110 lines
4.2 KiB
Python
110 lines
4.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from collections.abc import Iterator
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import torch
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from vllm.config import VllmConfig
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from vllm.platforms import current_platform
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from vllm.v1.attention.backend import AttentionBackend
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from vllm.v1.kv_cache_interface import KVCacheConfig
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from vllm.v1.kv_offload.abstract import LoadStoreSpec, OffloadingManager
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from vllm.v1.kv_offload.arc_manager import ARCOffloadingManager
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from vllm.v1.kv_offload.backends.cpu import CPUBackend
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from vllm.v1.kv_offload.lru_manager import LRUOffloadingManager
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from vllm.v1.kv_offload.mediums import CPULoadStoreSpec, GPULoadStoreSpec
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from vllm.v1.kv_offload.spec import OffloadingSpec
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from vllm.v1.kv_offload.worker.cpu_gpu import CpuGpuOffloadingHandlers
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from vllm.v1.kv_offload.worker.worker import OffloadingHandler
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class CPUOffloadingSpec(OffloadingSpec):
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def __init__(self, vllm_config: VllmConfig, kv_cache_config: KVCacheConfig):
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super().__init__(vllm_config, kv_cache_config)
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cpu_bytes_to_use = self.extra_config.get("cpu_bytes_to_use")
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if not cpu_bytes_to_use:
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raise Exception(
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"cpu_bytes_to_use must be specified in kv_connector_extra_config"
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)
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# calculate kv_bytes_per_offloaded_block
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assert kv_cache_config is not None
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page_sizes = {
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kv_cache_group.kv_cache_spec.page_size_bytes
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for kv_cache_group in kv_cache_config.kv_cache_groups
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}
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assert len(page_sizes) == 1
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page_size_bytes = page_sizes.pop()
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kv_bytes_per_block = (
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page_size_bytes
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* len(kv_cache_config.kv_cache_tensors)
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* vllm_config.parallel_config.world_size
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)
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kv_bytes_per_offloaded_block = kv_bytes_per_block * (
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self.offloaded_block_size // self.gpu_block_size
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)
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self.num_blocks = (
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int(cpu_bytes_to_use) // kv_bytes_per_offloaded_block
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if kv_bytes_per_offloaded_block > 0
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else 0
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)
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# scheduler-side
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self._manager: OffloadingManager | None = None
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# worker-side
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self._handlers: CpuGpuOffloadingHandlers | None = None
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self.eviction_policy: str = self.extra_config.get("eviction_policy", "lru")
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def get_manager(self) -> OffloadingManager:
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if not self._manager:
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kv_events_config = self.vllm_config.kv_events_config
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enable_events = (
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kv_events_config is not None and kv_events_config.enable_kv_cache_events
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)
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backend = CPUBackend(
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block_size=self.offloaded_block_size, num_blocks=self.num_blocks
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)
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if self.eviction_policy == "lru":
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self._manager = LRUOffloadingManager(
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backend=backend, enable_events=enable_events
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)
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elif self.eviction_policy == "arc":
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self._manager = ARCOffloadingManager(
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backend=backend, enable_events=enable_events
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)
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else:
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raise ValueError(
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f"Unknown eviction policy: {self.eviction_policy}. "
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f"Supported policies: lru, arc"
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)
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return self._manager
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def get_handlers(
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self,
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kv_caches: dict[str, torch.Tensor],
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attn_backends: dict[str, type[AttentionBackend]],
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) -> Iterator[tuple[type[LoadStoreSpec], type[LoadStoreSpec], OffloadingHandler]]:
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if not self._handlers:
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if not current_platform.is_cuda_alike():
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raise Exception(
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"CPU Offloading is currently only supported on CUDA-alike GPUs"
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)
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self._handlers = CpuGpuOffloadingHandlers(
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attn_backends=attn_backends,
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gpu_block_size=self.gpu_block_size,
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cpu_block_size=self.offloaded_block_size,
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num_cpu_blocks=self.num_blocks,
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gpu_caches=kv_caches,
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)
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assert self._handlers is not None
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yield GPULoadStoreSpec, CPULoadStoreSpec, self._handlers.gpu_to_cpu_handler
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yield CPULoadStoreSpec, GPULoadStoreSpec, self._handlers.cpu_to_gpu_handler
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