Refactor JAX implementations of `RandomVariable`

pytensor/link/jax/dispatch/random.py

+from functools import singledispatch
+
 import jax
-import jax.numpy as jnp
 from numpy.random import Generator, RandomState
 from numpy.random.bit_generator import (  # type: ignore[attr-defined]
     _coerce_to_uint32_array,
 )
 
+import pytensor.tensor.random.basic as aer
 from pytensor.link.jax.dispatch.basic import jax_funcify, jax_typify
-from pytensor.tensor.random.op import RandomVariable
+from pytensor.tensor.shape import Shape
 
 
 numpy_bit_gens = {"MT19937": 0, "PCG64": 1, "Philox": 2, "SFC64": 3}
@@ -46,25 +48,189 @@ def jax_typify_Generator(rng, **kwargs):
     return state
 
 
-@jax_funcify.register(RandomVariable)
+@jax_funcify.register(aer.RandomVariable)
 def jax_funcify_RandomVariable(op, node, **kwargs):
+    """JAX implementation of random variables."""
+    rv = node.outputs[1]
+    out_dtype = rv.type.dtype
+    out_size = rv.type.shape
+
+    if isinstance(op, aer.MvNormalRV):
+        # PyTensor sets the `size` to the concatenation of the support shape
+        # and the batch shape, while JAX explicitly requires the batch
+        # shape only for the multivariate normal.
+        out_size = node.outputs[1].type.shape[:-1]
+
+    # If one dimension has unknown size, either the size is determined
+    # by a `Shape` operator in which case JAX will compile, or it is
+    # not and we fail gracefully.
+    if None in out_size:
+        if not isinstance(node.inputs[1].owner.op, Shape):
+            raise NotImplementedError(
+                """JAX random variables require concrete values for the `size` parameter of the distributions.
+                Concrete values are either constants, or the shape of an array.
+                """
+            )
+
+        def sample_fn(rng, size, dtype, *parameters):
+            return jax_sample_fn(op)(rng, size, out_dtype, *parameters)
+
+    else:
+
+        def sample_fn(rng, size, dtype, *parameters):
+            return jax_sample_fn(op)(rng, out_size, out_dtype, *parameters)
+
+    return sample_fn
+
+
+@singledispatch
+def jax_sample_fn(op):
+    name = op.name
+    raise NotImplementedError(
+        f"No JAX implementation for the given distribution: {name}"
+    )
+
+
+@jax_sample_fn.register(aer.BetaRV)
+@jax_sample_fn.register(aer.DirichletRV)
+@jax_sample_fn.register(aer.PoissonRV)
+@jax_sample_fn.register(aer.MvNormalRV)
+def jax_sample_fn_generic(op):
+    """Generic JAX implementation of random variables."""
+    name = op.name
+    jax_op = getattr(jax.random, name)
+
+    def sample_fn(rng, size, dtype, *parameters):
+        rng_key = rng["jax_state"]
+        sample = jax_op(rng_key, *parameters, shape=size, dtype=dtype)
+        rng["jax_state"] = jax.random.split(rng_key, num=1)[0]
+        return (rng, sample)
+
+    return sample_fn
+
+
+@jax_sample_fn.register(aer.CauchyRV)
+@jax_sample_fn.register(aer.LaplaceRV)
+@jax_sample_fn.register(aer.LogisticRV)
+@jax_sample_fn.register(aer.NormalRV)
+def jax_sample_fn_loc_scale(op):
+    """JAX implementation of random variables in the loc-scale families.
+
+    JAX only implements the standard version of random variables in the
+    loc-scale family. We thus need to translate and rescale the results
+    manually.
+
+    """
+    name = op.name
+    jax_op = getattr(jax.random, name)
+
+    def sample_fn(rng, size, dtype, *parameters):
+        rng_key = rng["jax_state"]
+        loc, scale = parameters
+        sample = loc + jax_op(rng_key, size, dtype) * scale
+        rng["jax_state"] = jax.random.split(rng_key, num=1)[0]
+        return (rng, sample)
+
+    return sample_fn
+
+
+@jax_sample_fn.register(aer.BernoulliRV)
+@jax_sample_fn.register(aer.CategoricalRV)
+def jax_sample_fn_no_dtype(op):
+    """Generic JAX implementation of random variables."""
+    name = op.name
+    jax_op = getattr(jax.random, name)
+
+    def sample_fn(rng, size, dtype, *parameters):
+        rng_key = rng["jax_state"]
+        sample = jax_op(rng_key, *parameters, shape=size)
+        rng["jax_state"] = jax.random.split(rng_key, num=1)[0]
+        return (rng, sample)
+
+    return sample_fn
+
+
+@jax_sample_fn.register(aer.RandIntRV)
+@jax_sample_fn.register(aer.UniformRV)
+def jax_sample_fn_uniform(op):
+    """JAX implementation of random variables with uniform density.
+
+    We need to pass the arguments as keyword arguments since the order
+    of arguments is not the same.
+
+    """
+    name = op.name
+    jax_op = getattr(jax.random, name)
+
+    def sample_fn(rng, size, dtype, *parameters):
+        rng_key = rng["jax_state"]
+        minval, maxval = parameters
+        sample = jax_op(rng_key, shape=size, dtype=dtype, minval=minval, maxval=maxval)
+        rng["jax_state"] = jax.random.split(rng_key, num=1)[0]
+        return (rng, sample)
+
+    return sample_fn
+
+
+@jax_sample_fn.register(aer.ParetoRV)
+@jax_sample_fn.register(aer.GammaRV)
+def jax_sample_fn_shape_rate(op):
+    """JAX implementation of random variables in the shape-rate family.
+
+    JAX only implements the standard version of random variables in the
+    shape-rate family. We thus need to rescale the results manually.
+
+    """
     name = op.name
+    jax_op = getattr(jax.random, name)
 
-    # TODO Make sure there's a 1-to-1 correspondance with names
-    if not hasattr(jax.random, name):
-        raise NotImplementedError(
-            f"No JAX conversion for the given distribution: {name}"
-        )
-
-    dtype = node.outputs[1].dtype
-
-    def random_variable(rng, size, dtype_num, *args):
-        if not op.inplace:
-            rng = rng.copy()
-        prng = rng["jax_state"]
-        data = getattr(jax.random, name)(key=prng, shape=size)
-        smpl_value = jnp.array(data, dtype=dtype)
-        rng["jax_state"] = jax.random.split(prng, num=1)[0]
+    def sample_fn(rng, size, dtype, *parameters):
+        rng_key = rng["jax_state"]
+        (shape, rate) = parameters
+        sample = jax_op(rng_key, shape, size, dtype) / rate
+        rng["jax_state"] = jax.random.split(rng_key, num=1)[0]
+        return (rng, sample)
+
+    return sample_fn
+
+
+@jax_sample_fn.register(aer.ExponentialRV)
+def jax_sample_fn_exponential(op):
+    """JAX implementation of `ExponentialRV`."""
+
+    def sample_fn(rng, size, dtype, *parameters):
+        rng_key = rng["jax_state"]
+        (scale,) = parameters
+        sample = jax.random.exponential(rng_key, size, dtype) * scale
+        rng["jax_state"] = jax.random.split(rng_key, num=1)[0]
+        return (rng, sample)
+
+    return sample_fn
+
+
+@jax_sample_fn.register(aer.ChoiceRV)
+def jax_funcify_choice(op):
+    """JAX implementation of `ChoiceRV`."""
+
+    def sample_fn(rng, size, dtype, *parameters):
+        rng_key = rng["jax_state"]
+        (a, p, replace) = parameters
+        smpl_value = jax.random.choice(rng_key, a, size, replace, p)
+        rng["jax_state"] = jax.random.split(rng_key, num=1)[0]
         return (rng, smpl_value)
 
-    return random_variable
+    return sample_fn
+
+
+@jax_sample_fn.register(aer.PermutationRV)
+def jax_sample_fn_permutation(op):
+    """JAX implementation of `PermutationRV`."""
+
+    def sample_fn(rng, size, dtype, *parameters):
+        rng_key = rng["jax_state"]
+        (x,) = parameters
+        sample = jax.random.permutation(rng_key, x)
+        rng["jax_state"] = jax.random.split(rng_key, num=1)[0]
+        return (rng, sample)
+
+    return sample_fn