diff --git a/tests/python/unittest/test_ndarray.py b/tests/python/unittest/test_ndarray.py
index e55fa1af90e8..ac6ee1561c47 100644
--- a/tests/python/unittest/test_ndarray.py
+++ b/tests/python/unittest/test_ndarray.py
@@ -1308,25 +1308,31 @@ def test_norm(ctx=default_context()):
 
     def l1norm(input_data, axis=0, keepdims=False):
         return np.sum(abs(input_data), axis=axis, keepdims=keepdims)
-    def l2norm(input_data, axis=0, keepdims=False): 
+    def l2norm(input_data, axis=0, keepdims=False):
         return sp_norm(input_data, axis=axis, keepdims=keepdims)
 
     in_data_dim = random_sample([4,5,6], 1)[0]
-    in_data_shape = rand_shape_nd(in_data_dim)
-    np_arr = np.random.uniform(-1, 1, in_data_shape).astype(np.float32)
-    mx_arr = mx.nd.array(np_arr, ctx=ctx)
-    for ord in [1,2]:
-        for keep_dims in [True, False]:
-            for i in range(4):
-                npy_out = l1norm(np_arr, i, keep_dims) if ord==1 else l2norm(np_arr, i, keep_dims)
-                mx_out = mx.nd.norm(mx_arr, ord=ord, axis=i, keepdims=keep_dims)
-                assert npy_out.shape == mx_out.shape
-                mx.test_utils.assert_almost_equal(npy_out, mx_out.asnumpy())
-                if (i < 3):
-                    npy_out = l1norm(np_arr, (i, i+1), keep_dims) if ord==1 else l2norm(np_arr, (i, i+1), keep_dims)
-                    mx_out = mx.nd.norm(mx_arr, ord=ord, axis=(i, i+1), keepdims=keep_dims)
+    for force_reduce_dim1 in [True, False]:
+        in_data_shape = rand_shape_nd(in_data_dim)
+        if force_reduce_dim1:
+            in_data_shape = in_data_shape[:3] + (1, ) + in_data_shape[4:]
+        np_arr = np.random.uniform(-1, 1, in_data_shape).astype(np.float32)
+        mx_arr = mx.nd.array(np_arr, ctx=ctx)
+        for ord in [1, 2]:
+            for keep_dims in [True, False]:
+                for i in range(4):
+                    npy_out = l1norm(np_arr, i, keep_dims) if ord == 1 else l2norm(
+                        np_arr, i, keep_dims)
+                    mx_out = mx.nd.norm(mx_arr, ord=ord, axis=i, keepdims=keep_dims)
                     assert npy_out.shape == mx_out.shape
                     mx.test_utils.assert_almost_equal(npy_out, mx_out.asnumpy())
+                    if (i < 3):
+                        npy_out = l1norm(np_arr, (i, i + 1), keep_dims) if ord == 1 else l2norm(
+                            np_arr, (i, i + 1), keep_dims)
+                        mx_out = mx.nd.norm(mx_arr, ord=ord, axis=(i, i + 1), keepdims=keep_dims)
+                        assert npy_out.shape == mx_out.shape
+                        mx.test_utils.assert_almost_equal(npy_out, mx_out.asnumpy())
+
 
 @with_seed()
 def test_ndarray_cpu_shared_ctx():