[mlir] Fix bug in UnPackOp tiling implementation causing infinite loop (

#113571)

This fixes a bug in the tiling implementation of tensor.unpack that was
causing an infinite loop when certain unpack ops get tiled and fused as
a producer. The tiled implementation of tensor.unpack sometimes needs to
create an additional tensor.extract_slice on the result of the tiled
unpack op, but this slice was getting added to the `generatedSlices` of
the tiling result. The `generatedSlices` are used to find the next
producers to fuse, so it caused an infinite loop of fusing the same
unpack op after it was already in the loop. This fixes the bug by adding
the slice of the source instead of the result.

Signed-off-by: Max Dawkins <max.dawkins@gmail.com>

mlir/lib/Dialect/Tensor/IR/TensorTilingInterfaceImpl.cpp

@@ -554,13 +554,14 @@ struct UnPackOpTiling
     sliceSrcIndices.append(numInnerTiles, zeroAttr);
     sliceSrcSizes.append(unpackOp.getMixedTiles());
     sliceSrcStrides.append(numInnerTiles, oneAttr);
-    Value sliceSource =
+    SmallVector<Operation *> generatedSlices;
+    ExtractSliceOp sliceSource =
         b.create<ExtractSliceOp>(loc, unpackOp.getSource(), sliceSrcIndices,
                                  sliceSrcSizes, sliceSrcStrides);
+    generatedSlices.push_back(sliceSource);
 
     SmallVector<OpFoldResult> destStrides(destRank, oneAttr);
     Value sliceDest;
-    SmallVector<Operation *> generatedSlices;
     if (isPerfectTilingCase) {
       auto destSliceOp = b.create<ExtractSliceOp>(loc, unpackOp.getDest(),
                                                   offsets, sizes, destStrides);
@@ -571,7 +572,7 @@ struct UnPackOpTiling
                                     unpackOp.getDestType().getElementType());
     }
 
-    SmallVector<Value> tiledOperands = {sliceSource, sliceDest};
+    SmallVector<Value> tiledOperands = {sliceSource.getResult(), sliceDest};
     for (auto tile : unpackOp.getInnerTiles())
       tiledOperands.push_back(tile);
 
@@ -586,7 +587,6 @@ struct UnPackOpTiling
     auto extractSlice =
         b.create<ExtractSliceOp>(loc, tiledUnpackOp->getResult(0),
                                  resultOffsetsFromDest, sizes, destStrides);
-    generatedSlices.push_back(extractSlice);
     return TilingResult{
         {tiledUnpackOp}, {extractSlice.getResult()}, generatedSlices};
   }

mlir/test/Interfaces/TilingInterface/tile-and-fuse-using-interface.mlir

@@ -587,3 +587,50 @@ module attributes {transform.with_named_sequence} {
 //       CHECK:     %[[INSERT_SLICE:.+]] = tensor.insert_slice %[[IF_RESULT]]
 //       CHECK:     scf.yield %[[INSERT_SLICE]]
 //       CHECK:   return %[[FOR_RESULT]]
+
+// -----
+
+func.func @imperfect_unpack_producer_fusion(%source: tensor<1x1x288x8x4xf32>, %dest: tensor<1x2x1152xf32>) -> tensor<1x2x1152xf32> {
+  %0 = tensor.unpack %source
+      outer_dims_perm = [0, 1, 2]
+      inner_dims_pos = [1, 2]
+      inner_tiles = [8, 4] into %dest
+      : tensor<1x1x288x8x4xf32> -> tensor<1x2x1152xf32>
+  %1 = tensor.empty() : tensor<1x2x1152xf32>
+  %cst = arith.constant 1.0 : f32
+  %2 = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
+                                        affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
+                       iterator_types = ["parallel", "parallel", "parallel"]}
+                       ins(%0 : tensor<1x2x1152xf32>)
+                       outs(%1 : tensor<1x2x1152xf32>) {
+  ^bb0(%in: f32, %out: f32):
+    %7 = arith.addf %in, %cst : f32
+    linalg.yield %7 : f32
+  } -> tensor<1x2x1152xf32>
+  return %2 : tensor<1x2x1152xf32>
+}
+
+module attributes {transform.with_named_sequence} {
+  transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {
+    %matmul = transform.structured.match ops{["linalg.generic"]} in %arg1
+      : (!transform.any_op) -> !transform.any_op
+    %a, %b = transform.structured.fuse %matmul [0, 1, 0]
+      : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+    transform.yield
+  }
+}
+
+// CHECK-LABEL: func @imperfect_unpack_producer_fusion
+//  CHECK-SAME:     %[[ARG0:.+]]: tensor<1x1x288x8x4xf32>
+//  CHECK-SAME:     %[[ARG1:.+]]: tensor<1x2x1152xf32>
+//       CHECK:   %[[FOR_RESULT:.+]] = scf.for{{.*}}iter_args(%[[ITER_ARG:.+]] = {{.*}})
+//       CHECK:     %[[SLICE:.+]] = tensor.extract_slice %[[ARG0]]
+//       CHECK:     %[[UNPACK:.+]] = tensor.unpack %[[SLICE]]
+//   CHECK-DAG:     %[[UNPACK_SLICE:.+]] = tensor.extract_slice %[[UNPACK]]
+//   CHECK-DAG:     %[[INIT_SLICE:.+]] = tensor.extract_slice %[[ITER_ARG]]
+//       CHECK:     %[[GENERIC:.+]] = linalg.generic
+//  CHECK-SAME:         ins(%[[UNPACK_SLICE]]
+//  CHECK-SAME:         outs(%[[INIT_SLICE]]
+//       CHECK:     %[[INSERT_SLICE:.+]] = tensor.insert_slice %[[GENERIC]] into %[[ITER_ARG]]
+//       CHECK:     scf.yield %[[INSERT_SLICE]]
+//       CHECK:   return %[[FOR_RESULT]]