| # |
| # Licensed to the Apache Software Foundation (ASF) under one or more |
| # contributor license agreements. See the NOTICE file distributed with |
| # this work for additional information regarding copyright ownership. |
| # The ASF licenses this file to You 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. |
| # |
| |
| """Utility functions for all microbenchmarks.""" |
| |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| import collections |
| import gc |
| import os |
| import time |
| |
| import numpy |
| |
| |
| def check_compiled(module): |
| """Check whether given module has been compiled. |
| Args: |
| module: string, module name |
| """ |
| check_module = __import__(module, globals(), locals()) |
| ext = os.path.splitext(check_module.__file__)[-1] |
| if ext in ('.py', '.pyc'): |
| raise RuntimeError( |
| "Profiling uncompiled code.\n" |
| "To compile beam, run " |
| "'pip install Cython; python setup.py build_ext --inplace'") |
| |
| |
| class BenchmarkConfig( |
| collections.namedtuple( |
| "BenchmarkConfig", ["benchmark", "size", "num_runs"])): |
| """ |
| Attributes: |
| benchmark: a callable that takes an int argument - benchmark size, |
| and returns a callable. A returned callable must run the code being |
| benchmarked on an input of specified size. |
| |
| For example, one can implement a benchmark as: |
| |
| class MyBenchmark(object): |
| def __init__(self, size): |
| [do necessary initialization] |
| def __call__(self): |
| [run the code in question] |
| |
| size: int, a size of the input. Aggregated per-element metrics |
| are counted based on the size of the input. |
| num_runs: int, number of times to run each benchmark. |
| """ |
| |
| def __str__(self): |
| return "%s, %s element(s)" % ( |
| getattr(self.benchmark, '__name__', str(self.benchmark)), |
| str(self.size)) |
| |
| |
| def run_benchmarks(benchmark_suite, verbose=True): |
| """Runs benchmarks, and collects execution times. |
| |
| A simple instrumentation to run a callable several times, collect and print |
| its execution times. |
| |
| Args: |
| benchmark_suite: A list of BenchmarkConfig. |
| verbose: bool, whether to print benchmark results to stdout. |
| |
| Returns: |
| A dictionary of the form string -> list of floats. Keys of the dictionary |
| are benchmark names, values are execution times in seconds for each run. |
| """ |
| |
| def run(benchmark_fn, size): |
| # Contain each run of a benchmark inside a function so that any temporary |
| # objects can be garbage-collected after the run. |
| benchmark_instance_callable = benchmark_fn(size) |
| start = time.time() |
| _ = benchmark_instance_callable() |
| return time.time() - start |
| |
| cost_series = collections.defaultdict(list) |
| for benchmark_config in benchmark_suite: |
| name = str(benchmark_config) |
| num_runs = benchmark_config.num_runs |
| size = benchmark_config.size |
| for run_id in range(num_runs): |
| # Do a proactive GC before each run to minimize side-effects of different |
| # runs. |
| gc.collect() |
| time_cost = run(benchmark_config.benchmark, size) |
| cost_series[name].append(time_cost) |
| if verbose: |
| per_element_cost = time_cost / size |
| print("%s: run %d of %d, per element time cost: %g sec" % ( |
| name, run_id + 1, num_runs, per_element_cost)) |
| if verbose: |
| print("") |
| |
| if verbose: |
| pad_length = max([len(str(bc)) for bc in benchmark_suite]) |
| |
| for benchmark_config in benchmark_suite: |
| name = str(benchmark_config) |
| per_element_median_cost = ( |
| numpy.median(cost_series[name]) / benchmark_config.size) |
| std = numpy.std(cost_series[name]) / benchmark_config.size |
| |
| print("%s: per element median time cost: %g sec, relative std: %.2f%%" % ( |
| name.ljust(pad_length, " "), per_element_median_cost, |
| std * 100 / per_element_median_cost)) |
| |
| return cost_series |