segment_nt_config.py

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

from dataclasses import asdict, dataclass
from typing import List, Optional

from transformers import PretrainedConfig, logging

logger = logging.get_logger(__name__)

# TODO Update this
ESM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "facebook/esm-1b": "https://huggingface.co/facebook/esm-1b/resolve/main/config.json",
    # See all ESM models at https://huggingface.co/models?filter=esm
}


class SegmentNTConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`ESMModel`]. It is used to instantiate a ESM 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 ESM
    [facebook/esm-1b](https://huggingface.co/facebook/esm-1b) 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 ESM model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`ESMModel`].
        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 ESM 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 EsmModel, EsmConfig

    >>> # Initializing a ESM facebook/esm-1b style configuration >>> configuration = EsmConfig()

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

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

    def __init__(
        self,
        features=None,
        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,
        esmfold_config=None,
        vocab_list=None,
        add_bias_fnn=True,
        rescaling_factor=None,
        num_layers_head=2,
        **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
        # Arguments needed for dcnuc v2
        self.add_bias_fnn = add_bias_fnn
        # Arguments needed for Segment NT
        self.num_layers_head = num_layers_head
        self.features = features
        self.rescaling_factor = rescaling_factor
        if is_folding_model:
            if esmfold_config is None:
                logger.info(
                    "No esmfold_config supplied for folding model, using default values."
                )
                esmfold_config = EsmFoldConfig()
            elif isinstance(esmfold_config, dict):
                esmfold_config = EsmFoldConfig(**esmfold_config)
            self.esmfold_config = esmfold_config
            if vocab_list is None:
                logger.warning(
                    "No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!"
                )
                self.vocab_list = get_default_vocab_list()
            else:
                self.vocab_list = vocab_list
        else:
            self.esmfold_config = None
            self.vocab_list = None
        if self.esmfold_config is not None and getattr(
            self.esmfold_config, "use_esm_attn_map", False
        ):
            raise ValueError(
                "The HuggingFace port of ESMFold does not support use_esm_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()
        if isinstance(self.esmfold_config, EsmFoldConfig):
            output["esmfold_config"] = self.esmfold_config.to_dict()
        return output


@dataclass
class EsmFoldConfig:
    esm_type: str = None
    fp16_esm: bool = True
    use_esm_attn_map: bool = False
    esm_ablate_pairwise: bool = False
    esm_ablate_sequence: bool = False
    esm_input_dropout: float = 0

    embed_aa: bool = True
    bypass_lm: bool = False

    lddt_head_hid_dim: int = 128
    trunk: "TrunkConfig" = None

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

    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["trunk"] = self.trunk.to_dict()
        return output




def get_default_vocab_list():
    return (
        "<cls>",
        "<pad>",
        "<eos>",
        "<unk>",
        "L",
        "A",
        "G",
        "V",
        "S",
        "E",
        "R",
        "T",
        "I",
        "D",
        "P",
        "K",
        "Q",
        "N",
        "F",
        "Y",
        "M",
        "H",
        "W",
        "C",
        "X",
        "B",
        "U",
        "Z",
        "O",
        ".",
        "-",
        "<null_1>",
        "<mask>",
    )