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"""CUDA rotary embedding kernels."""

from typing import Any

import torch
from tokenspeed_kernel.platform import CapabilityRequirement, current_platform
from tokenspeed_kernel.registry import Priority, register_kernel
from tokenspeed_kernel.signature import format_signatures

platform = current_platform()

if platform.is_nvidia:
    from tokenspeed_kernel.thirdparty.cuda.rope import (
        apply_rope_with_cos_sin_cache_inplace,
    )

    @register_kernel(
        "embedding",
        "rope",
        name="cuda_embedding_rope",
        solution="cuda",
        capability=CapabilityRequirement(vendors=frozenset({"nvidia"})),
        signatures=format_signatures(
            ("q", "k"), "dense", {torch.float16, torch.bfloat16}
        ),
        priority=Priority.PERFORMANT,
        traits={
            "head_size": frozenset({64, 128, 256, 512}),
            "partial_rotary": frozenset({True, False}),
            "is_neox": frozenset({True, False}),
            "has_fused_kv": frozenset({True, False}),
            "has_q_out": frozenset({True, False}),
            "has_k_out": frozenset({True, False}),
        },
        tags={"latency"},
    )
    def cuda_embedding_rope(
        *,
        positions: torch.Tensor,
        q: torch.Tensor,
        k: torch.Tensor,
        head_size: int,
        cos_sin_cache: torch.Tensor,
        is_neox: bool = True,
        fused_set_kv_buffer_arg: Any = None,
        q_rope_out: torch.Tensor | None = None,
        k_rope_out: torch.Tensor | None = None,
        enable_pdl: bool = False,
    ) -> None:
        apply_rope_with_cos_sin_cache_inplace(
            positions=positions,
            query=q,
            key=k,
            head_size=head_size,
            cos_sin_cache=cos_sin_cache,
            is_neox=is_neox,
            fused_set_kv_buffer_arg=fused_set_kv_buffer_arg,
            output_q_rope=q_rope_out,
            output_k_rope=k_rope_out,
            enable_pdl=enable_pdl,
        )