def test_convert_variable():
t1 = MyType(1)
v1 = Variable(MyType(1), None, None)
v2 = Variable(MyType(2), None, None)
v3 = Variable(MyType2(0), None, None)
assert t1.convert_variable(v1) is v1
assert t1.convert_variable(v2) is None
with pytest.raises(NotImplementedError):
t1.convert_variable(v3)
def make_node(self, value: Variable, *conds: Tuple[Variable]):
"""
Parameters
==========
value
The value to return if `conds` all evaluate to ``True``; otherwise,
`self.exc_type` is raised.
conds
The conditions to evaluate.
"""
import aesara.tensor as at
if not isinstance(value, Variable):
value = at.as_tensor_variable(value)
conds = [at.as_tensor_variable(c) for c in conds]
assert all(c.type.ndim == 0 for c in conds)
return Apply(
self,
[value] + conds,
[value.type()],
)
def test_shape_basic():
s = shape([])
assert s.type.broadcastable == (True, )
s = shape([10])
assert s.type.broadcastable == (True, )
s = shape(lscalar())
assert s.type.broadcastable == (False, )
class MyType(Type):
def filter(self, *args, **kwargs):
raise NotImplementedError()
def __eq__(self, other):
return isinstance(other, MyType) and other.thingy == self.thingy
s = shape(Variable(MyType()))
assert s.type.broadcastable == (False, )
s = shape(np.array(1))
assert np.array_equal(eval_outputs([s]), [])
s = shape(np.ones((5, 3)))
assert np.array_equal(eval_outputs([s]), [5, 3])
s = shape(np.ones(2))
assert np.array_equal(eval_outputs([s]), [2])
s = shape(np.ones((5, 3, 10)))
assert np.array_equal(eval_outputs([s]), [5, 3, 10])
def test_invalid_modes(self):
# Modes 'r+', 'r', and 'w+' cannot work with Aesara, becausei
# the output array may be modified inplace, and that should not
# modify the original file.
path = Variable(Generic())
for mmap_mode in ("r+", "r", "w+", "toto"):
with pytest.raises(ValueError):
load(path, "int32", (False,), mmap_mode)
def test_basic(self):
path = Variable(Generic())
# Not specifying mmap_mode defaults to None, and the data is
# copied into main memory
x = load(path, "int32", (False,))
y = x * 2
fn = function([path], y)
assert (fn(self.filename) == (self.data * 2)).all()
def make_node(self):
return Apply(
self,
[],
[
Variable(Generic()),
tensor(self.dtype, shape=self.broadcastable),
],
)
def test1(self):
path = Variable(Generic())
# 'c' means "copy-on-write", which allow the array to be overwritten
# by an inplace Op in the graph, without modifying the underlying
# file.
x = load(path, "int32", (False,), "c")
# x ** 2 has been chosen because it will work inplace.
y = (x ** 2).sum()
fn = function([path], y)
# Call fn() twice, to check that inplace ops do not cause trouble
assert (fn(self.filename) == (self.data ** 2).sum()).all()
assert (fn(self.filename) == (self.data ** 2).sum()).all()
def get_test_value(v: Variable) -> Any:
"""Get the test value for `v`.
If input `v` is not already a variable, it is turned into one by calling
`as_tensor_variable(v)`.
Raises
------
AttributeError if no test value is set.
"""
if not isinstance(v, Variable):
v = aesara.tensor.as_tensor_variable(v)
return v.get_test_value()
def double(name):
return Variable(tdouble, None, None, name=name)
def safe_new(x: Variable,
tag: str = "",
dtype: Optional[Union[str, np.dtype]] = None) -> Variable:
"""Clone variables.
Internal function that constructs a new variable from `x` with the same
type, but with a different name (old name + tag). This function is used
by `gradient`, or the R-op to construct new variables for the inputs of
the inner graph such that there is no interference between the original
graph and the newly constructed graph.
"""
if hasattr(x, "name") and x.name is not None:
nw_name = x.name + tag
else:
nw_name = None
if isinstance(x, Constant):
# TODO: Do something better about this
assert isinstance(x.type, HasDataType)
if dtype and x.type.dtype != dtype:
casted_x = cast(x, dtype)
nwx = type(x)(casted_x.type, x.data, x.name)
nwx.tag = copy.copy(x.tag)
return nwx
else:
return x
# Note, `as_tensor_variable` will convert the `ScalarType` into a
# `TensorScalar` that will require a `ScalarFromTensor` `Op`, making the
# push-out optimization fail
elif isinstance(x, aes.ScalarVariable):
if dtype:
nw_x = aes.get_scalar_type(dtype=dtype)()
else:
nw_x = x.type()
nw_x.name = nw_name
if config.compute_test_value != "off":
# Copy test value, cast it if necessary
try:
x_test_value = get_test_value(x)
except TestValueError:
pass
else:
# This clause is executed if no exception was raised
nw_x.tag.test_value = nw_x.type.filter(x_test_value)
return nw_x
else:
try:
x = at.as_tensor_variable(x)
except TypeError:
# This could happen for example for random states
pass
# Cast `x` if needed. If `x` has a test value, this will also cast it.
if dtype:
# TODO: Do something better about this
assert isinstance(x.type, HasDataType)
if x.type.dtype != dtype:
x = cast(x, dtype)
nw_x = x.type()
nw_x.name = nw_name
# Preserve test values so that the `compute_test_value` option can be used.
# The test value is deep-copied to ensure there can be no interactions
# between test values, due to inplace operations for instance. This may
# not be the most efficient memory-wise, though.
if config.compute_test_value != "off":
try:
nw_x.tag.test_value = copy.deepcopy(get_test_value(x))
except TestValueError:
pass
return nw_x
def MyVariable2(name):
return Variable(MyType2(), None, None, name=name)
def make_node(self, request, data):
return Apply(self, [request, data], [Variable(Generic())])
def make_node(self, data):
return Apply(self, [data], [Variable(Generic()), data.type()])
def MyVariable(thingy):
return Variable(MyType(thingy), None, None)
def test_memmap(self):
path = Variable(Generic())
x = load(path, "int32", (False,), mmap_mode="c")
fn = function([path], x)
assert type(fn(self.filename)) == np.core.memmap
def MyVariable(name):
return Variable(MyType(name), None, None)
