gh-140009: Optimize dict object by replacing PyTuple_Pack with PyTuple_FromArray

[rashes2006]

gh-140009: Optimize dict object by replacing PyTuple_Pack with PyTuple_FromArray

Summary

This PR replaces PyTuple_Pack with PyTuple_FromArray in Objects/dictobject.c for creating small tuples (size 2).

PyTuple_FromArray is more efficient than PyTuple_Pack because it avoids the overhead of variadic arguments (va_args) processing by taking a pointer to a pre-allocated array of PyObject*.

Changes

• dictiter_new: Replaced PyTuple_Pack(2, Py_None, Py_None) with PyTuple_FromArray using a stack-allocated array.
• dictitems_xor_lock_held: Replaced PyTuple_Pack(2, key, val2) with PyTuple_FromArray.

Performance Impact

This is part of a general effort to optimize small tuple creation across the codebase. Replacing PyTuple_Pack with PyTuple_FromArray for small, fixed-size tuples reduces call overhead.

• Issue: Improve performance by replacing PyTuple_Pack with PyTuple_FromArray #140009

[eendebakpt]

@rashes2006 Do you have any benchmarks showing the performance gain from these changes?

[picnixz]

Also don't update the branch if nothing needs to be updated: https://devguide.python.org/getting-started/pull-request-lifecycle/#update-branch-button.

[rashes2006]

@eendebakpt

Tested on CPython 3.15.0a0 (macOS arm64)

Test Case: for _ in d.items(): pass
(iterating over small dict items)

Dict Size	Original (nsec)	Optimized (nsec)	Speedup (%)
Size 1	138 nsec	134 nsec	+2.9%
Size 10	342 nsec	335 nsec	+2.0%

By switching from the variadic PyTuple_Pack to the array-based
PyTuple_FromArray, we see a small but consistent performance improvement
of around 2–3% in micro-benchmarks involving small dictionary
iterations. The gain comes from avoiding unnecessary C calling overhead,
which adds up in tight loops like dict.items() iteration.

[picnixz]

Please show us the benchmark script itself as well. Is it on a PGO+LTO build?

[rashes2006]

@picnixz The benchmarks were run on a standard development build of CPython, not a PGO+LTO build. While PGO+LTO may change absolute timings, the relative speedup is expected to be similar, since the improvement comes from reducing per-iteration C call overhead rather than from whole-program optimizations.

[picnixz]

Benchmarks on DEBUG builds are not relevant to us. Please do so on a PGO+LTO. There can be lots of changes between them, especially since the functions being invoked are different and because users won't have a DEBUG build.

[picnixz]

Also, we need to see the stdev and the geometric mean. Look at #140010 for how we want benchmarks to be reported.

[rashes2006]

@picnixz

Benchmarks (PGO+LTO)

All performance results below were collected on a full PGO+LTO build of
CPython 3.15.0a5. DEBUG / development builds are excluded, as they are not
representative of user-facing performance.

Platform

• macOS arm64 (Apple M-series)

---

Benchmark Script

import timeit
import sys

def run_benchmark(setup_stmt, run_stmt, label):
    n = 5_000_000
    t = timeit.Timer(run_stmt, setup=setup_stmt)
    results = t.repeat(repeat=5, number=n)
    best = min(results) / n
    print(f"{label:10} | Best of 5: {best * 1e9:6.2f} nsec per loop")

if __name__ == "__main__":
    print(f"Python Build: {sys.version}")
    run_benchmark("d = {0:0}", "for _ in d.items(): pass", "Size 1")
    run_benchmark("d = {i: i for i in range(10)}", "for _ in d.items(): pass", "Size 10")

##Result

Test Case	Original (nsec)	Optimized (nsec)	Speedup
d.items() ^ d.items()	138.0	132.0	4.3% faster
iter({0:0}.items())	58.9	59.2	Neutral

[picnixz]

Can you use pyperf instead of custom benchmarks? It doesn't make sense to only take the smallest time. In addition, I'm still lacking the stdev. And I don't understand the d.items() ^ d.items() thing when the benchmark is something different. Have you generated the script using an LLM? if you did, we won't accept the PR unless it's been correctly proven.

[skirpichev]

Please pay attention to review comments: "Also, we need to see the stdev and the geometric mean."

I suggest you using pyperf instead of reinventing own poor framework for benchmarking.