def test_shared_mutable(self):
bval = np.arange(5)
b = shared(bval)
b_out = b * 2
# shared vars copy args.
assert b.get_value(borrow=True) is not bval
# so we do this to get at the underlying data
bval = data_of(b)
# by default, shared are not mutable unless doing an explicit update
f = pfunc([], [b_out], mode="FAST_RUN")
assert (f() == np.arange(5) * 2).all()
assert np.all(b.get_value(borrow=True) == np.arange(5))
# using updates, b is now a mutable parameter
f = pfunc([], [b_out], updates=[(b, b_out)], mode="FAST_RUN")
assert (f() == (np.arange(5) * 2)).all()
# because of the update
assert (b.get_value(borrow=True) == (np.arange(5) * 2)).all()
assert (bval == (np.arange(5) * 2)).all() # because of mutable=True
# do not depend on updates being in-place though!
bval = np.arange(5)
b.set_value(bval, borrow=True)
bval = data_of(b)
f = pfunc([], [b_out], updates=[(b, (b_out + 3))], mode="FAST_RUN")
assert (f() == (np.arange(5) * 2)).all()
# because of the update
assert (b.get_value(borrow=True) == ((np.arange(5) * 2) + 3)).all()
# bval got modified to something...
assert not (bval == np.arange(5)).all()
# ... but not to b.value !
assert not (bval == b.get_value(borrow=True)).all()
def test_allow_downcast_floatX(self):
a = fscalar("a")
b = fvector("b")
f = pfunc([a, b], (a + b), allow_input_downcast=True)
g = pfunc([a, b], (a + b), allow_input_downcast=False)
h = pfunc([a, b], (a + b), allow_input_downcast=None)
# If the values can be accurately represented, OK
assert np.all(f(0, [0]) == 0)
assert np.all(g(0, [0]) == 0)
assert np.all(h(0, [0]) == 0)
# For the vector: OK iff allow_input_downcast is True
assert np.allclose(f(0, [0.1]), 0.1)
with pytest.raises(TypeError):
g(0, [0.1])
with pytest.raises(TypeError):
h(0, [0.1])
# For the scalar: OK if allow_input_downcast is True,
# or None and floatX==float32
assert np.allclose(f(0.1, [0]), 0.1)
with pytest.raises(TypeError):
g(0.1, [0])
if config.floatX == "float32":
assert np.allclose(h(0.1, [0]), 0.1)
else:
with pytest.raises(TypeError):
h(0.1, [0])
def test_doc(self):
# Ensure the code given in pfunc.txt works as expected
# Example #1.
a = lscalar()
b = shared(1)
f1 = pfunc([a], (a + b))
f2 = pfunc([In(a, value=44)], a + b, updates={b: b + 1})
assert b.get_value() == 1
assert f1(3) == 4
assert f2(3) == 4
assert b.get_value() == 2
assert f1(3) == 5
b.set_value(0)
assert f1(3) == 3
# Example #2.
a = lscalar()
b = shared(7)
f1 = pfunc([a], a + b)
f2 = pfunc([a], a * b)
assert f1(5) == 12
b.set_value(8)
assert f1(5) == 13
assert f2(4) == 32
def test_default_updates_multiple(self):
x = shared(0)
y = shared(1)
x.default_update = x - 1
y.default_update = y + 1
f1 = pfunc([], [x, y])
f1()
assert x.get_value() == -1
assert y.get_value() == 2
f2 = pfunc([], [x, y], updates=[(x, (x - 2))], no_default_updates=[y])
f2()
assert x.get_value() == -3
assert y.get_value() == 2
f3 = pfunc([], [x, y], updates=[(x, (x - 2))], no_default_updates=True)
f3()
assert x.get_value() == -5
assert y.get_value() == 2
f4 = pfunc([], [x, y], updates=[(y, (y - 2))])
f4()
assert x.get_value() == -6
assert y.get_value() == 0
def test_allow_input_downcast_int(self):
a = wvector("a") # int16
b = bvector("b") # int8
c = bscalar("c") # int8
f = pfunc([a, b, c], (a + b + c), allow_input_downcast=True)
# Value too big for a, b, or c, silently ignored
assert f([2 ** 20], [1], 0) == 1
assert f([3], [312], 0) == 59
assert f([3], [1], 806) == 42
g = pfunc([a, b, c], (a + b + c), allow_input_downcast=False)
# All values are in range. Since they're not ndarrays (but lists
# or scalars), they will be converted, and their value checked.
assert np.all(g([3], [6], 0) == 9)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
with pytest.raises(TypeError):
g([3], np.array([6], dtype="int16"), 0)
# Value too big for b, raises TypeError
with pytest.raises(TypeError):
g([3], [312], 0)
h = pfunc([a, b, c], (a + b + c)) # Default: allow_input_downcast=None
# Everything here should behave like with False
assert np.all(h([3], [6], 0) == 9)
with pytest.raises(TypeError):
h([3], np.array([6], dtype="int16"), 0)
with pytest.raises(TypeError):
h([3], [312], 0)
def test_shared(self):
# CHECK: two functions (f1 and f2) can share w
w = shared(np.random.rand(2, 2), "w")
wval = w.get_value(borrow=False)
x = dmatrix()
out1 = w + x
out2 = w * x
f1 = pfunc([x], [out1])
f2 = pfunc([x], [out2])
xval = np.random.rand(2, 2)
assert np.all(f1(xval) == xval + wval)
assert np.all(f2(xval) == xval * wval)
# CHECK: updating a shared value
f3 = pfunc([x], out1, updates=[(w, (w - 1))])
# f3 changes the value of w
assert np.all(f3(xval) == xval + wval)
# this same value is read by f1
assert np.all(f1(xval) == xval + (wval - 1))
w.set_value(w.get_value(borrow=True) * 10, borrow=True)
# this same value is read by f1
assert np.all(f1(xval) == xval + w.get_value(borrow=True))
def test_no_shared_as_input(self):
# Test that shared variables cannot be used as function inputs.
w_init = np.random.rand(2, 2)
w = shared(w_init.copy(), "w")
with pytest.raises(
TypeError, match=r"^Cannot use a shared variable \(w\) as explicit input"
):
pfunc([w], aet_sum(w * w))
def test_default_updates(self):
x = shared(0)
x.default_update = x + 1
f = pfunc([], [x])
f()
assert x.get_value() == 1
del x.default_update
f()
assert x.get_value() == 2
g = pfunc([], [x])
g()
assert x.get_value() == 2
def test_givens(self):
x = shared(0)
assign = pfunc([], x, givens={x: 3})
assert assign() == 3
assert x.get_value(borrow=True) == 0
y = ivector()
f = pfunc([y], (y * x), givens={x: 6})
assert np.all(f([1, 1, 1]) == [6, 6, 6])
assert x.get_value() == 0
z = ivector()
c = z * y
f = pfunc([y], (c + 7), givens={z: _asarray([4, 4, 4], dtype="int32")})
assert np.all(f([1, 1, 1]) == [11, 11, 11])
assert x.get_value() == 0
def test_param_allow_downcast_int(self):
a = wvector("a") # int16
b = bvector("b") # int8
c = bscalar("c") # int8
f = pfunc(
[
In(a, allow_downcast=True),
In(b, allow_downcast=False),
In(c, allow_downcast=None),
],
(a + b + c),
)
# Both values are in range. Since they're not ndarrays (but lists),
# they will be converted, and their value checked.
assert np.all(f([3], [6], 1) == 10)
# Values are in range, but a dtype too large has explicitly been given
# For performance reasons, no check of the data is explicitly performed
# (It might be OK to change this in the future.)
with pytest.raises(TypeError):
f([3], np.array([6], dtype="int16"), 1)
# Value too big for a, silently ignored
assert np.all(f([2 ** 20], np.ones(1, dtype="int8"), 1) == 2)
# Value too big for b, raises TypeError
with pytest.raises(TypeError):
f([3], [312], 1)
# Value too big for c, raises TypeError
with pytest.raises(TypeError):
f([3], [6], 806)
def test_param_allow_downcast_floatX(self):
a = fscalar("a")
b = fscalar("b")
c = fscalar("c")
f = pfunc(
[
In(a, allow_downcast=True),
In(b, allow_downcast=False),
In(c, allow_downcast=None),
],
(a + b + c),
)
# If the values can be accurately represented, everything is OK
assert np.all(f(0, 0, 0) == 0)
# If allow_downcast is True, idem
assert np.allclose(f(0.1, 0, 0), 0.1)
# If allow_downcast is False, nope
with pytest.raises(TypeError):
f(0, 0.1, 0)
# If allow_downcast is None, it should work iff floatX=float32
if config.floatX == "float32":
assert np.allclose(f(0, 0, 0.1), 0.1)
else:
with pytest.raises(TypeError):
f(0, 0, 0.1)
def test_param_allow_downcast_vector_floatX(self):
a = fvector("a")
b = fvector("b")
c = fvector("c")
f = pfunc(
[
In(a, allow_downcast=True),
In(b, allow_downcast=False),
In(c, allow_downcast=None),
],
(a + b + c),
)
# If the values can be accurately represented, everything is OK
z = [0]
assert np.all(f(z, z, z) == 0)
# If allow_downcast is True, idem
assert np.allclose(f([0.1], z, z), 0.1)
# If allow_downcast is False, nope
with pytest.raises(TypeError):
f(z, [0.1], z)
# If allow_downcast is None, like False
with pytest.raises(TypeError):
f(z, z, [0.1])
def test_givens_replaces_shared_variable2(self):
a = shared(1.0, "a")
a.default_update = a + 3
c = a + 10
f = pfunc([], c, givens={a: (a + 10)})
assert f() == 21
assert f() == 34
def test_default_scalar_container(self):
# Similar in spirit to test_default_container, but updating a scalar
# variable. This is a sanity check for non mutable types.
x = shared(0.0, "x")
f = pfunc([], x)
assert f() == 0
x.set_value(x.get_value(borrow=True) + 1, borrow=True)
assert f() == 1
def test_no_aliasing_0(self):
# B is a shared variable, A is updated with B's contents
# we need A to be copied to avoid aliasing
A = self.shared(np.zeros((2, 2)) + 0.5)
B = self.shared(np.zeros((2, 2)) - 0.5)
f = pfunc([], [], updates=[(A, B)])
f()
assert not np.may_share_memory(data_of(A), data_of(B))
def test_param_mutable(self):
a = dvector()
a_out = a * 2 # assuming the op which makes this "in place" triggers
# using mutable=True will let fip change the value in aval
fip = pfunc([In(a, mutable=True)], [a_out], mode="FAST_RUN")
aval = np.random.rand(10)
aval2 = aval.copy()
assert np.all(fip(aval) == (aval2 * 2))
assert not np.all(aval == aval2)
# using mutable=False should leave the input untouched
f = pfunc([In(a, mutable=False)], [a_out], mode="FAST_RUN")
aval = np.random.rand(10)
aval2 = aval.copy()
assert np.all(f(aval) == (aval2 * 2))
assert np.all(aval == aval2)
def test_default_updates_partial_graph(self):
a = shared(0)
a.default_update = a + 1 # Increment a each time it is used
b = 2 * a
# Use only the tip of the graph, a is not used
f = pfunc([b], b)
assert a.get_value() == 0
f(21)
assert a.get_value() == 0
def test_param_strict(self):
a = dvector()
b = shared(7)
out = a + b
f = pfunc([In(a, strict=False)], [out])
# works, rand generates float64 by default
f(np.random.rand(8))
# works, casting is allowed
f(np.array([1, 2, 3, 4], dtype="int32"))
f = pfunc([In(a, strict=True)], [out])
try:
# fails, f expects float64
f(np.array([1, 2, 3, 4], dtype="int32"))
except TypeError:
pass
def test_givens_replaces_shared_variable(self):
a = shared(1.0, "a")
a.default_update = a + 3.0
b = dscalar("b")
c = a + 10
f = pfunc([b], c, givens={a: b})
assert len(f.maker.fgraph.inputs) == 1
assert len(f.maker.fgraph.outputs) == 1
def test_default_updates_expressions(self):
x = shared(0)
y = shared(1)
a = lscalar("a")
z = a * x
x.default_update = x + y
f1 = pfunc([a], z)
f1(12)
assert x.get_value() == 1
f2 = pfunc([a], z, no_default_updates=True)
assert f2(7) == 7
assert x.get_value() == 1
f3 = pfunc([a], z, no_default_updates=[x])
assert f3(9) == 9
assert x.get_value() == 1
def test_update(self):
# Test update mechanism in different settings.
# Simple value assignment.
x = shared(0)
assign = pfunc([], [], updates={x: 3})
assign()
assert x.get_value() == 3
# Basic increment function.
x.set_value(0)
inc = pfunc([], [], updates={x: x + 1})
inc()
assert x.get_value() == 1
# Increment by a constant value.
x.set_value(-1)
y = shared(2)
inc_by_y = pfunc([], [], updates={x: x + y})
inc_by_y()
assert x.get_value() == 1
def test_clone0(self):
x = shared(np.asarray([4, 4, 4]))
y = shared(np.asarray([4, 4, 4]))
z = shared(np.asarray([2, 2, 2]))
up = pfunc(
[], [], updates={x: (x * 5), y: ((x * 5) + y), z: (((x * 5) + y) ** z)}
)
up()
assert np.all(x.get_value() == 20)
assert np.all(y.get_value() == 24)
assert np.all(z.get_value() == (24 ** 2))
def test_default_updates_chained(self):
x = shared(2)
y = shared(1)
z = shared(-1)
x.default_update = x - y
y.default_update = z
z.default_update = z - 1
f1 = pfunc([], [x])
f1()
assert x.get_value() == 1
assert y.get_value() == -1
assert z.get_value() == -2
f2 = pfunc([], [x, y])
f2()
assert x.get_value() == 2
assert y.get_value() == -2
assert z.get_value() == -3
f3 = pfunc([], [y])
f3()
assert x.get_value() == 2
assert y.get_value() == -3
assert z.get_value() == -4
f4 = pfunc([], [x, y], no_default_updates=[x])
f4()
assert x.get_value() == 2
assert y.get_value() == -4
assert z.get_value() == -5
f5 = pfunc([], [x, y, z], no_default_updates=[z])
f5()
assert x.get_value() == 6
assert y.get_value() == -5
assert z.get_value() == -5
def test_potential_output_aliasing_induced_by_updates(self):
A = self.shared(np.zeros((2, 2)))
B = self.shared(np.zeros((2, 2)))
C = np.zeros((2, 2))
D = dmatrix()
DD = D + 5
f = pfunc([D], [], updates=[(A, D), (B, D)])
f(C)
assert not np.may_share_memory(data_of(A), data_of(B))
f = pfunc([D], [], updates=[(A, D[:]), (B, D)])
f(C)
assert not np.may_share_memory(data_of(A), data_of(B))
f = pfunc([D], [], updates=[(A, (D + 5)), (B, D[:])])
f(C)
assert not np.may_share_memory(data_of(A), data_of(B))
f = pfunc([D], [], updates=[(A, (D + 5)), (B, D)])
f(C)
assert not np.may_share_memory(data_of(A), data_of(B))
f = pfunc([D], DD, updates=[(A, DD[:1]), (B, DD)])
R = f(C)
assert not np.may_share_memory(data_of(A), data_of(B))
assert not np.may_share_memory(R, data_of(B))
assert not np.may_share_memory(R, data_of(A))
f = pfunc([D], DD, updates=[(A, DD[:1]), (B, (DD[:1] * 2))])
R = f(C)
assert not np.may_share_memory(data_of(A), data_of(B))
assert not np.may_share_memory(R, data_of(B))
assert not np.may_share_memory(R, data_of(A))
f = pfunc([D], (DD * 4),
updates=[(A, (DD[:1] * 3)), (B, (DD[:1] * 2))])
R = f(C)
assert not np.may_share_memory(data_of(A), data_of(B))
assert not np.may_share_memory(R, data_of(B))
assert not np.may_share_memory(R, data_of(A))
f = pfunc([D], (DD * 4),
updates=[(A, (DD[:1] * 3)), (B, (DD[:1] * 3))])
R = f(C)
assert not np.may_share_memory(data_of(A), data_of(B))
assert not np.may_share_memory(R, data_of(B))
assert not np.may_share_memory(R, data_of(A))
def test_default_container(self):
# Ensure it is possible to (implicitly) use a shared variable in a
# function, as a 'state' that can be updated at will.
rng = np.random.RandomState(1827)
w_init = rng.rand(5)
w = shared(w_init.copy(), "w")
reg = aet_sum(w * w)
f = pfunc([], reg)
assert f() == np.sum(w_init * w_init)
# Change the value of w and ensure the output changes accordingly.
w.set_value(w.get_value(borrow=True) + 1.0, borrow=True)
assert f() == np.sum((w_init + 1) ** 2)
def test_no_aliasing_1(self):
# B is a shared variable, A is updated with B's contents
# since B is being updated as well, we don't need to copy anything
# to avoid aliasing shared variables.
A = self.shared(np.zeros((2, 2)) + 0.5)
B = self.shared(np.zeros((2, 2)) - 0.5)
C = dmatrix()
f = pfunc([C], [], updates=[(A, B), (B, C)])
z = np.zeros((2, 2))
f(z)
assert not np.may_share_memory(data_of(A), data_of(B))
# Aesara tries to maintain its own memory space.
assert not np.may_share_memory(z, data_of(B))
assert np.all(data_of(B) == z)
def test_no_aliasing_2b(self):
# B and A take one another's values
# no copying is necessary since each one is updated.
# The twist one `test_no_aliasing_2` is that each shared var is updated
# with a view of the other one.
orig_a = np.zeros((2, 2)) + 0.5
orig_b = np.zeros((2, 2)) - 0.5
A = self.shared(orig_a)
B = self.shared(orig_b)
data_of_a = data_of(A)
data_of_b = data_of(B)
f = pfunc([], [], updates=[(A, B[:, ::-1]), (B, A.T)])
# aesara.printing.debugprint(f)
f()
# correctness (doesn't actually test the view...)
assert np.all(data_of(A) == -0.5)
assert np.all(data_of(B) == +0.5)
# shared vars may not be aliased
assert not np.may_share_memory(data_of(A), data_of(B))
# aesara should have been smart enough to not make copies
if aesara.config.mode not in [
"DebugMode", "DEBUG_MODE", "FAST_COMPILE"
]:
# We don't ask DebugMode and FAST_COMPILE not to make copy.
# We have the right to do so.
assert np.all(data_of(A) < 5)
data_of_b += 10
assert np.all(data_of(A) > 5)
data_of_b -= 10
assert np.all(data_of(B) < 5)
data_of_a += 10
assert np.all(data_of(B) > 5)
data_of_a -= 10
# N.B. may_share_memory is what we mean, but does it work?
assert np.may_share_memory(data_of(A), data_of_b)
assert np.may_share_memory(data_of(B), data_of_a)
def test_no_aliasing_2(self):
# B and A take one another's values
# no copying is necessary since each one is updated.
orig_a = np.zeros((2, 2)) + 0.5
orig_b = np.zeros((2, 2)) - 0.5
A = self.shared(orig_a)
B = self.shared(orig_b)
data_of_a = data_of(A)
data_of_b = data_of(B)
f = pfunc([], [], updates=[(A, B), (B, A)])
f()
# correctness
assert np.all(data_of(A) == -0.5)
assert np.all(data_of(B) == +0.5)
# shared vars may not be aliased
assert not np.may_share_memory(data_of(A), data_of(B))
# aesara should have been smart enough to not make copies
assert np.may_share_memory(data_of(A), data_of_b)
assert np.may_share_memory(data_of(B), data_of_a)
def test_default_updates_input(self):
x = shared(0)
y = shared(1)
if PYTHON_INT_BITWIDTH == 32:
a = iscalar("a")
else:
a = lscalar("a")
x.default_update = y
y.default_update = y + a
f1 = pfunc([], x, no_default_updates=True)
f1()
assert x.get_value() == 0
assert y.get_value() == 1
f2 = pfunc([], x, no_default_updates=[x])
f2()
assert x.get_value() == 0
assert y.get_value() == 1
f3 = pfunc([], x, no_default_updates=[y])
f3()
assert x.get_value() == 1
assert y.get_value() == 1
f4 = pfunc([a], x)
f4(2)
assert x.get_value() == 1
assert y.get_value() == 3
f5 = pfunc([], x, updates={y: (y - 1)})
f5()
assert x.get_value() == 3
assert y.get_value() == 2
# a is needed as input if y.default_update is used
with pytest.raises(MissingInputError):
pfunc([], x)
def test_no_default_updates(self):
x = shared(0)
y = shared(1)
x.default_update = x + 2
# Test that the default update is taken into account in the right cases
f1 = pfunc([], [x], no_default_updates=True)
f1()
assert x.get_value() == 0
f2 = pfunc([], [x], no_default_updates=[x])
f2()
assert x.get_value() == 0
f3 = pfunc([], [x], no_default_updates=[x, y])
f3()
assert x.get_value() == 0
f4 = pfunc([], [x], no_default_updates=[y])
f4()
assert x.get_value() == 2
f5 = pfunc([], [x], no_default_updates=[])
f5()
assert x.get_value() == 4
f5 = pfunc([], [x], no_default_updates=False)
f5()
assert x.get_value() == 6
with pytest.raises(TypeError):
pfunc([], [x], no_default_updates=(x))
with pytest.raises(TypeError):
pfunc([], [x], no_default_updates=x)
with pytest.raises(TypeError):
pfunc([], [x], no_default_updates="canard")
# Mix explicit updates and no_default_updates
g1 = pfunc([], [x], updates=[(x, (x - 1))], no_default_updates=True)
g1()
assert x.get_value() == 5
g2 = pfunc([], [x], updates=[(x, (x - 1))], no_default_updates=[x])
g2()
assert x.get_value() == 4
g3 = pfunc([], [x], updates=[(x, (x - 1))], no_default_updates=[x, y])
g3()
assert x.get_value() == 3
g4 = pfunc([], [x], updates=[(x, (x - 1))], no_default_updates=[y])
g4()
assert x.get_value() == 2
g5 = pfunc([], [x], updates=[(x, (x - 1))], no_default_updates=[])
g5()
assert x.get_value() == 1
g5 = pfunc([], [x], updates=[(x, (x - 1))], no_default_updates=False)
g5()
assert x.get_value() == 0
