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3623765dd3
* Add `setuptools_scm` for dynamic zero-config Python versioning This removes the need for manually bumping versions in the Python bindings. For the wheel uploads, the correct semver version is inferred in the case of tagged commits, which is exactly the case in GitHub CI. The docs were updated to reflect the changes in the release workflow. * Add separate version variable and module, use PEP484-compliant exports This is the best practice mentioned in the `setuptools_scm` docs, see https://setuptools-scm.readthedocs.io/en/latest/usage/#version-at-runtime.
141 lines
4.3 KiB
Python
141 lines
4.3 KiB
Python
# Copyright 2020 Google Inc. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Python benchmarking utilities.
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Example usage:
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import google_benchmark as benchmark
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@benchmark.register
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def my_benchmark(state):
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... # Code executed outside `while` loop is not timed.
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while state:
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... # Code executed within `while` loop is timed.
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if __name__ == '__main__':
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benchmark.main()
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"""
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import atexit
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from absl import app
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from google_benchmark import _benchmark
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from google_benchmark._benchmark import (
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Counter as Counter,
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State as State,
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kMicrosecond as kMicrosecond,
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kMillisecond as kMillisecond,
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kNanosecond as kNanosecond,
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kSecond as kSecond,
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o1 as o1,
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oAuto as oAuto,
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oLambda as oLambda,
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oLogN as oLogN,
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oN as oN,
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oNCubed as oNCubed,
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oNLogN as oNLogN,
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oNone as oNone,
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oNSquared as oNSquared,
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)
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from google_benchmark.version import __version__ as __version__
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class __OptionMaker:
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"""A stateless class to collect benchmark options.
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Collect all decorator calls like @option.range(start=0, limit=1<<5).
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"""
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class Options:
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"""Pure data class to store options calls, along with the benchmarked function."""
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def __init__(self, func):
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self.func = func
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self.builder_calls = []
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@classmethod
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def make(cls, func_or_options):
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"""Make Options from Options or the benchmarked function."""
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if isinstance(func_or_options, cls.Options):
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return func_or_options
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return cls.Options(func_or_options)
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def __getattr__(self, builder_name):
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"""Append option call in the Options."""
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# The function that get returned on @option.range(start=0, limit=1<<5).
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def __builder_method(*args, **kwargs):
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# The decorator that get called, either with the benchmared function
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# or the previous Options
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def __decorator(func_or_options):
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options = self.make(func_or_options)
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options.builder_calls.append((builder_name, args, kwargs))
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# The decorator returns Options so it is not technically a decorator
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# and needs a final call to @register
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return options
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return __decorator
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return __builder_method
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# Alias for nicer API.
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# We have to instantiate an object, even if stateless, to be able to use __getattr__
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# on option.range
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option = __OptionMaker()
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def register(undefined=None, *, name=None):
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"""Register function for benchmarking."""
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if undefined is None:
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# Decorator is called without parenthesis so we return a decorator
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return lambda f: register(f, name=name)
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# We have either the function to benchmark (simple case) or an instance of Options
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# (@option._ case).
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options = __OptionMaker.make(undefined)
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if name is None:
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name = options.func.__name__
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# We register the benchmark and reproduce all the @option._ calls onto the
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# benchmark builder pattern
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benchmark = _benchmark.RegisterBenchmark(name, options.func)
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for name, args, kwargs in options.builder_calls[::-1]:
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getattr(benchmark, name)(*args, **kwargs)
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# return the benchmarked function because the decorator does not modify it
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return options.func
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def _flags_parser(argv):
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argv = _benchmark.Initialize(argv)
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return app.parse_flags_with_usage(argv)
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def _run_benchmarks(argv):
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if len(argv) > 1:
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raise app.UsageError("Too many command-line arguments.")
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return _benchmark.RunSpecifiedBenchmarks()
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def main(argv=None):
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return app.run(_run_benchmarks, argv=argv, flags_parser=_flags_parser)
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# Methods for use with custom main function.
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initialize = _benchmark.Initialize
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run_benchmarks = _benchmark.RunSpecifiedBenchmarks
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atexit.register(_benchmark.ClearRegisteredBenchmarks)
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