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MAINT: normalize NDArray to tensors, add a special-case for out= NDArrays #108

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Apr 6, 2023
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MAINT: normalize NDArray to tensors, add a special-case for out= NDArrays #108

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66 changes: 35 additions & 31 deletions torch_np/_funcs_impl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,7 @@
AxisLike,
DTypeLike,
NDArray,
OutArray,
SubokLike,
normalize_array_like,
)
Expand All @@ -41,8 +42,8 @@ def copy(a: ArrayLike, order="K", subok: SubokLike = False):
def copyto(dst: NDArray, src: ArrayLike, casting="same_kind", where=NoValue):
if where is not NoValue:
raise NotImplementedError
(src,) = _util.typecast_tensors((src,), dst.tensor.dtype, casting=casting)
dst.tensor.copy_(src)
(src,) = _util.typecast_tensors((src,), dst.dtype, casting=casting)
dst.copy_(src)


def atleast_1d(*arys: ArrayLike):
Expand Down Expand Up @@ -114,7 +115,7 @@ def _concatenate(tensors, axis=0, out=None, dtype=None, casting="same_kind"):
def concatenate(
ar_tuple: Sequence[ArrayLike],
axis=0,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
dtype: DTypeLike = None,
casting="same_kind",
):
Expand Down Expand Up @@ -160,7 +161,7 @@ def column_stack(
def stack(
arrays: Sequence[ArrayLike],
axis=0,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
*,
dtype: DTypeLike = None,
casting="same_kind",
Expand Down Expand Up @@ -754,7 +755,7 @@ def nanmean(
a: ArrayLike,
axis=None,
dtype: DTypeLike = None,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
keepdims=NoValue,
*,
where=NoValue,
Expand Down Expand Up @@ -892,7 +893,7 @@ def take(
a: ArrayLike,
indices: ArrayLike,
axis=None,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
mode="raise",
):
if mode != "raise":
Expand Down Expand Up @@ -975,7 +976,7 @@ def clip(
a: ArrayLike,
min: Optional[ArrayLike] = None,
max: Optional[ArrayLike] = None,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
):
# np.clip requires both a_min and a_max not None, while ndarray.clip allows
# one of them to be None. Follow the more lax version.
Expand Down Expand Up @@ -1070,7 +1071,7 @@ def trace(
axis1=0,
axis2=1,
dtype: DTypeLike = None,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
):
result = torch.diagonal(a, offset, dim1=axis1, dim2=axis2).sum(-1, dtype=dtype)
return result
Expand Down Expand Up @@ -1180,7 +1181,7 @@ def tensordot(a: ArrayLike, b: ArrayLike, axes=2):
return torch.tensordot(a, b, dims=axes)


def dot(a: ArrayLike, b: ArrayLike, out: Optional[NDArray] = None):
def dot(a: ArrayLike, b: ArrayLike, out: Optional[OutArray] = None):
dtype = _dtypes_impl.result_type_impl((a.dtype, b.dtype))
a = _util.cast_if_needed(a, dtype)
b = _util.cast_if_needed(b, dtype)
Expand Down Expand Up @@ -1215,7 +1216,7 @@ def inner(a: ArrayLike, b: ArrayLike, /):
return result


def outer(a: ArrayLike, b: ArrayLike, out: Optional[NDArray] = None):
def outer(a: ArrayLike, b: ArrayLike, out: Optional[OutArray] = None):
return torch.outer(a, b)


Expand Down Expand Up @@ -1382,7 +1383,7 @@ def imag(a: ArrayLike):
return result


def round_(a: ArrayLike, decimals=0, out: Optional[NDArray] = None):
def round_(a: ArrayLike, decimals=0, out: Optional[OutArray] = None):
if a.is_floating_point():
result = torch.round(a, decimals=decimals)
elif a.is_complex():
Expand All @@ -1408,7 +1409,7 @@ def sum(
a: ArrayLike,
axis: AxisLike = None,
dtype: DTypeLike = None,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
keepdims=NoValue,
initial=NoValue,
where=NoValue,
Expand All @@ -1423,7 +1424,7 @@ def prod(
a: ArrayLike,
axis: AxisLike = None,
dtype: DTypeLike = None,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
keepdims=NoValue,
initial=NoValue,
where=NoValue,
Expand All @@ -1441,7 +1442,7 @@ def mean(
a: ArrayLike,
axis: AxisLike = None,
dtype: DTypeLike = None,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
keepdims=NoValue,
*,
where=NoValue,
Expand All @@ -1454,7 +1455,7 @@ def var(
a: ArrayLike,
axis: AxisLike = None,
dtype: DTypeLike = None,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
ddof=0,
keepdims=NoValue,
*,
Expand All @@ -1470,7 +1471,7 @@ def std(
a: ArrayLike,
axis: AxisLike = None,
dtype: DTypeLike = None,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
ddof=0,
keepdims=NoValue,
*,
Expand All @@ -1485,7 +1486,7 @@ def std(
def argmin(
a: ArrayLike,
axis: AxisLike = None,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
*,
keepdims=NoValue,
):
Expand All @@ -1496,7 +1497,7 @@ def argmin(
def argmax(
a: ArrayLike,
axis: AxisLike = None,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
*,
keepdims=NoValue,
):
Expand All @@ -1507,7 +1508,7 @@ def argmax(
def amax(
a: ArrayLike,
axis: AxisLike = None,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
keepdims=NoValue,
initial=NoValue,
where=NoValue,
Expand All @@ -1522,7 +1523,7 @@ def amax(
def amin(
a: ArrayLike,
axis: AxisLike = None,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
keepdims=NoValue,
initial=NoValue,
where=NoValue,
Expand All @@ -1535,7 +1536,10 @@ def amin(


def ptp(
a: ArrayLike, axis: AxisLike = None, out: Optional[NDArray] = None, keepdims=NoValue
a: ArrayLike,
axis: AxisLike = None,
out: Optional[OutArray] = None,
keepdims=NoValue,
):
result = _impl.ptp(a, axis=axis, keepdims=keepdims)
return result
Expand All @@ -1544,7 +1548,7 @@ def ptp(
def all(
a: ArrayLike,
axis: AxisLike = None,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
keepdims=NoValue,
*,
where=NoValue,
Expand All @@ -1556,7 +1560,7 @@ def all(
def any(
a: ArrayLike,
axis: AxisLike = None,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
keepdims=NoValue,
*,
where=NoValue,
Expand All @@ -1574,7 +1578,7 @@ def cumsum(
a: ArrayLike,
axis: AxisLike = None,
dtype: DTypeLike = None,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
):
result = _impl.cumsum(a, axis=axis, dtype=dtype)
return result
Expand All @@ -1584,7 +1588,7 @@ def cumprod(
a: ArrayLike,
axis: AxisLike = None,
dtype: DTypeLike = None,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
):
result = _impl.cumprod(a, axis=axis, dtype=dtype)
return result
Expand All @@ -1597,7 +1601,7 @@ def quantile(
a: ArrayLike,
q: ArrayLike,
axis: AxisLike = None,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
overwrite_input=False,
method="linear",
keepdims=False,
Expand All @@ -1620,7 +1624,7 @@ def percentile(
a: ArrayLike,
q: ArrayLike,
axis: AxisLike = None,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
overwrite_input=False,
method="linear",
keepdims=False,
Expand All @@ -1642,7 +1646,7 @@ def percentile(
def median(
a: ArrayLike,
axis=None,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
overwrite_input=False,
keepdims=False,
):
Expand Down Expand Up @@ -1726,7 +1730,7 @@ def imag(a: ArrayLike):
return result


def round_(a: ArrayLike, decimals=0, out: Optional[NDArray] = None):
def round_(a: ArrayLike, decimals=0, out: Optional[OutArray] = None):
if a.is_floating_point():
result = torch.round(a, decimals=decimals)
elif a.is_complex():
Expand Down Expand Up @@ -1786,11 +1790,11 @@ def isrealobj(x: ArrayLike):
return not torch.is_complex(x)


def isneginf(x: ArrayLike, out: Optional[NDArray] = None):
def isneginf(x: ArrayLike, out: Optional[OutArray] = None):
return torch.isneginf(x, out=out)


def isposinf(x: ArrayLike, out: Optional[NDArray] = None):
def isposinf(x: ArrayLike, out: Optional[OutArray] = None):
return torch.isposinf(x, out=out)


Expand Down
19 changes: 19 additions & 0 deletions torch_np/_normalizations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@
SubokLike = typing.TypeVar("SubokLike")
AxisLike = typing.TypeVar("AxisLike")
NDArray = typing.TypeVar("NDarray")
OutArray = typing.TypeVar("OutArray")


import inspect
Expand Down Expand Up @@ -60,6 +61,19 @@ def normalize_axis_like(arg, name=None):


def normalize_ndarray(arg, name=None):
# check the arg is an ndarray, extract its tensor attribute
if arg is None:
return arg

from ._ndarray import ndarray

if not isinstance(arg, ndarray):
raise TypeError("'out' must be an array")
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We should improve the error message using the name. Just patch this into the next PR.

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gh-109

return arg.tensor


def normalize_outarray(arg, name=None):
# almost normalize_ndarray, only return the array, not its tensor
if arg is None:
return arg

Expand All @@ -75,6 +89,8 @@ def normalize_ndarray(arg, name=None):
Optional[ArrayLike]: normalize_optional_array_like,
Sequence[ArrayLike]: normalize_seq_array_like,
Optional[NDArray]: normalize_ndarray,
Optional[OutArray]: normalize_outarray,
NDArray: normalize_ndarray,
DTypeLike: normalize_dtype,
SubokLike: normalize_subok_like,
AxisLike: normalize_axis_like,
Expand Down Expand Up @@ -164,6 +180,9 @@ def wrapped(*args, **kwds):

if "out" in params:
out = sig.bind(*args, **kwds).arguments.get("out")

### if out is not None: breakpoint()

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result = maybe_copy_to(out, result, promote_scalar_result)
result = wrap_tensors(result)

Expand Down
14 changes: 7 additions & 7 deletions torch_np/_ufuncs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@

from . import _binary_ufuncs_impl, _helpers, _unary_ufuncs_impl
from ._detail import _dtypes_impl, _util
from ._normalizations import ArrayLike, DTypeLike, NDArray, SubokLike, normalizer
from ._normalizations import ArrayLike, DTypeLike, OutArray, SubokLike, normalizer


def _ufunc_preprocess(tensors, where, casting, order, dtype, subok, signature, extobj):
Expand Down Expand Up @@ -46,7 +46,7 @@ def wrapped(
x1: ArrayLike,
x2: ArrayLike,
/,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
*,
where=True,
casting="same_kind",
Expand Down Expand Up @@ -80,7 +80,7 @@ def matmul(
x1: ArrayLike,
x2: ArrayLike,
/,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
*,
casting="same_kind",
order="K",
Expand Down Expand Up @@ -109,10 +109,10 @@ def matmul(
def divmod(
x1: ArrayLike,
x2: ArrayLike,
out1: Optional[NDArray] = None,
out2: Optional[NDArray] = None,
out1: Optional[OutArray] = None,
out2: Optional[OutArray] = None,
/,
out: tuple[Optional[NDArray], Optional[NDArray]] = (None, None),
out: tuple[Optional[OutArray], Optional[OutArray]] = (None, None),
*,
where=True,
casting="same_kind",
Expand Down Expand Up @@ -181,7 +181,7 @@ def deco_unary_ufunc(torch_func):
def wrapped(
x: ArrayLike,
/,
out: Optional[NDArray] = None,
out: Optional[OutArray] = None,
*,
where=True,
casting="same_kind",
Expand Down
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