[mlir][tensor] Support parallel_insert_slice in reassociative reshape folder

mlir/lib/Dialect/Tensor/Transforms/ReshapePatterns.cpp

LogicalResult matchAndRewrite(ExpandShapeOp expandShapeOp,
     rewriter.replaceOpWithNewOp<tensor::ExtractSliceOp>(
         expandShapeOp, extractSliceOp.getSource(), mixedOffsets, mixedSizes,
         mixedStrides);
     return success();
   }
 };
 
 /// Fold insert_slice(collapse_shape) ops that cancel itself out.
-struct FoldInsertOfRankReducingInsert : public OpRewritePattern<InsertSliceOp> {
-  using OpRewritePattern<InsertSliceOp>::OpRewritePattern;
+template <typename OpTy>
+struct FoldInsertOfRankReducingInsert : public OpRewritePattern<OpTy> {
+  using OpRewritePattern<OpTy>::OpRewritePattern;
 
-  LogicalResult matchAndRewrite(InsertSliceOp insertSliceOp,
+  LogicalResult matchAndRewrite(OpTy insertSliceOp,
                                 PatternRewriter &rewriter) const override {
     auto collapseShapeOp =
-        insertSliceOp.getSource().getDefiningOp<CollapseShapeOp>();
+        insertSliceOp.getSource().template getDefiningOp<CollapseShapeOp>();
     if (!collapseShapeOp)
       return failure();
     RankedTensorType srcType = collapseShapeOp.getSrcType();
 
     // Only cases where the CollapseShapeOp can be folded away entirely are
     // supported. Moreover, only simple cases where the resulting InsertSliceOp
     // has no rank-reduction anymore are supported at the moment.
     RankedTensorType nonReducingInsertType =
         RankedTensorType::get(insertSliceOp.getStaticSizes(),
-                              insertSliceOp.getType().getElementType());
+                              insertSliceOp.getDestType().getElementType());
     if (nonReducingInsertType != srcType)
       return failure();
 
     SmallVector<OpFoldResult> mixedOffsets = insertSliceOp.getMixedOffsets();
     SmallVector<OpFoldResult> mixedSizes = insertSliceOp.getMixedSizes();
     SmallVector<OpFoldResult> mixedStrides = insertSliceOp.getMixedStrides();
-    rewriter.replaceOpWithNewOp<tensor::InsertSliceOp>(
-        insertSliceOp, collapseShapeOp.getSrc(), insertSliceOp.getDest(),
-        mixedOffsets, mixedSizes, mixedStrides);
+    rewriter.replaceOpWithNewOp<OpTy>(insertSliceOp, collapseShapeOp.getSrc(),
+                                      insertSliceOp.getDest(), mixedOffsets,
+                                      mixedSizes, mixedStrides);
     return success();
   }
 };
 } // namespace
 
 void mlir::tensor::populateReassociativeReshapeFoldingPatterns(
     RewritePatternSet &patterns) {
-  patterns.add<FoldExpandOfRankReducingExtract, FoldInsertOfRankReducingInsert>(
+  patterns.add<FoldExpandOfRankReducingExtract,
+               FoldInsertOfRankReducingInsert<tensor::InsertSliceOp>,
+               FoldInsertOfRankReducingInsert<tensor::ParallelInsertSliceOp>>(
       patterns.getContext());
 }

mlir/test/Dialect/Tensor/fold-reassociative-reshapes.mlir

func.func @rank_reducing_insert_of_collapse_shape(
     %t: tensor<?x1x1x5xf32>, %d: tensor<?x?x?x?xf32>, %sz: index)
   -> tensor<?x?x?x?xf32> {
   %0 = tensor.collapse_shape %t [[0, 1], [2], [3]]
       : tensor<?x1x1x5xf32> into tensor<?x1x5xf32>
   %1 = tensor.insert_slice %0 into %d[0, 0, 0, 0][%sz, 1, 1, 5][1, 1, 1, 1]
       : tensor<?x1x5xf32> into tensor<?x?x?x?xf32>
   return %1 : tensor<?x?x?x?xf32>
 }
+
+// -----
+
+// CHECK-LABEL: func @rank_reducing_parallel_insert_of_collapse_shape(
+//  CHECK-SAME:     %[[t:.*]]: tensor<?x1x1x5xf32>
+//       CHECK:   tensor.parallel_insert_slice %[[t]] into %{{.*}}[0, 0, 0, 0] [%{{.*}}, 1, 1, 5] [1, 1, 1, 1] : tensor<?x1x1x5xf32> into tensor<?x?x?x?xf32>
+func.func @rank_reducing_parallel_insert_of_collapse_shape(
+    %t: tensor<?x1x1x5xf32>, %d: tensor<?x?x?x?xf32>, %sz: index, %thr: index)
+  -> tensor<?x?x?x?xf32> {
+  %0 = tensor.collapse_shape %t [[0, 1], [2], [3]]
+      : tensor<?x1x1x5xf32> into tensor<?x1x5xf32>
+  %1 = scf.foreach_thread (%iv) in (%thr) shared_outs(%o = %d) -> (tensor<?x?x?x?xf32>) {
+    scf.foreach_thread.perform_concurrently {
+      tensor.parallel_insert_slice %0 into %o[0, 0, 0, 0][%sz, 1, 1, 5][1, 1, 1, 1]
+          : tensor<?x1x5xf32> into tensor<?x?x?x?xf32>
+    }
+  }
+  return %1 : tensor<?x?x?x?xf32>
+}