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#!/usr/bin/env bash
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set -euo pipefail
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# ===== 可调参数 =====
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CKPT_ROOT="/home/test/checkpoints/q3-32b-lora" # 若实际是 .../checkpoint-62/global_step62，请把 CKPT_ROOT 改成 .../checkpoint-62
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TAG="global_step30"
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HOSTS=(tn01 tn02 tn03 tn04 tn05 tn06)
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AGGREGATOR_HOST="tn06" # 本脚本运行/汇总所在机器
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EXPECTED_SHARDS_PER_HOST=4 # 每机应写出分片数（按你的并行布局）
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MAX_SHARD_SIZE="5GB"
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# ★★★ 新增：参考模型目录（你用来做 LoRA 的 Qwen3-32B 或其 Instruct 变体） ★★★
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REF_MODEL_DIR="/home/test/Qwen3-32B"
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STRICT_PRECHECK=true # true: 预检不通过就退出；false: 仅告警
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SSH_OPTS="-o BatchMode=yes -o StrictHostKeyChecking=accept-new -o ConnectTimeout=8"
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RSYNC_OPTS="-a --info=progress2 --human-readable --partial --inplace"
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# ====================
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# ===== 派生参数（一般不用改） =====
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EXPECTED_TOTAL_SHARDS=$(( EXPECTED_SHARDS_PER_HOST * ${#HOSTS[@]} ))
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STAGING_BASE="${CKPT_ROOT}/_staging"
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STAGING_TAG_DIR="${STAGING_BASE}/${TAG}"
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OUT_DIR="${CKPT_ROOT}/merged-${TAG}"
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TMP_PT_DIR="${CKPT_ROOT}/_tmp-fp32-pt-${TAG}" # 临时 FP32（pytorch_model.bin）目录
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export OUT_DIR TMP_PT_DIR MAX_SHARD_SIZE REF_MODEL_DIR
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# =================================
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echo "== 预检查 SSH =="
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for h in "${HOSTS[@]}"; do
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ssh ${SSH_OPTS} "$h" "true" >/dev/null || { echo "!! 无法免密 SSH 到 $h"; exit 1; }
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done
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echo "== 0/7 逐节点分片预检（统计各机 ${CKPT_ROOT}/${TAG} 下的 *model_states.pt）=="
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remote_total=0
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agg_cnt=0
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for h in "${HOSTS[@]}"; do
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c=$(ssh ${SSH_OPTS} "$h" "find '${CKPT_ROOT}/${TAG}' -maxdepth 1 -type f -name '*model_states.pt' 2>/dev/null | wc -l" || echo 0)
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c=$(echo "$c" | tr -d ' ')
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printf " - %-8s: %s 分片\n" "$h" "$c"
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if [[ "$h" == "$AGGREGATOR_HOST" ]]; then
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agg_cnt=$c
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else
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remote_total=$(( remote_total + c ))
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if (( c < EXPECTED_SHARDS_PER_HOST )); then
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echo "!! 预警：$h 分片仅 $c 个（期望 ${EXPECTED_SHARDS_PER_HOST}）" >&2
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fi
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fi
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done
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expected_remote_total=$(( EXPECTED_TOTAL_SHARDS - EXPECTED_SHARDS_PER_HOST ))
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echo " - 远端合计（不含 ${AGGREGATOR_HOST}）：$remote_total（期望 ${expected_remote_total}）"
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echo " - ${AGGREGATOR_HOST} 自身：$agg_cnt（期望 ${EXPECTED_SHARDS_PER_HOST}）"
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precheck_ok=true
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if (( remote_total != expected_remote_total )); then
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echo "!! 远端总分片不等：实际 ${remote_total} / 期望 ${expected_remote_total}" >&2
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precheck_ok=false
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fi
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if (( agg_cnt < EXPECTED_SHARDS_PER_HOST )); then
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echo "!! ${AGGREGATOR_HOST} 本机分片不足：实际 ${agg_cnt} / 期望 ${EXPECTED_SHARDS_PER_HOST}" >&2
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precheck_ok=false
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fi
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if [[ "${STRICT_PRECHECK}" == "true" && "${precheck_ok}" == "false" ]]; then
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echo "!! STRICT_PRECHECK 开启：预检不通过，停止执行" >&2
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exit 2
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fi
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[[ "${precheck_ok}" == "true" ]] && echo "OK: 预检通过（远端=${remote_total}、本机=${agg_cnt}，总计期望=${EXPECTED_TOTAL_SHARDS}）" || echo "WARN: 预检未通过（分片数量与期望不符），已启用宽松模式，继续执行..."
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echo "== 1/7 准备 staging 目录（干净环境）=="
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rm -rf "${STAGING_TAG_DIR}"
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mkdir -p "${STAGING_TAG_DIR}"
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echo "== 2/7 收集分片到 staging =="
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for h in "${HOSTS[@]}"; do
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if ssh ${SSH_OPTS} "$h" "test -d '${CKPT_ROOT}/${TAG}'"; then
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echo " - 收集 ${h}:${CKPT_ROOT}/${TAG}/ -> ${STAGING_TAG_DIR}/"
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rsync ${RSYNC_OPTS} -e "ssh ${SSH_OPTS}" \
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"${h}:${CKPT_ROOT}/${TAG}/" "${STAGING_TAG_DIR}/" || true
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else
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echo " - ${h} 无 ${CKPT_ROOT}/${TAG}，跳过"
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fi
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done
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echo "== 3/7 在 staging 校验总分片数（应为 ${EXPECTED_TOTAL_SHARDS}）=="
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mapfile -t SHARDS < <(find "${STAGING_TAG_DIR}" -maxdepth 1 -type f -name "*model_states.pt" | sort -u)
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CNT=${#SHARDS[@]}
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echo " - staging 中发现分片数：${CNT}"
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if (( CNT != EXPECTED_TOTAL_SHARDS )); then
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echo "!! 分片总数不等：staging 实际 ${CNT} / 期望 ${EXPECTED_TOTAL_SHARDS}。请检查是否缺片或命名不一致。" >&2
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exit 3
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fi
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echo "== 4/7 合并分片 -> 临时 FP32（PyTorch .bin），避免共享权重导致 safetensors 报错 =="
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rm -rf "${TMP_PT_DIR}"
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mkdir -p "${TMP_PT_DIR}"
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python - <<PY
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from deepspeed.utils.zero_to_fp32 import convert_zero_checkpoint_to_fp32_state_dict
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convert_zero_checkpoint_to_fp32_state_dict(
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checkpoint_dir=r"${STAGING_BASE}",
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output_dir=r"${TMP_PT_DIR}",
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tag=r"${TAG}",
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safe_serialization=False, # 先落成 .bin（FP32）
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)
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print("合并完成（FP32 .bin）：", r"${TMP_PT_DIR}")
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PY
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# ★★★ 改动点：从参考模型复制 config/tokenizer，且强制覆盖（不要从 CKPT_ROOT 拷，LoRA 目录通常没 config.json）★★★
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echo "== 4.1/7 从参考模型复制 config/tokenizer 到临时 FP32 目录（装载需要）=="
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for f in config.json generation_config.json tokenizer_config.json tokenizer.json merges.txt vocab.json special_tokens_map.json added_tokens.json; do
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[[ -f "${REF_MODEL_DIR}/${f}" ]] && cp -f "${REF_MODEL_DIR}/${f}" "${TMP_PT_DIR}/" || true
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done
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echo "== 5/7 装载 REF 模型结构 + 灌入 FP32 权重；如检测到 LoRA，则导出 adapter；否则保存 BF16 分片 safetensors =="
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python - <<'PY'
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import os, re, json, sys, torch, shutil
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from transformers import AutoConfig, AutoModelForCausalLM
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from safetensors.torch import save_file
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TMP_PT_DIR = os.environ["TMP_PT_DIR"]
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REF_DIR = os.environ["REF_MODEL_DIR"]
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OUT_DIR = os.environ["OUT_DIR"]
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MAX_SHARD_SIZE = os.environ.get("MAX_SHARD_SIZE","5GB")
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print("[load] ref model from:", REF_DIR)
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cfg = AutoConfig.from_pretrained(REF_DIR, trust_remote_code=True) # 确保 model_type=qwen3
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model = AutoModelForCausalLM.from_pretrained(
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REF_DIR, config=cfg, trust_remote_code=True,
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torch_dtype=torch.float32, low_cpu_mem_usage=False, device_map={"": "cpu"}
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)
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sd_path = os.path.join(TMP_PT_DIR, "pytorch_model.bin")
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if not os.path.exists(sd_path):
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print("ERR: 未找到", sd_path, file=sys.stderr); sys.exit(9)
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state = torch.load(sd_path, map_location="cpu")
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# 去掉可能的 'module.' 前缀
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state = { (k.split("module.",1)[-1]): v for k, v in state.items() }
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# 识别 LoRA
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has_lora = any((".lora_A" in k) or (".lora_B" in k) or (".lora_alpha" in k) for k in state)
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print("[check] contains LoRA keys:", has_lora)
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if has_lora:
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# ——导出 LoRA 适配器（不合到基座）——
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lora_state = {k: v for k, v in state.items()
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if (".lora_A" in k) or (".lora_B" in k) or (".lora_alpha" in k)}
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if not lora_state:
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print("ERR: 识别到 LoRA 但未筛出权重；中止。", file=sys.stderr); sys.exit(10)
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lora_A_keys = [k for k in lora_state if k.endswith(".lora_A.weight")]
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r = state[lora_A_keys[0]].shape[0] if lora_A_keys else 16
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# alpha：优先读到的第一个；没有就用 r
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alpha = r
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for k in lora_A_keys:
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a = state.get(k.replace(".lora_A.weight", ".lora_alpha"))
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if a is not None:
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alpha = int(a.item()); break
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# 统计 target_modules（叶子模块名）
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targets = sorted(set(re.sub(r"\.lora_(A|B)\.weight$", "", k) for k in lora_A_keys))
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target_modules = sorted(set(t.split(".")[-1] for t in targets)) or ["q_proj","k_proj","v_proj","o_proj","gate_proj","up_proj","down_proj"]
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adapters_dir = os.path.join(OUT_DIR, "adapters")
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os.makedirs(adapters_dir, exist_ok=True)
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save_file(lora_state, os.path.join(adapters_dir, "adapter_model.safetensors"))
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adapter_cfg = {
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"peft_type": "LORA",
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"base_model_name_or_path": REF_DIR,
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"r": int(r),
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"lora_alpha": int(alpha),
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"lora_dropout": 0.0,
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"bias": "none",
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"task_type": "CAUSAL_LM",
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"target_modules": target_modules
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}
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with open(os.path.join(adapters_dir, "adapter_config.json"), "w", encoding="utf-8") as f:
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json.dump(adapter_cfg, f, ensure_ascii=False, indent=2)
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# 复制 tokenizer/generation/config，便于推理端直接使用
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for f in ("tokenizer_config.json","tokenizer.json","merges.txt","vocab.json","special_tokens_map.json","added_tokens.json","generation_config.json","config.json"):
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src = os.path.join(REF_DIR, f)
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if os.path.exists(src):
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dst = os.path.join(adapters_dir, f)
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if not os.path.exists(dst):
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try: shutil.copy(src, dst)
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except Exception: pass
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print("[save] 导出了 LoRA 适配器 →", adapters_dir)
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print("INFO: 可用 Transformers/vLLM/SGLang 以『REF_MODEL + adapters/adapter_model.safetensors』方式推理。")
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else:
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# ——无 LoRA：按密集权重流程保存 BF16 分片 safetensors——
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missing, unexpected = model.load_state_dict(state, strict=False)
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print(f"[load] missing={len(missing)} unexpected={len(unexpected)}")
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# untie（如需要）
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try:
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emb = model.get_input_embeddings().weight if hasattr(model, "get_input_embeddings") else None
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head = model.lm_head.weight if hasattr(model, "lm_head") else None
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if emb is not None and head is not None and emb.data_ptr() == head.data_ptr():
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with torch.no_grad():
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model.lm_head.weight = torch.nn.Parameter(head.detach().clone())
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print("[fix] untied lm_head from embed_tokens")
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except Exception as e:
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print("[fix] skip untie check:", e)
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model.to(dtype=torch.bfloat16)
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os.makedirs(OUT_DIR, exist_ok=True)
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model.save_pretrained(OUT_DIR, safe_serialization=True, max_shard_size=MAX_SHARD_SIZE)
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print("[save] BF16 safetensors →", OUT_DIR)
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PY
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echo "== 5.1/7 拷贝（/补齐）最终目录的 tokenizer/config 工件（如存在）=="
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for f in config.json generation_config.json tokenizer_config.json tokenizer.json merges.txt vocab.json special_tokens_map.json added_tokens.json; do
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[[ -f "${REF_MODEL_DIR}/${f}" ]] && cp -n "${REF_MODEL_DIR}/${f}" "${OUT_DIR}/" || true
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done
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echo "== 6/7 自检（索引与 config）=="
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python - <<'PY'
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import os, json, sys
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out_dir = os.environ.get("OUT_DIR")
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idx = os.path.join(out_dir, "model.safetensors.index.json")
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if os.path.exists(idx):
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with open(idx) as f: j = json.load(f)
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print(f"OK: 找到 safetensors 索引：{idx}（参数条目 {len(j.get('weight_map', {}))}）")
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else:
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sfts = [x for x in os.listdir(out_dir) if x.endswith(".safetensors")]
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if len(sfts) == 1:
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print(f"NOTE: 单分片 safetensors：{sfts[0]}")
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elif len(sfts) == 0:
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# LoRA 分支下可能没有模型分片（只导出 adapters）
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print("NOTE: 未发现模型分片（若已导出 adapters/ 则属正常）")
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else:
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print("WARN: 未找到 model.safetensors.index.json，且分片数 != 1", file=sys.stderr)
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try:
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from transformers import AutoConfig
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cfg = AutoConfig.from_pretrained(out_dir, trust_remote_code=True)
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print("OK: 读取到 config：", cfg.model_type, "hidden:", getattr(cfg,'hidden_size',None), "layers:", getattr(cfg,'num_hidden_layers',None))
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except Exception as e:
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print("NOTE: 最终目录无 config（若为纯 adapters 导出则正常）：", e)
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PY
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echo "== 7/7 清理提示 =="
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echo "临时 FP32 目录：${TMP_PT_DIR}"
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echo "输出目录（无 LoRA=密集权重；有 LoRA=adapters/）：${OUT_DIR}"
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echo "完成。"
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