[llvm] 9908a3b - Revert "[llvm] Native size estimator for training -Oz inliner"
Davide Italiano via llvm-commits
llvm-commits at lists.llvm.org
Mon Jul 13 13:13:50 PDT 2020
Author: Davide Italiano
Date: 2020-07-13T13:13:36-07:00
New Revision: 9908a3b9f521c954cbf6adcec35b14b2f6c8da49
URL: https://github.com/llvm/llvm-project/commit/9908a3b9f521c954cbf6adcec35b14b2f6c8da49
DIFF: https://github.com/llvm/llvm-project/commit/9908a3b9f521c954cbf6adcec35b14b2f6c8da49.diff
LOG: Revert "[llvm] Native size estimator for training -Oz inliner"
This reverts commit 83080a294ad7d145d758821bcf4354ad0cb7d299 as
it breaks the macOS modules build.
Added:
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:
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
################################################################################
diff --git a/llvm/CMakeLists.txt b/llvm/CMakeLists.txt
index 4e14e61fcacd..de2887b64c2a 100644
--- a/llvm/CMakeLists.txt
+++ b/llvm/CMakeLists.txt
@@ -981,18 +981,6 @@ 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
deleted file mode 100644
index 29a6f5914674..000000000000
--- a/llvm/include/llvm/Analysis/InlineSizeEstimatorAnalysis.h
+++ /dev/null
@@ -1,35 +0,0 @@
-//===- 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
deleted file mode 100644
index a1d7108b149f..000000000000
--- a/llvm/include/llvm/Analysis/Utils/TFUtils.h
+++ /dev/null
@@ -1,136 +0,0 @@
-//===- 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
-
-#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_ANALYSIS_UTILS_TFUTILS_H
\ No newline at end of file
diff --git a/llvm/lib/Analysis/CMakeLists.txt b/llvm/lib/Analysis/CMakeLists.txt
index 703623396d96..a317579ecc83 100644
--- a/llvm/lib/Analysis/CMakeLists.txt
+++ b/llvm/lib/Analysis/CMakeLists.txt
@@ -1,35 +1,17 @@
set(CommonMLSources MLInlineAdvisor.cpp)
set(ReleaseModeMLSources ReleaseModeModelRunner.cpp)
-set(DevelopmentModeMLSources TFUtils.cpp)
-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()
+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})
else()
- LIST(APPEND LLVM_OPTIONAL_SOURCES
- ${CommonMLSources}
- ${DevelopmentModeMLSources}
- ${ReleaseModeMLSources}
- )
+ set(LLVM_OPTIONAL_SOURCES ${CommonMLSources} ${ReleaseModeMLSources})
endif()
-
add_llvm_component_library(LLVMAnalysis
AliasAnalysis.cpp
@@ -75,7 +57,6 @@ add_llvm_component_library(LLVMAnalysis
InlineCost.cpp
InlineAdvisor.cpp
InlineFeaturesAnalysis.cpp
- InlineSizeEstimatorAnalysis.cpp
InstCount.cpp
InstructionPrecedenceTracking.cpp
InstructionSimplify.cpp
@@ -143,7 +124,4 @@ 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
deleted file mode 100644
index 1d1952ae6cbb..000000000000
--- a/llvm/lib/Analysis/InlineSizeEstimatorAnalysis.cpp
+++ /dev/null
@@ -1,299 +0,0 @@
-//===- 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
deleted file mode 100644
index 6cd5b5c9b4ea..000000000000
--- a/llvm/lib/Analysis/TFUtils.cpp
+++ /dev/null
@@ -1,143 +0,0 @@
-//===- 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 537d300fee55..53158e7aabab 100644
--- a/llvm/lib/Passes/PassBuilder.cpp
+++ b/llvm/lib/Passes/PassBuilder.cpp
@@ -35,7 +35,6 @@
#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 dfdfc3d05976..eb2b740db561 100644
--- a/llvm/lib/Passes/PassRegistry.def
+++ b/llvm/lib/Passes/PassRegistry.def
@@ -133,7 +133,6 @@ 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 59ad444d32fb..42f7dd3c0610 100644
--- a/llvm/unittests/Analysis/CMakeLists.txt
+++ b/llvm/unittests/Analysis/CMakeLists.txt
@@ -6,13 +6,7 @@ set(LLVM_LINK_COMPONENTS
TransformUtils
)
-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
+add_llvm_unittest(AnalysisTests
AliasAnalysisTest.cpp
AliasSetTrackerTest.cpp
AssumeBundleQueriesTest.cpp
@@ -28,7 +22,6 @@ add_llvm_unittest_with_input_files(AnalysisTests
DomTreeUpdaterTest.cpp
GlobalsModRefTest.cpp
InlineFeaturesAnalysisTest.cpp
- InlineSizeEstimatorAnalysisTest.cpp
IVDescriptorsTest.cpp
LazyCallGraphTest.cpp
LoadsTest.cpp
@@ -47,7 +40,4 @@ add_llvm_unittest_with_input_files(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
deleted file mode 100644
index 377590be016a..000000000000
--- a/llvm/unittests/Analysis/InlineSizeEstimatorAnalysisTest.cpp
+++ /dev/null
@@ -1,101 +0,0 @@
-//===- 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
deleted file mode 100644
index 6efdad51083d..000000000000
--- a/llvm/unittests/Analysis/Inputs/ir2native_x86_64_model/saved_model.pbtxt
+++ /dev/null
@@ -1,10596 +0,0 @@
-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
- }
- attr {
- name: "f"
- type: "func"
- }
- attr {
- name: "config"
- type: "string"
- default_value {
- s: ""
- }
- }
- attr {
- name: "config_proto"
- type: "string"
- default_value {
- s: ""
- }
- }
- attr {
- name: "executor_type"
- type: "string"
- default_value {
- s: ""
- }
- }
- is_stateful: true
- }
- op {
- name: "VarHandleOp"
- output_arg {
- name: "resource"
- type: DT_RESOURCE
- }
- attr {
- name: "container"
- type: "string"
- default_value {
- s: ""
- }
- }
- attr {
- name: "shared_name"
- type: "string"
- default_value {
- s: ""
- }
- }
- attr {
- name: "dtype"
- type: "type"
- }
- attr {
- name: "shape"
- type: "shape"
- }
- is_stateful: true
- }
- }
- tags: "serve"
- tensorflow_version: "1.15.0"
- tensorflow_git_version: "unknown"
- stripped_default_attrs: true
- }
- graph_def {
- node {
- name: "dense/kernel"
- op: "VarHandleOp"
- attr {
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diff --git a/llvm/unittests/Analysis/Inputs/ir2native_x86_64_model/variables/variables.data-00000-of-00001 b/llvm/unittests/Analysis/Inputs/ir2native_x86_64_model/variables/variables.data-00000-of-00001
deleted file mode 100644
index 98807d26ee9f..000000000000
Binary files a/llvm/unittests/Analysis/Inputs/ir2native_x86_64_model/variables/variables.data-00000-of-00001 and /dev/null
diff er
diff --git a/llvm/unittests/Analysis/Inputs/ir2native_x86_64_model/variables/variables.index b/llvm/unittests/Analysis/Inputs/ir2native_x86_64_model/variables/variables.index
deleted file mode 100644
index c20d8afabf38..000000000000
Binary files a/llvm/unittests/Analysis/Inputs/ir2native_x86_64_model/variables/variables.index and /dev/null
diff er
diff --git a/llvm/unittests/Analysis/TFUtilsTest.cpp b/llvm/unittests/Analysis/TFUtilsTest.cpp
deleted file mode 100644
index 4c775c4c0b93..000000000000
--- a/llvm/unittests/Analysis/TFUtilsTest.cpp
+++ /dev/null
@@ -1,98 +0,0 @@
-//===- TFUtilsTest.cpp - test for TFUtils ---------------------------------===//
-//
-// 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/Utils/TFUtils.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/Path.h"
-#include "llvm/Support/SourceMgr.h"
-#include "llvm/Testing/Support/SupportHelpers.h"
-#include "gtest/gtest.h"
-
-using namespace llvm;
-
-extern const char *TestMainArgv0;
-
-static std::string getModelPath() {
- SmallString<128> InputsDir = unittest::getInputFileDirectory(TestMainArgv0);
- llvm::sys::path::append(InputsDir, "ir2native_x86_64_model");
- return std::string(InputsDir);
-}
-
-// Test observable behavior when no model is provided.
-TEST(TFUtilsTest, NoModel) {
- TFModelEvaluator Evaluator("", {}, {});
- EXPECT_FALSE(Evaluator.isValid());
-}
-
-// Test we can correctly load a savedmodel and evaluate it.
-TEST(TFUtilsTest, LoadAndExecuteTest) {
- // We use the ir2native model for test. We know it has one feature of
- // dimension (1, 214)
- std::vector<std::string> InputNames{"serving_default_input_1"};
- std::vector<std::string> OutputName{"StatefulPartitionedCall"};
- const static int64_t KnownSize = 214;
-
- TFModelEvaluator Evaluator(getModelPath(), InputNames, OutputName);
- static const std::vector<int64_t> Dim{1, KnownSize};
-
- EXPECT_TRUE(Evaluator.isValid());
- Evaluator.initInput(0, TF_INT32, Dim);
-
- int32_t *V = static_cast<int32_t *>(TF_TensorData(Evaluator.getInput()[0]));
- // Fill it up with 1's, we know the output.
- for (auto I = 0; I < KnownSize; ++I) {
- V[I] = 1;
- }
- {
- auto ER = Evaluator.evaluate();
- EXPECT_TRUE(ER.hasValue());
- float Ret = *ER->getTensorValue<float>(0);
- EXPECT_EQ(static_cast<size_t>(Ret), 80);
- }
- // The input vector should be unchanged
- for (auto I = 0; I < KnownSize; ++I) {
- EXPECT_EQ(V[I], 1);
- }
- // Zero-out the unused position '0' of the instruction histogram, which is
- // after the first 9 calculated values. Should the the same result.
- V[9] = 0;
- {
- auto ER = Evaluator.evaluate();
- EXPECT_TRUE(ER.hasValue());
- float Ret = *ER->getTensorValue<float>(0);
- EXPECT_EQ(static_cast<size_t>(Ret), 80);
- }
-}
-
-// Test incorrect input setup
-TEST(TFUtilsTest, EvalError) {
- // We use the ir2native model for test. We know it has one feature of
- // dimension (1, 214)
- std::vector<std::string> InputNames{"serving_default_input_1"};
- std::vector<std::string> OutputName{"StatefulPartitionedCall"};
- const static int64_t KnownSize = 213;
-
- TFModelEvaluator Evaluator(getModelPath(), InputNames, OutputName);
- static const std::vector<int64_t> Dim{1, KnownSize};
-
- EXPECT_TRUE(Evaluator.isValid());
- Evaluator.initInput(0, TF_INT32, Dim);
-
- int32_t *V = static_cast<int32_t *>(TF_TensorData(Evaluator.getInput()[0]));
- // Fill it up with 1's, we know the output.
- for (auto I = 0; I < KnownSize; ++I) {
- V[I] = 1;
- }
- auto ER = Evaluator.evaluate();
- EXPECT_FALSE(ER.hasValue());
- EXPECT_FALSE(Evaluator.isValid());
-}
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