Revert [KV-Sharing] Support KV-Sharing feature in CLA models (#4138) (#5317)

### What this PR does / why we need it?
Revert [KV-Sharing] Support KV-Sharing feature in CLA models (#4138) as
it causes deepseek v3.2 hang error


- vLLM version: release/v0.13.0
- vLLM main:
5fbfa8d9ef

---------

Signed-off-by: MengqingCao <cmq0113@163.com>

.github/workflows/_e2e_test.yaml

@@ -116,7 +116,6 @@ jobs:
           pytest -sv --durations=0 tests/e2e/singlecard/test_xlite.py
           pytest -sv --durations=0 tests/e2e/singlecard/pooling/
           pytest -sv --durations=0 tests/e2e/singlecard/compile/test_norm_quant_fusion.py
-          pytest -sv --durations=0 tests/e2e/singlecard/test_cross_layer_attn_model.py
           pytest -sv --durations=0 tests/e2e/singlecard/test_multistream_overlap_shared_expert.py
 
           # ------------------------------------ v1 spec decode test ------------------------------------ #

requirements-dev.txt

@@ -23,4 +23,3 @@ mindstudio-probe>=8.3.0
 arctic-inference==0.1.1
 xlite
 uc-manager
-timm

tests/e2e/singlecard/test_cross_layer_attn_model.py

@@ -1,69 +0,0 @@
-#
-# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
-# Copyright 2023 The vLLM team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-#
-"""
-Compare the outputs of cross layer attention model with and without aclgraph.
-
-Run `pytest tests/e2e/singlecard/test_cross_layer_attn_model.py`.
-"""
-
-import os
-
-import pytest
-
-from tests.e2e.conftest import VllmRunner
-from tests.e2e.model_utils import check_outputs_equal
-
-os.environ["VLLM_USE_MODELSCOPE"] = "True"
-os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
-
-MODELS = [
-    "google/gemma-3n-E2B-it",
-]
-
-
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("max_tokens", [32])
-def test_models_with_aclgraph(
-    model: str,
-    max_tokens: int,
-) -> None:
-    prompts = [
-        "Hello, my name is", "The president of the United States is",
-        "The capital of France is", "The future of AI is"
-    ]
-
-    with VllmRunner(
-            model,
-            max_model_len=1024,
-            enforce_eager=False,
-            cudagraph_capture_sizes=[4],
-    ) as vllm_model:
-        vllm_aclgraph_outputs = vllm_model.generate_greedy(prompts, max_tokens)
-
-    with VllmRunner(
-            model,
-            max_model_len=1024,
-            enforce_eager=True,
-    ) as vllm_model:
-        vllm_eager_outputs = vllm_model.generate_greedy(prompts, max_tokens)
-
-    check_outputs_equal(
-        outputs_0_lst=vllm_eager_outputs,
-        outputs_1_lst=vllm_aclgraph_outputs,
-        name_0="vllm_eager_outputs",
-        name_1="vllm_aclgraph_outputs",
-    )

vllm_ascend/attention/attention_v1.py

@@ -307,7 +307,6 @@ class AscendAttentionBackendImpl(AttentionImpl):
                                         device="npu")
         self.alibi_slopes = alibi_slopes
         self.attn_type = attn_type
-        self.kv_sharing_target_layer_name = kv_sharing_target_layer_name
 
         assert self.num_heads % self.num_kv_heads == 0
         self.num_queries_per_kv = self.num_heads // self.num_kv_heads
@@ -619,7 +618,6 @@ class AscendAttentionBackendImpl(AttentionImpl):
         if len(kv_cache) > 1:
             if self.key_cache is None:
                 self.key_cache, self.value_cache = kv_cache[0], kv_cache[1]
-            if self.kv_sharing_target_layer_name is None:
             slots = attn_metadata.slot_mapping
             if get_ascend_device_type() == AscendDeviceType.A5:
                 # TODO: Once eagle running to here, it may has error because of the 0 dim of slot_mapping.
@@ -627,8 +625,7 @@ class AscendAttentionBackendImpl(AttentionImpl):
                 # If it's necessary, the slots should be sliced.
                 torch_npu.npu_scatter_pa_kv_cache(
                     key=key[:attn_metadata.num_actual_tokens],
-                        value=value[:attn_metadata.
+                    value=value[:attn_metadata.num_actual_tokens].contiguous(),
-                                    num_actual_tokens].contiguous(),
                     key_cache=self.key_cache,
                     value_cache=self.value_cache,
                     slot_mapping=slots)

vllm_ascend/worker/model_runner_v1.py

@@ -1195,10 +1195,6 @@ class NPUModelRunner(GPUModelRunner):
 
     def _build_attn_state(self, num_reqs, num_scheduled_tokens,
                           num_valid_tokens):
-        if self.shared_kv_cache_layers is not None:
-            # sharing kv across layers need to read the kvcache,
-            # directly return chunked prefill in this scenario
-            return AscendAttentionState.ChunkedPrefill
         if np.array_equal(self.seq_lens.np[:num_reqs], num_scheduled_tokens):
             attn_state = AscendAttentionState.PrefillNoCache
         # We assume it is the decode stage, where prefill occurs but only one token is not hit in cache.