diff --git a/python/tvm/relax/frontend/onnx/onnx_frontend.py b/python/tvm/relax/frontend/onnx/onnx_frontend.py
index 24217184b57c..32b2c508f009 100644
--- a/python/tvm/relax/frontend/onnx/onnx_frontend.py
+++ b/python/tvm/relax/frontend/onnx/onnx_frontend.py
@@ -2489,6 +2489,10 @@ def _impl_v17(cls, bb, inputs, attr, params):
         axis = attr.get("axis", -1)
         epsilon = attr.get("epsilon", 1e-05)
 
+        if bias is None:
+            seq_len = data.struct_info.shape[1].value
+            bias = relax.const([0.0] * seq_len, dtype="float32")
+
         output = relax.op.nn.layer_norm(data, scale, bias, axis, epsilon)
         # Onnx layernorm has 3 outputs but only the first is used.
         # We construct two empty constants for this.
diff --git a/tests/python/relax/test_frontend_onnx.py b/tests/python/relax/test_frontend_onnx.py
index f533c7945548..b7305d4810ed 100644
--- a/tests/python/relax/test_frontend_onnx.py
+++ b/tests/python/relax/test_frontend_onnx.py
@@ -1303,6 +1303,24 @@ def test_layer_norm():
     model = helper.make_model(graph, producer_name="layer_norm_test")
     check_correctness(model)
 
+    # Test case with no bias that is an optional input
+    layer_norm_node = helper.make_node("LayerNormalization", ["a", "b"], ["d"], epsilon=1e-12)
+
+    graph = helper.make_graph(
+        [layer_norm_node],
+        "layer_norm_test",
+        inputs=[
+            helper.make_tensor_value_info("a", TensorProto.FLOAT, [32, 32]),
+            helper.make_tensor_value_info("b", TensorProto.FLOAT, [32]),
+        ],
+        outputs=[
+            helper.make_tensor_value_info("d", TensorProto.FLOAT, [32, 32]),
+        ],
+    )
+
+    model = helper.make_model(graph, producer_name="layer_norm_test")
+    check_correctness(model)
+
 
 # TODO Enable dynamism
 @pytest.mark.parametrize("dynamic", [False])