Apply w/a for expected result in spacing test only on non-Intel NumPy (#2453)

The stock NumPy returns incorrect result for `numpy.spacing(-0.0)`,
while Intel NumPy returns the expected one (the same as dpnp also).
It might be the case when dpnp test is running with the stock NumPy.
Thus the PR add a conditional check to update expected result in
`TestSpacing::test_zeros` in case when non-Intel NumPy is used.

Previously the same logic was based on NumPy version, assuming Intel
NumPy has always `version < "2.0.0"`, but it is not the case anymore.

Author: antonwolfy

dpnp/tests/helper.py

@@ -414,6 +414,26 @@ def is_gpu_device(device=None):
     return dev.has_aspect_gpu
 
 
+def is_intel_numpy():
+    """
+    Return True if Intel NumPy is used during testing.
+
+    The check is based on MKL backend name stored in Build Dependencies, where
+    in case of Intel Numpy there "mkl" is expected at the beginning of the name
+    for both BLAS and LAPACK (the full name is "mkl-dynamic-ilp64-iomp").
+
+    """
+
+    build_deps = numpy.show_config(mode="dicts")["Build Dependencies"]
+    blas = build_deps["blas"]
+    lapack = build_deps["lapack"]
+
+    if numpy_version() < "2.0.0":
+        # numpy 1.26.4 has LAPACK name equals to 'dep140030038112336'
+        return blas["name"].startswith("mkl")
+    return all(dep["name"].startswith("mkl") for dep in [blas, lapack])
+
+
 def is_win_platform():
     """
     Return True if a test is running on Windows OS, False otherwise.

dpnp/tests/test_mathematical.py

@@ -32,6 +32,7 @@
     get_integer_float_dtypes,
     has_support_aspect16,
     has_support_aspect64,
+    is_intel_numpy,
     numpy_version,
 )
 from .third_party.cupy import testing
@@ -1751,11 +1752,11 @@ def test_zeros(self, dt):
 
         result = dpnp.spacing(ia)
         expected = numpy.spacing(a)
-        if numpy_version() < "2.0.0":
+        if is_intel_numpy():
             assert_allclose(result, expected)
         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
+            # numpy.spacing(-0.0) == numpy.spacing(0.0), i.e. the stock NumPy
+            # returns positive value (looks as a bug), because for any other
             # negative input the NumPy result will be also a negative value.
             expected[1] *= -1
             assert_allclose(result, expected)