llava_instruct_dataset

torchtune.datasets.multimodal.llava_instruct_dataset(model_transform: Transform, *, source: str = 'liuhaotian/LLaVA-Instruct-150K', image_dir: str = 'coco/train2017/', column_map: Optional[Dict[str, str]] = None, new_system_prompt: Optional[str] = None, packed: bool = False, split: str = 'train', data_files: str = 'llava_instruct_150k.json', **load_dataset_kwargs: Dict[str, Any]) → SFTDataset

Support for family of image + text datasets similar to LLaVA-Instruct-150K from Hugging Face Datasets.

To use this dataset, you must first download the COCO Train 2017 image dataset. You can do so by visiting https://cocodataset.org/#download or downloading it directly:

wget -c http://images.cocodataset.org/zips/train2017.zip
unzip train2017.zip -d coco/

The resulting directory should be passed into the model transform for loading and processing of the images.

The model transform is expected to be a callable that applies pre-processing steps specific to a model. For multimodal datasets, this is expected to be at minimum a tokenizer and an image transform. The tokenizer will convert text sequences into token IDs after the dataset is converted to a list of Message. The image transform will load the image and process it in accordance to the model’s requirements.

Here is a minimal example for illustrative purposes:

from torchtune.models.llama3 import llama3_tokenizer
from torchtune.models.clip import CLIPImageTransform
from torchtune.modules.transforms import Transform

class MyModelTransform(Transform):
    def __init__(
        self,
        tokenizer_path: str,
        max_seq_len: Optional[int] = None,
    ):
        self.tokenizer = llama3_tokenizer(tokenizer_path)
        self.image_transform = CLIPImageTransform()

    def __call__(self, sample: Mapping[str, Any]) -> Mapping[str, Any]:
        tokens, mask = self.tokenizer.tokenize_messages(sample["messages"])
        images = self.image_transform(sample["images"])
        return {
            "tokens": tokens,
            "mask": mask,
            "images": images,
        }

See SFTDataset for more details about model transforms and message transforms.

Parameters:

• model_transform (Transform) – model-specific transform class that takes in a sample dict and applies custom transforms on the keys. It should consist of at minimum two components: text tokenization (called on the “messages” field) and image transform (called on the “images” field). The keys returned by the model transform should be aligned with the expected inputs into the model.

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

• image_dir (str) – path to the directory containing the images as you are expected to download the COCO dataset before using. Default is “coco/”.

• column_map (Optional[Dict[str, str]]) – a mapping from the expected columns (“conversations”) to the new column names in the dataset. If None, assume these are identical. Default is None.

• 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.

• packed (bool) – Whether or not to pack the dataset to max_seq_len prior to training. Default is False.

• 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”.

• data_files (str) – path to the json file to load as dataset. See the dataset repo for options. Default is “llava_instruct_150k.json”.

• **load_dataset_kwargs (Dict[str, Any]) – additional keyword arguments to pass to load_dataset. See Hugging Face’s API ref for more details.

Returns:

dataset configured with source data and transform

Return type:

SFTDataset

Raises:

ValueError – If packed is True, they are not supported for multimodal datasets yet.

Example

>>> llava_instruct_ds = llava_instruct_dataset(model_transform=model_transform)
>>> for batch in Dataloader(llava_instruct_ds, batch_size=8):
>>>     print(f"Batch size: {len(batch)}")
>>> Batch size: 8