95 lines
3.0 KiB
Bash
Executable File
95 lines
3.0 KiB
Bash
Executable File
#!/usr/bin/env bash
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#
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# Llama 4 统一容器入口：
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# 1. 幂等应用 CoreX vLLM 的 Llama 4 补丁
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# 2. 读取 /models/llama4/config.json，自动判断 Scout / Maverick
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# 3. 选择对应的 TP / PP / 端口 / served name，再启动 vLLM
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#
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# AUTO_DETECT=1（默认）时根据模型 config 自动选择；显式设置的
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# TP / PP / PORT / SERVED_NAME 会覆盖检测结果。AUTO_DETECT=0 可完全手动。
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#
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set -euo pipefail
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PATCH_SCRIPT="${LLAMA4_PATCH_SCRIPT:-/opt/llama4/apply_patch.py}"
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DETECT_SCRIPT="${LLAMA4_DETECT_SCRIPT:-/opt/llama4/detect_model.py}"
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VLLM_BIN="${VLLM_BIN:-vllm}"
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python3 "${PATCH_SCRIPT}"
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MODEL_PATH="${MODEL_PATH:-/models/llama4}"
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AUTO_DETECT="${AUTO_DETECT:-1}"
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MODEL_DESCRIPTION=""
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if [[ ! -f "${MODEL_PATH}/config.json" ]]; then
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echo "ERROR: 模型目录不完整：${MODEL_PATH}/config.json 不存在" >&2
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echo "请确认已挂载模型，例如：" >&2
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echo " -v /mnt/disk0/models/LLM-Research/Llama-4-Scout-17B-16E-Instruct:/models/llama4:ro" >&2
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echo " -v /mnt/disk0/models/LLM-Research/Llama-4-Maverick-17B-128E-Instruct-FP8:/models/llama4:ro" >&2
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exit 1
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fi
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if [[ "${AUTO_DETECT}" == "1" ]]; then
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DETECT_OUTPUT="$(python3 "${DETECT_SCRIPT}" "${MODEL_PATH}")" || exit 1
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eval "${DETECT_OUTPUT}"
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MODEL_KIND="${MODEL_KIND:-${DETECTED_MODEL_KIND}}"
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TP="${TP:-${DETECTED_TP}}"
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PP="${PP:-${DETECTED_PP}}"
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PORT="${PORT:-${DETECTED_PORT}}"
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SERVED_NAME="${SERVED_NAME:-${DETECTED_SERVED_NAME}}"
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MODEL_DESCRIPTION="${DETECTED_DESCRIPTION:-}"
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else
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MODEL_KIND="${MODEL_KIND:-unknown}"
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fi
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HOST="${HOST:-0.0.0.0}"
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TP="${TP:-8}"
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PP="${PP:-1}"
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PORT="${PORT:-8091}"
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SERVED_NAME="${SERVED_NAME:-llama4}"
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MAX_MODEL_LEN="${MAX_MODEL_LEN:-4096}"
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MAX_NUM_SEQS="${MAX_NUM_SEQS:-1}"
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GPU_MEM_UTIL="${GPU_MEM_UTIL:-0.90}"
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ENFORCE_EAGER="${ENFORCE_EAGER:-1}"
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EXTRA_ARGS="${EXTRA_ARGS:-}"
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PP_ARGS=()
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if (( PP > 1 )); then
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PP_ARGS=(--pipeline-parallel-size "${PP}")
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fi
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EAGER_ARGS=()
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if [[ "${ENFORCE_EAGER}" == "1" ]]; then
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EAGER_ARGS=(--enforce-eager)
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fi
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echo "============================================================"
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echo "Llama 4 (BI-V150 unified image)"
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echo " model kind : ${MODEL_KIND}"
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echo " model : ${MODEL_PATH}"
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echo " served name: ${SERVED_NAME}"
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echo " listen : ${HOST}:${PORT}"
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echo " TP / PP : ${TP} / ${PP} (total $((TP * PP)) GPUs)"
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echo " max len : ${MAX_MODEL_LEN}"
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echo " max seqs : ${MAX_NUM_SEQS}"
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echo " gpu util : ${GPU_MEM_UTIL}"
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echo " eager : ${ENFORCE_EAGER}"
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echo " visible : ${CUDA_VISIBLE_DEVICES:-all}"
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echo " extra : ${EXTRA_ARGS}"
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if [[ -n "${MODEL_DESCRIPTION}" ]]; then
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echo " detected : ${MODEL_DESCRIPTION}"
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fi
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echo "============================================================"
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# shellcheck disable=SC2086
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exec "${VLLM_BIN}" serve "${MODEL_PATH}" \
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--host "${HOST}" \
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--port "${PORT}" \
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--served-model-name "${SERVED_NAME}" \
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--tensor-parallel-size "${TP}" \
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"${PP_ARGS[@]}" \
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--max-model-len "${MAX_MODEL_LEN}" \
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--max-num-seqs "${MAX_NUM_SEQS}" \
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--gpu-memory-utilization "${GPU_MEM_UTIL}" \
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"${EAGER_ARGS[@]}" \
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${EXTRA_ARGS}
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