modelzoo.transformers.pytorch.gpt2.input.GptHDF5DataProcessor.GptHDF5DataProcessor#
- class modelzoo.transformers.pytorch.gpt2.input.GptHDF5DataProcessor.GptHDF5DataProcessor[source]#
Bases:
modelzoo.transformers.data_processing.HDF5IterableDataProcessor.HDF5IterableDataProcessorA HDF5 dataset processor for GPT pre-training. Loads data from HDF5 files. :param dict params: dict containing training
input parameters for creating dataset.
Expects the following fields: - “data_dir” (str or list of str): Path to dataset HDF5 files - “batch_size” (int): Batch size. - “shuffle” (bool): Flag to enable data shuffling. - “shuffle_buffer” (int): Size of shuffle buffer in samples. - “shuffle_seed” (int): Shuffle seed. - “num_workers” (int): How many subprocesses to use for data loading. - “drop_last” (bool): If True and the dataset size is not divisible
by the batch size, the last incomplete batch will be dropped.
“prefetch_factor” (int): Number of batches loaded in advance by each worker.
- “persistent_workers” (bool): If True, the data loader will not shutdown
the worker processes after a dataset has been consumed once.
- “use_vsl” (bool): Flag to enable variable sequence length training.
It requires the dataset to have two extra features: the attention_span of keys and the position_ids of tokens. Defaults to False.
Methods
collate_fnClassmethod to create the dataloader object.
- create_dataloader()#
Classmethod to create the dataloader object.