add Qwen2.5-VL tutorials

docs/source/tutorials/Qwen2.5-VL-32B.md

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+# Multi-NPU (Qwen2.5-VL-32B-Instruct)
+
+## Introduction
+
+Key Enhancements:
+- Understand things visually: Qwen2.5-VL is not only proficient in recognizing common objects such as flowers, birds, fish, and insects, but it is highly capable of analyzing texts, charts, icons, graphics, and layouts within images.
+
+- Being agentic: Qwen2.5-VL directly plays as a visual agent that can reason and dynamically direct tools, which is capable of computer use and phone use.
+
+- Understanding long videos and capturing events: Qwen2.5-VL can comprehend videos of over 1 hour, and this time it has a new ability of cpaturing event by pinpointing the relevant video segments.
+
+- Capable of visual localization in different formats: Qwen2.5-VL can accurately localize objects in an image by generating bounding boxes or points, and it can provide stable JSON outputs for coordinates and attributes.
+
+- Generating structured outputs: for data like scans of invoices, forms, tables, etc. Qwen2.5-VL supports structured outputs of their contents, benefiting usages in finance, commerce, etc.
+
+This document will demonstrate the main validation steps of the model, including supported features, feature configuration, environment preparation, single-node deployment, as well as accuracy and performance evaluation.
+
+## Supported Features
+
+Refer to [supported features](../user_guide/support_matrix/supported_models.md) to get the model's supported feature matrix.
+
+Refer to [feature guide](../user_guide/feature_guide/index.md) to get the feature's configuration.
+
+## Environment Preparation
+
+### Model Weight
+
+- A sample Qwen2.5-VL quantization script can be found in the modelslim code repository. [Qwen2.5-VL Quantization Script Example](https://gitcode.com/Ascend/msit/blob/master/msmodelslim/example/multimodal_vlm/Qwen2.5-VL/README.md)
+
+- `Qwen2.5-VL-32B-Instruct-w8a8`(Quantized version): require 1 Atlas 800 A2 (64G × 8) node. 
+
+It is recommended to download the model weight to the shared directory of multiple nodes, such as `/root/.cache/`
+
+### Verify Multi-node Communication(Optional)
+
+If you want to deploy multi-node environment, you need to verify multi-node communication according to [verify multi-node communication environment](../installation.md#verify-multi-node-communication).
+
+
+## Deployment
+### Run docker container
+```shell
+export IMAGE=quay.io/ascend/vllm-ascend:0.11.0rc1
+docker run --rm \
+--shm-size=1g \
+--net=host \
+--name vllm-ascend-qwen25_VL \
+--device /dev/davinci0 \
+--device /dev/davinci1 \
+--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 \
+-v /data:/data \
+-it $IMAGE bash
+```
+
+### Single-node Deployment
+
+Run the following script to execute online inference. Recommend two NPU cards for deploying the Qwen2.5-VL-32B-Instruct-w8a8 model.
+
+```shell
+#!/bin/sh
+# apt install libjemalloc2 or yum install jemalloc
+export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libjemalloc.so.2:$LD_PRELOAD
+export HCCL_OP_EXPANSION_MODE="AIV"
+export VLLM_USE_V1=1
+
+vllm serve /data/Qwen2.5-VL-32B-Instruct-w8a8 \
+    --host 0.0.0.0 \
+    --port 8888 \
+    --served-model-name qwen25_vl \
+    --quantization ascend \
+    --async-scheduling \
+    --tensor-parallel-size 2 \
+    --max_model_len 15000 \
+    --max-num-batched-tokens 30000 \
+    --max-num-seqs 30 \
+    --no-enable-prefix-caching \
+    --trust-remote-code \
+    --additional-config '{"enable_weight_nz_layout":true}'
+
+```
+
+
+### Prefill-Decode Disaggregation
+
+Not supported yet.
+
+## Functional Verification
+
+Once your server is started, you can query the model with input prompts:
+
+```shell
+curl http://localhost:8000/v1/chat/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+    "model": "qwen25_vl",
+    "messages": [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": [
+        {"type": "image_url", "image_url": {"url": "https://modelscope.oss-cn-beijing.aliyuncs.com/resource/qwen.png"}},
+        {"type": "text", "text": "What is the text in the illustrate?"}
+    ]}
+    ]
+    }'
+```
+
+## Accuracy Evaluation
+
+Here are two accuracy evaluation methods.
+
+### Using AISBench
+
+1. Refer to [Using AISBench](../developer_guide/evaluation/using_ais_bench.md) for details.
+
+
+## Performance
+
+### Using AISBench
+
+Refer to [Using AISBench for performance evaluation](../developer_guide/evaluation/using_ais_bench.md#execute-performance-evaluation) for details.
+
+### Using vLLM Benchmark
+
+Run performance evaluation of `Qwen2.5-VL-32B-Instruct-w8a8` as an example.
+
+Refer to [vllm benchmark](https://docs.vllm.ai/en/latest/contributing/benchmarks.html) for more details.
+
+There are three `vllm bench` subcommand:
+- `latency`: Benchmark the latency of a single batch of requests.
+- `serve`: Benchmark the online serving throughput.
+- `throughput`: Benchmark offline inference throughput.
+
+Take the `serve` as an example. Run the code as follows.
+
+```shell
+export VLLM_USE_MODELSCOPE=true
+vllm bench serve --model /data/Qwen2.5-VL-32B-Instruct-w8a8  --dataset-name random --random-input 200 --num-prompt 200 --request-rate 1 --save-result --result-dir ./
+```
+
+After about several minutes, you can get the performance evaluation result.