问题内容

今天在炼丹的时候，我发现模型跑到140步的时候保存权重突然报了个问题，详细内容如下：

Traceback (most recent call last):
  File "/public/home/dyedd/.conda/envs/diffusers/lib/python3.8/site-packages/torch/serialization.py", line 423, in save
    _save(obj, opened_zipfile, pickle_module, pickle_protocol)
  File "/public/home/dyedd/.conda/envs/diffusers/lib/python3.8/site-packages/torch/serialization.py", line 650, in _save
    zip_file.write_record(name, storage.data_ptr(), num_bytes)
RuntimeError: [enforce fail at inline_container.cc:450] . PytorchStreamWriter failed writing file data/1125: file write failed

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "ds_train.py", line 160, in <module>
    main()
  File "ds_train.py", line 135, in main
    model_engine.save_checkpoint(f"{cfg.output_dir}")
  File "/public/home/dyedd/.conda/envs/diffusers/lib/python3.8/site-packages/deepspeed/runtime/engine.py", line 2890, in save_checkpoint
    self._save_checkpoint(save_dir, tag, client_state=client_state)
  File "/public/home/dyedd/.conda/envs/diffusers/lib/python3.8/site-packages/deepspeed/runtime/engine.py", line 3092, in _save_checkpoint
    self.checkpoint_engine.save(state, save_path)
  File "/public/home/dyedd/.conda/envs/diffusers/lib/python3.8/site-packages/deepspeed/runtime/checkpoint_engine/torch_checkpoint_engine.py", line 22, in save
    torch.save(state_dict, path)
  File "/public/home/dyedd/.conda/envs/diffusers/lib/python3.8/site-packages/torch/serialization.py", line 424, in save
    return
  File "/public/home/dyedd/.conda/envs/diffusers/lib/python3.8/site-packages/torch/serialization.py", line 290, in __exit__
    self.file_like.write_end_of_file()
RuntimeError: [enforce fail at inline_container.cc:325] . unexpected pos 286145984 vs 286145872
terminate called after throwing an instance of 'c10::Error'
  what():  [enforce fail at inline_container.cc:325] . unexpected pos 286145984 vs 286145872
frame #0: c10::ThrowEnforceNotMet(char const*, int, char const*, std::string const&, void const*) + 0x47 (0x2b48b09ef7d7 in /public/home/dyedd/.conda/envs/diffusers/lib/python3.8/site-packages/torch/lib/libc10.so)
frame #1: <unknown function> + 0x2fd16e0 (0x2b487a93e6e0 in /public/home/dyedd/.conda/envs/diffusers/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so)
frame #2: mz_zip_writer_add_mem_ex_v2 + 0x723 (0x2b487a9392c3 in /public/home/dyedd/.conda/envs/diffusers/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so)
frame #3: caffe2::serialize::PyTorchStreamWriter::writeRecord(std::string const&, void const*, unsigned long, bool) + 0xb5 (0x2b487a941835 in /public/home/dyedd/.conda/envs/diffusers/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so)
frame #4: caffe2::serialize::PyTorchStreamWriter::writeEndOfFile() + 0x2c3 (0x2b487a941d43 in /public/home/dyedd/.conda/envs/diffusers/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so)
frame #5: caffe2::serialize::PyTorchStreamWriter::~PyTorchStreamWriter() + 0x125 (0x2b487a941ff5 in /public/home/dyedd/.conda/envs/diffusers/lib/python3.8/site-packages/torch/lib/libtorch_cpu.so)
frame #6: <unknown function> + 0x66a353 (0x2b486ea82353 in /public/home/dyedd/.conda/envs/diffusers/lib/python3.8/site-packages/torch/lib/libtorch_python.so)
frame #7: <unknown function> + 0x23c986 (0x2b486e654986 in /public/home/dyedd/.conda/envs/diffusers/lib/python3.8/site-packages/torch/lib/libtorch_python.so)
frame #8: <unknown function> + 0x23debe (0x2b486e655ebe in /public/home/dyedd/.conda/envs/diffusers/lib/python3.8/site-packages/torch/lib/libtorch_python.so)
frame #9: <unknown function> + 0x110632 (0x560fa3677632 in /public/home/dyedd/.conda/envs/diffusers/bin/python)
frame #10: <unknown function> + 0x110059 (0x560fa3677059 in /public/home/dyedd/.conda/envs/diffusers/bin/python)
frame #11: <unknown function> + 0x110043 (0x560fa3677043 in /public/home/dyedd/.conda/envs/diffusers/bin/python)
frame #12: <unknown function> + 0x110043 (0x560fa3677043 in /public/home/dyedd/.conda/envs/diffusers/bin/python)
frame #13: <unknown function> + 0x110043 (0x560fa3677043 in /public/home/dyedd/.conda/envs/diffusers/bin/python)
frame #14: <unknown function> + 0x110043 (0x560fa3677043 in /public/home/dyedd/.conda/envs/diffusers/bin/python)
frame #15: <unknown function> + 0x110043 (0x560fa3677043 in /public/home/dyedd/.conda/envs/diffusers/bin/python)
frame #16: <unknown function> + 0x177ce7 (0x560fa36dece7 in /public/home/dyedd/.conda/envs/diffusers/bin/python)
frame #17: PyDict_SetItemString + 0x4c (0x560fa36e1d8c in /public/home/dyedd/.conda/envs/diffusers/bin/python)
frame #18: PyImport_Cleanup + 0xaa (0x560fa3754a2a in /public/home/dyedd/.conda/envs/diffusers/bin/python)
frame #19: Py_FinalizeEx + 0x79 (0x560fa37ba4c9 in /public/home/dyedd/.conda/envs/diffusers/bin/python)
frame #20: Py_RunMain + 0x1bc (0x560fa37bd83c in /public/home/dyedd/.conda/envs/diffusers/bin/python)
frame #21: Py_BytesMain + 0x39 (0x560fa37bdc29 in /public/home/dyedd/.conda/envs/diffusers/bin/python)
frame #22: __libc_start_main + 0xf5 (0x2b4852ea13d5 in /lib64/libc.so.6)
frame #23: <unknown function> + 0x1f9ad7 (0x560fa3760ad7 in /public/home/dyedd/.conda/envs/diffusers/bin/python)

问题分析

这个问题实际上是说，模型权重在保存的时候不完整。

这时候我惊呆了，我的模型都已经保存了140次了，怎么回事？难道是我的程序写出了隐藏BUG？

吃惊的同时召唤魔法去搜索，果然网友也有我的这个问题，但是他们说保存的目录有问题。我立马转回去看我的权重保存路径，没错呢，程序也不可能自动删除目录呀。看来我们遇到的不是同一个问题。

直到…

我看了下的硬盘，好家伙。怪不得模型权重保存不完整，原先是我的硬盘被前面的140次保存给吃饱了，一丁点空间都没有~

哎，十分感叹，果然大模型的时代，训练的东西都不是小孩，在以前就是保存几千次都没有这么多问题。

解决思路

所以这个问题如何解决呢？

那就是删除被占用的空间，当然你还可以设置保存权重的频率，例如在deepspeed：

for epoch in range(cfg.num_epochs) :
    model_engine.train()
    for i, data in enumerate(training_dataloader):
    	if i % cfg.save_interval == 0:
        	# save checkpoint
            model_engine.save_checkpoint(f"{cfg.output_dir}")

又回到刚刚说的删除被占用的空间，我建议不要全删了，因为deepspeed会把目前最优的权重文件夹保存在latest文件，你只要双击查看，然后删除多余的其它文件即可。

这就是典型的排他思想，哈哈，又回想起学JS的时候Pink老师说的。

代码我用GPT4写好了，并且经过了充分的验证：

import os
import shutil

def cleanup_except_latest(directory_path):
    # 读取 latest 文件的内容
    latest_file_path = os.path.join(directory_path, "latest")
    with open(latest_file_path, "r") as file:
        # 读取要保留的文件夹名称
        folders_to_keep = file.read().strip().split('\n')

    # 添加"latest"到保留列表
    folders_to_keep.append("latest")

    # 获取目录下的所有文件和文件夹
    all_items = os.listdir(directory_path)

    # 过滤出所有文件夹
    folders = [item for item in all_items if os.path.isdir(os.path.join(directory_path, item))]

    # 删除不在保留列表中的文件夹
    for folder in folders:
        if folder not in folders_to_keep:
            folder_path = os.path.join(directory_path, folder)
            shutil.rmtree(folder_path)

    # 返回更新后的目录内容
    print(os.listdir(directory_path))


if __name__ == '__main__':
    cleanup_except_latest("/home/dcuuser/dxm/diffusers/train/FineTunedStableDiffusion-lora")

在deepspeed重新训练的时候会检测到当前保留的最优值，然后继续开始训练的，所以不要担心删除了会造成什么影响。

• 转载于染念的博客