mirror of
https://github.com/vale981/ray
synced 2025-03-12 06:06:39 -04:00
60d4d5e1aa
* Remove all __future__ imports from RLlib. * Remove (object) again from tf_run_builder.py::TFRunBuilder. * Fix 2xLINT warnings. * Fix broken appo_policy import (must be appo_tf_policy) * Remove future imports from all other ray files (not just RLlib). * Remove future imports from all other ray files (not just RLlib). * Remove future import blocks that contain `unicode_literals` as well. Revert appo_tf_policy.py to appo_policy.py (belongs to another PR). * Add two empty lines before Schedule class. * Put back __future__ imports into determine_tests_to_run.py. Fails otherwise on a py2/print related error.
279 lines
11 KiB
Python
279 lines
11 KiB
Python
import hashlib
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import logging
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import pickle
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import sys
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import time
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import ray
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import ray.streaming.runtime.transfer as transfer
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from ray.streaming.config import Config
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from ray.streaming.operator import PStrategy
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from ray.streaming.runtime.transfer import ChannelID
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logger = logging.getLogger(__name__)
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logging.basicConfig(level=logging.INFO)
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# Forward and broadcast stream partitioning strategies
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forward_broadcast_strategies = [PStrategy.Forward, PStrategy.Broadcast]
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# Used to choose output channel in case of hash-based shuffling
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def _hash(value):
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if isinstance(value, int):
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return value
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try:
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return int(hashlib.sha1(value.encode("utf-8")).hexdigest(), 16)
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except AttributeError:
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return int(hashlib.sha1(value).hexdigest(), 16)
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class DataChannel:
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"""A data channel for actor-to-actor communication.
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Attributes:
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env (Environment): The environment the channel belongs to.
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src_operator_id (UUID): The id of the source operator of the channel.
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src_instance_index (int): The id of the source instance.
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dst_operator_id (UUID): The id of the destination operator of the
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channel.
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dst_instance_index (int): The id of the destination instance.
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"""
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def __init__(self, src_operator_id, src_instance_index, dst_operator_id,
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dst_instance_index, str_qid):
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self.src_operator_id = src_operator_id
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self.src_instance_index = src_instance_index
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self.dst_operator_id = dst_operator_id
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self.dst_instance_index = dst_instance_index
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self.str_qid = str_qid
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self.qid = ChannelID(str_qid)
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def __repr__(self):
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return "(src({},{}),dst({},{}), qid({}))".format(
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self.src_operator_id, self.src_instance_index,
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self.dst_operator_id, self.dst_instance_index, self.str_qid)
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_CLOSE_FLAG = b" "
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# Pulls and merges data from multiple input channels
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class DataInput:
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"""An input gate of an operator instance.
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The input gate pulls records from all input channels in a round-robin
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fashion.
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Attributes:
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input_channels (list): The list of input channels.
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channel_index (int): The index of the next channel to pull from.
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max_index (int): The number of input channels.
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closed (list): A list of flags indicating whether an input channel
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has been marked as 'closed'.
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all_closed (bool): Denotes whether all input channels have been
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closed (True) or not (False).
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"""
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def __init__(self, env, channels):
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assert len(channels) > 0
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self.env = env
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self.reader = None # created in `init` method
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self.input_channels = channels
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self.channel_index = 0
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self.max_index = len(channels)
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# Tracks the channels that have been closed. qid: close status
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self.closed = {}
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def init(self):
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channels = [c.str_qid for c in self.input_channels]
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input_actors = []
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for c in self.input_channels:
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actor = self.env.execution_graph.get_actor(c.src_operator_id,
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c.src_instance_index)
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input_actors.append(actor)
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logger.info("DataInput input_actors %s", input_actors)
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conf = {
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Config.TASK_JOB_ID: ray.runtime_context._get_runtime_context()
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.current_driver_id,
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Config.CHANNEL_TYPE: self.env.config.channel_type
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}
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self.reader = transfer.DataReader(channels, input_actors, conf)
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def pull(self):
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# pull from channel
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item = self.reader.read(100)
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while item is None:
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time.sleep(0.001)
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item = self.reader.read(100)
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msg_data = item.body()
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if msg_data == _CLOSE_FLAG:
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self.closed[item.channel_id] = True
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if len(self.closed) == len(self.input_channels):
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return None
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else:
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return self.pull()
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else:
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return pickle.loads(msg_data)
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def close(self):
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self.reader.stop()
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# Selects output channel(s) and pushes data
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class DataOutput:
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"""An output gate of an operator instance.
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The output gate pushes records to output channels according to the
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user-defined partitioning scheme.
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Attributes:
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partitioning_schemes (dict): A mapping from destination operator ids
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to partitioning schemes (see: PScheme in operator.py).
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forward_channels (list): A list of channels to forward records.
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shuffle_channels (list(list)): A list of output channels to shuffle
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records grouped by destination operator.
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shuffle_key_channels (list(list)): A list of output channels to
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shuffle records by a key grouped by destination operator.
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shuffle_exists (bool): A flag indicating that there exists at least
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one shuffle_channel.
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shuffle_key_exists (bool): A flag indicating that there exists at
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least one shuffle_key_channel.
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"""
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def __init__(self, env, channels, partitioning_schemes):
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assert len(channels) > 0
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self.env = env
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self.writer = None # created in `init` method
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self.channels = channels
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self.key_selector = None
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self.round_robin_indexes = [0]
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self.partitioning_schemes = partitioning_schemes
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# Prepare output -- collect channels by type
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self.forward_channels = [] # Forward and broadcast channels
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slots = sum(1 for scheme in self.partitioning_schemes.values()
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if scheme.strategy == PStrategy.RoundRobin)
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self.round_robin_channels = [[]] * slots # RoundRobin channels
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self.round_robin_indexes = [-1] * slots
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slots = sum(1 for scheme in self.partitioning_schemes.values()
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if scheme.strategy == PStrategy.Shuffle)
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# Flag used to avoid hashing when there is no shuffling
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self.shuffle_exists = slots > 0
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self.shuffle_channels = [[]] * slots # Shuffle channels
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slots = sum(1 for scheme in self.partitioning_schemes.values()
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if scheme.strategy == PStrategy.ShuffleByKey)
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# Flag used to avoid hashing when there is no shuffling by key
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self.shuffle_key_exists = slots > 0
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self.shuffle_key_channels = [[]] * slots # Shuffle by key channels
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# Distinct shuffle destinations
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shuffle_destinations = {}
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# Distinct shuffle by key destinations
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shuffle_by_key_destinations = {}
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# Distinct round robin destinations
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round_robin_destinations = {}
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index_1 = 0
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index_2 = 0
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index_3 = 0
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for channel in channels:
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p_scheme = self.partitioning_schemes[channel.dst_operator_id]
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strategy = p_scheme.strategy
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if strategy in forward_broadcast_strategies:
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self.forward_channels.append(channel)
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elif strategy == PStrategy.Shuffle:
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pos = shuffle_destinations.setdefault(channel.dst_operator_id,
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index_1)
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self.shuffle_channels[pos].append(channel)
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if pos == index_1:
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index_1 += 1
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elif strategy == PStrategy.ShuffleByKey:
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pos = shuffle_by_key_destinations.setdefault(
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channel.dst_operator_id, index_2)
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self.shuffle_key_channels[pos].append(channel)
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if pos == index_2:
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index_2 += 1
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elif strategy == PStrategy.RoundRobin:
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pos = round_robin_destinations.setdefault(
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channel.dst_operator_id, index_3)
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self.round_robin_channels[pos].append(channel)
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if pos == index_3:
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index_3 += 1
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else: # TODO (john): Add support for other strategies
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sys.exit("Unrecognized or unsupported partitioning strategy.")
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# A KeyedDataStream can only be shuffled by key
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assert not (self.shuffle_exists and self.shuffle_key_exists)
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def init(self):
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"""init DataOutput which creates DataWriter"""
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channel_ids = [c.str_qid for c in self.channels]
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to_actors = []
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for c in self.channels:
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actor = self.env.execution_graph.get_actor(c.dst_operator_id,
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c.dst_instance_index)
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to_actors.append(actor)
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logger.info("DataOutput output_actors %s", to_actors)
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conf = {
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Config.TASK_JOB_ID: ray.runtime_context._get_runtime_context()
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.current_driver_id,
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Config.CHANNEL_TYPE: self.env.config.channel_type
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}
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self.writer = transfer.DataWriter(channel_ids, to_actors, conf)
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def close(self):
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"""Close the channel (True) by propagating _CLOSE_FLAG
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_CLOSE_FLAG is used as special type of record that is propagated from
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sources to sink to notify that the end of data in a stream.
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"""
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for c in self.channels:
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self.writer.write(c.qid, _CLOSE_FLAG)
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# must ensure DataWriter send None flag to peer actor
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self.writer.stop()
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def push(self, record):
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target_channels = []
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# Forward record
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for c in self.forward_channels:
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logger.debug("[writer] Push record '{}' to channel {}".format(
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record, c))
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target_channels.append(c)
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# Forward record
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index = 0
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for channels in self.round_robin_channels:
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self.round_robin_indexes[index] += 1
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if self.round_robin_indexes[index] == len(channels):
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self.round_robin_indexes[index] = 0 # Reset index
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c = channels[self.round_robin_indexes[index]]
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logger.debug("[writer] Push record '{}' to channel {}".format(
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record, c))
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target_channels.append(c)
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index += 1
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# Hash-based shuffling by key
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if self.shuffle_key_exists:
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key, _ = record
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h = _hash(key)
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for channels in self.shuffle_key_channels:
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num_instances = len(channels) # Downstream instances
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c = channels[h % num_instances]
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logger.debug(
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"[key_shuffle] Push record '{}' to channel {}".format(
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record, c))
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target_channels.append(c)
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elif self.shuffle_exists: # Hash-based shuffling per destination
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h = _hash(record)
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for channels in self.shuffle_channels:
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num_instances = len(channels) # Downstream instances
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c = channels[h % num_instances]
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logger.debug("[shuffle] Push record '{}' to channel {}".format(
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record, c))
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target_channels.append(c)
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else: # TODO (john): Handle rescaling
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pass
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msg_data = pickle.dumps(record)
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for c in target_channels:
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# send data to channel
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self.writer.write(c.qid, msg_data)
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def push_all(self, records):
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for record in records:
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self.push(record)
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