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Merge f53e8a160 into 39f012d3d

Pull Request Pull Request #1032: Feature/support sycl queue in array constructor functions

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94.17

/numba_dpex/dpnp_iface/_intrinsic.py

# SPDX-FileCopyrightText: 2020 - 2023 Intel Corporation
#
# SPDX-License-Identifier: Apache-2.0

from collections import namedtuple

from llvmlite import ir as llvmir
from llvmlite.ir import Constant
from llvmlite.ir.types import DoubleType, FloatType
from numba import types
from numba.core import cgutils
from numba.core import config as numba_config
from numba.core.typing import signature
from numba.extending import intrinsic, overload_classmethod
from numba.np.arrayobj import (
    _parse_empty_args,
    _parse_empty_like_args,
    get_itemsize,
    make_array,
    populate_array,
)

from numba_dpex.core.runtime import context as dpexrt
from numba_dpex.core.types import DpnpNdArray
from numba_dpex.core.types.dpctl_types import DpctlSyclQueue


# XXX: The function should be moved into DpexTargetContext
def make_queue(context, builder, arrtype):
    """Utility function used for allocating a new queue.

    This function will allocates a new queue (e.g. SYCL queue)
    during LLVM code generation (lowering). Given a target context,
    builder, array type, returns a LLVM value pointing at a numba-dpex
    runtime allocated queue.

    Args:
        context (numba.core.base.BaseContext): Any of the context
            derived from Numba's BaseContext
            (e.g. `numba.core.cpu.CPUContext`).
        builder (llvmlite.ir.builder.IRBuilder): The IR builder
            from `llvmlite` for code generation.
        arrtype (numba_dpex.core.types.dpnp_ndarray_type.DpnpNdArray):
            Any of the array types derived from
            `numba.core.types.nptypes.Array`,
            e.g. `numba_dpex.core.types.dpnp_ndarray_type.DpnpNdArray`.
            Refer to `numba_dpex.dpnp_iface._intrinsic.alloc_empty_arrayobj()`
            function for details on how to construct this argument.

    Returns:
        ret (namedtuple): A namedtuple containing
        `llvmlite.ir.instructions.ExtractValue` as `queue_ref`,
        `llvmlite.ir.instructions.CastInstr` as `queue_address_ptr`
        and `numba.core.pythonapi.PythonAPI` as `pyapi`.
    """

    pyapi = context.get_python_api(builder)
    queue_struct_proxy = cgutils.create_struct_proxy(
        DpctlSyclQueue(arrtype.queue)
    )(context, builder)
    queue_struct_ptr = queue_struct_proxy._getpointer()
    queue_struct_voidptr = builder.bitcast(queue_struct_ptr, cgutils.voidptr_t)

    address = context.get_constant(types.intp, id(arrtype.queue))
    queue_address_ptr = builder.inttoptr(address, cgutils.voidptr_t)

    dpexrtCtx = dpexrt.DpexRTContext(context)
    dpexrtCtx.queuestruct_from_python(
        pyapi, queue_address_ptr, queue_struct_voidptr
    )

    queue_struct = builder.load(queue_struct_ptr)
    queue_ref = builder.extract_value(queue_struct, 1)

    return_values = namedtuple(
        "return_values", "queue_ref queue_address_ptr pyapi"
    )
    ret = return_values(queue_ref, queue_address_ptr, pyapi)

    return ret


def _empty_nd_impl(context, builder, arrtype, shapes):
    """Utility function used for allocating a new array.

    This function is used for allocating a new array during LLVM code
    generation (lowering).  Given a target context, builder, array
    type, and a tuple or list of lowered dimension sizes, returns a
    LLVM value pointing at a Numba runtime allocated array.
    """

    arycls = make_array(arrtype)
    ary = arycls(context, builder)

    datatype = context.get_data_type(arrtype.dtype)
    itemsize = context.get_constant(types.intp, get_itemsize(context, arrtype))

    # compute array length
    arrlen = context.get_constant(types.intp, 1)
    overflow = Constant(llvmir.IntType(1), 0)
    for s in shapes:
        arrlen_mult = builder.smul_with_overflow(arrlen, s)
        arrlen = builder.extract_value(arrlen_mult, 0)
        overflow = builder.or_(overflow, builder.extract_value(arrlen_mult, 1))

    if arrtype.ndim == 0:
        strides = ()
    elif arrtype.layout == "C":
        strides = [itemsize]
        for dimension_size in reversed(shapes[1:]):
            strides.append(builder.mul(strides[-1], dimension_size))
        strides = tuple(reversed(strides))
    elif arrtype.layout == "F":
        strides = [itemsize]
        for dimension_size in shapes[:-1]:
            strides.append(builder.mul(strides[-1], dimension_size))
        strides = tuple(strides)
    else:
        raise NotImplementedError(
            "Don't know how to allocate array with layout '{0}'.".format(
                arrtype.layout
            )
        )

    # Check overflow, numpy also does this after checking order
    allocsize_mult = builder.smul_with_overflow(arrlen, itemsize)
    allocsize = builder.extract_value(allocsize_mult, 0)
    overflow = builder.or_(overflow, builder.extract_value(allocsize_mult, 1))

    with builder.if_then(overflow, likely=False):
        # Raise same error as numpy, see:
        # https://github.com/numpy/numpy/blob/2a488fe76a0f732dc418d03b452caace161673da/numpy/core/src/multiarray/ctors.c#L1095-L1101    # noqa: E501
        context.call_conv.return_user_exc(
            builder,
            ValueError,
            (
                "array is too big; `arr.size * arr.dtype.itemsize` is larger "
                "than the maximum possible size.",
            ),
        )

    (queue_ref, queue_ptr, pyapi) = make_queue(context, builder, arrtype)

    # The queue_ref returned by make_queue if used to allocate a MemInfo
    # object needs to be copied first. The reason for the copy is to
    # properly manage the lifetime of the queue_ref object. The original
    # object is owned by the parent dpctl.SyclQueue object and is deleted
    # when the dpctl.SyclQueue is garbage collected. Whereas, the copied
    # queue_ref is to be owned by the NRT_External_Allocator object of
    # MemInfo, and its lifetime is tied to the MemInfo object.

    dpexrtCtx = dpexrt.DpexRTContext(context)
    queue_ref_copy = dpexrtCtx.copy_queue(builder, queue_ref)

    usm_ty = arrtype.usm_type
    usm_ty_map = {"device": 1, "shared": 2, "host": 3}
    usm_type = context.get_constant(
        types.uint64, usm_ty_map[usm_ty] if usm_ty in usm_ty_map else 0
    )

    args = (
        context.get_dummy_value(),
        allocsize,
        usm_type,
        queue_ref_copy,
    )
    mip = types.MemInfoPointer(types.voidptr)
    arytypeclass = types.TypeRef(type(arrtype))
    sig = signature(
        mip,
        arytypeclass,
        types.intp,
        types.uint64,
        types.voidptr,
    )
    from numba_dpex.decorators import dpjit

    op = dpjit(_call_usm_allocator)
    fnop = context.typing_context.resolve_value_type(op)
    # The _call_usm_allocator function will be compiled and added to
    # registry when the get_call_type function is invoked.
    fnop.get_call_type(context.typing_context, sig.args, {})
    eqfn = context.get_function(fnop, sig)
    meminfo = eqfn(builder, args)
    pyapi.decref(queue_ptr)
    data = context.nrt.meminfo_data(builder, meminfo)

    intp_t = context.get_value_type(types.intp)
    shape_array = cgutils.pack_array(builder, shapes, ty=intp_t)
    strides_array = cgutils.pack_array(builder, strides, ty=intp_t)

    populate_array(
        ary,
        data=builder.bitcast(data, datatype.as_pointer()),
        shape=shape_array,
        strides=strides_array,
        itemsize=itemsize,
        meminfo=meminfo,
    )

    return_values = namedtuple("return_values", "ary queue_ref")
    ret = return_values(ary, queue_ref)

    return ret


@overload_classmethod(DpnpNdArray, "_usm_allocate")
def _ol_array_allocate(cls, allocsize, usm_type, queue):
    """Implements an allocator for dpnp.ndarrays."""

    def impl(cls, allocsize, usm_type, queue):
        return intrin_usm_alloc(allocsize, usm_type, queue)

    return impl


numba_config.DISABLE_PERFORMANCE_WARNINGS = 0


def _call_usm_allocator(arrtype, size, usm_type, queue):
    """Trampoline to call the intrinsic used for allocation"""
    return arrtype._usm_allocate(size, usm_type, queue)


numba_config.DISABLE_PERFORMANCE_WARNINGS = 1


@intrinsic
def intrin_usm_alloc(typingctx, allocsize, usm_type, queue):
    """Intrinsic to call into the allocator for Array"""

    def codegen(context, builder, signature, args):
        [allocsize, usm_type, queue] = args
        dpexrtCtx = dpexrt.DpexRTContext(context)
        meminfo = dpexrtCtx.meminfo_alloc(builder, allocsize, usm_type, queue)
        return meminfo

    mip = types.MemInfoPointer(types.voidptr)  # return untyped pointer
    sig = signature(mip, allocsize, usm_type, queue)
    return sig, codegen


def alloc_empty_arrayobj(context, builder, sig, args, is_like=False):
    """Construct an empty numba.np.arrayobj.make_array.<locals>.ArrayStruct

    Args:
        context (numba.core.base.BaseContext): One of the class derived
            from numba's BaseContext, e.g. CPUContext
        builder (llvmlite.ir.builder.IRBuilder): IR builder object from
            llvmlite.
        sig (numba.core.typing.templates.Signature): A numba's function
            signature object.
        args (tuple): A tuple of args to be parsed as the arguments of
            an np.empty(), np.zeros() or np.ones() call.
        is_like (bool, optional): Decides on how to parse the args.
            Defaults to False.

    Returns:
        tuple(numba.np.arrayobj.make_array.<locals>.ArrayStruct,
            numba_dpex.core.types.dpnp_ndarray_type.DpnpNdArray):
                A tuple of allocated array and constructed array type info
                in DpnpNdArray.
    """

    arrtype = (
        _parse_empty_like_args(context, builder, sig, args)
        if is_like
        else _parse_empty_args(context, builder, sig, args)
    )
    ary, queue = _empty_nd_impl(context, builder, *arrtype)

    return ary, arrtype, queue


def fill_arrayobj(context, builder, ary, arrtype, queue_ref, fill_value):
    """Fill a numba.np.arrayobj.make_array.<locals>.ArrayStruct
        with a specified value.

    Args:
        context (numba.core.base.BaseContext): One of the class derived
            from numba's BaseContext, e.g. CPUContext
        builder (llvmlite.ir.builder.IRBuilder): IR builder object from
            llvmlite.
        ary (numba.np.arrayobj.make_array.<locals>.ArrayStruct): A numba
            arrystruct allocated by numba's `make_array()` function.
        arrtype (tuple): Parsed arguments by numba's `_parse_empty_args`
            like functions for different numpy/dpnp methods, e.g. `zeros()`,
            `ones()`, `empty()`, and their corresponding `_like()` methods.
        fill_value (llvmlite.ir.values.Argument): An LLVMLite IR `Argument`
            object that specifies the values to be filled in.

    Returns:
        tuple(numba.np.arrayobj.make_array.<locals>.ArrayStruct,
            numba_dpex.core.types.dpnp_ndarray_type.DpnpNdArray):
                A tuple of allocated array and constructed array type info
                in DpnpNdArray.
    """

    itemsize = context.get_constant(
        types.intp, get_itemsize(context, arrtype[0])
    )

    if isinstance(fill_value.type, DoubleType) or isinstance(
        fill_value.type, FloatType
    ):
        value_is_float = context.get_constant(types.boolean, 1)
    else:
        value_is_float = context.get_constant(types.boolean, 0)

    if isinstance(arrtype[0].dtype, types.scalars.Float):
        dest_is_float = context.get_constant(types.boolean, 1)
    else:
        dest_is_float = context.get_constant(types.boolean, 0)

    # Do a bitcast of the input to a 64-bit int.
    value = builder.bitcast(fill_value, llvmir.IntType(64))

    dpexrtCtx = dpexrt.DpexRTContext(context)
    dpexrtCtx.meminfo_fill(
        builder,
        ary.meminfo,
        itemsize,
        dest_is_float,
        value_is_float,
        value,
        queue_ref,
    )
    return ary, arrtype


@intrinsic
def impl_dpnp_empty(
    ty_context,
    ty_shape,
    ty_dtype,
    ty_order,
    ty_device,
    ty_usm_type,
    ty_sycl_queue,
    ty_retty_ref,
):
    """A numba "intrinsic" function to inject code for dpnp.empty().

    Args:
        ty_context (numba.core.typing.context.Context): The typing context
            for the codegen.
        ty_shape (numba.core.types.scalars.Integer or
            numba.core.types.containers.UniTuple): Numba type for the shape
            of the array.
        ty_dtype (numba.core.types.functions.NumberClass): Numba type for
            dtype.
        ty_order (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_device (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_usm_type (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_sycl_queue (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_retty_ref (numba.core.types.abstract.TypeRef): Reference to
            a type from numba, used when a type is passed as a value.

    Returns:
        tuple(numba.core.typing.templates.Signature, function): A tuple of
            numba function signature type and a function object.
    """

    ty_retty = ty_retty_ref.instance_type
    sig = ty_retty(
        ty_shape,
        ty_dtype,
        ty_order,
        ty_device,
        ty_usm_type,
        ty_sycl_queue,
        ty_retty_ref,
    )

    def codegen(context, builder, sig, args):
        ary, _, _ = alloc_empty_arrayobj(context, builder, sig, args)
        return ary._getvalue()

    return sig, codegen


@intrinsic
def impl_dpnp_zeros(
    ty_context,
    ty_shape,
    ty_dtype,
    ty_order,
    ty_device,
    ty_usm_type,
    ty_sycl_queue,
    ty_retty_ref,
):
    """A numba "intrinsic" function to inject code for dpnp.zeros().

    Args:
        ty_context (numba.core.typing.context.Context): The typing context
            for the codegen.
        ty_shape (numba.core.types.scalars.Integer or
            numba.core.types.containers.UniTuple): Numba type for the shape
            of the array.
        ty_dtype (numba.core.types.functions.NumberClass): Numba type for
            dtype.
        ty_order (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_device (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_usm_type (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_sycl_queue (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_retty_ref (numba.core.types.abstract.TypeRef): Reference to
            a type from numba, used when a type is passed as a value.

    Returns:
        tuple(numba.core.typing.templates.Signature, function): A tuple of
            numba function signature type and a function object.
    """

    ty_retty = ty_retty_ref.instance_type
    sig = ty_retty(
        ty_shape,
        ty_dtype,
        ty_order,
        ty_device,
        ty_usm_type,
        ty_sycl_queue,
        ty_retty_ref,
    )

    def codegen(context, builder, sig, args):
        ary, arrtype, queue_ref = alloc_empty_arrayobj(
            context, builder, sig, args
        )
        fill_value = context.get_constant(types.intp, 0)
        ary, _ = fill_arrayobj(
            context, builder, ary, arrtype, queue_ref, fill_value
        )
        return ary._getvalue()

    return sig, codegen


@intrinsic
def impl_dpnp_ones(
    ty_context,
    ty_shape,
    ty_dtype,
    ty_order,
    ty_device,
    ty_usm_type,
    ty_sycl_queue,
    ty_retty_ref,
):
    """A numba "intrinsic" function to inject code for dpnp.ones().

    Args:
        ty_context (numba.core.typing.context.Context): The typing context
            for the codegen.
        ty_shape (numba.core.types.scalars.Integer or
            numba.core.types.containers.UniTuple): Numba type for the shape
            of the array.
        ty_dtype (numba.core.types.functions.NumberClass): Numba type for
            dtype.
        ty_order (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_device (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_usm_type (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_sycl_queue (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_retty_ref (numba.core.types.abstract.TypeRef): Reference to
            a type from numba, used when a type is passed as a value.

    Returns:
        tuple(numba.core.typing.templates.Signature, function): A tuple of
            numba function signature type and a function object.
    """

    ty_retty = ty_retty_ref.instance_type
    sig = ty_retty(
        ty_shape,
        ty_dtype,
        ty_order,
        ty_device,
        ty_usm_type,
        ty_sycl_queue,
        ty_retty_ref,
    )

    def codegen(context, builder, sig, args):
        ary, arrtype, queue_ref = alloc_empty_arrayobj(
            context, builder, sig, args
        )
        fill_value = context.get_constant(types.intp, 1)
        ary, _ = fill_arrayobj(
            context, builder, ary, arrtype, queue_ref, fill_value
        )
        return ary._getvalue()

    return sig, codegen


@intrinsic
def impl_dpnp_full(
    ty_context,
    ty_shape,
    ty_fill_value,
    ty_dtype,
    ty_order,
    ty_like,
    ty_device,
    ty_usm_type,
    ty_sycl_queue,
    ty_retty_ref,
):
    """A numba "intrinsic" function to inject code for dpnp.full().

    Args:
        ty_context (numba.core.typing.context.Context): The typing context
            for the codegen.
        ty_shape (numba.core.types.scalars.Integer or
            numba.core.types.containers.UniTuple): Numba type for the shape
            of the array.
        ty_fill_value (numba.core.types.scalars): One of the Numba scalar
            types.
        ty_dtype (numba.core.types.functions.NumberClass): Numba type for
            dtype.
        ty_order (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_like (numba.core.types.npytypes.Array): Numba type for array.
        ty_device (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_usm_type (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_sycl_queue (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_retty_ref (numba.core.types.abstract.TypeRef): Reference to
            a type from numba, used when a type is passed as a value.

    Returns:
        tuple(numba.core.typing.templates.Signature, function): A tuple of
            numba function signature type and a function object.
    """

    ty_retty = ty_retty_ref.instance_type
    signature = ty_retty(
        ty_shape,
        ty_fill_value,
        ty_dtype,
        ty_order,
        ty_like,
        ty_device,
        ty_usm_type,
        ty_sycl_queue,
        ty_retty_ref,
    )

    def codegen(context, builder, sig, args):
        ary, arrtype, queue_ref = alloc_empty_arrayobj(
            context, builder, sig, args
        )
        fill_value = context.get_argument_value(builder, sig.args[1], args[1])
        ary, _ = fill_arrayobj(
            context, builder, ary, arrtype, queue_ref, fill_value
        )
        return ary._getvalue()

    return signature, codegen


@intrinsic
def impl_dpnp_empty_like(
    ty_context,
    ty_x1,
    ty_dtype,
    ty_order,
    ty_subok,
    ty_shape,
    ty_device,
    ty_usm_type,
    ty_sycl_queue,
    ty_retty_ref,
):
    """A numba "intrinsic" function to inject code for dpnp.empty_like().

    Args:
        ty_context (numba.core.typing.context.Context): The typing context
            for the codegen.
        ty_x1 (numba.core.types.npytypes.Array): Numba type class for ndarray.
        ty_dtype (numba.core.types.functions.NumberClass): Numba type for
            dtype.
        ty_order (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_subok (numba.core.types.scalars.Boolean): Numba type class for
            subok.
        ty_shape (numba.core.types.scalars.Integer or
            numba.core.types.containers.UniTuple): Numba type for the shape
            of the array. Not supported.
        ty_device (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_usm_type (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_sycl_queue (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_retty_ref (numba.core.types.abstract.TypeRef): Reference to
            a type from numba, used when a type is passed as a value.

    Returns:
        tuple(numba.core.typing.templates.Signature, function): A tuple of
            numba function signature type and a function object.
    """

    ty_retty = ty_retty_ref.instance_type
    sig = ty_retty(
        ty_x1,
        ty_dtype,
        ty_order,
        ty_subok,
        ty_shape,
        ty_device,
        ty_usm_type,
        ty_sycl_queue,
        ty_retty_ref,
    )

    def codegen(context, builder, sig, args):
        ary, _, _ = alloc_empty_arrayobj(
            context, builder, sig, args, is_like=True
        )
        return ary._getvalue()

    return sig, codegen


@intrinsic
def impl_dpnp_zeros_like(
    ty_context,
    ty_x1,
    ty_dtype,
    ty_order,
    ty_subok,
    ty_shape,
    ty_device,
    ty_usm_type,
    ty_sycl_queue,
    ty_retty_ref,
):
    """A numba "intrinsic" function to inject code for dpnp.zeros_like().

    Args:
        ty_context (numba.core.typing.context.Context): The typing context
            for the codegen.
        ty_x1 (numba.core.types.npytypes.Array): Numba type class for ndarray.
        ty_dtype (numba.core.types.functions.NumberClass): Numba type for
            dtype.
        ty_order (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_subok (numba.core.types.scalars.Boolean): Numba type class for
            subok.
        ty_shape (numba.core.types.scalars.Integer or
            numba.core.types.containers.UniTuple): Numba type for the shape
            of the array. Not supported.
        ty_device (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_usm_type (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_sycl_queue (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_retty_ref (numba.core.types.abstract.TypeRef): Reference to
            a type from numba, used when a type is passed as a value.

    Returns:
        tuple(numba.core.typing.templates.Signature, function): A tuple of
            numba function signature type and a function object.
    """

    ty_retty = ty_retty_ref.instance_type
    sig = ty_retty(
        ty_x1,
        ty_dtype,
        ty_order,
        ty_subok,
        ty_shape,
        ty_device,
        ty_usm_type,
        ty_sycl_queue,
        ty_retty_ref,
    )

    def codegen(context, builder, sig, args):
        ary, arrtype, queue_ref = alloc_empty_arrayobj(
            context, builder, sig, args, is_like=True
        )
        fill_value = context.get_constant(types.intp, 0)
        ary, _ = fill_arrayobj(
            context, builder, ary, arrtype, queue_ref, fill_value
        )
        return ary._getvalue()

    return sig, codegen


@intrinsic
def impl_dpnp_ones_like(
    ty_context,
    ty_x1,
    ty_dtype,
    ty_order,
    ty_subok,
    ty_shape,
    ty_device,
    ty_usm_type,
    ty_sycl_queue,
    ty_retty_ref,
):
    """A numba "intrinsic" function to inject code for dpnp.ones_like().

    Args:
        ty_context (numba.core.typing.context.Context): The typing context
            for the codegen.
        ty_x1 (numba.core.types.npytypes.Array): Numba type class for ndarray.
        ty_dtype (numba.core.types.functions.NumberClass): Numba type for
            dtype.
        ty_order (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_subok (numba.core.types.scalars.Boolean): Numba type class for
            subok.
        ty_shape (numba.core.types.scalars.Integer or
            numba.core.types.containers.UniTuple): Numba type for the shape
            of the array. Not supported.
        ty_device (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_usm_type (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_sycl_queue (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_retty_ref (numba.core.types.abstract.TypeRef): Reference to
            a type from numba, used when a type is passed as a value.

    Returns:
        tuple(numba.core.typing.templates.Signature, function): A tuple of
            numba function signature type and a function object.
    """

    ty_retty = ty_retty_ref.instance_type
    sig = ty_retty(
        ty_x1,
        ty_dtype,
        ty_order,
        ty_subok,
        ty_shape,
        ty_device,
        ty_usm_type,
        ty_sycl_queue,
        ty_retty_ref,
    )

    def codegen(context, builder, sig, args):
        ary, arrtype, queue_ref = alloc_empty_arrayobj(
            context, builder, sig, args, is_like=True
        )
        fill_value = context.get_constant(types.intp, 1)
        ary, _ = fill_arrayobj(
            context, builder, ary, arrtype, queue_ref, fill_value
        )
        return ary._getvalue()

    return sig, codegen


@intrinsic
def impl_dpnp_full_like(
    ty_context,
    ty_x1,
    ty_fill_value,
    ty_dtype,
    ty_order,
    ty_subok,
    ty_shape,
    ty_device,
    ty_usm_type,
    ty_sycl_queue,
    ty_retty_ref,
):
    """A numba "intrinsic" function to inject code for dpnp.full_like().

    Args:
        ty_context (numba.core.typing.context.Context): The typing context
            for the codegen.
        ty_x1 (numba.core.types.npytypes.Array): Numba type class for ndarray.
        ty_fill_value (numba.core.types.scalars): One of the Numba scalar
            types.
        ty_dtype (numba.core.types.functions.NumberClass): Numba type for
            dtype.
        ty_order (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_subok (numba.core.types.scalars.Boolean): Numba type class for
            subok.
        ty_shape (numba.core.types.scalars.Integer or
            numba.core.types.containers.UniTuple): Numba type for the shape
            of the array. Not supported.
        ty_device (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_usm_type (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_sycl_queue (numba.core.types.misc.UnicodeType): UnicodeType
            from numba for strings.
        ty_retty_ref (numba.core.types.abstract.TypeRef): Reference to
            a type from numba, used when a type is passed as a value.

    Returns:
        tuple(numba.core.typing.templates.Signature, function): A tuple of
            numba function signature type and a function object.
    """

    ty_retty = ty_retty_ref.instance_type
    signature = ty_retty(
        ty_x1,
        ty_fill_value,
        ty_dtype,
        ty_order,
        ty_subok,
        ty_shape,
        ty_device,
        ty_usm_type,
        ty_sycl_queue,
        ty_retty_ref,
    )

    def codegen(context, builder, sig, args):
        ary, arrtype, queue_ref = alloc_empty_arrayobj(
            context, builder, sig, args, is_like=True
        )
        fill_value = context.get_argument_value(builder, sig.args[1], args[1])
        ary, _ = fill_arrayobj(
            context, builder, ary, arrtype, queue_ref, fill_value
        )
        return ary._getvalue()

    return signature, codegen

IntelPython / numba-dpex / 5060157068

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