Update on "Save foundation weights separately"

This diff:
1. Introduces SerializationConfig to llm_config. Currently, this allows user to save the foundation weights in a separate file; majorly useful for lora case.
2. Adds a pass to tag foundation (non-lora) weights. This is at the top-level (export_llama_lib). The tags are preserved through run_decomps/other passes, and do not affect functionality.
3. Tags are read when placing constants into the named_data_store.
4. Tagged weights are serialized to a separate file.


Notes
1. Adding tags to node.meta['custom']['blah'] means that they will not be discarded by run_decompositions
2. Adding tags to the lifted model (ep.graph_module) requires the EP to check is_param_node for xnnpack constants. Instead, add tags to the unlifted model (ep.module()), so we do not need to go through a re-export to get the EP.
3. Not an issue for this diff as llama doesn't have any higher order ops. Adding tags to models with higher-order ops is problematic due to nested submodules.

Differential Revision: [D79181064](https://our.internmc.facebook.com/intern/diff/D79181064/)

[ghstack-poisoned]

Author: lucylq

exir/program/_program.py

@@ -1892,7 +1892,9 @@ def write_tensor_data_to_file(self, outdir) -> None:
         """
         assert self._tensor_data is not None
         for filename, cord in self._tensor_data.items():
-            with open(os.path.join(outdir, f"{filename}.ptd"), "wb") as f:
+            if not filename.endswith(".ptd"):
+                filename += ".ptd"
+            with open(os.path.join(outdir, f"{filename}"), "wb") as f:
                 logging.info(f"Writing data file to {filename}")
                 cord.write_to_file(f)