diff --git a/src/relay/pass/pattern_util.h b/src/relay/pass/pattern_util.h
index 82ca544a9f66..5e93ea1ff0aa 100644
--- a/src/relay/pass/pattern_util.h
+++ b/src/relay/pass/pattern_util.h
@@ -199,6 +199,28 @@ inline int64_t GetConv2DSuperChannelsDim(const CallNode* call) {
     return *channels;
 }
 
+/*!
+ * \brief Is single value tensor (scalar).
+ * \param expr The expr.
+ * \return True if single value tensor.
+ */
+inline bool IsScalar(const Expr& expr) {
+  if (auto tensor_type = expr->checked_type().as<TensorTypeNode>()) {
+    for (auto dim_index_expr : tensor_type->shape) {
+      if (auto dim_index = dim_index_expr.as<IntImm>()) {
+        if (dim_index->value != 1) {
+          return false;
+        }
+      } else {
+        return false;
+      }
+    }
+  } else {
+    return false;
+  }
+  return true;
+}
+
 /*!
  * \brief Create a Constant with a scalar
  *
diff --git a/src/relay/pass/transform_layout.h b/src/relay/pass/transform_layout.h
index 21a82a603c20..f6c5e9af6d62 100644
--- a/src/relay/pass/transform_layout.h
+++ b/src/relay/pass/transform_layout.h
@@ -119,6 +119,11 @@ class TransformMemorizer : public NodeRef {
     Expr input_expr = raw;
     Layout new_src_layout = src_layout;
     if (src_layout.ndim_primal() < dst_layout.ndim_primal()) {
+      // If scalar, then no need of layout transformation as scalar can be broadcasted easily even
+      // if the other operand has a transformed layout.
+      if (IsScalar(input_expr)) {
+        return raw;
+      }
       int num_new_axis = dst_layout.ndim_primal() - src_layout.ndim_primal();
       new_src_layout = src_layout.ExpandPrimal(dst_layout);
       input_expr = MakeExpandDims(input_expr, 0, num_new_axis);
diff --git a/tests/python/relay/test_pass_alter_op_layout.py b/tests/python/relay/test_pass_alter_op_layout.py
index 9ab582d5b3e2..3f02e1db625e 100644
--- a/tests/python/relay/test_pass_alter_op_layout.py
+++ b/tests/python/relay/test_pass_alter_op_layout.py
@@ -318,6 +318,70 @@ def expected():
 
     assert analysis.alpha_equal(a, b), "Actual = \n" + str(a)
 
+
+def test_alter_layout_broadcast_scalar_op():
+    """Test alternating the layout of a conv2d.
+    The layout of broadcast operators and the weight should be changed accordingly.
+    """
+    def before():
+        x = relay.var("x", shape=(1, 500, 500, 64))
+        kernel = relay.var('kernel', shape=(3, 3, 64, 64), dtype='float32')
+        bias = relay.var("bias", shape=(64,))
+        multiplier1 = relay.var('multiplier1', shape=(1, ), dtype='float32')
+        multiplier2 = relay.var('multiplier2', shape=(1, 1), dtype='float32')
+
+        y = relay.nn.conv2d(x, kernel,
+                            data_layout='NHWC',
+                            kernel_layout="HWIO",
+                            kernel_size=(3, 3))
+        y = relay.add(bias, y)
+        y = relay.nn.relu(y)
+
+        y = relay.multiply(multiplier1, y)
+        y = relay.multiply(y, multiplier2)
+        y = relay.Function(analysis.free_vars(y), y)
+        return y
+
+    def alter_conv2d(attrs, inputs, tinfos):
+        data, weight = inputs
+        new_attrs = dict(attrs)
+        new_attrs['data_layout'] = 'NCHW16c'
+        return relay.nn.conv2d(data, weight, **new_attrs)
+
+    def expected():
+        x = relay.var("x", shape=(1, 500, 500, 64))
+        kernel = relay.var('kernel', shape=(3, 3, 64, 64), dtype='float32')
+        bias = relay.var("bias", shape=(64,))
+        multiplier1 = relay.var('multiplier1', shape=(1, ), dtype='float32')
+        multiplier2 = relay.var('multiplier2', shape=(1, 1), dtype='float32')
+
+        b = relay.expand_dims(bias, axis=0, num_newaxis=3)
+        b = relay.layout_transform(b, "NHWC", "NCHW16c")
+
+        y = relay.layout_transform(x, "NHWC", "NCHW16c")
+        y = relay.nn.conv2d(y, kernel,
+                            data_layout='NCHW16c',
+                            kernel_layout="HWIO",
+                            kernel_size=(3, 3))
+
+        y = relay.add(b, y)
+        y = relay.nn.relu(y)
+
+        y = relay.multiply(multiplier1, y)
+        y = relay.multiply(y, multiplier2)
+        y = relay.layout_transform(y, "NCHW16c", "NHWC")
+        y = relay.Function(analysis.free_vars(y), y)
+        return y
+
+    with TempOpAttr("nn.conv2d", "FTVMAlterOpLayout", alter_conv2d):
+        a = before()
+        a = run_opt_pass(a, [transform.CanonicalizeOps(),
+                             transform.AlterOpLayout()])
+        b = run_opt_pass(expected(), transform.InferType())
+
+    assert analysis.alpha_equal(a, b), "Actual = \n" + str(a)
+
+
 def test_alter_layout_scalar():
     """Test alternating the layout of a conv2d.
     The layout of broadcast operators and the weight should be changed accordingly.
@@ -980,6 +1044,7 @@ def expected():
     test_alter_layout_dual_path()
     test_alter_layout_resnet()
     test_alter_layout_broadcast_op()
+    test_alter_layout_broadcast_scalar_op()
     test_alter_layout_scalar()
     test_alter_layout_concatenate()
     test_alter_layout_nchw_upsamping_op()