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llvm-project/llvm/lib/Analysis/TFUtils.cpp

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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/Debug.h"
#include "llvm/Support/JSON.h"
#include "llvm/Support/ManagedStatic.h"
#include "llvm/Support/raw_ostream.h"
#include "tensorflow/c/c_api.h"
#include "tensorflow/c/c_api_experimental.h"
#include <cassert>
#include <numeric>
using namespace llvm;
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);
}
/// Write the values of one tensor as a list.
template <typename T>
void writeTensorValues(raw_ostream &OutFile, const char *TensorData,
size_t ElemCount) {
OutFile << "[";
const T *TypedData = reinterpret_cast<const T *>(TensorData);
for (size_t I = 0; I < ElemCount; ++I) {
if (I > 0)
OutFile << ", ";
OutFile << TypedData[I];
}
OutFile << "]";
}
/// 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(raw_ostream &OutFile,
const Logger::LoggedFeatureSpec &LoggedSpec,
const char *TensorData, size_t TensorCount,
bool FinalReward = false) {
const char *FieldName = "<invalid>";
std::function<void(const char *)> ValueWriter;
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.
if (Spec.isElementType<int64_t>()) {
FieldName = "int64_list";
ValueWriter = [&](const char *Data) {
writeTensorValues<int64_t>(OutFile, Data, Spec.getElementCount());
};
} else if (Spec.isElementType<int32_t>()) {
FieldName = "int64_list";
ValueWriter = [&](const char *Data) {
writeTensorValues<int32_t>(OutFile, Data, Spec.getElementCount());
};
} else if (Spec.isElementType<float>()) {
FieldName = "float_list";
ValueWriter = [&](const char *Data) {
writeTensorValues<float>(OutFile, Data, Spec.getElementCount());
};
} else {
llvm_unreachable("Unsupported tensor type.");
}
OutFile << " feature_list: {\n";
OutFile << " key: "
<< "\""
<< (LoggedSpec.LoggingName ? *LoggedSpec.LoggingName : Spec.name())
<< "\" ";
OutFile << "value: {\n";
size_t TensorByteSize = Spec.getElementCount() * Spec.getElementByteSize();
auto WriteFeatureProto = [&](const char *P) {
OutFile << " feature: { " << FieldName << ": { value: ";
ValueWriter(P);
OutFile << " } }\n";
};
const char *CurrentTensor = TensorData;
static int64_t Zero = 0;
// Write all but the last value. If this is the final reward, don't increment
// the CurrentTensor, and just write 0.
for (size_t I = 0; I < TensorCount - 1; ++I) {
if (FinalReward)
WriteFeatureProto(reinterpret_cast<const char *>(&Zero));
else {
WriteFeatureProto(CurrentTensor);
CurrentTensor += TensorByteSize;
}
}
WriteFeatureProto(CurrentTensor);
OutFile << " }\n";
OutFile << " }\n";
}
} // 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;
}
class TFModelEvaluatorImpl {
public:
TFModelEvaluatorImpl(StringRef SavedModelPath,
const std::vector<TensorSpec> &InputSpecs,
const std::vector<TensorSpec> &OutputSpecs,
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);
};
} // namespace llvm
TFModelEvaluatorImpl::TFModelEvaluatorImpl(
StringRef SavedModelPath, const std::vector<TensorSpec> &InputSpecs,
const std::vector<TensorSpec> &OutputSpecs, const char *Tags)
: Graph(createTFGraph()), Options(createTFSessionOptions()),
InputFeed(InputSpecs.size()), Input(InputSpecs.size()),
OutputFeed(OutputSpecs.size()) {
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 < OutputSpecs.size(); ++I) {
auto &OutputSpec = OutputSpecs[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,
const std::vector<TensorSpec> &OutputSpecs,
const char *Tags)
: Impl(new TFModelEvaluatorImpl(SavedModelPath, InputSpecs, OutputSpecs,
Tags)) {
if (!Impl->isValid())
Impl.reset();
}
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() {}
void Logger::print(raw_ostream &OS) {
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;
OS << "feature_lists: {\n";
for (size_t I = 0; I < FeatureSpecs.size(); ++I)
writeRawTensorsAsFeatureLists(OS, FeatureSpecs[I], RawLogData[I].data(),
NumberOfRecords);
if (IncludeReward)
writeRawTensorsAsFeatureLists(OS, {RewardSpec, None},
RawLogData.back().data(), NumberOfRecords,
NumberOfRewards == 1);
OS << "}\n";
}
#endif // defined(LLVM_HAVE_TF_API)
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