GLM-5.2

이 페이지는 Ascend NPU에서 GLM-5.2의 최적 구성과 벤치마크 결과에 집중해요. 환경 설정, 모델 가중치 다운로드, 기능 구성, 배포 지침 등은 GLM-5.2 모델 튜토리얼을 참고하세요.

A3 시리즈에서는 각 카드에 2개의 die가 있어서 --tp-size가 카드 수의 2배예요. 자세한 내용은 Ascend NPU 참조를 확인하세요.

출처: 문서

본문

고처리량 (High Throughput)

모델	하드웨어	카드	배포 모드	데이터셋	TPOT	양자화	구성
GLM-5.2	Ascend A3 Series Products	32	PD Disaggregation	16k+1k	50ms	W4A8 INT8	최적 구성

최적 구성 (Optimal Configuration)

GLM-5.2 W4A8 3P1D 32P IN16K OUT1K 50ms

모델: GLM-5.2

하드웨어: Ascend A3 Series Products

카드: 32

배포 모드: PD Disaggregation

양자화: W4A8 INT8

데이터셋: 16k+1k

TPOT: 50ms

모델 배포 (Model Deployment)

# ============================================================
# Before running, update the following variables:
#   P_IP: prefill node IP address
#   D_IP: decode node IP address
#   ASCEND_MF_STORE_URL: prefill node IP with port
#   MODEL_PATH: path to the model weights directory
#   HCCL_SOCKET_IFNAME: network interface name for HCCL
#   GLOO_SOCKET_IFNAME: network interface name for Gloo
# ============================================================


echo performance | tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
sysctl -w vm.swappiness=0
sysctl -w kernel.numa_balancing=0
sysctl -w kernel.sched_migration_cost_ns=50000

unset https_proxy
unset http_proxy
unset HTTPS_PROXY
unset HTTP_PROXY
unset ASCEND_LAUNCH_BLOCKING

source /usr/local/Ascend/ascend-toolkit/set_env.sh
source /usr/local/Ascend/nnal/atb/set_env.sh

export PYTORCH_NPU_ALLOC_CONF=expandable_segments:True
export SGLANG_DISAGGREGATION_BOOTSTRAP_TIMEOUT=1200
export SGLANG_DISAGGREGATION_WAITING_TIMEOUT=1200
export SGLANG_SET_CPU_AFFINITY=1
export STREAMS_PER_DEVICE=32
export TRANSFORMERS_VERBOSITY=error

P_IP=('<your prefill ip1>' '<your prefill ip2>' '<your prefill ip3>')
D_IP=('<your decode ip>')

export ASCEND_MF_STORE_URL="tcp://<your prefill ip1>:24670"

MODEL_PATH=/path/to/model-weights

LOCAL_HOST1=`hostname -I|awk -F " " '{print$1}'`
LOCAL_HOST2=`hostname -I|awk -F " " '{print$2}'`
echo "${LOCAL_HOST1}"
echo "${LOCAL_HOST2}"
# prefill
for i in "${!P_IP[@]}";
do
    if [[ "$LOCAL_HOST1" == "${P_IP[$i]}" || "$LOCAL_HOST2" == "${P_IP[$i]}" ]];
    then
        echo "${P_IP[$i]}"
        export DEEP_NORMAL_MODE_USE_INT8_QUANT=1
        export DEEP_USE_ALLTOALL_MODE=1
        export GLOO_SOCKET_IFNAME=<network-interface>
        export HCCL_BUFFSIZE=128
        export HCCL_SOCKET_IFNAME=<network-interface>
        export SGLANG_ENABLE_TP_MEMORY_INBALANCE_CHECK=0
        export SGLANG_PP_LAYER_PARTITION=18,20,24,16
        export SGLANG_ZBAL_LOCAL_MEM_SIZE=61184
        export TASK_QUEUE_ENABLE=2
        export ZBAL_HCCL_OP=send,recv
        export ZBAL_NPU_ALLOC_CONF=use_vmm_for_static_memory:True

        python3 -m sglang.launch_server \
        --model-path ${MODEL_PATH} \
        --disaggregation-mode prefill \
        --host ${P_IP[$i]} \
        --port 8000 \
        --disaggregation-bootstrap-port $((8998 + $i)) \
        --trust-remote-code \
        --tp-size 4 \
        --mem-fraction-static 0.72 \
        --attention-backend ascend \
        --device npu \
        --quantization modelslim \
        --disaggregation-transfer-backend ascend \
        --max-running-requests 16 \
        --served-model-name glm-5 \
        --chunked-prefill-size 4096 \
        --max-prefill-tokens 180000 \
        --moe-a2a-backend deepep \
        --deepep-mode normal \
        --disable-shared-experts-fusion \
        --disable-cuda-graph \
        --dtype bfloat16 \
        --speculative-draft-model-quantization unquant \
        --enable-nsa-prefill-context-parallel \
        --nsa-prefill-cp-mode in-seq-split \
        --attn-cp-size 4 \
        --enable-dp-lm-head \
        --moe-dense-tp 1 \
        --pp-size 4 \
        --speculative-algorithm NEXTN \
        --speculative-num-steps 1 \
        --speculative-eagle-topk 1 \
        --speculative-num-draft-tokens 2
        break
    fi
done

# decode
for i in "${!D_IP[@]}";
do
    if [[ "$LOCAL_HOST1" == "${D_IP[$i]}" || "$LOCAL_HOST2" == "${D_IP[$i]}" ]];
    then
        echo "${D_IP[$i]}"
        export GLOO_SOCKET_IFNAME=<network-interface>
        export HCCL_BUFFSIZE=300
        export HCCL_SOCKET_IFNAME=<network-interface>
        export SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=40
        export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
        export SGLANG_NPU_USE_MULTI_STREAM=1
        export SGLANG_SPEC_ENABLE_OVERLAP_REFLOW=1
        export TASK_QUEUE_ENABLE=0

        python3 -m sglang.launch_server \
        --model-path ${MODEL_PATH} \
        --disaggregation-mode decode \
        --host ${D_IP[$i]} \
        --port 8001 \
        --trust-remote-code \
        --tp-size 16 \
        --dp-size 16 \
        --enable-dp-attention \
        --ep-size 16 \
        --mem-fraction-static 0.895 \
        --max-running-requests 128 \
        --attention-backend ascend \
        --device npu \
        --quantization modelslim \
        --served-model-name glm-5 \
        --moe-a2a-backend deepep \
        --deepep-mode low_latency \
        --cuda-graph-max-bs-decode 8 \
        --disaggregation-transfer-backend ascend \
        --watchdog-timeout 9000 \
        --context-length 180000 \
        --tokenizer-worker-num 8 \
        --disable-shared-experts-fusion \
        --dtype bfloat16 \
        --load-balance-method round_robin \
        --speculative-draft-model-quantization unquant \
        --speculative-algorithm NEXTN \
        --speculative-num-steps 3 \
        --speculative-eagle-topk 1 \
        --speculative-num-draft-tokens 4
        break
    fi
done

# ============================================================
# Before running, replace the following placeholders:
#   <your prefill ip1>, <your prefill ip2>, <your prefill ip3>: prefill node IP addresses
#   <your decode ip>: decode node IP address
# ============================================================

python -m sglang_router.launch_router \
    --pd-disaggregation \
    --prefill http://<your prefill ip1>:8000 8998 \
    --prefill http://<your prefill ip2>:8000 8999 \
    --prefill http://<your prefill ip3>:8000 9000 \
    --decode http://<your decode ip>:8001 \
    --host 127.0.0.1 \
    --port 6688 \
    --policy round_robin

벤치마크 (Benchmark)

RANDOM 데이터셋을 기반으로 테스트했어요.

python -m sglang.bench_serving \
    --dataset-name random \
    --backend sglang \
    --host 127.0.0.1 \
    --port 6688 \
    --max-concurrency 64 \
    --num-prompts 200 \
    --random-input-len 16000 \
    --random-output-len 1000 \
    --random-range-ratio 1

더 알아보기 (Learn more)

SGLang GLM-5.2 최적 구성 공식 문서