852 lines
28 KiB
Python
852 lines
28 KiB
Python
"""
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Copyright 2023-2024 SGLang Team
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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from sglang.srt.torch_memory_saver_adapter import TorchMemorySaverAdapter
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"""
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Memory pool.
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SGLang has two levels of memory pool.
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ReqToTokenPool maps a request to its token locations.
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TokenToKVPoolAllocator manages the indices to kv cache data.
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KVCache actually holds the physical kv cache.
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"""
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import abc
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import logging
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import os
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from contextlib import nullcontext
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from typing import List, Optional, Tuple, Union
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import numpy as np
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import torch
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import triton
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import triton.language as tl
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from sglang.srt.constants import GPU_MEMORY_TYPE_KV_CACHE
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.utils import debug_timing, get_bool_env_var, is_cuda, next_power_of_2
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logger = logging.getLogger(__name__)
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GB = 1024 * 1024 * 1024
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_is_cuda = is_cuda()
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class ReqToTokenPool:
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"""A memory pool that maps a request to its token locations."""
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def __init__(
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self,
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size: int,
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max_context_len: int,
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device: str,
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enable_memory_saver: bool,
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):
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memory_saver_adapter = TorchMemorySaverAdapter.create(
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enable=enable_memory_saver
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)
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self.size = size
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self.max_context_len = max_context_len
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self.device = device
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with memory_saver_adapter.region(GPU_MEMORY_TYPE_KV_CACHE):
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self.req_to_token = torch.zeros(
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(size, max_context_len), dtype=torch.int32, device=device
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)
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self.free_slots = list(range(size))
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def write(self, indices, values):
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self.req_to_token[indices] = values
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def available_size(self):
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return len(self.free_slots)
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def alloc(self, need_size: int) -> List[int]:
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if need_size > len(self.free_slots):
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return None
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select_index = self.free_slots[:need_size]
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self.free_slots = self.free_slots[need_size:]
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return select_index
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def free(self, free_index: Union[int, List[int]]):
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if isinstance(free_index, (int,)):
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self.free_slots.append(free_index)
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else:
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self.free_slots.extend(free_index)
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def clear(self):
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self.free_slots = list(range(self.size))
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class KVCache(abc.ABC):
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@abc.abstractmethod
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def __init__(
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self,
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size: int,
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page_size: int,
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dtype: torch.dtype,
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layer_num: int,
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device: str,
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enable_memory_saver: bool,
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start_layer: Optional[int] = None,
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end_layer: Optional[int] = None,
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):
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self.size = size
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self.page_size = page_size
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self.dtype = dtype
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self.device = device
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if dtype in (torch.float8_e5m2, torch.float8_e4m3fn):
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# NOTE: Store as torch.uint8 because Tensor.index_put is not implemented for torch.float8_e5m2
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self.store_dtype = torch.uint8
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else:
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self.store_dtype = dtype
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self.layer_num = layer_num
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self.start_layer = start_layer or 0
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self.end_layer = end_layer or layer_num - 1
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self.memory_saver_adapter = TorchMemorySaverAdapter.create(
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enable=enable_memory_saver
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)
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@abc.abstractmethod
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def get_key_buffer(self, layer_id: int) -> torch.Tensor:
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raise NotImplementedError()
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@abc.abstractmethod
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def get_value_buffer(self, layer_id: int) -> torch.Tensor:
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raise NotImplementedError()
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@abc.abstractmethod
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def get_kv_buffer(self, layer_id: int) -> Tuple[torch.Tensor, torch.Tensor]:
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raise NotImplementedError()
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@abc.abstractmethod
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def set_kv_buffer(
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self,
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layer: RadixAttention,
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loc: torch.Tensor,
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cache_k: torch.Tensor,
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cache_v: torch.Tensor,
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) -> None:
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raise NotImplementedError()
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def get_flat_data(self, indices):
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raise NotImplementedError()
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def transfer(self, indices, flat_data):
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raise NotImplementedError()
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def transfer_per_layer(self, indices, flat_data, layer_id):
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raise NotImplementedError()
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def register_layer_transfer_counter(self, layer_transfer_counter):
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self.layer_transfer_counter = layer_transfer_counter
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class TokenToKVPoolAllocator:
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"""An allocator managing the indices to kv cache data."""
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def __init__(
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self,
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size: int,
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dtype: torch.dtype,
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device: str,
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kvcache: KVCache,
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):
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self.size = size
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self.dtype = dtype
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self.device = device
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self.page_size = 1
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self.free_slots = None
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self.is_not_in_free_group = True
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self.free_group = []
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self.clear()
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self._kvcache = kvcache
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def available_size(self):
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return len(self.free_slots)
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def debug_print(self) -> str:
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return ""
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def get_kvcache(self):
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return self._kvcache
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def alloc(self, need_size: int):
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if need_size > len(self.free_slots):
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return None
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select_index = self.free_slots[:need_size]
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self.free_slots = self.free_slots[need_size:]
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return select_index
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def free(self, free_index: torch.Tensor):
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if free_index.numel() == 0:
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return
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if self.is_not_in_free_group:
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self.free_slots = torch.cat((self.free_slots, free_index))
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else:
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self.free_group.append(free_index)
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def free_group_begin(self):
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self.is_not_in_free_group = False
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self.free_group = []
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def free_group_end(self):
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self.is_not_in_free_group = True
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if self.free_group:
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self.free(torch.cat(self.free_group))
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def backup_state(self):
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return self.free_slots
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def restore_state(self, free_slots):
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self.free_slots = free_slots
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def clear(self):
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# The padded slot 0 is used for writing dummy outputs from padded tokens.
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self.free_slots = torch.arange(
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1, self.size + 1, dtype=torch.int64, device=self.device
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)
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self.is_not_in_free_group = True
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self.free_group = []
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def get_cpu_copy(self, indices):
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return self._kvcache.get_cpu_copy(indices)
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def load_cpu_copy(self, kv_cache_cpu, indices):
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return self._kvcache.load_cpu_copy(kv_cache_cpu, indices)
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class MHATokenToKVPool(KVCache):
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def __init__(
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self,
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size: int,
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page_size: int,
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dtype: torch.dtype,
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head_num: int,
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head_dim: int,
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layer_num: int,
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device: str,
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enable_memory_saver: bool,
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start_layer: Optional[int] = None,
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end_layer: Optional[int] = None,
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):
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super().__init__(
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size,
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page_size,
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dtype,
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layer_num,
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device,
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enable_memory_saver,
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start_layer,
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end_layer,
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)
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self.head_num = head_num
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self.head_dim = head_dim
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# for disagg with nvlink
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self.enable_custom_mem_pool = get_bool_env_var(
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"SGLANG_MOONCAKE_CUSTOM_MEM_POOL", "false"
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)
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if self.enable_custom_mem_pool:
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# TODO(shangming): abstract custom allocator class for more backends
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from mooncake.allocator import NVLinkAllocator
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allocator = NVLinkAllocator.get_allocator(self.device)
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self.custom_mem_pool = torch.cuda.MemPool(allocator.allocator())
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else:
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self.custom_mem_pool = None
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self._create_buffers()
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# used for chunked cpu-offloading
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self.chunk_size = 8192
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self.layer_transfer_counter = None
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self.device_module = torch.get_device_module(self.device)
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self.alt_stream = self.device_module.Stream() if _is_cuda else None
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k_size, v_size = self.get_kv_size_bytes()
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logger.info(
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f"KV Cache is allocated. #tokens: {size}, K size: {k_size / GB:.2f} GB, V size: {v_size / GB:.2f} GB"
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)
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def _create_buffers(self):
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with self.memory_saver_adapter.region(GPU_MEMORY_TYPE_KV_CACHE):
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with (
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torch.cuda.use_mem_pool(self.custom_mem_pool)
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if self.enable_custom_mem_pool
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else nullcontext()
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):
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# [size, head_num, head_dim] for each layer
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# The padded slot 0 is used for writing dummy outputs from padded tokens.
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self.k_buffer = [
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torch.zeros(
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(self.size + self.page_size, self.head_num, self.head_dim),
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dtype=self.store_dtype,
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device=self.device,
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)
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for _ in range(self.layer_num)
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]
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self.v_buffer = [
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torch.zeros(
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(self.size + self.page_size, self.head_num, self.head_dim),
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dtype=self.store_dtype,
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device=self.device,
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)
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for _ in range(self.layer_num)
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]
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self.data_ptrs = torch.tensor(
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[x.data_ptr() for x in self.k_buffer + self.v_buffer],
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dtype=torch.uint64,
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device=self.device,
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)
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self.data_strides = torch.tensor(
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[
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np.prod(x.shape[1:]) * x.dtype.itemsize
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for x in self.k_buffer + self.v_buffer
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],
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device=self.device,
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)
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def _clear_buffers(self):
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del self.k_buffer
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del self.v_buffer
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def get_kv_size_bytes(self):
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assert hasattr(self, "k_buffer")
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assert hasattr(self, "v_buffer")
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k_size_bytes = 0
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for k_cache in self.k_buffer:
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k_size_bytes += np.prod(k_cache.shape) * k_cache.dtype.itemsize
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v_size_bytes = 0
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for v_cache in self.v_buffer:
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v_size_bytes += np.prod(v_cache.shape) * v_cache.dtype.itemsize
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return k_size_bytes, v_size_bytes
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# for disagg
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def get_contiguous_buf_infos(self):
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# layer_num x [seq_len, head_num, head_dim]
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# layer_num x [page_num, page_size, head_num, head_dim]
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kv_data_ptrs = [
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self.get_key_buffer(i).data_ptr()
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for i in range(self.start_layer, self.start_layer + self.layer_num)
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] + [
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self.get_value_buffer(i).data_ptr()
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for i in range(self.start_layer, self.start_layer + self.layer_num)
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]
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kv_data_lens = [
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self.get_key_buffer(i).nbytes
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for i in range(self.start_layer, self.start_layer + self.layer_num)
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] + [
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self.get_value_buffer(i).nbytes
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for i in range(self.start_layer, self.start_layer + self.layer_num)
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]
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kv_item_lens = [
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self.get_key_buffer(i)[0].nbytes * self.page_size
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for i in range(self.start_layer, self.start_layer + self.layer_num)
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] + [
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self.get_value_buffer(i)[0].nbytes * self.page_size
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for i in range(self.start_layer, self.start_layer + self.layer_num)
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]
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return kv_data_ptrs, kv_data_lens, kv_item_lens
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def maybe_get_custom_mem_pool(self):
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return self.custom_mem_pool
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def get_cpu_copy(self, indices):
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torch.cuda.synchronize()
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kv_cache_cpu = []
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for layer_id in range(self.layer_num):
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kv_cache_cpu.append([])
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for i in range(0, len(indices), self.chunk_size):
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chunk_indices = indices[i : i + self.chunk_size]
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k_cpu = self.k_buffer[layer_id][chunk_indices].to(
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"cpu", non_blocking=True
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)
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v_cpu = self.v_buffer[layer_id][chunk_indices].to(
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"cpu", non_blocking=True
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)
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kv_cache_cpu[-1].append([k_cpu, v_cpu])
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torch.cuda.synchronize()
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return kv_cache_cpu
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def load_cpu_copy(self, kv_cache_cpu, indices):
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torch.cuda.synchronize()
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for layer_id in range(self.layer_num):
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for i in range(0, len(indices), self.chunk_size):
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chunk_indices = indices[i : i + self.chunk_size]
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k_cpu, v_cpu = (
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kv_cache_cpu[layer_id][i // self.chunk_size][0],
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kv_cache_cpu[layer_id][i // self.chunk_size][1],
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)
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assert k_cpu.shape[0] == v_cpu.shape[0] == len(chunk_indices)
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k_chunk = k_cpu.to(self.k_buffer[0].device, non_blocking=True)
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v_chunk = v_cpu.to(self.v_buffer[0].device, non_blocking=True)
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self.k_buffer[layer_id][chunk_indices] = k_chunk
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self.v_buffer[layer_id][chunk_indices] = v_chunk
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torch.cuda.synchronize()
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# Todo: different memory layout
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def get_flat_data(self, indices):
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# prepare a large chunk of contiguous data for efficient transfer
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flatten = torch.stack(
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[
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torch.stack([self.k_buffer[i][indices] for i in range(self.layer_num)]),
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torch.stack([self.v_buffer[i][indices] for i in range(self.layer_num)]),
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]
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)
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return flatten
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@debug_timing
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def transfer(self, indices, flat_data):
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# transfer prepared data from host to device
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flat_data = flat_data.to(device=self.device, non_blocking=False)
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k_data, v_data = flat_data[0], flat_data[1]
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for i in range(self.layer_num):
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self.k_buffer[i][indices] = k_data[i]
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self.v_buffer[i][indices] = v_data[i]
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def transfer_per_layer(self, indices, flat_data, layer_id):
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# transfer prepared data from host to device
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flat_data = flat_data.to(device=self.device, non_blocking=False)
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k_data, v_data = flat_data[0], flat_data[1]
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self.k_buffer[layer_id - self.start_layer][indices] = k_data
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self.v_buffer[layer_id - self.start_layer][indices] = v_data
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def get_key_buffer(self, layer_id: int):
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if self.layer_transfer_counter is not None:
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self.layer_transfer_counter.wait_until(layer_id - self.start_layer)
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if self.store_dtype != self.dtype:
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return self.k_buffer[layer_id - self.start_layer].view(self.dtype)
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return self.k_buffer[layer_id - self.start_layer]
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def get_value_buffer(self, layer_id: int):
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if self.layer_transfer_counter is not None:
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self.layer_transfer_counter.wait_until(layer_id - self.start_layer)
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if self.store_dtype != self.dtype:
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return self.v_buffer[layer_id - self.start_layer].view(self.dtype)
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return self.v_buffer[layer_id - self.start_layer]
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def get_kv_buffer(self, layer_id: int):
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return self.get_key_buffer(layer_id), self.get_value_buffer(layer_id)
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def set_kv_buffer(
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self,
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layer: RadixAttention,
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loc: torch.Tensor,
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cache_k: torch.Tensor,
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cache_v: torch.Tensor,
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k_scale: Optional[float] = None,
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v_scale: Optional[float] = None,
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):
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from sglang.srt.model_executor.cuda_graph_runner import get_is_capture_mode
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layer_id = layer.layer_id
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if cache_k.dtype != self.dtype:
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if k_scale is not None:
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cache_k.div_(k_scale)
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if v_scale is not None:
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cache_v.div_(v_scale)
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cache_k = cache_k.to(self.dtype)
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cache_v = cache_v.to(self.dtype)
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if self.store_dtype != self.dtype:
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cache_k = cache_k.view(self.store_dtype)
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cache_v = cache_v.view(self.store_dtype)
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if get_is_capture_mode() and self.alt_stream is not None:
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# Overlap the copy of K and V cache for small batch size
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current_stream = self.device_module.current_stream()
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self.alt_stream.wait_stream(current_stream)
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self.k_buffer[layer_id - self.start_layer][loc] = cache_k
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with self.device_module.stream(self.alt_stream):
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self.v_buffer[layer_id - self.start_layer][loc] = cache_v
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current_stream.wait_stream(self.alt_stream)
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else:
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self.k_buffer[layer_id - self.start_layer][loc] = cache_k
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self.v_buffer[layer_id - self.start_layer][loc] = cache_v
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|
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:
|
|
# TODO(shangming): abstract custom allocator class for more backends
|
|
from mooncake.allocator import NVLinkAllocator
|
|
|
|
allocator = NVLinkAllocator.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,
|
|
)
|