use ruda_kernel::dsl as kernel_dsl;
use ruda_kernel::dsl::Runtime;
use ruda_kernel::tensor::layout::address_type;
use ruda_kernel::tensor::layout::broadcast_strides;
use ruda_kernel::tensor::layout::shape_divmod;
use ruda_kernel::tensor::allocation::empty_device_dtype;
use ruda_kernel::tensor::RudaTensor;
use ruda_core::tensor::TensorMetadata;
use ruda_kernel::dsl::frontend::ABSOLUTE_POS;
use ruda_kernel::dsl::frontend::Numeric;
use ruda_kernel::dsl::frontend::Tensor;
use ruda_kernel::library::FastDivmod;
use ruda_kernel::library::tensor::index_offset_contiguous_fastdivmod;
use ruda_kernel::dsl::RudaDim;
use ruda_kernel::library::tensor::layout::linear::LinearView;
use ruda_kernel::dsl::calculate_ruda_count_elemwise;
use ruda_kernel::dsl::prelude::*;
#[ruda(launch_unchecked, address_type = "dynamic")]
fn gather_kernel<T: Numeric, I: Numeric>(
input: &Tensor<T>,
indices: &LinearView<I>,
output: &mut LinearView<T, ReadWrite>,
in_strides: Sequence<usize>, out_shape: Sequence<FastDivmod<usize>>,
dim: usize,
#[define(T, I)] _dtypes: [StorageType; 2],
) {
if !indices.is_in_bounds(ABSOLUTE_POS) {
terminate!();
}
let mut offset = index_offset_contiguous_fastdivmod(
ABSOLUTE_POS,
&out_shape,
&in_strides,
input.vector_size(),
);
offset += usize::cast_from(indices[ABSOLUTE_POS]) * input.stride(dim);
output[ABSOLUTE_POS] = input[offset];
}
pub fn gather<R: Runtime>(
dim: usize,
tensor: RudaTensor<R>,
indices: RudaTensor<R>,
) -> RudaTensor<R> {
let shape_output = indices.shape();
let total_elem = shape_output.num_elements();
let output = empty_device_dtype(
tensor.client.clone(),
tensor.device.clone(),
shape_output,
tensor.dtype,
);
if total_elem == 0 {
return output;
}
let ruda_dim = RudaDim::new(tensor.client.properties(), total_elem);
let ruda_count = calculate_ruda_count_elemwise(&tensor.client, total_elem, ruda_dim);
let mut in_strides = broadcast_strides(&output, &tensor);
in_strides.values[dim] = 0;
let (dtype, indices_dtype) = (tensor.dtype, indices.dtype);
unsafe {
gather_kernel::launch_unchecked(
&output.client,
ruda_count,
ruda_dim,
address_type!(tensor, indices, output),
tensor.into_tensor_arg(),
indices.into_linear_view(),
output.clone().into_linear_view(),
in_strides,
shape_divmod(&output),
dim,
[dtype.into(), indices_dtype.into()],
)
}
output
}