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[typing] better annotations for jnp.ufunc

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This commit is contained in:
Jake VanderPlas 2023-09-20 15:50:15 -07:00
parent 6a551a1efa
commit 7b6a88c9aa
2 changed files with 51 additions and 25 deletions
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72
jax/_src/numpy/ufunc_api.py
View File

@ -24,13 +24,14 @@ import operator
from typing import Any, Callable, Optional
import jax
from jax._src.typing import Array, ArrayLike, DTypeLike
from jax._src.lax import lax as lax_internal
from jax._src.numpy import reductions
from jax._src.numpy.lax_numpy import _eliminate_deprecated_list_indexing, append, take
from jax._src.numpy.reductions import _moveaxis
from jax._src.numpy.util import _wraps, check_arraylike, _broadcast_to, _where
from jax._src.numpy.vectorize import vectorize
from jax._src.util import canonicalize_axis
from jax._src.util import canonicalize_axis, set_module
import numpy as np
@ -56,28 +57,35 @@ def get_if_single_primitive(fun: Callable[..., Any], *args: Any) -> Optional[jax
return None
_primitive_reducers = {
_primitive_reducers: dict[jax.core.Primitive, Callable[..., Any]] = {
lax_internal.add_p: reductions.sum,
lax_internal.mul_p: reductions.prod,
}
_primitive_accumulators = {
_primitive_accumulators: dict[jax.core.Primitive, Callable[..., Any]] = {
lax_internal.add_p: reductions.cumsum,
lax_internal.mul_p: reductions.cumprod,
}
@set_module('jax.numpy')
class ufunc:
"""Functions that operate element-by-element on whole arrays.
This is a class for LAX-backed implementations of numpy ufuncs.
"""
def __init__(self, func, /, nin, nout, *, name=None, nargs=None, identity=None):
def __init__(self, func: Callable[..., Any], /,
nin: int, nout: int, *,
name: Optional[str] = None,
nargs: Optional[int] = None,
identity: Any = None, update_doc=False):
# We want ufunc instances to work properly when marked as static,
# and for this reason it's important that their properties not be
# mutated. We prevent this by storing them in a dunder attribute,
# and accessing them via read-only properties.
if update_doc:
self.__doc__ = func.__doc__
self.__name__ = name or func.__name__
self.__static_props = {
'func': func,
@ -95,21 +103,23 @@ class ufunc:
nargs = property(lambda self: self.__static_props['nargs'])
identity = property(lambda self: self.__static_props['identity'])
def __hash__(self):
def __hash__(self) -> int:
# Do not include _call, because it is computed from _func.
return hash((self._func, self.__name__, self.identity,
self.nin, self.nout, self.nargs))
def __eq__(self, other):
def __eq__(self, other: Any) -> bool:
# Do not include _call, because it is computed from _func.
return isinstance(other, ufunc) and (
(self._func, self.__name__, self.identity, self.nin, self.nout, self.nargs) ==
(other._func, other.__name__, other.identity, other.nin, other.nout, other.nargs))
def __repr__(self):
def __repr__(self) -> str:
return f"<jnp.ufunc '{self.__name__}'>"
def __call__(self, *args, out=None, where=None, **kwargs):
def __call__(self, *args: ArrayLike,
out: None = None, where: None = None,
**kwargs: Any) -> Any:
if out is not None:
raise NotImplementedError(f"out argument of {self}")
if where is not None:
@ -118,7 +128,9 @@ class ufunc:
@_wraps(np.ufunc.reduce, module="numpy.ufunc")
@partial(jax.jit, static_argnames=['self', 'axis', 'dtype', 'out', 'keepdims'])
def reduce(self, a, axis=0, dtype=None, out=None, keepdims=False, initial=None, where=None):
def reduce(self, a: ArrayLike, axis: int = 0, dtype: Optional[DTypeLike] = None,
out: None = None, keepdims: bool = False, initial: Optional[ArrayLike] = None,
where: Optional[ArrayLike] = None) -> Array:
check_arraylike(f"{self.__name__}.reduce", a)
if self.nin != 2:
raise ValueError("reduce only supported for binary ufuncs")
@ -136,10 +148,15 @@ class ufunc:
if lax_internal._dtype(where) != bool:
raise ValueError(f"where argument must have dtype=bool; got dtype={lax_internal._dtype(where)}")
primitive = get_if_single_primitive(self._call, *(self.nin * [lax_internal._one(a)]))
reducer = _primitive_reducers.get(primitive, self._reduce_via_scan)
if primitive is None:
reducer = self._reduce_via_scan
else:
reducer = _primitive_reducers.get(primitive, self._reduce_via_scan)
return reducer(a, axis=axis, dtype=dtype, keepdims=keepdims, initial=initial, where=where)
def _reduce_via_scan(self, arr, axis=0, dtype=None, keepdims=False, initial=None, where=None):
def _reduce_via_scan(self, arr: ArrayLike, axis: int = 0, dtype: Optional[DTypeLike] = None,
keepdims: bool = False, initial: Optional[ArrayLike] = None,
where: Optional[ArrayLike] = None) -> Array:
assert self.nin == 2 and self.nout == 1
arr = lax_internal.asarray(arr)
if initial is None:
@ -182,6 +199,7 @@ class ufunc:
else:
return _where(where[i], self._call(val, arr[i].astype(dtype)), val)
start_value: ArrayLike
if initial is None:
start_index = 1
start_value = arr[0]
@ -198,7 +216,8 @@ class ufunc:
@_wraps(np.ufunc.accumulate, module="numpy.ufunc")
@partial(jax.jit, static_argnames=['self', 'axis', 'dtype'])
def accumulate(self, a, axis=0, dtype=None, out=None):
def accumulate(self, a: ArrayLike, axis: int = 0, dtype: Optional[DTypeLike] = None,
out: None = None) -> Array:
if self.nin != 2:
raise ValueError("accumulate only supported for binary ufuncs")
if self.nout != 1:
@ -206,10 +225,14 @@ class ufunc:
if out is not None:
raise NotImplementedError(f"out argument of {self.__name__}.accumulate()")
primitive = get_if_single_primitive(self._call, *(self.nin * [lax_internal._one(a)]))
accumulator = _primitive_accumulators.get(primitive, self._accumulate_via_scan)
if primitive is None:
accumulator = self._accumulate_via_scan
else:
accumulator = _primitive_accumulators.get(primitive, self._accumulate_via_scan)
return accumulator(a, axis=axis, dtype=dtype)
def _accumulate_via_scan(self, arr, axis=0, dtype=None):
def _accumulate_via_scan(self, arr: ArrayLike, axis: int = 0,
dtype: Optional[DTypeLike] = None) -> Array:
assert self.nin == 2 and self.nout == 1
check_arraylike(f"{self.__name__}.accumulate", arr)
arr = lax_internal.asarray(arr)
@ -231,7 +254,8 @@ class ufunc:
@_wraps(np.ufunc.accumulate, module="numpy.ufunc")
@partial(jax.jit, static_argnums=[0], static_argnames=['inplace'])
def at(self, a, indices, b=None, /, *, inplace=True):
def at(self, a: ArrayLike, indices: Any, b: Optional[ArrayLike] = None, /, *,
inplace: bool = True) -> Array:
if inplace:
raise NotImplementedError(_AT_INPLACE_WARNING)
if b is None:
@ -239,7 +263,7 @@ class ufunc:
else:
return self._at_via_scan(a, indices, b)
def _at_via_scan(self, a, indices, *args):
def _at_via_scan(self, a: ArrayLike, indices: Any, *args: Any) -> Array:
check_arraylike(f"{self.__name__}.at", a, *args)
dtype = jax.eval_shape(self._func, lax_internal._one(a), *(lax_internal._one(arg) for arg in args)).dtype
a = lax_internal.asarray(a).astype(dtype)
@ -266,7 +290,8 @@ class ufunc:
@_wraps(np.ufunc.reduceat, module="numpy.ufunc")
@partial(jax.jit, static_argnames=['self', 'axis', 'dtype'])
def reduceat(self, a, indices, axis=0, dtype=None, out=None):
def reduceat(self, a: ArrayLike, indices: Any, axis: int = 0,
dtype: Optional[DTypeLike] = None, out: None = None) -> Array:
if self.nin != 2:
raise ValueError("reduceat only supported for binary ufuncs")
if self.nout != 1:
@ -275,7 +300,8 @@ class ufunc:
raise NotImplementedError(f"out argument of {self.__name__}.reduceat()")
return self._reduceat_via_scan(a, indices, axis=axis, dtype=dtype)
def _reduceat_via_scan(self, a, indices, axis=0, dtype=None):
def _reduceat_via_scan(self, a: ArrayLike, indices: Any, axis: int = 0,
dtype: Optional[DTypeLike] = None) -> Array:
check_arraylike(f"{self.__name__}.reduceat", a, indices)
a = lax_internal.asarray(a)
idx_tuple = _eliminate_deprecated_list_indexing(indices)
@ -292,7 +318,7 @@ class ufunc:
axis = canonicalize_axis(axis, a.ndim)
out = take(a, indices, axis=axis)
ind = jax.lax.expand_dims(append(indices, a.shape[axis]),
np.delete(np.arange(out.ndim), axis))
list(np.delete(np.arange(out.ndim), axis)))
ind_start = jax.lax.slice_in_dim(ind, 0, ind.shape[axis] - 1, axis=axis)
ind_end = jax.lax.slice_in_dim(ind, 1, ind.shape[axis], axis=axis)
def loop_body(i, out):
@ -303,7 +329,7 @@ class ufunc:
@_wraps(np.ufunc.outer, module="numpy.ufunc")
@partial(jax.jit, static_argnums=[0])
def outer(self, A, B, /, **kwargs):
def outer(self, A: ArrayLike, B: ArrayLike, /, **kwargs) -> Array:
if self.nin != 2:
raise ValueError("outer only supported for binary ufuncs")
if self.nout != 1:
@ -314,7 +340,8 @@ class ufunc:
return result.reshape(*np.shape(A), *np.shape(B))
def frompyfunc(func, /, nin, nout, *, identity=None):
def frompyfunc(func: Callable[..., Any], /, nin: int, nout: int,
*, identity: Any = None) -> ufunc:
"""Create a JAX ufunc from an arbitrary JAX-compatible scalar function.
Args:
@ -326,5 +353,4 @@ def frompyfunc(func, /, nin, nout, *, identity=None):
Returns:
wrapped : jax.numpy.ufunc wrapper of func.
"""
# TODO(jakevdp): use functools.wraps or similar to wrap the docstring?
return ufunc(func, nin, nout, identity=identity)
return ufunc(func, nin, nout, identity=identity, update_doc=True)

4
jax/_src/util.py
View File

@ -527,8 +527,8 @@ def _original_func(f):
return f
def set_module(module):
def wrapper(func):
def set_module(module: str) -> Callable[[T], T]:
def wrapper(func: T) -> T:
if module is not None:
func.__module__ = module
return func