[Feature][main]reconstruction kvpool connector to ascend connector (#4438)

### What this PR does / why we need it?
1.In short, we renamed the existing MooncakeStoreConnector to
AscendStoreConnector and extracted the storage engine interaction logic
into a new Backend class.
Associated RFC：https://github.com/vllm-project/vllm-ascend/issues/4329
2.Fixed the issue where the number of input parameters for the connector
was incorrect, introduced in vllm 0.11.2
### Does this PR introduce _any_ user-facing change?
change MooncakeStoreConnector to AscendStoreConnector
### How was this patch tested?

- vLLM version: v0.11.2

---------

Signed-off-by: fems14 <1804143737@qq.com>

vllm_ascend/distributed/kvpool/backend/memcache_backend.py

@@ -0,0 +1,74 @@
+# Standard
+from enum import Enum
+
+import torch
+from vllm.config import ParallelConfig
+from vllm.utils import logger
+
+from vllm_ascend.distributed.kvpool.backend.backend import Backend
+
+
+class MmcDirect(Enum):
+    COPY_L2G = 0
+    COPY_G2L = 1
+    COPY_G2H = 2
+    COPY_H2G = 3
+
+
+class MemcacheBackend(Backend):
+
+    def __init__(self, parallel_config: ParallelConfig):
+        try:
+            from memcache 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
+        try:
+            self.rank = parallel_config.rank
+            self.store = DistributedObjectStore()
+            res = self.store.init(self.rank)
+            assert res == 0
+        except ValueError as e:
+            logger.error("Configuration loading failed: %s", e)
+            raise
+        except Exception as exc:
+            logger.error(
+                "An error occurred while loading the configuration: %s", exc)
+            raise
+
+    def set_device(self):
+        device = torch.device(f"npu:{self.rank}")
+        torch.npu.set_device(device)
+
+    def register_buffer(self, ptrs: list[int], sizes: list[int]):
+        for ptr, size in zip(ptrs, sizes):
+            ret_value = self.store.register_buffer(ptr, size)
+            if ret_value != 0:
+                raise RuntimeError("Memcache memory registration failed.")
+
+    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]]):
+        try:
+            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]]):
+        try:
+            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}")
+        except Exception as e:
+            logger.error(f"Failed to put key {key},error:{e}")