Create score_clawbenchpro.sh

sc1/score_clawbenchpro.sh

@@ -0,0 +1,203 @@
+#!/bin/bash
+# ClawBenchPro base100/hard100 后处理评分。
+# 可选在本机启动 OpenAI-compatible vLLM Judge，然后调用每题自带 verifier。
+
+set -x
+
+SCRIPT_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
+BUNDLE_ROOT=/opt/huawei/dataset/zyr_yuyin/lyf/datasets/testClawBenchPro/upload_clawbenchpro_base100_hard100_npu
+
+export WORK_DIR=${WORK_DIR:-${BUNDLE_ROOT}/verl}
+export BASE_TASKS=${BASE_TASKS:-${BUNDLE_ROOT}/data/ClawBenchPro_base100_hard100_quality}
+export INFERENCE_ROOT=${INFERENCE_ROOT:-/opt/huawei/dataset/zyr_yuyin/lyf/datasets/testClawBenchPro/output/qwen35_2b}
+export SCORE_OUTPUT_ROOT=${SCORE_OUTPUT_ROOT:-${INFERENCE_ROOT}/scores}
+export SETUP_ENVIRONMENT=${SETUP_ENVIRONMENT:-1}
+
+# 留空表示评测 INFERENCE_ROOT 下所有含 step_1/ 的模型目录；也可逗号分隔。
+export MODELS=${MODELS:-}
+
+# JUDGE_ENABLED=0 只评 66 条规则任务，134 条 LLM Judge 任务会明确标记为 skipped。
+export JUDGE_ENABLED=${JUDGE_ENABLED:-1}
+# START_JUDGE_SERVER=1 在本机启动 vLLM；设为 0 时连接已有的兼容服务。
+export START_JUDGE_SERVER=${START_JUDGE_SERVER:-1}
+export JUDGE_MODEL_PATH=${JUDGE_MODEL_PATH:-/opt/huawei/dataset/zyr_yuyin/models/Qwen/Qwen3___5-9B}
+export JUDGE_SERVED_MODEL_NAME=${JUDGE_SERVED_MODEL_NAME:-qwen35_9b_judge}
+export JUDGE_BIND_HOST=${JUDGE_BIND_HOST:-127.0.0.1}
+export JUDGE_PORT=${JUDGE_PORT:-8000}
+export JUDGE_BASE_URL=${JUDGE_BASE_URL:-http://${JUDGE_BIND_HOST}:${JUDGE_PORT}/v1}
+export JUDGE_API_KEY=${JUDGE_API_KEY:-dummy_key}
+export JUDGE_DEVICES=${JUDGE_DEVICES:-0,1,2,3,4,5,6,7}
+export JUDGE_TP=${JUDGE_TP:-8}
+export JUDGE_DTYPE=${JUDGE_DTYPE:-bfloat16}
+export JUDGE_MAX_MODEL_LEN=${JUDGE_MAX_MODEL_LEN:-32768}
+export JUDGE_MAX_NUM_BATCHED_TOKENS=${JUDGE_MAX_NUM_BATCHED_TOKENS:-32768}
+export JUDGE_MAX_NUM_SEQS=${JUDGE_MAX_NUM_SEQS:-128}
+export JUDGE_GPU_MEMORY_UTILIZATION=${JUDGE_GPU_MEMORY_UTILIZATION:-0.80}
+export JUDGE_STARTUP_TIMEOUT=${JUDGE_STARTUP_TIMEOUT:-1800}
+export JUDGE_LOG=${JUDGE_LOG:-${SCORE_OUTPUT_ROOT}/judge.log}
+
+export PARALLEL=${PARALLEL:-128}
+export VERIFIER_TIMEOUT=${VERIFIER_TIMEOUT:-600}
+export PASS_THRESHOLD=${PASS_THRESHOLD:-0.75}
+export RESUME=${RESUME:-1}
+export OVERWRITE=${OVERWRITE:-0}
+export FAIL_ON_ERROR=${FAIL_ON_ERROR:-1}
+
+if [ ! -f "${WORK_DIR}/recipe/nanoclaw/score_clawbenchpro.py" ]; then
+    echo "ERROR: scorer not found: ${WORK_DIR}/recipe/nanoclaw/score_clawbenchpro.py" >&2
+    exit 2
+fi
+if [ ! -f "${BASE_TASKS}/benchmark_manifest.json" ]; then
+    echo "ERROR: benchmark manifest not found: ${BASE_TASKS}/benchmark_manifest.json" >&2
+    exit 2
+fi
+if [ ! -d "${INFERENCE_ROOT}" ]; then
+    echo "ERROR: inference root not found: ${INFERENCE_ROOT}" >&2
+    exit 2
+fi
+if [ ! -f "${BUNDLE_ROOT}/scripts/setup_full_npu_environment.sh" ]; then
+    echo "ERROR: full NPU environment installer not found: ${BUNDLE_ROOT}/scripts/setup_full_npu_environment.sh" >&2
+    exit 2
+fi
+
+mkdir -p "${SCORE_OUTPUT_ROOT}" "$(dirname "${JUDGE_LOG}")"
+
+# Triton/vLLM 在 Judge 初始化 KV cache 前会通过 tempfile 创建 hivmc
+# 探测文件。训练环境安装脚本历史上默认使用 /cache/ray_tmp，但该目录在
+# 某些独立评分节点上不存在、不可写或已耗尽配额。评分任务使用独立临时
+# 目录，允许通过 JUDGE_TMPDIR 覆盖，避免把 Judge 启动失败误报成评分失败。
+export JUDGE_TMPDIR=${JUDGE_TMPDIR:-/tmp/clawbenchpro_judge_${USER:-unknown}_$$}
+if [ -z "${TMPDIR:-}" ]; then
+    export TMPDIR="${JUDGE_TMPDIR}"
+fi
+if ! mkdir -p "${TMPDIR}" 2>/dev/null || [ ! -d "${TMPDIR}" ] || [ ! -w "${TMPDIR}" ]; then
+    echo "WARNING: configured TMPDIR is unavailable: ${TMPDIR}; falling back to ${JUDGE_TMPDIR}" >&2
+    export TMPDIR="${JUDGE_TMPDIR}"
+    mkdir -p "${TMPDIR}"
+fi
+if [ ! -d "${TMPDIR}" ] || [ ! -w "${TMPDIR}" ]; then
+    echo "ERROR: Judge temporary directory is not writable: ${TMPDIR}" >&2
+    df -h "$(dirname "${TMPDIR}")" >&2 || true
+    df -i "$(dirname "${TMPDIR}")" >&2 || true
+    exit 2
+fi
+export TEMP="${TMPDIR}"
+export TMP="${TMPDIR}"
+echo "Judge temporary directory: ${TMPDIR}"
+
+# 默认与训练/推理一样完整安装 GCC、CANN、torch-npu、vLLM-Ascend、Triton、
+# VERL 和 verifier 依赖。使用 source，确保导出的动态库和 Python 路径对 Judge 生效。
+source "${BUNDLE_ROOT}/scripts/setup_full_npu_environment.sh"
+cd "${BUNDLE_ROOT}"
+
+# Probe the same Python tempfile path used by Triton before launching all TP
+# workers. This produces a short actionable error instead of a long vLLM
+# multiprocess traceback.
+python3 - "${TMPDIR}" <<'PY'
+import shutil
+import sys
+import tempfile
+from pathlib import Path
+
+tmpdir = Path(sys.argv[1])
+try:
+    probe = Path(tempfile.mkdtemp(prefix="clawbenchpro_probe_", dir=str(tmpdir)))
+    (probe / "probe").write_text("ok", encoding="utf-8")
+    shutil.rmtree(probe)
+except Exception as exc:
+    print(f"ERROR: Python/Triton temporary-file probe failed in {tmpdir}: {type(exc).__name__}: {exc}", file=sys.stderr)
+    raise SystemExit(2)
+PY
+
+judge_pid=""
+cleanup() {
+    exit_code=$?
+    if [ -n "${judge_pid}" ]; then
+        kill "${judge_pid}" 2>/dev/null || true
+        wait "${judge_pid}" 2>/dev/null || true
+    fi
+    exit "${exit_code}"
+}
+trap cleanup EXIT INT TERM
+
+check_judge() {
+    python3 -c 'import json,sys,urllib.request; req=urllib.request.Request(sys.argv[1].rstrip("/")+"/models",headers={"Authorization":"Bearer "+sys.argv[2]}); data=json.load(urllib.request.urlopen(req,timeout=10)); ids={str(x.get("id")) for x in data.get("data",[])}; raise SystemExit(0 if sys.argv[3] in ids else 1)' \
+        "${JUDGE_BASE_URL}" "${JUDGE_API_KEY}" "${JUDGE_SERVED_MODEL_NAME}"
+}
+
+if [ "${JUDGE_ENABLED}" = "1" ]; then
+    if [ "${START_JUDGE_SERVER}" = "1" ]; then
+        if [ -z "${JUDGE_MODEL_PATH}" ] || [ ! -d "${JUDGE_MODEL_PATH}" ]; then
+            echo "ERROR: set JUDGE_MODEL_PATH to a local Hugging Face Judge model directory." >&2
+            exit 2
+        fi
+        visible_count=$(awk -F, '{print NF}' <<< "${JUDGE_DEVICES}")
+        if [ "${JUDGE_TP}" -gt "${visible_count}" ]; then
+            echo "ERROR: JUDGE_TP=${JUDGE_TP} exceeds JUDGE_DEVICES count=${visible_count}." >&2
+            exit 2
+        fi
+        export ASCEND_RT_VISIBLE_DEVICES=${JUDGE_DEVICES}
+        judge_args=(
+            --model "${JUDGE_MODEL_PATH}"
+            --tokenizer "${JUDGE_MODEL_PATH}"
+            --served-model-name "${JUDGE_SERVED_MODEL_NAME}"
+            --host "${JUDGE_BIND_HOST}"
+            --port "${JUDGE_PORT}"
+            --tensor-parallel-size "${JUDGE_TP}"
+            --dtype "${JUDGE_DTYPE}"
+            --max-model-len "${JUDGE_MAX_MODEL_LEN}"
+            --max-num-batched-tokens "${JUDGE_MAX_NUM_BATCHED_TOKENS}"
+            --max-num-seqs "${JUDGE_MAX_NUM_SEQS}"
+            --gpu-memory-utilization "${JUDGE_GPU_MEMORY_UTILIZATION}"
+        )
+        echo "Starting Judge model: ${JUDGE_MODEL_PATH}"
+        python3 -m vllm.entrypoints.openai.api_server "${judge_args[@]}" >"${JUDGE_LOG}" 2>&1 &
+        judge_pid=$!
+    fi
+
+    started=$(date +%s)
+    until check_judge; do
+        if [ -n "${judge_pid}" ] && ! kill -0 "${judge_pid}" 2>/dev/null; then
+            echo "ERROR: Judge process exited before becoming ready. Log: ${JUDGE_LOG}" >&2
+            tail -n 160 "${JUDGE_LOG}" >&2 || true
+            exit 2
+        fi
+        elapsed=$(($(date +%s) - started))
+        if [ "${elapsed}" -ge "${JUDGE_STARTUP_TIMEOUT}" ]; then
+            echo "ERROR: Judge API did not become ready within ${JUDGE_STARTUP_TIMEOUT}s." >&2
+            tail -n 160 "${JUDGE_LOG}" >&2 || true
+            exit 2
+        fi
+        echo "Waiting for Judge API ${JUDGE_BASE_URL}; elapsed=${elapsed}s"
+        sleep 5
+    done
+    echo "Judge API ready: ${JUDGE_BASE_URL}, model=${JUDGE_SERVED_MODEL_NAME}"
+else
+    echo "WARNING: JUDGE_ENABLED=0; 134 LLM Judge tasks will be skipped explicitly."
+fi
+
+score_args=(
+    python3 "${WORK_DIR}/recipe/nanoclaw/score_clawbenchpro.py"
+    --inference-root "${INFERENCE_ROOT}"
+    --base-tasks "${BASE_TASKS}"
+    --output-root "${SCORE_OUTPUT_ROOT}"
+    --models "${MODELS}"
+    --judge-enabled "${JUDGE_ENABLED}"
+    --judge-base-url "${JUDGE_BASE_URL}"
+    --judge-api-key "${JUDGE_API_KEY}"
+    --judge-model "${JUDGE_SERVED_MODEL_NAME}"
+    --parallel "${PARALLEL}"
+    --timeout "${VERIFIER_TIMEOUT}"
+    --pass-threshold "${PASS_THRESHOLD}"
+    --resume "${RESUME}"
+    --overwrite "${OVERWRITE}"
+)
+if [ "${FAIL_ON_ERROR}" = "1" ]; then
+    score_args+=(--fail-on-error)
+fi
+"${score_args[@]}"
+
+echo "Scoring completed:"
+echo "  ${SCORE_OUTPUT_ROOT}/summary.json"
+echo "  ${SCORE_OUTPUT_ROOT}/leaderboard.csv"
+echo "  ${SCORE_OUTPUT_ROOT}/<model>/scoring_summary.json"