torch/_numpy/_ufuncs.py

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