from __future__ import annotations

"""
Copyright 2023-2025 SGLang Team
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
    http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""

import logging
import math
import threading
import time
from queue import Empty, Full, PriorityQueue, Queue
from typing import TYPE_CHECKING, List, Optional

import torch

if TYPE_CHECKING:
    from sglang.srt.mem_cache.allocator import BaseTokenToKVPoolAllocator
    from sglang.srt.mem_cache.memory_pool_host import HostKVCache

from sglang.srt.distributed import get_tensor_model_parallel_rank
from sglang.srt.mem_cache.memory_pool import MHATokenToKVPool, MLATokenToKVPool

logger = logging.getLogger(__name__)


class LayerDoneCounter:
    def __init__(self, num_layers):
        self.num_layers = num_layers
        # extra producer and consumer counters for overlap mode
        self.num_counters = 3
        self.counters = [num_layers] * self.num_counters
        self.conditions = [threading.Condition() for _ in range(self.num_counters)]
        self.producer_index = 0
        self.consumer_index = 0

    def next_producer(self):
        return (self.producer_index + 1) % self.num_counters

    def update_producer(self):
        self.producer_index = self.next_producer()
        return self.producer_index

    def set_consumer(self, index):
        self.consumer_index = index

    def increment(self):
        with self.conditions[self.producer_index]:
            self.counters[self.producer_index] += 1
            self.conditions[self.producer_index].notify_all()

    def wait_until(self, threshold):
        with self.conditions[self.consumer_index]:
            while self.counters[self.consumer_index] <= threshold:
                self.conditions[self.consumer_index].wait()

    def reset(self):
        with self.conditions[self.producer_index]:
            self.counters[self.producer_index] = 0


class CacheOperation:

    counter = 0

    def __init__(
        self,
        host_indices: torch.Tensor,
        device_indices: torch.Tensor,
        node_id: int,
        priority: Optional[int] = None,
    ):
        self.host_indices = host_indices
        self.device_indices = device_indices
        self.node_ids = [node_id]
        self.data = None

        self.id = CacheOperation.counter
        CacheOperation.counter += 1
        # default priority is the order of creation
        self.priority = priority if priority is not None else self.id

    def merge(self, other: "CacheOperation") -> None:
        # multiple operations can be merged into a single operation for batch processing
        self.host_indices = torch.cat([self.host_indices, other.host_indices])
        self.device_indices = torch.cat([self.device_indices, other.device_indices])
        self.priority = min(self.priority, other.priority)
        self.node_ids.extend(other.node_ids)

    def split(self, factor) -> List["CacheOperation"]:
        # split an operation into smaller operations to reduce the size of intermediate buffers
        if factor <= 1:
            return [self]

        chunk_size = math.ceil(len(self.host_indices) / factor)
        split_ops = []
        for i in range(0, len(self.host_indices), chunk_size):
            split_ops.append(
                CacheOperation(
                    host_indices=self.host_indices[i : i + chunk_size],
                    device_indices=self.device_indices[i : i + chunk_size],
                    node_id=0,
                )
            )
        # Inherit the node_ids on the final chunk
        if split_ops:
            split_ops[-1].node_ids = self.node_ids

        return split_ops

    def __lt__(self, other: "CacheOperation"):
        return self.priority < other.priority


class TransferBuffer:
    """
    Overlapping buffer preparation and transfer operations to improve throughput.
    """

    def __init__(
        self, stop_event, buffer_count: int = 3, max_buffer_size: int = 1024
    ) -> None:
        self.stop_event = stop_event
        self.buffers = Queue(maxsize=buffer_count)
        # todo: adjust the buffer size based on throughput profile of the system
        self.max_buffer_size = max_buffer_size

    def full(self) -> bool:
        return self.buffers.full()

    def empty(self) -> bool:
        return self.buffers.empty()

    def put(self, item, block=True, timeout=1) -> None:
        while not self.stop_event.is_set():
            try:
                self.buffers.put(item, block=block, timeout=timeout)
                break
            except Full:
                if not block:
                    break
                continue
            except Exception as e:
                logger.error(e)

    def get(self, block=True, timeout=1) -> Optional[CacheOperation]:
        try:
            return self.buffers.get(block=block, timeout=timeout)
        except Empty:
            return None
        except Exception as e:
            logger.error(e)

    def clear(self):
        self.buffers.queue.clear()


class StorageOperation:
    counter = 0

    def __init__(
        self,
        host_indices: torch.Tensor,
        token_ids: List[int],
        last_hash: Optional[str] = None,
        hash_value: Optional[List[str]] = None,
    ):
        self.host_indices = host_indices
        self.token_ids = token_ids
        self.last_hash = last_hash
        self.completed_tokens = 0
        self.hash_value = hash_value if hash_value is not None else []

        self.id = StorageOperation.counter
        StorageOperation.counter += 1

    def __lt__(self, other: "StorageOperation"):
        return self.id < other.id


class PrefetchOperation(StorageOperation):
    def __init__(
        self,
        request_id: str,
        host_indices: torch.Tensor,
        token_ids: List[int],
        last_hash: Optional[str] = None,
    ):
        self.request_id = request_id

        self._done_flag = False
        self._lock = threading.Lock()

        self.start_time = time.monotonic()

        super().__init__(host_indices, token_ids, last_hash)

    def increment(self, num_tokens: int):
        with self._lock:
            if self._done_flag:
                return False
            self.completed_tokens += num_tokens
            return True

    def mark_done(self):
        with self._lock:
            self._done_flag = True

    def is_done(self) -> bool:
        return self._done_flag


class HiCacheController:

    def __init__(
        self,
        token_to_kv_pool_allocator: BaseTokenToKVPoolAllocator,
        mem_pool_host: HostKVCache,
        page_size: int,
        tp_group: torch.distributed.ProcessGroup,
        load_cache_event: threading.Event = None,
        write_policy: str = "write_through_selective",
        io_backend: str = "",
        storage_backend: Optional[str] = None,
        prefetch_threshold: int = 256,
    ):
        self.mem_pool_device_allocator = token_to_kv_pool_allocator
        self.mem_pool_device = token_to_kv_pool_allocator.get_kvcache()
        self.mem_pool_host = mem_pool_host
        self.write_policy = write_policy
        self.page_size = page_size
        self.io_backend = io_backend

        self.enable_storage = False

        # todo: move backend initialization to storage backend module
        if storage_backend is not None:
            self.storage_backend_type = storage_backend
            from sglang.srt.mem_cache.hicache_storage import get_hash_str

            self.get_hash_str = get_hash_str

            # Currently, AscendMLAPagedTokenToKVPool is the subclass of MLATokenToKVPool.
            is_mla_backend = isinstance(self.mem_pool_device, MLATokenToKVPool)
            # In MLA backend, only one rank needs to backup the KV cache
            self.backup_skip = (
                is_mla_backend
                # todo: for load balancing, decide which rank to backup the KV cache by hash value
                and get_tensor_model_parallel_rank() != 0
                # todo: support other storage backends
                and self.storage_backend_type in ["file", "mooncake"]
            )
            if storage_backend == "file":
                from sglang.srt.mem_cache.hicache_storage import HiCacheFile

                self.storage_backend = HiCacheFile(is_mla_backend=is_mla_backend)
            elif storage_backend == "nixl":
                from sglang.srt.mem_cache.storage.nixl.hicache_nixl import HiCacheNixl

                self.storage_backend = HiCacheNixl()
            elif storage_backend == "mooncake":
                from sglang.srt.mem_cache.storage.mooncake_store.mooncake_store import (
                    MooncakeStore,
                )

                self.storage_backend = MooncakeStore(is_mla_backend=is_mla_backend)
                self.storage_backend.register_buffer(self.mem_pool_host.kv_buffer)
                assert self.mem_pool_host.layout == "page_first"
            elif storage_backend == "hf3fs":
                from sglang.srt.mem_cache.storage.hf3fs.storage_hf3fs import (
                    HiCacheHF3FS,
                )

                if self.mem_pool_host.layout == "page_first":
                    bytes_per_page = (
                        mem_pool_host.get_ksize_per_token() * mem_pool_host.page_size
                    )
                elif self.mem_pool_host.layout == "layer_first":
                    bytes_per_page = (
                        mem_pool_host.get_size_per_token() * mem_pool_host.page_size
                    )
                dtype = mem_pool_host.dtype
                self.storage_backend = HiCacheHF3FS.from_env_config(
                    bytes_per_page, dtype
                )
            else:
                raise NotImplementedError(
                    f"Unsupported storage backend: {storage_backend}"
                )
            self.enable_storage = True
            # todo: threshold policy for prefetching
            self.prefetch_threshold = max(prefetch_threshold, self.page_size)
            self.prefetch_capacity_limit = int(
                0.8 * (self.mem_pool_host.size - self.mem_pool_device.size)
            )
            # tracking the number of tokens locked in prefetching, updated by the main scheduler thread
            self.prefetch_tokens_occupied = 0

            # create a new communication group for synchronizing storage operations across TP workers
            self.tp_world_size = torch.distributed.get_world_size(group=tp_group)
            if self.tp_world_size > 1:
                group_ranks = torch.distributed.get_process_group_ranks(tp_group)
                self.prefetch_tp_group = torch.distributed.new_group(
                    group_ranks, backend="gloo"
                )
                self.prefetch_io_tp_group = torch.distributed.new_group(
                    group_ranks, backend="gloo"
                )
                self.backup_tp_group = torch.distributed.new_group(
                    group_ranks, backend="gloo"
                )

        self.load_cache_event = load_cache_event
        self.layer_done_counter = LayerDoneCounter(self.mem_pool_device.layer_num)
        self.mem_pool_device.register_layer_transfer_counter(self.layer_done_counter)

        if write_policy not in [
            "write_through",
            "write_through_selective",
            "write_back",
        ]:
            raise ValueError(f"Invalid write policy: {write_policy}")

        self.write_queue = PriorityQueue()
        self.load_queue = PriorityQueue()

        self.ack_write_queue = Queue()
        self.ack_load_queue = Queue()

        self.stop_event = threading.Event()
        self.write_buffer = TransferBuffer(self.stop_event)
        self.load_buffer = TransferBuffer(
            self.stop_event, buffer_count=10, max_buffer_size=100
        )

        self.write_stream = torch.cuda.Stream()
        self.load_stream = torch.cuda.Stream()

        self.write_thread = threading.Thread(
            target=self.write_thread_func_direct, daemon=True
        )
        self.load_thread = threading.Thread(
            target=self.load_thread_func_layer_by_layer, daemon=True
        )

        self.write_thread.start()
        self.load_thread.start()

        if self.enable_storage:
            self.prefetch_thread = threading.Thread(
                target=self.prefetch_thread_func, daemon=True
            )
            self.backup_thread = threading.Thread(
                target=self.backup_thread_func, daemon=True
            )
            self.prefetch_queue = Queue()
            self.backup_queue = Queue()

            self.prefetch_revoke_queue = Queue()
            self.ack_backup_queue = Queue()

            self.prefetch_thread.start()
            self.backup_thread.start()

    def reset(self):
        self.stop_event.set()
        self.write_thread.join()
        self.load_thread.join()

        self.write_queue.queue.clear()
        self.load_queue.queue.clear()
        self.write_buffer.clear()
        self.load_buffer.clear()
        self.ack_write_queue.queue.clear()
        self.ack_load_queue.queue.clear()
        if self.enable_storage:
            self.prefetch_thread.join()
            self.backup_thread.join()
            self.prefetch_queue.queue.clear()
            self.backup_queue.queue.clear()
            self.prefetch_revoke_queue.queue.clear()
            self.ack_backup_queue.queue.clear()

        self.write_thread = threading.Thread(
            target=self.write_thread_func_direct, daemon=True
        )
        self.load_thread = threading.Thread(
            target=self.load_thread_func_layer_by_layer, daemon=True
        )
        self.stop_event.clear()
        self.write_thread.start()
        self.load_thread.start()

        if self.enable_storage:
            self.prefetch_thread = threading.Thread(
                target=self.prefetch_thread_func, daemon=True
            )
            self.backup_thread = threading.Thread(
                target=self.backup_thread_func, daemon=True
            )
            self.prefetch_thread.start()
            self.backup_thread.start()

    def write(
        self,
        device_indices: torch.Tensor,
        priority: Optional[int] = None,
        node_id: int = 0,
    ) -> Optional[torch.Tensor]:
        """
        Back up KV caches from device memory to host memory.
        """
        host_indices = self.mem_pool_host.alloc(len(device_indices))
        if host_indices is None:
            return None
        self.mem_pool_host.protect_write(host_indices)
        torch.cuda.current_stream().synchronize()
        self.write_queue.put(
            CacheOperation(host_indices, device_indices, node_id, priority)
        )
        return host_indices

    def load(
        self,
        host_indices: torch.Tensor,
        priority: Optional[int] = None,
        node_id: int = 0,
    ) -> Optional[torch.Tensor]:
        """
        Load KV caches from host memory to device memory.
        """
        device_indices = self.mem_pool_device_allocator.alloc(len(host_indices))
        if device_indices is None:
            return None
        self.mem_pool_host.protect_load(host_indices)
        # to ensure the device indices are ready before accessed by another CUDA stream
        torch.cuda.current_stream().synchronize()
        self.load_queue.put(
            CacheOperation(host_indices, device_indices, node_id, priority)
        )
        return device_indices

    def move_indices(self, host_indices, device_indices):
        # move indices to GPU if using kernels, to host if using direct indexing
        if self.io_backend == "kernel":
            return host_indices.to(self.mem_pool_device.device), device_indices
        elif self.io_backend == "direct":
            device_indices = device_indices.cpu()
            host_indices, idx = host_indices.sort()
            return host_indices, device_indices.index_select(0, idx)
        else:
            raise ValueError(f"Unsupported io backend")

    def write_thread_func_direct(self):
        """
        Directly write through KV caches to host memory without buffering.
        """
        torch.cuda.set_stream(self.write_stream)
        while not self.stop_event.is_set():
            try:
                operation = self.write_queue.get(block=True, timeout=1)
                host_indices, device_indices = self.move_indices(
                    operation.host_indices, operation.device_indices
                )
                self.mem_pool_host.backup_from_device_all_layer(
                    self.mem_pool_device, host_indices, device_indices, self.io_backend
                )
                self.write_stream.synchronize()
                self.mem_pool_host.complete_io(operation.host_indices)
                for node_id in operation.node_ids:
                    if node_id != 0:
                        self.ack_write_queue.put(node_id)
            except Empty:
                continue
            except Exception as e:
                logger.error(e)

    def load_thread_func_layer_by_layer(self):
        """
        Load KV caches from host memory to device memory layer by layer.
        """
        torch.cuda.set_stream(self.load_stream)
        while not self.stop_event.is_set():
            self.load_cache_event.wait(timeout=1)
            if not self.load_cache_event.is_set():
                continue
            self.load_cache_event.clear()
            self.layer_done_counter.update_producer()

            batch_operation = None
            while self.load_queue.qsize() > 0:
                op = self.load_queue.get(block=True)
                if batch_operation is None:
                    batch_operation = op
                else:
                    batch_operation.merge(op)
            if batch_operation is None:
                continue

            # start layer-wise KV cache transfer from CPU to GPU
            self.layer_done_counter.reset()
            host_indices, device_indices = self.move_indices(
                batch_operation.host_indices, batch_operation.device_indices
            )
            for i in range(self.mem_pool_host.layer_num):
                self.mem_pool_host.load_to_device_per_layer(
                    self.mem_pool_device,
                    host_indices,
                    device_indices,
                    i,
                    self.io_backend,
                )
                self.load_stream.synchronize()
                self.layer_done_counter.increment()

            self.mem_pool_host.complete_io(batch_operation.host_indices)
            for node_id in batch_operation.node_ids:
                if node_id != 0:
                    self.ack_load_queue.put(node_id)

    def evict_device(
        self, device_indices: torch.Tensor, host_indices: torch.Tensor
    ) -> int:
        if self.mem_pool_host.is_synced(host_indices):
            self.mem_pool_device_allocator.free(device_indices)
            self.mem_pool_host.update_backup(host_indices)
            return len(device_indices)
        else:
            raise ValueError(
                f"Inconsistent states: {self.mem_pool_host.get_state(host_indices)}"
            )

    def evict_host(self, host_indices: torch.Tensor, backup_only: bool = True) -> int:
        if not backup_only:
            raise ValueError("Other eviction policies are not supported yet.")

        if self.mem_pool_host.is_backup(host_indices):
            self.mem_pool_host.free(host_indices)
            return len(host_indices)
        else:
            raise ValueError(
                f"Inconsistent states: {self.mem_pool_host.get_state(host_indices)}"
            )

    def prefetch(
        self,
        request_id: str,
        host_indices: torch.Tensor,
        new_input_tokens: List[int],
        last_hash: Optional[str] = None,
    ) -> PrefetchOperation:
        """
        Prefetch KV caches from storage backend to host memory.
        """
        operation = PrefetchOperation(
            request_id, host_indices, new_input_tokens, last_hash
        )
        self.prefetch_queue.put(operation)
        return operation

    def terminate_prefetch(self, operation):
        operation.mark_done()
        return operation.completed_tokens, operation.hash_value

    # zero copy
    def _3fs_zero_copy_page_get(self, operation, hash_values, host_indices):
        hashes, dsts = self.mem_pool_host.get_buffer_with_hash(
            hash_values, host_indices
        )
        page_data = self.storage_backend.batch_get(hashes, dsts)
        if page_data:
            operation.increment(self.page_size * len(hashes))
        else:
            logger.warning(
                f"Prefetch operation {operation.request_id} failed to retrieve page {hashes}."
            )

    # zero copy
    def _mooncake_page_get(self, operation, hash_values, host_indices):
        key_strs, buffer_ptrs, buffer_sizes = self.mem_pool_host.get_buffer_meta(
            hash_values,
            host_indices,
        )
        get_result = self.storage_backend.batch_get(
            key_strs,
            target_location=buffer_ptrs,
            target_sizes=buffer_sizes,
        )
        if get_result != len(hash_values):
            logger.warning(
                f"Prefetch operation {operation.request_id} failed or partially failed."
            )
        if get_result != 0:
            operation.increment(get_result * self.page_size)

    # non-zero copy
    def _generic_page_get(self, operation, hash_values, host_indices):
        # todo: zero copy
        dummy_page_dst = [self.mem_pool_host.get_dummy_flat_data_page()] * len(
            hash_values
        )
        page_data = self.storage_backend.batch_get(hash_values, dummy_page_dst)
        if page_data is None:
            return
        for i in range(len(hash_values)):
            if page_data[i] is None:
                logger.warning(
                    f"Prefetch operation {operation.request_id} failed to retrieve page {hash_values[i]}."
                )
                break
            if operation.increment(self.page_size):
                self.mem_pool_host.set_from_flat_data_page(
                    host_indices[i * self.page_size],
                    page_data[i],
                )
            else:
                break

    def _page_transfer(self, operation):
        # Select the get function and batch size
        if self.is_mooncake_backend():
            get_func = self._mooncake_page_get
            batch_size = 128
        elif self.storage_backend_type == "hf3fs":
            if self.mem_pool_host.layout == "page_first":
                get_func = self._3fs_zero_copy_page_get
            elif self.mem_pool_host.layout == "layer_first":
                get_func = self._generic_page_get
            batch_size = 128
        else:
            get_func = self._generic_page_get
            batch_size = 8

        # Transfer batch by batch
        for i in range(0, len(operation.hash_value), batch_size):
            batch_hashes = operation.hash_value[i : i + batch_size]
            batch_host_indices = operation.host_indices[
                i * self.page_size : (i + len(batch_hashes)) * self.page_size
            ]
            prev_completed_tokens = operation.completed_tokens
            # Get one batch token, and update the completed_tokens if succeed
            get_func(operation, batch_hashes, batch_host_indices)
            # Check termination
            if (
                operation.completed_tokens
                != prev_completed_tokens + len(batch_hashes) * self.page_size
            ):
                break  # Some operations fail or operation terminated by controller
        # release pre-allocated memory
        self.mem_pool_host.free(operation.host_indices[operation.completed_tokens :])

    def is_mooncake_backend(self):
        return self.storage_backend_type == "mooncake"

    def prefetch_io_aux_func(self):
        """
        Auxiliary function conducting IO operations for prefetching.
        """
        while not self.stop_event.is_set():
            try:
                operation = self.prefetch_buffer.get(block=True, timeout=1)
                self._page_transfer(operation)

                if self.tp_world_size > 1:
                    # to ensure all TP workers release the host memory at the same time
                    torch.distributed.barrier(group=self.prefetch_io_tp_group)
                # operation terminated by controller, release pre-allocated memory
                self.mem_pool_host.free(
                    operation.host_indices[operation.completed_tokens :]
                )
            except Empty:
                continue

    def prefetch_rate_limit_check(self) -> bool:
        """
        Rate limit the prefetching operations to avoid overwhelming the storage backend.
        """
        # cancel prefetch if too much memory is occupied
        if self.prefetch_tokens_occupied >= self.prefetch_capacity_limit:
            return False
        # todo: more sophisticated rate limiting based on storage backend performance
        return True

    def _generic_storage_hit_query(self, operation) -> tuple[list[str], int]:
        last_hash = operation.last_hash
        tokens_to_fetch = operation.token_ids

        storage_query_count = 0
        remaining_tokens = len(tokens_to_fetch)
        hash_value = []
        while remaining_tokens >= self.page_size:
            last_hash = self.get_hash_str(
                tokens_to_fetch[
                    storage_query_count : storage_query_count + self.page_size
                ],
                last_hash,
            )
            hash_value.append(last_hash)
            storage_query_count += self.page_size
            remaining_tokens -= self.page_size
        # deferring to batch exists
        hit_page_num = self.storage_backend.batch_exists(hash_value)
        return hash_value[:hit_page_num], hit_page_num * self.page_size

    def prefetch_thread_func(self):
        """
        Manage prefetching operations from storage backend to host memory.
        """
        self.prefetch_buffer = Queue()
        aux_thread = threading.Thread(target=self.prefetch_io_aux_func, daemon=True)
        aux_thread.start()
        while (not self.stop_event.is_set()) or not self.prefetch_queue.empty():
            try:
                operation = self.prefetch_queue.get(block=True, timeout=1)
                if operation is None:
                    continue

                if (
                    operation.host_indices is not None
                ) and self.prefetch_rate_limit_check():
                    hash_value, storage_hit_count = self._generic_storage_hit_query(
                        operation
                    )

                if self.tp_world_size > 1:
                    storage_hit_count_tensor = torch.tensor(
                        storage_hit_count, dtype=torch.int
                    )
                    torch.distributed.all_reduce(
                        storage_hit_count_tensor,
                        op=torch.distributed.ReduceOp.MIN,
                        group=self.prefetch_tp_group,
                    )
                    storage_hit_count = storage_hit_count_tensor.item()

                if storage_hit_count < self.prefetch_threshold:
                    # not to prefetch if not enough benefits
                    self.prefetch_revoke_queue.put(operation.request_id)
                    if operation.host_indices is not None:
                        self.mem_pool_host.free(operation.host_indices)
                    logger.debug(
                        f"Revoking prefetch for request {operation.request_id} due to insufficient hits ({storage_hit_count})."
                    )
                else:
                    operation.hash_value = hash_value[
                        : (storage_hit_count // self.page_size)
                    ]
                    # free the pre-allocated memory for pages that are not hit
                    self.mem_pool_host.free(operation.host_indices[storage_hit_count:])
                    operation.host_indices = operation.host_indices[:storage_hit_count]
                    logger.debug(
                        f"Prefetching {len(operation.hash_value)} pages for request {operation.request_id}."
                    )
                    self.prefetch_buffer.put(operation)

            except Empty:
                continue

    def write_storage(
        self,
        host_indices: torch.Tensor,
        token_ids: List[int],
        hash_value: Optional[List[str]] = None,
    ) -> int:
        """
        Write KV caches from host memory to storage backend.
        """
        operation = StorageOperation(host_indices, token_ids, hash_value=hash_value)
        self.backup_queue.put(operation)
        return operation.id

    # non-zero copy
    def _generic_page_set(self, hash_values, host_indices) -> bool:
        data = [
            self.mem_pool_host.get_flat_data_page(host_indices[i * self.page_size])
            for i in range(len(hash_values))
        ]
        return self.storage_backend.batch_set(hash_values, data)

    # zero copy
    def _mooncake_page_set(self, hash_values, host_indices) -> bool:
        key_strs, buffer_ptrs, buffer_sizes = self.mem_pool_host.get_buffer_meta(
            hash_values,
            host_indices,
        )
        success = self.storage_backend.batch_set(
            key_strs,
            target_location=buffer_ptrs,
            target_sizes=buffer_sizes,
        )
        return success

    # zero copy
    def _3fs_zero_copy_page_set(self, hash_values, host_indices) -> bool:
        hashes, dsts = self.mem_pool_host.get_buffer_with_hash(
            hash_values, host_indices
        )
        return self.storage_backend.batch_set(hashes, dsts)

    # Backup batch by batch
    def _page_backup(self, operation):
        # Select the set function and batch size
        if self.is_mooncake_backend():
            backup_set_func = self._mooncake_page_set
            batch_size = 128
        elif self.storage_backend_type == "hf3fs":
            if self.mem_pool_host.layout == "page_first":
                backup_set_func = self._3fs_zero_copy_page_set
            elif self.mem_pool_host.layout == "layer_first":
                backup_set_func = self._generic_page_set
            batch_size = 128
        else:
            backup_set_func = self._generic_page_set
            batch_size = 8
        # Backup batch by batch
        for i in range(0, len(operation.hash_value), batch_size):
            batch_hashes = operation.hash_value[i : i + batch_size]
            batch_host_indices = operation.host_indices[
                i * self.page_size : (i + len(batch_hashes)) * self.page_size
            ]
            # Set one batch token, and record if success.
            # todo: allow partial success
            success = backup_set_func(batch_hashes, batch_host_indices)
            if not success:
                logger.warning(
                    f"Write page to storage: {len(batch_hashes)} pages failed."
                )
                break
            operation.completed_tokens += self.page_size * len(batch_hashes)

    def backup_thread_func(self):
        """
        Manage backup operations from host memory to storage backend.
        """
        while not self.stop_event.is_set():
            try:
                operation = self.backup_queue.get(block=True, timeout=1)
                if operation is None:
                    continue

                if not self.backup_skip:
                    self._page_backup(operation)
                    min_completed_tokens = operation.completed_tokens
                else:
                    min_completed_tokens = len(operation.token_ids)

                if self.tp_world_size > 1:
                    completed_tokens_tensor = torch.tensor(
                        min_completed_tokens, dtype=torch.int
                    )
                    torch.distributed.all_reduce(
                        completed_tokens_tensor,
                        op=torch.distributed.ReduceOp.MIN,
                        group=self.backup_tp_group,
                    )
                    min_completed_tokens = completed_tokens_tensor.item()

                self.ack_backup_queue.put(
                    (
                        operation.id,
                        min_completed_tokens,
                    )
                )

            except Empty:
                continue
