boostr 0.2.0

ML framework built on numr - attention, quantization, model architectures
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//! CPU implementation of RoPEPackedOps — delegates to impl_generic.

use crate::error::Result;
use crate::ops::impl_generic::position::apply_rope_packed_impl;
use crate::ops::traits::position::RoPEPackedOps;
use numr::autograd::Var;
use numr::runtime::cpu::{CpuClient, CpuRuntime};
use numr::tensor::Tensor;

impl RoPEPackedOps<CpuRuntime> for CpuClient {
    fn apply_rope_packed(
        &self,
        x: &Var<CpuRuntime>,
        cos_cache: &Var<CpuRuntime>,
        sin_cache: &Var<CpuRuntime>,
        position_ids: &Tensor<CpuRuntime>,
    ) -> Result<Var<CpuRuntime>> {
        apply_rope_packed_impl(self, x, cos_cache, sin_cache, position_ids)
    }
}