from __future__ import annotations

import abc
import logging
import threading
from collections import defaultdict
from dataclasses import dataclass
from functools import wraps
from typing import TYPE_CHECKING, Any, Callable, Optional

if TYPE_CHECKING:
    from sglang.srt.mem_cache.hicache_storage import PoolName

import numpy as np
import psutil
import torch

from sglang.jit_kernel.hicache import (
    can_use_hicache_jit_kernel,
)
from sglang.jit_kernel.hicache import (
    transfer_hicache_all_layer as jit_transfer_hicache_all_layer,
)
from sglang.jit_kernel.hicache import (
    transfer_hicache_all_layer_mla as jit_transfer_hicache_all_layer_mla,
)
from sglang.jit_kernel.hicache import (
    transfer_hicache_one_layer as jit_transfer_hicache_one_layer,
)
from sglang.jit_kernel.hicache import (
    transfer_hicache_one_layer_mla as jit_transfer_hicache_one_layer_mla,
)
from sglang.srt.mem_cache.memory_pool import (
    KVCache,
    MambaPool,
    MHATokenToKVPool,
    MLATokenToKVPool,
    NSATokenToKVPool,
)
from sglang.srt.utils import is_cuda, is_mps, is_npu, is_xpu

_is_cuda = is_cuda()
_is_npu = is_npu()
_is_xpu = is_xpu()
_is_mps = is_mps()
if not (_is_npu or _is_xpu or _is_mps):
    from sgl_kernel.kvcacheio import (
        transfer_kv_all_layer,
        transfer_kv_all_layer_direct_lf_pf,
        transfer_kv_all_layer_lf_pf,
        transfer_kv_all_layer_lf_ph,
        transfer_kv_all_layer_mla,
        transfer_kv_all_layer_mla_lf_pf,
        transfer_kv_direct,
        transfer_kv_per_layer,
        transfer_kv_per_layer_direct_pf_lf,
        transfer_kv_per_layer_mla,
        transfer_kv_per_layer_mla_pf_lf,
        transfer_kv_per_layer_pf_lf,
        transfer_kv_per_layer_ph_lf,
    )
if _is_npu:
    from sgl_kernel_npu.kvcacheio import TransferDirection, transfer_kv_dim_exchange

logger = logging.getLogger(__name__)


def synchronized(func):
    @wraps(func)
    def wrapper(self, *args, **kwargs):
        with self.lock:
            return func(self, *args, **kwargs)

    return wrapper


class HostTensorAllocator(abc.ABC):
    def __init__(self):
        """Initialize the HostTensorAllocator."""
        self.dtype = None
        self.dims = None

    def allocate(self, dims: tuple, dtype: torch.dtype, device: str) -> torch.Tensor:
        """Allocate a tensor of given dims and dtype on the memory."""
        self.dtype = dtype
        self.dims = dims
        tensor = torch.empty(dims, dtype=dtype, device=device)
        return tensor


def get_allocator_from_storage(allocator_type):
    if allocator_type == "mooncake":
        try:
            from sglang.srt.mem_cache.storage.mooncake_store.mooncake_store import (
                MooncakeHostTensorAllocator,
            )

            return MooncakeHostTensorAllocator()
        except ImportError:
            logger.warning(
                "Mooncake's tensor allocator requires mooncake >= 0.3.8.post1. "
                "Please upgrade Mooncake by 'pip install mooncake-transfer-engine --upgrade'. "
                "Fallback to use default allocator."
            )
            return HostTensorAllocator()
    else:
        return HostTensorAllocator()


def alloc_with_host_register(
    dims,
    dtype: torch.dtype,
    device: str,
    pin_memory: bool,
    allocator: HostTensorAllocator,
) -> torch.Tensor:
    """
    Allocate tensor and register host memory with cudaHostRegister.
    CudaHostRegister only applies when pin_memory=True.
    """
    buffer = allocator.allocate(dims, dtype=dtype, device=device)
    if pin_memory:
        torch.cuda.cudart().cudaHostRegister(
            buffer.data_ptr(), buffer.numel() * buffer.element_size(), 0
        )
    return buffer


def alloc_with_pin_memory(
    dims,
    dtype: torch.dtype,
    device: str,
    pin_memory: bool,
    allocator: None,
) -> torch.Tensor:
    """
    Allocate tensor using PyTorch's built-in pin_memory flag.
    """
    buffer = torch.empty(dims, dtype=dtype, device=device, pin_memory=pin_memory)
    return buffer


ALLOC_MEMORY_FUNCS = defaultdict(
    lambda: alloc_with_host_register,
    {
        "npu": alloc_with_pin_memory,
    },
)


class HostKVCache(abc.ABC):

    def __init__(
        self,
        device_pool: KVCache,
        host_to_device_ratio: float,
        host_size: int,
        page_size: int,
        layout: str,
        pin_memory: bool,
        device: str,
        allocator_type: str = "default",
    ):
        self.device_pool = device_pool
        self.page_size = page_size
        self.layout = layout
        self.pin_memory = pin_memory
        self.device = device
        self.allocator = get_allocator_from_storage(allocator_type)

        self.dtype = device_pool.store_dtype
        self.size_per_token = self.get_size_per_token()
        if host_size > 0:
            self.size = int(host_size * 1e9 // self.size_per_token)
        else:
            self.size = int(device_pool.size * host_to_device_ratio)
        # Align up the host memory pool size to the page size
        self.page_num = self.size // self.page_size + 1
        self.size = self.page_num * self.page_size
        self.start_layer = device_pool.start_layer
        self.end_layer = device_pool.end_layer

        assert (
            self.size > device_pool.size
        ), "The host memory should be larger than the device memory with the current protocol"

        # Verify there is enough available host memory.
        host_mem = psutil.virtual_memory()
        requested_bytes = self.size * self.size_per_token
        # preserve at least 10GB for other usage
        ten_gb = 10 * (1024**3)
        available_bytes = host_mem.available - ten_gb
        if requested_bytes > available_bytes:
            raise ValueError(
                f"Not enough host memory available. Requesting "
                f"{requested_bytes / 1e9:.2f} GB but only have "
                f"{available_bytes / 1e9:.2f} GB free. Please reduce the "
                f"size of the hierarchical cache."
            )
        else:
            logger.info(
                f"Allocating {requested_bytes / 1e9:.2f} GB host memory for hierarchical KV cache."
            )

        self.kv_buffer = self.init_kv_buffer()

        # A lock for synchronized operations on memory allocation and state transitions.
        self.lock = threading.RLock()
        self.clear()

    @abc.abstractmethod
    def get_size_per_token(self):
        raise NotImplementedError()

    @abc.abstractmethod
    def init_kv_buffer(self):
        raise NotImplementedError()

    @abc.abstractmethod
    def load_to_device_per_layer(
        self, device_pool, host_indices, device_indices, layer_id, io_backend
    ) -> None:
        """
        Load KV data from the host memory pool to the device memory pool for a specific layer.
        """
        raise NotImplementedError()

    @abc.abstractmethod
    def backup_from_device_all_layer(
        self, device_pool, host_indices, device_indices, io_backend
    ) -> None:
        """
        Backup KV data from the device memory pool to the host memory pool for all layers.
        """
        raise NotImplementedError()

    @abc.abstractmethod
    def get_data_page(self, index, flat: bool = True) -> torch.Tensor:
        """
        Get a flat data page from the host memory pool.
        """
        raise NotImplementedError()

    @abc.abstractmethod
    def get_dummy_flat_data_page(self) -> torch.Tensor:
        """
        Get a dummy flat data page from the host memory pool.
        This is used for prefetching or initializing empty pages.
        """
        raise NotImplementedError()

    @abc.abstractmethod
    def set_from_flat_data_page(self, index: int, data_page: torch.Tensor) -> None:
        """
        Set a flat data page to the host memory pool.
        """
        raise NotImplementedError()

    @synchronized
    def clear(self):
        # Initialize memory states and tracking structures.
        self.mem_state = torch.zeros(
            (self.size,), dtype=torch.uint8, device=self.device
        )
        self.free_slots = torch.arange(self.size, dtype=torch.int64)

    def available_size(self):
        return len(self.free_slots)

    @synchronized
    def alloc(self, need_size: int) -> Optional[torch.Tensor]:
        assert (
            need_size % self.page_size == 0
        ), "The requested size should be a multiple of the page size."
        if need_size > self.available_size():
            return None

        select_index = self.free_slots[:need_size]
        self.free_slots = self.free_slots[need_size:]

        return select_index

    @synchronized
    def free(self, indices: torch.Tensor) -> int:
        self.free_slots = torch.cat([self.free_slots, indices.cpu()])
        return len(indices)


class MHATokenToKVPoolHost(HostKVCache):
    device_pool: MHATokenToKVPool

    def __init__(
        self,
        device_pool: MHATokenToKVPool,
        host_to_device_ratio: float,
        host_size: int,
        page_size: int,
        layout: str,
        pin_memory: bool = True,
        device: str = "cpu",
        allocator_type: str = "default",
    ):
        super().__init__(
            device_pool,
            host_to_device_ratio,
            host_size,
            page_size,
            layout,
            pin_memory,
            device,
            allocator_type,
        )
        self.element_dim = self.device_pool.head_num * self.device_pool.head_dim
        self.can_use_jit = _is_cuda and can_use_hicache_jit_kernel(
            element_size=self.element_dim * self.dtype.itemsize
        )

        if self.layout == "page_first":
            # Transpose [page, layer, ...] -> [layer, page, ...] to get per-layer views
            # This swaps strides without copying data
            k_transposed = self.k_buffer.transpose(0, 1)
            v_transposed = self.v_buffer.transpose(0, 1)
            self.k_data_refs = [k_transposed[i] for i in range(self.layer_num)]
            self.v_data_refs = [v_transposed[i] for i in range(self.layer_num)]
        else:
            self.k_data_refs = [self.k_buffer[i] for i in range(self.layer_num)]
            self.v_data_refs = [self.v_buffer[i] for i in range(self.layer_num)]
        self.k_data_ptrs = torch.tensor(
            [x.data_ptr() for x in self.k_data_refs],
            dtype=torch.uint64,
            device=self.device_pool.device,
        )
        self.v_data_ptrs = torch.tensor(
            [x.data_ptr() for x in self.v_data_refs],
            dtype=torch.uint64,
            device=self.device_pool.device,
        )

    def get_size_per_token(self):
        self.head_num = self.device_pool.head_num
        self.head_dim = self.device_pool.head_dim
        self.layer_num = self.device_pool.layer_num

        return self.head_dim * self.head_num * self.layer_num * self.dtype.itemsize * 2

    def get_ksize_per_token(self):
        return self.get_size_per_token() // 2

    def init_kv_buffer(self):
        if self.layout == "layer_first":
            dims = (2, self.layer_num, self.size, self.head_num, self.head_dim)
        elif self.layout == "page_first":
            dims = (2, self.size, self.layer_num, self.head_num, self.head_dim)
        elif self.layout == "page_first_direct":
            dims = (
                2,
                self.page_num,
                self.layer_num,
                self.page_size,
                self.head_num,
                self.head_dim,
            )
        elif self.layout == "page_head":
            dims = (
                2,
                self.page_num,
                self.head_num,
                self.page_size,
                self.layer_num,
                self.head_dim,
            )
        else:
            raise ValueError(f"Unsupported layout: {self.layout}")
        self.token_stride_size = self.head_num * self.head_dim * self.dtype.itemsize
        self.layout_dim = self.token_stride_size * self.layer_num

        alloc_func = ALLOC_MEMORY_FUNCS[self.device_pool.device]
        buffer = alloc_func(
            dims,
            dtype=self.dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            allocator=self.allocator,
        )
        return buffer

    @property
    def k_buffer(self):
        return self.kv_buffer[0]

    @property
    def v_buffer(self):
        return self.kv_buffer[1]

    def load_to_device_per_layer(
        self,
        device_pool,
        host_indices,
        device_indices,
        layer_id,
        io_backend,
    ):
        if io_backend == "kernel":
            if self.layout == "layer_first":
                if self.can_use_jit:
                    jit_transfer_hicache_one_layer(
                        k_cache_dst=device_pool.k_buffer[layer_id],
                        v_cache_dst=device_pool.v_buffer[layer_id],
                        k_cache_src=self.k_buffer[layer_id],
                        v_cache_src=self.v_buffer[layer_id],
                        indices_dst=device_indices,
                        indices_src=host_indices,
                        element_dim=self.element_dim,
                    )
                else:
                    transfer_kv_per_layer(
                        src_k=self.k_buffer[layer_id],
                        dst_k=device_pool.k_buffer[layer_id],
                        src_v=self.v_buffer[layer_id],
                        dst_v=device_pool.v_buffer[layer_id],
                        src_indices=host_indices,
                        dst_indices=device_indices,
                        item_size=self.token_stride_size,
                    )
            elif self.layout == "page_first":
                if self.can_use_jit:
                    # Transpose [page, layer, ...] -> [layer, page, ...] then
                    # index by layer_id to get a per-layer view with strided layout.
                    # The kernel handles different src/dst strides automatically.
                    jit_transfer_hicache_one_layer(
                        k_cache_dst=device_pool.k_buffer[layer_id],
                        v_cache_dst=device_pool.v_buffer[layer_id],
                        k_cache_src=self.k_data_refs[layer_id],
                        v_cache_src=self.v_data_refs[layer_id],
                        indices_dst=device_indices,
                        indices_src=host_indices,
                        element_dim=self.element_dim,
                    )
                else:
                    transfer_kv_per_layer_pf_lf(
                        src_k=self.k_buffer,
                        dst_k=device_pool.k_buffer[layer_id],
                        src_v=self.v_buffer,
                        dst_v=device_pool.v_buffer[layer_id],
                        src_indices=host_indices,
                        dst_indices=device_indices,
                        layer_id=layer_id,
                        item_size=self.token_stride_size,
                        src_layout_dim=self.layout_dim,
                    )
            elif self.layout == "page_head":
                transfer_kv_per_layer_ph_lf(
                    src_k=self.k_buffer,
                    dst_k=device_pool.k_buffer[layer_id],
                    src_v=self.v_buffer,
                    dst_v=device_pool.v_buffer[layer_id],
                    src_indices=host_indices,
                    dst_indices=device_indices,
                    layer_id=layer_id,
                    item_size=self.token_stride_size,
                    src_layout_dim=self.layout_dim,
                    page_size=self.page_size,
                    head_num=self.head_num,
                )
            else:
                raise ValueError(f"Unsupported layout: {self.layout}")
        elif io_backend == "direct":
            if self.layout == "layer_first":
                transfer_kv_direct(
                    src_layers=[self.k_buffer[layer_id], self.v_buffer[layer_id]],
                    dst_layers=[
                        device_pool.k_buffer[layer_id],
                        device_pool.v_buffer[layer_id],
                    ],
                    src_indices=host_indices,
                    dst_indices=device_indices,
                    page_size=self.page_size,
                )
            elif self.layout == "page_first_direct":
                transfer_kv_per_layer_direct_pf_lf(
                    src_ptrs=[self.k_buffer, self.v_buffer],
                    dst_ptrs=[
                        device_pool.k_buffer[layer_id],
                        device_pool.v_buffer[layer_id],
                    ],
                    src_indices=host_indices,
                    dst_indices=device_indices,
                    layer_id=layer_id,
                    page_size=self.page_size,
                )
            else:
                raise ValueError(f"Unsupported layout: {self.layout}")
        elif io_backend == "kernel_ascend":
            if self.layout == "page_first_direct":
                # Ascend-specific: transfer KV data for all layers when layer_id == 0
                if layer_id == 0:
                    transfer_kv_dim_exchange(
                        device_indices=device_indices,
                        host_indices=host_indices,
                        device_k=device_pool.k_buffer,
                        host_k=self.k_buffer,
                        device_v=device_pool.v_buffer,
                        host_v=self.v_buffer,
                        page_size=self.page_size,
                        direction=TransferDirection.H2D,
                    )
            else:
                raise ValueError(f"Unsupported layout: {self.layout}")
        else:
            raise ValueError(f"Unsupported IO backend: {io_backend}")

    def backup_from_device_all_layer(
        self, device_pool, host_indices, device_indices, io_backend
    ):
        if io_backend == "kernel":
            if self.layout == "layer_first":
                if self.can_use_jit:
                    jit_transfer_hicache_all_layer(
                        k_ptr_dst=self.k_data_ptrs,
                        v_ptr_dst=self.v_data_ptrs,
                        indices_dst=host_indices,
                        k_ptr_src=device_pool.k_data_ptrs,
                        v_ptr_src=device_pool.v_data_ptrs,
                        indices_src=device_indices,
                        kv_cache_dst_stride_bytes=self.token_stride_size,
                        kv_cache_src_stride_bytes=self.token_stride_size,
                        element_size=self.element_dim * self.dtype.itemsize,
                    )
                else:
                    transfer_kv_all_layer(
                        src_k_layers=device_pool.k_data_ptrs,
                        dst_k_layers=self.k_data_ptrs,
                        src_v_layers=device_pool.v_data_ptrs,
                        dst_v_layers=self.v_data_ptrs,
                        src_indices=device_indices,
                        dst_indices=host_indices,
                        item_size=self.token_stride_size,
                        num_layers=self.layer_num,
                    )
            elif self.layout == "page_first":
                if self.can_use_jit:
                    # Use transposed data ptrs so the kernel writes to
                    # [layer, page, item] view with stride layout_dim per token.
                    jit_transfer_hicache_all_layer(
                        k_ptr_dst=self.k_data_ptrs,
                        v_ptr_dst=self.v_data_ptrs,
                        indices_dst=host_indices,
                        k_ptr_src=device_pool.k_data_ptrs,
                        v_ptr_src=device_pool.v_data_ptrs,
                        indices_src=device_indices,
                        kv_cache_src_stride_bytes=self.token_stride_size,
                        kv_cache_dst_stride_bytes=self.layout_dim,
                        element_size=self.element_dim * self.dtype.itemsize,
                    )
                else:
                    transfer_kv_all_layer_lf_pf(
                        src_k_layers=device_pool.k_data_ptrs,
                        dst_k=self.k_buffer,
                        src_v_layers=device_pool.v_data_ptrs,
                        dst_v=self.v_buffer,
                        src_indices=device_indices,
                        dst_indices=host_indices,
                        item_size=self.token_stride_size,
                        dst_layout_dim=self.layout_dim,
                        num_layers=self.layer_num,
                    )
            elif self.layout == "page_head":
                transfer_kv_all_layer_lf_ph(
                    src_k_layers=device_pool.k_data_ptrs,
                    dst_k=self.k_buffer,
                    src_v_layers=device_pool.v_data_ptrs,
                    dst_v=self.v_buffer,
                    src_indices=device_indices,
                    dst_indices=host_indices,
                    item_size=self.token_stride_size,
                    dst_layout_dim=self.layout_dim,
                    num_layers=self.layer_num,
                    page_size=self.page_size,
                    head_num=self.head_num,
                )
            else:
                raise ValueError(f"Unsupported layout: {self.layout}")
        elif io_backend == "direct":
            if self.layout == "layer_first":
                transfer_kv_direct(
                    src_layers=device_pool.k_buffer + device_pool.v_buffer,
                    dst_layers=self.k_data_refs + self.v_data_refs,
                    src_indices=device_indices,
                    dst_indices=host_indices,
                    page_size=self.page_size,
                )
            elif self.layout == "page_first_direct":
                transfer_kv_all_layer_direct_lf_pf(
                    src_ptrs=device_pool.k_buffer + device_pool.v_buffer,
                    dst_ptrs=[self.k_buffer, self.v_buffer],
                    src_indices=device_indices,
                    dst_indices=host_indices,
                    page_size=self.page_size,
                )
            else:
                raise ValueError(f"Unsupported layout: {self.layout}")
        elif io_backend == "kernel_ascend":
            if self.layout == "page_first_direct":
                transfer_kv_dim_exchange(
                    device_indices=device_indices,
                    host_indices=host_indices,
                    device_k=device_pool.k_buffer,
                    host_k=self.k_buffer,
                    device_v=device_pool.v_buffer,
                    host_v=self.v_buffer,
                    page_size=self.page_size,
                    direction=TransferDirection.D2H,
                )
            else:
                raise ValueError(f"Unsupported layout: {self.layout}")
        else:
            raise ValueError(f"Unsupported IO backend: {io_backend}")

    def get_data_page(self, index, flat: bool = True) -> torch.Tensor:
        if self.layout == "layer_first":
            data_page = self.kv_buffer[:, :, index : index + self.page_size, :, :]
        elif self.layout == "page_first":
            data_page = self.kv_buffer[:, index : index + self.page_size, :, :, :]
        elif self.layout in ["page_first_direct", "page_head"]:
            real_index = index // self.page_size
            data_page = self.kv_buffer[:, real_index : real_index + 1, :, :, :, :]
        else:
            raise ValueError(f"Unsupported layout: {self.layout}")
        if flat:
            data_page = data_page.flatten()
        return data_page

    def get_dummy_flat_data_page(self) -> torch.Tensor:
        return torch.zeros(
            (2, self.layer_num, self.page_size, self.head_num, self.head_dim),
            dtype=self.dtype,
            device=self.device,
            pin_memory=self.pin_memory,
        ).flatten()

    def set_from_flat_data_page(self, index: int, data_page: torch.Tensor) -> None:
        if self.layout == "layer_first":
            self.kv_buffer[:, :, index : index + self.page_size, :, :] = (
                data_page.reshape(
                    2,
                    self.layer_num,
                    self.page_size,
                    self.head_num,
                    self.head_dim,
                )
            )
        elif self.layout == "page_first":
            self.kv_buffer[:, index : index + self.page_size, :, :, :] = (
                data_page.reshape(
                    2, self.page_size, self.layer_num, self.head_num, self.head_dim
                )
            )
        elif self.layout == "page_first_direct":
            real_index = index // self.page_size
            self.kv_buffer[:, real_index : real_index + 1, :, :, :, :] = (
                data_page.reshape(
                    2, 1, self.layer_num, self.page_size, self.head_num, self.head_dim
                )
            )
        elif self.layout == "page_head":
            real_index = index // self.page_size
            self.kv_buffer[:, real_index : real_index + 1, :, :, :, :] = (
                data_page.reshape(
                    2, 1, self.head_num, self.page_size, self.layer_num, self.head_dim
                )
            )
        else:
            raise ValueError(f"Unsupported layout: {self.layout}")

    def get_split_heads_page_buffer_meta(
        self, indices: torch.Tensor, split_factor: int
    ):
        """
        get meta data for zero copy of heterogeneous ranks' KVCache
        """
        assert self.layout == "page_head"
        assert len(indices) % self.page_size == 0
        assert self.head_num % split_factor == 0
        ptr_list = []
        kv_buffer_data_ptr = self.kv_buffer.data_ptr()
        indices = indices.tolist()
        v_offset = (
            self.layer_num
            * self.size
            * self.head_num
            * self.head_dim
            * self.dtype.itemsize
        )
        for index in range(0, len(indices), self.page_size):
            for head_id in range(0, self.head_num, self.head_num // split_factor):
                k_ptr = (
                    kv_buffer_data_ptr
                    + indices[index]
                    * self.layer_num
                    * self.head_num
                    * self.head_dim
                    * self.dtype.itemsize
                    + head_id
                    * self.page_size
                    * self.layer_num
                    * self.head_dim
                    * self.dtype.itemsize
                )
                v_ptr = k_ptr + v_offset
                ptr_list.append(k_ptr)
                ptr_list.append(v_ptr)
        element_size = (
            self.layer_num
            * self.dtype.itemsize
            * self.page_size
            * self.head_num
            * self.head_dim
            // split_factor
        )
        element_size_list = [element_size] * len(ptr_list)
        return ptr_list, element_size_list

    def get_page_buffer_meta(self, indices):
        """ "
        meta data for zero copy
        """
        assert len(indices) % self.page_size == 0
        ptr_list = []
        kv_buffer_data_ptr = self.kv_buffer.data_ptr()
        indices = indices.tolist()
        v_offset = (
            self.layer_num
            * self.size
            * self.head_num
            * self.head_dim
            * self.dtype.itemsize
        )
        if self.layout == "layer_first":
            for index in range(0, len(indices), self.page_size):
                for layer_id in range(self.layer_num):
                    k_ptr = (
                        kv_buffer_data_ptr
                        + indices[index]
                        * self.head_num
                        * self.head_dim
                        * self.dtype.itemsize
                        + layer_id
                        * self.size
                        * self.head_num
                        * self.head_dim
                        * self.dtype.itemsize
                    )
                    v_ptr = k_ptr + v_offset
                    ptr_list.append(k_ptr)
                    ptr_list.append(v_ptr)
            element_size = (
                self.dtype.itemsize * self.page_size * self.head_num * self.head_dim
            )
            element_size_list = [element_size] * len(ptr_list)
        elif self.layout in ["page_first", "page_first_direct", "page_head"]:
            for index in range(0, len(indices), self.page_size):
                k_ptr = (
                    kv_buffer_data_ptr
                    + indices[index]
                    * self.layer_num
                    * self.head_num
                    * self.head_dim
                    * self.dtype.itemsize
                )
                v_ptr = k_ptr + v_offset
                ptr_list.append(k_ptr)
                ptr_list.append(v_ptr)
            element_size = (
                self.layer_num
                * self.dtype.itemsize
                * self.page_size
                * self.head_num
                * self.head_dim
            )
            element_size_list = [element_size] * len(ptr_list)
        else:
            raise ValueError(f"Unsupported layout: {self.layout}")
        return ptr_list, element_size_list


class MLATokenToKVPoolHost(HostKVCache):
    device_pool: MLATokenToKVPool

    def __init__(
        self,
        device_pool: MLATokenToKVPool,
        host_to_device_ratio: float,
        host_size: int,
        page_size: int,
        layout: str,
        pin_memory: bool = True,
        device: str = "cpu",
        allocator_type: str = "default",
        override_kv_cache_dim: Optional[int] = None,
    ):
        self.override_kv_cache_dim = override_kv_cache_dim
        super().__init__(
            device_pool,
            host_to_device_ratio,
            host_size,
            page_size,
            layout,
            pin_memory,
            device,
            allocator_type,
        )
        self.can_use_jit = _is_cuda and can_use_hicache_jit_kernel(
            element_size=self.kv_cache_dim * self.dtype.itemsize
        )

        if self.layout == "page_first" and self.can_use_jit:
            # Transpose [page, layer, ...] -> [layer, page, ...] to get per-layer views
            # This swaps strides without copying data
            transposed = self.kv_buffer.transpose(0, 1)
            self.data_refs = [transposed[i] for i in range(self.layer_num)]
        else:
            self.data_refs = [self.kv_buffer[i] for i in range(self.layer_num)]
        self.data_ptrs = torch.tensor(
            [x.data_ptr() for x in self.data_refs],
            dtype=torch.uint64,
            device=self.device_pool.device,
        )

    def get_contiguous_buf_infos(self):
        """Return (data_ptrs, data_lens, item_lens) in the same format as device pool,
        for registering host memory with the disaggregation transfer engine."""
        data_ptrs = [int(self.data_ptrs[i].item()) for i in range(self.layer_num)]
        data_lens = [self.kv_buffer[i].nbytes for i in range(self.layer_num)]
        item_lens = [self.token_stride_size] * self.layer_num
        return data_ptrs, data_lens, item_lens

    def get_size_per_token(self):
        self.kv_lora_rank = self.device_pool.kv_lora_rank
        self.qk_rope_head_dim = self.device_pool.qk_rope_head_dim
        self.layer_num = self.device_pool.layer_num
        self.kv_cache_dim = self.override_kv_cache_dim or (
            self.kv_lora_rank + self.qk_rope_head_dim
        )
        return self.kv_cache_dim * self.dtype.itemsize * self.layer_num

    def get_ksize_per_token(self):
        return self.get_size_per_token()

    def init_kv_buffer(self):
        if self.layout == "layer_first":
            dims = (
                self.layer_num,
                self.size,
                1,
                self.kv_cache_dim,
            )
        elif self.layout == "page_first":
            dims = (
                self.size,
                self.layer_num,
                1,
                self.kv_cache_dim,
            )
        elif self.layout == "page_first_direct":
            dims = (
                self.page_num,
                self.layer_num,
                self.page_size,
                1,
                self.kv_cache_dim,
            )
        # Ascend-specific: Aligns with NPUMLATokenToKVPool layout
        # Separately allocate k_buffer and v_buffer for easier data transfer.
        elif self.layout == "page_first_kv_split":
            base_dims = (
                self.page_num,
                self.layer_num,
                self.page_size,
                1,
            )
            alloc_func = ALLOC_MEMORY_FUNCS[self.device_pool.device]
            self.k_buffer = alloc_func(
                (*base_dims, self.kv_lora_rank),
                dtype=self.dtype,
                device=self.device,
                pin_memory=self.pin_memory,
                allocator=self.allocator,
            )
            self.v_buffer = alloc_func(
                (*base_dims, self.qk_rope_head_dim),
                dtype=self.dtype,
                device=self.device,
                pin_memory=self.pin_memory,
                allocator=self.allocator,
            )
            self.index_k_buffer = None
            if self.device_pool.index_head_dim is not None:
                self.index_k_buffer = alloc_func(
                    (*base_dims, self.device_pool.index_head_dim),
                    dtype=self.dtype,
                    device=self.device,
                    pin_memory=self.pin_memory,
                    allocator=self.allocator,
                )
            # Return k_buffer to preserve original kv_buffer and data_refs init logic,
            # though Ascend doesn't use these parameters.
            return self.k_buffer
        else:
            raise ValueError(f"Unsupported layout: {self.layout}")
        self.token_stride_size = self.kv_cache_dim * self.dtype.itemsize
        self.layout_dim = self.token_stride_size * self.layer_num

        alloc_func = ALLOC_MEMORY_FUNCS[self.device_pool.device]
        buffer = alloc_func(
            dims,
            dtype=self.dtype,
            device=self.device,
            pin_memory=self.pin_memory,
            allocator=self.allocator,
        )
        return buffer

    def load_to_device_per_layer(
        self, device_pool, host_indices, device_indices, layer_id, io_backend
    ):
        if io_backend == "kernel":
            if self.layout == "layer_first":
                if self.can_use_jit:
                    jit_transfer_hicache_one_layer_mla(
                        cache_dst=device_pool.kv_buffer[layer_id],
                        cache_src=self.kv_buffer[layer_id],
                        indices_dst=device_indices,
                        indices_src=host_indices,
                        element_dim=self.kv_cache_dim,
                    )
                else:
                    transfer_kv_per_layer_mla(
                        src=self.kv_buffer[layer_id],
                        dst=device_pool.kv_buffer[layer_id],
                        src_indices=host_indices,
                        dst_indices=device_indices,
                        item_size=self.token_stride_size,
                    )
            elif self.layout == "page_first":
                if self.can_use_jit:
                    jit_transfer_hicache_one_layer_mla(
                        cache_dst=device_pool.kv_buffer[layer_id],
                        cache_src=self.data_refs[layer_id],
                        indices_dst=device_indices,
                        indices_src=host_indices,
                        element_dim=self.kv_cache_dim,
                    )
                else:
                    transfer_kv_per_layer_mla_pf_lf(
                        src=self.kv_buffer,
                        dst=device_pool.kv_buffer[layer_id],
                        src_indices=host_indices,
                        dst_indices=device_indices,
                        layer_id=layer_id,
                        item_size=self.token_stride_size,
                        src_layout_dim=self.layout_dim,
                    )
            else:
                raise ValueError(f"Unsupported layout: {self.layout}")
        elif io_backend == "direct":
            if self.layout == "layer_first":
                transfer_kv_direct(
                    src_layers=[self.kv_buffer[layer_id]],
                    dst_layers=[device_pool.kv_buffer[layer_id]],
                    src_indices=host_indices,
                    dst_indices=device_indices,
                    page_size=self.page_size,
                )
            elif self.layout == "page_first_direct":
                transfer_kv_per_layer_direct_pf_lf(
                    src_ptrs=[self.kv_buffer],
                    dst_ptrs=[device_pool.kv_buffer[layer_id]],
                    src_indices=host_indices,
                    dst_indices=device_indices,
                    layer_id=layer_id,
                    page_size=self.page_size,
                )
            else:
                raise ValueError(f"Unsupported layout: {self.layout}")
        elif io_backend == "kernel_ascend":
            if self.layout == "page_first_kv_split":
                # Ascend-specific: transfer KV data for all layers when layer_id == 0
                if layer_id == 0:
                    transfer_kv_dim_exchange(
                        device_indices=device_indices,
                        host_indices=host_indices,
                        device_k=device_pool.k_buffer,
                        host_k=self.k_buffer,
                        device_v=device_pool.v_buffer,
                        host_v=self.v_buffer,
                        device_index_k=device_pool.index_k_buffer,
                        host_index_k=self.index_k_buffer,
                        page_size=self.page_size,
                        direction=TransferDirection.H2D,
                    )
            else:
                raise ValueError(f"Unsupported layout: {self.layout}")
        else:
            raise ValueError(f"Unsupported IO backend: {io_backend}")

    def backup_from_device_all_layer(
        self, device_pool, host_indices, device_indices, io_backend
    ):
        if io_backend == "kernel":
            if self.layout == "layer_first":
                if self.can_use_jit:
                    jit_transfer_hicache_all_layer_mla(
                        ptr_dst=self.data_ptrs,
                        indices_dst=host_indices,
                        ptr_src=device_pool.data_ptrs,
                        indices_src=device_indices,
                        cache_dst_stride_bytes=self.token_stride_size,
                        cache_src_stride_bytes=self.token_stride_size,
                        element_size=self.kv_cache_dim * self.dtype.itemsize,
                    )
                else:
                    transfer_kv_all_layer_mla(
                        src_layers=device_pool.data_ptrs,
                        dst_layers=self.data_ptrs,
                        src_indices=device_indices,
                        dst_indices=host_indices,
                        item_size=self.token_stride_size,
                        num_layers=self.layer_num,
                    )
            elif self.layout == "page_first":
                if self.can_use_jit:
                    jit_transfer_hicache_all_layer_mla(
                        ptr_dst=self.data_ptrs,
                        indices_dst=host_indices,
                        ptr_src=device_pool.data_ptrs,
                        indices_src=device_indices,
                        cache_src_stride_bytes=self.token_stride_size,
                        cache_dst_stride_bytes=self.layout_dim,
                        element_size=self.kv_cache_dim * self.dtype.itemsize,
                    )
                else:
                    transfer_kv_all_layer_mla_lf_pf(
                        src_layers=device_pool.data_ptrs,
                        dst=self.kv_buffer,
                        src_indices=device_indices,
                        dst_indices=host_indices,
                        item_size=self.token_stride_size,
                        dst_layout_dim=self.layout_dim,
                        num_layers=self.layer_num,
                    )
            else:
                raise ValueError(f"Unsupported layout: {self.layout}")
        elif io_backend == "direct":
            if self.layout == "layer_first":
                transfer_kv_direct(
                    src_layers=device_pool.kv_buffer,
                    dst_layers=self.data_refs,
                    src_indices=device_indices,
                    dst_indices=host_indices,
                    page_size=self.page_size,
                )
            elif self.layout == "page_first_direct":
                transfer_kv_all_layer_direct_lf_pf(
                    src_ptrs=device_pool.kv_buffer,
                    dst_ptrs=[self.kv_buffer],
                    src_indices=device_indices,
                    dst_indices=host_indices,
                    page_size=self.page_size,
                )
            else:
                raise ValueError(f"Unsupported layout: {self.layout}")
        elif io_backend == "kernel_ascend":
            if self.layout == "page_first_kv_split":
                transfer_kv_dim_exchange(
                    device_indices=device_indices,
                    host_indices=host_indices,
                    device_k=device_pool.k_buffer,
                    host_k=self.k_buffer,
                    device_v=device_pool.v_buffer,
                    host_v=self.v_buffer,
                    device_index_k=device_pool.index_k_buffer,
                    host_index_k=self.index_k_buffer,
                    page_size=self.page_size,
                    direction=TransferDirection.D2H,
                )
            else:
                raise ValueError(f"Unsupported layout: {self.layout}")
        else:
            raise ValueError(f"Unsupported IO backend: {io_backend}")

    def get_data_page(self, index, flat: bool = True) -> torch.Tensor:
        if self.layout == "layer_first":
            data_page = self.kv_buffer[:, index : index + self.page_size, :, :]
        elif self.layout == "page_first":
            data_page = self.kv_buffer[index : index + self.page_size, :, :, :]
        elif self.layout == "page_first_direct":
            real_index = index // self.page_size
            data_page = self.kv_buffer[real_index : real_index + 1, :, :, :, :]
        else:
            raise ValueError(f"Unsupported layout: {self.layout}")
        if flat:
            data_page = data_page.flatten()
        return data_page

    def get_dummy_flat_data_page(self) -> torch.Tensor:
        return torch.zeros(
            (
                self.layer_num,
                self.page_size,
                1,
                self.kv_cache_dim,
            ),
            dtype=self.dtype,
            device=self.device,
            pin_memory=self.pin_memory,
        ).flatten()

    def set_from_flat_data_page(self, index: int, data_page: torch.Tensor) -> None:
        if self.layout == "layer_first":
            self.kv_buffer[:, index : index + self.page_size, :, :] = data_page.reshape(
                self.layer_num,
                self.page_size,
                1,
                self.kv_cache_dim,
            )
        elif self.layout == "page_first":
            self.kv_buffer[index : index + self.page_size, :, :, :] = data_page.reshape(
                self.page_size,
                self.layer_num,
                1,
                self.kv_cache_dim,
            )
        elif self.layout == "page_first_direct":
            real_index = index // self.page_size
            self.kv_buffer[real_index : real_index + 1, :, :, :, :] = data_page.reshape(
                1,
                self.layer_num,
                self.page_size,
                1,
                self.kv_cache_dim,
            )
        else:
            raise ValueError(f"Unsupported layout: {self.layout}")

    def get_page_buffer_meta(self, indices):
        """ "
        meta data for zero copy
        """
        assert len(indices) % self.page_size == 0
        ptr_list = []
        kv_buffer_data_ptr = self.kv_buffer.data_ptr()
        indices = indices.tolist()
        if self.layout == "layer_first":
            for index in range(0, len(indices), self.page_size):
                for layer_id in range(self.layer_num):
                    k_ptr = (
                        kv_buffer_data_ptr
                        + indices[index] * self.kv_cache_dim * self.dtype.itemsize
                        + layer_id * self.size * self.kv_cache_dim * self.dtype.itemsize
                    )
                    ptr_list.append(k_ptr)
            element_size = self.dtype.itemsize * self.page_size * self.kv_cache_dim
            element_size_list = [element_size] * len(ptr_list)
        elif self.layout in ["page_first", "page_first_direct"]:
            for index in range(0, len(indices), self.page_size):
                k_ptr = (
                    kv_buffer_data_ptr
                    + indices[index]
                    * self.layer_num
                    * self.kv_cache_dim
                    * self.dtype.itemsize
                )
                ptr_list.append(k_ptr)
            element_size = (
                self.layer_num
                * self.dtype.itemsize
                * self.page_size
                * self.kv_cache_dim
            )
            element_size_list = [element_size] * len(ptr_list)
        else:
            raise ValueError(f"Unsupported layout: {self.layout}")
        return ptr_list, element_size_list


class MambaPoolHost(HostKVCache):

    def __init__(
        self,
        device_pool: MambaPool,
        host_to_device_ratio: float,
        host_size: int,
        pin_memory: bool = True,
        device: str = "cpu",
        allocator_type: str = "default",
        layout: str = "layer_first",
    ):
        self.device_pool = device_pool
        self.page_size = 1
        assert layout in [
            "page_first",
            "page_first_direct",
            "layer_first",
        ], "Unsupported layout: {layout}"

        self.layout = layout
        self.pin_memory = pin_memory
        self.device = device
        self.allocator = get_allocator_from_storage(allocator_type)
        self.num_mamba_layers = device_pool.num_mamba_layers

        self.conv_state_shapes = [
            conv_state.shape[2:] for conv_state in device_pool.mamba_cache.conv
        ]
        self.temporal_state_shape = device_pool.mamba_cache.temporal.shape[2:]
        self.conv_dtype = device_pool.mamba_cache.conv[0].dtype
        self.temporal_dtype = device_pool.mamba_cache.temporal.dtype
        self.dtype = self.conv_dtype
        self.size_per_token = self.get_size_per_token()

        if host_size > 0:
            self.size = int(host_size * 1e9 // self.size_per_token)
        else:
            self.size = int(device_pool.size * host_to_device_ratio)

        self.page_num = self.size // self.page_size + 1
        self.size = self.page_num * self.page_size

        assert (
            self.size > device_pool.size
        ), "The host memory should be larger than the device memory with the current protocol"

        host_mem = psutil.virtual_memory()
        requested_bytes = self.size * self.size_per_token
        ten_gb = 10 * (1024**3)
        available_bytes = host_mem.available - ten_gb
        if requested_bytes > available_bytes:
            raise ValueError(
                f"Not enough host memory available. Requesting "
                f"{requested_bytes / 1e9:.2f} GB but only have "
                f"{available_bytes / 1e9:.2f} GB free. Please reduce the "
                f"size of the hierarchical cache."
            )
        logger.info(
            "Allocating %.2f GB host memory for hierarchical Mamba cache (layout=%s).",
            requested_bytes / 1e9,
            self.layout,
        )

        self.init_kv_buffer()
        self.lock = threading.RLock()
        self.clear()

    def init_kv_buffer(self):
        alloc_func = ALLOC_MEMORY_FUNCS[self.device_pool.device]

        if self.layout in ["page_first", "page_first_direct"]:
            # page-first: (page_num, num_layers, 1, *shape) — per-page data is contiguous
            temporal_dims = (
                self.size,
                self.num_mamba_layers,
                1,
            ) + self.temporal_state_shape
            self.temporal_buffer = alloc_func(
                temporal_dims,
                dtype=self.temporal_dtype,
                device=self.device,
                pin_memory=self.pin_memory,
                allocator=self.allocator,
            )
            self.conv_buffer = []
            for conv_shape in self.conv_state_shapes:
                conv_dims = (self.size, self.num_mamba_layers, 1) + conv_shape
                self.conv_buffer.append(
                    alloc_func(
                        conv_dims,
                        dtype=self.conv_dtype,
                        device=self.device,
                        pin_memory=self.pin_memory,
                        allocator=self.allocator,
                    )
                )
        else:
            # layer-first: (num_layers, size, *shape)
            temporal_dims = (
                self.num_mamba_layers,
                self.size,
            ) + self.temporal_state_shape
            self.temporal_buffer = alloc_func(
                temporal_dims,
                dtype=self.temporal_dtype,
                device=self.device,
                pin_memory=self.pin_memory,
                allocator=self.allocator,
            )
            self.conv_buffer = []
            for conv_shape in self.conv_state_shapes:
                conv_dims = (self.num_mamba_layers, self.size) + conv_shape
                self.conv_buffer.append(
                    alloc_func(
                        conv_dims,
                        dtype=self.conv_dtype,
                        device=self.device,
                        pin_memory=self.pin_memory,
                        allocator=self.allocator,
                    )
                )

    def _iter_page_tensors(self, index: int):
        if self.layout in ["page_first", "page_first_direct"]:
            yield self.temporal_buffer[index]
            for conv_buf in self.conv_buffer:
                yield conv_buf[index]
        else:
            yield self.temporal_buffer[:, index : index + self.page_size]
            for conv_buf in self.conv_buffer:
                yield conv_buf[:, index : index + self.page_size]

    @staticmethod
    def _flatten_tensor_bytes(tensor: torch.Tensor) -> torch.Tensor:
        return tensor.contiguous().view(torch.uint8).reshape(-1)

    @synchronized
    def clear(self):
        self.mem_state = torch.zeros(
            (self.size,), dtype=torch.uint8, device=self.device
        )
        self.free_slots = torch.arange(self.size, dtype=torch.int64)

    def available_size(self):
        return len(self.free_slots)

    @synchronized
    def alloc(self, need_size: int) -> Optional[torch.Tensor]:
        assert (
            need_size % self.page_size == 0
        ), "The requested size should be a multiple of the page size."
        if need_size > self.available_size():
            return None
        select_index = self.free_slots[:need_size]
        self.free_slots = self.free_slots[need_size:]
        return select_index

    @synchronized
    def free(self, indices: torch.Tensor) -> int:
        self.free_slots = torch.cat([self.free_slots, indices])
        return len(indices)

    def get_size_per_token(self):
        conv_total_size = 0
        for conv_shape in self.conv_state_shapes:
            conv_total_size += int(np.prod(conv_shape)) * self.conv_dtype.itemsize
        temporal_size = (
            int(np.prod(self.temporal_state_shape)) * self.temporal_dtype.itemsize
        )
        return (conv_total_size + temporal_size) * self.num_mamba_layers

    def get_ksize_per_token(self):
        return self.get_size_per_token()

    @staticmethod
    def _item_size_per_index(tensor: torch.Tensor) -> int:
        if tensor.shape[0] == 0:
            return 0
        return int(tensor[0].numel() * tensor.element_size())

    @staticmethod
    def _copy_tensor(
        src: torch.Tensor,
        dst: torch.Tensor,
        src_indices: torch.Tensor,
        dst_indices: torch.Tensor,
        io_backend: str,
    ) -> None:
        if src_indices.numel() == 0:
            return
        if io_backend == "kernel":
            # TODO: Rename the interface for clarity.
            # Here, transfer_kv_per_layer_mla is reused to transfer the Mamba state.
            # This has nothing to do with MLA; it's only reused because this interface happens to transfer a single Pool.
            transfer_kv_per_layer_mla(
                src=src,
                dst=dst,
                src_indices=src_indices,
                dst_indices=dst_indices,
                item_size=MambaPoolHost._item_size_per_index(src),
            )
        elif io_backend == "direct":
            transfer_kv_direct(
                src_layers=[src],
                dst_layers=[dst],
                src_indices=src_indices,
                dst_indices=dst_indices,
                page_size=1,
            )
        else:
            raise ValueError(f"Unsupported io_backend: {io_backend}")

    @staticmethod
    def _copy_tensor_pf_lf(
        src: torch.Tensor,
        dst: torch.Tensor,
        src_indices: torch.Tensor,
        dst_indices: torch.Tensor,
        layer_id: int,
        num_layers: int,
        io_backend: str,
    ) -> None:
        if src_indices.numel() == 0:
            return
        if io_backend == "kernel":
            item_size = MambaPoolHost._item_size_per_index(dst)
            transfer_kv_per_layer_mla_pf_lf(
                src=src,
                dst=dst,
                src_indices=src_indices,
                dst_indices=dst_indices,
                layer_id=layer_id,
                item_size=item_size,
                src_layout_dim=item_size * num_layers,
            )
        elif io_backend == "direct":
            transfer_kv_per_layer_direct_pf_lf(
                src_ptrs=[src],
                dst_ptrs=[dst],
                src_indices=src_indices,
                dst_indices=dst_indices,
                layer_id=layer_id,
                page_size=1,
            )
        else:
            raise ValueError(f"Unsupported io_backend: {io_backend}")

    @staticmethod
    def _copy_tensor_all_layers_lf_pf(
        src_layers: torch.Tensor,
        dst: torch.Tensor,
        src_indices: torch.Tensor,
        dst_indices: torch.Tensor,
        num_layers: int,
        device: str,
        io_backend: str,
    ) -> None:
        if src_indices.numel() == 0:
            return
        if io_backend == "kernel":
            item_size = MambaPoolHost._item_size_per_index(src_layers[0])
            src_ptrs = torch.tensor(
                [src_layers[i].data_ptr() for i in range(num_layers)],
                dtype=torch.uint64,
                device=device,
            )
            transfer_kv_all_layer_mla_lf_pf(
                src_layers=src_ptrs,
                dst=dst,
                src_indices=src_indices,
                dst_indices=dst_indices,
                item_size=item_size,
                dst_layout_dim=item_size * num_layers,
                num_layers=num_layers,
            )
        elif io_backend == "direct":
            src_ptrs = [src_layers[i] for i in range(num_layers)]
            transfer_kv_all_layer_direct_lf_pf(
                src_ptrs=src_ptrs,
                dst_ptrs=[dst],
                src_indices=src_indices,
                dst_indices=dst_indices,
                page_size=1,
            )
        else:
            raise ValueError(f"Unsupported io_backend: {io_backend}")

    def load_to_device_per_layer(
        self,
        device_pool,
        host_indices,
        device_indices,
        layer_id,
        io_backend="kernel",
    ):
        if self.layout in ["page_first", "page_first_direct"]:
            self._copy_tensor_pf_lf(
                src=self.temporal_buffer,
                dst=device_pool.mamba_cache.temporal[layer_id],
                src_indices=host_indices,
                dst_indices=device_indices,
                layer_id=layer_id,
                num_layers=self.num_mamba_layers,
                io_backend=io_backend,
            )
            for conv_idx in range(len(self.conv_state_shapes)):
                self._copy_tensor_pf_lf(
                    src=self.conv_buffer[conv_idx],
                    dst=device_pool.mamba_cache.conv[conv_idx][layer_id],
                    src_indices=host_indices,
                    dst_indices=device_indices,
                    layer_id=layer_id,
                    num_layers=self.num_mamba_layers,
                    io_backend=io_backend,
                )
        else:
            self._copy_tensor(
                self.temporal_buffer[layer_id],
                device_pool.mamba_cache.temporal[layer_id],
                host_indices,
                device_indices,
                io_backend,
            )
            for conv_idx in range(len(self.conv_state_shapes)):
                self._copy_tensor(
                    self.conv_buffer[conv_idx][layer_id],
                    device_pool.mamba_cache.conv[conv_idx][layer_id],
                    host_indices,
                    device_indices,
                    io_backend,
                )

    def backup_from_device_all_layer(
        self, device_pool, host_indices, device_indices, io_backend="kernel"
    ):
        if self.layout in ["page_first", "page_first_direct"]:
            self._copy_tensor_all_layers_lf_pf(
                src_layers=device_pool.mamba_cache.temporal,
                dst=self.temporal_buffer,
                src_indices=device_indices,
                dst_indices=host_indices,
                num_layers=self.num_mamba_layers,
                device=self.device_pool.device,
                io_backend=io_backend,
            )
            for conv_idx in range(len(self.conv_state_shapes)):
                self._copy_tensor_all_layers_lf_pf(
                    src_layers=device_pool.mamba_cache.conv[conv_idx],
                    dst=self.conv_buffer[conv_idx],
                    src_indices=device_indices,
                    dst_indices=host_indices,
                    num_layers=self.num_mamba_layers,
                    device=self.device_pool.device,
                    io_backend=io_backend,
                )
        else:
            for layer_id in range(self.num_mamba_layers):
                self._copy_tensor(
                    device_pool.mamba_cache.temporal[layer_id],
                    self.temporal_buffer[layer_id],
                    device_indices,
                    host_indices,
                    io_backend,
                )
                for conv_idx in range(len(self.conv_state_shapes)):
                    self._copy_tensor(
                        device_pool.mamba_cache.conv[conv_idx][layer_id],
                        self.conv_buffer[conv_idx][layer_id],
                        device_indices,
                        host_indices,
                        io_backend,
                    )

    def get_data_page(self, index, flat: bool = True) -> torch.Tensor:
        data_page = torch.cat(
            [
                self._flatten_tensor_bytes(tensor)
                for tensor in self._iter_page_tensors(index)
            ]
        )
        return data_page.flatten() if flat else data_page

    def get_dummy_flat_data_page(self) -> torch.Tensor:
        return torch.zeros(
            self.page_size * self.size_per_token,
            dtype=torch.uint8,
            device=self.device,
            pin_memory=self.pin_memory,
        )

    def set_from_flat_data_page(
        self,
        index: int,
        data_page: torch.Tensor,
    ) -> None:
        flat_bytes = data_page.contiguous().view(torch.uint8).reshape(-1)
        start = 0
        for tensor in self._iter_page_tensors(index):
            num_bytes = tensor.numel() * tensor.element_size()
            tensor_bytes = flat_bytes[start : start + num_bytes]
            start += num_bytes
            restored = tensor_bytes.view(dtype=tensor.dtype).reshape(tensor.shape)
            tensor.copy_(restored)


@dataclass
class PoolEntry:
    name: PoolName
    host_pool: Any
    device_pool: Any
    layer_mapper: Callable[[int], Optional[int]]
    is_primary_index_anchor: bool = False
    # Optional eviction callbacks for auto-alloc in HybridCacheController.
    # host_evict_fn(n): evict n slots from the host pool (used by write()).
    # device_evict_fn(n): evict n slots from the device pool (used by load()).
    host_evict_fn: Optional[Callable] = None
    device_evict_fn: Optional[Callable] = None


class HostPoolGroup:
    def __init__(self, entries: list[PoolEntry]):
        if not entries:
            raise ValueError("HostPoolGroup requires at least one pool entry.")
        self.entries = entries
        self.entry_map = {entry.name: entry for entry in entries}
        self.anchor_entry = next(
            (entry for entry in entries if entry.is_primary_index_anchor),
            entries[0],
        )

        self.layout = self.anchor_entry.host_pool.layout
        self.page_size = self.anchor_entry.host_pool.page_size
        self.device = self.anchor_entry.host_pool.device
        self.size = self.anchor_entry.host_pool.size

    def clear(self) -> None:
        for entry in self.entries:
            entry.host_pool.clear()

    def alloc(self, need_size: int) -> Optional[torch.Tensor]:
        return self.anchor_entry.host_pool.alloc(need_size)

    def free(self, indices: torch.Tensor) -> int:
        return self.anchor_entry.host_pool.free(indices)

    def get_data_page(self, index, flat: bool = True):
        return self.anchor_entry.host_pool.get_data_page(index, flat)

    def get_dummy_flat_data_page(self):
        return self.anchor_entry.host_pool.get_dummy_flat_data_page()

    def set_from_flat_data_page(self, index: int, data_page) -> None:
        return self.anchor_entry.host_pool.set_from_flat_data_page(index, data_page)

    def load_to_device_per_layer(
        self,
        device_pool,
        host_indices,
        device_indices,
        layer_id,
        io_backend,
        pool_transfers: Optional[list] = None,
    ) -> None:
        # 1. Anchor (KV) transfer
        anchor = self.anchor_entry
        local_layer_id = anchor.layer_mapper(layer_id)
        if local_layer_id is not None and host_indices.numel() > 0:
            anchor.host_pool.load_to_device_per_layer(
                anchor.device_pool,
                host_indices,
                device_indices,
                local_layer_id,
                io_backend,
            )

        # 2. Extra pool transfers
        for transfer in pool_transfers or []:
            entry = self.entry_map.get(transfer.name)
            if entry is None or transfer.host_indices is None:
                continue
            local_layer_id = entry.layer_mapper(layer_id)
            if local_layer_id is None:
                continue
            entry.host_pool.load_to_device_per_layer(
                entry.device_pool,
                transfer.host_indices,
                transfer.device_indices,
                local_layer_id,
                io_backend,
            )

    def backup_from_device_all_layer(
        self,
        device_pool,
        host_indices,
        device_indices,
        io_backend,
        pool_transfers: Optional[list] = None,
    ) -> None:
        # 1. Anchor (KV) backup
        self.anchor_entry.host_pool.backup_from_device_all_layer(
            self.anchor_entry.device_pool,
            host_indices,
            device_indices,
            io_backend,
        )
        # 2. Extra pool backup
        for transfer in pool_transfers or []:
            entry = self.entry_map.get(transfer.name)
            if entry is None or transfer.host_indices is None:
                continue
            entry.host_pool.backup_from_device_all_layer(
                entry.device_pool,
                transfer.host_indices,
                transfer.device_indices,
                io_backend,
            )


class NSATokenToKVPoolHost(MLATokenToKVPoolHost):
    device_pool: NSATokenToKVPool

    def __init__(
        self,
        device_pool: NSATokenToKVPool,
        host_to_device_ratio: float,
        host_size: int,
        page_size: int,
        layout: str,
        pin_memory: bool = True,
        device: str = "cpu",
        allocator_type: str = "default",
    ):
        # Initialize indexer metadata before HostKVCache.__init__ calls get_size_per_token.
        self.index_head_dim = device_pool.index_head_dim
        self.indexer_quant_block_size = device_pool.quant_block_size
        self.indexer_dtype = NSATokenToKVPool.index_k_with_scale_buffer_dtype
        self.indexer_size_per_token = (
            self.index_head_dim
            + self.index_head_dim // self.indexer_quant_block_size * 4
        )
        super().__init__(
            device_pool,
            host_to_device_ratio,
            host_size,
            page_size,
            layout,
            pin_memory,
            device,
            allocator_type,
            override_kv_cache_dim=device_pool.kv_cache_dim,
        )
        self.indexer_page_stride_size = (
            self.indexer_size_per_token * self.page_size * self.indexer_dtype.itemsize
        )
        self.indexer_layout_dim = self.indexer_page_stride_size * self.layer_num
        self.indexer_page_num = (self.size + self.page_size + 1) // self.page_size
        self._init_indexer_buffers()
        logger.info(
            f"NSATokenToKVPoolHost initialized with indexer page stride size: {self.indexer_page_stride_size}, page num: {self.indexer_page_num}"
        )

    def get_size_per_token(self):
        base = super().get_size_per_token()
        return (
            base
            + self.indexer_size_per_token * self.layer_num * self.indexer_dtype.itemsize
        )

    def _init_indexer_buffers(self):
        alloc_func = ALLOC_MEMORY_FUNCS[self.device_pool.device]
        self.index_k_device_ptrs = torch.tensor(
            [x.data_ptr() for x in self.device_pool.index_k_with_scale_buffer],
            dtype=torch.uint64,
            device=self.device_pool.device,
        )
        if self.layout == "layer_first":
            self.index_k_with_scale_buffer = [
                alloc_func(
                    (self.indexer_page_num, self.indexer_page_stride_size),
                    dtype=self.indexer_dtype,
                    device=self.device,
                    pin_memory=self.pin_memory,
                    allocator=self.allocator,
                )
                for _ in range(self.layer_num)
            ]
            self.index_k_data_refs = [
                self.index_k_with_scale_buffer[i] for i in range(self.layer_num)
            ]
            self.index_k_data_ptrs = torch.tensor(
                [x.data_ptr() for x in self.index_k_data_refs],
                dtype=torch.uint64,
                device=self.device_pool.device,
            )
        elif self.layout in ["page_first", "page_first_direct"]:
            self.index_k_with_scale_buffer = alloc_func(
                (
                    self.indexer_page_num,
                    self.layer_num,
                    1,
                    self.indexer_page_stride_size,
                ),
                dtype=self.indexer_dtype,
                device=self.device,
                pin_memory=self.pin_memory,
                allocator=self.allocator,
            )
        else:
            raise ValueError(f"Unsupported layout: {self.layout}")

    def _get_indexer_page_indices(self, host_indices, device_indices):
        if host_indices.numel() == 0:
            return host_indices, device_indices
        if host_indices.numel() % self.page_size != 0:
            raise ValueError(
                "Index buffer transfer expects page-aligned indices for NSA."
            )
        host_page_indices = (
            host_indices.reshape(-1, self.page_size)[:, 0] // self.page_size
        )
        device_page_indices = (
            device_indices.reshape(-1, self.page_size)[:, 0] // self.page_size
        )
        return host_page_indices, device_page_indices

    def _load_indexer_to_device_per_layer(
        self, device_pool, host_indices, device_indices, layer_id, io_backend
    ):
        host_page_indices, device_page_indices = self._get_indexer_page_indices(
            host_indices, device_indices
        )
        use_kernel = io_backend == "kernel" and self.indexer_page_stride_size % 8 == 0
        if use_kernel:
            if self.layout == "layer_first":
                transfer_kv_per_layer_mla(
                    src=self.index_k_with_scale_buffer[layer_id],
                    dst=device_pool.index_k_with_scale_buffer[layer_id],
                    src_indices=host_page_indices,
                    dst_indices=device_page_indices,
                    item_size=self.indexer_page_stride_size,
                )
            elif self.layout == "page_first":
                transfer_kv_per_layer_mla_pf_lf(
                    src=self.index_k_with_scale_buffer,
                    dst=device_pool.index_k_with_scale_buffer[layer_id],
                    src_indices=host_page_indices,
                    dst_indices=device_page_indices,
                    layer_id=layer_id,
                    item_size=self.indexer_page_stride_size,
                    src_layout_dim=self.indexer_layout_dim,
                )
            else:
                raise ValueError(f"Unsupported layout: {self.layout}")
        elif io_backend == "direct":
            if self.layout == "layer_first":
                transfer_kv_direct(
                    src_layers=[self.index_k_with_scale_buffer[layer_id]],
                    dst_layers=[device_pool.index_k_with_scale_buffer[layer_id]],
                    src_indices=host_page_indices,
                    dst_indices=device_page_indices,
                    page_size=1,
                )
            elif self.layout == "page_first_direct":
                transfer_kv_per_layer_direct_pf_lf(
                    src_ptrs=[self.index_k_with_scale_buffer],
                    dst_ptrs=[device_pool.index_k_with_scale_buffer[layer_id]],
                    src_indices=host_page_indices,
                    dst_indices=device_page_indices,
                    layer_id=layer_id,
                    page_size=1,
                )
            else:
                raise ValueError(f"Unsupported layout: {self.layout}")
        else:
            raise ValueError(f"Unsupported IO backend: {io_backend}")

    def _backup_indexer_from_device_all_layer(
        self, device_pool, host_indices, device_indices, io_backend
    ):
        host_page_indices, device_page_indices = self._get_indexer_page_indices(
            host_indices, device_indices
        )
        use_kernel = io_backend == "kernel" and self.indexer_page_stride_size % 8 == 0
        if use_kernel:
            if self.layout == "layer_first":
                transfer_kv_all_layer_mla(
                    src_layers=self.index_k_device_ptrs,
                    dst_layers=self.index_k_data_ptrs,
                    src_indices=device_page_indices,
                    dst_indices=host_page_indices,
                    item_size=self.indexer_page_stride_size,
                    num_layers=self.layer_num,
                )
            elif self.layout == "page_first":
                transfer_kv_all_layer_mla_lf_pf(
                    src_layers=self.index_k_device_ptrs,
                    dst=self.index_k_with_scale_buffer,
                    src_indices=device_page_indices,
                    dst_indices=host_page_indices,
                    item_size=self.indexer_page_stride_size,
                    dst_layout_dim=self.indexer_layout_dim,
                    num_layers=self.layer_num,
                )
            else:
                raise ValueError(f"Unsupported layout: {self.layout}")
        elif io_backend == "direct":
            if self.layout == "layer_first":
                transfer_kv_direct(
                    src_layers=device_pool.index_k_with_scale_buffer,
                    dst_layers=self.index_k_with_scale_buffer,
                    src_indices=device_page_indices,
                    dst_indices=host_page_indices,
                    page_size=1,
                )
            elif self.layout == "page_first_direct":
                transfer_kv_all_layer_direct_lf_pf(
                    src_ptrs=device_pool.index_k_with_scale_buffer,
                    dst_ptrs=[self.index_k_with_scale_buffer],
                    src_indices=device_page_indices,
                    dst_indices=host_page_indices,
                    page_size=1,
                )
            else:
                raise ValueError(f"Unsupported layout: {self.layout}")
        else:
            raise ValueError(f"Unsupported IO backend: {io_backend}")

    def load_to_device_per_layer(
        self,
        device_pool,
        host_indices,
        device_indices,
        layer_id,
        io_backend,
    ):
        super().load_to_device_per_layer(
            device_pool, host_indices, device_indices, layer_id, io_backend
        )
        self._load_indexer_to_device_per_layer(
            device_pool, host_indices, device_indices, layer_id, io_backend
        )

    def backup_from_device_all_layer(
        self, device_pool, host_indices, device_indices, io_backend
    ):
        super().backup_from_device_all_layer(
            device_pool, host_indices, device_indices, io_backend
        )
        self._backup_indexer_from_device_all_layer(
            device_pool, host_indices, device_indices, io_backend
        )
