"""

.. currentmodule:: arraycontext

.. autofunction:: make_loopy_program

References

----------

.. class:: InstructionBase

See :class:`loopy.InstructionBase`.

.. class:: SubstitutionRule

See :class:`loopy.SubstitutionRule`.

.. class:: ValueArg

See :class:`loopy.ValueArg`.

.. class:: ArrayArg

See :class:`loopy.ArrayArg`.

.. class:: TemporaryVariable

See :class:`loopy.TemporaryVariable`.

.. class:: EllipsisType

See :data:`types.EllipsisType`.

"""

from __future__ import annotations

__copyright__ = """

Copyright (C) 2020-1 University of Illinois Board of Trustees

"""

__license__ = """

Permission is hereby granted, free of charge, to any person obtaining a copy

of this software and associated documentation files (the "Software"), to deal

in the Software without restriction, including without limitation the rights

to use, copy, modify, merge, publish, distribute, sublicense, and/or sell

copies of the Software, and to permit persons to whom the Software is

furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in

all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR

IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,

FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE

AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER

LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,

OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN

THE SOFTWARE.

"""

from abc import ABC

from typing import TYPE_CHECKING, ClassVar, cast

import numpy as np

import loopy as lp

from loopy.version import MOST_RECENT_LANGUAGE_VERSION

from pytools import memoize_in

from arraycontext.container.traversal import multimapped_over_array_containers

from arraycontext.fake_numpy import BaseFakeNumpyNamespace

if TYPE_CHECKING:

from collections.abc import Mapping, Sequence

from types import EllipsisType

import islpy as isl

from loopy.kernel.data import (

ArrayArg,

SubstitutionRule,

TemporaryVariable,

ValueArg,

)

from loopy.kernel.instruction import InstructionBase

from pytools.tag import ToTagSetConvertible

from arraycontext import ArrayContext

from arraycontext.typing import ArrayOrScalar, ScalarLike

# {{{ loopy

_DEFAULT_LOOPY_OPTIONS = lp.Options(

no_numpy=True,

return_dict=True)

def make_loopy_program(

domains: str | Sequence[str | isl.BasicSet],

statements: str | Sequence[InstructionBase | SubstitutionRule | str],

kernel_data: Sequence[

ValueArg | ArrayArg | TemporaryVariable | EllipsisType | str

] | None = None,

name: str = "mm_actx_kernel",

tags: ToTagSetConvertible = None):

"""Return a :class:`loopy.LoopKernel` suitable for use with

:meth:`ArrayContext.call_loopy`.

"""

if kernel_data is None:

kernel_data = ["..."]

return lp.make_kernel(

domains,

statements,

kernel_data=kernel_data,

options=_DEFAULT_LOOPY_OPTIONS,

default_offset=lp.auto,

name=name,

lang_version=MOST_RECENT_LANGUAGE_VERSION,

tags=tags)

def get_default_entrypoint(t_unit: lp.TranslationUnit) -> lp.LoopKernel:

try:

# main and "kernel callables" branch

return t_unit.default_entrypoint

except AttributeError:

try:

return t_unit.root_kernel

except AttributeError as err:

raise TypeError("unable to find default entry point for loopy "

"translation unit") from err

def _get_scalar_func_loopy_program(

actx: ArrayContext, c_name: str, nargs: int, naxes: int,

) -> lp.TranslationUnit:

@memoize_in(actx, _get_scalar_func_loopy_program)

def get(c_name: str, nargs: int, naxes: int) -> lp.TranslationUnit:

from pymbolic.primitives import Subscript, Variable

var_names = [f"i{i}" for i in range(naxes)]

size_names = [f"n{i}" for i in range(naxes)]

subscript = tuple(Variable(vname) for vname in var_names)

from islpy import make_zero_and_vars

v = make_zero_and_vars(var_names, params=size_names)

domain = v[0].domain()

for vname, sname in zip(var_names, size_names, strict=True):

domain = domain & v[0].le_set(v[vname]) & v[vname].lt_set(v[sname])

domain_bset, = domain.get_basic_sets()

import loopy as lp

from arraycontext.transform_metadata import ElementwiseMapKernelTag

def sub(name: str) -> Variable | Subscript:

return Subscript(Variable(name), subscript) if subscript else Variable(name)

return make_loopy_program(

[domain_bset], [

lp.Assignment(

sub("out"),

Variable(c_name)(*[sub(f"inp{i}") for i in range(nargs)]))

], [

lp.GlobalArg("out", dtype=None, shape=lp.auto, offset=lp.auto)

] + [

lp.GlobalArg(f"inp{i}", dtype=None, shape=lp.auto, offset=lp.auto)

for i in range(nargs)

] + [...],

name=f"actx_special_{c_name}",

tags=(ElementwiseMapKernelTag(),))

return get(c_name, nargs, naxes)

class LoopyBasedFakeNumpyNamespace(BaseFakeNumpyNamespace, ABC):

_numpy_to_c_arc_functions: ClassVar[Mapping[str, str]] = {

"arcsin": "asin",

"arccos": "acos",

"arctan": "atan",

"arctan2": "atan2",

"arcsinh": "asinh",

"arccosh": "acosh",

"arctanh": "atanh",

}

_c_to_numpy_arc_functions: ClassVar[Mapping[str, str]] = {c_name: numpy_name

for numpy_name, c_name in _numpy_to_c_arc_functions.items()}

def __getattr__(self, name: str):

def loopy_implemented_elwise_func(*args: ArrayOrScalar) -> ArrayOrScalar:

if all(np.isscalar(ary) for ary in args):

result = getattr(

np, self._c_to_numpy_arc_functions.get(name, name)

)(*args)

return cast("ScalarLike", result)

actx = self._array_context

prg = _get_scalar_func_loopy_program(actx,

c_name, nargs=len(args), naxes=len(args[0].shape))

outputs = actx.call_loopy(prg,

**{f"inp{i}": arg for i, arg in enumerate(args)})

return outputs["out"]

if name in self._c_to_numpy_arc_functions:

raise RuntimeError(f"'{name}' in ArrayContext.np has been removed: "

f"use '{self._c_to_numpy_arc_functions[name]}' (as in numpy)")

# normalize to C names anyway

c_name = self._numpy_to_c_arc_functions.get(name, name)

# limit which functions we try to hand off to loopy

if (name in self._numpy_math_functions

or name in self._c_to_numpy_arc_functions):

return multimapped_over_array_containers(loopy_implemented_elwise_func)

else:

raise AttributeError(

f"'{type(self._array_context).__name__}.np' object "

f"has no attribute '{name}'")

# }}}

# vim: foldmethod=marker