d904d663a4
- vLLM adaptive concurrency benchmark adapted from H200 version - Key changes: - Model path: /data/hf_models/DeepSeek-V4-Flash - Venv path: /root/.miniconda3/envs/sglang - Dataset path: /data/yy/sskj/datasets/ - DSL renamed to OSL (Output Sequence Length) - Includes: config.env, adaptive_config.env, run_adaptive_concurrency.sh, start_vllm_dp.sh, start_vllm_docker.sh
77 lines
2.9 KiB
Bash
77 lines
2.9 KiB
Bash
# TP×DP matrix experiment for DeepSeek-V4-Flash on RTX 6000D (8 GPUs) using vLLM.
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# Tests vLLM with three parallel configurations:
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# TP=2, DP=4 -> 2 GPUs per replica, 4 replicas
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# TP=4, DP=2 -> 4 GPUs per replica, 2 replicas
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# TP=8, DP=1 -> 8 GPUs, no data parallelism
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EXPERIMENT="dsv4_pro6000_vllm_tp_dp_matrix"
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MODEL_NAME="DeepSeek-V4-Flash"
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MODEL_PATH="/data/hf_models/DeepSeek-V4-Flash"
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SERVED_MODEL_NAME="deepseek-v4-flash"
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VLLM_PORT="${VLLM_PORT:-30030}"
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# Python interpreter for orchestration scripts (parse_backend.py, compare.py, etc.)
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# and the benchmark client. Defaults to the system python3 if the sglang venv
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# does not exist on the host.
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VENV_CLIENT="${VENV_CLIENT:-/root/.miniconda3/envs/sglang}"
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# Run the benchmark client natively (0) or inside Docker (1).
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USE_DOCKER_CLIENT="${USE_DOCKER_CLIENT:-1}"
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export CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES:-0,1,2,3,4,5,6,7}"
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# Runtime working directory for logs, pid files, and tmp. Defaults to a local
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# directory under this experiment so the benchmark is self-contained.
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RUNTIME_BASE="${RUNTIME_BASE:-${SCRIPT_DIR}/runtime}"
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# Parallel configurations to test. Format: "TP DP"
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declare -a PARALLEL_CONFIGS=(
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"2 4"
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"4 2"
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"8 1"
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)
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# vLLM server settings
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MAX_MODEL_LEN="${MAX_MODEL_LEN:-1048576}"
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MAX_NUM_SEQS="${MAX_NUM_SEQS:-128}"
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GPU_MEMORY_UTILIZATION="${GPU_MEMORY_UTILIZATION:-0.9}"
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KV_CACHE_DTYPE="${KV_CACHE_DTYPE:-fp8}"
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BLOCK_SIZE="${BLOCK_SIZE:-256}"
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# Deployment switch. 0 = native vllm venv, 1 = Docker.
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USE_DOCKER="${USE_DOCKER:-1}"
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DOCKER_IMAGE="${DOCKER_IMAGE:-vllm/vllm-openai:latest}"
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# Benchmark client Docker image. vLLM's image does not include sglang.bench_serving,
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# so the client runs inside the SGLang image and targets the vLLM backend via the
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# OpenAI-compatible API.
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DOCKER_CLIENT_IMAGE="${DOCKER_CLIENT_IMAGE:-lmsysorg/sglang:latest}"
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# Dataset used by sglang.bench_serving --dataset-name random.
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# The random sampler needs a ShareGPT-style JSON file locally; it falls back to
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# downloading from HuggingFace, which usually fails on offline nodes.
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DATASET_PATH="${DATASET_PATH:-/data/yy/sskj/datasets/ShareGPT_V3_unfiltered_cleaned_split.json}"
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# Matrix and concurrency rules are defined in matrix.json by default.
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MATRIX_FILE="${MATRIX_FILE:-${SCRIPT_DIR:-.}/matrix.json}"
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MATRIX_MODE="${MATRIX_MODE:-Y}"
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# Sampling density for concurrency.
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# 0 = use the default heuristic in generate_scenarios.py (6-8 points).
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# 2 = only test the low and high endpoints.
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export CONCURRENCY_SAMPLES="${CONCURRENCY_SAMPLES:-2}"
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# Per-scenario timeout to avoid hangs (seconds).
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SCENARIO_TIMEOUT_S="${SCENARIO_TIMEOUT_S:-1800}"
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# GPU memory sampling interval (seconds).
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GPU_MEM_SAMPLE_INTERVAL_S="${GPU_MEM_SAMPLE_INTERVAL_S:-1}"
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# Dry-run mode: if 1, only log the server args and scenario plan without starting
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# any server or sending requests.
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DRY_RUN="${DRY_RUN:-0}"
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# Per-config scenario limit for quick smoke tests. 0 = run all generated scenarios.
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GRID_LIMIT="${GRID_LIMIT:-0}"
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