jax implementation of log1mexp op

aesara/link/jax/dispatch.py

@@ -17,7 +17,7 @@ from aesara.link.utils import fgraph_to_python
 from aesara.raise_op import CheckAndRaise
 from aesara.scalar import Softplus
 from aesara.scalar.basic import Cast, Clip, Composite, Identity, ScalarOp, Second
-from aesara.scalar.math import Erf, Erfc, Erfinv, Psi
+from aesara.scalar.math import Erf, Erfc, Erfinv, Log1mexp, Psi
 from aesara.scan.op import Scan
 from aesara.scan.utils import ScanArgs
 from aesara.tensor.basic import (
@@ -1119,6 +1119,16 @@ def jax_funcify_Erfc(op, **kwargs):
     return erfc
 
 
+@jax_funcify.register(Log1mexp)
+def jax_funcify_Log1mexp(op, node, **kwargs):
+    def log1mexp(x):
+        return jnp.where(
+            x < jnp.log(0.5), jnp.log1p(-jnp.exp(x)), jnp.log(-jnp.expm1(x))
+        )
+
+    return log1mexp
+
+
 # Commented out because jax.scipy does not have erfcx,
 # but leaving the implementation in here just in case we ever see
 # a JAX implementation of Erfcx.

tests/link/test_jax.py

@@ -32,7 +32,7 @@ from aesara.tensor import subtensor as at_subtensor
 from aesara.tensor.elemwise import Elemwise
 from aesara.tensor.math import MaxAndArgmax
 from aesara.tensor.math import all as at_all
-from aesara.tensor.math import clip, cosh, erf, erfc, erfinv, gammaln, log
+from aesara.tensor.math import clip, cosh, erf, erfc, erfinv, gammaln, log, log1mexp
 from aesara.tensor.math import max as at_max
 from aesara.tensor.math import maximum, prod, psi, sigmoid, softplus
 from aesara.tensor.math import sum as at_sum
@@ -1394,3 +1394,11 @@ def test_psi():
     out = psi(x)
     fg = FunctionGraph([x], [out])
     compare_jax_and_py(fg, [3.0])
+
+
+def test_log1mexp():
+    x = vector("x")
+    out = log1mexp(x)
+    fg = FunctionGraph([x], [out])
+
+    compare_jax_and_py(fg, [[-1.0, -0.75, -0.5, -0.25]])