# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import ctypes
import functools
import os
from collections.abc import Callable
from typing import TYPE_CHECKING, Any

import torch
import torch.distributed as dist
from torch._ops import OpOverload

import vllm.envs as envs
from vllm.logger import init_logger
from vllm.platforms import current_platform
from vllm.utils.torch_utils import direct_register_custom_op
from vllm.v1.attention.ops.rocm_aiter_mla_sparse import (
    rocm_aiter_sparse_attn_indexer,
    rocm_aiter_sparse_attn_indexer_fake,
)

if TYPE_CHECKING:
    from aiter import ActivationType, QuantType

logger = init_logger(__name__)

# fp8_dtype is not cached.
# on ROCm the fp8_dtype always calls is_fp8_fnuz
# which is a host op, so we cache it once here.
FP8_DTYPE = current_platform.fp8_dtype()
_HIPB_MM_INITIALIZED_DEVICES: set[int] = set()
_FP8_E4M3_DTYPES = {
    dtype
    for dtype in (
        getattr(torch, "float8_e4m3fn", None),
        getattr(torch, "float8_e4m3fnuz", None),
    )
    if dtype is not None
}
KB = 1024
MB = 1024 * KB


def _is_fp8_e4m3_tensor(t: torch.Tensor | None) -> bool:
    return t is not None and t.dtype in _FP8_E4M3_DTYPES


def _get_or_create_aiter_qr_rmsnorm_comm(
    device_comm: Any,
) -> tuple[int, int, bool] | None:
    """Create the AITER QR communicator used by fused QR+RMSNorm.

    vLLM's normal QuickReduce communicator owns vLLM C++ QR buffers. The fused
    QR+RMSNorm kernel added in AITER needs an AITER DeviceComms instance, so we
    lazily create one on the same TP CPU group and keep it on the vLLM device
    communicator for reuse.
    """
    existing = getattr(device_comm, "_aiter_qr_rmsnorm_comm", None)
    if existing is False:
        return None
    if existing is not None:
        return existing

    try:
        import aiter
    except Exception:
        device_comm._aiter_qr_rmsnorm_comm = False
        return None

    if not hasattr(aiter, "qr_all_reduce_rmsnorm"):
        device_comm._aiter_qr_rmsnorm_comm = False
        return None

    group = getattr(device_comm, "cpu_group", None)
    if group is None:
        device_comm._aiter_qr_rmsnorm_comm = False
        return None

    world_size = dist.get_world_size(group=group)
    rank = dist.get_rank(group=group)
    if world_size not in (2, 4, 8):
        device_comm._aiter_qr_rmsnorm_comm = False
        return None

    qr_max_size_mb = envs.VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB
    qr_max_size = None if qr_max_size_mb is None else qr_max_size_mb * MB

    try:
        ptr = aiter.init_custom_qr(rank, world_size, qr_max_size)
        handle = aiter.qr_get_handle(ptr)
        handles = [None] * world_size
        dist.all_gather_object(handles, handle, group=group)
        aiter.qr_open_handles(ptr, handles)
    except Exception:
        device_comm._aiter_qr_rmsnorm_comm = False
        return None

    comm = (ptr, world_size, envs.VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16)
    device_comm._aiter_qr_rmsnorm_comm = comm
    return comm


def _ensure_hipb_mm_extension_initialized() -> None:
    import aiter

    device = torch.accelerator.current_device_index()
    if device not in _HIPB_MM_INITIALIZED_DEVICES:
        aiter.hipb_create_extension()
        _HIPB_MM_INITIALIZED_DEVICES.add(device)


def is_aiter_found() -> bool:
    from importlib.util import find_spec

    return find_spec("aiter") is not None


# `find_spec` is not torch.compile compatible.
# In cases where aiter availability might have
# been checked in forward passes that are torch compiled.
# we keep this global outside to not cause torch compile breaks.
IS_AITER_FOUND = is_aiter_found()


class _DlInfo(ctypes.Structure):
    _fields_ = [
        ("dli_fname", ctypes.c_char_p),
        ("dli_fbase", ctypes.c_void_p),
        ("dli_sname", ctypes.c_char_p),
        ("dli_saddr", ctypes.c_void_p),
    ]


_HIP_RUNTIME_HANDLE: ctypes.CDLL | None = None


def _find_torch_hip_runtime_path() -> str | None:
    """Find the HIP runtime already selected by PyTorch's extension graph."""
    try:
        torch_extension = ctypes.CDLL(torch._C.__file__)
        hip_symbol = torch_extension.hipGetDeviceCount
        dladdr = ctypes.CDLL(None).dladdr
        dladdr.argtypes = [ctypes.c_void_p, ctypes.POINTER(_DlInfo)]
        dladdr.restype = ctypes.c_int
        info = _DlInfo()
        if not dladdr(ctypes.cast(hip_symbol, ctypes.c_void_p), ctypes.byref(info)):
            return None
    except (AttributeError, OSError, TypeError, ctypes.ArgumentError):
        return None
    if info.dli_fname is None:
        return None

    runtime_path = os.fsdecode(info.dli_fname)
    if "libamdhip64.so" not in os.path.basename(runtime_path):
        return None
    return runtime_path


def _promote_torch_hip_runtime() -> None:
    """Make PyTorch's HIP runtime symbols visible to AITER JIT extensions.

    PyTorch loads its native extension with ``RTLD_LOCAL``. Some AITER JIT
    extensions rely on HIP registration and launch symbols being available in
    the process-global lookup scope. Reopening the exact runtime that provides
    PyTorch's ``hipGetDeviceCount`` symbol promotes the existing DSO instead of
    selecting or initializing a second HIP runtime.
    """
    global _HIP_RUNTIME_HANDLE
    if _HIP_RUNTIME_HANDLE is not None:
        return

    runtime_path = _find_torch_hip_runtime_path()
    if runtime_path is None:
        logger.warning_once(
            "Could not locate PyTorch's HIP runtime; AITER JIT extensions may "
            "fail to resolve HIP symbols."
        )
        return

    try:
        # Retain the handle for the process lifetime. Besides preventing a
        # future dlclose, this documents that the RTLD_GLOBAL promotion is
        # intentional and must outlive every lazily imported AITER extension.
        _HIP_RUNTIME_HANDLE = ctypes.CDLL(runtime_path, mode=ctypes.RTLD_GLOBAL)
    except OSError as err:
        logger.warning_once(
            "Could not promote PyTorch's HIP runtime at %s; AITER JIT "
            "extensions may fail to resolve HIP symbols: %s",
            runtime_path,
            err,
        )


def _maybe_promote_torch_hip_runtime() -> None:
    if current_platform.is_rocm() and IS_AITER_FOUND:
        _promote_torch_hip_runtime()


_maybe_promote_torch_hip_runtime()


def is_aiter_found_and_supported() -> bool:
    """Check if AITER library is available and platform supports it.

    Checks: platform (ROCm), device arch is CDNA 3 or better, and library existence.
    Does NOT check environment variables - that's handled by rocm_aiter_ops.is_enabled().

    This function determines if aiter CAN be used, not if it SHOULD be used.

    Separation of concerns:
    - This function: Can aiter work on this system? (platform + library availability)
    - rocm_aiter_ops.is_enabled(): Should aiter be used by default? (adds env var check)
    - Backend selection: Can explicitly request aiter regardless of env var

    This allows explicit backend selection via attention_config to work even when
    VLLM_ROCM_USE_AITER=0, while preventing unwanted JIT warnings for auto-discovery.
    """
    if current_platform.is_rocm() and IS_AITER_FOUND:
        from vllm.platforms.rocm import get_cdna_version

        return get_cdna_version() > 2
    return False


def is_aiter_found_and_supported_on_rdna4() -> bool:
    """RDNA4 (gfx12) analog of `is_aiter_found_and_supported()`.

    gfx12 has no aiter CK build, so this deliberately stays off the gfx9
    `@if_aiter_supported` umbrella; it reports only that aiter's Triton
    kernels are usable here. Like its gfx9 counterpart it checks platform +
    arch + library availability and does not check environment variables.
    """
    if current_platform.is_rocm() and IS_AITER_FOUND:
        from vllm.platforms.rocm import on_rdna4

        return on_rdna4()
    return False


def _triton_gemm_config_is_tuned(config_name: str, N: int, K: int) -> bool:
    from aiter.ops.triton.utils.gemm_config_utils import get_gemm_config

    # any M shape under 1024 should be tuned
    M_TUNE_PROBE = 128

    return get_gemm_config(config_name, M_TUNE_PROBE, N, K)[1]


def _ck_gemm_shape_is_tuned(
    N: int, K: int, q_dtype_w: torch.dtype, csv_attr: str
) -> bool:
    l_m = (
        [1, 2, 4]
        + list(range(8, 513, 8))
        + [1024, 1536]
        + [2**i for i in range(11, 19)]
    )
    try:
        from aiter.ops.gemm_op_a8w8 import (
            AITER_CONFIGS,
            get_GEMM_config_with_quant_type,
        )

        csv_path = getattr(AITER_CONFIGS, csv_attr)
        return any(
            get_GEMM_config_with_quant_type(M, N, K, q_dtype_w, csv_path) is not None
            for M in l_m
        )
    except (AttributeError, ImportError, OSError):
        logger.warning_once(
            "Could not read aiter CK GEMM configs from AITER_CONFIGS.%s; "
            "treating all shapes as untuned.",
            csv_attr,
        )
        return False


def if_aiter_supported(func: Callable) -> Callable:
    """Decorator that only executes the function if
    ROCm AITER package is supported and enabled on gfx9 archs.
    """

    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        if is_aiter_found_and_supported():
            return func(*args, **kwargs)

        return None

    return wrapper


def _validate_rocm_aiter_fused_moe_shared_expert_args(
    shared_w1: torch.Tensor | None,
    shared_w2: torch.Tensor | None,
    shared_w1_scale: torch.Tensor | None,
    shared_w2_scale: torch.Tensor | None,
    shared_expert_id: int,
) -> bool:
    shared_tensors = (shared_w1, shared_w2, shared_w1_scale, shared_w2_scale)
    has_shared_expert = any(tensor is not None for tensor in shared_tensors)
    if has_shared_expert:
        if not all(tensor is not None for tensor in shared_tensors):
            raise ValueError(
                "Heterogeneous fused MoE requires both shared weights and scales."
            )
        if shared_expert_id < 0:
            raise ValueError(
                "Heterogeneous fused MoE requires a non-negative shared expert ID."
            )
    elif shared_expert_id >= 0:
        raise ValueError(
            "A non-negative shared expert ID requires shared weights and scales."
        )
    return has_shared_expert


def _rocm_aiter_fused_moe_impl(
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weight: torch.Tensor,
    topk_ids: torch.Tensor,
    expert_mask: torch.Tensor | None = None,
    activation_method: int = 0,
    quant_method: int = 0,
    doweight_stage1: bool = False,
    w1_scale: torch.Tensor | None = None,
    w2_scale: torch.Tensor | None = None,
    a1_scale: torch.Tensor | None = None,
    a2_scale: torch.Tensor | None = None,
    num_local_tokens: torch.Tensor | None = None,
    output_dtype: torch.dtype | None = None,
    hidden_pad: int = 0,
    intermediate_pad: int = 0,
    gate_mode: str = "",
    bias1: torch.Tensor | None = None,
    bias2: torch.Tensor | None = None,
    moe_sorting_dispatch_policy: int = 0,
    swiglu_limit: float = 0.0,
    beta: float | None = None,
    linear_beta: float | None = None,
    shared_w1: torch.Tensor | None = None,
    shared_w2: torch.Tensor | None = None,
    shared_w1_scale: torch.Tensor | None = None,
    shared_w2_scale: torch.Tensor | None = None,
    shared_expert_id: int = -1,
    q_dtype_a: torch.dtype | None = None,
) -> torch.Tensor:
    has_shared_expert = _validate_rocm_aiter_fused_moe_shared_expert_args(
        shared_w1,
        shared_w2,
        shared_w1_scale,
        shared_w2_scale,
        shared_expert_id,
    )

    from aiter import ActivationType, QuantType
    from aiter.fused_moe import fused_moe

    activation = ActivationType(activation_method)
    quant_type = QuantType(quant_method)

    extra_kwargs: dict = {}
    if gate_mode and rocm_aiter_ops.fused_moe_supports_gate_mode():
        extra_kwargs["gate_mode"] = gate_mode
    if (
        getattr(ActivationType, "Situv2", None) is not None
        and activation == ActivationType.Situv2
    ):
        extra_kwargs["beta"] = beta
        extra_kwargs["linear_beta"] = linear_beta

    if has_shared_expert:
        extra_kwargs.update(
            shared_w1=shared_w1,
            shared_w2=shared_w2,
            shared_w1_scale=shared_w1_scale,
            shared_w2_scale=shared_w2_scale,
            shared_expert_id=shared_expert_id,
        )
    if q_dtype_a is not None:
        # DeepSeek V4.1 a4w4 override (use_mxfp4_w4a4_dsv4 in
        # rocm_aiter_moe.py). rocm_aiter_ops.fused_moe_supports_quant_dtype_a()
        # is checked at config time, so this is only reached on an AITER
        # build new enough to accept it.
        extra_kwargs["quant_dtype_a"] = q_dtype_a

    return fused_moe(
        hidden_states,
        w1,
        w2,
        topk_weight,
        topk_ids,
        expert_mask,
        activation,
        quant_type,
        doweight_stage1,
        w1_scale,
        w2_scale,
        a1_scale,
        a2_scale,
        num_local_tokens=num_local_tokens,
        dtype=output_dtype,
        hidden_pad=hidden_pad,
        intermediate_pad=intermediate_pad,
        bias1=bias1,
        bias2=bias2,
        moe_sorting_dispatch_policy=moe_sorting_dispatch_policy,
        swiglu_limit=swiglu_limit,
        **extra_kwargs,
    )


def _rocm_aiter_fused_moe_fake(
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weight: torch.Tensor,
    topk_ids: torch.Tensor,
    expert_mask: torch.Tensor | None = None,
    activation_method: int = 0,
    quant_method: int = 0,
    doweight_stage1: bool = False,
    w1_scale: torch.Tensor | None = None,
    w2_scale: torch.Tensor | None = None,
    a1_scale: torch.Tensor | None = None,
    a2_scale: torch.Tensor | None = None,
    num_local_tokens: torch.Tensor | None = None,
    output_dtype: torch.dtype | None = None,
    hidden_pad: int = 0,
    intermediate_pad: int = 0,
    gate_mode: str = "",
    bias1: torch.Tensor | None = None,
    bias2: torch.Tensor | None = None,
    moe_sorting_dispatch_policy: int = 0,
    swiglu_limit: float = 0.0,
    beta: float | None = None,
    linear_beta: float | None = None,
    shared_w1: torch.Tensor | None = None,
    shared_w2: torch.Tensor | None = None,
    shared_w1_scale: torch.Tensor | None = None,
    shared_w2_scale: torch.Tensor | None = None,
    shared_expert_id: int = -1,
    q_dtype_a: torch.dtype | None = None,
) -> torch.Tensor:
    if output_dtype is not None:
        return torch.empty_like(hidden_states, dtype=output_dtype)
    return torch.empty_like(hidden_states)


def _rocm_aiter_asm_moe_tkw1_impl(
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    fc1_scale: torch.Tensor | None = None,
    fc2_scale: torch.Tensor | None = None,
    fc1_smooth_scale: torch.Tensor | None = None,
    fc2_smooth_scale: torch.Tensor | None = None,
    a16: bool = False,
    per_tensor_quant_scale: torch.Tensor | None = None,
    expert_mask: torch.Tensor | None = None,
    activation_method: int = 0,
) -> torch.Tensor:
    from aiter import ActivationType
    from aiter.fused_moe_bf16_asm import asm_moe_tkw1

    activation = ActivationType(activation_method)

    return asm_moe_tkw1(
        hidden_states,
        w1,
        w2,
        topk_weights,
        topk_ids,
        fc1_scale=fc1_scale,
        fc2_scale=fc2_scale,
        fc1_smooth_scale=fc1_smooth_scale,
        fc2_smooth_scale=fc2_smooth_scale,
        a16=a16,
        per_tensor_quant_scale=per_tensor_quant_scale,
        expert_mask=expert_mask,
        activation=activation,
    )


def _rocm_aiter_asm_moe_tkw1_fake(
    hidden_states: torch.Tensor,
    w1: torch.Tensor,
    w2: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    fc1_scale: torch.Tensor | None = None,
    fc2_scale: torch.Tensor | None = None,
    fc1_smooth_scale: torch.Tensor | None = None,
    fc2_smooth_scale: torch.Tensor | None = None,
    a16: bool = False,
    per_tensor_quant_scale: torch.Tensor | None = None,
    expert_mask: torch.Tensor | None = None,
    activation_method: int = 0,
) -> torch.Tensor:
    return torch.empty_like(hidden_states)


def _rocm_aiter_topk_softmax_impl(
    topk_weights: torch.Tensor,
    topk_indices: torch.Tensor,
    token_expert_indices: torch.Tensor,
    gating_output: torch.Tensor,
    renormalize: bool,
    num_shared_experts: int = 0,
    shared_expert_scoring_func: str = "",
) -> None:
    from aiter import topk_softmax

    topk_softmax(
        topk_weights,
        topk_indices,
        token_expert_indices,
        gating_output,
        renormalize,
        num_shared_experts,
        shared_expert_scoring_func,
    )


def _rocm_aiter_topk_sigmoid_impl(
    topk_weights: torch.Tensor,
    topk_indices: torch.Tensor,
    gating_output: torch.Tensor,
) -> None:
    from aiter import topk_sigmoid

    topk_sigmoid(topk_weights, topk_indices, gating_output)


def _rocm_aiter_biased_grouped_topk_impl(
    gating_output: torch.Tensor,
    correction_bias: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    num_expert_group: int,
    topk_group: int,
    need_renorm: bool,
    routed_scaling_factor: float = 1.0,  # mul to topk_weights
) -> None:
    from aiter import biased_grouped_topk

    biased_grouped_topk(
        gating_output,
        correction_bias,
        topk_weights,
        topk_ids,
        num_expert_group,
        topk_group,
        need_renorm,
        routed_scaling_factor,
    )


def _rocm_aiter_grouped_topk_impl(
    gating_output: torch.Tensor,
    topk_weights: torch.Tensor,
    topk_ids: torch.Tensor,
    num_expert_group: int,
    topk_group: int,
    need_renorm: bool,
    scoring_func: str = "softmax",
    routed_scaling_factor: float = 1.0,  # mul to topk_weights
) -> None:
    is_softmax = scoring_func == "softmax"
    from aiter import grouped_topk

    grouped_topk(
        gating_output,
        topk_weights,
        topk_ids,
        num_expert_group,
        topk_group,
        need_renorm,
        is_softmax,
        routed_scaling_factor,
    )


def _rocm_aiter_fused_topk_impl(
    x: torch.Tensor,
    router_logits: torch.Tensor,
    top_k: int,
    gate_up: bool,
) -> tuple[torch.Tensor, torch.Tensor]:
    from aiter.fused_moe import fused_topk

    # fused_topk returns (topk_weights, topk_indices)
    return fused_topk(x, router_logits, top_k, gate_up)


def _rocm_aiter_fused_topk_fake(
    x: torch.Tensor,
    router_logits: torch.Tensor,
    top_k: int,
    gate_up: bool,
) -> tuple[torch.Tensor, torch.Tensor]:
    num_tokens = x.shape[0]
    topk_weights = torch.empty(
        (num_tokens, top_k), dtype=torch.float32, device=x.device
    )
    topk_indices = torch.empty((num_tokens, top_k), dtype=torch.int32, device=x.device)
    return topk_weights, topk_indices


# Cache whether aiter supports FP8 MLA parameters
_AITER_MLA_SUPPORTS_FP8: bool | None = None


def _check_aiter_mla_fp8_support() -> bool:
    """Check if aiter.mla.mla_decode_fwd supports q_scale and kv_scale parameters."""
    global _AITER_MLA_SUPPORTS_FP8
    if _AITER_MLA_SUPPORTS_FP8 is None:
        try:
            import inspect

            from aiter.mla import mla_decode_fwd

            sig = inspect.signature(mla_decode_fwd)
            _AITER_MLA_SUPPORTS_FP8 = (
                "q_scale" in sig.parameters and "kv_scale" in sig.parameters
            )
        except (
            ImportError,
            ModuleNotFoundError,
            AttributeError,
            ValueError,
            TypeError,
        ):
            # ImportError/ModuleNotFoundError: aiter.mla module not available
            # AttributeError: mla_decode_fwd doesn't exist
            # ValueError: mla_decode_fwd has no signature (e.g., built-in)
            # TypeError: mla_decode_fwd is not a callable
            _AITER_MLA_SUPPORTS_FP8 = False
    return _AITER_MLA_SUPPORTS_FP8


def _rocm_aiter_gather_kv_b_proj_impl(
    kv_cache: torch.Tensor,
    kv_scale: torch.Tensor,
    kv_indptr: torch.Tensor,
    kv_indices: torch.Tensor,
    cu_seq_lens: torch.Tensor,
    weight: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
) -> None:
    from aiter.ops.triton.gather_kv_b_proj import gather_kv_b_proj

    gather_kv_b_proj(
        kv_cache, kv_scale, kv_indptr, kv_indices, cu_seq_lens, weight, None, k, v
    )


def _rocm_aiter_gather_kv_b_proj_fake(
    kv_cache: torch.Tensor,
    kv_scale: torch.Tensor,
    kv_indptr: torch.Tensor,
    kv_indices: torch.Tensor,
    cu_seq_lens: torch.Tensor,
    weight: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
) -> None:
    pass


def _rocm_aiter_mla_decode_fwd_impl(
    q: torch.Tensor,
    kv_buffer: torch.Tensor,
    o: torch.Tensor,
    qo_indptr: torch.Tensor,
    max_seqlen_qo: int,
    kv_indptr: torch.Tensor | None = None,
    kv_indices: torch.Tensor | None = None,
    kv_last_page_lens: torch.Tensor | None = None,
    sm_scale: float = 1.0,
    logit_cap: float = 0.0,
    q_scale: torch.Tensor | None = None,
    kv_scale: torch.Tensor | None = None,
    work_meta_data: torch.Tensor | None = None,
    work_indptr: torch.Tensor | None = None,
    work_info_set: torch.Tensor | None = None,
    reduce_indptr: torch.Tensor | None = None,
    reduce_final_map: torch.Tensor | None = None,
    reduce_partial_map: torch.Tensor | None = None,
    causal: bool = True,
) -> None:
    from aiter.mla import mla_decode_fwd

    kwargs: dict[str, float | torch.Tensor | None | bool] = {
        "sm_scale": sm_scale,
        "logit_cap": logit_cap,
    }

    # Only pass q_scale and kv_scale if the aiter library supports them
    if _check_aiter_mla_fp8_support():
        kwargs["q_scale"] = q_scale
        kwargs["kv_scale"] = kv_scale

    kwargs["causal"] = causal

    if work_meta_data is not None:
        assert work_indptr is not None, (
            "work_indptr must be provided with work_meta_data"
        )
        assert work_info_set is not None, (
            "work_info_set must be provided with work_meta_data"
        )
        assert reduce_indptr is not None, (
            "reduce_indptr must be provided with work_meta_data"
        )
        assert reduce_final_map is not None, (
            "reduce_final_map must be provided with work_meta_data"
        )
        assert reduce_partial_map is not None, (
            "reduce_partial_map must be provided with work_meta_data"
        )
        kwargs["work_meta_data"] = work_meta_data
        kwargs["work_indptr"] = work_indptr
        kwargs["work_info_set"] = work_info_set
        kwargs["reduce_indptr"] = reduce_indptr
        kwargs["reduce_final_map"] = reduce_final_map
        kwargs["reduce_partial_map"] = reduce_partial_map

    mla_decode_fwd(
        q,
        kv_buffer.view(-1, 1, 1, q.shape[-1]),
        o,
        qo_indptr,
        kv_indptr,
        kv_indices,
        kv_last_page_lens,
        max_seqlen_qo,
        **kwargs,
    )


def _rocm_aiter_mla_decode_fwd_lse_impl(
    q: torch.Tensor,
    kv_buffer: torch.Tensor,
    o: torch.Tensor,
    qo_indptr: torch.Tensor,
    max_seqlen_qo: int,
    kv_indptr: torch.Tensor | None = None,
    kv_indices: torch.Tensor | None = None,
    kv_last_page_lens: torch.Tensor | None = None,
    sm_scale: float = 1.0,
    logit_cap: float = 0.0,
    q_scale: torch.Tensor | None = None,
    kv_scale: torch.Tensor | None = None,
) -> torch.Tensor:
    """Run non-persistent AITER MLA decode and return natural-log LSE.

    gfx942 persistent MLA kernels do not provide an LSE code object. Keeping
    this as a separate op makes that constraint structural: callers that need
    LSE cannot accidentally forward persistent work metadata.
    """
    from aiter.mla import get_meta_param, mla_decode_fwd

    kwargs: dict[str, float | int | torch.Tensor | None | bool] = {
        "sm_scale": sm_scale,
        "logit_cap": logit_cap,
        "return_lse": True,
    }
    if _check_aiter_mla_fp8_support():
        kwargs["q_scale"] = q_scale
        kwargs["kv_scale"] = kv_scale

    if (
        q.dtype == torch.bfloat16
        and kv_buffer.dtype == torch.bfloat16
        and q.shape[1] == 32
    ):
        from vllm.platforms.rocm import on_gfx950

        if on_gfx950():
            # The gfx950 H32 BF16 kernel mishandles ragged tails with split KV.
            # Its single-split path returns correct output and natural-log LSE.
            kwargs["num_kv_splits"] = 1
            kwargs["num_kv_splits_indptr"] = torch.arange(
                qo_indptr.shape[0], dtype=torch.int32, device=q.device
            )

    if q.dtype == FP8_DTYPE:
        assert kv_indices is not None
        num_kv_splits, num_kv_splits_indptr = get_meta_param(
            None,
            qo_indptr.shape[0] - 1,
            kv_indices.numel(),
            q.shape[1],
            max_seqlen_qo,
            q.dtype,
        )
        if num_kv_splits == 1:
            # gfx942's one-split FP8 asm writes the final output directly but
            # does not write either LSE buffer. Force the normal split reducer,
            # which produces both the same output and an accurate natural LSE.
            num_kv_splits = 2
            num_kv_splits_indptr = torch.arange(
                0,
                (qo_indptr.shape[0]) * num_kv_splits,
                num_kv_splits,
                dtype=torch.int32,
                device=q.device,
            )
        kwargs["num_kv_splits"] = num_kv_splits
        kwargs["num_kv_splits_indptr"] = num_kv_splits_indptr

    _, final_lse = mla_decode_fwd(
        q,
        kv_buffer.view(-1, 1, 1, q.shape[-1]),
        o,
        qo_indptr,
        kv_indptr,
        kv_indices,
        kv_last_page_lens,
        max_seqlen_qo,
        **kwargs,
    )
    if final_lse is None:
        raise RuntimeError("AITER MLA decode did not return the requested LSE")
    return final_lse


def _rocm_aiter_mla_decode_fwd_lse_fake(
    q: torch.Tensor,
    kv_buffer: torch.Tensor,
    o: torch.Tensor,
    qo_indptr: torch.Tensor,
    max_seqlen_qo: int,
    kv_indptr: torch.Tensor | None = None,
    kv_indices: torch.Tensor | None = None,
    kv_last_page_lens: torch.Tensor | None = None,
    sm_scale: float = 1.0,
    logit_cap: float = 0.0,
    q_scale: torch.Tensor | None = None,
    kv_scale: torch.Tensor | None = None,
) -> torch.Tensor:
    return torch.empty(q.shape[:-1], dtype=torch.float32, device=q.device)


def _rocm_aiter_w8a8_gemm_impl(
    A: torch.Tensor,
    B: torch.Tensor,
    As: torch.Tensor,
    Bs: torch.Tensor,
    bias: torch.Tensor | None = None,
    output_dtype: torch.dtype = torch.float16,
) -> torch.Tensor:
    from aiter import gemm_a8w8_CK

    # gemm_a8w8_CK(a, b, scale_a, scale_b, bias) expects
    # a to be [M, K]
    # b to be [N, K]
    # CutlassInt8ScaledMMLinearKernel prepare weight `w_q` in [K, N] format
    return gemm_a8w8_CK(A, B, As, Bs, bias, output_dtype)


def _rocm_aiter_w8a8_gemm_fake(
    A: torch.Tensor,
    B: torch.Tensor,
    As: torch.Tensor,
    Bs: torch.Tensor,
    bias: torch.Tensor | None = None,
    output_dtype: torch.dtype = torch.float16,
) -> torch.Tensor:
    m = A.shape[0]
    n = B.shape[0]
    Y = torch.empty(m, n, dtype=output_dtype, device=A.device)
    return Y


def _rocm_aiter_preshuffled_per_token_w8a8_gemm_impl(
    A: torch.Tensor,
    B: torch.Tensor,
    As: torch.Tensor,
    Bs: torch.Tensor,
    bias: torch.Tensor | None = None,
    output_dtype: torch.dtype = torch.float16,
) -> torch.Tensor:
    from aiter import gemm_a8w8_bpreshuffle

    output = gemm_a8w8_bpreshuffle(A, B, As, Bs, None, output_dtype)
    if bias is not None:
        output.add_(bias)
    return output


def _rocm_aiter_preshuffled_per_token_w8a8_gemm_fake(
    A: torch.Tensor,
    B: torch.Tensor,
    As: torch.Tensor,
    Bs: torch.Tensor,
    bias: torch.Tensor | None = None,
    output_dtype: torch.dtype = torch.float16,
) -> torch.Tensor:
    m = A.shape[0]
    n = B.shape[0]
    return torch.empty(m, n, dtype=output_dtype, device=A.device)


def _rocm_aiter_hipb_mm_fp8_impl(
    A: torch.Tensor,
    B: torch.Tensor,
    As: torch.Tensor,
    Bs: torch.Tensor,
    bias: torch.Tensor | None = None,
    output_dtype: torch.dtype = torch.bfloat16,
) -> torch.Tensor:
    from aiter import hipb_mm

    _ensure_hipb_mm_extension_initialized()
    return hipb_mm(
        A,
        B,
        solution_index=-1,
        bias=bias,
        out_dtype=output_dtype,
        scaleA=As,
        scaleB=Bs,
        scaleOut=None,
        bpreshuffle=True,
    )


def _rocm_aiter_hipb_mm_fp8_fake(
    A: torch.Tensor,
    B: torch.Tensor,
    As: torch.Tensor,
    Bs: torch.Tensor,
    bias: torch.Tensor | None = None,
    output_dtype: torch.dtype = torch.bfloat16,
) -> torch.Tensor:
    m = A.shape[0]
    n = B.shape[1]
    return torch.empty(m, n, dtype=output_dtype, device=A.device)


def _rocm_aiter_triton_gemm_a8w8_blockscale_impl(
    A: torch.Tensor,
    B: torch.Tensor,
    As: torch.Tensor,
    Bs: torch.Tensor,
    output_dtype: torch.dtype = torch.float16,
) -> torch.Tensor:
    from aiter.ops.triton.gemm_a8w8_blockscale import gemm_a8w8_blockscale

    return gemm_a8w8_blockscale(A, B, As, Bs, dtype=output_dtype)


def _rocm_aiter_triton_gemm_a8w8_blockscale_fake(
    A: torch.Tensor,
    B: torch.Tensor,
    As: torch.Tensor,
    Bs: torch.Tensor,
    output_dtype: torch.dtype = torch.float16,
) -> torch.Tensor:
    m = A.shape[0]
    n = B.shape[0]
    Y = torch.empty(m, n, dtype=output_dtype, device=A.device)
    return Y


def _rocm_aiter_gemm_a8w8_blockscale_impl(
    A: torch.Tensor,
    B: torch.Tensor,
    As: torch.Tensor,
    Bs: torch.Tensor,
    output_dtype: torch.dtype = torch.float16,
) -> torch.Tensor:
    from aiter import gemm_a8w8_blockscale

    return gemm_a8w8_blockscale(A, B, As, Bs, dtype=output_dtype)


def _rocm_aiter_gemm_a8w8_blockscale_fake(
    A: torch.Tensor,
    B: torch.Tensor,
    As: torch.Tensor,
    Bs: torch.Tensor,
    output_dtype: torch.dtype = torch.float16,
) -> torch.Tensor:
    m = A.shape[0]
    n = B.shape[0]
    Y = torch.empty(m, n, dtype=output_dtype, device=A.device)
    return Y


def _rocm_aiter_gemm_a8w8_blockscale_bpreshuffle_impl(
    A: torch.Tensor,
    B: torch.Tensor,
    As: torch.Tensor,
    Bs: torch.Tensor,
    output_dtype: torch.dtype = torch.float16,
) -> torch.Tensor:
    from aiter import gemm_a8w8_blockscale_bpreshuffle

    # B preshuffled (shuffle_weight (16,16)); As column-major group scale.
    return gemm_a8w8_blockscale_bpreshuffle(A, B, As, Bs, dtype=output_dtype)


def _rocm_aiter_gemm_a8w8_blockscale_bpreshuffle_fake(
    A: torch.Tensor,
    B: torch.Tensor,
    As: torch.Tensor,
    Bs: torch.Tensor,
    output_dtype: torch.dtype = torch.float16,
) -> torch.Tensor:
    m = A.shape[0]
    n = B.shape[0]
    Y = torch.empty(m, n, dtype=output_dtype, device=A.device)
    return Y


def _rocm_aiter_rmsnorm_fused_add_dynamic_quant_impl(
    x: torch.Tensor,
    residual: torch.Tensor,
    weight: torch.Tensor,
    epsilon: float,
    quant_dtype: torch.dtype,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    import aiter as rocm_aiter

    assert quant_dtype in [torch.int8, FP8_DTYPE]

    y_scale = torch.empty(x.shape[0], 1, dtype=torch.float32, device=x.device)
    out = torch.empty(x.shape, dtype=quant_dtype, device=x.device)
    residual_out = torch.empty_like(x)

    rocm_aiter.rmsnorm2d_fwd_with_add_dynamicquant(
        out,
        x,
        residual,
        residual_out,
        y_scale,
        weight,
        epsilon,
        use_model_sensitive_rmsnorm=0,
    )

    return out, residual_out, y_scale


def _rocm_aiter_rmsnorm_fused_add_dynamic_quant_fake(
    x: torch.Tensor,
    residual: torch.Tensor,
    weight: torch.Tensor,
    epsilon: float,
    quant_dtype: torch.dtype,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    y_scale = torch.empty(x.shape[0], 1, dtype=torch.float32, device=x.device)
    out = torch.empty(x.shape, dtype=quant_dtype, device=x.device)
    residual_out = torch.empty_like(x)

    return out, residual_out, y_scale


def _rocm_aiter_rmsnorm_fused_dynamic_quant_impl(
    x: torch.Tensor,
    weight: torch.Tensor,
    epsilon: float,
    quant_dtype: torch.dtype,
) -> tuple[torch.Tensor, torch.Tensor]:
    import aiter as rocm_aiter

    assert quant_dtype in [torch.int8, FP8_DTYPE]

    y_scale = torch.empty(x.shape[0], 1, dtype=torch.float32, device=x.device)
    out = torch.empty(x.shape, dtype=quant_dtype, device=x.device)

    rocm_aiter.rmsnorm2d_fwd_with_dynamicquant(
        out, x, y_scale, weight, epsilon, use_model_sensitive_rmsnorm=0
    )

    return out, y_scale


def _rocm_aiter_rmsnorm_fused_dynamic_quant_fake(
    x: torch.Tensor,
    weight: torch.Tensor,
    epsilon: float,
    quant_dtype: torch.dtype,
) -> tuple[torch.Tensor, torch.Tensor]:
    y_scale = torch.empty(x.shape[0], 1, dtype=torch.float32, device=x.device)
    out = torch.empty(x.shape, dtype=quant_dtype, device=x.device)

    return out, y_scale


def _rocm_aiter_fused_allreduce_rmsnorm_impl(
    input_: torch.Tensor,
    residual: torch.Tensor,
    weight: torch.Tensor,
    epsilon: float,
    gemma_norm: bool = False,
) -> tuple[torch.Tensor, torch.Tensor]:
    aiter_ar = rocm_aiter_ops.get_aiter_allreduce()
    assert aiter_ar is not None, "aiter allreduce must be initialized"
    ca = aiter_ar.aiter_ca
    use_1stage = aiter_ar.use_1stage_fused_ar_rms(input_)

    if not use_1stage:
        from vllm.distributed import get_tp_group

        device_comm = get_tp_group().device_communicator
        qr_comm = getattr(device_comm, "qr_comm", None)
        hidden_dim = input_.shape[-1]
        row_size = hidden_dim * input_.element_size()
        fused_qr_rmsnorm_ok = (
            # aiter.qr_all_reduce_rmsnorm does not implement Gemma's
            # (1 + weight) scaling.
            not gemma_norm
            and qr_comm is not None
            and not getattr(qr_comm, "disabled", True)
            and hasattr(qr_comm, "should_quick_allreduce")
            and qr_comm.should_quick_allreduce(input_)
            and input_.shape == residual.shape
            and input_.dtype in (torch.bfloat16, torch.float16)
            and input_.dtype == residual.dtype
            and input_.dtype == weight.dtype
            and weight.dim() == 1
            and weight.numel() == hidden_dim
            and input_.numel() % hidden_dim == 0
            and row_size > 0
            and row_size <= 32 * KB
            and (32 * KB) % row_size == 0
        )
        if fused_qr_rmsnorm_ok:
            assert qr_comm is not None
            aiter_qr_comm = _get_or_create_aiter_qr_rmsnorm_comm(device_comm)
            if aiter_qr_comm is not None:
                import aiter

                ptr, _, cast_bf2half = aiter_qr_comm
                quant_level = qr_comm._get_qr_quant_level(input_)
                out = torch.empty_like(input_)
                residual_out = torch.empty_like(residual)
                aiter.qr_all_reduce_rmsnorm(
                    ptr,
                    input_,
                    residual,
                    residual_out,
                    out,
                    weight,
                    epsilon,
                    hidden_dim,
                    quant_level,
                    cast_bf2half,
                )
                return out, residual_out

    result = ca.custom_fused_ar_rms(
        input_,
        residual,
        weight,
        epsilon,
        use_1stage=use_1stage,
        gemma_norm=gemma_norm,
    )
    if result is None:
        from vllm.distributed import tensor_model_parallel_all_reduce

        return torch.ops.vllm_aiter.fused_add_rms_norm(
            tensor_model_parallel_all_reduce(input_),
            residual,
            weight,
            epsilon,
            gemma_norm,
        )
    return result[0], result[1]


def _rocm_aiter_fused_allreduce_rmsnorm_fake(
    input_: torch.Tensor,
    residual: torch.Tensor,
    weight: torch.Tensor,
    epsilon: float,
    gemma_norm: bool = False,
) -> tuple[torch.Tensor, torch.Tensor]:
    return torch.empty_like(input_), torch.empty_like(residual)


def _rocm_aiter_fused_allreduce_rmsnorm_quant_per_group_impl(
    input_: torch.Tensor,
    residual: torch.Tensor,
    weight: torch.Tensor,
    epsilon: float,
    group_size: int,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    """Fused AllReduce + RMSNorm + per-group FP8 quant."""
    aiter_ar = rocm_aiter_ops.get_aiter_allreduce()
    assert aiter_ar is not None, "aiter allreduce must be initialized"
    ca = aiter_ar.aiter_ca

    use_1stage = aiter_ar.use_1stage_fused_ar_rms(input_)

    result = ca.fused_ar_rms_per_group_quant(
        input_,
        residual,
        w=weight,
        eps=epsilon,
        group_size=group_size,
        registered=torch.cuda.is_current_stream_capturing()
        and ca.enable_register_for_capturing,
        use_1stage=use_1stage,
    )
    assert result is not None
    return result[0], result[1], result[2]


def _rocm_aiter_fused_allreduce_rmsnorm_quant_per_group_fake(
    input_: torch.Tensor,
    residual: torch.Tensor,
    weight: torch.Tensor,
    epsilon: float,
    group_size: int,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    hidden_dim = input_.shape[-1]
    num_groups = hidden_dim // group_size
    quant_out = torch.empty(input_.shape, dtype=FP8_DTYPE, device=input_.device)
    residual_out = torch.empty_like(residual)
    scale_out = torch.empty(
        input_.shape[:-1] + (num_groups,),
        dtype=torch.float32,
        device=input_.device,
    )
    return quant_out, residual_out, scale_out


def _rocm_aiter_fused_allreduce_rmsnorm_quant_per_group_with_bf16_norm_impl(
    input_: torch.Tensor,
    residual: torch.Tensor,
    weight: torch.Tensor,
    epsilon: float,
    group_size: int,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
    """Fused AllReduce + add-RMSNorm + per-group FP8 quant + bf16 normed act.

    Wraps the same AITER launcher as ``_rocm_aiter_fused_allreduce_rmsnorm_
    quant_per_group_impl`` with ``emit_bf16=True``, emitting the pre-quant
    bf16/fp16 normed activation for a parallel consumer (DeepSeek V3.2 sparse
    indexer ``wk_weights_proj``).
    """
    aiter_ar = rocm_aiter_ops.get_aiter_allreduce()
    assert aiter_ar is not None, "aiter allreduce must be initialized"
    ca = aiter_ar.aiter_ca

    use_1stage = aiter_ar.use_1stage_fused_ar_rms(input_)

    result = ca.fused_ar_rms_per_group_quant(
        input_,
        residual,
        w=weight,
        eps=epsilon,
        group_size=group_size,
        registered=torch.cuda.is_current_stream_capturing()
        and ca.enable_register_for_capturing,
        use_1stage=use_1stage,
        emit_bf16=True,
    )
    assert result is not None
    assert len(result) == 4, "emit_bf16=True must return four tensors from aiter"
    return result[0], result[1], result[2], result[3]


def _rocm_aiter_fused_allreduce_rmsnorm_quant_per_group_with_bf16_norm_fake(
    input_: torch.Tensor,
    residual: torch.Tensor,
    weight: torch.Tensor,
    epsilon: float,
    group_size: int,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
    hidden_dim = input_.shape[-1]
    num_groups = hidden_dim // group_size
    quant_out = torch.empty(input_.shape, dtype=FP8_DTYPE, device=input_.device)
    residual_out = torch.empty_like(residual)
    scale_out = torch.empty(
        input_.shape[:-1] + (num_groups,),
        dtype=torch.float32,
        device=input_.device,
    )
    bf16_norm_out = torch.empty_like(input_)
    return quant_out, residual_out, scale_out, bf16_norm_out


def _rocm_aiter_per_tensor_quant_impl(
    out: torch.Tensor,
    x: torch.Tensor,
    scale: torch.Tensor,
    is_dynamic: bool,
) -> None:
    from aiter.ops.quant import dynamic_per_tensor_quant, static_per_tensor_quant

    if is_dynamic:
        dynamic_per_tensor_quant(out, x, scale)
    else:
        static_per_tensor_quant(out, x, scale)


def _rocm_aiter_per_token_quant_impl(
    x: torch.Tensor, quant_dtype: torch.dtype, scale: torch.Tensor | None = None
) -> tuple[torch.Tensor, torch.Tensor]:
    from aiter.ops.quant import dynamic_per_token_scaled_quant

    assert quant_dtype in [torch.int8, FP8_DTYPE]

    out_shape = x.shape
    out = torch.empty(x.shape, dtype=quant_dtype, device=x.device)
    if scale is None:
        scale = torch.empty((*out_shape[:-1], 1), dtype=torch.float32, device=x.device)
    dynamic_per_token_scaled_quant(
        out,
        x,
        scale,
        scale_ub=None,
        shuffle_scale=False,
        num_rows=None,
        num_rows_factor=1,
    )
    return out, scale


def _rocm_aiter_per_token_quant_fake(
    x: torch.Tensor, quant_dtype: torch.dtype, scale: torch.Tensor | None = None
) -> tuple[torch.Tensor, torch.Tensor]:
    out_shape = x.shape
    return (
        torch.empty(x.shape, dtype=quant_dtype, device=x.device),
        torch.empty((*out_shape[:-1], 1), dtype=torch.float32, device=x.device),
    )


def _rocm_aiter_rmsnorm_with_add_fp8_group_quant_impl(
    x: torch.Tensor,
    residual: torch.Tensor,
    weight: torch.Tensor,
    variance_epsilon: float,
    group_size: int,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    from aiter.ops.triton.fused_fp8_quant import fused_rms_fp8_group_quant

    (x_quant, x_quant_scales), _, _, res = fused_rms_fp8_group_quant(
        x,
        weight,
        variance_epsilon,
        None,
        None,
        None,
        group_size=group_size,
        dtype_quant=FP8_DTYPE,
        res1=residual,
    )
    return (
        x_quant,
        res,
        x_quant_scales,
    )


def _rocm_aiter_rmsnorm_with_add_fp8_group_quant_fake(
    x: torch.Tensor,
    residual: torch.Tensor,
    weight: torch.Tensor,
    variance_epsilon: float,
    group_size: int,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    M, N = x.shape
    scale_shape = (M, (N + group_size - 1) // group_size)
    return (
        torch.empty_like(x, dtype=FP8_DTYPE, device=x.device),
        torch.empty_like(residual, device=residual.device),
        torch.empty(scale_shape, dtype=torch.float32, device=x.device),
    )


def _rocm_aiter_rmsnorm_fp8_group_quant_impl(
    x: torch.Tensor,
    weight: torch.Tensor,
    variance_epsilon: float,
    group_size: int,
) -> tuple[torch.Tensor, torch.Tensor]:
    from aiter.ops.triton.fused_fp8_quant import fused_rms_fp8_group_quant

    (x_quant, x_quant_scales), _, _, res = fused_rms_fp8_group_quant(
        x,
        weight,
        variance_epsilon,
        None,
        None,
        None,
        group_size=group_size,
        dtype_quant=FP8_DTYPE,
        res1=None,
    )
    return (x_quant, x_quant_scales)


def _rocm_aiter_rmsnorm_fp8_group_quant_fake(
    x: torch.Tensor,
    weight: torch.Tensor,
    variance_epsilon: float,
    group_size: int,
) -> tuple[torch.Tensor, torch.Tensor]:
    M, N = x.shape
    scale_shape = (M, (N + group_size - 1) // group_size)
    return (
        torch.empty_like(x, dtype=FP8_DTYPE, device=x.device),
        torch.empty(scale_shape, dtype=torch.float32, device=x.device),
    )


def _rocm_aiter_fused_rms_gated_fp8_group_quant_impl(
    x: torch.Tensor,
    weight: torch.Tensor,
    bias: torch.Tensor | None,
    z: torch.Tensor,
    eps: float,
    norm_before_gate: bool,
    activation: str,
    group_size: int,
) -> tuple[torch.Tensor, torch.Tensor]:
    """Fused gated-RMSNorm + FP8 group quantization via aiter Triton kernel."""
    from aiter.ops.triton.quant import fused_rms_gated_fp8_group_quant

    return fused_rms_gated_fp8_group_quant(
        x,
        weight,
        bias,
        z,
        eps,
        norm_before_gate=norm_before_gate,
        activation=activation,
        out_dtype=FP8_DTYPE,
        group_size=group_size,
    )


def _rocm_aiter_fused_rms_gated_fp8_group_quant_fake(
    x: torch.Tensor,
    weight: torch.Tensor,
    bias: torch.Tensor | None,
    z: torch.Tensor,
    eps: float,
    norm_before_gate: bool,
    activation: str,
    group_size: int,
) -> tuple[torch.Tensor, torch.Tensor]:
    M, N = x.shape
    scale_shape = (M, (N + group_size - 1) // group_size)
    return (
        torch.empty_like(x, dtype=FP8_DTYPE, device=x.device),
        torch.empty(scale_shape, dtype=torch.float32, device=x.device),
    )


def _rocm_aiter_group_fp8_quant_impl(
    x: torch.Tensor,
    group_size: int,
    transpose_scale: bool = False,
) -> tuple[torch.Tensor, torch.Tensor]:
    assert x.shape[-1] % group_size == 0, "Input shape must be divisible by group size"
    from aiter import QuantType, get_hip_quant

    aiter_per1x128_quant = get_hip_quant(QuantType.per_1x128)
    return aiter_per1x128_quant(
        x.contiguous(), quant_dtype=FP8_DTYPE, transpose_scale=transpose_scale
    )


def _rocm_aiter_group_fp8_quant_fake(
    x: torch.Tensor,
    group_size: int,
    transpose_scale: bool = False,
) -> tuple[torch.Tensor, torch.Tensor]:
    M, N = x.shape
    x_fp8 = torch.empty((M, N), dtype=FP8_DTYPE, device=x.device)
    out_bs = torch.empty(
        (
            M,
            (N + group_size - 1) // group_size,
        ),
        dtype=torch.float32,
        device=x.device,
    )
    return x_fp8, out_bs


def _rocm_aiter_act_mul_and_fp8_group_quant_impl(
    x: torch.Tensor,
    group_size: int,
) -> tuple[torch.Tensor, torch.Tensor]:
    from aiter.ops.triton.activation import act_mul_and_fp8_group_quant

    return act_mul_and_fp8_group_quant(
        x,
        activation="silu",
        group_size=group_size,
        dtype_quant=FP8_DTYPE,
    )


def _rocm_aiter_act_mul_and_fp8_group_quant_fake(
    x: torch.Tensor,
    group_size: int,
) -> tuple[torch.Tensor, torch.Tensor]:
    M, N = x.shape
    assert N % 2 == 0
    N_half = N // 2
    x_fp8 = torch.empty((M, N_half), dtype=FP8_DTYPE, device=x.device)
    out_bs = torch.empty(
        (
            M,
            (N_half + group_size - 1) // group_size,
        ),
        dtype=torch.float32,
        device=x.device,
    )
    return x_fp8, out_bs


def _rocm_aiter_triton_add_rmsnorm_pad_impl(
    x: torch.Tensor,
    weight: torch.Tensor,
    variance_epsilon: float,
    residual: torch.Tensor,
    x_pad_to_multiple: int,
) -> tuple[torch.Tensor, torch.Tensor]:
    from aiter.ops.triton.fused_add_rmsnorm_pad import fused_add_rmsnorm_pad

    return fused_add_rmsnorm_pad(
        x,
        weight,
        variance_epsilon,
        residual,
        x_pad_to_multiple=x_pad_to_multiple,
    )


def _rocm_aiter_triton_add_rmsnorm_pad_fake(
    x: torch.Tensor,
    weight: torch.Tensor,
    variance_epsilon: float,
    residual: torch.Tensor,
    x_pad_to_multiple: int,
) -> tuple[torch.Tensor, torch.Tensor]:
    M, N = x.shape
    if x_pad_to_multiple > 0:
        N_out = (N + x_pad_to_multiple - 1) // x_pad_to_multiple * x_pad_to_multiple
    else:
        N_out = N
    out = torch.empty((M, N_out), dtype=x.dtype, device=x.device)
    residual_out = torch.empty_like(residual)
    return out, residual_out


def _fused_mla_dual_rms_norm_impl(
    x1: torch.Tensor,
    x1_weight: torch.Tensor,
    x2: torch.Tensor,
    x2_weight: torch.Tensor,
    x1_epsilon: float,
    x2_epsilon: float,
) -> tuple[torch.Tensor, torch.Tensor]:
    from aiter.ops.fused_qk_norm_rope_cache_quant import _fused_qk_rmsnorm

    return _fused_qk_rmsnorm(
        q_out=None,
        q=x1,
        q_weight=x1_weight,
        q_eps=x1_epsilon,
        k_out=None,
        k=x2,
        k_weight=x2_weight,
        k_eps=x2_epsilon,
    )


def _fused_mla_dual_rms_norm_fake(
    x1: torch.Tensor,
    x1_weight: torch.Tensor,
    x2: torch.Tensor,
    x2_weight: torch.Tensor,
    x1_epsilon: float,
    x2_epsilon: float,
) -> tuple[torch.Tensor, torch.Tensor]:
    return (torch.empty_like(x1), torch.empty_like(x2))


def _fused_mla_dual_rms_norm_per_token_quant_impl(
    q: torch.Tensor,
    q_weight: torch.Tensor,
    kv: torch.Tensor,
    kv_weight: torch.Tensor,
    q_epsilon: float,
    kv_epsilon: float,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    """Fused MLA q/kv RMSNorm (+ FP8 per-token quant on q) via AITER.

    Backs the ``fused_mla_dual_rms_norm_per_token_quant`` custom op used by the
    MLA FP8 attention fusion when the q latent is quantized *per token* (a single
    ``(M, 1)`` scale). Only the *q* latent is FP8 quantized (it feeds the
    FP8 ``q_b_proj`` GEMM); the *kv* latent is RMS-normed and consumed by attention as bf16.
    """
    from aiter.ops.fused_qk_rmsnorm_group_quant import (
        fused_qk_rmsnorm_per_token_quant,
    )

    mq, nq = q.shape
    q_out = torch.empty((mq, nq), dtype=FP8_DTYPE, device=q.device)
    q_scale = torch.empty((mq, 1), dtype=torch.float32, device=q.device)
    kv_normed = torch.empty(kv.shape, dtype=kv.dtype, device=kv.device)

    # q -> RMSNorm + FP8 per-token quant (q slot); kv -> RMSNorm only (k slot).
    # `split` views are accepted directly (unit inner stride); the kernel
    # handles strided inputs, matching the aiter op-test usage.
    fused_qk_rmsnorm_per_token_quant(
        q_out_quantized=q_out,
        q_out_scale=q_scale,
        q=q,
        q_weight=q_weight,
        q_epsilon=q_epsilon,
        k_out=kv_normed,
        k=kv,
        k_weight=kv_weight,
        k_epsilon=kv_epsilon,
        gemma_norm=False,
    )
    return q_out, q_scale, kv_normed


def _fused_mla_dual_rms_norm_per_token_quant_fake(
    q: torch.Tensor,
    q_weight: torch.Tensor,
    kv: torch.Tensor,
    kv_weight: torch.Tensor,
    q_epsilon: float,
    kv_epsilon: float,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    mq, nq = q.shape
    q_out = torch.empty((mq, nq), dtype=FP8_DTYPE, device=q.device)
    q_scale = torch.empty((mq, 1), dtype=torch.float32, device=q.device)
    kv_normed = torch.empty(kv.shape, dtype=kv.dtype, device=kv.device)
    return q_out, q_scale, kv_normed


def _fused_mla_dual_rms_norm_group_quant_impl(
    q: torch.Tensor,
    q_weight: torch.Tensor,
    kv: torch.Tensor,
    kv_weight: torch.Tensor,
    q_epsilon: float,
    kv_epsilon: float,
    group_size: int,
    transpose_scale: bool,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    """Fused MLA q/kv RMSNorm (+ FP8 per-1x128 group quant on q) via AITER.

    Backs the ``fused_mla_dual_rms_norm_group_quant`` custom op used by the
    DeepSeek-V4 ROCm attention path when the q latent is group-quantized
    (``(M, N/128)`` scales) for the block-scaled FP8 wq_b GEMMs. The q latent
    is quantized directly from the fp32 norm accumulator, skipping the bf16
    intermediate the standalone quant path would re-read; the kv latent is
    RMS-normed to bf16 and consumed by the fused insert kernel unchanged.
    """
    from aiter.ops.fused_qk_rmsnorm_group_quant import (
        fused_qk_rmsnorm_group_quant,
    )

    mq, nq = q.shape
    q_out = torch.empty((mq, nq), dtype=FP8_DTYPE, device=q.device)
    q_scale = torch.empty((mq, nq // group_size), dtype=torch.float32, device=q.device)
    kv_normed = torch.empty(kv.shape, dtype=kv.dtype, device=kv.device)

    # q -> RMSNorm + FP8 group quant (q slot); kv -> RMSNorm only (k slot).
    # `split` views are accepted directly (unit inner stride); the kernel
    # handles strided inputs, matching the aiter op-test usage.
    fused_qk_rmsnorm_group_quant(
        q_out_quantized=q_out,
        q_out_scale=q_scale,
        q=q,
        q_weight=q_weight,
        q_epsilon=q_epsilon,
        k_out=kv_normed,
        k=kv,
        k_weight=kv_weight,
        k_epsilon=kv_epsilon,
        group_size=group_size,
        transpose_scale=transpose_scale,
    )
    return q_out, q_scale, kv_normed


def _fused_mla_dual_rms_norm_group_quant_fake(
    q: torch.Tensor,
    q_weight: torch.Tensor,
    kv: torch.Tensor,
    kv_weight: torch.Tensor,
    q_epsilon: float,
    kv_epsilon: float,
    group_size: int,
    transpose_scale: bool,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    mq, nq = q.shape
    q_out = torch.empty((mq, nq), dtype=FP8_DTYPE, device=q.device)
    q_scale = torch.empty((mq, nq // group_size), dtype=torch.float32, device=q.device)
    kv_normed = torch.empty(kv.shape, dtype=kv.dtype, device=kv.device)
    return q_out, q_scale, kv_normed


def _rocm_aiter_gemm_a8wfp4_impl(
    x: torch.Tensor,
    w: torch.Tensor,
    x_scales: torch.Tensor,
    w_scales: torch.Tensor,
    out_dtype: torch.dtype,
) -> torch.Tensor:
    from aiter.ops.triton.gemm_a8wfp4 import gemm_a8wfp4

    M, N = x.shape[0], w.shape[0]
    y = torch.empty(M, N, dtype=out_dtype, device=x.device)
    gemm_a8wfp4(
        x=x,
        w=w,
        y=y,
        x_scales=x_scales,
        w_scales=w_scales,
        dtype=out_dtype,
        config=None,
    )
    return y


def _rocm_aiter_gemm_a8wfp4_fake(
    x: torch.Tensor,
    w: torch.Tensor,
    x_scales: torch.Tensor,
    w_scales: torch.Tensor,
    out_dtype: torch.dtype,
) -> torch.Tensor:
    return torch.empty(x.shape[0], w.shape[0], dtype=out_dtype, device=x.device)


def _triton_rotary_embedding_impl(
    positions: torch.Tensor,
    query: torch.Tensor,
    key: torch.Tensor,
    head_size: int,
    cos_sin_cache: torch.Tensor,
    is_neox: bool,
    offsets: torch.Tensor | None = None,
) -> None:
    # Modifies query and key in-place
    from aiter.ops.triton.rope.rope import (
        rope_cached_thd_positions_offsets_2c_fwd_inplace,
    )

    num_tokens = positions.numel()
    cos, sin = cos_sin_cache.chunk(2, dim=-1)
    query_shape = query.shape
    key_shape = key.shape
    rotate_style = 0 if is_neox else 1
    rotary_dim = head_size

    query = query.view(num_tokens, -1, head_size)
    key = key.view(num_tokens, -1, head_size)
    query_ = query[..., :rotary_dim]
    key_ = key[..., :rotary_dim]
    positions = positions.view(*query.shape[:1])
    rope_cached_thd_positions_offsets_2c_fwd_inplace(
        query_,
        key_,
        cos,
        sin,
        positions,
        offsets,
        rotate_style,
        reuse_freqs_front_part=True,
        nope_first=False,
    )
    query = query.view(query_shape)
    key = key.view(key_shape)


def _rocm_aiter_fp8_attn_impl(
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    q_descale: torch.Tensor,
    k_descale: torch.Tensor,
    v_descale: torch.Tensor,
    batch_size: int,
    output_dtype: torch.dtype,
    scale: float | None = None,
    cu_seqlens: torch.Tensor | None = None,
    max_seqlen: torch.Tensor | None = None,
) -> torch.Tensor:
    """Run AITER FP8 attention for fixed or packed inputs."""
    from aiter import flash_attn_varlen_fp8_pertensor_func

    q_len = q.size(1)
    if cu_seqlens is None:
        cu_seqlens = torch.arange(
            0, (batch_size + 1) * q_len, step=q_len, dtype=torch.int32, device=q.device
        )
    max_seqlen_value = q_len if max_seqlen is None else max_seqlen.item()

    q, k, v = (x.flatten(0, 1) for x in (q, k, v))
    output = flash_attn_varlen_fp8_pertensor_func(
        q,
        k,
        v,
        q_descale=q_descale,
        k_descale=k_descale,
        v_descale=v_descale,
        cu_seqlens_q=cu_seqlens,
        cu_seqlens_k=cu_seqlens,
        max_seqlen_q=max_seqlen_value,
        max_seqlen_k=max_seqlen_value,
        causal=False,
        softmax_scale=scale,
    )
    return output.to(output_dtype).reshape(batch_size, q_len, *output.shape[1:])


def _rocm_aiter_fp8_attn_fake(
    q: torch.Tensor,
    k: torch.Tensor,
    v: torch.Tensor,
    q_descale: torch.Tensor,
    k_descale: torch.Tensor,
    v_descale: torch.Tensor,
    batch_size: int,
    output_dtype: torch.dtype,
    scale: float | None = None,
    cu_seqlens: torch.Tensor | None = None,
    max_seqlen: torch.Tensor | None = None,
) -> torch.Tensor:
    return torch.empty(
        (*q.shape[:-1], v.shape[-1]), device=q.device, dtype=output_dtype
    )


def _mhc_delayed_pre_tail(
    gemm_out: torch.Tensor,
    sqrsum: torch.Tensor,
    residual: torch.Tensor,
    hc_scale: torch.Tensor,
    hc_base: torch.Tensor,
    pre_mix: torch.Tensor | None,
    rms_eps: float,
    hc_pre_eps: float,
    hc_sinkhorn_eps: float,
    hc_post_mult_value: float,
    sinkhorn_repeat: int,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
    """Finish a delayed mHC pre once AITER has produced the projection.

    Shared by the plain and the post-fused entry points, which differ only in
    which kernel computes ``gemm_out`` / ``sqrsum`` and the residual they
    project. ``mhc_pre_big_fuse`` also writes a collapse against its own
    pre-mix, which the delayed formulation cannot use and so discards.
    Returns ``(post_mix, comb_mix, layer_input, next_pre_mix)``.
    """
    from aiter.ops.mhc import mhc_pre_big_fuse

    num_tokens, hc_mult, hidden_size = residual.shape
    device = residual.device
    with torch.device(device):
        post_mix = torch.empty(
            num_tokens, hc_mult, 1, dtype=torch.float32, device=device
        )
        comb_mix = torch.empty(
            num_tokens, hc_mult, hc_mult, dtype=torch.float32, device=device
        )
        unused_collapse = torch.empty(
            num_tokens, hidden_size, dtype=torch.bfloat16, device=device
        )
        mhc_pre_big_fuse(
            post_mix,
            comb_mix,
            unused_collapse,
            gemm_out,
            sqrsum,
            hc_scale,
            hc_base,
            residual,
            rms_eps,
            hc_pre_eps,
            hc_sinkhorn_eps,
            hc_post_mult_value,
            sinkhorn_repeat,
        )

    next_pre_mix = torch.ops.vllm.mhc_pre_mix_triton(
        gemm_out,
        sqrsum,
        hc_scale,
        hc_base,
        hc_mult,
        hc_mult * hidden_size,
        rms_eps,
        hc_pre_eps,
    )
    if pre_mix is None:
        # Model entry selects residual stream zero.
        layer_input = residual[:, 0]
    else:
        layer_input = torch.ops.vllm.hc_collapse_triton(residual, pre_mix)
    return post_mix, comb_mix, layer_input, next_pre_mix


# Global flag to ensure ops are registered only once
_OPS_REGISTERED = False


_AITER_SITUV2_ACT_ENV = {
    "a8w4": "AITER_SITUV2_A8W4",
    "a4w4": "AITER_SITUV2_A4W4",
}


def _resolve_situv2_activation() -> str:
    """VLLM_ROCM_USE_AITER_MOE_SITUV2 -> a4w4 | a8w4 | a16w4.

    auto (default) and the legacy 1 mean a4w4; legacy 0 means a16w4.
    """
    value = envs.VLLM_ROCM_USE_AITER_MOE_SITUV2.lower()
    if value in ("auto", "1"):
        return "a4w4"
    if value == "0":
        return "a16w4"
    return value


def _sync_aiter_situv2_moe_env() -> None:
    """Set the AITER_SITUV2_* env matching VLLM_ROCM_USE_AITER_MOE_SITUV2.

    AITER reads AITER_SITUV2_A8W4 / AITER_SITUV2_A4W4 and defaults to a16w4,
    so exactly one is set for a8w4/a4w4 and both are cleared for a16w4.
    """
    import os

    selected = _AITER_SITUV2_ACT_ENV.get(_resolve_situv2_activation())
    for name in _AITER_SITUV2_ACT_ENV.values():
        if name == selected:
            os.environ[name] = "1"
        else:
            os.environ.pop(name, None)


_GFX950_C4A_AITER_MAX_COMPRESSED_SEQ_LEN = 64 * 1024
_GFX950_C4A_NATIVE_MAX_ROWS = 256
_GFX950_DSV4_NATIVE_MAX_COLUMNS = 1024 * 1024


class rocm_aiter_ops:
    """ROCm AITER operations wrapper for AMD GPU acceleration in vLLM.

    This class centralizes the import and registration of AITER ops,
    and provides a unified interface for checking if AITER is enabled.
    Operations are only available on supported gfx9
    architectures when aiter is installed.

    The class uses environment variables to control which features are enabled,
    allowing fine-grained control over which AITER optimizations are used.

    Environment Variables:
        VLLM_ROCM_USE_AITER: Main toggle for all AITER operations.
        VLLM_ROCM_USE_AITER_LINEAR: Controls GEMM and quantization ops.
        VLLM_ROCM_USE_AITER_RMSNORM: Controls RMSNorm operations.
        VLLM_ROCM_USE_AITER_MOE: Controls MoE (Mixture of Experts) ops.
        VLLM_ROCM_USE_AITER_MLA: Controls MLA (Multi-head Latent Attention) ops.
        VLLM_ROCM_USE_AITER_MHA: Controls MHA ops including flash_attn_varlen.
        VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION: Controls Triton unified attention.
        VLLM_ROCM_USE_AITER_FP8BMM: Controls FP8 batched matrix multiply.
        VLLM_ROCM_USE_AITER_FP4_ASM_GEMM: Controls FP4 assembly GEMM.
        VLLM_ROCM_USE_AITER_TRITON_ROPE: Controls Triton rotary embeddings.
        VLLM_ROCM_USE_AITER_TRITON_SPARSE_MLA: Controls Triton sparse MLA (gfx950).
        VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS: Controls shared expert fusion.
        VLLM_ROCM_USE_AITER_MOE_SITUV2: SiTUv2 FlyDSL MoE activation
            dtype (a16w4 | a8w4 | a4w4).
        VLLM_ROCM_USE_AITER_TRITON_GEMM: Controls Triton unquantized GEMM.

    Note:
        The environment variables are assigned when the module is imported,
        so you can't change the environment variables after the module is imported.
        This is done out of performance consideration. Accessing environment variables
        is expensive as described in issue https://github.com/vllm-project/vllm/issues/17067
        so we don't want to do it repeatedly, especially in the hot path (the forward pass).
        You can call the refresh_env_variables() function to reload the env variables
        after monkey patching the env variables in the unit test.

    Check Functions:
        All check functions (is_*_enabled) are decorated with @if_aiter_supported,
        which verifies: (1) platform is ROCm, (2) device arch is gfx9, and
        (3) aiter library is installed. The check function then also verifies
        the corresponding environment variable is enabled.
        i.e.                                             ___
        is_enabled() == current_platform.is_rocm() and      |     checked by
                        current_platform.is_on_gfx9() and   | @if_aiter_supported
                        IS_AITER_FOUND and   _______________|
                        cls._AITER_ENABLED   -----> Check by the logic in `is_enabled()`

    Example:
        from vllm._aiter_ops import rocm_aiter_ops

        # Check if aiter is enabled before using operations
        if rocm_aiter_ops.is_enabled():
            result = rocm_aiter_ops.per_token_quant(x, FP8_DTYPE)

    Operations:
        - GEMM operations: gemm_a8w8, gemm_a8w8_blockscale
        - Fused MoE: fused_moe, asm_moe_tkw1
        - Routing: topk_softmax, biased_grouped_topk, grouped_topk
        - MLA decode: mla_decode_fwd
        - Quantization: per_tensor_quant, per_token_quant, group_fp8_quant
        - Triton ops: triton_rotary_embed, triton_fp8_bmm, triton_gemm_a8w8_blockscale

    """

    _MOE_DISPATCH_POLICY: int | None = None

    @classmethod
    @if_aiter_supported
    def get_moe_dispatch_policy(cls) -> int:
        """Cached MoE sorting dispatch policy."""
        if cls._MOE_DISPATCH_POLICY is None:
            import vllm.envs as envs

            cls._MOE_DISPATCH_POLICY = envs.VLLM_ROCM_AITER_MOE_DISPATCH_POLICY
        return cls._MOE_DISPATCH_POLICY

    # Check if the env variable is set
    _AITER_ENABLED = envs.VLLM_ROCM_USE_AITER
    # Chunk length the AITER FlyDSL K5 prefill kernels are compiled for.
    GDN_FLYDSL_CHUNK_SIZE = 64
    _CUSTOM_ALL_REDUCE_ENABLED = envs.VLLM_ROCM_USE_AITER_CUSTOM_AR
    _LINEAR_ENABLED = envs.VLLM_ROCM_USE_AITER_LINEAR
    _FMOE_ENABLED = envs.VLLM_ROCM_USE_AITER_MOE
    _MLA_ENABLED = envs.VLLM_ROCM_USE_AITER_MLA
    _MHA_ENABLED = envs.VLLM_ROCM_USE_AITER_MHA
    _SHUFFLE_KV_CACHE_ENABLED = envs.VLLM_ROCM_SHUFFLE_KV_CACHE_LAYOUT
    _TRITON_UNIFIED_ATTN_ENABLED = envs.VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION
    # TODO: Consolidate under _LINEAR_ENABLED
    _FP8BMM_ENABLED = envs.VLLM_ROCM_USE_AITER_FP8BMM
    _FP4BMM_ENABLED = envs.VLLM_ROCM_USE_AITER_FP4BMM
    _LINEAR_HIPBMM_ENABLED = envs.VLLM_ROCM_USE_AITER_LINEAR_HIPBMM
    # TODO: Consolidate under _LINEAR_ENABLED
    _FP4_GEMM_DYNAMIC_QUANT_ASM = envs.VLLM_ROCM_USE_AITER_FP4_ASM_GEMM
    # TODO: Consolidate under VLLM_ROCM_USE_AITER_ROPE
    _TRITON_ROTARY_EMBED = envs.VLLM_ROCM_USE_AITER_TRITON_ROPE
    _TRITON_SPARSE_MLA = envs.VLLM_ROCM_USE_AITER_TRITON_SPARSE_MLA
    _MOE_SHARED_EXPERTS_ENABLED = envs.VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS
    _MOE_SITUV2 = _resolve_situv2_activation()
    # TODO: Consolidate under _LINEAR_ENABLED
    _TRITON_UNQUANT_GEMM = envs.VLLM_ROCM_USE_AITER_TRITON_GEMM
    # Lazily probed: whether aiter.topk_softmax supports the
    # num_shared_experts / shared_expert_scoring_func args (7-arg form).
    _TOPK_SOFTMAX_FUSED_SIGMOID: bool | None = None

    @classmethod
    def refresh_env_variables(cls):
        """Since the environment variables are assigned when the module is imported,
        This is a helper function to reload all the env variables from
        the environment variables.
        for example, after monkey patching the env variables in the unit test,
        you can call this function to reload the env variables.
        """
        cls._AITER_ENABLED = envs.VLLM_ROCM_USE_AITER
        cls._CUSTOM_ALL_REDUCE_ENABLED = envs.VLLM_ROCM_USE_AITER_CUSTOM_AR
        cls._LINEAR_ENABLED = envs.VLLM_ROCM_USE_AITER_LINEAR
        cls._FMOE_ENABLED = envs.VLLM_ROCM_USE_AITER_MOE
        cls._MLA_ENABLED = envs.VLLM_ROCM_USE_AITER_MLA
        cls._MHA_ENABLED = envs.VLLM_ROCM_USE_AITER_MHA
        cls._SHUFFLE_KV_CACHE_ENABLED = envs.VLLM_ROCM_SHUFFLE_KV_CACHE_LAYOUT
        cls._TRITON_UNIFIED_ATTN_ENABLED = envs.VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION
        cls._FP8BMM_ENABLED = envs.VLLM_ROCM_USE_AITER_FP8BMM
        cls._FP4BMM_ENABLED = envs.VLLM_ROCM_USE_AITER_FP4BMM
        cls._LINEAR_HIPBMM_ENABLED = envs.VLLM_ROCM_USE_AITER_LINEAR_HIPBMM
        cls._FP4_GEMM_DYNAMIC_QUANT_ASM = envs.VLLM_ROCM_USE_AITER_FP4_ASM_GEMM
        cls._TRITON_ROTARY_EMBED = envs.VLLM_ROCM_USE_AITER_TRITON_ROPE
        cls._TRITON_SPARSE_MLA = envs.VLLM_ROCM_USE_AITER_TRITON_SPARSE_MLA
        cls._MOE_SHARED_EXPERTS_ENABLED = envs.VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS
        cls._MOE_SITUV2 = _resolve_situv2_activation()
        _sync_aiter_situv2_moe_env()
        cls._TRITON_UNQUANT_GEMM = envs.VLLM_ROCM_USE_AITER_TRITON_GEMM
        cls._MOE_DISPATCH_POLICY = envs.VLLM_ROCM_AITER_MOE_DISPATCH_POLICY

    @staticmethod
    def get_aiter_activation_type(activation_str: str) -> "ActivationType | None":
        """Given an activation type as a string, returns the corresponding aiter ActivationType enum.
        Supported activation types: "no", "none", "silu", "gelu", "swiglu".
        Returns None if the mapping fails.

        Args:
            activation_str (str): Activation type as string.

        Returns:
            Aiter ActivationType enum value, or None if not found.

        """
        # Import only locally, since aiter may not always be available.
        try:
            from aiter import ActivationType
        except ImportError:
            return None

        if not isinstance(activation_str, str):
            return None

        name = activation_str.strip().lower()
        mapping = {
            "none": ActivationType.No,
            "no": ActivationType.No,
            "silu": ActivationType.Silu,
            "gelu": ActivationType.Gelu,
            "swiglu": ActivationType.Swiglu,
            "situ": getattr(ActivationType, "Situv2", None),
        }
        return mapping.get(name)

    @staticmethod
    def get_aiter_quant_type(quant_type_str: str) -> "QuantType | None":
        """Given a quantization type as a string, returns the corresponding aiter QuantType enum.
        Supported quantization types: "no", "per_tensor", "per_token", "per_1x32", "per_1x128", "per_128x128".
        Returns None if the mapping fails.

        Args:
            quant_type_str (str): Quantization type as string.

        Returns:
            Aiter QuantType enum value, or None if not found.

        """
        try:
            from aiter import QuantType
        except ImportError:
            return None

        if not isinstance(quant_type_str, str):
            return None

        name = quant_type_str.strip().lower()
        mapping = {
            "no": QuantType.No,
            "per_tensor": QuantType.per_Tensor,
            "per_token": QuantType.per_Token,
            "per_1x32": QuantType.per_1x32,
            "per_1x128": QuantType.per_1x128,
            "per_128x128": QuantType.per_128x128,
        }
        return mapping.get(name)

    @classmethod
    @if_aiter_supported
    def is_enabled(cls) -> bool:
        return cls._AITER_ENABLED

    @classmethod
    def is_rdna_aiter_enabled(cls) -> bool:
        """AITER on RDNA4 (gfx12): library present, arch is rdna4, and the user
        enabled aiter. Only aiter's Triton kernels exist on gfx12 (no CK build),
        so this deliberately stays off the gfx9/CK `@if_aiter_supported` umbrella
        and gates only the Triton paths rdna4 uses. The gfx12 analog of
        `is_enabled()`."""
        if not current_platform.is_rocm() or not IS_AITER_FOUND:
            return False
        from vllm.platforms.rocm import on_rdna4

        return on_rdna4() and cls._AITER_ENABLED

    @classmethod
    def is_rdna_linear_enabled(cls) -> bool:
        """RDNA4 (gfx12) analog of is_linear_enabled() (aiter Triton blockscale)."""
        return cls.is_rdna_aiter_enabled() and cls._LINEAR_ENABLED

    @classmethod
    @if_aiter_supported
    def is_linear_enabled(cls) -> bool:
        return cls._AITER_ENABLED and cls._LINEAR_ENABLED

    @classmethod
    @if_aiter_supported
    def is_linear_fp8_enabled(cls) -> bool:
        return cls.is_linear_enabled()

    @classmethod
    @if_aiter_supported
    def is_fused_moe_enabled(cls) -> bool:
        return cls._AITER_ENABLED and cls._FMOE_ENABLED

    @classmethod
    @if_aiter_supported
    def is_fusion_moe_shared_experts_enabled(cls) -> bool:
        return cls.is_fused_moe_enabled() and cls._MOE_SHARED_EXPERTS_ENABLED

    @classmethod
    @if_aiter_supported
    def is_fused_moe_situv2_enabled(cls) -> bool:
        """True when a low-precision (a8w4/a4w4) SiTUv2 activation is selected."""
        return cls.is_fused_moe_enabled() and cls._MOE_SITUV2 != "a16w4"

    @classmethod
    @if_aiter_supported
    def get_fused_moe_situv2_activation(cls) -> str:
        """SiTUv2 activation dtype name: a16w4, a8w4 or a4w4."""
        return cls._MOE_SITUV2

    @classmethod
    @if_aiter_supported
    def is_fused_moe_situv2_gate_up_interleaved(cls) -> bool:
        """Whether the SiTUv2 kernels expect gate/up-interleaved w13.

        The a8w4 FlyDSL stage-1 kernels (_gui_) are interleaved; a16w4 and
        a4w4 are separated. Weight shuffle (oracle/mxfp4.py) and gate_mode
        (experts/rocm_aiter_moe.py) must both use this.
        """
        return cls._MOE_SITUV2 == "a8w4"

    @classmethod
    @if_aiter_supported
    def topk_softmax_supports_fused_sigmoid(cls) -> bool:
        """Check if topk_softmax supports fused shared expert activation."""
        if cls._TOPK_SOFTMAX_FUSED_SIGMOID is None:
            try:
                import inspect

                from aiter import topk_softmax

                params = inspect.signature(topk_softmax).parameters
                if "num_shared_experts" in params:
                    cls._TOPK_SOFTMAX_FUSED_SIGMOID = True
                else:
                    # @compile_ops wrapper loses the original signature.
                    # Fall back to the torch custom op schema.
                    import torch

                    schema = getattr(
                        getattr(torch.ops.aiter, "topk_softmax", None), "default", None
                    )
                    schema_str = str(getattr(schema, "_schema", ""))
                    cls._TOPK_SOFTMAX_FUSED_SIGMOID = "num_shared_experts" in schema_str
            except (ImportError, ValueError):
                cls._TOPK_SOFTMAX_FUSED_SIGMOID = False
        return cls._TOPK_SOFTMAX_FUSED_SIGMOID

    @classmethod
    @if_aiter_supported
    def fuse_sigmoid_in_kernel(cls, aiter_topK_meta_data: object) -> bool:
        """Whether fused shared-expert sigmoid in the topk kernel is usable.

        Combines the cached static capability checks (FSE enabled, fused-moe
        enabled, topk_softmax supports fused sigmoid) with the runtime
        readiness check (topK meta-data buffer initialized).

        ``aiter_topK_meta_data`` is accepted as a parameter rather than
        imported internally so callers cannot hit initialization-order
        issues where the module-level global has not been set yet.
        """
        return (
            cls.is_fusion_moe_shared_experts_enabled()
            and cls.topk_softmax_supports_fused_sigmoid()
            and aiter_topK_meta_data is not None
        )

    @classmethod
    @if_aiter_supported
    def is_mla_enabled(cls) -> bool:
        return cls._AITER_ENABLED and cls._MLA_ENABLED

    @classmethod
    @if_aiter_supported
    def mla_decode_supports_non_causal(cls) -> bool:
        """Whether installed aiter.mla.mla_decode_fwd accepts `causal`.

        Added in aiter v0.1.20. A missing argument is a hard error rather
        than a boolean the caller can use to pick another MLA backend.
        """
        import inspect

        from aiter.mla import mla_decode_fwd

        if "causal" not in inspect.signature(mla_decode_fwd).parameters:
            raise RuntimeError(
                "ROCM_AITER_MLA requires aiter.mla.mla_decode_fwd(..., causal=). "
                "The installed aiter is causal-only; upgrade aiter rather than "
                "falling back to another MLA backend."
            )
        return True

    @classmethod
    @if_aiter_supported
    def is_mha_enabled(cls) -> bool:
        return cls._AITER_ENABLED and cls._MHA_ENABLED

    @classmethod
    @if_aiter_supported
    def is_custom_all_reduce_enabled(cls) -> bool:
        return cls._AITER_ENABLED and cls._CUSTOM_ALL_REDUCE_ENABLED

    @classmethod
    @if_aiter_supported
    def is_shuffle_kv_cache_enabled(cls) -> bool:
        return cls._SHUFFLE_KV_CACHE_ENABLED

    @classmethod
    @if_aiter_supported
    def fused_qknorm_idxrqknorm_enabled(
        cls,
        kv_cache_dtype: str,
        k_scale: torch.Tensor | None = None,
        v_scale: torch.Tensor | None = None,
    ) -> bool:
        """Whether MiniMax-M3 can use AITER's consolidated fused QK-norm.

        Requires AITER to be installed and enabled. bf16 (``auto``) always
        qualifies; fp8 e4m3 also needs K/V scales. Other cache dtypes fall
        back to vLLM's fused kernel.
        """
        if not cls._AITER_ENABLED:
            return False
        if kv_cache_dtype == "auto":
            return True
        if kv_cache_dtype in ("fp8", "fp8_e4m3"):
            return k_scale is not None and v_scale is not None
        return False

    @classmethod
    def fused_qknorm_idxrqknorm(
        cls,
        qkv: torch.Tensor,
        q_norm_weight: torch.Tensor,
        k_norm_weight: torch.Tensor,
        cos_sin_cache: torch.Tensor,
        positions: torch.Tensor,
        num_heads: int,
        num_kv_heads: int,
        rotary_dim: int,
        eps: float,
        slot_mapping: torch.Tensor,
        kv_cache_k: torch.Tensor,
        kv_cache_v: torch.Tensor,
        q_out: torch.Tensor,
        kv_cache_dtype: str,
        k_scale: torch.Tensor | None = None,
        v_scale: torch.Tensor | None = None,
        index_q_norm_weight: torch.Tensor | None = None,
        index_k_norm_weight: torch.Tensor | None = None,
        num_index_heads: int = 0,
        index_cache: torch.Tensor | None = None,
        index_q_out: torch.Tensor | None = None,
        index_slot_mapping: torch.Tensor | None = None,
        skip_index_branch: bool = False,
    ) -> None:
        """Run consolidated MiniMax-M3 QK-norm fusion.

        Callers must check ``fused_qknorm_idxrqknorm_enabled`` first. Runtime
        failures from the AITER op are deliberately propagated.
        """
        if kv_cache_dtype == "auto":
            aiter_kv_cache_dtype = "auto"
            aiter_k_scale = None
            aiter_v_scale = None
        elif kv_cache_dtype in ("fp8", "fp8_e4m3"):
            aiter_kv_cache_dtype = "fp8_e4m3_static"
            aiter_k_scale = k_scale
            aiter_v_scale = v_scale
        else:
            raise ValueError(
                "AITER fused QK-norm requires kv_cache_dtype 'auto', 'fp8', "
                f"or 'fp8_e4m3', got {kv_cache_dtype!r}"
            )
        aiter_index_cache_dtype = "fp8" if _is_fp8_e4m3_tensor(index_cache) else "auto"

        from aiter import fused_qknorm_idxrqknorm

        fused_qknorm_idxrqknorm(
            qkv,
            q_norm_weight,
            k_norm_weight,
            cos_sin_cache,
            positions,
            num_heads,
            num_kv_heads,
            rotary_dim,
            eps,
            index_q_norm_weight=index_q_norm_weight,
            index_k_norm_weight=index_k_norm_weight,
            num_index_heads=num_index_heads,
            slot_mapping=slot_mapping,
            kv_cache_k=kv_cache_k,
            kv_cache_v=kv_cache_v,
            index_cache=index_cache,
            block_size=16,
            q_out=q_out,
            index_q_out=index_q_out,
            index_slot_mapping=index_slot_mapping,
            kv_cache_dtype=aiter_kv_cache_dtype,
            index_cache_dtype=aiter_index_cache_dtype,
            k_scale=aiter_k_scale,
            v_scale=aiter_v_scale,
            asm_layout=True,
            skip_index_branch=skip_index_branch,
        )

    @classmethod
    @if_aiter_supported
    def is_triton_unified_attn_enabled(cls) -> bool:
        return cls._AITER_ENABLED and cls._TRITON_UNIFIED_ATTN_ENABLED

    @classmethod
    @if_aiter_supported
    def is_fp8bmm_enabled(cls) -> bool:
        return cls._AITER_ENABLED and cls._FP8BMM_ENABLED

    @classmethod
    @if_aiter_supported
    def is_fp4bmm_enabled(cls) -> bool:
        from vllm.platforms.rocm import get_cdna_version

        # TODO GFX1250: Enable for cdna 4+ when aiter supports batched_gemm_a16wfp4 on gfx1250
        return cls._AITER_ENABLED and cls._FP4BMM_ENABLED and get_cdna_version() == 4

    @classmethod
    @if_aiter_supported
    def is_linear_hipbmm_enabled(cls) -> bool:
        from vllm.platforms.rocm import get_cdna_version

        return (
            cls.is_linear_enabled()
            and (get_cdna_version() > 2)
            and cls._LINEAR_HIPBMM_ENABLED
        )

    @classmethod
    @if_aiter_supported
    def is_asm_fp4_gemm_dynamic_quant_enabled(cls) -> bool:
        from vllm.platforms.rocm import on_gfx950

        return cls._AITER_ENABLED and cls._FP4_GEMM_DYNAMIC_QUANT_ASM and on_gfx950()

    @classmethod
    @if_aiter_supported
    def is_triton_rotary_embed_enabled(cls) -> bool:
        return cls._AITER_ENABLED and cls._TRITON_ROTARY_EMBED

    @classmethod
    @if_aiter_supported
    def is_triton_sparse_mla_enabled(cls) -> bool:
        if not cls._TRITON_SPARSE_MLA:
            return False
        from vllm.platforms.rocm import on_gfx950

        if not cls._AITER_ENABLED:
            reason = "VLLM_ROCM_USE_AITER is off"
        elif not on_gfx950():
            reason = "the kernel is gfx950-only"
        else:
            return True
        logger.warning_once(
            "VLLM_ROCM_USE_AITER_TRITON_SPARSE_MLA is set, but %s; ignoring it.",
            reason,
        )
        return False

    @classmethod
    @if_aiter_supported
    def is_triton_gemm_enabled(cls) -> bool:
        return cls._AITER_ENABLED and cls._TRITON_UNQUANT_GEMM

    @classmethod
    @if_aiter_supported
    def is_tgemm_enabled(cls) -> bool:
        from vllm.platforms.rocm import on_gfx950

        return cls.is_linear_enabled() and on_gfx950()

    @classmethod
    def get_aiter_allreduce(cls):
        """Return the TP device communicator's AITER custom-allreduce if it has
        one, return None otherwise
        """
        from vllm.distributed.device_communicators.aiter_custom_all_reduce import (
            AiterCustomAllreduce,
        )
        from vllm.distributed.parallel_state import get_tp_group

        device_comm = get_tp_group().device_communicator
        aiter_ar_comm = getattr(device_comm, "aiter_ar_comm", None)
        return (
            aiter_ar_comm if isinstance(aiter_ar_comm, AiterCustomAllreduce) else None
        )

    @staticmethod
    def _gdn_triton_kernels_importable() -> bool:
        try:
            import aiter.ops.triton.causal_conv1d_update_single_token  # noqa: F401
            import aiter.ops.triton.gated_delta_net  # noqa: F401
            from aiter.ops.triton.quant import (  # noqa: F401
                fused_rms_gated_fp8_group_quant,
            )

            return True
        except (ImportError, ModuleNotFoundError):
            return False

    @staticmethod
    def _gdn_flydsl_prefill_kernels_importable() -> bool:
        try:
            from aiter.ops.flydsl.linear_attention_prefill_kernels import (  # noqa: F401
                chunk_gated_delta_rule_fwd_h_flydsl_opt,
                gdn_prepare_flydsl_supported,
                gdn_prepare_fwd_flydsl,
            )
            from aiter.ops.triton.gated_delta_net import (  # noqa: F401
                build_gated_delta_rule_prefill_metadata,
                chunk_gated_delta_rule_opt_vk,
            )

            return True
        except (ImportError, ModuleNotFoundError):
            return False

    @classmethod
    @if_aiter_supported
    def are_gdn_triton_kernels_available(cls) -> bool:
        """Check if AITER Triton kernels for GDN attention are importable.

        These are optional Triton kernels (conv1d fast-path, gated delta net)
        used by GatedDeltaNetAttention's decode fast-path.  They may be absent
        in older aiter builds.
        """
        return cls._AITER_ENABLED and cls._gdn_triton_kernels_importable()

    @classmethod
    @functools.cache
    def is_gdn_flydsl_prefill_available(cls) -> bool:
        """Whether the opt-in AITER FlyDSL GDN prefill path can be used.

        Selecting the backend is an explicit opt-in in itself, but it still
        runs AITER kernels, so VLLM_ROCM_USE_AITER remains the one switch that
        turns all of them off.
        """
        return (
            cls._AITER_ENABLED
            and is_aiter_found_and_supported()
            and cls._gdn_flydsl_prefill_kernels_importable()
        )

    @classmethod
    def build_gdn_flydsl_prefill_metadata(
        cls,
        seq_lens_cpu: list[int],
        cu_seqlens: torch.Tensor,
    ) -> object:
        """Build the reusable varlen metadata FlyDSL GDN prefill runs against.

        The K5 recurrence is compiled for a fixed 64-token chunk, so the chunk
        size is a property of the AITER kernel rather than of FLA, and is not
        the caller's to choose.
        """
        from aiter.ops.triton.gated_delta_net import (
            build_gated_delta_rule_prefill_metadata,
        )

        return build_gated_delta_rule_prefill_metadata(
            seq_lens_cpu,
            cu_seqlens=cu_seqlens,
            chunk_size=cls.GDN_FLYDSL_CHUNK_SIZE,
        )

    @classmethod
    def gdn_flydsl_prefill(
        cls,
        q: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
        g: torch.Tensor,
        beta: torch.Tensor,
        initial_state: torch.Tensor,
        output_final_state: bool,
        cu_seqlens: torch.Tensor | None,
        aiter_prefill_metadata: object | None,
        use_qk_l2norm_in_kernel: bool,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        """Run GDN prefill through AITER's VK path with the FlyDSL kernels.

        Every argument AITER takes is named here rather than forwarded as
        ``**kwargs``, so a rename or reordering on the AITER side fails at this
        call instead of silently binding to the wrong parameter.
        """
        from aiter.ops.flydsl.linear_attention_prefill_kernels import (
            gdn_prepare_flydsl_supported,
        )
        from aiter.ops.triton.gated_delta_net import chunk_gated_delta_rule_opt_vk

        if cu_seqlens is not None and aiter_prefill_metadata is None:
            raise RuntimeError(
                "AITER FlyDSL GDN prefill requires reusable varlen prefill metadata."
            )
        if not gdn_prepare_flydsl_supported(k, v):
            raise RuntimeError(
                "AITER FlyDSL GDN prepare does not support the runtime input shape, "
                "dtype, or device."
            )
        logger.info_once(
            "Dispatching AITER FlyDSL GDN prefill (prepare=flydsl, chunk=flydsl)."
        )
        return chunk_gated_delta_rule_opt_vk(
            q=q,
            k=k,
            v=v,
            g=g,
            beta=beta,
            initial_state=initial_state,
            output_final_state=output_final_state,
            cu_seqlens=cu_seqlens,
            use_chunk_flydsl=True,
            use_prepare_flydsl=True,
            state_dtype=initial_state.dtype,
            prefill_metadata=aiter_prefill_metadata,
            # The caller stages the state densely and writes it back, so the
            # kernel gets a plain [N, H, V, K] buffer and never an index into
            # the pool; there is correspondingly nothing to update in place.
            inplace_final_state=False,
            use_qk_l2norm_in_kernel=use_qk_l2norm_in_kernel,
        )

    @classmethod
    def is_rdna_gdn_triton_kernels_available(cls) -> bool:
        """RDNA4 (gfx12) analog of are_gdn_triton_kernels_available()."""
        return cls.is_rdna_aiter_enabled() and cls._gdn_triton_kernels_importable()

    @classmethod
    @if_aiter_supported
    @functools.cache
    def fused_moe_supports_gate_mode(cls) -> bool:
        """Probe whether the installed aiter.fused_moe accepts `gate_mode`.

        Added in https://github.com/ROCm/aiter/pull/3123 (>=0.1.14).
        Builds with older AITER must omit this argument.
        """
        import inspect

        from aiter.fused_moe import fused_moe

        return "gate_mode" in inspect.signature(fused_moe).parameters

    @classmethod
    @if_aiter_supported
    @functools.cache
    def fused_moe_supports_quant_dtype_a(cls) -> bool:
        """Probe whether the installed aiter.fused_moe accepts `quant_dtype_a`.

        Added in https://github.com/ROCm/aiter/pull/5439 (unreleased at
        merge time). Older AITER can't override the activation quant dtype.
        """
        import inspect

        from aiter.fused_moe import fused_moe

        return "quant_dtype_a" in inspect.signature(fused_moe).parameters

    @staticmethod
    def _probe_dsv4_i384_fhmoe_capability(num_tokens: int) -> bool:
        """Probe AITER's CSV-backed DSV4 native-I384 FHMoE contract."""
        if type(num_tokens) is not int or num_tokens <= 0:
            return False

        try:
            import inspect

            import aiter.fhmoe as fhmoe
            from aiter.fused_moe import fused_moe

            params = inspect.signature(fused_moe).parameters
            supports_dsv4_i384_fhmoe = getattr(fhmoe, "supports_dsv4_i384_fhmoe", None)
            if not callable(supports_dsv4_i384_fhmoe):
                return False
            supports_num_tokens = supports_dsv4_i384_fhmoe(num_tokens)
        except Exception:
            return False

        return supports_num_tokens is True and all(
            name in params
            for name in (
                "shared_w1",
                "shared_w2",
                "shared_w1_scale",
                "shared_w2_scale",
                "shared_expert_id",
            )
        )

    @classmethod
    @if_aiter_supported
    @functools.cache
    def fused_moe_supports_heterogeneous_shared_expert(cls, num_tokens: int) -> bool:
        """Whether AITER has DSV4 native-I384 configs through the given M."""
        return cls._probe_dsv4_i384_fhmoe_capability(num_tokens)

    @classmethod
    @if_aiter_supported
    def is_indexer_top_k_enabled(cls) -> bool:
        """gfx950 is the only arch with tuned AITER indexer top-k kernels."""
        from vllm.platforms.rocm import on_gfx950

        return cls._AITER_ENABLED and on_gfx950()

    @classmethod
    def is_indexer_top_k_supported(
        cls,
        *,
        is_prefill: bool,
        compress_ratio: int,
        num_rows: int,
        max_valid_seq_len: int | None = None,
    ) -> bool:
        """Whether AITER's sparse indexer top-k beats the in-tree kernel for
        this shape."""
        if compress_ratio <= 1 or not cls.is_indexer_top_k_enabled():
            return False

        if not is_prefill:
            assert max_valid_seq_len is not None
            # AITER v0.1.19 decode is one-block only. This measured gfx950
            # FP32/k=1024 compressed-row boundary is independent of the native
            # split-count boundary in sampler.cu.
            if (
                num_rows <= _GFX950_C4A_NATIVE_MAX_ROWS
                and max_valid_seq_len > _GFX950_C4A_AITER_MAX_COMPRESSED_SEQ_LEN
            ):
                return False

        return True

    @staticmethod
    def dsv4_indexer_prefers_native_top_k(
        *,
        num_rows: int,
        num_columns: int,
        topk_tokens: int,
    ) -> bool:
        """The in-tree decode kernel's measured advantage window over AITER.
        Tuned on DSV4's compressed-KV logits, so it applies to that indexer
        only."""
        return (
            topk_tokens == 512
            and 0 < num_rows <= 384
            and num_columns <= _GFX950_DSV4_NATIVE_MAX_COLUMNS
        )

    @staticmethod
    def indexer_top_k_decode(
        logits: torch.Tensor,
        next_n: int,
        seq_lens: torch.Tensor,
        topk_indices: torch.Tensor,
        topk_tokens: int,
    ) -> None:
        from aiter.ops.topk import top_k_per_row_decode

        top_k_per_row_decode(
            logits,
            next_n,
            seq_lens,
            topk_indices,
            logits.shape[0],
            logits.stride(0),
            logits.stride(1),
            k=topk_tokens,
        )

    @staticmethod
    def indexer_top_k_prefill(
        logits: torch.Tensor,
        row_starts: torch.Tensor,
        row_ends: torch.Tensor,
        indices: torch.Tensor,
        topk_tokens: int,
    ) -> None:
        from aiter.ops.topk import top_k_per_row_prefill

        top_k_per_row_prefill(
            logits,
            row_starts,
            row_ends,
            indices,
            None,
            logits.shape[0],
            logits.stride(0),
            logits.stride(1),
            k=topk_tokens,
        )

    @staticmethod
    def register_ops_once() -> None:
        global _OPS_REGISTERED

        if not (
            is_aiter_found_and_supported() or is_aiter_found_and_supported_on_rdna4()
        ):
            if not current_platform.is_rocm():
                return

            from vllm.platforms.rocm import on_gfx11, on_gfx950

            # This op has self-contained Triton/C++ implementations on gfx11
            # and gfx950.  Only its optional top-k fast path comes from aiter.
            if (on_gfx11() or on_gfx950()) and not _OPS_REGISTERED:
                direct_register_custom_op(
                    op_name="rocm_aiter_sparse_attn_indexer",
                    op_func=rocm_aiter_sparse_attn_indexer,
                    mutates_args=["topk_indices_buffer", "candidate_blocks"],
                    fake_impl=rocm_aiter_sparse_attn_indexer_fake,
                    dispatch_key=current_platform.dispatch_key,
                )
                _OPS_REGISTERED = True
            return

        if not _OPS_REGISTERED:
            # register all the custom ops here
            direct_register_custom_op(
                op_name="rocm_aiter_asm_moe_tkw1",
                op_func=_rocm_aiter_asm_moe_tkw1_impl,
                mutates_args=[],
                fake_impl=_rocm_aiter_asm_moe_tkw1_fake,
                dispatch_key=current_platform.dispatch_key,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_fused_moe",
                op_func=_rocm_aiter_fused_moe_impl,
                mutates_args=[],
                fake_impl=_rocm_aiter_fused_moe_fake,
                dispatch_key=current_platform.dispatch_key,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_topk_softmax",
                op_func=_rocm_aiter_topk_softmax_impl,
                mutates_args=["topk_weights", "topk_indices", "token_expert_indices"],
                dispatch_key=current_platform.dispatch_key,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_topk_sigmoid",
                op_func=_rocm_aiter_topk_sigmoid_impl,
                mutates_args=["topk_weights", "topk_indices"],
                dispatch_key=current_platform.dispatch_key,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_biased_grouped_topk",
                op_func=_rocm_aiter_biased_grouped_topk_impl,
                mutates_args=["topk_weights", "topk_ids"],
                dispatch_key=current_platform.dispatch_key,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_grouped_topk",
                op_func=_rocm_aiter_grouped_topk_impl,
                mutates_args=["topk_weights", "topk_ids"],
                dispatch_key=current_platform.dispatch_key,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_fused_topk",
                op_func=_rocm_aiter_fused_topk_impl,
                mutates_args=[],
                fake_impl=_rocm_aiter_fused_topk_fake,
                dispatch_key=current_platform.dispatch_key,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_gather_kv_b_proj",
                op_func=_rocm_aiter_gather_kv_b_proj_impl,
                mutates_args=["k", "v"],
                fake_impl=_rocm_aiter_gather_kv_b_proj_fake,
                dispatch_key=current_platform.dispatch_key,
            )
            direct_register_custom_op(
                op_name="rocm_aiter_mla_decode_fwd",
                op_func=_rocm_aiter_mla_decode_fwd_impl,
                mutates_args=["o"],
            )

            direct_register_custom_op(
                op_name="rocm_aiter_mla_decode_fwd_lse",
                op_func=_rocm_aiter_mla_decode_fwd_lse_impl,
                mutates_args=["o"],
                fake_impl=_rocm_aiter_mla_decode_fwd_lse_fake,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_w8a8_gemm",
                op_func=_rocm_aiter_w8a8_gemm_impl,
                fake_impl=_rocm_aiter_w8a8_gemm_fake,
            )

            direct_register_custom_op(
                op_name="_rocm_aiter_preshuffled_per_token_w8a8_gemm",
                op_func=_rocm_aiter_preshuffled_per_token_w8a8_gemm_impl,
                fake_impl=_rocm_aiter_preshuffled_per_token_w8a8_gemm_fake,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_hipb_mm_fp8",
                op_func=_rocm_aiter_hipb_mm_fp8_impl,
                fake_impl=_rocm_aiter_hipb_mm_fp8_fake,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_triton_gemm_a8w8_blockscale",
                op_func=_rocm_aiter_triton_gemm_a8w8_blockscale_impl,
                fake_impl=_rocm_aiter_triton_gemm_a8w8_blockscale_fake,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_gemm_a8w8_blockscale",
                op_func=_rocm_aiter_gemm_a8w8_blockscale_impl,
                fake_impl=_rocm_aiter_gemm_a8w8_blockscale_fake,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_gemm_a8w8_blockscale_bpreshuffle",
                op_func=_rocm_aiter_gemm_a8w8_blockscale_bpreshuffle_impl,
                fake_impl=_rocm_aiter_gemm_a8w8_blockscale_bpreshuffle_fake,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_rmsnorm_fused_dynamic_quant",
                op_func=_rocm_aiter_rmsnorm_fused_dynamic_quant_impl,
                fake_impl=_rocm_aiter_rmsnorm_fused_dynamic_quant_fake,
                dispatch_key=current_platform.dispatch_key,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_rmsnorm_fused_add_dynamic_quant",
                op_func=_rocm_aiter_rmsnorm_fused_add_dynamic_quant_impl,
                fake_impl=_rocm_aiter_rmsnorm_fused_add_dynamic_quant_fake,
                dispatch_key=current_platform.dispatch_key,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_rmsnorm_fp8_group_quant",
                op_func=_rocm_aiter_rmsnorm_fp8_group_quant_impl,
                fake_impl=_rocm_aiter_rmsnorm_fp8_group_quant_fake,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_fused_rms_gated_fp8_group_quant",
                op_func=_rocm_aiter_fused_rms_gated_fp8_group_quant_impl,
                fake_impl=_rocm_aiter_fused_rms_gated_fp8_group_quant_fake,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_rmsnorm_with_add_fp8_group_quant",
                op_func=_rocm_aiter_rmsnorm_with_add_fp8_group_quant_impl,
                fake_impl=_rocm_aiter_rmsnorm_with_add_fp8_group_quant_fake,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_act_mul_and_fp8_group_quant",
                op_func=_rocm_aiter_act_mul_and_fp8_group_quant_impl,
                fake_impl=_rocm_aiter_act_mul_and_fp8_group_quant_fake,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_triton_add_rmsnorm_pad",
                op_func=_rocm_aiter_triton_add_rmsnorm_pad_impl,
                fake_impl=_rocm_aiter_triton_add_rmsnorm_pad_fake,
                dispatch_key=current_platform.dispatch_key,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_group_fp8_quant",
                op_func=_rocm_aiter_group_fp8_quant_impl,
                fake_impl=_rocm_aiter_group_fp8_quant_fake,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_per_tensor_quant",
                op_func=_rocm_aiter_per_tensor_quant_impl,
                mutates_args=["out", "scale"],
                dispatch_key=current_platform.dispatch_key,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_per_token_quant",
                op_func=_rocm_aiter_per_token_quant_impl,
                fake_impl=_rocm_aiter_per_token_quant_fake,
                dispatch_key=current_platform.dispatch_key,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_sparse_attn_indexer",
                op_func=rocm_aiter_sparse_attn_indexer,
                mutates_args=["topk_indices_buffer", "candidate_blocks"],
                fake_impl=rocm_aiter_sparse_attn_indexer_fake,
                dispatch_key=current_platform.dispatch_key,
            )

            direct_register_custom_op(
                op_name="aiter_fp8_attn_wrapper",
                op_func=_rocm_aiter_fp8_attn_impl,
                mutates_args=[],
                fake_impl=_rocm_aiter_fp8_attn_fake,
                dispatch_key=current_platform.dispatch_key,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_gemm_a8wfp4",
                op_func=_rocm_aiter_gemm_a8wfp4_impl,
                mutates_args=[],
                fake_impl=_rocm_aiter_gemm_a8wfp4_fake,
                dispatch_key=current_platform.dispatch_key,
            )

            # Register rocm aiter rotary embedding custom op
            direct_register_custom_op(
                op_name="rocm_aiter_triton_rotary_embedding",
                op_func=_triton_rotary_embedding_impl,
                mutates_args=["query", "key"],  # These tensors are modified in-place
            )

            direct_register_custom_op(
                op_name="rocm_aiter_fused_allreduce_rmsnorm",
                op_func=_rocm_aiter_fused_allreduce_rmsnorm_impl,
                fake_impl=_rocm_aiter_fused_allreduce_rmsnorm_fake,
            )

            direct_register_custom_op(
                op_name="rocm_aiter_fused_allreduce_rmsnorm_quant_per_group",
                op_func=(_rocm_aiter_fused_allreduce_rmsnorm_quant_per_group_impl),
                fake_impl=(_rocm_aiter_fused_allreduce_rmsnorm_quant_per_group_fake),
            )

            direct_register_custom_op(
                op_name="rocm_aiter_fused_allreduce_rmsnorm_quant_per_group_with_bf16_norm",  # noqa: E501
                op_func=_rocm_aiter_fused_allreduce_rmsnorm_quant_per_group_with_bf16_norm_impl,  # noqa: E501
                fake_impl=_rocm_aiter_fused_allreduce_rmsnorm_quant_per_group_with_bf16_norm_fake,  # noqa: E501
            )

            direct_register_custom_op(
                op_name="fused_mla_dual_rms_norm",
                op_func=_fused_mla_dual_rms_norm_impl,
                mutates_args=[],
                fake_impl=_fused_mla_dual_rms_norm_fake,
            )

            direct_register_custom_op(
                op_name="fused_mla_dual_rms_norm_per_token_quant",
                op_func=_fused_mla_dual_rms_norm_per_token_quant_impl,
                mutates_args=[],
                fake_impl=_fused_mla_dual_rms_norm_per_token_quant_fake,
            )

            direct_register_custom_op(
                op_name="fused_mla_dual_rms_norm_group_quant",
                op_func=_fused_mla_dual_rms_norm_group_quant_impl,
                mutates_args=[],
                fake_impl=_fused_mla_dual_rms_norm_group_quant_fake,
            )

            _OPS_REGISTERED = True

    @staticmethod
    def get_rmsnorm_fused_add_dynamic_quant_op() -> OpOverload:
        return torch.ops.vllm.rocm_aiter_rmsnorm_fused_add_dynamic_quant.default

    @staticmethod
    def get_rmsnorm_fused_dynamic_quant_op() -> OpOverload:
        return torch.ops.vllm.rocm_aiter_rmsnorm_fused_dynamic_quant.default

    @staticmethod
    def get_rmsnorm_group_fused_quant_op() -> OpOverload:
        return torch.ops.vllm.rocm_aiter_rmsnorm_fp8_group_quant.default

    @staticmethod
    def get_fused_rms_gated_fp8_group_quant_op() -> OpOverload:
        """Return the fused gated-RMSNorm + FP8 group quant custom op."""
        return torch.ops.vllm.rocm_aiter_fused_rms_gated_fp8_group_quant.default

    @staticmethod
    def get_rmsnorm_group_add_fused_quant_op() -> OpOverload:
        return torch.ops.vllm.rocm_aiter_rmsnorm_with_add_fp8_group_quant.default

    @staticmethod
    def get_per_token_quant_op() -> OpOverload:
        return torch.ops.vllm.rocm_aiter_per_token_quant.default

    @staticmethod
    def get_group_quant_op() -> OpOverload:
        return torch.ops.vllm.rocm_aiter_group_fp8_quant.default

    @staticmethod
    def get_act_mul_fused_fp8_group_quant_op() -> OpOverload:
        return torch.ops.vllm.rocm_aiter_act_mul_and_fp8_group_quant.default

    @staticmethod
    def get_triton_add_rmsnorm_pad_op() -> OpOverload:
        return torch.ops.vllm.rocm_aiter_triton_add_rmsnorm_pad.default

    @staticmethod
    def get_triton_rotary_embedding_op() -> OpOverload:
        return torch.ops.vllm.rocm_aiter_triton_rotary_embedding.default

    @staticmethod
    def get_fused_allreduce_rmsnorm_op() -> OpOverload:
        return torch.ops.vllm.rocm_aiter_fused_allreduce_rmsnorm.default

    @staticmethod
    def get_fused_allreduce_rmsnorm_quant_per_group_op() -> OpOverload:
        return torch.ops.vllm.rocm_aiter_fused_allreduce_rmsnorm_quant_per_group.default

    @staticmethod
    def get_fused_allreduce_rmsnorm_quant_per_group_with_bf16_norm_op() -> OpOverload:  # noqa: E501
        return torch.ops.vllm.rocm_aiter_fused_allreduce_rmsnorm_quant_per_group_with_bf16_norm.default  # noqa: E501

    @staticmethod
    def get_fused_mla_dual_rms_norm_op() -> OpOverload:
        return torch.ops.vllm.fused_mla_dual_rms_norm.default

    @staticmethod
    def get_fused_mla_dual_rms_norm_per_token_quant_op() -> OpOverload:
        return torch.ops.vllm.fused_mla_dual_rms_norm_per_token_quant.default

    @staticmethod
    def fused_qk_rmsnorm_group_quant(
        q: torch.Tensor,
        q_weight: torch.Tensor,
        q_epsilon: float,
        kv: torch.Tensor,
        kv_weight: torch.Tensor,
        kv_epsilon: float,
        group_size: int = 128,
        transpose_scale: bool = False,
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """Fused q/kv RMSNorm + per-1x128 FP8 group quant on q via AITER."""
        return torch.ops.vllm.fused_mla_dual_rms_norm_group_quant(
            q,
            q_weight,
            kv,
            kv_weight,
            q_epsilon,
            kv_epsilon,
            group_size,
            transpose_scale,
        )

    @staticmethod
    def get_fused_mla_dual_rms_norm_group_quant_op() -> OpOverload:
        return torch.ops.vllm.fused_mla_dual_rms_norm_group_quant.default

    @staticmethod
    def rms_norm(
        x: torch.Tensor,
        weight: torch.Tensor,
        epsilon: float,
    ) -> torch.Tensor:
        """RMSNorm via AITER kernel."""
        import aiter

        return aiter.rms_norm(x, weight, epsilon)

    @staticmethod
    def rms_norm2d_with_add(
        x: torch.Tensor,
        residual: torch.Tensor,
        weight: torch.Tensor,
        epsilon: float,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        """Fused residual-add + RMSNorm via AITER kernel.

        Returns (normalized_output, residual_sum).
        """
        import aiter

        out = torch.empty_like(x)
        residual_out = torch.empty_like(x)
        aiter.rmsnorm2d_fwd_with_add(out, x, residual, residual_out, weight, epsilon, 0)
        return out, residual_out

    @staticmethod
    def w8a8_gemm(
        A: torch.Tensor,
        B: torch.Tensor,
        As: torch.Tensor,
        Bs: torch.Tensor,
        bias: torch.Tensor | None = None,
        output_dtype: torch.dtype = torch.float16,
    ) -> torch.Tensor:
        return torch.ops.vllm.rocm_aiter_w8a8_gemm(A, B, As, Bs, bias, output_dtype)

    @staticmethod
    def preshuffled_per_token_w8a8_gemm(
        A: torch.Tensor,
        B: torch.Tensor,
        As: torch.Tensor,
        Bs: torch.Tensor,
        bias: torch.Tensor | None = None,
        output_dtype: torch.dtype = torch.float16,
    ) -> torch.Tensor:
        return torch.ops.vllm._rocm_aiter_preshuffled_per_token_w8a8_gemm(
            A, B, As, Bs, bias, output_dtype
        )

    @staticmethod
    def hipb_mm_fp8(
        A: torch.Tensor,
        B: torch.Tensor,
        As: torch.Tensor,
        Bs: torch.Tensor,
        bias: torch.Tensor | None = None,
        output_dtype: torch.dtype = torch.bfloat16,
    ) -> torch.Tensor:
        return torch.ops.vllm.rocm_aiter_hipb_mm_fp8(A, B, As, Bs, bias, output_dtype)

    @staticmethod
    def triton_gemm_a8w8_blockscale(
        A: torch.Tensor,
        B: torch.Tensor,
        As: torch.Tensor,
        Bs: torch.Tensor,
        block_size: list[int],
        output_dtype: torch.dtype = torch.float16,
    ) -> torch.Tensor:
        return torch.ops.vllm.rocm_aiter_triton_gemm_a8w8_blockscale(
            A, B, As, Bs, output_dtype
        )

    @staticmethod
    def gemm_a8w8_blockscale(
        A: torch.Tensor,
        B: torch.Tensor,
        As: torch.Tensor,
        Bs: torch.Tensor,
        block_size: list[int],
        output_dtype: torch.dtype = torch.float16,
    ) -> torch.Tensor:
        return torch.ops.vllm.rocm_aiter_gemm_a8w8_blockscale(
            A, B, As, Bs, output_dtype
        )

    @staticmethod
    def gemm_a8w8_blockscale_bpreshuffle(
        A: torch.Tensor,
        B: torch.Tensor,
        As: torch.Tensor,
        Bs: torch.Tensor,
        output_dtype: torch.dtype = torch.float16,
    ) -> torch.Tensor:
        return torch.ops.vllm.rocm_aiter_gemm_a8w8_blockscale_bpreshuffle(
            A, B, As, Bs, output_dtype
        )

    @staticmethod
    def fused_moe(
        hidden_states: torch.Tensor,
        w1: torch.Tensor,
        w2: torch.Tensor,
        topk_weight: torch.Tensor,
        topk_ids: torch.Tensor,
        expert_mask: torch.Tensor | None = None,
        activation_method: int = 0,
        quant_method: int = 0,
        doweight_stage1: bool = False,
        w1_scale: torch.Tensor | None = None,
        w2_scale: torch.Tensor | None = None,
        a1_scale: torch.Tensor | None = None,
        a2_scale: torch.Tensor | None = None,
        num_local_tokens: torch.Tensor | None = None,
        output_dtype: torch.dtype | None = None,
        hidden_pad: int = 0,
        intermediate_pad: int = 0,
        gate_mode: str = "",
        bias1: torch.Tensor | None = None,
        bias2: torch.Tensor | None = None,
        moe_sorting_dispatch_policy: int = 0,
        swiglu_limit: float = 0.0,
        beta: float | None = None,
        linear_beta: float | None = None,
        shared_w1: torch.Tensor | None = None,
        shared_w2: torch.Tensor | None = None,
        shared_w1_scale: torch.Tensor | None = None,
        shared_w2_scale: torch.Tensor | None = None,
        shared_expert_id: int = -1,
        q_dtype_a: torch.dtype | None = None,
    ) -> torch.Tensor:
        return torch.ops.vllm.rocm_aiter_fused_moe(
            hidden_states,
            w1,
            w2,
            topk_weight,
            topk_ids,
            expert_mask,
            activation_method,
            quant_method,
            doweight_stage1,
            w1_scale,
            w2_scale,
            a1_scale,
            a2_scale,
            num_local_tokens,
            output_dtype,
            hidden_pad,
            intermediate_pad,
            gate_mode,
            bias1,
            bias2,
            moe_sorting_dispatch_policy,
            swiglu_limit,
            beta,
            linear_beta,
            shared_w1,
            shared_w2,
            shared_w1_scale,
            shared_w2_scale,
            shared_expert_id,
            q_dtype_a,
        )

    @staticmethod
    def asm_moe_tkw1(
        hidden_states: torch.Tensor,
        w1: torch.Tensor,
        w2: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        fc1_scale: torch.Tensor | None = None,
        fc2_scale: torch.Tensor | None = None,
        fc1_smooth_scale: torch.Tensor | None = None,
        fc2_smooth_scale: torch.Tensor | None = None,
        a16: bool = False,
        per_tensor_quant_scale: torch.Tensor | None = None,
        expert_mask: torch.Tensor | None = None,
        activation_method: int = 0,
    ) -> torch.Tensor:
        return torch.ops.vllm.rocm_aiter_asm_moe_tkw1(
            hidden_states,
            w1,
            w2,
            topk_weights,
            topk_ids,
            fc1_scale,
            fc2_scale,
            fc1_smooth_scale,
            fc2_smooth_scale,
            a16,
            per_tensor_quant_scale,
            expert_mask,
            activation_method,
        )

    @staticmethod
    def topk_softmax(
        topk_weights: torch.Tensor,
        topk_indices: torch.Tensor,
        token_expert_indices: torch.Tensor,
        gating_output: torch.Tensor,
        renormalize: bool,
        num_shared_experts: int = 0,
        shared_expert_scoring_func: str = "",
    ) -> tuple[torch.Tensor, ...]:
        torch.ops.vllm.rocm_aiter_topk_softmax(
            topk_weights,
            topk_indices,
            token_expert_indices,
            gating_output,
            renormalize,
            num_shared_experts,
            shared_expert_scoring_func,
        )
        return topk_weights, topk_indices

    @staticmethod
    def topk_sigmoid(
        topk_weights: torch.Tensor,
        topk_indices: torch.Tensor,
        token_expert_indices: torch.Tensor,
        gating_output: torch.Tensor,
        renormalize: bool,
    ) -> tuple[torch.Tensor, ...]:
        torch.ops.vllm.rocm_aiter_topk_sigmoid(
            topk_weights, topk_indices, gating_output
        )
        return topk_weights, topk_indices

    @staticmethod
    def biased_grouped_topk(
        gating_output: torch.Tensor,
        correction_bias: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        num_expert_group: int,
        topk_group: int,
        need_renorm: bool,
        routed_scaling_factor: float = 1.0,
    ) -> None:
        if correction_bias.dtype != gating_output.dtype:
            correction_bias = correction_bias.to(gating_output.dtype)
        torch.ops.vllm.rocm_aiter_biased_grouped_topk(
            gating_output,
            correction_bias,
            topk_weights,
            topk_ids,
            num_expert_group,
            topk_group,
            need_renorm,
            routed_scaling_factor,
        )

    @staticmethod
    def grouped_topk(
        gating_output: torch.Tensor,
        topk_weights: torch.Tensor,
        topk_ids: torch.Tensor,
        num_expert_group: int,
        topk_group: int,
        need_renorm: bool,
        scoring_func: str = "softmax",
        routed_scaling_factor: float = 1.0,
    ) -> None:
        torch.ops.vllm.rocm_aiter_grouped_topk(
            gating_output,
            topk_weights,
            topk_ids,
            num_expert_group,
            topk_group,
            need_renorm,
            scoring_func,
            routed_scaling_factor,
        )

    @staticmethod
    def fused_topk(
        x: torch.Tensor,
        router_logits: torch.Tensor,
        top_k: int,
        gate_up: bool,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        return torch.ops.vllm.rocm_aiter_fused_topk(x, router_logits, top_k, gate_up)

    @staticmethod
    def triton_sparse_mla_fwd(
        q: torch.Tensor,
        kv_buffer: torch.Tensor,
        o: torch.Tensor,
        sm_scale: float,
        kv_indptr: torch.Tensor,
        kv_indices: torch.Tensor,
        kv_lora_rank: int = 512,
        qk_rope_head_dim: int = 64,
        q_scale: torch.Tensor | None = None,
        kv_scale: torch.Tensor | None = None,
        attn_sink: torch.Tensor | None = None,
        extra_kv_buffer: torch.Tensor | None = None,
        extra_kv_indptr: torch.Tensor | None = None,
        extra_kv_indices: torch.Tensor | None = None,
        has_invalid: bool = True,
    ) -> None:
        """Sparse MLA read straight from the KV cache, for prefill and decode.

        kv_indices are ragged global slot ids. With has_invalid they may hold
        -1, which the kernel masks; a caller whose stream never holds a
        negative slot passes False, which lets the fp8 kernel stage K straight
        into LDS. The cache format (bf16, per-tensor fp8, or DeepSeek V4's
        paged fp8_ds_mla) is inferred from kv_buffer and kv_scale. An fp8 q
        must come with its q_scale and runs both dots in fp8. The extra segment
        is DeepSeek V4's second cache: SWA window in kv_buffer, top-k
        compressed tokens here.
        """
        from aiter.ops.triton.attention.sparse_mla import sparse_mla_fwd

        fp8_q = q.dtype == FP8_DTYPE
        sparse_mla_fwd(
            q,
            kv_buffer,
            kv_indptr,
            kv_indices,
            sm_scale,
            kv_scale=kv_scale,
            kv_lora_rank=kv_lora_rank,
            qk_rope_head_dim=qk_rope_head_dim,
            has_invalid=has_invalid,
            dot_precision="fp8" if fp8_q else "bf16",
            q_scale=q_scale if fp8_q else None,
            out=o,
            attn_sink=attn_sink,
            extra_kv=extra_kv_buffer,
            extra_indptr=extra_kv_indptr,
            extra_indices=extra_kv_indices,
        )

    @staticmethod
    def gather_kv_b_proj(
        kv_cache: torch.Tensor,
        kv_scale: torch.Tensor,
        kv_indptr: torch.Tensor,
        kv_indices: torch.Tensor,
        cu_seq_lens: torch.Tensor,
        weight: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
    ) -> None:
        torch.ops.vllm.rocm_aiter_gather_kv_b_proj(
            kv_cache, kv_scale, kv_indptr, kv_indices, cu_seq_lens, weight, k, v
        )

    @staticmethod
    def mla_decode_fwd(
        q: torch.Tensor,
        kv_buffer: torch.Tensor,
        o: torch.Tensor,
        sm_scale: float,
        qo_indptr: torch.Tensor,
        max_seqlen_qo: int,
        kv_indptr: torch.Tensor | None = None,
        kv_indices: torch.Tensor | None = None,
        kv_last_page_lens: torch.Tensor | None = None,
        logit_cap: float = 0.0,
        q_scale: torch.Tensor | None = None,
        kv_scale: torch.Tensor | None = None,
        work_meta_data: torch.Tensor | None = None,
        work_indptr: torch.Tensor | None = None,
        work_info_set: torch.Tensor | None = None,
        reduce_indptr: torch.Tensor | None = None,
        reduce_final_map: torch.Tensor | None = None,
        reduce_partial_map: torch.Tensor | None = None,
        causal: bool = True,
    ):
        torch.ops.vllm.rocm_aiter_mla_decode_fwd(
            q,
            kv_buffer.view(-1, 1, 1, q.shape[-1]),
            o,
            qo_indptr,
            max_seqlen_qo,
            kv_indptr,
            kv_indices,
            kv_last_page_lens,
            sm_scale=sm_scale,
            logit_cap=logit_cap,
            q_scale=q_scale,
            kv_scale=kv_scale,
            work_meta_data=work_meta_data,
            work_indptr=work_indptr,
            work_info_set=work_info_set,
            reduce_indptr=reduce_indptr,
            reduce_final_map=reduce_final_map,
            reduce_partial_map=reduce_partial_map,
            causal=causal,
        )

    @staticmethod
    def mla_decode_fwd_lse(
        q: torch.Tensor,
        kv_buffer: torch.Tensor,
        o: torch.Tensor,
        sm_scale: float,
        qo_indptr: torch.Tensor,
        max_seqlen_qo: int,
        kv_indptr: torch.Tensor | None = None,
        kv_indices: torch.Tensor | None = None,
        kv_last_page_lens: torch.Tensor | None = None,
        logit_cap: float = 0.0,
        q_scale: torch.Tensor | None = None,
        kv_scale: torch.Tensor | None = None,
    ) -> torch.Tensor:
        """Run MLA decode without persistent metadata and return its LSE."""
        return torch.ops.vllm.rocm_aiter_mla_decode_fwd_lse(
            q,
            kv_buffer.view(-1, 1, 1, q.shape[-1]),
            o,
            qo_indptr,
            max_seqlen_qo,
            kv_indptr,
            kv_indices,
            kv_last_page_lens,
            sm_scale=sm_scale,
            logit_cap=logit_cap,
            q_scale=q_scale,
            kv_scale=kv_scale,
        )

    @staticmethod
    def per_tensor_quant(
        x: torch.Tensor,
        quant_dtype: torch.dtype,
        scale: torch.Tensor | None = None,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        out = torch.empty_like(x, dtype=quant_dtype)
        is_dynamic = scale is None
        if is_dynamic:
            scale = torch.empty(1, dtype=torch.float32, device=x.device)
        torch.ops.vllm.rocm_aiter_per_tensor_quant(out, x, scale, is_dynamic)
        return out, scale

    @staticmethod
    def per_token_quant(
        x: torch.Tensor,
        quant_dtype: torch.dtype,
        scale: torch.Tensor | None = None,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        return torch.ops.vllm.rocm_aiter_per_token_quant(x, quant_dtype, scale)

    @staticmethod
    def gemm_a8wfp4(
        x: torch.Tensor,
        w: torch.Tensor,
        x_scales: torch.Tensor,
        w_scales: torch.Tensor,
        out_dtype: torch.dtype,
    ) -> torch.Tensor:
        return torch.ops.vllm.rocm_aiter_gemm_a8wfp4(
            x, w, x_scales, w_scales, out_dtype
        )

    @staticmethod
    def triton_fp4_gemm_dynamic_quant(
        x: torch.Tensor,
        weight: torch.Tensor,
        weight_scale: torch.Tensor,
        out_dtype: torch.dtype | None = torch.bfloat16,
        x_scales: torch.Tensor | None = None,
    ) -> torch.Tensor:
        from aiter.ops.triton.gemm_afp4wfp4 import gemm_afp4wfp4
        from aiter.ops.triton.quant import dynamic_mxfp4_quant

        if x_scales is None:
            x_q, x_s = dynamic_mxfp4_quant(x)
        else:
            x_q = x
            x_s = x_scales

        y = torch.empty(
            x_q.shape[0], weight.shape[0], device=x_q.device, dtype=out_dtype
        )

        gemm_afp4wfp4(x_q, weight, x_s, weight_scale.T, out_dtype, y)
        return y

    @staticmethod
    def fused_qk_norm_rope_and_cache(
        qkv: torch.Tensor,
        q_weight: torch.Tensor,
        k_weight: torch.Tensor,
        cos_sin_cache: torch.Tensor,
        positions: torch.Tensor,
        num_heads_q: int,
        num_heads_k: int,
        num_heads_v: int,
        head_dim: int,
        is_neox: bool,
        rms_norm_eps: float,
        q_out: torch.Tensor,
        k_cache: torch.Tensor,
        v_cache: torch.Tensor,
        slot_mapping: torch.Tensor,
        k_scale: torch.Tensor,
        v_scale: torch.Tensor,
        k_out: torch.Tensor | None,
        v_out: torch.Tensor | None,
        return_kv: bool,
        use_shuffle_layout: bool,
        block_size: int,
        x: int,
        rotary_dim: int = 0,
    ):
        from aiter.ops.fused_qk_norm_rope_cache_quant import (
            fused_qk_norm_rope_cache_pts_quant_shuffle,
        )

        fused_qk_norm_rope_cache_pts_quant_shuffle(
            qkv,
            q_weight,
            k_weight,
            cos_sin_cache,
            positions,
            qkv.size(0),
            num_heads_q,
            num_heads_k,
            num_heads_v,
            head_dim,
            is_neox,
            rms_norm_eps,
            q_out,
            k_cache,
            v_cache,
            slot_mapping,
            k_scale,
            v_scale,
            k_out,
            v_out,
            return_kv,
            use_shuffle_layout,
            block_size,
            x,
            rotary_dim,
        )

    @staticmethod
    def do_qk_norm_rope_kvcache_update(
        qkv: torch.Tensor,
        q_weight: torch.Tensor,
        k_weight: torch.Tensor,
        cos_sin_cache: torch.Tensor,
        positions: torch.Tensor,
        num_heads_q: int,
        num_heads_k: int,
        head_dim: int,
        is_neox: bool,
        rms_norm_eps: float,
        q_out: torch.Tensor,
        k_out: torch.Tensor,
        key_cache: torch.Tensor,
        value_cache: torch.Tensor,
        slot_mapping: torch.Tensor,
        k_scale: torch.Tensor,
        v_scale: torch.Tensor,
        kv_cache_dtype: str,
        use_shuffle_layout: bool,
    ) -> None:
        """Run the fused QK-norm+RoPE+KV-cache op on already-split k/v caches.

        Shared by the AITER FA and unified-attention impls. The caller splits
        kv_cache, since the unbind dim depends on the layout (e.g. the unified
        encoder-decoder path is K/V-first), and passes use_shuffle_layout
        (unified reads NHD and must pass False).
        """
        if kv_cache_dtype.startswith("fp8"):
            key_cache = key_cache.view(current_platform.fp8_dtype())
            value_cache = value_cache.view(current_platform.fp8_dtype())
        # Partial-rotary support (e.g. GLM-4.7 applies rotary to only a prefix
        # of each head's channel dim).
        rotary_dim = cos_sin_cache.shape[-1]
        kernel_rotary_dim = 0 if rotary_dim == head_dim else rotary_dim
        rocm_aiter_ops.fused_qk_norm_rope_and_cache(
            qkv=qkv,
            q_weight=q_weight,
            k_weight=k_weight,
            cos_sin_cache=cos_sin_cache,
            positions=positions,
            num_heads_q=num_heads_q,
            num_heads_k=num_heads_k,
            num_heads_v=num_heads_k,
            head_dim=head_dim,
            is_neox=is_neox,
            rms_norm_eps=rms_norm_eps,
            q_out=q_out,
            k_cache=key_cache,
            v_cache=value_cache,
            slot_mapping=slot_mapping,
            k_scale=k_scale,
            v_scale=v_scale,
            k_out=k_out,
            v_out=None,
            return_kv=True,
            use_shuffle_layout=use_shuffle_layout,
            block_size=key_cache.shape[1],
            x=16 // key_cache.element_size(),
            rotary_dim=kernel_rotary_dim,
        )

    @staticmethod
    def fused_qk_norm_mrope_and_cache(
        qkv: torch.Tensor,
        q_weight: torch.Tensor,
        k_weight: torch.Tensor,
        cos_sin_cache: torch.Tensor,
        positions: torch.Tensor,
        num_heads_q: int,
        num_heads_k: int,
        head_dim: int,
        is_neox: bool,
        mrope_section: list[int],
        is_interleaved: bool,
        rms_norm_eps: float,
        q_out: torch.Tensor,
        k_cache: torch.Tensor,
        v_cache: torch.Tensor,
        slot_mapping: torch.Tensor,
        k_scale: torch.Tensor,
        v_scale: torch.Tensor,
        block_size: int,
        x: int,
        rotary_dim: int = 0,
    ) -> None:
        from aiter.ops.fused_qk_norm_mrope_cache_quant import (
            fused_qk_norm_mrope_3d_cache_pts_quant_shuffle,
        )

        fused_qk_norm_mrope_3d_cache_pts_quant_shuffle(
            qkv,
            q_weight,
            k_weight,
            cos_sin_cache,
            positions,
            qkv.size(0),
            num_heads_q,
            num_heads_k,
            num_heads_k,
            head_dim,
            is_neox,
            mrope_section,
            is_interleaved,
            rms_norm_eps,
            q_out,
            k_cache,
            v_cache,
            slot_mapping,
            k_scale,
            v_scale,
            None,
            None,
            False,
            False,
            block_size,
            x,
            rotary_dim,
        )

    @staticmethod
    def do_qk_norm_mrope_kvcache_update(
        qkv: torch.Tensor,
        q_weight: torch.Tensor,
        k_weight: torch.Tensor,
        cos_sin_cache: torch.Tensor,
        positions: torch.Tensor,
        num_heads_q: int,
        num_heads_k: int,
        head_dim: int,
        is_neox: bool,
        mrope_section: list[int],
        is_interleaved: bool,
        rms_norm_eps: float,
        q_out: torch.Tensor,
        key_cache: torch.Tensor,
        value_cache: torch.Tensor,
        slot_mapping: torch.Tensor,
        k_scale: torch.Tensor,
        v_scale: torch.Tensor,
        kv_cache_dtype: str,
        rotary_dim: int = 0,
    ) -> None:
        if kv_cache_dtype.startswith("fp8"):
            key_cache = key_cache.view(current_platform.fp8_dtype())
            value_cache = value_cache.view(current_platform.fp8_dtype())

        rocm_aiter_ops.fused_qk_norm_mrope_and_cache(
            qkv=qkv,
            q_weight=q_weight,
            k_weight=k_weight,
            cos_sin_cache=cos_sin_cache,
            positions=positions,
            num_heads_q=num_heads_q,
            num_heads_k=num_heads_k,
            head_dim=head_dim,
            is_neox=is_neox,
            mrope_section=mrope_section,
            is_interleaved=is_interleaved,
            rms_norm_eps=rms_norm_eps,
            q_out=q_out,
            k_cache=key_cache,
            v_cache=value_cache,
            slot_mapping=slot_mapping,
            k_scale=k_scale,
            v_scale=v_scale,
            block_size=key_cache.shape[1],
            x=16 // key_cache.element_size(),
            rotary_dim=rotary_dim,
        )

    @staticmethod
    def triton_rope_and_cache(
        query: torch.Tensor,
        key: torch.Tensor,
        value: torch.Tensor,
        positions: torch.Tensor,
        cos_sin_cache: torch.Tensor,
        is_neox: bool,
        key_cache: torch.Tensor,
        value_cache: torch.Tensor,
        layer_slot_mapping: torch.Tensor,
        k_scale: torch.Tensor,
        v_scale: torch.Tensor,
        flash_layout: bool,
        apply_scale: bool,
    ):
        from aiter.ops.triton.fused_kv_cache import fused_qk_rope_reshape_and_cache

        cos, sin = cos_sin_cache.chunk(2, dim=-1)
        fused_qk_rope_reshape_and_cache(
            query,
            key,
            value,
            key_cache,
            value_cache,
            layer_slot_mapping,
            positions,
            cos,
            sin,
            k_scale,
            v_scale,
            is_neox,
            flash_layout=flash_layout,
            apply_scale=apply_scale,
            q_out=query,
            k_out=key,
            output_zeros=False,
        )

    @staticmethod
    def batched_gemm_a16wfp4(
        X: torch.Tensor,
        W: torch.Tensor,
        w_scale: torch.Tensor,
        Y: torch.Tensor | None = None,
        transpose_bm: bool | None = False,
        prequant: bool | None = False,
        y_scale: torch.Tensor | None = None,
    ) -> torch.Tensor:
        # ruff: noqa: E501 # isort: skip
        from aiter.ops.triton.batched_gemm_a16wfp4 import batched_gemm_a16wfp4

        return batched_gemm_a16wfp4(
            X,
            W,
            w_scale,
            y=Y,
            transpose_bm=transpose_bm,
            prequant=prequant,
            y_scale=y_scale,
        )

    @staticmethod
    def triton_fp8_bmm(
        X: torch.Tensor,
        WQ: torch.Tensor,
        w_scale: torch.Tensor,
        group_size: int = 128,
        bias: torch.Tensor | None = None,
        dtype: torch.dtype | None = torch.bfloat16,
        splitK: int | None = None,
        YQ: torch.Tensor | None = None,
        transpose_bm: bool | None = False,
        config: dict | None = None,
    ) -> torch.Tensor:
        # ruff: noqa: E501 # isort: skip
        from aiter.ops.triton.batched_gemm_a8w8_a_per_token_group_prequant_w_per_batched_tensor_quant import (
            batched_gemm_a8w8_a_per_token_group_prequant_w_per_batched_tensor_quant as aiter_triton_fp8_bmm,
        )

        return aiter_triton_fp8_bmm(
            X,
            WQ,
            w_scale,
            group_size=group_size,
            bias=bias,
            dtype=dtype,
            splitK=splitK,
            YQ=YQ,
            transpose_bm=transpose_bm,
            config=config,
        )

    @staticmethod
    def group_fp8_quant(
        input_2d: torch.Tensor,
        group_size: int = 128,
        transpose_scale: bool = False,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        assert group_size == 128, "Group size must be 128"
        return torch.ops.vllm.rocm_aiter_group_fp8_quant(
            input_2d, group_size, transpose_scale
        )

    @staticmethod
    @functools.cache
    def is_triton_gemm_w8a8_tuned(n: int, k: int) -> bool:
        if not current_platform.is_rocm():
            return False
        try:
            return _triton_gemm_config_is_tuned("GEMM-A8W8_BLOCKSCALE", n, k)
        except (AssertionError, ImportError):
            return False

    @staticmethod
    @functools.cache
    def is_triton_gemm_afp4wfp4_presh_ws_tuned(n: int, k_bytes: int) -> bool:
        if not current_platform.is_rocm():
            return False
        # Input = weight.shape[1] (bytes); *4 for fp4 and aiter config 2*k
        try:
            return _triton_gemm_config_is_tuned(
                "GEMM-AFP4WFP4_PRESHUFFLED", n, 4 * k_bytes
            )
        except (AssertionError, ImportError):
            return False

    @staticmethod
    @functools.cache
    def is_shuffled_per_token_w8a8_gemm_tuned(
        N: int, K: int, q_dtype_w: torch.dtype
    ) -> bool:
        return _ck_gemm_shape_is_tuned(
            N, K, q_dtype_w, "AITER_CONFIG_GEMM_A8W8_BPRESHUFFLE_FILE"
        )

    @staticmethod
    @functools.cache
    def is_per_token_w8a8_gemm_tuned(N: int, K: int, q_dtype_w: torch.dtype) -> bool:
        return _ck_gemm_shape_is_tuned(N, K, q_dtype_w, "AITER_CONFIG_GEMM_A8W8_FILE")

    @staticmethod
    def shuffle_weight(
        tensor: torch.Tensor, layout: tuple[int, int] = (16, 16)
    ) -> torch.Tensor:
        from aiter.ops.shuffle import shuffle_weight

        return shuffle_weight(tensor, layout=layout)

    @staticmethod
    def shuffle_weight_a16w4(
        tensor: "torch.Tensor",
        nLane: int,
        gate_up: bool,
    ) -> "torch.Tensor":
        """Shuffles the weight tensor into (A16W4) layout for AITER kernels.

        Args:
            tensor: The input weight tensor to be shuffled.
            nLane: Number of lanes in the shuffle layout.
            gate_up: Whether the weight is for w13 (True) or w2 (False).

        Returns:
            torch.Tensor: The shuffled tensor.

        """
        from aiter.ops.shuffle import shuffle_weight_a16w4

        return shuffle_weight_a16w4(tensor, nLane, gate_up)

    @staticmethod
    def shuffle_scale_a16w4(
        tensor: "torch.Tensor",
        num_experts: int,
        gate_up: bool,
    ) -> "torch.Tensor":
        """Shuffles the scale tensor into (A16W4) layout for AITER kernels.

        Args:
            tensor: The input scale tensor to be shuffled.
            num_experts: Number of experts, needed for reshaping logic.
            gate_up: Whether the scale is for w13 (True) or w2 (False).

        Returns:
            torch.Tensor: The shuffled scale tensor.

        """
        from aiter.ops.shuffle import shuffle_scale_a16w4

        return shuffle_scale_a16w4(tensor, num_experts, gate_up)

    @staticmethod
    def shuffle_scale(tensor: torch.Tensor) -> torch.Tensor:
        """Shuffle a non-gate/up E8M0 scale tensor for AITER."""
        from aiter.ops.shuffle import shuffle_scale

        return shuffle_scale(tensor)

    @staticmethod
    def shuffle_weights(
        *tensors: torch.Tensor, layout: tuple[int, int] = (16, 16)
    ) -> tuple[torch.Tensor, ...]:
        """Applies shuffle_weight function from AITER to each
        input tensor and returns them.

        Rearranges (shuffles) the input tensor/s
        into a specified block layout for optimized computation.

        Args:
            *tensors: Variable number of torch.Tensor objects.
            layout: A pair of integers specifying the block sizes used to divide
                the tensors during shuffling. Default is (16, 16).

        Returns:
        A Tuple of shuffled tensors.

        """
        from aiter.ops.shuffle import shuffle_weight

        return tuple(shuffle_weight(tensor, layout=layout) for tensor in tensors)

    @staticmethod
    def shuffle_mxfp8_moe_weights(
        w13: torch.Tensor,
        w2: torch.Tensor,
        w13_scale: torch.Tensor,
        w2_scale: torch.Tensor,
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
        """Preshuffle MXFP8 MoE weights + E8M0 scales into AITER's FlyDSL layout:
        gate/up-interleaved weights, interleaved scale for w13 (gate/up), plain
        scale for w2 (the interleaved variant is gate/up-only and misaligns w2).
        """
        from aiter.ops.shuffle import shuffle_scale, shuffle_weight

        num_experts = w13.shape[0]
        w13 = shuffle_weight(w13, is_guinterleave=True, gate_up=True)
        w2 = shuffle_weight(w2, is_guinterleave=True, gate_up=False)
        w13_scale = shuffle_scale(
            w13_scale.reshape(-1, w13_scale.shape[-1]),
            num_experts,
            is_guinterleave=True,
            gate_up=True,
        )
        w2_scale = shuffle_scale(w2_scale.reshape(-1, w2_scale.shape[-1]))
        return w13, w2, w13_scale, w2_scale

    @staticmethod
    def flash_attn_varlen_func(
        q: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
        cu_seqlens_q: torch.Tensor,
        cu_seqlens_k: torch.Tensor,
        max_seqlen_q: int,
        max_seqlen_k: int,
        min_seqlen_q: int | None = None,
        dropout_p: float = 0.0,
        softmax_scale: float | None = None,
        causal: bool = False,
        window_size: tuple[int, int] | None = None,
        alibi_slopes: torch.Tensor | None = None,
        return_lse: bool = False,
        out: torch.Tensor | None = None,
        sink_ptr: torch.Tensor | None = None,
    ):
        """Flash attention with variable length sequences.

        This function is NOT wrapped with @is_aiter_supported decorator
        to allow explicit backend selection via attention_config to work
        even when VLLM_ROCM_USE_AITER=0.

        Note: This performs lazy import of aiter.flash_attn_varlen_func
        """
        from aiter import flash_attn_varlen_func

        return flash_attn_varlen_func(
            q=q,
            k=k,
            v=v,
            cu_seqlens_q=cu_seqlens_q,
            cu_seqlens_k=cu_seqlens_k,
            max_seqlen_q=max_seqlen_q,
            max_seqlen_k=max_seqlen_k,
            min_seqlen_q=min_seqlen_q,
            dropout_p=dropout_p,
            softmax_scale=softmax_scale,
            causal=causal,
            window_size=window_size,
            alibi_slopes=alibi_slopes,
            return_lse=return_lse,
            out=out,
            sink_ptr=sink_ptr,
        )

    @staticmethod
    def fp8_attn_wrapper(
        q: torch.Tensor,
        k: torch.Tensor,
        v: torch.Tensor,
        q_descale: torch.Tensor,
        k_descale: torch.Tensor,
        v_descale: torch.Tensor,
        batch_size: int,
        output_dtype: torch.dtype,
        scale: float | None = None,
        cu_seqlens: torch.Tensor | None = None,
        max_seqlen: torch.Tensor | None = None,
    ) -> torch.Tensor:
        return torch.ops.vllm.aiter_fp8_attn_wrapper(
            q,
            k,
            v,
            q_descale,
            k_descale,
            v_descale,
            batch_size,
            output_dtype,
            scale,
            cu_seqlens,
            max_seqlen,
        )

    @staticmethod
    def pa_fwd_asm(
        Q: torch.Tensor,
        K: torch.Tensor,
        V: torch.Tensor,
        block_tables: torch.Tensor,
        context_lens: torch.Tensor,
        block_tables_stride0: int,
        K_QScale: torch.Tensor,
        V_QScale: torch.Tensor,
        out_: torch.Tensor,
    ):
        """Paged attention forward pass using assembly kernel.

        This function is NOT wrapped with @is_aiter_supported decorator
        to allow explicit backend selection via attention_config to work
        even when VLLM_ROCM_USE_AITER=0.

        Note: This performs lazy import of aiter.pa_fwd_asm
        """
        from aiter import pa_fwd_asm

        return pa_fwd_asm(
            Q=Q,
            K=K,
            V=V,
            block_tables=block_tables,
            context_lens=context_lens,
            block_tables_stride0=block_tables_stride0,
            K_QScale=K_QScale,
            V_QScale=V_QScale,
            out_=out_,
        )

    @staticmethod
    def paged_attention_common(
        Q: torch.Tensor,
        K: torch.Tensor,
        V: torch.Tensor,
        tmp_out: torch.Tensor,
        max_logits: torch.Tensor,
        exp_sums: torch.Tensor,
        max_seq_len: int,
        block_tables: torch.Tensor,
        context_lens: torch.Tensor,
        block_tables_stride0: int,
        scale: float,
        K_QScale_hip: torch.Tensor,
        V_QScale_hip: torch.Tensor,
        K_QScale_asm: torch.Tensor,
        V_QScale_asm: torch.Tensor,
        out_: torch.Tensor,
        kv_cache_dtype: str,
    ):
        """Paged attention common function.

        This function is NOT wrapped with @is_aiter_supported decorator
        to allow explicit backend selection via attention_config to work
        even when VLLM_ROCM_USE_AITER=0.

        Note: This performs lazy import of aiter.paged_attention_common
        """
        from aiter import paged_attention_common

        return paged_attention_common(
            Q=Q,
            K=K,
            V=V,
            tmp_out=tmp_out,
            max_logits=max_logits,
            exp_sums=exp_sums,
            max_seq_len=max_seq_len,
            block_tables=block_tables,
            context_lens=context_lens,
            block_tables_stride0=block_tables_stride0,
            scale=scale,
            K_QScale_hip=K_QScale_hip,
            V_QScale_hip=V_QScale_hip,
            K_QScale_asm=K_QScale_asm,
            V_QScale_asm=V_QScale_asm,
            out_=out_,
            kv_cache_dtype=kv_cache_dtype,
        )

    @staticmethod
    def mhc_pre(
        residual: torch.Tensor,
        fn: torch.Tensor,
        hc_scale: torch.Tensor,
        hc_base: torch.Tensor,
        rms_eps: float,
        hc_pre_eps: float,
        hc_sinkhorn_eps: float,
        hc_post_mult_value: float,
        sinkhorn_repeat: int,
        norm_weight: torch.Tensor | None = None,
        norm_eps: float = 0.0,
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """Forward pass for mHC pre block.

        Args:
            residual: shape (..., hc_mult, hidden_size), dtype torch.bfloat16
            fn: shape (hc_mult3, hc_mult * hidden_size), dtype torch.float32
            hc_scale: shape (3,), dtype torch.float32
            hc_base: shape (hc_mult3,), dtype torch.float32
            rms_eps: RMS normalization epsilon
            hc_pre_eps: pre-mix epsilon
            hc_sinkhorn_eps: sinkhorn epsilon
            hc_post_mult_value: post-mix multiplier value
            sinkhorn_repeat: number of sinkhorn iterations
            norm_weight: optional RMSNorm weight fused into the pre kernel
            norm_eps: epsilon for the fused RMSNorm when norm_weight is set

        Returns:
            post_mix: shape (..., hc_mult), dtype torch.float32
            comb_mix: shape (..., hc_mult, hc_mult), dtype torch.float32
            layer_input: shape (..., hidden_size), dtype torch.bfloat16

        """
        from aiter.ops.mhc import mhc_pre

        # Validate shapes
        assert residual.dtype == torch.bfloat16
        assert fn.dtype == torch.float32
        assert hc_scale.dtype == torch.float32
        assert hc_base.dtype == torch.float32

        hc_mult = residual.shape[-2]
        hidden_size = residual.shape[-1]
        hc_mult2 = hc_mult * hc_mult
        hc_mult3 = hc_mult * 2 + hc_mult2

        hc_hidden_size = hc_mult * hidden_size
        assert fn.shape[0] == hc_mult3
        assert fn.shape[1] == hc_hidden_size
        assert hc_scale.shape == (3,)
        assert hc_base.shape == (hc_mult3,)

        outer_shape = residual.shape[:-2]

        residual_flat = residual.view(-1, hc_mult, hidden_size)

        num_tokens = residual_flat.shape[0]
        if num_tokens == 0:
            return (
                torch.empty(
                    num_tokens,
                    hc_mult,
                    1,
                    dtype=torch.float32,
                    device=residual_flat.device,
                ),
                torch.empty(
                    num_tokens,
                    hc_mult,
                    hc_mult,
                    dtype=torch.float32,
                    device=residual_flat.device,
                ),
                torch.empty(
                    num_tokens,
                    hidden_size,
                    dtype=torch.bfloat16,
                    device=residual_flat.device,
                ),
            )

        # AITER's Python wrapper allocates intermediate/output tensors without
        # explicit device arguments, so run it under the residual tensor's device.
        with torch.device(residual_flat.device):
            args = (
                residual_flat,
                fn,
                hc_scale,
                hc_base,
                rms_eps,
                hc_pre_eps,
                hc_sinkhorn_eps,
                hc_post_mult_value,
                sinkhorn_repeat,
            )
            if norm_weight is None:
                post_mix, comb_mix, layer_input = mhc_pre(*args)
            else:
                post_mix, comb_mix, layer_input = mhc_pre(*args, norm_weight, norm_eps)
        return (
            post_mix.view(*outer_shape, hc_mult, 1),
            comb_mix.view(*outer_shape, hc_mult, hc_mult),
            layer_input.view(*outer_shape, hidden_size),
        )

    @staticmethod
    def mhc_pre_delayed(
        residual: torch.Tensor,
        fn: torch.Tensor,
        hc_scale: torch.Tensor,
        hc_base: torch.Tensor,
        rms_eps: float,
        hc_pre_eps: float,
        hc_sinkhorn_eps: float,
        hc_post_mult_value: float,
        sinkhorn_repeat: int,
        pre_mix: torch.Tensor | None = None,
        sublayer_out: torch.Tensor | None = None,
        post_layer_mix: torch.Tensor | None = None,
        comb_res_mix: torch.Tensor | None = None,
        residual_out: torch.Tensor | None = None,
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
        """MHC pre using the pre-mix carried from the previous sublayer.

        Same gates as :meth:`mhc_pre`, but the stream collapse applies the
        caller's ``pre_mix`` instead of the one computed here, and the one
        computed here is returned for the next sublayer seam. AITER has no
        single kernel for that shape, so drive its two stages directly:
        ``mhc_pre_big_fuse`` still produces the post and comb gates (including
        every sinkhorn iteration), while the pre gate is recovered from the
        same split-k GEMM output and the collapse is done by the Triton
        kernel. ``mhc_pre_big_fuse`` also writes a collapse we do not use;
        that redundant store is the price of not having a native delayed
        kernel, and is small next to the ~140 launches it replaces.

        Passing ``sublayer_out`` / ``post_layer_mix`` / ``comb_res_mix``
        also applies the preceding post block. AITER then projects the new
        residual in the same kernel that computes it
        (``mhc_fused_post_pre_gemm_sqrsum``), which saves one launch; every
        later stage is identical either way. That new residual is written to
        ``residual_out``, which the caller must supply in that case; it is an
        output buffer rather than a return value so the op never has to hand
        back one of its own inputs on the unfused path.

        Returns:
            post_mix: shape (..., hc_mult, 1), dtype torch.float32
            comb_mix: shape (..., hc_mult, hc_mult), dtype torch.float32
            layer_input: shape (..., hidden_size), dtype torch.bfloat16
            next_pre_mix: shape (..., hc_mult), dtype torch.float32

        """
        from aiter.ops.mhc import (
            get_mhc_fused_post_pre_config,
            get_mhc_pre_splitk,
            mhc_fused_post_pre_gemm_sqrsum,
            mhc_pre_gemm_sqrsum,
        )

        assert residual.dtype == torch.bfloat16
        assert fn.dtype == torch.float32
        assert hc_scale.dtype == torch.float32
        assert hc_base.dtype == torch.float32

        hc_mult = residual.shape[-2]
        hidden_size = residual.shape[-1]
        hc_mult3 = hc_mult * 2 + hc_mult * hc_mult
        hc_hidden_size = hc_mult * hidden_size

        assert fn.shape == (hc_mult3, hc_hidden_size)
        assert hc_scale.shape == (3,)
        assert hc_base.shape == (hc_mult3,)

        outer_shape = residual.shape[:-2]
        residual_flat = residual.view(-1, hc_mult, hidden_size)
        num_tokens = residual_flat.shape[0]
        device = residual_flat.device

        if num_tokens == 0:
            return (
                torch.empty(0, hc_mult, 1, dtype=torch.float32, device=device),
                torch.empty(0, hc_mult, hc_mult, dtype=torch.float32, device=device),
                torch.empty(0, hidden_size, dtype=torch.bfloat16, device=device),
                torch.empty(0, hc_mult, dtype=torch.float32, device=device),
            )

        pre_mix_flat = None if pre_mix is None else pre_mix.view(-1, hc_mult)

        fuse_post = sublayer_out is not None
        # AITER's Python wrappers allocate without explicit device arguments.
        with torch.device(device):
            if fuse_post:
                # Keyed on the per-stream K, i.e. hidden_size, not hc_mult * it.
                splitk, tile_m, tile_n, tile_k = get_mhc_fused_post_pre_config(
                    num_tokens, hidden_size
                )
            else:
                splitk, tile_k = get_mhc_pre_splitk(num_tokens, hc_hidden_size)
            # AITER pads the GEMM output to a multiple of 32 columns.
            gemm_pad = torch.empty(
                splitk,
                num_tokens,
                (hc_mult3 + 31) // 32 * 32,
                dtype=torch.float32,
                device=device,
            )
            gemm_out = gemm_pad[:, :, :hc_mult3]
            sqrsum = torch.empty(splitk, num_tokens, dtype=torch.float32, device=device)
            if fuse_post:
                # Restated for the type checker, which cannot narrow the
                # optional arguments through the fuse_post flag.
                assert sublayer_out is not None
                assert post_layer_mix is not None and comb_res_mix is not None
                assert residual_out is not None
                projected = residual_out.view(-1, hc_mult, hidden_size)
                mhc_fused_post_pre_gemm_sqrsum(
                    gemm_out,
                    sqrsum,
                    projected,
                    sublayer_out.view(-1, hidden_size),
                    residual_flat,
                    # The kernel wants the post mix squeezed to (tokens, hc_mult).
                    post_layer_mix.view(-1, hc_mult, 1).squeeze(-1),
                    comb_res_mix.view(-1, hc_mult, hc_mult),
                    fn,
                    tile_m,
                    tile_n,
                    tile_k,
                    0,
                )
            else:
                projected = residual_flat
                mhc_pre_gemm_sqrsum(gemm_out, sqrsum, projected, fn, tile_k, 0)

        post_mix, comb_mix, layer_input, next_pre_mix = _mhc_delayed_pre_tail(
            gemm_out,
            sqrsum,
            projected,
            hc_scale,
            hc_base,
            pre_mix_flat,
            rms_eps,
            hc_pre_eps,
            hc_sinkhorn_eps,
            hc_post_mult_value,
            sinkhorn_repeat,
        )

        return (
            post_mix.view(*outer_shape, hc_mult, 1),
            comb_mix.view(*outer_shape, hc_mult, hc_mult),
            layer_input.view(*outer_shape, hidden_size),
            next_pre_mix.view(*outer_shape, hc_mult),
        )

    @staticmethod
    def mhc_fused_post_pre_delayed_rms_norm(
        residual: torch.Tensor,
        fn: torch.Tensor,
        hc_scale: torch.Tensor,
        hc_base: torch.Tensor,
        rms_eps: float,
        hc_pre_eps: float,
        hc_sinkhorn_eps: float,
        hc_post_mult_value: float,
        sinkhorn_repeat: int,
        pre_mix: torch.Tensor | None,
        sublayer_out: torch.Tensor | None,
        post_layer_mix: torch.Tensor | None,
        comb_res_mix: torch.Tensor | None,
        norm_weight: torch.Tensor,
        norm_eps: float,
        residual_out: torch.Tensor | None,
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
        """:meth:`mhc_pre_delayed` with the RMSNorm of the collapse folded in.

        Runs aiter's fused Triton kernel ``mhc_fused_post_pre_delayed_rmsnorm``;
        the returned ``layer_input`` is already normalised with ``norm_weight`` /
        ``norm_eps``.
        """
        from aiter.ops.triton.fusions.mhc_fused_post_pre_delayed_rmsnorm import (
            mhc_fused_post_pre_delayed_rmsnorm,
        )

        _, post_mix, comb_mix, layer_input, next_pre_mix = (
            mhc_fused_post_pre_delayed_rmsnorm(
                residual,
                fn,
                hc_scale,
                hc_base,
                rms_eps,
                hc_pre_eps,
                hc_sinkhorn_eps,
                hc_post_mult_value,
                sinkhorn_repeat,
                pre_mix,
                sublayer_out,
                post_layer_mix,
                comb_res_mix,
                norm_weight,
                norm_eps,
                residual_out=residual_out,
            )
        )
        return post_mix, comb_mix, layer_input, next_pre_mix

    @staticmethod
    def mhc_fused_post_pre_delayed_prefers_unfused(num_tokens: int) -> bool:
        """True when AITER's own heuristic favours separate post and pre.

        Mirrors the ``fused_m_upper_bound`` table in ``aiter.ops.mhc``: the
        fused GEMM wins for decode-sized batches and loses for large ones.
        """
        from aiter.jit.utils.chip_info import get_gfx_runtime

        return num_tokens >= {
            "gfx950": 1024,
            "gfx942": 128,
            "gfx1250": 1024,
        }.get(get_gfx_runtime(), 1024)

    @staticmethod
    def hc_head(
        hs_flat: torch.Tensor,
        fn: torch.Tensor,
        hc_scale: torch.Tensor,
        hc_base: torch.Tensor,
        out: torch.Tensor,
        hidden_size: int,
        rms_eps: float,
        hc_eps: float,
        hc_mult: int,
    ) -> None:
        """Run hc_head through AITER mhc_pre and write the result to out."""
        assert hs_flat.dtype == torch.bfloat16
        assert fn.dtype == torch.float32
        assert hc_scale.dtype == torch.float32
        assert hc_base.dtype == torch.float32
        assert hs_flat.shape[-2:] == (hc_mult, hidden_size)
        assert fn.shape == (hc_mult, hc_mult * hidden_size)
        assert hc_scale.shape == (1,)
        assert hc_base.shape == (hc_mult,)

        num_tokens = hs_flat.shape[0]
        if num_tokens == 0:
            return

        hc_mult3 = hc_mult * 2 + hc_mult * hc_mult

        full_fn = torch.zeros(
            hc_mult3,
            hc_mult * hidden_size,
            dtype=fn.dtype,
            device=fn.device,
        )
        full_fn[:hc_mult] = fn

        full_base = torch.zeros(hc_mult3, dtype=hc_base.dtype, device=hc_base.device)
        full_base[:hc_mult] = hc_base

        full_scale = torch.zeros(3, dtype=hc_scale.dtype, device=hc_scale.device)
        full_scale[0] = hc_scale[0]

        _, _, layer_input = rocm_aiter_ops.mhc_pre(
            hs_flat,
            full_fn,
            full_scale,
            full_base,
            rms_eps,
            hc_eps,
            0.0,
            1.0,
            0,
        )
        out.copy_(layer_input)

    @staticmethod
    def mhc_post(
        x: torch.Tensor,
        residual: torch.Tensor,
        post_layer_mix: torch.Tensor,
        comb_res_mix: torch.Tensor,
    ) -> torch.Tensor:
        from aiter.ops.mhc import mhc_post

        hc_mult = residual.shape[-2]
        hidden_size = residual.shape[-1]
        residual_flat = residual.view(-1, hc_mult, hidden_size)
        num_tokens = residual_flat.shape[0]
        out = torch.empty_like(residual_flat)
        mhc_post(
            out,
            x.view(num_tokens, hidden_size),
            residual_flat,
            post_layer_mix.view(num_tokens, hc_mult, 1),
            comb_res_mix.view(num_tokens, hc_mult, hc_mult),
        )
        return out.view_as(residual)

    @staticmethod
    def mhc_fused_post_pre(
        x: torch.Tensor,
        residual: torch.Tensor,
        post_layer_mix: torch.Tensor,
        comb_res_mix: torch.Tensor,
        fn: torch.Tensor,
        hc_scale: torch.Tensor,
        hc_base: torch.Tensor,
        rms_eps: float,
        hc_pre_eps: float,
        hc_sinkhorn_eps: float,
        hc_post_mult_value: float,
        sinkhorn_repeat: int,
        norm_weight: torch.Tensor | None = None,
        norm_eps: float = 0.0,
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
        """Fused mHC post + next mHC pre via AITER.

        Returns residual_cur, post_mix, comb_mix, layer_input in vLLM order
        (AITER returns post_mix, comb_mix, layer_input, next_residual).
        """
        from aiter.ops.mhc import mhc_fused_post_pre

        assert x.dtype == torch.bfloat16
        assert residual.dtype == torch.bfloat16
        assert fn.dtype == torch.float32
        assert hc_scale.dtype == torch.float32
        assert hc_base.dtype == torch.float32

        hc_mult = residual.shape[-2]
        hidden_size = residual.shape[-1]
        outer_shape = residual.shape[:-2]

        residual_flat = residual.view(-1, hc_mult, hidden_size)
        num_tokens = residual_flat.shape[0]
        x_flat = x.view(num_tokens, hidden_size)
        post_flat = post_layer_mix.view(num_tokens, hc_mult, 1)
        comb_flat = comb_res_mix.view(num_tokens, hc_mult, hc_mult)

        if num_tokens == 0:
            return (
                torch.empty_like(residual_flat).view_as(residual),
                torch.empty(
                    *outer_shape,
                    hc_mult,
                    1,
                    dtype=torch.float32,
                    device=residual.device,
                ),
                torch.empty(
                    *outer_shape,
                    hc_mult,
                    hc_mult,
                    dtype=torch.float32,
                    device=residual.device,
                ),
                torch.empty(
                    *outer_shape,
                    hidden_size,
                    dtype=torch.bfloat16,
                    device=residual.device,
                ),
            )

        args = (
            x_flat,
            residual_flat,
            post_flat,
            comb_flat,
            fn,
            hc_scale,
            hc_base,
            rms_eps,
            hc_pre_eps,
            hc_sinkhorn_eps,
            hc_post_mult_value,
            sinkhorn_repeat,
        )
        with torch.device(residual_flat.device):
            if norm_weight is None:
                result = mhc_fused_post_pre(*args)
            else:
                result = mhc_fused_post_pre(*args, norm_weight, norm_eps)
            post_mix, comb_mix, layer_input, next_residual = result

        return (
            next_residual.view_as(residual),
            post_mix.view(*outer_shape, hc_mult, 1),
            comb_mix.view(*outer_shape, hc_mult, hc_mult),
            layer_input.view(*outer_shape, hidden_size),
        )


rocm_aiter_ops.register_ops_once()
_sync_aiter_situv2_moe_env()