update tests

Author: Vahid Tavanashad

dpnp/tests/test_linalg.py

@@ -2096,7 +2096,12 @@ def test_empty(self, shape, ord, axis, keepdims):
             # Improper number of dimensions to norm
             assert_raises(ValueError, dpnp.linalg.norm, ia, **kwarg)
             assert_raises(ValueError, numpy.linalg.norm, a, **kwarg)
-        elif axis is None and a.ndim != 1 and a.shape[-1] == 0:
+        elif (
+            axis is None
+            and ord is not None
+            and a.ndim != 1
+            and a.shape[-1] == 0
+        ):
             if ord in [-2, -1, 0, 3]:
                 # reduction cannot be performed over zero-size axes
                 assert_raises(ValueError, dpnp.linalg.norm, ia, **kwarg)
@@ -2106,6 +2111,7 @@ def test_empty(self, shape, ord, axis, keepdims):
                 # of assert_equal with zero, we should compare with numpy
                 # ord in [None, 1, 2]
                 assert_equal(dpnp.linalg.norm(ia, **kwarg), 0)
+                assert_raises(ValueError, numpy.linalg.norm, a, **kwarg)
         else:
             result = dpnp.linalg.norm(ia, **kwarg)
             expected = numpy.linalg.norm(a, **kwarg)
@@ -2297,29 +2303,23 @@ def test_matrix_norm(self, ord, keepdims):
     @pytest.mark.parametrize(
         "shape_axis", [[(2, 0), None], [(2, 0), (0, 1)], [(0, 2), (0, 1)]]
     )
-    def test_matrix_norm_empty(self, dtype, shape_axis):
+    @pytest.mark.parametrize("ord", [None, "fro", "nuc", 1, 2, dpnp.inf])
+    def test_matrix_norm_empty(self, dtype, shape_axis, ord):
         shape, axis = shape_axis[0], shape_axis[1]
         x = dpnp.zeros(shape, dtype=dtype)
 
         # TODO: when similar changes in numpy are available,
         # instead of assert_equal with zero, we should compare with numpy
-        assert_equal(dpnp.linalg.norm(x, axis=axis), 0)
-        assert_equal(dpnp.linalg.norm(x, axis=axis, ord="fro"), 0)
-        assert_equal(dpnp.linalg.norm(x, axis=axis, ord="nuc"), 0)
-        assert_equal(dpnp.linalg.norm(x, axis=axis, ord=2), 0)
-        assert_equal(dpnp.linalg.norm(x, axis=axis, ord=1), 0)
-        assert_equal(dpnp.linalg.norm(x, axis=axis, ord=dpnp.inf), 0)
+        assert_equal(dpnp.linalg.norm(x, axis=axis, ord=ord), 0)
 
     @pytest.mark.parametrize("dtype", [dpnp.float32, dpnp.int32])
     @pytest.mark.parametrize("axis", [None, 0])
-    def test_vector_norm_empty(self, dtype, axis):
+    @pytest.mark.parametrize("ord", [None, 1, 2, dpnp.inf])
+    def test_vector_norm_empty(self, dtype, axis, ord):
         x = dpnp.zeros(0, dtype=dtype)
         # TODO: when similar changes in numpy are available,
         # instead of assert_equal with zero, we should compare with numpy
-        assert_equal(dpnp.linalg.vector_norm(x, axis=axis), 0)
-        assert_equal(dpnp.linalg.vector_norm(x, axis=axis, ord=1), 0)
-        assert_equal(dpnp.linalg.vector_norm(x, axis=axis, ord=2), 0)
-        assert_equal(dpnp.linalg.vector_norm(x, axis=axis, ord=dpnp.inf), 0)
+        assert_equal(dpnp.linalg.vector_norm(x, axis=axis, ord=ord), 0)
 
     @testing.with_requires("numpy>=2.0")
     @pytest.mark.parametrize(