Add some missing jax.numpy documentation

jax/_src/dtypes.py

@@ -651,7 +651,8 @@ def _least_upper_bound(jax_numpy_dtype_promotion: str, *nodes: JAXType) -> JAXTy
 def promote_types(a: DTypeLike, b: DTypeLike) -> DType:
   """Returns the type to which a binary operation should cast its arguments.
 
-  For details of JAX's type promotion semantics, see :ref:`type-promotion`.
+  JAX implementation of :func:`numpy.promote_types`. For details of JAX's
+  type promotion semantics, see :ref:`type-promotion`.
 
   Args:
     a: a :class:`numpy.dtype` or a dtype specifier.
@@ -659,6 +660,35 @@ def promote_types(a: DTypeLike, b: DTypeLike) -> DType:
 
   Returns:
     A :class:`numpy.dtype` object.
+
+  Examples:
+    Type specifiers may be strings, dtypes, or scalar types, and the return
+    value is always a dtype:
+
+    >>> jnp.promote_types('int32', 'float32')  # strings
+    dtype('float32')
+    >>> jnp.promote_types(jnp.dtype('int32'), jnp.dtype('float32'))  # dtypes
+    dtype('float32')
+    >>> jnp.promote_types(jnp.int32, jnp.float32)  # scalar types
+    dtype('float32')
+
+    Built-in scalar types (:type:`int`, :type:`float`, or :type:`complex`) are
+    treated as weakly-typed and will not change the bit width of a strongly-typed
+    counterpart (see discussion in :ref:`type-promotion`):
+
+    >>> jnp.promote_types('uint8', int)
+    dtype('uint8')
+    >>> jnp.promote_types('float16', float)
+    dtype('float16')
+
+    This differs from the NumPy version of this function, which treats built-in scalar
+    types as equivalent to 64-bit types:
+
+    >>> import numpy
+    >>> numpy.promote_types('uint8', int)
+    dtype('int64')
+    >>> numpy.promote_types('float16', float)
+    dtype('float64')
   """
   # Note: we deliberately avoid `if a in _weak_types` here because we want to check
   # object identity, not object equality, due to the behavior of np.dtype.__eq__

jax/_src/numpy/lax_numpy.py

@@ -194,6 +194,12 @@ def _make_scalar_type(np_scalar_type: type) -> _ScalarMeta:
   meta = _ScalarMeta(np_scalar_type.__name__, (object,),
                      {"dtype": np.dtype(np_scalar_type)})
   meta.__module__ = _PUBLIC_MODULE_NAME
+  meta.__doc__ =\
+  f"""A JAX scalar constructor of type {np_scalar_type.__name__}.
+
+  While NumPy defines scalar types for each data type, JAX represents
+  scalars as zero-dimensional arrays.
+  """
   return meta
 
 bool_ = _make_scalar_type(np.bool_)

jax/_src/numpy/ufunc_api.py

@@ -598,5 +598,28 @@ def frompyfunc(func: Callable[..., Any], /, nin: int, nout: int,
 
   Returns:
     wrapped : jax.numpy.ufunc wrapper of func.
+
+  Examples:
+    Here is an example of creating a ufunc similar to :obj:`jax.numpy.add`:
+
+    >>> import operator
+    >>> add = frompyfunc(operator.add, nin=2, nout=1, identity=0)
+
+    Now all the standard :class:`jax.numpy.ufunc` methods are available:
+
+    >>> x = jnp.arange(4)
+    >>> add(x, 10)
+    Array([10, 11, 12, 13], dtype=int32)
+    >>> add.outer(x, x)
+    Array([[0, 1, 2, 3],
+           [1, 2, 3, 4],
+           [2, 3, 4, 5],
+           [3, 4, 5, 6]], dtype=int32)
+    >>> add.reduce(x)
+    Array(6, dtype=int32)
+    >>> add.accumulate(x)
+    Array([0, 1, 3, 6], dtype=int32)
+    >>> add.at(x, 1, 10, inplace=False)
+    Array([ 0, 11,  2,  3], dtype=int32)
   """
   return ufunc(func, nin, nout, identity=identity)