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[NFC][MLGO] Just use the underlying protobuf object for logging

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Avoid buffering just to copy the buffered data, in 'development
mode', when logging. Instead, just populate the underlying protobuf.

Differential Revision: https://reviews.llvm.org/D106592
This commit is contained in:
Mircea Trofin 2021-07-22 12:45:32 -07:00
parent 89d6157c2c
commit 4559a48614
4 changed files with 229 additions and 130 deletions
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69
include/llvm/Analysis/Utils/TFUtils.h
View File

@ -12,6 +12,7 @@
#include "llvm/Config/llvm-config.h"
#ifdef LLVM_HAVE_TF_API
#include "llvm/ADT/StringMap.h"
#include "llvm/IR/LLVMContext.h"
#include "llvm/Support/JSON.h"
@ -120,56 +121,62 @@ loadOutputSpecs(LLVMContext &Ctx, StringRef ExpectedDecisionName,
/// 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:
/// 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:
/// logTensorValue(0, ...)
/// logTensorValue(1, ...)
/// logFloatValue(0, ...)
/// logInt64Value(1, ...)
/// ...
/// logReward(...)
/// logFloatReward(...)
/// event 1:
/// logTensorValue(0, ...)
/// logTensorValue(1, ...)
/// logFloatValue(0, ...)
/// logInt64Value(1, ...)
/// ...
/// logReward(...)
/// logFloatReward(...)
///
/// At the end, call print to generate the protobuf.
/// 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 shouldn't
/// be called, and the reward feature won't be printed out.
/// Construct a Logger. If IncludeReward is false, then logReward or
/// logFinalReward shouldn't be called, and the reward feature won't be
/// printed out.
Logger(const std::vector<LoggedFeatureSpec> &FeatureSpecs,
const TensorSpec &RewardSpec, bool IncludeReward)
: FeatureSpecs(FeatureSpecs), RewardSpec(RewardSpec),
RawLogData(FeatureSpecs.size() + IncludeReward),
IncludeReward(IncludeReward) {}
const TensorSpec &RewardSpec, bool IncludeReward);
template <typename T> void logReward(T Value) {
assert(IncludeReward);
logTensorValue(RawLogData.size() - 1, &Value);
}
~Logger();
template <typename T> void logFinalReward(T Value) {
assert(RawLogData.back().empty());
logReward(Value);
}
void logFloatReward(float Value);
void logInt32Reward(int32_t Value);
void logInt64Reward(int64_t Value);
template <typename T>
void logTensorValue(size_t FeatureID, const T *Value, size_t Size = 1) {
const char *Start = reinterpret_cast<const char *>(Value);
const char *End = Start + sizeof(T) * Size;
RawLogData[FeatureID].insert(RawLogData[FeatureID].end(), Start, End);
}
void logFloatFinalReward(float Value);
void logInt32FinalReward(int32_t Value);
void logInt64FinalReward(int64_t Value);
void logFloatValue(size_t FeatureID, const float *Value);
void logInt32Value(size_t FeatureID, const int32_t *Value);
void logInt64Value(size_t FeatureID, const int64_t *Value);
void logSpecifiedTensorValue(size_t FeatureID, const char *RawData);
// Warning! For int32_t, the return is set up for int64_t, so the caller needs
// to piecemeal cast their int32_t values.
// FIXME: let's drop int32_t support. While it's supported by evaluator, it's
// not supported by the tensorflow::SequenceExample proto. For small values,
// we can consider using bytes.
char *addEntryAndGetFloatOrInt64Buffer(size_t FeatureID);
void print(raw_ostream &OS);
private:
std::vector<LoggedFeatureSpec> FeatureSpecs;
TensorSpec RewardSpec;
/// RawData has one entry per feature, plus one more for the reward.
/// Each feature's values are then stored in a vector, in succession.
/// This means the ith event is stored at [*][i]
std::vector<std::vector<char>> RawLogData;
const bool IncludeReward;
std::unique_ptr<LoggerDataImpl> LoggerData;
};
class TFModelEvaluator final {

12
lib/Analysis/DevelopmentModeInlineAdvisor.cpp
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@ -353,24 +353,22 @@ void TrainingLogger::logInlineEvent(const InlineEvent &Event,
size_t CurrentFeature = 0;
for (; CurrentFeature < NumberOfFeatures; ++CurrentFeature) {
int64_t F = ModelRunner.getFeature(CurrentFeature);
L->logTensorValue(CurrentFeature, &F);
L->logInt64Value(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());
L->logSpecifiedTensorValue(CurrentFeature, RawData);
++CurrentFeature;
}
assert(CurrentFeature == DefaultDecisionPos);
L->logTensorValue(DefaultDecisionPos, &Event.DefaultDecision);
L->logTensorValue(DecisionPos, &Event.AdvisedDecision);
L->logInt64Value(DefaultDecisionPos, &Event.DefaultDecision);
L->logInt64Value(DecisionPos, &Event.AdvisedDecision);
if (InlineSizeEstimatorAnalysis::isEvaluatorRequested())
L->logReward(Event.Reward);
L->logInt64Reward(Event.Reward);
// For debugging / later use
Effects.push_back(Event.Effect);

214
lib/Analysis/TFUtils.cpp
View File

@ -32,6 +32,9 @@
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."));
@ -70,55 +73,6 @@ TFStatusPtr createTFStatus() {
TFSessionOptionsPtr createTFSessionOptions() {
return TFSessionOptionsPtr(TF_NewSessionOptions(), &TF_DeleteSessionOptions);
}
/// Write a list of tensors as a sequence of TensorFlow FeatureList protobufs.
/// The tensors are assumed to be stored contiguously, in row-major format,
/// in the TensorData buffer. Each tensor has the shape given by Spec. The
/// feature name in the output is either the provided LoggingName, if
/// specified, otherwise it's the name of the tensor (as given by Spec).
void writeRawTensorsAsFeatureLists(tensorflow::FeatureLists *FE,
const LoggedFeatureSpec &LoggedSpec,
const char *TensorData, size_t TensorCount,
bool FinalReward = false) {
const auto &Spec = LoggedSpec.Spec;
// The 'Feature' protobuf only has 3 possible fields: float_list,
// int64_list, or bytes_list, so we capture int32 values as int64. We don't
// support any other types.
tensorflow::FeatureList &FL = (*FE->mutable_feature_list())[(
LoggedSpec.LoggingName ? *LoggedSpec.LoggingName : Spec.name())];
const char *CurrentTensor = TensorData;
const size_t TensorByteSize =
Spec.getElementCount() * Spec.getElementByteSize();
const size_t ElemCount = Spec.getElementCount();
for (size_t E = 0; E < TensorCount; ++E) {
const bool ShouldWrite = E + 1 == TensorCount || !FinalReward;
if (Spec.isElementType<int64_t>()) {
auto *MF = FL.add_feature()->mutable_int64_list()->mutable_value();
MF->Resize(ElemCount, 0);
if (ShouldWrite)
memcpy(MF->mutable_data(), CurrentTensor, TensorByteSize);
} else if (Spec.isElementType<int32_t>()) {
auto *MF = FL.add_feature()->mutable_int64_list()->mutable_value();
MF->Resize(ElemCount, 0);
if (ShouldWrite) {
const int32_t *TD = reinterpret_cast<const int32_t *>(CurrentTensor);
for (size_t I = 0; I < ElemCount; ++I)
(*MF)[I] = TD[I];
}
} else if (Spec.isElementType<float>()) {
auto *MF = FL.add_feature()->mutable_float_list()->mutable_value();
MF->Resize(ElemCount, 0.0);
if (ShouldWrite)
memcpy(MF->mutable_data(), CurrentTensor, TensorByteSize);
} else {
llvm_unreachable("Unsupported tensor type.");
}
if (ShouldWrite)
CurrentTensor += TensorByteSize;
}
}
} // namespace
namespace llvm {
@ -304,6 +258,76 @@ private:
bool checkReportAndInvalidate(const TF_Output &Output,
const TensorSpec &OutputSpec);
};
class LoggerDataImpl {
const std::vector<LoggedFeatureSpec> LoggedFeatureSpecs;
const TensorSpec RewardSpec;
tensorflow::SequenceExample SE;
std::vector<tensorflow::FeatureList *> FeatureLists;
tensorflow::FeatureList *Reward = nullptr;
public:
LoggerDataImpl(const std::vector<LoggedFeatureSpec> &LoggedSpecs,
const TensorSpec &RewardSpec, bool IncludeReward)
: LoggedFeatureSpecs(LoggedSpecs), RewardSpec(RewardSpec) {
auto *FL = SE.mutable_feature_lists()->mutable_feature_list();
if (IncludeReward)
Reward = &(*FL)[RewardSpec.name()];
// Allocate first the map entries, then capture their address. We will not
// mutate the set of features after this (i.e. the pointers won't dangle).
for (const auto &LFS : LoggedSpecs) {
(*FL)[LFS.LoggingName ? *LFS.LoggingName : LFS.Spec.name()] = {};
}
for (const auto &LFS : LoggedSpecs)
FeatureLists.push_back(
&(*FL)[LFS.LoggingName ? *LFS.LoggingName : LFS.Spec.name()]);
}
void print(raw_ostream &OS) {
std::string OutStr;
if (ProtobufTextMode)
google::protobuf::TextFormat::PrintToString(SE, &OutStr);
else
OutStr = SE.SerializeAsString();
OS << OutStr;
}
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) {
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
TFModelEvaluatorImpl::TFModelEvaluatorImpl(
@ -448,37 +472,71 @@ TFUTILS_SUPPORTED_TYPES(TFUTILS_GETDATATYPE_IMPL)
TFModelEvaluator::EvaluationResult::~EvaluationResult() {}
TFModelEvaluator::~TFModelEvaluator() {}
void Logger::print(raw_ostream &OS) {
tensorflow::SequenceExample SE;
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)) {}
if (RawLogData.empty())
return;
if (RawLogData[0].empty())
return;
size_t Tensor0Size = FeatureSpecs[0].Spec.getElementCount() *
FeatureSpecs[0].Spec.getElementByteSize();
size_t NumberOfRecords = RawLogData[0].size() / Tensor0Size;
if (NumberOfRecords == 0)
return;
size_t RewardSize =
RewardSpec.getElementCount() * RewardSpec.getElementByteSize();
size_t NumberOfRewards = RawLogData.back().size() / RewardSize;
Logger::~Logger() {}
tensorflow::FeatureLists *FE = SE.mutable_feature_lists();
for (size_t I = 0; I < FeatureSpecs.size(); ++I)
writeRawTensorsAsFeatureLists(FE, FeatureSpecs[I], RawLogData[I].data(),
NumberOfRecords);
if (IncludeReward)
writeRawTensorsAsFeatureLists(FE, {RewardSpec, None},
RawLogData.back().data(), NumberOfRecords,
NumberOfRewards == 1);
std::string OutStr;
if (ProtobufTextMode) {
google::protobuf::TextFormat::PrintToString(SE, &OutStr);
} else {
OutStr = SE.SerializeAsString();
#define LOG_REWARD(NAME, TYPE) \
void Logger::log##NAME##Reward(TYPE Value) { \
assert(IncludeReward); \
LoggerData->logReward(Value); \
}
OS << OutStr;
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::print(raw_ostream &OS) { LoggerData->print(OS); }
#endif // defined(LLVM_HAVE_TF_API)

64
unittests/Analysis/TFUtilsTest.cpp
View File

@ -169,14 +169,14 @@ TEST(TFUtilsTest, Logger) {
const float F00[]{0.0, 0.1, 0.2, 0.3, 0.4, 0.5};
const int64_t F01[]{2, 3};
L.logTensorValue(0, F00, 6);
L.logTensorValue(1, F01, 2);
L.logReward<float>(3.4);
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.logTensorValue(0, F10, 6);
L.logTensorValue(1, F11, 2);
L.logReward<float>(-3.0);
L.logFloatValue(0, F10);
L.logInt64Value(1, F11);
L.logFloatReward(-3.0);
std::string Result;
raw_string_ostream OS(Result);
L.print(OS);
@ -193,6 +193,42 @@ TEST(TFUtilsTest, Logger) {
PROTO_CHECKER("reward", float_list, 1, R1);
}
TEST(TFUtilsTest, LoggerInt32FeaturesAndReward) {
std::vector<LoggedFeatureSpec> Features;
Features.push_back(
{TensorSpec::createSpec<float>("the_float", {2, 3}), None});
Features.push_back({TensorSpec::createSpec<int32_t>("the_int", {2}),
std::string("alternate_name")});
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.print(OS);
tensorflow::SequenceExample Expected;
EXPECT_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(TFUtilsTest, LoggerNoReward) {
std::vector<LoggedFeatureSpec> Features;
Features.push_back(
@ -205,12 +241,12 @@ TEST(TFUtilsTest, LoggerNoReward) {
const float F00[]{0.0, 0.1, 0.2, 0.3, 0.4, 0.5};
const int64_t F01[]{2, 3};
L.logTensorValue(0, F00, 6);
L.logTensorValue(1, F01, 2);
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.logTensorValue(0, F10, 6);
L.logTensorValue(1, F11, 2);
L.logFloatValue(0, F10);
L.logInt64Value(1, F11);
std::string Result;
raw_string_ostream OS(Result);
@ -230,12 +266,12 @@ TEST(TFUtilsTest, LoggerFinalReward) {
auto Rewards = TensorSpec::createSpec<float>("reward", {1});
Logger L(Features, Rewards, true);
for (size_t I = 0; I < 3; ++I) {
for (int64_t I = 0; I < 3; ++I) {
float F = static_cast<float>(I);
L.logTensorValue(0, &F);
L.logTensorValue(1, &I);
L.logFloatValue(0, &F);
L.logInt64Value(1, &I);
}
L.logFinalReward<float>(3.14);
L.logFloatFinalReward(3.14);
std::string Result;
raw_string_ostream OS(Result);
L.print(OS);