ConcatDataset

class torchtune.datasets.ConcatDataset(datasets: List[Dataset])

A dataset class for concatenating multiple sub-datasets into a single dataset. This class enables the unified handling of different datasets as if they were a single dataset, simplifying tasks such as training models on multiple sources of data simultaneously.

The class internally manages the aggregation of different datasets and allows transparent indexing across them. However, it requires all constituent datasets to be fully loaded into memory, which might not be optimal for very large datasets.

Upon initialization, this class computes the cumulative length of all datasets and maintains an internal mapping of indices to the respective datasets. This approach allows the ConcatDataset to delegate data retrieval to the appropriate sub-dataset transparently when a particular index is accessed.

Note

Using this class with very large datasets can lead to high memory consumption, as it requires all datasets to be loaded into memory. For large-scale scenarios, consider other strategies that might stream data on demand.

Parameters:

datasets (List[Dataset]) – A list of datasets to concatenate. Each dataset must be an instance of a class derived from Dataset.

Examples

>>> dataset1 = MyCustomDataset(params1)
>>> dataset2 = MyCustomDataset(params2)
>>> concat_dataset = ConcatDataset([dataset1, dataset2])
>>> print(len(concat_dataset))  # Total length of both datasets
>>> data_point = concat_dataset[1500]  # Accesses an element from the appropriate dataset

This can also be accomplished by passing in a list of datasets to the YAML config:

dataset:
  - _component_: torchtune.datasets.instruct_dataset
    source: vicgalle/alpaca-gpt4
    template: torchtune.data.AlpacaInstructTemplate
    split: train
    train_on_input: True
  - _component_: torchtune.datasets.instruct_dataset
    source: samsum
    template: torchtune.data.SummarizeTemplate
    column_map: {"output": "summary"}
    output: summary
    split: train
    train_on_input: False

This class primarily focuses on providing a unified interface to access elements from multiple datasets, enhancing the flexibility in handling diverse data sources for training machine learning models.