diff --git a/mlir/test/Dialect/Linalg/transform-tile-and-fuse.mlir b/mlir/test/Dialect/Linalg/transform-tile-and-fuse.mlir
new file mode 100644
index 000000000000..a2547d5973a5
--- /dev/null
+++ b/mlir/test/Dialect/Linalg/transform-tile-and-fuse.mlir
@@ -0,0 +1,58 @@
+// RUN: mlir-opt %s --test-transform-dialect-interpreter -canonicalize | FileCheck %s
+
+// This is a simple tile-and-fuse example with a single fusion group.
+
+module {
+  // CHECK: func @foo
+  // CHECK:   scf.foreach_thread {{.*}} {
+  // CHECK:     linalg.fill
+  // CHECK:     linalg.matmul
+  // CHECK:     linalg.generic
+  // CHECK:   }
+  func.func @foo(%A: tensor<?x?xf32>, %B: tensor<?x?xf32>, %C: tensor<?xf32>,
+                 %D: tensor<?x?xf32>, %sz0: index, %sz1: index)
+      -> tensor<?x?xf32>
+  {
+    %cst = arith.constant 0.000000e+00 : f32
+    %5 = linalg.fill
+        {__producer__}
+        ins(%cst : f32)
+        outs(%D : tensor<?x?xf32>) -> tensor<?x?xf32>
+    %6 = linalg.matmul
+        {__producer__}
+        ins(%A, %B : tensor<?x?xf32>, tensor<?x?xf32>)
+        outs(%5 : tensor<?x?xf32>) -> tensor<?x?xf32>
+    %7 = linalg.generic 
+        {__root__,
+         indexing_maps = [affine_map<(d0, d1) -> (d0)>,
+                          affine_map<(d0, d1) -> (d0, d1)>,
+                          affine_map<(d0, d1) -> (d0, d1)>],
+         iterator_types = ["parallel", "parallel"]
+        }
+        ins(%C, %6 : tensor<?xf32>, tensor<?x?xf32>)
+        outs(%D : tensor<?x?xf32>) {
+    ^bb0(%arg2: f32, %arg3: f32, %arg4: f32):
+      %16 = arith.maxf %arg3, %cst : f32
+      %17 = arith.cmpf ogt, %arg2, %cst : f32
+      %18 = arith.select %17, %cst, %16 : f32
+      linalg.yield %18 : f32
+    } -> tensor<?x?xf32>
+    return %7 : tensor<?x?xf32>
+  }
+
+  transform.with_pdl_patterns {
+  ^bb0(%arg0: !pdl.operation):
+    transform.sequence %arg0 {
+    ^bb1(%arg1: !pdl.operation):
+      // Find the root and all producers.
+      %root = transform.structured.match attribute{"__root__"} in %arg1
+      %producers = transform.structured.match attribute{"__producer__"} in %arg1
+
+      // Tile the root.
+      %foreach_thread_op, %tiled_op = transform.structured.tile_to_foreach_thread_op %root num_threads [10, 20]
+
+      // Fuse all producers.
+      transform.structured.fuse_into_containing_op %producers into %foreach_thread_op
+    }
+  }
+}