# Disaggregated Prefill-Decode Deployment Guide ## Overview This demo document provides instructions for running a disaggregated vLLM-ascend service with separate prefill and decode stages across 4 nodes, uses 16 Ascend NPUs for two prefill nodes (P1/P2) and 16 Ascend NPUS for two decode nodes (D1/D2). ## Prerequisites - Ascend NPU environment with vLLM 0.9.1 installed - Network interfaces configured for distributed communication (eg: eth0) - Model weights located at `/models/deepseek_r1_w8a8` ## Rank table generation The rank table is a JSON file that specifies the mapping of Ascend NPU ranks to nodes. The following command generates a rank table for all nodes with 16 cards prefill and 16 cards decode: Run the following command on every node to generate the rank table: ```shell cd /vllm-workspace/vllm-ascend/examples/disaggregated_prefill_v1/ bash gen_ranktable.sh --ips 172.19.32.175 172.19.241.49 172.19.123.51 172.19.190.36 \ --npus-per-node 8 --network-card-name eth0 --prefill-device-cnt 16 --decode-device-cnt 16 ``` Rank table will generated at `/vllm-workspace/vllm-ascend/examples/disaggregated_prefill_v1/ranktable.json` ## Start disaggregated vLLM-ascend service For demonstration purposes, we will utilize the quantized version of Deepseek-R1. Recommended Parallelization Strategies: - P-node: DP2-TP8-EP16 (Data Parallelism 2, Tensor Parallelism 8, Expert Parallelism 16) - D-node: DP4-TP4-EP16 (Data Parallelism 4, Tensor Parallelism 4, Expert Parallelism 16) Execution Sequence - 4 configured node ip are: 172.19.32.175 172.19.241.49 172.19.123.51 172.19.190.36 - Start Prefill on Node 1 (P1) - Start Prefill on Node 2 (P2) - Start Decode on Node 1 (D1) - Start Decode on Node 2 (D2) - Start proxy server on Node1 Run prefill server P1 on first node: ```shell export HCCL_IF_IP=172.19.32.175 # 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=/vllm-workspace/vllm-ascend/examples/disaggregated_prefill_v1/ranktable.json export OMP_PROC_BIND=false export OMP_NUM_THREADS=100 export VLLM_USE_V1=1 export VLLM_LLMDD_RPC_PORT=5559 vllm serve /models/deepseek_r1_w8a8 \ --host 0.0.0.0 \ --port 20002 \ --data-parallel-size 2 \ --data-parallel-size-local 1 \ --api-server-count 2 \ --data-parallel-address 172.19.32.175 \ --data-parallel-rpc-port 13356 \ --tensor-parallel-size 8 \ --enable-expert-parallel \ --seed 1024 \ --served-model-name deepseek \ --max-model-len 32768 \ --max-num-batched-tokens 32768 \ --max-num-seqs 256 \ --trust-remote-code \ --enforce-eager \ --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" }' \ --additional-config \ '{"chunked_prefill_for_mla":true}' ``` Run prefill server P2 on second node: ```shell export HCCL_IF_IP=172.19.241.49 export GLOO_SOCKET_IFNAME="eth0" export TP_SOCKET_IFNAME="eth0" export HCCL_SOCKET_IFNAME="eth0" export DISAGGREGATED_PREFILL_RANK_TABLE_PATH=/vllm-workspace/vllm-ascend/examples/disaggregated_prefill_v1/ranktable.json export OMP_PROC_BIND=false export OMP_NUM_THREADS=100 export VLLM_USE_V1=1 export VLLM_LLMDD_RPC_PORT=5659 vllm serve /models/deepseek_r1_w8a8 \ --host 0.0.0.0 \ --port 20002 \ --headless \ --data-parallel-size 2 \ --data-parallel-start-rank 1 \ --data-parallel-size-local 1 \ --data-parallel-address 172.19.32.175 \ --data-parallel-rpc-port 13356 \ --tensor-parallel-size 8 \ --enable-expert-parallel \ --seed 1024 \ --served-model-name deepseek \ --max-model-len 32768 \ --max-num-batched-tokens 32768 \ --max-num-seqs 256 \ --trust-remote-code \ --enforce-eager \ --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" }' \ --additional-config \ '{"chunked_prefill_for_mla":true}' ``` Run decode server d1 on third node: * In the D node, the `max-num-batched-tokens` parameter can be set to a smaller value since the D node processes at most `max-num-seqs` batches concurrently. As the `profile_run` only needs to handle `max-num-seqs` sequences at a time, we can safely set `max-num-batched-tokens` equal to `max-num-seqs`. This optimization will help reduce activation memory consumption. ```shell export HCCL_IF_IP=172.19.123.51 export GLOO_SOCKET_IFNAME="eth0" export TP_SOCKET_IFNAME="eth0" export HCCL_SOCKET_IFNAME="eth0" export DISAGGREGATED_PREFILL_RANK_TABLE_PATH=/vllm-workspace/vllm-ascend/examples/disaggregated_prefill_v1/ranktable.json export OMP_PROC_BIND=false export OMP_NUM_THREADS=100 export VLLM_USE_V1=1 export VLLM_LLMDD_RPC_PORT=5759 vllm serve /models/deepseek_r1_w8a8 \ --host 0.0.0.0 \ --port 20002 \ --data-parallel-size 4 \ --data-parallel-size-local 2 \ --api-server-count 2 \ --data-parallel-address 172.19.123.51 \ --data-parallel-rpc-port 13356 \ --tensor-parallel-size 4 \ --enable-expert-parallel \ --seed 1024 \ --served-model-name deepseek \ --max-model-len 32768 \ --max-num-batched-tokens 256 \ --max-num-seqs 256 \ --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" }' \ --additional-config \ '{"torchair_graph_config": {"enabled":true}}' ``` Run decode server d2 on last node: ```shell export HCCL_IF_IP=172.19.190.36 export GLOO_SOCKET_IFNAME="eth0" export TP_SOCKET_IFNAME="eth0" export HCCL_SOCKET_IFNAME="eth0" export DISAGGREGATED_PREFILL_RANK_TABLE_PATH=/vllm-workspace/vllm-ascend/examples/disaggregated_prefill_v1/ranktable.json export OMP_PROC_BIND=false export OMP_NUM_THREADS=100 export VLLM_USE_V1=1 export VLLM_LLMDD_RPC_PORT=5859 vllm serve /models/deepseek_r1_w8a8 \ --host 0.0.0.0 \ --port 20002 \ --headless \ --data-parallel-size 4 \ --data-parallel-start-rank 2 \ --data-parallel-size-local 2 \ --data-parallel-address 172.19.123.51 \ --data-parallel-rpc-port 13356 \ --tensor-parallel-size 4 \ --enable-expert-parallel \ --seed 1024 \ --served-model-name deepseek \ --max-model-len 32768 \ --max-num-batched-tokens 256 \ --max-num-seqs 256 \ --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" }' \ --additional-config \ '{"torchair_graph_config": {"enabled":true}}' ``` Run proxy server on the first node: ```shell cd /vllm-workspace/vllm-ascend/examples/disaggregated_prefill_v1 python toy_proxy_server.py --host 172.19.32.175 --port 1025 --prefiller-hosts 172.19.241.49 --prefiller-port 20002 --decoder-hosts 172.19.123.51 --decoder-ports 20002 ``` Verification Check service health using the proxy server endpoint: ```shell curl http://localhost:1025/v1/completions \ -H "Content-Type: application/json" \ -d '{ "model": "deepseek", "prompt": "Who are you?", "max_tokens": 100, "temperature": 0 }' ``` Performance Test performance with vllm benchmark: ```shell cd /vllm-workspace/vllm/benchmarks python3 benchmark_serving.py \ --backend vllm \ --dataset-name random \ --random-input-len 4096 \ --random-output-len 1536 \ --num-prompts 256 \ --ignore-eos \ --model deepseek \ --tokenizer /models/deepseek_r1_w8a8 \ --host localhost \ --port 1025 \ --endpoint /v1/completions \ --max-concurrency 4 \ --request-rate 4 ```