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# Copyright 2020 Google Inc. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Python benchmarking utilities.
Example usage:
import google_benchmark as benchmark
@benchmark.register
def my_benchmark(state):
... # Code executed outside `while` loop is not timed.
while state:
... # Code executed within `while` loop is timed.
if __name__ == '__main__':
benchmark.main()
"""
from absl import app
from google_benchmark import _benchmark
from google_benchmark._benchmark import (
Counter,
kNanosecond,
kMicrosecond,
kMillisecond,
kSecond,
oNone,
o1,
oN,
oNSquared,
oNCubed,
oLogN,
oNLogN,
oAuto,
oLambda,
)
__all__ = [
"register",
"main",
"Counter",
"kNanosecond",
"kMicrosecond",
"kMillisecond",
"kSecond",
"oNone",
"o1",
"oN",
"oNSquared",
"oNCubed",
"oLogN",
"oNLogN",
"oAuto",
"oLambda",
]
__version__ = "0.2.0"
class __OptionMaker:
"""A stateless class to collect benchmark options.
Collect all decorator calls like @option.range(start=0, limit=1<<5).
"""
class Options:
"""Pure data class to store options calls, along with the benchmarked function."""
def __init__(self, func):
self.func = func
self.builder_calls = []
@classmethod
def make(cls, func_or_options):
"""Make Options from Options or the benchmarked function."""
if isinstance(func_or_options, cls.Options):
return func_or_options
return cls.Options(func_or_options)
def __getattr__(self, builder_name):
"""Append option call in the Options."""
# The function that get returned on @option.range(start=0, limit=1<<5).
def __builder_method(*args, **kwargs):
# The decorator that get called, either with the benchmared function
# or the previous Options
def __decorator(func_or_options):
options = self.make(func_or_options)
options.builder_calls.append((builder_name, args, kwargs))
# The decorator returns Options so it is not technically a decorator
# and needs a final call to @regiser
return options
return __decorator
return __builder_method
# Alias for nicer API.
# We have to instantiate an object, even if stateless, to be able to use __getattr__
# on option.range
option = __OptionMaker()
def register(undefined=None, *, name=None):
"""Register function for benchmarking."""
if undefined is None:
# Decorator is called without parenthesis so we return a decorator
return lambda f: register(f, name=name)
# We have either the function to benchmark (simple case) or an instance of Options
# (@option._ case).
options = __OptionMaker.make(undefined)
if name is None:
name = options.func.__name__
# We register the benchmark and reproduce all the @option._ calls onto the
# benchmark builder pattern
benchmark = _benchmark.RegisterBenchmark(name, options.func)
for name, args, kwargs in options.builder_calls[::-1]:
getattr(benchmark, name)(*args, **kwargs)
# return the benchmarked function because the decorator does not modify it
return options.func
def _flags_parser(argv):
argv = _benchmark.Initialize(argv)
return app.parse_flags_with_usage(argv)
def _run_benchmarks(argv):
if len(argv) > 1:
raise app.UsageError("Too many command-line arguments.")
return _benchmark.RunSpecifiedBenchmarks()
def main(argv=None):
return app.run(_run_benchmarks, argv=argv, flags_parser=_flags_parser)
# Methods for use with custom main function.
initialize = _benchmark.Initialize
run_benchmarks = _benchmark.RunSpecifiedBenchmarks
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