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cpython/Lib/profile.py

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#! /usr/bin/env python
#
# Class for profiling python code. rev 1.0 6/2/94
#
# Based on prior profile module by Sjoerd Mullender...
# which was hacked somewhat by: Guido van Rossum
#
# See profile.doc for more information
# Copyright 1994, by InfoSeek Corporation, all rights reserved.
# Written by James Roskind
#
# Permission to use, copy, modify, and distribute this Python software
# and its associated documentation for any purpose (subject to the
# restriction in the following sentence) without fee is hereby granted,
# provided that the above copyright notice appears in all copies, and
# that both that copyright notice and this permission notice appear in
# supporting documentation, and that the name of InfoSeek not be used in
# advertising or publicity pertaining to distribution of the software
# without specific, written prior permission. This permission is
# explicitly restricted to the copying and modification of the software
# to remain in Python, compiled Python, or other languages (such as C)
# wherein the modified or derived code is exclusively imported into a
# Python module.
#
# INFOSEEK CORPORATION DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS
# SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
# FITNESS. IN NO EVENT SHALL INFOSEEK CORPORATION BE LIABLE FOR ANY
# SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER
# RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF
# CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN
# CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
import sys
import os
import time
import string
import marshal
# Global variables
func_norm_dict = {}
func_norm_counter = 0
if hasattr(os, 'getpid'):
pid_string = `os.getpid()`
else:
pid_string = ''
# Sample timer for use with
#i_count = 0
#def integer_timer():
# global i_count
# i_count = i_count + 1
# return i_count
#itimes = integer_timer # replace with C coded timer returning integers
#**************************************************************************
# The following are the static member functions for the profiler class
# Note that an instance of Profile() is *not* needed to call them.
#**************************************************************************
# simplified user interface
def run(statement, *args):
prof = Profile()
try:
prof = prof.run(statement)
except SystemExit:
pass
if args:
prof.dump_stats(args[0])
else:
return prof.print_stats()
# print help
def help():
for dirname in sys.path:
fullname = os.path.join(dirname, 'profile.doc')
if os.path.exists(fullname):
sts = os.system('${PAGER-more} '+fullname)
if sts: print '*** Pager exit status:', sts
break
else:
print 'Sorry, can\'t find the help file "profile.doc"',
print 'along the Python search path'
#**************************************************************************
# class Profile documentation:
#**************************************************************************
# self.cur is always a tuple. Each such tuple corresponds to a stack
# frame that is currently active (self.cur[-2]). The following are the
# definitions of its members. We use this external "parallel stack" to
# avoid contaminating the program that we are profiling. (old profiler
# used to write into the frames local dictionary!!) Derived classes
# can change the definition of some entries, as long as they leave
# [-2:] intact.
#
# [ 0] = Time that needs to be charged to the parent frame's function. It is
# used so that a function call will not have to access the timing data
# for the parents frame.
# [ 1] = Total time spent in this frame's function, excluding time in
# subfunctions
# [ 2] = Cumulative time spent in this frame's function, including time in
# all subfunctions to this frame.
# [-3] = Name of the function that corresonds to this frame.
# [-2] = Actual frame that we correspond to (used to sync exception handling)
# [-1] = Our parent 6-tuple (corresonds to frame.f_back)
#**************************************************************************
# Timing data for each function is stored as a 5-tuple in the dictionary
# self.timings[]. The index is always the name stored in self.cur[4].
# The following are the definitions of the members:
#
# [0] = The number of times this function was called, not counting direct
# or indirect recursion,
# [1] = Number of times this function appears on the stack, minus one
# [2] = Total time spent internal to this function
# [3] = Cumulative time that this function was present on the stack. In
# non-recursive functions, this is the total execution time from start
# to finish of each invocation of a function, including time spent in
# all subfunctions.
# [5] = A dictionary indicating for each function name, the number of times
# it was called by us.
#**************************************************************************
# We produce function names via a repr() call on the f_code object during
# profiling. This save a *lot* of CPU time. This results in a string that
# always looks like:
# <code object main at 87090, file "/a/lib/python-local/myfib.py", line 76>
# After we "normalize it, it is a tuple of filename, line, function-name.
# We wait till we are done profiling to do the normalization.
# *IF* this repr format changes, then only the normalization routine should
# need to be fixed.
#**************************************************************************
class Profile:
def __init__(self, timer=None):
self.timings = {}
self.cur = None
self.cmd = ""
self.dispatch = { \
'call' : self.trace_dispatch_call, \
'return' : self.trace_dispatch_return, \
'exception': self.trace_dispatch_exception, \
}
if not timer:
if os.name == 'mac':
import MacOS
self.timer = MacOS.GetTicks
self.dispatcher = self.trace_dispatch_mac
self.get_time = self.get_time_mac
elif hasattr(time, 'clock'):
self.timer = time.clock
self.dispatcher = self.trace_dispatch_i
elif hasattr(os, 'times'):
self.timer = os.times
self.dispatcher = self.trace_dispatch
else:
self.timer = time.time
self.dispatcher = self.trace_dispatch_i
else:
self.timer = timer
t = self.timer() # test out timer function
try:
if len(t) == 2:
self.dispatcher = self.trace_dispatch
else:
self.dispatcher = self.trace_dispatch_l
except TypeError:
self.dispatcher = self.trace_dispatch_i
self.t = self.get_time()
self.simulate_call('profiler')
def get_time(self): # slow simulation of method to acquire time
t = self.timer()
if type(t) == type(()) or type(t) == type([]):
t = reduce(lambda x,y: x+y, t, 0)
return t
def get_time_mac(self):
return self.timer()/60.0
# Heavily optimized dispatch routine for os.times() timer
def trace_dispatch(self, frame, event, arg):
t = self.timer()
t = t[0] + t[1] - self.t # No Calibration constant
# t = t[0] + t[1] - self.t - .00053 # Calibration constant
if self.dispatch[event](frame,t):
t = self.timer()
self.t = t[0] + t[1]
else:
r = self.timer()
self.t = r[0] + r[1] - t # put back unrecorded delta
return
# Dispatch routine for best timer program (return = scalar integer)
def trace_dispatch_i(self, frame, event, arg):
t = self.timer() - self.t # - 1 # Integer calibration constant
if self.dispatch[event](frame,t):
self.t = self.timer()
else:
self.t = self.timer() - t # put back unrecorded delta
return
# Dispatch routine for macintosh (timer returns time in ticks of 1/60th second)
def trace_dispatch_mac(self, frame, event, arg):
t = self.timer()/60.0 - self.t # - 1 # Integer calibration constant
if self.dispatch[event](frame,t):
self.t = self.timer()/60.0
else:
self.t = self.timer()/60.0 - t # put back unrecorded delta
return
# SLOW generic dispatch rountine for timer returning lists of numbers
def trace_dispatch_l(self, frame, event, arg):
t = self.get_time() - self.t
if self.dispatch[event](frame,t):
self.t = self.get_time()
else:
self.t = self.get_time()-t # put back unrecorded delta
return
def trace_dispatch_exception(self, frame, t):
rt, rtt, rct, rfn, rframe, rcur = self.cur
if (not rframe is frame) and rcur:
return self.trace_dispatch_return(rframe, t)
return 0
def trace_dispatch_call(self, frame, t):
fn = `frame.f_code`
# The following should be about the best approach, but
# we would need a function that maps from id() back to
# the actual code object.
# fn = id(frame.f_code)
# Note we would really use our own function, which would
# return the code address, *and* bump the ref count. We
# would then fix up the normalize function to do the
# actualy repr(fn) call.
# The following is an interesting alternative
# It doesn't do as good a job, and it doesn't run as
# fast 'cause repr() is written in C, and this is Python.
#fcode = frame.f_code
#code = fcode.co_code
#if ord(code[0]) == 127: # == SET_LINENO
# # see "opcode.h" in the Python source
# fn = (fcode.co_filename, ord(code[1]) | \
# ord(code[2]) << 8, fcode.co_name)
#else:
# fn = (fcode.co_filename, 0, fcode.co_name)
self.cur = (t, 0, 0, fn, frame, self.cur)
if self.timings.has_key(fn):
cc, ns, tt, ct, callers = self.timings[fn]
self.timings[fn] = cc, ns + 1, tt, ct, callers
else:
self.timings[fn] = 0, 0, 0, 0, {}
return 1
def trace_dispatch_return(self, frame, t):
# if not frame is self.cur[-2]: raise "Bad return", self.cur[3]
# Prefix "r" means part of the Returning or exiting frame
# Prefix "p" means part of the Previous or older frame
rt, rtt, rct, rfn, frame, rcur = self.cur
rtt = rtt + t
sft = rtt + rct
pt, ptt, pct, pfn, pframe, pcur = rcur
self.cur = pt, ptt+rt, pct+sft, pfn, pframe, pcur
cc, ns, tt, ct, callers = self.timings[rfn]
if not ns:
ct = ct + sft
cc = cc + 1
if callers.has_key(pfn):
callers[pfn] = callers[pfn] + 1 # hack: gather more
# stats such as the amount of time added to ct courtesy
# of this specific call, and the contribution to cc
# courtesy of this call.
else:
callers[pfn] = 1
self.timings[rfn] = cc, ns - 1, tt+rtt, ct, callers
return 1
# The next few function play with self.cmd. By carefully preloading
# our paralell stack, we can force the profiled result to include
# an arbitrary string as the name of the calling function.
# We use self.cmd as that string, and the resulting stats look
# very nice :-).
def set_cmd(self, cmd):
if self.cur[-1]: return # already set
self.cmd = cmd
self.simulate_call(cmd)
class fake_code:
def __init__(self, filename, line, name):
self.co_filename = filename
self.co_line = line
self.co_name = name
self.co_code = '\0' # anything but 127
def __repr__(self):
return (self.co_filename, self.co_line, self.co_name)
class fake_frame:
def __init__(self, code, prior):
self.f_code = code
self.f_back = prior
def simulate_call(self, name):
code = self.fake_code('profile', 0, name)
if self.cur:
pframe = self.cur[-2]
else:
pframe = None
frame = self.fake_frame(code, pframe)
a = self.dispatch['call'](frame, 0)
return
# collect stats from pending stack, including getting final
# timings for self.cmd frame.
def simulate_cmd_complete(self):
t = self.get_time() - self.t
while self.cur[-1]:
# We *can* cause assertion errors here if
# dispatch_trace_return checks for a frame match!
a = self.dispatch['return'](self.cur[-2], t)
t = 0
self.t = self.get_time() - t
def print_stats(self):
import pstats
pstats.Stats(self).strip_dirs().sort_stats(-1). \
print_stats()
def dump_stats(self, file):
f = open(file, 'wb')
self.create_stats()
marshal.dump(self.stats, f)
f.close()
def create_stats(self):
self.simulate_cmd_complete()
self.snapshot_stats()
def snapshot_stats(self):
self.stats = {}
for func in self.timings.keys():
cc, ns, tt, ct, callers = self.timings[func]
nor_func = self.func_normalize(func)
nor_callers = {}
nc = 0
for func_caller in callers.keys():
nor_callers[self.func_normalize(func_caller)]=\
callers[func_caller]
nc = nc + callers[func_caller]
self.stats[nor_func] = cc, nc, tt, ct, nor_callers
# Override the following function if you can figure out
# a better name for the binary f_code entries. I just normalize
# them sequentially in a dictionary. It would be nice if we could
# *really* see the name of the underlying C code :-). Sometimes
# you can figure out what-is-what by looking at caller and callee
# lists (and knowing what your python code does).
def func_normalize(self, func_name):
global func_norm_dict
global func_norm_counter
global func_sequence_num
if func_norm_dict.has_key(func_name):
return func_norm_dict[func_name]
if type(func_name) == type(""):
long_name = string.split(func_name)
file_name = long_name[-3][1:-2]
func = long_name[2]
lineno = long_name[-1][:-1]
if '?' == func: # Until I find out how to may 'em...
file_name = 'python'
func_norm_counter = func_norm_counter + 1
func = pid_string + ".C." + `func_norm_counter`
result = file_name , string.atoi(lineno) , func
else:
result = func_name
func_norm_dict[func_name] = result
return result
# The following two methods can be called by clients to use
# a profiler to profile a statement, given as a string.
def run(self, cmd):
import __main__
dict = __main__.__dict__
return self.runctx(cmd, dict, dict)
def runctx(self, cmd, globals, locals):
self.set_cmd(cmd)
sys.setprofile(self.dispatcher)
try:
exec cmd in globals, locals
finally:
sys.setprofile(None)
return self
# This method is more useful to profile a single function call.
def runcall(self, func, *args):
self.set_cmd(`func`)
sys.setprofile(self.dispatcher)
try:
return apply(func, args)
finally:
sys.setprofile(None)
#******************************************************************
# The following calculates the overhead for using a profiler. The
# problem is that it takes a fair amount of time for the profiler
# to stop the stopwatch (from the time it recieves an event).
# Similarly, there is a delay from the time that the profiler
# re-starts the stopwatch before the user's code really gets to
# continue. The following code tries to measure the difference on
# a per-event basis. The result can the be placed in the
# Profile.dispatch_event() routine for the given platform. Note
# that this difference is only significant if there are a lot of
# events, and relatively little user code per event. For example,
# code with small functions will typically benefit from having the
# profiler calibrated for the current platform. This *could* be
# done on the fly during init() time, but it is not worth the
# effort. Also note that if too large a value specified, then
# execution time on some functions will actually appear as a
# negative number. It is *normal* for some functions (with very
# low call counts) to have such negative stats, even if the
# calibration figure is "correct."
#
# One alternative to profile-time calibration adjustments (i.e.,
# adding in the magic little delta during each event) is to track
# more carefully the number of events (and cumulatively, the number
# 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). Currintly, 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.)
#
# Plugging in the calibration constant doesn't slow down the
# profiler very much, and the accuracy goes way up.
#**************************************************************
def calibrate(self, m):
n = m
s = self.timer()
while n:
self.simple()
n = n - 1
f = self.timer()
my_simple = f[0]+f[1]-s[0]-s[1]
#print "Simple =", my_simple,
n = m
s = self.timer()
while n:
self.instrumented()
n = n - 1
f = self.timer()
my_inst = f[0]+f[1]-s[0]-s[1]
# print "Instrumented =", my_inst
avg_cost = (my_inst - my_simple)/m
#print "Delta/call =", avg_cost, "(profiler fixup constant)"
return avg_cost
# simulate a program with no profiler activity
def simple(self):
a = 1
pass
# simulate a program with call/return event processing
def instrumented(self):
a = 1
self.profiler_simulation(a, a, a)
# simulate an event processing activity (from user's perspective)
def profiler_simulation(self, x, y, z):
t = self.timer()
t = t[0] + t[1]
self.ut = t
#****************************************************************************
# OldProfile class documentation
#****************************************************************************
#
# The following derived profiler simulates the old style profile, providing
# errant results on recursive functions. The reason for the usefulnes of this
# profiler is that it runs faster (i.e., less overhead). It still creates
# all the caller stats, and is quite useful when there is *no* recursion
# in the user's code.
#
# This code also shows how easy it is to create a modified profiler.
#****************************************************************************
class OldProfile(Profile):
def trace_dispatch_exception(self, frame, t):
rt, rtt, rct, rfn, rframe, rcur = self.cur
if rcur and not rframe is frame:
return self.trace_dispatch_return(rframe, t)
return 0
def trace_dispatch_call(self, frame, t):
fn = `frame.f_code`
self.cur = (t, 0, 0, fn, frame, self.cur)
if self.timings.has_key(fn):
tt, ct, callers = self.timings[fn]
self.timings[fn] = tt, ct, callers
else:
self.timings[fn] = 0, 0, {}
return 1
def trace_dispatch_return(self, frame, t):
rt, rtt, rct, rfn, frame, rcur = self.cur
rtt = rtt + t
sft = rtt + rct
pt, ptt, pct, pfn, pframe, pcur = rcur
self.cur = pt, ptt+rt, pct+sft, pfn, pframe, pcur
tt, ct, callers = self.timings[rfn]
if callers.has_key(pfn):
callers[pfn] = callers[pfn] + 1
else:
callers[pfn] = 1
self.timings[rfn] = tt+rtt, ct + sft, callers
return 1
def snapshot_stats(self):
self.stats = {}
for func in self.timings.keys():
tt, ct, callers = self.timings[func]
nor_func = self.func_normalize(func)
nor_callers = {}
nc = 0
for func_caller in callers.keys():
nor_callers[self.func_normalize(func_caller)]=\
callers[func_caller]
nc = nc + callers[func_caller]
self.stats[nor_func] = nc, nc, tt, ct, nor_callers
#****************************************************************************
# HotProfile class documentation
#****************************************************************************
#
# This profiler is the fastest derived profile example. It does not
# calculate caller-callee relationships, and does not calculate cumulative
# time under a function. It only calculates time spent in a function, so
# it runs very quickly (re: very low overhead)
#****************************************************************************
class HotProfile(Profile):
def trace_dispatch_exception(self, frame, t):
rt, rtt, rfn, rframe, rcur = self.cur
if rcur and not rframe is frame:
return self.trace_dispatch_return(rframe, t)
return 0
def trace_dispatch_call(self, frame, t):
self.cur = (t, 0, frame, self.cur)
return 1
def trace_dispatch_return(self, frame, t):
rt, rtt, frame, rcur = self.cur
rfn = `frame.f_code`
pt, ptt, pframe, pcur = rcur
self.cur = pt, ptt+rt, pframe, pcur
if self.timings.has_key(rfn):
nc, tt = self.timings[rfn]
self.timings[rfn] = nc + 1, rt + rtt + tt
else:
self.timings[rfn] = 1, rt + rtt
return 1
def snapshot_stats(self):
self.stats = {}
for func in self.timings.keys():
nc, tt = self.timings[func]
nor_func = self.func_normalize(func)
self.stats[nor_func] = nc, nc, tt, 0, {}
#****************************************************************************
def Stats(*args):
print 'Report generating functions are in the "pstats" module\a'
# When invoked as main program, invoke the profiler on a script
if __name__ == '__main__':
import sys
import os
if not sys.argv[1:]:
print "usage: profile.py scriptfile [arg] ..."
sys.exit(2)
filename = sys.argv[1] # Get script filename
del sys.argv[0] # Hide "profile.py" from argument list
# Insert script directory in front of module search path
sys.path.insert(0, os.path.dirname(filename))
run('execfile(' + `filename` + ')')
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