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[llvm][NFC] Moved implementation of TrainingLogger outside of its decl

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Also renamed a method - printTensor - to print; and added comments.
This commit is contained in:
Mircea Trofin 2020-08-04 14:32:07 -07:00
parent 15b7474538
commit d0d6d07a22
1 changed files with 54 additions and 41 deletions
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95
lib/Analysis/DevelopmentModeInlineAdvisor.cpp
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@ -71,54 +71,19 @@ struct InlineEvent {
/// lines up with how TF SequenceExample represents it.
class TrainingLogger final {
public:
TrainingLogger() {
for (size_t I = 0; I < NumberOfFeatures; ++I) {
Features.push_back(InlineFeatures());
}
}
TrainingLogger();
/// Log one inlining event.
void logInlineEvent(const InlineEvent &Event,
const MLModelRunner &ModelRunner) {
for (size_t I = 0; I < NumberOfFeatures; ++I) {
Features[I].push_back(ModelRunner.getFeature(I));
}
Decisions.push_back(Event.AdvisedDecision);
Effects.push_back(Event.Effect);
Rewards.push_back(Event.Reward);
DefaultDecisions.push_back(Event.DefaultDecision);
}
const MLModelRunner &ModelRunner);
void printTensor(raw_fd_ostream &OutFile) {
if (DefaultDecisions.empty())
return;
OutFile << "feature_lists: {\n";
for (size_t I = 0; I < Features.size(); I++) {
writeTensor(OutFile, FeatureNameMap.at(I), Features[I]);
}
writeTensor(OutFile, DefaultDecisionName, DefaultDecisions);
writeTensor(OutFile, DecisionName, Decisions);
writeTensor(OutFile, RewardName, Rewards);
OutFile << "}\n";
}
/// Print the stored tensors.
void print(raw_fd_ostream &OutFile);
private:
template <typename T>
void writeTensor(raw_fd_ostream &OutFile, StringRef TensorName,
const std::vector<T> &Tensor) {
OutFile << " feature_list: {\n";
OutFile << " key: "
<< "\"" << TensorName << "\" ";
OutFile << "value: {\n";
for (const auto &Feature : Tensor) {
OutFile << " feature: { int64_list: { value: [" << Feature
<< "] } }\n";
}
OutFile << " }\n";
OutFile << " }\n";
}
const std::vector<T> &Tensor);
std::vector<InlineFeatures> Features;
std::vector<bool> DefaultDecisions;
@ -307,6 +272,54 @@ private:
};
} // namespace
TrainingLogger::TrainingLogger() {
for (size_t I = 0; I < NumberOfFeatures; ++I) {
Features.push_back(InlineFeatures());
}
}
/// Log one inlining event.
void TrainingLogger::logInlineEvent(const InlineEvent &Event,
const MLModelRunner &ModelRunner) {
for (size_t I = 0; I < NumberOfFeatures; ++I) {
Features[I].push_back(ModelRunner.getFeature(I));
}
Decisions.push_back(Event.AdvisedDecision);
Effects.push_back(Event.Effect);
Rewards.push_back(Event.Reward);
DefaultDecisions.push_back(Event.DefaultDecision);
}
void TrainingLogger::print(raw_fd_ostream &OutFile) {
if (DefaultDecisions.empty())
return;
OutFile << "feature_lists: {\n";
for (size_t I = 0; I < Features.size(); I++) {
writeTensor(OutFile, FeatureNameMap.at(I), Features[I]);
}
writeTensor(OutFile, DefaultDecisionName, DefaultDecisions);
writeTensor(OutFile, DecisionName, Decisions);
writeTensor(OutFile, RewardName, Rewards);
OutFile << "}\n";
}
template <typename T>
void TrainingLogger::writeTensor(raw_fd_ostream &OutFile, StringRef TensorName,
const std::vector<T> &Tensor) {
OutFile << " feature_list: {\n";
OutFile << " key: "
<< "\"" << TensorName << "\" ";
OutFile << "value: {\n";
for (const auto &Feature : Tensor) {
OutFile << " feature: { int64_list: { value: [" << Feature
<< "] } }\n";
}
OutFile << " }\n";
OutFile << " }\n";
}
DevelopmentModeMLInlineAdvisor::DevelopmentModeMLInlineAdvisor(
Module &M, ModuleAnalysisManager &MAM,
std::unique_ptr<MLModelRunner> ModelRunner,
@ -324,7 +337,7 @@ DevelopmentModeMLInlineAdvisor::~DevelopmentModeMLInlineAdvisor() {
return;
std::error_code ErrorCode;
raw_fd_ostream OutFile(TrainingLog, ErrorCode);
Logger.printTensor(OutFile);
Logger.print(OutFile);
}
size_t