bpo-44953: Add vectorcall for itemgetter and attrgetter

[sweeneyde]

https://bugs.python.org/issue44953

[sweeneyde]

This was inspired by GH-27782, which does the same, and more, for methodcaller.

[sweeneyde]

Benchmarks for this change:

itemgetter: Mean +- std dev: [operator_main] 45.3 ns +- 1.3 ns -> [operator_vec] 29.5 ns +- 0.7 ns: 1.54x faster
attrgetter: Mean +- std dev: [operator_main] 61.6 ns +- 1.7 ns -> [operator_vec] 43.8 ns +- 0.9 ns: 1.41x faster

[bedevere-bot]

🤖 New build scheduled with the buildbot fleet by @rhettinger for commit f9fc83a 🤖

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[github-actions]

This PR is stale because it has been open for 30 days with no activity.

[sweeneyde]

The __vectorcalloffset__ behavior was added in GH-20026. I don't think I understand that design decision rather than adding a new PyType_Slot.slot value called something like Py_tp_vectorcall, but it appears to be what we have. The corresponding behavior of __dictoffset__ and __weaklistoffset__ goes back to 2019 in GH-16076. Are there any plans to change these, or is there anything holding this up?

[sweeneyde]

Any plans for this? It may also be relevant to bpo-46148 about optimizing Pathlib.

[sweeneyde]

Does anyone with a commit bit want to merge this? This is the kind of callable that's often called in loops.

[sweeneyde]

I re-ran some benchmarks, and they still look good. I plan to merge this in about 24 hours if there are no more objections.

fromoperatorimportitemgetter, attrgetterfromitertoolsimportrepeatfromcollectionsimportnamedtuple, dequefrompyperfimportRunnerclassDefaultClass:
def__init__(self, a, b):
self.a=aself.b=bclassSlotsClass:
__slots__="a", "b"def__init__(self, a, b):
self.a=aself.b=bNamedTuple=namedtuple("NT", ["a", "b"])
MAP_LOOPS=10_000attr_classes= {
'DefaultClass': DefaultClass,
'SlotsClass': SlotsClass,
'NamedTuple': NamedTuple,
}
item_classes= {
'tuple': tuple,
'list': list,
'dict': dict.fromkeys,
}
namespace= {
'IG': itemgetter(1),
'AG': attrgetter('a'),
'repeat': repeat,
'deque': deque,
} |attr_classes|item_classesrunner=Runner()
forclassnameinattr_classes:
runner.timeit(
name=f"{classname}-1",
setup=f"obj = {classname}(11, 22)",
stmt="AG(obj)",
globals=namespace
)
runner.timeit(
name=f"{classname}-map",
setup=f"obj = {classname}(11, 22)",
stmt=f"deque(map(AG, repeat(obj, {MAP_LOOPS})), maxlen=0)",
globals=namespace,
inner_loops=MAP_LOOPS,
)
forclassnameinitem_classes:
runner.timeit(
name=f"{classname}-1",
setup=f"obj = {classname}((1, 2, 3, 4, 5))",
stmt="IG(obj)",
globals=namespace,
)
runner.timeit(
name=f"{classname}-map",
setup=f"obj = {classname}((1, 2, 3, 4, 5))",
stmt=f"deque(map(IG, repeat(obj, {MAP_LOOPS})), maxlen=0)",
globals=namespace,
inner_loops=MAP_LOOPS,
)

Results:

Benchmark	main_bench	PR-27828
tuple-map	27.6 ns	13.2 ns: 2.09x faster
dict-map	39.4 ns	24.2 ns: 1.62x faster
NamedTuple-map	40.0 ns	24.8 ns: 1.61x faster
list-map	38.2 ns	23.8 ns: 1.61x faster
SlotsClass-map	42.8 ns	28.1 ns: 1.53x faster
DefaultClass-map	45.5 ns	31.0 ns: 1.47x faster
tuple-1	56.7 ns	41.4 ns: 1.37x faster
NamedTuple-1	70.1 ns	52.7 ns: 1.33x faster
dict-1	69.4 ns	52.2 ns: 1.33x faster
SlotsClass-1	75.1 ns	56.6 ns: 1.33x faster
list-1	68.7 ns	52.1 ns: 1.32x faster
DefaultClass-1	76.9 ns	59.4 ns: 1.29x faster
Geometric mean	(ref)	1.48x faster