[mlir][Linalg] Allow collapsing subset of the reassociations when fusing by collapsing.

[mravishankar]

Summary

This change generalizes the fusion of tensor.expand_shape ->
linalg.generic op by collapsing to handle cases where only a subset
of the reassociations specified in the tensor.expand_shape are valid
to be collapsed.
The method that does the collapsing is refactored to allow it to be a
generic utility when required.

[okkwon]

I left a few comments mostly related to naming and comments. I will review the rest later.

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

1158

The indexing map is already checked outside, we can simply return a non-optional vector.
To guard a case in future for dev, we can use assert on the projected permutation condition.

Also, the result is a vector of dimensions on the iteration space, so I am not clear of using ReassociationIndices.

1218–1219

The function now returns a vector, so the function name seems to need a change.

1218–1219

It would be great if you can add fuse-able and un-fuse-able examples here.

1291

This can never happen knowing its implementation because the condition is already bailed out earlier.

1295

Please add why.

1298

We are mixing of the terms of domain and iteration. Sticking to one would make the code more readable.

1312

Please add TODO.

[gysit]

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

1170

nit: repetition

1311

nit:associative

1345–1362

origNumLoops since we have a lot of dims already?

1353

nit:Repetition

1410

...OpMapping[0] = ...
...OpMapping[1] = ...
...OpMapping[2] = ...
etc.?

1413

I wonder if a struct could help to clarify things a bit by naming the members collapsedOpDim and foldingIdx or similar?

mlir/test/Dialect/Linalg/fuse-with-reshape-by-collapsing.mlir

426

nit: probably should be [2, 3]{{\]}} with an additional closing bracket? (below as well?)

[okkwon]

Adding more comments.

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

1309

nit: dimension -> dimensions

1355

Is there a case that the number of loops is different from the rank of the iteration space? It would be great to give an example as comment.

1357

Do we hit this case? I thought the case is already bailed out when producing foldedIterationDims.

1437

It is irrelevant to this PR, but can we make it const CollapsingInfo &?
Same for getCollapsedOpIndexingMap() below.

1589

nit: collapse

1690–1693

Check control function first before calling isFusableWith...().

[mravishankar]

Address comments.

[okkwon]

Adding more comments.

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

1230

%2 -> %3 in the examples.

Also, need an expand_shape after the generic op for this generic op.

1334

Can we just move the whole vector like emplace_back(std::move(foldedIterationSpaceDims))?

1352

nit: contains -> contain, thats -> that's

1400

nit: [0, 1, 2], [3, 4] and remove the last tick at the end.

1455

use const AffineMap &.

1480–1481

use const AffineMap &.

[mravishankar]

Address comments.

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

1158

Dropped the Optional and made it assert that map is projected permutation. I am using Reassociation here cause just like expand/collapse shape op. the listed dimensions are "folded".

1218–1219

Those are typically documented in lit tests? There are quite a few examples there.. Added descrition.

1312

Actually not sure about TODO. Its not a gaurantee of the linalg op that this is allowed. So TODO would be misleading.

1355

I wrote this method with a view that this could be moved into a separate utility method by itself. So added some extra checks here independent of whats checked earlier.

1357

It is possible if this method is made a utility and then passed in dims for collapsing iteration space has an error.

1413

I think pair does the job here. It is documented what the pair represents. I found the naming and description of such a struct hard to write (let alone convey meaning effectively)

1455

For such objects use of const I think is not recommended. (https://mlir.llvm.org/docs/Rationale/UsageOfConst/)

1480–1481

See above.

1690–1693

I first check if it is fusable at all structurally, then call the control function...

[gysit]

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

1413

Alright!

mlir/test/Dialect/Linalg/fuse-with-reshape-by-collapsing.mlir

501–502

nit: I think the dims and the constants they use are unused?

526

I think 2 and 3 are not collapsed since they are not in order? If yes it may be helpful to add a short comment here?

[mravishankar]

Address comments, and make reduction dimension folding predicated on them being contiguous instead of monotonic.

mlir/test/Dialect/Linalg/fuse-with-reshape-by-collapsing.mlir

526

Actually, took a look again at this case. The reduction dimensions folded have to be contiguous in terms of reduction dims. So fixed that in code and modified test accordingly.

[gysit]

mlir/test/Dialect/Linalg/fuse-with-reshape-by-collapsing.mlir

526

Ah right that makes sense! That way the reduction order should be unchanged...

[mravishankar]

Closed by b40e901333b903fd71f17c3314d3e40f8abde074

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

 };
 } // namespace
 
 //===---------------------------------------------------------------------===//
 // Methods and patterns to fuse reshape with linalg.generic operations by
 // contraction of dimensions.
 //===---------------------------------------------------------------------===//
 
-/// For an `indexingMap` that is a projected permutation, if the range is to be
-/// collapsed using the given `reassociation`, get the reassociation in the
-/// domain that would keep the map a projected permutation.
-static SmallVector<ReassociationIndices>
+/// For a given list of indices in the range of the `indexingMap` that are
+/// folded, return the indices of the corresponding domain. Return `llvm::None`
+/// on failure. Ensures that all the elements of the returned reassociation are
+/// distinct.
+static ReassociationIndices
 getDomainReassociation(AffineMap indexingMap,
-                       ArrayRef<ReassociationIndices> rangeReassociation) {
+                       ReassociationIndicesRef rangeReassociation) {
   assert(indexingMap.isProjectedPermutation() &&
-         "expected projected permutation map");
-  unsigned counter = 0;
-  SmallVector<ReassociationIndices> domainReassociation;
-  llvm::SmallDenseSet<unsigned, 4> processedDomainDims;
-  // Iterate over the reassociation indices.
-  for (ReassociationIndicesRef foldedRangeDims : rangeReassociation) {
-    ReassociationIndices foldedDomainDims;
-    for (auto rangeDim : foldedRangeDims) {
-      (void)rangeDim;
-      AffineDimExpr dimExpr =
-          indexingMap.getResult(counter++).cast<AffineDimExpr>();
-      foldedDomainDims.push_back(dimExpr.getPosition());
-      processedDomainDims.insert(dimExpr.getPosition());
-    }
-    domainReassociation.emplace_back(std::move(foldedDomainDims));
-  }
-  // Fill in the missing domain dims.
-  for (auto dim : llvm::seq<unsigned>(0, indexingMap.getNumDims())) {
-    if (processedDomainDims.count(dim))
-      continue;
-    ReassociationIndices vec = {dim};
-    domainReassociation.emplace_back(std::move(vec));
-  }
-
-  // Sort the reassociation using the first dimension of the folded range to
-  // not create unnecessary transposes.
-  llvm::sort(domainReassociation,
-             [](ReassociationIndicesRef lhs, ReassociationIndicesRef rhs) {
-               return lhs[0] < rhs[0];
-             });
+         "expected projected permutation");
+
+  ReassociationIndices domainReassociation = llvm::to_vector<4>(
+      llvm::map_range(rangeReassociation, [&](int64_t pos) -> int64_t {
+        return indexingMap.getResults()[pos]
+            .cast<AffineDimExpr>()
+            .getPosition();
+      }));
+  // The projected permutation semantics ensures that there is no repetition of
+  // the domain indices.
   return domainReassociation;
 }
 
 /// For a given `dimSequence`, check if the sequence is conserved in the
 /// `indexingMap`. `indexingMap` is expected to be a projected permutation.
 /// Non-existence of the sequence returns true as well.
 static bool isDimSequencePreserved(AffineMap indexingMap,
                                    ReassociationIndicesRef dimSequence) {
for (const auto &expr : enumerate(indexingMap.getResults())) {
     // of sequence, return false here. This implies the entire sequence does not
     // exist in the indexing map.
     if (sequenceElements.count(dimInMapStart))
       return false;
   }
   // 3. No element of sequence found. Return true.
   return true;
 }
 
-// Check if a generic op can be fused along an operand by collapsing dimensions.
-static bool isFusableWithReshapeByDimCollapse(
-    GenericOp genericOp, OpOperand *fusableOperand,
+// Return the list of dimensions of the iteration domain that can be
+// collapsed to allow for fusion with the a producer that is an expand_shape
+// operation. If all dimensions created by expansion can be collapsed in the
+// iteration space then the reshape is defunct.
+//
+// Example:
+//
+// ```mlir
+// #map = affine_map<(d0, d1) -> (d0, d1)>
+// %1 = tensor.expand_shape %0 [[0, 1]] : tensor<?xf32> into tensor<?x4xf32>
+// %2 = linalg.init_tensor [..] : tensor<?x4xf32>
+// %3 = linalg.generic {
+//     indexing_maps = [#map, #map],
+//     iterator_types = ["parallel" ,"parallel"]}
+//     ins(%1 : tensor<?x4xf32>) outs(%2 : tensor<?x4xf32>) {.. }
+// ```
+//
+// can be fused by collapsing the dimensions of the iteration space.
+//
+// ```mlir
+// #map = affine_map<(d0) -> (d0)>
+// %2 = linalg.init_tensor [..] : tensor<?xf32>
+// %3 = linalg.generic {
+//     indexing_maps = [#map, #map],
+//     iterator_types = ["parallel"]}
+//     ins(%1 : tensor<?xf32>) outs(%2 : tensor<?xf32>) {.. }
+// %4 = tensor.expand_shape %3 [[0, 1]] : tensor<?xf32> into tensor<?x4xf32>
+// ```
+//
+// In the following example,
+//
+// ```mlir
+// #map0 = affine_map<(d0, d1) -> (d0, d1)>
+// #map1 = affine_map<(d0, d1) -> (d1, d0)>
+// %1 = tensor.expand_shape %0 [[0, 1]] : tensor<?xf32> into tensor<?x4xf32>
+// %2 = linalg.init_tensor [..] : tensor<4x?xf32>
+// %2 = linalg.generic {
+//     indexing_maps = [#map0, #map1],
+//     iterator_types = ["parallel" ,"parallel"]}
+//     ins(%1 : tensor<?x4xf32>) outs(%2 : tensor<4x?xf32>) {.. }
+// ```
+//
+// the reshape cannot be fused with the generic op by collapsing the op
+// dimensions since the indexing maps will have to contain mods and divs
+// to preserve the accesses pattern. When no dimensions of the iteration
+// space are collapsable and empty vector is returned.
+static SmallVector<ReassociationIndices>
+getCollapsableIterationSpaceDims(GenericOp genericOp, OpOperand *fusableOperand,
                                  ArrayRef<ReassociationIndices> reassociation) {
   // Some basic checks for this fusion to be valid.
   if (!genericOp.hasTensorSemantics() || genericOp.getNumOutputs() != 1)
-    return false;
+    return {};
 
   if (!llvm::all_of(genericOp.getIndexingMaps(), [](AffineMap map) {
         return map.isProjectedPermutation();
       })) {
-    return false;
+    return {};
   }
 
-  // Get the reassociation for the iteration space.
-  SmallVector<ReassociationIndices> iterationReassociation =
-      getDomainReassociation(genericOp.getTiedIndexingMap(fusableOperand),
-                             reassociation);
-  if (iterationReassociation.empty()) {
-    // If the domain reassociation indices is empty, then this is a scalar op.
-    // Nothing to do.
-    return false;
+  llvm::SmallDenseSet<unsigned, 4> processedIterationDims;
+  AffineMap indexingMap = genericOp.getTiedIndexingMap(fusableOperand);
+  auto iteratorTypes = genericOp.iterator_types().getValue();
+  SmallVector<ReassociationIndices> iterationSpaceReassociation;
+  for (ReassociationIndicesRef foldedRangeDims : reassociation) {
+    assert(!foldedRangeDims.empty() && "unexpected empty reassociation");
+
+    // Ignore dims that are not folded.
+    if (foldedRangeDims.size() == 1)
+      continue;
+
+    ReassociationIndices foldedIterationSpaceDims =
+        getDomainReassociation(indexingMap, foldedRangeDims);
+
+    // Check that the folded iteration dims do not contain already processed
+    // dims.
+    if (llvm::any_of(foldedIterationSpaceDims, [&](int64_t dim) {
+          return processedIterationDims.count(dim);
+        }))
+      continue;
+
+    // Check that all folded iterator types are all parallel or all reductions.
+    Attribute startIteratorType = iteratorTypes[foldedIterationSpaceDims[0]];
+    if (!isParallelIterator(startIteratorType) &&
+        !isReductionIterator(startIteratorType))
+      continue;
+    if (llvm::any_of(foldedIterationSpaceDims, [&](int64_t dim) {
+          return iteratorTypes[dim] != startIteratorType;
+        }))
+      continue;
+
+    // If the folded dimensions correspond to a "reduction" iterator type,
+    // the folded dimensions need to be "in-order". Strictly speaking this is
+    // not necessary, for reductions that are associative and commutative,  but
+    // using a more strict definition of reduction for now.
+    if (isReductionIterator(startIteratorType)) {
+      bool isMonotonic = true;
+      for (auto i :
+           llvm::seq<unsigned>(0, foldedIterationSpaceDims.size() - 1)) {
+        if (foldedIterationSpaceDims[i] > foldedIterationSpaceDims[i + 1]) {
+          isMonotonic = false;
+          break;
+        }
+      }
+      if (!isMonotonic)
+        continue;
     }
 
-  auto iteratorTypes = genericOp.iterator_types().getValue();
-  ArrayRef<Attribute> iteratorTypesRef(iteratorTypes);
-  for (ReassociationIndicesRef foldedIterDims : iterationReassociation) {
-    // Check that for all indexing maps, the folded dimensions sequence is
-    // preserved.
-    if (!llvm::all_of(genericOp.getIndexingMaps(), [&](AffineMap indexingMap) {
-          return isDimSequencePreserved(indexingMap, foldedIterDims);
+    // Check that the sequence is preserved in all indexing maps.
+    if (llvm::any_of(genericOp.getIndexingMaps(), [&](AffineMap indexingMap) {
+          return !isDimSequencePreserved(indexingMap, foldedIterationSpaceDims);
         }))
-      return false;
-    unsigned startDim = foldedIterDims[0];
-    ArrayRef<Attribute> foldedIteratorTypes =
-        iteratorTypesRef.drop_front(startDim).take_front(foldedIterDims.size());
-    // Check that all folded iterator types are either all parallel, or all
-    // reduction.
-    if (!llvm::all_of(
-            foldedIteratorTypes,
-            [](Attribute attr) { return isParallelIterator(attr); }) &&
-        !llvm::all_of(foldedIteratorTypes,
-                      [](Attribute attr) { return isReductionIterator(attr); }))
-      return false;
+      continue;
+
+    processedIterationDims.insert(foldedIterationSpaceDims.begin(),
+                                  foldedIterationSpaceDims.end());
+    iterationSpaceReassociation.emplace_back(
+        std::move(foldedIterationSpaceDims));
   }
-  return true;
+
+  return iterationSpaceReassociation;
 }
 
 /// Helper class to carry state while collapsing the `linalg.generic` op.
 namespace {
 class CollapsingInfo {
 public:
-  CollapsingInfo(SmallVector<ReassociationIndices> &&reassociation) {
-    iterationReassociation = std::move(reassociation);
-    for (const auto &foldedIterDims : enumerate(iterationReassociation)) {
-      foldedDimStartToSequenceMap[foldedIterDims.value()[0]] =
-          foldedIterDims.index();
+  LogicalResult initialize(unsigned origNumLoops,
+                           ArrayRef<ReassociationIndices> foldedIterationDims) {
+    llvm::SmallDenseSet<int64_t, 4> processedDims;
+    // Find all the dims that are folded.
+    for (ReassociationIndicesRef foldedIterationDim : foldedIterationDims) {
+      if (foldedIterationDim.empty())
+        continue;
+      // If the folded dims contain dims already folded, that's illegal
+      // specification. Repetition within a list is also illegal.
+      for (auto dim : foldedIterationDim) {
+        if (dim >= origNumLoops)
+          return failure();
+        if (processedDims.count(dim))
+          return failure();
+        processedDims.insert(dim);
       }
+      collapsedOpToOrigOpIterationDim.emplace_back(foldedIterationDim.begin(),
+                                                   foldedIterationDim.end());
     }
+    if (processedDims.size() > origNumLoops)
+      return failure();
 
-  // Returns the iteration space reassociation.
-  ArrayRef<ReassociationIndices> getReassociationIndices() {
-    return iterationReassociation;
+    // Add all the preserved dims of the original op as single
+    // elements to `collapsedOpToOrigOpIterationDim`.
+    for (auto dim : llvm::seq<int64_t>(0, origNumLoops)) {
+      if (processedDims.count(dim))
+        continue;
+      collapsedOpToOrigOpIterationDim.emplace_back(ReassociationIndices{dim});
     }
 
-  // Returns true if the given dimension is the start of a sequence of folded
-  // dimensions.
-  bool isDimStartOfFoldedDims(unsigned dim) {
-    return foldedDimStartToSequenceMap.count(dim);
+    llvm::sort(collapsedOpToOrigOpIterationDim,
+               [&](ReassociationIndicesRef lhs, ReassociationIndicesRef rhs) {
+                 return lhs[0] < rhs[0];
+               });
+    origOpToCollapsedOpIterationDim.resize(origNumLoops);
+    for (auto foldedDims : llvm::enumerate(collapsedOpToOrigOpIterationDim)) {
+      for (auto dim : enumerate(foldedDims.value()))
+        origOpToCollapsedOpIterationDim[dim.value()] =
+            std::make_pair<int64_t, unsigned>(foldedDims.index(), dim.index());
     }
-
-  // Return the folded dimensions starting at `dim`.
-  ReassociationIndicesRef getFoldedDimsStartingAt(unsigned dim) {
-    assert(foldedDimStartToSequenceMap.count(dim) &&
-           "invalid start dim of folded dim "
-           "sequence");
-    return iterationReassociation[foldedDimStartToSequenceMap[dim]];
+    return success();
   }
 
-  // For a dim in the original op, return the dim in the collapsed op, that it
-  // is mapped to. Expectes `dim` to be start of a folded dimension sequence.
-  unsigned getDimInCollapsedOpForStartOfFoldedDims(unsigned dim) {
-    assert(foldedDimStartToSequenceMap.count(dim) &&
-           "invalid start dim of folded dim sequence");
-    return foldedDimStartToSequenceMap[dim];
+  /// Return mapping from collapsed loop domain to original loop domain.
+  ArrayRef<ReassociationIndices> getCollapsedOpToOrigOpMapping() const {
+    return collapsedOpToOrigOpIterationDim;
+  }
+
+  /// Return mapping from original loop domain to collapsed loop domain. The
+  /// mapping is a pair. First value is the dimension in the collapsed loop that
+  /// the original loop is mapped to. Second is the relative position in folded
+  /// list of this domain. For example if the original loop domain is 3D, and
+  /// the collapsed loop domain is folding all of it, i.e.
+  ///
+  /// ```
+  /// collapsedOpToOrigOpMapping = [[0, 1, 2] [3, 4]]`
+  /// ```
+  ///
+  /// then
+  ///
+  /// ```
+  ///  origOpToCollapsedOpMapping[0] = {0, 0};
+  ///  origOpToCollapsedOpMapping[1] = {0, 1};
+  ///  origOpToCollapsedOpMapping[2] = {0, 2};
+  ///  origOpToCollapsedOpMapping[3] = {1, 0};
+  ///  origOpToCollapsedOpMapping[4] = {1, 1};
+  /// ```
+  ///
+  ArrayRef<std::pair<int64_t, unsigned>> getOrigOpToCollapsedOpMapping() const {
+    return origOpToCollapsedOpIterationDim;
+  }
+
+  /// Return the collapsed op iteration domain rank.
+  unsigned getCollapsedOpIterationRank() const {
+    return collapsedOpToOrigOpIterationDim.size();
   }
 
 private:
-  /// Reassociation describing the folded iteration space dimensions.
-  SmallVector<ReassociationIndices> iterationReassociation;
-
-  /// Map from the starting dimensions of the folded dimension sequences to
-  /// their index in `iterationReassociation`.
-  llvm::DenseMap<unsigned, unsigned> foldedDimStartToSequenceMap;
+  /// Map from the iteration domain index in collapsed op to the iteration
+  /// domain indices in the original op.
+  SmallVector<ReassociationIndices> collapsedOpToOrigOpIterationDim;
+
+  /// Map from iteration domain index in the original op to the iteration domain
+  /// index in the collapsed op.
+  SmallVector<std::pair<int64_t, unsigned>> origOpToCollapsedOpIterationDim;
 };
 } // namespace
 
 /// Get the iterator types for the collapsed operation given the original
 /// iterator types and collapsed dimensions.
 static SmallVector<StringRef>
 getCollapsedOpIteratorTypes(ArrayRef<Attribute> iteratorTypes,
-                            CollapsingInfo &collapsingInfo) {
+                            const CollapsingInfo &collapsingInfo) {
   SmallVector<StringRef> collapsedIteratorTypes;
   for (ReassociationIndicesRef foldedIterDims :
-       collapsingInfo.getReassociationIndices()) {
+       collapsingInfo.getCollapsedOpToOrigOpMapping()) {
     assert(!foldedIterDims.empty() &&
            "reassociation indices expected to have non-empty sets");
     // Just pick the iterator type of the first folded dim. Pre-condition checks
     // expected to have checked that iterator types of all folded dimensions are
     // the same.
     collapsedIteratorTypes.push_back(
         iteratorTypes[foldedIterDims[0]].cast<StringAttr>().getValue());
   }
   return collapsedIteratorTypes;
 }
 
 /// Compute the indexing map in the collapsed op that corresponds to the given
 /// `indexingMap` of the original operation.
-static AffineMap getCollapsedOpIndexingMap(AffineMap indexingMap,
-                                           CollapsingInfo &collapsingInfo) {
+static AffineMap
+getCollapsedOpIndexingMap(AffineMap indexingMap,
+                          const CollapsingInfo &collapsingInfo) {
   MLIRContext *context = indexingMap.getContext();
   assert(indexingMap.isProjectedPermutation() &&
          "expected indexing map to be projected permutation");
   SmallVector<AffineExpr> resultExprs;
+  auto origOpToCollapsedOpMapping =
+      collapsingInfo.getOrigOpToCollapsedOpMapping();
   for (auto expr : indexingMap.getResults()) {
     unsigned dim = expr.cast<AffineDimExpr>().getPosition();
-    if (collapsingInfo.isDimStartOfFoldedDims(dim)) {
-      resultExprs.push_back(getAffineDimExpr(
-          collapsingInfo.getDimInCollapsedOpForStartOfFoldedDims(dim),
-          context));
-    }
+    // If the dim is not the first of the collapsed dim, do nothing.
+    if (origOpToCollapsedOpMapping[dim].second != 0)
+      continue;
+    // The next n-dims are guaranteed to be collapsed. So just use the
+    // iteration dimension of the collapsed op.
+    resultExprs.push_back(
+        getAffineDimExpr(origOpToCollapsedOpMapping[dim].first, context));
   }
-  return AffineMap::get(collapsingInfo.getReassociationIndices().size(), 0,
+  return AffineMap::get(collapsingInfo.getCollapsedOpIterationRank(), 0,
                         resultExprs, context);
 }
 
 /// Return the `reassociation` indices to use to collapse the operand when the
 /// iteration space of a generic op is collapsed.
 static SmallVector<ReassociationIndices>
-getOperandReassociation(AffineMap indexingMap, CollapsingInfo &collapsingInfo) {
+getOperandReassociation(AffineMap indexingMap,
+                        const CollapsingInfo &collapsingInfo) {
   unsigned counter = 0;
   SmallVector<ReassociationIndices> operandReassociation;
-  for (auto expr : indexingMap.getResults()) {
-    unsigned dim = expr.cast<AffineDimExpr>().getPosition();
-    if (collapsingInfo.isDimStartOfFoldedDims(dim)) {
+  auto origOpToCollapsedOpMapping =
+      collapsingInfo.getOrigOpToCollapsedOpMapping();
+  auto collapsedOpToOrigOpMapping =
+      collapsingInfo.getCollapsedOpToOrigOpMapping();
+  while (counter < indexingMap.getNumResults()) {
+    unsigned dim =
+        indexingMap.getResult(counter).cast<AffineDimExpr>().getPosition();
+    if (origOpToCollapsedOpMapping[dim].second == 0) {
+      // This is the start of a collapsed dimensions of the iteration that
+      // is gauranteed to be preserved in the indexing map. The number of folded
+      // dims is obtained from the collapsed op to original op mapping.
       unsigned numFoldedDims =
-          collapsingInfo.getFoldedDimsStartingAt(dim).size();
+          collapsedOpToOrigOpMapping[origOpToCollapsedOpMapping[dim].first]
+              .size();
       auto range = llvm::seq<unsigned>(counter, counter + numFoldedDims);
       operandReassociation.emplace_back(range.begin(), range.end());
       counter += numFoldedDims;
     }
   }
   return operandReassociation;
 }
 
 /// Get the new value to use for a given `OpOperand` in the collapsed operation.
 static Value getCollapsedOpOperand(Location loc, GenericOp genericOp,
                                    OpOperand *opOperand,
-                                   CollapsingInfo &collapsingInfo,
+                                   const CollapsingInfo &collapsingInfo,
                                    OpBuilder &builder) {
   AffineMap indexingMap = genericOp.getTiedIndexingMap(opOperand);
   SmallVector<ReassociationIndices> operandReassociation =
       getOperandReassociation(indexingMap, collapsingInfo);
 
   // If the number of entries in the reassocation for the operand is same as the
   // number of results of the indexing map, then nothing to do for this operand.
   Value operand = opOperand->get();
   if (operandReassociation.size() == indexingMap.getNumResults())
     return operand;
 
   // Insert a reshape to collapse the dimensions.
   auto reshapeOp = builder.create<tensor::CollapseShapeOp>(
       loc, operand, operandReassociation);
   return reshapeOp.getResult();
 }
 
 /// Modify the `linalg.index` operations in the original generic op, to its
 /// value in the collapsed operation.
 void generateCollapsedIndexingRegion(Location loc, Block *block,
-                                     CollapsingInfo &collapsingInfo,
+                                     const CollapsingInfo &collapsingInfo,
                                      ValueRange loopRange,
                                      PatternRewriter &rewriter) {
   OpBuilder::InsertionGuard g(rewriter);
   rewriter.setInsertionPointToStart(block);
 
   // Collect all the original index ops.
   auto indexOps = llvm::to_vector(block->getOps<linalg::IndexOp>());
 
   // For each folded dimension list resolve the original induction variable
   // values in terms of the folded dimension induction variable.
   //   i_{folded} = (i_0 * d1 + i1) * d2 + i2.
   // can be inverted to
   //   i2 = i_{folded} % d2
   //   i1 = (i_{folded} / d2) % d1
   //   i0 = i_{folded} / (d1 * d2)
   llvm::DenseMap<unsigned, Value> indexReplacementVals;
-  for (auto &foldedDims : enumerate(collapsingInfo.getReassociationIndices())) {
+  for (auto &foldedDims :
+       enumerate(collapsingInfo.getCollapsedOpToOrigOpMapping())) {
     ReassociationIndicesRef foldedDimsRef(foldedDims.value());
     Value newIndexVal =
         rewriter.create<linalg::IndexOp>(loc, foldedDims.index());
     for (auto dim : llvm::reverse(foldedDimsRef.drop_front())) {
       indexReplacementVals[dim] =
           rewriter.create<arith::RemUIOp>(loc, newIndexVal, loopRange[dim]);
       newIndexVal =
           rewriter.create<arith::DivUIOp>(loc, newIndexVal, loopRange[dim]);
     }
     indexReplacementVals[foldedDims.value().front()] = newIndexVal;
   }
 
   for (auto indexOp : indexOps) {
     auto dim = indexOp.dim();
     rewriter.replaceOp(indexOp, indexReplacementVals[dim]);
   }
 }
 
 /// Implementation of fusion with reshape operation by collapsing dimensions.
-static Optional<SmallVector<Value>>
-fuseWithReshapeByCollapsing(GenericOp genericOp, Operation *reshapeOp,
-                            OpOperand *fusableOpOperand,
-                            PatternRewriter &rewriter) {
-  SmallVector<ReassociationIndices> reassociation =
-      isa<tensor::CollapseShapeOp>(reshapeOp)
-          ? cast<tensor::CollapseShapeOp>(reshapeOp).getReassociationIndices()
-          : cast<tensor::ExpandShapeOp>(reshapeOp).getReassociationIndices();
-  assert(isFusableWithReshapeByDimCollapse(genericOp, fusableOpOperand,
-                                           reassociation) &&
-         "preconditions for fusing with reshape by collapse failed");
-
-  CollapsingInfo collapsingInfo(getDomainReassociation(
-      genericOp.getTiedIndexingMap(fusableOpOperand), reassociation));
-  // Check for trivial no transformation cases. In that case return nothing.
-  if (collapsingInfo.getReassociationIndices().size() ==
-      genericOp.getNumLoops())
-    return llvm::None;
+static FailureOr<SmallVector<Value>> collapseGenericOpIterationDims(
+    GenericOp genericOp, ArrayRef<ReassociationIndices> foldedIterationDims,
+    OpOperand *fusableOpOperand, PatternRewriter &rewriter) {
+  // Bail on trivial no-op cases.
+  if (genericOp.getNumLoops() <= 1 || foldedIterationDims.empty() ||
+      llvm::all_of(foldedIterationDims, [](ReassociationIndicesRef foldedDims) {
+        return foldedDims.size() <= 1;
+      }))
+    return failure();
+
+  CollapsingInfo collapsingInfo;
+  if (failed(collapsingInfo.initialize(genericOp.getNumLoops(),
+                                       foldedIterationDims))) {
+    return rewriter.notifyMatchFailure(
+        genericOp, "illegal to collapse specified dimensions");
+  }
 
   // Get the iterator types for the operand.
   SmallVector<StringRef> iteratorTypes = getCollapsedOpIteratorTypes(
       genericOp.iterator_types().getValue(), collapsingInfo);
 
   // Get the indexing maps.
   auto indexingMaps = llvm::to_vector(
       llvm::map_range(genericOp.getIndexingMaps(), [&](AffineMap map) {
         return getCollapsedOpIndexingMap(map, collapsingInfo);
       }));
 
-  Location loc =
-      rewriter.getFusedLoc({genericOp->getLoc(), reshapeOp->getLoc()});
+  Location loc = genericOp->getLoc();
 
   // Get the input operands.
   auto inputOperands = llvm::to_vector(
       llvm::map_range(genericOp.getInputOperands(), [&](OpOperand *opOperand) {
         return getCollapsedOpOperand(loc, genericOp, opOperand, collapsingInfo,
                                      rewriter);
       }));
 
public:
   LogicalResult matchAndRewrite(GenericOp genericOp,
                                 PatternRewriter &rewriter) const override {
     for (OpOperand *opOperand : genericOp.getInputTensorOperands()) {
       tensor::ExpandShapeOp reshapeOp =
           opOperand->get().getDefiningOp<tensor::ExpandShapeOp>();
       if (!reshapeOp)
         continue;
 
-      if (!isFusableWithReshapeByDimCollapse(
-              genericOp, opOperand, reshapeOp.getReassociationIndices()) ||
+      SmallVector<ReassociationIndices> collapsableIterationDims =
+          getCollapsableIterationSpaceDims(genericOp, opOperand,
+                                           reshapeOp.getReassociationIndices());
+      if (collapsableIterationDims.empty() ||
           !controlFoldingReshapes(reshapeOp->getResult(0), *opOperand)) {
         continue;
       }
 
-      Optional<SmallVector<Value>> replacements = fuseWithReshapeByCollapsing(
-          genericOp, reshapeOp, opOperand, rewriter);
+      Optional<SmallVector<Value>> replacements =
+          collapseGenericOpIterationDims(genericOp, collapsableIterationDims,
+                                         opOperand, rewriter);
       if (!replacements) {
         return rewriter.notifyMatchFailure(
             genericOp, "failed to do the fusion by collapsing transformation");
       }
 
       rewriter.replaceOp(genericOp, replacements.getValue());
       return success();
     }

mlir/test/Dialect/Linalg/fuse-with-reshape-by-collapsing.mlir

func @zero_D_test(%arg0: tensor<f32>) -> tensor<1xf32> {
   return %1 : tensor<1xf32>
 }
 //      CHECK: func @zero_D_test
 // CHECK-SAME:     %[[ARG0:.+]]: tensor<f32>
 //      CHECK:   %[[EXPAND:.+]] = tensor.expand_shape %[[ARG0]]
 //      CHECK:   %[[GENERIC:.+]] = linalg.generic
 // CHECK-SAME:       ins(%[[EXPAND]] :
 //      CHECK:   return %[[GENERIC]]
+
+// -----
+
+#map0 = affine_map<(d0, d1, d2, d3) -> (d1, d0, d2, d3)>
+#map1 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+func @fuse_only_one_reassociation(%arg0 : tensor<?x?xf32>, %arg1 : tensor<4x?x?x8xf32>) -> tensor<4x?x?x8xf32> {
+  %0 = tensor.expand_shape %arg0 [[0, 1], [2, 3]] : tensor<?x?xf32> into tensor<?x4x?x8xf32>
+  %1 = linalg.generic {
+      indexing_maps = [#map0, #map1, #map1],
+      iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
+      ins(%0, %arg1 : tensor<?x4x?x8xf32>, tensor<4x?x?x8xf32>)
+      outs(%arg1 : tensor<4x?x?x8xf32>) {
+    ^bb0(%b0: f32, %b1 : f32, %b2 : f32):
+      %2 = arith.addf %b0, %b1 : f32
+      linalg.yield %2 : f32
+    } -> tensor<4x?x?x8xf32>
+  return %1 : tensor<4x?x?x8xf32>
+}
+//  CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2) -> (d1, d0, d2)>
+//  CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
+//      CHECK: func @fuse_only_one_reassociation(
+// CHECK-SAME:     %[[ARG0:.+]]: tensor<?x?xf32>
+// CHECK-SAME:     %[[ARG1:.+]]: tensor<4x?x?x8xf32>
+//  CHECK-DAG:   %[[EXPAND_ARG0:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1], [2, 3]{{\]}}
+//  CHECK-DAG:   %[[COLLAPSE_ARG0:.+]] = tensor.collapse_shape %[[EXPAND_ARG0]] {{\[}}[0], [1], [2, 3]{{\]}}
+//  CHECK-DAG:   %[[COLLAPSE_ARG1_0:.+]] = tensor.collapse_shape %[[ARG1]] {{\[}}[0], [1], [2, 3]{{\]}}
+//  CHECK-DAG:   %[[COLLAPSE_ARG1_1:.+]] = tensor.collapse_shape %[[ARG1]] {{\[}}[0], [1], [2, 3]{{\]}}
+//      CHECK:   %[[GENERIC:.+]] = linalg.generic
+// CHECK-SAME:       indexing_maps = [#[[MAP0]], #[[MAP1]], #[[MAP1]]]
+// CHECK-SAME:       iterator_types = ["parallel", "parallel", "parallel"]
+// CHECK-SAME:       ins(%[[COLLAPSE_ARG0]], %[[COLLAPSE_ARG1_0]] :
+// CHECK-SAME:       outs(%[[COLLAPSE_ARG1_1]] :
+//      CHECK:   %[[EXPAND_GENERIC:.+]] = tensor.expand_shape %[[GENERIC]] {{\[}}[0], [1], [2, 3]{{\]}}
+//      CHECK:   return %[[EXPAND_GENERIC]]
+
+// -----
+
+#map0 = affine_map<(d0, d1, d2, d3) -> (d0, d2, d3, d1)>
+#map1 = affine_map<(d0, d1, d2, d3) -> (d3, d1, d0, d2)>
+func @fold_non_consecutive_dims(%arg0 : tensor<?x?xi32>) -> tensor<?x8x?x4xi32> {
+  %c0 = arith.constant 0 : index
+  %c2 = arith.constant 2 : index
+  %0 = tensor.expand_shape %arg0 [[0, 1], [2, 3]] : tensor<?x?xi32> into tensor<?x4x?x8xi32>
+  %d0 = tensor.dim %0, %c0 : tensor<?x4x?x8xi32>
+  %d1 = tensor.dim %0, %c2 : tensor<?x4x?x8xi32>
+  %init = linalg.init_tensor [%d1, 8, %d0, 4] : tensor<?x8x?x4xi32>
+  %1 = linalg.generic {
+      indexing_maps = [#map0, #map1],
+      iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
+      ins(%0 : tensor<?x4x?x8xi32>) outs(%init : tensor<?x8x?x4xi32>) {
+    ^bb0(%b0 : i32, %b1 : i32):
+      %2 = linalg.index 0 : index
+      %3 = linalg.index 1 : index
+      %4 = linalg.index 2 : index
+      %5 = linalg.index 3 : index
+      %6 = arith.addi %2, %3 : index
+      %7 = arith.addi %6, %4 : index
+      %8 = arith.addi %7, %5 : index
+      %9 = arith.index_cast %8 : index to i32
+      linalg.yield %9: i32
+    } -> tensor<?x8x?x4xi32>
+  return %1 : tensor<?x8x?x4xi32>
+}
+//  CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1) -> (d0, d1)>
+//  CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1) -> (d1, d0)>
+//      CHECK: func @fold_non_consecutive_dims(
+// CHECK-SAME:     %[[ARG0:.+]]: tensor<?x?xi32>)
+//  CHECK-DAG:   %[[C4:.+]] = arith.constant 4 : index
+//  CHECK-DAG:   %[[C8:.+]] = arith.constant 8 : index
+//      CHECK:   %[[INIT:.+]] = linalg.init_tensor
+//      CHECK:   %[[COLLAPSE_INIT:.+]] = tensor.collapse_shape %[[INIT]] {{\[}}[0, 1], [2, 3]{{\]}}
+//      CHECK:   %[[GENERIC:.+]] = linalg.generic
+// CHECK-SAME:       indexing_maps = [#[[MAP0]], #[[MAP1]]]
+// CHECK-SAME:       iterator_types = ["parallel", "parallel"]
+// CHECK-SAME:       ins(%[[ARG0]] :
+// CHECK-SAME:       outs(%[[COLLAPSE_INIT]] :
+// CHECK-NEXT:   ^bb{{[0-9]}}
+//      CHECK:       %[[ID0:.+]] = linalg.index 0
+//  CHECK-DAG:       %[[T0:.+]] = arith.remui %[[ID0]], %[[C4]]
+//  CHECK-DAG:       %[[T1:.+]] = arith.divui %[[ID0]], %[[C4]]
+//      CHECK:       %[[ID1:.+]] = linalg.index 1
+//  CHECK-DAG:       %[[T2:.+]] = arith.remui %[[ID1]], %[[C8]]
+//  CHECK-DAG:       %[[T3:.+]] = arith.divui %[[ID1]], %[[C8]]
+//  CHECK-DAG:       %[[T4:.+]] = arith.addi %[[T1]], %[[T2]]
+//  CHECK-DAG:       %[[T5:.+]] = arith.addi %[[T4]], %[[T0]]
+//  CHECK-DAG:       %[[T6:.+]] = arith.addi %[[T5]], %[[T3]]
+//  CHECK-DAG:       %[[T7:.+]] = arith.index_cast %[[T6]]
+//      CHECK:       linalg.yield %[[T7]]
+//      CHECK:   %[[EXPAND_GENERIC:.+]] = tensor.expand_shape %[[GENERIC]] {{\[}}[0, 1], [2, 3]{{\]}}
+//      CHECK:   return %[[EXPAND_GENERIC]]
+
+// -----
+
+#map0 = affine_map<(d0, d1, d2, d3) -> (d0, d2, d3, d1)>
+#map1 = affine_map<(d0, d1, d2, d3) -> ()>
+func @fold_non_consecutive_reduction_dims(%arg0 : tensor<?x?xi32>) -> tensor<i32> {
+  %c0 = arith.constant 0 : index
+  %c2 = arith.constant 2 : index
+  %0 = tensor.expand_shape %arg0 [[0, 1], [2, 3]] : tensor<?x?xi32> into tensor<?x4x?x8xi32>
+  %d0 = tensor.dim %0, %c0 : tensor<?x4x?x8xi32>
+  %d1 = tensor.dim %0, %c2 : tensor<?x4x?x8xi32>
+  %init = linalg.init_tensor [] : tensor<i32>
+  %1 = linalg.generic {
+      indexing_maps = [#map0, #map1],
+      iterator_types = ["reduction", "reduction", "reduction", "reduction"]}
+      ins(%0 : tensor<?x4x?x8xi32>) outs(%init : tensor<i32>) {
+    ^bb0(%b0 : i32, %b1 : i32):
+      %2 = linalg.index 0 : index
+      %3 = linalg.index 1 : index
+      %4 = linalg.index 2 : index
+      %5 = linalg.index 3 : index
+      %6 = arith.addi %2, %3 : index
+      %7 = arith.addi %6, %4 : index
+      %8 = arith.addi %7, %5 : index
+      %9 = arith.index_cast %8 : index to i32
+      linalg.yield %9: i32
+    } -> tensor<i32>
+  return %1 : tensor<i32>
+}
+//  CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2) -> (d0, d2, d1)>
+//  CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2) -> ()>
+//      CHECK: func @fold_non_consecutive_reduction_dims(
+// CHECK-SAME:   %[[ARG0:.+]]: tensor<?x?xi32>)
+//      CHECK:   %[[EXPAND_ARG0:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1], [2, 3]{{\]}}
+//      CHECK:   %[[COLLAPSE_ARG0:.+]] = tensor.collapse_shape %[[EXPAND_ARG0]] {{\[}}[0, 1], [2], [3]{{\]}}
+//      CHECK:   %[[GENERIC:.+]] = linalg.generic
+// CHECK-SAME:       indexing_maps = [#[[MAP0]], #[[MAP1]]]
+// CHECK-NEXT:   ^bb{{[0-9]}}
+//      CHECK:       %[[ID0:.+]] = linalg.index 0
+//  CHECK-DAG:       %[[T0:.+]] = arith.remui %[[ID0]], %[[C4]]
+//  CHECK-DAG:       %[[T1:.+]] = arith.divui %[[ID0]], %[[C4]]
+//      CHECK:       %[[ID1:.+]] = linalg.index 1
+//  CHECK-DAG:       %[[ID2:.+]] = linalg.index 2
+//  CHECK-DAG:       %[[T2:.+]] = arith.addi %[[T1]], %[[ID1]]
+//  CHECK-DAG:       %[[T3:.+]] = arith.addi %[[T2]], %[[T0]]
+//  CHECK-DAG:       %[[T4:.+]] = arith.addi %[[T3]], %[[ID2]]
+//  CHECK-DAG:       %[[T5:.+]] = arith.index_cast %[[T4]]
+//      CHECK:       linalg.yield %[[T5]]