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提交 c193af5c authored 作者: Frederic Bastien's avatar Frederic Bastien
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Remove ProfileMode

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theano/compile/mode.py
浏览文件 @ c193af5c
......@@ -391,7 +391,7 @@ def get_mode(orig_string):
default_mode_class):
return instantiated_default_mode
if string in ['Mode', 'ProfileMode', 'DebugMode', 'NanGuardMode']:
if string in ['Mode', 'DebugMode', 'NanGuardMode']:
if string == 'DebugMode':
# need to import later to break circular dependency.
from .debugmode import DebugMode
......@@ -403,9 +403,6 @@ def get_mode(orig_string):
# NanGuardMode use its own linker.
ret = NanGuardMode(True, True, True, optimizer=config.optimizer)
else:
# This might be required if the string is 'ProfileMode'
from .profilemode import ProfileMode # noqa
from .profilemode import prof_mode_instance_to_print
# TODO: Can't we look up the name and invoke it rather than using eval here?
ret = eval(string +
'(linker=config.linker, optimizer=config.optimizer)')
......@@ -424,11 +421,6 @@ def get_mode(orig_string):
ret = ret.requiring(*theano.config.optimizer_requiring.split(':'))
instantiated_default_mode = ret
# must tell python to print the summary at the end.
if string == 'ProfileMode':
# need to import later to break circular dependency.
prof_mode_instance_to_print.append(ret)
return ret
......
theano/compile/profilemode.py deleted 100644 → 0
浏览文件 @ ee4c4e21
from __future__ import absolute_import, print_function, division
import atexit
import copy
import os
import time
import warnings
import theano
from theano.gof.link import WrapLinker
from six import string_types, iteritems, itervalues
from theano.compile.mode import (Mode, register_mode,
predefined_modes, predefined_linkers,
predefined_optimizers)
from theano.configparser import config
from theano.compile.function_module import FunctionMaker
from .profiling import ProfileStats
run_cthunk = None # Will be imported only when needed.
import_time = time.time()
class Profile_Maker(FunctionMaker):
def create(self, input_storage=None, trustme=False, storage_map=None):
ret = super(Profile_Maker, self).create(input_storage, trustme,
storage_map)
if (hasattr(theano, 'sandbox') and
hasattr(theano.sandbox, 'cuda') and
theano.sandbox.cuda.cuda_enabled):
if os.environ.get('CUDA_LAUNCH_BLOCKING', '0') != '1':
raise Exception(
"You are running the Theano profiler with CUDA enabled."
" Theano GPU ops execution is asynchronous by default."
" So by default, the profile is useless."
" You must set the environment variable"
" CUDA_LAUNCH_BLOCKING to 1 to tell the CUDA driver to"
" synchronize the execution to get a meaningful profile.")
# create a function-specific storage container for profiling info
profile = ProfileStats(atexit_print=False)
self.mode.profile_stats[ret] = profile
ret.profile = profile
# initialize the timers
for i, node in enumerate(ret.maker.fgraph.toposort()):
profile.apply_time[node] = 0.0
# a thunk_group is a list of the thunks from each linker
# corresponding to the i'th position in the toposort.
assert len(ret.fn.thunk_groups[i]) == 1
profile.apply_cimpl[node] = hasattr(
ret.fn.thunk_groups[i][0],
'cthunk')
# Here we replace the linker function.
# This ugliness makes WrapLinker (an object that *generates*
# functions and is not function-specific) work with ProfileStats
# objects which are function-specific.
# capture old fn in closure. This is important since new_fn is about to
# take its place as ret.fn.
ret_fn = ret.fn
def new_fn():
self.mode.apply_time = self.mode.profile_stats[ret].apply_time
self.mode.variable_shape = \
self.mode.profile_stats[ret].variable_shape
ret_fn()
# delete the old apply_time variable
# because it doesn't mean the same thing anymore.
# This prevents old code from looking like it still works.
del self.mode.apply_time
del self.mode.variable_shape
ret.fn = new_fn
global run_cthunk
if run_cthunk is None and any(profile.apply_cimpl.values()):
# Lazy import to avoid compilation when importing theano.
from theano.gof.cutils import run_cthunk # noqa
warnings.warn(
"DEPRECATION WARNING: The ProfileMode is deprecated. "
"Use the Theano flags/parameter to theano.function "
"'profile=True' instead of 'mode=ProfileMode'")
return ret
class ProfileMode(Mode):
def __init__(self, linker=None, optimizer='default'):
if linker is None:
linker = config.linker
if optimizer is 'default':
optimizer = config.optimizer
message = ""
profile_stats = {}
self.__setstate__((linker,
optimizer,
message,
profile_stats))
def function_maker(self, i, o, m, *args, **kwargs):
"""
Return an instance of `Profiler_Maker` which init the count.
"""
assert m is self
return Profile_Maker(i, o, self, *args, **kwargs)
def __get_local_time(self):
rval = 0
for ps in itervalues(self.profile_stats):
rval += sum(ps.apply_time.values())
return rval
local_time = property(__get_local_time)
def __getstate__(self):
# print "__getstate__",self.provided_linker,self.provided_optimizer
return (self.provided_linker,
self.provided_optimizer,
self.message,
self.profile_stats)
def __setstate__(self, state):
linker, optimizer, message, profile_stats = state
self.message = message
self.profile_stats = profile_stats
def profile_thunk(i, node, th):
"""
Profile only the execution time.
"""
global run_cthunk
if hasattr(th, 'cthunk'):
t0 = time.time()
failure = run_cthunk(th.cthunk)
dt = time.time() - t0
if failure:
raise RuntimeError(
('A C Op raised an exception. ProfileMode cannot'
' tell you what it was though. Use a standard mode'
' such as FAST_RUN to correct the problem.'))
else:
t0 = time.time()
th()
dt = time.time() - t0
# Some Op are so fast that the time.time() resolution is
# insufficient to measure it. So we add an epsilon.
self.apply_time[node] += max(dt, 1e-14)
def profile_thunk2(i, node, th):
"""
Profile the execution time and the memory size.
"""
global run_cthunk
if hasattr(th, 'cthunk'):
t0 = time.time()
failure = run_cthunk(th.cthunk)
dt = time.time() - t0
if failure:
raise RuntimeError(
('A C Op raised an exception. ProfileMode cannot'
' tell you what it was though. Use a standard mode'
' such as FAST_RUN to correct the problem.'))
else:
t0 = time.time()
th()
dt = time.time() - t0
for var, data in zip(node.outputs, th.outputs):
sh = getattr(data[0], 'shape', 'input no shape')
self.variable_shape[var] = sh
self.apply_time[node] += max(dt, 1e-14)
self.provided_linker = linker
self.provided_optimizer = optimizer
if isinstance(linker, string_types) or linker is None:
linker = predefined_linkers[linker]
if not config.ProfileMode.profile_memory:
p_thunk = profile_thunk
else:
p_thunk = profile_thunk2
linker = WrapLinker([linker], p_thunk)
self.linker = linker
if isinstance(optimizer, string_types) or optimizer is None:
optimizer = predefined_optimizers[optimizer]
self._optimizer = optimizer
self.call_time = 0
self.fn_time = 0
def print_summary(self, **kwargs):
"""
Print 3 summaries that show where time is spent. The first shows
an Apply-wise summary, the second an Op-wise summary and the
third a type-Op-wise summary.
The Apply-wise summary prints the timing information for the
worst offending Apply nodes. This corresponds to individual Op
applications within your graph which take the longest to
execute (so if you use dot twice, you will see two entries
there).
The Op-wise summary prints the execution time of all Apply
nodes executing the same Op grouped together and the total
execution time per Op is shown (so if you use dot twice, you
will see only one entry there corresponding to the sum of the
time spent in each of them). If two Ops have different hash
value, they will be separate.
The type-Op-wise summary group the result by type of op. So
event if two Op have different hash value, they will be
merged.
There is an hack with the Op-wise summary. Go see it if you
want to know more.
Parameters
----------
kwargs
They are passed to print_summary_ expanded. Currently there is
n_apply_to_print, n_ops_to_print and min_memory_size that are
accepted.
"""
compile_time = sum([ps.compile_time for ps
in self.profile_stats.values()])
fct_call = dict([(fn, ps.fct_callcount)
for (fn, ps) in iteritems(self.profile_stats)])
fct_call_time = dict([(fn, ps.fct_call_time)
for (fn, ps) in iteritems(self.profile_stats)])
apply_time = {}
for fn, ps in iteritems(self.profile_stats):
for (i, node) in enumerate(fn.maker.fgraph.toposort()):
apply_time[(i, node)] = ps.apply_time[node]
for (i, n), t in iteritems(apply_time):
if t == 0:
print(i, n)
apply_cimpl = {}
for ps in itervalues(self.profile_stats):
apply_cimpl.update(ps.apply_cimpl)
message = self.message
variable_shape = {}
for ps in itervalues(self.profile_stats):
variable_shape.update(ps.variable_shape)
other_time = dict(
linker_time=sum(
[ps.linker_time for ps in self.profile_stats.values()]),
optimizer_time=sum(
[ps.optimizer_time for ps in self.profile_stats.values()]))
self.print_summary_("print_summary",
compile_time, fct_call_time, fct_call,
apply_time, apply_cimpl, message, variable_shape,
self.local_time, other_time,
**kwargs)
def print_diff_summary(self, other, **kwargs):
"""
As print_summary, but print the difference on two different
profile mode.
TODO: Also we don't print the Apply-wise summary as it don't
work for now.
TODO: make comparaison with gpu code.
Parameters
----------
other
The other instance of ProfileMode that we want to be compared to.
kwargs
They are passed to print_summary_ expanded.
Currently there is n_apply_to_print, n_ops_to_print and
min_memory_size that are accepted.
"""
def diff_dict(a_time, b_time_):
r = {}
b_time = copy.copy(b_time_)
for a, ta in iteritems(a_time):
r.setdefault(a, 0)
tb = b_time.pop(a, 0)
r[a] += ta - tb
# they are missing in a
for a, t in iteritems(b_time):
r.setdefault(a, 0)
r[a] += t
return r
compile_time = self.compile_time - other.compile_time
fct_call_time = diff_dict(self.fct_call_time, other.fct_call_time)
fct_call = diff_dict(self.fct_call, other.fct_call)
apply_time = diff_dict(self.apply_time, other.apply_time)
apply_cimpl = self.apply_cimpl and other.apply_cimpl
message = self.message
variable_shape = diff_dict(self.variable_shape, other.variable_shape)
self_linker_time = sum([ps.linker_time for ps
in self.profile_stats.values()])
other_linker_time = sum([ps.linker_time for ps
in other.profile_stats.values()])
self_optimizer_time = sum([ps.optimizer_time for ps
in self.profile_stats.values()])
other_optimizer_time = sum([ps.optimizer_time for ps
in other.profile_stats.values()])
other_time = {'linker_time': self_linker_time - other_linker_time,
'optimizer_time': self_optimizer_time -
other_optimizer_time}
self.print_summary_("print_diff_summary", compile_time,
fct_call_time, fct_call,
apply_time, apply_cimpl, message, variable_shape,
print_apply=False, other_time=other_time,
**kwargs)
@staticmethod
def print_summary_(fct_name, compile_time, fct_call_time, fct_call,
apply_time, apply_cimpl, message, variable_shape,
local_time, other_time,
n_apply_to_print=config.ProfileMode.n_apply_to_print,
n_ops_to_print=config.ProfileMode.n_ops_to_print,
print_apply=True,
min_memory_size=config.ProfileMode.min_memory_size,
):
"""
Do the actual printing of print_summary and print_diff_summary.
Parameters
----------
n_apply_to_print
The number of apply to print. Default 15.
n_ops_to_print
The number of ops to print. Default 20.
min_memory_size
Don't print memory profile of apply whose outputs memory size is
lower than that.
"""
print("ProfileMode is deprecated! Use the new profiler.")
print(" The Theano flags to enable it ise: profile=True")
print(" The Theano flags for the memory profile to it is: "
"profile_memory=True")
total_time = time.time() - import_time
total_fct_time = sum(fct_call_time.values())
total_fct_call = sum(fct_call.values())
unknown_time = total_time - total_fct_time - compile_time
overhead_time = total_fct_time - local_time
if total_fct_time > 0:
time_pr_in_fct = local_time / total_fct_time * 100
overhead_time_pourcent_fct_time = (overhead_time / total_fct_time *
100)
time_per_call = total_fct_time / total_fct_call
else:
time_pr_in_fct = 0
overhead_time_pourcent_fct_time = 0
time_per_call = 0
print()
print('ProfileMode.%s(%s)' % (fct_name, message))
print('---------------------------')
print()
print('Time since import %.3fs' % (total_time))
print('Theano compile time: %.3fs (%.1f%% since import)' %
(compile_time, compile_time / total_time * 100))
print(' Optimization time: %.3fs' % (other_time['optimizer_time']))
print(' Linker time: %.3fs' % (other_time['linker_time']))
print('Theano fct call %.3fs (%.1f%% since import)' %
(total_fct_time, total_fct_time / total_time * 100))
print(' Theano Op time %.3fs %.1f%%(since import) %.1f%%'
'(of fct call)' % (local_time, local_time / total_time * 100,
time_pr_in_fct))
print(' Theano function overhead in ProfileMode %.3fs %.1f%%'
'(since import) %.1f%%(of fct call)' % (
overhead_time, overhead_time / total_time * 100,
overhead_time_pourcent_fct_time))
print('%i Theano fct call, %.3fs per call' %
(total_fct_call, time_per_call))
print('Rest of the time since import %.3fs %.1f%%' %
(unknown_time, unknown_time / total_time * 100))
print()
print('Theano fct summary:')
print('<% total fct time> <total time> <time per call> <nb call> '
'<fct name>')
for key in fct_call:
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)
# Compute stats per op.
op_time = {}
op_call = {}
op_apply = {}
op_cimpl = {}
sop_apply = {}
for (i, a), t in iteritems(apply_time):
op = a.op
op_time.setdefault(op, 0)
op_call.setdefault(op, 0)
op_apply.setdefault(op, 0)
sop_apply.setdefault(type(a.op), 0)
op_time[op] += t
nb_call = [v for k, v in iteritems(fct_call)
if k.maker.fgraph is a.fgraph][0]
op_cimpl.setdefault(a.op, True)
op_cimpl[a.op] = op_cimpl[a.op] and apply_cimpl.get(a, False)
if t == 0:
assert nb_call == 0, nb_call
else:
op_call[op] += nb_call
op_apply[op] += 1
sop_apply[type(a.op)] += 1
# Compute stats per op class
sop_time = {}
sop_call = {}
sop_op = {}
# map each op class to Bool. True iff all applies were done in c.
sop_cimpl = {}
for a, t in iteritems(op_time):
typ = type(a)
sop_time.setdefault(typ, 0)
sop_time[typ] += t
sop_op.setdefault(typ, 0)
sop_op[typ] += 1
sop_cimpl.setdefault(typ, True)
sop_cimpl[typ] = sop_cimpl[typ] and op_cimpl.get(a, False)
sop_call[typ] = sop_call.get(typ, 0) + op_call[a]
# Print the summary per op class.
print()
print('Single Op-wise summary:')
print('<% of local_time spent on this kind of Op> <cumulative %> '
'<self seconds> <cumulative seconds> <time per call> [*] '
'<nb_call> <nb_op> <nb_apply> <Op name>')
sotimes = [(t * 100 / local_time, t, a, sop_cimpl[a], sop_call[a],
sop_op[a], sop_apply[a]) for a, t in iteritems(sop_time)]
sotimes.sort()
sotimes.reverse()
tot = 0
for f, t, a, ci, nb_call, nb_op, nb_apply in sotimes[:n_ops_to_print]:
if nb_call == 0:
assert t == 0
continue
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 '
'%2d %s' % (f, ftot, t, tot, t / nb_call, msg, nb_call,
nb_op, nb_apply, a))
print(' ... (remaining %i single Op account for %.2f%%(%.2fs) of '
'the runtime)' %
(max(0, len(sotimes) - n_ops_to_print),
sum(soinfo[0] for soinfo in sotimes[n_ops_to_print:]),
sum(soinfo[1] for soinfo in sotimes[n_ops_to_print:])))
print('(*) Op is running a c implementation')
# The summary per op
op_flops = {}
for a, t in iteritems(op_time):
if hasattr(a, 'flops'):
op_flops[a] = a.flops * op_call[a] / t / 1e6
flops_msg = ''
if op_flops:
flops_msg = ' <MFlops/s>'
print("\nHACK WARNING: we print the flops for some OP, but the "
"logic doesn't always work. You need to know the "
"internals of Theano to make it work correctly. "
"Otherwise don't use it!")
print()
print('Op-wise summary:')
print('<%% 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))
otimes = [(t * 100 / local_time, t, a, op_cimpl.get(a, 0),
op_call.get(a, 0), op_apply.get(a, 0))
for a, t in iteritems(op_time)]
otimes.sort()
otimes.reverse()
tot = 0
for f, t, a, ci, nb_call, nb_apply in otimes[:n_ops_to_print]:
if nb_call == 0:
assert t == 0
continue
tot += t
ftot = tot * 100 / local_time
if ci:
msg = '*'
else:
msg = ' '
if op_flops:
print(' %4.1f%% %5.1f%% %5.3fs %5.3fs %.2es %s %7.1f '
'%5d %2d %s' % (f, ftot, t, tot, t / nb_call, msg,
op_flops.get(a, -1), nb_call, nb_apply,
a))
else:
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_apply, a))
print(' ... (remaining %i Op account for %6.2f%%(%.2fs) of the '
'runtime)' %
(max(0, len(otimes) - n_ops_to_print),
sum(f for f, t, a, ci, nb_call, nb_op in
otimes[n_ops_to_print:]),
sum(t for f, t, a, ci, nb_call, nb_op in
otimes[n_ops_to_print:])))
print('(*) Op is running a c implementation')
if print_apply:
print()
print('Apply-wise summary:')
print('<% of local_time spent at this position> <cumulative %%> '
'<apply time> <cumulative seconds> <time per call> [*] '
'<nb_call> <Apply position> <Apply Op name>')
atimes = [(t * 100 / local_time, t, a,
[v for k, v in iteritems(fct_call)
if k.maker.fgraph is a[1].fgraph][0])
for a, t in iteritems(apply_time)]
atimes.sort()
atimes.reverse()
tot = 0
for f, t, a, nb_call in atimes[:n_apply_to_print]:
tot += t
ftot = tot * 100 / local_time
if nb_call == 0:
continue
if apply_cimpl.get(a[1], False):
msg = '*'
else:
msg = ' '
print(' %4.1f%% %5.1f%% %5.3fs %5.3fs %.2es %s %i '
'%2i %s' %
(f, ftot, t, tot, t / nb_call, msg, nb_call, a[0],
str(a[1])))
print(' ... (remaining %i Apply instances account for '
'%.2f%%(%.2fs) of the runtime)' %
(max(0, len(atimes) - n_apply_to_print),
sum(f for f, t, a, nb_call in atimes[n_apply_to_print:]),
sum(t for f, t, a, nb_call in atimes[n_apply_to_print:])))
print('(*) Op is running a c implementation')
for printer in profiler_printers:
printer(fct_name, compile_time, fct_call_time, fct_call,
apply_time, apply_cimpl, message, variable_shape,
other_time)
if not variable_shape:
print("\nProfile of Theano intermediate memory disabled. "
"To enable, set the Theano flag ProfileMode.profile_memory "
"to True.")
else:
print("""
The memory profile in ProfileMode is removed!
Use the new profiler. Use the Theano flags
profile=True,profile_memory=True to enable it.""")
print()
print("""Here are tips to potentially make your code run faster
(if you think of new ones, suggest them on the mailing list).
Test them first, as they are not guaranteed to always provide a speedup.""")
from theano import tensor as T
from theano.tensor.raw_random import RandomFunction
import theano
import theano.scalar as scal
scalar_op_amdlibm_no_speed_up = [scal.LT, scal.GT, scal.LE, scal.GE,
scal.EQ, scal.NEQ, scal.InRange,
scal.Switch, scal.OR, scal.XOR,
scal.AND, scal.Invert, scal.Maximum,
scal.Minimum, scal.Add, scal.Mul,
scal.Sub, scal.TrueDiv, scal.IntDiv,
scal.Clip, scal.Second, scal.Identity,
scal.Cast, scal.Sgn, scal.Neg,
scal.Inv, scal.Sqr]
scalar_op_amdlibm_speed_up = [scal.Mod, scal.Pow, scal.Ceil,
scal.Floor, scal.RoundHalfToEven,
scal.RoundHalfAwayFromZero, scal.Log,
scal.Log2, scal.Log10, scal.Log1p,
scal.Exp, scal.Sqrt, scal.Abs, scal.Cos,
scal.Sin, scal.Tan, scal.Tanh,
scal.Cosh, scal.Sinh,
T.nnet.sigm.ScalarSigmoid,
T.nnet.sigm.ScalarSoftplus]
def get_scalar_ops(s):
if isinstance(s, theano.scalar.Composite):
l = []
for node in s.fgraph.toposort():
l += get_scalar_ops(node.op)
return l
else:
return [s]
def list_scalar_op(op):
if isinstance(op.scalar_op, theano.scalar.Composite):
return get_scalar_ops(op.scalar_op)
else:
return [op.scalar_op]
def amdlibm_speed_up(op):
if not isinstance(op, T.Elemwise):
return False
else:
l = list_scalar_op(op)
for s_op in l:
if s_op.__class__ in scalar_op_amdlibm_speed_up:
return True
elif s_op.__class__ not in scalar_op_amdlibm_no_speed_up:
print("We don't know if amdlibm will accelerate "
"this scalar op.", s_op)
return False
def exp_float32_op(op):
if not isinstance(op, T.Elemwise):
return False
else:
l = list_scalar_op(op)
return any([s_op.__class__ in [scal.Exp] for s_op in l])
printed_tip = False
# tip 1
if config.floatX == 'float64':
print(" - Try the Theano flag floatX=float32")
printed_tip = True
# tip 2
if not config.lib.amdlibm and any([amdlibm_speed_up(a.op) for i, a
in apply_time]):
print(" - Try installing amdlibm and set the Theano flag "
"lib.amdlibm=True. This speeds up only some Elemwise "
"operation.")
printed_tip = True
# tip 3
if not config.lib.amdlibm and any([exp_float32_op(a.op) and
a.inputs[0].dtype == 'float32'
for i, a in apply_time]):
print(" - With the default gcc libm, exp in float32 is slower "
"than in float64! Try Theano flag floatX=float64, or "
"install amdlibm and set the theano flags lib.amdlibm=True")
printed_tip = True
# tip 4
for a, t in iteritems(apply_time):
node = a[1]
if (isinstance(node.op, T.Dot) and
all([len(i.type.broadcastable) == 2
for i in node.inputs])):
print(" - You have a dot operation that was not optimized to"
" dot22 (which is faster). Make sure the inputs are "
"float32 or float64, and are the same for both inputs. "
"Currently they are: %s" %
[i.type for i in node.inputs])
printed_tip = True
# tip 5
for a, t in iteritems(apply_time):
node = a[1]
if isinstance(node.op, RandomFunction):
printed_tip = True
print(" - Replace the default random number generator by "
"'from theano.sandbox.rng_mrg import MRG_RandomStreams "
"as RandomStreams', as this is is faster. It is still "
"experimental, but seems to work correctly.")
if config.device.startswith("gpu"):
print(" - MRG_RandomStreams is the only random number"
" generator supported on the GPU.")
break
# tip 6
import theano.sandbox.cuda as cuda
from theano.tensor.nnet import LogSoftmax
import theano.tensor.signal.pool as pool
import theano.gpuarray
for a, t in iteritems(apply_time):
node = a[1]
if (isinstance(node.op, pool.Pool)):
if (not cuda.dnn.dnn_available() and not theano.gpuarray.dnn.dnn_present()):
print("Install CuDNN to do pooling faster"
"this allows the operation to run on GPU")
if (isinstance(node.op, LogSoftmax)):
if (not cuda.dnn.dnn_available() and not theano.gpuarray.dnn.dnn_present()):
print("Install CuDNN to do LogSoftmax faster"
"this allows the operation to run on GPU")
if not printed_tip:
print(" Sorry, no tip for today.")
def clone(self, link_kwargs=None, optimizer="", message=None):
"""
Create a new instance of this Mode.
Keyword arguments can be provided for the linker, in which case its
`clone` method will be called with these arguments.
"""
new_linker = self.linker.clone(**link_kwargs)
new_optimizer = optimizer
if optimizer == "":
new_optimizer = self.provided_optimizer
new_mode = type(self)(linker=new_linker,
optimizer=new_optimizer)
# If self is in the list or profiles to print, then add the
# new one as well
if self in prof_mode_instance_to_print:
prof_mode_instance_to_print.append(new_mode)
if message:
new_mode.message = message
return new_mode
register_mode('PROFILE_MODE', ProfileMode())
# needed to print the profile at the end automatically
prof_mode_instance_to_print = [predefined_modes["PROFILE_MODE"]]
def atexit_print_default_profile_mode():
"""
Print the summary of the predefined mode ProfileMode if used.
This all to have the summary printed at exit when config.mode=ProfileMode.
"""
for prof_mode in prof_mode_instance_to_print:
if prof_mode.local_time > 0:
prof_mode.print_summary()
# Register atexit_print_default_profile_mode to have the summary of the
# predefined mode ProfileMode if it is used printed when the program terminate.
atexit.register(atexit_print_default_profile_mode)
# Here we define an hook that allow to print extra profiling information
profiler_printers = []
def register_profiler_printer(fct):
profiler_printers.append(fct)
return fct
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