MegEngine/imperative/python/megengine/jit/tracing.py

# -*- coding: utf-8 -*-
# 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 collections
import contextlib
import functools
import itertools
import json
import os

import numpy as np

from ..core._imperative_rt import GraphProfiler
from ..core._imperative_rt.core2 import Tensor as RawTensor
from ..core._imperative_rt.core2 import (
    TensorWeakRef,
    apply,
    set_tracing,
    skip_tracing,
    unset_tracing,
)
from ..core._imperative_rt.ops import (
    AssertEqual,
    CollectiveComm,
    RemoteRecv,
    RemoteSend,
)
from ..core._trace_option import set_symbolic_shape
from ..core._wrap import device as as_device
from ..core.ops.builtin import BatchNorm, OpDef
from ..core.ops.special import Const
from ..core.tensor import megbrain_graph as G
from ..core.tensor.utils import setscalar
from ..utils.naming import AutoNaming
from .dtr_config import DTRConfig
from .graph_opt_config import GraphOptimizationConfig
from .sublinear_memory_config import SublinearMemoryConfig


def _input_node_use_static_shape():
    return os.environ.get("MEGENGINE_INPUT_NODE_USE_STATIC_SHAPE") is not None


class TraceMismatchError(RuntimeError):
    pass


active_trace = None


def is_tracing():
    if active_trace is None:
        return False
    else:
        return not skip_tracing


@contextlib.contextmanager
def exclude_from_trace():
    global skip_tracing
    if skip_tracing:
        yield
        return
    try:
        skip_tracing = True
        unset_tracing()
        if active_trace is not None:
            active_trace._begin_excluded_region()
        yield
    finally:
        skip_tracing = False
        set_tracing()


class TensorInfo:
    __slots__ = (
        # collected attributes
        "name",
        "external",
        "data_read",
        "shape_read",
        "value_read",
        "exported",
        "device",
        "dtype",
        "shape",
        "is_const",
        "bound_data",
        # resources for execution
        "varnode",
        "data_setter",
        "shape_reader",
        "value_reader",
        "data_reader",
    )

    def __init__(self):
        self.name = None
        self.exported = None
        self.data_read = None
        self.shape_read = None
        self.value_read = None
        self.bound_data = None

        self.data_setter = None
        self.shape_reader = None
        self.value_reader = None
        self.data_reader = None


_io_op_types = {AssertEqual, CollectiveComm, RemoteSend, RemoteRecv}


class trace:
    """
    Wraps a callable and provide:

    * tracing via :meth:`.trace` and :meth:`.dump`
    * accelerated evalutaion via :meth:`.__call__`

    :param function: the function will be traced.
    :param symbolic: whether to apply symbolic execution for tracing. Default: False
    :param capture_as_const: capture global vars or closures as const value. Default: False
    :param sublinear_memory_config: configuration for sublinear memory optimization.
        If not None, it enables sublinear memory optimization with given setting.
    :param profiling: whether to profile compiled trace. Default: False
    :param opt_level: optimization level for compiling trace. Default: 2
    :param graph_opt_config: configuration for graph optimization. Default: None
    :param symbolic_shape: whether to use symbolic shape for tracing. Default: True
    """

    def __new__(cls, *args, **kwargs):
        if not args:
            return functools.partial(cls, **kwargs)
        return super().__new__(cls)

    def __init__(
        self,
        function,
        symbolic=False,
        capture_as_const=False,
        sublinear_memory_config: SublinearMemoryConfig = None,
        dtr_config: DTRConfig = None,
        profiling: bool = False,
        opt_level: int = 2,
        graph_opt_config: GraphOptimizationConfig = None,
        symbolic_shape: bool = True,
    ):
        self.__wrapped__ = function
        self._symbolic = symbolic
        self._capture_as_const = capture_as_const
        self._sublinear_memory_config = sublinear_memory_config
        self._dtr_config = dtr_config
        self._profiling = profiling
        self._profiler = None
        self._graph_opt_level = opt_level
        self._graph_opt_config = graph_opt_config
        self._symbolic_shape = symbolic_shape
        self._output_handles = set()

        self._reset()

    def _reset(self):
        self._untraced = True
        self._tinfo = []  # handle -> TensorInfo
        self._seq = []
        self._pc = 0
        self._graph = None
        self._need_reset_nodes = None
        self._lazy_eval_graph = None
        self._lazy_eval_tensors = set()
        self._lazy_eval_links = None
        self._active_tensors = set()
        self._tensor_remaps = None
        self._inputs_to_restore = None
        self._arg_bindings = None
        self._kwarg_bindings = None
        self._output_bindings = None
        self._output_names = None

    def _new_handle(self):
        handle = len(self._tinfo)
        info = TensorInfo()
        self._tinfo.append(info)
        return handle, info

    def _apply_op(self, op, args):
        assert not self._untraced
        # check against trace
        if self._pc >= len(self._seq):
            raise TraceMismatchError("trace should end here, but more op observed")
        record = self._seq[self._pc]
        op_, ihandles, ohandles = record
        if (isinstance(op_, str) and op_ == "Const") or (op != op_):
            raise TraceMismatchError("op different from last time")
        if len(ihandles) != len(args):
            raise TraceMismatchError("op input size different from last time")

        # check all inputs of crrent op
        for h, x in zip(ihandles, args):
            info = self._tinfo[h]
            if info.external:
                if (
                    x._compiled_info is not None
                    and not self._tinfo[x._mixin_handle].exported
                ):
                    raise TraceMismatchError(
                        "failed to capture: input was an external tensor "
                        "last time, got an internal tensor this time"
                    )
                if info.bound_data:
                    if x._compiled_info is not None:
                        raise TraceMismatchError(
                            "const capture violated: was an external tensor "
                            "last time, got an internal tensor this time"
                        )
                    if x._handle != info.bound_data._handle:
                        if not np.array_equal(x.numpy(), info.bound_data.numpy()):
                            raise TraceMismatchError(
                                "const capture violated: got "
                                "a different tensor this time"
                            )
                else:
                    if info.dtype != x.dtype:
                        raise TraceMismatchError(
                            "failed to capture: different dtype from last time"
                        )
                    if info.device != x.device:
                        raise TraceMismatchError(
                            "failed to capture: different device from last time"
                        )
                    info.data_setter.set_value(x._dev_tensor())
            else:
                if x._mixin_handle == -1:
                    if x._handle not in self._tensor_remaps:
                        raise TraceMismatchError(
                            "unexpected capture: trying to use an external tensor as "
                            "input, but that input was an internal tensor last time"
                        )
                    else:
                        x._mixin_handle = self._tensor_remaps[
                            x._handle
                        ]._CompiledTensorProxy__handle
                if x._mixin_handle != h:
                    raise TraceMismatchError(
                        "mis-wiring: input edge to an data flow "
                        "graph node is different from last time"
                    )

        self._pc += 1
        outputs = []
        for h in ohandles:
            info = self._tinfo[h]
            # generate output tensor and create compied info
            y = RawTensor(info.varnode)
            y._compiled_info = CompiledTensorProxy(h)
            y._mixin_handle = h
            outputs += [y]
            self._active_tensors.add(TensorWeakRef(y))
        self._output_handles.update(ohandles)
        return outputs

    def _apply_const(self, value, dtype, device):
        assert not self._untraced
        # check against trace
        if self._pc >= len(self._seq):
            raise TraceMismatchError("trace should end here, but more op observed")
        record = self._seq[self._pc]
        op_, ihandles, ohandles = record
        # Const op is represented by a str
        assert isinstance(op_, str) and op_ == "Const"

        eq = np.all(np.atleast_1d(value) == self._tinfo[ohandles[0]].bound_data.numpy())
        if not eq:
            raise TraceMismatchError(
                "const tensor violated: got a different tensor this time"
            )

        self._pc += 1
        (h,) = ohandles
        outputs = [self._tinfo[h].bound_data]
        return outputs

    # run in first step, record information for trace
    def _record_op(self, op, inputs, outputs):
        if skip_tracing:
            for x in inputs:
                h = getattr(x, "_mixin_handle", -1)
                if h >= 0:
                    self._tinfo[h].data = True
            return

        ihandles = []
        for x in inputs:
            h = getattr(x, "_mixin_handle", -1)
            if h < 0 or (not self._capture_as_const and self._tinfo[h].exported):
                h, info = self._new_handle()
                name = AutoNaming.gen_name(x)
                info.name = name
                info.external = True
                info.device = x.device
                info.dtype = x.dtype
                info.shape = x.shape
                if self._capture_as_const:
                    info.bound_data = RawTensor(
                        x.numpy(), x.dtype, x.device, False, name
                    )

            ihandles.append(h)

        ohandles = []
        for x in outputs:
            h, info = self._new_handle()
            ohandles.append(h)
            info.external = False
            x._mixin_handle = h
            x._recording = True
            x._trace_mixin_info = info
            self._active_tensors.add(TensorWeakRef(x))
            if self._symbolic:
                self._lazy_eval_tensors.add(TensorWeakRef(x))

        self._seq.append((op, tuple(ihandles), tuple(ohandles)))

    def _record_const(self, outputs):
        if skip_tracing:
            (x,) = outputs
            h = getattr(x, "_mixin_handle", -1)
            if h >= 0:
                self._tinfo[h].data_read = True
            return

        (x,) = outputs
        h, info = self._new_handle()
        ohandles = [h]
        info.external = True
        info.device = x.device
        info.dtype = x.dtype
        info.shape = x.shape
        info.bound_data = x
        info.is_const = True
        x._mixin_handle = h
        x._recording = True
        x._trace_mixin_info = info
        if self._symbolic:
            self._lazy_eval_tensors.add(TensorWeakRef(x))
        self._seq.append(("Const", tuple(), tuple(ohandles)))

    def _set_active(self, active: bool):
        global active_trace
        if active:
            if active_trace:
                raise NotImplementedError("sorry, not implemented: nested trace")
            active_trace = self
        else:
            assert active_trace is self
            active_trace = None

    def _init_trace(self, symbolic: bool):
        if symbolic:
            self._lazy_eval_graph = G.Graph()
            self._apply_graph_options(self._lazy_eval_graph)
            self._lazy_eval_links = ()

    def _take_escaped_tensors(self):
        escaped_tensors = tuple(filter(lambda x: x() is not None, self._active_tensors))
        self._active_tensors.clear()
        return escaped_tensors

    def _lazy_eval(self, lazy_eval_graph, lazy_eval_tensors, lazy_eval_links):
        lazy_eval_tensors = [x() for x in lazy_eval_tensors]
        lazy_eval_tensors = [x for x in lazy_eval_tensors if x is not None]
        readers = [G.OutputNode(x._varnode).outputs[0] for x in lazy_eval_tensors]
        self._apply_graph_options(lazy_eval_graph)
        lazy_eval_graph.options.graph_opt_level = self._graph_opt_level
        lazy_eval_graph._set_priority_to_id([*lazy_eval_links, *readers])
        lazy_eval_graph.compile(*lazy_eval_links, *readers)
        lazy_eval_graph()
        for r, x in zip(readers, lazy_eval_tensors):
            # get values from lazy_eval_graph and assign to lazy_eval tensor
            x._handle = RawTensor(r.op.get_value())._handle
            x._reset_varnode()

    @contextlib.contextmanager
    def _setup(self):
        interrupted = False

        def do_enter():
            set_tracing()
            self._save_symbolic_shape = set_symbolic_shape(self._symbolic_shape)
            self._set_active(True)
            if self._untraced:
                self._init_trace(self._symbolic)
            else:
                if self._graph is None:
                    self._compile()
                self._graph.execute()

        def do_finalize():
            escaped_tensors = self._take_escaped_tensors()
            if self._untraced:
                for x in escaped_tensors:
                    if x():
                        info = self._tinfo[x()._mixin_handle]
                        info.data_read = True
                        x()._mixin_handle = -1
                        x()._recording = False
                if self._inputs_to_restore:
                    for x in self._inputs_to_restore:
                        x._mixin_handle = -1
                        x._recording = False
                if self._symbolic and (
                    self._lazy_eval_tensors or self._lazy_eval_links
                ):
                    # eval lazy eval tensors
                    self._lazy_eval(
                        self._lazy_eval_graph,
                        self._lazy_eval_tensors,
                        self._lazy_eval_links,
                    )
                    self._lazy_eval_graph = None
                    self._lazy_eval_tensors = None
                    self._lazy_eval_links = None
                self._untraced = False
            else:
                # compiled_tensor leaks
                if self._pc == len(self._seq):
                    for x in escaped_tensors:
                        try:
                            assign_raw_tensor(x(), RawTensor(x()._dev_tensor()))
                        except RuntimeError:
                            # TraceMismatchError thrown in do_exit
                            pass
                    self._graph.wait()
                    self._reset_exec_env()

            # reset status
            self._pc = 0
            self._tensor_remaps = None
            self._set_active(False)
            set_symbolic_shape(self._save_symbolic_shape)
            unset_tracing()

        def do_exit():
            unset_tracing()
            if not self._untraced and self._pc != len(self._seq):
                raise TraceMismatchError("premature end")
            if not self._symbolic or not self._untraced:
                # reset output tensors
                for x in self._active_tensors.copy():
                    strong_x = x()
                    if strong_x is not None:
                        strong_x._dev_tensor()
                        strong_x._reset_varnode()
                        strong_x._mixin_handle = -1
                        strong_x._recording = False
                        strong_x._trace_mixin_info = None

        try:
            do_enter()
            yield
            do_exit()
        except:
            interrupted = True
            raise
        finally:
            do_finalize()
            if interrupted:
                self._reset()

    def _begin_excluded_region(self):
        if self._capture_as_const:
            raise RuntimeError(
                "exclude_from_trace cannot be used with capture_as_const"
            )
        if self._untraced:
            # conditionally reading a compiled tensor in excluded region
            # is permitted, so we have to assume every tensor might be read
            for x in self._active_tensors:
                strong_x = x()
                if strong_x:
                    info = self._tinfo[strong_x._mixin_handle]
                    info.exported = True
                    info.data_read = True
        else:
            for x in self._active_tensors:
                strong_x = x()
                if strong_x:
                    strong_x._dev_tensor()

    def _apply_graph_options(self, graph):

        graph.options.no_force_inplace = True
        graph.options.seq_opt.enable_seq_comp_node_opt = False
        graph.options.graph_opt_level = self._graph_opt_level
        if self._dtr_config is not None:
            graph.options.enable_dtr_memory_opt = True
            graph.options.dtr_config.eviction_threshold = (
                self._dtr_config.eviction_threshold
            )
            graph.options.dtr_config.evictee_minimum_size = (
                self._dtr_config.evictee_minimum_size
            )
        # graph optimization
        if self._graph_opt_config is not None:
            mapping = {None: 0, False: 1, True: 2}
            jit_config = graph.options.graph_opt.jit_config
            jit_config.fuse_dimshuffle = mapping[
                self._graph_opt_config.jit_fuse_dimshuffle
            ]
            jit_config.fuse_reduce = mapping[self._graph_opt_config.jit_fuse_reduce]
        # sublinear
        if self._sublinear_memory_config is not None:
            graph.options.enable_sublinear_memory_opt = True
            sublinear_config = graph.options.sublinear_mem_config
            sublinear_config.lb_memory = self._sublinear_memory_config.lb_memory
            sublinear_config.genetic_nr_iter = (
                self._sublinear_memory_config.genetic_nr_iter
            )
            sublinear_config.genetic_pool_size = (
                self._sublinear_memory_config.genetic_pool_size
            )
            sublinear_config.thresh_nr_try = self._sublinear_memory_config.thresh_nr_try
            sublinear_config.num_worker = self._sublinear_memory_config.num_worker
        # profile
        if self._profiling:
            self._profiler = GraphProfiler(graph)
        if int(os.getenv("MEGENGINE_INPLACE_UPDATE", "0")):
            graph.options.var_sanity_check_first_run = False

    def _compile(self):
        graph = self._graph = G.Graph()
        graph.options.async_exec_level = 0b100
        self._apply_graph_options(graph)
        need_reset_nodes = self._need_reset_nodes = []
        # links enforce ordering of I/O nodes
        in_out_links = ()
        io_links = ()
        readers = []

        if self._capture_as_const:
            for h in itertools.chain(self._arg_bindings, self._kwarg_bindings.values()):
                info = self._tinfo[h]
                opnode = info.data_setter = G.InputNode(
                    device=info.device,
                    dtype=info.dtype,
                    shape=info.shape or (1,),
                    graph=graph,
                    use_static_shape=_input_node_use_static_shape(),
                )
                need_reset_nodes.append(opnode)
                info.varnode = opnode.outputs[0]
                in_out_links += opnode.outputs[1:]

        for op, ihandles, ohandles in self._seq:
            if isinstance(op, str) and op == "Const":
                assert len(ihandles) == 0
                (h,) = ohandles
                info = self._tinfo[h]
                if not hasattr(info, "varnode"):
                    assert info.external
                    assert info.bound_data
                    info.varnode = graph.make_const(
                        info.bound_data.numpy(),
                        info.bound_data.dtype,
                        info.bound_data.device,
                    )
                continue

            require_links = type(op) in _io_op_types
            ivars = []
            for i, h in enumerate(ihandles):
                info = self._tinfo[h]
                if not hasattr(info, "varnode"):
                    assert info.external
                    if info.bound_data:
                        if getattr(info, "is_const", False):
                            info.varnode = graph.make_const(
                                info.bound_data.numpy(),
                                info.bound_data.dtype,
                                info.bound_data.device,
                            )
                        else:
                            info.varnode = graph.make_const(
                                info.bound_data._dev_tensor()
                                # info.bound_data.numpy()
                            )
                    else:
                        opnode = info.data_setter = G.InputNode(
                            *in_out_links,
                            device=info.device,
                            dtype=info.dtype,
                            shape=info.shape or (1,),
                            graph=graph,
                            use_static_shape=_input_node_use_static_shape(),
                        )
                        need_reset_nodes.append(opnode)
                        info.varnode, *in_out_links = opnode.outputs
                if require_links and i == 0 and len(io_links) > 0:
                    opnode = G.VirtualDepNode(
                        [info.varnode, *io_links], str(io_links[0].device)
                    )
                    info.varnode = opnode.outputs[0]
                    io_links = (info.varnode,)

                ivars.append(info.varnode)

            ovars = G.apply_normal_varnode(op, *ivars)

            if require_links and len(ovars) > 0:
                io_links = (ovars[0],)
            assert len(ovars) == len(ohandles)
            for h, v in zip(ohandles, ovars):
                info = self._tinfo[h]
                info.varnode = v

                def add_reader(opnode):
                    nonlocal in_out_links
                    need_reset_nodes.append(opnode)
                    readers.append(opnode.outputs[0])
                    in_out_links = opnode.outputs

                if info.data_read:
                    # Shape can be obtained from data so doesn't need its own
                    # output node. On the other hand, value is read separately
                    # to leverage eager h2d copy
                    info.shape_read = False
                    opnode = info.data_reader = G.OutputNode(v, *in_out_links)
                    add_reader(opnode)
                if info.value_read:
                    opnode = info.value_reader = G.ValueOutputNode(v, *in_out_links)
                    add_reader(opnode)
                if info.shape_read:
                    opnode = info.shape_reader = G.AttrOutputNode(v, *in_out_links)
                    add_reader(opnode)

        graph.options.graph_opt_level = self._graph_opt_level
        graph._set_priority_to_id([*readers, *in_out_links, *io_links])
        graph.compile(*readers, *in_out_links, *io_links)

    def _reset_exec_env(self):
        for opnode in self._need_reset_nodes:
            opnode.reset()

    def __call__(self, *args, **kwargs):
        with self._setup():
            if self._capture_as_const:
                self._process_inputs(*args, **kwargs)
            outputs = self.__wrapped__(*args, **kwargs)
            if self._capture_as_const:
                self._process_outputs(outputs)
            return outputs

    def dump(
        self,
        file,
        *,
        arg_names=None,
        output_names=None,
        append=False,
        keep_var_name: int = 1,
        keep_opr_name: bool = False,
        keep_param_name: bool = False,
        keep_opr_priority: bool = False,
        strip_info_file=None,
        append_json=False,
        optimize_for_inference=True,
        **kwargs
    ):
        r"""
        Serializes trace to file system.

        :param file: output file, could be file object or filename.
        :param arg_names: names of the input tensors in the traced function.
        :param output_names: names of the output tensors in the traced function,
            use the default name if not specified.
        :param append: whether output is appended to ``file``.
            Only works when ``file`` is str.
        :param keep_var_name: level for keeping variable names:

            * 0: none of the names are kept
            * 1: (default)keep names of output vars
            * 2: keep names of all (output and internal) vars
        :param keep_opr_name: whether to keep operator names.
        :param keep_param_name: whether to keep param names, so param values can be
            easily manipulated after loading model
        :param keep_opr_priority: whether to keep priority setting for operators
        :param strip_info_file: a string for path or a file handler. if is not None,
            then the dump information for code strip would be written to ``strip_info_file``
        :param append_json: will be check when `strip_info_file` is not None. if set
            true, the information for code strip will be append to strip_info_file.
            if set false, will rewrite strip_info_file
        :param optimize_for_inference: enbale optmizations,
            will skip all optimize options if this is False. Default: True

        :Keyword Arguments:

            * enable_io16xc32 --
                whether to use float16 for I/O between oprs and use
                float32 as internal computation precision. Note the output var would be
                changed to float16.
            * enable_ioc16 --
                whether to use float16 for both I/O and computation
                precision.

            * enable_hwcd4 --
                whether to use NHWCD4 data layout. This is faster on some
                OpenCL backend.
            * enable_nchw88 --
                whether to use NCHW88 data layout, currently
                used in X86 AVX backend.
            * enable_nchw44 --
                whether to use NCHW44 data layout, currently
                used in arm backend.
            * enable_nchw44_dot --
                whether to use NCHW44_dot data layout, currently
                used in armv8.2+dotprod backend.
            * enable_nchw4 --
                whether to use NCHW4 data layout, currently
                used in nvidia backend(based on cudnn).
            * enable_nchw32 --
                whether to use NCHW32 data layout, currently
                used in nvidia backend with tensorcore(based on cudnn).
            * enable_chwn4 --
                whether to use CHWN4 data layout, currently
                used in nvidia backend with tensorcore.

            * enable_fuse_conv_bias_nonlinearity: whether to fuse conv+bias+nonlinearty
                into one opr.
            * enable_fuse_conv_bias_with_z: whether to fuse conv_bias with z
                input for inference on nvidia backend(this optimization pass will
                result in mismatch of the precision of output of training and
                inference)
        """
        if not self._capture_as_const:
            raise ValueError(
                "you must specify capture_as_const=True at __init__ to use dump"
            )
        if self._untraced:
            raise RuntimeError("should run at least once before calling dump")
        if self._output_names and output_names:
            raise TypeError(
                "cannot specify output_names when output is already in dict format"
            )
        if output_names and not isinstance(output_names, collections.abc.Sequence):
            output_names = (output_names,)
        if output_names and len(output_names) != len(self._output_bindings):
            raise ValueError(
                "wrong number of output_names, should be {} values".format(
                    len(self._output_bindings)
                )
            )
        without_arg_names = arg_names is None
        if without_arg_names:
            arg_names = ["arg_%d" % i for i in range(len(self._arg_bindings))]
        if arg_names and not isinstance(arg_names, collections.abc.Sequence):
            arg_names = (arg_names,)
        if arg_names and len(arg_names) != len(self._arg_bindings):
            raise ValueError(
                "wrong number of arg_names, should be {} values".format(
                    len(self._arg_bindings)
                )
            )
        output_names = output_names or self._output_names

        def dumped_device(info):
            device_name = info.device.logical_name
            if device_name[:3] in ("cpu", "gpu", "xpu"):
                return as_device("xpux")
            return info.device

        h2v = {}
        graph = G.Graph()

        # apply graph_opt_level in dump
        if self._graph_opt_level is not None:
            graph.options.graph_opt_level = self._graph_opt_level
        for i, h in enumerate(self._arg_bindings):
            info = self._tinfo[h]
            h2v[h] = graph.make_h2d(
                dtype=info.dtype,
                device=dumped_device(info),
                shape=info.shape or (1,),
                name=info.name if without_arg_names and info.name else arg_names[i],
            )
        for k, h in self._kwarg_bindings.items():
            info = self._tinfo[h]
            h2v[h] = graph.make_h2d(
                dtype=info.dtype,
                device=dumped_device(info),
                shape=info.shape or (1,),
                name=k,
            )

        for op, ihandles, ohandles in self._seq:
            if isinstance(op, str) and op == "Const":
                assert len(ihandles) == 0
                (h,) = ohandles
                info = self._tinfo[h]
                if h not in h2v:
                    assert info.external
                    assert info.bound_data
                    h2v[h] = graph.make_const(
                        info.bound_data.numpy(),
                        dtype=info.dtype,
                        device=info.device,
                        name=info.name,
                    )
                continue
            ivars = []
            for h in ihandles:
                info = self._tinfo[h]
                if h not in h2v:
                    assert info.external
                    assert info.bound_data
                    h2v[h] = graph.make_const(
                        info.bound_data.numpy(),
                        dtype=info.dtype,
                        device=dumped_device(info),
                        name=info.name,
                    )
                ivars.append(h2v[h])
            if isinstance(op, BatchNorm):
                assert (
                    op.fwd_mode == BatchNorm.FwdMode.INFERENCE
                ), "can not dump BatchNorm in training mode, maybe you forget to do model.eval()?"
            ovars = G.apply_normal_varnode(op, *ivars)

            AutoNaming.record_opnode(ovars[0].op)

            assert len(ovars) == len(ohandles)
            h2v.update(zip(ohandles, ovars))

            for i in ohandles:
                name = AutoNaming.get_var_name(i)
                if name is not None:
                    h2v[i].name = name

        AutoNaming.remove_duplicate_names()

        dest_vars = []
        for i, h in enumerate(self._output_bindings):
            v = h2v[h]
            if output_names:
                v.name = output_names[i]
            dest_vars.append(v)

        if optimize_for_inference:
            dest_vars = G.optimize_for_inference(dest_vars, **kwargs)

        if isinstance(file, str):
            permission = "wb" if append == False else "ab"
            file = open(file, permission)
        dump_content, dump_info = G.dump_graph(
            dest_vars,
            keep_var_name=keep_var_name,
            keep_opr_name=keep_opr_name,
            keep_param_name=keep_param_name,
            keep_opr_priority=keep_opr_priority,
            strip_info_file=strip_info_file,
            append_json=append_json,
        )
        file.write(dump_content)
        return dump_info

    def _process_inputs(self, *args, **kwargs):
        if self._untraced:
            self._inputs_to_restore = []

            def record_input(x):
                if x is None:
                    return
                h, info = self._new_handle()
                info.external = False
                info.name = x.c_name
                info.device = x.device
                info.dtype = x.dtype
                info.shape = x.numpy().shape
                x._mixin_handle = h
                x._recording = True
                x._trace_mixin_info = info
                self._inputs_to_restore.append(x)
                return h

            self._arg_bindings = []
            for i, x in enumerate(args):
                if not isinstance(x, RawTensor):
                    raise TypeError(
                        "positional arguments should all be tensor "
                        "but args[%d] cannot be recognized as one" % i
                    )
                self._arg_bindings.append(record_input(x))

            self._kwarg_bindings = {}
            for k, x in kwargs.items():
                if isinstance(x, RawTensor):
                    self._kwarg_bindings[k] = record_input(x)
        else:
            if len(args) != len(self._arg_bindings):
                raise TraceMismatchError("positional argument length mismatch")

            self._tensor_remaps = {}

            for i, (h, x) in enumerate(zip(self._arg_bindings, args)):
                if not isinstance(x, RawTensor):
                    raise TypeError(
                        "positional arguments should all be tensor "
                        "but args[%d] cannot be recognized as one" % i
                    )
                info = self._tinfo[h]
                if x.dtype != info.dtype:
                    raise TypeError("args[%d].dtype different from last time" % i)
                if x.device != info.device:
                    raise TypeError("args[%d].device different from last time" % i)
                info.data_setter.set_value(x._dev_tensor())
                self._tensor_remaps[x._handle] = CompiledTensorProxy(h)

            kwargs_tensors = {}
            for k, x in kwargs.items():
                if isinstance(x, RawTensor):
                    kwargs_tensors[k] = x
            if set(kwargs_tensors) != set(self._kwarg_bindings):
                too_many = set(kwargs_tensors) - set(self._kwarg_bindings)
                too_few = set(self._kwarg_bindings) - set(kwargs_tensors)
                if too_many:
                    raise TraceMismatchError(
                        "keyword arguments found to be tensor this time "
                        "but were non-tensor previously: %s" % " ".join(too_many)
                    )
                if too_few:
                    raise TraceMismatchError(
                        "keyword arguments found to be non-tensor this time "
                        "but were tensor previously: %s" % " ".join(too_few)
                    )
            for k, h in self._kwarg_bindings.items():
                x = kwargs_tensors[k]
                info = self._tinfo[h]
                if x.dtype != info.dtype:
                    raise TypeError("kwargs[%s].dtype different from last time" % k)
                if x.device != info.device:
                    raise TypeError("kwargs[%s].device different from last time" % k)
                info.data_setter.set_value(x._dev_tensor())
                self._tensor_remaps[x._handle] = CompiledTensorProxy(h)

    def _process_outputs(self, outputs):
        output_names = None
        if isinstance(outputs, collections.abc.Mapping):
            output_names, outputs = zip(*sorted(outputs.items()))
        elif not isinstance(outputs, collections.abc.Sequence):
            outputs = (outputs,)

        if not self._untraced:
            if output_names != self._output_names:
                too_many = set(output_names) - set(self._output_names)
                too_few = set(self._output_names) - set(output_names)
                if too_many:
                    raise TraceMismatchError(
                        "output has more keys than last time: %s" % " ".join(too_many)
                    )
                if too_few:
                    raise TraceMismatchError(
                        "output has less keys than last time: %s" % " ".join(too_few)
                    )
            if len(outputs) != len(self._output_bindings):
                raise TraceMismatchError("output size differs from last time")
        else:
            self._output_names = output_names
            self._output_bindings = []

        for i, x in enumerate(outputs):
            if not isinstance(x, RawTensor):
                raise TypeError("every item of return value should be tensor")
            if self._untraced:
                h = x._mixin_handle
                if h < 0:
                    raise RuntimeError("output is not computed from inputs")
                self._output_bindings.append(h)
            else:
                h = x._mixin_handle
                if h not in self._output_handles:
                    raise RuntimeError("output is not computed from inputs")
                if h != self._output_bindings[i]:
                    raise TraceMismatchError(
                        "retval[%s] is a different tensor than last time"
                        % (output_names and output_names[i] or i)
                    )

    def get_profile(self):
        """
        Get profiling result for compiled trace.

        :return: a json compatible object.
        """
        if not self._profiler:
            raise RuntimeError("trace is not set with profiling=True")
        return json.loads(self._profiler.get())

    def trace(self, *args, **kwargs):
        raise NotImplementedError(
            "trace is deemed unbeneficial with the new "
            "tracing mechanism. You should alwasy use __call__."
        )


class CompiledTensorProxy:
    """
    Duck-typed RawTensor
    """

    def __init__(self, handle):
        self.__handle = handle
        self._isscalar = False
        self.__info = active_trace._tinfo[handle]
        self.__shape = None
        self.__data = None
        self.__value = None

    @property
    def dtype(self):
        return self.__info.varnode.dtype

    @property
    def device(self):
        return self.__info.varnode.device

    @property
    def shape(self):
        if self._isscalar:
            return ()
        if self.__shape is None:
            if self.__info.shape_read:
                self.__shape = self.__info.shape_reader.get_value().shape
            elif self.__info.data_read:
                self.__shape = self._dev_tensor().shape
            else:
                # c++ will throw TraceReadError
                return None
        return self.__shape

    def numpy(self):
        if self.__value is None:
            if self.__info.value_read:
                self.__value = self.__info.value_reader.get_value()
            elif self.__info.data_read:
                self.__value = self._dev_tensor().numpy()
            else:
                # c++ will throw TraceReadError
                return None
        # c++ side will handle scalar case
        return self.__value

    def _dev_tensor(self):
        if self.__data is None:
            if not self.__info.data_read:
                # c++ will throw TraceReadError
                return None
            self.__data = self.__info.data_reader.get_value()
        return self.__data

    def __del__(self):
        if self.__info.shape_read and self.__shape is not None:
            self.__info.shape_reader.drop_value()
        if self.__info.value_read and self.__value is not None:
            self.__info.value_reader.drop_value()
        if self.__info.data_read and self.__data is not None:
            self.__info.data_reader.drop_value()


def assign_raw_tensor(lhs, rhs):
    lhs.__init__(rhs)


def apply_symbolic_mode(op: OpDef, *args: RawTensor):
    graph = active_trace._lazy_eval_graph
    ivars = []
    for x in args:
        var = getattr(x, "_varnode", None)
        if var:
            ivars.append(var)
        else:
            data_setter = G.InputNode(
                device=x.device,
                dtype=x.dtype,
                shape=x.numpy().shape or (1,),
                graph=graph,
                use_static_shape=True,
            )
            var = data_setter.outputs[0]
            ivars.append(var)
            data_setter.set_value(x._dev_tensor())

    require_links = type(op) in _io_op_types

    if require_links and active_trace._lazy_eval_links:
        assert len(ivars) > 0, "op should has at least one input"
        opnode = G.VirtualDepNode(
            [ivars[0], *active_trace._lazy_eval_links],
            str(active_trace._lazy_eval_links[0].device),
        )
        ivars[0] = opnode.outputs[0]
        active_trace._lazy_eval_links = (ivars[0],)

    ovars = G.apply_normal_varnode(op, *ivars)
    outputs = [RawTensor(o) for o in ovars]

    if require_links:
        active_trace._lazy_eval_links = (G.VarNode(outputs[0]._varnode),)

    return outputs


def apply_const_symbolic_mode(value, dtype, device, name):
    graph = active_trace._lazy_eval_graph
    # don't need to unset tracing
    # because varnode construction will ignore tracing flag
    ret = RawTensor(graph.make_const(value, dtype=dtype, device=device, name=name))
    if np.array(value).ndim == 0:
        setscalar(ret)
    return (ret,)


def apply_compiled_mode(op: OpDef, *args: RawTensor):
    if skip_tracing:
        args = [
            RawTensor(x._dev_tensor()) if x.__class__ is CompiledTensorProxy else x
            for x in args
        ]
        unset_tracing()
        ret = apply(op, *args)
        set_tracing()
        return ret
    return active_trace._apply_op(op, args)


def apply_const_compiled_mode(value, dtype, device, is_const, no_cache, name):
    if skip_tracing:
        unset_tracing()
        ret = RawTensor(value, dtype, device, False, name)
        set_tracing()
        return ret
    return active_trace._apply_const(value, dtype, device)


def apply_with_tracing(op: OpDef, *args: RawTensor):
    if active_trace._graph:
        # if member _graph exits, then is_compiled
        return apply_compiled_mode(op, *args)
    if hasattr(op, "scope"):
        op.scope = AutoNaming.get_scope()
    if active_trace._symbolic:
        outputs = apply_symbolic_mode(op, *args)
    else:
        unset_tracing()
        outputs = apply(op, *args)
        set_tracing()

    active_trace._record_op(op, args, outputs)
    return list(outputs)


def apply_const_with_tracing(value, dtype, device, is_const, no_cache, name):
    if active_trace._graph:
        return apply_const_compiled_mode(value, dtype, device, is_const, no_cache, name)
    if active_trace._symbolic:
        outputs = apply_const_symbolic_mode(value, dtype, device, name)
    else:
        unset_tracing()
        outputs = RawTensor(value, dtype, device, False, name)
        if np.array(value).ndim == 0:
            setscalar(outputs)
        outputs = (outputs,)
        set_tracing()
    active_trace._record_const(outputs)
    return list(outputs)