[mlir][Linalg] NFC: Refactor fusion of LinalgOp with TensorReshapeOp by expansion.

[mravishankar]

Summary

Change the implementation of LinalgOp with TensorReshapeOp by
expansion to be more modular and easier to follow.

Depends On D93724

[hanchung]

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

466–504

Look into the implementation, I feel this is more like a constructer, but you want to check if it construct successfully or not. The function assumes some things, e.g., reassociation is empty. I think if the function is expected to call only once, at least we should write it down to the documentation, or add example usage.

I suggest to do it in this way:

1. Add a constructor which takes LinalgOp and fusedTensorIndex.
2. Add something like std::call_once (or a flag) to compute, which make sure it is only called once.
3. Add a comment to state the expected usage is call compute at least once...

521–522

Can't this be for (int64_t shape : expandedShape.drop_front())?

638

s/Region/region

660

auto is enough

672

auto is enough

[mravishankar]

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

466–504

It is a constructor, but I dont know if there is a way to return an error from a constructor. AFAIK, thats not possible without exceptions. So keeping the constructor simple, and adding a compute method. Ill add comments and checks to verify that it either is called once, or that it resets state if called multiple times.

521–522

Thanks!

[mravishankar]

Rebase

[hanchung]

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

466–504

Yes, I think we typically don't return an error or exception from a constructor, and we should avoid that. The suggestion actually keeps the constructor simple, it only takes two variables and store them. The compute function is also simple, only need an extra bool variable to make sure it's called once.

I think both are okay, this doesn't make things harder since it's only used in this file, and one case.

nit: how about rename it with init?

660

missed this one? I would prefer at least l.660 and l.672 being consistent, ie, both use unsigned or both use auto.

[mravishankar]

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

466–504

If its fine by you, I would prefer to leave it as is for now. It is defined and used in the same file, and adapting it to your recommendation is a minor refactoring. We can revisit this later.
I prefer compute cause it makes it explicit that this is computing the information needed for expansion.

660

Will make this auto. Yes, I missed this one. Thanks

[mravishankar]

Rebase

[mravishankar]

Minor comment addressed

[mravishankar]

Addressed remaining minor comments

mlir/include/mlir/Dialect/Linalg/IR/LinalgOps.h

 /// patterns of the form `(m, n)[s] -> (m + n - s floordiv 2)`.
 /// A more advanced Tensor-Comprehension like inference is possible but has
 /// proven to be ambiguous in unfavorable case.
 /// As a consequence, we relax the default behavior very conservatively and
 /// provide an op-specified hook so that Linalg ops may override the behavior.
 LoopRangeBuilder defaultLoopRangesBuilder(LinalgOp op);
 
 using ReassociationIndices = SmallVector<int64_t, 2>;
+using ReassociationIndicesRef = ArrayRef<int64_t>;
 using ReassociationExprs = SmallVector<AffineExpr, 2>;
 
 /// Returns the name mangled library call name to disambiguate between different
 /// overloads at the C level. The name mangling scheme is basic and uses MLIR
 /// type names:
 ///   1. form a string which is the concatenation of the linalg op name with all
 ///      the operand type names, separate by underscores;
 ///   2. drop the `linalg.` prefix, and the `<`, `>`, `?` symbols from the type.

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

return isa<GenericOp, IndexedGenericOp>(linalgOp.getOperation()) &&
                       }) &&
          linalgOp.getIndexingMap(fusedTensorIndex).getNumResults() > 0 &&
          llvm::all_of(linalgOp.iterator_types(), [](Attribute attr) {
            return attr.cast<StringAttr>().getValue() ==
                   getParallelIteratorTypeName();
          });
 }
 
-// Get the output tensor to use for the expanded operation. Creates an
-// `linalg.init_tensor` operation to materialize the tensor that carries the
-// shape information.
-static Value getOutputValueForExpansion(
-    OpBuilder &builder, Location loc, AffineMap outputIndexingMap, Value result,
-    ArrayRef<SmallVector<int64_t, 4>> origDimToExpandedShapeMap) {
+namespace {
+/// Information needed to expand a generic/indexed_generic operation to fold the
+/// reshape with it.
+class ExpansionInfo {
+public:
+  // Computes the mapping from original dimensions of the op to the dimensions
+  // of the expanded op given the `indexingMap` of the fused operand/result of
+  // the generic/indexed_generic op, the `reassocationMaps` of the reshape op
+  // and the shape of the expanded op.
+  LogicalResult compute(LinalgOp linalgOp, unsigned fusedTensorIndex,
+                        ArrayRef<AffineMap> reassociationMaps,
+                        ArrayRef<int64_t> expandedShape);
+  unsigned getOrigOpNumDims() const { return reassociation.size(); }
+  unsigned getExpandedOpNumDims() const { return expandedOpNumDims; }
+  ReassociationIndicesRef getExpandedDims(unsigned i) const {
+    return reassociation[i];
+  }
+  ArrayRef<int64_t> getExpandedShapeOfDim(unsigned i) const {
+    return expandedShapeMap[i];
+  }
+
+private:
+  /// Reassociation from the dimensions in the original operation to the
+  /// dimension of the expanded operation.
+  SmallVector<ReassociationIndices, 4> reassociation;
+  /// Mapping from extent of loops in the original operation, to the extent of
+  /// loops in the expanded operation.
+  SmallVector<SmallVector<int64_t, 4>, 4> expandedShapeMap;
+  unsigned expandedOpNumDims;
+};
+} // namespace
+
+LogicalResult ExpansionInfo::compute(LinalgOp linalgOp,
+                                     unsigned fusedTensorIndex,
+                                     ArrayRef<AffineMap> reassociationMaps,
+                                     ArrayRef<int64_t> expandedShape) {
+  if (reassociationMaps.empty())
+    return failure();
+  AffineMap fusedIndexMap = linalgOp.getIndexingMap(fusedTensorIndex);
+
+  Optional<SmallVector<int64_t, 4>> originalLoopRange =
+      getStaticLoopRanges(linalgOp);
+  if (!originalLoopRange)
+    return linalgOp.emitError("unable to find loop range for operation");
+
+  reassociation.clear();
+  expandedShapeMap.clear();
+  // Compute the number of dimension in the expanded op that correspond to each
+  // dimension of the original op.
+  SmallVector<unsigned, 4> numExpandedDims(fusedIndexMap.getNumDims(), 1);
+  expandedShapeMap.resize(fusedIndexMap.getNumDims());
+  for (auto resultExpr : llvm::enumerate(fusedIndexMap.getResults())) {
+    unsigned pos = resultExpr.value().cast<AffineDimExpr>().getPosition();
+    AffineMap foldedDims = reassociationMaps[resultExpr.index()];
+    numExpandedDims[pos] = foldedDims.getNumResults();
+    ArrayRef<int64_t> shape =
+        expandedShape.slice(foldedDims.getDimPosition(0), numExpandedDims[pos]);
+    expandedShapeMap[pos].assign(shape.begin(), shape.end());
+  }
+  // The remaining dimensions remain the same.
+  for (unsigned i : llvm::seq<unsigned>(0, fusedIndexMap.getNumDims()))
+    if (expandedShapeMap[i].empty())
+      expandedShapeMap[i] = {(*originalLoopRange)[i]};
+
+  // Compute reassociation map from the original op to the expanded op.
+  unsigned sum = 0;
+  reassociation.reserve(fusedIndexMap.getNumDims());
+  for (auto numFoldedDim : llvm::enumerate(numExpandedDims)) {
+    auto seq = llvm::seq<int64_t>(sum, sum + numFoldedDim.value());
+    reassociation.emplace_back(seq.begin(), seq.end());
+    sum += numFoldedDim.value();
+  }
+  expandedOpNumDims = sum;
+  return success();
+}
+
+/// To expand an indexed_generic operation, the body of the indexed generic op
+/// need to be modified appropriately. Specifically, uses of arguments for
+/// induction variables in the original operation need to be replaced with
+/// linearization of the corresponding arguments in the expanded op. That
+/// requires the shape of the expanded dimensions (at least all but the most
+/// significant. For now check that these are all statically sized. Note that
+/// this could be extended to handle dynamic case, but the implementation below
+/// uses `affine.apply` which seems to have issues when the shapes are not
+/// static.
+LogicalResult isIndexedGenericOpExpandable(LinalgOp linalgOp,
+                                           const ExpansionInfo &expansionInfo) {
+  for (unsigned i : llvm::seq<unsigned>(0, expansionInfo.getOrigOpNumDims())) {
+    ArrayRef<int64_t> expandedShape = expansionInfo.getExpandedShapeOfDim(i);
+    if (expandedShape.size() == 1)
+      continue;
+    for (int64_t shape : expandedShape.drop_front()) {
+      if (ShapedType::isDynamic(shape)) {
+        return linalgOp.emitError(
+            "unable to fuse indexed generic op where the expanded dim is "
+            "dynamic");
+      }
+    }
+  }
+  return success();
+}
+
+/// Return the indexing map to use in the expanded op for a given the
+/// `indexingMap` of the original operation.
+static AffineMap
+getIndexingMapInExpandedOp(OpBuilder &builder, AffineMap indexingMap,
+                           const ExpansionInfo &expansionInfo) {
+  SmallVector<AffineExpr, 4> newExprs;
+  for (AffineExpr expr : indexingMap.getResults()) {
+    unsigned pos = expr.cast<AffineDimExpr>().getPosition();
+    SmallVector<AffineExpr, 4> expandedExprs = llvm::to_vector<4>(
+        llvm::map_range(expansionInfo.getExpandedDims(pos), [&](int64_t v) {
+          return builder.getAffineDimExpr(static_cast<unsigned>(v));
+        }));
+    newExprs.append(expandedExprs.begin(), expandedExprs.end());
+  }
+  return AffineMap::get(expansionInfo.getExpandedOpNumDims(),
+                        indexingMap.getNumSymbols(), newExprs,
+                        builder.getContext());
+}
+
+/// Return the type of the operand/result to use in the expanded op given the
+/// type in the original op.
+static RankedTensorType getExpandedType(RankedTensorType originalType,
+                                        AffineMap indexingMap,
+                                        const ExpansionInfo &expansionInfo) {
+  SmallVector<int64_t, 4> expandedShape;
+  for (AffineExpr expr : indexingMap.getResults()) {
+    unsigned dim = expr.cast<AffineDimExpr>().getPosition();
+    auto dimExpansion = expansionInfo.getExpandedShapeOfDim(dim);
+    expandedShape.append(dimExpansion.begin(), dimExpansion.end());
+  }
+  return RankedTensorType::get(expandedShape, originalType.getElementType());
+}
+
+/// Get the value to use for the output in the expanded operation given the
+/// `indexingMap` for the output in the original op. Creates an
+/// `linalg.init_tensor` operation to materialize the tensor that carries the
+/// shape information. This is only used when the tensor_reshape is expanding
+/// and is a consumer. In such cases, the tensor_reshape op semantics gaurantees
+/// that the shape of the output is computable from the shape of the input since
+/// at most one of the expanded dims can be dynamic.
+static Value getOutputValueForExpandedOp(OpBuilder &builder, Location loc,
+                                         AffineMap indexingMap, Value result,
+                                         const ExpansionInfo &expansionInfo) {
   SmallVector<Value, 4> dynamicDims;
   SmallVector<int64_t, 4> staticDims;
   ShapedType resultType = result.getType().cast<ShapedType>();
   ArrayRef<int64_t> origShape = resultType.getShape();
-  for (AffineExpr expr : outputIndexingMap.getResults()) {
+  for (AffineExpr expr : indexingMap.getResults()) {
     unsigned origDimPos = expr.cast<AffineDimExpr>().getPosition();
-    ArrayRef<int64_t> expandedShape(origDimToExpandedShapeMap[origDimPos]);
     bool foundDynamic = false;
     int64_t linearizedShape = 1;
-    for (int64_t extent : expandedShape) {
+    for (int64_t extent : expansionInfo.getExpandedShapeOfDim(origDimPos)) {
       if (ShapedType::isDynamic(extent)) {
         assert(!foundDynamic &&
                "Expanded dimensions of reshape can have only one dynamic dim");
         staticDims.push_back(ShapedType::kDynamicSize);
         foundDynamic = true;
         continue;
       }
       staticDims.push_back(extent);
       linearizedShape *= extent;
     }
     if (ShapedType::isDynamic(origShape[origDimPos])) {
       Value origDim = builder.create<DimOp>(loc, result, origDimPos);
       dynamicDims.push_back(builder.create<UnsignedDivIOp>(
           loc, origDim, builder.create<ConstantIndexOp>(loc, linearizedShape)));
     }
   }
   return builder.create<linalg::InitTensorOp>(loc, dynamicDims, staticDims,
                                               resultType.getElementType());
 }
 
+/// Returns the reassociation maps to use in the `linalg.tensor_reshape`
+/// operation to convert the operands of the origial operation to operands of
+/// the expanded operation. The same method is used to compute the
+/// `linalg.tensor_reshape` used to collapse the result of the expanded op to
+/// get the value that can replace all uses of the results of the original op.
+static SmallVector<ReassociationIndices, 4>
+getReassociationForExpansion(AffineMap indexingMap,
+                             const ExpansionInfo &expansionInfo) {
+  SmallVector<ReassociationIndices, 4> reassociation;
+  unsigned numReshapeDims = 0;
+  for (AffineExpr expr : indexingMap.getResults()) {
+    unsigned dim = expr.cast<AffineDimExpr>().getPosition();
+    auto numExpandedDims = expansionInfo.getExpandedDims(dim).size();
+    auto indices = llvm::to_vector<2>(
+        llvm::seq<int64_t>(numReshapeDims, numReshapeDims + numExpandedDims));
+    reassociation.emplace_back(std::move(indices));
+    numReshapeDims += numExpandedDims;
+  }
+  return reassociation;
+}
+
+/// Build the body of the expanded IndexedGenericOp. The arguments for the
+/// induction variables of the original operation need to be recovered by
+/// linearizing the arguments of the corresponding dimensions of the expanded
+/// op. For now it is assumed that the shapes of the expanded op needed for
+/// linearization are static.
+static void buildExpandedIndexedGenericOpRegion(
+    PatternRewriter &rewriter, Location loc, Region &originalOpRegion,
+    Region &fusedOpRegion, const ExpansionInfo &expansionInfo) {
+  assert(fusedOpRegion.empty() && "expected fused op to have empty region");
+  // Create an entry block in the fused region with same number of arguments
+  // as the fused op
+  Block *fusedEntryBlock = new Block;
+  fusedOpRegion.push_back(fusedEntryBlock);
+  rewriter.cloneRegionBefore(originalOpRegion, fusedOpRegion,
+                             fusedOpRegion.end());
+
+  // Merge the entry block of the fused op with the cloned blocks. For this
+  // compute the value for arguments of the region in the original operation
+  // in terms of the arguments of the fused op. Since the original operation
+  // is expanded, the expanded dimensions need to be folded back to get the
+  // replacement value for the arguments corresponding to interation index.
+  // For now this expects that all the loop ranges are constants, which is
+  // true if the shapes are all static. This has already been checked in the
+  // precondition.
+  using namespace edsc::op;
+  using namespace edsc::intrinsics;
+  OpBuilder::InsertionGuard guard(rewriter);
+  SmallVector<Value, 4> argReplacements(originalOpRegion.getNumArguments());
+  rewriter.setInsertionPointToStart(fusedEntryBlock);
+  edsc::ScopedContext scopedContext(rewriter, loc);
+  IndexType indexType = rewriter.getIndexType();
+  for (unsigned i : llvm::seq<unsigned>(0, expansionInfo.getOrigOpNumDims())) {
+    Value linearizedIndex = fusedEntryBlock->addArgument(indexType);
+    ArrayRef<int64_t> expandedDimsShape =
+        expansionInfo.getExpandedShapeOfDim(i).drop_front();
+    for (unsigned shape : expandedDimsShape) {
+      assert(!ShapedType::isDynamic(shape));
+      linearizedIndex = linearizedIndex * std_constant_index(shape);
+      linearizedIndex =
+          linearizedIndex + fusedEntryBlock->addArgument(indexType);
+    }
+    argReplacements[i] = linearizedIndex;
+  }
+  for (auto i : llvm::seq<unsigned>(expansionInfo.getOrigOpNumDims(),
+                                    argReplacements.size())) {
+    argReplacements[i] =
+        fusedEntryBlock->addArgument(originalOpRegion.getArgument(i).getType());
+  }
+  rewriter.mergeBlocks(fusedEntryBlock->getNextNode(), fusedEntryBlock,
+                       argReplacements);
+}
+
 /// Implements the fusion of a tensor_reshape op and a generic/indexed_generic
 /// op as explained in `isFusableWithReshapeByExpansion`. Assumes that those
 /// conditions have been satisfied.
 static Optional<SmallVector<Value, 1>>
 fuseWithReshapeByExpansion(LinalgOp linalgOp, TensorReshapeOp reshapeOp,
                            unsigned fusedTensorIndex,
                            PatternRewriter &rewriter) {
   assert(isFusableWithReshapeByDimExpansion(linalgOp, fusedTensorIndex) &&
          "preconditions for fuse operation failed");
   // Check if reshape is expanding or collapsing.
   bool isExpanding =
       reshapeOp.getSrcType().getRank() < reshapeOp.getResultType().getRank();
   RankedTensorType expandedType =
       isExpanding ? reshapeOp.getResultType() : reshapeOp.getSrcType();
-  AffineMap fusedIndexMap = linalgOp.getIndexingMap(fusedTensorIndex);
 
-  // The reshape is folding/expanding consecutive dimensions. Given the indexing
-  // map of the fused tensor find the number of dimensions each of the loops of
-  // the original op is expanded into. Also record the shape of the expanded
-  // dimensions.
-  ArrayRef<int64_t> expandedShape = expandedType.getShape();
-  Optional<SmallVector<int64_t, 4>> origOpLoopRange =
-      getStaticLoopRanges(linalgOp);
-  if (!origOpLoopRange) {
-    linalgOp.emitError("unable to find loop range for operation");
+  ExpansionInfo expansionInfo;
+  if (failed(expansionInfo.compute(linalgOp, fusedTensorIndex,
+                                   reshapeOp.getReassociationMaps(),
+                                   expandedType.getShape())))
     return llvm::None;
-  }
-  SmallVector<unsigned, 4> numFoldedDims(fusedIndexMap.getNumDims(), 1);
-  SmallVector<SmallVector<int64_t, 4>, 4> expandedDimsShape(
-      fusedIndexMap.getNumDims());
-  auto reassociationMaps = reshapeOp.getReassociationMaps();
-  for (auto resultExpr : llvm::enumerate(fusedIndexMap.getResults())) {
-    unsigned pos = resultExpr.value().cast<AffineDimExpr>().getPosition();
-    AffineMap foldedDims = reassociationMaps[resultExpr.index()];
-    numFoldedDims[pos] = foldedDims.getNumResults();
-    ArrayRef<int64_t> shape =
-        expandedShape.slice(foldedDims.getDimPosition(0), numFoldedDims[pos]);
-    expandedDimsShape[pos].assign(shape.begin(), shape.end());
-  }
-  // The remaining dimensions remain the same.
-  for (unsigned i : llvm::seq<unsigned>(0, fusedIndexMap.getNumDims()))
-    if (expandedDimsShape[i].empty())
-      expandedDimsShape[i] = {(*origOpLoopRange)[i]};
 
-  if (isa<IndexedGenericOp>(linalgOp.getOperation())) {
-    // For indexed generic op, the region contains arguments that represent the
-    // induction variable value of the loops. In the fused op these values are
-    // obtained by linearizing the expanded dimensions. For now just check that
-    // the extents used in the linearization (all the expanded dims except the
-    // front) are statically know. For dynamic case, we would need shape
-    // information on these dimensions to get these.
-    for (auto &expandedShape : expandedDimsShape) {
-      if (expandedShape.size() == 1)
-        continue;
-      for (int64_t expandedDimShape : llvm::make_range(
-               std::next(expandedShape.begin()), expandedShape.end())) {
-        if (ShapedType::isDynamic(expandedDimShape)) {
-          linalgOp.emitError(
-              "unable to fuse indexed generic op where the expanded dim is "
-              "dynamic");
-          return llvm::None;
-        }
-      }
-    }
-  }
+  if (isa<IndexedGenericOp>(linalgOp.getOperation()) &&
+      failed(isIndexedGenericOpExpandable(linalgOp, expansionInfo)))
 
-  // The remapping of the indices is then the prefix sum (inclusive) of the
-  // numFoldedDims.
-  SmallVector<unsigned, 4> remapping(numFoldedDims.size() + 1, 0);
-  unsigned sum = 0;
-  for (auto numFoldedDim : llvm::enumerate(numFoldedDims)) {
-    sum += numFoldedDim.value();
-    remapping[numFoldedDim.index() + 1] = sum;
-  }
+  SmallVector<AffineMap, 4> expandedOpIndexingMaps = llvm::to_vector<4>(
+      llvm::map_range(linalgOp.getIndexingMaps(), [&](AffineMap m) {
+        return getIndexingMapInExpandedOp(rewriter, m, expansionInfo);
+      }));
 
-  SmallVector<AffineMap, 4> expandedOpIndexingMaps;
-  // Compute the modified indexing maps by replacing every loop (AffineDimExpr)
-  // in the original indexing map with the sequence of loops that it is expanded
-  // to.
-  for (AffineMap indexingMap : linalgOp.getIndexingMaps()) {
-    SmallVector<AffineExpr, 4> newExprs;
-    for (AffineExpr expr : indexingMap.getResults()) {
-      unsigned pos = expr.cast<AffineDimExpr>().getPosition();
-      for (unsigned newPos :
-           llvm::seq<unsigned>(remapping[pos], remapping[pos + 1])) {
-        newExprs.push_back(rewriter.getAffineDimExpr(newPos));
-      }
-    }
-    expandedOpIndexingMaps.push_back(
-        AffineMap::get(remapping.back(), indexingMap.getNumSymbols(), newExprs,
-                       rewriter.getContext()));
-  }
-
-  // The operands of the expanded op are computed by reshaping the original
-  // operands. The reshape depends on the ordering of the loop used to access
-  // the tensor in the original operation, and are expanded into as many
-  // dimensions as the loop is expanded into (as computed by `remapping`).
-  auto getReshapeInfo =
-      [&](AffineMap operandIndexingMap,
-          SmallVectorImpl<ReassociationIndices> &reassociation,
-          SmallVectorImpl<int64_t> &expandedOpOperandShape) {
-        unsigned reshapeDims = 0;
-        for (AffineExpr expr : operandIndexingMap.getResults()) {
-          unsigned origDim = expr.cast<AffineDimExpr>().getPosition();
-          auto foldedDims = llvm::seq<int64_t>(
-              reshapeDims, reshapeDims + numFoldedDims[origDim]);
-          reassociation.emplace_back(foldedDims.begin(), foldedDims.end());
-          expandedOpOperandShape.append(expandedDimsShape[origDim].begin(),
-                                        expandedDimsShape[origDim].end());
-          reshapeDims += numFoldedDims[origDim];
-        }
-      };
   SmallVector<Value, 4> expandedOpOperands;
   for (auto operand : llvm::enumerate(linalgOp.getInputs())) {
     if (operand.index() == fusedTensorIndex) {
       expandedOpOperands.push_back(reshapeOp.src());
       continue;
     }
     AffineMap indexingMap = linalgOp.getInputIndexingMap(operand.index());
-    SmallVector<ReassociationIndices, 4> reassociation;
-    SmallVector<int64_t, 4> expandedOperandShape;
-    getReshapeInfo(indexingMap, reassociation, expandedOperandShape);
-    Type expandedOperandType = RankedTensorType::get(
-        expandedOperandShape,
-        operand.value().getType().cast<ShapedType>().getElementType());
+    RankedTensorType expandedOperandType =
+        getExpandedType(operand.value().getType().cast<RankedTensorType>(),
+                        indexingMap, expansionInfo);
     if (expandedOperandType != operand.value().getType()) {
+      // Reshape the operand to get the right type.
+      SmallVector<ReassociationIndices, 4> reassociation =
+          getReassociationForExpansion(indexingMap, expansionInfo);
       expandedOpOperands.push_back(rewriter.create<TensorReshapeOp>(
           linalgOp.getLoc(), expandedOperandType, operand.value(),
           reassociation));
-    } else {
-      expandedOpOperands.push_back(operand.value());
+      continue;
     }
+    expandedOpOperands.push_back(operand.value());
   }
 
   Location loc = linalgOp.getLoc();
   SmallVector<Value, 1> outputs;
-  SmallVector<SmallVector<ReassociationIndices, 4>, 1> resultReassociation;
   for (auto result : llvm::enumerate(linalgOp.getOutputs())) {
     AffineMap indexingMap = linalgOp.getOutputIndexingMap(result.index());
-    SmallVector<ReassociationIndices, 4> reassociation;
-    SmallVector<int64_t, 4> expandedResultShape;
-    getReshapeInfo(indexingMap, reassociation, expandedResultShape);
-    outputs.push_back(getOutputValueForExpansion(
-        rewriter, loc, indexingMap, result.value(), expandedDimsShape));
-    resultReassociation.emplace_back(std::move(reassociation));
+    outputs.push_back(getOutputValueForExpandedOp(
+        rewriter, loc, indexingMap, result.value(), expansionInfo));
   }
 
   // The iterator types of the expanded op are all parallel.
-  SmallVector<StringRef, 4> iteratorTypes(remapping.back(),
+  SmallVector<StringRef, 4> iteratorTypes(expansionInfo.getExpandedOpNumDims(),
                                           getParallelIteratorTypeName());
 
   TypeRange resultTypes = ValueRange(outputs).getTypes();
   LinalgOp fusedOp = createLinalgOpOfSameType(
       linalgOp, rewriter, linalgOp.getLoc(), resultTypes,
       /*inputs=*/expandedOpOperands, outputs, expandedOpIndexingMaps,
       iteratorTypes);
   Region &fusedRegion = fusedOp->getRegion(0);
   Region &originalRegion = linalgOp->getRegion(0);
 
   if (isa<GenericOp>(linalgOp.getOperation())) {
     rewriter.cloneRegionBefore(originalRegion, fusedRegion,
                                fusedRegion.begin());
   } else {
     assert(isa<IndexedGenericOp>(linalgOp.getOperation()));
-    // Create an entry block in the fused Region with same number of arguments
-    // as the fused op
-    Block *fusedEntryBlock = new Block;
-    fusedRegion.push_back(fusedEntryBlock);
-    rewriter.cloneRegionBefore(originalRegion, fusedRegion, fusedRegion.end());
-
-    // Merge the entry block of the fused op with the cloned blocks. For this
-    // compute the value for arguments of the region in the original operation
-    // in terms of the arguments of the fused op. Since the original operation
-    // is expanded, the expanded dimensions need to be folded back to get the
-    // replacement value for the arguments corresponding to interation index.
-    // For now this expects that all the loop ranges are constants, which is
-    // true if the shapes are all static. This has already been checked in the
-    // precondition.
-    using namespace edsc::op;
-    using namespace edsc::intrinsics;
-    OpBuilder::InsertionGuard guard(rewriter);
-    SmallVector<Value, 4> argReplacements(originalRegion.getNumArguments());
-    rewriter.setInsertionPointToStart(fusedEntryBlock);
-    edsc::ScopedContext scopedContext(rewriter, fusedOp.getLoc());
-    IndexType indexType = rewriter.getIndexType();
-    for (unsigned i : llvm::seq<unsigned>(0, numFoldedDims.size())) {
-      Value linearizedIndex = fusedEntryBlock->addArgument(indexType);
-      for (unsigned foldedDim = remapping[i] + 1; foldedDim != remapping[i + 1];
-           foldedDim++) {
-        int64_t expandedDimExtent =
-            expandedDimsShape[i][foldedDim - remapping[i]];
-        assert(!ShapedType::isDynamic(expandedDimExtent));
-        linearizedIndex =
-            linearizedIndex * std_constant_index(expandedDimExtent);
-        linearizedIndex =
-            linearizedIndex + fusedEntryBlock->addArgument(indexType);
-      }
-      argReplacements[i] = linearizedIndex;
-    }
-    for (unsigned i :
-         llvm::seq<unsigned>(numFoldedDims.size(), argReplacements.size())) {
-      argReplacements[i] =
-          fusedEntryBlock->addArgument(originalRegion.getArgument(i).getType());
-    }
-    rewriter.mergeBlocks(fusedEntryBlock->getNextNode(), fusedEntryBlock,
-                         argReplacements);
+    buildExpandedIndexedGenericOpRegion(rewriter, loc, originalRegion,
+                                        fusedRegion, expansionInfo);
   }
 
   // Reshape the result values to their original shape if this is a collapsing
   // reshape folded into its consumer.
   SmallVector<Value, 1> resultVals;
   for (auto result : llvm::enumerate(linalgOp->getResults())) {
     if (!isExpanding &&
         resultTypes[result.index()] != result.value().getType()) {
+      SmallVector<ReassociationIndices, 4> reassociation =
+          getReassociationForExpansion(
+              linalgOp.getOutputIndexingMap(result.index()), expansionInfo);
       resultVals.push_back(rewriter.create<TensorReshapeOp>(
           linalgOp.getLoc(), result.value().getType(),
-          fusedOp->getResult(result.index()),
-          resultReassociation[result.index()]));
+          fusedOp->getResult(result.index()), reassociation));
     } else {
       resultVals.push_back(fusedOp->getResult(result.index()));
     }
   }
   // Assuming a single result.
   return resultVals;
 }