pythongh-108303: Move Lib/test/sortperf.py to Tools/scripts (pyth…

…onGH-114687)

(cherry picked from commit f9154f8)

Co-authored-by: Nikita Sobolev <[email protected]>

Tools/scripts/sortperf.py

@@ -0,0 +1,196 @@
+"""
+List sort performance test.
+
+To install `pyperf` you would need to:
+
+    python3 -m pip install pyperf
+
+To run:
+
+    python3 Tools/scripts/sortperf
+
+Options:
+
+    * `benchmark` name to run
+    * `--rnd-seed` to set random seed
+    * `--size` to set the sorted list size
+
+Based on https://github.com/python/cpython/blob/963904335e579bfe39101adf3fd6a0cf705975ff/Lib/test/sortperf.py
+"""
+
+from __future__ import annotations
+
+import argparse
+import time
+import random
+
+
+# ===============
+# Data generation
+# ===============
+
+def _random_data(size: int, rand: random.Random) -> list[float]:
+    result = [rand.random() for _ in range(size)]
+    # Shuffle it a bit...
+    for i in range(10):
+        i = rand.randrange(size)
+        temp = result[:i]
+        del result[:i]
+        temp.reverse()
+        result.extend(temp)
+        del temp
+    assert len(result) == size
+    return result
+
+
+def list_sort(size: int, rand: random.Random) -> list[float]:
+    return _random_data(size, rand)
+
+
+def list_sort_descending(size: int, rand: random.Random) -> list[float]:
+    return list(reversed(list_sort_ascending(size, rand)))
+
+
+def list_sort_ascending(size: int, rand: random.Random) -> list[float]:
+    return sorted(_random_data(size, rand))
+
+
+def list_sort_ascending_exchanged(size: int, rand: random.Random) -> list[float]:
+    result = list_sort_ascending(size, rand)
+    # Do 3 random exchanges.
+    for _ in range(3):
+        i1 = rand.randrange(size)
+        i2 = rand.randrange(size)
+        result[i1], result[i2] = result[i2], result[i1]
+    return result
+
+
+def list_sort_ascending_random(size: int, rand: random.Random) -> list[float]:
+    assert size >= 10, "This benchmark requires size to be >= 10"
+    result = list_sort_ascending(size, rand)
+    # Replace the last 10 with random floats.
+    result[-10:] = [rand.random() for _ in range(10)]
+    return result
+
+
+def list_sort_ascending_one_percent(size: int, rand: random.Random) -> list[float]:
+    result = list_sort_ascending(size, rand)
+    # Replace 1% of the elements at random.
+    for _ in range(size // 100):
+        result[rand.randrange(size)] = rand.random()
+    return result
+
+
+def list_sort_duplicates(size: int, rand: random.Random) -> list[float]:
+    assert size >= 4
+    result = list_sort_ascending(4, rand)
+    # Arrange for lots of duplicates.
+    result = result * (size // 4)
+    # Force the elements to be distinct objects, else timings can be
+    # artificially low.
+    return list(map(abs, result))
+
+
+def list_sort_equal(size: int, rand: random.Random) -> list[float]:
+    # All equal.  Again, force the elements to be distinct objects.
+    return list(map(abs, [-0.519012] * size))
+
+
+def list_sort_worst_case(size: int, rand: random.Random) -> list[float]:
+    # This one looks like [3, 2, 1, 0, 0, 1, 2, 3].  It was a bad case
+    # for an older implementation of quicksort, which used the median
+    # of the first, last and middle elements as the pivot.
+    half = size // 2
+    result = list(range(half - 1, -1, -1))
+    result.extend(range(half))
+    # Force to float, so that the timings are comparable.  This is
+    # significantly faster if we leave them as ints.
+    return list(map(float, result))
+
+
+# =========
+# Benchmark
+# =========
+
+class Benchmark:
+    def __init__(self, name: str, size: int, seed: int) -> None:
+        self._name = name
+        self._size = size
+        self._seed = seed
+        self._random = random.Random(self._seed)
+
+    def run(self, loops: int) -> float:
+        all_data = self._prepare_data(loops)
+        start = time.perf_counter()
+
+        for data in all_data:
+            data.sort()  # Benching this method!
+
+        return time.perf_counter() - start
+
+    def _prepare_data(self, loops: int) -> list[float]:
+        bench = BENCHMARKS[self._name]
+        return [bench(self._size, self._random)] * loops
+
+
+def add_cmdline_args(cmd: list[str], args) -> None:
+    if args.benchmark:
+        cmd.append(args.benchmark)
+    cmd.append(f"--size={args.size}")
+    cmd.append(f"--rng-seed={args.rng_seed}")
+
+
+def add_parser_args(parser: argparse.ArgumentParser) -> None:
+    parser.add_argument(
+        "benchmark",
+        choices=BENCHMARKS,
+        nargs="?",
+        help="Can be any of: {0}".format(", ".join(BENCHMARKS)),
+    )
+    parser.add_argument(
+        "--size",
+        type=int,
+        default=DEFAULT_SIZE,
+        help=f"Size of the lists to sort (default: {DEFAULT_SIZE})",
+    )
+    parser.add_argument(
+        "--rng-seed",
+        type=int,
+        default=DEFAULT_RANDOM_SEED,
+        help=f"Random number generator seed (default: {DEFAULT_RANDOM_SEED})",
+    )
+
+
+DEFAULT_SIZE = 1 << 14
+DEFAULT_RANDOM_SEED = 0
+BENCHMARKS = {
+    "list_sort": list_sort,
+    "list_sort_descending": list_sort_descending,
+    "list_sort_ascending": list_sort_ascending,
+    "list_sort_ascending_exchanged": list_sort_ascending_exchanged,
+    "list_sort_ascending_random": list_sort_ascending_random,
+    "list_sort_ascending_one_percent": list_sort_ascending_one_percent,
+    "list_sort_duplicates": list_sort_duplicates,
+    "list_sort_equal": list_sort_equal,
+    "list_sort_worst_case": list_sort_worst_case,
+}
+
+if __name__ == "__main__":
+    # This needs `pyperf` 3rd party library:
+    import pyperf
+
+    runner = pyperf.Runner(add_cmdline_args=add_cmdline_args)
+    add_parser_args(runner.argparser)
+    args = runner.parse_args()
+
+    runner.metadata["description"] = "Test `list.sort()` with different data"
+    runner.metadata["list_sort_size"] = args.size
+    runner.metadata["list_sort_random_seed"] = args.rng_seed
+
+    if args.benchmark:
+        benchmarks = (args.benchmark,)
+    else:
+        benchmarks = sorted(BENCHMARKS)
+    for bench in benchmarks:
+        benchmark = Benchmark(bench, args.size, args.rng_seed)
+        runner.bench_time_func(bench, benchmark.run)