Provide JAX Ops from Optional tfp dependency

.github/workflows/test.yml

@@ -145,7 +145,7 @@ jobs:
           # PyTensor next, pip installs a lower version of numpy via the PyPI.
           if [[ $INSTALL_NUMBA == "1" ]] && [[ $PYTHON_VERSION == "3.9" ]]; then mamba install --yes -q -c conda-forge "python~=${PYTHON_VERSION}=*_cpython" "numpy<1.23" "numba>=0.57" numba-scipy; fi
           if [[ $INSTALL_NUMBA == "1" ]] && [[ $PYTHON_VERSION != "3.9" ]]; then mamba install --yes -q -c conda-forge "python~=${PYTHON_VERSION}=*_cpython" "numba>=0.57" numba-scipy; fi
-          if [[ $INSTALL_JAX == "1" ]]; then mamba install --yes -q -c conda-forge "python~=${PYTHON_VERSION}=*_cpython" jax jaxlib numpyro; fi
+          if [[ $INSTALL_JAX == "1" ]]; then mamba install --yes -q -c conda-forge "python~=${PYTHON_VERSION}=*_cpython" jax jaxlib numpyro && pip install tensorflow-probability; fi
           pip install -e ./
           mamba list && pip freeze
           python -c 'import pytensor; print(pytensor.config.__str__(print_doc=False))'

pytensor/link/jax/dispatch/scalar.py

 import functools
+import typing
+from typing import Callable, Optional
 
 import jax
 import jax.numpy as jnp
@@ -18,7 +20,21 @@ from pytensor.scalar.basic import (
     Second,
     Sub,
 )
-from pytensor.scalar.math import Erf, Erfc, Erfinv, Log1mexp, Psi
+from pytensor.scalar.math import Erf, Erfc, Erfcinv, Erfcx, Erfinv, Iv, Log1mexp, Psi
+
+
+def try_import_tfp_jax_op(op: ScalarOp, jax_op_name: Optional[str] = None) -> Callable:
+    try:
+        import tensorflow_probability.substrates.jax.math as tfp_jax_math
+    except ModuleNotFoundError:
+        raise NotImplementedError(
+            f"No JAX implementation for Op {op.name}. "
+            "Implementation is available if TensorFlow Probability is installed"
+        )
+
+    if jax_op_name is None:
+        jax_op_name = op.name
+    return typing.cast(Callable, getattr(tfp_jax_math, jax_op_name))
 
 
 def check_if_inputs_scalars(node):
@@ -211,6 +227,24 @@ def jax_funcify_Erfinv(op, **kwargs):
     return erfinv
 
 
+@jax_funcify.register(Erfcx)
+@jax_funcify.register(Erfcinv)
+def jax_funcify_from_tfp(op, **kwargs):
+    tfp_jax_op = try_import_tfp_jax_op(op)
+
+    return tfp_jax_op
+
+
+@jax_funcify.register(Iv)
+def jax_funcify_Iv(op, **kwargs):
+    ive = try_import_tfp_jax_op(op, jax_op_name="bessel_ive")
+
+    def iv(v, x):
+        return ive(v, x) / jnp.exp(-jnp.abs(jnp.real(x)))
+
+    return iv
+
+
 @jax_funcify.register(Log1mexp)
 def jax_funcify_Log1mexp(op, node, **kwargs):
     def log1mexp(x):

tests/link/jax/test_scalar.py

@@ -7,13 +7,17 @@ from pytensor.configdefaults import config
 from pytensor.graph.fg import FunctionGraph
 from pytensor.graph.op import get_test_value
 from pytensor.scalar.basic import Composite
+from pytensor.tensor import as_tensor
 from pytensor.tensor.elemwise import Elemwise
 from pytensor.tensor.math import all as at_all
 from pytensor.tensor.math import (
     cosh,
     erf,
     erfc,
+    erfcinv,
+    erfcx,
     erfinv,
+    iv,
     log,
     log1mexp,
     psi,
@@ -28,6 +32,14 @@ jax = pytest.importorskip("jax")
 from pytensor.link.jax.dispatch import jax_funcify
 
 
+try:
+    pass
+
+    TFP_INSTALLED = True
+except ModuleNotFoundError:
+    TFP_INSTALLED = False
+
+
 def test_second():
     a0 = scalar("a0")
     b = scalar("b")
@@ -134,6 +146,23 @@ def test_erfinv():
     compare_jax_and_py(fg, [0.95])
 
 
+@pytest.mark.parametrize(
+    "op, test_values",
+    [
+        (erfcx, (0.7,)),
+        (erfcinv, (0.7,)),
+        (iv, (0.3, 0.7)),
+    ],
+)
+@pytest.mark.skipif(not TFP_INSTALLED, reason="Test requires tensorflow-probability")
+def test_tfp_ops(op, test_values):
+    inputs = [as_tensor(test_value).type() for test_value in test_values]
+    output = op(*inputs)
+
+    fg = FunctionGraph(inputs, [output])
+    compare_jax_and_py(fg, test_values)
+
+
 def test_psi():
     x = scalar("x")
     out = psi(x)