Summary

We improve performance of attrs.asdict by:

• Using a fast check for atomic types (list int, float or str)
• Using issubclass instead of isinstance for various type checks

Benchmark:

asdict: Mean +- std dev: [main] 4.07 us +- 0.41 us -> [pr] 2.48 us +- 0.21 us: 1.64x faster

Benchmark hidden because not significant (1): instance creation

Geometric mean: 1.28x faster

import pyperf

setup = """
from attrs import define, asdict

@define
class Simple:
     i : int
     s : str
     l : list

s = Simple(10, 'hi', [3, 1, 4])

"""

runner = pyperf.Runner()
runner.timeit(name="instance creation", stmt="Simple(10, 'hi', [3, 1, 4])", setup=setup)
runner.timeit(name="asdict", stmt="asdict(s)", setup=setup)

Notes:

• a similar PR was added to cpython dataclasses as well gh-103000: Optimise dataclasses asdict/astuple for common types python/cpython#103005
• With a similar approach we can improve performance of astuple. That is left to a separate PR though. Also see attrs.astuple() leaves performance on the table compared to operator.attrgetter #1129

Pull Request Check List

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• Added tests for changed code.
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• New features have been added to our Hypothesis testing strategy.
• Changes or additions to public APIs are reflected in our type stubs (files ending in .pyi).
  • ...and used in the stub test file tests/typing_example.py.
  • If they've been added to attr/__init__.pyi, they've also been re-imported in attrs/__init__.pyi.
• Updated documentation for changed code.
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