def test_nested():
notimpl = NotImplementedOp()
ifelseifelseif = IfElseIfElseIf()
x1 = tt.scalar("x1")
x2 = tt.scalar("x2")
c1 = tt.scalar("c1")
c2 = tt.scalar("c2")
t1 = ifelse(c1, x1, notimpl(x2))
t1.name = "t1"
t2 = t1 * 10
t2.name = "t2"
t3 = ifelse(c2, t2, x1 + t1)
t3.name = "t3"
t4 = ifelseifelseif(tt.eq(x1, x2), x1, tt.eq(x1, 5), x2, c2, t3, t3 + 0.5)
t4.name = "t4"
linker = aesara.gof.vm.VM_Linker(lazy=False)
f = function([c1, c2, x1, x2],
t4,
mode=Mode(linker=linker, optimizer="fast_run"))
with pytest.raises(NotImplementedOpException):
f(1, 0, np.array(10, dtype=x1.dtype), 0)
linker = aesara.gof.vm.VM_Linker(lazy=True)
f = function([c1, c2, x1, x2],
t4,
mode=Mode(linker=linker, optimizer="fast_run"))
assert f(1, 0, np.array(10, dtype=x1.dtype), 0) == 20.5
def test_ifelse(self):
config1 = aesara.config.profile
config2 = aesara.config.profile_memory
try:
aesara.config.profile = True
aesara.config.profile_memory = True
a, b = tt.scalars("a", "b")
x, y = tt.scalars("x", "y")
z = ifelse(tt.lt(a, b), x * 2, y * 2)
p = aesara.ProfileStats(False, gpu_checks=False)
if aesara.config.mode in ["DebugMode", "DEBUG_MODE", "FAST_COMPILE"]:
m = "FAST_RUN"
else:
m = None
f_ifelse = aesara.function(
[a, b, x, y], z, profile=p, name="test_ifelse", mode=m
)
val1 = 0.0
val2 = 1.0
big_mat1 = 10
big_mat2 = 11
f_ifelse(val1, val2, big_mat1, big_mat2)
finally:
aesara.config.profile = config1
aesara.config.profile_memory = config2
def test_jax_ifelse():
true_vals = np.r_[1, 2, 3]
false_vals = np.r_[-1, -2, -3]
x = ifelse(np.array(True), true_vals, false_vals)
x_fg = FunctionGraph([], [x])
compare_jax_and_py(x_fg, [])
a = dscalar("a")
a.tag.test_value = np.array(0.2, dtype=config.floatX)
x = ifelse(a < 0.5, true_vals, false_vals)
x_fg = FunctionGraph([a], [x]) # I.e. False
compare_jax_and_py(x_fg, [get_test_value(i) for i in x_fg.inputs])
def test_ifelse():
a = tt.scalar()
b = generic()
c = generic()
notimpl = NotImplementedOp()
lazys = [True]
# We need lazy to end up being True for this test.
if aesara.config.vm.lazy in [True, None]:
lazys = [True, None]
cloops = [True, False]
if aesara.config.cxx == "":
cloops = [False]
for cloop in cloops:
for lazy in lazys:
linker = aesara.gof.vm.VM_Linker(use_cloop=cloop, lazy=lazy)
f = function(
[a, b, c],
ifelse(a, notimpl(b), c),
mode=Mode(linker=linker, optimizer="fast_run"),
)
with pytest.raises(NotImplementedOpException):
f(1, "a", "b")
assert f(0, "a", "b") == "b"
def test_callback_with_ifelse(self):
a, b, c = scalars("abc")
f = function(
[a, b, c],
ifelse(a, 2 * b, 2 * c),
mode=Mode(optimizer=None, linker=VMLinker(callback=self.callback)),
)
f(1, 2, 3)
assert self.n_callbacks["IfElse"] == 2
def test_c_thunks():
a = tensor.scalars("a")
b, c = tensor.vectors("bc")
cases = [False]
if aesara.config.cxx:
cases.append(True)
for c_thunks in cases:
f = function(
[a, b, c],
ifelse(a, a * b, b * c),
mode=Mode(optimizer=None,
linker=vm.VM_Linker(c_thunks=c_thunks, use_cloop=False)),
)
f(1, [2], [3, 2])
with pytest.raises(ValueError):
f(0, [2], [3, 4])
assert any([hasattr(t, "cthunk") for t in f.fn.thunks]) == c_thunks
def build_graph(x, depth=5):
z = x
for d in range(depth):
z = ifelse(z.mean() > 0.5, -z, z)
return z
def build_graph(x, depth=5):
z = x
for d in range(depth):
z = ifelse(z[0] > 0, -z, z)
return z
import time
import numpy as np
import aesara
from aesara import tensor as tt
from aesara.ifelse import ifelse
a, b = tt.scalars("a", "b")
x, y = tt.matrices("x", "y")
z_switch = tt.switch(tt.lt(a, b), tt.mean(x), tt.mean(y))
z_lazy = ifelse(tt.lt(a, b), tt.mean(x), tt.mean(y))
f_switch = aesara.function([a, b, x, y], z_switch)
f_lazyifelse = aesara.function([a, b, x, y], z_lazy)
val1 = 0.0
val2 = 1.0
big_mat1 = np.ones((10000, 1000))
big_mat2 = np.ones((10000, 1000))
n_times = 10
tic = time.clock()
for i in range(n_times):
f_switch(val1, val2, big_mat1, big_mat2)
print("time spent evaluating both values %f sec" % (time.clock() - tic))
tic = time.clock()
