Update TestSpacing and TestTensordot (#2251)

This PR suggests using `assert_allclose` instead of `assert_equal` with
a resolution-based tolerance in `TestSpacing::test_zeros` to fix test
failures on CUDA caused by excessive precision for float32.

And updates `TestTensordot::test_axes` and `TestTensordot::test_linalg`
to increase a factor parameter in `assert_dtype_allclose` for scaling
tolerance

Author: vlad-perevezentsev

dpnp/tests/test_mathematical.py

@@ -2111,14 +2111,15 @@ def test_zeros(self, dt):
 
         result = dpnp.spacing(ia)
         expected = numpy.spacing(a)
+        tol = numpy.finfo(expected.dtype).resolution
         if numpy.lib.NumpyVersion(numpy.__version__) < "2.0.0":
-            assert_equal(result, expected)
+            assert_allclose(result, expected, rtol=tol, atol=tol)
         else:
             # numpy.spacing(-0.0) == numpy.spacing(0.0), i.e. NumPy returns
             # positive value (looks as a bug in NumPy), because for any other
             # negative input the NumPy result will be also a negative value.
             expected[1] *= -1
-            assert_equal(result, expected)
+            assert_allclose(result, expected, rtol=tol, atol=tol)
 
     @pytest.mark.parametrize("dt", get_float_dtypes(no_float16=False))
     @pytest.mark.parametrize("val", [1, 1e-5, 1000])

dpnp/tests/test_product.py

@@ -799,7 +799,7 @@ def test_axes(self, dtype, axes):
 
         result = dpnp.tensordot(ia, ib, axes=axes)
         expected = numpy.tensordot(a, b, axes=axes)
-        assert_dtype_allclose(result, expected)
+        assert_dtype_allclose(result, expected, factor=9)
 
     @pytest.mark.parametrize("dtype1", get_all_dtypes())
     @pytest.mark.parametrize("dtype2", get_all_dtypes())
@@ -844,7 +844,7 @@ def test_linalg(self, axes):
 
         result = dpnp.linalg.tensordot(ia, ib, axes=axes)
         expected = numpy.linalg.tensordot(a, b, axes=axes)
-        assert_dtype_allclose(result, expected)
+        assert_dtype_allclose(result, expected, factor=9)
 
     def test_error(self):
         a = 5