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Added the cProfile module. 

Based on lsprof (patch #1212837) by Brett Rosen and Ted Czotter.
With further editing by Michael Hudson and myself.
History in svn repo: http://codespeak.net/svn/user/arigo/hack/misc/lsprof

* Module/_lsprof.c is the internal C module, Lib/cProfile.py a wrapper.
* pstats.py updated to display cProfile's caller/callee timings if available.
* setup.py and NEWS updated.
* documentation updates in the profiler section:
   - explain the differences between the three profilers that we have now
   - profile and cProfile can use a unified documentation, like (c)Pickle
   - mention that hotshot is "for specialized usage" now
   - removed references to the "old profiler" that no longer exists
* test updates:
   - extended test_profile to cover delicate cases like recursion
   - added tests for the caller/callee displays
   - added test_cProfile, performing the same tests for cProfile
* TO-DO:
   - cProfile gives a nicer name to built-in, particularly built-in methods,
     which could be backported to profile.
   - not tested on Windows recently!
This commit is contained in:
Armin Rigo 2006-02-08 12:53:56 +00:00
parent 5eefdca654
commit a871ef2b3e
14 changed files with 1701 additions and 109 deletions
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2
Doc/lib/lib.tex
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@ -358,7 +358,7 @@ and how to embed it in other applications.
\input{libpdb} % The Python Debugger
\input{libprofile} % The Python Profiler
\input{libhotshot} % New profiler
\input{libhotshot} % unmaintained C profiler
\input{libtimeit}

11
Doc/lib/libhotshot.tex
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@ -14,6 +14,17 @@ Hotshot is a replacement for the existing \refmodule{profile} module. As it's
written mostly in C, it should result in a much smaller performance impact
than the existing \refmodule{profile} module.
\begin{notice}[note]
The \module{hotshot} module focuses on minimizing the overhead
while profiling, at the expense of long data post-processing times.
For common usages it is recommended to use \module{cProfile} instead.
\module{hotshot} is not maintained and might be removed from the
standard library in the future.
\end{notice}
\versionchanged[the results should be more meaningful than in the
past: the timing core contained a critical bug]{2.5}
\begin{notice}[warning]
The \module{hotshot} profiler does not yet work well with threads.
It is useful to use an unthreaded script to run the profiler over

153
Doc/lib/libprofile.tex
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@ -1,4 +1,4 @@
\chapter{The Python Profiler \label{profile}}
\chapter{The Python Profilers \label{profile}}
\sectionauthor{James Roskind}{}
@ -6,8 +6,9 @@ Copyright \copyright{} 1994, by InfoSeek Corporation, all rights reserved.
\index{InfoSeek Corporation}
Written by James Roskind.\footnote{
Updated and converted to \LaTeX\ by Guido van Rossum. The references to
the old profiler are left in the text, although it no longer exists.}
Updated and converted to \LaTeX\ by Guido van Rossum.
Further updated by Armin Rigo to integrate the documentation for the new
\module{cProfile} module of Python 2.5.}
Permission to use, copy, modify, and distribute this Python software
and its associated documentation for any purpose (subject to the
@ -41,7 +42,7 @@ ways at times. Please send suggestions for improvements to:
I'd appreciate the feedback.
\section{Introduction to the profiler}
\section{Introduction to the profilers}
\nodename{Profiler Introduction}
A \dfn{profiler} is a program that describes the run time performance
@ -54,6 +55,31 @@ examine the results of a profile operation.
\index{deterministic profiling}
\index{profiling, deterministic}
The Python standard library provides three different profilers:
\begin{enumerate}
\item \module{profile}, a pure Python module, described in the sequel.
Copyright \copyright{} 1994, by InfoSeek Corporation.
\versionchanged[also reports the time spent in calls to built-in
functions and methods]{2.4}
\item \module{cProfile}, a module written in C, with a reasonable
overhead that makes it suitable for profiling long-running programs.
Based on \module{lsprof}, contributed by Brett Rosen and Ted Czotter.
\versionadded{2.5}
\item \module{hotshot}, a C module focusing on minimizing the overhead
while profiling, at the expense of long data post-processing times.
\versionchanged[the results should be more meaningful than in the
past: the timing core contained a critical bug]{2.5}
\end{enumerate}
The \module{profile} and \module{cProfile} modules export the same
interface, so they are mostly interchangeables; \module{cProfile} has a
much lower overhead but is not so far as well-tested and might not be
available on all systems. \module{cProfile} is really a compatibility
layer on top of the internal \module{_lsprof} module. The
\module{hotshot} module is reserved to specialized usages.
%\section{How Is This Profiler Different From The Old Profiler?}
%\nodename{Profiler Changes}
@ -108,10 +134,13 @@ To profile an application with a main entry point of \function{foo()},
you would add the following to your module:
\begin{verbatim}
import profile
profile.run('foo()')
import cProfile
cProfile.run('foo()')
\end{verbatim}
(Use \module{profile} instead of \module{cProfile} if the latter is not
available on your system.)
The above action would cause \function{foo()} to be run, and a series of
informative lines (the profile) to be printed. The above approach is
most useful when working with the interpreter. If you would like to
@ -120,21 +149,21 @@ can supply a file name as the second argument to the \function{run()}
function:
\begin{verbatim}
import profile
profile.run('foo()', 'fooprof')
import cProfile
cProfile.run('foo()', 'fooprof')
\end{verbatim}
The file \file{profile.py} can also be invoked as
The file \file{cProfile.py} can also be invoked as
a script to profile another script. For example:
\begin{verbatim}
python -m profile myscript.py
python -m cProfile myscript.py
\end{verbatim}
\file{profile.py} accepts two optional arguments on the command line:
\file{cProfile.py} accepts two optional arguments on the command line:
\begin{verbatim}
profile.py [-o output_file] [-s sort_order]
cProfile.py [-o output_file] [-s sort_order]
\end{verbatim}
\programopt{-s} only applies to standard output (\programopt{-o} is
@ -153,7 +182,7 @@ p = pstats.Stats('fooprof')
The class \class{Stats} (the above code just created an instance of
this class) has a variety of methods for manipulating and printing the
data that was just read into \code{p}. When you ran
\function{profile.run()} above, what was printed was the result of three
\function{cProfile.run()} above, what was printed was the result of three
method calls:
\begin{verbatim}
@ -162,8 +191,9 @@ p.strip_dirs().sort_stats(-1).print_stats()
The first method removed the extraneous path from all the module
names. The second method sorted all the entries according to the
standard module/line/name string that is printed (this is to comply
with the semantics of the old profiler). The third method printed out
standard module/line/name string that is printed.
%(this is to comply with the semantics of the old profiler).
The third method printed out
all the statistics. You might try the following sort calls:
\begin{verbatim}
@ -268,15 +298,17 @@ times in this profiler allows statistics for recursive implementations
of algorithms to be directly compared to iterative implementations.
\section{Reference Manual}
\section{Reference Manual -- \module{profile} and \module{cProfile}}
\declaremodule{standard}{profile}
\declaremodule{standard}{cProfile}
\modulesynopsis{Python profiler}
The primary entry point for the profiler is the global function
\function{profile.run()}. It is typically used to create any profile
\function{profile.run()} (resp. \function{cProfile.run()}).
It is typically used to create any profile
information. The reports are formatted and printed using methods of
the class \class{pstats.Stats}. The following is a description of all
of these standard entry points and functions. For a more in-depth
@ -296,7 +328,6 @@ standard name string (file/line/function-name) that is presented in
each line. The following is a typical output from such a call:
\begin{verbatim}
main()
2706 function calls (2004 primitive calls) in 4.504 CPU seconds
Ordered by: standard name
@ -307,9 +338,7 @@ ncalls tottime percall cumtime percall filename:lineno(function)
...
\end{verbatim}
The first line indicates that this profile was generated by the call:\\
\code{profile.run('main()')}, and hence the exec'ed string is
\code{'main()'}. The second line indicates that 2706 calls were
The first line indicates that 2706 calls were
monitored. Of those calls, 2004 were \dfn{primitive}. We define
\dfn{primitive} to mean that the call was not induced via recursion.
The next line: \code{Ordered by:\ standard name}, indicates that
@ -350,7 +379,7 @@ figure is printed.
\end{funcdesc}
\begin{funcdesc}{runctx}{command, globals, locals\optional{, filename}}
This function is similar to \function{profile.run()}, with added
This function is similar to \function{run()}, with added
arguments to supply the globals and locals dictionaries for the
\var{command} string.
\end{funcdesc}
@ -368,10 +397,12 @@ from a \var{filename} (or set of filenames). \class{Stats} objects are
manipulated by methods, in order to print useful reports.
The file selected by the above constructor must have been created by
the corresponding version of \module{profile}. To be specific, there is
the corresponding version of \module{profile} or \module{cProfile}.
To be specific, there is
\emph{no} file compatibility guaranteed with future versions of this
profiler, and there is no compatibility with files produced by other
profilers (such as the old system profiler).
profilers.
%(such as the old system profiler).
If several files are provided, all the statistics for identical
functions will be coalesced, so that an overall view of several
@ -403,7 +434,8 @@ statistics for these two entries are accumulated into a single entry.
This method of the \class{Stats} class accumulates additional
profiling information into the current profiling object. Its
arguments should refer to filenames created by the corresponding
version of \function{profile.run()}. Statistics for identically named
version of \function{profile.run()} or \function{cProfile.run()}.
Statistics for identically named
(re: file, line, name) functions are automatically accumulated into
single function statistics.
\end{methoddesc}
@ -412,7 +444,8 @@ single function statistics.
Save the data loaded into the \class{Stats} object to a file named
\var{filename}. The file is created if it does not exist, and is
overwritten if it already exists. This is equivalent to the method of
the same name on the \class{profile.Profile} class.
the same name on the \class{profile.Profile} and
\class{cProfile.Profile} classes.
\versionadded{2.3}
\end{methoddesc}
@ -456,7 +489,8 @@ string order 20, 3 and 40. In contrast, \code{'nfl'} does a numeric
compare of the line numbers. In fact, \code{sort_stats('nfl')} is the
same as \code{sort_stats('name', 'file', 'line')}.
For compatibility with the old profiler, the numeric arguments
%For compatibility with the old profiler,
For backward-compatibility reasons, the numeric arguments
\code{-1}, \code{0}, \code{1}, and \code{2} are permitted. They are
interpreted as \code{'stdname'}, \code{'calls'}, \code{'time'}, and
\code{'cumulative'} respectively. If this old style format (numeric)
@ -467,10 +501,10 @@ additional arguments will be silently ignored.
\begin{methoddesc}[Stats]{reverse_order}{}
This method for the \class{Stats} class reverses the ordering of the basic
list within the object. This method is provided primarily for
compatibility with the old profiler. Its utility is questionable
now that ascending vs descending order is properly selected based on
the sort key of choice.
list within the object. %This method is provided primarily for
%compatibility with the old profiler.
Note that by default ascending vs descending order is properly selected
based on the sort key of choice.
\end{methoddesc}
\begin{methoddesc}[Stats]{print_stats}{\optional{restriction, \moreargs}}
@ -512,10 +546,21 @@ and then proceed to only print the first 10\% of them.
This method for the \class{Stats} class prints a list of all functions
that called each function in the profiled database. The ordering is
identical to that provided by \method{print_stats()}, and the definition
of the restricting argument is also identical. For convenience, a
number is shown in parentheses after each caller to show how many
times this specific call was made. A second non-parenthesized number
is the cumulative time spent in the function at the right.
of the restricting argument is also identical. Each caller is reported on
its own line. The format differs slightly depending on the profiler that
produced the stats:
\begin{itemize}
\item With \module{profile}, a number is shown in parentheses after each
caller to show how many times this specific call was made. For
convenience, a second non-parenthesized number repeats the cumulative
time spent in the function at the right.
\item With \module{cProfile}, each caller is preceeded by three numbers:
the number of times this specific call was made, and the total and
cumulative times spent in the current function while it was invoked by
this specific caller.
\end{itemize}
\end{methoddesc}
\begin{methoddesc}[Stats]{print_callees}{\optional{restriction, \moreargs}}
@ -546,7 +591,10 @@ is once again executing. As a result, functions that are called many
times, or call many functions, will typically accumulate this error.
The error that accumulates in this fashion is typically less than the
accuracy of the clock (less than one clock tick), but it
\emph{can} accumulate and become very significant. This profiler
\emph{can} accumulate and become very significant.
The problem is more important with \module{profile} than with the
lower-overhead \module{cProfile}. For this reason, \module{profile}
provides a means of calibrating itself for a given platform so that
this error can be probabilistically (on the average) removed.
After the profiler is calibrated, it will be more accurate (in a least
@ -560,7 +608,7 @@ calibration.
\section{Calibration \label{profile-calibration}}
The profiler subtracts a constant from each
The profiler of the \module{profile} module subtracts a constant from each
event handling time to compensate for the overhead of calling the time
function, and socking away the results. By default, the constant is 0.
The following procedure can
@ -614,11 +662,12 @@ statistics.
\section{Extensions --- Deriving Better Profilers}
\nodename{Profiler Extensions}
The \class{Profile} class of module \module{profile} was written so that
The \class{Profile} class of both modules, \module{profile} and
\module{cProfile}, were written so that
derived classes could be developed to extend the profiler. The details
are not described here, as doing this successfully requires an expert
understanding of how the \class{Profile} class works internally. Study
the source code of module \module{profile} carefully if you want to
the source code of the module carefully if you want to
pursue this.
If all you want to do is change how current time is determined (for
@ -630,8 +679,11 @@ constructor:
pr = profile.Profile(your_time_func)
\end{verbatim}
The resulting profiler will then call \code{your_time_func()}.
The function should return a single number, or a list of
The resulting profiler will then call \function{your_time_func()}.
\begin{description}
\item[\class{profile.Profile}]
\function{your_time_func()} should return a single number, or a list of
numbers whose sum is the current time (like what \function{os.times()}
returns). If the function returns a single time number, or the list of
returned numbers has length 2, then you will get an especially fast
@ -646,3 +698,22 @@ you want to substitute a better timer in the cleanest fashion,
derive a class and hardwire a replacement dispatch method that best
handles your timer call, along with the appropriate calibration
constant.
\item[\class{cProfile.Profile}]
\function{your_time_func()} should return a single number. If it returns
plain integers, you can also invoke the class constructor with a second
argument specifying the real duration of one unit of time. For example,
if \function{your_integer_time_func()} returns times measured in thousands
of seconds, you would constuct the \class{Profile} instance as follows:
\begin{verbatim}
pr = profile.Profile(your_integer_time_func, 0.001)
\end{verbatim}
As the \module{cProfile.Profile} class cannot be calibrated, custom
timer functions should be used with care and should be as fast as
possible. For the best results with a custom timer, it might be
necessary to hard-code it in the C source of the internal
\module{_lsprof} module.
\end{description}

190
Lib/cProfile.py Executable file
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@ -0,0 +1,190 @@
#! /usr/bin/env python
"""Python interface for the 'lsprof' profiler.
Compatible with the 'profile' module.
"""
__all__ = ["run", "runctx", "help", "Profile"]
import _lsprof
# ____________________________________________________________
# Simple interface
def run(statement, filename=None, sort=-1):
"""Run statement under profiler optionally saving results in filename
This function takes a single argument that can be passed to the
"exec" statement, and an optional file name. In all cases this
routine attempts to "exec" its first argument and gather profiling
statistics from the execution. If no file name is present, then this
function automatically prints a simple profiling report, sorted by the
standard name string (file/line/function-name) that is presented in
each line.
"""
prof = Profile()
result = None
try:
try:
prof = prof.run(statement)
except SystemExit:
pass
finally:
if filename is not None:
prof.dump_stats(filename)
else:
result = prof.print_stats(sort)
return result
def runctx(statement, globals, locals, filename=None):
"""Run statement under profiler, supplying your own globals and locals,
optionally saving results in filename.
statement and filename have the same semantics as profile.run
"""
prof = Profile()
result = None
try:
try:
prof = prof.runctx(statement, globals, locals)
except SystemExit:
pass
finally:
if filename is not None:
prof.dump_stats(filename)
else:
result = prof.print_stats()
return result
# Backwards compatibility.
def help():
print "Documentation for the profile/cProfile modules can be found "
print "in the Python Library Reference, section 'The Python Profiler'."
# ____________________________________________________________
class Profile(_lsprof.Profiler):
"""Profile(custom_timer=None, time_unit=None, subcalls=True, builtins=True)
Builds a profiler object using the specified timer function.
The default timer is a fast built-in one based on real time.
For custom timer functions returning integers, time_unit can
be a float specifying a scale (i.e. how long each integer unit
is, in seconds).
"""
# Most of the functionality is in the base class.
# This subclass only adds convenient and backward-compatible methods.
def print_stats(self, sort=-1):
import pstats
pstats.Stats(self).strip_dirs().sort_stats(sort).print_stats()
def dump_stats(self, file):
import marshal
f = open(file, 'wb')
self.create_stats()
marshal.dump(self.stats, f)
f.close()
def create_stats(self):
self.disable()
self.snapshot_stats()
def snapshot_stats(self):
entries = self.getstats()
self.stats = {}
callersdicts = {}
# call information
for entry in entries:
func = label(entry.code)
nc = entry.callcount # ncalls column of pstats (before '/')
cc = nc - entry.reccallcount # ncalls column of pstats (after '/')
tt = entry.inlinetime # tottime column of pstats
ct = entry.totaltime # cumtime column of pstats
callers = {}
callersdicts[id(entry.code)] = callers
self.stats[func] = cc, nc, tt, ct, callers
# subcall information
for entry in entries:
if entry.calls:
func = label(entry.code)
for subentry in entry.calls:
try:
callers = callersdicts[id(subentry.code)]
except KeyError:
continue
nc = subentry.callcount
cc = nc - subentry.reccallcount
tt = subentry.inlinetime
ct = subentry.totaltime
if func in callers:
prev = callers[func]
nc += prev[0]
cc += prev[1]
tt += prev[2]
ct += prev[3]
callers[func] = nc, cc, tt, ct
# 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.enable()
try:
exec cmd in globals, locals
finally:
self.disable()
return self
# This method is more useful to profile a single function call.
def runcall(self, func, *args, **kw):
self.enable()
try:
return func(*args, **kw)
finally:
self.disable()
# ____________________________________________________________
def label(code):
if isinstance(code, str):
return ('~', 0, code) # built-in functions ('~' sorts at the end)
else:
return (code.co_filename, code.co_firstlineno, code.co_name)
# ____________________________________________________________
def main():
import os, sys
from optparse import OptionParser
usage = "cProfile.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()

44
Lib/pstats.py
View File

@ -371,27 +371,47 @@ class Stats:
self.print_call_heading(width, "was called by...")
for func in list:
cc, nc, tt, ct, callers = self.stats[func]
self.print_call_line(width, func, callers)
self.print_call_line(width, func, callers, "<-")
print
print
return self
def print_call_heading(self, name_size, column_title):
print "Function ".ljust(name_size) + column_title
# print sub-header only if we have new-style callers
subheader = False
for cc, nc, tt, ct, callers in self.stats.itervalues():
if callers:
value = callers.itervalues().next()
subheader = isinstance(value, tuple)
break
if subheader:
print " "*name_size + " ncalls tottime cumtime"
def print_call_line(self, name_size, source, call_dict):
print func_std_string(source).ljust(name_size),
def print_call_line(self, name_size, source, call_dict, arrow="->"):
print func_std_string(source).ljust(name_size) + arrow,
if not call_dict:
print "--"
print
return
clist = call_dict.keys()
clist.sort()
name_size = name_size + 1
indent = ""
for func in clist:
name = func_std_string(func)
print indent*name_size + name + '(%r)' % (call_dict[func],), \
f8(self.stats[func][3])
value = call_dict[func]
if isinstance(value, tuple):
nc, cc, tt, ct = value
if nc != cc:
substats = '%d/%d' % (nc, cc)
else:
substats = '%d' % (nc,)
substats = '%s %s %s %s' % (substats.rjust(7+2*len(indent)),
f8(tt), f8(ct), name)
left_width = name_size + 1
else:
substats = '%s(%r) %s' % (name, value, f8(self.stats[func][3]))
left_width = name_size + 3
print indent*left_width + substats
indent = " "
def print_title(self):
@ -448,7 +468,15 @@ def func_get_function_name(func):
return func[2]
def func_std_string(func_name): # match what old profile produced
return "%s:%d(%s)" % func_name
if func_name[:2] == ('~', 0):
# special case for built-in functions
name = func_name[2]
if name.startswith('<') and name.endswith('>'):
return '{%s}' % name[1:-1]
else:
return name
else:
return "%s:%d(%s)" % func_name
#**************************************************************************
# The following functions combine statists for pairs functions.

79
Lib/test/output/test_cProfile Normal file
View File

@ -0,0 +1,79 @@
test_cProfile
126 function calls (106 primitive calls) in 1.000 CPU seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 1.000 1.000 <string>:1(<module>)
8 0.064 0.008 0.080 0.010 test_cProfile.py:103(subhelper)
28 0.028 0.001 0.028 0.001 test_cProfile.py:115(__getattr__)
1 0.270 0.270 1.000 1.000 test_cProfile.py:30(testfunc)
23/3 0.150 0.007 0.170 0.057 test_cProfile.py:40(factorial)
20 0.020 0.001 0.020 0.001 test_cProfile.py:53(mul)
2 0.040 0.020 0.600 0.300 test_cProfile.py:60(helper)
4 0.116 0.029 0.120 0.030 test_cProfile.py:78(helper1)
2 0.000 0.000 0.140 0.070 test_cProfile.py:89(helper2_indirect)
8 0.312 0.039 0.400 0.050 test_cProfile.py:93(helper2)
12 0.000 0.000 0.012 0.001 {hasattr}
4 0.000 0.000 0.000 0.000 {method 'append' of 'list' objects}
1 0.000 0.000 0.000 0.000 {method 'disable' of '_lsprof.Profiler' objects}
8 0.000 0.000 0.000 0.000 {range}
4 0.000 0.000 0.000 0.000 {sys.exc_info}
Ordered by: standard name
Function called...
ncalls tottime cumtime
<string>:1(<module>) -> 1 0.270 1.000 test_cProfile.py:30(testfunc)
test_cProfile.py:103(subhelper) -> 16 0.016 0.016 test_cProfile.py:115(__getattr__)
8 0.000 0.000 {range}
test_cProfile.py:115(__getattr__) ->
test_cProfile.py:30(testfunc) -> 1 0.014 0.130 test_cProfile.py:40(factorial)
2 0.040 0.600 test_cProfile.py:60(helper)
test_cProfile.py:40(factorial) -> 20/3 0.130 0.147 test_cProfile.py:40(factorial)
20 0.020 0.020 test_cProfile.py:53(mul)
test_cProfile.py:53(mul) ->
test_cProfile.py:60(helper) -> 4 0.116 0.120 test_cProfile.py:78(helper1)
2 0.000 0.140 test_cProfile.py:89(helper2_indirect)
6 0.234 0.300 test_cProfile.py:93(helper2)
test_cProfile.py:78(helper1) -> 4 0.000 0.004 {hasattr}
4 0.000 0.000 {method 'append' of 'list' objects}
4 0.000 0.000 {sys.exc_info}
test_cProfile.py:89(helper2_indirect) -> 2 0.006 0.040 test_cProfile.py:40(factorial)
2 0.078 0.100 test_cProfile.py:93(helper2)
test_cProfile.py:93(helper2) -> 8 0.064 0.080 test_cProfile.py:103(subhelper)
8 0.000 0.008 {hasattr}
{hasattr} -> 12 0.012 0.012 test_cProfile.py:115(__getattr__)
{method 'append' of 'list' objects} ->
{method 'disable' of '_lsprof.Profiler' objects} ->
{range} ->
{sys.exc_info} ->
Ordered by: standard name
Function was called by...
ncalls tottime cumtime
<string>:1(<module>) <-
test_cProfile.py:103(subhelper) <- 8 0.064 0.080 test_cProfile.py:93(helper2)
test_cProfile.py:115(__getattr__) <- 16 0.016 0.016 test_cProfile.py:103(subhelper)
12 0.012 0.012 {hasattr}
test_cProfile.py:30(testfunc) <- 1 0.270 1.000 <string>:1(<module>)
test_cProfile.py:40(factorial) <- 1 0.014 0.130 test_cProfile.py:30(testfunc)
20/3 0.130 0.147 test_cProfile.py:40(factorial)
2 0.006 0.040 test_cProfile.py:89(helper2_indirect)
test_cProfile.py:53(mul) <- 20 0.020 0.020 test_cProfile.py:40(factorial)
test_cProfile.py:60(helper) <- 2 0.040 0.600 test_cProfile.py:30(testfunc)
test_cProfile.py:78(helper1) <- 4 0.116 0.120 test_cProfile.py:60(helper)
test_cProfile.py:89(helper2_indirect) <- 2 0.000 0.140 test_cProfile.py:60(helper)
test_cProfile.py:93(helper2) <- 6 0.234 0.300 test_cProfile.py:60(helper)
2 0.078 0.100 test_cProfile.py:89(helper2_indirect)
{hasattr} <- 4 0.000 0.004 test_cProfile.py:78(helper1)
8 0.000 0.008 test_cProfile.py:93(helper2)
{method 'append' of 'list' objects} <- 4 0.000 0.000 test_cProfile.py:78(helper1)
{method 'disable' of '_lsprof.Profiler' objects} <-
{range} <- 8 0.000 0.000 test_cProfile.py:103(subhelper)
{sys.exc_info} <- 4 0.000 0.000 test_cProfile.py:78(helper1)

78
Lib/test/output/test_profile
View File

@ -1,20 +1,84 @@
test_profile
74 function calls in 1.000 CPU seconds
127 function calls (107 primitive calls) in 1.000 CPU seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
4 0.000 0.000 0.000 0.000 :0(append)
4 0.000 0.000 0.000 0.000 :0(exc_info)
12 0.000 0.000 0.012 0.001 :0(hasattr)
8 0.000 0.000 0.000 0.000 :0(range)
1 0.000 0.000 0.000 0.000 :0(setprofile)
1 0.000 0.000 1.000 1.000 <string>:1(<module>)
0 0.000 0.000 profile:0(profiler)
1 0.000 0.000 1.000 1.000 profile:0(testfunc())
1 0.400 0.400 1.000 1.000 test_profile.py:23(testfunc)
2 0.080 0.040 0.600 0.300 test_profile.py:32(helper)
4 0.116 0.029 0.120 0.030 test_profile.py:50(helper1)
8 0.312 0.039 0.400 0.050 test_profile.py:58(helper2)
8 0.064 0.008 0.080 0.010 test_profile.py:68(subhelper)
28 0.028 0.001 0.028 0.001 test_profile.py:80(__getattr__)
8 0.064 0.008 0.080 0.010 test_profile.py:103(subhelper)
28 0.028 0.001 0.028 0.001 test_profile.py:115(__getattr__)
1 0.270 0.270 1.000 1.000 test_profile.py:30(testfunc)
23/3 0.150 0.007 0.170 0.057 test_profile.py:40(factorial)
20 0.020 0.001 0.020 0.001 test_profile.py:53(mul)
2 0.040 0.020 0.600 0.300 test_profile.py:60(helper)
4 0.116 0.029 0.120 0.030 test_profile.py:78(helper1)
2 0.000 0.000 0.140 0.070 test_profile.py:89(helper2_indirect)
8 0.312 0.039 0.400 0.050 test_profile.py:93(helper2)
Ordered by: standard name
Function called...
:0(append) ->
:0(exc_info) ->
:0(hasattr) -> test_profile.py:115(__getattr__)(12) 0.028
:0(range) ->
:0(setprofile) ->
<string>:1(<module>) -> test_profile.py:30(testfunc)(1) 1.000
profile:0(profiler) -> profile:0(testfunc())(1) 1.000
profile:0(testfunc()) -> :0(setprofile)(1) 0.000
<string>:1(<module>)(1) 1.000
test_profile.py:103(subhelper) -> :0(range)(8) 0.000
test_profile.py:115(__getattr__)(16) 0.028
test_profile.py:115(__getattr__) ->
test_profile.py:30(testfunc) -> test_profile.py:40(factorial)(1) 0.170
test_profile.py:60(helper)(2) 0.600
test_profile.py:40(factorial) -> test_profile.py:40(factorial)(20) 0.170
test_profile.py:53(mul)(20) 0.020
test_profile.py:53(mul) ->
test_profile.py:60(helper) -> test_profile.py:78(helper1)(4) 0.120
test_profile.py:89(helper2_indirect)(2) 0.140
test_profile.py:93(helper2)(6) 0.400
test_profile.py:78(helper1) -> :0(append)(4) 0.000
:0(exc_info)(4) 0.000
:0(hasattr)(4) 0.012
test_profile.py:89(helper2_indirect) -> test_profile.py:40(factorial)(2) 0.170
test_profile.py:93(helper2)(2) 0.400
test_profile.py:93(helper2) -> :0(hasattr)(8) 0.012
test_profile.py:103(subhelper)(8) 0.080
Ordered by: standard name
Function was called by...
:0(append) <- test_profile.py:78(helper1)(4) 0.120
:0(exc_info) <- test_profile.py:78(helper1)(4) 0.120
:0(hasattr) <- test_profile.py:78(helper1)(4) 0.120
test_profile.py:93(helper2)(8) 0.400
:0(range) <- test_profile.py:103(subhelper)(8) 0.080
:0(setprofile) <- profile:0(testfunc())(1) 1.000
<string>:1(<module>) <- profile:0(testfunc())(1) 1.000
profile:0(profiler) <-
profile:0(testfunc()) <- profile:0(profiler)(1) 0.000
test_profile.py:103(subhelper) <- test_profile.py:93(helper2)(8) 0.400
test_profile.py:115(__getattr__) <- :0(hasattr)(12) 0.012
test_profile.py:103(subhelper)(16) 0.080
test_profile.py:30(testfunc) <- <string>:1(<module>)(1) 1.000
test_profile.py:40(factorial) <- test_profile.py:30(testfunc)(1) 1.000
test_profile.py:40(factorial)(20) 0.170
test_profile.py:89(helper2_indirect)(2) 0.140
test_profile.py:53(mul) <- test_profile.py:40(factorial)(20) 0.170
test_profile.py:60(helper) <- test_profile.py:30(testfunc)(2) 1.000
test_profile.py:78(helper1) <- test_profile.py:60(helper)(4) 0.600
test_profile.py:89(helper2_indirect) <- test_profile.py:60(helper)(2) 0.600
test_profile.py:93(helper2) <- test_profile.py:60(helper)(6) 0.600
test_profile.py:89(helper2_indirect)(2) 0.140

123
Lib/test/test_cProfile.py Normal file
View File

@ -0,0 +1,123 @@
"""Test suite for the cProfile module."""
import cProfile, pstats, sys
# In order to have reproducible time, we simulate a timer in the global
# variable 'ticks', which represents simulated time in milliseconds.
# (We can't use a helper function increment the timer since it would be
# included in the profile and would appear to consume all the time.)
ticks = 0
# IMPORTANT: this is an output test. *ALL* NUMBERS in the expected
# output are relevant. If you change the formatting of pstats,
# please don't just regenerate output/test_cProfile without checking
# very carefully that not a single number has changed.
def test_main():
global ticks
ticks = 42000
prof = cProfile.Profile(timer, 0.001)
prof.runctx("testfunc()", globals(), locals())
assert ticks == 43000, ticks
st = pstats.Stats(prof)
st.strip_dirs().sort_stats('stdname').print_stats()
st.print_callees()
st.print_callers()
def timer():
return ticks
def testfunc():
# 1 call
# 1000 ticks total: 270 ticks local, 730 ticks in subfunctions
global ticks
ticks += 99
helper() # 300
helper() # 300
ticks += 171
factorial(14) # 130
def factorial(n):
# 23 calls total
# 170 ticks total, 150 ticks local
# 3 primitive calls, 130, 20 and 20 ticks total
# including 116, 17, 17 ticks local
global ticks
if n > 0:
ticks += n
return mul(n, factorial(n-1))
else:
ticks += 11
return 1
def mul(a, b):
# 20 calls
# 1 tick, local
global ticks
ticks += 1
return a * b
def helper():
# 2 calls
# 300 ticks total: 20 ticks local, 260 ticks in subfunctions
global ticks
ticks += 1
helper1() # 30
ticks += 2
helper1() # 30
ticks += 6
helper2() # 50
ticks += 3
helper2() # 50
ticks += 2
helper2() # 50
ticks += 5
helper2_indirect() # 70
ticks += 1
def helper1():
# 4 calls
# 30 ticks total: 29 ticks local, 1 tick in subfunctions
global ticks
ticks += 10
hasattr(C(), "foo") # 1
ticks += 19
lst = []
lst.append(42) # 0
sys.exc_info() # 0
def helper2_indirect():
helper2() # 50
factorial(3) # 20
def helper2():
# 8 calls
# 50 ticks local: 39 ticks local, 11 ticks in subfunctions
global ticks
ticks += 11
hasattr(C(), "bar") # 1
ticks += 13
subhelper() # 10
ticks += 15
def subhelper():
# 8 calls
# 10 ticks total: 8 ticks local, 2 ticks in subfunctions
global ticks
ticks += 2
for i in range(2): # 0
try:
C().foo # 1 x 2
except AttributeError:
ticks += 3 # 3 x 2
class C:
def __getattr__(self, name):
# 28 calls
# 1 tick, local
global ticks
ticks += 1
raise AttributeError
if __name__ == "__main__":
test_main()

106
Lib/test/test_profile.py
View File

@ -1,8 +1,6 @@
"""Test suite for the profile module."""
import profile
import os
from test.test_support import TESTFN, vereq
import profile, pstats, sys
# In order to have reproducible time, we simulate a timer in the global
# variable 'ticks', which represents simulated time in milliseconds.
@ -10,50 +8,87 @@ from test.test_support import TESTFN, vereq
# included in the profile and would appear to consume all the time.)
ticks = 0
def test_1():
# IMPORTANT: this is an output test. *ALL* NUMBERS in the expected
# output are relevant. If you change the formatting of pstats,
# please don't just regenerate output/test_profile without checking
# very carefully that not a single number has changed.
def test_main():
global ticks
ticks = 0
ticks = 42000
prof = profile.Profile(timer)
prof.runctx("testfunc()", globals(), globals())
prof.print_stats()
prof.runctx("testfunc()", globals(), locals())
assert ticks == 43000, ticks
st = pstats.Stats(prof)
st.strip_dirs().sort_stats('stdname').print_stats()
st.print_callees()
st.print_callers()
def timer():
return ticks*0.001
def testfunc():
# 1 call
# 1000 ticks total: 400 ticks local, 600 ticks in subfunctions
# 1000 ticks total: 270 ticks local, 730 ticks in subfunctions
global ticks
ticks += 199
ticks += 99
helper() # 300
helper() # 300
ticks += 201
ticks += 171
factorial(14) # 130
def factorial(n):
# 23 calls total
# 170 ticks total, 150 ticks local
# 3 primitive calls, 130, 20 and 20 ticks total
# including 116, 17, 17 ticks local
global ticks
if n > 0:
ticks += n
return mul(n, factorial(n-1))
else:
ticks += 11
return 1
def mul(a, b):
# 20 calls
# 1 tick, local
global ticks
ticks += 1
return a * b
def helper():
# 2 calls
# 300 ticks total: 40 ticks local, 260 ticks in subfunctions
# 300 ticks total: 20 ticks local, 260 ticks in subfunctions
global ticks
ticks += 1
helper1() # 30
ticks += 3
ticks += 2
helper1() # 30
ticks += 6
helper2() # 50
ticks += 3
helper2() # 50
ticks += 2
helper2() # 50
ticks += 5
helper2() # 50
ticks += 4
helper2() # 50
ticks += 7
helper2() # 50
ticks += 14
helper2_indirect() # 70
ticks += 1
def helper1():
# 4 calls
# 30 ticks total: 29 ticks local, 1 tick in subfunctions
global ticks
ticks += 10
hasattr(C(), "foo")
hasattr(C(), "foo") # 1
ticks += 19
lst = []
lst.append(42) # 0
sys.exc_info() # 0
def helper2_indirect():
helper2() # 50
factorial(3) # 20
def helper2():
# 8 calls
@ -70,7 +105,7 @@ def subhelper():
# 10 ticks total: 8 ticks local, 2 ticks in subfunctions
global ticks
ticks += 2
for i in range(2):
for i in range(2): # 0
try:
C().foo # 1 x 2
except AttributeError:
@ -84,36 +119,5 @@ class C:
ticks += 1
raise AttributeError
def test_2():
d = globals().copy()
def testfunc():
global x
x = 1
d['testfunc'] = testfunc
profile.runctx("testfunc()", d, d, TESTFN)
vereq (x, 1)
os.unlink (TESTFN)
def test_3():
result = []
def testfunc1():
try: len(None)
except: pass
try: len(None)
except: pass
result.append(True)
def testfunc2():
testfunc1()
testfunc1()
profile.runctx("testfunc2()", locals(), locals(), TESTFN)
vereq(result, [True, True])
os.unlink(TESTFN)
def test_main():
test_1()
test_2()
test_3()
if __name__ == "__main__":
test_main()

5
Misc/NEWS
View File

@ -2019,6 +2019,11 @@ Extension modules
Library
-------
- Added a new module: cProfile, a C profiler with the same interface as the
profile module. cProfile avoids some of the drawbacks of the hotshot
profiler and provides a bit more information than the other two profilers.
Based on "lsprof" (patch #1212837).
- Bug #1266283: The new function "lexists" is now in os.path.__all__.
- Bug #981530: Fix UnboundLocalError in shutil.rmtree(). This affects

867
Modules/_lsprof.c Normal file
View File

@ -0,0 +1,867 @@
#include "Python.h"
#include "compile.h"
#include "frameobject.h"
#include "structseq.h"
#include "rotatingtree.h"
#if !defined(HAVE_LONG_LONG)
#error "This module requires long longs!"
#endif
/*** Selection of a high-precision timer ***/
#ifdef MS_WINDOWS
#include <windows.h>
static PY_LONG_LONG
hpTimer(void)
{
LARGE_INTEGER li;
QueryPerformanceCounter(&li);
return li.QuadPart;
}
static double
hpTimerUnit(void)
{
LARGE_INTEGER li;
if (QueryPerformanceFrequency(&li))
return 1000.0 / li.QuadPart;
else
return 0.001; /* unlikely */
}
#else /* !MS_WINDOWS */
#ifndef HAVE_GETTIMEOFDAY
#error "This module requires gettimeofday() on non-Windows platforms!"
#endif
#if (defined(PYOS_OS2) && defined(PYCC_GCC))
#include <sys/time.h>
#else
#include <sys/resource.h>
#include <sys/times.h>
#endif
static PY_LONG_LONG
hpTimer(void)
{
struct timeval tv;
PY_LONG_LONG ret;
#ifdef GETTIMEOFDAY_NO_TZ
gettimeofday(&tv);
#else
gettimeofday(&tv, (struct timezone *)NULL);
#endif
ret = tv.tv_sec;
ret = ret * 1000000 + tv.tv_usec;
return ret;
}
static double
hpTimerUnit(void)
{
return 0.001;
}
#endif /* MS_WINDOWS */
/************************************************************/
/* Written by Brett Rosen and Ted Czotter */
struct _ProfilerEntry;
/* represents a function called from another function */
typedef struct _ProfilerSubEntry {
rotating_node_t header;
PY_LONG_LONG tt;
PY_LONG_LONG it;
long callcount;
long recursivecallcount;
long recursionLevel;
} ProfilerSubEntry;
/* represents a function or user defined block */
typedef struct _ProfilerEntry {
rotating_node_t header;
PyObject *userObj; /* PyCodeObject, or a descriptive str for builtins */
PY_LONG_LONG tt; /* total time in this entry */
PY_LONG_LONG it; /* inline time in this entry (not in subcalls) */
long callcount; /* how many times this was called */
long recursivecallcount; /* how many times called recursively */
long recursionLevel;
rotating_node_t *calls;
} ProfilerEntry;
typedef struct _ProfilerContext {
PY_LONG_LONG t0;
PY_LONG_LONG subt;
struct _ProfilerContext *previous;
ProfilerEntry *ctxEntry;
} ProfilerContext;
typedef struct {
PyObject_HEAD
rotating_node_t *profilerEntries;
ProfilerContext *currentProfilerContext;
ProfilerContext *freelistProfilerContext;
int flags;
PyObject *externalTimer;
double externalTimerUnit;
} ProfilerObject;
#define POF_ENABLED 0x001
#define POF_SUBCALLS 0x002
#define POF_BUILTINS 0x004
#define POF_NOMEMORY 0x100
staticforward PyTypeObject PyProfiler_Type;
#define PyProfiler_Check(op) PyObject_TypeCheck(op, &PyProfiler_Type)
#define PyProfiler_CheckExact(op) ((op)->ob_type == &PyProfiler_Type)
/*** External Timers ***/
#define DOUBLE_TIMER_PRECISION 4294967296.0
static PyObject *empty_tuple;
static PY_LONG_LONG CallExternalTimer(ProfilerObject *pObj)
{
PY_LONG_LONG result;
PyObject *o = PyObject_Call(pObj->externalTimer, empty_tuple, NULL);
if (o == NULL) {
PyErr_WriteUnraisable(pObj->externalTimer);
return 0;
}
if (pObj->externalTimerUnit > 0.0) {
/* interpret the result as an integer that will be scaled
in profiler_getstats() */
result = PyLong_AsLongLong(o);
}
else {
/* interpret the result as a double measured in seconds.
As the profiler works with PY_LONG_LONG internally
we convert it to a large integer */
double val = PyFloat_AsDouble(o);
/* error handling delayed to the code below */
result = (PY_LONG_LONG) (val * DOUBLE_TIMER_PRECISION);
}
Py_DECREF(o);
if (PyErr_Occurred()) {
PyErr_WriteUnraisable((PyObject *) pObj);
return 0;
}
return result;
}
#define CALL_TIMER(pObj) ((pObj)->externalTimer ? \
CallExternalTimer(pObj) : \
hpTimer())
/*** ProfilerObject ***/
static PyObject *
normalizeUserObj(PyObject *obj)
{
PyCFunctionObject *fn;
if (!PyCFunction_Check(obj)) {
Py_INCREF(obj);
return obj;
}
/* Replace built-in function objects with a descriptive string
because of built-in methods -- keeping a reference to
__self__ is probably not a good idea. */
fn = (PyCFunctionObject *)obj;
if (fn->m_self == NULL) {
/* built-in function: look up the module name */
PyObject *mod = fn->m_module;
char *modname;
if (mod && PyString_Check(mod)) {
modname = PyString_AS_STRING(mod);
}
else if (mod && PyModule_Check(mod)) {
modname = PyModule_GetName(mod);
if (modname == NULL) {
PyErr_Clear();
modname = "__builtin__";
}
}
else {
modname = "__builtin__";
}
if (strcmp(modname, "__builtin__") != 0)
return PyString_FromFormat("<%s.%s>",
modname,
fn->m_ml->ml_name);
else
return PyString_FromFormat("<%s>",
fn->m_ml->ml_name);
}
else {
/* built-in method: try to return
repr(getattr(type(__self__), __name__))
*/
PyObject *self = fn->m_self;
PyObject *name = PyString_FromString(fn->m_ml->ml_name);
if (name != NULL) {
PyObject *mo = _PyType_Lookup(self->ob_type, name);
Py_XINCREF(mo);
Py_DECREF(name);
if (mo != NULL) {
PyObject *res = PyObject_Repr(mo);
Py_DECREF(mo);
if (res != NULL)
return res;
}
}
PyErr_Clear();
return PyString_FromFormat("<built-in method %s>",
fn->m_ml->ml_name);
}
}
static ProfilerEntry*
newProfilerEntry(ProfilerObject *pObj, void *key, PyObject *userObj)
{
ProfilerEntry *self;
self = (ProfilerEntry*) malloc(sizeof(ProfilerEntry));
if (self == NULL) {
pObj->flags |= POF_NOMEMORY;
return NULL;
}
userObj = normalizeUserObj(userObj);
if (userObj == NULL) {
PyErr_Clear();
free(self);
pObj->flags |= POF_NOMEMORY;
return NULL;
}
self->header.key = key;
self->userObj = userObj;
self->tt = 0;
self->it = 0;
self->callcount = 0;
self->recursivecallcount = 0;
self->recursionLevel = 0;
self->calls = EMPTY_ROTATING_TREE;
RotatingTree_Add(&pObj->profilerEntries, &self->header);
return self;
}
static ProfilerEntry*
getEntry(ProfilerObject *pObj, void *key)
{
return (ProfilerEntry*) RotatingTree_Get(&pObj->profilerEntries, key);
}
static ProfilerSubEntry *
getSubEntry(ProfilerObject *pObj, ProfilerEntry *caller, ProfilerEntry* entry)
{
return (ProfilerSubEntry*) RotatingTree_Get(&caller->calls,
(void *)entry);
}
static ProfilerSubEntry *
newSubEntry(ProfilerObject *pObj, ProfilerEntry *caller, ProfilerEntry* entry)
{
ProfilerSubEntry *self;
self = (ProfilerSubEntry*) malloc(sizeof(ProfilerSubEntry));
if (self == NULL) {
pObj->flags |= POF_NOMEMORY;
return NULL;
}
self->header.key = (void *)entry;
self->tt = 0;
self->it = 0;
self->callcount = 0;
self->recursivecallcount = 0;
self->recursionLevel = 0;
RotatingTree_Add(&caller->calls, &self->header);
return self;
}
static int freeSubEntry(rotating_node_t *header, void *arg)
{
ProfilerSubEntry *subentry = (ProfilerSubEntry*) header;
free(subentry);
return 0;
}
static int freeEntry(rotating_node_t *header, void *arg)
{
ProfilerEntry *entry = (ProfilerEntry*) header;
RotatingTree_Enum(entry->calls, freeSubEntry, NULL);
Py_DECREF(entry->userObj);
free(entry);
return 0;
}
static void clearEntries(ProfilerObject *pObj)
{
RotatingTree_Enum(pObj->profilerEntries, freeEntry, NULL);
pObj->profilerEntries = EMPTY_ROTATING_TREE;
/* release the memory hold by the free list of ProfilerContexts */
while (pObj->freelistProfilerContext) {
ProfilerContext *c = pObj->freelistProfilerContext;
pObj->freelistProfilerContext = c->previous;
free(c);
}
}
static void
initContext(ProfilerObject *pObj, ProfilerContext *self, ProfilerEntry *entry)
{
self->ctxEntry = entry;
self->subt = 0;
self->previous = pObj->currentProfilerContext;
pObj->currentProfilerContext = self;
++entry->recursionLevel;
if ((pObj->flags & POF_SUBCALLS) && self->previous) {
/* find or create an entry for me in my caller's entry */
ProfilerEntry *caller = self->previous->ctxEntry;
ProfilerSubEntry *subentry = getSubEntry(pObj, caller, entry);
if (subentry == NULL)
subentry = newSubEntry(pObj, caller, entry);
if (subentry)
++subentry->recursionLevel;
}
self->t0 = CALL_TIMER(pObj);
}
static void
Stop(ProfilerObject *pObj, ProfilerContext *self, ProfilerEntry *entry)
{
PY_LONG_LONG tt = CALL_TIMER(pObj) - self->t0;
PY_LONG_LONG it = tt - self->subt;
if (self->previous)
self->previous->subt += tt;
pObj->currentProfilerContext = self->previous;
if (--entry->recursionLevel == 0)
entry->tt += tt;
else
++entry->recursivecallcount;
entry->it += it;
entry->callcount++;
if ((pObj->flags & POF_SUBCALLS) && self->previous) {
/* find or create an entry for me in my caller's entry */
ProfilerEntry *caller = self->previous->ctxEntry;
ProfilerSubEntry *subentry = getSubEntry(pObj, caller, entry);
if (subentry) {
if (--subentry->recursionLevel == 0)
subentry->tt += tt;
else
++subentry->recursivecallcount;
subentry->it += it;
++subentry->callcount;
}
}
}
static void
ptrace_enter_call(PyObject *self, void *key, PyObject *userObj)
{
/* entering a call to the function identified by 'key'
(which can be a PyCodeObject or a PyMethodDef pointer) */
ProfilerObject *pObj = (ProfilerObject*)self;
ProfilerEntry *profEntry;
ProfilerContext *pContext;
profEntry = getEntry(pObj, key);
if (profEntry == NULL) {
profEntry = newProfilerEntry(pObj, key, userObj);
if (profEntry == NULL)
return;
}
/* grab a ProfilerContext out of the free list */
pContext = pObj->freelistProfilerContext;
if (pContext) {
pObj->freelistProfilerContext = pContext->previous;
}
else {
/* free list exhausted, allocate a new one */
pContext = (ProfilerContext*)
malloc(sizeof(ProfilerContext));
if (pContext == NULL) {
pObj->flags |= POF_NOMEMORY;
return;
}
}
initContext(pObj, pContext, profEntry);
}
static void
ptrace_leave_call(PyObject *self, void *key)
{
/* leaving a call to the function identified by 'key' */
ProfilerObject *pObj = (ProfilerObject*)self;
ProfilerEntry *profEntry;
ProfilerContext *pContext;
pContext = pObj->currentProfilerContext;
if (pContext == NULL)
return;
profEntry = getEntry(pObj, key);
if (profEntry) {
Stop(pObj, pContext, profEntry);
}
else {
pObj->currentProfilerContext = pContext->previous;
}
/* put pContext into the free list */
pContext->previous = pObj->freelistProfilerContext;
pObj->freelistProfilerContext = pContext;
}
static int
profiler_callback(PyObject *self, PyFrameObject *frame, int what,
PyObject *arg)
{
switch (what) {
/* the 'frame' of a called function is about to start its execution */
case PyTrace_CALL:
ptrace_enter_call(self, (void *)frame->f_code,
(PyObject *)frame->f_code);
break;
/* the 'frame' of a called function is about to finish
(either normally or with an exception) */
case PyTrace_RETURN:
ptrace_leave_call(self, (void *)frame->f_code);
break;
/* case PyTrace_EXCEPTION:
If the exception results in the function exiting, a
PyTrace_RETURN event will be generated, so we don't need to
handle it. */
#ifdef PyTrace_C_CALL /* not defined in Python <= 2.3 */
/* the Python function 'frame' is issuing a call to the built-in
function 'arg' */
case PyTrace_C_CALL:
if ((((ProfilerObject *)self)->flags & POF_BUILTINS)
&& PyCFunction_Check(arg)) {
ptrace_enter_call(self,
((PyCFunctionObject *)arg)->m_ml,
arg);
}
break;
/* the call to the built-in function 'arg' is returning into its
caller 'frame' */
case PyTrace_C_RETURN: /* ...normally */
case PyTrace_C_EXCEPTION: /* ...with an exception set */
if ((((ProfilerObject *)self)->flags & POF_BUILTINS)
&& PyCFunction_Check(arg)) {
ptrace_leave_call(self,
((PyCFunctionObject *)arg)->m_ml);
}
break;
#endif
default:
break;
}
return 0;
}
static int
pending_exception(ProfilerObject *pObj)
{
if (pObj->flags & POF_NOMEMORY) {
pObj->flags -= POF_NOMEMORY;
PyErr_SetString(PyExc_MemoryError,
"memory was exhausted while profiling");
return -1;
}
return 0;
}
/************************************************************/
static PyStructSequence_Field profiler_entry_fields[] = {
{"code", "code object or built-in function name"},
{"callcount", "how many times this was called"},
{"reccallcount", "how many times called recursively"},
{"totaltime", "total time in this entry"},
{"inlinetime", "inline time in this entry (not in subcalls)"},
{"calls", "details of the calls"},
{0}
};
static PyStructSequence_Field profiler_subentry_fields[] = {
{"code", "called code object or built-in function name"},
{"callcount", "how many times this is called"},
{"reccallcount", "how many times this is called recursively"},
{"totaltime", "total time spent in this call"},
{"inlinetime", "inline time (not in further subcalls)"},
{0}
};
static PyStructSequence_Desc profiler_entry_desc = {
"_lsprof.profiler_entry", /* name */
NULL, /* doc */
profiler_entry_fields,
6
};
static PyStructSequence_Desc profiler_subentry_desc = {
"_lsprof.profiler_subentry", /* name */
NULL, /* doc */
profiler_subentry_fields,
5
};
static PyTypeObject StatsEntryType;
static PyTypeObject StatsSubEntryType;
typedef struct {
PyObject *list;
PyObject *sublist;
double factor;
} statscollector_t;
static int statsForSubEntry(rotating_node_t *node, void *arg)
{
ProfilerSubEntry *sentry = (ProfilerSubEntry*) node;
statscollector_t *collect = (statscollector_t*) arg;
ProfilerEntry *entry = (ProfilerEntry*) sentry->header.key;
int err;
PyObject *sinfo;
sinfo = PyObject_CallFunction((PyObject*) &StatsSubEntryType,
"((Olldd))",
entry->userObj,
sentry->callcount,
sentry->recursivecallcount,
collect->factor * sentry->tt,
collect->factor * sentry->it);
if (sinfo == NULL)
return -1;
err = PyList_Append(collect->sublist, sinfo);
Py_DECREF(sinfo);
return err;
}
static int statsForEntry(rotating_node_t *node, void *arg)
{
ProfilerEntry *entry = (ProfilerEntry*) node;
statscollector_t *collect = (statscollector_t*) arg;
PyObject *info;
int err;
if (entry->callcount == 0)
return 0; /* skip */
if (entry->calls != EMPTY_ROTATING_TREE) {
collect->sublist = PyList_New(0);
if (collect->sublist == NULL)
return -1;
if (RotatingTree_Enum(entry->calls,
statsForSubEntry, collect) != 0) {
Py_DECREF(collect->sublist);
return -1;
}
}
else {
Py_INCREF(Py_None);
collect->sublist = Py_None;
}
info = PyObject_CallFunction((PyObject*) &StatsEntryType,
"((OllddO))",
entry->userObj,
entry->callcount,
entry->recursivecallcount,
collect->factor * entry->tt,
collect->factor * entry->it,
collect->sublist);
Py_DECREF(collect->sublist);
if (info == NULL)
return -1;
err = PyList_Append(collect->list, info);
Py_DECREF(info);
return err;
}
PyDoc_STRVAR(getstats_doc, "\
getstats() -> list of profiler_entry objects\n\
\n\
Return all information collected by the profiler.\n\
Each profiler_entry is a tuple-like object with the\n\
following attributes:\n\
\n\
code code object\n\
callcount how many times this was called\n\
reccallcount how many times called recursively\n\
totaltime total time in this entry\n\
inlinetime inline time in this entry (not in subcalls)\n\
calls details of the calls\n\
\n\
The calls attribute is either None or a list of\n\
profiler_subentry objects:\n\
\n\
code called code object\n\
callcount how many times this is called\n\
reccallcount how many times this is called recursively\n\
totaltime total time spent in this call\n\
inlinetime inline time (not in further subcalls)\n\
");
static PyObject*
profiler_getstats(ProfilerObject *pObj, PyObject* noarg)
{
statscollector_t collect;
if (pending_exception(pObj))
return NULL;
if (!pObj->externalTimer)
collect.factor = hpTimerUnit();
else if (pObj->externalTimerUnit > 0.0)
collect.factor = pObj->externalTimerUnit;
else
collect.factor = 1.0 / DOUBLE_TIMER_PRECISION;
collect.list = PyList_New(0);
if (collect.list == NULL)
return NULL;
if (RotatingTree_Enum(pObj->profilerEntries, statsForEntry, &collect)
!= 0) {
Py_DECREF(collect.list);
return NULL;
}
return collect.list;
}
static int
setSubcalls(ProfilerObject *pObj, int nvalue)
{
if (nvalue == 0)
pObj->flags &= ~POF_SUBCALLS;
else if (nvalue > 0)
pObj->flags |= POF_SUBCALLS;
return 0;
}
static int
setBuiltins(ProfilerObject *pObj, int nvalue)
{
if (nvalue == 0)
pObj->flags &= ~POF_BUILTINS;
else if (nvalue > 0) {
#ifndef PyTrace_C_CALL
PyErr_SetString(PyExc_ValueError,
"builtins=True requires Python >= 2.4");
return -1;
#else
pObj->flags |= POF_BUILTINS;
#endif
}
return 0;
}
PyDoc_STRVAR(enable_doc, "\
enable(subcalls=True, builtins=True)\n\
\n\
Start collecting profiling information.\n\
If 'subcalls' is True, also records for each function\n\
statistics separated according to its current caller.\n\
If 'builtins' is True, records the time spent in\n\
built-in functions separately from their caller.\n\
");
static PyObject*
profiler_enable(ProfilerObject *self, PyObject *args, PyObject *kwds)
{
int subcalls = -1;
int builtins = -1;
static const char *kwlist[] = {"subcalls", "builtins", 0};
if (!PyArg_ParseTupleAndKeywords(args, kwds, "|ii:enable",
kwlist, &subcalls, &builtins))
return NULL;
if (setSubcalls(self, subcalls) < 0 || setBuiltins(self, builtins) < 0)
return NULL;
PyEval_SetProfile(profiler_callback, (PyObject*)self);
self->flags |= POF_ENABLED;
Py_INCREF(Py_None);
return Py_None;
}
static void
flush_unmatched(ProfilerObject *pObj)
{
while (pObj->currentProfilerContext) {
ProfilerContext *pContext = pObj->currentProfilerContext;
ProfilerEntry *profEntry= pContext->ctxEntry;
if (profEntry)
Stop(pObj, pContext, profEntry);
else
pObj->currentProfilerContext = pContext->previous;
if (pContext)
free(pContext);
}
}
PyDoc_STRVAR(disable_doc, "\
disable()\n\
\n\
Stop collecting profiling information.\n\
");
static PyObject*
profiler_disable(ProfilerObject *self, PyObject* noarg)
{
self->flags &= ~POF_ENABLED;
PyEval_SetProfile(NULL, NULL);
flush_unmatched(self);
if (pending_exception(self))
return NULL;
Py_INCREF(Py_None);
return Py_None;
}
PyDoc_STRVAR(clear_doc, "\
clear()\n\
\n\
Clear all profiling information collected so far.\n\
");
static PyObject*
profiler_clear(ProfilerObject *pObj, PyObject* noarg)
{
clearEntries(pObj);
Py_INCREF(Py_None);
return Py_None;
}
static void
profiler_dealloc(ProfilerObject *op)
{
if (op->flags & POF_ENABLED)
PyEval_SetProfile(NULL, NULL);
flush_unmatched(op);
clearEntries(op);
Py_XDECREF(op->externalTimer);
op->ob_type->tp_free(op);
}
static int
profiler_init(ProfilerObject *pObj, PyObject *args, PyObject *kw)
{
PyObject *o;
PyObject *timer = NULL;
double timeunit = 0.0;
int subcalls = 1;
#ifdef PyTrace_C_CALL
int builtins = 1;
#else
int builtins = 0;
#endif
static const char *kwlist[] = {"timer", "timeunit",
"subcalls", "builtins", 0};
if (!PyArg_ParseTupleAndKeywords(args, kw, "|Odii:Profiler", kwlist,
&timer, &timeunit,
&subcalls, &builtins))
return -1;
if (setSubcalls(pObj, subcalls) < 0 || setBuiltins(pObj, builtins) < 0)
return -1;
o = pObj->externalTimer;
pObj->externalTimer = timer;
Py_XINCREF(timer);
Py_XDECREF(o);
pObj->externalTimerUnit = timeunit;
return 0;
}
static PyMethodDef profiler_methods[] = {
{"getstats", (PyCFunction)profiler_getstats,
METH_NOARGS, getstats_doc},
{"enable", (PyCFunction)profiler_enable,
METH_VARARGS | METH_KEYWORDS, enable_doc},
{"disable", (PyCFunction)profiler_disable,
METH_NOARGS, disable_doc},
{"clear", (PyCFunction)profiler_clear,
METH_NOARGS, clear_doc},
{NULL, NULL}
};
PyDoc_STRVAR(profiler_doc, "\
Profiler(custom_timer=None, time_unit=None, subcalls=True, builtins=True)\n\
\n\
Builds a profiler object using the specified timer function.\n\
The default timer is a fast built-in one based on real time.\n\
For custom timer functions returning integers, time_unit can\n\
be a float specifying a scale (i.e. how long each integer unit\n\
is, in seconds).\n\
");
statichere PyTypeObject PyProfiler_Type = {
PyObject_HEAD_INIT(NULL)
0, /* ob_size */
"_lsprof.Profiler", /* tp_name */
sizeof(ProfilerObject), /* tp_basicsize */
0, /* tp_itemsize */
(destructor)profiler_dealloc, /* tp_dealloc */
0, /* tp_print */
0, /* tp_getattr */
0, /* tp_setattr */
0, /* tp_compare */
0, /* tp_repr */
0, /* tp_as_number */
0, /* tp_as_sequence */
0, /* tp_as_mapping */
0, /* tp_hash */
0, /* tp_call */
0, /* tp_str */
0, /* tp_getattro */
0, /* tp_setattro */
0, /* tp_as_buffer */
Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE, /* tp_flags */
profiler_doc, /* tp_doc */
0, /* tp_traverse */
0, /* tp_clear */
0, /* tp_richcompare */
0, /* tp_weaklistoffset */
0, /* tp_iter */
0, /* tp_iternext */
profiler_methods, /* tp_methods */
0, /* tp_members */
0, /* tp_getset */
0, /* tp_base */
0, /* tp_dict */
0, /* tp_descr_get */
0, /* tp_descr_set */
0, /* tp_dictoffset */
(initproc)profiler_init, /* tp_init */
PyType_GenericAlloc, /* tp_alloc */
PyType_GenericNew, /* tp_new */
PyObject_Del, /* tp_free */
};
static PyMethodDef moduleMethods[] = {
{NULL, NULL}
};
PyMODINIT_FUNC
init_lsprof(void)
{
PyObject *module, *d;
module = Py_InitModule3("_lsprof", moduleMethods, "Fast profiler");
d = PyModule_GetDict(module);
if (PyType_Ready(&PyProfiler_Type) < 0)
return;
PyDict_SetItemString(d, "Profiler", (PyObject *)&PyProfiler_Type);
PyStructSequence_InitType(&StatsEntryType, &profiler_entry_desc);
PyStructSequence_InitType(&StatsSubEntryType, &profiler_subentry_desc);
Py_INCREF((PyObject*) &StatsEntryType);
Py_INCREF((PyObject*) &StatsSubEntryType);
PyModule_AddObject(module, "profiler_entry",
(PyObject*) &StatsEntryType);
PyModule_AddObject(module, "profiler_subentry",
(PyObject*) &StatsSubEntryType);
empty_tuple = PyTuple_New(0);
}

121
Modules/rotatingtree.c Normal file
View File

@ -0,0 +1,121 @@
#include "rotatingtree.h"
#define KEY_LOWER_THAN(key1, key2) ((char*)(key1) < (char*)(key2))
/* The randombits() function below is a fast-and-dirty generator that
* is probably irregular enough for our purposes. Note that it's biased:
* I think that ones are slightly more probable than zeroes. It's not
* important here, though.
*/
static unsigned int random_value = 1;
static unsigned int random_stream = 0;
static int
randombits(int bits)
{
int result;
if (random_stream < (1<<bits)) {
random_value *= 1082527;
random_stream = random_value;
}
result = random_stream & ((1<<bits)-1);
random_stream >>= bits;
return result;
}
/* Insert a new node into the tree.
(*root) is modified to point to the new root. */
void
RotatingTree_Add(rotating_node_t **root, rotating_node_t *node)
{
while (*root != NULL) {
if (KEY_LOWER_THAN(node->key, (*root)->key))
root = &((*root)->left);
else
root = &((*root)->right);
}
node->left = NULL;
node->right = NULL;
*root = node;
}
/* Locate the node with the given key. This is the most complicated
function because it occasionally rebalances the tree to move the
resulting node closer to the root. */
rotating_node_t *
RotatingTree_Get(rotating_node_t **root, void *key)
{
if (randombits(3) != 4) {
/* Fast path, no rebalancing */
rotating_node_t *node = *root;
while (node != NULL) {
if (node->key == key)
return node;
if (KEY_LOWER_THAN(key, node->key))
node = node->left;
else
node = node->right;
}
return NULL;
}
else {
rotating_node_t **pnode = root;
rotating_node_t *node = *pnode;
rotating_node_t *next;
int rotate;
if (node == NULL)
return NULL;
while (1) {
if (node->key == key)
return node;
rotate = !randombits(1);
if (KEY_LOWER_THAN(key, node->key)) {
next = node->left;
if (next == NULL)
return NULL;
if (rotate) {
node->left = next->right;
next->right = node;
*pnode = next;
}
else
pnode = &(node->left);
}
else {
next = node->right;
if (next == NULL)
return NULL;
if (rotate) {
node->right = next->left;
next->left = node;
*pnode = next;
}
else
pnode = &(node->right);
}
node = next;
}
}
}
/* Enumerate all nodes in the tree. The callback enumfn() should return
zero to continue the enumeration, or non-zero to interrupt it.
A non-zero value is directly returned by RotatingTree_Enum(). */
int
RotatingTree_Enum(rotating_node_t *root, rotating_tree_enum_fn enumfn,
void *arg)
{
int result;
rotating_node_t *node;
while (root != NULL) {
result = RotatingTree_Enum(root->left, enumfn, arg);
if (result != 0) return result;
node = root->right;
result = enumfn(root, arg);
if (result != 0) return result;
root = node;
}
return 0;
}

27
Modules/rotatingtree.h Normal file
View File

@ -0,0 +1,27 @@
/* "Rotating trees" (Armin Rigo)
*
* Google "splay trees" for the general idea.
*
* It's a dict-like data structure that works best when accesses are not
* random, but follow a strong pattern. The one implemented here is for
* accesses patterns where the same small set of keys is looked up over
* and over again, and this set of keys evolves slowly over time.
*/
#include <stdlib.h>
#define EMPTY_ROTATING_TREE ((rotating_node_t *)NULL)
typedef struct rotating_node_s rotating_node_t;
typedef int (*rotating_tree_enum_fn) (rotating_node_t *node, void *arg);
struct rotating_node_s {
void *key;
rotating_node_t *left;
rotating_node_t *right;
};
void RotatingTree_Add(rotating_node_t **root, rotating_node_t *node);
rotating_node_t* RotatingTree_Get(rotating_node_t **root, void *key);
int RotatingTree_Enum(rotating_node_t *root, rotating_tree_enum_fn enumfn,
void *arg);

4
setup.py
View File

@ -328,7 +328,6 @@ class PyBuildExt(build_ext):
# Some modules that are normally always on:
exts.append( Extension('regex', ['regexmodule.c', 'regexpr.c']) )
exts.append( Extension('_hotshot', ['_hotshot.c']) )
exts.append( Extension('_weakref', ['_weakref.c']) )
# array objects
@ -363,6 +362,9 @@ class PyBuildExt(build_ext):
exts.append( Extension("functional", ["functionalmodule.c"]) )
# Python C API test module
exts.append( Extension('_testcapi', ['_testcapimodule.c']) )
# profilers (_lsprof is for cProfile.py)
exts.append( Extension('_hotshot', ['_hotshot.c']) )
exts.append( Extension('_lsprof', ['_lsprof.c', 'rotatingtree.c']) )
# static Unicode character database
if have_unicode:
exts.append( Extension('unicodedata', ['unicodedata.c']) )