feat(traced_module): support traced module backward compatible serialization

GitOrigin-RevId: aaa9e51c74c11fa7955ae7bbfac476fa9bcf0d7d

imperative/python/megengine/traced_module/__init__.py

@@ -7,6 +7,7 @@
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 
 from ..core._imperative_rt.core2 import set_cpp_apply_module_trace
+from . import compat
 from .traced_module import (
     TracedModule,
     _register_all_builtin_module,

imperative/python/megengine/traced_module/compat.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 numpy as np
+
+from .. import tensor
+from ..core.ops.builtin import BatchNorm
+from .expr import CallMethod, Constant
+from .node import TensorNode
+from .serialization import (
+    register_functional_loader,
+    register_module_loader,
+    register_opdef_loader,
+    register_tensor_method_loader,
+)
+
+
+"""
+# Expr loaders examples
+
+from ..core.ops.builtin import Elemwise
+
+@register_opdef_loader(Elemwise)
+def add_opdef_loader(expr):
+    if expr.opdef_state["mode"] == "ADD":
+        expr.opdef_state["mode"] == "MUL"
+        node = expr.inputs[1]
+        astype_expr = CallMethod(node, "astype")
+        oup = TensorNode(
+            astype_expr,
+            shape=node.shape,
+            dtype=expr.inputs[0].dtype,
+            qparams=node.qparams,
+        )
+
+        astype_expr.set_args_kwargs(node, expr.inputs[0].dtype)
+        astype_expr.return_val = (oup,)
+        expr.inputs[1] = oup
+
+
+@register_functional_loader(("megengine.functional.nn", "conv2d"))
+def conv2df_loader(expr):
+    # expr.func = ("megengine.functional.nn","conv2d")
+    kwargs = expr.kwargs
+    orig_weight = expr.named_args["weight"]
+
+    astype_expr = CallMethod(orig_weight, "astype")
+    oup = TensorNode(
+        astype_expr,
+        shape=orig_weight.shape,
+        dtype=orig_weight.dtype,
+        qparams=orig_weight.qparams,
+    )
+
+    astype_expr.set_args_kwargs(orig_weight, expr.named_args["inp"].dtype)
+    astype_expr.return_val = (oup,)
+
+    expr.set_arg("weight", oup)
+
+
+@register_module_loader(("megengine.module.conv", "Conv2d"))
+def conv2dm_loader(expr):
+    module = expr.inputs[0].owner
+    args = list(expr.args)
+    orig_inp = args[1]
+    astype_expr = CallMethod(orig_inp, "astype")
+    oup = TensorNode(
+        astype_expr,
+        shape=orig_inp.shape,
+        dtype=orig_inp.dtype,
+        qparams=orig_inp.qparams,
+    )
+    astype_expr.set_args_kwargs(orig_inp, module.weight.dtype)
+    astype_expr.return_val = (oup,)
+    args[1] = oup
+    expr.set_args_kwargs(*args)
+
+
+@register_tensor_method_loader("__add__")
+def add_loader(expr):
+    args = list(expr.args)
+    if not isinstance(args[1], TensorNode):
+        args[1] = tensor(args[1])
+        node = Constant(args[1], "const").outputs[0]
+
+        astype_expr = CallMethod(node, "astype")
+        oup = TensorNode(
+            astype_expr, shape=node.shape, dtype=node.dtype, qparams=node.qparams,
+        )
+
+        astype_expr.set_args_kwargs(node, expr.inputs[0].dtype)
+        astype_expr.return_val = (oup,)
+        args[1] = oup
+        expr.set_args_kwargs(*args)
+"""
+
+
+@register_module_loader(
+    ("megengine.module.batchnorm", "BatchNorm1d"),
+    ("megengine.module.batchnorm", "BatchNorm2d"),
+    ("megengine.module.batchnorm", "SyncBatchNorm"),
+)
+def bn2d_module_loader(expr):
+    # mge 1.6
+    if not hasattr(expr, "version"):
+        module = expr.inputs[0].owner
+        if not hasattr(module, "param_dim"):
+            module.param_dim = "dim_1c11"
+
+
+@register_module_loader(
+    ("megengine.module.conv_bn", "ConvBn2d"),
+    ("megengine.module.conv_bn", "ConvBnRelu2d"),
+    ("megengine.module.qat.conv_bn", "ConvBn2d"),
+    ("megengine.module.qat.conv_bn", "ConvBnRelu2d"),
+)
+def convbn2d_module_loader(expr):
+    # mge 1.6
+    if not hasattr(expr, "version"):
+        module = expr.inputs[0].owner
+        if not hasattr(module.bn, "param_dim"):
+            module.bn.param_dim = "dim_1c11"
+
+
+@register_opdef_loader(BatchNorm)
+def bn_opdef_loader(expr):
+    # mge 1.6
+    if not hasattr(expr, "version"):
+        output = expr.outputs[-1]
+        oup = TensorNode(expr, shape=(0,), dtype=None, qparams=output._qparams,)
+        expr.outputs.insert(4, oup)

imperative/python/megengine/traced_module/expr.py

@@ -11,19 +11,28 @@ import collections
 import copy
 import inspect
 import re
-from typing import Callable, Dict, List, Optional, Union
+import weakref
+from importlib import import_module
+from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union
 
 from ..core._imperative_rt import OpDef
 from ..core._imperative_rt.core2 import Tensor as RawTensor
-from ..core._imperative_rt.core2 import apply, set_module_tracing, unset_module_tracing
+from ..core._imperative_rt.core2 import (
+    apply,
+    is_tracing_module,
+    set_module_tracing,
+    unset_module_tracing,
+)
 from ..core.ops.builtin import FakeQuant
 from ..core.ops.special import Const
 from ..module import Module
 from ..tensor import Parameter, Tensor
+from ..version import __version__
 from .module_tracer import active_module_tracer, module_tracer
 from .node import ModuleNode, Node, NodeMixin, TensorNode
 from .pytree import ArgsIndex, TreeDef, _is_const_leaf, _is_leaf, tree_flatten
-from .serialization import get_opdef_state, load_opdef_from_state
+from .serialization import _ModuleState
+from .utils import _check_builtin_module_attr, _check_obj_attr, _convert_kwargs_to_args
 
 
 def rstrip(s: str, __chars: str):
@@ -112,6 +121,7 @@ class Expr:
                 node.users.append(self)
             else:
                 assert node is None
+                assert not isinstance(val, (Module, RawTensor))
                 assert _is_leaf(val) and _is_const_leaf(val)
                 idx = len(self.inputs) + len(self.const_val)
                 self.const_val.append((idx, val))
@@ -132,14 +142,14 @@ class Expr:
         current_graph._namespace.auto_naming_for_outputs(self)
 
     def unflatten_args(self, inputs):
-        if self.arg_def is not None:
-            inputs = list(inputs)
-            for idx, val in self.const_val:
-                inputs.insert(idx, val)
-            args, kwargs = self.arg_def.unflatten(inputs)
-            return args, kwargs
-        else:
-            return inputs, {}
+        assert self.arg_def is not None, "{} expr doesn't have args/kwargs".format(
+            type(self).__name__
+        )
+        inputs = list(inputs)
+        for idx, val in self.const_val:
+            inputs.insert(idx, val)
+        args, kwargs = self.arg_def.unflatten(inputs)
+        return args, kwargs
 
     def replace_inputs(self, repl_dict: Dict[Node, Node]):
         r"""Replace the input Nodes of this Expr.
@@ -165,6 +175,39 @@ class Expr:
             node.users.remove(self)
             repl_node.users.append(self)
 
+    @property
+    def _support_set_args_kwargs(self):
+        return False
+
+    def set_args_kwargs(self, *args, **kwargs):
+        r""" Set args and kwargs for Expr.
+        """
+        assert (
+            self._support_set_args_kwargs
+        ), "Doesn't support set args/kwargs for {} expr".format(type(self).__name__)
+        args, kwargs = _convert_kwargs_to_args(self._get_func(), args, kwargs)
+        inputs, arg_def = tree_flatten((args, kwargs))
+        orig_inputs = self.inputs
+        self.inputs = []
+        self.const_val = []
+        for val in inputs:
+            if isinstance(val, (TensorNode, ModuleNode)):
+                self.inputs.append(val)
+            else:
+                assert _is_leaf(val) and _is_const_leaf(val)
+                idx = len(self.inputs) + len(self.const_val)
+                self.const_val.append((idx, val))
+
+        for n in orig_inputs:
+            if n not in self.inputs:
+                n.users.remove(self)
+
+        for n in self.inputs:
+            if n not in orig_inputs:
+                n.users.append(self)
+
+        self.arg_def = arg_def
+
     @property
     def kwargs(self):
         r"""Get the keyword arguments of the operation corresponding to this Expr."""
@@ -177,6 +220,61 @@ class Expr:
         args, _ = self.unflatten_args(self.inputs)
         return args
 
+    def _get_func(self):
+        # get called function when the expr is interpreted
+        raise NotImplementedError
+
+    @property
+    def named_args(self):
+        func = self._get_func()
+        return inspect.getcallargs(func, *self.args, **self.kwargs)
+
+    def set_arg(self, name, val):
+        func = self._get_func()
+        if name in self.kwargs:
+            new_kwargs = self.kwargs
+            new_kwargs[name] = val
+            self.set_args_kwargs(*self.args, **new_kwargs)
+        else:
+            arg_spec = inspect.getfullargspec(func)
+            if name in arg_spec.args:
+                ind = arg_spec.args.index(name)
+                new_args = list(self.args)
+                new_args[ind] = val
+                self.set_args_kwargs(*new_args)
+            elif name == arg_spec.varargs:
+                assert arg_spec.varargs is not None
+                assert len(self.args) >= len(arg_spec.args)
+                val = (val,) if not isinstance(val, Sequence) else val
+                self.set_args_kwargs(*self.args[0 : len(arg_spec.args)], *val)
+            else:
+                assert (
+                    arg_spec.varkw is not None
+                ), "func {} does't have argument named {}".format(func, name)
+                new_kwargs = self.kwargs
+                new_kwargs[name] = val
+                self.set_args_kwargs(*self.args, **new_kwargs)
+
+    @property
+    def return_val(self):
+        return self.out_def.unflatten(self.outputs)
+
+    @return_val.setter
+    def return_val(self, new_outputs):
+        outputs, out_def = tree_flatten(
+            new_outputs, is_leaf=lambda x: isinstance(x, Node)
+        )
+        assert all(
+            isinstance(o, Node) for o in outputs
+        ), "Return values of expr must be ModuleNode or TensorNode or Container with them"
+        assert all(
+            o.expr in (None, self) for o in outputs
+        ), "Some nodes are produced by other expr, can not be output of expr {}".format(
+            self
+        )
+        self.outputs = outputs
+        self.out_def = out_def
+
     @property
     def top_graph(self):
         r"""Get the parent graph of this Expr."""
@@ -184,12 +282,6 @@ class Expr:
             return self._top_graph()
         return None
 
-    def __getstate__(self):
-        state = self.__dict__.copy()
-        if "_top_graph" in state:
-            state.pop("_top_graph")
-        return state
-
     @classmethod
     def _get_next_id(cls):
         return cls.__total_id
@@ -199,6 +291,23 @@ class Expr:
         assert isinstance(id, int)
         cls.__total_id = id
 
+    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)
+        return result
+
 
 # expr: None (i.e. fake expression which is used to mark input)
 class Input(Expr):
@@ -229,6 +338,17 @@ class Input(Expr):
     def __repr__(self):
         return "%{}:\t{} = Input()".format(self._id, self.outputs[0])
 
+    def __getstate__(self):
+        state = {
+            "_id": self._id,
+            "_disable_remove": self._disable_remove,
+            "inputs": self.inputs,
+            "outputs": self.outputs,
+            "name": self.name,
+        }
+        _check_obj_attr(state)
+        return state
+
 
 # expr: outputs = getattr(inputs[0], self.name)
 class GetAttr(Expr):
@@ -276,11 +396,23 @@ class GetAttr(Expr):
     def __repr__(self):
         out_type = "Tensor"
         if isinstance(self.outputs[0], ModuleNode):
-            out_type = self.outputs[0].module_type.__name__
+            m_type = self.outputs[0].module_type
+            out_type = m_type.__name__ if isinstance(m_type, type) else m_type[1]
         return '%{}:\t{} = getattr({}, "{}") -> ({})'.format(
             self._id, self.outputs[0], self.inputs[0], self.name, out_type
         )
 
+    def __getstate__(self):
+        state = {
+            "_id": self._id,
+            "_disable_remove": self._disable_remove,
+            "inputs": self.inputs,
+            "outputs": self.outputs,
+            "name": self.name,
+        }
+        _check_obj_attr(state)
+        return state
+
 
 # expr: outputs = inputs[0].__call__(*inputs[1:])
 class CallMethod(Expr):
@@ -307,6 +439,7 @@ class CallMethod(Expr):
                 node,
             ]
             self.const_val = []
+        self.arg_def = tree_flatten(((node,), {}))[1]
         self.method = method
 
     @classmethod
@@ -342,6 +475,27 @@ class CallMethod(Expr):
         outputs, _ = tree_flatten(outputs, is_leaf=lambda x: isinstance(x, RawTensor))
         return outputs
 
+    def _get_func(self):
+        if isinstance(self.args[0], type):
+            obj_type = self.args[0]
+        elif isinstance(self.args[0], ModuleNode):
+            obj_type = self.args[0].module_type
+        else:
+            assert isinstance(self.args[0], TensorNode)
+            obj_type = Tensor
+        meth = getattr(
+            obj_type, "forward" if issubclass(obj_type, Module) else self.method
+        )
+        return meth
+
+    @property
+    def _support_set_args_kwargs(self):
+        # only expr call tensor method or builtin module support modify args/kwargs
+        return (
+            isinstance(self.args[0], (TensorNode, type))
+            or self.args[0].module_type is not Module
+        )
+
     def __repr__(self):
         args = ", ".join(str(i) for i in self.args[1:])
         kwargs = ", ".join("{}={}".format(k, v) for k, v in self.kwargs.items())
@@ -359,6 +513,21 @@ class CallMethod(Expr):
             ", ".join([args, kwargs]),
         )
 
+    def __getstate__(self):
+        state = {
+            "_id": self._id,
+            "_disable_remove": self._disable_remove,
+            "inputs": self.inputs,
+            "const_val": self.const_val,
+            "method": self.method,
+            "arg_def": self.arg_def,
+            "out_def": self.out_def,
+            "outputs": self.outputs,
+            "version": __version__,
+        }
+        _check_obj_attr(state)
+        return state
+
 
 # expr: outputs = apply(self.opdef, *inputs)
 class Apply(Expr):
@@ -394,14 +563,32 @@ class Apply(Expr):
         )
 
     def __getstate__(self):
-        state = super().__getstate__()
-        state["opdef"] = get_opdef_state(state["opdef"])
+        opdef_state = self.opdef.__getstate__()
+        opdef_state["opdef_type"] = type(self.opdef)
+        state = {
+            "_id": self._id,
+            "_disable_remove": self._disable_remove,
+            "opdef_state": opdef_state,
+            "inputs": self.inputs,
+            "outputs": self.outputs,
+            "version": __version__,
+        }
+        _check_obj_attr(state)
         return state
 
     def __setstate__(self, state):
-        state["opdef"] = load_opdef_from_state(state["opdef"])
-        for k, v in state.items():
-            setattr(self, k, v)
+        # compat with mge 1.6
+        if "opdef" in state and "opdef_state" not in state:
+            opdef_state = state.pop("opdef")
+            opdef_state["opdef_type"] = opdef_state.pop("type")
+            state["opdef_state"] = opdef_state
+        self.__dict__.update(state)
+        assert isinstance(state["opdef_state"], dict)
+        opdef_state = state["opdef_state"].copy()
+        opdef_type = opdef_state.pop("opdef_type")
+        opdef_obj = opdef_type()
+        opdef_obj.__setstate__(opdef_state)
+        setattr(self, "opdef", opdef_obj)
 
     @classmethod
     def apply_module_trace_hook(cls, opdef, *inputs):
@@ -458,12 +645,24 @@ class CallFunction(Expr):
 
     def interpret(self, *inputs):
         args, kwargs = self.unflatten_args(inputs)
-        outputs = self.func(*args, **kwargs)
+        func = (
+            self.func
+            if not is_tracing_module()
+            else active_module_tracer().patcher.wrap_fn(self.func)
+        )
+        outputs = func(*args, **kwargs)
         if outputs is None:
             return outputs
         outputs, _ = tree_flatten(outputs, is_leaf=lambda x: isinstance(x, RawTensor))
         return outputs
 
+    def _get_func(self):
+        return self.func
+
+    @property
+    def _support_set_args_kwargs(self):
+        return True
+
     def __repr__(self):
         args = ", ".join(str(i) for i in self.args)
         kwargs = ", ".join("{}={}".format(k, v) for k, v in self.kwargs.items())
@@ -477,6 +676,33 @@ class CallFunction(Expr):
             ", ".join([args, kwargs]),
         )
 
+    def __getstate__(self):
+        state = {
+            "_id": self._id,
+            "_disable_remove": self._disable_remove,
+            "func": (self.func.__module__, self.func.__qualname__),
+            "const_val": self.const_val,
+            "inputs": self.inputs,
+            "arg_def": self.arg_def,
+            "out_def": self.out_def,
+            "outputs": self.outputs,
+            "version": __version__,
+        }
+        _check_obj_attr(state)
+        return state
+
+    def __setstate__(self, state):
+        self.__dict__.update(state)
+        try:
+            if isinstance(self.func, tuple):
+                mname, fname = self.func
+                f = import_module(mname)
+                for i in fname.split("."):
+                    f = getattr(f, i)
+                self.func = f
+        except Exception:
+            pass
+
 
 # expr outputs = self.value
 class Constant(Expr):
@@ -496,6 +722,13 @@ class Constant(Expr):
         assert isinstance(c, (RawTensor, Module))
         if isinstance(c, Module):
             assert module_tracer.is_builtin(c) or c.is_qat
+        if isinstance(c, RawTensor):
+            if is_tracing_module():
+                unset_module_tracing()
+                c = Tensor(c)
+                set_module_tracing()
+            else:
+                c = Tensor(c)
         self.value = c
         self.name = name
         self.inputs = []
@@ -530,9 +763,25 @@ class Constant(Expr):
         )
 
     def __getstate__(self):
-        state = self.__dict__.copy()
-        if "_top_graph" in state:
-            state.pop("_top_graph")
+        state = {
+            "_id": self._id,
+            "_disable_remove": self._disable_remove,
+            "value": self.value,
+            "name": self.name,
+            "inputs": self.inputs,
+            "outputs": self.outputs,
+        }
+        _check_obj_attr(state)
         if isinstance(self.value, RawTensor):
             state["value"] = Tensor(self.value)
+        if isinstance(self.value, Module) and module_tracer.is_builtin(self.value):
+            _check_builtin_module_attr(self.value)
+            state["value"] = _ModuleState.get_module_state(self.value)
+
         return state
+
+    def __setstate__(self, state):
+        for k, v in state.items():
+            if isinstance(v, _ModuleState):
+                state[k] = v.to_module()
+        self.__dict__.update(state)

imperative/python/megengine/traced_module/module_tracer.py

@@ -72,7 +72,6 @@ BUILTIN_ARRAY_METHOD = [
     "astype",
     "reshape",
     "_broadcast",
-    "transpose",
     "flatten",
     "sum",
     "prod",

imperative/python/megengine/traced_module/node.py

@@ -6,7 +6,9 @@
 # software distributed under the License is distributed on an
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 import abc
+import copy
 import weakref
+from importlib import import_module
 from typing import Any, Dict, List, Tuple, Type
 
 import numpy
@@ -14,7 +16,9 @@ import numpy
 from .. import get_logger
 from ..core._imperative_rt.core2 import Tensor as RawTensor
 from ..module import Module
+from ..quantization.utils import QParams
 from ..tensor import Tensor
+from .utils import _check_obj_attr
 
 logger = get_logger(__name__)
 
@@ -145,6 +149,23 @@ class Node:
         assert isinstance(id, int)
         cls.__total_id = id
 
+    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) and k != "actual_node":
+                state[k] = copy.deepcopy(v, memo)
+        result.__dict__.update(state)
+        return result
+
 
 class ModuleNode(Node):
     r"""``ModuleNode`` represents the Module objects."""
@@ -157,19 +178,28 @@ class ModuleNode(Node):
         super().__init__(expr, name, qualname)
 
     def __getstate__(self):
-        return {
+        state = {
             "expr": self.expr,
             "users": self.users,
             "_id": self._id,
             "_name": self._name,
             "_qualname": self._qualname,
-            "module_type": self.module_type,
+            "module_type": (self.module_type.__module__, self.module_type.__qualname__),
         }
+        _check_obj_attr(state)
+        return state
 
     def __setstate__(self, state):
         if "_orig_name" in state:
             state["_qualname"] = state.pop("_orig_name")
         self.__dict__.update(state)
+        try:
+            if isinstance(self.module_type, tuple):
+                mname, classname = self.module_type
+                mtype = getattr(import_module(mname), classname)
+                self.module_type = mtype
+        except Exception:
+            pass
 
     @property
     def owner(self):
@@ -185,12 +215,26 @@ class TensorNode(Node):
 
     _shape = None  # type: Tuple[int]
     _dtype = None  # type: numpy.dtype
-    _qparams = None
+    _qparams = None  # type: QParams
     _device = None
     _value = None  # type: Tensor
 
+    def __init__(
+        self,
+        expr: "Expr",
+        name: str = None,
+        qualname: str = None,
+        shape: Tuple[int] = None,
+        dtype: numpy.dtype = None,
+        qparams: QParams = None,
+    ):
+        super().__init__(expr, name, qualname)
+        self._shape = shape
+        self._dtype = shape
+        self._qparams = qparams
+
     def __getstate__(self):
-        return {
+        state = {
             "expr": self.expr,
             "users": self.users,
             "_id": self._id,
@@ -201,6 +245,8 @@ class TensorNode(Node):
             "_name": self._name,
             "_qualname": self._qualname,
         }
+        _check_obj_attr(state)
+        return state
 
     def __setstate__(self, state):
         if "_orig_name" in state:
@@ -276,7 +322,10 @@ class NodeMixin(abc.ABC):
         assert isinstance(node, TensorNode)
         assert isinstance(value, RawTensor)
         if isinstance(value, RawTensor):
-            node._dtype = value.dtype
+            try:
+                node._dtype = value.dtype
+            except RuntimeError:
+                node._dtype = None
             node._shape = (
                 value._tuple_shape if isinstance(value, Tensor) else value.shape
             )

imperative/python/megengine/traced_module/pytree.py

@@ -7,15 +7,18 @@
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 
 import collections
-from collections import OrderedDict
+from collections import OrderedDict, defaultdict
+from functools import partial
 from typing import Callable, NamedTuple
 
 import numpy as np
 
+from ..core._imperative_rt import OpDef
 from ..core._imperative_rt.common import CompNode
 from ..core._imperative_rt.core2 import Tensor as RawTensor
 from ..core._wrap import Device
 from ..core.tensor.dtype import QuantDtypeMeta
+from ..distributed import Group
 from ..module import Module
 from ..quantization.utils import LSQParams, QParams, QuantMode
 from ..tensor import Parameter, Tensor
@@ -49,45 +52,54 @@ SUPPORTED_LEAF_TYPE = {
     type(Ellipsis),
     QuantMode,
     ArgsIndex,
+    Group,
 }
 
+USER_REGISTERED_LEAF_TYPE = []
+USER_REGISTERED_CONTAINER_TYPE = []
 # if isinstance(object, SUPPORTED_LEAF_CLS) or issubclass(obj, SUPPORTED_LEAF_CLS) is True, the object could be threated as leaf node of pytree
-SUPPORTED_LEAF_CLS = [Module, Node, NodeMixin, np.dtype, np.ndarray, np.number]
+SUPPORTED_LEAF_CLS = [
+    Module,
+    Node,
+    NodeMixin,
+    np.dtype,
+    np.ndarray,
+    np.number,
+    np.bool_,
+    OpDef,
+]
 
 NodeType = NamedTuple("NodeType", [("flatten", Callable), ("unflatten", Callable)])
 
 
 def register_supported_type(type, flatten=None, unflatten=None):
+    tp_info = (type.__module__, type.__qualname__)
     if flatten and unflatten:
-        SUPPORTED_TYPE[type] = NodeType(flatten, unflatten)
+        USER_REGISTERED_CONTAINER_TYPE.append(tp_info)
     else:
-        SUPPORTED_LEAF_CLS.append(type)
-
-
-def _dict_flatten(inp):
-    aux_data = []
-    results = []
-    for key, value in sorted(inp.items()):
-        results.append(value)
-        aux_data.append(key)
-    return results, tuple(aux_data)
+        USER_REGISTERED_LEAF_TYPE.append(tp_info)
+    _register_supported_type(type, flatten, unflatten)
 
 
-def _dict_unflatten(inps, aux_data):
-    return dict(zip(aux_data, inps))
+def _register_supported_type(type, flatten=None, unflatten=None):
+    if flatten and unflatten:
+        SUPPORTED_TYPE[type] = NodeType(flatten, unflatten)
+    else:
+        SUPPORTED_LEAF_CLS.append(type)
 
 
-def _ordereddict_flatten(inp):
+def _dict_flatten(ordered, inp):
     aux_data = []
     results = []
-    for key, value in inp.items():
+    dict_items = inp.items() if ordered else sorted(inp.items())
+    for key, value in dict_items:
         results.append(value)
         aux_data.append(key)
     return results, tuple(aux_data)
 
 
-def _ordereddict_unflatten(inps, aux_data):
-    return OrderedDict(zip(aux_data, inps))
+def _dict_unflatten(dict_type, inps, aux_data):
+    return dict_type(zip(aux_data, inps))
 
 
 def qparams_flatten(inp):
@@ -99,33 +111,41 @@ def qparams_flatten(inp):
     return results, tuple(aux_data)
 
 
-def qparams_unflatten(inp, aux_data):
-    obj = QParams.__new__(QParams)
+def qparams_unflatten(qparam_type, inp, aux_data):
+    obj = qparam_type.__new__(qparam_type)
     for k, v in zip(aux_data, inp):
         setattr(obj, k, v)
     return obj
 
 
-register_supported_type(list, lambda x: (x, None), lambda x, aux_data: list(x))
-register_supported_type(tuple, lambda x: (x, None), lambda x, aux_data: tuple(x))
-register_supported_type(dict, _dict_flatten, _dict_unflatten)
-register_supported_type(
-    collections.OrderedDict, _ordereddict_flatten, _ordereddict_unflatten
+_register_supported_type(list, lambda x: (x, None), lambda x, aux_data: list(x))
+_register_supported_type(tuple, lambda x: (x, None), lambda x, aux_data: tuple(x))
+_register_supported_type(
+    dict, partial(_dict_flatten, False), partial(_dict_unflatten, dict)
+)
+_register_supported_type(
+    defaultdict, partial(_dict_flatten, False), partial(_dict_unflatten, defaultdict)
 )
-register_supported_type(
+_register_supported_type(
+    OrderedDict, partial(_dict_flatten, True), partial(_dict_unflatten, OrderedDict)
+)
+_register_supported_type(
     slice,
     lambda x: ([x.start, x.stop, x.step], None),
     lambda x, aux_data: slice(x[0], x[1], x[2]),
 )
 
-register_supported_type(QParams, qparams_flatten, qparams_unflatten)
+_register_supported_type(QParams, qparams_flatten, partial(qparams_unflatten, QParams))
+_register_supported_type(
+    LSQParams, qparams_flatten, partial(qparams_unflatten, LSQParams)
+)
 
 
 def _is_leaf(obj):
-    if isinstance(obj, type):
-        return issubclass(obj, tuple(SUPPORTED_LEAF_CLS)) or obj in SUPPORTED_LEAF_TYPE
+    obj_type = obj if isinstance(obj, type) else type(obj)
     return (
-        isinstance(obj, tuple(SUPPORTED_LEAF_CLS)) or type(obj) in SUPPORTED_LEAF_TYPE
+        issubclass(obj_type, tuple(SUPPORTED_LEAF_CLS))
+        or obj_type in SUPPORTED_LEAF_TYPE
     )
 
 

imperative/python/megengine/traced_module/serialization.py

@@ -5,30 +5,158 @@
 # 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.
-from typing import Dict
+from importlib import import_module
+from typing import Dict, Tuple
 
 from ..core._imperative_rt import OpDef
 from ..core.ops import builtin
+from ..tensor import Tensor
 from ..version import __version__
+from .utils import _convert_kwargs_to_args
 
-OPDEF_PARAM_LOADER = {}
+OPDEF_LOADER = {}
+FUNCTIONAL_LOADER = {}
+TENSORMETHOD_LOADER = {}
+MODULE_LOADER = {}
 
 
-def get_opdef_state(obj: OpDef) -> Dict:
-    state = obj.__getstate__()
-    state["type"] = type(obj)
-    state["version"] = __version__
-    return state
+class _ModuleState:
+    obj = None
 
+    def __init__(self, module: Tuple, state: Dict, version: str):
+        self.module = module
+        self.state = state
+        self.version = version
 
-def load_opdef_from_state(state: Dict) -> OpDef:
-    assert "type" in state and issubclass(state["type"], OpDef)
-    assert "version" in state
-    opdef_type = state.pop("type")
-    if opdef_type in OPDEF_PARAM_LOADER:
-        loader = OPDEF_PARAM_LOADER[opdef_type]
-        state = loader(state)
-    state.pop("version")
-    opdef_obj = opdef_type()
-    opdef_obj.__setstate__(state)
-    return opdef_obj
+    @classmethod
+    def get_module_state(cls, module):
+        typem = (type(module).__module__, type(module).__qualname__)
+        state = module.__dict__.copy()
+        state.pop("_m_dump_modulestate", None)
+        if hasattr(module, "_m_dump_modulestate"):
+            assert isinstance(module._m_dump_modulestate, cls)
+            module._m_dump_modulestate.__init__(typem, state, __version__)
+        else:
+            module.__dict__["_m_dump_modulestate"] = _ModuleState(
+                typem, state, __version__
+            )
+
+        return module._m_dump_modulestate
+
+    def __getstate__(self):
+        return {"module": self.module, "state": self.state, "version": self.version}
+
+    def to_module(self):
+        if self.obj is None:
+            typem = getattr(import_module(self.module[0]), self.module[1])
+            m_obj = typem.__new__(typem)
+            m_obj.__dict__.update(self.state)
+            self.obj = m_obj
+        return self.obj
+
+
+def register_opdef_loader(*opdefs):
+    def callback(loader):
+        for opdef in opdefs:
+            assert opdef not in OPDEF_LOADER
+            OPDEF_LOADER[opdef] = loader
+        return loader
+
+    return callback
+
+
+def register_functional_loader(*funcs):
+    def callback(loader):
+        for func in funcs:
+            assert func not in FUNCTIONAL_LOADER
+            FUNCTIONAL_LOADER[func] = loader
+        return loader
+
+    return callback
+
+
+def register_module_loader(*module_types):
+    def callback(loader):
+        for module_type in module_types:
+            assert module_type not in MODULE_LOADER
+            MODULE_LOADER[module_type] = loader
+        return loader
+
+    return callback
+
+
+def register_tensor_method_loader(*methods):
+    def callback(loader):
+        for method in methods:
+            assert method not in TENSORMETHOD_LOADER
+            TENSORMETHOD_LOADER[method] = loader
+        return loader
+
+    return callback
+
+
+def _replace_args_kwargs(expr, new_args, new_kwargs):
+    if len(new_args) != len(expr.args) or set(new_kwargs.keys()) != set(
+        expr.kwargs.keys()
+    ):
+        expr.set_args_kwargs(*new_args, **new_kwargs)
+
+
+def load_functional(expr):
+    func = (
+        (expr.func.__module__, expr.func.__qualname__)
+        if callable(expr.func)
+        else expr.func
+    )
+    assert isinstance(func, tuple)
+    if func in FUNCTIONAL_LOADER:
+        loader = FUNCTIONAL_LOADER[func]
+        loader(expr)
+        mname, fname = func
+        f = import_module(mname)
+        for i in fname.split("."):
+            f = getattr(f, i)
+        expr.func = f
+    assert callable(expr.func)
+    if not hasattr(expr, "version") or expr.version != __version__:
+        args, kwargs = _convert_kwargs_to_args(expr.func, expr.args, expr.kwargs)
+        _replace_args_kwargs(expr, args, kwargs)
+
+
+def load_call_module_expr(expr):
+    m_type = expr.inputs[0].module_type
+    if isinstance(m_type, type):
+        m_type = (m_type.__module__, m_type.__qualname__)
+    if m_type in MODULE_LOADER:
+        MODULE_LOADER[m_type](expr)
+    if isinstance(expr.inputs[0].module_type, tuple):
+        mname, classname = expr.inputs[0].module_type
+        expr.inputs[0].module_type = getattr(import_module(mname), classname)
+    if not hasattr(expr, "version") or expr.version != __version__:
+        fwd_func = getattr(expr.inputs[0].module_type, "forward")
+        args, kwargs = _convert_kwargs_to_args(fwd_func, expr.args, expr.kwargs)
+        _replace_args_kwargs(expr, args, kwargs)
+
+
+def load_call_tensor_method_expr(expr):
+    if expr.method in TENSORMETHOD_LOADER:
+        loader = TENSORMETHOD_LOADER[expr.method]
+        loader(expr)
+    if not hasattr(expr, "version") or expr.version != __version__:
+        tmethod = (
+            getattr(expr.args[0], expr.method)
+            if isinstance(expr.args[0], type)
+            else getattr(Tensor, expr.method)
+        )
+        args, kwargs = _convert_kwargs_to_args(tmethod, expr.args, expr.kwargs)
+        _replace_args_kwargs(expr, args, kwargs)
+
+
+def load_apply_expr(expr):
+    opdef_type = type(expr.opdef)
+    if opdef_type in OPDEF_LOADER:
+        OPDEF_LOADER[opdef_type](expr)
+        opdef_state = expr.opdef_state
+        opdef_obj = opdef_state.pop("opdef_type")()
+        opdef_obj.__setstate__(opdef_state)
+        expr.opdef = opdef_obj

imperative/python/megengine/traced_module/traced_module.py

@@ -14,6 +14,7 @@ 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
@@ -53,6 +54,7 @@ from ..quantization.observer import (
     SyncMinMaxObserver,
 )
 from ..tensor import Tensor
+from ..version import __version__
 from .expr import (
     Apply,
     CallFunction,
@@ -80,8 +82,27 @@ from .module_tracer import (
     set_active_module_tracer,
 )
 from .node import ModuleNode, Node, NodeMixin, TensorNode
-from .pytree import ArgsIndex, tree_flatten
-from .utils import replace_container_with_module_container
+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 .utils import (
+    _check_builtin_module_attr,
+    _check_obj_attr,
+    _convert_kwargs_to_args,
+    replace_container_with_module_container,
+)
 
 logger = get_logger(__name__)
 
@@ -341,7 +362,7 @@ class NameSpace:
     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:
+        if name in self._used_names and (self._used_names[name] is node):
             return name
 
         name = re.sub("[^0-9a-zA-Z_]+", "_", name)
@@ -1067,6 +1088,7 @@ class InternalGraph:
                 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)]
@@ -1105,13 +1127,27 @@ class InternalGraph:
         return res
 
     def __getstate__(self):
-        state = self.__dict__.copy()
-        if "_top_graph" in state:
-            state.pop("_top_graph")
+        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")
@@ -1144,6 +1180,25 @@ class InternalGraph:
             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)
@@ -1157,9 +1212,7 @@ def _get_meth_name(obj, func):
 def _wrapped_function(orig_func):
     @functools.wraps(orig_func)
     def wrapped_fn(*args, **kwargs):
-        method_func = wrapped_fn
-        if "method_func" in kwargs:
-            method_func = kwargs.pop("method_func")
+        method_func = kwargs.pop("method_func", wrapped_fn)
         if is_tracing_module():
             unset_module_tracing()
             inputs, tree_def = tree_flatten((args, kwargs))
@@ -1167,11 +1220,11 @@ def _wrapped_function(orig_func):
                 if not NodeMixin.get(i, None):
                     if isinstance(i, (RawTensor, NodeMixin)):
                         NodeMixin.wrap_safe(i, Constant.make(i))
-            meth_name, arg_type = None, None
-            if args:
-                meth_name = _get_meth_name(args[0], method_func)
-                arg_type = args[0] if isinstance(args[0], type) else type(args[0])
+            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]):
@@ -1190,6 +1243,7 @@ def _wrapped_function(orig_func):
                     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)
 
@@ -1228,9 +1282,11 @@ class TracedModuleBuilder(NodeMixin):
         "_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):
@@ -1301,22 +1357,18 @@ class TracedModuleBuilder(NodeMixin):
                 qat_module.weight_fake_quant.set_qparams(qparams)
 
     def build(self):
-        if self._is_builtin or isinstance(self._mod, TracedModule):
-            if module_tracer.is_builtin(self._mod) or isinstance(
-                self._mod, TracedModule
-            ):
-                mod_type = type(self._mod)
-            else:
-                assert isinstance(self._mod, (Observer, _FakeQuantize))
-                mod_type = (
-                    Observer if isinstance(self._mod, Observer) else _FakeQuantize
-                )
+        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
         else:
-            is_qat = isinstance(self._mod, QATModule)
+            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
             )
@@ -1338,15 +1390,18 @@ class TracedModuleBuilder(NodeMixin):
                 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_fakequant = None
+                    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_fakequant = None
+                    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):
@@ -1357,6 +1412,7 @@ class TracedModuleBuilder(NodeMixin):
         # 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)
@@ -1372,11 +1428,7 @@ class TracedModuleBuilder(NodeMixin):
 
         callnode.arg_def = tree_def
 
-        if (
-            self._is_builtin
-            or tree_def in self._argdef_graph_map
-            or isinstance(self._mod, TracedModule)
-        ):
+        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)
@@ -1385,33 +1437,7 @@ class TracedModuleBuilder(NodeMixin):
                 self._body = None
             elif tree_def in self._argdef_graph_map:
                 self._body = self._argdef_graph_map[tree_def]
-            else:
-                self._mod._is_top = False
-                self._body = self._mod.argdef_graph_map[tree_def]
-                module_qualname = NodeMixin.get(self).qualname
-                if module_qualname != self._body.qualname:
-                    src_name, dst_name = self._body.qualname, module_qualname
-
-                    def replace_qualname(g):
-                        attr_name = get_suffix_name(src_name, g.qualname)
-                        if attr_name is not None:
-                            g._qualname = (
-                                ("%s.%s" % (dst_name, attr_name))
-                                if attr_name
-                                else dst_name
-                            )
-                        assert get_suffix_name(dst_name, g.qualname) is not None
-
-                    for mod in self._mod.modules():
-                        if not hasattr(mod, "argdef_graph_map"):
-                            continue
-                        for g in mod.argdef_graph_map.values():
-                            replace_qualname(g)
-                            g._namespace.qualname = g.qualname
-                            for n in g.nodes(False):
-                                replace_qualname(n)
         else:
-            self_node = None
             orig_self = NodeMixin.get(self)
             parent_graph = active_module_tracer().current_scope()
             module_qualname = orig_self._qualname
@@ -1423,20 +1449,14 @@ class TracedModuleBuilder(NodeMixin):
             active_module_tracer().push_scope(self._body)
             # rebind self to new input node
 
-            if self_node:
-                NodeMixin.wrap_safe(self, self_node)
-                active_module_tracer().current_scope()._add_input(self_node)
-            else:
-                NodeMixin.wrap_safe(
-                    self,
-                    self_node
-                    if self_node
-                    else Input.make(
-                        name="self",
-                        qualname=module_qualname,
-                        type=NodeMixin.get_wrapped_type(self),
-                    ),
-                )
+            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
@@ -1470,8 +1490,23 @@ class TracedModuleBuilder(NodeMixin):
                 return x
 
             args = [self]
-            for i, v in enumerate(inputs[1:]):
-                args.append(wrap(v, idx2key[i + 1]))
+            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(
@@ -1514,7 +1549,6 @@ class TracedModuleBuilder(NodeMixin):
             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
@@ -1568,7 +1602,7 @@ class TracedModuleBuilder(NodeMixin):
                         wrapped = self.__getattr__(name)
 
                 if isinstance(wrapped, TracedModuleBuilder):
-                    if not isinstance(mod_attr, (List, Dict)):
+                    if not isinstance(mod_attr, (List, Dict, QATModule)):
                         assert mod_attr is wrapped._mod
                 else:
                     assert mod_attr is wrapped
@@ -1977,8 +2011,6 @@ class TracedModule(Module):
     def graph(self) -> InternalGraph:
         """Return the ``InternalGraph`` of this ``TracedModule``.
         """
-        if self._is_top:
-            self._update_ref()
         assert len(self.argdef_graph_map) == 1
         return list(self.argdef_graph_map.values())[0]
 
@@ -2112,7 +2144,7 @@ class TracedModule(Module):
                             if hasattr(obj, "argdef_graph_map")
                             else None
                         )
-                        if expr_graph is not 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:
@@ -2137,26 +2169,119 @@ class TracedModule(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__
+        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)
 
 
@@ -2181,6 +2306,7 @@ def wrap(func: Callable):
     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
@@ -2247,6 +2373,8 @@ def trace_module(
             NodeMixin.wrap_safe(
                 builder, Input.make(name="top", type=ModuleNode, qualname=net_name)
             )
+            args, kwargs = _convert_kwargs_to_args(mod.forward, args, kwargs, True)
+
             inputs, _ = tree_flatten((args, kwargs))
             for _, i in enumerate(inputs):
                 # assert isinstance(i, Tensor), "not support "

imperative/python/megengine/traced_module/utils.py

@@ -5,12 +5,17 @@
 # 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 collections
 import copy
+import inspect
 from collections.abc import MutableMapping, MutableSequence
-from typing import Dict, Iterable, List, Optional, Sequence
+from typing import Dict, Iterable, List, Optional, Sequence, Type
 
+from .. import get_logger
 from ..module import Module
 
+logger = get_logger(__name__)
+
 
 def replace_container_with_module_container(container):
     has_module = False
@@ -52,6 +57,101 @@ def replace_container_with_module_container(container):
     return has_module, module_container
 
 
+def _convert_kwargs_to_args(func, args, kwargs, is_bounded=False):
+    # is_bounded = True when func is a method and provided args don't include 'self'
+    arg_specs = inspect.getfullargspec(func)
+    arg_specs_args = arg_specs.args
+    if is_bounded:
+        arg_specs_args = arg_specs.args[1:]
+    new_args = []
+    new_kwargs = {}
+    new_args.extend(args)
+    if set(arg_specs_args[0 : len(new_args)]) & set(kwargs.keys()):
+        repeated_arg_name = set(arg_specs_args[0 : len(new_args)]) & set(kwargs.keys())
+        raise TypeError(
+            "{} got multiple values for argument {}".format(
+                func.__qualname__, ", ".join(repeated_arg_name)
+            )
+        )
+    if len(new_args) < len(arg_specs.args):
+        for ind in range(len(new_args), len(arg_specs_args)):
+            arg_name = arg_specs_args[ind]
+            if arg_name in kwargs:
+                new_args.append(kwargs[arg_name])
+            else:
+                index = ind - len(arg_specs_args) + len(arg_specs.defaults)
+                assert index < len(arg_specs.defaults) and index >= 0
+                new_args.append(arg_specs.defaults[index])
+
+    for kwarg_name in arg_specs.kwonlyargs:
+        if kwarg_name in kwargs:
+            new_kwargs[kwarg_name] = kwargs[kwarg_name]
+        else:
+            assert kwarg_name in arg_specs.kwonlydefaults
+            new_kwargs[kwarg_name] = arg_specs.kwonlydefaults[kwarg_name]
+    for k, v in kwargs.items():
+        if k not in arg_specs.args and k not in arg_specs.kwonlyargs:
+            if arg_specs.varkw is None:
+                raise TypeError(
+                    "{} got an unexpected keyword argument {}".format(
+                        func.__qualname__, k
+                    )
+                )
+            new_kwargs[k] = v
+    return tuple(new_args), new_kwargs
+
+
+def _check_obj_attr(obj):
+    # check if all the attributes of a obj is serializable
+    from .pytree import tree_flatten
+    from .pytree import SUPPORTED_LEAF_CLS, SUPPORTED_LEAF_TYPE, TreeDef
+    from .expr import Expr
+    from .traced_module import TracedModule, InternalGraph, NameSpace
+
+    def _check_leaf_type(leaf):
+        leaf_type = leaf if isinstance(leaf, type) else type(leaf)
+        traced_module_types = [Expr, TreeDef, TracedModule, InternalGraph, NameSpace]
+        return (
+            issubclass(leaf_type, tuple(SUPPORTED_LEAF_CLS + traced_module_types))
+            or leaf_type in SUPPORTED_LEAF_TYPE
+        )
+
+    for _, v in obj.items():
+        leafs, _ = tree_flatten(v, is_leaf=lambda _: True)
+        for leaf in leafs:
+            assert _check_leaf_type(
+                leaf
+            ), "Type {} is not supported by traced module".format(
+                leaf if isinstance(leaf, type) else type(leaf)
+            )
+
+
+def _check_builtin_module_attr(mod):
+    from .pytree import _is_leaf as _check_leaf_type
+    from .pytree import tree_flatten
+
+    # check if all the attributes of a builtin module is serializable
+    is_non_serializable_module = lambda m: isinstance(
+        m, Module
+    ) and not _check_builtin_module_attr(m)
+    for k, v in mod.__dict__.items():
+        if k == "_m_dump_modulestate":
+            continue
+        if is_non_serializable_module(v):
+            return False
+        elif not isinstance(v, Module):
+            leafs, _ = tree_flatten(v, is_leaf=lambda _: True)
+            for leaf in leafs:
+                if not _check_leaf_type(leaf) or is_non_serializable_module(leaf):
+                    logger.warn(
+                        "Type {} is not supported by traced module".format(
+                            leaf if isinstance(leaf, type) else type(leaf)
+                        )
+                    )
+                    return False
+    return True
+
+
 class _ModuleList(Module, MutableSequence):
     r"""A List-like container.
 

imperative/python/test/unit/core/test_serialization.py

@@ -15,7 +15,6 @@ import numpy as np
 import megengine as mge
 from megengine import Parameter, Tensor
 from megengine.core.ops import builtin
-from megengine.traced_module.serialization import get_opdef_state, load_opdef_from_state
 
 
 def test_tensor_serialization():
@@ -88,25 +87,3 @@ def test_compatibility():
 
     test_old_tensor("tensor_v1_1.mge")
     test_old_tensor("tensor_v1_2.mge")
-
-
-def test_opdef_serialization():
-    with TemporaryFile() as f:
-        x = builtin.Elemwise(mode="Add")
-        pickle.dump(get_opdef_state(x), f)
-        f.seek(0)
-        load_x = load_opdef_from_state(pickle.load(f))
-        assert x == load_x
-
-    with TemporaryFile() as f:
-        x = builtin.Convolution(stride_h=9, compute_mode="float32")
-        x.strategy = (
-            builtin.Convolution.Strategy.PROFILE
-            | builtin.Convolution.Strategy.HEURISTIC
-            | builtin.Convolution.Strategy.REPRODUCIBLE
-        )
-        pickle.dump(get_opdef_state(x), f)
-        f.seek(0)
-        load_x = load_opdef_from_state(pickle.load(f))
-        assert x.strategy == load_x.strategy
-        assert x == load_x

imperative/python/test/unit/traced_module/test_modification.py

@@ -85,12 +85,12 @@ class NewModule(M.Module):
         return x
 
 
-def _check_expr_users(traced_module):
+def _check_expr_users(flattened_module):
     node_user = defaultdict(list)
-    for expr in traced_module.graph._exprs:
+    for expr in flattened_module.graph._exprs:
         for node in expr.inputs:
             node_user[node].append(expr)
-    for node in traced_module.graph.nodes():
+    for node in flattened_module.graph.nodes():
         node.users.sort(key=lambda m: m._id)
         node_user[node].sort(key=lambda m: m._id)
         assert node.users == node_user[node]

imperative/python/test/unit/traced_module/test_qat_module.py

@@ -8,6 +8,7 @@ import numpy as np
 import megengine as mge
 import megengine.functional as F
 import megengine.module as M
+import megengine.module.qat as QM
 import megengine.quantization as Q
 from megengine import Tensor
 from megengine.module.qat.module import QATModule
@@ -28,10 +29,18 @@ def get_subattr(self: M.Module, name: str):
     return getattr(self, name)
 
 
+class MyConvBnRelu2d(M.ConvBnRelu2d):
+    pass
+
+
+class MyQATConvBnRelu2d(QM.ConvBnRelu2d):
+    pass
+
+
 class Myblcok(M.Module):
     def __init__(self,):
         super().__init__()
-        self.conv0 = M.ConvBnRelu2d(3, 3, 3, 1, 1)
+        self.conv0 = MyConvBnRelu2d(3, 3, 3, 1, 1)
         self.conv1 = M.ConvBn2d(3, 3, 1, 1, 0)
         self.conv2 = M.ConvBn2d(3, 3, 1, 1, 0)
         self.add = M.Elemwise("FUSE_ADD_RELU")
@@ -106,7 +115,11 @@ def check_qparams(qparmsa: Q.QParams, qparmsb: Q.QParams):
 
 
 def build_observered_net(net: M.Module, observer_cls):
-    qat_net = Q.quantize_qat(net, qconfig=get_observer_config(observer_cls))
+    qat_net = Q.quantize_qat(
+        net,
+        qconfig=get_observer_config(observer_cls),
+        mapping={MyConvBnRelu2d: MyQATConvBnRelu2d},
+    )
     Q.enable_observer(qat_net)
     inp = Tensor(np.random.random(size=(5, 3, 32, 32)))
     qat_net(inp)
@@ -134,6 +147,15 @@ def test_trace_qat():
                 check_qparams(weight_qparams, traced_weight_qparams)
             if act_qparams:
                 check_qparams(act_qparams, traced_act_qparams)
+        flatten_traced_net = traced_net.flatten()
+        conv0_node = flatten_traced_net.graph.get_node_by_name(
+            "MyModule_block0_conv0"
+        ).as_unique()
+        conv0_out_node = flatten_traced_net.graph.get_node_by_name(
+            "MyModule_block0_conv0_out"
+        ).as_unique()
+        assert isinstance(conv0_node.owner, TracedModule)
+        assert conv0_out_node.expr.inputs[0] is conv0_node
 
     _check_qat_module(build_observered_net(MyModule(), Q.MinMaxObserver))
     _check_qat_module(build_observered_net(MyModule(), MyMinMaxObserver))

imperative/python/test/unit/traced_module/test_serialization.py

@@ -6,14 +6,59 @@
 # software distributed under the License is distributed on an
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 import pickle
+from collections import defaultdict
+from tempfile import TemporaryFile
 
 import numpy as np
 
 import megengine.functional as F
 import megengine.module as M
+import megengine.traced_module.serialization as S
 from megengine import Tensor
+from megengine.core._imperative_rt.core2 import apply
+from megengine.core.ops import builtin
+from megengine.core.ops.builtin import Elemwise
 from megengine.module import Module
 from megengine.traced_module import trace_module
+from megengine.traced_module.expr import CallMethod, Constant
+from megengine.traced_module.node import TensorNode
+from megengine.traced_module.serialization import (
+    register_functional_loader,
+    register_module_loader,
+    register_opdef_loader,
+    register_tensor_method_loader,
+)
+from megengine.traced_module.utils import _convert_kwargs_to_args
+
+
+def _check_id(traced_module):
+    _total_ids = traced_module.graph._total_ids
+    node_ids = [n._id for n in traced_module.graph.nodes().as_list()]
+    assert len(set(node_ids)) == len(node_ids)
+    assert max(node_ids) + 1 == _total_ids[0]
+
+    expr_ids = [n._id for n in traced_module.graph.exprs().as_list()]
+    assert len(set(expr_ids)) == len(expr_ids)
+    assert max(expr_ids) + 1 == _total_ids[1]
+
+
+def _check_name(flatened_module):
+    node_names = [n._name for n in flatened_module.graph.nodes().as_list()]
+    assert len(set(node_names)) == len(node_names)
+
+
+def _check_expr_users(traced_module):
+    node_user = defaultdict(list)
+    for expr in traced_module.graph._exprs:
+        for node in expr.inputs:
+            node_user[node].append(expr)
+        if isinstance(expr, CallMethod) and expr.graph:
+            _check_expr_users(expr.inputs[0].owner)
+
+    for node in traced_module.graph.nodes(False):
+        node.users.sort(key=lambda m: m._id)
+        node_user[node].sort(key=lambda m: m._id)
+        assert node.users == node_user[node]
 
 
 class MyBlock(Module):
@@ -48,5 +93,274 @@ def test_dump_and_load():
     traced_module = trace_module(module, x)
     np.testing.assert_array_equal(expect, traced_module(x))
     obj = pickle.dumps(traced_module)
-    pickle.loads(obj)
+    new_tm = pickle.loads(obj)
+    _check_id(new_tm)
+    _check_expr_users(new_tm)
+    traced_module.graph._reset_ids()
+    old_nodes = traced_module.graph.nodes().as_list()
+    new_nodes = new_tm.graph.nodes().as_list()
+    old_exprs = traced_module.graph.exprs().as_list()
+    new_exprs = new_tm.graph.exprs().as_list()
+    assert len(old_nodes) == len(new_nodes)
+    for i, j in zip(old_nodes, new_nodes):
+        assert i._name == j._name
+        assert i._qualname == j._qualname
+        assert i._id == j._id
+    assert len(old_exprs) == len(new_exprs)
+    for i, j in zip(old_exprs, new_exprs):
+        assert i._id == j._id
+
     np.testing.assert_array_equal(expect, traced_module(x))
+
+
+def test_opdef_loader():
+    class MyModule1(Module):
+        def forward(self, x, y):
+            op = Elemwise("ADD")
+            return apply(op, x, y)[0]
+
+    m = MyModule1()
+    x = Tensor(np.ones((20)))
+    y = Tensor(np.ones((20)))
+    traced_module = trace_module(m, x, y)
+    orig_loader_dict = S.OPDEF_LOADER
+    S.OPDEF_LOADER = {}
+
+    @register_opdef_loader(Elemwise)
+    def add_opdef_loader(expr):
+        if expr.opdef_state["mode"] == "ADD":
+            expr.opdef_state["mode"] = "MUL"
+            node = expr.inputs[1]
+            astype_expr = CallMethod(node, "astype")
+            oup = TensorNode(
+                astype_expr,
+                shape=node.shape,
+                dtype=expr.inputs[0].dtype,
+                qparams=node.qparams,
+            )
+            astype_expr.set_args_kwargs(node, expr.inputs[0].dtype)
+            astype_expr.return_val = (oup,)
+            expr.inputs[1] = oup
+
+    obj = pickle.dumps(traced_module)
+    new_module = pickle.loads(obj)
+    _check_id(new_module)
+    _check_expr_users(new_module)
+    _check_name(new_module.flatten())
+    assert (
+        isinstance(new_module.graph._exprs[0], CallMethod)
+        and new_module.graph._exprs[1].opdef.mode == "MUL"
+        and len(new_module.graph._exprs) == 2
+    )
+    result = new_module(x, y)
+    np.testing.assert_equal(result.numpy(), x.numpy())
+    S.OPDEF_LOADER = orig_loader_dict
+
+
+def test_functional_loader():
+    class MyModule2(Module):
+        def forward(self, x, y):
+            return F.conv2d(x, y)
+
+    m = MyModule2()
+    x = Tensor(np.random.random((1, 3, 32, 32)))
+    y = Tensor(np.random.random((3, 3, 3, 3)))
+    traced_module = trace_module(m, x, y)
+    orig_loader_dict = S.FUNCTIONAL_LOADER
+    S.FUNCTIONAL_LOADER = {}
+
+    @register_functional_loader(("megengine.functional.nn", "conv2d"))
+    def conv2df_loader(expr):
+        # expr.func = ("megengine.functional.nn","conv2d")
+        kwargs = expr.kwargs
+        orig_weight = expr.named_args["weight"]
+
+        astype_expr = CallMethod(orig_weight, "astype")
+        oup = TensorNode(
+            astype_expr,
+            shape=orig_weight.shape,
+            dtype=orig_weight.dtype,
+            qparams=orig_weight.qparams,
+        )
+
+        astype_expr.set_args_kwargs(orig_weight, expr.named_args["inp"].dtype)
+        astype_expr.return_val = (oup,)
+
+        expr.set_arg("weight", oup)
+
+    obj = pickle.dumps(traced_module)
+    new_module = pickle.loads(obj)
+    _check_expr_users(new_module)
+    _check_id(new_module)
+    result = new_module(x, y)
+    gt = m(x, y)
+    assert (
+        isinstance(new_module.graph._exprs[0], CallMethod)
+        and len(new_module.graph._exprs) == 2
+    )
+    np.testing.assert_equal(result.numpy(), gt.numpy())
+    S.FUNCTIONAL_LOADER = orig_loader_dict
+
+
+def test_tensor_method_loader():
+    class MyModule3(Module):
+        def forward(self, x):
+            return x + 1
+
+    m = MyModule3()
+    x = Tensor(np.ones((20)))
+    traced_module = trace_module(m, x)
+    orig_loader_dict = S.TENSORMETHOD_LOADER
+    S.TENSORMETHOD_LOADER = {}
+
+    @register_tensor_method_loader("__add__")
+    def add_loader(expr):
+        args = list(expr.args)
+        if not isinstance(args[1], TensorNode):
+            args[1] = Tensor(args[1])
+            node = Constant(args[1], "const").outputs[0]
+
+            astype_expr = CallMethod(node, "astype")
+            oup = TensorNode(
+                astype_expr, shape=node.shape, dtype=node.dtype, qparams=node.qparams,
+            )
+            astype_expr.set_args_kwargs(node, expr.inputs[0].dtype)
+            astype_expr.return_val = (oup,)
+
+            add_expr = CallMethod(oup, "__add__")
+            add_expr.set_args_kwargs(oup, oup)
+            oup1 = TensorNode(
+                add_expr, shape=oup.shape, dtype=oup.dtype, qparams=node.qparams,
+            )
+            add_expr.return_val = oup1
+            args[1] = oup1
+            expr.set_args_kwargs(*args)
+
+    obj = pickle.dumps(traced_module)
+    new_module = pickle.loads(obj)
+    _check_expr_users(new_module)
+    _check_id(new_module)
+    result = new_module(x)
+    gt = m(x)
+    assert (
+        isinstance(new_module.graph._exprs[0], Constant)
+        and len(new_module.graph._exprs) == 4
+    )
+    np.testing.assert_equal(result.numpy(), (x + 2).numpy())
+    S.TENSORMETHOD_LOADER = orig_loader_dict
+
+
+def test_module_loader():
+    class MyModule4(Module):
+        def __init__(self):
+            super().__init__()
+            self.conv = M.Conv2d(3, 3, 3)
+
+        def forward(self, x):
+            return self.conv(x)
+
+    m = MyModule4()
+    x = Tensor(np.random.random((1, 3, 32, 32)))
+    traced_module = trace_module(m, x)
+    orig_loader_dict = S.MODULE_LOADER
+    S.MODULE_LOADER = {}
+
+    @register_module_loader(("megengine.module.conv", "Conv2d"))
+    def conv2dm_loader(expr):
+        module = expr.inputs[0].owner
+        args = list(expr.args)
+        orig_inp = args[1]
+        astype_expr = CallMethod(orig_inp, "astype")
+        oup = TensorNode(
+            astype_expr,
+            shape=orig_inp.shape,
+            dtype=orig_inp.dtype,
+            qparams=orig_inp.qparams,
+        )
+        astype_expr.set_args_kwargs(orig_inp, module.weight.dtype)
+        astype_expr.return_val = (oup,)
+        args[1] = oup
+        expr.set_args_kwargs(*args)
+
+    obj = pickle.dumps(traced_module)
+    new_module = pickle.loads(obj)
+    result = new_module(x)
+    gt = m(x)
+    assert (
+        isinstance(new_module.graph._exprs[1], CallMethod)
+        and len(new_module.graph._exprs) == 3
+    )
+    np.testing.assert_equal(result.numpy(), gt.numpy())
+    S.MODULE_LOADER = orig_loader_dict
+
+
+def test_shared_module():
+    class MyModule(M.Module):
+        def __init__(self):
+            super().__init__()
+            self.a = M.Elemwise("ADD")
+            self.b = self.a
+
+        def forward(self, x, y):
+            z = self.a(x, y)
+            z = self.b(z, y)
+            return z
+
+    x = Tensor(1)
+    y = Tensor(2)
+    m = MyModule()
+    tm = trace_module(m, x, y)
+    obj = pickle.dumps(tm)
+    load_tm = pickle.loads(obj)
+    _check_expr_users(load_tm)
+    _check_name(load_tm.flatten())
+    _check_id(load_tm)
+    assert load_tm.a is load_tm.b
+
+
+def test_convert_kwargs_to_args():
+    def func(a, b, c=4, *, d, e=3, f=4):
+        pass
+
+    args = (1,)
+    kwargs = {"b": 1, "d": 6}
+    new_args, new_kwargs = _convert_kwargs_to_args(func, args, kwargs)
+    assert new_args == (1, 1, 4)
+    assert new_kwargs == {"d": 6, "e": 3, "f": 4}
+
+    args = (1,)
+    kwargs = {"d": 6}
+    new_args, new_kwargs = _convert_kwargs_to_args(func, args, kwargs, is_bounded=True)
+    assert new_args == (1, 4)
+    assert new_kwargs == {"d": 6, "e": 3, "f": 4}
+
+    def func1(a, b, c, d, e, *, f):
+        pass
+
+    args = ()
+    kwargs = {"a": 1, "b": 2, "c": 3, "d": 4, "e": 5, "f": 6}
+    new_args, new_kwargs = _convert_kwargs_to_args(func1, args, kwargs)
+    assert new_args == (1, 2, 3, 4, 5)
+    assert new_kwargs == {"f": 6}
+
+
+def test_opdef_serialization():
+    with TemporaryFile() as f:
+        x = builtin.Elemwise(mode="Add")
+        pickle.dump(x, f)
+        f.seek(0)
+        load_x = pickle.load(f)
+        assert x == load_x
+
+    with TemporaryFile() as f:
+        x = builtin.Convolution(stride_h=9, compute_mode="float32")
+        x.strategy = (
+            builtin.Convolution.Strategy.PROFILE
+            | builtin.Convolution.Strategy.HEURISTIC
+            | builtin.Convolution.Strategy.REPRODUCIBLE
+        )
+        pickle.dump(x, f)
+        f.seek(0)
+        load_x = pickle.load(f)
+        assert x.strategy == load_x.strategy
+        assert x == load_x