E2E calculation per output message, plotting of E2E over time, E2E breakdown charts, E2E message flow charts
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Maximilian Schmeller
parent
241a7c3bf2
commit
8eee45c79a
7 changed files with 1212 additions and 339 deletions
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@ -1,58 +1,34 @@
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from bisect import bisect_left, bisect
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from dataclasses import dataclass
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from itertools import combinations
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from multiprocessing import Pool
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from typing import Optional, Set, List, Iterable, Dict, Tuple
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import numpy as np
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from tqdm.notebook import tqdm
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from tqdm.contrib import concurrent
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from matching.subscriptions import sanitize
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from tracing_interop.tr_types import TrContext, TrCallbackObject, TrCallbackSymbol, TrNode, TrPublisher, TrSubscription, \
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TrTimer, TrPublishInstance, TrSubscriptionObject, TrTopic, TrCallbackInstance
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TrTimer, TrPublishInstance, TrSubscriptionObject, TrTopic, TrCallbackInstance, Timestamp
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TOPIC_FILTERS = ["/parameter_events", "/tf_static", "/robot_description", "diagnostics"]
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def _map_cb_times(args):
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cb_id, inst_times, pub_timestamps = args
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pub_cb_overlaps = {i: set() for i in range(len(pub_timestamps))}
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inst_times.sort(key=lambda tup: tup[0]) # tup[0] is start time
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inst_iter = iter(inst_times)
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pub_iter = iter(enumerate(pub_timestamps))
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inst_start, inst_end = next(inst_iter, (None, None))
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i, t = next(pub_iter, (None, None))
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while inst_start is not None and i is not None:
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if inst_start <= t <= inst_end:
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pub_cb_overlaps[i].add(cb_id)
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if t <= inst_end:
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i, t = next(pub_iter, (None, None))
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else:
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inst_start, inst_end = next(inst_iter, (None, None))
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return pub_cb_overlaps
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TOPIC_FILTERS = ["/parameter_events", "/tf_static", "/robot_description", "diagnostics", "/rosout"]
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def _get_cb_owner_node(cb: TrCallbackObject) -> TrNode | None:
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match cb.owner:
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case TrTimer(nodes=nodes):
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owner_nodes = nodes
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case TrTimer(node=node):
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owner_node = node
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case TrSubscriptionObject(subscription=sub):
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owner_nodes = [sub.node]
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owner_node = sub.node
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case _:
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owner_nodes = []
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owner_node = None
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if len(owner_nodes) > 1:
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raise RuntimeError(f"CB has owners {', '.join(map(lambda n: n.path, owner_nodes))}")
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elif not owner_nodes:
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if not owner_node:
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print("[WARN] CB has no owners")
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return None
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return owner_nodes[0]
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return owner_node
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def _hierarchize(lg_nodes: Iterable['LGHierarchyLevel']):
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@ -79,33 +55,25 @@ def _hierarchize(lg_nodes: Iterable['LGHierarchyLevel']):
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def inst_runtime_interval(cb_inst: TrCallbackInstance):
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inst_t_min = cb_inst.timestamp.timestamp()
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inst_t_max = inst_t_min + cb_inst.duration.total_seconds()
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return inst_t_min, inst_t_max
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start_time = cb_inst.timestamp
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end_time = start_time + cb_inst.duration
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return start_time, end_time
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def _get_publishing_cbs(cbs: Set[TrCallbackObject], pub: TrPublisher):
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"""
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Counts number of publication instances that lie within one of the cb_intervals.
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"""
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pub_timestamps = [inst.timestamp * 1e-9 for inst in pub.instances]
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pub_insts = pub.instances
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pub_cb_overlaps = {i: set() for i in range(len(pub_insts))}
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# Algorithm: Two-pointer method
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# With both the pub_timestamps and cb_intervals sorted ascending,
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# we can cut down the O(m*n) comparisons to O(m+n).
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pub_timestamps.sort()
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cb_id_to_cb = {cb.id: cb for cb in cbs}
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_map_args = [(cb.id, [inst_runtime_interval(inst) for inst in cb.callback_instances], pub_timestamps) for cb in cbs]
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with Pool() as p:
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cb_wise_overlaps = p.map(_map_cb_times, _map_args)
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pub_cb_overlaps = {i: set() for i in range(len(pub_timestamps))}
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for overlap_dict in cb_wise_overlaps:
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for i, cb_ids in overlap_dict.items():
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cbs = [cb_id_to_cb[cb_id] for cb_id in cb_ids]
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pub_cb_overlaps[i].update(cbs)
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for cb in cbs:
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cb_intervals = map(inst_runtime_interval, cb.callback_instances)
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for t_start, t_end in cb_intervals:
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i_overlap_begin = bisect_left(pub_insts, t_start, key=lambda x: x.timestamp)
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i_overlap_end = bisect(pub_insts, t_end, key=lambda x: x.timestamp)
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for i in range(i_overlap_begin, i_overlap_end):
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pub_cb_overlaps[i].add(cb)
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pub_cbs = set()
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cb_cb_overlaps = set()
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@ -168,21 +136,24 @@ def _get_cb_topic_deps(nodes_to_cbs: Dict[TrNode, Set[TrCallbackObject]]):
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# For topics published to during the runtime of the callback's instances,
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# assume that they are published by the callback
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cbs_publishing_topic: Dict[TrTopic, Set[TrCallbackObject]] = {}
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p = tqdm(desc="Processing node publications", total=len(nodes_to_cbs))
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for node, cbs in nodes_to_cbs.items():
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p.update()
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cb_publishers: Dict[TrCallbackObject, Set[TrPublisher]] = {}
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for node, cbs in tqdm(nodes_to_cbs.items(), desc="Processing node publications"):
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if node is None:
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continue
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for pub in node.publishers:
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if any(f in pub.topic_name for f in TOPIC_FILTERS):
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continue
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pub_cbs = _get_publishing_cbs(cbs, pub)
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for cb in pub_cbs:
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if cb not in cb_publishers:
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cb_publishers[cb] = set()
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cb_publishers[cb].add(pub)
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if pub.topic not in cbs_publishing_topic:
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cbs_publishing_topic[pub.topic] = set()
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cbs_publishing_topic[pub.topic].update(pub_cbs)
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return cbs_subbed_to_topic, cbs_publishing_topic
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return cbs_subbed_to_topic, cbs_publishing_topic, cb_publishers
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@dataclass
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@ -224,6 +195,7 @@ class LGHierarchyLevel:
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class LGEdge:
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start: LGCallback
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end: LGCallback
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topic: TrTopic
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@dataclass
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@ -231,6 +203,9 @@ class LatencyGraph:
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top_node: LGHierarchyLevel
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edges: List[LGEdge]
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cb_pubs: Dict[TrCallbackObject, Set[TrPublisher]]
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pub_cbs: Dict[TrPublisher, Set[TrCallbackObject]]
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def __init__(self, tr: TrContext):
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##################################################
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# Annotate nodes with their callbacks
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@ -238,9 +213,7 @@ class LatencyGraph:
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# Note that nodes can also be None!
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nodes_to_cbs = {}
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p = tqdm(desc="Finding CB nodes", total=len(tr.callback_objects))
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for cb in tr.callback_objects.values():
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p.update()
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for cb in tqdm(tr.callback_objects, desc="Finding CB nodes"):
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node = _get_cb_owner_node(cb)
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if node not in nodes_to_cbs:
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@ -251,31 +224,23 @@ class LatencyGraph:
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# Find in/out topics for each callback
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##################################################
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cbs_subbed_to_topic, cbs_publishing_topic = _get_cb_topic_deps(nodes_to_cbs)
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cbs_subbed_to_topic, cbs_publishing_topic, cb_pubs = _get_cb_topic_deps(nodes_to_cbs)
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pub_cbs = {}
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for cb, pubs in cb_pubs.items():
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for pub in pubs:
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if pub not in pub_cbs:
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pub_cbs[pub] = set()
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pub_cbs[pub].add(cb)
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##################################################
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# Map topics to their messages
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##################################################
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topics_to_messages = {}
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p = tqdm(desc="Mapping messages to topics", total=len(tr.publish_instances))
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for pub_inst in tr.publish_instances:
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p.update()
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try:
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topic = pub_inst.publisher.topic
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except KeyError:
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continue
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if topic not in topics_to_messages:
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topics_to_messages[topic] = []
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topics_to_messages[topic].append(pub_inst)
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self.cb_pubs = cb_pubs
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self.pub_cbs = pub_cbs
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##################################################
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# Define nodes and edges on lowest level
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##################################################
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input = LGCallback("INPUT", [], [topic for topic in tr.topics.values() if not topic.publishers])
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output = LGCallback("OUTPUT", [topic for topic in tr.topics.values() if not topic.subscriptions], [])
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input = LGCallback("INPUT", [], [topic for topic in tr.topics if not topic.publishers])
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output = LGCallback("OUTPUT", [topic for topic in tr.topics if not topic.subscriptions], [])
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in_node = LGHierarchyLevel(None, [], "INPUT", [input])
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out_node = LGHierarchyLevel(None, [], "OUTPUT", [output])
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@ -284,17 +249,17 @@ class LatencyGraph:
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tr_to_lg_cb = {}
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p = tqdm("Building graph nodes", total=sum(map(len, nodes_to_cbs.values())))
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p = tqdm(desc="Building graph nodes", total=sum(map(len, nodes_to_cbs.values())))
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for node, cbs in nodes_to_cbs.items():
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node_callbacks = []
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for cb in cbs:
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p.update()
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try:
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sym = cb.callback_symbol
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sym = cb.callback_symbol
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if sym is not None:
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pretty_sym = sanitize(sym.symbol)
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except KeyError:
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sym = None
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else:
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pretty_sym = cb.id
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in_topics = [topic for topic, cbs in cbs_subbed_to_topic.items() if cb in cbs]
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out_topics = [topic for topic, cbs in cbs_publishing_topic.items() if cb in cbs]
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@ -306,15 +271,13 @@ class LatencyGraph:
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lg_nodes.append(lg_node)
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edges = []
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p = tqdm("Building graph edges", total=len(tr.topics))
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for topic in tr.topics.values():
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p.update()
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for topic in tqdm(tr.topics, desc="Building graph edges"):
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sub_cbs = cbs_subbed_to_topic[topic] if topic in cbs_subbed_to_topic else []
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pub_cbs = cbs_publishing_topic[topic] if topic in cbs_publishing_topic else []
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for sub_cb in sub_cbs:
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for pub_cb in pub_cbs:
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lg_edge = LGEdge(tr_to_lg_cb[pub_cb], tr_to_lg_cb[sub_cb])
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lg_edge = LGEdge(tr_to_lg_cb[pub_cb], tr_to_lg_cb[sub_cb], topic)
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edges.append(lg_edge)
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self.edges = edges
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@ -324,6 +287,3 @@ class LatencyGraph:
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##################################################
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self.top_node = _hierarchize(lg_nodes)
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def to_gv(self):
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pass
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31
latency_graph/message_tree.py
Normal file
31
latency_graph/message_tree.py
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@ -0,0 +1,31 @@
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from dataclasses import dataclass
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from typing import List
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from tracing_interop.tr_types import TrPublishInstance, TrCallbackInstance
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@dataclass
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class DepTree:
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head: TrCallbackInstance | TrPublishInstance
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deps: List['DepTree']
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def depth(self):
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return 1 + max(map(DepTree.depth, self.deps), default=0)
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def size(self):
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return 1 + sum(map(DepTree.size, self.deps))
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def fanout(self):
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if not self.deps:
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return 1
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return sum(map(DepTree.fanout, self.deps))
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def e2e_lat(self):
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return self.head.timestamp - self.critical_path()[-1].timestamp
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def critical_path(self):
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if not self.deps:
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return [self.head]
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return [self.head, *min(map(DepTree.critical_path, self.deps), key=lambda ls: ls[-1].timestamp)]
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