Implement PositivePowerTransform

docs/source/conf.py

@@ -196,7 +196,7 @@
     "funsor": ("http://funsor.pyro.ai/en/stable/", None),
     "opt_einsum": ("https://optimized-einsum.readthedocs.io/en/stable/", None),
     "scipy": ("https://docs.scipy.org/doc/scipy/reference/", None),
-    "Bio": ("https://biopython.readthedocs.io/en/latest/", None),
+    "Bio": ("https://biopython.org/docs/latest/api/", None),
     "horovod": ("https://horovod.readthedocs.io/en/stable/", None),
 }
 

docs/source/distributions.rst

@@ -508,6 +508,13 @@ Permute
     :undoc-members:
     :show-inheritance:
 
+PositivePowerTransform
+----------------------
+.. autoclass:: pyro.distributions.transforms.PositivePowerTransform
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 SoftplusLowerCholeskyTransform
 ------------------------------
 .. autoclass:: pyro.distributions.transforms.SoftplusLowerCholeskyTransform

pyro/distributions/transforms/__init__.py

@@ -67,6 +67,7 @@
 from .permute import Permute, permute
 from .planar import ConditionalPlanar, Planar, conditional_planar, planar
 from .polynomial import Polynomial, polynomial
+from .power import PositivePowerTransform
 from .radial import ConditionalRadial, Radial, conditional_radial, radial
 from .softplus import SoftplusLowerCholeskyTransform, SoftplusTransform
 from .spline import ConditionalSpline, Spline, conditional_spline, spline
@@ -180,6 +181,7 @@ def iterated(repeats, base_fn, *args, **kwargs):
     "Permute",
     "Planar",
     "Polynomial",
+    "PositivePowerTransform",
     "Radial",
     "SoftplusLowerCholeskyTransform",
     "SoftplusTransform",

pyro/distributions/transforms/power.py

@@ -0,0 +1,60 @@
+# Copyright Contributors to the Pyro project.
+# SPDX-License-Identifier: Apache-2.0
+
+import torch
+from torch.distributions import Distribution, constraints
+from torch.distributions.transforms import Transform
+
+
+class PositivePowerTransform(Transform):
+    r"""
+    Transform via the mapping
+    :math:`y=\operatorname{sign}(x)|x|^{\text{exponent}}`.
+
+    Whereas :class:`~torch.distributions.transforms.PowerTransform` allows
+    arbitrary ``exponent`` and restricts domain and codomain to postive values,
+    this class restricts ``exponent > 0`` and allows real domain and codomain.
+
+    .. warning:: The Jacobian is typically zero or infinite at the origin.
+    """
+    domain = constraints.real
+    codomain = constraints.real
+    bijective = True
+    sign = +1
+
+    def __init__(self, exponent, *, cache_size=0, validate_args=None):
+        super().__init__(cache_size=cache_size)
+        if isinstance(exponent, int):
+            exponent = float(exponent)
+        exponent = torch.as_tensor(exponent)
+        self.exponent = exponent
+        if validate_args is None:
+            validate_args = Distribution._validate_args
+        if validate_args:
+            if not exponent.gt(0).all():
+                raise ValueError(f"Expected exponent > 0 but got:{exponent}")
+
+    def with_cache(self, cache_size=1):
+        if self._cache_size == cache_size:
+            return self
+        return PositivePowerTransform(self.exponent, cache_size=cache_size)
+
+    def __eq__(self, other):
+        if not isinstance(other, PositivePowerTransform):
+            return False
+        return self.exponent.eq(other.exponent).all().item()
+
+    def _call(self, x):
+        return x.abs().pow(self.exponent) * x.sign()
+
+    def _inverse(self, y):
+        return y.abs().pow(self.exponent.reciprocal()) * y.sign()
+
+    def log_abs_det_jacobian(self, x, y):
+        return self.exponent.log() + (y / x).log()
+
+    def forward_shape(self, shape):
+        return torch.broadcast_shapes(shape, getattr(self.exponent, "shape", ()))
+
+    def inverse_shape(self, shape):
+        return torch.broadcast_shapes(shape, getattr(self.exponent, "shape", ()))

tests/distributions/test_transforms.py

@@ -195,7 +195,7 @@ def _test(
                 if event_dim > 1:
                     transform = Flatten(transform, event_shape)
                 self._test_jacobian(reduce(operator.mul, event_shape, 1), transform)
-            if autodiff:
+            if isinstance(transform, dist.TransformModule) and autodiff:
                 # If the function doesn't have an explicit inverse, then use the forward op for autodiff
                 self._test_autodiff(
                     reduce(operator.mul, event_shape, 1), transform, inverse=not inverse
@@ -390,10 +390,14 @@ def test_sylvester(self):
         self._test(T.sylvester, inverse=False)
 
     def test_normalize_transform(self):
-        self._test(lambda p: T.Normalize(p=p), autodiff=False)
+        self._test(lambda p: T.Normalize(p=p))
 
     def test_softplus(self):
-        self._test(lambda _: T.SoftplusTransform(), autodiff=False)
+        self._test(lambda _: T.SoftplusTransform())
+
+    def test_positive_power(self):
+        for p in [0.3, 1.0, 3.0]:
+            self._test(lambda _: T.PositivePowerTransform(p), event_dim=0)
 
 
 @pytest.mark.parametrize("batch_shape", [(), (7,), (6, 5)])