Lib/profile.py

#
# Class for profiling python code. rev 1.0  6/2/94
#
# Based on prior profile module by Sjoerd Mullender...
#   which was hacked somewhat by: Guido van Rossum
#
# See profile.doc for more information


# Copyright 1994, by InfoSeek Corporation, all rights reserved.
# Written by James Roskind
# 
# Permission to use, copy, modify, and distribute this Python software
# and its associated documentation for any purpose (subject to the
# restriction in the following sentence) without fee is hereby granted,
# provided that the above copyright notice appears in all copies, and
# that both that copyright notice and this permission notice appear in
# supporting documentation, and that the name of InfoSeek not be used in
# advertising or publicity pertaining to distribution of the software
# without specific, written prior permission.  This permission is
# explicitly restricted to the copying and modification of the software
# to remain in Python, compiled Python, or other languages (such as C)
# wherein the modified or derived code is exclusively imported into a
# Python module.
# 
# INFOSEEK CORPORATION DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS
# SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
# FITNESS. IN NO EVENT SHALL INFOSEEK CORPORATION BE LIABLE FOR ANY
# SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER
# RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF
# CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN
# CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.



import sys
import os
import time
import string
import marshal


# Global variables
func_norm_dict = {}
func_norm_counter = 0
pid_string = `os.getpid()`


# Optimized intermodule references
ostimes = os.times


# Sample timer for use with 
#i_count = 0
#def integer_timer():
#	global i_count
#	i_count = i_count + 1
#	return i_count
#itimes = integer_timer # replace with C coded timer returning integers

#**************************************************************************
# The following are the static member functions for the profiler class
# Note that an instance of Profile() is *not* needed to call them.
#**************************************************************************


# simplified user interface
def run(statement, *args):
	prof = Profile()
	try:
		prof = prof.run(statement)
	except SystemExit:
		pass
	if args:
		prof.dump_stats(args[0])
	else:
		return prof.print_stats()

# print help
def help():
	for dirname in sys.path:
		fullname = os.path.join(dirname, 'profile.doc')
		if os.path.exists(fullname):
			sts = os.system('${PAGER-more} '+fullname)
			if sts: print '*** Pager exit status:', sts
			break
	else:
		print 'Sorry, can\'t find the help file "profile.doc"',
		print 'along the Python search path'


#**************************************************************************
# class Profile documentation:
#**************************************************************************
# self.cur is always a tuple.  Each such tuple corresponds to a stack
# frame that is currently active (self.cur[-2]).  The following are the
# definitions of its members.  We use this external "parallel stack" to
# avoid contaminating the program that we are profiling. (old profiler
# used to write into the frames local dictionary!!) Derived classes
# can change the definition of some entries, as long as they leave
# [-2:] intact.
#
# [ 0] = Time that needs to be charged to the parent frame's function.  It is
#        used so that a function call will not have to access the timing data
#        for the parents frame.
# [ 1] = Total time spent in this frame's function, excluding time in
#        subfunctions
# [ 2] = Cumulative time spent in this frame's function, including time in
#        all subfunctions to this frame.
# [-3] = Name of the function that corresonds to this frame.  
# [-2] = Actual frame that we correspond to (used to sync exception handling)
# [-1] = Our parent 6-tuple (corresonds to frame.f_back)
#**************************************************************************
# Timing data for each function is stored as a 5-tuple in the dictionary
# self.timings[].  The index is always the name stored in self.cur[4].
# The following are the definitions of the members:
#
# [0] = The number of times this function was called, not counting direct
#       or indirect recursion,
# [1] = Number of times this function appears on the stack, minus one
# [2] = Total time spent internal to this function
# [3] = Cumulative time that this function was present on the stack.  In
#       non-recursive functions, this is the total execution time from start
#       to finish of each invocation of a function, including time spent in
#       all subfunctions.
# [5] = A dictionary indicating for each function name, the number of times
#       it was called by us.
#**************************************************************************
# We produce function names via a repr() call on the f_code object during
# profiling. This save a *lot* of CPU time.  This results in a string that
# always looks like:
#   <code object main at 87090, file "/a/lib/python-local/myfib.py", line 76>
# After we "normalize it, it is a tuple of filename, line, function-name.
# We wait till we are done profiling to do the normalization.
# *IF* this repr format changes, then only the normalization routine should
# need to be fixed.
#**************************************************************************
class Profile:

	def __init__(self, *arg):
		self.timings = {}
		self.cur = None
		self.cmd = ""

		self.dispatch = {  \
			  'call'     : self.trace_dispatch_call, \
			  'return'   : self.trace_dispatch_return, \
			  'exception': self.trace_dispatch_exception, \
			  }

		if not arg:
			self.timer = os.times
			self.dispatcher = self.trace_dispatch
		else:
			self.timer = arg[0]
			t = self.timer() # test out timer function
			try:
				if len(t) == 2:
					self.dispatcher = self.trace_dispatch
				else:
					self.dispatcher = self.trace_dispatch_r
			except:
				self.dispatcher = self.trace_dispatch_i
		self.t = self.get_time()
		self.simulate_call('profiler')


	def get_time(self): # slow simulation of method to acquire time
		t = self.timer()
		if type(t) == type(()) or type(t) == type([]):
			t = reduce(lambda x,y: x+y, t, 0)
		return t
		

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

	def trace_dispatch(self, frame, event, arg):
		t = self.timer()
		t = t[0] + t[1] - self.t        # No Calibration constant
		# t = t[0] + t[1] - self.t - .00053 # Calibration constant

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



	# Dispatch routine for best timer program (return = scalar integer)

	def trace_dispatch_i(self, frame, event, arg):
		t = self.timer() - self.t # - 1 # Integer calibration constant
		if self.dispatch[event](frame,t):
			self.t = self.timer()
		else:
			self.t = self.timer() - t  # put back unrecorded delta
		return


	# SLOW generic dispatch rountine for timer returning lists of numbers

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

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


	def trace_dispatch_exception(self, frame, t):
		rt, rtt, rct, rfn, rframe, rcur = self.cur
		if (not rframe is frame) and rcur:
			return self.trace_dispatch_return(rframe, t)
		return 0


	def trace_dispatch_call(self, frame, t):
		fn = `frame.f_code` 

		# The following should be about the best approach, but
		# we would need a function that maps from id() back to
		# the actual code object.  
		#     fn = id(frame.f_code)
		# Note we would really use our own function, which would
		# return the code address, *and* bump the ref count.  We
		# would then fix up the normalize function to do the
		# actualy repr(fn) call.

		# The following is an interesting alternative
		# It doesn't do as good a job, and it doesn't run as
		# fast 'cause repr() is written in C, and this is Python.
		#fcode = frame.f_code
		#code = fcode.co_code
		#if ord(code[0]) == 127: #  == SET_LINENO
		#	# see "opcode.h" in the Python source
		#	fn = (fcode.co_filename, ord(code[1]) | \
		#		  ord(code[2]) << 8, fcode.co_name)
		#else:
		#	fn = (fcode.co_filename, 0, fcode.co_name)

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

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

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

		rt, rtt, rct, rfn, frame, rcur = self.cur
		rtt = rtt + t
		sft = rtt + rct

		pt, ptt, pct, pfn, pframe, pcur = rcur
		self.cur = pt, ptt+rt, pct+sft, pfn, pframe, pcur

		cc, ns, tt, ct, callers = self.timings[rfn]
		if not ns:
			ct = ct + sft
			cc = cc + 1
		if callers.has_key(pfn):
			callers[pfn] = callers[pfn] + 1  # hack: gather more
			# stats such as the amount of time added to ct courtesy
			# of this specific call, and the contribution to cc
			# courtesy of this call.
		else:
			callers[pfn] = 1
		self.timings[rfn] = cc, ns - 1, tt+rtt, ct, callers

		return 1

	# The next few function play with self.cmd. By carefully preloading
	# our paralell stack, we can force the profiled result to include
	# an arbitrary string as the name of the calling function.
	# We use self.cmd as that string, and the resulting stats look
	# very nice :-).

	def set_cmd(self, cmd):
		if self.cur[-1]: return   # already set
		self.cmd = cmd
		self.simulate_call(cmd)

	class fake_code:
		def __init__(self, filename, line, name):
			self.co_filename = filename
			self.co_line = line
			self.co_name = name
			self.co_code = '\0'  # anything but 127

		def __repr__(self):
			return (self.co_filename, self.co_line, self.co_name)

	class fake_frame:
		def __init__(self, code, prior):
			self.f_code = code
			self.f_back = prior
			
	def simulate_call(self, name):
		code = self.fake_code('profile', 0, name)
		if self.cur:
			pframe = self.cur[-2]
		else:
			pframe = None
		frame = self.fake_frame(code, pframe)
		a = self.dispatch['call'](frame, 0)
		return

	# collect stats from pending stack, including getting final
	# timings for self.cmd frame.
	
	def simulate_cmd_complete(self):   
		t = self.get_time() - self.t
		while self.cur[-1]:
			# We *can* cause assertion errors here if
			# dispatch_trace_return checks for a frame match!
			a = self.dispatch['return'](self.cur[-2], t)
			t = 0
		self.t = self.get_time() - t

	
	def print_stats(self):
		import pstats
		pstats.Stats(self).strip_dirs().sort_stats(-1). \
			  print_stats()

	def dump_stats(self, file):
		f = open(file, 'w')
		self.create_stats()
		marshal.dump(self.stats, f)
		f.close()

	def create_stats(self):
		self.simulate_cmd_complete()
		self.snapshot_stats()

	def snapshot_stats(self):
		self.stats = {}
		for func in self.timings.keys():
			cc, ns, tt, ct, callers = self.timings[func]
			nor_func = self.func_normalize(func)
			nor_callers = {}
			nc = 0
			for func_caller in callers.keys():
				nor_callers[self.func_normalize(func_caller)]=\
					  callers[func_caller]
				nc = nc + callers[func_caller]
			self.stats[nor_func] = cc, nc, tt, ct, nor_callers


	# Override the following function if you can figure out
	# a better name for the binary f_code entries.  I just normalize
	# them sequentially in a dictionary.  It would be nice if we could
	# *really* see the name of the underlying C code :-).  Sometimes
	#  you can figure out what-is-what by looking at caller and callee
	# lists (and knowing what your python code does).
	
	def func_normalize(self, func_name):
		global func_norm_dict
		global func_norm_counter
		global func_sequence_num

		if func_norm_dict.has_key(func_name):
			return func_norm_dict[func_name]
		if type(func_name) == type(""):
			long_name = string.split(func_name)
			file_name = long_name[6][1:-2]
			func = long_name[2]
			lineno = long_name[8][:-1]
			if '?' == func:   # Until I find out how to may 'em...
				file_name = 'python'
				func_norm_counter = func_norm_counter + 1
				func = pid_string + ".C." + `func_norm_counter`
			result =  file_name ,  string.atoi(lineno) , func
		else:
			result = func_name
		func_norm_dict[func_name] = result
		return result


	# The following two methods can be called by clients to use
	# a profiler to profile a statement, given as a string.
	
	def run(self, cmd):
		import __main__
		dict = __main__.__dict__
		self.runctx(cmd, dict, dict)
		return self
	
	def runctx(self, cmd, globals, locals):
		self.set_cmd(cmd)
		sys.setprofile(self.trace_dispatch)
		try:
			exec(cmd, globals, locals)
		finally:
			sys.setprofile(None)

	# This method is more useful to profile a single function call.
	def runcall(self, func, *args):
		self.set_cmd(`func`)
		sys.setprofile(self.trace_dispatch)
		try:
			apply(func, args)
		finally:
			sys.setprofile(None)
		return self


        #******************************************************************
	# The following calculates the overhead for using a profiler.  The
	# problem is that it takes a fair amount of time for the profiler
	# to stop the stopwatch (from the time it recieves an event).
	# Similarly, there is a delay from the time that the profiler
	# re-starts the stopwatch before the user's code really gets to
	# continue.  The following code tries to measure the difference on
	# a per-event basis. The result can the be placed in the
	# Profile.dispatch_event() routine for the given platform.  Note
	# that this difference is only significant if there are a lot of
	# events, and relatively little user code per event.  For example,
	# code with small functions will typically benefit from having the
	# profiler calibrated for the current platform.  This *could* be
	# done on the fly during init() time, but it is not worth the
	# effort.  Also note that if too large a value specified, then
	# execution time on some functions will actually appear as a
	# negative number.  It is *normal* for some functions (with very
	# low call counts) to have such negative stats, even if the
	# calibration figure is "correct." 
	#
	# One alternative to profile-time calibration adjustments (i.e.,
	# adding in the magic little delta during each event) is to track
	# more carefully the number of events (and cumulatively, the number
	# of events during sub functions) that are seen.  If this were
	# done, then the arithmetic could be done after the fact (i.e., at
	# display time).  Currintly, we track only call/return events.
	# These values can be deduced by examining the callees and callers
	# vectors for each functions.  Hence we *can* almost correct the
	# internal time figure at print time (note that we currently don't
	# track exception event processing counts).  Unfortunately, there
	# is currently no similar information for cumulative sub-function
	# time.  It would not be hard to "get all this info" at profiler
	# time.  Specifically, we would have to extend the tuples to keep
	# counts of this in each frame, and then extend the defs of timing
	# tuples to include the significant two figures. I'm a bit fearful
	# that this additional feature will slow the heavily optimized
	# event/time ratio (i.e., the profiler would run slower, fur a very
	# low "value added" feature.) 
	#
	# Plugging in the calibration constant doesn't slow down the
	# profiler very much, and the accuracy goes way up.
	#**************************************************************
	
        def calibrate(self, m):
		n = m
		s = self.timer()
		while n:
			self.simple()
			n = n - 1
		f = self.timer()
		my_simple = f[0]+f[1]-s[0]-s[1]
		#print "Simple =", my_simple,

		n = m
		s = self.timer()
		while n:
			self.instrumented()
			n = n - 1
		f = self.timer()
		my_inst = f[0]+f[1]-s[0]-s[1]
		# print "Instrumented =", my_inst
		avg_cost = (my_inst - my_simple)/m
		#print "Delta/call =", avg_cost, "(profiler fixup constant)"
		return avg_cost

	# simulate a program with no profiler activity
        def simple(self):      
		a = 1
		pass

	# simulate a program with call/return event processing
        def instrumented(self):
		a = 1
		self.profiler_simulation(a, a, a)

	# simulate an event processing activity (from user's perspective)
	def profiler_simulation(self, x, y, z):  
		t = self.timer()
		t = t[0] + t[1]
		self.ut = t



#****************************************************************************
# OldProfile class documentation
#****************************************************************************
#
# The following derived profiler simulates the old style profile, providing
# errant results on recursive functions. The reason for the usefulnes of this
# profiler is that it runs faster (i.e., less overhead).  It still creates
# all the caller stats, and is quite useful when there is *no* recursion
# in the user's code.
#
# This code also shows how easy it is to create a modified profiler.
#****************************************************************************
class OldProfile(Profile):
	def trace_dispatch_exception(self, frame, t):
		rt, rtt, rct, rfn, rframe, rcur = self.cur
		if rcur and not rframe is frame:
			return self.trace_dispatch_return(rframe, t)
		return 0

	def trace_dispatch_call(self, frame, t):
		fn = `frame.f_code`
		
		self.cur = (t, 0, 0, fn, frame, self.cur)
		if self.timings.has_key(fn):
			tt, ct, callers = self.timings[fn]
			self.timings[fn] = tt, ct, callers
		else:
			self.timings[fn] = 0, 0, {}
		return 1

	def trace_dispatch_return(self, frame, t):
		rt, rtt, rct, rfn, frame, rcur = self.cur
		rtt = rtt + t
		sft = rtt + rct

		pt, ptt, pct, pfn, pframe, pcur = rcur
		self.cur = pt, ptt+rt, pct+sft, pfn, pframe, pcur

		tt, ct, callers = self.timings[rfn]
		if callers.has_key(pfn):
			callers[pfn] = callers[pfn] + 1
		else:
			callers[pfn] = 1
		self.timings[rfn] = tt+rtt, ct + sft, callers

		return 1


	def snapshot_stats(self):
		self.stats = {}
		for func in self.timings.keys():
			tt, ct, callers = self.timings[func]
			nor_func = self.func_normalize(func)
			nor_callers = {}
			nc = 0
			for func_caller in callers.keys():
				nor_callers[self.func_normalize(func_caller)]=\
					  callers[func_caller]
				nc = nc + callers[func_caller]
			self.stats[nor_func] = nc, nc, tt, ct, nor_callers

		

#****************************************************************************
# HotProfile class documentation
#****************************************************************************
#
# This profiler is the fastest derived profile example.  It does not
# calculate caller-callee relationships, and does not calculate cumulative
# time under a function.  It only calculates time spent in a function, so
# it runs very quickly (re: very low overhead)
#****************************************************************************
class HotProfile(Profile):
	def trace_dispatch_exception(self, frame, t):
		rt, rtt, rfn, rframe, rcur = self.cur
		if rcur and not rframe is frame:
			return self.trace_dispatch_return(rframe, t)
		return 0

	def trace_dispatch_call(self, frame, t):
		self.cur = (t, 0, frame, self.cur)
		return 1

	def trace_dispatch_return(self, frame, t):
		rt, rtt, frame, rcur = self.cur

		rfn = `frame.f_code`

		pt, ptt, pframe, pcur = rcur
		self.cur = pt, ptt+rt, pframe, pcur

		if self.timings.has_key(rfn):
			nc, tt = self.timings[rfn]
			self.timings[rfn] = nc + 1, rt + rtt + tt
		else:
			self.timings[rfn] =      1, rt + rtt

		return 1


	def snapshot_stats(self):
		self.stats = {}
		for func in self.timings.keys():
			nc, tt = self.timings[func]
			nor_func = self.func_normalize(func)
			self.stats[nor_func] = nc, nc, tt, 0, {}

		

#****************************************************************************
def Stats(*args):
	print 'Report generating functions are in the "pstats" module\a'