307 lines
11 KiB
Python
307 lines
11 KiB
Python
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from __future__ import division
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from __future__ import print_function
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import cProfile
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import gc
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import pstats
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import sys
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import time
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import traceback
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from math import ceil
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from .compat import INT
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from .compat import XRANGE
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from .timers import compute_timer_precision
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from .utils import NameWrapper
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from .utils import format_time
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try:
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import statistics
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except (ImportError, SyntaxError):
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statistics_error = traceback.format_exc()
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statistics = None
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else:
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statistics_error = None
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from .stats import Metadata
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class FixtureAlreadyUsed(Exception):
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pass
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class BenchmarkFixture(object):
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_precisions = {}
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def __init__(self, node, disable_gc, timer, min_rounds, min_time, max_time, warmup, warmup_iterations,
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calibration_precision, add_stats, logger, warner, disabled, cprofile, group=None):
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self.name = node.name
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self.fullname = node._nodeid
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self.disabled = disabled
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if hasattr(node, 'callspec'):
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self.param = node.callspec.id
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self.params = node.callspec.params
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else:
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self.param = None
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self.params = None
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self.group = group
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self.has_error = False
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self.extra_info = {}
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self.skipped = False
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self._disable_gc = disable_gc
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self._timer = timer.target
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self._min_rounds = min_rounds
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self._max_time = float(max_time)
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self._min_time = float(min_time)
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self._add_stats = add_stats
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self._calibration_precision = calibration_precision
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self._warmup = warmup and warmup_iterations
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self._logger = logger
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self._warner = warner
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self._cleanup_callbacks = []
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self._mode = None
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self.cprofile = cprofile
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self.cprofile_stats = None
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self.stats = None
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@property
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def enabled(self):
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return not self.disabled
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def _get_precision(self, timer):
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if timer in self._precisions:
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timer_precision = self._precisions[timer]
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else:
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timer_precision = self._precisions[timer] = compute_timer_precision(timer)
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self._logger.debug("")
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self._logger.debug("Computing precision for %s ... %ss." % (
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NameWrapper(timer), format_time(timer_precision)), blue=True, bold=True)
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return timer_precision
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def _make_runner(self, function_to_benchmark, args, kwargs):
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def runner(loops_range, timer=self._timer):
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gc_enabled = gc.isenabled()
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if self._disable_gc:
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gc.disable()
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tracer = sys.gettrace()
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sys.settrace(None)
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try:
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if loops_range:
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start = timer()
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for _ in loops_range:
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function_to_benchmark(*args, **kwargs)
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end = timer()
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return end - start
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else:
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start = timer()
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result = function_to_benchmark(*args, **kwargs)
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end = timer()
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return end - start, result
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finally:
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sys.settrace(tracer)
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if gc_enabled:
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gc.enable()
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return runner
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def _make_stats(self, iterations):
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bench_stats = Metadata(self, iterations=iterations, options={
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"disable_gc": self._disable_gc,
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"timer": self._timer,
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"min_rounds": self._min_rounds,
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"max_time": self._max_time,
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"min_time": self._min_time,
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"warmup": self._warmup,
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})
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self._add_stats(bench_stats)
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self.stats = bench_stats
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return bench_stats
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def __call__(self, function_to_benchmark, *args, **kwargs):
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if self._mode:
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self.has_error = True
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raise FixtureAlreadyUsed(
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"Fixture can only be used once. Previously it was used in %s mode." % self._mode)
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try:
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self._mode = 'benchmark(...)'
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return self._raw(function_to_benchmark, *args, **kwargs)
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except Exception:
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self.has_error = True
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raise
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def pedantic(self, target, args=(), kwargs=None, setup=None, rounds=1, warmup_rounds=0, iterations=1):
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if self._mode:
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self.has_error = True
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raise FixtureAlreadyUsed(
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"Fixture can only be used once. Previously it was used in %s mode." % self._mode)
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try:
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self._mode = 'benchmark.pedantic(...)'
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return self._raw_pedantic(target, args=args, kwargs=kwargs, setup=setup, rounds=rounds,
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warmup_rounds=warmup_rounds, iterations=iterations)
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except Exception:
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self.has_error = True
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raise
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def _raw(self, function_to_benchmark, *args, **kwargs):
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if self.enabled:
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runner = self._make_runner(function_to_benchmark, args, kwargs)
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duration, iterations, loops_range = self._calibrate_timer(runner)
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# Choose how many time we must repeat the test
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rounds = int(ceil(self._max_time / duration))
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rounds = max(rounds, self._min_rounds)
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rounds = min(rounds, sys.maxsize)
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stats = self._make_stats(iterations)
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self._logger.debug(" Running %s rounds x %s iterations ..." % (rounds, iterations), yellow=True, bold=True)
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run_start = time.time()
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if self._warmup:
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warmup_rounds = min(rounds, max(1, int(self._warmup / iterations)))
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self._logger.debug(" Warmup %s rounds x %s iterations ..." % (warmup_rounds, iterations))
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for _ in XRANGE(warmup_rounds):
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runner(loops_range)
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for _ in XRANGE(rounds):
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stats.update(runner(loops_range))
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self._logger.debug(" Ran for %ss." % format_time(time.time() - run_start), yellow=True, bold=True)
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if self.enabled and self.cprofile:
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profile = cProfile.Profile()
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function_result = profile.runcall(function_to_benchmark, *args, **kwargs)
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self.stats.cprofile_stats = pstats.Stats(profile)
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else:
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function_result = function_to_benchmark(*args, **kwargs)
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return function_result
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def _raw_pedantic(self, target, args=(), kwargs=None, setup=None, rounds=1, warmup_rounds=0, iterations=1):
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if kwargs is None:
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kwargs = {}
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has_args = bool(args or kwargs)
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if not isinstance(iterations, INT) or iterations < 1:
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raise ValueError("Must have positive int for `iterations`.")
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if not isinstance(rounds, INT) or rounds < 1:
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raise ValueError("Must have positive int for `rounds`.")
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if not isinstance(warmup_rounds, INT) or warmup_rounds < 0:
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raise ValueError("Must have positive int for `warmup_rounds`.")
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if iterations > 1 and setup:
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raise ValueError("Can't use more than 1 `iterations` with a `setup` function.")
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def make_arguments(args=args, kwargs=kwargs):
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if setup:
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maybe_args = setup()
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if maybe_args:
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if has_args:
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raise TypeError("Can't use `args` or `kwargs` if `setup` returns the arguments.")
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args, kwargs = maybe_args
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return args, kwargs
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if self.disabled:
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args, kwargs = make_arguments()
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return target(*args, **kwargs)
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stats = self._make_stats(iterations)
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loops_range = XRANGE(iterations) if iterations > 1 else None
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for _ in XRANGE(warmup_rounds):
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args, kwargs = make_arguments()
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runner = self._make_runner(target, args, kwargs)
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runner(loops_range)
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for _ in XRANGE(rounds):
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args, kwargs = make_arguments()
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runner = self._make_runner(target, args, kwargs)
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if loops_range:
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duration = runner(loops_range)
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else:
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duration, result = runner(loops_range)
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stats.update(duration)
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if loops_range:
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args, kwargs = make_arguments()
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result = target(*args, **kwargs)
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if self.cprofile:
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profile = cProfile.Profile()
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args, kwargs = make_arguments()
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profile.runcall(target, *args, **kwargs)
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self.stats.cprofile_stats = pstats.Stats(profile)
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return result
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def weave(self, target, **kwargs):
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try:
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import aspectlib
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except ImportError as exc:
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raise ImportError(exc.args, "Please install aspectlib or pytest-benchmark[aspect]")
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def aspect(function):
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def wrapper(*args, **kwargs):
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return self(function, *args, **kwargs)
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return wrapper
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self._cleanup_callbacks.append(aspectlib.weave(target, aspect, **kwargs).rollback)
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patch = weave
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def _cleanup(self):
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while self._cleanup_callbacks:
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callback = self._cleanup_callbacks.pop()
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callback()
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if not self._mode and not self.skipped:
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self._logger.warn("Benchmark fixture was not used at all in this test!",
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warner=self._warner, suspend=True)
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def _calibrate_timer(self, runner):
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timer_precision = self._get_precision(self._timer)
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min_time = max(self._min_time, timer_precision * self._calibration_precision)
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min_time_estimate = min_time * 5 / self._calibration_precision
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self._logger.debug("")
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self._logger.debug(" Calibrating to target round %ss; will estimate when reaching %ss "
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"(using: %s, precision: %ss)." % (
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format_time(min_time),
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format_time(min_time_estimate),
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NameWrapper(self._timer),
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format_time(timer_precision)
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), yellow=True, bold=True)
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loops = 1
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while True:
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loops_range = XRANGE(loops)
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duration = runner(loops_range)
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if self._warmup:
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warmup_start = time.time()
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warmup_iterations = 0
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warmup_rounds = 0
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while time.time() - warmup_start < self._max_time and warmup_iterations < self._warmup:
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duration = min(duration, runner(loops_range))
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warmup_rounds += 1
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warmup_iterations += loops
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self._logger.debug(" Warmup: %ss (%s x %s iterations)." % (
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format_time(time.time() - warmup_start),
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warmup_rounds, loops
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))
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self._logger.debug(" Measured %s iterations: %ss." % (loops, format_time(duration)), yellow=True)
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if duration >= min_time:
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break
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if duration >= min_time_estimate:
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# coarse estimation of the number of loops
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loops = int(ceil(min_time * loops / duration))
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self._logger.debug(" Estimating %s iterations." % loops, green=True)
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if loops == 1:
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# If we got a single loop then bail early - nothing to calibrate if the the
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# test function is 100 times slower than the timer resolution.
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loops_range = XRANGE(loops)
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break
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else:
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loops *= 10
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return duration, loops, loops_range
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