This commit is contained in:
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@ -9,7 +9,7 @@ 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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# ★★★ 参考模型目录（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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@ -22,7 +22,7 @@ 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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TMP_PT_DIR="${CKPT_ROOT}/_tmp-fp32-pt-${TAG}" # 临时 FP32 输出目录
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export OUT_DIR TMP_PT_DIR MAX_SHARD_SIZE REF_MODEL_DIR
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# =================================
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@ -90,30 +90,62 @@ if (( CNT != EXPECTED_TOTAL_SHARDS )); then
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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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echo "== 4/7 合并分片 -> 临时 FP32（优先单文件 pytorch_model.bin；不支持则分片）=="
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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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import os, json, glob, torch
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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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TMP_PT_DIR = r"${TMP_PT_DIR}"
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STAGING_BASE = r"${STAGING_BASE}"
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TAG = r"${TAG}"
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sd_single = os.path.join(TMP_PT_DIR, "pytorch_model.bin")
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idx_json = os.path.join(TMP_PT_DIR, "pytorch_model.bin.index.json")
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# 优先：新式接口，直接写单文件
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ok = False
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try:
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convert_zero_checkpoint_to_fp32_state_dict(
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checkpoint_dir=STAGING_BASE,
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tag=TAG,
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output_file=sd_single,
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safe_serialization=False,
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)
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ok = True
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except TypeError:
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# 回退：写目录（多分片）
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convert_zero_checkpoint_to_fp32_state_dict(
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checkpoint_dir=STAGING_BASE,
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tag=TAG,
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output_dir=TMP_PT_DIR,
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safe_serialization=False,
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)
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# 若写成分片且存在索引，记录一下
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if os.path.exists(sd_single):
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print("合并完成（FP32 单文件）：", sd_single)
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else:
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shards = sorted(glob.glob(os.path.join(TMP_PT_DIR, "pytorch_model-*.bin")))
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if os.path.exists(idx_json):
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with open(idx_json) as f: j = json.load(f)
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n = len(set(j.get("weight_map", {}).values()))
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print(f"合并完成（FP32 多分片）：{n} 片，索引 {idx_json}")
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else:
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print(f"合并完成（FP32 多分片）：{len(shards)} 片（无 index.json）")
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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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# ★★★ 从参考模型复制 config/tokenizer 到临时 FP32 目录（装载需要）★★★
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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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echo "== 5/7 装载 REF 结构 + 灌入 FP32；若检测到 LoRA → 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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import os, re, json, sys, torch, shutil, glob
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from transformers import AutoConfig, AutoModelForCausalLM
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from safetensors.torch import save_file
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@ -122,42 +154,152 @@ 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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sd_single = os.path.join(TMP_PT_DIR, "pytorch_model.bin")
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idx_json = os.path.join(TMP_PT_DIR, "pytorch_model.bin.index.json")
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def log(*a, **k): print(*a, **k, flush=True)
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def parse_index(idx_path):
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with open(idx_path) as f:
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j = json.load(f)
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weight_map = j.get("weight_map", {})
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shard_to_keys = {}
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for k, shard in weight_map.items():
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shard_to_keys.setdefault(shard, []).append(k)
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return weight_map, shard_to_keys
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def detect_lora_from_index(weight_map):
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# 直接从权重名判断，不用先加载大权重
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has = any((".lora_A" in k) or (".lora_B" in k) or (".lora_alpha" in k) for k in weight_map)
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return has
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def stream_collect_lora_from_shards(shard_to_keys):
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lora_state = {}
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lora_keys = []
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for shard, keys in sorted(shard_to_keys.items()):
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pick = [k for k in keys if (".lora_A" in k) or (".lora_B" in k) or (".lora_alpha" in k)]
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if not pick: continue
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part = torch.load(os.path.join(TMP_PT_DIR, shard), map_location="cpu")
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for k in pick:
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if k in part: lora_state[k] = part[k]
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else:
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# 极少数 DS 版本权重名不完全一致，容错跳过
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pass
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lora_keys.extend(pick)
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return lora_state, lora_keys
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def stream_load_full_into_model(model, shard_to_keys):
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missing_total = 0
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unexpected_total = 0
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for shard, keys in sorted(shard_to_keys.items()):
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part = torch.load(os.path.join(TMP_PT_DIR, shard), map_location="cpu")
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m, u = model.load_state_dict(part, strict=False)
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missing_total += len(m)
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unexpected_total += len(u)
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log(f"[load] missing_total={missing_total} unexpected_total={unexpected_total}")
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log("[load] ref model from:", REF_DIR)
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cfg = AutoConfig.from_pretrained(REF_DIR, trust_remote_code=True)
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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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state = None
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has_lora = False
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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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if os.path.exists(sd_single):
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log("[fp32] detected single file:", sd_single)
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state = torch.load(sd_single, map_location="cpu")
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# 去 'module.' 前缀
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state = { (k.split("module.",1)[-1]): v for k, v in state.items() }
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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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elif os.path.exists(idx_json):
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log("[fp32] detected sharded weights with index:", idx_json)
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weight_map, shard_to_keys = parse_index(idx_json)
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has_lora = detect_lora_from_index(weight_map)
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if has_lora:
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log("[check] contains LoRA keys: True (stream collecting)")
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lora_state, lora_keys = stream_collect_lora_from_shards(shard_to_keys)
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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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# 估 r / alpha
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lora_A_keys = [k for k in lora_keys if k.endswith(".lora_A.weight")]
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if lora_A_keys:
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# 找到第一个 lora_A 所在分片并读取 shape
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k0 = lora_A_keys[0]
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shard = next(s for s, ks in shard_to_keys.items() if k0 in ks)
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part = torch.load(os.path.join(TMP_PT_DIR, shard), map_location="cpu")
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r = part[k0].shape[0]
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a = part.get(k0.replace(".lora_A.weight", ".lora_alpha"))
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alpha = int(a.item()) if (a is not None and hasattr(a, "item")) else int(r)
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else:
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r, alpha = 16, 16
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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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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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if has_lora:
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# ——导出 LoRA 适配器（不合到基座）——
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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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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
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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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log("[save] 导出了 LoRA 适配器 →", adapters_dir)
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log("INFO: 可用 Transformers/vLLM/SGLang 以『REF_MODEL + adapters/adapter_model.safetensors』方式推理。")
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sys.exit(0)
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else:
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log("[check] contains LoRA keys: False (stream loading into model)")
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stream_load_full_into_model(model, shard_to_keys)
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else:
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# 无单文件也无 index.json，尝试兜底按分片名加载
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shard_glob = sorted(glob.glob(os.path.join(TMP_PT_DIR, "pytorch_model-*.bin")))
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if not shard_glob:
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print("ERR: 未找到单文件或分片 FP32（pytorch_model.bin / .index.json / 分片）", file=sys.stderr); sys.exit(9)
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log(f"[fp32] detected {len(shard_glob)} shards (no index.json), brute-load")
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# 粗暴合并（可能占内存，但作为兜底）
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state = {}
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for sf in shard_glob:
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part = torch.load(sf, map_location="cpu")
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state.update(part)
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state = { (k.split("module.",1)[-1]): v for k, v in state.items() }
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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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if state is not None and has_lora:
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log("[check] contains LoRA keys: True")
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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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if lora_A_keys:
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r = state[lora_A_keys[0]].shape[0]
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a = state.get(lora_A_keys[0].replace(".lora_A.weight", ".lora_alpha"))
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alpha = int(a.item()) if (a is not None and hasattr(a, "item")) else int(r)
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else:
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r, alpha = 16, 16
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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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@ -178,7 +320,6 @@ if has_lora:
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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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@ -187,27 +328,30 @@ if has_lora:
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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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log("[save] 导出了 LoRA 适配器 →", adapters_dir)
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log("INFO: 可用 Transformers/vLLM/SGLang 以『REF_MODEL + adapters/adapter_model.safetensors』方式推理。")
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sys.exit(0)
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# ——走到这里表示“无 LoRA”，把 FP32 权重注入模型并保存 BF16 safetensors——
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if state is not None:
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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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log(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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log("[fix] untied lm_head from embed_tokens")
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except Exception as e:
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log("[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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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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log("[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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@ -217,7 +361,7 @@ 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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import os, json
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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")
|
||||
if os.path.exists(idx):
|
||||
|
|
@ -231,8 +375,7 @@ else:
|
|||
# LoRA 分支下可能没有模型分片（只导出 adapters）
|
||||
print("NOTE: 未发现模型分片（若已导出 adapters/ 则属正常）")
|
||||
else:
|
||||
print("WARN: 未找到 model.safetensors.index.json，且分片数 != 1", file=sys.stderr)
|
||||
|
||||
print("WARN: 未找到 model.safetensors.index.json，且分片数 != 1")
|
||||
try:
|
||||
from transformers import AutoConfig
|
||||
cfg = AutoConfig.from_pretrained(out_dir, trust_remote_code=True)
|
||||
|
|
|
|||
Loading…
Reference in New Issue