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 pickle
import re
import struct
from typing import Any

import cv2
import numpy as np

from megengine.logger import get_logger

from .. import tensor
from ..core import _imperative_rt as rt
from ..core._imperative_rt import GraphProfiler, GraphProfiler2, SerializationMetadata
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,
    ExternOpr,
    RemoteRecv,
    RemoteSend,
)
from ..core._trace_option import set_symbolic_shape
from ..core._wrap import as_device
from ..core.ops.builtin import BatchNorm, OpDef
from ..core.tensor import megbrain_graph as G
from ..core.tensor.utils import setscalar
from ..utils import comp_graph_tools as cgtools
from ..utils.naming import AutoNaming
from ..utils.profiler import is_profiling
from .dtr_config import DTRConfig
from .graph_opt_config import GraphOptimizationConfig
from .sublinear_memory_config import SublinearMemoryConfig

logger = get_logger(__name__)


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 or (active_trace is None):
        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",
        "bound_data_numpy",
        # 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.bound_data_numpy = None

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

    def get_numpy(self):
        if self.bound_data_numpy is None:
            self.bound_data_numpy = self.bound_data.numpy()
        return self.bound_data_numpy


_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__`

    Args:
        function: the function will be traced.
        symbolic: whether to apply symbolic execution for tracing. Default: False
        capture_as_const: capture global vars or closures as const value. Default: False
        record_only: if True, won't run even if call the function. Default: False
        sublinear_memory_config: configuration for sublinear memory optimization.
            If not None, it enables sublinear memory optimization with given setting.
        profiling: whether to profile compiled trace. Default: False
        opt_level: optimization level for compiling trace. Default: 2
        graph_opt_config: configuration for graph optimization. Default: None
        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,
        record_only=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 or record_only
        self._capture_as_const = capture_as_const or record_only
        self._record_only = record_only
        self._sublinear_memory_config = sublinear_memory_config
        self._dtr_config = dtr_config
        self._profiling = profiling
        self._profiler = None
        self._profiler2 = 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"

        expected = self._tinfo[ohandles[0]].get_numpy()
        shape = value.shape
        if shape != expected.shape or dtype != expected.dtype:
            eq = False
        elif shape == ():
            eq = expected.item() == value.item()
        elif shape == (1,):
            eq = expected[0] == value[0]
        else:
            eq = np.all(value == expected)
        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.bound_data_numpy = None
        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)
        self._execute_graph(lazy_eval_graph)
        lazy_eval_graph.wait()
        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._execute_graph(self._graph)

        def do_finalize():
            escaped_tensors = self._take_escaped_tensors()
            if self._untraced:
                if self._record_only:
                    self._lazy_eval_graph = None
                    self._lazy_eval_tensors = None
                    self._lazy_eval_links = None
                else:
                    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:
                            x().__init__(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.options.dtr_config.recomp_memory_factor = (
                self._dtr_config.recomp_memory_factor
            )
            graph.options.dtr_config.recomp_time_factor = (
                self._dtr_config.recomp_time_factor
            )
        # 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_mb = self._sublinear_memory_config.lb_memory_mb
            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)
        self._profiler2 = None
        if int(os.getenv("MEGENGINE_INPLACE_UPDATE", "0")):
            graph.options.var_sanity_check_first_run = False

    def _execute_graph(self, graph: G.Graph, *args):
        if is_profiling() and (self._profiler2 is None):
            self._profiler2 = GraphProfiler2(graph)
        elif not is_profiling() and (self._profiler2 is not None):
            self._profiler2 = None
        graph.execute(*args)

    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.get_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.get_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 _make_feed(
        self,
        graph,
        outputs,
        input_data,
        repeat,
        silent,
        no_assert,
        maxerr,
        resize_input,
        input_transform,
    ):
        def auto_reformat_image(path, data, dst_shape):
            """reformat image to target shape

            :param data: image data as numpy array
            :param dst_shape: target shape
            """
            dim3_format = False  # required input format does not contain batch
            hwc_format = False  # required input format is NHWC

            if not dst_shape:  # input tensor shape is not predefined
                if len(data.shape) == 2:
                    chl = 1
                    h = data.shape[0]
                    w = data.shape[1]
                else:
                    assert (
                        len(data.shape) == 3
                    ), "Input image must be of dimension 2 or 3"
                    h, w, chl = data.shape
                dst_shape = (1, chl, h, w)

            if len(dst_shape) == 3:
                dst_shape = (1,) + dst_shape
                dim3_format = True

            assert len(dst_shape) == 4, "bad dst_shape: {}".format(dst_shape)
            chl = dst_shape[1]
            if chl in [1, 3]:
                n, c, h, w = dst_shape
                dst_shape = (n, h, w, c)
            else:
                chl = dst_shape[3]
                assert chl in [
                    1,
                    3,
                ], "can not infer input format from shape: {}".format(dst_shape)
                hwc_format = True

            # dst_shape has now been normalized to NHWC format

            if resize_input:
                h, w = dst_shape[1:3]
                data = cv2.resize(data, (w, h))
                logger.info("input {} resized to {}".format(path, data.shape))

            if chl == 1:
                data = cv2.cvtColor(data, cv2.COLOR_BGR2GRAY)
                data = data[:, :, np.newaxis]

            assert data.ndim == 3
            data = data[np.newaxis]
            # data normalized to NHWC format

            if not hwc_format:
                data = np.transpose(data, (0, 3, 1, 2))

            if dim3_format:
                data = np.squeeze(data, 0)

            return data

        def read_input_data(dst_shape, dtype, path):
            def check_shape_equal(dst_shape, data_shape):
                if len(dst_shape):
                    assert len(data_shape) == len(
                        dst_shape
                    ), "input/data shapes mismatch: {} vs {}".format(
                        dst_shape, data_shape
                    )

                    if data_shape[1:] != dst_shape[1:]:
                        logger.warning(
                            "dst_shape is {}; data_shape is {}".format(
                                dst_shape, data_shape
                            )
                        )

            if path.startswith("#"):
                assert not resize_input
                assert not input_transform
                spec = path
                m = re.match(
                    r"^#rand\(([-0-9.]*)\s*,\s*([-0-9.]*)\s*(,[^\)]+)?\)$", spec
                )
                assert m, "bad spec {}".format(spec)

                rng_min = float(m.group(1))
                rng_max = float(m.group(2))
                if m.group(3):
                    shape_str = m.group(3)
                    try:
                        shape = shape_str[1:].split(",")
                        if shape[-1].strip() == "...":
                            shape = shape[:-1]
                            shape.extend(list(dst_shape[len(shape) :]))
                        data_shape = tuple(map(int, shape))
                    except ValueError as e:
                        raise ValueError("bad spec {}: {}".format(spec, e.args))
                else:
                    data_shape = dst_shape

                check_shape_equal(dst_shape, data_shape)
                return np.random.uniform(rng_min, rng_max, data_shape).astype(dtype)

            # try to load image
            data = cv2.imread(path, cv2.IMREAD_COLOR)
            if data is None:
                assert not resize_input
                data = np.load(path)
                assert isinstance(data, np.ndarray)
            else:
                # load image succeeds, so we expect input format is image format
                data = auto_reformat_image(path, data, dst_shape)

            data = np.repeat(data, repeat, axis=0)
            if repeat > 1:
                logger.info(
                    "repeat input for {} times, data shape is {}".format(
                        repeat, data.shape
                    )
                )

            check_shape_equal(dst_shape, data.shape)

            if input_transform:
                data = eval(input_transform, {"data": data, "np": np})

            return data

        def gen_one_testcase(inputs, spec):
            paths = spec.split(";")
            if len(paths) != len(inputs):
                if len(paths) == 1 and paths[0].startswith("#"):
                    paths = ["{}:{}".format(name, paths[0]) for name in inputs.keys()]
            assert len(paths) == len(
                inputs
            ), "required inputs: {}; data paths: {}".format(inputs.keys(), paths)
            if len(paths) == 1 and ":" not in paths[0]:
                paths[0] = next(iter(inputs.keys())) + ":" + paths[0]

            ret = {}
            for path in paths:
                var, path = path.split(":")
                ret[var] = read_input_data(inputs[var].shape, inputs[var].dtype, path)
            return ret

        inputs = cgtools.get_dep_vars(outputs, "Host2DeviceCopy")
        inputs = {i.name: i for i in inputs}

        if not no_assert:

            replace_varmap = {}
            inp_map = {}
            # replace var use InputNode
            for name, var in inputs.items():
                inp = G.InputNode(
                    device="xpux", dtype=var.dtype, shape=var.shape, graph=graph
                )
                replace_varmap[var] = inp.outputs[0]._node
                inp_map[name] = inp

            new = cgtools.replace_vars(outputs, replace_varmap)
            if isinstance(new, rt.VarNode):
                new = list(new)

            output_nodes = [G.OutputNode(var) for var in new]
            func = graph.compile(*[node.outputs[0]._node for node in output_nodes])

            def make_dev_tensor(value, dtype=None, device=None):
                return tensor(value, dtype=dtype, device=device)._dev_tensor()

            def calculate(*args, **kwargs):
                output_val = []
                # set inputs value
                for name, var in inputs.items():
                    val = kwargs.pop(name, None)
                    assert val is not None, "miss input name{}".format(name)
                    dev_tensor = make_dev_tensor(val, dtype=var.dtype, device="xpux")
                    inp_map[name].set_value(dev_tensor)

                func.execute()

                for res in output_nodes:
                    output_val.append(res.get_value().numpy())
                return output_val

            def expect_name(var):
                return "{}:expect".format(var.name)

        testcases = []

        np.set_printoptions(precision=2, threshold=4, suppress=True)

        data_list = []
        for item in input_data:
            if item.startswith("@"):
                with open(item[1:], "r") as f:
                    data_list.extend(
                        [line.rstrip() for line in f if line.rstrip() != ""]
                    )
            else:
                data_list.append(item)

        for inp_spec in data_list:
            cur_testcase = gen_one_testcase(inputs, inp_spec)
            assert len(cur_testcase) == len(
                inputs
            ), "required inputs: {}; given data: {}".format(
                inputs.keys(), cur_testcase.keys()
            )

            if not no_assert:
                outputs_get = calculate(**cur_testcase)
                for var, val in zip(outputs, outputs_get):
                    cur_testcase[expect_name(var)] = val
                    logger.info(
                        "generate test groundtruth: var={} shape={} range=({}, {})"
                        " mean={} var={}".format(
                            var,
                            val.shape,
                            val.min(),
                            val.max(),
                            np.mean(val),
                            np.var(val),
                        )
                    )
            testcases.append(cur_testcase)
            logger.info(
                "add testcase: \n {}".format(
                    "\n ".join(
                        "{}: shape={} dtype={} range=({:.2f},{:.2f}) "
                        "mean={:.2f} sd={:.2f}".format(
                            k, v.shape, v.dtype, v.min(), v.max(), np.mean(v), np.std(v)
                        )
                        for k, v in sorted(cur_testcase.items())
                    )
                )
            )

        if not no_assert:

            def expect_shp(var):
                ret = var.shape
                if ret:
                    return ret
                return testcases[0][expect_name(var)].shape

            def assert_equal(expect, real, **kwargs):
                op = AssertEqual(**kwargs)
                (res,) = G.apply_normal_varnode(op, expect, real)
                return res._node

            verbose = not silent

            outputs_new = []
            for i in outputs:
                device = rt.CompNode("xpux")
                dtype = i.dtype
                name = expect_name(i)
                shape = expect_shp(i)
                # make expect output as one input of model.
                expect_get = rt.make_h2d(graph, device, dtype, shape, name)
                # insert assert opr to check expect and real.
                outputs_new.append(
                    assert_equal(expect_get, i, verbose=verbose, maxerr=maxerr,)
                )
                inputs[expect_name(i)] = expect_get
            outputs = outputs_new

        return {"outputs": outputs, "testcases": testcases}

    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,
        user_info: Any = None,
        enable_metadata: bool = True,
        input_data=None,
        repeat=1,
        silent=False,
        no_assert=False,
        maxerr=1e-4,
        resize_input=False,
        input_transform=None,
        dump_format: str = None,
        **kwargs
    ):
        r"""Serializes trace to file system.

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

            keep_opr_name: whether to keep operator names.
            keep_param_name: whether to keep param names, so param values can be
                easily manipulated after loading model
            keep_opr_priority: whether to keep priority setting for operators
            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``
            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
            optimize_for_inference: enbale optmizations,
                will skip all optimize options if this is False. Default: True
            user_info: any type object, which will be pickled to bytes.
            enable_metadata: whether to save metadata into output file.
            input_data: input test data and current network output would be used as groundtruth.
                The format is "var0:file0;var1:file1..." to specify data files for input vars.
                It can also be "#rand(min,max,shape...)" for generating random input data, for
                example, "#rand(0,255)", "#rand(0,255,1,3,224,224)" or "#rand(0, 255, 1, ...)"
                where `...` means the remaining part of the original shape. If the shape is not
                specified, the shape of corresponding input tensors in the network will be used.
                If there is only one input var, its name can be omitted. Each data file can either
                be an image which can be loaded by opencv, or a pickled numpy.ndarray. This option
                can be given multiple times to add multiple testcases. If you start the data
                with the letter @, the rest should be a filename, and each line in the file should
                be a single datum in the format described above. *NOTE* If `input_data` is not None,
                you can only use load-and-run to run the output file.
            repeat: how many times the input image is repeated. Useful when running benchmark for
                batch size other than one. Have no effect on randomly generated input data.
            silent: whether set verbose to False in assert_equal opr.
            no_assert: whether insert assert_equal opr to check result; this option is useful for
                benchmarking.
            maxerr: max error for assert_equal check during runtime.
            resize_input: whether resize input image to fit input var shape.
            input_transform: a python expression to transform the input data.
                Example: data / np.std(data)
            dump_format: using different dump formats. the open source MegEngine defaults to the FBS 
                format. internal MegEngine have a choice of FBS and internal proprietary formats

        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_nchw64 --
          whether to use NCHW64 data layout, used for fast int4
          support on Nvidia GPU.
        * 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)
        * enable_fuse_preprocess: whether to fuse astype\pad_channel\dimshuffle and
          etc opr
        """
        if not self._capture_as_const:
            raise ValueError(
                "you must specify capture_as_const=True at __init__ to use dump"
            )
        if self._untraced and len(self._seq) == 0:
            raise RuntimeError("should do record first before 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.get_numpy(),
                        dtype=info.dtype,
                        device=dumped_device(info),
                        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.get_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)

        dest_vars = [i._node for i in dest_vars]

        if input_data is not None:
            feeds = self._make_feed(
                graph,
                dest_vars,
                input_data,
                repeat,
                silent,
                no_assert,
                maxerr,
                resize_input,
                input_transform,
            )
            assert (
                isinstance(feeds, dict) and feeds["testcases"]
            ), "testcases can not be empty"
            dest_vars = feeds["outputs"]

        if optimize_for_inference:
            dest_vars, optimize_options = G.optimize_for_inference(dest_vars, **kwargs)
            dest_vars = [i._node for i in dest_vars]

        metadata = SerializationMetadata()
        if enable_metadata:
            metadata.user_info = pickle.dumps(user_info)
            metadata.is_valid = True
            metadata.graph_modified = False
            if optimize_for_inference:
                metadata.optimize_options = optimize_options

        if isinstance(file, str):
            permission = "wb" if append == False else "ab"
            file = open(file, permission)

        if keep_opr_priority:
            graph._set_priority_to_id(dest_vars)

        if input_data is not None:
            file.write(b"mgbtest0")
            file.write(struct.pack("I", len(feeds["testcases"])))
        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,
            metadata=metadata,
            dump_format=dump_format,
        )
        file.write(dump_content)

        if input_data is not None:
            inputs = cgtools.get_dep_vars(dest_vars, "Host2DeviceCopy")
            inputs = sorted((i.name, i.dtype) for i in inputs)

            def make_dev_tensor(value, dtype=None, device=None):
                return tensor(value, dtype=dtype, device=device)._dev_tensor()

            for testcase in feeds["testcases"]:
                assert isinstance(testcase, dict)
                cg = G.Graph()
                output_mgbvars = []
                for name, dtype in inputs:
                    output_mgbvars.append(
                        cg.make_const(
                            make_dev_tensor(
                                testcase.pop(name), dtype=dtype, device="cpux"
                            )
                        )
                    )
                assert not testcase, "extra inputs provided in testcase: {}".format(
                    testcase.keys()
                )
                dump_content, _ = G.dump_graph(
                    output_mgbvars, strip_info_file=strip_info_file, append_json=True,
                )
                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):
        r"""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())


class CompiledTensorProxy:
    r"""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 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)