[Feat] Add Euler xlite graph wrapper support (#4526)

### What this PR does / why we need it?
This patch adds support for the xlite graph wrapper to vllm_ascend.
Xlite provides operator implementations of the transformer network on
Ascend hardware. For details about xlite, please refer to the following
link: https://gitee.com/openeuler/GVirt/blob/master/xlite/README.md
The latest performance comparison data between xlite and the default
aclgraph mode is as follows:

## Qwen3 32B TPS 910B3(A2) Online Inference Performance Comparison
- aclgraph: main(c4a71fc6) 
- xlite-full: main(c4a71fc6) + xlite-full
- xlite-decode-only: main(c4a71fc6) + xlite-decode-only
- diff1: Performance comparison between xlite-full and aclgraph
- diff2: Performance comparison between xlite-decode-only and aclgraph


### Does this PR introduce _any_ user-facing change?
Enable the xlite graph mode by setting xlite_graph_config:
--additional-config='{"xlite_graph_config": {"enabled": true}}' #
Enabled for decode only
--additional-config='{"xlite_graph_config": {"enabled": true,
"full_mode": true}}' # Enabled for prefill and decode

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

---------

Signed-off-by: lulina <lina.lulina@huawei.com>
Co-authored-by: wangxiyuan <wangxiyuan1007@gmail.com>

vllm_ascend/xlite/xlite_model_runner.py

@@ -0,0 +1,36 @@
+#
+# 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.
+# This file is a part of the vllm-ascend project.
+# Adapted from vllm-project/vllm/vllm/worker/gpu_model_runner.py
+# isort: skip_file
+import torch.nn as nn
+from vllm.v1.kv_cache_interface import KVCacheConfig
+from vllm_ascend.worker.model_runner_v1 import NPUModelRunner
+
+
+class XliteModelRunner(NPUModelRunner):
+
+    def get_model(self) -> nn.Module:
+        return self.model.unwrap()
+
+    def load_model(self) -> None:
+        super().load_model()
+        from vllm_ascend.xlite.xlite import XliteWrapper
+        self.model = XliteWrapper(self.model, self.vllm_config)
+
+    def initialize_kv_cache(self, kv_cache_config: KVCacheConfig) -> None:
+        super().initialize_kv_cache(kv_cache_config)
+        self.model.register_kv_caches(self.kv_caches)