There is a bug in PyTorch/Numpy where when loading batches in parallel with a DataLoader
(i.e. setting num_workers > 1
), the same NumPy random seed is used for each worker, resulting in any random functions applied being identical across parallelized batches.
Minimal example:
import numpy as np
from torch.utils.data import Dataset, DataLoader
class RandomDataset(Dataset):
def __getitem__(self, index):
return np.random.randint(0, 1000, 2)
def __len__(self):
return 9
dataset = RandomDataset()
dataloader = DataLoader(dataset, batch_size=1, num_workers=3)
for batch in dataloader:
print(batch)
As you can see, for each parallelized set of batches (3), the results are the same:
# First 3 batches
tensor([[891, 674]])
tensor([[891, 674]])
tensor([[891, 674]])
# Second 3 batches
tensor([[545, 977]])
tensor([[545, 977]])
tensor([[545, 977]])
# Third 3 batches
tensor([[880, 688]])
tensor([[880, 688]])
tensor([[880, 688]])
What is the recommended/most elegant way to fix this? i.e. have each batch produce a different randomization, irrespective of the number of workers.
It seems this works, at least in Colab:
dataloader = DataLoader(dataset, batch_size=1, num_workers=3,
worker_init_fn = lambda id: np.random.seed(id) )
EDIT:
it produces identical output (i.e. the same problem) when iterated over epochs. – iacob
Best fix I have found so far:
...
dataloader = DataLoader(ds, num_workers= num_w,
worker_init_fn = lambda id: np.random.seed(id + epoch * num_w ))
for epoch in range ( 2 ):
for batch in dataloader:
print(batch)
print()
Still can't suggest closed form, thing depends on a var (epoch
) then called. Ideally It must be something like worker_init_fn = lambda id: np.random.seed(id + EAGER_EVAL(np.random.randint(10000) )
where EAGER_EVAL evaluate seed on loader construction, before lambda is passed as parameter. Is it possible in python, I wonder.