@albanD, Hi, I have managed to reproduce it with a self-contained example adapted from  distributed mnist 1
And I drew a sketch to illustrate my situation that may differ from the original one.

15760724624731470×930 125 KB

shown above, the aux branch’s loss wont affect the backbone. That’s what I think the “unused_parameters” means, But I dont know why it cant be done in DDP since it works fine in normal setting, also tested (you can try it out by comment the init_process_group part of code to disable the DDP)
then comes the code, it’s a bit of lengthy:grimacing:
from __future__ import division, print_function

import argparse

import torch
import torch.nn.functional as F
from torch import distributed, nn
from torch.utils import data
from torchvision import datasets, transforms


def distributed_is_initialized():
    if distributed.is_available():
        if distributed.is_initialized():
            return True
    return False


class Average(object):

    def __init__(self):
        self.sum = 0
        self.count = 0

    def __str__(self):
        return '{:.6f}'.format(self.average)

    @property
    def average(self):
        return self.sum / self.count

    def update(self, value, number):
        self.sum += value * number
        self.count += number


class Accuracy(object):

    def __init__(self):
        self.correct = 0
        self.count = 0

    def __str__(self):
        return '{:.2f}%'.format(self.accuracy * 100)

    @property
    def accuracy(self):
        return self.correct / self.count

    def update(self, output, target):
        with torch.no_grad():
            pred = output.argmax(dim=1)
            correct = pred.eq(target).sum().item()

        self.correct += correct
        self.count += output.size(0)


class Trainer(object):

    def __init__(self, model, optimizer1, optimizer2 , train_loader, test_loader, device):
        self.model = model
        self.optimizer1 = optimizer1
        self.optimizer2 = optimizer2
        self.train_loader = train_loader
        self.test_loader = test_loader
        self.device = device

    def fit(self, epochs):
        for epoch in range(1, epochs + 1):
            train_loss, train_acc = self.train()
            test_loss, test_acc = self.evaluate()

            print(
                'Epoch: {}/{},'.format(epoch, epochs),
                'train loss: {}, train acc: {},'.format(train_loss, train_acc),
                'test loss: {}, test acc: {}.'.format(test_loss, test_acc),
            )

    def train(self):
        self.model.train()

        train_loss = Average()
        train_acc = Accuracy()

        for data, target in self.train_loader:
            data = data.to(self.device)
            target = target.to(self.device)

            output1, output2 = self.model(data)

            loss1 = F.cross_entropy(output1, target)


            self.optimizer1.zero_grad()
            loss1.backward(retain_graph=True)
            self.optimizer1.step()

            loss2 = F.cross_entropy(output2, target)
            self.optimizer2.zero_grad()
            loss2.backward(retain_graph=True)
            self.optimizer2.step()

            train_loss.update(loss1.item(), data.size(0))
            train_acc.update(output1, target)

        return train_loss, train_acc

    def evaluate(self):
        self.model.eval()

        test_loss = Average()
        test_acc = Accuracy()

        with torch.no_grad():
            for data, target in self.test_loader:
                data = data.to(self.device)
                target = target.to(self.device)

                output,_ = self.model(data)
                loss = F.cross_entropy(output, target)

                test_loss.update(loss.item(), data.size(0))
                test_acc.update(output, target)

        return test_loss, test_acc


class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        self.backbone = nn.Linear(784,64)
        self.main_branch = nn.Linear(64, 10)
        self.aux_branch = nn.Linear(64, 10)


    def forward(self, x):
        x = self.backbone(x.view(x.size(0), -1))
        return self.main_branch(x), self.aux_branch(x)


class MNISTDataLoader(data.DataLoader):

    def __init__(self, root, batch_size, train=True):
        transform = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize((0.1307,), (0.3081,)),
        ])

        dataset = datasets.MNIST(root, train=train, transform=transform, download=True)
        sampler = None
        if train and distributed_is_initialized():
            sampler = data.DistributedSampler(dataset)

        super(MNISTDataLoader, self).__init__(
            dataset,
            batch_size=batch_size,
            shuffle=(sampler is None),
            sampler=sampler,
        )


def run(args):
    device = torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu')

    model = Net()
    main_param_list = list(model.main_branch.parameters())+list(model.backbone.parameters())
    aux_param_list = list(model.aux_branch.parameters())
    optimizer1 = torch.optim.Adam(main_param_list, lr=args.learning_rate)
    optimizer2 = torch.optim.Adam(aux_param_list, lr=args.learning_rate)

    if distributed_is_initialized():
        model.to(device)
        model = nn.parallel.DistributedDataParallel(model)
    else:
        model = nn.DataParallel(model)
        model.to(device)





    train_loader = MNISTDataLoader(args.root, args.batch_size, train=True)
    test_loader = MNISTDataLoader(args.root, args.batch_size, train=False)

    trainer = Trainer(model, optimizer1,optimizer2, train_loader, test_loader, device)
    trainer.fit(args.epochs)


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--backend', type=str, default='nccl', help='Name of the backend to use.')
    parser.add_argument(
        '-i',
        '--init-method',
        type=str,
        default='tcp://127.0.0.1:23456',
        help='URL specifying how to initialize the package.')
    parser.add_argument('-s', '--world-size', type=int, default=2, help='Number of processes participating in the job.')
    parser.add_argument('-r', '--local_rank', type=int, default=0, help='Rank of the current process.')
    parser.add_argument('--epochs', type=int, default=20)
    parser.add_argument('--no-cuda', action='store_true')
    parser.add_argument('-lr', '--learning-rate', type=float, default=1e-3)
    parser.add_argument('--root', type=str, default='/mnt/EXTRA/remote/cosine/data')
    parser.add_argument('--batch-size', type=int, default=128)
    args = parser.parse_args()
    print(args)

    if args.world_size > 1:
        distributed.init_process_group(
            backend=args.backend,
            init_method=args.init_method,
            world_size=args.world_size,
            rank=args.local_rank,
        )

    run(args)


if __name__ == '__main__':
    main()

the error log is:
Traceback (most recent call last):
  File "mnist.py", line 222, in <module>
    main()
  File "mnist.py", line 218, in main
    run(args)
  File "mnist.py", line 188, in run
    trainer.fit(args.epochs)
  File "mnist.py", line 71, in fit
    train_loss, train_acc = self.train()
  File "mnist.py", line 101, in train
    loss2.backward(retain_graph=True)
  File "/home/lxs/anaconda3/envs/torch10/lib/python3.6/site-packages/torch/tensor.py", line 107, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph)
  File "/home/lxs/anaconda3/envs/torch10/lib/python3.6/site-packages/torch/autograd/__init__.py", line 93, in backward
    allow_unreachable=True)  # allow_unreachable flag
RuntimeError: has_marked_unused_parameters_ ASSERT FAILED at /opt/conda/conda-bld/pytorch_1556653183467/work/torch/csrc/distributed/c10d/reducer.cpp:181, please report a bug to PyTorch.

I am using two 1080TI on a single machine, using command python -m torch.distributed.launch --nproc_per_node=2 mnist.py to run it.
And I am using pytorch ver 1.1.0, and you may need to change the root path. I used the abs path.