Distributed sequential windowed data in pytorch
At every epoch of my training, I need to split my dataset in n batches of t consecutive samples. For example, if my data is [1,2,3,4,5,6,7,8,9,10], n = 2 and t = 3 then valid batches would be
[1-2-3, 4-5-6] and [7-8-9, 10-1-2]
[2-3-4, 8-9-10] and [5-6-7, 1-2-3]

My old version is the following, but it samples every point in the data, meaning that I would parse the whole dataset t times per epoch.
train_dataset = list(range(n))
train_sampler = None
if distributed:
    train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
train_loader = torch.utils.data.DataLoader(
        train_dataset, batch_size=bsize, shuffle=(train_sampler is None),
        pin_memory=True, sampler=train_sampler)

for epoch in range(epochs):
    if distributed: 
        train_sampler.set_epoch(epoch)

    for starting_i in train_loader:
        batch = np.array([np.mod(np.arange(i, i + t), n) for i in starting_i])

I have now implemented my own sampling function that splits the data into random batches where each sample is far from the two closest exactly t.  In the non-distributed scenario, I can do
for epoch in range(epochs):
    pad = np.random.randint(n)
    train_loader = np.mod(np.arange(pad, n + pad, t), n)
    np.random.shuffle(train_loader)
    train_loader = np.array_split(train_loader,
        np.ceil(len(train_loader) / bsize))

    for starting_i in train_loader:
        batch = np.array([np.mod(np.arange(i, i + t), n) for i in starting_i])

How do I make this version distributed? Do I need to make a custom torch.nn.parallel.DistributedDataParallel or torch.utils.data.DataLoader?
I have checked the DistributedSampler class
and my guess is that I have to override the __iter__ method. Am I right?
How does DistributedSampler split the dataset? Is it sequentially among num_replicas?
Say num_replicas = 2. Would my dataset be split into [1,2,3,4,5] and [6,7,8,9,10] between the 2 workers? Or is it random? Like [1,4,7,3,10] and [2,9,5,8,6]? First case would be ok for me because keeps samples sequential, but second would not.

I ended up making my own Dataset where the data is [t, t + window, ... t + n * window]. Every time it is called it randomizes the starting indices of the window. Then the sampler does the shuffling as usual. For reproducibility, it has a set_seed method similar to set_epoch of samplers.
class SequentialWindowedDataset(Dataset):
    def __init__(self, size, window):
        self.size = size
        self.window = window
        self.seed = 0
        self.data = np.arange(0, self.size, self.window)

    def __getitem__(self, index):
        rng = np.random.default_rng(self.seed)
        pad = rng.integers(0, self.size)
        data = (self.data + pad) % self.size
        return data[index]

    def __len__(self):
        return len(self.data)

    def set_seed(self, seed):
        self.seed = seed

The following version randomizes the data outside the call and it is much much faster.
class SequentialWindowedDataset(Dataset):
    def __init__(self, size, window):
        self.size = size
        self.window = window
        self.data = np.arange(0, self.size, self.window)

    def __getitem__(self, index):
        return self.data[index]

    def __len__(self):
        return len(self.data)

    def randomize(self, seed):
        rng = np.random.default_rng(seed)
        pad = rng.integers(0, self.size)
        self.data = (self.data + pad) % self.size

