def check_force_gemv_init():
if check_force_gemv_init._force_init_beta is None:
"""
Test issue 1569.
Namely when evaulating
beta*aa + alpha*dot(xx, yy)
where we set aa = betas = zeros of the correct dimensions we do not
actually set aa = zeros and instead let the BLAS perform beta*aa with
uninitialized memory for speed. Occasionally the memory contains values
that are equivalent to NaN in which case the product beta*aa contains
NaN's for correctly implemented BLAS libraries. In this situation, since
we are introducing the NaN's, we need to test whether the BLAS performs
correctly. If it *does*, i.e. it actually performs the multiplication
beta*aa which will result in NaN's in the result, then we need intialize
the memory to zeros.
"""
tv = theano.config.compute_test_value
tvo = theano.config.compute_test_value_opt
theano.config.compute_test_value = 'off'
theano.config.compute_test_value_opt = 'off'
try:
aa = T.vector('aa')
yy = T.vector('yy')
xx = T.matrix('xx')
f = theano.function(
[aa, yy, xx],
gemv_no_inplace(aa, 1., xx, yy, 0.),
theano.compile.Mode(optimizer='fast_compile').excluding('gpu',
'gpuarray'),
profile=False
)
finally:
theano.config.compute_test_value = tv
theano.config.compute_test_value_opt = tvo
# Here we introduce NaNs into the data, if they are returned by the BLAS
# then we want gemv_c_code to initiliaze the memory to 0 so that we
# don't inadvertantly introduce NaNs to the users data.
aa_data = numpy.array(
float('NaN')*numpy.ones((2,)),
dtype=theano.config.floatX
)
yy_data = numpy.array(
numpy.ones((2,))*2,
dtype=theano.config.floatX
)
xx_data = numpy.array(
numpy.ones((2, 2)),
dtype=theano.config.floatX
)
zz = f(aa_data, yy_data, xx_data)
check_force_gemv_init._force_init_beta = numpy.isnan(zz).any()
return check_force_gemv_init._force_init_beta
def check_force_gemv_init():
if check_force_gemv_init._force_init_beta is None:
"""
Test issue 1569.
Namely when evaulating
beta*aa + alpha*dot(xx, yy)
where we set aa = betas = zeros of the correct dimensions we do not
actually set aa = zeros and instead let the BLAS perform beta*aa with
uninitialized memory for speed. Occasionally the memory contains values
that are equivalent to NaN in which case the product beta*aa contains
NaN's for correctly implemented BLAS libraries. In this situation, since
we are introducing the NaN's, we need to test whether the BLAS performs
correctly. If it *does*, i.e. it actually performs the multiplication
beta*aa which will result in NaN's in the result, then we need intialize
the memory to zeros.
"""
tv = theano.config.compute_test_value
tvo = theano.config.compute_test_value_opt
theano.config.compute_test_value = 'off'
theano.config.compute_test_value_opt = 'off'
try:
aa = T.vector('aa')
yy = T.vector('yy')
xx = T.matrix('xx')
f = theano.function(
[aa, yy, xx],
gemv_no_inplace(aa, 1., xx, yy, 0.),
theano.compile.Mode(optimizer='fast_compile').excluding(
'gpu', 'gpuarray'),
profile=False)
finally:
theano.config.compute_test_value = tv
theano.config.compute_test_value_opt = tvo
# Here we introduce NaNs into the data, if they are returned by the BLAS
# then we want gemv_c_code to initiliaze the memory to 0 so that we
# don't inadvertantly introduce NaNs to the users data.
aa_data = numpy.array(float('NaN') * numpy.ones((2, )),
dtype=theano.config.floatX)
yy_data = numpy.array(numpy.ones((2, )) * 2,
dtype=theano.config.floatX)
xx_data = numpy.array(numpy.ones((2, 2)), dtype=theano.config.floatX)
zz = f(aa_data, yy_data, xx_data)
check_force_gemv_init._force_init_beta = numpy.isnan(zz).any()
return check_force_gemv_init._force_init_beta
def test_bad_shape(self):
# Test that at run time we raise an exception when the shape
# is not the one specified
specify_shape = SpecifyShape()
x = vector()
xval = np.random.rand(2).astype(config.floatX)
f = theano.function([x], specify_shape(x, [2]), mode=self.mode)
f(xval)
xval = np.random.rand(3).astype(config.floatX)
with pytest.raises(AssertionError):
f(xval)
assert isinstance(
[n for n in f.maker.fgraph.toposort() if isinstance(n.op, SpecifyShape)][0]
.inputs[0]
.type,
self.input_type,
)
x = matrix()
xval = np.random.rand(2, 3).astype(config.floatX)
f = theano.function([x], specify_shape(x, [2, 3]), mode=self.mode)
assert isinstance(
[n for n in f.maker.fgraph.toposort() if isinstance(n.op, SpecifyShape)][0]
.inputs[0]
.type,
self.input_type,
)
f(xval)
for shape_ in [(1, 3), (2, 2), (5, 5)]:
xval = np.random.rand(*shape_).astype(config.floatX)
with pytest.raises(AssertionError):
f(xval)
def test_is_1pexp(self):
backup = config.warn.identify_1pexp_bug
config.warn.identify_1pexp_bug = False
try:
x = tensor.vector('x')
exp = tensor.exp
assert is_1pexp(1 + exp(x)) == (False, x)
assert is_1pexp(exp(x) + 1) == (False, x)
for neg, exp_arg in imap(is_1pexp, [(1 + exp(-x)), (exp(-x) + 1)]):
assert not neg and theano.gof.graph.is_same_graph(exp_arg, -x)
assert is_1pexp(1 - exp(x)) is None
assert is_1pexp(2 + exp(x)) is None
assert is_1pexp(exp(x) + 2) is None
assert is_1pexp(exp(x) - 1) is None
assert is_1pexp(-1 + exp(x)) is None
assert is_1pexp(1 + 2 * exp(x)) is None
finally:
config.warn.identify_1pexp_bug = backup
def test_bad_number_of_shape(self):
# Test that the number of dimensions provided is good
specify_shape = SpecifyShape()
x = vector()
shape_vec = ivector()
xval = np.random.rand(2).astype(config.floatX)
with pytest.raises(AssertionError):
specify_shape(x, [])
with pytest.raises(AssertionError):
specify_shape(x, [2, 2])
f = theano.function([x, shape_vec],
specify_shape(x, shape_vec),
mode=self.mode)
assert isinstance(
[
n for n in f.maker.fgraph.toposort()
if isinstance(n.op, SpecifyShape)
][0].inputs[0].type,
self.input_type,
)
with pytest.raises(AssertionError):
f(xval, [])
with pytest.raises(AssertionError):
f(xval, [2, 2])
x = matrix()
xval = np.random.rand(2, 3).astype(config.floatX)
for shape_ in [(), (1, ), (2, 3, 4)]:
with pytest.raises(AssertionError):
specify_shape(x, shape_)
f = theano.function([x, shape_vec],
specify_shape(x, shape_vec),
mode=self.mode)
assert isinstance(
[
n for n in f.maker.fgraph.toposort()
if isinstance(n.op, SpecifyShape)
][0].inputs[0].type,
self.input_type,
)
with pytest.raises(AssertionError):
f(xval, shape_)
def test_is_1pexp(self):
backup = config.warn.identify_1pexp_bug
config.warn.identify_1pexp_bug = False
try:
x = tensor.vector('x')
exp = tensor.exp
assert is_1pexp(1 + exp(x), False) == (False, x)
assert is_1pexp(exp(x) + 1, False) == (False, x)
for neg, exp_arg in imap(
lambda x: is_1pexp(x, only_process_constants=False),
[(1 + exp(-x)), (exp(-x) + 1)]):
assert not neg and theano.gof.graph.is_same_graph(exp_arg, -x)
assert is_1pexp(1 - exp(x), False) is None
assert is_1pexp(2 + exp(x), False) is None
assert is_1pexp(exp(x) + 2, False) is None
assert is_1pexp(exp(x) - 1, False) is None
assert is_1pexp(-1 + exp(x), False) is None
assert is_1pexp(1 + 2 * exp(x), False) is None
finally:
config.warn.identify_1pexp_bug = backup
def test_is_1pexp(self):
backup = config.warn.identify_1pexp_bug
config.warn.identify_1pexp_bug = False
try:
x = tensor.vector('x')
exp = tensor.exp
assert is_1pexp(1 + exp(x), False) == (False, x)
assert is_1pexp(exp(x) + 1, False) == (False, x)
for neg, exp_arg in imap(lambda x:
is_1pexp(x, only_process_constants=False),
[(1 + exp(-x)), (exp(-x) + 1)]):
assert not neg and theano.gof.graph.is_same_graph(exp_arg, -x)
assert is_1pexp(1 - exp(x), False) is None
assert is_1pexp(2 + exp(x), False) is None
assert is_1pexp(exp(x) + 2, False) is None
assert is_1pexp(exp(x) - 1, False) is None
assert is_1pexp(-1 + exp(x), False) is None
assert is_1pexp(1 + 2 * exp(x), False) is None
finally:
config.warn.identify_1pexp_bug = backup
