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

### 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:
vllm-project@2c24bc6

---------

Signed-off-by: MrZ20 <2609716663@qq.com>
Signed-off-by: SILONG ZENG <2609716663@qq.com>

Author: MrZ20

mypy.ini

@@ -1,6 +1,8 @@
 [mypy]
 ; warn_return_any = True
 warn_unused_configs = True
+; disable errors about unchecked annotations for now.
+disable_error_code = annotation-unchecked
 
 ; Suppress all missing import errors from torch_npu for mypy.
 [mypy-torch_npu.*]
@@ -31,4 +33,4 @@ ignore_missing_imports = True
 ignore_missing_imports = True
 
 [mypy-ucm.*]
-ignore_missing_imports = True
+ignore_missing_imports = True

pyproject.toml

@@ -51,11 +51,6 @@ line-length = 120
 # Folder to be modified
 exclude = [
     "tests/**",
-    # (5)
-    "vllm_ascend/distributed/kv_transfer/kv_pool/**",
-    "vllm_ascend/distributed/kv_transfer/utils/**",
-    "vllm_ascend/kv_offload/**",
-    "vllm_ascend/lora/**",
     # (7)
     "vllm_ascend/quantization/**",
     "vllm_ascend/sample/*.py",

vllm_ascend/distributed/kv_transfer/kv_pool/ascend_store/ascend_store_connector.py

@@ -1,11 +1,10 @@
 import threading
-from typing import Any, Optional
+from typing import Any
 
 import torch
 import zmq
 from vllm.config import VllmConfig
-from vllm.distributed.kv_transfer.kv_connector.v1.base import (
-    KVConnectorBase_V1, KVConnectorMetadata, KVConnectorRole)
+from vllm.distributed.kv_transfer.kv_connector.v1.base import KVConnectorBase_V1, KVConnectorMetadata, KVConnectorRole
 from vllm.forward_context import ForwardContext
 from vllm.logger import logger
 from vllm.utils.network_utils import make_zmq_socket
@@ -17,40 +16,35 @@
 from vllm.v1.serial_utils import MsgpackDecoder
 
 from vllm_ascend.distributed.kv_transfer.kv_pool.ascend_store.pool_scheduler import (
-    KVPoolScheduler, get_zmq_rpc_path_lookup)
-from vllm_ascend.distributed.kv_transfer.kv_pool.ascend_store.pool_worker import \
-    KVPoolWorker
+    KVPoolScheduler,
+    get_zmq_rpc_path_lookup,
+)
+from vllm_ascend.distributed.kv_transfer.kv_pool.ascend_store.pool_worker import KVPoolWorker
 
 
 class AscendStoreConnector(KVConnectorBase_V1):
-
-    def __init__(self,
-                 vllm_config: VllmConfig,
-                 role: KVConnectorRole,
-                 kv_cache_config: Optional[KVCacheConfig] = None):
-        super().__init__(vllm_config=vllm_config,
-                         role=role,
-                         kv_cache_config=kv_cache_config)
+    def __init__(self, vllm_config: VllmConfig, role: KVConnectorRole, kv_cache_config: KVCacheConfig | None = None):
+        super().__init__(vllm_config=vllm_config, role=role, kv_cache_config=kv_cache_config)
         self.kv_role = vllm_config.kv_transfer_config.kv_role
 
-        self.use_layerwise = vllm_config.kv_transfer_config.kv_connector_extra_config.get(
-            "use_layerwise", False)
+        self.use_layerwise = vllm_config.kv_transfer_config.kv_connector_extra_config.get("use_layerwise", False)
         self.consumer_is_to_put = vllm_config.kv_transfer_config.kv_connector_extra_config.get(
-            "consumer_is_to_put", False)
+            "consumer_is_to_put", False
+        )
 
         connector_name = vllm_config.kv_transfer_config.kv_connector
         if connector_name == "MooncakeConnectorStoreV1":
             logger.warning(
-                "It is recommended to use the AscendStoreConnector, as the MoonCakeStoreConnector will be removed in the future."
+                "It is recommended to use the AscendStoreConnector, "
+                "as the MoonCakeStoreConnector will be removed in the future."
             )
 
         self.kv_caches: dict[str, torch.Tensor] = {}
 
         self.sended_but_unfinished_reqs: set[str] = set()
 
         if role == KVConnectorRole.SCHEDULER:
-            self.connector_scheduler = KVPoolScheduler(vllm_config,
-                                                       self.use_layerwise)
+            self.connector_scheduler = KVPoolScheduler(vllm_config, self.use_layerwise)
         else:
             self.connector_worker = KVPoolWorker(
                 vllm_config,
@@ -59,27 +53,19 @@ def __init__(self,
 
             assert self.connector_worker is not None
             if vllm_config.parallel_config.rank == 0:
-                self.lookup_server = LookupKeyServer(self.connector_worker,
-                                                     vllm_config,
-                                                     self.use_layerwise)
+                self.lookup_server = LookupKeyServer(self.connector_worker, vllm_config, self.use_layerwise)
 
     ############################################################
     # Scheduler Side Methods
     ############################################################
 
-    def get_num_new_matched_tokens(
-            self, request: "Request",
-            num_computed_tokens: int) -> tuple[int, bool]:
+    def get_num_new_matched_tokens(self, request: "Request", num_computed_tokens: int) -> tuple[int, bool]:
         assert self.connector_scheduler is not None
-        return self.connector_scheduler.get_num_new_matched_tokens(
-            request, num_computed_tokens)
+        return self.connector_scheduler.get_num_new_matched_tokens(request, num_computed_tokens)
 
-    def update_state_after_alloc(self, request: "Request",
-                                 blocks: "KVCacheBlocks",
-                                 num_external_tokens: int):
+    def update_state_after_alloc(self, request: "Request", blocks: "KVCacheBlocks", num_external_tokens: int):
         assert self.connector_scheduler is not None
-        return self.connector_scheduler.update_state_after_alloc(
-            request, blocks, num_external_tokens)
+        return self.connector_scheduler.update_state_after_alloc(request, blocks, num_external_tokens)
 
     def build_connector_meta(
         self,
@@ -92,7 +78,7 @@ def request_finished(
         self,
         request: "Request",
         block_ids: list[int],
-    ) -> tuple[bool, Optional[dict[str, Any]]]:
+    ) -> tuple[bool, dict[str, Any] | None]:
         assert self.connector_scheduler is not None
         return self.connector_scheduler.request_finished(request, block_ids)
 
@@ -103,8 +89,7 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
         assert self.connector_worker is not None
         self.connector_worker.register_kv_caches(kv_caches)
 
-    def start_load_kv(self, forward_context: "ForwardContext",
-                      **kwargs) -> None:
+    def start_load_kv(self, forward_context: "ForwardContext", **kwargs) -> None:
         assert self.connector_worker is not None
         self.connector_worker.start_load_kv(self._get_connector_metadata())
 
@@ -113,8 +98,9 @@ def wait_for_layer_load(self, layer_name: str) -> None:
             return
         self.connector_worker.wait_for_layer_load()
 
-    def save_kv_layer(self, layer_name: str, kv_layer: torch.Tensor,
-                      attn_metadata: "AttentionMetadata", **kwargs) -> None:
+    def save_kv_layer(
+        self, layer_name: str, kv_layer: torch.Tensor, attn_metadata: "AttentionMetadata", **kwargs
+    ) -> None:
         if not self.use_layerwise:
             return
 
@@ -133,17 +119,16 @@ def wait_for_save(self):
 
         self.connector_worker.wait_for_save(self._get_connector_metadata())
 
-    def get_finished(self,
-                     finished_req_ids: set[str]) -> tuple[set[str], set[str]]:
+    def get_finished(self, finished_req_ids: set[str]) -> tuple[set[str], set[str]]:
         """Get the finished recving and sending requests."""
         assert self.connector_worker is not None
         done_sending, done_recving = self.connector_worker.get_finished(
-            finished_req_ids, self._get_connector_metadata())
+            finished_req_ids, self._get_connector_metadata()
+        )
         return done_sending, done_recving
 
 
 class LookupKeyServer:
-
     def __init__(
         self,
         pool_worker: KVPoolWorker,
@@ -171,8 +156,7 @@ def process_request():
                 token_len = int.from_bytes(all_frames[0], byteorder="big")
                 hash_frames = all_frames[1:]
                 hashes_str = self.decoder.decode(hash_frames)
-                result = self.pool_worker.lookup_scheduler(
-                    token_len, hashes_str, self.use_layerwise)
+                result = self.pool_worker.lookup_scheduler(token_len, hashes_str, self.use_layerwise)
                 response = result.to_bytes(4, "big")
                 self.socket.send(response)
 

vllm_ascend/distributed/kv_transfer/kv_pool/ascend_store/backend/backend.py

@@ -4,13 +4,15 @@
 
 
 class Backend(ABC):
-
+    @abstractmethod
     def __init__(self, parallel_config: ParallelConfig):
         pass
 
+    @abstractmethod
     def set_device(self):
         pass
 
+    @abstractmethod
     def register_buffer(self, ptrs: list[int], lengths: list[int]):
         pass
 
@@ -19,11 +21,9 @@ def exists(self, keys: list[str]) -> list[int]:
         pass
 
     @abstractmethod
-    def put(self, keys: list[str], addrs: list[list[int]],
-            sizes: list[list[int]]):
+    def put(self, keys: list[str], addrs: list[list[int]], sizes: list[list[int]]):
         pass
 
     @abstractmethod
-    def get(self, keys: list[str], addrs: list[list[int]],
-            sizes: list[list[int]]):
+    def get(self, keys: list[str], addrs: list[list[int]], sizes: list[list[int]]):
         pass

vllm_ascend/distributed/kv_transfer/kv_pool/ascend_store/backend/memcache_backend.py

@@ -5,8 +5,7 @@
 from vllm.config import ParallelConfig
 from vllm.logger import logger
 
-from vllm_ascend.distributed.kv_transfer.kv_pool.ascend_store.backend.backend import \
-    Backend
+from vllm_ascend.distributed.kv_transfer.kv_pool.ascend_store.backend.backend import Backend
 from vllm_ascend.utils import AscendDeviceType, get_ascend_device_type
 
 
@@ -18,29 +17,24 @@ class MmcDirect(Enum):
 
 
 class MemcacheBackend(Backend):
-
     def __init__(self, parallel_config: ParallelConfig):
         try:
             from memcache_hybrid import DistributedObjectStore  # type: ignore
         except ImportError as e:
             raise ImportError(
                 "Please install memcache by following the instructions at "
                 "https://gitee.com/ascend/memfabric_hybrid "  # noqa: E501
-                "to run vLLM with MemcacheConnector.") from e
+                "to run vLLM with MemcacheConnector."
+            ) from e
         try:
             soc_version = get_ascend_device_type()
             if soc_version in {AscendDeviceType.A2}:
                 import torch
                 from vllm.distributed import get_world_group
+
                 tmp_tensor = torch.zeros(1, device="npu")
-                output_tensor_list = [
-                    torch.empty_like(tmp_tensor)
-                    for _ in range(torch.distributed.get_world_size())
-                ]
-                torch.distributed.all_gather(
-                    output_tensor_list,
-                    tmp_tensor,
-                    group=get_world_group().device_group)
+                output_tensor_list = [torch.empty_like(tmp_tensor) for _ in range(torch.distributed.get_world_size())]
+                torch.distributed.all_gather(output_tensor_list, tmp_tensor, group=get_world_group().device_group)
                 self.rank = parallel_config.rank
                 self.store = DistributedObjectStore()
                 res = self.store.init(self.rank)
@@ -54,8 +48,7 @@ def __init__(self, parallel_config: ParallelConfig):
             logger.error("Configuration loading failed: %s", e)
             raise
         except Exception as exc:
-            logger.error(
-                "An error occurred while loading the configuration: %s", exc)
+            logger.error("An error occurred while loading the configuration: %s", exc)
             raise
 
     def set_device(self):
@@ -73,22 +66,18 @@ def register_buffer(self, ptrs: list[int], sizes: list[int]):
     def exists(self, keys: list[str]) -> list[int]:
         return self.store.batch_is_exist(keys)
 
-    def get(self, key: list[str], addr: list[list[int]],
-            size: list[list[int]]):
+    def get(self, key: list[str], addr: list[list[int]], size: list[list[int]]):
         try:
-            res = self.store.batch_get_into_layers(key, addr, size,
-                                                   MmcDirect.COPY_G2L.value)
+            res = self.store.batch_get_into_layers(key, addr, size, MmcDirect.COPY_G2L.value)
             for value in res:
                 if value != 0:
                     logger.error(f"Failed to get key {key},res:{res}")
         except Exception as e:
             logger.error(f"Failed to get key {key}. {e}")
 
-    def put(self, key: list[str], addr: list[list[int]],
-            size: list[list[int]]):
+    def put(self, key: list[str], addr: list[list[int]], size: list[list[int]]):
         try:
-            res = self.store.batch_put_from_layers(key, addr, size,
-                                                   MmcDirect.COPY_L2G.value)
+            res = self.store.batch_put_from_layers(key, addr, size, MmcDirect.COPY_L2G.value)
             for value in res:
                 if value != 0:
                     logger.error(f"Failed to get key {key},res:{res}")