Add Pangu MoE Pro docs (#1512)

### What this PR does / why we need it?
This PR add Pangu MoE Pro 72B docs

[1] https://gitcode.com/ascend-tribe/pangu-pro-moe-model

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
CI passed

Signed-off-by: Yikun Jiang <yikunkero@gmail.com>

Author: Yikun

docs/source/tutorials/index.md

@@ -7,6 +7,7 @@ single_npu
 single_npu_multimodal
 single_npu_audio
 multi_npu
+multi_npu_moge
 multi_npu_quantization
 single_node_300i
 multi_node

docs/source/tutorials/multi_npu_moge.md

@@ -0,0 +1,136 @@
+# Multi-NPU (Pangu Pro MoE)
+
+## Run vllm-ascend on Multi-NPU
+
+Run container:
+
+```{code-block} bash
+   :substitutions:
+# Update the vllm-ascend image
+export IMAGE=quay.io/ascend/vllm-ascend:|vllm_ascend_version|
+docker run --rm \
+--name vllm-ascend \
+--device /dev/davinci0 \
+--device /dev/davinci1 \
+--device /dev/davinci2 \
+--device /dev/davinci3 \
+--device /dev/davinci_manager \
+--device /dev/devmm_svm \
+--device /dev/hisi_hdc \
+-v /usr/local/dcmi:/usr/local/dcmi \
+-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
+-v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ \
+-v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
+-v /etc/ascend_install.info:/etc/ascend_install.info \
+-v /root/.cache:/root/.cache \
+-p 8000:8000 \
+-it $IMAGE bash
+```
+
+Setup environment variables:
+
+```bash
+# Set `max_split_size_mb` to reduce memory fragmentation and avoid out of memory
+export PYTORCH_NPU_ALLOC_CONF=max_split_size_mb:256
+```
+
+Download the model:
+
+```bash
+git lfs install
+git clone https://gitcode.com/ascend-tribe/pangu-pro-moe-model.git
+```
+
+### Online Inference on Multi-NPU
+
+Run the following script to start the vLLM server on Multi-NPU:
+
+```bash
+vllm serve /path/to/pangu-pro-moe-model \
+--tensor-parallel-size 4 \
+--trust-remote-code \
+--enforce-eager
+```
+
+Once your server is started, you can query the model with input prompts:
+
+```bash
+export question="你是谁？"
+curl http://localhost:8000/v1/completions \
+  -H "Content-Type: application/json" \
+  -d '{
+    "prompt": "[unused9]系统：[unused10][unused9]用户：'${question}'[unused10][unused9]助手：",
+    "max_tokens": 64,
+    "top_p": 0.95,
+    "top_k": 50,
+    "temperature": 0.6
+  }'
+```
+
+If you run this successfully, you can see the info shown below:
+
+```json
+{"id":"cmpl-2cd4223228ab4be9a91f65b882e65b32","object":"text_completion","created":1751255067,"model":"/root/.cache/pangu-pro-moe-model","choices":[{"index":0,"text":" [unused16] 好的，用户问我是谁，我需要根据之前的设定来回答。用户提到我是华为开发的“盘古Reasoner”，属于盘古大模型系列，作为智能助手帮助解答问题和提供 信息支持。现在用户再次询问，可能是在确认我的身份或者测试我的回答是否一致。\n\n首先，我要确保","logprobs":null,"finish_reason":"length","stop_reason":null,"prompt_logprobs":null}],"usage":{"prompt_tokens":15,"total_tokens":79,"completion_tokens":64,"prompt_tokens_details":null},"kv_transfer_params":null}
+```
+
+### Offline Inference on Multi-NPU
+
+Run the following script to execute offline inference on multi-NPU:
+
+```python
+import gc
+from transformers import AutoTokenizer
+import torch
+
+from vllm import LLM, SamplingParams
+from vllm.distributed.parallel_state import (destroy_distributed_environment,
+                                             destroy_model_parallel)
+
+def clean_up():
+    destroy_model_parallel()
+    destroy_distributed_environment()
+    gc.collect()
+    torch.npu.empty_cache()
+
+
+if __name__ == "__main__":
+
+    tokenizer = AutoTokenizer.from_pretrained("/path/to/pangu-pro-moe-model", trust_remote_code=True)
+    tests = [
+        "Hello, my name is",
+        "The future of AI is",
+    ]
+    prompts = []
+    for text in tests:
+        messages = [
+        {"role": "system", "content": ""},    # Optionally customize system content
+        {"role": "user", "content": text}
+    ]
+        prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)        # 推荐使用官方的template
+        prompts.append(prompt)
+
+    sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=40)
+
+    llm = LLM(model="/path/to/pangu-pro-moe-model",
+            tensor_parallel_size=4,
+            distributed_executor_backend="mp",
+            max_model_len=1024,
+            trust_remote_code=True,
+            enforce_eager=True)
+
+    outputs = llm.generate(prompts, sampling_params)
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+    del llm
+    clean_up()
+```
+
+If you run this script successfully, you can see the info shown below:
+
+```bash
+Prompt: 'Hello, my name is', Generated text: ' Daniel and I am an 8th grade student at York Middle School. I'
+Prompt: 'The future of AI is', Generated text: ' following you. As the technology advances, a new report from the Institute for the'
+```