Python 分布式采样器&x2014；预期为‘；cuda&x2019；生成索引时生成器的设备类型

执行分布式培训时，我有如下代码：

training_sampler = DistributedSampler(training_set, num_replicas=2, rank=0)
training_generator = data.DataLoader(training_set, **params, sampler=training_sampler)
for x, y, z in training_generator:  # Error occurs here.
    ...

总的来说，我得到了以下信息：

-- Process 0 terminated with the following error:
Traceback (most recent call last):
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/torch/multiprocessing/spawn.py", line 19, in _wrap
    fn(i, *args)
  File "/home/ubuntu/VC/ppg_training_extraction/ppg_training_scripts/train_ASR_trim_scp.py", line 336, in train
    for local_batch_src, local_batch_tgt, lengths in dataloaders[phase]:
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 352, in __iter__
    return self._get_iterator()
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 294, in _get_iterator
    return _MultiProcessingDataLoaderIter(self)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 827, in __init__
    self._reset(loader, first_iter=True)
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 857, in _reset
    self._try_put_index()
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 1091, in _try_put_index
    index = self._next_index()
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 427, in _next_index
    return next(self._sampler_iter)  # may raise StopIteration
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/torch/utils/data/sampler.py", line 227, in __iter__
    for idx in self.sampler:
  File "/home/ubuntu/anaconda3/lib/python3.7/site-packages/torch/utils/data/distributed.py", line 97, in __iter__
    indices = torch.randperm(len(self.dataset), generator=g).tolist()  # type: ignore
RuntimeError: Expected a 'cuda' device type for generator but found 'cpu'

现在在那一行，我在pdb中运行了以下说明：

(Pdb) g = torch.Generator()
(Pdb) g.manual_seed(0)
<torch._C.Generator object at 0x7ff7f8143110>
(Pdb) indices = torch.randperm(4556, generator=g).tolist()
(Pdb) indices = torch.randperm(455604, generator=g).tolist()
*** RuntimeError: Expected a 'cuda' device type for generator but found 'cpu'

（Pdb）g=torch.Generator（）
（Pdb）g.手动种子（0）
（Pdb）索引=torch.randperm（4556，generator=g）.tolist（）
（Pdb）索引=torch.randperm（455604，generator=g）.tolist（）
***RuntimeError:生成器应为“cuda”设备类型，但找到“cpu”

为什么当上限整数很高时会出现运行时错误，而当上限整数足够低时却没有

注意，我运行了一个干净的Python会话，发现

>>> import torch
>>> g = torch.Generator()
>>> g.manual_seed(0)
<torch._C.Generator object at 0x7f9d2dfb39f0>
>>> indices = torch.randperm(455604, generator=g).tolist()

导入火炬 >>>g=火炬发生器（） >>>g.人工种子（0） >>>索引=torch.randperm（455604，generator=g）.tolist（） 这很有效。我是如何在多个GPU之间处理分布式培训的？任何形式的见解都将不胜感激