10

正如我在这里概述的那样，由于硬件原因，例如使用 ppc64le IBM 架构，我被困在使用旧版本的 pytorch 和 torchvision。

出于这个原因，我在不同计算机、集群和我的个人 Mac 之间发送和接收检查点时遇到问题。我想知道是否有任何方法可以避免这个问题来加载模型？例如，在使用 1.6.x 时，可能会以新旧格式保存模型。当然，对于 1.3.1 到 1.6.x 来说是不可能的，但至少我希望有些东西能奏效。

有什么建议吗？当然，我理想的解决方案是我不必担心它，我总是可以加载和保存我的检查点以及我通常在所有硬件上统一腌制的所有内容。

我得到的第一个错误是 zip jit 错误：

RuntimeError: /home/miranda9/data/f.pt is a zip archive (did you mean to use torch.jit.load()?)

所以我使用了它（和其他泡菜库）：

# %%
import torch
from pathlib import Path


def load(path):
    import torch
    import pickle
    import dill

    path = str(path)
    try:
        db = torch.load(path)
        f = db['f']
    except Exception as e:
        db = torch.jit.load(path)
        f = db['f']
        #with open():
        # db = pickle.load(open(path, "r+"))
        # db = dill.load(open(path, "r+"))
        #raise ValueError(f'FAILED: {e}')
    return db, f

p = "~/data/f.pt"
path = Path(p).expanduser()

db, f = load(path)

Din, nb_examples = 1, 5
x = torch.distributions.Normal(loc=0.0, scale=1.0).sample(sample_shape=(nb_examples, Din))

y = f(x)

print(y)
print('Success!\a')

但我抱怨我被迫使用不同的 pytorch 版本：

Traceback (most recent call last):
  File "hal_pg.py", line 27, in <module>
    db, f = load(path)
  File "hal_pg.py", line 16, in load
    db = torch.jit.load(path)
  File "/home/miranda9/.conda/envs/wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/jit/__init__.py", line 239, in load
    cpp_module = torch._C.import_ir_module(cu, f, map_location, _extra_files)
RuntimeError: version_number <= kMaxSupportedFileFormatVersion INTERNAL ASSERT FAILED at /opt/anaconda/conda-bld/pytorch-base_1581395437985/work/caffe2/serialize/inline_container.cc:131, please report a bug to PyTorch. Attempted to read a PyTorch file with version 3, but the maximum supported version for reading is 1. Your PyTorch installation may be too old. (init at /opt/anaconda/conda-bld/pytorch-base_1581395437985/work/caffe2/serialize/inline_container.cc:131)
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0xbc (0x7fff7b527b9c in /home/miranda9/.conda/envs/wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #1: caffe2::serialize::PyTorchStreamReader::init() + 0x1d98 (0x7fff1d293c78 in /home/miranda9/.conda/envs/wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #2: caffe2::serialize::PyTorchStreamReader::PyTorchStreamReader(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0x88 (0x7fff1d2950d8 in /home/miranda9/.conda/envs/wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #3: torch::jit::import_ir_module(std::shared_ptr<torch::jit::script::CompilationUnit>, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, c10::optional<c10::Device>, std::unordered_map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::hash<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::equal_to<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > >&) + 0x64 (0x7fff1e624664 in /home/miranda9/.conda/envs/wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #4: <unknown function> + 0x70e210 (0x7fff7c0ae210 in /home/miranda9/.conda/envs/wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
frame #5: <unknown function> + 0x28efc4 (0x7fff7bc2efc4 in /home/miranda9/.conda/envs/wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
<omitting python frames>
frame #26: <unknown function> + 0x25280 (0x7fff84b35280 in /lib64/libc.so.6)
frame #27: __libc_start_main + 0xc4 (0x7fff84b35474 in /lib64/libc.so.6)

任何想法如何使集群中的所有内容保持一致？我什至无法打开泡菜文件。

也许这对于我被迫使用的当前 pytorch 版本来说是不可能的:(

RuntimeError: version_number <= kMaxSupportedFileFormatVersion INTERNAL ASSERT FAILED at /opt/anaconda/conda-bld/pytorch-base_1581395437985/work/caffe2/serialize/inline_container.cc:131, please report a bug to PyTorch. Attempted to read a PyTorch file with version 3, but the maximum supported version for reading is 1. Your PyTorch installation may be too old. (init at /opt/anaconda/conda-bld/pytorch-base_1581395437985/work/caffe2/serialize/inline_container.cc:131)
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0xbc (0x7fff83ba7b9c in /home/miranda9/.conda/envs/automl-meta-learning_wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libc10.so)
frame #1: caffe2::serialize::PyTorchStreamReader::init() + 0x1d98 (0x7fff25993c78 in /home/miranda9/.conda/envs/automl-meta-learning_wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #2: caffe2::serialize::PyTorchStreamReader::PyTorchStreamReader(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0x88 (0x7fff259950d8 in /home/miranda9/.conda/envs/automl-meta-learning_wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #3: torch::jit::import_ir_module(std::shared_ptr<torch::jit::script::CompilationUnit>, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, c10::optional<c10::Device>, std::unordered_map<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >, std::hash<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::equal_to<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > >, std::allocator<std::pair<std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > > > >&) + 0x64 (0x7fff26d24664 in /home/miranda9/.conda/envs/automl-meta-learning_wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libtorch.so)
frame #4: <unknown function> + 0x70e210 (0x7fff8472e210 in /home/miranda9/.conda/envs/automl-meta-learning_wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
frame #5: <unknown function> + 0x28efc4 (0x7fff842aefc4 in /home/miranda9/.conda/envs/automl-meta-learning_wmlce-v1.7.0-py3.7/lib/python3.7/site-packages/torch/lib/libtorch_python.so)
<omitting python frames>
frame #23: <unknown function> + 0x25280 (0x7fff8d335280 in /lib64/libc.so.6)
frame #24: __libc_start_main + 0xc4 (0x7fff8d335474 in /lib64/libc.so.6)

使用代码：

from pathlib import Path

import torch

path = '/home/miranda9/data/dataset/'
path = Path(path).expanduser() / 'fi_db.pt'
path = str(path)

# db = torch.load(path)
# torch.jit.load(path)
db = torch.jit.load(str(path))

print(db)

相关链接：

4

4 回答 4

1

基于@maxim velikanov 的答案，我创建了一个单独的 OrderedDict，其中的键与模型的原始状态字典相同，但每个张量值都转换为一个列表。

这个 OrderedDict 是他们转储到 JSON 文件中的。

def save_model_json(model, path):
    actual_dict = OrderedDict()
    for k, v in model.state_dict().items():
      actual_dict[k] = v.tolist()
    with open(path, 'w') as f:
      json.dump(actual_dict, f)

然后加载器可以将文件作为 JSON 加载，每个列表/整数将在将其值复制到原始状态字典之前转换回张量。

def load_model_json(model, path):
  data_dict = OrderedDict()
  with open(path, 'r') as f:
    data_dict = json.load(f)    
  own_state = model.state_dict()
  for k, v in data_dict.items():
    print('Loading parameter:', k)
    if not k in own_state:
      print('Parameter', k, 'not found in own_state!!!')
    if type(v) == list or type(v) == int:
      v = torch.tensor(v)
    own_state[k].copy_(v)
  model.load_state_dict(own_state)
  print('Model loaded')
于 2021-01-28T08:50:18.460 回答
1

这不是一个理想的解决方案，但它适用于将检查点从新版本转移到旧版本。

我也使用 ppc64le 并面临同样的问题。可以将模型保存为任何 PyTorch 版本都可以读取的文本格式。我在 ppc64le 机器上安装了 PyTorch v1.3.0，在我的笔记本电脑上安装了 v1.7.0（不需要显卡）。

步骤 1. 通过更新的 PyTorch 版本保存模型

def save_model_txt(model, path):
    fout = open(path, 'w')
    for k, v in model.state_dict().items():
        fout.write(str(k) + '\n')
        fout.write(str(v.tolist()) + '\n')
    fout.close()

在保存之前，我像这样加载模型

checkpoint = torch.load(path, map_location=torch.device('cpu'))
model.load_state_dict(checkpoint, strict=False)

步骤 2. 传输文本文件

步骤 3. 在旧 PyTorch 中加载文本文件

def load_model_txt(model, path):
    data_dict = {}
    fin = open(path, 'r')
    i = 0
    odd = 1
    prev_key = None
    while True:
        s = fin.readline().strip()
        if not s:
            break
        if odd:
            prev_key = s
        else:
            print('Iter', i)
            val = eval(s)
            if type(val) != type([]):
                data_dict[prev_key] = torch.FloatTensor([eval(s)])[0]
            else:
                data_dict[prev_key] = torch.FloatTensor(eval(s))
            i += 1
        odd = (odd + 1) % 2

    # Replace existing values with loaded

    print('Loading...')
    own_state = model.state_dict()
    print('Items:', len(own_state.items()))
    for k, v in data_dict.items():
        if not k in own_state:
            print('Parameter', k, 'not found in own_state!!!')
        else:
            try:
                own_state[k].copy_(v)
            except:
                print('Key:', k)
                print('Old:', own_state[k])
                print('New:', v)
                sys.exit(0)
    print('Model loaded')

加载前必须初始化模型。空模型被传递到函数中。

限制

如果您的模型 state_dict 包含 (str: torch.Tensor) 值以外的其他内容，则此方法将不起作用。您可以检查您的 state_dict 内容

for k, v in model.state_dict().items():
    ...

阅读这些以了解：

https://pytorch.org/tutorials/recipes/recipes/saving_and_loading_models_for_inference.html

https://discuss.pytorch.org/t/how-to-load-part-of-pre-trained-model/1113

于 2020-11-29T22:24:18.880 回答
0

我相信开发人员的意图是传递一个保存为泡菜的标志。只是默认行为更改。

对于以前检查点的文件，在较新的环境中重新加载 zip 文件保存的权重（使用 pytorch>=1.6），然后再次检查点作为泡菜（无需重新训练）；

更新您的代码并从下次添加标志

从 1.6 版弃用：

我们已将 torch.save 切换为默认使用基于 zip 文件的格式，而不是旧的基于 Pickle 的格式。torch.load 保留了加载旧格式的能力，但建议使用新格式。新格式是：

更易于检查和构建用于操作保存文件的工具修复了一个长期存在的问题，其中依赖于序列化 Tensor 值的模块上的序列化（getstate，setstate）函数获取与 TorchScript 序列化格式相同的错误数据，使序列化更多在 PyTorch 中保持一致

用法如下：

m = MyMod()
torch.save(m.state_dict(), 'mymod.pt') # Saves a zipfile to mymod.pt

要使用旧格式，请传递标志_use_new_zipfile_serialization=False

m = MyMod()
torch.save(m.state_dict(), 'mymod.pt', _use_new_zipfile_serialization=False) # Saves pickle
于 2021-09-14T03:27:36.087 回答
0

我在加载处理后的数据时遇到了类似的问题。我之前在torch 1.8 中将数据保存为“xxx.pt”，但在torch 1.2 中加载了它。即使通过 torch.jit.load()，我也无法成功加载它。我唯一的解决方案是在旧版本中再次保存数据。

于 2021-03-07T09:27:42.203 回答