def test_nesting():
def testing():
with pyro.markov():
v1 = pyro.to_data(Tensor(torch.ones(2), OrderedDict([(str(1), funsor.Bint[2])]), 'real'))
print(1, v1.shape) # shapes should alternate
assert v1.shape == (2,)
with pyro.markov():
v2 = pyro.to_data(Tensor(torch.ones(2), OrderedDict([(str(2), funsor.Bint[2])]), 'real'))
print(2, v2.shape) # shapes should alternate
assert v2.shape == (2, 1)
with pyro.markov():
v3 = pyro.to_data(Tensor(torch.ones(2), OrderedDict([(str(3), funsor.Bint[2])]), 'real'))
print(3, v3.shape) # shapes should alternate
assert v3.shape == (2,)
with pyro.markov():
v4 = pyro.to_data(Tensor(torch.ones(2), OrderedDict([(str(4), funsor.Bint[2])]), 'real'))
print(4, v4.shape) # shapes should alternate
assert v4.shape == (2, 1)
with pyro_backend("contrib.funsor"), NamedMessenger(first_available_dim=-1):
testing()
def test_fresh_inputs_to_funsor():
def testing():
x = pyro.to_funsor(torch.tensor([0., 1.]), funsor.Real, dim_to_name={-1: "x"})
assert set(x.inputs) == {"x"}
px = pyro.to_funsor(torch.ones(2, 3), funsor.Real, dim_to_name={-2: "x", -1: "y"})
assert px.inputs["x"].dtype == 2 and px.inputs["y"].dtype == 3
with pyro_backend("contrib.funsor"), NamedMessenger():
testing()
def test_staggered_fresh():
def testing():
for i in pyro.markov(range(12)):
if i % 4 == 0:
fv2 = pyro.to_funsor(torch.zeros(2), funsor.Real, dim_to_name={-1: "a"})
v2 = pyro.to_data(fv2)
assert v2.shape == (2,)
print("a", v2.shape)
print("a", fv2.inputs)
with pyro_backend("contrib.funsor"), NamedMessenger(first_available_dim=-1):
testing()
def test_staggered():
def testing():
for i in pyro.markov(range(12)):
if i % 4 == 0:
v2 = pyro.to_data(Tensor(torch.zeros(2), OrderedDict([('a', funsor.Bint[2])]), 'real'))
fv2 = pyro.to_funsor(v2, funsor.Real)
assert v2.shape == (2,)
print('a', v2.shape)
print('a', fv2.inputs)
with pyro_backend("contrib.funsor"), NamedMessenger(first_available_dim=-1):
testing()
def test_iteration_fresh():
def testing():
for i in pyro.markov(range(5)):
fv1 = pyro.to_funsor(torch.zeros(2), funsor.Real, dim_to_name={-1: str(i)})
fv2 = pyro.to_funsor(torch.ones(2), funsor.Real, dim_to_name={-1: "a"})
v1 = pyro.to_data(fv1)
v2 = pyro.to_data(fv2)
print(i, v1.shape) # shapes should alternate
if i % 2 == 0:
assert v1.shape == (2,)
else:
assert v1.shape == (2, 1, 1)
assert v2.shape == (2, 1)
print(i, fv1.inputs)
print("a", v2.shape) # shapes should stay the same
print("a", fv2.inputs)
with pyro_backend("contrib.funsor"), NamedMessenger(first_available_dim=-1):
testing()
def test_iteration():
def testing():
for i in pyro.markov(range(5)):
v1 = pyro.to_data(Tensor(torch.ones(2), OrderedDict([(str(i), funsor.Bint[2])]), 'real'))
v2 = pyro.to_data(Tensor(torch.zeros(2), OrderedDict([('a', funsor.Bint[2])]), 'real'))
fv1 = pyro.to_funsor(v1, funsor.Real)
fv2 = pyro.to_funsor(v2, funsor.Real)
print(i, v1.shape) # shapes should alternate
if i % 2 == 0:
assert v1.shape == (2,)
else:
assert v1.shape == (2, 1, 1)
assert v2.shape == (2, 1)
print(i, fv1.inputs)
print('a', v2.shape) # shapes should stay the same
print('a', fv2.inputs)
with pyro_backend("contrib.funsor"), NamedMessenger(first_available_dim=-1):
testing()