I ran the relevant pyperformance deepcopy benchmark which does seem to suggest that this change makes the non empty case slightly slower:EDIT: I re-ran the pyperformance benchmarks with the latest changes in d87901e and I now can't see any performance regression for the common case.

Performance version: 1.11.0
Python version: 3.14.0a0 (64-bit) revision 1a84bdc237
Report on macOS-14.5-arm64-arm-64bit-Mach-O
Number of logical CPUs: 8
Start date: 2024-07-02 01:23:14.690599
End date: 2024-07-02 01:23:51.844329

### deepcopy ###
Mean +- std dev: 123 us +- 12 us -> 120 us +- 1 us: 1.03x faster
Significant (t=2.25)

### deepcopy_memo ###
Mean +- std dev: 14.1 us +- 0.2 us -> 14.1 us +- 0.3 us: 1.00x slower
Not significant

### deepcopy_reduce ###
Mean +- std dev: 1.22 us +- 0.06 us -> 1.21 us +- 0.01 us: 1.00x faster
Not significant

The optimization applied in the latest iteration of the PR is only applied when no memo is passed to deepcopy. The advantage is that any recursive structures like {'a': [1, 2, 3, 4, ,5]} will have no (or near zero) overhead. So I agree that performance regression for many common cases is not a big concern.

The question then is whether a deepcopy directly on an empty builtin like like and set (and not a deepcopy on a recursive structure containing empty builtins, e.g, ( {}, [], ())) is a common enough operation to apply this PR. I am not convinced on this, In the corresponding issue an example is given for pydantic basemodels. But there the deepcopy overhead can be avoided by using a default factory. Also the corresponding situation for plain dataclasses cannot occur:

from dataclasses import dataclass

@dataclass
class DC:
    a : list = []
    
x=DC()

raises a ValueError: mutable default <class 'list'> for field a is not allowed: use default_factory. With attrs an empty list is allowed, but no deepcopy is performed on instantiation. For example

import attrs
@attrs.define
class DC:
    a : list = []
    
x=DC()
y=DC()

x.a.append(1)

print(x)
print(y)

results in

DC(a=[1])
DC(a=[1])

For reference: I performed a benchmark of main, this PR and the deepcopy C implementation from #91610. On benchmark

import pyperf
runner = pyperf.Runner()

setup="""
import copy
from dataclasses import dataclass

@dataclass
class A:
    a : list
    b : str
    c : bool
    d: tuple
    e : set
    
dc=A([1,2,3], 'hello', True, (), {})

empty_tuple = tuple()
empty_list = []
"""

runner.timeit(name="deepcopy empty tuple", stmt="b=copy.deepcopy(empty_tuple)", setup=setup)
runner.timeit(name="deepcopy empty list", stmt="b=copy.deepcopy(empty_list)", setup=setup)
runner.timeit(name="deepcopy dataclass", stmt="b=copy.deepcopy(dc)", setup=setup)

Results are

deepcopy empty tuple: Mean +- std dev: [dc_main] 827 ns +- 37 ns -> [dc_fastpath_empty] 203 ns +- 7 ns: 4.06x faster
deepcopy empty list: Mean +- std dev: [dc_main] 1.00 us +- 0.09 us -> [dc_fastpath_empty] 233 ns +- 24 ns: 4.29x faster
deepcopy dataclass: Mean +- std dev: [dc_main] 8.02 us +- 0.12 us -> [dc_fastpath_empty] 8.22 us +- 0.07 us: 1.02x slower

Geometric mean: 2.57x faster

(env312) c:\projects\misc\cpython\PCbuild>python -m pyperf compare_to dc_main.json dc_c.json
deepcopy empty tuple: Mean +- std dev: [dc_main] 827 ns +- 37 ns -> [dc_c] 90.6 ns +- 9.8 ns: 9.13x faster
deepcopy empty list: Mean +- std dev: [dc_main] 1.00 us +- 0.09 us -> [dc_c] 216 ns +- 24 ns: 4.63x faster
deepcopy dataclass: Mean +- std dev: [dc_main] 8.02 us +- 0.12 us -> [dc_c] 4.06 us +- 0.43 us: 1.97x faster

Geometric mean: 4.37x faster

@eendebakpt it can happen for dataclass, example:

from dataclasses import dataclass, as_dict

@dataclass 
class Some:
    field: set[int]

as_dict(Some(set()))

@eendebakpt it can happen for dataclass, example:

Indeed. Here a more complete example, including the cases where this PR helps

from dataclasses import dataclass, asdict

@dataclass 
class Some:
    a: str
    t: tuple[int]
    field: set[int] # benefits
    l : list[int]
    d : dict[str, str]
    fs: frozenset[int] # benefits
    nonempty : set[int]

s=Some('x', tuple(), set(), [], d={}, fs=frozenset(), nonempty={1, 2})
asdict(s)

Indeed. Here a more complete example, including the cases where this PR helps

Thanks for the example! This PR indeed shows a decent improvement here:

import pyperf
runner = pyperf.Runner()

setup = """
from dataclasses import dataclass, asdict

@dataclass
class Some:
    a: str
    t: tuple[int]
    field: set[int] # benefits
    l : list[int]
    d : dict[str, str]
    fs: frozenset[int] # benefits
    nonempty : set[int]

s = Some('x', tuple(), set(), [], d={}, fs=frozenset(), nonempty={1, 2})
"""

runner.timeit(name="dataclass asdict", stmt=f"b = asdict(s)", setup=setup)

Mean +- std dev: [asdict-baseline] 6.26 us +- 0.07 us -> [asdict-opt] 3.39 us +- 0.18 us: 1.85x faster

Other use-cases that we can find in our own code:

• dataclass:

  cpython/Lib/dataclasses.py

  Line 1487 in 26d24ee

  return copy.deepcopy(obj)

  also as_tuple for set and frozenset
• weakref: WeakValueDictionary

  cpython/Lib/weakref.py

  Line 191 in 26d24ee

  new[deepcopy(key, memo)] = o

  and WeakKeyDictionary:

  cpython/Lib/weakref.py

  Line 448 in 26d24ee

  new[o] = deepcopy(value, memo)

  where values can be any of the given types, and keys can be empty tuples and empty frozendicts (which is rather rare, I agree)
• http:

  cpython/Lib/http/cookiejar.py

  Line 1819 in 26d24ee

  old_state = copy.deepcopy(self._cookies)

  where self._cookies is empty by default

  cpython/Lib/http/cookiejar.py

  Line 1267 in 26d24ee

  self._cookies = {}

• coping nested structures where keys/values are empty collections, like: {'data': []}