text: /**
   * Copyright (c) Glow Contributors. See CONTRIBUTORS file.
   *
   * Licensed under the Apache License, Version 2.0 (the "License");
   * you may not use this file except in compliance with the License.
   * You may obtain a copy of the License at
   *
   *     http://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  
  #include "glow/Optimizer/GraphOptimizer/GraphOptimizer.h"
  
  #include "glow/Backend/Backend.h"
  #include "glow/Converter/Float16Converter.h"
  #include "glow/Converter/FusedRowwiseConverter.h"
  #include "glow/Converter/TypeAToTypeBFunctionConverter.h"
  #include "glow/Graph/Graph.h"
  #include "glow/Graph/Log.h"
  #include "glow/Graph/Node.h"
  #include "glow/Graph/Nodes.h"
  #include "glow/Graph/PlaceholderBindings.h"
  #include "glow/Graph/TensorLayout.h"
  #include "glow/Graph/Utils.h"
  #include "glow/Graph/VerifierHelper.h"
  #include "glow/Optimizer/GraphOptimizer/FunctionPassPipeline.h"
  #include "glow/Optimizer/GraphOptimizer/FunctionPasses.h"
  #include "glow/Optimizer/Lower/Lower.h"
  #include "glow/PassManager/PassManager.h"
  #include "glow/Quantization/Base/Base.h"
  #include "glow/Quantization/Quantization.h"
  #include "glow/Runtime/RuntimeTypes.h"
  
  #include "llvm/Support/Casting.h"
  #include "llvm/Support/CommandLine.h"
  
  #include <algorithm>
  #include <numeric>
  #include <unordered_map>
  #include <unordered_set>
  #include <vector>
  
  llvm::cl::OptionCategory graphOptCat("Graph Optimizations Options");
  llvm::cl::opt<unsigned> constDedupSizeOpt(
      "const-dedup-size",
      llvm::cl::desc(
          "Max number of elements allowed for deduplicating Constants. "
          "A value equal to 0 means no limit. Default is 0."),
      llvm::cl::Optional, llvm::cl::init(0), llvm::cl::cat(graphOptCat));
  
  using namespace glow;
  using llvm::cast;
  using llvm::dyn_cast;
  using llvm::isa;
  
  static bool shouldDeleteNode(Node *N) {
    // In general, nodes who have side effects are retained.
    if (N->hasSideEffects()) {
      return false;
    }
  
    // Don't delete nodes that have users.
    if (N->hasUsers()) {
      return false;
    }
  
    return true;
  }
  
  ConstantModificationPreventer::ConstantModificationPreventer(
      Module &mod, CompilationContext &cctx)
      : ScopeGuard([&]() {
          // Ensure we cleanup Placeholder-Constant swap if necessary.
          auto &PHs = mod_.getPlaceholders();
          for (auto &pair : tmpPHToConstMap_) {
            Placeholder *tmpPH = pair.first;
            Constant *C = pair.second;
            tmpPH->getOutput().replaceAllUsesOfWith(C->getOutput());
            mod_.erasePlaceholder(std::find(PHs.begin(), PHs.end(), tmpPH));
          }
          cctx_.optimizationOpts.enableConstantFolding =
              origEnableConstantFolding_;
        }),
        mod_(mod), cctx_(cctx),
        origEnableConstantFolding_(cctx.optimizationOpts.enableConstantFolding) {
    // By default dismiss until explicitly activated.
    dismissed_ = true;
  }
  
  void ConstantModificationPreventer::activate() {
    dismissed_ = false;
    // Prevent Constant modification by temporarily replacing them with PHs.
    for (Constant *C : mod_.getConstants()) {
      // Note: These temp Placeholders are more like static Placeholders, but we
      // don't want to set them as static here because optimizations may kick in
      // to modify the type of the static Placeholder (see
      // cctx.optimizationOpts.foldStaticPlaceholderConversions).
      Placeholder *tmpPH = mod_.createPlaceholder(
          C->getType(), C->getName().str() + "_SWAP_CONST_FOLD",
          /* isTrainable */ false, C->getLayout());
      tmpPHToConstMap_[tmpPH] = C;
      cctx_.optimizationOpts.tempPHsForConstants.insert(tmpPH);
      C->getOutput().replaceAllUsesOfWith(tmpPH->getOutput());
    }
    // Disable constant folding temporarily; restored later by the scope guard.
    cctx_.optimizationOpts.enableConstantFolding = false;
  }
  
  /// Helper that \returns true if all functions in \p mod are loaded.
  static bool areAllFunctionsLoaded(Module *mod) {
    for (auto *MF : mod->getFunctions()) {
      if (MF->getState() < FunctionState::FuncLoaded) {
        return false;
      }
    }
    return true;
  }
  
  /// Helper that \returns the shuffle that inverts \p shuffle. For example, if
  /// \p shuffle is {3, 0, 1, 2}, then this function returns {1, 2, 3, 0}.
  static llvm::SmallVector<unsigned_t, max_tensor_dimensions>
  invertShuffle(llvm::ArrayRef<unsigned_t> shuffle) {
    llvm::SmallVector<unsigned_t, max_tensor_dimensions> invertedShuffle;
    invertedShuffle.resize(shuffle.size());
  
    for (size_t i = 0; i < shuffle.size(); ++i) {
      invertedShuffle[shuffle[i]] = i;
    }
  
    return invertedShuffle;
  }
  
  /// Add a TranposeNode after \p C in \p F that has the same shuffle as \p TR.
  /// This funcion assumes that the type of \p C and the output type of \p TR are
  /// the same. \returns the newly added TransposeNode.
  static TransposeNode *insertMatchingTransposeAfterConstant(Function *F,
                                                             Constant *C,
                                                             TransposeNode *TR) {
    const auto *CT = C->getOutput().getType();
    const auto *TRT = TR->getResult().getType();
    DCHECK(CT->isEqual(*TRT, /* allowDifferentShape */ false,
                       /* allowDifferentStride */ false,
                       /* allowDifferentScaleOffset */ true));
  
    auto &T = C->getPayload();
  
    // In order for a new Transpose node with the same shuffle as TR to be created
    // at the output of the Constant, a new Constant with the same dimension as
    // the input of TR should be created. Note that the original scale and offset
    // should be kept for quantized types.
    auto newConstTy =
        F->getParent()->uniqueTypeWithNewShape(CT, TR->getInput().dims());
    auto *NC = F->getParent()->createConstant(newConstTy,
                                              C->getName().str() + ".transposed");
  
    // The payload of the original Constant C has the same type as the
    // output of TR. In order to preserve correctness, this payload must
    // be transposed using the inverse of the shuffle of TR and stored
    // into the payload of the new Constant.
    //
    // Another way to think of this is that we are inserting two
    // Transposes that are inverses of each other back to back after the original
    // Constant. The shuffle of the second Transpose must match that of TR.
    // In order to preserve correctness, the shuffle
    // of the first Transpose must be the inverse of that shuffle of the
    // second Transpose. The statement below statically computes this
    // first Transpose.
    T.transpose(&NC->getPayloadMutable(), invertShuffle(TR->getShuffle()));
  
    // Create Transpose on the LHS that has the same shuffle as TR.
    return F->createTranspose("transpose", NC, TR->getShuffle());
  }
  
  bool EmptyPass::run(Function *F, const CompilationContext &cctx) {
    return false;
  }
  
  bool DCE::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
  
    auto &nodes = F->getNodes();
  
    std::vector<NodesList::iterator> erasedNodes{};
  
    bool changed = false;
  
    // Remove unused nodes.
    while (true) {
      bool changedLocally = false;
      for (auto it = nodes.begin(), e = nodes.end(); it != e;) {
        if (!shouldDeleteNode(&*it)) {
          ++it;
          continue;
        }
  
        erasedNodes.push_back(it);
        ++it;
        changedLocally = true;
        changed = true;
      }
  
      while (!erasedNodes.empty()) {
        auto it = erasedNodes.back();
        F->eraseNode(it);
        erasedNodes.pop_back();
      }
  
      if (!changedLocally) {
        break;
      }
    }
  
    // Don't remove unused Constants since many may be temporarily unused during
    // optimizations.
    if (cctx.optimizationOpts.delayAndRecordConstantModification) {
      return changed;
    }
  
    if (!areAllFunctionsLoaded(F->getParent())) {
      return changed;
    }
  
    // Delete unused Constants.
    deleteUnusedConstants(*F->getParent());
  
    return changed;
  }
  
  void glow::deleteUnusedConstants(Module &mod) {
    auto &consts = mod.getConstants();
    std::vector<ConstList::iterator> erasedConsts{};
    for (auto it = consts.begin(), e = consts.end(); it != e;) {
      if (!shouldDeleteNode(*it)) {
        ++it;
        continue;
      }
      erasedConsts.push_back(it);
      ++it;
    }
  
    while (!erasedConsts.empty()) {
      auto it = erasedConsts.back();
      mod.eraseConstant(it);
      erasedConsts.pop_back();
    }
  }
  
  /// \returns true if the \p shuffle corresponds to an identity operation, false
  /// otherwise.
  static bool isIdentityShuffle(llvm::ArrayRef<unsigned> shuffle) {
    for (size_t i = 0, e = shuffle.size(); i < e; i++) {
      if (shuffle[i] != i) {
        return false;
      }
    }
    return true;
  }
  
  /// \returns True if the node \p N always evaluates to \p val.
  bool isSplatOfVal(Node *N, float val) {
    SplatNode *Z = dyn_cast<SplatNode>(N);
    if (!Z) {
      return false;
    }
    return (Z->getValue() == val);
  }
  
  /// \returns True if the node returns a constant value.
  bool isConstant(Node *N) { return isa<SplatNode>(N); }
  
  /// \returns the new simplified NodeValue or the original node's first result.
  static NodeValue simplifyNode(Node *node, Function *F) {
  // Simplify commutative nodes by moving the constant operator to the right-hand
  // side.
  // Example:  C + X  =>  X + C
  #define COMMUTE_CONST_TO_RHS(NodeKind)                                         \
    if (auto *NN = dyn_cast<NodeKind##Node>(node))                               \
      if (isConstant(NN->getLHS()) && !isConstant(NN->getRHS())) {               \
        return F->create##NodeKind(NN->getName(), NN->getResult().getType(),     \
                                   NN->getRHS(), NN->getLHS());                  \
      }
  
    COMMUTE_CONST_TO_RHS(Add)
    COMMUTE_CONST_TO_RHS(Mul)
    COMMUTE_CONST_TO_RHS(Max)
    COMMUTE_CONST_TO_RHS(Min)
  #undef COMMUTE_CONST_TO_RHS
  
    if (auto *AN = dyn_cast<AddNode>(node)) {
      // X + 0 => X
      if (isSplatOfVal(AN->getRHS(), 0)) {
        return AN->getLHS();
      }
    }
  
    if (auto *MN = dyn_cast<MulNode>(node)) {
      // X * 0 => 0
      if (isSplatOfVal(MN->getRHS(), 0)) {
        return MN->getRHS();
      }
      // X * 1 => X
      if (isSplatOfVal(MN->getRHS(), 1)) {
        return MN->getLHS();
      }
    }
  
    if (auto *DN = dyn_cast<DivNode>(node)) {
      // 0 / X => 0
      if (isSplatOfVal(DN->getLHS(), 0)) {
        return DN->getLHS();
      }
      // X / 1 => X
      if (isSplatOfVal(DN->getRHS(), 1)) {
        return DN->getLHS();
      }
    }
  
    // X - 0 => X
    if (auto *SN = dyn_cast<SubNode>(node)) {
      if (isSplatOfVal(SN->getRHS(), 0)) {
        return SN->getLHS();
      }
    }
  
    return node;
  }
  
  /// Sink Transpose below ChannelShuffle node.
  static bool sinkTranposeBelowChannelShuffle(Function *F,
                                              ChannelShuffleNode *CS) {
    auto *TR = dyn_cast<TransposeNode>(CS->getInput());
    if (!TR) {
      return false;
    }
  
    // Create a new ChannelShuffle with kernel parameter transposed by the
    // sinking TR's shuffle because that Transpose will now be moved below this
    // ChannelShuffle operator.
    auto *newCS =
        F->createChannelShuffle(CS->getName(), TR->getInput(), CS->getGroup(),
                                TR->getShuffle()[CS->getKernel()]);
  
    // Create a copy of sinkingTR and insert after newChannelShuffle.
    auto *newTR = F->createTranspose(TR->getName(), newCS, TR->getShuffle(),
                                     TR->getLayout());
  
    CS->getResult().replaceAllUsesOfWith(newTR);
  
    return true;
  }
  
  /// Given \p CN from \p F, determines if all inputs are either Quantize or
  /// Dequantize (depending on \p QuantNodeClass) that use the same
  /// scale/offset/kind, and if so creates and \returns a new concat with all
  /// inputs as the inputs from the Quantize or Dequantize inputs. Otherwise
  /// \returns nullptr.
  template <class QuantNodeClass>
  static ConcatNode *setupQuantDequantSinkBelowConcat(Function *F,
                                                      ConcatNode *CN) {
    constexpr bool isQuant = std::is_same<QuantizeNode, QuantNodeClass>::value;
    constexpr bool isDeq = std::is_same<DequantizeNode, QuantNodeClass>::value;
    static_assert(isQuant || isDeq, "setupQuantDequantSinkBelowConcat() only "
                                    "supports Quantize/Dequantize nodes.");
    // Check if all inputs are Quantize with the same input
    // scale/offset/ElemKind.
    std::vector<QuantNodeClass *> qNodes;
    qNodes.reserve(CN->getInputs().size());
    for (auto &concatInput : CN->getInputs()) {
      QuantNodeClass *Q = dyn_cast<QuantNodeClass>(concatInput);
      if (!Q) {
        return nullptr;
      }
      qNodes.push_back(Q);
    }
  
    // Gather all inputs of the nodes in qNodes here.
    std::vector<NodeValue> newInputs;
    newInputs.reserve(qNodes.size());
    newInputs.push_back(qNodes[0]->getInput());
  
    // Check the CN's first input's type to check against all other inputs. Use
    // the output of Quantize or input of Dequantize.
    const TypeRef firstTy = isQuant ? qNodes[0]->getResult().getType()
                                    : qNodes[0]->getInput().getType();
  
    // Check that all inputs have the same scale/offset/type.
    for (size_t i = 1, e = qNodes.size(); i < e; i++) {
      const TypeRef currTy = isQuant ? qNodes[i]->getResult().getType()
                                     : qNodes[i]->getInput().getType();
      if (currTy->getScale() != firstTy->getScale() ||
          currTy->getOffset() != firstTy->getOffset() ||
          currTy->getElementType() != firstTy->getElementType()) {
        return nullptr;
      }
      newInputs.push_back(qNodes[i]->getInput());
    }
  
    // Create and return a new ConcatNode with newInputs.
    return F->createConcat(CN->getName(), newInputs, CN->getDim());
  }
  
  bool SinkConversions::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    auto &nodes = F->getNodes();
    // For each node:
    for (auto &N : nodes) {
      ConcatNode *CN = dyn_cast<ConcatNode>(&N);
      if (!CN) {
        continue;
      }
      const Node *firstNode = CN->getInputs().front().getNode();
  
      // Sink Dequantize below Concat nodes.
      if (firstNode->getKind() == Kinded::Kind::DequantizeNodeKind) {
        ConcatNode *newCN =
            setupQuantDequantSinkBelowConcat<DequantizeNode>(F, CN);
        if (!newCN) {
          continue;
        }
  
        DequantizeNode *newDequantize =
            F->createDequantize(CN->getName().str() + "_dequantize", newCN,
                                CN->getResult().getType());
  
        CN->getResult().replaceAllUsesOfWith(newDequantize->getResult());
        changed = true;
        continue;
      }
  
      // Sink Quantize below Concat nodes.
      if (firstNode->getKind() == Kinded::Kind::QuantizeNodeKind) {
        ConcatNode *newCN = setupQuantDequantSinkBelowConcat<QuantizeNode>(F, CN);
        if (!newCN) {
          continue;
        }
  
        const TypeRef QTy =
            llvm::cast<QuantizeNode>(firstNode)->getResult().getType();
        const TypeRef concatQTy = F->getParent()->uniqueType(
            QTy->getElementType(), newCN->getResult().dims(), QTy->getScale(),
            QTy->getOffset());
        QuantizeNode *newQuantize = F->createQuantize(
            CN->getName().str() + "_quantize", newCN, concatQTy);
  
        CN->getResult().replaceAllUsesOfWith(newQuantize->getResult());
        changed = true;
        continue;
      }
    }
  
    return changed;
  }
  
  /// Sink Quantize(Concat(...)) -> Concat(Quantize(...)). This allows for
  /// concatenating less data, and if there are some inputs that are already
  /// quantized and are being dequantized just for the concat then we can skip
  /// this conversion.
  bool SinkConcatBelowQuantize::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    auto &nodes = F->getNodes();
    // For each node:
    for (auto &N : nodes) {
      QuantizeNode *QN = dyn_cast<QuantizeNode>(&N);
      if (!QN) {
        continue;
      }
  
      ConcatNode *CN = dyn_cast<ConcatNode>(QN->getInput());
      if (!CN || CN->getNumUsers() > 1) {
        continue;
      }
  
      // For all inputs to the current CN, add quantize nodes to them all using
      // the same scale/offset as QN and put the quantize nodes in newQuantInputs.
      std::vector<NodeValue> newQuantInputs;
      for (const NodeValue &inCN : CN->getInputs()) {
        TypeRef newOutTy = F->getParent()->uniqueTypeWithNewShape(
            QN->getResult().getType(), inCN.dims());
        QuantizeNode *quantInCN = F->createQuantize(
            inCN.getNode()->getName().str() + "_quant", inCN, newOutTy);
        newQuantInputs.push_back(quantInCN);
      }
  
      // Create a new CN with the quantized inputs and replace QN with it.
      ConcatNode *newCN =
          F->createConcat(CN->getName(), newQuantInputs, CN->getDim());
      QN->getResult().replaceAllUsesOfWith(newCN->getResult());
      changed = true;
    }
  
    return changed;
  }
  
  /// Code Sinking.
  bool SinkCode::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    auto &nodes = F->getNodes();
    // For each node:
    for (auto &N : nodes) {
      auto *node = &N;
  
      // Sink Reshape/Transpose below BatchNormalization.
      if (auto *BN = dyn_cast<BatchNormalizationNode>(node)) {
  
        // Sink Reshape below BatchNormalization.
        if (auto *RS = dyn_cast<ReshapeNode>(BN->getInput())) {
          auto inDims = RS->getInput().dims();
          auto outDims = RS->getResult().dims();
          unsigned_t newChannelIdx;
  
          // Skip sinking if: 1) the input was less than 3 dimensions,
          // because we need spatial dimensions in addition to batch
          // and channel or 2) if it is 3D data because the reshapes
          // are deliberately introduced to phrase 3D BatchNormalization
          // as a 2D one.
          if (RS->getInput().dims().size() < 3 ||
              RS->getInput().dims().size() == 5) {
            continue;
          }
  
          // Reshape should not change the BatchNorm ChannelIdx dimensions.
          // Only NH[W]C and NCH[W] are allowed.
          if (BN->getChannelIdx() == outDims.size() - 1) {
            if (inDims[inDims.size() - 1] != outDims[outDims.size() - 1]) {
              continue;
            }
            newChannelIdx = inDims.size() - 1;
          } else if (BN->getChannelIdx() == 1) {
            // Note: index '1' maps to C in NCH[W] layout.
            if (inDims[1] != outDims[1]) {
              continue;
            }
            newChannelIdx = 1;
          } else {
            continue;
          }
  
          // Reshape should not change the batch dimension.
          if (inDims[0] != outDims[0]) {
            continue;
          }
  
          if (!RS->hasOneUse()) {
            continue;
          }
  
          auto bnOutTy = BN->getResult().getType();
          auto rsInputType = RS->getInput().getType();
          glow::TypeRef outTy = F->getParent()->uniqueTypeWithNewShape(
              bnOutTy, rsInputType->dims());
          auto *newBN = F->createBatchNormalization(
              BN->getName(), outTy, RS->getInput(), BN->getBias(), BN->getScale(),
              BN->getMean(), BN->getVar(), newChannelIdx, BN->getEpsilon(),
              BN->getMomentum());
          RS->setNthInput(ReshapeNode::InputIdx, newBN);
          BN->getResult().replaceAllUsesOfWith(RS);
          changed = true;
          continue;
        }
  
        // Sink Transpose below batch normalization nodes:
        if (auto *TR = dyn_cast<TransposeNode>(BN->getInput())) {
  
          // Figure out where we transposed the channel index for batch
          // normalization.
          unsigned_t idx = BN->getChannelIdx();
          unsigned_t newChannelIdx = TR->getShuffle()[idx];
  
          auto bnOutTy = BN->getResult().getType();
          auto trInputType = TR->getInput().getType();
          glow::TypeRef outTy = F->getParent()->uniqueTypeWithNewShape(
              bnOutTy, trInputType->dims());
  
          auto *NewBN = F->createBatchNormalization(
              BN->getName(), outTy, TR->getInput(), BN->getBias(), BN->getScale(),
              BN->getMean(), BN->getVar(), newChannelIdx, BN->getEpsilon(),
              BN->getMomentum());
          NewBN->setPredicate(node->getPredicate());
          auto *newTR = F->createTranspose(TR->getName(), NewBN, TR->getShuffle(),
                                           TR->getLayout());
          newTR->setPredicate(node->getPredicate());
  
          BN->getResult().replaceAllUsesOfWith(newTR);
          changed = true;
          continue;
        }
      }
  
      if (auto *RL = dyn_cast<ReluNode>(node)) {
        // Sink Transpose below batch RELU nodes.
        if (auto *TR = dyn_cast<TransposeNode>(RL->getInput())) {
          // Keep the same quantization parameters for ReLU output, but
          // change the shape to appropriate value.
          auto reluOutTy = F->getParent()->uniqueTypeWithNewShape(
              RL->getResult().getType(), TR->getInput().getType());
          auto *NRL = F->createRELU(RL->getName(), TR->getInput(), reluOutTy);
          NRL->setPredicate(node->getPredicate());
          auto *newTR = F->createTranspose(TR->getName(), NRL, TR->getShuffle(),
                                           TR->getLayout());
          newTR->setPredicate(node->getPredicate());
          RL->getResult().replaceAllUsesOfWith(newTR);
          changed = true;
          continue;
        }
  
        // Sink Clip below RELU nodes.
        if (ClipNode *CN = dyn_cast<ClipNode>(RL->getInput())) {
          assert(!RL->getResult().getType()->isQuantizedType() &&
                 "Relu(Clip) means Relu should not be quantized.");
          ReluNode *newRL = F->createRELU(RL->getName(), CN->getInput());
          ClipNode *newCN =
              F->createClip(CN->getName(), newRL->getResult(),
                            std::max(CN->getMin(), 0.0f), CN->getMax());
          RL->getResult().replaceAllUsesOfWith(newCN);
          changed = true;
          continue;
        }
      }
  
      // Sink Transpose below Clip nodes.
      if (auto *CL = dyn_cast<ClipNode>(node)) {
        auto *TR = dyn_cast<TransposeNode>(CL->getInput());
  
        if (!TR) {
          continue;
        }
  
        // Keep the same quantization parameters for Clip output, but
        // change the shape to appropriate value.
        auto clipOutTy = F->getParent()->uniqueTypeWithNewShape(
            CL->getResult().getType(), TR->getInput().getType());
        auto *NCL = F->createClip(CL->getName(), TR->getInput(), clipOutTy,
                                  CL->getMin(), CL->getMax());
        NCL->setPredicate(node->getPredicate());
        auto *newTR = F->createTranspose(TR->getName(), NCL, TR->getShuffle());
        newTR->setPredicate(node->getPredicate());
        CL->getResult().replaceAllUsesOfWith(newTR);
        changed = true;
        continue;
      }
  
      // Sink Transpose below LeakyRelu nodes.
      if (auto *LR = dyn_cast<LeakyReluNode>(node)) {
        auto *TR = dyn_cast<TransposeNode>(LR->getInput());
        if (!TR) {
          continue;
        }
        auto newLROutTy = F->getParent()->uniqueTypeWithNewShape(
            LR->getResult().getType(), TR->getInput().getType());
        auto *newLR = F->createLeakyRELU(LR->getName(), newLROutTy,
                                         TR->getInput(), LR->getAlpha());
        newLR->setPredicate(node->getPredicate());
        auto *newTR = F->createTranspose(TR->getName(), newLR, TR->getShuffle());
        newTR->setPredicate(node->getPredicate());
        LR->getResult().replaceAllUsesOfWith(newTR);
        changed = true;
        continue;
      }
  
      // Sink Transpose below PRelu with Splat.
      if (auto *PN = dyn_cast<PReluNode>(node)) {
        auto *TR = dyn_cast<TransposeNode>(PN->getInput());
        if (!TR) {
          continue;
        }
        auto *SN = dyn_cast<SplatNode>(PN->getSlope());
        if (!SN) {
          continue;
        }
        auto newSNOutTy = F->getParent()->uniqueTypeWithNewShape(
            SN->getResult().getType(), TR->getInput().getType());
        auto newPNOutTy = F->getParent()->uniqueTypeWithNewShape(
            PN->getResult().getType(), TR->getInput().getType());
        auto *newSN = F->createSplat(SN->getName(), newSNOutTy, SN->getValue());
        auto *newPN =
            F->createPRELU(PN->getName(), TR->getInput(), newSN, newPNOutTy);
        auto *newTR = F->createTranspose(TR->getName(), newPN, TR->getShuffle());
        newPN->setPredicate(node->getPredicate());
        newTR->setPredicate(node->getPredicate());
        PN->getResult().replaceAllUsesOfWith(newTR);
        changed = true;
        continue;
      }
  
      // Sink Transpose below Sigmoid nodes.
      if (auto *SI = dyn_cast<SigmoidNode>(node)) {
        auto *TR = dyn_cast<TransposeNode>(SI->getInput());
  
        if (!TR) {
          continue;
        }
  
        auto *NSI = F->createSigmoid(SI->getName(), TR->getInput());
        NSI->setPredicate(node->getPredicate());
        auto *newTR = F->createTranspose(TR->getName(), NSI, TR->getShuffle(),
                                         TR->getLayout());
        newTR->setPredicate(node->getPredicate());
        SI->getResult().replaceAllUsesOfWith(newTR);
        changed = true;
        continue;
      }
  
      // Sink Transpose below Pad nodes.
      if (auto *padNode = dyn_cast<PadNode>(node)) {
        auto *transposeNode = dyn_cast<TransposeNode>(padNode->getInput());
  
        if (!transposeNode) {
          continue;
        }
  
        // The transpose shuffle specifies the source dimension.
        // When sinking Transpose below Pad, shuffle describes the target
        // dimension.
        auto shuffle = transposeNode->getShuffle();
  
        // Shuffle the Pad output type and the padding attribute.
        auto outPadType = padNode->getResult().getType();
        auto outPadShape = outPadType->dims();
        auto pads = padNode->getPads();
        size_t numDims = outPadShape.size();
        std::vector<dim_t> newOutPadShape(numDims);
        std::vector<int> newPads(2 * numDims);
        for (size_t i = 0; i < outPadShape.size(); i++) {
          newOutPadShape[shuffle[i]] = outPadShape[i];
          newPads[shuffle[i]] = pads[i];
          newPads[shuffle[i] + numDims] = pads[i + numDims];
        }
  
        // New pad
        auto newOutPadType =
            F->getParent()->uniqueTypeWithNewShape(outPadType, newOutPadShape);
        auto *NewPadNode = F->createPad(
            padNode->getName(), transposeNode->getInput(), newOutPadType,
            padNode->getMode(), newPads, padNode->getValue());
        NewPadNode->setPredicate(node->getPredicate());
        auto *newTransposeNode =
            F->createTranspose(transposeNode->getName(), NewPadNode, shuffle);
        newTransposeNode->setPredicate(node->getPredicate());
        padNode->getResult().replaceAllUsesOfWith(newTransposeNode);
        changed = true;
        continue;
      }
  
      // Sink Transpose below Tanh nodes.
      if (auto *TN = dyn_cast<TanhNode>(node)) {
        auto *TR = dyn_cast<TransposeNode>(TN->getInput());
  
        if (!TR) {
          continue;
        }
  
        auto *NTN = F->createTanh(TN->getName(), TR->getInput());
        NTN->setPredicate(node->getPredicate());
        auto *newTR = F->createTranspose(TR->getName(), NTN, TR->getShuffle(),
                                         TR->getLayout());
        newTR->setPredicate(node->getPredicate());
        TN->getResult().replaceAllUsesOfWith(newTR);
        changed = true;
        continue;
      }
  
      // Remove 'identity' transpose operations.
      if (auto *TR = dyn_cast<TransposeNode>(node)) {
        auto mask = TR->getShuffle();
  
        if (isIdentityShuffle(mask)) {
          TR->getResult().replaceAllUsesOfWith(TR->getInput());
          changed = true;
          continue;
        }
      }
  
      // Merge consecutive Transpose operations.
      if (auto *TR1 = dyn_cast<TransposeNode>(node)) {
        auto *TR2 = dyn_cast<TransposeNode>(TR1->getInput());
  
        if (!TR2) {
          continue;
        }
  
        auto mask1 = TR1->getShuffle();
        auto mask2 = TR2->getShuffle();
        assert(mask1.size() == mask2.size() && "Invalid mask size");
  
        llvm::SmallVector<unsigned_t, max_tensor_dimensions> newMask;
        newMask.resize(mask2.size());
  
        for (size_t i = 0, end = mask2.size(); i < end; i++) {
          newMask[i] = mask2[mask1[i]];
        }
  
        auto *newTR = F->createTranspose("tranpose", TR2->getInput(), newMask);
        TR1->getResult().replaceAllUsesOfWith(newTR->getResult());
        changed = true;
        continue;
      }
  
      if (auto *CS = dyn_cast<ChannelShuffleNode>(node)) {
        // Sink Transpose below ChannelShuffle.
        if (sinkTranposeBelowChannelShuffle(F, CS)) {
          changed = true;
          continue;
        }
      }
  
      // Sink Transpose below Arithmetic nodes.
      if (node->isArithmetic()) {
        TransposeNode *LTR =
            dyn_cast<TransposeNode>(node->getNthInput(ArithmeticNode::LHSIdx));
        TransposeNode *RTR =
            dyn_cast<TransposeNode>(node->getNthInput(ArithmeticNode::RHSIdx));
  
        if (!LTR || !RTR) {
          // If one of the sides is a splat, it can be seen as
          // transpose (splat'). Similarly, if one of the sides is a Constant,
          // it can be seen as tranpose (Constant').
          if (isa<SplatNode>(node->getNthInput(ArithmeticNode::LHSIdx)) && RTR) {
            // Build splat' for LHS.
            auto *SN =
                dyn_cast<SplatNode>(node->getNthInput(ArithmeticNode::LHSIdx));
            auto *NS = F->createSplat("splat", RTR->getInput().getType(),
                                      SN->getValue());
            LTR = F->createTranspose("transpose", NS, RTR->getShuffle(),
                                     RTR->getLayout());
            changed = true;
          } else if (isa<SplatNode>(node->getNthInput(ArithmeticNode::RHSIdx)) &&
                     LTR) {
            // Build splat' for RHS.
            auto *SN =
                dyn_cast<SplatNode>(node->getNthInput(ArithmeticNode::RHSIdx));
            auto *NS = F->createSplat("splat", LTR->getInput().getType(),
                                      SN->getValue());
            RTR = F->createTranspose("transpose", NS, LTR->getShuffle(),
                                     LTR->getLayout());
            changed = true;
          } else if (isa<Constant>(node->getNthInput(ArithmeticNode::LHSIdx)) &&
                     RTR) {
            // Build Constant' for for LHS.
            auto *C = cast<Constant>(node->getNthInput(ArithmeticNode::LHSIdx));
            LTR = insertMatchingTransposeAfterConstant(F, C, RTR);
            changed = true;
          } else if (isa<Constant>(node->getNthInput(ArithmeticNode::RHSIdx)) &&
                     LTR) {
            // Build Constant' for for RHS.
            auto *C = cast<Constant>(node->getNthInput(ArithmeticNode::RHSIdx));
            RTR = insertMatchingTransposeAfterConstant(F, C, LTR);
            changed = true;
          } else {
            continue;
          }
        }
        // The masks of the transposes on both sizes must match.
        if (LTR->getShuffle() != RTR->getShuffle()) {
          continue;
        }
  
        Node *newAN = nullptr;
  
  #define ARITHMETIC_CASE(NODE_NAME_)                                            \
    case glow::Kinded::Kind::NODE_NAME_##NodeKind:                               \
      newAN =                                                                    \
          F->create##NODE_NAME_(node->getName(),                                 \
                                F->getParent()->uniqueTypeWithNewShape(          \
                                    node->getType(ArithmeticNode::ResultIdx),    \
                                    LTR->getInput().getType()),                  \
                                LTR->getInput(), RTR->getInput());               \
      break;
  
  #define BOOLEAN_OP_CASE(NODE_NAME_)                                            \
    case glow::Kinded::Kind::NODE_NAME_##NodeKind:                               \
      newAN = F->create##NODE_NAME_(node->getName(), LTR->getInput(),            \
                                    RTR->getInput());                            \
      break;
  
        switch (node->getKind()) {
          ARITHMETIC_CASE(Add);
          ARITHMETIC_CASE(Mul);
          ARITHMETIC_CASE(Sub);
          ARITHMETIC_CASE(Div);
          ARITHMETIC_CASE(Fmod);
          ARITHMETIC_CASE(Max);
          ARITHMETIC_CASE(Min);
          ARITHMETIC_CASE(Pow);
          BOOLEAN_OP_CASE(CmpLTE);
          BOOLEAN_OP_CASE(CmpEQ);
        default:
          llvm_unreachable("Unhandled node");
        }
  #undef BOOLEAN_OP_CASE
  #undef ARITHMETIC_CASE
  
        newAN->setPredicate(node->getPredicate());
        changed = true;
        auto *newTR = F->createTranspose(LTR->getName(), newAN, LTR->getShuffle(),
                                         LTR->getLayout());
        newTR->setPredicate(node->getPredicate());
        node->getNthResult(ArithmeticNode::ResultIdx).replaceAllUsesOfWith(newTR);
      }
  
      if (auto *Q = dyn_cast<QuantizeNode>(node)) {
        // Sink TransposeNode below QuantizedNode.
        // If it doesn't work out it will be re-sinked later.
        if (auto *TR = dyn_cast<TransposeNode>(Q->getInput())) {
          auto newQType = F->getParent()->uniqueTypeWithNewShape(
              Q->getResult().getType(), TR->getInput().dims());
          auto *newQ = F->createQuantize(Q->getName(), TR->getInput(), newQType);
          auto *newTR = F->createTranspose(TR->getName(), newQ, TR->getShuffle());
          Q->getResult().replaceAllUsesOfWith(newTR);
          changed = true;
          continue;
        }
  
        // Sink Reshape below Quantize.
        if (auto *RN = dyn_cast<ReshapeNode>(Q->getInput())) {
          auto newQType = F->getParent()->uniqueTypeWithNewShape(
              Q->getResult().getType(), RN->getInput().dims());
          auto *newQ = F->createQuantize(Q->getName(), RN->getInput(), newQType);
          auto *newRN = F->createReshape(RN->getName(), newQ,
                                         RN->getResult().dims(), RN->getLayout());
          Q->getResult().replaceAllUsesOfWith(newRN->getResult());
          changed = true;
          continue;
        }
      }
  
      // Sink Reshape below ConvertTo.
      if (auto *CN = dyn_cast<ConvertToNode>(node)) {
        auto *RN = dyn_cast<ReshapeNode>(CN->getInput());
        if (!RN) {
          continue;
        }
        auto *newCN = F->createConvertTo(CN->getName(), RN->getInput(),
                                         CN->getResult().getElementType());
        auto *newRN = F->createReshape(RN->getName(), newCN,
                                       RN->getResult().dims(), RN->getLayout());
        CN->getResult().replaceAllUsesOfWith(newRN->getResult());
        changed = true;
        continue;
      }
  
      // Sink TransposeNode below DequantizedNode.
      // If it doesn't work out it will be re-sinked later.
      if (auto *D = dyn_cast<DequantizeNode>(node)) {
        auto *TR = dyn_cast<TransposeNode>(D->getInput());
        if (!TR) {
          continue;
        }
  
        auto newDType = F->getParent()->uniqueTypeWithNewShape(
            D->getResult().getType(), TR->getInput().dims());
        auto *newD = F->createDequantize(D->getName(), TR->getInput(), newDType);
        auto *newTR = F->createTranspose(TR->getName(), newD, TR->getShuffle());
        D->getResult().replaceAllUsesOfWith(newTR);
        changed = true;
      }
  
      // Sink Transpose below RescaleQuantized.
      // Potentially exposes opportunity to be combined up with Convolution.
      // If it doesn't work out it will be re-sinked later.
      if (auto *RQ = dyn_cast<RescaleQuantizedNode>(node)) {
        auto *TR = dyn_cast<TransposeNode>(RQ->getInput());
        if (!TR) {
          continue;
        }
  
        auto newRQType = F->getParent()->uniqueTypeWithNewShape(
            RQ->getResult().getType(), TR->getInput().getType());
        auto *newRQ =
            F->createRescaleQuantized(RQ->getName(), TR->getInput(), newRQType);
        auto *newTR = F->createTranspose(TR->getName(), newRQ, TR->getShuffle(),
                                         TR->getLayout());
        RQ->getResult().replaceAllUsesOfWith(newTR);
        changed = true;
      }
  
      if (auto *CN = dyn_cast<ConcatNode>(node)) {
        const Node *firstNode = CN->getInputs().front().getNode();
        // Sink RELU below batch concat nodes.
        if (firstNode->getKind() == Kinded::Kind::ReluNodeKind) {
          llvm::SmallVector<NodeValue, 6> CNInputs;
          for (auto &input : CN->getInputs()) {
            auto *inputRL = dyn_cast<ReluNode>(input);
            if (!inputRL) {
              break;
            }
            CNInputs.push_back(inputRL->getInput());
          }
  
          if (CNInputs.size() == CN->getNumInputs()) {
            auto *newCN = F->createConcat(CN->getName(), CNInputs, CN->getDim());
            newCN->setPredicate(node->getPredicate());
            auto name = CN->getNthInput(0).getNode()->getName();
            auto *newRL = F->createRELU(name, newCN, CN->getResult().getType());
            newRL->setPredicate(node->getPredicate());
            CN->getResult().replaceAllUsesOfWith(newRL);
            changed = true;
          }
          continue;
        }
  
        // Sink Transpose below concat nodes.
        if (firstNode->getKind() == Kinded::Kind::TransposeNodeKind) {
          llvm::SmallVector<NodeValue, 6> transVector;
          auto inputIter = CN->getInputs().begin();
          auto *firstInput = dyn_cast<TransposeNode>(*inputIter);
          if (!firstInput) {
            continue;
          }
  
          transVector.push_back(firstInput->getInput());
          auto shuffle = firstInput->getShuffle();
          // If the shuffle masks don't agree or not all inputs are Transpose then
          // bail out.
          for (++inputIter; inputIter != CN->getInputs().end(); ++inputIter) {
            auto *tTR = dyn_cast<TransposeNode>(*inputIter);
            if (!tTR || tTR->getShuffle() != shuffle) {
              break;
            }
            transVector.push_back(tTR->getInput());
          }
  
          if (transVector.size() != CN->getNumInputs()) {
            continue;
          }
  
          // Figure out where we transposed the channel index for batch
          // normalization.
          unsigned_t idx = CN->getDim();
          unsigned_t newChannelIdx = shuffle[idx];
  
          auto *newCN =
              F->createConcat(CN->getName(), transVector, newChannelIdx);
          newCN->setPredicate(node->getPredicate());
          auto *newTR = F->createTranspose(firstInput->getName(), newCN,
                                           firstInput->getShuffle(),
                                           firstInput->getLayout());
          newTR->setPredicate(node->getPredicate());
          CN->getResult().replaceAllUsesOfWith(newTR);
          changed = true;
          continue;
        }
      }
  
      // Sink Clip below Reshape nodes.
      if (auto *RN = dyn_cast<ReshapeNode>(node)) {
        auto *CN = dyn_cast<ClipNode>(RN->getInput());
        if (!CN) {
          continue;
        }
  
        ReshapeNode *newRN = F->createReshape(RN->getName(), CN->getInput(),
                                              RN->getDims(), RN->getLayout());
        ClipNode *newCN = F->createClip(CN->getName(), newRN->getResult(),
                                        CN->getMin(), CN->getMax());
        RN->getResult().replaceAllUsesOfWith(newCN->getResult());
        newRN->setPredicate(RN->getPredicate());
        newCN->setPredicate(CN->getPredicate());
        changed = true;
        continue;
      }
    } // For all nodes in the graph.
  
    // Transformations to sink nodes below Slice. Outlined into a separate loop to
    // prevent Transpose/Slice sinking to affect them.
    for (auto &N : nodes) {
      auto *node = &N;
      // Sink BatchNorm below Slice.
      if (auto *SN = dyn_cast<SliceNode>(node)) {
        auto *BN = dyn_cast<BatchNormalizationNode>(SN->getInput());
        if (!BN || !BN->hasOneUse()) {
          continue;
        }
  
        // Don't support sinking below Slice which affects depth.
        if (SN->getInput().dims()[BN->getChannelIdx()] !=
            SN->getResult().dims()[BN->getChannelIdx()]) {
          continue;
        }
  
        auto newSNType = F->getParent()->uniqueTypeWithNewShape(
            BN->getInput().getType(), SN->getResult().dims());
        auto *newSN = F->createSlice(SN->getName(), BN->getInput(),
                                     SN->getStart(), newSNType);
        auto *newBN = F->createBatchNormalization(
            BN->getName(), SN->getResult().getType(), newSN, BN->getBias(),
            BN->getScale(), BN->getMean(), BN->getVar(), BN->getChannelIdx(),
            BN->getEpsilon(), BN->getMomentum());
        SN->getResult().replaceAllUsesOfWith(newBN);
        changed = true;
      }
    }
  
    return changed;
  }
  
  /// Remove unnecessary padding and reduce filters for Convolution nodes with
  /// small input tensors.
  bool OptimizeSmallConv::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    for (auto &N : F->getNodes()) {
      auto *CN = dyn_cast<ConvolutionNode>(&N);
      if (!CN) {
        continue;
      }
  
      // Consider a Convolution "small", if its output is 1x1.
      // The transformation doesn't support dilation.
      auto dilation = CN->getDilation();
      ShapeNHWC odim(CN->getResult().dims());
      if (odim.h > 1 || odim.w > 1 || !isUniformArray(dilation, 1u)) {
        continue;
      }
  
      // Dealing with stride=1 Convoltuion nodes is generally easier, so try
      // to canonicalize stride to 1 if possible.
      ShapeNHWC idim(CN->getInput().dims());
      std::vector<unsigned_t> strides(CN->getStrides());
      std::vector<unsigned_t> kernels(CN->getKernels());
      std::vector<unsigned_t> pads(CN->getPads());
      auto newOutHW = calculateConvPoolOutputDims(idim.h, idim.w, kernels, {1, 1},
                                                  pads, dilation);
      if (newOutHW.first == 1 && newOutHW.second == 1) {
        strides = {1, 1};
      }
  
      // Slice off redundant filter parts.
      auto filters = CN->getFilter();
      auto *C = dyn_cast<Constant>(filters);
      if (C && isUniformArray(llvm::makeArrayRef(strides), 1u) &&
          !isUniformArray(llvm::makeArrayRef(pads), 0u)) {
        ShapeNHWC fdim(filters.dims());
        PaddingTLBR p(llvm::makeArrayRef(pads));
        dim_t start[] = {0u, p.top, p.left, 0u};
        dim_t end[] = {fdim.n, fdim.h - p.bottom, fdim.w - p.right, fdim.c};
        auto *SN = F->createSlice(C->getName(), C, start, end);
        filters = SN->getResult();
        kernels = {unsigned_t(idim.h), unsigned_t(idim.w)};
        pads = {0, 0, 0, 0};
      }
  
      // Check if this node needs any changes.
      if (filters == CN->getFilter() &&
          llvm::makeArrayRef(strides) == CN->getStrides()) {
        continue;
      }
  
      auto *newCN =
          F->createConv(CN->getName(), CN->getInput(), filters, CN->getBias(),
                        CN->getResult().getType(), kernels, strides, pads,
                        CN->getGroup(), dilation);
  
      CN->getResult().replaceAllUsesOfWith(newCN->getResult());
      changed = true;
    }
  
    return changed;
  }
  
  /// Fold a Convolution dilated manually using Transpose, SpaceToDepth and
  /// DepthToSpace nodes into a single Convolution node. Pattern:
  /// NHWC2CHWN -> S2D -> CHWN2NHWC -> Conv -> NHWC2CHWN -> D2S -> CHWN2NHWC
  bool FoldDilatedConv::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    for (auto &N : F->getNodes()) {
      // Do matching starting from the last node of the pattern and go backwards.
  
      // 1. Transpose CHWN2NHWC.
      // Do not match this Transpose, since it has likely sank and got separated
      // from the rest of the nodes in the pattern. Instead, we'll generate a
      // reverse Transpose after new Convolution for them to be optimized out.
  
      // 2. DepthToSpace (represented as Reshape + Transpose[6 dims] + Reshape).
      // Ignore the last Reshape for the same reasons as Transpose above and start
      // matching from 6 dim Transpose.
      auto *D2S = dyn_cast<TransposeNode>(&N);
      if (!D2S || !D2S->hasOneUse() ||
          D2S->getShuffle() != llvm::makeArrayRef(D2S_DCR)) {
        continue;
      }
      unsigned_t block = D2S->getInput().dims()[3];
      NodeValue output = D2S->getResult();
  
      auto *RN = dyn_cast<ReshapeNode>(D2S->getInput());
      if (!RN || !RN->hasOneUse()) {
        continue;
      }
      llvm::ArrayRef<dim_t> idim = RN->getInput().dims();
      llvm::SmallVector<dim_t, 6> odim = {
          idim[0], idim[1], idim[2], block, block, idim[3] / (block * block)};
      if (RN->getResult().dims() != llvm::makeArrayRef(odim)) {
        continue;
      }
  
      // 3. Transpose NHWC2CHWN.
      auto *T1 = dyn_cast<TransposeNode>(RN->getInput());
      if (!T1 || !T1->hasOneUse() ||
          T1->getShuffle() != llvm::makeArrayRef(NHWC2CHWN)) {
        continue;
      }
  
      // 4. Convolution.
      auto *CN = dyn_cast<ConvolutionNode>(T1->getInput());
      if (!CN || !CN->hasOneUse()) {
        continue;
      }
      llvm::StringRef name = CN->getName();
      auto kernels = CN->getKernels();
      auto strides = CN->getStrides();
      auto pads = CN->getPads();
      if (!isUniformArray(strides, 1u) || !isUniformArray(pads, 0u) ||
          !isUniformArray(CN->getDilation(), 1u)) {
        continue;
      }
  
      // 5. Transpose CHWN2NHWC.
      auto *T2 = dyn_cast<TransposeNode>(CN->getInput());
      if (!T2 || T2->getShuffle() != llvm::makeArrayRef(CHWN2NHWC)) {
        continue;
      }
  
      // 6. SpaceToDepth.
      auto *S2D = dyn_cast<SpaceToDepthNode>(T2->getInput());
      if (!S2D || S2D->getBlockSize() != block) {
        continue;
      }
      NodeValue input = S2D->getInput();
  
      // 7. Transpose NHWC2CHWN.
      // Can potentially be changed/removed by merging with other Transpose nodes,
      // so don't match it. Instead, will generate a reverse Transpose later.
  
      // Create CHWN2NHWC -> Conv -> NHWC2CHWN -> Reshape.
      // Reshape and Transposes are created to cancel the ones we did not match.
      auto *newT1 =
          F->createTranspose(name.str() + "_chwn2nhwc", input, CHWN2NHWC);
  
      auto trOutDims = newT1->getResult().dims();
      auto outHW = calculateConvPoolOutputDims(
          trOutDims[1], trOutDims[2], kernels, strides, pads, {block, block});
      auto convOutTy = F->getParent()->uniqueTypeWithNewShape(
          CN->getResult().getType(),
          {trOutDims[0], outHW.first, outHW.second, CN->getResult().dims()[3]});
      auto *newCN = F->createConv(name, newT1->getResult(), CN->getFilter(),
                                  CN->getBias(), convOutTy, kernels, strides,
                                  pads, CN->getGroup(), {block, block});
  
      auto *newT2 = F->createTranspose(name.str() + "_nhwc2chwn",
                                       newCN->getResult(), NHWC2CHWN);
  
      idim = newT2->getResult().dims();
      odim = {idim[0], idim[1] / block, block, idim[2] / block, block, idim[3]};
      auto *newRN =
          F->createReshape(name.str() + "_reshape", newT2->getResult(), odim);
  
      output.replaceAllUsesOfWith(newRN->getResult());
      changed = true;
    }
  
    return changed;
  }
  
  /// \returns True if node A may depend on the result of B. The relationship
  /// between the nodes does not have to be direct. For example, A can depend on
  /// X which depends on B. In that case the method needs to return True.
  /// Check the use-def dependency up to a depth of \p depth.
  static bool mayDepend(Node *A, Node *B, unsigned depth = 6) {
    // We define the identify as a dependency.
    if (A == B) {
      return true;
    }
  
    // A does not depend on anything.
    if (A->getNumInputs() == 0) {
      return false;
    }
  
    // B has no users. Nothing can depend on it.
    if (B->getNumResults() == 0) {
      return false;
    }
  
    // We can't continue the search. Assume that the nodes depend on one another.
    if (depth == 0) {
      return true;
    }
  
    // Check all inputs of A. None of them may depend on B.
    for (int i = 0, e = A->getNumInputs(); i < e; i++) {
      auto *input = A->getNthInput(i).getNode();
      // The inputs of A must not depend on B.
      if (mayDepend(input, B, depth - 1)) {
        return true;
      }
    }
  
    // We checked all inputs of A and none of them depend on B.
    return false;
  }
  
  /// \returns True if the node \p N depends on any of the values in \p list, or
  /// if any of the values in list depend on \p N.
  static bool mayDependOnAny(llvm::ArrayRef<NodeValue> list, Node *N) {
    for (auto &ll : list) {
      if (mayDepend(ll.getNode(), N) || mayDepend(N, ll.getNode())) {
        return true;
      }
    }
  
    return false;
  }
  
  // Merge several two or more multiple matrix multiplications into a single
  // large matmul. The large matmul is more likely to utilize the hardware. The
  // result of the big matmul is the concatenated results.
  //
  //            ____      _________        _________
  //   ----    |    |    |         |     M|  A * C  |
  // M| A  |  T| B  | * K|    C    | =    |---------|
  //   ---- ,  |    |    |         |     T|  B * C  |
  //    K       ----      ---------        ---------
  //             K            R                R
  bool MergeMatMul::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    auto &nodes = F->getNodes();
  
    // These two maps record the list of matrix multipliers that use each node
    // value either as a right-hand-side user or a left-hand-user.
    llvm::DenseMap<Node *, std::vector<MatMulNode *>> rightMatrixUsers;
    llvm::DenseMap<Node *, std::vector<MatMulNode *>> leftMatrixUsers;
  
    // Collect the list of nodes that are used by the matrix multiplier.
    for (auto &node : nodes) {
      if (auto *MM = dyn_cast<MatMulNode>(&node)) {
        // Do not try to merge quantized matrix multiplications because their
        // quantized parameters may not match. Until we implement the logic to
        // match the scale and offset just avoid the optimization.
        if (MM->getResult().getType()->isQuantizedType()) {
          continue;
        }
  
        rightMatrixUsers[MM->getRHS().getNode()].push_back(MM);
        leftMatrixUsers[MM->getLHS().getNode()].push_back(MM);
      }
    }
  
    // Merge RHS matrices.
    for (auto &it : rightMatrixUsers) {
      auto &MMs = it.second;
  
      // Collects the LHS values to merge.
      std::vector<NodeValue> LHS;
  
      // For each matmul that depends on the rhs matrix.
      for (auto &MM : MMs) {
        auto L = MM->getLHS();
        // The operands to the matrix multiplier should not depend on one another
        // or else we won't be able to get rid of the original matrix
        // multiplication.
        if (mayDependOnAny(LHS, L.getNode())) {
          continue;
        }
        LHS.push_back(L);
      }
  
      // We need to have at least two matrices to merge.
      if (LHS.size() < 2) {
        continue;
      }
  
      // Merge the matmul:
      auto *CC = F->createConcat("mergeLHS", LHS, 0);
      auto *MM = F->createMatMul("bigMatMul", CC, it.first);
  
      dim_t R = MM->getResult().dims()[1];
      dim_t start = 0;
      for (auto *origMM : MMs) {
        dim_t H = origMM->getResult().dims()[0];
        auto *ex = F->createSlice("extract", MM, {start, 0}, {start + H, R});
        start += H;
        origMM->getResult().replaceAllUsesOfWith(ex);
        changed = true;
      }
    }
    return changed;
  }
  
  bool MergePadIntoConvolution::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    for (auto &node : F->getNodes()) {
      auto *CN = dyn_cast<ConvolutionNode>(&node);
      if (!CN) {
        continue;
      }
  
      auto *PN = dyn_cast<PadNode>(CN->getInput());
      if (!PN) {
        continue;
      }
  
      // Convolution only supports padding with 0 constant
      if ((PN->getMode() != PaddingMode::CONSTANT) || (PN->getValue() != 0.f)) {
        continue;
      }
  
      // The convolution needs to be the unique user
      if (!PN->hasOneUse()) {
        continue;
      }
  
      // Compute the new padding.
      // Note: - convolution only supports positive padding
      //       - the convolution takes NHWC input tensors.
      bool canMerge = true;
      auto padPads = PN->getPads();
      auto convPads = CN->getPads();
  
      // For now, there is a different interpretation of the ONNX spec for
      // Pad and Convolution. The 'pads' array won't have the same size because
      // only spatial dimensions are specified for the convolution while all
      // dimensions are specified for Pad.
  
      // The merge can apply only if no padding is requested for non spatial
      // dimensions.
      if ((padPads[0] != 0) || (padPads[3] != 0) || (padPads[4] != 0) ||
          (padPads[7] != 0)) {
        continue;
      }
  
      // Compute new spatial padding.
      const int H_INDEX = 1;
      std::vector<unsigned_t> newConvPads(4);
      auto numDims = PN->getResult().dims().size();
      for (size_t i = 0; i < 2; i++) {
        // Two pad integers per dimension (begin and end padding).
        for (size_t j = 0; j < 2; j++) {
          int newConvPadSigned =
              padPads[(i + H_INDEX) + j * numDims] + int(convPads[i + j * 2]);
          if (newConvPadSigned < 0) {
            canMerge = false;
            break;
          }
          newConvPads[i + j * 2] = unsigned_t(newConvPadSigned);
        }
      }
      if (!canMerge) {
        continue;
      }
  
      // New Convolution
      auto *newCN = F->createConv(CN->getName(), PN->getInput(), CN->getFilter(),
                                  CN->getBias(), CN->getResult().getType(),
                                  CN->getKernels(), CN->getStrides(), newConvPads,
                                  CN->getGroup(), CN->getDilation());
      newCN->setFusedActivation(CN->getFusedActivation());
      newCN->setFusedActivationArgs(CN->getFusedActivationArgs());
  
      CN->getResult().replaceAllUsesOfWith(newCN);
      changed = true;
    }
  
    return changed;
  }
  
  /// Merge Transpose into MatMul or FC.
  /// MatMul/FC(Reshape(Transpose(X)), Weights) ->
  /// -> MatMul/FC(Reshape(X), reordered Weights)
  /// Common sequence while using NCHW as input layout, because GLOW convolution
  /// layout is NHWC:
  /// Transpose([N, H, W, C]) -> [N, C, H, W]
  /// Reshape([N, C, H, W]) -> [N, C * H * W]
  /// MatMul/FC([N, C * H * W], [C * H * W, K]) -> [N, K]
  bool MergeTransposeIntoMatMulOrFC::run(Function *F,
                                         const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    for (auto &node : F->getNodes()) {
      auto *MMN = dyn_cast<MatMulNode>(&node);
      auto *FCN = dyn_cast<FullyConnectedNode>(&node);
      Constant *W;
      ReshapeNode *RN;
  
      // Node is either MatMul or FC.
      if (MMN) {
        W = dyn_cast<Constant>(MMN->getRHS());
        RN = dyn_cast<ReshapeNode>(MMN->getLHS());
      } else if (FCN) {
        W = dyn_cast<Constant>(FCN->getWeights());
        RN = dyn_cast<ReshapeNode>(FCN->getInput());
      } else {
        continue;
      }
  
      // Weights node (or MatMul RHS) is constant.
      if (!W) {
        continue;
      }
  
      // Linearizing Reshape precedes MatMul/FC.
      // The first dimension must be kept unchanged, the others are linearized.
      if (!RN || !RN->hasOneUse() ||
          RN->getInput().dims()[0] != RN->getDims()[0]) {
        continue;
      }
  
      // Transpose precedes Reshape.
      // The first dimension must be kept unchanged, the others can be shuffled
      // in any way.
      auto *TN = dyn_cast<TransposeNode>(RN->getInput());
      if (!TN || !TN->hasOneUse() || TN->getShuffle()[0] != 0) {
        continue;
      }
  
      // MatMul/FC weights tensor is 2D. De-linearize the first dimension
      // according to Transpose output layout (original shape) and input layout
      // (reordered shape). Then we can do weights reordering by simply
      // transposing the tensor from original shape to reordered shape.
      //
      // Example for [N, H, W, C] -> [N, C, H, W] transpose (common case):
      //   De-linearized original shape: [C * H * W, K] -> [C, H, W, K]
      //   De-linearized reordered shape: [C * H * W, K] -> [H, W, C, K]
      //   Reorder weights by transposing them: [C, H, W, K] -> [H, W, C, K]
      ShapeVector shape, newShape;
      llvm::SmallVector<unsigned_t, max_tensor_dimensions> shuffle;
      shuffle.resize(TN->getShuffle().size() - 1);
      for (size_t i = 1; i < TN->getShuffle().size(); i++) {
        shape.push_back(TN->getResult().getType()->dims()[i]);
        newShape.push_back(TN->getInput().getType()->dims()[i]);
        shuffle[TN->getShuffle()[i] - 1] = i - 1;
      }
      shape.push_back(W->dims()[1]);
      newShape.push_back(W->dims()[1]);
      shuffle.push_back(TN->getShuffle().size() - 1);
      auto reshapedWTy =
          F->getParent()->uniqueTypeWithNewShape(W->getType(), shape);
      auto reshapedNewWTy =
          F->getParent()->uniqueTypeWithNewShape(W->getType(), newShape);
  
      // New reordered weights.
      auto *newW = F->getParent()->createConstant(W->getType(), W->getName(),
                                                  W->getLayout());
      Tensor reshapedSrc(W->getPayload().getUnsafePtr(), reshapedWTy);
      Tensor reshapedDst(newW->getPayload().getUnsafePtr(), reshapedNewWTy);
      reshapedSrc.transpose(&reshapedDst, shuffle);
  
      // New Reshape and MatMul/FC.
      auto *newRN =
          F->createReshape(RN->getName(), TN->getInput(), RN->getDims());
      if (MMN) {
        auto *newMMN = F->createMatMul(MMN->getName(), MMN->getResult().getType(),
                                       newRN, newW);
        MMN->getResult().replaceAllUsesOfWith(newMMN);
      } else if (FCN) {
        auto *newFCN =
            F->createFullyConnected(FCN->getName(), newRN, newW, FCN->getBias(),
                                    FCN->getResult().getType());
        FCN->getResult().replaceAllUsesOfWith(newFCN);
      } else {
        llvm_unreachable("Unexpected node kind");
      }
  
      changed = true;
    }
  
    return changed;
  }
  
  /// \returns True if the two slices \p A and \p B access consecutive spacial
  /// regions on the \p dim dimension. For example Slice(0..10) Slice(10..50)
  /// are consecutive but Slice(0..10) Slice(20..30) are not.
  static bool areSlicesConsecutive(SliceNode *A, SliceNode *B, unsigned_t dim) {
    // The slices must extract from the same input.
    if (A->getInput() != B->getInput()) {
      return false;
    }
  
    auto aStart = A->getStart();
    auto bStart = B->getStart();
  
    assert(aStart.size() > dim && "Invalid dimension");
  
    for (size_t i = 0, e = aStart.size(); i < e; i++) {
      if (i == dim) {
        auto resSize = A->getResult().dims();
        // This is the stride (the delta between the two slices on the requested
        // dimension).
        auto delta = bStart[i] - aStart[i];
        // The distance between the two slices must be identical to the size of
        // the result.
        if (resSize[dim] != delta) {
          return false;
        }
  
        continue;
      }
  
      // The non-consecutive dimensions must be identical.
      if (aStart[i] != bStart[i]) {
        return false;
      }
    }
  
    return true;
  }
  
  /// \returns True if the two slices \p A and \p B access consecutive spacial
  /// regions along some dimension. The dimension is stored in \p dim.
  /// For example, Slice((0, 0)..(1, 10)) Slice((1, 0)..(2, 10)) are consecutive
  /// along dim=0.
  static bool findConsecutiveSliceDim(SliceNode *A, SliceNode *B, int *dim) {
    // The slices must extract from the same input.
    if (A->getInput() != B->getInput()) {
      return false;
    }
  
    for (size_t i = 0, e = A->getStart().size(); i < e; i++) {
      if (areSlicesConsecutive(A, B, i)) {
        *dim = i;
        return true;
      }
    }
  
    return false;
  }
  
  bool ConvertBroadcastedBatchMatMul::run(Function *F,
                                          const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    for (auto &node : F->getNodes()) {
      BatchMatMulNode *BMMN = dyn_cast<BatchMatMulNode>(&node);
      if (!BMMN) {
        continue;
      }
  
      NodeValue LHS = BMMN->getLHS();
      NodeValue RHS = BMMN->getRHS();
  
      // If RHS is a Tile/Broadcast along axis 0 and the input's dims()[0] == 1,
      // then the RHS is fully broadcasted and we can perform the optimization.
      TileNode *TN = dyn_cast<TileNode>(RHS);
      BroadcastNode *BN = dyn_cast<BroadcastNode>(RHS);
      if (!TN && !BN) {
        continue;
      }
      const unsigned_t axis = TN ? TN->getAxis() : BN->getAxis();
      const dim_t dim0 = TN ? TN->getInput().dims()[0] : BN->getInput().dims()[0];
      if (axis != 0 || dim0 != 1) {
        continue;
      }
  
      // If this is a Broadcast, check if the first dimension is the only one
      // that's tiled. If so, then we can treat this as the same as a
      // Tile. Otherwise we must keep around a Broadcast for everything but the
      // first dimension.
      NodeValue singleTileNV;
      if (BN) {
        ShapeVector newBNDims(BN->getResult().dims().begin(),
                              BN->getResult().dims().end());
        newBNDims[0] = 1;
        if (!BN->getInput().dims().equals(newBNDims)) {
          BroadcastNode *newBN = F->createBroadcast(BN->getName(), BN->getInput(),
                                                    newBNDims, /* axis */ 0);
          singleTileNV = newBN->getResult();
        } else {
          // This Broadcast is equivalent to a Tile.
          singleTileNV = BN->getInput();
        }
      } else {
        singleTileNV = TN->getInput();
      }
  
      // Can now convert the broadcasted BatchMatMul to a MatMul.
      // LHS = {numBatches, N, M}
      // RHS = {M, P}
      // Multiply each LHS matrix {N, M} by RHS {M, P} to get final matrix
      // {numBatches, N, P}
      const dim_t numBatches = LHS.dims()[0];
      const dim_t N = LHS.dims()[1];
      const dim_t M = LHS.dims()[2];
      const dim_t P = RHS.dims()[2];
      auto name = BMMN->getName();
  
      // Reshape the LHS to be a two-dimensional matrix, where each batch is
      // essentially concatenated onto itself in the 0th dimension.
      ReshapeNode *reshapeLHS =
          F->createReshape(name.str() + ".reshapeLHS", LHS, {numBatches * N, M});
      // Squeeze out the first dimension of the original Tile's input.
      ReshapeNode *squeezedRHS =
          F->createSqueeze(name.str() + ".squeezedRHS", singleTileNV, {0});
  
      // Perform a normal matmul, implementing the batch matmul.
      MatMulNode *MMN = F->createMatMul(name, reshapeLHS, squeezedRHS);
  
      assert(MMN->getResult().dims()[0] == (numBatches * N) &&
             "Incorrect resulting dimension for batch matmul");
      assert(MMN->getResult().dims()[1] == P &&
             "Incorrect resulting dimension for batch matmul");
  
      // Reshape the result back to the expected batch output shape, with the
      // first dimension the number of batches.
      ReshapeNode *finalReshape = F->createReshape(name.str() + ".reshapeResult",
                                                   MMN, {numBatches, N, P});
      BMMN->getResult().replaceAllUsesOfWith(finalReshape);
      changed = true;
    }
    return changed;
  }
  
  /// Find a sequence of slices in \p input that span the whole input.
  /// \returns True if a group of slices that span the whole input was found.
  /// The order of the slices is recorded in \p order.
  static bool findSlicesThatSpanInput(llvm::ArrayRef<SliceNode *> input,
                                      unsigned_t dimension,
                                      std::vector<SliceNode *> &order) {
    // This is the 'last' slice to be found in the sequence of slices.
    SliceNode *lastSlice = nullptr;
  
    // Find the 'first' slice in the sequence.
    for (SliceNode *SN : input) {
      auto start = SN->getStart();
  
      // Invalid dimension.
      if (start.size() <= dimension) {
        return false;
      }
  
      // Check if this slice extract the first element.
      if (start[dimension] == 0) {
        // We found the first element.
        lastSlice = SN;
        order.push_back(lastSlice);
        break;
      }
    }
  
    // We could not find a 'first' slice.
    if (!lastSlice) {
      return false;
    }
  
    // Now that we've found the first slice in the sequence, try to order the
    // rest of the slices after the first one.
    bool addedSlice = true;
    while (addedSlice) {
      addedSlice = false;
  
      // For each slice:
      for (SliceNode *SN : input) {
        // Ignore slices of invalid types. Ignore shapes for now, that's checked
        // next while ignoring the axis dimension.
        if (!lastSlice->getResult().getType()->isEqual(
                *SN->getResult().getType(),
                /* allowDifferentShape */ true)) {
          continue;
        }
  
        // Check if shapes match except for the axis dimension.
        bool skip = false;
        for (size_t i = 0, e = lastSlice->getResult().dims().size(); i < e; ++i) {
          if (i != dimension &&
              lastSlice->getResult().dims()[i] != SN->getResult().dims()[i]) {
            skip = true;
            break;
          }
        }
        if (skip) {
          continue;
        }
  
        // Check if SN comes after the last slice in the sequence.
        if (areSlicesConsecutive(lastSlice, SN, dimension)) {
          // Add the consecutive slice and schedule another iteration.
          lastSlice = SN;
          order.push_back(lastSlice);
          addedSlice = true;
          continue;
        }
      }
    } // While adding new slices.
  
    // Check that the last slice completes the tensor.
    auto startCoor = lastSlice->getStart();
    auto resDim = lastSlice->getResult().getType()->dims();
    auto inDim = lastSlice->getInput().getType()->dims();
  
    // Check if for all dimensions, the size of the result tensor plus the start
    // coordinate matches the size of the tensor.
    for (int i = 0, e = startCoor.size(); i < e; i++) {
      if (startCoor[i] + resDim[i] != inDim[i]) {
        return false;
      }
    }
  
    // Report success if we found at least two slices that extract from the
    // input.
    return order.size() > 1;
  }
  
  /// Merge multiple batched add nodes into a large batched-add node.
  bool MergeBatchedAdd::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    auto &nodes = F->getNodes();
  
    // We index the batched add nodes by the slice operand.
    llvm::DenseMap<Node *, std::vector<BatchedAddNode *>> rightBAUsers;
  
    // Collect all of the batched add nodes and index them by the 'slice'
    // operand.
    for (auto &node : nodes) {
      if (auto *BA = dyn_cast<BatchedAddNode>(&node)) {
        rightBAUsers[BA->getSlice().getNode()].push_back(BA);
      }
    }
  
    // For each 'slice' that batched add nodes access:
    for (auto &it : rightBAUsers) {
      auto &BAs = it.second;
  
      // Collects the left-hand-side operands that the batched-adds add into. We
      // only collect 'slice' nodes.
      std::vector<SliceNode *> slices;
  
      for (auto *BA : BAs) {
        if (auto *S = dyn_cast<SliceNode>(BA->getBatch().getNode())) {
          slices.push_back(S);
        }
      }
  
      // Check if the slice nodes that we've collected cover a whole tensor.
      std::vector<SliceNode *> order;
      bool found = findSlicesThatSpanInput(slices, 0, order);
  
      if (!found) {
        continue;
      }
  
      // We found a sequence of batched-add-slice that cover the input tensor.
      // We can transform the graph and create one big batched-add.
      std::vector<Node *> newSlices;
      assert(order.size() > 1 && "order must contain at least 2 SliceNodes.");
      SliceNode *S = llvm::cast<SliceNode>(order[0]);
      auto *mergedBA = F->createBatchedAdd("mergedBA", S->getInput(), it.first);
  
      // Create the new slices. These slices will replace the original scalar
      // batched-add nodes.
      for (auto *orig : order) {
        newSlices.push_back(F->createSlice(orig->getName(), mergedBA,
                                           orig->getStart(),
                                           orig->getResult().getType()));
      }
  
      // Replace the original individual batched adds with corresponding slices
      // from the new merged batch add.
      for (auto *BA : BAs) {
        for (int i = 0, e = order.size(); i < e; i++) {
          if (BA->getBatch().getNode() == order[i]) {
            BA->getResult().replaceAllUsesOfWith(newSlices[i]);
            changed = true;
            break;
          }
        }
      }
  
    } // for each batched-add group.
    return changed;
  }
  
  /// Optimize ReduceMean configuration with AvgPool if possible: last two axes
  /// in a 4D input must be reduced.
  bool OptimizeReduceMean::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    auto &nodes = F->getNodes();
  
    // For each node:
    for (auto &node : nodes) {
      if (auto *RM = dyn_cast<BatchedReduceMeanNode>(&node)) {
  
        // Input shape must be 4D.
        if (RM->getBatch().dims().size() != 4) {
          continue;
        }
  
        // Last two axes must be reduced.
        auto axes = RM->getAxes();
        if (axes.size() != 2 || std::count(axes.begin(), axes.end(), 2) != 1 ||
            std::count(axes.begin(), axes.end(), 3) != 1) {
          continue;
        }
  
        // RM is already shaped to have the required output shape.
        NodeValue in = RM->getBatch();
  
        std::vector<unsigned_t> kernels = {static_cast<unsigned_t>(in.dims()[2]),
                                           static_cast<unsigned_t>(in.dims()[3])};
        std::vector<unsigned_t> strides = {1, 1};
        std::vector<unsigned_t> pads = {0, 0, 0, 0};
  
        // TODO: Fix bad assumption? See issue 3499, for now workaround it.
        // In Glow, AvgPool expects NHWC.
        auto *TR1 = F->createTranspose(
            RM->getName().str() + ".transposeNCHW2NHWC", in, NCHW2NHWC, "NHWC");
        auto *AP = F->createAvgPool(RM->getName().str() + ".avgPool", TR1,
                                    kernels, strides, pads);
        auto *TR2 = F->createTranspose(
            RM->getName().str() + ".transposeNHWC2NCHW", AP, NHWC2NCHW, "NCHW");
  
        // AvgPool keeps original shape. Add reshape to match expected output.
        std::vector<dim_t> shape = TR2->getResult().dims();
  
        ShapeVector shapeAxes(axes.begin(), axes.end());
  
        // Axes must be sorted for correct erase.
        std::sort(shapeAxes.rbegin(), shapeAxes.rend());
        for (const auto &axis : shapeAxes) {
          shape.erase(shape.begin() + axis);
        }
  
        auto *RN = F->createReshape(RM->getName().str() + ".reshape", TR2, shape);
  
        RM->getResult().replaceAllUsesOfWith(RN);
        changed = true;
        continue;
      }
    } // For all nodes in the graph.
  
    return changed;
  }
  
  /// \returns a uniquely used Constant with the same contents as \p node. If \p
  /// node is not a Constant then \returns a nullptr. If \node is a Constant which
  /// has a single use, \p node is returned. If \node is a Constant which has
  /// multiple uses, then \returns a new duplicate Constant that has the same
  /// contents as \p node contained in \p M.
  static Constant *getUniquelyUsedConstant(Module *M, Node &node) {
    Constant *constant = dyn_cast<Constant>(&node);
    if (!constant) {
      return nullptr;
    }
  
    if (constant->hasOneUse() && areAllFunctionsLoaded(M)) {
      return constant;
    }
  
    // If constant has more than one use, duplicate it and return the duplicate.
    auto *NC = M->createConstant(constant->getType(), constant->getName(),
                                 constant->getLayout());
    NC->getPayloadMutable().assign(&constant->getPayload());
    return NC;
  }
  
  /// Normalize the weight of \p CV with what \p BN is doing, given containing
  /// Module \p M. \returns whether or not the normalization was possible.
  template <typename ElemTy>
  bool normalizeWeights(Module *M, ConvolutionNode &CV,
                        BatchNormalizationNode &BN) {
    static_assert(
        std::is_floating_point<ElemTy>::value ||
            std::is_same<float16_t,
                         typename std::remove_cv<ElemTy>::type>::value ||
            std::is_same<bfloat16_t,
                         typename std::remove_cv<ElemTy>::type>::value,
        "This implementation is for floating-point values only");
  
    Constant *filterC = getUniquelyUsedConstant(M, *CV.getFilter().getNode());
    Constant *cbiasC = getUniquelyUsedConstant(M, *CV.getBias().getNode());
  
    if (!filterC || !cbiasC) {
      return false;
    }
  
    // Perform normalization when Convolution layout is NHWC and BatchNorm
    // ChannelIdx points to C.
    if (BN.getChannelIdx() != 3) {
      return false;
    }
  
    // Set the new filter and bias on CV if necessary.
    if (filterC != CV.getFilter().getNode()) {
      CV.getParent()->getLogContext()->logNodeInputChange(
          CV, CV.getNthInput(ConvolutionNode::FilterIdx), filterC);
      CV.setNthInput(ConvolutionNode::FilterIdx, filterC);
    }
    if (cbiasC != CV.getBias().getNode()) {
      CV.getParent()->getLogContext()->logNodeInputChange(
          CV, CV.getNthInput(ConvolutionNode::BiasIdx), cbiasC);
      CV.setNthInput(ConvolutionNode::BiasIdx, cbiasC);
    }
  
    // First, BN computation can be phrased as follows:
    //
    // (X - mean) * (1.0 / sqrt(var + eps)) * bn_scale + bias
    //
    // Thus, we can rewrite bn_scale as:
    //  X * bn_scale * 1.0 / (sqrt(var + eps)) +
    //    (bias - mean * (1.0 / sqrt(var + eps)) * bn_scale)
    //
    // Thus, can just have the affine transform:
    //
    //  X * A + B
    //
    //  where
    //
    //  A = bn_scale * 1.0 / (sqrt(running_var + eps))
    //  B =  (bias - mean * (1.0 / sqrt(var + eps)) * bn_scale)
    //
    // Now, we have that the computation made is the following:
    //
    // ((X `conv` W) + b) * A + B
    //
    // Then, we can simply fuse this as follows:
    //
    // (X `conv` (W * A)) + b * A + B
    //
    // which is simply
    //
    // (X `conv` Q) + C
    //
    // where
    //
    // Q = W * A
    // C = b * A + B
  
    Constant *scaleC = cast<Constant>(BN.getScale());
    Constant *biasC = cast<Constant>(BN.getBias());
    Constant *meanC = cast<Constant>(BN.getMean());
    Constant *var = cast<Constant>(BN.getVar());
  
    auto filterH = filterC->getHandle<ElemTy>();
  
    auto cbiasH = cbiasC->getHandle<ElemTy>();
  
    auto scaleH = scaleC->getHandle<ElemTy>();
    auto biasH = biasC->getHandle<ElemTy>();
    auto meanH = meanC->getHandle<ElemTy>();
    auto varH = var->getHandle<ElemTy>();
  
    // Update the filter/bias constants of the Conv node.
    auto epsilon = BN.getEpsilon();
    for (size_t i = 0, e = filterH.size(); i < e; i++) {
      // Dimension zero is the 'channel' dimension. If we ever change the
      // layout of the filter then we need to change this optimization.
      dim_t channelId = filterH.getDimForPtr(0, i);
      float value = varH.at({channelId});
      float stdvar = 1.0f / std::sqrt(value + epsilon);
      float gamma = scaleH.at({channelId});
      float A = gamma * stdvar;
      filterH.raw(i) = ElemTy(float(filterH.raw(i)) * A);
    }
  
    for (size_t i = 0, e = cbiasH.size(); i < e; i++) {
      // Dimension zero is the 'channel' dimension. If we ever change the
      // layout of the filter then we need to change this optimization.
      dim_t channelId = cbiasH.getDimForPtr(0, i);
      float mu = meanH.at({channelId});
      float value = varH.at({channelId});
      float stdvar = 1.0f / std::sqrt(value + epsilon);
      float gamma = scaleH.at({channelId});
      float beta = biasH.at({channelId});
      float A = gamma * stdvar;
      float B = beta - mu * A;
      cbiasH.raw(i) = ElemTy(float(cbiasH.raw(i)) * A + B);
    }
    return true;
  }
  
  bool OptimizeBatchNorm::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    auto &nodes = F->getNodes();
    auto *M = F->getParent();
  
    // For each node:
    for (auto &node : nodes) {
      // Merge the Batch Normalization operation into the convolution that comes
      // before it by updating the weights of the filter and bias.
      if (auto *BN = dyn_cast<BatchNormalizationNode>(&node)) {
        auto *CV = dyn_cast<ConvolutionNode>(BN->getInput());
        if (!CV) {
          continue;
        }
  
        // We can't modify conv operators that have multiple users.
        if (!CV->hasOneUse()) {
          continue;
        }
  
        bool normalizationHappened = false;
        switch (CV->getElementType(ConvolutionNode::ResultIdx)) {
        case ElemKind::FloatTy:
          normalizationHappened = normalizeWeights<float>(M, *CV, *BN);
          break;
        case ElemKind::Float16Ty:
          normalizationHappened = normalizeWeights<float16_t>(M, *CV, *BN);
          break;
        case ElemKind::BFloat16Ty:
          normalizationHappened = normalizeWeights<bfloat16_t>(M, *CV, *BN);
          break;
        default:
          llvm_unreachable("Type not supported");
        }
  
        if (!normalizationHappened) {
          continue;
        }
  
        // Take the predicate of what was expected for the output.
        CV->setPredicate(BN->getPredicate());
        BN->getResult().replaceAllUsesOfWith(CV);
        changed = true;
        continue;
      }
    } // For all nodes in the graph.
    return changed;
  }
  
  /// If \p node has uses, and all of them have one user node, return this user
  /// node. Otherwise, return nullptr.
  static Node *getOnlyUser(Node &node) {
    if (!node.hasUsers()) {
      // No users.
      return nullptr;
    }
    Node *first = node.getUsers().front().getUser();
    for (auto &U : node.getUsers()) {
      if (U.getUser() != first) {
        // Multiple users.
        return nullptr;
      }
    }
    // One user.
    return first;
  }
  
  /// Checks that \p tile sub-tensors along \p chId axis repeat.
  /// It also extracts the repeated dimension values into \p result.
  static bool isConstBroadcasted(std::vector<float> &result, const Constant &tile,
                                 int32_t chId) {
    // TODO: This limitation can be lifted, but that is for simplicity.
    if (tile.getType()->dims().size() != 4) {
      return false;
    }
    // TODO: We can also support quantized constants if there is a need in it.
    if (tile.getType()->getElementType() != ElemKind::FloatTy) {
      return false;
    }
    auto handle = tile.getPayload().getHandle<float>();
    glow::dim_t n, h, w, c;
    for (c = 0; c < tile.getType()->dims()[chId]; c++) {
      std::vector<glow::dim_t> dims = {c, 0, 0, 0};
      std::rotate(dims.begin(), dims.begin() + dims.size() - chId, dims.end());
      const float expected = handle.at(llvm::ArrayRef<glow::dim_t>(dims));
      for (n = 0; n < tile.getType()->dims()[(chId + 1) % 4]; n++) {
        for (h = 0; h < tile.getType()->dims()[(chId + 2) % 4]; h++) {
          for (w = 0; w < tile.getType()->dims()[(chId + 3) % 4]; w++) {
            std::vector<glow::dim_t> dimsE = {c, n, h, w};
            std::rotate(dimsE.begin(), dimsE.begin() + dimsE.size() - chId,
                        dimsE.end());
            if (handle.at(llvm::ArrayRef<glow::dim_t>(dims)) != expected) {
              return false;
            }
          }
        }
      }
      result[c] = expected;
    }
    return true;
  }
  
  /// Collects the longest chain of arithmetic operations with constants starting
  /// from \p start. Updates \p scale and \p bias as it collects the operands.
  /// \p returns the last node in the chain.
  static NodeValue collectArithmeticChain(Function *F, NodeValue start,
                                          Constant &scale, Constant &bias,
                                          int32_t chIdx) {
  
    Node *user = getOnlyUser(*start.getNode());
    NodeValue chainEnd = start;
  
    auto isSupportedForMerge = [](const Node *n) {
      return isa<MulNode>(n) || isa<AddNode>(n) || isa<SubNode>(n) ||
             isa<DivNode>(n);
    };
  
    while (user && isSupportedForMerge(user)) {
      // Paranoid
      assert(user->isArithmetic() && "Not an arithmetic node!");
  
      auto lhs = user->getNthInput(ArithmeticNode::LHSIdx);
      auto rhs = user->getNthInput(ArithmeticNode::RHSIdx);
  
      // Paranoid.
      assert(((lhs == chainEnd) || (rhs == chainEnd)) && "Not a user?");
  
      auto out = user->getNthResult(ArithmeticNode::ResultIdx);
  
      // Quantized arithmetic operations may change scale of result, we don't want
      // to deal with that. May be supported later if needed.
      if (lhs.getType() != out.getType() || rhs.getType() != out.getType()) {
        break;
      }
  
      // Only take this one if its other argument is a constant.
      // TODO: We can also support Splat here if needed.
      auto *c = dyn_cast<Constant>(lhs == chainEnd ? rhs : lhs);
      if (!c) {
        break;
      }
  
      const dim_t numChannels = c->dims()[chIdx];
  
      std::vector<float> toMerge(c->dims()[chIdx]);
      if (!isConstBroadcasted(toMerge, *c, chIdx)) {
        break;
      }
  
      auto biasH = bias.getPayloadMutable().getHandle();
      auto scaleH = scale.getPayloadMutable().getHandle();
  
      for (dim_t i = 0; i < numChannels; i++) {
        if (isa<DivNode>(user)) {
          scaleH.raw(i) /= toMerge[i];
          biasH.raw(i) /= toMerge[i];
        } else if (isa<MulNode>(user)) {
          scaleH.raw(i) *= toMerge[i];
          biasH.raw(i) *= toMerge[i];
        } else if (isa<SubNode>(user)) {
          // TODO: Can we support Sub(Constant, Chain)?
          if (chainEnd == rhs) {
            break;
          }
          biasH.raw(i) -= toMerge[i];
        } else if (isa<AddNode>(user)) {
          biasH.raw(i) += toMerge[i];
        } else {
          llvm_unreachable("Unsupported type!");
        }
      }
      chainEnd = user->getNthResult(ArithmeticNode::ResultIdx);
      user = getOnlyUser(*user);
    }
    return chainEnd;
  }
  
  /// Find the longest chain of Mul/Sub/Add/Div under a Convolution node that
  /// operate on Constant and fold them into a new BatchNormalization node.
  bool FoldArithmeticChainUnderConvIntoBN::run(Function *F,
                                               const CompilationContext &cctx) {
    bool changed = false;
  
    for (auto &node : F->getNodes()) {
      auto *CN = dyn_cast<ConvolutionNode>(&node);
      if (!CN) {
        continue;
      }
      auto bias = CN->getBias();
      // Conv is in NHWC format - channel is dim 3.
      int32_t chIdx = 3;
  
      // TODO: Support quantized constants if needed.
      if (bias.getType()->getElementType() != ElemKind::FloatTy) {
        continue;
      }
  
      // Provide collectArithmeticChain w/ bias/scale that have identity values
      // as we are creating new BN consisted of the arithmetic nodes that the
      // function will find.
      auto *newScale = F->getParent()->createConstant(
          bias.getType(), CN->getName().str() + "_BN.scale");
      auto *newBias = F->getParent()->createConstant(
          bias.getType(), CN->getName().str() + "_BN.bias");
  
      newScale->getPayloadMutable().getHandle<float>().clear(1.f);
      newBias->getPayloadMutable().getHandle<float>().clear(0.f);
  
      // Collect the chain and compute the new scale and bias.
      NodeValue chainEnd =
          collectArithmeticChain(F, CN->getResult(), *newScale, *newBias, chIdx);
      if (chainEnd == CN->getResult()) {
        F->getParent()->eraseConstant(newScale);
        F->getParent()->eraseConstant(newBias);
        continue;
      }
  
      // Compute the shape of batch normalization constants (array of
      // {depth} elements).
      glow::dim_t size = newScale->getPayloadMutable().getHandle<float>().size();
      auto depthTy =
          F->getParent()->uniqueTypeWithNewShape(bias.getType(), {size});
  
      Tensor varianceT(depthTy);
      varianceT.init(glow::Tensor::InitKind::Broadcast, 1.0f, F->getPRNG());
      auto variance = F->getParent()->createConstant(
          CN->getName().str() + "_BN.var", varianceT);
  
      Tensor meanT(depthTy);
      meanT.zero();
      auto mean =
          F->getParent()->createConstant(CN->getName().str() + "_BN.mean", meanT);
  
      // Create a BN with new parameters.
      auto *nBN =
          F->createBatchNormalization("BatchNorm", chainEnd.getType(), &node,
                                      newBias, newScale, mean, variance, 3, 0, 0);
      chainEnd.replaceAllUsesOfWith(nBN);
      changed = true;
    }
    return changed;
  }
  
  /// For each BatchNormalization node in \p F, find the longest chain of
  /// Mul/Sub/Add/Div operations with constants that use it and merge all those
  /// operations into the BatchNormalization.
  bool FoldBatchNormalizationWithArithmeticChain::run(
      Function *F, const CompilationContext &cctx) {
    bool changed = false;
    for (auto &node : F->getNodes()) {
      auto *BN = dyn_cast<BatchNormalizationNode>(&node);
      if (!BN) {
        continue;
      }
  
      // Expecting constant as const folding took place already.
      auto *scaleC = dyn_cast<Constant>(BN->getScale());
      auto *biasC = dyn_cast<Constant>(BN->getBias());
      if (!scaleC || !biasC) {
        continue;
      }
  
      // TODO: Support quantized constants if needed.
      if (scaleC->getType()->getElementType() != ElemKind::FloatTy ||
          biasC->getType()->getElementType() != ElemKind::FloatTy) {
        continue;
      }
  
      auto *newScaleC =
          F->getParent()->createConstant(scaleC->getType(), scaleC->getName());
      Tensor scaleT = scaleC->getPayload().getUnowned();
      newScaleC->assign(&scaleT);
  
      auto *newBiasC =
          F->getParent()->createConstant(biasC->getType(), biasC->getName());
      Tensor biasT = biasC->getPayload().getUnowned();
      newBiasC->assign(&biasT);
  
      // Collect the chain and compute the new scale and bias.
      NodeValue chainEnd = collectArithmeticChain(F, BN->getResult(), *newScaleC,
                                                  *newBiasC, BN->getChannelIdx());
      if (chainEnd == BN->getResult()) {
        F->getParent()->eraseConstant(newScaleC);
        F->getParent()->eraseConstant(newBiasC);
        continue;
      }
  
      Node *newScaleN = newScaleC, *newBiasN = newBiasC;
      if (isa<QuantizeNode>(BN->getScale())) {
        newScaleN = F->createQuantize(newScaleN->getName(), newScaleN,
                                      BN->getScale().getType());
      }
      if (isa<QuantizeNode>(BN->getBias())) {
        newBiasN = F->createQuantize(newBiasN->getName(), newBiasN,
                                     BN->getBias().getType());
      }
  
      // Create a BN with new parameters.
      auto *newBN = F->createBatchNormalization(
          BN->getName(), chainEnd.getType(), BN->getInput(), newBiasN, newScaleN,
          BN->getMean(), BN->getVar(), BN->getChannelIdx(), BN->getEpsilon(),
          BN->getMomentum());
  
      chainEnd.replaceAllUsesOfWith(newBN);
      changed = true;
    }
  
    return changed;
  }
  
  /// Fold MatMul + Add into FullyConnected. This is useful for backends which
  /// have an atomic implementation for the FullyConnected node. It is also needed
  /// for ONNX which does not have a representation for the FullyConnected node.
  bool FoldMatMulAddIntoFullyConnected::run(Function *F,
                                            const CompilationContext &cctx) {
    bool changed = false;
    for (auto &node : F->getNodes()) {
      auto *addNode = dyn_cast<AddNode>(&node);
      if (!addNode) {
        continue;
      }
  
      // Check for MatMul node being either RHS or LHS.
      auto *matMulNode_LHS = dyn_cast<MatMulNode>(addNode->getLHS());
      auto *matMulNode_RHS = dyn_cast<MatMulNode>(addNode->getRHS());
      auto *matMulNode = matMulNode_LHS ? matMulNode_LHS : matMulNode_RHS;
      NodeValue bias = matMulNode_LHS ? addNode->getRHS() : addNode->getLHS();
      if (!matMulNode) {
        continue;
      }
  
      // Folding is allowed only if MatMul has one use.
      if (!matMulNode->getResult().hasOneUse()) {
        continue;
      }
  
      // The corresponding batch/length of the FullyConnected Bias operand.
      assert(bias.dims().size() == 2 && "Bias should be 2D!");
      auto biasBatch = bias.dims()[0];
      auto biasLength = bias.dims()[1];
      if (biasBatch == 1) {
        // If bias is not batched then reshape to 1D.
        bias = F->createReshape(bias.getNode()->getName().str() + ".reshape",
                                bias, {bias.getType()->size()});
      } else {
        // If bias is batched then we must verify that the bias data
        // is same for all batches. For this the bias must be a Constant.
        auto *biasC = llvm::dyn_cast<Constant>(bias.getNode());
        if (!biasC) {
          continue;
        }
        if (!biasC->getPayload().isTiled(0)) {
          continue;
        }
        // Slice batched 2D bias and reshape to 1D.
        bias = F->createSlice(bias.getNode()->getName().str() + ".slice", bias,
                              {0, 0}, {1, biasLength});
        bias = F->createReshape(bias.getNode()->getName().str() + ".reshape",
                                bias, {biasLength});
      }
  
      // Create a new FullyConnected node.
      auto *newFC = F->createFullyConnected(
          matMulNode->getName(), matMulNode->getLHS(), matMulNode->getRHS(), bias,
          addNode->getResult().getType());
      addNode->getResult().replaceAllUsesOfWith(newFC);
      changed = true;
    }
  
    return changed;
  }
  
  // Fold Add after ConvTranspose into ConvTranspose's bias, if such Add was a
  // broadcasted Add. Examine by looking into Tensor repetitions. Fold this:
  //
  //    CONST1   Input
  //         \     |
  // CONST2  ConvTranspose
  //      \   /
  //      Add
  //       |
  //     Output
  //
  // into this:
  //
  //      CONST1  (CONST2 SQUEEZED)
  //          |  /
  // Input   ADD
  //     \   /
  //   ConvTranspose
  //       |
  //     Output
  //
  // Optimizations are going to take care of folding CONST1/CONST2/ADD
  // into one const bias.
  bool ConvTransposeBiasAddFold::run(Function *F,
                                     const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    for (auto &node : F->getNodes()) {
  
      auto *AN = dyn_cast<AddNode>(&node);
      if (!AN) {
        continue;
      }
  
      // Check for Transpose node being either RHS or LHS.
      auto *DN_L = dyn_cast<ConvTransposeNode>(AN->getLHS());
      auto *DN_R = dyn_cast<ConvTransposeNode>(AN->getRHS());
      auto *DN = DN_L ? DN_L : DN_R;
      if (!(!DN_R ^ !DN_L)) {
        continue;
      }
      auto *biasTile = dyn_cast<Constant>(DN_L ? AN->getRHS() : AN->getLHS());
      if (!biasTile || (biasTile->dims().size() != 4)) {
        continue;
      }
      auto *bias = dyn_cast<Constant>(DN->getBias());
      if (!bias) {
        continue;
      }
  
      // Check if Add is a broadcasted Add.
      std::vector<float> origConst(biasTile->dims()[3]);
      if (!isConstBroadcasted(origConst, *biasTile, 3)) {
        continue;
      }
  
      // Expect Bias Add so allocate a new bias to fill as do checking.
      auto *newBias = F->getParent()->createConstant(
          ElemKind::FloatTy, {biasTile->dims()[3]}, biasTile->getName());
      newBias->getHandle() = origConst;
  
      auto *add = F->createAdd(bias->getName(), bias, newBias);
      DN->setNthInput(ConvTransposeNode::BiasIdx, add);
      AN->getResult().replaceAllUsesOfWith(DN);
  
      changed = true;
    } // For all nodes in the graph.
  
    return changed;
  }
  
  /// \returns true if all dimensions of the \p input tensors are the same
  /// except for the provided \p dimension, otherwise return false.
  static bool checkConcatNodeUniformDims(llvm::ArrayRef<NodeValue> inputs,
                                         unsigned_t dimension) {
    for (size_t i = 1; i < inputs.size(); i++) {
      for (size_t j = 0; j < inputs[0].dims().size(); j++) {
        if (j == dimension) {
          continue;
        }
        if (inputs[0].dims()[j] != inputs[i].dims()[j]) {
          return false;
        }
      }
    }
    return true;
  }
  
  /// Given a tensor's dims \p firstDims and the desired leading/trailing dims
  /// sizes \p leadingDimsProdOriginalConcatNode, \p
  /// trailingDimsProdOriginalConcatNode. \returns the dimension, at which the
  /// trailing/leading dimensions match the desired sizes, otherwise returns -1.
  /// Example: Given a tensor <1,2,3,4,5>, and a desired trailing dimensions
  /// size of 20, and a desired leading dimensions size of 2, this function will
  /// return dimension 1 as the trailing dimensions after it are <4,5>, which
  /// matches the size 20, and the leading dimensions are <1,2>, which matches
  /// the size 2.
  static ssize_t findMatchingConcatDimForSameTrailingAndLeadingDims(
      llvm::ArrayRef<dim_t> firstDims, size_t leadingDimsProdOriginalConcatNode,
      size_t trailingDimsProdOriginalConcatNode) {
    size_t trailingDimsProdCurNode = 1;
    for (ssize_t i = firstDims.size() - 1; i >= 0; i--) {
      if (trailingDimsProdCurNode == trailingDimsProdOriginalConcatNode) {
        size_t leadingDimsProdCurNode = 1;
        for (ssize_t j = 0; j < i; j++) {
          leadingDimsProdCurNode *= firstDims[j];
        }
        if (leadingDimsProdCurNode == leadingDimsProdOriginalConcatNode) {
          return i;
        }
      }
      trailingDimsProdCurNode *= firstDims[i];
    }
    return -1;
  }
  
  /// Given input tensors \p inputs and a original ConcatNode \p origConcatN,
  /// try to find out if there is a dimension in the input tensors, with which
  /// we can meet two requirements:
  ///   1) Input tensors are concatenate-able along this dimension.
  ///   2) The trailing/leading dimensions sizes after/before this dimension in
  ///      the input tensors, are of the same size as the trailing/leading
  ///      dimensions of the input of the original Concat node after/before the
  ///      concatenation dimension. It is required, because they ensure that the
  ///      payload of the new concat node should be the same as the payload of
  ///      the original concat node, and also won't affect the data order of the
  ///      entire tensor.
  /// \returns this dimension if found, otherwise -1.
  static int
  findConcatDimForSameTrailingAndLeadingDims(llvm::ArrayRef<NodeValue> inputs,
                                             ConcatNode *originalConcatNode) {
    // For the purpose of the optimiztion
    // Concat(Reshape(X)*N)->Reshape(Concat(N*X)), we want to make sure the new
    // ConcatNode can concatenate on the trailing/leading dimensions which are
    // of the same size of those of the original Concate node.
  
    auto firstDims = inputs.front().dims();
    auto origConcatNInputDims = originalConcatNode->getInputs().front().dims();
    // The sizes of the trailing/leading dimensions of the original ConcatNode,
    // which are being concatenated. This sizes are simply the products of
    // dimensions following/before the dimension used for concatenation.
    dim_t trailingDimsProdOriginalConcatNode = 1;
    dim_t leadingDimsProdOriginalConcatNode = 1;
    for (size_t i = 0; i < origConcatNInputDims.size(); ++i) {
      if (i < originalConcatNode->getDim()) {
        leadingDimsProdOriginalConcatNode *= origConcatNInputDims[i];
      } else if (i > originalConcatNode->getDim()) {
        trailingDimsProdOriginalConcatNode *= origConcatNInputDims[i];
      }
    }
  
    // Try to find the dimension in the first input such that the
    // trailing/leading dimensions sizes are the same as the sizes of the
    // trailing/leading dimensions based on the concatenation dimension used by
    // the original ConcatNode.
    ssize_t dim = findMatchingConcatDimForSameTrailingAndLeadingDims(
        firstDims, leadingDimsProdOriginalConcatNode,
        trailingDimsProdOriginalConcatNode);
    if (dim == -1) {
      return -1;
    }
  
    // Now we have found the dimension, we need to check if all inputs can be
    // concatenated along this dimension.
    if (!checkConcatNodeUniformDims(inputs, dim)) {
      return -1;
    }
    return dim;
  }
  
  /// Given the inputs \p originalConcatInputs of one Concat Nodes, \returns
  /// true if they are all ReshapeNode, and the input tensors of these input
  /// nodes have same number of dimensions, otherwise returns false.
  static bool
  tryToGetNewConcatInputs(NodeValueArrayRef originalConcatInputs,
                          llvm::SmallVectorImpl<NodeValue> &newConcatInputs) {
    // Go through the input nodes of CN, check if they are all ReshapeNode,
    // and if the input tensors of these input nodes have same number of
    // dimensions.
    for (auto &I : originalConcatInputs) {
      if (auto *R = dyn_cast<ReshapeNode>(I)) {
        if (newConcatInputs.empty() || newConcatInputs.front().dims().size() ==
                                           R->getInput().dims().size()) {
          newConcatInputs.push_back(R->getInput());
          continue;
        }
      }
      return false;
    }
    return true;
  }
  
  /// Concat(Reshape(x) * N) -> Reshape(Concat(x * N)).
  /// \returns a new simplified Concat node or nullptr.
  static NodeValue tryToOptimizeConcatOfRehapes(Function *F, ConcatNode *CN) {
    llvm::SmallVector<NodeValue, 16> newConcatInputs;
    // The inputs of the collected input reshape nodes. They will be used as
    // inputs for the new Concat node if possible.
    if (!tryToGetNewConcatInputs(CN->getInputs(), newConcatInputs)) {
      return NodeValue(nullptr);
    }
  
    // Try to concatenate along the same size trailing/leading dimensions as of
    // the original Concat node.
    auto dim = findConcatDimForSameTrailingAndLeadingDims(newConcatInputs, CN);
    if (dim == -1) {
      return NodeValue(nullptr);
    }
    auto *newCN = F->createConcat(CN->getName(), newConcatInputs, dim);
    return F->createReshape(
        CN->getInputs().front().getNode()->getName(), newCN,
        CN->getResult().dims(),
        CanonicalTensorLayout::getInstance().getNthResultLayoutRequirements(
            CN, ConcatNode::ResultIdx));
  }
  
  /// Simplify concat node.
  /// \returns a new simplified Concat node or nullptr.
  static NodeValue simplifyConcatNode(Function *F, ConcatNode *CN,
                                      const CompilationContext &cctx) {
    /// concat(dim1, concat(dim2, X, Y), Z) -> concat(dim1, X, Y, Z),
    /// but only if dim1 == dim2
  
    LOG_SCOPE(F->getLogContext(), "simplifyConcatNode")
  
    if (!cctx.optimizationOpts.skipConcatMerging) {
      auto inputs = CN->getInputs();
      // Check if any of the inputs are ConcatNode.
      llvm::SmallVector<NodeValue, 16> newInputs;
      bool merged = false;
      for (auto &input : inputs) {
        newInputs.push_back(input);
        auto *CNI = dyn_cast<ConcatNode>(input);
        // Bail if it is not a ConcatNode or it is a concat node with a diffrent
        // dimension.
        if (!CNI || CNI->getDim() != CN->getDim()) {
          continue;
        }
  
        // Preventing this from kicking in.
        // Otherwise we will end up sequence of concats with more and more inputs,
        // without really eliminating any.
        if (CNI->getResult().getNumUsers() > 1) {
          continue;
        }
  
        merged = true;
        // Replace current input by its own inputs, i.e. merge them into the
        // parent concat node.
        newInputs.pop_back();
        newInputs.append(CNI->getInputs().begin(), CNI->getInputs().end());
      }
      if (merged) {
        // Return a new simplified Concat node.
        return F->createConcat(CN->getName(), newInputs, CN->getDim());
      }
    }
  
    // If all of the inputs to the concat are extracted from the same input in
    // the right order then we can just use the extract-input instead of the
    // concat. Concat(Slice(X, 0..10), Slice(X, 10..20)) -> X.
    {
      std::vector<SliceNode *> order;
      std::vector<SliceNode *> slices;
      // Collect all of the inputs that are SliceNode.
      for (auto &I : CN->getInputs()) {
        if (auto *S = dyn_cast<SliceNode>(I.getNode())) {
          slices.push_back(S);
        }
      }
      // Check if the slices span the input value.
      bool found = findSlicesThatSpanInput(slices, CN->getDim(), order);
      if (found && order.size() == slices.size()) {
        // Check that the ordered Slices that span the input are in order.
        bool ordered = true;
        for (size_t i = 0, e = slices.size(); i < e; i++) {
          if (order[i] != slices[i]) {
            ordered = false;
            break;
          }
        }
  
        auto orig = order[0]->getInput();
        // The original value that we extract from must be of the same shape as
        // the concat.
        if (ordered && CN->getResult().getType() == orig.getType()) {
          return orig;
        }
      }
    }
  
    // Try the optimization Concat(Reshape(x) * N) -> Reshape(Concat(x * N)).
    if (auto transformedConcatNode = tryToOptimizeConcatOfRehapes(F, CN)) {
      return transformedConcatNode;
    }
  
    // If the concat has a single input, replace the concat with that input.
    if (CN->getNumInputs() == 1) {
      return CN->getInputs()[0];
    }
  
    return NodeValue(nullptr);
  }
  
  /// If all of the outputs of \p CN are essentially piped from the inputs of the
  /// concat (i.e. same shape, axis, order) then we can get rid of the slices and
  /// concat. \returns true if this optimization is successful and changes the
  /// Function.
  static bool combineConcatSlices(ConcatNode *CN) {
    auto inputsToCN = CN->getInputs();
    std::vector<SliceNode *> slices;
    std::vector<SliceNode *> orderedSlices;
    for (auto &user : CN->getUsers()) {
      if (SliceNode *SN = dyn_cast<SliceNode>(user.getUser())) {
        slices.push_back(SN);
      }
    }
  
    // Check if the slices span the input value.
    bool found = findSlicesThatSpanInput(slices, CN->getDim(), orderedSlices);
    if (!found || orderedSlices.size() != slices.size() ||
        orderedSlices.size() != inputsToCN.size()) {
      return false;
    }
  
    // Now verify that all of the inputs to CN have the same shape as all of the
    // slices for the result of CN.
    for (size_t i = 0, e = orderedSlices.size(); i < e; ++i) {
      if (orderedSlices[i]->getResult().dims() != inputsToCN[i].dims()) {
        return false;
      }
    }
  
    // We can now replace all of the inputs to the concat to the result of
    // each slice.
    for (size_t i = 0, e = inputsToCN.size(); i < e; ++i) {
      orderedSlices[i]->getResult().replaceAllUsesOfWith(inputsToCN[i]);
    }
    return true;
  }
  
  /// Eliminate Concat-Slice patterns which are unnecessary. E.g.:
  /// NodeA   NodeB             NodeA   NodeB
  ///     \   /                   |       |
  ///    ConcatC                  |       |
  ///     /   \         ----->    |       |
  /// SliceD  SliceE              |       |
  ///   |       |                 |       |
  /// NodeF   NodeG             NodeF   NodeG
  bool EliminateConcatSlice::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    auto &nodes = F->getNodes();
  
    // For each node:
    for (auto &node : nodes) {
      auto *CN = dyn_cast<ConcatNode>(&node);
      if (!CN) {
        continue;
      }
      if (combineConcatSlices(CN)) {
        changed = true;
        continue;
      }
    }
    return changed;
  }
  
  /// Eliminate Slice-Concat patterns which are unnecessary. E.g.:
  ///            --  NodeSrc ---                               -NodeSrc-
  ///          /        |       |                            /          |
  ///       SliceA   SliceB   SliceC     ----->           SliceAB       SliceC
  ///           \       /      |                             |           |
  /// NodeE  -  ConcatABE       NodeD              NodeE - ConcatABE   NodeD
  bool EliminateSliceConcat::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    auto &nodes = F->getNodes();
  
    for (auto it = nodes.rbegin(), e = nodes.rend(); it != e; it++) {
      Node &node = *it;
      auto *CN = dyn_cast<ConcatNode>(&node);
      if (!CN) {
        continue;
      }
      // avoid 1) merging through operators other than Slices
      // e.g. Slice(A)-Other-Slice(B), A and B are consecutive
      // 2) merging Slices from different sources
      // e.g. Slice(A1)-Slice(B1)-Slice(A2)-Slice(B2), A1 and A2, B1 and B2 are
      // consecutive respectively
      //
      // Store consecutive slices along *any* dimension. If the consecutive
      // slices' dimension is the same as the concat, the concat can be removed.
      // If the slices' dimension is different from the concat, the nodes
      // can be replaced with slice+reshape OR slice+transpose+reshape.
      // The extra transpose is needed when the consecutive dimension is
      // after the concat dimension.
      std::vector<
          std::pair<int /* dimension of slicing */, std::vector<SliceNode *>>>
          consecutiveSlices;
      std::vector<SliceNode *> currConsecutiveSlices;
      SliceNode *lastSN = nullptr;
      int lastDim = -1;
      for (auto &concatInput : CN->getInputs()) {
        auto *SN = dyn_cast<SliceNode>(concatInput.getNode());
        // slices with multiple users will not be considered
        if (!SN || SN->getResult().getNumUsers() > 1) {
          if (currConsecutiveSlices.size()) {
            consecutiveSlices.emplace_back(lastDim, currConsecutiveSlices);
            currConsecutiveSlices.clear();
          }
          lastDim = -1;
          lastSN = nullptr;
          continue;
        }
        // slices with different sources will not be considered
        int dim = -1;
        if (lastSN && (lastSN->getInput() != SN->getInput() ||
                       !findConsecutiveSliceDim(lastSN, SN, &dim))) {
          consecutiveSlices.emplace_back(lastDim, currConsecutiveSlices);
          currConsecutiveSlices.clear();
        }
        lastDim = dim;
        lastSN = SN;
        currConsecutiveSlices.emplace_back(SN);
      }
      if (currConsecutiveSlices.size()) {
        consecutiveSlices.emplace_back(lastDim, currConsecutiveSlices);
      }
  
      // Mapping from old Slices to new Nodes where a Node can either be
      // i) a merged Slice
      // ii) a merged Slice + Reshape
      // iii) a merged Slice + Transpose + Reshape
      std::unordered_map<SliceNode *, Node *> oldSlicesToNewNodes;
  
      for (const auto &slicePairs : consecutiveSlices) {
        unsigned_t slicesDim = slicePairs.first;
        auto &slices = slicePairs.second;
  
        if (slices.size() <= 1) {
          continue;
        }
        if (slicesDim != CN->getDim() &&
            ((slicesDim != CN->getDim() + 1 && slicesDim != CN->getDim() - 1) ||
             slices[0]->getResult().dims()[slicesDim] != 1)) {
          // Optimizations are possible only if:
          // 1) slices consecutive dimension is the same as concat dimension, or
          // 2) slices consecutive dimension is adjacent to the concat dimension,
          //    and the size of each slice along the consecutive dimension is 1.
          //    NOTE: Checking the slicesDim dimension of 0th slice is
          //    sufficient, as opposed to checking every slice. If the slices
          //    can be concatenated (and they're being concatenated along a
          //    different dimension), then each slicesDim dim must be equal.
          continue;
        }
        if ((slicesDim == CN->getDim() + 1 && slices.size() <= 3) ||
            (slicesDim == CN->getDim() - 1 && slices.size() <= 2)) {
          // Optimization does not decrease the number of nodes.
          continue;
        }
  
        SliceNode *firstSlice = slices.front();
        auto *srcNode = firstSlice->getInput().getNode();
        std::vector<dim_t> endDims;
  
        for (size_t i = 0, e2 = firstSlice->getResult().dims().size(); i < e2;
             i++) {
          endDims.emplace_back(slices.back()->getStart()[i] +
                               slices.back()->getResult().dims()[i]);
        }
        Node *newNode = nullptr;
        auto *newSlice = F->createSlice(firstSlice->getName(), srcNode,
                                        firstSlice->getStart(), endDims);
  
        // Create a reshape node based on consecutive slice dimension and
        // concat dimension.
        if (slicesDim == CN->getDim() + 1 || slicesDim == CN->getDim() - 1) {
          auto outputDimVec = newSlice->getResult().dims().vec();
          outputDimVec[CN->getDim()] *= outputDimVec[slicesDim];
          outputDimVec[slicesDim] = 1;
          auto outputDims = llvm::makeArrayRef(outputDimVec);
  
          Node *inputToReshape = nullptr;
          if (slicesDim == CN->getDim() + 1) {
            std::vector<unsigned_t> shuffle(outputDimVec.size());
            std::iota(shuffle.begin(), shuffle.end(), 0);
            std::swap(shuffle[slicesDim], shuffle[CN->getDim()]);
            inputToReshape = F->createTranspose(
                newSlice->getName().str() + "_Transpose", newSlice, shuffle);
          } else {
            inputToReshape = newSlice;
          }
          newNode = F->createReshape(newSlice->getName().str() + "_Reshape",
                                     inputToReshape, outputDims);
        } else {
          newNode = newSlice;
        }
  
        for (auto *slice : slices) {
          oldSlicesToNewNodes[slice] = newNode;
        }
  
        changed = true;
      }
      if (!oldSlicesToNewNodes.size()) {
        continue;
      }
      // Replace the input Slices to CN with the merged Nodes.
      std::vector<NodeValue> newConcatInputs;
      const Node *lastNewNode = nullptr;
      for (const auto &concatInput : CN->getInputs()) {
        auto *SN = dyn_cast<SliceNode>(concatInput.getNode());
        if (!SN || !oldSlicesToNewNodes.count(SN)) {
          newConcatInputs.emplace_back(concatInput);
        } else {
          auto *newNode = oldSlicesToNewNodes[SN];
          if (newNode != lastNewNode) {
            lastNewNode = newNode;
            newConcatInputs.emplace_back(newNode);
          }
        }
      }
      if (newConcatInputs.size() != CN->getInputs().size()) {
        auto *newConcat =
            F->createConcat(CN->getName(), newConcatInputs, CN->getDim());
        CN->getResult().replaceAllUsesOfWith(newConcat);
      }
    }
  
    return changed;
  }
  
  /// Optimize Concat nodes.
  bool OptimizeConcatNodes::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    auto &nodes = F->getNodes();
  
    // For each node:
    for (auto &node : nodes) {
      auto *CN = dyn_cast<ConcatNode>(&node);
      if (!CN) {
        continue;
      }
      NodeValue newCN = simplifyConcatNode(F, CN, cctx);
      if (newCN.getNode()) {
        CN->getResult().replaceAllUsesOfWith(newCN);
        changed = true;
        continue;
      }
    }
    return changed;
  }
  
  /// Fold Slices into Constants. This will create new Constants if necessary.
  bool FoldSlicesIntoConstants::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    auto &nodes = F->getNodes();
  
    // For each node:
    for (auto &node : nodes) {
      auto *SN = dyn_cast<SliceNode>(&node);
      if (!SN) {
        continue;
      }
      auto *C = dyn_cast<Constant>(SN->getInput());
      if (!C) {
        continue;
      }
  
      // Create new slice of the Constant.
      Tensor outT = Tensor(SN->getResult().getType());
  
      ElemKind k = outT.getElementType();
  #define TYPED_INSERT(TY, TYPEKIND)                                             \
    if (k == TYPEKIND) {                                                         \
      auto OH = outT.getHandle<TY>();                                            \
      auto IH = C->getPayloadMutable().getHandle<TY>();                          \
      IH.extractTensors(OH, SN->getStart());                                     \
    }
  
      TYPED_INSERT(float, ElemKind::FloatTy);
      TYPED_INSERT(float16_t, ElemKind::Float16Ty);
      TYPED_INSERT(bfloat16_t, ElemKind::BFloat16Ty);
      TYPED_INSERT(int8_t, ElemKind::Int8QTy);
      TYPED_INSERT(int16_t, ElemKind::Int16QTy);
      TYPED_INSERT(int32_t, ElemKind::Int32QTy);
      TYPED_INSERT(int32_t, ElemKind::Int32ITy);
      TYPED_INSERT(int64_t, ElemKind::Int64ITy);
      TYPED_INSERT(bool, ElemKind::BoolTy);
  #undef TYPED_INSERT
  
      // Create a new Constant NC to hold the sliced result.
      auto *NC = F->getParent()->createConstant(C->getName(), std::move(outT));
      // Connect all Slice users with the new Slice.
      SN->getResult().replaceAllUsesOfWith(NC);
      changed = true;
    }
  
    return changed;
  }
  
  /// Simplify and canonicalize arithmetic nodes by detecting simple arithmetic
  /// identities.
  bool OptimizeArithmeticNodes::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    // A worklist that contains the nodes to process.
    std::vector<Node *> worklist;
  
    // Add all of the arithmetic nodes to the worklist, with a node's
    // dependencies added after itself so they are processed before the node.
    GraphPreOrderVisitor visitor(*F);
    worklist.reserve(visitor.getPreOrder().size());
    for (auto *N : visitor.getPreOrder()) {
      if (N->isArithmetic()) {
        worklist.push_back(N);
      }
    }
    while (!worklist.empty()) {
      Node *N = worklist.back();
      assert(N->isArithmetic() && "Must be an Arithmetic node.");
      worklist.pop_back();
  
      auto SNV = simplifyNode(N, F);
      if (SNV.getNode() != N) {
        N->getNthResult(ArithmeticNode::ResultIdx).replaceAllUsesOfWith(SNV);
        changed = true;
  
        auto *SN = SNV.getNode();
  
        // The simplified node could be further simplified. Note that the
        // simplified node might not be arithmetic; it could be a splat.
        if (SN->isArithmetic()) {
          worklist.push_back(SN);
        }
  
        // The simplified node's operands could be further simplified as well.
        // Push them after the node so they are processed before the node.
        for (size_t i = 0, e = SN->getNumInputs(); i < e; i++) {
          Node *input = SN->getNthInput(i).getNode();
          if (input->isArithmetic()) {
            worklist.push_back(input);
          }
        }
        continue;
      }
    }
    return changed;
  }
  
  /// Statically transpose Constants.
  bool TransposeConstants::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    auto &nodes = F->getNodes();
    bool changed = false;
    for (auto &node : nodes) {
      auto *TN = dyn_cast<TransposeNode>(&node);
      if (!TN) {
        continue;
      }
      auto *Q = dyn_cast<QuantizeNode>(TN->getInput());
      auto *C = dyn_cast<Constant>(Q ? Q->getInput() : TN->getInput());
      if (!C || (Q && !Q->hasOneUse())) {
        continue;
      }
      // Create a new Constant NC to hold the transposed result.
      auto cTy = F->getParent()->uniqueTypeWithNewShape(
          C->getType(), TN->getResult().getType());
      auto *NC =
          F->getParent()->createConstant(cTy, C->getName(), TN->getLayout());
      // Transpose the value of C into NC.
      genericTranspose(&C->getPayload(), &NC->getPayloadMutable(),
                       TN->getShuffle());
      NC->getPayloadMutable().setType(NC->getType());
      // Create a new transposed Quantize, if needed.
      NodeValue NN(NC);
      if (Q) {
        auto qTy = F->getParent()->uniqueTypeWithNewShape(
            Q->getResult().getType(), TN->getResult().dims());
        auto *NQ = F->createQuantize(Q->getName(), NC, qTy);
        NN = NodeValue(NQ);
      }
      // Rewrite uses of TN to reference NC or NQ.
      TN->getResult().replaceAllUsesOfWith(NN);
      changed = true;
    }
    return changed;
  }
  
  namespace {
  
  /// A helper type for hasing Node pointers when they are used as keys in hash
  /// maps.
  struct NodeHasher {
    size_t operator()(Node *N) const { return N->getHash(); }
  };
  
  /// A helper type implementing the Node equality predicate that can be used
  /// when Node pointers are used as keys in hash maps.
  struct NodeEq {
    bool operator()(const Node *lhs, const Node *rhs) const {
      return lhs->isEqual(*rhs);
    }
  };
  
  /// This visitor is used to walk the graph and perform a common subexpression
  /// evaluation.
  struct CSEVisitor : NodeWalker {
    // Mapping from the original node to its canonical representation under CSE.
    std::unordered_map<Node *, Node *, NodeHasher, NodeEq> cseNodes_;
    // Set of visited nodes.
    std::unordered_set<Node *> visitedNodes_;
  
    /// This callback is called before visiting the children of \p N.
    void pre(Node *parent, Node *N) override {
      // Put the node into a visited set to make sure it is visited
      // only once.
      visitedNodes_.insert(N);
    }
  
    /// This callback is called after visiting the children of \p N.
    /// It means that all of its dependencies are processed already.
    void post(Node *parent, Node *N) override {
      // Try to find a node equivalent to the current one.
      auto FoundI = cseNodes_.find(N);
      if (FoundI == cseNodes_.end()) {
        // No node CSE-equivalent to the current one has been seen yet.
        // Remember this node, so that the next occurrence can be
        // replaced by this one.
        cseNodes_.insert({N, N});
        assert(cseNodes_.find(N) != cseNodes_.end());
        return;
      }
      Node *foundN = FoundI->second;
  
      // Same node cannot be visited.
      assert(N != foundN);
      // Replace current node by a found node, which is
      // equivalent to it.
      assert(N->isEqual(*foundN));
      for (unsigned i = 0; i < N->getNumResults(); i++) {
        NodeValue FV(foundN, i);
        N->getNthResult(i).replaceAllUsesOfWith(FV);
      }
      // TODO: Erase N during CSE? If we don't do it here,
      // DCE will remove it later anyways.
    }
  
    /// Make sure that each node is processed only once.
    bool shouldVisit(Node *parent, Node *N) override {
      return visitedNodes_.count(N) == 0;
    }
  };
  
  /// A helper type for hashing Constant pointers when they are used as keys in
  /// hash maps for deduplication. The hash is based on the type of the Constant
  /// (element type, dimensions), as well as a constant number of elements from
  /// the backing Tensor to balance collisions with hash calclulation time.
  struct ConstsHasherDedup {
    size_t operator()(Constant *V) const {
      auto hash = llvm::hash_value(V->getType());
      auto &T = V->getPayload();
      // Only use the first 8 elements in the hash. It's likely that if two
      // tensors have different content they will diverge quickly. Fall back to
      // full equality check in ConstsEqDedup.
      constexpr dim_t maxNumEls = 8;
      dim_t numEls = std::min((dim_t)T.getType().size(), maxNumEls);
      dim_t bufSize = T.getType().getElementSize() * numEls;
      auto *data = T.getUnsafePtr();
      for (size_t i = 0; i < bufSize; i++) {
        hash = llvm::hash_combine(hash, data[i]);
      }
      return hash;
    }
  };
  
  /// A helper type implementing the Constants equality predicate that can be
  /// used when Constant pointers are used as keys in hash maps for
  /// deduplication.
  struct ConstsEqDedup {
    bool operator()(const Constant *lhs, const Constant *rhs) const {
      // Only consider Constants for deduplication if they have the same type.
      if (lhs->getType() != rhs->getType()) {
        return false;
      }
      // Only dedup Constants if they're bit exact matches.
      return lhs->getPayload().isBitwiseEqual(rhs->getPayload());
    }
  };
  
  } // namespace
  
  /// Deduplicates Constants in the Module \p M. Applicable Constants for
  /// deduplication must have the same data. \returns whether any Constants were
  /// deduplicated.
  static bool deduplicateConstants(Module *M) {
    // Map from Constants to other Constants that are equivalent for purposes of
    // deduplication.
    std::unordered_map<Constant *, Constant *, ConstsHasherDedup, ConstsEqDedup>
        duplicateConstants;
  
    bool changed = false;
    for (auto &C : M->getConstants()) {
      // Only perform deduplication of consts with given max number of elements.
      size_t maxNumEls = constDedupSizeOpt;
      size_t numEls = C->getType()->size();
      if ((maxNumEls != 0) && (numEls > maxNumEls)) {
        continue;
      }
  
      // Try to find a Constant that has the same data as the current one.
      auto foundI = duplicateConstants.find(C);
      if (foundI == duplicateConstants.end()) {
        // No node equivalent to the current one has been seen yet. Remember
        // this Constant, so that the next occurrence can be replaced by this
        // one.
        duplicateConstants.emplace(C, C);
        assert(duplicateConstants.find(C) != duplicateConstants.end());
        continue;
      }
      Constant *foundC = foundI->second;
      assert(C != foundC && "Constants should not be visited multiple times.");
  
      // Replace current Constant by a found Constant, which is equivalent to
      // it.
      C->getOutput().replaceAllUsesOfWith(foundC);
      changed = true;
    }
    return changed;
  }
  
  /// Common Subexpression Elimination.
  bool CSE::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    CSEVisitor visitor;
  
    bool changed = false;
    if (cctx.optimizationOpts.enableConstantDeduplication) {
      changed |= deduplicateConstants(F->getParent());
    }
  
    // Perform CSE on all nodes.
    for (auto &N : F->getNodes()) {
      N.visit(nullptr, &visitor);
    }
    // TODO: Change Visitors to return whether they modified the Function they
    // are contained in. For now conservatively set changed to true;
    changed = true;
    return changed;
  }
  
  /// Fold Nodes into SplatNodes.
  bool OptimizeSplat::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    for (Node &node : F->getNodes()) {
      // Slice(Splat(args)) -> Splat(args')
      if (SliceNode *sliceNode = dyn_cast<SliceNode>(&node)) {
        SplatNode *splatNode = dyn_cast<SplatNode>(sliceNode->getInput());
        if (!splatNode) {
          continue;
        }
        SplatNode *newSplatNode =
            F->createSplat(sliceNode->getName(), sliceNode->getResult().getType(),
                           splatNode->getValue());
        sliceNode->getResult().replaceAllUsesOfWith(newSplatNode);
        changed = true;
        continue;
      }
  
      // Clip(Splat(args)) -> Splat(args')
      if (ClipNode *clipNode = dyn_cast<ClipNode>(&node)) {
        SplatNode *splatNode = dyn_cast<SplatNode>(clipNode->getInput());
        if (!splatNode) {
          continue;
        }
        const float newSplatVal =
            std::min(std::max(splatNode->getValue(), clipNode->getMin()),
                     clipNode->getMax());
  
        SplatNode *newSplatNode = nullptr;
        if (newSplatVal == splatNode->getValue()) {
          // No need to crate a new Splat.
          newSplatNode = splatNode;
        } else {
          newSplatNode = F->createSplat(
              splatNode->getName().str() + clipNode->getName().str(),
              splatNode->getResult().getType(), newSplatVal);
        }
  
        clipNode->getResult().replaceAllUsesOfWith(newSplatNode->getResult());
        changed = true;
        continue;
      }
    }
    return changed;
  }
  
  /// Optimize TransposeNode into ReshapeNode when it actually moves no data.
  bool OptimizeTransposeIntoReshape::run(Function *F,
                                         const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
  
    for (auto &node : F->getNodes()) {
      auto *TR = dyn_cast<TransposeNode>(&node);
      if (!TR) {
        continue;
      }
      auto inputNode = TR->getInput();
      auto inputDims = inputNode.dims();
      auto outputDims = TR->getResult().dims();
      // The transformation is not possible if alignments different from 1 are
      // used for any dimension.
      if (!inputNode.getType()->isEqual(F->getParent()->uniqueTypeWithNewShape(
              inputNode.getType(), inputDims))) {
        continue;
      }
      if (!TR->getResult().getType()->isEqual(
              F->getParent()->uniqueTypeWithNewShape(TR->getResult().getType(),
                                                     outputDims))) {
        continue;
      }
      // Transpose moves no data if input/output dimensions match after they both
      // drop dimensions of size 1. E.g. transposing [1 5 1 15] into [5 15 1 1]
      // produces vectors (1, 3) for both dimensions so optimization is executed.
      auto shuffle = TR->getShuffle();
      ShapeVector inDims;
      ShapeVector outDims;
      for (size_t i = 0; i < inputDims.size(); i++) {
        if (inputDims[i] != 1) {
          inDims.push_back(i);
        }
        if (outputDims[i] != 1) {
          outDims.push_back(shuffle[i]);
        }
      }
      if (inDims != outDims) {
        continue;
      }
      auto *RS =
          F->createReshape(TR->getName(), inputNode, outputDims, TR->getLayout());
      TR->getResult().replaceAllUsesOfWith(RS);
      changed = true;
    }
  
    return changed;
  }
  
  /// Gets Constant or returns nullptr if input is not Constant.
  /// Skips QuantizeNode if present.
  static Constant *getConstant(const NodeValue &NV) {
    Node *N = NV.getNode();
    if (isa<QuantizeNode>(N)) {
      N = N->getNthInput(QuantizeNode::InputIdx);
    }
    return dyn_cast<Constant>(N);
  }
  
  /// Eliminate nodes which don't do anything useful.
  bool EliminateNoop::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
  
    auto isNoop = [](const Node &node, NodeValue &input,
                     NodeValue &output) -> bool {
      // Auto-select input/output, if there is just one. For other cases it
      // will be handled on per-operator basis below.
      if (node.getNumInputs() == 1) {
        input = node.getNthInput(0);
      }
      if (node.getNumResults() == 1) {
        output = node.getNthResult(0);
      }
  
      // For some nodes it's enough just to compare input and output types to
      // determine if they are noop.
      if (isa<PadNode>(&node) || isa<SliceNode>(&node) || isa<TileNode>(&node) ||
          isa<BroadcastNode>(&node)) {
        return input.getType() == output.getType();
      }
  
      // Operator-specific analysis.
      if (auto *APN = dyn_cast<AvgPoolNode>(&node)) {
        input = APN->getInput();
        return isUniformArray(APN->getKernels(), 1u) &&
               isUniformArray(APN->getStrides(), 1u) &&
               isUniformArray(APN->getPads(), 0u);
      } else if (auto *MPN = dyn_cast<MaxPoolNode>(&node)) {
        input = MPN->getInput();
        output = MPN->getResult();
        return isUniformArray(MPN->getKernels(), 1u) &&
               isUniformArray(MPN->getStrides(), 1u) &&
               isUniformArray(MPN->getPads(), 0u);
      } else if (auto *SN = dyn_cast<SelectNode>(&node)) {
        auto *cond = getConstant(SN->getCond());
        bool val = false;
        if (cond && isUniformConstant<bool>(*cond, val)) {
          input = val ? SN->getLHS() : SN->getRHS();
          return true;
        }
      }
  
      return false;
    };
  
    for (auto &node : F->getNodes()) {
      NodeValue input, output;
      if (isNoop(node, input, output)) {
        assert(input != NodeValue() && output != NodeValue() &&
               "Sanity check that input and output are set");
        output.replaceAllUsesOfWith(input);
        changed = true;
      }
    }
  
    return changed;
  }
  
  /// Optimize reshape nodes.
  bool OptimizeReshape::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    for (auto &node : F->getNodes()) {
      auto *reshapeNode = dyn_cast<ReshapeNode>(&node);
      if (!reshapeNode) {
        continue;
      }
      auto inputNode = reshapeNode->getNthInput(ReshapeNode::InputIdx);
      // Eliminate ReshapeNode when the input is already the correct shape.
      if (inputNode.dims() == reshapeNode->getResult().dims()) {
        reshapeNode->getResult().replaceAllUsesOfWith(inputNode);
        continue;
      }
      // Reshape(Splat(args)) -> Splat(args').
      auto *splatNode = dyn_cast<SplatNode>(inputNode);
      if (splatNode && splatNode->hasOneUse()) {
        // Splat node with more than one use can not be transformed, otherwise
        // we would increase the number of splats, which may lead to increased
        // memory consumption during the execution of the NN model.
        auto *newSplatNode = F->createSplat(splatNode->getName(),
                                            reshapeNode->getResult().getType(),
                                            splatNode->getValue());
        reshapeNode->getResult().replaceAllUsesOfWith(newSplatNode);
        changed = true;
        continue;
      }
      // Reshape(Reshape(x)) -> Reshape(x).
      auto *reshapeNodeInput = dyn_cast<ReshapeNode>(inputNode);
      if (reshapeNodeInput && reshapeNodeInput->hasOneUse()) {
        auto *newReshape = F->createReshape(
            reshapeNode->getName(), reshapeNodeInput->getInput(),
            reshapeNode->getResult().dims(), reshapeNode->getLayout());
        reshapeNode->getResult().replaceAllUsesOfWith(newReshape);
        changed = true;
        continue;
      }
      // Reshape(Constant) -> Constant' or
      // Reshape(Quantize(Constant)) -> Quantize'(Constant').
      auto *Q = dyn_cast<QuantizeNode>(inputNode);
      auto *C = dyn_cast<Constant>(Q ? Q->getInput() : inputNode);
      if (C && (!Q || Q->hasOneUse())) {
        // Create a new Constant with the dims of the reshape.
        auto layout =
            CanonicalTensorLayout::getInstance().getNthResultLayoutRequirements(
                reshapeNode, ReshapeNode::ResultIndices::ResultIdx);
        auto cTy = F->getParent()->uniqueTypeWithNewShape(
            C->getType(), reshapeNode->getResult().getType());
        auto *newC = F->getParent()->createConstant(cTy, C->getName(), layout);
        // Create an unowned view of the original tensor with the correct shape,
        // and assign it to the new Constant.
        Tensor reshapedT = C->getPayload().getUnowned(reshapeNode->getDims());
        newC->assign(&reshapedT);
        // Create a new Quantize with the dims of the reshape, if needed.
        NodeValue newN(newC);
        if (Q) {
          auto qTy = F->getParent()->uniqueTypeWithNewShape(
              Q->getResult().getType(), reshapeNode->getResult().dims());
          auto *newQ = F->createQuantize(Q->getName(), newC, qTy);
          newN = NodeValue(newQ);
        }
        reshapeNode->getResult().replaceAllUsesOfWith(newN);
        changed = true;
        continue;
      }
    }
    return changed;
  }
  
  /// Optimize: Max(Splat(), otherInput) or Max(otherInput, Splat()) for
  /// quantized operations.
  /// Splat and Max can be eliminated if Splat value cannot impact the result.
  /// For example, Max and Splat can be removed if splat value is smaller
  /// than quantization range [min, max].
  /// \returns if anything was changed in the given function.
  static bool optimizeQuantizedMaxSplat(Function *F) {
    LOG_SCOPE(F->getLogContext(), "optimizeQuantizedMaxSplat")
  
    bool changed = false;
    // The following optimizations need to be performed after all
    // quantize/dequantize/rescale optimizations are done.
    for (auto &node : F->getNodes()) {
      // Potentially nop quantized Max can be eliminated.
      // Likely MaxNode has same types for LHS/RHS and Result, make sure
      // it's the case.
      if (auto *MN = dyn_cast<MaxNode>(&node)) {
        if (!MN->getResult().getType()->isQuantizedType() ||
            MN->getResult().getType() != MN->getLHS().getType() ||
            MN->getResult().getType() != MN->getRHS().getType()) {
          continue;
        }
  
        // Check for input Splat node.
        if (!isa<SplatNode>(MN->getLHS()) && !isa<SplatNode>(MN->getRHS())) {
          continue;
        }
  
        Node *splatNode =
            isa<SplatNode>(MN->getLHS()) ? MN->getLHS() : MN->getRHS();
        Node *otherInput =
            isa<SplatNode>(MN->getLHS()) ? MN->getRHS() : MN->getLHS();
  
        // If splat value is smaller than values that can be covered by
        // quantization [min,max] range then just remove MaxNode operation.
        float splatValue = (dyn_cast<SplatNode>(splatNode))->getValue();
        float min = MN->getResult().getType()->getQuantizedValueRange().first;
        if (splatValue <= min) {
          changed = true;
          MN->getResult().replaceAllUsesOfWith(otherInput);
        }
        continue;
      }
      // Potentially nop quantized ReLU can be eliminated.
      if (auto *RN = dyn_cast<ReluNode>(&node)) {
        if (!RN->getResult().getType()->isQuantizedType() ||
            RN->getResult().getType() != RN->getInput().getType()) {
          continue;
        }
  
        Node *input = RN->getInput();
  
        // If zero is smaller or equal than values that can be covered by
        // quantization [min,max] range then just remove ReluNode operation.
        float min = RN->getResult().getType()->getQuantizedValueRange().first;
        if (0.0f <= min) {
          changed = true;
          RN->getResult().replaceAllUsesOfWith(input);
        }
        continue;
      }
    }
    return changed;
  }
  
  /// \returns A value representing the given \p constant converted
  /// to the destination type \p dstTy. If the conversion is not
  /// possible, this method returns NodeValue().
  static NodeValue convertConstant(Module &mod, Constant &constant,
                                   TypeRef dstTy) {
    // Sort out the easy case first.
    if (constant.getType() == dstTy) {
      return constant.getOutput();
    }
    auto modifyConstantTyAndGet = [&]() -> Constant & {
      Constant *oneUseCst = getUniquelyUsedConstant(&mod, constant);
      assert(oneUseCst &&
             "We should always be able to get a constant from a constant!");
      // This type setting updates the type of the node.
      // The underlying tensor still needs to be converted after this call.
      oneUseCst->setType(Storage::OutputIdx, dstTy);
      return *oneUseCst;
    };
    const Tensor &tensor = constant.getPayload();
    switch (tensor.getElementType()) {
    case ElemKind::FloatTy:
    case ElemKind::Float16Ty:
    case ElemKind::BFloat16Ty:
      switch (dstTy->getElementType()) {
      case ElemKind::FloatTy:
      case ElemKind::Float16Ty:
      case ElemKind::BFloat16Ty: {
        // Plain conversion:
        // {FloatTy, Float16Ty, BFloat16Ty} -> {FloatTy, Float16Ty, BFloat16Ty}.
        Constant &constantToBeModified = modifyConstantTyAndGet();
        constantToBeModified.getPayloadMutable().convertToType(
            dstTy->getElementType());
        return constantToBeModified.getOutput();
      }
      case ElemKind::Int32QTy:
      case ElemKind::Int16QTy:
      case ElemKind::Int8QTy: {
        // Quantization: {FloatTy, Float16Ty, BFloat16Ty} -> Quantized type.
        Constant &constantToBeModified = modifyConstantTyAndGet();
        TensorQuantizationParams params{dstTy->getScale(), dstTy->getOffset()};
        Tensor &tensorToBeModified = constantToBeModified.getPayloadMutable();
        // Right now we only quantize fp32 value.
        // Add an assert on that, so that if it changes, we adapt the
        // following code. Adapting the code would required to
        // teach quantizeTensor how to deal with Float16Ty.
        assert(tensor.getType().isFPType() &&
               "Type quantization not implemented");
        tensorToBeModified = quantization::quantizeTensor(
            tensorToBeModified, params, dstTy->getElementType());
        return constantToBeModified.getOutput();
      }
      case ElemKind::Int32ITy: {
        // Plain conversion: {FloatTy} -> {Int32ITy}.
        Constant &constantToBeModified = modifyConstantTyAndGet();
        constantToBeModified.getPayloadMutable().convertToType(
            dstTy->getElementType());
        return constantToBeModified.getOutput();
      }
      default:
        // Quantization: {FloatTy, Float16Ty, BFloat16Ty} -> Int[16|32]QTy.
        // Plain conversion: {FloatTy, Float16Ty, BFloat16Ty} -> Int64ITy.
        return NodeValue();
      }
    case ElemKind::UInt8FusedQTy: {
      if (dstTy->getElementType() != ElemKind::UInt8FusedFP16QTy) {
        return NodeValue();
      }
      auto *NC =
          mod.createConstant(dstTy, constant.getName(), constant.getLayout());
      NC->getPayloadMutable() =
          tensor.getCopyConvertedToType(dstTy->getElementType());
      return NC->getOutput();
    }
    case ElemKind::Int64ITy:
    case ElemKind::Int32ITy:
      switch (dstTy->getElementType()) {
      case ElemKind::Int32ITy:
      case ElemKind::Int64ITy: {
        // Plain conversion: {Int64ITy, Int32ITy} -> {Int64ITy, Int32ITy}.
        Constant &constantToBeModified = modifyConstantTyAndGet();
        constantToBeModified.getPayloadMutable().convertToType(
            dstTy->getElementType());
        return constantToBeModified.getOutput();
      }
      case ElemKind::FloatTy: {
        Constant &constantToBeModified = modifyConstantTyAndGet();
        constantToBeModified.getPayloadMutable().convertToType(
            dstTy->getElementType());
        return constantToBeModified.getOutput();
      }
  
      default:
        return NodeValue();
      }
    default:
      // For now we don't see other quantize, dequantize, or rescale nodes
      // directly attached to constants.
      // Thus don't add code that will never be executed.
      // Dequantization: Int[8|16|32]QTy -> {FloatTy, Float16Ty, BFloat16Ty,
      // Int64I}. Rescale: Int[8|16|32]QTy -> Int[8|16|32]QTy. Plain conversion:
      // Int64ITy -> {FloatTy, Float16Ty, BFloat16Ty}. Quantization: Int64ITy ->
      // Int[8|16|32]QTy.
      return NodeValue();
    }
  }
  
  /// Compute number of significant bits that are used to represent data of type
  /// \p kind. For FP, it is the number of bits in mantissa, for integers it's the
  /// number of bits except sign bit.
  /// \p returns number of significant bits of \p kind.
  /// TODO: Currently, for all supported types wider mantissa also means wider
  /// exponent. If we add a type for which this is not true, we should check both
  /// mantissa and exponent.
  static size_t numSignificantBits(ElemKind kind) {
    switch (kind) {
    case ElemKind::BoolTy:
      return std::numeric_limits<bool>::digits;
    case ElemKind::Int8QTy:
      return std::numeric_limits<int8_t>::digits;
    case ElemKind::UInt8QTy:
    case ElemKind::UInt8FusedQTy:
      return std::numeric_limits<uint8_t>::digits;
    case ElemKind::Float16Ty:
      // Custom type with layout 0 00000 0000000000.
      return 10;
    case ElemKind::BFloat16Ty:
      // bfloat16 has 8 significant bits.
      return 8;
    case ElemKind::Int16QTy:
      return std::numeric_limits<int16_t>::digits;
    case ElemKind::FloatTy:
      return std::numeric_limits<float>::digits;
    case ElemKind::Int32QTy:
    case ElemKind::Int32ITy:
      return std::numeric_limits<int32_t>::digits;
    case ElemKind::Int64ITy:
      return std::numeric_limits<int64_t>::digits;
    default:
      // Avoid compiler warning.
      break;
    }
    llvm_unreachable("Unknown type!");
  }
  
  /// Returns true if casting value from \p srcTy to \p destTy may change it. As
  /// implication of this, casting value from \p srcTy to \p destTy and back may
  /// produce different value than before cast.
  static bool isValueChangingCast(TypeRef srcTy, TypeRef destTy) {
    // FP-to-Int conversion may lead to loss of fraction, so it's not NOOP.
    if (srcTy->isFPType() && !destTy->isFPType()) {
      return true;
    }
    // Narrowing transform (e.g. int64 to int32) may lead to loss of
    // significant senior bits, so it's not NOOP.
    ElemKind srcElKind = srcTy->getElementType();
    ElemKind convElKind = destTy->getElementType();
    if (numSignificantBits(srcElKind) > numSignificantBits(convElKind)) {
      return true;
    }
    return false;
  }
  
  /// Optimize away redundant ClipNodes.
  /// We basically turn "Clip(Clip(Clip(A)))" to "Clip(A)".
  bool OptimizeClips::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
  
    bool changed = false;
    for (Node &node : F->getNodes()) {
      ClipNode *clip = dyn_cast<ClipNode>(&node);
      if (!clip) {
        continue;
      }
      float min = clip->getMin();
      float max = clip->getMax();
      if (auto *clipPrev = dyn_cast<ClipNode>(clip->getInput().getNode())) {
        float minPrev = clipPrev->getMin();
        float maxPrev = clipPrev->getMax();
        auto *newClip =
            F->createClip(clipPrev->getName(), clipPrev->getInput().getNode(),
                          std::max(minPrev, min), std::min(maxPrev, max));
        clip->getResult().replaceAllUsesOfWith(newClip);
        changed = true;
        continue;
      }
  
      // We can fold Clip(Relu) -> Clip'
      if (ReluNode *relu = dyn_cast<ReluNode>(clip->getInput())) {
        const float newMin = std::max(0.0f, min);
        ClipNode *newClip = F->createClip(clip->getName().str() + "_relu",
                                          relu->getInput(), newMin, max);
        clip->getResult().replaceAllUsesOfWith(newClip->getResult());
        changed = true;
        continue;
      }
    }
  
    return changed;
  }
  
  /// \returns whether \p N used used by any Nodes with side effects.
  static bool isUsedByNodeWithSideEffects(Node *N) {
    for (const auto &user : N->getUsers()) {
      if (user.getUser()->hasSideEffects()) {
        return true;
      }
    }
    return false;
  }
  
  /// Helper that \returns whether \p NV cannot have its output quantization
  /// parameters changed. For example, Concats require all inputs to have the same
  /// quantization parameters, so we cannot change the quantization parameters of
  /// a Node if it is input into a Concat.
  static bool disallowQuantParamChange(const NodeValue &NV) {
    for (auto &user : NV.getUsers()) {
      if (isa<ConcatNode>(user.getUser())) {
        return true;
      }
    }
    return false;
  }
  
  /// This is a specialized pass to use where we assume that quantized ranges are
  /// all inside the FP16 range. This means that if we have any clips outside the
  /// FP16 range we can safely remove them if adjacent to a quantized op.
  bool EliminateClipsOutsideFP16Range::run(Function *F,
                                           const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
  
    if (!cctx.precisionConfig.clipQuantRangeToFP16) {
      return false;
    }
  
    bool changed = false;
    for (Node &node : F->getNodes()) {
      // Clip(Dequantize(Node)) -> Dequantize(Node)
      if (ClipNode *clip = dyn_cast<ClipNode>(&node)) {
        DequantizeNode *DQN = dyn_cast<DequantizeNode>(clip->getInput());
        if (!DQN) {
          continue;
        }
  
        // Can only eliminate the clip if its outside the FP16 range.
        if (clip->getMin() > kMinFP16 || clip->getMax() < kMaxFP16) {
          continue;
        }
  
        // We can safely skip the Clip at this point.
        clip->getResult().replaceAllUsesOfWith(DQN->getResult());
        changed = true;
        continue;
      }
  
      // Quantize(Clip(Node)) -> Quantize(Node)
      if (QuantizeNode *QN = dyn_cast<QuantizeNode>(&node)) {
        ClipNode *clip = dyn_cast<ClipNode>(QN->getInput());
        if (!clip) {
          continue;
        }
  
        // Can only eliminate the clip if its outside the FP16 range.
        if (clip->getMin() > kMinFP16 || clip->getMax() < kMaxFP16) {
          continue;
        }
  
        // We can safely skip the Clip at this point.
        QN->setNthInput(QuantizeNode::InputIdx, clip->getInput());
        changed = true;
        continue;
      }
    }
  
    return changed;
  }
  
  /// When quantized operators and Clips are used together, we can often merge the
  /// Clip range and the Quantized range and remove the Clip.
  bool OptimizeQuantizeClip::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
  
    // All of the optimizations here depend on the quantization parameters. If
    // we've loaded dummy qparams then none should be performed.
    if (cctx.precisionConfig.loadUniquedDummyQParams) {
      return false;
    }
  
    bool changed = false;
  
    // Change a quantized result type qResult to account for the range from clip.
    auto updateQuantizeNodeType = [](Function *F, NodeValue qResult,
                                     ClipNode *clip, bool skipIfQuantParamChange,
                                     bool allowQParamChange) {
      const auto qMinMax = qResult.getType()->getQuantizedValueRange();
      const float newMin = std::max(clip->getMin(), qMinMax.first);
      const float newMax = std::min(clip->getMax(), qMinMax.second);
  
      // If the quantization parameters do not change then we can always elide the
      // Clip and do not need to change the type of qResult.
      if (newMin == qMinMax.first && newMax == qMinMax.second) {
        return true;
      }
  
      if (disallowQuantParamChange(qResult)) {
        return false;
      }
  
      // At this point the quantization parameters must be changing, so if we do
      // not allow for that then return false.
      if (!allowQParamChange || skipIfQuantParamChange) {
        return false;
      }
  
      // Replace the old quantized type with the new type with different
      // min/max.
      const TypeRef oldTy = qResult.getType();
      const auto qParams =
          quantization::chooseQuantizationParams({newMin, newMax});
      const TypeRef newTy = F->getParent()->uniqueType(
          oldTy->getElementType(), oldTy->dims(), qParams.scale, qParams.offset);
      qResult.getNode()->setType(qResult.getResNo(), newTy);
      return true;
    };
  
    for (Node &node : F->getNodes()) {
      // Clip(Dequantize(Node)) -> Dequantize(Node)
      if (ClipNode *clip = dyn_cast<ClipNode>(&node)) {
        DequantizeNode *DQN = dyn_cast<DequantizeNode>(clip->getInput());
        if (!DQN) {
          continue;
        }
  
        // Cannot perform this optimization if there are multiple users of DQN or
        // DQN's input, as otherwise they'd have incorrect quantization params.
        NodeValue qResult = DQN->getInput();
        const bool skipIfQuantParamChange =
            DQN->getNumUsers() != 1 || qResult.getNode()->getNumUsers() != 1;
  
        // Try to update the quantize's type, otherwise skip this one.
        if (!updateQuantizeNodeType(
                F, qResult, clip, skipIfQuantParamChange,
                cctx.optimizationOpts.enableQuantParamChanges)) {
          continue;
        }
  
        // Now we skip the Clip since the node prior to DQN has included the
        // Clip's range in its quantization parameters.
        clip->getResult().replaceAllUsesOfWith(DQN->getResult());
        changed = true;
        continue;
      }
  
      // Quantize(Clip(Node)) -> Quantize(Node)
      if (QuantizeNode *QN = dyn_cast<QuantizeNode>(&node)) {
        ClipNode *clip = dyn_cast<ClipNode>(QN->getInput());
        if (!clip) {
          continue;
        }
  
        // Cannot set the type of quantized nodes if they're used by a Node with
        // side effects, as they may be expecting a specific type.
        const bool skipIfQuantParamChange = isUsedByNodeWithSideEffects(QN);
  
        // Try to update the quantize's type, otherwise skip this one.
        if (!updateQuantizeNodeType(
                F, QN->getResult(), clip, skipIfQuantParamChange,
                cctx.optimizationOpts.enableQuantParamChanges)) {
          continue;
        }
  
        // Now we can skip the Clip since the QN has accounted for the Clip's
        // range in its quantization parameters.
        QN->setNthInput(QuantizeNode::InputIdx, clip->getInput());
        changed = true;
        continue;
      }
    }
  
    return changed;
  }
  
  /// Optimize away ConvertToNode.
  /// This basically turns "conversion(conversion A to B) to C"
  /// into noop if all of the conditions below are met:
  ///  - the type of A and C are the same;
  ///  - A->B is not a FP-to-Int conversion;
  ///  - A->B is not a narrowing conversion.
  bool OptimizeConversions::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
  
    bool changed = false;
    for (auto &node : F->getNodes()) {
      if (auto *CN = llvm::dyn_cast<ConvertToNode>(&node)) {
  
        // Eliminate no-op conversion.
        if (CN->getInput().getType() == CN->getResult().getType()) {
          CN->getResult().replaceAllUsesOfWith(CN->getInput());
          changed = true;
          continue;
        }
  
        // Perform conversion of constants.
        if (auto *BN = llvm::dyn_cast<Constant>(CN->getInput())) {
          auto newConst =
              convertConstant(*F->getParent(), *BN, CN->getResult().getType());
          if (newConst == NodeValue()) {
            continue;
          }
          CN->getResult().replaceAllUsesOfWith(newConst, F);
          changed = true;
          continue;
        }
  
        // Simplify a chain of conversions A -> B -> C to A -> C, unless A -> B
        // is a narrowing cast.
        if (auto *BN = llvm::dyn_cast<ConvertToNode>(CN->getInput())) {
          auto AN = BN->getInput();
  
          // Do not optimize away narrowing casts.
          if (!isValueChangingCast(AN.getType(), BN->getResult().getType())) {
            auto *newCast =
                F->createConvertTo(CN->getName(), AN, CN->getResult().getType());
            CN->getResult().replaceAllUsesOfWith(newCast);
            changed = true;
            continue;
          }
        }
      }
    }
    return changed;
  }
  
  /// Optimize patterns of Int8 quantization/dequantization with ConvertTo. This
  /// may have numerical differences but since Int8 has a small range it's likely
  /// fine. This is opt in by a backend.
  bool OptimizeOutIntermediateConversions::run(Function *F,
                                               const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
  
    bool changed = false;
    for (auto &node : F->getNodes()) {
      // Quantize(ConvertTo(Node)) -> Quantize(Node), where Quantize is int8
      if (QuantizeNode *QN = llvm::dyn_cast<QuantizeNode>(&node)) {
        if (QN->getResult().getType()->getElementType() != ElemKind::Int8QTy) {
          continue;
        }
  
        ConvertToNode *CN = llvm::dyn_cast<ConvertToNode>(QN->getInput());
        if (!CN) {
          continue;
        }
  
        QN->setNthInput(QuantizeNode::InputIdx, CN->getInput());
        changed = true;
        continue;
      }
  
      // ConvertTo(Dequantize(Node)) -> Dequantize(Node), where Dequantize is int8
      if (ConvertToNode *CN = llvm::dyn_cast<ConvertToNode>(&node)) {
        DequantizeNode *DN = llvm::dyn_cast<DequantizeNode>(CN->getInput());
        if (!DN ||
            DN->getInput().getType()->getElementType() != ElemKind::Int8QTy) {
          continue;
        }
  
        // Create new Dequantize node, dequantizing directly to the kind of the
        // ConverTo that originally consumed it.
        DequantizeNode *newDN = F->createDequantize(
            DN->getName(), DN->getInput(), CN->getResult().getElementType());
        CN->getResult().replaceAllUsesOfWith(newDN->getResult());
        changed = true;
        continue;
      }
    }
  
    return changed;
  }
  
  // Look for float Relus that we can fuse up into quantized FCs. This is either
  // with a Dequantize between them, or a Concat with multiple FCs being
  // dequantized and concatenated together.
  bool OptimizeQuantFCFloatRelu::run(Function *F,
                                     const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
  
    // This opt implies there to be changes to quantization, because we create an
    // int relu that was previously float.
    if (!cctx.optimizationOpts.enableQuantParamChanges) {
      return false;
    }
  
    bool changed = false;
    for (auto &node : F->getNodes()) {
      auto *relu = llvm::dyn_cast<ReluNode>(&node);
      // Look for Float relus to start.
      if (!relu ||
          !isFloatElemKind(relu->getResult().getType()->getElementType())) {
        continue;
      }
  
      // Now look for dequantize nodes. We may need to move this above a Concat.
      // Check if necessary.
      std::vector<FullyConnectedNode *> nodesToFuse;
      if (auto *CN = llvm::dyn_cast<ConcatNode>(relu->getInput())) {
        if (CN->getNumUsers() != 1) {
          continue;
        }
  
        // Check if all the concat inputs are dequantized FCs.
        for (const NodeValue &NV : CN->getInputs()) {
          auto *DQ = llvm::dyn_cast<DequantizeNode>(NV);
          if (!DQ || DQ->getNumUsers() != 1) {
            break;
          }
          auto *FC = llvm::dyn_cast<FullyConnectedNode>(DQ->getInput());
          if (!FC || FC->getNumUsers() != 1) {
            break;
          }
          nodesToFuse.push_back(FC);
        }
        if (nodesToFuse.size() != CN->getInputs().size()) {
          continue;
        }
      } else if (auto *DQ = llvm::dyn_cast<DequantizeNode>(relu->getInput())) {
        if (DQ->getNumUsers() != 1) {
          continue;
        }
  
        auto *FC = llvm::dyn_cast<FullyConnectedNode>(DQ->getInput());
        if (!FC || FC->getNumUsers() != 1) {
          break;
        }
        nodesToFuse.push_back(FC);
      } else {
        continue;
      }
  
      // Did not find any quantized FCs to fuse, so continue.
      if (!nodesToFuse.size()) {
        continue;
      }
  
      // If the quant FC is used by nodes with side effects then skip, since we
      // may be changing the user's input qparam type.
      bool skip = false;
      for (FullyConnectedNode *FC : nodesToFuse) {
        if (isUsedByNodeWithSideEffects(FC)) {
          skip = true;
          break;
        }
      }
      if (skip) {
        continue;
      }
  
      // Now add quantized relus onto all of the FCs.
      for (FullyConnectedNode *FC : nodesToFuse) {
        const TypeRef FCTy = FC->getResult().getType();
        TypeRef qReluTy = nullptr;
        if (cctx.precisionConfig.loadUniquedDummyQParams) {
          // Reuse the FC type, since we don't know its actual qparams during AOT
          // optimization. When we the actual type later before deploying, we will
          // do a final processing pass to set min to 0 via updateReluTypes().
          qReluTy = FCTy;
        } else {
          // Use the same type as the FC for the Relu but with 0 as min.
          const auto qParams = quantization::chooseQuantizationParams(
              {0, FCTy->getQuantizedValueRange().second});
          qReluTy =
              F->getParent()->uniqueType(FCTy->getElementType(), FCTy->dims(),
                                         qParams.scale, qParams.offset);
        }
        ReluNode *qRelu = F->createRELU(relu->getName().str() + "_quant",
                                        FC->getResult(), qReluTy);
        FC->getResult().typeUnsafeReplaceAllUsesOfWith(qRelu->getResult(), F,
                                                       qRelu);
      }
  
      // Now we can get rid of the relu.
      relu->getResult().replaceAllUsesOfWith(relu->getInput());
      changed = true;
      continue;
    }
  
    return changed;
  }
  
  /// Look for Concats with all Dequantization as input and Quantization as
  /// output, and change the Quantization/Dequantization into a rescale.
  bool OptimizeConcatQuantization::run(Function *F,
                                       const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
  
    bool changed = false;
    for (auto &node : F->getNodes()) {
      auto *CN = dyn_cast<ConcatNode>(&node);
      if (!CN) {
        continue;
      }
  
      // Look for a single Quantize user.
      if (CN->getUsers().size() != 1) {
        continue;
      }
      auto *QN = dyn_cast<QuantizeNode>((*CN->getUsers().begin()).getUser());
      if (!QN) {
        continue;
      }
  
      // Gather/check all of the inputs are DequantizeNodes.
      std::vector<DequantizeNode *> DNs;
      DNs.reserve(CN->getInputs().size());
      for (const NodeValue &NV : CN->getInputs()) {
        auto *DN = dyn_cast<DequantizeNode>(NV);
        if (!DN || DN->getNumUsers() != 1) {
          break;
        }
        DNs.push_back(DN);
      }
  
      // If not all CN inputs are Dequantizes then skip.
      if (DNs.size() != CN->getInputs().size()) {
        continue;
      }
  
      // Now create Rescales instead of Dequantizes for all CN inputs.
      std::vector<NodeValue> newConcatInputs;
      newConcatInputs.reserve(DNs.size());
      TypeRef QNTy = QN->getResult().getType();
      for (DequantizeNode *DN : DNs) {
        if (DN->getInput().getType()->getScale() == QNTy->getScale() &&
            DN->getInput().getType()->getOffset() == QNTy->getOffset()) {
          // Don't need to rescale as it already has the right scale/offset.
          newConcatInputs.push_back(DN->getInput());
        } else {
          TypeRef newTy = F->getParent()->uniqueTypeWithNewShape(
              QNTy, DN->getResult().dims());
          auto *RS = F->createRescaleQuantized(DN->getName().str() + "_rescale",
                                               DN->getInput(), newTy);
          newConcatInputs.push_back(RS->getResult());
        }
      }
  
      auto *newCN = F->createConcat(CN->getName(), newConcatInputs, CN->getDim());
  
      // Now we can get rid of the Quantize after the CN.
      QN->getResult().replaceAllUsesOfWith(newCN->getResult());
      changed = true;
      continue;
    }
  
    return changed;
  }
  
  /// \returns a cloned version of node \p N, but with each of the cloned node's
  /// output types set to the corresponding type in \p types. The new node is
  /// added to Function \p F.
  /// \pre types.size() == N->getNumResults()
  static Node *cloneNodeWithNewTypes(Function *F, Node *N,
                                     llvm::ArrayRef<TypeRef> types) {
    assert(N->getNumResults() == types.size() &&
           "Number of types must equal number of results of the node.");
  
    Node *newNode = F->addNode(N->clone());
    for (size_t i = 0; i < types.size(); i++) {
      newNode->setType(i, types[i]);
    }
  
    return newNode;
  }
  
  template <class T, class U>
  using enable_if_same_t = std::enable_if<std::is_same<T, U>::value, U>;
  #define FUNCTION_ENABLE_IF_TEMPLATE(NODE_NAME_)                                \
    template <class T, typename... Args>                                         \
    typename enable_if_same_t<T, NODE_NAME_##Node>::type static
  
  FUNCTION_ENABLE_IF_TEMPLATE(AvgPool) * createNode(Function &F, Args... args) {
    return F.createAvgPool(args...);
  }
  FUNCTION_ENABLE_IF_TEMPLATE(MaxPool) * createNode(Function &F, Args... args) {
    return F.createMaxPool(args...);
  }
  FUNCTION_ENABLE_IF_TEMPLATE(Add)
  *createNode(Function &F, Args... args) { return F.createAdd(args...); }
  FUNCTION_ENABLE_IF_TEMPLATE(Sub)
  *createNode(Function &F, Args... args) { return F.createSub(args...); }
  FUNCTION_ENABLE_IF_TEMPLATE(Mul)
  *createNode(Function &F, Args... args) { return F.createMul(args...); }
  FUNCTION_ENABLE_IF_TEMPLATE(Div)
  *createNode(Function &F, Args... args) { return F.createDiv(args...); }
  FUNCTION_ENABLE_IF_TEMPLATE(Min)
  *createNode(Function &F, Args... args) { return F.createMin(args...); }
  FUNCTION_ENABLE_IF_TEMPLATE(Max)
  *createNode(Function &F, Args... args) { return F.createMax(args...); }
  FUNCTION_ENABLE_IF_TEMPLATE(MatMul)
  *createNode(Function &F, Args... args) { return F.createMatMul(args...); }
  
  FUNCTION_ENABLE_IF_TEMPLATE(AvgPool) *
      createNewPool(Function &F, T *PN, RescaleQuantizedNode *rescale) {
    return createNode<T>(F, PN->getName(), rescale->getInput(), PN->getKernels(),
                         PN->getStrides(), PN->getPads(), NCHW,
                         PN->getCountIncludePads());
  }
  FUNCTION_ENABLE_IF_TEMPLATE(MaxPool) *
      createNewPool(Function &F, T *PN, RescaleQuantizedNode *rescale) {
    return createNode<T>(F, PN->getName(), rescale->getInput(), PN->getKernels(),
                         PN->getStrides(), PN->getPads(),
                         PN->getArgmax().getElementType());
  }
  
  /// Sink Rescale down with Pooling node.
  /// PoolingNode(Rescale(X)) -> Rescale(PoolingNode(X)).
  /// Apply this transformation for AvgPool and MaxPool.
  template <typename T>
  static bool sinkDownRescaleToPoolingNode(Function &F, T *PN) {
    LOG_SCOPE(F.getLogContext(), "sinkDownRescaleToPoolingNode")
  
    bool changed = false;
  
    if (auto *rescale = dyn_cast<RescaleQuantizedNode>(PN->getInput())) {
      T *newPN = createNewPool(F, PN, rescale);
      auto rescaleOutTy = F.getParent()->uniqueTypeWithNewShape(
          rescale->getResult().getType(), PN->getResult().getType());
      auto *newRescale = F.createRescaleQuantized(
          rescale->getName(), newPN->getResult(), rescaleOutTy);
      PN->getResult().replaceAllUsesOfWith(newRescale);
      for (size_t i = 1; i < PN->getNumResults(); i++) {
        PN->getNthResult(i).replaceAllUsesOfWith(newPN->getNthResult(i));
      }
      changed = true;
    }
  
    return changed;
  }
  
  /// Combine Rescale down with Arithmetic node.
  ///   ArithmeticNode(Rescale(X), Rescale(Y)) -> ArithmeticNode(X, Y).
  ///   ArithmeticNode(Rescale(X), Y) -> ArithmeticNode(X, Y).
  ///   ArithmeticNode(X, Rescale(Y)) -> ArithmeticNode(X, Y).
  /// Apply this optimization for Add, Sub, Mul, Div, Min, Max.
  template <typename T>
  static bool combineDownRescaleToArithmeticNode(Function &F, T *AN) {
    LOG_SCOPE(F.getLogContext(), "combineDownRescaleToArithmeticNode")
  
    bool changed = false;
  
    if (auto *rescale = dyn_cast<RescaleQuantizedNode>(AN->getLHS())) {
      T *newAN = createNode<T>(F, AN->getName(), AN->getResult().getType(),
                               rescale->getInput(), AN->getRHS());
      AN->getResult().replaceAllUsesOfWith(newAN);
      AN = newAN;
      changed = true;
    }
    if (auto *rescale = dyn_cast<RescaleQuantizedNode>(AN->getRHS())) {
      T *newAN = createNode<T>(F, AN->getName(), AN->getResult().getType(),
                               AN->getLHS(), rescale->getInput());
      AN->getResult().replaceAllUsesOfWith(newAN);
      changed = true;
    }
  
    return changed;
  }
  
  /// Sink Rescale nodes down when possible.
  /// \returns if anything was changed in the given function.
  static bool sinkRescaleQuantizedNode(Function *F,
                                       const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), "sinkRescaleQuantizedNode");
    bool changed = false;
    for (auto &node : F->getNodes()) {
      // Sink Rescale below Reshape node.
      // Reshape(Rescale(X)) -> Rescale(Reshape(X)).
      if (auto *reshape = dyn_cast<ReshapeNode>(&node)) {
        auto *rescale = dyn_cast<RescaleQuantizedNode>(reshape->getInput());
        if (!rescale) {
          continue;
        }
  
        auto *newReshape =
            F->createReshape(reshape->getName(), rescale->getInput(),
                             reshape->getResult().dims(), reshape->getLayout());
        auto *newRescale = F->createRescaleQuantized(
            rescale->getName(), newReshape, reshape->getResult().getType());
        reshape->getResult().replaceAllUsesOfWith(newRescale);
  
        changed = true;
        continue;
      }
  
      // Sink Rescale below Slice node.
      // Slice(Rescale(X)) -> Rescale(Slice(X)).
      if (auto *slice = dyn_cast<SliceNode>(&node)) {
        auto *rescale = dyn_cast<RescaleQuantizedNode>(slice->getInput());
        if (!rescale) {
          continue;
        }
  
        auto sliceOutTy = F->getParent()->uniqueTypeWithNewShape(
            rescale->getInput().getType(), slice->getResult().getType());
        auto *newSlice = F->createSlice(slice->getName(), rescale->getInput(),
                                        slice->getStart(), sliceOutTy);
        auto *newRescale = F->createRescaleQuantized(
            rescale->getName(), newSlice, slice->getResult().getType());
        slice->getResult().replaceAllUsesOfWith(newRescale);
  
        changed = true;
        continue;
      }
  
      // Sink Rescale below Transpose node.
      // Transpose(Rescale(X)) -> Rescale(Transpose(X)).
      if (auto *transpose = dyn_cast<TransposeNode>(&node)) {
        auto *rescale = dyn_cast<RescaleQuantizedNode>(transpose->getInput());
        if (!rescale) {
          continue;
        }
  
        auto *newTranspose =
            F->createTranspose(transpose->getName(), rescale->getInput(),
                               transpose->getShuffle(), transpose->getLayout());
        auto rescaleOutTy = F->getParent()->uniqueTypeWithNewShape(
            rescale->getResult().getType(), transpose->getResult().getType());
        auto *newRescale = F->createRescaleQuantized(rescale->getName(),
                                                     newTranspose, rescaleOutTy);
        transpose->getResult().replaceAllUsesOfWith(newRescale);
  
        changed = true;
        continue;
      }
  
      if (auto *PN = dyn_cast<AvgPoolNode>(&node)) {
        // AvgPool input and output scale/bias may differ.
        if (!cctx.optimizationOpts.enableQuantParamChanges) {
          changed |= sinkDownRescaleToPoolingNode<AvgPoolNode>(*F, PN);
        }
        continue;
      }
  
      if (auto *PN = dyn_cast<MaxPoolNode>(&node)) {
        changed |= sinkDownRescaleToPoolingNode<MaxPoolNode>(*F, PN);
        continue;
      }
  
      // Combine Rescale down with FullyConnected node.
      // FullyConnected(Rescale(X)) -> FullyConnected(X).
      if (auto *FC = dyn_cast<FullyConnectedNode>(&node)) {
        auto *rescale = dyn_cast<RescaleQuantizedNode>(FC->getInput());
        if (!rescale) {
          continue;
        }
  
        auto *newFC = F->createFullyConnected(FC->getName(), rescale->getInput(),
                                              FC->getWeights(), FC->getBias(),
                                              FC->getResult().getType());
        FC->getResult().replaceAllUsesOfWith(newFC);
  
        changed = true;
        continue;
      }
  
      // Combine Rescale down with Convolution node.
      // Convolution(Rescale(X), F, B) -> Convolution(X, F, B).
      // Convolution(X, Rescale(F), B) -> Convolution(X, F, B).
      // Convolution(X, F, Rescale(B)) -> Convolution(X, F, B).
      // ... and different combinations.
      if (auto *CN = dyn_cast<ConvolutionNode>(&node)) {
        auto *rescaleX = dyn_cast<RescaleQuantizedNode>(CN->getInput());
        auto *rescaleF = dyn_cast<RescaleQuantizedNode>(CN->getFilter());
        auto *rescaleB = dyn_cast<RescaleQuantizedNode>(CN->getBias());
        auto newX = rescaleX ? rescaleX->getInput() : CN->getInput();
        auto newF = rescaleF ? rescaleF->getInput() : CN->getFilter();
        auto newB = rescaleB ? rescaleB->getInput() : CN->getBias();
        if (rescaleX || rescaleF || rescaleB) {
          auto *newCN = F->createConv(CN->getName(), newX, newF, newB,
                                      CN->getResult().getType(), CN->getKernels(),
                                      CN->getStrides(), CN->getPads(),
                                      CN->getGroup(), CN->getDilation());
          newCN->setFusedActivation(CN->getFusedActivation());
          newCN->setFusedActivationArgs(CN->getFusedActivationArgs());
  
          CN->getResult().replaceAllUsesOfWith(newCN);
          changed = true;
        }
        continue;
      }
  
      if (auto *AN = dyn_cast<AddNode>(&node)) {
        changed |= combineDownRescaleToArithmeticNode<AddNode>(*F, AN);
        continue;
      }
      if (auto *AN = dyn_cast<SubNode>(&node)) {
        changed |= combineDownRescaleToArithmeticNode<SubNode>(*F, AN);
        continue;
      }
      if (auto *AN = dyn_cast<MulNode>(&node)) {
        changed |= combineDownRescaleToArithmeticNode<MulNode>(*F, AN);
        continue;
      }
      if (auto *AN = dyn_cast<DivNode>(&node)) {
        changed |= combineDownRescaleToArithmeticNode<DivNode>(*F, AN);
        continue;
      }
      if (auto *AN = dyn_cast<MinNode>(&node)) {
        changed |= combineDownRescaleToArithmeticNode<MinNode>(*F, AN);
        continue;
      }
      if (auto *AN = dyn_cast<MaxNode>(&node)) {
        changed |= combineDownRescaleToArithmeticNode<MaxNode>(*F, AN);
        continue;
      }
  
      // Combine Rescale down with Relu node.
      //   ReluNode(Rescale(in)) -> ReluNode(in).
      if (auto *RN = dyn_cast<ReluNode>(&node)) {
        if (auto *rescale = dyn_cast<RescaleQuantizedNode>(RN->getInput())) {
          auto *newRN = F->createRELU(RN->getName(), rescale->getInput(),
                                      RN->getResult().getType());
          RN->getResult().replaceAllUsesOfWith(newRN);
          changed = true;
        }
        continue;
      }
  
      if (auto *MN = dyn_cast<MatMulNode>(&node)) {
        changed |= combineDownRescaleToArithmeticNode<MatMulNode>(*F, MN);
        continue;
      }
    }
  
    return changed;
  }
  
  /// Eliminate node sequences that are related to quantization.
  /// \returns if anything was changed in the given function.
  bool OptimizeQuantization::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    // A worklist that contains the nodes to process.
    std::vector<Node *> worklist;
  
    // Add all of the interesting nodes to the worklist.
    for (auto &node : F->getNodes()) {
      if (isa<QuantizeNode>(node) || isa<DequantizeNode>(node) ||
          isa<RescaleQuantizedNode>(node)) {
        worklist.push_back(&node);
      }
    }
  
    while (!worklist.empty()) {
      // Take a node from the worklist.
      Node *node = worklist.back();
      worklist.pop_back();
  
      if (auto *Q = dyn_cast<QuantizeNode>(node)) {
        if (auto *DQ = dyn_cast<DequantizeNode>(Q->getInput())) {
          // Quantize(Dequantize(X)) -> RescaleQuantized(X)
          // If the quantization-dequantization sequence does not change the
          // type then we can simply drop them without adding a requantization
          // node.
          changed = true;
          if (DQ->getInput().getType() == Q->getResult().getType()) {
            Q->getResult().replaceAllUsesOfWith(DQ->getInput());
            continue;
          }
  
          auto *RS = F->createRescaleQuantized(Q->getName(), DQ->getInput(),
                                               Q->getResult().getType());
          Q->getResult().replaceAllUsesOfWith(RS);
  
          // We may be able to optimize this rescale node. Remember to visit
          // this new node and try to optimize it later.
          worklist.push_back(RS);
          continue;
        }
  
        if (auto *SN = dyn_cast<SplatNode>(Q->getInput())) {
          // Quantize(Splat) -> Splat'
          changed = true;
          SplatNode *newSN = F->createSplat(
              SN->getName(), Q->getResult().getType(), SN->getValue());
          Q->getResult().replaceAllUsesOfWith(newSN);
          continue;
        }
      }
  
      if (auto *DQ = dyn_cast<DequantizeNode>(node)) {
        if (auto *Q = dyn_cast<QuantizeNode>(DQ->getInput())) {
          // Dequantize(Quantize(X)) -> X
          changed = true;
          DQ->getResult().replaceAllUsesOfWith(Q->getInput());
          continue;
        }
        // Fold the rescale into the following Dequantize.
        // Dequantize(rescale) -> Dequantize()
        if (auto *RS = dyn_cast<RescaleQuantizedNode>(DQ->getInput())) {
          changed = true;
          auto *newRS = F->createDequantize(DQ->getName(), RS->getInput(),
                                            DQ->getResult().getType());
          DQ->getResult().replaceAllUsesOfWith(newRS);
  
          // We may be able to optimize this rescale node. Remember to visit
          // this new node and try to optimize it later.
          worklist.push_back(newRS);
          continue;
        }
        if (auto *SN = dyn_cast<SplatNode>(DQ->getInput())) {
          // Dequantize(Splat) -> Splat'
          changed = true;
          SplatNode *newSN = F->createSplat(
              SN->getName(), DQ->getResult().getType(), SN->getValue());
          DQ->getResult().replaceAllUsesOfWith(newSN);
          continue;
        }
      }
  
      if (auto *RS = dyn_cast<RescaleQuantizedNode>(node)) {
        // All cases below tend to change the output scale/bias of a Node. This
        // may change the numerics of the op (even the range is narrower and so it
        // should be more accurate).
        if (!cctx.optimizationOpts.enableQuantParamChanges) {
          continue;
        }
  
        if (RS->getInput().getType() == RS->getResult().getType()) {
          // If rescale does not change the type, then simply drop it.
          changed = true;
          RS->getResult().replaceAllUsesOfWith(RS->getInput());
          continue;
        }
  
        // All optimizations in this scope below combine a Rescale up into or
        // above the Rescale's input X. If X has multiple users then this merging
        // will duplicate X, just with a different output scale/offset. If X is
        // not a Splat then this is likely not desired, as it means a
        // computational node (e.g. Add) is duplicated.
        if (!RS->getInput().hasOneUse() && !isa<SplatNode>(RS->getInput())) {
          continue;
        }
  
        // Combine the rescale node up into its parent node.
        // Rescale(Node()) -> 'Node().
        bool addNewNodeToWorklist = false;
        switch (RS->getInput().getNode()->getKind()) {
        case Kinded::Kind::RescaleQuantizedNodeKind:
        case Kinded::Kind::QuantizeNodeKind:
          addNewNodeToWorklist = true;
        case Kinded::Kind::SplatNodeKind:
        case Kinded::Kind::AddNodeKind:
        case Kinded::Kind::SubNodeKind:
        case Kinded::Kind::MulNodeKind:
        case Kinded::Kind::DivNodeKind:
        case Kinded::Kind::FmodNodeKind:
        case Kinded::Kind::MinNodeKind:
        case Kinded::Kind::MatMulNodeKind:
        case Kinded::Kind::ConvolutionNodeKind:
        case Kinded::Kind::FullyConnectedNodeKind:
        case Kinded::Kind::SparseLengthsWeightedSumNodeKind: {
          changed = true;
          Node *newNode =
              cloneNodeWithNewTypes(F, RS->getInput(), RS->getResult().getType());
          RS->getResult().replaceAllUsesOfWith(newNode);
          if (addNewNodeToWorklist) {
            worklist.push_back(newNode);
          }
          continue;
        }
        default:;
        }
  
        if (auto *MN = dyn_cast<MaxNode>(RS->getInput())) {
          // Rescale(MAX(X, Y)) -> MAX(Rescale(X), Rescale(Y)).
          // It's okay to rescale the operands because even if the output range
          // is smaller then truncation would have happened during the rescale.
          // On values that are outside of the range we just moved the
          // truncation to a different location.
          changed = true;
          auto name = RS->getName();
          auto *L = F->createRescaleQuantized(name, MN->getLHS(),
                                              RS->getResult().getType());
          auto *R = F->createRescaleQuantized(name, MN->getRHS(),
                                              RS->getResult().getType());
          auto *newMN = F->createMax(MN->getName(), L, R);
          worklist.push_back(L);
          worklist.push_back(R);
          RS->getResult().replaceAllUsesOfWith(newMN);
          continue;
        }
      } // Handle RescaleQuantizedNode
    }   // For each item in the worklist.
  
    // This pass is based on real qparams, so skip this opt if using dummies.
    if (!cctx.precisionConfig.loadUniquedDummyQParams) {
      changed |= optimizeQuantizedMaxSplat(F);
    }
  
    // If nothing has changed then sink rescale quantization nodes.
    if (!changed) {
      changed = sinkRescaleQuantizedNode(F, cctx);
    }
    return changed;
  }
  
  void glow::convertQuantizedConstants(Function *F, CompilationContext &cctx) {
    for (auto &node : F->getNodes()) {
      auto *Q = dyn_cast<QuantizeNode>(&node);
      if (!Q) {
        continue;
      }
      auto *C = dyn_cast<Constant>(Q->getInput());
      if (!C) {
        continue;
      }
  
      // Quantize(Constant) -> Constant
      // Note, it does not really matter how many usages this Constant has.
      // Quantized graph will use optimized Constant and other functions will
      // refer to the floating point original Constant.
      NodeValue NC =
          convertConstant(*F->getParent(), *C, Q->getResult().getType());
      if (NC == NodeValue()) {
        continue;
      }
      Q->getResult().replaceAllUsesOfWith(NC);
    }
  
    // Perform Dead Code Elimination.
    runDCEPass(F, cctx);
  }
  
  void glow::convertPlaceholdersToConstants(Function *F,
                                            const PlaceholderBindings &bindings,
                                            llvm::ArrayRef<Placeholder *> phs) {
    LOG_SCOPE(F->getLogContext(), "convertPlaceholdersToConstants")
  
    auto *M = F->getParent();
    for (auto &PH : F->findPlaceholders()) {
      if (std::find(phs.begin(), phs.end(), PH) != phs.end()) {
        continue;
      }
      auto *tensor = bindings.get(PH);
      if (!tensor) {
        continue;
      }
      auto *constant = M->createConstant(PH->getName(), *tensor, PH->getLayout());
      PH->getOutput().replaceAllUsesOfWith(constant, F);
    }
  }
  
  /// \returns True if the \p node sub-tree corresponds to a scalar
  /// (Constant or Splat) and return the float value in \p retFloat.
  static bool getFloatScalar(Node *node, float *retFloat) {
    // Iterate across potential Tile Nodes (implied by broadcasting if any).
    auto *n = node;
    while (auto *TN = dyn_cast<TileNode>(n)) {
      n = TN->getInput();
    }
  
    // After potential Tile nodes, it should be a singleton constant scalar node
    // with any shape corresponding to one single element.
    if (auto *constNode = dyn_cast<Constant>(n)) {
      if ((constNode->getType()->getElementType() != ElemKind::FloatTy) ||
          (constNode->getType()->size() != 1)) {
        return false;
      }
      auto valueH = constNode->getHandle<float>();
      std::vector<dim_t> coord(constNode->getType()->dims().size(), 0);
      *retFloat = valueH.at(coord);
      return true;
    }
    if (auto *splatNode = dyn_cast<SplatNode>(n)) {
      *retFloat = splatNode->getValue();
      return true;
    }
  
    return false;
  }
  
  /// Fold leakyRelu operations expressed as a sub-graph Max(A, Mul(A, scalar))
  /// and replace it by PRelu(Splat).
  bool FoldLeakyRelu::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
    auto &nodes = F->getNodes();
    for (auto &node : nodes) {
      auto *maxNode = dyn_cast<MaxNode>(&node);
      if (!maxNode) {
        continue;
      }
      NodeValue otherMaxOperand;
      MulNode *mulNode;
      if ((mulNode = dyn_cast<MulNode>(maxNode->getRHS()))) {
        otherMaxOperand = maxNode->getLHS();
      } else if ((mulNode = dyn_cast<MulNode>(maxNode->getLHS()))) {
        otherMaxOperand = maxNode->getRHS();
      } else {
        continue;
      }
      NodeValue otherMulOperand;
      float value;
      if (getFloatScalar(mulNode->getRHS(), &value)) {
        otherMulOperand = maxNode->getLHS();
      } else if (getFloatScalar(mulNode->getLHS(), &value)) {
        otherMulOperand = maxNode->getRHS();
      } else {
        continue;
      }
      if ((value <= 1.0f) && (otherMulOperand == otherMaxOperand)) {
        // The sub-tree is a Leaky-Relu, express it as a PRelu.
        auto *splat = F->createSplat(maxNode->getName(),
                                     mulNode->getResult().getType(), value);
        auto *PRelu = F->createPRELU(maxNode->getName(), otherMaxOperand, splat);
        maxNode->getResult().replaceAllUsesOfWith(PRelu);
        changed = true;
        continue;
      }
    }
    return changed;
  }
  
  /// Parameters that are used to define ChannelShuffle operators.
  struct ChannelShuffleParams {
    size_t group;
    size_t kernel;
  };
  
  /// Compute the original parameters to the ChannelShuffle operator (represented
  /// as ReshapeNode->TransposeNode->ReshapeNode) for which \p node is the leading
  /// ReshapeNode. \returns The original ChannelShuffle parameters if possible and
  /// empty Optional otherwise.
  static llvm::Optional<ChannelShuffleParams>
  getChannelShuffleParams(const ReshapeNode &node) {
    auto resM = llvm::Optional<ChannelShuffleParams>();
  
    llvm::ArrayRef<dim_t> inputDims = node.getInput().dims();
    llvm::ArrayRef<dim_t> resultDims = node.getDims();
  
    // Check that there is one more output dimension than input dimension.
    if (resultDims.size() != inputDims.size() + 1) {
      return resM;
    }
  
    // Find the first output dimension that doesn't match its corresponding input
    // dimension.
    ChannelShuffleParams params;
    bool found = false;
    for (size_t i = 0, e = resultDims.size(); i < e - 1; ++i) {
      if (inputDims[i] != resultDims[i]) {
        params.kernel = i;
        params.group = resultDims[i];
        found = true;
        break;
      }
    }
  
    // Double check the property that the mismatched output found dimension and
    // its successor together evenly multiply to the input dimension they
    // mismatched on.
    if (found && resultDims[params.kernel] * resultDims[params.kernel + 1] ==
                     inputDims[params.kernel]) {
      resM = params;
    }
  
    return resM;
  }
  
  // Fold Reshape->Transpose->Reshape into ChannelShuffle when applicable.
  bool FoldChannelShuffle::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
  
    bool changed = false;
    auto &nodes = F->getNodes();
    for (auto &node : nodes) {
      auto *RN2 = dyn_cast<ReshapeNode>(&node);
      if (!RN2) {
        continue;
      }
  
      auto *TR = dyn_cast<TransposeNode>(RN2->getInput());
      if (!TR) {
        continue;
      }
  
      auto *RN1 = dyn_cast<ReshapeNode>(TR->getInput());
      if (!RN1) {
        continue;
      }
  
      // Check that the input and output shapes match:
      if (RN1->getInput().getType() != RN2->getResult().getType()) {
        continue;
      }
  
      // Compute the original parameters to ChannelShuffle.
      auto paramsM = getChannelShuffleParams(*RN1);
      if (!paramsM.hasValue()) {
        continue;
      }
  
      // Create a new ChannelShuffle with kernel parameter tranposed by the
      // TR's shuffle.
      auto *newCS = F->createChannelShuffle("channel_shuffle", RN1->getInput(),
                                            paramsM->group, paramsM->kernel);
      RN2->getResult().replaceAllUsesOfWith(newCS);
      changed = true;
    }
    return changed;
  }
  
  // Fold Tile -> Add into BatchedAdd wherever applicable.
  bool FoldTileAddIntoBatchedAdd::run(Function *F,
                                      const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
  
    bool changed = false;
    for (const auto &node : F->getNodes()) {
      const auto *addNode = dyn_cast<AddNode>(&node);
      if (!addNode) {
        continue;
      }
  
      NodeValue batchNode, addedNode;
      const auto &LHS = addNode->getLHS();
      const auto &RHS = addNode->getRHS();
      const TileNode *tileNode = nullptr;
  
      // Check if LHS is a tile.
      if ((tileNode = dyn_cast<TileNode>(LHS))) {
        batchNode = RHS;
        addedNode = tileNode->getInput();
      }
      // Check if RHS is a tile.
      else if ((tileNode = dyn_cast<TileNode>(RHS))) {
        batchNode = LHS;
        addedNode = tileNode->getInput();
      }
      // If neither LHS or RHS is a tile, nothing to do.
      else {
        continue;
      }
  
      // If the tiling of the added node is not along the 0th axis,
      // 'Add' cannot be replaced with 'BatchedAdd'.
      if (tileNode->getAxis() != 0) {
        continue;
      }
  
      auto oldDims = addedNode.dims();
      // If the 0th dimension of the added node is not 1,
      // then reducing dimension via reshaping is more complicated.
      // Hence, Add will not be replaced with BatchedAdd.
      if (oldDims.size() == 0 || oldDims[0] != 1) {
        continue;
      }
  
      // Reshape the added node to create a slice for the batched add
      // such that its dim size is one less than that of the batch.
      const auto newDims = oldDims.take_back(oldDims.size() - 1);
      auto *slice = F->createReshape(tileNode->getName().str() + "_reshape",
                                     addedNode, newDims);
  
      // Create a new batched add node to replace existing add node.
      auto *newBA = F->createBatchedAdd(addNode->getName().str() + "_batched_add",
                                        batchNode, slice);
      addNode->getResult().replaceAllUsesOfWith(newBA);
      changed = true;
    }
    return changed;
  }
  
  /// Raise ClipNodes above shaping ops, e.g. Reshape, Transpose, Slice. Other
  /// passes will sink Clips to try to eliminate redundant ones. This pass should
  /// happen after sinking of Clips in order to try to get Clips to directly
  /// consume compute Nodes outputs.
  bool RaiseClipsAboveShapeNodes::run(Function *F,
                                      const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
    bool changed = false;
  
    // Keep track of what nodes will have oneLessUser due to DCE eventually. As an
    // example, we know that after we replace all users of OrigSlice with NewClip,
    // then OrigSlice and OrigClip are dead, and so Input1 will have one less user
    // after DCE.
    //  Input                  Input
    //    |                    /    \
    //  OrigSlice        NewClip   OrigSlice  <--|
    //    |        -->      |         |          |-- (These two are now dead.)
    //  OrigClip         NewSlice   OrigClip  <--|
    //    |                 |
    //  Save              Save
    std::unordered_set<Node *> oneLessUser;
  
    for (auto &N : F->getNodes()) {
      ClipNode *CN = dyn_cast<ClipNode>(&N);
      if (!CN) {
        continue;
      }
  
      // If the Clip's input has multiple users then do not raise the Clip, as
      // otherwise this will impact other Nodes. We subtract off an extra user
      // here if we know one user will be eliminated due to DCE eventually (see
      // above pic).
      unsigned numUsers = CN->getInput().getNode()->getNumUsers();
      if (oneLessUser.count(CN->getInput().getNode())) {
        numUsers -= 1;
      }
      if (numUsers != 1) {
        continue;
      }
  
      // Sink Reshape below Clip.
      if (ReshapeNode *RN = dyn_cast<ReshapeNode>(CN->getInput())) {
        ClipNode *newCN = F->createClip(CN->getName(), RN->getInput(),
                                        CN->getMin(), CN->getMax());
        ReshapeNode *newRN = F->createReshape(RN->getName(), newCN->getResult(),
                                              RN->getDims(), RN->getLayout());
        CN->getResult().replaceAllUsesOfWith(newRN->getResult());
        oneLessUser.insert(RN->getInput().getNode());
        changed = true;
        continue;
      }
  
      // Sink Transpose below Clip.
      if (TransposeNode *TN = dyn_cast<TransposeNode>(CN->getInput())) {
        ClipNode *newCN = F->createClip(CN->getName(), TN->getInput(),
                                        CN->getMin(), CN->getMax());
        TransposeNode *newTN = F->createTranspose(
            TN->getName(), newCN->getResult(), TN->getShuffle(), TN->getLayout());
        CN->getResult().replaceAllUsesOfWith(newTN->getResult());
        oneLessUser.insert(TN->getInput().getNode());
        changed = true;
        continue;
      }
  
      // Sink Slice below Clip.
      if (SliceNode *SN = dyn_cast<SliceNode>(CN->getInput())) {
        ClipNode *newCN = F->createClip(CN->getName(), SN->getInput(),
                                        CN->getMin(), CN->getMax());
        SliceNode *newSN =
            F->createSlice(SN->getName(), newCN->getResult(), SN->getStart(),
                           SN->getResult().getType());
        CN->getResult().replaceAllUsesOfWith(newSN->getResult());
        oneLessUser.insert(SN->getInput().getNode());
        changed = true;
        continue;
      }
    } // For all nodes in the graph.
  
    return changed;
  }
  
  /// Fold ElemKind conversion nodes (ConvertTo, Quantize) into
  /// single-user Placeholders. Note that this changes the semantics
  /// of the IO of the Function and so must be done carefully, i.e. should always
  /// be opt-in and done alongside conversion of corresponding Tensors in
  /// PlaceholderBindings. If
  /// cctx.optimizationOpts.foldStaticPlaceholderConversions is set this will
  /// only change Placeholders marked as static.
  bool FoldElemKindConversionIntoInputs::run(Function *F,
                                             const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
  
    bool changed = false;
    auto &nodes = F->getNodes();
  
    for (auto it = nodes.begin(), e = nodes.end(); it != e; it++) {
      Node *N = &*it;
      // Handle conversion of inputs (conversion of Placeholders):
      ConvertToNode *CTN = llvm::dyn_cast<ConvertToNode>(N);
      QuantizeNode *QN = llvm::dyn_cast<QuantizeNode>(N);
      if (CTN || QN) {
        NodeValue in = CTN ? CTN->getInput() : QN->getInput();
        Placeholder *P = llvm::dyn_cast<Placeholder>(in);
        if (!P || P->getUsers().size() != 1) {
          continue;
        }
        // If foldElemKindConversionIntoIO is not set and this is not a static
        // placeholder then skip.
        if (!cctx.optimizationOpts.foldElemKindConversionIntoIO &&
            !P->isStatic()) {
          continue;
        }
  
        // We have a conversion of a single-use placeholder to some other type, so
        // it is safe to do the requested conversion.
        NodeValue res = CTN ? CTN->getResult() : QN->getResult();
  
        // Convert the type of the Placeholder to the conversion type. If target
        // type is fused call setTypeUnsafe because the shape can change in this
        // case.
        if (isFusedQuantizedElemKind(res.getElementType())) {
          P->setTypeUnsafe(Storage::OutputIdx, res.getType());
        } else {
          P->setType(Storage::OutputIdx, res.getType());
        }
  
        // Replace all uses of the original ConvertTo to the Placeholder.
        res.replaceAllUsesOfWith(P);
  
        changed = true;
        continue;
      }
    }
    return changed;
  }
  
  /// Fold ElemKind conversion nodes (ConvertTo, Dequantize) into SaveNodes. Note
  /// that this changes the semantics of the IO of the Function and so must be
  /// done carefully, i.e. should always be opt-in and done alongside conversion
  /// of corresponding Tensors in PlaceholderBindings.
  bool FoldElemKindConversionIntoOutputs::run(Function *F,
                                              const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
  
    std::unordered_set<SaveNode *> deadSaves;
  
    bool changed = false;
    // Since we will be adding in new SaveNodes, reverse iterate to be safe.
    auto &nodes = F->getNodes();
    for (auto it = nodes.rbegin(), e = nodes.rend(); it != e; it++) {
      Node *N = &*it;
  
      // Handle conversion of outputs (SaveNodes + Placeholders):
      if (SaveNode *SN = llvm::dyn_cast<SaveNode>(N)) {
        if (!SN) {
          continue;
        }
        if (SN->getPlaceholder()->getUsers().size() != 1) {
          continue;
        }
        ConvertToNode *CTN = llvm::dyn_cast<ConvertToNode>(SN->getInput());
        DequantizeNode *DQN = llvm::dyn_cast<DequantizeNode>(SN->getInput());
        if (!CTN && !DQN) {
          continue;
        }
        NodeValue in = CTN ? CTN->getInput() : DQN->getInput();
  
        // Set the type of the Placeholder to be same the conversion's input.
        SN->getPlaceholder()->setType(Storage::OutputIdx, in.getType());
  
        // Create a new SaveNode directly using the conversion's input.
        F->createSave(SN->getName(), in, SN->getPlaceholder());
  
        // Queue up deleting the original SaveNode as it won't be deleted via DCE.
        deadSaves.insert(SN);
        changed = true;
        continue;
      }
    }
  
    // Delete all the dead saves.
    for (SaveNode *SN : deadSaves) {
      F->eraseNode(SN);
    }
  
    return changed;
  }
  
  /// Broadcasts are implemented via 1) Reshape followed by a series of Tiles 2)
  /// BroadcastNode. This helper unwinds Broadcast operation -- it \returns the
  /// original Node before the broadcasting \p N if the broadcast takes place
  /// between the 0th dimension to \p endDim. Otherwise, \p return \p N.
  static NodeValue unwindBroadcast(NodeValue N, unsigned_t endDim) {
    if (auto *BN = dyn_cast<BroadcastNode>(N)) {
      const auto newShape = BN->getTargetDim();
      const auto axis = BN->getAxis();
      const auto &origDims = BN->getInput().dims();
  
      if (origDims.size() + axis != newShape.size()) {
        return N;
      }
  
      for (dim_t i = endDim; i < newShape.size(); i++) {
        if (!(i >= axis && origDims[i - axis] == newShape[i])) {
          return N;
        }
      }
  
      return BN->getInput();
    }
  
    // All non-BroadcastNode broadcasts must Tile at least once.
    if (!isa<TileNode>(N)) {
      return N;
    }
  
    while (TileNode *TN = dyn_cast<TileNode>(N)) {
      // Check that the axis of the current Tile is inside of the expected
      // provided endDim.
      if (TN->getAxis() >= endDim) {
        return N;
      }
      // Applicable only if original dim is 1 in the Broadcast's Tile.
      if (TN->getInput().dims()[TN->getAxis()] != 1) {
        return N;
      }
      N = TN->getInput();
    }
    if (ReshapeNode *RN = dyn_cast<ReshapeNode>(N)) {
      return RN->getInput();
    }
  
    return N;
  }
  
  /// Looks for supported arithmetic ops following LayerNorm and folds them into
  /// the scale/bias of the LayerNorm if the scale/bias are single-use
  /// Splat/Constant, as we can then later on constant fold them in.
  bool FoldLayerNormArithmetic::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
  
    bool changed = false;
    for (auto &N : F->getNodes()) {
      if (!isa<MulNode>(&N) && !isa<AddNode>(&N)) {
        continue;
      }
      // Currently only support floating point, as otherwise there will be
      // quantization parameter mismatches.
      if (!isFloatElemKind(N.getElementType(ArithmeticNode::ResultIdx))) {
        continue;
      }
  
      // Check if the Mul/Add is consuming an LN.
      LayerNormalizationNode *LN =
          dyn_cast<LayerNormalizationNode>(N.getNthInput(ArithmeticNode::LHSIdx));
      if (!LN) {
        continue;
      }
  
      // Check if the RHS is a Splat, or Constant, or temp PH (to be Constant
      // later). It may have been broadcasted to the correct shape.
      NodeValue RHS = unwindBroadcast(N.getNthInput(ArithmeticNode::RHSIdx),
                                      LN->getResult().dims().size() -
                                          LN->getScale().dims().size());
  
      auto *P = dyn_cast<Placeholder>(RHS);
      if (!isa<SplatNode>(RHS) && !isa<Constant>(RHS) &&
          !(P && cctx.optimizationOpts.tempPHsForConstants.count(P))) {
        continue;
      }
  
      // Make sure the RHS that we want to merge into the LN's Scale/Bias have the
      // same number of elements, since we're about to reshape them to match.
      if (RHS.getType()->size() != LN->getScale().getType()->size()) {
        continue;
      }
  
      // RHS may have already been fused with a Reshape to get ready for
      // Tiling. Reshape RHS back here if necessary.
      if (RHS.dims() != LN->getScale().dims()) {
        RHS = F->createReshape(RHS.getNode()->getName().str() + "_squeezed", RHS,
                               LN->getScale().dims());
      }
  
      if (MulNode *MN = dyn_cast<MulNode>(&N)) {
        // Merge the Mul into a new Scale and Bias, multiplying by the original
        // Mul RHS that followed the LayerNorm.
        MulNode *newScale =
            F->createMul(LN->getScale().getNode()->getName().str() + "_fuse_" +
                             MN->getName().data(),
                         LN->getScale(), RHS);
        MulNode *newBias = F->createMul(LN->getBias().getNode()->getName().str() +
                                            "_fuse_" + MN->getName().data(),
                                        LN->getBias(), RHS);
        LN->getScale().replaceAllUsesOfWith(newScale->getResult(), F, newScale);
        LN->getBias().replaceAllUsesOfWith(newBias->getResult(), F, newBias);
        MN->getResult().replaceAllUsesOfWith(LN->getResult());
        changed = true;
        continue;
      }
  
      if (AddNode *AN = dyn_cast<AddNode>(&N)) {
        // Merge the Add into a new Bias, adding the original Add RHS that
        // followed the LayerNorm.
        AddNode *newBias = F->createAdd(LN->getBias().getNode()->getName().str() +
                                            "_fuse_" + AN->getName().data(),
                                        LN->getBias(), RHS);
        LN->getBias().replaceAllUsesOfWith(newBias->getResult(), F, newBias);
        AN->getResult().replaceAllUsesOfWith(LN->getResult());
        changed = true;
        continue;
      }
    }
  
    return changed;
  }
  
  /// Looks for an activation directly following \p N from \p F that the backend
  /// \p B supports for fusion.
  template <class T> bool fuseActivation(T *N, Function *F, const Backend *B) {
    if (!N || N->hasFusedActivation() || !N->getResult().hasOneUse()) {
      return false;
    }
  
    // We know there is one result user so we can just deref the first result.
    Node *activation = (*N->getResult().getUsers().begin()).getUser();
    if (!B || !B->supportsFusedActivation(N, activation)) {
      return false;
    }
  
    NodeValue activationNV;
    switch (activation->getKind()) {
    case Kinded::Kind::ReluNodeKind:
      activationNV = cast<ReluNode>(activation)->getResult();
      N->setFusedActivation(FusedActivation::RELU);
      break;
    case Kinded::Kind::ClipNodeKind:
      activationNV = cast<ClipNode>(activation)->getResult();
      N->setFusedActivation(FusedActivation::CLIP);
      N->setFusedActivationArgs({cast<ClipNode>(activation)->getMin(),
                                 cast<ClipNode>(activation)->getMax()});
      break;
    case Kinded::Kind::SigmoidNodeKind:
      activationNV = cast<SigmoidNode>(activation)->getResult();
      N->setFusedActivation(FusedActivation::SIGMOID);
      break;
    case Kinded::Kind::TanhNodeKind:
      activationNV = cast<TanhNode>(activation)->getResult();
      N->setFusedActivation(FusedActivation::TANH);
      break;
    case Kinded::Kind::LeakyReluNodeKind:
      activationNV = cast<LeakyReluNode>(activation)->getResult();
      N->setFusedActivation(FusedActivation::LEAKY_RELU);
      N->setFusedActivationArgs({cast<LeakyReluNode>(activation)->getAlpha()});
      break;
    default:
      return false;
    }
  
    // Modify the node output type to that of the activation.
    if (!(activationNV.getType()->isEqual(N->getResult().getType()))) {
      N->getResult().setType(activationNV.getType());
    }
  
    activationNV.replaceAllUsesOfWith(N->getResult());
    return true;
  }
  
  static bool foldActivations(Function *F, const CompilationContext &cctx,
                              const Backend *B) {
    bool changed = false;
    for (auto &node : F->getNodes()) {
      if (fuseActivation(dyn_cast<ConvolutionNode>(&node), F, B)) {
        changed = true;
        continue;
      }
      if (fuseActivation(dyn_cast<ChannelwiseQuantizedConvolutionNode>(&node), F,
                         B)) {
        changed = true;
        continue;
      }
    }
    return changed;
  }
  
  void glow::fold(Function *F, const CompilationContext &cctx, const Backend *B) {
    LOG_SCOPE(F->getLogContext(), "glow::fold")
  
    FunctionPassManager FPM("FoldFPM", createDefaultFoldPassPipeline());
    FPM.run(F, cctx);
  
    foldActivations(F, cctx, B);
  }
  
  void glow::optimize(Function *F, const CompilationContext &cctx,
                      const Backend &B) {
    LOG_SCOPE(F->getLogContext(), "glow::optimize")
  
    FunctionPassManager FPM("TargetDependentGraphOptzFPM",
                            B.getOptimizationPipeline(), &B);
    FPM.run(F, cctx);
  }
  
  void glow::optimize(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), "glow::optimize")
  
    // Indicates if the given function is completely loaded. A temporary
    // workaround until #3213 is complete.
    F->setState(FunctionState::FuncLoaded);
  
    FunctionPassManager FPM("TargetIndependentGraphOptzFPM",
                            createDefaultGraphOptimizationPassPipeline());
    FPM.run(F, cctx);
  }
  
  void glow::optimize(Function *F, CompilationMode mode) {
    CompilationContext cctx;
    cctx.compMode = mode;
    optimize(F, cctx);
  }
  
  /// Helper to pass over all Nodes in \p F and set FP16 accumulation to true for
  /// those Nodes in the SLS family which support and need it. \p precConfig
  /// contains the black/whitelist for skipping.
  static void setFP16AccumSLS(Function *F,
                              const PrecisionConfiguration &precConfig) {
    // Iterate from original end to beginning to avoid processing new Nodes added
    // during the pass.
    auto nodeIt = F->getNodes().end();
    auto stopIt = F->getNodes().begin();
    do {
      --nodeIt;
      Node &node = *nodeIt;
      // Only update allowed nodes based on black/whitelist.
      const bool inSet = precConfig.precisionModeKindSet.count(node.getKind());
      const bool allowConversion = precConfig.useSetAsWhitelist ? inSet : !inSet;
      if (!allowConversion) {
        continue;
      }
  
  #define CASE_SET_SLS_FP16_ACCUM(NODE_)                                         \
    case Kinded::Kind::NODE_##NodeKind: {                                        \
      NODE_##Node *SLS = llvm::cast<NODE_##Node>(&node);                         \
      if (SLS->getResult().getElementType() != ElemKind::Float16Ty) {            \
        continue;                                                                \
      }                                                                          \
      SLS->setUseFP16Accumulation(true);                                         \
      continue;                                                                  \
    }
  
      switch (node.getKind()) {
        CASE_SET_SLS_FP16_ACCUM(RowwiseQuantizedSparseLengthsWeightedSum);
        CASE_SET_SLS_FP16_ACCUM(FusedRowwiseQuantizedSparseLengthsWeightedSum);
        CASE_SET_SLS_FP16_ACCUM(FusedRowwiseQuantizedSparseLengthsSum);
        CASE_SET_SLS_FP16_ACCUM(EmbeddingBagByteRowwiseOffsets);
      default:
        continue;
      }
    } while (nodeIt != stopIt);
  }
  
  /// Look for Dequantize -> Swish -> Quantize, replace it with a quantized Swish.
  bool QuantizeSwish::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
  
    if (!cctx.optimizationOpts.enableQuantParamChanges) {
      return false;
    }
  
    bool changed = false;
    for (auto &N : F->getNodes()) {
      auto *SN = dyn_cast<SwishNode>(&N);
      if (!SN || SN->getNumUsers() != 1) {
        continue;
      }
  
      QuantizeNode *QN =
          dyn_cast<QuantizeNode>((*SN->getUsers().begin()).getUser());
      if (!QN) {
        continue;
      }
  
      DequantizeNode *DN = dyn_cast<DequantizeNode>(SN->getInput());
      if (!DN) {
        continue;
      }
  
      SwishNode *newSN =
          F->createSwish(SN->getName().str() + "_int", DN->getInput(),
                         QN->getResult().getType());
      QN->getResult().replaceAllUsesOfWith(newSN);
      changed = true;
    }
    return changed;
  }
  
  /// Convert a FullyConnected node to a 1x1 Convolution.
  bool ConvertFullyConnectedToConvolution::run(Function *F,
                                               const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
  
    bool changed = false;
    for (auto &N : F->getNodes()) {
      auto *FCN = dyn_cast<FullyConnectedNode>(&N);
      if (!FCN) {
        continue;
      }
  
      NodeValue output = FCN->getResult();
      NodeValue input = FCN->getInput();
      NodeValue filter = FCN->getWeights();
      NodeValue bias = FCN->getBias();
  
      // Reshape input from 2D to 4D.
      auto inpDims = ShapeHW(input.getType()->dims());
      std::vector<dim_t> inpDimsCN = {inpDims.height, 1, 1, inpDims.width};
      input = F->createReshape(FCN->getName(), input, inpDimsCN);
  
      // Transpose filter and reshape from 2D to 4D.
      filter = F->createTranspose(FCN->getName(), filter, {1, 0});
      auto filterDims = ShapeHW(filter.getType()->dims());
      std::vector<dim_t> filterDimsCN = {filterDims.height, 1, 1,
                                         filterDims.width};
      filter = F->createReshape(FCN->getName(), filter, filterDimsCN);
  
      // Create Conv2D node with same output type but 4D shape.
      auto outDims = ShapeHW(output.getType()->dims());
      std::vector<dim_t> outDimsCN = {outDims.height, 1, 1, outDims.width};
      auto outTyCN =
          F->getParent()->uniqueTypeWithNewShape(output.getType(), outDimsCN);
      NodeValue outputCN =
          F->createConv(FCN->getName(), input, filter, bias, outTyCN,
                        /* kernels */ {1, 1},
                        /* strides */ {1, 1},
                        /* pads */ {0, 0, 0, 0},
                        /* group */ 1);
  
      // Reshape the 4D output back to its original 2D shape.
      outputCN =
          F->createReshape(FCN->getName(), outputCN, output.getType()->dims());
      FCN->getResult().replaceAllUsesOfWith(outputCN);
      changed = true;
    }
    return changed;
  }
  
  /// Fold Min -> Max or Max -> Min to Clip
  /// We need to place this function after `OptimizeArithmeticNodes` in which the
  /// constant operator in commutative nodes is moved to the RHS, so that we can
  /// assume SplatNode is on the RHS of MinNode (MaxNode).
  /// Only if it's the following structure we do this optimization
  ///
  ///   someOtherInput  SplatNode
  ///              \     /
  ///  SplatNode   MinNode (MaxNode)
  ///         \     /
  ///         MaxNode (MinNode)
  ///            |
  ///           Out
  ///
  /// ClipNode's min will be the SplatNode (maxSN) connected to the MaxNode.
  /// ClipNode's max will be the SplatNode (minSN) connected to the MinNode.
  bool FoldMinMaxToClip::run(Function *F, const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
  
    bool changed = false;
    for (auto &N : F->getNodes()) {
      MaxNode *maxNode = dyn_cast<MaxNode>(&N);
      MinNode *minNode = dyn_cast<MinNode>(&N);
      NodeValue otherInput;
      NodeValue resultNV;
      SplatNode *minSN = nullptr;
      SplatNode *maxSN = nullptr;
  
      // We assume SplatNode is on the RHS.
      // If currect node is MinNode
      //  - try casting the input to MaxNode and SplatNode.
      //  - If LHS of MinNode is MaxNode
      //    - try casting RHS of maxNode to SplatNode.
      if (minNode) {
        resultNV = minNode->getResult();
        maxNode = dyn_cast<MaxNode>(
            unwindBroadcast(minNode->getLHS(), minNode->getLHS().dims().size()));
        minSN = dyn_cast<SplatNode>(
            unwindBroadcast(minNode->getRHS(), minNode->getRHS().dims().size()));
  
        if (maxNode) {
          maxSN = dyn_cast<SplatNode>(unwindBroadcast(
              maxNode->getRHS(), maxNode->getRHS().dims().size()));
          otherInput =
              unwindBroadcast(maxNode->getLHS(), maxNode->getLHS().dims().size());
        }
      } else if (maxNode) { // vice versa for MaxNode
        resultNV = maxNode->getResult();
        minNode = dyn_cast<MinNode>(
            unwindBroadcast(maxNode->getLHS(), maxNode->getLHS().dims().size()));
        maxSN = dyn_cast<SplatNode>(
            unwindBroadcast(maxNode->getRHS(), maxNode->getRHS().dims().size()));
  
        if (minNode) {
          minSN = dyn_cast<SplatNode>(unwindBroadcast(
              minNode->getRHS(), minNode->getRHS().dims().size()));
          otherInput =
              unwindBroadcast(minNode->getLHS(), minNode->getLHS().dims().size());
        }
      }
  
      // If any of these is nullptr, it means the structure is not the same as
      // above example.
      if (!(minNode && maxNode && minSN && maxSN)) {
        continue;
      }
  
      // If minSN is smaller than maxSN, which is really weird because every entry
      // of the result will be a same number. In this case, we don't fold them.
      if (minSN->getValue() < maxSN->getValue()) {
        DLOG(INFO) << "Catch a combination of MinNode and MaxNode while MinSplat "
                      "is smaller than MaxSplat, which would make all entries of "
                      "the result tensor to be the same!";
        continue;
      }
  
      // The second node is broadcasted and we need to broadcast the otherInput.
      if (otherInput.dims() != resultNV.dims()) {
        otherInput = F->createBroadcast(
            otherInput.getNode()->getName().str() + ".broadcast", otherInput,
            resultNV.dims(), resultNV.dims().size() - otherInput.dims().size());
      }
  
      // MinSN is the SplatNode input of MinNode while MaxSN is the SplatNode
      // input of MaxNode. MinSN should be greater than MaxSN so we put MaxSN as
      // the min of ClipNode.
      auto minValue = maxSN->getValue();
      auto maxValue = minSN->getValue();
      ClipNode *CN = F->createClip(resultNV.getNode()->getName(), otherInput,
                                   resultNV.getType(),
                                   /* min */ minValue, /* max */ maxValue);
      resultNV.replaceAllUsesOfWith(CN->getResult());
      changed = true;
    }
  
    return changed;
  }
  
  /// Look for qparams with scale (when casted to fp16) == 0 and replace them with
  /// zero Splats.
  bool ReplaceZeroScaleFP16QuantNodes::run(Function *F,
                                           const CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), getName());
  
    // Cannot run this opt if we're using dummy qparams.
    if (cctx.precisionConfig.loadUniquedDummyQParams) {
      return false;
    }
  
    auto processNV = [](Function *F, NodeValue resNV) {
      const TypeRef resTy = resNV.getType();
      if (resTy->isFusedQuantizedType() || !resTy->isQuantizedType()) {
        return false;
      }
  
      // Check if we have a scale that's below the minimum allowed FP16 val.
      if (resTy->getScale() >= kMinScaleFP16) {
        return false;
      }
  
      // Skip if used by node with side effects, since we cannot change the
      // qparams in such cases.
      if (isUsedByNodeWithSideEffects(resNV.getNode())) {
        return false;
      }
  
      // This NodeValue has scale = 0.f, which means the equivalent float result
      // will always be equal to 0.f. So create a splat with value 0.f, scale 1.f,
      // offset 0 instead.
      auto splatTy = F->getParent()->uniqueType(resTy->getElementType(),
                                                resTy->dims(), 1.f, 0);
      auto *SN = F->createSplat(resNV.getNode()->getName().str() + ".splatted",
                                splatTy, 0.f);
  
      // Note: must use type unsafe replace because we've changed the qparams.
      resNV.typeUnsafeReplaceAllUsesOfWith(SN->getResult(), F);
      return true;
    };
  
    bool changed = false;
    // Since we will be adding in new SplatNodes, reverse iterate to be safe.
    auto &nodes = F->getNodes();
    for (auto it = nodes.rbegin(), e = nodes.rend(); it != e; it++) {
      Node *N = &*it;
      // For now only support nodes with single outputs.
      if (N->getNumResults() != 1) {
        continue;
      }
      NodeValue resNV = N->getNthResult(0);
      changed |= processNV(F, resNV);
    }
    for (Placeholder *PH : F->findPlaceholders()) {
      changed |= processNV(F, PH->getOutput());
    }
    for (Constant *C : F->findConstants()) {
      changed |= processNV(F, C->getOutput());
    }
  
    return changed;
  }
  
  /// This funciton uses TypeAToTypeBFunctionConverter to do a whole graph
  /// demotion of Index type from INT64 to INT32.
  static void transformIndexTypeDemotion(const Backend &B, Function *F,
                                         CompilationContext &cctx) {
  
    // Does a coarse  check to make sure none of the indices potentially can
    // overflow 32 bit. For now we just give up on the whole optimization, since
    // this is probably a corner case.
    for (auto &n : F->getNodes()) {
      for (int i = 0, nOutputs = n.getNumResults(); i < nOutputs; ++i) {
        if (n.getNthResult(i).getType()->actualSize() >=
            size_t(std::numeric_limits<int32_t>::max())) {
          return;
        }
      }
    }
  
    PrecisionConfiguration precConfig;
    if (B.canDoIndexTypeDemotion(ElemKind::Int64ITy, ElemKind::Int32ITy,
                                 precConfig) &&
        cctx.optimizationOpts.enableTypeDemotion) {
      precConfig.precisionModeKindSet.insert(Kinded::Kind::TraceEventNodeKind);
      TypeAToTypeBFunctionConverter converter(*F, ElemKind::Int64ITy,
                                              ElemKind::Int32ITy, precConfig);
      converter.convert();
    }
  }
  
  void glow::transformForPrecisionMode(const Backend &B, Function *F,
                                       CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), "transformForPrecisionMode")
    const PrecisionConfiguration &precConfig = cctx.precisionConfig;
  
    switch (precConfig.quantMode) {
    case QuantizationMode::Profile: {
      assert(cctx.bindings);
  
      LOG_SCOPE(F->getLogContext(), "glow::profileQuantization")
  
      glow::profileQuantization(*cctx.bindings, F, precConfig.profConfig);
      break;
    }
  
    case QuantizationMode::Quantize: {
      LOG_SCOPE(F->getLogContext(), "quantization::quantizeFunction")
  
      quantization::quantizeFunction(F, precConfig.quantConfig, B,
                                     *cctx.loweredInfoMap,
                                     precConfig.precisionModeKindSet);
      break;
    }
  
    case QuantizationMode::None: {
      break;
    }
    }
  
    if (precConfig.convertToFP16) {
      LOG_SCOPE(F->getLogContext(), "glow::convertFunctionToFloat16")
      convertFunctionToFloat16(F, precConfig);
      FunctionPassManager FPM("FP16GraphOptzFPM",
                              createFP16GraphOptimizationPassPipeline());
      FPM.run(F, cctx);
    }
  
    // By default, FP16 SLS accumulation is not enabled.
    // If requested, Force all ops in the SLS family to use FP16 accumulation.
    if (precConfig.forceFP16AccumSLS) {
      setFP16AccumSLS(F, precConfig);
    }
  
    // Convert UInt4FusedFP16QTy/UInt8FusedFP16QTy to UInt8FusedQTy.
    if (precConfig.convert4BitFusedToFP32 || precConfig.convert8BitFusedToFP32) {
      LOG_SCOPE(F->getLogContext(), "glow::convertFunctionToFP32ScaleOffset");
      convertFunctionToFP32ScaleOffset(F, precConfig);
    }
  
    // In FusedRowwiseQSLWS, convert its indices from Int32(if there is any) to
    // Int64.
    if (precConfig.convertIndicesToInt64) {
      LOG_SCOPE(F->getLogContext(), "glow::convertFunctionIndicesToInt64");
      convertFunctionIndicesToInt64(F, precConfig);
    }
  }
  
  Error glow::optimizeFunctionBeforeLowering(Function *F,
                                             CompilationContext &cctx) {
    LOG_SCOPE(F->getLogContext(), "glow::optimizeFunctionBeforeLowering")
  
    // If we only want to lower the Function, do nothing here.
    if (cctx.optimizationOpts.onlyLowerFuns.count(F)) {
      return Error::success();
    }
  
    // Verify the function pre-optimization/lowering.
    assert(F->verify() && "Function must be valid");
  
    // Verify that the CompilationContext is set up correctly.
    RETURN_IF_ERR(cctx.verify());
  
    // Fold low-level operators into higher-level operators.
    // This is useful when compiling an input model where some high-level
    // operators have been lowered (this can be for instance a side effect of
    // model converters, like converters from Tensorflow to ONNX). In this
    // situation, such folding can then enable more optimizations and also improve
    // the performance backends that support natively such high-level operators.
    ::glow::fold(F, cctx);
  
    // Optimize the graph. Only runs optimizations that are target-independent.
    ::glow::optimize(F, cctx);
    return Error::success();
  }
  
  /// Error message to print when there is a graph hash checking error.
  static const char *graphPreLowerHashCheckErrMsg =
      R"RAW(Graph hash mismatch!
  %s
  %s
  Potential causes:
  1. The profile YAML file was produced with an older version of the Glow tools
     while the quantization of the model is performed with a newer version.
  2. The profile YAML file was produced for a different model than the model used
     for quantization.
  3. The profile YAML file was produced for the same model but for a different
     batch size. If the profile was generated using the 'image-classifier' Glow
     tool you can select the batch size of the model during profiling using the
     'minibatch' option. During quantization you can choose the batch size of the
     model by choosing for each input placeholder the tensor size using the
     'model-input' option.
  )RAW";
  
  // NOTE: When updating this function, please also update the documentation in
  // docs/GraphOptimizationPipeline.md
  Error glow::optimizeFunction(Function *F, const Backend &B,
                               CompilationContext &cctx,
                               const glow::runtime::DeviceInfo *devInfo) {
    LOG_SCOPE(F->getLogContext(), "glow::optimizeFunction")
  
    // If requested only lower the Function and early return.
    if (cctx.optimizationOpts.onlyLowerFuns.count(F)) {
      ::glow::lower(F, cctx, &B);
      // Cleanup from lowering via DCE.
      runDCEPass(F, cctx);
  
      if (!B.verify(*F, cctx.verboseCompile)) {
        return MAKE_ERR(
            ErrorValue::ErrorCode::COMPILE_UNSUPPORTED_NODE_AFTER_OPTIMIZE,
            "Unsupported node(s) found after only-lowering path for Function " +
                F->getName().str() + " for backend " + B.getBackendName());
      }
      return Error::success();
    }
  
    RETURN_IF_ERR(optimizeFunctionBeforeLowering(F, cctx));
  
    // Graph hash check:
    // - During PROFILING store the hash of the graph at this point of the
    //   optimization pipeline. The hash will be exported in the YAML profile.
    // - During QUANTIZATION the hash imported from the YAML profile is used to
    //   verify the hash of the graph. This is helpful to catch mismatches between
    //   the graph used during profiling/quantization.
    const PrecisionConfiguration &precConfig = cctx.precisionConfig;
    if (precConfig.quantMode == QuantizationMode::Profile) {
      cctx.info.graphPreLowerHash = F->getHash();
    } else if (precConfig.quantMode == QuantizationMode::Quantize) {
      const auto &quantConfig = cctx.precisionConfig.quantConfig;
      if (quantConfig.checkGraphPreLowerHash) {
        auto profileHash = quantConfig.graphPreLowerHash;
        auto currentHash = F->getHash();
        auto profileHashStr =
            strFormat("Profile graph hash: 0x%" PRIX64, (uint64_t)(profileHash));
        auto currentHashStr =
            strFormat("Current graph hash: 0x%" PRIX64, (uint64_t)(currentHash));
        RETURN_ERR_IF_NOT(profileHash == currentHash,
                          strFormat(graphPreLowerHashCheckErrMsg,
                                    profileHashStr.c_str(),
                                    currentHashStr.c_str()));
      }
    }
  
    // Lower the graph into a sequence of low-level linear algebra operations.
    if (precConfig.quantMode == QuantizationMode::Profile) {
      // When profiling, pass a nullptr for the backend, signaling that all nodes
      // should be lowered. loweredInfoMap logs what is lowered from what for
      // later use when creating quantization infos. Also pass the precision mode
      // kind set as nodes to not lower, specified higher up in the stack.
      ::glow::lower(F, cctx, /* backend */ nullptr,
                    precConfig.precisionModeKindSet);
    } else {
      // Lower based on the backend's preferences.
      ::glow::lower(F, cctx, &B);
    }
  
    // Transforms the graph by demoting i64 to i32.
    transformIndexTypeDemotion(B, F, cctx);
  
    // Transform given precision mode; may quantize, convert to fp16, or
    // instrument with profiling nodes. This must be done after lowering.
    transformForPrecisionMode(B, F, cctx);
  
    // Optimize the quantized graph because quantization nodes should be optimized
    // before folding Activation into Conv.
    ::glow::optimize(F, cctx);
  
    // Fold activations before lowering to enable cases which would not fuse after
    // lowering. This concerns particularly convolution&relu since relu will be
    // lowered to max(0, x).
    foldActivations(F, cctx, &B);
  
    // Lower once more, in case precision transform has introduced operators that
    // need to be lowered, e.g., Clip.
    ::glow::lower(F, cctx, &B);
  
    // Optimize the graph again now that we have a lowered representation.
    ::glow::optimize(F, cctx);
  
    // If requested fold ElemKind conversion Nodes into static Placeholders,
    // inputs, and outputs (Placeholders and SaveNodes).
    if (cctx.optimizationOpts.foldStaticPlaceholderConversions ||
        cctx.optimizationOpts.foldElemKindConversionIntoIO) {
      std::unique_ptr<FunctionPassPipeline> pipeline =
          glow::make_unique<FunctionPassPipeline>();
      pipeline->pushBack({FunctionPassID::FoldElemKindConversionIntoInputs});
  
      if (cctx.optimizationOpts.foldElemKindConversionIntoIO) {
        pipeline->pushBack({FunctionPassID::FoldElemKindConversionIntoOutputs});
      }
      FunctionPassManager FPM("FoldElemKindConversionIntoIO",
                              std::move(pipeline));
      if (FPM.run(F, cctx)) {
        ::glow::optimize(F, cctx);
      }
    }
  
    if (B.shouldPreQuantizeConstants()) {
      // Do the actual float ->fix-point conversion of constant tensors before
      // Post-lowering.
      ::glow::convertQuantizedConstants(F, cctx);
    }
  
    // Allow the backend to transform the graph after lowering.
    RETURN_IF_EXPECTED_IS_ERR(B.transformPostLowering(F, cctx, devInfo));
  
    if (!B.shouldPreQuantizeConstants()) {
      // Do the actual float ->fix-point conversion of constant tensors after
      // Post-lowering.
      ::glow::convertQuantizedConstants(F, cctx);
    }
  
    // Optimize the graph again after the backend transformation.
    // In particular, DCE is very likely to be useful.
    ::glow::optimize(F, cctx, B);
  
    // We already started using backend specific verification when the function
    // state became lowered. Do one more verification pass to make sure everything
    // is in order and to bail if it is not.
    if (cctx.optimizationOpts.delayAndRecordConstantModification ||
        cctx.optimizationOpts.skipBackendSupportCheck) {
      // Only do verification without checking the backend's support if requested,
      // or if we are disallowing constant modification (since this flag may have
      // prevented a backend from supporting some Nodes, and may be supported
      // after constant folding finishes). Expect the caller to verify that Nodes
      // are supported by the backend later on in such cases.
      RETURN_ERR_IF_NOT(F->verify(&B),
                        "Verification after optimization failed for Function " +
                            F->getName().str() + " and Backend " +
                            B.getBackendName());
    } else if (!B.verify(*F, cctx.verboseCompile)) {
      return MAKE_ERR(
          ErrorValue::ErrorCode::COMPILE_UNSUPPORTED_NODE_AFTER_OPTIMIZE,
          "Unsupported node(s) found after optimizing Function " +
              F->getName().str() + " for backend " + B.getBackendName());
    }
    return Error::success();
  }
  
  bool glow::executeVerticalFCWeightsSplit(Function *F, unsigned numOfChunks,
                                           unsigned minKToSplit) {
    DCHECK(numOfChunks > 0) << "numOfChunks must be a positive number, given: "
                            << numOfChunks;
    DCHECK(minKToSplit > 0) << "minKToSplit must be a positive number, given: "
                            << minKToSplit;
  
    bool changed = false;
    for (auto it = F->getNodes().begin(), e = F->getNodes().end(); it != e;
         ++it) {
      auto *FC = dyn_cast<FullyConnectedNode>(it);
      if (!FC) {
        continue;
      }
  
      size_t K = FC->getWeights().dims()[1];
      if (K < minKToSplit) {
        continue;
      }
  
      auto input = FC->getInput();
      auto weights = FC->getWeights();
      auto bias = FC->getBias();
  
      dim_t elemPerChunk = (bias.dims()[0] + numOfChunks - 1) / numOfChunks;
      dim_t sliceStart = 0;
      std::vector<NodeValue> fcs(numOfChunks);
  
      // Split weights across second dimension into numOfChunks pieces.
      // Input dimension is [M;K] and kept untouched.
      // Bias dimension is [N], split into chunks.
      // Weight dimension is [K;N], split into numOfChunks chunks,
      // [K;N/numOfChunks] each.
      // Last chunk might require special handling in case
      // N is not divisible by numOfChunks.
      auto *fcType = F->getParent()->uniqueTypeWithNewShape(
          FC->getResult().getType(), {FC->getResult().dims()[0], elemPerChunk});
  
      for (unsigned i = 0; i < numOfChunks; ++i) {
        // Last chunk might need special handling if bias dimension
        // is not divisible by numOfChunks.
        if (i == numOfChunks - 1 && bias.dims()[0] % numOfChunks != 0) {
          elemPerChunk = bias.dims()[0] - (numOfChunks - 1) * elemPerChunk;
          fcType = F->getParent()->uniqueTypeWithNewShape(
              FC->getResult().getType(),
              {FC->getResult().dims()[0], elemPerChunk});
        }
  
        auto *weightSlice = F->createSlice(
            "weight_slice." + weights.getNode()->getName().str(), weights,
            {0, sliceStart}, {weights.dims()[0], sliceStart + elemPerChunk});
        auto *biasSlice =
            F->createSlice("bias_slice." + bias.getNode()->getName().str(), bias,
                           {sliceStart}, {sliceStart + elemPerChunk});
        fcs[i] = F->createFullyConnected("fc_slice." + FC->getName().str(), input,
                                         weightSlice->getResult(),
                                         biasSlice->getResult(), fcType);
        sliceStart += elemPerChunk;
      }
  
      auto *concat =
          F->createConcat("concat." + FC->getName().str(), fcs, /*dimension*/ 1);
      FC->getResult().replaceAllUsesOfWith(concat);
      changed = true;
    }
  
    return changed;
  }
  
  /// Helper to parallelize a node \p curNode from \p F into \p numOfChunksNode
  /// Nodes by slicing its inputs, creating clones of it and changing the inputs
  /// of the clones to the slices, and then concatenating all of the clones
  /// together and replacing \p curNode with the concat. \p inputBatchIdx is the
  /// input idx from \p curNode that will be split (there may be more than one
  /// input to split, but their splitDim should all have the same size).
  /// \p splitDim represents what dimension to split for each of the inputs to
  /// \p curNode. \p resultDim is the dimension on which we are splitting and then
  /// concatenating the results. \p resultIdx represents the result index from
  /// \p curNode that is being split and later concatenated. The size of the
  /// splits will be increased to a multiple of \p modelParallelSplitAlignment, if
  /// possible. If the result after aligning the splits is that the new aligned
  /// splits are larger than the original requested num splits, then the number of
  /// resulting splits may be less than requested. \returns an Expected of the
  /// ConcatNode that is created and replaces \p curNode, or otherwise an Error if
  /// parallelization had some issue.
  static Expected<ConcatNode *>
  parallelizeAndReplaceNode(Function *F, Node *curNode, dim_t numOfChunksNode,
                            dim_t inputBatchIdx, dim_t resultIdx,
                            llvm::ArrayRef<int> splitDims, size_t resultDim,
                            dim_t modelParallelSplitAlignment = 1) {
    const int inputIdx = splitDims[inputBatchIdx];
    CHECK_GE(inputIdx, 0) << "Input batch idx must be split";
    const dim_t batchSize = curNode->getNthInput(inputBatchIdx).dims()[inputIdx];
    const dim_t elemPerChunk = batchSize / numOfChunksNode;
    const dim_t remain = batchSize % numOfChunksNode;
    // This alignment will create aligned splits. So for example, if we're
    // splitting 190 by 3, then without alignment it would be {64, 63, 63}.
    // With alignment of 64, it will be {64, 64, 62}
    const dim_t alignedElemPerChunk =
        ((elemPerChunk + (modelParallelSplitAlignment - 1)) /
         modelParallelSplitAlignment) *
        modelParallelSplitAlignment;
  
    RETURN_ERR_IF_NOT(
        batchSize >= numOfChunksNode,
        "Invalid parallelization; batchSize " + std::to_string(batchSize) +
            " must be >= numOfChunksNode " + std::to_string(numOfChunksNode) +
            " for node " + curNode->getName().str() + " with kind " +
            curNode->getKindName());
  
    // Potentially modify numOfChunksNode, if the aligned size times current
    // numOfChunksNode exceeds the total size
    if (modelParallelSplitAlignment > 1) {
      numOfChunksNode =
          (batchSize + alignedElemPerChunk - 1) / alignedElemPerChunk;
    }
  
    std::vector<NodeValue> newNodes(numOfChunksNode);
    for (dim_t i = 0; i < numOfChunksNode; ++i) {
      // Calculate the out type of this chunk.
      dim_t sliceStart, sliceEnd;
  
      if (modelParallelSplitAlignment > 1) {
        // If we are using aligned splits, then slice by multiples of the
        // alignment, leaving the rest to the last split. The last split is
        // necessarily smaller than the other splits.
        sliceStart = i * alignedElemPerChunk;
        sliceEnd = (i < numOfChunksNode - 1) ? sliceStart + alignedElemPerChunk
                                             : batchSize;
      } else {
        // Otherwise, distribute elements evenly across the splits and sprinkle
        // the remainder evenly as well
        sliceStart = i * elemPerChunk + std::min(i, remain);
        sliceEnd = sliceStart + elemPerChunk + ((i < remain) ? 1 : 0);
      }
      VLOG(1) << "\tChunk " << i << ": start: " << sliceStart
              << " end: " << sliceEnd << "\n";
      auto outDims = curNode->dims(resultIdx).vec();
      outDims[resultDim] = (sliceEnd - sliceStart);
      for (auto outDim : outDims) {
        VLOG(1) << "outDim: " << outDim << "\n";
      }
  
      // Clone the original Node, so that it keeps all of the inputs/members of
      // the original Node. Then modify the output type so that its new shape is
      // correct, and below change the inputs the sliced inputs.
      Node *clone = curNode->clone();
      clone->getNthResult(resultIdx).setTypeUnsafe(
          F->getParent()->uniqueTypeWithNewShape(curNode->getType(resultIdx),
                                                 outDims));
      F->addNode(clone);
  
      // Loop over all of the inputs and slice those inputs that need to be
      // sliced, and set them on the clone.
      for (int j = 0, e = curNode->getNumInputs(); j < e; j++) {
        int dim = splitDims[j];
        if (dim == -1) {
          continue;
        }
  
        NodeValue currInput = curNode->getNthInput(j);
        auto sliceDimsStart = std::vector<dim_t>(currInput.dims().size(), 0);
        sliceDimsStart[dim] = sliceStart;
        auto sliceDimsEnd = currInput.dims().vec();
        sliceDimsEnd[dim] = sliceEnd;
        VLOG(1) << "start: ";
        for (auto sliceDimStart : sliceDimsStart) {
          VLOG(1) << sliceDimStart << "\n";
        }
        VLOG(1) << "end: ";
        for (auto sliceDimEnd : sliceDimsEnd) {
          VLOG(1) << sliceDimEnd << "\n";
        }
        VLOG(1) << "Input name: " << currInput.getNode()->getName().str() << "\n";
  
        auto *inputSlice =
            F->createSlice("dp_slice." + currInput.getNode()->getName().str() +
                               "." + std::to_string(i),
                           currInput, sliceDimsStart, sliceDimsEnd);
        clone->setNthInput(j, inputSlice);
  
        newNodes[i] = clone;
      }
    }
  
    // Now that we have split the node into many, concat all of the pieces back
    // together and replace the original by the concat.
    VLOG(1) << "Creating Concat";
    auto *concat = F->createConcat("concat." + curNode->getName().str(), newNodes,
                                   resultDim);
    curNode->getNthResult(resultIdx).replaceAllUsesOfWith(concat);
    return concat;
  }
  
  /// Specialized helper for parallelizing ConcatNode \p CN from \p F into
  /// \p numOfChunks.
  static Expected<ConcatNode *>
  parallelizeAndReplaceConcat(Function *F, ConcatNode *CN, dim_t numOfChunks) {
    auto in = CN->getInputs();
  
    const dim_t inputSize = in.size();
    const dim_t elemPerChunk = inputSize / numOfChunks;
    const dim_t remain = inputSize % numOfChunks;
  
    RETURN_ERR_IF_NOT(
        elemPerChunk > 0,
        "When parallelizing a Concat, inputSize must be larger than numOfChunks");
  
    auto startIt = in.begin();
    std::vector<NodeValue> finalConcatInputs;
    for (dim_t i = 0; i < numOfChunks; ++i) {
      const dim_t sliceStart = i * elemPerChunk + std::min(i, remain);
      const dim_t sliceEnd = sliceStart + elemPerChunk + ((i < remain) ? 1 : 0);
  
      // Slice out the original Concat chunk's inputs, create a new Concat with
      // the slice, and then add the new Concat to the final Concat's inputs.
      std::vector<NodeValue> newInputs(startIt + sliceStart, startIt + sliceEnd);
      ConcatNode *concatSlice = F->createConcat(CN->getName().str() + "_slice",
                                                newInputs, CN->getDim());
      finalConcatInputs.push_back(concatSlice);
    }
    ConcatNode *finalConcat = F->createConcat(CN->getName().str() + "_merge",
                                              finalConcatInputs, CN->getDim());
    CN->getResult().replaceAllUsesOfWith(finalConcat);
    return finalConcat;
  }
  
  #define SPLIT_ELTWISE_UNARY_OP_HELPER(NodeKind, Axis)                          \
    if (curNode->getNthInput(NodeKind##Node::InputIdx).dims().size() <= Axis) {  \
      break;                                                                     \
    }                                                                            \
    splitDims[NodeKind##Node::InputIdx] = Axis;                                  \
    ASSIGN_VALUE_OR_RETURN_ERR(                                                  \
        CN, parallelizeAndReplaceNode(                                           \
                F, curNode, curNumOfChunks, NodeKind##Node::InputIdx,            \
                NodeKind##Node::ResultIdx, splitDims, /*resultDim*/ Axis,        \
                modelParallelSplitAlignment));
  
  #define SPLIT_ELTWISE_BINARY_OP_HELPER(NodeKind, Axis)                         \
    if (curNode->getNthInput(NodeKind##Node::LHSIdx).dims().size() <= Axis) {    \
      break;                                                                     \
    }                                                                            \
    splitDims[NodeKind##Node::LHSIdx] = Axis;                                    \
    splitDims[NodeKind##Node::RHSIdx] = Axis;                                    \
    ASSIGN_VALUE_OR_RETURN_ERR(                                                  \
        CN, parallelizeAndReplaceNode(                                           \
                F, curNode, curNumOfChunks, NodeKind##Node::LHSIdx,              \
                NodeKind##Node::ResultIdx, splitDims, /*resultDim*/ Axis,        \
                modelParallelSplitAlignment));
  
  Expected<std::unordered_map<Node *, ConcatNode *>> glow::parallelizeOps(
      Function *F, const llvm::DenseMap<Node *, size_t> &numOfChunksMap,
      const llvm::DenseMap<Node *, ParallelTransformKind> &parOpts,
      size_t numOfChunks, size_t modelParallelSplitAlignment) {
    // Since we will be transforming the original list of nodes, reverse iterate.
    auto &nodes = F->getNodes();
    size_t numProcessedNodes = 0;
    std::unordered_map<Node *, ConcatNode *> replacedMap;
    for (auto it = nodes.rbegin(), e = nodes.rend(); it != e; it++) {
      Node *curNode = &*it;
      size_t curNumOfChunks = numOfChunks;
      auto numOfChunksIt = numOfChunksMap.find(curNode);
      if (numOfChunksIt != numOfChunksMap.end()) {
        curNumOfChunks = numOfChunksIt->second;
      }
  
      ParallelTransformKind parTransformMode = ParallelTransformKind::None;
      auto parOptsIt = parOpts.find(curNode);
      if (parOptsIt != parOpts.end()) {
        parTransformMode = parOptsIt->second;
        ++numProcessedNodes;
      }
  
      VLOG(1) << "Attempting to Parallelizing Node: " << curNode->getName().str()
              << "\n";
  
      ConcatNode *CN = nullptr;
  
      // Use this vector to communicate what dims to split to
      // parallelizeAndReplaceNode(). -1 represents not splitting at all.
      llvm::SmallVector<int, 3> splitDims(curNode->getNumInputs(), -1);
  
      // Set model parallelization axis
      // Default model parallelization is along axis = 1, hence the default value.
      dim_t modelParAxis = 1;
      switch (parTransformMode) {
  #define MODEL_AXIS_CASE(_N)                                                    \
    case ParallelTransformKind::Model_Axis##_N:                                  \
      modelParAxis = _N;                                                         \
      parTransformMode = ParallelTransformKind::Model;                           \
      break;
        MODEL_AXIS_CASE(1)
        MODEL_AXIS_CASE(2)
        MODEL_AXIS_CASE(3)
        MODEL_AXIS_CASE(4)
        MODEL_AXIS_CASE(5)
      default:
        break;
      }
  
      switch (parTransformMode) {
      case ParallelTransformKind::Data: {
        switch (curNode->getKind()) {
        case Kinded::Kind::FullyConnectedNodeKind: {
          splitDims[FullyConnectedNode::InputIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(
                      F, curNode, curNumOfChunks, FullyConnectedNode::InputIdx,
                      FullyConnectedNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::RowwiseQuantizedFullyConnectedNodeKind: {
          splitDims[RowwiseQuantizedFullyConnectedNode::InputIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN,
              parallelizeAndReplaceNode(
                  F, curNode, curNumOfChunks,
                  RowwiseQuantizedFullyConnectedNode::InputIdx,
                  RowwiseQuantizedFullyConnectedNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::MatMulNodeKind: {
          splitDims[MatMulNode::LHSIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(F, curNode, curNumOfChunks,
                                            MatMulNode::LHSIdx,
                                            MatMulNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::ChannelwiseQuantizedConvolutionNodeKind: {
          splitDims[ChannelwiseQuantizedConvolutionNode::InputIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN,
              parallelizeAndReplaceNode(
                  F, curNode, curNumOfChunks,
                  ChannelwiseQuantizedConvolutionNode::InputIdx,
                  ChannelwiseQuantizedConvolutionNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::AdaptiveAvgPoolNodeKind: {
          splitDims[AdaptiveAvgPoolNode::InputIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(
                      F, curNode, curNumOfChunks, AdaptiveAvgPoolNode::InputIdx,
                      AdaptiveAvgPoolNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::ReshapeNodeKind: {
          splitDims[ReshapeNode::InputIdx] = 0;
          const dim_t batchSize =
              curNode->getNthInput(ReshapeNode::InputIdx).dims()[0];
          // Do nothing if reshape applies to the first batch dimension
          if (batchSize != curNode->getNthResult(0).dims()[0]) {
            LOG(INFO) << "Reshape changes batch dimension; Disabling data "
                         "parallel split";
            break;
          }
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(
                      F, curNode, curNumOfChunks, ReshapeNode::InputIdx,
                      ReshapeNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::AddNodeKind: {
          splitDims[AddNode::LHSIdx] = 0;
          splitDims[AddNode::RHSIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(F, curNode, curNumOfChunks,
                                            AddNode::LHSIdx, AddNode::ResultIdx,
                                            splitDims, 0));
          break;
        }
        case Kinded::Kind::SubNodeKind: {
          splitDims[SubNode::LHSIdx] = 0;
          splitDims[SubNode::RHSIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(F, curNode, curNumOfChunks,
                                            SubNode::LHSIdx, SubNode::ResultIdx,
                                            splitDims, 0));
          break;
        }
        case Kinded::Kind::BatchMatMulNodeKind: {
          splitDims[BatchMatMulNode::LHSIdx] = 0;
          splitDims[BatchMatMulNode::RHSIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(
                      F, curNode, curNumOfChunks, BatchMatMulNode::LHSIdx,
                      BatchMatMulNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::MulNodeKind: {
          splitDims[AddNode::LHSIdx] = 0;
          splitDims[AddNode::RHSIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(F, curNode, curNumOfChunks,
                                            MulNode::LHSIdx, MulNode::ResultIdx,
                                            splitDims, 0));
          break;
        }
        case Kinded::Kind::SelectNodeKind: {
          splitDims[SelectNode::LHSIdx] = 0;
          splitDims[SelectNode::RHSIdx] = 0;
          splitDims[SelectNode::CondIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(F, curNode, curNumOfChunks,
                                            SelectNode::LHSIdx,
                                            SelectNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::SigmoidNodeKind: {
          splitDims[SigmoidNode::InputIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(
                      F, curNode, curNumOfChunks, SigmoidNode::InputIdx,
                      SigmoidNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::SoftMaxNodeKind: {
          splitDims[SoftMaxNode::InputIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(
                      F, curNode, curNumOfChunks, SoftMaxNode::InputIdx,
                      SoftMaxNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::LogSoftMaxNodeKind: {
          splitDims[LogSoftMaxNode::InputIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(
                      F, curNode, curNumOfChunks, LogSoftMaxNode::InputIdx,
                      LogSoftMaxNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::TanhNodeKind: {
          splitDims[TanhNode::InputIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(F, curNode, curNumOfChunks,
                                            TanhNode::InputIdx,
                                            TanhNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::SwishNodeKind: {
          splitDims[SwishNode::InputIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(F, curNode, curNumOfChunks,
                                            SwishNode::InputIdx,
                                            SwishNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::TransposeNodeKind: {
          auto shuffleVec = cast<TransposeNode>(curNode)->getShuffle();
          unsigned_t inputDim = shuffleVec[0];
          splitDims[TransposeNode::InputIdx] = inputDim;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(
                      F, curNode, curNumOfChunks, TransposeNode::InputIdx,
                      TransposeNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::ReluNodeKind: {
          splitDims[ReluNode::InputIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(F, curNode, curNumOfChunks,
                                            ReluNode::InputIdx,
                                            ReluNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::GeluNodeKind: {
          splitDims[GeluNode::InputIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(F, curNode, curNumOfChunks,
                                            GeluNode::InputIdx,
                                            GeluNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::ClipNodeKind: {
          splitDims[ClipNode::InputIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(F, curNode, curNumOfChunks,
                                            ClipNode::InputIdx,
                                            ClipNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::TileNodeKind: {
          TileNode *TN = llvm::dyn_cast<TileNode>(curNode);
          RETURN_ERR_IF_NOT(
              TN->getAxis() != 0,
              "Tile node cannot be split on axis 0 which is being replicated");
          splitDims[TileNode::InputIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(F, curNode, curNumOfChunks,
                                            TileNode::InputIdx,
                                            TileNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::BatchedReduceAddNodeKind: {
          BatchedReduceAddNode *BR = llvm::cast<BatchedReduceAddNode>(curNode);
          splitDims[BatchedReduceAddNode::BatchIdx] =
              (BR->getAxis() == 0) ? 1 : 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(
                      F, curNode, curNumOfChunks, BatchedReduceAddNode::BatchIdx,
                      BatchedReduceAddNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::ConcatNodeKind: {
          ConcatNode *concat = llvm::cast<ConcatNode>(curNode);
          RETURN_ERR_IF_NOT(concat->getDim() == 0,
                            "Expected to Data parallelize for concat on dim 0");
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceConcat(F, concat, curNumOfChunks));
          break;
        }
        case Kinded::Kind::LayerNormalizationNodeKind: {
          splitDims[LayerNormalizationNode::InputIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(F, curNode, curNumOfChunks,
                                            LayerNormalizationNode::InputIdx,
                                            LayerNormalizationNode::ResultIdx,
                                            splitDims, 0));
          break;
        }
        case Kinded::Kind::QuantizeNodeKind: {
          splitDims[QuantizeNode::InputIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(
                      F, curNode, curNumOfChunks, QuantizeNode::InputIdx,
                      QuantizeNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::DequantizeNodeKind: {
          splitDims[DequantizeNode::InputIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(
                      F, curNode, curNumOfChunks, DequantizeNode::InputIdx,
                      DequantizeNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::RescaleQuantizedNodeKind: {
          splitDims[RescaleQuantizedNode::InputIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(
                      F, curNode, curNumOfChunks, RescaleQuantizedNode::InputIdx,
                      RescaleQuantizedNode::ResultIdx, splitDims, 0));
          break;
        }
        case Kinded::Kind::ConvertToNodeKind: {
          splitDims[ConvertToNode::InputIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(
                      F, curNode, curNumOfChunks, ConvertToNode::InputIdx,
                      ConvertToNode::ResultIdx, splitDims, 0));
          break;
        }
        default:
          VLOG(1) << "Attempted to parallelize op type " << curNode->getKindName()
                  << "not yet supported"
                  << "\n";
          break;
        }
        break;
      }
  
      case ParallelTransformKind::Model: {
        switch (curNode->getKind()) {
        case Kinded::Kind::FullyConnectedNodeKind: {
          if (modelParAxis != 1) {
            break;
          }
          splitDims[FullyConnectedNode::WeightsIdx] = 1;
          splitDims[FullyConnectedNode::BiasIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(
                      F, curNode, curNumOfChunks, FullyConnectedNode::WeightsIdx,
                      FullyConnectedNode::ResultIdx, splitDims, /*resultDim*/ 1,
                      modelParallelSplitAlignment));
          break;
        }
        case Kinded::Kind::MatMulNodeKind: {
          if (modelParAxis != 1) {
            break;
          }
          splitDims[MatMulNode::RHSIdx] = 1;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(
                      F, curNode, curNumOfChunks, MatMulNode::RHSIdx,
                      MatMulNode::ResultIdx, splitDims, /*resultDim*/ 1,
                      modelParallelSplitAlignment));
          break;
        }
        case Kinded::Kind::ReluNodeKind: {
          SPLIT_ELTWISE_UNARY_OP_HELPER(Relu, modelParAxis);
          break;
        }
        case Kinded::Kind::ClipNodeKind: {
          SPLIT_ELTWISE_UNARY_OP_HELPER(Clip, modelParAxis);
          break;
        }
        case Kinded::Kind::SelectNodeKind: {
          if (modelParAxis != 1) {
            break;
          }
          auto *SL = llvm::cast<SelectNode>(curNode);
          if (SL->getNthInput(SelectNode::LHSIdx).dims().size() < 2) {
            break;
          }
          splitDims[SelectNode::LHSIdx] = 1;
          splitDims[SelectNode::RHSIdx] = 1;
          splitDims[SelectNode::CondIdx] = 1;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(
                      F, curNode, curNumOfChunks, SelectNode::LHSIdx,
                      SelectNode::ResultIdx, splitDims,
                      /*resultDim*/ 1, modelParallelSplitAlignment));
          break;
        }
        case Kinded::Kind::AddNodeKind: {
          SPLIT_ELTWISE_BINARY_OP_HELPER(Add, modelParAxis);
          break;
        }
        case Kinded::Kind::TileNodeKind: {
          if (modelParAxis != 1) {
            break;
          }
          if (curNode->getNthInput(TileNode::InputIdx).dims().size() < 2) {
            break;
          }
          TileNode *TN = llvm::dyn_cast<TileNode>(curNode);
          RETURN_ERR_IF_NOT(
              TN->getAxis() != 1,
              "Tile node cannot be split on axis 1 which is being replicated");
          splitDims[TileNode::InputIdx] = 1;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(
                      F, curNode, curNumOfChunks, TileNode::InputIdx,
                      TileNode::ResultIdx, splitDims,
                      /*resultDim*/ 1, modelParallelSplitAlignment));
          break;
        }
        case Kinded::Kind::ConcatNodeKind: {
          if (modelParAxis != 1) {
            break;
          }
          ConcatNode *concat = llvm::cast<ConcatNode>(curNode);
          RETURN_ERR_IF_NOT(concat->getDim() == 1,
                            "Expected to Model parallelize for concat on dim 1");
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceConcat(F, concat, curNumOfChunks));
          break;
        }
        case Kinded::Kind::QuantizeNodeKind: {
          SPLIT_ELTWISE_UNARY_OP_HELPER(Quantize, modelParAxis);
          break;
        }
        case Kinded::Kind::DequantizeNodeKind: {
          SPLIT_ELTWISE_UNARY_OP_HELPER(Dequantize, modelParAxis);
          break;
        }
        case Kinded::Kind::BatchedReduceMeanNodeKind: {
          if (curNode->getNthInput(BatchedReduceMeanNode::BatchIdx)
                  .dims()
                  .size() <= modelParAxis) {
            break;
          }
          splitDims[BatchedReduceMeanNode::BatchIdx] = modelParAxis;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(
                      F, curNode, curNumOfChunks, BatchedReduceMeanNode::BatchIdx,
                      BatchedReduceMeanNode::ResultIdx, splitDims,
                      /*resultDim*/ modelParAxis, modelParallelSplitAlignment));
          break;
        }
        case Kinded::Kind::RescaleQuantizedNodeKind: {
          SPLIT_ELTWISE_UNARY_OP_HELPER(RescaleQuantized, modelParAxis);
          break;
        }
        case Kinded::Kind::BatchNormalizationNodeKind: {
          auto *BN = llvm::cast<BatchNormalizationNode>(curNode);
  
          if (modelParAxis != BN->getChannelIdx()) {
            break;
          }
          if (BN->getInput().dims().size() <= modelParAxis) {
            break;
          }
          splitDims[BatchNormalizationNode::InputIdx] = modelParAxis;
          splitDims[BatchNormalizationNode::ScaleIdx] = 0;
          splitDims[BatchNormalizationNode::BiasIdx] = 0;
          splitDims[BatchNormalizationNode::MeanIdx] = 0;
          splitDims[BatchNormalizationNode::VarIdx] = 0;
  
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN,
              parallelizeAndReplaceNode(
                  F, curNode, curNumOfChunks, BatchNormalizationNode::InputIdx,
                  BatchNormalizationNode::ResultIdx, splitDims,
                  /*resultDim*/ modelParAxis, modelParallelSplitAlignment));
          break;
        }
        case Kinded::Kind::ResizeNearestNodeKind: {
          SPLIT_ELTWISE_UNARY_OP_HELPER(ResizeNearest, modelParAxis);
          break;
        }
        case Kinded::Kind::ConvolutionNodeKind: {
          if (modelParAxis != 3) {
            break;
          }
          splitDims[ConvolutionNode::FilterIdx] = 0;
          splitDims[ConvolutionNode::BiasIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(
                      F, curNode, curNumOfChunks, ConvolutionNode::FilterIdx,
                      ConvolutionNode::ResultIdx, splitDims,
                      /*resultDim*/ 3, modelParallelSplitAlignment));
          break;
        }
        case Kinded::Kind::Convolution3DNodeKind: {
          if (modelParAxis != 4) {
            break;
          }
          splitDims[Convolution3DNode::FilterIdx] = 0;
          splitDims[Convolution3DNode::BiasIdx] = 0;
          ASSIGN_VALUE_OR_RETURN_ERR(
              CN, parallelizeAndReplaceNode(
                      F, curNode, curNumOfChunks, Convolution3DNode::FilterIdx,
                      Convolution3DNode::ResultIdx, splitDims,
                      /*resultDim*/ 4, modelParallelSplitAlignment));
          break;
        }
        case Kinded::Kind::AvgPoolNodeKind: {
          auto *APN = llvm::cast<AvgPoolNode>(curNode);
          if (APN->getLayout() != 2) {
            break;
          }
          if (modelParAxis != 4) {
            break;
          }
          SPLIT_ELTWISE_UNARY_OP_HELPER(AvgPool, 4);
          break;
        }
        default:
          VLOG(1) << "Attempted to parallelize op type " << curNode->getKindName()
                  << "not yet supported"
                  << "\n";
          break;
        }
        break;
      }
  
      default:
        break;
      }
  
      if (CN) {
        replacedMap[curNode] = CN;
      }
    }
  
    // Because we transformed Node types unsafely, make sure all types of the
    // Function still are valid.
    RETURN_ERR_IF_NOT(F->verify(), "Verification issue post parallelization");
  
    RETURN_ERR_IF_NOT(numProcessedNodes == parOpts.size(),
                      "Not all Nodes specified in parOpts were processed.");
  
    return replacedMap;
  }
  
  void glow::updateQuantReluTypes(Function *F) {
    // A worklist that contains the nodes to process.
    std::vector<Node *> worklist;
    auto needsQuantTyUpdate = [](const Node *N) {
      return isa<ConcatNode>(N) || isa<SliceNode>(N) || isa<ReshapeNode>(N) ||
             isa<TileNode>(N) || isa<BroadcastNode>(N) || isa<TransposeNode>(N);
    };
  
    for (Node &N : F->getNodes()) {
      // Look for quantized Relus that have negative min, and update their min to
      // be zero.
      auto *RN = llvm::dyn_cast<ReluNode>(&N);
      if (!RN || !RN->getResult().getType()->isQuantizedType() ||
          isUsedByNodeWithSideEffects(RN)) {
        continue;
      }
      const TypeRef RNTy = RN->getResult().getType();
  
      const auto qRange = RNTy->getQuantizedValueRange();
      if (qRange.first >= 0) {
        continue;
      }
      const auto qParams =
          quantization::chooseQuantizationParams({0, qRange.second});
      const TypeRef qReluTy = F->getParent()->uniqueType(
          RNTy->getElementType(), RNTy->dims(), qParams.scale, qParams.offset);
      RN->setType(ReluNode::ResultIdx, qReluTy);
  
      // Now look for any users of the Relu which set their type based directly on
      // the type of the Relu, and update them as well. These tend to be shape
      // changes such as Concat, Slice, Reshape, Tile, Broadcast, Transpose, etc.
      for (auto &user : RN->getUsers()) {
        auto *U = user.getUser();
        if (needsQuantTyUpdate(U)) {
          worklist.push_back(U);
        }
      }
    }
  
    // Now we need to update all nodes following the relus which directly took
    // their output types from the relu.
    while (!worklist.empty()) {
      Node *N = worklist.back();
      assert(needsQuantTyUpdate(N) && "Unsupported node for quant update.");
      worklist.pop_back();
  
      // Look for other users that also need updates and add to worklist.
      for (auto &user : N->getUsers()) {
        auto *U = user.getUser();
        if (needsQuantTyUpdate(U)) {
          worklist.push_back(U);
        }
      }
  
      // Note: We can unconditionally get the 0th result because all nodes we
      // currently support to update have a single output.
      assert(N->getNumResults() == 1 && "Unsupported multi-output Node");
      constexpr unsigned resultIdx = 0;
      const TypeRef T = N->getNthResult(resultIdx).getType();
  
      // We must still be in the quantized domain, because we are only following
      // chains starting from quantized relus down through shape nodes.
      assert(T->isQuantizedType());
  
      // This likely represents an issue because it means e.g. a Reshape will
      // change the scale/bias, but continue for now and assume a verifier will
      // catch the issue if it is one.
      if (isUsedByNodeWithSideEffects(N)) {
        continue;
      }
  
      // Update the output type just like we did for the original relu.
      const auto qRange = T->getQuantizedValueRange();
      if (qRange.first >= 0) {
        continue;
      }
      const auto qParams =
          quantization::chooseQuantizationParams({0, qRange.second});
      const TypeRef qReluTy = F->getParent()->uniqueType(
          T->getElementType(), T->dims(), qParams.scale, qParams.offset);
      N->setType(resultIdx, qReluTy);
    }
  }
  