[llvm] caf395e - Reapply "[llvm] Native size estimator for training -Oz inliner"
Mircea Trofin via llvm-commits
llvm-commits at lists.llvm.org
Tue Jul 14 07:04:26 PDT 2020
Could you paste the cmake command line and the build command line, for
repro? Thanks!
On Tue, Jul 14, 2020, 06:21 Florian Hahn <florian_hahn at apple.com> wrote:
> Hi,
>
>
> The patch below seems to cause a build-failure on macOS caused by some
> undefined symbols (see below).
>
> It would be great if you could take a look.
>
> Cheers,
> Florian
>
> Undefined symbols for architecture x86_64:
> "llvm::InlineSizeEstimatorAnalysis::Key", referenced from:
>
> llvm::PassBuilder::registerFunctionAnalyses(llvm::AnalysisManager<llvm::Function>&)
> in libLLVMPasses.a(PassBuilder.cpp.o)
> llvm::detail::AnalysisResultModel<llvm::Function,
> llvm::InlineSizeEstimatorAnalysis, llvm::Optional<unsigned long>,
> llvm::PreservedAnalyses,
> llvm::AnalysisManager<llvm::Function>::Invalidator,
> false>::invalidate(llvm::Function&, llvm::PreservedAnalyses const&,
> llvm::AnalysisManager<llvm::Function>::Invalidator&) in
> libLLVMPasses.a(PassBuilder.cpp.o)
> llvm::RequireAnalysisPass<llvm::InlineSizeEstimatorAnalysis,
> llvm::Function, llvm::AnalysisManager<llvm::Function>
> >::run(llvm::Function&, llvm::AnalysisManager<llvm::Function>&) in
> libLLVMPasses.a(PassBuilder.cpp.o)
> llvm::detail::PassModel<llvm::Function,
> llvm::InvalidateAnalysisPass<llvm::InlineSizeEstimatorAnalysis>,
> llvm::PreservedAnalyses, llvm::AnalysisManager<llvm::Function>
> >::run(llvm::Function&, llvm::AnalysisManager<llvm::Function>&) in
> libLLVMPasses.a(PassBuilder.cpp.o)
> "llvm::InlineSizeEstimatorAnalysis::run(llvm::Function const&,
> llvm::AnalysisManager<llvm::Function>&)", referenced from:
> llvm::detail::AnalysisPassModel<llvm::Function,
> llvm::InlineSizeEstimatorAnalysis, llvm::PreservedAnalyses,
> llvm::AnalysisManager<llvm::Function>::Invalidator>::run(llvm::Function&,
> llvm::AnalysisManager<llvm::Function>&) in
> libLLVMPasses.a(PassBuilder.cpp.o)
>
> "llvm::InlineSizeEstimatorAnalysis::InlineSizeEstimatorAnalysis(llvm::InlineSizeEstimatorAnalysis&&)",
> referenced from:
>
> llvm::PassBuilder::registerFunctionAnalyses(llvm::AnalysisManager<llvm::Function>&)
> in libLLVMPasses.a(PassBuilder.cpp.o)
> "llvm::InlineSizeEstimatorAnalysis::InlineSizeEstimatorAnalysis()",
> referenced from:
>
> llvm::PassBuilder::registerFunctionAnalyses(llvm::AnalysisManager<llvm::Function>&)
> in libLLVMPasses.a(PassBuilder.cpp.o)
> "llvm::InlineSizeEstimatorAnalysis::~InlineSizeEstimatorAnalysis()",
> referenced from:
>
> llvm::PassBuilder::registerFunctionAnalyses(llvm::AnalysisManager<llvm::Function>&)
> in libLLVMPasses.a(PassBuilder.cpp.o)
> llvm::detail::AnalysisPassModel<llvm::Function,
> llvm::InlineSizeEstimatorAnalysis, llvm::PreservedAnalyses,
> llvm::AnalysisManager<llvm::Function>::Invalidator>::~AnalysisPassModel()
> in libLLVMPasses.a(PassBuilder.cpp.o)
> llvm::detail::AnalysisPassModel<llvm::Function,
> llvm::InlineSizeEstimatorAnalysis, llvm::PreservedAnalyses,
> llvm::AnalysisManager<llvm::Function>::Invalidator>::~AnalysisPassModel()
> in libLLVMPasses.a(PassBuilder.cpp.o)
>
> > On Jul 14, 2020, at 00:26, Mircea Trofin via llvm-commits <
> llvm-commits at lists.llvm.org> wrote:
> >
> >
> > Author: Mircea Trofin
> > Date: 2020-07-13T16:26:26-07:00
> > New Revision: caf395ee8c28028d5af0f1455cd5ef134432124c
> >
> > URL:
> https://github.com/llvm/llvm-project/commit/caf395ee8c28028d5af0f1455cd5ef134432124c
> > DIFF:
> https://github.com/llvm/llvm-project/commit/caf395ee8c28028d5af0f1455cd5ef134432124c.diff
> >
> > LOG: Reapply "[llvm] Native size estimator for training -Oz inliner"
> >
> > This reverts commit 9908a3b9f521c954cbf6adcec35b14b2f6c8da49.
> >
> > The fix was to exclude the content of TFUtils.h (automatically
> > included in the LLVM_Analysis module, when LLVM_ENABLE_MODULES is
> enabled).
> >
> > Differential Revision: https://reviews.llvm.org/D82817
> >
> > Added:
> > llvm/include/llvm/Analysis/InlineSizeEstimatorAnalysis.h
> > llvm/include/llvm/Analysis/Utils/TFUtils.h
> > llvm/lib/Analysis/InlineSizeEstimatorAnalysis.cpp
> > llvm/lib/Analysis/TFUtils.cpp
> > llvm/unittests/Analysis/InlineSizeEstimatorAnalysisTest.cpp
> >
> llvm/unittests/Analysis/Inputs/ir2native_x86_64_model/saved_model.pbtxt
> >
> llvm/unittests/Analysis/Inputs/ir2native_x86_64_model/variables/variables.data-00000-of-00001
> >
> llvm/unittests/Analysis/Inputs/ir2native_x86_64_model/variables/variables.index
> > llvm/unittests/Analysis/TFUtilsTest.cpp
> >
> > Modified:
> > llvm/CMakeLists.txt
> > llvm/lib/Analysis/CMakeLists.txt
> > llvm/lib/Passes/PassBuilder.cpp
> > llvm/lib/Passes/PassRegistry.def
> > llvm/unittests/Analysis/CMakeLists.txt
> >
> > Removed:
> >
> >
> >
> >
> ################################################################################
> > diff --git a/llvm/CMakeLists.txt b/llvm/CMakeLists.txt
> > index de2887b64c2a..4e14e61fcacd 100644
> > --- a/llvm/CMakeLists.txt
> > +++ b/llvm/CMakeLists.txt
> > @@ -981,6 +981,18 @@ if (NOT TENSORFLOW_AOT_PATH STREQUAL "")
> > ${CMAKE_ARCHIVE_OUTPUT_DIRECTORY}/tf_runtime)
> > endif()
> >
> > +set(TENSORFLOW_C_LIB_PATH "" CACHE PATH "Path to TensorFlow C library
> install")
> > +find_library(tensorflow_c_api tensorflow PATHS
> ${TENSORFLOW_C_LIB_PATH}/lib)
> > +
> > +# Similar to the above Tensorflow dependency, please refer to the same
> script.
> > +# In this case, the latest C API library is available for download from
> > +# https://www.tensorflow.org/install/lang_c
> > +if (tensorflow_c_api)
> > + set(LLVM_HAVE_TF_API "ON" CACHE BOOL "Full Tensorflow API available")
> > + add_definitions("-DLLVM_HAVE_TF_API")
> > + include_directories(${TENSORFLOW_C_LIB_PATH}/include)
> > +endif()
> > +
> > # Put this before tblgen. Else we have a circular dependence.
> > add_subdirectory(lib/Demangle)
> > add_subdirectory(lib/Support)
> >
> > diff --git a/llvm/include/llvm/Analysis/InlineSizeEstimatorAnalysis.h
> b/llvm/include/llvm/Analysis/InlineSizeEstimatorAnalysis.h
> > new file mode 100644
> > index 000000000000..29a6f5914674
> > --- /dev/null
> > +++ b/llvm/include/llvm/Analysis/InlineSizeEstimatorAnalysis.h
> > @@ -0,0 +1,35 @@
> > +//===- InlineSizeEstimatorAnalysis.h - ML size estimator --------*- C++
> -*-===//
> > +//
> > +// 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
> > +//
> >
> +//===----------------------------------------------------------------------===//
> > +//
> > +
> > +#ifndef LLVM_ANALYSIS_INLINESIZEESTIMATORANALYSIS_H
> > +#define LLVM_ANALYSIS_INLINESIZEESTIMATORANALYSIS_H
> > +
> > +#include "llvm/IR/PassManager.h"
> > +
> > +namespace llvm {
> > +class Function;
> > +
> > +class TFModelEvaluator;
> > +class InlineSizeEstimatorAnalysis
> > + : public AnalysisInfoMixin<InlineSizeEstimatorAnalysis> {
> > +public:
> > + InlineSizeEstimatorAnalysis();
> > + InlineSizeEstimatorAnalysis(InlineSizeEstimatorAnalysis &&);
> > + ~InlineSizeEstimatorAnalysis();
> > +
> > + static AnalysisKey Key;
> > + using Result = Optional<size_t>;
> > + Result run(const Function &F, FunctionAnalysisManager &FAM);
> > + static bool isEvaluatorRequested();
> > +
> > +private:
> > + std::unique_ptr<TFModelEvaluator> Evaluator;
> > +};
> > +} // namespace llvm
> > +#endif // LLVM_ANALYSIS_INLINESIZEESTIMATORANALYSIS_H
> > \ No newline at end of file
> >
> > diff --git a/llvm/include/llvm/Analysis/Utils/TFUtils.h
> b/llvm/include/llvm/Analysis/Utils/TFUtils.h
> > new file mode 100644
> > index 000000000000..b7de199753a6
> > --- /dev/null
> > +++ b/llvm/include/llvm/Analysis/Utils/TFUtils.h
> > @@ -0,0 +1,138 @@
> > +//===- TFUtils.h - utilities for tensorflow C API ---------------*- C++
> -*-===//
> > +//
> > +// 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
> > +//
> >
> +//===----------------------------------------------------------------------===//
> > +//
> > +#ifndef LLVM_ANALYSIS_UTILS_TFUTILS_H
> > +#define LLVM_ANALYSIS_UTILS_TFUTILS_H
> > +
> > +#ifdef LLVM_HAVE_TF_API
> > +#include "tensorflow/c/c_api.h"
> > +#include "llvm/IR/LLVMContext.h"
> > +
> > +#include <memory>
> > +#include <vector>
> > +
> > +namespace llvm {
> > +
> > +/// Load a SavedModel, find the given inputs and outputs, and setup
> storage
> > +/// for input tensors. The user is responsible for correctly
> dimensioning the
> > +/// input tensors and setting their values before calling evaluate().
> > +/// To initialize:
> > +/// - construct the object
> > +/// - initialize the input tensors using initInput. Indices must
> correspond to
> > +/// indices in the InputNames used at construction.
> > +/// To use:
> > +/// - set input values by using getInput to get each input tensor, and
> then
> > +/// setting internal scalars, for all dimensions (tensors are
> row-major:
> > +///
> https://github.com/tensorflow/tensorflow/blob/r1.5/tensorflow/c/c_api.h#L205
> )
> > +/// - prepare an output vector of TF_Output* type, with the correct
> number of
> > +/// outputs (i.e. same as OutputNames). Initialize the vector with
> nullptr
> > +/// values.
> > +/// - call evaluate. The input tensors' values are not consumed after
> this, and
> > +/// may still be read.
> > +/// - use the outputs in the output vector
> > +/// - deallocate each output tensor in the output vector, using
> TF_DeleteTensor.
> > +class TFModelEvaluator final {
> > +public:
> > + /// The result of a model evaluation. Handles the lifetime of the
> output
> > + /// TF_Tensor objects, which means that their values need to be used
> before
> > + /// the EvaluationResult's dtor is called.
> > + class EvaluationResult {
> > + public:
> > + ~EvaluationResult() {
> > + for (auto *P : Output)
> > + if (P)
> > + TF_DeleteTensor(P);
> > + }
> > +
> > + EvaluationResult(const EvaluationResult &) = delete;
> > + EvaluationResult(EvaluationResult &&Other)
> > + : OutputSize(Other.OutputSize), Output(std::move(Other.Output))
> {
> > + Other.Output.clear();
> > + };
> > +
> > + /// Get a pointer to the first element of the tensor at Index.
> > + template <typename T> T *getTensorValue(size_t Index) {
> > + return static_cast<T *>(TF_TensorData(Output[Index]));
> > + }
> > +
> > + private:
> > + friend class TFModelEvaluator;
> > + EvaluationResult(size_t OutputSize)
> > + : OutputSize(OutputSize), Output(OutputSize){};
> > +
> > + const size_t OutputSize;
> > + std::vector<TF_Tensor *> Output;
> > + };
> > +
> > + 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)>;
> > +
> > + TFModelEvaluator(StringRef SavedModelPath,
> > + const std::vector<std::string> &InputNames,
> > + const std::vector<std::string> &OutputNames,
> > + const char *Tags = "serve");
> > + ~TFModelEvaluator();
> > + TFModelEvaluator(const TFModelEvaluator &) = delete;
> > + TFModelEvaluator(TFModelEvaluator &&) = delete;
> > +
> > + /// Evaluate the model, assuming it is valid. Returns None if the
> evaluation
> > + /// fails or the model is invalid, or an EvaluationResult otherwise.
> The
> > + /// inputs are assumed to have been already provided via getInput().
> When
> > + /// returning None, it also marks the object invalid. Pass an Output
> vector
> > + /// with the same size as OutputNames, but with nullptr values.
> evaluate()
> > + /// will populate it with tensors, matching in index the corresponding
> > + /// OutputNames. The caller is responsible for the deallocation of
> those
> > + /// tensors, using TF_DeleteTensor.
> > + Optional<EvaluationResult> evaluate();
> > +
> > + /// Provides access to the input vector. It is already dimensioned
> correctly,
> > + /// but the values need to be allocated by the user.
> > + std::vector<TF_Tensor *> &getInput() { return Input; }
> > +
> > + /// Returns true if the tensorflow model was loaded successfully,
> false
> > + /// otherwise.
> > + bool isValid() const { return !!Session; }
> > +
> > + /// Initialize the input at Index as a tensor of the given type and
> dimensions
> > + void initInput(int Index, TF_DataType Type,
> > + const std::vector<int64_t> &Dimensions);
> > +
> > +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;
> > +
> > + /// Reusable utility for deleting the session.
> > + void deleteSession();
> > +
> > + /// Reusable utility for ensuring we can bind the requested Name to a
> node in
> > + /// the SavedModel Graph.
> > + bool checkReportAndReset(const TF_Output &Output, StringRef Name);
> > +};
> > +} // namespace llvm
> > +
> > +#endif // LLVM_HAVE_TF_API
> > +#endif // LLVM_ANALYSIS_UTILS_TFUTILS_H
> >
> > diff --git a/llvm/lib/Analysis/CMakeLists.txt
> b/llvm/lib/Analysis/CMakeLists.txt
> > index a317579ecc83..703623396d96 100644
> > --- a/llvm/lib/Analysis/CMakeLists.txt
> > +++ b/llvm/lib/Analysis/CMakeLists.txt
> > @@ -1,17 +1,35 @@
> > set(CommonMLSources MLInlineAdvisor.cpp)
> > set(ReleaseModeMLSources ReleaseModeModelRunner.cpp)
> > +set(DevelopmentModeMLSources TFUtils.cpp)
> >
> > -if (DEFINED LLVM_HAVE_TF_AOT)
> > - include(TensorFlowCompile)
> > - tfcompile(models/inliner serve action InlinerSizeModel
> llvm::InlinerSizeModel)
> > - list(APPEND ReleaseModeMLSources
> > - $<TARGET_OBJECTS:tf_xla_runtime_objects>
> > - ${GENERATED_OBJS}
> > - )
> > - set(MLPolicySources ${CommonMLSources} ${ReleaseModeMLSources})
> > +if (DEFINED LLVM_HAVE_TF_AOT OR DEFINED LLVM_HAVE_TF_API)
> > + set(MLPolicySources ${CommonMLSources})
> > + if (DEFINED LLVM_HAVE_TF_AOT)
> > + include(TensorFlowCompile)
> > + tfcompile(models/inliner serve action InlinerSizeModel
> llvm::InlinerSizeModel)
> > + list(APPEND ReleaseModeMLSources
> > + $<TARGET_OBJECTS:tf_xla_runtime_objects>
> > + ${GENERATED_OBJS}
> > + )
> > + LIST(APPEND MLPolicySources ${ReleaseModeMLSources})
> > + else()
> > + LIST(APPEND LLVM_OPTIONAL_SOURCES ${ReleaseModeMLSources})
> > + endif()
> > +
> > + if (DEFINED LLVM_HAVE_TF_API)
> > + LIST(APPEND MLPolicySources ${DevelopmentModeMLSources})
> > + LIST(APPEND MLLinkDeps ${tensorflow_c_api})
> > + else()
> > + LIST(APPEND LLVM_OPTIONAL_SOURCES ${DevelopmentModeMLSources})
> > + endif()
> > else()
> > - set(LLVM_OPTIONAL_SOURCES ${CommonMLSources} ${ReleaseModeMLSources})
> > + LIST(APPEND LLVM_OPTIONAL_SOURCES
> > + ${CommonMLSources}
> > + ${DevelopmentModeMLSources}
> > + ${ReleaseModeMLSources}
> > + )
> > endif()
> > +
> >
> > add_llvm_component_library(LLVMAnalysis
> > AliasAnalysis.cpp
> > @@ -57,6 +75,7 @@ add_llvm_component_library(LLVMAnalysis
> > InlineCost.cpp
> > InlineAdvisor.cpp
> > InlineFeaturesAnalysis.cpp
> > + InlineSizeEstimatorAnalysis.cpp
> > InstCount.cpp
> > InstructionPrecedenceTracking.cpp
> > InstructionSimplify.cpp
> > @@ -124,4 +143,7 @@ add_llvm_component_library(LLVMAnalysis
> >
> > DEPENDS
> > intrinsics_gen
> > +
> > + LINK_LIBS
> > + ${MLLinkDeps}
> > )
> >
> > diff --git a/llvm/lib/Analysis/InlineSizeEstimatorAnalysis.cpp
> b/llvm/lib/Analysis/InlineSizeEstimatorAnalysis.cpp
> > new file mode 100644
> > index 000000000000..1d1952ae6cbb
> > --- /dev/null
> > +++ b/llvm/lib/Analysis/InlineSizeEstimatorAnalysis.cpp
> > @@ -0,0 +1,299 @@
> > +//===- InlineSizeEstimatorAnalysis.cpp - IR to native size from ML
> model --===//
> > +//
> > +// The LLVM Compiler Infrastructure
> > +//
> > +// This file is distributed under the University of Illinois Open Source
> > +// License. See LICENSE.TXT for details.
> > +//
> >
> +//===----------------------------------------------------------------------===//
> > +//
> > +// This implements feature and label extraction for offline supervised
> learning
> > +// of a IR to native size model.
> > +//
> >
> +//===----------------------------------------------------------------------===//
> > +#include "llvm/Analysis/InlineSizeEstimatorAnalysis.h"
> > +
> > +#ifdef LLVM_HAVE_TF_API
> > +#include "llvm/Analysis/Utils/TFUtils.h"
> > +#endif
> > +#include "llvm/Analysis/LoopInfo.h"
> > +#include "llvm/Analysis/TargetLibraryInfo.h"
> > +#include "llvm/Analysis/TargetTransformInfo.h"
> > +#include "llvm/IR/BasicBlock.h"
> > +#include "llvm/IR/Dominators.h"
> > +#include "llvm/IR/Function.h"
> > +#include "llvm/IR/Instructions.h"
> > +#include "llvm/IR/PassManager.h"
> > +#include "llvm/MC/MCAsmLayout.h"
> > +#include "llvm/Support/Casting.h"
> > +#include "llvm/Support/CommandLine.h"
> > +#include "llvm/Support/raw_ostream.h"
> > +
> > +#include <algorithm>
> > +#include <deque>
> > +
> > +using namespace llvm;
> > +
> > +AnalysisKey InlineSizeEstimatorAnalysis::Key;
> > +
> > +#define DEBUG_TYPE "inline-size-estimator"
> > +
> > +#ifdef LLVM_HAVE_TF_API
> > +cl::opt<std::string> TFIR2NativeModelPath(
> > + "ml-inliner-ir2native-model", cl::Hidden,
> > + cl::desc("Path to saved model evaluating native size from IR."));
> > +
> > +namespace {
> > +unsigned getMaxInstructionID() {
> > +#define LAST_OTHER_INST(NR) return NR;
> > +#include "llvm/IR/Instruction.def"
> > +}
> > +
> > +class IRToNativeSizeLearning {
> > +public:
> > + enum class NamedFeatureIndex : size_t {
> > + InitialSize,
> > + Blocks,
> > + Calls,
> > + IsLocal,
> > + IsLinkOnceODR,
> > + IsLinkOnce,
> > + Loops,
> > + MaxLoopDepth,
> > + MaxDomTreeLevel,
> > +
> > + NumNamedFeatures
> > + };
> > + static const size_t NumNamedFeatures =
> > + static_cast<size_t>(NamedFeatureIndex::NumNamedFeatures);
> > + struct FunctionFeatures {
> > + static std::vector<std::pair<size_t, size_t>>
> > + ImportantInstructionSuccessions;
> > + static const size_t FeatureCount;
> > +
> > + std::array<int32_t, NumNamedFeatures> NamedFeatures = {0};
> > + std::vector<int32_t> InstructionHistogram;
> > + std::vector<int32_t> InstructionPairHistogram;
> > +
> > + void fillTensor(int32_t *Ptr) const;
> > + int32_t &operator[](NamedFeatureIndex Pos) {
> > + return NamedFeatures[static_cast<size_t>(Pos)];
> > + }
> > + };
> > + IRToNativeSizeLearning() = default;
> > +
> > + static FunctionFeatures getFunctionFeatures(Function &F,
> > + FunctionAnalysisManager
> &FAM);
> > +
> > +private:
> > + /// Sort once the feature tuples.
> > + struct SortFeatureTuples {
> > + bool IsSorted = false;
> > + SortFeatureTuples() {
> > +
> std::sort(FunctionFeatures::ImportantInstructionSuccessions.begin(),
> > +
> FunctionFeatures::ImportantInstructionSuccessions.end());
> > + IsSorted = true;
> > + }
> > + };
> > +
> > + static llvm::ManagedStatic<SortFeatureTuples> TupleSorter;
> > +
> > + static bool ensureSortedTuples() { return TupleSorter->IsSorted; }
> > +};
> > +llvm::ManagedStatic<IRToNativeSizeLearning::SortFeatureTuples>
> > + IRToNativeSizeLearning::TupleSorter;
> > +
> > +// This is a point in time - we determined including these pairs of
> > +// consecutive instructions (in the IR layout available at inline time)
> as
> > +// features improves the model performance. We want to move away from
> manual
> > +// feature selection.
> > +// The vector is given in opcode pairs rather than labels because 1)
> labels
> > +// weren't readily available, and 2) the successions were hand -
> extracted
> > +std::vector<std::pair<size_t, size_t>>
> > +
> IRToNativeSizeLearning::FunctionFeatures::ImportantInstructionSuccessions =
> > + {{1, 34}, {15, 27}, {53, 53}, {53, 34}, {1, 11}, {32, 2},
> {2, 48},
> > + {28, 48}, {1, 45}, {49, 32}, {57, 56}, {55, 53}, {1, 28},
> {57, 34},
> > + {1, 1}, {32, 28}, {32, 15}, {49, 28}, {53, 1}, {2, 53},
> {48, 34},
> > + {28, 53}, {2, 32}, {1, 40}, {32, 48}, {29, 56}, {56, 32},
> {55, 56},
> > + {48, 56}, {1, 31}, {33, 34}, {2, 28}, {1, 12}, {55, 1},
> {31, 31},
> > + {65, 1}, {33, 56}, {32, 32}, {13, 13}, {1, 26}, {13, 26},
> {2, 1},
> > + {1, 33}, {47, 49}, {64, 1}, {2, 38}, {34, 53}, {48, 2},
> {55, 34},
> > + {34, 32}, {1, 5}, {56, 13}, {2, 2}, {2, 49}, {33, 2},
> {49, 39},
> > + {56, 49}, {33, 49}, {32, 39}, {39, 57}, {29, 33}, {31, 34},
> {32, 29},
> > + {47, 15}, {13, 34}, {2, 33}, {32, 49}, {49, 34}, {56, 33},
> {1, 30},
> > + {33, 33}, {31, 33}, {2, 29}, {56, 7}, {32, 13}, {2, 55},
> {56, 56},
> > + {2, 34}, {1, 42}, {34, 49}, {1, 20}, {32, 33}, {1, 25},
> {53, 28},
> > + {1, 14}, {31, 49}, {28, 2}, {2, 13}, {2, 56}, {1, 32},
> {56, 53},
> > + {65, 65}, {33, 53}, {64, 64}, {13, 2}, {34, 33}, {1, 4},
> {49, 2},
> > + {1, 9}, {56, 1}, {33, 1}, {53, 57}, {32, 53}, {13, 56},
> {32, 56},
> > + {55, 55}, {1, 18}, {49, 56}, {34, 34}, {1, 7}, {56, 64},
> {32, 1},
> > + {13, 33}, {55, 28}, {49, 33}, {57, 57}, {56, 34}, {34, 56},
> {33, 32},
> > + {32, 40}, {1, 29}, {53, 2}, {34, 1}, {32, 34}, {49, 49},
> {1, 24},
> > + {40, 34}, {1, 13}, {38, 34}, {29, 2}, {34, 2}, {1, 39},
> {1, 22},
> > + {1, 27}, {49, 1}, {1, 8}, {56, 2}};
> > +
> > +// We have: 9 calculated features (the features here); 1 feature for
> each
> > +// instruction opcode; and 1 feature for each manually-identified
> sequence.
> > +// For the latter 2, we build a histogram: we count the number of
> > +// occurrences of each instruction opcode or succession of instructions,
> > +// respectively.
> > +// Note that instruction opcodes start from 1. For convenience, we also
> have an
> > +// always 0 feature for the '0' opcode, hence the extra 1.
> > +const size_t IRToNativeSizeLearning::FunctionFeatures::FeatureCount =
> > +
> IRToNativeSizeLearning::FunctionFeatures::ImportantInstructionSuccessions
> > + .size() +
> > + getMaxInstructionID() + 1 +
> IRToNativeSizeLearning::NumNamedFeatures;
> > +
> > +size_t getSize(Function &F, TargetTransformInfo &TTI) {
> > + size_t Ret = 0;
> > + for (auto &BB : F)
> > + for (auto &I : BB)
> > + Ret += TTI.getInstructionCost(
> > + &I, TargetTransformInfo::TargetCostKind::TCK_CodeSize);
> > + return Ret;
> > +}
> > +
> > +size_t getSize(Function &F, FunctionAnalysisManager &FAM) {
> > + auto &TTI = FAM.getResult<TargetIRAnalysis>(F);
> > + return getSize(F, TTI);
> > +}
> > +
> > +unsigned getMaxDominatorTreeDepth(const Function &F,
> > + const DominatorTree &Tree) {
> > + unsigned Ret = 0;
> > + for (auto &BB : F)
> > + if (auto *TN = Tree.getNode(&BB))
> > + Ret = std::max(Ret, TN->getLevel());
> > + return Ret;
> > +}
> > +} // namespace
> > +
> > +IRToNativeSizeLearning::FunctionFeatures
> > +IRToNativeSizeLearning::getFunctionFeatures(Function &F,
> > + FunctionAnalysisManager
> &FAM) {
> > + assert(ensureSortedTuples() && "expected lazy initialization");
> > +
> > + auto &DomTree = FAM.getResult<DominatorTreeAnalysis>(F);
> > + FunctionFeatures FF;
> > + size_t InstrCount = getMaxInstructionID() + 1;
> > + FF.InstructionHistogram.resize(InstrCount);
> > +
> > + FF.InstructionPairHistogram.resize(
> > + FunctionFeatures::ImportantInstructionSuccessions.size());
> > +
> > + auto StartID = 0;
> > + auto LastID = StartID;
> > + auto getPairIndex = [](size_t a, size_t b) {
> > + auto I =
> > +
> std::find(FunctionFeatures::ImportantInstructionSuccessions.begin(),
> > +
> FunctionFeatures::ImportantInstructionSuccessions.end(),
> > + std::make_pair(a, b));
> > + if (I == FunctionFeatures::ImportantInstructionSuccessions.end())
> > + return -1;
> > + return static_cast<int>(std::distance(
> > + FunctionFeatures::ImportantInstructionSuccessions.begin(), I));
> > + };
> > +
> > + // We don't want debug calls, because they'd just add noise.
> > + for (auto &BB : F) {
> > + for (auto I = BB.instructionsWithoutDebug().begin(),
> > + E = BB.instructionsWithoutDebug().end();
> > + I != E; ++I) {
> > + auto ID = I->getOpcode();
> > +
> > + ++FF.InstructionHistogram[ID];
> > + int PairIndex = getPairIndex(LastID, ID);
> > + if (PairIndex >= 0)
> > + ++FF.InstructionPairHistogram[PairIndex];
> > + LastID = ID;
> > + if (isa<CallBase>(*I))
> > + ++FF[NamedFeatureIndex::Calls];
> > + }
> > + }
> > +
> > + FF[NamedFeatureIndex::InitialSize] = getSize(F, FAM);
> > + FF[NamedFeatureIndex::IsLocal] = F.hasLocalLinkage();
> > + FF[NamedFeatureIndex::IsLinkOnceODR] = F.hasLinkOnceODRLinkage();
> > + FF[NamedFeatureIndex::IsLinkOnce] = F.hasLinkOnceLinkage();
> > + FF[NamedFeatureIndex::Blocks] =
> > + std::distance(F.getBasicBlockList().begin(),
> F.getBasicBlockList().end());
> > + auto &LI = FAM.getResult<LoopAnalysis>(F);
> > + FF[NamedFeatureIndex::Loops] = std::distance(LI.begin(), LI.end());
> > + for (auto &L : LI)
> > + FF[NamedFeatureIndex::MaxLoopDepth] =
> > + std::max(FF[NamedFeatureIndex::MaxLoopDepth],
> > + static_cast<int32_t>(L->getLoopDepth()));
> > + FF[NamedFeatureIndex::MaxDomTreeLevel] = getMaxDominatorTreeDepth(F,
> DomTree);
> > + return FF;
> > +}
> > +
> > +void IRToNativeSizeLearning::FunctionFeatures::fillTensor(int32_t *Ptr)
> const {
> > + std::copy(NamedFeatures.begin(), NamedFeatures.end(), Ptr);
> > + Ptr += NamedFeatures.size();
> > + std::copy(InstructionHistogram.begin(), InstructionHistogram.end(),
> Ptr);
> > + Ptr += InstructionHistogram.size();
> > + std::copy(InstructionPairHistogram.begin(),
> InstructionPairHistogram.end(),
> > + Ptr);
> > +}
> > +
> > +bool InlineSizeEstimatorAnalysis::isEvaluatorRequested() {
> > + return !TFIR2NativeModelPath.empty();
> > +}
> > +
> > +InlineSizeEstimatorAnalysis::InlineSizeEstimatorAnalysis() {
> > + if (!isEvaluatorRequested()) {
> > + return;
> > + }
> > + std::vector<std::string> InputNames{"serving_default_input_1"};
> > + std::vector<std::string> OutputName{"StatefulPartitionedCall"};
> > + Evaluator = std::make_unique<TFModelEvaluator>(
> > + TFIR2NativeModelPath.getValue().c_str(), InputNames, OutputName);
> > + if (!Evaluator || !Evaluator->isValid()) {
> > + Evaluator.reset();
> > + return;
> > + }
> > + static const std::vector<int64_t> Dim{
> > + 1, static_cast<int64_t>(
> > + IRToNativeSizeLearning::FunctionFeatures::FeatureCount)};
> > +
> > + Evaluator->initInput(0, TF_INT32, Dim);
> > +}
> > +
> > +InlineSizeEstimatorAnalysis::Result
> > +InlineSizeEstimatorAnalysis::run(const Function &F,
> > + FunctionAnalysisManager &FAM) {
> > + if (!Evaluator)
> > + return None;
> > + auto Features = IRToNativeSizeLearning::getFunctionFeatures(
> > + const_cast<Function &>(F), FAM);
> > + int32_t *V = static_cast<int32_t
> *>(TF_TensorData(Evaluator->getInput()[0]));
> > + Features.fillTensor(V);
> > + auto ER = Evaluator->evaluate();
> > + if (!ER)
> > + return None;
> > + float Ret = *ER->getTensorValue<float>(0);
> > + if (Ret < 0.0)
> > + Ret = 0.0;
> > + return static_cast<size_t>(Ret);
> > +}
> > +
> > +InlineSizeEstimatorAnalysis::~InlineSizeEstimatorAnalysis() {}
> > +InlineSizeEstimatorAnalysis::InlineSizeEstimatorAnalysis(
> > + InlineSizeEstimatorAnalysis &&Other)
> > + : Evaluator(std::move(Other.Evaluator)) {}
> > +
> > +#else
> > +namespace llvm {
> > +class TFModelEvaluator {};
> > +} // namespace llvm
> > +InlineSizeEstimatorAnalysis::InlineSizeEstimatorAnalysis() {}
> > +InlineSizeEstimatorAnalysis ::InlineSizeEstimatorAnalysis(
> > + InlineSizeEstimatorAnalysis &&) {}
> > +InlineSizeEstimatorAnalysis::~InlineSizeEstimatorAnalysis() {}
> > +InlineSizeEstimatorAnalysis::Result
> > +InlineSizeEstimatorAnalysis::run(const Function &F,
> > + FunctionAnalysisManager &FAM) {
> > + return None;
> > +}
> > +bool InlineSizeEstimatorAnalysis::isEvaluatorRequested() { return
> false; }
> > +#endif
> > \ No newline at end of file
> >
> > diff --git a/llvm/lib/Analysis/TFUtils.cpp
> b/llvm/lib/Analysis/TFUtils.cpp
> > new file mode 100644
> > index 000000000000..6cd5b5c9b4ea
> > --- /dev/null
> > +++ b/llvm/lib/Analysis/TFUtils.cpp
> > @@ -0,0 +1,143 @@
> > +//===- 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_experimental.h"
> > +
> > +#include <cassert>
> > +
> > +using namespace llvm;
> > +
> > +namespace {
> > +
> > +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; }
> > +
> > +TFModelEvaluator::TFGraphPtr createTFGraph() {
> > + return TFModelEvaluator::TFGraphPtr(TF_NewGraph(), &TF_DeleteGraph);
> > +}
> > +
> > +TFModelEvaluator::TFStatusPtr createTFStatus() {
> > + return TFModelEvaluator::TFStatusPtr(TF_NewStatus(),
> &TF_DeleteStatus);
> > +}
> > +
> > +TFModelEvaluator::TFSessionOptionsPtr createTFSessionOptions() {
> > + return TFModelEvaluator::TFSessionOptionsPtr(TF_NewSessionOptions(),
> > +
> &TF_DeleteSessionOptions);
> > +}
> > +} // namespace
> > +
> > +TFModelEvaluator::TFModelEvaluator(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());
> > + deleteSession();
> > + }
> > + for (size_t I = 0; I < InputNames.size(); ++I) {
> > + InputFeed[I] = {
> > + TF_GraphOperationByName(Graph.get(), (InputNames[I]).c_str()),
> 0};
> > + if (!checkReportAndReset(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 (!checkReportAndReset(OutputFeed[I], OutputNames[I]))
> > + return;
> > + }
> > +}
> > +
> > +TFModelEvaluator::~TFModelEvaluator() {
> > + for (auto *T : Input) {
> > + TF_DeleteTensor(T);
> > + }
> > + deleteSession();
> > +}
> > +
> > +bool TFModelEvaluator::checkReportAndReset(const TF_Output &Output,
> > + StringRef Name) {
> > + if (Output.oper)
> > + return true;
> > + errs() << "Could not find TF_Output named: " + Name;
> > + deleteSession();
> > + return false;
> > +}
> > +
> > +void TFModelEvaluator::deleteSession() {
> > + 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";
> > +}
> > +
> > +Optional<TFModelEvaluator::EvaluationResult>
> TFModelEvaluator::evaluate() {
> > + if (!isValid())
> > + return None;
> > + EvaluationResult Ret(OutputFeed.size());
> > + auto Status = createTFStatus();
> > + TF_SessionRun(Session, nullptr, InputFeed.data(), Input.data(),
> Input.size(),
> > + OutputFeed.data(), Ret.Output.data(), Ret.Output.size(),
> > + nullptr, 0, nullptr, Status.get());
> > + if (TF_GetCode(Status.get()) != TF_Code::TF_OK) {
> > + errs() << TF_Message(Status.get());
> > + deleteSession();
> > + return None;
> > + }
> > + return Ret;
> > +}
> > +
> > +void TFModelEvaluator::initInput(int 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);
> > +}
> > \ No newline at end of file
> >
> > diff --git a/llvm/lib/Passes/PassBuilder.cpp
> b/llvm/lib/Passes/PassBuilder.cpp
> > index 53158e7aabab..537d300fee55 100644
> > --- a/llvm/lib/Passes/PassBuilder.cpp
> > +++ b/llvm/lib/Passes/PassBuilder.cpp
> > @@ -35,6 +35,7 @@
> > #include "llvm/Analysis/IVUsers.h"
> > #include "llvm/Analysis/InlineAdvisor.h"
> > #include "llvm/Analysis/InlineFeaturesAnalysis.h"
> > +#include "llvm/Analysis/InlineSizeEstimatorAnalysis.h"
> > #include "llvm/Analysis/LazyCallGraph.h"
> > #include "llvm/Analysis/LazyValueInfo.h"
> > #include "llvm/Analysis/LoopAccessAnalysis.h"
> >
> > diff --git a/llvm/lib/Passes/PassRegistry.def
> b/llvm/lib/Passes/PassRegistry.def
> > index eb2b740db561..dfdfc3d05976 100644
> > --- a/llvm/lib/Passes/PassRegistry.def
> > +++ b/llvm/lib/Passes/PassRegistry.def
> > @@ -133,6 +133,7 @@ FUNCTION_ANALYSIS("loops", LoopAnalysis())
> > FUNCTION_ANALYSIS("lazy-value-info", LazyValueAnalysis())
> > FUNCTION_ANALYSIS("da", DependenceAnalysis())
> > FUNCTION_ANALYSIS("inliner-features", InlineFeaturesAnalysis())
> > +FUNCTION_ANALYSIS("inliner-size-estimator",
> InlineSizeEstimatorAnalysis())
> > FUNCTION_ANALYSIS("memdep", MemoryDependenceAnalysis())
> > FUNCTION_ANALYSIS("memoryssa", MemorySSAAnalysis())
> > FUNCTION_ANALYSIS("phi-values", PhiValuesAnalysis())
> >
> > diff --git a/llvm/unittests/Analysis/CMakeLists.txt
> b/llvm/unittests/Analysis/CMakeLists.txt
> > index 42f7dd3c0610..59ad444d32fb 100644
> > --- a/llvm/unittests/Analysis/CMakeLists.txt
> > +++ b/llvm/unittests/Analysis/CMakeLists.txt
> > @@ -6,7 +6,13 @@ set(LLVM_LINK_COMPONENTS
> > TransformUtils
> > )
> >
> > -add_llvm_unittest(AnalysisTests
> > +if (DEFINED LLVM_HAVE_TF_API)
> > + LIST(APPEND EXTRA_TESTS TFUtilsTest.cpp)
> > +else()
> > + LIST(APPEND LLVM_OPTIONAL_SOURCES TFUtilsTest.cpp)
> > +endif()
> > +
> > +add_llvm_unittest_with_input_files(AnalysisTests
> > AliasAnalysisTest.cpp
> > AliasSetTrackerTest.cpp
> > AssumeBundleQueriesTest.cpp
> > @@ -22,6 +28,7 @@ add_llvm_unittest(AnalysisTests
> > DomTreeUpdaterTest.cpp
> > GlobalsModRefTest.cpp
> > InlineFeaturesAnalysisTest.cpp
> > + InlineSizeEstimatorAnalysisTest.cpp
> > IVDescriptorsTest.cpp
> > LazyCallGraphTest.cpp
> > LoadsTest.cpp
> > @@ -40,4 +47,7 @@ add_llvm_unittest(AnalysisTests
> > ValueLatticeTest.cpp
> > ValueTrackingTest.cpp
> > VectorUtilsTest.cpp
> > + ${EXTRA_TESTS}
> > )
> > +
> > + target_link_libraries(AnalysisTests PRIVATE LLVMTestingSupport)
> >
> > diff --git
> a/llvm/unittests/Analysis/InlineSizeEstimatorAnalysisTest.cpp
> b/llvm/unittests/Analysis/InlineSizeEstimatorAnalysisTest.cpp
> > new file mode 100644
> > index 000000000000..377590be016a
> > --- /dev/null
> > +++ b/llvm/unittests/Analysis/InlineSizeEstimatorAnalysisTest.cpp
> > @@ -0,0 +1,101 @@
> > +//===- InlineSizeEstimatorAnalysisTest.cpp - test for ir2native
> -----------===//
> > +//
> > +// 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
> > +//
> >
> +//===----------------------------------------------------------------------===//
> > +
> > +#include "llvm/Analysis/InlineSizeEstimatorAnalysis.h"
> > +#include "llvm/Analysis/LoopInfo.h"
> > +#include "llvm/Analysis/TargetLibraryInfo.h"
> > +#include "llvm/Analysis/TargetTransformInfo.h"
> > +#include "llvm/AsmParser/Parser.h"
> > +#include "llvm/IR/Dominators.h"
> > +#include "llvm/IR/Instructions.h"
> > +#include "llvm/IR/LLVMContext.h"
> > +#include "llvm/IR/Module.h"
> > +#include "llvm/Support/CommandLine.h"
> > +#include "llvm/Support/Path.h"
> > +#include "llvm/Support/SourceMgr.h"
> > +#include "llvm/Testing/Support/SupportHelpers.h"
> > +#include "gtest/gtest.h"
> > +
> > +using namespace llvm;
> > +
> > +extern const char *TestMainArgv0;
> > +extern cl::opt<std::string> TFIR2NativeModelPath;
> > +
> > +#if LLVM_HAVE_TF_API
> > +static std::string getModelPath() {
> > + SmallString<128> InputsDir =
> unittest::getInputFileDirectory(TestMainArgv0);
> > + llvm::sys::path::append(InputsDir, "ir2native_x86_64_model");
> > + return std::string(InputsDir);
> > +}
> > +#endif
> > +
> > +static std::unique_ptr<Module> parseIR(LLVMContext &C, const char *IR) {
> > + SMDiagnostic Err;
> > + std::unique_ptr<Module> Mod = parseAssemblyString(IR, Err, C);
> > + if (!Mod)
> > + Err.print("MLAnalysisTests", errs());
> > + return Mod;
> > +}
> > +
> > +static FunctionAnalysisManager buildFAM() {
> > + FunctionAnalysisManager FAM;
> > + FAM.registerPass([&] { return DominatorTreeAnalysis(); });
> > + FAM.registerPass([&] { return PassInstrumentationAnalysis(); });
> > + FAM.registerPass([&] { return TargetIRAnalysis(); });
> > + FAM.registerPass([&] { return LoopAnalysis(); });
> > + return FAM;
> > +}
> > +
> > +// Test model loading and evaluation.
> > +TEST(InlineSizeEstimatorAnalysis, SizeIsValidTest) {
> > + LLVMContext C;
> > + std::unique_ptr<Module> M = parseIR(C,
> > + R"IR(
> > +target datalayout = "e-m:e-i64:64-f80:128-n8:16:32:64-S128"
> > +target triple = "x86_64-pc-linux-gnu"
> > +
> > +declare i32 @f1(i32)
> > +declare i32 @f2(i32)
> > +
> > +define i32 @branches(i32) {
> > + %cond = icmp slt i32 %0, 3
> > + br i1 %cond, label %then, label %else
> > +
> > +then:
> > + %ret.1 = call i32 @f1(i32 %0)
> > + br label %last.block
> > +
> > +else:
> > + %ret.2 = call i32 @f2(i32 %0)
> > + br label %last.block
> > +
> > +last.block:
> > + %ret = phi i32 [%ret.1, %then], [%ret.2, %else]
> > + ret i32 %ret
> > +}
> > +
> > +define internal i32 @top() {
> > + %1 = call i32 @branches(i32 2)
> > + %2 = call i32 @f1(i32 %1)
> > + ret i32 %2
> > +}
> > +)IR");
> > +
> > + FunctionAnalysisManager FAM = buildFAM();
> > +#if LLVM_HAVE_TF_API
> > + TFIR2NativeModelPath = getModelPath();
> > +#endif
> > +
> > + InlineSizeEstimatorAnalysis FA;
> > + auto SizeEstimate = FA.run(*M->getFunction("branches"), FAM);
> > +#if LLVM_HAVE_TF_API
> > + EXPECT_GT(*SizeEstimate, 0);
> > +#else
> > + EXPECT_FALSE(SizeEstimate.hasValue());
> > +#endif
> > +}
> >
> > diff --git
> a/llvm/unittests/Analysis/Inputs/ir2native_x86_64_model/saved_model.pbtxt
> b/llvm/unittests/Analysis/Inputs/ir2native_x86_64_model/saved_model.pbtxt
> > new file mode 100644
> > index 000000000000..6efdad51083d
> > --- /dev/null
> > +++
> b/llvm/unittests/Analysis/Inputs/ir2native_x86_64_model/saved_model.pbtxt
> > @@ -0,0 +1,10596 @@
> > +saved_model_schema_version: 1
> > +meta_graphs {
> > + meta_info_def {
> > + stripped_op_list {
> > + op {
> > + name: "Const"
> > + output_arg {
> > + name: "output"
> > + type_attr: "dtype"
> > + }
> > + attr {
> > + name: "value"
> > + type: "tensor"
> > + }
> > + attr {
> > + name: "dtype"
> > + type: "type"
> > + }
> > + }
> > + op {
> > + name: "NoOp"
> > + }
> > + op {
> > + name: "Placeholder"
> > + output_arg {
> > + name: "output"
> > + type_attr: "dtype"
> > + }
> > + attr {
> > + name: "dtype"
> > + type: "type"
> > + }
> > + attr {
> > + name: "shape"
> > + type: "shape"
> > + default_value {
> > + shape {
> > + unknown_rank: true
> > + }
> > + }
> > + }
> > + }
> > + op {
> > + name: "ReadVariableOp"
> > + input_arg {
> > + name: "resource"
> > + type: DT_RESOURCE
> > + }
> > + output_arg {
> > + name: "value"
> > + type_attr: "dtype"
> > + }
> > + attr {
> > + name: "dtype"
> > + type: "type"
> > + }
> > + is_stateful: true
> > + }
> > + op {
> > + name: "StatefulPartitionedCall"
> > + input_arg {
> > + name: "args"
> > + type_list_attr: "Tin"
> > + }
> > + output_arg {
> > + name: "output"
> > + type_list_attr: "Tout"
> > + }
> > + attr {
> > + name: "Tin"
> > + type: "list(type)"
> > + has_minimum: true
> > + }
> > + attr {
> > + name: "Tout"
> > + type: "list(type)"
> > + has_minimum: true
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