[mlgo] Remove the protobuf dependency

[mtrofin]

Summary

The dependency was due to the log format. This change switches to the
previously-introduced (D139370) "dependency-free" logger instead of the
protobuf-based one.

A subsequent change will clean out the unnecessary abstraction left
behind.

This change drops the logger unittest, we have sufficient test coverage
via lit tests, and a unit test would require adding, unnecesarily, a log
reader (the reader is expected to be python, for the ML side, and there
is a reader for that under Analysis/models, used for tests).

[aidengrossman]

LGTM
One really minor nit.

I'm assuming the performance difference is pretty minimal between this and the TF Protobuf implementation within the overall training process (although most of the overhead would probably by in parsing on the Python side I would think)?

llvm/test/CodeGen/MLRegalloc/dev-mode-extra-features-logging.ll

25–26

Maybe note here explicitly we're regexing most of the opcodes due to exact values causing test failures over minor differences? Otherwise the regex might be somewhat hard to parse without additional context.

[mtrofin]

nit

[mtrofin]

In D141720#4052669, @aidengrossman wrote:

LGTM
One really minor nit.

I'm assuming the performance difference is pretty minimal between this and the TF Protobuf implementation within the overall training process (although most of the overhead would probably by in parsing on the Python side I would think)?

Right, the main value is in removing a dependency, enable the compiler stream out observations without needing to cache them, which also means we can reuse this code to implement the interactive model evaluator for gym environments. The last 2 will happen in subsequent patches, this just achieves the first bit.

llvm/test/CodeGen/MLRegalloc/dev-mode-extra-features-logging.ll

25–26

done

[ChrisCummins]

Changes LGTM having read through the diff, though I'm not in any position to sign off on it :)

llvm/CMakeLists.txt

if(LLVM_INCLUDE_TESTS)
   include(GetErrcMessages)
   get_errc_messages(LLVM_LIT_ERRC_MESSAGES)
 endif()
 
 # For up-to-date instructions for installing the TFLite dependency, refer to
 # the bot setup script: https://github.com/google/ml-compiler-opt/blob/main/buildbot/buildbot_init.sh
 set(LLVM_HAVE_TFLITE "" CACHE BOOL "Use tflite")
 if (LLVM_HAVE_TFLITE)
-  find_package(protobuf REQUIRED)
   find_package(tensorflow-lite REQUIRED)
-  set(LLVM_PROTOBUF_OUT_DIR ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/protobuf_gen)
-  include_directories(${LLVM_PROTOBUF_OUT_DIR})
 endif()
 
 # For up-to-date instructions for installing the Tensorflow dependency, refer to
 # the bot setup script: https://github.com/google/ml-compiler-opt/blob/main/buildbot/buildbot_init.sh
 # Specifically, assuming python3 is installed:
 # python3 -m pip install --upgrade pip && python3 -m pip install --user tf_nightly==2.3.0.dev20200528
 # Then set TENSORFLOW_AOT_PATH to the package install - usually it's ~/.local/lib/python3.7/site-packages/tensorflow
 #
 set(TENSORFLOW_AOT_PATH "" CACHE PATH "Path to TensorFlow pip install dir")
 
 if (NOT TENSORFLOW_AOT_PATH STREQUAL "")
   set(LLVM_HAVE_TF_AOT "ON" CACHE BOOL "Tensorflow AOT available")
   set(TENSORFLOW_AOT_COMPILER
     "${TENSORFLOW_AOT_PATH}/../../../../bin/saved_model_cli"
     CACHE PATH "Path to the Tensorflow AOT compiler")
-  # This needs to happen to avoid clashing protobuf codegen when building both AOT and development mode.
-  # We plan to replace protobuf with a simpler alternative, so this will go away.
-  file(COPY ${TENSORFLOW_AOT_PATH}/include DESTINATION ${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/tensorflow
-    NO_SOURCE_PERMISSIONS
-    PATTERN "google/*" EXCLUDE
-    PATTERN "*.pb.h" EXCLUDE)
-  include_directories(${CMAKE_LIBRARY_OUTPUT_DIRECTORY}/tensorflow/include)
+  include_directories(${TENSORFLOW_AOT_PATH}/include)
   add_subdirectory(${TENSORFLOW_AOT_PATH}/xla_aot_runtime_src
     ${CMAKE_ARCHIVE_OUTPUT_DIRECTORY}/tf_runtime)
   install(TARGETS tf_xla_runtime EXPORT LLVMExports
     ARCHIVE DESTINATION lib${LLVM_LIBDIR_SUFFIX} COMPONENT tf_xla_runtime)
   set_property(GLOBAL APPEND PROPERTY LLVM_EXPORTS tf_xla_runtime)
   # Once we add more modules, we should handle this more automatically.
   if (DEFINED LLVM_OVERRIDE_MODEL_HEADER_INLINERSIZEMODEL)
     set(LLVM_INLINER_MODEL_PATH "none")

llvm/cmake/modules/TensorFlowCompile.cmake

else()
     tf_compile(${LLVM_ML_MODELS_ABSOLUTE} ${tag_set} ${signature_def_key} ${fname} ${cpp_class} ${hdr_file} ${obj_file})
   endif()
 
   set(GeneratedMLSources ${GeneratedMLSources} ${GENERATED_OBJS} ${GENERATED_HEADERS} PARENT_SCOPE)
   set(MLDeps ${MLDeps} tf_xla_runtime PARENT_SCOPE)
   set(MLLinkDeps ${MLLinkDeps} tf_xla_runtime PARENT_SCOPE)
   add_definitions(-DLLVM_HAVE_TF_AOT_${fname_allcaps})
 endfunction()
-
-function(build_proto)
-  foreach (P ${ARGV})
-    set(PB_SRCS ${PB_SRCS} ${LLVM_PROTOBUF_OUT_DIR}/${P}.pb.cc)
-    set(PB_HDRS ${PB_HDRS} ${LLVM_PROTOBUF_OUT_DIR}/${P}.pb.h)
-    set(PBS ${PBS} ${TENSORFLOW_SRC_DIR}/${P}.proto)
-  endforeach()
-  add_custom_command(OUTPUT ${PB_SRCS} ${PB_HDRS}
-    COMMAND protobuf::protoc 
-    ARGS --proto_path=${TENSORFLOW_SRC_DIR} --cpp_out=${LLVM_PROTOBUF_OUT_DIR} ${PBS})
-  set_source_files_properties(${PB_SRCS} PROPERTIES
-    GENERATED 1)
-  set(GeneratedMLSources ${GeneratedMLSources} ${PB_SRCS} PARENT_SCOPE)
-  set(MLDeps ${MLDeps} ${MLDeps} PARENT_SCOPE)
-endfunction()

llvm/include/llvm/Analysis/Utils/TrainingLogger.h

 //===- TrainingLogger.h - mlgo feature/reward logging  ----------*- C++ -*-===//
 //
 // 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
 //
 //===----------------------------------------------------------------------===//
 //
 #ifndef LLVM_ANALYSIS_UTILS_TRAININGLOGGER_H
 #define LLVM_ANALYSIS_UTILS_TRAININGLOGGER_H
 
 #include "llvm/Config/llvm-config.h"
 
-#ifdef LLVM_HAVE_TFLITE
 #include "llvm/ADT/StringMap.h"
 #include "llvm/Analysis/TensorSpec.h"
 #include "llvm/IR/LLVMContext.h"
 #include "llvm/Support/JSON.h"
 
 #include <memory>
 #include <vector>
 
 namespace llvm {
 
 /// Logging utility - given an ordered specification of features, and assuming
-/// a scalar reward, allow logging feature values and rewards, and then print
-/// as tf.train.SequenceExample text protobuf.
+/// a scalar reward, allow logging feature values and rewards.
 /// The assumption is that, for an event to be logged (i.e. a set of feature
 /// values and a reward), the user calls the log* API for each feature exactly
 /// once, providing the index matching the position in the feature spec list
 /// provided at construction. The example assumes the first feature's element
 /// type is float, the second is int64, and the reward is float:
 ///
 /// event 0:
 ///   logFloatValue(0, ...)
 ///   logInt64Value(1, ...)
 ///   ...
 ///   logFloatReward(...)
 /// event 1:
 ///   logFloatValue(0, ...)
 ///   logInt64Value(1, ...)
 ///   ...
 ///   logFloatReward(...)
 ///
-/// At the end, call print to generate the protobuf.
+/// At the end, call print to generate the log.
 /// Alternatively, don't call logReward at the end of each event, just
 /// log{Float|Int32|Int64}FinalReward at the end.
 class LoggerDataImpl;
 class Logger final {
 public:
   /// Construct a Logger. If IncludeReward is false, then logReward or
   /// logFinalReward shouldn't be called, and the reward feature won't be
   /// printed out.
public:
   char *addEntryAndGetFloatOrInt64Buffer(size_t FeatureID);
 
   // Flush the content of the log to the stream, clearing the stored data in the
   // process.
   void flush(std::string *Str);
   void flush(raw_ostream &OS);
 
   // Flush a set of logs that are produced from the same module, e.g.
-  // per-function regalloc traces, as a google::protobuf::Struct message.
+  // per-function regalloc traces.
   static void flushLogs(raw_ostream &OS,
                         const StringMap<std::unique_ptr<Logger>> &Loggers);
 
 private:
   std::vector<TensorSpec> FeatureSpecs;
   TensorSpec RewardSpec;
   const bool IncludeReward;
   std::unique_ptr<LoggerDataImpl> LoggerData;
 };
 
 } // namespace llvm
-
-#endif // LLVM_HAVE_TFLITE
 #endif // LLVM_ANALYSIS_UTILS_TRAININGLOGGER_H

llvm/lib/Analysis/CMakeLists.txt

tf_find_and_compile(
       serve
       action
       InlinerSizeModel
       llvm::InlinerSizeModel
     )
   endif()
 
   if (LLVM_HAVE_TFLITE)
-    build_proto(
-      tensorflow/core/protobuf/error_codes
-      tensorflow/core/example/feature
-      tensorflow/core/example/example)
     list(APPEND MLLinkDeps
-      tensorflow-lite::tensorflow-lite
-      protobuf::libprotobuf)
+      tensorflow-lite::tensorflow-lite)
   endif()
 endif()
 
 add_llvm_component_library(LLVMAnalysis
   AliasAnalysis.cpp
   AliasAnalysisEvaluator.cpp
   AliasAnalysisSummary.cpp
   AliasSetTracker.cpp

llvm/lib/Analysis/DevelopmentModeInlineAdvisor.cpp

struct InlineEvent {
   /// 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
-/// (https://www.tensorflow.org/api_docs/python/tf/train/SequenceExample)
-/// protobuf (https://developers.google.com/protocol-buffers).
-/// 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.
+/// Collect data we may use for training a model.
 class TrainingLogger final {
 public:
   TrainingLogger(StringRef LogFileName, const ModelUnderTrainingRunner *MUTR);
 
   /// Log one inlining event.
   void logInlineEvent(const InlineEvent &Event,
                       const MLModelRunner &ModelRunner);
 

llvm/lib/Analysis/TrainingLogger.cpp

 //===- 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/Analysis/TensorSpec.h"
 #include "llvm/Config/config.h"
-#if defined(LLVM_HAVE_TFLITE)
 
 #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."));
-
+// FIXME(mtrofin): remove the flag altogether
 static cl::opt<bool>
-    UseSimpleLogger("tfutils-use-simplelogger", cl::init(false), cl::Hidden,
+    UseSimpleLogger("tfutils-use-simplelogger", cl::init(true), cl::Hidden,
                     cl::desc("Output simple (non-protobuf) log."));
 
-namespace {
-
-void serialize(const Message &SE, std::string *OutStr) {
-  if (ProtobufTextMode) {
-    TextFormat::PrintToString(SE, OutStr);
-  } else {
-    *OutStr = SE.SerializeAsString();
-  }
-}
-} // namespace
-
 namespace llvm {
 
 class LoggerDataImpl {
 protected:
   const std::vector<TensorSpec> LoggedFeatureSpecs;
   const TensorSpec RewardSpec;
   const bool IncludeReward;
   LoggerDataImpl(const std::vector<TensorSpec> &LoggedSpecs,
for (size_t I = 0; I < FeatureStorage.size(); ++I) {
                LoggedFeatureSpecs[TensorID].getTotalTensorBufferSize());
       if (TensorID == LoggedFeatureSpecs.size() - 1) {
         writeOutcome(OS, CurrentObservationID);
       }
     }
     return OS;
   }
 };
-
-class TFSequenceExampleLoggerDataImpl : public LoggerDataImpl {
-  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.name();
-      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.name()] = std::move(FeatureLists[I]);
-    }
-  }
-
-public:
-  TFSequenceExampleLoggerDataImpl(const std::vector<TensorSpec> &LoggedSpecs,
-                                  const TensorSpec &RewardSpec,
-                                  bool IncludeReward)
-      : LoggerDataImpl(LoggedSpecs, RewardSpec, IncludeReward),
-        FeatureLists(LoggedFeatureSpecs.size()) {}
-
-  // flush the logged info to a stream and clear the log contents.
-  void flush(std::string *Str) override {
-    size_t NrRecords = getNrRecords();
-    (void)NrRecords;
-    tensorflow::SequenceExample SE;
-    transferLog(SE);
-    assert(isSelfConsistent(SE, NrRecords));
-    serialize(SE, Str);
-  }
-
-  char *addNewTensor(size_t FeatureID) override {
-    const auto &Spec = LoggedFeatureSpecs[FeatureID];
-    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.");
-  }
-
-  void logRewardImpl(const char *Value, size_t Size) override {
-    assert(IncludeReward);
-    if (RewardSpec.isElementType<float>())
-      Reward.add_feature()->mutable_float_list()->add_value(
-          *reinterpret_cast<const float *>(Value));
-    else if (RewardSpec.isElementType<int32_t>())
-      Reward.add_feature()->mutable_int64_list()->add_value(
-          *reinterpret_cast<const int32_t *>(Value));
-    else if (RewardSpec.isElementType<int64_t>())
-      Reward.add_feature()->mutable_int64_list()->add_value(
-          *reinterpret_cast<const int64_t *>(Value));
-    else
-      llvm_unreachable("Unsupported tensor type.");
-  }
-
-  size_t getNrRecords() const override {
-    return FeatureLists.empty() ? 0 : FeatureLists[0].feature().size();
-  }
-};
 } // namespace llvm
 
 Logger::Logger(const std::vector<TensorSpec> &FeatureSpecs,
                const TensorSpec &RewardSpec, bool IncludeReward)
     : FeatureSpecs(FeatureSpecs), RewardSpec(RewardSpec),
       IncludeReward(IncludeReward) {
-  if (UseSimpleLogger)
-    LoggerData = std::make_unique<SimpleLoggerDataImpl>(
-        FeatureSpecs, RewardSpec, IncludeReward);
-  else
-    LoggerData = std::make_unique<TFSequenceExampleLoggerDataImpl>(
-        FeatureSpecs, RewardSpec, IncludeReward);
+  LoggerData = std::make_unique<SimpleLoggerDataImpl>(FeatureSpecs, RewardSpec,
+                                                      IncludeReward);
 }
 
 Logger::~Logger() {}
 
 #define LOG_REWARD(NAME, TYPE)                                                 \
   void Logger::log##NAME##Reward(TYPE Value) {                                 \
     assert(IncludeReward);                                                     \
     LoggerData->logReward(Value);                                              \
if (UseSimpleLogger) {
     std::string Buff;
     LoggerData->flush(&Buff);
     OS << Buff;
   }
 }
 
 void Logger::flushLogs(raw_ostream &OS,
                        const StringMap<std::unique_ptr<Logger>> &Loggers) {
-  if (UseSimpleLogger) {
-    bool IsFirst = true;
-    for (const auto &NamedLogger : Loggers) {
-      auto *Impl = NamedLogger.second->LoggerData.get();
-      reinterpret_cast<const SimpleLoggerDataImpl *>(Impl)->flush(
-          OS, IsFirst, NamedLogger.first());
-      IsFirst = false;
-    }
-  } else {
-    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_TFLITE)

llvm/test/CodeGen/MLRegalloc/Inputs/reference-log-noml.txt

-fields {
- key: "SyFgets"
- value {
- string_value: "feature_lists {
- feature_list {
- key: \"end_bb_freq_by_max\"  value {
- feature {  float_list {  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 1  value: 2.27244862e-10  value: 2.27244862e-10  value: 8.80573836e-10  value: 0.97600925  value: 0.97600925  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0.97600925  }  }
- feature {  float_list {  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 1  value: 2.27244862e-10  value: 0  value: 0.97600925  value: 0.97600925  value: 2.27244862e-10  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 8.80573836e-10  }  }
- feature {  float_list {  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 1  value: 2.27244862e-10  value: 0.97600925  value: 0.97600925  value: 0.97600925  value: 2.27244862e-10  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0.97600925  }  }
- feature {  float_list {  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 1  value: 2.27244862e-10  value: 0.97600925  value: 0  value: 0.97600925  value: 2.27244862e-10  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0.97600925  }  }
- feature {  float_list {  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 1  value: 2.27244862e-10  value: 2.27244862e-10  value: 0.97600925  value: 0.97600925  value: 0.97600925  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0.97600925  }  }
- feature {  float_list {  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 1  value: 2.27244862e-10  value: 0.97600925  value: 0.97600925  value: 0  value: 2.27244862e-10  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0.97600925  }  }
- feature {  float_list {  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 1  value: 2.27244862e-10  value: 0.97600925  value: 0.97600925  value: 0.97600925  value: 2.27244862e-10  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0.97600925  }  }
- feature {  float_list {  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 1  value: 2.27244862e-10  value: 0.97600925  value: 0.97600925  value: 0.97600925  value: 2.27244862e-10  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0.97600925  }  }
- feature {  float_list {  value: 0.97600925  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 1  value: 2.27244862e-10  value: 0.97600925  value: 0.97600925  value: 2.27244862e-10  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0.51107651  }  }
- feature {  float_list {  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 1  value: 2.27244862e-10  value: 0.97600925  value: 0.97600925  value: 2.27244862e-10  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0.97600925  }  }
- feature {  float_list {  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 1  value: 0  value: 0.97600925  value: 0  value: 0.97600925  value: 2.27244862e-10  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 0  value: 2.27244862e-10  }  }
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- }
-}
+context: SyFgets
+observation: 0
+mask: 0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
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+nr_broken_hints: 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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llvm/test/CodeGen/MLRegalloc/Inputs/reference-prio-log-noml.txt

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llvm/test/CodeGen/MLRegalloc/dev-mode-extra-features-logging.ll

 ; REQUIRES: have_tflite
 ; REQUIRES: x86_64-linux
 ;
 ; Check that we log the currently in development features correctly with both the default
 ; case and with a learned policy.
 ;
 ; RUN: llc -o /dev/null -mtriple=x86_64-linux-unknown -regalloc=greedy \
 ; RUN:   -regalloc-enable-advisor=development \
-; RUN:   -regalloc-training-log=%t1 -tfutils-text-log \
+; RUN:   -regalloc-training-log=%t1 \
 ; RUN:   -regalloc-enable-development-features < %S/Inputs/input.ll
-; RUN: sed -i 's/ \+/ /g' %t1
-; RUN: sed -i 's/\\n key:/\n key:/g' %t1
-; RUN: sed -i 's/\\n feature/\n feature/g' %t1
-; RUN: sed -i 's/\\n/ /g' %t1
-; RUN: FileCheck --input-file %t1 %s
+; RUN: %python %S/../../../lib/Analysis/models/log_reader.py %t1 > %t1.readable
+; RUN: FileCheck --input-file %t1.readable %s
 
 ; RUN: rm -rf %t && mkdir %t
 ; RUN: %python %S/../../../lib/Analysis/models/gen-regalloc-eviction-test-model.py %t_savedmodel
 ; RUN: %python %S/../../../lib/Analysis/models/saved-model-to-tflite.py %t_savedmodel %t
 ; RUN: llc -o /dev/null -mtriple=x86_64-linux-unknown -regalloc=greedy \
 ; RUN:   -regalloc-enable-advisor=development \
-; RUN:   -regalloc-training-log=%t2 -tfutils-text-log -regalloc-model=%t \
+; RUN:   -regalloc-training-log=%t2 -regalloc-model=%t \
 ; RUN:   -regalloc-enable-development-features < %S/Inputs/input.ll
-; RUN: sed -i 's/ \+/ /g' %t2
-; RUN: sed -i 's/\\n key:/\n key:/g' %t2
-; RUN: sed -i 's/\\n feature/\n feature/g' %t2
-; RUN: sed -i 's/\\n/ /g' %t2
-; RUN: FileCheck --input-file %t2 %s
+; RUN: %python %S/../../../lib/Analysis/models/log_reader.py %t2 > %t2.readable
+; RUN: FileCheck --input-file %t2.readable %s
 
 ; CHECK-NOT: nan
-; CHECK-LABEL: key: \"instructions\"
 ; Check the first five opcodes in the first eviction problem
-; CHECK-NEXT: value: 19
-; CHECK-SAME: value: {{([0-9]{4})}}
-; CHECK-SAME: value: 12{{([0-9]{2})}}
-; CHECK-SAME: value: 12{{([0-9]{2})}}
-; The first eviction problem is significantly less than 300 instructions. Check
-; that there is a zero value
-; CHECK-SAME: value: 0
+; Also, the first eviction problem is significantly less than 300 instructions. Check
+; that there is a zero value.
+; Note: we're regex-ing some of the opcodes to avoid test flakyness.
+; CHECK: instructions: 19,{{([0-9]{4})}},12{{([0-9]{2})}},12{{([0-9]{2})}},{{.*}},0,
 ; Only the candidate virtreg and the 10th LR are included in this problem. Make
-; sure the other LRs have values of zero.
-; CHECK-LABEL: key: \"instructions_mapping\"
-; CHECK-COUNT-2700: value: 0
-; CHECK-SAME: value: 1
+; sure the other LRs have values of zero. There are 2700 0s followed by some 1s.
+; There's a limit to how many repetitions can be matched.
+; CHECK: instructions_mapping: {{(((0,){27}){100})}}
+; CHECK-SAME: 1
 ; Indexing 300 back from where the candidate vr actual resides due to the fact
 ; that not all the values between the 10th LR and the candidate are zero.
-; CHECK-COUNT-6600: value: 0
-; CHECK-SAME: value: 1
+; CHECK-SAME-COUNT-6600: 0,
+; CHECK-SAME: 1
 ; Ensure that we can still go through the mapping matrices for the rest of the
 ; eviction problems to make sure we haven't hit the end of the matrix above.
 ; There are a total of 23 eviction problems with this test.
-; CHECK-COUNT-15: int64_list
-; CHECK: key: \"is_free\"
+; CHECK-LABEL: observation: 16
 ; Make sure that we're exporting the mbb_frequencies. Don't actually check
 ; values due to all values being floating point/liable to change very easily.
-; CHECK: key: \"mbb_frequencies\"
+; CHECK: mbb_frequencies:
 ; Make sure that we have the mbb_mapping feature, and that the first couple
 ; of values are correct.
-; CHECK: key: \"mbb_mapping\"
-; CHECK-NEXT: 0
-; CHECK-SAME: 0
-; CHECK-SAME: 0
-; CHECK-SAME: 0
-; CHECK-SAME: 0
-; CHECK-SAME: 1
-; CHECK-SAME: 1
+; CHECK: mbb_mapping: 0,0,0,0,0,1,1

llvm/test/CodeGen/MLRegalloc/dev-mode-log-2-fcts.ll

 ; REQUIRES: have_tflite
 ; REQUIRES: x86_64-linux
 ;
 ; Check that we can log more than 1 function.
 ;
 ; RUN: llc -mtriple=x86_64-linux-unknown -regalloc=greedy -regalloc-enable-advisor=development \
-; RUN:   -regalloc-training-log=%t1 -tfutils-text-log < %s
-; RUN: sed -i 's/ \+/ /g' %t1
-; RUN: sed -i 's/\\n key:/\n key:/g' %t1
-; RUN: sed -i 's/\\n feature/\n feature/g' %t1
-; RUN: sed -i 's/\\n/ /g' %t1
+; RUN:   -regalloc-training-log=%t1 < %s
 ; RUN: FileCheck --input-file %t1 %s
 
 ; RUN: rm -rf %t %t_savedmodel
 ; RUN: %python %S/../../../lib/Analysis/models/gen-regalloc-eviction-test-model.py %t_savedmodel
 ; RUN: %python %S/../../../lib/Analysis/models/saved-model-to-tflite.py %t_savedmodel %t
 ; RUN: llc -mtriple=x86_64-linux-unknown -regalloc=greedy -regalloc-enable-advisor=development \
-; RUN:   -regalloc-training-log=%t2 -tfutils-text-log -regalloc-model=%t < %s
-; RUN: sed -i 's/ \+/ /g' %t2
-; RUN: sed -i 's/\\n key:/\n key:/g' %t2
-; RUN: sed -i 's/\\n feature/\n feature/g' %t2
-; RUN: sed -i 's/\\n/ /g' %t2
+; RUN:   -regalloc-training-log=%t2 -regalloc-model=%t < %s
 ; RUN: FileCheck --input-file %t2 %s
 
 declare void @f();
 
 define void @f1(i64 %lhs, i64 %rhs, i64* %addr) !prof !15 {
   %sum = add i64 %lhs, %rhs
   call void @f();
   store i64 %sum, i64* %addr
   ret void
 }
 
 define void @f2(i64 %lhs, i64 %rhs, i64* %addr) !prof !16 {
   %sum = add i64 %lhs, %rhs
   store i64 %sum, i64* %addr
   ret void
 }
 
-; CHECK:  key: "f1"
-; CHECK:  key: "f2"
+; CHECK:  {"context":"f1"}
+; CHECK:  {"context":"f2"}
 
 !llvm.module.flags = !{!1}
 !1 = !{i32 1, !"ProfileSummary", !2}
 !2 = !{!3, !4, !5, !6, !7, !8, !9, !10}
 !3 = !{!"ProfileFormat", !"InstrProf"}
 !4 = !{!"TotalCount", i64 10000}
 !5 = !{!"MaxCount", i64 10}
 !6 = !{!"MaxInternalCount", i64 1}

llvm/test/CodeGen/MLRegalloc/dev-mode-logging.ll

 ; REQUIRES: have_tflite
 ; REQUIRES: x86_64-linux
 ;
 ; Check that we log correctly, both with a learned policy, and the default policy
 ;
 ; RUN: llc -o /dev/null -mtriple=x86_64-linux-unknown -regalloc=greedy \
-; RUN:   -regalloc-enable-advisor=development -regalloc-training-log=%t1 \
-; RUN:   -tfutils-text-log < %S/Inputs/input.ll
-; RUN: sed -i 's/ \+/ /g' %t1
-; RUN: sed -i 's/\\n key:/\n key:/g' %t1
-; RUN: sed -i 's/\\n feature/\n feature/g' %t1
-; RUN: sed -i 's/\\n/ /g' %t1
-; RUN: FileCheck --input-file %t1 %s --check-prefixes=CHECK,NOML
-; RUN: diff %t1 %S/Inputs/reference-log-noml.txt
+; RUN:   -regalloc-enable-advisor=development -regalloc-training-log=%t1 < %S/Inputs/input.ll
+; RUN: %python %S/../../../lib/Analysis/models/log_reader.py %t1 > %t1.readable
+; RUN: FileCheck --input-file %t1.readable %s --check-prefixes=CHECK,NOML
+; RUN: diff %t1.readable %S/Inputs/reference-log-noml.txt
 
 ; RUN: rm -rf %t_savedmodel %t
 ; RUN: %python %S/../../../lib/Analysis/models/gen-regalloc-eviction-test-model.py %t_savedmodel
 ; RUN: %python %S/../../../lib/Analysis/models/saved-model-to-tflite.py %t_savedmodel %t
 ; RUN: llc -o /dev/null -mtriple=x86_64-linux-unknown -regalloc=greedy \
 ; RUN:   -regalloc-enable-advisor=development -regalloc-training-log=%t2 \
-; RUN:   -tfutils-text-log -regalloc-model=%t < %S/Inputs/input.ll
-; RUN: sed -i 's/ \+/ /g' %t2
-; RUN: sed -i 's/\\n key:/\n key:/g' %t2
-; RUN: sed -i 's/\\n feature/\n feature/g' %t2
-; RUN: sed -i 's/\\n/ /g' %t2
-; RUN: FileCheck --input-file %t2 %s --check-prefixes=CHECK,ML
+; RUN:   -regalloc-model=%t < %S/Inputs/input.ll
+; RUN: %python %S/../../../lib/Analysis/models/log_reader.py %t2 > %t2.readable
+; RUN: FileCheck --input-file %t2.readable %s --check-prefixes=CHECK,ML
 
 ; RUN: llc -o /dev/null -mtriple=x86_64-linux-unknown -regalloc=greedy \
-; RUN:   -regalloc-enable-advisor=development -regalloc-training-log=%t3.log \
-; RUN:   -tfutils-use-simplelogger < %S/Inputs/two-large-fcts.ll
-; RUN: %python %S/../../../lib/Analysis/models/log_reader.py %t3.log | FileCheck %s --check-prefixes=CHECK-LOG
+; RUN:   -regalloc-enable-advisor=development -regalloc-training-log=%t3.log < %S/Inputs/two-large-fcts.ll
+; RUN: %python %S/../../../lib/Analysis/models/log_reader.py %t3.log | FileCheck %s --check-prefixes=CHECK-TWO-FCTS
 
 ; CHECK-NOT: nan
-; CHECK-LABEL: key: \"index_to_evict\"
-; ML-NEXT:    value: 9
-; NOML-NEXT:  value: 12
-; CHECK-LABEL: key: \"reward\"
-; ML:   value: 37.06
-; NOML: value: 36.64
-; CHECK-NEXT: feature_list
-; CHECK-NEXT: key: \"start_bb_freq_by_max\"
+; CHECK-LABEL: context: SyFgets
+; CHECK-NEXT: observation: 0
+; ML: index_to_evict: 9
+; NOML: index_to_evict: 12
+; CHECK-NEXT: reward: 0
+; CHECK-NEXT: observation: 1
+; CHECK-NEXT: mask:
+; NOML:      observation: 16
+; ML:      observation: 83
+; ML: reward: 37.06
+; NOML: reward: 36.64
 
-; CHECK-LOG: context: SyFgetsCopy
-; CHECK-LOG-NEXT: observation: 0
-; CHECK-LOG-NEXT: mask: 0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
-; CHECK-LOG: index_to_evict: 12
-; CHECK-LOG: observation: 16
-; CHECK-LOG: reward: 36.64
-; CHECK-LOG: context: SyFgets
-; CHECK-LOG-NEXT: observation: 0
-; CHECK-LOG-NEXT: mask: 0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
-; CHECK-LOG: index_to_evict: 12
-; CHECK-LOG: observation: 16
-; CHECK-LOG: reward: 36.64
+
+; CHECK-TWO-FCTS: context: SyFgetsCopy
+; CHECK-TWO-FCTS-NEXT: observation: 0
+; CHECK-TWO-FCTS-NEXT: mask: 0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
+; CHECK-TWO-FCTS: index_to_evict: 12
+; CHECK-TWO-FCTS: observation: 16
+; CHECK-TWO-FCTS: reward: 36.64
+; CHECK-TWO-FCTS: context: SyFgets
+; CHECK-TWO-FCTS-NEXT: observation: 0
+; CHECK-TWO-FCTS-NEXT: mask: 0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
+; CHECK-TWO-FCTS: index_to_evict: 12
+; CHECK-TWO-FCTS: observation: 16
+; CHECK-TWO-FCTS: reward: 36.64

llvm/test/CodeGen/MLRegalloc/dev-mode-prio-logging.ll

 ; REQUIRES: have_tflite
 ; REQUIRES: x86_64-linux
 ;
 ; Check that we log correctly, both with a learned policy, and the default policy
 ;
 ; RUN: llc -o /dev/null -mtriple=x86_64-linux-unknown -regalloc=greedy \
 ; RUN:   -regalloc-enable-priority-advisor=development \
-; RUN:   -regalloc-priority-training-log=%t1 -tfutils-text-log \
+; RUN:   -regalloc-priority-training-log=%t1 \
 ; RUN:   < %S/Inputs/input.ll
-; RUN: sed -i 's/ \+/ /g' %t1
-; RUN: sed -i 's/\\n key:/\n key:/g' %t1
-; RUN: sed -i 's/\\n feature/\n feature/g' %t1
-; RUN: sed -i 's/\\n/ /g' %t1
-; RUN: FileCheck --input-file %t1 %s --check-prefixes=CHECK,NOML
-; RUN: diff %t1 %S/Inputs/reference-prio-log-noml.txt
+; RUN: %python %S/../../../lib/Analysis/models/log_reader.py %t1 > %t1.readable
+; RUN: FileCheck --input-file %t1.readable %s --check-prefixes=CHECK,NOML
+; RUN: diff %t1.readable %S/Inputs/reference-prio-log-noml.txt
 
 ; RUN: rm -rf %t && mkdir %t
 ; RUN: %python %S/../../../lib/Analysis/models/gen-regalloc-priority-test-model.py %t_savedmodel
 ; RUN: %python %S/../../../lib/Analysis/models/saved-model-to-tflite.py %t_savedmodel %t
 ; RUN: llc -o /dev/null -mtriple=x86_64-linux-unknown -regalloc=greedy \
 ; RUN:   -regalloc-enable-priority-advisor=development \
-; RUN:   -regalloc-priority-training-log=%t2 -tfutils-text-log \
+; RUN:   -regalloc-priority-training-log=%t2 \
 ; RUN:   -regalloc-priority-model=%t < %S/Inputs/input.ll
-; RUN: sed -i 's/ \+/ /g' %t2
-; RUN: sed -i 's/\\n key:/\n key:/g' %t2
-; RUN: sed -i 's/\\n feature/\n feature/g' %t2
-; RUN: sed -i 's/\\n/ /g' %t2
-; RUN: FileCheck --input-file %t2 %s --check-prefixes=CHECK,ML
+; RUN: %python %S/../../../lib/Analysis/models/log_reader.py %t2 > %t2.readable
+; RUN: FileCheck --input-file %t2.readable %s --check-prefixes=CHECK,ML
 
 ; CHECK-NOT: nan
-; CHECK-LABEL: key: \"priority\"
-; NOML-NEXT: feature {  float_list {  value: 2.68435814e+09  }  }
-; ML-NEXT: feature {  float_list {  value: 3551  }  }
-; CHECK-LABEL: key: \"reward\"
+; CHECK-LABEL: priority:
+; NOML-SAME: 2684358144.0
+; ML-SAME: 3551
+; CHECK-LABEL: reward:

llvm/test/Transforms/Inline/ML/bounds-checks-rewards.ll

 ; Test behavior when inlining policy grows size out of control.
 ; In all cases, the end result is the same: mandatory inlinings must happen.
 ; Also in all cases, we don't record the mandatory inlining (there's nothing to
 ; learn from it).
 ; However, when we discover we 'trip' over the artificially-low size increase 
 ; factor, we penalize the 'bad' decision.
 ; REQUIRES: have_tflite
 ;
 ; Generate mock model
 ; RUN: rm -rf %t
 ; RUN: rm -rf %t_savedmodel
 ; RUN: %python %S/../../../../lib/Analysis/models/gen-inline-oz-test-model.py %t_savedmodel
 ; RUN: %python %S/../../../../lib/Analysis/models/saved-model-to-tflite.py %t_savedmodel %t
 ;
 ; When the bounds are very wide ("no bounds"), all inlinings happen.
-; RUN: opt -passes=scc-oz-module-inliner -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -ml-inliner-model-under-training=%t -training-log=- -tfutils-text-log -enable-ml-inliner=development -ml-advisor-size-increase-threshold=10.0 -S < %s 2>&1 | FileCheck %s --check-prefix=CHECK --check-prefix=NOBOUNDS
+; RUN: opt -passes=scc-oz-module-inliner -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -ml-inliner-model-under-training=%t -training-log=%t1 -enable-ml-inliner=development -ml-advisor-size-increase-threshold=10.0 -S < %s | FileCheck %s --check-prefixes=NOBOUNDS-OUT,CHECK
+; RUN: %python %S/../../../../lib/Analysis/models/log_reader.py %t1 | FileCheck %s --check-prefix=NOBOUNDS
 ;
 ; When the bounds are very restrictive, the first inlining happens but it's
 ; considered as "bad" (since it trips over the bounds) and its reward is a
 ; penalty. However, the mandatory inlining, which is considered next, happens.
 ; No other inlinings happend.
-; RUN: opt -passes=scc-oz-module-inliner -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -ml-inliner-model-under-training=%t -training-log=- -tfutils-text-log -enable-ml-inliner=development -ml-advisor-size-increase-threshold=1.0 -S < %s 2>&1 | FileCheck %s --check-prefix=CHECK --check-prefix=BOUNDS
+; RUN: opt -passes=scc-oz-module-inliner -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -ml-inliner-model-under-training=%t -training-log=%t2 -enable-ml-inliner=development -ml-advisor-size-increase-threshold=1.0 -S < %s | FileCheck %s --check-prefixes=BOUNDS-OUT,CHECK
+; RUN: %python %S/../../../../lib/Analysis/models/log_reader.py %t2 | FileCheck %s --check-prefix=BOUNDS
 ;
 ; With more restrictive bounds, the first inlining happens and is OK. The
 ; mandatory inlining happens next, and it trips over the bounds, which then
 ; forces no further inlinings.
-; RUN: opt -passes=scc-oz-module-inliner -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -ml-inliner-model-under-training=%t -training-log=- -tfutils-text-log -enable-ml-inliner=development -ml-advisor-size-increase-threshold=1.1 -S < %s 2>&1 | FileCheck %s --check-prefix=CHECK --check-prefix=RELAXED-BOUNDS
+; RUN: opt -passes=scc-oz-module-inliner -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -ml-inliner-model-under-training=%t -training-log=%t3 -enable-ml-inliner=development -ml-advisor-size-increase-threshold=1.1 -S < %s | FileCheck %s --check-prefixes=RELAXED-BOUNDS-OUT,CHECK
+; RUN: %python %S/../../../../lib/Analysis/models/log_reader.py %t3 | FileCheck %s --check-prefix=RELAXED-BOUNDS
+
 target datalayout = "e-m:e-p270:32:32-p271:32:32-p272:64:64-i64:64-f80:128-n8:16:32:64-S128"
 target triple = "x86_64-grtev4-linux-gnu"
 declare i64 @f1()
 define i64 @may_not_be_inlined() {
   %r = call i64 @f1()
   %r2 = add i64 13, %r
   ret i64 %r2
 }
 define i64 @must_be_inlined() #0 {
   %r = call i64 @may_not_be_inlined()
   %r2 = add i64 13, %r
   ret i64 %r2
 }
 define i64 @top() {
   %r = call i64 @must_be_inlined()
   %r2 = call i64 @may_not_be_inlined()
   %r3 = call i64 @may_not_be_inlined()
   %r4 = add i64 %r, %r2
   %r5 = add i64 %r3, %r4
   ret i64 %r5
 }
 attributes #0 = { alwaysinline }
-; CHECK:       key: "delta_size"
-; CHECK-NEXT:     value {
-; CHECK-NEXT:       feature {
-; CHECK-NEXT:         int64_list {
-; NOBOUNDS-NEXT:        value: 6
-; RELAXED-BOUNDS-NEXT:  value: 6
-; NOBOUNDS-NEXT:      }
-; NOBOUNDS-NEXT:    }
-; NOBOUNDS-NEXT:    feature {
-; NOBOUNDS-NEXT:      int64_list {
-; NOBOUNDS-NEXT:        value: -11
-; NOBOUNDS-NEXT:      }
-; NOBOUNDS-NEXT:    }
-; NOBOUNDS-NEXT:    feature {
-; NOBOUNDS-NEXT:      int64_list {
-; NOBOUNDS-NEXT:        value: 4
-; BOUNDS-NEXT:          value: 2147483647
-; CHECK-NEXT:         }
+; NOBOUNDS: observation: 0
+; NOBOUNDS: delta_size: 6
+; RELAXED-BOUNDS: delta_size: 6
+; BOUNDS: delta_size: 2147483647
+; NOBOUNDS: observation: 1
+; BOUNDS-NOT: observation
+; RELAXED-BOUNDS-NOT: observation
+; NOBOUNDS: delta_size: -11
+; NOBOUNDS: observation: 2
+; NOBOUNDS: delta_size: 4
+
 ; CHECK-LABEL: @top
 ; must_be_inlined must always be inlined, so we won't find a call to it in @top()
 ; CHECK-NOT: call i64 @must_be_inlined
 ; @some-function isn't mandatory, and when we set the increase threshold too low,
 ; it won't be inlined.
-; NOBOUNDS-NOT: @may_not_be_inlined
-; RELAXED-BOUNDS: call i64 @may_not_be_inlined
-; BOUNDS: call i64 @may_not_be_inlined
+; NOBOUNDS-OUT-NOT: @may_not_be_inlined
+; RELAXED-BOUNDS-OUT: call i64 @may_not_be_inlined
+; BOUNDS-OUT: call i64 @may_not_be_inlined

llvm/test/Transforms/Inline/ML/dev-mode-log-deleted.ll

 ; REQUIRES: have_tflite
 ; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner \
-; RUN:     -training-log=- -tfutils-text-log  -S < %s | FileCheck %s 
+; RUN:     -training-log=%t -S < %s 
+; RUN: %python %S/../../../../lib/Analysis/models/log_reader.py %t | FileCheck %s 
 
 define i32 @top() {
     %a = call i32 @to_be_deleted()
     %b = call i32 @externally_visible()
     %ret = add i32 %a, %b
     ret i32 %ret
 }
 
 define internal i32 @to_be_deleted() {
     ret i32 1
 }
 
 define i32 @externally_visible() {
     ret i32 2
 }
 
-; CHECK:        key: "inlining_decision"
-; CHECK-NEXT:   value {
-; CHECK-NEXT:     feature {
-; CHECK-NEXT:       int64_list {
-; CHECK-NEXT:         value: 1
-; CHECK-NEXT:       }
-; CHECK-NEXT:     }
-; CHECK-NEXT:     feature {
-; CHECK-NEXT:       int64_list {
-; CHECK-NEXT:         value: 1
-; CHECK-NEXT:       }
-; CHECK-NEXT:     }
-; CHECK-NEXT:   }
+; CHECK: observation: 0
+; CHECK: inlining_decision: 1
+; CHECK: observation: 1
+; CHECK: inlining_decision: 1
 

llvm/test/Transforms/Inline/ML/development-training-log.ll

 ; Test that we can produce a log if we have or do not have a model, in development mode.
 ; REQUIRES: have_tflite
 ; Generate mock model
 ; RUN: rm -rf %t_savedmodel %t
 ; RUN: %python %S/../../../../lib/Analysis/models/gen-inline-oz-test-model.py %t_savedmodel
 ; RUN: %python %S/../../../../lib/Analysis/models/saved-model-to-tflite.py %t_savedmodel %t
 ;
-; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=- -tfutils-text-log -ml-inliner-model-under-training=%t -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=- -tfutils-text-log -ml-inliner-model-under-training=%t -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=- -tfutils-text-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=- -tfutils-text-log -ml-inliner-model-under-training=%t -S < %s | FileCheck %s --check-prefix=NOREWARD
-; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=- -tfutils-text-log -S < %s | FileCheck %s --check-prefix=NOREWARD
+; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=%t1 -ml-inliner-model-under-training=%t -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -S < %s
+; RUN: %python %S/../../../../lib/Analysis/models/log_reader.py %t1 | FileCheck %s
+; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=%t2 -ml-inliner-model-under-training=%t -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
+; RUN: %python %S/../../../../lib/Analysis/models/log_reader.py %t2 | FileCheck %s --check-prefixes=EXTRA-OUTPUTS,CHECK
+; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=%t3 -ml-inliner-ir2native-model=%S/../../../../unittests/Analysis/Inputs/ir2native_x86_64_model -S < %s
+; RUN: %python %S/../../../../lib/Analysis/models/log_reader.py %t3 | FileCheck %s
+; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=%t4 -ml-inliner-model-under-training=%t -S < %s
+; RUN: %python %S/../../../../lib/Analysis/models/log_reader.py %t4 | FileCheck %s --check-prefix=NOREWARD
+; RUN: opt -enable-ml-inliner=development -passes=scc-oz-module-inliner -training-log=%t5 -S < %s
+; RUN: %python %S/../../../../lib/Analysis/models/log_reader.py %t5| 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"
 declare i32 @f1(i32)
 declare i32 @f2(i32)
 define dso_local i32 @branches(i32) {
   %cond = icmp slt i32 %0, 3
   br i1 %cond, label %then, label %else
 then:

 define dso_local i32 @top() {
   %1 = call i32 @branches(i32 2)
   ret i32 %1
 }
 !llvm.module.flags = !{!0}
 !llvm.ident = !{!1}
 !0 = !{i32 1, !"wchar_size", i32 4}
 !1 = !{!"clang version 7.0.0-6 (tags/RELEASE_700/final)"}
-; Check we produce a protobuf that has inlining decisions and rewards.
-; CHECK:                  key: "delta_size"
-; CHECK-NEXT:               value {
-; CHECK-NEXT:                 feature {
-; CHECK-NEXT:                   int64_list {
-; CHECK-NEXT:                     value: 0
-; CHECK-NEXT:                   }
-; CHECK-NEXT:                 }
-; CHECK-NOT: fake_extra_output
-; EXTRA-OUTPUTS:          key: "fake_extra_output"
-; EXTRA-OUTPUTS-NEXT:       value {
-; EXTRA-OUTPUTS-NEXT:         feature {
-; EXTRA-OUTPUTS-NEXT:           int64_list {
-; EXTRA-OUTPUTS-NEXT:             value: {{[0-9]+}}
-; CHECK:                  key: "inlining_decision"
-; CHECK-NEXT:               value {
-; CHECK-NEXT:                 feature {
-; CHECK-NEXT:                   int64_list {
-; CHECK-NEXT:                     value: 1
-; NOREWARD-NOT: key: "delta_size"
+; Check we produce a log that has inlining decisions and rewards.
+; CHECK-NOT:        fake_extra_output:
+; EXTRA-OUTPUTS:    fake_extra_output: {{[0-9]+}}
+; CHECK:            inlining_decision: 1
+; CHECK:            delta_size: 0
+; NOREWARD-NOT:     delta_size:

llvm/test/Transforms/Inline/ML/ml-test-development-mode.ll

 ; The default inliner doesn't elide @adder, it believes it's too costly to inline 
 ; adder into switcher. The ML inliner carries out that inlining, resulting in
 ; a smaller result (part of it is that adder gets elided).
 ;
 ; This test uses Inputs/test-module.ll, as it shares it with a similar test
 ; for the 'release' mode.
 ;
 ; REQUIRES: have_tflite
 ; RUN: rm -rf %t
 ; RUN: rm -rf %t_savedmodel
 ; RUN: %python %S/../../../../lib/Analysis/models/gen-inline-oz-test-model.py %t_savedmodel
 ; RUN: %python %S/../../../../lib/Analysis/models/saved-model-to-tflite.py %t_savedmodel %t
 ; RUN: opt -passes=scc-oz-module-inliner -enable-ml-inliner=default -S < %S/Inputs/test-module.ll 2>&1 | FileCheck %S/Inputs/test-module.ll --check-prefix=DEFAULT
 ; RUN: opt -passes=scc-oz-module-inliner -enable-ml-inliner=development -ml-inliner-model-under-training=%t -S < %S/Inputs/test-module.ll 2>&1 | FileCheck %S/Inputs/test-module.ll --check-prefix=CHECK
-; RUN: opt -passes=scc-oz-module-inliner -enable-ml-inliner=development -training-log=%t.log -tfutils-use-simplelogger -S < %S/Inputs/test-module.ll 2>&1
+; RUN: opt -passes=scc-oz-module-inliner -enable-ml-inliner=development -training-log=%t.log -S < %S/Inputs/test-module.ll 2>&1
 ; RUN: %python %S/../../../../lib/Analysis/models/log_reader.py %t.log | FileCheck %s --check-prefix=CHECK-LOG
 
 CHECK-LOG: observation: 0
 CHECK-LOG-NEXT: {{^sroa_savings:}} 0
 CHECK-LOG: {{^cost_estimate:}} -30
 CHECK-LOG: {{^inlining_decision:}} 1
 CHECK-LOG-NEXT: observation: 1
 CHECK-LOG: observation: 6

llvm/unittests/Analysis/CMakeLists.txt

set(ANALYSIS_TEST_SOURCES
   VectorUtilsTest.cpp
   )
 
 # The unit tests target does not use InlineAdvisorPlugin.cpp
 # so if not added to LLVM_OPTIONAL_SOURCES, FileCheck will
 # complain about unused file.
 set(LLVM_OPTIONAL_SOURCES InlineAdvisorPlugin.cpp)
 
-set(MLGO_TESTS TFUtilsTest.cpp TrainingLoggerTest.cpp)
+set(MLGO_TESTS TFUtilsTest.cpp)
 if (LLVM_HAVE_TFLITE)
   LIST(APPEND ANALYSIS_TEST_SOURCES ${MLGO_TESTS})
 else()
   LIST(APPEND LLVM_OPTIONAL_SOURCES ${MLGO_TESTS})
 endif()
 
 add_llvm_unittest_with_input_files(AnalysisTests
   ${ANALYSIS_TEST_SOURCES}

llvm/unittests/Analysis/TrainingLoggerTest.cpp

-//===- TrainingLoggerTest.cpp - test for TrainingLogger -------------------===//
-//
-// 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
-//
-//===----------------------------------------------------------------------===//
-
-#include "llvm/Analysis/Utils/TrainingLogger.h"
-#include "google/protobuf/struct.pb.h"
-#include "tensorflow/core/example/example.pb.h"
-#include "tensorflow/core/example/feature.pb.h"
-#include "llvm/Analysis/TensorSpec.h"
-#include "llvm/AsmParser/Parser.h"
-#include "llvm/IR/Dominators.h"
-#include "llvm/IR/Instructions.h"
-#include "llvm/IR/LLVMContext.h"
-#include "llvm/IR/Module.h"
-#include "llvm/Support/Path.h"
-#include "llvm/Support/SourceMgr.h"
-#include "llvm/Testing/Support/SupportHelpers.h"
-#include "gtest/gtest.h"
-
-using namespace llvm;
-
-extern const char *TestMainArgv0;
-
-// NOTE! This test model is currently also used by test/Transforms/Inline/ML
-// tests
-//- relevant if updating this model.
-
-#define PROTO_CHECKER(FNAME, TYPE, INDEX, EXP)                                 \
-  do {                                                                         \
-    const auto &V = Expected.feature_lists()                                   \
-                        .feature_list()                                        \
-                        .at(FNAME)                                             \
-                        .feature(INDEX)                                        \
-                        .TYPE()                                                \
-                        .value();                                              \
-    for (auto I = 0; I < V.size(); ++I)                                        \
-      EXPECT_EQ(V.at(I), EXP[I]);                                              \
-  } while (false)
-
-TEST(TrainingLoggerTest, Logger) {
-  std::vector<TensorSpec> Features{
-      TensorSpec::createSpec<float>("the_float", {2, 3}),
-      TensorSpec::createSpec<int64_t>("alternate_name", {2})};
-
-  auto Rewards = TensorSpec::createSpec<float>("reward", {1});
-  Logger L(Features, Rewards, true);
-  const float F00[]{0.0, 0.1, 0.2, 0.3, 0.4, 0.5};
-  const int64_t F01[]{2, 3};
-
-  L.logFloatValue(0, F00);
-  L.logInt64Value(1, F01);
-  L.logFloatReward(3.4);
-  const float F10[]{0.0, 1.0, 2.0, 3.0, 4.0, 5.0};
-  const int64_t F11[]{-2, -3};
-  L.logFloatValue(0, F10);
-  L.logInt64Value(1, F11);
-  L.logFloatReward(-3.0);
-  std::string Result;
-  raw_string_ostream OS(Result);
-  L.flush(OS);
-
-  tensorflow::SequenceExample Expected;
-  ASSERT_TRUE(Expected.ParseFromString(Result));
-  PROTO_CHECKER("the_float", float_list, 0, F00);
-  PROTO_CHECKER("the_float", float_list, 1, F10);
-  PROTO_CHECKER("alternate_name", int64_list, 0, F01);
-  PROTO_CHECKER("alternate_name", int64_list, 1, F11);
-  float R0[]{3.4};
-  float R1[]{-3.0};
-  PROTO_CHECKER("reward", float_list, 0, R0);
-  PROTO_CHECKER("reward", float_list, 1, R1);
-}
-
-TEST(TrainingLoggerTest, LoggerInt32FeaturesAndReward) {
-  std::vector<TensorSpec> Features{
-      TensorSpec::createSpec<float>("the_float", {2, 3}),
-      TensorSpec::createSpec<int32_t>("alternate_name", {2})};
-
-  auto Rewards = TensorSpec::createSpec<int32_t>("reward", {1});
-  Logger L(Features, Rewards, true);
-  const float F00[]{0.0, 0.1, 0.2, 0.3, 0.4, 0.5};
-  const int32_t F01[]{2, 3};
-
-  L.logFloatValue(0, F00);
-  L.logInt32Value(1, F01);
-  L.logInt32Reward(3);
-  const float F10[]{0.0, 1.0, 2.0, 3.0, 4.0, 5.0};
-  const int32_t F11[]{-2, -3};
-  L.logFloatValue(0, F10);
-  L.logInt32Value(1, F11);
-  L.logInt32Reward(-3);
-  std::string Result;
-  raw_string_ostream OS(Result);
-  L.flush(OS);
-
-  tensorflow::SequenceExample Expected;
-  ASSERT_TRUE(Expected.ParseFromString(Result));
-  PROTO_CHECKER("the_float", float_list, 0, F00);
-  PROTO_CHECKER("the_float", float_list, 1, F10);
-  PROTO_CHECKER("alternate_name", int64_list, 0, F01);
-  PROTO_CHECKER("alternate_name", int64_list, 1, F11);
-  int32_t R0[]{3};
-  int32_t R1[]{-3};
-  PROTO_CHECKER("reward", int64_list, 0, R0);
-  PROTO_CHECKER("reward", int64_list, 1, R1);
-}
-
-TEST(TrainingLoggerTest, LoggerNoReward) {
-  std::vector<TensorSpec> Features{
-      TensorSpec::createSpec<float>("the_float", {2, 3}),
-      TensorSpec::createSpec<int64_t>("alternate_name", {2})};
-
-  auto Rewards = TensorSpec::createSpec<float>("reward", {1});
-  Logger L(Features, Rewards, false);
-  const float F00[]{0.0, 0.1, 0.2, 0.3, 0.4, 0.5};
-  const int64_t F01[]{2, 3};
-
-  L.logFloatValue(0, F00);
-  L.logInt64Value(1, F01);
-  const float F10[]{0.0, 1.0, 2.0, 3.0, 4.0, 5.0};
-  const int64_t F11[]{-2, -3};
-  L.logFloatValue(0, F10);
-  L.logInt64Value(1, F11);
-
-  std::string Result;
-  raw_string_ostream OS(Result);
-  L.flush(OS);
-  tensorflow::SequenceExample Expected;
-  ASSERT_TRUE(Expected.ParseFromString(Result));
-  PROTO_CHECKER("the_float", float_list, 0, F00);
-  PROTO_CHECKER("the_float", float_list, 1, F10);
-  PROTO_CHECKER("alternate_name", int64_list, 0, F01);
-  PROTO_CHECKER("alternate_name", int64_list, 1, F11);
-}
-
-TEST(TrainingLoggerTest, LoggerFinalReward) {
-  std::vector<TensorSpec> Features{
-      TensorSpec::createSpec<float>("the_float", {1}),
-      TensorSpec::createSpec<int64_t>("the_int", {1})};
-
-  auto Rewards = TensorSpec::createSpec<float>("reward", {1});
-  Logger L(Features, Rewards, true);
-  for (int64_t I = 0; I < 3; ++I) {
-    float F = static_cast<float>(I);
-    L.logFloatValue(0, &F);
-    L.logInt64Value(1, &I);
-  }
-  L.logFloatFinalReward(3.14);
-  std::string Result;
-  raw_string_ostream OS(Result);
-  L.flush(OS);
-  const float Zero[]{0.0};
-  const float R[]{3.14};
-  tensorflow::SequenceExample Expected;
-  ASSERT_TRUE(Expected.ParseFromString(Result));
-  PROTO_CHECKER("reward", float_list, 0, Zero);
-  PROTO_CHECKER("reward", float_list, 1, Zero);
-  PROTO_CHECKER("reward", float_list, 2, R);
-}
-
-TEST(TrainingLoggerTest, LoggerGroup) {
-  std::vector<TensorSpec> Features{
-      TensorSpec::createSpec<float>("the_float", {1}),
-      TensorSpec::createSpec<int64_t>("the_int", {1})};
-
-  auto Rewards = TensorSpec::createSpec<float>("reward", {1});
-  StringMap<std::unique_ptr<Logger>> Loggers;
-  std::vector<std::string> Names{"a", "b"};
-  size_t Bump = 0;
-  for (auto Name : Names) {
-    auto L = std::make_unique<Logger>(Features, Rewards, true);
-    for (int64_t I = 0; I < 3; ++I) {
-      float F = static_cast<float>(I) + Bump;
-      L->logFloatValue(0, &F);
-      L->logInt64Value(1, &I);
-    }
-    L->logFloatFinalReward(3.14 + Bump);
-    Loggers.insert(std::make_pair(Name, std::move(L)));
-  }
-  std::string Result;
-  raw_string_ostream OS(Result);
-  Logger::flushLogs(OS, Loggers);
-  google::protobuf::Struct Expected;
-  ASSERT_TRUE(Expected.ParseFromString(Result));
-  EXPECT_EQ(Expected.fields_size(), 2);
-  EXPECT_TRUE(Expected.fields().contains("a"));
-  EXPECT_TRUE(Expected.fields().contains("b"));
-}