Lightstar/venv/Lib/site-packages/torch/export/_trace.py

# mypy: allow-untyped-defs
import dataclasses
import functools
import inspect
import logging
import re
import time
import warnings
from contextlib import contextmanager, nullcontext
from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union

import torch
import torch._dynamo
import torch.fx

import torch.utils._pytree as pytree
from torch._dynamo.exc import UserError, UserErrorType
from torch._export.non_strict_utils import (
    _fakify_script_objects,
    _gather_constant_attrs,
    make_constraints,
    make_fake_inputs,
    make_fake_params_buffers,
    produce_guards_and_solve_constraints,
)
from torch._export.passes._node_metadata_hook import (
    _node_metadata_hook,
    _set_node_metadata_hook,
)
from torch._export.passes.add_runtime_assertions_for_constraints_pass import (
    _AddRuntimeAssertionsForInlineConstraintsPass,
)
from torch._export.passes.collect_tracepoints_pass import CollectTracepointsPass
from torch._export.passes.lift_constants_pass import (
    ConstantAttrMap,
    lift_constants_pass,
    rewrite_script_object_meta,
)
from torch._export.utils import placeholder_naming_pass, placeholder_prefixes
from torch._export.verifier import SpecViolationError
from torch._export.wrappers import _wrap_submodules
from torch._functorch.aot_autograd import aot_export_module
from torch._guards import detect_fake_mode

from torch._library.fake_class_registry import FakeScriptObject
from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
from torch._utils_internal import log_export_usage
from torch.export.dynamic_shapes import _combine_args
from torch.export.exported_program import OutputKind
from torch.fx._utils import first_call_function_nn_module_stack
from torch.fx.experimental.symbolic_shapes import (
    ConstraintViolationError,
    free_unbacked_symbols,
    GuardOnDataDependentSymNode,
    ShapeEnv,
)
from torch.fx.graph import _PyTreeCodeGen, _PyTreeInfo
from torch.fx.passes.runtime_assert import insert_deferred_runtime_asserts
from torch.utils._pytree import TreeSpec
from torch.utils._sympy.value_ranges import ValueRangeError

from ._safeguard import AutogradStateOpsFailSafeguard

from .exported_program import (
    _disable_prexisiting_fake_mode,
    ExportedProgram,
    InputKind,
    ModuleCallEntry,
    ModuleCallSignature,
)
from .graph_signature import (
    _sig_to_specs,
    ArgumentSpec,
    ConstantArgument,
    CustomObjArgument,
    ExportGraphSignature,
    SymIntArgument,
    TensorArgument,
    TokenArgument,
)

log = logging.getLogger(__name__)


@dataclasses.dataclass
class ExportDynamoConfig:
    """
    Manage Export-specific configurations of Dynamo.
    """

    allow_rnn: bool = True
    reorderable_logging_functions: Set[Callable] = dataclasses.field(
        default_factory=set
    )


@dataclasses.dataclass
class ExportedArtifact:
    gm: torch.fx.GraphModule
    sig: ExportGraphSignature
    constants: Dict[
        str,
        Union[
            torch.Tensor,
            FakeScriptObject,
            torch.ScriptObject,
        ],
    ]
    out_spec: Optional[TreeSpec] = None  # type: ignore[Incompatible types in assignment]
    fake_mode: Optional[FakeTensorMode] = None  # type: ignore[Incompatible types in assignment]
    module_call_specs: Optional[Dict[str, Dict[str, pytree.TreeSpec]]] = None  # type: ignore[Incompatible types in assignment]


DEFAULT_EXPORT_DYNAMO_CONFIG = ExportDynamoConfig()
DEFAULT_EXPORT_DYNAMO_CONFIG.reorderable_logging_functions = {
    logging.critical,
    logging.debug,
    logging.error,
    logging.exception,
    logging.info,
    logging.log,
    logging.warning,
    print,
    warnings.warn,
}


@contextmanager
def _ignore_backend_decomps():
    orig_mkldnn_flag = torch.backends.mkldnn.set_flags(False)
    orig_nnpack_flag = torch.backends.nnpack.set_flags(False)
    try:
        yield
    finally:
        torch.backends.mkldnn.set_flags(*orig_mkldnn_flag)
        torch.backends.nnpack.set_flags(*orig_nnpack_flag)


def _fixup_key(x):
    return "L__self__" + _strip_root(x)


def _strip_root(x):
    if isinstance(x, str) and x.startswith("_export_root"):
        stripped = x[len("_export_root") :]
        return stripped[1:] if stripped.startswith(".") else stripped
    return x


def _add_runtime_assertions_to_cond_in_subgraph(range_constraints, gm, fake_mode):
    # We can't get rid of this yet, since for some reason
    # insert_deferred_runtime_assertions doesn't add assertions to cond
    # subgraphs
    if len(range_constraints) > 0:
        stack_trace = (
            'File "torch/_export/passes/add_runtime_assertions_for_constraints_pass.py", line 46, '
            "in _AddRuntimeAssertionsForInlineConstraintsPass"
        )
        with fake_mode, _set_node_metadata_hook(
            gm, functools.partial(_node_metadata_hook, stack_trace=stack_trace)
        ):
            res = _AddRuntimeAssertionsForInlineConstraintsPass(range_constraints)(gm)
        assert res is not None
        gm = res.graph_module


def _rewrite_node(gm):
    for node in gm.graph.nodes:
        if node.target == torch.ops.higher_order._export_tracepoint:
            if "path" in node.kwargs:
                path = _strip_root(node.kwargs["path"])
                with gm.graph.inserting_before(node):
                    new_node = gm.graph.create_node(
                        "call_function",
                        torch.ops.higher_order._export_tracepoint,
                        args=node.args,
                        kwargs={
                            "path": path,
                            "kind": node.kwargs["kind"],
                        },
                    )
                    new_node.meta = node.meta
                    node.replace_all_uses_with(new_node)
                    gm.graph.erase_node(node)


def _convert_input_to_fake(gm, args, kwargs):
    params_buffers = _get_params_buffers(gm)
    fake_inps: List[torch.Tensor] = []
    for node in gm.graph.nodes:
        if node.op == "placeholder" and "val" in node.meta:
            fake_val = node.meta["val"]
            if fake_val is not None and isinstance(fake_val, torch.Tensor):
                fake_inps.append(fake_val)

    if detected_fake_mode := detect_fake_mode(fake_inps):
        fake_mode = detected_fake_mode
    else:
        fake_mode = FakeTensorMode(shape_env=ShapeEnv(), export=True)

    if len(args) == 0 and len(kwargs) == 0:
        return (), {}, params_buffers, fake_mode

    count = 0

    def convert_to_fake(x):
        nonlocal count
        val = fake_inps[count]
        count += 1
        return val

    fake_args = pytree.tree_map_only(torch.Tensor, convert_to_fake, args)
    # TODO properly use the cached fake tensor
    fake_kwargs = pytree.tree_map_only(torch.Tensor, fake_mode.from_tensor, kwargs)
    fake_params_buffers = pytree.tree_map_only(
        torch.Tensor,
        functools.partial(fake_mode.from_tensor, static_shapes=True),
        params_buffers,
    )
    return fake_args, fake_kwargs, fake_params_buffers, fake_mode


def _replace_param_buffer_names(param_buffer_table, sig):
    for spec in sig.input_specs:
        if spec.kind in (
            InputKind.PARAMETER,
            InputKind.BUFFER,
        ):
            spec.target = param_buffer_table[spec.target]
    for spec in sig.output_specs:
        if spec.kind in (
            OutputKind.BUFFER_MUTATION,
            OutputKind.GRADIENT_TO_PARAMETER,
        ):
            spec.target = param_buffer_table[spec.target]


def _convert_to_positional_args(orig_arg_names, args, kwargs):
    assert len(orig_arg_names) == len(args) + len(kwargs), (
        f"Total number of arg names is expected to be {len(orig_arg_names)} "
        f"but got {len(args)} positional args, {len(kwargs)} kwargs."
    )
    reordered_kwargs = [kwargs[kw_name] for kw_name in orig_arg_names[len(args) :]]
    return (
        *args,
        *reordered_kwargs,
    )


def _normalize_nn_module_stack(gm_torch_level, root_cls):
    # Append a root module to every nn_module_stack.
    root = "L['self']"
    root_key = re.sub(r"[^a-zA-Z0-9]", "_", root)
    for gm in gm_torch_level.modules():
        if not isinstance(gm, torch.fx.GraphModule):
            continue
        for node in gm.graph.nodes:
            if node.op in ["placeholder", "output"]:
                continue
            add_root = True
            if nn_module_stack := node.meta.get("nn_module_stack", {}):
                path, ty = next(iter(nn_module_stack.values()))
                # After deserializing the class `ty` might not exist anymore so
                # it could be a string
                if inspect.isclass(ty) and issubclass(ty, torch.nn.Module):
                    # TODO Figure out why sometimes we have root sometimes we don't.
                    if path == root and ty is root_cls:
                        add_root = False
                else:
                    assert isinstance(ty, str)
            if add_root:

                def normalize_path(path):
                    try:
                        parts = []

                        class Path:
                            def __getattr__(self, name):
                                parts.append(name)
                                return self

                            def __getitem__(self, idx):
                                parts.append(str(idx))
                                return self

                        eval(path, {"L": {"self": Path()}})
                        return ".".join(parts)
                    except Exception:  # TODO(zhxchen17) Remove this.
                        return path

                nn_module_stack = {
                    root_key: (root, root_cls.__module__ + "." + root_cls.__qualname__),
                    **nn_module_stack,
                }
                node.meta["nn_module_stack"] = {
                    key: (normalize_path(path), ty)
                    for key, (path, ty) in nn_module_stack.items()
                }


def _get_param_buffer_mapping(
    original_module: torch.nn.Module,
    traced_module: torch.nn.Module,
) -> Dict[str, str]:
    """
    Returns a mapping of parameter/buffer names from the new module to the
    original model. This is to help with restoring the FQN for parameter/buffers
    of a traced module to what the original module contains.
    """

    param_lookup: Dict[int, List[str]] = {}
    buffer_lookup: Dict[int, List[str]] = {}
    for name, param in original_module.named_parameters(remove_duplicate=False):
        param_lookup.setdefault(id(param), []).append(name)
    for name, buffer in original_module.named_buffers(remove_duplicate=False):
        buffer_lookup.setdefault(id(buffer), []).append(name)

    # reverse lists so FQN assignment is FIFO wrt model structure
    for name, fqns in param_lookup.items():
        param_lookup[name] = fqns[::-1]
    for name, fqns in buffer_lookup.items():
        buffer_lookup[name] = fqns[::-1]

    param_buffer_table: Dict[str, str] = {}
    for dynamo_name, dynamo_param in traced_module.named_parameters(
        remove_duplicate=False
    ):
        assert dynamo_name not in param_buffer_table
        if id(dynamo_param) in param_lookup:
            param_buffer_table[dynamo_name] = param_lookup[id(dynamo_param)].pop()

    for dynamo_name, dynamo_buffer in traced_module.named_buffers(
        remove_duplicate=False
    ):
        assert dynamo_name not in param_buffer_table
        if id(dynamo_buffer) in buffer_lookup:
            param_buffer_table[dynamo_name] = buffer_lookup[id(dynamo_buffer)].pop()

    return param_buffer_table


def _remap_constants(
    orig_constant_attrs: ConstantAttrMap,
    graph_signature: ExportGraphSignature,
    constants: Dict[str, Union[torch.Tensor, torch.ScriptObject]],
) -> None:
    """Rewrite the graph signature and constants table to use the FQN from the original module."""
    remap_table: Dict[str, List[str]] = {}
    for name, value in constants.items():
        if value in orig_constant_attrs:
            remap_table[name] = orig_constant_attrs[value]

    for spec in graph_signature.input_specs:
        if spec.kind in (
            InputKind.CONSTANT_TENSOR,
            InputKind.CUSTOM_OBJ,
        ):
            orig_target = spec.target
            assert orig_target is not None
            targets = remap_table.get(orig_target, [orig_target])
            spec.target = targets[0]

            constant = constants[orig_target]
            del constants[orig_target]
            for target in targets:
                constants[target] = constant


def _rename_constants_nodes(
    gm: torch.fx.GraphModule,
    graph_signature: ExportGraphSignature,
) -> None:
    """
    For strict mode, rename constants nodes that were previously annotated as buffers.
    """
    # handle name collisions with existing constants
    node_names = {node.name for node in gm.graph.nodes}

    def rename_constant(name):
        if name in node_names:
            n = 1
            while (dup_name := f"{name}_{n}") in node_names:
                n += 1
            name = dup_name
        node_names.add(name)
        return name

    # use input specs to map names from buffers to constants
    buffer_prefix = placeholder_prefixes[InputKind.BUFFER]
    const_prefix = placeholder_prefixes[InputKind.CONSTANT_TENSOR]
    buffer_to_constant = {}
    for spec in graph_signature.input_specs:
        if spec.kind == InputKind.CONSTANT_TENSOR and not spec.arg.name.startswith(
            const_prefix
        ):
            if spec.arg.name.startswith(buffer_prefix):  # map from buffer to constants
                c_name = rename_constant(
                    const_prefix + spec.arg.name[len(buffer_prefix) :]
                )
            else:  # lifted constant
                c_name = rename_constant(const_prefix + spec.arg.name)
            buffer_to_constant[spec.arg.name] = c_name
            spec.arg.name = c_name
    for spec in graph_signature.output_specs:
        if spec.arg.name in buffer_to_constant:
            spec.arg.name = buffer_to_constant[spec.arg.name]

    # Rename constants nodes for all modules
    for mod in gm.modules():
        if not isinstance(mod, torch.fx.GraphModule):
            continue
        for node in mod.graph.nodes:
            if node.name in buffer_to_constant:
                node.name = node.target = buffer_to_constant[node.name]
        mod.recompile()


def _restore_state_dict(
    original_module: torch.nn.Module, traced_module: torch.fx.GraphModule
) -> None:
    """
    Restores the state dict of the traced module to that of the original module.
    """
    param_buffer_table = _get_param_buffer_mapping(original_module, traced_module)
    # Since the graph module is flattened (no module heirarchy), we
    # need to noramlize the module by replacing "." with "_". If we
    # don't, it will try to save the weight to a submodule which no
    # longer exists.
    for name, fqn in param_buffer_table.items():
        param_buffer_table[name] = fqn.replace(".", "_")

    # Replace state dict attr names with the fqn
    for name, fqn in param_buffer_table.items():
        if not hasattr(traced_module, name):
            continue

        attr = getattr(traced_module, name)
        if isinstance(attr, torch.Tensor) and not isinstance(attr, torch.nn.Parameter):
            traced_module.register_buffer(fqn, attr)
        else:
            setattr(traced_module, fqn, attr)
        delattr(traced_module, name)

    # Replace graph getattr nodes with the correct name
    for node in traced_module.graph.nodes:
        if node.op == "get_attr":
            attr_name = node.target
            if attr_name in param_buffer_table:
                node.target = param_buffer_table[attr_name]

    traced_module.recompile()


def _get_module_hierarchy(mod: torch.nn.Module) -> Dict[str, str]:
    return {
        name: type(m).__name__ for name, m in mod.named_modules(remove_duplicate=False)
    }


def _make_module_call_graph(
    module_hierarchy: Dict[str, str],
    in_spec: TreeSpec,
    out_spec: TreeSpec,
    module_call_signatures: Dict[str, ModuleCallSignature],
) -> List[ModuleCallEntry]:
    ret = [
        ModuleCallEntry(fqn=fqn, signature=module_call_signatures.get(fqn))
        for fqn in module_hierarchy
    ]
    assert ret[0].fqn == ""
    ret[0].signature = ModuleCallSignature(
        inputs=[], outputs=[], in_spec=in_spec, out_spec=out_spec
    )
    return ret


def _export_to_torch_ir(
    f: Callable,
    args: Tuple[Any, ...],
    kwargs: Optional[Dict[str, Any]] = None,
    dynamic_shapes: Optional[Union[Dict[str, Any], Tuple[Any], List[Any]]] = None,
    *,
    preserve_module_call_signature: Tuple[str, ...] = (),
    disable_constraint_solver: bool = False,
    _allow_complex_guards_as_runtime_asserts: bool = False,
    restore_fqn: bool = True,
    _log_export_usage: bool = True,
    same_signature: bool = True,
) -> torch.fx.GraphModule:
    """
    Traces either an nn.Module's forward function or just a callable with PyTorch
    operations inside and produce a torch.fx.GraphModule in torch IR.
    """

    if _log_export_usage:
        log_export_usage(event="export.private_api", flags={"_export_to_torch_ir"})

    if not isinstance(args, tuple):
        raise UserError(
            UserErrorType.INVALID_INPUT,
            f"Expecting `args` to be a tuple of example positional inputs, got {type(args)}",
        )

    kwargs = kwargs or {}

    with torch._dynamo.config.patch(dataclasses.asdict(DEFAULT_EXPORT_DYNAMO_CONFIG)):
        try:
            module_call_specs: Dict[str, Dict[str, pytree.TreeSpec]] = {}
            with _wrap_submodules(
                f, preserve_module_call_signature, module_call_specs
            ), _ignore_backend_decomps():
                gm_torch_level, _ = torch._dynamo.export(
                    f,
                    dynamic_shapes=dynamic_shapes,  # type: ignore[arg-type]
                    assume_static_by_default=True,
                    tracing_mode="symbolic",
                    disable_constraint_solver=disable_constraint_solver,
                    # currently the following 2 flags are tied together for export purposes,
                    # but untangle for sake of dynamo export api
                    prefer_deferred_runtime_asserts_over_guards=_allow_complex_guards_as_runtime_asserts,
                    _allow_complex_guards_as_runtime_asserts=_allow_complex_guards_as_runtime_asserts,
                    _log_export_usage=_log_export_usage,
                    same_signature=same_signature,
                )(
                    *args,
                    **kwargs,
                )
        except (ConstraintViolationError, ValueRangeError) as e:
            raise UserError(UserErrorType.CONSTRAINT_VIOLATION, str(e))  # noqa: B904
        except GuardOnDataDependentSymNode as e:
            raise UserError(  # noqa: B904
                UserErrorType.ANTI_PATTERN,
                f"Consider annotating your code using torch._check*(). {str(e)}",
                case_name="constrain_as_size_example",
            )

    gm_torch_level.meta["module_call_specs"] = module_call_specs

    if isinstance(f, torch.nn.Module) and restore_fqn:
        _restore_state_dict(f, gm_torch_level)

    return gm_torch_level


def _export_to_aten_ir(
    mod: torch.nn.Module,
    fake_args,
    fake_kwargs,
    fake_params_buffers,
    constant_attrs: ConstantAttrMap,
    *,
    transform=lambda x: x,  # TODO(zhxchen17) Revisit if this is needed later.
    pre_dispatch=False,
    _is_torch_jit_trace=False,
):
    # [NOTE] If the user is exporting under training mode, we want to detect if there is any
    # state change in the autograd global state and error. If the user is exporting under inference
    # mode, we don't care. At predispatch level, we don't care about the state change.
    is_grad_enabled = torch._C.is_grad_enabled()
    grad_safe_guard = nullcontext()
    if not pre_dispatch and is_grad_enabled:
        grad_safe_guard = AutogradStateOpsFailSafeguard()  # type: ignore[assignment]

    @contextmanager
    def _compiling_state_context():
        old_value = torch.compiler._is_compiling_flag
        try:
            torch.compiler._is_compiling_flag = True
            yield
        finally:
            torch.compiler._is_compiling_flag = old_value

    # This _reparametrize_module makes sure inputs and module.params/buffers have the same fake_mode,
    # otherwise aot_export_module will error out because it sees a mix of fake_modes.
    # And we want aot_export_module to use the fake_tensor mode in dynamo to keep the pipeline easy to reason about.
    with torch.nn.utils.stateless._reparametrize_module(
        mod,
        fake_params_buffers,
        tie_weights=True,
        strict=True,
        stack_weights=True,
    ), grad_safe_guard, _ignore_backend_decomps(), _compiling_state_context():  # type: ignore[attr-defined]
        gm, graph_signature = transform(aot_export_module)(
            mod,
            fake_args,
            trace_joint=False,
            pre_dispatch=pre_dispatch,
            kwargs=fake_kwargs,
        )
    # TODO unfortunately preserving graph-level metadata is not
    # working well with aot_export. So we manually copy it.
    # (The node-level meta is addressed above.)
    if isinstance(mod, torch.fx.GraphModule) and hasattr(mod, "meta"):
        gm.meta.update(mod.meta)

    def make_argument_spec(i, node) -> ArgumentSpec:
        if isinstance(node, (int, bool, float, type(None))):
            # For const outputs we just directly return this
            return ConstantArgument(name="", value=node)

        assert (
            "val" in node.meta
        ), f"{node} is not a constant or a node with a 'val' metadata field"
        val = node.meta["val"]
        if i < len(graph_signature.input_tokens):
            # TODO: We should be checking for a different type, once we add a new type
            return TokenArgument(name=node.name)
        elif isinstance(val, FakeTensor):
            return TensorArgument(name=node.name)
        elif isinstance(val, torch.SymInt):
            return SymIntArgument(name=node.name)
        elif isinstance(val, torch.ScriptObject):
            return CustomObjArgument(name=node.name, class_fqn=val._type().qualified_name())  # type: ignore[attr-defined]
        elif isinstance(val, FakeScriptObject):
            return CustomObjArgument(name=node.name, class_fqn=val.script_class_name)
        elif isinstance(val, (int, bool, str, float, type(None))):
            return ConstantArgument(name=node.name, value=val)
        else:
            raise AssertionError(
                f"Encountered an unsupported object of type {type(val)} "
                f"while writing the metadata for exported program"
            )

    is_joint = graph_signature.backward_signature is not None

    # NOTE: aot_export adds symint metadata for placeholders with int values;
    # since these become specialized, we replace such metadata with the original values
    flat_args = pytree.tree_leaves((fake_args, fake_kwargs))
    index = 0
    total_non_user_inputs = (
        len(graph_signature.parameters)
        + len(graph_signature.buffers)
        + len(graph_signature.input_tokens)
    )
    for node in gm.graph.nodes:
        if node.op == "placeholder":
            if index >= total_non_user_inputs:
                user_arg = flat_args[index - total_non_user_inputs]
                if not isinstance(user_arg, torch.Tensor):
                    node.meta["val"] = user_arg
            index += 1

    input_specs, output_specs = _sig_to_specs(
        user_inputs=set(graph_signature.user_inputs),
        inputs_to_parameters=graph_signature.inputs_to_parameters,  # type: ignore[arg-type]
        inputs_to_buffers=graph_signature.inputs_to_buffers,  # type: ignore[arg-type]
        user_outputs=set(graph_signature.user_outputs),  # type: ignore[arg-type]
        buffer_mutations=graph_signature.buffers_to_mutate,  # type: ignore[arg-type]
        user_input_mutations=graph_signature.user_inputs_to_mutate,  # type: ignore[arg-type]
        grad_params=graph_signature.backward_signature.gradients_to_parameters if is_joint else {},  # type: ignore[arg-type, union-attr]
        grad_user_inputs=graph_signature.backward_signature.gradients_to_user_inputs if is_joint else {},  # type: ignore[arg-type, union-attr]
        loss_output=graph_signature.backward_signature.loss_output if is_joint else None,  # type: ignore[arg-type, union-attr]
        inputs=[
            make_argument_spec(i, node)
            for i, node in enumerate(gm.graph.nodes)
            if node.op == "placeholder"
        ],
        outputs=[
            make_argument_spec(i, node)
            for i, node in enumerate(
                pytree.tree_leaves(next(iter(reversed(gm.graph.nodes))).args)
            )
        ],
        input_tokens=graph_signature.input_tokens,
        output_tokens=graph_signature.output_tokens,
    )
    export_graph_signature = ExportGraphSignature(
        input_specs=input_specs, output_specs=output_specs
    )

    from torch._guards import detect_fake_mode

    fake_mode = detect_fake_mode(flat_args)

    from torch._dynamo import config as _dynamo_config

    if not _dynamo_config.do_not_emit_runtime_asserts:
        stack_trace = (
            'File "torch/fx/passes/runtime_assert.py", line 24, '
            "in insert_deferred_runtime_asserts"
        )
        with _set_node_metadata_hook(
            gm, functools.partial(_node_metadata_hook, stack_trace=stack_trace)
        ):
            insert_deferred_runtime_asserts(
                gm,
                fake_mode.shape_env,
                f"exported program: {first_call_function_nn_module_stack(gm.graph)}",
                export=True,
            )

    if pre_dispatch:
        from torch._export.passes.replace_set_grad_with_hop_pass import (
            replace_set_grad_with_hop_pass,
        )

        gm = replace_set_grad_with_hop_pass(gm, export_graph_signature)

    # Remove nn_module_stack, stack_trace metadata from all placeholders/inputs nodes.
    for _mod in gm.modules():
        if not isinstance(_mod, torch.fx.GraphModule):
            continue
        for node in _mod.graph.nodes:
            if node.op in ["placeholder", "output"]:
                node.meta.pop("nn_module_stack", None)
                node.meta.pop("stack_trace", None)

    constants = rewrite_script_object_meta(gm)
    constants.update(lift_constants_pass(gm, export_graph_signature, constant_attrs))

    # Prettify names for placeholder nodes.
    placeholder_naming_pass(
        gm,
        export_graph_signature,
        mod,
        fake_args,
        fake_kwargs,
        fake_params_buffers,
        constants,
    )

    return ExportedArtifact(
        gm,
        export_graph_signature,
        constants,
    )


def _get_params_buffers(mod: torch.nn.Module) -> Dict[str, torch.Tensor]:
    params_buffers: Dict[str, torch.Tensor] = {}
    for name, param in mod.named_parameters(remove_duplicate=False):
        params_buffers[name] = param

    for name, buffer in mod.named_buffers(remove_duplicate=False):
        params_buffers[name] = buffer
    return params_buffers


def _get_forward_arg_names(
    mod: torch.nn.Module,
    args: Tuple[Any, ...],
    kwargs: Optional[Dict[str, Any]] = None,
) -> List[str]:
    """
    Gets the argument names to forward that are used, for restoring the
    original signature when unlifting the exported program module.
    - Positional args: retain the original argument names, and enumerate
        *args as args_0, args_1, ...
    - Keyword args: retain the original kwarg names in the order specified
        by the user. This order seems to matter for the current state of
        export lifted modules.
    """
    sig = inspect.signature(mod.forward)
    _args = sig.bind_partial(*args).arguments

    names: List[str] = []
    for name, value in _args.items():
        # handle variable number of positional args
        if sig.parameters[name].kind == inspect._ParameterKind.VAR_POSITIONAL:
            names.extend([f"{name}_{i}" for i, _ in enumerate(value)])
        else:
            names.append(name)
    # order of kwargs matters for input spec
    if kwargs:
        names.extend([kwarg for kwarg, _ in kwargs.items()])

    return names


def _rewrite_dynamo_tensor_constants(
    orig_mod_buffers: Set[torch.Tensor],
    traced_mod_buffers: Dict[str, torch.Tensor],
    graph_signature: ExportGraphSignature,
    constants: Dict[str, Union[torch.Tensor, torch.ScriptObject]],
):
    """Dynamo erroneously marks tensor attributes on modules as a buffers.

    Rewrite them to be tensor constants.
    """
    for spec in graph_signature.input_specs:
        if spec.kind == InputKind.BUFFER:
            assert spec.target is not None
            value = traced_mod_buffers[spec.target]
            if value not in orig_mod_buffers:
                # This was a tensor constant erroneously marked as a buffer.
                # Convert it int oa constant in the graph signature, and add its
                # value to the constants table.
                spec.kind = InputKind.CONSTANT_TENSOR
                constants[spec.target] = value


def _rewrite_non_persistent_buffers(
    orig_mod: torch.nn.Module,
    graph_signature: ExportGraphSignature,
    constants: Dict[str, Union[torch.Tensor, torch.ScriptObject]],
):
    """Dynamo erroneously drops the persistent flag on buffers.

    Rewrite non-persistent buffers to reflect the original module.
    """
    state_dict = orig_mod.state_dict()
    for spec in graph_signature.input_specs:
        if spec.kind == InputKind.BUFFER:
            assert spec.target is not None
            if spec.target not in state_dict:
                assert spec.target not in constants
                spec.persistent = False
                constants[spec.target] = orig_mod.get_buffer(spec.target)


def _verify_nn_module_stack(graph_module: torch.fx.GraphModule) -> None:
    """
    Perform nn_module_stack checks on the graph.
    Current constraints:
        For the top level graph:
        - populated for 'call_function', 'get_attr'
        - None for 'placeholder', 'output'
        For submodule graphs:
        - None for 'placeholder', output'

    TODO(pianpwk): make this a consistent node-level check once nn_module_stack is populated for cond submodules.
    """
    # Check top-level graph for all nodes, all graphs for placeholder & output nodes
    for i, mod in enumerate([graph_module] + list(graph_module.modules())):
        if not isinstance(mod, torch.fx.GraphModule):
            continue
        for node in mod.graph.nodes:
            if node.op in ["call_function", "get_attr"]:
                if i == 0:
                    if (
                        nn_module_stack := node.meta.get("nn_module_stack", None)
                    ) is None:
                        raise SpecViolationError(
                            f"Node {node} of type {node.op} is missing nn_module_stack metadata"
                        )
                    if not all(
                        isinstance(k, str)
                        and isinstance(v, tuple)
                        and len(v) == 2
                        and all(isinstance(x, str) for x in v)
                        for k, v in nn_module_stack.items()
                    ):
                        raise SpecViolationError(
                            f"Node {node} of type {node.op} has incorrect nn_module_stack metadata format"
                            f"expected Dict[str, Tuple[str, str]], but got {nn_module_stack}"
                        )
            elif node.op in ["placeholder", "output"]:
                if node.meta.get("nn_module_stack", None):
                    raise SpecViolationError(
                        f"Node {node} of type {node.op} contains nn_module_stack metadata, this should be None"
                    )


def _verify_stack_trace(graph_module: torch.fx.GraphModule) -> None:
    """
    Perform stack trace checks on the graph.
    Constraints:
        - None or non-empty str for 'call_function', 'get_attr'
        - None for 'placeholder', 'output'
    """
    for i, mod in enumerate([graph_module] + list(graph_module.modules())):
        if not isinstance(mod, torch.fx.GraphModule):
            continue
        for node in graph_module.graph.nodes:
            stack_trace = node.meta.get("stack_trace", None)
            if node.op in ["call_function", "get_attr"]:
                if not (stack_trace is None or isinstance(stack_trace, str)):
                    raise SpecViolationError(
                        f"Node {node} of type {node.op} has invalid stack_trace metadata, "
                        f"expected a string or None but instead found: {stack_trace}"
                    )
            elif node.op in ["placeholder", "output"]:
                if stack_trace:
                    raise SpecViolationError(
                        f"Node {node} of type {node.op} contains stack_trace metadata, "
                        f"expected None but instead found: {stack_trace}"
                    )


def _verify_placeholder_names(gm: torch.fx.GraphModule, sig: ExportGraphSignature):
    """
    Performs a sanity check on the placeholder node names.
    - User input nodes: no restrictions, should match the original forward() signature
    - Params/buffers/constants/custom_obj/token nodes: should start with prefixes defined in <placeholder_prefixes>
    """
    name_to_kind = {spec.arg.name: spec.kind for spec in sig.input_specs}
    for mod in gm.modules():
        if not isinstance(mod, torch.fx.GraphModule):
            continue
        for node in mod.graph.nodes:
            if node.op == "placeholder":
                if node.name not in name_to_kind:
                    continue
                node_kind = name_to_kind[node.name]
                prefix = placeholder_prefixes[node_kind]
                if not node.name.startswith(prefix):
                    raise SpecViolationError(
                        f"Placeholder node name {node.name} does not follow spec for {node_kind}, name should have prefix: {prefix}"
                    )


def get_ep_stats(ep: ExportedProgram) -> Dict[str, Any]:
    op_count = 0
    op_set = set()
    for m in ep.graph_module.modules():
        if not isinstance(m, torch.fx.GraphModule):
            continue
        for node in m.graph.nodes:
            if node.op != "call_function":
                continue
            op_count += 1
            assert hasattr(node.target, "__module__")
            assert hasattr(node.target, "__name__")
            op_set.add(f"{node.target.__module__}.{node.target.__name__}")
    return {"op_count": op_count, "op_set": op_set}


_EXPORT_FLAGS: Optional[Set[str]] = None
_EXPORT_MODULE_HIERARCHY: Optional[Dict[str, str]] = None


def _log_export_wrapper(fn):
    @functools.wraps(fn)
    def wrapper(*args, **kwargs):
        global _EXPORT_FLAGS, _EXPORT_MODULE_HIERARCHY
        try:
            start = time.time()
            ep = fn(*args, **kwargs)
            end = time.time()
            log_export_usage(
                event="export.time",
                metrics=end - start,
                flags=_EXPORT_FLAGS,
                **get_ep_stats(ep),
            )
        except Exception as e:
            t = type(e)
            error_type = t.__module__ + "." + t.__qualname__
            log_export_usage(
                event="export.error",
                type=error_type,
                message=str(e),
                flags=_EXPORT_FLAGS,
            )
            raise e
        finally:
            _EXPORT_FLAGS = None
            _EXPORT_MODULE_HIERARCHY = None

        return ep

    return wrapper


def _process_jit_trace_inputs_for_export(example_inputs, example_kwarg_inputs):
    if not isinstance(example_inputs, (tuple, list, dict)):
        example_inputs = (example_inputs,)

    elif isinstance(example_inputs, list):
        example_inputs = tuple(example_inputs)

    elif (
        isinstance(example_inputs, (torch.Tensor, dict))
        and example_kwarg_inputs is None
    ):
        example_inputs = (example_inputs,)

    if example_kwarg_inputs is None:
        example_kwarg_inputs = {}
    return example_inputs, example_kwarg_inputs


@contextmanager
def patch_forward(obj: torch.nn.Module, new_method):
    """Helper method to make it easier to cleanly torch.export() a method on a
    module that is not `forward`.
    """
    # Save the original method
    original_method = obj.forward

    # Patch the method
    obj.forward = new_method.__get__(obj, obj.__class__)

    try:
        yield
    finally:
        # Restore the original method
        obj.forward = original_method


@contextmanager
def _temp_disable_texpr_fuser():
    original_state = torch._C._jit_texpr_fuser_enabled()
    torch._C._jit_set_texpr_fuser_enabled(False)
    try:
        yield
    finally:
        torch._C._jit_set_texpr_fuser_enabled(original_state)


class _WrapperModule(torch.nn.Module):
    def __init__(self, f):
        super().__init__()
        self.f = f

    def forward(self, *args, **kwargs):
        return self.f(*args, **kwargs)


def _convert_ts_to_export_experimental(traced_callable, args, kwargs=None):
    with _temp_disable_texpr_fuser():
        from torch.jit._trace import TopLevelTracedModule

        export_args, export_kwargs = _process_jit_trace_inputs_for_export(args, kwargs)

        if isinstance(traced_callable, (TopLevelTracedModule, torch._C.ScriptModule)):  # type: ignore[operator]
            return _export(
                traced_callable,
                export_args,
                export_kwargs,
                strict=False,
                _is_torch_jit_trace=True,
            ).module()

        elif isinstance(traced_callable, torch.ScriptMethod) and isinstance(
            traced_callable.owner(), (torch._C.ScriptModule, torch.nn.Module)  # type: ignore[operator]
        ):
            with patch_forward(traced_callable.owner(), traced_callable):  # type: ignore[operator]
                return _export(
                    traced_callable.owner(),  # type: ignore[operator]
                    export_args,
                    export_kwargs,
                    strict=False,
                    _is_torch_jit_trace=True,
                ).module()

        else:
            return _export(
                _WrapperModule(traced_callable),
                export_args,
                export_kwargs,
                strict=False,
                _is_torch_jit_trace=True,
            ).module()


def _strict_export(
    mod: torch.nn.Module,
    args: Tuple[Any, ...],
    kwargs: Dict[str, Any],
    dynamic_shapes: Optional[Union[Dict[str, Any], Tuple[Any], List[Any]]],
    preserve_module_call_signature: Tuple[str, ...],
    pre_dispatch: bool,
    original_state_dict: Dict[str, Any],
    orig_in_spec: TreeSpec,
    _allow_complex_guards_as_runtime_asserts: bool,
    _disable_forced_specializations: Optional[bool],
    _is_torch_jit_trace: bool,
):
    gm_torch_level = _export_to_torch_ir(
        mod,
        args,
        kwargs,
        dynamic_shapes,
        preserve_module_call_signature=preserve_module_call_signature,
        restore_fqn=False,  # don't need to restore because we will do it later
        _allow_complex_guards_as_runtime_asserts=_allow_complex_guards_as_runtime_asserts,
        _log_export_usage=False,
    )

    # We detect the fake_mode by looking at gm_torch_level's placeholders, this is the fake_mode created in dynamo.
    (
        fake_args,
        fake_kwargs,
        fake_params_buffers,
        dynamo_fake_mode,
    ) = _convert_input_to_fake(gm_torch_level, args, kwargs)

    # First, we want to pass through the graph to try populating
    # val field for getattr if there is anything missing.
    # This can happen when quantization adds extra params and forgets
    # to update "val"
    for node in gm_torch_level.graph.nodes:
        if node.op == "get_attr" and "val" not in node.meta:
            attr = getattr(gm_torch_level, node.target)
            # Checks if it is not a HigherOrderOp branch or a module
            if not isinstance(attr, torch.nn.Module):
                assert (
                    dynamo_fake_mode is not None
                ), "Cannot find dynamo_fake_mode. This could be due to the exported graph module have no placeholders."
                node.meta["val"] = dynamo_fake_mode.from_tensor(
                    attr, static_shapes=True
                )

    # When aot_export lifts the params, we lose metadata (e.g. source_fn_stack, stack_trace)
    # from the param nodes as they are treated as fresh inputs
    # Therefore, we manually extract them before calling into aot_export
    params_buffers_to_node_meta = {}
    for node in gm_torch_level.graph.nodes:
        target = node.target
        meta = node.meta
        if node.op == "call_module":
            submodule = getattr(gm_torch_level, target)
            if isinstance(submodule, torch.nn.Module):
                for name, _ in submodule.named_parameters(
                    recurse=True, remove_duplicate=False
                ):
                    params_buffers_to_node_meta[target + "." + name] = meta

                for name, _ in submodule.named_buffers(
                    recurse=True, remove_duplicate=False
                ):
                    params_buffers_to_node_meta[target + "." + name] = meta

        if node.op == "get_attr":
            submodule = getattr(gm_torch_level, target)
            if not isinstance(submodule, torch.fx.GraphModule):
                params_buffers_to_node_meta[target] = meta

        # If the call_function uses param as input, we also need to update params' meta
        # with this call_function node's meta.
        # This is basically the same flow as torch.fx.traceback.preserve_meta()
        if node.op == "call_function" and not isinstance(
            node.target, torch._ops.HigherOrderOperator
        ):
            for arg in node._input_nodes:
                if arg.op == "get_attr":
                    for entry in torch.fx.proxy._COPY_META_FIELDS:
                        if entry in meta:
                            params_buffers_to_node_meta[arg.target][entry] = meta[entry]

    # Fix the graph output signature to be tuple if scalar
    out_spec = orig_out_spec = gm_torch_level._out_spec

    # Used to get rid of lint type error.
    assert out_spec is not None

    # aot_export expect the return type to always be a tuple.
    if out_spec.type not in (list, tuple):
        out_spec = pytree.TreeSpec(tuple, None, [out_spec])

    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.recompile()

    _normalize_nn_module_stack(gm_torch_level, type(mod))

    # NOTE: graph module expects only positional args
    constant_attrs = _gather_constant_attrs(mod)
    with dynamo_fake_mode:
        aten_export_artifact = _export_to_aten_ir(
            gm_torch_level,
            _convert_to_positional_args(orig_arg_names, fake_args, fake_kwargs),
            {},
            fake_params_buffers,
            constant_attrs,
            pre_dispatch=pre_dispatch,
        )

    # Decompose for readability.
    gm = aten_export_artifact.gm
    export_graph_signature = aten_export_artifact.sig
    constants = aten_export_artifact.constants

    # Don't copy over nn_module_stack, stack_trace metadata for params/buffers nodes
    for metadata in params_buffers_to_node_meta.values():
        metadata.pop("nn_module_stack", None)
        metadata.pop("stack_trace", None)

    # After aot_export, set the param/buffer metadata back into placeholders
    # Technically, users can still construct this data from param names
    # without relying on this metadata
    for node in gm.graph.nodes:
        if node.op == "placeholder":
            if node.target in export_graph_signature.inputs_to_parameters:
                param_name = export_graph_signature.inputs_to_parameters[node.target]
                if param_name in params_buffers_to_node_meta:
                    for k, v in params_buffers_to_node_meta[param_name].items():
                        node.meta[k] = v
            if node.target in export_graph_signature.inputs_to_buffers:
                buffer_name = export_graph_signature.inputs_to_buffers[node.target]
                if buffer_name in params_buffers_to_node_meta:
                    for k, v in params_buffers_to_node_meta[buffer_name].items():
                        node.meta[k] = v

    # Do some cleanups on the graph module to restore the state dict to the
    # expected form. Each of these steps should probably get fixed upstream.
    # 1. Remove tensor constants that were added as buffers.
    _rewrite_dynamo_tensor_constants(
        orig_mod_buffers=set(mod.buffers()),
        traced_mod_buffers=dict(gm_torch_level.named_buffers()),
        graph_signature=export_graph_signature,
        constants=constants,
    )
    # 2. Restore FQN of param/buffers
    param_buffer_table: Dict[str, str] = _get_param_buffer_mapping(mod, gm_torch_level)
    _replace_param_buffer_names(param_buffer_table, export_graph_signature)

    # 3. Remove non-persistent buffers from the graph signature
    _rewrite_non_persistent_buffers(mod, export_graph_signature, constants)

    # 4. Rewrite constants to have the same FQN as the original module.
    _remap_constants(constant_attrs, export_graph_signature, constants)

    # 5. Rename constants nodes in graph module from buffers to constants
    _rename_constants_nodes(gm, export_graph_signature)

    aten_export_artifact.out_spec = orig_out_spec
    aten_export_artifact.fake_mode = dynamo_fake_mode
    aten_export_artifact.module_call_specs = gm_torch_level.meta["module_call_specs"]
    return aten_export_artifact


def _non_strict_export(
    mod: torch.nn.Module,
    args: Tuple[Any, ...],
    kwargs: Dict[str, Any],
    dynamic_shapes: Optional[Union[Dict[str, Any], Tuple[Any], List[Any]]],
    preserve_module_call_signature: Tuple[str, ...],
    pre_dispatch: bool,
    original_state_dict: Dict[str, Any],
    orig_in_spec: TreeSpec,
    _allow_complex_guards_as_runtime_asserts: bool,
    _disable_forced_specializations: Optional[bool],
    _is_torch_jit_trace: bool,
):
    out_spec = None

    module_call_specs: Dict[str, Dict[str, pytree.TreeSpec]] = {}

    def _tuplify_outputs(aot_export):
        def _aot_export_non_strict(mod, args, kwargs=None, **flags):
            kwargs = kwargs or {}

            class Wrapper(torch.nn.Module):
                def __init__(self, mod):
                    super().__init__()
                    self._export_root = mod

                def forward(self, *args, **kwargs):
                    nonlocal out_spec
                    if isinstance(self._export_root, torch.fx.GraphModule):
                        with torch.fx.traceback.preserve_node_meta():
                            tree_out = torch.fx.Interpreter(self._export_root).run(
                                *args, **kwargs
                            )
                    else:
                        tree_out = self._export_root(*args, **kwargs)
                    flat_outs, out_spec = pytree.tree_flatten(tree_out)
                    return tuple(flat_outs)

            wrapped_mod = Wrapper(mod)
            # Patch export_root to the signatures so that wrapper module correctly populates the
            # in/out spec
            new_preserved_call_signatures = [
                "_export_root." + i for i in preserve_module_call_signature
            ]
            with _wrap_submodules(
                wrapped_mod, new_preserved_call_signatures, module_call_specs
            ):
                gm, sig = aot_export(wrapped_mod, args, kwargs=kwargs, **flags)
                log.debug("Exported program from AOTAutograd:\n%s", gm)

            sig.parameters = pytree.tree_map(_strip_root, sig.parameters)
            sig.buffers = pytree.tree_map(_strip_root, sig.buffers)
            sig.inputs_to_buffers = pytree.tree_map(_strip_root, sig.inputs_to_buffers)
            sig.inputs_to_parameters = pytree.tree_map(
                _strip_root, sig.inputs_to_parameters
            )
            sig.buffers_to_mutate = pytree.tree_map(_strip_root, sig.buffers_to_mutate)
            for node in gm.graph.nodes:
                if "nn_module_stack" in node.meta:
                    nn_module_stack = node.meta["nn_module_stack"]
                    node.meta["nn_module_stack"] = {
                        _fixup_key(key): val
                        for key, val in pytree.tree_map(
                            _strip_root, nn_module_stack
                        ).items()
                    }

            return gm, sig

        return _aot_export_non_strict

    (
        fake_mode,
        fake_args,
        fake_kwargs,
        equalities_inputs,
        original_signature,
    ) = make_fake_inputs(
        mod,
        args,
        kwargs,
        dynamic_shapes,
        _is_torch_jit_trace=_is_torch_jit_trace,
        _allow_complex_guards_as_runtime_asserts=_allow_complex_guards_as_runtime_asserts,  # for shape env initialization
    )

    fake_params_buffers = make_fake_params_buffers(fake_mode, _get_params_buffers(mod))

    with fake_mode:
        with _fakify_script_objects(mod, fake_args, fake_kwargs, fake_mode) as (
            patched_mod,
            new_fake_args,
            new_fake_kwargs,
            new_fake_constant_attrs,
            map_fake_to_real,
        ):
            aten_export_artifact = _export_to_aten_ir(
                patched_mod,
                new_fake_args,
                new_fake_kwargs,
                fake_params_buffers,
                new_fake_constant_attrs,
                pre_dispatch=pre_dispatch,
                transform=_tuplify_outputs,
                _is_torch_jit_trace=_is_torch_jit_trace,
            )
            # aten_export_artifact.constants contains only fake script objects, we need to map them back
            aten_export_artifact.constants = {
                fqn: map_fake_to_real[obj] if isinstance(obj, FakeScriptObject) else obj
                for fqn, obj in aten_export_artifact.constants.items()
            }

    try:
        produce_guards_and_solve_constraints(
            fake_mode,
            aten_export_artifact.gm,
            dynamic_shapes,
            equalities_inputs,
            original_signature,
            _disable_forced_specializations=_disable_forced_specializations,
            _is_torch_jit_trace=_is_torch_jit_trace,
        )
    except (ConstraintViolationError, ValueRangeError) as e:
        raise UserError(UserErrorType.CONSTRAINT_VIOLATION, str(e))  # noqa: B904

    _rewrite_non_persistent_buffers(
        mod, aten_export_artifact.sig, aten_export_artifact.constants
    )

    aten_export_artifact.out_spec = out_spec
    aten_export_artifact.fake_mode = fake_mode
    aten_export_artifact.module_call_specs = module_call_specs
    return aten_export_artifact


@_log_export_wrapper
@_disable_prexisiting_fake_mode
def _export(
    mod: torch.nn.Module,
    args: Tuple[Any, ...],
    kwargs: Optional[Dict[str, Any]] = None,
    dynamic_shapes: Optional[Union[Dict[str, Any], Tuple[Any], List[Any]]] = None,
    *,
    strict: bool = True,
    preserve_module_call_signature: Tuple[str, ...] = (),
    pre_dispatch: bool = False,
    _allow_complex_guards_as_runtime_asserts: bool = False,
    _disable_forced_specializations: Optional[bool] = False,
    _is_torch_jit_trace: bool = False,
) -> ExportedProgram:
    """
    Traces either an nn.Module's forward function or just a callable with PyTorch
    operations inside and produce a ExportedProgram.

    Args:
        f: the `nn.Module` to trace.

        args: example positional inputs.

        kwargs: optional example keyword inputs.

        dynamic_shapes:
         An optional argument where the type should either be:
         1) a dict from argument names of ``f`` to their dynamic shape specifications,
         2) a tuple that specifies dynamic shape specifications for each input in original order.
         If you are specifying dynamism on keyword args, you will need to pass them in the order that
         is defined in the original function signature.

         The dynamic shape of a tensor argument can be specified as either
         (1) a dict from dynamic dimension indices to :func:`Dim` types, where it is
         not required to include static dimension indices in this dict, but when they are,
         they should be mapped to None; or (2) a tuple / list of :func:`Dim` types or None,
         where the :func:`Dim` types correspond to dynamic dimensions, and static dimensions
         are denoted by None. Arguments that are dicts or tuples / lists of tensors are
         recursively specified by using mappings or sequences of contained specifications.

        preserve_module_call_signature: A list of submodule paths for which the original
            calling conventions are preserved as metadata.

        _allow_complex_guards_as_runtime_asserts:
         With the current dynamic shapes language for dims and derived dims, we can run into constraints
         that are not expressible with the language. For example, flattening a matrix and adding to a vector,
         both fully dynamic (i.e. x.reshape([-1]) + y) emits a guard s0 * s1 = s2, which is not expressible.
         By default, we either raise a constraint violation error or specialize to static values.
         If this flag is set to True, we avoid erroring out and instead allow complex constraints to exist as runtime
         assertions in the graph. The sympy interpreter (torch/utils/_sympy/interp.py) will produce the math ops
         required to compute and assert the value of the guard (e.g. sym_size_int, eq, _assert_scalar).
         Additionally, if TORCH_DYNAMO_DO_NOT_EMIT_RUNTIME_ASSERTS=1 is specified, we will allow complex constraints
         while not emitting runtime asserts, returning a cleaner graph with lesser guarantees around dynamic shapes.

        _disable_forced_specializations:
         Similar to _allow_complex_guards_as_runtime_asserts, but only avoids specializing to static values if set to True.
         For complex guards that don't specialize, this flag doesn't have any effect. Ideally this would be subsumed by
         _allow_complex_guards_as_runtime_asserts, but this handles one additional case: single-variable equalities where
         the symbol is solvable for a concrete value (e.g. Eq(s0 // 4, 400) -> s0 = 1600). If set to True, this flag will
         avoid specializations. Direct equalities (e.g. s0 = 4), will still specialize.

    Returns:
        An ExportedProgram containing the traced method.
    """
    if not isinstance(args, tuple):
        raise UserError(
            UserErrorType.INVALID_INPUT,
            f"Expecting `args` to be a tuple of example positional inputs, got {type(args)}",
        )

    if _disable_forced_specializations and strict:
        raise UserError(
            UserErrorType.INVALID_INPUT,
            "_disable_forced_specializations can be only be specified in non-strict mode.",
        )

    global _EXPORT_FLAGS, _EXPORT_MODULE_HIERARCHY
    _EXPORT_MODULE_HIERARCHY = _get_module_hierarchy(mod)

    flags = set()
    flags.add("strict" if strict else "non_strict")
    flags.add("pre_dispatch" if pre_dispatch else "aot_dispatch")
    log_export_usage(event="export.enter", flags=flags)
    _EXPORT_FLAGS = flags

    kwargs = kwargs or {}
    if isinstance(dynamic_shapes, torch.export.ShapesCollection):
        dynamic_shapes = dynamic_shapes.dynamic_shapes(mod, args, kwargs)

    flat_args, orig_in_spec = pytree.tree_flatten((args, kwargs))
    original_state_dict = mod.state_dict(keep_vars=True)
    if not _is_torch_jit_trace:
        forward_arg_names = _get_forward_arg_names(mod, args, kwargs)
    else:
        forward_arg_names = None

    # Call the appropriate export function based on the strictness of tracing.
    export_func = _strict_export if strict else _non_strict_export
    aten_export_artifact = export_func(
        mod,
        args,
        kwargs,
        dynamic_shapes,
        preserve_module_call_signature,
        pre_dispatch,
        original_state_dict,
        orig_in_spec,
        _allow_complex_guards_as_runtime_asserts,
        _disable_forced_specializations,
        _is_torch_jit_trace,
    )

    # Decompose here for readability.
    gm = aten_export_artifact.gm
    export_graph_signature = aten_export_artifact.sig
    out_spec = aten_export_artifact.out_spec
    constants = aten_export_artifact.constants
    fake_mode = aten_export_artifact.fake_mode
    module_call_specs = aten_export_artifact.module_call_specs

    # Add forward args metadata.
    gm.meta["forward_arg_names"] = forward_arg_names

    # The unbacked symint symbols are updated in aot_export
    # so we serialize them here instead of inside dynamo.
    gm.meta["inline_constraints"] = {
        k: v
        for k, v in fake_mode.shape_env.var_to_range.items()
        if free_unbacked_symbols(k)
    }
    num_lifted = next(
        (
            i
            for i, s in enumerate(export_graph_signature.input_specs)
            if s.kind == InputKind.USER_INPUT
        ),
        len(export_graph_signature.input_specs),
    )
    combined_args = _combine_args(
        mod, args, kwargs, _is_torch_jit_trace=_is_torch_jit_trace
    )
    range_constraints = make_constraints(
        fake_mode,
        gm,
        combined_args,
        dynamic_shapes,
        num_lifted,
    )
    if strict:
        _add_runtime_assertions_to_cond_in_subgraph(
            range_constraints,
            gm,
            fake_mode,
        )

    # Make module signatures.
    module_call_signatures = {}
    for fqn, specs in module_call_specs.items():
        mod_fqn = _strip_root(fqn) if not strict else fqn
        module_call_signatures[mod_fqn] = ModuleCallSignature(
            inputs=[], outputs=[], **specs
        )

    if len(preserve_module_call_signature) > 0:
        if not strict:
            _rewrite_node(gm)
        res = CollectTracepointsPass(module_call_signatures, export_graph_signature)(gm)
        assert res is not None
        gm = res.graph_module

    assert out_spec is not None

    _verify_nn_module_stack(gm)
    _verify_stack_trace(gm)
    if not _is_torch_jit_trace:
        _verify_placeholder_names(gm, export_graph_signature)
    exported_program = ExportedProgram(
        root=gm,
        graph=gm.graph,
        graph_signature=export_graph_signature,
        state_dict=original_state_dict,
        range_constraints=range_constraints,
        module_call_graph=_make_module_call_graph(
            _EXPORT_MODULE_HIERARCHY,
            orig_in_spec,
            out_spec,
            module_call_signatures,
        ),
        example_inputs=(args, kwargs),
        constants=aten_export_artifact.constants,
    )

    return exported_program