[HybridKV][Bugfix] Fix Hybrid kvcache sharing bug in same attention type (vllm-project#3760)

### What this PR does / why we need it?
Part of vllm-project#3106
Fix Hybrid kvcache sharing bug in same attention type
Change the `shared_by` logic so that the same attention spec could share
the same buffer instead of allocating more hbm.
After this pr, kvcache memory saved 50% in qwen3-next compared with
before (`self_attn:linear_attn=1:3` in an `attn_group`), and
`gpu_memory_utilization` could increase to `0.8` on Qwen3-Next when
running on A2 64G/card with tp4

<img width="2833" height="1540" alt="image"
src="https://github.com/user-attachments/assets/2a91fa99-fb0f-447c-9e8b-acd587890fbe"
/>

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?
Test pass with the latest e2e test case on qwen3-next

- vLLM version: v0.11.0rc3
- vLLM main:
vllm-project/vllm@c9461e0

---------

Signed-off-by: MengqingCao <[email protected]>
Signed-off-by: hwhaokun <[email protected]>

Author: MengqingCao

tests/e2e/multicard/test_qwen3_next.py

@@ -27,12 +27,12 @@
 def test_models_distributed_Qwen3_NEXT_TP4():
     example_prompts = [
         "Hello, my name is",
-    ]
+    ] * 4
     max_tokens = 5
     with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
                     tensor_parallel_size=4,
                     max_model_len=4096,
-                    gpu_memory_utilization=0.7,
+                    gpu_memory_utilization=0.8,
                     distributed_executor_backend="mp",
                     enforce_eager=True) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)

vllm_ascend/worker/model_runner_v1.py

@@ -3225,25 +3225,26 @@ def initialize_kv_cache_tensors(
             # TODO: REFACTOR ME to sharing hybrid cache
             for idx in range(len(kv_cache_tensor.shared_by)):
                 layer_name = kv_cache_tensor.shared_by[idx]
-                if "linear_attn" in layer_name:
+                if "linear_attn" in layer_name and layer_name not in kv_cache_raw_tensors.keys(
+                ):
                     # for mamba linear attention
+                    if self.vllm_config.kv_transfer_config is None:
+                        tensor = torch.zeros(kv_cache_tensor.size,
+                                             dtype=torch.int8,
+                                             device=self.device)
+                    else:
+                        cache_size_aligned = kv_cache_tensor.size + alignment
+                        tensor = torch.zeros(cache_size_aligned,
+                                             dtype=torch.int8,
+                                             device=self.device)
+                        tensor = self._align_memory(
+                            tensor, alignment)[:kv_cache_tensor.size]
                     for layer_name_inner in kv_cache_tensor.shared_by:
-                        if ("attn" in layer_name_inner and "linear_attn" not in layer_name_inner) or \
-                            layer_name_inner in kv_cache_raw_tensors.keys():
-                            continue
-                        if self.vllm_config.kv_transfer_config is None:
-                            tensor = torch.zeros(kv_cache_tensor.size,
-                                                 dtype=torch.int8,
-                                                 device=self.device)
-                        else:
-                            cache_size_aligned = kv_cache_tensor.size + alignment
-                            tensor = torch.zeros(cache_size_aligned,
-                                                 dtype=torch.int8,
-                                                 device=self.device)
-                            tensor = self._align_memory(
-                                tensor, alignment)[:kv_cache_tensor.size]
-                        kv_cache_raw_tensors[layer_name_inner] = tensor
-                elif "attn" in layer_name:
+                        # shared the kvcache between the linear_attn specs in the same group
+                        if "linear_attn" in layer_name_inner:
+                            kv_cache_raw_tensors[layer_name_inner] = tensor
+                elif "attn" in layer_name and layer_name not in kv_cache_raw_tensors.keys(
+                ):
                     # for other attentions, e.g., self_attn, sliding window attn
                     if self.vllm_config.kv_transfer_config is None:
                         k_tensor = torch.zeros(kv_cache_tensor.size // 2,
@@ -3265,7 +3266,12 @@ def initialize_kv_cache_tensors(
                                                       alignment)[:cache_size]
                         v_tensor = self._align_memory(v_tensor,
                                                       alignment)[:cache_size]
-                    kv_cache_raw_tensors[layer_name] = (k_tensor, v_tensor)
+                    for layer_name_inner in kv_cache_tensor.shared_by:
+                        # shared the kvcache between the self_attn specs in the same group
+                        if ("attn" in layer_name_inner
+                                and "linear_attn" not in layer_name_inner):
+                            kv_cache_raw_tensors[layer_name_inner] = (k_tensor,
+                                                                      v_tensor)
 
         layer_names = set()
         for group in kv_cache_config.kv_cache_groups: