Merge branch 'master' into impl-blackman

Author: vtavana

dpnp/dpnp_iface_counting.py

@@ -58,16 +58,19 @@ def count_nonzero(a, axis=None, *, keepdims=False, out=None):
         Axis or tuple of axes along which to count non-zeros.
         Default value means that non-zeros will be counted along a flattened
         version of `a`.
+
         Default: ``None``.
-    keepdims : bool, optional
+    keepdims : {None, bool}, optional
         If this is set to ``True``, the axes that are counted are left in the
         result as dimensions with size one. With this option, the result will
         broadcast correctly against the input array.
+
         Default: ``False``.
     out : {None, dpnp.ndarray, usm_ndarray}, optional
         The array into which the result is written. The data type of `out` must
         match the expected shape and the expected data type of the result.
         If ``None`` then a new array is returned.
+
         Default: ``None``.
 
     Returns

dpnp/tests/test_indexing.py

@@ -1200,15 +1200,17 @@ def test_empty_indices_error(self):
         )
 
     def test_empty_indices(self):
-        assert_equal(
-            dpnp.ravel_multi_index(
-                (dpnp.array([], dtype=int), dpnp.array([], dtype=int)), (5, 3)
-            ),
-            [],
-        )
-        assert_equal(
-            dpnp.ravel_multi_index(dpnp.array([[], []], dtype=int), (5, 3)), []
-        )
+        a = numpy.array([], dtype=int)
+        ia = dpnp.array(a)
+        result = dpnp.ravel_multi_index((ia, ia), (5, 3))
+        expected = numpy.ravel_multi_index((a, a), (5, 3))
+        assert_equal(result, expected)
+
+        a = numpy.array([[], []], dtype=int)
+        ia = dpnp.array(a)
+        result = dpnp.ravel_multi_index(ia, (5, 3))
+        expected = numpy.ravel_multi_index(a, (5, 3))
+        assert_equal(result, expected)
 
 
 class TestUnravelIndex:

dpnp/tests/test_linalg.py

@@ -293,7 +293,9 @@ def test_empty(self, shape, p):
         expected = numpy.linalg.cond(a, p=p)
         assert_dtype_allclose(result, expected)
 
-    @pytest.mark.parametrize("dtype", get_all_dtypes(no_bool=True))
+    @pytest.mark.parametrize(
+        "dtype", get_all_dtypes(no_none=True, no_bool=True)
+    )
     @pytest.mark.parametrize(
         "shape", [(4, 4), (2, 4, 3, 3)], ids=["(4, 4)", "(2, 4, 3, 3)"]
     )
@@ -2408,10 +2410,8 @@ def test_qr(self, dtype, shape, mode):
             if mode in ("complete", "reduced"):
                 result = dpnp.linalg.qr(ia, mode)
                 dpnp_q, dpnp_r = result.Q, result.R
-                assert_almost_equal(
-                    dpnp.matmul(dpnp_q, dpnp_r),
-                    a,
-                    decimal=5,
+                assert dpnp.allclose(
+                    dpnp.matmul(dpnp_q, dpnp_r), ia, atol=1e-05
                 )
             else:  # mode=="raw"
                 dpnp_q, dpnp_r = dpnp.linalg.qr(ia, mode)
@@ -2424,15 +2424,13 @@ def test_qr(self, dtype, shape, mode):
     @pytest.mark.parametrize(
         "shape",
         [(32, 32), (8, 16, 16)],
-        ids=[
-            "(32, 32)",
-            "(8, 16, 16)",
-        ],
+        ids=["(32, 32)", "(8, 16, 16)"],
     )
     @pytest.mark.parametrize("mode", ["r", "raw", "complete", "reduced"])
     def test_qr_large(self, dtype, shape, mode):
         a = generate_random_numpy_array(shape, dtype, seed_value=81)
         ia = dpnp.array(a)
+
         if mode == "r":
             np_r = numpy.linalg.qr(a, mode)
             dpnp_r = dpnp.linalg.qr(ia, mode)
@@ -2443,11 +2441,7 @@ def test_qr_large(self, dtype, shape, mode):
             if mode in ("complete", "reduced"):
                 result = dpnp.linalg.qr(ia, mode)
                 dpnp_q, dpnp_r = result.Q, result.R
-                assert_almost_equal(
-                    dpnp.matmul(dpnp_q, dpnp_r),
-                    a,
-                    decimal=5,
-                )
+                assert dpnp.allclose(dpnp.matmul(dpnp_q, dpnp_r), ia, atol=1e-5)
             else:  # mode=="raw"
                 dpnp_q, dpnp_r = dpnp.linalg.qr(ia, mode)
                 assert_allclose(dpnp_q, np_q, atol=1e-4)
@@ -3169,9 +3163,7 @@ def test_test_tensorinv_errors(self):
 class TestTensorsolve:
     @pytest.mark.parametrize("dtype", get_all_dtypes())
     @pytest.mark.parametrize(
-        "axes",
-        [None, (1,), (2,)],
-        ids=["None", "(1,)", "(2,)"],
+        "axes", [None, (1,), (2,)], ids=["None", "(1,)", "(2,)"]
     )
     def test_tensorsolve_axes(self, dtype, axes):
         a = numpy.eye(12).reshape(12, 3, 4).astype(dtype)