Files
enginex-mthreads-vllm/vllm/v1/kv_offload/spec.py
2026-01-19 10:38:50 +08:00

67 lines
2.1 KiB
Python

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from abc import ABC, abstractmethod
from collections.abc import Iterator
from typing import TYPE_CHECKING
import torch
from vllm.attention.backends.abstract import AttentionBackend
from vllm.logger import init_logger
from vllm.v1.kv_offload.abstract import LoadStoreSpec, OffloadingManager
from vllm.v1.kv_offload.worker.worker import OffloadingHandler
if TYPE_CHECKING:
from vllm.config import VllmConfig
logger = init_logger(__name__)
class OffloadingSpec(ABC):
"""Spec for an offloading connector"""
def __init__(self, vllm_config: "VllmConfig"):
logger.warning(
"Initializing OffloadingSpec. This API is experimental and "
"subject to change in the future as we iterate the design."
)
self.vllm_config = vllm_config
kv_transfer_config = vllm_config.kv_transfer_config
assert kv_transfer_config is not None
self.extra_config = kv_transfer_config.kv_connector_extra_config
self.gpu_block_size = vllm_config.cache_config.block_size
self.offloaded_block_size = int(
self.extra_config.get("block_size", self.gpu_block_size)
)
assert self.offloaded_block_size % self.gpu_block_size == 0
@abstractmethod
def get_manager(self) -> OffloadingManager:
"""
Get an OffloadingManager that will be used
by the scheduler-side offloading connector to track
offloaded blocks and manage evictions.
"""
pass
@abstractmethod
def get_handlers(
self,
kv_caches: dict[str, torch.Tensor],
attn_backends: dict[str, type[AttentionBackend]],
) -> Iterator[tuple[type[LoadStoreSpec], type[LoadStoreSpec], OffloadingHandler]]:
"""
Get offloading handlers along with their respective src and dst types.
Args:
kv_caches: A dictionary of layer_name -> gpu_kv_cache tensor.
attn_backends: A dictionary of layer_name -> AttentionBackend.
Yields:
Tuples of (src_type, dst_type, offloading_handler).
"""
pass