configuration_omnigenome.py

# coding=utf-8
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""" OmniGenome model configuration"""

from dataclasses import asdict, dataclass
from typing import Optional

from transformers import PretrainedConfig

from transformers.utils import logging

logger = logging.get_logger(__name__)

# TODO Update this
OmniGenome_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "yangheng/OmniGenome-52M": "https://huggingface.co/yangheng/OmniGenome-52M/resolve/main/config.json",
    "yangheng/OmniGenome-186M": "https://huggingface.co/yangheng/OmniGenome-186M/resolve/main/config.json",
    # See all OmniGenome models at https://huggingface.co/models?filter=OmniGenome
}


class OmniGenomeConfig(PretrainedConfig):
    r"""

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

    [yangheng/OmniGenome-52M](https://huggingface.co/yangheng/OmniGenome-52M) 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*):

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

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

        mask_token_id (`int`, *optional*):

            The index of the mask token in the vocabulary. This must be included in the config because of the

            "mask-dropout" scaling trick, which will scale the inputs depending on the number of masked tokens.

        pad_token_id (`int`, *optional*):

            The index of the padding token in the vocabulary. This must be included in the config because certain parts

            of the OmniGenome code use this instead of the attention mask.

        hidden_size (`int`, *optional*, defaults to 768):

            Dimensionality of the encoder layers and the pooler layer.

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

            Number of hidden layers in the Transformer encoder.

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

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

        intermediate_size (`int`, *optional*, defaults to 3072):

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

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

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

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

            The dropout ratio for the attention probabilities.

        max_position_embeddings (`int`, *optional*, defaults to 1026):

            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).

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

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

        layer_norm_eps (`float`, *optional*, defaults to 1e-12):

            The epsilon used by the layer normalization layers.

        position_embedding_type (`str`, *optional*, defaults to `"absolute"`):

            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query", "rotary"`.

            For positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to

            [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).

            For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models

            with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).

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

            Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.

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

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

            relevant if `config.is_decoder=True`.

        emb_layer_norm_before (`bool`, *optional*):

            Whether to apply layer normalization after embeddings but before the main stem of the network.

        token_dropout (`bool`, defaults to `False`):

            When this is enabled, masked tokens are treated as if they had been dropped out by input dropout.



    Examples:



    ```python

    # >>> from transformers import OmniGenomeModel, OmniGenomeConfig

    #

    # >>> # Initializing a OmniGenome yangheng/OmniGenome-52M style configuration >>> configuration = OmniGenomeConfig()

    #

    # >>> # Initializing a model from the configuration >>> model = OmniGenomeModel(configuration)

    #

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

    ```"""

    model_type = "mprna"

    def __init__(

        self,

        vocab_size=None,

        mask_token_id=None,

        pad_token_id=None,

        hidden_size=768,

        num_hidden_layers=12,

        num_attention_heads=12,

        intermediate_size=3072,

        hidden_dropout_prob=0.1,

        attention_probs_dropout_prob=0.1,

        max_position_embeddings=1026,

        initializer_range=0.02,

        layer_norm_eps=1e-12,

        position_embedding_type="absolute",

        use_cache=True,

        emb_layer_norm_before=None,

        token_dropout=False,

        is_folding_model=False,

        OmniGenomefold_config=None,

        vocab_list=None,

        **kwargs,

    ):
        super().__init__(
            pad_token_id=pad_token_id, mask_token_id=mask_token_id, **kwargs
        )

        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.position_embedding_type = position_embedding_type
        self.use_cache = use_cache
        self.emb_layer_norm_before = emb_layer_norm_before
        self.token_dropout = token_dropout
        self.is_folding_model = is_folding_model
        self.OmniGenomefold_config = None
        self.vocab_list = None
        if self.OmniGenomefold_config is not None and getattr(
            self.OmniGenomefold_config, "use_OmniGenome_attn_map", False
        ):
            raise ValueError(
                "The HuggingFace port of OmniGenomeFold does not support use_OmniGenome_attn_map at this time!"
            )

    def to_dict(self):
        """

        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].



        Returns:

            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,

        """
        output = super().to_dict()
        return output


@dataclass
class TrunkConfig:
    num_blocks: int = 48
    sequence_state_dim: int = 1024
    pairwise_state_dim: int = 128
    sequence_head_width: int = 32
    pairwise_head_width: int = 32
    position_bins: int = 32
    dropout: float = 0
    layer_drop: float = 0
    cpu_grad_checkpoint: bool = False
    max_recycles: int = 4
    chunk_size: Optional[int] = 128
    structure_module: "StructureModuleConfig" = None

    def __post_init__(self):
        if self.structure_module is None:
            self.structure_module = StructureModuleConfig()
        elif isinstance(self.structure_module, dict):
            self.structure_module = StructureModuleConfig(**self.structure_module)

        if self.max_recycles <= 0:
            raise ValueError(
                f"`max_recycles` should be positive, got {self.max_recycles}."
            )
        if self.sequence_state_dim % self.sequence_state_dim != 0:
            raise ValueError(
                "`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
                f" {self.sequence_state_dim} and {self.sequence_state_dim}."
            )
        if self.pairwise_state_dim % self.pairwise_state_dim != 0:
            raise ValueError(
                "`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
                f" {self.pairwise_state_dim} and {self.pairwise_state_dim}."
            )

        sequence_num_heads = self.sequence_state_dim // self.sequence_head_width
        pairwise_num_heads = self.pairwise_state_dim // self.pairwise_head_width

        if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
            raise ValueError(
                "`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
                f" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."
            )
        if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
            raise ValueError(
                "`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
                f" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."
            )
        if self.pairwise_state_dim % 2 != 0:
            raise ValueError(
                f"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."
            )

        if self.dropout >= 0.4:
            raise ValueError(
                f"`dropout` should not be greater than 0.4, got {self.dropout}."
            )

    def to_dict(self):
        """

        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].



        Returns:

            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,

        """
        output = asdict(self)
        output["structure_module"] = self.structure_module.to_dict()
        return output


@dataclass
class StructureModuleConfig:
    """

    Args:

        sequence_dim:

            Single representation channel dimension

        pairwise_dim:

            Pair representation channel dimension

        ipa_dim:

            IPA hidden channel dimension

        resnet_dim:

            Angle resnet (Alg. 23 lines 11-14) hidden channel dimension

        num_heads_ipa:

            Number of IPA heads

        num_qk_points:

            Number of query/key points to generate during IPA

        num_v_points:

            Number of value points to generate during IPA

        dropout_rate:

            Dropout rate used throughout the layer

        num_blocks:

            Number of structure module blocks

        num_transition_layers:

            Number of layers in the single representation transition (Alg. 23 lines 8-9)

        num_resnet_blocks:

            Number of blocks in the angle resnet

        num_angles:

            Number of angles to generate in the angle resnet

        trans_scale_factor:

            Scale of single representation transition hidden dimension

        epsilon:

            Small number used in angle resnet normalization

        inf:

            Large number used for attention masking

    """

    sequence_dim: int = 384
    pairwise_dim: int = 128
    ipa_dim: int = 16
    resnet_dim: int = 128
    num_heads_ipa: int = 12
    num_qk_points: int = 4
    num_v_points: int = 8
    dropout_rate: float = 0.1
    num_blocks: int = 8
    num_transition_layers: int = 1
    num_resnet_blocks: int = 2
    num_angles: int = 7
    trans_scale_factor: int = 10
    epsilon: float = 1e-8
    inf: float = 1e5

    def to_dict(self):
        return asdict(self)


def get_default_vocab_list():
    return (
        "<cls>",
        "<pad>",
        "<eos>",
        "<unk>",
        "A",
        "C",
        "G",
        "T",
        "U",
        "N",
        " ",
        "<mask>",
    )