def test_multMatVect():
A1 = tensor.lmatrix("A1")
s1 = tensor.ivector("s1")
m1 = tensor.iscalar("m1")
A2 = tensor.lmatrix("A2")
s2 = tensor.ivector("s2")
m2 = tensor.iscalar("m2")
g0 = rng_mrg.DotModulo()(A1, s1, m1, A2, s2, m2)
f0 = aesara.function([A1, s1, m1, A2, s2, m2], g0)
i32max = np.iinfo(np.int32).max
A1 = np.random.randint(0, i32max, (3, 3)).astype("int64")
s1 = np.random.randint(0, i32max, 3).astype("int32")
m1 = np.asarray(np.random.randint(i32max), dtype="int32")
A2 = np.random.randint(0, i32max, (3, 3)).astype("int64")
s2 = np.random.randint(0, i32max, 3).astype("int32")
m2 = np.asarray(np.random.randint(i32max), dtype="int32")
f0.input_storage[0].storage[0] = A1
f0.input_storage[1].storage[0] = s1
f0.input_storage[2].storage[0] = m1
f0.input_storage[3].storage[0] = A2
f0.input_storage[4].storage[0] = s2
f0.input_storage[5].storage[0] = m2
r_a1 = rng_mrg.matVecModM(A1, s1, m1)
r_a2 = rng_mrg.matVecModM(A2, s2, m2)
f0.fn()
r_b = f0.output_storage[0].value
assert np.allclose(r_a1, r_b[:3])
assert np.allclose(r_a2, r_b[3:])
def multMatVect(v, A, m1, B, m2):
# TODO : need description for parameter and return
"""
Multiply the first half of v by A with a modulo of m1 and the second half
by B with a modulo of m2.
Notes
-----
The parameters of dot_modulo are passed implicitly because passing them
explicitly takes more time than running the function's C-code.
"""
if multMatVect.dot_modulo is None:
A_sym = tensor.lmatrix("A")
s_sym = tensor.ivector("s")
m_sym = tensor.iscalar("m")
A2_sym = tensor.lmatrix("A2")
s2_sym = tensor.ivector("s2")
m2_sym = tensor.iscalar("m2")
o = DotModulo()(A_sym, s_sym, m_sym, A2_sym, s2_sym, m2_sym)
multMatVect.dot_modulo = function(
[A_sym, s_sym, m_sym, A2_sym, s2_sym, m2_sym], o, profile=False)
# This way of calling the Aesara fct is done to bypass Aesara overhead.
f = multMatVect.dot_modulo
f.input_storage[0].storage[0] = A
f.input_storage[1].storage[0] = v[:3]
f.input_storage[2].storage[0] = m1
f.input_storage[3].storage[0] = B
f.input_storage[4].storage[0] = v[3:]
f.input_storage[5].storage[0] = m2
f.fn()
r = f.output_storage[0].storage[0]
return r
def create_test_hmm():
srng = at.random.RandomStream()
N_tt = at.iscalar("N")
N_tt.tag.test_value = 10
M_tt = at.iscalar("M")
M_tt.tag.test_value = 2
mus_tt = at.matrix("mus")
mus_tt.tag.test_value = np.stack(
[np.arange(0.0, 10), np.arange(0.0, -10, -1)],
axis=-1).astype(aesara.config.floatX)
sigmas_tt = at.ones((N_tt, ))
sigmas_tt.name = "sigmas"
pi_0_rv = srng.dirichlet(at.ones((M_tt, )), name="pi_0")
Gamma_rv = srng.dirichlet(at.ones((M_tt, M_tt)), name="Gamma")
S_0_rv = srng.categorical(pi_0_rv, name="S_0")
def scan_fn(mus_t, sigma_t, S_tm1, Gamma_t):
S_t = srng.categorical(Gamma_t[S_tm1], name="S_t")
Y_t = srng.normal(mus_t[S_t], sigma_t, name="Y_t")
return S_t, Y_t
(S_rv, Y_rv), scan_updates = aesara.scan(
fn=scan_fn,
sequences=[mus_tt, sigmas_tt],
non_sequences=[Gamma_rv],
outputs_info=[{
"initial": S_0_rv,
"taps": [-1]
}, {}],
strict=True,
name="scan_rv",
)
Y_rv.name = "Y_rv"
scan_op = Y_rv.owner.op
scan_args = ScanArgs.from_node(Y_rv.owner)
Gamma_in = scan_args.inner_in_non_seqs[0]
Y_t = scan_args.inner_out_nit_sot[0]
mus_t = scan_args.inner_in_seqs[0]
sigmas_t = scan_args.inner_in_seqs[1]
S_t = scan_args.inner_out_sit_sot[0]
rng_in = scan_args.inner_out_shared[0]
mus_in = Y_rv.owner.inputs[1]
mus_in.name = "mus_in"
sigmas_in = Y_rv.owner.inputs[2]
sigmas_in.name = "sigmas_in"
# The output `S_rv` is really `S_rv[1:]`, so we have to extract the actual
# `Scan` output: `S_rv`.
S_in = S_rv.owner.inputs[0]
S_in.name = "S_in"
return locals()
def test_swap_SharedVariable_with_given(self):
# A special testcase for logistic_sgd.py in Deep Learning Tutorial
# This test assert that SharedVariable in different function have same storage
train_x = aesara.shared(value=np.random.rand(10, 10).astype(config.floatX))
test_x = aesara.shared(value=np.random.rand(10, 10).astype(config.floatX))
train_y = aesara.shared(value=np.random.rand(10, 1).astype(config.floatX))
test_y = aesara.shared(value=np.random.rand(10, 1).astype(config.floatX))
i = tt.iscalar("index")
x = tt.vector("x")
y = tt.vector("y")
# this formular has no sense but for a test
out = (tt.sum(x) - y) ** 2
train = aesara.function(
[i],
out,
givens={x: train_x[i], y: train_y[i]},
updates={train_x: train_x + 0.1},
)
test_def = aesara.function([i], out, givens={x: test_x[i], y: test_y[i]})
test_cpy = train.copy(
swap={train_x: test_x, train_y: test_y}, delete_updates=True
)
for in1, in2 in zip(test_def.maker.inputs, test_cpy.maker.inputs):
assert in1.value is in2.value
def test_select_proportional_to_weight(self):
# Tests that ChoiceFromUniform selects elements, on average,
# proportional to the their probabilities
p = tensor.fmatrix()
u = tensor.fvector()
n = tensor.iscalar()
m = multinomial.ChoiceFromUniform(odtype="auto")(p, u, n)
f = function([p, u, n], m, allow_input_downcast=True)
n_elements = 100
n_selected = 10
mean_rtol = 0.0005
np.random.seed(12345)
pvals = np.random.randint(1, 100,
(1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
avg_pvals = np.zeros((n_elements, ), dtype=config.floatX)
for rep in range(10000):
uni = np.random.rand(n_selected).astype(config.floatX)
res = f(pvals, uni, n_selected)
res = np.squeeze(res)
avg_pvals[res] += 1
avg_pvals /= avg_pvals.sum()
avg_diff = np.mean(abs(avg_pvals - pvals))
assert avg_diff < mean_rtol, avg_diff
def test_merge_ifs_true_false(self):
x1 = tensor.scalar("x1")
x2 = tensor.scalar("x2")
y1 = tensor.scalar("y1")
y2 = tensor.scalar("y2")
w1 = tensor.scalar("w1")
w2 = tensor.scalar("w2")
c = tensor.iscalar("c")
out = ifelse(
c,
ifelse(c, x1, x2) + ifelse(c, y1, y2) + w1,
ifelse(c, x1, x2) + ifelse(c, y1, y2) + w2,
)
f = aesara.function([x1, x2, y1, y2, w1, w2, c],
out,
allow_input_downcast=True)
assert (len([
x for x in f.maker.fgraph.toposort() if isinstance(x.op, IfElse)
]) == 1)
rng = np.random.RandomState(utt.fetch_seed())
vx1 = rng.uniform()
vx2 = rng.uniform()
vy1 = rng.uniform()
vy2 = rng.uniform()
vw1 = rng.uniform()
vw2 = rng.uniform()
assert np.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 1), vx1 + vy1 + vw1)
assert np.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 0), vx2 + vy2 + vw2)
def test_not_lazy_if_inplace(self):
# Tests that if the outputs are scalars and the graph is big,
# we disable the inplace opt to speed up optimization
x = tensor.vector("x", dtype=self.dtype)
y = tensor.vector("y", dtype=self.dtype)
c = tensor.iscalar("c")
mode = aesara.compile.get_mode(self.mode).excluding(
# Disable many opt to keep the graph big enough to disable
# the opt.
"fusion",
"local_add_canonizer",
"inplace",
"constant_folding",
"constant_folding",
)
y2 = reduce(lambda x, y: x + y, [y] + list(range(200)))
f = aesara.function([c, x, y], ifelse(c, x, y2), mode=mode)
# For not inplace ifelse
ifnode = [
n for n in f.maker.fgraph.toposort() if isinstance(n.op, IfElse)
]
assert len(ifnode) == 1
assert not ifnode[0].op.as_view
rng = np.random.RandomState(utt.fetch_seed())
xlen = rng.randint(200)
ylen = rng.randint(200)
vx = np.asarray(rng.uniform(size=(xlen, )), self.dtype)
vy = np.asarray(rng.uniform(size=(ylen, )), self.dtype)
assert np.allclose(vx, f(1, vx, vy))
assert np.allclose(vy + sum(range(200)), f(0, vx, vy))
def test_select_proportional_to_weight(self):
# Tests that multinomial_wo_replacement selects elements, on average,
# proportional to the their probabilities
th_rng = RandomStreams(12345)
p = tensor.fmatrix()
n = tensor.iscalar()
m = th_rng.multinomial_wo_replacement(pvals=p, n=n)
f = function([p, n], m, allow_input_downcast=True)
n_elements = 100
n_selected = 10
mean_rtol = 0.0005
np.random.seed(12345)
pvals = np.random.randint(1, 100,
(1, n_elements)).astype(config.floatX)
pvals /= pvals.sum(1)
avg_pvals = np.zeros((n_elements, ), dtype=config.floatX)
for rep in range(10000):
res = f(pvals, n_selected)
res = np.squeeze(res)
avg_pvals[res] += 1
avg_pvals /= avg_pvals.sum()
avg_diff = np.mean(abs(avg_pvals - pvals))
assert avg_diff < mean_rtol
def test_multiple_out(self):
x1 = tensor.vector("x1", dtype=self.dtype)
x2 = tensor.vector("x2", dtype=self.dtype)
y1 = tensor.vector("y1", dtype=self.dtype)
y2 = tensor.vector("y2", dtype=self.dtype)
c = tensor.iscalar("c")
z = ifelse(c, (x1, x2), (y1, y2))
f = aesara.function([c, x1, x2, y1, y2], z, mode=self.mode)
self.assertFunctionContains1(f, self.get_ifelse(2))
ifnode = [
x for x in f.maker.fgraph.toposort() if isinstance(x.op, IfElse)
][0]
assert len(ifnode.outputs) == 2
rng = np.random.RandomState(utt.fetch_seed())
x1len = rng.randint(200)
x2len = rng.randint(200)
y1len = rng.randint(200)
y2len = rng.randint(200)
vx1 = np.asarray(rng.uniform(size=(x1len, )), self.dtype)
vx2 = np.asarray(rng.uniform(size=(x2len, )), self.dtype)
vy1 = np.asarray(rng.uniform(size=(y1len, )), self.dtype)
vy2 = np.asarray(rng.uniform(size=(y2len, )), self.dtype)
ovx1, ovx2 = f(1, vx1, vx2, vy1, vy2)
ovy1, ovy2 = f(0, vx1, vx2, vy1, vy2)
assert np.allclose(vx1, ovx1)
assert np.allclose(vy1, ovy1)
assert np.allclose(vx2, ovx2)
assert np.allclose(vy2, ovy2)
def test_multiple_out_grad(self):
# Tests that we can compute the gradients through lazy if
x1 = tensor.vector("x1")
x2 = tensor.vector("x2")
y1 = tensor.vector("y1")
y2 = tensor.vector("y2")
c = tensor.iscalar("c")
z = ifelse(c, (x1, x2), (y1, y2))
grads = tensor.grad(z[0].sum() + z[1].sum(), [x1, x2, y1, y2])
f = aesara.function([c, x1, x2, y1, y2], grads)
rng = np.random.RandomState(utt.fetch_seed())
lens = [rng.randint(200) for i in range(4)]
values = [
np.asarray(rng.uniform(size=(l, )), aesara.config.floatX)
for l in lens
]
outs_1 = f(1, *values)
assert all([x.shape[0] == y for x, y in zip(outs_1, lens)])
assert np.all(outs_1[0] == 1.0)
assert np.all(outs_1[1] == 1.0)
assert np.all(outs_1[2] == 0.0)
assert np.all(outs_1[3] == 0.0)
outs_0 = f(0, *values)
assert all([x.shape[0] == y for x, y in zip(outs_1, lens)])
assert np.all(outs_0[0] == 0.0)
assert np.all(outs_0[1] == 0.0)
assert np.all(outs_0[2] == 1.0)
assert np.all(outs_0[3] == 1.0)
def test_mixed_dtype(self):
x1 = tensor.vector("x1", dtype="int32")
x2 = tensor.vector("x2", dtype=self.dtype)
y1 = tensor.vector("y1", dtype="int32")
y2 = tensor.vector("y2", dtype=self.dtype)
c = tensor.iscalar("c")
f = aesara.function([c, x1, x2, y1, y2],
ifelse(c, (x1, x2), (y1, y2)),
mode=self.mode)
self.assertFunctionContains1(f, self.get_ifelse(2))
rng = np.random.RandomState(utt.fetch_seed())
xlen = rng.randint(200)
ylen = rng.randint(200)
vx1 = np.asarray(rng.uniform(size=(xlen, )) * 3, "int32")
vx2 = np.asarray(rng.uniform(size=(xlen, )), self.dtype)
vy1 = np.asarray(rng.uniform(size=(ylen, )) * 3, "int32")
vy2 = np.asarray(rng.uniform(size=(ylen, )), self.dtype)
o1, o2 = f(1, vx1, vx2, vy1, vy2)
assert np.allclose(vx1, o1)
assert np.allclose(vx2, o2)
o1, o2 = f(0, vx1, vx2, vy1, vy2)
assert np.allclose(vy1, o1)
assert np.allclose(vy2, o2)
def test_grad_lazy_if(self):
# Tests that we can compute the gradients through lazy if
x = tensor.vector("x", dtype=self.dtype)
y = tensor.vector("y", dtype=self.dtype)
c = tensor.iscalar("c")
z = ifelse(c, x, y)
gx, gy = tensor.grad(z.sum(), [x, y])
f = aesara.function(
[c, x, y],
[self.cast_output(gx), self.cast_output(gy)],
mode=self.mode)
# There is only 2 of the 3 ifelse that are moved on the GPU.
# The one that stay on the CPU is for the shape.
self.assertFunctionContains(f, self.get_ifelse(1), min=2, max=3)
rng = np.random.RandomState(utt.fetch_seed())
xlen = rng.randint(200)
ylen = rng.randint(200)
vx = np.asarray(rng.uniform(size=(xlen, )), self.dtype)
vy = np.asarray(rng.uniform(size=(ylen, )), self.dtype)
gx0, gy0 = f(1, vx, vy)
assert np.allclose(gx0.shape, vx.shape)
assert np.allclose(gy0.shape, vy.shape)
assert np.all(np.asarray(gx0) == 1.0)
assert np.all(np.asarray(gy0) == 0.0)
gx0, gy0 = f(0, vx, vy)
assert np.allclose(gx0.shape, vx.shape)
assert np.allclose(gy0.shape, vy.shape)
assert np.all(np.asarray(gx0) == 0.0)
assert np.all(np.asarray(gy0) == 1.0)
def setUp(self):
extra1 = at.iscalar("extra1")
extra1_ = np.array(0, dtype=extra1.dtype)
extra1.dshape = tuple()
extra1.dsize = 1
val1 = at.vector("val1")
val1_ = np.zeros(3, dtype=val1.dtype)
val1.dshape = (3, )
val1.dsize = 3
val2 = at.matrix("val2")
val2_ = np.zeros((2, 3), dtype=val2.dtype)
val2.dshape = (2, 3)
val2.dsize = 6
self.val1, self.val1_ = val1, val1_
self.val2, self.val2_ = val2, val2_
self.extra1, self.extra1_ = extra1, extra1_
self.cost = extra1 * val1.sum() + val2.sum()
self.f_grad = ValueGradFunction([self.cost], [val1, val2],
{extra1: extra1_},
mode="FAST_COMPILE")
def test_pushout2(self):
x1 = tensor.scalar("x1")
x2 = tensor.scalar("x2")
y1 = tensor.scalar("y1")
y2 = tensor.scalar("y2")
w1 = tensor.scalar("w1")
w2 = tensor.scalar("w2")
c = tensor.iscalar("c")
x, y = ifelse(c, (x1, y1), (x2, y2), name="f1")
z = ifelse(x > y, w1, w2, name="f2")
out = x * z * y
f = aesara.function([x1, x2, y1, y2, w1, w2, c],
out,
allow_input_downcast=True)
assert isinstance(f.maker.fgraph.toposort()[-1].op, IfElse)
rng = np.random.RandomState(utt.fetch_seed())
vx1 = rng.uniform()
vx2 = rng.uniform()
vy1 = rng.uniform()
vy2 = rng.uniform()
vw1 = rng.uniform()
vw2 = rng.uniform()
if vx1 > vy1:
vw = vw1
else:
vw = vw2
assert np.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 1), vx1 * vy1 * vw)
if vx2 > vy2:
vw = vw1
else:
vw = vw2
assert np.allclose(f(vx1, vx2, vy1, vy2, vw1, vw2, 0), vx2 * vy2 * vw)
def test_copy_delete_updates(self):
w = tt.iscalar("w")
x = tt.fscalar("x")
# SharedVariable for tests, one of them has update
y = aesara.shared(value=1, name="y")
z = aesara.shared(value=2, name="z")
out = x + y + z
# Test for different linkers
# for mode in ["FAST_RUN","FAST_COMPILE"]:
# second_time = False
for mode in ["FAST_RUN", "FAST_COMPILE"]:
ori = aesara.function([x], out, mode=mode, updates={z: z * 2})
cpy = ori.copy(delete_updates=True)
assert cpy(1)[0] == 4
assert cpy(1)[0] == 4
assert cpy(1)[0] == 4
# Test if unused implicit and explicit inputs from delete_updates
# are ignored as intended.
for mode in ["FAST_RUN", "FAST_COMPILE"]:
ori = aesara.function([x], x, mode=mode, updates={z: z * 2})
cpy = ori.copy(delete_updates=True)
ori = aesara.function([x, w], x, mode=mode, updates={z: z + w})
cpy = ori.copy(delete_updates=True)
def check(dtype, N, M_=None, k=0):
# Aesara does not accept None as a tensor.
# So we must use a real value.
M = M_
# Currently DebugMode does not support None as inputs even if this is
# allowed.
if M is None:
M = N
N_symb = tt.iscalar()
M_symb = tt.iscalar()
k_symb = tt.iscalar()
out = tt.tri(N_symb, M_symb, k_symb, dtype=dtype) + np.array(1).astype(dtype)
f = aesara.function([N_symb, M_symb, k_symb], out, mode=mode_with_gpu)
result = np.asarray(f(N, M, k)) - np.array(1).astype(dtype)
assert np.allclose(result, np.tri(N, M_, k, dtype=dtype))
assert result.dtype == np.dtype(dtype)
assert any([isinstance(node.op, GpuTri) for node in f.maker.fgraph.toposort()])
def test_grad_cast_input(self):
# Tests the gradient when both inputs are on the GPU.
x = tensor.vector("x", dtype=self.dtype)
y = tensor.vector("y", dtype=self.dtype)
c = tensor.iscalar("c")
z = ifelse(c, self.cast_output(x), self.cast_output(y))
gx, gy = tensor.grad(z.sum(), [x, y])
aesara.function([c, x, y], [gx, gy], mode=self.mode)
def test_record_mode_bad():
# Like test_record_bad, but some events are recorded by the
# aesara RecordMode, as is the event that triggers the mismatch
# error.
# Record a sequence of events
output = StringIO()
recorder = Record(file_object=output, replay=False)
record_mode = RecordMode(recorder)
i = iscalar()
f = function([i], i, mode=record_mode, name="f")
num_lines = 10
for i in range(num_lines):
recorder.handle_line(str(i) + "\n")
f(i)
# Make sure that the playback functionality doesn't raise any errors
# when we repeat them
output_value = output.getvalue()
output = StringIO(output_value)
playback_checker = Record(file_object=output, replay=True)
playback_mode = RecordMode(playback_checker)
i = iscalar()
f = function([i], i, mode=playback_mode, name="f")
for i in range(num_lines // 2):
playback_checker.handle_line(str(i) + "\n")
f(i)
# Make sure a wrong event causes a MismatchError
try:
f(0)
except MismatchError:
return
raise AssertionError("Failed to detect a mismatch.")
def test_lazy_if_on_generics(self):
x = aesara.generic()
y = aesara.generic()
c = tensor.iscalar("c")
f = aesara.function([c, x, y], ifelse(c, x, y))
vx = ["testX"]
vy = ["testY"]
assert f(1, vx, vy) == vx
assert f(0, vx, vy) == vy
def check_u(m, k=0):
m_symb = tt.matrix(dtype=m.dtype)
k_symb = tt.iscalar()
f = aesara.function(
[m_symb, k_symb], tt.triu(m_symb, k_symb), mode=mode_with_gpu
)
result = f(m, k)
assert np.allclose(result, np.triu(m, k))
assert result.dtype == np.dtype(dtype)
assert any([isinstance(node.op, GpuTri) for node in f.maker.fgraph.toposort()])
def test_remove_useless_inputs1(self):
x = tensor.vector("x")
y = tensor.vector("y")
c = tensor.iscalar("c")
z = ifelse(c, (x, x), (y, y))
f = aesara.function([c, x, y], z)
ifnode = [
n for n in f.maker.fgraph.toposort() if isinstance(n.op, IfElse)
][0]
assert len(ifnode.inputs) == 3
def test_merge(self):
x = tensor.vector("x")
y = tensor.vector("y")
c = tensor.iscalar("c")
z1 = ifelse(c, x + 1, y + 1)
z2 = ifelse(c, x + 2, y + 2)
z = z1 + z2
f = aesara.function([c, x, y], z)
assert (len([
n for n in f.maker.fgraph.toposort() if isinstance(n.op, IfElse)
]) == 1)
def test_scan_debugprint1():
k = tensor.iscalar("k")
A = tensor.dvector("A")
# Symbolic description of the result
result, updates = aesara.scan(
fn=lambda prior_result, A: prior_result * A,
outputs_info=tensor.ones_like(A),
non_sequences=A,
n_steps=k,
)
final_result = result[-1]
output_str = aesara.printing.debugprint(final_result, file="str")
lines = output_str.split("\n")
expected_output = """Subtensor{int64} [id A] ''
|Subtensor{int64::} [id B] ''
| |for{cpu,scan_fn} [id C] ''
| | |k [id D]
| | |IncSubtensor{Set;:int64:} [id E] ''
| | | |AllocEmpty{dtype='float64'} [id F] ''
| | | | |Elemwise{add,no_inplace} [id G] ''
| | | | | |k [id D]
| | | | | |Subtensor{int64} [id H] ''
| | | | | |Shape [id I] ''
| | | | | | |Rebroadcast{0} [id J] ''
| | | | | | |InplaceDimShuffle{x,0} [id K] ''
| | | | | | |Elemwise{second,no_inplace} [id L] ''
| | | | | | |A [id M]
| | | | | | |InplaceDimShuffle{x} [id N] ''
| | | | | | |TensorConstant{1.0} [id O]
| | | | | |Constant{0} [id P]
| | | | |Subtensor{int64} [id Q] ''
| | | | |Shape [id R] ''
| | | | | |Rebroadcast{0} [id J] ''
| | | | |Constant{1} [id S]
| | | |Rebroadcast{0} [id J] ''
| | | |ScalarFromTensor [id T] ''
| | | |Subtensor{int64} [id H] ''
| | |A [id M]
| |Constant{1} [id U]
|Constant{-1} [id V]
Inner graphs of the scan ops:
for{cpu,scan_fn} [id C] ''
>Elemwise{mul,no_inplace} [id W] ''
> |<TensorType(float64, vector)> [id X] -> [id E]
> |A_copy [id Y] -> [id M]"""
for truth, out in zip(expected_output.split("\n"), lines):
assert truth.strip() == out.strip()
def test_n_samples_1():
p = tensor.fmatrix()
u = tensor.fvector()
n = tensor.iscalar()
m = multinomial.MultinomialFromUniform("auto")(p, u, n)
f = function([p, u, n], m, allow_input_downcast=True)
np.random.seed(12345)
for i in [1, 5, 10, 100, 1000, 10000]:
uni = np.random.rand(2 * i).astype(config.floatX)
res = f([[1.0, 0.0], [0.0, 1.0]], uni, i)
utt.assert_allclose(res, [[i * 1.0, 0.0], [0.0, i * 1.0]])
def test_cpu_contiguous():
a = tt.fmatrix("a")
i = tt.iscalar("i")
a_val = np.asarray(np.random.rand(4, 5), dtype="float32")
f = aesara.function([a, i], cpu_contiguous(a.reshape((5, 4))[::i]))
topo = f.maker.fgraph.toposort()
assert any([isinstance(node.op, CpuContiguous) for node in topo])
assert f(a_val, 1).flags["C_CONTIGUOUS"]
assert f(a_val, 2).flags["C_CONTIGUOUS"]
assert f(a_val, 3).flags["C_CONTIGUOUS"]
# Test the grad:
utt.verify_grad(cpu_contiguous, [np.random.rand(5, 7, 2)])
def test_remove_useless_inputs2(self):
x1 = tensor.vector("x1")
x2 = tensor.vector("x2")
y1 = tensor.vector("y1")
y2 = tensor.vector("y2")
c = tensor.iscalar("c")
z = ifelse(c, (x1, x1, x1, x2, x2), (y1, y1, y2, y2, y2))
f = aesara.function([c, x1, x2, y1, y2], z)
ifnode = [
x for x in f.maker.fgraph.toposort() if isinstance(x.op, IfElse)
][0]
assert len(ifnode.outputs) == 3
def test_gpu_contiguous():
a = tt.fmatrix("a")
i = tt.iscalar("i")
a_val = np.asarray(np.random.rand(4, 5), dtype="float32")
# The reshape is needed otherwise we make the subtensor on the CPU
# to transfer less data.
f = aesara.function(
[a, i], gpu_contiguous(a.reshape((5, 4))[::i]), mode=mode_with_gpu
)
topo = f.maker.fgraph.toposort()
assert any([isinstance(node.op, GpuSubtensor) for node in topo])
assert any([isinstance(node.op, GpuContiguous) for node in topo])
assert f(a_val, 1).flags.c_contiguous
assert f(a_val, 2).flags.c_contiguous
assert f(a_val, 2).flags.c_contiguous
def test_local_alloc_dimshuffle():
alloc_dimshuffle = out2in(local_alloc_dimshuffle)
x = tensor.vector("x")
m = tensor.iscalar("m")
y = x.dimshuffle("x", 0)
out = tensor.alloc(y, m, 1, x.shape[0])
g = FunctionGraph([x, m], [out])
alloc_dimshuffle(g)
topo = g.toposort()
assert any([not isinstance(x, DimShuffle) for x in topo])
def test_record_mode_good():
# Like test_record_good, but some events are recorded by the
# aesara RecordMode. We don't attempt to check the
# exact string value of the record in this case.
# Record a sequence of events
output = StringIO()
recorder = Record(file_object=output, replay=False)
record_mode = RecordMode(recorder)
i = iscalar()
f = function([i], i, mode=record_mode, name="f")
num_lines = 10
for i in range(num_lines):
recorder.handle_line(str(i) + "\n")
f(i)
# Make sure that the playback functionality doesn't raise any errors
# when we repeat them
output_value = output.getvalue()
output = StringIO(output_value)
playback_checker = Record(file_object=output, replay=True)
playback_mode = RecordMode(playback_checker)
i = iscalar()
f = function([i], i, mode=playback_mode, name="f")
for i in range(num_lines):
playback_checker.handle_line(str(i) + "\n")
f(i)
def test_functions():
xvals = list(map(np.atleast_1d, [0.01, 0.1, 2, 100, 10000]))
x = aet.dvector("x")
x.tag.test_value = xvals[0]
p = aet.iscalar("p")
p.tag.test_value = 1
gammaln = function([x], ps.gammaln(x))
psi = function([x], ps.psi(x))
function([x, p], ps.multigammaln(x, p))
for x in xvals:
check_vals(gammaln, ss.gammaln, x)
for x in xvals[1:]:
check_vals(psi, ss.psi, x)