mirror of
https://github.com/JHUAPL/SIMoN.git
synced 2026-01-10 07:18:05 -05:00
1ab0d1ffc7
This reverts commit0b0e37dcdf[formerly 0c34f85f763e2d795cb6de42dd882b40075d79ff], reversing changes made to7005670aff[formerly 9575e6df8d92d6221a6268dca3bc709304e1216d]. Former-commit-id: d70511bb14eb0f4af5d03dfe8068a4e38733011f
513 lines
21 KiB
Python
513 lines
21 KiB
Python
import zmq
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import json
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from jsonschema import validate, ValidationError
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import time
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import glob
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from threading import Thread, Event
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from queue import Queue, Full, Empty
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from abc import ABC, abstractmethod
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import os
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import sys
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import logging
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import networkx as nx
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from collections import defaultdict
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class Graph(nx.DiGraph):
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def __init__(self, filename=None):
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super().__init__()
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self.functions = {"simple_sum": self.simple_sum, "distribute_uniformly": self.distribute_uniformly, "distribute_by_area": self.distribute_by_area}
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self.default_aggregator = self.simple_sum
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self.default_disaggregator = self.distribute_by_area
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if filename:
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with open(filename, mode='r') as json_file:
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data = json.load(json_file)
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for node in data['nodes']:
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self.add_node(node['id'], name=node.get('name'), type=node.get('type'), shape=node.get('shape'), area=node.get('area'))
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for edge in data['links']:
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self.add_edge(edge['source'], edge['target'], a=edge.get('a'), d=edge.get('d'))
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def simple_sum(self, values):
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"""
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aggregator for an instance graph
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:param values: a list of values
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:return: a scalar value, the summation of the values
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"""
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return sum(values)
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def distribute_uniformly(self, value, instance, child_granularity):
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"""
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disaggregator for an instance graph
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:param value:
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:param instance:
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:param child_granularity: the intended granularity of the transformation. the child node of the instance in the abstract graph
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:return:
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"""
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if instance not in self.nodes:
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logging.critical("instance {} not in instance graph".format(instance))
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return {}
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children = [child for child in self.successors(instance) if self.nodes[child]['type'] == child_granularity]
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mean = value / len(children)
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distributed = {child: mean for child in children}
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return distributed
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def distribute_by_area(self, value, instance, child_granularity):
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"""
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disaggregator for an instance graph
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:param value:
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:param instance:
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:param child_granularity: the intended granularity of the transformation. the child node of the instance in the abstract graph
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:return:
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"""
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if instance not in self.nodes:
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logging.critical("instance {} not in instance graph".format(instance))
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return {}
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children = [child for child in self.successors(instance) if self.nodes[child]['type'] == child_granularity]
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parent_area = self.nodes[instance]["area"]
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distributed = {child: value * self.nodes[child]["area"] / parent_area for child in children}
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return distributed
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class OuterWrapper(ABC):
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def __init__(self, model_id, num_expected_inputs):
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"""
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constructor for the outer wrapper, an abstract base class inherited by the inner wrapper
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:param model_id: the ID / unique name of the model, as defined in the inner wrapper
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:param num_expected_inputs: the number of unique types of input data messages the model needs in order to perform
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an increment, as defined in the inner wrapper. Should equal the number of input schemas,
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that is, the number of .json files in the model's schemas/input/ directory
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"""
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self.model_id = model_id
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self.num_expected_inputs = num_expected_inputs
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self.status = 'booting'
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self.incstep = 0
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self.increment_flag = False
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self.connected_to_broker = False
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self.pub_queue = Queue()
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self.broker_queue = Queue()
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self.action_queue = Queue()
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self.input_schemas = None
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self.output_schemas = None
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self.validated_schemas = {}
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self.initial_conditions = None
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self.generic_output_schema = '{' \
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' "type": "object",' \
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' "patternProperties": {' \
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' ".*": {' \
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' "type": "object", ' \
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' "properties": {' \
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' "data": {"type": "object"}, "granularity": {"type": "string"}' \
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' },' \
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' "required": ["data", "granularity"]' \
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' }' \
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' }' \
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'}'
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logging.basicConfig(level=logging.DEBUG, stream=sys.stdout,
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format='%(asctime)s - %(levelname)s - %(filename)s:%(funcName)s:%(lineno)d - %(message)s')
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def meet(self, a, b, graph=None):
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sort = sorted((a, b))
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return "{}^{}".format(sort[0], sort[1])
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def aggregate(self, data, src, dest, variable):
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if src == dest:
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return data
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path = nx.shortest_path(self.abstract_graph, dest, src)
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path.reverse()
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logging.info(path)
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if path:
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assert path[0] == src
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assert path[-1] == dest
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parents = defaultdict(list)
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for instance, value in data.items():
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if instance not in self.instance_graph.nodes:
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logging.critical("instance {} not in instance graph".format(instance))
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continue
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parent = [parent for parent in self.instance_graph.predecessors(instance) if self.instance_graph.nodes[parent]['type'] == path[1]]
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assert len(parent) == 1
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parents[parent[0]].append((instance, value))
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translated = {}
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for instance, values, in parents.items():
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trans_func_name = self.abstract_graph[path[1]][path[0]]['a'].get(variable)
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trans_func = self.instance_graph.functions.get(trans_func_name, self.instance_graph.default_aggregator)
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translated[instance] = trans_func([value[1] for value in values])
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return self.aggregate(translated, path[1], dest, variable)
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else:
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raise Exception("aggregation error")
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def disaggregate(self, data, src, dest, variable):
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if src == dest:
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return data
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path = nx.shortest_path(self.abstract_graph, src, dest)
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if path:
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assert path[0] == src
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assert path[-1] == dest
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translated = {}
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for instance, value in data.items():
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trans_func_name = self.abstract_graph[path[0]][path[1]]['d'].get(variable)
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trans_func = self.instance_graph.functions.get(trans_func_name, self.instance_graph.default_disaggregator)
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tmp = trans_func(value, instance, path[1])
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translated = {**translated, **tmp}
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return self.disaggregate(translated, path[1], dest, variable)
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else:
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raise Exception("disaggregation error")
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def translate(self, data, src, dest, variable):
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"""
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:param data: dictionary mapping instances to their values
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:param src: granularity of the data (the abstract graph must have a node with the same name)
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:param dest: granularity to translate the data to (the abstract graph must have a node with the same name)
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:return: dictionary mapping new instances to their values
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"""
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if src == dest:
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return data
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elif nx.has_path(self.abstract_graph, src, dest):
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return self.disaggregate(data, src, dest, variable)
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elif nx.has_path(self.abstract_graph, dest, src):
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return self.aggregate(data, src, dest, variable)
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elif nx.has_path(self.abstract_graph, src, self.meet(src, dest)) and nx.has_path(self.abstract_graph, dest, self.meet(src, dest)):
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disaggregated = self.disaggregate(data, src, self.meet(src, dest), variable)
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aggregated = self.aggregate(disaggregated, self.meet(src, dest), dest, variable)
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return aggregated
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else:
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logging.critical("error translating {} from {} to {}".format(variable, src, dest))
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raise Exception("error translating {} from {} to {}".format(variable, src, dest))
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def load_json_objects(self, dir_path):
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"""
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load JSON objects from .json files
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:param dir_path: path to a directory with .json files
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:return: a dictionary that a maps a schema's filename (without the .json extension) to the corresponding JSON
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object / schema
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"""
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schemas = {}
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for schema in glob.glob('{}/*.json'.format(dir_path)):
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with open(schema) as schema_file:
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file_name = os.path.splitext(os.path.basename(schema))[0]
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schemas[file_name] = json.load(schema_file)
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return schemas
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@abstractmethod
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def configure(self, **kwargs):
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"""
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implemented in the inner wrapper. Initializes the model with its parameters, needed before any increments
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:param kwargs:
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:return:
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"""
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raise NotImplementedError("configure() has to be implemented in the {} inner wrapper".format(self.model_id))
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@abstractmethod
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def increment(self, **kwargs):
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"""
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implemented in the inner wrapper
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:param kwargs:
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:return:
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"""
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raise NotImplementedError("increment() has to be implemented in the {} inner wrapper".format(self.model_id))
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def increment_handler(self, event, incstep=0):
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"""
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Calls increment() after validating inputs, then validates the results of the increment
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:param event: the shutdown event for managing threads
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:param incstep: the increment number that is currently being performed, as understood by the broker
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:return: if the increment is successful, returns its results in a dictionary
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"""
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self.increment_flag = True
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logging.info("about to increment, incstep {}".format(incstep))
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# validate against input schemas
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if incstep > 0 and len(self.validated_schemas) != self.num_expected_inputs:
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logging.critical("number of validated schemas {} != num_expected_inputs {}".format(len(self.validated_schemas), self.num_expected_inputs))
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event.set()
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raise RuntimeError
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# call the inner wrapper
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schemas = self.validated_schemas.copy()
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self.validated_schemas.clear()
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results = self.increment(**schemas)
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# validate against output schemas
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for schema_name, data_msg in results.items():
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try:
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validate(data_msg, json.loads(self.generic_output_schema))
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validate(data_msg, self.output_schemas[schema_name])
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except Exception as e:
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logging.critical("message {} failed to validate schema {}".format(data_msg, schema_name))
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event.set()
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raise RuntimeError
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if len(results) != len(self.output_schemas):
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logging.critical("didn't validate against every output schema")
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event.set()
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raise RuntimeError
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self.increment_flag = False
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self.incstep += 1
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for schema, data in results.items():
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data_msg = {}
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data_msg['schema'] = schema
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data_msg['payload'] = data
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data_msg['signal'] = 'data'
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data_msg['source'] = self.model_id
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self.pub_queue.put(data_msg)
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logging.info("finished the increment")
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def send_status(self, event):
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"""
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creates a status message and puts it into the publish queue. A status message must include the model's ID,
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a signal / message type of "status", the increment step, and the current status:
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booting: model is waiting for the broker to boot (receive status messages from all models)
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waiting: model is waiting for its needed input data messages from other models
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ready: model can begin incrementing if it receives an increment pulse; it is at the first increment step,
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or it has received all input messages and has validated them against input schemas
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incrementing: model has received an increment pulse and is performing the increment in the handler
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:param event: the shutdown event for managing threads
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:return: runs continuously until the shutdown event is set
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"""
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count = 0
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while not event.is_set():
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count += 1
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time.sleep(1)
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if self.connected_to_broker:
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if self.increment_flag:
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# waiting for the increment to finish
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self.status = 'incrementing'
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else:
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if self.incstep == 0:
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# kickstart the model for the first increment
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self.status = 'ready'
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elif len(self.validated_schemas) == self.num_expected_inputs:
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# all input messages have been received and all input schemas have been validated
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self.status = 'ready'
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else:
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# still waiting for messages from other models
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self.status = 'waiting'
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else:
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self.status = 'booting'
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message = {}
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message['source'] = self.model_id
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message['id'] = count
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message['time'] = time.time()
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message['date'] = time.ctime()
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message['signal'] = 'status'
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message['incstep'] = self.incstep
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message['status'] = self.status
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self.pub_queue.put(message)
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logging.debug(json.dumps(message))
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def pub(self, event):
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"""
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publishes messages to the broker, including status messages and data messages
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Sets the shutdown event if an outgoing data message matches more than one output schema.
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:param event: the shutdown event for managing threads
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:return: runs continuously until shutdown event is set, then closes its zmq socket
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"""
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# connect to zmq
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context = zmq.Context()
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sock = context.socket(zmq.PUB)
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sock.setsockopt(zmq.LINGER, 1000)
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sock.connect('tcp://{}:{}'.format('broker', '5555'))
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while not event.is_set():
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# get message from the queue
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try:
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message = self.pub_queue.get(timeout=0.1)
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except Empty:
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continue
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# send status messages
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if message.get('signal') == 'status':
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sock.send_json(message)
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continue
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# validate data messages
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matched = []
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for name, schema in self.output_schemas.items():
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try:
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validate(message['payload'], schema)
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logging.info("validated outgoing message: {}".format(message))
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matched.append(schema)
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except ValidationError:
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logging.info("validation error")
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except json.JSONDecodeError:
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logging.warning("json decode error")
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if len(matched) == 0:
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logging.info("message didn't match any output schemas: {}".format(message))
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elif len(matched) == 1:
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logging.info("message matched an output schema: {}".format(message))
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sock.send_json(message)
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else:
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logging.critical("more than one output schema was matched: {}".format(message))
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event.set()
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sock.close()
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context.term()
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def sub(self, event):
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"""
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connects to the broker's PUB as a subscriber and receives all messages sent from the broker,
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and all messages sent by other models and forwarded by the broker.
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Status messages from the broker go into the broker queue, for the watchdog.
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:param event: the shutdown event for managing threads
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:return: runs continuously until shutdown event is set, then closes its zmq socket
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"""
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# connect to zmq
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context = zmq.Context()
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sock = context.socket(zmq.SUB)
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sock.setsockopt(zmq.SUBSCRIBE, b"")
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sock.setsockopt(zmq.RCVTIMEO, 0)
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sock.setsockopt(zmq.LINGER, 1000)
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sock.connect('tcp://{}:{}'.format('broker', '5556'))
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while not event.is_set():
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try:
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message = sock.recv_json()
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except zmq.ZMQError:
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continue
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logging.info(json.dumps(message))
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signal = message.get('signal')
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if signal == 'status' and message.get('source') == 'broker':
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self.broker_queue.put(message)
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elif signal == 'data':
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if not self.insert_data_message(message['payload']):
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event.set()
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else:
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self.action_queue.put(message)
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sock.close()
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context.term()
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def insert_data_message(self, message):
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"""
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validates a data message against the input schemas
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:param message: the payload of the data message to insert into the queue
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:return: False if message insertion throws an error, otherwise True.
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returns True if the message is validated by 0 or 1 schemas
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returns False if the data message is a duplicate
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(was validated by a schema already used since the last increment)
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"""
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matched = []
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for name, schema in self.input_schemas.items():
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try:
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validate(message, schema)
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logging.info("schema {} validated incoming message: {}".format(name, message))
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if name in self.validated_schemas:
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logging.error("schema {} already validated a message: {}".format(name, message))
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return False
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else:
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matched.append(schema)
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self.validated_schemas[name] = message
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except ValidationError:
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logging.info("validation error")
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except json.JSONDecodeError:
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logging.warning("json decode error")
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if len(matched) == 0:
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logging.info("message didn't match any input schemas: {}".format(message))
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return True
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def action_worker(self, event):
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"""
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move logic for parsing results and putting into pub_queue into self.increment_handler.
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gets messages from the action queue and performs the respective action. currently, just the increment action
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:param event: the shutdown event for managing threads
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:return: runs continuously until the shutdown event is set
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"""
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while not event.is_set():
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try:
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message = self.action_queue.get(timeout=0.1)
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except Empty:
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continue
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if message['signal'] == 'increment':
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try:
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self.increment_handler(event, message['incstep'])
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except Exception as e:
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logging.critical(e)
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event.set()
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raise RuntimeError
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def watchdog(self, event):
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"""
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verifies that the broker is running by receiving its status messages. Sets the connected_to_broker flag
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if the broker's status is booted, that is, if the broker has received status messages from all models,
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so that models can safely receive increment pulses and publish their data.
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Will set the shutdown event if a status message has not been received within the timeout interval
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:param event: the shutdown event for managing threads
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:return: runs continuously until the shutdown event is set
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"""
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while not event.is_set():
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try:
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message = self.broker_queue.get(timeout=10)
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if message.get('status') == 'booted':
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self.connected_to_broker = True
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except Empty:
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logging.critical("Timed out waiting for broker message")
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event.set()
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def run(self):
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"""
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main thread of the outer wrapper. Launches all sub threads. Called from the inner wrapper
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:return: runs continuously until shutdown event is set
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"""
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# initialize the model
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self.input_schemas = self.load_json_objects('/opt/schemas/input')
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self.output_schemas = self.load_json_objects('/opt/schemas/output')
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self.initial_conditions = self.load_json_objects('/opt/config')
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self.configure(**self.initial_conditions)
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# start the threads
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shutdown = Event()
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subscribe_thread = Thread(target=self.sub, args=(shutdown,))
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subscribe_thread.start()
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publish_thread = Thread(target=self.pub, args=(shutdown,))
|
|
publish_thread.start()
|
|
|
|
action_thread = Thread(target=self.action_worker, args=(shutdown,))
|
|
action_thread.start()
|
|
|
|
watchdog_thread = Thread(target=self.watchdog, args=(shutdown,))
|
|
watchdog_thread.start()
|
|
|
|
status_thread = Thread(target=self.send_status, args=(shutdown,))
|
|
status_thread.start()
|
|
|
|
try:
|
|
while not shutdown.is_set():
|
|
time.sleep(1)
|
|
except Exception as e:
|
|
logging.critical(e)
|
|
finally:
|
|
shutdown.set()
|
|
logging.critical("{} model has shut down".format(self.model_id))
|