Problem in training iterable dataset
### Describe the bug

I am using PyTorch DDP (Distributed Data Parallel) to train my model. Since the data is too large to load into memory at once, I am using load_dataset to read the data as an iterable dataset. I have used datasets.distributed.split_dataset_by_node to distribute the dataset. However, I have noticed that this distribution results in different processes having different amounts of data to train on. As a result, when the earliest process finishes training and starts predicting on the test set, other processes are still training, causing the overall training speed to be very slow.

### Steps to reproduce the bug
```
def train(args, model, device, train_loader, optimizer, criterion, epoch, length):
    model.train()
    idx_length = 0
    for batch_idx, data in enumerate(train_loader):
        s_time = time.time()
        X = data['X']
        target = data['y'].reshape(-1, 28)
        X, target = X.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(X)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        idx_length += 1
        if batch_idx % args.log_interval == 0:
            # print('Train Epoch: {} Batch_idx: {} Process: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
            #     epoch, batch_idx, torch.distributed.get_rank(), batch_idx * len(X), length / torch.distributed.get_world_size(),
            #                                          100. * batch_idx * len(
            #                                              X) * torch.distributed.get_world_size() / length, loss.item()))
            print('Train Epoch: {} Batch_idx: {} Process: {} [{}/{} ({:.0f}%)]\t'.format(
                epoch, batch_idx, torch.distributed.get_rank(), batch_idx * len(X), length / torch.distributed.get_world_size(),
                                                                100. * batch_idx * len(
                                                                    X) * torch.distributed.get_world_size() / length))
            if args.dry_run:
                break
    print('Process %s length: %s time: %s' % (torch.distributed.get_rank(), idx_length, datetime.datetime.now()))

train_iterable_dataset = load_dataset("parquet", data_files=data_files, split="train", streaming=True)
test_iterable_dataset = load_dataset("parquet", data_files=data_files, split="test", streaming=True)
train_iterable_dataset = train_iterable_dataset.map(process_fn)
test_iterable_dataset = test_iterable_dataset.map(process_fn)
train_iterable_dataset = train_iterable_dataset.map(scale)
test_iterable_dataset = test_iterable_dataset.map(scale)

train_iterable_dataset = datasets.distributed.split_dataset_by_node(train_iterable_dataset,
                                                                    world_size=world_size, rank=local_rank).shuffle(seed=1234)
test_iterable_dataset = datasets.distributed.split_dataset_by_node(test_iterable_dataset,
                                                                   world_size=world_size, rank=local_rank).shuffle(seed=1234)
print(torch.distributed.get_rank(), train_iterable_dataset.n_shards, test_iterable_dataset.n_shards)

train_kwargs = {'batch_size': args.batch_size}
test_kwargs = {'batch_size': args.test_batch_size}
if use_cuda:
    cuda_kwargs = {'num_workers': 3,#ngpus_per_node,
                   'pin_memory': True,
                   'shuffle': False}
    train_kwargs.update(cuda_kwargs)
    test_kwargs.update(cuda_kwargs)
train_loader = torch.utils.data.DataLoader(train_iterable_dataset, **train_kwargs,
                                           # sampler=torch.utils.data.distributed.DistributedSampler(
                                           #     train_iterable_dataset,
                                           #     num_replicas=ngpus_per_node,
                                           #     rank=0)
                                           )
test_loader = torch.utils.data.DataLoader(test_iterable_dataset, **test_kwargs,
                                          # sampler=torch.utils.data.distributed.DistributedSampler(
                                          #     test_iterable_dataset,
                                          #     num_replicas=ngpus_per_node,
                                          #     rank=0)
                                          )
for epoch in range(1, args.epochs + 1):
    start_time = time.time()
    train_iterable_dataset.set_epoch(epoch)
    test_iterable_dataset.set_epoch(epoch)
    train(args, model, device, train_loader, optimizer, criterion, epoch, train_len)
    test(args, model, device, criterion2, test_loader)

```



And here’s the part of output:
```
Train Epoch: 1 Batch_idx: 5000 Process: 0 [320000/4710975.0 (7%)]	
Train Epoch: 1 Batch_idx: 5000 Process: 1 [320000/4710975.0 (7%)]	
Train Epoch: 1 Batch_idx: 5000 Process: 2 [320000/4710975.0 (7%)]	
Train Epoch: 1 Batch_idx: 5862 Process: 3 Data_length: 12 coststime: 0.04095172882080078
Train Epoch: 1 Batch_idx: 5862 Process: 0 Data_length: 3 coststime: 0.0751960277557373
Train Epoch: 1 Batch_idx: 5867 Process: 3 Data_length: 49 coststime: 0.0032558441162109375
Train Epoch: 1 Batch_idx: 5872 Process: 1 Data_length: 2 coststime: 0.022842884063720703
Train Epoch: 1 Batch_idx: 5876 Process: 3 Data_length: 63 coststime: 0.002694845199584961
Process 3 length: 5877 time: 2023-11-17 17:03:26.582317
Train epoch 1 costTime: 241.72063446044922s . Process 3 Start to test.
3 0 tensor(45508.8516, device='cuda:3')
3 100 tensor(45309.0469, device='cuda:3')
3 200 tensor(45675.3047, device='cuda:3')
3 300 tensor(45263.0273, device='cuda:3')
Process 3 Reduce metrics.
Train Epoch: 2 Batch_idx: 0 Process: 3 [0/4710975.0 (0%)]	
Train Epoch: 1 Batch_idx: 5882 Process: 1 Data_length: 63 coststime: 0.05185818672180176
Train Epoch: 1 Batch_idx: 5887 Process: 1 Data_length: 12 coststime: 0.006895303726196289
Process 1 length: 5888 time: 2023-11-17 17:20:48.578204
Train epoch 1 costTime: 1285.7279663085938s . Process 1 Start to test.
1 0 tensor(45265.9141, device='cuda:1')
```



### Expected behavior

I'd like to know how to fix this problem.

### Environment info
```
torch==2.0
datasets==2.14.0
```
