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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

80
vllm_ascend/lora/utils.py
View File

@@ -1,91 +1,75 @@
from typing import Optional
import vllm
from torch import nn
from transformers import PretrainedConfig
from vllm.config import LoRAConfig
from vllm.lora.layers import (ColumnParallelLinearWithLoRA,
MergedColumnParallelLinearWithLoRA,
MergedQKVParallelLinearWithLoRA,
QKVParallelLinearWithLoRA,
RowParallelLinearWithLoRA,
VocabParallelEmbeddingWithLoRA)
from vllm.lora.layers import (
ColumnParallelLinearWithLoRA,
MergedColumnParallelLinearWithLoRA,
MergedQKVParallelLinearWithLoRA,
QKVParallelLinearWithLoRA,
RowParallelLinearWithLoRA,
VocabParallelEmbeddingWithLoRA,
)
from vllm.lora.layers.utils import _not_fully_sharded_can_replace
from vllm_ascend.ops.linear import (AscendColumnParallelLinear,
AscendMergedColumnParallelLinear,
AscendQKVParallelLinear,
AscendRowParallelLinear)
from vllm_ascend.ops.vocab_parallel_embedding import \
AscendVocabParallelEmbedding
from vllm_ascend.ops.linear import (
AscendColumnParallelLinear,
AscendMergedColumnParallelLinear,
AscendQKVParallelLinear,
AscendRowParallelLinear,
)
from vllm_ascend.ops.vocab_parallel_embedding import AscendVocabParallelEmbedding
class AscendColumnParallelLinearWithLoRA(ColumnParallelLinearWithLoRA):
@classmethod
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
model_config: PretrainedConfig | None,
) -> bool:
return type(source_layer) is AscendColumnParallelLinear
class AscendMergedColumnParallelLinearWithLoRA(
MergedColumnParallelLinearWithLoRA):
class AscendMergedColumnParallelLinearWithLoRA(MergedColumnParallelLinearWithLoRA):
@classmethod
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
model_config: PretrainedConfig | None,
) -> bool:
return type(source_layer) is AscendMergedColumnParallelLinear
class AscendRowParallelLinearWithLoRA(RowParallelLinearWithLoRA):
@classmethod
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
model_config: PretrainedConfig | None,
) -> bool:
return type(source_layer) is AscendRowParallelLinear
class AscendVocabParallelEmbeddingWithLoRA(VocabParallelEmbeddingWithLoRA):
@classmethod
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
model_config: PretrainedConfig | None,
) -> bool:
return type(source_layer) is AscendVocabParallelEmbedding
class AscendQKVParallelLinearWithLoRA(QKVParallelLinearWithLoRA):
@classmethod
@_not_fully_sharded_can_replace
def can_replace_layer(cls, source_layer: nn.Module,
lora_config: LoRAConfig, packed_modules_list: list,
model_config: Optional[PretrainedConfig]) -> bool:
return type(source_layer) is AscendQKVParallelLinear and len(
packed_modules_list) == 1
class AscendMergedQKVParallelLinearWithLoRA(MergedQKVParallelLinearWithLoRA):
@classmethod
@_not_fully_sharded_can_replace
def can_replace_layer(
@@ -93,18 +77,28 @@ class AscendMergedQKVParallelLinearWithLoRA(MergedQKVParallelLinearWithLoRA):
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: Optional[PretrainedConfig],
model_config: PretrainedConfig | None,
) -> bool:
return (type(source_layer) is AscendQKVParallelLinear
and len(packed_modules_list) == 3)
return type(source_layer) is AscendQKVParallelLinear and len(packed_modules_list) == 1
class AscendMergedQKVParallelLinearWithLoRA(MergedQKVParallelLinearWithLoRA):
@classmethod
@_not_fully_sharded_can_replace
def can_replace_layer(
cls,
source_layer: nn.Module,
lora_config: LoRAConfig,
packed_modules_list: list,
model_config: PretrainedConfig | None,
) -> bool:
return type(source_layer) is AscendQKVParallelLinear and len(packed_modules_list) == 3
def refresh_all_lora_classes():
vllm.lora.utils._all_lora_classes.add(AscendColumnParallelLinearWithLoRA)
vllm.lora.utils._all_lora_classes.add(
AscendMergedColumnParallelLinearWithLoRA)
vllm.lora.utils._all_lora_classes.add(AscendMergedColumnParallelLinearWithLoRA)
vllm.lora.utils._all_lora_classes.add(AscendRowParallelLinearWithLoRA)
vllm.lora.utils._all_lora_classes.add(AscendVocabParallelEmbeddingWithLoRA)
vllm.lora.utils._all_lora_classes.add(AscendQKVParallelLinearWithLoRA)
vllm.lora.utils._all_lora_classes.add(
AscendMergedQKVParallelLinearWithLoRA)
vllm.lora.utils._all_lora_classes.add(AscendMergedQKVParallelLinearWithLoRA)