DevelopmentModeMLInlineAdvisor::getNativeSizeEstimate(const Function &F) const {
if (!InlineSizeEstimatorAnalysis::isEvaluatorRequested())
return std::nullopt;
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 ? static_cast<bool>(ModelRunner->evaluate<int64_t>())
: 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())
Ret += *getNativeSizeEstimate(F);
return Ret;
std::unique_ptr<InlineAdvisor> llvm::getDevelopmentModeAdvisor(
Module &M, ModuleAnalysisManager &MAM,
std::function<bool(CallBase &)> GetDefaultAdvice) {
auto &Ctx = M.getContext();
std::unique_ptr<MLModelRunner> Runner;
if (TFModelUnderTrainingPath.empty())
Runner.reset(new NoInferenceModelRunner(Ctx, getInputFeatures()));
Runner = ModelUnderTrainingRunner::createAndEnsureValid(
Ctx, TFModelUnderTrainingPath, DecisionName, getInputFeatures(),
if (!Runner)
return nullptr;
std::unique_ptr<TrainingLogger> Logger;
if (!TrainingLog.empty())
Logger = std::make_unique<TrainingLogger>(
TrainingLog, dyn_cast<ModelUnderTrainingRunner>(Runner.get()));
return std::make_unique<DevelopmentModeMLInlineAdvisor>(
M, MAM, std::move(Runner), GetDefaultAdvice, std::move(Logger));
#endif // defined(LLVM_HAVE_TFLITE)