MLPY/Lib/site-packages/torch/onnx/utils.py

"""Functions to export models into the ONNX IR format.

These models can be loaded with the ONNX library and then
converted to models which run on other deep learning frameworks.
"""
from __future__ import annotations

import contextlib
import copy
import inspect
import io
import re
import textwrap
import typing
import warnings
from typing import (
    Any,
    Callable,
    cast,
    Collection,
    Dict,
    List,
    Mapping,
    Optional,
    Sequence,
    Set,
    Tuple,
    Type,
    Union,
)

import torch
import torch._C._onnx as _C_onnx
import torch.jit._trace
import torch.serialization
from torch import _C
from torch.onnx import (  # noqa: F401
    _constants,
    _exporter_states,
    errors,
    symbolic_caffe2,
    symbolic_helper,
)
from torch.onnx._globals import GLOBALS
from torch.onnx._internal import (
    _beartype,
    diagnostics,
    jit_utils,
    onnx_proto_utils,
    registration,
)

__all__ = [
    "is_in_onnx_export",
    "select_model_mode_for_export",
    "disable_apex_o2_state_dict_hook",
    "setup_onnx_logging",
    "exporter_context",
    "export",
    "model_signature",
    "warn_on_static_input_change",
    "unpack_quantized_tensor",
    "export_to_pretty_string",
    "unconvertible_ops",
    "register_custom_op_symbolic",
    "unregister_custom_op_symbolic",
]


def is_in_onnx_export() -> bool:
    """Returns whether it is in the middle of ONNX export."""
    return GLOBALS.in_onnx_export


# TODO(justinchuby): Remove dependency to this global variable from constant_fold.cpp
# Skip check due to cannot import IValue from torch._C
_params_dict = {}  # type: ignore[var-annotated]


@contextlib.contextmanager
@_beartype.beartype
def select_model_mode_for_export(model, mode: _C_onnx.TrainingMode):
    r"""A context manager to temporarily set the training mode of ``model``
    to ``mode``, resetting it when we exit the with-block.

    Args:
        model: Same type and meaning as ``model`` arg to :func:`export`.
        mode: Same type and meaning as ``training`` arg to :func:`export`.
    """
    if not isinstance(mode, _C_onnx.TrainingMode):
        raise TypeError(
            f"'mode' should be a torch.onnx.TrainingMode enum, but got '{type(mode)}'."
        )
    originally_training: bool = False

    if hasattr(model, "training"):
        originally_training = model.training

        # ONNX opset 12 has better support for training amenable models, with updated
        # versions of the dropout and batch_norm operators
        if mode == _C_onnx.TrainingMode.TRAINING or (
            mode == _C_onnx.TrainingMode.PRESERVE and originally_training
        ):
            GLOBALS.export_training = True
            if GLOBALS.export_onnx_opset_version < 12:
                warnings.warn(
                    "You are exporting the model in training mode with onnx opset "
                    f"version {GLOBALS.export_onnx_opset_version}. "
                    "Opset versions lower than opset 12 will not be able to export "
                    "nodes such as Dropout and BatchNorm correctly."
                )
        else:
            GLOBALS.export_training = False

        GLOBALS.training_mode = mode
        if mode == _C_onnx.TrainingMode.TRAINING:
            model.train(True)
        elif mode == _C_onnx.TrainingMode.EVAL:
            model.train(False)
        # else mode == _C_onnx.TrainingMode.PRESERVE, do nothing

    try:
        yield
    finally:
        if hasattr(model, "training") and not mode == _C_onnx.TrainingMode.PRESERVE:
            model.train(originally_training)


@contextlib.contextmanager
@_beartype.beartype
def disable_apex_o2_state_dict_hook(
    model: Union[torch.nn.Module, torch.jit.ScriptFunction]
):
    # Apex O2 hook state_dict to return fp16 weights as fp32.
    # Exporter cannot identify them as same tensors.
    # Since this hook is only used by optimizer, it is safe to
    # remove this hook while exporting.
    if not isinstance(model, torch.jit.ScriptFunction):
        model_hooks = {}  # type: ignore[var-annotated]
        for module in model.modules():
            for key, hook in module._state_dict_hooks.items():
                if type(hook).__name__ == "O2StateDictHook":
                    if module not in model_hooks:
                        model_hooks[module] = {}
                    model_hooks[module][key] = hook
            if module in model_hooks:
                for key in model_hooks[module]:
                    module._state_dict_hooks.pop(key)
        try:
            yield
        finally:
            # Add the hooks back
            for module, m_map in model_hooks.items():
                for key, hook in m_map.items():
                    module._state_dict_hooks[key] = hook
    else:
        try:
            yield
        finally:
            pass


@contextlib.contextmanager
@_beartype.beartype
def setup_onnx_logging(verbose: bool):
    is_originally_enabled = torch.onnx.is_onnx_log_enabled()
    if is_originally_enabled or verbose:
        torch.onnx.enable_log()
    try:
        yield
    finally:
        if not is_originally_enabled:
            torch.onnx.disable_log()


@contextlib.contextmanager
@_beartype.beartype
def exporter_context(model, mode: _C_onnx.TrainingMode, verbose: bool):
    with select_model_mode_for_export(
        model, mode
    ) as mode_ctx, disable_apex_o2_state_dict_hook(
        model
    ) as apex_ctx, setup_onnx_logging(
        verbose
    ) as log_ctx, diagnostics.create_export_diagnostic_context() as diagnostic_ctx:
        yield (mode_ctx, apex_ctx, log_ctx, diagnostic_ctx)


@_beartype.beartype
def export(
    model: Union[torch.nn.Module, torch.jit.ScriptModule, torch.jit.ScriptFunction],
    args: Union[Tuple[Any, ...], torch.Tensor],
    f: Union[str, io.BytesIO],
    export_params: bool = True,
    verbose: bool = False,
    training: _C_onnx.TrainingMode = _C_onnx.TrainingMode.EVAL,
    input_names: Optional[Sequence[str]] = None,
    output_names: Optional[Sequence[str]] = None,
    operator_export_type: _C_onnx.OperatorExportTypes = _C_onnx.OperatorExportTypes.ONNX,
    opset_version: Optional[int] = None,
    do_constant_folding: bool = True,
    dynamic_axes: Optional[
        Union[Mapping[str, Mapping[int, str]], Mapping[str, Sequence[int]]]
    ] = None,
    keep_initializers_as_inputs: Optional[bool] = None,
    custom_opsets: Optional[Mapping[str, int]] = None,
    export_modules_as_functions: Union[bool, Collection[Type[torch.nn.Module]]] = False,
    autograd_inlining: Optional[bool] = True,
) -> None:
    r"""Exports a model into ONNX format.

    If ``model`` is not a :class:`torch.jit.ScriptModule` nor a
    :class:`torch.jit.ScriptFunction`, this runs
    ``model`` once in order to convert it to a TorchScript graph to be exported
    (the equivalent of :func:`torch.jit.trace`). Thus this has the same limited support
    for dynamic control flow as :func:`torch.jit.trace`.

    Args:
        model (:class:`torch.nn.Module`, :class:`torch.jit.ScriptModule` or :class:`torch.jit.ScriptFunction`):
            the model to be exported.
        args (tuple or torch.Tensor):

            args can be structured either as:

            1. ONLY A TUPLE OF ARGUMENTS::

                args = (x, y, z)

            The tuple should contain model inputs such that ``model(*args)`` is a valid
            invocation of the model. Any non-Tensor arguments will be hard-coded into the
            exported model; any Tensor arguments will become inputs of the exported model,
            in the order they occur in the tuple.

            2. A TENSOR::

                args = torch.Tensor([1])

            This is equivalent to a 1-ary tuple of that Tensor.

            3. A TUPLE OF ARGUMENTS ENDING WITH A DICTIONARY OF NAMED ARGUMENTS::

                args = (
                    x,
                    {
                        "y": input_y,
                        "z": input_z
                    }
                )

            All but the last element of the tuple will be passed as non-keyword arguments,
            and named arguments will be set from the last element. If a named argument is
            not present in the dictionary, it is assigned the default value, or None if a
            default value is not provided.

            .. note::
                If a dictionary is the last element of the args tuple, it will be
                interpreted as containing named arguments. In order to pass a dict as the
                last non-keyword arg, provide an empty dict as the last element of the args
                tuple. For example, instead of::

                    torch.onnx.export(
                        model,
                        (
                            x,
                            # WRONG: will be interpreted as named arguments
                            {y: z}
                        ),
                        "test.onnx.pb"
                    )

                Write::

                    torch.onnx.export(
                        model,
                        (
                            x,
                            {y: z},
                            {}
                        ),
                        "test.onnx.pb"
                    )

        f: a file-like object (such that ``f.fileno()`` returns a file descriptor)
            or a string containing a file name.  A binary protocol buffer will be written
            to this file.
        export_params (bool, default True): if True, all parameters will
            be exported. Set this to False if you want to export an untrained model.
            In this case, the exported model will first take all of its parameters
            as arguments, with the ordering as specified by ``model.state_dict().values()``
        verbose (bool, default False): if True, prints a description of the
            model being exported to stdout. In addition, the final ONNX graph will include the
            field ``doc_string``` from the exported model which mentions the source code locations
            for ``model``. If True, ONNX exporter logging will be turned on.
        training (enum, default TrainingMode.EVAL):
            * ``TrainingMode.EVAL``: export the model in inference mode.
            * ``TrainingMode.PRESERVE``: export the model in inference mode if model.training is
                False and in training mode if model.training is True.
            * ``TrainingMode.TRAINING``: export the model in training mode. Disables optimizations
                which might interfere with training.
        input_names (list of str, default empty list): names to assign to the
            input nodes of the graph, in order.
        output_names (list of str, default empty list): names to assign to the
            output nodes of the graph, in order.
        operator_export_type (enum, default OperatorExportTypes.ONNX):

            * ``OperatorExportTypes.ONNX``: Export all ops as regular ONNX ops
                (in the default opset domain).
            * ``OperatorExportTypes.ONNX_FALLTHROUGH``: Try to convert all ops
                to standard ONNX ops in the default opset domain. If unable to do so
                (e.g. because support has not been added to convert a particular torch op to ONNX),
                fall back to exporting the op into a custom opset domain without conversion. Applies
                to `custom ops <https://pytorch.org/tutorials/advanced/torch_script_custom_ops.html>`_
                as well as ATen ops. For the exported model to be usable, the runtime must support
                these non-standard ops.
            * ``OperatorExportTypes.ONNX_ATEN``: All ATen ops (in the TorchScript namespace "aten")
                are exported as ATen ops (in opset domain "org.pytorch.aten").
                `ATen <https://pytorch.org/cppdocs/#aten>`_ is PyTorch's built-in tensor library, so
                this instructs the runtime to use PyTorch's implementation of these ops.

                .. warning::

                    Models exported this way are probably runnable only by Caffe2.

                    This may be useful if the numeric differences in implementations of operators are
                    causing large differences in behavior between PyTorch and Caffe2 (which is more
                    common on untrained models).

            * ``OperatorExportTypes.ONNX_ATEN_FALLBACK``: Try to export each ATen op
                (in the TorchScript namespace "aten") as a regular ONNX op. If we are unable to do so
                (e.g. because support has not been added to convert a particular torch op to ONNX),
                fall back to exporting an ATen op. See documentation on OperatorExportTypes.ONNX_ATEN for
                context.
                For example::

                    graph(%0 : Float):
                    %3 : int = prim::Constant[value=0]()
                    # conversion unsupported
                    %4 : Float = aten::triu(%0, %3)
                    # conversion supported
                    %5 : Float = aten::mul(%4, %0)
                    return (%5)

                Assuming ``aten::triu`` is not supported in ONNX, this will be exported as::

                    graph(%0 : Float):
                    %1 : Long() = onnx::Constant[value={0}]()
                    # not converted
                    %2 : Float = aten::ATen[operator="triu"](%0, %1)
                    # converted
                    %3 : Float = onnx::Mul(%2, %0)
                    return (%3)

                If PyTorch was built with Caffe2 (i.e. with ``BUILD_CAFFE2=1``), then
                Caffe2-specific behavior will be enabled, including special support
                for ops are produced by the modules described in
                `Quantization <https://pytorch.org/docs/stable/quantization.html>`_.

                .. warning::

                    Models exported this way are probably runnable only by Caffe2.

        opset_version (int, default 17): The version of the
            `default (ai.onnx) opset <https://github.com/onnx/onnx/blob/master/docs/Operators.md>`_
            to target. Must be >= 7 and <= 17.
        do_constant_folding (bool, default True): Apply the constant-folding optimization.
            Constant-folding will replace some of the ops that have all constant inputs
            with pre-computed constant nodes.
        dynamic_axes (dict[string, dict[int, string]] or dict[string, list(int)], default empty dict):

            By default the exported model will have the shapes of all input and output tensors
            set to exactly match those given in ``args``. To specify axes of tensors as
            dynamic (i.e. known only at run-time), set ``dynamic_axes`` to a dict with schema:

            * KEY (str): an input or output name. Each name must also be provided in ``input_names`` or
                ``output_names``.
            * VALUE (dict or list): If a dict, keys are axis indices and values are axis names. If a
                list, each element is an axis index.

            For example::

                class SumModule(torch.nn.Module):
                    def forward(self, x):
                        return torch.sum(x, dim=1)

                torch.onnx.export(
                    SumModule(),
                    (torch.ones(2, 2),),
                    "onnx.pb",
                    input_names=["x"],
                    output_names=["sum"]
                )

            Produces::

                input {
                  name: "x"
                  ...
                      shape {
                        dim {
                          dim_value: 2  # axis 0
                        }
                        dim {
                          dim_value: 2  # axis 1
                ...
                output {
                  name: "sum"
                  ...
                      shape {
                        dim {
                          dim_value: 2  # axis 0
                ...

            While::

                torch.onnx.export(
                    SumModule(),
                    (torch.ones(2, 2),),
                    "onnx.pb",
                    input_names=["x"],
                    output_names=["sum"],
                    dynamic_axes={
                        # dict value: manually named axes
                        "x": {0: "my_custom_axis_name"},
                        # list value: automatic names
                        "sum": [0],
                    }
                )

            Produces::

                input {
                  name: "x"
                  ...
                      shape {
                        dim {
                          dim_param: "my_custom_axis_name"  # axis 0
                        }
                        dim {
                          dim_value: 2  # axis 1
                ...
                output {
                  name: "sum"
                  ...
                      shape {
                        dim {
                          dim_param: "sum_dynamic_axes_1"  # axis 0
                ...

        keep_initializers_as_inputs (bool, default None): If True, all the
            initializers (typically corresponding to parameters) in the
            exported graph will also be added as inputs to the graph. If False,
            then initializers are not added as inputs to the graph, and only
            the non-parameter inputs are added as inputs.
            This may allow for better optimizations (e.g. constant folding) by
            backends/runtimes.

            If True, `deduplicate_initializers` pass will not be executed. This means
            initializers with duplicated values will not be deduplicated and
            will be treated as distinct inputs to the graph. This allows different
            input initializers to be supplied at the runtime following export.

            If ``opset_version < 9``, initializers MUST be part of graph
            inputs and this argument will be ignored and the behavior will be
            equivalent to setting this argument to True.

            If None, then the behavior is chosen automatically as follows:

            * If ``operator_export_type=OperatorExportTypes.ONNX``, the behavior is equivalent
                to setting this argument to False.
            * Else, the behavior is equivalent to setting this argument to True.

        custom_opsets (dict[str, int], default empty dict): A dict with schema:

            * KEY (str): opset domain name
            * VALUE (int): opset version

            If a custom opset is referenced by ``model`` but not mentioned in this dictionary,
            the opset version is set to 1. Only custom opset domain name and version should be
            indicated through this argument.

        export_modules_as_functions (bool or set of type of nn.Module, default False): Flag to enable
            exporting all ``nn.Module`` forward calls as local functions in ONNX. Or a set to indicate the
            particular types of modules to export as local functions in ONNX.
            This feature requires ``opset_version`` >= 15, otherwise the export will fail. This is because
            ``opset_version`` < 15 implies IR version < 8, which means no local function support.
            Module variables will be exported as function attributes. There are two categories of function
            attributes.

            1. Annotated attributes: class variables that have type annotations via
            `PEP 526-style <https://www.python.org/dev/peps/pep-0526/#class-and-instance-variable-annotations>`_
            will be exported as attributes.
            Annotated attributes are not used inside the subgraph of ONNX local function because
            they are not created by PyTorch JIT tracing, but they may be used by consumers
            to determine whether or not to replace the function with a particular fused kernel.

            2. Inferred attributes: variables that are used by operators inside the module. Attribute names
            will have prefix "inferred::". This is to differentiate from predefined attributes retrieved from
            python module annotations. Inferred attributes are used inside the subgraph of ONNX local function.

            * ``False`` (default): export ``nn.Module`` forward calls as fine grained nodes.
            * ``True``: export all ``nn.Module`` forward calls as local function nodes.
            * Set of type of nn.Module: export ``nn.Module`` forward calls as local function nodes,
                only if the type of the ``nn.Module`` is found in the set.

        autograd_inlining (bool, default True): Flag used to control whether to inline autograd functions.
            Refer to https://github.com/pytorch/pytorch/pull/74765 for more details.

    Raises:
        :class:`torch.onnx.errors.CheckerError`: If the ONNX checker detects an invalid ONNX graph.
        :class:`torch.onnx.errors.UnsupportedOperatorError`: If the ONNX graph cannot be exported because it
            uses an operator that is not supported by the exporter.
        :class:`torch.onnx.errors.OnnxExporterError`: Other errors that can occur during export.
            All errors are subclasses of :class:`errors.OnnxExporterError`.
    """

    _export(
        model,
        args,
        f,
        export_params,
        verbose,
        training,
        input_names,
        output_names,
        operator_export_type=operator_export_type,
        opset_version=opset_version,
        do_constant_folding=do_constant_folding,
        dynamic_axes=dynamic_axes,
        keep_initializers_as_inputs=keep_initializers_as_inputs,
        custom_opsets=custom_opsets,
        export_modules_as_functions=export_modules_as_functions,
        autograd_inlining=autograd_inlining,
    )


@_beartype.beartype
def _is_constant_tensor_list(node):
    if node.kind() != "prim::Constant":
        return False
    output_type = node.output().type()
    if output_type.isSubtypeOf(_C.ListType.ofTensors()):
        return True
    if output_type.isSubtypeOf(_C.ListType(_C.OptionalType.ofTensor())):
        return True


# ONNX can't handle constants that are lists of tensors, which can
# get generated in constant prop. So we split them back into prim::ListConstructs


@_beartype.beartype
def _split_tensor_list_constants(g, block):
    for node in block.nodes():
        for subblock in node.blocks():
            _split_tensor_list_constants(g, subblock)
        if _is_constant_tensor_list(node):
            inputs = []
            for val in node.output().toIValue():
                input = g.insertConstant(val)
                input.node().moveBefore(node)
                input.node().copyMetadata(node)
                inputs.append(input)

            lc = (
                g.create("prim::ListConstruct", inputs)
                .insertBefore(node)
                .output()
                .setType(_C.ListType.ofTensors())
            )
            lc.node().copyMetadata(node)
            node.output().replaceAllUsesWith(lc)


@_beartype.beartype
def _optimize_graph(
    graph: _C.Graph,
    operator_export_type: _C_onnx.OperatorExportTypes,
    _disable_torch_constant_prop: bool = False,
    fixed_batch_size: bool = False,
    params_dict=None,
    dynamic_axes=None,
    input_names=None,
    module=None,
):
    if params_dict is None:
        params_dict = {}

    # Inline everything
    _C._jit_pass_inline(graph)

    # Remove fork/wait nodes
    _C._jit_pass_inline_fork_wait(graph)
    _C._jit_pass_lint(graph)
    if GLOBALS.autograd_inlining:
        _C._jit_pass_onnx_autograd_function_process(graph)
    _C._jit_pass_lower_all_tuples(graph)

    # we now record some ops like ones/zeros
    # into a trace where we previously recorded constants.
    # use constant prop to maintain our current level of onnx support
    # without implementing symbolics for all of them
    if _disable_torch_constant_prop is False:
        _C._jit_pass_constant_propagation(graph)

    _split_tensor_list_constants(graph, graph)
    # run dce to eliminate dead parts of the graph that might have been
    # left behind by things like symbolic_override
    _C._jit_pass_dce(graph)
    _C._jit_pass_lint(graph)

    # CSE should improve perf when Autocast is used with disabled cache
    # Autocast is disabled due to a limitation on tracer as described at https://github.com/pytorch/pytorch/issues/84092
    # Must run before _C._jit_pass_erase_number_types to prevent type substitution
    if _C._jit_pass_cse(graph):
        _C._jit_pass_onnx_lint(graph)

    _C._jit_pass_canonicalize_graph_fuser_ops(graph)
    _C._jit_pass_lint(graph)
    _C._jit_pass_peephole(graph, True)
    _C._jit_pass_fuse_addmm(graph)
    _C._jit_pass_lint(graph)

    _C._jit_pass_peephole(graph, True)
    _C._jit_pass_lower_all_tuples(graph)
    # in _jit_pass_onnx, symbolic functions are called for each node for conversion.
    # However, there are nodes that cannot be converted without additional context.
    # For example, the number of outputs from split (and whether it is static or dynamic) is unknown
    # until the point where it is unpacked by listUnpack node.
    # This pass does a preprocess, and prepares the nodes such that enough context can be received
    # by the symbolic function.
    _C._jit_pass_onnx_remove_inplace_ops_for_onnx(graph, module)
    _C._jit_pass_onnx_preprocess(graph)

    # onnx does not support tuples, so try to remove them
    _C._jit_pass_lint(graph)

    # onnx only supports tensors, but 1 / 2 = 0.5 and tensor(1) / tensor(2) = 0
    _C._jit_pass_prepare_division_for_onnx(graph)

    _C._jit_pass_onnx_remove_print(graph)
    _C._jit_pass_onnx_preprocess_caffe2(graph)

    symbolic_helper._quantized_ops.clear()
    # Unpack quantized weights for conv and linear ops and insert into graph.
    _C._jit_pass_onnx_unpack_quantized_weights(
        graph, params_dict, symbolic_helper.is_caffe2_aten_fallback()
    )
    if symbolic_helper.is_caffe2_aten_fallback():
        # Insert permutes before and after each conv op to ensure correct order.
        _C._jit_pass_onnx_quantization_insert_permutes(graph, params_dict)

        # Find consecutive permutes that are no-ops and remove them.
        _C._jit_pass_custom_pattern_based_rewrite_graph(
            textwrap.dedent(
                """\
                graph(%Pi):
                    %Pq = quantized::nhwc2nchw(%Pi)
                    %Pr = quantized::nchw2nhwc(%Pq)
                    return (%Pr)"""
            ),
            textwrap.dedent(
                """\
                graph(%Ri):
                    return (%Ri)"""
            ),
            graph,
        )

    # onnx only supports tensors, so we turn all out number types into tensors
    _C._jit_pass_erase_number_types(graph)
    if GLOBALS.onnx_shape_inference:
        input_names = [] if input_names is None else input_names
        dynamic_axes = {} if dynamic_axes is None else dynamic_axes
        _C._jit_pass_onnx_set_dynamic_input_shape(graph, dynamic_axes, input_names)
    _C._jit_pass_onnx_lint(graph)

    graph = _C._jit_pass_onnx(graph, operator_export_type)
    _C._jit_pass_onnx_lint(graph)
    _C._jit_pass_lint(graph)

    _C._jit_pass_onnx_scalar_type_analysis(
        graph, True, GLOBALS.export_onnx_opset_version
    )
    _C._jit_pass_lint(graph)

    _C._jit_pass_onnx_peephole(
        graph, GLOBALS.export_onnx_opset_version, fixed_batch_size
    )
    _C._jit_pass_lint(graph)

    # graph is not a valid jit graph anymore because types have been replaced
    # (e.g. int with Tensor), so it now contains operators that don't actually
    # exist. We can't run normal dead code elimination because it'd fail trying
    # to look up if an operator has side effects, but we can run a dead code
    # elimination variant that doesn't need to look up if an op has side effects.
    _C._jit_pass_dce_allow_deleting_nodes_with_side_effects(graph)
    _C._jit_pass_lint(graph)
    graph = _C._jit_pass_canonicalize(graph)
    _C._jit_pass_lint(graph)
    if GLOBALS.onnx_shape_inference:
        try:
            _C._jit_pass_onnx_graph_shape_type_inference(
                graph, params_dict, GLOBALS.export_onnx_opset_version
            )
        except RuntimeError as exc:
            if (
                _C_onnx._CAFFE2_ATEN_FALLBACK
                and exc.args[0]
                == "ScalarType UNKNOWN_SCALAR is an unexpected tensor scalar type!"
            ):
                # Caffe2 builds can have UNKNOWN_SCALAR for some tensors
                pass

    return graph


@_beartype.beartype
def warn_on_static_input_change(input_states):
    """Warns that changes to input dictionaries and strings won't take effect in the traced ONNX graph.

    We accept dictionaries and strings as ONNX inputs, but they should be only for
    configuration use. we detect here if these inputs are modified, and if so we warn
    the user that the changes won't take effect in the traced ONNX graph.
    """
    for input, traced_input in zip(input_states[0], input_states[1]):
        if isinstance(input, dict):
            if list(input.keys()) != list(traced_input.keys()):
                warning = (
                    "We detected that you are modifying a dictionary that is an input to your "
                    "model. "
                    "Note that dictionaries are allowed as inputs in ONNX but they should be "
                    "handled with care. "
                    "Usages of dictionaries is not recommended, and should not be used except "
                    "for configuration use. "
                    "Also note that the order and values of the keys must remain the same. "
                )
                warnings.warn(warning)
        elif isinstance(input, str):
            if input != traced_input:
                warning = (
                    "The model seems to have string inputs/outputs. "
                    "Note that strings will not appear as inputs/outputs of the ONNX graph. "
                )
                warnings.warn(warning)


@_beartype.beartype
def _resolve_args_by_export_type(arg_name, arg_value, operator_export_type):
    """Resolves the arguments that are ignored when export_type != operator_export_type.ONNX."""
    if (
        operator_export_type is not operator_export_type.ONNX
        and _C_onnx._CAFFE2_ATEN_FALLBACK
    ):
        if arg_value is True:
            warnings.warn(
                f"'{arg_name}' can be set to True only when 'operator_export_type' is "
                "`ONNX`. Since 'operator_export_type' is not set to 'ONNX', "
                f"'{arg_name}' argument will be ignored."
            )
        arg_value = False
    return arg_value


@_beartype.beartype
def _decide_keep_init_as_input(
    keep_initializers_as_inputs: Optional[bool],
    operator_export_type: _C_onnx.OperatorExportTypes,
    opset_version: int,
):
    """Decides whether the initializers in the graph should be listed as ONNX graph inputs.

    This method encapsulates the logic to decide whether the initializers in the graph
    should be listed as ONNX graph inputs (i.e., whether to choose ONNX IR v3 or v4).
    If keep_initializers_as_inputs is not specified (None), then we decide whether to keep
    initializers as graph inputs (val_keep_init_as_ip) based on export type. If export type
    is ONNX, then do not keep initializers as input (val_keep_init_as_ip=False). For all other
    export types keep initializers as input (val_keep_init_as_ip=True).
    If keep_initializers_as_inputs is specified, then respect it. Unless opset version <= 8,
    in which case it must be ignored because for opset version <= 8, all initializers MUST be
    part of graph input (only ONNX IR v3 is allowed), i.e. val_keep_init_as_ip=True.

    Special handling is needed for opset version 8 or lower, because irrespective
    of user input for keep_initializers_as_inputs, the graph must follow ONNX IR v3
    semantics, i.e. all initializers must be listed as ONNX graph input.
    """

    if opset_version < 9:
        if keep_initializers_as_inputs is False:
            warnings.warn(
                "Setting 'keep_initializers_as_inputs=False' for opset version"
                "8 or lower would lead to an invalid ONNX graph. Therefore, "
                "'keep_initializers_as_inputs=False' is ignored during export."
                "Exported model will have initializers as graph inputs (compliant "
                " to ONNX IR v3)."
            )
        return True  # i.e. True == initializers are part of graph input (ONNX IR v3)
    val_keep_init_as_ip = (
        True if keep_initializers_as_inputs is None else keep_initializers_as_inputs
    )
    if (
        keep_initializers_as_inputs is None
        and operator_export_type is _C_onnx.OperatorExportTypes.ONNX
    ):
        val_keep_init_as_ip = False
    return val_keep_init_as_ip


@_beartype.beartype
def _decide_add_node_names(add_node_names, operator_export_type):
    return _resolve_args_by_export_type(
        "add_node_names", add_node_names, operator_export_type
    )


@_beartype.beartype
def _decide_constant_folding(do_constant_folding, operator_export_type, training):
    do_constant_folding = _resolve_args_by_export_type(
        "do_constant_folding", do_constant_folding, operator_export_type
    )
    if do_constant_folding and (
        training is not None and training is not _C_onnx.TrainingMode.EVAL
    ):
        warnings.warn(
            "It is recommended that constant folding be turned off ('do_constant_folding=False') "
            "when exporting the model in training-amenable mode, i.e. with 'training=TrainingMode.TRAIN' "
            "or 'training=TrainingMode.PRESERVE' (when model is in training mode). Otherwise, some "
            "learnable model parameters may not translate correctly in the exported ONNX model "
            "because constant folding mutates model parameters. Please consider "
            "turning off constant folding or setting the training=TrainingMode.EVAL."
        )
    return do_constant_folding


@_beartype.beartype
def _signature(model) -> inspect.Signature:
    should_be_callable = getattr(model, "forward", model)
    if callable(should_be_callable):
        return inspect.signature(should_be_callable)
    raise ValueError("model has no forward method and is not callable")


@_beartype.beartype
def _decide_input_format(model, args):
    try:
        sig = _signature(model)
    except ValueError as e:
        warnings.warn(f"{e}, skipping _decide_input_format")
        return args
    try:
        ordered_list_keys = list(sig.parameters.keys())
        if ordered_list_keys[0] == "self":
            ordered_list_keys = ordered_list_keys[1:]
        args_dict: Dict = {}
        if isinstance(args, list):
            args_list = args
        elif isinstance(args, tuple):
            args_list = list(args)
        else:
            args_list = [args]
        if isinstance(args_list[-1], dict):
            args_dict = args_list[-1]
            args_list = args_list[:-1]
        n_nonkeyword = len(args_list)
        for optional_arg in ordered_list_keys[n_nonkeyword:]:
            if optional_arg in args_dict:
                args_list.append(args_dict[optional_arg])
            # Check if this arg has a default value
            else:
                param = sig.parameters[optional_arg]
                if param.default != param.empty:
                    args_list.append(param.default)
        args = args_list if isinstance(args, list) else tuple(args_list)
    # Cases of models with no input args
    except IndexError:
        warnings.warn("No input args, skipping _decide_input_format")
    except Exception as e:
        warnings.warn(f"Skipping _decide_input_format\n {e.args[0]}")

    return args


@_beartype.beartype
def _trace(func, args, operator_export_type, return_outs=False):
    # Special case for common case of passing a single Tensor
    if isinstance(args, torch.Tensor):
        args = (args,)

    trace_graph, torch_out, inputs_states = torch.jit._get_trace_graph(
        func,
        args,
        strict=False,
        _force_outplace=False,
        _return_inputs_states=True,
    )
    warn_on_static_input_change(inputs_states)

    trace_graph = _optimize_graph(trace_graph, operator_export_type, params_dict={})
    if return_outs:
        return trace_graph, torch_out
    return trace_graph


@_beartype.beartype
def _trace_and_get_graph_from_model(model, args):
    # A basic sanity check: make sure the state_dict keys are the same
    # before and after running the model.  Fail fast!
    orig_state_dict_keys = torch.jit._unique_state_dict(model).keys()

    # Disable Autocast cache because it replaces kernel's weight and bias
    # by (undesired) constants.
    # No perf impact for when there are reused weights since https://github.com/pytorch/pytorch/pull/85665
    prev_autocast_cache_enabled = torch.is_autocast_cache_enabled()
    torch.set_autocast_cache_enabled(False)
    trace_graph, torch_out, inputs_states = torch.jit._get_trace_graph(
        model,
        args,
        strict=False,
        _force_outplace=False,
        _return_inputs_states=True,
    )
    torch.set_autocast_cache_enabled(prev_autocast_cache_enabled)

    warn_on_static_input_change(inputs_states)

    if orig_state_dict_keys != torch.jit._unique_state_dict(model).keys():
        raise RuntimeError(
            "state_dict changed after running the tracer; "
            "something weird is happening in your model!"
        )

    return trace_graph, torch_out


@_beartype.beartype
def _get_param_count_list(method_graph, args_params):
    param_count_list = []
    for input_, arg_params_ in zip(method_graph.inputs(), args_params):
        if "PackedParams" in str(input_.type()):
            in_vars, _ = torch.jit._flatten(arg_params_)
            param_count_list.append(len(in_vars))
        else:
            param_count_list.append(arg_params_ is not None)

    return param_count_list


@_beartype.beartype
def _check_flatten_did_not_remove(original, jit_flattened):
    """torch.jit._flatten removes None. Check if it did so in this case."""

    @_beartype.beartype
    def flatten(x):
        if isinstance(x, (list, tuple)):
            for inner in x:
                yield from flatten(inner)
        elif isinstance(x, dict):
            for inner in x.values():
                yield from flatten(inner)
        else:
            yield x

    flattened_with_none = list(flatten(original))
    num_none = len(flattened_with_none) - len(jit_flattened)
    assert num_none >= 0
    if num_none:
        raise ValueError(
            f"args contained {num_none} None's after flattening. "
            "When exporting a ScriptModule or ScriptFunction, no args may "
            "be None because that breaks type propagation."
        )


def _create_jit_graph(
    model: Union[torch.nn.Module, torch.jit.ScriptFunction], args: Sequence[Any]
) -> Tuple[_C.Graph, List[_C.IValue], Optional[Any], Optional[_C.ScriptModule]]:
    if isinstance(model, (torch.jit.ScriptFunction, torch.jit.ScriptModule)):
        flattened_args = tuple(torch.jit._flatten(tuple(args))[0])
        _check_flatten_did_not_remove(args, flattened_args)
        torch_out = None

        if isinstance(model, torch.jit.ScriptModule):
            try:
                graph = model.forward.graph  # type: ignore[attr-defined]
            except AttributeError as e:
                raise RuntimeError("'forward' method must be a script method") from e
            _C._jit_pass_onnx_function_substitution(graph)
            freezed_module = _C._freeze_module(
                cast(_C.ScriptModule, model._c), preserveParameters=True
            )
            module, params = _C._jit_onnx_list_model_parameters(freezed_module)
            method_graph = module._get_method("forward").graph
            args_params = tuple(args) + tuple(params)
            param_count_list = _get_param_count_list(method_graph, args_params)
            in_vars, _ = torch.jit._flatten(args_params)
            graph = _C._propagate_and_assign_input_shapes(
                method_graph, tuple(in_vars), param_count_list, False, False
            )
            return graph, params, torch_out, module

        # torch.jit.ScriptFunction
        params = []
        graph = model.graph
        _C._jit_pass_onnx_function_substitution(graph)
        param_count_list = _get_param_count_list(graph, args)
        graph = _C._propagate_and_assign_input_shapes(
            graph, flattened_args, param_count_list, False, False
        )
        return graph, params, torch_out, None

    graph, torch_out = _trace_and_get_graph_from_model(model, args)
    _C._jit_pass_onnx_lint(graph)
    state_dict = torch.jit._unique_state_dict(model)
    params = list(state_dict.values())
    graph_inputs = list(graph.inputs())
    user_input_num = len(graph_inputs) - len(state_dict)
    param_names = list(state_dict.keys())
    for i, inp in enumerate(graph_inputs):
        if i >= user_input_num:
            inp.setDebugName(param_names[i - user_input_num])
    _C._jit_pass_onnx_function_substitution(graph)
    return graph, params, torch_out, None


@_beartype.beartype
def _get_named_param_dict(graph, params):
    input_and_param_names = [val.debugName() for val in graph.inputs()]
    param_names = input_and_param_names[len(input_and_param_names) - len(params) :]
    _params_dict = dict(zip(param_names, params))
    return _params_dict


@_beartype.beartype
def _get_example_outputs(model, args):
    input_args = copy.deepcopy(args)
    input_kwargs = {}
    if input_args and isinstance(input_args[-1], dict):
        input_kwargs = input_args[-1]
        input_args = input_args[:-1]

    example_outputs = model(*input_args, **input_kwargs)
    if isinstance(example_outputs, list):
        example_outputs = [example_outputs]
    elif not isinstance(example_outputs, tuple):
        example_outputs = (example_outputs,)

    return example_outputs


_qtype_vtype_map = {
    torch.quint8: torch.uint8,
    torch.qint8: torch.int8,
    torch.qint32: torch.int32,
    torch.quint4x2: torch.int8,
}


@_beartype.beartype
def unpack_quantized_tensor(value, cast_onnx_accepted=True):
    if isinstance(value, torch.Tensor) and value.dtype in _qtype_vtype_map:
        q_value_dequantize = value.dequantize()
        q_scale = (
            torch.tensor(value.q_scale(), dtype=torch.double)
            if cast_onnx_accepted
            else torch.tensor(value.q_scale(), dtype=torch.float32)
        )
        q_zero_point = (
            torch.tensor(value.q_zero_point(), dtype=torch.int64)
            if cast_onnx_accepted
            else torch.tensor(value.q_zero_point(), dtype=_qtype_vtype_map[value.dtype])
        )
        q_value = q_value_dequantize / q_scale + q_zero_point
        q_value = q_value.to(dtype=_qtype_vtype_map[value.dtype])
        return q_value, q_scale, q_zero_point
    else:
        return (value,)


@_beartype.beartype
def _pre_trace_quant_model(model, args):
    r"""Returns `torch.jit.trace(model, args)` if model is quantized. Otherwise do nothing and return
    original model.

    This is due to https://github.com/pytorch/pytorch/issues/75761.
    """
    if any(
        hasattr(m, "_packed_params") for m in getattr(model, "modules", list)()
    ) or any(getattr(arg, "is_quantized", False) for arg in args):
        return torch.jit.trace(model, args)
    return model


@_beartype.beartype
def _model_to_graph(
    model,
    args,
    verbose=False,
    input_names=None,
    output_names=None,
    operator_export_type=_C_onnx.OperatorExportTypes.ONNX,
    do_constant_folding=True,
    _disable_torch_constant_prop=False,
    fixed_batch_size=False,
    training=_C_onnx.TrainingMode.EVAL,
    dynamic_axes=None,
) -> Tuple[
    _C.Graph,
    Dict[str, torch.Tensor],
    Optional[
        Union[
            torch.Tensor,
            Tuple[torch.Tensor, ...],
            List[torch.Tensor],
            Dict[str, torch.Tensor],
            Any,  # Can be nested tuples etc.
        ]
    ],
]:
    """Converts model into an ONNX graph.

    Returns:
        graph: A TorchScript IR Graph with ONNX nodes.
        params_dict: Dict from input param name to param value.
        torch_out: The output tensors resulting from the trace of ``model``.
            If ``model`` is a :class:`torch.jit.ScriptModule` or :class:`torch.jit.ScriptFunction`,
            this will be None, since we are not doing any tracing.
    """
    # TODO: can we simplify this to always return a tuple of Tensor or None?

    # Special case for common case of passing a single Tensor
    if isinstance(args, (torch.Tensor, int, float, bool)):
        args = (args,)

    model = _pre_trace_quant_model(model, args)
    graph, params, torch_out, module = _create_jit_graph(model, args)
    params_dict = _get_named_param_dict(graph, params)

    try:
        graph = _optimize_graph(
            graph,
            operator_export_type,
            _disable_torch_constant_prop=_disable_torch_constant_prop,
            fixed_batch_size=fixed_batch_size,
            params_dict=params_dict,
            dynamic_axes=dynamic_axes,
            input_names=input_names,
            module=module,
        )
    except Exception as e:
        torch.onnx.log("Torch IR graph at exception: ", graph)
        raise

    is_script = isinstance(model, (torch.jit.ScriptFunction, torch.jit.ScriptModule))
    if is_script:
        example_outputs = _get_example_outputs(model, args)
        example_outputs_final = ()
        for example_output in example_outputs:
            example_outputs_final += unpack_quantized_tensor(example_output)
        out_vars, desc = torch.jit._flatten(example_outputs_final)
        _C._jit_pass_onnx_assign_output_shape(
            graph,
            out_vars,
            desc,
            GLOBALS.onnx_shape_inference,
            is_script,
            GLOBALS.export_onnx_opset_version,
        )

    # NB: ONNX requires complete information about output types, which might be
    # erased by some optimizations, so we need to set it explicitly again.
    else:
        if not isinstance(torch_out, (list, tuple)):
            output_wrapped = [torch_out]
        else:
            output_wrapped = torch_out  # type: ignore[assignment]

        output_tensors, out_desc = torch.jit._flatten(tuple(output_wrapped))
        # assign_output_shape pass is not compatible with quantized outputs.
        # Quantized outputs are flattened to 3 values in ONNX, while packed as
        # single value in PyTorch.
        if not any(getattr(out, "is_quantized", False) for out in output_tensors):
            _C._jit_pass_onnx_assign_output_shape(
                graph,
                output_tensors,
                out_desc,
                GLOBALS.onnx_shape_inference,
                is_script,
                GLOBALS.export_onnx_opset_version,
            )

    _set_input_and_output_names(graph, input_names, output_names)
    params_dict = _get_named_param_dict(graph, params)

    if (
        do_constant_folding
        and GLOBALS.export_onnx_opset_version
        >= _constants.ONNX_CONSTANT_FOLDING_MIN_OPSET
    ):
        if training is None or training == _C_onnx.TrainingMode.EVAL:
            params_dict = _C._jit_pass_onnx_eval_peephole(graph, params_dict)

        params_dict = _C._jit_pass_onnx_constant_fold(
            graph, params_dict, GLOBALS.export_onnx_opset_version
        )
        _C._jit_pass_dce_allow_deleting_nodes_with_side_effects(graph)

    if GLOBALS.onnx_shape_inference:
        try:
            _C._jit_pass_onnx_graph_shape_type_inference(
                graph, params_dict, GLOBALS.export_onnx_opset_version
            )
        except RuntimeError as exc:
            if (
                _C_onnx._CAFFE2_ATEN_FALLBACK
                and exc.args[0]
                == "ScalarType UNKNOWN_SCALAR is an unexpected tensor scalar type!"
            ):
                # Caffe2 builds can have UNKNOWN_SCALAR for some tensors
                pass

    params_dict = _C._jit_pass_onnx_eliminate_unused_items(graph, params_dict)

    # For ONNX opset < 9, constants only have three data types: float16, float, double.
    # In this pass transform constants of other data types to float/double + cast operator.
    if GLOBALS.export_onnx_opset_version < 9:
        _C._jit_pass_onnx_cast_all_constant_to_floating(graph)

    params_dict = _C._jit_pass_filter_non_tensor_arguments(params_dict)
    _C._jit_decay_packed_param_input_types(graph)

    # If output names lack a proper name and are identified only by their unique
    # give them a legible name for debugging purposes
    _apply_friendly_debug_names(graph, params_dict)

    return graph, params_dict, torch_out


@_beartype.beartype
@torch._disable_dynamo
def export_to_pretty_string(
    model,
    args,
    export_params=True,
    verbose=False,
    training=_C_onnx.TrainingMode.EVAL,
    input_names=None,
    output_names=None,
    operator_export_type=_C_onnx.OperatorExportTypes.ONNX,
    export_type=None,
    google_printer=False,
    opset_version=None,
    keep_initializers_as_inputs=None,
    custom_opsets=None,
    add_node_names=True,
    do_constant_folding=True,
    dynamic_axes=None,
):
    r"""
    Similar to :func:`export`, but returns a text representation of the ONNX
    model. Only differences in args listed below. All other args are the same
    as :func:`export`.

    Args:
        add_node_names (bool, default True): Whether or not to set
            NodeProto.name. This makes no difference unless
            ``google_printer=True``.
        google_printer (bool, default False): If False, will return a custom,
            compact representation of the model. If True will return the
            protobuf's `Message::DebugString()`, which is more verbose.

    Returns:
        A UTF-8 str containing a human-readable representation of the ONNX model.
    """
    if opset_version is None:
        opset_version = _constants.ONNX_DEFAULT_OPSET
    if custom_opsets is None:
        custom_opsets = {}
    GLOBALS.export_onnx_opset_version = opset_version
    GLOBALS.operator_export_type = operator_export_type

    with exporter_context(model, training, verbose):
        val_keep_init_as_ip = _decide_keep_init_as_input(
            keep_initializers_as_inputs, operator_export_type, opset_version
        )
        val_add_node_names = _decide_add_node_names(
            add_node_names, operator_export_type
        )
        val_do_constant_folding = _decide_constant_folding(
            do_constant_folding, operator_export_type, training
        )
        args = _decide_input_format(model, args)
        graph, params_dict, torch_out = _model_to_graph(
            model,
            args,
            verbose,
            input_names,
            output_names,
            operator_export_type,
            val_do_constant_folding,
            training=training,
            dynamic_axes=dynamic_axes,
        )

        return graph._pretty_print_onnx(  # type: ignore[attr-defined]
            params_dict,
            opset_version,
            False,
            operator_export_type,
            google_printer,
            val_keep_init_as_ip,
            custom_opsets,
            val_add_node_names,
        )


@_beartype.beartype
def unconvertible_ops(
    model,
    args,
    training: _C_onnx.TrainingMode = _C_onnx.TrainingMode.EVAL,
    opset_version: Optional[int] = None,
) -> Tuple[_C.Graph, List[str]]:
    """Returns an approximated list of all ops that are yet supported by :mod:`torch.onnx`.

    The list is approximated because some ops may be removed during the conversion
    process and don't need to be converted. Some other ops may have partial support
    that will fail conversion with particular inputs. Please open a Github Issue
    for op support requests.

    Args:
        model: Same as the `model` parameter in :func:`torch.onnx.export`.
        args: Same as the `args` parameter in :func:`torch.onnx.export`.
        training: Same as the `training` parameter in :func:`torch.onnx.export`.
        opset_version: Same as the `opset_version` parameter in :func:`torch.onnx.export`.

    Returns:
        The JIT graph and a list of unconvertible ops in the format of "domain::op".
    """

    opset_version = opset_version or _constants.ONNX_DEFAULT_OPSET
    GLOBALS.export_onnx_opset_version = opset_version

    try:
        with exporter_context(model, training, verbose=False):
            # Create a mostly clean JIT graph that contains the plain aten and
            # other ops we can check with the symbolic registry.
            # NOTE: We don't want to actually convert any ops to ONNX or run any
            # symbolic functions because there is a higher chance that a pass
            # fails or an unconvertible op messes up the graph during ONNX conversion.
            # This way we can always generate a list just by looking at the names
            # of the ops in the graph.
            args = _decide_input_format(model, args)
            model = _pre_trace_quant_model(model, args)
            graph, _, _, module = _create_jit_graph(model, args)
            _C._jit_pass_inline(graph)
            _C._jit_pass_onnx_remove_inplace_ops_for_onnx(graph, module)
            _C._jit_pass_erase_number_types(graph)
            _C._jit_pass_dce_allow_deleting_nodes_with_side_effects(graph)
    except Exception as e:
        raise errors.OnnxExporterError(
            "Failed to discover unconvertible ops because of errors during the JIT graph "
            "generation process."
        ) from e

    unsupported_ops = []
    for node in graph.nodes():
        domain_op = node.kind()
        if domain_op.startswith(("onnx::", "prim::")):
            # We consider onnx and prim ops as supported ops, even though some "prim"
            # ops are not implemented as symbolic functions, because they may be
            # eliminated in the conversion passes. Users may still see errors caused
            # by prim ops even though they don't show up in the list.
            continue
        if not registration.registry.is_registered_op(
            domain_op.rstrip("_"), opset_version
        ):
            # We consider all registered ops supported, even though some of them are
            # only partially supported, because there is not yet a good way to check
            # if an op is fully supported.
            # TODO(justinchuby): Create a way to check if an op is fully supported.
            unsupported_ops.append(domain_op)
    return graph, unsupported_ops


@_beartype.beartype
def _setup_trace_module_map(
    model: Union[torch.nn.Module, torch.jit.ScriptModule],
    export_modules_as_functions: Union[bool, Collection[Type[torch.nn.Module]]],
) -> Set[str]:
    def __register_attribute_hook():
        attr_name = "_onnx_attrs"

        def _track_module_attributes_forward_pre_hook(module, input):
            setattr(module, attr_name, _get_module_attributes(module))

        def _track_module_attributes_forward_hook(module, input, output):
            tracing_state = _C._get_tracing_state()
            if not tracing_state:
                return

            graph = tracing_state.graph()
            onnx_attrs = {}
            if hasattr(module, attr_name):
                onnx_attrs = getattr(module, attr_name)
                delattr(module, attr_name)

            _C._jit_pass_onnx_track_scope_attributes(graph, onnx_attrs)

        for m in model.modules():
            m.register_forward_hook(_track_module_attributes_forward_hook)
            m.register_forward_pre_hook(_track_module_attributes_forward_pre_hook)

    def _unqualified_variable_name(qualified_name: str) -> str:
        """
        Parse qualified variable name and return the unqualified version.

        Pure numeric atoms are considered inadequate, so this function will look past them,
        and start from the first non-numeric atom.

        Example:
            >>> _unqualified_variable_name('__main__.Foo.bar')
            'bar'
            >>> _unqualified_variable_name('__main__.Foo.bar.0')
            'bar.0'
        """
        name_atoms = qualified_name.split(".")
        for i, atom in reversed(list(enumerate(name_atoms))):
            if not atom.isnumeric():
                return ".".join(name_atoms[i:])
        return qualified_name

    trace_module_map = {
        _m: torch._C._jit_onnx_create_full_scope_name(
            torch.typename(type(_m)), _unqualified_variable_name(_n)
        )
        for _n, _m in model.named_modules()
    }
    torch.jit._trace._trace_module_map = trace_module_map
    if isinstance(export_modules_as_functions, bool) and export_modules_as_functions:
        module_typenames = {torch.typename(type(module)) for module in trace_module_map}
    elif isinstance(export_modules_as_functions, set) and export_modules_as_functions:

        def _find_typename(v):
            if isinstance(v, type):
                return torch.typename(v)
            else:
                raise RuntimeError(
                    "Only type of the `nn.Module` should be "
                    "passed in the set for argument `export_modules_as_functions`. "
                    "Got `%s`." % (type(v).__name__)
                )

        module_typenames = {_find_typename(v) for v in export_modules_as_functions}
    else:
        module_typenames = set()

    if module_typenames:
        __register_attribute_hook()

    return module_typenames


@_beartype.beartype
def _reset_trace_module_map():
    torch.jit._trace._trace_module_map = None
    _C._jit_pass_onnx_clear_scope_records()


@_beartype.beartype
def _get_module_attributes(module):
    annotations = typing.get_type_hints(type(module))
    base_m_annotations = typing.get_type_hints(torch.nn.Module)
    [annotations.pop(k, None) for k in base_m_annotations]
    # Check whether module attributes can be accessed. Some classes
    # define attributes but don't provide access to them in their
    # constructor.
    #
    # For example, torch.nn.Embedding has the `freeze` variable and its
    # type specified in the class but the attribute is not created in the
    # constructor. In other words, there is no `self.freeze = <True | False>`
    # in the constructor.
    #
    # Reference: https://github.com/pytorch/pytorch/blob/92de1d322223fb5584e384971b32c46b93bc2f4b/torch/nn/modules/sparse.py#L120
    attrs = {}
    for k in annotations:
        try:
            attrs[k] = getattr(module, k)
        except AttributeError:
            torch.onnx.log(f"Skipping module attribute '{k}'")
            continue
    return attrs


@_beartype.beartype
def _export(
    model,
    args,
    f,
    export_params=True,
    verbose=False,
    training=_C_onnx.TrainingMode.EVAL,
    input_names=None,
    output_names=None,
    operator_export_type=_C_onnx.OperatorExportTypes.ONNX,
    export_type=None,
    opset_version=None,
    do_constant_folding=True,
    dynamic_axes=None,
    keep_initializers_as_inputs=None,
    fixed_batch_size=False,
    custom_opsets=None,
    add_node_names=True,
    onnx_shape_inference=True,
    export_modules_as_functions=False,
    autograd_inlining=True,
):
    assert GLOBALS.in_onnx_export is False

    if export_type is None:
        export_type = _exporter_states.ExportTypes.PROTOBUF_FILE

    # Discussed deprecation with Nikita Shulga and Sergii Dymchenko from Meta
    if _C_onnx._CAFFE2_ATEN_FALLBACK:
        warnings.warn(
            "Caffe2 ONNX exporter is deprecated in version 2.0 and will be "
            "removed in 2.2. Please use PyTorch 2.1 or older for this capability.",
            category=FutureWarning,
            stacklevel=2,
        )

    if isinstance(model, torch.nn.DataParallel):
        raise ValueError(
            "torch.nn.DataParallel is not supported by ONNX "
            "exporter, please use 'attribute' module to "
            "unwrap model from torch.nn.DataParallel. Try "
            "torch.onnx.export(model.module, ...)"
        )

    GLOBALS.onnx_shape_inference = onnx_shape_inference

    if opset_version is None:
        opset_version = _constants.ONNX_DEFAULT_OPSET

    # torch.onnx.export does not support opset versions >=18
    if opset_version > _constants.ONNX_TORCHSCRIPT_EXPORTER_MAX_OPSET:
        # We do not want to fail because we should still allow users to create
        # custom symbolic functions for opset>17
        warnings.warn(
            f"Exporting to ONNX opset version {opset_version} is not supported. "
            f"by 'torch.onnx.export()'. "
            f"The highest opset version supported is {_constants.ONNX_TORCHSCRIPT_EXPORTER_MAX_OPSET}. "
            f"To use a newer opset version, consider 'torch.onnx.dynamo_export()'. "
            f"Note that dynamo_export() is in preview. Please report errors with "
            f"dynamo_export() as Github issues to https://github.com/pytorch/pytorch/issues.",
            category=errors.OnnxExporterWarning,
        )

    if export_modules_as_functions and opset_version < 15:
        raise ValueError(
            "`export_modules_as_functions` is not supported for `opset_version` < 15."
            "This is because `opset_version` < 15 implies IR version < 8, which means "
            "no local function support. "
        )
    if not operator_export_type:
        if _C_onnx._CAFFE2_ATEN_FALLBACK:
            operator_export_type = _C_onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK
        else:
            operator_export_type = _C_onnx.OperatorExportTypes.ONNX

    # By default, training=TrainingMode.EVAL,
    # which is good because running a model in training mode could result in
    # internal buffers getting updated, dropout getting applied, etc.
    # If you really know what you're doing, you can turn
    # training=TrainingMode.TRAINING or training=TrainingMode.PRESERVE,
    # (to preserve whatever the original training mode was.)
    GLOBALS.export_onnx_opset_version = opset_version
    GLOBALS.operator_export_type = operator_export_type

    try:
        GLOBALS.in_onnx_export = True
        _autograd_inlining_previous = GLOBALS.autograd_inlining
        GLOBALS.autograd_inlining = autograd_inlining

        module_typenames_to_export_as_functions: Set[str] = set()
        if isinstance(model, (torch.nn.Module, torch.jit.ScriptModule)):
            module_typenames_to_export_as_functions = _setup_trace_module_map(
                model, export_modules_as_functions
            )

        with exporter_context(model, training, verbose):
            val_keep_init_as_ip = _decide_keep_init_as_input(
                keep_initializers_as_inputs,
                operator_export_type,
                opset_version,
            )
            val_add_node_names = _decide_add_node_names(
                add_node_names, operator_export_type
            )
            val_do_constant_folding = _decide_constant_folding(
                do_constant_folding, operator_export_type, training
            )
            # Normally f can be a file-like object, but for large models, the external data format requires a
            # valid `model_file_location`. Code in export.cpp will enforce this.
            if isinstance(f, str):
                model_file_location = f
            else:
                model_file_location = ""
            args = _decide_input_format(model, args)
            if dynamic_axes is None:
                dynamic_axes = {}
            _validate_dynamic_axes(dynamic_axes, model, input_names, output_names)

            graph, params_dict, torch_out = _model_to_graph(
                model,
                args,
                verbose,
                input_names,
                output_names,
                operator_export_type,
                val_do_constant_folding,
                fixed_batch_size=fixed_batch_size,
                training=training,
                dynamic_axes=dynamic_axes,
            )

            # TODO: Don't allocate a in-memory string for the protobuf
            defer_weight_export = (
                export_type is not _exporter_states.ExportTypes.PROTOBUF_FILE
            )
            if custom_opsets is None:
                custom_opsets = {}

            _C._jit_pass_dce_allow_deleting_nodes_with_side_effects(graph)
            node_attr_to_name = {}  # type: ignore[var-annotated]
            if module_typenames_to_export_as_functions:
                # NOTE: cannot call DCE after this pass. DCE will remove function definition nodes.
                node_attr_to_name = _C._jit_pass_onnx_function_extraction(
                    graph,
                    module_typenames_to_export_as_functions,
                    list(params_dict.keys()),
                )

            if keep_initializers_as_inputs is not True:
                params_dict = _C._jit_pass_onnx_deduplicate_initializers(  # type: ignore[assignment]
                    graph, params_dict, getattr(model, "training", False)  # type: ignore[arg-type]
                )
            _C._jit_pass_onnx_assign_scoped_names_for_node_and_value(graph)
            if export_params:
                (
                    proto,
                    export_map,
                    val_use_external_data_format,
                    node_names,
                ) = graph._export_onnx(  # type: ignore[attr-defined]
                    params_dict,
                    opset_version,
                    dynamic_axes,
                    defer_weight_export,
                    operator_export_type,
                    not verbose,
                    val_keep_init_as_ip,
                    custom_opsets,
                    val_add_node_names,
                    model_file_location,
                    node_attr_to_name,
                )
            else:
                (
                    proto,
                    export_map,
                    val_use_external_data_format,
                    node_names,
                ) = graph._export_onnx(  # type: ignore[attr-defined]
                    {},
                    opset_version,
                    dynamic_axes,
                    False,
                    operator_export_type,
                    not verbose,
                    val_keep_init_as_ip,
                    custom_opsets,
                    val_add_node_names,
                    model_file_location,
                    node_attr_to_name,
                )
            # insert function_proto into model_proto.
            proto = onnx_proto_utils._add_onnxscript_fn(
                proto,
                custom_opsets,
            )
            if verbose:
                torch.onnx.log("Exported graph: ", graph)
            onnx_proto_utils._export_file(proto, f, export_type, export_map)
            # The ONNX checker only works for ONNX graph. So if the operator_export_type is not ONNX,
            # we can skip this check.
            # If large model format export is enabled, proto will only contain data location instead of
            # raw data and _check_onnx_proto() will fail because it can only handle the raw ONNX proto
            # string in memory.
            if (operator_export_type is _C_onnx.OperatorExportTypes.ONNX) and (
                not val_use_external_data_format
            ):
                try:
                    _C._check_onnx_proto(proto)
                except RuntimeError as e:
                    raise errors.CheckerError(e) from e
    finally:
        assert GLOBALS.in_onnx_export
        GLOBALS.in_onnx_export = False
        GLOBALS.autograd_inlining = _autograd_inlining_previous
        _reset_trace_module_map()

    return torch_out


@_beartype.beartype
def _apply_friendly_debug_names(graph, params):
    for n in graph.nodes():
        for v in n.inputs():
            old_name = v.debugName()
            if old_name != str(v.unique()):
                continue
            new_name = f"{n.kind()}_{v.unique()}"
            v.setDebugName(new_name)
            if old_name in params:
                params[new_name] = params.pop(old_name)


@_beartype.beartype
def _set_input_and_output_names(graph, input_names, output_names):
    @_beartype.beartype
    def set_names(node_list, name_list, descriptor):
        if name_list is None:
            return
        if len(name_list) > len(node_list):
            raise RuntimeError(
                "number of %s names provided (%d) exceeded number of %ss (%d)"
                % (descriptor, len(name_list), descriptor, len(node_list))
            )

        # Mark if the output node DebugName is set before.
        output_node_set = set()
        for i, (name, node) in enumerate(zip(name_list, node_list)):
            # Duplicated output node, insert onnx::Identity to avoid setting the same DebugName after setDebugName().
            if descriptor == "output":
                if node in output_node_set:
                    identity_node = graph.create("onnx::Identity")
                    identity_node.insertAfter(node.node())
                    identity_node.addInput(node)
                    identity_node.output().setType(node.type())
                    graph.return_node().replaceInput(i, identity_node.output())
                    node = identity_node.output()
                output_node_set.add(node)

            if node.debugName() != name:
                node.setDebugName(name)

    set_names(list(graph.inputs()), input_names, "input")
    set_names(list(graph.outputs()), output_names, "output")


@_beartype.beartype
def _run_symbolic_method(g, op_name, symbolic_fn, args):
    r"""
    This trampoline function gets invoked for every symbolic method
    call from C++.
    """
    try:
        graph_context = jit_utils.GraphContext(
            graph=g,
            block=g.block(),
            opset=GLOBALS.export_onnx_opset_version,
            original_node=None,  # type: ignore[arg-type]
            params_dict=_params_dict,
            env={},
        )
        return symbolic_fn(graph_context, *args)
    except TypeError as e:
        # Handle the specific case where we didn't successfully dispatch
        # to symbolic_fn.  Otherwise, the backtrace will have the clues
        # you need.
        e.args = (f"{e.args[0]} (occurred when translating {op_name})",)
        raise


@_beartype.beartype
def _add_block(node: _C.Node) -> _C.Block:
    return node.addBlock()


@_beartype.beartype
def _add_input_to_block(block: _C.Block):
    return block.addInputToBlock()  # type: ignore[attr-defined]


@_beartype.beartype
def _add_output_to_block(block: _C.Block, value: _C.Value) -> int:
    return block.registerOutput(value)


@_beartype.beartype
def _should_aten_fallback(
    name: str, opset_version: int, operator_export_type: _C_onnx.OperatorExportTypes
):
    # For BUILD_CAFFE2=0 builds, if domain=="aten" and operator_export_type==ONNX_ATEN,
    #   an aten::ATen operator is created regardless of symbolics existence
    # For BUILD_CAFFE2=1, the same applies only if there is no symbolic available

    is_exportable_aten_op = registration.registry.is_registered_op(name, opset_version)
    is_onnx_aten_export = operator_export_type == _C_onnx.OperatorExportTypes.ONNX_ATEN
    is_aten_fallback_export = (
        operator_export_type == _C_onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK
    )
    is_caffe2_build = _C_onnx._CAFFE2_ATEN_FALLBACK

    if not name.startswith("aten::"):
        return False

    if is_caffe2_build:
        if (
            is_onnx_aten_export or is_aten_fallback_export
        ) and not is_exportable_aten_op:
            return True
    else:
        if is_onnx_aten_export or (
            is_aten_fallback_export and not is_exportable_aten_op
        ):
            return True

    return False


@_beartype.beartype
def _need_symbolic_context(symbolic_fn: Callable) -> bool:
    """Checks if the first argument to symbolic_fn is annotated as type `torch.onnx.SymbolicContext`."""
    params = tuple(inspect.signature(symbolic_fn).parameters.values())
    # When the annotation is postpone-evaluated, the annotation is a string
    # and not a type. We need to use get_type_hints to get the real type.
    if not params:
        return False
    first_param_name = params[0].name
    type_hints = typing.get_type_hints(symbolic_fn)
    if first_param_name not in type_hints:
        return False
    param_type = type_hints[first_param_name]
    return issubclass(param_type, _exporter_states.SymbolicContext)


@_beartype.beartype
def _symbolic_context_handler(symbolic_fn: Callable) -> Callable:
    """Decorator that provides the symbolic context to the symbolic function if needed."""
    if _need_symbolic_context(symbolic_fn):
        # TODO(justinchuby): Update the module name of GraphContext when it is public
        warnings.warn(
            "The first argument to symbolic functions is deprecated in 1.13 and will be "
            "removed in the future. Please annotate treat the first argument (g) as GraphContext "
            "and use context information from the object instead.",
            category=FutureWarning,
        )

        def wrapper(graph_context: jit_utils.GraphContext, *args, **kwargs):
            symbolic_context = _exporter_states.SymbolicContext(
                params_dict=graph_context.params_dict,
                env=graph_context.env,
                cur_node=graph_context.original_node,
                onnx_block=graph_context.block,
            )
            return symbolic_fn(symbolic_context, graph_context, *args, **kwargs)

        return wrapper
    return symbolic_fn


@_beartype.beartype
def _get_aten_op_overload_name(n: _C.Node) -> str:
    # Returns `overload_name` attribute to ATen ops on non-Caffe2 builds
    schema = n.schema()
    if not schema.startswith("aten::") or symbolic_helper.is_caffe2_aten_fallback():
        return ""
    return _C.parse_schema(schema).overload_name


@_beartype.beartype
def _run_symbolic_function(
    graph: _C.Graph,
    block: _C.Block,
    node: _C.Node,
    inputs: Any,
    env: Dict[_C.Value, _C.Value],
    operator_export_type=_C_onnx.OperatorExportTypes.ONNX,
) -> Optional[Union[_C.Value, Sequence[Optional[_C.Value]]]]:
    """Runs a symbolic function.

    The function is used in C++ to export the node to ONNX.

    Returns:
        A single or a tuple of Values.
        None when the node gets cloned as is into the new graph.
    """

    opset_version = GLOBALS.export_onnx_opset_version

    # See Note [Export inplace]
    node_kind = node.kind()
    if node_kind.endswith("_"):
        # Treat relu_ -> relu; add_ -> add etc.
        ns_op_name = node_kind[:-1]
    else:
        ns_op_name = node_kind

    namespace, op_name = jit_utils.parse_node_kind(ns_op_name)

    graph_context = jit_utils.GraphContext(
        graph=graph,
        block=block,
        opset=opset_version,
        original_node=node,
        params_dict=_params_dict,
        env=env,
    )

    # Direct ATen export requested
    if _should_aten_fallback(ns_op_name, opset_version, operator_export_type):
        attrs = {
            k + "_" + node.kindOf(k)[0]: symbolic_helper._node_get(node, k)
            for k in node.attributeNames()
        }
        outputs = node.outputsSize()
        attrs["outputs"] = outputs
        return graph_context.aten_op(
            op_name,
            *inputs,
            overload_name=_get_aten_op_overload_name(node),
            **attrs,
        )

    try:
        # Caffe2-specific: Quantized op symbolics are registered for opset 9 only.
        if symbolic_helper.is_caffe2_aten_fallback() and opset_version == 9:
            symbolic_caffe2.register_quantized_ops("caffe2", opset_version)

        if namespace == "quantized" and symbolic_helper.is_caffe2_aten_fallback():
            domain = "caffe2"
        else:
            domain = namespace
        symbolic_function_name = f"{domain}::{op_name}"

        symbolic_function_group = registration.registry.get_function_group(
            symbolic_function_name
        )
        if symbolic_function_group is not None:
            symbolic_fn = symbolic_function_group.get(opset_version)
            if symbolic_fn is not None:
                # TODO Wrap almost identical attrs assignment or comment the difference.
                attrs = {
                    k: symbolic_helper._node_get(node, k) for k in node.attributeNames()
                }
                return symbolic_fn(graph_context, *inputs, **attrs)

        attrs = {
            k + "_" + node.kindOf(k)[0]: symbolic_helper._node_get(node, k)
            for k in node.attributeNames()
        }
        if namespace == "onnx":
            # Clone node to trigger ONNX shape inference
            return graph_context.op(op_name, *inputs, **attrs, outputs=node.outputsSize())  # type: ignore[attr-defined]

        raise errors.UnsupportedOperatorError(
            symbolic_function_name,
            opset_version,
            symbolic_function_group.get_min_supported()
            if symbolic_function_group
            else None,
        )

    except RuntimeError:
        if operator_export_type == _C_onnx.OperatorExportTypes.ONNX_FALLTHROUGH:
            return None
        elif (
            operator_export_type == _C_onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK
            and not symbolic_helper.is_caffe2_aten_fallback()
        ):
            # Emit ATen op for non-Caffe2 builds when `operator_export_type==ONNX_ATEN_FALLBACK`
            attrs = {
                k + "_" + node.kindOf(k)[0]: symbolic_helper._node_get(node, k)
                for k in node.attributeNames()
            }
            return graph_context.aten_op(
                op_name,
                *inputs,
                overload_name=_get_aten_op_overload_name(node),
                **attrs,
            )
        raise
    except TypeError as e:
        # Handle the specific case where we didn't successfully dispatch.
        # Otherwise, the backtrace will have the clues you need.
        e.args = (f"{e.args[0]} \n(Occurred when translating {op_name}).",)
        raise


@_beartype.beartype
def _verify_custom_op_name(symbolic_name: str):
    if not re.match(r"^[a-zA-Z0-9-_]+::[a-zA-Z-_]+[a-zA-Z0-9-_]*$", symbolic_name):
        raise errors.OnnxExporterError(
            f"Failed to register operator {symbolic_name}. "
            "The symbolic name must match the format domain::name, "
            "and should start with a letter and contain only "
            "alphanumerical characters"
        )

    ns, _ = jit_utils.parse_node_kind(symbolic_name)
    if ns == "onnx":
        raise ValueError(
            f"Failed to register operator {symbolic_name}. {ns} domain cannot be modified."
        )


@_beartype.beartype
def register_custom_op_symbolic(
    symbolic_name: str,
    symbolic_fn: Callable,
    opset_version: int,
):
    """Registers a symbolic function for a custom operator.

    When the user registers symbolic for custom/contrib ops,
    it is highly recommended to add shape inference for that operator via setType API,
    otherwise the exported graph may have incorrect shape inference in some extreme cases.
    An example of setType is `test_aten_embedding_2` in `test_operators.py`.

    See "Custom Operators" in the module documentation for an example usage.

    Args:
        symbolic_name (str): The name of the custom operator in "<domain>::<op>"
            format.
        symbolic_fn (Callable): A function that takes in the ONNX graph and
            the input arguments to the current operator, and returns new
            operator nodes to add to the graph.
        opset_version (int): The ONNX opset version in which to register.
    """
    if symbolic_name.startswith("::"):
        symbolic_name = f"aten{symbolic_name}"

    _verify_custom_op_name(symbolic_name)

    registration.custom_onnx_symbolic(
        symbolic_name,
        opset_version,
        decorate=[
            _symbolic_context_handler,
        ],
    )(symbolic_fn)


@_beartype.beartype
def unregister_custom_op_symbolic(symbolic_name: str, opset_version: int):
    """Unregisters ``symbolic_name``.

    See "Custom Operators" in the module documentation for an example usage.

    Args:
        symbolic_name (str): The name of the custom operator in "<domain>::<op>"
            format.
        opset_version (int): The ONNX opset version in which to unregister.
    """
    if symbolic_name.startswith("::"):
        symbolic_name = f"aten{symbolic_name}"

    _verify_custom_op_name(symbolic_name)

    registration.registry.unregister(symbolic_name, opset_version)


@_beartype.beartype
def _validate_dynamic_axes(dynamic_axes, model, input_names, output_names):
    """Ensures dynamic axes argument is follows the expected format."""
    if len(dynamic_axes) == 0:
        return

    if hasattr(model, "graph"):
        # Extracting set of valid input/output names that shall be used for dynamic_axes
        if (input_names is None) or len(input_names) == 0:
            input_names = [x.debugName() for x in model.graph.inputs()]
        if (output_names is None) or len(output_names) == 0:
            output_names = [y.debugName() for y in model.graph.outputs()]

    valid_names = set((input_names or []) + (output_names or []))

    # If dynamic axes are provided as a list rather than dictionary, they should
    # first get converted to a dictionary in expected format. If desired axes names
    # are not provided for dynamic axes, automatic names shall be generated for
    # provided dynamic axes of specified input/output
    for key, value in dynamic_axes.items():
        if key not in valid_names:
            warnings.warn(
                f"Provided key {key} for dynamic axes is not a valid input/output name"
            )
        if isinstance(value, list):
            warnings.warn(
                "No names were found for specified dynamic axes of provided input."
                f"Automatically generated names will be applied to each dynamic axes of input {key}"
            )

            value_dict = {}
            for i, x in enumerate(value):
                if not isinstance(x, int):
                    raise ValueError(
                        "The type of axis index is expected to be an integer"
                    )
                if x in value_dict:
                    warnings.warn(
                        f"Duplicate dynamic axis index {x} was provided for input {key}."
                    )
                else:
                    value_dict[x] = str(key) + "_dynamic_axes_" + str(i + 1)
            dynamic_axes[key] = value_dict


def model_signature(model: Union[torch.nn.Module, Callable]) -> inspect.Signature:
    return inspect.signature(
        model.forward if isinstance(model, torch.nn.Module) else model
    )