Linalg/Transforms/Fusion.cpp

//===- Fusion.cpp - Implementation of linalg Fusion -----------------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
//
// This file implements the linalg dialect Fusion pass.
//
//===----------------------------------------------------------------------===//

#include "PassDetail.h"
#include "mlir/Analysis/Dominance.h"
#include "mlir/Dialect/Linalg/Analysis/DependenceAnalysis.h"
#include "mlir/Dialect/Linalg/EDSC/FoldedIntrinsics.h"
#include "mlir/Dialect/Linalg/IR/LinalgOps.h"
#include "mlir/Dialect/Linalg/IR/LinalgTypes.h"
#include "mlir/Dialect/Linalg/Passes.h"
#include "mlir/Dialect/Linalg/Utils/Utils.h"
#include "mlir/Dialect/StandardOps/EDSC/Intrinsics.h"
#include "mlir/IR/AffineExpr.h"
#include "mlir/IR/AffineMap.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/Support/LLVM.h"
#include "mlir/Transforms/FoldUtils.h"
#include "llvm/ADT/SetVector.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/Debug.h"

#define DEBUG_TYPE "linalg-fusion"

using namespace mlir;
using namespace mlir::edsc;
using namespace mlir::edsc::intrinsics;
using namespace mlir::linalg;

using folded_std_constant_index = FoldedValueBuilder<ConstantIndexOp>;

using llvm::dbgs;

/// Implements a simple high-level fusion pass of linalg library operations.
///
/// In each block, linalg ops are processed in reverse textual order.
/// Given a linalg op `O`, fusion occurs by:
///   1. inspecting the linalg ops that write into the views read by `O`. This
///      uses the SSA value of the views and a simple subview/slice analysis to
///      determine producer-consumer dependences;
///   2. greedily fuse the linalg ops that produce subview
///   3. inspect the fused ops and determine whether they have other remaining
///      LinalgOp uses. If not, then erase the original producing linalg op.
///
/// More advanced use cases, analyses as well as profitability heuristics are
/// left for future work.

// Return a cloned version of `op` that operates on `loopRanges`, assumed to be
// a subset of the original loop ranges of `op`.
// This is achieved by applying the `loopToOperandRangesMaps` permutation maps
// to the `loopRanges` in order to obtain view ranges.
static LinalgOp cloneWithLoopRanges(OpBuilder &b, Location loc, LinalgOp op,
                                    ArrayRef<SubViewOp::Range> loopRanges) {
  assert(op.hasBufferSemantics() && "expected linalg op with buffer semantics");
  auto maps = op.indexing_maps();
  SmallVector<Value, 8> clonedViews;
  clonedViews.reserve(op.getNumInputsAndOutputs());
  // Iterate over the inputs and outputs in order.
  // Extract the subranges from the linearized ranges.
  SmallVector<Value, 8> ios(op.getInputsAndOutputBuffers());
  for (auto en : llvm::enumerate(ios)) {
    unsigned idx = en.index();
    auto map = maps[idx].cast<AffineMapAttr>().getValue();
    LLVM_DEBUG(dbgs() << "map: " << map << "\n");
    Value view = en.value();
    SmallVector<SubViewOp::Range, 4> viewRanges(map.getNumResults());
    for (auto en2 : llvm::enumerate(map.getResults())) {
      unsigned d = en2.index();
      // loopToOperandRangesMaps are permutations-only.
      unsigned loopPos = en2.value().cast<AffineDimExpr>().getPosition();
      viewRanges[d] = loopRanges[loopPos];
      LLVM_DEBUG(dbgs() << "\ni,j: " << en.index() << ", " << en2.index()
                        << "\t"
                        << "loopPos: " << loopPos << "\t" << viewRanges[d]);
    }
    // Construct a new subview for the tile.
    unsigned rank = viewRanges.size();
    SmallVector<Value, 4> offsets, sizes, strides;
    offsets.reserve(rank);
    sizes.reserve(rank);
    strides.reserve(rank);
    for (auto r : viewRanges) {
      offsets.push_back(r.offset);
      sizes.push_back(r.size);
      strides.push_back(r.stride);
    }
    clonedViews.push_back(
        b.create<SubViewOp>(loc, view, offsets, sizes, strides));
  }
  auto operands = getAssumedNonViewOperands(op);
  clonedViews.append(operands.begin(), operands.end());

  Operation *clonedOp = op.clone(b, loc, clonedViews);
  // When the producer is an IndexedGenercOp, we have to transform its block
  // IV arguments according to the tiling of the consumer, i.e. offset them by
  // the values computed in `loopRanges`.
  if (auto indexedGenericOp = dyn_cast<IndexedGenericOp>(clonedOp)) {
    auto &block = indexedGenericOp.region().front();

    OpBuilder::InsertionGuard g(b);
    b.setInsertionPointToStart(&block);
    for (unsigned i = 0, e = indexedGenericOp.getNumLoops(); i < e; ++i) {
      Value oldIndex = block.getArgument(i);
      AddIOp newIndex = b.create<AddIOp>(indexedGenericOp.getLoc(), oldIndex,
                                         loopRanges[i].offset);
      oldIndex.replaceAllUsesExcept(newIndex,
                                    SmallPtrSet<Operation *, 1>{newIndex});
    }
  }
  return clonedOp;
}

struct ViewDimension {
  Value view;
  unsigned dimension;
};

// Given an `op`, returns the first (`view`, `dimension`) pair that identifies
// the loop range at `loopDepth`. The semantics of the loopToOperandRangesMaps
// guarantees at least one such dimension is found. If multiple candidates exist
// they must agree by construction (i.e. have the same size) and we just return
// the first one.
static ViewDimension getViewDefiningLoopRange(LinalgOp op, unsigned loopDepth) {
  assert(op.hasBufferSemantics() && "expected linalg op with buffer semantics");
  auto maps = op.indexing_maps();
  // Iterate over the inputs and outputs in order.
  // Extract the subranges from the linearized ranges.
  SmallVector<Value, 8> ios(op.getInputsAndOutputBuffers());
  for (auto en : llvm::enumerate(ios)) {
    unsigned idx = en.index();
    auto map = maps[idx].cast<AffineMapAttr>().getValue();
    LLVM_DEBUG(dbgs() << "getViewDefiningLoopRange I/O idx: " << idx << "\n");
    LLVM_DEBUG(dbgs() << "getViewDefiningLoopRange map: " << map << "\n");
    Value view = en.value();
    SmallVector<Value, 8> viewRanges(map.getNumResults(), nullptr);
    for (auto en2 : llvm::enumerate(map.getResults())) {
      if (loopDepth == en2.value().cast<AffineDimExpr>().getPosition()) {
        LLVM_DEBUG(dbgs() << "getViewDefiningLoopRange loopDepth: " << loopDepth
                          << "\n");
        LLVM_DEBUG(dbgs() << "getViewDefiningLoopRange view: " << view << "\n");
        return ViewDimension{view, static_cast<unsigned>(en2.index())};
      }
    }
  }
  llvm_unreachable("Expect to be able to extract a view defining loop range");
}

static LinalgOp fuse(Value producedView, LinalgOp producer, LinalgOp consumer,
                     unsigned consumerIdx, unsigned producerIdx,
                     OperationFolder *folder) {
  assert(producer.hasBufferSemantics() &&
         "expected linalg op with buffer semantics");
  assert(consumer.hasBufferSemantics() &&
         "expected linalg op with buffer semantics");

  auto subView = dyn_cast_or_null<SubViewOp>(
      consumer.getBuffer(consumerIdx).getDefiningOp());
  auto slice = dyn_cast_or_null<SliceOp>(
      consumer.getBuffer(consumerIdx).getDefiningOp());
  assert(subView || slice);
  (void)subView;
  (void)slice;

  // loopToOperandRangesMaps are permutations-only by construction:
  //   we can always identify a data dimension with a (at least one) loop
  //   dimension.
  AffineMap producerMap =
      producer.indexing_maps()[producer.getNumInputs() + producerIdx]
          .cast<AffineMapAttr>()
          .getValue();
  LLVM_DEBUG(dbgs() << "Producer Idx: " << producerIdx
                    << ", producer map: " << producerMap << "\n");

  unsigned nPar = producer.getNumParallelLoops();
  unsigned nRed = producer.getNumReductionLoops();
  unsigned nWin = producer.getNumWindowLoops();
  SmallVector<SubViewOp::Range, 8> loopRanges(nPar + nRed + nWin);

  // Iterate over dimensions identified by the producer map for `producerIdx`.
  // This defines a subset of the loop ranges that we need to complete later.
  for (auto en : llvm::enumerate(producerMap.getResults())) {
    unsigned posInProducerLoop = en.value().cast<AffineDimExpr>().getPosition();
    loopRanges[posInProducerLoop] = subView.getRanges()[en.index()];
  }

  OpBuilder b(consumer.getOperation());
  auto loc = consumer.getLoc();
  // Iterate over all dimensions. For the dimensions not identified by the
  // producer map for `producerIdx`, we need to explicitly compute the view that
  // defines the loop ranges using the `producer`.
  for (unsigned i = 0, nLoops = loopRanges.size(); i < nLoops; ++i) {
    if (loopRanges[i].offset)
      LLVM_DEBUG(llvm::dbgs()
                 << "existing LoopRange: " << loopRanges[i] << "\n");
    else {
      auto viewDim = getViewDefiningLoopRange(producer, i);
      loopRanges[i] = SubViewOp::Range{folded_std_constant_index(folder, 0),
                                       std_dim(viewDim.view, viewDim.dimension),
                                       folded_std_constant_index(folder, 1)};
      LLVM_DEBUG(llvm::dbgs() << "new LoopRange: " << loopRanges[i] << "\n");
    }
  }

  return cloneWithLoopRanges(b, loc, producer, loopRanges);
}

// Encode structural fusion safety preconditions.
// Some of these will be lifted in the future with better analysis.
static bool isStructurallyFusableProducer(LinalgOp producer, Value consumedView,
                                          LinalgOp consumer) {
  assert(producer.hasBufferSemantics() &&
         "expected linalg op with buffer semantics");
  assert(consumer.hasBufferSemantics() &&
         "expected linalg op with buffer semantics");
  if (producer.getNumOutputs() != 1) {
    LLVM_DEBUG(dbgs() << "\nNot structurally fusable (multi-output)");
    return false;
  }
  // Only fuse when the producer block dominates.
  DominanceInfo dom(producer.getOperation());
  if (!dom.dominates(producer.getOperation()->getBlock(),
                     consumer.getOperation()->getBlock())) {
    LLVM_DEBUG(
        dbgs()
        << "\nNot structurally fusable (producer block does not dominate)");
    return false;
  }
  return true;
}

bool mlir::linalg::isProducerLastWriteOfView(const LinalgDependenceGraph &graph,
                                             LinalgOp consumer,
                                             Value consumedView,
                                             LinalgOp producer) {
  assert(producer.hasBufferSemantics() &&
         "expected linalg op with buffer semantics");
  assert(consumer.hasBufferSemantics() &&
         "expected linalg op with buffer semantics");
  // Make some simple structural checks that alleviate the need for more
  // complex analyses.
  if (!isStructurallyFusableProducer(producer, consumedView, consumer)) {
    LLVM_DEBUG(dbgs() << "\n***Not static last write due to structure:\t"
                      << *producer.getOperation());
    return false;
  }
  // Check for any interleaved write to consumedView.
  if (!graph.findCoveringWrites(producer, consumer, consumedView).empty()) {
    LLVM_DEBUG(dbgs() << "\n***Not fusable due to interleaved write:\t"
                      << *producer.getOperation());
    return false;
  }
  return true;
}

bool mlir::linalg::isFusableInto(const LinalgDependenceGraph &graph,
                                 LinalgOp consumer, Value consumedView,
                                 LinalgOp producer) {
  assert(producer.hasBufferSemantics() &&
         "expected linalg op with buffer semantics");
  assert(consumer.hasBufferSemantics() &&
         "expected linalg op with buffer semantics");
  if (!isProducerLastWriteOfView(graph, consumer, consumedView, producer))
    return false;
  // Check for any fusion-preventing dependence to any view read/written that
  // would violate dependences.
  if (!graph.findCoveringDependences(producer, consumer).empty()) {
    LLVM_DEBUG(dbgs() << "\n***Not fusable due to an interleaved dependence:\t"
                      << *producer.getOperation());
    return false;
  }
  if (auto convOp = dyn_cast<linalg::ConvOp>(producer.getOperation())) {
    // TODO(ntv): add a level of indirection to linalg.generic.
    if (convOp.padding())
      return false;
  }
  if (auto convOp = dyn_cast<linalg::ConvOp>(consumer.getOperation())) {
    // TODO(ntv): add a level of indirection to linalg.generic.
    if (convOp.padding())
      return false;
  }
  return true;
}

static Optional<FusionInfo>
fuseProducerOfDep(OpBuilder &b, LinalgOp consumer, unsigned consumerIdx,
                  const LinalgDependenceGraph &graph, OperationFolder *folder,
                  LinalgDependenceGraph::DependenceType depType) {
  assert(consumer.hasBufferSemantics() &&
         "expected linalg op with buffer semantics");
  LLVM_DEBUG(dbgs() << "\nStart examining consumer: "
                    << *consumer.getOperation());
  for (auto dependence : graph.getDependencesInto(consumer, depType)) {
    LLVM_DEBUG(dbgs() << "\n***Consider producer:\t"
                      << *dependence.dependentOpView.op << "\n");
    auto producer = cast<LinalgOp>(dependence.dependentOpView.op);

    // Check that the dependence is indeed on the input `consumerIdx` view.
    auto consumedView = dependence.indexingView;
    if (consumer.getBuffer(consumerIdx) != consumedView)
      continue;

    // Consumer consumes this view, `isStructurallyFusableProducer` also checks
    // whether it is a strict subview of the producer view.
    auto producedView = dependence.dependentOpView.view;
    auto producerIdx = producer.getIndexOfOutputBuffer(producedView).getValue();
    // `consumerIdx` and `producerIdx` exist by construction.
    LLVM_DEBUG(dbgs() << "\n"
                      << LinalgDependenceGraph::getDependenceTypeStr(depType)
                      << "producer: " << *producer.getOperation() << " view: "
                      << producedView << " output index: " << producerIdx);

    // Must be a subview or a slice to guarantee there are loops we can fuse
    // into.
    auto subView = dyn_cast_or_null<SubViewOp>(consumedView.getDefiningOp());
    auto slice = dyn_cast_or_null<SliceOp>(consumedView.getDefiningOp());
    if (!subView && !slice) {
      LLVM_DEBUG(dbgs() << "\nNot fusable (not a subview or slice)");
      continue;
    }

    // Simple fusability checks.
    if (!isFusableInto(graph, consumer, consumedView, producer))
      continue;

    // Fuse `producer` just before `consumer`.
    OpBuilder::InsertionGuard g(b);
    b.setInsertionPoint(consumer.getOperation());
    ScopedContext scope(b, consumer.getLoc());
    LLVM_DEBUG(dbgs() << "Fuse into consumer: " << *consumer << "\n");
    auto fusedProducer = fuse(producedView, producer, consumer, consumerIdx,
                              producerIdx, folder);

    return FusionInfo{producer, fusedProducer};
  }
  return llvm::None;
}

// Only consider RAW and WAW atm.
Optional<FusionInfo> mlir::linalg::fuseProducerOf(
    OpBuilder &b, LinalgOp consumer, unsigned consumerIdx,
    const LinalgDependenceGraph &graph, OperationFolder *folder) {
  SmallVector<LinalgDependenceGraph::DependenceType, 4> deps = {
      LinalgDependenceGraph::DependenceType::RAW,
      LinalgDependenceGraph::DependenceType::WAW,
  };
  for (auto dep : deps) {
    if (auto res =
            fuseProducerOfDep(b, consumer, consumerIdx, graph, folder, dep))
      return res;
  }
  return llvm::None;
}

static void fuseLinalgOpsGreedily(FuncOp f) {
  LLVM_DEBUG(f.print(dbgs() << "\nBefore linalg-fusion: \n"));

  OpBuilder b(f);
  OperationFolder folder(f.getContext());
  DenseSet<Operation *> eraseSet;

  // Save original Linalg ops, we only want to make a pass over those.
  SmallVector<Operation *, 8> linalgOps;
  f.walk([&](LinalgOp op) {
    if (op.hasBufferSemantics())
      linalgOps.push_back(op);
  });

  // TODO(pifon, ntv): LinalgDependenceGraph should be able to update itself.
  // The current naive and expensive reconstruction of the graph should be
  // removed.
  for (auto *op : llvm::reverse(linalgOps)) {
    for (unsigned id = 0, e = LinalgOp(op).getNumInputsAndOutputBuffers();
         id < e; ++id) {
      linalg::Aliases aliases;
      linalg::LinalgDependenceGraph graph(aliases, linalgOps);
      if (auto info = fuseProducerOf(b, op, id, graph, &folder)) {
        auto *originalOp = info->originalProducer.getOperation();
        eraseSet.insert(originalOp);
        auto *originalOpInLinalgOpsVector =
            std::find(linalgOps.begin(), linalgOps.end(), originalOp);
        *originalOpInLinalgOpsVector = info->fusedProducer.getOperation();
      }
    }
  }
  // The `fuseProducerOf` function performs structural checks and in particular
  // that no covering read or write exist between the consumer and the producer.
  // As a consequence, the only fusions that may occur preserve subsequent
  // dependences and are guaranteed by construction to produce the whole view.
  // We may thus erase the producer once it is fused.
  for (auto *e : eraseSet)
    e->erase();
  LLVM_DEBUG(f.print(dbgs() << "\nAfter linalg-fusion: \n"));
}

//====---------------------------------------------------------------------===//
// Fusion on Tensor operation.
//====---------------------------------------------------------------------===//

namespace {

/// Implementation of fusion of generic ops.
struct FuseGenericOpsOnTensors {
  static bool isFusible(GenericOp producer, GenericOp consumer,
                        unsigned consumerIdx) {
    // Verify that
    // - the producer has all "parallel" iterator type.
    if (producer.getNumParallelLoops() != producer.getNumLoops())
      return false;

    // Get the consumer index map. The number of results of the consumer index
    // map must match the number of loops of the producer.
    AffineMap consumerIndexMap = consumer.getIndexingMap(consumerIdx);
    if (consumerIndexMap.getNumResults() != producer.getNumLoops())
      return false;

    // Finally the index_map for the result must be invertible. For now just
    // verify it is a permutation.
    AffineMap producerResultIndexMap = producer.getOutputIndexingMap(0);
    return producerResultIndexMap.isPermutation();
  }

  static Operation *fuse(GenericOp producer, GenericOp consumer,
                         unsigned consumerIdx, PatternRewriter &rewriter,
                         OperationFolder *folder = nullptr) {
    if (!isFusible(producer, consumer, consumerIdx))
      return nullptr;

    unsigned numFusedOperands = producer.getOperation()->getNumOperands() +
                                consumer.getOperation()->getNumOperands() - 1;

    // Compute the fused operands list,
    SmallVector<Value, 2> fusedOperands;
    fusedOperands.reserve(numFusedOperands);
    auto consumerOperands = consumer.getOperation()->getOperands();
    auto producerOperands = producer.getOperation()->getOperands();
    fusedOperands.assign(consumerOperands.begin(),
                         std::next(consumerOperands.begin(), consumerIdx));
    fusedOperands.append(producerOperands.begin(), producerOperands.end());
    fusedOperands.append(std::next(consumerOperands.begin(), consumerIdx + 1),
                         consumerOperands.end());

    // Compute indexing_maps for the fused operation. The indexing_maps for the
    // operands of the consumers that arent fused are the same. The
    // indexing_maps for the producers need to be computed based on the
    // indexing_map of the operand at consumerIdx in the consumer.
    SmallVector<Attribute, 4> fusedIndexMaps;
    auto consumerIndexMaps = consumer.indexing_maps();
    fusedIndexMaps.reserve(fusedOperands.size() + consumer.getNumResults());
    fusedIndexMaps.assign(consumerIndexMaps.begin(),
                          std::next(consumerIndexMaps.begin(), consumerIdx));
    // Compute indexing maps for the producer args in the fused operation.
    computeProducerOperandIndex(
        producer, consumer.getInputIndexingMap(consumerIdx), fusedIndexMaps);

    // Append the indexing maps for the remaining consumer operands.
    fusedIndexMaps.append(std::next(consumerIndexMaps.begin(), consumerIdx + 1),
                          consumerIndexMaps.end());

    // Generate the fused op.
    auto fusedOp = rewriter.create<GenericOp>(
        rewriter.getUnknownLoc(), consumer.getResultTypes(), fusedOperands,
        rewriter.getI64IntegerAttr(fusedOperands.size()),
        rewriter.getI64IntegerAttr(consumer.getNumResults()),
        rewriter.getArrayAttr(fusedIndexMaps), consumer.iterator_types(),
        /*doc=*/nullptr,
        /*library_call=*/nullptr);
    generateFusedRegion(rewriter, fusedOp.region(), producer.region(),
                        consumer.region(), consumerIdx);
    return fusedOp;
  }

private:
  /// Append to `fusedOpIndexingMapAttrs` the indexing maps for the operands of
  /// the `producer` to use in the fused operation given the indexing map of the
  /// result of the producer in the consumer.
  static void computeProducerOperandIndex(
      GenericOp producer, AffineMap fusedConsumerArgIndexMap,
      SmallVectorImpl<Attribute> &fusedOpIndexingMapAttrs) {
    // The indexing map in the consumer op (fusedConsumerArgIndexMap) is a map
    // from consumer loop -> consumer arg tensor index/producer result tensor
    // index. The fused loop is same as the consumer loop. For each producer arg
    // the indexing map to be computed is a map from consumer loop -> producer
    // arg tensor index.

    AffineMap producerResultIndexMap = producer.getOutputIndexingMap(0);
    // producerResultIndexMap is a map from producer loop -> tensor index.
    // Compute the inverse to get map from tensor index -> producer loop.
    // The inverse is a map from producer result tensor index -> producer loop.
    AffineMap invProducerResultIndexMap =
        inversePermutation(producerResultIndexMap);
    assert(invProducerResultIndexMap &&
           "expected producer result indexig map to be invertible");
    for (unsigned argNum : llvm::seq<unsigned>(0, producer.getNumInputs())) {
      // argMap is a map from producer loop -> producer arg tensor index.
      AffineMap argMap = producer.getInputIndexingMap(argNum);

      // Compose argMap with invProducerResultIndexMap to get a map from
      // producer result tensor index -> producer arg tensor index.
      AffineMap t1 = argMap.compose(invProducerResultIndexMap);

      // Compose t1 with fusedConsumerArgIndexMap gives an indexing map from
      // consumer loop/ fused loop -> producer arg tensor index.
      AffineMap indexingMap = t1.compose(fusedConsumerArgIndexMap);
      fusedOpIndexingMapAttrs.push_back(AffineMapAttr::get(indexingMap));
    }
  }

  /// Generate the region of the fused operation. The region of the fused op
  /// must be empty.
  static void generateFusedRegion(PatternRewriter &rewriter,
                                  Region &fusedRegion, Region &producerRegion,
                                  Region &consumerRegion,
                                  unsigned consumerIdx) {
    // Build the region of the fused op.
    Block &producerBlock = producerRegion.front();
    Block &consumerBlock = consumerRegion.front();
    Block *fusedBlock = new Block();
    fusedRegion.push_back(fusedBlock);
    BlockAndValueMapping mapper;
    OpBuilder::InsertionGuard guard(rewriter);
    rewriter.setInsertionPointToStart(fusedBlock);
    // Map the arguments for the unmodified args from the consumer.
    for (auto consumerArg : llvm::enumerate(consumerBlock.getArguments())) {
      if (consumerArg.index() == consumerIdx) {
        // Map the arguments for the args from the producer.
        for (auto producerArg : producerBlock.getArguments())
          mapper.map(producerArg,
                     fusedBlock->addArgument(producerArg.getType()));
        continue;
      }
      mapper.map(consumerArg.value(),
                 fusedBlock->addArgument(consumerArg.value().getType()));
    }

    // Add operations from producer (except the yield operation) to the fused
    // op.
    for (auto &op : producerBlock.getOperations()) {
      if (auto yieldOp = dyn_cast<YieldOp>(op)) {
        // Lookup the value the yield operation is mapped to.
        Value yieldVal = yieldOp.getOperand(0);
        auto clonedVal = mapper.lookup(yieldVal);
        mapper.map(consumerBlock.getArgument(consumerIdx), clonedVal);
        continue;
      }
      rewriter.clone(op, mapper);
    }
    for (auto &op : consumerBlock.getOperations())
      rewriter.clone(op, mapper);
  }
};
} // namespace

/// Linearize the expressions in `sourceMap` based on the `reassociationMaps`
/// provided, given the shape of the source tensor that corresponds to the
/// `sourceMap`. Note that this implicitly assumes that the tensors dimensions
/// are "row-major" ordered logically.
///
/// For example:
///
/// %0 = op ... : tensor<?x?x4x5xf32>
/// with output index_map `affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>`
///
/// and reshape:
/// %1 = linalg.tensor_reshape %0 [affine_map<(i, j, k, l) -> (i)>,
///                                affine_map<(i, j, k, l) -> (j, k, l)>] :
///        tensor<?x?x4x5xf32> into tensor<?x?xf32>
///
/// would be rewritten into:
/// %0 = op ... : tensor<?x?x4x5xf32>
/// with output index_map
///   `affine_map<(d0, d1, d2, d3) -> (d0, d1 * 20 + d2 * 5 + d3)>`
static AffineMap linearizeCollapsedDims(AffineMap sourceMap,
                                        ArrayRef<int64_t> sourceShape,
                                        ArrayRef<AffineMap> reassociationMaps) {
  SmallVector<AffineExpr, 4> resultExprs;
  resultExprs.reserve(reassociationMaps.size());
  ArrayRef<AffineExpr> sourceExprs = sourceMap.getResults();
  MLIRContext *context = sourceMap.getContext();

  // Compute the result exprs based on the reassociation maps.
  for (AffineMap map : reassociationMaps) {
    ArrayRef<AffineExpr> collapsedDims = map.getResults();
    // Assume that they are in-order and contiguous (already checked in
    // verifier).
    assert(!collapsedDims.empty());
    unsigned startDim =
        collapsedDims.front().cast<AffineDimExpr>().getPosition();
    AffineExpr linearizedExpr = makeCanonicalStridedLayoutExpr(
        sourceShape.slice(startDim, collapsedDims.size()),
        sourceExprs.slice(startDim, collapsedDims.size()), context);
    resultExprs.push_back(linearizedExpr);
  }
  return AffineMap::get(sourceMap.getNumDims(), sourceMap.getNumSymbols(),
                        resultExprs, context);
}

/// Checks if the `reshapeOp` can be fused with it consumer (if `asProducer` is
/// true) or its producer (if `asProducer` is false) given the indexing map at
/// its use.
static bool isTensorReshapeOpFusible(TensorReshapeOp reshapeOp,
                                     AffineMap useIndexMap, bool asProducer) {
  RankedTensorType returnType = reshapeOp.getResultType();
  RankedTensorType operandType = reshapeOp.getSrcType();
  // Reshape is fusible with its consumer (i.e. reshape as a producer) when its
  // operand is of lesser rank than the result. Fusing when operand has higher
  // rank will require use of mods and divs in the indexing maps of the fused op
  // which would make it non-invertible. Similarly reshape is fused with its
  // producer (i.e. reshape as consumer) only if the return type has lesser
  // rank.
  if ((asProducer && returnType.getRank() < operandType.getRank()) ||
      (!asProducer && operandType.getRank() < returnType.getRank()))
    return false;
  return useIndexMap.isIdentity();
}

namespace {
/// Implementation of fusion on tensor ops when producer is a TensorReshapeOp.
template <typename LinalgOpTy> struct FuseTensorReshapeOpAsProducer {
  static bool isFusible(TensorReshapeOp producer, LinalgOpTy consumer,
                        unsigned consumerIdx) {
    return isTensorReshapeOpFusible(
        producer, consumer.getInputIndexingMap(consumerIdx), true);
  }

  static Operation *fuse(TensorReshapeOp producer, LinalgOpTy consumer,
                         unsigned consumerIdx, PatternRewriter &rewriter,
                         OperationFolder *folder = nullptr) {
    if (!isFusible(producer, consumer, consumerIdx))
      return nullptr;

    // Compute the fused operands list,
    SmallVector<Value, 2> fusedOperands(consumer.operand_begin(),
                                        consumer.operand_end());
    fusedOperands[consumerIdx] = producer.src();

    // Compute indexing_maps for the fused operation. The indexing_maps for the
    // operands of the consumers that arent fused are the same.
    SmallVector<AffineMap, 4> fusedIndexMaps =
        llvm::to_vector<4>(llvm::map_range(
            consumer.indexing_maps(), [](Attribute attr) -> AffineMap {
              return attr.cast<AffineMapAttr>().getValue();
            }));

    // Compute the indexing map to use for the operand of the producer.
    AffineMap modifiedMap = linearizeCollapsedDims(
        fusedIndexMaps[consumerIdx], producer.getResultType().getShape(),
        producer.getReassociationMaps());
    for (AffineExpr expr : modifiedMap.getResults()) {
      if (!expr.isPureAffine())
        return nullptr;
    }
    fusedIndexMaps[consumerIdx] = modifiedMap;

    // Further check that the resulting index maps can be fused and
    // inverted. Without this the resultant op is not legal.
    if (!inversePermutation(concatAffineMaps(fusedIndexMaps)))
      return nullptr;

    SmallVector<Attribute, 4> indexMapAttrs = llvm::to_vector<4>(
        llvm::map_range(fusedIndexMaps, [](AffineMap map) -> Attribute {
          return AffineMapAttr::get(map);
        }));
    auto fusedOp = rewriter.create<LinalgOpTy>(
        rewriter.getUnknownLoc(), consumer.getResultTypes(), fusedOperands,
        rewriter.getI64IntegerAttr(fusedOperands.size()),
        rewriter.getI64IntegerAttr(consumer.getNumResults()),
        rewriter.getArrayAttr(indexMapAttrs), consumer.iterator_types(),
        /*doc=*/nullptr,
        /*library_call=*/nullptr);
    auto &fusedRegion = fusedOp.region();
    rewriter.cloneRegionBefore(consumer.region(), fusedRegion,
                               fusedRegion.begin());
    return fusedOp;
  }
};

/// Implementation of fusion on tensor ops when consumer is a TensorReshapeOp.
template <typename LinalgOpTy> struct FuseTensorReshapeOpAsConsumer {
  static bool isFusible(LinalgOpTy producer, TensorReshapeOp consumer,
                        unsigned consumerIdx) {
    return isTensorReshapeOpFusible(consumer, producer.getOutputIndexingMap(0),
                                    false);
  }

  static Operation *fuse(LinalgOpTy producer, TensorReshapeOp consumer,
                         unsigned consumerIdx, PatternRewriter &rewriter,
                         OperationFolder *folder = nullptr) {
    if (!isFusible(producer, consumer, consumerIdx))
      return nullptr;

    // The indexing_maps for the operands of the fused operation are same as
    // those for the operands of the producer.
    SmallVector<AffineMap, 4> fusedIndexMaps =
        llvm::to_vector<4>(llvm::map_range(
            producer.indexing_maps(), [](Attribute attr) -> AffineMap {
              return attr.cast<AffineMapAttr>().getValue();
            }));
    // Compute the indexing map to use for the operand of the producer.
    AffineMap modifiedMap = linearizeCollapsedDims(
        producer.getOutputIndexingMap(0), consumer.getSrcType().getShape(),
        consumer.getReassociationMaps());
    for (AffineExpr expr : modifiedMap.getResults()) {
      if (!expr.isPureAffine())
        return nullptr;
    }
    fusedIndexMaps.back() = modifiedMap;

    // Further check that the resulting index maps can be fused and
    // inverted. Without this the resultant op is not legal.
    if (!inversePermutation(concatAffineMaps(fusedIndexMaps)))
      return nullptr;

    SmallVector<Attribute, 4> indexMapAttrs = llvm::to_vector<4>(
        llvm::map_range(fusedIndexMaps, [](AffineMap map) -> Attribute {
          return AffineMapAttr::get(map);
        }));

    auto fusedOp = rewriter.create<LinalgOpTy>(
        rewriter.getUnknownLoc(), consumer.getResultType(),
        producer.getOperands(),
        rewriter.getI64IntegerAttr(producer.getNumOperands()),
        rewriter.getI64IntegerAttr(1), rewriter.getArrayAttr(indexMapAttrs),
        producer.iterator_types(),
        /*doc=*/nullptr,
        /*library_call=*/nullptr);
    auto &fusedRegion = fusedOp.region();
    rewriter.cloneRegionBefore(producer.region(), fusedRegion,
                               fusedRegion.begin());
    return fusedOp;
  }
};
} // namespace

Operation *mlir::linalg::fuseTensorOps(PatternRewriter &rewriter,
                                       Operation *consumer,
                                       unsigned consumerIdx,
                                       OperationFolder *folder) {
  if (consumerIdx >= consumer->getNumOperands())
    return nullptr;
  Operation *producer = consumer->getOperand(consumerIdx).getDefiningOp();
  if (!producer || producer->getNumResults() != 1)
    return nullptr;

  // Fuse when consumer is GenericOp.
  if (GenericOp genericOp = dyn_cast<GenericOp>(consumer)) {
    if (!genericOp.hasTensorSemantics())
      return nullptr;
    if (auto genericOpProducer = dyn_cast<GenericOp>(producer)) {
      if (genericOpProducer.hasTensorSemantics())
        return FuseGenericOpsOnTensors::fuse(genericOpProducer, genericOp,
                                             consumerIdx, rewriter, folder);
    } else if (auto reshapeOpProducer = dyn_cast<TensorReshapeOp>(producer)) {
      return FuseTensorReshapeOpAsProducer<GenericOp>::fuse(
          reshapeOpProducer, genericOp, consumerIdx, rewriter, folder);
    }
    return nullptr;
  }

  // Fuse when consumer is a TensorReshapeOp.
  if (TensorReshapeOp reshapeOp = dyn_cast<TensorReshapeOp>(consumer)) {
    if (auto genericOpProducer = dyn_cast<GenericOp>(producer)) {
      if (genericOpProducer.hasTensorSemantics())
        return FuseTensorReshapeOpAsConsumer<GenericOp>::fuse(
            genericOpProducer, reshapeOp, consumerIdx, rewriter, folder);
    }
    return nullptr;
  }
  return nullptr;
}

namespace {
/// Patterns to fuse a generic op, with the producer of its operands.
template <typename LinalgOpTy>
struct FuseTensorOps : public OpRewritePattern<LinalgOpTy> {
  using OpRewritePattern<LinalgOpTy>::OpRewritePattern;

  LogicalResult matchAndRewrite(LinalgOpTy op,
                                PatternRewriter &rewriter) const override {
    // Find the first operand that is defined by another generic op on tensors.
    for (auto operandNum :
         llvm::seq<unsigned>(0, op.getOperation()->getNumOperands())) {
      Operation *producer =
          op.getOperation()->getOperand(operandNum).getDefiningOp();
      if (Operation *fusedOp = fuseTensorOps(rewriter, op, operandNum)) {
        rewriter.replaceOp(op, fusedOp->getResults());
        if (producer && llvm::all_of(producer->getResults(),
                                     [](Value val) { return val.use_empty(); }))
          rewriter.eraseOp(producer);
        return success();
      }
    }
    return failure();
  }
};

/// Pass that fuses generic ops on tensors. Used only for testing.
struct FusionOfTensorOpsPass
    : public LinalgFusionOfTensorOpsBase<FusionOfTensorOpsPass> {
  void runOnOperation() override {
    OwningRewritePatternList patterns;
    Operation *op = getOperation();
    populateLinalgTensorOpsFusionPatterns(op->getContext(), patterns);
    applyPatternsAndFoldGreedily(op->getRegions(), patterns);
  };
};

struct LinalgFusionPass : public LinalgFusionBase<LinalgFusionPass> {
  void runOnFunction() override { fuseLinalgOpsGreedily(getFunction()); }
};
} // namespace

void mlir::populateLinalgTensorOpsFusionPatterns(
    MLIRContext *context, OwningRewritePatternList &patterns) {
  patterns.insert<FuseTensorOps<GenericOp>, FuseTensorOps<TensorReshapeOp>>(
      context);
}

std::unique_ptr<OperationPass<FuncOp>> mlir::createLinalgFusionPass() {
  return std::make_unique<LinalgFusionPass>();
}

std::unique_ptr<Pass> mlir::createLinalgFusionOfTensorOpsPass() {
  return std::make_unique<FusionOfTensorOpsPass>();
}