It’s a known issue, since you are using other libraries (numpy in your case) to generate random numbers.
Each worker will duplicate numpy’s PRNG, so that you’ll see the same numbers.
It’s described in the FAQ 155 a bit better.
You could sample random numbers using torch.randint or use worker_init_fn to set the seed for each numpy process.