enhance test_matrix_power.py/cholesky.py/norm.py (#1038)

* enhance test_matrix_power.py

* enhance test_ch

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---------

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cunumeric/linalg/exception.py

@@ -14,5 +14,4 @@
 #
 
 
-class LinAlgError(Exception):
-    pass
+from numpy.linalg.linalg import LinAlgError  # noqa: F401

cunumeric/linalg/linalg.py

@@ -207,8 +207,6 @@ def matrix_power(a: ndarray, n: int) -> ndarray:
         a = empty_like(a)
         a[...] = eye(a.shape[-2], dtype=a.dtype)
         return a
-    elif n == 1:
-        return a.copy()
 
     # Invert if necessary
     if n < 0:
@@ -219,7 +217,7 @@ def matrix_power(a: ndarray, n: int) -> ndarray:
 
     # Fast paths
     if n == 1:
-        return a
+        return a.copy()
     elif n == 2:
         return matmul(a, a)
     elif n == 3:

tests/integration/test_cholesky.py

@@ -41,6 +41,15 @@ def test_array_negative_3dim():
         num.linalg.cholesky(arr)
 
 
+def test_array_negative():
+    arr = num.random.randint(0, 9, size=(3, 2, 3))
+    expected_exc = ValueError
+    with pytest.raises(expected_exc):
+        num.linalg.cholesky(arr)
+    with pytest.raises(expected_exc):
+        np.linalg.cholesky(arr)
+
+
 def test_diagonal():
     a = num.eye(10) * 10.0
     b = num.linalg.cholesky(a)

tests/integration/test_matrix_power.py

@@ -73,28 +73,37 @@ class TestMatrixPowerErrors:
     def test_matrix_ndim_smaller_than_two(self, ndim):
         shape = (3,) * ndim
         a_num = mk_0to1_array(num, shape)
-        msg = "Expected at least 2d array"
-        with pytest.raises(num.linalg.LinAlgError, match=msg):
+        a_np = mk_0to1_array(np, shape)
+        expected_exc = num.linalg.LinAlgError
+        with pytest.raises(expected_exc):
             num.linalg.matrix_power(a_num, 1)
+        with pytest.raises(expected_exc):
+            np.linalg.matrix_power(a_np, 1)
 
     @pytest.mark.parametrize(
         "shape", ((2, 1), (2, 2, 1)), ids=lambda shape: f"(shape={shape})"
     )
     def test_matrix_not_square(self, shape):
         a_num = mk_0to1_array(num, shape)
-        msg = "Last 2 dimensions of the array must be square"
-        with pytest.raises(num.linalg.LinAlgError, match=msg):
+        a_np = mk_0to1_array(np, shape)
+        expected_exc = num.linalg.LinAlgError
+        with pytest.raises(expected_exc):
             num.linalg.matrix_power(a_num, 1)
+        with pytest.raises(expected_exc):
+            np.linalg.matrix_power(a_np, 1)
 
     @pytest.mark.parametrize(
         "n", (-1.0, 1.0, [1], None), ids=lambda n: f"(n={n})"
     )
     def test_n_not_int(self, n):
         shape = (2, 2)
         a_num = mk_0to1_array(num, shape)
-        msg = "exponent must be an integer"
-        with pytest.raises(TypeError, match=msg):
+        a_np = mk_0to1_array(np, shape)
+        expected_exc = TypeError
+        with pytest.raises(expected_exc):
             num.linalg.matrix_power(a_num, n)
+        with pytest.raises(expected_exc):
+            np.linalg.matrix_power(a_np, n)
 
     def test_n_negative_int(self):
         shape = (2, 2)

tests/integration/test_norm.py

@@ -141,6 +141,17 @@ def test_axis_invalid_value(self, axis):
         with pytest.raises(expected_exc):
             num.linalg.norm(num_arrays[ndim], axis=axis)
 
+    def test_axis_out_of_bounds(self):
+        # raise ValueError("Improper number of dimensions to norm")
+        expected_exc = ValueError
+        ndim = 3
+
+        with pytest.raises(expected_exc):
+            np.linalg.norm(np_arrays[ndim], ord=1)
+
+        with pytest.raises(expected_exc):
+            num.linalg.norm(num_arrays[ndim], ord=1)
+
     @pytest.mark.parametrize(
         "ndim_axis",
         ((1, None), (2, 0)),