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#! /usr/bin/env python # # Class for profiling python code. rev 1.0 6/2/94 # # Written by James Roskind # Based on prior profile module by Sjoerd Mullender... # which was hacked somewhat by: Guido van Rossum """Class for profiling Python code.""" # Copyright Disney Enterprises, Inc. All Rights Reserved. # Licensed to PSF under a Contributor Agreement # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, # either express or implied. See the License for the specific language # governing permissions and limitations under the License. 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, sort=-1): """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(sort) # Backwards compatibility. def help(): print "Documentation for the profile module can be found " print "in the Python Library Reference, section 'The Python Profiler'." 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 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 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_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(args) > 0: progname = args[0] sys.path.insert(0, os.path.dirname(progname)) with open(progname, 'rb') as fp: code = compile(fp.read(), progname, 'exec') globs = { '__file__': progname, '__name__': '__main__', '__package__': None, } runctx(code, globs, None, 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()
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