[model] Support PanguUltraMoE (#4615)

### What this PR does / why we need it?
To support PanguUltraMoE model

### Test result
#### Start serving using W8A8 quantized model and ACL graph:
Master node:
```
vllm serve $LOCAL_CKPT_DIR \
        --host 0.0.0.0 \
        --port 8000 \
        --data-parallel-size 2 \
        --data-parallel-size-local 1 \
        --data-parallel-address $MASTER_NODE_IP \
        --data-parallel-rpc-port 13389 \
        --tensor-parallel-size 16 \
        --seed 1024 \
        --enable-expert-parallel \
        --served-model-name $NAME \
        --max-model-len 4096 \
        --max-num-batched-tokens 256 \
        --max-num-seqs 18 \
        --trust-remote-code \
        --gpu-memory-utilization 0.90 \
        --quantization ascend \
        --additional-config '{"ascend_scheduler_config":{"enabled":false, "enable_chunked_prefill":true, "chunked_prefill_enabled":true},"torchair_graph_config":{"enabled":false}}' \
        --speculative_config '{"method": "pangu_ultra_moe_mtp", "num_speculative_tokens": 1}' \
```
Other nodes:
```
vllm serve $LOCAL_CKPT_DIR \
        --host 0.0.0.0 \
        --port 8000 \
        --headless \
        --data-parallel-size 2 \
        --data-parallel-size-local 1 \
        --data-parallel-start-rank 1 \
        --data-parallel-address $MASTER_NODE_IP \
        --data-parallel-rpc-port 13389 \
        --tensor-parallel-size 16 \
        --seed 1024 \
        --enable-expert-parallel \
        --served-model-name $NAME \
        --max-model-len 4096 \
        --max-num-batched-tokens 256 \
        --max-num-seqs 18 \
        --trust-remote-code \
        --gpu-memory-utilization 0.90 \
        --quantization ascend \
        --additional-config '{"ascend_scheduler_config":{"enabled":false, "enable_chunked_prefill":true, "chunked_prefill_enabled":true},"torchair_graph_config":{"enabled":false}}' \
        --speculative_config '{"method": "pangu_ultra_moe_mtp", "num_speculative_tokens": 1}' \
```
Request & Response:

- Request
```
curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "messages": [
      {"role": "system", "content": ""},
      {"role": "user", "content": "你是谁？"}
    ],
        "max_tokens": "64",
        "top_p": "0.95",
        "top_k": "50",
        "temperature": "0.6",
        "add_special_tokens" : true
    }'
```
- Response
```
[unused16] 好的，用户问我是谁，我需要按照之前的设定来回答。首先，我的角色是盘古，由华为开发，属于推理模型。要强调我的主要功能是解答问题和提供信息支持，特别是通过逻辑推理和数据分析处理复杂任务。需要保持回答简洁，用中文，并且符合用户的
```


- vLLM version: v0.12.0
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.12.0

Signed-off-by: lijifu <[email protected]>
Co-authored-by: lijifu <[email protected]>

Author: JeffLee1874

vllm_ascend/quantization/quant_config.py

@@ -221,6 +221,12 @@ def get_scaled_act_names(self) -> List[str]:
         ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"],
         "fused_qkv_a_proj": ["q_a_proj", "kv_a_proj_with_mqa"]
     },
+    "pangu_ultra_moe": {
+        "gate_up_proj": ["gate_proj", "up_proj"],
+        "experts":
+        ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"],
+        "fused_qkv_a_proj": ["q_a_proj", "kv_a_proj_with_mqa"]
+    },
     "kimi_k2": {
         "gate_up_proj": ["gate_proj", "up_proj"],
         "experts":
@@ -241,6 +247,12 @@ def get_scaled_act_names(self) -> List[str]:
         "experts":
         ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"]
     },
+    "pangu_ultra_moe_mtp": {
+        "gate_up_proj": ["gate_proj", "up_proj"],
+        "experts":
+        ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"],
+        "fused_qkv_a_proj": ["q_a_proj", "kv_a_proj_with_mqa"]
+    },
     "qwen3_next": {
         "qkv_proj": [
             "q_proj",

vllm_ascend/spec_decode/mtp_proposer.py

@@ -44,6 +44,8 @@
 _MTP_MODELS = {
     "DeepseekV3ForCausalLM":
     ("vllm.model_executor.models.deepseek_mtp", "DeepSeekMTP"),
+    "PanguUltraMoEForCausalLM":
+    ("vllm.model_executor.models.openpangu_mtp", "OpenPanguMTP"),
     "DeepseekV32ForCausalLM":
     ("vllm.model_executor.models.deepseek_mtp", "DeepSeekMTP"),
     "Qwen3NextForCausalLM":