Move autoparallel to use leaner export API

autoparallel/api.py

@@ -8,9 +8,10 @@
 import warnings
 from contextlib import ExitStack, contextmanager
 from types import MethodType
-from typing import Optional, Union
+from typing import Any, Optional, Tuple, Union
 
 import torch
+from torch._dynamo.functional_export import _dynamo_graph_capture_for_export
 from torch._functorch.aot_autograd import (
     aot_compile_joint_with_descriptors,
     aot_export_joint_with_descriptors,
@@ -22,6 +23,7 @@
 from torch._subclasses import FakeTensorMode
 from torch.distributed.fsdp import MixedPrecisionPolicy
 from torch.distributed.tensor import DeviceMesh
+from torch.export._trace import _restore_state_dict
 from torch.export._unlift import _assign_attr
 from torch.export.unflatten import _AttrKind
 
@@ -163,6 +165,21 @@ def enable_local_map_wrapping():
         yield
 
 
+def _export(model: torch.nn.Module, inputs: Tuple[Any]) -> torch.nn.Module:
+    """
+    Thin wrapper around graph capture output that restores the
+    original calling convention and attribute fqn. TODO:
+    1) Use bytecode for calling convention instead of pytree for more
+       seamless UX.
+    2) Attach guards
+    3) Be more careful about tensor constants names.
+    """
+    with torch._dynamo.config.patch(install_free_tensors=True):
+        gm = _dynamo_graph_capture_for_export(model)(*inputs)
+        _restore_state_dict(model, gm)
+        return gm
+
+
 class AutoParallel:
     """
     Args:
@@ -279,13 +296,10 @@ def build_model_graph(self):
         with set_dtype_cast(
             True
         ), enable_local_map_wrapping(), torch._dynamo.utils._disable_saved_tensors_hooks_during_tracing():
-            with torch._dynamo.config.patch(
-                install_free_tensors=True
-            ), monkey_patch_export_verifier():
-                ep = torch.export.export(self.model, inputs, strict=True)
+            torch_ir_with_fqn = _export(self.model, inputs)
             self.joint_with_descriptors = aot_export_joint_with_descriptors(
                 self.stack,
-                ep.module(),
+                torch_ir_with_fqn,
                 inputs,
                 decompositions=decomp_table,
                 fw_compiler=self.compiler_fn,