### What this PR does / why we need it? add single node PD disaggregation instructions for Qwen 2.5VL model. ### Does this PR introduce _any_ user-facing change? no --------- Signed-off-by: mazhixin <mazhixin7@huawei.com> Signed-off-by: mazhixin000 <mazhixinkorea@163.com> Co-authored-by: mazhixin <mazhixin7@huawei.com>
5.9 KiB
Prefill-Decode Disaggregation Llmdatadist Verification (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
- 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
# View NPU network configuration
cat /etc/hccn.conf
- Get NPU IP Addresses
for i in {0..7}; do hccn_tool -i $i -ip -g;done
Generate Ranktable
The rank table is a JSON file that specifies the mapping of Ascend NPU ranks to nodes. For more details, please refer to the vllm-ascend examples. Execute the following commands for reference.
cd vllm-ascend/examples/disaggregate_prefill_v1/
bash gen_ranktable.sh --ips 192.0.0.1 \
--npus-per-node 2 --network-card-name eth0 --prefill-device-cnt 1 --decode-device-cnt 1
If you want to run "2P1D", please set npus-per-node to 3 and prefill-device-cnt to 2. The rank table will be generated at /vllm-workspace/vllm-ascend/examples/disaggregate_prefill_v1/ranktable.json
| Parameter | Meaning |
|---|---|
| --ips | Each node's local IP address (prefiller nodes should be in front of decoder nodes) |
| --npus-per-node | Each node's NPU clips |
| --network-card-name | The physical machines' NIC |
| --prefill-device-cnt | NPU clips used for prefill |
| --decode-device-cnt | NPU clips used for decode |
Prefiller/Decoder Deployment
We can run the following scripts to launch a server on the prefiller/decoder NPU, respectively.
:::::{tab-set}
::::{tab-item} Prefiller
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 DISAGGREGATED_PREFILL_RANK_TABLE_PATH="/path/to/your/generated/ranktable.json"
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export VLLM_ASCEND_LLMDD_RPC_PORT=5959
vllm serve /model/Qwen2.5-VL-7B-Instruct \
--host 0.0.0.0 \
--port 13700 \
--tensor-parallel-size 1 \
--no-enable-prefix-caching \
--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": "LLMDataDistCMgrConnector",
"kv_buffer_device": "npu",
"kv_role": "kv_producer",
"kv_parallel_size": 1,
"kv_port": "20001",
"engine_id": "0",
"kv_connector_module_path": "vllm_ascend.distributed.llmdatadist_c_mgr_connector"
}'
::::
::::{tab-item} Decoder
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 DISAGGREGATED_PREFILL_RANK_TABLE_PATH="/path/to/your/generated/ranktable.json"
export OMP_PROC_BIND=false
export OMP_NUM_THREADS=10
export VLLM_ASCEND_LLMDD_RPC_PORT=5979
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": "LLMDataDistCMgrConnector",
"kv_buffer_device": "npu",
"kv_role": "kv_consumer",
"kv_parallel_size": 1,
"kv_port": "20001",
"engine_id": "0",
"kv_connector_module_path": "vllm_ascend.distributed.llmdatadist_c_mgr_connector"
}'
::::
:::::
If you want to run "2P1D", please set ASCEND_RT_VISIBLE_DEVICES, VLLM_ASCEND_LLMDD_RPC_PORT 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 illustrate?"}
]}
],
"max_tokens": 100,
"temperature": 0
}'