[export] Update dynamo_graph_capture_for_export to return GraphModule.

test/distributed/tensor/test_dtensor_export.py

@@ -442,13 +442,22 @@ class DTensorExportTest(TestCase):
 
         # Run model to verify it works
         output = model(*inputs)
-        with torch._dynamo.config.patch(install_free_tensors=True):
+        with torch._dynamo.config.patch(
+            install_free_tensors=(export_fn is _dynamo_graph_capture_for_export)
+        ):
             # TODO: switch to use the official graph_capture API once it is ready
             gm = export_fn(model)(*inputs)
         output_gm = gm(*inputs)
         self.assertEqual(output, output_gm)
 
-    def test_flex_attention_dtensor_export(self):
+    @parametrize(
+        "export_fn",
+        [
+            graph_capture_and_aot_export_joint_with_descriptors_v2,
+            graph_capture_and_aot_export_joint_with_descriptors,
+        ],
+    )
+    def test_flex_attention_dtensor_export(self, export_fn):
         device_mesh = init_device_mesh(self.device_type, mesh_shape=(self.world_size,))
         model = FlexAttentionModel(self.device_type)
 
@@ -485,9 +494,7 @@ class DTensorExportTest(TestCase):
 
         flex_kwargs = {"block_mask": block_mask}
 
-        joint_gm = graph_capture_and_aot_export_joint_with_descriptors(
-            tp_model, inputs, flex_kwargs
-        )
+        joint_gm = export_fn(tp_model, inputs, flex_kwargs)
 
         self.assertTrue(
             _count_op(joint_gm, torch.ops.higher_order.flex_attention),

test/export/test_experimental.py

@@ -3,16 +3,19 @@
 import copy
 import types
 import unittest
+from dataclasses import dataclass
 from typing import Dict, List, Tuple
 
 import torch
 import torch._dynamo
+from torch._dynamo.functional_export import dynamo_graph_capture_for_export
 from torch._dynamo.test_case import run_tests, TestCase
 from torch._functorch.aot_autograd import aot_export_module
 from torch.export import export
 from torch.export.experimental import _export_forward_backward, _sticky_export
 from torch.export.graph_signature import OutputKind
 from torch.testing import FileCheck
+from torch.testing._internal.common_utils import TEST_CUDA
 
 
 @unittest.skipIf(not torch._dynamo.is_dynamo_supported(), "dynamo isn't supported")
@@ -403,8 +406,6 @@ def forward(self, x):
         self.assertEqual(res_export, res_eager)
 
     def test_dynamo_graph_capture(self):
-        from torch._dynamo.functional_export import dynamo_graph_capture_for_export
-
         class Foo(torch.nn.Module):
             def forward(self, dct, lst, bleh):
                 x = dct["a"] * lst[1][0]
@@ -439,6 +440,151 @@ def forward(self, x):
         test_inputs = make_inputs()
         self.assertEqual(gm(*test_inputs), foo(*test_inputs))
 
+    def test_dynamo_graph_capture_custom_pytree_type(self):
+        import torch.utils._pytree as pytree
+
+        @dataclass
+        class Bar:
+            x: torch.Tensor
+            y: torch.Tensor
+
+        class Foo(torch.nn.Module):
+            def forward(self, bar: Bar):
+                return bar.x + bar.y
+
+        foo = Foo()
+
+        def make_inputs():
+            return (Bar(torch.randn(2, 3), torch.randn(2, 3)),)
+
+        pytree.register_dataclass(Bar)
+        try:
+            trace_inputs = make_inputs()
+            gm = dynamo_graph_capture_for_export(foo)(*trace_inputs)
+            test_inputs = make_inputs()
+            self.assertExpectedInline(
+                gm._in_shuffle_graph.code.strip("\r\n "),
+                """\
+def forward(self, arg0_1, arg1_1, arg2_1):
+    return (arg1_1, arg2_1)""",
+            )
+            self.assertExpectedInline(
+                gm.code.strip("\r\n "),
+                """\
+def forward(self, args_0):
+    _tree_leaf_0, _tree_leaf_1, _tree_leaf_2, = pytree.tree_leaves((self, args_0,))
+    L_bar_x , L_bar_y , = self._in_shuffle_graph(_tree_leaf_0, _tree_leaf_1, _tree_leaf_2)
+    l_bar_x = L_bar_x
+    l_bar_y = L_bar_y
+    add = l_bar_x + l_bar_y;  l_bar_x = l_bar_y = None
+    return pytree.tree_unflatten(self._out_shuffle_graph(_tree_leaf_0, _tree_leaf_1, _tree_leaf_2, add), self._out_spec)""",
+            )
+            self.assertEqual(gm(*test_inputs), foo(*test_inputs))
+        finally:
+            pytree._deregister_pytree_node(Bar)
+
+    def test_dynamo_graph_capture_closure(self):
+        from torch.export import Dim
+
+        N = 3
+        outer = torch.randn(10, 32)
+
+        class MyModel(torch.nn.Module):
+            def forward(self, x):
+                z = x + outer
+                y = z[:-1, :]  # [s0 - 1, 32]
+                stacked = torch.stack([y] * N, dim=0)  # [N * (s0 - 1), 32]
+                reshaped = stacked.reshape(-1, N, 32)  # [(s0 - 1), N, 32]
+                return reshaped
+
+        inps = (torch.randn(10, 32),)
+        ep = dynamo_graph_capture_for_export(MyModel())(*inps)
+        self.assertExpectedInline(
+            ep._in_shuffle_graph.code.strip("\r\n "),
+            """\
+def forward(self, arg0_1, arg1_1):
+    _tensor_constant0 = self._tensor_constant0
+    return (arg1_1, _tensor_constant0)""",
+        )
+        self.assertExpectedInline(
+            ep.code.strip("\r\n "),
+            """\
+def forward(self, args_0):
+    _tree_leaf_0, _tree_leaf_1, = pytree.tree_leaves((self, args_0,))
+    L_x_ , L_outer_ , = self._in_shuffle_graph(_tree_leaf_0, _tree_leaf_1)
+    l_x_ = L_x_
+    l_outer_ = L_outer_
+    z = l_x_ + l_outer_;  l_x_ = l_outer_ = None
+    y = z[(slice(None, -1, None), slice(None, None, None))];  z = None
+    stacked = torch.stack([y, y, y], dim = 0);  y = None
+    reshaped = stacked.reshape(-1, 3, 32);  stacked = None
+    return pytree.tree_unflatten(self._out_shuffle_graph(_tree_leaf_0, _tree_leaf_1, reshaped), self._out_spec)""",
+        )
+        self.assertEqual(ep(*inps), MyModel()(*inps))
+
+    @unittest.skipIf(not TEST_CUDA, "CUDA not available")
+    def test_dynamo_graph_capture_fx_graph_annotate_overlap_pass(self):
+        class DummyOp(torch.autograd.Function):
+            @staticmethod
+            def forward(ctx, x, scalar):
+                ctx.save_for_backward(x)
+                return x + scalar
+
+            @staticmethod
+            def backward(ctx, grad_out):
+                return grad_out, None
+
+        def mock_fw_compute(x):
+            with fx_traceback.annotate({"compute": 0}):
+                return DummyOp.apply(x, 10)
+
+        def mock_bw_comm(x):
+            with fx_traceback.annotate({"comm": 0}):
+                return DummyOp.apply(x, 20)
+
+        def mock_bw_compute(x):
+            return DummyOp.apply(x, 30)
+
+        class Model(torch.nn.Module):
+            def forward(self, fw_in, bw_in):
+                fw_out = mock_fw_compute(fw_in)
+                # bw_in blocks bw_out
+                bw_in = mock_bw_comm(bw_in)
+                bw_out = mock_bw_compute(bw_in)
+                return fw_out, bw_out
+
+        def input_fn():
+            inputs = (torch.rand(2, 128, device="cuda", requires_grad=True),)
+            grad_ins = (torch.rand(2, 128, device="cuda"),)
+            return (
+                *inputs,
+                *grad_ins,
+            )
+
+        with torch.device("meta"):
+            model = Model()
+
+        import torch.fx.traceback as fx_traceback
+
+        with fx_traceback.preserve_node_meta():
+            gm = dynamo_graph_capture_for_export(model)(*input_fn())
+
+        """
+        def forward(self, args_0, args_1):
+            _tree_leaf_0, _tree_leaf_1, _tree_leaf_2, = pytree.tree_leaves((self, args_0, args_1,))
+            L_fw_in_ , L_bw_in_ , = self._in_shuffle_graph(_tree_leaf_0, _tree_leaf_1, _tree_leaf_2)
+            l_fw_in_ = L_fw_in_
+            l_bw_in_ = L_bw_in_
+            fwd_body_0 = self.fwd_body_0
+            bwd_body_0 = self.bwd_body_0
+            fw_out = torch.ops.higher_order.autograd_function_apply(fwd_body_0, bwd_body_0, l_fw_in_, args_tensor_mask = [True, False], non_differentiable_idx = []);  fwd_body_0 = bwd_body_0 = l_fw_in_ = None
+            bw_in = l_bw_in_ + 20;  l_bw_in_ = None
+            bw_out = bw_in + 30;  bw_in = None
+            return pytree.tree_unflatten(self._out_shuffle_graph(_tree_leaf_0, _tree_leaf_1, _tree_leaf_2, fw_out, bw_out), self._out_spec)
+        """
+        test_inputs = input_fn()
+        self.assertEqual(gm(*test_inputs), model(*test_inputs))
+
 
 if __name__ == "__main__":
     run_tests()

torch/_dynamo/convert_frame.py

@@ -896,6 +896,7 @@ class DynamoOutput:
             output_graph.import_sources,
             output_graph.traced_code,
             self.bytecode,
+            self.tracer_output.closure,
         )
 
 
@@ -927,6 +928,7 @@ class GraphCaptureOutput:
     import_sources: dict[str, str]
     traced_code: list[CodeType]
     bytecode: CodeType
+    closure: Optional[tuple[Any, ...]]
 
     def build_guards(
         self,
@@ -981,7 +983,7 @@ class CaptureOutput:
         return types.FunctionType(
             self.graph_capture_output.bytecode,
             f_globals,
-            closure=(),
+            closure=self.graph_capture_output.closure,
         )
 
 

torch/_dynamo/eval_frame.py

@@ -1211,7 +1211,9 @@ class _NullDecorator(contextlib.nullcontext):  # type: ignore[type-arg]
 
 # Make dynamo graph to have same input/output spec as user code
 def argument_names(
-    f_sig: inspect.Signature, args: list[Any], kwargs: dict[str, Any]
+    f_sig: inspect.Signature,
+    args: Union[list[Any], tuple[Any, ...]],
+    kwargs: dict[str, Any],
 ) -> list[str]:
     def signature_to_fullargspec(sig: inspect.Signature) -> inspect.FullArgSpec:
         # Get a list of Parameter objects from the Signature object

torch/_dynamo/functional_export.py

@@ -1,10 +1,9 @@
-import copy
 import inspect
 import logging
 import traceback
-import types
 from collections import namedtuple
 from collections.abc import Callable
+from dataclasses import dataclass
 from typing import Any, Optional, TYPE_CHECKING, Union
 
 import sympy
@@ -19,17 +18,19 @@ from torch._dynamo.utils import dynamo_timed, get_metrics_context
 from torch._export.utils import _compiling_state_context
 from torch.export.dynamic_shapes import _RelaxedConstraint, Constraint
 from torch.fx import Node
+from torch.fx.experimental.proxy_tensor import make_fx
 from torch.fx.experimental.symbolic_shapes import (
     ConstraintViolationError,
     DimDynamic,
+    ShapeEnv,
     StatelessSymbolicContext,
 )
-from torch.fx.graph import _PyTreeCodeGen, _PyTreeInfo
+from torch.fx.graph import _ExportCodeGen, _PyTreeCodeGen, _PyTreeInfo
+from torch.utils._pytree import TreeSpec
 
 
 if TYPE_CHECKING:
     from torch._subclasses.fake_tensor import FakeTensorMode
-    from torch.utils._pytree import TreeSpec
 
 
 log = logging.getLogger(__name__)
@@ -448,9 +449,20 @@ def _suggest_or_raise_constraint_violation(
         raise constraint_violation_error
 
 
+@dataclass(frozen=True)
+class PyTreeifyOutput:
+    graph_module: torch.fx.GraphModule
+    in_spec: TreeSpec
+    in_shuffle_graph: torch.fx.GraphModule
+    num_flat_args: int
+    out_spec: TreeSpec
+    out_shuffle_graph: torch.fx.GraphModule
+    root: Optional[torch.nn.Module] = None
+
+
 def pytreeify(
     out: CaptureOutput, mod: Any, args: tuple[Any, ...], kwargs: dict[str, Any]
-) -> Any:
+) -> PyTreeifyOutput:
     """
     Given a dynamo capture output, return a callable graph module that
     contain the following information:
@@ -468,10 +480,13 @@ def pytreeify(
     backend_input = out.backend_input
     backend = out.backend_input.graph_module
 
+    root = None
     if isinstance(mod, torch.nn.Module):
         args = (mod,) + args
+        root = mod
     elif inspect.ismethod(mod):
         args = (mod.__self__,) + args
+        root = mod.__self__
 
     flat_real_args, in_spec = pytree.tree_flatten((args, kwargs))
 
@@ -504,15 +519,21 @@ def pytreeify(
                 backend_input.graph_module = backend
             raise RuntimeError
 
-    in_shuffle_graph = torch.fx.symbolic_trace(InShuffle())
+    fake_mode = torch._dynamo.utils.detect_fake_mode(flat_real_args)
+    if fake_mode and fake_mode.shape_env is None:
+        fake_mode.shape_env = ShapeEnv()
+    in_shuffle_graph = make_fx(
+        InShuffle(), tracing_mode="symbolic", proxy_module_inputs=True
+    )(*flat_real_args)
+
+    output_node = next(iter(reversed(backend_input.graph_module.graph.nodes)))
 
     class OutShuffle(torch.nn.Module):
         def __init__(self):
             super().__init__()
             self.num_inputs = len(flat_real_args)
-            self.num_outputs = len(
-                next(iter(reversed(backend_input.graph_module.graph.nodes))).args[0]
-            )
+
+            self.num_outputs = len(output_node.args[0])
             self.out_spec: Optional[TreeSpec] = None
 
         def forward(self, *flat_proxy_args):
@@ -535,49 +556,101 @@ def pytreeify(
             return ret
 
     out_shuffle = OutShuffle()
-    out_shuffle_graph = torch.fx.symbolic_trace(out_shuffle)
+    flat_out_shuffle_args = [
+        *flat_real_args,
+        *pytree.tree_map_only(
+            torch.fx.Node,
+            lambda x: fake_mode.from_tensor(x.meta["example_value"])
+            if fake_mode
+            else x.meta["example_value"],
+            output_node.args[0],
+        ),
+    ]
+    fake_mode = torch._dynamo.utils.detect_fake_mode(flat_out_shuffle_args)
+    if fake_mode and fake_mode.shape_env is None:
+        fake_mode.shape_env = ShapeEnv()
+    out_shuffle_graph = make_fx(
+        out_shuffle, tracing_mode="symbolic", proxy_module_inputs=True
+    )(*flat_out_shuffle_args)
 
-    def pytree_call(*args, **kwargs):
-        import torch.export._unlift
+    assert out_shuffle.out_spec is not None
+    return PyTreeifyOutput(
+        backend_input.graph_module,
+        in_spec,
+        in_shuffle_graph,
+        len(flat_real_args),
+        out_shuffle.out_spec,
+        out_shuffle_graph,
+        root=root,  # type: ignore[arg-type]
+    )
 
-        flat_args, in_spec_runtime = pytree.tree_flatten((args, kwargs))
-        if not torch.export._unlift.eq_spec(in_spec_runtime, in_spec):
-            raise RuntimeError(
-                f"Model input mismatch. Expected input spec: {in_spec}. Actual input spec: {in_spec_runtime}"
-            )
-        flat_outs = backend_input.graph_module(*in_shuffle_graph(*flat_args))
-        assert out_shuffle.out_spec is not None
-        return pytree.tree_unflatten(
-            out_shuffle_graph(*flat_args, *flat_outs), out_shuffle.out_spec
-        )
 
-    if isinstance(mod, torch.nn.Module):
-        compiled_mod = copy.copy(mod)
-        compiled_mod.forward = types.MethodType(pytree_call, compiled_mod)
-        if not hasattr(compiled_mod, "meta"):
-            compiled_mod.meta = {}  # type: ignore[attr-defined]
-        if isinstance(compiled_mod.meta, dict) and "fake_mode" not in compiled_mod.meta:
-            compiled_mod.meta["fake_mode"] = out.backend_input.fake_mode
-        return compiled_mod
-    elif inspect.ismethod(mod):
-        return types.MethodType(pytree_call, mod.__self__)
-    else:
-        return pytree_call
+def normalize_graph_module(gm):
+    for node in gm.graph.nodes:
+        if node.op == "placeholder":
+            node.meta["val"] = node.meta["example_value"]
 
 
 def dynamo_graph_capture_for_export(
     mod: Callable[..., Any],
+    constraints: Optional[list[Constraint]] = None,
 ) -> Callable[..., Any]:
     def inner(*args: Any, **kwargs: Any) -> Any:
+        assert not torch._dynamo.config.install_free_tensors
         with (
             get_metrics_context(),
             dynamo_timed("fullgraph_capture"),
         ):
-            out = fullgraph_capture(mod, args, kwargs)
+            out = fullgraph_capture(
+                mod,
+                args,
+                kwargs,
+                constraints=constraints,
+            )
 
         # TODO filter out side effects.
+        pyt = pytreeify(out, mod, args, kwargs)
 
-        return pytreeify(out, mod, args, kwargs)
+        graph_module = pyt.graph_module
+        tree_leaf_names = [
+            graph_module.graph._graph_namespace.create_name(f"_tree_leaf_{i}", None)
+            for i in range(pyt.num_flat_args)
+        ]
+        graph_module.graph._codegen = _ExportCodeGen(
+            _PyTreeInfo(
+                # TODO we should be able to use the names from dynamo graph directly.
+                argument_names(inspect.signature(mod), args, kwargs),
+                pyt.in_spec,
+                pyt.out_spec,
+            ),
+            pyt.in_shuffle_graph,
+            pyt.out_shuffle_graph,
+            tree_leaf_names,
+            pyt.root,
+        )  # type: ignore[attr-defined]
+        normalize_graph_module(graph_module)
+        if pyt.root is not None:
+            graph_module._parameters = pyt.root._parameters.copy()
+            graph_module._buffers = pyt.root._buffers.copy()
+            assert all(not hasattr(graph_module, m) for m in pyt.root._modules)
+            graph_module._modules.update(pyt.root._modules)
+            graph_module._non_persistent_buffers_set = (
+                pyt.root._non_persistent_buffers_set.copy()
+            )
+        graph_module._in_spec = pyt.in_spec
+        graph_module._out_spec = pyt.out_spec
+        assert not hasattr(graph_module, "_in_shuffle_graph")
+        assert not hasattr(graph_module, "_out_shuffle_graph")
+        graph_module._in_shuffle_graph = pyt.in_shuffle_graph
+        graph_module._out_shuffle_graph = pyt.out_shuffle_graph
+        delattr(graph_module, "_param_name_to_source")
+        graph_module.recompile()
+        graph_module.meta["module_call_specs"] = (
+            out.graph_capture_output.output_graph.export_metadata.module_call_spec
+        )
+        assert out.backend_input is not None
+        graph_module.meta["fake_mode"] = out.backend_input.fake_mode  # type: ignore[attr-defined]
+        return graph_module
 
     return inner
 

torch/_dynamo/output_graph.py

@@ -2747,12 +2747,14 @@ class DynamoTracerOutput:
     error_on_graph_break: bool
     is_tracing_resume_prologue: bool
     output_graph: Optional[OutputGraph]
+    closure: Optional[tuple[Any, ...]]
 
     def __init__(
         self, tracer: "InstructionTranslatorBase", error: Optional[Any] = None
     ) -> None:
         self.error_on_graph_break = tracer.error_on_graph_break
         self.is_tracing_resume_prologue = tracer.is_tracing_resume_prologue
+        self.closure = tracer.closure
         if error:
             self.output_graph = None
         else:

torch/_dynamo/symbolic_convert.py

@@ -4120,6 +4120,7 @@ class InstructionTranslatorBase(
         self.f_builtins: dict[str, Any] = f_builtins
         self.code_options: dict[str, Any] = code_options
         self.f_code: types.CodeType = f_code
+        self.closure = closure
 
         # Execution record for replaying errors
         if closure is not None and config.replay_record_enabled:

torch/export/_trace.py

@@ -97,7 +97,7 @@ from torch.fx.experimental.symbolic_shapes import (
     GuardOnDataDependentSymNode,
     ShapeEnv,
 )
-from torch.fx.graph import _PyTreeCodeGen, _PyTreeInfo
+from torch.fx.graph import _PyTreeInfo
 from torch.utils._pytree import TreeSpec
 from torch.utils._sympy.value_ranges import ValueRangeError
 
@@ -1537,12 +1537,10 @@ def _strict_export(
 
     orig_arg_names = gm_torch_level.graph._codegen.pytree_info.orig_args  # type: ignore[attr-defined]
 
-    gm_torch_level.graph._codegen = _PyTreeCodeGen(
-        _PyTreeInfo(
-            orig_arg_names,
-            gm_torch_level._in_spec,
-            out_spec,
-        )
+    gm_torch_level.graph._codegen.pytree_info = _PyTreeInfo(
+        orig_arg_names,
+        gm_torch_level._in_spec,
+        out_spec,
     )
     gm_torch_level.recompile()
 

torch/fx/experimental/proxy_tensor.py

@@ -1489,12 +1489,20 @@ def wrap_key(
 
     @functools.wraps(f)
     def wrapped(*proxies: _P.args, **_unused: _P.kwargs) -> R:
+        nonlocal tensors
+
         flat_proxies, _proxies_spec = pytree.tree_flatten(proxies)
         assert len(flat_proxies) == len(flat_tensors)
         with disable_proxy_modes_tracing() as m:
             assert isinstance(m, ProxyTorchDispatchMode)
             track_tensor_tree(flat_tensors, flat_proxies, constant=None, tracer=tracer)
 
+        if getattr(tracer, "proxy_module_inputs", False):
+            tensors = [  # type: ignore[assignment, var-annotated]
+                p if isinstance(t, torch.nn.Module) else t
+                for t, p in zip(tensors, proxies)  # type: ignore[arg-type]
+            ]
+
         def get_tensor_proxy_slot(t: Tensor) -> Union[Tensor, Proxy]:
             return get_proxy_slot(t, tracer, t, lambda x: x.proxy)  # type: ignore[attr-defined]
 
@@ -2208,6 +2216,7 @@ class _MakefxTracer:
         _error_on_data_dependent_ops: bool,
         record_stack_traces: bool = False,
         parent_tracer: Optional[_MakefxTracer] = None,
+        proxy_module_inputs: bool = False,
     ) -> None:
         # Configurations that are used to initialize the context managers and their states.
         # Should not modify them during tracing.
@@ -2240,6 +2249,7 @@ class _MakefxTracer:
         )
         self.record_stack_traces = record_stack_traces
         self.parent_tracer: Optional[_MakefxTracer] = parent_tracer
+        self.proxy_module_inputs = proxy_module_inputs
 
     def _checkpoint_modes(self) -> list[Any]:
         return [
@@ -2349,6 +2359,7 @@ class _MakefxTracer:
             self.python_dispatcher_mode = enable_python_dispatcher()
 
         self.torch_fn_metadata_mode = TorchFunctionMetadataMode(fx_tracer)
+        fx_tracer.proxy_module_inputs = self.proxy_module_inputs  # type: ignore[union-attr]
 
     @contextmanager
     def _init_modes_from_parent(
@@ -2551,6 +2562,7 @@ def make_fx(
     _allow_fake_constant: bool = False,
     _error_on_data_dependent_ops: bool = True,
     record_stack_traces: bool = False,
+    proxy_module_inputs: bool = False,
 ) -> Callable[..., GraphModule]:
     """
     Given a function f, return a new function which when executed with valid
@@ -2574,6 +2586,7 @@ def make_fx(
         _error_on_data_dependent_ops,
         record_stack_traces=record_stack_traces
         or config.trace.provenance_tracking_level == 1,
+        proxy_module_inputs=proxy_module_inputs,
     )
 
     @functools.wraps(f)

torch/fx/graph.py

@@ -930,6 +930,42 @@ class _PyTreeCodeGen(CodeGen):
         else:
             return "\n    " + "".join(x + "; " for x in has_annotation) + "\n"
 
+    def gen_var_bindings(self, fn_args, free_vars, expanded_def) -> str:
+        # when kwargs is present, in_spec is tuple(args, kwargs)
+        has_args_kwargs_tuple = (
+            self.pytree_info.in_spec.type is tuple
+            and self.pytree_info.in_spec.num_children == 2
+            and self.pytree_info.in_spec.children_specs[0].type is tuple
+            and self.pytree_info.in_spec.children_specs[1].type is dict
+        )
+        fn_kwargs = "{}"
+        fn_signature = f"[{', '.join(fn_args)}], self._in_spec"
+        if has_args_kwargs_tuple:
+            count_args = self.pytree_info.in_spec.children_specs[0].num_children
+            fn_args = self.pytree_info.orig_args[:count_args]
+            fn_kwargs = (
+                "{"
+                + ", ".join(
+                    f"'{k}':{v}"
+                    for k, v in zip(
+                        self.pytree_info.in_spec.children_specs[1].context,
+                        self.pytree_info.orig_args[count_args:],
+                    )
+                )
+                + "}"
+            )
+            fn_signature = f"([{', '.join(fn_args)}], {fn_kwargs}), self._in_spec"
+
+        # in Python, `var1: annotation1, var2: annotation2 = function_call()` is invalid.
+        # we need to split it to two lines:
+        # one for annotation: `var1: annotation1; var2: annotation2;` (note the semicolon)
+        # one for code: `var1, var2, = function_call()`
+        without_annotation = [x.split(":")[0].split("#")[0] for x in free_vars]
+        bindings = self._format_annotations(free_vars, expanded_def)
+        bindings += f"""
+    {", ".join(without_annotation)}, = fx_pytree.tree_flatten_spec({fn_signature})"""
+        return bindings
+
     def gen_fn_def(
         self, free_vars, maybe_return_annotation, *, expanded_def: bool = False
     ):
@@ -962,39 +998,7 @@ class _PyTreeCodeGen(CodeGen):
         )
 
         if len(free_vars) > 0:  # pytree has placeholders in it
-            # when kwargs is present, in_spec is tuple(args, kwargs)
-            has_args_kwargs_tuple = (
-                self.pytree_info.in_spec.type is tuple
-                and self.pytree_info.in_spec.num_children == 2
-                and self.pytree_info.in_spec.children_specs[0].type is tuple
-                and self.pytree_info.in_spec.children_specs[1].type is dict
-            )
-            fn_kwargs = "{}"
-            fn_signature = f"[{', '.join(fn_args)}], self._in_spec"
-            if has_args_kwargs_tuple:
-                count_args = self.pytree_info.in_spec.children_specs[0].num_children
-                fn_args = self.pytree_info.orig_args[:count_args]
-                fn_kwargs = (
-                    "{"
-                    + ", ".join(
-                        f"'{k}':{v}"
-                        for k, v in zip(
-                            self.pytree_info.in_spec.children_specs[1].context,
-                            self.pytree_info.orig_args[count_args:],
-                        )
-                    )
-                    + "}"
-                )
-                fn_signature = f"([{', '.join(fn_args)}], {fn_kwargs}), self._in_spec"
-
-            # in Python, `var1: annotation1, var2: annotation2 = function_call()` is invalid.
-            # we need to split it to two lines:
-            # one for annotation: `var1: annotation1; var2: annotation2;` (note the semicolon)
-            # one for code: `var1, var2, = function_call()`
-            without_annotation = [x.split(":")[0].split("#")[0] for x in free_vars]
-            fn_definition += self._format_annotations(free_vars, expanded_def)
-            fn_definition += f"""
-    {", ".join(without_annotation)}, = fx_pytree.tree_flatten_spec({fn_signature})"""
+            fn_definition += self.gen_var_bindings(fn_args, free_vars, expanded_def)
         return fn_definition
 
     def generate_output(self, output_args, *, descs: Optional[Any] = None):
@@ -1014,6 +1018,52 @@ class _PyTreeCodeGen(CodeGen):
             return super().generate_output(output_args, descs=descs)
 
 
+class _ExportCodeGen(_PyTreeCodeGen):
+    def __init__(
+        self,
+        pytree_info: _PyTreeInfo,
+        in_shuffle_graph: "GraphModule",
+        out_shuffle_graph: "GraphModule",
+        tree_leaf_names: list[str],
+        root: Optional[torch.nn.Module],
+    ):
+        super().__init__(pytree_info)
+        self.in_shuffle_graph = in_shuffle_graph
+        self.out_shuffle_graph = out_shuffle_graph
+        self.tree_leaf_names = tree_leaf_names
+        self.root = root
+
+    def process_inputs(self, *inputs: Any) -> Any:
+        flat_args = super().process_inputs(*inputs)
+        if self.root is not None:
+            flat_args = (self.root, *flat_args)
+        self.flat_args = flat_args
+        return self.in_shuffle_graph(*flat_args)
+
+    def process_outputs(self, out: Any) -> Any:
+        flat_outs = self.out_shuffle_graph(*self.flat_args, *out)
+        del self.flat_args
+        ret = super().process_outputs(flat_outs)
+        return ret
+
+    def gen_fn_def(self, *args, **kwargs) -> str:
+        fn_def = super().gen_fn_def(*args, **kwargs)
+        return fn_def
+
+    def gen_var_bindings(self, fn_args, free_vars, expanded_def) -> str:
+        without_annotation = [x.split(":")[0].split("#")[0] for x in free_vars]
+        fn_signature: str = f"{', '.join(fn_args)}"
+        if self.root is not None:
+            fn_signature = f"self, {fn_signature}"
+        return f"""
+    {", ".join(self.tree_leaf_names)}, = pytree.tree_leaves(({fn_signature},))
+    {", ".join(without_annotation)}, = self._in_shuffle_graph({", ".join(self.tree_leaf_names)})"""
+
+    def generate_output(self, output_args, *args, **kwargs) -> str:
+        output = f"self._out_shuffle_graph({', '.join(self.tree_leaf_names)}, {', '.join([str(a) for a in output_args])})"
+        return f"return pytree.tree_unflatten({output}, self._out_spec)"
+
+
 class _FindNodesLookupTable:
     """
     Side table for the graph for the purpose of doing fast queries