Source

cpython-withatomic / Lib / profile.py

The branch 'legacy-trunk' does not exist.
  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
#! /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

"""Class for profiling Python code."""

# 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 marshal
from optparse import OptionParser

__all__ = ["run", "runctx", "help", "Profile"]

# 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.
#**************************************************************************

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()
    try:
        prof = prof.run(statement)
    except SystemExit:
        pass
    if filename is not None:
        prof.dump_stats(filename)
    else:
        return prof.print_stats(sort)

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()
    try:
        prof = prof.runctx(statement, globals, locals)
    except SystemExit:
        pass

    if filename is not None:
        prof.dump_stats(filename)
    else:
        return prof.print_stats()

# Backwards compatibility.
def help():
    print "Documentation for the profile module can be found "
    print "in the Python Library Reference, section 'The Python Profiler'."

if os.name == "mac":
    import MacOS
    def _get_time_mac(timer=MacOS.GetTicks):
        return timer() / 60.0

if hasattr(os, "times"):
    def _get_time_times(timer=os.times):
        t = timer()
        return t[0] + t[1]

# Using getrusage(3) is better than clock(3) if available:
# on some systems (e.g. FreeBSD), getrusage has a higher resolution
# Furthermore, on a POSIX system, returns microseconds, which
# wrap around after 36min.
_has_res = 0
try:
    import resource
    resgetrusage = lambda: resource.getrusage(resource.RUSAGE_SELF)
    def _get_time_resource(timer=resgetrusage):
        t = timer()
        return t[0] + t[1]
    _has_res = 1
except ImportError:
    pass

class Profile:
    """Profiler class.

    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 (frame and previous tuple).  In case an internal error is
    detected, the -3 element is used as the function name.

    [ 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 parent frame.
    [ 1] = Total time spent in this frame's function, excluding time in
           subfunctions (this latter is tallied in cur[2]).
    [ 2] = Total time spent in subfunctions, excluding time executing the
           frame's function (this latter is tallied in cur[1]).
    [-3] = Name of the function that corresponds to this frame.
    [-2] = Actual frame that we correspond to (used to sync exception handling).
    [-1] = Our parent 6-tuple (corresponds 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[-3].
    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.
    [4] = A dictionary indicating for each function name, the number of times
          it was called by us.
    """

    bias = 0  # calibration constant

    def __init__(self, timer=None, bias=None):
        self.timings = {}
        self.cur = None
        self.cmd = ""
        self.c_func_name = ""

        if bias is None:
            bias = self.bias
        self.bias = bias     # Materialize in local dict for lookup speed.

        if not timer:
            if _has_res:
                self.timer = resgetrusage
                self.dispatcher = self.trace_dispatch
                self.get_time = _get_time_resource
            elif os.name == 'mac':
                self.timer = MacOS.GetTicks
                self.dispatcher = self.trace_dispatch_mac
                self.get_time = _get_time_mac
            elif hasattr(time, 'clock'):
                self.timer = self.get_time = time.clock
                self.dispatcher = self.trace_dispatch_i
            elif hasattr(os, 'times'):
                self.timer = os.times
                self.dispatcher = self.trace_dispatch
                self.get_time = _get_time_times
            else:
                self.timer = self.get_time = time.time
                self.dispatcher = self.trace_dispatch_i
        else:
            self.timer = timer
            t = self.timer() # test out timer function
            try:
                length = len(t)
            except TypeError:
                self.get_time = timer
                self.dispatcher = self.trace_dispatch_i
            else:
                if length == 2:
                    self.dispatcher = self.trace_dispatch
                else:
                    self.dispatcher = self.trace_dispatch_l
                # This get_time() implementation needs to be defined
                # here to capture the passed-in timer in the parameter
                # list (for performance).  Note that we can't assume
                # the timer() result contains two values in all
                # cases.
                def get_time_timer(timer=timer, sum=sum):
                    return sum(timer())
                self.get_time = get_time_timer
        self.t = self.get_time()
        self.simulate_call('profiler')

    # Heavily optimized dispatch routine for os.times() timer

    def trace_dispatch(self, frame, event, arg):
        timer = self.timer
        t = timer()
        t = t[0] + t[1] - self.t - self.bias

        if event == "c_call":
            self.c_func_name = arg.__name__

        if self.dispatch[event](self, frame,t):
            t = timer()
            self.t = t[0] + t[1]
        else:
            r = timer()
            self.t = r[0] + r[1] - t # put back unrecorded delta

    # Dispatch routine for best timer program (return = scalar, fastest if
    # an integer but float works too -- and time.clock() relies on that).

    def trace_dispatch_i(self, frame, event, arg):
        timer = self.timer
        t = timer() - self.t - self.bias

        if event == "c_call":
            self.c_func_name = arg.__name__

        if self.dispatch[event](self, frame, t):
            self.t = timer()
        else:
            self.t = timer() - t  # put back unrecorded delta

    # Dispatch routine for macintosh (timer returns time in ticks of
    # 1/60th second)

    def trace_dispatch_mac(self, frame, event, arg):
        timer = self.timer
        t = timer()/60.0 - self.t - self.bias

        if event == "c_call":
            self.c_func_name = arg.__name__

        if self.dispatch[event](self, frame, t):
            self.t = timer()/60.0
        else:
            self.t = timer()/60.0 - t  # put back unrecorded delta

    # SLOW generic dispatch routine for timer returning lists of numbers

    def trace_dispatch_l(self, frame, event, arg):
        get_time = self.get_time
        t = get_time() - self.t - self.bias

        if event == "c_call":
            self.c_func_name = arg.__name__

        if self.dispatch[event](self, frame, t):
            self.t = get_time()
        else:
            self.t = get_time() - t # put back unrecorded delta

    # In the event handlers, the first 3 elements of self.cur are unpacked
    # into vrbls w/ 3-letter names.  The last two characters are meant to be
    # mnemonic:
    #     _pt  self.cur[0] "parent time"   time to be charged to parent frame
    #     _it  self.cur[1] "internal time" time spent directly in the function
    #     _et  self.cur[2] "external time" time spent in subfunctions

    def trace_dispatch_exception(self, frame, t):
        rpt, rit, ret, rfn, rframe, rcur = self.cur
        if (rframe is not frame) and rcur:
            return self.trace_dispatch_return(rframe, t)
        self.cur = rpt, rit+t, ret, rfn, rframe, rcur
        return 1


    def trace_dispatch_call(self, frame, t):
        if self.cur and frame.f_back is not self.cur[-2]:
            rpt, rit, ret, rfn, rframe, rcur = self.cur
            if not isinstance(rframe, Profile.fake_frame):
                assert rframe.f_back is frame.f_back, ("Bad call", rfn,
                                                       rframe, rframe.f_back,
                                                       frame, frame.f_back)
                self.trace_dispatch_return(rframe, 0)
                assert (self.cur is None or \
                        frame.f_back is self.cur[-2]), ("Bad call",
                                                        self.cur[-3])
        fcode = frame.f_code
        fn = (fcode.co_filename, fcode.co_firstlineno, fcode.co_name)
        self.cur = (t, 0, 0, fn, frame, self.cur)
        timings = self.timings
        if fn in timings:
            cc, ns, tt, ct, callers = timings[fn]
            timings[fn] = cc, ns + 1, tt, ct, callers
        else:
            timings[fn] = 0, 0, 0, 0, {}
        return 1

    def trace_dispatch_c_call (self, frame, t):
        fn = ("", 0, self.c_func_name)
        self.cur = (t, 0, 0, fn, frame, self.cur)
        timings = self.timings
        if timings.has_key(fn):
            cc, ns, tt, ct, callers = timings[fn]
            timings[fn] = cc, ns+1, tt, ct, callers
        else:
            timings[fn] = 0, 0, 0, 0, {}
        return 1

    def trace_dispatch_return(self, frame, t):
        if frame is not self.cur[-2]:
            assert frame is self.cur[-2].f_back, ("Bad return", self.cur[-3])
            self.trace_dispatch_return(self.cur[-2], 0)

        # Prefix "r" means part of the Returning or exiting frame.
        # Prefix "p" means part of the Previous or Parent or older frame.

        rpt, rit, ret, rfn, frame, rcur = self.cur
        rit = rit + t
        frame_total = rit + ret

        ppt, pit, pet, pfn, pframe, pcur = rcur
        self.cur = ppt, pit + rpt, pet + frame_total, pfn, pframe, pcur

        timings = self.timings
        cc, ns, tt, ct, callers = timings[rfn]
        if not ns:
            # This is the only occurrence of the function on the stack.
            # Else this is a (directly or indirectly) recursive call, and
            # its cumulative time will get updated when the topmost call to
            # it returns.
            ct = ct + frame_total
            cc = cc + 1

        if pfn in callers:
            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

        timings[rfn] = cc, ns - 1, tt + rit, ct, callers

        return 1


    dispatch = {
        "call": trace_dispatch_call,
        "exception": trace_dispatch_exception,
        "return": trace_dispatch_return,
        "c_call": trace_dispatch_c_call,
        "c_exception": trace_dispatch_return,  # the C function returned
        "c_return": trace_dispatch_return,
        }


    # The next few functions play with self.cmd. By carefully preloading
    # our parallel 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_firstlineno = 0

        def __repr__(self):
            return repr((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)
        self.dispatch['call'](self, frame, 0)

    # collect stats from pending stack, including getting final
    # timings for self.cmd frame.

    def simulate_cmd_complete(self):
        get_time = self.get_time
        t = get_time() - self.t
        while self.cur[-1]:
            # We *can* cause assertion errors here if
            # dispatch_trace_return checks for a frame match!
            self.dispatch['return'](self, self.cur[-2], t)
            t = 0
        self.t = get_time() - t


    def print_stats(self, sort=-1):
        import pstats
        pstats.Stats(self).strip_dirs().sort_stats(sort). \
                  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, (cc, ns, tt, ct, callers) in self.timings.iteritems():
            callers = callers.copy()
            nc = 0
            for callcnt in callers.itervalues():
                nc += callcnt
            self.stats[func] = cc, nc, tt, ct, callers


    # 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, **kw):
        self.set_cmd(repr(func))
        sys.setprofile(self.dispatcher)
        try:
            return func(*args, **kw)
        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 receives 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.
    #
    # 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).  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()
Tip: Filter by directory path e.g. /media app.js to search for public/media/app.js.
Tip: Use camelCasing e.g. ProjME to search for ProjectModifiedEvent.java.
Tip: Filter by extension type e.g. /repo .js to search for all .js files in the /repo directory.
Tip: Separate your search with spaces e.g. /ssh pom.xml to search for src/ssh/pom.xml.
Tip: Use ↑ and ↓ arrow keys to navigate and return to view the file.
Tip: You can also navigate files with Ctrl+j (next) and Ctrl+k (previous) and view the file with Ctrl+o.
Tip: You can also navigate files with Alt+j (next) and Alt+k (previous) and view the file with Alt+o.