Wan2.1/models/hyvideo/text_encoder/llava/configuration_llava.py

# coding=utf-8
# Copyright 2023 Microsoft Research & University of Wisconsin-Madison and the HuggingFace Inc. team. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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"""Llava model configuration"""

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
from transformers.models.auto import CONFIG_MAPPING, AutoConfig


logger = logging.get_logger(__name__)


class LlavaConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`LlavaForConditionalGeneration`]. It is used to instantiate an
    Llava model according to the specified arguments, defining the model architecture. Instantiating a configuration
    with the defaults will yield a similar configuration to that of the Llava-9B.

    e.g. [llava-hf/llava-9b](https://huggingface.co/llava-hf/llava-9b)

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        vision_config (`Union[AutoConfig, dict]`,  *optional*, defaults to `CLIPVisionConfig`):
            The config object or dictionary of the vision backbone.
        text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `LlamaConfig`):
            The config object or dictionary of the text backbone.
        image_token_index (`int`, *optional*, defaults to 32000):
            The image token index to encode the image prompt.
        projector_hidden_act (`str`, *optional*, defaults to `"gelu"`):
            The activation function used by the multimodal projector.
        vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
            The feature selection strategy used to select the vision feature from the vision backbone.
            Can be one of `"default"` or `"full"`.
        vision_feature_layer (`Union[int, List[int]]`, *optional*, defaults to -2):
            The index of the layer to select the vision feature. If multiple indices are provided,
            the vision feature of the corresponding indices will be concatenated to form the
            vision features.
        image_seq_length (`int`, *optional*, defaults to 576):
            Sequence length of one image embedding.
        multimodal_projector_bias (`bool`, *optional*, defaults to `True`):
            Whether to use bias in the multimodal projector.

    Example:

    ```python
    >>> from transformers import LlavaForConditionalGeneration, LlavaConfig, CLIPVisionConfig, LlamaConfig

    >>> # Initializing a CLIP-vision config
    >>> vision_config = CLIPVisionConfig()

    >>> # Initializing a Llama config
    >>> text_config = LlamaConfig()

    >>> # Initializing a Llava llava-1.5-7b style configuration
    >>> configuration = LlavaConfig(vision_config, text_config)

    >>> # Initializing a model from the llava-1.5-7b style configuration
    >>> model = LlavaForConditionalGeneration(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "llava"
    sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}
    is_composition = True

    def __init__(
        self,
        vision_config=None,
        text_config=None,
        image_token_index=32000,
        projector_hidden_act="gelu",
        vision_feature_select_strategy="default",
        vision_feature_layer=-2,
        image_seq_length=576,
        multimodal_projector_bias=True,
        **kwargs,
    ):
        self.image_token_index = image_token_index
        self.projector_hidden_act = projector_hidden_act
        self.image_seq_length = image_seq_length

        if vision_feature_select_strategy not in ["default", "full"]:
            raise ValueError(
                "vision_feature_select_strategy should be one of 'default', 'full'."
                f"Got: {vision_feature_select_strategy}"
            )

        self.vision_feature_select_strategy = vision_feature_select_strategy
        self.vision_feature_layer = vision_feature_layer

        if isinstance(vision_config, dict):
            vision_config["model_type"] = (
                vision_config["model_type"] if "model_type" in vision_config else "clip_vision_model"
            )
            vision_config = CONFIG_MAPPING[vision_config["model_type"]](**vision_config)
        elif vision_config is None:
            vision_config = CONFIG_MAPPING["clip_vision_model"](
                intermediate_size=4096,
                hidden_size=1024,
                patch_size=14,
                image_size=336,
                num_hidden_layers=24,
                num_attention_heads=16,
                vocab_size=32000,
                projection_dim=768,
            )

        self.vision_config = vision_config

        if isinstance(text_config, dict):
            text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "llama"
            text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
        elif text_config is None:
            text_config = CONFIG_MAPPING["llama"]()

        self.text_config = text_config
        self.multimodal_projector_bias = multimodal_projector_bias

        super().__init__(**kwargs)


__all__ = ["LlavaConfig"]