operator.py

#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# pylint: disable=doc-string-missing
from time import time as _time
import time
import threading
import multiprocessing
from paddle_serving_client import MultiLangClient, Client
from concurrent import futures
import logging
import func_timeout
import os
import sys
import collections
import numpy as np
from numpy import *
if sys.version_info.major == 2:
    import Queue
elif sys.version_info.major == 3:
    import queue as Queue
else:
    raise Exception("Error Python version")

from .proto import pipeline_service_pb2
from .channel import (ThreadChannel, ProcessChannel, ChannelDataEcode,
                      ChannelData, ChannelDataType, ChannelStopError,
                      ChannelTimeoutError)
from .util import NameGenerator
from .profiler import UnsafeTimeProfiler as TimeProfiler
from . import local_service_handler

_LOGGER = logging.getLogger(__name__)
_op_name_gen = NameGenerator("Op")


class Op(object):
    def __init__(self,
                 name=None,
                 input_ops=[],
                 server_endpoints=None,
                 fetch_list=None,
                 client_config=None,
                 client_type=None,
                 concurrency=None,
                 timeout=None,
                 retry=None,
                 batch_size=None,
                 auto_batching_timeout=None,
                 local_service_handler=None):
        # In __init__, all the parameters are just saved and Op is not initialized
        if name is None:
            name = _op_name_gen.next()
        self.name = name  # to identify the type of OP, it must be globally unique
        self.concurrency = concurrency  # amount of concurrency
        self.set_input_ops(input_ops)

        self._local_service_handler = local_service_handler
        self._server_endpoints = server_endpoints
        self._fetch_names = fetch_list
        self._client_config = client_config
        self.client_type = client_type
        self._timeout = timeout
        self._retry = max(1, retry)
        self._batch_size = batch_size
        self._auto_batching_timeout = auto_batching_timeout

        self._input = None
        self._outputs = []

        self._server_use_profile = False
        self._tracer = None

        # only for thread op
        self._for_init_op_lock = threading.Lock()
        self._for_close_op_lock = threading.Lock()
        self._succ_init_op = False
        self._succ_close_op = False

    def init_from_dict(self, conf):
        # init op
        if self.concurrency is None:
            self.concurrency = conf["concurrency"]
        if self._retry is None:
            self._retry = conf["retry"]
        if self._fetch_names is None:
            self._fetch_names = conf.get("fetch_list")
        if self._client_config is None:
            self._client_config = conf.get("client_config")

        if self._timeout is None:
            self._timeout = conf["timeout"]
        if self._timeout > 0:
            self._timeout = self._timeout / 1000.0
        else:
            self._timeout = -1

        if self._batch_size is None:
            self._batch_size = conf["batch_size"]
        if self._auto_batching_timeout is None:
            self._auto_batching_timeout = conf["auto_batching_timeout"]
        if self._auto_batching_timeout <= 0 or self._batch_size == 1:
            _LOGGER.warning(
                self._log(
                    "Because auto_batching_timeout <= 0 or batch_size == 1,"
                    " set auto_batching_timeout to None."))
            self._auto_batching_timeout = None
        else:
            self._auto_batching_timeout = self._auto_batching_timeout / 1000.0

        if self._server_endpoints is None:
            server_endpoints = conf.get("server_endpoints", [])
            if len(server_endpoints) != 0:
                # remote service
                self.with_serving = True
                self._server_endpoints = server_endpoints
            else:
                if self._local_service_handler is None:
                    local_service_conf = conf.get("local_service_conf")
                    _LOGGER.info("local_service_conf: {}".format(
                        local_service_conf))
                    model_config = local_service_conf.get("model_config")
                    self.client_type = local_service_conf.get("client_type")
                    _LOGGER.info("model_config: {}".format(model_config))
                    if model_config is None:
                        self.with_serving = False
                    else:
                        # local rpc service
                        self.with_serving = True
                        if self.client_type == "brpc" or self.client_type == "grpc":
                            service_handler = local_service_handler.LocalServiceHandler(
                                model_config=model_config,
                                client_type=self.client_type,
                                workdir=local_service_conf["workdir"],
                                thread_num=local_service_conf["thread_num"],
                                devices=local_service_conf["devices"],
                                mem_optim=local_service_conf["mem_optim"],
                                ir_optim=local_service_conf["ir_optim"])
                            service_handler.prepare_server()  # get fetch_list
                            serivce_ports = service_handler.get_port_list()
                            self._server_endpoints = [
                                "127.0.0.1:{}".format(p) for p in serivce_ports
                            ]
                            if self._client_config is None:
                                self._client_config = service_handler.get_client_config(
                                )
                            if self._fetch_names is None:
                                self._fetch_names = service_handler.get_fetch_list(
                                )
                        elif self.client_type == "local_predictor":
                            service_handler = local_service_handler.LocalServiceHandler(
                                model_config=model_config,
                                client_type=self.client_type,
                                workdir=local_service_conf["workdir"],
                                thread_num=local_service_conf["thread_num"],
                                devices=local_service_conf["devices"])
                            #service_handler.prepare_server()  # get fetch_list
                            self.local_predictor = service_handler.get_client()
                            if self._client_config is None:
                                self._client_config = service_handler.get_client_config(
                                )
                            if self._fetch_names is None:
                                self._fetch_names = service_handler.get_fetch_list(
                                )
                        self._local_service_handler = service_handler
                else:
                    self.with_serving = True
                    self._local_service_handler.prepare_server(
                    )  # get fetch_list
                    serivce_ports = self._local_service_handler.get_port_list()
                    self._server_endpoints = [
                        "127.0.0.1:{}".format(p) for p in serivce_ports
                    ]
                    if self._client_config is None:
                        self._client_config = self._local_service_handler.get_client_config(
                        )
                    if self._fetch_names is None:
                        self._fetch_names = self._local_service_handler.get_fetch_list(
                        )
        else:
            self.with_serving = True

        if not isinstance(self, RequestOp) and not isinstance(self, ResponseOp):
            _LOGGER.info(
                self._log("\n\tinput_ops: {},"
                          "\n\tserver_endpoints: {}"
                          "\n\tfetch_list: {}"
                          "\n\tclient_config: {}"
                          "\n\tconcurrency: {},"
                          "\n\ttimeout(s): {},"
                          "\n\tretry: {},"
                          "\n\tbatch_size: {},"
                          "\n\tauto_batching_timeout(s): {}".format(
                              ", ".join([op.name for op in self._input_ops
                                         ]), self._server_endpoints,
                              self._fetch_names, self._client_config,
                              self.concurrency, self._timeout, self._retry,
                              self._batch_size, self._auto_batching_timeout)))

    def launch_local_rpc_service(self):
        if self._local_service_handler is None:
            _LOGGER.warning(
                self._log("Failed to launch local rpc"
                          " service: local_service_handler is None."))
            return
        port = self._local_service_handler.get_port_list()
        #if self._local_service_handler.client_type == "local_predictor":
        #    _LOGGER.info("Op({}) use local predictor.")
        #    return
        self._local_service_handler.start_server()
        _LOGGER.info("Op({}) use local rpc service at port: {}"
                     .format(self.name, port))

    def use_default_auto_batching_config(self):
        if self._batch_size != 1:
            _LOGGER.warning("Op({}) reset batch_size=1 (original: {})"
                            .format(self.name, self._batch_size))
            self._batch_size = 1
        if self._auto_batching_timeout != None:
            _LOGGER.warning(
                "Op({}) reset auto_batching_timeout=None (original: {})"
                .format(self.name, self._auto_batching_timeout))
            self._auto_batching_timeout = None

    def use_profiler(self, use_profile):
        self._server_use_profile = use_profile

    def set_tracer(self, tracer):
        self._tracer = tracer

    def init_client(self, client_config, server_endpoints):
        if self.with_serving == False:
            _LOGGER.info("Op({}) has no client (and it also do not "
                         "run the process function)".format(self.name))
            return None
        if self.client_type == 'brpc':
            client = Client()
            client.load_client_config(client_config)
        elif self.client_type == 'grpc':
            client = MultiLangClient()
        elif self.client_type == 'local_predictor':
            if self.local_predictor is None:
                raise ValueError("local predictor not yet created")
            client = self.local_predictor
        else:
            raise ValueError("Failed to init client: unknow client "
                             "type {}".format(self.client_type))
        if self._fetch_names is None:
            self._fetch_names = client.fetch_names_
            _LOGGER.info("Op({}) has no fetch name set. So fetch all vars")
        if self.client_type != "local_predictor":
            client.connect(server_endpoints)
        return client

    def get_input_ops(self):
        return self._input_ops

    def set_input_ops(self, ops):
        if not isinstance(ops, list):
            ops = [] if ops is None else [ops]
        self._input_ops = []
        for op in ops:
            if not isinstance(op, Op):
                _LOGGER.critical(
                    self._log("Failed to set input_ops: input op "
                              "must be Op type, not {}".format(type(op))))
                os._exit(-1)
            self._input_ops.append(op)

    def add_input_channel(self, channel):
        if not isinstance(channel, (ThreadChannel, ProcessChannel)):
            _LOGGER.critical(
                self._log("Failed to set input_channel: input "
                          "channel must be Channel type, not {}".format(
                              type(channel))))
            os._exit(-1)
        channel.add_consumer(self.name)
        self._input = channel

    def clean_input_channel(self):
        self._input = None

    def _get_input_channel(self):
        return self._input

    def add_output_channel(self, channel):
        if not isinstance(channel, (ThreadChannel, ProcessChannel)):
            _LOGGER.critical(
                self._log("Failed to add output_channel: output channel "
                          "must be Channel type, not {}".format(type(channel))))
            os._exit(-1)
        channel.add_producer(self.name)
        self._outputs.append(channel)

    def clean_output_channels(self):
        self._outputs = []

    def _get_output_channels(self):
        return self._outputs

    def preprocess(self, input_dicts):
        # multiple previous Op
        if len(input_dicts) != 1:
            _LOGGER.critical(
                self._log(
                    "Failed to run preprocess: this Op has multiple previous "
                    "inputs. Please override this func."))
            os._exit(-1)

        (_, input_dict), = input_dicts.items()
        return input_dict

    def process(self, feed_batch, typical_logid):
        err, err_info = ChannelData.check_batch_npdata(feed_batch)
        if err != 0:
            _LOGGER.critical(
                self._log("Failed to run process: {}. Please override "
                          "preprocess func.".format(err_info)))
            os._exit(-1)
        if self.client_type == "local_predictor":
            call_result = self.client.predict(
                feed=feed_batch[0],
                fetch=self._fetch_names,
                batch=True,
                log_id=typical_logid)
        else:
            call_result = self.client.predict(
                feed=feed_batch,
                fetch=self._fetch_names,
                batch=True,
                log_id=typical_logid)
        if isinstance(self.client, MultiLangClient):
            if call_result is None or call_result["serving_status_code"] != 0:
                return None
            call_result.pop("serving_status_code")
        return call_result

    def postprocess(self, input_dict, fetch_dict):
        return fetch_dict

    def _parse_channeldata(self, channeldata_dict):
        data_id, error_channeldata = None, None
        client_need_profile, profile_set = False, set()
        parsed_data = {}

        key = list(channeldata_dict.keys())[0]
        data_id = channeldata_dict[key].id
        client_need_profile = channeldata_dict[key].client_need_profile

        for name, data in channeldata_dict.items():
            if data.ecode != ChannelDataEcode.OK.value:
                error_channeldata = data
                break
            parsed_data[name] = data.parse()
            if client_need_profile:
                profile_set |= data.profile_data_set
        return (data_id, error_channeldata, parsed_data, client_need_profile,
                profile_set)

    def _push_to_output_channels(self,
                                 data,
                                 channels,
                                 name=None,
                                 profile_str=None,
                                 client_need_profile=False,
                                 profile_set=None):
        if name is None:
            name = self.name

        # add profile into channeldata
        if client_need_profile and profile_set is not None:
            if profile_str is not None:
                profile_set.add(profile_str)
            data.add_profile(profile_set)

        for channel in channels:
            channel.push(data, name)

    def start_with_process(self):
        trace_buffer = None
        if self._tracer is not None:
            trace_buffer = self._tracer.data_buffer()
        process = []
        for concurrency_idx in range(self.concurrency):
            p = multiprocessing.Process(
                target=self._run,
                args=(concurrency_idx, self._get_input_channel(),
                      self._get_output_channels(), False, trace_buffer))
            p.daemon = True
            p.start()
            process.append(p)
        return process

    def start_with_thread(self):
        trace_buffer = None
        if self._tracer is not None:
            trace_buffer = self._tracer.data_buffer()
        threads = []
        for concurrency_idx in range(self.concurrency):
            t = threading.Thread(
                target=self._run,
                args=(concurrency_idx, self._get_input_channel(),
                      self._get_output_channels(), True, trace_buffer))
            # When a process exits, it attempts to terminate
            # all of its daemonic child processes.
            t.daemon = True
            t.start()
            threads.append(t)
        return threads

    def init_op(self):
        pass

    def _run_preprocess(self, parsed_data_dict, op_info_prefix):
        _LOGGER.debug("{} Running preprocess".format(op_info_prefix))
        preped_data_dict = collections.OrderedDict()
        err_channeldata_dict = collections.OrderedDict()
        for data_id, parsed_data in parsed_data_dict.items():
            preped_data, error_channeldata = None, None
            try:
                preped_data = self.preprocess(parsed_data)
            except TypeError as e:
                # Error type in channeldata.datatype
                error_info = "(logid={}) {} Failed to preprocess: {}".format(
                    data_id, op_info_prefix, e)
                _LOGGER.error(error_info, exc_info=True)
                error_channeldata = ChannelData(
                    ecode=ChannelDataEcode.TYPE_ERROR.value,
                    error_info=error_info,
                    data_id=data_id)
            except Exception as e:
                error_info = "(logid={}) {} Failed to preprocess: {}".format(
                    data_id, op_info_prefix, e)
                _LOGGER.error(error_info, exc_info=True)
                error_channeldata = ChannelData(
                    ecode=ChannelDataEcode.UNKNOW.value,
                    error_info=error_info,
                    data_id=data_id)
            if error_channeldata is not None:
                err_channeldata_dict[data_id] = error_channeldata
            else:
                preped_data_dict[data_id] = preped_data
        _LOGGER.debug("{} Succ preprocess".format(op_info_prefix))
        return preped_data_dict, err_channeldata_dict

    def _run_process(self, preped_data_dict, op_info_prefix):
        _LOGGER.debug("{} Running process".format(op_info_prefix))
        midped_data_dict = collections.OrderedDict()
        err_channeldata_dict = collections.OrderedDict()
        if self.with_serving:
            data_ids = preped_data_dict.keys()
            typical_logid = data_ids[0]
            if len(data_ids) != 1:
                for data_id in data_ids:
                    _LOGGER.info(
                        "(logid={}) {} During access to PaddleServingService,"
                        " we selected logid={} (from batch: {}) as a "
                        "representative for logging.".format(
                            data_id, op_info_prefix, typical_logid, data_ids))

            # combine samples to batch
            one_input = preped_data_dict[data_ids[0]]
            feed_batch = []
            input_offset = None
            if isinstance(one_input, dict):
                # sample input
                feed_batch = [preped_data_dict[data_id] for data_id in data_ids]
                input_offset = list(range(len(data_ids) + 1))
            elif isinstance(one_input, list):
                # batch input
                input_offset = [0]
                for data_id in data_ids:
                    batch_input = preped_data_dict[data_id]
                    offset = input_offset[-1] + len(batch_input)
                    feed_batch += batch_input
                    input_offset.append(offset)
            else:
                _LOGGER.critical(
                    "{} Failed to process: expect input type is dict(sample"
                    " input) or list(batch input), but get {}".format(
                        op_info_prefix, type(one_input)))
                os._exit(-1)

            midped_batch = None
            ecode = ChannelDataEcode.OK.value
            if self._timeout <= 0:
                try:
                    midped_batch = self.process(feed_batch, typical_logid)
                except Exception as e:
                    ecode = ChannelDataEcode.UNKNOW.value
                    error_info = "(logid={}) {} Failed to process(batch: {}): {}".format(
                        typical_logid, op_info_prefix, data_ids, e)
                    _LOGGER.error(error_info, exc_info=True)
            else:
                for i in range(self._retry):
                    try:
                        midped_batch = func_timeout.func_timeout(
                            self._timeout,
                            self.process,
                            args=(feed_batch, typical_logid))
                    except func_timeout.FunctionTimedOut as e:
                        if i + 1 >= self._retry:
                            ecode = ChannelDataEcode.TIMEOUT.value
                            error_info = "(logid={}) {} Failed to process(batch: {}): " \
                                    "exceeded retry count.".format(
                                            typical_logid, op_info_prefix, data_ids)
                            _LOGGER.error(error_info)
                        else:
                            _LOGGER.warning(
                                "(logid={}) {} Failed to process(batch: {}): timeout,"
                                " and retrying({}/{})...".format(
                                    typical_logid, op_info_prefix, data_ids, i +
                                    1, self._retry))
                    except Exception as e:
                        ecode = ChannelDataEcode.UNKNOW.value
                        error_info = "(logid={}) {} Failed to process(batch: {}): {}".format(
                            typical_logid, op_info_prefix, data_ids, e)
                        _LOGGER.error(error_info, exc_info=True)
                        break
                    else:
                        break
            if ecode != ChannelDataEcode.OK.value:
                for data_id in data_ids:
                    err_channeldata_dict[data_id] = ChannelData(
                        ecode=ecode, error_info=error_info, data_id=data_id)
            elif midped_batch is None:
                # op client return None
                error_info = "(logid={}) {} Failed to predict, please check if " \
                        "PaddleServingService is working properly.".format(
                                typical_logid, op_info_prefix)
                _LOGGER.error(error_info)
                for data_id in data_ids:
                    err_channeldata_dict[data_id] = ChannelData(
                        ecode=ChannelDataEcode.CLIENT_ERROR.value,
                        error_info=error_info,
                        data_id=data_id)
            else:
                # transform np format to dict format
                var_names = midped_batch.keys()
                lod_var_names = set()
                lod_offset_names = set()
                for name in var_names:
                    lod_offset_name = "{}.lod".format(name)
                    if lod_offset_name in var_names:
                        _LOGGER.debug("(logid={}) {} {} is LodTensor".format(
                            typical_logid, op_info_prefix, name))
                        lod_var_names.add(name)
                        lod_offset_names.add(lod_offset_name)

                for idx, data_id in enumerate(data_ids):
                    midped_data_dict[data_id] = {}

                for name, value in midped_batch.items():
                    if name in lod_offset_names:
                        continue
                    if name in lod_var_names:
                        # lodtensor
                        lod_offset_name = "{}.lod".format(name)
                        lod_offset = midped_batch[lod_offset_name]
                        for idx, data_id in enumerate(data_ids):
                            data_offset_left = input_offset[idx]
                            data_offset_right = input_offset[idx + 1]
                            lod_offset_left = lod_offset[data_offset_left]
                            lod_offset_right = lod_offset[data_offset_right]
                            midped_data_dict[data_id][name] = value[
                                lod_offset_left:lod_offset_right]
                            midped_data_dict[data_id][lod_offset_name] = \
                                    lod_offset[data_offset_left:data_offset_right + 1] - lod_offset[data_offset_left]
                    else:
                        # normal tensor
                        for idx, data_id in enumerate(data_ids):
                            left = input_offset[idx]
                            right = input_offset[idx + 1]
                            midped_data_dict[data_id][name] = value[left:right]
        else:
            midped_data_dict = preped_data_dict
        _LOGGER.debug("{} Succ process".format(op_info_prefix))
        return midped_data_dict, err_channeldata_dict

    def _run_postprocess(self, parsed_data_dict, midped_data_dict,
                         op_info_prefix):
        _LOGGER.debug("{} Running postprocess".format(op_info_prefix))
        postped_data_dict = collections.OrderedDict()
        err_channeldata_dict = collections.OrderedDict()
        for data_id, midped_data in midped_data_dict.items():
            postped_data, err_channeldata = None, None
            try:
                postped_data = self.postprocess(parsed_data_dict[data_id],
                                                midped_data)
            except Exception as e:
                error_info = "(logid={}) {} Failed to postprocess: {}".format(
                    data_id, op_info_prefix, e)
                _LOGGER.error(error_info, exc_info=True)
                err_channeldata = ChannelData(
                    ecode=ChannelDataEcode.UNKNOW.value,
                    error_info=error_info,
                    data_id=data_id)
            if err_channeldata is not None:
                err_channeldata_dict[data_id] = err_channeldata
                continue
            else:
                if not isinstance(postped_data, dict):
                    error_info = "(logid={}) {} Failed to postprocess: " \
                            "output of postprocess funticon must be " \
                            "dict type, but get {}".format(
                                data_id, op_info_prefix,
                                type(postped_data))
                    _LOGGER.error(error_info)
                    err_channeldata = ChannelData(
                        ecode=ChannelDataEcode.UNKNOW.value,
                        error_info=error_info,
                        data_id=data_id)
                    err_channeldata_dict[data_id] = err_channeldata
                    continue

                output_data = None
                err, _ = ChannelData.check_npdata(postped_data)
                if err == 0:
                    output_data = ChannelData(
                        ChannelDataType.CHANNEL_NPDATA.value,
                        npdata=postped_data,
                        data_id=data_id)
                else:
                    output_data = ChannelData(
                        ChannelDataType.DICT.value,
                        dictdata=postped_data,
                        data_id=data_id)
                postped_data_dict[data_id] = output_data
        _LOGGER.debug("{} Succ postprocess".format(op_info_prefix))
        return postped_data_dict, err_channeldata_dict

    def _auto_batching_generator(self, input_channel, op_name, batch_size,
                                 timeout, op_info_prefix):
        while True:
            batch = []
            while len(batch) == 0:
                endtime = None
                if timeout is not None:
                    endtime = _time() + timeout
                for idx in range(batch_size):
                    try:
                        channeldata_dict = None
                        if timeout is not None:
                            remaining = endtime - _time()
                            if remaining <= 0.0:
                                _LOGGER.debug("{} Failed to generate batch: "
                                              "timeout".format(op_info_prefix))
                                break
                            channeldata_dict = input_channel.front(op_name,
                                                                   timeout)
                        else:
                            channeldata_dict = input_channel.front(op_name)
                        batch.append(channeldata_dict)
                    except ChannelTimeoutError:
                        _LOGGER.debug("{} Failed to generate batch: "
                                      "timeout".format(op_info_prefix))
                        break
            _LOGGER.debug("{} Got actual batch_size: {}".format(op_info_prefix,
                                                                len(batch)))
            yield batch

    def _parse_channeldata_batch(self, batch, output_channels):
        parsed_data_dict = collections.OrderedDict()
        need_profile_dict = {}
        profile_dict = {}
        for channeldata_dict in batch:
            (data_id, error_channeldata, parsed_data,
                    client_need_profile, profile_set) = \
                            self._parse_channeldata(channeldata_dict)
            if error_channeldata is None:
                parsed_data_dict[data_id] = parsed_data
                need_profile_dict[data_id] = client_need_profile
                profile_dict[data_id] = profile_set
            else:
                # error data in predecessor Op
                # (error_channeldata with profile info)
                self._push_to_output_channels(error_channeldata,
                                              output_channels)

        return parsed_data_dict, need_profile_dict, profile_dict

    def _run(self, concurrency_idx, input_channel, output_channels,
             is_thread_op, trace_buffer):
        op_info_prefix = "[{}|{}]".format(self.name, concurrency_idx)
        tid = threading.current_thread().ident

        # init op
        profiler = None
        try:
            profiler = self._initialize(is_thread_op, concurrency_idx)
        except Exception as e:
            _LOGGER.critical(
                "{} Failed to init op: {}".format(op_info_prefix, e),
                exc_info=True)
            os._exit(-1)
        _LOGGER.info("{} Succ init".format(op_info_prefix))

        batch_generator = self._auto_batching_generator(
            input_channel=input_channel,
            op_name=self.name,
            batch_size=self._batch_size,
            timeout=self._auto_batching_timeout,
            op_info_prefix=op_info_prefix)

        start, end = None, None
        trace_que = collections.deque()
        while True:
            start = int(round(_time() * 1000000))
            try:
                channeldata_dict_batch = next(batch_generator)
            except ChannelStopError:
                _LOGGER.debug("{} Stop.".format(op_info_prefix))
                self._finalize(is_thread_op)
                break
            end = int(round(_time() * 1000000))
            in_time = end - start

            # parse channeldata batch
            try:
                parsed_data_dict, need_profile_dict, profile_dict \
                        = self._parse_channeldata_batch(
                                channeldata_dict_batch, output_channels)
            except ChannelStopError:
                _LOGGER.debug("{} Stop.".format(op_info_prefix))
                self._finalize(is_thread_op)
                break
            if len(parsed_data_dict) == 0:
                # data in the whole batch is all error data
                continue

            # preprecess
            start = profiler.record("prep#{}_0".format(op_info_prefix))
            preped_data_dict, err_channeldata_dict \
                    = self._run_preprocess(parsed_data_dict, op_info_prefix)
            end = profiler.record("prep#{}_1".format(op_info_prefix))
            prep_time = end - start
            try:
                for data_id, err_channeldata in err_channeldata_dict.items():
                    self._push_to_output_channels(
                        data=err_channeldata,
                        channels=output_channels,
                        client_need_profile=need_profile_dict[data_id],
                        profile_set=profile_dict[data_id])
            except ChannelStopError:
                _LOGGER.debug("{} Stop.".format(op_info_prefix))
                self._finalize(is_thread_op)
                break
            if len(preped_data_dict) == 0:
                continue

            # process
            start = profiler.record("midp#{}_0".format(op_info_prefix))
            midped_data_dict, err_channeldata_dict \
                    = self._run_process(preped_data_dict, op_info_prefix)
            end = profiler.record("midp#{}_1".format(op_info_prefix))
            midp_time = end - start
            try:
                for data_id, err_channeldata in err_channeldata_dict.items():
                    self._push_to_output_channels(
                        data=err_channeldata,
                        channels=output_channels,
                        client_need_profile=need_profile_dict[data_id],
                        profile_set=profile_dict[data_id])
            except ChannelStopError:
                _LOGGER.debug("{} Stop.".format(op_info_prefix))
                self._finalize(is_thread_op)
                break
            if len(midped_data_dict) == 0:
                continue

            # postprocess
            start = profiler.record("postp#{}_0".format(op_info_prefix))
            postped_data_dict, err_channeldata_dict \
                    = self._run_postprocess(
                            parsed_data_dict, midped_data_dict, op_info_prefix)
            end = profiler.record("postp#{}_1".format(op_info_prefix))
            postp_time = end - start
            try:
                for data_id, err_channeldata in err_channeldata_dict.items():
                    self._push_to_output_channels(
                        data=err_channeldata,
                        channels=output_channels,
                        client_need_profile=need_profile_dict[data_id],
                        profile_set=profile_dict[data_id])
            except ChannelStopError:
                _LOGGER.debug("{} Stop.".format(op_info_prefix))
                self._finalize(is_thread_op)
                break
            if len(postped_data_dict) == 0:
                continue

            # push data to channel (if run succ)
            start = int(round(_time() * 1000000))
            try:
                profile_str = profiler.gen_profile_str()
                for data_id, postped_data in postped_data_dict.items():
                    if self._server_use_profile:
                        sys.stderr.write(profile_str)
                    self._push_to_output_channels(
                        data=postped_data,
                        channels=output_channels,
                        profile_str=profile_str,
                        client_need_profile=need_profile_dict[data_id],
                        profile_set=profile_dict[data_id])
            except ChannelStopError:
                _LOGGER.debug("{} Stop.".format(op_info_prefix))
                self._finalize(is_thread_op)
                break
            end = int(round(_time() * 1000000))
            out_time = end - start
            if trace_buffer is not None:
                trace_que.append({
                    "name": self.name,
                    "actions": {
                        "in": in_time,
                        "prep": prep_time,
                        "midp": midp_time,
                        "postp": postp_time,
                        "out": out_time,
                    }
                })
                while trace_que:
                    info = trace_que[0]
                    try:
                        trace_buffer.put_nowait(info)
                        trace_que.popleft()
                    except Queue.Full:
                        break

    def _initialize(self, is_thread_op, concurrency_idx):
        if is_thread_op:
            with self._for_init_op_lock:
                if not self._succ_init_op:
                    # for the threaded version of Op, each thread cannot get its concurrency_idx
                    self.concurrency_idx = None
                    # init client
                    self.client = self.init_client(self._client_config,
                                                   self._server_endpoints)
                    # user defined
                    self.init_op()
                    self._succ_init_op = True
                    self._succ_close_op = False
        else:
            self.concurrency_idx = concurrency_idx
            # init client
            self.client = self.init_client(self._client_config,
                                           self._server_endpoints)
            # user defined
            self.init_op()

        # use a separate TimeProfiler per thread or process
        profiler = TimeProfiler()
        profiler.enable(True)
        return profiler

    def _finalize(self, is_thread_op):
        if is_thread_op:
            with self._for_close_op_lock:
                if not self._succ_close_op:
                    self._profiler = None
                    self.client = None
                    self._succ_init_op = False
                    self._succ_close_op = True

    def _log(self, info):
        return "{} {}".format(self.name, info)


class RequestOp(Op):
    """ RequestOp do not run preprocess, process, postprocess. """

    def __init__(self):
        # PipelineService.name = "@DAGExecutor"
        super(RequestOp, self).__init__(name="@DAGExecutor", input_ops=[])
        # init op
        try:
            self.init_op()
        except Exception as e:
            _LOGGER.critical("Op(Request) Failed to init: {}".format(e))
            os._exit(-1)

    def unpack_request_package(self, request):
        dictdata = {}
        for idx, key in enumerate(request.key):
            data = request.value[idx]
            try:
                evaled_data = eval(data)
                if isinstance(evaled_data, np.ndarray):
                    data = evaled_data
            except Exception as e:
                pass
            dictdata[key] = data
        return dictdata


class ResponseOp(Op):
    """ ResponseOp do not run preprocess, process, postprocess. """

    def __init__(self, input_ops):
        super(ResponseOp, self).__init__(
            name="@DAGExecutor", input_ops=input_ops)
        # init op
        try:
            self.init_op()
        except Exception as e:
            _LOGGER.critical("Op(ResponseOp) Failed to init: {}".format(
                e, exc_info=True))
            os._exit(-1)

    def pack_response_package(self, channeldata):
        resp = pipeline_service_pb2.Response()
        resp.ecode = channeldata.ecode
        if resp.ecode == ChannelDataEcode.OK.value:
            if channeldata.datatype == ChannelDataType.CHANNEL_NPDATA.value:
                feed = channeldata.parse()
                # ndarray to string:
                # https://stackoverflow.com/questions/30167538/convert-a-numpy-ndarray-to-stringor-bytes-and-convert-it-back-to-numpy-ndarray
                np.set_printoptions(threshold=sys.maxsize)
                for name, var in feed.items():
                    resp.value.append(var.__repr__())
                    resp.key.append(name)
            elif channeldata.datatype == ChannelDataType.DICT.value:
                feed = channeldata.parse()
                for name, var in feed.items():
                    if not isinstance(var, str):
                        resp.ecode = ChannelDataEcode.TYPE_ERROR.value
                        resp.error_info = self._log(
                            "fetch var type must be str({}).".format(
                                type(var)))
                        _LOGGER.error("(logid={}) Failed to pack RPC "
                                      "response package: {}".format(
                                          channeldata.id, resp.error_info))
                        break
                    resp.value.append(var)
                    resp.key.append(name)
            else:
                resp.ecode = ChannelDataEcode.TYPE_ERROR.value
                resp.error_info = self._log(
                    "error type({}) in datatype.".format(channeldata.datatype))
                _LOGGER.error("(logid={}) Failed to pack RPC response"
                              " package: {}".format(channeldata.id,
                                                    resp.error_info))
        else:
            resp.error_info = channeldata.error_info
        return resp


class VirtualOp(Op):
    ''' For connecting two channels. '''

    def __init__(self, name, concurrency=1):
        super(VirtualOp, self).__init__(
            name=name, input_ops=None, concurrency=concurrency)
        self._virtual_pred_ops = []

    def add_virtual_pred_op(self, op):
        self._virtual_pred_ops.append(op)

    def _actual_pred_op_names(self, op):
        # can use disjoint-set, but it's not necessary
        if not isinstance(op, VirtualOp):
            return [op.name]
        names = []
        for x in op._virtual_pred_ops:
            names.extend(self._actual_pred_op_names(x))
        return names

    def add_output_channel(self, channel):
        if not isinstance(channel, (ThreadChannel, ProcessChannel)):
            _LOGGER.critical(
                self._log("Failed to add output_channel: output_channel"
                          " must be Channel type, not {}".format(
                              type(channel))))
            os._exit(-1)
        for op in self._virtual_pred_ops:
            for op_name in self._actual_pred_op_names(op):
                channel.add_producer(op_name)
        self._outputs.append(channel)

    def _run(self, concurrency_idx, input_channel, output_channels, client_type,
             is_thread_op):
        op_info_prefix = "[{}|{}]".format(self.name, concurrency_idx)
        log = get_log_func(op_info_prefix)
        tid = threading.current_thread().ident

        batch_generator = self._auto_batching_generator(
            input_channel=input_channel,
            op_name=self.name,
            batch_size=1,
            timeout=None,
            log_func=log)

        while True:
            try:
                channeldata_dict_batch = next(batch_generator)
            except ChannelStopError:
                _LOGGER.debug("{} Stop.".format(op_info_prefix))
                self._finalize(is_thread_op)
                break

            try:
                for channeldata_dict in channeldata_dict_batch:
                    for name, data in channeldata_dict.items():
                        self._push_to_output_channels(
                            data, channels=output_channels, name=name)
            except ChannelStopError:
                _LOGGER.debug("{} Stop.".format(op_info_prefix))
                self._finalize(is_thread_op)
                break