def _get_func(self):
"""
Return a function that makes a value from an integer.
The integer value is assumed to be a valid pointer for the
type and no check is done to ensure that.
"""
from aesara.scalar import get_scalar_type
if self._fn is None:
with change_flags(compute_test_value="off"):
v = get_scalar_type("int64")()
self._fn = aesara.function(
[v],
_make_cdata(self)(v),
mode=aesara.Mode(optimizer=None),
profile=False,
)
return self._fn
def c_code_cache_version(self):
scalar_version = aes.get_scalar_type(self.dtype).c_code_cache_version()
if scalar_version:
return (11, ) + scalar_version
else:
return ()
def c_init_code(self, **kwargs):
return aes.get_scalar_type(self.dtype).c_init_code(**kwargs)
def c_compile_args(self, **kwargs):
return aes.get_scalar_type(self.dtype).c_compile_args(**kwargs)
def c_libraries(self, **kwargs):
return aes.get_scalar_type(self.dtype).c_libraries(**kwargs)
def c_headers(self, **kwargs):
return aes.get_scalar_type(self.dtype).c_headers(**kwargs)
def to_scalar_type(self):
return aes.get_scalar_type(dtype=self.dtype)
def safe_new(x, tag="", dtype=None):
"""
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, aesara.Constant):
if dtype and x.dtype != dtype:
casted_x = x.astype(dtype)
nwx = x.__class__(casted_x.type, x.data, x.name)
nwx.tag = copy.copy(x.tag)
return nwx
else:
return x.clone()
# Note, as_tensor_variable will convert the Scalar into a
# TensorScalar that will require a ScalarFromTensor op,
# making the pushout optimization fail
elif isinstance(x, scalar.ScalarVariable):
if dtype:
nw_x = scalar.get_scalar_type(dtype=dtype)()
else:
nw_x = x.type()
nw_x.name = nw_name
if aesara.config.compute_test_value != "off":
# Copy test value, cast it if necessary
try:
x_test_value = gof.op.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 = tensor.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 and x.dtype != dtype:
x = x.astype(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 aesara.config.compute_test_value != "off":
try:
nw_x.tag.test_value = copy.deepcopy(gof.op.get_test_value(x))
except TestValueError:
pass
return nw_x
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
