update vector_norm and matrix_norm tests for empty arrays and improve coverage

Author: Vahid Tavanashad

dpnp/tests/test_linalg.py

@@ -2298,49 +2298,44 @@ def test_matrix_norm(self, ord, keepdims):
         expected = numpy.linalg.matrix_norm(a, ord=ord, keepdims=keepdims)
         assert_dtype_allclose(result, expected)
 
-    @pytest.mark.parametrize(
-        "xp",
-        [
-            dpnp,
-            pytest.param(
-                numpy,
-                marks=pytest.mark.skipif(
-                    numpy_version() < "2.3.0",
-                    reason="numpy raises an error",
-                ),
-            ),
-        ],
-    )
+    @testing.with_requires("numpy>=2.3")
     @pytest.mark.parametrize("dtype", [dpnp.float32, dpnp.int32])
     @pytest.mark.parametrize(
         "shape, axis", [[(2, 0), None], [(2, 0), (0, 1)], [(0, 2), (0, 1)]]
     )
     @pytest.mark.parametrize("ord", [None, "fro", "nuc", 1, 2, dpnp.inf])
-    def test_matrix_norm_empty(self, xp, dtype, shape, axis, ord):
-        x = xp.zeros(shape, dtype=dtype)
-        sc = dtype(0.0) if dtype == dpnp.float32 else 0.0
-        assert_equal(xp.linalg.norm(x, axis=axis, ord=ord), sc)
+    @pytest.mark.parametrize("keepdims", [True, False])
+    def test_matrix_norm_empty(self, dtype, shape, axis, ord, keepdims):
+        a = numpy.zeros(shape, dtype=dtype)
+        ia = dpnp.array(a)
+        result = dpnp.linalg.matrix_norm(
+            ia, axis=axis, ord=ord, keepdims=keepdims
+        )
+        expected = dpnp.linalg.matrix_norm(
+            a, axis=axis, ord=ord, keepdims=keepdims
+        )
+        assert_dtype_allclose(result, expected)
 
-    @pytest.mark.parametrize(
-        "xp",
-        [
-            dpnp,
-            pytest.param(
-                numpy,
-                marks=pytest.mark.skipif(
-                    numpy_version() < "2.3.0",
-                    reason="numpy raises an error",
-                ),
-            ),
-        ],
-    )
+    @testing.with_requires("numpy>=2.3")
     @pytest.mark.parametrize("dtype", [dpnp.float32, dpnp.int32])
     @pytest.mark.parametrize("axis", [None, 0])
     @pytest.mark.parametrize("ord", [None, 1, 2, dpnp.inf])
-    def test_vector_norm_empty(self, xp, dtype, axis, ord):
-        x = xp.zeros(0, dtype=dtype)
-        sc = dtype(0.0) if dtype == dpnp.float32 else 0.0
-        assert_equal(xp.linalg.vector_norm(x, axis=axis, ord=ord), sc)
+    @pytest.mark.parametrize("keepdims", [True, False])
+    def test_vector_norm_empty(self, dtype, axis, ord, keepdims):
+        a = numpy.zeros(0, dtype=dtype)
+        ia = dpnp.array(a)
+        result = dpnp.linalg.vector_norm(
+            ia, axis=axis, ord=ord, keepdims=keepdims
+        )
+        expected = numpy.linalg.vector_norm(
+            a, axis=axis, ord=ord, keepdims=keepdims
+        )
+        assert_dtype_allclose(result, expected)
+        if keepdims:
+            # norm and vector_norm have different paths in dpnp when keepdims=True,
+            # to cover both of them test with norm as well
+            result = dpnp.linalg.norm(ia, axis=axis, ord=ord, keepdims=keepdims)
+            assert_dtype_allclose(result, expected)
 
     @testing.with_requires("numpy>=2.0")
     @pytest.mark.parametrize(