xc-llm-ascend/docs/source/tutorials/models/Qwen2.5-Omni.md

Qwen2.5-Omni-7B

Introduction

Qwen2.5-Omni is an end-to-end multimodal model designed to perceive diverse modalities, including text, images, audio, and video, while simultaneously generating text and natural speech responses in a streaming manner.

The Qwen2.5-Omni model was supported since vllm-ascend:v0.11.0rc0. This document will show the main verification steps of the model, including supported features, feature configuration, environment preparation, single-NPU and multi-NPU deployment, accuracy and performance evaluation.

Supported Features

Refer to supported features to get the model's supported feature matrix.

Refer to feature guide to get the feature's configuration.

Environment Preparation

Model Weight

• Qwen2.5-Omni-3B(BF16): Download model weight
• Qwen2.5-Omni-7B(BF16): Download model weight

Following examples use the 7B version by default.

Installation

You can use our official docker image to run Qwen2.5-Omni directly.

Select an image based on your machine type and start the docker image on your node, refer to using docker.

   :substitutions:
# Update --device according to your device (Atlas A2: /dev/davinci[0-7] Atlas A3:/dev/davinci[0-15]).
# Update the vllm-ascend image according to your environment.
# Note you should download the weight to /root/.cache in advance.
# Update the vllm-ascend image
export IMAGE=m.daocloud.io/quay.io/ascend/vllm-ascend:|vllm_ascend_version|
export NAME=vllm-ascend
# Run the container using the defined variables
# Note: If you are running bridge network with docker, please expose available ports for multiple nodes communication in advance
docker run --rm \
--name $NAME \
--net=host \
--shm-size=1g \
--device /dev/davinci0 \
--device /dev/davinci1 \
--device /dev/davinci2 \
--device /dev/davinci3 \
--device /dev/davinci4 \
--device /dev/davinci5 \
--device /dev/davinci6 \
--device /dev/davinci7 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/Ascend/driver/tools/hccn_tool:/usr/local/Ascend/driver/tools/hccn_tool \
-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 /mnt/sfs_turbo/.cache:/root/.cache \
-it $IMAGE bash

Deployment

Single-node Deployment

Single NPU (Qwen2.5-Omni-7B)

:::{note} The env LOCAL_MEDIA_PATH which allowing API requests to read local images or videos from directories specified by the server file system. Please note this is a security risk. Should only be enabled in trusted environments.

:::

export VLLM_USE_MODELSCOPE=true
export MODEL_PATH="Qwen/Qwen2.5-Omni-7B"
export LOCAL_MEDIA_PATH=$HOME/.cache/vllm/assets/vllm_public_assets/

vllm serve "${MODEL_PATH}" \
--host 0.0.0.0 \
--port 8000 \
--served-model-name Qwen-Omni \
--allowed-local-media-path ${LOCAL_MEDIA_PATH} \
--trust-remote-code \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--no-enable-prefix-caching

:::{note} Now vllm-ascend docker image should contain vllm[audio] build part, if you encounter audio not supported issue by any chance, please re-build vllm with [audio] flag.

VLLM_TARGET_DEVICE=empty pip install -v ".[audio]"

:::

--allowed-local-media-path is optional, only set it if you need infer model with local media file.

--gpu-memory-utilization should not be set manually only if you know what this parameter aims to.

Multiple NPU (Qwen2.5-Omni-7B)

export VLLM_USE_MODELSCOPE=true
export MODEL_PATH=Qwen/Qwen2.5-Omni-7B
export LOCAL_MEDIA_PATH=$HOME/.cache/vllm/assets/vllm_public_assets/
export DP_SIZE=8

vllm serve ${MODEL_PATH} \
--host 0.0.0.0 \
--port 8000 \
--served-model-name Qwen-Omni \
--allowed-local-media-path ${LOCAL_MEDIA_PATH} \
--trust-remote-code \
--compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY"}' \
--data-parallel-size ${DP_SIZE} \
--no-enable-prefix-caching

--tensor_parallel_size no need to set for this 7B model, but if you really need tensor parallel, tp size can be one of 1/2/4.

Prefill-Decode Disaggregation

Not supported yet.

Functional Verification

If your service start successfully, you can see the info shown below:

INFO:     Started server process [2736]
INFO:     Waiting for application startup.
INFO:     Application startup complete.

Once your server is started, you can query the model with input prompts:

curl http://localhost:8000/v1/chat/completions   -H "Content-Type: application/json"   -H "Authorization: Bearer EMPTY"   -d '{
    "model": "Qwen-Omni",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "What is the text in the illustration?"
          },
          {
            "type": "image_url",
            "image_url": {
              "url": "https://modelscope.oss-cn-beijing.aliyuncs.com/resource/qwen.png"
            }
          }
        ]
      }
    ],
    "max_completion_tokens": 100,
    "temperature": 0.7
  }'

If you query the server successfully, you can see the info shown below (client):

{"id":"chatcmpl-a70a719c12f7445c8204390a8d0d8c97","object":"chat.completion","created":1764056861,"model":"Qwen-Omni","choices":[{"index":0,"message":{"role":"assistant","content":"The text in the illustration is \"TONGYI Qwen\".","refusal":null,"annotations":null,"audio":null,"function_call":null,"tool_calls":[],"reasoning_content":null},"logprobs":null,"finish_reason":"stop","stop_reason":null,"token_ids":null}],"service_tier":null,"system_fingerprint":null,"usage":{"prompt_tokens":73,"total_tokens":88,"completion_tokens":15,"prompt_tokens_details":null},"prompt_logprobs":null,"prompt_token_ids":null,"kv_transfer_params":null}

Accuracy Evaluation

Qwen2.5-Omni on vllm-ascend has been tested on AISBench.

Using AISBench

1. Refer to Using AISBench for details.

2. After execution, you can get the result, here is the result of Qwen2.5-Omni-7B with vllm-ascend:0.11.0rc0 for reference only.

dataset	platform	metric	mode	vllm-api-stream-chat
textVQA	A2	accuracy	gen_base64	83.47
textVQA	A3	accuracy	gen_base64	84.04

Performance Evaluation

Using AISBench

Refer to Using AISBench for performance evaluation for details.

Using vLLM Benchmark

Run performance evaluation of Qwen2.5-Omni-7B as an example.

Refer to vllm benchmark for more details.

There are three vllm bench subcommands:

• 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.

vllm bench serve --model Qwen/Qwen2.5-Omni-7B --dataset-name random --random-input 1024 --num-prompts 200 --request-rate 1 --save-result --result-dir ./

After about several minutes, you can get the performance evaluation result.