2c8595098c
git-svn-id: http://google-refine.googlecode.com/svn/trunk@883 7d457c2a-affb-35e4-300a-418c747d4874
620 lines
23 KiB
Python
620 lines
23 KiB
Python
#! /usr/bin/env python
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#
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# Class for profiling python code. rev 1.0 6/2/94
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#
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# Based on prior profile module by Sjoerd Mullender...
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# which was hacked somewhat by: Guido van Rossum
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"""Class for profiling Python code."""
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# Copyright 1994, by InfoSeek Corporation, all rights reserved.
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# Written by James Roskind
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#
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# Permission to use, copy, modify, and distribute this Python software
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# and its associated documentation for any purpose (subject to the
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# restriction in the following sentence) without fee is hereby granted,
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# provided that the above copyright notice appears in all copies, and
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# that both that copyright notice and this permission notice appear in
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# supporting documentation, and that the name of InfoSeek not be used in
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# advertising or publicity pertaining to distribution of the software
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# without specific, written prior permission. This permission is
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# explicitly restricted to the copying and modification of the software
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# to remain in Python, compiled Python, or other languages (such as C)
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# wherein the modified or derived code is exclusively imported into a
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# Python module.
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#
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# INFOSEEK CORPORATION DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS
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# SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
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# FITNESS. IN NO EVENT SHALL INFOSEEK CORPORATION BE LIABLE FOR ANY
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# SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER
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# RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF
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# CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN
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# CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
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import sys
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import os
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import time
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import marshal
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from optparse import OptionParser
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__all__ = ["run", "runctx", "help", "Profile"]
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# Sample timer for use with
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#i_count = 0
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#def integer_timer():
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# global i_count
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# i_count = i_count + 1
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# return i_count
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#itimes = integer_timer # replace with C coded timer returning integers
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#**************************************************************************
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# The following are the static member functions for the profiler class
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# Note that an instance of Profile() is *not* needed to call them.
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#**************************************************************************
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def run(statement, filename=None, sort=-1):
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"""Run statement under profiler optionally saving results in filename
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This function takes a single argument that can be passed to the
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"exec" statement, and an optional file name. In all cases this
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routine attempts to "exec" its first argument and gather profiling
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statistics from the execution. If no file name is present, then this
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function automatically prints a simple profiling report, sorted by the
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standard name string (file/line/function-name) that is presented in
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each line.
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"""
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prof = Profile()
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try:
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prof = prof.run(statement)
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except SystemExit:
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pass
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if filename is not None:
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prof.dump_stats(filename)
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else:
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return prof.print_stats(sort)
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def runctx(statement, globals, locals, filename=None):
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"""Run statement under profiler, supplying your own globals and locals,
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optionally saving results in filename.
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statement and filename have the same semantics as profile.run
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"""
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prof = Profile()
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try:
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prof = prof.runctx(statement, globals, locals)
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except SystemExit:
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pass
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if filename is not None:
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prof.dump_stats(filename)
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else:
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return prof.print_stats()
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# Backwards compatibility.
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def help():
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print "Documentation for the profile module can be found "
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print "in the Python Library Reference, section 'The Python Profiler'."
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if os.name == "mac":
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import MacOS
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def _get_time_mac(timer=MacOS.GetTicks):
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return timer() / 60.0
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if hasattr(os, "times"):
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def _get_time_times(timer=os.times):
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t = timer()
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return t[0] + t[1]
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# Using getrusage(3) is better than clock(3) if available:
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# on some systems (e.g. FreeBSD), getrusage has a higher resolution
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# Furthermore, on a POSIX system, returns microseconds, which
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# wrap around after 36min.
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_has_res = 0
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try:
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import resource
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resgetrusage = lambda: resource.getrusage(resource.RUSAGE_SELF)
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def _get_time_resource(timer=resgetrusage):
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t = timer()
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return t[0] + t[1]
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_has_res = 1
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except ImportError:
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pass
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class Profile:
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"""Profiler class.
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self.cur is always a tuple. Each such tuple corresponds to a stack
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frame that is currently active (self.cur[-2]). The following are the
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definitions of its members. We use this external "parallel stack" to
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avoid contaminating the program that we are profiling. (old profiler
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used to write into the frames local dictionary!!) Derived classes
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can change the definition of some entries, as long as they leave
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[-2:] intact (frame and previous tuple). In case an internal error is
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detected, the -3 element is used as the function name.
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[ 0] = Time that needs to be charged to the parent frame's function.
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It is used so that a function call will not have to access the
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timing data for the parent frame.
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[ 1] = Total time spent in this frame's function, excluding time in
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subfunctions (this latter is tallied in cur[2]).
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[ 2] = Total time spent in subfunctions, excluding time executing the
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frame's function (this latter is tallied in cur[1]).
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[-3] = Name of the function that corresponds to this frame.
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[-2] = Actual frame that we correspond to (used to sync exception handling).
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[-1] = Our parent 6-tuple (corresponds to frame.f_back).
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Timing data for each function is stored as a 5-tuple in the dictionary
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self.timings[]. The index is always the name stored in self.cur[-3].
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The following are the definitions of the members:
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[0] = The number of times this function was called, not counting direct
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or indirect recursion,
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[1] = Number of times this function appears on the stack, minus one
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[2] = Total time spent internal to this function
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[3] = Cumulative time that this function was present on the stack. In
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non-recursive functions, this is the total execution time from start
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to finish of each invocation of a function, including time spent in
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all subfunctions.
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[4] = A dictionary indicating for each function name, the number of times
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it was called by us.
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"""
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bias = 0 # calibration constant
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def __init__(self, timer=None, bias=None):
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self.timings = {}
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self.cur = None
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self.cmd = ""
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self.c_func_name = ""
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if bias is None:
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bias = self.bias
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self.bias = bias # Materialize in local dict for lookup speed.
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if not timer:
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if _has_res:
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self.timer = resgetrusage
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self.dispatcher = self.trace_dispatch
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self.get_time = _get_time_resource
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elif os.name == 'mac':
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self.timer = MacOS.GetTicks
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self.dispatcher = self.trace_dispatch_mac
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self.get_time = _get_time_mac
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elif hasattr(time, 'clock'):
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self.timer = self.get_time = time.clock
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self.dispatcher = self.trace_dispatch_i
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elif hasattr(os, 'times'):
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self.timer = os.times
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self.dispatcher = self.trace_dispatch
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self.get_time = _get_time_times
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else:
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self.timer = self.get_time = time.time
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self.dispatcher = self.trace_dispatch_i
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else:
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self.timer = timer
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t = self.timer() # test out timer function
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try:
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length = len(t)
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except TypeError:
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self.get_time = timer
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self.dispatcher = self.trace_dispatch_i
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else:
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if length == 2:
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self.dispatcher = self.trace_dispatch
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else:
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self.dispatcher = self.trace_dispatch_l
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# This get_time() implementation needs to be defined
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# here to capture the passed-in timer in the parameter
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# list (for performance). Note that we can't assume
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# the timer() result contains two values in all
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# cases.
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def get_time_timer(timer=timer, sum=sum):
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return sum(timer())
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self.get_time = get_time_timer
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self.t = self.get_time()
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self.simulate_call('profiler')
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# Heavily optimized dispatch routine for os.times() timer
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def trace_dispatch(self, frame, event, arg):
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timer = self.timer
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t = timer()
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t = t[0] + t[1] - self.t - self.bias
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if event == "c_call":
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self.c_func_name = arg.__name__
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if self.dispatch[event](self, frame,t):
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t = timer()
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self.t = t[0] + t[1]
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else:
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r = timer()
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self.t = r[0] + r[1] - t # put back unrecorded delta
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# Dispatch routine for best timer program (return = scalar, fastest if
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# an integer but float works too -- and time.clock() relies on that).
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def trace_dispatch_i(self, frame, event, arg):
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timer = self.timer
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t = timer() - self.t - self.bias
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if event == "c_call":
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self.c_func_name = arg.__name__
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if self.dispatch[event](self, frame, t):
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self.t = timer()
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else:
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self.t = timer() - t # put back unrecorded delta
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# Dispatch routine for macintosh (timer returns time in ticks of
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# 1/60th second)
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def trace_dispatch_mac(self, frame, event, arg):
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timer = self.timer
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t = timer()/60.0 - self.t - self.bias
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if event == "c_call":
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self.c_func_name = arg.__name__
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if self.dispatch[event](self, frame, t):
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self.t = timer()/60.0
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else:
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self.t = timer()/60.0 - t # put back unrecorded delta
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# SLOW generic dispatch routine for timer returning lists of numbers
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def trace_dispatch_l(self, frame, event, arg):
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get_time = self.get_time
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t = get_time() - self.t - self.bias
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if event == "c_call":
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self.c_func_name = arg.__name__
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if self.dispatch[event](self, frame, t):
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self.t = get_time()
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else:
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self.t = get_time() - t # put back unrecorded delta
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# In the event handlers, the first 3 elements of self.cur are unpacked
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# into vrbls w/ 3-letter names. The last two characters are meant to be
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# mnemonic:
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# _pt self.cur[0] "parent time" time to be charged to parent frame
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# _it self.cur[1] "internal time" time spent directly in the function
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# _et self.cur[2] "external time" time spent in subfunctions
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def trace_dispatch_exception(self, frame, t):
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rpt, rit, ret, rfn, rframe, rcur = self.cur
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if (rframe is not frame) and rcur:
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return self.trace_dispatch_return(rframe, t)
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self.cur = rpt, rit+t, ret, rfn, rframe, rcur
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return 1
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def trace_dispatch_call(self, frame, t):
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if self.cur and frame.f_back is not self.cur[-2]:
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rpt, rit, ret, rfn, rframe, rcur = self.cur
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if not isinstance(rframe, Profile.fake_frame):
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assert rframe.f_back is frame.f_back, ("Bad call", rfn,
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rframe, rframe.f_back,
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frame, frame.f_back)
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self.trace_dispatch_return(rframe, 0)
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assert (self.cur is None or \
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frame.f_back is self.cur[-2]), ("Bad call",
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self.cur[-3])
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fcode = frame.f_code
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fn = (fcode.co_filename, fcode.co_firstlineno, fcode.co_name)
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self.cur = (t, 0, 0, fn, frame, self.cur)
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timings = self.timings
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if fn in timings:
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cc, ns, tt, ct, callers = timings[fn]
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timings[fn] = cc, ns + 1, tt, ct, callers
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else:
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timings[fn] = 0, 0, 0, 0, {}
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return 1
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def trace_dispatch_c_call (self, frame, t):
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fn = ("", 0, self.c_func_name)
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self.cur = (t, 0, 0, fn, frame, self.cur)
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timings = self.timings
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if timings.has_key(fn):
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cc, ns, tt, ct, callers = timings[fn]
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timings[fn] = cc, ns+1, tt, ct, callers
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else:
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timings[fn] = 0, 0, 0, 0, {}
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return 1
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def trace_dispatch_return(self, frame, t):
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if frame is not self.cur[-2]:
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assert frame is self.cur[-2].f_back, ("Bad return", self.cur[-3])
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self.trace_dispatch_return(self.cur[-2], 0)
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# Prefix "r" means part of the Returning or exiting frame.
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# Prefix "p" means part of the Previous or Parent or older frame.
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rpt, rit, ret, rfn, frame, rcur = self.cur
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rit = rit + t
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frame_total = rit + ret
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ppt, pit, pet, pfn, pframe, pcur = rcur
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self.cur = ppt, pit + rpt, pet + frame_total, pfn, pframe, pcur
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timings = self.timings
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cc, ns, tt, ct, callers = timings[rfn]
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if not ns:
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# This is the only occurrence of the function on the stack.
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# Else this is a (directly or indirectly) recursive call, and
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# its cumulative time will get updated when the topmost call to
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# it returns.
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ct = ct + frame_total
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cc = cc + 1
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if pfn in callers:
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callers[pfn] = callers[pfn] + 1 # hack: gather more
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# stats such as the amount of time added to ct courtesy
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# of this specific call, and the contribution to cc
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# courtesy of this call.
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else:
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callers[pfn] = 1
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timings[rfn] = cc, ns - 1, tt + rit, ct, callers
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return 1
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dispatch = {
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"call": trace_dispatch_call,
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"exception": trace_dispatch_exception,
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"return": trace_dispatch_return,
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"c_call": trace_dispatch_c_call,
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"c_exception": trace_dispatch_return, # the C function returned
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"c_return": trace_dispatch_return,
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}
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# The next few functions play with self.cmd. By carefully preloading
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# our parallel stack, we can force the profiled result to include
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# an arbitrary string as the name of the calling function.
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# We use self.cmd as that string, and the resulting stats look
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# very nice :-).
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def set_cmd(self, cmd):
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if self.cur[-1]: return # already set
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self.cmd = cmd
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self.simulate_call(cmd)
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class fake_code:
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def __init__(self, filename, line, name):
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self.co_filename = filename
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self.co_line = line
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self.co_name = name
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self.co_firstlineno = 0
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def __repr__(self):
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return repr((self.co_filename, self.co_line, self.co_name))
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class fake_frame:
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def __init__(self, code, prior):
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self.f_code = code
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self.f_back = prior
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def simulate_call(self, name):
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code = self.fake_code('profile', 0, name)
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if self.cur:
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pframe = self.cur[-2]
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else:
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pframe = None
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frame = self.fake_frame(code, pframe)
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self.dispatch['call'](self, frame, 0)
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# collect stats from pending stack, including getting final
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# timings for self.cmd frame.
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def simulate_cmd_complete(self):
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get_time = self.get_time
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t = get_time() - self.t
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while self.cur[-1]:
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# We *can* cause assertion errors here if
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# dispatch_trace_return checks for a frame match!
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self.dispatch['return'](self, self.cur[-2], t)
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t = 0
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self.t = get_time() - t
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def print_stats(self, sort=-1):
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import pstats
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pstats.Stats(self).strip_dirs().sort_stats(sort). \
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print_stats()
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def dump_stats(self, file):
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f = open(file, 'wb')
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self.create_stats()
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marshal.dump(self.stats, f)
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f.close()
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def create_stats(self):
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self.simulate_cmd_complete()
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self.snapshot_stats()
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def snapshot_stats(self):
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self.stats = {}
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for func, (cc, ns, tt, ct, callers) in self.timings.iteritems():
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callers = callers.copy()
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nc = 0
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for callcnt in callers.itervalues():
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nc += callcnt
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self.stats[func] = cc, nc, tt, ct, callers
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# The following two methods can be called by clients to use
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# a profiler to profile a statement, given as a string.
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def run(self, cmd):
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import __main__
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dict = __main__.__dict__
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return self.runctx(cmd, dict, dict)
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def runctx(self, cmd, globals, locals):
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self.set_cmd(cmd)
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sys.setprofile(self.dispatcher)
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try:
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exec cmd in globals, locals
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finally:
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sys.setprofile(None)
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return self
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# This method is more useful to profile a single function call.
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def runcall(self, func, *args, **kw):
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self.set_cmd(repr(func))
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sys.setprofile(self.dispatcher)
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try:
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return func(*args, **kw)
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finally:
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sys.setprofile(None)
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#******************************************************************
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# The following calculates the overhead for using a profiler. The
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# problem is that it takes a fair amount of time for the profiler
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# to stop the stopwatch (from the time it receives an event).
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# Similarly, there is a delay from the time that the profiler
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# re-starts the stopwatch before the user's code really gets to
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# continue. The following code tries to measure the difference on
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# a per-event basis.
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#
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# Note that this difference is only significant if there are a lot of
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# events, and relatively little user code per event. For example,
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# code with small functions will typically benefit from having the
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# profiler calibrated for the current platform. This *could* be
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# done on the fly during init() time, but it is not worth the
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# effort. Also note that if too large a value specified, then
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# execution time on some functions will actually appear as a
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# negative number. It is *normal* for some functions (with very
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# low call counts) to have such negative stats, even if the
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# calibration figure is "correct."
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#
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# One alternative to profile-time calibration adjustments (i.e.,
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# adding in the magic little delta during each event) is to track
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# more carefully the number of events (and cumulatively, the number
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# of events during sub functions) that are seen. If this were
|
|
# done, then the arithmetic could be done after the fact (i.e., at
|
|
# display time). Currently, we track only call/return events.
|
|
# These values can be deduced by examining the callees and callers
|
|
# vectors for each functions. Hence we *can* almost correct the
|
|
# internal time figure at print time (note that we currently don't
|
|
# track exception event processing counts). Unfortunately, there
|
|
# is currently no similar information for cumulative sub-function
|
|
# time. It would not be hard to "get all this info" at profiler
|
|
# time. Specifically, we would have to extend the tuples to keep
|
|
# counts of this in each frame, and then extend the defs of timing
|
|
# tuples to include the significant two figures. I'm a bit fearful
|
|
# that this additional feature will slow the heavily optimized
|
|
# event/time ratio (i.e., the profiler would run slower, fur a very
|
|
# low "value added" feature.)
|
|
#**************************************************************
|
|
|
|
def calibrate(self, m, verbose=0):
|
|
if self.__class__ is not Profile:
|
|
raise TypeError("Subclasses must override .calibrate().")
|
|
|
|
saved_bias = self.bias
|
|
self.bias = 0
|
|
try:
|
|
return self._calibrate_inner(m, verbose)
|
|
finally:
|
|
self.bias = saved_bias
|
|
|
|
def _calibrate_inner(self, m, verbose):
|
|
get_time = self.get_time
|
|
|
|
# Set up a test case to be run with and without profiling. Include
|
|
# lots of calls, because we're trying to quantify stopwatch overhead.
|
|
# Do not raise any exceptions, though, because we want to know
|
|
# exactly how many profile events are generated (one call event, +
|
|
# one return event, per Python-level call).
|
|
|
|
def f1(n):
|
|
for i in range(n):
|
|
x = 1
|
|
|
|
def f(m, f1=f1):
|
|
for i in range(m):
|
|
f1(100)
|
|
|
|
f(m) # warm up the cache
|
|
|
|
# elapsed_noprofile <- time f(m) takes without profiling.
|
|
t0 = get_time()
|
|
f(m)
|
|
t1 = get_time()
|
|
elapsed_noprofile = t1 - t0
|
|
if verbose:
|
|
print "elapsed time without profiling =", elapsed_noprofile
|
|
|
|
# elapsed_profile <- time f(m) takes with profiling. The difference
|
|
# is profiling overhead, only some of which the profiler subtracts
|
|
# out on its own.
|
|
p = Profile()
|
|
t0 = get_time()
|
|
p.runctx('f(m)', globals(), locals())
|
|
t1 = get_time()
|
|
elapsed_profile = t1 - t0
|
|
if verbose:
|
|
print "elapsed time with profiling =", elapsed_profile
|
|
|
|
# reported_time <- "CPU seconds" the profiler charged to f and f1.
|
|
total_calls = 0.0
|
|
reported_time = 0.0
|
|
for (filename, line, funcname), (cc, ns, tt, ct, callers) in \
|
|
p.timings.items():
|
|
if funcname in ("f", "f1"):
|
|
total_calls += cc
|
|
reported_time += tt
|
|
|
|
if verbose:
|
|
print "'CPU seconds' profiler reported =", reported_time
|
|
print "total # calls =", total_calls
|
|
if total_calls != m + 1:
|
|
raise ValueError("internal error: total calls = %d" % total_calls)
|
|
|
|
# reported_time - elapsed_noprofile = overhead the profiler wasn't
|
|
# able to measure. Divide by twice the number of calls (since there
|
|
# are two profiler events per call in this test) to get the hidden
|
|
# overhead per event.
|
|
mean = (reported_time - elapsed_noprofile) / 2.0 / total_calls
|
|
if verbose:
|
|
print "mean stopwatch overhead per profile event =", mean
|
|
return mean
|
|
|
|
#****************************************************************************
|
|
def Stats(*args):
|
|
print 'Report generating functions are in the "pstats" module\a'
|
|
|
|
def main():
|
|
usage = "profile.py [-o output_file_path] [-s sort] scriptfile [arg] ..."
|
|
parser = OptionParser(usage=usage)
|
|
parser.allow_interspersed_args = False
|
|
parser.add_option('-o', '--outfile', dest="outfile",
|
|
help="Save stats to <outfile>", default=None)
|
|
parser.add_option('-s', '--sort', dest="sort",
|
|
help="Sort order when printing to stdout, based on pstats.Stats class", default=-1)
|
|
|
|
if not sys.argv[1:]:
|
|
parser.print_usage()
|
|
sys.exit(2)
|
|
|
|
(options, args) = parser.parse_args()
|
|
sys.argv[:] = args
|
|
|
|
if (len(sys.argv) > 0):
|
|
sys.path.insert(0, os.path.dirname(sys.argv[0]))
|
|
run('execfile(%r)' % (sys.argv[0],), options.outfile, options.sort)
|
|
else:
|
|
parser.print_usage()
|
|
return parser
|
|
|
|
# When invoked as main program, invoke the profiler on a script
|
|
if __name__ == '__main__':
|
|
main()
|