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This makes profiling more flexible since we can now call multiple functions when a profiler is active. But the real reason for this is to enable a future consumer to profile a function that returns a generator. We can't do this from the profiling function itself because functions can either be generators or have return values: they can't be both. So therefore it isn't possible to have a generic profiling function that can both consume and re-emit a generator and return a value.
146 lines
4.1 KiB
Python
146 lines
4.1 KiB
Python
# profiling.py - profiling functions
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#
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# Copyright 2016 Gregory Szorc <gregory.szorc@gmail.com>
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#
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# This software may be used and distributed according to the terms of the
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# GNU General Public License version 2 or any later version.
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from __future__ import absolute_import, print_function
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import contextlib
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import os
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import sys
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import time
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from .i18n import _
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from . import (
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error,
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util,
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)
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@contextlib.contextmanager
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def lsprofile(ui, fp):
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format = ui.config('profiling', 'format', default='text')
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field = ui.config('profiling', 'sort', default='inlinetime')
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limit = ui.configint('profiling', 'limit', default=30)
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climit = ui.configint('profiling', 'nested', default=0)
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if format not in ['text', 'kcachegrind']:
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ui.warn(_("unrecognized profiling format '%s'"
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" - Ignored\n") % format)
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format = 'text'
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try:
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from . import lsprof
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except ImportError:
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raise error.Abort(_(
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'lsprof not available - install from '
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'http://codespeak.net/svn/user/arigo/hack/misc/lsprof/'))
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p = lsprof.Profiler()
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p.enable(subcalls=True)
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try:
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yield
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finally:
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p.disable()
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if format == 'kcachegrind':
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from . import lsprofcalltree
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calltree = lsprofcalltree.KCacheGrind(p)
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calltree.output(fp)
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else:
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# format == 'text'
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stats = lsprof.Stats(p.getstats())
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stats.sort(field)
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stats.pprint(limit=limit, file=fp, climit=climit)
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@contextlib.contextmanager
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def flameprofile(ui, fp):
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try:
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from flamegraph import flamegraph
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except ImportError:
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raise error.Abort(_(
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'flamegraph not available - install from '
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'https://github.com/evanhempel/python-flamegraph'))
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# developer config: profiling.freq
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freq = ui.configint('profiling', 'freq', default=1000)
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filter_ = None
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collapse_recursion = True
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thread = flamegraph.ProfileThread(fp, 1.0 / freq,
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filter_, collapse_recursion)
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start_time = time.clock()
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try:
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thread.start()
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yield
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finally:
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thread.stop()
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thread.join()
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print('Collected %d stack frames (%d unique) in %2.2f seconds.' % (
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time.clock() - start_time, thread.num_frames(),
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thread.num_frames(unique=True)))
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@contextlib.contextmanager
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def statprofile(ui, fp):
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try:
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import statprof
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except ImportError:
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raise error.Abort(_(
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'statprof not available - install using "easy_install statprof"'))
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freq = ui.configint('profiling', 'freq', default=1000)
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if freq > 0:
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statprof.reset(freq)
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else:
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ui.warn(_("invalid sampling frequency '%s' - ignoring\n") % freq)
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statprof.start()
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try:
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yield
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finally:
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statprof.stop()
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statprof.display(fp)
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@contextlib.contextmanager
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def profile(ui):
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"""Start profiling.
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Profiling is active when the context manager is active. When the context
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manager exits, profiling results will be written to the configured output.
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"""
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profiler = os.getenv('HGPROF')
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if profiler is None:
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profiler = ui.config('profiling', 'type', default='ls')
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if profiler not in ('ls', 'stat', 'flame'):
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ui.warn(_("unrecognized profiler '%s' - ignored\n") % profiler)
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profiler = 'ls'
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output = ui.config('profiling', 'output')
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if output == 'blackbox':
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fp = util.stringio()
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elif output:
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path = ui.expandpath(output)
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fp = open(path, 'wb')
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else:
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fp = sys.stderr
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try:
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if profiler == 'ls':
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proffn = lsprofile
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elif profiler == 'flame':
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proffn = flameprofile
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else:
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proffn = statprofile
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with proffn(ui, fp):
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yield
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finally:
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if output:
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if output == 'blackbox':
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val = 'Profile:\n%s' % fp.getvalue()
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# ui.log treats the input as a format string,
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# so we need to escape any % signs.
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val = val.replace('%', '%%')
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ui.log('profile', val)
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fp.close()
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