Update jnp.fabs to emulate the behavior of np.fabs for complex inputs

CHANGELOG.md

@@ -22,6 +22,8 @@ When releasing, please add the new-release-boilerplate to docs/pallas/CHANGELOG.
     `jax.config.update('jax_cpu_enable_async_dispatch', False)`.
   * Added new {func}`jax.process_indices` function to replace the
     `jax.host_ids()` function that was deprecated in JAX v0.2.13.
+  * To align with the behavior of `numpy.fabs`, `jax.numpy.fabs` has been
+    modified to no longer support `complex dtypes`.
 
 * Breaking changes
   * The MHLO MLIR dialect (`jax.extend.mlir.mhlo`) has been removed. Use the

jax/_src/numpy/ufuncs.py

@@ -52,9 +52,48 @@ def _replace_inf(x: ArrayLike) -> Array:
 def _to_bool(x: Array) -> Array:
   return x if x.dtype == bool else lax.ne(x, _lax_const(x, 0))
 
-@implements(np.fabs, module='numpy')
+
 @partial(jit, inline=True)
 def fabs(x: ArrayLike, /) -> Array:
+  """Compute the element-wise absolute values of the real-valued input.
+
+  JAX implementation of :func:`numpy.fabs`.
+
+  Args:
+    x: input array or scalar. Must not have a complex dtype.
+
+  Returns:
+    An array with same shape as ``x`` and dtype float, containing the element-wise
+    absolute values.
+
+  See also:
+    - :func:`jax.numpy.absolute`: Computes the absolute values of the input including
+      complex dtypes.
+    - :func:`jax.numpy.abs`: Computes the absolute values of the input including
+      complex dtypes.
+
+  Examples:
+    For integer inputs:
+
+    >>> x = jnp.array([-5, -9, 1, 10, 15])
+    >>> jnp.fabs(x)
+    Array([ 5.,  9.,  1., 10., 15.], dtype=float32)
+
+    For float type inputs:
+
+    >>> x1 = jnp.array([-1.342, 5.649, 3.927])
+    >>> jnp.fabs(x1)
+    Array([1.342, 5.649, 3.927], dtype=float32)
+
+    For boolean inputs:
+
+    >>> x2 = jnp.array([True, False])
+    >>> jnp.fabs(x2)
+    Array([1., 0.], dtype=float32)
+  """
+  check_arraylike('fabs', x)
+  if dtypes.issubdtype(dtypes.dtype(x), np.complexfloating):
+    raise TypeError("ufunc 'fabs' does not support complex dtypes")
   return lax.abs(*promote_args_inexact('fabs', x))
 
 @implements(getattr(np, 'bitwise_invert', np.invert), module='numpy')