191 lines
8.0 KiB
Bash
Executable File
191 lines
8.0 KiB
Bash
Executable File
#!/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_step110"
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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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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
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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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# 每台机最简单的 sanity：至少应有 EXPECTED_SHARDS_PER_HOST 个
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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 "${TMP_PT_DIR}"
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# 直接走 API：safe_serialization=False -> 生成 pytorch_model.bin（FP32）
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python - <<PY
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from deepspeed.utils.zero_to_fp32 import convert_zero_checkpoint_to_fp32_state_dict
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convert_zero_checkpoint_to_fp32_state_dict(
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checkpoint_dir=r"${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, # 关键：先落成 .bin（FP32），绕开共享权重的 safetensors 限制
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)
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print("合并完成（FP32 .bin）：", r"${TMP_PT_DIR}")
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PY
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echo "== 4.1/7 将 config/tokenizer 工件拷贝到临时 FP32 目录（装载需要）=="
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for f in config.json generation_config.json tokenizer_config.json tokenizer.json merges.txt vocab.json special_tokens_map.json added_tokens.json; do
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[[ -f "${CKPT_ROOT}/${f}" ]] && cp -n "${CKPT_ROOT}/${f}" "${TMP_PT_DIR}/" || true
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done
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echo "== 5/7 FP32 -> BF16，并解开 lm_head <-> embed_tokens 共享存储，保存为分片 safetensors（${MAX_SHARD_SIZE}）=="
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python - <<'PY'
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import os, sys, torch
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from transformers import AutoConfig, AutoModelForCausalLM
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TMP_PT_DIR = os.environ["TMP_PT_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] from:", TMP_PT_DIR)
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cfg = AutoConfig.from_pretrained(TMP_PT_DIR, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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TMP_PT_DIR,
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config=cfg,
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trust_remote_code=True,
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torch_dtype=torch.bfloat16, # 目标 BF16
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low_cpu_mem_usage=True,
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device_map={"": "cpu"}, # 全在 CPU 装载，避免吃显存
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)
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# —— 如 lm_head 与 embed_tokens 权重共享，则手动 untie，防止后续 safetensors 报共享存储 —— #
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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 shared weights: lm_head.weight cloned from embed_tokens.weight")
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else:
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print("[fix] No shared storage detected between lm_head and embed_tokens")
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except Exception as e:
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print("[fix] Skip untie check:", e, file=sys.stderr)
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# 再确保全模型 dtype 为 BF16
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model.to(dtype=torch.bfloat16)
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# 分片 safetensors（支持大模型）
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os.makedirs(OUT_DIR, exist_ok=True)
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model.save_pretrained(
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OUT_DIR,
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safe_serialization=True, # 写 safetensors
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max_shard_size=MAX_SHARD_SIZE, # 分片上限
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)
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print("[save] BF16 safetensors saved to:", 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 工件（如存在）=="
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for f in tokenizer_config.json tokenizer.json merges.txt vocab.json special_tokens_map.json added_tokens.json; do
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[[ -f "${CKPT_ROOT}/${f}" ]] && cp -n "${CKPT_ROOT}/${f}" "${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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# 单分片也可能没有 index.json
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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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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("WARN: 读取 config 失败（若无 config.json 可忽略）：", e, file=sys.stderr)
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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：${TMP_PT_DIR}"
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echo "BF16 safetensors 输出：${OUT_DIR}"
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echo "完成。"
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