examples/multimodal/multimodal_args.py

# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
from megatron.core.models.multimodal.llava_model import IMAGE_TOKEN


def add_multimodal_extra_args(parser):
    """Extra arguments."""
    group = parser.add_argument_group(title='multimodal arguments')
    group.add_argument('--dataset-config', type=str, default=None)
    group.add_argument("--prompt-path", type=str, default=None)
    group.add_argument('--freeze-LM', action='store_true', default=False)
    group.add_argument('--freeze-ViT', action='store_true', default=False)
    group.add_argument('--language-model-type', type=str, required=True)
    group.add_argument('--vision-model-type', type=str, default="clip")
    group.add_argument("--disable-vision-class-token", action="store_true", default=False)
    group.add_argument(
        "--allow-missing-vision-projection-checkpoint", action="store_true", default=False
    )
    group.add_argument("--use-te", action="store_true", default=False)
    group.add_argument(
        "--dataloader-save", type=str, default=None, help="Energon dataloader state save path"
    )
    group.add_argument(
        "--use-tiling", action="store_true", default=False, help="Use input image tiling"
    )
    group.add_argument("--max-num-tiles", type=int, default=1, help="Maximum number of image tiles")
    group.add_argument(
        "--use-thumbnail", action="store_true", default=False, help="Add image thumbnail as a tile"
    )
    group.add_argument(
        "--dataloader-seq-length",
        type=int,
        help="Make dataloader to produce sequences of specific length.",
    )
    group.add_argument(
        "--num-frames",
        type=int,
        default=1,
        help="Number of frames to regularly sample from the video as input to the model.",
    )
    group.add_argument(
        "--online-evaluation-config", type=str, help="Config file for online evaluation."
    )
    group.add_argument(
        "--special-tokens",
        nargs="*",
        default=[IMAGE_TOKEN],
        help="Special tokens used in the multimodal model",
    )
    group.add_argument(
        "--tokenizer-prompt-format",
        type=str,
        choices=["mistral", "llama3", "chatml", "nvlm-yi-34b", "qwen2p0", "qwen2p5", "llama3p1", "nemotron5",
                 "nemotron5-aligned"],
        required=True,
        help="Prompt format to use with the tokenizer.",
    )
    group.add_argument("--pixel-shuffle", action="store_true", default=False)
    group.add_argument(
        "--image-tag-type",
        type=str,
        choices=["nvlm", "internvl", ""],
        default="",  # Default: Image tag not used.
        help="Surround image tokens with tags.",
    )
    group.add_argument("--use-tile-tags", action="store_true", default=False, help="Use tile tags")
    group.add_argument(
        "--packing-buffer-size",
        type=int,
        default=None,   # Packing is disabled by default.
        help="Enable sample packing by setting the buffer size to > 0",
    )
    group.add_argument(
        "--packing-seq-length", type=int, default=0, help="Packing sequence length. Must be > 0 if using packing."
    )
    group.add_argument(
        "--recompute-vision", action="store_true", default=False, help="Enable activation checkpointing in the vision model"
    )
    group.add_argument(
        "--use-loss-scaling", action="store_true", default=False, help="Scale loss based on conversation turn length (in tokens)."
    )
    group.add_argument(
        "--force-system-message", action="store_true", default=False, help="Force a specific system message"
    )
    group.add_argument("--eos-id", type=int, help="termination id for MultiModal Tokenizer")
    group.add_argument(
        "--use-area-weighted-aspect-ratio", action="store_true", default=False,
        help=(
            "When --use-tiling is True, find the aspect ratio to use based on the original ",
            "image aspect ratio and the area covered by the tiles.")
    )
    group.add_argument("--use-mcore-inference", action="store_true", default=False, help="Use the MCore inference API")

    return parser