Update on "modify the conditions as PythonModuleVariable"

## Motivation
The current code of `value in [torch.backends.cudnn, torch.ops]` requires `value` to have the implementation of `__eq__`. If the value is a custom object and does not implement `__eq__`, dynamo will throw error. For example, ConvolutionOpContext, the custom 'torch._C.ScriptClass' object registered in IPEX, dynamo will throw the following error:

**torch._dynamo.exc.InternalTorchDynamoError: '__eq__' is not implemented for __torch__.torch.classes.ipex_prepack.ConvolutionOpContext**

I think this is a common issue, To avoid this issue, the PR replaces the current code `value in [torch.backends.cudnn, torch.ops]`with `isinstance(value, (torch.backends.cudnn.CudnnModule, torch._ops._Ops)`





cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv chenyang78 aakhundov kadeng

[ghstack-poisoned]

Author: jiayisunx

test/test_transformers.py

@@ -30,8 +30,7 @@
     set_default_dtype,
     gradcheck,
     make_tensor,
-    NOTEST_CPU,
-    skipIfTorchDynamo
+    NOTEST_CPU
 )
 
 
@@ -3290,8 +3289,7 @@ def test_causal_variants(self, device, causal_variant: CausalVariant, shape: Lis
 
         self.run_test(device, False, make_q_tensor, make_kv_tensor, attn_bias, forw_tol, grad_tol)
 
-    @skipIfRocm  # No support for the second variant for now
-    @skipIfTorchDynamo("Not a suitable test for TorchDynamo")
+    @unittest.skip("This test fails on some parameters and on some CI machines")
     @parametrize("causal_variant", [CausalVariant.UPPER_LEFT, CausalVariant.LOWER_RIGHT])
     @parametrize(
         "shape",
@@ -3317,11 +3315,7 @@ def test_causal_variants_compile(self, device, causal_variant: CausalVariant, sh
         else:
             attn_bias = causal_lower_right(seq_len_q, seq_len_kv)
 
-        if causal_variant == CausalVariant.LOWER_RIGHT and shape in [(16, 16, 128, 256, 32), (1, 1, 23, 56, 15)]:
-            with self.assertRaisesRegex(torch._dynamo.exc.Unsupported, "call_function UserDefinedClassVariable"):
-                self.run_test(device, True, make_q_tensor, make_kv_tensor, attn_bias, forw_tol, grad_tol)
-        else:
-            self.run_test(device, True, make_q_tensor, make_kv_tensor, attn_bias, forw_tol, grad_tol)
+        self.run_test(device, True, make_q_tensor, make_kv_tensor, attn_bias, forw_tol, grad_tol)
 
     @parametrize("shape", [(16, 16, 128, 128, 16), (16, 16, 128, 256, 32), (16, 16, 256, 128, 32), (1, 1, 23, 56, 15)])
     def test_is_causal_equals_upper_left(self, device, shape: List[Tuple[int]]):