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Nonprojective dependency entropy #103

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Nonprojective dependency entropy #103

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609c233
add nonprojective entropy implementation
Aug 30, 2021
d1d19f9
add nonprojective entropy implementation
Aug 30, 2021
cad728f
update docstring
Sep 1, 2021
b3ea558
Update distributions.py
srush Sep 3, 2021
3e17a50
refactor central entropy, xent, kl using marginals instead of semiring
Sep 14, 2021
ba1f502
:Merge branch 'master' of https://github.com/harvardnlp/pytorch-struc…
Sep 14, 2021
69c0db9
refactor central entropy, xent, kl using marginals instead of semiring
Sep 14, 2021
d3fa0d6
attempt to fix docstr lint error and derivation
Sep 14, 2021
e2cdc41
attempt to fix docstr lint error and derivation
Sep 14, 2021
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62 changes: 32 additions & 30 deletions torch_struct/distributions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,11 +61,7 @@ def log_prob(self, value):

d = value.dim()
batch_dims = range(d - len(self.event_shape))
v = self._struct().score(
self.log_potentials,
value.type_as(self.log_potentials),
batch_dims=batch_dims,
)
v = self._struct().score(self.log_potentials, value.type_as(self.log_potentials), batch_dims=batch_dims,)

return v - self.partition

Expand All @@ -91,9 +87,7 @@ def cross_entropy(self, other):
cross entropy (*batch_shape*)
"""

return self._struct(CrossEntropySemiring).sum(
[self.log_potentials, other.log_potentials], self.lengths
)
return self._struct(CrossEntropySemiring).sum([self.log_potentials, other.log_potentials], self.lengths)

def kl(self, other):
"""
Expand All @@ -105,9 +99,7 @@ def kl(self, other):
Returns:
cross entropy (*batch_shape*)
"""
return self._struct(KLDivergenceSemiring).sum(
[self.log_potentials, other.log_potentials], self.lengths
)
return self._struct(KLDivergenceSemiring).sum([self.log_potentials, other.log_potentials], self.lengths)

@lazy_property
def max(self):
Expand Down Expand Up @@ -140,9 +132,7 @@ def kmax(self, k):
kmax (*k x batch_shape*)
"""
with torch.enable_grad():
return self._struct(KMaxSemiring(k)).sum(
self.log_potentials, self.lengths, _raw=True
)
return self._struct(KMaxSemiring(k)).sum(self.log_potentials, self.lengths, _raw=True)

def topk(self, k):
r"""
Expand All @@ -155,9 +145,7 @@ def topk(self, k):
kmax (*k x batch_shape x event_shape*)
"""
with torch.enable_grad():
return self._struct(KMaxSemiring(k)).marginals(
self.log_potentials, self.lengths, _raw=True
)
return self._struct(KMaxSemiring(k)).marginals(self.log_potentials, self.lengths, _raw=True)

@lazy_property
def mode(self):
Expand Down Expand Up @@ -186,9 +174,7 @@ def count(self):

def gumbel_crf(self, temperature=1.0):
with torch.enable_grad():
st_gumbel = self._struct(GumbelCRFSemiring(temperature)).marginals(
self.log_potentials, self.lengths
)
st_gumbel = self._struct(GumbelCRFSemiring(temperature)).marginals(self.log_potentials, self.lengths)
return st_gumbel

# @constraints.dependent_property
Expand Down Expand Up @@ -219,9 +205,7 @@ def sample(self, sample_shape=torch.Size()):
samples = []
for k in range(nsamples):
if k % 10 == 0:
sample = self._struct(MultiSampledSemiring).marginals(
self.log_potentials, lengths=self.lengths
)
sample = self._struct(MultiSampledSemiring).marginals(self.log_potentials, lengths=self.lengths)
sample = sample.detach()
tmp_sample = MultiSampledSemiring.to_discrete(sample, (k % 10) + 1)
samples.append(tmp_sample)
Expand Down Expand Up @@ -301,9 +285,7 @@ def __init__(self, log_potentials, local=False, lengths=None, max_gap=None):
super().__init__(log_potentials, lengths)

def _struct(self, sr=None):
return self.struct(
sr if sr is not None else LogSemiring, self.local, max_gap=self.max_gap
)
return self.struct(sr if sr is not None else LogSemiring, self.local, max_gap=self.max_gap)


class HMM(StructDistribution):
Expand Down Expand Up @@ -440,9 +422,7 @@ def __init__(self, log_potentials, lengths=None):
event_shape = log_potentials[0].shape[1:]
self.log_potentials = log_potentials
self.lengths = lengths
super(StructDistribution, self).__init__(
batch_shape=batch_shape, event_shape=event_shape
)
super(StructDistribution, self).__init__(batch_shape=batch_shape, event_shape=event_shape)


class NonProjectiveDependencyCRF(StructDistribution):
Expand Down Expand Up @@ -504,4 +484,26 @@ def argmax(self):

@lazy_property
def entropy(self):
pass
r"""
Compute entropy efficiently using arc-factorization property.

Algorithm derivation:
..math::
{{
\begin{align}
H[p] &= E_{p(T)}[-\log p(T)]\\
&= -E_{p(T)}\big[ \log [\frac{1}{Z} \prod\limits_{(i,j) \in T} \exp\{\phi_{i,j}\}] \big]\\
&= -E_{p(T)}\big[ \sum\limits_{(i,j) \in T} \phi_{i,j} - \log Z \big]\\
&= \log Z -E_{p(T)}\big[\sum\limits_{(i,j) \in A} 1\{(i,j) \in T\} \phi_{i,j}\big]\\
&= \log Z - \sum\limits_{(i,j) \in A} p\big((i,j) \in T\big) \phi_{i,j}
\end{align}
}}

Returns:
entropy (*batch_shape)
"""
logZ = self.partition
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@justinchiu points out that Instead of grouping this with non-proj we should just have it be the default entropy function for all of the models to materialize the marginals. (nothing non-projective specific).

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Yes, it's a general property of all exponential family / sum-product models. See also this Twitter discussion https://twitter.com/RanZmigrod/status/1300832956701970434?s=20

p = self.marginals
phi = self.log_potentials
H = logZ - (p * phi).reshape(phi.shape[0], -1).sum(-1)
return H