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# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
"""
The objective of optimization is to remove as many tasks from the graph as
possible, as efficiently as possible, thereby delivering useful results as
quickly as possible. For example, ideally if only a test script is modified in
a push, then the resulting graph contains only the corresponding test suite
task.
See ``taskcluster/docs/optimization.rst`` for more information.
"""
import datetime
import logging
from abc import ABCMeta, abstractmethod
from collections import defaultdict
from slugid import nice as slugid
from taskgraph.graph import Graph
from taskgraph.taskgraph import TaskGraph
from taskgraph.util.parameterization import resolve_task_references, resolve_timestamps
from taskgraph.util.python_path import import_sibling_modules
from taskgraph.util.taskcluster import find_task_id_batched, status_task_batched
logger = logging.getLogger("optimization")
registry = {}
def register_strategy(name, args=(), kwargs=None):
kwargs = kwargs or {}
def wrap(cls):
if name not in registry:
registry[name] = cls(*args, **kwargs)
if not hasattr(registry[name], "description"):
registry[name].description = name
return cls
return wrap
def optimize_task_graph(
target_task_graph,
requested_tasks,
params,
do_not_optimize,
decision_task_id,
existing_tasks=None,
strategy_override=None,
):
"""
Perform task optimization, returning a taskgraph and a map from label to
assigned taskId, including replacement tasks.
"""
# avoid circular import
from taskgraph.optimize.strategies import IndexSearch
label_to_taskid = {}
if not existing_tasks:
existing_tasks = {}
# instantiate the strategies for this optimization process
strategies = registry.copy()
if strategy_override:
strategies.update(strategy_override)
optimizations = _get_optimizations(target_task_graph, strategies)
removed_tasks = remove_tasks(
target_task_graph=target_task_graph,
requested_tasks=requested_tasks,
optimizations=optimizations,
params=params,
do_not_optimize=do_not_optimize,
)
# Gather each relevant task's index
indexes = set()
for label in target_task_graph.graph.visit_postorder():
if label in do_not_optimize:
continue
_, strategy, arg = optimizations(label)
if isinstance(strategy, IndexSearch) and arg is not None:
indexes.update(arg)
index_to_taskid = {}
taskid_to_status = {}
if indexes:
# Find their respective status using TC index/queue batch APIs
indexes = list(indexes)
index_to_taskid = find_task_id_batched(indexes)
taskid_to_status = status_task_batched(list(index_to_taskid.values()))
replaced_tasks = replace_tasks(
target_task_graph=target_task_graph,
optimizations=optimizations,
params=params,
do_not_optimize=do_not_optimize,
label_to_taskid=label_to_taskid,
existing_tasks=existing_tasks,
removed_tasks=removed_tasks,
index_to_taskid=index_to_taskid,
taskid_to_status=taskid_to_status,
)
return (
get_subgraph(
target_task_graph,
removed_tasks,
replaced_tasks,
label_to_taskid,
decision_task_id,
),
label_to_taskid,
)
def _get_optimizations(target_task_graph, strategies):
def optimizations(label):
task = target_task_graph.tasks[label]
if task.optimization:
opt_by, arg = list(task.optimization.items())[0]
strategy = strategies[opt_by]
if hasattr(strategy, "description"):
opt_by += f" ({strategy.description})"
return (opt_by, strategy, arg)
else:
return ("never", strategies["never"], None)
return optimizations
def _log_optimization(verb, opt_counts, opt_reasons=None):
if opt_reasons:
message = "optimize: {label} {action} because of {reason}"
for label, (action, reason) in opt_reasons.items():
logger.debug(message.format(label=label, action=action, reason=reason))
if opt_counts:
logger.info(
f"{verb.title()} "
+ ", ".join(f"{c} tasks by {b}" for b, c in sorted(opt_counts.items()))
+ " during optimization."
)
else:
logger.info(f"No tasks {verb} during optimization")
def remove_tasks(
target_task_graph, requested_tasks, params, optimizations, do_not_optimize
):
"""
Implement the "Removing Tasks" phase, returning a set of task labels of all removed tasks.
"""
opt_counts = defaultdict(int)
opt_reasons = {}
removed = set()
dependents_of = target_task_graph.graph.reverse_links_dict()
tasks = target_task_graph.tasks
prune_candidates = set()
# Traverse graph so dependents (child nodes) are guaranteed to be processed
# first.
for label in target_task_graph.graph.visit_preorder():
# Dependents that can be pruned away (shouldn't cause this task to run).
# Only dependents that either:
# A) Explicitly reference this task in their 'if_dependencies' list, or
# B) Don't have an 'if_dependencies' attribute (i.e are in 'prune_candidates'
# because they should be removed but have prune_deps themselves)
# should be considered.
prune_deps = {
l
for l in dependents_of[label]
if l in prune_candidates
if not tasks[l].if_dependencies or label in tasks[l].if_dependencies
}
def _keep(reason):
"""Mark a task as being kept in the graph. Also recursively removes
any dependents from `prune_candidates`, assuming they should be
kept because of this task.
"""
opt_reasons[label] = ("kept", reason)
# Removes dependents that were in 'prune_candidates' from a task
# that ended up being kept (and therefore the dependents should
# also be kept).
queue = list(prune_deps)
while queue:
l = queue.pop()
# If l is a prune_dep of multiple tasks it could be queued up
# multiple times. Guard against it being already removed.
if l not in prune_candidates:
continue
# If a task doesn't set 'if_dependencies' itself (rather it was
# added to 'prune_candidates' due to one of its depenendents),
# then we shouldn't remove it.
if not tasks[l].if_dependencies:
continue
prune_candidates.remove(l)
queue.extend([r for r in dependents_of[l] if r in prune_candidates])
def _remove(reason):
"""Potentially mark a task as being removed from the graph. If the
task has dependents that can be pruned, add this task to
`prune_candidates` rather than removing it.
"""
if prune_deps:
# If there are prune_deps, unsure if we can remove this task yet.
prune_candidates.add(label)
else:
opt_reasons[label] = ("removed", reason)
opt_counts[reason] += 1
removed.add(label)
# if we're not allowed to optimize, that's easy..
if label in do_not_optimize:
_keep("do not optimize")
continue
# If there are remaining tasks depending on this one, do not remove.
if any(
l for l in dependents_of[label] if l not in removed and l not in prune_deps
):
_keep("dependent tasks")
continue
# Some tasks in the task graph only exist because they were required
# by a task that has just been optimized away. They can now be removed.
if label not in requested_tasks:
_remove("dependents optimized")
continue
# Call the optimization strategy.
task = tasks[label]
opt_by, opt, arg = optimizations(label)
if opt.should_remove_task(task, params, arg):
_remove(opt_by)
continue
# Some tasks should only run if their dependency was also run. Since we
# haven't processed dependencies yet, we add them to a list of
# candidate tasks for pruning.
if task.if_dependencies:
opt_reasons[label] = ("kept", opt_by)
prune_candidates.add(label)
else:
_keep(opt_by)
if prune_candidates:
reason = "if-dependencies pruning"
for label in prune_candidates:
# There's an edge case where a triangle graph can cause a
# dependency to stay in 'prune_candidates' when the dependent
# remains. Do a final check to ensure we don't create any bad
# edges.
dependents = any(
d
for d in dependents_of[label]
if d not in prune_candidates
if d not in removed
)
if dependents:
opt_reasons[label] = ("kept", "dependent tasks")
continue
removed.add(label)
opt_counts[reason] += 1
opt_reasons[label] = ("removed", reason)
_log_optimization("removed", opt_counts, opt_reasons)
return removed
def replace_tasks(
target_task_graph,
params,
optimizations,
do_not_optimize,
label_to_taskid,
removed_tasks,
existing_tasks,
index_to_taskid,
taskid_to_status,
):
"""
Implement the "Replacing Tasks" phase, returning a set of task labels of
all replaced tasks. The replacement taskIds are added to label_to_taskid as
a side-effect.
"""
# avoid circular import
from taskgraph.optimize.strategies import IndexSearch
opt_counts = defaultdict(int)
replaced = set()
dependents_of = target_task_graph.graph.reverse_links_dict()
dependencies_of = target_task_graph.graph.links_dict()
for label in target_task_graph.graph.visit_postorder():
logger.debug(f"replace_tasks: {label}")
# if we're not allowed to optimize, that's easy..
if label in do_not_optimize:
logger.debug(f"replace_tasks: {label} is in do_not_optimize")
continue
# if this task depends on un-replaced, un-removed tasks, do not replace
if any(
l not in replaced and l not in removed_tasks for l in dependencies_of[label]
):
logger.debug(
f"replace_tasks: {label} depends on an unreplaced or unremoved task"
)
continue
# if the task already exists, that's an easy replacement
repl = existing_tasks.get(label)
if repl:
label_to_taskid[label] = repl
replaced.add(label)
opt_counts["existing_tasks"] += 1
logger.debug(f"replace_tasks: {label} replaced from existing_tasks")
continue
# call the optimization strategy
task = target_task_graph.tasks[label]
opt_by, opt, arg = optimizations(label)
# compute latest deadline of dependents (if any)
dependents = [target_task_graph.tasks[l] for l in dependents_of[label]]
deadline = None
if dependents:
now = datetime.datetime.utcnow()
deadline = max(
resolve_timestamps(now, task.task["deadline"])
for task in dependents # type: ignore
)
if isinstance(opt, IndexSearch):
arg = arg, index_to_taskid, taskid_to_status
repl = opt.should_replace_task(task, params, deadline, arg)
if repl:
if repl is True:
logger.debug(f"replace_tasks: {label} removed by optimization strategy")
# True means remove this task; get_subgraph will catch any
# problems with removed tasks being depended on
removed_tasks.add(label)
else:
logger.debug(
f"replace_tasks: {label} replaced with {repl} by optimization strategy"
)
label_to_taskid[label] = repl
replaced.add(label)
opt_counts[opt_by] += 1
continue
else:
logger.debug(f"replace_tasks: {label} kept by optimization strategy")
_log_optimization("replaced", opt_counts)
return replaced
def get_subgraph(
target_task_graph,
removed_tasks,
replaced_tasks,
label_to_taskid,
decision_task_id,
):
"""
Return the subgraph of target_task_graph consisting only of
non-optimized tasks and edges between them.
To avoid losing track of taskIds for tasks optimized away, this method
simultaneously substitutes real taskIds for task labels in the graph, and
populates each task definition's `dependencies` key with the appropriate
taskIds. Task references are resolved in the process.
"""
# check for any dependency edges from included to removed tasks
bad_edges = [
(l, r, n)
for l, r, n in target_task_graph.graph.edges
if l not in removed_tasks and r in removed_tasks
]
if bad_edges:
probs = ", ".join(
f"{l} depends on {r} as {n} but it has been removed"
for l, r, n in bad_edges
)
raise Exception("Optimization error: " + probs)
# fill in label_to_taskid for anything not removed or replaced
assert replaced_tasks <= set(label_to_taskid)
for label in sorted(
target_task_graph.graph.nodes - removed_tasks - set(label_to_taskid)
):
label_to_taskid[label] = slugid()
# resolve labels to taskIds and populate task['dependencies']
tasks_by_taskid = {}
named_links_dict = target_task_graph.graph.named_links_dict()
omit = removed_tasks | replaced_tasks
for label, task in target_task_graph.tasks.items():
if label in omit:
continue
task.task_id = label_to_taskid[label]
named_task_dependencies = {
name: label_to_taskid[label]
for name, label in named_links_dict.get(label, {}).items()
}
# Add remaining soft dependencies
if task.soft_dependencies:
named_task_dependencies.update(
{
label: label_to_taskid[label]
for label in task.soft_dependencies
if label in label_to_taskid and label not in omit
}
)
task.task = resolve_task_references(
task.label,
task.task,
task_id=task.task_id,
decision_task_id=decision_task_id,
dependencies=named_task_dependencies,
)
deps = task.task.setdefault("dependencies", []) # type: ignore
deps.extend(sorted(named_task_dependencies.values()))
tasks_by_taskid[task.task_id] = task
# resolve edges to taskIds
edges_by_taskid = (
(label_to_taskid.get(left), label_to_taskid.get(right), name)
for (left, right, name) in target_task_graph.graph.edges
)
# ..and drop edges that are no longer entirely in the task graph
# (note that this omits edges to replaced tasks, but they are still in task.dependnecies)
edges_by_taskid = {
(left, right, name)
for (left, right, name) in edges_by_taskid
if left in tasks_by_taskid and right in tasks_by_taskid
}
return TaskGraph(tasks_by_taskid, Graph(set(tasks_by_taskid), edges_by_taskid)) # type: ignore
@register_strategy("never")
class OptimizationStrategy:
def should_remove_task(self, task, params, arg):
"""Determine whether to optimize this task by removing it. Returns
True to remove."""
return False
def should_replace_task(self, task, params, deadline, arg):
"""Determine whether to optimize this task by replacing it. Returns a
taskId to replace this task, True to replace with nothing, or False to
keep the task."""
return False
@register_strategy("always")
class Always(OptimizationStrategy):
def should_remove_task(self, task, params, arg):
return True
class CompositeStrategy(OptimizationStrategy, metaclass=ABCMeta):
def __init__(self, *substrategies, **kwargs):
self.substrategies = []
missing = set()
for sub in substrategies:
if isinstance(sub, str):
if sub not in registry.keys():
missing.add(sub)
continue
sub = registry[sub]
self.substrategies.append(sub)
if missing:
raise TypeError(
"substrategies aren't registered: {}".format(
", ".join(sorted(missing))
)
)
self.split_args = kwargs.pop("split_args", None)
if not self.split_args:
self.split_args = lambda arg, substrategies: [arg] * len(substrategies)
if kwargs:
raise TypeError("unexpected keyword args")
@property
@abstractmethod
def description(self):
"""A textual description of the combined substrategies."""
@abstractmethod
def reduce(self, results):
"""Given all substrategy results as a generator, return the overall
result."""
def _generate_results(self, fname, *args):
*passthru, arg = args
for sub, arg in zip(
self.substrategies, self.split_args(arg, self.substrategies)
):
yield getattr(sub, fname)(*passthru, arg)
def should_remove_task(self, *args):
results = self._generate_results("should_remove_task", *args)
return self.reduce(results)
def should_replace_task(self, *args):
results = self._generate_results("should_replace_task", *args)
return self.reduce(results)
class Any(CompositeStrategy):
"""Given one or more optimization strategies, remove or replace a task if any of them
says to.
Replacement will use the value returned by the first strategy that says to replace.
"""
@property
def description(self):
return "-or-".join([s.description for s in self.substrategies])
@classmethod
def reduce(cls, results):
for rv in results:
if rv:
return rv
return False
class All(CompositeStrategy):
"""Given one or more optimization strategies, remove or replace a task if all of them
says to.
Replacement will use the value returned by the first strategy passed in.
Note the values used for replacement need not be the same, as long as they
all say to replace.
"""
@property
def description(self):
return "-and-".join([s.description for s in self.substrategies])
@classmethod
def reduce(cls, results):
for rv in results:
if not rv:
return rv
return True
class Alias(CompositeStrategy):
"""Provides an alias to an existing strategy.
This can be useful to swap strategies in and out without needing to modify
the task transforms.
"""
def __init__(self, strategy):
super().__init__(strategy)
@property
def description(self):
return self.substrategies[0].description
def reduce(self, results):
return next(results)
class Not(CompositeStrategy):
"""Given a strategy, returns the opposite."""
def __init__(self, strategy):
super().__init__(strategy)
@property
def description(self):
return "not-" + self.substrategies[0].description
def reduce(self, results):
return not next(results)
# Trigger registration in sibling modules.
import_sibling_modules()