diff --git a/.github/configs/nvidia-master.yaml b/.github/configs/nvidia-master.yaml index 33751270b..56d95539e 100644 --- a/.github/configs/nvidia-master.yaml +++ b/.github/configs/nvidia-master.yaml @@ -1789,6 +1789,24 @@ qwen3.5-fp8-b200-sglang: - { tp: 8, ep: 1, conc-start: 4, conc-end: 16 } - { tp: 4, ep: 4, conc-start: 16, conc-end: 128 } +qwen3.5-fp4-b200-sglang: + image: lmsysorg/sglang:v0.5.9-cu129-amd64 + model: nvidia/Qwen3.5-397B-A17B-NVFP4 + model-prefix: qwen3.5 + runner: b200 + precision: fp4 + framework: sglang + multinode: false + seq-len-configs: + - isl: 1024 + osl: 1024 + search-space: + - { tp: 4, ep: 1, conc-start: 4, conc-end: 128 } + - isl: 8192 + osl: 1024 + search-space: + - { tp: 4, ep: 1, conc-start: 4, conc-end: 128 } + glm5-fp8-b200-sglang: image: lmsysorg/sglang:nightly-dev-cu13-20260317-1eea7448 model: zai-org/GLM-5-FP8 diff --git a/benchmarks/single_node/qwen3.5_fp4_b200.sh b/benchmarks/single_node/qwen3.5_fp4_b200.sh new file mode 100755 index 000000000..c26421059 --- /dev/null +++ b/benchmarks/single_node/qwen3.5_fp4_b200.sh @@ -0,0 +1,101 @@ +#!/usr/bin/env bash + +source "$(dirname "$0")/../benchmark_lib.sh" + +check_env_vars \ + MODEL \ + TP \ + CONC \ + ISL \ + OSL \ + RANDOM_RANGE_RATIO \ + RESULT_FILENAME \ + EP_SIZE + +if [[ -n "$SLURM_JOB_ID" ]]; then + echo "JOB $SLURM_JOB_ID running on $SLURMD_NODENAME" +fi + +nvidia-smi + +hf download "$MODEL" + +export NCCL_NVLS_ENABLE=1 +export SGL_ENABLE_JIT_DEEPGEMM=false +export SGLANG_ENABLE_FLASHINFER_GEMM=true +export PYTHONUNBUFFERED=1 + +SERVER_LOG=/workspace/server.log +PORT=${PORT:-8888} + +# Default: recv every ~10 requests; if CONC >= 16, relax to ~30 requests between scheduler recv polls. +if [[ $CONC -ge 16 ]]; then + SCHEDULER_RECV_INTERVAL=30 +else + SCHEDULER_RECV_INTERVAL=10 +fi + +MEM_FRAC_STATIC=0.85 +CHUNKED_PREFILL_SIZE=32768 +MAX_PREFILL_TOKENS=32768 +CUDA_GRAPH_MAX_BATCH_SIZE=$CONC +MAX_RUNNING_REQUESTS=128 +CONTEXT_LENGTH=$((ISL + OSL + 20)) +if [ "${EVAL_ONLY}" = "true" ]; then + setup_eval_context + CONTEXT_LENGTH="$EVAL_MAX_MODEL_LEN" +fi + +if [[ $TP -eq 8 ]]; then + EXTRA_ARGS="--enable-flashinfer-allreduce-fusion" +else + EXTRA_ARGS="" +fi + +echo "SCHEDULER_RECV_INTERVAL: $SCHEDULER_RECV_INTERVAL, CONC: $CONC, ISL: $ISL, OSL: $OSL" + +# Start GPU monitoring (power, temperature, clocks every second) +start_gpu_monitor + +set -x +PYTHONNOUSERSITE=1 python3 -m sglang.launch_server --model-path=$MODEL --host=0.0.0.0 --port=$PORT \ +--trust-remote-code \ +--tensor-parallel-size=$TP --data-parallel-size=1 --ep-size $EP_SIZE \ +--quantization modelopt_fp4 --fp4-gemm-backend flashinfer_cutlass \ +--kv-cache-dtype fp8_e4m3 \ +--mamba-ssm-dtype bfloat16 \ +--cuda-graph-max-bs $CUDA_GRAPH_MAX_BATCH_SIZE --max-running-requests $MAX_RUNNING_REQUESTS \ +--mem-fraction-static $MEM_FRAC_STATIC --chunked-prefill-size $CHUNKED_PREFILL_SIZE --max-prefill-tokens $MAX_PREFILL_TOKENS \ +--context-length $CONTEXT_LENGTH --disable-radix-cache \ +--attention-backend trtllm_mha --moe-runner-backend flashinfer_trtllm \ +$EXTRA_ARGS --scheduler-recv-interval $SCHEDULER_RECV_INTERVAL \ +--tokenizer-worker-num 6 --stream-interval 30 > $SERVER_LOG 2>&1 & + +SERVER_PID=$! + +# Wait for server to be ready +wait_for_server_ready --port "$PORT" --server-log "$SERVER_LOG" --server-pid "$SERVER_PID" + +pip install -q datasets pandas + +run_benchmark_serving \ + --model "$MODEL" \ + --port "$PORT" \ + --backend vllm \ + --input-len "$ISL" \ + --output-len "$OSL" \ + --random-range-ratio "$RANDOM_RANGE_RATIO" \ + --num-prompts "$((CONC * 10))" \ + --max-concurrency "$CONC" \ + --result-filename "$RESULT_FILENAME" \ + --result-dir /workspace/ + +# After throughput, run evaluation only if RUN_EVAL is true +if [ "${RUN_EVAL}" = "true" ]; then + run_eval --framework lm-eval --port "$PORT" + append_lm_eval_summary +fi + +# Stop GPU monitoring +stop_gpu_monitor +set +x diff --git a/perf-changelog.yaml b/perf-changelog.yaml index b3720abc8..5a35d3914 100644 --- a/perf-changelog.yaml +++ b/perf-changelog.yaml @@ -1198,3 +1198,12 @@ description: - "Add --disable-radix-cache to SGLang server launch command for qwen3.5 MI300X and MI325X benchmark scripts" pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/970 + +- config-keys: + - qwen3.5-fp4-b200-sglang + description: + - "Add Qwen3.5-397B-A17B NVFP4 B200 SGLang benchmark config and launch script" + - "Image: lmsysorg/sglang:v0.5.9-cu129-amd64" + - "Model: nvidia/Qwen3.5-397B-A17B-NVFP4" + - "Configs: 1k1k (TP4 conc 4-128), 8k1k (TP4 conc 4-128)" + pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/820