autoregressive: explicitly clone rng for reuse

PiperOrigin-RevId: 614079072

tensorflow_probability/python/distributions/autoregressive.py

@@ -294,11 +294,18 @@ def _sample_n(self, n, seed=None):
     if num_steps_static is not None:
       for _ in range(num_steps_static):
         # pylint: disable=not-callable
-        samples = self.distribution_fn(samples).sample(seed=seed)
+        samples = self.distribution_fn(samples).sample(
+            seed=samplers.clone_seed(seed)
+        )
     else:
       # pylint: disable=not-callable
-      samples = tf.foldl(lambda s, _: self.distribution_fn(s).sample(seed=seed),
-                         elems=tf.range(0, num_steps), initializer=samples)
+      samples = tf.foldl(
+          lambda s, _: self.distribution_fn(s).sample(
+              seed=samplers.clone_seed(seed)
+          ),
+          elems=tf.range(0, num_steps),
+          initializer=samples,
+      )
     return samples
 
   def _log_prob(self, value):

tensorflow_probability/python/internal/samplers.py

@@ -31,6 +31,7 @@
 
 __all__ = [
     'categorical',
+    'clone_seed',
     'fold_in',
     'gamma',
     'is_stateful_seed',
@@ -229,6 +230,16 @@ def split_seed(seed, n=2, salt=None, name=None):
     return seeds
 
 
+def clone_seed(seed):
+  """Clones a seed so it can be reused without causing a JAX KeyReuseError."""
+  if JAX_MODE:
+    from jax import random as jaxrand  # pylint: disable=g-import-not-at-top
+    if hasattr(jaxrand, 'clone'):
+      # JAX v0.4.26+
+      return jaxrand.clone(seed)
+  return seed
+
+
 def categorical(
     logits,
     num_samples,