Implement faster Multinomial JAX dispatch

pytensor/link/jax/dispatch/random.py

@@ -1,6 +1,7 @@
 from functools import singledispatch
 
 import jax
+import jax.numpy as jnp
 import numpy as np
 from numpy.random import Generator
 from numpy.random.bit_generator import (  # type: ignore[attr-defined]
@@ -394,21 +395,47 @@
 
 @jax_sample_fn.register(ptr.MultinomialRV)
 def jax_sample_fn_multinomial(op, node):
-    if not numpyro_available:
-        raise NotImplementedError(
-            f"No JAX implementation for the given distribution: {op.name}. "
-            "Implementation is available if NumPyro is installed."
-        )
-
-    from numpyro.distributions.util import multinomial
-
     def sample_fn(rng_key, size, dtype, n, p):
-        sample = multinomial(key=rng_key, n=n, p=p, shape=size)
+        sample = _jax_multinomial(key=rng_key, n=n, p=p, size=size)
         return sample
 
     return sample_fn
 
 
+def _jax_multinomial(n, p, size=None, key=None):
+    if size is not None:
+        broadcast_shape_n = jax.lax.broadcast_shapes(jnp.shape(n), size)
+        n = jnp.broadcast_to(n, broadcast_shape_n)
+
+        broadcast_shape_p = jax.lax.broadcast_shapes(jnp.shape(p)[:-1], size)
+        p = jnp.broadcast_to(p, broadcast_shape_p + jnp.shape(p)[-1:])
+
+    else:
+        broadcast_shape = jax.lax.broadcast_shapes(jnp.shape(n), jnp.shape(p)[:-1])
+        n = jnp.broadcast_to(n, broadcast_shape)
+        p = jnp.broadcast_to(p, broadcast_shape + jnp.shape(p)[-1:])
+
+    binom_p = jnp.moveaxis(p, -1, 0)[:-1, ...]
+    sampling_rng = jax.random.split(key, binom_p.shape[0])
+
+    def _binomial_sample_fn(carry, p_rng):
+        s, rho = carry
+        p, rng = p_rng
+        samples = jax.random.binomial(rng, s, p / rho)
+        s = s - samples
+        rho = rho - p
+        return ((s, rho), samples)
+
+    (remain, _), samples = jax.lax.scan(
+        _binomial_sample_fn,
+        (n.astype(np.float64), jnp.ones(binom_p.shape[1:])),
+        (binom_p, sampling_rng),
+    )
+    return jnp.concatenate(
+        [jnp.moveaxis(samples, 0, -1), jnp.expand_dims(remain, -1)], axis=-1
+    )
+
+
 @jax_sample_fn.register(ptr.VonMisesRV)
 def jax_sample_fn_vonmises(op, node):
     if not numpyro_available:

tests/link/jax/test_random.py

@@ -703,21 +703,33 @@ def test_beta_binomial():
     )
 
 
-@pytest.mark.skipif(
-    not numpyro_available, reason="Multinomial dispatch requires numpyro"
-)
 def test_multinomial():
     rng = shared(np.random.default_rng(123))
+
+    # test with 'size' argument and n.shape == p.shape[:-1]
     n = np.array([10, 40])
     p = np.array([[0.3, 0.7, 0.0], [0.1, 0.4, 0.5]])
-    g = pt.random.multinomial(n, p, size=(10_000, 2), rng=rng)
+    size = (10_000, 2)
+
+    g = pt.random.multinomial(n, p, size=size, rng=rng)
     g_fn = compile_random_function([], g, mode="JAX")
     samples = g_fn()
     np.testing.assert_allclose(samples.mean(axis=0), n[..., None] * p, rtol=0.1)
     np.testing.assert_allclose(
         samples.std(axis=0), np.sqrt(n[..., None] * p * (1 - p)), rtol=0.1
     )
 
+    # test with no 'size' argument and n.shape != p.shape[:-1]
+    n = np.broadcast_to(np.array([10, 40]), size)
+
+    g = pt.random.multinomial(n, p, rng=rng)
+    g_fn = compile_random_function([], g, mode="JAX")
+    samples = g_fn()
+    np.testing.assert_allclose(samples.mean(axis=0), n[0, :, None] * p, rtol=0.1)
+    np.testing.assert_allclose(
+        samples.std(axis=0), np.sqrt(n[0, :, None] * p * (1 - p)), rtol=0.1
+    )
+
 
 @pytest.mark.skipif(not numpyro_available, reason="VonMises dispatch requires numpyro")
 def test_vonmises_mu_outside_circle():