Module infinibatch.torch.data

Classes

class IterableCheckpointedDataset (source: CheckpointableIterator)

Wraps a CheckpointableIterator into a PyTorch IterableDataset, which is recognized by its type by PyTorch's DataLoader class.

Expand source code

class IterableCheckpointedDataset(torch.utils.data.IterableDataset):
    """
    Wraps a CheckpointableIterator into a PyTorch IterableDataset, which is recognized by its type by
    PyTorch's DataLoader class.
    """
    def __init__(self, source: CheckpointableIterator):
        super().__init__()
        self._source = source

    def __iter__(self):  # this is called in the forked clone
        worker_info = torch.utils.data.get_worker_info()
        assert worker_info is None or worker_info.num_workers == 1  # not supported since we can't get at the checkpoint for each worker
        return iter(self._source)

Ancestors

• torch.utils.data.dataset.IterableDataset
• torch.utils.data.dataset.Dataset

class IterableChunkedDataset (paths: Union[str, Iterable[str]], shuffle: bool = True, buffer_size: int = 1048576, transform=None, seed: int = None, world_size: int = 1, rank: int = 0, num_workers_per_rank: int = 1)

An iterable Dataset.

All datasets that represent an iterable of data samples should subclass it. Such form of datasets is particularly useful when data come from a stream.

All subclasses should overwrite :meth:__iter__, which would return an iterator of samples in this dataset.

When a subclass is used with :class:~torch.utils.data.DataLoader, each item in the dataset will be yielded from the :class:~torch.utils.data.DataLoader iterator. When :attr:num_workers > 0, each worker process will have a different copy of the dataset object, so it is often desired to configure each copy independently to avoid having duplicate data returned from the workers. :func:~torch.utils.data.get_worker_info, when called in a worker process, returns information about the worker. It can be used in either the dataset's :meth:__iter__ method or the :class:~torch.utils.data.DataLoader 's :attr:worker_init_fn option to modify each copy's behavior.

Example 1: splitting workload across all workers in :meth:__iter__::

>>> class MyIterableDataset(torch.utils.data.IterableDataset):
...     def __init__(self, start, end):
...         super(MyIterableDataset).__init__()
...         assert end > start, "this example code only works with end >= start"
...         self.start = start
...         self.end = end
...
...     def __iter__(self):
...         worker_info = torch.utils.data.get_worker_info()
...         if worker_info is None:  # single-process data loading, return the full iterator
...             iter_start = self.start
...             iter_end = self.end
...         else:  # in a worker process
...             # split workload
...             per_worker = int(math.ceil((self.end - self.start) / float(worker_info.num_workers)))
...             worker_id = worker_info.id
...             iter_start = self.start + worker_id * per_worker
...             iter_end = min(iter_start + per_worker, self.end)
...         return iter(range(iter_start, iter_end))
...
>>> # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6].
>>> ds = MyIterableDataset(start=3, end=7)

>>> # Single-process loading
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
[3, 4, 5, 6]

>>> # Mult-process loading with two worker processes
>>> # Worker 0 fetched [3, 4].  Worker 1 fetched [5, 6].
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=2)))
[3, 5, 4, 6]

>>> # With even more workers
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=20)))
[3, 4, 5, 6]

Example 2: splitting workload across all workers using :attr:worker_init_fn::

>>> class MyIterableDataset(torch.utils.data.IterableDataset):
...     def __init__(self, start, end):
...         super(MyIterableDataset).__init__()
...         assert end > start, "this example code only works with end >= start"
...         self.start = start
...         self.end = end
...
...     def __iter__(self):
...         return iter(range(self.start, self.end))
...
>>> # should give same set of data as range(3, 7), i.e., [3, 4, 5, 6].
>>> ds = MyIterableDataset(start=3, end=7)

>>> # Single-process loading
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
[3, 4, 5, 6]
>>>
>>> # Directly doing multi-process loading yields duplicate data
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=2)))
[3, 3, 4, 4, 5, 5, 6, 6]

>>> # Define a `worker_init_fn` that configures each dataset copy differently
>>> def worker_init_fn(worker_id):
...     worker_info = torch.utils.data.get_worker_info()
...     dataset = worker_info.dataset  # the dataset copy in this worker process
...     overall_start = dataset.start
...     overall_end = dataset.end
...     # configure the dataset to only process the split workload
...     per_worker = int(math.ceil((overall_end - overall_start) / float(worker_info.num_workers)))
...     worker_id = worker_info.id
...     dataset.start = overall_start + worker_id * per_worker
...     dataset.end = min(dataset.start + per_worker, overall_end)
...

>>> # Mult-process loading with the custom `worker_init_fn`
>>> # Worker 0 fetched [3, 4].  Worker 1 fetched [5, 6].
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=2, worker_init_fn=worker_init_fn)))
[3, 5, 4, 6]

>>> # With even more workers
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=20, worker_init_fn=worker_init_fn)))
[3, 4, 5, 6]

Expand source code

class IterableChunkedDataset(torch.utils.data.IterableDataset):
    def __init__(self, paths: Union[str, Iterable[str]], shuffle: bool=True, buffer_size: int=2**20, transform=None, seed: int=None, world_size: int=1, rank: int=0, num_workers_per_rank: int=1):
        super().__init__()
        self.rank = rank
        self.num_workers_per_rank = num_workers_per_rank
        # instance_rank is set assuming that num_workers_per_rank = 1 and adapted dynamically in __iter__
        self.dataset = chunked_dataset_iterator(paths, shuffle=shuffle, buffer_size=buffer_size, transform=transform, seed=seed, num_instances=world_size*num_workers_per_rank, instance_rank=rank)

    def __iter__(self):
        worker_info = torch.utils.data.get_worker_info()
        if worker_info is None:  # single-process data loading
            self.dataset._instance_rank = self.rank
        else:
            assert worker_info.num_workers == self.num_workers_per_rank
            self.dataset._instance_rank = self.rank * self.num_workers_per_rank + worker_info.id
        return iter(self.dataset)

Ancestors

• torch.utils.data.dataset.IterableDataset
• torch.utils.data.dataset.Dataset