diff --git a/mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp b/mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp
index f97ed3d6d5111..7bffac5425f8f 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp
@@ -172,8 +172,9 @@ bool mlir::linalg::areElementwiseOpsFusable(OpOperand *fusedOperand) {
 
   // Finally the index_map for the result must be invertible. For now just
   // verify it is a permutation.
+  auto producerResult = cast<OpResult>(fusedOperand->get());
   AffineMap producerResultIndexMap =
-      producer.getMatchingIndexingMap(producer.getDpsInitOperand(0));
+      producer.getIndexingMapMatchingResult(producerResult);
   if (!producerResultIndexMap.isPermutation())
     return false;
 
diff --git a/mlir/test/Dialect/Linalg/fusion-elementwise-ops.mlir b/mlir/test/Dialect/Linalg/fusion-elementwise-ops.mlir
index 66fc55fadf8fa..bc55c12c02f29 100644
--- a/mlir/test/Dialect/Linalg/fusion-elementwise-ops.mlir
+++ b/mlir/test/Dialect/Linalg/fusion-elementwise-ops.mlir
@@ -1014,3 +1014,69 @@ module {
 //   CHECK-DAG:     %[[T3:.+]] = arith.addf %[[T2]], %[[B1]]
 //       CHECK:     linalg.yield %[[T3]] : f32
 //       CHECK:   return %[[GENERIC]]
+
+// -----
+
+// In this test we expect the first two linalg.generic operations to be fused into one, but the third one (the matmul) to remain separate. 
+// The reason is that when the pattern is applied the 1st time, the fusion of the first two operations produces a fused operation with 
+// an additional result and ana dditional output indexing map that is not a permutation / not invertible. 
+// The fused op will still produce also the original result (and its output indexing map), which is preserved because the new indexing map 
+// is not invertible. Thus the fused op will have 2 results, but only the 2nd one will be used by the following matmul op as an input argument.
+// When trying to apply the fusion pattern again, the matmul op won't be fused because the operand to fuse was not produced with an invertible indexing map.
+
+#map = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+#map1 = affine_map<(d0, d1, d2, d3) -> (d0 * 4 + d1 * 2 + d2 + d3, 0, 0, 0)>
+#map2 = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1, d2, d6)>
+#map3 = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d3, d4, d5, d6)>
+#map4 = affine_map<(d0, d1, d2, d3, d4, d5, d6) -> (d0, d1, d2, d3, d4, d5)>
+module {
+  func.func @fuse_only_as_long_as_result_map_is_permutation(%arg0: tensor<1x1x2x1xf32>, %arg1: tensor<1x1x2x1xf32>) -> tensor<1x1x2x4xf32> {
+    %c2 = arith.constant 2 : index
+    %c1 = arith.constant 1 : index
+    %cst = arith.constant 0.000000e+00 : f32
+    %c0 = arith.constant 0 : index
+    %0 = tensor.empty() : tensor<1x2x2x1xf32>
+    %1 = linalg.generic {indexing_maps = [#map], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} outs(%0 : tensor<1x2x2x1xf32>) {
+    ^bb0(%out: f32):
+      %6 = linalg.index 1 : index
+      %7 = linalg.index 2 : index
+      %8 = arith.cmpi ult, %6, %c1 : index
+      %9 = arith.cmpi ult, %7, %c2 : index
+      %10 = arith.andi %8, %9 : i1
+      %11 = scf.if %10 -> (f32) {
+        %extracted = tensor.extract %arg1[%c0, %6, %7, %c0] : tensor<1x1x2x1xf32>
+        scf.yield %extracted : f32
+      } else {
+        scf.yield %cst : f32
+      }
+      linalg.yield %11 : f32
+    } -> tensor<1x2x2x1xf32>
+    %2 = tensor.empty() : tensor<4x1x1x1xf32>
+    %3 = linalg.generic {indexing_maps = [#map, #map1], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%1 : tensor<1x2x2x1xf32>) outs(%2 : tensor<4x1x1x1xf32>) {
+    ^bb0(%in: f32, %out: f32):
+      linalg.yield %in : f32
+    } -> tensor<4x1x1x1xf32>
+    %4 = tensor.empty() : tensor<1x1x2x4xf32>
+    %expanded = tensor.expand_shape %4 [[0], [1], [2], [3, 4, 5]] output_shape [1, 1, 2, 4, 1, 1] : tensor<1x1x2x4xf32> into tensor<1x1x2x4x1x1xf32>
+    %5 = linalg.generic {indexing_maps = [#map2, #map3, #map4], iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel", "parallel", "reduction"]} ins(%arg0, %3 : tensor<1x1x2x1xf32>, tensor<4x1x1x1xf32>) outs(%expanded : tensor<1x1x2x4x1x1xf32>) {
+    ^bb0(%in: f32, %in_0: f32, %out: f32):
+      %6 = arith.mulf %in, %in_0 : f32
+      %7 = arith.addf %6, %out : f32
+      linalg.yield %7 : f32
+    } -> tensor<1x1x2x4x1x1xf32>
+    %collapsed = tensor.collapse_shape %5 [[0], [1], [2], [3, 4, 5]] : tensor<1x1x2x4x1x1xf32> into tensor<1x1x2x4xf32>
+    return %collapsed : tensor<1x1x2x4xf32>
+  }
+}
+// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0 * 4 + d1 * 2 + d2 + d3, 0, 0, 0)>
+// CHECK:     func.func @fuse_only_as_long_as_result_map_is_permutation
+// CHECK-SAME:  (%[[ARG0:.*]]: tensor<1x1x2x1xf32>, %[[ARG1:.*]]: tensor<1x1x2x1xf32>) -> tensor<1x1x2x4xf32> {
+// CHECK-DAG:     %[[OUT0:.+]] = tensor.empty() : tensor<1x2x2x1xf32>
+// CHECK-DAG:     %[[OUT1:.+]] = tensor.empty() : tensor<4x1x1x1xf32>
+// CHECK:         %[[FUSED:.+]]:2 = linalg.generic {indexing_maps = [#[[MAP0]], #[[MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
+// CHECK-SAME:     outs(%[[OUT0]], %[[OUT1]] : tensor<1x2x2x1xf32>, tensor<4x1x1x1xf32>)
+// CHECK-NOT:     linalg.generic
+// CHECK:         tensor.expand_shape
+// CHECK:         linalg.generic {{.*}}, iterator_types = ["parallel", "parallel", "parallel", "parallel", "parallel", "parallel", "reduction"]}
+// CHECK-SAME:     ins(%[[ARG0]], %[[FUSED]]#1 : tensor<1x1x2x1xf32>, tensor<4x1x1x1xf32>)
\ No newline at end of file