"""
Copyright 2023-2024 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.
"""

from sglang.srt.torch_memory_saver_adapter import TorchMemorySaverAdapter

"""
Memory pool.

SGLang has two levels of memory pool.
ReqToTokenPool maps a request to its token locations.
TokenToKVPoolAllocator manages the indices to kv cache data.
KVCache actually holds the physical kv cache.
"""

import abc
import logging
import os
from contextlib import nullcontext
from typing import List, Optional, Tuple, Union

import numpy as np
import torch
import triton
import triton.language as tl

from sglang.srt.constants import GPU_MEMORY_TYPE_KV_CACHE
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.utils import debug_timing, get_bool_env_var, is_cuda, next_power_of_2

logger = logging.getLogger(__name__)

GB = 1024 * 1024 * 1024
_is_cuda = is_cuda()


class ReqToTokenPool:
    """A memory pool that maps a request to its token locations."""

    def __init__(
        self,
        size: int,
        max_context_len: int,
        device: str,
        enable_memory_saver: bool,
    ):

        memory_saver_adapter = TorchMemorySaverAdapter.create(
            enable=enable_memory_saver
        )

        self.size = size
        self.max_context_len = max_context_len
        self.device = device
        with memory_saver_adapter.region(GPU_MEMORY_TYPE_KV_CACHE):
            self.req_to_token = torch.zeros(
                (size, max_context_len), dtype=torch.int32, device=device
            )
        self.free_slots = list(range(size))

    def write(self, indices, values):
        self.req_to_token[indices] = values

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

    def alloc(self, need_size: int) -> List[int]:
        if need_size > len(self.free_slots):
            return None

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

        return select_index

    def free(self, free_index: Union[int, List[int]]):
        if isinstance(free_index, (int,)):
            self.free_slots.append(free_index)
        else:
            self.free_slots.extend(free_index)

    def clear(self):
        self.free_slots = list(range(self.size))


class KVCache(abc.ABC):
    @abc.abstractmethod
    def __init__(
        self,
        size: int,
        page_size: int,
        dtype: torch.dtype,
        layer_num: int,
        device: str,
        enable_memory_saver: bool,
        start_layer: Optional[int] = None,
        end_layer: Optional[int] = None,
    ):
        self.size = size
        self.page_size = page_size
        self.dtype = dtype
        self.device = device
        if dtype in (torch.float8_e5m2, torch.float8_e4m3fn):
            # NOTE: Store as torch.uint8 because Tensor.index_put is not implemented for torch.float8_e5m2
            self.store_dtype = torch.uint8
        else:
            self.store_dtype = dtype
        self.layer_num = layer_num
        self.start_layer = start_layer or 0
        self.end_layer = end_layer or layer_num - 1
        self.memory_saver_adapter = TorchMemorySaverAdapter.create(
            enable=enable_memory_saver
        )

    @abc.abstractmethod
    def get_key_buffer(self, layer_id: int) -> torch.Tensor:
        raise NotImplementedError()

    @abc.abstractmethod
    def get_value_buffer(self, layer_id: int) -> torch.Tensor:
        raise NotImplementedError()

    @abc.abstractmethod
    def get_kv_buffer(self, layer_id: int) -> Tuple[torch.Tensor, torch.Tensor]:
        raise NotImplementedError()

    @abc.abstractmethod
    def set_kv_buffer(
        self,
        layer: RadixAttention,
        loc: torch.Tensor,
        cache_k: torch.Tensor,
        cache_v: torch.Tensor,
    ) -> None:
        raise NotImplementedError()

    def get_flat_data(self, indices):
        raise NotImplementedError()

    def transfer(self, indices, flat_data):
        raise NotImplementedError()

    def transfer_per_layer(self, indices, flat_data, layer_id):
        raise NotImplementedError()

    def register_layer_transfer_counter(self, layer_transfer_counter):
        self.layer_transfer_counter = layer_transfer_counter


class TokenToKVPoolAllocator:
    """An allocator managing the indices to kv cache data."""

    def __init__(
        self,
        size: int,
        dtype: torch.dtype,
        device: str,
        kvcache: KVCache,
    ):
        self.size = size
        self.dtype = dtype
        self.device = device
        self.page_size = 1

        self.free_slots = None
        self.is_not_in_free_group = True
        self.free_group = []
        self.clear()

        self._kvcache = kvcache

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

    def debug_print(self) -> str:
        return ""

    def get_kvcache(self):
        return self._kvcache

    def alloc(self, need_size: int):
        if need_size > len(self.free_slots):
            return None

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

    def free(self, free_index: torch.Tensor):
        if free_index.numel() == 0:
            return

        if self.is_not_in_free_group:
            self.free_slots = torch.cat((self.free_slots, free_index))
        else:
            self.free_group.append(free_index)

    def free_group_begin(self):
        self.is_not_in_free_group = False
        self.free_group = []

    def free_group_end(self):
        self.is_not_in_free_group = True
        if self.free_group:
            self.free(torch.cat(self.free_group))

    def backup_state(self):
        return self.free_slots

    def restore_state(self, free_slots):
        self.free_slots = free_slots

    def clear(self):
        # The padded slot 0 is used for writing dummy outputs from padded tokens.
        self.free_slots = torch.arange(
            1, self.size + 1, dtype=torch.int64, device=self.device
        )
        self.is_not_in_free_group = True
        self.free_group = []

    def get_cpu_copy(self, indices):
        return self._kvcache.get_cpu_copy(indices)

    def load_cpu_copy(self, kv_cache_cpu, indices):
        return self._kvcache.load_cpu_copy(kv_cache_cpu, indices)


class MHATokenToKVPool(KVCache):

    def __init__(
        self,
        size: int,
        page_size: int,
        dtype: torch.dtype,
        head_num: int,
        head_dim: int,
        layer_num: int,
        device: str,
        enable_memory_saver: bool,
        start_layer: Optional[int] = None,
        end_layer: Optional[int] = None,
    ):
        super().__init__(
            size,
            page_size,
            dtype,
            layer_num,
            device,
            enable_memory_saver,
            start_layer,
            end_layer,
        )

        self.head_num = head_num
        self.head_dim = head_dim

        # for disagg with nvlink
        self.enable_custom_mem_pool = get_bool_env_var(
            "SGLANG_MOONCAKE_CUSTOM_MEM_POOL", "false"
        )
        if self.enable_custom_mem_pool:
            from sglang.srt.disaggregation.mooncake.memory_pool import (
                MooncakeNVLinkAllocator,
            )

            # TODO(shangming): abstract custom allocator class for more backends
            allocator = MooncakeNVLinkAllocator.get_allocator(self.device)
            self.custom_mem_pool = torch.cuda.MemPool(allocator.allocator())
        else:
            self.custom_mem_pool = None

        self._create_buffers()

        # used for chunked cpu-offloading
        self.chunk_size = 8192
        self.layer_transfer_counter = None
        self.device_module = torch.get_device_module(self.device)
        self.alt_stream = self.device_module.Stream() if _is_cuda else None

        k_size, v_size = self.get_kv_size_bytes()
        logger.info(
            f"KV Cache is allocated. #tokens: {size}, K size: {k_size / GB:.2f} GB, V size: {v_size / GB:.2f} GB"
        )

    def _create_buffers(self):
        with self.memory_saver_adapter.region(GPU_MEMORY_TYPE_KV_CACHE):
            with (
                torch.cuda.use_mem_pool(self.custom_mem_pool)
                if self.enable_custom_mem_pool
                else nullcontext()
            ):
                # [size, head_num, head_dim] for each layer
                # The padded slot 0 is used for writing dummy outputs from padded tokens.
                self.k_buffer = [
                    torch.zeros(
                        (self.size + self.page_size, self.head_num, self.head_dim),
                        dtype=self.store_dtype,
                        device=self.device,
                    )
                    for _ in range(self.layer_num)
                ]
                self.v_buffer = [
                    torch.zeros(
                        (self.size + self.page_size, self.head_num, self.head_dim),
                        dtype=self.store_dtype,
                        device=self.device,
                    )
                    for _ in range(self.layer_num)
                ]

        self.data_ptrs = torch.tensor(
            [x.data_ptr() for x in self.k_buffer + self.v_buffer],
            dtype=torch.uint64,
            device=self.device,
        )
        self.data_strides = torch.tensor(
            [
                np.prod(x.shape[1:]) * x.dtype.itemsize
                for x in self.k_buffer + self.v_buffer
            ],
            device=self.device,
        )

    def _clear_buffers(self):
        del self.k_buffer
        del self.v_buffer

    def get_kv_size_bytes(self):
        assert hasattr(self, "k_buffer")
        assert hasattr(self, "v_buffer")
        k_size_bytes = 0
        for k_cache in self.k_buffer:
            k_size_bytes += np.prod(k_cache.shape) * k_cache.dtype.itemsize
        v_size_bytes = 0
        for v_cache in self.v_buffer:
            v_size_bytes += np.prod(v_cache.shape) * v_cache.dtype.itemsize
        return k_size_bytes, v_size_bytes

    # for disagg
    def get_contiguous_buf_infos(self):
        # layer_num x [seq_len, head_num, head_dim]
        # layer_num x [page_num, page_size, head_num, head_dim]
        kv_data_ptrs = [
            self.get_key_buffer(i).data_ptr()
            for i in range(self.start_layer, self.start_layer + self.layer_num)
        ] + [
            self.get_value_buffer(i).data_ptr()
            for i in range(self.start_layer, self.start_layer + self.layer_num)
        ]
        kv_data_lens = [
            self.get_key_buffer(i).nbytes
            for i in range(self.start_layer, self.start_layer + self.layer_num)
        ] + [
            self.get_value_buffer(i).nbytes
            for i in range(self.start_layer, self.start_layer + self.layer_num)
        ]
        kv_item_lens = [
            self.get_key_buffer(i)[0].nbytes * self.page_size
            for i in range(self.start_layer, self.start_layer + self.layer_num)
        ] + [
            self.get_value_buffer(i)[0].nbytes * self.page_size
            for i in range(self.start_layer, self.start_layer + self.layer_num)
        ]
        return kv_data_ptrs, kv_data_lens, kv_item_lens

    def maybe_get_custom_mem_pool(self):
        return self.custom_mem_pool

    def get_cpu_copy(self, indices):
        torch.cuda.synchronize()
        kv_cache_cpu = []
        for layer_id in range(self.layer_num):
            kv_cache_cpu.append([])
            for i in range(0, len(indices), self.chunk_size):
                chunk_indices = indices[i : i + self.chunk_size]
                k_cpu = self.k_buffer[layer_id][chunk_indices].to(
                    "cpu", non_blocking=True
                )
                v_cpu = self.v_buffer[layer_id][chunk_indices].to(
                    "cpu", non_blocking=True
                )
                kv_cache_cpu[-1].append([k_cpu, v_cpu])
        torch.cuda.synchronize()
        return kv_cache_cpu

    def load_cpu_copy(self, kv_cache_cpu, indices):
        torch.cuda.synchronize()
        for layer_id in range(self.layer_num):
            for i in range(0, len(indices), self.chunk_size):
                chunk_indices = indices[i : i + self.chunk_size]
                k_cpu, v_cpu = (
                    kv_cache_cpu[layer_id][i // self.chunk_size][0],
                    kv_cache_cpu[layer_id][i // self.chunk_size][1],
                )
                assert k_cpu.shape[0] == v_cpu.shape[0] == len(chunk_indices)
                k_chunk = k_cpu.to(self.k_buffer[0].device, non_blocking=True)
                v_chunk = v_cpu.to(self.v_buffer[0].device, non_blocking=True)
                self.k_buffer[layer_id][chunk_indices] = k_chunk
                self.v_buffer[layer_id][chunk_indices] = v_chunk
        torch.cuda.synchronize()

    # Todo: different memory layout
    def get_flat_data(self, indices):
        # prepare a large chunk of contiguous data for efficient transfer
        flatten = torch.stack(
            [
                torch.stack([self.k_buffer[i][indices] for i in range(self.layer_num)]),
                torch.stack([self.v_buffer[i][indices] for i in range(self.layer_num)]),
            ]
        )
        return flatten

    @debug_timing
    def transfer(self, indices, flat_data):
        # transfer prepared data from host to device
        flat_data = flat_data.to(device=self.device, non_blocking=False)
        k_data, v_data = flat_data[0], flat_data[1]
        for i in range(self.layer_num):
            self.k_buffer[i][indices] = k_data[i]
            self.v_buffer[i][indices] = v_data[i]

    def transfer_per_layer(self, indices, flat_data, layer_id):
        # transfer prepared data from host to device
        flat_data = flat_data.to(device=self.device, non_blocking=False)
        k_data, v_data = flat_data[0], flat_data[1]
        self.k_buffer[layer_id - self.start_layer][indices] = k_data
        self.v_buffer[layer_id - self.start_layer][indices] = v_data

    def get_key_buffer(self, layer_id: int):
        if self.layer_transfer_counter is not None:
            self.layer_transfer_counter.wait_until(layer_id - self.start_layer)

        if self.store_dtype != self.dtype:
            return self.k_buffer[layer_id - self.start_layer].view(self.dtype)
        return self.k_buffer[layer_id - self.start_layer]

    def get_value_buffer(self, layer_id: int):
        if self.layer_transfer_counter is not None:
            self.layer_transfer_counter.wait_until(layer_id - self.start_layer)

        if self.store_dtype != self.dtype:
            return self.v_buffer[layer_id - self.start_layer].view(self.dtype)
        return self.v_buffer[layer_id - self.start_layer]

    def get_kv_buffer(self, layer_id: int):
        return self.get_key_buffer(layer_id), self.get_value_buffer(layer_id)

    def set_kv_buffer(
        self,
        layer: RadixAttention,
        loc: torch.Tensor,
        cache_k: torch.Tensor,
        cache_v: torch.Tensor,
        k_scale: Optional[float] = None,
        v_scale: Optional[float] = None,
    ):
        from sglang.srt.model_executor.cuda_graph_runner import get_is_capture_mode

        layer_id = layer.layer_id
        if cache_k.dtype != self.dtype:
            if k_scale is not None:
                cache_k.div_(k_scale)
            if v_scale is not None:
                cache_v.div_(v_scale)
            cache_k = cache_k.to(self.dtype)
            cache_v = cache_v.to(self.dtype)

        if self.store_dtype != self.dtype:
            cache_k = cache_k.view(self.store_dtype)
            cache_v = cache_v.view(self.store_dtype)

        if get_is_capture_mode() and self.alt_stream is not None:
            # Overlap the copy of K and V cache for small batch size
            current_stream = self.device_module.current_stream()
            self.alt_stream.wait_stream(current_stream)
            self.k_buffer[layer_id - self.start_layer][loc] = cache_k
            with self.device_module.stream(self.alt_stream):
                self.v_buffer[layer_id - self.start_layer][loc] = cache_v
            current_stream.wait_stream(self.alt_stream)
        else:
            self.k_buffer[layer_id - self.start_layer][loc] = cache_k
            self.v_buffer[layer_id - self.start_layer][loc] = cache_v

    def move_kv_cache(self, tgt_loc: torch.Tensor, src_loc: torch.Tensor):
        copy_all_layer_kv_cache[(len(self.data_ptrs),)](
            self.data_ptrs,
            self.data_strides,
            tgt_loc,
            src_loc,
            len(tgt_loc),
            next_power_of_2(len(tgt_loc)),
        )


@triton.jit
def set_mla_kv_buffer_kernel(
    kv_buffer_ptr,
    cache_k_nope_ptr,
    cache_k_rope_ptr,
    loc_ptr,
    buffer_stride: tl.constexpr,
    nope_stride: tl.constexpr,
    rope_stride: tl.constexpr,
    nope_dim: tl.constexpr,
    rope_dim: tl.constexpr,
    BLOCK: tl.constexpr,
):
    pid_loc = tl.program_id(0)
    pid_blk = tl.program_id(1)

    base = pid_blk * BLOCK
    offs = base + tl.arange(0, BLOCK)
    total_dim = nope_dim + rope_dim
    mask = offs < total_dim

    loc = tl.load(loc_ptr + pid_loc)
    dst_ptr = kv_buffer_ptr + loc * buffer_stride + offs

    if base + BLOCK <= nope_dim:
        src = tl.load(
            cache_k_nope_ptr + pid_loc * nope_stride + offs,
            mask=mask,
        )
    else:
        offs_rope = offs - nope_dim
        src = tl.load(
            cache_k_rope_ptr + pid_loc * rope_stride + offs_rope,
            mask=mask,
        )

    tl.store(dst_ptr, src, mask=mask)


def set_mla_kv_buffer_triton(
    kv_buffer: torch.Tensor,
    loc: torch.Tensor,
    cache_k_nope: torch.Tensor,
    cache_k_rope: torch.Tensor,
):
    nope_dim = cache_k_nope.shape[-1]
    rope_dim = cache_k_rope.shape[-1]
    total_dim = nope_dim + rope_dim
    BLOCK = 128
    n_loc = loc.numel()
    grid = (n_loc, triton.cdiv(total_dim, BLOCK))

    set_mla_kv_buffer_kernel[grid](
        kv_buffer,
        cache_k_nope,
        cache_k_rope,
        loc,
        kv_buffer.stride(0),
        cache_k_nope.stride(0),
        cache_k_rope.stride(0),
        nope_dim,
        rope_dim,
        BLOCK=BLOCK,
    )


class MLATokenToKVPool(KVCache):
    def __init__(
        self,
        size: int,
        page_size: int,
        dtype: torch.dtype,
        kv_lora_rank: int,
        qk_rope_head_dim: int,
        layer_num: int,
        device: str,
        enable_memory_saver: bool,
        start_layer: Optional[int] = None,
        end_layer: Optional[int] = None,
    ):
        super().__init__(
            size,
            page_size,
            dtype,
            layer_num,
            device,
            enable_memory_saver,
            start_layer,
            end_layer,
        )

        self.kv_lora_rank = kv_lora_rank
        self.qk_rope_head_dim = qk_rope_head_dim

        # for disagg with nvlink
        self.enable_custom_mem_pool = get_bool_env_var(
            "SGLANG_MOONCAKE_CUSTOM_MEM_POOL", "false"
        )
        if self.enable_custom_mem_pool:
            from sglang.srt.disaggregation.mooncake.memory_pool import (
                MooncakeNVLinkAllocator,
            )

            # TODO(shangming): abstract custom allocator class for more backends
            allocator = MooncakeNVLinkAllocator.get_allocator(self.device)
            self.custom_mem_pool = torch.cuda.MemPool(allocator.allocator())
        else:
            self.custom_mem_pool = None

        with self.memory_saver_adapter.region(GPU_MEMORY_TYPE_KV_CACHE):
            with (
                torch.cuda.use_mem_pool(self.custom_mem_pool)
                if self.custom_mem_pool
                else nullcontext()
            ):
                # The padded slot 0 is used for writing dummy outputs from padded tokens.
                self.kv_buffer = [
                    torch.zeros(
                        (size + page_size, 1, kv_lora_rank + qk_rope_head_dim),
                        dtype=self.store_dtype,
                        device=device,
                    )
                    for _ in range(layer_num)
                ]

        self.layer_transfer_counter = None

        kv_size = self.get_kv_size_bytes()
        logger.info(
            f"KV Cache is allocated. #tokens: {size}, KV size: {kv_size / GB:.2f} GB"
        )

    def get_kv_size_bytes(self):
        assert hasattr(self, "kv_buffer")
        kv_size_bytes = 0
        for kv_cache in self.kv_buffer:
            kv_size_bytes += np.prod(kv_cache.shape) * kv_cache.dtype.itemsize
        return kv_size_bytes

    # for disagg
    def get_contiguous_buf_infos(self):
        # MLA has only one kv_buffer, so only the information of this buffer needs to be returned.
        kv_data_ptrs = [self.kv_buffer[i].data_ptr() for i in range(self.layer_num)]
        kv_data_lens = [self.kv_buffer[i].nbytes for i in range(self.layer_num)]
        kv_item_lens = [
            self.kv_buffer[i][0].nbytes * self.page_size for i in range(self.layer_num)
        ]
        return kv_data_ptrs, kv_data_lens, kv_item_lens

    def maybe_get_custom_mem_pool(self):
        return self.custom_mem_pool

    def get_key_buffer(self, layer_id: int):
        if self.layer_transfer_counter is not None:
            self.layer_transfer_counter.wait_until(layer_id - self.start_layer)

        if self.store_dtype != self.dtype:
            return self.kv_buffer[layer_id - self.start_layer].view(self.dtype)
        return self.kv_buffer[layer_id - self.start_layer]

    def get_value_buffer(self, layer_id: int):
        if self.layer_transfer_counter is not None:
            self.layer_transfer_counter.wait_until(layer_id - self.start_layer)

        if self.store_dtype != self.dtype:
            return self.kv_buffer[layer_id - self.start_layer][
                ..., : self.kv_lora_rank
            ].view(self.dtype)
        return self.kv_buffer[layer_id - self.start_layer][..., : self.kv_lora_rank]

    def get_kv_buffer(self, layer_id: int):
        return self.get_key_buffer(layer_id), self.get_value_buffer(layer_id)

    def set_kv_buffer(
        self,
        layer: RadixAttention,
        loc: torch.Tensor,
        cache_k: torch.Tensor,
        cache_v: torch.Tensor,
    ):
        layer_id = layer.layer_id
        if cache_k.dtype != self.dtype:
            cache_k = cache_k.to(self.dtype)
        if self.store_dtype != self.dtype:
            self.kv_buffer[layer_id - self.start_layer][loc] = cache_k.view(
                self.store_dtype
            )
        else:
            self.kv_buffer[layer_id - self.start_layer][loc] = cache_k

    def set_mla_kv_buffer(
        self,
        layer: RadixAttention,
        loc: torch.Tensor,
        cache_k_nope: torch.Tensor,
        cache_k_rope: torch.Tensor,
    ):
        layer_id = layer.layer_id
        if cache_k_nope.dtype != self.dtype:
            cache_k_nope = cache_k_nope.to(self.dtype)
            cache_k_rope = cache_k_rope.to(self.dtype)
        if self.store_dtype != self.dtype:
            cache_k_nope = cache_k_nope.view(self.store_dtype)
            cache_k_rope = cache_k_rope.view(self.store_dtype)

        set_mla_kv_buffer_triton(
            self.kv_buffer[layer_id], loc, cache_k_nope, cache_k_rope
        )

    def get_flat_data(self, indices):
        # prepare a large chunk of contiguous data for efficient transfer
        return torch.stack([self.kv_buffer[i][indices] for i in range(self.layer_num)])

    @debug_timing
    def transfer(self, indices, flat_data):
        # transfer prepared data from host to device
        flat_data = flat_data.to(device=self.device, non_blocking=False)
        for i in range(self.layer_num):
            self.kv_buffer[i][indices] = flat_data[i]

    def transfer_per_layer(self, indices, flat_data, layer_id):
        # transfer prepared data from host to device
        flat_data = flat_data.to(device=self.device, non_blocking=False)
        self.kv_buffer[layer_id - self.start_layer][indices] = flat_data


class DoubleSparseTokenToKVPool(KVCache):
    def __init__(
        self,
        size: int,
        page_size: int,
        dtype: torch.dtype,
        head_num: int,
        head_dim: int,
        layer_num: int,
        device: str,
        heavy_channel_num: int,
        enable_memory_saver: bool,
        start_layer: Optional[int] = None,
        end_layer: Optional[int] = None,
    ):
        super().__init__(
            size,
            page_size,
            dtype,
            layer_num,
            device,
            enable_memory_saver,
            start_layer,
            end_layer,
        )

        with self.memory_saver_adapter.region(GPU_MEMORY_TYPE_KV_CACHE):
            # [size, head_num, head_dim] for each layer
            self.k_buffer = [
                torch.zeros(
                    (size + page_size, head_num, head_dim), dtype=dtype, device=device
                )
                for _ in range(layer_num)
            ]
            self.v_buffer = [
                torch.zeros(
                    (size + page_size, head_num, head_dim), dtype=dtype, device=device
                )
                for _ in range(layer_num)
            ]

            # [size, head_num, heavy_channel_num] for each layer
            self.label_buffer = [
                torch.zeros(
                    (size + 1, head_num, heavy_channel_num), dtype=dtype, device=device
                )
                for _ in range(layer_num)
            ]

    def get_key_buffer(self, layer_id: int):
        return self.k_buffer[layer_id - self.start_layer]

    def get_value_buffer(self, layer_id: int):
        return self.v_buffer[layer_id - self.start_layer]

    def get_label_buffer(self, layer_id: int):
        return self.label_buffer[layer_id - self.start_layer]

    def get_kv_buffer(self, layer_id: int):
        return (
            self.k_buffer[layer_id - self.start_layer],
            self.v_buffer[layer_id - self.start_layer],
        )

    def set_kv_buffer(
        self,
        layer: RadixAttention,
        loc: torch.Tensor,
        cache_k: torch.Tensor,
        cache_v: torch.Tensor,
        cache_label: torch.Tensor,
    ):
        # NOTE(Andy): ignore the dtype check
        layer_id = layer.layer_id
        self.k_buffer[layer_id - self.start_layer][loc] = cache_k
        self.v_buffer[layer_id - self.start_layer][loc] = cache_v
        self.label_buffer[layer_id - self.start_layer][loc] = cache_label

    def get_flat_data(self, indices):
        pass

    def transfer(self, indices, flat_data):
        pass

    def transfer_per_layer(self, indices, flat_data, layer_id):
        pass


@triton.jit
def copy_all_layer_kv_cache(
    data_ptrs,
    strides,
    tgt_loc_ptr,
    src_loc_ptr,
    num_locs,
    num_locs_upper: tl.constexpr,
):
    BLOCK_SIZE: tl.constexpr = 128

    bid = tl.program_id(0)
    stride = tl.load(strides + bid)

    data_ptr = tl.load(data_ptrs + bid)
    data_ptr = tl.cast(data_ptr, tl.pointer_type(tl.uint8))

    num_locs_offset = tl.arange(0, num_locs_upper)
    tgt_locs = tl.load(tgt_loc_ptr + num_locs_offset, mask=num_locs_offset < num_locs)
    src_locs = tl.load(src_loc_ptr + num_locs_offset, mask=num_locs_offset < num_locs)

    # NOTE: we cannot parallelize over the tgt_loc_ptr dim with cuda blocks
    # because this copy is an inplace operation.

    num_loop = tl.cdiv(stride, BLOCK_SIZE)
    for i in range(num_loop):
        copy_offset = tl.arange(0, BLOCK_SIZE) + i * BLOCK_SIZE
        mask = (num_locs_offset < num_locs)[:, None] and (copy_offset < stride)[None, :]
        value = tl.load(
            data_ptr + src_locs[:, None] * stride + copy_offset[None, :], mask=mask
        )
        tl.store(
            data_ptr + tgt_locs[:, None] * stride + copy_offset[None, :],
            value,
            mask=mask,
        )
