d72dbff689
- 新增 P 组(prefill)/D 组(decode) deploy profiles（mooncake RDMA + 计算网 mlx5_0~3） - 新增 experiments/pro6000/kimi3_pro6000_pd_rdma/ 编排脚本（mc-master+P+D+router） - 关键修复: 必须关闭 PYTORCH_CUDA_ALLOC_CONF=expandable_segments （mooncake RDMA 注册 expandable GPU 段报 Bad address，GitHub #2511） - 容器内升级 mooncake 0.3.12.post1（含 dmabuf 修复 #2035） - docs/KIMI_K3_DEPLOY.md 新增附录 B，README 更新实验索引
55 lines
3.0 KiB
Bash
55 lines
3.0 KiB
Bash
# Kimi-K3 PD 分离 - D 组 (decode) deployment profile (4x RTX 6000D).
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# Nodes: 174.1.60.5~8 (rank 0~3), 32x NVIDIA RTX 6000D (85GB, sm_120).
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#
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# 这是 PD (Prefill/Decode) 分离部署的 D 组 (decode) 端 profile。
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# 配套 P 组 profile: kimi3_pro6000_pd_prefill.env
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# 配套编排脚本: experiments/pro6000/kimi3_pro6000_pd_rdma/deploy_pd.sh
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#
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# 与 P 组相同的传输/网络配置（mooncake RDMA + 计算网），区别仅在:
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# - 节点是 174.1.60.5~8
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# - --disaggregation-mode decode --port 30000
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# - dist-init-addr 174.1.60.5:20000
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# - 必须 D 组在 P 组之后启动（见 deploy_pd.sh）
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#
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# Model-team only. Ops only run benchmark against the served router URL.
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PLATFORM=pro6000
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EXPERIMENT=kimi3_pro6000_pd_decode
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MODEL_NAME=Kimi-K3
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ENGINE=sglang
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RUNTIME=docker
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DOCKER_IMAGE=lmsysorg/sglang:kimi-k3
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CONTAINER_NAME=${EXPERIMENT}_node${NODE_RANK}
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MODEL_PATH=/data/hf_models/Kimi-K3
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SERVED_MODEL_NAME=kimi-k3
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PORT=30000
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HEALTH_PATH=/health
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HEALTH_HOST=174.1.60.5
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HEALTH_WAIT_S=2400
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CONTAINER_PYTHON=python3
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# ---- 并行度（固定，勿改）----
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TP=32
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DP=1
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EP_SIZE=32
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# ---- Multi-node topology (D 组 = decode 4 节点) ----
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NNODES=4
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NODE_HOSTS="174.1.60.5 174.1.60.6 174.1.60.7 174.1.60.8"
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NODE_SSH_USER=root
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LOCAL_NODE_RANK=0
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MASTER_IP=174.1.60.5
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DIST_PORT=20000
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DEVICE_VARS="CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7"
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ENGINE_ENV="MOONCAKE_MASTER=174.1.60.1:50051 MOONCAKE_PROTOCOL=rdma NCCL_SOCKET_IFNAME=bond1 GLOO_SOCKET_IFNAME=bond1 NCCL_IB_HCA=mlx5_0,mlx5_1,mlx5_2,mlx5_3 NCCL_IB_GID_INDEX=3 NCCL_IB_TIMEOUT=22 NCCL_IB_RETRY_CNT=7 NCCL_CUMEM_ENABLE=1 SGLANG_ENABLE_TP_MEMORY_INBALANCE_CHECK=0 SGLANG_MOE_FUSED_GATE_RADIX=1 SGLANG_ENABLE_REQUEST_HEADER_OVERRIDES=1"
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DOCKER_FLAGS="--gpus all --network host --ipc=host --ulimit memlock=-1 --device /dev/infiniband --shm-size 32g --entrypoint ''"
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VOLUMES="${MODEL_PATH}:${MODEL_PATH}:ro"
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PATCH_MOUNTS="/tmp/patch_k3_sm120.py:/tmp/patch_k3_sm120.py:ro /data/flashkda_deploy/wheels:/mc_wheels:ro"
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# BOOTSTRAP: 打补丁 → 装 flashkda + mooncake wheel → 按 rank 设 SGLANG_HOST_IP → 启动 decode。
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BOOTSTRAP="python3 /tmp/patch_k3_sm120.py && pip install /flash_kda-0.0.1-cp312-cp312-linux_x86_64.whl --no-deps -q && pip install /mc_wheels/mooncake_transfer_engine_cuda13-0.3.12.post1-cp312-cp312-manylinux_2_28_x86_64.whl --no-deps -q && export SGLANG_HOST_IP=\"174.1.60.$((5 + ${NODE_RANK}))\" && exec python3 -m sglang.launch_server ${LAUNCH_ARGS}"
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LAUNCH_ARGS="--model-path ${MODEL_PATH} --served-model-name ${SERVED_MODEL_NAME} --tp-size ${TP} --ep-size 32 --nnodes ${NNODES} --node-rank ${NODE_RANK} --dist-init-addr ${MASTER_IP}:${DIST_PORT} --trust-remote-code --moe-runner-backend marlin --mem-fraction-static 0.88 --cuda-graph-max-bs-decode 16 --mamba-radix-cache-strategy extra_buffer --dist-timeout 3600 --mamba-full-memory-ratio 0.36 --disaggregation-transfer-backend mooncake --disaggregation-bootstrap-port 28800 --disaggregation-ib-device mlx5_0,mlx5_1,mlx5_2,mlx5_3 --disaggregation-mode decode --host 0.0.0.0 --port ${PORT}"
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