burn-cubecl 0.22.0-pre.4

use crate::kernel::memory_order::in_memory_order;
use crate::{
    kernel::utils::address_type,
    ops::{max_vector_size, numeric::empty_device_dtype},
    tensor::CubeTensor,
};
use burn_backend::TensorMetadata;
use burn_backend::cubecl::dtype_to_storage_type;
use cubecl::{
    calculate_cube_count_elemwise,
    prelude::*,
    std::tensor::layout::linear::{LinearView, LinearViewMut},
};

pub(crate) trait NumericUnaryOpFamily: 'static + Send + Sync {
    type Options: LaunchArg;
    type Unary<T: Numeric, N: Size>: NumericUnaryOp<T, N, Options = Self::Options>;
}

#[cube]
pub(crate) trait NumericUnaryOp<T: Scalar, N: Size>: 'static + Send + Sync {
    type Options: LaunchArg;

    fn execute(input: Vector<T, N>, options: &Self::Options) -> Vector<T, N>;
}

#[cube(launch_unchecked, address_type = "dynamic")]
pub(crate) fn unary_numeric<T: Numeric, N: Size, O: NumericUnaryOpFamily>(
    input: LinearView<'_, Vector<T, N>>,
    mut output: LinearViewMut<'_, Vector<T, N>>,
    options: &O::Options,
    #[define(T)] _dtype: ElemType,
) {
    if !output.is_in_bounds(ABSOLUTE_POS) {
        terminate!();
    }

    output.write(
        ABSOLUTE_POS,
        O::Unary::<T, N>::execute(input.read(ABSOLUTE_POS), options),
    );
}

pub(crate) fn launch_unary_numeric<O, Args>(tensor: CubeTensor, args: Args) -> CubeTensor
where
    // Magic fix for lifetime, the closure is supposed to capture everything required to create the
    // argument.
    for<'a> Args: FnOnce(&'a ()) -> RuntimeArg<O::Options>,
    O: NumericUnaryOpFamily,
{
    let output_shape = tensor.shape();
    in_memory_order([tensor], output_shape, |[tensor], shape_out| {
        let vector_size = max_vector_size(&tensor);
        let client = tensor.client.clone();
        let num_elems = tensor.meta.num_elements();

        let working_units = num_elems / vector_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);
        let dtype = tensor.dtype;

        unsafe {
            if tensor.can_mut() && tensor.is_nonoverlapping() {
                unary_numeric::launch_unchecked::<O>(
                    &client,
                    cube_count,
                    cube_dim,
                    address_type!(tensor),
                    vector_size,
                    tensor.clone().into_linear_view(),
                    tensor.as_linear_view_alias(0),
                    args(&()),
                    dtype_to_storage_type(dtype),
                );

                tensor
            } else {
                let output = empty_device_dtype(
                    tensor.client.clone(),
                    tensor.device.clone(),
                    shape_out,
                    tensor.dtype,
                );

                unary_numeric::launch_unchecked::<O>(
                    &client,
                    cube_count,
                    cube_dim,
                    address_type!(tensor, output),
                    vector_size,
                    tensor.into_linear_view(),
                    output.clone().into_linear_view(),
                    args(&()),
                    dtype_to_storage_type(dtype),
                );

                output
            }
        }
    })
}