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rlax: Replace rlax categorical cross entropy computation with distrax components. #103

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rlax: Replace rlax categorical cross entropy computation with distrax components. #103

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15 changes: 13 additions & 2 deletions distrax/_src/distributions/categorical.py
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Original file line number Diff line number Diff line change
Expand Up @@ -165,6 +165,13 @@ def _kl_divergence_categorical_categorical(
) -> Array:
"""Obtain the KL divergence `KL(dist1 || dist2)` between two Categoricals.

This is useful if we want to e.g. compute the cross entropy between logits and
labels as:

H(labels, logits) = KL(labels | logits) - H(labels)

where we parametrize labels and logits as categorical distributions.

Args:
dist1: A Categorical distribution.
dist2: A Categorical distribution.
Expand Down Expand Up @@ -193,8 +200,12 @@ def _kl_divergence_categorical_categorical(
else:
probs1 = dist1.probs

log_probs1 = jax.nn.log_softmax(logits1, axis=-1)
log_probs2 = jax.nn.log_softmax(logits2, axis=-1)
# When there are NaNs in the logits, e.g. when we take log(prob=0), we ignore
# those components and set them to 0.
log_probs1 = jnp.where(
jnp.isfinite(logits1), jax.nn.log_softmax(logits1, axis=-1), 0.)
log_probs2 = jnp.where(
jnp.isfinite(logits2), jax.nn.log_softmax(logits2, axis=-1), 0.)

return jnp.sum((probs1 * (log_probs1 - log_probs2)), axis=-1)

Expand Down