Re-enable FakeTensor caching for SymInts (#152662)

Summary:

This backs out D60320595 which itself turned off FakeTensor caching when a SymInt was present.

There has been a lot of dynamic shape fixes done this year and tests pass so I'm assuming some of that work fixed what was breaking previously.

Test Plan: Reran the tests listed in T196779132 and they pass.

## Perf
### Instruction Counter Benchmark:
- 26% win on add_loop_eager_dynamic
- 13% win on add_loop_inductor_dynamic_gpu
### Perf Dashboard
Compilation Latency wins across the board but especially strong on the dynamic tests (like cudagraphs_dynamic) - for example MobileBertForMaskedLM went from 66s -> 50s.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/152662
Approved by: https://github.com/anijain2305

torch/_subclasses/fake_tensor.py

@@ -1521,6 +1521,10 @@ class FakeTensorMode(TorchDispatchMode):
             # where it wasn't seen on a previous instance of the same op.
             self.shape_env.settings if self.shape_env else None,
         ]
+        if state.known_symbols:
+            # If there are symbols then include the epoch - this is really more
+            # of a Shape env var which lives on the FakeTensorMode.
+            key_values.append(self.epoch)
         # Collect the id_hashed objects to attach a weakref finalize later
         id_hashed_objects: list[object] = []
         # Translate any FakeTensor args to metadata.
@@ -1632,10 +1636,6 @@ class FakeTensorMode(TorchDispatchMode):
                     raise _BypassDispatchCache("constant attribute")
                 if is_sparse_any(arg):
                     raise _BypassDispatchCache(f"{arg.layout} tensor")
-                # FIXME: For now back out caching when there are symbolic nbytes
-                # - this doesn't seem to play nice with set(). See T196779132 for examples.
-                if isinstance(arg.untyped_storage().nbytes(), SymInt):
-                    raise _BypassDispatchCache("symbolic nbytes")
                 metadata = extract_tensor_metadata(arg)
                 metadata._flatten_into(result, self, state)
             elif isinstance(arg, Tensor):
@@ -1937,11 +1937,7 @@ class FakeTensorMode(TorchDispatchMode):
         if entry.is_output_tuple:
             outputs = [
                 self._get_output_tensor_from_cache_entry(
-                    state,
-                    output_info,
-                    key,
-                    func,
-                    args,
+                    state, output_info, key, func, args
                 )
                 for output_info in entry.output_infos
             ]