MegEngine/imperative/python/megengine/traced_module/traced_module.py

# MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
#
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
import builtins
import collections
import copy
import fnmatch
import functools
import inspect
import keyword
import re
import weakref
from importlib import import_module
from inspect import getcallargs, getmembers, isclass, ismethod
from itertools import chain
from types import FunctionType
from typing import (
    Any,
    Callable,
    Dict,
    Iterable,
    List,
    Optional,
    Sequence,
    Tuple,
    Type,
    Union,
)

from .. import functional as F
from .. import get_logger
from .. import module as M
from ..core._imperative_rt.core2 import Tensor as RawTensor
from ..core._imperative_rt.core2 import (
    apply,
    is_tracing_module,
    set_module_tracing,
    unset_module_tracing,
)
from ..core._trace_option import set_symbolic_shape
from ..core.ops.builtin import Copy
from ..core.tensor.utils import isscalar, setscalar
from ..module import Module
from ..module import external as MExternal
from ..module.qat import QATModule
from ..quantization.fake_quant import LSQ, TQT, FakeQuantize, _FakeQuantize
from ..quantization.observer import (
    ExponentialMovingAverageObserver,
    HistogramObserver,
    MinMaxObserver,
    Observer,
    PassiveObserver,
    SyncExponentialMovingAverageObserver,
    SyncMinMaxObserver,
)
from ..tensor import Tensor
from ..version import __version__
from .expr import (
    Apply,
    CallFunction,
    CallMethod,
    Constant,
    Expr,
    GetAttr,
    Input,
    get_suffix_name,
    is_apply_def,
    is_call_function,
    is_call_module,
    is_call_tensor_method,
    is_constant,
    is_getattr,
    is_input,
)
from .fake_quant import FakeQuantize as TM_FakeQuant
from .module_tracer import (
    PatchedFn,
    Patcher,
    active_module_tracer,
    get_tensor_wrapable_method,
    module_tracer,
    set_active_module_tracer,
)
from .node import ModuleNode, Node, NodeMixin, TensorNode
from .pytree import (
    USER_REGISTERED_CONTAINER_TYPE,
    USER_REGISTERED_LEAF_TYPE,
    ArgsIndex,
    TreeDef,
    _register_supported_type,
    tree_flatten,
)
from .serialization import (
    _ModuleState,
    load_apply_expr,
    load_call_module_expr,
    load_call_tensor_method_expr,
    load_functional,
)
from .tm_config import (
    _exclude_from_trace,
    _get_default_checker,
    _get_expr_checker,
    _graph_surgery_mode,
    _set_graph_surgery_mode,
)
from .utils import (
    _check_builtin_module_attr,
    _check_obj_attr,
    _convert_kwargs_to_args,
    replace_container_with_module_container,
)

logger = get_logger(__name__)


def _is_builtin_name(name: str) -> bool:
    return (
        name in builtins.__dict__
        or name in keyword.kwlist
        or name in {"inf", "nan", "NoneType"}
    )


def _is_leaf(node):
    assert isinstance(
        node, RawTensor
    ), 'doesn\'t support {} in return values, MUST use Tensor or use "register_supported_type" method to register self-defined type'.format(
        type(node)
    )
    return isinstance(node, RawTensor)


def _node_to_tensor(*args, **kwargs):
    tensors = []
    nodes, tree_def = tree_flatten((args, kwargs))
    for n in nodes:
        if isinstance(n, TensorNode):
            if n.top_graph is not None:
                active_module_tracer().current_scope()._add_input(n)
            value = n.value
            if value is None:
                flag = _set_graph_surgery_mode(False)
                unset_module_tracing()
                value = F.zeros(shape=n._shape, dtype=n._dtype)
                set_module_tracing()
                _set_graph_surgery_mode(flag)
            orig_n = NodeMixin.get(value, None)
            if orig_n is None or "setitem" not in orig_n._name:
                NodeMixin.wrap_safe(value, n)
            tensors.append(value)
        else:
            tensors.append(n)
    tensors = tree_def.unflatten(tensors)
    return tensors


def _tensor_to_node(tensors):
    if tensors is None:
        return None
    nodes = []
    tensors, out_def = tree_flatten(tensors)
    for t in tensors:
        if isinstance(t, Tensor):
            n = NodeMixin.get(t, None)
            if isinstance(n, TensorNode):
                n.value = t
                nodes.append(n)
            else:
                nodes.append(t)
        else:
            nodes.append(t)
    nodes = out_def.unflatten(nodes)
    return nodes


def _name_setter(node: Node, new_name: str):
    surgery_mode = _set_graph_surgery_mode(False)
    graph = active_module_tracer().current_scope()

    if node.top_graph is not None:
        top_graph = active_module_tracer().top_scope()
        if node is top_graph._namespace.used_names.get(node._name, None):
            graph = top_graph
        else:
            graph = node.top_graph

    assert (
        graph._namespace.used_names.get(new_name, None) is None
    ), "The name(%s) is already in use. Please try a different one again." % (new_name)
    graph._namespace.unassociate_name_with_obj(node)
    node._name = graph._namespace.create_unique_name(new_name, node)
    _set_graph_surgery_mode(surgery_mode)


def _wrap_method_to_tensor_node():
    def _any_method(name, func):
        def _any(*args, **kwargs):
            if is_tracing_module() and _graph_surgery_mode():
                args, kwargs = _node_to_tensor(*args, **kwargs)
                attr = getattr(args[0], name)
                outs = attr
                if callable(attr):
                    outs = attr(*(args[1:]), **kwargs)
                if name == "__setitem__":
                    _node_to_tensor(outs)
                    return None
                outs = _tensor_to_node(outs)
                return outs
            else:
                outs = func
                if callable(func):
                    outs = func(*args, **kwargs)
                if isinstance(func, property):
                    outs = func.__get__(*args, **kwargs)
            return outs

        return _any

    tensor_method_patch = []
    for method in get_tensor_wrapable_method():
        patch = PatchedFn(TensorNode, method)
        if type(getattr(Tensor, method)) == property:
            # Only support property.getter
            patch.set_func(property(_any_method(method, patch.origin_fn)))
        else:
            patch.set_func(_any_method(method, patch.origin_fn))
        tensor_method_patch.append(patch)

    patch = PatchedFn(Node, "name")
    patch.set_func(property(patch.origin_fn.fget, _name_setter))
    tensor_method_patch.append(patch)
    return tensor_method_patch


def _convert_node_and_tensor(orig_func):
    @functools.wraps(orig_func)
    def _convert(*args, **kwargs):
        if is_tracing_module() and _graph_surgery_mode():
            args, kwargs = _node_to_tensor(*args, **kwargs)
            rst = orig_func(*args, **kwargs, method_func=_convert)
            rst = _tensor_to_node(rst)
            return rst
        else:
            rst = orig_func(*args, **kwargs)
        return rst

    return _convert


def _wrap_mnode_getattr(orig_getattr):
    @functools.wraps(orig_getattr)
    def wraped_fn(self, name):
        if is_tracing_module() and _graph_surgery_mode():
            obj = self.owner
            current_graph = active_module_tracer().current_scope()
            if self.top_graph is not None:
                current_graph._add_input(self)
            attr = getattr(obj, name)
            node = attr
            if not isinstance(attr, TracedModuleBuilder):
                if isinstance(attr, Module):
                    attr = TracedModuleBuilder(attr)
                    setattr(obj, name, attr)

                if isinstance(attr, (NodeMixin, RawTensor)):
                    NodeMixin.wrap(
                        attr,
                        lambda: GetAttr.make(
                            self,
                            type=NodeMixin.get_wrapped_type(attr),
                            attr_name=name,
                            name="",
                        ),
                    )
            if isinstance(attr, (NodeMixin, RawTensor)):
                node = NodeMixin.get(attr)
            if isinstance(node, ModuleNode) and isinstance(attr, (NodeMixin, Module)):
                node._owner = weakref.ref(attr)
            return node
        else:
            node = object.__getattribute__(self, name)
        return node

    return wraped_fn


def _wrap_mnode_call(orig_call):
    @functools.wraps(orig_call)
    def wraped_fn(self, *args, **kwargs):
        if is_tracing_module() and _graph_surgery_mode():
            obj = self.owner
            if self.top_graph is not None:
                active_module_tracer().current_scope()._add_input(self)
            rst = obj(*args, **kwargs)
        else:
            raise TypeError("'ModuleNode' object is not callable")
        return rst

    return wraped_fn


class _InsertExprs:
    def __init__(self, graph, expr: Optional[Expr] = None):
        self.graph = graph
        while graph.top_graph is not None:
            graph = graph.top_graph
        assert graph.inputs[0].owner._is_top
        self.root_graph = graph
        self.global_scope = InternalGraph(self.graph._name, self.graph._qualname)
        self.global_scope._namespace.merge(self.graph._namespace)
        self.expr = expr
        self._tensor_method_patch = None

    def __enter__(self):
        self.use_sym_shape = set_symbolic_shape(True)
        node_id, expr_id = self.root_graph._total_ids
        Node._set_next_id(node_id)
        Expr._set_next_id(expr_id)
        set_module_tracing()
        _set_graph_surgery_mode(True)
        assert active_module_tracer() is None
        set_active_module_tracer(
            module_tracer(lambda x: _convert_node_and_tensor(_wrapped_function(x)))
        )
        active_module_tracer().patcher.__enter__()
        for cls, name, func in [
            [ModuleNode, "__getattr__", _wrap_mnode_getattr],
            [ModuleNode, "__call__", _wrap_mnode_call],
            [TracedModuleBuilder, "__call__", _convert_node_and_tensor],
        ]:
            active_module_tracer().patcher.patch_function(cls, name, func)
        self._tensor_method_patch = _wrap_method_to_tensor_node()
        active_module_tracer().push_scope(self.global_scope)

    def __exit__(self, ty, va, tr):
        if va is not None:
            return False
        active_module_tracer().patcher.__exit__(ty, va, tr)

        while self._tensor_method_patch:
            pf = self._tensor_method_patch.pop()
            pf.set_func(pf.origin_fn)

        # delete ModuleNode.__call__ to avoid entering the
        # ModuleNode.__init__ method when call a ModuleNode object.
        delattr(ModuleNode, "__call__")

        module = self.graph.inputs[0].owner

        def build_traced_module(
            module: TracedModuleBuilder, target_module: TracedModule
        ):
            for k, v in module.__dict__.items():
                if isinstance(v, TracedModuleBuilder):
                    traced_v = v.build()
                    build_traced_module(v, traced_v)
                    setattr(target_module, k, traced_v)

        build_traced_module(module, module)

        set_symbolic_shape(self.use_sym_shape)
        _set_graph_surgery_mode(False)
        set_active_module_tracer(None)
        unset_module_tracing()

        extra_inp_nodes = set(self.global_scope.inputs)
        max_inp_expr_idx = -1
        for node in extra_inp_nodes:
            assert (
                node.top_graph == self.graph
            ), "The input node ({}) is not in the graph ({})".format(node, self.graph)
            if node.expr in self.graph._exprs:
                max_inp_expr_idx = max(
                    max_inp_expr_idx, self.graph._exprs.index(node.expr)
                )
        max_inp_expr_idx += 1

        insert_index = -1
        if self.expr in self.graph._exprs:
            insert_index = self.graph._exprs.index(self.expr)
        insert_index += 1

        if insert_index < max_inp_expr_idx:
            insert_index = max_inp_expr_idx

        for expr in self.global_scope._exprs:
            self.graph._exprs.insert(insert_index, expr)
            insert_index += 1

        self.graph._namespace.merge(self.global_scope._namespace)
        self.root_graph._total_ids = (Node._get_next_id(), Expr._get_next_id())
        self.root_graph.inputs[0].owner._update_ref()
        for node in self.root_graph.nodes():
            if isinstance(node, TensorNode):
                node.value = None
        return True


class NameSpace:
    def __init__(self, name, qualname):
        self.name = name
        self.qualname = qualname
        self._used_names = {}

    def create_unique_name(self, name: str, node: Any = None) -> str:
        assert isinstance(name, str), "The name must be a string"

        if name in self._used_names and (self._used_names[name] is node):
            return name

        name = re.sub("[^0-9a-zA-Z_]+", "_", name)
        if name[0].isdigit():
            name = "_{}".format(name)

        while (
            name in self._used_names and self._used_names[name] is not None
        ) or _is_builtin_name(name):
            match = re.match(r"(.*)_(\d+)$", name)
            if match is None:
                name = name + "_1"
            else:
                base, num = match.group(1, 2)
                name = "{}_{}".format(base, int(num) + 1)

        self._used_names.setdefault(name)

        if node is not None:
            self.associate_name_with_obj(name, node)

        return name

    def auto_naming_for_outputs(self, expr: Expr):
        _add_suffix = lambda x: x + "_out"
        if is_call_module(expr):
            call_node = expr.inputs[0]
            qualname = "%s.[out]" % (call_node.qualname)
            name = call_node.name
        elif is_call_tensor_method(expr):
            name = expr.method.strip("_")
            qualname = "{}.[{}]".format(
                self.qualname, self.create_unique_name("method_%s" % (name)),
            )
        elif is_call_function(expr):
            name = expr.func.__name__
            qualname = "{}.[{}]".format(
                self.qualname, self.create_unique_name("func_%s" % name),
            )
        elif is_apply_def(expr):
            name = str(expr.opdef).lower()
            qualname = "{}.[{}]".format(
                self.qualname, self.create_unique_name("def_%s" % name),
            )
        elif is_getattr(expr):
            qualname = "{}.{}".format(expr.inputs[0].qualname, expr.name)
            name = get_suffix_name(self.qualname, qualname)
            _add_suffix = lambda x: x
        elif is_constant(expr) or is_input(expr):
            name = (
                expr.name if expr.name else "const_" + type(expr.value).__name__.lower()
            )
            qualname = "{}.[{}]".format(self.qualname, name)
            _add_suffix = lambda x: x

        for node in expr.outputs:
            cur_name = node._name if node._name else _add_suffix(name)
            node._name = self.create_unique_name(cur_name, node)
            if node._qualname == "":
                node._qualname = qualname
            assert get_suffix_name(self.qualname, qualname) is not None

    def merge(self, other: "NameSpace"):
        self._used_names.update(other.used_names)

    def associate_name_with_obj(self, name: str, node: Node):
        assert name in self.used_names
        assert self.used_names[name] is None, "The name(%s) is already in use" % (name)
        self._used_names[name] = node

    def unassociate_name_with_obj(self, node: Node):
        assert node.name in self.used_names
        # assert self.used_names[node.name] is node
        self._used_names[node.name] = None

    @property
    def used_names(self):
        return self._used_names


class InternalGraph:
    r"""``InternalGraph`` is the main data structure used in the TracedModule.
    It is used to represent the execution procedure of Module's forward method.

    For example, the following code

    .. code-block::

        import megengine.random as rand
        import megengine.functional as F
        import megengine.module as M

        import megengine.traced_module as tm

        class MyModule(M.Module):
            def __init__(self):
                super().__init__()
                self.param = rand.normal(size=(3, 4))
                self.linear = M.Linear(4, 5)

            def forward(self, x):
                return F.relu(self.linear(x + self.param))

        net = MyModule()

        inp = F.zeros(shape = (3, 4))
        traced_module = tm.trace_module(net, inp)

    Will produce the following ``InternalGraph``::

        print(traced_module.graph)

    .. code-block:: text

        MyModule.Graph (self, x) {
                %2:     linear = getattr(self, "linear") -> (Linear)
                %3:     param = getattr(self, "param") -> (Tensor)
                %4:     add_out = x.__add__(param, )
                %5:     linear_out = linear(add_out, )
                %6:     relu_out = nn.relu(linear_out, )
                return relu_out
        }
    """

    _exprs = None  # type: List[Expr]
    _inputs = None  # type: List[Node]
    _outputs = None  # type: List[Node]
    _top_graph = None  # type: InternalGraph
    _total_ids = None  # type: List[int]

    def __init__(self, name: str, qualname: str):
        self._exprs = []
        self._inputs = []
        self._outputs = []
        self._watch_point = []
        self._end_point = []
        self._namespace = NameSpace(name, qualname)
        self._rst = collections.defaultdict(list)
        self._name = name
        self._qualname = qualname

    def _insert(self, expr):
        self._exprs.append(expr)

    @property
    def name(self) -> str:
        r"""Get the name of this graph."""
        return self._name

    @name.setter
    def name(self, new_name: str):
        r"""Set a new name to this graph."""
        mod = self.inputs[0].owner
        graph = self.top_graph
        assert graph is not None or mod._is_top, "The parent graph cannot be None."
        if graph is not None:
            assert graph._namespace.used_names.get(new_name, None) is None, (
                "The name(%s) is already in use. Please try a different one again."
                % (new_name)
            )
            new_name = graph._namespace.create_unique_name(new_name, self)
        self._name = new_name

    @property
    def qualname(self) -> str:
        r"""Get the `qualname` of this graph. The `qualname` can be used to get the
        submodule from the traced Module or Module.

        Example:
            .. code-block::

                import megengine.module as M
                import megengine.traced_module as tm
                import megengine as mge

                class block(M.Module):
                    def __init__(self):
                        super().__init__()
                        self.relu = M.ReLU()

                    def forward(self, x):
                        return self.relu(x)

                class module(M.Module):
                    def __init__(self):
                        super().__init__()
                        self.block = block()

                    def forward(self, x):
                        x = self.block(x)
                        return x

                net = module()
                traced_net = tm.trace_module(net, mge.Tensor([0.]))

                qualname = traced_net.block.graph.qualname  # qualname = "module.block"
                qualname = qualname.split(".", 1)[-1]  # qualname = "block"

                assert qualname in list(map(lambda x: x[0], net.named_modules()))
                assert qualname in list(map(lambda x: x[0], traced_net.named_modules()))
        """
        return self._qualname

    @property
    def inputs(self) -> List[Node]:
        r"""Get the list of input Nodes of this graph.

        Returns:
            A list of ``Node``.
        """
        return self._inputs

    @property
    def outputs(self) -> List[Node]:
        r"""Get the list of output Nodes of this graph.

        Returns:
            A list of ``Node``.
        """
        return self._outputs

    @property
    def top_graph(self):
        r"""Get the parent graph of this graph.

        Returns:
            An ``InternalGraph``.
        """
        if self._top_graph:
            return self._top_graph()
        return None

    def exprs(self, recursive=True):
        r"""Get the Exprs that constitute this graph.

        Args:
            recursive: whether to get the Exprs in the subgraph.
                Default: True
        Returns:
            A ``ExprFilter`` containing all Exprs of this graph.
        """
        return ExprFilter(_expr_iter(self, recursive))

    def nodes(self, recursive=True):
        r"""Get the Nodes that constitute this graph.

        Args:
            recursive: whether to get the Nodes in the subgraph.
                Default: True
        Returns:
            A ``NodeFilter`` containing all Nodes of this graph.
        """
        return NodeFilter(_node_iter(self, recursive))

    def get_function_by_type(self, func: Callable = None, recursive=True):
        r"""Filter Exprs by the type of ``CallFunction``.

        Args:
            func: a built-in function, such as ``F.relu``.
            recursive: whether to get the Exprs in the subgraph.
                Default: True
        Returns:
            A :class:`~.TracedModule.ExprFilterCallFunction`.
        """
        return self.exprs(recursive).call_function(func)

    def get_method_by_type(self, method: str = None, recursive=True):
        r"""Filter Exprs by the type of ``CallMethod``.

        Args:
            method: a method string, such as "__add__".
            recursive: whether to get the Exprs in the subgraph.
                Default: True
        Returns:
            A :class:`~.TracedModule.ExprFilterCallMethod`.
        """
        return self.exprs(recursive).call_method(method)

    def get_expr_by_id(self, expr_id: List[int] = None, recursive=True):
        r"""Filter Exprs by their ``id``.

        Args:
            expr_id: a list of :class:`int`.
            recursive: whether to get the Exprs in the subgraph.
                Default: True
        Returns:
            A :class:`~.TracedModule.ExprFilterExprId`.
        """
        return self.exprs(recursive).expr_id(expr_id)

    def get_module_by_type(self, module_cls: Module, recursive=True):
        r"""Filter Nodes by the ``module_type`` of ``ModuleNode``.

        Args:
            module_cls: a subclass of :class:`~.Module`.
            recursive: whether to get the Nodes in the subgraph.
                Default: True
        Returns:
            A :class:`~.TracedModule.NodeFilterType`.
        """
        return self.nodes(recursive).type(module_cls)

    def get_node_by_id(self, node_id: List[int] = None, recursive=True):
        r"""Filter Nodes by their ``id``.

        The ``id`` of the ``Node`` can be obtained by the following code

        .. code-block::

            # node : Node
            print("{:i}".format(node))
            print(node.__format__("i"))
            # graph : InternalGraph
            print("{:i}".format(graph))
            print(graph.__format__("i"))

        Args:
            node_id: a list of :class:`int`.
            recursive: whether to get the Nodes in the subgraph.
                Default: True
        Returns:
            A :class:`~.TracedModule.NodeFilterNodeId`.
        """
        return self.nodes(recursive).node_id(node_id)

    def get_node_by_name(
        self, name: str = None, ignorecase: bool = True, recursive=True
    ):
        r"""Filter Nodes by their full name.

        The full name of the ``Node`` can be obtained by the following code

        .. code-block::

            # node : Node
            print("{:p}".format(node))
            print(node.__format__("p"))
            # graph : InternalGraph
            print("{:p}".format(graph))
            print(graph.__format__("p"))

        Args:
            name: a string in glob syntax that can contain ``?`` and
             ``*`` to match a single or arbitrary characters.
            ignorecase: whether to ignroe case.
                Default: True
            recursive: whether to get the Nodes in the subgraph.
                Default: True
        Returns:
            A :class:`~.TracedModule.NodeFilterName`.
        """
        return self.nodes(recursive).name(name, ignorecase)

    def _add_input(self, i):
        self._inputs.append(i)

    def _add_output(self, o):
        self._outputs.append(o)

    def get_dep_exprs(self, nodes: Sequence[Node]) -> List[Expr]:
        r"""Get the dependent Exprs of the ``nodes``.

        Args:
            nodes: a list of :class:`Node`.
        Returns:
            A list of dependent :class:`Expr`.
        """
        if not isinstance(nodes, Sequence):
            nodes = (nodes,)
        ret = list()
        queue = list(nodes)
        visited_queue = list()
        while queue:
            node = queue.pop()
            visited_queue.append(node)

            expr = node.expr

            if expr not in ret:
                ret.append(expr)

            for i in expr.inputs:
                if i not in queue and i not in visited_queue:
                    queue.append(i)
        return ret

    def reset_inputs(self, *args, **kwargs):
        forma_mnode = self.inputs[0]
        moudle = forma_mnode.owner
        assert moudle._is_top, "reset_inputs only supports top graph"

        inputs, tree_def = tree_flatten(((moudle, *args), kwargs))

        def create_node(val: Tensor):
            name = self._namespace.create_unique_name("args")
            node = Input(
                type=TensorNode, name=name, qualname="%s.[%s]" % (self._qualname, name)
            ).outputs[0]
            self._namespace.associate_name_with_obj(node.name, node)
            node.shape = val.shape
            node.dtype = val.dtype
            return node

        formal_node_inputs = [
            forma_mnode,
        ]

        org_argdef = list(moudle.argdef_graph_map.keys())[0]

        for v in inputs[1:]:
            assert isinstance(v, RawTensor)
            formal_node_inputs.append(create_node(v))

        self._inputs[:] = formal_node_inputs
        moudle.argdef_graph_map[tree_def] = moudle.argdef_graph_map.pop(org_argdef)
        moudle.argdef_outdef_map[tree_def] = moudle.argdef_outdef_map.pop(org_argdef)
        return formal_node_inputs[1:]

    def add_input_node(
        self, shape: Tuple[int], dtype: str = "float32", name: str = "args"
    ):
        r"""Add an input node to the graph.

        The new Node will be the last of the positional arguments.

        Args:
            shape: the shape of the new input Node.
            dtype: the dtype of the new input Node.
                Default: float32
            name: the name of the new input Node. When the name is used in the graph,
             a suffix will be added to it.
        """
        forma_mnode = self.inputs[0]
        moudle = forma_mnode.owner
        assert moudle._is_top, "add_input_node only supports top graph"

        def create_node(name=None):
            name = self._namespace.create_unique_name(name)
            node = Input(
                type=TensorNode, name=name, qualname="%s.[%s]" % (self._qualname, name)
            ).outputs[0]
            self._namespace.associate_name_with_obj(node.name, node)
            node.shape = shape
            node.dtype = dtype
            return node

        org_argdef = list(moudle.argdef_graph_map.keys())[0]

        args, kwargs = org_argdef.unflatten(self._inputs)
        formal_inp_node = create_node(name)
        inputs, tree_def = tree_flatten(
            ((*args, formal_inp_node), kwargs),
            is_const_leaf=lambda x: not isinstance(x, (TensorNode, ModuleNode)),
        )
        self._inputs[:] = inputs[:]

        moudle.argdef_graph_map[tree_def] = moudle.argdef_graph_map.pop(org_argdef)
        moudle.argdef_outdef_map[tree_def] = moudle.argdef_outdef_map.pop(org_argdef)
        return formal_inp_node

    def reset_outputs(self, outputs):
        r"""Reset the output Nodes of the graph.

        .. note::

            This method only supports resetting the output of graphs
            that do not have a parent graph.

        Args:
            outputs: an object which inner element is Node. Support tuple, list
             dict, etc.

        For example, the following code

        .. code-block::

            import megengine.functional as F
            import megengine.module as M
            import megengine.traced_module as tm

            class MyModule(M.Module):
                def forward(self, x):
                    x = x + 1
                    return x

            net = MyModule()

            inp = F.zeros(shape = (1, ))
            traced_module = tm.trace_module(net, inp)
            graph = traced_module.graph
            inp_node = graph.inputs[1]
            out_node = graph.outputs[0]
            graph.reset_outputs((out_node, {"input": inp_node}))
            out = traced_module(inp)

        Will produce the following ``InternalGraph`` and ``out``::

            print(graph)
            print(out)

        .. code-block:: text

            MyModule.Graph (self, x) {
                    %2:     add_out = x.__add__(1, )
                    return add_out, x
            }
            (Tensor([1.], device=xpux:0), {'input': Tensor([0.], device=xpux:0)})
        """
        outputs, out_def = tree_flatten(
            outputs, is_leaf=lambda x: isinstance(x, TensorNode),
        )
        forma_mnode = self.inputs[0]
        moudle = forma_mnode.owner
        assert moudle._is_top, "reset_outputs only supports top graph"

        tree_def = list(moudle.argdef_graph_map.keys())[0]

        self._outputs[:] = outputs
        moudle.argdef_outdef_map[tree_def] = out_def

    def add_output_node(self, node: TensorNode):
        r"""Add an output node to the Graph.

        The Graph output will become a ``tuple`` after calling ``add_output_node``.
        The first element of the ``tuple`` is the original output, and the second
        is the ``node``.

        For example, the following code

        .. code-block::

            import megengine.functional as F
            import megengine.module as M
            import megengine.traced_module as tm

            class MyModule(M.Module):
                def forward(self, x):
                    x = x + 1
                    return x

            net = MyModule()

            inp = F.zeros(shape = (1, ))
            traced_module = tm.trace_module(net, inp)
            graph = traced_module.graph
            inp_node = graph.inputs[1]
            out_node = graph.outputs[0]
            graph.add_output_node(inp_node)
            graph.add_output_node(out_node)
            out = traced_module(inp)

        Will produce the following ``InternalGraph`` and ``out``::

            print(graph)
            print(out)

        .. code-block:: text

            MyModule.Graph (self, x) {
                    %2:     add_out = x.__add__(1, )
                    return add_out, x, add_out
            }
            ((Tensor([1.], device=xpux:0), Tensor([0.], device=xpux:0)), Tensor([1.], device=xpux:0))
        """
        forma_mnode = self.inputs[0]
        moudle = forma_mnode.owner
        assert moudle._is_top, "add_output_node only supports top graph"

        tree_def = list(moudle.argdef_graph_map.keys())[0]

        org_out_def = moudle.argdef_outdef_map[tree_def]
        org_outs = org_out_def.unflatten(self._outputs)
        outputs, out_def = tree_flatten(
            (org_outs, node), is_leaf=lambda x: isinstance(x, TensorNode),
        )
        self._outputs[:] = outputs
        moudle.argdef_outdef_map[tree_def] = out_def

    def insert_exprs(self, expr: Optional[Expr] = None):
        r"""Initialize the trace mode and insertion position.

        When used within a 'with' statement, this will temporary set the trace mode and
        then restore normal mode when the with statement exits::

            with graph.insert_exprs(e): # set the trace mode
                ... # trace function or module
            ... # inert exprs into graph and resotre normal mode

        Args:
            expr: the ``expr`` after which to insert. If None, the insertion position will be
                automatically set based on the input node.

        Returns:
            A resource manager that will initialize trace mode on ``__enter__`` and
            restore normal mode on ``__exit__``.
        """
        if expr is not None:
            assert expr.top_graph == self, "Expr to insert after is not in graph."
        return _InsertExprs(self, expr)

    def replace_node(self, repl_dict: Dict[Node, Node]):
        r"""Replace the Nodes in the graph.

        Args:
            repl_dict: the map {old_Node: new_Node} that specifies how to replace the Nodes.
        """
        while repl_dict:
            node, repl_node = repl_dict.popitem()
            assert type(node) == type(
                repl_node
            ), "The type of {}({}) and {}({}) are not the same".format(
                node, type(node).__name__, repl_node, type(repl_node).__name__
            )
            # check graph inputs and outputs
            for i, n in enumerate(self.outputs):
                if n is node:
                    self.outputs[i] = repl_node
            # update users of node and repl_node
            # update inputs of expr in node.users
            graph = repl_node.top_graph
            assert graph is not None
            assert graph is self
            index = -1
            if not isinstance(repl_node.expr, Input):
                index = graph._exprs.index(repl_node.expr)
            dep_exprs = self.get_dep_exprs(repl_node)
            i = 0
            while i < len(node.users):
                n = node.users[i]
                if n in graph._exprs and index >= graph._exprs.index(n):
                    i += 1
                    continue
                if n in dep_exprs:
                    logger.info("Find a loop: ignore this replacement once")
                    logger.info("node: %s" % node.__repr__())
                    logger.info("expr: %s" % n.__repr__())
                    i += 1
                    continue
                repl_node.users.append(n)
                node.users.pop(i)
                idx = n.inputs.index(node)
                n.inputs[idx] = repl_node

    def _merge_getattr_expr(self):
        getattr_nodes_map = dict()  # Dcit[(Node, str), Node]
        node_to_attrname = dict()  # Dict[Node, (Node, Str)]
        for expr in filter(lambda x: isinstance(x, GetAttr), self._exprs):
            base_node, attr_name = expr.inputs[0], expr.name
            if expr.inputs[0] in node_to_attrname:
                base_node, base_name = node_to_attrname[expr.inputs[0]]
                attr_name = "{}.{}".format(base_name, expr.name)

            if get_suffix_name(self.qualname, expr.outputs[0].qualname) != attr_name:
                expected_qualname = base_node.qualname + "." + attr_name
                logger.warning(
                    "{}.qualname expects {}, got {} actually. You can re-trace this "
                    "TracedModel to make the name correct.".format(
                        expr.outputs[0], expected_qualname, expr.outputs[0].qualname
                    )
                )
                expr.outputs[0]._qualname = expected_qualname

            key = (base_node, attr_name)
            node_to_attrname[expr.outputs[0]] = key
            if key in getattr_nodes_map:
                existed_node = getattr_nodes_map[key]
                repl_node = expr.outputs[0]
                for expr in repl_node.users:
                    existed_node.users.append(expr)
                    idx = expr.inputs.index(repl_node)
                    expr.inputs[idx] = existed_node
                repl_node.users = []
            else:
                if attr_name != expr.name:
                    expr.name = attr_name
                    expr.inputs[0].users.remove(expr)
                    self.inputs[0].users.append(expr)
                    expr.inputs[0] = self.inputs[0]
                getattr_nodes_map[key] = expr.outputs[0]

    def compile(self):
        r"""Delete unused expr."""
        self._merge_getattr_expr()
        dep_exprs = self.get_dep_exprs(self.outputs)
        i = 0
        while i < len(self._exprs):
            expr = self._exprs[i]
            if expr in dep_exprs or expr._disable_remove:
                i += 1
                continue
            for n in expr.inputs:
                n.users.remove(expr)
            self._exprs.remove(expr)
            for n in expr.outputs:
                self._namespace.unassociate_name_with_obj(n)

    def _reset_ids(self):
        for total_expr_id, expr in enumerate(self.exprs()):
            expr._id = total_expr_id
        for total_node_id, node in enumerate(self.nodes()):
            node._id = total_node_id
        self._total_ids = (total_node_id + 1, total_expr_id + 1)

    def _re_associate_name(self):
        self._namespace.used_names.clear()
        for node in self.nodes(False):
            node._name = self._namespace.create_unique_name(node.name, node)

    def interpret(self, *inputs):
        node2value = {}
        end_nodes_set = set(self._end_point)
        endnode2value = {}

        def get_all_endnode_val(n, v):
            if n in end_nodes_set:
                endnode2value[n] = v
                end_nodes_set.remove(n)
                return not end_nodes_set
            return False

        ref_count = lambda n: len(n.users) + (1 if n in self._outputs else 0)

        for n, v in zip(self._inputs, inputs):
            if ref_count(n) > 0:
                node2value[n] = [v, ref_count(n)]
            if n in self._watch_point:
                self._rst[n].append(v)
            if n in self._end_point and get_all_endnode_val(n, v):
                return list(endnode2value[i] for i in self._end_point)

        for expr in self._exprs:
            values = expr.interpret(*list(node2value[i][0] for i in expr.inputs))
            for n in expr.inputs:
                node2value[n][1] -= 1
                if node2value[n][1] == 0:
                    node2value.pop(n)
            if values is not None:
                assert len(values) == len(expr.outputs)
                for n, v in zip(expr.outputs, values):
                    if ref_count(n) > 0:
                        node2value[n] = [v, ref_count(n)]
                    if n in self._watch_point:
                        self._rst[n] = v
                    if self._end_point and get_all_endnode_val(n, v):
                        return list(endnode2value[i] for i in self._end_point)

        return list(node2value[i][0] for i in self._outputs)

    def eval(self, *inputs: Tuple[Tensor]):
        r"""Call this method to execute the graph.

        Args:
            inputs: the tensors corresponding to the ``graph.inputs[1:]``.
        """
        assert len(inputs) == len(self._inputs) - 1
        inp = [self._inputs[0].owner] + list(inputs)
        return self.interpret(*inp)

    def __repr__(self):
        return self.__format__()

    def __format__(self, format_spec: str = "") -> str:
        saved_format_spec = Node._set_format_spec(format_spec)
        name = ""
        if self._name:
            name = "%s.Graph" % self._name
        res = "{} ({}) {{\n\t{}\n\treturn {}\n}}".format(
            name,
            ", ".join(str(i) for i in self._inputs),
            "\n\t".join("{}".format(str(i)) for i in self._exprs),
            ", ".join(str(i) for i in self._outputs),
        )
        Node._set_format_spec(saved_format_spec)
        return res

    def __getstate__(self):
        state = {
            "_exprs": self._exprs,
            "_inputs": self._inputs,
            "_outputs": self._outputs,
            "_watch_point": [],
            "_end_point": [],
            "_namespace": self._namespace,
            "_rst": collections.defaultdict(list),
            "_name": self._name,
            "_qualname": self._qualname,
        }
        if self._total_ids:
            state["_total_ids"] = self._total_ids

        _check_obj_attr(state)

        return state

    def __setstate__(self, state):
        old_version = False

        if "_module_name" in state:
            old_version = True
            state["_qualname"] = state.pop("_module_name")
            prefix_name = state.pop("_prefix_name")
            if prefix_name:
                state["_name"] = "{}_{}".format(prefix_name, state["_name"])

        self.__dict__.update(state)

        if old_version:
            self.inputs[0]._qualname = self._qualname
            for e in self.exprs(False):
                if isinstance(e, GetAttr):
                    e.outputs[0]._qualname = "{}.{}".format(
                        e.inputs[0]._qualname, e.name
                    )

            for n in self.nodes(False):
                if isinstance(n.expr, CallMethod) and isinstance(
                    n.expr.inputs[0], ModuleNode
                ):
                    n._qualname = n.expr.inputs[0]._qualname + ".[out]"
                    continue
                if (
                    not isinstance(n.expr, GetAttr)
                    and isinstance(n, TensorNode)
                    and n._qualname
                ):
                    n._qualname = "{}.{}".format(self._qualname, n._qualname)
            self._namespace = NameSpace(self._name, self._qualname)
            self._re_associate_name()

    def __copy__(self):
        cls = self.__class__
        result = cls.__new__(cls)
        result.__dict__.update(self.__dict__)
        return result

    def __deepcopy__(self, memo):
        if id(self) in memo:
            return memo[id(self)]
        cls = self.__class__
        result = cls.__new__(cls)
        state = {}
        memo[id(self)] = result
        for k, v in self.__dict__.items():
            if not isinstance(v, weakref.ReferenceType):
                state[k] = copy.deepcopy(v, memo)
        result.__dict__.update(state)
        return result


def _get_meth_name(obj, func):
    tp = obj if isinstance(obj, type) else type(obj)
    for cls in tp.mro():
        for k, v in cls.__dict__.items():
            if v == func:
                return k
    return None


def _wrapped_function(orig_func):
    @functools.wraps(orig_func)
    def wrapped_fn(*args, **kwargs):
        method_func = kwargs.pop("method_func", wrapped_fn)
        if is_tracing_module():
            unset_module_tracing()
            inputs, tree_def = tree_flatten((args, kwargs))
            for i in inputs:
                if not NodeMixin.get(i, None):
                    if isinstance(i, (RawTensor, NodeMixin)):
                        NodeMixin.wrap_safe(i, Constant.make(i))
            args, kwargs = _convert_kwargs_to_args(orig_func, args, kwargs)
            meth_name = _get_meth_name(args[0], method_func)
            arg_type = args[0] if isinstance(args[0], type) else type(args[0])
            if meth_name and arg_type and issubclass(arg_type, RawTensor):
                inputs, tree_def = tree_flatten((args, kwargs))
                self = inputs[0]
                if meth_name == "__new__":
                    if all([not isinstance(i, RawTensor) for i in inputs]):
                        # only trace Tensor.__new__() when there are tensors in args
                        set_module_tracing()
                        return orig_func(*args, **kwargs)
                    if isinstance(args[1], RawTensor):
                        node = NodeMixin.get(inputs[1])
                        is_scalar = isscalar(inputs[1])
                        inputs[1] = apply(
                            Copy(comp_node=inputs[1].device), Tensor(inputs[1])
                        )[0]
                        if is_scalar:
                            setscalar(inputs[1])
                        # copy inputs[1] to avoid tensor and Tensor(tensor) share same m_tensor,
                        # which will cause they have same _NodeMixin__node in tracing.
                        NodeMixin.wrap_safe(inputs[1], node)
                        args, kwargs = tree_def.unflatten(inputs)
                    call_node = CallMethod.make(self, meth_name)
                else:
                    call_node = CallMethod.make(NodeMixin.get(self), meth_name)
                call_node.add_inputs(inputs[1:])
            else:
                inputs, tree_def = tree_flatten((args, kwargs))
                call_node = CallFunction.make(orig_func)
                call_node.add_inputs(inputs)

            call_node.arg_def = tree_def
            rst = orig_func(*args, **kwargs)
            if meth_name == "__setitem__":
                rst = self
            if rst is not None:
                outputs, out_def = tree_flatten(rst, is_leaf=_is_leaf)
                call_node.out_def = out_def
            else:
                outputs = None
            call_node.add_outputs(outputs)

            if _get_expr_checker():
                with _exclude_from_trace():
                    active_module_tracer().checker.check_expr_interpret(
                        call_node, outputs
                    )

            set_module_tracing()
            return rst
        return orig_func(*args, **kwargs)

    return wrapped_fn


class TracedModuleBuilder(NodeMixin):

    _mod = None  # type: Module
    _body = None  # type: InternalGraph
    _is_builtin = None  # type: bool
    _argdef_graph_map = None  # type: Dict[Treedef, "InternalGraph"]
    _argdef_outdef_map = None  # type: Dict[Treedef, Treedef]
    nodes = None

    __builder_attributes__ = [
        "_mod",
        "_body",
        "_NodeMixin__node",
        "_is_builtin",
        "build",
        "_record_wrapped_nodes",
        "_argdef_graph_map",
        "_argdef_outdef_map",
        "_check_qat_module",
        "nodes",
        "__class__",
        "__dict__",
        "_is_top",
    ]

    def __init__(self, mod, is_top_module=False):
        super(TracedModuleBuilder, self).__init__()
        assert isinstance(mod, Module)
        self._mod = mod
        self._body = None
        self._is_top = is_top_module
        self._is_builtin = (
            True
            if isinstance(mod, (Observer, _FakeQuantize))
            else module_tracer.is_builtin(mod)
        )
        if isinstance(self._mod, QATModule):
            unset_module_tracing()
            self._check_qat_module(self._mod)
            set_module_tracing()
        self._argdef_graph_map = {}
        self._argdef_outdef_map = {}

        self.nodes = set()
        # The builder will be passed to self._mod.forward as 'self' argument. If the 'forward' uses super().xxx to call method of its base classes, the trace procedure will throw exceprion, because the builder doesn't inherit from self._mod.__bases__.
        # modify self.__class__ and let the builder inherit from TracedModuleBuilder and mod.__class__.
        self.__class__ = type(
            "TracedModuleBuilder",
            (TracedModuleBuilder, mod.__class__),
            dict(TracedModuleBuilder.__dict__),
        )

    def _check_qat_module(self, qat_module):
        def isbuiltin(m):
            return m is None or module_tracer.is_builtin(m)

        if qat_module.with_act:
            act_observer = qat_module.act_observer
            act_fakequant = qat_module.act_fake_quant
            if not isbuiltin(act_observer) or not isbuiltin(act_fakequant):
                qparams = (
                    act_observer.get_qparams()
                    if hasattr(act_observer, "get_qparams")
                    else act_fakequant.get_qparams()
                )
                dtype = (
                    act_observer.dtype
                    if hasattr(act_observer, "dtype")
                    else act_fakequant.dtype
                )
                qat_module.act_observer = None
                qat_module.act_fake_quant = TM_FakeQuant(dtype)
                qat_module.act_fake_quant.set_qparams(qparams)

        if qat_module.with_weight:
            weight_observer = qat_module.weight_observer
            weight_fakequant = qat_module.weight_fake_quant
            if not isbuiltin(weight_observer) or not isbuiltin(weight_fakequant):
                qparams = (
                    weight_observer.get_qparams()
                    if hasattr(weight_observer, "get_qparams")
                    else weight_fakequant.get_qparams()
                )
                dtype = (
                    weight_observer.dtype
                    if hasattr(weight_observer, "dtype")
                    else weight_fakequant.dtype
                )
                qat_module.weight_observer = None
                qat_module.weight_fake_quant = TM_FakeQuant(dtype)
                qat_module.weight_fake_quant.set_qparams(qparams)

    def build(self):
        if self._is_builtin:
            assert module_tracer.is_builtin(self._mod)
            mod_type = type(self._mod)

            for node in self.nodes:
                node.module_type = mod_type

            return self._mod
        elif isinstance(self._mod, TracedModule) and _graph_surgery_mode():
            return self._mod
        else:
            is_qat = isinstance(self._mod, QATModule) or (
                isinstance(self._mod, TracedModule) and self._mod.is_qat
            )
            traced_module = TracedModule(
                self._is_top, self._argdef_graph_map, self._argdef_outdef_map, is_qat
            )
            for _, g in self._argdef_graph_map.items():
                g.compile()
                if self._is_top:
                    g._total_ids = (Node._get_next_id(), Expr._get_next_id())

            for k, v in self.__dict__.items():
                if k not in TracedModuleBuilder.__builder_attributes__:
                    if isinstance(v, TracedModuleBuilder):
                        v = v.build()
                        setattr(traced_module, k, v)
                    elif isinstance(v, RawTensor):
                        setattr(traced_module, k, v)

            if isinstance(self._mod, QATModule):
                unset_module_tracing()
                traced_module.with_act = self._mod.with_act
                traced_module.with_weight = self._mod.with_weight
                if not hasattr(traced_module, "act_fake_quant"):
                    traced_module.act_fake_quant = None
                if not hasattr(traced_module, "act_observer"):
                    traced_module.act_observer = None
                if not hasattr(traced_module, "weight_fake_quant"):
                    traced_module.weight_fake_quant = None
                if not hasattr(traced_module, "weight_observer"):
                    traced_module.weight_observer = None
                set_module_tracing()

            if self._is_top:
                traced_module._update_ref()

            return traced_module

    def _record_wrapped_nodes(self, node):
        self.nodes.add(node)

    def __call__(self, *args, **kwargs):
        assert isinstance(self._mod, Module)
        is_graph_surgery_mode = _graph_surgery_mode()
        if isinstance(self._mod, TracedModule) and is_graph_surgery_mode:
            _set_graph_surgery_mode(False)

        # prepare args and kwargs for inner graph
        if "method_func" in kwargs:
            kwargs.pop("method_func")
        args, kwargs = _convert_kwargs_to_args(self._mod.forward, args, kwargs, True)

        def mark_constant(x):
            node = NodeMixin.get(x, None)
            if node is None:  # capture as constant
                NodeMixin.wrap(x, lambda: Constant.make(x))

        inputs, tree_def = tree_flatten(((self, *args), kwargs))
        for i in inputs:
            mark_constant(i)
        callnode = CallMethod.make(NodeMixin.get(self))

        callnode.add_inputs(inputs[1:])

        callnode.arg_def = tree_def

        if self._is_builtin or tree_def in self._argdef_graph_map:
            unset_module_tracing()
            rst = self._mod(*args, **kwargs)
            outputs, out_def = tree_flatten(rst, is_leaf=_is_leaf)
            if _get_expr_checker():
                with _exclude_from_trace():
                    tmp = self.build()
                    active_module_tracer().checker.check_builtin_module(
                        tmp, callnode, outputs
                    )
            set_module_tracing()
            if self._is_builtin:
                self._body = None
            elif tree_def in self._argdef_graph_map:
                self._body = self._argdef_graph_map[tree_def]
        else:
            orig_self = NodeMixin.get(self)
            parent_graph = active_module_tracer().current_scope()
            module_qualname = orig_self._qualname
            self._body = InternalGraph(
                name=parent_graph._namespace.create_unique_name(module_qualname),
                qualname=module_qualname,
            )
            parent_graph._namespace.associate_name_with_obj(self._body.name, self._body)
            active_module_tracer().push_scope(self._body)
            # rebind self to new input node

            NodeMixin.wrap_safe(
                self,
                Input.make(
                    name="self",
                    qualname=module_qualname,
                    type=NodeMixin.get_wrapped_type(self),
                ),
            )

            origin_inp_node = [NodeMixin.get(i, None) for i in inputs[1:]]
            # prepare args and kwargs for inner graph
            index_args, index_kwargs = tree_def.unflatten(
                [
                    ArgsIndex(0),
                    *list(ArgsIndex(i + 1) for i in range(len(origin_inp_node))),
                ]
            )
            key2idx = getcallargs(type(self._mod).forward, *index_args, **index_kwargs)
            idx2key = {}
            for k, v in key2idx.items():
                if isinstance(v, ArgsIndex):
                    idx2key[v.index] = k
                else:
                    flatten_argidx, _ = tree_flatten(v)
                    for _i, v in enumerate(flatten_argidx):
                        if isinstance(v, ArgsIndex):
                            idx2key[v.index] = k + "_%d" % _i

            def wrap(x, name):
                if isinstance(x, (RawTensor, NodeMixin)):
                    NodeMixin.wrap(
                        x,
                        lambda: Input.make(
                            type=NodeMixin.get_wrapped_type(x),
                            name=name,
                            qualname="%s.[%s]" % (module_qualname, name),
                        ),
                    )
                return x

            args = [self]
            orig_traced_inputs = (
                None
                if not isinstance(self._mod, TracedModule)
                else self._mod.argdef_graph_map[tree_def].inputs
            )
            ind = 1
            for v in inputs[1:]:
                if isinstance(v, (RawTensor, NodeMixin)):
                    args_name = (
                        orig_traced_inputs[ind]._name
                        if orig_traced_inputs
                        else idx2key[ind]
                    )
                    ind += 1
                    args.append(wrap(v, args_name))
                else:
                    args.append(v)

            args, kwargs = tree_def.unflatten(args)
            active_module_tracer().patcher.auto_patch(
                getattr(getattr(self._mod, "forward", self._mod), "__globals__", {})
            )
            rst = type(self._mod).forward(*args, **kwargs)

            if _graph_surgery_mode():
                rst = _node_to_tensor(rst)[0][0]

            outputs, out_def = tree_flatten(rst, is_leaf=_is_leaf)

            for i in (
                outputs if isinstance(outputs, collections.abc.Sequence) else (outputs,)
            ):
                mark_constant(i)
                active_module_tracer().current_scope()._add_output(NodeMixin.get(i))

            NodeMixin.wrap_safe(self, orig_self)
            for arg, node in zip(inputs[1:], origin_inp_node):
                if node:
                    NodeMixin.wrap_safe(arg, node)
            active_module_tracer().pop_scope()

        # rebind output to outer graph
        callnode.out_def = out_def
        callnode.add_outputs(outputs)
        self._argdef_graph_map[callnode.arg_def] = self._body
        self._argdef_outdef_map[callnode.arg_def] = out_def
        _set_graph_surgery_mode(is_graph_surgery_mode)
        return rst

    def __setattr__(self, name, value):
        object.__setattr__(self, name, value)

    def __repr__(self):
        return repr(self._mod)

    def __getattr__(self, name):
        if name not in self._mod.__dict__:
            attr = getattr(type(self._mod), name).__get__(self, type(self))
        else:
            attr = getattr(self._mod, name)
            if (
                isinstance(attr, FunctionType)
                and id(attr) in active_module_tracer().patcher.patched_fn_ids
            ):
                return active_module_tracer().patcher.wrap_fn(attr)

            if isinstance(attr, (List, Dict)):
                flag = _set_graph_surgery_mode(False)
                unset_module_tracing()
                has_module, m_container = replace_container_with_module_container(attr)
                if m_container:
                    attr = m_container
                if has_module and not m_container:
                    raise ValueError(
                        "Can not trace the module that uses the same container to store"
                        " Module and Non-Module objects."
                    )
                set_module_tracing()
                _set_graph_surgery_mode(flag)

            if isinstance(attr, Module):
                attr = TracedModuleBuilder(attr)

            if isinstance(attr, (Module, RawTensor)):
                setattr(self, name, attr)

            NodeMixin.wrap(
                attr,
                lambda: GetAttr.make(
                    NodeMixin.get(self),
                    type=NodeMixin.get_wrapped_type(attr),
                    attr_name=name,
                    name="",
                ),
            )
        return attr

    def __getattribute__(self, name):
        if name in TracedModuleBuilder.__builder_attributes__:
            return object.__getattribute__(self, name)
        else:
            wrapped = object.__getattribute__(self, name)
            class_members = dict(inspect.getmembers(self.__class__))
            if name in self._mod.__dict__:
                mod_attr = getattr(self._mod, name)
                if name in class_members:
                    if (
                        not isinstance(wrapped, TracedModuleBuilder)
                        and wrapped is not mod_attr
                    ):
                        wrapped = self.__getattr__(name)

                if isinstance(wrapped, TracedModuleBuilder):
                    if not isinstance(mod_attr, (List, Dict, QATModule)):
                        assert mod_attr is wrapped._mod
                else:
                    assert (
                        mod_attr is wrapped
                    ), "TracedModule do not support modify attributes, please check your code."

                if isinstance(wrapped, (NodeMixin, RawTensor)):
                    NodeMixin.wrap(
                        wrapped,
                        lambda: GetAttr.make(
                            NodeMixin.get(self),
                            type=NodeMixin.get_wrapped_type(wrapped),
                            attr_name=name,
                            name="",
                        ),
                    )

            return wrapped


class _expr_iter:
    def __init__(self, graph: InternalGraph, recursive: bool = True):
        self.graph = graph
        self.recursive = recursive
        self._visited_graph = set()

    def __iter__(self):
        yield from self._gen_expr(self.graph)

    def _gen_expr(self, graph: InternalGraph):
        visit_inp = set()
        for inp_node in graph.inputs:
            if inp_node not in visit_inp:
                yield inp_node.expr
            visit_inp.add(inp_node)

        for expr in graph._exprs:
            yield expr
            if (
                self.recursive
                and hasattr(expr, "graph")
                and expr.graph is not None
                and id(expr.graph) not in self._visited_graph
            ):
                self._visited_graph.add(id(expr.graph))
                yield from self._gen_expr(expr.graph)


class _node_iter:
    def __init__(self, graph: InternalGraph, recursive: bool = True) -> None:
        nodes = []
        node_ids = set()
        for expr in graph.exprs(recursive):
            for n in expr.outputs:
                assert id(n) not in node_ids
                nodes.append(n)
                node_ids.add(id(n))
        self.nodes = nodes

    def __iter__(self):
        for node in self.nodes:
            yield node


class BaseFilter:
    r"""``BaseFilter`` exposes some methods for converting ``_node_iter/_expr_iter`` to ``list``, ``dict``, etc."""

    def __init__(self, iter: Iterable):
        self._iter = iter

    def __iter__(self):
        return iter(self._iter)

    def as_list(self):
        r"""Consume this iterator and return its content as a list.

        Returns:
            A list of ``Node`` or ``Expr``.
        """
        return list(self)

    def as_dict(self):
        r"""Construct an ordered dict to map from ``id`` to objects in this iterator.

        Returns:
            An :class:`OrderedDict`.
        """
        return collections.OrderedDict((i._id, i) for i in self)

    def as_unique(self):
        """Assert that this iterator yields only one ``Node`` or ``Expr`` and return it.

        Rerurns:
            A ``Node`` or ``Expr``.
        """
        rst = self.as_list()
        assert len(rst) == 1, "{} elements found".format(len(rst))
        (elem,) = self
        return elem

    def as_count(self):
        r"""Consume this iterator and get the number of elements."""
        return sum(1 for _ in self)


class ExprFilter(BaseFilter):
    """Filter on Expr iterator.
    This class is an iterator of :class:`.Expr` objects and multiple
    filtering conditions and mappers can be chained.
    """

    def call_function(self, func):
        r"""Filter by specific ``CallFunction.func``.
        See :meth:`~.InternalGraph.get_function_by_type` for details.
        """
        return ExprFilterCallFunction(self, func)

    def call_method(self, method):
        r"""Filter by specific ``CallMethod.method``.
        See :meth:`~.InternalGraph.get_function_by_type` for details.
        """
        return ExprFilterCallMethod(self, method)

    def expr_id(self, expr_id: List[int]):
        r"""Filter Exprs by their ``id``.
        See :meth:`~.InternalGraph.get_function_by_type` for details.
        """
        return ExprFilterExprId(self, expr_id)


class NodeFilter(BaseFilter):
    """Filter on Node iterator.
    This class is an iterator of :class:`~.traced_module.Node` objects and multiple
    filtering conditions and mappers can be chained.
    """

    def type(self, owner_type):
        r"""Filter by specific Module type.
        See :meth:`~.InternalGraph.get_module_by_type` for details.
        """
        return NodeFilterType(self, owner_type)

    def node_id(self, node_id: List[int]):
        r"""Filter Nodes by their ``id``.
        See :meth:`~.InternalGraph.get_node_by_id` for details.
        """
        return NodeFilterNodeId(self, node_id)

    def name(self, name: str, ignorecase: bool = True):
        r"""Filter Nodes by their full name.
        See :meth:`~.InternalGraph.get_node_by_name` for details.
        """
        return NodeFilterName(self, name, ignorecase)


class NodeFilterType(NodeFilter):
    """See :meth:`~.InternalGraph.get_module_by_type`"""

    def __init__(self, expr_iter, owner_type):
        super().__init__(expr_iter)
        self.owner_type = owner_type

    def __iter__(self):
        for node in self._iter:
            if not isinstance(node, ModuleNode):
                continue
            if not hasattr(node, "owner"):
                continue
            if isinstance(node.owner, self.owner_type):
                yield node


class NodeFilterNodeId(NodeFilter):
    """See :meth:`~.InternalGraph.get_node_by_id`"""

    def __init__(self, expr_iter, node_id: List[int]):
        super().__init__(expr_iter)
        if not isinstance(node_id, Sequence):
            node_id = [node_id]
        self.node_id = node_id

    def __iter__(self):
        for node in self._iter:
            if node._id in self.node_id:
                yield node


class NodeFilterName(NodeFilter):
    """See :meth:`~.InternalGraph.get_node_by_name`"""

    _re = None

    def __init__(self, node_iter, pattern, ignorecase):
        super().__init__(node_iter)
        self.pattern = pattern
        self._re = self.make_re(pattern, ignorecase)

    @classmethod
    def make_re(cls, pattern, ignorecase=True):
        assert isinstance(pattern, str), "bad pattern: {!r}".format(pattern)
        assert isinstance(ignorecase, bool)
        flags = 0
        if ignorecase:
            flags |= re.IGNORECASE
        return re.compile(fnmatch.translate(pattern), flags=flags)

    def __iter__(self):
        for i in self._iter:
            graph = i.top_graph
            name = "{}_{}".format(graph._name, i._name)
            if self.pattern == name or self._re.match(name):
                yield i


class ExprFilterCallFunction(ExprFilter):
    """See :meth:`~.InternalGraph.get_function_by_type`"""

    def __init__(self, expr_iter, func: Callable = None):
        super().__init__(expr_iter)
        self.func = func

    def __iter__(self):
        for expr in self._iter:
            if not isinstance(expr, CallFunction):
                continue
            if self.func is None or expr.func == self.func:
                yield expr


class ExprFilterCallMethod(ExprFilter):
    """See :meth:`~.InternalGraph.get_method_by_type`"""

    def __init__(self, expr_iter, method: str = None):
        super().__init__(expr_iter)
        self.method = method

    def __iter__(self):
        for expr in self._iter:
            if not isinstance(expr, CallMethod):
                continue
            if self.method is None or expr.method == self.method:
                yield expr


class ExprFilterExprId(ExprFilter):
    """See :meth:`~.InternalGraph.get_expr_by_id`"""

    def __init__(self, expr_iter, expr_id: List[int]):
        super().__init__(expr_iter)
        if not isinstance(expr_id, Sequence):
            expr_id = [expr_id]
        self.expr_id = expr_id

    def __iter__(self):
        for expr in self._iter:
            if expr._id in self.expr_id:
                yield expr


class TracedModule(Module):
    r"""``TracedModule`` is the Module created by tracing normal module.

    It owns an argdef to graph(InternalGraph) map. The forward method of ``TracedModule``
    will get a graph from ``argdef_graph_map`` according to the argdef of input ``args/kwargs``
    and interpret it.

    .. note::
        ``TracedModule`` can only be created by :func:`~.trace_module`. See :func:`~.trace_module`
        for more details.
    """
    # m_node = None  # type: ModuleNode
    argdef_graph_map = None
    argdef_outdef_map = None

    def __init__(self, is_top, argdef_graph_map, argdef_outdef_map, is_qat=False):
        super(TracedModule, self).__init__()
        self.argdef_graph_map = argdef_graph_map
        self.argdef_outdef_map = argdef_outdef_map
        self._is_top = is_top
        self.watch_points = []
        self.watch_node_value = {}
        self.end_points = []
        self.is_qat = is_qat
        self.argspec = None

    def forward(self, *args, **kwargs):
        if hasattr(self, "argspec") and self.argspec is not None:
            args, kwargs = _convert_kwargs_to_args(self.argspec, args, kwargs, True)
        inputs, treedef = tree_flatten(((self, *args), kwargs))
        assert treedef in self.argdef_graph_map
        inputs = filter(
            lambda i: isinstance(i, (Module, TracedModuleBuilder, RawTensor)), inputs
        )  # allow TracedModuleBuilder for retrace.
        outputs = self.argdef_graph_map[treedef].interpret(*inputs)
        if self.watch_points:
            self.watch_node_value = {}
            for n in self.watch_points:
                self.watch_node_value[n] = n.top_graph._rst.pop(n)

        if self.end_points:
            return outputs

        out_def = self.argdef_outdef_map[treedef]
        outputs = out_def.unflatten(outputs)

        return outputs

    def set_watch_points(self, nodes):
        r"""Initialize the :attr:`~.TracedModule.watch_points`.

        You can call this function to get the ``Tensor/Module`` corresponding to a ``Node`` at runtime.

        Args:
            nodes: a list of ``Node``.

        For example, the following code

        .. code-block::

            import megengine.module as M
            import megengine as mge
            import megengine.traced_module as tm

            class MyModule(M.Module):
                def forward(self, x):
                    x = x + 1 + 2
                    return x

            net = MyModule()

            inp = mge.Tensor([0])
            traced_module = tm.trace_module(net, inp)
            add_1_node = traced_module.graph.get_node_by_id(2).as_unique()
            traced_module.set_watch_points(add_1_node)

            out = traced_module(inp)

        Will get the following ``watch_node_value``::

            print(traced_module.watch_node_value)

        .. code-block:: text

            {add_out: Tensor([1.], device=xpux:0)}
        """
        if not isinstance(nodes, Sequence):
            nodes = [nodes]
        self.watch_points = nodes
        if nodes:
            nodes[0].top_graph._watch_point = []
        for n in nodes:
            n.top_graph._watch_point.append(n)

    def clear_watch_points(self):
        r"""Clear the :attr:`~.TracedModule.watch_points` and :attr:`~.TracedModule.watch_node_value`.
        """
        for n in self.watch_points:
            n.top_graph._watch_point = []
        self.watch_points = []
        self.watch_node_value = {}

    def set_end_points(self, nodes: Sequence[Node]):
        r"""Initialize the :attr:`~.TracedModule.end_points`.

        When all the ``nodes`` are generated, the Module will stop execution and return directly.

        Args:
            nodes: a list of ``Node``.

        For example, the following code

        .. code-block::

            import megengine.module as M
            import megengine as mge
            import megengine.traced_module as tm

            class MyModule(M.Module):
                def forward(self, x):
                    x = x + 1 + 2
                    return x

            net = MyModule()

            inp = mge.Tensor([0])
            traced_module = tm.trace_module(net, inp)
            add_1_node = traced_module.graph.get_node_by_id(2).as_unique()
            traced_module.set_end_points(add_1_node)

            out = traced_module(inp)

        Will get the following ``out``::

            print(out)

        .. code-block:: text

            [Tensor([1.], device=xpux:0)]
        """
        if not isinstance(nodes, Sequence):
            nodes = [nodes]
        self.end_points = nodes
        graphs = list(self.argdef_graph_map.values())
        for n in nodes:
            assert n.top_graph in graphs
            n.top_graph._end_point.append(n)

    def clear_end_points(self):
        r"""Clear the :attr:`~.TracedModule.end_points`.
        """
        for n in self.end_points:
            n.top_graph._end_point = []
        self.end_points = []

    @property
    def graph(self) -> InternalGraph:
        """Return the ``InternalGraph`` of this ``TracedModule``.
        """
        assert len(self.argdef_graph_map) == 1
        return list(self.argdef_graph_map.values())[0]

    def _update_ref(self, actual_node_map: Union[Dict] = None, top_graph=None):
        for inp_def, graph in self.argdef_graph_map.items():
            if top_graph is not None:
                graph._top_graph = weakref.ref(top_graph)
            for n in graph._inputs + graph._outputs:
                n.expr._top_graph = weakref.ref(graph)
                n._top_graph = weakref.ref(graph)
            graph._inputs[0]._owner = weakref.ref(self)
            for i, n in enumerate(graph._inputs):
                n.actual_node = []
                if actual_node_map is not None and inp_def in actual_node_map.keys():
                    n.actual_node = list(list(zip(*(actual_node_map[inp_def])))[i])
            node2obj = {}
            next_actual_node_map = collections.defaultdict(
                lambda: collections.defaultdict(list)
            )
            node2obj[graph._inputs[0]] = self
            for expr in graph._exprs:
                for n in expr.inputs + expr.outputs:
                    n._top_graph = weakref.ref(graph)
                expr._top_graph = weakref.ref(graph)
                if isinstance(expr, GetAttr) and isinstance(
                    expr.outputs[0], ModuleNode
                ):
                    obj = expr.interpret(node2obj[expr.inputs[0]])[0]
                    expr.outputs[0]._owner = weakref.ref(obj)
                    node2obj[expr.outputs[0]] = obj
                if isinstance(expr, Constant) and isinstance(
                    expr.outputs[0], ModuleNode
                ):
                    obj = expr.value
                    expr.outputs[0]._owner = weakref.ref(obj)
                    node2obj[expr.outputs[0]] = obj
                if (
                    isinstance(expr, CallMethod)
                    and expr.method == "__call__"
                    and isinstance(expr.inputs[0], ModuleNode)
                ):
                    obj = node2obj[expr.inputs[0]]
                    if expr.arg_def is not None:
                        next_actual_node_map[obj][expr.arg_def].append(expr.inputs)

            for obj in node2obj.values():
                if obj is self:
                    continue
                mnode_map = None
                if obj in next_actual_node_map.keys():
                    mnode_map = next_actual_node_map[obj]
                if isinstance(obj, TracedModule):
                    obj._update_ref(mnode_map, graph)

    def flatten(self):
        r"""Get a new TracedModule, which eliminates ``GetAttr`` and has no hierarchy.

        Retruns:
            A new :class:`TracedModule`.
        """
        new_module = copy.deepcopy(self)

        def _replace_inputs_and_outputs(expr: Expr, repl_dict: Dict[Node, Node]):
            inputs, outputs = expr.inputs, expr.outputs
            for i, node in enumerate(inputs):
                if node in repl_dict:
                    inputs[i] = repl_dict[node]
            for i, node in enumerate(outputs):
                if node in repl_dict:
                    outputs[i] = repl_dict[node]
                    outputs[i].expr = expr

        def _flatten_subgraph(
            parent_graph: InternalGraph,
            graph: InternalGraph,
            call: CallMethod,
            module: Module,
        ):
            repl_dict, node2obj, rename_blacklist = {}, {}, []

            if call is not None:
                graph = copy.deepcopy(graph)
                node2obj[call.inputs[0]] = module

                repl_dict = dict(zip(graph._inputs, call.inputs))
                for ind, out in enumerate(graph.outputs):
                    if isinstance(out.expr, Input):
                        assert out in repl_dict
                        call_out = call.outputs[ind]
                        for expr in call.outputs[ind].users:
                            for index, inp in enumerate(expr.inputs):
                                if inp is call_out:
                                    expr.inputs[index] = repl_dict[out]
                                    repl_dict[out].users.append(expr)
                        if parent_graph is not None:
                            for index, parent_out in enumerate(parent_graph._outputs):
                                if parent_out is call_out:
                                    parent_graph._outputs[index] = repl_dict[out]
                        continue
                    repl_dict[out] = call.outputs[ind]
                    if isinstance(out, TensorNode):
                        call.outputs[ind]._qualname = out._qualname

                for node, repl_node in repl_dict.items():
                    assert node in graph._inputs or node in graph._outputs
                    repl_node.users.extend(node.users)

                rename_blacklist = list(chain(call.inputs, call.outputs))

            node2obj[graph._inputs[0]] = module
            prefix_name = call.inputs[0]._name if call else ""
            flattened_exprs = []

            for expr in graph._exprs:
                exprs = [expr]

                if call is not None:
                    _replace_inputs_and_outputs(expr, repl_dict)

                if isinstance(expr, GetAttr):
                    mnode = expr.inputs[0]
                    node2obj[expr.outputs[0]] = expr.interpret(node2obj[mnode])[0]

                if isinstance(expr, CallMethod):
                    obj_node = expr.inputs[0]
                    if isinstance(obj_node, ModuleNode) and isinstance(
                        obj_node.expr, GetAttr
                    ):
                        obj = node2obj[obj_node]
                        expr_graph = (
                            obj.argdef_graph_map[expr.arg_def]
                            if hasattr(obj, "argdef_graph_map")
                            else None
                        )
                        if expr_graph is not None and not obj.is_qat:
                            exprs = _flatten_subgraph(graph, expr_graph, expr, obj)

                if parent_graph is not None:
                    for node in expr.outputs:
                        name = node._name
                        if node not in rename_blacklist:
                            name = "{}_{}".format(prefix_name, name)
                        node._name = parent_graph._namespace.create_unique_name(
                            name, node
                        )

                flattened_exprs.extend(exprs)

            if call is not None:
                for i in call.inputs:
                    i.users.remove(call)

            return flattened_exprs

        new_module.graph._exprs = _flatten_subgraph(
            None, new_module.graph, None, new_module
        )
        new_module.graph._re_associate_name()
        new_module.graph.compile()
        new_module._update_ref()
        new_module.graph._reset_ids()
        return new_module

    def __getstate__(self):
        d = self.__dict__.copy()
        for k in Module.__dict__:
            d.pop(k, None)
        _check_obj_attr(d)
        for k in d:
            if module_tracer.is_builtin(d[k]):
                assert _check_builtin_module_attr(
                    d[k]
                ), "Module {} can not be serialized. ".format(type(d[k]))
                d[k] = _ModuleState.get_module_state(d[k])
        dump_info = {
            "version": __version__,
            "register_type": USER_REGISTERED_LEAF_TYPE,
            "register_container_type": USER_REGISTERED_CONTAINER_TYPE,
            "register_mdule": USER_REGISTERED_MODULE,
            "register_function": USER_REGISTERED_FUNCTION,
        }
        d["dump_info"] = dump_info
        return d

    def __setstate__(self, state):

        for k, v in state.items():
            if isinstance(v, _ModuleState):
                state[k] = v.to_module()
        self.__dict__.update(state)
        self._update_ref()

        for _, graph in self.argdef_graph_map.items():
            for expr in graph._exprs:
                if isinstance(expr, CallFunction):
                    load_functional(expr)
                if isinstance(expr, CallMethod):
                    if expr.method == "__call__":
                        load_call_module_expr(expr)
                    else:
                        load_call_tensor_method_expr(expr)
                if isinstance(expr, Apply):
                    load_apply_expr(expr)

        for _, graph in self.argdef_graph_map.items():
            ind = 0
            while ind < len(graph._exprs):
                cur_expr = graph._exprs[ind]
                has_new_expr = False
                for i in cur_expr.inputs:
                    if i.expr not in graph._exprs and not isinstance(i.expr, Input):
                        graph._exprs.insert(ind, i.expr)
                        has_new_expr = True
                if not has_new_expr:
                    ind += 1
            for expr in graph._exprs:
                for i in expr.inputs:
                    if expr.inputs.count(i) != i.users.count(expr):
                        add_or_del_count = expr.inputs.count(i) - i.users.count(expr)
                        if add_or_del_count > 0:
                            i.users.extend([expr] * add_or_del_count)
                        else:
                            [i.users.remove(expr) for i in range(-add_or_del_count)]

                for o in expr.outputs:
                    if o.expr is not expr:
                        assert o not in o.expr.outputs
                        o.expr = expr
            for node in graph.nodes(False):
                # remove users of node which doesn't use node as input
                node.users = [e for e in node.users if node in e.inputs]

            for expr in graph._exprs:
                graph._namespace.auto_naming_for_outputs(expr)
        self._update_ref()
        for _, graph in self.argdef_graph_map.items():
            graph._reset_ids()

    def __copy__(self):
        cls = self.__class__
        result = cls.__new__(cls)
        result.__dict__.update(self.__dict__)
        return result

    def __deepcopy__(self, memo):
        cls = self.__class__
        result = cls.__new__(cls)
        state = {}
        memo[id(self)] = result
        for k, v in self.__dict__.items():
            if not isinstance(v, weakref.ReferenceType):
                state[k] = copy.deepcopy(v, memo)
        result.__dict__.update(state)
        result._update_ref()
        return result


def cpp_apply_module_trace(opdef, *args):
    return Apply.apply_module_trace_hook(opdef, *args)


USER_REGISTERED_MODULE = []
USER_REGISTERED_FUNCTION = []


def register_as_builtin(mod_cls: Type[Module]) -> None:
    r"""Registers class ``mod_cls`` (subclass of :class:`~.Module`) as builtin module.

    Args:
        mod_cls: the module class which will be treated as builtin module in tracing.
    """
    USER_REGISTERED_MODULE.append((mod_cls.__module__, mod_cls.__qualname__))
    module_tracer.register_as_builtin(mod_cls)


def wrap(func: Callable):
    r"""Call this function to register ``func`` as a builtin function.

    This function can be called at module-level scope to register ``func`` as a builtin function.
    A builtin function will be converted to a :class:`CallFunction` Expr in tracing::

        def my_func(x, y):
            return x + y

        import megengine.traced_module as tm
        tm.wrap(my_func)

    This function can also equivalently be used as a decorator::

        @tm.wrap
        def my_func(x, y):
            return x + y

    Args:
        func: the function of the global function to insert into the graph when it's called.
    """
    USER_REGISTERED_FUNCTION.append((func.__module__, func.__qualname__))
    assert callable(func), "func must be a callable"
    assert hasattr(func, "__code__")
    fn_name = func.__code__.co_name
    currentframe = inspect.currentframe()
    assert currentframe is not None
    f = currentframe.f_back
    assert f is not None
    assert (
        f.f_code.co_name == "<module>"
    ), "wrap must be called at the top level of a module"
    Patcher._builtin_functions.append((f.f_globals, fn_name))
    return func


def _register_all_builtin_module():

    for sub_mod in [M, M.qat, M.quantized, MExternal]:
        for m in getmembers(sub_mod):
            if (
                isclass(m[1])
                and issubclass(m[1], M.Module)
                and m[1] is not M.Sequential
            ):
                module_tracer.register_as_builtin(m[1])

    module_tracer.register_as_builtin(Observer)
    module_tracer.register_as_builtin(MinMaxObserver)
    module_tracer.register_as_builtin(SyncMinMaxObserver)
    module_tracer.register_as_builtin(ExponentialMovingAverageObserver)
    module_tracer.register_as_builtin(SyncExponentialMovingAverageObserver)
    module_tracer.register_as_builtin(HistogramObserver)
    module_tracer.register_as_builtin(PassiveObserver)

    module_tracer.register_as_builtin(LSQ)
    module_tracer.register_as_builtin(TQT)
    module_tracer.register_as_builtin(FakeQuantize)
    module_tracer.register_as_builtin(TM_FakeQuant)


def trace_module(
    mod: Module, *args: Tuple[Any], **kwargs: Dict[str, Any]
) -> TracedModule:
    r"""Traces module ``mod`` and returns corresponding :class:`TracedModule`.

    Args:
        mod: the module will be converted to :class:`TracedModule`.
        args: the positional arguments passed to forward method of ``mod``.
        kwargs: the keyword arguments passed to forward method of ``mod``.
    """
    assert active_module_tracer() is None
    assert isinstance(mod, Module)
    try:
        net_name = mod._name if mod._name else mod.__class__.__name__
        use_sym_shape = set_symbolic_shape(True)
        set_module_tracing()
        set_active_module_tracer(module_tracer(_wrapped_function))
        for cls in [Expr, Node]:
            cls._set_next_id(0)
        with active_module_tracer().patcher:
            global_scope = InternalGraph(name="top", qualname=net_name)
            active_module_tracer().push_scope(global_scope)
            builder = TracedModuleBuilder(mod, True)

            NodeMixin.wrap_safe(
                builder, Input.make(name="top", type=ModuleNode, qualname=net_name)
            )
            forward_argspec = (
                mod.argspec
                if hasattr(mod, "argspec")
                else inspect.getfullargspec(mod.forward)
            )
            args, kwargs = _convert_kwargs_to_args(forward_argspec, args, kwargs, True)
            inputs, _ = tree_flatten((args, kwargs))
            for _, i in enumerate(inputs):
                # assert isinstance(i, Tensor), "not support "
                if isinstance(i, RawTensor):
                    NodeMixin.wrap_safe(
                        i,
                        Input.make(
                            name="arg_{}".format(_),
                            type=NodeMixin.get_wrapped_type(i),
                            qualname="{}.[{}]".format(net_name, "arg_{}".format(_)),
                        ),
                    )
            rst = builder(*copy.deepcopy(args), **copy.deepcopy(kwargs))
            active_module_tracer().pop_scope()
            traced_mod = builder.build()
            traced_mod.argspec = forward_argspec
            traced_mod.graph._reset_ids()

            has_expr_not_check = False
            if _get_expr_checker():
                has_expr_not_check = (
                    active_module_tracer().checker.check_node_not_in_scope()
                )
            if _get_default_checker() or has_expr_not_check:
                with _exclude_from_trace():
                    tm_res = traced_mod(*args, **kwargs)
                    tm_res, _ = tree_flatten(tm_res, is_leaf=_is_leaf)
                    rst, _ = tree_flatten(rst, is_leaf=_is_leaf)
                    active_module_tracer().checker.check_net_outputs(tm_res, rst)
            return traced_mod
    finally:
        set_symbolic_shape(use_sym_shape)
        set_active_module_tracer(None)
        unset_module_tracing()