Create export_edge_to_executorch, call export_to_executorch in testing flow, and call print_ops_info in export_to_executorch (#3863)

Summary:
Pull Request resolved: https://github.com/pytorch/executorch/pull/3863

The current setup is unbalanced in the way we call different APIs, especially because we would love to (and should) use the `print_ops_info` function better. Currently, it's not used in the calls most people would want to do on Bento for example.

For one-liner compilation (e.g. calling `export_to_executorch`), the information should be printed. `export_to_executorch` should also be called in the testing flow.

This diff refactors both the APIs in `__init__.py` and `utils.py`, so that the breakdown makes more sense. Arguably it should be a stack of diffs, but it mostly all goes hand in hand IMO so I kept it as one.

Main changes:
- create an `export_edge_to_executorch` API, which takes in an `EdgeProgramManager`. This is useful because we want to keep the edge graph module around to pass it in `print_ops_count`, and now we can use it in `export_to_executorch` (see next point)
- calls `print_ops_info` in `export_to_executorch`, now that the edge graph is exposed there
- call `export_to_executorch` in `run_and_verify`, using the exported module. This required changing the checks for `eval()` mode, see next point.
- introduce a `model_is_quantized()` util to call the right API when trying to make models eval. The check on the `GraphModule` type is not robust enough, since other models could be `GraphModule`s but not be quantized. If that's the case, we assert that they have been exported already, which makes the `eval()` requirement moot.

Reviewed By: dulinriley, zonglinpengmeta

Differential Revision: D58101124

fbshipit-source-id: 9822411c2a832d539c96ab61aff99586da206d01