vqa_dataset

torchtune.datasets.multimodal.vqa_dataset(model_transform: Transform, *, source: str, image_dir: str = None, column_map: Optional[Dict[str, str]] = None, new_system_prompt: Optional[str] = None, filter_fn: Optional[Callable] = None, split: str = 'train', **load_dataset_kwargs: Dict[str, Any]) → SFTDataset

Configure a custom visual question answer dataset with separate columns for user question, image, and model response.

This builder function can be used to configure a custom visual question answer dataset directly from the yaml config as an alternative to SFTDataset, as it is made to be config friendly.

The dataset should follow this format:

|  input          |  image          |  output          |
|-----------------|-----------------|------------------|
| "user prompt"   | images/1.jpg    | "model response" |

If your column names are different, you can use the column_map parameter to change the expected column names. For example, if your dataset has columns "question", "answer" and "picture" you can use:

column_map = {“input”: “question”, “output”: “answer”, “image”: “picture”}

Parameters:

• model_transform (Transform) – callable that applies model-specific pre-processing to the sample. This includes tokenization and any modality-specific transforms. It is expected to return at minimum "tokens" and "mask" keys.

• source (str) – path to dataset repository on Hugging Face. For local datasets, define source as the data file type (e.g. “json”, “csv”, “text”), pass in the filepath in data_files, and set split="train". See Hugging Face’s load_dataset for more details.

• image_dir (str) – path to the directory containing the images that is prepended to all image paths in the dataset. For example, if image_dir="/home/user/dataset/"` and the sample image path was ``"images/1.jpg", the final image path that will be loaded is "/home/user/dataset/images/1.jpg". If None, assume images are available in current working directory or are located on a remote url. For text-only, leave as None. Default is None.

• column_map (Optional[Dict[str, str]]) – a mapping to change the expected “input”, “output”, and “image” column names to the actual column names in the dataset. Keys should be “input”, “output”, and “image, and values should be the actual column names. Default is None, keeping the default “input” and “output”, and “image” column names.

• new_system_prompt (Optional[str]) – if specified, prepend a system message. This can serve as instructions to guide the model response. Setting this will OVERRIDE any system messages already present in the dataset. Default is None.

• filter_fn (Optional[Callable]) – callable used to filter the dataset prior to any pre-processing. See the Hugging Face docs for more details.

• split (str) – split argument for datasets.load_dataset. You can use this argument to load a subset of a given split, e.g. split="train[:10%]". Default is “train”.

• **load_dataset_kwargs (Dict[str, Any]) – additional keyword arguments to pass to load_dataset, such as data_files or split.

Examples:

my_dataset.json
[
    {
        "question": "What is presented on the image?",
        "answer": "PyTorch logo.",
        "picture": "rgb_pytorch.png"
    },
    {
        ...
    },
    ...,
]

>>> from torchtune.datasets.multimodal import vqa_dataset
>>> dataset = vqa_dataset(
...     model_transform=model_transform,
...     source="json",
...     data_files="my_dataset.json",
...     column_map={
...         "input": "question",
...         "output": "answer",
...         "image": "picture"
...     },
...     split="train",
... )
>>> tokens = dataset[0]["tokens"]
>>> model_transform.decode(tokens)
"What is presented on the image?PyTorch logo."

This can also be accomplished via the yaml config:

dataset:
  _component_: torchtune.datasets.multimodal.vqa_dataset
  source: json
  data_files: my_dataset.json
  column_map:
    input: question
    output: answer
    image: picture
  split: train

Returns:

the configured SFTDataset

Return type:

SFTDataset