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llvm-mirror/lib/Analysis/TFUtils.cpp
Mircea Trofin 4559a48614 [NFC][MLGO] Just use the underlying protobuf object for logging 
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
2021-07-23 10:56:48 -07:00

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//===- TFUtils.cpp - tensorflow evaluation utilities ----------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
//
// This file implements utilities for interfacing with tensorflow C APIs.
//
//===----------------------------------------------------------------------===//
#include "llvm/Config/config.h"
#if defined(LLVM_HAVE_TF_API)
#include "llvm/ADT/Twine.h"
#include "llvm/Analysis/Utils/TFUtils.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/JSON.h"
#include "llvm/Support/ManagedStatic.h"
#include "llvm/Support/MemoryBuffer.h"
#include "llvm/Support/Path.h"
#include "llvm/Support/raw_ostream.h"
#include "google/protobuf/text_format.h"
#include "tensorflow/c/c_api.h"
#include "tensorflow/c/c_api_experimental.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 {
using TFGraphPtr = std::unique_ptr<TF_Graph, decltype(&TF_DeleteGraph)>;
using TFSessionOptionsPtr =
std::unique_ptr<TF_SessionOptions, decltype(&TF_DeleteSessionOptions)>;
using TFStatusPtr = std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)>;
struct TFInitializer {
TFInitializer() {
assert(!IsInitialized && "TFInitialized should be called only once");
int Argc = 1;
const char *Name = "";
const char **NamePtr = &Name;
TF_InitMain(Name, &Argc, const_cast<char ***>(&NamePtr));
IsInitialized = true;
}
bool IsInitialized = false;
};
llvm::ManagedStatic<TFInitializer> TFLibInitializer;
bool ensureInitTF() { return TFLibInitializer->IsInitialized; }
TFGraphPtr createTFGraph() {
return TFGraphPtr(TF_NewGraph(), &TF_DeleteGraph);
}
TFStatusPtr createTFStatus() {
return TFStatusPtr(TF_NewStatus(), &TF_DeleteStatus);
}
TFSessionOptionsPtr createTFSessionOptions() {
return TFSessionOptionsPtr(TF_NewSessionOptions(), &TF_DeleteSessionOptions);
}
} // namespace
namespace llvm {
class EvaluationResultImpl {
public:
EvaluationResultImpl(size_t OutputSize)
: OutputSize(OutputSize), Output(OutputSize){};
~EvaluationResultImpl() {
for (auto *P : Output)
if (P)
TF_DeleteTensor(P);
}
EvaluationResultImpl(const EvaluationResultImpl &) = delete;
EvaluationResultImpl(EvaluationResultImpl &&Other) = delete;
std::vector<TF_Tensor *> &getOutput() { return Output; }
private:
const size_t OutputSize;
std::vector<TF_Tensor *> Output;
};
size_t TensorSpec::getElementByteSize() const {
return TF_DataTypeSize(static_cast<TF_DataType>(TypeIndex));
}
TensorSpec::TensorSpec(const std::string &Name, int Port, int TypeIndex,
const std::vector<int64_t> &Shape)
: Name(Name), Port(Port), TypeIndex(TypeIndex), Shape(Shape),
ElementCount(std::accumulate(Shape.begin(), Shape.end(), 1,
std::multiplies<int64_t>())) {}
Optional<TensorSpec> getTensorSpecFromJSON(LLVMContext &Ctx,
const json::Value &Value) {
auto EmitError = [&](const llvm::Twine &Message) -> Optional<TensorSpec> {
std::string S;
llvm::raw_string_ostream OS(S);
OS << Value;
Ctx.emitError("Unable to parse JSON Value as spec (" + Message + "): " + S);
return None;
};
// FIXME: accept a Path as a parameter, and use it for error reporting.
json::Path::Root Root("tensor_spec");
json::ObjectMapper Mapper(Value, Root);
if (!Mapper)
return EmitError("Value is not a dict");
std::string TensorName;
int TensorPort = -1;
std::string TensorType;
std::vector<int64_t> TensorShape;
if (!Mapper.map<std::string>("name", TensorName))
return EmitError("'name' property not present or not a string");
if (!Mapper.map<std::string>("type", TensorType))
return EmitError("'type' property not present or not a string");
if (!Mapper.map<int>("port", TensorPort))
return EmitError("'port' property not present or not an int");
if (!Mapper.map<std::vector<int64_t>>("shape", TensorShape))
return EmitError("'shape' property not present or not an int array");
#define PARSE_TYPE(T, E) \
if (TensorType == #T) \
return TensorSpec::createSpec<T>(TensorName, TensorShape, TensorPort);
TFUTILS_SUPPORTED_TYPES(PARSE_TYPE)
#undef PARSE_TYPE
return None;
}
Optional<std::vector<LoggedFeatureSpec>>
loadOutputSpecs(LLVMContext &Ctx, StringRef ExpectedDecisionName,
StringRef ModelPath, StringRef SpecFileOverride) {
SmallVector<char, 128> OutputSpecsPath;
StringRef FileName = SpecFileOverride;
if (FileName.empty()) {
llvm::sys::path::append(OutputSpecsPath, ModelPath, "output_spec.json");
FileName = {OutputSpecsPath.data(), OutputSpecsPath.size()};
}
auto BufferOrError = MemoryBuffer::getFileOrSTDIN(FileName);
if (!BufferOrError) {
Ctx.emitError("Error opening output specs file: " + FileName + " : " +
BufferOrError.getError().message());
return None;
}
auto ParsedJSONValues = json::parse(BufferOrError.get()->getBuffer());
if (!ParsedJSONValues) {
Ctx.emitError("Could not parse specs file: " + FileName);
return None;
}
auto ValuesArray = ParsedJSONValues->getAsArray();
if (!ValuesArray) {
Ctx.emitError("Expected an array of {tensor_spec:<TensorSpec>, "
"logging_name:<name>} dictionaries");
return None;
}
std::vector<LoggedFeatureSpec> Ret;
for (const auto &Value : *ValuesArray)
if (const auto *Obj = Value.getAsObject())
if (const auto *SpecPart = Obj->get("tensor_spec"))
if (auto TensorSpec = getTensorSpecFromJSON(Ctx, *SpecPart))
if (auto LoggingName = Obj->getString("logging_name")) {
if (!TensorSpec->isElementType<int64_t>() &&
!TensorSpec->isElementType<int32_t>() &&
!TensorSpec->isElementType<float>()) {
Ctx.emitError(
"Only int64, int32, and float tensors are supported. "
"Found unsupported type for tensor named " +
TensorSpec->name());
return None;
}
Ret.push_back({*TensorSpec, LoggingName->str()});
}
if (ValuesArray->size() != Ret.size()) {
Ctx.emitError(
"Unable to parse output spec. It should be a json file containing an "
"array of dictionaries. Each dictionary must have a 'tensor_spec' key, "
"with a json object describing a TensorSpec; and a 'logging_name' key, "
"which is a string to use as name when logging this tensor in the "
"training log.");
return None;
}
if (Ret.empty() || *Ret[0].LoggingName != ExpectedDecisionName) {
Ctx.emitError("The first output spec must describe the decision tensor, "
"and must have the logging_name " +
StringRef(ExpectedDecisionName));
return None;
}
return Ret;
}
class TFModelEvaluatorImpl {
public:
TFModelEvaluatorImpl(StringRef SavedModelPath,
const std::vector<TensorSpec> &InputSpecs,
function_ref<TensorSpec(size_t)> GetOutputSpecs,
size_t OutputSpecsSize, const char *Tags);
bool isValid() const { return IsValid; }
size_t OutputSize() const { return OutputFeed.size(); }
void evaluate(TF_Tensor **Output, TF_Status *Status) {
TF_SessionRun(Session, nullptr, InputFeed.data(), Input.data(),
Input.size(), OutputFeed.data(), Output, OutputFeed.size(),
nullptr, 0, nullptr, Status);
}
void initInput(size_t Index, TF_DataType Type,
const std::vector<int64_t> &Dimensions);
const std::vector<TF_Tensor *> &getInput() const { return Input; }
~TFModelEvaluatorImpl();
private:
/// The objects necessary for carrying out an evaluation of the SavedModel.
/// They are expensive to set up, and we maintain them accross all the
/// evaluations of the model.
TF_Session *Session = nullptr;
TFGraphPtr Graph;
TFSessionOptionsPtr Options;
/// The specification of the input nodes.
std::vector<TF_Output> InputFeed;
/// The input tensors. They must match by index of the corresponding InputFeed
/// value. We set up the tensors once and just mutate theirs scalars before
/// each evaluation. The input tensors keep their value after an evaluation.
std::vector<TF_Tensor *> Input;
/// The specification of the output nodes. When evaluating, the tensors in the
/// output tensor vector must match by index the corresponding element in the
/// OutputFeed.
std::vector<TF_Output> OutputFeed;
void invalidate() { IsValid = false; }
bool IsValid = true;
/// Reusable utility for ensuring we can bind the requested Name to a node in
/// the SavedModel Graph.
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(
StringRef SavedModelPath, const std::vector<TensorSpec> &InputSpecs,
function_ref<TensorSpec(size_t)> GetOutputSpecs, size_t OutputSpecsSize,
const char *Tags = "serve")
: Graph(createTFGraph()), Options(createTFSessionOptions()),
InputFeed(InputSpecs.size()), Input(InputSpecs.size()),
OutputFeed(OutputSpecsSize) {
if (!ensureInitTF()) {
errs() << "Tensorflow should have been initialized";
return;
}
auto Status = createTFStatus();
Session = TF_LoadSessionFromSavedModel(Options.get(), nullptr,
SavedModelPath.str().c_str(), &Tags, 1,
Graph.get(), nullptr, Status.get());
if (TF_GetCode(Status.get()) != TF_Code::TF_OK) {
errs() << TF_Message(Status.get());
invalidate();
}
for (size_t I = 0; I < InputSpecs.size(); ++I) {
auto &InputSpec = InputSpecs[I];
InputFeed[I] = {
TF_GraphOperationByName(Graph.get(), (InputSpec.name()).c_str()),
InputSpec.port()};
if (!checkReportAndInvalidate(InputFeed[I], InputSpec))
return;
initInput(I, static_cast<TF_DataType>(InputSpec.typeIndex()),
InputSpec.shape());
}
for (size_t I = 0; I < OutputSpecsSize; ++I) {
auto OutputSpec = GetOutputSpecs(I);
OutputFeed[I] = {
TF_GraphOperationByName(Graph.get(), (OutputSpec.name()).c_str()),
OutputSpec.port()};
if (!checkReportAndInvalidate(OutputFeed[I], OutputSpec))
return;
}
}
TFModelEvaluator::TFModelEvaluator(
StringRef SavedModelPath, const std::vector<TensorSpec> &InputSpecs,
function_ref<TensorSpec(size_t)> GetOutputSpecs, size_t OutputSpecsSize,
const char *Tags)
: Impl(new TFModelEvaluatorImpl(SavedModelPath, InputSpecs, GetOutputSpecs,
OutputSpecsSize, Tags)) {
if (!Impl->isValid())
Impl.reset();
}
TFModelEvaluator::TFModelEvaluator(StringRef SavedModelPath,
const std::vector<TensorSpec> &InputSpecs,
const std::vector<TensorSpec> &OutputSpecs,
const char *Tags)
: TFModelEvaluator(
SavedModelPath, InputSpecs, [&](size_t I) { return OutputSpecs[I]; },
OutputSpecs.size(), Tags) {}
TFModelEvaluatorImpl::~TFModelEvaluatorImpl() {
for (auto *T : Input) {
TF_DeleteTensor(T);
}
if (Session == nullptr)
return;
auto Status = createTFStatus();
TF_DeleteSession(Session, Status.get());
Session = nullptr;
if (TF_GetCode(Status.get()) != TF_Code::TF_OK)
errs() << "Could not delete TF session";
}
bool TFModelEvaluatorImpl::checkReportAndInvalidate(
const TF_Output &Output, const TensorSpec &OutputSpec) {
if (Output.oper)
return true;
errs() << "Could not find TF_Output named: " + OutputSpec.name();
IsValid = false;
return IsValid;
}
Optional<TFModelEvaluator::EvaluationResult> TFModelEvaluator::evaluate() {
if (!isValid())
return None;
std::unique_ptr<EvaluationResultImpl> Ret =
std::make_unique<EvaluationResultImpl>(Impl->OutputSize());
auto Status = createTFStatus();
Impl->evaluate(Ret->getOutput().data(), Status.get());
if (TF_GetCode(Status.get()) != TF_Code::TF_OK) {
errs() << TF_Message(Status.get());
Impl.reset();
return None;
}
return EvaluationResult(std::move(Ret));
}
void TFModelEvaluatorImpl::initInput(size_t Index, TF_DataType Type,
const std::vector<int64_t> &Dimensions) {
int64_t TotalSize = TF_DataTypeSize(Type);
for (auto &D : Dimensions)
TotalSize *= D;
Input[Index] =
TF_AllocateTensor(Type, Dimensions.data(), Dimensions.size(), TotalSize);
std::memset(TF_TensorData(Input[Index]), 0, TotalSize);
}
void *TFModelEvaluator::getUntypedInput(size_t Index) {
return TF_TensorData(Impl->getInput()[Index]);
}
TFModelEvaluator::EvaluationResult::EvaluationResult(
std::unique_ptr<EvaluationResultImpl> Impl)
: Impl(std::move(Impl)) {}
TFModelEvaluator::EvaluationResult::EvaluationResult(EvaluationResult &&Other)
: Impl(std::move(Other.Impl)) {}
TFModelEvaluator::EvaluationResult &
TFModelEvaluator::EvaluationResult::operator=(EvaluationResult &&Other) {
Impl = std::move(Other.Impl);
return *this;
}
void *TFModelEvaluator::EvaluationResult::getUntypedTensorValue(size_t Index) {
return TF_TensorData(Impl->getOutput()[Index]);
}
const void *
TFModelEvaluator::EvaluationResult::getUntypedTensorValue(size_t Index) const {
return TF_TensorData(Impl->getOutput()[Index]);
}
#define TFUTILS_GETDATATYPE_IMPL(T, E) \
template <> int TensorSpec::getDataType<T>() { return E; }
TFUTILS_SUPPORTED_TYPES(TFUTILS_GETDATATYPE_IMPL)
#undef TFUTILS_GETDATATYPE_IMPL
TFModelEvaluator::EvaluationResult::~EvaluationResult() {}
TFModelEvaluator::~TFModelEvaluator() {}
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::print(raw_ostream &OS) { LoggerData->print(OS); }
#endif // defined(LLVM_HAVE_TF_API)
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