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提交 dbefcd29 authored 作者: memimo's avatar memimo
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remove if block

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theano/compile/profiling.py
浏览文件 @ dbefcd29
......@@ -1395,320 +1395,6 @@ class ProfileStats(object):
print(" Sorry, no tip for today.", file = file)
if False: # old code still to be ported from ProfileMode
def long_print(self, file=sys.stderr, fct_name=None, message=None,
n_apply_to_print=15, n_ops_to_print=20, print_apply=False):
"""
Print a readable summary of the stats.
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.
"""
local_time = sum(self.apply_time.values())
print('')
print('ProfileMode.long_print()')
print('name = %s' % fct_name)
print('msg = %s' % message)
print('---------------------------')
print('')
print('Total time spent running thunks: %.3fs' % local_time)
sop_time = {}
sop_call = {}
sop_op = {}
# map each op class to Bool. True iff all applies were done in c.
sop_c = {}
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_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 iteritems(sop_time)]
sotimes.sort(key=lambda t: (t[1], t[4], t[5]), reverse=True)
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
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:])))
total_time = time.time() - theano_imported_time
total_fct_time = sum(fct_call_time.values())
total_fct_call = sum(fct_call.values())
other_time = total_time - total_fct_time - compile_time
print()
print('Theano fct summary: <% 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)
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
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('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))
print()
print("List of apply that don't have float64 as input but have float64 in outputs. Usefull to know if we forgot some cast when using floatX=float32 or gpu code.")
print('<Apply> <Apply position> <fct name> <inputs type> <outputs type>')
for fct in fct_call:
for idx, node in enumerate(fct.maker.fgraph.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):
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 = []
for so in sotimes:
if so[2].__name__ in ["HostFromGpu", "GpuFromHost"]:
trans.append(so)
elif so[2].__name__.startswith("Gpu"):
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)
print()
print("Spent %.3fs(%.3f%%) in cpu Op, %.3fs(%.3f%%) in gpu Op and %.3fs(%.3f%%) transfert Op" % (
sum_cpu, sum_cpu / local_time * 100, sum_gpu, sum_gpu / local_time * 100, sum_trans, sum_trans / local_time * 100))
print("Theano function input that are float64")
print("<fct name> <input name> <input type> <str input>")
for fct in fct_call:
for i in fct.input_storage:
if hasattr(i.type, 'dtype') and i.type.dtype == 'float64':
print(fct.name, i.name, i.type, i)
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.First, 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] # Abs, Mod in float{32,64} only
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])
# tip 1
if config.floatX == 'float64':
print(" - Try the Theano flag floatX=float32")
# 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 speed up only some Elemwise operation.")
# 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 flags floatX=float64 or install amdlibm and set the theano flags lib.amdlibm=True")
# tip 4
for a, t in iteritems(apply_time):
node = a
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 that 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]))
# tip 5
for a, t in iteritems(apply_time):
node = a
if isinstance(node.op, RandomFunction):
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 seams to work correctly.")
if config.device.startswith("gpu"):
print (" - MRG_RandomStreams is the only random number"
" supported on the GPU.")
break
def print_summary(self,
n_apply_to_print=config.ProfileMode.n_apply_to_print,
n_ops_to_print=config.ProfileMode.n_ops_to_print):
"""
Print 3 summaries that show where the time is spent. The first shows an
Apply-wise summary, the second shows an Op-wise summary, the third
shows an type-Op-wise summary.
The Apply-wise summary print 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 print the execution time of all Apply nodes
executing the same Op are 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 Op 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 a hack with the Op-wise summary. Go see it if you want to know
more.
Parameters
----------
n_apply_to_print
The number of apply to print. Default 15, or n_ops_to_print flag.
n_ops_to_print
The number of ops to print. Default 20, or n_apply_to_print flag.
"""
fct_call_time = self.mode.fct_call_time
fct_call = self.mode.fct_call
apply_time = self.apply_time
op_cimpl = self.op_cimpl
message = self.message
outputs_size = self.outputs_size
self.print_summary_("print_summary",
None,
None,
None,
apply_time,
op_cimpl,
message,
outputs_size,
n_apply_to_print,
n_ops_to_print)
def print_diff_summary(self, other, n_apply_to_print=15,
n_ops_to_print=20):
"""
As print_summary, but print the difference on two different profile
mode.
TODO: Also we don't print the Apply-wise summary as it doesn't work for
now.
TODO: make comparaison with gpu code.
Parameters
----------
other
The other instance of ProfileMode that we want to be compared to.
n_apply_to_print
The number of apply to print. Default 15.
n_ops_to_print
The number of ops to print. Default 20.
"""
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)
op_cimpl = self.op_cimpl and other.op_cimpl
message = self.message
outputs_size = diff_dict(self.outputs_size, other.outputs_size)
self.print_summary_(
"print_diff_summary", compile_time, fct_call_time, fct_call,
apply_time, op_cimpl, message, outputs_size,
n_apply_to_print=n_apply_to_print,
n_ops_to_print=n_ops_to_print, print_apply=False)
class ScanProfileStats(ProfileStats):
callcount = 0.0
nbsteps = 0.0
......
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