修改

2026-05-20 15:04:19 +08:00
parent c4b935692c
commit 1a94cec822
27 changed files with 4831 additions and 1693 deletions
@@ -4,11 +4,9 @@

 用法:
  # 仅组装数据
-  AUTORESEARCH_ROOT=/mnt/wangsenhao/autoresearch-zk \
-    python prepare_and_train_sft.py prepare --output_dir /mnt/wangsenhao/autoresearch-zk/sft_data
+  python prepare_and_train_sft.py prepare --output_dir /mnt/wangsenhao/autoresearch-zk/sft_data

  # 启动训练（需先 prepare）
-  AUTORESEARCH_ROOT=/mnt/wangsenhao/autoresearch-zk \
  python prepare_and_train_sft.py train \
    --data_dir /mnt/wangsenhao/autoresearch-zk/sft_data \
    --model_path /mnt/wangsenhao/verl_zk/Qwen3-4B-Instruct-2507 \
@@ -28,14 +26,7 @@ from pathlib import Path
 logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
 log = logging.getLogger(__name__)

-
-def _path_from_env(name: str, fallback: Path) -> Path:
-    return Path(os.environ.get(name, str(fallback))).expanduser().resolve()
-
-
-AUTORESEARCH_ROOT = _path_from_env("AUTORESEARCH_ROOT", Path("/mnt/wangsenhao/autoresearch-zk"))
-AI_PLANNING = _path_from_env("AI_PLANNING_DIR", AUTORESEARCH_ROOT / "ai-planning")
-ZK_TRAINER_DIR = _path_from_env("ZK_TRAINER_DIR", AUTORESEARCH_ROOT / "zk_trainer")
+AI_PLANNING = Path(__file__).resolve().parent / "ai-planning"
 TRAIN_SET_DIR = AI_PLANNING / "data" / "train_set"
 DATA_TRAIN_DIR = AI_PLANNING / "data_train"
 GENERATE_TRAIN_SCRIPT = DATA_TRAIN_DIR / "generate_train.py"
@@ -157,7 +148,7 @@ def run_sft_training(data_dir: str, model_path: str, model_type: str, train_outp
                     epochs: int = 3, lr: float = 1e-5, max_seq_length: int = 1024,
                     per_device_batch: int = 1, grad_accum: int = 1):
    """基于 zk_trainer 的 accelerate launch 方式启动 SFT 训练。"""
-    zk_trainer_dir = str(ZK_TRAINER_DIR)
+    zk_trainer_dir = str(Path(__file__).resolve().parent / "zk_trainer")
    if not os.path.isdir(zk_trainer_dir):
        log.error(f"zk_trainer 目录不存在: {zk_trainer_dir}")
        return None
@@ -1,117 +0,0 @@
-jobName: "sft-train-r{RUNDIC}"
-description: "AutoResearch R{RUNDIC} SFT training (40k+ samples, qwen3-4B base, 3 epochs FSDP2)"
-accessType: PUBLIC
-
-imageConfig:
-  imageUrl: micr.cloud.mioffice.cn/vllm-image/ai-arch-llm-prod:vllm-v0.12.0-f098b188
-  imageCommand: |-
-    set -ex
-    cd {AUTORESEARCH_ROOT}
-
-    # 0a. 验环境 + 装缺的包（vllm 镜像 py3.12 + torch 已含）
-    python3 --version
-    python3 -c "import torch; print(f'torch={torch.__version__}')"
-    pip install -q --no-deps "accelerate==1.7.0" 2>&1 | tail -3
-    pip install -q --ignore-installed blinker 2>&1 | tail -2
-    pip install -q peft 2>&1 | tail -3
-    pip install -q wandb bitsandbytes 2>&1 | tail -3
-    pip install -q luigi mlflow scikit-learn openpyxl pyyaml sentencepiece tiktoken protobuf pynvml datasets 2>&1 | tail -3
-    pip install -q "transformers>=4.45" 2>&1 | tail -3
-    python3 -c 'import torch, accelerate, transformers, peft; print(torch.__version__, accelerate.__version__, transformers.__version__, peft.__version__)'
-
-    # 0c. 把 HF cache 重定向到容器本地大盘（默认在 ~/.cache 可能是 juicefs，mmap 易 SIGBUS）
-    export HF_HOME=/tmp/hf_cache
-    export HF_DATASETS_CACHE=/tmp/hf_cache/datasets
-    export TRANSFORMERS_CACHE=/tmp/hf_cache/transformers
-    mkdir -p /tmp/hf_cache/datasets /tmp/hf_cache/transformers
-    df -h /tmp /dev/shm 2>/dev/null || true
-
-    # 强制 datasets 加载进内存而不是 arrow mmap（避免 /dev/shm 溢出 SIGBUS）
-    export HF_DATASETS_IN_MEMORY_MAX_SIZE=20000000000
-    export HF_DATASETS_NUM_PROC=1
-    export TOKENIZERS_PARALLELISM=false
-    export OMP_NUM_THREADS=1
-    export MKL_NUM_THREADS=1
-    # 关闭 NCCL 用 shm 通信（改 socket 通道）
-    export NCCL_SHM_DISABLE=1
-    export NCCL_P2P_DISABLE=0
-    export NCCL_DEBUG=WARN
-
-    # 0b. 把上一轮产出搬走（cml job 内幂等，本地搬过就跳过）
-    if [ -d sft_output ] && [ ! -d sft_output_r{PREV_RUNDIC} ]; then
-      mv sft_output sft_output_r{PREV_RUNDIC}
-    fi
-    rm -rf sft_output
-
-    # 1. 组装数据
-    python3 {AUTORESEARCH_ROOT}/prepare_and_train_sft.py prepare \
-      --output_dir {AUTORESEARCH_ROOT}/sft_data
-
-    # 2. 启动训练（绝对路径，避免相对路径在容器内找不到 zk_trainer）
-    python3 {AUTORESEARCH_ROOT}/prepare_and_train_sft.py train \
-      --data_dir {AUTORESEARCH_ROOT}/sft_data \
-      --model_path /mnt/wangsenhao/verl_zk/Qwen3-4B-Instruct-2507 \
-      --model_type qwen3 \
-      --train_output {AUTORESEARCH_ROOT}/sft_output \
-      --epochs 3 --lr 1e-5
-
-    # 3. 训练成功标记（被 watcher 检测）
-    [ -f {AUTORESEARCH_ROOT}/sft_output/config.json ] && \
-      touch {AUTORESEARCH_ROOT}/sft_output/_SUCCESS
-
-# 挂载 wangsenhao + xiaoai-zk-model-train-tj5 + verl_zk 所在卷
-juiceFsMountConfigs:
-  - volume: wangsenhao
-    juiceFsCluster: tj5-common
-    subPath: /
-    mountPath: /mnt/wangsenhao
-    readOnly: false
-  - volume: xiaoai-zk-model-train-tj5
-    juiceFsCluster: tj5-common
-    subPath: /
-    mountPath: /mnt/xiaoai-zk-model-train-tj5
-    readOnly: false
-
-envConfigs:
-  - key: PYTHONPATH
-    value: {AUTORESEARCH_ROOT}/zk_trainer
-  - key: WANDB_DISABLED
-    value: "true"
-  - key: WANDB_MODE
-    value: offline
-  - key: VOLUME_PREFIX
-    value: /mnt/xiaoai-zk-model-train-tj5
-  - key: HF_HOME
-    value: /mnt/wangsenhao/.hf_cache
-
-# h20-96g 8 卡 FSDP2
-queueId: "6052"
-priority: 5
-preemptible: false
-framework: pytorch
-resourceConfigs:
-  - nodeRole: worker
-    nodeNumber: 1
-    perNodeResourceSpec:
-      resourcePriority: GUARANTEED
-      resourceName: cloudml.ng2h20-8-8.20-199
-      resourceNumber: 8
-
-# 故障自动重试（节点级失败）
-retryConfig:
-  enableRetry: true
-  maxRetryTimes: 2
-  policySets:
-    - NodeFailure
-
-# 失败/完成飞书告警
-alertConfig:
-  enableAlert: true
-  alertItems:
-    - alertConditions:
-        - FAILED
-        - SUCCEED
-      alertLevel: P2
-      alertReceivers:
-        persons:
-          - wangsenhao
@@ -1,104 +0,0 @@
-#!/bin/bash
-# 用 cml custom_train submit 提交 SFT 训练，训练完成后自动起 cml workflow run 评测
-# 用法: ./submit_sft_via_cml.sh <RUNDIC>
-# 例:   ./submit_sft_via_cml.sh 17756
-
-set -euo pipefail
-
-RUNDIC=${1:?usage: $0 <RUNDIC>}
-PREV_RUNDIC=${2:-$((RUNDIC-1))}
-SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
-ROOT=${AUTORESEARCH_ROOT:-/mnt/wangsenhao/autoresearch-zk}
-MODEL_OLD=${MODEL_OLD:-/mnt/wangsenhao/verl_zk/qwen4b_cispo_wokl_add_bvt_2/global_step_5/actor/huggingface}
-TPL=${SFT_TRAIN_JOB_TEMPLATE:-$SCRIPT_DIR/sft_train_job.yaml.tpl}
-YAML=/tmp/sft_train_job_r${RUNDIC}.yaml
-
-if [ -f ~/.cloudml-cli/.profile ]; then
-  source ~/.cloudml-cli/.profile
-else
-  echo "[cml-sft] ❌ 未找到 ~/.cloudml-cli/.profile，请先安装并初始化 cml" >&2
-  exit 1
-fi
-
-# 1. 渲染 yaml 模板
-sed \
-  -e "s|{RUNDIC}|${RUNDIC}|g" \
-  -e "s|{PREV_RUNDIC}|${PREV_RUNDIC}|g" \
-  -e "s|{AUTORESEARCH_ROOT}|${ROOT}|g" \
-  "$TPL" > "$YAML"
-echo "[cml-sft] yaml: $YAML"
-
-# 2. 提交训练任务
-SUBMIT_OUT=$(cml custom_train submit --filename "$YAML" 2>&1)
-echo "$SUBMIT_OUT"
-JOB_ID=$(echo "$SUBMIT_OUT" | grep -oE 't-[0-9]+-[a-z0-9]+' | head -1)
-if [ -z "$JOB_ID" ]; then
-  echo "[cml-sft] ❌ 提交失败" >&2
-  exit 1
-fi
-echo "[cml-sft] ✅ JobID: $JOB_ID"
-
-# 3. 后台 watcher：等训练成功 → 自动起评测
-WATCHER_LOG=/tmp/r${RUNDIC}_logs/cml_watcher.log
-mkdir -p "$(dirname "$WATCHER_LOG")"
-
-nohup bash -c '
-JOB_ID='"$JOB_ID"'
-RUNDIC='"$RUNDIC"'
-LOG='"$WATCHER_LOG"'
-ROOT='"$ROOT"'
-MODEL_NEW="$ROOT/sft_output"
-MODEL_OLD='"$MODEL_OLD"'
-EVAL_WORKFLOW_ID=f-20260408161444-wu3pz
-EVAL_VERSION=v28
-
-source ~/.cloudml-cli/.profile
-
-echo "[$(date)] 等待 cml job $JOB_ID 完成..." >> "$LOG"
-while true; do
-  STATE=$(cml custom_train describe "$JOB_ID" 2>/dev/null | grep -o "\"state\": \"[a-z]*\"" | head -1 | sed "s/.*: \"//;s/\"//")
-  case "$STATE" in
-    succeed)
-      echo "[$(date)] 训练成功" >> "$LOG"
-      break ;;
-    failed|killed)
-      echo "[$(date)] ❌ 训练 $STATE" >> "$LOG"
-      exit 1 ;;
-    *)
-      sleep 60 ;;
-  esac
-done
-
-# 验证产出
-[ ! -f "$MODEL_NEW/_SUCCESS" ] && [ ! -f "$MODEL_NEW/config.json" ] && {
-  echo "[$(date)] ❌ 产出缺失" >> "$LOG"; exit 1
-}
-
-# 起评测
-echo "[$(date)] 启动 CML 评测 runDic=$RUNDIC" >> "$LOG"
-cml workflow run \
-  --workflow_id $EVAL_WORKFLOW_ID --version $EVAL_VERSION \
-  --global_inputs runDic=$RUNDIC \
-  --global_inputs model_path_new=$MODEL_NEW \
-  --global_inputs model_path_old=$MODEL_OLD >> "$LOG" 2>&1
-echo "[$(date)] cml workflow run 提交完成" >> "$LOG"
-' > /tmp/r${RUNDIC}_logs/watcher_runner.log 2>&1 &
-
-WATCHER_PID=$!
-echo "[cml-sft] watcher PID: $WATCHER_PID（log: $WATCHER_LOG）"
-
-# 4. 立即输出可查看命令
-cat <<EOF
-
-[cml-sft] 任务提交完成，关键命令：
-  查看任务状态:  cml custom_train describe $JOB_ID
-  查看实时日志:  cml custom_train logs $JOB_ID --follow
-  停止任务:      cml custom_train kill $JOB_ID
-  watcher log:   tail -f $WATCHER_LOG
-
-训练完成后，evaluation workflow 会自动启动；评测产物在
-  /mnt/xiaoai-zk-model-train-tj5/workflow5/workflow${RUNDIC}/
-
-JobID: $JOB_ID
-RUNDIC: $RUNDIC
-EOF