From 531d0e6fff8331224ef76aeea0f7614fcdfd8abc Mon Sep 17 00:00:00 2001
From: DreamerLeader <88812830+DreamerLeader@users.noreply.github.com>
Date: Thu, 9 Apr 2026 21:55:56 +0800
Subject: [PATCH] =?UTF-8?q?[v0.18.0][BugFix][KV=20Pool]Fix=20the=20conflic?=
 =?UTF-8?q?t=20between=20pooling=20scenarios=20=E2=80=A6=20(#8101)?=
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…and PCP across machines

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Signed-off-by: DreamLeader <2270923832@qq.com>
---
 .../ascend_store/backend/memcache_backend.py  | 19 ++++++-------------
 .../ascend_store/backend/mooncake_backend.py  |  5 +++--
 2 files changed, 9 insertions(+), 15 deletions(-)

diff --git a/vllm_ascend/distributed/kv_transfer/kv_pool/ascend_store/backend/memcache_backend.py b/vllm_ascend/distributed/kv_transfer/kv_pool/ascend_store/backend/memcache_backend.py
index fc5bc070..a04c59f8 100644
--- a/vllm_ascend/distributed/kv_transfer/kv_pool/ascend_store/backend/memcache_backend.py
+++ b/vllm_ascend/distributed/kv_transfer/kv_pool/ascend_store/backend/memcache_backend.py
@@ -3,6 +3,7 @@ from enum import Enum
 
 import torch
 from vllm.config import ParallelConfig
+from vllm.distributed.parallel_state import get_world_group
 from vllm.logger import logger
 
 from vllm_ascend.distributed.kv_transfer.kv_pool.ascend_store.backend.backend import Backend
@@ -29,21 +30,13 @@ class MemcacheBackend(Backend):
         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)
-                self.rank = parallel_config.rank
-                self.store = DistributedObjectStore()
-                res = self.store.init(self.rank)
-                assert res == 0
-            else:
-                self.rank = parallel_config.rank
-                self.store = DistributedObjectStore()
-                res = self.store.init(self.rank)
-                assert res == 0
+            self.local_rank = get_world_group().local_rank
+            self.store = DistributedObjectStore()
+            res = self.store.init(self.local_rank)
+            assert res == 0
         except ValueError as e:
             logger.error("Configuration loading failed: %s", e)
             raise
@@ -52,7 +45,7 @@ class MemcacheBackend(Backend):
             raise
 
     def set_device(self):
-        device = torch.device(f"npu:{self.rank}")
+        device = torch.device(f"npu:{self.local_rank}")
         torch.npu.set_device(device)
 
     def register_buffer(self, ptrs: list[int], sizes: list[int]):
diff --git a/vllm_ascend/distributed/kv_transfer/kv_pool/ascend_store/backend/mooncake_backend.py b/vllm_ascend/distributed/kv_transfer/kv_pool/ascend_store/backend/mooncake_backend.py
index 078590f6..1e584aa4 100644
--- a/vllm_ascend/distributed/kv_transfer/kv_pool/ascend_store/backend/mooncake_backend.py
+++ b/vllm_ascend/distributed/kv_transfer/kv_pool/ascend_store/backend/mooncake_backend.py
@@ -8,6 +8,7 @@ import torch
 
 # Third Party
 from vllm.config import ParallelConfig
+from vllm.distributed.parallel_state import get_world_group
 from vllm.logger import logger
 from vllm.utils.network_utils import get_ip
 
@@ -30,7 +31,6 @@ class MooncakeBackend(Backend):
             ) from e
         self.config = MooncakeStoreConfig.load_from_env()
         self.store = MooncakeDistributedStore()
-        self.rank = parallel_config.rank
         if self.config.protocol == "ascend":
             local_hostname = get_ip()
             # ASCEND_ENABLE_USE_FABRIC_MEM: Enable unified memory address direct transmission scheme
@@ -67,7 +67,8 @@ class MooncakeBackend(Backend):
             raise RuntimeError(msg)
 
     def set_device(self):
-        device = torch.device(f"npu:{self.rank}")
+        local_rank = get_world_group().local_rank
+        device = torch.device(f"npu:{local_rank}")
         torch.npu.set_device(device)
 
     def register_buffer(self, ptrs: list[int], lengths: list[int]):