MLPY/Lib/site-packages/torch/export/unflatten.py

import abc
import copy
import operator
from copy import deepcopy
from enum import Enum
from itertools import chain
from typing import Any, cast, Dict, List, Optional, Union

import torch
import torch.fx._pytree as fx_pytree
import torch.utils._pytree as pytree
from torch.export._tree_utils import reorder_kwargs
from torch.export.exported_program import (
    ConstantArgument,
    ExportedProgram,
    ModuleCallSignature,
    SymIntArgument,
    TensorArgument,
)
from torch.fx._symbolic_trace import is_fx_tracing
from torch.utils._pytree import GetAttrKey, SequenceKey

__all__ = ["InterpreterModule", "UnflattenedModule", "unflatten", "FlatArgsAdapter"]


class _AttrKind(Enum):
    PARAMETER = "parameter"
    BUFFER = "buffer"
    CONSTANT = "constant"


# Assign attribute 'from_obj' to the qualified name 'target' on 'to_module
# This installs empty Modules where none exist yet if they are subpaths of target
def _assign_attr(

    from_obj: Union[torch.Tensor, torch.ScriptObject],

    to_module: torch.nn.Module,

    target: str,

    attr_kind: _AttrKind,

    persistent: bool = True,

):
    *prefix, field = target.split(".")
    for item in prefix:
        t = getattr(to_module, item, None)

        if t is None:
            t = torch.nn.Module()
            setattr(to_module, item, t)
        to_module = t

    if attr_kind == _AttrKind.PARAMETER:
        assert isinstance(from_obj, torch.nn.Parameter)
        to_module.register_parameter(field, from_obj)
    elif attr_kind == _AttrKind.BUFFER:
        assert isinstance(from_obj, torch.Tensor)
        to_module.register_buffer(field, from_obj, persistent=persistent)
    elif attr_kind == _AttrKind.CONSTANT:
        assert isinstance(from_obj, (torch.Tensor, torch.ScriptObject))
        setattr(to_module, field, from_obj)


class InterpreterModule(torch.nn.Module):
    """A module that uses torch.fx.Interpreter to execute instead of the usual

    codegen that GraphModule uses. This provides better stack trace information

    and makes it easier to debug execution.

    """

    def __init__(

        self,

        graph: torch.fx.Graph,

    ):
        super().__init__()
        self.graph = graph
        self.graph.owning_module = self

    def forward(self, *args, **kwargs):
        assert self.graph_module is not None, "Didn't finalize this InterpreterModule"
        if torch.compiler.is_dynamo_compiling():
            # Dynamo cannot trace through torch.fx.Interpreter, so fall back to
            # GraphModule codegen in this instance.
            return self.graph_module(*args, **kwargs)
        else:
            if kwargs:
                # Handle **kwargs. FX only natively supports positional
                # arguments (through placeholders). So in order to pass in
                # kwargs, we must correspond the names of the placeholders with
                # the keys in the kwarg dict.
                arg_list = list(args)
                kwarg_names = self.arg_names[len(arg_list) :]
                for kwarg_name in kwarg_names:
                    if kwarg_name in kwargs:
                        arg_list.append(kwargs[kwarg_name])

                # Assert that the kwargs passed in exactly match the positional
                # arguments specified by the GraphModule. This should be
                # guaranteed by the unflattening process.
                assert len(kwarg_names) == len(kwargs)
                assert len(arg_list) == len(self.arg_names)
                args = tuple(arg_list)

            return torch.fx.Interpreter(self, graph=self.graph).run(
                *args, enable_io_processing=False
            )

    def finalize(self):
        # We need to "finalize" because GraphModule populates its own state_dict
        # based on the get_attrs observed in the graph. So we need to fully
        # construct the graph and call _sink_params before generating this
        # GraphModule.

        # need to set `graph_module` directly on the dict to avoid it getting
        # registered as a submodule.
        self.__dict__["graph_module"] = torch.fx.GraphModule(self, self.graph)
        self.graph.lint()

        # Cache arg names for kwarg handling (see forward())
        self.arg_names = []
        for node in self.graph.nodes:
            if node.op == "placeholder":
                self.arg_names.append(node.target)


class FlatArgsAdapter(abc.ABC):
    """

    Adapts input arguments with ``input_spec`` to align ``target_spec``.

    """

    @abc.abstractmethod
    def adapt(

        self,

        target_spec: pytree.TreeSpec,

        input_spec: pytree.TreeSpec,

        input_args: List[Any],

    ) -> List[Any]:
        """NOTE: This adapter may mutate given ``input_args_with_path``."""
        ...


class UnflattenedModule(torch.nn.Module):
    def __init__(

        self,

        export_module: ExportedProgram,

        flat_args_adapter: Optional[FlatArgsAdapter] = None,

    ):
        super().__init__()
        if export_module.graph_signature.backward_signature is not None:
            raise ValueError("Unflattening on JointExportModule NYI")

        export_graph = deepcopy(export_module.graph)
        self.graph_signature = deepcopy(export_module.graph_signature)
        self.graph = torch.fx.Graph()
        self.module_call_graph = deepcopy(export_module.module_call_graph)
        self.flat_args_adapter = flat_args_adapter
        # Flag to indicate whether args have been adapted.
        self.adapted = False

        _inplace_buffer_mutations(export_graph, self.graph_signature)
        _outline_submodules(export_graph, self)

        self.range_constraints = export_module.range_constraints
        self.equality_constraints: List = []

        state_dict = export_module.state_dict
        for name in self.graph_signature.parameters:
            cloned = torch.nn.Parameter(state_dict[name].clone())
            _assign_attr(
                cloned,
                self,
                name,
                attr_kind=_AttrKind.PARAMETER,
            )

        non_persistent_buffers = set(self.graph_signature.non_persistent_buffers)
        for name in self.graph_signature.buffers:
            if name in non_persistent_buffers:
                persistent = False
                cloned = export_module.constants[name].clone()
            else:
                persistent = True
                cloned = state_dict[name].clone()

            _assign_attr(
                cloned,
                self,
                name,
                attr_kind=_AttrKind.BUFFER,
                persistent=persistent,
            )

        for fqn in chain(
            self.graph_signature.lifted_tensor_constants,
            self.graph_signature.lifted_custom_objs,
        ):
            constant = export_module.constants[fqn]
            if isinstance(constant, torch.Tensor):
                constant = constant.clone()
            _assign_attr(
                constant,
                self,
                fqn,
                attr_kind=_AttrKind.CONSTANT,
            )

        inputs_to_state: Dict[str, str] = {
            **self.graph_signature.inputs_to_parameters,
            **self.graph_signature.inputs_to_buffers,
            **self.graph_signature.inputs_to_lifted_tensor_constants,
            **self.graph_signature.inputs_to_lifted_custom_objs,
        }

        _sink_params(self, inputs_to_state, [])
        # Check all input nodes has been processed.
        for module in self.modules():
            if not isinstance(module, torch.fx.GraphModule):
                continue
            for node in module.graph.nodes:
                if node.op != "placeholder":
                    continue
                assert node.name not in inputs_to_state

        # Cache so we don't have to compute this every time.
        # NOTE: this needs to be kept in sync with the placeholders in
        # self.graph, but currently we have no way to guarantee that.
        self.input_placeholders = [
            node for node in self.graph.nodes if node.op == "placeholder"
        ]
        self.check_input_constraints = True
        assert self.module_call_graph[0].fqn == ""

    def forward(self, *args, **kwargs):
        signature = self.module_call_graph[0].signature

        reordered_kwargs = reorder_kwargs(kwargs, signature.in_spec)

        flat_args_with_path, in_spec = pytree.tree_flatten_with_path(
            (args, reordered_kwargs)
        )
        flat_args = [x[1] for x in flat_args_with_path]
        if is_fx_tracing():
            return_val = torch.fx.Interpreter(self, graph=self.graph).run(
                *flat_args, enable_io_processing=False
            )
            # For scalar return value, fx.Graph wraps in a tuple
            if isinstance(return_val, tuple) and len(return_val) == 1:
                return return_val[0]
            return return_val

        if in_spec != signature.in_spec:
            if not self.adapted:
                print(
                    "Input treespec does not match with exported module's: \n"
                    f"Input treespec: {in_spec}. ",
                    f"Exported module treespec: {signature.in_spec}",
                )
            if self.flat_args_adapter is None:
                raise TypeError(
                    "There is no flat args adapter sepcified. "
                    "Are you sure you are calling this with the right arguments? "
                )
            else:
                if not self.adapted:
                    print("Adapting flat arg to match exported module's treespec")
                flat_args = self.flat_args_adapter.adapt(
                    target_spec=signature.in_spec,
                    input_spec=in_spec,
                    input_args=flat_args,
                )
                self.adapted = True
                if len(flat_args) != signature.in_spec.num_leaves:
                    raise TypeError(
                        f"Flat args adaption failed, number of args mismatch "
                        f"Adatped: {len(flat_args)} \n"
                        f"Exported module: {signature.in_spec.num_leaves}"
                    )

        if self.check_input_constraints:
            # Import here to avoid an unfortunate circular dependency.
            # TODO(suo): untangle this.
            from torch._export.utils import _check_input_constraints_for_graph

            if self.adapted is True:
                # TODO(suo): The FlatArgsAdapter returns a list of flat args,
                # which we don't have keypaths for. For now, just create a dummy
                # keypath to associate with the arg.
                new_flat_args_with_path = [  # type: ignore[var-annotated]
                    ((SequenceKey(idx=0), GetAttrKey(name="<unknown location>")), arg)
                    for arg in flat_args
                ]
            else:
                new_flat_args_with_path = flat_args_with_path  # type: ignore[assignment]

            _check_input_constraints_for_graph(
                self.input_placeholders, new_flat_args_with_path, self.range_constraints
            )
        tree_out = torch.fx.Interpreter(self, graph=self.graph).run(
            *flat_args, enable_io_processing=False
        )
        return pytree.tree_unflatten(tree_out, signature.out_spec)


def unflatten(

    module: ExportedProgram, flat_args_adapter: Optional[FlatArgsAdapter] = None

) -> UnflattenedModule:
    """Unflatten an ExportedProgram, producing a module with the same module

    hierarchy as the original eager module. This can be useful if you are trying

    to use :mod:`torch.export` with another system that expects a module

    hierachy instead of the flat graph that :mod:`torch.export` usually produces.



    .. note:: The args/kwargs of unflattened modules will not necessarily match

        the eager module, so doing a module swap (e.g. :code:`self.submod =

        new_mod`) will not necessarily work. If you need to swap a module out, you

        need to set the :code:`preserve_module_call_signature` parameter of

        :func:`torch.export.export`.



    Args:

        module (ExportedProgram): The ExportedProgram to unflatten.

        flat_args_adapter (Optional[FlatArgsAdapter]): Adapt flat args if input TreeSpec does not match with exported module's.



    Returns:

        An instance of :class:`UnflattenedModule`, which has the same module

        hierarchy as the original eager module pre-export.

    """
    return UnflattenedModule(module, flat_args_adapter)


def _inplace_buffer_mutations(graph: torch.fx.Graph, graph_signature) -> None:
    """Transform buffer mutations from their functionalized form into a copy_

    node in the graph.



    Functionalization represents buffer mutation by passing the buffer as an input and output. So for example, the eager code:

        def forward(self, x):

            self.buffer += x

            return x * x



    Will become a graph that looks like:

        def forward(self, buffer, x):

            mutated_buffer = aten.add(buffer, x)

            mul = aten.mul(x, x)

            return (mutated_buffer, mul)



    We want to inplace this into something that looks like the original eager code:

        def forward(self, buffer, x):

            mutated_buffer = aten.add(buffer, x)

            buffer.copy_(mutated_buffer)

            mul = aten.mul(x, x)

            return (mul,)

    """
    output_node = next(iter(reversed(graph.nodes)))
    assert output_node.op == "output" and len(output_node.args) == 1
    return_args = output_node.args[0]

    mutation_node_to_buffer = graph_signature.buffers_to_mutate
    mutations = return_args[: len(mutation_node_to_buffer)]
    buffers_to_inputs = {v: k for k, v in graph_signature.inputs_to_buffers.items()}
    input_name_to_node = {
        node.name: node for node in graph.nodes if node.op == "placeholder"
    }

    for mutation in mutations:
        buffer_name = mutation_node_to_buffer[mutation.name]
        input_name = buffers_to_inputs[buffer_name]
        input_node = input_name_to_node[input_name]

        with graph.inserting_after(mutation):
            new_node = graph.create_node(
                "call_function", torch.ops.aten.copy_, (input_node, mutation)
            )
            for k, v in mutation.meta.items():
                new_node.meta[k] = v
        # Replace all uses of the previously functional mutation with our copy_ output.
        mutation.replace_all_uses_with(new_node, lambda x: x is not new_node)

    # Remove the mutated buffer from the graph outputs, since we don't need to
    # thread it through anymore. We don't need to handle the inputs, which will
    # be handled by _sink_params.
    user_outputs = tuple(
        return_args[len(mutation_node_to_buffer) :],
    )
    output_node.args = ((user_outputs),)


def _is_prefix(candidate, target):
    """Check whether `candidate` is a prefix of `target`."""
    return len(candidate) < len(target) and target[: len(candidate)] == candidate


def _compute_accessor(parent_fqn: str, child_fqn: str) -> str:
    if parent_fqn == "":
        # Handle the root module correctly.
        return child_fqn

    parent_split = parent_fqn.split(".")
    child_split = child_fqn.split(".")

    assert (
        child_split[: len(parent_split)] == parent_split
    ), f"Child module '{child_fqn}' is not a descendant of parent module '{parent_fqn}'"
    return ".".join(child_split[len(parent_split) :])


def _verify_graph_equivalence(x: torch.nn.Module, y: torch.nn.Module):
    def graph_dump(graph: torch.fx.Graph) -> str:
        ret = []
        nodes_idx: Dict[int, int] = {}

        def arg_dump(arg) -> str:
            if isinstance(arg, torch.fx.Node):
                return "%" + str(nodes_idx[id(arg)])
            return str(arg)

        for i, node in enumerate(graph.nodes):
            args_dump = [str(arg) for arg in pytree.tree_map(arg_dump, node.args)]
            args_dump += [
                f"{key}={value}"
                for key, value in pytree.tree_map(arg_dump, node.kwargs).items()
            ]
            target = node.target if node.op == "call_function" else ""
            ret.append(f"{i}: {node.op}[{target}]({', '.join(args_dump)})")
            nodes_idx[id(node)] = i
        return "\n".join(ret)

    assert graph_dump(x.graph) == graph_dump(y.graph)


def _add_spec(gm: torch.nn.Module, spec) -> str:
    i = 0
    while hasattr(gm, f"_spec_{i}"):
        i += 1
    name = f"_spec_{i}"
    setattr(gm, name, spec)
    return name


def _generate_flatten(gm: torch.nn.Module, node, spec) -> torch.fx.Node:
    name = _add_spec(gm, spec)
    spec_node = gm.graph.get_attr(name)
    return gm.graph.call_function(fx_pytree.tree_flatten_spec, (node, spec_node))


def _generate_unflatten(gm: torch.nn.Module, nodes, spec) -> torch.fx.Node:
    name = _add_spec(gm, spec)
    spec_node = gm.graph.get_attr(name)
    return gm.graph.call_function(pytree.tree_unflatten, (nodes, spec_node))


def _add_submodule(mod: torch.nn.Module, target: str, module_to_add: torch.nn.Module):
    *prefix, field = target.split(".")

    for item in prefix:
        submod = getattr(mod, item, None)

        if submod is None:
            submod = torch.nn.Module()
            setattr(mod, item, submod)

        if not isinstance(submod, torch.nn.Module):
            return False

        mod = submod

    mod.add_module(field, module_to_add)


class _ModuleFrame:
    def __init__(

        self,

        flat_graph,

        nodes,

        seen_nodes,

        seen_modules,

        parent,

        module_stack,

        module_id,

        module_call_graph: Dict[str, ModuleCallSignature],

        module: Optional[torch.nn.Module] = None,

    ):
        self.flat_graph = flat_graph
        self.nodes = nodes
        self.seen_nodes = seen_nodes
        self.seen_modules = seen_modules
        self.parent = parent
        self.module_stack = module_stack
        self.module_id = module_id

        self.module_call_graph = module_call_graph
        self.verbose = False

        self.fqn = self.module_stack[-1]
        if module is not None:
            self.module = module
        else:
            self.module = InterpreterModule(torch.fx.Graph())
        if self.module_id in self.seen_modules:
            self.cached_graph_module = self.seen_modules[self.module_id]
        else:
            self.cached_graph_module = None
            self.seen_modules[self.module_id] = self.module

        self.graph = self.module.graph

        # Mapping of nodes in the flat graph to nodes in this graph.
        self.node_map: Dict[torch.fx.Node, torch.fx.Node] = {}
        self.node_to_placeholder = {}

        self.parent_call_module: Optional[torch.fx.Node] = None
        if parent is not None:
            accessor = _compute_accessor(parent.fqn, self.fqn)
            _add_submodule(
                parent.module,
                accessor,
                self.module
                if self.cached_graph_module is None
                else self.cached_graph_module,
            )
            self.parent_call_module = parent.graph.call_module(accessor)

        signature = module_call_graph.get(self.fqn)
        if signature is not None and self.parent is not None:
            assert signature.in_spec.num_children == 2
            args_spec = signature.in_spec.children_specs[0]
            kwargs_spec = signature.in_spec.children_specs[1]
            assert args_spec.context is None
            assert kwargs_spec.context is not None

            with self.graph.inserting_after(None):
                arg_nodes = []
                for idx in range(args_spec.num_children):
                    arg_nodes.append(self.graph.placeholder(f"_positional_arg_{idx}"))
                kwarg_nodes = {}
                for name in kwargs_spec.context:
                    kwarg_nodes[name] = self.graph.placeholder(name)
                flat_args = _generate_flatten(
                    self.module,
                    (tuple(arg_nodes), kwarg_nodes),
                    signature.in_spec,
                )
                for idx, arg in enumerate(signature.inputs):
                    flat_arg_node = self.graph.create_node(
                        op="call_function",
                        target=operator.getitem,
                        args=(flat_args, idx),
                        name=arg.name
                        if not isinstance(arg, ConstantArgument)
                        else f"_constant_{idx}",
                    )
                    if isinstance(arg, ConstantArgument):
                        continue
                    flat_arg_node.meta = copy.copy(self.seen_nodes[arg.name].meta)
                    self.node_to_placeholder[self.seen_nodes[arg.name]] = flat_arg_node

            with self.parent.graph.inserting_before(self.parent_call_module):
                input_nodes: List[Optional[torch.fx.Node]] = []
                for input in signature.inputs:
                    if isinstance(input, ConstantArgument) and input.value is None:
                        input_nodes.append(None)
                    else:
                        assert isinstance(input, (TensorArgument, SymIntArgument))
                        input_nodes.append(
                            self.parent.remap_input(self.seen_nodes[input.name])
                        )

                inputs_node = _generate_unflatten(
                    self.parent.module,
                    input_nodes,
                    signature.in_spec,
                )

                args_node = self.parent.graph.call_function(
                    operator.getitem, (inputs_node, 0)
                )
                kwargs_node = self.parent.graph.call_function(
                    operator.getitem, (inputs_node, 1)
                )
                arg_nodes = [
                    self.parent.graph.call_function(operator.getitem, (args_node, i))
                    for i in range(args_spec.num_children)
                ]
                kwarg_nodes = {
                    k: self.parent.graph.call_function(
                        operator.getitem, (kwargs_node, k)
                    )
                    for k in kwargs_spec.context
                }
            assert self.parent_call_module is not None
            self.parent_call_module.args = tuple(arg_nodes)
            self.parent_call_module.kwargs = kwarg_nodes

    def add_placeholder(self, x):
        assert x.graph is self.flat_graph
        # x is not in subgraph, create a new placeholder for subgraph
        with self.graph.inserting_before(None):
            placeholder_node = self.graph.placeholder(x.name, type_expr=x.type)
        # copy all meta fields, even if some fields might be irrelvant for
        # the placeholder node
        placeholder_node.meta = copy.copy(x.meta)
        self.node_to_placeholder[x] = placeholder_node

    def remap_input(self, x):
        assert x.graph is self.flat_graph
        if x in self.node_map:
            return self.node_map[x]
        if x not in self.node_to_placeholder:
            self.add_placeholder(x)
            if self.parent_call_module is not None:
                # Important to *prepend* the output to match how we are
                # inserting placeholder nodes.
                self.parent_call_module.insert_arg(0, self.parent.remap_input(x))
        return self.node_to_placeholder[x]

    def finalize_outputs(self):
        orig_outputs = []

        signature = self.module_call_graph.get(self.fqn)
        if signature is not None and self.parent is not None:
            for output in signature.outputs:
                if isinstance(output, (TensorArgument, SymIntArgument)):
                    orig_outputs.append(self.seen_nodes[output.name])
                else:
                    raise RuntimeError(
                        f"Unsupported data type for output node: {output}"
                    )

            tree_out_node = _generate_unflatten(
                self.module,
                tuple(
                    self.node_map[self.seen_nodes[output.name]]
                    for output in orig_outputs
                ),
                signature.out_spec,
            )
            parent_out: Optional[torch.fx.Node] = _generate_flatten(
                self.parent.module, self.parent_call_module, signature.out_spec
            )
            graph_outputs: Union[torch.fx.Node, List[torch.fx.Node]] = tree_out_node
        else:
            graph_outputs = []
            # Iterate through nodes we have copied into self.graph.
            for orig_node in self.node_map.keys():
                for user_node in orig_node.users:
                    if user_node.name not in self.seen_nodes:
                        # external user node, need to expose as an output
                        orig_outputs.append(orig_node)
                        graph_outputs.append(self.node_map[orig_node])
                        break

            parent_out = self.parent_call_module
            if len(graph_outputs) == 1:
                graph_outputs = graph_outputs[0]

        assert isinstance(graph_outputs, (list, torch.fx.Node))

        self.graph.output(graph_outputs)

        # Rewrite outputs in parent module
        if parent_out is None:
            return

        parent_out.meta["val"] = (
            graph_outputs.meta.get("val")
            if isinstance(graph_outputs, torch.fx.Node)
            else [o.meta.get("val") for o in graph_outputs]
        )

        if len(orig_outputs) == 1 and signature is None:
            self.parent.node_map[orig_outputs[0]] = parent_out
        else:
            for i, orig_output in enumerate(orig_outputs):
                # Use Proxy to record getitem access.
                proxy_out = torch.fx.Proxy(parent_out)[i].node  # type: ignore[index]
                proxy_out.meta["val"] = orig_output.meta.get("val")
                self.parent.node_map[orig_output] = proxy_out

        if self.cached_graph_module is not None:
            _verify_graph_equivalence(self.cached_graph_module, self.module)

    def copy_node(self, node):
        self.print("copying", node.format_node())
        self.node_map[node] = self.graph.node_copy(node, self.remap_input)
        self.seen_nodes[node.name] = node

    def run_outer(self):
        i = 0
        for node in self.flat_graph.nodes:
            self.print(i, node.meta.get("nn_module_stack"), node.format_node())
            i += 1

        # Copy all graph inputs
        node_idx: int = 0
        node = self.nodes[node_idx]
        while node.op == "placeholder":
            self.copy_node(node)
            node_idx += 1
            node = self.nodes[node_idx]

        self.run_from(node_idx)

        # Copy graph outputs
        for node in self.flat_graph.nodes:
            if node.op == "output":
                self.copy_node(node)

    def print(self, *args, **kwargs):
        if self.verbose:
            print(*args, **kwargs)

    def run_from(self, node_idx):
        module_idx = 0
        # Walk through the graph, building up a new graph with the right submodules
        while node_idx < len(self.nodes):
            node = self.nodes[node_idx]
            assert node.op != "placeholder"

            self.print()
            self.print("STEP", node_idx, node.format_node())
            self.print(self.module_stack)
            if node.op == "output":
                if len(self.module_stack) == 1:
                    # We want the output node of the original graph to be handled
                    # specially by the outermost stack frame (in run_outer). So
                    # skip finalization here.
                    return node_idx

                # We've reached the end of the graph. Wrap up all the existing stack frames.
                self.finalize_outputs()
                return node_idx

            node_module_stack = (
                [path for path, ty in node.meta["nn_module_stack"].values()]
                if "nn_module_stack" in node.meta
                else self.module_stack
            )
            if node_module_stack[: len(self.module_stack)] != self.module_stack:
                # This means that the current module is done executing and the
                # current node is the beginning of a new module.
                #
                # In this case, we should finalize this module and return without
                # incrementing the node counter.
                self.finalize_outputs()
                self.print("outlining", self.fqn)
                self.print(self.graph)
                return node_idx

            assert node_module_stack is not None

            if _is_prefix(self.module_stack, node_module_stack):
                # This means that the current node represents the execution of a new
                # module.
                next_module = node_module_stack[len(self.module_stack)]
                self.print("Creating new stack frame for", next_module)
                # Run a nested version of module outliner from the current node
                # counter. Once it is complete, continue from that point.
                node_idx = _ModuleFrame(
                    self.flat_graph,
                    self.nodes,
                    self.seen_nodes,
                    self.seen_modules,
                    self,
                    self.module_stack + [next_module],
                    list(node.meta["nn_module_stack"].keys())[len(self.module_stack)],
                    self.module_call_graph,
                ).run_from(node_idx)
                module_idx += 1
                continue

            # The only remaining possibility is that we are in the right stack
            # frame. Copy the node into this frame's graph and increment the node counter.
            assert node_module_stack == self.module_stack
            self.copy_node(node)
            node_idx += 1


def _outline_submodules(orig_graph: torch.fx.Graph, root_module: UnflattenedModule):
    seen_nodes: Dict[str, torch.fx.Node] = {}
    seen_modules: Dict[int, torch.nn.Module] = {}
    _ModuleFrame(
        orig_graph,
        tuple(orig_graph.nodes),
        seen_nodes,
        seen_modules,
        None,
        [""],
        "",
        {
            entry.fqn: entry.signature
            for entry in root_module.module_call_graph
            if entry.signature
        },
        module=root_module,
    ).run_outer()


def _sink_params(

    module: torch.nn.Module,

    inputs_to_state: Dict[str, str],

    scope: List[str],

):
    """Sink params, buffers, and constants from graph inputs into get_attr nodes.



    Exported modules are purely functional, so they pass their parameters and

    buffers in as inputs to the graph.



    To replicate eager's semantics, we need to get them from the module state

    via get_attr instead.



    module: GraphModule, potentially containining nested submodules.

    inputs_to_state: mapping graph input names to the corresponding key in the state_dict.

    scope: tracks where we are in the module hierarchy, so that we can emit the

        right `getattr(self, "foo.bar")` calls, etc.

    """
    # We need to use _modules here instead of named_children(), because we
    # explicitly want duplicate modules to show up in the traversal.
    for name, submodule in module._modules.items():
        _sink_params(cast(torch.nn.Module, submodule), inputs_to_state, scope + [name])

    if not hasattr(module, "graph"):
        # Not all modules have graphs defined, if they are empty modules with no operations (like ParameterList)
        return

    graph = module.graph
    inputs = list(filter(lambda n: n.op == "placeholder", graph.nodes))
    the_last_input = inputs[-1]

    # Also remove from call_module nodes
    call_module_nodes = filter(lambda n: n.op == "call_module", graph.nodes)
    for node in call_module_nodes:
        node.args = tuple(filter(lambda n: n.name not in inputs_to_state, node.args))

    for node in inputs:
        if node.name not in inputs_to_state:
            continue

        if len(node.users) > 0:
            state_name = inputs_to_state[node.name].split(".")
            # If there's a mismatch beteewn scope name and state name, then there must be multuple scopes
            # pointing to the same state name, meaning some modules are shared. In such case, we can simply
            # skip updating the current node because another later iteration will take care of this input
            # node when the unique match between scope and state name occurs.
            # To make sure this always happen, we should enforce the invariant that no placeholder node
            # in the unflattened graph appears in inputs_to_state dict, which means all the extra input
            # nodes have been handled.
            if state_name[: len(scope)] != scope:
                continue
            attr_path = state_name[len(scope) :]
            state_attr = _recursive_getattr(module, attr_path)
            assert isinstance(state_attr, (torch.Tensor, torch.ScriptObject))

            # Make sure the newly created get_attr node is placed after the last placeholder node
            with graph.inserting_after(the_last_input):
                new_node = graph.create_node("get_attr", ".".join(attr_path))

            node.replace_all_uses_with(new_node, propagate_meta=True)
        graph.erase_node(node)
    if isinstance(module, InterpreterModule):
        module.finalize()


def _recursive_getattr(obj, attr_path):
    for attr in attr_path:
        obj = getattr(obj, attr)

    return obj