llvm/lib/Analysis/TFUtils.cpp - third_party/llvm-project - Git at Google

 //===- 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);
 }
 } // 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;
   };
   json::ObjectMapper Mapper(Value);
   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() {}
 #endif // defined(LLVM_HAVE_TF_API)
	//===- 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);
	}
	} // 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;
	};
	json::ObjectMapper Mapper(Value);
	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() {}
	#endif // defined(LLVM_HAVE_TF_API)