def test_skip_param_selection(): d = {'repo': 'foo'} d.update(ASV_CONF_JSON) conf = config.Config.from_json(d) class DummyEnv(object): name = 'env' d = [{ 'name': 'test_nonparam', 'params': [], 'version': '1' }, { 'name': 'test_param', 'params': [['1', '2', '3']], 'param_names': ['n'], 'version': '1' }] results = Results.unnamed() b = benchmarks.Benchmarks(conf, d, [r'test_nonparam', r'test_param\([23]\)']) results.add_result( b['test_param'], runner.BenchmarkResult(result=[1, 2, 3], samples=[None] * 3, number=[None] * 3, errcode=0, stderr='', profile=None)) runner.skip_benchmarks(b, DummyEnv(), results) assert results._results.get('test_nonparam') == None assert results._results['test_param'] == [1, None, None]
def test_run_benchmarks(benchmarks_fixture, tmpdir): conf, repo, envs, commit_hash = benchmarks_fixture start_timestamp = datetime.datetime.utcnow() b = benchmarks.Benchmarks.discover(conf, repo, envs, [commit_hash]) # Old results to append to results = Results.unnamed() name = 'time_examples.TimeSuite.time_example_benchmark_1' results.add_result(b[name], runner.BenchmarkResult(result=[1], samples=[[42.0, 24.0]], number=[1], errcode=0, stderr='', profile=None), record_samples=True) # Run runner.run_benchmarks(b, envs[0], results=results, profile=True, show_stderr=True, append_samples=True, record_samples=True) times = ResultsWrapper(results, b) end_timestamp = datetime.datetime.utcnow() assert len(times) == len(b) assert times[ 'time_examples.TimeSuite.time_example_benchmark_1'].result != [None] stats = results.get_result_stats(name, b[name]['params']) assert isinstance(stats[0]['std'], float) # The exact number of samples may vary if the calibration is not fully accurate samples = results.get_result_samples(name, b[name]['params']) assert len(samples[0]) >= 4 # Explicitly provided 'prev_samples` should come first assert samples[0][:2] == [42.0, 24.0] # Benchmarks that raise exceptions should have a time of "None" assert times['time_secondary.TimeSecondary.time_exception'].result == [ None ] assert times['subdir.time_subdir.time_foo'].result != [None] if not ON_PYPY: # XXX: the memory benchmarks don't work on Pypy, since asizeof # is CPython-only assert times['mem_examples.mem_list'].result[0] > 1000 assert times['time_secondary.track_value'].result == [42.0] assert times['time_secondary.track_value'].profile is not None assert isinstance(times['time_examples.time_with_warnings'].stderr, type('')) assert times['time_examples.time_with_warnings'].errcode != 0 assert times['time_examples.TimeWithBadTimer.time_it'].result == [0.0] assert times['params_examples.track_param'].params == [[ "<class 'benchmark.params_examples.ClassOne'>", "<class 'benchmark.params_examples.ClassTwo'>" ]] assert times['params_examples.track_param'].result == [42, 42] assert times['params_examples.mem_param'].params == [['10', '20'], ['2', '3']] assert len(times['params_examples.mem_param'].result) == 2 * 2 assert times['params_examples.ParamSuite.track_value'].params == [[ "'a'", "'b'", "'c'" ]] assert times['params_examples.ParamSuite.track_value'].result == [ 1 + 0, 2 + 0, 3 + 0 ] assert isinstance(times['params_examples.TuningTest.time_it'].result[0], float) assert isinstance(times['params_examples.TuningTest.time_it'].result[1], float) assert isinstance(times['params_examples.time_skip'].result[0], float) assert isinstance(times['params_examples.time_skip'].result[1], float) assert util.is_nan(times['params_examples.time_skip'].result[2]) assert times['peakmem_examples.peakmem_list'].result[0] >= 4 * 2**20 assert times['cache_examples.ClassLevelSetup.track_example'].result == [ 500 ] assert times['cache_examples.ClassLevelSetup.track_example2'].result == [ 500 ] assert times['cache_examples.track_cache_foo'].result == [42] assert times['cache_examples.track_cache_bar'].result == [12] assert times['cache_examples.track_my_cache_foo'].result == [0] assert times['cache_examples.ClassLevelSetupFail.track_fail'].result == [ None ] assert 'raise RuntimeError()' in times[ 'cache_examples.ClassLevelSetupFail.track_fail'].stderr assert times[ 'cache_examples.ClassLevelCacheTimeout.track_fail'].result == [None] assert times[ 'cache_examples.ClassLevelCacheTimeoutSuccess.track_success'].result == [ 0 ] assert times['cache_examples.time_fail_second_run'].result == [None] assert times['cache_examples.time_fail_second_run'].samples == [None] profile_path = join(six.text_type(tmpdir), 'test.profile') with open(profile_path, 'wb') as fd: fd.write(times['time_secondary.track_value'].profile) pstats.Stats(profile_path) # Check for running setup on each repeat (one extra run from profile) # The output would contain error messages if the asserts in the benchmark fail. expected = ["<%d>" % j for j in range(1, 12)] assert times['time_examples.TimeWithRepeat.time_it'].stderr.split( ) == expected # Calibration of iterations should not rerun setup expected = (['setup'] * 2, ['setup'] * 3) assert times['time_examples.TimeWithRepeatCalibrate.time_it'].stderr.split( ) in expected # Check tuple-form repeat attribute produced results assert 2 <= len(times['time_examples.time_auto_repeat'].samples[0]) <= 4 # Check run time timestamps for name, result in times.items(): assert result.started_at >= util.datetime_to_js_timestamp( start_timestamp) assert result.ended_at >= result.started_at assert result.ended_at <= util.datetime_to_js_timestamp(end_timestamp)