chore: import upstream snapshot with attribution
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This commit is contained in:
@@ -0,0 +1,34 @@
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load("@nanobind_bazel//:build_defs.bzl", "nanobind_extension", "nanobind_stubgen")
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load("@rules_python//python:defs.bzl", "py_library", "py_test")
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py_library(
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name = "google_benchmark",
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srcs = ["__init__.py"],
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visibility = ["//visibility:public"],
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deps = [
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":_benchmark",
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],
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)
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nanobind_extension(
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name = "_benchmark",
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srcs = ["benchmark.cc"],
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deps = ["//:benchmark"],
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)
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nanobind_stubgen(
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name = "benchmark_stubgen",
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marker_file = "bindings/python/google_benchmark/py.typed",
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module = ":_benchmark",
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)
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py_test(
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name = "example",
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srcs = ["example.py"],
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python_version = "PY3",
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srcs_version = "PY3",
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visibility = ["//visibility:public"],
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deps = [
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":google_benchmark",
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],
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)
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@@ -0,0 +1,136 @@
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# 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 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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__version__ = "1.9.5"
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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
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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
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# decorator 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
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# __getattr__ 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
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# Options (@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 main(argv: list[str] | None = None) -> None:
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import sys
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_benchmark.Initialize(argv or sys.argv)
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return _benchmark.RunSpecifiedBenchmarks()
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# FIXME: can we rerun with disabled ASLR?
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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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add_custom_context = _benchmark.AddCustomContext
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atexit.register(_benchmark.ClearRegisteredBenchmarks)
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@@ -0,0 +1,189 @@
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// Benchmark for Python.
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#include "benchmark/benchmark.h"
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#include "nanobind/nanobind.h"
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#include "nanobind/operators.h"
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#include "nanobind/stl/bind_map.h"
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#include "nanobind/stl/string.h"
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#include "nanobind/stl/vector.h"
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NB_MAKE_OPAQUE(benchmark::UserCounters);
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namespace {
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namespace nb = nanobind;
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std::vector<std::string> Initialize(const std::vector<std::string>& argv) {
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std::vector<char*> ptrs;
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ptrs.reserve(argv.size());
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for (auto& arg : argv) {
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ptrs.push_back(const_cast<char*>(arg.c_str()));
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}
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if (!ptrs.empty()) {
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// The `argv` pointers here become invalid when this function returns, but
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// benchmark holds the pointer to `argv[0]`. We create a static copy of it
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// so it persists, and replace the pointer below.
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static std::string executable_name(argv[0]);
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ptrs[0] = const_cast<char*>(executable_name.c_str());
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}
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int argc = static_cast<int>(argv.size());
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benchmark::Initialize(&argc, ptrs.data());
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std::vector<std::string> remaining_argv;
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remaining_argv.reserve(argc);
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for (int i = 0; i < argc; ++i) {
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remaining_argv.emplace_back(ptrs[i]);
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}
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return remaining_argv;
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}
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benchmark::Benchmark* RegisterBenchmark(const std::string& name,
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nb::callable f) {
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return benchmark::RegisterBenchmark(
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name, [f](benchmark::State& state) { f(&state); });
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}
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NB_MODULE(_benchmark, m) {
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using benchmark::TimeUnit;
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nb::enum_<TimeUnit>(m, "TimeUnit")
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.value("kNanosecond", TimeUnit::kNanosecond)
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.value("kMicrosecond", TimeUnit::kMicrosecond)
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.value("kMillisecond", TimeUnit::kMillisecond)
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.value("kSecond", TimeUnit::kSecond)
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.export_values();
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using benchmark::BigO;
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nb::enum_<BigO>(m, "BigO")
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.value("oNone", BigO::oNone)
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.value("o1", BigO::o1)
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.value("oN", BigO::oN)
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.value("oNSquared", BigO::oNSquared)
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.value("oNCubed", BigO::oNCubed)
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.value("oLogN", BigO::oLogN)
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.value("oNLogN", BigO::oNLogN)
|
||||
.value("oAuto", BigO::oAuto)
|
||||
.value("oLambda", BigO::oLambda)
|
||||
.export_values();
|
||||
|
||||
using benchmark::Benchmark;
|
||||
nb::class_<Benchmark>(m, "Benchmark")
|
||||
// For methods returning a pointer to the current object, reference
|
||||
// return policy is used to ask nanobind not to take ownership of the
|
||||
// returned object and avoid calling delete on it.
|
||||
// https://pybind11.readthedocs.io/en/stable/advanced/functions.html#return-value-policies
|
||||
//
|
||||
// For methods taking a const std::vector<...>&, a copy is created
|
||||
// because a it is bound to a Python list.
|
||||
// https://pybind11.readthedocs.io/en/stable/advanced/cast/stl.html
|
||||
.def("unit", &Benchmark::Unit, nb::rv_policy::reference)
|
||||
.def("arg", &Benchmark::Arg, nb::rv_policy::reference)
|
||||
.def("args", &Benchmark::Args, nb::rv_policy::reference)
|
||||
.def("range", &Benchmark::Range, nb::rv_policy::reference,
|
||||
nb::arg("start"), nb::arg("limit"))
|
||||
.def("dense_range", &Benchmark::DenseRange, nb::rv_policy::reference,
|
||||
nb::arg("start"), nb::arg("limit"), nb::arg("step") = 1)
|
||||
.def("ranges", &Benchmark::Ranges, nb::rv_policy::reference)
|
||||
.def("args_product", &Benchmark::ArgsProduct, nb::rv_policy::reference)
|
||||
.def("arg_name", &Benchmark::ArgName, nb::rv_policy::reference)
|
||||
.def("arg_names", &Benchmark::ArgNames, nb::rv_policy::reference)
|
||||
.def("range_pair", &Benchmark::RangePair, nb::rv_policy::reference,
|
||||
nb::arg("lo1"), nb::arg("hi1"), nb::arg("lo2"), nb::arg("hi2"))
|
||||
.def("range_multiplier", &Benchmark::RangeMultiplier,
|
||||
nb::rv_policy::reference)
|
||||
.def("min_time", &Benchmark::MinTime, nb::rv_policy::reference)
|
||||
.def("min_warmup_time", &Benchmark::MinWarmUpTime,
|
||||
nb::rv_policy::reference)
|
||||
.def("iterations", &Benchmark::Iterations, nb::rv_policy::reference)
|
||||
.def("repetitions", &Benchmark::Repetitions, nb::rv_policy::reference)
|
||||
.def("report_aggregates_only", &Benchmark::ReportAggregatesOnly,
|
||||
nb::rv_policy::reference, nb::arg("value") = true)
|
||||
.def("display_aggregates_only", &Benchmark::DisplayAggregatesOnly,
|
||||
nb::rv_policy::reference, nb::arg("value") = true)
|
||||
.def("measure_process_cpu_time", &Benchmark::MeasureProcessCPUTime,
|
||||
nb::rv_policy::reference)
|
||||
.def("use_real_time", &Benchmark::UseRealTime, nb::rv_policy::reference)
|
||||
.def("use_manual_time", &Benchmark::UseManualTime,
|
||||
nb::rv_policy::reference)
|
||||
.def(
|
||||
"complexity",
|
||||
(Benchmark * (Benchmark::*)(benchmark::BigO)) & Benchmark::Complexity,
|
||||
nb::rv_policy::reference, nb::arg("complexity") = benchmark::oAuto);
|
||||
|
||||
using benchmark::Counter;
|
||||
nb::class_<Counter> py_counter(m, "Counter");
|
||||
|
||||
nb::enum_<Counter::Flags>(py_counter, "Flags", nb::is_arithmetic(),
|
||||
nb::is_flag())
|
||||
.value("kDefaults", Counter::Flags::kDefaults)
|
||||
.value("kIsRate", Counter::Flags::kIsRate)
|
||||
.value("kAvgThreads", Counter::Flags::kAvgThreads)
|
||||
.value("kAvgThreadsRate", Counter::Flags::kAvgThreadsRate)
|
||||
.value("kIsIterationInvariant", Counter::Flags::kIsIterationInvariant)
|
||||
.value("kIsIterationInvariantRate",
|
||||
Counter::Flags::kIsIterationInvariantRate)
|
||||
.value("kAvgIterations", Counter::Flags::kAvgIterations)
|
||||
.value("kAvgIterationsRate", Counter::Flags::kAvgIterationsRate)
|
||||
.value("kInvert", Counter::Flags::kInvert)
|
||||
.export_values();
|
||||
|
||||
nb::enum_<Counter::OneK>(py_counter, "OneK")
|
||||
.value("kIs1000", Counter::OneK::kIs1000)
|
||||
.value("kIs1024", Counter::OneK::kIs1024)
|
||||
.export_values();
|
||||
|
||||
py_counter
|
||||
.def(nb::init<double, Counter::Flags, Counter::OneK>(),
|
||||
nb::arg("value") = 0., nb::arg("flags") = Counter::kDefaults,
|
||||
nb::arg("k") = Counter::kIs1000)
|
||||
.def("__init__",
|
||||
([](Counter* c, double value) { new (c) Counter(value); }))
|
||||
.def_rw("value", &Counter::value)
|
||||
.def_rw("flags", &Counter::flags)
|
||||
.def_rw("oneK", &Counter::oneK)
|
||||
.def(nb::init_implicit<double>());
|
||||
|
||||
nb::implicitly_convertible<nb::int_, Counter>();
|
||||
|
||||
nb::bind_map<benchmark::UserCounters>(m, "UserCounters");
|
||||
|
||||
using benchmark::State;
|
||||
nb::class_<State>(m, "State")
|
||||
.def("__bool__", &State::KeepRunning)
|
||||
.def_prop_ro("keep_running", &State::KeepRunning)
|
||||
.def("pause_timing", &State::PauseTiming)
|
||||
.def("resume_timing", &State::ResumeTiming)
|
||||
.def("skip_with_error", &State::SkipWithError)
|
||||
.def_prop_ro("error_occurred", &State::error_occurred)
|
||||
.def("set_iteration_time", &State::SetIterationTime)
|
||||
.def_prop_rw("bytes_processed", &State::bytes_processed,
|
||||
&State::SetBytesProcessed)
|
||||
.def_prop_rw("complexity_n", &State::complexity_length_n,
|
||||
&State::SetComplexityN)
|
||||
.def_prop_rw("items_processed", &State::items_processed,
|
||||
&State::SetItemsProcessed)
|
||||
.def("set_label", &State::SetLabel)
|
||||
.def(
|
||||
"range",
|
||||
[](const State& state, std::size_t pos = 0) -> int64_t {
|
||||
if (pos < state.range_size()) {
|
||||
return state.range(pos);
|
||||
}
|
||||
throw nb::index_error("pos is out of range");
|
||||
},
|
||||
nb::arg("pos") = 0)
|
||||
.def_prop_ro("iterations", &State::iterations)
|
||||
.def_prop_ro("name", &State::name)
|
||||
.def_rw("counters", &State::counters)
|
||||
.def_prop_ro("thread_index", &State::thread_index)
|
||||
.def_prop_ro("threads", &State::threads);
|
||||
|
||||
m.def("Initialize", Initialize);
|
||||
m.def("RegisterBenchmark", RegisterBenchmark, nb::rv_policy::reference);
|
||||
m.def("RunSpecifiedBenchmarks",
|
||||
[]() { benchmark::RunSpecifiedBenchmarks(); });
|
||||
m.def("ClearRegisteredBenchmarks", benchmark::ClearRegisteredBenchmarks);
|
||||
m.def("AddCustomContext", benchmark::AddCustomContext, nb::arg("key"),
|
||||
nb::arg("value"),
|
||||
"Add a key-value pair to output as part of the context stanza in the "
|
||||
"report.");
|
||||
};
|
||||
} // namespace
|
||||
@@ -0,0 +1,142 @@
|
||||
# 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.
|
||||
"""Example of Python using C++ benchmark framework.
|
||||
|
||||
To run this example, you must first install the `google_benchmark` Python
|
||||
package.
|
||||
|
||||
To install using `setup.py`, download and extract the `google_benchmark` source.
|
||||
In the extracted directory, execute:
|
||||
python setup.py install
|
||||
"""
|
||||
|
||||
import random
|
||||
import sys
|
||||
import time
|
||||
|
||||
import google_benchmark as benchmark
|
||||
from google_benchmark import Counter
|
||||
|
||||
|
||||
@benchmark.register
|
||||
def empty(state):
|
||||
while state:
|
||||
pass
|
||||
|
||||
|
||||
@benchmark.register
|
||||
def sum_million(state):
|
||||
while state:
|
||||
sum(range(1_000_000))
|
||||
|
||||
|
||||
@benchmark.register
|
||||
def pause_timing(state):
|
||||
"""Pause timing every iteration."""
|
||||
while state:
|
||||
# Construct a list of random ints every iteration without timing it
|
||||
state.pause_timing()
|
||||
random_list = [random.randint(0, 100) for _ in range(100)]
|
||||
state.resume_timing()
|
||||
# Time the in place sorting algorithm
|
||||
random_list.sort()
|
||||
|
||||
|
||||
@benchmark.register
|
||||
def skipped(state):
|
||||
if True: # Test some predicate here.
|
||||
state.skip_with_error("some error")
|
||||
return # NOTE: You must explicitly return, or benchmark will continue.
|
||||
|
||||
# Benchmark code would be here.
|
||||
|
||||
|
||||
@benchmark.register
|
||||
@benchmark.option.use_manual_time()
|
||||
def manual_timing(state):
|
||||
while state:
|
||||
# Manually count Python CPU time
|
||||
start = time.perf_counter() # perf_counter_ns() in Python 3.7+
|
||||
# Something to benchmark
|
||||
time.sleep(0.01)
|
||||
end = time.perf_counter()
|
||||
state.set_iteration_time(end - start)
|
||||
|
||||
|
||||
@benchmark.register
|
||||
def custom_counters(state):
|
||||
"""Collect custom metric using benchmark.Counter."""
|
||||
num_foo = 0.0
|
||||
while state:
|
||||
# Benchmark some code here
|
||||
# Collect some custom metric named foo
|
||||
num_foo += 0.13
|
||||
|
||||
# Automatic Counter from numbers.
|
||||
state.counters["foo"] = num_foo
|
||||
# Set a counter as a rate.
|
||||
state.counters["foo_rate"] = Counter(num_foo, Counter.kIsRate)
|
||||
# Set a counter as an inverse of rate.
|
||||
state.counters["foo_inv_rate"] = Counter(
|
||||
num_foo, Counter.kIsRate | Counter.kInvert
|
||||
)
|
||||
# Set a counter as a thread-average quantity.
|
||||
state.counters["foo_avg"] = Counter(num_foo, Counter.kAvgThreads)
|
||||
# There's also a combined flag:
|
||||
state.counters["foo_avg_rate"] = Counter(num_foo, Counter.kAvgThreadsRate)
|
||||
|
||||
|
||||
@benchmark.register
|
||||
@benchmark.option.measure_process_cpu_time()
|
||||
@benchmark.option.use_real_time()
|
||||
def with_options(state):
|
||||
while state:
|
||||
sum(range(1_000_000))
|
||||
|
||||
|
||||
@benchmark.register(name="sum_million_microseconds")
|
||||
@benchmark.option.unit(benchmark.kMicrosecond)
|
||||
def with_options2(state):
|
||||
while state:
|
||||
sum(range(1_000_000))
|
||||
|
||||
|
||||
@benchmark.register
|
||||
@benchmark.option.arg(100)
|
||||
@benchmark.option.arg(1000)
|
||||
def passing_argument(state):
|
||||
while state:
|
||||
sum(range(state.range(0)))
|
||||
|
||||
|
||||
@benchmark.register
|
||||
@benchmark.option.range(8, limit=8 << 10)
|
||||
def using_range(state):
|
||||
while state:
|
||||
sum(range(state.range(0)))
|
||||
|
||||
|
||||
@benchmark.register
|
||||
@benchmark.option.range_multiplier(2)
|
||||
@benchmark.option.range(1 << 10, 1 << 18)
|
||||
@benchmark.option.complexity(benchmark.oN)
|
||||
def computing_complexity(state):
|
||||
while state:
|
||||
sum(range(state.range(0)))
|
||||
state.complexity_n = state.range(0)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
benchmark.add_custom_context("python", sys.version)
|
||||
benchmark.main()
|
||||
Reference in New Issue
Block a user