Refactorings, better console output.
* Removed spam-like warnings (mostly replaced by printing a single warning counter) * Removed inst_runtime_interval() from latency_graph.py as it was out of that file's responsibilities. Moved into TrCallbackInstance directly * Fixed coloring of latency graph (planning was misspelled)
This commit is contained in:
Maximilian Schmeller
parent
e9a00d51c9
commit
130c99e56f
3 changed files with 106 additions and 75 deletions
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@ -1,8 +1,10 @@
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from bisect import bisect_left, bisect_right
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from collections import defaultdict
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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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from functools import cache
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import numpy as np
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from tqdm import tqdm
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@ -15,21 +17,16 @@ from tracing_interop.tr_types import TrContext, TrCallbackObject, TrCallbackSymb
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TOPIC_FILTERS = ["/parameter_events", "/tf_static", "/robot_description", "diagnostics", "/rosout"]
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@cache
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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(node=node):
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owner_node = node
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return node
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case TrSubscriptionObject(subscription=sub):
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owner_node = sub.node
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return sub.node
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case _:
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owner_node = None
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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_node
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def _hierarchize(lg_nodes: Iterable['LGHierarchyLevel']):
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base = LGHierarchyLevel(None, [], "", [])
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@ -54,12 +51,6 @@ def _hierarchize(lg_nodes: Iterable['LGHierarchyLevel']):
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return base
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def inst_runtime_interval(cb_inst: TrCallbackInstance):
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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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@ -68,7 +59,7 @@ def _get_publishing_cbs(cbs: Set[TrCallbackObject], pub: TrPublisher):
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pub_cb_overlaps = {i: set() for i in range(len(pub_insts))}
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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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cb_intervals = map(lambda inst: (inst.t_start, inst.t_end), 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_right(pub_insts, t_end, key=lambda x: x.timestamp)
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@ -77,14 +68,20 @@ def _get_publishing_cbs(cbs: Set[TrCallbackObject], pub: TrPublisher):
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pub_cbs = set()
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cb_cb_overlaps = set()
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cb_less_pubs = defaultdict(lambda: 0)
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for i, i_cbs in pub_cb_overlaps.items():
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if not i_cbs:
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print(f"[WARN] Publication on {pub.topic_name} without corresponding callback!")
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cb_less_pubs[pub.topic_name] += 1
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elif len(i_cbs) == 1:
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pub_cbs.update(i_cbs)
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else: # Multiple CBs in i_cbs
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cb_cb_overlaps.update(iter(combinations(i_cbs, 2)))
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if cb_less_pubs:
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print(f"[ENTKÄFERN] There are {len(cb_less_pubs)} topics which have publications from untracked callbacks:")
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for topic_name, count in cb_less_pubs.items():
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print(f"--{pub.topic_name:.<120s}: {count:>20d} ownerless publications")
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for cb1, cb2 in cb_cb_overlaps:
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cb1_subset_of_cb2 = True
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cb2_subset_of_cb1 = True
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@ -213,13 +210,19 @@ class LatencyGraph:
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# Note that nodes can also be None!
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nodes_to_cbs = {}
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ownerless_cbs = 0
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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 is None:
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ownerless_cbs += 1
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continue
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if node not in nodes_to_cbs:
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nodes_to_cbs[node] = set()
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nodes_to_cbs[node].add(cb)
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print(f"[ENTKÄFERN] {ownerless_cbs} callbacks have no owner, filtering them out.")
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##################################################
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# Find in/out topics for each callback
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##################################################
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@ -26,6 +26,8 @@
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"\n",
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"from misc.utils import cached, parse_as\n",
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"\n",
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"plt.rcParams['figure.dpi'] = 300\n",
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"\n",
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"%matplotlib inline"
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],
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"metadata": {
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@ -58,6 +60,7 @@
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"# Path to trace directory (e.g. ~/.ros/my-trace/ust) or to a converted trace file.\n",
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"# Using the path \"/ust\" at the end is optional but greatly reduces processing time\n",
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"# if kernel traces are also present.\n",
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"# TR_PATH = \"/home/max/Downloads/iteration2_worker1/aw_replay/tracing/scenario-trace/ust\"\n",
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"TR_PATH = \"data/trace-awsim-x86/ust\"\n",
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"\n",
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"# Path to the folder all artifacts from this notebook are saved to.\n",
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@ -83,9 +86,9 @@
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"\n",
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"# Whether to compute data flow graphs.\n",
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"# If you are only interested in E2E latencies, set this to False\n",
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"DFG_ENABLED = False\n",
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"DFG_ENABLED = True\n",
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"# Whether to plot data flow graphs (ignored if DFG_ENABLED is False)\n",
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"DFG_PLOT = False\n",
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"DFG_PLOT = True\n",
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"\n",
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"# The maximum node namespace hierarchy level to be plotted.\n",
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"# Top-level (1): e.g. /sensing, /control, etc.\n",
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@ -116,10 +119,10 @@
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"\n",
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"# All topics containing any of these RegEx patterns are considered output topics in E2E latency calculations\n",
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"# E.g. r\"^/control/\" will cover all control topics\n",
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"E2E_OUTPUT_TOPIC_PATTERNS = [r\"^/control/.*?/control_cmd\"]\n",
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"E2E_OUTPUT_TOPIC_PATTERNS = [r\"emergency/control_cmd\"]\n",
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"# All topics containing any of these RegEx patterns are considered input topics in E2E latency calculations\n",
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"# E.g. r\"^/sensing/\" will cover all sensing topics\n",
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"E2E_INPUT_TOPIC_PATTERNS = [r\"^/vehicle/status/\", r\"^/sensing/\"]\n",
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"E2E_INPUT_TOPIC_PATTERNS = [r\"^/vehicle/status/\", r\"^/sensing/(lidar/[^c]|[^l])\"]\n",
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"\n",
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"# E2E paths are uniquely identified by a string like \"/topic/1 -> void(Node1)(args1) -> /topic/2 -> void(Node2)(args2) -> void(Node2)(args3) -> ...\".\n",
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"# Certain patterns only occur in initial setup or in scenario switching and can be excluded via RegEx patterns here.\n",
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@ -192,7 +195,6 @@
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"execution_count": null,
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"outputs": [],
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"source": [
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"\n",
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"def _load_traces():\n",
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" file = load_file(TR_PATH)\n",
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" handler = Ros2Handler.process(file)\n",
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@ -229,13 +231,13 @@
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"for t in sorted(topics, key=lambda t: t.name):\n",
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" for f in E2E_INPUT_TOPIC_PATTERNS:\n",
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" if re.search(f, t.name):\n",
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" print(f\"--[ENTKÄFERN] {f} :ß: {t.name}\")\n",
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" print(f\"--[ENTKÄFERN] {f:<30s}:{t.name:.<89s} | {sum(map(lambda p: len(p.instances), t.publishers))}\")\n",
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"\n",
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"print(\"\\n[ENTKÄFERN] OUTPUT TOPICS\")\n",
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"for t in sorted(topics, key=lambda t: t.name):\n",
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" for f in E2E_OUTPUT_TOPIC_PATTERNS:\n",
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" if re.search(f, t.name):\n",
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" print(f\"--[ENTKÄFERN] {f} :ß: {t.name}\")"
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" print(f\"--[ENTKÄFERN] {f:<30s}:{t.name:.<89s} | {sum(map(lambda p: len(p.instances), t.publishers))}\")"
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],
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"metadata": {
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"collapsed": false
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@ -255,7 +257,6 @@
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"execution_count": null,
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"outputs": [],
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"source": [
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"\n",
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"from latency_graph import latency_graph as lg\n",
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"\n",
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"\n",
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@ -274,7 +275,6 @@
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"execution_count": null,
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"outputs": [],
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"source": [
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"\n",
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"%%skip_if_false DFG_ENABLED\n",
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"%%skip_if_false DFG_PLOT\n",
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"\n",
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@ -297,11 +297,11 @@
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"node_namespace_mapping = {\n",
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" 'perception': 'perception',\n",
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" 'sensing': 'sensing',\n",
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" 'plannitokenizeng': 'planning',\n",
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" 'planning': 'planning',\n",
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" 'control': 'control',\n",
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" 'awapi': None,\n",
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" 'autoware_api': None,\n",
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" 'map': None,\n",
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" 'awapi': 'system',\n",
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" 'autoware_api': 'system',\n",
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" 'map': 'system',\n",
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" 'system': 'system',\n",
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" 'localization': 'localization',\n",
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" 'robot_state_publisher': None,\n",
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@ -370,7 +370,6 @@
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"execution_count": null,
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"outputs": [],
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"source": [
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"\n",
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"%%skip_if_false DFG_ENABLED\n",
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"%%skip_if_false DFG_PLOT\n",
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"\n",
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@ -384,7 +383,7 @@
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" if cur_lvl == DFG_MAX_HIER_LEVEL:\n",
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" return [node]\n",
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"\n",
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" if not node.children and cur_lvl < DFG_MAX_HIER_LEVEL:\n",
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" if not node.children:\n",
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" return [node]\n",
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"\n",
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" collected_nodes = []\n",
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@ -555,7 +554,7 @@
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" i_inst_latest = bisect_right(cb.callback_instances, inst.timestamp, key=lambda x: x.timestamp)\n",
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"\n",
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" for inst_before in cb.callback_instances[i_inst_latest::-1]:\n",
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" if lg.inst_runtime_interval(inst_before)[-1] < inst.timestamp:\n",
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" if inst_before.t_end < inst.timestamp:\n",
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" return inst_before\n",
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"\n",
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" return None\n",
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@ -606,11 +605,10 @@
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" i_inst_after = bisect_right(cb.callback_instances, msg.timestamp, key=lambda x: x.timestamp)\n",
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"\n",
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" for inst in cb.callback_instances[:i_inst_after]:\n",
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" inst_start, inst_end = lg.inst_runtime_interval(inst)\n",
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" if msg.timestamp > inst_end:\n",
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" if msg.timestamp > inst.t_end:\n",
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" continue\n",
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"\n",
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" assert inst_start <= msg.timestamp <= inst_end\n",
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" assert inst.t_start <= msg.timestamp <= inst.t_end\n",
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"\n",
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" cb_inst_candidates.append(inst)\n",
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"\n",
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@ -674,7 +672,6 @@
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"execution_count": null,
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"outputs": [],
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"source": [
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"\n",
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"%%skip_if_false E2E_ENABLED\n",
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"\n",
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"end_topics = [t for t in _tracing_context.topics if any(re.search(f, t.name) for f in E2E_OUTPUT_TOPIC_PATTERNS)]\n",
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@ -710,12 +707,10 @@
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"execution_count": null,
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"outputs": [],
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"source": [
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"\n",
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"%%skip_if_false E2E_ENABLED\n",
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"%%skip_if_false BW_ENABLED\n",
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"\n",
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"\n",
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"\n",
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"def parse_bytes(string):\n",
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" match string[-1]:\n",
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" case 'K':\n",
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@ -758,7 +753,7 @@
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" msg_sizes[topic] = {'bw': parse_bytes(m.group(\"bw\")),\n",
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" 'min': parse_bytes(m.group(\"min\")),\n",
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" 'mean': parse_bytes(m.group(\"mean\")),\n",
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" 'max': parse_bytes(m.group(\"max\"))}\n"
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" 'max': parse_bytes(m.group(\"max\"))}"
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],
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"metadata": {
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"collapsed": false
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@ -828,23 +823,28 @@
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"\n",
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"\n",
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"e2ess = []\n",
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"e2ess_trees = []\n",
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"e2e_pathss = []\n",
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"for tree in trees:\n",
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" try:\n",
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" e2es, e2e_paths = zip(*relevant_e2es(tree, E2E_INPUT_TOPIC_PATTERNS))\n",
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" except ValueError as e:\n",
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" print(e)\n",
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" print(end='!')\n",
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" continue\n",
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" e2ess.append(e2es)\n",
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" e2ess_trees.append(tree)\n",
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" e2e_pathss.append(e2e_paths)\n",
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"\n",
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"print()\n",
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"print(f\"[ENTKÄFERN] IN-TOPICS FOUND in {len(e2ess)}/{len(trees)} trees\")\n",
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"in_topics_found = \"\\n \".join(map(lambda pair: f\"{pair[0]:.<110s} {pair[1]:>10d}\", in_topics_found.items()))\n",
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"print(f\" {in_topics_found}\")\n",
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"\n",
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"print(f\"[ENTKÄFERN] LEAF TOPICS FOUND\")\n",
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"leaf_topics_found = \"\\n \".join(map(lambda pair: f\"{pair[0]:.<110s} {pair[1]:>10d}\", leaf_topics_found.items()))\n",
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"print(f\" {leaf_topics_found}\")"
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"print(f\" {leaf_topics_found}\")\n",
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"\n",
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"trees = e2ess_trees # Filter non-E2E trees"
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],
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"metadata": {
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"collapsed": false
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@ -855,12 +855,11 @@
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"execution_count": null,
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"outputs": [],
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"source": [
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"\n",
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"%%skip_if_false E2E_ENABLED\n",
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"%%skip_if_false E2E_PLOT\n",
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"\n",
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"\n",
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"fig, ax = plt.subplots(figsize=(18, 9), num=\"e2e_plot\")\n",
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"fig, ax = plt.subplots(num=\"e2e_plot\")\n",
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"DS = 1\n",
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"times = [tree.head.timestamp - trees[0].head.timestamp for tree in trees[::DS]]\n",
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"\n",
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@ -905,7 +904,6 @@
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"execution_count": null,
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"outputs": [],
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"source": [
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"\n",
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"%%skip_if_false E2E_ENABLED\n",
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"\n",
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"def critical_path(self):\n",
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@ -971,7 +969,6 @@
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"execution_count": null,
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"outputs": [],
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"source": [
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"\n",
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"%%skip_if_false E2E_ENABLED\n",
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"%%skip_if_false E2E_PLOT\n",
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"\n",
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@ -981,7 +978,7 @@
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" dictionary[key].append(value)\n",
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"\n",
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"\n",
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"fig, (ax, ax_rel) = plt.subplots(2, 1, sharex=True, figsize=(60, 30), num=\"crit_plot\")\n",
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"fig, (ax, ax_rel) = plt.subplots(2, 1, sharex=True, num=\"crit_plot\")\n",
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"ax.set_prop_cycle(cycler('color', [plt.cm.nipy_spectral(i / 4) for i in range(5)]))\n",
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"ax_rel.set_prop_cycle(cycler('color', [plt.cm.nipy_spectral(i / 4) for i in range(5)]))\n",
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"\n",
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@ -1068,17 +1065,17 @@
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"%%skip_if_false E2E_ENABLED\n",
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"%%skip_if_false E2E_PLOT\n",
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"\n",
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"fig, ax = plt.subplots(figsize=(60, 15), num=\"crit_pdf\")\n",
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"fig, ax = plt.subplots(num=\"crit_pdf\")\n",
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"ax.set_prop_cycle(cycler('color', [plt.cm.nipy_spectral(i / 4) for i in range(5)]))\n",
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"\n",
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"kde = stats.gaussian_kde(timestep_mags)\n",
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"xs = np.linspace(timestep_mags.min(), timestep_mags.max(), 1000)\n",
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"ax.plot(xs, kde(xs), label=\"End-to-End Latency\")\n",
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"perc = 90\n",
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"perc = 50\n",
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"ax.axvline(np.percentile(timestep_mags, perc), label=f\"{perc}th percentile\")\n",
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"\n",
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"ax2 = ax.twinx()\n",
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"ax2.hist(timestep_mags, 200)\n",
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"ax2.hist(timestep_mags, 50)\n",
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"ax2.set_ylim(0, ax2.get_ylim()[1])\n",
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"\n",
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"ax.set_title(\"Time Distribution for E2E Breakdown\")\n",
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@ -1099,7 +1096,6 @@
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"execution_count": null,
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"outputs": [],
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"source": [
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"\n",
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"%%skip_if_false E2E_ENABLED\n",
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"%%skip_if_false E2E_PLOT\n",
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"\n",
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@ -1257,7 +1253,7 @@
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" return height\n",
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"\n",
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"\n",
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"fig, ax = plt.subplots(figsize=(36, 20), num=\"path_viz\")\n",
|
||||
"fig, ax = plt.subplots(num=\"path_viz\")\n",
|
||||
"ax.set_ylim(0, len(e2es))\n",
|
||||
"\n",
|
||||
"y_labels = []\n",
|
||||
|
|
@ -1299,14 +1295,8 @@
|
|||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"%%skip_if_false E2E_ENABLED\n",
|
||||
"\n",
|
||||
"critical_paths = {}\n",
|
||||
"print(len(relevant_trees))\n",
|
||||
"for tree in tqdm(relevant_trees):\n",
|
||||
" crit = critical_path(tree)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"def _owner(inst):\n",
|
||||
" match inst:\n",
|
||||
|
|
@ -1325,6 +1315,11 @@
|
|||
" raise ValueError()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"critical_paths = {}\n",
|
||||
"print(len(relevant_trees))\n",
|
||||
"for tree in tqdm(relevant_trees):\n",
|
||||
" crit = critical_path(tree)\n",
|
||||
"\n",
|
||||
" key = tuple(map(_owner, crit[::-1]))\n",
|
||||
" if key not in critical_paths:\n",
|
||||
" critical_paths[key] = []\n",
|
||||
|
|
@ -1387,6 +1382,24 @@
|
|||
"name": "#%%%%skip_if_false E2E_ENABLED\n"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
from collections import namedtuple, UserList
|
||||
from collections import namedtuple, UserList, defaultdict
|
||||
from dataclasses import dataclass, field
|
||||
from functools import cached_property
|
||||
from typing import List, Dict, Optional, Set, TypeVar, Generic, Iterable
|
||||
|
||||
from tracetools_analysis.processor.ros2 import Ros2Handler
|
||||
|
|
@ -21,20 +22,26 @@ class Index(Generic[IdxItemType]):
|
|||
for idx_name, is_multi in idx_fields.items():
|
||||
index = {}
|
||||
self.__indices[idx_name] = index
|
||||
|
||||
if is_multi:
|
||||
for item in self.__items:
|
||||
key = getattr(item, idx_name)
|
||||
if key not in index:
|
||||
index[key] = []
|
||||
index[key].append(item) # Also sorted since items are processed in order and only filtered here
|
||||
else:
|
||||
else: # Unique index
|
||||
duplicate_indices = defaultdict(lambda: 0)
|
||||
|
||||
for item in self.__items:
|
||||
key = getattr(item, idx_name)
|
||||
if key in index:
|
||||
print(repr(ValueError(f"Duplicate key: {idx_name}={key}; old={index[key]}; new={item}")))
|
||||
duplicate_indices[key] += 1
|
||||
index[key] = item
|
||||
|
||||
if duplicate_indices:
|
||||
print(f"[ENTKÄFERN] Duplicate Indices in {idx_name}:")
|
||||
# for key, count in duplicate_indices.items():
|
||||
# print(f"--{key:<20d}'s last candidate: {repr(index[key])}")
|
||||
|
||||
def __iter__(self):
|
||||
return iter(self.__items)
|
||||
|
||||
|
|
@ -331,6 +338,14 @@ class TrCallbackInstance:
|
|||
def callback_obj(self) -> Optional['TrCallbackObject']:
|
||||
return self._c.callback_objects.by_callback_object.get(self.callback_object)
|
||||
|
||||
@property
|
||||
def t_start(self):
|
||||
return self.timestamp
|
||||
|
||||
@cached_property
|
||||
def t_end(self):
|
||||
return self.timestamp + self.duration
|
||||
|
||||
def __hash__(self):
|
||||
return hash((self.callback_object, self.timestamp, self.duration))
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue