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

 //===- TFUtils.cpp - tensorflow evaluation utilities ----------------------===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//
 //
 // 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/Base64.h"
 #include "llvm/Support/CommandLine.h"
 #include "llvm/Support/Debug.h"
 #include "llvm/Support/JSON.h"
 #include "llvm/Support/MemoryBuffer.h"
 #include "llvm/Support/Path.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() {
     int Argc = 1;
     const char *Name = "";
     const char **NamePtr = &Name;
     TF_InitMain(Name, &Argc, const_cast<char ***>(&NamePtr));
   }
 };

 bool ensureInitTF() {
   static TFInitializer TFLibInitializer;
   return true;
 }

 TFGraphPtr createTFGraph() {
   return TFGraphPtr(TF_NewGraph(), &TF_DeleteGraph);
 }

 TFStatusPtr createTFStatus() {
   return TFStatusPtr(TF_NewStatus(), &TF_DeleteStatus);
 }

 TFSessionOptionsPtr createTFSessionOptions() {
   return TFSessionOptionsPtr(TF_NewSessionOptions(), &TF_DeleteSessionOptions);
 }

 int getTFTypeIndex(TensorType TType) {
   switch (TType) {
   case TensorType::Double:
     return TF_DOUBLE;
   case TensorType::Float:
     return TF_FLOAT;
   case TensorType::Int8:
     return TF_INT8;
   case TensorType::UInt8:
     return TF_UINT8;
   case TensorType::Int16:
     return TF_INT16;
   case TensorType::UInt16:
     return TF_UINT16;
   case TensorType::Int32:
     return TF_INT32;
   case TensorType::UInt32:
     return TF_UINT32;
   case TensorType::Int64:
     return TF_INT64;
   case TensorType::UInt64:
     return TF_UINT64;
   case TensorType::Invalid:
     llvm_unreachable("Unknown tensor type");
   }
 }
 } // 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<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);
 };

 } // 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();
   }
   size_t NrSupported = 0;
   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 (!InputFeed[I].oper) {
       continue;
     }
     if (NrSupported++ != I) {
       errs()
           << "Unsupported features must be placed at the end of the InputSpecs";
       invalidate();
       return;
     }
     if (!checkReportAndInvalidate(InputFeed[I], InputSpec))
       return;
     initInput(I, static_cast<TF_DataType>(getTFTypeIndex(InputSpec.type())),
               InputSpec.shape());
   }
   InputFeed.resize(NrSupported);
   Input.resize(NrSupported);

   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) {
   if (Index < Impl->getInput().size())
     return TF_TensorData(Impl->getInput()[Index]);
   return nullptr;
 }

 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]);
 }

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

 #endif // defined(LLVM_HAVE_TF_API)
	//===- TFUtils.cpp - tensorflow evaluation utilities ----------------------===//
	//
	// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
	// See https://llvm.org/LICENSE.txt for license information.
	// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
	//
	//===----------------------------------------------------------------------===//
	//
	// 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/Base64.h"
	#include "llvm/Support/CommandLine.h"
	#include "llvm/Support/Debug.h"
	#include "llvm/Support/JSON.h"
	#include "llvm/Support/MemoryBuffer.h"
	#include "llvm/Support/Path.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() {
	int Argc = 1;
	const char *Name = "";
	const char **NamePtr = &Name;
	TF_InitMain(Name, &Argc, const_cast<char ***>(&NamePtr));
	}
	};

	bool ensureInitTF() {
	static TFInitializer TFLibInitializer;
	return true;
	}

	TFGraphPtr createTFGraph() {
	return TFGraphPtr(TF_NewGraph(), &TF_DeleteGraph);
	}

	TFStatusPtr createTFStatus() {
	return TFStatusPtr(TF_NewStatus(), &TF_DeleteStatus);
	}

	TFSessionOptionsPtr createTFSessionOptions() {
	return TFSessionOptionsPtr(TF_NewSessionOptions(), &TF_DeleteSessionOptions);
	}

	int getTFTypeIndex(TensorType TType) {
	switch (TType) {
	case TensorType::Double:
	return TF_DOUBLE;
	case TensorType::Float:
	return TF_FLOAT;
	case TensorType::Int8:
	return TF_INT8;
	case TensorType::UInt8:
	return TF_UINT8;
	case TensorType::Int16:
	return TF_INT16;
	case TensorType::UInt16:
	return TF_UINT16;
	case TensorType::Int32:
	return TF_INT32;
	case TensorType::UInt32:
	return TF_UINT32;
	case TensorType::Int64:
	return TF_INT64;
	case TensorType::UInt64:
	return TF_UINT64;
	case TensorType::Invalid:
	llvm_unreachable("Unknown tensor type");
	}
	}
	} // 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<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);
	};

	} // 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();
	}
	size_t NrSupported = 0;
	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 (!InputFeed[I].oper) {
	continue;
	}
	if (NrSupported++ != I) {
	errs()
	<< "Unsupported features must be placed at the end of the InputSpecs";
	invalidate();
	return;
	}
	if (!checkReportAndInvalidate(InputFeed[I], InputSpec))
	return;
	initInput(I, static_cast<TF_DataType>(getTFTypeIndex(InputSpec.type())),
	InputSpec.shape());
	}
	InputFeed.resize(NrSupported);
	Input.resize(NrSupported);

	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) {
	if (Index < Impl->getInput().size())
	return TF_TensorData(Impl->getInput()[Index]);
	return nullptr;
	}

	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]);
	}

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

	#endif // defined(LLVM_HAVE_TF_API)