Add reparameterization support to OneHotCategorical

test/distributions/test_distributions.py

@@ -47,8 +47,9 @@
                                  Independent, Laplace, LogisticNormal,
                                  LogNormal, LowRankMultivariateNormal,
                                  MixtureSameFamily, Multinomial, MultivariateNormal,
-                                 NegativeBinomial, Normal, OneHotCategorical, Pareto,
-                                 Poisson, RelaxedBernoulli, RelaxedOneHotCategorical,
+                                 NegativeBinomial, Normal,
+                                 OneHotCategorical, OneHotCategoricalStraightThrough,
+                                 Pareto, Poisson, RelaxedBernoulli, RelaxedOneHotCategorical,
                                  StudentT, TransformedDistribution, Uniform,
                                  VonMises, Weibull, constraints, kl_divergence)
 from torch.distributions.constraint_registry import transform_to
@@ -335,6 +336,11 @@ def is_all_nan(tensor):
         {'probs': torch.tensor([[1.0, 0.0], [0.0, 1.0]], requires_grad=True)},
         {'logits': torch.tensor([[0.0, 0.0], [0.0, 0.0]], requires_grad=True)},
     ]),
+    Example(OneHotCategoricalStraightThrough, [
+        {'probs': torch.tensor([[0.1, 0.2, 0.3], [0.5, 0.3, 0.2]], requires_grad=True)},
+        {'probs': torch.tensor([[1.0, 0.0], [0.0, 1.0]], requires_grad=True)},
+        {'logits': torch.tensor([[0.0, 0.0], [0.0, 0.0]], requires_grad=True)},
+    ]),
     Example(Pareto, [
         {
             'scale': 1.0,
@@ -604,6 +610,10 @@ def is_all_nan(tensor):
         {'probs': torch.tensor([[0.1, 0.2, 0.3], [0.1, -10.0, 0.2]], requires_grad=True)},
         {'probs': torch.tensor([[0.0, 0.0], [0.0, 0.0]], requires_grad=True)},
     ]),
+    Example(OneHotCategoricalStraightThrough, [
+        {'probs': torch.tensor([[0.1, 0.2, 0.3], [0.1, -10.0, 0.2]], requires_grad=True)},
+        {'probs': torch.tensor([[0.0, 0.0], [0.0, 0.0]], requires_grad=True)},
+    ]),
     Example(Pareto, [
         {
             'scale': 0.0,
@@ -3755,13 +3765,21 @@ def test_entropy_exponential_family(self):
 
 class TestConstraints(TestCase):
     def test_params_constraints(self):
+        normalize_probs_dists = (
+            Categorical,
+            Multinomial,
+            OneHotCategorical,
+            OneHotCategoricalStraightThrough,
+            RelaxedOneHotCategorical
+        )
+
         for Dist, params in EXAMPLES:
             for i, param in enumerate(params):
                 dist = Dist(**param)
                 for name, value in param.items():
                     if isinstance(value, numbers.Number):
                         value = torch.tensor([value])
-                    if Dist in (Categorical, OneHotCategorical, Multinomial) and name == 'probs':
+                    if Dist in normalize_probs_dists and name == 'probs':
                         # These distributions accept positive probs, but elsewhere we
                         # use a stricter constraint to the simplex.
                         value = value / value.sum(-1, True)

torch/distributions/__init__.py

@@ -100,7 +100,7 @@
 from .multivariate_normal import MultivariateNormal
 from .negative_binomial import NegativeBinomial
 from .normal import Normal
-from .one_hot_categorical import OneHotCategorical
+from .one_hot_categorical import OneHotCategorical, OneHotCategoricalStraightThrough
 from .pareto import Pareto
 from .poisson import Poisson
 from .relaxed_bernoulli import RelaxedBernoulli
@@ -142,6 +142,7 @@
     'NegativeBinomial',
     'Normal',
     'OneHotCategorical',
+    'OneHotCategoricalStraightThrough',
     'Pareto',
     'RelaxedBernoulli',
     'RelaxedOneHotCategorical',

torch/distributions/one_hot_categorical.py

@@ -96,3 +96,18 @@ def enumerate_support(self, expand=True):
         if expand:
             values = values.expand((n,) + self.batch_shape + (n,))
         return values
+
+class OneHotCategoricalStraightThrough(OneHotCategorical):
+    r"""
+    Creates a reparameterizable :class:`OneHotCategorical` distribution based on the straight-
+    through gradient estimator from [1].
+
+    [1] Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
+    (Bengio et al, 2013)
+    """
+    has_rsample = True
+
+    def rsample(self, sample_shape=torch.Size()):
+        samples = self.sample(sample_shape)
+        probs = self._categorical.probs  # cached via @lazy_property
+        return samples + (probs - probs.detach())