diff --git a/python/tvm/relax/frontend/onnx/onnx_frontend.py b/python/tvm/relax/frontend/onnx/onnx_frontend.py
index 3d9dfba9a16b..113193263d86 100644
--- a/python/tvm/relax/frontend/onnx/onnx_frontend.py
+++ b/python/tvm/relax/frontend/onnx/onnx_frontend.py
@@ -3834,8 +3834,7 @@ def _impl_v17(cls, bb, inputs, attr, params):
         gamma_shape = get_const_tuple(scale.struct_info.shape)
 
         if bias is None:
-            seq_len = data.struct_info.shape[1].value
-            bias = relax.const([0.0] * seq_len, dtype="float32")
+            bias = relax.const(_np.zeros(gamma_shape), dtype=scale.struct_info.dtype)
         else:
             beta_shape = get_const_tuple(bias.struct_info.shape)
             if gamma_shape != beta_shape:
diff --git a/tests/python/relax/test_frontend_onnx.py b/tests/python/relax/test_frontend_onnx.py
index 8d8c1bc54b9b..7f77dac0c876 100644
--- a/tests/python/relax/test_frontend_onnx.py
+++ b/tests/python/relax/test_frontend_onnx.py
@@ -2330,6 +2330,72 @@ def test_layer_norm():
     model = helper.make_model(graph, producer_name="layer_norm_test")
     check_correctness(model)
 
+    # No bias with a non-square input where data.shape[1] differs from the scale
+    # shape, see https://github.com/apache/tvm/issues/19691.
+    layer_norm_node = helper.make_node(
+        "LayerNormalization", ["input", "scale"], ["Y"], axis=-1, epsilon=1e-12
+    )
+
+    graph = helper.make_graph(
+        [layer_norm_node],
+        "layer_norm_test",
+        inputs=[
+            helper.make_tensor_value_info("input", TensorProto.FLOAT, [2, 3, 4, 8]),
+            helper.make_tensor_value_info("scale", TensorProto.FLOAT, [8]),
+        ],
+        outputs=[
+            helper.make_tensor_value_info("Y", TensorProto.FLOAT, [2, 3, 4, 8]),
+        ],
+    )
+
+    model = helper.make_model(graph, producer_name="layer_norm_test")
+    check_correctness(model)
+
+    # No bias with a non-square fp16 input. The synthesized zero bias must match
+    # the scale dtype, otherwise layer_norm rejects the float32 bias, see
+    # https://github.com/apache/tvm/issues/19691.
+    layer_norm_node = helper.make_node(
+        "LayerNormalization", ["input", "scale"], ["Y"], axis=-1, epsilon=1e-12
+    )
+
+    graph = helper.make_graph(
+        [layer_norm_node],
+        "layer_norm_test",
+        inputs=[
+            helper.make_tensor_value_info("input", TensorProto.FLOAT16, [2, 3, 4, 8]),
+            helper.make_tensor_value_info("scale", TensorProto.FLOAT16, [8]),
+        ],
+        outputs=[
+            helper.make_tensor_value_info("Y", TensorProto.FLOAT16, [2, 3, 4, 8]),
+        ],
+    )
+
+    model = helper.make_model(graph, producer_name="layer_norm_test")
+    check_correctness(model, opset=17, atol=1e-2, rtol=1e-2)
+
+    # Same no-bias path for bf16. ONNX Runtime's CPU provider has no bf16
+    # LayerNormalization kernel, so this only checks the importer builds the
+    # graph with a bf16 zero bias (the dtype the fix derives from the scale).
+    layer_norm_node = helper.make_node(
+        "LayerNormalization", ["input", "scale"], ["Y"], axis=-1, epsilon=1e-12
+    )
+
+    graph = helper.make_graph(
+        [layer_norm_node],
+        "layer_norm_test",
+        inputs=[
+            helper.make_tensor_value_info("input", TensorProto.BFLOAT16, [2, 3, 4, 8]),
+            helper.make_tensor_value_info("scale", TensorProto.BFLOAT16, [8]),
+        ],
+        outputs=[
+            helper.make_tensor_value_info("Y", TensorProto.BFLOAT16, [2, 3, 4, 8]),
+        ],
+    )
+
+    model = helper.make_model(graph, producer_name="layer_norm_test")
+    model.opset_import[0].version = 17
+    from_onnx(model, opset=17, keep_params_in_input=True)
+
 
 def test_layer_norm_with_nd_gamma_beta():
     layer_norm_node = helper.make_node(