torch.backends.mkldnn.flags() CM should not warn (pytorch#150358)

By returning `None` rather than `False` from `THPModule_allowTF32OneDNN` when USE_XPU is not defined

Added regression test

Fixes pytorch#149829

Fixes #ISSUE_NUMBER

Pull Request resolved: pytorch#150358
Approved by: https://github.com/atalman

Author: malfet

test/test_mkldnn.py

@@ -4,6 +4,7 @@
 import itertools
 import functools
 import unittest
+import warnings
 from contextlib import nullcontext
 
 try:
@@ -1612,6 +1613,16 @@ def common(self, shape1, shape2, op, dtype):
             ]:
                 common(self, shape1, shape2, op, dtype)
 
+    def test_mkldnn_setflags_nowarn(self, device):
+        # Regression test for https://github.com/pytorch/pytorch/issues/149829
+        with warnings.catch_warnings(record=True) as w:
+            rc = torch.backends.mkldnn.set_flags()
+            # torch.backends.mkldnn. returns previously set flags
+            # That one should be able to set back without cauinsg a warning
+            torch.backends.mkldnn.set_flags(*rc)
+        # Above should trigger no warnings regardless of configuration
+        self.assertEqual(len(w), 0)
+
 
 instantiate_device_type_tests(TestMkldnn, globals(), only_for=('cpu',))
 

torch/csrc/Module.cpp

@@ -965,10 +965,14 @@ static PyObject* THPModule_setAllowTF32OneDNN(
 static PyObject* THPModule_allowTF32OneDNN(
     PyObject* _unused,
     PyObject* noargs) {
+#ifdef USE_XPU
   if (at::globalContext().allowTF32OneDNN())
     Py_RETURN_TRUE;
   else
     Py_RETURN_FALSE;
+#else
+  Py_RETURN_NONE;
+#endif
 }
 
 static PyObject* THPModule_deterministicAlgorithms(