# Copyright (c) 2025, Tri Dao.

import math
import hashlib
import inspect
from typing import Type, Callable, Optional, Tuple, overload

import cutlass
import cutlass.cute as cute

from cutlass import Float32, const_expr
from cutlass.cutlass_dsl import T, dsl_user_op
from cutlass._mlir.dialects import nvvm, llvm
from cutlass.cute.runtime import from_dlpack


import quack.activation

_MIXER_ATTRS = ("__vec_size__",)

# Obtained from sollya:
# fpminimax(exp(x * log(2.0)), 1, [|1,24...|],[0;1],relative);
POLY_EX2 = {
    0: (1.0),
    1: (
        1.0,
        0.922497093677520751953125,
    ),
    2: (
        1.0,
        0.6657850742340087890625,
        0.330107033252716064453125,
    ),
    3: (
        1.0,
        0.695146143436431884765625,
        0.227564394474029541015625,
        0.077119089663028717041015625,
    ),
    4: (
        1.0,
        0.693042695522308349609375,
        0.2412912547588348388671875,
        5.2225358784198760986328125e-2,
        1.3434938155114650726318359375e-2,
    ),
    5: (
        1.0,
        0.693151414394378662109375,
        0.24016360938549041748046875,
        5.5802188813686370849609375e-2,
        9.01452265679836273193359375e-3,
        1.86810153536498546600341796875e-3,
    ),
}


def _compute_base_hash(func: Callable) -> str:
    """Compute hash from source code or bytecode and closure values."""
    try:
        data = inspect.getsource(func).encode()
    except (OSError, TypeError):
        if hasattr(func, "__code__") and func.__code__ is not None:
            data = func.__code__.co_code
        else:
            data = repr(func).encode()

    hasher = hashlib.sha256(data)

    if hasattr(func, "__closure__") and func.__closure__ is not None:
        for cell in func.__closure__:
            hasher.update(repr(cell.cell_contents).encode())

    return hasher.hexdigest()


def hash_callable(
    func: Callable, mixer_attrs: Tuple[str] = _MIXER_ATTRS, set_cute_hash: bool = True
) -> str:
    """Hash a callable based on the source code or bytecode and closure values.
    Fast-path: if the callable (or its __wrapped__ base) has a ``__cute_hash__``
    attribute, that value is returned immediately as the base hash, then
    metadata dunders are mixed in to produce the final dict-key hash.
    set_cute_hash: whether or not to set func.__cute_hash__
    """
    # Resolve base hash
    if hasattr(func, "__cute_hash__"):
        base_hash = func.__cute_hash__
    else:
        # Unwrap decorated functions (e.g., cute.jit wrappers).
        base_func = getattr(func, "__wrapped__", func)

        if hasattr(base_func, "__cute_hash__"):
            base_hash = base_func.__cute_hash__
        else:
            base_hash = _compute_base_hash(base_func)

            if set_cute_hash:
                base_func.__cute_hash__ = base_hash  # type: ignore[union-attr]

    # Mix in mutable metadata dunders
    mixer_values = tuple(getattr(func, attr, None) for attr in mixer_attrs)

    if all(v is None for v in mixer_values):
        return base_hash

    hasher = hashlib.sha256(base_hash.encode())

    for attr, val in zip(_MIXER_ATTRS, mixer_values, strict=False):
        hasher.update(f"{attr}={val!r}".encode())

    return hasher.hexdigest()


LOG2_E = math.log2(math.e)


def compute_softmax_scale_log2(softmax_scale):
    """Compute softmax_scale_log2 from softmax_scale.

    Returns (softmax_scale_log2, None).
    """
    return softmax_scale * LOG2_E, None


def convert_from_dlpack(x, leading_dim, alignment=16, divisibility=1) -> cute.Tensor:
    return (
        from_dlpack(x, assumed_align=alignment)
        .mark_layout_dynamic(leading_dim=leading_dim)
        .mark_compact_shape_dynamic(
            mode=leading_dim, stride_order=x.dim_order(), divisibility=divisibility
        )
    )


def make_tiled_copy_A(
    copy_atom: cute.CopyAtom,
    tiled_mma: cute.TiledMma,
    swapAB: cutlass.Constexpr[bool] = False,
) -> cute.TiledCopy:
    if const_expr(swapAB):
        return cute.make_tiled_copy_B(copy_atom, tiled_mma)
    else:
        return cute.make_tiled_copy_A(copy_atom, tiled_mma)


def make_tiled_copy_B(
    copy_atom: cute.CopyAtom,
    tiled_mma: cute.TiledMma,
    swapAB: cutlass.Constexpr[bool] = False,
) -> cute.TiledCopy:
    if const_expr(swapAB):
        return cute.make_tiled_copy_A(copy_atom, tiled_mma)
    else:
        return cute.make_tiled_copy_B(copy_atom, tiled_mma)


def mma_make_fragment_A(
    smem: cute.Tensor,
    thr_mma: cute.ThrMma,
    swapAB: cutlass.Constexpr[bool] = False,
) -> cute.Tensor:
    if const_expr(swapAB):
        return mma_make_fragment_B(smem, thr_mma)
    else:
        return thr_mma.make_fragment_A(thr_mma.partition_A(smem))


def mma_make_fragment_B(
    smem: cute.Tensor,
    thr_mma: cute.ThrMma,
    swapAB: cutlass.Constexpr[bool] = False,
) -> cute.Tensor:
    if const_expr(swapAB):
        return mma_make_fragment_A(smem, thr_mma)
    else:
        return thr_mma.make_fragment_B(thr_mma.partition_B(smem))


def get_smem_store_atom(
    arch: cutlass.Constexpr[int],
    element_type: Type[cute.Numeric],
    transpose: bool = False,
) -> cute.CopyAtom:
    if const_expr(arch < 90 or element_type.width != 16):
        return cute.make_copy_atom(
            cute.nvgpu.CopyUniversalOp(),
            element_type,
            num_bits_per_copy=2 * element_type.width,
        )
    else:
        return cute.make_copy_atom(
            cute.nvgpu.warp.StMatrix8x8x16bOp(transpose=transpose, num_matrices=4),
            element_type,
        )


@cute.jit
def warp_reduce(
    val: cute.TensorSSA | cute.Numeric,
    op: Callable,
    width: cutlass.Constexpr[int] = cute.arch.WARP_SIZE,
) -> cute.TensorSSA | cute.Numeric:
    if const_expr(isinstance(val, cute.TensorSSA)):
        res = cute.make_rmem_tensor(val.shape, val.dtype)
        res.store(val)
        for i in cutlass.range_constexpr(cute.size(val.shape)):
            res[i] = warp_reduce(res[i], op, width)
        return res.load()
    else:
        for i in cutlass.range_constexpr(int(math.log2(width))):
            val = op(val, cute.arch.shuffle_sync_bfly(val, offset=1 << i))
    return val


@dsl_user_op
def fmax(
    a: float | Float32,
    b: float | Float32,
    c: float | Float32 | None = None,
    *,
    loc=None,
    ip=None,
) -> Float32:
    from cutlass import CUDA_VERSION

    # * NVVM call based on nvvm version
    if CUDA_VERSION.major == 12 and CUDA_VERSION.minor == 9:
        # Old API: requires explicit result type as first positional argument
        return Float32(
            nvvm.fmax(
                T.f32(),
                Float32(a).ir_value(loc=loc, ip=ip),
                Float32(b).ir_value(loc=loc, ip=ip),
                c=Float32(c).ir_value(loc=loc, ip=ip) if c is not None else None,
                loc=loc,
                ip=ip,
            )
        )
    else:
        # New API: infers result type automatically
        return Float32(
            nvvm.fmax(
                Float32(a).ir_value(loc=loc, ip=ip),
                Float32(b).ir_value(loc=loc, ip=ip),
                c=Float32(c).ir_value(loc=loc, ip=ip) if c is not None else None,
                loc=loc,
                ip=ip,
            )
        )


@cute.jit
def fmax_reduce(
    x: cute.TensorSSA,
    init_val: float | Float32 | None = None,
    arch: cutlass.Constexpr[int] = 80,
) -> Float32:
    if const_expr(arch < 100 or cute.size(x.shape) % 8 != 0):
        res = cute.make_rmem_tensor(x.shape, Float32)
        res.store(x)
        local_max = [res[0], res[1], res[2], res[3]]
        for i in cutlass.range_constexpr(4, cute.size(x.shape), 4):
            local_max[0] = fmax(local_max[0], res[i + 0])
            local_max[1] = fmax(local_max[1], res[i + 1])
            local_max[2] = fmax(local_max[2], res[i + 2])
            local_max[3] = fmax(local_max[3], res[i + 3])
        local_max[0] = fmax(local_max[0], local_max[1])
        local_max[2] = fmax(local_max[2], local_max[3])
        local_max[0] = fmax(local_max[0], local_max[2])
        return (
            local_max[0]
            if const_expr(init_val is None)
            else fmax(local_max[0], init_val)
        )
    else:
        res = cute.make_rmem_tensor(x.shape, Float32)
        res.store(x)
        local_max_0 = (
            fmax(init_val, res[0], res[1])
            if const_expr(init_val is not None)
            else fmax(res[0], res[1])
        )
        local_max = [
            local_max_0,
            fmax(res[2], res[3]),
            fmax(res[4], res[5]),
            fmax(res[6], res[7]),
        ]
        for i in cutlass.range_constexpr(8, cute.size(x.shape), 8):
            local_max[0] = fmax(local_max[0], res[i], res[i + 1])
            local_max[1] = fmax(local_max[1], res[i + 2], res[i + 3])
            local_max[2] = fmax(local_max[2], res[i + 4], res[i + 5])
            local_max[3] = fmax(local_max[3], res[i + 6], res[i + 7])
        local_max[0] = fmax(local_max[0], local_max[1])
        return fmax(local_max[0], local_max[2], local_max[3])


@cute.jit
def fadd_reduce(
    x: cute.TensorSSA,
    init_val: float | Float32 | None = None,
    arch: cutlass.Constexpr[int] = 80,
) -> Float32:
    if const_expr(arch < 100 or cute.size(x.shape) % 8 != 0):
        if const_expr(init_val is None):
            init_val = Float32.zero
        return x.reduce(cute.ReductionOp.ADD, init_val, 0)
    else:
        res = cute.make_rmem_tensor(x.shape, Float32)
        res.store(x)
        local_sum_0 = (
            cute.arch.add_packed_f32x2((init_val, 0.0), (res[0], res[1]))
            if const_expr(init_val is not None)
            else (res[0], res[1])
        )
        local_sum = [local_sum_0, (res[2], res[3]), (res[4], res[5]), (res[6], res[7])]
        for i in cutlass.range_constexpr(8, cute.size(x.shape), 8):
            local_sum[0] = cute.arch.add_packed_f32x2(
                local_sum[0], (res[i + 0], res[i + 1])
            )
            local_sum[1] = cute.arch.add_packed_f32x2(
                local_sum[1], (res[i + 2], res[i + 3])
            )
            local_sum[2] = cute.arch.add_packed_f32x2(
                local_sum[2], (res[i + 4], res[i + 5])
            )
            local_sum[3] = cute.arch.add_packed_f32x2(
                local_sum[3], (res[i + 6], res[i + 7])
            )
        local_sum[0] = cute.arch.add_packed_f32x2(local_sum[0], local_sum[1])
        local_sum[2] = cute.arch.add_packed_f32x2(local_sum[2], local_sum[3])
        local_sum[0] = cute.arch.add_packed_f32x2(local_sum[0], local_sum[2])
        return local_sum[0][0] + local_sum[0][1]


@dsl_user_op
def atomic_add_fp32(
    a: float | Float32, gmem_ptr: cute.Pointer, *, loc=None, ip=None
) -> None:
    nvvm.atomicrmw(
        res=T.f32(),
        op=nvvm.AtomicOpKind.FADD,
        ptr=gmem_ptr.llvm_ptr,
        a=Float32(a).ir_value(),
    )


@dsl_user_op
def elem_pointer(
    x: cute.Tensor, coord: cute.Coord, *, loc=None, ip=None
) -> cute.Pointer:
    return x.iterator + cute.crd2idx(coord, x.layout, loc=loc, ip=ip)


@cute.jit
def predicate_k(tAcA: cute.Tensor, limit: cutlass.Int32) -> cute.Tensor:
    # Only compute predicates for the "k" dimension. For the mn dimension, we will use "if"
    tApA = cute.make_rmem_tensor(
        cute.make_layout(
            (
                cute.size(tAcA, mode=[0, 1]),
                cute.size(tAcA, mode=[1]),
                cute.size(tAcA, mode=[2]),
            ),
            stride=(cute.size(tAcA, mode=[2]), 0, 1),
        ),
        cutlass.Boolean,
    )
    for rest_v in cutlass.range_constexpr(tApA.shape[0]):
        for rest_k in cutlass.range_constexpr(tApA.shape[2]):
            tApA[rest_v, 0, rest_k] = cute.elem_less(
                tAcA[(0, rest_v), 0, rest_k][1], limit
            )
    return tApA


def canonical_warp_group_idx(sync: bool = True) -> cutlass.Int32:
    warp_group_idx = cute.arch.thread_idx()[0] // 128
    if const_expr(sync):
        warp_group_idx = cute.arch.make_warp_uniform(warp_group_idx)
    return warp_group_idx


@cute.jit
def shuffle_sync(
    value: cute.Numeric,
    offset: cute.typing.Int,
    width: cutlass.Constexpr[int] = cute.arch.WARP_SIZE,
) -> cute.Numeric:
    assert value.width % 32 == 0, "value type must be a multiple of 32 bits"
    # 1 -> 0b11111, 2 -> 0b11110, 4 -> 0b11100, 8 -> 0b11000, 16 -> 0b10000, 32 -> 0b00000
    mask = cute.arch.WARP_SIZE - width
    clamp = cute.arch.WARP_SIZE - 1
    mask_and_clamp = mask << 8 | clamp
    # important: need stride 1 and not 0 for recast_tensor to work
    val = cute.make_rmem_tensor(cute.make_layout((1,), stride=(1,)), type(value))
    val[0] = value
    val_i32 = cute.recast_tensor(val, cutlass.Int32)
    for i in cutlass.range_constexpr(cute.size(val_i32)):
        val_i32[i] = cute.arch.shuffle_sync(
            val_i32[i], offset, mask_and_clamp=mask_and_clamp
        )
    return val[0]


@dsl_user_op
def shl_u32(
    val: cutlass.Uint32, shift: cutlass.Uint32, *, loc=None, ip=None
) -> cutlass.Uint32:
    """
    Left-shift val by shift bits using PTX shl.b32 (sign-agnostic).

    Named ``shl_u32`` (not ``shl_b32``) because python type annotations
    distinguish signed/unsigned.

    PTX semantics (9.7.8.8): "Shift amounts greater than the register width N
    are clamped to N."  So ``shl.b32 d, a, 32`` is well-defined and yields 0.

    This differs from C/C++ and LLVM IR, where shifting by >= the type width is
    undefined behavior.  CuTeDSL compiles through MLIR -> LLVM IR, so a plain
    Python-level ``Uint32(x) << Uint32(n)`` inherits LLVM's UB: the optimizer
    may treat the result as poison and eliminate dependent code.  Inline PTX
    bypasses the LLVM IR shift entirely -- the instruction is emitted verbatim
    into PTX where clamping makes it safe for all shift amounts.
    """
    return cutlass.Uint32(
        llvm.inline_asm(
            T.i32(),
            [
                cutlass.Uint32(val).ir_value(loc=loc, ip=ip),
                cutlass.Uint32(shift).ir_value(loc=loc, ip=ip),
            ],
            "shl.b32 $0, $1, $2;",
            "=r,r,r",
            has_side_effects=False,
            is_align_stack=False,
            asm_dialect=llvm.AsmDialect.AD_ATT,
        )
    )


@dsl_user_op
def shr_u32(
    val: cutlass.Uint32, shift: cutlass.Uint32, *, loc=None, ip=None
) -> cutlass.Uint32:
    """
    Unsigned right-shift val by shift bits using PTX shr.u32 (zero-fills).

    See ``shl_u32`` docstring for why inline PTX is used instead of plain
    CuTeDSL shift operators (LLVM shift-by-type-width UB).
    """
    return cutlass.Uint32(
        llvm.inline_asm(
            T.i32(),
            [
                cutlass.Uint32(val).ir_value(loc=loc, ip=ip),
                cutlass.Uint32(shift).ir_value(loc=loc, ip=ip),
            ],
            "shr.u32 $0, $1, $2;",
            "=r,r,r",
            has_side_effects=False,
            is_align_stack=False,
            asm_dialect=llvm.AsmDialect.AD_ATT,
        )
    )


@cute.jit
def warp_prefix_sum(
    val: cutlass.Int32, lane: Optional[cutlass.Int32] = None
) -> cutlass.Int32:
    if const_expr(lane is None):
        lane = cute.arch.lane_idx()
    for i in cutlass.range_constexpr(int(math.log2(cute.arch.WARP_SIZE))):
        offset = 1 << i
        # Very important that we set mask_and_clamp to 0
        partial_sum = cute.arch.shuffle_sync_up(val, offset=offset, mask_and_clamp=0)
        if lane >= offset:
            val += partial_sum
    return val


@dsl_user_op
def cvt_f16x2_f32(
    a: float | Float32, b: float | Float32, to_dtype: Type, *, loc=None, ip=None
) -> cutlass.Int32:
    assert to_dtype in [cutlass.BFloat16, cutlass.Float16], (
        "to_dtype must be BFloat16 or Float16"
    )
    return cutlass.Int32(
        llvm.inline_asm(
            T.i32(),
            [Float32(a).ir_value(loc=loc, ip=ip), Float32(b).ir_value(loc=loc, ip=ip)],
            f"cvt.rn.{'bf16x2' if to_dtype is cutlass.BFloat16 else 'f16x2'}.f32 $0, $2, $1;",
            "=r,f,f",
            has_side_effects=False,
            is_align_stack=False,
            asm_dialect=llvm.AsmDialect.AD_ATT,
        )
    )


@overload
def cvt_f16(src: cute.Tensor, dst: cute.Tensor) -> None: ...


@overload
def cvt_f16(src: cute.Tensor, dtype: Type[cute.Numeric]) -> cute.Tensor: ...


@cute.jit
def cvt_f16(src: cute.Tensor, dst_or_dtype):
    """Convert Float32 tensor to Float16/BFloat16.

    Args:
        src: Source tensor with Float32 element type
        dst_or_dtype: Either a destination tensor or a dtype (Float16/BFloat16)

    Returns:
        None if dst is a tensor, or a new tensor if dtype is provided
    """
    if const_expr(isinstance(dst_or_dtype, type)):
        # dtype variant: create new tensor and call the tensor variant
        dtype = dst_or_dtype
        dst = cute.make_rmem_tensor(src.shape, dtype)
        cvt_f16(src, dst)
        return dst
    else:
        # tensor variant: write to dst
        dst = dst_or_dtype
        assert cute.size(dst.shape) == cute.size(src.shape), (
            "dst and src must have the same size"
        )
        assert cute.size(src.shape) % 2 == 0, "src must have an even number of elements"
        assert dst.element_type in [cutlass.BFloat16, cutlass.Float16], (
            "dst must be BFloat16 or Float16"
        )
        assert src.element_type is Float32, "src must be Float32"
        dst_i32 = cute.recast_tensor(dst, cutlass.Int32)
        assert cute.size(dst_i32.shape) * 2 == cute.size(src.shape)
        for i in cutlass.range_constexpr(cute.size(dst_i32)):
            dst_i32[i] = cvt_f16x2_f32(src[2 * i], src[2 * i + 1], dst.element_type)


@dsl_user_op
@cute.jit
def evaluate_polynomial(
    x: Float32, poly: Tuple[Float32, ...], *, loc=None, ip=None
) -> Float32:
    deg = len(poly) - 1
    out = poly[deg]
    for i in cutlass.range_constexpr(deg - 1, -1, -1):
        out = out * x + poly[i]
    return out


@dsl_user_op
@cute.jit
def evaluate_polynomial_2(
    x: Float32, y: Float32, poly: Tuple[Float32, ...], *, loc=None, ip=None
) -> Tuple[Float32, Float32]:
    deg = len(poly) - 1
    out = (poly[deg], poly[deg])
    for i in cutlass.range_constexpr(deg - 1, -1, -1):
        out = cute.arch.fma_packed_f32x2(out, (x, y), (poly[i], poly[i]))
    return out


@dsl_user_op
def add_round_down(
    x: float | Float32, y: float | Float32, *, loc=None, ip=None
) -> Float32:
    # There's probably a way to call llvm or nvvm to do this instead of ptx
    return cutlass.Float32(
        llvm.inline_asm(
            T.f32(),
            [Float32(x).ir_value(loc=loc, ip=ip), Float32(y).ir_value(loc=loc, ip=ip)],
            "add.rm.ftz.f32 $0, $1, $2;",
            "=f,f,f",
            has_side_effects=False,
            is_align_stack=False,
            asm_dialect=llvm.AsmDialect.AD_ATT,
        )
    )


@dsl_user_op
def combine_int_frac_ex2(
    x_rounded: Float32, frac_ex2: Float32, *, loc=None, ip=None
) -> Float32:
    return cutlass.Float32(
        llvm.inline_asm(
            T.f32(),
            [
                Float32(x_rounded).ir_value(loc=loc, ip=ip),
                Float32(frac_ex2).ir_value(loc=loc, ip=ip),
            ],
            "{\n\t"
            ".reg .s32 x_rounded_i, frac_ex_i, x_rounded_e, out_i;\n\t"
            "mov.b32 x_rounded_i, $1;\n\t"
            "mov.b32 frac_ex_i, $2;\n\t"
            "shl.b32 x_rounded_e, x_rounded_i, 23;\n\t"
            # add.u32 generates IMAD instruction and add.s32 generates LEA instruction
            # IMAD uses the FMA pipeline and LEA uses the ALU pipeline, afaik
            "add.s32 out_i, x_rounded_e, frac_ex_i;\n\t"
            "mov.b32 $0, out_i;\n\t"
            "}\n",
            "=f,f,f",
            has_side_effects=False,
            is_align_stack=False,
            asm_dialect=llvm.AsmDialect.AD_ATT,
        )
    )


@dsl_user_op
def ex2_emulation(x: Float32, *, poly_degree: int = 3, loc=None, ip=None) -> Float32:
    assert poly_degree in POLY_EX2, f"Polynomial degree {poly_degree} not supported"
    # We assume x <= 127.0
    fp32_round_int = float(2**23 + 2**22)
    x_clamped = cute.arch.fmax(x, -127.0)
    # We want to round down here, so that the fractional part is in [0, 1)
    x_rounded = add_round_down(x_clamped, fp32_round_int, loc=loc, ip=ip)
    # The integer floor of x is now in the last 8 bits of x_rounded
    # We assume the next 2 ops round to nearest even. The rounding mode is important.
    x_rounded_back = x_rounded - fp32_round_int
    x_frac = x_clamped - x_rounded_back
    x_frac_ex2 = evaluate_polynomial(x_frac, POLY_EX2[poly_degree], loc=loc, ip=ip)
    return combine_int_frac_ex2(x_rounded, x_frac_ex2, loc=loc, ip=ip)


@dsl_user_op
def ex2_emulation_2(
    x: Float32, y: Float32, *, poly_degree: int = 3, loc=None, ip=None
) -> Tuple[Float32, Float32]:
    # We assume x <= 127.0 and y <= 127.0
    fp32_round_int = float(2**23 + 2**22)
    xy_clamped = (cute.arch.fmax(x, -127.0), cute.arch.fmax(y, -127.0))
    # We want to round down here, so that the fractional part is in [0, 1)
    xy_rounded = cute.arch.add_packed_f32x2(
        xy_clamped, (fp32_round_int, fp32_round_int), rnd="rm"
    )
    # The integer floor of x & y are now in the last 8 bits of xy_rounded
    # We want the next 2 ops to round to nearest even. The rounding mode is important.
    xy_rounded_back = quack.activation.sub_packed_f32x2(
        xy_rounded, (fp32_round_int, fp32_round_int)
    )
    xy_frac = quack.activation.sub_packed_f32x2(xy_clamped, xy_rounded_back)
    xy_frac_ex2 = evaluate_polynomial_2(*xy_frac, POLY_EX2[poly_degree], loc=loc, ip=ip)
    x_out = combine_int_frac_ex2(xy_rounded[0], xy_frac_ex2[0], loc=loc, ip=ip)
    y_out = combine_int_frac_ex2(xy_rounded[1], xy_frac_ex2[1], loc=loc, ip=ip)
    return x_out, y_out


@dsl_user_op
def e2e_asm2(x: Float32, y: Float32, *, loc=None, ip=None) -> Tuple[Float32, Float32]:
    out_f32x2 = llvm.inline_asm(
        llvm.StructType.get_literal([T.f32(), T.f32()]),
        [Float32(x).ir_value(loc=loc, ip=ip), Float32(y, loc=loc, ip=ip).ir_value()],
        "{\n\t"
        ".reg .f32 f1, f2, f3, f4, f5, f6, f7;\n\t"
        ".reg .b64 l1, l2, l3, l4, l5, l6, l7, l8, l9, l10;\n\t"
        ".reg .s32 r1, r2, r3, r4, r5, r6, r7, r8;\n\t"
        "max.ftz.f32 f1, $2, 0fC2FE0000;\n\t"
        "max.ftz.f32 f2, $3, 0fC2FE0000;\n\t"
        "mov.b64 l1, {f1, f2};\n\t"
        "mov.f32 f3, 0f4B400000;\n\t"
        "mov.b64 l2, {f3, f3};\n\t"
        "add.rm.ftz.f32x2 l7, l1, l2;\n\t"
        "sub.rn.ftz.f32x2 l8, l7, l2;\n\t"
        "sub.rn.ftz.f32x2 l9, l1, l8;\n\t"
        "mov.f32 f7, 0f3D9DF09D;\n\t"
        "mov.b64 l6, {f7, f7};\n\t"
        "mov.f32 f6, 0f3E6906A4;\n\t"
        "mov.b64 l5, {f6, f6};\n\t"
        "mov.f32 f5, 0f3F31F519;\n\t"
        "mov.b64 l4, {f5, f5};\n\t"
        "mov.f32 f4, 0f3F800000;\n\t"
        "mov.b64 l3, {f4, f4};\n\t"
        "fma.rn.ftz.f32x2 l10, l9, l6, l5;\n\t"
        "fma.rn.ftz.f32x2 l10, l10, l9, l4;\n\t"
        "fma.rn.ftz.f32x2 l10, l10, l9, l3;\n\t"
        "mov.b64 {r1, r2}, l7;\n\t"
        "mov.b64 {r3, r4}, l10;\n\t"
        "shl.b32 r5, r1, 23;\n\t"
        "add.s32 r7, r5, r3;\n\t"
        "shl.b32 r6, r2, 23;\n\t"
        "add.s32 r8, r6, r4;\n\t"
        "mov.b32 $0, r7;\n\t"
        "mov.b32 $1, r8;\n\t"
        "}\n",
        "=r,=r,f,f",
        has_side_effects=False,
        is_align_stack=False,
        asm_dialect=llvm.AsmDialect.AD_ATT,
    )
    out0 = Float32(llvm.extractvalue(T.f32(), out_f32x2, [0], loc=loc, ip=ip))
    out1 = Float32(llvm.extractvalue(T.f32(), out_f32x2, [1], loc=loc, ip=ip))
    return out0, out1


@dsl_user_op
def domain_offset_aligned(
    coord: cute.Coord, tensor: cute.Tensor, *, loc=None, ip=None
) -> cute.Tensor:
    assert isinstance(tensor.iterator, cute.Pointer)
    # We assume that applying the offset does not change the pointer alignment
    new_ptr = cute.make_ptr(
        tensor.element_type,
        elem_pointer(tensor, coord).toint(),
        tensor.memspace,
        assumed_align=tensor.iterator.alignment,
    )
    return cute.make_tensor(new_ptr, tensor.layout)


@cute.jit
def scalar_to_ssa(a: cute.Numeric, dtype) -> cute.TensorSSA:
    """Convert a scalar to a cute TensorSSA of shape (1,) and given dtype"""
    vec = cute.make_rmem_tensor(1, dtype)
    vec[0] = a
    return vec.load()


def ssa_to_scalar(val):
    """Could inline but nice for reflecting the above api"""
    return val[0]
