[mlir][Linalg] NFC: Refactor fusion on tensors to enable extending it to fusing different kinds of linalg operations on tensors.

[mravishankar]

Summary

The implementation of fusion on tensor was initially planned for just
GenericOps (and maybe IndexedGenericOps). With addition of
linalg.tensor_reshape, and potentially other such non-structured ops,
refactor the existing implementation to allow easier specification of
fusion between different linalg operations on tensors.

[nicolasvasilache]

Mostly looks good but the extra level of indirection through the template class was harder to follow than necessary I think.

Can we make it just an untyped entry point and drop the extra class ?

mlir/include/mlir/Dialect/Linalg/Utils/Utils.h

77

This looks like an easy way to get link errors.
Can you avoid templating here, just take Operation* as a consumer and do a switch underneath?

mlir/lib/Dialect/Linalg/Transforms/Fusion.cpp

415

This level of indirection does not seem to pay for itself.
Can we move the body of this function just above:

if (auto genericOp = dyn_cast<GenericOp>(producer)) {

on l. 590

[mravishankar]

mlir/lib/Dialect/Linalg/Transforms/Fusion.cpp

415

I want to keep the implementation of different fusions separate. This only has fusion of GenericOp with GenericOp. The follow up CL just adds the method to fuse GenericOp with TensorRehapeOp, and TensorReshapeOp with GenericOp. (see D78464)
I wasnt able to see a way in which you dont have to these pairwise implementations. They seem to have conditions to check and the exact steps to generate the fused op diverges. Part of the problem is that TensorReshapeOp is not a structure op (not saying it should be thats what made me go in this direction).
I could go further. You could fuse GenericOp with a ConstantOp which is a splat constant. So given all that this was what I thought would be most extendible.
The base case is just a place where common functionality can be kept and a common invocation method is defined. In this case though it is only the operands marshalling that is common (and checking that the ops are fusible).
That being said, happy to course correct. Ill try to simplify the structure anyway.

[mravishankar]

Addressing comments

Updating D78463: [mlir][Linalg] NFC: Refactor fusion on tensors to enable extending it

to fusing different kinds of linalg operations on tensors.

[mravishankar]

Remmoving indirection in class heirarchy.

Updating D78463: [mlir][Linalg] NFC: Refactor fusion on tensors to enable extending it

to fusing different kinds of linalg operations on tensors.

[mravishankar]

mlir/lib/Dialect/Linalg/Transforms/Fusion.cpp

415

Removed the level of indirection here. PTAL

[nicolasvasilache]

Thanks Mahesh!

[mehdi_amini]

What is the plan for the fusion in general? Is this intended to stay specific to Linalg? Is this supposed to be extensible composable? It does not seem to be designed with interfaces to abstract away the dialect operations right now (the path under lib/Dialect/Linalg/Transforms/ may also contribute there).
There is an XLA fusion pass getting tested in the TensorFlow repo but that is a good candidate for upstream at some point. However we need to figure a path for all of these to compose.

mlir/include/mlir/Dialect/Linalg/Utils/Utils.h

 #include "mlir/Dialect/StandardOps/IR/Ops.h"
 
 #include "llvm/ADT/SetVector.h"
 
 namespace mlir {
 class AffineExpr;
 class AffineMap;
 class OperationFolder;
+class PatternRewriter;
 
 namespace linalg {
 class LinalgDependenceGraph;
 
 struct FusionInfo {
   LinalgOp originalProducer;
   LinalgOp fusedProducer;
 };

 /// When non-null, the optional pointer `folder` is used to call into the
 /// `createAndFold` builder method. If `folder` is null, the regular `create`
 /// method is called.
 Optional<FusionInfo> fuseProducerOf(OpBuilder &b, LinalgOp consumer,
                                     unsigned consumerIdx,
                                     const LinalgDependenceGraph &graph,
                                     OperationFolder *folder = nullptr);
 
-/// Fuse linalg operation on tensors, where the result of the producer is used
-/// as the operand of the consumer at position `consumerIdx`.
-Optional<LinalgOp> fuseTensorOps(OpBuilder &b, LinalgOp producer,
-                                 LinalgOp consumer, unsigned consumerIdx,
-                                 OperationFolder *folder = nullptr);
+/// Fuse linalg operation on tensors, with the producer of the operand at
+/// position `consumerIdx` of the consumer.
+Operation *fuseTensorOps(PatternRewriter &rewriter, Operation *consumer,
+                         unsigned consumerIdx,
 
 /// Returns the linearized list of all view dimensions in a linalgOp. Applying
 /// the inverse, concatenated loopToOperandRangeMaps to this list allows the
 /// derivation of loop ranges for any linalgOp.
 template <typename ConcreteOp>
 SmallVector<Value, 8> getViewSizes(OpBuilder &builder, ConcreteOp linalgOp) {
   auto loc = linalgOp.getLoc();
   SmallVector<Value, 8> res;

mlir/lib/Dialect/Linalg/Transforms/Fusion.cpp

Optional<FusionInfo> mlir::linalg::fuseProducerOf(
   for (auto dep : deps) {
     if (auto res =
             fuseProducerOfDep(b, consumer, consumerIdx, graph, folder, dep))
       return res;
   }
   return llvm::None;
 }
 
-/// Checks if two Generic ops are fusible, when one is a producer and another is
-/// a consumer (with the result of the producer being the `consumerIdx` operand
-/// of the consumer).
-static bool areTensorOpsFusible(LinalgOp producer, LinalgOp consumer,
-                                unsigned consumerIdx) {
-  // Verify that the producer and consumer are ops on tensors.
-  if (!producer.hasTensorSemantics() || !consumer.hasTensorSemantics())
-    return false;
-
-  auto producerOp = dyn_cast<linalg::GenericOp>(producer.getOperation());
-  auto consumerOp = dyn_cast<linalg::GenericOp>(consumer.getOperation());
-  // Verify that
-  // - the producer and consumers are generic ops,
-  // - only handle cases where the producer has a single return value,
-  // - the producer return value should be the same as argument at `consumerIdx`
-  //   of the consumer,
-  // - the producer has all "parallel" iterator type.
-  // - only handle ops that use regions for specifying the scalar operations.
-  if (!producerOp || !consumerOp || producerOp.getNumOutputs() != 1 ||
-      producerOp.getResult(0) != consumerOp.getOperand(consumerIdx) ||
-      producerOp.getNumParallelLoops() != producerOp.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 = consumerOp.getIndexingMap(consumerIdx);
-  if (consumerIndexMap.getNumResults() != producerOp.getNumLoops())
-    return false;
-
-  // Finally the index_map for the result must be invertible. For now just
-  // verify it is a permutation.
-  AffineMap producerResultIndexMap = producerOp.getOutputIndexingMap(0);
-  return producerResultIndexMap.isPermutation();
-}
-
-/// Computes the indexing maps for arguments of a producer generic op when the
-/// result of the producer is fused with the consumer.
-/// - consumerIndexMap is the indexing_map for the argument in the consumer op
-///   that is the result of the producer op.
-/// - invProducerResultIndexMap is the inverse of the indexing_map for the
-///   result in the producer op.
-/// - producerArgIndexMap is the indexing_map of the argument of the producer
-///   op.
-/// The result is the indexing_map to use for the producer argument when the
-/// producer and consumer ops are fused.
-static AffineMap computeProducerArgMap(AffineMap consumerIndexMap,
-                                       AffineMap invProducerResultIndexMap,
-                                       AffineMap producerArgIndexMap) {
-  // t1 is map from producer result tensor index -> producer arg tensor index.
-  auto t1 = producerArgIndexMap.compose(invProducerResultIndexMap);
-  // The return is map from consumer loop -> producer arg tensor index,
-  // i.e. indexing_map for the producer argument in the fused operation.
-  return t1.compose(consumerIndexMap);
-}
-
-Optional<LinalgOp> mlir::linalg::fuseTensorOps(OpBuilder &b, LinalgOp producer,
-                                               LinalgOp consumer,
-                                               unsigned consumerIdx,
-                                               OperationFolder *folder) {
-  if (!areTensorOpsFusible(producer, consumer, consumerIdx))
-    return {};
-
-  MLIRContext *context = b.getContext();
-  auto producerOp = cast<linalg::GenericOp>(producer.getOperation());
-  auto consumerOp = cast<linalg::GenericOp>(consumer.getOperation());
-  AffineMap consumerIndexMap = consumerOp.getIndexingMap(consumerIdx);
-  AffineMap invProducerResultIndexMap =
-      inversePermutation(producerOp.getOutputIndexingMap(0));
-  if (!invProducerResultIndexMap)
-    return {};
-
-  // Compute the fused op operandslist by replacing the operand corresponding to
-  // the result of the producer, with the operands of the producer.
-  unsigned fusedArgsIn =
-      producerOp.getNumInputs() + consumerOp.getNumInputs() - 1;
-  auto fusedArgsOut = consumerOp.getNumOutputs();
-  SmallVector<Value, 2> fusedOperandsList(consumerOp.getOperands());
-  fusedOperandsList.erase(std::next(fusedOperandsList.begin(), consumerIdx));
-  fusedOperandsList.reserve(fusedArgsIn + fusedArgsOut);
-  fusedOperandsList.insert(
-      std::next(fusedOperandsList.begin(), consumerIdx),
-      producerOp.operand_begin(),
-      std::next(producerOp.operand_begin(), producerOp.getNumInputs()));
-
-  // Compute the fused indexing_maps of the operands/results of the fused op.
-  SmallVector<Attribute, 2> fusedIndexingMapAttrs;
-  fusedIndexingMapAttrs.reserve(fusedArgsIn + fusedArgsOut);
-  fusedIndexingMapAttrs.append(consumerOp.indexing_maps().begin(),
-                               consumerOp.indexing_maps().end());
-  fusedIndexingMapAttrs.erase(
-      std::next(fusedIndexingMapAttrs.begin(), consumerIdx));
-  auto *insertPos = std::next(fusedIndexingMapAttrs.begin(), consumerIdx);
-  for (auto producerArgIndexAttr :
-       llvm::enumerate(producerOp.indexing_maps())) {
-    if (producerArgIndexAttr.index() == producerOp.getNumInputs())
-      break;
-    auto composedIndexMap = computeProducerArgMap(
-        consumerIndexMap, invProducerResultIndexMap,
-        producerArgIndexAttr.value().cast<AffineMapAttr>().getValue());
-    insertPos = std::next(fusedIndexingMapAttrs.insert(
-        insertPos, AffineMapAttr::get(composedIndexMap)));
-  }
-
-  // Generate the fused op.
-  auto fusedLinalgOp = b.create<GenericOp>(
-      UnknownLoc::get(context), consumerOp.getResultTypes(), fusedOperandsList,
-      b.getI64IntegerAttr(fusedArgsIn), b.getI64IntegerAttr(fusedArgsOut),
-      b.getArrayAttr(fusedIndexingMapAttrs), consumerOp.iterator_types(),
-      /*doc=*/nullptr,
-      /*library_call=*/nullptr);
-
-  // Build the region of the fused op.
-  auto &fusedOpRegion = fusedLinalgOp.region();
-  Block &producerOpBlock = producerOp.region().front();
-  Block &consumerOpBlock = consumerOp.region().front();
-  Block *fusedBlock = new Block();
-  fusedOpRegion.push_back(fusedBlock);
-  BlockAndValueMapping mapper;
-  // Map the arguments for the unmodified args from the consumer.
-  for (auto consumerOpArg : llvm::enumerate(consumerOpBlock.getArguments())) {
-    if (consumerOpArg.index() == consumerIdx) {
-      // Map the arguments for the args from the producer.
-      for (auto producerOpArg : producerOpBlock.getArguments())
-        mapper.map(producerOpArg,
-                   fusedBlock->addArgument(producerOpArg.getType()));
-      continue;
-    }
-    mapper.map(consumerOpArg.value(),
-               fusedBlock->addArgument(consumerOpArg.value().getType()));
-  }
-
-  // Add operations from producer (except the yield operation) to the fused op.
-  for (auto &op : producerOpBlock.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(consumerOpBlock.getArgument(consumerIdx), clonedVal);
-      continue;
-    }
-    fusedBlock->push_back(op.clone(mapper));
-  }
-  for (auto &op : consumerOpBlock.getOperations())
-    fusedBlock->push_back(op.clone(mapper));
-
-  return cast<LinalgOp>(fusedLinalgOp.getOperation());
-}
-
 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.
static void fuseLinalgOpsGreedily(FuncOp f) {
   // 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 {
 
-/// Patterns to fuse a generic op, with the producer of its operands.
-struct FuseGenericTensorOps : public OpRewritePattern<GenericOp> {
-  using OpRewritePattern<GenericOp>::OpRewritePattern;
+/// 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;
 
-  LogicalResult matchAndRewrite(GenericOp op,
-                                PatternRewriter &rewriter) const override {
-    if (!op.hasTensorSemantics())
-      return failure();
+    // 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;
 
-    // Find the first operand that is defined by another generic op on tensors.
-    for (auto operand : llvm::enumerate(op.getOperation()->getOperands())) {
-      auto definingOp =
-          dyn_cast_or_null<GenericOp>(operand.value().getDefiningOp());
-      if (!definingOp || !definingOp.hasTensorSemantics())
+    // 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;
-      auto fusedOp =
-          fuseTensorOps(rewriter, cast<LinalgOp>(definingOp.getOperation()),
-                        cast<LinalgOp>(op.getOperation()), operand.index());
-      if (!fusedOp)
+      }
+      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.replaceOp(op, fusedOp.getValue().getOperation()->getResults());
-      if (llvm::all_of(definingOp.getResults(),
-                       [](Value val) -> bool { return val.use_empty(); }))
-        rewriter.eraseOp(definingOp);
+      }
+      rewriter.clone(op, mapper);
+    }
+    for (auto &op : consumerBlock.getOperations())
+      rewriter.clone(op, mapper);
+  }
+};
+} // 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;
+
+  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);
+    }
+  }
+  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();
-    patterns.insert<FuseGenericTensorOps>(op->getContext());
+    patterns.insert<FuseTensorOps<GenericOp>>(op->getContext());
     applyPatternsAndFoldGreedily(op->getRegions(), patterns);
   };
 };
 
 struct LinalgFusionPass : public LinalgFusionBase<LinalgFusionPass> {
   void runOnFunction() override { fuseLinalgOpsGreedily(getFunction()); }
 };
 } // namespace
 
 std::unique_ptr<OperationPass<FuncOp>> mlir::createLinalgFusionPass() {
   return std::make_unique<LinalgFusionPass>();
 }
 
 std::unique_ptr<Pass> mlir::createLinalgFusionOfTensorOpsPass() {
   return std::make_unique<FusionOfTensorOpsPass>();
 }