configuration_florence2.py

# coding=utf-8
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import warnings
""" Florence-2 configuration"""

from typing import Optional

from transformers import AutoConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging

logger = logging.get_logger(__name__)

class Florence2VisionConfig(PretrainedConfig):
    r"""

    This is the configuration class to store the configuration of a [`Florence2VisionModel`]. It is used to instantiate a Florence2VisionModel

    according to the specified arguments, defining the model architecture. Instantiating a configuration with the 

    defaults will yield a similar configuration to that of the Florence2VisionModel architecture.



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

    documentation from [`PretrainedConfig`] for more information.



    Args:

        drop_path_rate (`float`, *optional*, defaults to 0.1):

            The dropout rate of the drop path layer.

        patch_size (`List[int]`, *optional*, defaults to [7, 3, 3, 3]):

            The patch size of the image.

        patch_stride (`List[int]`, *optional*, defaults to [4, 2, 2, 2]):

            The patch stride of the image.

        patch_padding (`List[int]`, *optional*, defaults to [3, 1, 1, 1]):

            The patch padding of the image.

        patch_prenorm (`List[bool]`, *optional*, defaults to [false, true, true, true]):

            Whether to apply layer normalization before the patch embedding layer.

        enable_checkpoint (`bool`, *optional*, defaults to False):

            Whether to enable checkpointing.

        dim_embed (`List[int]`, *optional*, defaults to [256, 512, 1024, 2048]):

            The dimension of the embedding layer.

        num_heads (`List[int]`, *optional*, defaults to [8, 16, 32, 64]):

            The number of attention heads.

        num_groups (`List[int]`, *optional*, defaults to [8, 16, 32, 64]):

            The number of groups.

        depths (`List[int]`, *optional*, defaults to [1, 1, 9, 1]):

            The depth of the model.

        window_size (`int`, *optional*, defaults to 12):

            The window size of the model.

        projection_dim (`int`, *optional*, defaults to 1024):

            The dimension of the projection layer.

        visual_temporal_embedding (`dict`, *optional*):

            The configuration of the visual temporal embedding.

        image_pos_embed (`dict`, *optional*):

            The configuration of the image position embedding.

        image_feature_source (`List[str]`, *optional*, defaults to ["spatial_avg_pool", "temporal_avg_pool"]):

            The source of the image feature.

    Example:



    ```python

    >>> from transformers import Florence2VisionConfig, Florence2VisionModel



    >>> # Initializing a Florence2 Vision style configuration

    >>> configuration = Florence2VisionConfig()



    >>> # Initializing a model (with random weights)

    >>> model = Florence2VisionModel(configuration)



    >>> # Accessing the model configuration

    >>> configuration = model.config

    ```"""

    model_type = "florence2_vision"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(

        self,

        drop_path_rate=0.1,

        patch_size=[7, 3, 3, 3],

        patch_stride=[4, 2, 2, 2],

        patch_padding=[3, 1, 1, 1],

        patch_prenorm=[False, True, True, True],

        enable_checkpoint=False,

        dim_embed=[256, 512, 1024, 2048],

        num_heads=[8, 16, 32, 64],

        num_groups=[8, 16, 32, 64],

        depths=[1, 1, 9, 1],

        window_size=12,

        projection_dim=1024,

        visual_temporal_embedding=None,

        image_pos_embed=None,

        image_feature_source=["spatial_avg_pool", "temporal_avg_pool"],

        **kwargs,

    ):
        self.drop_path_rate = drop_path_rate
        self.patch_size = patch_size
        self.patch_stride = patch_stride
        self.patch_padding = patch_padding
        self.patch_prenorm = patch_prenorm
        self.enable_checkpoint = enable_checkpoint
        self.dim_embed = dim_embed
        self.num_heads = num_heads
        self.num_groups = num_groups
        self.depths = depths
        self.window_size = window_size
        self.projection_dim = projection_dim
        self.visual_temporal_embedding = visual_temporal_embedding
        self.image_pos_embed = image_pos_embed
        self.image_feature_source = image_feature_source

        super().__init__(**kwargs)



class Florence2LanguageConfig(PretrainedConfig):
    r"""

    This is the configuration class to store the configuration of a [`Florence2LanguagePreTrainedModel`]. It is used to instantiate a BART

    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 BART

    [facebook/bart-large](https://huggingface.co/facebook/bart-large) architecture.



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

    documentation from [`PretrainedConfig`] for more information.





    Args:

        vocab_size (`int`, *optional*, defaults to 51289):

            Vocabulary size of the Florence2Language model. Defines the number of different tokens that can be represented by the

            `inputs_ids` passed when calling [`Florence2LanguageModel`].

        d_model (`int`, *optional*, defaults to 1024):

            Dimensionality of the layers and the pooler layer.

        encoder_layers (`int`, *optional*, defaults to 12):

            Number of encoder layers.

        decoder_layers (`int`, *optional*, defaults to 12):

            Number of decoder layers.

        encoder_attention_heads (`int`, *optional*, defaults to 16):

            Number of attention heads for each attention layer in the Transformer encoder.

        decoder_attention_heads (`int`, *optional*, defaults to 16):

            Number of attention heads for each attention layer in the Transformer decoder.

        decoder_ffn_dim (`int`, *optional*, defaults to 4096):

            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.

        encoder_ffn_dim (`int`, *optional*, defaults to 4096):

            Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.

        activation_function (`str` or `function`, *optional*, defaults to `"gelu"`):

            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,

            `"relu"`, `"silu"` and `"gelu_new"` are supported.

        dropout (`float`, *optional*, defaults to 0.1):

            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

        attention_dropout (`float`, *optional*, defaults to 0.0):

            The dropout ratio for the attention probabilities.

        activation_dropout (`float`, *optional*, defaults to 0.0):

            The dropout ratio for activations inside the fully connected layer.

        classifier_dropout (`float`, *optional*, defaults to 0.0):

            The dropout ratio for classifier.

        max_position_embeddings (`int`, *optional*, defaults to 1024):

            The maximum sequence length that this model might ever be used with. Typically set this to something large

            just in case (e.g., 512 or 1024 or 2048).

        init_std (`float`, *optional*, defaults to 0.02):

            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

        encoder_layerdrop (`float`, *optional*, defaults to 0.0):

            The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)

            for more details.

        decoder_layerdrop (`float`, *optional*, defaults to 0.0):

            The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556)

            for more details.

        scale_embedding (`bool`, *optional*, defaults to `False`):

            Scale embeddings by diving by sqrt(d_model).

        use_cache (`bool`, *optional*, defaults to `True`):

            Whether or not the model should return the last key/values attentions (not used by all models).

        num_labels (`int`, *optional*, defaults to 3):

            The number of labels to use in [`Florence2LanguageForSequenceClassification`].

        forced_eos_token_id (`int`, *optional*, defaults to 2):

            The id of the token to force as the last generated token when `max_length` is reached. Usually set to

            `eos_token_id`.



    Example:



    ```python

    >>> from transformers import Florence2LanguageConfig, Florence2LanguageModel



    >>> # Initializing a Florence2 Language style configuration

    >>> configuration = Florence2LanguageConfig()



    >>> # Initializing a model (with random weights)

    >>> model = Florence2LangaugeModel(configuration)



    >>> # Accessing the model configuration

    >>> configuration = model.config

    ```"""

    model_type = "florence2_language"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

    def __init__(

        self,

        vocab_size=51289,

        max_position_embeddings=1024,

        encoder_layers=12,

        encoder_ffn_dim=4096,

        encoder_attention_heads=16,

        decoder_layers=12,

        decoder_ffn_dim=4096,

        decoder_attention_heads=16,

        encoder_layerdrop=0.0,

        decoder_layerdrop=0.0,

        activation_function="gelu",

        d_model=1024,

        dropout=0.1,

        attention_dropout=0.0,

        activation_dropout=0.0,

        init_std=0.02,

        classifier_dropout=0.0,

        scale_embedding=False,

        use_cache=True,

        num_labels=3,

        pad_token_id=1,

        bos_token_id=0,

        eos_token_id=2,

        is_encoder_decoder=True,

        decoder_start_token_id=2,

        forced_eos_token_id=2,

        **kwargs,

    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.d_model = d_model
        self.encoder_ffn_dim = encoder_ffn_dim
        self.encoder_layers = encoder_layers
        self.encoder_attention_heads = encoder_attention_heads
        self.decoder_ffn_dim = decoder_ffn_dim
        self.decoder_layers = decoder_layers
        self.decoder_attention_heads = decoder_attention_heads
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.activation_dropout = activation_dropout
        self.activation_function = activation_function
        self.init_std = init_std
        self.encoder_layerdrop = encoder_layerdrop
        self.decoder_layerdrop = decoder_layerdrop
        self.classifier_dropout = classifier_dropout
        self.use_cache = use_cache
        self.num_hidden_layers = encoder_layers
        self.scale_embedding = scale_embedding  # scale factor will be sqrt(d_model) if True

        super().__init__(
            num_labels=num_labels,
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            is_encoder_decoder=is_encoder_decoder,
            decoder_start_token_id=decoder_start_token_id,
            forced_eos_token_id=forced_eos_token_id,
            **kwargs,
        )

        # ensure backward compatibility for BART CNN models
        if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False):
            self.forced_bos_token_id = self.bos_token_id
            warnings.warn(
                f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
                "The config can simply be saved and uploaded again to be fixed."
            )

class Florence2Config(PretrainedConfig):
    r"""

    This is the configuration class to store the configuration of a [`Florence2ForConditionalGeneration`]. It is used to instantiate an

    Florence-2 model according to the specified arguments, defining the model architecture. 



    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 (`Florence2VisionConfig`,  *optional*):

            Custom vision config or dict

        text_config (`Union[AutoConfig, dict]`, *optional*):

            The config object of the text backbone. 

        ignore_index (`int`, *optional*, defaults to -100):

            The ignore index for the loss function.

        vocab_size (`int`, *optional*, defaults to 51289):

            Vocabulary size of the Florence2model. Defines the number of different tokens that can be represented by the

            `inputs_ids` passed when calling [`~Florence2ForConditionalGeneration`]

        projection_dim (`int`, *optional*, defaults to 1024):

            Dimension of the multimodal projection space.



    Example:



    ```python

    >>> from transformers import Florence2ForConditionalGeneration, Florence2Config, CLIPVisionConfig, BartConfig



    >>> # Initializing a clip-like vision config

    >>> vision_config = CLIPVisionConfig()



    >>> # Initializing a Bart config

    >>> text_config = BartConfig()



    >>> # Initializing a Florence-2 configuration

    >>> configuration = Florence2Config(vision_config, text_config)



    >>> # Initializing a model from the florence-2 configuration

    >>> model = Florence2ForConditionalGeneration(configuration)



    >>> # Accessing the model configuration

    >>> configuration = model.config

    ```"""

    model_type = "florence2"
    is_composition = False

    def __init__(

        self,

        vision_config=None,

        text_config=None,

        ignore_index=-100,

        vocab_size=51289,

        projection_dim=1024,

        **kwargs,

    ):
        self.ignore_index = ignore_index
        self.vocab_size = vocab_size
        self.projection_dim = projection_dim
        if vision_config is not None:
            vision_config = PretrainedConfig(**vision_config)
        self.vision_config = vision_config
        self.vocab_size = self.vocab_size

        self.text_config = text_config
        if text_config is not None:
            self.text_config = Florence2LanguageConfig(**text_config)


        super().__init__(**kwargs)