[bugfix] [main] Fix KV cache query inconsistency across different TP ranks in the KV Pool (#5030)

### What this PR does / why we need it?
In the current KV Pool scenario for models like MLA and GQA, where
different TP ranks generate identical KV caches, the system is designed
to store only a single copy. The previous approach allowed each card to
query storage requirements dynamically, but inconsistent query results
across cards led to incorrect storage. To fix this, the new solution
pre-allocates storage responsibilities; each card now simply stores its
pre-assigned blocks, bypassing the inconsistent query step and ensuring
data correctness.

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

---------

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

vllm_ascend/distributed/kvpool/backend/memcache_backend.py

@@ -19,7 +19,7 @@ class MemcacheBackend(Backend):
 
     def __init__(self, parallel_config: ParallelConfig):
         try:
-            from memcache import DistributedObjectStore  # type: ignore
+            from memcache_hybrid import DistributedObjectStore  # type: ignore
         except ImportError as e:
             raise ImportError(
                 "Please install memcache by following the instructions at "
@@ -43,10 +43,7 @@ class MemcacheBackend(Backend):
         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.")
+        pass
 
     def exists(self, keys: list[str]) -> list[int]:
         return self.store.batch_is_exist(keys)