GH-139389: Do not track immutable tuples in PyTuple_Pack

[sergey-miryanov]

When we use PyTuple_Pack all objects already well constructed. If we know that they immutable we can skip tracking it in GC, because GC will untrack them eventually.

I have a PR ready and benchmark results:

Geometric mean: 1.01x faster (Win11 x64, 11th Gen Intel(R) Core(TM) i5-11600K @ 3.90GHz, 48d0d0d)

All benchmarks:

+--------------------------+----------+------------------------+
| Benchmark                | main     | tuples                 |
+==========================+==========+========================+
| async_generators         | 435 ms   | 430 ms: 1.01x faster   |
+--------------------------+----------+------------------------+
| asyncio_tcp              | 750 ms   | 756 ms: 1.01x slower   |
+--------------------------+----------+------------------------+
| asyncio_tcp_ssl          | 1.91 sec | 1.92 sec: 1.01x slower |
+--------------------------+----------+------------------------+
| comprehensions           | 22.1 us  | 21.8 us: 1.01x faster  |
+--------------------------+----------+------------------------+
| bench_mp_pool            | 104 ms   | 103 ms: 1.01x faster   |
+--------------------------+----------+------------------------+
| bench_thread_pool        | 1.29 ms  | 1.27 ms: 1.01x faster  |
+--------------------------+----------+------------------------+
| coroutines               | 28.2 ms  | 27.7 ms: 1.02x faster  |
+--------------------------+----------+------------------------+
| coverage                 | 88.5 ms  | 86.3 ms: 1.02x faster  |
+--------------------------+----------+------------------------+
| crypto_pyaes             | 90.1 ms  | 86.7 ms: 1.04x faster  |
+--------------------------+----------+------------------------+
| deepcopy                 | 310 us   | 307 us: 1.01x faster   |
+--------------------------+----------+------------------------+
| deepcopy_memo            | 36.4 us  | 36.1 us: 1.01x faster  |
+--------------------------+----------+------------------------+
| deltablue                | 5.19 ms  | 4.85 ms: 1.07x faster  |
+--------------------------+----------+------------------------+
| django_template          | 45.5 ms  | 45.8 ms: 1.01x slower  |
+--------------------------+----------+------------------------+
| docutils                 | 2.47 sec | 2.45 sec: 1.01x faster |
+--------------------------+----------+------------------------+
| dulwich_log              | 86.2 ms  | 86.9 ms: 1.01x slower  |
+--------------------------+----------+------------------------+
| fannkuch                 | 449 ms   | 441 ms: 1.02x faster   |
+--------------------------+----------+------------------------+
| float                    | 85.3 ms  | 82.5 ms: 1.03x faster  |
+--------------------------+----------+------------------------+
| create_gc_cycles         | 1.17 ms  | 1.17 ms: 1.01x faster  |
+--------------------------+----------+------------------------+
| gc_traversal             | 2.97 ms  | 2.88 ms: 1.03x faster  |
+--------------------------+----------+------------------------+
| generators               | 43.0 ms  | 41.6 ms: 1.03x faster  |
+--------------------------+----------+------------------------+
| genshi_text              | 28.9 ms  | 28.7 ms: 1.01x faster  |
+--------------------------+----------+------------------------+
| go                       | 160 ms   | 153 ms: 1.04x faster   |
+--------------------------+----------+------------------------+
| hexiom                   | 8.39 ms  | 8.13 ms: 1.03x faster  |
+--------------------------+----------+------------------------+
| json_dumps               | 8.62 ms  | 8.69 ms: 1.01x slower  |
+--------------------------+----------+------------------------+
| logging_format           | 12.5 us  | 12.2 us: 1.02x faster  |
+--------------------------+----------+------------------------+
| logging_silent           | 139 ns   | 140 ns: 1.01x slower   |
+--------------------------+----------+------------------------+
| logging_simple           | 11.3 us  | 11.1 us: 1.01x faster  |
+--------------------------+----------+------------------------+
| mako                     | 14.2 ms  | 14.4 ms: 1.01x slower  |
+--------------------------+----------+------------------------+
| mdp                      | 1.47 sec | 1.50 sec: 1.02x slower |
+--------------------------+----------+------------------------+
| meteor_contest           | 104 ms   | 102 ms: 1.02x faster   |
+--------------------------+----------+------------------------+
| nbody                    | 114 ms   | 113 ms: 1.01x faster   |
+--------------------------+----------+------------------------+
| pickle_pure_python       | 439 us   | 436 us: 1.01x faster   |
+--------------------------+----------+------------------------+
| pprint_safe_repr         | 953 ms   | 916 ms: 1.04x faster   |
+--------------------------+----------+------------------------+
| pprint_pformat           | 1.95 sec | 1.88 sec: 1.04x faster |
+--------------------------+----------+------------------------+
| pyflate                  | 506 ms   | 492 ms: 1.03x faster   |
+--------------------------+----------+------------------------+
| python_startup           | 28.5 ms  | 27.4 ms: 1.04x faster  |
+--------------------------+----------+------------------------+
| python_startup_no_site   | 23.2 ms  | 22.2 ms: 1.05x faster  |
+--------------------------+----------+------------------------+
| raytrace                 | 361 ms   | 345 ms: 1.05x faster   |
+--------------------------+----------+------------------------+
| regex_compile            | 146 ms   | 146 ms: 1.01x faster   |
+--------------------------+----------+------------------------+
| regex_effbot             | 2.03 ms  | 2.02 ms: 1.01x faster  |
+--------------------------+----------+------------------------+
| regex_v8                 | 23.9 ms  | 22.7 ms: 1.06x faster  |
+--------------------------+----------+------------------------+
| richards                 | 66.1 ms  | 59.9 ms: 1.10x faster  |
+--------------------------+----------+------------------------+
| richards_super           | 71.6 ms  | 68.7 ms: 1.04x faster  |
+--------------------------+----------+------------------------+
| scimark_fft              | 300 ms   | 294 ms: 1.02x faster   |
+--------------------------+----------+------------------------+
| scimark_lu               | 135 ms   | 131 ms: 1.03x faster   |
+--------------------------+----------+------------------------+
| scimark_monte_carlo      | 83.3 ms  | 82.4 ms: 1.01x faster  |
+--------------------------+----------+------------------------+
| scimark_sor              | 157 ms   | 150 ms: 1.05x faster   |
+--------------------------+----------+------------------------+
| scimark_sparse_mat_mult  | 4.27 ms  | 4.35 ms: 1.02x slower  |
+--------------------------+----------+------------------------+
| spectral_norm            | 122 ms   | 118 ms: 1.03x faster   |
+--------------------------+----------+------------------------+
| sqlglot_optimize         | 60.7 ms  | 60.9 ms: 1.00x slower  |
+--------------------------+----------+------------------------+
| sympy_expand             | 501 ms   | 503 ms: 1.00x slower   |
+--------------------------+----------+------------------------+
| sympy_sum                | 143 ms   | 144 ms: 1.01x slower   |
+--------------------------+----------+------------------------+
| sympy_str                | 287 ms   | 292 ms: 1.02x slower   |
+--------------------------+----------+------------------------+
| telco                    | 7.26 ms  | 7.33 ms: 1.01x slower  |
+--------------------------+----------+------------------------+
| tomli_loads              | 2.23 sec | 2.25 sec: 1.01x slower |
+--------------------------+----------+------------------------+
| typing_runtime_protocols | 189 us   | 185 us: 1.02x faster   |
+--------------------------+----------+------------------------+
| unpack_sequence          | 65.4 ns  | 68.7 ns: 1.05x slower  |
+--------------------------+----------+------------------------+
| unpickle                 | 13.9 us  | 14.1 us: 1.01x slower  |
+--------------------------+----------+------------------------+
| unpickle_pure_python     | 303 us   | 300 us: 1.01x faster   |
+--------------------------+----------+------------------------+
| xml_etree_parse          | 130 ms   | 130 ms: 1.01x slower   |
+--------------------------+----------+------------------------+
| xml_etree_iterparse      | 107 ms   | 108 ms: 1.01x slower   |
+--------------------------+----------+------------------------+
| xml_etree_process        | 79.2 ms  | 78.6 ms: 1.01x faster  |
+--------------------------+----------+------------------------+
| Geometric mean           | (ref)    | 1.01x faster           |
+--------------------------+----------+------------------------+

Benchmark hidden because not significant (20): 2to3, chaos, deepcopy_reduce, genshi_xml, html5lib, json_loads, nqueens, pathlib, pickle, pickle_dict, pickle_list, pidigits, regex_dna, sqlglot_normalize, sqlglot_parse, sqlglot_transpile, sqlite_synth, sympy_integrate, unpickle_list, xml_etree_generate

It doesn't hurt performance, but can decrease number of objects in GC to check and untrack.

• Issue: Do not track immutable tuples in PyTuple_Pack #139389

[sergey-miryanov]

I'm not sure that this needs a NEWS entry because it is an implementation detail. But I'm here on triage/core decision.

[sergey-miryanov]

Sorry, misclick.

[ZeroIntensity]

PyTuple_Pack is user-facing, so let's add a blurb.

[eendebakpt]

Which cases would benefit from this change to PyTuple_Pack? We could apply a similar optimization to some other methods to construct a tuple (e.g. _PyTuple_FromArray, or inside dictiter_iternextitem), and it is not clear to me directly which ones would benefit (no gc tracking), and which ones would not (expensive to check all the arguments)

PyTuple_Pack itself is slow (due to varargs), so there should not be a lot of cases where this is used often.

Note: I used this code to check which cases are impacted

import gc
import random

a = (1, 2, 3, 4)
b = tuple([1,2,3])
c = tuple(list([1,2,3])) 
d = (2,3) * (random.randint(2, 4)+2)
          
print(f'{gc.is_tracked(a)=}') # still tracked
print(f'{gc.is_tracked(b)=}')
print(f'{gc.is_tracked(c)=}')
print(f'{gc.is_tracked(d)=}')

d={10: 1, 11: 2}

one_dict_item = next(iter(d.items()))
print(f'{gc.is_tracked(one_dict_item)=}') # still tracked!

l=[]
l.extend(d.items())
print(f'{gc.is_tracked(l[0])=}') # here it works! l[0] is not tracked

for tp in d.items():
    print(f'{gc.is_tracked(tp)=}')  # tracked, even though this might be a common use case

[sergey-miryanov]

@eendebakpt Yeah, I did the same for:

• _PyTuple_FromArray
• _PyTuple_FromStackRefStealOnSuccess
• _PyTuple_FromArraySteal
• tuple_concat
• tuple_repeat
• tuple_subscript

Collecting stats and microbenchmarking now.

[sergey-miryanov]

Tests fail because instrumentation a bit straightforward and work on my machine

[sergey-miryanov]

Tests will fail because I write stats to stderr.