Source code for cerebras.modelzoo.data_preparation.huggingface.HuggingFaceDataProcessor

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"""Pytorch HuggingFace Dataloader"""

import torch
from datasets.distributed import split_dataset_by_node

from cerebras.modelzoo.common.input_utils import get_streaming_batch_size
from cerebras.modelzoo.data.common.input_utils import num_tasks, task_id


[docs]class HuggingFaceDataProcessor: """ A HuggingFace map-style Data Processor. :param dict params: dict containing training input parameters for creating dataset. Expects the following fields: - "batch_size" (int): Batch size. - "shuffle" (bool): Flag to enable data shuffling. - "shuffle_seed" (int): Shuffle seed. - "shuffle_buffer" (int): Size of shuffle buffer in samples. - "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. """ def __init__(self, params): super(HuggingFaceDataProcessor, self).__init__() self.batch_size = get_streaming_batch_size(params["batch_size"]) self.shuffle = params["shuffle"] self.shuffle_seed = params.get("shuffle_seed", None) self.shuffle_buffer = params.get("shuffle_buffer", 10 * self.batch_size) self.num_workers = params.get("num_workers", 0) self.drop_last = params.get("drop_last", True) self.prefetch_factor = params.get("prefetch_factor", 10) self.persistent_workers = params.get("persistent_workers", True) assert self.batch_size > 0, "Batch size should be positive." if not hasattr(self, "dataset"): assert hasattr( self, "dataset" ), "The child class should implement self.dataset" if isinstance(self.dataset, torch.utils.data.IterableDataset): self.map_style_dataset = False else: self.map_style_dataset = True if not hasattr(self, "data_collator"): self.data_collator = None self.dataset = split_dataset_by_node( self.dataset, world_size=num_tasks(), rank=task_id() ) if self.shuffle and not self.map_style_dataset: self.dataset = self.dataset.shuffle( buffer_size=self.shuffle_buffer, seed=self.shuffle_seed ) else: torch.manual_seed(self.shuffle_seed)
[docs] def create_dataloader(self): """ Classmethod to create the dataloader object. """ data_loader = torch.utils.data.DataLoader( self.dataset, shuffle=self.shuffle if self.map_style_dataset else False, batch_size=self.batch_size, drop_last=self.drop_last, num_workers=self.num_workers, prefetch_factor=( self.prefetch_factor if self.num_workers > 0 else None ), persistent_workers=( self.persistent_workers if self.num_workers > 0 else False ), collate_fn=self.data_collator, ) return data_loader