update test_nanfunctions.py

Author: Vahid Tavanashad

dpnp/tests/conftest.py

@@ -32,6 +32,8 @@
 import numpy
 import pytest
 
+from . import config as dtype_config
+
 if numpy.lib.NumpyVersion(numpy.__version__) >= "2.0.0b1":
     from numpy.exceptions import ComplexWarning
 else:
@@ -128,20 +130,25 @@ def pytest_collection_modifyitems(config, items):
 
     dev = dpctl.select_default_device()
     is_cpu = dev.is_cpu
-    is_gpu_no_fp64 = not dev.has_aspect_fp64
+    is_gpu = dev.is_gpu
+    is_gpu_w_fp64 = dev.has_aspect_fp64
     is_cuda = dpnp.is_cuda_backend(dev)
 
     print("")
+    print(
+        f"DPNP Test scope includes all integer dtypes: {bool(dtype_config.all_int_types)}"
+    )
     print(f"DPNP current device is CPU: {is_cpu}")
-    print(f"DPNP current device is GPU without fp64 support: {is_gpu_no_fp64}")
+    print(f"DPNP current device is GPU: {is_gpu}")
+    print(f"DPNP current device is GPU with fp64 support: {is_gpu_w_fp64}")
     print(f"DPNP current device is GPU with cuda backend: {is_cuda}")
     print(f"DPNP version: {dpnp.__version__}, location: {dpnp}")
     print(f"NumPy version: {numpy.__version__}, location: {numpy}")
     print(f"Python version: {sys.version}")
     print("")
-    if not is_cpu or os.getenv("DPNP_QUEUE_GPU") == "1":
+    if is_gpu or os.getenv("DPNP_QUEUE_GPU") == "1":
         excluded_tests.extend(get_excluded_tests(test_exclude_file_gpu))
-        if is_gpu_no_fp64:
+        if not is_gpu_w_fp64:
             excluded_tests.extend(
                 get_excluded_tests(test_exclude_file_gpu_no_fp64)
             )

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)"]
     )
@@ -2408,11 +2410,7 @@ 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)
             else:  # mode=="raw"
                 dpnp_q, dpnp_r = dpnp.linalg.qr(ia, mode)
                 assert_dtype_allclose(dpnp_q, np_q, factor=24)
@@ -2424,15 +2422,13 @@ 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 +2439,7 @@ 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-4)
             else:  # mode=="raw"
                 dpnp_q, dpnp_r = dpnp.linalg.qr(ia, mode)
                 assert_allclose(dpnp_q, np_q, atol=1e-4)
@@ -3169,9 +3161,7 @@ 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)