def with_linker_inplace(self, linker, op, type, rand_val):
for shape_info in ("complete", "only_broadcastable", "none"):
for xsh, ysh in [
((5, 5), (5, 5)),
((5, 5), (1, 5)),
((5, 5), (5, 1)),
((1, 1), (1, 1)),
((2, 3, 4, 5), (2, 3, 4, 5)),
((2, 3, 4, 5), (1, 3, 1, 5)),
((2, 3, 4, 5), (1, 1, 1, 1)),
((), ()),
]:
if shape_info == "complete":
x_type = type(aesara.config.floatX, shape=xsh)
y_type = type(aesara.config.floatX, shape=ysh)
elif shape_info == "only_broadcastable":
# This condition is here for backwards compatibility, when the only
# type shape provided by Aesara was broadcastable/non-broadcastable
x_type = type(
aesara.config.floatX,
broadcastable=[(entry == 1) for entry in xsh],
)
y_type = type(
aesara.config.floatX,
broadcastable=[(entry == 1) for entry in ysh],
)
else:
x_type = type(aesara.config.floatX, shape=[None for _ in xsh])
y_type = type(aesara.config.floatX, shape=[None for _ in ysh])
x = x_type("x")
y = y_type("y")
e = op(aes.Add(aes.transfer_type(0)), {0: 0})(x, y)
f = make_function(copy(linker).accept(FunctionGraph([x, y], [e])))
xv = rand_val(xsh)
yv = rand_val(ysh)
zv = xv + yv
f(xv, yv)
assert (xv == zv).all()
# test Elemwise.infer_shape
# the Shape op don't implement c_code!
if isinstance(linker, PerformLinker):
x = x_type("x")
y = y_type("y")
e = op(aes.Add(aes.transfer_type(0)), {0: 0})(x, y)
f = make_function(
copy(linker).accept(FunctionGraph([x, y], [e.shape]))
)
xv = rand_val(xsh)
yv = rand_val(ysh)
zv = xv + yv
assert xv.shape == zv.shape
assert tuple(f(xv, yv)) == zv.shape
def with_linker_inplace(self, linker, op, type, rand_val):
for xsh, ysh in [
((5, 5), (5, 5)),
((5, 5), (1, 5)),
((5, 5), (5, 1)),
((1, 1), (1, 1)),
((2, 3, 4, 5), (2, 3, 4, 5)),
((2, 3, 4, 5), (1, 3, 1, 5)),
((2, 3, 4, 5), (1, 1, 1, 1)),
((), ()),
]:
x = type(aesara.config.floatX,
[(entry == 1) for entry in xsh])("x")
y = type(aesara.config.floatX,
[(entry == 1) for entry in ysh])("y")
e = op(aes.Add(aes.transfer_type(0)), {0: 0})(x, y)
f = copy(linker).accept(FunctionGraph([x, y], [e])).make_function()
xv = rand_val(xsh)
yv = rand_val(ysh)
zv = xv + yv
f(xv, yv)
assert (xv == zv).all()
# test Elemwise.infer_shape
# the Shape op don't implement c_code!
if isinstance(linker, PerformLinker):
x = type(aesara.config.floatX,
[(entry == 1) for entry in xsh])("x")
y = type(aesara.config.floatX,
[(entry == 1) for entry in ysh])("y")
e = op(aes.Add(aes.transfer_type(0)), {0: 0})(x, y)
f = (copy(linker).accept(FunctionGraph(
[x, y], [e.shape])).make_function())
xv = rand_val(xsh)
yv = rand_val(ysh)
zv = xv + yv
f(xv, yv)
assert xv.shape == zv.shape
def test_fill(self):
for linker, op, t, rval in zip(
self.linkers,
[self.op, self.cop],
[self.type, self.ctype],
[self.rand_val, self.rand_cval],
):
x = t(aesara.config.floatX, (False, False))("x")
y = t(aesara.config.floatX, (True, True))("y")
e = op(aes.Second(aes.transfer_type(0)), {0: 0})(x, y)
f = make_function(linker().accept(FunctionGraph([x, y], [e])))
xv = rval((5, 5))
yv = rval((1, 1))
f(xv, yv)
assert (xv == yv).all()
def _scal_inplace(symbol):
"""Replace a symbol definition with an elementwise version of the corresponding scalar Op"""
symbolname = symbol.__name__
inplace = symbolname.endswith("_inplace")
if inplace:
scalar_op = getattr(scal, symbolname[:-len("_inplace")])
inplace_scalar_op = scalar_op.__class__(scal.transfer_type(0))
rval = elemwise.Elemwise(inplace_scalar_op, {0: 0}, name=symbolname)
else:
scalar_op = getattr(scal, symbolname)
rval = elemwise.Elemwise(scalar_op, name=symbolname)
if getattr(symbol, "__doc__", False):
rval.__doc__ = symbol.__doc__ + "\n" + rval.__doc__
# for the meaning of this see the ./epydoc script
# it makes epydoc display rval as if it were a function, not an object
rval.__epydoc_asRoutine = symbol
rval.__module__ = "aesara.tensor.inplace"
pprint.assign(
rval, printing.FunctionPrinter(symbolname.replace("_inplace", "=")))
return rval
//%(z)s = 0.5*(ultrafasttanh(0.5*x)+1.);
%(z)s = 0.5*(%(z)s+1.);
}""" % locals())
@staticmethod
def c_code_cache_version():
return (5, )
ultra_fast_scalar_sigmoid = UltraFastScalarSigmoid(
aes.upgrade_to_float, name="ultra_fast_scalar_sigmoid")
ultra_fast_sigmoid = Elemwise(ultra_fast_scalar_sigmoid,
name="ultra_fast_sigmoid")
ultra_fast_sigmoid_inplace = Elemwise(
UltraFastScalarSigmoid(aes.transfer_type(0)),
inplace_pattern={0: 0},
name="ultra_fast_sigmoid_inplace",
)
pprint.assign(ultra_fast_sigmoid,
printing.FunctionPrinter(["ultra_fast_sigmoid"]))
# @opt.register_uncanonicalize
@local_optimizer(None)
def local_ultra_fast_sigmoid(fgraph, node):
"""
When enabled, change all sigmoid to ultra_fast_sigmoid.
For example do mode.including('local_ultra_fast_sigmoid')
"library",
"tensor",
"nnet",
"sigmoid_prec.png",
)
plt.savefig(fname)
print("New picture saved at", fname)
print(val_ultra.max())
print(val_ultra.min())
scalar_sigmoid = ScalarSigmoid(scalar.upgrade_to_float, name="scalar_sigmoid")
sigmoid = elemwise.Elemwise(scalar_sigmoid, name="sigmoid")
sigmoid_inplace = elemwise.Elemwise(
ScalarSigmoid(scalar.transfer_type(0)),
inplace_pattern={0: 0},
name="sigmoid_inplace",
)
pprint.assign(sigmoid, printing.FunctionPrinter("sigmoid"))
class UltraFastScalarSigmoid(scalar.UnaryScalarOp):
"""
This is just speed opt. Not for stability.
"""
nfunc_spec = ("scipy.special.expit", 1, 1)