gh-128118: Improve performance of copy.copy by using a fast lookup for atomic and container types

[eendebakpt]

Similar to the approached used for copy.deepcopy in #114266 we can simplifly the implementation of copy.copy and improve performance by checking on the type of the argument using a lookup.

Results:

copy int: Mean +- std dev: [main] 159 ns +- 10 ns -> [pr_v2] 104 ns +- 7 ns: 1.54x faster
copy slice: Mean +- std dev: [main] 184 ns +- 44 ns -> [pr_v2] 109 ns +- 13 ns: 1.69x faster
copy dict: Mean +- std dev: [main] 196 ns +- 18 ns -> [pr_v2] 182 ns +- 16 ns: 1.07x faster
copy dataclass: Mean +- std dev: [main] 1.88 us +- 0.13 us -> [pr_v2] 1.82 us +- 0.11 us: 1.04x faster
copy small list: Mean +- std dev: [main] 179 ns +- 18 ns -> [pr_v2] 134 ns +- 7 ns: 1.33x faster
copy small tuple: Mean +- std dev: [main] 155 ns +- 12 ns -> [pr_v2] 80.3 ns +- 6.1 ns: 1.93x faster
copy list dataclasses: Mean +- std dev: [main] 151 ns +- 11 ns -> [pr_v2] 160 ns +- 25 ns: 1.05x slower

Geometric mean: 1.32x faster

Benchmark script:

import pyperf

runner = pyperf.Runner()

setup = """
import copy

a={'list': [1,2,3,43], 't': (1,2,3), 'str': 'hello', 'subdict': {'a': True}}

from dataclasses import dataclass

lst = [1, 's']
tpl  =('a', 'b', 3)

i = 123123123
sl = slice(1,2,3)

@dataclass
class A:
    a : int
    
dc = A(123)
list_dc = [A(1), A(2), A(3), A(4)]
"""

runner.timeit(name="copy int", stmt="b=copy.copy(i)", setup=setup)
runner.timeit(name="copy slice", stmt="b=copy.copy(sl)", setup=setup)
runner.timeit(name="copy dict", stmt="b=copy.copy(a)", setup=setup)
runner.timeit(name="copy dataclass", stmt="b=copy.copy(dc)", setup=setup)
runner.timeit(name="copy small list", stmt="b=copy.copy(lst)", setup=setup)
runner.timeit(name="copy small tuple", stmt="b=copy.copy(tpl)", setup=setup)
runner.timeit(name="copy list dataclasses", stmt="b=copy.copy(list_dc)", setup=setup)

• Issue: Improve performance of copy.copy #128118