def assert_grads_agree(logits):
d1 = dist.OneOneMatching(logits)
d2 = dist.OneOneMatching(logits, bp_iters=BP_ITERS)
expected = torch.autograd.grad(d1.log_partition_function, [logits])[0]
actual = torch.autograd.grad(d2.log_partition_function, [logits])[0]
assert torch.allclose(actual, expected, atol=0.2, rtol=1e-3), \
f"Expected:\n{expected.numpy()}\nActual:\n{actual.numpy()}"
def test_sample_shape_smoke(num_nodes, sample_shape, dtype, bp_iters):
logits = torch.randn(num_nodes, num_nodes, dtype=dtype)
d = dist.OneOneMatching(logits, bp_iters=bp_iters)
with xfail_if_not_implemented():
values = d.sample(sample_shape)
assert values.shape == sample_shape + (num_nodes, )
assert d.support.check(values).all()
def test_log_prob_full(num_nodes, dtype, bp_iters):
logits = torch.randn(num_nodes, num_nodes, dtype=dtype) * 10
d = dist.OneOneMatching(logits, bp_iters=bp_iters)
values = d.enumerate_support()
log_total = d.log_prob(values).logsumexp(0).item()
logging.info(f"log_total = {log_total:0.3g}, " +
f"log_Z = {d.log_partition_function:0.3g}")
assert_close(log_total, 0.0, atol=2.0)
def test_enumerate(num_nodes, dtype):
logits = torch.randn(num_nodes, num_nodes, dtype=dtype)
d = dist.OneOneMatching(logits)
values = d.enumerate_support()
logging.info("destins = {}, suport size = {}".format(
num_nodes, len(values)))
assert d.support.check(values), "invalid"
assert len(set(map(_hash, values))) == len(values), "not unique"
def test_log_prob_hard(dtype, bp_iters):
logits = [[0.0, 0.0], [0.0, -math.inf]]
logits = torch.tensor(logits, dtype=dtype)
d = dist.OneOneMatching(logits, bp_iters=bp_iters)
values = d.enumerate_support()
log_total = d.log_prob(values).logsumexp(0).item()
logging.info(f"log_total = {log_total:0.3g}, " +
f"log_Z = {d.log_partition_function:0.3g}")
assert_close(log_total, 0.0, atol=0.5)
def test_mode(num_nodes, dtype):
pytest.importorskip("lap")
logits = torch.randn(num_nodes, num_nodes, dtype=dtype) * 10
d = dist.OneOneMatching(logits)
values = d.enumerate_support()
i = d.log_prob(values).max(0).indices.item()
expected = values[i]
actual = d.mode()
assert_equal(actual, expected)
def test_sample(num_nodes, dtype, bp_iters):
pytest.importorskip("lap")
logits = torch.randn(num_nodes, num_nodes, dtype=dtype) * 10
d = dist.OneOneMatching(logits, bp_iters=bp_iters)
# Compute an empirical mean.
num_samples = 1000
s = torch.arange(num_nodes)
actual = torch.zeros_like(logits)
with xfail_if_not_implemented():
for v in d.sample([num_samples]):
actual[s, v] += 1 / num_samples
# Compute truth via enumeration.
values = d.enumerate_support()
probs = d.log_prob(values).exp()
probs /= probs.sum()
expected = torch.zeros(num_nodes, num_nodes)
for v, p in zip(values, probs):
expected[s, v] += p
assert_close(actual, expected, atol=0.1)
def fn(logits):
d = dist.OneOneMatching(logits, bp_iters=bp_iters)
return d.log_partition_function
def test_mode_smoke(num_nodes, dtype):
pytest.importorskip("lap")
logits = torch.randn(num_nodes, num_nodes, dtype=dtype) * 10
d = dist.OneOneMatching(logits)
value = d.mode()
assert d.support.check(value)
