### What this PR does / why we need it?
**Scope of Changes**:
| File Path |
| :--- |
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`.../distributed/kv_transfer/kv_pool/ascend_store/ascend_store_connector.py`
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`vllm_ascend/distributed/kv_transfer/kv_pool/ascend_store/backend/backend.py`
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.../distributed/kv_transfer/kv_pool/ascend_store/backend/memcache_backend.py`
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.../distributed/kv_transfer/kv_pool/ascend_store/backend/mooncake_backend.py`
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| `
vllm_ascend/distributed/kv_transfer/kv_pool/ascend_store/config_data.py`
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| `
vllm_ascend/distributed/kv_transfer/kv_pool/ascend_store/kv_transfer.py`
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| `
vllm_ascend/distributed/kv_transfer/kv_pool/ascend_store/pool_scheduler.py`
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| `
vllm_ascend/distributed/kv_transfer/kv_pool/ascend_store/pool_worker.py`
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| `
.../distributed/kv_transfer/kv_pool/cpu_offload/cpu_kv_cache_manager.py`
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| `
.../distributed/kv_transfer/kv_pool/cpu_offload/cpu_offload_connector.py`
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| ` vllm_ascend/distributed/kv_transfer/kv_pool/cpu_offload/metadata.py`
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| ` vllm_ascend/distributed/kv_transfer/kv_pool/ucm_connector.py` |
| `
vllm_ascend/distributed/kv_transfer/utils/mooncake_transfer_engine.py` |
| ` vllm_ascend/distributed/kv_transfer/utils/utils.py` |
| ` vllm_ascend/kv_offload/cpu_npu.py` |
| ` vllm_ascend/kv_offload/npu.py` |
| ` vllm_ascend/lora/lora_ops.py` |
| ` vllm_ascend/lora/punica_npu.py` |
| ` vllm_ascend/lora/utils.py` |
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.13.0
- vLLM main:
2c24bc6996
---------
Signed-off-by: MrZ20 <2609716663@qq.com>
Signed-off-by: SILONG ZENG <2609716663@qq.com>
59 lines
2.4 KiB
Python
59 lines
2.4 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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self._manager = LRUOffloadingManager(
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CPUBackend(block_size=self.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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self._handler = CpuNpuOffloadingHandler(
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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_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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