diff --git a/mlir/lib/Dialect/Linalg/Transforms/FusionOnTensors.cpp b/mlir/lib/Dialect/Linalg/Transforms/FusionOnTensors.cpp
--- a/mlir/lib/Dialect/Linalg/Transforms/FusionOnTensors.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/FusionOnTensors.cpp
@@ -349,6 +349,12 @@
       consumerOp->getBlock() != rootOp->getBlock())
     return failure();
 
+  // Check ̀̀consumerOpOperand` is not shape-only to avoid fusion if the data is
+  // not used by the `consumerOp` computation.
+  BlockArgument bbArg = consumerOp.getTiedBlockArgument(consumerOpOperand);
+  if (bbArg.getUses().empty())
+    return failure();
+
   // Check if the producer is a LinalgOp possibly passed by iteration argument.
   OpOperand *iterArg = nullptr;
   auto producerResult = sliceOp.source().dyn_cast<OpResult>();
diff --git a/mlir/test/Dialect/Linalg/tile-and-fuse-no-fuse.mlir b/mlir/test/Dialect/Linalg/tile-and-fuse-no-fuse.mlir
--- a/mlir/test/Dialect/Linalg/tile-and-fuse-no-fuse.mlir
+++ b/mlir/test/Dialect/Linalg/tile-and-fuse-no-fuse.mlir
@@ -1,19 +1,40 @@
-// RUN: mlir-opt %s -test-linalg-codegen-strategy="anchor-op=linalg.matmul fuse tile-sizes=0,0,0 run-enable-pass=false" -split-input-file | FileCheck %s
+// RUN: mlir-opt %s -test-linalg-codegen-strategy="anchor-op=linalg.matmul fuse tile-sizes=0,0,0 run-enable-pass=false" -split-input-file | FileCheck --check-prefix=MATMUL %s
+// RUN: mlir-opt %s -test-linalg-codegen-strategy="anchor-op=linalg.elemwise_unary fuse tile-sizes=32,32,0 run-enable-pass=false" -split-input-file | FileCheck --check-prefix=UNARY %s
 
-func.func @no_fuse_gemm(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>) -> tensor<?x?xf32> {
+// MATMUL-LABEL: @tile_sizes_zero(
+func @tile_sizes_zero(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>) -> tensor<?x?xf32> {
   %c0 = arith.constant 0 : index
   %c1 = arith.constant 1 : index
   %cst = arith.constant 0.0 : f32
   %d0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
   %d1 = tensor.dim %arg1, %c1 : tensor<?x?xf32>
   %init = linalg.init_tensor [%d0, %d1] : tensor<?x?xf32>
+
+  //   MATMUL-NOT:   scf.for
+  //       MATMUL:   linalg.fill
   %fill = linalg.fill ins(%cst : f32) outs(%init : tensor<?x?xf32>) -> tensor<?x?xf32>
+
+  //   MATMUL-NOT:   scf.for
+  //       MATMUL:   linalg.matmul
   %result = linalg.matmul ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
       outs(%fill : tensor<?x?xf32>) -> tensor<?x?xf32>
   return %result : tensor<?x?xf32>
 }
-// CHECK-LABEL: @no_fuse_gemm(
-//   CHECK-NOT:   scf.for
-//       CHECK:   linalg.fill
-//   CHECK-NOT:   scf.for
-//       CHECK:   linalg.matmul
+
+// -----
+
+// UNARY_LABEL: @shape_only(
+func @shape_only(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>) -> tensor<?x?xf32> {
+  %cst = arith.constant 0.0 : f32
+
+  //       UNARY:   linalg.fill
+  %0 = linalg.fill ins(%cst : f32) outs(%arg1 : tensor<?x?xf32>) -> tensor<?x?xf32>
+
+  //       UNARY:   scf.for
+  //       UNARY:     scf.for
+  //   UNARY-NOT:       linalg.fill
+  //       UNARY:       linalg.elemwise_unary
+  %1 = linalg.elemwise_unary {fun = #linalg.unary_fn<exp>}
+      ins(%arg0 : tensor<?x?xf32>) outs(%0 : tensor<?x?xf32>) -> tensor<?x?xf32>
+  return %1 : tensor<?x?xf32>
+}