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提交 420a9b83 authored 作者: Pascal Lamblin's avatar Pascal Lamblin 提交者： GitHub
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Merge pull request #4376 from adbrebs/fix_stack_check_nnet

[WIP] Use check_stack_trace helper function in tensor/nnet/tests/
上级 b3f54e3a 8be3b304
隐藏空白字符变更
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正在显示 包含 133 行增加 和 86 行删除
theano/tensor/nnet/nnet.py
浏览文件 @ 420a9b83
......@@ -905,9 +905,11 @@ def softmax_simplifier(numerators, denominators):
matching_denom = denominator
break
if matching_denom:
softmax = softmax_op(x)
copy_stack_trace(numerator, softmax)
numerators.remove(numerator)
denominators.remove(matching_denom)
numerators.append(softmax_op(x))
numerators.append(softmax)
return numerators, denominators
opt.local_mul_canonizer.add_simplifier(softmax_simplifier, 'softmax_simplifier')
......
theano/tensor/nnet/sigm.py
浏览文件 @ 420a9b83
......@@ -612,6 +612,7 @@ def local_exp_over_1_plus_exp(node):
else:
# case: 1/(1+exp(x))
sigmoids.append(sigmoid(-t))
copy_stack_trace(node.outputs[0], sigmoids[-1])
if not sigmoids: # we didn't find any. abort
return
......@@ -625,12 +626,17 @@ def local_exp_over_1_plus_exp(node):
if num_neg ^ denom_neg:
new_num = -new_num
copy_stack_trace(num, new_num)
if len(denom_rest) == 0:
return [new_num]
elif len(denom_rest) == 1:
return [new_num / denom_rest[0]]
out = new_num / denom_rest[0]
else:
return [new_num / tensor.mul(*denom_rest)]
out = new_num / tensor.mul(*denom_rest)
copy_stack_trace(node.outputs[0], out)
return [out]
def parse_mul_tree(root):
......@@ -923,6 +929,7 @@ def local_sigm_times_exp(node):
exp(x) * sigm(-x) -> sigm(x)
exp(-x) * sigm(x) -> sigm(-x)
todo: add stack traces to the intermediate variables
"""
# Bail early if it is not a multiplication.
if node.op != tensor.mul:
......
theano/tensor/nnet/tests/test_abstract_conv.py
浏览文件 @ 420a9b83
......@@ -7,6 +7,7 @@ from nose.tools import assert_raises
import theano
from theano import tensor
from theano.gof.opt import check_stack_trace
from theano.tests import unittest_tools as utt
from theano.tensor.nnet import corr, abstract_conv as conv
from theano.tensor.nnet.abstract_conv import get_conv_output_shape
......@@ -98,7 +99,7 @@ class BaseTestConv2d(unittest.TestCase):
def run_fwd(self, inputs_shape, filters_shape, ref=conv_corr,
subsample=(1, 1), verify_grad=True, mode=None,
border_mode='valid', filter_flip=True, provide_shape=False,
target_op=None):
target_op=None, check_trace=False):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
......@@ -133,8 +134,9 @@ class BaseTestConv2d(unittest.TestCase):
if target_op is not None:
assert any([isinstance(n.op, target_op) for n
in f.maker.fgraph.toposort()])
if check_trace:
self.assertTrue(check_stack_trace(f, ops_to_check=target_op))
self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
res_ref = numpy.array(f_ref())
res = numpy.array(f())
utt.assert_allclose(res_ref, res)
......@@ -148,7 +150,7 @@ class BaseTestConv2d(unittest.TestCase):
def run_gradweight(self, inputs_shape, filters_shape, output_shape,
ref=conv_corr_gw, subsample=(1, 1), filter_flip=True,
verify_grad=True, mode=None, border_mode='valid',
provide_shape=False, target_op=None):
provide_shape=False, target_op=None, check_trace=False):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
output_val = numpy.random.random(output_shape).astype('float32')
......@@ -177,12 +179,13 @@ class BaseTestConv2d(unittest.TestCase):
subsample=subsample,
conv_mode=conv_mode)
f = theano.function([], c, mode=mode)
self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
f_ref = theano.function([], c_ref, mode='FAST_RUN')
if target_op is not None:
assert any([isinstance(n.op, target_op) for n
in f.maker.fgraph.toposort()])
if check_trace:
self.assertTrue(check_stack_trace(f, ops_to_check=target_op))
res_ref = numpy.array(f_ref())
res = numpy.array(f())
......@@ -201,7 +204,7 @@ class BaseTestConv2d(unittest.TestCase):
def run_gradinput(self, inputs_shape, filters_shape, output_shape,
ref=conv_corr_gi, subsample=(1, 1), filter_flip=True,
verify_grad=True, mode=None, border_mode='valid',
provide_shape=False, target_op=None):
provide_shape=False, target_op=None, check_trace=False):
output_val = numpy.random.random(output_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
......@@ -227,12 +230,13 @@ class BaseTestConv2d(unittest.TestCase):
border_mode=border_mode, subsample=subsample,
conv_mode=conv_mode)
f = theano.function([], c, mode=mode)
self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
f_ref = theano.function([], c_ref, mode='FAST_RUN')
if target_op is not None:
assert any([isinstance(n.op, target_op) for n
in f.maker.fgraph.toposort()])
if check_trace:
self.assertTrue(check_stack_trace(f, ops_to_check=target_op))
res_ref = numpy.array(f_ref())
res = numpy.array(f())
......@@ -291,15 +295,18 @@ class TestCorrConv2d(BaseTestConv2d):
raise SkipTest("Need blas to test conv2d")
self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
verify_grad=True, provide_shape=provide_shape,
border_mode=b, filter_flip=flip, target_op=CorrMM)
border_mode=b, filter_flip=flip, target_op=CorrMM,
check_trace=True)
self.run_gradweight(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s, verify_grad=True,
provide_shape=provide_shape, border_mode=b,
filter_flip=flip, target_op=CorrMM_gradWeights)
filter_flip=flip, target_op=CorrMM_gradWeights,
check_trace=True)
self.run_gradinput(inputs_shape=i, filters_shape=f,
output_shape=o, subsample=s, verify_grad=True,
provide_shape=provide_shape, border_mode=b,
filter_flip=flip, target_op=CorrMM_gradInputs)
filter_flip=flip, target_op=CorrMM_gradInputs,
check_trace=True)
class TestCpuConv2d(BaseTestConv2d):
......@@ -343,7 +350,8 @@ class TestCpuConv2d(BaseTestConv2d):
self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
verify_grad=(gradweight_OK and gradinput_OK),
mode=mode, provide_shape=provide_shape,
border_mode=b, filter_flip=flip, target_op=ConvOp)
border_mode=b, filter_flip=flip, target_op=ConvOp,
check_trace=True)
else:
self.assertRaises(AssertionError,
self.run_fwd,
......@@ -354,7 +362,8 @@ class TestCpuConv2d(BaseTestConv2d):
mode=mode,
provide_shape=provide_shape,
border_mode=b,
filter_flip=flip)
filter_flip=flip,
check_trace=True)
if gradweight_OK:
if not theano.config.blas.ldflags:
......@@ -364,7 +373,8 @@ class TestCpuConv2d(BaseTestConv2d):
verify_grad=False, mode=mode,
provide_shape=provide_shape, border_mode=b,
filter_flip=flip,
target_op=(ConvOp, ConvGrad3D))
target_op=(ConvOp, ConvGrad3D),
check_trace=True)
else:
self.assertRaises(AssertionError,
self.run_gradweight,
......@@ -376,7 +386,8 @@ class TestCpuConv2d(BaseTestConv2d):
mode=mode,
provide_shape=provide_shape,
border_mode=b,
filter_flip=flip)
filter_flip=flip,
check_trace=True)
if gradinput_OK:
if not theano.config.blas.ldflags:
......@@ -386,7 +397,8 @@ class TestCpuConv2d(BaseTestConv2d):
verify_grad=False, mode=mode,
provide_shape=provide_shape, border_mode=b,
filter_flip=flip,
target_op=(ConvOp, ConvTransp3D))
target_op=(ConvOp, ConvTransp3D),
check_trace=True)
else:
self.assertRaises(AssertionError,
self.run_gradinput,
......@@ -398,7 +410,8 @@ class TestCpuConv2d(BaseTestConv2d):
mode=mode,
provide_shape=provide_shape,
border_mode=b,
filter_flip=flip)
filter_flip=flip,
check_trace=True)
def test_constant_shapes():
......
theano/tensor/nnet/tests/test_conv3d2d.py
浏览文件 @ 420a9b83
......@@ -10,6 +10,7 @@ except ImportError:
from six.moves import xrange
import theano
from theano.gof.opt import check_stack_trace
from theano.tensor.nnet.conv3d2d import *
import theano.tests.unittest_tools as utt
......@@ -73,10 +74,11 @@ def pyconv3d(signals, filters):
r_i += o_i[Tf2:o_i_sh0-Tf2, Hf2:-Hf2, Wf2:-Wf2]
return rval
def check_diagonal_subtensor_view_traces(fn):
for apply_node in fn.maker.fgraph.apply_nodes:
if isinstance(apply_node.op, (DiagonalSubtensor, IncDiagonalSubtensor)):
assert hasattr(apply_node.outputs[0].tag, 'trace')
assert check_stack_trace(
fn, ops_to_check=(DiagonalSubtensor, IncDiagonalSubtensor))
def test_conv3d(mode=mode_without_gpu, shared=theano.tensor._shared):
if ndimage is None:
......@@ -150,7 +152,6 @@ def test_conv3d(mode=mode_without_gpu, shared=theano.tensor._shared):
newconv3d = theano.function([], [],
updates={s_output: out},
mode=mode)
check_diagonal_subtensor_view_traces(newconv3d)
t0 = time.time()
newconv3d()
......@@ -162,7 +163,6 @@ def test_conv3d(mode=mode_without_gpu, shared=theano.tensor._shared):
(s_signals, gsignals)],
mode=mode,
name='grad')
check_diagonal_subtensor_view_traces(gnewconv3d)
t0 = time.time()
gnewconv3d()
......
theano/tensor/nnet/tests/test_nnet.py
浏览文件 @ 420a9b83
......@@ -10,6 +10,7 @@ from theano import config
from theano import tensor as T
from theano import tensor
from theano import gof
from theano.gof.opt import check_stack_trace
from theano.tests import unittest_tools as utt
from theano import printing
from theano.tensor.nnet import (categorical_crossentropy,
......@@ -150,8 +151,7 @@ class T_SoftmaxWithBias(utt.InferShapeTester):
b = theano.shared(numpy.float32(numpy.random.randn()))
sm = T.nnet.softmax(a + b)
f = theano.function([], sm)
self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
print('f.maker.fgraph.outputs[0]: {0}'.format(f.maker.fgraph.outputs[0], ))
assert check_stack_trace(f, ops_to_check='last')
def test_infer_shape(self):
admat = matrix()
......@@ -256,9 +256,10 @@ class T_LogSoftmax(utt.InferShapeTester):
sm = tensor.nnet.softmax(x)
logsm = tensor.log(sm)
f = theano.function([x], logsm)
self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
assert isinstance(f.maker.fgraph.outputs[0].owner.op,
theano.tensor.nnet.nnet.LogSoftmax)
assert check_stack_trace(
f, ops_to_check=theano.tensor.nnet.nnet.LogSoftmax)
def test_local_softmax_grad_optimization_and_big_input(self):
"""Test the Logsoftmax's grad substitution.
......@@ -272,7 +273,8 @@ class T_LogSoftmax(utt.InferShapeTester):
m.check_isfinite = False
# some inputs that are large to make the gradient explode in the non
# optimized case
a = numpy.exp(10 * numpy.random.rand(5, 10).astype(theano.config.floatX))
a = numpy.exp(
10 * numpy.random.rand(5, 10).astype(theano.config.floatX))
def myfunc(x):
sm = tensor.nnet.softmax(x)
......@@ -280,8 +282,9 @@ class T_LogSoftmax(utt.InferShapeTester):
return logsm
# We set step to 0.1 because for big values we need a big epsilon
utt.verify_grad(myfunc, [a], eps=0.1, mode=m)
f = theano.function([], myfunc(a))
self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
sa = theano.shared(a)
f = theano.function([], myfunc(sa))
self.assertTrue(check_stack_trace(f, ops_to_check='all'))
class T_SoftmaxGrad(utt.InferShapeTester):
......@@ -659,7 +662,9 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
fgraph = gof.FunctionGraph(
[x, one_of_n],
[g_x])
self.assertTrue(hasattr(fgraph.outputs[0].tag, 'trace'))
assert check_stack_trace(
fgraph, ops_to_check=[crossentropy_softmax_1hot_with_bias_dx,
softmax_op])
# print 'BEFORE'
# for node in fgraph.toposort():
......@@ -755,7 +760,9 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
for expr in expressions:
# Verify the optimizer worked on the expressions
f = theano.function([x, y], expr, mode=mode)
self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
# todo: only the first output of the op has a stack trace
# assert check_stack_trace(
# f, ops_to_check=crossentropy_softmax_argmax_1hot_with_bias)
if verbose:
theano.printing.debugprint(f)
try:
......@@ -771,7 +778,9 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
# Also verify the gradient wrt x
g = theano.function([x, y], T.grad(expr, x), mode=mode)
self.assertTrue(hasattr(g.maker.fgraph.outputs[0].tag, 'trace'))
assert check_stack_trace(
g, ops_to_check=[crossentropy_softmax_1hot_with_bias_dx,
softmax_op])
if verbose:
theano.printing.debugprint(g)
try:
......@@ -794,7 +803,9 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
for expr in bias_expressions:
f = theano.function([x, b, y], expr, mode=mode)
self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
# todo: only the first output of the op has a stack trace
# assert check_stack_trace(
# f, ops_to_check=crossentropy_softmax_argmax_1hot_with_bias)
if verbose:
theano.printing.debugprint(f)
try:
......@@ -806,7 +817,9 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
theano.printing.debugprint(f)
raise
g = theano.function([x, b, y], T.grad(expr, x), mode=mode)
self.assertTrue(hasattr(g.maker.fgraph.outputs[0].tag, 'trace'))
assert check_stack_trace(
g, ops_to_check=[crossentropy_softmax_1hot_with_bias_dx,
softmax_with_bias])
if verbose:
theano.printing.debugprint(g)
try:
......@@ -829,7 +842,9 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
for expr in mean_expressions:
f = theano.function([x, y], expr, mode=mode)
self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
# todo: only the first output of the op has a stack trace
# assert check_stack_trace(
# f, ops_to_check=[crossentropy_softmax_argmax_1hot_with_bias])
if verbose:
theano.printing.debugprint(f)
try:
......@@ -844,7 +859,9 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
raise
g = theano.function([x, y], T.grad(expr, x), mode=mode)
self.assertTrue(hasattr(g.maker.fgraph.outputs[0].tag, 'trace'))
assert check_stack_trace(
g, ops_to_check=[crossentropy_softmax_1hot_with_bias_dx,
softmax_op])
if verbose:
theano.printing.debugprint(g)
try:
......@@ -868,7 +885,9 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
for expr in mean_bias_expressions:
f = theano.function([x, b, y], expr, mode=mode)
self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
# todo: only the first output of the op has a stack trace
# assert check_stack_trace(
# f, ops_to_check=crossentropy_softmax_argmax_1hot_with_bias)
if verbose:
theano.printing.debugprint(f)
try:
......@@ -881,7 +900,9 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
theano.printing.debugprint(f)
raise
g = theano.function([x, b, y], T.grad(expr, x), mode=mode)
self.assertTrue(hasattr(g.maker.fgraph.outputs[0].tag, 'trace'))
assert check_stack_trace(
g, ops_to_check=[crossentropy_softmax_1hot_with_bias_dx,
softmax_with_bias])
if verbose:
theano.printing.debugprint(g)
try:
......@@ -1287,6 +1308,8 @@ def test_argmax_pushdown():
assert len(fgraph.toposort()) == 2 # an output_guard is second
assert fgraph.toposort()[0].op == tensor.basic._max_and_argmax
assert str(fgraph.toposort()[1].op) == 'OutputGuard'
assert check_stack_trace(
fgraph, ops_to_check=tensor.basic._max_and_argmax)
x = tensor.matrix()
# test that the max_and_argmax is not pushed down if the max is used
out = tensor.max_and_argmax(
......@@ -1295,7 +1318,6 @@ def test_argmax_pushdown():
fgraph = gof.FunctionGraph(
[x],
[out])
assert hasattr(fgraph.outputs[0].tag, 'trace')
backup = config.warn.argmax_pushdown_bug
config.warn.argmax_pushdown_bug = False
......@@ -1324,8 +1346,6 @@ def test_argmax_pushdown_bias():
fgraph = gof.FunctionGraph(
[x, b],
[out])
f = theano.function([x, b], out)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
theano.compile.mode.optdb.query(
theano.compile.mode.OPT_FAST_RUN).optimize(fgraph)
......@@ -1333,11 +1353,12 @@ def test_argmax_pushdown_bias():
# print 'AFTER'
# for node in fgraph.toposort():
# print node.op
types_to_check = (tensor.DimShuffle, tensor.Elemwise, tensor.MaxAndArgmax)
assert len(fgraph.toposort()) == 4
assert isinstance(fgraph.toposort()[0].op, tensor.DimShuffle)
assert isinstance(fgraph.toposort()[1].op, tensor.Elemwise)
assert isinstance(fgraph.toposort()[2].op, tensor.MaxAndArgmax)
for i, type in enumerate(types_to_check):
assert isinstance(fgraph.toposort()[i].op, type)
assert str(fgraph.toposort()[3].op) == 'OutputGuard'
assert check_stack_trace(fgraph, ops_to_check=types_to_check)
x = tensor.matrix()
b = tensor.vector()
......@@ -1345,8 +1366,6 @@ def test_argmax_pushdown_bias():
fgraph = gof.FunctionGraph(
[x, b],
[out])
f = theano.function([x, b], out)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
backup = config.warn.argmax_pushdown_bug
config.warn.argmax_pushdown_bug = False
......@@ -1364,6 +1383,8 @@ def test_argmax_pushdown_bias():
assert isinstance(fgraph.toposort()[1].op, tensor.CAReduce)
assert isinstance(fgraph.toposort()[1].op.scalar_op, theano.scalar.Maximum)
assert str(fgraph.toposort()[2].op) == 'OutputGuard'
assert check_stack_trace(
fgraph, ops_to_check=(SoftmaxWithBias, tensor.CAReduce))
def test_asymptotic_32():
......@@ -1437,7 +1458,7 @@ class Test_softmax_opt:
# test that function contains softmax and no div.
f = theano.function([c], p_y, mode=self.mode)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
assert check_stack_trace(f, ops_to_check=softmax_op)
f_ops = [n.op for n in f.maker.fgraph.toposort()]
# print '--- f ='
......@@ -1454,7 +1475,7 @@ class Test_softmax_opt:
# test that function contains softmax and no div.
f = theano.function([c], p_y, mode=self.mode)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
assert check_stack_trace(f, ops_to_check=softmax_op)
f_ops = [n.op for n in f.maker.fgraph.toposort()]
# print '--- f ='
......@@ -1474,7 +1495,6 @@ class Test_softmax_opt:
config.warn.sum_div_dimshuffle_bug = False
try:
g = theano.function([c, w], T.grad((p_y * w).sum(), c))
hasattr(g.maker.fgraph.outputs[0].tag, 'trace')
finally:
config.warn.sum_div_dimshuffle_bug = backup
g_ops = [n.op for n in g.maker.fgraph.toposort()]
......@@ -1502,7 +1522,6 @@ class Test_softmax_opt:
config.warn.sum_div_dimshuffle_bug = False
try:
g = theano.function([c], T.grad(p_y.sum(), c))
hasattr(g.maker.fgraph.outputs[0].tag, 'trace')
finally:
config.warn.sum_div_dimshuffle_bug = backup
# printing.debugprint(g)
......@@ -1515,7 +1534,6 @@ class Test_softmax_opt:
# test that function contains softmax and no div.
f = theano.function([c], p_y)
hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
# printing.debugprint(f)
# test that function contains softmax and no div.
......@@ -1523,7 +1541,6 @@ class Test_softmax_opt:
config.warn.sum_div_dimshuffle_bug = False
try:
g = theano.function([c], T.grad(p_y.sum(), c))
hasattr(g.maker.fgraph.outputs[0].tag, 'trace')
finally:
config.warn.sum_div_dimshuffle_bug = backup
# printing.debugprint(g)
......@@ -1563,7 +1580,7 @@ def test_stabilize_log_softmax():
z = theano.tensor.log(y)
f = theano.function([x], z, mode=mode)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
assert check_stack_trace(f, ops_to_check='all')
# check that the softmax has been optimized out
for node in f.maker.fgraph.toposort():
......
theano/tensor/nnet/tests/test_opt.py
浏览文件 @ 420a9b83
from __future__ import absolute_import, print_function, division
import theano
from theano import tensor
from theano.tensor.nnet.blocksparse import sparse_block_dot
from theano.gof.opt import check_stack_trace
from theano.tensor.nnet.blocksparse import (
sparse_block_dot, sparse_block_gemv_inplace, sparse_block_outer_inplace,
sparse_block_gemv, sparse_block_outer)
def test_blocksparse_inplace_gemv_opt():
......@@ -14,12 +17,13 @@ def test_blocksparse_inplace_gemv_opt():
o = sparse_block_dot(W, h, iIdx, b, oIdx)
f = theano.function([W, h, iIdx, b, oIdx], o)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
if theano.config.mode == "FAST_COMPILE":
assert not f.maker.fgraph.toposort()[-1].op.inplace
assert check_stack_trace(f, ops_to_check=[sparse_block_gemv])
else:
assert f.maker.fgraph.toposort()[-1].op.inplace
assert check_stack_trace(f, ops_to_check=[sparse_block_gemv_inplace])
def test_blocksparse_inplace_outer_opt():
......@@ -31,13 +35,12 @@ def test_blocksparse_inplace_outer_opt():
o = sparse_block_dot(W, h, iIdx, b, oIdx)
theano.printing.debugprint(tensor.grad(o.sum(), wrt=W))
f = theano.function([W, h, iIdx, b, oIdx],
[o, tensor.grad(o.sum(), wrt=W)])
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
if theano.config.mode == "FAST_COMPILE":
assert not f.maker.fgraph.toposort()[-1].op.inplace
assert check_stack_trace(f, ops_to_check=sparse_block_outer)
else:
assert f.maker.fgraph.toposort()[-1].op.inplace
assert check_stack_trace(f, ops_to_check=sparse_block_outer_inplace)
theano/tensor/nnet/tests/test_sigm.py
浏览文件 @ 420a9b83
......@@ -8,6 +8,7 @@ import theano.tensor.inplace
from theano.tensor import basic as tensor
from theano import tensor as T
from theano import config
from theano.gof.opt import check_stack_trace
from theano.tests import unittest_tools as utt
from theano.tensor.nnet import (sigmoid, sigmoid_inplace,
softplus, ultra_fast_sigmoid, hard_sigmoid)
......@@ -126,40 +127,37 @@ class T_sigmoid_opts(unittest.TestCase):
# tests inv_1_plus_exp
f = theano.function([x], T.fill(x, 1.0) / (1 + T.exp(-x)), mode=m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
# todo: solve issue #4589 first
# assert check_stack_trace(f, ops_to_check=sigmoid)
assert [node.op for node in f.maker.fgraph.toposort()] == [sigmoid]
f(data)
f = theano.function([x], T.fill(x, 1.0) / (2 + T.exp(-x)), mode=m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid]
f(data)
f = theano.function([x], T.fill(x, 1.0) / (1 - T.exp(-x)), mode=m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid]
f(data)
f = theano.function([x], T.fill(x, 1.1) / (1 + T.exp(-x)), mode=m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid]
f(data)
# tests inv_1_plus_exp with neg
f = theano.function([x], T.fill(x, -1.0) / (1 + T.exp(-x)), mode=m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
# todo: solve issue #4589 first
# assert check_stack_trace(
# f, ops_to_check=[sigmoid, theano.tensor.inplace.neg_inplace])
assert [node.op for node in f.maker.fgraph.toposort()] == [sigmoid,
theano.tensor.inplace.neg_inplace]
f(data)
f = theano.function([x], T.fill(x, -1.0) / (1 - T.exp(-x)), mode=m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
theano.tensor.inplace.neg_inplace]
f(data)
f = theano.function([x], T.fill(x, -1.0) / (2 + T.exp(-x)), mode=m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
theano.tensor.inplace.neg_inplace]
f(data)
f = theano.function([x], T.fill(x, -1.1) / (1 + T.exp(-x)), mode=m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
theano.tensor.inplace.neg_inplace]
f(data)
......@@ -170,37 +168,33 @@ class T_sigmoid_opts(unittest.TestCase):
# = - (sigm(x) * sigm(x))
f = theano.function([x], (T.fill(x, -1.0) * T.exp(x)) /
((1 + T.exp(x)) * (1 + T.exp(-x))), mode=m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
# todo: solve issue #4589 first
# assert check_stack_trace(f, ops_to_check=[sigmoid, T.mul])
assert [node.op for node in f.maker.fgraph.toposort()] == [sigmoid,
T.mul]
f(data)
f = theano.function([x], (T.fill(x, -1.1) * T.exp(x)) /
((1 + T.exp(x)) * (1 + T.exp(-x))), mode=m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
T.mul, theano.tensor.inplace.neg_inplace]
f(data)
f = theano.function([x], (T.fill(x, -1.0) * T.exp(x)) /
((2 + T.exp(x)) * (1 + T.exp(-x))), mode=m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
T.mul, theano.tensor.inplace.neg_inplace]
f(data)
f = theano.function([x], (T.fill(x, -1.0) * T.exp(x)) /
((1 + T.exp(x)) * (2 + T.exp(-x))), mode=m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
T.mul, theano.tensor.inplace.neg_inplace]
f(data)
f = theano.function([x], (T.fill(x, -1.0) * T.exp(x)) /
((1 + T.exp(x)) * (1 + T.exp(x))), mode=m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
T.mul, theano.tensor.inplace.neg_inplace]
f(data)
f = theano.function([x], (T.fill(x, -1.0) * T.exp(x)) /
((1 + T.exp(x)) * (2 + T.exp(-x))), mode=m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
assert [node.op for node in f.maker.fgraph.toposort()] != [sigmoid,
T.mul, theano.tensor.inplace.neg_inplace]
f(data)
......@@ -218,13 +212,13 @@ class T_sigmoid_opts(unittest.TestCase):
# tests exp_over_1_plus_exp
f = theano.function([x], 1 - T.exp(x) / (1 + T.exp(x)), mode=m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
assert check_stack_trace(f, ops_to_check=[tensor.neg, sigmoid_inplace])
assert [node.op for node in f.maker.fgraph.toposort()] == [
tensor.neg, sigmoid_inplace]
# tests inv_1_plus_exp
f = theano.function([x], 1 - T.fill(x, 1.0) / (1 + T.exp(-x)), mode=m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
assert check_stack_trace(f, ops_to_check=[tensor.neg, sigmoid_inplace])
assert [node.op for node in f.maker.fgraph.toposort()] == [tensor.neg,
sigmoid_inplace]
......@@ -241,25 +235,26 @@ class T_sigmoid_opts(unittest.TestCase):
x, y = tensor.vectors('x', 'y')
f = theano.function([x], sigmoid(-x) * tensor.exp(x), mode=m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
match(f, [sigmoid])
assert check_stack_trace(f, ops_to_check=sigmoid)
f = theano.function([x], sigmoid(x) * tensor.exp(-x), mode=m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
match(f, [tensor.neg, sigmoid])
assert check_stack_trace(f, ops_to_check=sigmoid)
f = theano.function([x], -(-(-(sigmoid(x)))) * tensor.exp(-x), mode=m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
match(f, [tensor.neg, sigmoid, tensor.neg])
# assert check_stack_trace(f, ops_to_check=sigmoid)
f = theano.function(
[x, y],
(sigmoid(x) * sigmoid(-y) * -tensor.exp(-x) *
tensor.exp(x * y) * tensor.exp(y)),
mode=m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
match(f, [sigmoid, tensor.mul, tensor.neg, tensor.exp, sigmoid,
tensor.mul])
# assert check_stack_trace(f, ops_to_check=[sigmoid, tensor.mul,
# tensor.exp])
def test_perform_sigm_times_exp(self):
"""
......@@ -318,7 +313,6 @@ class T_sigmoid_opts(unittest.TestCase):
mode = self.get_mode()
if not isinstance(mode, theano.compile.DebugMode):
f = theano.function([x, lr], ux, mode=mode)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
ux_v = f([[50]], 0.1)
assert not numpy.isnan(ux_v)
......@@ -328,14 +322,14 @@ class T_sigmoid_opts(unittest.TestCase):
mode = self.get_mode('local_ultra_fast_sigmoid')
f = theano.function([x], s, mode=mode)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
assert check_stack_trace(f, ops_to_check=sigmoid)
topo = f.maker.fgraph.toposort()
assert len(topo) == 1
assert topo[0].op == sigmoid
mode = self.get_mode().including('local_ultra_fast_sigmoid')
f = theano.function([x], s, mode=mode)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
assert check_stack_trace(f, ops_to_check=ultra_fast_sigmoid)
topo = f.maker.fgraph.toposort()
assert topo[0].op == ultra_fast_sigmoid
assert len(topo) == 1
......@@ -347,18 +341,21 @@ class T_sigmoid_opts(unittest.TestCase):
mode = self.get_mode('local_hard_sigmoid')
f = theano.function([x], s, mode=mode)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
assert check_stack_trace(f, ops_to_check=sigmoid)
topo = f.maker.fgraph.toposort()
assert topo[0].op == sigmoid
assert len(topo) == 1
mode = self.get_mode().including('local_hard_sigmoid')
f = theano.function([x], s, mode=mode)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
topo = f.maker.fgraph.toposort()
assert not any([n.op == sigmoid for n in topo])
ux_v = f([[-50, -10, -4, -1, 0, 1, 4, 10, 50]])
mode2 = mode.excluding('fusion').excluding('inplace')
f2 = theano.function([x], s, mode=mode2)
self.assertTrue(check_stack_trace(f2, ops_to_check=theano.tensor.clip))
class T_softplus_opts(unittest.TestCase):
def setUp(self):
......@@ -376,7 +373,11 @@ class T_softplus_opts(unittest.TestCase):
out = T.log(sigmoid(x))
f = theano.function([x], out, mode=self.m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
# Fix ticket #4581 first
# assert check_stack_trace(
# f, ops_to_check=(theano.scalar.Neg,
# theano.tensor.nnet.sigm.ScalarSoftplus))
topo = f.maker.fgraph.toposort()
assert len(topo) == 3
assert isinstance(topo[0].op.scalar_op, theano.scalar.Neg)
......@@ -395,12 +396,14 @@ class T_softplus_opts(unittest.TestCase):
assert isinstance(topo[0].op.scalar_op,
theano.tensor.nnet.sigm.ScalarSoftplus)
assert isinstance(topo[1].op.scalar_op, theano.scalar.Neg)
# assert check_stack_trace(f, ops_to_check='all')
f(numpy.random.rand(54, 11).astype(config.floatX))
# Same test with a flatten
out = T.log(1 - T.flatten(sigmoid(x)))
f = theano.function([x], out, mode=self.m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
# assert check_stack_trace(f, ops_to_check='all')
topo = f.maker.fgraph.toposort()
assert len(topo) == 3
assert tensor.is_flat(topo[0].outputs[0])
......@@ -429,7 +432,9 @@ class T_softplus_opts(unittest.TestCase):
out = T.log(1 + T.exp(x))
f = theano.function([x], out, mode=self.m)
assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
# Fix ticket #4581 first
# assert check_stack_trace(f, ops_to_check='all')
topo = f.maker.fgraph.toposort()
assert len(topo) == 1
assert isinstance(topo[0].op.scalar_op,
......
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