Move Set to dicts.py (#110522)

Summary:
A set is more of a dict than a list if you ask me.
This comes before the refactor where we implement sets and dicts via the
same logic.

X-link: pytorch/pytorch#110522
Approved by: https://github.com/jansel

Reviewed By: izaitsevfb

Differential Revision: D50778786

fbshipit-source-id: fbaaa926fd2c571e3f7a1518ad9dd79994cf53f7

userbenchmark/dynamo/dynamobench/_dynamo/utils.py

@@ -913,6 +913,49 @@ def enum_repr(value, local):
     return local_name
 
 
+def _get_fake_tensor(vt):
+    fake_tensor = vt.as_proxy().node.meta.get("example_value")
+    if not is_fake(fake_tensor):
+        unimplemented("Cannot check Tensor object identity without its fake value")
+    return fake_tensor
+
+
+def iter_contains(items, search, tx, options, check_tensor_identity=False):
+    from .variables import BuiltinVariable, ConstantVariable, TensorVariable
+
+    if search.is_python_constant():
+        found = any(
+            x.is_python_constant()
+            and x.as_python_constant() == search.as_python_constant()
+            for x in items
+        )
+        return ConstantVariable.create(found, **options)
+
+    must_check_tensor_id = False
+    if check_tensor_identity and isinstance(search, TensorVariable):
+        must_check_tensor_id = True
+        # Match of Tensor means match of FakeTensor
+        search = _get_fake_tensor(search)
+
+    found = None
+    for x in items:
+        if must_check_tensor_id:
+            if isinstance(x, TensorVariable):
+                if search is _get_fake_tensor(x):  # Object equivalence
+                    return ConstantVariable.create(True)
+        else:
+            check = BuiltinVariable(operator.eq).call_function(tx, [x, search], {})
+            if found is None:
+                found = check
+            else:
+                found = BuiltinVariable(operator.or_).call_function(
+                    tx, [check, found], {}
+                )
+    if found is None:
+        found = ConstantVariable.create(False)
+    return found
+
+
 def dict_param_key_ids(value):
     return {
         id(k) for k in value.keys() if isinstance(k, (torch.nn.Parameter, torch.Tensor))