Torchrun using GPU memory on rank 0 for all the processes

For all the processes spanned by torchrun (DDP) a small amount of GPU on rank 0 is used for all the processes.

Test code:

import torch
import torch.distributed as dist
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel as DDP

class ToyModel(nn.Module):
    def __init__(self):
        super(ToyModel, self).__init__() = nn.Linear(10, 5)

rank = dist.get_rank()
print(f"Start running basic DDP example on rank {rank}.")
# One way to fix it
# import os
# os.environ["CUDA_VISIBLE_DEVICES"] = str(rank)
# torch.cuda.set_device(rank)

device_id = rank % torch.cuda.device_count()
model = ToyModel().to(device_id)
ddp_model = DDP(model, device_ids=[device_id])
if dist.get_rank() == 1:
    import pdb; pdb.set_trace()

Command used: OMP_NUM_THREADS=4 torchrun --standalone --nproc_per_node=3


There are similar issue posted:

  1. DistributedDataParallel: resume training from a checkpoint results in additional processes on GPU 0 · Issue #23138 · pytorch/pytorch · GitHub
  2. DDP taking up too much memory on rank 0

But all of them are because of some where in the code the tensors get allocated to cuda which default to rank 0. But it is not the case with the above bare minimal snippet.