def test_expand_new_dim(dist, sample_shape, shape_type):
for idx in range(dist.get_num_test_data()):
small = dist.pyro_dist(**dist.get_dist_params(idx))
with xfail_if_not_implemented():
large = small.expand(shape_type(sample_shape + small.batch_shape))
assert large.batch_shape == sample_shape + small.batch_shape
check_sample_shapes(small, large)
def test_expand_error(dist, initial_shape, proposed_shape):
for idx in range(dist.get_num_test_data()):
small = dist.pyro_dist(**dist.get_dist_params(idx))
with xfail_if_not_implemented():
large = small.expand(torch.Size(initial_shape) + small.batch_shape)
proposed_batch_shape = torch.Size(proposed_shape) + small.batch_shape
with pytest.raises(RuntimeError):
large.expand(proposed_batch_shape)
def test_expand_by(dist, sample_shape, shape_type):
for idx in range(dist.get_num_test_data()):
small = dist.pyro_dist(**dist.get_dist_params(idx))
large = small.expand_by(shape_type(sample_shape))
assert large.batch_shape == sample_shape + small.batch_shape
if dist.get_test_distribution_name() == 'Stable':
pytest.skip('Stable does not implement a log_prob method.')
check_sample_shapes(small, large)
def test_score_errors_non_broadcastable_data_shape(dist):
for idx in dist.get_batch_data_indices():
dist_params = dist.get_dist_params(idx)
d = dist.pyro_dist(**dist_params)
shape = d.shape()
non_broadcastable_shape = (shape[0] + 1,) + shape[1:]
test_data_non_broadcastable = torch.ones(non_broadcastable_shape)
with pytest.raises((ValueError, RuntimeError)):
d.log_prob(test_data_non_broadcastable)
def test_batch_entropy_shape(dist):
for idx in range(dist.get_num_test_data()):
dist_params = dist.get_dist_params(idx)
d = dist.pyro_dist(**dist_params)
with xfail_if_not_implemented():
# Get entropy shape after broadcasting.
expected_shape = _log_prob_shape(d)
entropy_obj = d.entropy()
assert entropy_obj.size() == expected_shape
def test_support_shape(dist):
for idx in range(dist.get_num_test_data()):
dist_params = dist.get_dist_params(idx)
d = dist.pyro_dist(**dist_params)
assert d.support.event_dim == d.event_dim
x = dist.get_test_data(idx)
ok = d.support.check(x)
assert ok.shape == broadcast_shape(d.batch_shape, x.shape[:x.dim() - d.event_dim])
assert ok.all()
def test_score_errors_non_broadcastable_data_shape(dist):
for idx in dist.get_batch_data_indices():
dist_params = dist.get_dist_params(idx)
d = dist.pyro_dist(**dist_params)
shape = d.shape()
non_broadcastable_shape = (shape[0] + 1,) + shape[1:]
test_data_non_broadcastable = torch.ones(non_broadcastable_shape)
with pytest.raises((ValueError, RuntimeError)):
d.log_prob(test_data_non_broadcastable)
def test_batch_log_prob(dist):
if dist.scipy_arg_fn is None:
pytest.skip('{}.log_prob_sum has no scipy equivalent'.format(dist.pyro_dist.__name__))
for idx in dist.get_batch_data_indices():
dist_params = dist.get_dist_params(idx)
d = dist.pyro_dist(**dist_params)
test_data = dist.get_test_data(idx)
log_prob_sum_pyro = d.log_prob(test_data).sum().item()
log_prob_sum_np = np.sum(dist.get_scipy_batch_logpdf(-1))
assert_equal(log_prob_sum_pyro, log_prob_sum_np)
def test_batch_log_prob_shape(dist):
for idx in range(dist.get_num_test_data()):
dist_params = dist.get_dist_params(idx)
d = dist.pyro_dist(**dist_params)
x = dist.get_test_data(idx)
with xfail_if_not_implemented():
# Get log_prob shape after broadcasting.
expected_shape = _log_prob_shape(d, x.size())
log_p_obj = d.log_prob(x)
assert log_p_obj.size() == expected_shape
def test_batch_log_prob(dist):
if dist.scipy_arg_fn is None:
pytest.skip('{}.log_prob_sum has no scipy equivalent'.format(dist.pyro_dist.__name__))
for idx in dist.get_batch_data_indices():
dist_params = dist.get_dist_params(idx)
d = dist.pyro_dist(**dist_params)
test_data = dist.get_test_data(idx)
log_prob_sum_pyro = d.log_prob(test_data).sum().item()
log_prob_sum_np = np.sum(dist.get_scipy_batch_logpdf(-1))
assert_equal(log_prob_sum_pyro, log_prob_sum_np)
def test_expand_twice(dist):
for idx in range(dist.get_num_test_data()):
small = dist.pyro_dist(**dist.get_dist_params(idx))
medium = small.expand(torch.Size((2, 1)) + small.batch_shape)
batch_shape = torch.Size((2, 3)) + small.batch_shape
with xfail_if_not_implemented():
large = medium.expand(batch_shape)
assert large.batch_shape == batch_shape
check_sample_shapes(small, large)
check_sample_shapes(medium, large)
def test_expand_twice(dist):
for idx in range(dist.get_num_test_data()):
small = dist.pyro_dist(**dist.get_dist_params(idx))
medium = small.expand(torch.Size((2, 1)) + small.batch_shape)
batch_shape = torch.Size((2, 3)) + small.batch_shape
with xfail_if_not_implemented():
large = medium.expand(batch_shape)
assert large.batch_shape == batch_shape
check_sample_shapes(small, large)
check_sample_shapes(medium, large)
def test_batch_log_prob_shape(dist):
for idx in range(dist.get_num_test_data()):
dist_params = dist.get_dist_params(idx)
d = dist.pyro_dist(**dist_params)
x = dist.get_test_data(idx)
with xfail_if_not_implemented():
# Get log_prob shape after broadcasting.
expected_shape = _log_prob_shape(d, x.size())
log_p_obj = d.log_prob(x)
assert log_p_obj.size() == expected_shape
def test_expand_error(dist, initial_shape, proposed_shape):
for idx in range(dist.get_num_test_data()):
small = dist.pyro_dist(**dist.get_dist_params(idx))
with xfail_if_not_implemented():
large = small.expand(torch.Size(initial_shape) + small.batch_shape)
proposed_batch_shape = torch.Size(proposed_shape) + small.batch_shape
if dist.get_test_distribution_name() == 'LKJCorrCholesky':
pytest.skip('LKJCorrCholesky can expand to a shape not' +
'broadcastable with its original batch_shape.')
with pytest.raises(RuntimeError):
large.expand(proposed_batch_shape)
def test_infer_shapes(dist):
if "LKJ" in dist.pyro_dist.__name__:
pytest.xfail(reason="cannot statically compute shape")
for idx in range(dist.get_num_test_data()):
dist_params = dist.get_dist_params(idx)
arg_shapes = {k: v.shape if isinstance(v, torch.Tensor) else ()
for k, v in dist_params.items()}
batch_shape, event_shape = dist.pyro_dist.infer_shapes(**arg_shapes)
d = dist.pyro_dist(**dist_params)
assert d.batch_shape == batch_shape
assert d.event_shape == event_shape
def test_expand_error(dist, initial_shape, proposed_shape, default):
for idx in range(dist.get_num_test_data()):
small = dist.pyro_dist(**dist.get_dist_params(idx))
if default:
large = TorchDistribution.expand(small, initial_shape + small.batch_shape)
else:
with xfail_if_not_implemented():
large = small.expand(torch.Size(initial_shape) + small.batch_shape)
proposed_batch_shape = torch.Size(proposed_shape) + small.batch_shape
with pytest.raises((RuntimeError, ValueError)):
large.expand(proposed_batch_shape)
def test_score_errors_non_broadcastable_data_shape(dist):
for idx in dist.get_batch_data_indices():
dist_params = dist.get_dist_params(idx)
d = dist.pyro_dist(**dist_params)
if dist.get_test_distribution_name() == "LKJCholesky":
pytest.skip("https://github.com/pytorch/pytorch/issues/52724")
shape = d.shape()
non_broadcastable_shape = (shape[0] + 1, ) + shape[1:]
test_data_non_broadcastable = torch.ones(non_broadcastable_shape)
with pytest.raises((ValueError, RuntimeError)):
d.log_prob(test_data_non_broadcastable)
def test_subsequent_expands_ok(dist, sample_shapes):
for idx in range(dist.get_num_test_data()):
d = dist.pyro_dist(**dist.get_dist_params(idx))
original_batch_shape = d.batch_shape
for shape in sample_shapes:
proposed_batch_shape = torch.Size(shape) + original_batch_shape
with xfail_if_not_implemented():
n = d.expand(proposed_batch_shape)
assert n.batch_shape == proposed_batch_shape
check_sample_shapes(d, n)
d = n
def test_score_errors_event_dim_mismatch(dist):
for idx in dist.get_batch_data_indices():
dist_params = dist.get_dist_params(idx)
d = dist.pyro_dist(**dist_params)
test_data_wrong_dims = torch.ones(d.shape() + (1,))
if len(d.event_shape) > 0:
if dist.get_test_distribution_name() == 'MultivariateNormal':
pytest.skip('MultivariateNormal does not do shape validation in log_prob.')
if dist.get_test_distribution_name() == 'LowRankMultivariateNormal':
pytest.skip('LowRankMultivariateNormal does not do shape validation in log_prob.')
with pytest.raises((ValueError, RuntimeError)):
d.log_prob(test_data_wrong_dims)
def test_score_errors_event_dim_mismatch(dist):
for idx in dist.get_batch_data_indices():
dist_params = dist.get_dist_params(idx)
d = dist.pyro_dist(**dist_params)
test_data_wrong_dims = torch.ones(d.shape() + (1,))
if len(d.event_shape) > 0:
if dist.get_test_distribution_name() == 'MultivariateNormal':
pytest.skip('MultivariateNormal does not do shape validation in log_prob.')
elif dist.get_test_distribution_name() == 'LowRankMultivariateNormal':
pytest.skip('LowRankMultivariateNormal does not do shape validation in log_prob.')
with pytest.raises((ValueError, RuntimeError)):
d.log_prob(test_data_wrong_dims)
def test_expand_new_dim(dist, sample_shape, shape_type, default):
for idx in range(dist.get_num_test_data()):
small = dist.pyro_dist(**dist.get_dist_params(idx))
if default:
large = TorchDistribution.expand(small, shape_type(sample_shape + small.batch_shape))
else:
with xfail_if_not_implemented():
large = small.expand(shape_type(sample_shape + small.batch_shape))
assert large.batch_shape == sample_shape + small.batch_shape
if dist.get_test_distribution_name() == 'Stable':
pytest.skip('Stable does not implement a log_prob method.')
check_sample_shapes(small, large)
def test_expand_existing_dim(dist, shape_type):
for idx in range(dist.get_num_test_data()):
small = dist.pyro_dist(**dist.get_dist_params(idx))
for dim, size in enumerate(small.batch_shape):
if size != 1:
continue
batch_shape = list(small.batch_shape)
batch_shape[dim] = 5
batch_shape = torch.Size(batch_shape)
with xfail_if_not_implemented():
large = small.expand(shape_type(batch_shape))
assert large.batch_shape == batch_shape
check_sample_shapes(small, large)
def test_expand_existing_dim(dist, shape_type):
for idx in range(dist.get_num_test_data()):
small = dist.pyro_dist(**dist.get_dist_params(idx))
for dim, size in enumerate(small.batch_shape):
if size != 1:
continue
batch_shape = list(small.batch_shape)
batch_shape[dim] = 5
batch_shape = torch.Size(batch_shape)
with xfail_if_not_implemented():
large = small.expand(shape_type(batch_shape))
assert large.batch_shape == batch_shape
check_sample_shapes(small, large)
def test_enumerate_support_shape(dist):
if not dist.pyro_dist.has_enumerate_support:
pytest.skip()
for idx in range(dist.get_num_test_data()):
dist_params = dist.get_dist_params(idx)
d = dist.pyro_dist(**dist_params)
with xfail_if_not_implemented():
support = d.enumerate_support()
n = support.shape[0]
assert support.shape == (n,) + d.batch_shape + d.event_shape
support_expanded = d.enumerate_support(expand=True)
assert_equal(support, support_expanded)
support_unexpanded = d.enumerate_support(expand=False)
assert support_unexpanded.shape == (n,) + (1,) * len(d.batch_shape) + d.event_shape
assert (support_expanded == support_unexpanded).all()
def test_expand_existing_dim(dist, shape_type, default):
for idx in range(dist.get_num_test_data()):
small = dist.pyro_dist(**dist.get_dist_params(idx))
for dim, size in enumerate(small.batch_shape):
if size != 1:
continue
batch_shape = list(small.batch_shape)
batch_shape[dim] = 5
batch_shape = torch.Size(batch_shape)
if default:
large = TorchDistribution.expand(small, shape_type(batch_shape))
else:
with xfail_if_not_implemented():
large = small.expand(shape_type(batch_shape))
assert large.batch_shape == batch_shape
if dist.get_test_distribution_name() == 'Stable':
pytest.skip('Stable does not implement a log_prob method.')
check_sample_shapes(small, large)
def test_expand_new_dim(dist, sample_shape, shape_type):
for idx in range(dist.get_num_test_data()):
small = dist.pyro_dist(**dist.get_dist_params(idx))
large = small.expand(shape_type(sample_shape + small.batch_shape))
assert large.batch_shape == sample_shape + small.batch_shape
check_sample_shapes(small, large)
def test_expand_by(dist, sample_shape, shape_type):
for idx in range(dist.get_num_test_data()):
small = dist.pyro_dist(**dist.get_dist_params(idx))
large = small.expand_by(shape_type(sample_shape))
assert large.batch_shape == sample_shape + small.batch_shape
check_sample_shapes(small, large)
