configuration_spec_vision.py

# coding=utf-8
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""" Spec-Vision model configuration"""

from typing import Dict, Optional, Union

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

logger = logging.get_logger(__name__)


class SpecVisionConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`SpecVisionModel`]. It is used to instantiate a Spec-Vision
    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:
        vocab_size (`int`, *optional*, defaults to 32064):
            Vocabulary size of the model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`SpecVisionModel`].
        hidden_size (`int`, *optional*, defaults to 3072):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 8192):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 32):
            Number of hidden layers in the Transformer decoder.
        num_attention_heads (`int`, *optional*, defaults to 32):
            Number of attention heads for each attention layer in the Transformer decoder.
        num_key_value_heads (`int`, *optional*):
            Number of key/value heads for implementing Grouped Query Attention.
        resid_pdrop (`float`, *optional*, defaults to 0.0):
            Dropout probability for MLP outputs.
        embd_pdrop (`float`, *optional*, defaults to 0.0):
            The dropout ratio for embeddings.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio after computing attention scores.
        hidden_act (`str`, *optional*, defaults to `"silu"`):
            The non-linear activation function in the decoder.
        max_position_embeddings (`int`, *optional*, defaults to 4096):
            The maximum sequence length that this model might ever be used with.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation for initializing all weight matrices.
        rms_norm_eps (`float`, *optional*, defaults to 1e-5):
            The epsilon value used for RMSNorm.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether to use the past key/values attentions for faster inference.
        rope_theta (`float`, *optional*, defaults to 10000.0):
            The base period of the RoPE embeddings.
        rope_scaling (`dict`, *optional*):
            Configuration for RoPE scaling strategy.
        embd_layer (`dict`, *optional*):
            Configuration for the embedding layer, including image embedding settings.
    """
    model_type = "spec_vision"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size: int = 32064,
        hidden_size: int = 3072,
        intermediate_size: int = 8192,
        num_hidden_layers: int = 32,
        num_attention_heads: int = 32,
        num_key_value_heads: Optional[int] = None,
        resid_pdrop: float = 0.0,
        embd_pdrop: float = 0.0,
        attention_dropout: float = 0.0,
        hidden_act: str = "silu",
        max_position_embeddings: int = 4096,
        initializer_range: float = 0.02,
        rms_norm_eps: float = 1e-5,
        use_cache: bool = True,
        rope_theta: float = 10000.0,
        rope_scaling: Optional[Dict] = None,
        embd_layer: Dict[str, Union[str, bool]] = {
            "embedding_cls": "image",
            "hd_transform_order": "sub_glb",
            "projection_cls": "mlp",
            "use_hd_transform": True,
            "with_learnable_separator": True
        },
        bos_token_id: int = 1,
        eos_token_id: int = 32000,
        pad_token_id: int = 32000,
        tie_word_embeddings: bool = False,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.num_key_value_heads = num_key_value_heads or num_attention_heads
        self.resid_pdrop = resid_pdrop
        self.embd_pdrop = embd_pdrop
        self.attention_dropout = attention_dropout
        self.hidden_act = hidden_act
        self.max_position_embeddings = max_position_embeddings
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.rope_theta = rope_theta
        self.rope_scaling = rope_scaling
        self.embd_layer = embd_layer

        super().__init__(
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            pad_token_id=pad_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )

    def _rope_scaling_validation(self):
        """
        Validate the `rope_scaling` configuration.
        """
        if self.rope_scaling is None:
            return

        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3:
            raise ValueError(
                "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "
                f"got {self.rope_scaling}"
            )
        
        rope_scaling_type = self.rope_scaling.get("type", None)
        rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
        rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
        
        if rope_scaling_type is None or rope_scaling_type not in ["su", "yarn"]:
            raise ValueError(f"`rope_scaling`'s type field must be one of ['su', 'yarn'], got {rope_scaling_type}")
            
        head_dim = self.hidden_size // self.num_attention_heads // 2
        
        for factor, name in [(rope_scaling_short_factor, "short_factor"), (rope_scaling_long_factor, "long_factor")]:
            if not (isinstance(factor, list) and all(isinstance(x, (int, float)) for x in factor)):
                raise ValueError(f"`rope_scaling`'s {name} field must be a list of numbers, got {factor}")
            if len(factor) != head_dim:
                raise ValueError(f"`rope_scaling`'s {name} field must have length {head_dim}, got {len(factor)}")