configuration_embed1.py

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"""Configuration class for Cosmos-Embed1."""

from typing import Any, Literal, Tuple, Union

from transformers import AutoConfig, PretrainedConfig


class CosmosEmbed1Config(PretrainedConfig):
    model_type = "cosmos-embed1"

    def __init__(
        self,
        embed_dim: int = 768,
        num_query_tokens: int = 32,
        max_txt_len: int = 128,
        num_video_frames: int = 8,
        temporal_encoding_type: Literal[
            "neighboring_token_propagation", "temporal_parameter"
        ] = "neighboring_token_propagation",
        resolution: Union[int, Tuple[int, int]] = 224,
        vocab_size: int = 30523,
        transformer_engine: bool = False,
        use_fp8: bool = False,
        **kwargs: Any,
    ) -> None:
        """Configuration for `CosmosEmbed1Config`.

        Args:
            embed_dim (int): the dimension of extracted text-visual embeddings.
            num_query_tokens (int): number of learnable query tokens.
            max_txt_len (int): max length of text token sequences before truncation.
            num_video_frames (int): number of input video frames.
            temporal_encoding_type (str): temporal encoding module type.
            resolution (Union[int, Tuple[int, int]]): input video frame resolution.
                Can be an integer for square images (height=width) or a tuple of (height, width) for non-square.
            vocab_size (int): vocab size for text tokenizer.
                The default is from `bert-base-uncased` with an extra [DEC] token.
            transformer_engine (bool): whether to use TransformerEngine for acceleration.
            use_fp8 (bool): whether to use FP8 precision (requires transformer_engine=True).
        """
        super().__init__(**kwargs)

        self.embed_dim = embed_dim
        self.num_query_tokens = num_query_tokens
        self.max_txt_len = max_txt_len
        self.num_video_frames = num_video_frames
        self.temporal_encoding_type = temporal_encoding_type
        self.resolution = resolution
        self.vocab_size = vocab_size
        self.transformer_engine = transformer_engine
        self.use_fp8 = use_fp8


AutoConfig.register("cosmos-embed1", CosmosEmbed1Config)