lib/Analysis/TrainingLogger.cpp - llvm-project/llvm - Git at Google

 //===- TrainingLogger.cpp - mlgo feature/reward logging -------------------===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 //
 // This file implements logging infrastructure for extracting features and
 // rewards for mlgo policy training.
 //
 //===----------------------------------------------------------------------===//
 #include "llvm/Config/config.h"
 #if defined(LLVM_HAVE_TF_API)

 #include "llvm/ADT/Twine.h"
 #include "llvm/Analysis/Utils/TrainingLogger.h"
 #include "llvm/Support/Base64.h"
 #include "llvm/Support/CommandLine.h"
 #include "llvm/Support/Debug.h"
 #include "llvm/Support/JSON.h"
 #include "llvm/Support/MemoryBuffer.h"
 #include "llvm/Support/Path.h"
 #include "llvm/Support/raw_ostream.h"

 #include "google/protobuf/struct.pb.h"
 #include "google/protobuf/text_format.h"
 #include "tensorflow/core/example/example.pb.h"
 #include <cassert>
 #include <numeric>

 using namespace llvm;

 using google::protobuf::Message;
 using google::protobuf::TextFormat;

 static cl::opt<bool>
     ProtobufTextMode("tfutils-text-log", cl::init(false), cl::Hidden,
                      cl::desc("Output textual (human-readable) protobuf."));

 namespace {

 void serialize(const Message &SE, std::string *OutStr) {
   if (ProtobufTextMode) {
     TextFormat::PrintToString(SE, OutStr);
   } else {
     *OutStr = SE.SerializeAsString();
   }
 }
 } // namespace

 namespace llvm {

 class LoggerDataImpl {
   const std::vector<LoggedFeatureSpec> LoggedFeatureSpecs;
   const TensorSpec RewardSpec;
   const bool IncludeReward;

   std::vector<tensorflow::FeatureList> FeatureLists;
   tensorflow::FeatureList Reward;

   bool isSelfConsistent(const tensorflow::SequenceExample &SE,
                         size_t NrRecords) const {
     bool Ret = true;
     for (const auto &TSpecs : LoggedFeatureSpecs) {
       const auto &Name = TSpecs.getLoggingName();
       const auto &FL = SE.feature_lists().feature_list().at(Name).feature();
       if (NrRecords != static_cast<size_t>(FL.size())) {
         dbgs() << "[TF-UTILS]: " << Name << " has missing records. Expected "
                << NrRecords << " got " << FL.size() << "\n";
         Ret = false;
       }
     }
     if (IncludeReward && static_cast<size_t>(SE.feature_lists()
                                                  .feature_list()
                                                  .at(RewardSpec.name())
                                                  .feature()
                                                  .size()) != NrRecords) {
       dbgs() << "[TF-UTILS]: reward is missing records.\n";
       Ret = false;
     }
     return Ret;
   }

   void transferLog(tensorflow::SequenceExample &SE) {
     auto *FL = SE.mutable_feature_lists()->mutable_feature_list();
     if (IncludeReward)
       (*FL)[RewardSpec.name()] = std::move(Reward);
     assert(FeatureLists.size() == LoggedFeatureSpecs.size());
     for (size_t I = 0; I < FeatureLists.size(); ++I) {
       const auto &LFS = LoggedFeatureSpecs[I];
       (*FL)[LFS.getLoggingName()] = std::move(FeatureLists[I]);
     }
   }

 public:
   LoggerDataImpl(const std::vector<LoggedFeatureSpec> &LoggedSpecs,
                  const TensorSpec &RewardSpec, bool IncludeReward)
       : LoggedFeatureSpecs(LoggedSpecs), RewardSpec(RewardSpec),
         IncludeReward(IncludeReward), FeatureLists(LoggedFeatureSpecs.size()) {}

   // flush the logged info to a stream and clear the log contents.
   void flush(std::string *Str) {
     size_t NrRecords = getNrRecords();
     (void)NrRecords;
     tensorflow::SequenceExample SE;
     transferLog(SE);
     assert(isSelfConsistent(SE, NrRecords));
     serialize(SE, Str);
   }

   char *addNewTensor(size_t FeatureID) {
     const auto &Spec = LoggedFeatureSpecs[FeatureID].Spec;
     if (Spec.isElementType<float>()) {
       auto *RF = FeatureLists[FeatureID]
                      .add_feature()
                      ->mutable_float_list()
                      ->mutable_value();
       RF->Resize(Spec.getElementCount(), 0.0);
       return reinterpret_cast<char *>(RF->mutable_data());
     } else if (Spec.isElementType<int32_t>() || Spec.isElementType<int64_t>()) {
       auto *RF = FeatureLists[FeatureID]
                      .add_feature()
                      ->mutable_int64_list()
                      ->mutable_value();
       RF->Resize(Spec.getElementCount(), 0);
       return reinterpret_cast<char *>(RF->mutable_data());
     }
     llvm_unreachable("Unsupported tensor type.");
   }

   template <typename T> void logReward(T Value) {
     assert(IncludeReward);
     if (RewardSpec.isElementType<float>())
       Reward.add_feature()->mutable_float_list()->add_value(Value);
     else if (RewardSpec.isElementType<int32_t>() ||
              RewardSpec.isElementType<int64_t>())
       Reward.add_feature()->mutable_int64_list()->add_value(Value);
     else
       llvm_unreachable("Unsupported tensor type.");
   }

   size_t getNrRecords() const {
     return FeatureLists.empty() ? 0 : FeatureLists[0].feature().size();
   }
 };
 } // namespace llvm

 Logger::Logger(const std::vector<LoggedFeatureSpec> &FeatureSpecs,
                const TensorSpec &RewardSpec, bool IncludeReward)
     : FeatureSpecs(FeatureSpecs), RewardSpec(RewardSpec),
       IncludeReward(IncludeReward),
       LoggerData(std::make_unique<LoggerDataImpl>(FeatureSpecs, RewardSpec,
                                                   IncludeReward)) {}

 Logger::~Logger() {}

 #define LOG_REWARD(NAME, TYPE)                                                 \
   void Logger::log##NAME##Reward(TYPE Value) {                                 \
     assert(IncludeReward);                                                     \
     LoggerData->logReward(Value);                                              \
   }

 LOG_REWARD(Float, float)
 LOG_REWARD(Int32, int32_t)
 LOG_REWARD(Int64, int64_t)
 #undef LOG_REWARD

 #define LOG_FINAL_REWARD(NAME, TYPE)                                           \
   void Logger::log##NAME##FinalReward(TYPE Value) {                            \
     assert(RewardSpec.isElementType<TYPE>());                                  \
     for (size_t I = 1; I < LoggerData->getNrRecords(); ++I)                    \
       log##NAME##Reward(0);                                                    \
     log##NAME##Reward(Value);                                                  \
   }

 LOG_FINAL_REWARD(Float, float)
 LOG_FINAL_REWARD(Int32, int32_t)
 LOG_FINAL_REWARD(Int64, int64_t)
 #undef LOG_FINAL_REWARD

 void Logger::logFloatValue(size_t FeatureID, const float *Value) {
   assert(FeatureSpecs[FeatureID].Spec.isElementType<float>());
   logSpecifiedTensorValue(FeatureID, reinterpret_cast<const char *>(Value));
 }

 void Logger::logInt64Value(size_t FeatureID, const int64_t *Value) {
   assert(FeatureSpecs[FeatureID].Spec.isElementType<int64_t>());
   logSpecifiedTensorValue(FeatureID, reinterpret_cast<const char *>(Value));
 }

 void Logger::logInt32Value(size_t FeatureID, const int32_t *Value) {
   assert(FeatureSpecs[FeatureID].Spec.isElementType<int32_t>());
   logSpecifiedTensorValue(FeatureID, reinterpret_cast<const char *>(Value));
 }

 void Logger::logSpecifiedTensorValue(size_t FeatureID, const char *RawData) {
   const auto &Spec = FeatureSpecs[FeatureID].Spec;
   char *Buff = addEntryAndGetFloatOrInt64Buffer(FeatureID);
   if (Spec.isElementType<int32_t>())
     for (size_t I = 0; I < Spec.getElementCount(); ++I)
       (reinterpret_cast<int64_t *>(Buff))[I] =
           static_cast<int64_t>((reinterpret_cast<const int32_t *>(RawData))[I]);
   else if (Spec.isElementType<int64_t>() || Spec.isElementType<float>())
     std::memcpy(Buff, RawData,
                 Spec.getElementCount() * Spec.getElementByteSize());
   else
     llvm_unreachable("Unsupported tensor type");
 }

 char *Logger::addEntryAndGetFloatOrInt64Buffer(size_t FeatureID) {
   return reinterpret_cast<char *>(LoggerData->addNewTensor(FeatureID));
 }

 void Logger::flush(std::string *Str) { LoggerData->flush(Str); }

 void Logger::flush(raw_ostream &OS) {
   std::string Buff;
   LoggerData->flush(&Buff);
   OS << Buff;
 }

 void Logger::flushLogs(raw_ostream &OS,
                        const StringMap<std::unique_ptr<Logger>> &Loggers) {
   google::protobuf::Struct Msg;
   for (const auto &NamedLogger : Loggers) {
     tensorflow::SequenceExample SE;
     const auto &Logger = NamedLogger.second;
     std::string Unencoded;
     if (Logger->LoggerData->getNrRecords() > 0)
       Logger->flush(&Unencoded);

     (*Msg.mutable_fields())[NamedLogger.first().str()]
         .mutable_string_value()
         ->append(ProtobufTextMode ? Unencoded : encodeBase64(Unencoded));
   }

   std::string OutStr;
   serialize(Msg, &OutStr);
   OS << OutStr;
 }
 #endif // defined(LLVM_HAVE_TF_API)
	//===- TrainingLogger.cpp - mlgo feature/reward logging -------------------===//
	//
	// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
	// See https://llvm.org/LICENSE.txt for license information.
	// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
	//
	//===----------------------------------------------------------------------===//
	//
	// This file implements logging infrastructure for extracting features and
	// rewards for mlgo policy training.
	//
	//===----------------------------------------------------------------------===//
	#include "llvm/Config/config.h"
	#if defined(LLVM_HAVE_TF_API)

	#include "llvm/ADT/Twine.h"
	#include "llvm/Analysis/Utils/TrainingLogger.h"
	#include "llvm/Support/Base64.h"
	#include "llvm/Support/CommandLine.h"
	#include "llvm/Support/Debug.h"
	#include "llvm/Support/JSON.h"
	#include "llvm/Support/MemoryBuffer.h"
	#include "llvm/Support/Path.h"
	#include "llvm/Support/raw_ostream.h"

	#include "google/protobuf/struct.pb.h"
	#include "google/protobuf/text_format.h"
	#include "tensorflow/core/example/example.pb.h"
	#include <cassert>
	#include <numeric>

	using namespace llvm;

	using google::protobuf::Message;
	using google::protobuf::TextFormat;

	static cl::opt<bool>
	ProtobufTextMode("tfutils-text-log", cl::init(false), cl::Hidden,
	cl::desc("Output textual (human-readable) protobuf."));

	namespace {

	void serialize(const Message &SE, std::string *OutStr) {
	if (ProtobufTextMode) {
	TextFormat::PrintToString(SE, OutStr);
	} else {
	*OutStr = SE.SerializeAsString();
	}
	}
	} // namespace

	namespace llvm {

	class LoggerDataImpl {
	const std::vector<LoggedFeatureSpec> LoggedFeatureSpecs;
	const TensorSpec RewardSpec;
	const bool IncludeReward;

	std::vector<tensorflow::FeatureList> FeatureLists;
	tensorflow::FeatureList Reward;

	bool isSelfConsistent(const tensorflow::SequenceExample &SE,
	size_t NrRecords) const {
	bool Ret = true;
	for (const auto &TSpecs : LoggedFeatureSpecs) {
	const auto &Name = TSpecs.getLoggingName();
	const auto &FL = SE.feature_lists().feature_list().at(Name).feature();
	if (NrRecords != static_cast<size_t>(FL.size())) {
	dbgs() << "[TF-UTILS]: " << Name << " has missing records. Expected "
	<< NrRecords << " got " << FL.size() << "\n";
	Ret = false;
	}
	}
	if (IncludeReward && static_cast<size_t>(SE.feature_lists()
	.feature_list()
	.at(RewardSpec.name())
	.feature()
	.size()) != NrRecords) {
	dbgs() << "[TF-UTILS]: reward is missing records.\n";
	Ret = false;
	}
	return Ret;
	}

	void transferLog(tensorflow::SequenceExample &SE) {
	auto *FL = SE.mutable_feature_lists()->mutable_feature_list();
	if (IncludeReward)
	(*FL)[RewardSpec.name()] = std::move(Reward);
	assert(FeatureLists.size() == LoggedFeatureSpecs.size());
	for (size_t I = 0; I < FeatureLists.size(); ++I) {
	const auto &LFS = LoggedFeatureSpecs[I];
	(*FL)[LFS.getLoggingName()] = std::move(FeatureLists[I]);
	}
	}

	public:
	LoggerDataImpl(const std::vector<LoggedFeatureSpec> &LoggedSpecs,
	const TensorSpec &RewardSpec, bool IncludeReward)
	: LoggedFeatureSpecs(LoggedSpecs), RewardSpec(RewardSpec),
	IncludeReward(IncludeReward), FeatureLists(LoggedFeatureSpecs.size()) {}

	// flush the logged info to a stream and clear the log contents.
	void flush(std::string *Str) {
	size_t NrRecords = getNrRecords();
	(void)NrRecords;
	tensorflow::SequenceExample SE;
	transferLog(SE);
	assert(isSelfConsistent(SE, NrRecords));
	serialize(SE, Str);
	}

	char *addNewTensor(size_t FeatureID) {
	const auto &Spec = LoggedFeatureSpecs[FeatureID].Spec;
	if (Spec.isElementType<float>()) {
	auto *RF = FeatureLists[FeatureID]
	.add_feature()
	->mutable_float_list()
	->mutable_value();
	RF->Resize(Spec.getElementCount(), 0.0);
	return reinterpret_cast<char *>(RF->mutable_data());
	} else if (Spec.isElementType<int32_t>() \|\| Spec.isElementType<int64_t>()) {
	auto *RF = FeatureLists[FeatureID]
	.add_feature()
	->mutable_int64_list()
	->mutable_value();
	RF->Resize(Spec.getElementCount(), 0);
	return reinterpret_cast<char *>(RF->mutable_data());
	}
	llvm_unreachable("Unsupported tensor type.");
	}

	template <typename T> void logReward(T Value) {
	assert(IncludeReward);
	if (RewardSpec.isElementType<float>())
	Reward.add_feature()->mutable_float_list()->add_value(Value);
	else if (RewardSpec.isElementType<int32_t>() \|\|
	RewardSpec.isElementType<int64_t>())
	Reward.add_feature()->mutable_int64_list()->add_value(Value);
	else
	llvm_unreachable("Unsupported tensor type.");
	}

	size_t getNrRecords() const {
	return FeatureLists.empty() ? 0 : FeatureLists[0].feature().size();
	}
	};
	} // namespace llvm

	Logger::Logger(const std::vector<LoggedFeatureSpec> &FeatureSpecs,
	const TensorSpec &RewardSpec, bool IncludeReward)
	: FeatureSpecs(FeatureSpecs), RewardSpec(RewardSpec),
	IncludeReward(IncludeReward),
	LoggerData(std::make_unique<LoggerDataImpl>(FeatureSpecs, RewardSpec,
	IncludeReward)) {}

	Logger::~Logger() {}

	#define LOG_REWARD(NAME, TYPE) \
	void Logger::log##NAME##Reward(TYPE Value) { \
	assert(IncludeReward); \
	LoggerData->logReward(Value); \
	}

	LOG_REWARD(Float, float)
	LOG_REWARD(Int32, int32_t)
	LOG_REWARD(Int64, int64_t)
	#undef LOG_REWARD

	#define LOG_FINAL_REWARD(NAME, TYPE) \
	void Logger::log##NAME##FinalReward(TYPE Value) { \
	assert(RewardSpec.isElementType<TYPE>()); \
	for (size_t I = 1; I < LoggerData->getNrRecords(); ++I) \
	log##NAME##Reward(0); \
	log##NAME##Reward(Value); \
	}

	LOG_FINAL_REWARD(Float, float)
	LOG_FINAL_REWARD(Int32, int32_t)
	LOG_FINAL_REWARD(Int64, int64_t)
	#undef LOG_FINAL_REWARD

	void Logger::logFloatValue(size_t FeatureID, const float *Value) {
	assert(FeatureSpecs[FeatureID].Spec.isElementType<float>());
	logSpecifiedTensorValue(FeatureID, reinterpret_cast<const char *>(Value));
	}

	void Logger::logInt64Value(size_t FeatureID, const int64_t *Value) {
	assert(FeatureSpecs[FeatureID].Spec.isElementType<int64_t>());
	logSpecifiedTensorValue(FeatureID, reinterpret_cast<const char *>(Value));
	}

	void Logger::logInt32Value(size_t FeatureID, const int32_t *Value) {
	assert(FeatureSpecs[FeatureID].Spec.isElementType<int32_t>());
	logSpecifiedTensorValue(FeatureID, reinterpret_cast<const char *>(Value));
	}

	void Logger::logSpecifiedTensorValue(size_t FeatureID, const char *RawData) {
	const auto &Spec = FeatureSpecs[FeatureID].Spec;
	char *Buff = addEntryAndGetFloatOrInt64Buffer(FeatureID);
	if (Spec.isElementType<int32_t>())
	for (size_t I = 0; I < Spec.getElementCount(); ++I)
	(reinterpret_cast<int64_t *>(Buff))[I] =
	static_cast<int64_t>((reinterpret_cast<const int32_t *>(RawData))[I]);
	else if (Spec.isElementType<int64_t>() \|\| Spec.isElementType<float>())
	std::memcpy(Buff, RawData,
	Spec.getElementCount() * Spec.getElementByteSize());
	else
	llvm_unreachable("Unsupported tensor type");
	}

	char *Logger::addEntryAndGetFloatOrInt64Buffer(size_t FeatureID) {
	return reinterpret_cast<char *>(LoggerData->addNewTensor(FeatureID));
	}

	void Logger::flush(std::string *Str) { LoggerData->flush(Str); }

	void Logger::flush(raw_ostream &OS) {
	std::string Buff;
	LoggerData->flush(&Buff);
	OS << Buff;
	}

	void Logger::flushLogs(raw_ostream &OS,
	const StringMap<std::unique_ptr<Logger>> &Loggers) {
	google::protobuf::Struct Msg;
	for (const auto &NamedLogger : Loggers) {
	tensorflow::SequenceExample SE;
	const auto &Logger = NamedLogger.second;
	std::string Unencoded;
	if (Logger->LoggerData->getNrRecords() > 0)
	Logger->flush(&Unencoded);

	(*Msg.mutable_fields())[NamedLogger.first().str()]
	.mutable_string_value()
	->append(ProtobufTextMode ? Unencoded : encodeBase64(Unencoded));
	}

	std::string OutStr;
	serialize(Msg, &OutStr);
	OS << OutStr;
	}
	#endif // defined(LLVM_HAVE_TF_API)