use crate::{
CubeRuntime,
kernel::utils::{address_type, linear_view, linear_view_alias},
ops::{max_line_size, numeric::empty_device_dtype},
tensor::CubeTensor,
};
use burn_backend::TensorMetadata;
use cubecl::{calculate_cube_count_elemwise, prelude::*, std::tensor::layout::linear::LinearView};
pub(crate) trait IntUnaryOpFamily: 'static + Send + Sync {
type Options: LaunchArg;
type Unary<I: Int>: IntUnaryOp<I, Options = Self::Options>;
}
#[cube]
pub(crate) trait IntUnaryOp<I: CubePrimitive>: 'static + Send + Sync {
type Options: LaunchArg;
fn execute(input: Line<I>, options: &Self::Options) -> Line<I>;
}
#[cube(launch_unchecked, address_type = "dynamic")]
pub(crate) fn unary_int<I: Int, O: IntUnaryOpFamily>(
input: &LinearView<Line<I>>,
output: &mut LinearView<Line<I>, ReadWrite>,
options: &O::Options,
#[define(I)] _dtype: StorageType,
) {
if !output.is_in_bounds(ABSOLUTE_POS) {
terminate!();
}
output[ABSOLUTE_POS] = O::Unary::<I>::execute(input[ABSOLUTE_POS], options);
}
pub(crate) fn launch_unary_int<R, O, Args>(tensor: CubeTensor<R>, args: Args) -> CubeTensor<R>
where
for<'a> Args: FnOnce(&'a ()) -> RuntimeArg<'a, O::Options, R>,
R: CubeRuntime,
O: IntUnaryOpFamily,
{
let line_size = max_line_size(&tensor);
let client = tensor.client.clone();
let num_elems = tensor.meta.num_elements();
let working_units = num_elems / line_size as usize;
let cube_dim = CubeDim::new(&tensor.client, working_units);
let cube_count = calculate_cube_count_elemwise(&tensor.client, working_units, cube_dim);
unsafe {
if tensor.can_mut() && tensor.is_nonoverlapping() {
unary_int::launch_unchecked::<O, R>(
&client,
cube_count,
cube_dim,
address_type!(tensor),
linear_view(&tensor, line_size),
linear_view_alias(&tensor, line_size, 0),
args(&()),
tensor.dtype.into(),
)
.expect("Kernel to never fail");
tensor
} else {
let output = empty_device_dtype(
tensor.client.clone(),
tensor.device.clone(),
tensor.shape(),
tensor.dtype,
);
unary_int::launch_unchecked::<O, R>(
&client,
cube_count,
cube_dim,
address_type!(tensor, output),
linear_view(&tensor, line_size),
linear_view(&output, line_size),
args(&()),
tensor.dtype.into(),
)
.expect("Kernel to never fail");
output
}
}
}
pub(crate) mod unary_basic_int {
use super::*;
pub(crate) fn launch<R, Args>(tensor: CubeTensor<R>, args: Args) -> CubeTensor<R>
where
R: CubeRuntime,
for<'a> Args: FnOnce(&'a ()) -> BasicIntUnaryKind,
{
launch_unary_int::<R, BasicIntUnary, _>(tensor, |input| {
BasicIntUnaryOptionsLaunch::new(args(input))
})
}
#[derive(Clone, Copy, Debug, Hash, Eq, PartialEq, serde::Serialize, serde::Deserialize)]
pub enum BasicIntUnaryKind {
BitwiseNot,
Sign,
}
#[derive(CubeLaunch, CubeType)]
struct BasicIntUnaryOptions {
#[cube(comptime)]
kind: BasicIntUnaryKind,
}
struct BasicIntUnary;
#[cube]
impl<I: Int> IntUnaryOp<I> for BasicIntUnary {
type Options = BasicIntUnaryOptions;
fn execute(input: Line<I>, options: &Self::Options) -> Line<I> {
match comptime![options.kind] {
BasicIntUnaryKind::BitwiseNot => !input,
BasicIntUnaryKind::Sign => {
let zero = Line::new(I::new(0));
let one = Line::new(I::new(1));
let minus_one = Line::new(I::new(-1));
let is_positive = input.greater_than(zero);
let is_negative = input.less_than(zero);
let sign = select_many(is_negative, minus_one, zero);
select_many(is_positive, one, sign)
}
}
}
}
impl IntUnaryOpFamily for BasicIntUnary {
type Options = BasicIntUnaryOptions;
type Unary<I: Int> = Self;
}
}