fix issues when running tests with all integer dtypes on GPU w/o support for fp64

dpnp/tests/test_linalg.py

@@ -311,7 +311,7 @@ def test_basic(self, dtype, shape, p):
 
         result = dpnp.linalg.cond(ia, p=p)
         expected = numpy.linalg.cond(a, p=p)
-        assert_dtype_allclose(result, expected)
+        assert_dtype_allclose(result, expected, factor=16)
 
     @pytest.mark.parametrize(
         "p", [None, -dpnp.inf, -2, -1, 1, 2, dpnp.inf, "fro"]
@@ -2841,10 +2841,8 @@ def get_tol(self, dtype):
         tol = 1e-06
         if dtype in (dpnp.float32, dpnp.complex64):
             tol = 1e-03
-        elif not has_support_aspect64() and dtype in (
-            dpnp.int32,
-            dpnp.int64,
-            None,
+        elif not has_support_aspect64() and (
+            dtype is None or dpnp.issubdtype(dtype, dpnp.integer)
         ):
             tol = 1e-03
         self._tol = tol
@@ -3019,10 +3017,8 @@ def get_tol(self, dtype):
         tol = 1e-06
         if dtype in (dpnp.float32, dpnp.complex64):
             tol = 1e-03
-        elif not has_support_aspect64() and dtype in (
-            dpnp.int32,
-            dpnp.int64,
-            None,
+        elif not has_support_aspect64() and (
+            dtype is None or dpnp.issubdtype(dtype, dpnp.integer)
         ):
             tol = 1e-03
         self._tol = tol

dpnp/tests/third_party/cupy/creation_tests/test_ranges.py

@@ -229,7 +229,7 @@ def test_linspace_mixed_start_stop2(self, xp, dtype_range, dtype_out):
         if xp.dtype(dtype_range).kind in "u":
             # to avoid overflow, limit `val` to be smaller
             # than xp.iinfo(dtype).max
-            if dtype_range == xp.uint8 or dtype_out == xp.uint8:
+            if dtype_range in [xp.uint8, xp.uint16] or dtype_out == xp.uint8:
                 val = 125
             else:
                 val = 160

dpnp/tests/third_party/cupy/math_tests/test_explog.py

@@ -16,8 +16,12 @@ def check_unary(self, name, xp, dtype, no_complex=False):
         a = testing.shaped_arange((2, 3), xp, dtype)
         return getattr(xp, name)(a)
 
+    # rtol=1e-3 is added for dpnp to pass the test when dtype is int8/unint8
+    # for such a case, output dtype is float16
     @testing.for_all_dtypes()
-    @testing.numpy_cupy_allclose(atol=1e-5, type_check=has_support_aspect64())
+    @testing.numpy_cupy_allclose(
+        rtol=1e-3, atol=1e-5, type_check=has_support_aspect64()
+    )
     def check_binary(self, name, xp, dtype, no_complex=False):
         if no_complex:
             if numpy.dtype(dtype).kind == "c":

dpnp/tests/third_party/cupy/math_tests/test_trigonometric.py

@@ -1,5 +1,7 @@
 import unittest
 
+import pytest
+
 from dpnp.tests.helper import has_support_aspect64
 from dpnp.tests.third_party.cupy import testing
 
@@ -76,12 +78,26 @@ def test_unwrap_1dim_with_discont(self, xp, dtype):
     @testing.for_all_dtypes(no_complex=True)
     @testing.numpy_cupy_allclose(type_check=has_support_aspect64())
     def test_unwrap_1dim_with_period(self, xp, dtype):
+        if not has_support_aspect64() and dtype in [xp.uint8, xp.uint16]:
+            # The unwrap function relies on the remainder function, and the
+            # result of remainder can vary significantly between float32 and
+            # float64. This discrepancy causes test failures when numpy uses
+            # float64 and dpnp uses float32, especially with uint8/uint16
+            # dtypes where overflow occurs
+            pytest.skip("skipping due to large difference of result")
         a = testing.shaped_random((5,), xp, dtype)
         return xp.unwrap(a, period=1.2)
 
     @testing.for_all_dtypes(no_complex=True)
     @testing.numpy_cupy_allclose(type_check=has_support_aspect64())
     def test_unwrap_1dim_with_discont_and_period(self, xp, dtype):
+        if not has_support_aspect64() and dtype in [xp.uint8, xp.uint16]:
+            # The unwrap function relies on the remainder function, and the
+            # result of remainder can vary significantly between float32 and
+            # float64. This discrepancy causes test failures when numpy uses
+            # float64 and dpnp uses float32, especially with uint8/uint16
+            # dtypes where overflow occurs
+            pytest.skip("skipping due to large difference of result")
         a = testing.shaped_random((5,), xp, dtype)
         return xp.unwrap(a, discont=1.0, period=1.2)