[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>

tests/e2e/singlecard/test_xlite.py

@@ -0,0 +1,130 @@
+#
+# 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 vLLM with and without xlite.
+
+Run `pytest tests/e2e/singlecard/test_xlite.py`.
+"""
+
+import pytest
+from vllm import SamplingParams
+
+from tests.e2e.conftest import VllmRunner
+from tests.e2e.model_utils import check_outputs_equal
+
+MODELS = [
+    "Qwen/Qwen3-0.6B",
+]
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("max_tokens", [32])
+def test_models_with_xlite_decode_only(
+    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"
+    ]
+
+    sampling_params = SamplingParams(max_tokens=max_tokens, temperature=0.0)
+    with VllmRunner(
+            model,
+            block_size=128,
+            max_model_len=1024,
+            enforce_eager=False,
+            additional_config={"xlite_graph_config": {
+                "enabled": True
+            }},
+    ) as runner:
+        vllm_xlite_outputs = runner.model.generate(prompts, sampling_params)
+
+    with VllmRunner(
+            model,
+            block_size=128,
+            max_model_len=1024,
+            enforce_eager=True,
+    ) as runner:
+        vllm_eager_outputs = runner.model.generate(prompts, sampling_params)
+    vllm_xlite_outputs_list = []
+    for output in vllm_xlite_outputs:
+        vllm_xlite_outputs_list.append(
+            (output.outputs[0].index, output.outputs[0].text))
+
+    vllm_eager_outputs_list = []
+    for output in vllm_eager_outputs:
+        vllm_eager_outputs_list.append(
+            (output.outputs[0].index, output.outputs[0].text))
+
+    check_outputs_equal(
+        outputs_0_lst=vllm_eager_outputs_list,
+        outputs_1_lst=vllm_xlite_outputs_list,
+        name_0="vllm_eager_outputs",
+        name_1="vllm_xlite_outputs",
+    )
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("max_tokens", [32])
+def test_models_with_xlite_full_mode(
+    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"
+    ]
+
+    sampling_params = SamplingParams(max_tokens=max_tokens, temperature=0.0)
+    with VllmRunner(
+            model,
+            block_size=128,
+            max_model_len=1024,
+            enforce_eager=False,
+            additional_config={
+                "xlite_graph_config": {
+                    "enabled": True,
+                    "full_mode": True
+                }
+            },
+    ) as runner:
+        vllm_xlite_outputs = runner.model.generate(prompts, sampling_params)
+
+    with VllmRunner(
+            model,
+            block_size=128,
+            max_model_len=1024,
+            enforce_eager=True,
+    ) as runner:
+        vllm_eager_outputs = runner.model.generate(prompts, sampling_params)
+    vllm_xlite_outputs_list = []
+    for output in vllm_xlite_outputs:
+        vllm_xlite_outputs_list.append(
+            (output.outputs[0].index, output.outputs[0].text))
+
+    vllm_eager_outputs_list = []
+    for output in vllm_eager_outputs:
+        vllm_eager_outputs_list.append(
+            (output.outputs[0].index, output.outputs[0].text))
+
+    check_outputs_equal(
+        outputs_0_lst=vllm_eager_outputs_list,
+        outputs_1_lst=vllm_xlite_outputs_list,
+        name_0="vllm_eager_outputs",
+        name_1="vllm_xlite_outputs",
+    )