cerebras.modelzoo.common.pytorch_utils.BufferedShuffleDataset#

class cerebras.modelzoo.common.pytorch_utils.BufferedShuffleDataset[source]#

Bases: torch.utils.data.IterableDataset

Dataset shuffled from the original dataset.

This class is useful to shuffle an existing instance of an IterableDataset. The buffer with buffer_size is filled with the items from the dataset first. Then, each item will be yielded from the buffer by reservoir sampling via iterator. buffer_size is required to be larger than 0. For buffer_size == 1, the dataset is not shuffled. In order to fully shuffle the whole dataset, buffer_size is required to be greater than or equal to the size of dataset. When it is used with DataLoader, each item in the dataset will be yielded from the DataLoader iterator. And, the method to set up a random seed is different based on num_workers. For single-process mode (num_workers == 0), the random seed is required to be set before the DataLoader in the main process.

Parameters

  • dataset (IterableDataset) – The original IterableDataset.

  • buffer_size (int) – The buffer size for shuffling.

Example

For multi-process mode (num_workers > 0), the random seed is set by a callable function in each worker.

>>> ds = BufferedShuffleDataset(dataset)
>>> random.seed(...)
>>> print(list(torch.utils.data.DataLoader(ds, num_workers=0)))
>>> ds = BufferedShuffleDataset(dataset)
>>> def init_fn(worker_id):
...     random.seed(...)
>>> print(list(torch.utils.data.DataLoader(ds, ..., num_workers=n, worker_init_fn=init_fn)))

Methods

__init__(dataset, buffer_size)[source]#
__call__(*args: Any, **kwargs: Any) → Any#

Call self as a function.

static __new__(cls, *args: Any, **kwargs: Any) → Any#

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