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[Lint]Style: Convert vllm-ascend/ to ruff format(Batch #5) (#5996)

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### What this PR does / why we need it?
**Scope of Changes**:
| File Path |
| :--- |
|
`.../distributed/kv_transfer/kv_pool/ascend_store/ascend_store_connector.py`
|
|
`vllm_ascend/distributed/kv_transfer/kv_pool/ascend_store/backend/backend.py`
|
| `
.../distributed/kv_transfer/kv_pool/ascend_store/backend/memcache_backend.py`
|
| `
.../distributed/kv_transfer/kv_pool/ascend_store/backend/mooncake_backend.py`
|
| `
vllm_ascend/distributed/kv_transfer/kv_pool/ascend_store/config_data.py`
|
| `
vllm_ascend/distributed/kv_transfer/kv_pool/ascend_store/kv_transfer.py`
|
| `
vllm_ascend/distributed/kv_transfer/kv_pool/ascend_store/pool_scheduler.py`
|
| `
vllm_ascend/distributed/kv_transfer/kv_pool/ascend_store/pool_worker.py`
|
| `
.../distributed/kv_transfer/kv_pool/cpu_offload/cpu_kv_cache_manager.py`
|
| `
.../distributed/kv_transfer/kv_pool/cpu_offload/cpu_offload_connector.py`
|
| ` vllm_ascend/distributed/kv_transfer/kv_pool/cpu_offload/metadata.py`
|
| ` 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>
This commit is contained in:
SILONG ZENG
2026-01-24 22:45:38 +08:00
committed by GitHub
parent 7faa6878a6
commit 6ccccad102
21 changed files with 866 additions and 1034 deletions
Download Patch File Download Diff File Expand all files Collapse all files

25
vllm_ascend/kv_offload/npu.py
View File

@@ -1,48 +1,40 @@
from collections.abc import Iterator
from typing import Optional
import torch
from vllm.config import VllmConfig
from vllm.v1.attention.backend import AttentionBackend # type: ignore
from vllm.v1.kv_cache_interface import KVCacheConfig
from vllm.v1.kv_offload.abstract import LoadStoreSpec, OffloadingManager
from vllm.v1.kv_offload.backends.cpu import CPUBackend
from vllm.v1.kv_offload.lru_manager import LRUOffloadingManager
from vllm.v1.kv_offload.mediums import CPULoadStoreSpec, GPULoadStoreSpec
from vllm.v1.kv_offload.spec import OffloadingSpec
from vllm.v1.kv_offload.worker.worker import OffloadingHandler
from vllm.v1.kv_cache_interface import KVCacheConfig
from vllm_ascend.kv_offload.cpu_npu import CpuNpuOffloadingHandler
class NPUOffloadingSpec(OffloadingSpec):
def __init__(self,
vllm_config: VllmConfig,
kv_cache_config: Optional[KVCacheConfig] = None):
def __init__(self, vllm_config: VllmConfig, kv_cache_config: KVCacheConfig | None = None):
super().__init__(vllm_config, kv_cache_config)
num_cpu_blocks = self.extra_config.get("num_cpu_blocks")
if not num_cpu_blocks:
raise Exception(
"num_cpu_blocks must be specified in kv_connector_extra_config"
)
raise Exception("num_cpu_blocks must be specified in kv_connector_extra_config")
self.num_cpu_blocks: int = num_cpu_blocks
# scheduler-side
self._manager: Optional[OffloadingManager] = None
self._manager: OffloadingManager | None = None
# worker-side
self._handler: Optional[OffloadingHandler] = None
self._handler: OffloadingHandler | None = None
def get_manager(self) -> OffloadingManager:
if not self._manager:
kv_events_config = self.vllm_config.kv_events_config
enable_events = (kv_events_config is not None
and kv_events_config.enable_kv_cache_events)
enable_events = kv_events_config is not None and kv_events_config.enable_kv_cache_events
self._manager = LRUOffloadingManager(
CPUBackend(block_size=self.offloaded_block_size,
num_blocks=self.num_cpu_blocks),
CPUBackend(block_size=self.offloaded_block_size, num_blocks=self.num_cpu_blocks),
enable_events=enable_events,
)
return self._manager
@@ -51,8 +43,7 @@ class NPUOffloadingSpec(OffloadingSpec):
self,
kv_caches: dict[str, torch.Tensor],
attn_backends: dict[str, type[AttentionBackend]],
) -> Iterator[tuple[type[LoadStoreSpec], type[LoadStoreSpec],
OffloadingHandler]]:
) -> Iterator[tuple[type[LoadStoreSpec], type[LoadStoreSpec], OffloadingHandler]]:
if not self._handler:
self._handler = CpuNpuOffloadingHandler(
attn_backends=attn_backends,