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Fix nan for valid parameters in jax implementation of Multinomial #1328

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Merged
merged 1 commit into from
Mar 30, 2025
Merged

Fix nan for valid parameters in jax implementation of Multinomial #1328

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12 changes: 7 additions & 5 deletions pytensor/link/jax/dispatch/random.py
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Original file line number Diff line number Diff line change
Expand Up @@ -409,12 +409,14 @@ def sample_fn(rng_key, size, dtype, n, p):
sampling_rng = jax.random.split(rng_key, binom_p.shape[0])

def _binomial_sample_fn(carry, p_rng):
s, rho = carry
remaining_n, remaining_p = carry
p, rng = p_rng
samples = jax.random.binomial(rng, s, p / rho)
s = s - samples
rho = rho - p
return ((s, rho), samples)
samples = jnp.where(
p == 0, 0, jax.random.binomial(rng, remaining_n, p / remaining_p)
)
remaining_n -= samples
remaining_p -= p
return ((remaining_n, remaining_p), samples)

(remain, _), samples = jax.lax.scan(
_binomial_sample_fn,
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12 changes: 12 additions & 0 deletions tests/link/jax/test_random.py
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Original file line number Diff line number Diff line change
Expand Up @@ -733,6 +733,18 @@ def test_multinomial():
samples.std(axis=0), np.sqrt(n[0, :, None] * p * (1 - p)), rtol=0.1
)

# Test with p=0
g = pt.random.multinomial(n=5, p=pt.eye(4))
g_fn = compile_random_function([], g, mode="JAX")
samples = g_fn()
np.testing.assert_array_equal(samples, np.eye(4) * 5)

# Test with n=0
g = pt.random.multinomial(n=0, p=np.ones(4) / 4)
g_fn = compile_random_function([], g, mode="JAX")
samples = g_fn()
np.testing.assert_array_equal(samples, np.zeros(4))


@pytest.mark.skipif(not numpyro_available, reason="VonMises dispatch requires numpyro")
def test_vonmises_mu_outside_circle():
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