[Feat]Xlite Qwen3-vl Support (#5228)

### What this PR does / why we need it?
This patch adds support for the Qwen3-VL model in Xlite. For more
details about Xlite, please refer to the following
link:https://atomgit.com/openeuler/GVirt/blob/master/xlite/README.md.
The latest performance comparison data between xlite and the default
aclgraph mode is as follows:

### Does this PR introduce _any_ user-facing change?
XLite graph mode supports the Qwen3-VL model.

### How was this patch tested?
vLLM version: v0.12.0 

- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c

Signed-off-by: lvjunqi <lvjunqi1@huawei.com>
Co-authored-by: lvjunqi <lvjunqi1@huawei.com>

docs/source/user_guide/configuration/additional_config.md

@@ -49,7 +49,7 @@ The details of each configuration option are as follows:
 **xlite_graph_config**
 | Name | Type | Default | Description |
 | ---- | ---- | ------- | ----------- |
-| `enabled` | bool | `False` | Whether to enable xlite graph mode. Currently only Llama or Qwen dense series models are supported. |
+| `enabled` | bool | `False` | Whether to enable xlite graph mode. Currently only Llama, Qwen dense series models, and Qwen3-vl are supported. |
 | `full_mode` | bool | `False` | Whether to enable xlite for both the prefill and decode stages. By default, xlite is only enabled for the decode stage. |
 
 **weight_prefetch_config**

docs/source/user_guide/feature_guide/graph_mode.md

@@ -12,7 +12,7 @@ From v0.9.1rc1 with V1 Engine, vLLM Ascend will run models in graph mode by defa
 
 There are two kinds for graph mode supported by vLLM Ascend:
 - **ACLGraph**: This is the default graph mode supported by vLLM Ascend. In v0.9.1rc1, Qwen and Deepseek series models are well tested.
-- **XliteGraph**: This is the euler xlite graph mode. In v0.11.0, only Llama and Qwen dense serise models are supported.
+- **XliteGraph**: This is the openeuler xlite graph mode. In v0.11.0, only Llama, Qwen dense series models, and Qwen3-vl are supported.
 
 ## Using ACLGraph
 ACLGraph is enabled by default. Take Qwen series models as an example, just set to use V1 Engine is enough.
@@ -36,7 +36,7 @@ vllm serve Qwen/Qwen2-7B-Instruct
 
 ## Using XliteGraph
 
-If you want to run Llama or Qwen dense series models with xlite graph mode, please install xlite, and set xlite_graph_config.
+If you want to run Llama, Qwen dense series models, or Qwen3-vl with xlite graph mode, please install xlite, and set xlite_graph_config.
 
 ```bash
 pip install xlite
@@ -59,7 +59,7 @@ Online example:
 vllm serve path/to/Qwen3-32B --tensor-parallel-size 8 --additional-config='{"xlite_graph_config": {"enabled": true, "full_mode": true}}'
 ```
 
-You can find more details abort xlite [here](https://gitee.com/openeuler/GVirt/blob/master/xlite/README.md)
+You can find more details abort xlite [here](https://atomgit.com/openeuler/GVirt/blob/master/xlite/README.md)
 
 ## Fallback to the Eager Mode
 

vllm_ascend/platform.py

@@ -281,7 +281,7 @@ class NPUPlatform(Platform):
             parallel_config.all2all_backend = "flashinfer_all2allv"
             if ascend_config.xlite_graph_config.enabled:
                 logger.info(
-                    "Euler Xlite enabled. See: https://gitee.com/openeuler/GVirt/tree/master/xlite"
+                    "openEuler Xlite enabled. See: https://atomgit.com/openeuler/GVirt/tree/master/xlite"
                 )
                 parallel_config.worker_cls = "vllm_ascend.xlite.xlite_worker.XliteWorker"
             else:

vllm_ascend/xlite/xlite.py

@@ -48,16 +48,23 @@ class LlamaXliteModel(XliteModel):
             vllm_config: VllmConfig) -> Tuple[Model, int, int, torch.dtype]:
         dtype = vllm_config.model_config.dtype
         params_dict = dict(runnable.named_parameters())
+
+        if hasattr(runnable, "language_model"):
+            layers = runnable.language_model.model.layers
+            model_prefix = "language_model."
+        else:
             layers = runnable.model.layers
+            model_prefix = ""
 
         config = self._build_model_config(vllm_config)
         xlite_model = Model()
-        xlite_model.embed = params_dict.get("model.embed_tokens.weight")
+        xlite_model.embed = params_dict.get(model_prefix +
-        xlite_model.norm = params_dict.get("model.norm.weight")
+                                            "model.embed_tokens.weight")
+        xlite_model.norm = params_dict.get(model_prefix + "model.norm.weight")
         if vllm_config.model_config.hf_config.tie_word_embeddings:
             xlite_model.head = xlite_model.embed
         else:
-            xlite_model.head = params_dict.get("lm_head.weight")
+            xlite_model.head = params_dict.get(model_prefix + "lm_head.weight")
         xlite_model.attn_norm = [
             layer.input_layernorm.weight for layer in layers
         ]
@@ -112,6 +119,8 @@ class LlamaXliteModel(XliteModel):
 
     def _build_model_config(self, vllm_config: VllmConfig) -> ModelConfig:
         hf_config = vllm_config.model_config.hf_config
+        if hasattr(hf_config, "text_config"):
+            hf_config = hf_config.text_config
         config = ModelConfig()
         config.vocab_size = hf_config.vocab_size
         config.hidden_size = hf_config.hidden_size
@@ -166,6 +175,7 @@ def xlite_model_init(
         "LlamaForCausalLM": LlamaXliteModel,
         "Qwen2ForCausalLM": LlamaXliteModel,
         "Qwen3ForCausalLM": LlamaXliteModel,
+        "Qwen3VLForConditionalGeneration": LlamaXliteModel,
     }
 
     architecture = vllm_config.model_config.architectures[0]