dag.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
import threading
import multiprocessing
import sys
import copy
if sys.version_info.major == 2:
    import Queue
elif sys.version_info.major == 3:
    import queue as Queue
else:
    raise Exception("Error Python version")
import os
import logging

from .operator import Op, RequestOp, ResponseOp, VirtualOp
from .channel import (ThreadChannel, ProcessChannel, ChannelData,
                      ChannelDataEcode, ChannelDataType, ChannelStopError)
from .profiler import TimeProfiler
from .util import NameGenerator

_LOGGER = logging.getLogger()


class DAGExecutor(object):
    def __init__(self, response_op, dag_config, show_info):
        default_conf = {
            "retry": 1,
            "client_type": "brpc",
            "use_profile": False,
            "channel_size": 0,
            "is_thread_op": True
        }

        for key, val in default_conf.items():
            if dag_config.get(key) is None:
                _LOGGER.warning("[CONF] {} not set, use default: {}"
                                .format(key, val))
                dag_config[key] = val

        self._retry = dag_config["retry"]
        client_type = dag_config["client_type"]
        self._server_use_profile = dag_config["use_profile"]
        channel_size = dag_config["channel_size"]
        self._is_thread_op = dag_config["is_thread_op"]
        build_dag_each_worker = dag_config["build_dag_each_worker"]

        if show_info:
            _LOGGER.info("=============== DAGExecutor ===============")
            for key in default_conf.keys():
                _LOGGER.info("{}: {}".format(key, dag_config[key]))
            _LOGGER.info("-------------------------------------------")

        self.name = "@G"
        self._profiler = TimeProfiler()
        self._profiler.enable(True)

        self._dag = DAG(self.name, response_op, self._server_use_profile,
                        self._is_thread_op, client_type, channel_size,
                        show_info, build_dag_each_worker)
        (in_channel, out_channel, pack_rpc_func,
         unpack_rpc_func) = self._dag.build()
        self._dag.start()

        self._set_in_channel(in_channel)
        self._set_out_channel(out_channel)
        self._pack_rpc_func = pack_rpc_func
        self._unpack_rpc_func = unpack_rpc_func

        self._id_lock = threading.Lock()
        self._id_counter = 0
        self._reset_max_id = 1000000000000000000
        self._cv_pool = {}
        self._cv_for_cv_pool = threading.Condition()
        self._fetch_buffer = None
        self._recive_func = None

        self._client_profile_key = "pipeline.profile"
        self._client_profile_value = "1"

    def start(self):
        self._recive_func = threading.Thread(
            target=DAGExecutor._recive_out_channel_func, args=(self, ))
        self._recive_func.start()
        _LOGGER.debug("[DAG Executor] start recive thread")

    def stop(self):
        self._dag.stop()
        self._dag.join()
        _LOGGER.info("[DAG Executor] succ stop")

    def _get_next_data_id(self):
        with self._id_lock:
            if self._id_counter >= self._reset_max_id:
                self._id_counter -= self._reset_max_id
            self._id_counter += 1
            return self._id_counter - 1

    def _set_in_channel(self, in_channel):
        if not isinstance(in_channel, (ThreadChannel, ProcessChannel)):
            raise TypeError("in_channel must be Channel type, but get {}".
                            format(type(in_channel)))
        in_channel.add_producer(self.name)
        self._in_channel = in_channel

    def _set_out_channel(self, out_channel):
        if not isinstance(out_channel, (ThreadChannel, ProcessChannel)):
            raise TypeError("iout_channel must be Channel type, but get {}".
                            format(type(out_channel)))
        out_channel.add_consumer(self.name)
        self._out_channel = out_channel

    def _recive_out_channel_func(self):
        cv = None
        while True:
            try:
                channeldata_dict = self._out_channel.front(self.name)
            except ChannelStopError:
                _LOGGER.debug("[DAG Executor] channel stop.")
                with self._cv_for_cv_pool:
                    for data_id, cv in self._cv_pool.items():
                        closed_errror_data = ChannelData(
                            ecode=ChannelDataEcode.CLOSED_ERROR.value,
                            error_info="dag closed.",
                            data_id=data_id)
                        with cv:
                            self._fetch_buffer = closed_errror_data
                            cv.notify_all()
                break

            if len(channeldata_dict) != 1:
                _LOGGER.error(
                    "[DAG Executor] out_channel cannot have multiple input ops")
                os._exit(-1)
            (_, channeldata), = channeldata_dict.items()
            if not isinstance(channeldata, ChannelData):
                _LOGGER.error(
                    '[DAG Executor] data must be ChannelData type, but get {}'
                    .format(type(channeldata)))
                os._exit(-1)

            data_id = channeldata.id
            _LOGGER.debug("recive thread fetch data[{}]".format(data_id))
            with self._cv_for_cv_pool:
                cv = self._cv_pool[data_id]
            with cv:
                self._fetch_buffer = channeldata
                cv.notify_all()

    def _get_channeldata_from_fetch_buffer(self, data_id):
        resp = None
        cv = threading.Condition()
        with self._cv_for_cv_pool:
            self._cv_pool[data_id] = cv
        with cv:
            cv.wait()
        with self._cv_for_cv_pool:
            resp = copy.deepcopy(self._fetch_buffer)
            _LOGGER.debug("resp thread get resp data[{}]".format(data_id))
            self._cv_pool.pop(data_id)
        return resp

    def _pack_channeldata(self, rpc_request, data_id):
        dictdata = None
        try:
            dictdata = self._unpack_rpc_func(rpc_request)
        except Exception as e:
            _LOGGER.error("parse RPC package to data[{}] Error: {}"
                          .format(data_id, e))
            return ChannelData(
                ecode=ChannelDataEcode.RPC_PACKAGE_ERROR.value,
                error_info="rpc package error: {}".format(e),
                data_id=data_id)
        else:
            # because unpack_rpc_func is rewritten by user, we need
            # to look for client_profile_key field in rpc_request
            profile_value = None
            for idx, key in enumerate(rpc_request.key):
                if key == self._client_profile_key:
                    profile_value = rpc_request.value[idx]
                    break
            client_need_profile = (profile_value == self._client_profile_value)
            _LOGGER.debug("request[{}] need profile: {}".format(
                data_id, client_need_profile))
            return ChannelData(
                datatype=ChannelDataType.DICT.value,
                dictdata=dictdata,
                data_id=data_id,
                client_need_profile=client_need_profile)

    def call(self, rpc_request):
        data_id = self._get_next_data_id()
        _LOGGER.debug("generate id: {}".format(data_id))

        if not self._is_thread_op:
            self._profiler.record("call_{}#DAG-{}_0".format(data_id, data_id))
        else:
            self._profiler.record("call_{}#DAG_0".format(data_id))

        _LOGGER.debug("try parse RPC package to channeldata[{}]".format(
            data_id))
        self._profiler.record("prepack_{}#{}_0".format(data_id, self.name))
        req_channeldata = self._pack_channeldata(rpc_request, data_id)
        self._profiler.record("prepack_{}#{}_1".format(data_id, self.name))

        resp_channeldata = None
        for i in range(self._retry):
            _LOGGER.debug("push data[{}] into Graph engine".format(data_id))
            try:
                self._in_channel.push(req_channeldata, self.name)
            except ChannelStopError:
                _LOGGER.debug("[DAG Executor] channel stop.")
                return self._pack_for_rpc_resp(
                    ChannelData(
                        ecode=ChannelDataEcode.CLOSED_ERROR.value,
                        error_info="dag closed.",
                        data_id=data_id))

            _LOGGER.debug("wait for Graph engine for data[{}]...".format(
                data_id))
            resp_channeldata = self._get_channeldata_from_fetch_buffer(data_id)

            if resp_channeldata.ecode == ChannelDataEcode.OK.value:
                _LOGGER.debug("Graph engine predict data[{}] succ".format(
                    data_id))
                break
            else:
                _LOGGER.warn("Graph engine predict data[{}] failed: {}"
                             .format(data_id, resp_channeldata.error_info))
                if resp_channeldata.ecode != ChannelDataEcode.TIMEOUT.value:
                    break

            if i + 1 < self._retry:
                _LOGGER.warn("retry({}/{}) data[{}]".format(i + 1, self._retry,
                                                            data_id))

        _LOGGER.debug("unpack channeldata[{}] into RPC resp package".format(
            data_id))
        self._profiler.record("postpack_{}#{}_0".format(data_id, self.name))
        rpc_resp = self._pack_for_rpc_resp(resp_channeldata)
        self._profiler.record("postpack_{}#{}_1".format(data_id, self.name))
        if not self._is_thread_op:
            self._profiler.record("call_{}#DAG-{}_1".format(data_id, data_id))
        else:
            self._profiler.record("call_{}#DAG_1".format(data_id))

        profile_str = self._profiler.gen_profile_str()
        if self._server_use_profile:
            sys.stderr.write(profile_str)

        # add profile info into rpc_resp
        profile_value = ""
        if resp_channeldata.client_need_profile:
            profile_set = resp_channeldata.profile_data_set
            profile_set.add(profile_str)
            profile_value = "".join(list(profile_set))
        rpc_resp.key.append(self._client_profile_key)
        rpc_resp.value.append(profile_value)

        return rpc_resp

    def _pack_for_rpc_resp(self, channeldata):
        return self._pack_rpc_func(channeldata)


class DAG(object):
    def __init__(self, request_name, response_op, use_profile, is_thread_op,
                 client_type, channel_size, show_info, build_dag_each_worker):
        self._request_name = request_name
        self._response_op = response_op
        self._use_profile = use_profile
        self._is_thread_op = is_thread_op
        self._channel_size = channel_size
        self._client_type = client_type
        self._show_info = show_info
        self._build_dag_each_worker = build_dag_each_worker
        if not self._is_thread_op:
            self._manager = multiprocessing.Manager()
        _LOGGER.info("[DAG] succ init")

    def get_use_ops(self, response_op):
        unique_names = set()
        used_ops = set()
        succ_ops_of_use_op = {}  # {op_name: succ_ops}
        que = Queue.Queue()
        que.put(response_op)
        while que.qsize() != 0:
            op = que.get()
            for pred_op in op.get_input_ops():
                if pred_op.name not in succ_ops_of_use_op:
                    succ_ops_of_use_op[pred_op.name] = []
                if op != response_op:
                    succ_ops_of_use_op[pred_op.name].append(op)
                if pred_op not in used_ops:
                    que.put(pred_op)
                    used_ops.add(pred_op)
                    # check the name of op is globally unique
                    if pred_op.name in unique_names:
                        raise Exception("the name of Op must be unique: {}".
                                        format(pred_op.name))
                    unique_names.add(pred_op.name)
        return used_ops, succ_ops_of_use_op

    def _gen_channel(self, name_gen):
        channel = None
        if self._is_thread_op:
            channel = ThreadChannel(
                name=name_gen.next(), maxsize=self._channel_size)
        else:
            channel = ProcessChannel(
                self._manager, name=name_gen.next(), maxsize=self._channel_size)
        _LOGGER.debug("[DAG] gen Channel: {}".format(channel.name))
        return channel

    def _gen_virtual_op(self, name_gen):
        vir_op = VirtualOp(name=name_gen.next())
        _LOGGER.debug("[DAG] gen VirtualOp: {}".format(vir_op.name))
        return vir_op

    def _topo_sort(self, used_ops, response_op, out_degree_ops):
        out_degree_num = {
            name: len(ops)
            for name, ops in out_degree_ops.items()
        }
        que_idx = 0  # scroll queue 
        ques = [Queue.Queue() for _ in range(2)]
        zero_indegree_num = 0
        for op in used_ops:
            if len(op.get_input_ops()) == 0:
                zero_indegree_num += 1
        if zero_indegree_num != 1:
            raise Exception("DAG contains multiple input Ops")
        last_op = response_op.get_input_ops()[0]
        ques[que_idx].put(last_op)

        # topo sort to get dag_views
        dag_views = []
        sorted_op_num = 0
        while True:
            que = ques[que_idx]
            next_que = ques[(que_idx + 1) % 2]
            dag_view = []
            while que.qsize() != 0:
                op = que.get()
                dag_view.append(op)
                sorted_op_num += 1
                for pred_op in op.get_input_ops():
                    out_degree_num[pred_op.name] -= 1
                    if out_degree_num[pred_op.name] == 0:
                        next_que.put(pred_op)
            dag_views.append(dag_view)
            if next_que.qsize() == 0:
                break
            que_idx = (que_idx + 1) % 2
        if sorted_op_num < len(used_ops):
            raise Exception("not legal DAG")

        return dag_views, last_op

    def _build_dag(self, response_op):
        if response_op is None:
            raise Exception("response_op has not been set.")
        used_ops, out_degree_ops = self.get_use_ops(response_op)
        if self._show_info:
            _LOGGER.info("================= USED OP =================")
            for op in used_ops:
                if op.name != self._request_name:
                    _LOGGER.info(op.name)
            _LOGGER.info("-------------------------------------------")
        if len(used_ops) <= 1:
            raise Exception(
                "Besides RequestOp and ResponseOp, there should be at least one Op in DAG."
            )
        if self._build_dag_each_worker:
            _LOGGER.info("Because `build_dag_each_worker` mode is used, "
                         "Auto-batching is set to the default config: "
                         "batch_size=1, auto_batching_timeout=None")
            for op in used_ops:
                op.use_default_auto_batching_config()

        dag_views, last_op = self._topo_sort(used_ops, response_op,
                                             out_degree_ops)
        dag_views = list(reversed(dag_views))
        if self._show_info:
            _LOGGER.info("================== DAG ====================")
            for idx, view in enumerate(dag_views):
                _LOGGER.info("(VIEW {})".format(idx))
                for op in view:
                    _LOGGER.info("  [{}]".format(op.name))
                    for out_op in out_degree_ops[op.name]:
                        _LOGGER.info("    - {}".format(out_op.name))
            _LOGGER.info("-------------------------------------------")

        # create channels and virtual ops
        virtual_op_name_gen = NameGenerator("vir")
        channel_name_gen = NameGenerator("chl")
        virtual_ops = []
        channels = []
        input_channel = None
        actual_view = None
        for v_idx, view in enumerate(dag_views):
            if v_idx + 1 >= len(dag_views):
                break
            next_view = dag_views[v_idx + 1]
            if actual_view is None:
                actual_view = view
            actual_next_view = []
            pred_op_of_next_view_op = {}
            for op in actual_view:
                # find actual succ op in next view and create virtual op
                for succ_op in out_degree_ops[op.name]:
                    if succ_op in next_view:
                        if succ_op not in actual_next_view:
                            actual_next_view.append(succ_op)
                        if succ_op.name not in pred_op_of_next_view_op:
                            pred_op_of_next_view_op[succ_op.name] = []
                        pred_op_of_next_view_op[succ_op.name].append(op)
                    else:
                        # create virtual op
                        virtual_op = self._gen_virtual_op(virtual_op_name_gen)
                        virtual_ops.append(virtual_op)
                        out_degree_ops[virtual_op.name] = [succ_op]
                        actual_next_view.append(virtual_op)
                        pred_op_of_next_view_op[virtual_op.name] = [op]
                        virtual_op.add_virtual_pred_op(op)
            actual_view = actual_next_view
            # create channel
            processed_op = set()
            for o_idx, op in enumerate(actual_next_view):
                if op.name in processed_op:
                    continue
                channel = self._gen_channel(channel_name_gen)
                channels.append(channel)
                _LOGGER.debug("[DAG] Channel({}) => Op({})"
                              .format(channel.name, op.name))
                op.add_input_channel(channel)
                pred_ops = pred_op_of_next_view_op[op.name]
                if v_idx == 0:
                    input_channel = channel
                else:
                    # if pred_op is virtual op, it will use ancestors as producers to channel
                    for pred_op in pred_ops:
                        _LOGGER.debug("[DAG] Op({}) => Channel({})"
                                      .format(pred_op.name, channel.name))
                        pred_op.add_output_channel(channel)
                processed_op.add(op.name)
                # find same input op to combine channel
                for other_op in actual_next_view[o_idx + 1:]:
                    if other_op.name in processed_op:
                        continue
                    other_pred_ops = pred_op_of_next_view_op[other_op.name]
                    if len(other_pred_ops) != len(pred_ops):
                        continue
                    same_flag = True
                    for pred_op in pred_ops:
                        if pred_op not in other_pred_ops:
                            same_flag = False
                            break
                    if same_flag:
                        _LOGGER.debug("[DAG] Channel({}) => Op({})"
                                      .format(channel.name, other_op.name))
                        other_op.add_input_channel(channel)
                        processed_op.add(other_op.name)
        output_channel = self._gen_channel(channel_name_gen)
        channels.append(output_channel)
        last_op.add_output_channel(output_channel)

        pack_func, unpack_func = None, None
        pack_func = response_op.pack_response_package

        actual_ops = virtual_ops
        for op in used_ops:
            if len(op.get_input_ops()) == 0:
                unpack_func = op.unpack_request_package
                continue
            actual_ops.append(op)

        for c in channels:
            _LOGGER.debug("Channel({}):\n -producers: {}\n -consumers: {}"
                          .format(c.name, c.get_producers(), c.get_consumers()))

        return (actual_ops, channels, input_channel, output_channel, pack_func,
                unpack_func)

    def build(self):
        (actual_ops, channels, input_channel, output_channel, pack_func,
         unpack_func) = self._build_dag(self._response_op)
        _LOGGER.info("[DAG] succ build dag")

        self._actual_ops = actual_ops
        self._channels = channels
        self._input_channel = input_channel
        self._output_channel = output_channel
        self._pack_func = pack_func
        self._unpack_func = unpack_func

        return self._input_channel, self._output_channel, self._pack_func, self._unpack_func

    def start(self):
        self._threads_or_proces = []
        for op in self._actual_ops:
            op.use_profiler(self._use_profile)
            if self._is_thread_op:
                self._threads_or_proces.extend(
                    op.start_with_thread(self._client_type))
            else:
                self._threads_or_proces.extend(
                    op.start_with_process(self._client_type))
        _LOGGER.info("[DAG] start")

        # not join yet
        return self._threads_or_proces

    def join(self):
        for x in self._threads_or_proces:
            x.join()

    def stop(self):
        for chl in self._channels:
            chl.stop()
        for op in self._actual_ops:
            op.clean_input_channel()
            op.clean_output_channels()