87 lines
3.7 KiB
Bash
87 lines
3.7 KiB
Bash
# TP×DP matrix experiment for DeepSeek-V4-Flash-INT8 on Kunlun P800 (8 XPUs)
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# using SGLang-XPU. Tests SGLang with three parallel configurations:
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# TP=2, DP=4 -> 2 XPUs per replica, 4 replicas
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# TP=4, DP=2 -> 4 XPUs per replica, 2 replicas
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# TP=8, DP=1 -> 8 XPUs, no data parallelism
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EXPERIMENT="dsv4_p800_sglang_tp_dp_matrix"
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MODEL_NAME="DeepSeek-V4-Flash-INT8"
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MODEL_PATH="${MODEL_PATH:-/data1/models/DeepSeek-V4-Flash-INT8}"
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SERVED_MODEL_NAME="deepseek-v4-flash-int8"
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SGLANG_PORT="${SGLANG_PORT:-30014}"
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# Dedicated container name so this experiment never touches the
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# `sglang-dsv4-flash` container used by the other P800 experiments.
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CONTAINER_NAME="sglang-dsv4-flash-tpdp"
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# Python interpreter inside the P800 SGLang container (used to run the
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# benchmark client via `docker exec`).
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CONTAINER_PYTHON="${CONTAINER_PYTHON:-/root/miniconda/envs/python310_torch25_cuda/bin/python}"
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export XPU_VISIBLE_DEVICES="${XPU_VISIBLE_DEVICES:-0,1,2,3,4,5,6,7}"
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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:-${SCRIPT_DIR}/runtime}"
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# Parallel configurations to test. Format: "TP DP"
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# Note: TP=2/DP=4 fails at startup with a weight-loading OOM. Root cause:
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# expert weights are sharded only across the TP group (no --ep-size is
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# passed, so experts fall back to TP sharding), and DP replicas do not
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# share expert shards. The INT8 model holds ~264 GiB of routed-expert
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# weights, so TP=2 needs ~137 GiB per XPU (>96 GiB). TP=2/DP=4
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# is kept in the default list so the matrix records the failure and moves on.
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# Override with a space-separated list of comma pairs, e.g.
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# PARALLEL_CONFIGS_STR="8,1" to run only TP=8/DP=1.
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if [[ -n "${PARALLEL_CONFIGS_STR:-}" ]]; then
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declare -a PARALLEL_CONFIGS=()
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for pair in $PARALLEL_CONFIGS_STR; do
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PARALLEL_CONFIGS+=("${pair//,/ }")
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done
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else
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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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fi
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# SGLang server settings. P800 INT8 supports at most ~131k input tokens, so
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# the context length is capped well below the H20 1M setting.
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CONTEXT_LENGTH="${CONTEXT_LENGTH:-140000}"
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MEM_FRACTION_STATIC="${MEM_FRACTION_STATIC:-0.8}"
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DOCKER_IMAGE="${DOCKER_IMAGE:-iregistry.baidu-int.com/xpu/sglang-p800-pd-disagg-0510:20260511_4202}"
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# ShareGPT seed dataset for the random workload. DATASET_PATH is the host
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# copy (also used by the adaptive search for capacity checks); it is mounted
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# into the container at CONTAINER_DATASET_PATH, which is what the benchmark
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# client references. Default matches the H20 experiments; the file is
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# gitignored, download it into ${ROOT_DIR}/datasets/ first.
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DATASET_PATH="${DATASET_PATH:-${ROOT_DIR}/datasets/ShareGPT_V3_unfiltered_cleaned_split.json}"
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CONTAINER_DATASET_PATH="${CONTAINER_DATASET_PATH:-/workspace/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="${MATRIX_MODE:-Y}"
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# Sampling density for concurrency (kept for parity with the H20 experiment;
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# generate_scenarios.py currently samples the low/high endpoints only).
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export CONCURRENCY_SAMPLES="${CONCURRENCY_SAMPLES:-2}"
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# Per-scenario timeout to avoid hangs (seconds).
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SCENARIO_TIMEOUT_S="${SCENARIO_TIMEOUT_S:-1800}"
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# XPU memory sampling interval (seconds).
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GPU_MEM_SAMPLE_INTERVAL_S="${GPU_MEM_SAMPLE_INTERVAL_S:-1}"
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# Dry-run mode: if 1, only log the server args and scenario plan without
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# starting any server or sending requests.
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DRY_RUN="${DRY_RUN:-0}"
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# Per-config scenario limit for quick smoke tests. 0 = run all generated
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# scenarios.
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GRID_LIMIT="${GRID_LIMIT:-0}"
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