Vehicle-cpp/hkpc/software/torch/_refs/special/__init__.py

import math
from typing import Optional, Union

import torch
import torch._prims as prims
import torch._prims_common as utils
import torch._refs as refs

from torch import Tensor
from torch._decomp import register_decomposition
from torch._prims_common import (
    ELEMENTWISE_TYPE_PROMOTION_KIND,
    Number,
    NumberType,
    TensorLike,
    TensorLikeType,
)
from torch._prims_common.wrappers import elementwise_type_promotion_wrapper, out_wrapper
from torch._refs import (
    _make_elementwise_binary_reference,
    _make_elementwise_unary_reference,
)


__all__ = [
    "bessel_j0",
    "bessel_j1",
    "entr",
    "erfcx",
    "expit",
    "i0e",
    "i1",
    "i1e",
    "log_ndtr",
    "logit",
    "log_softmax",
    "multigammaln",
    "ndtr",
    "ndtri",
    "softmax",
    "spherical_bessel_j0",
    "xlog1py",
    "zeta",
]
aten = torch._ops.ops.aten


@_make_elementwise_unary_reference(
    ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
)
def bessel_j0(a: TensorLikeType) -> TensorLikeType:
    return prims.bessel_j0(a)


@_make_elementwise_unary_reference(
    ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
)
def bessel_j1(a: TensorLikeType) -> TensorLikeType:
    return prims.bessel_j1(a)


@register_decomposition(aten.special_entr)
@out_wrapper()
@elementwise_type_promotion_wrapper(
    type_promoting_args=("a",),
    type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
)
def entr(a: TensorLikeType) -> TensorLikeType:
    return torch.where(
        torch.isnan(a),
        a,
        torch.where(a > 0, -a * torch.log(a), torch.where(a == 0, 0, -torch.inf)),
    )


@register_decomposition(aten.special_erfcx)
@out_wrapper()
@elementwise_type_promotion_wrapper(
    type_promoting_args=("a",),
    type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
)
def erfcx(a: TensorLikeType) -> TensorLikeType:
    return prims.erfcx(a)


# alias for sigmoid
expit = torch.sigmoid


@_make_elementwise_unary_reference(
    ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
)
def i0e(a: TensorLikeType) -> TensorLikeType:
    return prims.bessel_i0e(a)


@_make_elementwise_unary_reference(
    ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
)
def i1(a: TensorLikeType) -> TensorLikeType:
    return prims.bessel_i1(a)


@_make_elementwise_unary_reference(
    ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
)
def i1e(a: TensorLikeType) -> TensorLikeType:
    return prims.bessel_i1e(a)


@register_decomposition(aten.special_log_ndtr)
@out_wrapper()
@elementwise_type_promotion_wrapper(
    type_promoting_args=("a",),
    type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
)
def log_ndtr(a: TensorLikeType) -> TensorLikeType:
    # Note: M_SQRT1_2 is the value of 1 / √2
    M_SQRT1_2 = 0.707106781186547524400844362104849039
    t = a * M_SQRT1_2
    return torch.where(
        a < 1.0,
        torch.log(torch.special.erfcx(-t) / 2) - t * t,
        torch.log1p(-refs.erfc(t) / 2),
    )


@register_decomposition(aten.logit)
@out_wrapper()
@elementwise_type_promotion_wrapper(
    type_promoting_args=("self",),
    type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
)
def logit(self: TensorLikeType, eps: Optional[float] = None) -> TensorLikeType:
    if eps is None:
        eps = -1.0
    lo = eps
    hi = 1 - eps
    self = torch.clamp(self, lo, hi)
    return torch.log(torch.true_divide(self, torch.sub(1, self)))


@register_decomposition(aten.special_xlog1py)
@out_wrapper()
@elementwise_type_promotion_wrapper(
    type_promoting_args=("a", "b"),
    type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
)
def xlog1py(a: Union[TensorLikeType, NumberType], b: Union[TensorLikeType, NumberType]):
    utils.check(
        isinstance(a, TensorLike) or isinstance(b, TensorLike),
        lambda: 'Expected either argument a or b to be a Tensor"',
    )

    # Operations like eq and log do not handle scalar values, so we convert them to scalar_tensors.
    if isinstance(a, TensorLike) and isinstance(b, Number):
        b = refs.scalar_tensor(b, dtype=a.dtype, device=a.device)
    elif isinstance(b, TensorLike) and isinstance(a, Number):
        a = refs.scalar_tensor(a, dtype=b.dtype, device=b.device)

    # mypy: expected "Tensor"
    assert isinstance(a, TensorLike)
    assert isinstance(b, TensorLike)
    rhs = torch.where(torch.eq(a, 0), 0, torch.mul(a, refs.log1p(b)))
    return torch.where(torch.isnan(b), float("nan"), rhs)


@register_decomposition(aten.mvlgamma)
@out_wrapper()
@elementwise_type_promotion_wrapper(
    type_promoting_args=("a",),
    type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
)
def multigammaln(a: TensorLikeType, p: int) -> TensorLikeType:
    c = 0.25 * p * (p - 1) * math.log(math.pi)
    b = 0.5 * torch.arange(start=(1 - p), end=1, step=1, dtype=a.dtype, device=a.device)
    return torch.sum(torch.lgamma(a.unsqueeze(-1) + b), dim=-1) + c


@register_decomposition(aten.special_ndtr)
@out_wrapper()
@elementwise_type_promotion_wrapper(
    type_promoting_args=("a",),
    type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
)
def ndtr(a: TensorLikeType) -> TensorLikeType:
    # Note: M_SQRT1_2 is the value of 1 / √2
    M_SQRT1_2 = 0.707106781186547524400844362104849039
    a_sqrt_2 = a * M_SQRT1_2
    return (1 + torch.erf(a_sqrt_2)) * 0.5


@register_decomposition(aten.special_ndtri)
@out_wrapper()
@elementwise_type_promotion_wrapper(
    type_promoting_args=("a",),
    type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
)
def ndtri(a: TensorLikeType) -> TensorLikeType:
    return prims.ndtri(a)


# Forwarding alias: the special variant doesn't support the out kwarg
# CompositeImplicitAutograd - don't register decomp
def log_softmax(
    a: TensorLikeType,
    dim: int,
    dtype: Optional[torch.dtype] = None,
) -> TensorLikeType:
    return torch.log_softmax(a=a, dim=dim, dtype=dtype)  # type: ignore[call-overload]


# Forwarding alias: the special variant doesn't support the out kwarg
# CompositeImplicitAutograd - don't register decomp
def softmax(
    a: TensorLikeType,
    dim: int,
    dtype: Optional[torch.dtype] = None,
) -> TensorLikeType:
    return torch.softmax(a=a, dim=dim, dtype=dtype)  # type: ignore[call-overload]


@_make_elementwise_unary_reference(
    ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
)
def spherical_bessel_j0(a: TensorLikeType) -> TensorLikeType:
    return prims.spherical_bessel_j0(a)


# TODO: add docstring
@_make_elementwise_binary_reference(
    type_promotion_kind=utils.ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
)
def zeta(a: TensorLikeType, b: TensorLikeType) -> TensorLikeType:
    return prims.zeta(a, b)