bff4fbfca5
### What this PR does / why we need it?
1. upgrade to 0.18.0
2. ensure kernel_block_sizes is int for Eagle drafter
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.17.0
- vLLM main:
8b6325758c
---------
Signed-off-by: Meihan-chen <jcccx.cmh@gmail.com>
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
Co-authored-by: hfadzxy <starmoon_zhang@163.com>
64 lines
2.7 KiB
Python
64 lines
2.7 KiB
Python
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.v1.attention.backend import AttentionBackend # type: ignore
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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.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.worker import OffloadingHandler
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from vllm_ascend.kv_offload.cpu_npu import CpuNpuOffloadingHandler
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class NPUOffloadingSpec(OffloadingSpec):
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def __init__(self, vllm_config: VllmConfig, kv_cache_config: KVCacheConfig | None = None):
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super().__init__(vllm_config, kv_cache_config)
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num_cpu_blocks = self.extra_config.get("num_cpu_blocks")
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if not num_cpu_blocks:
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raise Exception("num_cpu_blocks must be specified in kv_connector_extra_config")
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self.num_cpu_blocks: int = num_cpu_blocks
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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._handler: OffloadingHandler | None = None
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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 = kv_events_config is not None and kv_events_config.enable_kv_cache_events
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assert len(self.gpu_block_size) == 1
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gpu_block_size = self.gpu_block_size[0]
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offloaded_block_size = gpu_block_size * self.block_size_factor
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self._manager = LRUOffloadingManager(
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CPUBackend(block_size=offloaded_block_size, num_blocks=self.num_cpu_blocks),
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enable_events=enable_events,
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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._handler:
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assert len(self.gpu_block_size) == 1
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gpu_block_size = self.gpu_block_size[0]
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self._handler = CpuNpuOffloadingHandler(
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attn_backends=attn_backends,
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gpu_block_size=gpu_block_size,
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cpu_block_size=gpu_block_size * self.block_size_factor,
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num_cpu_blocks=self.num_cpu_blocks,
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gpu_caches=kv_caches,
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)
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assert self._handler is not None
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yield GPULoadStoreSpec, CPULoadStoreSpec, self._handler
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yield CPULoadStoreSpec, GPULoadStoreSpec, self._handler
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