Prefill-Decode Disaggregation (Qwen2.5-VL)

Prefill-Decode Disaggregation (Qwen2.5-VL)#

Getting Start#

vLLM-Ascend now supports prefill-decode (PD) disaggregation. This guide takes one-by-one steps to verify these features with constrained resources.

Using the Qwen2.5-VL-7B-Instruct model as an example, use vllm-ascend v0.11.0rc1 (with vLLM v0.11.0) on 1 Atlas 800T A2 server to deploy the “1P1D” architecture. Assume the IP address is 192.0.0.1.

Verify Communication Environment#

Verification Process#

  1. Single Node Verification:

Execute the following commands in sequence. The results must all be success and the status must be UP:

# Check the remote switch ports
for i in {0..7}; do hccn_tool -i $i -lldp -g | grep Ifname; done
# Get the link status of the Ethernet ports (UP or DOWN)
for i in {0..7}; do hccn_tool -i $i -link -g ; done
# Check the network health status
for i in {0..7}; do hccn_tool -i $i -net_health -g ; done
# View the network detected IP configuration
for i in {0..7}; do hccn_tool -i $i -netdetect -g ; done
# View gateway configuration
for i in {0..7}; do hccn_tool -i $i -gateway -g ; done

  1. Check NPU HCCN Configuration:

Ensure that the hccn.conf file exists in the environment. If using Docker, mount it into the container.

cat /etc/hccn.conf

  1. Get NPU IP Addresses

for i in {0..7}; do hccn_tool -i $i -ip -g;done

  1. Cross-Node PING Test

# Execute on the target node (replace 'x.x.x.x' with actual npu ip address).
for i in {0..7}; do hccn_tool -i $i -ping -g address x.x.x.x;done

  1. Check NPU TLS Configuration

# The tls settings should be consistent across all nodes
for i in {0..7}; do hccn_tool -i $i -tls -g ; done | grep switch

Run with Docker#

Start a Docker container.

# Update the vllm-ascend image
export IMAGE=m.daocloud.io/quay.io/ascend/vllm-ascend:v0.15.0rc1
export NAME=vllm-ascend

# Run the container using the defined variables
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 /etc/hccn.conf:/etc/hccn.conf \
-v /mnt/sfs_turbo/.cache:/root/.cache \
-it $IMAGE bash

Install Mooncake#

Mooncake is the serving platform for Kimi, a leading LLM service provided by Moonshot AI. Installation and Compilation Guide: kvcache-ai/Mooncake. First, we need to obtain the Mooncake project. Refer to the following command:

git clone -b v0.3.8.post1 --depth 1 https://github.com/kvcache-ai/Mooncake.git

(Optional) Replace go install url if the network is poor.

cd Mooncake
sed -i 's|https://go.dev/dl/|https://golang.google.cn/dl/|g' dependencies.sh

Install mpi.

apt-get install mpich libmpich-dev -y

Install the relevant dependencies. The installation of Go is not required.

bash dependencies.sh -y

Compile and install.

mkdir build
cd build
cmake .. -DUSE_ASCEND_DIRECT=ON
make -j
make install

Set environment variables.

Note:

  • Adjust the Python path according to your specific Python installation

  • Ensure /usr/local/lib and /usr/local/lib64 are in your LD_LIBRARY_PATH

export LD_LIBRARY_PATH=/usr/local/lib64/python3.11/site-packages/mooncake:$LD_LIBRARY_PATH

Prefiller/Decoder Deployment#

We can run the following scripts to launch a server on the prefiller/decoder NPU, respectively.

export ASCEND_RT_VISIBLE_DEVICES=0
export HCCL_IF_IP=192.0.0.1  # node ip
export GLOO_SOCKET_IFNAME="eth0"  # network card name
export TP_SOCKET_IFNAME="eth0"
export HCCL_SOCKET_IFNAME="eth0"
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10

vllm serve /model/Qwen2.5-VL-7B-Instruct  \
  --host 0.0.0.0 \
  --port 13700 \
  --no-enable-prefix-caching \
  --tensor-parallel-size 1 \
  --seed 1024 \
  --served-model-name qwen25vl \
  --max-model-len 40000  \
  --max-num-batched-tokens 40000  \
  --trust-remote-code \
  --gpu-memory-utilization 0.9  \
  --kv-transfer-config \
  '{"kv_connector": "MooncakeConnectorV1",
  "kv_role": "kv_producer",
  "kv_port": "30000",
  "engine_id": "0",
  "kv_connector_extra_config": {
            "prefill": {
                    "dp_size": 1,
                    "tp_size": 1
             },
             "decode": {
                    "dp_size": 1,
                    "tp_size": 1
             }
      }
  }'

export ASCEND_RT_VISIBLE_DEVICES=1
export HCCL_IF_IP=192.0.0.1  # node ip
export GLOO_SOCKET_IFNAME="eth0"  # network card name
export TP_SOCKET_IFNAME="eth0"
export HCCL_SOCKET_IFNAME="eth0"
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10

vllm serve /model/Qwen2.5-VL-7B-Instruct  \
  --host 0.0.0.0 \
  --port 13701 \
  --no-enable-prefix-caching \
  --tensor-parallel-size 1 \
  --seed 1024 \
  --served-model-name qwen25vl \
  --max-model-len 40000  \
  --max-num-batched-tokens 40000  \
  --trust-remote-code \
  --gpu-memory-utilization 0.9  \
  --kv-transfer-config \
  '{"kv_connector": "MooncakeConnectorV1",
  "kv_role": "kv_consumer",
  "kv_port": "30100",
  "engine_id": "1",
  "kv_connector_extra_config": {
            "prefill": {
                    "dp_size": 1,
                    "tp_size": 1
             },
             "decode": {
                    "dp_size": 1,
                    "tp_size": 1
             }
      }
  }'

If you want to run “2P1D”, please set ASCEND_RT_VISIBLE_DEVICES and port to different values for each P process.

Example Proxy for Deployment#

Run a proxy server on the same node with the prefiller service instance. You can get the proxy program in the repository’s examples: load_balance_proxy_server_example.py

python load_balance_proxy_server_example.py \
    --host 192.0.0.1 \
    --port 8080 \
    --prefiller-hosts 192.0.0.1 \
    --prefiller-port 13700 \
    --decoder-hosts 192.0.0.1 \
    --decoder-ports 13701

Parameter

Meaning

–port

Port of proxy

–prefiller-port

All ports of prefill

–decoder-ports

All ports of decoder

Verification#

Check service health using the proxy server endpoint.

curl http://192.0.0.1:8080/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "qwen25vl",
        "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 illustration?"}
            ]}
            ],
        "max_completion_tokens": 100,
        "temperature": 0
    }'
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