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

//===- 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/Analysis/Utils/TFUtils.h"
#include "llvm/ADT/Twine.h"
#include "llvm/Support/Debug.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>

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);
}
} // 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;
};

class TFModelEvaluatorImpl {
public:
  TFModelEvaluatorImpl(StringRef SavedModelPath,
                       const std::vector<std::string> &InputNames,
                       const std::vector<std::string> &OutputNames,
                       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, StringRef Name);
};
} // namespace llvm

TFModelEvaluatorImpl::TFModelEvaluatorImpl(
    StringRef SavedModelPath, const std::vector<std::string> &InputNames,
    const std::vector<std::string> &OutputNames, const char *Tags)
    : Graph(createTFGraph()), Options(createTFSessionOptions()),
      InputFeed(InputNames.size()), Input(InputNames.size()),
      OutputFeed(OutputNames.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 < InputNames.size(); ++I) {
    InputFeed[I] = {
        TF_GraphOperationByName(Graph.get(), (InputNames[I]).c_str()), 0};
    if (!checkReportAndInvalidate(InputFeed[I], InputNames[I]))
      return;
  }
  for (size_t I = 0; I < OutputNames.size(); ++I) {
    OutputFeed[I] = {
        TF_GraphOperationByName(Graph.get(), (OutputNames[I]).c_str()), 0};
    if (!checkReportAndInvalidate(OutputFeed[I], OutputNames[I]))
      return;
  }
}

TFModelEvaluator::TFModelEvaluator(StringRef SavedModelPath,
                                   const std::vector<std::string> &InputNames,
                                   const std::vector<std::string> &OutputNames,
                                   const char *Tags)
    : Impl(new TFModelEvaluatorImpl(SavedModelPath, InputNames, OutputNames,
                                    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,
                                                    StringRef Name) {
  if (Output.oper)
    return true;
  errs() << "Could not find TF_Output named: " + 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)) {}

void *TFModelEvaluator::EvaluationResult::getUntypedTensorValue(size_t Index) {
  return TF_TensorData(Impl->getOutput()[Index]);
}

void TFModelEvaluator::initInput(size_t Index, int TypeIndex,
                                 const std::vector<int64_t> &Dimensions) {
  Impl->initInput(Index, static_cast<TF_DataType>(TypeIndex), Dimensions);
}

template <> int TFModelEvaluator::getModelTypeIndex<float>() {
  return TF_FLOAT;
}

template <> int TFModelEvaluator::getModelTypeIndex<double>() {
  return TF_DOUBLE;
}

template <> int TFModelEvaluator::getModelTypeIndex<int8_t>() {
  return TF_INT8;
}

template <> int TFModelEvaluator::getModelTypeIndex<uint8_t>() {
  return TF_UINT8;
}

template <> int TFModelEvaluator::getModelTypeIndex<int16_t>() {
  return TF_INT16;
}

template <> int TFModelEvaluator::getModelTypeIndex<uint16_t>() {
  return TF_UINT16;
}

template <> int TFModelEvaluator::getModelTypeIndex<int32_t>() {
  return TF_INT32;
}

template <> int TFModelEvaluator::getModelTypeIndex<uint32_t>() {
  return TF_UINT32;
}

template <> int TFModelEvaluator::getModelTypeIndex<int64_t>() {
  return TF_INT64;
}

template <> int TFModelEvaluator::getModelTypeIndex<uint64_t>() {
  return TF_UINT64;
}

TFModelEvaluator::EvaluationResult::~EvaluationResult() {}
TFModelEvaluator::~TFModelEvaluator() {}