Simplify dispatch of JAX random variables by handling rng split automatically

pytensor/link/jax/dispatch/random.py

@@ -105,14 +105,24 @@ def jax_funcify_RandomVariable(op: ptr.RandomVariable, node, **kwargs):
         assert_size_argument_jax_compatible(node)
 
         def sample_fn(rng, size, *parameters):
-            return jax_sample_fn(op, node=node)(rng, size, out_dtype, *parameters)
+            rng_key = rng["jax_state"]
+            rng_key, sampling_key = jax.random.split(rng_key, 2)
+            rng["jax_state"] = rng_key
+            sample = jax_sample_fn(op, node=node)(
+                sampling_key, size, out_dtype, *parameters
+            )
+            return (rng, sample)
 
     else:
 
         def sample_fn(rng, size, *parameters):
-            return jax_sample_fn(op, node=node)(
-                rng, static_size, out_dtype, *parameters
+            rng_key = rng["jax_state"]
+            rng_key, sampling_key = jax.random.split(rng_key, 2)
+            rng["jax_state"] = rng_key
+            sample = jax_sample_fn(op, node=node)(
+                sampling_key, static_size, out_dtype, *parameters
             )
+            return (rng, sample)
 
     return sample_fn
 
@@ -133,12 +143,9 @@ def jax_sample_fn_generic(op, node):
     name = op.name
     jax_op = getattr(jax.random, name)
 
-    def sample_fn(rng, size, dtype, *parameters):
-        rng_key = rng["jax_state"]
-        rng_key, sampling_key = jax.random.split(rng_key, 2)
-        sample = jax_op(sampling_key, *parameters, shape=size, dtype=dtype)
-        rng["jax_state"] = rng_key
-        return (rng, sample)
+    def sample_fn(rng_key, size, dtype, *parameters):
+        sample = jax_op(rng_key, *parameters, shape=size, dtype=dtype)
+        return sample
 
     return sample_fn
 
@@ -159,29 +166,23 @@ def jax_sample_fn_loc_scale(op, node):
     name = op.name
     jax_op = getattr(jax.random, name)
 
-    def sample_fn(rng, size, dtype, *parameters):
-        rng_key = rng["jax_state"]
-        rng_key, sampling_key = jax.random.split(rng_key, 2)
+    def sample_fn(rng_key, size, dtype, *parameters):
         loc, scale = parameters
         if size is None:
             size = jax.numpy.broadcast_arrays(loc, scale)[0].shape
-        sample = loc + jax_op(sampling_key, size, dtype) * scale
-        rng["jax_state"] = rng_key
-        return (rng, sample)
+        sample = loc + jax_op(rng_key, size, dtype) * scale
+        return sample
 
     return sample_fn
 
 
 @jax_sample_fn.register(ptr.MvNormalRV)
 def jax_sample_mvnormal(op, node):
-    def sample_fn(rng, size, dtype, mean, cov):
-        rng_key = rng["jax_state"]
-        rng_key, sampling_key = jax.random.split(rng_key, 2)
+    def sample_fn(rng_key, size, dtype, mean, cov):
         sample = jax.random.multivariate_normal(
-            sampling_key, mean, cov, shape=size, dtype=dtype, method=op.method
+            rng_key, mean, cov, shape=size, dtype=dtype, method=op.method
         )
-        rng["jax_state"] = rng_key
-        return (rng, sample)
+        return sample
 
     return sample_fn
 
@@ -191,12 +192,9 @@ def jax_sample_fn_bernoulli(op, node):
     """JAX implementation of `BernoulliRV`."""
 
     # We need a separate dispatch, because there is no dtype argument for Bernoulli in JAX
-    def sample_fn(rng, size, dtype, p):
-        rng_key = rng["jax_state"]
-        rng_key, sampling_key = jax.random.split(rng_key, 2)
-        sample = jax.random.bernoulli(sampling_key, p, shape=size)
-        rng["jax_state"] = rng_key
-        return (rng, sample)
+    def sample_fn(rng_key, size, dtype, p):
+        sample = jax.random.bernoulli(rng_key, p, shape=size)
+        return sample
 
     return sample_fn
 
@@ -206,14 +204,10 @@ def jax_sample_fn_categorical(op, node):
     """JAX implementation of `CategoricalRV`."""
 
     # We need a separate dispatch because Categorical expects logits in JAX
-    def sample_fn(rng, size, dtype, p):
-        rng_key = rng["jax_state"]
-        rng_key, sampling_key = jax.random.split(rng_key, 2)
-
+    def sample_fn(rng_key, size, dtype, p):
         logits = jax.scipy.special.logit(p)
-        sample = jax.random.categorical(sampling_key, logits=logits, shape=size)
-        rng["jax_state"] = rng_key
-        return (rng, sample)
+        sample = jax.random.categorical(rng_key, logits=logits, shape=size)
+        return sample
 
     return sample_fn
 
@@ -233,15 +227,10 @@ def jax_sample_fn_uniform(op, node):
         name = "randint"
     jax_op = getattr(jax.random, name)
 
-    def sample_fn(rng, size, dtype, *parameters):
-        rng_key = rng["jax_state"]
-        rng_key, sampling_key = jax.random.split(rng_key, 2)
+    def sample_fn(rng_key, size, dtype, *parameters):
         minval, maxval = parameters
-        sample = jax_op(
-            sampling_key, shape=size, dtype=dtype, minval=minval, maxval=maxval
-        )
-        rng["jax_state"] = rng_key
-        return (rng, sample)
+        sample = jax_op(rng_key, shape=size, dtype=dtype, minval=minval, maxval=maxval)
+        return sample
 
     return sample_fn
 
@@ -258,14 +247,11 @@ def jax_sample_fn_shape_scale(op, node):
     name = op.name
     jax_op = getattr(jax.random, name)
 
-    def sample_fn(rng, size, dtype, shape, scale):
-        rng_key = rng["jax_state"]
-        rng_key, sampling_key = jax.random.split(rng_key, 2)
+    def sample_fn(rng_key, size, dtype, shape, scale):
         if size is None:
             size = jax.numpy.broadcast_arrays(shape, scale)[0].shape
-        sample = jax_op(sampling_key, shape, size, dtype) * scale
-        rng["jax_state"] = rng_key
-        return (rng, sample)
+        sample = jax_op(rng_key, shape, size, dtype) * scale
+        return sample
 
     return sample_fn
 
@@ -274,14 +260,11 @@ def sample_fn(rng, size, dtype, shape, scale):
 def jax_sample_fn_exponential(op, node):
     """JAX implementation of `ExponentialRV`."""
 
-    def sample_fn(rng, size, dtype, scale):
-        rng_key = rng["jax_state"]
-        rng_key, sampling_key = jax.random.split(rng_key, 2)
+    def sample_fn(rng_key, size, dtype, scale):
         if size is None:
             size = jax.numpy.asarray(scale).shape
-        sample = jax.random.exponential(sampling_key, size, dtype) * scale
-        rng["jax_state"] = rng_key
-        return (rng, sample)
+        sample = jax.random.exponential(rng_key, size, dtype) * scale
+        return sample
 
     return sample_fn
 
@@ -290,14 +273,11 @@ def sample_fn(rng, size, dtype, scale):
 def jax_sample_fn_t(op, node):
     """JAX implementation of `StudentTRV`."""
 
-    def sample_fn(rng, size, dtype, df, loc, scale):
-        rng_key = rng["jax_state"]
-        rng_key, sampling_key = jax.random.split(rng_key, 2)
+    def sample_fn(rng_key, size, dtype, df, loc, scale):
         if size is None:
             size = jax.numpy.broadcast_arrays(df, loc, scale)[0].shape
-        sample = loc + jax.random.t(sampling_key, df, size, dtype) * scale
-        rng["jax_state"] = rng_key
-        return (rng, sample)
+        sample = loc + jax.random.t(rng_key, df, size, dtype) * scale
+        return sample
 
     return sample_fn
 
@@ -315,10 +295,7 @@ def jax_funcify_choice(op: ptr.ChoiceWithoutReplacement, node):
             "A default JAX rewrite should have materialized the implicit arange"
         )
 
-    def sample_fn(rng, size, dtype, *parameters):
-        rng_key = rng["jax_state"]
-        rng_key, sampling_key = jax.random.split(rng_key, 2)
-
+    def sample_fn(rng_key, size, dtype, *parameters):
         if op.has_p_param:
             a, p, core_shape = parameters
         else:
@@ -327,9 +304,7 @@ def sample_fn(rng, size, dtype, *parameters):
         core_shape = tuple(np.asarray(core_shape)[(0,) * batch_ndim])
 
         if batch_ndim == 0:
-            sample = jax.random.choice(
-                sampling_key, a, shape=core_shape, replace=False, p=p
-            )
+            sample = jax.random.choice(rng_key, a, shape=core_shape, replace=False, p=p)
 
         else:
             if size is None:
@@ -345,7 +320,7 @@ def sample_fn(rng, size, dtype, *parameters):
             if p is not None:
                 p = jax.numpy.broadcast_to(p, size + p.shape[batch_ndim:])
 
-            batch_sampling_keys = jax.random.split(sampling_key, np.prod(size))
+            batch_sampling_keys = jax.random.split(rng_key, np.prod(size))
 
             # Ravel the batch dimensions because vmap only works along a single axis
             raveled_batch_a = a.reshape((-1,) + a.shape[batch_ndim:])
@@ -366,8 +341,7 @@ def sample_fn(rng, size, dtype, *parameters):
             # Reshape the batch dimensions
             sample = raveled_sample.reshape(size + raveled_sample.shape[1:])
 
-        rng["jax_state"] = rng_key
-        return (rng, sample)
+        return sample
 
     return sample_fn
 
@@ -378,9 +352,7 @@ def jax_sample_fn_permutation(op, node):
 
     batch_ndim = op.batch_ndim(node)
 
-    def sample_fn(rng, size, dtype, *parameters):
-        rng_key = rng["jax_state"]
-        rng_key, sampling_key = jax.random.split(rng_key, 2)
+    def sample_fn(rng_key, size, dtype, *parameters):
         (x,) = parameters
         if batch_ndim:
             # jax.random.permutation has no concept of batch dims
@@ -389,17 +361,16 @@ def sample_fn(rng, size, dtype, *parameters):
             else:
                 x = jax.numpy.broadcast_to(x, size + x.shape[batch_ndim:])
 
-            batch_sampling_keys = jax.random.split(sampling_key, np.prod(size))
+            batch_sampling_keys = jax.random.split(rng_key, np.prod(size))
             raveled_batch_x = x.reshape((-1,) + x.shape[batch_ndim:])
             raveled_sample = jax.vmap(lambda key, x: jax.random.permutation(key, x))(
                 batch_sampling_keys, raveled_batch_x
             )
             sample = raveled_sample.reshape(size + raveled_sample.shape[1:])
         else:
-            sample = jax.random.permutation(sampling_key, x)
+            sample = jax.random.permutation(rng_key, x)
 
-        rng["jax_state"] = rng_key
-        return (rng, sample)
+        return sample
 
     return sample_fn
 
@@ -414,15 +385,9 @@ def jax_sample_fn_binomial(op, node):
 
     from numpyro.distributions.util import binomial
 
-    def sample_fn(rng, size, dtype, n, p):
-        rng_key = rng["jax_state"]
-        rng_key, sampling_key = jax.random.split(rng_key, 2)
-
-        sample = binomial(key=sampling_key, n=n, p=p, shape=size)
-
-        rng["jax_state"] = rng_key
-
-        return (rng, sample)
+    def sample_fn(rng_key, size, dtype, n, p):
+        sample = binomial(key=rng_key, n=n, p=p, shape=size)
+        return sample
 
     return sample_fn
 
@@ -437,15 +402,9 @@ def jax_sample_fn_multinomial(op, node):
 
     from numpyro.distributions.util import multinomial
 
-    def sample_fn(rng, size, dtype, n, p):
-        rng_key = rng["jax_state"]
-        rng_key, sampling_key = jax.random.split(rng_key, 2)
-
-        sample = multinomial(key=sampling_key, n=n, p=p, shape=size)
-
-        rng["jax_state"] = rng_key
-
-        return (rng, sample)
+    def sample_fn(rng_key, size, dtype, n, p):
+        sample = multinomial(key=rng_key, n=n, p=p, shape=size)
+        return sample
 
     return sample_fn
 
@@ -460,17 +419,12 @@ def jax_sample_fn_vonmises(op, node):
 
     from numpyro.distributions.util import von_mises_centered
 
-    def sample_fn(rng, size, dtype, mu, kappa):
-        rng_key = rng["jax_state"]
-        rng_key, sampling_key = jax.random.split(rng_key, 2)
-
+    def sample_fn(rng_key, size, dtype, mu, kappa):
         sample = von_mises_centered(
-            key=sampling_key, concentration=kappa, shape=size, dtype=dtype
+            key=rng_key, concentration=kappa, shape=size, dtype=dtype
         )
         sample = (sample + mu + np.pi) % (2.0 * np.pi) - np.pi
 
-        rng["jax_state"] = rng_key
-
-        return (rng, sample)
+        return sample
 
     return sample_fn

tests/link/jax/test_random.py

@@ -796,7 +796,7 @@ def rng_fn(cls, rng, size):
     @jax_sample_fn.register(CustomRV)
     def jax_sample_fn_custom(op, node):
         def sample_fn(rng, size, dtype, *parameters):
-            return (rng, 0)
+            return 0
 
         return sample_fn