Handle local measures in TransformedDistribution.

This change continues to set up the framework for tracking base measures and computing corrections on transformed densities. In `TransformedDistribution` we update `log_prob` to call a version of `experimental_local_measure` that keeps track of the base measure. We introduce a backwards-compatibility argument to control this rollout.

PiperOrigin-RevId: 385616650

Author: DistraxDev

distrax/_src/bijectors/tfp_compatible_bijector.py

@@ -21,13 +21,15 @@
 from distrax._src.utils import math
 import jax
 import jax.numpy as jnp
+from tensorflow_probability.python.experimental import tangent_spaces
 from tensorflow_probability.substrates import jax as tfp
 
 tfb = tfp.bijectors
 tfd = tfp.distributions
 
 Array = chex.Array
 Bijector = bijector.Bijector
+TangentSpace = tangent_spaces.TangentSpace
 
 
 def tfp_compatible_bijector(
@@ -175,4 +177,31 @@ def _check_shape(
                          f"{event_shape} which has only {len(event_shape)} "
                          f"dimensions instead.")
 
+    def experimental_compute_density_correction(
+        self,
+        x: Array,
+        tangent_space: TangentSpace,
+        backward_compat: bool = True,
+        **kwargs):
+      """Density correction for this transform wrt the tangent space, at x.
+
+      See `tfb.bijector.Bijector.experimental_compute_density_correction`, and
+      Radul and Alexeev, AISTATS 2021, “The Base Measure Problem and its
+      Solution”, https://arxiv.org/abs/2010.09647.
+
+      Args:
+        x: `float` or `double` `Array`.
+        tangent_space: `TangentSpace` or one of its subclasses.  The tangent to
+          the support manifold at `x`.
+        backward_compat: unused
+        **kwargs: Optional keyword arguments forwarded to tangent space methods.
+
+      Returns:
+        density_correction: `Array` representing the density correction---in log
+          space---under the transformation that this Bijector denotes. Assumes
+          the Bijector is dimension-preserving.
+      """
+      del backward_compat
+      return tangent_space.transform_dimension_preserving(x, self, **kwargs)
+
   return TFPCompatibleBijector()

distrax/_src/distributions/tfp_compatible_distribution.py

@@ -14,12 +14,13 @@
 # ==============================================================================
 """Wrapper to adapt a Distrax distribution for use in TFP."""
 
-from typing import Dict, Optional, Sequence, Union
+from typing import Dict, Optional, Sequence, Tuple, Union
 
 import chex
 from distrax._src.distributions import distribution
 import jax.numpy as jnp
 import numpy as np
+from tensorflow_probability.python.experimental import tangent_spaces
 from tensorflow_probability.substrates import jax as tfp
 
 tfd = tfp.distributions
@@ -29,6 +30,7 @@
 Distribution = distribution.Distribution
 IntLike = distribution.IntLike
 PRNGKey = chex.PRNGKey
+TangentSpace = tangent_spaces.TangentSpace
 
 
 def tfp_compatible_distribution(
@@ -136,4 +138,29 @@ def sample(self,
         sample_shape = tuple(sample_shape)
       return base_distribution.sample(sample_shape=sample_shape, seed=seed)
 
+    def experimental_local_measure(
+        self,
+        value: Array,
+        backward_compat: bool = True,
+        **unused_kwargs) -> Tuple[Array, TangentSpace]:
+      """Returns a log probability density together with a `TangentSpace`.
+
+      See `tfd.distribution.Distribution.experimental_local_measure`, and
+      Radul and Alexeev, AISTATS 2021, “The Base Measure Problem and its
+      Solution”, https://arxiv.org/abs/2010.09647.
+
+      Args:
+        value: `float` or `double` `Array`.
+        backward_compat: unused
+        **unused_kwargs: unused
+
+      Returns:
+        log_prob: see `log_prob`.
+        tangent_space: `tangent_spaces.FullSpace()`, representing R^n with the
+          standard basis.
+      """
+      del backward_compat
+      del unused_kwargs
+      return self.log_prob(value), tangent_spaces.FullSpace()
+
   return TFPCompatibleDistribution()