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# mypy: ignore-errors
from __future__ import annotations
from typing import Optional
import torch
from . import _binary_ufuncs_impl, _dtypes_impl, _unary_ufuncs_impl, _util
from ._normalizations import (
ArrayLike,
ArrayLikeOrScalar,
CastingModes,
DTypeLike,
normalizer,
NotImplementedType,
OutArray,
)
def _ufunc_postprocess(result, out, casting):
if out is not None:
result = _util.typecast_tensor(result, out.dtype.torch_dtype, casting)
result = torch.broadcast_to(result, out.shape)
return result
# ############# Binary ufuncs ######################
_binary = [
name
for name in dir(_binary_ufuncs_impl)
if not name.startswith("_") and name not in ["torch", "matmul", "divmod", "ldexp"]
]
NEP50_FUNCS = (
"add",
"subtract",
"multiply",
"floor_divide",
"true_divide",
"divide",
"remainder",
"bitwise_and",
"bitwise_or",
"bitwise_xor",
"bitwise_left_shift",
"bitwise_right_shift",
"hypot",
"arctan2",
"logaddexp",
"logaddexp2",
"heaviside",
"copysign",
"fmax",
"minimum",
"fmin",
"maximum",
"fmod",
"gcd",
"lcm",
"pow",
)
def deco_binary_ufunc(torch_func):
"""Common infra for binary ufuncs.
Normalize arguments, sort out type casting, broadcasting and delegate to
the pytorch functions for the actual work.
"""
@normalizer
def wrapped(
x1: ArrayLikeOrScalar,
x2: ArrayLikeOrScalar,
/,
out: Optional[OutArray] = None,
*,
where: NotImplementedType = True,
casting: Optional[CastingModes] = "same_kind",
order: NotImplementedType = "K",
dtype: Optional[DTypeLike] = None,
subok: NotImplementedType = False,
signature: NotImplementedType = None,
extobj: NotImplementedType = None,
):
if dtype is not None:
def cast(x, dtype):
if isinstance(x, torch.Tensor):
return _util.typecast_tensor(x, dtype, casting)
else:
return torch.as_tensor(x, dtype=dtype)
x1 = cast(x1, dtype)
x2 = cast(x2, dtype)
elif isinstance(x1, torch.Tensor) and isinstance(x2, torch.Tensor):
dtype = _dtypes_impl.result_type_impl(x1, x2)
x1, x2 = _util.typecast_tensors((x1, x2), dtype, casting)
else:
x1, x2 = _dtypes_impl.nep50_to_tensors(
x1, x2, torch_func.__name__ in NEP50_FUNCS, torch_func.__name__
)
result = torch_func(x1, x2)
return _ufunc_postprocess(result, out, casting)
wrapped.__qualname__ = torch_func.__name__
wrapped.__name__ = torch_func.__name__
return wrapped
# matmul's signature is _slightly_ different from other ufuncs:
# - no where=...
# - additional axis=..., axes=...
# - no NEP50 scalars in or out
@normalizer
def matmul(
x1: ArrayLike,
x2: ArrayLike,
/,
out: Optional[OutArray] = None,
*,
casting: Optional[CastingModes] = "same_kind",
order: NotImplementedType = "K",
dtype: Optional[DTypeLike] = None,
subok: NotImplementedType = False,
signature: NotImplementedType = None,
extobj: NotImplementedType = None,
axes: NotImplementedType = None,
axis: NotImplementedType = None,
):
if dtype is None:
dtype = _dtypes_impl.result_type_impl(x1, x2)
x1, x2 = _util.typecast_tensors((x1, x2), dtype, casting)
result = _binary_ufuncs_impl.matmul(x1, x2)
result = _ufunc_postprocess(result, out, casting)
return result
# ldexp casting is special : the dtype of the result == dtype of the 1st arg
@normalizer
def ldexp(
x1: ArrayLikeOrScalar,
x2: ArrayLikeOrScalar,
/,
out: Optional[OutArray] = None,
*,
where: NotImplementedType = True,
casting: Optional[CastingModes] = "same_kind",
order: NotImplementedType = "K",
dtype: Optional[DTypeLike] = None,
subok: NotImplementedType = False,
signature: NotImplementedType = None,
extobj: NotImplementedType = None,
):
if dtype is not None:
if isinstance(x1, torch.Tensor):
x1 = _util.typecast_tensor(x1, dtype, casting)
else:
x1 = torch.as_tensor(x1, dtype=dtype)
else:
if not isinstance(x1, torch.Tensor):
x1 = torch.as_tensor(x1)
x1 = _util.cast_int_to_float(x1)
x2 = torch.as_tensor(x2)
# the second arg must be integer
if _dtypes_impl._category(x2.dtype) != 1:
raise ValueError("ldexp 2nd arg must be integer")
result = _binary_ufuncs_impl.ldexp(x1, x2)
if x1.dtype == torch.float16:
# torch.ldexp(f16, int) -> f32, undo it
result = result.to(torch.float16)
return _ufunc_postprocess(result, out, casting)
# nin=2, nout=2
@normalizer
def divmod(
x1: ArrayLike,
x2: ArrayLike,
out1: Optional[OutArray] = None,
out2: Optional[OutArray] = None,
/,
out: tuple[Optional[OutArray], Optional[OutArray]] = (None, None),
*,
where: NotImplementedType = True,
casting: Optional[CastingModes] = "same_kind",
order: NotImplementedType = "K",
dtype: Optional[DTypeLike] = None,
subok: NotImplementedType = False,
signature: NotImplementedType = None,
extobj: NotImplementedType = None,
):
# make sure we either have no out arrays at all, or there is either
# out1, out2, or out=tuple, but not both
num_outs = sum(x is not None for x in [out1, out2])
if num_outs == 1:
raise ValueError("both out1 and out2 need to be provided")
elif num_outs == 2:
o1, o2 = out
if o1 is not None or o2 is not None:
raise TypeError(
"cannot specify 'out' as both a positional and keyword argument"
)
else:
out1, out2 = out
if dtype is None:
dtype = _dtypes_impl.result_type_impl(x1, x2)
x1, x2 = _util.typecast_tensors((x1, x2), dtype, casting)
quot, rem = _binary_ufuncs_impl.divmod(x1, x2)
quot = _ufunc_postprocess(quot, out1, casting)
rem = _ufunc_postprocess(rem, out2, casting)
return quot, rem
#
# Attach ufuncs to this module, for a further export to the public namespace in __init__.py
#
for name in _binary:
ufunc = getattr(_binary_ufuncs_impl, name)
vars()[name] = deco_binary_ufunc(ufunc)
def modf(x, /, *args, **kwds):
quot, rem = divmod(x, 1, *args, **kwds)
return rem, quot
_binary = _binary + ["divmod", "modf", "matmul", "ldexp"]
# ############# Unary ufuncs ######################
_unary = [
name
for name in dir(_unary_ufuncs_impl)
if not name.startswith("_") and name != "torch"
]
# these are ufunc(int) -> float
_fp_unary = [
"arccos",
"arccosh",
"arcsin",
"arcsinh",
"arctan",
"arctanh",
"cbrt",
"cos",
"cosh",
"deg2rad",
"degrees",
"exp",
"exp2",
"expm1",
"log",
"log10",
"log1p",
"log2",
"rad2deg",
"radians",
"reciprocal",
"sin",
"sinh",
"sqrt",
"square",
"tan",
"tanh",
"trunc",
]
def deco_unary_ufunc(torch_func):
"""Common infra for unary ufuncs.
Normalize arguments, sort out type casting, broadcasting and delegate to
the pytorch functions for the actual work.
"""
@normalizer
def wrapped(
x: ArrayLike,
/,
out: Optional[OutArray] = None,
*,
where=True,
casting: Optional[CastingModes] = "same_kind",
order="K",
dtype: Optional[DTypeLike] = None,
subok: NotImplementedType = False,
signature=None,
extobj=None,
):
if dtype is not None:
x = _util.typecast_tensor(x, dtype, casting)
if torch_func.__name__ in _fp_unary:
x = _util.cast_int_to_float(x)
result = torch_func(x)
result = _ufunc_postprocess(result, out, casting)
return result
wrapped.__qualname__ = torch_func.__name__
wrapped.__name__ = torch_func.__name__
return wrapped
#
# Attach ufuncs to this module, for a further export to the public namespace in __init__.py
#
for name in _unary:
ufunc = getattr(_unary_ufuncs_impl, name)
vars()[name] = deco_unary_ufunc(ufunc)
__all__ = _binary + _unary # noqa: PLE0605
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