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提交 34e499c8 authored 作者: Frederic's avatar Frederic
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theano/compile/profiling.py
浏览文件 @ 34e499c8
......@@ -7,11 +7,11 @@
# TODO: ensure field width for string fields makes columns line up
# TODO: what to do about 'diff summary'? (ask Fred?)
#
__authors__ = "James Bergstra"
__reviewer__ = "Razvan Pascanu"
__authors__ = "James Bergstra"
__reviewer__ = "Razvan Pascanu"
__copyright__ = "(c) 2011, Universite de Montreal"
__license__ = "3-clause BSD License"
__contact__ = "theano-dev <theano-dev@googlegroups.com>"
__license__ = "3-clause BSD License"
__contact__ = "theano-dev <theano-dev@googlegroups.com>"
__docformat__ = "restructuredtext en"
import atexit
......@@ -22,7 +22,7 @@ import time
import numpy
import theano
from theano.configparser import AddConfigVar, StrParam, BoolParam
from theano.configparser import AddConfigVar, BoolParam
import_time = time.time()
config = theano.config
......@@ -48,6 +48,8 @@ def _atexit_print_fn():
cum = copy.copy(_atexit_print_list[0])
cum.message = "Sum of all printed profile at exit"
for ps in _atexit_print_list[1:]:
# for ps in [ps for ps in _atexit_print_list[1:]
# if not isinstance(ps, ScanProfileStats)]:
for attr in ["compile_time", "fct_call_time", "fct_callcount",
"vm_call_time", "optimizer_time", "linker_time"]:
setattr(cum, attr, getattr(cum, attr) + getattr(ps, attr))
......@@ -62,6 +64,7 @@ def _atexit_print_fn():
atexit.register(_atexit_print_fn)
class ProfileStats(object):
"""
Object to store runtime and memory profiling information for all of
......@@ -119,6 +122,7 @@ class ProfileStats(object):
# time spent linking graph (FunctionMaker.create)
line_width = 140
# param is called flag_time_thunks because most other attributes with time
# in the name are times *of* something, rather than configuration flags.
def __init__(self, atexit_print=True, flag_time_thunks=None, **kwargs):
......@@ -185,20 +189,27 @@ class ProfileStats(object):
"""dict op -> total number of flops"""
# timing is stored by node, we compute timing by Op on demand
rval = {}
return rval #TODO: continue here
return rval # TODO: continue here
for node, count in self.apply_callcount.items():
rval.setdefault(node.op, 0)
rval[node.op] += 1
return rval
for a,t in op_time.items():
if hasattr(a,'flops'):
op_flops[a]=a.flops*op_call[a]/t/1e6
for a, t in self.op_time.items():
if hasattr(a, 'flops'):
op_flops[a] = a.flops * op_call[a] / t / 1e6
flops_msg=''
flops_msg = ''
if op_flops:
flops_msg=' <MFlops/s>'
print '\nHACK WARNING: we print the flops for some OP, but the logic don\' always work. You need to know the internal of Theano to make it work correctly. Otherwise don\'t use!'
print '\nOp-wise summary: <%% of local_time spent on this kind of Op> <cumulative %%> <self seconds> <cumulative seconds> <time per call> %s <nb_call> <nb apply> <Op name>'%(flops_msg)
flops_msg = ' <MFlops/s>'
print ('\nHACK WARNING: we print the flops for some OP, but the'
' logic don\' always work. You need to know the internal'
' of Theano to make it work correctly.'
' Otherwise don\'t use!')
print ('\nOp-wise summary:'
' <%% of local_time spent on this kind of Op>'
' <cumulative %%> <self seconds> <cumulative seconds>'
' <time per call> %s <nb_call> <nb apply> <Op name>' % (
flops_msg))
def summary_ops(self, file=sys.stderr, N=None):
if self.apply_time:
......@@ -216,19 +227,21 @@ class ProfileStats(object):
op_impl = self.op_impl()
if N is None:
N = len(self.op_flops)
otimes = [(t*100/local_time,
otimes = [(t * 100 / local_time,
t,
op,
op_impl.get(op, ' '),
op_call.get(op, 0),
op_apply.get(op,0))
op_apply.get(op, 0))
for op, t in op_time.items()]
otimes.sort()
otimes.reverse()
tot=0
tot = 0
print >> file, 'Ops'
print >> file, '---'
#print >> file, '<% time> <cumulative %%> <apply time> <cumulative seconds> <time per call> <nb_call> <Op name>'
#print >> file, '<% time> <cumulative %%> <apply time>,'
#print >>file, '<cumulative seconds> <time per call> <nb_call>'
#print >>file, '<Op name>'
hs = []
# formatting string
es = []
......@@ -263,14 +276,15 @@ class ProfileStats(object):
print >> file, header_str
for f,t,a,impl,nb_call,nb_apply in otimes[:N]:
for f, t, a, impl, nb_call, nb_apply in otimes[:N]:
if nb_call == 0:
assert t == 0
continue
tot+=t
ftot=tot*100/local_time
print >> file, format_str%(f,ftot,t,t/nb_call, impl, nb_call,
nb_apply, str(a)[:maxlen])
tot += t
ftot = tot * 100 / local_time
print >> file, format_str % (f, ftot, t, t / nb_call,
impl, nb_call,
nb_apply, str(a)[:maxlen])
# While this carries over less information, it is arranged such
# that it way more readeable that the previous output of the
# profiler
......@@ -281,7 +295,7 @@ class ProfileStats(object):
# print >>file, ' %4.1f%% %5.1f%% %5.3fs %5.3fs %.2es %s %5d %2d %s' % (
# f, ftot, t, tot, t/nb_call, impl, nb_call, nb_apply, a)
print >>file, ' ... (remaining %i Ops account for %6.2f%%(%.2fs) of the runtime)'\
%(max(0, len(otimes)-N),
% (max(0, len(otimes) - N),
sum(f for f, t, a, ci, nb_call, nb_op in otimes[N:]),
sum(t for f, t, a, ci, nb_call, nb_op in otimes[N:]))
print >> file, ''
......@@ -333,7 +347,7 @@ class ProfileStats(object):
print >> file, header_str
atimes = [(
t*100/local_time,
t * 100 / local_time,
t,
a,
a.env.toposort().index(a),
......@@ -341,13 +355,13 @@ class ProfileStats(object):
for a, t in self.apply_time.items()]
atimes.sort()
atimes.reverse()
tot=0
tot = 0
for (f, t, a, nd_id, nb_call) in atimes[:N]:
tot+=t
ftot=tot*100/local_time
if nb_call==0:
tot += t
ftot = tot * 100 / local_time
if nb_call == 0:
continue
print >> file, format_str %(f,ftot, t, t/nb_call, nb_call,
print >> file, format_str %(f, ftot, t, t / nb_call, nb_call,
nd_id,
str(a)[:maxlen])
# Same as before, this I've sacrificied some information making
......@@ -355,7 +369,7 @@ class ProfileStats(object):
#print >> file, ' %4.1f%% %5.1f%% %5.3fs %5.3fs %.2es %i %s'%(
# f, ftot, t, tot, t/nb_call,nb_call, str(a))
print >> file, ' ... (remaining %i Apply instances account for %.2f%%(%.2fs) of the runtime)'\
%(max(0, len(atimes)-N),
% (max(0, len(atimes) - N),
sum(f for f, t, a, nd_id, nb_call in atimes[N:]),
sum(t for f, t, a, nd_id, nb_call in atimes[N:]))
print >> file, ''
......@@ -363,31 +377,34 @@ class ProfileStats(object):
def summary_function(self, file):
print >> file, 'Function profiling'
print >> file, '=================='
print >> file, ' Message: %s'%self.message
print >> file, ' Message: %s' % self.message
print >> file, ' Time in %i calls to Function.__call__: %es' % (
self.fct_callcount, self.fct_call_time)
if self.fct_call_time>0:
print >> file, ' Time in Function.fn.__call__: %es (%.3f%%)' %(
self.vm_call_time, 100*self.vm_call_time / self.fct_call_time)
if self.fct_call_time > 0:
print >> file, ' Time in Function.fn.__call__: %es (%.3f%%)' % (
self.vm_call_time,
100 * self.vm_call_time / self.fct_call_time)
local_time = sum(self.apply_time.values())
if local_time > 0:
print >> file, ' Time in thunks: %es (%.3f%%)' %(
print >> file, ' Time in thunks: %es (%.3f%%)' % (
local_time, 100*local_time / self.fct_call_time)
print >> file, ' Total compile time: %es' % self.compile_time
print >> file, ' Theano Optimizer time: %es' % self.optimizer_time
print >> file, ' Theano Linker time (includes C, CUDA code generation/compiling): %es' % self.linker_time
print >> file, (' Theano Linker time (includes C,'
' CUDA code generation/compiling): %es' %
self.linker_time)
print >> file, ''
def summary(self, file=sys.stderr, n_ops_to_print=20, n_applies_to_print=20):
def summary(self, file=sys.stderr, n_ops_to_print=20,
n_applies_to_print=20):
self.summary_function(file)
local_time = sum(self.apply_time.values())
if local_time > 0:
self.summary_ops(file, n_ops_to_print)
self.summary_nodes(file, n_applies_to_print)
else:
print >> file, " No node time accumulated (hint: try config profiling.time_thunks=1)"
print >> file, (" No node time accumulated "
"(hint: try config profiling.time_thunks=1)")
if 0: # old code still to be ported from ProfileMode
......@@ -404,47 +421,50 @@ if 0: # old code still to be ported from ProfileMode
print ''
print 'ProfileMode.long_print()'
print 'name = %s'%fct_name
print 'msg = %s'%message
print 'name = %s' % fct_name
print 'msg = %s' % message
print '---------------------------'
print ''
print 'Total time spent running thunks: %.3fs'% local_time
print 'Total time spent running thunks: %.3fs' % local_time
sop_time={}
sop_call={}
sop_time = {}
sop_call = {}
sop_op = {}
sop_c={} #map each op class to Bool. True iff all applies were done in c.
for a,t in op_time.items():
#map each op class to Bool. True iff all applies were done in c.
sop_c = {}
for a, t in op_time.items():
typ = type(a)
sop_time.setdefault(typ,0)
sop_time[typ]+=t
sop_op.setdefault(typ,0)
sop_op[typ]+=1
sop_c.setdefault(typ,True)
sop_c[typ]=sop_c[typ] and op_cimpl.get(a, False)
sop_call[typ]=sop_call.get(typ,0)+op_call[a]
sop_time.setdefault(typ, 0)
sop_time[typ] += t
sop_op.setdefault(typ, 0)
sop_op[typ] += 1
sop_c.setdefault(typ, True)
sop_c[typ] = sop_c[typ] and op_cimpl.get(a, False)
sop_call[typ] = sop_call.get(typ, 0) + op_call[a]
print '\nSingle Op-wise summary: <% of local_time spent on this kind of Op> <cumulative %%> <self seconds> <cumulative seconds> <time per call> <nb_call> <nb_op> <nb_op> <Op name>'
sotimes = [(t*100/local_time, t, a, sop_c[a], sop_call[a], sop_op[a]) for a, t in sop_time.items()]
sotimes = [(t * 100 / local_time, t, a, sop_c[a],
sop_call[a], sop_op[a]) for a, t in sop_time.items()]
sotimes.sort()
sotimes.reverse()
tot=0
for f,t,a,ci, nb_call, nb_op in sotimes[:n_ops_to_print]:
tot = 0
for f, t, a, ci, nb_call, nb_op in sotimes[:n_ops_to_print]:
if nb_call == 0:
assert t == 0
continue
tot+=t
ftot=tot*100/local_time
tot += t
ftot = tot * 100 / local_time
if ci:
msg = '*'
else:
msg = ' '
print ' %4.1f%% %5.1f%% %5.3fs %5.3fs %.2es %s %5d %2d %s' % (f, ftot, t, tot, t/nb_call, msg, nb_call, nb_op, a)
print ' ... (remaining %i Ops account for %.2f%%(%.2fs) of the runtime)'\
%(max(0, len(sotimes)-n_ops_to_print),
sum(f for f, t, a, ci, nb_call, nb_op in sotimes[n_ops_to_print:]),
sum(t for f, t, a, ci, nb_call, nb_op in sotimes[n_ops_to_print:]))
% (max(0, len(sotimes) - n_ops_to_print),
sum(f for f, t, a, ci, nb_call, nb_op in
sotimes[n_ops_to_print:]),
sum(t for f, t, a, ci, nb_call, nb_op in
sotimes[n_ops_to_print:]))
total_time = time.time() - import_time
total_fct_time = sum(fct_call_time.values())
......@@ -453,24 +473,32 @@ if 0: # old code still to be ported from ProfileMode
print
print 'Theano fct summary: <% total fct time> <total time> <time per call> <nb call> <fct name>'
for key in fct_call.keys():
if fct_call[key]>0:
print ' %4.1f%% %.3fs %.2es %d %s'%(fct_call_time[key]/total_fct_time*100 ,fct_call_time[key],
fct_call_time[key]/fct_call[key], fct_call[key], key.name)
if fct_call[key] > 0:
print ' %4.1f%% %.3fs %.2es %d %s'%(
fct_call_time[key] / total_fct_time * 100,
fct_call_time[key],
fct_call_time[key] / fct_call[key],
fct_call[key], key.name)
else:
print ' NOT CALLED',key.name
if total_fct_time>0:
time_pr_in_fct=local_time/total_fct_time*100
time_per_call=total_fct_time/total_fct_call
if total_fct_time > 0:
time_pr_in_fct = local_time / total_fct_time * 100
time_per_call = total_fct_time / total_fct_call
else:
time_pr_in_fct=0
time_per_call=0
time_pr_in_fct = 0
time_per_call = 0
print
print 'Time since import %.3fs'%(total_time)
print 'Compile time: %.3fs %.1f%%'%(compile_time, compile_time/total_time*100)
print 'Theano fct call %.3fs %.1f%%'%(total_fct_time,total_fct_time/total_time*100)
print ' Theano Op time (included in fct call, Time spent running thunks) %.3fs %.1f%%(of total) %.1f%%(of fct call)'% (local_time,local_time/total_time*100, time_pr_in_fct)
print 'Time since import %.3fs' % (total_time)
print 'Compile time: %.3fs %.1f%%' % (compile_time,
compile_time / total_time * 100)
print 'Theano fct call %.3fs %.1f%%' % (total_fct_time,
total_fct_time / total_time *
100)
print ' Theano Op time (included in fct call, Time spent running thunks) %.3fs %.1f%%(of total) %.1f%%(of fct call)' % (local_time,
local_time / total_time * 100,
time_pr_in_fct)
print 'Other time since import %.3fs %.1f%%'%(other_time,other_time/total_time*100)
print '%i Theano fct call, %.3fs per call'%(total_fct_call, time_per_call)
......@@ -479,13 +507,13 @@ if 0: # old code still to be ported from ProfileMode
print '<Apply> <Apply position> <fct name> <inputs type> <outputs type>'
for fct in fct_call.keys():
for idx, node in enumerate(fct.maker.env.toposort()):
if any(hasattr(i,'dtype') and i.dtype=='float64' for i in node.outputs) and not any(hasattr(i,'dtype') and i.dtype=='float64' for i in node.inputs):
if any(hasattr(i, 'dtype') and i.dtype == 'float64' for i in node.outputs) and not any(hasattr(i, 'dtype') and i.dtype == 'float64' for i in node.inputs):
print str(node), idx, fct.name, str([getattr(i,'dtype',None) for i in node.inputs]),str([getattr(i,'dtype',None) for i in node.outputs])
if any([x[2].__name__.startswith("Gpu") for x in sotimes]):
cpu=[]
gpu=[]
trans=[]
cpu = []
gpu = []
trans = []
for so in sotimes:
if so[2].__name__ in ["HostFromGpu", "GpuFromHost"]:
trans.append(so)
......@@ -493,9 +521,9 @@ if 0: # old code still to be ported from ProfileMode
gpu.append(so)
else:
cpu.append(so)
sum_cpu=sum(so[1] for so in cpu)
sum_gpu=sum(so[1] for so in gpu)
sum_trans=sum(so[1] for so in trans)
sum_cpu = sum(so[1] for so in cpu)
sum_gpu = sum(so[1] for so in gpu)
sum_trans = sum(so[1] for so in trans)
print
print "Spent %.3fs(%.3f%%) in cpu Op, %.3fs(%.3f%%) in gpu Op and %.3fs(%.3f%%) transfert Op"%(
......@@ -505,7 +533,7 @@ if 0: # old code still to be ported from ProfileMode
print "<fct name> <input name> <input type> <str input>"
for fct in fct_call.keys():
for i in fct.input_storage:
if hasattr(i.type, 'dtype') and i.type.dtype=='float64':
if hasattr(i.type, 'dtype') and i.type.dtype == 'float64':
print fct.name, i.name, i.type, i
if outputs_size:
......
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